From 3d36ea94135aed3e88308ef1e9795cb181b036eb Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 30 May 2024 16:44:36 +0300 Subject: [PATCH 01/50] nn_models --- Tutorial_2_Neural_Networks.ipynb | 387 +++++++++++++++++++++++++ pyproject.toml | 4 +- tsururu/dataset/dataset.py | 49 +++- tsururu/model_training/trainer.py | 325 +++++++++++++++++++++ tsururu/model_training/validator.py | 68 +++++ tsururu/models/__init__.py | 6 +- tsururu/models/base.py | 79 ++--- tsururu/models/boost.py | 145 +++------ tsururu/models/torch_based/__init__.py | 5 + tsururu/models/torch_based/dlinear.py | 117 ++++++++ tsururu/strategies/base.py | 21 +- tsururu/strategies/direct.py | 108 ++++++- tsururu/strategies/flat_wide_mimo.py | 4 +- tsururu/strategies/mimo.py | 5 +- tsururu/strategies/recursive.py | 66 ++++- tsururu/torch_based/callbacks.py | 67 +++++ tsururu/torch_based/data_provider.py | 36 +++ 17 files changed, 1294 insertions(+), 198 deletions(-) create mode 100644 Tutorial_2_Neural_Networks.ipynb create mode 100644 tsururu/model_training/trainer.py create mode 100644 tsururu/model_training/validator.py create mode 100644 tsururu/models/torch_based/__init__.py create mode 100644 tsururu/models/torch_based/dlinear.py create mode 100644 tsururu/torch_based/callbacks.py create mode 100644 tsururu/torch_based/data_provider.py diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb new file mode 100644 index 0000000..5231e30 --- /dev/null +++ b/Tutorial_2_Neural_Networks.ipynb @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from pathlib import Path\n", + "from typing import List, Optional, Union\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", + "from tsururu.model_training.validator import HoldOutValidator\n", + "from tsururu.models.torch_based.dlinear import DLinear_NN\n", + "from tsururu.strategies import MIMOStrategy\n", + "from tsururu.transformers import (LagTransformer, SequentialTransformer,\n", + " TargetGenerator, UnionTransformer)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_results(\n", + " cv: int,\n", + " regime: str,\n", + " y_true: Optional[List[np.ndarray]] = None,\n", + " y_pred: Optional[List[np.ndarray]] = None,\n", + " ids: Optional[List[Union[float, str]]] = None,\n", + ") -> pd.DataFrame:\n", + " def _get_fold_value(\n", + " value: Optional[Union[float, np.ndarray]], idx: int\n", + " ) -> List[Optional[Union[float, np.ndarray]]]:\n", + " if value is None:\n", + " return [None]\n", + " if isinstance(value[idx], float):\n", + " return value[idx]\n", + " if isinstance(value[idx], np.ndarray):\n", + " return value[idx].reshape(-1)\n", + " raise TypeError(f\"Unexpected value type. Value: {value}\")\n", + "\n", + " df_res_dict = {}\n", + "\n", + " for idx_fold in range(cv):\n", + " # Fill df_res_dict\n", + " for name, value in [(\"y_true\", y_true), (\"y_pred\", y_pred)]:\n", + " df_res_dict[f\"{name}_{idx_fold+1}\"] = _get_fold_value(value, idx_fold)\n", + " if regime != \"local\":\n", + " df_res_dict[f\"id_{idx_fold+1}\"] = _get_fold_value(ids, idx_fold)\n", + "\n", + " # Save datasets to specified directory\n", + " df_res = pd.DataFrame(df_res_dict)\n", + " return df_res" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def expand_val_with_train(train_data, val_data, id_column, date_column, history):\n", + " L_split_data = train_data[date_column].values[(len(train_data) - history)]\n", + " L_last_train_data = train_data[train_data[date_column] >= L_split_data]\n", + " val_data_expanded = pd.concat((L_last_train_data, val_data))\n", + " val_data_expanded = val_data_expanded.sort_values([id_column, date_column]).reset_index(\n", + " drop=True\n", + " )\n", + " return val_data_expanded\n", + "\n", + "\n", + "def expand_test_with_val_and_train(\n", + " train_data, val_data, test_data, id_column, date_column, history\n", + "):\n", + " unqiue_id_cnt = val_data[id_column].nunique()\n", + " L_split_data = val_data[date_column].values[\n", + " (\n", + " (len(val_data) - history)\n", + " if (len(val_data) // val_data[id_column].nunique() - history) > 0\n", + " else 0\n", + " )\n", + " ]\n", + " L_last_val_data = val_data[val_data[date_column] >= L_split_data]\n", + " if len(val_data) // unqiue_id_cnt - history < 0:\n", + " if (len(train_data) - (history - len(L_last_val_data) / unqiue_id_cnt)) > 0:\n", + " L_split_data = train_data[date_column].values[\n", + " (\n", + " len(train_data) // unqiue_id_cnt\n", + " - (history - len(L_last_val_data) // unqiue_id_cnt)\n", + " )\n", + " ]\n", + " else:\n", + " L_split_data = 0\n", + " L_last_train_data = train_data[train_data[date_column] >= L_split_data]\n", + " test_data_expanded = pd.concat((L_last_train_data, L_last_val_data, test_data))\n", + " else:\n", + " test_data_expanded = pd.concat((L_last_val_data, test_data))\n", + " test_data_expanded = test_data_expanded.sort_values([id_column, date_column]).reset_index(\n", + " drop=True\n", + " )\n", + " return test_data_expanded\n", + "\n", + "\n", + "def get_train_val_test_datasets(dataset_path, columns_params, train_size, test_size, history):\n", + " data = pd.read_csv(dataset_path)\n", + "\n", + " date_column = columns_params[\"date\"][\"columns\"][0]\n", + " id_column = columns_params[\"id\"][\"columns\"][0]\n", + "\n", + " if dataset_path.parts[-1] in [\"ETTh1.csv\", \"ETTh2.csv\", \"ETTm1.csv\", \"ETTm2.csv\"]:\n", + " train_val_split_data = \"2017-06-25 23:00:00\"\n", + " val_test_slit_data = \"2017-10-23 23:00:00\"\n", + " else:\n", + " train_val_split_data = data[date_column].values[\n", + " int(data[date_column].nunique() * train_size)\n", + " ]\n", + " val_test_slit_data = data[date_column].values[\n", + " int(data[date_column].nunique() * (1 - test_size))\n", + " ]\n", + "\n", + " train_data = data[data[date_column] <= train_val_split_data]\n", + " val_data = data[\n", + " (data[date_column] > train_val_split_data) & (data[date_column] <= val_test_slit_data)\n", + " ]\n", + " test_data = data[data[date_column] > val_test_slit_data]\n", + " val_data = expand_val_with_train(train_data, val_data, id_column, date_column, history)\n", + " test_data_expanded = expand_test_with_val_and_train(\n", + " train_data, val_data, test_data, id_column, date_column, history\n", + " )\n", + "\n", + " # train, val and test TSDataset initialization\n", + " train_dataset = TSDataset(\n", + " data=train_data,\n", + " columns_params=columns_params,\n", + " )\n", + "\n", + " val_dataset = TSDataset(\n", + " data=val_data,\n", + " columns_params=columns_params,\n", + " )\n", + "\n", + " test_dataset = TSDataset(\n", + " data=test_data_expanded,\n", + " columns_params=columns_params,\n", + " )\n", + "\n", + " return train_dataset, val_dataset, test_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize TSDataset, Pipeline, Model, Validator, Strategy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TSDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "TRAIN_SIZE = 0.7\n", + "TEST_SIZE = 0.2\n", + "history = 7\n", + "\n", + "df_path = Path(\"datasets/global/simulated_data_to_check.csv\")\n", + "\n", + "columns_params = {\n", + " \"target\": {\n", + " \"columns\": [\"value\"],\n", + " \"type\": \"continious\",\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"date\"],\n", + " \"type\": \"datetime\",\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"type\": \"categorical\",\n", + " }\n", + "}\n", + "\n", + "train_dataset, val_dataset, test_dataset = get_train_val_test_datasets(\n", + " df_path, columns_params, TRAIN_SIZE, TEST_SIZE, history\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "lag = LagTransformer(lags=7)\n", + "target_generator = TargetGenerator()\n", + "\n", + "union_1 = UnionTransformer(transformers_list=[lag, target_generator])\n", + "seq_1 = SequentialTransformer(transformers_list=[union_1], input_features=[\"value\"])\n", + "union = UnionTransformer(transformers_list=[seq_1])\n", + "\n", + "pipeline = Pipeline(union, multivariate=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "horizon = 7\n", + "model_horizon = 7\n", + "history = 7\n", + "step = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure the model parameters\n", + "model_params = {\n", + " \"seq_len\": history,\n", + " \"pred_len\": model_horizon,\n", + " \"moving_avg\": 7,\n", + " \"individual\": False,\n", + " \"enc_in\": None\n", + "}\n", + "\n", + "model = DLinear_NN(model_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strategy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "strategy = MIMOStrategy(horizon, history, step, model, None, pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "trainer_params = {\n", + " \"device\": \"cpu\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f82ad85e8bba4e08a5999b77d03ef657", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Epochs: 0%| | 0/10 [00:00.'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstrategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrainer_params\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/utils.py:8\u001b[0m, in \u001b[0;36mtiming_decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m 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trainer_params)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtrainer_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDLTrainer\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 130\u001b[0m trainer \u001b[38;5;241m=\u001b[39m DLTrainer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidator, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtrainer_params)\n\u001b[0;32m--> 131\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m 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\u001b[38;5;28;01mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/model_training/trainer.py:183\u001b[0m, in \u001b[0;36mDLTrainer.train_model\u001b[0;34m(self, train_loader, val_loader)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m 181\u001b[0m running_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m--> 183\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inputs, targets \u001b[38;5;129;01min\u001b[39;00m tqdm(train_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining\u001b[39m\u001b[38;5;124m\"\u001b[39m, leave\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[1;32m 185\u001b[0m callback\u001b[38;5;241m.\u001b[39mon_batch_begin()\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/tqdm/notebook.py:250\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 249\u001b[0m it \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__iter__\u001b[39m()\n\u001b[0;32m--> 250\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m it:\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 253\u001b[0m \u001b[38;5;66;03m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m 1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 1182\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1184\u001b[0m \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py:439\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py:387\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_worker_number_rationality()\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MultiProcessingDataLoaderIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1040\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m 1033\u001b[0m w\u001b[38;5;241m.\u001b[39mdaemon \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;66;03m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;66;03m# start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;66;03m# Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;66;03m# it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;66;03m# before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;66;03m# AssertionError: can only join a started process.\u001b[39;00m\n\u001b[0;32m-> 1040\u001b[0m \u001b[43mw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1041\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_index_queues\u001b[38;5;241m.\u001b[39mappend(index_queue)\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_workers\u001b[38;5;241m.\u001b[39mappend(w)\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _current_process\u001b[38;5;241m.\u001b[39m_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemon\u001b[39m\u001b[38;5;124m'\u001b[39m), \\\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemonic processes are not allowed to have children\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 120\u001b[0m _cleanup()\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen\u001b[38;5;241m.\u001b[39msentinel\n\u001b[1;32m 123\u001b[0m \u001b[38;5;66;03m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;66;03m# reference to the process object (see bpo-30775)\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mProcess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/context.py:288\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpopen_spawn_posix\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Popen\n\u001b[0;32m--> 288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/popen_spawn_posix.py:32\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, process_obj):\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fds \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/popen_fork.py:19\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_launch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/popen_spawn_posix.py:47\u001b[0m, in \u001b[0;36mPopen._launch\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 46\u001b[0m reduction\u001b[38;5;241m.\u001b[39mdump(prep_data, fp)\n\u001b[0;32m---> 47\u001b[0m \u001b[43mreduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 49\u001b[0m set_spawning_popen(\u001b[38;5;28;01mNone\u001b[39;00m)\n", + "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/reduction.py:60\u001b[0m, in \u001b[0;36mdump\u001b[0;34m(obj, file, protocol)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdump\u001b[39m(obj, file, protocol\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 59\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m'''Replacement for pickle.dump() using ForkingPickler.'''\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m \u001b[43mForkingPickler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mAttributeError\u001b[0m: Can't pickle local object 'DLTrainer.fit..'" + ] + } + ], + "source": [ + "strategy.fit(train_dataset, val_dataset, trainer_params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyproject.toml b/pyproject.toml index b4ea07d..71acd1a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,8 +23,10 @@ scikit-learn = "^1.3.2" numpy = "^1.26.3" pandas = "^2.1.4" holidays = "^0.40" -catboost = "^1.2.2" +catboost = "^1.2.0" scipy = "^1.11.4" +torch = "^2.3.0" +tqdm = "^4.66.4" [build-system] requires = ["poetry-core"] diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index 957d9c6..f865319 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -157,6 +157,8 @@ def make_padded_test( horizon: int, history: int, test_last: bool = True, + test_all: bool = False, + model_horizon: Optional[int] = None, id_column_name: Optional[Union[str, Sequence[str]]] = None, ): """Generate a test dataframe with new rows with NaN targets. @@ -164,8 +166,13 @@ def make_padded_test( Args: horizon: number of points to prediction (number of new rows to add to each segment). - test_last: if True, return generated test data - corresponding to the last observation only. + history: number of previous for feature generating. + test_last: if True, return generated test data built by + the last point. + test_all: if True, return generated test data for all + points (like rolling forecast). + model_horizon: the number of points to predict in one step. + Needs for test_all=True. id_column_name: name of the column(s) by which the data is split (in some cases it is different from the original id column(s)). @@ -179,18 +186,43 @@ def make_padded_test( the padded test dataset. """ - index_slicer = IndexSlicer() + if test_all: + current_test_ids = slicer.create_idx_data( + self.data, + horizon, + self.history, + model_horizon, + date_column=self.date_column, + ) + extended_data = slicer.get_slice(self.data, (current_test_ids, None)) + extended_data = pd.DataFrame( + extended_data.reshape(-1, extended_data.shape[-1]), + columns=self.seq_data.columns, + ) + extended_data_nrows = extended_data.shape[0] + + extended_data["segment_col"] = np.repeat( + np.arange(extended_data_nrows // self.history), + self.history, + ) + id_column_name = ["segment_col", self.id_column] + else: + extended_data = self.data + columns = self.data.columns - date_col_id = index_slicer.get_cols_idx(self.data, self.date_column) + date_col_id = slicer.get_cols_idx(self.data, self.date_column) if id_column_name is None: id_column_name = self.id_column - id_col_id = index_slicer.get_cols_idx(self.data, id_column_name) + id_col_id = slicer.get_cols_idx(self.data, id_column_name) # Find indices for segments - ids, time_delta = index_slicer.ids_from_date( + ids, time_delta = slicer.ids_from_date( self.data, self.date_column, delta=self.delta, return_delta=True ) + if test_all: + ids = list(np.unique(extended_data.segment_col, return_index=True)[1])[1:] + data = self.data.to_numpy() segments = np.split(data, ids) @@ -206,7 +238,10 @@ def make_padded_test( # Concatenate together result = np.vstack(np.concatenate((segments, padded_segments_results), axis=1)) - result = pd.DataFrame(result, columns=columns) + if test_all: + result = pd.DataFrame(result, columns=list(columns) + ["segment_col"]) + else: + result = pd.DataFrame(result, columns=columns) result[self.date_column] = pd.to_datetime(result[self.date_column]) result[self.id_column] = result[self.id_column].astype("int") other = [col for col in columns if col not in [self.id_column, self.date_column]] diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py new file mode 100644 index 0000000..0a15c82 --- /dev/null +++ b/tsururu/model_training/trainer.py @@ -0,0 +1,325 @@ +import copy +from copy import deepcopy +from pathlib import Path +from typing import Callable, Dict, List, Optional, Union + +import numpy as np +import torch +from tqdm.notebook import tqdm + +from ..dataset.pipeline import Pipeline +from ..models.base import Estimator +from ..torch_based.callbacks import EarlyStopping, ModelCheckpoint +from ..torch_based.data_provider import nnDataset +from .validator import Validator + + +class MLTrainer: + """Class for training and predicting using a model and a validation strategy. + + Args: + model: the model estimator to be used for training. + validator: the validation strategy to be used for training. + + """ + + def __init__(self, model: Estimator, validator: Validator): + self.model = deepcopy(model) + self.validator = deepcopy(validator) + + self.models: List[Estimator] = [] + self.scores: List[float] = [] + + def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "MLTrainer": + """Fits the models using the input data and pipeline. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + pipeline: fitted data preprocessing pipeline. + val_data: dictionary with validation data. Structure is similar + to `data`. + + Returns: + the fitted models. + + """ + X, y = pipeline.generate(data) + if val_data: + X_val, y_val = pipeline.generate(val_data) + else: + X_val, y_val = None, None + + # Initialize columns' order and reorder columns + self.features_argsort = np.argsort(pipeline.output_features) + X = X[:, self.features_argsort] + + for fold_i, X_train, y_train, X_val, y_val in enumerate( + self.validator.get_split(X, y, X_val, y_val) + ): + model = self.model.fit_one_fold(X_train, y_train, X_val, y_val) + self.models.append(model) + self.scores.append(model.score) + + print(f"Fold {fold_i}. Score: {model.score}") + + print(f"Mean score: {np.mean(self.scores).round(4)}") + print(f"Std: {np.std(self.scores).round(4)}") + + def predict(self, X: np.ndarray) -> np.ndarray: + """Generates predictions using the trained model. + + Args: + X: features array. + + Returns: + array of predicted values. + """ + models_preds = [model.predict(X) for model in self.models] + y_pred = np.mean(models_preds, axis=0) + return y_pred + + +class DLTrainer: + def __init__( + self, + model: Estimator, + validator: Validator, + n_epochs: int = 10, + batch_size: int = 32, + drop_last: bool = False, + device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"), + device_ids: List[int] = [0], + num_workers: int = 4, + metric: Callable = torch.nn.MSELoss(), + criterion: torch.nn.Module = torch.nn.MSELoss(), + optimizer: Optional[torch.optim.Optimizer] = torch.optim.Adam, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, + scheduler_after_epoch: bool = True, + verbose: int = 1, + stop_by_metric: bool = False, + patience: int = 5, + pretrained_path: Optional[Union[str, Path]] = None, + checkpoint_path: Union[str, Path] = "checkpoints/", + callbacks: List[Callable] = [], + ): + self.model = deepcopy(model) + self.validator = deepcopy(validator) + self.n_epochs = n_epochs + self.batch_size = batch_size + self.drop_last = drop_last + self.device = device + self.device_ids = device_ids + self.num_workers = num_workers + self.metric = metric + self.criterion = criterion + self.optimizer = deepcopy(optimizer) + self.scheduler = deepcopy(scheduler) + self.scheduler_after_epoch = scheduler_after_epoch + self.verbose = verbose + # either by loss or metric + self.stop_by_metric = stop_by_metric + # how many epochs to wait for improvement before early stopping + self.patience = patience + self.pretrained_path = pretrained_path + self.checkpoint_path = checkpoint_path + self.callbacks = callbacks + + self.best_model_wts = deepcopy(model.state_dict()) + self.best_score = float("inf") + self.early_stopping_counter = 0 + + self.is_fitted = False + + def _build_model(self, model): + if len(self.device_ids) > 1: + model = torch.nn.DataParallel(model, device_ids=self.device_ids) + else: + model.to(self.device) + + return model + + def _multivariate_collator(data_list, batch_size): + date_groups = defaultdict(list) + for X, y, idx_X in data_list: + date = idx_X # Assuming idx_X is date for simplicity, adapt if needed + date_groups[date].append((X, y, idx_X)) + + batches = [] + for date, group in date_groups.items(): + while len(group) >= batch_size: + batches.append(custom_collator(group[:batch_size])) + group = group[batch_size:] + + return batches + + def load_state(self, path): + self.model.load_state_dict(torch.load(path)) + + def init_trainer(self): + self.model = self._build_model(self.model) + if self.optimizer is None: + self.optimizer = torch.optim.Adam(self.model.parameters()) + else: + self.optimizer = self.optimizer(self.model.parameters()) + + if self.scheduler is not None: + self.scheduler = self.scheduler(self.optimizer) + + def train_model(self, train_loader, val_loader): + if self.pretrained_path is not None: + self.load_state(self.pretrained_path) + else: + self.init_trainer() + + for epoch in tqdm(range(self.n_epochs), desc="Epochs"): + for callback in self.callbacks: + callback.on_epoch_begin(epoch) + + self.model.train() + running_loss = 0.0 + + for inputs, targets in tqdm(train_loader, desc="Training", leave=False): + for callback in self.callbacks: + callback.on_batch_begin() + + inputs, targets = inputs.to(self.device), targets.to(self.device) + + self.optimizer.zero_grad() + outputs = self.model(inputs) + loss = self.criterion(outputs, targets) + loss.backward() + self.optimizer.step() + running_loss += loss.item() * inputs.size(0) + + logs = {"loss": loss.item()} + for cb in self.callbacks: + cb.on_batch_end(logs) + + if self.scheduler_batch: + self.scheduler_batch.step() + + epoch_loss = running_loss / len(train_loader.dataset) + print(f"Epoch {epoch+1}/{self.n_epochs}, Loss: {epoch_loss:.4f}") + + val_loss, val_metric = self.validate_model(val_loader) + print(f"Validation, Loss: {val_loss:.4f}, Metric: {val_metric:.4f}") + + if self.scheduler_epoch: + self.scheduler_epoch.step(val_loss) + + # Сохранение модели и проверка early stopping + logs = { + "epoch": epoch, + "loss": epoch_loss, + "val_loss": val_loss, + "val_metric": val_metric, + "model_state_dict": self.model.state_dict(), + } + + for cb in self.callbacks: + cb.on_epoch_end(epoch, logs) + if getattr(cb, "stop_training", False): + for cb in self.callbacks: + cb.on_train_end() + return + + for cb in self.callbacks: + cb.on_train_end() + + def validate_model(self, val_loader): + self.model.eval() + + all_outputs = [] + all_targets = [] + + with torch.no_grad(): + for inputs, targets in val_loader: + outputs = self.model(inputs) + all_outputs.append(outputs) + all_targets.append(targets) + + all_outputs = torch.cat(all_outputs) + all_targets = torch.cat(all_targets) + loss = self.criterion(all_outputs, all_targets).item() + metric = self.metric_fn(all_outputs, all_targets).item() if self.metric_fn else 0.0 + + print(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") + + return loss, metric + + def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "DLTrainer": + """Fits the models using the input data and pipeline. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + pipeline: fitted data preprocessing pipeline. + val_data: dictionary with validation data. Structure is similar + to `data`. + + Returns: + the fitted models. + + """ + train_dataset = nnDataset(data, pipeline) + + if pipeline.multivariate: + # Custom DataLoader for multivariate case + train_loader = torch.utils.data.DataLoader( + train_dataset, + batch_size=self.batch_size * 4, # Initial larger batch size + shuffle=True, + drop_last=self.drop_last, + num_workers=self.num_workers, + collate_fn=lambda batch: self._multivariate_collator(batch, self.batch_size), + ) + else: + train_loader = torch.utils.data.DataLoader( + train_dataset, + batch_size=self.batch_size, + shuffle=True, + drop_last=self.drop_last, + num_workers=self.num_workers, + ) + + if val_data: + val_dataset = nnDataset(val_data, pipeline) + + if pipeline.multivariate: + val_loader = torch.utils.data.DataLoader( + val_dataset, + batch_size=self.batch_size * 4, # Initial larger batch size + shuffle=False, + drop_last=self.drop_last, + num_workers=self.num_workers, + collate_fn=lambda batch: self._multivariate_collator(batch, self.batch_size), + ) + else: + val_loader = torch.utils.data.DataLoader( + val_dataset, + batch_size=self.batch_size, + shuffle=False, + drop_last=self.drop_last, + num_workers=self.num_workers, + ) + else: + val_loader = None + + self.train_model(train_loader, val_loader) + self.is_fitted = True + + def predict(self, X: np.ndarray) -> np.ndarray: + """Generates predictions using the trained model. + + Args: + X: features array. + + Returns: + array of predicted values. + """ + models_preds = [model.predict(X) for model in self.models] + y_pred = np.mean(models_preds, axis=0) + return y_pred diff --git a/tsururu/model_training/validator.py b/tsururu/model_training/validator.py new file mode 100644 index 0000000..db02b61 --- /dev/null +++ b/tsururu/model_training/validator.py @@ -0,0 +1,68 @@ +from typing import Dict, Iterator, Optional, Tuple, Union + +import numpy as np +from sklearn.model_selection import KFold, TimeSeriesSplit + + +class Validator: + """Base class for validation strategy.""" + + def __init__(self, validation_params: Optional[Dict[str, Union[str, int]]] = None): + self.validation_params = validation_params + + def get_split( + self, + X: np.ndarray, + y: np.ndarray, + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + raise NotImplementedError() + + +class KFoldCrossValidator(Validator): + def __init__(self, n_splits: int = 3, shuffle: bool = True, random_state: int = 42): + self.n_splits = n_splits + self.shuffle = shuffle + self.random_state = random_state + + def get_split( + self, + X: np.ndarray, + y: np.ndarray, + **kwargs, + ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + cv = KFold(n_splits=self.n_splits, shuffle=self.shuffle, random_state=self.random_state) + for X_train_idx, X_val_idx in cv.split(X): + yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] + + +class TimeSeriesValidator(Validator): + def __init__(self, n_splits: int = 3): + self.n_splits = n_splits + + def get_split( + self, + X: np.ndarray, + y: np.ndarray, + **kwargs, + ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + cv = TimeSeriesSplit(n_splits=self.n_splits) + for X_train_idx, X_val_idx in cv.split(X): + yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] + + +class HoldOutValidator(Validator): + def __init__(self, validation_data: Dict[str, np.ndarray]): + self.validation_data = validation_data + + def get_split( + self, + X: np.ndarray, + y: np.ndarray, + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + train_idx = np.arange(len(X)) + val_idx = np.arange(len(self.validation_data["X_val"])) + return X[train_idx], y[train_idx], X_val[val_idx], y_val[val_idx] diff --git a/tsururu/models/__init__.py b/tsururu/models/__init__.py index 0d2f4d1..17cda87 100644 --- a/tsururu/models/__init__.py +++ b/tsururu/models/__init__.py @@ -1,14 +1,14 @@ """Algorithms for time series forecasting.""" from .base import Estimator -from .boost import CatBoostRegressor_CV +from .boost import CatBoost # Factory Object class ModelsFactory: def __init__(self): self.models = { - "CatBoostRegressor_CV": CatBoostRegressor_CV, + "CatBoost": CatBoost, } def get_allowed(self): @@ -24,4 +24,4 @@ def create_model(self, model_name, model_params): return self.models[model_name](**model_params) -__all__ = ["CatBoostRegressor_CV", "ModelsFactory", "Estimator"] +__all__ = ["CatBoost", "Estimator", "ModelsFactory"] diff --git a/tsururu/models/base.py b/tsururu/models/base.py index 272e977..515cb2c 100644 --- a/tsururu/models/base.py +++ b/tsururu/models/base.py @@ -1,71 +1,37 @@ -from typing import Dict, Union +from typing import Dict, Optional, Union import numpy as np -from sklearn.model_selection import KFold, TimeSeriesSplit class Estimator: """Base class for all models. Args: - validation_params: execution params (type, cv, loss), - for example: { - "type": "KFold", - "n_splits": 3, - "loss_function": "MAE", - }. - model_params: base model's params, - for example: { - "loss_function": "MultiRMSE", - "early_stopping_rounds": 100, - }. + model_params: parameters for the model. + Individually defined for each model. """ - def __init__( - self, - validation_params: Dict[str, Union[str, int]], - model_params: Dict[str, Union[str, int]], - ): - self.validation_params = validation_params + def __init__(self, model_params: Dict[str, Union[str, int]]): self.model_params = model_params - self.models = [] - self.scores = [] - self.columns = None - - def initialize_validator(self): - """Initialization of the sample generator. - - Returns: - generator object. - - """ - if self.validation_params["type"] == "KFold": - # Set default params if params are None - for param, default_value in [ - ("n_splits", 3), - ("shuffle", True), - ("random_state", 42), - ]: - if self.validation_params.get(param) is None: - self.validation_params[param] = default_value + self.model = None + self.score = None - cv = KFold(**{k: v for k, v in self.validation_params.items() if k != "type"}) - - elif self.validation_params["type"] == "TS_expanding_window": - cv = TimeSeriesSplit(n_splits=self.validation_params["n_splits"]) - - return cv - - def fit(self, data: dict, pipeline) -> "Estimator": - """Fits the model using the input data and pipeline. + def fit_one_fold( + self, + X_train: np.ndarray, + y_train: np.ndarray, + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> "Estimator": + """Fits the model on one fold using the input data. Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - pipeline: data preprocessing pipeline. + X_train: features array. + y_train: target array. + X_val: validation features array. + y_val: validation target array. Returns: the fitted model. @@ -73,17 +39,14 @@ def fit(self, data: dict, pipeline) -> "Estimator": """ raise NotImplementedError() - def predict(self, data: dict, pipeline) -> np.ndarray: + def predict(self, X: np.ndarray) -> np.ndarray: """Generates predictions using the trained model. Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - pipeline: data preprocessing pipeline. + X: features array. Returns: array of predicted values. """ - raise NotImplementedError() + return self.model.predict(X) diff --git a/tsururu/models/boost.py b/tsururu/models/boost.py index 20e82ef..f470ef8 100644 --- a/tsururu/models/boost.py +++ b/tsururu/models/boost.py @@ -1,29 +1,21 @@ -from typing import Dict, Union +from typing import Dict, Optional, Union import numpy as np try: from catboost import CatBoostRegressor, Pool -except: +except ImportError: Pool = None CatBoostRegressor = None - -from ..dataset import Pipeline from .base import Estimator -class CatBoostRegressor_CV(Estimator): - """CatBoostRegressor_CV is a class that performs cross-validation +class CatBoost(Estimator): + """CatBoost is a class that performs cross-validation using CatBoostRegressor. Args: - validation_params: execution params (type, cv, loss), - for example: { - "type": "KFold", - "n_splits": 3, - "loss_function": "MAE", - }. - model_params: parameters for the CatBoostRegressor model, + model_params: parameters for the model, for example: { "loss_function": "MultiRMSE", "early_stopping_rounds": 100, @@ -31,93 +23,42 @@ class CatBoostRegressor_CV(Estimator): """ - def __init__( - self, - validation_params: Dict[str, Union[str, int]], - model_params: Dict[str, Union[str, int]], - ): - super().__init__(validation_params, model_params) - - def fit(self, data: dict, pipeline: Pipeline) -> "CatBoostRegressor_CV": - """Fits the CatBoostRegressor models using the input data and - pipeline. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - pipeline: fitted data preprocessing pipeline. - - Returns: - the fitted model. - - """ - X, y = pipeline.generate(data) - - # Initialize columns' order and reorder columns - self.features_argsort = np.argsort(pipeline.output_features) - X = X[:, self.features_argsort] - - # Initialize cv object - cv = self.initialize_validator() - - # Fit models - for i, (train_idx, test_idx) in enumerate(cv.split(X)): - X_train, X_test = X[train_idx], X[test_idx] - y_train, y_test = y[train_idx], y[test_idx] - - train_dataset = Pool(data=X_train, label=y_train) - eval_dataset = Pool(data=X_test, label=y_test) - - # Set default params if params are None - for param, default_value in [ - ("loss_function", "MultiRMSE"), - ("thread_count", -1), - ("random_state", 42), - ("early_stopping_rounds", 100), - ]: - if self.model_params.get(param) is None: - self.model_params[param] = default_value + def __init__(self, model_params: Dict[str, Union[str, int]]): + super().__init__(model_params) + self.trainer_type = "MLTrainer" - model = CatBoostRegressor(**self.model_params) - - model.fit( - train_dataset, - eval_set=eval_dataset, - use_best_model=True, - plot=False, - ) - - self.models.append(model) - - score = model.best_score_["validation"][f"{self.model_params['loss_function']}"] - self.scores.append(score) - - print(f"Fold {i}:") - print(f"{self.model_params['loss_function']}: {score}") - - print(f"Mean {self.model_params['loss_function']}: {np.mean(self.scores).round(4)}") - print(f"Std: {np.std(self.scores).round(4)}") - - def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: - """Generates predictions using the trained model. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - pipeline: fitted data preprocessing pipeline. - - Returns: - array of predicted values. - - """ - X, _ = pipeline.generate(data) - - # Reorder columns - X = X[:, self.features_argsort] - - models_preds = [model.predict(X) for model in self.models] - y_pred = np.mean(models_preds, axis=0) - - return y_pred + def fit_one_fold( + self, + X_train: np.ndarray, + y_train: np.ndarray, + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> "CatBoost": + train_dataset = Pool(data=X_train, label=y_train) + eval_dataset = Pool(data=X_val, label=y_val) + + # Set default params if params are None + for param, default_value in [ + ("loss_function", "MultiRMSE"), + ("thread_count", -1), + ("random_state", 42), + ("early_stopping_rounds", 100), + ]: + if self.model_params.get(param) is None: + self.model_params[param] = default_value + + self.model = CatBoostRegressor(**self.model_params) + + self.model.fit( + train_dataset, + eval_set=eval_dataset, + use_best_model=True, + plot=False, + ) + + self.score = self.model.best_score_["validation"][f"{self.model_params['loss_function']}"] + + return self + + def predict(self, X: np.ndarray) -> np.ndarray: + return self.model.predict(X) diff --git a/tsururu/models/torch_based/__init__.py b/tsururu/models/torch_based/__init__.py new file mode 100644 index 0000000..52751c0 --- /dev/null +++ b/tsururu/models/torch_based/__init__.py @@ -0,0 +1,5 @@ +"""NN algorithms for time series forecasting.""" + +from .dlinear import DLinear_NN + +__all__ = ["Dlinear_NN"] diff --git a/tsururu/models/torch_based/dlinear.py b/tsururu/models/torch_based/dlinear.py new file mode 100644 index 0000000..aee25cb --- /dev/null +++ b/tsururu/models/torch_based/dlinear.py @@ -0,0 +1,117 @@ +import torch +import torch.nn as nn + + +class DLinear_NN(nn.Module): + """DLInear model from the paper https://arxiv.org/pdf/2205.13504.pdf. + + Args: + model_params: dict with parameters for the model: + - seq_len: int, the length of the input sequence. + - pred_len: int, the length of the output sequence. + - moving_avg: int, the size of the moving average window. + - individual: bool, whether shared model among different variates. + true may be better for time series with different trend and seasonal patterns. + - enc_in: int, the number of input time series. + Needs for individual=True. + + """ + + def __init__(self, model_params): + super(DLinear_NN, self).__init__() + # Params from model_params + self.seq_len = model_params["seq_len"] + self.pred_len = model_params["pred_len"] + self.decompsition = self.series_decomp(model_params.get("moving_avg", 7)) + self.individual = model_params.get("individual", False) + self.channels = model_params.get("enc_in", 1) + + if self.individual: + self.Linear_Seasonal = nn.ModuleList() + self.Linear_Trend = nn.ModuleList() + + for i in range(self.channels): + self.Linear_Seasonal.append(nn.Linear(self.seq_len, self.pred_len)) + self.Linear_Trend.append(nn.Linear(self.seq_len, self.pred_len)) + + self.Linear_Seasonal[i].weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + self.Linear_Trend[i].weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + else: + self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len) + self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len) + + self.Linear_Seasonal.weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + self.Linear_Trend.weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + + self.trainer_type = "DLTrainer" + + def encoder(self, x): + seasonal_init, trend_init = self.decompsition(x) + seasonal_init, trend_init = seasonal_init.permute(0, 2, 1), trend_init.permute(0, 2, 1) + if self.individual: + seasonal_output = torch.zeros( + [seasonal_init.size(0), seasonal_init.size(1), self.pred_len], + dtype=seasonal_init.dtype, + ).to(seasonal_init.device) + trend_output = torch.zeros( + [trend_init.size(0), trend_init.size(1), self.pred_len], dtype=trend_init.dtype + ).to(trend_init.device) + for i in range(self.channels): + seasonal_output[:, i, :] = self.Linear_Seasonal[i](seasonal_init[:, i, :]) + trend_output[:, i, :] = self.Linear_Trend[i](trend_init[:, i, :]) + else: + seasonal_output = self.Linear_Seasonal(seasonal_init) + trend_output = self.Linear_Trend(trend_init) + x = seasonal_output + trend_output + return x.permute(0, 2, 1) + + def forecast(self, x_enc): + return self.encoder(x_enc) + + def forward(self, x_enc): + dec_out = self.forecast(x_enc) + return dec_out[:, -self.pred_len :, :] + + @staticmethod + def my_Layernorm(channels): + layernorm = nn.LayerNorm(channels) + + def forward(x): + x_hat = layernorm(x) + bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1) + return x_hat - bias + + return forward + + @staticmethod + def moving_avg(kernel_size, stride): + avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) + + def forward(x): + front = x[:, 0:1, :].repeat(1, (kernel_size - 1) // 2, 1) + end = x[:, -1:, :].repeat(1, (kernel_size - 1) // 2, 1) + x = torch.cat([front, x, end], dim=1) + x = avg(x.permute(0, 2, 1)) + x = x.permute(0, 2, 1) + return x + + return forward + + @staticmethod + def series_decomp(kernel_size): + moving_avg = DLinear_NN.moving_avg(kernel_size, stride=1) + + def forward(x): + moving_mean = moving_avg(x) + res = x - moving_mean + return res, moving_mean + + return forward diff --git a/tsururu/strategies/base.py b/tsururu/strategies/base.py index 59669fc..46832c3 100644 --- a/tsururu/strategies/base.py +++ b/tsururu/strategies/base.py @@ -6,7 +6,8 @@ from ..dataset.dataset import TSDataset from ..dataset.pipeline import Pipeline from ..dataset.slice import IndexSlicer -from ..models import Estimator +from ..model_training.validator import Validator +from ..models.base import Estimator from .utils import timing_decorator @@ -22,6 +23,7 @@ class Strategy: step: in how many points to take the next observation while making samples' matrix. model: base model. + validator: validator for model training. pipeline: pipeline for feature and target generation. Notes: @@ -51,6 +53,7 @@ def __init__( history: int, step: int, model: Estimator, + validator: Validator, pipeline: Pipeline, ): self.check_step_param(step) @@ -59,9 +62,10 @@ def __init__( self.history = history self.step = step self.model = model + self.validator = validator self.pipeline = pipeline - self.models = [] + self.trainers = [] @staticmethod def _make_preds_df( @@ -176,11 +180,18 @@ def make_step(self, dataset: TSDataset): raise NotImplementedError() @timing_decorator - def fit(self, dataset: TSDataset): + def fit( + self, + dataset: TSDataset, + val_dataset: Optional[TSDataset] = None, + trainer_params: dict = {}, + ): """Fits the strategy to the given dataset. Args: dataset: The dataset to fit the strategy on. + val_dataset: The validation dataset. + trainer_params: additional parameters for the trainer. Returns: self. @@ -230,11 +241,13 @@ def back_test(self, dataset: TSDataset, cv: int) -> Union[List, np.ndarray]: return (ids_list, test_list, preds_list, fit_time_list, forecast_time_list) @timing_decorator - def predict(self, dataset: TSDataset) -> np.ndarray: + def predict(self, dataset: TSDataset, test_all: bool = False) -> np.ndarray: """Predicts the target values for the given dataset. Args: dataset: the dataset to make predictions on. + test_all: whether to predict all the target values + (like rolling forecast) or only the last one. Returns: a pandas DataFrame containing the predicted target values. diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index 732c489..e25dba5 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -1,8 +1,10 @@ -from copy import deepcopy +from typing import Optional from ..dataset.dataset import TSDataset from ..dataset.pipeline import Pipeline from ..dataset.slice import IndexSlicer +from ..model_training.trainer import DLTrainer, MLTrainer +from ..model_training.validator import Validator from ..models import Estimator from .recursive import RecursiveStrategy from .utils import timing_decorator @@ -22,6 +24,7 @@ class DirectStrategy(RecursiveStrategy): step: in how many points to take the next observation while making samples' matrix. model: base model. + validator: validator for model training. pipeline: pipeline for feature and target generation. model_horizon: how many points to predict at a time, if model_horizon > 1, then it's an intermediate strategy between @@ -43,6 +46,7 @@ def __init__( history: int, step: int, model: Estimator, + validator: Validator, pipeline: Pipeline, model_horizon: int = 1, equal_train_size: bool = False, @@ -52,17 +56,24 @@ def __init__( self.strategy_name = "direct" @timing_decorator - def fit(self, dataset: TSDataset) -> "DirectStrategy": + def fit( + self, + dataset: TSDataset, + val_dataset: Optional[TSDataset] = None, + trainer_params: dict = {}, + ) -> "DirectStrategy": """Fits the direct strategy to the given dataset. Args: dataset: The dataset to fit the strategy on. + val_dataset: The validation dataset. + trainer_params: The parameters for the trainer. Returns: self. """ - self.models = [] + self.trainers = [] if self.equal_train_size: features_idx = index_slicer.create_idx_data( @@ -86,6 +97,32 @@ def fit(self, dataset: TSDataset) -> "DirectStrategy": data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) data = self.pipeline.fit_transform(data, self.strategy_name) + if val_dataset: + val_features_idx = index_slicer.create_idx_data( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + ) + + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + ) + + val_data = self.pipeline.create_data_dict_for_pipeline( + val_dataset, val_features_idx, val_target_idx + ) + val_data = self.pipeline.transform(val_data) + else: + val_data = None + for horizon in range(1, self.horizon // self.model_horizon + 1): target_idx = index_slicer.create_idx_target( dataset.data, @@ -98,9 +135,27 @@ def fit(self, dataset: TSDataset) -> "DirectStrategy": data["target_idx"] = target_idx - current_model = deepcopy(self.model) - current_model.fit(data, self.pipeline) - self.models.append(current_model) + if val_dataset: + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon] + + val_data["target_idx"] = val_target_idx + + if self.model.trainer_type == "MLTrainer": + current_trainer = MLTrainer(self.model, self.validator, **trainer_params) + current_trainer.fit(data, self.pipeline, val_data) + + elif self.model.trainer_type == "DLTrainer": + current_trainer = DLTrainer(self.model, self.validator, **trainer_params) + current_trainer.fit(data, self.pipeline, val_data) + + self.trainers.append(current_trainer) else: for horizon in range(1, self.horizon // self.model_horizon + 1): @@ -128,9 +183,42 @@ def fit(self, dataset: TSDataset) -> "DirectStrategy": ) data = self.pipeline.fit_transform(data, self.strategy_name) - current_model = deepcopy(self.model) - current_model.fit(data, self.pipeline) - self.models.append(current_model) + if val_dataset: + val_features_idx = index_slicer.create_idx_data( + val_dataset.data, + self.model_horizon * horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + ) + + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.model_horizon * horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + n_last_horizon=self.model_horizon, + ) + + val_data = self.pipeline.create_data_dict_for_pipeline( + val_dataset, val_features_idx, val_target_idx + ) + val_data = self.pipeline.transform(val_data) + else: + val_data = None + + if self.model.trainer_type == "MLTrainer": + current_trainer = MLTrainer(self.model, self.validator, **trainer_params) + current_trainer.fit(data, self.pipeline, val_data) + + elif self.model.trainer_type == "DLTrainer": + current_trainer = DLTrainer(self.model, self.validator, **trainer_params) + current_trainer.fit(data, self.pipeline, val_data) + + self.trainers.append(current_trainer) return self @@ -165,7 +253,7 @@ def make_step(self, step, dataset): data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx) data = self.pipeline.transform(data) - pred = self.models[step].predict(data, self.pipeline) + pred = self.trainers[step].predict(data, self.pipeline) pred = self.pipeline.inverse_transform_y(pred) dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1) diff --git a/tsururu/strategies/flat_wide_mimo.py b/tsururu/strategies/flat_wide_mimo.py index 9c2508b..ee602e3 100644 --- a/tsururu/strategies/flat_wide_mimo.py +++ b/tsururu/strategies/flat_wide_mimo.py @@ -1,4 +1,5 @@ from ..dataset.pipeline import Pipeline +from ..model_training.validator import Validator from ..models import Estimator from .mimo import MIMOStrategy @@ -30,7 +31,8 @@ def __init__( history: int, step: int, model: Estimator, + validator: Validator, pipeline: Pipeline, ): - super().__init__(horizon, history, step, model, pipeline) + super().__init__(horizon, history, step, model, validator, pipeline) self.strategy_name = "FlatWideMIMOStrategy" diff --git a/tsururu/strategies/mimo.py b/tsururu/strategies/mimo.py index 4051914..f4c1fe6 100644 --- a/tsururu/strategies/mimo.py +++ b/tsururu/strategies/mimo.py @@ -1,4 +1,5 @@ from ..dataset.pipeline import Pipeline +from ..model_training.validator import Validator from ..models import Estimator from .recursive import RecursiveStrategy @@ -15,6 +16,7 @@ class MIMOStrategy(RecursiveStrategy): step: in how many points to take the next observation while making samples' matrix. model: base model. + validator: validator for model training. pipeline: pipeline for feature and target generation. Notes: @@ -34,7 +36,8 @@ def __init__( history: int, step: int, model: Estimator, + validator: Validator, pipeline: Pipeline, ): - super().__init__(horizon, history, step, model, pipeline, model_horizon=horizon) + super().__init__(horizon, history, step, model, validator, pipeline, model_horizon=horizon) self.strategy_name = "MIMOStrategy" diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 489f0a6..2737ff1 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -1,9 +1,11 @@ -from copy import deepcopy +from typing import Optional import pandas as pd from ..dataset import IndexSlicer, Pipeline, TSDataset -from ..models import Estimator +from ..model_training.trainer import MLTrainer, DLTrainer +from ..model_training.validator import Validator +from ..models.base import Estimator from .base import Strategy from .utils import timing_decorator @@ -22,6 +24,7 @@ class RecursiveStrategy(Strategy): step: in how many points to take the next observation while making samples' matrix. model: base model. + validator: validator for model training. pipeline: pipeline for feature and target generation. model_horizon: how many points to predict at a time, if model_horizon > 1, then it's an intermediate strategy @@ -44,21 +47,29 @@ def __init__( history: int, step: int, model: Estimator, + validator: Validator, pipeline: Pipeline, model_horizon: int = 1, reduced: bool = False, ): - super().__init__(horizon, history, step, model, pipeline) + super().__init__(horizon, history, step, model, validator, pipeline) self.model_horizon = model_horizon self.reduced = reduced self.strategy_name = "recursive" @timing_decorator - def fit(self, dataset: TSDataset) -> "RecursiveStrategy": + def fit( + self, + dataset: TSDataset, + val_dataset: Optional[TSDataset] = None, + trainer_params: dict = {}, + ) -> "RecursiveStrategy": """Fits the recursive strategy to the given dataset. Args: dataset: The dataset to fit the strategy on. + val_dataset: The validation dataset. + trainer_params: Additional parameters for the trainer. Returns: self. @@ -85,10 +96,41 @@ def fit(self, dataset: TSDataset) -> "RecursiveStrategy": data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) data = self.pipeline.fit_transform(data, self.strategy_name) - model = deepcopy(self.model) - model.fit(data, self.pipeline) + if val_dataset: + val_features_idx = index_slicer.create_idx_data( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + val_data = self.pipeline.create_data_dict_for_pipeline( + val_dataset, val_features_idx, val_target_idx + ) + val_data = self.pipeline.transform(val_data) + else: + val_data = None + + if self.model.trainer_type == "MLTrainer": + trainer = MLTrainer(self.model, self.validator, **trainer_params) + trainer.fit(data, self.pipeline, val_data) - self.models.append(model) + elif self.model.trainer_type == "DLTrainer": + trainer = DLTrainer(self.model, self.validator, **trainer_params) + trainer.fit(data, self.pipeline, val_data) + + self.trainers.append(trainer) return self def make_step(self, step: int, dataset: TSDataset) -> TSDataset: @@ -123,7 +165,7 @@ def make_step(self, step: int, dataset: TSDataset) -> TSDataset: data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx) data = self.pipeline.transform(data) - pred = self.models[0].predict(data, self.pipeline) + pred = self.trainers[0].predict(data, self.pipeline) pred = self.pipeline.inverse_transform_y(pred) dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1) @@ -131,7 +173,7 @@ def make_step(self, step: int, dataset: TSDataset) -> TSDataset: return dataset @timing_decorator - def predict(self, dataset: TSDataset) -> pd.DataFrame: + def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame: """Predicts the target values for the given dataset. Args: @@ -141,7 +183,9 @@ def predict(self, dataset: TSDataset) -> pd.DataFrame: a pandas DataFrame containing the predicted target values. """ - new_data = dataset.make_padded_test(self.horizon, self.history) + new_data = dataset.make_padded_test( + self.horizon, self.history, test_all, self.model_horizon + ) new_dataset = TSDataset(new_data, dataset.columns_params, dataset.delta) if self.reduced: @@ -168,7 +212,7 @@ def predict(self, dataset: TSDataset) -> pd.DataFrame: ) data = self.pipeline.transform(data) - pred = self.models[0].predict(data, self.pipeline) + pred = self.trainers[0].predict(data, self.pipeline) pred = self.pipeline.inverse_transform_y(pred) new_dataset.data.loc[target_ids.reshape(-1), dataset.target_column] = pred.reshape(-1) diff --git a/tsururu/torch_based/callbacks.py b/tsururu/torch_based/callbacks.py new file mode 100644 index 0000000..94218cf --- /dev/null +++ b/tsururu/torch_based/callbacks.py @@ -0,0 +1,67 @@ +import torch + + +class Callback: + def on_epoch_begin(self, epoch, logs=None): + pass + + def on_epoch_end(self, epoch, logs=None): + pass + + def on_batch_begin(self, logs=None): + pass + + def on_batch_end(self, logs=None): + pass + + def on_train_begin(self, logs=None): + pass + + def on_train_end(self, logs=None): + pass + + +class EarlyStopping(Callback): + def __init__(self, patience=5, verbose=1, stop_by_metric=True): + self.patience = patience + self.verbose = verbose + self.best_score = None + self.early_stopping_counter = 0 + self.stop_training = False + self.stop_by_metric = stop_by_metric + + def on_epoch_end(self, epoch, logs=None): + current_score = logs.get("val_metric") if self.stop_by_metric else logs.get("val_loss") + if self.best_score is None: + self.best_score = current_score + elif current_score > self.best_score: + self.early_stopping_counter += 1 + if self.early_stopping_counter >= self.patience: + if self.verbose: + print("Early stopping triggered") + self.stop_training = True + else: + self.best_score = current_score + self.early_stopping_counter = 0 + + +class ModelCheckpoint(Callback): + def __init__(self, filepath, monitor="val_loss", verbose=1, save_best_only=True): + self.filepath = filepath + self.monitor = monitor + self.verbose = verbose + self.save_best_only = save_best_only + self.best_score = None + + def on_epoch_end(self, epoch, logs=None): + current_score = logs.get(self.monitor) + if self.save_best_only: + if self.best_score is None or current_score < self.best_score: + self.best_score = current_score + torch.save(logs["model_state_dict"], f"{self.filepath}_epoch_{epoch}.pth") + if self.verbose: + print(f"Model saved to {self.filepath}_epoch_{epoch}.pth") + else: + torch.save(logs["model_state_dict"], f"{self.filepath}_epoch_{epoch}.pth") + if self.verbose: + print(f"Model saved to {self.filepath}_epoch_{epoch}.pth") diff --git a/tsururu/torch_based/data_provider.py b/tsururu/torch_based/data_provider.py new file mode 100644 index 0000000..0df9535 --- /dev/null +++ b/tsururu/torch_based/data_provider.py @@ -0,0 +1,36 @@ +import numpy as np +import torch +from torch.utils.data import Dataset + + +class nnDataset(Dataset): + def __init__(self, data, pipeline): + self.data = data + self.pipeline = pipeline + self.idx_X = self.data["idx_X"] + self.idx_y = self.data["idx_y"] + self.indices = np.arange(len(self.data["idx_X"])) + + def __getitem__(self, index): + data = { + "raw_ts_X": self.data["raw_ts_X"], + "raw_ts_y": self.data["raw_ts_y"], + "X": np.array([]), + "y": np.array([]), + "idx_X": self.idx_X[index], + "idx_y": self.idx_y[index], + "target_column": self.data["target_column"], + "date_column": self.data["date_column"], + "id_column": self.data["id_column"], + } + + # Копия всего дикта data, всего но для индекса (копия, но маленькой штуки) + X, y = self.pipeline.generate(data) + + X_tensor = torch.from_numpy(X).float() + y_tensor = torch.from_numpy(y).float() + + return X_tensor, y_tensor + + def __len__(self): + return len(self.indices) From 65f5770a30e4c4fd2bd3d73c7276babb64590a57 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Tue, 4 Jun 2024 17:37:44 +0300 Subject: [PATCH 02/50] fixes of torch-based parts --- .gitignore | 3 +- Tutorial_2_Neural_Networks.ipynb | 84 +++++----------------------- tsururu/__init__.py | 2 +- tsururu/dataset/pipeline.py | 7 +++ tsururu/model_training/trainer.py | 70 ++++++----------------- tsururu/torch_based/__init__.py | 3 + tsururu/torch_based/callbacks.py | 39 ++++++++++--- tsururu/torch_based/data_provider.py | 53 +++++++++++++++--- tsururu/torch_based/scheduler.py | 30 ++++++++++ 9 files changed, 149 insertions(+), 142 deletions(-) create mode 100644 tsururu/torch_based/__init__.py create mode 100644 tsururu/torch_based/scheduler.py diff --git a/.gitignore b/.gitignore index c558c4c..aeee479 100644 --- a/.gitignore +++ b/.gitignore @@ -151,4 +151,5 @@ catboost_info/ poetry.lock *check*.ipynb *temp* -*demand* \ No newline at end of file +*demand* +*test* \ No newline at end of file diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index 5231e30..23dd59a 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -2,11 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import warnings\n", + "import importlib\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", @@ -26,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -211,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -234,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -280,79 +281,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "trainer_params = {\n", " \"device\": \"cpu\",\n", + " \"num_workers\": 0,\n", "}" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f82ad85e8bba4e08a5999b77d03ef657", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Epochs: 0%| | 0/10 [00:00.'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstrategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrainer_params\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/utils.py:8\u001b[0m, in \u001b[0;36mtiming_decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 7\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 10\u001b[0m execution_time \u001b[38;5;241m=\u001b[39m end_time \u001b[38;5;241m-\u001b[39m start_time\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/recursive.py:131\u001b[0m, in \u001b[0;36mRecursiveStrategy.fit\u001b[0;34m(self, dataset, val_dataset, trainer_params)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtrainer_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDLTrainer\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 130\u001b[0m trainer \u001b[38;5;241m=\u001b[39m DLTrainer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidator, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtrainer_params)\n\u001b[0;32m--> 131\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainers\u001b[38;5;241m.\u001b[39mappend(trainer)\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/model_training/trainer.py:311\u001b[0m, in \u001b[0;36mDLTrainer.fit\u001b[0;34m(self, data, pipeline, val_data)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 309\u001b[0m val_loader \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_fitted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/model_training/trainer.py:183\u001b[0m, in \u001b[0;36mDLTrainer.train_model\u001b[0;34m(self, train_loader, val_loader)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m 181\u001b[0m running_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m--> 183\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inputs, targets \u001b[38;5;129;01min\u001b[39;00m tqdm(train_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining\u001b[39m\u001b[38;5;124m\"\u001b[39m, leave\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[1;32m 185\u001b[0m callback\u001b[38;5;241m.\u001b[39mon_batch_begin()\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/tqdm/notebook.py:250\u001b[0m, in \u001b[0;36mtqdm_notebook.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 249\u001b[0m it \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__iter__\u001b[39m()\n\u001b[0;32m--> 250\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m it:\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# return super(tqdm...) will not catch exception\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 253\u001b[0m \u001b[38;5;66;03m# NB: except ... [ as ...] breaks IPython async KeyboardInterrupt\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m 1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 1182\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m 1183\u001b[0m \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m 1184\u001b[0m \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py:439\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 437\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator\n\u001b[1;32m 438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py:387\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_worker_number_rationality()\n\u001b[0;32m--> 387\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MultiProcessingDataLoaderIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1040\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m 1033\u001b[0m w\u001b[38;5;241m.\u001b[39mdaemon \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 1034\u001b[0m \u001b[38;5;66;03m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;66;03m# start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;66;03m# Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;66;03m# it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;66;03m# before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[1;32m 1039\u001b[0m \u001b[38;5;66;03m# AssertionError: can only join a started process.\u001b[39;00m\n\u001b[0;32m-> 1040\u001b[0m \u001b[43mw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1041\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_index_queues\u001b[38;5;241m.\u001b[39mappend(index_queue)\n\u001b[1;32m 1042\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_workers\u001b[38;5;241m.\u001b[39mappend(w)\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _current_process\u001b[38;5;241m.\u001b[39m_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemon\u001b[39m\u001b[38;5;124m'\u001b[39m), \\\n\u001b[1;32m 119\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemonic processes are not allowed to have children\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 120\u001b[0m _cleanup()\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen\u001b[38;5;241m.\u001b[39msentinel\n\u001b[1;32m 123\u001b[0m \u001b[38;5;66;03m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[38;5;66;03m# reference to the process object (see bpo-30775)\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[0;32m--> 224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mProcess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/context.py:288\u001b[0m, in \u001b[0;36mSpawnProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m 285\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 286\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[1;32m 287\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpopen_spawn_posix\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Popen\n\u001b[0;32m--> 288\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/popen_spawn_posix.py:32\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, process_obj):\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fds \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/popen_fork.py:19\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_launch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/popen_spawn_posix.py:47\u001b[0m, in \u001b[0;36mPopen._launch\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 46\u001b[0m reduction\u001b[38;5;241m.\u001b[39mdump(prep_data, fp)\n\u001b[0;32m---> 47\u001b[0m \u001b[43mreduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 49\u001b[0m set_spawning_popen(\u001b[38;5;28;01mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/miniconda3/envs/py_3_10/lib/python3.10/multiprocessing/reduction.py:60\u001b[0m, in \u001b[0;36mdump\u001b[0;34m(obj, file, protocol)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdump\u001b[39m(obj, file, protocol\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 59\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m'''Replacement for pickle.dump() using ForkingPickler.'''\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m \u001b[43mForkingPickler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprotocol\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mAttributeError\u001b[0m: Can't pickle local object 'DLTrainer.fit..'" - ] - } - ], - "source": [ - "strategy.fit(train_dataset, val_dataset, trainer_params)" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", diff --git a/tsururu/__init__.py b/tsururu/__init__.py index 6687220..156cc84 100644 --- a/tsururu/__init__.py +++ b/tsururu/__init__.py @@ -1 +1 @@ -__all__ = ["dataset", "transformers", "models", "strategies", "validation"] +__all__ = ["dataset", "transformers", "models", "strategies", "validation", "torch_based"] diff --git a/tsururu/dataset/pipeline.py b/tsururu/dataset/pipeline.py index c1138d9..30296f1 100644 --- a/tsururu/dataset/pipeline.py +++ b/tsururu/dataset/pipeline.py @@ -370,6 +370,13 @@ def _make_multivariate_X_y(self, data: dict) -> dict: date_features_colname = X.columns[X.columns.str.contains(data["date_column_name"])].values id_features_colname = X.columns[X.columns.str.contains(data["id_column_name"])].values + + if id_features_colname.size == 0: + # add temporary column with id for make multivariate merging + id_idx = index_slicer.get_cols_idx(data["raw_ts_X"], data["id_column_name"]) + X["temp_ID"] = index_slicer.get_slice(data["raw_ts_X"], (data["idx_X"][:, 0], id_idx)) + id_features_colname = np.array(["temp_ID"]) + other_features_colname = np.setdiff1d( X.columns.values, np.hstack((id_features_colname, date_features_colname)), diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 0a15c82..3f6022e 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -1,4 +1,3 @@ -import copy from copy import deepcopy from pathlib import Path from typing import Callable, Dict, List, Optional, Union @@ -140,20 +139,6 @@ def _build_model(self, model): return model - def _multivariate_collator(data_list, batch_size): - date_groups = defaultdict(list) - for X, y, idx_X in data_list: - date = idx_X # Assuming idx_X is date for simplicity, adapt if needed - date_groups[date].append((X, y, idx_X)) - - batches = [] - for date, group in date_groups.items(): - while len(group) >= batch_size: - batches.append(custom_collator(group[:batch_size])) - group = group[batch_size:] - - return batches - def load_state(self, path): self.model.load_state_dict(torch.load(path)) @@ -173,14 +158,16 @@ def train_model(self, train_loader, val_loader): else: self.init_trainer() - for epoch in tqdm(range(self.n_epochs), desc="Epochs"): + # for epoch in tqdm(range(self.n_epochs), desc="Epochs"): + for epoch in range(self.n_epochs): for callback in self.callbacks: callback.on_epoch_begin(epoch) self.model.train() running_loss = 0.0 - for inputs, targets in tqdm(train_loader, desc="Training", leave=False): + # for inputs, targets in tqdm(train_loader, desc="Training", leave=False): + for inputs, targets in train_loader: for callback in self.callbacks: callback.on_batch_begin() @@ -266,45 +253,24 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "DLTr """ train_dataset = nnDataset(data, pipeline) - if pipeline.multivariate: - # Custom DataLoader for multivariate case - train_loader = torch.utils.data.DataLoader( - train_dataset, - batch_size=self.batch_size * 4, # Initial larger batch size - shuffle=True, - drop_last=self.drop_last, - num_workers=self.num_workers, - collate_fn=lambda batch: self._multivariate_collator(batch, self.batch_size), - ) - else: - train_loader = torch.utils.data.DataLoader( - train_dataset, - batch_size=self.batch_size, - shuffle=True, - drop_last=self.drop_last, - num_workers=self.num_workers, - ) + train_loader = torch.utils.data.DataLoader( + train_dataset, + batch_size=self.batch_size, + shuffle=True, + drop_last=self.drop_last, + num_workers=self.num_workers, + ) if val_data: val_dataset = nnDataset(val_data, pipeline) - if pipeline.multivariate: - val_loader = torch.utils.data.DataLoader( - val_dataset, - batch_size=self.batch_size * 4, # Initial larger batch size - shuffle=False, - drop_last=self.drop_last, - num_workers=self.num_workers, - collate_fn=lambda batch: self._multivariate_collator(batch, self.batch_size), - ) - else: - val_loader = torch.utils.data.DataLoader( - val_dataset, - batch_size=self.batch_size, - shuffle=False, - drop_last=self.drop_last, - num_workers=self.num_workers, - ) + val_loader = torch.utils.data.DataLoader( + val_dataset, + batch_size=self.batch_size, + shuffle=False, + drop_last=self.drop_last, + num_workers=self.num_workers, + ) else: val_loader = None diff --git a/tsururu/torch_based/__init__.py b/tsururu/torch_based/__init__.py new file mode 100644 index 0000000..1305abf --- /dev/null +++ b/tsururu/torch_based/__init__.py @@ -0,0 +1,3 @@ +from .data_provider import nnDataset + +__all__ = ["nnDataset"] diff --git a/tsururu/torch_based/callbacks.py b/tsururu/torch_based/callbacks.py index 94218cf..55e3736 100644 --- a/tsururu/torch_based/callbacks.py +++ b/tsururu/torch_based/callbacks.py @@ -1,3 +1,6 @@ +import heapq +import os + import torch @@ -45,23 +48,41 @@ def on_epoch_end(self, epoch, logs=None): self.early_stopping_counter = 0 -class ModelCheckpoint(Callback): - def __init__(self, filepath, monitor="val_loss", verbose=1, save_best_only=True): +class ModelCheckpoint: + def __init__(self, filepath, monitor="val_loss", verbose=1, save_best_only=True, k=5): self.filepath = filepath self.monitor = monitor self.verbose = verbose self.save_best_only = save_best_only - self.best_score = None + self.k = k + self.best_snapshots = [] + self.worst_best_score = float("inf") def on_epoch_end(self, epoch, logs=None): current_score = logs.get(self.monitor) + if current_score is None: + return + + model_path = f"{self.filepath}_epoch_{epoch}.pth" + if self.save_best_only: - if self.best_score is None or current_score < self.best_score: - self.best_score = current_score - torch.save(logs["model_state_dict"], f"{self.filepath}_epoch_{epoch}.pth") + if len(self.best_snapshots) < self.k or current_score < self.worst_best_score: + if len(self.best_snapshots) == self.k: + worst_snapshot = heapq.heappop(self.best_snapshots) + if self.verbose: + print(f"Removing worst model snapshot: {worst_snapshot[1]}") + os.remove(worst_snapshot[1]) + + heapq.heappush(self.best_snapshots, (current_score, model_path)) + self.worst_best_score = max(self.best_snapshots)[0] + torch.save(logs["model_state_dict"], model_path) + if self.verbose: - print(f"Model saved to {self.filepath}_epoch_{epoch}.pth") + print(f"Model saved to {model_path}") else: - torch.save(logs["model_state_dict"], f"{self.filepath}_epoch_{epoch}.pth") + torch.save(logs["model_state_dict"], model_path) if self.verbose: - print(f"Model saved to {self.filepath}_epoch_{epoch}.pth") + print(f"Model saved to {model_path}") + + def get_best_snapshots(self): + return [snapshot[1] for snapshot in sorted(self.best_snapshots)] diff --git a/tsururu/torch_based/data_provider.py b/tsururu/torch_based/data_provider.py index 0df9535..5d73168 100644 --- a/tsururu/torch_based/data_provider.py +++ b/tsururu/torch_based/data_provider.py @@ -1,5 +1,6 @@ import numpy as np import torch +from pandas import to_datetime from torch.utils.data import Dataset @@ -9,27 +10,61 @@ def __init__(self, data, pipeline): self.pipeline = pipeline self.idx_X = self.data["idx_X"] self.idx_y = self.data["idx_y"] - self.indices = np.arange(len(self.data["idx_X"])) + + if pipeline.multivariate: + self.indices = np.arange( + len(self.data["idx_X"]) // self.data["raw_ts_X"]["id"].nunique() + ) + unique_dates, inverse_indices = np.unique( + self.data["raw_ts_X"]["date"], return_inverse=True + ) + unique_dates = to_datetime(unique_dates) + self.date_indices = { + date: np.where(inverse_indices == idx)[0] for idx, date in enumerate(unique_dates) + } + + else: + self.indices = np.arange(len(self.data["idx_X"])) def __getitem__(self, index): + if self.pipeline.multivariate: + current_date = self.data["raw_ts_X"]["date"].iloc[self.idx_X[index][0]] + current_date = to_datetime(current_date) + first_idx = self.date_indices[current_date] + idx_X = self.idx_X[np.isin(self.idx_X[:, 0], first_idx)] + idx_y = self.idx_y[np.isin(self.idx_X[:, 0], first_idx)] + else: + idx_X = self.idx_X[index] + idx_y = self.idx_y[index] + + raw_ts_X_adjusted = self.data["raw_ts_X"].iloc[idx_X.flatten()].reset_index(drop=True) + raw_ts_y_adjusted = self.data["raw_ts_y"].iloc[idx_y.flatten()].reset_index(drop=True) + + idx_X_adjusted = np.arange(np.size(idx_X)).reshape(idx_X.shape) + idx_y_adjusted = np.arange(np.size(idx_y)).reshape(idx_y.shape) + data = { - "raw_ts_X": self.data["raw_ts_X"], - "raw_ts_y": self.data["raw_ts_y"], + "raw_ts_X": raw_ts_X_adjusted, + "raw_ts_y": raw_ts_y_adjusted, "X": np.array([]), "y": np.array([]), - "idx_X": self.idx_X[index], - "idx_y": self.idx_y[index], - "target_column": self.data["target_column"], - "date_column": self.data["date_column"], - "id_column": self.data["id_column"], + "idx_X": idx_X_adjusted, + "idx_y": idx_y_adjusted, + "target_column_name": self.data["target_column_name"], + "date_column_name": self.data["date_column_name"], + "id_column_name": self.data["id_column_name"], } - # Копия всего дикта data, всего но для индекса (копия, но маленькой штуки) X, y = self.pipeline.generate(data) X_tensor = torch.from_numpy(X).float() y_tensor = torch.from_numpy(y).float() + if self.pipeline.multivariate: + num_series = raw_ts_X_adjusted["id"].nunique() + X_tensor = X_tensor.view(num_series, -1).T.flip(0) + y_tensor = y_tensor.view(num_series, -1).T + return X_tensor, y_tensor def __len__(self): diff --git a/tsururu/torch_based/scheduler.py b/tsururu/torch_based/scheduler.py new file mode 100644 index 0000000..17bd3c0 --- /dev/null +++ b/tsururu/torch_based/scheduler.py @@ -0,0 +1,30 @@ +import torch +from torch.optim.lr_scheduler import _LRScheduler + + +class LRAdjScheduler(_LRScheduler): + def __init__(self, optimizer, lr_schedule_type, args, last_epoch=-1): + self.lr_schedule_type = lr_schedule_type + self.args = args + super(LRAdjScheduler, self).__init__(optimizer, last_epoch) + + def get_lr(self): + epoch = self.last_epoch + 1 + + if self.lr_schedule_type == "type1": + lr = self.args.learning_rate * (0.5 ** ((epoch - 1) // 1)) + elif self.lr_schedule_type == "type2": + lr_adjust = {2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6, 10: 5e-7, 15: 1e-7, 20: 5e-8} + lr = lr_adjust.get(epoch, self.base_lrs[0]) + elif self.lr_schedule_type == "3": + lr = self.args.learning_rate if epoch < 10 else self.args.learning_rate * 0.1 + elif self.lr_schedule_type == "4": + lr = self.args.learning_rate if epoch < 15 else self.args.learning_rate * 0.1 + elif self.lr_schedule_type == "5": + lr = self.args.learning_rate if epoch < 25 else self.args.learning_rate * 0.1 + elif self.lr_schedule_type == "6": + lr = self.args.learning_rate if epoch < 5 else self.args.learning_rate * 0.1 + else: + raise ValueError(f"Unknown lr_schedule_type: {self.lr_schedule_type}") + + return [lr for _ in self.optimizer.param_groups] From 4177376c2cad7eaff0d9a4666ef958c97bf5f7fe Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 7 Jun 2024 13:21:32 +0300 Subject: [PATCH 03/50] validations for nn --- Tutorial_2_Neural_Networks.ipynb | 97 ++++++---- .../torch_based/__init__.py | 0 .../torch_based/callbacks.py | 0 .../torch_based/data_provider.py | 0 .../torch_based/scheduler.py | 0 tsururu/model_training/trainer.py | 178 ++++++++++++------ tsururu/model_training/validator.py | 39 ++-- tsururu/strategies/base.py | 16 +- tsururu/strategies/direct.py | 38 ++-- tsururu/strategies/flat_wide_mimo.py | 13 +- tsururu/strategies/mimo.py | 13 +- tsururu/strategies/recursive.py | 30 +-- 12 files changed, 244 insertions(+), 180 deletions(-) rename tsururu/{ => model_training}/torch_based/__init__.py (100%) rename tsururu/{ => model_training}/torch_based/callbacks.py (100%) rename tsururu/{ => model_training}/torch_based/data_provider.py (100%) rename tsururu/{ => model_training}/torch_based/scheduler.py (100%) diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index 23dd59a..df4bf54 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,12 +22,14 @@ "from tsururu.models.torch_based.dlinear import DLinear_NN\n", "from tsururu.strategies import MIMOStrategy\n", "from tsururu.transformers import (LagTransformer, SequentialTransformer,\n", - " TargetGenerator, UnionTransformer)" + " TargetGenerator, UnionTransformer)\n", + "\n", + "from tsururu.model_training.trainer import DLTrainer" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -212,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -230,37 +232,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Model" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "horizon = 7\n", - "model_horizon = 7\n", - "history = 7\n", - "step = 1" + "### Trainer" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Configure the model parameters\n", - "model_params = {\n", - " \"seq_len\": history,\n", - " \"pred_len\": model_horizon,\n", - " \"moving_avg\": 7,\n", - " \"individual\": False,\n", - " \"enc_in\": None\n", + "model = DLinear_NN\n", + "model_params = {\"moving_avg\": 7, \"individual\": False, \"enc_in\": None}\n", + "\n", + "validation = HoldOutValidator\n", + "validation_params = {\"validation_data\": val_dataset}\n", + "\n", + "trainer_params = {\n", + " \"device\": \"cpu\",\n", + " \"num_workers\": 0,\n", "}\n", "\n", - "model = DLinear_NN(model_params)" + "trainer = DLTrainer(\n", + " model, \n", + " model_params, \n", + " validation, \n", + " validation_params, \n", + " trainer_params\n", + ")" ] }, { @@ -272,23 +271,53 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "strategy = MIMOStrategy(horizon, history, step, model, None, pipeline)" + "horizon = 7\n", + "model_horizon = 7\n", + "history = 7\n", + "step = 1" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "trainer_params = {\n", - " \"device\": \"cpu\",\n", - " \"num_workers\": 0,\n", - "}" + "strategy = MIMOStrategy(\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " horizon=horizon,\n", + " history=history,\n", + " step=step,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'params'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstrategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/utils.py:8\u001b[0m, in \u001b[0;36mtiming_decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 7\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 10\u001b[0m execution_time \u001b[38;5;241m=\u001b[39m end_time \u001b[38;5;241m-\u001b[39m start_time\n", + "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/recursive.py:119\u001b[0m, in \u001b[0;36mRecursiveStrategy.fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 117\u001b[0m val_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainers\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer)\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/model_training/trainer.py:327\u001b[0m, in \u001b[0;36mDLTrainer.fit\u001b[0;34m(self, data, pipeline, val_data)\u001b[0m\n\u001b[1;32m 322\u001b[0m X \u001b[38;5;241m=\u001b[39m X[:, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures_argsort]\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fold_i, (X_train, y_train, X_val, y_val) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidator_base(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidation_params)\u001b[38;5;241m.\u001b[39mget_split(X, y, X_val, y_val)\n\u001b[1;32m 326\u001b[0m ):\n\u001b[0;32m--> 327\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparams\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_one_fold(X_train, y_train, X_val, y_val)\n\u001b[1;32m 329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mappend(model)\n", + "\u001b[0;31mKeyError\u001b[0m: 'params'" + ] + } + ], + "source": [ + "strategy.fit(train_dataset)" ] }, { diff --git a/tsururu/torch_based/__init__.py b/tsururu/model_training/torch_based/__init__.py similarity index 100% rename from tsururu/torch_based/__init__.py rename to tsururu/model_training/torch_based/__init__.py diff --git a/tsururu/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py similarity index 100% rename from tsururu/torch_based/callbacks.py rename to tsururu/model_training/torch_based/callbacks.py diff --git a/tsururu/torch_based/data_provider.py b/tsururu/model_training/torch_based/data_provider.py similarity index 100% rename from tsururu/torch_based/data_provider.py rename to tsururu/model_training/torch_based/data_provider.py diff --git a/tsururu/torch_based/scheduler.py b/tsururu/model_training/torch_based/scheduler.py similarity index 100% rename from tsururu/torch_based/scheduler.py rename to tsururu/model_training/torch_based/scheduler.py diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 3f6022e..d88ef12 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -4,12 +4,13 @@ import numpy as np import torch +from torch.utils.data import Subset from tqdm.notebook import tqdm from ..dataset.pipeline import Pipeline from ..models.base import Estimator -from ..torch_based.callbacks import EarlyStopping, ModelCheckpoint -from ..torch_based.data_provider import nnDataset +from .torch_based.callbacks import EarlyStopping, ModelCheckpoint +from .torch_based.data_provider import nnDataset from .validator import Validator @@ -18,18 +19,29 @@ class MLTrainer: Args: model: the model estimator to be used for training. + model_params: the parameters for the model. validator: the validation strategy to be used for training. + validation_params: the parameters for the validation strategy. """ - def __init__(self, model: Estimator, validator: Validator): - self.model = deepcopy(model) - self.validator = deepcopy(validator) + def __init__( + self, + model: Estimator, + model_params: Dict, + validator: Optional[Validator] = None, + validation_params: Dict = {}, + ): + self.model = model + self.model_params = model_params + self.validation = validator + self.validation_params = validation_params self.models: List[Estimator] = [] self.scores: List[float] = [] + self.columns: List[str] = [] - def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "MLTrainer": + def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) -> "MLTrainer": """Fits the models using the input data and pipeline. Args: @@ -55,9 +67,10 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "MLTr X = X[:, self.features_argsort] for fold_i, X_train, y_train, X_val, y_val in enumerate( - self.validator.get_split(X, y, X_val, y_val) + self.validator(**self.validation_params).get_split(X, y, X_val, y_val) ): - model = self.model.fit_one_fold(X_train, y_train, X_val, y_val) + model = self.model(**self.model_params["params"]) + model.fit_one_fold(X_train, y_train, X_val, y_val) self.models.append(model) self.scores.append(model.score) @@ -66,17 +79,27 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "MLTr print(f"Mean score: {np.mean(self.scores).round(4)}") print(f"Std: {np.std(self.scores).round(4)}") - def predict(self, X: np.ndarray) -> np.ndarray: + def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: """Generates predictions using the trained model. Args: - X: features array. + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + pipeline: fitted data preprocessing pipeline. Returns: array of predicted values. + """ + X, _ = pipeline.generate(data) + + # Reorder columns + X = X[:, self.features_argsort] + models_preds = [model.predict(X) for model in self.models] y_pred = np.mean(models_preds, axis=0) + return y_pred @@ -84,7 +107,9 @@ class DLTrainer: def __init__( self, model: Estimator, - validator: Validator, + model_params: Dict, + validator: Optional[Validator] = None, + validation_params: Dict = {}, n_epochs: int = 10, batch_size: int = 32, drop_last: bool = False, @@ -94,17 +119,21 @@ def __init__( metric: Callable = torch.nn.MSELoss(), criterion: torch.nn.Module = torch.nn.MSELoss(), optimizer: Optional[torch.optim.Optimizer] = torch.optim.Adam, + optinizer_params: Dict = {}, scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, + scheduler_params: Dict = {}, scheduler_after_epoch: bool = True, verbose: int = 1, + early_stopping: bool = True, stop_by_metric: bool = False, patience: int = 5, pretrained_path: Optional[Union[str, Path]] = None, checkpoint_path: Union[str, Path] = "checkpoints/", - callbacks: List[Callable] = [], ): - self.model = deepcopy(model) - self.validator = deepcopy(validator) + self.model_base = model + self.model_params = model_params + self.validator_base = validator + self.validation_params = validation_params self.n_epochs = n_epochs self.batch_size = batch_size self.drop_last = drop_last @@ -113,44 +142,40 @@ def __init__( self.num_workers = num_workers self.metric = metric self.criterion = criterion - self.optimizer = deepcopy(optimizer) - self.scheduler = deepcopy(scheduler) + self.optimizer_base = optimizer + self.optinizer_params = optinizer_params + self.scheduler_base = scheduler + self.scheduler_params = scheduler_params self.scheduler_after_epoch = scheduler_after_epoch self.verbose = verbose + self.early_stopping = early_stopping # either by loss or metric self.stop_by_metric = stop_by_metric # how many epochs to wait for improvement before early stopping self.patience = patience self.pretrained_path = pretrained_path self.checkpoint_path = checkpoint_path - self.callbacks = callbacks - - self.best_model_wts = deepcopy(model.state_dict()) - self.best_score = float("inf") - self.early_stopping_counter = 0 + self.callbacks = [ + EarlyStopping(patience=patience, stop_by_metric=stop_by_metric), + ModelCheckpoint(checkpoint_path), + ] + self.models = None + self.optimizers = None + self.schedulers = None self.is_fitted = False - def _build_model(self, model): + def init_trainer_one_fold(self): + model = self.model_base(**self.model_params) if len(self.device_ids) > 1: model = torch.nn.DataParallel(model, device_ids=self.device_ids) else: model.to(self.device) - return model - - def load_state(self, path): - self.model.load_state_dict(torch.load(path)) - - def init_trainer(self): - self.model = self._build_model(self.model) - if self.optimizer is None: - self.optimizer = torch.optim.Adam(self.model.parameters()) - else: - self.optimizer = self.optimizer(self.model.parameters()) - - if self.scheduler is not None: - self.scheduler = self.scheduler(self.optimizer) + optimizer = self.optimizer_base(model.parameters(), **self.optinizer_params) + if self.scheduler_base is not None: + scheduler = self.scheduler_base(optimizer, **self.scheduler_params) + return model, optimizer, scheduler def train_model(self, train_loader, val_loader): if self.pretrained_path is not None: @@ -184,8 +209,8 @@ def train_model(self, train_loader, val_loader): for cb in self.callbacks: cb.on_batch_end(logs) - if self.scheduler_batch: - self.scheduler_batch.step() + if self.scheduler_after_epoch is False: + self.scheduler.step() epoch_loss = running_loss / len(train_loader.dataset) print(f"Epoch {epoch+1}/{self.n_epochs}, Loss: {epoch_loss:.4f}") @@ -193,8 +218,8 @@ def train_model(self, train_loader, val_loader): val_loss, val_metric = self.validate_model(val_loader) print(f"Validation, Loss: {val_loss:.4f}, Metric: {val_metric:.4f}") - if self.scheduler_epoch: - self.scheduler_epoch.step(val_loss) + if self.scheduler_after_epoch: + self.scheduler.step(val_loss) # Сохранение модели и проверка early stopping logs = { @@ -207,7 +232,7 @@ def train_model(self, train_loader, val_loader): for cb in self.callbacks: cb.on_epoch_end(epoch, logs) - if getattr(cb, "stop_training", False): + if getattr(cb, "stop_training", False): # TODO: Точно False? for cb in self.callbacks: cb.on_train_end() return @@ -223,6 +248,7 @@ def validate_model(self, val_loader): with torch.no_grad(): for inputs, targets in val_loader: + inputs, targets = inputs.to(self.device), targets.to(self.device) outputs = self.model(inputs) all_outputs.append(outputs) all_targets.append(targets) @@ -236,7 +262,7 @@ def validate_model(self, val_loader): return loss, metric - def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "DLTrainer": + def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) -> "DLTrainer": """Fits the models using the input data and pipeline. Args: @@ -252,40 +278,74 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict]) -> "DLTr """ train_dataset = nnDataset(data, pipeline) - - train_loader = torch.utils.data.DataLoader( - train_dataset, - batch_size=self.batch_size, - shuffle=True, - drop_last=self.drop_last, - num_workers=self.num_workers, - ) - + train_dataset_idx = np.arange(len(train_dataset)) if val_data: val_dataset = nnDataset(val_data, pipeline) + val_dataset_all_idx = np.arange(len(val_dataset)) + else: + val_dataset = None + val_dataset_all_idx = None + for fold_i, train_dataset_idx, _, val_dataset_idx, _ in enumerate( + self.validator_base(**self.validation_params).get_split( + val_dataset_all_idx, val_dataset_all_idx + ) + ): + train_subset = Subset(train_dataset, train_dataset_idx) + val_subset = Subset(val_dataset, val_dataset_idx) + train_loader = torch.utils.data.DataLoader( + train_subset, + batch_size=self.batch_size, + shuffle=True, + drop_last=self.drop_last, + num_workers=self.num_workers, + ) val_loader = torch.utils.data.DataLoader( - val_dataset, + val_subset, batch_size=self.batch_size, shuffle=False, drop_last=self.drop_last, num_workers=self.num_workers, ) - else: - val_loader = None - self.train_model(train_loader, val_loader) - self.is_fitted = True + model, optimizer, scheduler = self.init_trainer_one_fold() + model, optimizer, scheduler, score = self.fit_one_fold( + train_loader, val_loader, model, optimizer, scheduler + ) + + self.models.append(model) + self.optimizers.append(optimizer) + self.schedulers.append(scheduler) + self.scores.append(score) + + print(f"Fold {fold_i}. Score: {model.score}") + + print(f"Mean score: {np.mean(self.scores).round(4)}") + print(f"Std: {np.std(self.scores).round(4)}") - def predict(self, X: np.ndarray) -> np.ndarray: + def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: """Generates predictions using the trained model. Args: - X: features array. + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + pipeline: fitted data preprocessing pipeline. Returns: array of predicted values. + """ - models_preds = [model.predict(X) for model in self.models] + test_dataset = nnDataset(data, pipeline) + test_loader = torch.utils.data.DataLoader( + test_dataset, + batch_size=self.batch_size, + shuffle=True, + drop_last=self.drop_last, + num_workers=self.num_workers, + ) + + models_preds = [self.validate_model(test_loader, model) for model in self.models] y_pred = np.mean(models_preds, axis=0) + return y_pred diff --git a/tsururu/model_training/validator.py b/tsururu/model_training/validator.py index db02b61..7da8299 100644 --- a/tsururu/model_training/validator.py +++ b/tsururu/model_training/validator.py @@ -13,10 +13,10 @@ def __init__(self, validation_params: Optional[Dict[str, Union[str, int]]] = Non def get_split( self, X: np.ndarray, - y: np.ndarray, + y: Optional[np.ndarray], X_val: Optional[np.ndarray], y_val: Optional[np.ndarray], - ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: raise NotImplementedError() @@ -29,12 +29,16 @@ def __init__(self, n_splits: int = 3, shuffle: bool = True, random_state: int = def get_split( self, X: np.ndarray, - y: np.ndarray, - **kwargs, - ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + y: Optional[np.ndarray], + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: cv = KFold(n_splits=self.n_splits, shuffle=self.shuffle, random_state=self.random_state) for X_train_idx, X_val_idx in cv.split(X): - yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] + if y is None: + yield X[X_train_idx], None, X[X_val_idx], None + else: + yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] class TimeSeriesValidator(Validator): @@ -44,12 +48,16 @@ def __init__(self, n_splits: int = 3): def get_split( self, X: np.ndarray, - y: np.ndarray, - **kwargs, - ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + y: Optional[np.ndarray], + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: cv = TimeSeriesSplit(n_splits=self.n_splits) for X_train_idx, X_val_idx in cv.split(X): - yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] + if y is None: + yield X[X_train_idx], None, X[X_val_idx], None + else: + yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] class HoldOutValidator(Validator): @@ -59,10 +67,13 @@ def __init__(self, validation_data: Dict[str, np.ndarray]): def get_split( self, X: np.ndarray, - y: np.ndarray, + y: Optional[np.ndarray], X_val: Optional[np.ndarray], y_val: Optional[np.ndarray], - ) -> Iterator[Tuple[np.ndarray, np.ndarray]]: + ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: train_idx = np.arange(len(X)) - val_idx = np.arange(len(self.validation_data["X_val"])) - return X[train_idx], y[train_idx], X_val[val_idx], y_val[val_idx] + val_idx = np.arange(len(X_val)) + if y is None: + yield (X[train_idx], None, X_val[val_idx], None) + else: + yield (X[train_idx], y[train_idx], X_val[val_idx], y_val[val_idx]) diff --git a/tsururu/strategies/base.py b/tsururu/strategies/base.py index 46832c3..8d4fe9d 100644 --- a/tsururu/strategies/base.py +++ b/tsururu/strategies/base.py @@ -6,8 +6,7 @@ from ..dataset.dataset import TSDataset from ..dataset.pipeline import Pipeline from ..dataset.slice import IndexSlicer -from ..model_training.validator import Validator -from ..models.base import Estimator +from ..model_training.trainer import DLTrainer, MLTrainer from .utils import timing_decorator @@ -22,8 +21,7 @@ class Strategy: observations (y_{t-history}, ..., y_{t-1}). step: in how many points to take the next observation while making samples' matrix. - model: base model. - validator: validator for model training. + trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. Notes: @@ -52,8 +50,7 @@ def __init__( horizon: int, history: int, step: int, - model: Estimator, - validator: Validator, + trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, ): self.check_step_param(step) @@ -61,8 +58,7 @@ def __init__( self.horizon = horizon self.history = history self.step = step - self.model = model - self.validator = validator + self.trainer = trainer self.pipeline = pipeline self.trainers = [] @@ -183,15 +179,11 @@ def make_step(self, dataset: TSDataset): def fit( self, dataset: TSDataset, - val_dataset: Optional[TSDataset] = None, - trainer_params: dict = {}, ): """Fits the strategy to the given dataset. Args: dataset: The dataset to fit the strategy on. - val_dataset: The validation dataset. - trainer_params: additional parameters for the trainer. Returns: self. diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index e25dba5..1882aa1 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -1,11 +1,9 @@ -from typing import Optional +from typing import Optional, Union from ..dataset.dataset import TSDataset from ..dataset.pipeline import Pipeline from ..dataset.slice import IndexSlicer from ..model_training.trainer import DLTrainer, MLTrainer -from ..model_training.validator import Validator -from ..models import Estimator from .recursive import RecursiveStrategy from .utils import timing_decorator @@ -23,8 +21,7 @@ class DirectStrategy(RecursiveStrategy): (y_{t-history}, ..., y_{t-1}). step: in how many points to take the next observation while making samples' matrix. - model: base model. - validator: validator for model training. + trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. model_horizon: how many points to predict at a time, if model_horizon > 1, then it's an intermediate strategy between @@ -45,13 +42,12 @@ def __init__( horizon: int, history: int, step: int, - model: Estimator, - validator: Validator, + trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, model_horizon: int = 1, equal_train_size: bool = False, ): - super().__init__(horizon, history, step, model, pipeline, model_horizon) + super().__init__(horizon, history, step, trainer, pipeline, model_horizon) self.equal_train_size = equal_train_size self.strategy_name = "direct" @@ -59,15 +55,11 @@ def __init__( def fit( self, dataset: TSDataset, - val_dataset: Optional[TSDataset] = None, - trainer_params: dict = {}, ) -> "DirectStrategy": """Fits the direct strategy to the given dataset. Args: dataset: The dataset to fit the strategy on. - val_dataset: The validation dataset. - trainer_params: The parameters for the trainer. Returns: self. @@ -97,6 +89,8 @@ def fit( data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) data = self.pipeline.fit_transform(data, self.strategy_name) + val_dataset = self.trainer.validation_params.get("validation_data") + if val_dataset: val_features_idx = index_slicer.create_idx_data( val_dataset.data, @@ -147,13 +141,8 @@ def fit( val_data["target_idx"] = val_target_idx - if self.model.trainer_type == "MLTrainer": - current_trainer = MLTrainer(self.model, self.validator, **trainer_params) - current_trainer.fit(data, self.pipeline, val_data) - - elif self.model.trainer_type == "DLTrainer": - current_trainer = DLTrainer(self.model, self.validator, **trainer_params) - current_trainer.fit(data, self.pipeline, val_data) + current_trainer = self.trainer.copy() + current_trainer.fit(data, self.pipeline, val_data) self.trainers.append(current_trainer) @@ -183,6 +172,8 @@ def fit( ) data = self.pipeline.fit_transform(data, self.strategy_name) + val_dataset = self.trainer.validation_params.get("validation_data") + if val_dataset: val_features_idx = index_slicer.create_idx_data( val_dataset.data, @@ -210,13 +201,8 @@ def fit( else: val_data = None - if self.model.trainer_type == "MLTrainer": - current_trainer = MLTrainer(self.model, self.validator, **trainer_params) - current_trainer.fit(data, self.pipeline, val_data) - - elif self.model.trainer_type == "DLTrainer": - current_trainer = DLTrainer(self.model, self.validator, **trainer_params) - current_trainer.fit(data, self.pipeline, val_data) + current_trainer = self.trainer.copy() + current_trainer.fit(data, self.pipeline, val_data) self.trainers.append(current_trainer) diff --git a/tsururu/strategies/flat_wide_mimo.py b/tsururu/strategies/flat_wide_mimo.py index ee602e3..e3d168c 100644 --- a/tsururu/strategies/flat_wide_mimo.py +++ b/tsururu/strategies/flat_wide_mimo.py @@ -1,6 +1,7 @@ +from typing import Union + from ..dataset.pipeline import Pipeline -from ..model_training.validator import Validator -from ..models import Estimator +from ..model_training.trainer import DLTrainer, MLTrainer from .mimo import MIMOStrategy @@ -15,8 +16,7 @@ class FlatWideMIMOStrategy(MIMOStrategy): (y_{t-history}, ..., y_{t-1}). step: in how many points to take the next observation while making samples' matrix. - model: base model. - pipeline: pipeline for feature and target generation. + trainer: trainer with model params and validation params. Notes: 1. Fit: mixture of DirectStrategy and MIMOStrategy, fit one @@ -30,9 +30,8 @@ def __init__( horizon: int, history: int, step: int, - model: Estimator, - validator: Validator, + trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, ): - super().__init__(horizon, history, step, model, validator, pipeline) + super().__init__(horizon, history, step, trainer, pipeline) self.strategy_name = "FlatWideMIMOStrategy" diff --git a/tsururu/strategies/mimo.py b/tsururu/strategies/mimo.py index f4c1fe6..43f54fe 100644 --- a/tsururu/strategies/mimo.py +++ b/tsururu/strategies/mimo.py @@ -1,6 +1,7 @@ +from typing import Union + from ..dataset.pipeline import Pipeline -from ..model_training.validator import Validator -from ..models import Estimator +from ..model_training.trainer import DLTrainer, MLTrainer from .recursive import RecursiveStrategy @@ -15,8 +16,7 @@ class MIMOStrategy(RecursiveStrategy): (y_{t-history}, ..., y_{t-1}). step: in how many points to take the next observation while making samples' matrix. - model: base model. - validator: validator for model training. + trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. Notes: @@ -35,9 +35,8 @@ def __init__( horizon: int, history: int, step: int, - model: Estimator, - validator: Validator, + trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, ): - super().__init__(horizon, history, step, model, validator, pipeline, model_horizon=horizon) + super().__init__(horizon, history, step, trainer, pipeline, model_horizon=horizon) self.strategy_name = "MIMOStrategy" diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 2737ff1..0ca4d0d 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -1,11 +1,9 @@ -from typing import Optional +from typing import Optional, Union import pandas as pd from ..dataset import IndexSlicer, Pipeline, TSDataset -from ..model_training.trainer import MLTrainer, DLTrainer -from ..model_training.validator import Validator -from ..models.base import Estimator +from ..model_training.trainer import DLTrainer, MLTrainer from .base import Strategy from .utils import timing_decorator @@ -23,8 +21,7 @@ class RecursiveStrategy(Strategy): observations (y_{t-history}, ..., y_{t-1}). step: in how many points to take the next observation while making samples' matrix. - model: base model. - validator: validator for model training. + trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. model_horizon: how many points to predict at a time, if model_horizon > 1, then it's an intermediate strategy @@ -46,13 +43,12 @@ def __init__( horizon: int, history: int, step: int, - model: Estimator, - validator: Validator, + trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, model_horizon: int = 1, reduced: bool = False, ): - super().__init__(horizon, history, step, model, validator, pipeline) + super().__init__(horizon, history, step, trainer, pipeline) self.model_horizon = model_horizon self.reduced = reduced self.strategy_name = "recursive" @@ -61,15 +57,11 @@ def __init__( def fit( self, dataset: TSDataset, - val_dataset: Optional[TSDataset] = None, - trainer_params: dict = {}, ) -> "RecursiveStrategy": """Fits the recursive strategy to the given dataset. Args: dataset: The dataset to fit the strategy on. - val_dataset: The validation dataset. - trainer_params: Additional parameters for the trainer. Returns: self. @@ -96,6 +88,8 @@ def fit( data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) data = self.pipeline.fit_transform(data, self.strategy_name) + val_dataset = self.trainer.validation_params.get("validation_data") + if val_dataset: val_features_idx = index_slicer.create_idx_data( val_dataset.data, @@ -122,15 +116,9 @@ def fit( else: val_data = None - if self.model.trainer_type == "MLTrainer": - trainer = MLTrainer(self.model, self.validator, **trainer_params) - trainer.fit(data, self.pipeline, val_data) - - elif self.model.trainer_type == "DLTrainer": - trainer = DLTrainer(self.model, self.validator, **trainer_params) - trainer.fit(data, self.pipeline, val_data) + self.trainer.fit(data, self.pipeline, val_data) - self.trainers.append(trainer) + self.trainers.append(self.trainer) return self def make_step(self, step: int, dataset: TSDataset) -> TSDataset: From 4dc2709c2c475e6a7f752f90a4bcdbfb2159f412 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 7 Jun 2024 14:55:53 +0300 Subject: [PATCH 04/50] loop fix --- Tutorial_2_Neural_Networks.ipynb | 22 ++++---- .../model_training/torch_based/callbacks.py | 2 +- tsururu/model_training/trainer.py | 53 ++++++++++--------- tsururu/model_training/validator.py | 24 ++++----- tsururu/models/torch_based/dlinear.py | 14 ++--- tsururu/strategies/direct.py | 8 +++ tsururu/strategies/recursive.py | 4 ++ 7 files changed, 72 insertions(+), 55 deletions(-) diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index df4bf54..3e4af37 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -258,7 +258,7 @@ " model_params, \n", " validation, \n", " validation_params, \n", - " trainer_params\n", + " **trainer_params\n", ")" ] }, @@ -302,17 +302,15 @@ "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'params'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mstrategy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/utils.py:8\u001b[0m, in \u001b[0;36mtiming_decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 7\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 10\u001b[0m execution_time \u001b[38;5;241m=\u001b[39m end_time \u001b[38;5;241m-\u001b[39m start_time\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/recursive.py:119\u001b[0m, in \u001b[0;36mRecursiveStrategy.fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 117\u001b[0m val_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpipeline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainers\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer)\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/model_training/trainer.py:327\u001b[0m, in \u001b[0;36mDLTrainer.fit\u001b[0;34m(self, data, pipeline, val_data)\u001b[0m\n\u001b[1;32m 322\u001b[0m X \u001b[38;5;241m=\u001b[39m X[:, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeatures_argsort]\n\u001b[1;32m 324\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fold_i, (X_train, y_train, X_val, y_val) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\n\u001b[1;32m 325\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidator_base(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidation_params)\u001b[38;5;241m.\u001b[39mget_split(X, y, X_val, y_val)\n\u001b[1;32m 326\u001b[0m ):\n\u001b[0;32m--> 327\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparams\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 328\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_one_fold(X_train, y_train, X_val, y_val)\n\u001b[1;32m 329\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mappend(model)\n", - "\u001b[0;31mKeyError\u001b[0m: 'params'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10, Loss: 275.5199\n", + "Validation, Loss: 29.9555, Metric: 29.9555\n", + "Validation, Loss: 29.9555, Metric: 29.9555\n", + "Epoch 1/10, Loss: 32.3650\n", + "Validation, Loss: 13.7257, Metric: 13.7257\n", + "Validation, Loss: 13.7257, Metric: 13.7257\n" ] } ], diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index 55e3736..b8a88d2 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -48,7 +48,7 @@ def on_epoch_end(self, epoch, logs=None): self.early_stopping_counter = 0 -class ModelCheckpoint: +class ModelCheckpoint(Callback): def __init__(self, filepath, monitor="val_loss", verbose=1, save_best_only=True, k=5): self.filepath = filepath self.monitor = monitor diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index d88ef12..a374dfb 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -37,6 +37,9 @@ def __init__( self.validation = validator self.validation_params = validation_params + # Provide by strategy if needed + self.history = None + self.horizon = None self.models: List[Estimator] = [] self.scores: List[float] = [] self.columns: List[str] = [] @@ -160,12 +163,17 @@ def __init__( EarlyStopping(patience=patience, stop_by_metric=stop_by_metric), ModelCheckpoint(checkpoint_path), ] + # Provide by strategy if needed + self.history = None + self.horizon = None self.models = None self.optimizers = None self.schedulers = None - self.is_fitted = False def init_trainer_one_fold(self): + self.model_params["seq_len"] = self.history + self.model_params["pred_len"] = self.horizon + model = self.model_base(**self.model_params) if len(self.device_ids) > 1: model = torch.nn.DataParallel(model, device_ids=self.device_ids) @@ -175,20 +183,17 @@ def init_trainer_one_fold(self): optimizer = self.optimizer_base(model.parameters(), **self.optinizer_params) if self.scheduler_base is not None: scheduler = self.scheduler_base(optimizer, **self.scheduler_params) - return model, optimizer, scheduler - - def train_model(self, train_loader, val_loader): - if self.pretrained_path is not None: - self.load_state(self.pretrained_path) else: - self.init_trainer() + scheduler = None + return model, optimizer, scheduler + def train_model(self, train_loader, val_loader, model, optimizer, scheduler): # for epoch in tqdm(range(self.n_epochs), desc="Epochs"): for epoch in range(self.n_epochs): for callback in self.callbacks: callback.on_epoch_begin(epoch) - self.model.train() + model.train() running_loss = 0.0 # for inputs, targets in tqdm(train_loader, desc="Training", leave=False): @@ -198,28 +203,28 @@ def train_model(self, train_loader, val_loader): inputs, targets = inputs.to(self.device), targets.to(self.device) - self.optimizer.zero_grad() - outputs = self.model(inputs) + optimizer.zero_grad() + outputs = model(inputs) loss = self.criterion(outputs, targets) loss.backward() - self.optimizer.step() + optimizer.step() running_loss += loss.item() * inputs.size(0) logs = {"loss": loss.item()} for cb in self.callbacks: cb.on_batch_end(logs) - if self.scheduler_after_epoch is False: - self.scheduler.step() + if self.scheduler_after_epoch is False and scheduler is not None: + scheduler.step() epoch_loss = running_loss / len(train_loader.dataset) print(f"Epoch {epoch+1}/{self.n_epochs}, Loss: {epoch_loss:.4f}") - val_loss, val_metric = self.validate_model(val_loader) + val_loss, val_metric = self.validate_model(val_loader, model) print(f"Validation, Loss: {val_loss:.4f}, Metric: {val_metric:.4f}") - if self.scheduler_after_epoch: - self.scheduler.step(val_loss) + if self.scheduler_after_epoch and scheduler is not None: + scheduler.step(val_loss) # Сохранение модели и проверка early stopping logs = { @@ -227,7 +232,7 @@ def train_model(self, train_loader, val_loader): "loss": epoch_loss, "val_loss": val_loss, "val_metric": val_metric, - "model_state_dict": self.model.state_dict(), + "model_state_dict": model.state_dict(), } for cb in self.callbacks: @@ -240,8 +245,8 @@ def train_model(self, train_loader, val_loader): for cb in self.callbacks: cb.on_train_end() - def validate_model(self, val_loader): - self.model.eval() + def validate_model(self, val_loader, model): + model.eval() all_outputs = [] all_targets = [] @@ -249,14 +254,14 @@ def validate_model(self, val_loader): with torch.no_grad(): for inputs, targets in val_loader: inputs, targets = inputs.to(self.device), targets.to(self.device) - outputs = self.model(inputs) + outputs = model(inputs) all_outputs.append(outputs) all_targets.append(targets) all_outputs = torch.cat(all_outputs) all_targets = torch.cat(all_targets) loss = self.criterion(all_outputs, all_targets).item() - metric = self.metric_fn(all_outputs, all_targets).item() if self.metric_fn else 0.0 + metric = self.metric(all_outputs, all_targets).item() if self.metric else 0.0 print(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") @@ -286,9 +291,9 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - val_dataset = None val_dataset_all_idx = None - for fold_i, train_dataset_idx, _, val_dataset_idx, _ in enumerate( + for fold_i, (train_dataset_idx, _, val_dataset_idx, _) in enumerate( self.validator_base(**self.validation_params).get_split( - val_dataset_all_idx, val_dataset_all_idx + X=train_dataset_idx, X_val=val_dataset_all_idx ) ): train_subset = Subset(train_dataset, train_dataset_idx) @@ -309,7 +314,7 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - ) model, optimizer, scheduler = self.init_trainer_one_fold() - model, optimizer, scheduler, score = self.fit_one_fold( + model, optimizer, scheduler, score = self.train_model( train_loader, val_loader, model, optimizer, scheduler ) diff --git a/tsururu/model_training/validator.py b/tsururu/model_training/validator.py index 7da8299..210f3f4 100644 --- a/tsururu/model_training/validator.py +++ b/tsururu/model_training/validator.py @@ -13,9 +13,9 @@ def __init__(self, validation_params: Optional[Dict[str, Union[str, int]]] = Non def get_split( self, X: np.ndarray, - y: Optional[np.ndarray], - X_val: Optional[np.ndarray], - y_val: Optional[np.ndarray], + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: raise NotImplementedError() @@ -29,9 +29,9 @@ def __init__(self, n_splits: int = 3, shuffle: bool = True, random_state: int = def get_split( self, X: np.ndarray, - y: Optional[np.ndarray], - X_val: Optional[np.ndarray], - y_val: Optional[np.ndarray], + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: cv = KFold(n_splits=self.n_splits, shuffle=self.shuffle, random_state=self.random_state) for X_train_idx, X_val_idx in cv.split(X): @@ -48,9 +48,9 @@ def __init__(self, n_splits: int = 3): def get_split( self, X: np.ndarray, - y: Optional[np.ndarray], - X_val: Optional[np.ndarray], - y_val: Optional[np.ndarray], + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: cv = TimeSeriesSplit(n_splits=self.n_splits) for X_train_idx, X_val_idx in cv.split(X): @@ -67,9 +67,9 @@ def __init__(self, validation_data: Dict[str, np.ndarray]): def get_split( self, X: np.ndarray, - y: Optional[np.ndarray], - X_val: Optional[np.ndarray], - y_val: Optional[np.ndarray], + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: train_idx = np.arange(len(X)) val_idx = np.arange(len(X_val)) diff --git a/tsururu/models/torch_based/dlinear.py b/tsururu/models/torch_based/dlinear.py index aee25cb..a186520 100644 --- a/tsururu/models/torch_based/dlinear.py +++ b/tsururu/models/torch_based/dlinear.py @@ -17,14 +17,16 @@ class DLinear_NN(nn.Module): """ - def __init__(self, model_params): + def __init__(self, seq_len, pred_len, moving_avg=7, individual=False, enc_in=1): super(DLinear_NN, self).__init__() # Params from model_params - self.seq_len = model_params["seq_len"] - self.pred_len = model_params["pred_len"] - self.decompsition = self.series_decomp(model_params.get("moving_avg", 7)) - self.individual = model_params.get("individual", False) - self.channels = model_params.get("enc_in", 1) + self.seq_len = seq_len + self.pred_len = pred_len + + # Decomposition + self.decompsition = self.series_decomp(moving_avg) + self.individual = individual + self.channels = enc_in if self.individual: self.Linear_Seasonal = nn.ModuleList() diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index 1882aa1..3b0f45b 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -141,6 +141,10 @@ def fit( val_data["target_idx"] = val_target_idx + if isinstance(self.trainer, DLTrainer): + self.trainer.horizon = self.model_horizon + self.trainer.history = self.history + current_trainer = self.trainer.copy() current_trainer.fit(data, self.pipeline, val_data) @@ -201,6 +205,10 @@ def fit( else: val_data = None + if isinstance(self.trainer, DLTrainer): + self.trainer.horizon = self.model_horizon + self.trainer.history = self.history + current_trainer = self.trainer.copy() current_trainer.fit(data, self.pipeline, val_data) diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 0ca4d0d..f5ec876 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -116,6 +116,10 @@ def fit( else: val_data = None + if isinstance(self.trainer, DLTrainer): + self.trainer.horizon = self.model_horizon + self.trainer.history = self.history + self.trainer.fit(data, self.pipeline, val_data) self.trainers.append(self.trainer) From 3bc02a07954168c65de8aa6fcfd00cb5c9b5856e Mon Sep 17 00:00:00 2001 From: unknown Date: Sun, 9 Jun 2024 18:26:53 +0300 Subject: [PATCH 05/50] fix directory --- tsururu/model_training/torch_based/callbacks.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index b8a88d2..d1af967 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -57,6 +57,10 @@ def __init__(self, filepath, monitor="val_loss", verbose=1, save_best_only=True, self.k = k self.best_snapshots = [] self.worst_best_score = float("inf") + + directory = os.path.dirname(filepath) + if not os.path.exists(directory): + os.makedirs(directory) def on_epoch_end(self, epoch, logs=None): current_score = logs.get(self.monitor) From 497f9645a749f85a902cc315c3242c031ae12e57 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Sun, 9 Jun 2024 20:46:01 +0300 Subject: [PATCH 06/50] fix encoding for id column --- tsururu/dataset/dataset.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index f865319..00482c3 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -138,7 +138,7 @@ def _pad_segment( padded segment of data. """ - result = np.full((horizon, segment.shape[1]), np.nan) + result = np.full((horizon, segment.shape[1]), np.nan, dtype=object) last_date = segment[-1, date_col_id] new_dates = pd.date_range(last_date + time_delta, periods=horizon, freq=time_delta) @@ -243,7 +243,6 @@ def make_padded_test( else: result = pd.DataFrame(result, columns=columns) result[self.date_column] = pd.to_datetime(result[self.date_column]) - result[self.id_column] = result[self.id_column].astype("int") other = [col for col in columns if col not in [self.id_column, self.date_column]] result[other] = result[other].astype("float") From a691c3e399229bb93d13e096742dce83b615cb5f Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Sun, 9 Jun 2024 23:14:45 +0300 Subject: [PATCH 07/50] train fix --- .gitignore | 3 +- Tutorial_2_Neural_Networks.ipynb | 64 ++++++++++++++-- .../model_training/torch_based/callbacks.py | 74 +++++++++++-------- tsururu/model_training/torch_based/metrics.py | 12 +++ tsururu/model_training/trainer.py | 41 ++++++---- 5 files changed, 142 insertions(+), 52 deletions(-) create mode 100644 tsururu/model_training/torch_based/metrics.py diff --git a/.gitignore b/.gitignore index aeee479..5e95941 100644 --- a/.gitignore +++ b/.gitignore @@ -152,4 +152,5 @@ poetry.lock *check*.ipynb *temp* *demand* -*test* \ No newline at end of file +*test* +*checkpoints* \ No newline at end of file diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index 3e4af37..c057cfa 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -305,13 +305,65 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 275.5199\n", - "Validation, Loss: 29.9555, Metric: 29.9555\n", - "Validation, Loss: 29.9555, Metric: 29.9555\n", - "Epoch 1/10, Loss: 32.3650\n", - "Validation, Loss: 13.7257, Metric: 13.7257\n", - "Validation, Loss: 13.7257, Metric: 13.7257\n" + "Epoch 1/10, Loss: 277.0368\n", + "Validation, Loss: 35.0733, Metric: -35.0733\n", + "Validation, Loss: 35.0733, Metric: -35.0733\n", + "Model saved to checkpoints/fold_0/model_0.pth\n", + "Epoch 2/10, Loss: 34.0641\n", + "Validation, Loss: 15.1628, Metric: -15.1628\n", + "Validation, Loss: 15.1628, Metric: -15.1628\n", + "Model saved to checkpoints/fold_0/model_1.pth\n", + "Epoch 3/10, Loss: 13.4610\n", + "Validation, Loss: 10.8341, Metric: -10.8341\n", + "Validation, Loss: 10.8341, Metric: -10.8341\n", + "Model saved to checkpoints/fold_0/model_2.pth\n", + "Epoch 4/10, Loss: 10.7970\n", + "Validation, Loss: 9.4608, Metric: -9.4608\n", + "Validation, Loss: 9.4608, Metric: -9.4608\n", + "Model saved to checkpoints/fold_0/model_3.pth\n", + "Epoch 5/10, Loss: 10.4385\n", + "Validation, Loss: 9.3113, Metric: -9.3113\n", + "Validation, Loss: 9.3113, Metric: -9.3113\n", + "Model saved to checkpoints/fold_0/model_4.pth\n", + "Epoch 6/10, Loss: 10.3405\n", + "Validation, Loss: 9.2797, Metric: -9.2797\n", + "Validation, Loss: 9.2797, Metric: -9.2797\n", + "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", + "Model saved to checkpoints/fold_0/model_5.pth\n", + "Epoch 7/10, Loss: 10.2645\n", + "Validation, Loss: 9.1931, Metric: -9.1931\n", + "Validation, Loss: 9.1931, Metric: -9.1931\n", + "Removing worst model snapshot: checkpoints/fold_0/model_5.pth\n", + "Model saved to checkpoints/fold_0/model_6.pth\n", + "Epoch 8/10, Loss: 10.1901\n", + "Validation, Loss: 9.1556, Metric: -9.1556\n", + "Validation, Loss: 9.1556, Metric: -9.1556\n", + "Removing worst model snapshot: checkpoints/fold_0/model_6.pth\n", + "Model saved to checkpoints/fold_0/model_7.pth\n", + "Epoch 9/10, Loss: 10.1075\n", + "Validation, Loss: 9.0769, Metric: -9.0769\n", + "Validation, Loss: 9.0769, Metric: -9.0769\n", + "Removing worst model snapshot: checkpoints/fold_0/model_7.pth\n", + "Model saved to checkpoints/fold_0/model_8.pth\n", + "Epoch 10/10, Loss: 10.0234\n", + "Validation, Loss: 8.9797, Metric: -8.9797\n", + "Validation, Loss: 8.9797, Metric: -8.9797\n", + "Removing worst model snapshot: checkpoints/fold_0/model_8.pth\n", + "Model saved to checkpoints/fold_0/model_9.pth\n", + "Fold 0. Score: -8.97965145111084\n", + "Mean score: -8.9797\n", + "Std: 0.0\n" ] + }, + { + "data": { + "text/plain": [ + "(8.606657028198242, )" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index b8a88d2..d4d775b 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -1,5 +1,6 @@ import heapq import os +from pathlib import Path import torch @@ -24,59 +25,59 @@ def on_train_end(self, logs=None): pass -class EarlyStopping(Callback): - def __init__(self, patience=5, verbose=1, stop_by_metric=True): - self.patience = patience +class ES_Checkpoints_Manager(Callback): + def __init__(self, monitor="val_loss", verbose=1, save_best_only=True, k=5, patience=5, mode="min"): + self.monitor = monitor self.verbose = verbose + self.save_best_only = save_best_only + self.k = k + self.patience = patience + self.mode = mode + self.best_snapshots = [] self.best_score = None + self.worst_best_score = float('inf') if mode == "min" else float('-inf') self.early_stopping_counter = 0 self.stop_training = False - self.stop_by_metric = stop_by_metric - def on_epoch_end(self, epoch, logs=None): - current_score = logs.get("val_metric") if self.stop_by_metric else logs.get("val_loss") - if self.best_score is None: - self.best_score = current_score - elif current_score > self.best_score: - self.early_stopping_counter += 1 - if self.early_stopping_counter >= self.patience: - if self.verbose: - print("Early stopping triggered") - self.stop_training = True + def _is_improvement(self, current, best): + if self.mode == "min": + return current < best else: - self.best_score = current_score - self.early_stopping_counter = 0 + return current > best + def _should_save_checkpoint(self, current_score): + if len(self.best_snapshots) < self.k: + return True + if self.mode == "min": + return current_score < self.worst_best_score + else: + return current_score > self.worst_best_score -class ModelCheckpoint(Callback): - def __init__(self, filepath, monitor="val_loss", verbose=1, save_best_only=True, k=5): - self.filepath = filepath - self.monitor = monitor - self.verbose = verbose - self.save_best_only = save_best_only - self.k = k - self.best_snapshots = [] - self.worst_best_score = float("inf") + def _update_worst_best_score(self): + if self.mode == "min": + self.worst_best_score = max(self.best_snapshots)[0] + else: + self.worst_best_score = min(self.best_snapshots)[0] def on_epoch_end(self, epoch, logs=None): current_score = logs.get(self.monitor) if current_score is None: return - model_path = f"{self.filepath}_epoch_{epoch}.pth" + model_path = logs.get("filepath") / f"model_{epoch}.pth" + model_path.parent.mkdir(parents=True, exist_ok=True) if self.save_best_only: - if len(self.best_snapshots) < self.k or current_score < self.worst_best_score: + if self._should_save_checkpoint(current_score): if len(self.best_snapshots) == self.k: worst_snapshot = heapq.heappop(self.best_snapshots) + os.remove(worst_snapshot[1]) if self.verbose: print(f"Removing worst model snapshot: {worst_snapshot[1]}") - os.remove(worst_snapshot[1]) heapq.heappush(self.best_snapshots, (current_score, model_path)) - self.worst_best_score = max(self.best_snapshots)[0] + self._update_worst_best_score() torch.save(logs["model_state_dict"], model_path) - if self.verbose: print(f"Model saved to {model_path}") else: @@ -84,5 +85,16 @@ def on_epoch_end(self, epoch, logs=None): if self.verbose: print(f"Model saved to {model_path}") + # Early stopping logic + if self.best_score is None or self._is_improvement(current_score, self.best_score): + self.best_score = current_score + self.early_stopping_counter = 0 + else: + self.early_stopping_counter += 1 + if self.early_stopping_counter >= self.patience: + if self.verbose: + print("Early stopping triggered") + self.stop_training = True + def get_best_snapshots(self): - return [snapshot[1] for snapshot in sorted(self.best_snapshots)] + return [snapshot[1] for snapshot in sorted(self.best_snapshots, key=lambda x: x[0] if self.mode == "min" else -x[0])] \ No newline at end of file diff --git a/tsururu/model_training/torch_based/metrics.py b/tsururu/model_training/torch_based/metrics.py new file mode 100644 index 0000000..359e571 --- /dev/null +++ b/tsururu/model_training/torch_based/metrics.py @@ -0,0 +1,12 @@ +from torch import nn + + +class NegativeMSEMetric(nn.Module): + def __init__(self): + super(NegativeMSEMetric, self).__init__() + self.mse_loss = nn.MSELoss() + + def forward(self, input, target): + loss = self.mse_loss(input, target) + negative_loss = -1 * loss + return negative_loss diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index a374dfb..6917d5d 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -1,4 +1,3 @@ -from copy import deepcopy from pathlib import Path from typing import Callable, Dict, List, Optional, Union @@ -9,8 +8,9 @@ from ..dataset.pipeline import Pipeline from ..models.base import Estimator -from .torch_based.callbacks import EarlyStopping, ModelCheckpoint +from .torch_based.callbacks import ES_Checkpoints_Manager from .torch_based.data_provider import nnDataset +from .torch_based.metrics import NegativeMSEMetric from .validator import Validator @@ -119,7 +119,7 @@ def __init__( device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"), device_ids: List[int] = [0], num_workers: int = 4, - metric: Callable = torch.nn.MSELoss(), + metric: Callable = NegativeMSEMetric(), criterion: torch.nn.Module = torch.nn.MSELoss(), optimizer: Optional[torch.optim.Optimizer] = torch.optim.Adam, optinizer_params: Dict = {}, @@ -157,18 +157,27 @@ def __init__( # how many epochs to wait for improvement before early stopping self.patience = patience self.pretrained_path = pretrained_path + if isinstance(checkpoint_path, str): + checkpoint_path = Path(checkpoint_path) self.checkpoint_path = checkpoint_path self.callbacks = [ - EarlyStopping(patience=patience, stop_by_metric=stop_by_metric), - ModelCheckpoint(checkpoint_path), + ES_Checkpoints_Manager( + monitor="val_metric" if stop_by_metric else "val_loss", + verbose=verbose, + save_best_only=True, + k=5, + patience=patience, + mode="max" if stop_by_metric else "min" + ) ] # Provide by strategy if needed self.history = None self.horizon = None - self.models = None - self.optimizers = None - self.schedulers = None + self.models = [] + self.optimizers = [] + self.schedulers = [] + self.scores = [] def init_trainer_one_fold(self): self.model_params["seq_len"] = self.history @@ -188,7 +197,6 @@ def init_trainer_one_fold(self): return model, optimizer, scheduler def train_model(self, train_loader, val_loader, model, optimizer, scheduler): - # for epoch in tqdm(range(self.n_epochs), desc="Epochs"): for epoch in range(self.n_epochs): for callback in self.callbacks: callback.on_epoch_begin(epoch) @@ -196,7 +204,6 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): model.train() running_loss = 0.0 - # for inputs, targets in tqdm(train_loader, desc="Training", leave=False): for inputs, targets in train_loader: for callback in self.callbacks: callback.on_batch_begin() @@ -214,7 +221,7 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): for cb in self.callbacks: cb.on_batch_end(logs) - if self.scheduler_after_epoch is False and scheduler is not None: + if not self.scheduler_after_epoch and scheduler is not None: scheduler.step() epoch_loss = running_loss / len(train_loader.dataset) @@ -229,6 +236,7 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): # Сохранение модели и проверка early stopping logs = { "epoch": epoch, + "filepath": self.checkpoint_path, "loss": epoch_loss, "val_loss": val_loss, "val_metric": val_metric, @@ -237,14 +245,16 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): for cb in self.callbacks: cb.on_epoch_end(epoch, logs) - if getattr(cb, "stop_training", False): # TODO: Точно False? + if getattr(cb, "stop_training", False): for cb in self.callbacks: cb.on_train_end() - return + return model, optimizer, scheduler, val_metric for cb in self.callbacks: cb.on_train_end() + return model, optimizer, scheduler, val_metric + def validate_model(self, val_loader, model): model.eval() @@ -296,6 +306,8 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - X=train_dataset_idx, X_val=val_dataset_all_idx ) ): + checkpoint_path = self.checkpoint_path + self.checkpoint_path /= f"fold_{fold_i}" train_subset = Subset(train_dataset, train_dataset_idx) val_subset = Subset(val_dataset, val_dataset_idx) train_loader = torch.utils.data.DataLoader( @@ -323,7 +335,8 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - self.schedulers.append(scheduler) self.scores.append(score) - print(f"Fold {fold_i}. Score: {model.score}") + print(f"Fold {fold_i}. Score: {score}") + self.checkpoint_path = checkpoint_path print(f"Mean score: {np.mean(self.scores).round(4)}") print(f"Std: {np.std(self.scores).round(4)}") From a46feaafa2e82d7652ba721652c11dcad2bc2665 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Sun, 9 Jun 2024 23:36:25 +0300 Subject: [PATCH 08/50] train fix --- Tutorial_2_Neural_Networks.ipynb | 97 ++++++++++--------- .../model_training/torch_based/callbacks.py | 26 +++-- 2 files changed, 67 insertions(+), 56 deletions(-) diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index c057cfa..d32ff90 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -251,6 +251,7 @@ "trainer_params = {\n", " \"device\": \"cpu\",\n", " \"num_workers\": 0,\n", + " \"stop_by_metric\": True,\n", "}\n", "\n", "trainer = DLTrainer(\n", @@ -271,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -298,70 +299,70 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 277.0368\n", - "Validation, Loss: 35.0733, Metric: -35.0733\n", - "Validation, Loss: 35.0733, Metric: -35.0733\n", + "Epoch 1/10, Loss: 272.3933\n", + "Validation, Loss: 29.0868, Metric: -29.0868\n", + "Validation, Loss: 29.0868, Metric: -29.0868\n", "Model saved to checkpoints/fold_0/model_0.pth\n", - "Epoch 2/10, Loss: 34.0641\n", - "Validation, Loss: 15.1628, Metric: -15.1628\n", - "Validation, Loss: 15.1628, Metric: -15.1628\n", + "Epoch 2/10, Loss: 32.3303\n", + "Validation, Loss: 13.5448, Metric: -13.5448\n", + "Validation, Loss: 13.5448, Metric: -13.5448\n", "Model saved to checkpoints/fold_0/model_1.pth\n", - "Epoch 3/10, Loss: 13.4610\n", - "Validation, Loss: 10.8341, Metric: -10.8341\n", - "Validation, Loss: 10.8341, Metric: -10.8341\n", + "Epoch 3/10, Loss: 12.8174\n", + "Validation, Loss: 9.9666, Metric: -9.9666\n", + "Validation, Loss: 9.9666, Metric: -9.9666\n", "Model saved to checkpoints/fold_0/model_2.pth\n", - "Epoch 4/10, Loss: 10.7970\n", - "Validation, Loss: 9.4608, Metric: -9.4608\n", - "Validation, Loss: 9.4608, Metric: -9.4608\n", + "Epoch 4/10, Loss: 10.3944\n", + "Validation, Loss: 9.1675, Metric: -9.1675\n", + "Validation, Loss: 9.1675, Metric: -9.1675\n", "Model saved to checkpoints/fold_0/model_3.pth\n", - "Epoch 5/10, Loss: 10.4385\n", - "Validation, Loss: 9.3113, Metric: -9.3113\n", - "Validation, Loss: 9.3113, Metric: -9.3113\n", + "Epoch 5/10, Loss: 10.1024\n", + "Validation, Loss: 9.0681, Metric: -9.0681\n", + "Validation, Loss: 9.0681, Metric: -9.0681\n", "Model saved to checkpoints/fold_0/model_4.pth\n", - "Epoch 6/10, Loss: 10.3405\n", - "Validation, Loss: 9.2797, Metric: -9.2797\n", - "Validation, Loss: 9.2797, Metric: -9.2797\n", - "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", + "Epoch 6/10, Loss: 10.0179\n", + "Validation, Loss: 8.9633, Metric: -8.9633\n", + "Validation, Loss: 8.9633, Metric: -8.9633\n", + "Removing worst model snapshot: checkpoints/fold_0/model_0.pth\n", "Model saved to checkpoints/fold_0/model_5.pth\n", - "Epoch 7/10, Loss: 10.2645\n", - "Validation, Loss: 9.1931, Metric: -9.1931\n", - "Validation, Loss: 9.1931, Metric: -9.1931\n", - "Removing worst model snapshot: checkpoints/fold_0/model_5.pth\n", + "Epoch 7/10, Loss: 9.9462\n", + "Validation, Loss: 8.9418, Metric: -8.9418\n", + "Validation, Loss: 8.9418, Metric: -8.9418\n", + "Removing worst model snapshot: checkpoints/fold_0/model_1.pth\n", "Model saved to checkpoints/fold_0/model_6.pth\n", - "Epoch 8/10, Loss: 10.1901\n", - "Validation, Loss: 9.1556, Metric: -9.1556\n", - "Validation, Loss: 9.1556, Metric: -9.1556\n", - "Removing worst model snapshot: checkpoints/fold_0/model_6.pth\n", + "Epoch 8/10, Loss: 9.8689\n", + "Validation, Loss: 8.8533, Metric: -8.8533\n", + "Validation, Loss: 8.8533, Metric: -8.8533\n", + "Removing worst model snapshot: checkpoints/fold_0/model_2.pth\n", "Model saved to checkpoints/fold_0/model_7.pth\n", - "Epoch 9/10, Loss: 10.1075\n", - "Validation, Loss: 9.0769, Metric: -9.0769\n", - "Validation, Loss: 9.0769, Metric: -9.0769\n", - "Removing worst model snapshot: checkpoints/fold_0/model_7.pth\n", + "Epoch 9/10, Loss: 9.7912\n", + "Validation, Loss: 8.8337, Metric: -8.8337\n", + "Validation, Loss: 8.8337, Metric: -8.8337\n", + "Removing worst model snapshot: checkpoints/fold_0/model_3.pth\n", "Model saved to checkpoints/fold_0/model_8.pth\n", - "Epoch 10/10, Loss: 10.0234\n", - "Validation, Loss: 8.9797, Metric: -8.9797\n", - "Validation, Loss: 8.9797, Metric: -8.9797\n", - "Removing worst model snapshot: checkpoints/fold_0/model_8.pth\n", + "Epoch 10/10, Loss: 9.7140\n", + "Validation, Loss: 8.7207, Metric: -8.7207\n", + "Validation, Loss: 8.7207, Metric: -8.7207\n", + "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", "Model saved to checkpoints/fold_0/model_9.pth\n", - "Fold 0. Score: -8.97965145111084\n", - "Mean score: -8.9797\n", + "Fold 0. Score: -8.720703125\n", + "Mean score: -8.7207\n", "Std: 0.0\n" ] }, { "data": { "text/plain": [ - "(8.606657028198242, )" + "(8.068553924560547, )" ] }, - "execution_count": 9, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index ba40cd8..19fb810 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -25,7 +25,9 @@ def on_train_end(self, logs=None): class ES_Checkpoints_Manager(Callback): - def __init__(self, monitor="val_loss", verbose=1, save_best_only=True, k=5, patience=5, mode="min"): + def __init__( + self, monitor="val_loss", verbose=1, save_best_only=True, k=5, patience=5, mode="min" + ): self.monitor = monitor self.verbose = verbose self.save_best_only = save_best_only @@ -34,7 +36,6 @@ def __init__(self, monitor="val_loss", verbose=1, save_best_only=True, k=5, pati self.mode = mode self.best_snapshots = [] self.best_score = None - self.worst_best_score = float('inf') if mode == "min" else float('-inf') self.early_stopping_counter = 0 self.stop_training = False @@ -48,15 +49,15 @@ def _should_save_checkpoint(self, current_score): if len(self.best_snapshots) < self.k: return True if self.mode == "min": - return current_score < self.worst_best_score + return current_score < -self.best_snapshots[0][0] else: - return current_score > self.worst_best_score + return current_score > self.best_snapshots[0][0] def _update_worst_best_score(self): if self.mode == "min": - self.worst_best_score = max(self.best_snapshots)[0] + self.worst_best_score = -self.best_snapshots[0][0] else: - self.worst_best_score = min(self.best_snapshots)[0] + self.worst_best_score = self.best_snapshots[0][0] def on_epoch_end(self, epoch, logs=None): current_score = logs.get(self.monitor) @@ -74,7 +75,11 @@ def on_epoch_end(self, epoch, logs=None): if self.verbose: print(f"Removing worst model snapshot: {worst_snapshot[1]}") - heapq.heappush(self.best_snapshots, (current_score, model_path)) + if self.mode == "min": + heapq.heappush(self.best_snapshots, (-current_score, model_path)) + else: + heapq.heappush(self.best_snapshots, (current_score, model_path)) + self._update_worst_best_score() torch.save(logs["model_state_dict"], model_path) if self.verbose: @@ -96,4 +101,9 @@ def on_epoch_end(self, epoch, logs=None): self.stop_training = True def get_best_snapshots(self): - return [snapshot[1] for snapshot in sorted(self.best_snapshots, key=lambda x: x[0] if self.mode == "min" else -x[0])] \ No newline at end of file + return [ + snapshot[1] + for snapshot in sorted( + self.best_snapshots, key=lambda x: -x[0] if self.mode == "min" else x[0] + ) + ] From e95ba13374ba5f8e2dae07fe3024266ea56856c7 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Mon, 10 Jun 2024 00:54:40 +0300 Subject: [PATCH 09/50] fix predict --- Tutorial_2_Neural_Networks.ipynb | 126 +++++++++++++++++++----------- tsururu/dataset/dataset.py | 15 ++-- tsururu/model_training/trainer.py | 23 ++++-- tsururu/strategies/recursive.py | 5 +- 4 files changed, 108 insertions(+), 61 deletions(-) diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index d32ff90..e76dc8d 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -299,70 +299,70 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 272.3933\n", - "Validation, Loss: 29.0868, Metric: -29.0868\n", - "Validation, Loss: 29.0868, Metric: -29.0868\n", + "Epoch 1/10, Loss: 273.4190\n", + "Validation, Loss: 30.7949, Metric: -30.7949\n", + "Validation, Loss: 30.7949, Metric: -30.7949\n", "Model saved to checkpoints/fold_0/model_0.pth\n", - "Epoch 2/10, Loss: 32.3303\n", - "Validation, Loss: 13.5448, Metric: -13.5448\n", - "Validation, Loss: 13.5448, Metric: -13.5448\n", + "Epoch 2/10, Loss: 32.7996\n", + "Validation, Loss: 14.9507, Metric: -14.9507\n", + "Validation, Loss: 14.9507, Metric: -14.9507\n", "Model saved to checkpoints/fold_0/model_1.pth\n", - "Epoch 3/10, Loss: 12.8174\n", - "Validation, Loss: 9.9666, Metric: -9.9666\n", - "Validation, Loss: 9.9666, Metric: -9.9666\n", + "Epoch 3/10, Loss: 13.4258\n", + "Validation, Loss: 10.6110, Metric: -10.6110\n", + "Validation, Loss: 10.6110, Metric: -10.6110\n", "Model saved to checkpoints/fold_0/model_2.pth\n", - "Epoch 4/10, Loss: 10.3944\n", - "Validation, Loss: 9.1675, Metric: -9.1675\n", - "Validation, Loss: 9.1675, Metric: -9.1675\n", + "Epoch 4/10, Loss: 10.8625\n", + "Validation, Loss: 9.5021, Metric: -9.5021\n", + "Validation, Loss: 9.5021, Metric: -9.5021\n", "Model saved to checkpoints/fold_0/model_3.pth\n", - "Epoch 5/10, Loss: 10.1024\n", - "Validation, Loss: 9.0681, Metric: -9.0681\n", - "Validation, Loss: 9.0681, Metric: -9.0681\n", + "Epoch 5/10, Loss: 10.5123\n", + "Validation, Loss: 9.4560, Metric: -9.4560\n", + "Validation, Loss: 9.4560, Metric: -9.4560\n", "Model saved to checkpoints/fold_0/model_4.pth\n", - "Epoch 6/10, Loss: 10.0179\n", - "Validation, Loss: 8.9633, Metric: -8.9633\n", - "Validation, Loss: 8.9633, Metric: -8.9633\n", + "Epoch 6/10, Loss: 10.4202\n", + "Validation, Loss: 9.4053, Metric: -9.4053\n", + "Validation, Loss: 9.4053, Metric: -9.4053\n", "Removing worst model snapshot: checkpoints/fold_0/model_0.pth\n", "Model saved to checkpoints/fold_0/model_5.pth\n", - "Epoch 7/10, Loss: 9.9462\n", - "Validation, Loss: 8.9418, Metric: -8.9418\n", - "Validation, Loss: 8.9418, Metric: -8.9418\n", + "Epoch 7/10, Loss: 10.3471\n", + "Validation, Loss: 9.2806, Metric: -9.2806\n", + "Validation, Loss: 9.2806, Metric: -9.2806\n", "Removing worst model snapshot: checkpoints/fold_0/model_1.pth\n", "Model saved to checkpoints/fold_0/model_6.pth\n", - "Epoch 8/10, Loss: 9.8689\n", - "Validation, Loss: 8.8533, Metric: -8.8533\n", - "Validation, Loss: 8.8533, Metric: -8.8533\n", + "Epoch 8/10, Loss: 10.2655\n", + "Validation, Loss: 9.2938, Metric: -9.2938\n", + "Validation, Loss: 9.2938, Metric: -9.2938\n", "Removing worst model snapshot: checkpoints/fold_0/model_2.pth\n", "Model saved to checkpoints/fold_0/model_7.pth\n", - "Epoch 9/10, Loss: 9.7912\n", - "Validation, Loss: 8.8337, Metric: -8.8337\n", - "Validation, Loss: 8.8337, Metric: -8.8337\n", + "Epoch 9/10, Loss: 10.1888\n", + "Validation, Loss: 9.0976, Metric: -9.0976\n", + "Validation, Loss: 9.0976, Metric: -9.0976\n", "Removing worst model snapshot: checkpoints/fold_0/model_3.pth\n", "Model saved to checkpoints/fold_0/model_8.pth\n", - "Epoch 10/10, Loss: 9.7140\n", - "Validation, Loss: 8.7207, Metric: -8.7207\n", - "Validation, Loss: 8.7207, Metric: -8.7207\n", + "Epoch 10/10, Loss: 10.1022\n", + "Validation, Loss: 9.1226, Metric: -9.1226\n", + "Validation, Loss: 9.1226, Metric: -9.1226\n", "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", "Model saved to checkpoints/fold_0/model_9.pth\n", - "Fold 0. Score: -8.720703125\n", - "Mean score: -8.7207\n", + "Fold 0. Score: -9.122612953186035\n", + "Mean score: -9.1226\n", "Std: 0.0\n" ] }, { "data": { "text/plain": [ - "(8.068553924560547, )" + "(8.613364934921265, )" ] }, - "execution_count": 18, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -373,10 +373,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation, Loss: nan, Metric: nan\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.10042428970336914,\n", + " id date value\n", + " 0 0 2022-03-15 1800.882568\n", + " 1 0 2022-03-16 1801.434326\n", + " 2 0 2022-03-17 1801.189453\n", + " 3 0 2022-03-18 1801.897705\n", + " 4 0 2022-03-19 1801.974609\n", + " ... .. ... ...\n", + " 1955 9 2022-09-22 10998.390625\n", + " 1956 9 2022-09-23 10999.610352\n", + " 1957 9 2022-09-24 11001.310547\n", + " 1958 9 2022-09-25 11002.625\n", + " 1959 9 2022-09-26 11004.196289\n", + " \n", + " [1960 rows x 3 columns])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strategy.predict(test_dataset, test_all=True)" + ] }, { "cell_type": "code", diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index 00482c3..96e9fca 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -190,40 +190,39 @@ def make_padded_test( current_test_ids = slicer.create_idx_data( self.data, horizon, - self.history, + history, model_horizon, date_column=self.date_column, ) extended_data = slicer.get_slice(self.data, (current_test_ids, None)) extended_data = pd.DataFrame( extended_data.reshape(-1, extended_data.shape[-1]), - columns=self.seq_data.columns, + columns=self.data.columns, ) extended_data_nrows = extended_data.shape[0] extended_data["segment_col"] = np.repeat( - np.arange(extended_data_nrows // self.history), - self.history, + np.arange(extended_data_nrows // history), history ) id_column_name = ["segment_col", self.id_column] else: extended_data = self.data columns = self.data.columns - date_col_id = slicer.get_cols_idx(self.data, self.date_column) + date_col_id = slicer.get_cols_idx(extended_data, self.date_column) if id_column_name is None: id_column_name = self.id_column - id_col_id = slicer.get_cols_idx(self.data, id_column_name) + id_col_id = slicer.get_cols_idx(extended_data, id_column_name) # Find indices for segments ids, time_delta = slicer.ids_from_date( - self.data, self.date_column, delta=self.delta, return_delta=True + extended_data, self.date_column, delta=self.delta, return_delta=True ) if test_all: ids = list(np.unique(extended_data.segment_col, return_index=True)[1])[1:] - data = self.data.to_numpy() + data = extended_data.to_numpy() segments = np.split(data, ids) segments = [ diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 6917d5d..119c68f 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -255,7 +255,7 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): return model, optimizer, scheduler, val_metric - def validate_model(self, val_loader, model): + def validate_model(self, val_loader, model, return_outputs=False): model.eval() all_outputs = [] @@ -275,7 +275,10 @@ def validate_model(self, val_loader, model): print(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") - return loss, metric + if return_outputs: + return loss, metric, all_outputs, all_targets + else: + return loss, metric def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) -> "DLTrainer": """Fits the models using the input data and pipeline. @@ -358,12 +361,20 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=self.batch_size, - shuffle=True, + shuffle=False, drop_last=self.drop_last, num_workers=self.num_workers, ) - models_preds = [self.validate_model(test_loader, model) for model in self.models] + models_preds = [self.validate_model(test_loader, model, return_outputs=True)[2] for model in self.models] + y_pred = np.mean(models_preds, axis=0) - - return y_pred + y_pred = torch.tensor(y_pred) + + if pipeline.multivariate: + pipeline.y_original_shape = list(pipeline.y_original_shape) + pipeline.y_original_shape[0] *= y_pred.shape[0] + y_pred = y_pred.permute(0, 2, 1) + y_pred = y_pred.reshape(-1, y_pred.shape[2]) + + return y_pred.numpy() diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index f5ec876..2158f58 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -176,9 +176,12 @@ def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame: """ new_data = dataset.make_padded_test( - self.horizon, self.history, test_all, self.model_horizon + self.horizon, self.history, test_all=test_all, model_horizon=self.model_horizon ) new_dataset = TSDataset(new_data, dataset.columns_params, dataset.delta) + + if test_all: + new_dataset.data = new_dataset.data.sort_values([dataset.id_column, "segment_col", dataset.date_column]) if self.reduced: current_test_ids = index_slicer.create_idx_data( From 2d36616f3c5a93fddaff87867dbaf8bc38eee258 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Wed, 12 Jun 2024 13:22:30 +0300 Subject: [PATCH 10/50] ML models --- Tutorial_1_Quick_start.ipynb | 160 +++++++++++++++--------------- Tutorial_2_Neural_Networks.ipynb | 2 +- tsururu/model_training/trainer.py | 6 +- tsururu/strategies/direct.py | 5 +- tsururu/strategies/recursive.py | 12 ++- 5 files changed, 95 insertions(+), 90 deletions(-) diff --git a/Tutorial_1_Quick_start.ipynb b/Tutorial_1_Quick_start.ipynb index aa33804..e9c9d94 100644 --- a/Tutorial_1_Quick_start.ipynb +++ b/Tutorial_1_Quick_start.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 47, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,9 @@ "import pandas as pd\n", "\n", "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", - "from tsururu.models import CatBoostRegressor_CV\n", + "from tsururu.models.boost import CatBoost\n", + "from tsururu.model_training.validator import KFoldCrossValidator\n", + "from tsururu.model_training.trainer import MLTrainer\n", "from tsururu.strategies import (\n", " DirectStrategy,\n", " FlatWideMIMOStrategy,\n", @@ -37,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -133,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -226,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -249,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -273,13 +275,13 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'transformers': ,\n", + "{'transformers': ,\n", " 'multivariate': False,\n", " 'is_fitted': False,\n", " 'strategy_name': None,\n", @@ -287,7 +289,7 @@ " 'y_original_shape': None}" ] }, - "execution_count": 54, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -305,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -351,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -389,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -414,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -442,30 +444,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Model" + "#### Model, Validator and Trainer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The model is separate from the strategy. Any model can be run in any strategy if it supports this input and output format" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One of the easiest options – is to use GBM." + "- `Model`:\n", + " - The model is separate from the strategy. Any model can be run in any strategy if it supports this input and output format.\n", + " - You can use on of the implemented ML models (for instance, GBM (Gradient Boosting Machine)).\n", + "- `Validator`:\n", + " - The validator is responsible for setting up the validation process, which includes creating training and validation folds. It ensures that the data is split correctly so that the model’s performance can be accurately assessed. \n", + "- `Trainer`:\n", + " - The trainer is the component that trains the model with provided validator. \n", + " - It is necessary to choose a trainer in accordance with the type of model (ML, DL, stats)." ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Configure the model parameters\n", + "model = CatBoost\n", "model_params = {\n", " \"loss_function\": \"MultiRMSE\",\n", " \"early_stopping_rounds\": 100,\n", @@ -473,19 +476,19 @@ "}\n", "\n", "# Configure the validation parameters\n", + "validation = KFoldCrossValidator\n", "validation_params = {\n", - " \"type\": 'KFold',\n", " \"n_splits\": 2,\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "model = CatBoostRegressor_CV(validation_params, model_params)" + "}\n", + "\n", + "trainer_params = {}\n", + "\n", + "trainer = MLTrainer(\n", + " model,\n", + " model_params,\n", + " validation,\n", + " validation_params,\n", + ")" ] }, { @@ -519,51 +522,50 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "horizon = 3\n", "history = 7\n", - "step = 1\n", - "\n", - "strategy = RecursiveStrategy(horizon, history, step, model, pipeline)" + "step = 1" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "strategy = RecursiveStrategy(horizon, history, step, trainer, pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 8.15ms\tremaining: 8.14s\n", - "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 1.19s\tremaining: 1.18s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 2.4s\tremaining: 0us\n", + "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 56.7ms\tremaining: 56.6s\n", + "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 399ms\tremaining: 397ms\n", + "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 804ms\tremaining: 0us\n", "\n", "bestTest = 0.003047062205\n", "bestIteration = 999\n", "\n", - "Fold 0:\n", - "MultiRMSE: 0.003047062205400275\n", - "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 4.77ms\tremaining: 4.77s\n", - "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 1.18s\tremaining: 1.18s\n", - "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 2.4s\tremaining: 0us\n", + "Fold 0. Score: 0.003047062205400275\n", + "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 1.59ms\tremaining: 1.59s\n", + "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 369ms\tremaining: 368ms\n", + "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 710ms\tremaining: 0us\n", "\n", "bestTest = 0.002916854444\n", "bestIteration = 999\n", "\n", - "Fold 1:\n", - "MultiRMSE: 0.002916854443892958\n", - "Mean MultiRMSE: 0.003\n", + "Fold 1. Score: 0.002916854443892958\n", + "Mean score: 0.003\n", "Std: 0.0001\n" ] } @@ -574,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -583,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -831,7 +833,7 @@ "29 9 2022-09-29 10995.683994" ] }, - "execution_count": 65, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -849,32 +851,30 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 8.42ms\tremaining: 8.41s\n", - "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 1.2s\tremaining: 1.19s\n", - "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 2.44s\tremaining: 0us\n", + "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 1.06ms\tremaining: 1.06s\n", + "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 348ms\tremaining: 346ms\n", + "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 699ms\tremaining: 0us\n", "\n", "bestTest = 0.002972644218\n", "bestIteration = 999\n", "\n", - "Fold 0:\n", - "MultiRMSE: 0.0029726442175072565\n", - "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 5.31ms\tremaining: 5.31s\n", - "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 1.2s\tremaining: 1.19s\n", - "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 2.44s\tremaining: 0us\n", + "Fold 0. Score: 0.0029726442175072565\n", + "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 841us\tremaining: 840ms\n", + "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 384ms\tremaining: 382ms\n", + "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 718ms\tremaining: 0us\n", "\n", "bestTest = 0.002971764957\n", "bestIteration = 999\n", "\n", - "Fold 1:\n", - "MultiRMSE: 0.002971764957214351\n", - "Mean MultiRMSE: 0.003\n", + "Fold 1. Score: 0.002971764957214351\n", + "Mean score: 0.003\n", "Std: 0.0\n" ] } @@ -885,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1133,7 +1133,7 @@ "29 10999.0 10994.990313 9" ] }, - "execution_count": 67, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1181,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1234,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1278,7 +1278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.10.14" }, "orig_nbformat": 4 }, diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index e76dc8d..2cefbc3 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -17,7 +17,7 @@ "import numpy as np\n", "import pandas as pd\n", "\n", - "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", + "from tsururu.dataset import Pipeline, TSDataset\n", "from tsururu.model_training.validator import HoldOutValidator\n", "from tsururu.models.torch_based.dlinear import DLinear_NN\n", "from tsururu.strategies import MIMOStrategy\n", diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 119c68f..1854011 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -34,7 +34,7 @@ def __init__( ): self.model = model self.model_params = model_params - self.validation = validator + self.validator = validator self.validation_params = validation_params # Provide by strategy if needed @@ -69,10 +69,10 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - self.features_argsort = np.argsort(pipeline.output_features) X = X[:, self.features_argsort] - for fold_i, X_train, y_train, X_val, y_val in enumerate( + for fold_i, (X_train, y_train, X_val, y_val) in enumerate( self.validator(**self.validation_params).get_split(X, y, X_val, y_val) ): - model = self.model(**self.model_params["params"]) + model = self.model(self.model_params) model.fit_one_fold(X_train, y_train, X_val, y_val) self.models.append(model) self.scores.append(model.score) diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index 3b0f45b..6e93388 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -1,3 +1,4 @@ +from copy import deepcopy from typing import Optional, Union from ..dataset.dataset import TSDataset @@ -145,7 +146,7 @@ def fit( self.trainer.horizon = self.model_horizon self.trainer.history = self.history - current_trainer = self.trainer.copy() + current_trainer = deepcopy(self.trainer) current_trainer.fit(data, self.pipeline, val_data) self.trainers.append(current_trainer) @@ -209,7 +210,7 @@ def fit( self.trainer.horizon = self.model_horizon self.trainer.history = self.history - current_trainer = self.trainer.copy() + current_trainer = deepcopy(self.trainer) current_trainer.fit(data, self.pipeline, val_data) self.trainers.append(current_trainer) diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 2158f58..87f0d5b 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -1,3 +1,4 @@ +from copy import deepcopy from typing import Optional, Union import pandas as pd @@ -120,9 +121,10 @@ def fit( self.trainer.horizon = self.model_horizon self.trainer.history = self.history - self.trainer.fit(data, self.pipeline, val_data) + current_trainer = deepcopy(self.trainer) + current_trainer.fit(data, self.pipeline, val_data) - self.trainers.append(self.trainer) + self.trainers.append(current_trainer) return self def make_step(self, step: int, dataset: TSDataset) -> TSDataset: @@ -179,9 +181,11 @@ def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame: self.horizon, self.history, test_all=test_all, model_horizon=self.model_horizon ) new_dataset = TSDataset(new_data, dataset.columns_params, dataset.delta) - + if test_all: - new_dataset.data = new_dataset.data.sort_values([dataset.id_column, "segment_col", dataset.date_column]) + new_dataset.data = new_dataset.data.sort_values( + [dataset.id_column, "segment_col", dataset.date_column] + ) if self.reduced: current_test_ids = index_slicer.create_idx_data( From 29fd485aa7acde30c49fd6c95b9b4b2cae8c31b2 Mon Sep 17 00:00:00 2001 From: NickKar30 Date: Wed, 12 Jun 2024 14:40:43 +0300 Subject: [PATCH 11/50] add PatchTST and layers --- tsururu/models/torch_based/__init__.py | 10 +- tsururu/models/torch_based/dlinear.py | 239 +++++------ .../torch_based/layers/PatchTST_backbone.py | 379 ++++++++++++++++++ .../torch_based/layers/PatchTST_layers.py | 121 ++++++ tsururu/models/torch_based/layers/RevIN.py | 63 +++ tsururu/models/torch_based/layers/__init__.py | 0 tsururu/models/torch_based/patchTST.py | 94 +++++ 7 files changed, 782 insertions(+), 124 deletions(-) create mode 100644 tsururu/models/torch_based/layers/PatchTST_backbone.py create mode 100644 tsururu/models/torch_based/layers/PatchTST_layers.py create mode 100644 tsururu/models/torch_based/layers/RevIN.py create mode 100644 tsururu/models/torch_based/layers/__init__.py create mode 100644 tsururu/models/torch_based/patchTST.py diff --git a/tsururu/models/torch_based/__init__.py b/tsururu/models/torch_based/__init__.py index 52751c0..dd826bc 100644 --- a/tsururu/models/torch_based/__init__.py +++ b/tsururu/models/torch_based/__init__.py @@ -1,5 +1,5 @@ -"""NN algorithms for time series forecasting.""" - -from .dlinear import DLinear_NN - -__all__ = ["Dlinear_NN"] +"""NN algorithms for time series forecasting.""" + +from .dlinear import DLinear_NN + +__all__ = ["Dlinear_NN"] diff --git a/tsururu/models/torch_based/dlinear.py b/tsururu/models/torch_based/dlinear.py index a186520..0b2e3fb 100644 --- a/tsururu/models/torch_based/dlinear.py +++ b/tsururu/models/torch_based/dlinear.py @@ -1,119 +1,120 @@ -import torch -import torch.nn as nn - - -class DLinear_NN(nn.Module): - """DLInear model from the paper https://arxiv.org/pdf/2205.13504.pdf. - - Args: - model_params: dict with parameters for the model: - - seq_len: int, the length of the input sequence. - - pred_len: int, the length of the output sequence. - - moving_avg: int, the size of the moving average window. - - individual: bool, whether shared model among different variates. - true may be better for time series with different trend and seasonal patterns. - - enc_in: int, the number of input time series. - Needs for individual=True. - - """ - - def __init__(self, seq_len, pred_len, moving_avg=7, individual=False, enc_in=1): - super(DLinear_NN, self).__init__() - # Params from model_params - self.seq_len = seq_len - self.pred_len = pred_len - - # Decomposition - self.decompsition = self.series_decomp(moving_avg) - self.individual = individual - self.channels = enc_in - - if self.individual: - self.Linear_Seasonal = nn.ModuleList() - self.Linear_Trend = nn.ModuleList() - - for i in range(self.channels): - self.Linear_Seasonal.append(nn.Linear(self.seq_len, self.pred_len)) - self.Linear_Trend.append(nn.Linear(self.seq_len, self.pred_len)) - - self.Linear_Seasonal[i].weight = nn.Parameter( - (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) - ) - self.Linear_Trend[i].weight = nn.Parameter( - (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) - ) - else: - self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len) - self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len) - - self.Linear_Seasonal.weight = nn.Parameter( - (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) - ) - self.Linear_Trend.weight = nn.Parameter( - (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) - ) - - self.trainer_type = "DLTrainer" - - def encoder(self, x): - seasonal_init, trend_init = self.decompsition(x) - seasonal_init, trend_init = seasonal_init.permute(0, 2, 1), trend_init.permute(0, 2, 1) - if self.individual: - seasonal_output = torch.zeros( - [seasonal_init.size(0), seasonal_init.size(1), self.pred_len], - dtype=seasonal_init.dtype, - ).to(seasonal_init.device) - trend_output = torch.zeros( - [trend_init.size(0), trend_init.size(1), self.pred_len], dtype=trend_init.dtype - ).to(trend_init.device) - for i in range(self.channels): - seasonal_output[:, i, :] = self.Linear_Seasonal[i](seasonal_init[:, i, :]) - trend_output[:, i, :] = self.Linear_Trend[i](trend_init[:, i, :]) - else: - seasonal_output = self.Linear_Seasonal(seasonal_init) - trend_output = self.Linear_Trend(trend_init) - x = seasonal_output + trend_output - return x.permute(0, 2, 1) - - def forecast(self, x_enc): - return self.encoder(x_enc) - - def forward(self, x_enc): - dec_out = self.forecast(x_enc) - return dec_out[:, -self.pred_len :, :] - - @staticmethod - def my_Layernorm(channels): - layernorm = nn.LayerNorm(channels) - - def forward(x): - x_hat = layernorm(x) - bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1) - return x_hat - bias - - return forward - - @staticmethod - def moving_avg(kernel_size, stride): - avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) - - def forward(x): - front = x[:, 0:1, :].repeat(1, (kernel_size - 1) // 2, 1) - end = x[:, -1:, :].repeat(1, (kernel_size - 1) // 2, 1) - x = torch.cat([front, x, end], dim=1) - x = avg(x.permute(0, 2, 1)) - x = x.permute(0, 2, 1) - return x - - return forward - - @staticmethod - def series_decomp(kernel_size): - moving_avg = DLinear_NN.moving_avg(kernel_size, stride=1) - - def forward(x): - moving_mean = moving_avg(x) - res = x - moving_mean - return res, moving_mean - - return forward +import torch +import torch.nn as nn + + +class DLinear_NN(nn.Module): + """DLInear model from the paper https://arxiv.org/pdf/2205.13504.pdf. + + Args: + model_params: dict with parameters for the model: + - seq_len: int, the length of the input sequence. + - pred_len: int, the length of the output sequence. + - moving_avg: int, the size of the moving average window. + - individual: bool, whether shared model among different variates. + true may be better for time series with different trend and seasonal patterns. + - enc_in: int, the number of input time series. + Needs for individual=True. + + """ + + def __init__(self, seq_len, pred_len, moving_avg=7, individual=False, enc_in=1): + super(DLinear_NN, self).__init__() + # Params from model_params + self.seq_len = seq_len + self.pred_len = pred_len + + # Decomposition + self.decompsition = self.series_decomp(moving_avg) + self.individual = individual + self.channels = enc_in + + if self.individual: + self.Linear_Seasonal = nn.ModuleList() + self.Linear_Trend = nn.ModuleList() + + for i in range(self.channels): + self.Linear_Seasonal.append(nn.Linear(self.seq_len, self.pred_len)) + self.Linear_Trend.append(nn.Linear(self.seq_len, self.pred_len)) + + self.Linear_Seasonal[i].weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + self.Linear_Trend[i].weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + else: + self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len) + self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len) + + self.Linear_Seasonal.weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + self.Linear_Trend.weight = nn.Parameter( + (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]) + ) + + self.trainer_type = "DLTrainer" + + def encoder(self, x): + seasonal_init, trend_init = self.decompsition(x) + seasonal_init, trend_init = seasonal_init.permute(0, 2, 1), trend_init.permute(0, 2, 1) + if self.individual: + seasonal_output = torch.zeros( + [seasonal_init.size(0), seasonal_init.size(1), self.pred_len], + dtype=seasonal_init.dtype, + ).to(seasonal_init.device) + trend_output = torch.zeros( + [trend_init.size(0), trend_init.size(1), self.pred_len], dtype=trend_init.dtype + ).to(trend_init.device) + for i in range(self.channels): + seasonal_output[:, i, :] = self.Linear_Seasonal[i](seasonal_init[:, i, :]) + trend_output[:, i, :] = self.Linear_Trend[i](trend_init[:, i, :]) + else: + seasonal_output = self.Linear_Seasonal(seasonal_init) + trend_output = self.Linear_Trend(trend_init) + x = seasonal_output + trend_output + return x.permute(0, 2, 1) + + def forecast(self, x_enc): + return self.encoder(x_enc) + + def forward(self, x_enc): + dec_out = self.forecast(x_enc) + return dec_out[:, -self.pred_len :, :] + + @staticmethod + def my_Layernorm(channels): + layernorm = nn.LayerNorm(channels) + + def forward(x): + x_hat = layernorm(x) + bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1) + return x_hat - bias + + return forward + + @staticmethod + def moving_avg(kernel_size, stride): + avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) + + def forward(x): + front = x[:, 0:1, :].repeat(1, (kernel_size - 1) // 2, 1) + end = x[:, -1:, :].repeat(1, (kernel_size - 1) // 2, 1) + x = torch.cat([front, x, end], dim=1) + x = avg(x.permute(0, 2, 1)) + x = x.permute(0, 2, 1) + return x + + return forward + + @staticmethod + def series_decomp(kernel_size): + moving_avg = DLinear_NN.moving_avg(kernel_size, stride=1) + + def forward(x): + moving_mean = moving_avg(x) + res = x - moving_mean + return res, moving_mean + + return forward + diff --git a/tsururu/models/torch_based/layers/PatchTST_backbone.py b/tsururu/models/torch_based/layers/PatchTST_backbone.py new file mode 100644 index 0000000..22cbd0c --- /dev/null +++ b/tsururu/models/torch_based/layers/PatchTST_backbone.py @@ -0,0 +1,379 @@ +__all__ = ['PatchTST_backbone'] + +# Cell +from typing import Callable, Optional +import torch +from torch import nn +from torch import Tensor +import torch.nn.functional as F +import numpy as np + +#from collections import OrderedDict +from tsururu.models.torch_based.layers.PatchTST_layers import * +from tsururu.models.torch_based.layers.RevIN import RevIN + +# Cell +class PatchTST_backbone(nn.Module): + def __init__(self, c_in:int, context_window:int, target_window:int, patch_len:int, stride:int, max_seq_len:Optional[int]=1024, + n_layers:int=3, d_model=128, n_heads=16, d_k:Optional[int]=None, d_v:Optional[int]=None, + d_ff:int=256, norm:str='BatchNorm', attn_dropout:float=0., dropout:float=0., act:str="gelu", key_padding_mask:bool='auto', + padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False, + pe:str='zeros', learn_pe:bool=True, fc_dropout:float=0., head_dropout = 0, padding_patch = None, + pretrain_head:bool=False, head_type = 'flatten', individual = False, revin = True, affine = True, subtract_last = False, + verbose:bool=False, **kwargs): + + super().__init__() + + # RevIn + self.revin = revin + if self.revin: self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last) + + # Patching + self.patch_len = patch_len + self.stride = stride + self.padding_patch = padding_patch + patch_num = int((context_window - patch_len)/stride + 1) + if padding_patch == 'end': # can be modified to general case + self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) + patch_num += 1 + + # Backbone + self.backbone = TSTiEncoder(c_in, patch_num=patch_num, patch_len=patch_len, max_seq_len=max_seq_len, + n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, + attn_dropout=attn_dropout, dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, + attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, + pe=pe, learn_pe=learn_pe, verbose=verbose, **kwargs) + + # Head + self.head_nf = d_model * patch_num + self.n_vars = c_in + self.pretrain_head = pretrain_head + self.head_type = head_type + self.individual = individual + + if self.pretrain_head: + self.head = self.create_pretrain_head(self.head_nf, c_in, fc_dropout) # custom head passed as a partial func with all its kwargs + elif head_type == 'flatten': + self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window, head_dropout=head_dropout) + + + def forward(self, z): # z: [bs x nvars x seq_len] + # norm + if self.revin: + z = z.permute(0,2,1) + z = self.revin_layer(z, 'norm') + z = z.permute(0,2,1) + + # do patching + if self.padding_patch == 'end': + z = self.padding_patch_layer(z) + z = z.unfold(dimension=-1, size=self.patch_len, step=self.stride) # z: [bs x nvars x patch_num x patch_len] + z = z.permute(0,1,3,2) # z: [bs x nvars x patch_len x patch_num] + + # model + z = self.backbone(z) # z: [bs x nvars x d_model x patch_num] + z = self.head(z) # z: [bs x nvars x target_window] + + # denorm + if self.revin: + z = z.permute(0,2,1) + z = self.revin_layer(z, 'denorm') + z = z.permute(0,2,1) + return z + + def create_pretrain_head(self, head_nf, vars, dropout): + return nn.Sequential(nn.Dropout(dropout), + nn.Conv1d(head_nf, vars, 1) + ) + + +class Flatten_Head(nn.Module): + def __init__(self, individual, n_vars, nf, target_window, head_dropout=0): + super().__init__() + + self.individual = individual + self.n_vars = n_vars + + if self.individual: + self.linears = nn.ModuleList() + self.dropouts = nn.ModuleList() + self.flattens = nn.ModuleList() + for i in range(self.n_vars): + self.flattens.append(nn.Flatten(start_dim=-2)) + self.linears.append(nn.Linear(nf, target_window)) + self.dropouts.append(nn.Dropout(head_dropout)) + else: + self.flatten = nn.Flatten(start_dim=-2) + self.linear = nn.Linear(nf, target_window) + self.dropout = nn.Dropout(head_dropout) + + def forward(self, x): # x: [bs x nvars x d_model x patch_num] + if self.individual: + x_out = [] + for i in range(self.n_vars): + z = self.flattens[i](x[:,i,:,:]) # z: [bs x d_model * patch_num] + z = self.linears[i](z) # z: [bs x target_window] + z = self.dropouts[i](z) + x_out.append(z) + x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window] + else: + x = self.flatten(x) + x = self.linear(x) + x = self.dropout(x) + return x + + + + +class TSTiEncoder(nn.Module): #i means channel-independent + def __init__(self, c_in, patch_num, patch_len, max_seq_len=1024, + n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None, + d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act="gelu", store_attn=False, + key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False, + pe='zeros', learn_pe=True, verbose=False, **kwargs): + + + super().__init__() + + self.patch_num = patch_num + self.patch_len = patch_len + + # Input encoding + q_len = patch_num + self.W_P = nn.Linear(patch_len, d_model) # Eq 1: projection of feature vectors onto a d-dim vector space + self.seq_len = q_len + + # Positional encoding + self.W_pos = positional_encoding(pe, learn_pe, q_len, d_model) + + # Residual dropout + self.dropout = nn.Dropout(dropout) + + # Encoder + self.encoder = TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout, + pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=n_layers, store_attn=store_attn) + + + def forward(self, x) -> Tensor: # x: [bs x nvars x patch_len x patch_num] + + n_vars = x.shape[1] + # Input encoding + x = x.permute(0,1,3,2) # x: [bs x nvars x patch_num x patch_len] + x = self.W_P(x) # x: [bs x nvars x patch_num x d_model] + + u = torch.reshape(x, (x.shape[0]*x.shape[1],x.shape[2],x.shape[3])) # u: [bs * nvars x patch_num x d_model] + u = self.dropout(u + self.W_pos) # u: [bs * nvars x patch_num x d_model] + + # Encoder + z = self.encoder(u) # z: [bs * nvars x patch_num x d_model] + z = torch.reshape(z, (-1,n_vars,z.shape[-2],z.shape[-1])) # z: [bs x nvars x patch_num x d_model] + z = z.permute(0,1,3,2) # z: [bs x nvars x d_model x patch_num] + + return z + + + +# Cell +class TSTEncoder(nn.Module): + def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=None, + norm='BatchNorm', attn_dropout=0., dropout=0., activation='gelu', + res_attention=False, n_layers=1, pre_norm=False, store_attn=False): + super().__init__() + + self.layers = nn.ModuleList([TSTEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, + attn_dropout=attn_dropout, dropout=dropout, + activation=activation, res_attention=res_attention, + pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)]) + self.res_attention = res_attention + + def forward(self, src:Tensor, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None): + output = src + scores = None + if self.res_attention: + for mod in self.layers: output, scores = mod(output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + return output + else: + for mod in self.layers: output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + return output + + + +class TSTEncoderLayer(nn.Module): + def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False, + norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation="gelu", res_attention=False, pre_norm=False): + super().__init__() + assert not d_model%n_heads, f"d_model ({d_model}) must be divisible by n_heads ({n_heads})" + d_k = d_model // n_heads if d_k is None else d_k + d_v = d_model // n_heads if d_v is None else d_v + + # Multi-Head attention + self.res_attention = res_attention + self.self_attn = _MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention) + + # Add & Norm + self.dropout_attn = nn.Dropout(dropout) + if "batch" in norm.lower(): + self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2)) + else: + self.norm_attn = nn.LayerNorm(d_model) + + # Position-wise Feed-Forward + self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias), + get_activation_fn(activation), + nn.Dropout(dropout), + nn.Linear(d_ff, d_model, bias=bias)) + + # Add & Norm + self.dropout_ffn = nn.Dropout(dropout) + if "batch" in norm.lower(): + self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2)) + else: + self.norm_ffn = nn.LayerNorm(d_model) + + self.pre_norm = pre_norm + self.store_attn = store_attn + + + def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None) -> Tensor: + + # Multi-Head attention sublayer + if self.pre_norm: + src = self.norm_attn(src) + ## Multi-Head attention + if self.res_attention: + src2, attn, scores = self.self_attn(src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + else: + src2, attn = self.self_attn(src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + if self.store_attn: + self.attn = attn + ## Add & Norm + src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout + if not self.pre_norm: + src = self.norm_attn(src) + + # Feed-forward sublayer + if self.pre_norm: + src = self.norm_ffn(src) + ## Position-wise Feed-Forward + src2 = self.ff(src) + ## Add & Norm + src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout + if not self.pre_norm: + src = self.norm_ffn(src) + + if self.res_attention: + return src, scores + else: + return src + + + + +class _MultiheadAttention(nn.Module): + def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, attn_dropout=0., proj_dropout=0., qkv_bias=True, lsa=False): + """Multi Head Attention Layer + Input shape: + Q: [batch_size (bs) x max_q_len x d_model] + K, V: [batch_size (bs) x q_len x d_model] + mask: [q_len x q_len] + """ + super().__init__() + d_k = d_model // n_heads if d_k is None else d_k + d_v = d_model // n_heads if d_v is None else d_v + + self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v + + self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias) + self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias) + self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias) + + # Scaled Dot-Product Attention (multiple heads) + self.res_attention = res_attention + self.sdp_attn = _ScaledDotProductAttention(d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa) + + # Poject output + self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout)) + + + def forward(self, Q:Tensor, K:Optional[Tensor]=None, V:Optional[Tensor]=None, prev:Optional[Tensor]=None, + key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None): + + bs = Q.size(0) + if K is None: K = Q + if V is None: V = Q + + # Linear (+ split in multiple heads) + q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1,2) # q_s : [bs x n_heads x max_q_len x d_k] + k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0,2,3,1) # k_s : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3) + v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1,2) # v_s : [bs x n_heads x q_len x d_v] + + # Apply Scaled Dot-Product Attention (multiple heads) + if self.res_attention: + output, attn_weights, attn_scores = self.sdp_attn(q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + else: + output, attn_weights = self.sdp_attn(q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len] + + # back to the original inputs dimensions + output = output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) # output: [bs x q_len x n_heads * d_v] + output = self.to_out(output) + + if self.res_attention: return output, attn_weights, attn_scores + else: return output, attn_weights + + +class _ScaledDotProductAttention(nn.Module): + r"""Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) with optional residual attention from previous layer + (Realformer: Transformer likes residual attention by He et al, 2020) and locality self sttention (Vision Transformer for Small-Size Datasets + by Lee et al, 2021)""" + + def __init__(self, d_model, n_heads, attn_dropout=0., res_attention=False, lsa=False): + super().__init__() + self.attn_dropout = nn.Dropout(attn_dropout) + self.res_attention = res_attention + head_dim = d_model // n_heads + self.scale = nn.Parameter(torch.tensor(head_dim ** -0.5), requires_grad=lsa) + self.lsa = lsa + + def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None): + ''' + Input shape: + q : [bs x n_heads x max_q_len x d_k] + k : [bs x n_heads x d_k x seq_len] + v : [bs x n_heads x seq_len x d_v] + prev : [bs x n_heads x q_len x seq_len] + key_padding_mask: [bs x seq_len] + attn_mask : [1 x seq_len x seq_len] + Output shape: + output: [bs x n_heads x q_len x d_v] + attn : [bs x n_heads x q_len x seq_len] + scores : [bs x n_heads x q_len x seq_len] + ''' + + # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence + attn_scores = torch.matmul(q, k) * self.scale # attn_scores : [bs x n_heads x max_q_len x q_len] + + # Add pre-softmax attention scores from the previous layer (optional) + if prev is not None: attn_scores = attn_scores + prev + + # Attention mask (optional) + if attn_mask is not None: # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len + if attn_mask.dtype == torch.bool: + attn_scores.masked_fill_(attn_mask, -np.inf) + else: + attn_scores += attn_mask + + # Key padding mask (optional) + if key_padding_mask is not None: # mask with shape [bs x q_len] (only when max_w_len == q_len) + attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf) + + # normalize the attention weights + attn_weights = F.softmax(attn_scores, dim=-1) # attn_weights : [bs x n_heads x max_q_len x q_len] + attn_weights = self.attn_dropout(attn_weights) + + # compute the new values given the attention weights + output = torch.matmul(attn_weights, v) # output: [bs x n_heads x max_q_len x d_v] + + if self.res_attention: return output, attn_weights, attn_scores + else: return output, attn_weights + diff --git a/tsururu/models/torch_based/layers/PatchTST_layers.py b/tsururu/models/torch_based/layers/PatchTST_layers.py new file mode 100644 index 0000000..11b5bd6 --- /dev/null +++ b/tsururu/models/torch_based/layers/PatchTST_layers.py @@ -0,0 +1,121 @@ +__all__ = ['Transpose', 'get_activation_fn', 'moving_avg', 'series_decomp', 'PositionalEncoding', 'SinCosPosEncoding', 'Coord2dPosEncoding', 'Coord1dPosEncoding', 'positional_encoding'] + +import torch +from torch import nn +import math + +class Transpose(nn.Module): + def __init__(self, *dims, contiguous=False): + super().__init__() + self.dims, self.contiguous = dims, contiguous + def forward(self, x): + if self.contiguous: return x.transpose(*self.dims).contiguous() + else: return x.transpose(*self.dims) + + +def get_activation_fn(activation): + if callable(activation): return activation() + elif activation.lower() == "relu": return nn.ReLU() + elif activation.lower() == "gelu": return nn.GELU() + raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') + + +# decomposition + +class moving_avg(nn.Module): + """ + Moving average block to highlight the trend of time series + """ + def __init__(self, kernel_size, stride): + super(moving_avg, self).__init__() + self.kernel_size = kernel_size + self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) + + def forward(self, x): + # padding on the both ends of time series + front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1) + end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1) + x = torch.cat([front, x, end], dim=1) + x = self.avg(x.permute(0, 2, 1)) + x = x.permute(0, 2, 1) + return x + + +class series_decomp(nn.Module): + """ + Series decomposition block + """ + def __init__(self, kernel_size): + super(series_decomp, self).__init__() + self.moving_avg = moving_avg(kernel_size, stride=1) + + def forward(self, x): + moving_mean = self.moving_avg(x) + res = x - moving_mean + return res, moving_mean + + + +# pos_encoding + +def PositionalEncoding(q_len, d_model, normalize=True): + pe = torch.zeros(q_len, d_model) + position = torch.arange(0, q_len).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model)) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + if normalize: + pe = pe - pe.mean() + pe = pe / (pe.std() * 10) + return pe + +SinCosPosEncoding = PositionalEncoding + +def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False): + x = .5 if exponential else 1 + i = 0 + for i in range(100): + cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1 + pv(f'{i:4.0f} {x:5.3f} {cpe.mean():+6.3f}', verbose) + if abs(cpe.mean()) <= eps: break + elif cpe.mean() > eps: x += .001 + else: x -= .001 + i += 1 + if normalize: + cpe = cpe - cpe.mean() + cpe = cpe / (cpe.std() * 10) + return cpe + +def Coord1dPosEncoding(q_len, exponential=False, normalize=True): + cpe = (2 * (torch.linspace(0, 1, q_len).reshape(-1, 1)**(.5 if exponential else 1)) - 1) + if normalize: + cpe = cpe - cpe.mean() + cpe = cpe / (cpe.std() * 10) + return cpe + +def positional_encoding(pe, learn_pe, q_len, d_model): + # Positional encoding + if pe == None: + W_pos = torch.empty((q_len, d_model)) # pe = None and learn_pe = False can be used to measure impact of pe + nn.init.uniform_(W_pos, -0.02, 0.02) + learn_pe = False + elif pe == 'zero': + W_pos = torch.empty((q_len, 1)) + nn.init.uniform_(W_pos, -0.02, 0.02) + elif pe == 'zeros': + W_pos = torch.empty((q_len, d_model)) + nn.init.uniform_(W_pos, -0.02, 0.02) + elif pe == 'normal' or pe == 'gauss': + W_pos = torch.zeros((q_len, 1)) + torch.nn.init.normal_(W_pos, mean=0.0, std=0.1) + elif pe == 'uniform': + W_pos = torch.zeros((q_len, 1)) + nn.init.uniform_(W_pos, a=0.0, b=0.1) + elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True) + elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True) + elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True) + elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True) + elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True) + else: raise ValueError(f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \ + 'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)") + return nn.Parameter(W_pos, requires_grad=learn_pe) \ No newline at end of file diff --git a/tsururu/models/torch_based/layers/RevIN.py b/tsururu/models/torch_based/layers/RevIN.py new file mode 100644 index 0000000..f7fed81 --- /dev/null +++ b/tsururu/models/torch_based/layers/RevIN.py @@ -0,0 +1,63 @@ +# code from https://github.com/ts-kim/RevIN, with minor modifications + +import torch +import torch.nn as nn + +class RevIN(nn.Module): + def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False): + """ + :param num_features: the number of features or channels + :param eps: a value added for numerical stability + :param affine: if True, RevIN has learnable affine parameters + """ + super(RevIN, self).__init__() + self.num_features = num_features + self.eps = eps + self.affine = affine + self.subtract_last = subtract_last + if self.affine: + self._init_params() + + def forward(self, x, mode:str): + if mode == 'norm': + self._get_statistics(x) + x = self._normalize(x) + elif mode == 'denorm': + x = self._denormalize(x) + else: raise NotImplementedError + return x + + def _init_params(self): + # initialize RevIN params: (C,) + self.affine_weight = nn.Parameter(torch.ones(self.num_features)) + self.affine_bias = nn.Parameter(torch.zeros(self.num_features)) + + def _get_statistics(self, x): + dim2reduce = tuple(range(1, x.ndim-1)) + if self.subtract_last: + self.last = x[:,-1,:].unsqueeze(1) + else: + self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach() + self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach() + + def _normalize(self, x): + if self.subtract_last: + x = x - self.last + else: + x = x - self.mean + x = x / self.stdev + if self.affine: + x = x * self.affine_weight + x = x + self.affine_bias + return x + + def _denormalize(self, x): + if self.affine: + x = x - self.affine_bias + x = x / (self.affine_weight + self.eps*self.eps) + x = x * self.stdev + if self.subtract_last: + x = x + self.last + else: + x = x + self.mean + return x diff --git a/tsururu/models/torch_based/layers/__init__.py b/tsururu/models/torch_based/layers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tsururu/models/torch_based/patchTST.py b/tsururu/models/torch_based/patchTST.py new file mode 100644 index 0000000..cb512d4 --- /dev/null +++ b/tsururu/models/torch_based/patchTST.py @@ -0,0 +1,94 @@ +from typing import Callable, Optional +import torch +from torch import nn +from torch import Tensor +import torch.nn.functional as F +import numpy as np + +from tsururu.models.torch_based.layers.PatchTST_backbone import PatchTST_backbone +from tsururu.models.torch_based.layers.PatchTST_layers import series_decomp + + +class PatchTST_NN(nn.Module): + def __init__(self, c_in, context_window, target_window, n_layers, n_heads, d_model, d_ff, dropout, fc_dropout, head_dropout, individual, patch_len, stride, padding_patch, revin, affine, subtract_last, decomposition, kernel_size, max_seq_len:Optional[int]=1024, d_k:Optional[int]=None, d_v:Optional[int]=None, norm:str='BatchNorm', attn_dropout:float=0., act:str="gelu", key_padding_mask:bool='auto', padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False, pe:str='zeros', learn_pe:bool=True, pretrain_head:bool=False, head_type:str='flatten', verbose:bool=False, **kwargs): + + super().__init__() + + # load parameters + self.c_in = c_in + self.context_window = context_window + self.target_window = target_window + + self.n_layers = n_layers + self.n_heads = n_heads + self.d_model = d_model + self.d_ff = d_ff + self.dropout = dropout + self.fc_dropout = fc_dropout + self.head_dropout = head_dropout + + self.individual = individual + + self.patch_len = patch_len + self.stride = stride + self.padding_patch = padding_patch + + self.revin = revin + self.affine = affine + self.subtract_last = subtract_last + + self.decomposition = decomposition + self.kernel_size = kernel_size + + + # model + self.decomposition = decomposition + if self.decomposition: + self.decomp_module = series_decomp(kernel_size) + self.model_trend = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, + max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model, + n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, + dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, + attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, + pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch, + pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine, + subtract_last=subtract_last, verbose=verbose, **kwargs) + self.model_res = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, + max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model, + n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, + dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, + attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, + pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch, + pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine, + subtract_last=subtract_last, verbose=verbose, **kwargs) + else: + self.model = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, + max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model, + n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, + dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, + attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, + pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch, + pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine, + subtract_last=subtract_last, verbose=verbose, **kwargs) + + + + def forward(self, x): # x: [Batch, Input length, Channel] + if self.decomposition: + res_init, trend_init = self.decomp_module(x) + res_init, trend_init = res_init.permute(0,2,1), trend_init.permute(0,2,1) # x: [Batch, Channel, Input length] + res = self.model_res(res_init) + trend = self.model_trend(trend_init) + x = res + trend + x = x.permute(0,2,1) # x: [Batch, Input length, Channel] + else: + x = x.permute(0,2,1) # x: [Batch, Channel, Input length] + x = self.model(x) + x = x.permute(0,2,1) # x: [Batch, Input length, Channel] + return x + + + + + + From be36800230fc0fb71ddd55f7e1e59ca3c3dd94b5 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Wed, 12 Jun 2024 17:43:48 +0300 Subject: [PATCH 12/50] add_train_demand_example --- .gitignore | 1 - Example_Demand.ipynb | 498325 +++++++++++++++ Tutorial_1_Quick_start.ipynb | 80 +- Tutorial_2_Neural_Networks.ipynb | 88 +- datasets/global/train_demand.csv | 86917 +++ .../torch_based/data_provider.py | 4 +- .../torch_based/layers/PatchTST_backbone.py | 532 +- .../torch_based/layers/PatchTST_layers.py | 112 +- tsururu/models/torch_based/patchTST.py | 227 +- 9 files changed, 585955 insertions(+), 331 deletions(-) create mode 100644 Example_Demand.ipynb create mode 100644 datasets/global/train_demand.csv diff --git a/.gitignore b/.gitignore index 5e95941..7f46077 100644 --- a/.gitignore +++ b/.gitignore @@ -151,6 +151,5 @@ catboost_info/ poetry.lock *check*.ipynb *temp* -*demand* *test* *checkpoints* \ No newline at end of file diff --git a/Example_Demand.ipynb b/Example_Demand.ipynb new file mode 100644 index 0000000..120faa9 --- /dev/null +++ b/Example_Demand.ipynb @@ -0,0 +1,498325 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "from tqdm.contrib.itertools import product\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from typing import List, Optional, Union\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "from tsururu.models.torch_based.dlinear import DLinear_NN\n", + "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", + "from tsururu.model_training.trainer import MLTrainer\n", + "from tsururu.model_training.validator import KFoldCrossValidator\n", + "from tsururu.models.boost import CatBoost\n", + "from tsururu.strategies import (\n", + " DirectStrategy,\n", + " FlatWideMIMOStrategy,\n", + " MIMOStrategy,\n", + " RecursiveStrategy,\n", + ")\n", + "from tsururu.transformers import (\n", + " DateSeasonsGenerator,\n", + " DifferenceNormalizer,\n", + " LagTransformer,\n", + " LastKnownNormalizer,\n", + " SequentialTransformer,\n", + " StandardScalerTransformer,\n", + " TargetGenerator,\n", + " UnionTransformer,\n", + ")\n", + "\n", + "from sklearn.metrics import mean_absolute_error\n", + "\n", + "from tsururu.model_training.trainer import DLTrainer" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def symmetric_mean_absolute_percentage_error(y_true, y_pred):\n", + " return np.mean(np.abs(y_true - y_pred) / ((np.abs(y_true) + np.abs(y_pred)) / 2))\n", + "\n", + "\n", + "def get_metrics_by_id(test_df: pd.DataFrame, preds_df: pd.DataFrame):\n", + " merge_df = test_df.merge(preds_df, on=[\"id\", \"Date\"], suffixes=(\"_true\", \"_pred\"))\n", + " metrics_df = (\n", + " merge_df.groupby(\"id\")\n", + " .apply(\n", + " lambda x: symmetric_mean_absolute_percentage_error(x[\"Demand_true\"], x[\"Demand_pred\"])\n", + " )\n", + " .reset_index()\n", + " )\n", + " return metrics_df\n", + "\n", + "\n", + "def pad_multivariate_time_series(data, pad_with=np.nan):\n", + " data = data.copy()\n", + " min_date = data[\"Date\"].min()\n", + " max_date = data[\"Date\"].max()\n", + "\n", + " all_dates = pd.date_range(min_date, max_date)\n", + "\n", + " to_concat = []\n", + "\n", + " for unique_id in data[\"id\"].unique():\n", + " id_dates = data[data[\"id\"] == unique_id][\"Date\"]\n", + " missing_dates = all_dates.difference(id_dates)\n", + "\n", + " for missing_date in missing_dates:\n", + " to_concat.append(\n", + " pd.DataFrame(\n", + " {\n", + " \"Date\": [missing_date],\n", + " \"id\": [unique_id],\n", + " \"Demand\": [pad_with],\n", + " }\n", + " )\n", + " )\n", + "\n", + " data = pd.concat([data] + to_concat, ignore_index=True)\n", + " data = data.sort_values([\"id\", \"Date\"]).reset_index(drop=True)\n", + "\n", + " return data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and initial preprocessing data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the datasets\n", + "train_data = pd.read_csv(\"datasets/global/train_demand.csv\")\n", + "test_data = pd.read_csv(\"datasets/global/test_demand.csv\")\n", + "\n", + "# Convert 'Date' column to datetime format\n", + "train_data[\"Date\"] = pd.to_datetime(train_data[\"Date\"], format=\"%d.%m.%Y\", dayfirst=True)\n", + "test_data[\"Date\"] = pd.to_datetime(test_data[\"Date\"], format=\"%d.%m.%Y\", dayfirst=True)\n", + "\n", + "# Make val data\n", + "n_days_in_val = 28\n", + "val_data = train_data[\n", + " train_data[\"Date\"] > train_data[\"Date\"].max() - pd.Timedelta(days=n_days_in_val)\n", + "].reset_index(drop=True)\n", + "train_data = train_data[\n", + " train_data[\"Date\"] <= train_data[\"Date\"].max() - pd.Timedelta(days=n_days_in_val)\n", + "]\n", + "\n", + "# Convert each column to the appropriate data type\n", + "for df in [train_data, val_data, test_data]:\n", + " df[\"Store_id\"] = df[\"Store_id\"].astype(\"str\")\n", + " df[\"SKU_id\"] = df[\"SKU_id\"].astype(\"str\")\n", + " df[\"Promo\"] = df[\"Promo\"].fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DatePromoDemandRegular_PricePromo_Price
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mean2016-06-05 15:53:14.7738693120.253601NaN135.004390119.452272
min2016-05-23 00:00:000.000000NaN128.980000114.170000
25%2016-05-30 00:00:000.000000NaN131.700000114.170000
50%2016-06-06 00:00:000.000000NaN134.960000119.600000
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max2016-06-19 00:00:001.000000NaN145.560000127.760000
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" + ], + "text/plain": [ + " Date Promo Demand Regular_Price \\\n", + "count 5970 5970.000000 0.0 5970.000000 \n", + "mean 2016-06-05 15:53:14.773869312 0.253601 NaN 135.004390 \n", + "min 2016-05-23 00:00:00 0.000000 NaN 128.980000 \n", + "25% 2016-05-30 00:00:00 0.000000 NaN 131.700000 \n", + "50% 2016-06-06 00:00:00 0.000000 NaN 134.960000 \n", + "75% 2016-06-13 00:00:00 1.000000 NaN 138.500000 \n", + "max 2016-06-19 00:00:00 1.000000 NaN 145.560000 \n", + "std NaN 0.435109 NaN 3.490976 \n", + "\n", + " Promo_Price \n", + "count 1514.000000 \n", + "mean 119.452272 \n", + "min 114.170000 \n", + "25% 114.170000 \n", + "50% 119.600000 \n", + "75% 125.040000 \n", + "max 127.760000 \n", + "std 5.494316 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check for missing values\n", + "missing_values_train = train_data.isnull().sum()\n", + "missing_values_val = val_data.isnull().sum()\n", + "missing_values_test = test_data.isnull().sum()\n", + "\n", + "# Generate summary statistics\n", + "summary_stats_train = train_data.describe()\n", + "summary_stats_val = val_data.describe()\n", + "summary_stats_test = test_data.describe()\n", + "\n", + "print(\"Missing values in training data:\")\n", + "display(missing_values_train)\n", + "\n", + "print(\"Missing values in val data:\")\n", + "display(missing_values_val)\n", + "\n", + "print(\"Missing values in test data:\")\n", + "display(missing_values_test)\n", + "\n", + "print(\"Summary statistics for training data:\")\n", + "display(summary_stats_train)\n", + "\n", + "print(\"Summary statistics for val data:\")\n", + "display(summary_stats_val)\n", + "\n", + "print(\"Summary statistics for test data:\")\n", + "display(summary_stats_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__You can see that the targeting variable has heavy tails and a large range.__" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Demand Distribution (Train Data)')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(train_data[\"Demand\"], bins=50, kde=True)\n", + "plt.title(\"Demand Distribution (Train Data)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(14, 7))\n", + "\n", + "plt.subplot(3, 2, 1)\n", + "sns.histplot(train_data[\"Regular_Price\"], bins=50)\n", + "plt.title(\"Regular Price Distribution (Train Data)\")\n", + "\n", + "plt.subplot(3, 2, 2)\n", + "sns.histplot(train_data[\"Promo_Price\"], bins=50)\n", + "plt.title(\"Promo Price Distribution (Train Data)\")\n", + "\n", + "plt.subplot(3, 2, 3)\n", + "sns.histplot(val_data[\"Regular_Price\"], bins=50)\n", + "plt.title(\"Regular Price Distribution (Val Data)\")\n", + "\n", + "plt.subplot(3, 2, 4)\n", + "sns.histplot(val_data[\"Promo_Price\"], bins=50)\n", + "plt.title(\"Promo Price Distribution (Val Data)\")\n", + "\n", + "plt.subplot(3, 2, 5)\n", + "sns.histplot(test_data[\"Regular_Price\"], bins=50)\n", + "plt.title(\"Regular Price Distribution (Test Data)\")\n", + "\n", + "plt.subplot(3, 2, 6)\n", + "sns.histplot(test_data[\"Promo_Price\"], bins=50)\n", + "plt.title(\"Promo Price Distribution (Test Data)\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- __Demand Distribution:__\n", + "\n", + "\t- The demand is highly right-skewed. There are a few extremely high demand values, which could be considered outliers or exceptional cases.\n", + "\n", + "- __Regular Price Distribution:__\n", + "\n", + "\t- The regular price in the training data shows multiple peaks.\n", + "\t- The most common prices on train are around 125-135 and 139-142, indicating these are frequently used price points.\n", + "\n", + "- __Promo Price Distribution:__\n", + "\n", + "\t- The promo price also shows multiple peaks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Target feature normalization is likely to perform well on such data.__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph time series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Сombine store id and sku to get single id for rows.__" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_data[\"id\"] = train_data[\"Store_id\"] + \"_\" + train_data[\"SKU_id\"]\n", + "val_data[\"id\"] = val_data[\"Store_id\"] + \"_\" + val_data[\"SKU_id\"]\n", + "test_data[\"id\"] = test_data[\"Store_id\"] + \"_\" + test_data[\"SKU_id\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "name": "Value 1_1", + "type": "scatter", + "x": [ + "2015-01-01T00:00:00", + "2015-01-02T00:00:00", + "2015-01-03T00:00:00", + "2015-01-04T00:00:00", + "2015-01-05T00:00:00", + "2015-01-06T00:00:00", + "2015-01-07T00:00:00", + "2015-01-08T00:00:00", + "2015-01-09T00:00:00", + "2015-01-10T00:00:00", + "2015-01-11T00:00:00", + "2015-01-12T00:00:00", + "2015-01-13T00:00:00", + "2015-01-14T00:00:00", + "2015-01-15T00:00:00", + "2015-01-16T00:00:00", + "2015-01-17T00:00:00", + "2015-01-18T00:00:00", + "2015-01-19T00:00:00", + 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"gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Time Series Dynamics of Value, Promo, and Price" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.94 + ], + "title": { + "text": "Date" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Value/Price" + } + }, + "yaxis2": { + "anchor": "x", + "overlaying": "y", + "side": "right", + "title": { + "text": "Promo" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a line plot with Plotly\n", + "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", + "\n", + "# Add traces for each id\n", + "for unique_id in train_data[\"id\"].unique():\n", + " df = train_data[train_data[\"id\"] == unique_id]\n", + " fig.add_trace(\n", + " go.Scatter(x=df[\"Date\"], y=df[\"Demand\"], mode=\"lines\", name=f\"Value {unique_id}\"),\n", + " secondary_y=False,\n", + " )\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=df[\"Date\"],\n", + " y=df[\"Regular_Price\"],\n", + " mode=\"lines\",\n", + " name=f\"Regular Price {unique_id}\",\n", + " line=dict(dash=\"dash\"),\n", + " ),\n", + " secondary_y=False,\n", + " )\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=df[\"Date\"],\n", + " y=df[\"Promo_Price\"],\n", + " mode=\"lines\",\n", + " name=f\"Promo Price {unique_id}\",\n", + " line=dict(dash=\"dash\"),\n", + " ),\n", + " secondary_y=False,\n", + " )\n", + "\n", + "# Update layout\n", + "fig.update_layout(\n", + " title=\"Time Series Dynamics of Value, Promo, and Price\",\n", + " xaxis_title=\"Date\",\n", + " yaxis_title=\"Value/Price\",\n", + " yaxis2_title=\"Promo\",\n", + " legend_title=\"ID\",\n", + " hovermode=\"x unified\",\n", + ")\n", + "\n", + "# Show plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__It can be seen that demand rises strongly during promo periods, so it is good to take into account the exogenous variable promo.__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare data for tsururu and configure blocks" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "train_data_tsururu_1 = train_data[[\"Date\", \"id\", \"Demand\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_params = {\n", + " \"target\": {\n", + " \"columns\": [\"Demand\"],\n", + " \"type\": \"continious\",\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"Date\"],\n", + " \"type\": \"datetime\",\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"type\": \"categorical\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_params = {\n", + " \"target\": {\n", + " \"columns\": [\"Demand\"],\n", + " \"features\": {\n", + " \"LagTransformer\": {\"lags\": [0, 1, 2, 3, 4, 5, 6, 7, 14, 21, 28, 30]},\n", + " },\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"Date\"],\n", + " \"features\": {\n", + " \"DateSeasonsGenerator\": {\n", + " \"seasonalities\": [\"doy\", \"m\", \"wd\", \"d\"],\n", + " \"from_target_date\": True,\n", + " },\n", + " \"LagTransformer\": {\"lags\": 3},\n", + " },\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"features\": {\n", + " \"LabelEncodingTransformer\": {},\n", + " \"LagTransformer\": {\"lags\": 1},\n", + " },\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "model = CatBoost\n", + "model_params = {\n", + " \"loss_function\": \"MultiRMSE\",\n", + " \"early_stopping_rounds\": 100,\n", + " \"verbose\": 500,\n", + "}\n", + "\n", + "validation = KFoldCrossValidator\n", + "validation_params = {\n", + " \"n_splits\": 3,\n", + "}\n", + "\n", + "trainer = MLTrainer(\n", + " model,\n", + " model_params,\n", + " validation,\n", + " validation_params,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "horizon = 28\n", + "history = 31\n", + "step = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try Baselines!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try Statistical models!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try ML models in different strategies!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Try CatBoost!" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "padded_for_multivariate = pad_multivariate_time_series(train_data_tsururu_1, pad_with=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a0a3098315e5428fbec142ac91179342", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/8 [00:00,\n", + "{'transformers': ,\n", " 'multivariate': False,\n", " 'is_fitted': False,\n", " 'strategy_name': None,\n", @@ -289,7 +289,7 @@ " 'y_original_shape': None}" ] }, - "execution_count": 8, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -522,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -542,24 +542,24 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 56.7ms\tremaining: 56.6s\n", - "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 399ms\tremaining: 397ms\n", - "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 804ms\tremaining: 0us\n", + "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 6.36ms\tremaining: 6.35s\n", + "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 338ms\tremaining: 337ms\n", + "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 762ms\tremaining: 0us\n", "\n", "bestTest = 0.003047062205\n", "bestIteration = 999\n", "\n", "Fold 0. Score: 0.003047062205400275\n", - "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 1.59ms\tremaining: 1.59s\n", - "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 369ms\tremaining: 368ms\n", - "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 710ms\tremaining: 0us\n", + "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 842us\tremaining: 841ms\n", + "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 329ms\tremaining: 327ms\n", + "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 706ms\tremaining: 0us\n", "\n", "bestTest = 0.002916854444\n", "bestIteration = 999\n", @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -585,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ "29 9 2022-09-29 10995.683994" ] }, - "execution_count": 19, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -851,24 +851,24 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 1.06ms\tremaining: 1.06s\n", - "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 348ms\tremaining: 346ms\n", - "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 699ms\tremaining: 0us\n", + "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 1.67ms\tremaining: 1.67s\n", + "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 487ms\tremaining: 485ms\n", + "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 913ms\tremaining: 0us\n", "\n", "bestTest = 0.002972644218\n", "bestIteration = 999\n", "\n", "Fold 0. Score: 0.0029726442175072565\n", - "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 841us\tremaining: 840ms\n", - "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 384ms\tremaining: 382ms\n", - "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 718ms\tremaining: 0us\n", + "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 983us\tremaining: 983ms\n", + "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 370ms\tremaining: 369ms\n", + "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 696ms\tremaining: 0us\n", "\n", "bestTest = 0.002971764957\n", "bestIteration = 999\n", @@ -885,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1133,7 +1133,7 @@ "29 10999.0 10994.990313 9" ] }, - "execution_count": 21, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1181,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1234,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 48, "metadata": {}, "outputs": [ { diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index 2cefbc3..2572213 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -306,60 +306,60 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 273.4190\n", - "Validation, Loss: 30.7949, Metric: -30.7949\n", - "Validation, Loss: 30.7949, Metric: -30.7949\n", + "Epoch 1/10, Loss: 276.3597\n", + "Validation, Loss: 33.9238, Metric: -33.9238\n", + "Validation, Loss: 33.9238, Metric: -33.9238\n", "Model saved to checkpoints/fold_0/model_0.pth\n", - "Epoch 2/10, Loss: 32.7996\n", - "Validation, Loss: 14.9507, Metric: -14.9507\n", - "Validation, Loss: 14.9507, Metric: -14.9507\n", + "Epoch 2/10, Loss: 32.1673\n", + "Validation, Loss: 13.6875, Metric: -13.6875\n", + "Validation, Loss: 13.6875, Metric: -13.6875\n", "Model saved to checkpoints/fold_0/model_1.pth\n", - "Epoch 3/10, Loss: 13.4258\n", - "Validation, Loss: 10.6110, Metric: -10.6110\n", - "Validation, Loss: 10.6110, Metric: -10.6110\n", + "Epoch 3/10, Loss: 12.2549\n", + "Validation, Loss: 9.5308, Metric: -9.5308\n", + "Validation, Loss: 9.5308, Metric: -9.5308\n", "Model saved to checkpoints/fold_0/model_2.pth\n", - "Epoch 4/10, Loss: 10.8625\n", - "Validation, Loss: 9.5021, Metric: -9.5021\n", - "Validation, Loss: 9.5021, Metric: -9.5021\n", + "Epoch 4/10, Loss: 9.7045\n", + "Validation, Loss: 8.4738, Metric: -8.4738\n", + "Validation, Loss: 8.4738, Metric: -8.4738\n", "Model saved to checkpoints/fold_0/model_3.pth\n", - "Epoch 5/10, Loss: 10.5123\n", - "Validation, Loss: 9.4560, Metric: -9.4560\n", - "Validation, Loss: 9.4560, Metric: -9.4560\n", + "Epoch 5/10, Loss: 9.3849\n", + "Validation, Loss: 8.4111, Metric: -8.4111\n", + "Validation, Loss: 8.4111, Metric: -8.4111\n", "Model saved to checkpoints/fold_0/model_4.pth\n", - "Epoch 6/10, Loss: 10.4202\n", - "Validation, Loss: 9.4053, Metric: -9.4053\n", - "Validation, Loss: 9.4053, Metric: -9.4053\n", + "Epoch 6/10, Loss: 9.2967\n", + "Validation, Loss: 8.3393, Metric: -8.3393\n", + "Validation, Loss: 8.3393, Metric: -8.3393\n", "Removing worst model snapshot: checkpoints/fold_0/model_0.pth\n", "Model saved to checkpoints/fold_0/model_5.pth\n", - "Epoch 7/10, Loss: 10.3471\n", - "Validation, Loss: 9.2806, Metric: -9.2806\n", - "Validation, Loss: 9.2806, Metric: -9.2806\n", + "Epoch 7/10, Loss: 9.2303\n", + "Validation, Loss: 8.3204, Metric: -8.3204\n", + "Validation, Loss: 8.3204, Metric: -8.3204\n", "Removing worst model snapshot: checkpoints/fold_0/model_1.pth\n", "Model saved to checkpoints/fold_0/model_6.pth\n", - "Epoch 8/10, Loss: 10.2655\n", - "Validation, Loss: 9.2938, Metric: -9.2938\n", - "Validation, Loss: 9.2938, Metric: -9.2938\n", + "Epoch 8/10, Loss: 9.1627\n", + "Validation, Loss: 8.2604, Metric: -8.2604\n", + "Validation, Loss: 8.2604, Metric: -8.2604\n", "Removing worst model snapshot: checkpoints/fold_0/model_2.pth\n", "Model saved to checkpoints/fold_0/model_7.pth\n", - "Epoch 9/10, Loss: 10.1888\n", - "Validation, Loss: 9.0976, Metric: -9.0976\n", - "Validation, Loss: 9.0976, Metric: -9.0976\n", + "Epoch 9/10, Loss: 9.0904\n", + "Validation, Loss: 8.1742, Metric: -8.1742\n", + "Validation, Loss: 8.1742, Metric: -8.1742\n", "Removing worst model snapshot: checkpoints/fold_0/model_3.pth\n", "Model saved to checkpoints/fold_0/model_8.pth\n", - "Epoch 10/10, Loss: 10.1022\n", - "Validation, Loss: 9.1226, Metric: -9.1226\n", - "Validation, Loss: 9.1226, Metric: -9.1226\n", + "Epoch 10/10, Loss: 9.0140\n", + "Validation, Loss: 8.0992, Metric: -8.0992\n", + "Validation, Loss: 8.0992, Metric: -8.0992\n", "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", "Model saved to checkpoints/fold_0/model_9.pth\n", - "Fold 0. Score: -9.122612953186035\n", - "Mean score: -9.1226\n", + "Fold 0. Score: -8.099207878112793\n", + "Mean score: -8.0992\n", "Std: 0.0\n" ] }, { "data": { "text/plain": [ - "(8.613364934921265, )" + "(9.684648990631104, )" ] }, "execution_count": 9, @@ -386,19 +386,19 @@ { "data": { "text/plain": [ - "(0.10042428970336914,\n", + "(0.09302711486816406,\n", " id date value\n", - " 0 0 2022-03-15 1800.882568\n", - " 1 0 2022-03-16 1801.434326\n", - " 2 0 2022-03-17 1801.189453\n", - " 3 0 2022-03-18 1801.897705\n", - " 4 0 2022-03-19 1801.974609\n", + " 0 0 2022-03-15 1801.496216\n", + " 1 0 2022-03-16 1801.333862\n", + " 2 0 2022-03-17 1801.839233\n", + " 3 0 2022-03-18 1801.989624\n", + " 4 0 2022-03-19 1802.165771\n", " ... .. ... ...\n", - " 1955 9 2022-09-22 10998.390625\n", - " 1956 9 2022-09-23 10999.610352\n", - " 1957 9 2022-09-24 11001.310547\n", - " 1958 9 2022-09-25 11002.625\n", - " 1959 9 2022-09-26 11004.196289\n", + " 1955 9 2022-09-22 10997.84668\n", + " 1956 9 2022-09-23 10999.4375\n", + " 1957 9 2022-09-24 11001.006836\n", + " 1958 9 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+106,2,08.05.2016,1.0,79,138.5,106.01 +106,2,09.05.2016,1.0,53,138.5,106.01 +106,2,10.05.2016,,3,138.5, +106,2,11.05.2016,,0,138.5, +106,2,12.05.2016,,3,138.5, +106,2,13.05.2016,,3,138.5, +106,2,14.05.2016,,3,138.5, +106,2,15.05.2016,,0,138.5, +106,2,16.05.2016,,6,138.5, +106,2,17.05.2016,,0,138.5, +106,2,18.05.2016,,9,138.5, +106,2,19.05.2016,,3,138.5, +106,2,20.05.2016,,13,138.5, +106,2,21.05.2016,,3,138.5, +106,2,22.05.2016,,0,138.5, diff --git a/tsururu/model_training/torch_based/data_provider.py b/tsururu/model_training/torch_based/data_provider.py index 5d73168..f9fb10f 100644 --- a/tsururu/model_training/torch_based/data_provider.py +++ b/tsururu/model_training/torch_based/data_provider.py @@ -16,7 +16,7 @@ def __init__(self, data, pipeline): len(self.data["idx_X"]) // self.data["raw_ts_X"]["id"].nunique() ) unique_dates, inverse_indices = np.unique( - self.data["raw_ts_X"]["date"], return_inverse=True + self.data["raw_ts_X"][self.data["date_column_name"]], return_inverse=True ) unique_dates = to_datetime(unique_dates) self.date_indices = { @@ -28,7 +28,7 @@ def __init__(self, data, pipeline): def __getitem__(self, index): if self.pipeline.multivariate: - current_date = self.data["raw_ts_X"]["date"].iloc[self.idx_X[index][0]] + current_date = self.data["raw_ts_X"][self.data["date_column_name"]].iloc[self.idx_X[index][0]] current_date = to_datetime(current_date) first_idx = self.date_indices[current_date] idx_X = self.idx_X[np.isin(self.idx_X[:, 0], first_idx)] diff --git a/tsururu/models/torch_based/layers/PatchTST_backbone.py b/tsururu/models/torch_based/layers/PatchTST_backbone.py index 22cbd0c..8debb8e 100644 --- a/tsururu/models/torch_based/layers/PatchTST_backbone.py +++ b/tsururu/models/torch_based/layers/PatchTST_backbone.py @@ -1,48 +1,101 @@ -__all__ = ['PatchTST_backbone'] +__all__ = ["PatchTST_backbone"] # Cell -from typing import Callable, Optional +from typing import Optional import torch from torch import nn from torch import Tensor import torch.nn.functional as F import numpy as np -#from collections import OrderedDict -from tsururu.models.torch_based.layers.PatchTST_layers import * +# from collections import OrderedDict +from tsururu.models.torch_based.layers.PatchTST_layers import Transpose, positional_encoding, get_activation_fn from tsururu.models.torch_based.layers.RevIN import RevIN + # Cell class PatchTST_backbone(nn.Module): - def __init__(self, c_in:int, context_window:int, target_window:int, patch_len:int, stride:int, max_seq_len:Optional[int]=1024, - n_layers:int=3, d_model=128, n_heads=16, d_k:Optional[int]=None, d_v:Optional[int]=None, - d_ff:int=256, norm:str='BatchNorm', attn_dropout:float=0., dropout:float=0., act:str="gelu", key_padding_mask:bool='auto', - padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False, - pe:str='zeros', learn_pe:bool=True, fc_dropout:float=0., head_dropout = 0, padding_patch = None, - pretrain_head:bool=False, head_type = 'flatten', individual = False, revin = True, affine = True, subtract_last = False, - verbose:bool=False, **kwargs): - + def __init__( + self, + c_in: int, + context_window: int, + target_window: int, + patch_len: int, + stride: int, + max_seq_len: Optional[int] = 1024, + n_layers: int = 3, + d_model=128, + n_heads=16, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + d_ff: int = 256, + norm: str = "BatchNorm", + attn_dropout: float = 0.0, + dropout: float = 0.0, + act: str = "gelu", + key_padding_mask: bool = "auto", + padding_var: Optional[int] = None, + attn_mask: Optional[Tensor] = None, + res_attention: bool = True, + pre_norm: bool = False, + store_attn: bool = False, + pe: str = "zeros", + learn_pe: bool = True, + fc_dropout: float = 0.0, + head_dropout=0, + padding_patch=None, + pretrain_head: bool = False, + head_type="flatten", + individual=False, + revin=True, + affine=True, + subtract_last=False, + verbose: bool = False, + **kwargs, + ): + super().__init__() - + # RevIn self.revin = revin - if self.revin: self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last) - + if self.revin: + self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last) + # Patching self.patch_len = patch_len self.stride = stride self.padding_patch = padding_patch - patch_num = int((context_window - patch_len)/stride + 1) - if padding_patch == 'end': # can be modified to general case - self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) + patch_num = int((context_window - patch_len) / stride + 1) + if padding_patch == "end": # can be modified to general case + self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) patch_num += 1 - - # Backbone - self.backbone = TSTiEncoder(c_in, patch_num=patch_num, patch_len=patch_len, max_seq_len=max_seq_len, - n_layers=n_layers, d_model=d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, - attn_dropout=attn_dropout, dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, - attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, - pe=pe, learn_pe=learn_pe, verbose=verbose, **kwargs) + + # Backbone + self.backbone = TSTiEncoder( + c_in, + patch_num=patch_num, + patch_len=patch_len, + max_seq_len=max_seq_len, + n_layers=n_layers, + d_model=d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + attn_dropout=attn_dropout, + dropout=dropout, + act=act, + key_padding_mask=key_padding_mask, + padding_var=padding_var, + attn_mask=attn_mask, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + pe=pe, + learn_pe=learn_pe, + verbose=verbose, + **kwargs, + ) # Head self.head_nf = d_model * patch_num @@ -51,49 +104,56 @@ def __init__(self, c_in:int, context_window:int, target_window:int, patch_len:in self.head_type = head_type self.individual = individual - if self.pretrain_head: - self.head = self.create_pretrain_head(self.head_nf, c_in, fc_dropout) # custom head passed as a partial func with all its kwargs - elif head_type == 'flatten': - self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window, head_dropout=head_dropout) - - - def forward(self, z): # z: [bs x nvars x seq_len] + if self.pretrain_head: + self.head = self.create_pretrain_head( + self.head_nf, c_in, fc_dropout + ) # custom head passed as a partial func with all its kwargs + elif head_type == "flatten": + self.head = Flatten_Head( + self.individual, + self.n_vars, + self.head_nf, + target_window, + head_dropout=head_dropout, + ) + + def forward(self, z): # z: [bs x nvars x seq_len] # norm - if self.revin: - z = z.permute(0,2,1) - z = self.revin_layer(z, 'norm') - z = z.permute(0,2,1) - + if self.revin: + z = z.permute(0, 2, 1) + z = self.revin_layer(z, "norm") + z = z.permute(0, 2, 1) + # do patching - if self.padding_patch == 'end': + if self.padding_patch == "end": z = self.padding_patch_layer(z) - z = z.unfold(dimension=-1, size=self.patch_len, step=self.stride) # z: [bs x nvars x patch_num x patch_len] - z = z.permute(0,1,3,2) # z: [bs x nvars x patch_len x patch_num] - + z = z.unfold( + dimension=-1, size=self.patch_len, step=self.stride + ) # z: [bs x nvars x patch_num x patch_len] + z = z.permute(0, 1, 3, 2) # z: [bs x nvars x patch_len x patch_num] + # model - z = self.backbone(z) # z: [bs x nvars x d_model x patch_num] - z = self.head(z) # z: [bs x nvars x target_window] - + z = self.backbone(z) # z: [bs x nvars x d_model x patch_num] + z = self.head(z) # z: [bs x nvars x target_window] + # denorm - if self.revin: - z = z.permute(0,2,1) - z = self.revin_layer(z, 'denorm') - z = z.permute(0,2,1) + if self.revin: + z = z.permute(0, 2, 1) + z = self.revin_layer(z, "denorm") + z = z.permute(0, 2, 1) return z - + def create_pretrain_head(self, head_nf, vars, dropout): - return nn.Sequential(nn.Dropout(dropout), - nn.Conv1d(head_nf, vars, 1) - ) + return nn.Sequential(nn.Dropout(dropout), nn.Conv1d(head_nf, vars, 1)) class Flatten_Head(nn.Module): def __init__(self, individual, n_vars, nf, target_window, head_dropout=0): super().__init__() - + self.individual = individual self.n_vars = n_vars - + if self.individual: self.linears = nn.ModuleList() self.dropouts = nn.ModuleList() @@ -106,41 +166,62 @@ def __init__(self, individual, n_vars, nf, target_window, head_dropout=0): self.flatten = nn.Flatten(start_dim=-2) self.linear = nn.Linear(nf, target_window) self.dropout = nn.Dropout(head_dropout) - - def forward(self, x): # x: [bs x nvars x d_model x patch_num] + + def forward(self, x): # x: [bs x nvars x d_model x patch_num] if self.individual: x_out = [] for i in range(self.n_vars): - z = self.flattens[i](x[:,i,:,:]) # z: [bs x d_model * patch_num] - z = self.linears[i](z) # z: [bs x target_window] + z = self.flattens[i](x[:, i, :, :]) # z: [bs x d_model * patch_num] + z = self.linears[i](z) # z: [bs x target_window] z = self.dropouts[i](z) x_out.append(z) - x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window] + x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window] else: x = self.flatten(x) x = self.linear(x) x = self.dropout(x) return x - - - - -class TSTiEncoder(nn.Module): #i means channel-independent - def __init__(self, c_in, patch_num, patch_len, max_seq_len=1024, - n_layers=3, d_model=128, n_heads=16, d_k=None, d_v=None, - d_ff=256, norm='BatchNorm', attn_dropout=0., dropout=0., act="gelu", store_attn=False, - key_padding_mask='auto', padding_var=None, attn_mask=None, res_attention=True, pre_norm=False, - pe='zeros', learn_pe=True, verbose=False, **kwargs): - - + + +class TSTiEncoder(nn.Module): # i means channel-independent + def __init__( + self, + c_in, + patch_num, + patch_len, + max_seq_len=1024, + n_layers=3, + d_model=128, + n_heads=16, + d_k=None, + d_v=None, + d_ff=256, + norm="BatchNorm", + attn_dropout=0.0, + dropout=0.0, + act="gelu", + store_attn=False, + key_padding_mask="auto", + padding_var=None, + attn_mask=None, + res_attention=True, + pre_norm=False, + pe="zeros", + learn_pe=True, + verbose=False, + **kwargs, + ): + super().__init__() - + self.patch_num = patch_num self.patch_len = patch_len - + # Input encoding q_len = patch_num - self.W_P = nn.Linear(patch_len, d_model) # Eq 1: projection of feature vectors onto a d-dim vector space + self.W_P = nn.Linear( + patch_len, d_model + ) # Eq 1: projection of feature vectors onto a d-dim vector space self.seq_len = q_len # Positional encoding @@ -150,114 +231,208 @@ def __init__(self, c_in, patch_num, patch_len, max_seq_len=1024, self.dropout = nn.Dropout(dropout) # Encoder - self.encoder = TSTEncoder(q_len, d_model, n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, dropout=dropout, - pre_norm=pre_norm, activation=act, res_attention=res_attention, n_layers=n_layers, store_attn=store_attn) + self.encoder = TSTEncoder( + q_len, + d_model, + n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + pre_norm=pre_norm, + activation=act, + res_attention=res_attention, + n_layers=n_layers, + store_attn=store_attn, + ) + + def forward(self, x) -> Tensor: # x: [bs x nvars x patch_len x patch_num] - - def forward(self, x) -> Tensor: # x: [bs x nvars x patch_len x patch_num] - n_vars = x.shape[1] # Input encoding - x = x.permute(0,1,3,2) # x: [bs x nvars x patch_num x patch_len] - x = self.W_P(x) # x: [bs x nvars x patch_num x d_model] + x = x.permute(0, 1, 3, 2) # x: [bs x nvars x patch_num x patch_len] + x = self.W_P(x) # x: [bs x nvars x patch_num x d_model] - u = torch.reshape(x, (x.shape[0]*x.shape[1],x.shape[2],x.shape[3])) # u: [bs * nvars x patch_num x d_model] - u = self.dropout(u + self.W_pos) # u: [bs * nvars x patch_num x d_model] + u = torch.reshape( + x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]) + ) # u: [bs * nvars x patch_num x d_model] + u = self.dropout(u + self.W_pos) # u: [bs * nvars x patch_num x d_model] # Encoder - z = self.encoder(u) # z: [bs * nvars x patch_num x d_model] - z = torch.reshape(z, (-1,n_vars,z.shape[-2],z.shape[-1])) # z: [bs x nvars x patch_num x d_model] - z = z.permute(0,1,3,2) # z: [bs x nvars x d_model x patch_num] - - return z - - - + z = self.encoder(u) # z: [bs * nvars x patch_num x d_model] + z = torch.reshape( + z, (-1, n_vars, z.shape[-2], z.shape[-1]) + ) # z: [bs x nvars x patch_num x d_model] + z = z.permute(0, 1, 3, 2) # z: [bs x nvars x d_model x patch_num] + + return z + + # Cell class TSTEncoder(nn.Module): - def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=None, - norm='BatchNorm', attn_dropout=0., dropout=0., activation='gelu', - res_attention=False, n_layers=1, pre_norm=False, store_attn=False): + def __init__( + self, + q_len, + d_model, + n_heads, + d_k=None, + d_v=None, + d_ff=None, + norm="BatchNorm", + attn_dropout=0.0, + dropout=0.0, + activation="gelu", + res_attention=False, + n_layers=1, + pre_norm=False, + store_attn=False, + ): super().__init__() - self.layers = nn.ModuleList([TSTEncoderLayer(q_len, d_model, n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, - attn_dropout=attn_dropout, dropout=dropout, - activation=activation, res_attention=res_attention, - pre_norm=pre_norm, store_attn=store_attn) for i in range(n_layers)]) + self.layers = nn.ModuleList( + [ + TSTEncoderLayer( + q_len, + d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + activation=activation, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + ) + for i in range(n_layers) + ] + ) self.res_attention = res_attention - def forward(self, src:Tensor, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None): + def forward( + self, + src: Tensor, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ): output = src scores = None if self.res_attention: - for mod in self.layers: output, scores = mod(output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + for mod in self.layers: + output, scores = mod( + output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) return output else: - for mod in self.layers: output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + for mod in self.layers: + output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask) return output - class TSTEncoderLayer(nn.Module): - def __init__(self, q_len, d_model, n_heads, d_k=None, d_v=None, d_ff=256, store_attn=False, - norm='BatchNorm', attn_dropout=0, dropout=0., bias=True, activation="gelu", res_attention=False, pre_norm=False): + def __init__( + self, + q_len, + d_model, + n_heads, + d_k=None, + d_v=None, + d_ff=256, + store_attn=False, + norm="BatchNorm", + attn_dropout=0, + dropout=0.0, + bias=True, + activation="gelu", + res_attention=False, + pre_norm=False, + ): super().__init__() - assert not d_model%n_heads, f"d_model ({d_model}) must be divisible by n_heads ({n_heads})" + assert ( + not d_model % n_heads + ), f"d_model ({d_model}) must be divisible by n_heads ({n_heads})" d_k = d_model // n_heads if d_k is None else d_k d_v = d_model // n_heads if d_v is None else d_v # Multi-Head attention self.res_attention = res_attention - self.self_attn = _MultiheadAttention(d_model, n_heads, d_k, d_v, attn_dropout=attn_dropout, proj_dropout=dropout, res_attention=res_attention) + self.self_attn = _MultiheadAttention( + d_model, + n_heads, + d_k, + d_v, + attn_dropout=attn_dropout, + proj_dropout=dropout, + res_attention=res_attention, + ) # Add & Norm self.dropout_attn = nn.Dropout(dropout) if "batch" in norm.lower(): - self.norm_attn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2)) + self.norm_attn = nn.Sequential( + Transpose(1, 2), nn.BatchNorm1d(d_model), Transpose(1, 2) + ) else: self.norm_attn = nn.LayerNorm(d_model) # Position-wise Feed-Forward - self.ff = nn.Sequential(nn.Linear(d_model, d_ff, bias=bias), - get_activation_fn(activation), - nn.Dropout(dropout), - nn.Linear(d_ff, d_model, bias=bias)) + self.ff = nn.Sequential( + nn.Linear(d_model, d_ff, bias=bias), + get_activation_fn(activation), + nn.Dropout(dropout), + nn.Linear(d_ff, d_model, bias=bias), + ) # Add & Norm self.dropout_ffn = nn.Dropout(dropout) if "batch" in norm.lower(): - self.norm_ffn = nn.Sequential(Transpose(1,2), nn.BatchNorm1d(d_model), Transpose(1,2)) + self.norm_ffn = nn.Sequential( + Transpose(1, 2), nn.BatchNorm1d(d_model), Transpose(1, 2) + ) else: self.norm_ffn = nn.LayerNorm(d_model) self.pre_norm = pre_norm self.store_attn = store_attn - - def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None) -> Tensor: + def forward( + self, + src: Tensor, + prev: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ) -> Tensor: # Multi-Head attention sublayer if self.pre_norm: src = self.norm_attn(src) - ## Multi-Head attention + # Multi-Head attention if self.res_attention: - src2, attn, scores = self.self_attn(src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + src2, attn, scores = self.self_attn( + src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) else: - src2, attn = self.self_attn(src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + src2, attn = self.self_attn( + src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) if self.store_attn: self.attn = attn - ## Add & Norm - src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout + # Add & Norm + src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout if not self.pre_norm: src = self.norm_attn(src) # Feed-forward sublayer if self.pre_norm: src = self.norm_ffn(src) - ## Position-wise Feed-Forward + # Position-wise Feed-Forward src2 = self.ff(src) - ## Add & Norm - src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout + # Add & Norm + src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout if not self.pre_norm: src = self.norm_ffn(src) @@ -267,10 +442,19 @@ def forward(self, src:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optio return src - - class _MultiheadAttention(nn.Module): - def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, attn_dropout=0., proj_dropout=0., qkv_bias=True, lsa=False): + def __init__( + self, + d_model, + n_heads, + d_k=None, + d_v=None, + res_attention=False, + attn_dropout=0.0, + proj_dropout=0.0, + qkv_bias=True, + lsa=False, + ): """Multi Head Attention Layer Input shape: Q: [batch_size (bs) x max_q_len x d_model] @@ -289,37 +473,61 @@ def __init__(self, d_model, n_heads, d_k=None, d_v=None, res_attention=False, at # Scaled Dot-Product Attention (multiple heads) self.res_attention = res_attention - self.sdp_attn = _ScaledDotProductAttention(d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa) + self.sdp_attn = _ScaledDotProductAttention( + d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa + ) # Poject output self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout)) - - def forward(self, Q:Tensor, K:Optional[Tensor]=None, V:Optional[Tensor]=None, prev:Optional[Tensor]=None, - key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None): + def forward( + self, + Q: Tensor, + K: Optional[Tensor] = None, + V: Optional[Tensor] = None, + prev: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ): bs = Q.size(0) - if K is None: K = Q - if V is None: V = Q + if K is None: + K = Q + if V is None: + V = Q # Linear (+ split in multiple heads) - q_s = self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1,2) # q_s : [bs x n_heads x max_q_len x d_k] - k_s = self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0,2,3,1) # k_s : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3) - v_s = self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1,2) # v_s : [bs x n_heads x q_len x d_v] + q_s = ( + self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1, 2) + ) # q_s : [bs x n_heads x max_q_len x d_k] + k_s = ( + self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0, 2, 3, 1) + ) # k_s : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3) + v_s = ( + self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1, 2) + ) # v_s : [bs x n_heads x q_len x d_v] # Apply Scaled Dot-Product Attention (multiple heads) if self.res_attention: - output, attn_weights, attn_scores = self.sdp_attn(q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + output, attn_weights, attn_scores = self.sdp_attn( + q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) else: - output, attn_weights = self.sdp_attn(q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + output, attn_weights = self.sdp_attn( + q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len] # back to the original inputs dimensions - output = output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) # output: [bs x q_len x n_heads * d_v] + output = ( + output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) + ) # output: [bs x q_len x n_heads * d_v] output = self.to_out(output) - if self.res_attention: return output, attn_weights, attn_scores - else: return output, attn_weights + if self.res_attention: + return output, attn_weights, attn_scores + else: + return output, attn_weights class _ScaledDotProductAttention(nn.Module): @@ -327,16 +535,24 @@ class _ScaledDotProductAttention(nn.Module): (Realformer: Transformer likes residual attention by He et al, 2020) and locality self sttention (Vision Transformer for Small-Size Datasets by Lee et al, 2021)""" - def __init__(self, d_model, n_heads, attn_dropout=0., res_attention=False, lsa=False): + def __init__(self, d_model, n_heads, attn_dropout=0.0, res_attention=False, lsa=False): super().__init__() self.attn_dropout = nn.Dropout(attn_dropout) self.res_attention = res_attention head_dim = d_model // n_heads - self.scale = nn.Parameter(torch.tensor(head_dim ** -0.5), requires_grad=lsa) + self.scale = nn.Parameter(torch.tensor(head_dim**-0.5), requires_grad=lsa) self.lsa = lsa - def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None, key_padding_mask:Optional[Tensor]=None, attn_mask:Optional[Tensor]=None): - ''' + def forward( + self, + q: Tensor, + k: Tensor, + v: Tensor, + prev: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ): + """ Input shape: q : [bs x n_heads x max_q_len x d_k] k : [bs x n_heads x d_k x seq_len] @@ -348,32 +564,42 @@ def forward(self, q:Tensor, k:Tensor, v:Tensor, prev:Optional[Tensor]=None, key_ output: [bs x n_heads x q_len x d_v] attn : [bs x n_heads x q_len x seq_len] scores : [bs x n_heads x q_len x seq_len] - ''' + """ # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence - attn_scores = torch.matmul(q, k) * self.scale # attn_scores : [bs x n_heads x max_q_len x q_len] + attn_scores = ( + torch.matmul(q, k) * self.scale + ) # attn_scores : [bs x n_heads x max_q_len x q_len] # Add pre-softmax attention scores from the previous layer (optional) - if prev is not None: attn_scores = attn_scores + prev + if prev is not None: + attn_scores = attn_scores + prev # Attention mask (optional) - if attn_mask is not None: # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len + if ( + attn_mask is not None + ): # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len if attn_mask.dtype == torch.bool: attn_scores.masked_fill_(attn_mask, -np.inf) else: attn_scores += attn_mask # Key padding mask (optional) - if key_padding_mask is not None: # mask with shape [bs x q_len] (only when max_w_len == q_len) + if ( + key_padding_mask is not None + ): # mask with shape [bs x q_len] (only when max_w_len == q_len) attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf) # normalize the attention weights - attn_weights = F.softmax(attn_scores, dim=-1) # attn_weights : [bs x n_heads x max_q_len x q_len] + attn_weights = F.softmax( + attn_scores, dim=-1 + ) # attn_weights : [bs x n_heads x max_q_len x q_len] attn_weights = self.attn_dropout(attn_weights) # compute the new values given the attention weights - output = torch.matmul(attn_weights, v) # output: [bs x n_heads x max_q_len x d_v] - - if self.res_attention: return output, attn_weights, attn_scores - else: return output, attn_weights + output = torch.matmul(attn_weights, v) # output: [bs x n_heads x max_q_len x d_v] + if self.res_attention: + return output, attn_weights, attn_scores + else: + return output, attn_weights diff --git a/tsururu/models/torch_based/layers/PatchTST_layers.py b/tsururu/models/torch_based/layers/PatchTST_layers.py index 11b5bd6..fd9fc91 100644 --- a/tsururu/models/torch_based/layers/PatchTST_layers.py +++ b/tsururu/models/torch_based/layers/PatchTST_layers.py @@ -1,31 +1,50 @@ -__all__ = ['Transpose', 'get_activation_fn', 'moving_avg', 'series_decomp', 'PositionalEncoding', 'SinCosPosEncoding', 'Coord2dPosEncoding', 'Coord1dPosEncoding', 'positional_encoding'] +__all__ = [ + "Transpose", + "get_activation_fn", + "moving_avg", + "series_decomp", + "PositionalEncoding", + "SinCosPosEncoding", + "Coord2dPosEncoding", + "Coord1dPosEncoding", + "positional_encoding", +] import torch from torch import nn import math + class Transpose(nn.Module): - def __init__(self, *dims, contiguous=False): + def __init__(self, *dims, contiguous=False): super().__init__() self.dims, self.contiguous = dims, contiguous + def forward(self, x): - if self.contiguous: return x.transpose(*self.dims).contiguous() - else: return x.transpose(*self.dims) + if self.contiguous: + return x.transpose(*self.dims).contiguous() + else: + return x.transpose(*self.dims) + - def get_activation_fn(activation): - if callable(activation): return activation() - elif activation.lower() == "relu": return nn.ReLU() - elif activation.lower() == "gelu": return nn.GELU() - raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') - - + if callable(activation): + return activation() + elif activation.lower() == "relu": + return nn.ReLU() + elif activation.lower() == "gelu": + return nn.GELU() + raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') + + # decomposition + class moving_avg(nn.Module): """ Moving average block to highlight the trend of time series """ + def __init__(self, kernel_size, stride): super(moving_avg, self).__init__() self.kernel_size = kernel_size @@ -45,6 +64,7 @@ class series_decomp(nn.Module): """ Series decomposition block """ + def __init__(self, kernel_size): super(series_decomp, self).__init__() self.moving_avg = moving_avg(kernel_size, stride=1) @@ -53,11 +73,9 @@ def forward(self, x): moving_mean = self.moving_avg(x) res = x - moving_mean return res, moving_mean - - - -# pos_encoding + +# pos_encoding def PositionalEncoding(q_len, d_model, normalize=True): pe = torch.zeros(q_len, d_model) position = torch.arange(0, q_len).unsqueeze(1) @@ -69,53 +87,75 @@ def PositionalEncoding(q_len, d_model, normalize=True): pe = pe / (pe.std() * 10) return pe + SinCosPosEncoding = PositionalEncoding + def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False): - x = .5 if exponential else 1 + x = 0.5 if exponential else 1 i = 0 for i in range(100): - cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1 - pv(f'{i:4.0f} {x:5.3f} {cpe.mean():+6.3f}', verbose) - if abs(cpe.mean()) <= eps: break - elif cpe.mean() > eps: x += .001 - else: x -= .001 + cpe = ( + 2 + * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x) + * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) + - 1 + ) + pv(f"{i:4.0f} {x:5.3f} {cpe.mean():+6.3f}", verbose) + if abs(cpe.mean()) <= eps: + break + elif cpe.mean() > eps: + x += 0.001 + else: + x -= 0.001 i += 1 if normalize: cpe = cpe - cpe.mean() cpe = cpe / (cpe.std() * 10) return cpe + def Coord1dPosEncoding(q_len, exponential=False, normalize=True): - cpe = (2 * (torch.linspace(0, 1, q_len).reshape(-1, 1)**(.5 if exponential else 1)) - 1) + cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** (0.5 if exponential else 1)) - 1 if normalize: cpe = cpe - cpe.mean() cpe = cpe / (cpe.std() * 10) return cpe + def positional_encoding(pe, learn_pe, q_len, d_model): # Positional encoding - if pe == None: - W_pos = torch.empty((q_len, d_model)) # pe = None and learn_pe = False can be used to measure impact of pe + if pe is None: + W_pos = torch.empty( + (q_len, d_model) + ) # pe = None and learn_pe = False can be used to measure impact of pe nn.init.uniform_(W_pos, -0.02, 0.02) learn_pe = False - elif pe == 'zero': + elif pe == "zero": W_pos = torch.empty((q_len, 1)) nn.init.uniform_(W_pos, -0.02, 0.02) - elif pe == 'zeros': + elif pe == "zeros": W_pos = torch.empty((q_len, d_model)) nn.init.uniform_(W_pos, -0.02, 0.02) - elif pe == 'normal' or pe == 'gauss': + elif pe == "normal" or pe == "gauss": W_pos = torch.zeros((q_len, 1)) torch.nn.init.normal_(W_pos, mean=0.0, std=0.1) - elif pe == 'uniform': + elif pe == "uniform": W_pos = torch.zeros((q_len, 1)) nn.init.uniform_(W_pos, a=0.0, b=0.1) - elif pe == 'lin1d': W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True) - elif pe == 'exp1d': W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True) - elif pe == 'lin2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True) - elif pe == 'exp2d': W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True) - elif pe == 'sincos': W_pos = PositionalEncoding(q_len, d_model, normalize=True) - else: raise ValueError(f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \ - 'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)") - return nn.Parameter(W_pos, requires_grad=learn_pe) \ No newline at end of file + elif pe == "lin1d": + W_pos = Coord1dPosEncoding(q_len, exponential=False, normalize=True) + elif pe == "exp1d": + W_pos = Coord1dPosEncoding(q_len, exponential=True, normalize=True) + elif pe == "lin2d": + W_pos = Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True) + elif pe == "exp2d": + W_pos = Coord2dPosEncoding(q_len, d_model, exponential=True, normalize=True) + elif pe == "sincos": + W_pos = PositionalEncoding(q_len, d_model, normalize=True) + else: + raise ValueError( + f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \ + 'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)" + ) + return nn.Parameter(W_pos, requires_grad=learn_pe) diff --git a/tsururu/models/torch_based/patchTST.py b/tsururu/models/torch_based/patchTST.py index cb512d4..373b110 100644 --- a/tsururu/models/torch_based/patchTST.py +++ b/tsururu/models/torch_based/patchTST.py @@ -1,24 +1,61 @@ -from typing import Callable, Optional -import torch -from torch import nn -from torch import Tensor -import torch.nn.functional as F -import numpy as np +from typing import Optional -from tsururu.models.torch_based.layers.PatchTST_backbone import PatchTST_backbone +from torch import Tensor, nn + +from tsururu.models.torch_based.layers.PatchTST_backbone import \ + PatchTST_backbone from tsururu.models.torch_based.layers.PatchTST_layers import series_decomp class PatchTST_NN(nn.Module): - def __init__(self, c_in, context_window, target_window, n_layers, n_heads, d_model, d_ff, dropout, fc_dropout, head_dropout, individual, patch_len, stride, padding_patch, revin, affine, subtract_last, decomposition, kernel_size, max_seq_len:Optional[int]=1024, d_k:Optional[int]=None, d_v:Optional[int]=None, norm:str='BatchNorm', attn_dropout:float=0., act:str="gelu", key_padding_mask:bool='auto', padding_var:Optional[int]=None, attn_mask:Optional[Tensor]=None, res_attention:bool=True, pre_norm:bool=False, store_attn:bool=False, pe:str='zeros', learn_pe:bool=True, pretrain_head:bool=False, head_type:str='flatten', verbose:bool=False, **kwargs): - + def __init__( + self, + c_in, + context_window, + target_window, + n_layers, + n_heads, + d_model, + d_ff, + dropout, + fc_dropout, + head_dropout, + individual, + patch_len, + stride, + padding_patch, + revin, + affine, + subtract_last, + decomposition, + kernel_size, + max_seq_len: Optional[int] = 1024, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + norm: str = "BatchNorm", + attn_dropout: float = 0.0, + act: str = "gelu", + key_padding_mask: bool = "auto", + padding_var: Optional[int] = None, + attn_mask: Optional[Tensor] = None, + res_attention: bool = True, + pre_norm: bool = False, + store_attn: bool = False, + pe: str = "zeros", + learn_pe: bool = True, + pretrain_head: bool = False, + head_type: str = "flatten", + verbose: bool = False, + **kwargs + ): + super().__init__() - + # load parameters self.c_in = c_in self.context_window = context_window self.target_window = target_window - + self.n_layers = n_layers self.n_heads = n_heads self.d_model = d_model @@ -26,69 +63,149 @@ def __init__(self, c_in, context_window, target_window, n_layers, n_heads, d_mod self.dropout = dropout self.fc_dropout = fc_dropout self.head_dropout = head_dropout - + self.individual = individual - + self.patch_len = patch_len self.stride = stride self.padding_patch = padding_patch - + self.revin = revin self.affine = affine self.subtract_last = subtract_last - + self.decomposition = decomposition self.kernel_size = kernel_size - - + # model self.decomposition = decomposition if self.decomposition: self.decomp_module = series_decomp(kernel_size) - self.model_trend = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, - max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model, - n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, - dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, - attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, - pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch, - pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine, - subtract_last=subtract_last, verbose=verbose, **kwargs) - self.model_res = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, - max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model, - n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, - dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, - attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, - pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch, - pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine, - subtract_last=subtract_last, verbose=verbose, **kwargs) + self.model_trend = PatchTST_backbone( + c_in=c_in, + context_window=context_window, + target_window=target_window, + patch_len=patch_len, + stride=stride, + max_seq_len=max_seq_len, + n_layers=n_layers, + d_model=d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + act=act, + key_padding_mask=key_padding_mask, + padding_var=padding_var, + attn_mask=attn_mask, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + pe=pe, + learn_pe=learn_pe, + fc_dropout=fc_dropout, + head_dropout=head_dropout, + padding_patch=padding_patch, + pretrain_head=pretrain_head, + head_type=head_type, + individual=individual, + revin=revin, + affine=affine, + subtract_last=subtract_last, + verbose=verbose, + **kwargs + ) + self.model_res = PatchTST_backbone( + c_in=c_in, + context_window=context_window, + target_window=target_window, + patch_len=patch_len, + stride=stride, + max_seq_len=max_seq_len, + n_layers=n_layers, + d_model=d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + act=act, + key_padding_mask=key_padding_mask, + padding_var=padding_var, + attn_mask=attn_mask, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + pe=pe, + learn_pe=learn_pe, + fc_dropout=fc_dropout, + head_dropout=head_dropout, + padding_patch=padding_patch, + pretrain_head=pretrain_head, + head_type=head_type, + individual=individual, + revin=revin, + affine=affine, + subtract_last=subtract_last, + verbose=verbose, + **kwargs + ) else: - self.model = PatchTST_backbone(c_in=c_in, context_window = context_window, target_window=target_window, patch_len=patch_len, stride=stride, - max_seq_len=max_seq_len, n_layers=n_layers, d_model=d_model, - n_heads=n_heads, d_k=d_k, d_v=d_v, d_ff=d_ff, norm=norm, attn_dropout=attn_dropout, - dropout=dropout, act=act, key_padding_mask=key_padding_mask, padding_var=padding_var, - attn_mask=attn_mask, res_attention=res_attention, pre_norm=pre_norm, store_attn=store_attn, - pe=pe, learn_pe=learn_pe, fc_dropout=fc_dropout, head_dropout=head_dropout, padding_patch = padding_patch, - pretrain_head=pretrain_head, head_type=head_type, individual=individual, revin=revin, affine=affine, - subtract_last=subtract_last, verbose=verbose, **kwargs) - - - - def forward(self, x): # x: [Batch, Input length, Channel] + self.model = PatchTST_backbone( + c_in=c_in, + context_window=context_window, + target_window=target_window, + patch_len=patch_len, + stride=stride, + max_seq_len=max_seq_len, + n_layers=n_layers, + d_model=d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + act=act, + key_padding_mask=key_padding_mask, + padding_var=padding_var, + attn_mask=attn_mask, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + pe=pe, + learn_pe=learn_pe, + fc_dropout=fc_dropout, + head_dropout=head_dropout, + padding_patch=padding_patch, + pretrain_head=pretrain_head, + head_type=head_type, + individual=individual, + revin=revin, + affine=affine, + subtract_last=subtract_last, + verbose=verbose, + **kwargs + ) + + def forward(self, x): # x: [Batch, Input length, Channel] if self.decomposition: res_init, trend_init = self.decomp_module(x) - res_init, trend_init = res_init.permute(0,2,1), trend_init.permute(0,2,1) # x: [Batch, Channel, Input length] + res_init, trend_init = res_init.permute(0, 2, 1), trend_init.permute( + 0, 2, 1 + ) # x: [Batch, Channel, Input length] res = self.model_res(res_init) trend = self.model_trend(trend_init) x = res + trend - x = x.permute(0,2,1) # x: [Batch, Input length, Channel] + x = x.permute(0, 2, 1) # x: [Batch, Input length, Channel] else: - x = x.permute(0,2,1) # x: [Batch, Channel, Input length] + x = x.permute(0, 2, 1) # x: [Batch, Channel, Input length] x = self.model(x) - x = x.permute(0,2,1) # x: [Batch, Input length, Channel] + x = x.permute(0, 2, 1) # x: [Batch, Input length, Channel] return x - - - - - - From 3057b24f2dcec685cba780eb2c50c88e79d57fbc Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Wed, 12 Jun 2024 17:58:57 +0300 Subject: [PATCH 13/50] add dlinear example --- Example_Demand.ipynb | 1278 +++++------------------------ tsururu/model_training/trainer.py | 5 +- 2 files changed, 228 insertions(+), 1055 deletions(-) diff --git a/Example_Demand.ipynb b/Example_Demand.ipynb index 120faa9..bb88d57 100644 --- a/Example_Demand.ipynb +++ b/Example_Demand.ipynb @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -496994,6 +496994,15 @@ "step = 1" ] }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "padded_for_multivariate = pad_multivariate_time_series(train_data_tsururu_1, pad_with=0)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -497045,22 +497054,13 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "padded_for_multivariate = pad_multivariate_time_series(train_data_tsururu_1, pad_with=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0a3098315e5428fbec142ac91179342", + "model_id": "c9ab4698fe234b9fb09587090c2dac9f", "version_major": 2, "version_minor": 0 }, @@ -497081,7 +497081,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ca73ceee358b4aaaa59858979b632358", + "model_id": "285cfc5af3394d2eadfbedb15b122da4", "version_major": 2, "version_minor": 0 }, @@ -497096,1050 +497096,66 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 346.2034112\ttest: 347.0768727\tbest: 347.0768727 (0)\ttotal: 4.05ms\tremaining: 4.04s\n", - "500:\tlearn: 172.1386901\ttest: 185.8569856\tbest: 185.8569856 (500)\ttotal: 1.74s\tremaining: 1.73s\n", - "999:\tlearn: 153.6936412\ttest: 178.9742095\tbest: 178.9742095 (999)\ttotal: 3.34s\tremaining: 0us\n", - "\n", - "bestTest = 178.9742095\n", - "bestIteration = 999\n", - "\n", - "Fold 0. Score: 178.97420953750216\n", - "0:\tlearn: 341.8767075\ttest: 355.9989285\tbest: 355.9989285 (0)\ttotal: 3.48ms\tremaining: 3.47s\n", - "500:\tlearn: 166.6441137\ttest: 199.1733969\tbest: 199.1733969 (500)\ttotal: 1.65s\tremaining: 1.64s\n", - "999:\tlearn: 147.5946327\ttest: 192.3942796\tbest: 192.3942796 (999)\ttotal: 3.24s\tremaining: 0us\n", - "\n", - "bestTest = 192.3942796\n", - "bestIteration = 999\n", - "\n", - "Fold 1. Score: 192.39427963881423\n", - "0:\tlearn: 351.4081171\ttest: 336.2556421\tbest: 336.2556421 (0)\ttotal: 4.44ms\tremaining: 4.44s\n", - "500:\tlearn: 173.2543177\ttest: 182.3943422\tbest: 182.3943422 (500)\ttotal: 1.68s\tremaining: 1.67s\n", - "999:\tlearn: 152.7361359\ttest: 175.0576787\tbest: 175.0576787 (999)\ttotal: 3.36s\tremaining: 0us\n", - "\n", - "bestTest = 175.0576787\n", - "bestIteration = 999\n", - "\n", - "Fold 2. Score: 175.0576787318993\n", - "Mean score: 182.1421\n", - "Std: 7.4237\n", - "0:\tlearn: 917.8687542\ttest: 911.5997024\tbest: 911.5997024 (0)\ttotal: 20.1ms\tremaining: 20s\n", - "500:\tlearn: 535.7612102\ttest: 564.7598771\tbest: 564.7598771 (500)\ttotal: 7.46s\tremaining: 7.43s\n", - "999:\tlearn: 479.8340882\ttest: 530.2893138\tbest: 530.2893138 (999)\ttotal: 14.1s\tremaining: 0us\n", - "\n", - "bestTest = 530.2893138\n", - "bestIteration = 999\n", - "\n", - "Fold 0. Score: 530.2893138064202\n", - "0:\tlearn: 918.5932042\ttest: 910.1355458\tbest: 910.1355458 (0)\ttotal: 17.5ms\tremaining: 17.4s\n", - "500:\tlearn: 536.5823972\ttest: 559.4462437\tbest: 559.4462437 (500)\ttotal: 6.85s\tremaining: 6.82s\n", - "999:\tlearn: 483.5649036\ttest: 524.9460420\tbest: 524.9448426 (998)\ttotal: 13.8s\tremaining: 0us\n", - "\n", - "bestTest = 524.9448426\n", - "bestIteration = 998\n", - "\n", - "Shrink model to first 999 iterations.\n", - "Fold 1. Score: 524.9448426347514\n", - "0:\tlearn: 911.0934008\ttest: 925.9288464\tbest: 925.9288464 (0)\ttotal: 20.2ms\tremaining: 20.2s\n", - "500:\tlearn: 533.5854572\ttest: 564.9233539\tbest: 564.9233539 (500)\ttotal: 7.73s\tremaining: 7.7s\n", - "999:\tlearn: 480.9918463\ttest: 529.8566101\tbest: 529.8566101 (999)\ttotal: 14.7s\tremaining: 0us\n", - "\n", - "bestTest = 529.8566101\n", - "bestIteration = 999\n", - "\n", - "Fold 2. Score: 529.856610064411\n", - "Mean score: 528.3636\n", - "Std: 2.4239\n", - "0:\tlearn: 1280.0761008\ttest: 1299.0851667\tbest: 1299.0851667 (0)\ttotal: 59.8ms\tremaining: 59.7s\n", - "500:\tlearn: 760.5376289\ttest: 807.9681597\tbest: 807.9681597 (500)\ttotal: 29.7s\tremaining: 29.6s\n", - "999:\tlearn: 689.6433063\ttest: 758.8821034\tbest: 758.8821034 (999)\ttotal: 59.4s\tremaining: 0us\n", - "\n", - "bestTest = 758.8821034\n", - "bestIteration = 999\n", - "\n", - "Fold 0. Score: 758.8821034235361\n", - "0:\tlearn: 1286.1634528\ttest: 1286.7639329\tbest: 1286.7639329 (0)\ttotal: 60.6ms\tremaining: 1m\n", - "500:\tlearn: 764.4878009\ttest: 797.9376441\tbest: 797.9376441 (500)\ttotal: 30s\tremaining: 29.9s\n", - "999:\tlearn: 692.8140407\ttest: 748.4606953\tbest: 748.4606953 (999)\ttotal: 59.9s\tremaining: 0us\n", - "\n", - "bestTest = 748.4606953\n", - "bestIteration = 999\n", - "\n", - "Fold 1. Score: 748.4606953439309\n", - "0:\tlearn: 1292.7683312\ttest: 1273.2430869\tbest: 1273.2430869 (0)\ttotal: 52.6ms\tremaining: 52.5s\n", - "500:\tlearn: 769.7002951\ttest: 788.2455151\tbest: 788.2455151 (500)\ttotal: 29.5s\tremaining: 29.4s\n", - "999:\tlearn: 697.5121973\ttest: 740.8226168\tbest: 740.8226168 (999)\ttotal: 1m 1s\tremaining: 0us\n", - "\n", - "bestTest = 740.8226168\n", - "bestIteration = 999\n", - "\n", - "Fold 2. Score: 740.8226168213073\n", - "Mean score: 749.3885\n", - "Std: 7.4019\n", - "freq: Day; period: 1\n" + "0:\tlearn: 346.2034112\ttest: 347.0768727\tbest: 347.0768727 (0)\ttotal: 64.8ms\tremaining: 1m 4s\n", + "500:\tlearn: 172.1386901\ttest: 185.8569856\tbest: 185.8569856 (500)\ttotal: 1.76s\tremaining: 1.75s\n" ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "24b620d1aae64929b206b477b2082a4b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/3 [00:00 25\u001b[0m \u001b[43mstrategy_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m _, preds[model_name] \u001b[38;5;241m=\u001b[39m strategy_instance\u001b[38;5;241m.\u001b[39mpredict(dataset)\n\u001b[1;32m 27\u001b[0m results[model_name] \u001b[38;5;241m=\u001b[39m get_metrics_by_id(val_data, preds[model_name])\n", + "File 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75\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_params)\n\u001b[0;32m---> 76\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_one_fold\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_val\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mappend(model)\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscores\u001b[38;5;241m.\u001b[39mappend(model\u001b[38;5;241m.\u001b[39mscore)\n", + "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/models/boost.py:52\u001b[0m, in 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save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m 5824\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m params:\n\u001b[1;32m 5825\u001b[0m CatBoostRegressor\u001b[38;5;241m.\u001b[39m_check_is_compatible_loss(params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m-> 5827\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcat_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mlog_cout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_cerr\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/catboost/core.py:2400\u001b[0m, in \u001b[0;36mCatBoost._fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m 2397\u001b[0m allow_clear_pool \u001b[38;5;241m=\u001b[39m train_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_clear_pool\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 2399\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m plot_wrapper(plot, plot_file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTraining plots\u001b[39m\u001b[38;5;124m'\u001b[39m, [_get_train_dir(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_params())]):\n\u001b[0;32m-> 2400\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2401\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2402\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meval_sets\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2403\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2404\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_clear_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2405\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minit_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 2406\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2408\u001b[0m \u001b[38;5;66;03m# Have property feature_importance possibly set\u001b[39;00m\n\u001b[1;32m 2409\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_object\u001b[38;5;241m.\u001b[39m_get_loss_function_name()\n", + "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/catboost/core.py:1780\u001b[0m, in \u001b[0;36m_CatBoostBase._train\u001b[0;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_train\u001b[39m(\u001b[38;5;28mself\u001b[39m, train_pool, test_pool, params, allow_clear_pool, init_model):\n\u001b[0;32m-> 1780\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_clear_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_object\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1781\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_trained_model_attributes()\n", + "File \u001b[0;32m_catboost.pyx:4833\u001b[0m, in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m_catboost.pyx:4882\u001b[0m, in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "multivariate_list = [False, True]\n", - "strategy_list = [\"recursive\", \"direct\", \"mimo\", \"flatwidemimo\"]\n", - "model_horizon_list = [1, 7, 14]\n", + "# multivariate_list = [False, True]\n", + "# strategy_list = [\"recursive\", \"direct\", \"mimo\", \"flatwidemimo\"]\n", + "# model_horizon_list = [1, 7, 14]\n", "\n", - "preds = {}\n", - "results = {}\n", + "# preds = {}\n", + "# results = {}\n", "\n", - "for multivariate, strategy in product(multivariate_list, strategy_list):\n", - " current_data = padded_for_multivariate\n", + "# for multivariate, strategy in product(multivariate_list, strategy_list):\n", + "# current_data = padded_for_multivariate\n", "\n", - " dataset = TSDataset(\n", - " data=current_data,\n", - " columns_params=dataset_params,\n", - " print_freq_period_info=True,\n", - " )\n", - " pipeline = Pipeline.from_dict(pipeline_params, multivariate=multivariate)\n", + "# dataset = TSDataset(\n", + "# data=current_data,\n", + "# columns_params=dataset_params,\n", + "# print_freq_period_info=True,\n", + "# )\n", + "# pipeline = Pipeline.from_dict(pipeline_params, multivariate=multivariate)\n", "\n", - " if strategy in [\"recursive\", \"direct\"]:\n", - " strategy_class = RecursiveStrategy if strategy == \"recursive\" else DirectStrategy\n", - " for model_horizon in tqdm(model_horizon_list):\n", - " model_name = f\"CatBoost_{multivariate}_{strategy}_{model_horizon}\"\n", - " strategy_instance = strategy_class(\n", - " horizon, history, step, trainer, pipeline, model_horizon\n", - " )\n", - " strategy_instance.fit(dataset)\n", - " _, preds[model_name] = strategy_instance.predict(dataset)\n", - " results[model_name] = get_metrics_by_id(val_data, preds[model_name])\n", - " else:\n", - " model_name = f\"CatBoost_{multivariate}_{strategy}\"\n", - " strategy_class = MIMOStrategy if strategy == \"mimo\" else FlatWideMIMOStrategy\n", - " strategy_instance = strategy_class(horizon, history, step, trainer, pipeline)\n", - " strategy_instance.fit(dataset)\n", - " _, preds[model_name] = strategy_instance.predict(dataset)\n", - " results[model_name] = get_metrics_by_id(val_data, preds[model_name])" + "# if strategy in [\"recursive\", \"direct\"]:\n", + "# strategy_class = RecursiveStrategy if strategy == \"recursive\" else DirectStrategy\n", + "# for model_horizon in tqdm(model_horizon_list):\n", + "# model_name = f\"CatBoost_{multivariate}_{strategy}_{model_horizon}\"\n", + "# strategy_instance = strategy_class(\n", + "# horizon, history, step, trainer, pipeline, model_horizon\n", + "# )\n", + "# strategy_instance.fit(dataset)\n", + "# _, preds[model_name] = strategy_instance.predict(dataset)\n", + "# results[model_name] = get_metrics_by_id(val_data, preds[model_name])\n", + "# else:\n", + "# model_name = f\"CatBoost_{multivariate}_{strategy}\"\n", + "# strategy_class = MIMOStrategy if strategy == \"mimo\" else FlatWideMIMOStrategy\n", + "# strategy_instance = strategy_class(horizon, history, step, trainer, pipeline)\n", + "# strategy_instance.fit(dataset)\n", + "# _, preds[model_name] = strategy_instance.predict(dataset)\n", + "# results[model_name] = get_metrics_by_id(val_data, preds[model_name])" ] }, { @@ -498179,9 +497195,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + } + ], "source": [ "dataset = TSDataset(\n", " data=padded_for_multivariate,\n", @@ -498192,7 +497216,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -498208,7 +497232,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -498238,7 +497262,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -498250,7 +497274,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -498265,13 +497289,159 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10, Loss: 77022.8143\n", + "Validation, Loss: 88605.5391, Metric: -88605.5391\n", + "Validation, Loss: 88605.5391, Metric: -88605.5391\n", + "Model saved to checkpoints/fold_0/model_0.pth\n", + "Epoch 2/10, Loss: 75997.1524\n", + "Validation, Loss: 88353.4141, Metric: -88353.4141\n", + "Validation, Loss: 88353.4141, Metric: -88353.4141\n", + "Model saved to checkpoints/fold_0/model_1.pth\n", + "Epoch 3/10, Loss: 75493.1131\n", + "Validation, Loss: 88131.5859, Metric: -88131.5859\n", + "Validation, Loss: 88131.5859, Metric: -88131.5859\n", + "Model saved to checkpoints/fold_0/model_2.pth\n", + "Epoch 4/10, Loss: 75091.6221\n", + "Validation, Loss: 88008.4922, Metric: -88008.4922\n", + "Validation, Loss: 88008.4922, Metric: -88008.4922\n", + "Model saved to checkpoints/fold_0/model_3.pth\n", + "Epoch 5/10, Loss: 74739.4823\n", + "Validation, Loss: 88012.4844, Metric: -88012.4844\n", + "Validation, Loss: 88012.4844, Metric: -88012.4844\n", + "Model saved to checkpoints/fold_0/model_4.pth\n", + "Epoch 6/10, Loss: 74490.1450\n", + "Validation, Loss: 87961.9688, Metric: -87961.9688\n", + "Validation, Loss: 87961.9688, Metric: -87961.9688\n", + "Removing worst model snapshot: checkpoints/fold_0/model_0.pth\n", + "Model saved to checkpoints/fold_0/model_5.pth\n", + "Epoch 7/10, Loss: 74279.9838\n", + "Validation, Loss: 87934.3047, Metric: -87934.3047\n", + "Validation, Loss: 87934.3047, Metric: -87934.3047\n", + "Removing worst model snapshot: checkpoints/fold_0/model_1.pth\n", + "Model saved to checkpoints/fold_0/model_6.pth\n", + "Epoch 8/10, Loss: 74146.2284\n", + "Validation, Loss: 87924.1172, Metric: -87924.1172\n", + "Validation, Loss: 87924.1172, Metric: -87924.1172\n", + "Removing worst model snapshot: checkpoints/fold_0/model_2.pth\n", + "Model saved to checkpoints/fold_0/model_7.pth\n", + "Epoch 9/10, Loss: 73984.6355\n", + "Validation, Loss: 87928.1328, Metric: -87928.1328\n", + "Validation, Loss: 87928.1328, Metric: -87928.1328\n", + "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", + "Model saved to checkpoints/fold_0/model_8.pth\n", + "Epoch 10/10, Loss: 73872.0578\n", + "Validation, Loss: 87890.6797, Metric: -87890.6797\n", + "Validation, Loss: 87890.6797, Metric: -87890.6797\n", + "Removing worst model snapshot: checkpoints/fold_0/model_3.pth\n", + "Model saved to checkpoints/fold_0/model_9.pth\n", + "Fold 0. Score: -87890.6796875\n", + "Epoch 1/10, Loss: 85199.6444\n", + "Validation, Loss: 71910.2891, Metric: -71910.2891\n", + "Validation, Loss: 71910.2891, Metric: -71910.2891\n", + "Removing worst model snapshot: checkpoints/fold_0/model_5.pth\n", + "Model saved to checkpoints/fold_1/model_0.pth\n", + "Epoch 2/10, Loss: 84203.7885\n", + "Validation, Loss: 71741.6719, Metric: -71741.6719\n", + "Validation, Loss: 71741.6719, Metric: -71741.6719\n", + "Removing worst model snapshot: checkpoints/fold_0/model_6.pth\n", + "Model saved to checkpoints/fold_1/model_1.pth\n", + "Epoch 3/10, Loss: 83587.3179\n", + "Validation, Loss: 71790.6016, Metric: -71790.6016\n", + "Validation, Loss: 71790.6016, Metric: -71790.6016\n", + "Removing worst model snapshot: checkpoints/fold_0/model_8.pth\n", + "Model saved to checkpoints/fold_1/model_2.pth\n", + "Epoch 4/10, Loss: 83060.4770\n", + "Validation, Loss: 71882.2031, Metric: -71882.2031\n", + "Validation, Loss: 71882.2031, Metric: -71882.2031\n", + "Removing worst model snapshot: checkpoints/fold_0/model_7.pth\n", + "Model saved to checkpoints/fold_1/model_3.pth\n", + "Epoch 5/10, Loss: 82721.1268\n", + "Validation, Loss: 72010.3828, Metric: -72010.3828\n", + "Validation, Loss: 72010.3828, Metric: -72010.3828\n", + "Removing worst model snapshot: checkpoints/fold_0/model_9.pth\n", + "Model saved to checkpoints/fold_1/model_4.pth\n", + "Epoch 6/10, Loss: 82375.5125\n", + "Validation, Loss: 72040.5547, Metric: -72040.5547\n", + "Validation, Loss: 72040.5547, Metric: -72040.5547\n", + "Epoch 7/10, Loss: 82099.6802\n", + "Validation, Loss: 72074.7656, Metric: -72074.7656\n", + "Validation, Loss: 72074.7656, Metric: -72074.7656\n", + "Early stopping triggered\n", + "Fold 1. Score: -72074.765625\n", + "Epoch 1/10, Loss: 80639.1106\n", + "Validation, Loss: 81399.9219, Metric: -81399.9219\n", + "Validation, Loss: 81399.9219, Metric: -81399.9219\n", + "Early stopping triggered\n", + "Fold 2. Score: -81399.921875\n", + "Mean score: -80455.1224\n", + "Std: 6491.29\n" + ] + }, + { + "data": { + "text/plain": [ + "(35.62223482131958, )" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "strategy.fit(dataset)" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.2591209411621094,\n", + " id Date Demand\n", + " 0 105_1 2016-04-25 129.049103\n", + " 1 105_1 2016-04-26 115.711586\n", + " 2 105_1 2016-04-27 107.117775\n", + " 3 105_1 2016-04-28 91.924202\n", + " 4 105_1 2016-04-29 63.435825\n", + " ... ... ... ...\n", + " 5539 9_2 2016-05-18 38.332611\n", + " 5540 9_2 2016-05-19 37.380253\n", + " 5541 9_2 2016-05-20 38.566143\n", + " 5542 9_2 2016-05-21 37.990238\n", + " 5543 9_2 2016-05-22 37.946899\n", + " \n", + " [5544 rows x 3 columns])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strategy.predict(dataset)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 1854011..18dbd8d 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -312,7 +312,10 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - checkpoint_path = self.checkpoint_path self.checkpoint_path /= f"fold_{fold_i}" train_subset = Subset(train_dataset, train_dataset_idx) - val_subset = Subset(val_dataset, val_dataset_idx) + if val_dataset is not None: + val_subset = Subset(val_dataset, val_dataset_idx) + elif val_dataset_idx is not None: + val_subset = Subset(train_dataset, val_dataset_idx) train_loader = torch.utils.data.DataLoader( train_subset, batch_size=self.batch_size, From 5266261c621089424e5c7946ec5958708a6ff316 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 20 Jun 2024 14:43:02 +0300 Subject: [PATCH 14/50] add TimesNet --- .../models/torch_based/layers/Conv_Block.py | 30 +++++ tsururu/models/torch_based/layers/Embed.py | 126 ++++++++++++++++++ 2 files changed, 156 insertions(+) create mode 100644 tsururu/models/torch_based/layers/Conv_Block.py create mode 100644 tsururu/models/torch_based/layers/Embed.py diff --git a/tsururu/models/torch_based/layers/Conv_Block.py b/tsururu/models/torch_based/layers/Conv_Block.py new file mode 100644 index 0000000..b98f8ed --- /dev/null +++ b/tsururu/models/torch_based/layers/Conv_Block.py @@ -0,0 +1,30 @@ +import torch +import torch.nn as nn + + +class Inception_Block_V1(nn.Module): + def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True): + super(Inception_Block_V1, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.num_kernels = num_kernels + kernels = [] + for i in range(self.num_kernels): + kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i)) + self.kernels = nn.ModuleList(kernels) + if init_weight: + self._initialize_weights() + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + res_list = [] + for i in range(self.num_kernels): + res_list.append(self.kernels[i](x)) + res = torch.stack(res_list, dim=-1).mean(-1) + return res \ No newline at end of file diff --git a/tsururu/models/torch_based/layers/Embed.py b/tsururu/models/torch_based/layers/Embed.py new file mode 100644 index 0000000..4a8d40f --- /dev/null +++ b/tsururu/models/torch_based/layers/Embed.py @@ -0,0 +1,126 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils import weight_norm +import math + + +class TokenEmbedding(nn.Module): + def __init__(self, c_in, d_model): + super(TokenEmbedding, self).__init__() + padding = 1 if torch.__version__ >= '1.5.0' else 2 + self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model, + kernel_size=3, padding=padding, padding_mode='circular', bias=False) + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_( + m.weight, mode='fan_in', nonlinearity='leaky_relu') + + def forward(self, x): + x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2) + return x + + +class PositionalEmbedding(nn.Module): + def __init__(self, d_model, max_len=5000): + super(PositionalEmbedding, self).__init__() + # Compute the positional encodings once in log space. + pe = torch.zeros(max_len, d_model).float() + pe.require_grad = False + + position = torch.arange(0, max_len).float().unsqueeze(1) + div_term = (torch.arange(0, d_model, 2).float() + * -(math.log(10000.0) / d_model)).exp() + + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + pe = pe.unsqueeze(0) + self.register_buffer('pe', pe) + + def forward(self, x): + return self.pe[:, :x.size(1)] + + +class FixedEmbedding(nn.Module): + def __init__(self, c_in, d_model): + super(FixedEmbedding, self).__init__() + + w = torch.zeros(c_in, d_model).float() + w.require_grad = False + + position = torch.arange(0, c_in).float().unsqueeze(1) + div_term = (torch.arange(0, d_model, 2).float() + * -(math.log(10000.0) / d_model)).exp() + + w[:, 0::2] = torch.sin(position * div_term) + w[:, 1::2] = torch.cos(position * div_term) + + self.emb = nn.Embedding(c_in, d_model) + self.emb.weight = nn.Parameter(w, requires_grad=False) + + def forward(self, x): + return self.emb(x).detach() + + +class TemporalEmbedding(nn.Module): + def __init__(self, d_model, embed_type='fixed', freq='h'): + super(TemporalEmbedding, self).__init__() + + minute_size = 4 + hour_size = 24 + weekday_size = 7 + day_size = 32 + month_size = 13 + + Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding + if freq == 't': + self.minute_embed = Embed(minute_size, d_model) + self.hour_embed = Embed(hour_size, d_model) + self.weekday_embed = Embed(weekday_size, d_model) + self.day_embed = Embed(day_size, d_model) + self.month_embed = Embed(month_size, d_model) + + def forward(self, x): + x = x.long() + minute_x = self.minute_embed(x[:, :, 4]) if hasattr( + self, 'minute_embed') else 0. + hour_x = self.hour_embed(x[:, :, 3]) + weekday_x = self.weekday_embed(x[:, :, 2]) + day_x = self.day_embed(x[:, :, 1]) + month_x = self.month_embed(x[:, :, 0]) + + return hour_x + weekday_x + day_x + month_x + minute_x + + +class TimeFeatureEmbedding(nn.Module): + def __init__(self, d_model, embed_type='timeF', freq='h'): + super(TimeFeatureEmbedding, self).__init__() + + freq_map = {'h': 4, 't': 5, 's': 6, + 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3} + d_inp = freq_map[freq] + self.embed = nn.Linear(d_inp, d_model, bias=False) + + def forward(self, x): + return self.embed(x) + + +class DataEmbedding(nn.Module): + def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1): + super(DataEmbedding, self).__init__() + + self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) + self.position_embedding = PositionalEmbedding(d_model=d_model) + self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, + freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding( + d_model=d_model, embed_type=embed_type, freq=freq) + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x, x_mark): + if x_mark is None: + x = self.value_embedding(x) + self.position_embedding(x) + else: + x = self.value_embedding( + x) + self.temporal_embedding(x_mark) + self.position_embedding(x) + return self.dropout(x) From 2bee8fd01ac5e26c30931e680153c4d1a6734154 Mon Sep 17 00:00:00 2001 From: unknown Date: Thu, 20 Jun 2024 14:50:36 +0300 Subject: [PATCH 15/50] add TimesNet --- tsururu/models/torch_based/TimesNet.py | 102 +++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 tsururu/models/torch_based/TimesNet.py diff --git a/tsururu/models/torch_based/TimesNet.py b/tsururu/models/torch_based/TimesNet.py new file mode 100644 index 0000000..9db6c91 --- /dev/null +++ b/tsururu/models/torch_based/TimesNet.py @@ -0,0 +1,102 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.fft +from tsururu.models.torch_based.layers.Embed import DataEmbedding +from tsururu.models.torch_based.layers.Conv_Block import Inception_Block_V1 + + +def FFT_for_Period(x, k=2): + # [B, T, C] + xf = torch.fft.rfft(x, dim=1) + # find period by amplitudes + frequency_list = abs(xf).mean(0).mean(-1) + frequency_list[0] = 0 + _, top_list = torch.topk(frequency_list, k) + top_list = top_list.detach().cpu().numpy() + period = x.shape[1] // top_list + return period, abs(xf).mean(-1)[:, top_list] + + +class TimesBlock(nn.Module): + def __init__(self, seq_len, pred_len, top_k, d_model, d_ff, num_kernels): + super(TimesBlock, self).__init__() + + self.seq_len = seq_len + self.pred_len = pred_len + self.k = top_k + + self.conv = nn.Sequential( + Inception_Block_V1(d_model, d_ff, num_kernels=num_kernels), + nn.GELU(), + Inception_Block_V1(d_ff, d_model, num_kernels=num_kernels) + ) + + def forward(self, x): + B, T, N = x.size() + period_list, period_weight = FFT_for_Period(x, self.k) + + res = [] + for i in range(self.k): + period = period_list[i] + if (self.seq_len + self.pred_len) % period != 0: + length = (((self.seq_len + self.pred_len) // period) + 1) * period + padding = torch.zeros([B, (length - (self.seq_len + self.pred_len)), N]).to(x.device) + out = torch.cat([x, padding], dim=1) + else: + length = self.seq_len + self.pred_len + out = x + out = out.reshape(B, length // period, period, N).permute(0, 3, 1, 2).contiguous() + out = self.conv(out) + out = out.permute(0, 2, 3, 1).reshape(B, -1, N) + res.append(out[:, :(self.seq_len + self.pred_len), :]) + res = torch.stack(res, dim=-1) + period_weight = F.softmax(period_weight, dim=1) + period_weight = period_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1) + res = torch.sum(res * period_weight, -1) + res = res + x + return res + + +class TimesNet_NN(nn.Module): + def __init__(self, seq_len, pred_len, e_layers, enc_in, d_model, d_ff, num_kernels, top_k, c_out, dropout, embed, freq): + super(TimesNet_NN, self).__init__() + + self.seq_len = seq_len + self.pred_len = pred_len + self.e_layers = e_layers + self.d_model = d_model + self.d_ff = d_ff + self.num_kernels = num_kernels + self.top_k = top_k + self.c_out = c_out + self.dropout = dropout + self.embed = embed + self.freq = freq + + self.model = nn.ModuleList([TimesBlock(seq_len, pred_len, top_k, d_model, d_ff, num_kernels) + for _ in range(self.e_layers)]) + + self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout) + self.layer_norm = nn.LayerNorm(d_model) + self.predict_linear = nn.Linear(seq_len, pred_len + seq_len) + self.projection = nn.Linear(d_model, c_out, bias=True) + + def forward(self, x_enc, x_mark_enc): + means = x_enc.mean(1, keepdim=True).detach() + x_enc = x_enc - means + stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) + x_enc /= stdev + + enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] + enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(0, 2, 1) + + for i in range(self.e_layers): + enc_out = self.layer_norm(self.model) + + dec_out = self.projection(enc_out) + + dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)) + dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)) + + return dec_out[:, -self.pred_len:, :] # [B, L, D] From a0ce8ad28461f4cec17c2b2fd9108ddd0e5399c3 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 9 Aug 2024 00:02:39 +0300 Subject: [PATCH 16/50] add new transformers and fix bugs with nn --- .gitignore | 308 +- Example_Demand.ipynb | 497495 --------------- LICENSE | 402 +- README.md | 100 +- Tutorial_1_Quick_start.ipynb | 84 +- Tutorial_2_Neural_Networks.ipynb | 142 +- .../global/demand_forecasting_kernels.csv | 18261 + datasets/global/simulated_data_to_check.csv | 20002 +- .../global/simulated_data_to_check_28D.csv | 2002 +- datasets/global/train_demand.csv | 86917 --- .../simulated_data_to_check_0.csv | 2002 +- .../simulated_data_to_check_1.csv | 2002 +- .../simulated_data_to_check_2.csv | 2002 +- .../simulated_data_to_check_3.csv | 2002 +- .../simulated_data_to_check_4.csv | 2002 +- .../simulated_data_to_check_5.csv | 2002 +- .../simulated_data_to_check_6.csv | 2002 +- .../simulated_data_to_check_7.csv | 2002 +- .../simulated_data_to_check_8.csv | 2002 +- .../simulated_data_to_check_9.csv | 2002 +- pyproject.toml | 77 +- tsururu/__init__.py | 2 +- tsururu/dataset/__init__.py | 14 +- tsururu/dataset/dataset.py | 500 +- tsururu/dataset/pipeline.py | 1070 +- tsururu/dataset/slice.py | 928 +- .../model_training/torch_based/__init__.py | 6 +- .../model_training/torch_based/callbacks.py | 436 +- .../torch_based/data_provider.py | 221 +- tsururu/model_training/torch_based/metrics.py | 43 +- .../model_training/torch_based/scheduler.py | 30 - tsururu/model_training/trainer.py | 283 +- tsururu/model_training/validator.py | 214 +- tsururu/models/__init__.py | 54 +- tsururu/models/base.py | 104 +- tsururu/models/boost.py | 128 +- tsururu/models/torch_based/TimesNet.py | 102 - tsururu/models/torch_based/dlinear.py | 102 +- .../models/torch_based/layers/Conv_Block.py | 30 - tsururu/models/torch_based/layers/Embed.py | 126 - tsururu/models/torch_based/layers/RevIN.py | 63 - .../models/torch_based/layers/convolution.py | 64 + .../torch_based/layers/decomposition.py | 70 + .../models/torch_based/layers/embedding.py | 254 + .../{PatchTST_backbone.py => patch_tst.py} | 1466 +- ...chTST_layers.py => positional_encoding.py} | 145 +- tsururu/models/torch_based/layers/rev_in.py | 115 + tsururu/models/torch_based/layers/utils.py | 68 + .../torch_based/{patchTST.py => patch_tst.py} | 160 +- tsururu/models/torch_based/times_net.py | 205 + tsururu/strategies/__init__.py | 70 +- tsururu/strategies/base.py | 496 +- tsururu/strategies/direct.py | 512 +- tsururu/strategies/flat_wide_mimo.py | 74 +- tsururu/strategies/mimo.py | 84 +- tsururu/strategies/recursive.py | 453 +- tsururu/strategies/utils.py | 26 +- tsururu/transformers/__init__.py | 119 +- tsururu/transformers/base.py | 1200 +- tsururu/transformers/categorical.py | 284 +- tsururu/transformers/datetime.py | 408 +- tsururu/transformers/impute.py | 169 + tsururu/transformers/numeric.py | 843 +- tsururu/transformers/seq.py | 320 +- tsururu/transformers/utils.py | 76 +- 65 files changed, 46697 insertions(+), 611250 deletions(-) delete mode 100644 Example_Demand.ipynb create mode 100644 datasets/global/demand_forecasting_kernels.csv delete mode 100644 datasets/global/train_demand.csv delete mode 100644 tsururu/model_training/torch_based/scheduler.py delete mode 100644 tsururu/models/torch_based/TimesNet.py delete mode 100644 tsururu/models/torch_based/layers/Conv_Block.py delete mode 100644 tsururu/models/torch_based/layers/Embed.py delete mode 100644 tsururu/models/torch_based/layers/RevIN.py create mode 100644 tsururu/models/torch_based/layers/convolution.py create mode 100644 tsururu/models/torch_based/layers/decomposition.py create mode 100644 tsururu/models/torch_based/layers/embedding.py rename tsururu/models/torch_based/layers/{PatchTST_backbone.py => patch_tst.py} (53%) rename tsururu/models/torch_based/layers/{PatchTST_layers.py => positional_encoding.py} (54%) create mode 100644 tsururu/models/torch_based/layers/rev_in.py create mode 100644 tsururu/models/torch_based/layers/utils.py rename tsururu/models/torch_based/{patchTST.py => patch_tst.py} (58%) create mode 100644 tsururu/models/torch_based/times_net.py create mode 100644 tsururu/transformers/impute.py diff --git a/.gitignore b/.gitignore index 7f46077..82ab42a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,155 +1,155 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# DS_store -.DS_Store - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -# Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# VSCode -.vscode/ - -temp/ -additional_datasets/ -scripts/ -results/ -catboost_info/ -poetry.lock -*check*.ipynb -*temp* -*test* +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# DS_store +.DS_Store + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +# Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# VSCode +.vscode/ + +temp/ +additional_datasets/ +scripts/ +results/ +catboost_info/ +poetry.lock +*check*.ipynb +*temp* +*test* *checkpoints* \ No newline at end of file diff --git a/Example_Demand.ipynb b/Example_Demand.ipynb deleted file mode 100644 index bb88d57..0000000 --- a/Example_Demand.ipynb +++ /dev/null @@ -1,497495 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "from tqdm.contrib.itertools import product\n", - "from tqdm.notebook import tqdm\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "from typing import List, Optional, Union\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import plotly.graph_objects as go\n", - "from plotly.subplots import make_subplots\n", - "\n", - "from tsururu.models.torch_based.dlinear import DLinear_NN\n", - "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", - "from tsururu.model_training.trainer import MLTrainer\n", - "from tsururu.model_training.validator import KFoldCrossValidator\n", - "from tsururu.models.boost import CatBoost\n", - "from tsururu.strategies import (\n", - " DirectStrategy,\n", - " FlatWideMIMOStrategy,\n", - " MIMOStrategy,\n", - " RecursiveStrategy,\n", - ")\n", - "from tsururu.transformers import (\n", - " DateSeasonsGenerator,\n", - " DifferenceNormalizer,\n", - " LagTransformer,\n", - " LastKnownNormalizer,\n", - " SequentialTransformer,\n", - " StandardScalerTransformer,\n", - " TargetGenerator,\n", - " UnionTransformer,\n", - ")\n", - "\n", - "from sklearn.metrics import mean_absolute_error\n", - "\n", - "from tsururu.model_training.trainer import DLTrainer" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def symmetric_mean_absolute_percentage_error(y_true, y_pred):\n", - " return np.mean(np.abs(y_true - y_pred) / ((np.abs(y_true) + np.abs(y_pred)) / 2))\n", - "\n", - "\n", - "def get_metrics_by_id(test_df: pd.DataFrame, preds_df: pd.DataFrame):\n", - " merge_df = test_df.merge(preds_df, on=[\"id\", \"Date\"], suffixes=(\"_true\", \"_pred\"))\n", - " metrics_df = (\n", - " merge_df.groupby(\"id\")\n", - " .apply(\n", - " lambda x: symmetric_mean_absolute_percentage_error(x[\"Demand_true\"], x[\"Demand_pred\"])\n", - " )\n", - " .reset_index()\n", - " )\n", - " return metrics_df\n", - "\n", - "\n", - "def pad_multivariate_time_series(data, pad_with=np.nan):\n", - " data = data.copy()\n", - " min_date = data[\"Date\"].min()\n", - " max_date = data[\"Date\"].max()\n", - "\n", - " all_dates = pd.date_range(min_date, max_date)\n", - "\n", - " to_concat = []\n", - "\n", - " for unique_id in data[\"id\"].unique():\n", - " id_dates = data[data[\"id\"] == unique_id][\"Date\"]\n", - " missing_dates = all_dates.difference(id_dates)\n", - "\n", - " for missing_date in missing_dates:\n", - " to_concat.append(\n", - " pd.DataFrame(\n", - " {\n", - " \"Date\": [missing_date],\n", - " \"id\": [unique_id],\n", - " \"Demand\": [pad_with],\n", - " }\n", - " )\n", - " )\n", - "\n", - " data = pd.concat([data] + to_concat, ignore_index=True)\n", - " data = data.sort_values([\"id\", \"Date\"]).reset_index(drop=True)\n", - "\n", - " return data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading and initial preprocessing data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the datasets\n", - "train_data = pd.read_csv(\"datasets/global/train_demand.csv\")\n", - "test_data = pd.read_csv(\"datasets/global/test_demand.csv\")\n", - "\n", - "# Convert 'Date' column to datetime format\n", - "train_data[\"Date\"] = pd.to_datetime(train_data[\"Date\"], format=\"%d.%m.%Y\", dayfirst=True)\n", - "test_data[\"Date\"] = pd.to_datetime(test_data[\"Date\"], format=\"%d.%m.%Y\", dayfirst=True)\n", - "\n", - "# Make val data\n", - "n_days_in_val = 28\n", - "val_data = train_data[\n", - " train_data[\"Date\"] > train_data[\"Date\"].max() - pd.Timedelta(days=n_days_in_val)\n", - "].reset_index(drop=True)\n", - "train_data = train_data[\n", - " train_data[\"Date\"] <= train_data[\"Date\"].max() - pd.Timedelta(days=n_days_in_val)\n", - "]\n", - "\n", - "# Convert each column to the appropriate data type\n", - "for df in [train_data, val_data, test_data]:\n", - " df[\"Store_id\"] = df[\"Store_id\"].astype(\"str\")\n", - " df[\"SKU_id\"] = df[\"SKU_id\"].astype(\"str\")\n", - " df[\"Promo\"] = df[\"Promo\"].fillna(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DatePromoDemandRegular_PricePromo_Price
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min2016-05-23 00:00:000.000000NaN128.980000114.170000
25%2016-05-30 00:00:000.000000NaN131.700000114.170000
50%2016-06-06 00:00:000.000000NaN134.960000119.600000
75%2016-06-13 00:00:001.000000NaN138.500000125.040000
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" - ], - "text/plain": [ - " Date Promo Demand Regular_Price \\\n", - "count 5970 5970.000000 0.0 5970.000000 \n", - "mean 2016-06-05 15:53:14.773869312 0.253601 NaN 135.004390 \n", - "min 2016-05-23 00:00:00 0.000000 NaN 128.980000 \n", - "25% 2016-05-30 00:00:00 0.000000 NaN 131.700000 \n", - "50% 2016-06-06 00:00:00 0.000000 NaN 134.960000 \n", - "75% 2016-06-13 00:00:00 1.000000 NaN 138.500000 \n", - "max 2016-06-19 00:00:00 1.000000 NaN 145.560000 \n", - "std NaN 0.435109 NaN 3.490976 \n", - "\n", - " Promo_Price \n", - "count 1514.000000 \n", - "mean 119.452272 \n", - "min 114.170000 \n", - "25% 114.170000 \n", - "50% 119.600000 \n", - "75% 125.040000 \n", - "max 127.760000 \n", - "std 5.494316 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check for missing values\n", - "missing_values_train = train_data.isnull().sum()\n", - "missing_values_val = val_data.isnull().sum()\n", - "missing_values_test = test_data.isnull().sum()\n", - "\n", - "# Generate summary statistics\n", - "summary_stats_train = train_data.describe()\n", - "summary_stats_val = val_data.describe()\n", - "summary_stats_test = test_data.describe()\n", - "\n", - "print(\"Missing values in training data:\")\n", - "display(missing_values_train)\n", - "\n", - "print(\"Missing values in val data:\")\n", - "display(missing_values_val)\n", - "\n", - "print(\"Missing values in test data:\")\n", - "display(missing_values_test)\n", - "\n", - "print(\"Summary statistics for training data:\")\n", - "display(summary_stats_train)\n", - "\n", - "print(\"Summary statistics for val data:\")\n", - "display(summary_stats_val)\n", - "\n", - "print(\"Summary statistics for test data:\")\n", - "display(summary_stats_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__You can see that the targeting variable has heavy tails and a large range.__" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Demand Distribution (Train Data)')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.histplot(train_data[\"Demand\"], bins=50, kde=True)\n", - "plt.title(\"Demand Distribution (Train Data)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(14, 7))\n", - "\n", - "plt.subplot(3, 2, 1)\n", - "sns.histplot(train_data[\"Regular_Price\"], bins=50)\n", - "plt.title(\"Regular Price Distribution (Train Data)\")\n", - "\n", - "plt.subplot(3, 2, 2)\n", - "sns.histplot(train_data[\"Promo_Price\"], bins=50)\n", - "plt.title(\"Promo Price Distribution (Train Data)\")\n", - "\n", - "plt.subplot(3, 2, 3)\n", - "sns.histplot(val_data[\"Regular_Price\"], bins=50)\n", - "plt.title(\"Regular Price Distribution (Val Data)\")\n", - "\n", - "plt.subplot(3, 2, 4)\n", - "sns.histplot(val_data[\"Promo_Price\"], bins=50)\n", - "plt.title(\"Promo Price Distribution (Val Data)\")\n", - "\n", - "plt.subplot(3, 2, 5)\n", - "sns.histplot(test_data[\"Regular_Price\"], bins=50)\n", - "plt.title(\"Regular Price Distribution (Test Data)\")\n", - "\n", - "plt.subplot(3, 2, 6)\n", - "sns.histplot(test_data[\"Promo_Price\"], bins=50)\n", - "plt.title(\"Promo Price Distribution (Test Data)\")\n", - "\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- __Demand Distribution:__\n", - "\n", - "\t- The demand is highly right-skewed. There are a few extremely high demand values, which could be considered outliers or exceptional cases.\n", - "\n", - "- __Regular Price Distribution:__\n", - "\n", - "\t- The regular price in the training data shows multiple peaks.\n", - "\t- The most common prices on train are around 125-135 and 139-142, indicating these are frequently used price points.\n", - "\n", - "- __Promo Price Distribution:__\n", - "\n", - "\t- The promo price also shows multiple peaks." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Target feature normalization is likely to perform well on such data.__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Graph time series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Сombine store id and sku to get single id for rows.__" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "train_data[\"id\"] = train_data[\"Store_id\"] + \"_\" + train_data[\"SKU_id\"]\n", - "val_data[\"id\"] = val_data[\"Store_id\"] + \"_\" + val_data[\"SKU_id\"]\n", - "test_data[\"id\"] = test_data[\"Store_id\"] + \"_\" + test_data[\"SKU_id\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "mode": "lines", - "name": "Value 1_1", - "type": "scatter", - "x": [ - "2015-01-01T00:00:00", - "2015-01-02T00:00:00", - "2015-01-03T00:00:00", - "2015-01-04T00:00:00", - "2015-01-05T00:00:00", - "2015-01-06T00:00:00", - "2015-01-07T00:00:00", - "2015-01-08T00:00:00", - "2015-01-09T00:00:00", - "2015-01-10T00:00:00", - "2015-01-11T00:00:00", - "2015-01-12T00:00:00", - "2015-01-13T00:00:00", - "2015-01-14T00:00:00", - "2015-01-15T00:00:00", - "2015-01-16T00:00:00", - "2015-01-17T00:00:00", - "2015-01-18T00:00:00", - "2015-01-19T00:00:00", - "2015-01-20T00:00:00", - "2015-01-21T00:00:00", - "2015-01-22T00:00:00", - 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}, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Time Series Dynamics of Value, Promo, and Price" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 0.94 - ], - "title": { - "text": "Date" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Value/Price" - } - }, - "yaxis2": { - "anchor": "x", - "overlaying": "y", - "side": "right", - "title": { - "text": "Promo" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create a line plot with Plotly\n", - "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n", - "\n", - "# Add traces for each id\n", - "for unique_id in train_data[\"id\"].unique():\n", - " df = train_data[train_data[\"id\"] == unique_id]\n", - " fig.add_trace(\n", - " go.Scatter(x=df[\"Date\"], y=df[\"Demand\"], mode=\"lines\", name=f\"Value {unique_id}\"),\n", - " secondary_y=False,\n", - " )\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=df[\"Date\"],\n", - " y=df[\"Regular_Price\"],\n", - " mode=\"lines\",\n", - " name=f\"Regular Price {unique_id}\",\n", - " line=dict(dash=\"dash\"),\n", - " ),\n", - " secondary_y=False,\n", - " )\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=df[\"Date\"],\n", - " y=df[\"Promo_Price\"],\n", - " mode=\"lines\",\n", - " name=f\"Promo Price {unique_id}\",\n", - " line=dict(dash=\"dash\"),\n", - " ),\n", - " secondary_y=False,\n", - " )\n", - "\n", - "# Update layout\n", - "fig.update_layout(\n", - " title=\"Time Series Dynamics of Value, Promo, and Price\",\n", - " xaxis_title=\"Date\",\n", - " yaxis_title=\"Value/Price\",\n", - " yaxis2_title=\"Promo\",\n", - " legend_title=\"ID\",\n", - " hovermode=\"x unified\",\n", - ")\n", - "\n", - "# Show plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__It can be seen that demand rises strongly during promo periods, so it is good to take into account the exogenous variable promo.__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prepare data for tsururu and configure blocks" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "train_data_tsururu_1 = train_data[[\"Date\", \"id\", \"Demand\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_params = {\n", - " \"target\": {\n", - " \"columns\": [\"Demand\"],\n", - " \"type\": \"continious\",\n", - " },\n", - " \"date\": {\n", - " \"columns\": [\"Date\"],\n", - " \"type\": \"datetime\",\n", - " },\n", - " \"id\": {\n", - " \"columns\": [\"id\"],\n", - " \"type\": \"categorical\",\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_params = {\n", - " \"target\": {\n", - " \"columns\": [\"Demand\"],\n", - " \"features\": {\n", - " \"LagTransformer\": {\"lags\": [0, 1, 2, 3, 4, 5, 6, 7, 14, 21, 28, 30]},\n", - " },\n", - " },\n", - " \"date\": {\n", - " \"columns\": [\"Date\"],\n", - " \"features\": {\n", - " \"DateSeasonsGenerator\": {\n", - " \"seasonalities\": [\"doy\", \"m\", \"wd\", \"d\"],\n", - " \"from_target_date\": True,\n", - " },\n", - " \"LagTransformer\": {\"lags\": 3},\n", - " },\n", - " },\n", - " \"id\": {\n", - " \"columns\": [\"id\"],\n", - " \"features\": {\n", - " \"LabelEncodingTransformer\": {},\n", - " \"LagTransformer\": {\"lags\": 1},\n", - " },\n", - " },\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "model = CatBoost\n", - "model_params = {\n", - " \"loss_function\": \"MultiRMSE\",\n", - " \"early_stopping_rounds\": 100,\n", - " \"verbose\": 500,\n", - "}\n", - "\n", - "validation = KFoldCrossValidator\n", - "validation_params = {\n", - " \"n_splits\": 3,\n", - "}\n", - "\n", - "trainer = MLTrainer(\n", - " model,\n", - " model_params,\n", - " validation,\n", - " validation_params,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "horizon = 28\n", - "history = 31\n", - "step = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "padded_for_multivariate = pad_multivariate_time_series(train_data_tsururu_1, pad_with=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try Baselines!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try Statistical models!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try ML models in different strategies!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try CatBoost!" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c9ab4698fe234b9fb09587090c2dac9f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/8 [00:00 25\u001b[0m \u001b[43mstrategy_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m _, preds[model_name] \u001b[38;5;241m=\u001b[39m strategy_instance\u001b[38;5;241m.\u001b[39mpredict(dataset)\n\u001b[1;32m 27\u001b[0m results[model_name] \u001b[38;5;241m=\u001b[39m get_metrics_by_id(val_data, preds[model_name])\n", - "File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/strategies/utils.py:8\u001b[0m, in \u001b[0;36mtiming_decorator..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 7\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m----> 8\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 10\u001b[0m execution_time \u001b[38;5;241m=\u001b[39m end_time \u001b[38;5;241m-\u001b[39m start_time\n", - 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"File \u001b[0;32m~/Documents/Work/tsururu/tsururu-statmodels/tsururu_nn/tsururu/models/boost.py:52\u001b[0m, in \u001b[0;36mCatBoost.fit_one_fold\u001b[0;34m(self, X_train, y_train, X_val, y_val)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_params[param] \u001b[38;5;241m=\u001b[39m default_value\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m=\u001b[39m CatBoostRegressor(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_params)\n\u001b[0;32m---> 52\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_set\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_best_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43mplot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscore \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mbest_score_[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/catboost/core.py:5827\u001b[0m, in \u001b[0;36mCatBoostRegressor.fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m 5824\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m params:\n\u001b[1;32m 5825\u001b[0m CatBoostRegressor\u001b[38;5;241m.\u001b[39m_check_is_compatible_loss(params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss_function\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m-> 5827\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcat_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbaseline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5828\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_best_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meval_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogging_level\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn_description\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5829\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose_eval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetric_period\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msilent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mearly_stopping_rounds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5830\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_snapshot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msnapshot_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msnapshot_interval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_cout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_cerr\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/catboost/core.py:2400\u001b[0m, in \u001b[0;36mCatBoost._fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, plot_file, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m 2397\u001b[0m allow_clear_pool \u001b[38;5;241m=\u001b[39m train_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_clear_pool\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 2399\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m plot_wrapper(plot, plot_file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTraining plots\u001b[39m\u001b[38;5;124m'\u001b[39m, [_get_train_dir(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_params())]):\n\u001b[0;32m-> 2400\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2401\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2402\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meval_sets\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2403\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2404\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_clear_pool\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2405\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minit_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 2406\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2408\u001b[0m \u001b[38;5;66;03m# Have property feature_importance possibly set\u001b[39;00m\n\u001b[1;32m 2409\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_object\u001b[38;5;241m.\u001b[39m_get_loss_function_name()\n", - "File \u001b[0;32m~/Documents/Work/tsururu/.venv/lib/python3.10/site-packages/catboost/core.py:1780\u001b[0m, in \u001b[0;36m_CatBoostBase._train\u001b[0;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_train\u001b[39m(\u001b[38;5;28mself\u001b[39m, train_pool, test_pool, params, allow_clear_pool, init_model):\n\u001b[0;32m-> 1780\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_train\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_clear_pool\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_object\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43minit_model\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 1781\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_trained_model_attributes()\n", - "File \u001b[0;32m_catboost.pyx:4833\u001b[0m, in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m_catboost.pyx:4882\u001b[0m, in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# multivariate_list = [False, True]\n", - "# strategy_list = [\"recursive\", \"direct\", \"mimo\", \"flatwidemimo\"]\n", - "# model_horizon_list = [1, 7, 14]\n", - "\n", - "# preds = {}\n", - "# results = {}\n", - "\n", - "# for multivariate, strategy in product(multivariate_list, strategy_list):\n", - "# current_data = padded_for_multivariate\n", - "\n", - "# dataset = TSDataset(\n", - "# data=current_data,\n", - "# columns_params=dataset_params,\n", - "# print_freq_period_info=True,\n", - "# )\n", - "# pipeline = Pipeline.from_dict(pipeline_params, multivariate=multivariate)\n", - "\n", - "# if strategy in [\"recursive\", \"direct\"]:\n", - "# strategy_class = RecursiveStrategy if strategy == \"recursive\" else DirectStrategy\n", - "# for model_horizon in tqdm(model_horizon_list):\n", - "# model_name = f\"CatBoost_{multivariate}_{strategy}_{model_horizon}\"\n", - "# strategy_instance = strategy_class(\n", - "# horizon, history, step, trainer, pipeline, model_horizon\n", - "# )\n", - "# strategy_instance.fit(dataset)\n", - "# _, preds[model_name] = strategy_instance.predict(dataset)\n", - "# results[model_name] = get_metrics_by_id(val_data, preds[model_name])\n", - "# else:\n", - "# model_name = f\"CatBoost_{multivariate}_{strategy}\"\n", - "# strategy_class = MIMOStrategy if strategy == \"mimo\" else FlatWideMIMOStrategy\n", - "# strategy_instance = strategy_class(horizon, history, step, trainer, pipeline)\n", - "# strategy_instance.fit(dataset)\n", - "# _, preds[model_name] = strategy_instance.predict(dataset)\n", - "# results[model_name] = get_metrics_by_id(val_data, preds[model_name])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try normalizers on best strategy!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try exogeneous features on best strategy + normalizer!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try DLinear!" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "freq: Day; period: 1\n" - ] - } - ], - "source": [ - "dataset = TSDataset(\n", - " data=padded_for_multivariate,\n", - " columns_params=dataset_params,\n", - " print_freq_period_info=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "lag = LagTransformer(lags=30)\n", - "target_generator = TargetGenerator()\n", - "\n", - "union_1 = UnionTransformer(transformers_list=[lag, target_generator])\n", - "seq_1 = SequentialTransformer(transformers_list=[union_1], input_features=[\"Demand\"])\n", - "union = UnionTransformer(transformers_list=[seq_1])\n", - "\n", - "pipeline = Pipeline(union, multivariate=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# Configure the model parameters\n", - "model = DLinear_NN\n", - "model_params = {\"moving_avg\": 7, \"individual\": False, \"enc_in\": None}\n", - "\n", - "validation = KFoldCrossValidator\n", - "validation_params = {\n", - " \"n_splits\": 3,\n", - "}\n", - "\n", - "trainer_params = {\n", - " \"device\": \"cpu\",\n", - " \"num_workers\": 0,\n", - " \"stop_by_metric\": True,\n", - "}\n", - "\n", - "trainer = DLTrainer(\n", - " model, \n", - " model_params, \n", - " validation, \n", - " validation_params, \n", - " **trainer_params\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "horizon = 28\n", - "model_horizon = 28\n", - "history = 30\n", - "step = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "strategy = MIMOStrategy(\n", - " pipeline=pipeline,\n", - " trainer=trainer,\n", - " horizon=horizon,\n", - " history=history,\n", - " step=step,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/10, Loss: 77022.8143\n", - "Validation, Loss: 88605.5391, Metric: -88605.5391\n", - "Validation, Loss: 88605.5391, Metric: -88605.5391\n", - "Model saved to checkpoints/fold_0/model_0.pth\n", - "Epoch 2/10, Loss: 75997.1524\n", - "Validation, Loss: 88353.4141, Metric: -88353.4141\n", - "Validation, Loss: 88353.4141, Metric: -88353.4141\n", - "Model saved to checkpoints/fold_0/model_1.pth\n", - "Epoch 3/10, Loss: 75493.1131\n", - "Validation, Loss: 88131.5859, Metric: -88131.5859\n", - "Validation, Loss: 88131.5859, Metric: -88131.5859\n", - "Model saved to checkpoints/fold_0/model_2.pth\n", - "Epoch 4/10, Loss: 75091.6221\n", - "Validation, Loss: 88008.4922, Metric: -88008.4922\n", - "Validation, Loss: 88008.4922, Metric: -88008.4922\n", - "Model saved to checkpoints/fold_0/model_3.pth\n", - "Epoch 5/10, Loss: 74739.4823\n", - "Validation, Loss: 88012.4844, Metric: -88012.4844\n", - "Validation, Loss: 88012.4844, Metric: -88012.4844\n", - "Model saved to checkpoints/fold_0/model_4.pth\n", - "Epoch 6/10, Loss: 74490.1450\n", - "Validation, Loss: 87961.9688, Metric: -87961.9688\n", - "Validation, Loss: 87961.9688, Metric: -87961.9688\n", - "Removing worst model snapshot: checkpoints/fold_0/model_0.pth\n", - "Model saved to checkpoints/fold_0/model_5.pth\n", - "Epoch 7/10, Loss: 74279.9838\n", - "Validation, Loss: 87934.3047, Metric: -87934.3047\n", - "Validation, Loss: 87934.3047, Metric: -87934.3047\n", - "Removing worst model snapshot: checkpoints/fold_0/model_1.pth\n", - "Model saved to checkpoints/fold_0/model_6.pth\n", - "Epoch 8/10, Loss: 74146.2284\n", - "Validation, Loss: 87924.1172, Metric: -87924.1172\n", - "Validation, Loss: 87924.1172, Metric: -87924.1172\n", - "Removing worst model snapshot: checkpoints/fold_0/model_2.pth\n", - "Model saved to checkpoints/fold_0/model_7.pth\n", - "Epoch 9/10, Loss: 73984.6355\n", - "Validation, Loss: 87928.1328, Metric: -87928.1328\n", - "Validation, Loss: 87928.1328, Metric: -87928.1328\n", - "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", - "Model saved to checkpoints/fold_0/model_8.pth\n", - "Epoch 10/10, Loss: 73872.0578\n", - "Validation, Loss: 87890.6797, Metric: -87890.6797\n", - "Validation, Loss: 87890.6797, Metric: -87890.6797\n", - "Removing worst model snapshot: checkpoints/fold_0/model_3.pth\n", - "Model saved to checkpoints/fold_0/model_9.pth\n", - "Fold 0. Score: -87890.6796875\n", - "Epoch 1/10, Loss: 85199.6444\n", - "Validation, Loss: 71910.2891, Metric: -71910.2891\n", - "Validation, Loss: 71910.2891, Metric: -71910.2891\n", - "Removing worst model snapshot: checkpoints/fold_0/model_5.pth\n", - "Model saved to checkpoints/fold_1/model_0.pth\n", - "Epoch 2/10, Loss: 84203.7885\n", - "Validation, Loss: 71741.6719, Metric: -71741.6719\n", - "Validation, Loss: 71741.6719, Metric: -71741.6719\n", - "Removing worst model snapshot: checkpoints/fold_0/model_6.pth\n", - "Model saved to checkpoints/fold_1/model_1.pth\n", - "Epoch 3/10, Loss: 83587.3179\n", - "Validation, Loss: 71790.6016, Metric: -71790.6016\n", - "Validation, Loss: 71790.6016, Metric: -71790.6016\n", - "Removing worst model snapshot: checkpoints/fold_0/model_8.pth\n", - "Model saved to checkpoints/fold_1/model_2.pth\n", - "Epoch 4/10, Loss: 83060.4770\n", - "Validation, Loss: 71882.2031, Metric: -71882.2031\n", - "Validation, Loss: 71882.2031, Metric: -71882.2031\n", - "Removing worst model snapshot: checkpoints/fold_0/model_7.pth\n", - "Model saved to checkpoints/fold_1/model_3.pth\n", - "Epoch 5/10, Loss: 82721.1268\n", - "Validation, Loss: 72010.3828, Metric: -72010.3828\n", - "Validation, Loss: 72010.3828, Metric: -72010.3828\n", - "Removing worst model snapshot: checkpoints/fold_0/model_9.pth\n", - "Model saved to checkpoints/fold_1/model_4.pth\n", - "Epoch 6/10, Loss: 82375.5125\n", - "Validation, Loss: 72040.5547, Metric: -72040.5547\n", - "Validation, Loss: 72040.5547, Metric: -72040.5547\n", - "Epoch 7/10, Loss: 82099.6802\n", - "Validation, Loss: 72074.7656, Metric: -72074.7656\n", - "Validation, Loss: 72074.7656, Metric: -72074.7656\n", - "Early stopping triggered\n", - "Fold 1. Score: -72074.765625\n", - "Epoch 1/10, Loss: 80639.1106\n", - "Validation, Loss: 81399.9219, Metric: -81399.9219\n", - "Validation, Loss: 81399.9219, Metric: -81399.9219\n", - "Early stopping triggered\n", - "Fold 2. Score: -81399.921875\n", - "Mean score: -80455.1224\n", - "Std: 6491.29\n" - ] - }, - { - "data": { - "text/plain": [ - "(35.62223482131958, )" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy.fit(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.2591209411621094,\n", - " id Date Demand\n", - " 0 105_1 2016-04-25 129.049103\n", - " 1 105_1 2016-04-26 115.711586\n", - " 2 105_1 2016-04-27 107.117775\n", - " 3 105_1 2016-04-28 91.924202\n", - " 4 105_1 2016-04-29 63.435825\n", - " ... ... ... ...\n", - " 5539 9_2 2016-05-18 38.332611\n", - " 5540 9_2 2016-05-19 37.380253\n", - " 5541 9_2 2016-05-20 38.566143\n", - " 5542 9_2 2016-05-21 37.990238\n", - " 5543 9_2 2016-05-22 37.946899\n", - " \n", - " [5544 rows x 3 columns])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy.predict(dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try Ensembles on best models!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Try Hierarchy!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/LICENSE b/LICENSE index 261eeb9..29f81d8 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,201 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. For the purposes of this definition, - "control" means (i) the power, direct or indirect, to cause the - direction or management of such entity, whether by contract or - otherwise, or (ii) ownership of fifty percent (50%) or more of the - outstanding shares, or (iii) beneficial ownership of such entity. - - "You" (or "Your") shall mean an individual or Legal Entity - exercising permissions granted by this License. - - "Source" form shall mean the preferred form for making modifications, - including but not limited to software source code, documentation - source, and configuration files. - - "Object" form shall mean any form resulting from mechanical - transformation or translation of a Source form, including but - not limited to compiled object code, generated documentation, - and conversions to other media types. - - "Work" shall mean the work of authorship, whether in Source or - Object form, made available under the License, as indicated by a - copyright notice that is included in or attached to the work - (an example is provided in the Appendix below). - - "Derivative Works" shall mean any work, whether in Source or Object - form, that is based on (or derived from) the Work and for which the - editorial revisions, annotations, elaborations, or other modifications - represent, as a whole, an original work of authorship. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright [yyyy] [name of copyright owner] - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. 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Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md index 29e5874..180e6ac 100644 --- a/README.md +++ b/README.md @@ -1,50 +1,50 @@ -![tsururu_logo](tsururu_logo.png) - -# Tsururu – a time series forecasting strategies framework - -Much attention is now paid to what models to use for time series forecasting, but not to how exactly to perform training and inference. - -Tsururu is a Python-based library which aims at overcoming the aforementioned problems and provides a large number of multi-series and multi-point-ahead prediction strategies that can be used with any underlying model, including neural networks. - -Also tsururu provides various preprocessing techniques. - -## Multi-series prediction strategies: -- _Local-modelling_: - - Individual model for each time series. -- _Global-modelling_: - - One model for all time series; - - features made up of individual series do not overlap. -- _Multivariate-modelling_: - - One model for all time series; - - features made up of individual series corresponding to the same time point are concatenated for all time series. - -## Multi-point-ahead prediction strategies: -- _Recursive:_ - - one model for all points of the forecast horizon; - - *training*: the model is trained to predict one point ahead; - - *prediction*: a prediction is iteratively made one point ahead, and then this prediction is used to further shape the features in the test data. -- _Recursive-reduced:_ - - one model for all points in the prediction horizon; - - *training*: the model is trained to predict one point ahead; - - *prediction*: features are generated for all test observations at once, unavailable values are replaced by NaN. -- _Direct:_ - - individual models for each point in the prediction horizon. -- _MultiOutput (MIMO - Multi-input-multi-output):_ - - one model that learns to predict the entire prediction horizon. - - __Also, this strategy supports the presence of exogenous features (only for local- or global-modelling).__ -- _FlatWideMIMO:_. - - mixture of Direct and MIMO, fit one model, but uses deployed over horizon Direct's features. - -## Installation - -To install tsururu via pip you can use: - -`pip install -U tsururu` - -## Quick tour - -For usage example please see: - -* [Tutorial_1_Quick_Start](https://github.com/sb-ai-lab/tsururu/blob/main/Tutorial_1_Quick_start.ipynb) for simple usage examples - -More examples are coming soon. +![tsururu_logo](tsururu_logo.png) + +# Tsururu – a time series forecasting strategies framework + +Much attention is now paid to what models to use for time series forecasting, but not to how exactly to perform training and inference. + +Tsururu is a Python-based library which aims at overcoming the aforementioned problems and provides a large number of multi-series and multi-point-ahead prediction strategies that can be used with any underlying model, including neural networks. + +Also tsururu provides various preprocessing techniques. + +## Multi-series prediction strategies: +- _Local-modelling_: + - Individual model for each time series. +- _Global-modelling_: + - One model for all time series; + - features made up of individual series do not overlap. +- _Multivariate-modelling_: + - One model for all time series; + - features made up of individual series corresponding to the same time point are concatenated for all time series. + +## Multi-point-ahead prediction strategies: +- _Recursive:_ + - one model for all points of the forecast horizon; + - *training*: the model is trained to predict one point ahead; + - *prediction*: a prediction is iteratively made one point ahead, and then this prediction is used to further shape the features in the test data. +- _Recursive-reduced:_ + - one model for all points in the prediction horizon; + - *training*: the model is trained to predict one point ahead; + - *prediction*: features are generated for all test observations at once, unavailable values are replaced by NaN. +- _Direct:_ + - individual models for each point in the prediction horizon. +- _MultiOutput (MIMO - Multi-input-multi-output):_ + - one model that learns to predict the entire prediction horizon. + - __Also, this strategy supports the presence of exogenous features (only for local- or global-modelling).__ +- _FlatWideMIMO:_. + - mixture of Direct and MIMO, fit one model, but uses deployed over horizon Direct's features. + +## Installation + +To install tsururu via pip you can use: + +`pip install -U tsururu` + +## Quick tour + +For usage example please see: + +* [Tutorial_1_Quick_Start](https://github.com/sb-ai-lab/tsururu/blob/main/Tutorial_1_Quick_start.ipynb) for simple usage examples + +More examples are coming soon. diff --git a/Tutorial_1_Quick_start.ipynb b/Tutorial_1_Quick_start.ipynb index 3dcaf09..b5b77c0 100644 --- a/Tutorial_1_Quick_start.ipynb +++ b/Tutorial_1_Quick_start.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -16,9 +16,9 @@ "import pandas as pd\n", "\n", "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", - "from tsururu.models.boost import CatBoost\n", - "from tsururu.model_training.validator import KFoldCrossValidator\n", "from tsururu.model_training.trainer import MLTrainer\n", + "from tsururu.model_training.validator import KFoldCrossValidator\n", + "from tsururu.models.boost import CatBoost\n", "from tsururu.strategies import (\n", " DirectStrategy,\n", " FlatWideMIMOStrategy,\n", @@ -39,7 +39,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -275,13 +275,13 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'transformers': ,\n", + "{'transformers': ,\n", " 'multivariate': False,\n", " 'is_fitted': False,\n", " 'strategy_name': None,\n", @@ -289,7 +289,7 @@ " 'y_original_shape': None}" ] }, - "execution_count": 32, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -307,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -369,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -416,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -522,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -533,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -542,24 +542,24 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 6.36ms\tremaining: 6.35s\n", - "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 338ms\tremaining: 337ms\n", - "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 762ms\tremaining: 0us\n", + "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 56.5ms\tremaining: 56.4s\n", + "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 420ms\tremaining: 418ms\n", + "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 763ms\tremaining: 0us\n", "\n", "bestTest = 0.003047062205\n", "bestIteration = 999\n", "\n", "Fold 0. Score: 0.003047062205400275\n", - "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 842us\tremaining: 841ms\n", - "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 329ms\tremaining: 327ms\n", - "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 706ms\tremaining: 0us\n", + "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 1.65ms\tremaining: 1.65s\n", + "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 342ms\tremaining: 340ms\n", + "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 705ms\tremaining: 0us\n", "\n", "bestTest = 0.002916854444\n", "bestIteration = 999\n", @@ -576,7 +576,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -585,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -833,7 +833,7 @@ "29 9 2022-09-29 10995.683994" ] }, - "execution_count": 43, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -851,24 +851,24 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 1.67ms\tremaining: 1.67s\n", - "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 487ms\tremaining: 485ms\n", - "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 913ms\tremaining: 0us\n", + "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 1.06ms\tremaining: 1.06s\n", + "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 371ms\tremaining: 370ms\n", + "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 747ms\tremaining: 0us\n", "\n", "bestTest = 0.002972644218\n", "bestIteration = 999\n", "\n", "Fold 0. Score: 0.0029726442175072565\n", - "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 983us\tremaining: 983ms\n", - "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 370ms\tremaining: 369ms\n", - "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 696ms\tremaining: 0us\n", + "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 1.12ms\tremaining: 1.12s\n", + "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 386ms\tremaining: 384ms\n", + "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 748ms\tremaining: 0us\n", "\n", "bestTest = 0.002971764957\n", "bestIteration = 999\n", @@ -885,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1133,7 +1133,7 @@ "29 10999.0 10994.990313 9" ] }, - "execution_count": 45, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1181,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1234,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 24, "metadata": {}, "outputs": [ { diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb index 2572213..0f0e861 100644 --- a/Tutorial_2_Neural_Networks.ipynb +++ b/Tutorial_2_Neural_Networks.ipynb @@ -6,8 +6,8 @@ "metadata": {}, "outputs": [], "source": [ - "import warnings\n", "import importlib\n", + "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", @@ -18,13 +18,16 @@ "import pandas as pd\n", "\n", "from tsururu.dataset import Pipeline, TSDataset\n", + "from tsururu.model_training.trainer import DLTrainer\n", "from tsururu.model_training.validator import HoldOutValidator\n", "from tsururu.models.torch_based.dlinear import DLinear_NN\n", "from tsururu.strategies import MIMOStrategy\n", - "from tsururu.transformers import (LagTransformer, SequentialTransformer,\n", - " TargetGenerator, UnionTransformer)\n", - "\n", - "from tsururu.model_training.trainer import DLTrainer" + "from tsururu.transformers import (\n", + " LagTransformer,\n", + " SequentialTransformer,\n", + " TargetGenerator,\n", + " UnionTransformer,\n", + ")" ] }, { @@ -251,7 +254,7 @@ "trainer_params = {\n", " \"device\": \"cpu\",\n", " \"num_workers\": 0,\n", - " \"stop_by_metric\": True,\n", + " \"best_by_metric\": True,\n", "}\n", "\n", "trainer = DLTrainer(\n", @@ -306,60 +309,82 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1/10, Loss: 276.3597\n", - "Validation, Loss: 33.9238, Metric: -33.9238\n", - "Validation, Loss: 33.9238, Metric: -33.9238\n", + "length of train dataset: 688\n", + "length of val dataset: 94\n", + "Epoch 1/10, cost time: 0.77s\n", + "train loss: 274.1573\n", + "Validation, Loss: 30.1381, Metric: -30.1381\n", + "val loss: 30.1381\n", "Model saved to checkpoints/fold_0/model_0.pth\n", - "Epoch 2/10, Loss: 32.1673\n", - "Validation, Loss: 13.6875, Metric: -13.6875\n", - "Validation, Loss: 13.6875, Metric: -13.6875\n", + "Optimizer saved to checkpoints/fold_0/opt_0.pth\n", + "Epoch 2/10, cost time: 0.72s\n", + "train loss: 32.8188\n", + "Validation, Loss: 14.3539, Metric: -14.3539\n", + "val loss: 14.3539\n", "Model saved to checkpoints/fold_0/model_1.pth\n", - "Epoch 3/10, Loss: 12.2549\n", - "Validation, Loss: 9.5308, Metric: -9.5308\n", - "Validation, Loss: 9.5308, Metric: -9.5308\n", + "Optimizer saved to checkpoints/fold_0/opt_1.pth\n", + "Epoch 3/10, cost time: 0.72s\n", + "train loss: 12.8709\n", + "Validation, Loss: 10.0110, Metric: -10.0110\n", + "val loss: 10.0110\n", "Model saved to checkpoints/fold_0/model_2.pth\n", - "Epoch 4/10, Loss: 9.7045\n", - "Validation, Loss: 8.4738, Metric: -8.4738\n", - "Validation, Loss: 8.4738, Metric: -8.4738\n", + "Optimizer saved to checkpoints/fold_0/opt_2.pth\n", + "Epoch 4/10, cost time: 0.73s\n", + "train loss: 10.2632\n", + "Validation, Loss: 8.9626, Metric: -8.9626\n", + "val loss: 8.9626\n", "Model saved to checkpoints/fold_0/model_3.pth\n", - "Epoch 5/10, Loss: 9.3849\n", - "Validation, Loss: 8.4111, Metric: -8.4111\n", - "Validation, Loss: 8.4111, Metric: -8.4111\n", + "Optimizer saved to checkpoints/fold_0/opt_3.pth\n", + "Epoch 5/10, cost time: 0.72s\n", + "train loss: 9.9284\n", + "Validation, Loss: 8.8743, Metric: -8.8743\n", + "val loss: 8.8743\n", "Model saved to checkpoints/fold_0/model_4.pth\n", - "Epoch 6/10, Loss: 9.2967\n", - "Validation, Loss: 8.3393, Metric: -8.3393\n", - "Validation, Loss: 8.3393, Metric: -8.3393\n", - "Removing worst model snapshot: checkpoints/fold_0/model_0.pth\n", + "Optimizer saved to checkpoints/fold_0/opt_4.pth\n", + "Epoch 6/10, cost time: 0.72s\n", + "train loss: 9.8400\n", + "Validation, Loss: 8.8730, Metric: -8.8730\n", + "val loss: 8.8730\n", + "Removing worst model snapshot: from epoch 0\n", "Model saved to checkpoints/fold_0/model_5.pth\n", - "Epoch 7/10, Loss: 9.2303\n", - "Validation, Loss: 8.3204, Metric: -8.3204\n", - "Validation, Loss: 8.3204, Metric: -8.3204\n", - "Removing worst model snapshot: checkpoints/fold_0/model_1.pth\n", + "Optimizer saved to checkpoints/fold_0/opt_5.pth\n", + "Epoch 7/10, cost time: 0.72s\n", + "train loss: 9.7701\n", + "Validation, Loss: 8.8641, Metric: -8.8641\n", + "val loss: 8.8641\n", + "Removing worst model snapshot: from epoch 1\n", "Model saved to checkpoints/fold_0/model_6.pth\n", - "Epoch 8/10, Loss: 9.1627\n", - "Validation, Loss: 8.2604, Metric: -8.2604\n", - "Validation, Loss: 8.2604, Metric: -8.2604\n", - "Removing worst model snapshot: checkpoints/fold_0/model_2.pth\n", + "Optimizer saved to checkpoints/fold_0/opt_6.pth\n", + "Epoch 8/10, cost time: 0.72s\n", + "train loss: 9.7031\n", + "Validation, Loss: 8.6850, Metric: -8.6850\n", + "val loss: 8.6850\n", + "Removing worst model snapshot: from epoch 2\n", "Model saved to checkpoints/fold_0/model_7.pth\n", - "Epoch 9/10, Loss: 9.0904\n", - "Validation, Loss: 8.1742, Metric: -8.1742\n", - "Validation, Loss: 8.1742, Metric: -8.1742\n", - "Removing worst model snapshot: checkpoints/fold_0/model_3.pth\n", + "Optimizer saved to checkpoints/fold_0/opt_7.pth\n", + "Epoch 9/10, cost time: 0.75s\n", + "train loss: 9.6208\n", + "Validation, Loss: 8.6171, Metric: -8.6171\n", + "val loss: 8.6171\n", + "Removing worst model snapshot: from epoch 3\n", "Model saved to checkpoints/fold_0/model_8.pth\n", - "Epoch 10/10, Loss: 9.0140\n", - "Validation, Loss: 8.0992, Metric: -8.0992\n", - "Validation, Loss: 8.0992, Metric: -8.0992\n", - "Removing worst model snapshot: checkpoints/fold_0/model_4.pth\n", + "Optimizer saved to checkpoints/fold_0/opt_8.pth\n", + "Epoch 10/10, cost time: 0.83s\n", + "train loss: 9.5413\n", + "Validation, Loss: 8.5182, Metric: -8.5182\n", + "val loss: 8.5182\n", + "Removing worst model snapshot: from epoch 4\n", "Model saved to checkpoints/fold_0/model_9.pth\n", - "Fold 0. Score: -8.099207878112793\n", - "Mean score: -8.0992\n", + "Optimizer saved to checkpoints/fold_0/opt_9.pth\n", + "Fold 0. Score: -8.518207550048828\n", + "Mean score: -8.5182\n", "Std: 0.0\n" ] }, { "data": { "text/plain": [ - "(9.684648990631104, )" + "(8.324044942855835, )" ] }, "execution_count": 9, @@ -380,27 +405,28 @@ "name": "stdout", "output_type": "stream", "text": [ + "length of test dataset: 193\n", "Validation, Loss: nan, Metric: nan\n" ] }, { "data": { "text/plain": [ - "(0.09302711486816406,\n", - " id date value\n", - " 0 0 2022-03-15 1801.496216\n", - " 1 0 2022-03-16 1801.333862\n", - " 2 0 2022-03-17 1801.839233\n", - " 3 0 2022-03-18 1801.989624\n", - " 4 0 2022-03-19 1802.165771\n", - " ... .. ... ...\n", - " 1955 9 2022-09-22 10997.84668\n", - " 1956 9 2022-09-23 10999.4375\n", - " 1957 9 2022-09-24 11001.006836\n", - " 1958 9 2022-09-25 11002.239258\n", - " 1959 9 2022-09-26 11003.734375\n", + "(0.6019949913024902,\n", + " id date value\n", + " 0 0 2022-03-12 1798.098267\n", + " 1 0 2022-03-13 1798.139038\n", + " 2 0 2022-03-14 1798.520996\n", + " 3 0 2022-03-15 1798.796387\n", + " 4 0 2022-03-16 1799.245605\n", + " ... .. ... ...\n", + " 13505 9 2022-09-22 10997.950195\n", + " 13506 9 2022-09-23 10999.390625\n", + " 13507 9 2022-09-24 11000.709961\n", + " 13508 9 2022-09-25 11002.036133\n", + " 13509 9 2022-09-26 11003.500977\n", " \n", - " [1960 rows x 3 columns])" + " [13510 rows x 3 columns])" ] }, "execution_count": 10, diff --git a/datasets/global/demand_forecasting_kernels.csv b/datasets/global/demand_forecasting_kernels.csv new file mode 100644 index 0000000..06c539c --- /dev/null +++ b/datasets/global/demand_forecasting_kernels.csv @@ -0,0 +1,18261 @@ +date,value,id +2013-01-01,48,0 +2013-01-02,44,0 +2013-01-03,60,0 +2013-01-04,56,0 +2013-01-05,50,0 +2013-01-06,63,0 +2013-01-07,27,0 +2013-01-08,54,0 +2013-01-09,55,0 +2013-01-10,45,0 +2013-01-11,53,0 +2013-01-12,60,0 +2013-01-13,7888,0 +2013-01-14,44,0 +2013-01-15,41,0 +2013-01-16,54,0 +2013-01-17,51,0 +2013-01-18,54,0 +2013-01-19,71,0 +2013-01-20,57,0 +2013-01-21,36,0 +2013-01-22,56,0 +2013-01-23,52,0 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-2,1,17.01.2015,,135,176.82, -2,1,18.01.2015,,170,176.82, -2,1,19.01.2015,,119,176.82, -2,1,20.01.2015,,217,176.82, -2,1,21.01.2015,,113,176.82, -2,1,22.01.2015,,138,176.82, -2,1,23.01.2015,,91,168.4, -2,1,24.01.2015,,148,168.4, -2,1,25.01.2015,,148,168.4, -2,1,26.01.2015,,104,168.4, -2,1,27.01.2015,,132,168.4, -2,1,28.01.2015,,289,154.26, -2,1,29.01.2015,,261,154.26, -2,1,30.01.2015,,210,154.26, -2,1,31.01.2015,,236,154.26, -2,1,01.02.2015,,245,154.26, -2,1,02.02.2015,,110,154.26, -2,1,03.02.2015,,210,154.26, -2,1,04.02.2015,,261,154.26, -2,1,05.02.2015,,248,154.26, -2,1,06.02.2015,,207,154.26, -2,1,07.02.2015,,254,154.26, -2,1,08.02.2015,,270,154.26, -2,1,09.02.2015,,214,154.26, -2,1,10.02.2015,,305,154.26, -2,1,11.02.2015,,295,154.26, -2,1,12.02.2015,,254,154.26, -2,1,13.02.2015,,223,156.98, -2,1,14.02.2015,,229,156.98, -2,1,15.02.2015,,267,156.98, -2,1,16.02.2015,,173,156.98, -2,1,17.02.2015,1.0,1178,156.98,143.8 -2,1,18.02.2015,1.0,952,156.98,135.64 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-2,1,19.04.2015,,239,138.5, -2,1,20.04.2015,,204,138.5, -2,1,21.04.2015,,173,138.5, -2,1,22.04.2015,,339,128.98, -2,1,23.04.2015,,317,128.98, -2,1,24.04.2015,,264,128.98, -2,1,25.04.2015,,352,128.98, -2,1,26.04.2015,,471,128.98, -2,1,27.04.2015,,302,128.98, -2,1,28.04.2015,,245,128.98, -2,1,29.04.2015,,210,131.7, -2,1,30.04.2015,,393,131.7, -2,1,01.05.2015,,383,131.7, -2,1,02.05.2015,,311,131.7, -2,1,03.05.2015,,295,131.7, -2,1,04.05.2015,,333,131.7, -2,1,05.05.2015,,320,131.7, -2,1,06.05.2015,,302,131.7, -2,1,07.05.2015,,298,131.7, -2,1,08.05.2015,,355,131.7, -2,1,09.05.2015,,192,131.7, -2,1,10.05.2015,,258,131.7, -2,1,11.05.2015,,242,131.7, -2,1,12.05.2015,,217,131.7, -2,1,13.05.2015,,232,131.7, -2,1,14.05.2015,,471,131.7, -2,1,15.05.2015,,386,131.7, -2,1,16.05.2015,,405,131.7, -2,1,17.05.2015,,525,131.7, -2,1,18.05.2015,,427,131.7, -2,1,19.05.2015,1.0,1706,131.7,119.33 -2,1,20.05.2015,1.0,1571,131.7,119.33 -2,1,21.05.2015,1.0,1775,131.7,119.33 -2,1,22.05.2015,1.0,1539,131.7,119.33 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-3,1,23.06.2015,,226,128.17, -3,1,24.06.2015,,182,128.17, -3,1,25.06.2015,,242,128.17, -3,1,26.06.2015,,201,128.17, -3,1,27.06.2015,,236,128.17, -3,1,28.06.2015,,396,130.34, -3,1,29.06.2015,,204,130.34, -3,1,30.06.2015,1.0,562,130.34,119.33 -3,1,01.07.2015,1.0,1235,130.34,119.33 -3,1,02.07.2015,1.0,553,130.34,119.33 -3,1,03.07.2015,1.0,748,130.34,119.33 -3,1,04.07.2015,1.0,503,130.34,119.33 -3,1,05.07.2015,1.0,522,130.34,119.33 -3,1,06.07.2015,1.0,877,130.34,119.33 -3,1,07.07.2015,,276,130.34, -3,1,08.07.2015,,141,130.34, -3,1,09.07.2015,,371,121.64, -3,1,10.07.2015,,635,121.64, -3,1,11.07.2015,,518,123.55, -3,1,12.07.2015,,474,123.55, -3,1,13.07.2015,,581,123.55, -3,1,14.07.2015,,396,123.55, -3,1,15.07.2015,,553,125.45, -3,1,16.07.2015,,556,125.45, -3,1,17.07.2015,,496,125.45, -3,1,18.07.2015,,311,127.62, -3,1,19.07.2015,,600,127.62, -3,1,20.07.2015,,719,127.62, -3,1,21.07.2015,,393,127.62, -3,1,22.07.2015,,789,127.62, -3,1,23.07.2015,,864,127.62, -3,1,24.07.2015,,763,127.62, -3,1,25.07.2015,,839,127.62, -3,1,26.07.2015,,933,130.07, -3,1,27.07.2015,,895,130.07, -3,1,28.07.2015,1.0,1128,130.07,124.77 -3,1,29.07.2015,1.0,1417,130.07,124.77 -3,1,30.07.2015,1.0,1294,130.07,124.77 -3,1,31.07.2015,1.0,1417,130.07,124.77 -3,1,01.08.2015,1.0,861,130.07,124.77 -3,1,02.08.2015,1.0,1703,130.07,124.77 -3,1,03.08.2015,1.0,437,130.07,124.77 -3,1,04.08.2015,,289,138.5, -3,1,05.08.2015,,320,138.5, -3,1,06.08.2015,,559,138.5, -3,1,07.08.2015,,311,138.5, -3,1,08.08.2015,,327,141.21, -3,1,09.08.2015,,232,141.21, -3,1,10.08.2015,,311,141.21, -3,1,11.08.2015,,214,141.21, -3,1,12.08.2015,,311,142.85, -3,1,13.08.2015,,415,142.85, -3,1,14.08.2015,,399,142.85, -3,1,15.08.2015,,295,142.85, -3,1,16.08.2015,,330,142.85, -3,1,17.08.2015,,405,142.85, -3,1,18.08.2015,,474,142.85, -3,1,19.08.2015,,575,142.85, -3,1,20.08.2015,,302,142.85, -3,1,21.08.2015,,493,142.85, -3,1,22.08.2015,,371,142.85, -3,1,23.08.2015,,336,142.85, -3,1,24.08.2015,,694,142.85, -3,1,25.08.2015,,528,142.85, -3,1,26.08.2015,,509,142.85, -3,1,27.08.2015,,380,137.68, -3,1,28.08.2015,,518,137.68, -3,1,29.08.2015,,864,137.68, -3,1,30.08.2015,,707,137.68, -3,1,31.08.2015,,578,137.68, -3,1,01.09.2015,,157,137.68, -3,1,02.09.2015,,707,135.78, -3,1,03.09.2015,,368,135.78, -3,1,04.09.2015,,537,135.78, -3,1,05.09.2015,,732,133.6, -3,1,06.09.2015,,628,133.6, -3,1,07.09.2015,,581,133.6, -3,1,08.09.2015,,597,133.6, -3,1,09.09.2015,,481,135.51, -3,1,10.09.2015,,471,135.51, -3,1,11.09.2015,,597,135.51, -3,1,12.09.2015,,613,135.51, -3,1,13.09.2015,,440,135.51, -3,1,14.09.2015,,317,135.51, -3,1,15.09.2015,,603,135.51, -3,1,16.09.2015,,346,138.5, -3,1,17.09.2015,,302,138.5, -3,1,18.09.2015,,198,138.5, -3,1,19.09.2015,,214,138.5, -3,1,20.09.2015,,286,138.5, -3,1,21.09.2015,,254,138.5, -3,1,22.09.2015,,157,138.5, -3,1,23.09.2015,,135,141.21, -3,1,24.09.2015,,251,141.21, -3,1,25.09.2015,,261,141.21, -3,1,26.09.2015,,163,141.21, -3,1,27.09.2015,,185,141.21, -3,1,28.09.2015,,342,141.21, -3,1,29.09.2015,,408,141.21, -3,1,30.09.2015,,173,141.21, -3,1,01.10.2015,,342,141.21, -3,1,02.10.2015,,145,141.21, -3,1,03.10.2015,,195,141.21, -3,1,04.10.2015,,173,141.21, -3,1,05.10.2015,,210,141.21, -3,1,06.10.2015,1.0,779,141.21,132.92 -3,1,07.10.2015,1.0,1034,141.21,130.21 -3,1,08.10.2015,1.0,701,141.11,130.21 -3,1,09.10.2015,1.0,565,141.21,130.21 -3,1,10.10.2015,1.0,405,141.21,130.21 -3,1,11.10.2015,1.0,481,141.21,130.21 -3,1,12.10.2015,1.0,528,141.21,130.21 -3,1,13.10.2015,,94,141.21, -3,1,14.10.2015,,154,141.21, -3,1,15.10.2015,,79,142.57, -3,1,16.10.2015,,101,142.57, -3,1,17.10.2015,,151,142.57, -3,1,18.10.2015,,88,142.57, -3,1,19.10.2015,,132,142.57, -3,1,20.10.2015,,182,142.57, -3,1,21.10.2015,,371,130.34, -3,1,22.10.2015,,748,130.34, -3,1,23.10.2015,,553,130.34, -3,1,24.10.2015,,424,130.34, -3,1,25.10.2015,,364,130.34, -3,1,26.10.2015,,314,130.34, -3,1,27.10.2015,1.0,914,130.34,122.32 -3,1,28.10.2015,1.0,892,130.34,124.77 -3,1,29.10.2015,1.0,672,130.34,124.77 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-3,1,02.01.2016,,97,126.54, -3,1,03.01.2016,,123,126.54, -3,1,04.01.2016,,179,126.54, -3,1,05.01.2016,,163,126.54, -3,1,06.01.2016,,157,126.54, -3,1,07.01.2016,,167,126.54, -3,1,08.01.2016,,154,126.54, -3,1,09.01.2016,,320,126.54, -3,1,10.01.2016,,47,126.54, -3,1,11.01.2016,,38,126.54, -3,1,12.01.2016,,141,126.54, -3,1,13.01.2016,,141,126.54, -3,1,14.01.2016,,141,126.54, -3,1,15.01.2016,,141,126.54, -3,1,16.01.2016,,141,126.54, -3,1,17.01.2016,,141,126.54, -3,1,18.01.2016,,141,126.54, -3,1,19.01.2016,,141,126.54, -3,1,20.01.2016,,141,126.54, -3,1,21.01.2016,,141,127.9, -3,1,22.01.2016,,141,127.9, -3,1,23.01.2016,,141,127.9, -3,1,24.01.2016,,141,127.9, -3,1,25.01.2016,,141,127.9, -3,1,26.01.2016,,396,127.9, -3,1,27.01.2016,,333,127.9, -3,1,28.01.2016,,342,127.9, -3,1,29.01.2016,,355,127.9, -3,1,30.01.2016,,264,127.9, -3,1,31.01.2016,,248,127.9, -3,1,01.02.2016,,408,130.34, -3,1,02.02.2016,,167,130.34, -3,1,03.02.2016,,248,130.34, -3,1,04.02.2016,,154,133.06, -3,1,05.02.2016,,192,133.06, 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-4,1,20.01.2015,,63,168.4, -4,1,21.01.2015,,53,168.4, -4,1,22.01.2015,,63,168.4, -4,1,23.01.2015,,107,160.24, -4,1,24.01.2015,,104,160.24, -4,1,25.01.2015,,97,160.24, -4,1,26.01.2015,,53,160.24, -4,1,27.01.2015,,57,160.24, -4,1,28.01.2015,,129,146.92, -4,1,29.01.2015,,141,146.92, -4,1,30.01.2015,,82,146.92, -4,1,31.01.2015,,214,146.92, -4,1,01.02.2015,,176,146.92, -4,1,02.02.2015,,88,146.92, -4,1,03.02.2015,,226,146.92, -4,1,04.02.2015,,126,146.92, -4,1,05.02.2015,,185,146.92, -4,1,06.02.2015,,182,146.92, -4,1,07.02.2015,,286,146.92, -4,1,08.02.2015,,148,146.92, -4,1,09.02.2015,,141,146.92, -4,1,10.02.2015,,248,146.92, -4,1,11.02.2015,,254,146.92, -4,1,12.02.2015,,242,146.92, -4,1,13.02.2015,,258,149.37, -4,1,14.02.2015,,201,149.37, -4,1,15.02.2015,,242,149.37, -4,1,16.02.2015,,185,149.37, -4,1,17.02.2015,1.0,594,149.37,143.8 -4,1,18.02.2015,1.0,446,149.37,135.64 -4,1,19.02.2015,1.0,512,149.37,135.64 -4,1,20.02.2015,1.0,682,149.37,135.64 -4,1,21.02.2015,1.0,625,149.37,135.64 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-4,1,24.04.2015,,201,128.98, -4,1,25.04.2015,,239,128.98, -4,1,26.04.2015,,79,128.98, -4,1,27.04.2015,,116,128.98, -4,1,28.04.2015,,207,128.98, -4,1,29.04.2015,,69,131.7, -4,1,30.04.2015,,145,131.7, -4,1,01.05.2015,,110,131.7, -4,1,02.05.2015,,129,131.7, -4,1,03.05.2015,,104,131.7, -4,1,04.05.2015,,163,131.7, -4,1,05.05.2015,,170,131.7, -4,1,06.05.2015,,101,131.7, -4,1,07.05.2015,,94,131.7, -4,1,08.05.2015,,113,131.7, -4,1,09.05.2015,,79,131.7, -4,1,10.05.2015,,91,131.7, -4,1,11.05.2015,,75,131.7, -4,1,12.05.2015,,185,131.7, -4,1,13.05.2015,,182,131.7, -4,1,14.05.2015,,154,131.7, -4,1,15.05.2015,,204,131.7, -4,1,16.05.2015,,204,131.7, -4,1,17.05.2015,,104,131.7, -4,1,18.05.2015,,204,131.7, -4,1,19.05.2015,1.0,2265,131.7,119.33 -4,1,20.05.2015,1.0,1338,131.7,119.33 -4,1,21.05.2015,1.0,968,131.7,119.33 -4,1,22.05.2015,1.0,823,131.7,119.33 -4,1,23.05.2015,1.0,958,131.7,119.33 -4,1,24.05.2015,1.0,958,131.7,119.33 -4,1,25.05.2015,1.0,961,131.7,119.33 -4,1,26.05.2015,,129,131.7, -4,1,27.05.2015,,123,129.25, -4,1,28.05.2015,,110,129.25, -4,1,29.05.2015,,160,129.25, -4,1,30.05.2015,,151,129.25, -4,1,31.05.2015,,148,129.25, -4,1,01.06.2015,,101,129.25, -4,1,02.06.2015,,66,129.25, -4,1,03.06.2015,,94,129.25, -4,1,04.06.2015,,170,129.25, -4,1,05.06.2015,,185,129.25, -4,1,06.06.2015,,198,131.7, -4,1,07.06.2015,,160,131.7, -4,1,08.06.2015,,132,131.7, -4,1,09.06.2015,1.0,1451,131.7,119.33 -4,1,10.06.2015,1.0,1376,131.7,119.33 -4,1,11.06.2015,1.0,1602,131.7,119.33 -4,1,12.06.2015,1.0,562,131.7,119.33 -4,1,13.06.2015,1.0,682,131.7,119.33 -4,1,14.06.2015,1.0,402,131.7,119.33 -4,1,15.06.2015,1.0,581,131.7,119.33 -4,1,16.06.2015,,264,131.7, -4,1,17.06.2015,,251,131.7, -4,1,18.06.2015,,276,128.17, -4,1,19.06.2015,,355,128.17, -4,1,20.06.2015,,236,128.17, -4,1,21.06.2015,,204,128.17, -4,1,22.06.2015,,204,128.17, -4,1,23.06.2015,,358,128.17, -4,1,24.06.2015,,490,128.17, -4,1,25.06.2015,,543,128.17, -4,1,26.06.2015,,267,128.17, -4,1,27.06.2015,,430,128.17, -4,1,28.06.2015,,220,130.34, -4,1,29.06.2015,,289,130.34, -4,1,30.06.2015,1.0,487,130.34,119.33 -4,1,01.07.2015,1.0,707,130.34,119.33 -4,1,02.07.2015,1.0,1263,130.34,119.33 -4,1,03.07.2015,1.0,789,130.34,119.33 -4,1,04.07.2015,1.0,594,130.34,119.33 -4,1,05.07.2015,1.0,694,130.34,119.33 -4,1,06.07.2015,1.0,418,130.34,119.33 -4,1,07.07.2015,,163,130.34, -4,1,08.07.2015,,160,130.34, -4,1,09.07.2015,,484,121.64, -4,1,10.07.2015,,622,121.64, -4,1,11.07.2015,,763,123.55, -4,1,12.07.2015,,185,123.55, -4,1,13.07.2015,,361,123.55, -4,1,14.07.2015,,506,123.55, -4,1,15.07.2015,,974,125.45, -4,1,16.07.2015,,543,125.45, -4,1,17.07.2015,,374,125.45, -4,1,18.07.2015,,336,127.62, -4,1,19.07.2015,,500,127.62, -4,1,20.07.2015,,650,127.62, -4,1,21.07.2015,,405,127.62, -4,1,22.07.2015,,540,127.62, -4,1,23.07.2015,,484,127.62, -4,1,24.07.2015,,1052,127.62, -4,1,25.07.2015,,653,127.62, -4,1,26.07.2015,,782,130.07, -4,1,27.07.2015,,801,130.07, -4,1,28.07.2015,1.0,2551,130.07,124.77 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-4,1,30.08.2015,,468,137.68, -4,1,31.08.2015,,223,137.68, -4,1,01.09.2015,,251,137.68, -4,1,02.09.2015,,390,135.78, -4,1,03.09.2015,,355,135.78, -4,1,04.09.2015,,355,135.78, -4,1,05.09.2015,,320,133.6, -4,1,06.09.2015,,320,133.6, -4,1,07.09.2015,,320,133.6, -4,1,08.09.2015,,320,133.6, -4,1,09.09.2015,,320,135.51, -4,1,10.09.2015,,320,135.51, -4,1,11.09.2015,,320,135.51, -4,1,12.09.2015,,320,135.51, -4,1,13.09.2015,,320,135.51, -4,1,14.09.2015,,320,135.51, -4,1,15.09.2015,,320,135.51, -4,1,16.09.2015,,320,138.5, -4,1,17.09.2015,,320,138.5, -4,1,18.09.2015,,613,138.5, -4,1,19.09.2015,,283,138.5, -4,1,20.09.2015,,308,138.5, -4,1,21.09.2015,,229,138.5, -4,1,22.09.2015,,254,138.5, -4,1,23.09.2015,,154,141.21, -4,1,24.09.2015,,176,141.21, -4,1,25.09.2015,,324,141.21, -4,1,26.09.2015,,201,141.21, -4,1,27.09.2015,,280,141.21, -4,1,28.09.2015,,283,141.21, -4,1,29.09.2015,,160,141.21, -4,1,30.09.2015,,280,141.21, -4,1,01.10.2015,,210,141.21, -4,1,02.10.2015,,214,141.21, -4,1,03.10.2015,,195,141.21, -4,1,04.10.2015,,141,141.21, -4,1,05.10.2015,,245,141.21, -4,1,06.10.2015,1.0,817,141.21,122.32 -4,1,07.10.2015,1.0,694,141.21,122.32 -4,1,08.10.2015,1.0,751,141.11,122.32 -4,1,09.10.2015,1.0,889,141.21,122.32 -4,1,10.10.2015,1.0,870,141.21,122.32 -4,1,11.10.2015,1.0,540,141.21,122.32 -4,1,12.10.2015,1.0,741,141.21,122.32 -4,1,13.10.2015,,145,141.21, -4,1,14.10.2015,,113,141.21, -4,1,15.10.2015,,157,142.57, -4,1,16.10.2015,,198,142.57, -4,1,17.10.2015,,72,142.57, -4,1,18.10.2015,,148,142.57, -4,1,19.10.2015,,91,142.57, -4,1,20.10.2015,,138,142.57, -4,1,21.10.2015,,575,130.34, -4,1,22.10.2015,,829,130.34, -4,1,23.10.2015,,129,130.34, -4,1,24.10.2015,,53,130.34, -4,1,25.10.2015,,267,130.34, -4,1,26.10.2015,,220,130.34, -4,1,27.10.2015,1.0,1913,130.34,122.32 -4,1,28.10.2015,1.0,1191,130.34,122.32 -4,1,29.10.2015,1.0,1109,130.34,122.32 -4,1,30.10.2015,1.0,616,130.34,122.32 -4,1,31.10.2015,1.0,609,130.34,122.32 -4,1,01.11.2015,1.0,386,130.34,122.32 -4,1,02.11.2015,1.0,292,130.34,122.32 -4,1,03.11.2015,,151,130.34, -4,1,04.11.2015,,141,134.69, -4,1,05.11.2015,,129,134.69, -4,1,06.11.2015,,129,134.69, -4,1,07.11.2015,,157,134.69, -4,1,08.11.2015,,53,134.69, -4,1,09.11.2015,,229,134.69, -4,1,10.11.2015,,173,134.69, -4,1,11.11.2015,,280,130.34, -4,1,12.11.2015,,223,130.34, -4,1,13.11.2015,,207,130.34, -4,1,14.11.2015,,267,130.34, -4,1,15.11.2015,,273,130.34, -4,1,16.11.2015,,242,130.34, -4,1,17.11.2015,,204,130.34, -4,1,18.11.2015,,160,130.34, -4,1,19.11.2015,,223,130.34, -4,1,20.11.2015,,163,130.34, -4,1,21.11.2015,,129,130.34, -4,1,22.11.2015,,141,130.34, -4,1,23.11.2015,,195,130.34, -4,1,24.11.2015,1.0,6905,130.34,116.61 -4,1,25.11.2015,1.0,2658,130.34,116.61 -4,1,26.11.2015,1.0,1338,130.34,116.61 -4,1,27.11.2015,1.0,1275,130.34,116.61 -4,1,28.11.2015,1.0,1222,130.34,116.61 -4,1,29.11.2015,1.0,1703,130.34,116.61 -4,1,30.11.2015,1.0,2070,130.34,116.61 -4,1,01.12.2015,,198,130.34, -4,1,02.12.2015,,110,130.34, -4,1,03.12.2015,,188,130.34, -4,1,04.12.2015,,151,130.34, -4,1,05.12.2015,1.0,1313,130.34,116.61 -4,1,06.12.2015,1.0,845,130.34,116.61 -4,1,07.12.2015,1.0,1297,130.34,116.61 -4,1,08.12.2015,,176,130.34, -4,1,09.12.2015,,167,130.34, -4,1,10.12.2015,,223,130.34, -4,1,11.12.2015,,148,130.34, -4,1,12.12.2015,,163,130.34, -4,1,13.12.2015,,104,130.34, -4,1,14.12.2015,,129,130.34, -4,1,15.12.2015,,173,130.34, -4,1,16.12.2015,,91,130.34, -4,1,17.12.2015,,204,130.34, -4,1,18.12.2015,,251,130.34, -4,1,19.12.2015,,129,130.34, -4,1,20.12.2015,,163,130.34, -4,1,21.12.2015,,107,130.34, -4,1,22.12.2015,,232,130.34, -4,1,23.12.2015,,104,130.34, -4,1,24.12.2015,,85,126.54, -4,1,25.12.2015,,198,126.54, -4,1,26.12.2015,,138,126.54, -4,1,27.12.2015,,60,126.54, -4,1,28.12.2015,,113,126.54, -4,1,29.12.2015,,173,126.54, -4,1,30.12.2015,,217,126.54, -4,1,31.12.2015,,72,126.54, -4,1,01.01.2016,,19,126.54, -4,1,02.01.2016,,41,126.54, -4,1,03.01.2016,,79,126.54, -4,1,04.01.2016,,110,126.54, -4,1,05.01.2016,,101,126.54, -4,1,06.01.2016,,135,126.54, -4,1,07.01.2016,,104,126.54, -4,1,08.01.2016,,110,126.54, -4,1,09.01.2016,,258,126.54, -4,1,10.01.2016,,113,126.54, -4,1,11.01.2016,,342,126.54, -4,1,12.01.2016,,358,126.54, -4,1,13.01.2016,,320,126.54, -4,1,14.01.2016,,478,126.54, -4,1,15.01.2016,,437,126.54, -4,1,16.01.2016,,327,126.54, -4,1,17.01.2016,,358,126.54, -4,1,18.01.2016,,393,126.54, -4,1,19.01.2016,,188,126.54, -4,1,20.01.2016,,459,126.54, -4,1,21.01.2016,,358,127.9, -4,1,22.01.2016,,833,127.9, -4,1,23.01.2016,,534,127.9, -4,1,24.01.2016,,214,127.9, -4,1,25.01.2016,,336,127.9, -4,1,26.01.2016,,364,127.9, -4,1,27.01.2016,,361,127.9, -4,1,28.01.2016,,352,127.9, -4,1,29.01.2016,,311,127.9, -4,1,30.01.2016,,330,127.9, -4,1,31.01.2016,,273,127.9, -4,1,01.02.2016,,361,130.34, -4,1,02.02.2016,,242,130.34, -4,1,03.02.2016,,270,130.34, -4,1,04.02.2016,,182,133.06, -4,1,05.02.2016,,170,133.06, -4,1,06.02.2016,,339,133.06, -4,1,07.02.2016,,286,141.76, -4,1,08.02.2016,,185,141.76, -4,1,09.02.2016,1.0,804,141.76,130.75 -4,1,10.02.2016,1.0,675,141.76,130.75 -4,1,11.02.2016,1.0,701,141.76,130.75 -4,1,12.02.2016,1.0,559,141.76,130.75 -4,1,13.02.2016,1.0,704,141.76,130.75 -4,1,14.02.2016,1.0,336,141.76,130.75 -4,1,15.02.2016,1.0,798,141.76,130.75 -4,1,16.02.2016,1.0,509,141.76,130.75 -4,1,17.02.2016,1.0,528,141.76,130.75 -4,1,18.02.2016,,176,141.76, -4,1,19.02.2016,,154,141.76, -4,1,20.02.2016,,135,141.76, -4,1,21.02.2016,,97,141.76, -4,1,22.02.2016,,232,141.76, -4,1,23.02.2016,,116,141.76, -4,1,24.02.2016,,207,141.76, -4,1,25.02.2016,,154,141.76, -4,1,26.02.2016,,229,141.76, -4,1,27.02.2016,,126,141.76, -4,1,28.02.2016,,138,141.76, -4,1,29.02.2016,,163,141.76, -4,1,01.03.2016,,154,141.76, -4,1,02.03.2016,,101,141.76, -4,1,03.03.2016,,157,141.76, -4,1,04.03.2016,,126,141.76, -4,1,05.03.2016,,198,141.21, -4,1,06.03.2016,,119,141.21, -4,1,07.03.2016,,154,141.21, -4,1,08.03.2016,1.0,204,141.21,132.92 -4,1,09.03.2016,1.0,707,141.21,132.92 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-4,1,10.04.2016,,110,141.21, -4,1,11.04.2016,,188,141.21, -4,1,12.04.2016,,163,141.21, -4,1,13.04.2016,,157,141.21, -4,1,14.04.2016,,421,135.78, -4,1,15.04.2016,,562,135.78, -4,1,16.04.2016,,223,135.78, -4,1,17.04.2016,,295,135.78, -4,1,18.04.2016,,204,135.78, -4,1,19.04.2016,1.0,5721,135.78,122.32 -4,1,20.04.2016,1.0,251,135.78,122.32 -4,1,21.04.2016,1.0,694,135.78,122.32 -4,1,22.04.2016,1.0,889,135.78,122.32 -4,1,23.04.2016,1.0,848,135.78,122.32 -4,1,24.04.2016,1.0,817,135.78,122.32 -4,1,25.04.2016,1.0,905,135.78,122.32 -4,1,26.04.2016,,364,135.78, -4,1,27.04.2016,,339,135.78, -4,1,28.04.2016,,179,135.78, -4,1,29.04.2016,,229,131.16, -4,1,30.04.2016,,336,131.16, -4,1,01.05.2016,,107,131.16, -4,1,02.05.2016,,85,131.16, -4,1,03.05.2016,,113,131.16, -4,1,04.05.2016,,264,131.16, -4,1,05.05.2016,,195,131.16, -4,1,06.05.2016,,173,131.16, -4,1,07.05.2016,,148,131.16, -4,1,08.05.2016,,141,131.16, -4,1,09.05.2016,,116,131.16, -4,1,10.05.2016,,126,131.16, -4,1,11.05.2016,,217,131.16, -4,1,12.05.2016,,251,131.16, -4,1,13.05.2016,,324,128.98, -4,1,14.05.2016,,308,128.98, -4,1,15.05.2016,,289,128.98, -4,1,16.05.2016,,324,128.98, -4,1,17.05.2016,1.0,2425,128.98,119.6 -4,1,18.05.2016,1.0,1304,128.98,119.6 -4,1,19.05.2016,1.0,1963,128.98,119.6 -4,1,20.05.2016,1.0,789,128.98,119.6 -4,1,21.05.2016,1.0,1074,128.98,119.6 -4,1,22.05.2016,1.0,1021,128.98,119.6 -5,1,01.01.2015,,22,163.78, -5,1,02.01.2015,,44,163.78, -5,1,03.01.2015,,38,163.78, -5,1,04.01.2015,,47,163.78, -5,1,05.01.2015,,72,163.78, -5,1,06.01.2015,,85,163.78, -5,1,07.01.2015,,82,163.78, -5,1,08.01.2015,,91,163.78, -5,1,09.01.2015,,63,163.78, -5,1,10.01.2015,,101,163.78, -5,1,11.01.2015,,116,163.78, -5,1,12.01.2015,,126,163.78, -5,1,13.01.2015,,116,163.78, -5,1,14.01.2015,,75,168.4, -5,1,15.01.2015,,88,168.4, -5,1,16.01.2015,,88,168.4, -5,1,17.01.2015,,101,168.4, -5,1,18.01.2015,,88,168.4, -5,1,19.01.2015,,94,168.4, -5,1,20.01.2015,,75,168.4, -5,1,21.01.2015,,79,168.4, -5,1,22.01.2015,,91,168.4, -5,1,23.01.2015,,75,160.24, -5,1,24.01.2015,,129,160.24, -5,1,25.01.2015,,167,160.24, -5,1,26.01.2015,,107,160.24, -5,1,27.01.2015,,123,160.24, -5,1,28.01.2015,,220,146.92, -5,1,29.01.2015,,204,146.92, -5,1,30.01.2015,,276,146.92, -5,1,31.01.2015,,358,146.92, -5,1,01.02.2015,,280,146.92, -5,1,02.02.2015,,217,146.92, -5,1,03.02.2015,,258,146.92, -5,1,04.02.2015,,361,146.92, -5,1,05.02.2015,,280,146.92, -5,1,06.02.2015,,493,146.92, -5,1,07.02.2015,,349,146.92, -5,1,08.02.2015,,283,146.92, -5,1,09.02.2015,,217,146.92, -5,1,10.02.2015,,346,146.92, -5,1,11.02.2015,,434,146.92, -5,1,12.02.2015,,273,146.92, -5,1,13.02.2015,,261,149.37, -5,1,14.02.2015,,280,149.37, -5,1,15.02.2015,,239,149.37, -5,1,16.02.2015,,192,149.37, -5,1,17.02.2015,1.0,609,149.37,143.8 -5,1,18.02.2015,1.0,958,149.37,135.64 -5,1,19.02.2015,1.0,1913,149.37,135.64 -5,1,20.02.2015,1.0,1464,149.37,135.64 -5,1,21.02.2015,1.0,1027,149.37,135.64 -5,1,22.02.2015,1.0,691,149.37,135.64 -5,1,23.02.2015,1.0,478,149.37,135.64 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-5,1,27.04.2015,,276,128.98, -5,1,28.04.2015,,204,128.98, -5,1,29.04.2015,,185,131.7, -5,1,30.04.2015,,248,131.7, -5,1,01.05.2015,,201,131.7, -5,1,02.05.2015,,173,131.7, -5,1,03.05.2015,,204,131.7, -5,1,04.05.2015,,289,131.7, -5,1,05.05.2015,,267,131.7, -5,1,06.05.2015,,207,131.7, -5,1,07.05.2015,,176,131.7, -5,1,08.05.2015,,386,131.7, -5,1,09.05.2015,,267,131.7, -5,1,10.05.2015,,245,131.7, -5,1,11.05.2015,,204,131.7, -5,1,12.05.2015,,236,131.7, -5,1,13.05.2015,,201,131.7, -5,1,14.05.2015,,210,131.7, -5,1,15.05.2015,,390,131.7, -5,1,16.05.2015,,305,131.7, -5,1,17.05.2015,,314,131.7, -5,1,18.05.2015,,264,131.7, -5,1,19.05.2015,1.0,2479,131.7,119.33 -5,1,20.05.2015,1.0,1945,131.7,119.33 -5,1,21.05.2015,1.0,1920,131.7,119.33 -5,1,22.05.2015,1.0,1649,131.7,119.33 -5,1,23.05.2015,1.0,1074,131.7,119.33 -5,1,24.05.2015,1.0,864,131.7,119.33 -5,1,25.05.2015,1.0,782,131.7,119.33 -5,1,26.05.2015,,248,131.7, -5,1,27.05.2015,,336,129.25, -5,1,28.05.2015,,302,129.25, -5,1,29.05.2015,,204,129.25, -5,1,30.05.2015,,210,129.25, -5,1,31.05.2015,,182,129.25, -5,1,01.06.2015,,415,129.25, -5,1,02.06.2015,,185,129.25, -5,1,03.06.2015,,192,129.25, -5,1,04.06.2015,,176,129.25, -5,1,05.06.2015,,220,129.25, -5,1,06.06.2015,,223,131.7, -5,1,07.06.2015,,232,131.7, -5,1,08.06.2015,,311,131.7, -5,1,09.06.2015,1.0,1910,131.7,119.33 -5,1,10.06.2015,1.0,2322,131.7,119.33 -5,1,11.06.2015,1.0,1913,131.7,119.33 -5,1,12.06.2015,1.0,983,131.7,119.33 -5,1,13.06.2015,1.0,666,131.7,119.33 -5,1,14.06.2015,1.0,952,131.7,119.33 -5,1,15.06.2015,1.0,609,131.7,119.33 -5,1,16.06.2015,,204,131.7, -5,1,17.06.2015,,245,131.7, -5,1,18.06.2015,,261,128.17, -5,1,19.06.2015,,242,128.17, -5,1,20.06.2015,,182,128.17, -5,1,21.06.2015,,308,128.17, -5,1,22.06.2015,,302,128.17, -5,1,23.06.2015,,298,128.17, -5,1,24.06.2015,,361,128.17, -5,1,25.06.2015,,603,128.17, -5,1,26.06.2015,,396,128.17, -5,1,27.06.2015,,298,128.17, -5,1,28.06.2015,,358,130.34, -5,1,29.06.2015,,349,130.34, -5,1,30.06.2015,1.0,807,130.34,119.33 -5,1,01.07.2015,1.0,1090,130.34,119.33 -5,1,02.07.2015,1.0,1084,130.34,119.33 -5,1,03.07.2015,1.0,1483,130.34,119.33 -5,1,04.07.2015,1.0,933,130.34,119.33 -5,1,05.07.2015,1.0,1084,130.34,119.33 -5,1,06.07.2015,1.0,1037,130.34,119.33 -5,1,07.07.2015,,151,130.34, -5,1,08.07.2015,,283,130.34, -5,1,09.07.2015,,493,121.64, -5,1,10.07.2015,,628,121.64, -5,1,11.07.2015,,515,123.55, -5,1,12.07.2015,,540,123.55, -5,1,13.07.2015,,547,123.55, -5,1,14.07.2015,,741,123.55, -5,1,15.07.2015,,553,125.45, -5,1,16.07.2015,,487,125.45, -5,1,17.07.2015,,459,125.45, -5,1,18.07.2015,,204,127.62, -5,1,19.07.2015,,371,127.62, -5,1,20.07.2015,,528,127.62, -5,1,21.07.2015,,430,127.62, -5,1,22.07.2015,,478,127.62, -5,1,23.07.2015,,487,127.62, -5,1,24.07.2015,,613,127.62, -5,1,25.07.2015,,657,127.62, -5,1,26.07.2015,,1068,130.07, -5,1,27.07.2015,,1112,130.07, -5,1,28.07.2015,1.0,1087,130.07,124.77 -5,1,29.07.2015,1.0,2353,130.07,124.77 -5,1,30.07.2015,1.0,459,130.07,124.77 -5,1,31.07.2015,1.0,977,130.07,124.77 -5,1,01.08.2015,1.0,531,130.07,124.77 -5,1,02.08.2015,1.0,1448,130.07,124.77 -5,1,03.08.2015,1.0,1891,130.07,124.77 -5,1,04.08.2015,,368,138.5, -5,1,05.08.2015,,352,138.5, -5,1,06.08.2015,,342,138.5, -5,1,07.08.2015,,484,138.5, -5,1,08.08.2015,,248,141.21, -5,1,09.08.2015,,292,141.21, -5,1,10.08.2015,,352,141.21, -5,1,11.08.2015,,336,141.21, -5,1,12.08.2015,,320,142.85, -5,1,13.08.2015,,569,142.85, -5,1,14.08.2015,,298,142.85, -5,1,15.08.2015,,330,142.85, -5,1,16.08.2015,,710,142.85, -5,1,17.08.2015,,509,142.85, -5,1,18.08.2015,,547,142.85, -5,1,19.08.2015,,569,142.85, -5,1,20.08.2015,,531,142.85, -5,1,21.08.2015,,405,142.85, -5,1,22.08.2015,,553,142.85, -5,1,23.08.2015,,405,142.85, -5,1,24.08.2015,,484,142.85, -5,1,25.08.2015,,418,142.85, -5,1,26.08.2015,,380,142.85, -5,1,27.08.2015,,578,137.68, -5,1,28.08.2015,,490,137.68, -5,1,29.08.2015,,732,137.68, -5,1,30.08.2015,,581,137.68, -5,1,31.08.2015,,603,137.68, -5,1,01.09.2015,,584,137.68, -5,1,02.09.2015,,635,135.78, -5,1,03.09.2015,,682,135.78, -5,1,04.09.2015,,798,135.78, -5,1,05.09.2015,,911,133.6, -5,1,06.09.2015,,631,133.6, -5,1,07.09.2015,,606,133.6, -5,1,08.09.2015,,889,133.6, -5,1,09.09.2015,,587,135.51, -5,1,10.09.2015,,902,135.51, -5,1,11.09.2015,,864,135.51, -5,1,12.09.2015,,619,135.51, -5,1,13.09.2015,,412,135.51, -5,1,14.09.2015,,584,135.51, -5,1,15.09.2015,,540,135.51, -5,1,16.09.2015,,386,138.5, -5,1,17.09.2015,,324,138.5, -5,1,18.09.2015,,418,138.5, -5,1,19.09.2015,,537,138.5, -5,1,20.09.2015,,415,138.5, -5,1,21.09.2015,,386,138.5, -5,1,22.09.2015,,352,138.5, -5,1,23.09.2015,,317,141.21, -5,1,24.09.2015,,239,141.21, -5,1,25.09.2015,,229,141.21, -5,1,26.09.2015,,336,141.21, -5,1,27.09.2015,,311,141.21, -5,1,28.09.2015,,314,141.21, -5,1,29.09.2015,,383,141.21, -5,1,30.09.2015,,393,141.21, -5,1,01.10.2015,,248,141.21, -5,1,02.10.2015,,336,141.21, -5,1,03.10.2015,,295,141.21, -5,1,04.10.2015,,355,141.21, -5,1,05.10.2015,,214,141.21, -5,1,06.10.2015,1.0,522,141.21,132.92 -5,1,07.10.2015,1.0,694,141.21,130.21 -5,1,08.10.2015,1.0,1678,141.11,130.21 -5,1,09.10.2015,1.0,908,141.21,130.21 -5,1,10.10.2015,1.0,1008,141.21,130.21 -5,1,11.10.2015,1.0,594,141.21,130.21 -5,1,12.10.2015,1.0,556,141.21,130.21 -5,1,13.10.2015,,308,141.21, -5,1,14.10.2015,,214,141.21, -5,1,15.10.2015,,170,142.57, -5,1,16.10.2015,,229,142.57, -5,1,17.10.2015,,248,142.57, -5,1,18.10.2015,,198,142.57, -5,1,19.10.2015,,198,142.57, -5,1,20.10.2015,,223,142.57, -5,1,21.10.2015,,877,130.34, -5,1,22.10.2015,,738,130.34, -5,1,23.10.2015,,682,130.34, -5,1,24.10.2015,,597,130.34, -5,1,25.10.2015,,625,130.34, -5,1,26.10.2015,,628,130.34, -5,1,27.10.2015,1.0,2391,130.34,122.32 -5,1,28.10.2015,1.0,1819,130.34,124.77 -5,1,29.10.2015,1.0,1436,130.34,124.77 -5,1,30.10.2015,1.0,804,130.34,124.77 -5,1,31.10.2015,1.0,785,130.34,124.77 -5,1,01.11.2015,1.0,525,130.34,124.77 -5,1,02.11.2015,1.0,562,130.34,124.77 -5,1,03.11.2015,,383,130.34, -5,1,04.11.2015,,229,134.69, -5,1,05.11.2015,,207,134.69, -5,1,06.11.2015,,210,134.69, -5,1,07.11.2015,,157,134.69, -5,1,08.11.2015,,226,134.69, -5,1,09.11.2015,,254,134.69, -5,1,10.11.2015,,214,134.69, -5,1,11.11.2015,,415,130.34, -5,1,12.11.2015,,390,130.34, -5,1,13.11.2015,,251,130.34, -5,1,14.11.2015,,298,130.34, -5,1,15.11.2015,,289,130.34, -5,1,16.11.2015,,276,130.34, -5,1,17.11.2015,,289,130.34, -5,1,18.11.2015,,229,130.34, -5,1,19.11.2015,,377,130.34, -5,1,20.11.2015,,314,130.34, -5,1,21.11.2015,,393,130.34, -5,1,22.11.2015,,289,130.34, -5,1,23.11.2015,,267,130.34, -5,1,24.11.2015,1.0,6732,130.34,116.61 -5,1,25.11.2015,1.0,1012,130.34,116.61 -5,1,26.11.2015,1.0,1370,130.34,116.61 -5,1,27.11.2015,1.0,2934,130.34,116.61 -5,1,28.11.2015,1.0,2614,130.34,116.61 -5,1,29.11.2015,1.0,2403,130.34,116.61 -5,1,30.11.2015,1.0,2944,130.34,116.61 -5,1,01.12.2015,,236,130.34, -5,1,02.12.2015,,176,130.34, -5,1,03.12.2015,,226,130.34, -5,1,04.12.2015,,214,130.34, -5,1,05.12.2015,1.0,2108,130.34,116.61 -5,1,06.12.2015,1.0,1608,130.34,116.61 -5,1,07.12.2015,1.0,2143,130.34,116.61 -5,1,08.12.2015,,226,130.34, -5,1,09.12.2015,,251,130.34, -5,1,10.12.2015,,232,130.34, -5,1,11.12.2015,,298,130.34, -5,1,12.12.2015,,276,130.34, -5,1,13.12.2015,,210,130.34, -5,1,14.12.2015,,204,130.34, -5,1,15.12.2015,,283,130.34, -5,1,16.12.2015,,298,130.34, -5,1,17.12.2015,,239,130.34, -5,1,18.12.2015,,286,130.34, -5,1,19.12.2015,,276,130.34, -5,1,20.12.2015,,245,130.34, -5,1,21.12.2015,,220,130.34, -5,1,22.12.2015,,204,130.34, -5,1,23.12.2015,,160,130.34, -5,1,24.12.2015,,236,126.54, -5,1,25.12.2015,,327,126.54, -5,1,26.12.2015,,333,126.54, -5,1,27.12.2015,,305,126.54, -5,1,28.12.2015,,396,126.54, -5,1,29.12.2015,,302,126.54, -5,1,30.12.2015,,368,126.54, -5,1,31.12.2015,,254,126.54, -5,1,01.01.2016,,22,126.54, -5,1,02.01.2016,,163,126.54, -5,1,03.01.2016,,242,126.54, -5,1,04.01.2016,,229,126.54, -5,1,05.01.2016,,286,126.54, -5,1,06.01.2016,,399,126.54, -5,1,07.01.2016,,264,126.54, -5,1,08.01.2016,,396,126.54, -5,1,09.01.2016,,390,126.54, -5,1,10.01.2016,,509,126.54, -5,1,11.01.2016,,534,126.54, -5,1,12.01.2016,,559,126.54, -5,1,13.01.2016,,553,126.54, -5,1,14.01.2016,,487,126.54, -5,1,15.01.2016,,540,126.54, -5,1,16.01.2016,,578,126.54, -5,1,17.01.2016,,408,126.54, -5,1,18.01.2016,,622,126.54, -5,1,19.01.2016,,408,126.54, -5,1,20.01.2016,,619,126.54, -5,1,21.01.2016,,418,127.9, -5,1,22.01.2016,,509,127.9, -5,1,23.01.2016,,754,127.9, -5,1,24.01.2016,,581,127.9, -5,1,25.01.2016,,833,127.9, -5,1,26.01.2016,,443,127.9, -5,1,27.01.2016,,534,127.9, -5,1,28.01.2016,,547,127.9, -5,1,29.01.2016,,537,127.9, -5,1,30.01.2016,,412,127.9, -5,1,31.01.2016,,471,127.9, -5,1,01.02.2016,,437,130.34, -5,1,02.02.2016,,569,130.34, -5,1,03.02.2016,,437,130.34, -5,1,04.02.2016,,352,133.06, -5,1,05.02.2016,,320,133.06, -5,1,06.02.2016,,500,133.06, -5,1,07.02.2016,,418,141.76, -5,1,08.02.2016,,446,141.76, -5,1,09.02.2016,1.0,848,141.76,130.75 -5,1,10.02.2016,1.0,801,141.76,130.75 -5,1,11.02.2016,1.0,729,141.76,130.75 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-6,1,25.01.2015,,195,168.4, -6,1,26.01.2015,,132,168.4, -6,1,27.01.2015,,148,168.4, -6,1,28.01.2015,,148,168.4, -6,1,29.01.2015,,119,168.4, -6,1,30.01.2015,,104,168.4, -6,1,31.01.2015,,154,168.4, -6,1,01.02.2015,,116,168.4, -6,1,02.02.2015,,88,168.4, -6,1,03.02.2015,,97,168.4, -6,1,04.02.2015,,151,168.4, -6,1,05.02.2015,,236,144.2, -6,1,06.02.2015,,280,144.2, -6,1,07.02.2015,,223,144.2, -6,1,08.02.2015,,437,144.2, -6,1,09.02.2015,,273,144.2, -6,1,10.02.2015,,245,144.2, -6,1,11.02.2015,,333,144.2, -6,1,12.02.2015,,305,144.2, -6,1,13.02.2015,,327,144.2, -6,1,14.02.2015,,352,144.2, -6,1,15.02.2015,,525,144.2, -6,1,16.02.2015,,298,144.2, -6,1,17.02.2015,1.0,418,144.2,143.8 -6,1,18.02.2015,1.0,675,144.2,135.64 -6,1,19.02.2015,1.0,1517,144.2,135.64 -6,1,20.02.2015,1.0,1253,144.2,135.64 -6,1,21.02.2015,1.0,1291,144.2,135.64 -6,1,22.02.2015,1.0,572,144.2,135.64 -6,1,23.02.2015,1.0,763,144.2,135.64 -6,1,24.02.2015,,305,144.2, -6,1,25.02.2015,,236,144.2, -6,1,26.02.2015,,298,144.2, 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-6,1,05.11.2015,,248,134.69, -6,1,06.11.2015,,390,134.69, -6,1,07.11.2015,,289,134.69, -6,1,08.11.2015,,368,134.69, -6,1,09.11.2015,,145,134.69, -6,1,10.11.2015,,308,134.69, -6,1,11.11.2015,,151,130.34, -6,1,12.11.2015,,377,130.34, -6,1,13.11.2015,,258,130.34, -6,1,14.11.2015,,506,130.34, -6,1,15.11.2015,,280,130.34, -6,1,16.11.2015,,236,130.34, -6,1,17.11.2015,,195,130.34, -6,1,18.11.2015,,270,130.34, -6,1,19.11.2015,,324,130.34, -6,1,20.11.2015,,386,130.34, -6,1,21.11.2015,,355,130.34, -6,1,22.11.2015,,305,130.34, -6,1,23.11.2015,,276,130.34, -6,1,24.11.2015,1.0,2457,130.34,116.61 -6,1,25.11.2015,1.0,2215,130.34,116.61 -6,1,26.11.2015,1.0,2209,130.34,116.61 -6,1,27.11.2015,1.0,1718,130.34,116.61 -6,1,28.11.2015,1.0,462,130.34,116.61 -6,1,29.11.2015,1.0,952,130.34,116.61 -6,1,30.11.2015,1.0,1898,130.34,116.61 -6,1,01.12.2015,,280,130.34, -6,1,02.12.2015,,339,130.34, -6,1,03.12.2015,,481,130.34, -6,1,04.12.2015,,292,130.34, -6,1,05.12.2015,1.0,1819,130.34,116.61 -6,1,06.12.2015,1.0,1078,130.34,116.61 -6,1,07.12.2015,1.0,1335,130.34,116.61 -6,1,08.12.2015,,336,130.34, -6,1,09.12.2015,,292,130.34, -6,1,10.12.2015,,276,130.34, -6,1,11.12.2015,,50,130.34, -6,1,12.12.2015,,248,130.34, -6,1,13.12.2015,,226,130.34, -6,1,14.12.2015,,393,130.34, -6,1,15.12.2015,,443,130.34, -6,1,16.12.2015,,449,130.34, -6,1,17.12.2015,,320,130.34, -6,1,18.12.2015,,355,130.34, -6,1,19.12.2015,,383,130.34, -6,1,20.12.2015,,371,130.34, -6,1,21.12.2015,,342,130.34, -6,1,22.12.2015,,270,130.34, -6,1,23.12.2015,,302,130.34, -6,1,24.12.2015,,380,126.54, -6,1,25.12.2015,,402,126.54, -6,1,26.12.2015,,336,126.54, -6,1,27.12.2015,,352,126.54, -6,1,28.12.2015,,399,126.54, -6,1,29.12.2015,,276,126.54, -6,1,30.12.2015,,449,126.54, -6,1,31.12.2015,,289,126.54, -6,1,01.01.2016,,57,126.54, -6,1,02.01.2016,,226,126.54, -6,1,03.01.2016,,148,126.54, -6,1,04.01.2016,,311,126.54, -6,1,05.01.2016,,280,126.54, -6,1,06.01.2016,,236,126.54, -6,1,07.01.2016,,223,126.54, -6,1,08.01.2016,,245,126.54, -6,1,09.01.2016,,349,126.54, -6,1,10.01.2016,,327,126.54, -6,1,11.01.2016,,352,126.54, -6,1,12.01.2016,,298,126.54, -6,1,13.01.2016,,242,126.54, -6,1,14.01.2016,,302,126.54, -6,1,15.01.2016,,424,126.54, -6,1,16.01.2016,,261,126.54, -6,1,17.01.2016,,478,126.54, -6,1,18.01.2016,,405,126.54, -6,1,19.01.2016,,276,126.54, -6,1,20.01.2016,,393,126.54, -6,1,21.01.2016,,349,127.9, -6,1,22.01.2016,,336,127.9, -6,1,23.01.2016,,336,127.9, -6,1,24.01.2016,,317,127.9, -6,1,25.01.2016,,217,127.9, -6,1,26.01.2016,,349,127.9, -6,1,27.01.2016,,317,127.9, -6,1,28.01.2016,,487,127.9, -6,1,29.01.2016,,383,127.9, -6,1,30.01.2016,,434,127.9, -6,1,31.01.2016,,336,127.9, -6,1,01.02.2016,,264,130.34, -6,1,02.02.2016,,295,130.34, -6,1,03.02.2016,,371,130.34, -6,1,04.02.2016,,587,133.06, -6,1,05.02.2016,,594,133.06, -6,1,06.02.2016,,402,133.06, -6,1,07.02.2016,,317,141.76, -6,1,08.02.2016,,261,141.76, -6,1,09.02.2016,1.0,553,141.76,130.75 -6,1,10.02.2016,1.0,622,141.76,130.75 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-6,1,12.04.2016,,182,141.21, -6,1,13.04.2016,,179,141.21, -6,1,14.04.2016,,336,135.78, -6,1,15.04.2016,,324,135.78, -6,1,16.04.2016,,377,135.78, -6,1,17.04.2016,,308,135.78, -6,1,18.04.2016,,320,135.78, -6,1,19.04.2016,1.0,920,135.78,122.32 -6,1,20.04.2016,1.0,1464,135.78,122.32 -6,1,21.04.2016,1.0,1458,135.78,122.32 -6,1,22.04.2016,1.0,1002,135.78,122.32 -6,1,23.04.2016,1.0,839,135.78,122.32 -6,1,24.04.2016,1.0,1222,135.78,122.32 -6,1,25.04.2016,1.0,823,135.78,122.32 -6,1,26.04.2016,,371,135.78, -6,1,27.04.2016,,264,135.78, -6,1,28.04.2016,,311,135.78, -6,1,29.04.2016,,509,131.16, -6,1,30.04.2016,,390,131.16, -6,1,01.05.2016,,119,131.16, -6,1,02.05.2016,,198,131.16, -6,1,03.05.2016,,119,131.16, -6,1,04.05.2016,,229,131.16, -6,1,05.05.2016,,176,131.16, -6,1,06.05.2016,,547,131.16, -6,1,07.05.2016,,339,131.16, -6,1,08.05.2016,,449,131.16, -6,1,09.05.2016,,195,131.16, -6,1,10.05.2016,,261,131.16, -6,1,11.05.2016,,267,131.16, -6,1,12.05.2016,,198,131.16, -6,1,13.05.2016,,226,128.98, -6,1,14.05.2016,,302,128.98, -6,1,15.05.2016,,286,128.98, -6,1,16.05.2016,,273,128.98, -6,1,17.05.2016,1.0,986,128.98,119.6 -6,1,18.05.2016,1.0,1514,128.98,119.6 -6,1,19.05.2016,1.0,1247,128.98,119.6 -6,1,20.05.2016,1.0,1169,128.98,119.6 -6,1,21.05.2016,1.0,1232,128.98,119.6 -6,1,22.05.2016,1.0,1225,128.98,119.6 -7,1,01.01.2015,,28,152.36, -7,1,02.01.2015,,60,152.36, -7,1,03.01.2015,,82,152.36, -7,1,04.01.2015,,63,152.36, -7,1,05.01.2015,,145,152.36, -7,1,06.01.2015,,217,152.36, -7,1,07.01.2015,,60,152.36, -7,1,08.01.2015,,210,152.36, -7,1,09.01.2015,,173,152.36, -7,1,10.01.2015,,179,152.36, -7,1,11.01.2015,,220,152.36, -7,1,12.01.2015,,509,152.36, -7,1,13.01.2015,,402,152.36, -7,1,14.01.2015,,506,152.36, -7,1,15.01.2015,,333,152.36, -7,1,16.01.2015,,305,168.4, -7,1,17.01.2015,,261,168.4, -7,1,18.01.2015,,126,168.4, -7,1,19.01.2015,,239,168.4, -7,1,20.01.2015,,267,168.4, -7,1,21.01.2015,,223,168.4, -7,1,22.01.2015,,135,168.4, -7,1,23.01.2015,,195,168.4, -7,1,24.01.2015,,210,168.4, -7,1,25.01.2015,,317,168.4, -7,1,26.01.2015,,226,168.4, -7,1,27.01.2015,,188,168.4, -7,1,28.01.2015,,167,168.4, -7,1,29.01.2015,,201,168.4, -7,1,30.01.2015,,126,168.4, -7,1,31.01.2015,,91,168.4, -7,1,01.02.2015,,188,168.4, -7,1,02.02.2015,,101,168.4, -7,1,03.02.2015,,119,168.4, -7,1,04.02.2015,,75,168.4, -7,1,05.02.2015,,412,144.2, -7,1,06.02.2015,,333,144.2, -7,1,07.02.2015,,330,144.2, -7,1,08.02.2015,,267,144.2, -7,1,09.02.2015,,248,144.2, -7,1,10.02.2015,,324,144.2, -7,1,11.02.2015,,157,144.2, -7,1,12.02.2015,,207,144.2, -7,1,13.02.2015,,289,144.2, -7,1,14.02.2015,,245,144.2, -7,1,15.02.2015,,173,144.2, -7,1,16.02.2015,,302,144.2, -7,1,17.02.2015,1.0,314,144.2,143.8 -7,1,18.02.2015,1.0,503,144.2,135.64 -7,1,19.02.2015,1.0,606,144.2,135.64 -7,1,20.02.2015,1.0,666,144.2,135.64 -7,1,21.02.2015,1.0,462,144.2,135.64 -7,1,22.02.2015,1.0,292,144.2,135.64 -7,1,23.02.2015,1.0,386,144.2,135.64 -7,1,24.02.2015,,201,144.2, -7,1,25.02.2015,,295,144.2, -7,1,26.02.2015,,245,144.2, -7,1,27.02.2015,,267,144.2, -7,1,28.02.2015,,421,144.2, -7,1,01.03.2015,,418,144.2, -7,1,02.03.2015,,418,144.2, -7,1,03.03.2015,1.0,132,144.2,138.36 -7,1,04.03.2015,1.0,474,144.2,135.64 -7,1,05.03.2015,1.0,101,144.2,135.64 -7,1,06.03.2015,1.0,270,144.2,135.64 -7,1,07.03.2015,1.0,679,144.2,135.64 -7,1,08.03.2015,1.0,352,144.2,135.64 -7,1,09.03.2015,,170,144.2, -7,1,10.03.2015,,160,144.2, -7,1,11.03.2015,,217,144.2, -7,1,12.03.2015,,198,144.2, -7,1,13.03.2015,,308,144.2, -7,1,14.03.2015,,214,144.2, -7,1,15.03.2015,,173,144.2, -7,1,16.03.2015,,179,144.2, -7,1,17.03.2015,1.0,1137,144.2,127.49 -7,1,18.03.2015,1.0,1932,144.2,127.49 -7,1,19.03.2015,1.0,1291,144.2,127.49 -7,1,20.03.2015,1.0,1495,144.2,127.49 -7,1,21.03.2015,1.0,905,140.67,127.49 -7,1,22.03.2015,1.0,779,140.67,127.49 -7,1,23.03.2015,1.0,682,140.67,127.49 -7,1,24.03.2015,,192,140.67, -7,1,25.03.2015,,204,140.67, -7,1,26.03.2015,,377,131.97, -7,1,27.03.2015,,383,131.97, -7,1,28.03.2015,,330,131.97, -7,1,29.03.2015,,258,131.97, 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-7,1,03.06.2015,,292,128.98, -7,1,04.06.2015,,210,128.98, -7,1,05.06.2015,,311,128.98, -7,1,06.06.2015,,261,128.98, -7,1,07.06.2015,,434,128.98, -7,1,08.06.2015,,289,128.98, -7,1,09.06.2015,1.0,609,128.98,119.33 -7,1,10.06.2015,1.0,1062,128.98,119.33 -7,1,11.06.2015,1.0,807,128.98,119.33 -7,1,12.06.2015,1.0,537,128.98,119.33 -7,1,13.06.2015,1.0,760,128.98,119.33 -7,1,14.06.2015,1.0,456,128.98,119.33 -7,1,15.06.2015,1.0,522,128.98,119.33 -7,1,16.06.2015,,547,128.98, -7,1,17.06.2015,,292,128.98, -7,1,18.06.2015,,342,128.98, -7,1,19.06.2015,,317,128.98, -7,1,20.06.2015,,207,128.98, -7,1,21.06.2015,,258,128.98, -7,1,22.06.2015,,468,128.98, -7,1,23.06.2015,,663,128.98, -7,1,24.06.2015,,430,128.98, -7,1,25.06.2015,,503,128.98, -7,1,26.06.2015,,418,128.98, -7,1,27.06.2015,,434,128.98, -7,1,28.06.2015,,286,128.98, -7,1,29.06.2015,,371,128.98, -7,1,30.06.2015,1.0,591,128.98,119.33 -7,1,01.07.2015,1.0,763,128.98,119.33 -7,1,02.07.2015,1.0,873,128.98,119.33 -7,1,03.07.2015,1.0,1178,128.98,119.33 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-7,1,11.12.2015,,317,130.34, -7,1,12.12.2015,,251,130.34, -7,1,13.12.2015,,148,130.34, -7,1,14.12.2015,,314,130.34, -7,1,15.12.2015,,176,130.34, -7,1,16.12.2015,,302,130.34, -7,1,17.12.2015,,264,130.34, -7,1,18.12.2015,,368,130.34, -7,1,19.12.2015,,229,130.34, -7,1,20.12.2015,,107,130.34, -7,1,21.12.2015,,223,130.34, -7,1,22.12.2015,,226,130.34, -7,1,23.12.2015,,223,130.34, -7,1,24.12.2015,,317,126.54, -7,1,25.12.2015,,267,126.54, -7,1,26.12.2015,,229,126.54, -7,1,27.12.2015,,188,126.54, -7,1,28.12.2015,,145,126.54, -7,1,29.12.2015,,210,126.54, -7,1,30.12.2015,,163,126.54, -7,1,31.12.2015,,185,126.54, -7,1,01.01.2016,,22,126.54, -7,1,02.01.2016,,110,126.54, -7,1,03.01.2016,,160,126.54, -7,1,04.01.2016,,129,126.54, -7,1,05.01.2016,,229,126.54, -7,1,06.01.2016,,232,126.54, -7,1,07.01.2016,,170,126.54, -7,1,08.01.2016,,210,126.54, -7,1,09.01.2016,,167,126.54, -7,1,10.01.2016,,154,126.54, -7,1,11.01.2016,,204,126.54, -7,1,12.01.2016,,405,126.54, -7,1,13.01.2016,,232,126.54, -7,1,14.01.2016,,355,126.54, -7,1,15.01.2016,,424,126.54, -7,1,16.01.2016,,487,126.54, -7,1,17.01.2016,,371,126.54, -7,1,18.01.2016,,308,126.54, -7,1,19.01.2016,,371,126.54, -7,1,20.01.2016,,609,126.54, -7,1,21.01.2016,,236,127.9, -7,1,22.01.2016,,361,127.9, -7,1,23.01.2016,,547,127.9, -7,1,24.01.2016,,421,127.9, -7,1,25.01.2016,,468,127.9, -7,1,26.01.2016,,446,127.9, -7,1,27.01.2016,,324,127.9, -7,1,28.01.2016,,465,127.9, -7,1,29.01.2016,,506,127.9, -7,1,30.01.2016,,459,127.9, -7,1,31.01.2016,,327,127.9, -7,1,01.02.2016,,377,130.34, -7,1,02.02.2016,,298,130.34, -7,1,03.02.2016,,236,130.34, -7,1,04.02.2016,,515,133.06, -7,1,05.02.2016,,280,133.06, -7,1,06.02.2016,,396,133.06, -7,1,07.02.2016,,264,141.76, -7,1,08.02.2016,,305,141.76, -7,1,09.02.2016,1.0,591,141.76,130.75 -7,1,10.02.2016,1.0,911,141.76,130.75 -7,1,11.02.2016,1.0,569,141.76,130.75 -7,1,12.02.2016,1.0,666,141.76,130.75 -7,1,13.02.2016,1.0,650,141.76,130.75 -7,1,14.02.2016,1.0,547,141.76,130.75 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-8,1,30.01.2015,,88,168.13, -8,1,31.01.2015,,129,168.13, -8,1,01.02.2015,,79,168.13, -8,1,02.02.2015,,110,168.13, -8,1,03.02.2015,,104,168.13, -8,1,04.02.2015,,66,168.13, -8,1,05.02.2015,,446,143.93, -8,1,06.02.2015,,591,143.93, -8,1,07.02.2015,,459,143.93, -8,1,08.02.2015,,295,143.93, -8,1,09.02.2015,,547,143.93, -8,1,10.02.2015,,553,143.93, -8,1,11.02.2015,,481,143.93, -8,1,12.02.2015,,537,143.93, -8,1,13.02.2015,,638,143.93, -8,1,14.02.2015,,522,143.93, -8,1,15.02.2015,,572,143.93, -8,1,16.02.2015,,741,143.93, -8,1,17.02.2015,1.0,823,143.93,143.8 -8,1,18.02.2015,1.0,619,143.93,135.64 -8,1,19.02.2015,1.0,1404,143.93,135.64 -8,1,20.02.2015,1.0,1756,143.93,135.64 -8,1,21.02.2015,1.0,1392,143.93,135.64 -8,1,22.02.2015,1.0,716,143.93,135.64 -8,1,23.02.2015,1.0,578,143.93,135.64 -8,1,24.02.2015,,289,143.93, -8,1,25.02.2015,,349,143.93, -8,1,26.02.2015,,302,143.93, -8,1,27.02.2015,,317,143.93, -8,1,28.02.2015,,550,143.93, -8,1,01.03.2015,,462,143.93, -8,1,02.03.2015,,478,143.93, 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-8,1,02.04.2015,,408,133.33, -8,1,03.04.2015,,543,133.33, -8,1,04.04.2015,,456,133.33, -8,1,05.04.2015,,358,133.33, -8,1,06.04.2015,,254,133.33, -8,1,07.04.2015,1.0,2614,133.33,119.33 -8,1,08.04.2015,1.0,2491,133.33,119.33 -8,1,09.04.2015,1.0,1502,133.33,119.33 -8,1,10.04.2015,1.0,1433,133.33,119.33 -8,1,11.04.2015,1.0,1398,133.33,119.33 -8,1,12.04.2015,1.0,1316,133.33,119.33 -8,1,13.04.2015,1.0,280,133.33,119.33 -8,1,14.04.2015,,346,130.34, -8,1,15.04.2015,,368,130.34, -8,1,16.04.2015,,364,130.34, -8,1,17.04.2015,,188,138.5, -8,1,18.04.2015,,229,138.5, -8,1,19.04.2015,,176,138.5, -8,1,20.04.2015,,170,138.5, -8,1,21.04.2015,,157,138.5, -8,1,22.04.2015,,248,128.98, -8,1,23.04.2015,,330,128.98, -8,1,24.04.2015,,352,128.98, -8,1,25.04.2015,,352,128.98, -8,1,26.04.2015,,327,128.98, -8,1,27.04.2015,,437,128.98, -8,1,28.04.2015,,358,128.98, -8,1,29.04.2015,,239,131.7, -8,1,30.04.2015,,317,131.7, -8,1,01.05.2015,,104,131.7, -8,1,02.05.2015,,154,131.7, -8,1,03.05.2015,,185,131.7, -8,1,04.05.2015,,220,131.7, -8,1,05.05.2015,,276,131.7, -8,1,06.05.2015,,242,131.7, -8,1,07.05.2015,,396,131.7, -8,1,08.05.2015,,214,131.7, -8,1,09.05.2015,,141,131.7, -8,1,10.05.2015,,188,131.7, -8,1,11.05.2015,,242,131.7, -8,1,12.05.2015,,248,131.7, -8,1,13.05.2015,,280,131.7, -8,1,14.05.2015,,270,131.7, -8,1,15.05.2015,,412,131.7, -8,1,16.05.2015,,405,131.7, -8,1,17.05.2015,,283,131.7, -8,1,18.05.2015,,396,131.7, -8,1,19.05.2015,1.0,1392,131.7,119.33 -8,1,20.05.2015,1.0,1319,131.7,119.33 -8,1,21.05.2015,1.0,1357,131.7,119.33 -8,1,22.05.2015,1.0,980,131.7,119.33 -8,1,23.05.2015,1.0,547,131.7,119.33 -8,1,24.05.2015,1.0,754,131.7,119.33 -8,1,25.05.2015,1.0,820,131.7,119.33 -8,1,26.05.2015,,239,131.7, -8,1,27.05.2015,,163,129.25, -8,1,28.05.2015,,157,129.25, -8,1,29.05.2015,,273,129.25, -8,1,30.05.2015,,245,129.25, -8,1,31.05.2015,,182,129.25, -8,1,01.06.2015,,104,129.25, -8,1,02.06.2015,,170,128.98, -8,1,03.06.2015,,217,128.98, -8,1,04.06.2015,,270,128.98, -8,1,05.06.2015,,210,128.98, -8,1,06.06.2015,,226,128.98, -8,1,07.06.2015,,207,128.98, -8,1,08.06.2015,,248,128.98, -8,1,09.06.2015,1.0,707,128.98,119.33 -8,1,10.06.2015,1.0,949,128.98,119.33 -8,1,11.06.2015,1.0,996,128.98,119.33 -8,1,12.06.2015,1.0,506,128.98,119.33 -8,1,13.06.2015,1.0,729,128.98,119.33 -8,1,14.06.2015,1.0,336,128.98,119.33 -8,1,15.06.2015,1.0,584,128.98,119.33 -8,1,16.06.2015,,261,128.98, -8,1,17.06.2015,,226,128.98, -8,1,18.06.2015,,195,128.98, -8,1,19.06.2015,,242,128.98, -8,1,20.06.2015,,239,128.98, -8,1,21.06.2015,,239,128.98, -8,1,22.06.2015,,440,128.98, -8,1,23.06.2015,,317,128.98, -8,1,24.06.2015,,396,128.98, -8,1,25.06.2015,,446,128.98, -8,1,26.06.2015,,484,128.98, -8,1,27.06.2015,,254,128.98, -8,1,28.06.2015,,522,128.98, -8,1,29.06.2015,,628,128.98, -8,1,30.06.2015,1.0,873,128.98,119.33 -8,1,01.07.2015,1.0,641,128.98,119.33 -8,1,02.07.2015,1.0,977,128.98,119.33 -8,1,03.07.2015,1.0,833,128.98,119.33 -8,1,04.07.2015,1.0,807,128.98,119.33 -8,1,05.07.2015,1.0,342,128.98,119.33 -8,1,06.07.2015,1.0,657,128.98,119.33 -8,1,07.07.2015,,273,128.98, -8,1,08.07.2015,,264,128.98, -8,1,09.07.2015,,160,128.98, -8,1,10.07.2015,,295,128.98, -8,1,11.07.2015,,223,128.98, -8,1,12.07.2015,,399,128.98, -8,1,13.07.2015,,314,128.98, -8,1,14.07.2015,,232,128.98, -8,1,15.07.2015,,474,128.98, -8,1,16.07.2015,,427,128.98, -8,1,17.07.2015,,478,128.98, -8,1,18.07.2015,,437,128.98, -8,1,19.07.2015,,449,128.98, -8,1,20.07.2015,,631,128.98, -8,1,21.07.2015,,949,128.98, -8,1,22.07.2015,,600,128.98, -8,1,23.07.2015,,603,128.98, -8,1,24.07.2015,,383,128.98, -8,1,25.07.2015,,531,128.98, -8,1,26.07.2015,,729,128.98, -8,1,27.07.2015,,1015,128.98, -8,1,28.07.2015,1.0,1257,128.98,124.77 -8,1,29.07.2015,1.0,1301,128.98,124.77 -8,1,30.07.2015,1.0,1247,128.98,124.77 -8,1,31.07.2015,1.0,1169,128.98,124.77 -8,1,01.08.2015,1.0,839,128.98,124.77 -8,1,02.08.2015,1.0,990,128.98,124.77 -8,1,03.08.2015,1.0,1166,128.98,124.77 -8,1,04.08.2015,,1115,128.98, -8,1,05.08.2015,,1049,128.98, -8,1,06.08.2015,,1169,128.98, -8,1,07.08.2015,,1065,128.98, -8,1,08.08.2015,,990,128.98, -8,1,09.08.2015,,776,128.98, -8,1,10.08.2015,,1495,128.98, -8,1,11.08.2015,,1294,128.98, -8,1,12.08.2015,,1335,128.98, -8,1,13.08.2015,,1005,128.98, -8,1,14.08.2015,,873,128.98, -8,1,15.08.2015,,569,128.98, -8,1,16.08.2015,,763,128.98, -8,1,17.08.2015,,1210,128.98, -8,1,18.08.2015,,1068,128.98, -8,1,19.08.2015,,974,128.98, -8,1,20.08.2015,,1153,128.98, -8,1,21.08.2015,,1433,128.98, -8,1,22.08.2015,,911,128.98, -8,1,23.08.2015,,977,128.98, -8,1,24.08.2015,,1404,128.98, -8,1,25.08.2015,,1646,128.98, -8,1,26.08.2015,,1527,128.98, -8,1,27.08.2015,,748,128.98, -8,1,28.08.2015,,1392,128.98, -8,1,29.08.2015,,920,128.98, -8,1,30.08.2015,,1140,128.98, -8,1,31.08.2015,,999,128.98, -8,1,01.09.2015,,889,128.98, -8,1,02.09.2015,,1304,128.98, -8,1,03.09.2015,,1455,128.98, -8,1,04.09.2015,,1122,128.98, -8,1,05.09.2015,,704,128.98, -8,1,06.09.2015,,767,128.98, -8,1,07.09.2015,,980,128.98, -8,1,08.09.2015,,1106,128.98, -8,1,09.09.2015,,1297,128.98, -8,1,10.09.2015,,952,128.98, -8,1,11.09.2015,,1565,128.98, -8,1,12.09.2015,,1122,128.98, -8,1,13.09.2015,,861,128.98, -8,1,14.09.2015,,1448,128.98, -8,1,15.09.2015,,1351,128.98, -8,1,16.09.2015,,1147,128.98, -8,1,17.09.2015,,1081,128.98, -8,1,18.09.2015,,1090,128.98, -8,1,19.09.2015,,789,128.98, -8,1,20.09.2015,,848,128.98, -8,1,21.09.2015,,1326,128.98, -8,1,22.09.2015,,1137,128.98, -8,1,23.09.2015,,1115,128.98, -8,1,24.09.2015,,999,128.98, -8,1,25.09.2015,,1156,128.98, -8,1,26.09.2015,,669,128.98, -8,1,27.09.2015,,1068,128.98, -8,1,28.09.2015,,1423,128.98, -8,1,29.09.2015,,1147,141.21, -8,1,30.09.2015,,855,141.21, -8,1,01.10.2015,,1348,141.21, -8,1,02.10.2015,,1172,141.21, -8,1,03.10.2015,,572,141.21, -8,1,04.10.2015,,1021,141.21, -8,1,05.10.2015,,352,141.21, -8,1,06.10.2015,1.0,961,141.21,132.92 -8,1,07.10.2015,1.0,939,141.21,130.21 -8,1,08.10.2015,1.0,889,141.11,130.21 -8,1,09.10.2015,1.0,3468,141.21,130.21 -8,1,10.10.2015,1.0,719,141.21,130.21 -8,1,11.10.2015,1.0,239,141.21,130.21 -8,1,12.10.2015,1.0,1084,141.21,130.21 -8,1,13.10.2015,,873,141.21, -8,1,14.10.2015,,792,141.21, -8,1,15.10.2015,,723,142.57, -8,1,16.10.2015,,638,142.57, -8,1,17.10.2015,,264,142.57, -8,1,18.10.2015,,214,142.57, -8,1,19.10.2015,,204,142.57, -8,1,20.10.2015,,214,142.57, -8,1,21.10.2015,,644,130.34, -8,1,22.10.2015,,474,130.34, -8,1,23.10.2015,,434,130.34, -8,1,24.10.2015,,126,130.34, -8,1,25.10.2015,,471,130.34, -8,1,26.10.2015,,459,130.34, -8,1,27.10.2015,1.0,1461,130.34,122.32 -8,1,28.10.2015,1.0,1901,130.34,122.32 -8,1,29.10.2015,1.0,1059,130.34,122.32 -8,1,30.10.2015,1.0,861,130.34,122.32 -8,1,31.10.2015,1.0,719,130.34,122.32 -8,1,01.11.2015,1.0,522,130.34,122.32 -8,1,02.11.2015,1.0,679,130.34,122.32 -8,1,03.11.2015,,295,130.34, -8,1,04.11.2015,,217,134.69, -8,1,05.11.2015,,217,134.69, -8,1,06.11.2015,,226,134.69, -8,1,07.11.2015,,223,134.69, -8,1,08.11.2015,,248,134.69, -8,1,09.11.2015,,160,134.69, -8,1,10.11.2015,,289,134.69, -8,1,11.11.2015,,298,130.34, -8,1,12.11.2015,,342,130.34, -8,1,13.11.2015,,377,130.34, -8,1,14.11.2015,,195,130.34, -8,1,15.11.2015,,258,130.34, -8,1,16.11.2015,,283,130.34, -8,1,17.11.2015,,349,130.34, -8,1,18.11.2015,,399,130.34, -8,1,19.11.2015,,324,130.34, -8,1,20.11.2015,,430,130.34, -8,1,21.11.2015,,449,130.34, -8,1,22.11.2015,,314,130.34, -8,1,23.11.2015,,528,130.34, -8,1,24.11.2015,1.0,3541,130.34,116.61 -8,1,25.11.2015,1.0,3547,130.34,116.61 -8,1,26.11.2015,1.0,1338,130.34,116.61 -8,1,27.11.2015,1.0,2262,130.34,116.61 -8,1,28.11.2015,1.0,2689,130.34,116.61 -8,1,29.11.2015,1.0,2695,130.34,116.61 -8,1,30.11.2015,1.0,2944,130.34,116.61 -8,1,01.12.2015,,324,130.34, -8,1,02.12.2015,,220,130.34, -8,1,03.12.2015,,264,130.34, -8,1,04.12.2015,,217,130.34, -8,1,05.12.2015,1.0,1499,130.34,116.61 -8,1,06.12.2015,1.0,1436,130.34,116.61 -8,1,07.12.2015,1.0,1731,130.34,116.61 -8,1,08.12.2015,,368,130.34, -8,1,09.12.2015,,167,130.34, -8,1,10.12.2015,,170,130.34, -8,1,11.12.2015,,217,130.34, -8,1,12.12.2015,,176,130.34, -8,1,13.12.2015,,91,130.34, -8,1,14.12.2015,,25,130.34, -8,1,15.12.2015,,157,130.34, -8,1,16.12.2015,,280,130.34, -8,1,17.12.2015,,330,130.34, -8,1,18.12.2015,,352,130.34, -8,1,19.12.2015,,270,130.34, -8,1,20.12.2015,,292,130.34, -8,1,21.12.2015,,324,130.34, -8,1,22.12.2015,,251,130.34, -8,1,23.12.2015,,371,130.34, -8,1,24.12.2015,,305,126.54, -8,1,25.12.2015,,456,126.54, -8,1,26.12.2015,,478,126.54, -8,1,27.12.2015,,418,126.54, -8,1,28.12.2015,,408,126.54, -8,1,29.12.2015,,415,126.54, -8,1,30.12.2015,,481,126.54, -8,1,31.12.2015,,320,126.54, -8,1,01.01.2016,,35,126.54, -8,1,02.01.2016,,229,126.54, -8,1,03.01.2016,,273,126.54, -8,1,04.01.2016,,333,126.54, -8,1,05.01.2016,,487,126.54, -8,1,06.01.2016,,371,126.54, -8,1,07.01.2016,,418,126.54, -8,1,08.01.2016,,452,126.54, -8,1,09.01.2016,,528,126.54, -8,1,10.01.2016,,336,126.54, -8,1,11.01.2016,,675,126.54, -8,1,12.01.2016,,597,126.54, -8,1,13.01.2016,,553,126.54, -8,1,14.01.2016,,493,126.54, -8,1,15.01.2016,,427,126.54, -8,1,16.01.2016,,609,126.54, -8,1,17.01.2016,,707,126.54, -8,1,18.01.2016,,528,126.54, -8,1,19.01.2016,,845,126.54, -8,1,20.01.2016,,506,126.54, -8,1,21.01.2016,,547,127.9, -8,1,22.01.2016,,481,127.9, -8,1,23.01.2016,,565,127.9, -8,1,24.01.2016,,534,127.9, -8,1,25.01.2016,,691,127.9, -8,1,26.01.2016,,653,127.9, -8,1,27.01.2016,,657,127.9, -8,1,28.01.2016,,842,127.9, -8,1,29.01.2016,,584,127.9, -8,1,30.01.2016,,587,127.9, -8,1,31.01.2016,,346,127.9, -8,1,01.02.2016,,402,130.34, -8,1,02.02.2016,,600,130.34, -8,1,03.02.2016,,543,130.34, -8,1,04.02.2016,,421,133.06, -8,1,05.02.2016,,452,133.06, -8,1,06.02.2016,,745,133.06, -8,1,07.02.2016,,330,141.76, -8,1,08.02.2016,,358,141.76, -8,1,09.02.2016,1.0,782,141.76,130.75 -8,1,10.02.2016,1.0,1008,141.76,130.75 -8,1,11.02.2016,1.0,933,141.76,130.75 -8,1,12.02.2016,1.0,770,141.76,130.75 -8,1,13.02.2016,1.0,550,141.76,130.75 -8,1,14.02.2016,1.0,421,141.76,130.75 -8,1,15.02.2016,1.0,653,141.76,130.75 -8,1,16.02.2016,1.0,682,141.76,130.75 -8,1,17.02.2016,1.0,657,141.76,130.75 -8,1,18.02.2016,,317,141.76, -8,1,19.02.2016,,380,141.76, -8,1,20.02.2016,,179,141.76, -8,1,21.02.2016,,157,141.76, -8,1,22.02.2016,,217,141.76, -8,1,23.02.2016,,110,141.76, -8,1,24.02.2016,,179,141.76, -8,1,25.02.2016,,210,141.76, -8,1,26.02.2016,,264,141.76, -8,1,27.02.2016,,192,141.76, -8,1,28.02.2016,,248,141.76, -8,1,29.02.2016,,188,141.76, -8,1,01.03.2016,,292,141.76, -8,1,02.03.2016,,248,141.76, -8,1,03.03.2016,,251,141.76, -8,1,04.03.2016,,264,141.76, -8,1,05.03.2016,,374,141.21, -8,1,06.03.2016,,286,141.21, -8,1,07.03.2016,,465,141.21, -8,1,08.03.2016,1.0,512,141.21,132.92 -8,1,09.03.2016,1.0,886,141.21,132.92 -8,1,10.03.2016,1.0,647,141.21,132.92 -8,1,11.03.2016,1.0,814,141.21,132.92 -8,1,12.03.2016,1.0,663,141.21,132.92 -8,1,13.03.2016,1.0,569,141.21,132.92 -8,1,14.03.2016,1.0,694,141.21,132.92 -8,1,15.03.2016,1.0,886,141.21,130.21 -8,1,16.03.2016,1.0,1002,141.21,130.21 -8,1,17.03.2016,1.0,908,141.21,130.21 -8,1,18.03.2016,1.0,908,141.21,130.21 -8,1,19.03.2016,1.0,616,141.21,130.21 -8,1,20.03.2016,1.0,710,141.21,130.21 -8,1,21.03.2016,,201,141.21, -8,1,22.03.2016,,386,141.21, -8,1,23.03.2016,,336,141.21, -8,1,24.03.2016,,198,141.21, -8,1,25.03.2016,,210,141.21, -8,1,26.03.2016,,210,141.21, -8,1,27.03.2016,,220,141.21, -8,1,28.03.2016,,167,141.21, -8,1,29.03.2016,,220,141.21, -8,1,30.03.2016,,160,141.21, -8,1,31.03.2016,,207,141.21, -8,1,01.04.2016,,198,141.21, -8,1,02.04.2016,,270,141.21, -8,1,03.04.2016,,157,141.21, -8,1,04.04.2016,,295,141.21, -8,1,05.04.2016,,182,141.21, -8,1,06.04.2016,,160,141.21, -8,1,07.04.2016,,267,141.21, -8,1,08.04.2016,,167,141.21, -8,1,09.04.2016,,242,141.21, -8,1,10.04.2016,,188,141.21, -8,1,11.04.2016,,204,141.21, -8,1,12.04.2016,,182,141.21, -8,1,13.04.2016,,185,141.21, -8,1,14.04.2016,,399,135.78, -8,1,15.04.2016,,352,135.78, -8,1,16.04.2016,,195,135.78, -8,1,17.04.2016,,210,135.78, -8,1,18.04.2016,,229,135.78, -8,1,19.04.2016,1.0,1612,135.78,122.32 -8,1,20.04.2016,1.0,1448,135.78,122.32 -8,1,21.04.2016,1.0,942,135.78,122.32 -8,1,22.04.2016,1.0,1140,135.78,122.32 -8,1,23.04.2016,1.0,826,135.78,122.32 -8,1,24.04.2016,1.0,176,135.78,122.32 -8,1,25.04.2016,1.0,873,135.78,122.32 -8,1,26.04.2016,,273,135.78, -8,1,27.04.2016,,198,135.78, -8,1,28.04.2016,,349,135.78, -8,1,29.04.2016,,248,131.16, -8,1,30.04.2016,,289,131.16, -8,1,01.05.2016,,85,131.16, -8,1,02.05.2016,,82,131.16, -8,1,03.05.2016,,173,131.16, -8,1,04.05.2016,,229,131.16, -8,1,05.05.2016,,336,131.16, -8,1,06.05.2016,,342,131.16, -8,1,07.05.2016,,327,131.16, -8,1,08.05.2016,,232,131.16, -8,1,09.05.2016,,141,131.16, -8,1,10.05.2016,,320,131.16, -8,1,11.05.2016,,223,131.16, -8,1,12.05.2016,,305,131.16, -8,1,13.05.2016,,380,128.98, -8,1,14.05.2016,,198,128.98, -8,1,15.05.2016,,207,128.98, -8,1,16.05.2016,,386,128.98, -8,1,17.05.2016,1.0,1235,128.98,119.6 -8,1,18.05.2016,1.0,1825,128.98,119.6 -8,1,19.05.2016,1.0,1492,128.98,119.6 -8,1,20.05.2016,1.0,1363,128.98,119.6 -8,1,21.05.2016,1.0,936,128.98,119.6 -8,1,22.05.2016,1.0,701,128.98,119.6 -9,1,01.01.2015,,16,163.78, -9,1,02.01.2015,,63,163.78, -9,1,03.01.2015,,107,163.78, -9,1,04.01.2015,,113,163.78, -9,1,05.01.2015,,167,163.78, -9,1,06.01.2015,,141,163.78, -9,1,07.01.2015,,126,163.78, -9,1,08.01.2015,,157,163.78, -9,1,09.01.2015,,107,163.78, -9,1,10.01.2015,,226,163.78, -9,1,11.01.2015,,94,163.78, -9,1,12.01.2015,,129,163.78, -9,1,13.01.2015,,148,163.78, -9,1,14.01.2015,,72,168.4, -9,1,15.01.2015,,66,168.4, -9,1,16.01.2015,,110,168.4, -9,1,17.01.2015,,132,168.4, -9,1,18.01.2015,,94,168.4, -9,1,19.01.2015,,129,168.4, -9,1,20.01.2015,,126,168.4, -9,1,21.01.2015,,110,168.4, -9,1,22.01.2015,,141,168.4, -9,1,23.01.2015,,97,160.24, -9,1,24.01.2015,,210,160.24, -9,1,25.01.2015,,126,160.24, -9,1,26.01.2015,,126,160.24, -9,1,27.01.2015,,138,160.24, -9,1,28.01.2015,,254,146.92, -9,1,29.01.2015,,358,146.92, -9,1,30.01.2015,,182,146.92, -9,1,31.01.2015,,302,146.92, -9,1,01.02.2015,,229,146.92, -9,1,02.02.2015,,129,146.92, -9,1,03.02.2015,,251,146.92, -9,1,04.02.2015,,179,146.92, -9,1,05.02.2015,,201,146.92, -9,1,06.02.2015,,188,146.92, -9,1,07.02.2015,,339,146.92, -9,1,08.02.2015,,283,146.92, -9,1,09.02.2015,,239,146.92, -9,1,10.02.2015,,245,146.92, -9,1,11.02.2015,,298,146.92, -9,1,12.02.2015,,314,146.92, -9,1,13.02.2015,,386,149.37, -9,1,14.02.2015,,280,149.37, -9,1,15.02.2015,,292,149.37, -9,1,16.02.2015,,346,149.37, -9,1,17.02.2015,1.0,547,149.37,143.8 -9,1,18.02.2015,1.0,735,149.37,135.64 -9,1,19.02.2015,1.0,1275,149.37,135.64 -9,1,20.02.2015,1.0,1319,149.37,135.64 -9,1,21.02.2015,1.0,1106,149.37,135.64 -9,1,22.02.2015,1.0,707,149.37,135.64 -9,1,23.02.2015,1.0,688,149.37,135.64 -9,1,24.02.2015,,148,149.37, -9,1,25.02.2015,,317,149.37, -9,1,26.02.2015,,242,149.37, -9,1,27.02.2015,,204,143.93, -9,1,28.02.2015,,415,143.93, -9,1,01.03.2015,,509,143.93, -9,1,02.03.2015,,449,143.93, -9,1,03.03.2015,1.0,390,143.93,138.36 -9,1,04.03.2015,1.0,377,143.93,135.64 -9,1,05.03.2015,1.0,390,138.5,135.64 -9,1,06.03.2015,1.0,449,138.5,135.64 -9,1,07.03.2015,1.0,669,138.5,135.64 -9,1,08.03.2015,1.0,220,138.5,135.64 -9,1,09.03.2015,,264,140.67, -9,1,10.03.2015,,308,140.67, -9,1,11.03.2015,,254,140.67, -9,1,12.03.2015,,280,140.67, -9,1,13.03.2015,,280,140.67, -9,1,14.03.2015,,383,140.67, -9,1,15.03.2015,,330,140.67, -9,1,16.03.2015,,305,140.67, -9,1,17.03.2015,1.0,3522,140.67,127.49 -9,1,18.03.2015,1.0,3647,140.67,127.49 -9,1,19.03.2015,1.0,2259,140.67,127.49 -9,1,20.03.2015,1.0,1401,140.67,127.49 -9,1,21.03.2015,1.0,600,140.67,127.49 -9,1,22.03.2015,1.0,1093,140.67,127.49 -9,1,23.03.2015,1.0,1046,140.67,127.49 -9,1,24.03.2015,,220,140.67, -9,1,25.03.2015,,283,140.67, -9,1,26.03.2015,,333,131.97, -9,1,27.03.2015,,371,131.97, -9,1,28.03.2015,,408,131.97, -9,1,29.03.2015,,418,131.97, -9,1,30.03.2015,,430,131.97, -9,1,31.03.2015,,305,131.97, -9,1,01.04.2015,,346,133.33, -9,1,02.04.2015,,207,133.33, -9,1,03.04.2015,,333,133.33, -9,1,04.04.2015,,462,133.33, -9,1,05.04.2015,,320,133.33, -9,1,06.04.2015,,258,133.33, -9,1,07.04.2015,1.0,1668,133.33,119.33 -9,1,08.04.2015,1.0,2055,133.33,119.33 -9,1,09.04.2015,1.0,1392,133.33,119.33 -9,1,10.04.2015,1.0,1338,133.33,119.33 -9,1,11.04.2015,1.0,1125,133.33,119.33 -9,1,12.04.2015,1.0,500,133.33,119.33 -9,1,13.04.2015,1.0,500,133.33,119.33 -9,1,14.04.2015,,170,130.34, -9,1,15.04.2015,,214,130.34, -9,1,16.04.2015,,311,130.34, -9,1,17.04.2015,,214,138.5, -9,1,18.04.2015,,135,138.5, -9,1,19.04.2015,,126,138.5, -9,1,20.04.2015,,160,138.5, -9,1,21.04.2015,,167,138.5, -9,1,22.04.2015,,270,128.98, -9,1,23.04.2015,,157,128.98, -9,1,24.04.2015,,320,128.98, -9,1,25.04.2015,,182,128.98, -9,1,26.04.2015,,207,128.98, -9,1,27.04.2015,,264,128.98, -9,1,28.04.2015,,239,128.98, -9,1,29.04.2015,,286,131.7, -9,1,30.04.2015,,280,131.7, -9,1,01.05.2015,,207,131.7, -9,1,02.05.2015,,119,131.7, -9,1,03.05.2015,,195,131.7, -9,1,04.05.2015,,185,131.7, -9,1,05.05.2015,,201,131.7, -9,1,06.05.2015,,210,131.7, -9,1,07.05.2015,,258,131.7, -9,1,08.05.2015,,185,131.7, -9,1,09.05.2015,,229,131.7, -9,1,10.05.2015,,210,131.7, -9,1,11.05.2015,,188,131.7, -9,1,12.05.2015,,254,131.7, -9,1,13.05.2015,,188,131.7, -9,1,14.05.2015,,273,131.7, -9,1,15.05.2015,,283,131.7, -9,1,16.05.2015,,280,131.7, -9,1,17.05.2015,,320,131.7, -9,1,18.05.2015,,471,131.7, -9,1,19.05.2015,1.0,1382,131.7,119.33 -9,1,20.05.2015,1.0,1775,131.7,119.33 -9,1,21.05.2015,1.0,964,131.7,119.33 -9,1,22.05.2015,1.0,1618,131.7,119.33 -9,1,23.05.2015,1.0,1225,131.7,119.33 -9,1,24.05.2015,1.0,493,131.7,119.33 -9,1,25.05.2015,1.0,779,131.7,119.33 -9,1,26.05.2015,,229,131.7, -9,1,27.05.2015,,167,129.25, -9,1,28.05.2015,,157,129.25, -9,1,29.05.2015,,251,129.25, -9,1,30.05.2015,,182,129.25, -9,1,31.05.2015,,465,129.25, -9,1,01.06.2015,,217,129.25, -9,1,02.06.2015,,188,129.25, -9,1,03.06.2015,,232,129.25, -9,1,04.06.2015,,333,129.25, -9,1,05.06.2015,,292,129.25, -9,1,06.06.2015,,371,131.7, -9,1,07.06.2015,,308,131.7, -9,1,08.06.2015,,286,131.7, -9,1,09.06.2015,1.0,1703,131.7,119.33 -9,1,10.06.2015,1.0,1794,131.7,119.33 -9,1,11.06.2015,1.0,1414,131.7,119.33 -9,1,12.06.2015,1.0,2095,131.7,119.33 -9,1,13.06.2015,1.0,1162,131.7,119.33 -9,1,14.06.2015,1.0,1128,131.7,119.33 -9,1,15.06.2015,1.0,1093,131.7,119.33 -9,1,16.06.2015,,188,131.7, -9,1,17.06.2015,,289,131.7, -9,1,18.06.2015,,424,128.17, -9,1,19.06.2015,,412,128.17, -9,1,20.06.2015,,210,128.17, -9,1,21.06.2015,,352,128.17, -9,1,22.06.2015,,292,128.17, -9,1,23.06.2015,,311,128.17, -9,1,24.06.2015,,280,128.17, -9,1,25.06.2015,,327,128.17, -9,1,26.06.2015,,594,128.17, -9,1,27.06.2015,,355,128.17, -9,1,28.06.2015,,512,130.34, -9,1,29.06.2015,,320,130.34, -9,1,30.06.2015,1.0,1316,130.34,119.33 -9,1,01.07.2015,1.0,1659,130.34,119.33 -9,1,02.07.2015,1.0,1131,130.34,119.33 -9,1,03.07.2015,1.0,829,130.34,119.33 -9,1,04.07.2015,1.0,1024,130.34,119.33 -9,1,05.07.2015,1.0,1652,130.34,119.33 -9,1,06.07.2015,1.0,770,130.34,119.33 -9,1,07.07.2015,,364,130.34, -9,1,08.07.2015,,185,130.34, -9,1,09.07.2015,,346,121.64, -9,1,10.07.2015,,748,121.64, -9,1,11.07.2015,,672,123.55, -9,1,12.07.2015,,412,123.55, -9,1,13.07.2015,,490,123.55, -9,1,14.07.2015,,653,123.55, -9,1,15.07.2015,,503,125.45, -9,1,16.07.2015,,653,125.45, -9,1,17.07.2015,,553,125.45, -9,1,18.07.2015,,298,127.62, -9,1,19.07.2015,,556,127.62, -9,1,20.07.2015,,679,127.62, -9,1,21.07.2015,,751,127.62, -9,1,22.07.2015,,817,127.62, -9,1,23.07.2015,,770,127.62, -9,1,24.07.2015,,939,127.62, -9,1,25.07.2015,,578,127.62, -9,1,26.07.2015,,465,130.07, -9,1,27.07.2015,,1131,130.07, -9,1,28.07.2015,1.0,1734,130.07,124.77 -9,1,29.07.2015,1.0,1235,130.07,124.77 -9,1,30.07.2015,1.0,374,130.07,124.77 -9,1,31.07.2015,1.0,845,130.07,124.77 -9,1,01.08.2015,1.0,842,130.07,124.77 -9,1,02.08.2015,1.0,88,130.07,124.77 -9,1,03.08.2015,1.0,1272,130.07,124.77 -9,1,04.08.2015,,449,138.5, -9,1,05.08.2015,,440,138.5, -9,1,06.08.2015,,298,138.5, -9,1,07.08.2015,,339,138.5, -9,1,08.08.2015,,465,141.21, -9,1,09.08.2015,,597,141.21, -9,1,10.08.2015,,396,141.21, -9,1,11.08.2015,,371,141.21, -9,1,12.08.2015,,547,142.85, -9,1,13.08.2015,,393,142.85, -9,1,14.08.2015,,500,142.85, -9,1,15.08.2015,,333,142.85, -9,1,16.08.2015,,490,142.85, -9,1,17.08.2015,,478,142.85, -9,1,18.08.2015,,330,142.85, -9,1,19.08.2015,,311,142.85, -9,1,20.08.2015,,320,142.85, -9,1,21.08.2015,,412,142.85, -9,1,22.08.2015,,550,142.85, -9,1,23.08.2015,,528,142.85, -9,1,24.08.2015,,317,142.85, -9,1,25.08.2015,,644,142.85, -9,1,26.08.2015,,594,142.85, -9,1,27.08.2015,,757,137.68, -9,1,28.08.2015,,814,137.68, -9,1,29.08.2015,,465,137.68, -9,1,30.08.2015,,600,137.68, -9,1,31.08.2015,,569,137.68, -9,1,01.09.2015,,390,137.68, -9,1,02.09.2015,,418,135.78, -9,1,03.09.2015,,594,135.78, -9,1,04.09.2015,,679,135.78, -9,1,05.09.2015,,280,133.6, -9,1,06.09.2015,,490,133.6, -9,1,07.09.2015,,509,133.6, -9,1,08.09.2015,,522,133.6, -9,1,09.09.2015,,540,135.51, -9,1,10.09.2015,,591,135.51, -9,1,11.09.2015,,478,135.51, -9,1,12.09.2015,,600,135.51, -9,1,13.09.2015,,550,135.51, -9,1,14.09.2015,,584,135.51, -9,1,15.09.2015,,500,135.51, -9,1,16.09.2015,,540,138.5, -9,1,17.09.2015,,456,138.5, -9,1,18.09.2015,,390,138.5, -9,1,19.09.2015,,465,138.5, -9,1,20.09.2015,,506,138.5, -9,1,21.09.2015,,327,138.5, -9,1,22.09.2015,,327,138.5, -9,1,23.09.2015,,374,141.21, -9,1,24.09.2015,,264,141.21, -9,1,25.09.2015,,383,141.21, -9,1,26.09.2015,,283,141.21, -9,1,27.09.2015,,283,141.21, -9,1,28.09.2015,,352,141.21, -9,1,29.09.2015,,346,141.21, -9,1,30.09.2015,,270,141.21, -9,1,01.10.2015,,342,141.21, -9,1,02.10.2015,,236,141.21, -9,1,03.10.2015,,412,141.21, -9,1,04.10.2015,,261,141.21, -9,1,05.10.2015,,330,141.21, -9,1,06.10.2015,1.0,779,141.21,132.92 -9,1,07.10.2015,1.0,924,141.21,130.21 -9,1,08.10.2015,1.0,1096,141.11,130.21 -9,1,09.10.2015,1.0,1056,141.21,130.21 -9,1,10.10.2015,1.0,964,141.21,130.21 -9,1,11.10.2015,1.0,833,141.21,130.21 -9,1,12.10.2015,1.0,688,141.21,130.21 -9,1,13.10.2015,,308,141.21, -9,1,14.10.2015,,214,141.21, -9,1,15.10.2015,,258,142.57, -9,1,16.10.2015,,176,142.57, -9,1,17.10.2015,,154,142.57, -9,1,18.10.2015,,182,142.57, -9,1,19.10.2015,,273,142.57, -9,1,20.10.2015,,226,142.57, -9,1,21.10.2015,,584,130.34, -9,1,22.10.2015,,688,130.34, -9,1,23.10.2015,,798,130.34, -9,1,24.10.2015,,675,130.34, -9,1,25.10.2015,,531,130.34, -9,1,26.10.2015,,358,130.34, -9,1,27.10.2015,1.0,1530,130.34,122.32 -9,1,28.10.2015,1.0,1323,130.34,124.77 -9,1,29.10.2015,1.0,732,130.34,124.77 -9,1,30.10.2015,1.0,694,130.34,124.77 -9,1,31.10.2015,1.0,578,130.34,124.77 -9,1,01.11.2015,1.0,528,130.34,124.77 -9,1,02.11.2015,1.0,286,130.34,124.77 -9,1,03.11.2015,,123,130.34, -9,1,04.11.2015,,210,134.69, -9,1,05.11.2015,,286,134.69, -9,1,06.11.2015,,258,134.69, -9,1,07.11.2015,,207,134.69, -9,1,08.11.2015,,214,134.69, -9,1,09.11.2015,,182,134.69, -9,1,10.11.2015,,220,134.69, -9,1,11.11.2015,,371,130.34, -9,1,12.11.2015,,333,130.34, -9,1,13.11.2015,,320,130.34, -9,1,14.11.2015,,496,130.34, -9,1,15.11.2015,,443,130.34, -9,1,16.11.2015,,258,130.34, -9,1,17.11.2015,,311,130.34, -9,1,18.11.2015,,280,130.34, -9,1,19.11.2015,,188,130.34, -9,1,20.11.2015,,371,130.34, -9,1,21.11.2015,,314,130.34, -9,1,22.11.2015,,478,130.34, -9,1,23.11.2015,,302,130.34, -9,1,24.11.2015,1.0,1841,130.34,116.61 -9,1,25.11.2015,1.0,2535,130.34,116.61 -9,1,26.11.2015,1.0,2026,130.34,116.61 -9,1,27.11.2015,1.0,679,130.34,116.61 -9,1,28.11.2015,1.0,1021,130.34,116.61 -9,1,29.11.2015,1.0,1021,130.34,116.61 -9,1,30.11.2015,1.0,1021,130.34,116.61 -9,1,01.12.2015,,245,130.34, -9,1,02.12.2015,,368,130.34, -9,1,03.12.2015,,207,130.34, -9,1,04.12.2015,,339,130.34, -9,1,05.12.2015,1.0,1194,130.34,116.61 -9,1,06.12.2015,1.0,1288,130.34,116.61 -9,1,07.12.2015,1.0,776,130.34,116.61 -9,1,08.12.2015,,242,130.34, -9,1,09.12.2015,,204,130.34, -9,1,10.12.2015,,258,130.34, -9,1,11.12.2015,,283,130.34, -9,1,12.12.2015,,239,130.34, -9,1,13.12.2015,,336,130.34, -9,1,14.12.2015,,302,130.34, -9,1,15.12.2015,,390,130.34, -9,1,16.12.2015,,295,130.34, -9,1,17.12.2015,,302,130.34, -9,1,18.12.2015,,330,130.34, -9,1,19.12.2015,,242,130.34, -9,1,20.12.2015,,245,130.34, -9,1,21.12.2015,,245,130.34, -9,1,22.12.2015,,320,130.34, -9,1,23.12.2015,,217,130.34, -9,1,24.12.2015,,333,126.54, -9,1,25.12.2015,,364,126.54, -9,1,26.12.2015,,619,126.54, -9,1,27.12.2015,,430,126.54, -9,1,28.12.2015,,399,126.54, -9,1,29.12.2015,,587,126.54, -9,1,30.12.2015,,500,126.54, -9,1,31.12.2015,,261,126.54, -9,1,01.01.2016,,101,126.54, -9,1,02.01.2016,,113,126.54, -9,1,03.01.2016,,145,126.54, -9,1,04.01.2016,,223,126.54, -9,1,05.01.2016,,217,126.54, -9,1,06.01.2016,,295,126.54, -9,1,07.01.2016,,207,126.54, -9,1,08.01.2016,,333,126.54, -9,1,09.01.2016,,327,126.54, -9,1,10.01.2016,,368,126.54, -9,1,11.01.2016,,292,126.54, -9,1,12.01.2016,,339,126.54, -9,1,13.01.2016,,380,126.54, -9,1,14.01.2016,,437,126.54, -9,1,15.01.2016,,685,126.54, -9,1,16.01.2016,,955,126.54, -9,1,17.01.2016,,562,126.54, -9,1,18.01.2016,,415,126.54, -9,1,19.01.2016,,657,126.54, -9,1,20.01.2016,,402,126.54, -9,1,21.01.2016,,471,127.9, -9,1,22.01.2016,,779,127.9, -9,1,23.01.2016,,584,127.9, -9,1,24.01.2016,,682,127.9, -9,1,25.01.2016,,405,127.9, -9,1,26.01.2016,,525,127.9, -9,1,27.01.2016,,663,127.9, -9,1,28.01.2016,,402,127.9, -9,1,29.01.2016,,399,127.9, -9,1,30.01.2016,,635,127.9, -9,1,31.01.2016,,531,127.9, -9,1,01.02.2016,,807,130.34, -9,1,02.02.2016,,380,130.34, -9,1,03.02.2016,,468,130.34, -9,1,04.02.2016,,628,133.06, -9,1,05.02.2016,,543,133.06, -9,1,06.02.2016,,478,133.06, -9,1,07.02.2016,,305,141.76, -9,1,08.02.2016,,245,141.76, -9,1,09.02.2016,1.0,968,141.76,130.75 -9,1,10.02.2016,1.0,817,141.76,130.75 -9,1,11.02.2016,1.0,688,141.76,130.75 -9,1,12.02.2016,1.0,738,141.76,130.75 -9,1,13.02.2016,1.0,883,141.76,130.75 -9,1,14.02.2016,1.0,883,141.76,130.75 -9,1,15.02.2016,1.0,393,141.76,130.75 -9,1,16.02.2016,1.0,754,141.76,130.75 -9,1,17.02.2016,1.0,688,141.76,130.75 -9,1,18.02.2016,,273,141.76, -9,1,19.02.2016,,229,141.76, -9,1,20.02.2016,,239,141.76, -9,1,21.02.2016,,229,141.76, -9,1,22.02.2016,,261,141.76, -9,1,23.02.2016,,141,141.76, -9,1,24.02.2016,,170,141.76, -9,1,25.02.2016,,254,141.76, -9,1,26.02.2016,,226,141.76, -9,1,27.02.2016,,232,141.76, -9,1,28.02.2016,,270,141.76, -9,1,29.02.2016,,236,141.76, -9,1,01.03.2016,,314,141.76, -9,1,02.03.2016,,390,141.76, -9,1,03.03.2016,,267,141.76, -9,1,04.03.2016,,452,141.76, -9,1,05.03.2016,,355,141.21, -9,1,06.03.2016,,242,141.21, -9,1,07.03.2016,,305,141.21, -9,1,08.03.2016,1.0,635,141.21,132.92 -9,1,09.03.2016,1.0,917,141.21,132.92 -9,1,10.03.2016,1.0,1068,141.21,132.92 -9,1,11.03.2016,1.0,833,141.21,132.92 -9,1,12.03.2016,1.0,936,141.21,132.92 -9,1,13.03.2016,1.0,745,141.21,132.92 -9,1,14.03.2016,1.0,594,141.21,132.92 -9,1,15.03.2016,1.0,1389,141.21,130.21 -9,1,16.03.2016,1.0,1140,141.21,130.21 -9,1,17.03.2016,1.0,902,141.21,130.21 -9,1,18.03.2016,1.0,1030,141.21,130.21 -9,1,19.03.2016,1.0,339,141.21,130.21 -9,1,20.03.2016,1.0,192,141.21,130.21 -9,1,21.03.2016,,192,141.21, -9,1,22.03.2016,,251,141.21, -9,1,23.03.2016,,396,141.21, -9,1,24.03.2016,,324,141.21, -9,1,25.03.2016,,182,141.21, -9,1,26.03.2016,,236,141.21, -9,1,27.03.2016,,207,141.21, -9,1,28.03.2016,,198,141.21, -9,1,29.03.2016,,254,141.21, -9,1,30.03.2016,,185,141.21, -9,1,31.03.2016,,204,141.21, -9,1,01.04.2016,,264,141.21, -9,1,02.04.2016,,236,141.21, -9,1,03.04.2016,,192,141.21, -9,1,04.04.2016,,217,141.21, -9,1,05.04.2016,,283,141.21, -9,1,06.04.2016,,182,141.21, -9,1,07.04.2016,,207,141.21, -9,1,08.04.2016,,248,141.21, -9,1,09.04.2016,,289,141.21, -9,1,10.04.2016,,305,141.21, -9,1,11.04.2016,,273,141.21, -9,1,12.04.2016,,305,141.21, -9,1,13.04.2016,,261,141.21, -9,1,14.04.2016,,368,135.78, -9,1,15.04.2016,,405,135.78, -9,1,16.04.2016,,248,135.78, -9,1,17.04.2016,,487,135.78, -9,1,18.04.2016,,276,135.78, -9,1,19.04.2016,1.0,2234,135.78,122.32 -9,1,20.04.2016,1.0,2378,135.78,122.32 -9,1,21.04.2016,1.0,936,135.78,122.32 -9,1,22.04.2016,1.0,983,135.78,122.32 -9,1,23.04.2016,1.0,983,135.78,122.32 -9,1,24.04.2016,1.0,983,135.78,122.32 -9,1,25.04.2016,1.0,983,135.78,122.32 -9,1,26.04.2016,,113,135.78, -9,1,27.04.2016,,273,135.78, -9,1,28.04.2016,,352,135.78, -9,1,29.04.2016,,559,131.16, -9,1,30.04.2016,,509,131.16, -9,1,01.05.2016,,132,131.16, -9,1,02.05.2016,,248,131.16, -9,1,03.05.2016,,220,131.16, -9,1,04.05.2016,,267,131.16, -9,1,05.05.2016,,311,131.16, -9,1,06.05.2016,,258,131.16, -9,1,07.05.2016,,346,131.16, -9,1,08.05.2016,,346,131.16, -9,1,09.05.2016,,119,131.16, -9,1,10.05.2016,,292,131.16, -9,1,11.05.2016,,289,131.16, -9,1,12.05.2016,,537,131.16, -9,1,13.05.2016,,374,128.98, -9,1,14.05.2016,,371,128.98, -9,1,15.05.2016,,506,128.98, -9,1,16.05.2016,,525,128.98, -9,1,17.05.2016,1.0,1316,128.98,119.6 -9,1,18.05.2016,1.0,2224,128.98,119.6 -9,1,19.05.2016,1.0,1162,128.98,119.6 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-11,1,18.01.2015,,50,168.4, -11,1,19.01.2015,,44,168.4, -11,1,20.01.2015,,85,168.4, -11,1,21.01.2015,,91,168.4, -11,1,22.01.2015,,63,168.4, -11,1,23.01.2015,,41,160.24, -11,1,24.01.2015,,113,160.24, -11,1,25.01.2015,,75,160.24, -11,1,26.01.2015,,82,160.24, -11,1,27.01.2015,,85,160.24, -11,1,28.01.2015,,82,146.92, -11,1,29.01.2015,,188,146.92, -11,1,30.01.2015,,210,146.92, -11,1,31.01.2015,,107,146.92, -11,1,01.02.2015,,188,146.92, -11,1,02.02.2015,,35,146.92, -11,1,03.02.2015,,138,146.92, -11,1,04.02.2015,,167,146.92, -11,1,05.02.2015,,207,146.92, -11,1,06.02.2015,,182,146.92, -11,1,07.02.2015,,135,146.92, -11,1,08.02.2015,,148,146.92, -11,1,09.02.2015,,182,146.92, -11,1,10.02.2015,,214,146.92, -11,1,11.02.2015,,110,146.92, -11,1,12.02.2015,,210,146.92, -11,1,13.02.2015,,176,149.37, -11,1,14.02.2015,,214,149.37, -11,1,15.02.2015,,226,149.37, -11,1,16.02.2015,,154,149.37, -11,1,17.02.2015,1.0,286,149.37,143.8 -11,1,18.02.2015,1.0,349,149.37,135.64 -11,1,19.02.2015,1.0,848,149.37,135.64 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-11,1,23.06.2015,,126,128.17, -11,1,24.06.2015,,104,128.17, -11,1,25.06.2015,,94,128.17, -11,1,26.06.2015,,123,128.17, -11,1,27.06.2015,,151,128.17, -11,1,28.06.2015,,198,130.34, -11,1,29.06.2015,,145,130.34, -11,1,30.06.2015,1.0,157,130.34,119.33 -11,1,01.07.2015,1.0,430,130.34,119.33 -11,1,02.07.2015,1.0,371,130.34,119.33 -11,1,03.07.2015,1.0,569,130.34,119.33 -11,1,04.07.2015,1.0,298,130.34,119.33 -11,1,05.07.2015,1.0,386,130.34,119.33 -11,1,06.07.2015,1.0,242,130.34,119.33 -11,1,07.07.2015,,60,130.34, -11,1,08.07.2015,,135,130.34, -11,1,09.07.2015,,69,121.64, -11,1,10.07.2015,,157,121.64, -11,1,11.07.2015,,141,123.55, -11,1,12.07.2015,,157,123.55, -11,1,13.07.2015,,214,123.55, -11,1,14.07.2015,,264,123.55, -11,1,15.07.2015,,311,125.45, -11,1,16.07.2015,,135,125.45, -11,1,17.07.2015,,145,125.45, -11,1,18.07.2015,,75,127.62, -11,1,19.07.2015,,204,127.62, -11,1,20.07.2015,,185,127.62, -11,1,21.07.2015,,283,127.62, -11,1,22.07.2015,,254,127.62, -11,1,23.07.2015,,236,127.62, 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-11,1,26.12.2015,,176,126.54, -11,1,27.12.2015,,116,126.54, -11,1,28.12.2015,,248,126.54, -11,1,29.12.2015,,148,126.54, -11,1,30.12.2015,,141,126.54, -11,1,31.12.2015,,101,126.54, -11,1,01.01.2016,,22,126.54, -11,1,02.01.2016,,35,126.54, -11,1,03.01.2016,,126,126.54, -11,1,04.01.2016,,116,126.54, -11,1,05.01.2016,,129,126.54, -11,1,06.01.2016,,185,126.54, -11,1,07.01.2016,,94,126.54, -11,1,08.01.2016,,97,126.54, -11,1,09.01.2016,,138,126.54, -11,1,10.01.2016,,151,126.54, -11,1,11.01.2016,,167,126.54, -11,1,12.01.2016,,129,126.54, -11,1,13.01.2016,,170,126.54, -11,1,14.01.2016,,207,126.54, -11,1,15.01.2016,,170,126.54, -11,1,16.01.2016,,192,126.54, -11,1,17.01.2016,,232,126.54, -11,1,18.01.2016,,201,126.54, -11,1,19.01.2016,,167,126.54, -11,1,20.01.2016,,226,126.54, -11,1,21.01.2016,,324,127.9, -11,1,22.01.2016,,132,127.9, -11,1,23.01.2016,,135,127.9, -11,1,24.01.2016,,126,127.9, -11,1,25.01.2016,,192,127.9, -11,1,26.01.2016,,248,127.9, -11,1,27.01.2016,,214,127.9, -11,1,28.01.2016,,355,127.9, -11,1,29.01.2016,,327,127.9, -11,1,30.01.2016,,239,127.9, -11,1,31.01.2016,,173,127.9, -11,1,01.02.2016,,148,130.34, -11,1,02.02.2016,,302,130.34, -11,1,03.02.2016,,280,130.34, -11,1,04.02.2016,,163,133.06, -11,1,05.02.2016,,85,133.06, -11,1,06.02.2016,,264,133.06, -11,1,07.02.2016,,79,141.76, -11,1,08.02.2016,,91,141.76, -11,1,09.02.2016,1.0,308,141.76,130.75 -11,1,10.02.2016,1.0,613,141.76,130.75 -11,1,11.02.2016,1.0,748,141.76,130.75 -11,1,12.02.2016,1.0,333,141.76,130.75 -11,1,13.02.2016,1.0,528,141.76,130.75 -11,1,14.02.2016,1.0,355,141.76,130.75 -11,1,15.02.2016,1.0,600,141.76,130.75 -11,1,16.02.2016,1.0,267,141.76,130.75 -11,1,17.02.2016,1.0,148,141.76,130.75 -11,1,18.02.2016,,113,141.76, -11,1,19.02.2016,,110,141.76, -11,1,20.02.2016,,110,141.76, -11,1,21.02.2016,,66,141.76, -11,1,22.02.2016,,97,141.76, -11,1,23.02.2016,,57,141.76, -11,1,24.02.2016,,57,141.76, -11,1,25.02.2016,,101,141.76, -11,1,26.02.2016,,88,141.76, -11,1,27.02.2016,,129,141.76, -11,1,28.02.2016,,82,141.76, -11,1,29.02.2016,,97,141.76, -11,1,01.03.2016,,107,141.76, -11,1,02.03.2016,,66,141.76, -11,1,03.03.2016,,60,141.76, -11,1,04.03.2016,,60,141.76, -11,1,05.03.2016,,141,141.21, -11,1,06.03.2016,,145,141.21, -11,1,07.03.2016,,110,141.21, -11,1,08.03.2016,1.0,355,141.21,132.92 -11,1,09.03.2016,1.0,399,141.21,132.92 -11,1,10.03.2016,1.0,487,141.21,132.92 -11,1,11.03.2016,1.0,402,141.21,132.92 -11,1,12.03.2016,1.0,352,141.21,132.92 -11,1,13.03.2016,1.0,298,141.21,132.92 -11,1,14.03.2016,1.0,333,141.21,132.92 -11,1,15.03.2016,1.0,653,141.21,130.21 -11,1,16.03.2016,1.0,1426,141.21,130.21 -11,1,17.03.2016,1.0,713,141.21,130.21 -11,1,18.03.2016,1.0,650,141.21,130.21 -11,1,19.03.2016,1.0,704,141.21,130.21 -11,1,20.03.2016,1.0,113,141.21,130.21 -11,1,21.03.2016,,116,141.21, -11,1,22.03.2016,,101,141.21, -11,1,23.03.2016,,85,141.21, -11,1,24.03.2016,,129,141.21, -11,1,25.03.2016,,138,141.21, -11,1,26.03.2016,,79,141.21, -11,1,27.03.2016,,85,141.21, -11,1,28.03.2016,,97,141.21, -11,1,29.03.2016,,135,141.21, -11,1,30.03.2016,,79,141.21, -11,1,31.03.2016,,104,141.21, -11,1,01.04.2016,,66,141.21, -11,1,02.04.2016,,63,141.21, -11,1,03.04.2016,,88,141.21, -11,1,04.04.2016,,88,141.21, -11,1,05.04.2016,,69,141.21, -11,1,06.04.2016,,82,141.21, -11,1,07.04.2016,,66,141.21, -11,1,08.04.2016,,60,141.21, -11,1,09.04.2016,,72,141.21, -11,1,10.04.2016,,72,141.21, -11,1,11.04.2016,,66,141.21, -11,1,12.04.2016,,217,141.21, -11,1,13.04.2016,,72,141.21, -11,1,14.04.2016,,75,135.78, -11,1,15.04.2016,,72,135.78, -11,1,16.04.2016,,138,135.78, -11,1,17.04.2016,,116,135.78, -11,1,18.04.2016,,132,135.78, -11,1,19.04.2016,1.0,729,135.78,122.32 -11,1,20.04.2016,1.0,1125,135.78,122.32 -11,1,21.04.2016,1.0,707,135.78,122.32 -11,1,22.04.2016,1.0,792,135.78,122.32 -11,1,23.04.2016,1.0,594,135.78,122.32 -11,1,24.04.2016,1.0,537,135.78,122.32 -11,1,25.04.2016,1.0,798,135.78,122.32 -11,1,26.04.2016,,179,135.78, -11,1,27.04.2016,,72,135.78, -11,1,28.04.2016,,110,135.78, -11,1,29.04.2016,,167,131.16, -11,1,30.04.2016,,107,131.16, -11,1,01.05.2016,,25,131.16, -11,1,02.05.2016,,47,131.16, -11,1,03.05.2016,,47,131.16, -11,1,04.05.2016,,31,131.16, -11,1,05.05.2016,,85,131.16, -11,1,06.05.2016,,107,131.16, -11,1,07.05.2016,,85,131.16, -11,1,08.05.2016,,25,131.16, -11,1,09.05.2016,,57,131.16, -11,1,10.05.2016,,88,131.16, -11,1,11.05.2016,,113,131.16, -11,1,12.05.2016,,91,131.16, -11,1,13.05.2016,,53,128.98, -11,1,14.05.2016,,97,128.98, -11,1,15.05.2016,,75,128.98, -11,1,16.05.2016,,170,128.98, -11,1,17.05.2016,1.0,559,128.98,119.6 -11,1,18.05.2016,1.0,833,128.98,119.6 -11,1,19.05.2016,1.0,1021,128.98,119.6 -11,1,20.05.2016,1.0,553,128.98,119.6 -11,1,21.05.2016,1.0,688,128.98,119.6 -11,1,22.05.2016,1.0,430,128.98,119.6 -12,1,01.01.2015,,25,171.93, -12,1,02.01.2015,,35,171.93, -12,1,03.01.2015,,69,171.93, -12,1,04.01.2015,,50,171.93, -12,1,05.01.2015,,107,171.93, -12,1,06.01.2015,,57,171.93, -12,1,07.01.2015,,63,171.93, -12,1,08.01.2015,,119,171.93, -12,1,09.01.2015,,75,171.93, -12,1,10.01.2015,,82,171.93, -12,1,11.01.2015,,72,171.93, -12,1,12.01.2015,,72,171.93, -12,1,13.01.2015,,82,171.93, -12,1,14.01.2015,,91,176.82, -12,1,15.01.2015,,75,176.82, -12,1,16.01.2015,,60,176.82, -12,1,17.01.2015,,72,176.82, -12,1,18.01.2015,,85,176.82, -12,1,19.01.2015,,75,176.82, -12,1,20.01.2015,,85,176.82, -12,1,21.01.2015,,79,176.82, -12,1,22.01.2015,,88,176.82, -12,1,23.01.2015,,94,168.4, -12,1,24.01.2015,,107,168.4, -12,1,25.01.2015,,116,168.4, -12,1,26.01.2015,,82,168.4, -12,1,27.01.2015,,53,168.4, -12,1,28.01.2015,,97,154.26, -12,1,29.01.2015,,113,154.26, -12,1,30.01.2015,,101,154.26, -12,1,31.01.2015,,110,154.26, -12,1,01.02.2015,,129,154.26, -12,1,02.02.2015,,60,154.26, -12,1,03.02.2015,,97,154.26, -12,1,04.02.2015,,126,154.26, -12,1,05.02.2015,,82,154.26, -12,1,06.02.2015,,157,154.26, -12,1,07.02.2015,,220,154.26, -12,1,08.02.2015,,138,154.26, -12,1,09.02.2015,,104,154.26, -12,1,10.02.2015,,91,154.26, 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-12,1,13.07.2015,,210,123.55, -12,1,14.07.2015,,459,123.55, -12,1,15.07.2015,,402,125.45, -12,1,16.07.2015,,506,125.45, -12,1,17.07.2015,,496,125.45, -12,1,18.07.2015,,565,127.62, -12,1,19.07.2015,,226,127.62, -12,1,20.07.2015,,578,127.62, -12,1,21.07.2015,,408,127.62, -12,1,22.07.2015,,575,127.62, -12,1,23.07.2015,,776,127.62, -12,1,24.07.2015,,487,127.62, -12,1,25.07.2015,,622,127.62, -12,1,26.07.2015,,405,130.07, -12,1,27.07.2015,,434,130.07, -12,1,28.07.2015,1.0,471,130.07,124.77 -12,1,29.07.2015,1.0,408,130.07,124.77 -12,1,30.07.2015,1.0,352,130.07,124.77 -12,1,31.07.2015,1.0,622,130.07,124.77 -12,1,01.08.2015,1.0,625,130.07,124.77 -12,1,02.08.2015,1.0,726,130.07,124.77 -12,1,03.08.2015,1.0,512,130.07,124.77 -12,1,04.08.2015,,327,138.5, -12,1,05.08.2015,,236,138.5, -12,1,06.08.2015,,154,138.5, -12,1,07.08.2015,,192,138.5, -12,1,08.08.2015,,229,141.21, -12,1,09.08.2015,,201,141.21, -12,1,10.08.2015,,198,141.21, -12,1,11.08.2015,,204,141.21, -12,1,12.08.2015,,192,142.85, 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-12,1,15.09.2015,,242,135.51, -12,1,16.09.2015,,220,138.5, -12,1,17.09.2015,,198,138.5, -12,1,18.09.2015,,182,138.5, -12,1,19.09.2015,,163,138.5, -12,1,20.09.2015,,119,138.5, -12,1,21.09.2015,,167,138.5, -12,1,22.09.2015,,195,138.5, -12,1,23.09.2015,,167,141.21, -12,1,24.09.2015,,229,141.21, -12,1,25.09.2015,,145,141.21, -12,1,26.09.2015,,107,141.21, -12,1,27.09.2015,,132,141.21, -12,1,28.09.2015,,132,141.21, -12,1,29.09.2015,,88,141.21, -12,1,30.09.2015,,132,141.21, -12,1,01.10.2015,,123,141.21, -12,1,02.10.2015,,145,141.21, -12,1,03.10.2015,,157,141.21, -12,1,04.10.2015,,97,141.21, -12,1,05.10.2015,,151,141.21, -12,1,06.10.2015,1.0,584,141.21,132.92 -12,1,07.10.2015,1.0,179,141.21,130.21 -12,1,08.10.2015,1.0,349,141.11,130.21 -12,1,09.10.2015,1.0,82,141.21,130.21 -12,1,10.10.2015,1.0,421,141.21,130.21 -12,1,11.10.2015,1.0,94,141.21,130.21 -12,1,12.10.2015,1.0,254,141.21,130.21 -12,1,13.10.2015,,91,141.21, -12,1,14.10.2015,,104,141.21, -12,1,15.10.2015,,126,142.57, 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-12,1,16.12.2015,,173,130.34, -12,1,17.12.2015,,101,130.34, -12,1,18.12.2015,,85,130.34, -12,1,19.12.2015,,258,130.34, -12,1,20.12.2015,,217,130.34, -12,1,21.12.2015,,167,130.34, -12,1,22.12.2015,,119,130.34, -12,1,23.12.2015,,195,130.34, -12,1,24.12.2015,,104,126.54, -12,1,25.12.2015,,160,126.54, -12,1,26.12.2015,,157,126.54, -12,1,27.12.2015,,132,126.54, -12,1,28.12.2015,,97,126.54, -12,1,29.12.2015,,113,126.54, -12,1,30.12.2015,,192,126.54, -12,1,31.12.2015,,126,126.54, -12,1,01.01.2016,,41,126.54, -12,1,02.01.2016,,126,126.54, -12,1,03.01.2016,,85,126.54, -12,1,04.01.2016,,119,126.54, -12,1,05.01.2016,,204,126.54, -12,1,06.01.2016,,104,126.54, -12,1,07.01.2016,,57,126.54, -12,1,08.01.2016,,210,126.54, -12,1,09.01.2016,,123,126.54, -12,1,10.01.2016,,188,126.54, -12,1,11.01.2016,,317,126.54, -12,1,12.01.2016,,154,126.54, -12,1,13.01.2016,,148,126.54, -12,1,14.01.2016,,116,126.54, -12,1,15.01.2016,,201,126.54, -12,1,16.01.2016,,173,126.54, -12,1,17.01.2016,,182,126.54, -12,1,18.01.2016,,188,126.54, -12,1,19.01.2016,,141,126.54, -12,1,20.01.2016,,270,126.54, -12,1,21.01.2016,,201,127.9, -12,1,22.01.2016,,210,127.9, -12,1,23.01.2016,,251,127.9, -12,1,24.01.2016,,223,127.9, -12,1,25.01.2016,,248,127.9, -12,1,26.01.2016,,119,127.9, -12,1,27.01.2016,,173,127.9, -12,1,28.01.2016,,170,127.9, -12,1,29.01.2016,,154,127.9, -12,1,30.01.2016,,267,127.9, -12,1,31.01.2016,,210,127.9, -12,1,01.02.2016,,179,130.34, -12,1,02.02.2016,,232,130.34, -12,1,03.02.2016,,305,130.34, -12,1,04.02.2016,,101,133.06, -12,1,05.02.2016,,132,133.06, -12,1,06.02.2016,,138,133.06, -12,1,07.02.2016,,160,141.76, -12,1,08.02.2016,,141,141.76, -12,1,09.02.2016,1.0,298,141.76,130.75 -12,1,10.02.2016,1.0,408,141.76,130.75 -12,1,11.02.2016,1.0,481,141.76,130.75 -12,1,12.02.2016,1.0,283,141.76,130.75 -12,1,13.02.2016,1.0,198,141.76,130.75 -12,1,14.02.2016,1.0,207,141.76,130.75 -12,1,15.02.2016,1.0,305,141.76,130.75 -12,1,16.02.2016,1.0,242,141.76,130.75 -12,1,17.02.2016,1.0,226,141.76,130.75 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-13,1,06.10.2015,1.0,694,141.21,132.92 -13,1,07.10.2015,1.0,889,141.21,130.21 -13,1,08.10.2015,1.0,1200,141.11,130.21 -13,1,09.10.2015,1.0,716,141.21,130.21 -13,1,10.10.2015,1.0,789,141.21,130.21 -13,1,11.10.2015,1.0,792,141.21,130.21 -13,1,12.10.2015,1.0,468,141.21,130.21 -13,1,13.10.2015,,239,141.21, -13,1,14.10.2015,,239,141.21, -13,1,15.10.2015,,207,142.57, -13,1,16.10.2015,,192,142.57, -13,1,17.10.2015,,192,142.57, -13,1,18.10.2015,,154,142.57, -13,1,19.10.2015,,167,142.57, -13,1,20.10.2015,,151,142.57, -13,1,21.10.2015,,364,130.34, -13,1,22.10.2015,,327,130.34, -13,1,23.10.2015,,465,130.34, -13,1,24.10.2015,,660,130.34, -13,1,25.10.2015,,446,130.34, -13,1,26.10.2015,,371,130.34, -13,1,27.10.2015,1.0,1407,130.34,122.32 -13,1,28.10.2015,1.0,933,130.34,124.77 -13,1,29.10.2015,1.0,685,130.34,124.77 -13,1,30.10.2015,1.0,402,130.34,124.77 -13,1,31.10.2015,1.0,339,130.34,124.77 -13,1,01.11.2015,1.0,245,130.34,124.77 -13,1,02.11.2015,1.0,540,130.34,124.77 -13,1,03.11.2015,,236,130.34, 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-13,1,05.12.2015,1.0,606,130.34,116.61 -13,1,06.12.2015,1.0,1005,130.34,116.61 -13,1,07.12.2015,1.0,1056,130.34,116.61 -13,1,08.12.2015,,232,130.34, -13,1,09.12.2015,,273,130.34, -13,1,10.12.2015,,248,130.34, -13,1,11.12.2015,,210,130.34, -13,1,12.12.2015,,254,130.34, -13,1,13.12.2015,,167,130.34, -13,1,14.12.2015,,267,130.34, -13,1,15.12.2015,,352,130.34, -13,1,16.12.2015,,258,130.34, -13,1,17.12.2015,,245,130.34, -13,1,18.12.2015,,201,130.34, -13,1,19.12.2015,,214,130.34, -13,1,20.12.2015,,258,130.34, -13,1,21.12.2015,,270,130.34, -13,1,22.12.2015,,393,130.34, -13,1,23.12.2015,,232,130.34, -13,1,24.12.2015,,317,126.54, -13,1,25.12.2015,,311,126.54, -13,1,26.12.2015,,214,126.54, -13,1,27.12.2015,,210,126.54, -13,1,28.12.2015,,298,126.54, -13,1,29.12.2015,,339,126.54, -13,1,30.12.2015,,364,126.54, -13,1,31.12.2015,,311,126.54, -13,1,01.01.2016,,60,126.54, -13,1,02.01.2016,,254,126.54, -13,1,03.01.2016,,201,126.54, -13,1,04.01.2016,,314,126.54, -13,1,05.01.2016,,270,126.54, -13,1,06.01.2016,,270,126.54, -13,1,07.01.2016,,232,126.54, -13,1,08.01.2016,,276,126.54, -13,1,09.01.2016,,280,126.54, -13,1,10.01.2016,,270,126.54, -13,1,11.01.2016,,327,126.54, -13,1,12.01.2016,,402,126.54, -13,1,13.01.2016,,292,126.54, -13,1,14.01.2016,,371,126.54, -13,1,15.01.2016,,386,126.54, -13,1,16.01.2016,,377,126.54, -13,1,17.01.2016,,317,126.54, -13,1,18.01.2016,,468,126.54, -13,1,19.01.2016,,437,126.54, -13,1,20.01.2016,,324,126.54, -13,1,21.01.2016,,446,127.9, -13,1,22.01.2016,,361,127.9, -13,1,23.01.2016,,264,127.9, -13,1,24.01.2016,,377,127.9, -13,1,25.01.2016,,317,127.9, -13,1,26.01.2016,,440,127.9, -13,1,27.01.2016,,500,127.9, -13,1,28.01.2016,,361,127.9, -13,1,29.01.2016,,258,127.9, -13,1,30.01.2016,,380,127.9, -13,1,31.01.2016,,393,127.9, -13,1,01.02.2016,,305,130.34, -13,1,02.02.2016,,342,130.34, -13,1,03.02.2016,,251,130.34, -13,1,04.02.2016,,289,133.06, -13,1,05.02.2016,,377,133.06, -13,1,06.02.2016,,358,133.06, -13,1,07.02.2016,,226,141.76, 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-13,1,07.04.2016,,192,141.21, -13,1,08.04.2016,,182,141.21, -13,1,09.04.2016,,179,141.21, -13,1,10.04.2016,,185,141.21, -13,1,11.04.2016,,217,141.21, -13,1,12.04.2016,,261,141.21, -13,1,13.04.2016,,245,141.21, -13,1,14.04.2016,,210,135.78, -13,1,15.04.2016,,258,135.78, -13,1,16.04.2016,,245,135.78, -13,1,17.04.2016,,295,135.78, -13,1,18.04.2016,,248,135.78, -13,1,19.04.2016,1.0,1872,135.78,122.32 -13,1,20.04.2016,1.0,1816,135.78,122.32 -13,1,21.04.2016,1.0,1304,135.78,122.32 -13,1,22.04.2016,1.0,811,135.78,122.32 -13,1,23.04.2016,1.0,767,135.78,122.32 -13,1,24.04.2016,1.0,729,135.78,122.32 -13,1,25.04.2016,1.0,1056,135.78,122.32 -13,1,26.04.2016,,339,135.78, -13,1,27.04.2016,,251,135.78, -13,1,28.04.2016,,223,135.78, -13,1,29.04.2016,,311,131.16, -13,1,30.04.2016,,223,131.16, -13,1,01.05.2016,,245,131.16, -13,1,02.05.2016,,116,131.16, -13,1,03.05.2016,,182,131.16, -13,1,04.05.2016,,232,131.16, -13,1,05.05.2016,,210,131.16, -13,1,06.05.2016,,226,131.16, -13,1,07.05.2016,,182,131.16, -13,1,08.05.2016,,132,131.16, -13,1,09.05.2016,,148,131.16, -13,1,10.05.2016,,330,131.16, -13,1,11.05.2016,,214,131.16, -13,1,12.05.2016,,342,131.16, -13,1,13.05.2016,,547,128.98, -13,1,14.05.2016,,245,128.98, -13,1,15.05.2016,,390,128.98, -13,1,16.05.2016,,342,128.98, -13,1,17.05.2016,1.0,1596,128.98,119.6 -13,1,18.05.2016,1.0,1505,128.98,119.6 -13,1,19.05.2016,1.0,1210,128.98,119.6 -13,1,20.05.2016,1.0,1329,128.98,119.6 -13,1,21.05.2016,1.0,688,128.98,119.6 -13,1,22.05.2016,1.0,217,128.98,119.6 -14,1,01.01.2015,,3,163.78, -14,1,02.01.2015,,72,163.78, -14,1,03.01.2015,,31,163.78, -14,1,04.01.2015,,41,163.78, -14,1,05.01.2015,,79,163.78, -14,1,06.01.2015,,22,163.78, -14,1,07.01.2015,,44,163.78, -14,1,08.01.2015,,69,163.78, -14,1,09.01.2015,,35,163.78, -14,1,10.01.2015,,82,163.78, -14,1,11.01.2015,,88,163.78, -14,1,12.01.2015,,66,163.78, -14,1,13.01.2015,,79,163.78, -14,1,14.01.2015,,47,168.4, -14,1,15.01.2015,,47,168.4, -14,1,16.01.2015,,85,168.4, -14,1,17.01.2015,,63,168.4, -14,1,18.01.2015,,101,168.4, -14,1,19.01.2015,,88,168.4, -14,1,20.01.2015,,79,168.4, -14,1,21.01.2015,,60,168.4, -14,1,22.01.2015,,60,168.4, -14,1,23.01.2015,,104,160.24, -14,1,24.01.2015,,148,160.24, -14,1,25.01.2015,,91,160.24, -14,1,26.01.2015,,110,160.24, -14,1,27.01.2015,,113,160.24, -14,1,28.01.2015,,167,146.92, -14,1,29.01.2015,,126,146.92, -14,1,30.01.2015,,66,146.92, -14,1,31.01.2015,,141,146.92, -14,1,01.02.2015,,126,146.92, -14,1,02.02.2015,,226,146.92, -14,1,03.02.2015,,217,146.92, -14,1,04.02.2015,,160,146.92, -14,1,05.02.2015,,116,146.92, -14,1,06.02.2015,,91,146.92, -14,1,07.02.2015,,154,146.92, -14,1,08.02.2015,,113,146.92, -14,1,09.02.2015,,91,146.92, -14,1,10.02.2015,,204,146.92, -14,1,11.02.2015,,141,146.92, -14,1,12.02.2015,,107,146.92, -14,1,13.02.2015,,110,149.37, -14,1,14.02.2015,,82,149.37, -14,1,15.02.2015,,104,149.37, -14,1,16.02.2015,,123,149.37, -14,1,17.02.2015,1.0,983,149.37,143.8 -14,1,18.02.2015,1.0,232,149.37,135.64 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-14,1,19.04.2015,,53,138.5, -14,1,20.04.2015,,82,138.5, -14,1,21.04.2015,,47,138.5, -14,1,22.04.2015,,101,128.98, -14,1,23.04.2015,,141,128.98, -14,1,24.04.2015,,107,128.98, -14,1,25.04.2015,,88,128.98, -14,1,26.04.2015,,101,128.98, -14,1,27.04.2015,,97,128.98, -14,1,28.04.2015,,91,128.98, -14,1,29.04.2015,,63,131.7, -14,1,30.04.2015,,66,131.7, -14,1,01.05.2015,,60,131.7, -14,1,02.05.2015,,50,131.7, -14,1,03.05.2015,,63,131.7, -14,1,04.05.2015,,53,131.7, -14,1,05.05.2015,,69,131.7, -14,1,06.05.2015,,82,131.7, -14,1,07.05.2015,,88,131.7, -14,1,08.05.2015,,72,131.7, -14,1,09.05.2015,,38,131.7, -14,1,10.05.2015,,66,131.7, -14,1,11.05.2015,,44,131.7, -14,1,12.05.2015,,82,131.7, -14,1,13.05.2015,,53,131.7, -14,1,14.05.2015,,82,131.7, -14,1,15.05.2015,,66,131.7, -14,1,16.05.2015,,94,131.7, -14,1,17.05.2015,,101,131.7, -14,1,18.05.2015,,104,131.7, -14,1,19.05.2015,1.0,129,131.7,119.33 -14,1,20.05.2015,1.0,349,131.7,119.33 -14,1,21.05.2015,1.0,421,131.7,119.33 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-14,1,22.06.2015,,107,128.17, -14,1,23.06.2015,,145,128.17, -14,1,24.06.2015,,138,128.17, -14,1,25.06.2015,,113,128.17, -14,1,26.06.2015,,50,128.17, -14,1,27.06.2015,,129,128.17, -14,1,28.06.2015,,157,130.34, -14,1,29.06.2015,,204,130.34, -14,1,30.06.2015,1.0,399,130.34,119.33 -14,1,01.07.2015,1.0,478,130.34,119.33 -14,1,02.07.2015,1.0,232,130.34,119.33 -14,1,03.07.2015,1.0,443,130.34,119.33 -14,1,04.07.2015,1.0,214,130.34,119.33 -14,1,05.07.2015,1.0,198,130.34,119.33 -14,1,06.07.2015,1.0,314,130.34,119.33 -14,1,07.07.2015,,79,130.34, -14,1,08.07.2015,,151,130.34, -14,1,09.07.2015,,157,121.64, -14,1,10.07.2015,,182,121.64, -14,1,11.07.2015,,185,123.55, -14,1,12.07.2015,,157,123.55, -14,1,13.07.2015,,110,123.55, -14,1,14.07.2015,,352,123.55, -14,1,15.07.2015,,201,125.45, -14,1,16.07.2015,,173,125.45, -14,1,17.07.2015,,148,125.45, -14,1,18.07.2015,,113,127.62, -14,1,19.07.2015,,170,127.62, -14,1,20.07.2015,,188,127.62, -14,1,21.07.2015,,434,127.62, -14,1,22.07.2015,,471,127.62, 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-14,1,29.03.2016,,38,141.21, -14,1,30.03.2016,,47,141.21, -14,1,31.03.2016,,50,141.21, -14,1,01.04.2016,,38,141.21, -14,1,02.04.2016,,57,141.21, -14,1,03.04.2016,,38,141.21, -14,1,04.04.2016,,38,141.21, -14,1,05.04.2016,,44,141.21, -14,1,06.04.2016,,44,141.21, -14,1,07.04.2016,,41,141.21, -14,1,08.04.2016,,25,141.21, -14,1,09.04.2016,,28,141.21, -14,1,10.04.2016,,66,141.21, -14,1,11.04.2016,,38,141.21, -14,1,12.04.2016,,47,141.21, -14,1,13.04.2016,,82,141.21, -14,1,14.04.2016,,47,135.78, -14,1,15.04.2016,,47,135.78, -14,1,16.04.2016,,41,135.78, -14,1,17.04.2016,,41,135.78, -14,1,18.04.2016,,50,135.78, -14,1,19.04.2016,1.0,578,135.78,122.32 -14,1,20.04.2016,1.0,603,135.78,122.32 -14,1,21.04.2016,1.0,364,135.78,122.32 -14,1,22.04.2016,1.0,493,135.78,122.32 -14,1,23.04.2016,1.0,443,135.78,122.32 -14,1,24.04.2016,1.0,484,135.78,122.32 -14,1,25.04.2016,1.0,264,135.78,122.32 -14,1,26.04.2016,,63,135.78, -14,1,27.04.2016,,60,135.78, -14,1,28.04.2016,,138,135.78, -14,1,29.04.2016,,57,131.16, 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-19,1,26.06.2015,,116,128.17, -19,1,27.06.2015,,113,128.17, -19,1,28.06.2015,,207,130.34, -19,1,29.06.2015,,167,130.34, -19,1,30.06.2015,1.0,327,130.34,119.33 -19,1,01.07.2015,1.0,515,130.34,119.33 -19,1,02.07.2015,1.0,418,130.34,119.33 -19,1,03.07.2015,1.0,522,130.34,119.33 -19,1,04.07.2015,1.0,399,130.34,119.33 -19,1,05.07.2015,1.0,371,130.34,119.33 -19,1,06.07.2015,1.0,440,130.34,119.33 -19,1,07.07.2015,,119,130.34, -19,1,08.07.2015,,135,130.34, -19,1,09.07.2015,,270,121.64, -19,1,10.07.2015,,179,121.64, -19,1,11.07.2015,,201,123.55, -19,1,12.07.2015,,305,123.55, -19,1,13.07.2015,,270,123.55, -19,1,14.07.2015,,239,123.55, -19,1,15.07.2015,,280,125.45, -19,1,16.07.2015,,264,125.45, -19,1,17.07.2015,,349,125.45, -19,1,18.07.2015,,239,127.62, -19,1,19.07.2015,,286,127.62, -19,1,20.07.2015,,324,127.62, -19,1,21.07.2015,,251,127.62, -19,1,22.07.2015,,295,127.62, -19,1,23.07.2015,,405,127.62, -19,1,24.07.2015,,311,127.62, -19,1,25.07.2015,,232,127.62, -19,1,26.07.2015,,327,130.07, -19,1,27.07.2015,,572,130.07, -19,1,28.07.2015,1.0,503,130.07,124.77 -19,1,29.07.2015,1.0,481,130.07,124.77 -19,1,30.07.2015,1.0,380,130.07,124.77 -19,1,31.07.2015,1.0,569,130.07,124.77 -19,1,01.08.2015,1.0,459,130.07,124.77 -19,1,02.08.2015,1.0,386,130.07,124.77 -19,1,03.08.2015,1.0,415,130.07,124.77 -19,1,04.08.2015,,317,138.5, -19,1,05.08.2015,,314,138.5, -19,1,06.08.2015,,157,138.5, -19,1,07.08.2015,,236,138.5, -19,1,08.08.2015,,192,141.21, -19,1,09.08.2015,,305,141.21, -19,1,10.08.2015,,336,141.21, -19,1,11.08.2015,,261,141.21, -19,1,12.08.2015,,226,142.85, -19,1,13.08.2015,,104,142.85, -19,1,14.08.2015,,157,142.85, -19,1,15.08.2015,,185,142.85, -19,1,16.08.2015,,151,142.85, -19,1,17.08.2015,,132,142.85, -19,1,18.08.2015,,220,142.85, -19,1,19.08.2015,,220,142.85, -19,1,20.08.2015,,220,142.85, -19,1,21.08.2015,,220,142.85, -19,1,22.08.2015,,220,142.85, -19,1,23.08.2015,,220,142.85, -19,1,24.08.2015,,220,142.85, -19,1,25.08.2015,,220,142.85, -19,1,26.08.2015,,220,142.85, -19,1,27.08.2015,,220,137.68, -19,1,28.08.2015,,525,137.68, -19,1,29.08.2015,,320,137.68, -19,1,30.08.2015,,289,137.68, -19,1,31.08.2015,,308,137.68, -19,1,01.09.2015,,261,137.68, -19,1,02.09.2015,,286,135.78, -19,1,03.09.2015,,160,135.78, -19,1,04.09.2015,,170,135.78, -19,1,05.09.2015,,223,133.6, -19,1,06.09.2015,,317,133.6, -19,1,07.09.2015,,474,133.6, -19,1,08.09.2015,,418,133.6, -19,1,09.09.2015,,245,135.51, -19,1,10.09.2015,,308,135.51, -19,1,11.09.2015,,383,135.51, -19,1,12.09.2015,,176,135.51, -19,1,13.09.2015,,308,135.51, -19,1,14.09.2015,,226,135.51, -19,1,15.09.2015,,251,135.51, -19,1,16.09.2015,,207,138.5, -19,1,17.09.2015,,261,138.5, -19,1,18.09.2015,,251,138.5, -19,1,19.09.2015,,214,138.5, -19,1,20.09.2015,,198,138.5, -19,1,21.09.2015,,302,138.5, -19,1,22.09.2015,,151,138.5, -19,1,23.09.2015,,223,141.21, -19,1,24.09.2015,,135,141.21, -19,1,25.09.2015,,170,141.21, -19,1,26.09.2015,,182,141.21, -19,1,27.09.2015,,135,141.21, -19,1,28.09.2015,,179,141.21, 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-19,1,29.12.2015,,207,126.54, -19,1,30.12.2015,,182,126.54, -19,1,31.12.2015,,188,126.54, -19,1,01.01.2016,,47,126.54, -19,1,02.01.2016,,104,126.54, -19,1,03.01.2016,,138,126.54, -19,1,04.01.2016,,217,126.54, -19,1,05.01.2016,,188,126.54, -19,1,06.01.2016,,163,126.54, -19,1,07.01.2016,,182,126.54, -19,1,08.01.2016,,157,126.54, -19,1,09.01.2016,,239,126.54, -19,1,10.01.2016,,223,126.54, -19,1,11.01.2016,,239,126.54, -19,1,12.01.2016,,198,126.54, -19,1,13.01.2016,,368,126.54, -19,1,14.01.2016,,434,126.54, -19,1,15.01.2016,,276,126.54, -19,1,16.01.2016,,327,126.54, -19,1,17.01.2016,,314,126.54, -19,1,18.01.2016,,302,126.54, -19,1,19.01.2016,,377,126.54, -19,1,20.01.2016,,396,126.54, -19,1,21.01.2016,,377,127.9, -19,1,22.01.2016,,254,127.9, -19,1,23.01.2016,,317,127.9, -19,1,24.01.2016,,295,127.9, -19,1,25.01.2016,,333,127.9, -19,1,26.01.2016,,327,127.9, -19,1,27.01.2016,,358,127.9, -19,1,28.01.2016,,358,127.9, -19,1,29.01.2016,,251,127.9, -19,1,30.01.2016,,364,127.9, -19,1,31.01.2016,,223,127.9, -19,1,01.02.2016,,201,130.34, -19,1,02.02.2016,,336,130.34, -19,1,03.02.2016,,198,130.34, -19,1,04.02.2016,,207,133.06, -19,1,05.02.2016,,242,133.06, -19,1,06.02.2016,,308,133.06, -19,1,07.02.2016,,273,141.76, -19,1,08.02.2016,,145,141.76, -19,1,09.02.2016,1.0,440,141.76,130.75 -19,1,10.02.2016,1.0,518,141.76,130.75 -19,1,11.02.2016,1.0,292,141.76,130.75 -19,1,12.02.2016,1.0,242,141.76,130.75 -19,1,13.02.2016,1.0,449,141.76,130.75 -19,1,14.02.2016,1.0,484,141.76,130.75 -19,1,15.02.2016,1.0,298,141.76,130.75 -19,1,16.02.2016,1.0,462,141.76,130.75 -19,1,17.02.2016,1.0,368,141.76,130.75 -19,1,18.02.2016,,116,141.76, -19,1,19.02.2016,,135,141.76, -19,1,20.02.2016,,179,141.76, -19,1,21.02.2016,,154,141.76, -19,1,22.02.2016,,195,141.76, -19,1,23.02.2016,,129,141.76, -19,1,24.02.2016,,167,141.76, -19,1,25.02.2016,,201,141.76, -19,1,26.02.2016,,119,141.76, -19,1,27.02.2016,,119,141.76, -19,1,28.02.2016,,188,141.76, -19,1,29.02.2016,,129,141.76, -19,1,01.03.2016,,232,141.76, -19,1,02.03.2016,,170,141.76, -19,1,03.03.2016,,192,141.76, -19,1,04.03.2016,,204,141.76, -19,1,05.03.2016,,210,141.21, -19,1,06.03.2016,,157,141.21, -19,1,07.03.2016,,167,141.21, -19,1,08.03.2016,1.0,245,141.21,132.92 -19,1,09.03.2016,1.0,556,141.21,132.92 -19,1,10.03.2016,1.0,638,141.21,132.92 -19,1,11.03.2016,1.0,569,141.21,132.92 -19,1,12.03.2016,1.0,393,141.21,132.92 -19,1,13.03.2016,1.0,581,141.21,132.92 -19,1,14.03.2016,1.0,518,141.21,132.92 -19,1,15.03.2016,1.0,754,141.21,130.21 -19,1,16.03.2016,1.0,609,141.21,130.21 -19,1,17.03.2016,1.0,635,141.21,130.21 -19,1,18.03.2016,1.0,565,141.21,130.21 -19,1,19.03.2016,1.0,741,141.21,130.21 -19,1,20.03.2016,,185,141.21, -19,1,21.03.2016,,126,141.21, -19,1,22.03.2016,,239,141.21, -19,1,23.03.2016,,145,141.21, -19,1,24.03.2016,,188,141.21, -19,1,25.03.2016,,110,141.21, -19,1,26.03.2016,,91,141.21, -19,1,27.03.2016,,151,141.21, -19,1,28.03.2016,,94,141.21, -19,1,29.03.2016,,154,141.21, -19,1,30.03.2016,,113,141.21, -19,1,31.03.2016,,101,141.21, -19,1,01.04.2016,,151,141.21, -19,1,02.04.2016,,116,141.21, -19,1,03.04.2016,,132,141.21, -19,1,04.04.2016,,173,141.21, -19,1,05.04.2016,,116,141.21, -19,1,06.04.2016,,110,141.21, -19,1,07.04.2016,,129,141.21, -19,1,08.04.2016,,129,141.21, -19,1,09.04.2016,,138,141.21, -19,1,10.04.2016,,82,141.21, -19,1,11.04.2016,,145,141.21, -19,1,12.04.2016,,132,141.21, -19,1,13.04.2016,,138,141.21, -19,1,14.04.2016,,148,135.78, -19,1,15.04.2016,,217,135.78, -19,1,16.04.2016,,201,135.78, -19,1,17.04.2016,,188,135.78, -19,1,18.04.2016,,135,135.78, -19,1,19.04.2016,1.0,1071,135.78,122.32 -19,1,20.04.2016,1.0,1125,135.78,122.32 -19,1,21.04.2016,1.0,625,135.78,122.32 -19,1,22.04.2016,1.0,584,135.78,122.32 -19,1,23.04.2016,1.0,540,135.78,122.32 -19,1,24.04.2016,1.0,500,135.78,122.32 -19,1,25.04.2016,1.0,471,135.78,122.32 -19,1,26.04.2016,,245,135.78, -19,1,27.04.2016,,198,135.78, -19,1,28.04.2016,,148,135.78, -19,1,29.04.2016,,232,131.16, -19,1,30.04.2016,,157,131.16, -19,1,01.05.2016,,75,131.16, -19,1,02.05.2016,,88,131.16, -19,1,03.05.2016,,151,131.16, -19,1,04.05.2016,,151,131.16, -19,1,05.05.2016,,94,131.16, -19,1,06.05.2016,,126,131.16, -19,1,07.05.2016,,270,131.16, -19,1,08.05.2016,,88,131.16, -19,1,09.05.2016,,145,131.16, -19,1,10.05.2016,,170,131.16, -19,1,11.05.2016,,217,131.16, -19,1,12.05.2016,,179,131.16, -19,1,13.05.2016,,280,128.98, -19,1,14.05.2016,,176,128.98, -19,1,15.05.2016,,88,128.98, -19,1,16.05.2016,,261,128.98, -19,1,17.05.2016,1.0,1860,128.98,119.6 -19,1,18.05.2016,1.0,1926,128.98,119.6 -19,1,19.05.2016,1.0,798,128.98,119.6 -19,1,20.05.2016,1.0,685,128.98,119.6 -19,1,21.05.2016,1.0,757,128.98,119.6 -19,1,22.05.2016,1.0,396,128.98,119.6 -20,1,01.01.2015,,19,168.67, -20,1,02.01.2015,,38,168.67, -20,1,03.01.2015,,60,168.67, -20,1,04.01.2015,,69,168.67, -20,1,05.01.2015,,31,168.67, -20,1,06.01.2015,,50,168.67, -20,1,07.01.2015,,31,168.67, -20,1,08.01.2015,,38,168.67, -20,1,09.01.2015,,44,168.67, 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-23,1,18.09.2015,,145,138.5, -23,1,19.09.2015,,160,138.5, -23,1,20.09.2015,,188,138.5, -23,1,21.09.2015,,160,138.5, -23,1,22.09.2015,,195,138.5, -23,1,23.09.2015,,176,141.21, -23,1,24.09.2015,,148,141.21, -23,1,25.09.2015,,138,141.21, -23,1,26.09.2015,,145,141.21, -23,1,27.09.2015,,167,141.21, -23,1,28.09.2015,,173,141.21, -23,1,29.09.2015,,148,141.21, -23,1,30.09.2015,,132,141.21, -23,1,01.10.2015,,163,141.21, -23,1,02.10.2015,,129,141.21, -23,1,03.10.2015,,176,141.21, -23,1,04.10.2015,,157,141.21, -23,1,05.10.2015,,126,141.21, -23,1,06.10.2015,1.0,226,141.21,132.92 -23,1,07.10.2015,1.0,258,141.21,130.21 -23,1,08.10.2015,1.0,361,141.11,130.21 -23,1,09.10.2015,1.0,440,141.21,130.21 -23,1,10.10.2015,1.0,261,141.21,130.21 -23,1,11.10.2015,1.0,245,141.21,130.21 -23,1,12.10.2015,1.0,276,141.21,130.21 -23,1,13.10.2015,,138,141.21, -23,1,14.10.2015,,126,141.21, -23,1,15.10.2015,,132,142.57, -23,1,16.10.2015,,148,142.57, -23,1,17.10.2015,,107,142.57, -23,1,18.10.2015,,138,142.57, 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-23,1,19.12.2015,,135,130.34, -23,1,20.12.2015,,145,130.34, -23,1,21.12.2015,,113,130.34, -23,1,22.12.2015,,141,130.34, -23,1,23.12.2015,,116,130.34, -23,1,24.12.2015,,107,126.54, -23,1,25.12.2015,,160,126.54, -23,1,26.12.2015,,207,126.54, -23,1,27.12.2015,,195,126.54, -23,1,28.12.2015,,141,126.54, -23,1,29.12.2015,,242,126.54, -23,1,30.12.2015,,167,126.54, -23,1,31.12.2015,,101,126.54, -23,1,01.01.2016,,38,126.54, -23,1,02.01.2016,,88,126.54, -23,1,03.01.2016,,101,126.54, -23,1,04.01.2016,,192,126.54, -23,1,05.01.2016,,170,126.54, -23,1,06.01.2016,,201,126.54, -23,1,07.01.2016,,135,126.54, -23,1,08.01.2016,,126,126.54, -23,1,09.01.2016,,132,126.54, -23,1,10.01.2016,,154,126.54, -23,1,11.01.2016,,176,126.54, -23,1,12.01.2016,,173,126.54, -23,1,13.01.2016,,217,126.54, -23,1,14.01.2016,,170,126.54, -23,1,15.01.2016,,289,126.54, -23,1,16.01.2016,,232,126.54, -23,1,17.01.2016,,236,126.54, -23,1,18.01.2016,,226,126.54, -23,1,19.01.2016,,141,126.54, -23,1,20.01.2016,,214,126.54, -23,1,21.01.2016,,245,127.9, -23,1,22.01.2016,,267,127.9, -23,1,23.01.2016,,179,127.9, -23,1,24.01.2016,,239,127.9, -23,1,25.01.2016,,210,127.9, -23,1,26.01.2016,,138,127.9, -23,1,27.01.2016,,151,127.9, -23,1,28.01.2016,,170,127.9, -23,1,29.01.2016,,129,127.9, -23,1,30.01.2016,,151,127.9, -23,1,31.01.2016,,138,127.9, -23,1,01.02.2016,,176,130.34, -23,1,02.02.2016,,273,130.34, -23,1,03.02.2016,,173,130.34, -23,1,04.02.2016,,145,133.06, -23,1,05.02.2016,,390,133.06, -23,1,06.02.2016,,289,133.06, -23,1,07.02.2016,,157,141.76, -23,1,08.02.2016,,167,141.76, -23,1,09.02.2016,1.0,220,141.76,130.75 -23,1,10.02.2016,1.0,223,141.76,130.75 -23,1,11.02.2016,1.0,292,141.76,130.75 -23,1,12.02.2016,1.0,317,141.76,130.75 -23,1,13.02.2016,1.0,210,141.76,130.75 -23,1,14.02.2016,1.0,188,141.76,130.75 -23,1,15.02.2016,1.0,349,141.76,130.75 -23,1,16.02.2016,1.0,289,141.76,130.75 -23,1,17.02.2016,1.0,195,141.76,130.75 -23,1,18.02.2016,,176,141.76, -23,1,19.02.2016,,123,141.76, -23,1,20.02.2016,,119,141.76, 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-24,1,06.04.2016,,276,141.21, -24,1,07.04.2016,,280,141.21, -24,1,08.04.2016,,283,141.21, -24,1,09.04.2016,,358,141.21, -24,1,10.04.2016,,292,141.21, -24,1,11.04.2016,,459,141.21, -24,1,12.04.2016,,355,141.21, -24,1,13.04.2016,,273,141.21, -24,1,14.04.2016,,459,135.78, -24,1,15.04.2016,,719,135.78, -24,1,16.04.2016,,565,135.78, -24,1,17.04.2016,,471,135.78, -24,1,18.04.2016,,525,135.78, -24,1,19.04.2016,1.0,8724,135.78,122.32 -24,1,20.04.2016,1.0,1341,135.78,122.32 -24,1,21.04.2016,1.0,1294,135.78,122.32 -24,1,22.04.2016,1.0,556,135.78,122.32 -24,1,23.04.2016,1.0,1184,135.78,122.32 -24,1,24.04.2016,1.0,1184,135.78,122.32 -24,1,25.04.2016,1.0,1184,135.78,122.32 -24,1,26.04.2016,,258,135.78, -24,1,27.04.2016,,386,135.78, -24,1,28.04.2016,,412,135.78, -24,1,29.04.2016,,390,131.16, -24,1,30.04.2016,,474,131.16, -24,1,01.05.2016,,418,131.16, -24,1,02.05.2016,,201,131.16, -24,1,03.05.2016,,188,131.16, -24,1,04.05.2016,,298,131.16, -24,1,05.05.2016,,276,131.16, -24,1,06.05.2016,,283,131.16, 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-30,1,25.06.2015,,273,128.17, -30,1,26.06.2015,,145,128.17, -30,1,27.06.2015,,292,128.17, -30,1,28.06.2015,,207,130.34, -30,1,29.06.2015,,116,130.34, -30,1,30.06.2015,1.0,327,130.34,119.33 -30,1,01.07.2015,1.0,465,130.34,119.33 -30,1,02.07.2015,1.0,361,130.34,119.33 -30,1,03.07.2015,1.0,415,130.34,119.33 -30,1,04.07.2015,1.0,182,130.34,119.33 -30,1,05.07.2015,1.0,267,130.34,119.33 -30,1,06.07.2015,1.0,408,130.34,119.33 -30,1,07.07.2015,,91,130.34, -30,1,08.07.2015,,160,130.34, -30,1,09.07.2015,,170,121.64, -30,1,10.07.2015,,418,121.64, -30,1,11.07.2015,,386,123.55, -30,1,12.07.2015,,308,123.55, -30,1,13.07.2015,,302,123.55, -30,1,14.07.2015,,390,123.55, -30,1,15.07.2015,,487,125.45, -30,1,16.07.2015,,496,125.45, -30,1,17.07.2015,,616,125.45, -30,1,18.07.2015,,500,125.45, -30,1,19.07.2015,,515,127.62, -30,1,20.07.2015,,386,127.62, -30,1,21.07.2015,,509,127.62, -30,1,22.07.2015,,449,127.62, -30,1,23.07.2015,,261,127.62, -30,1,24.07.2015,,543,127.62, -30,1,25.07.2015,,446,127.62, 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-30,1,28.10.2015,1.0,361,130.34,124.77 -30,1,29.10.2015,1.0,198,130.34,124.77 -30,1,30.10.2015,1.0,402,130.34,124.77 -30,1,31.10.2015,1.0,254,130.34,124.77 -30,1,01.11.2015,1.0,254,130.34,124.77 -30,1,02.11.2015,1.0,179,130.34,124.77 -30,1,03.11.2015,,167,130.34, -30,1,04.11.2015,,110,134.69, -30,1,05.11.2015,,82,134.69, -30,1,06.11.2015,,123,134.69, -30,1,07.11.2015,,104,134.69, -30,1,08.11.2015,,138,134.69, -30,1,09.11.2015,,123,134.69, -30,1,10.11.2015,,66,134.69, -30,1,11.11.2015,,82,130.34, -30,1,12.11.2015,,101,130.34, -30,1,13.11.2015,,123,130.34, -30,1,14.11.2015,,119,130.34, -30,1,15.11.2015,,126,130.34, -30,1,16.11.2015,,123,130.34, -30,1,17.11.2015,,97,130.34, -30,1,18.11.2015,,101,130.34, -30,1,19.11.2015,,72,130.34, -30,1,20.11.2015,,75,130.34, -30,1,21.11.2015,,129,130.34, -30,1,22.11.2015,,160,130.34, -30,1,23.11.2015,,119,130.34, -30,1,24.11.2015,1.0,575,130.34,116.61 -30,1,25.11.2015,1.0,603,130.34,116.61 -30,1,26.11.2015,1.0,556,130.34,116.61 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-30,1,31.03.2016,,138,141.21, -30,1,01.04.2016,,107,141.21, -30,1,02.04.2016,,75,141.21, -30,1,03.04.2016,,72,141.21, -30,1,04.04.2016,,85,141.21, -30,1,05.04.2016,,60,141.21, -30,1,06.04.2016,,94,141.21, -30,1,07.04.2016,,66,141.21, -30,1,08.04.2016,,113,141.21, -30,1,09.04.2016,,63,141.21, -30,1,10.04.2016,,119,141.21, -30,1,11.04.2016,,60,141.21, -30,1,12.04.2016,,63,141.21, -30,1,13.04.2016,,119,141.21, -30,1,14.04.2016,,72,135.78, -30,1,15.04.2016,,60,135.78, -30,1,16.04.2016,,79,135.78, -30,1,17.04.2016,,69,135.78, -30,1,18.04.2016,,72,135.78, -30,1,19.04.2016,1.0,352,135.78,122.32 -30,1,20.04.2016,1.0,317,135.78,122.32 -30,1,21.04.2016,1.0,160,135.78,122.32 -30,1,22.04.2016,1.0,474,135.78,122.32 -30,1,23.04.2016,1.0,361,135.78,122.32 -30,1,24.04.2016,1.0,179,135.78,122.32 -30,1,25.04.2016,1.0,192,135.78,122.32 -30,1,26.04.2016,,94,135.78, -30,1,27.04.2016,,53,135.78, -30,1,28.04.2016,,82,135.78, -30,1,29.04.2016,,82,131.16, -30,1,30.04.2016,,97,131.16, -30,1,01.05.2016,,72,131.16, -30,1,02.05.2016,,60,131.16, -30,1,03.05.2016,,79,131.16, -30,1,04.05.2016,,63,131.16, -30,1,05.05.2016,,173,131.16, -30,1,06.05.2016,,75,131.16, -30,1,07.05.2016,,119,131.16, -30,1,08.05.2016,,60,131.16, -30,1,09.05.2016,,69,131.16, -30,1,10.05.2016,,148,131.16, -30,1,11.05.2016,,69,131.16, -30,1,12.05.2016,,75,131.16, -30,1,13.05.2016,,85,128.98, -30,1,14.05.2016,,154,128.98, -30,1,15.05.2016,,185,128.98, -30,1,16.05.2016,,132,128.98, -30,1,17.05.2016,1.0,270,128.98,119.6 -30,1,18.05.2016,1.0,355,128.98,119.6 -30,1,19.05.2016,1.0,302,128.98,119.6 -30,1,20.05.2016,1.0,204,128.98,119.6 -30,1,21.05.2016,1.0,531,128.98,119.6 -30,1,22.05.2016,1.0,430,128.98,119.6 -31,1,01.01.2015,,28,163.78, -31,1,02.01.2015,,57,163.78, -31,1,03.01.2015,,101,163.78, -31,1,04.01.2015,,75,163.78, -31,1,05.01.2015,,72,163.78, -31,1,06.01.2015,,116,163.78, -31,1,07.01.2015,,113,163.78, -31,1,08.01.2015,,154,163.78, -31,1,09.01.2015,,88,163.78, -31,1,10.01.2015,,88,163.78, -31,1,11.01.2015,,116,163.78, -31,1,12.01.2015,,107,163.78, -31,1,13.01.2015,,104,163.78, -31,1,14.01.2015,,60,168.4, -31,1,15.01.2015,,116,168.4, -31,1,16.01.2015,,113,168.4, -31,1,17.01.2015,,145,168.4, -31,1,18.01.2015,,94,168.4, -31,1,19.01.2015,,101,168.4, -31,1,20.01.2015,,116,168.4, -31,1,21.01.2015,,119,168.4, -31,1,22.01.2015,,104,168.4, -31,1,23.01.2015,,123,160.24, -31,1,24.01.2015,,119,160.24, -31,1,25.01.2015,,116,160.24, -31,1,26.01.2015,,101,160.24, -31,1,27.01.2015,,91,160.24, -31,1,28.01.2015,,119,146.92, -31,1,29.01.2015,,113,146.92, -31,1,30.01.2015,,91,146.92, -31,1,31.01.2015,,126,146.92, -31,1,01.02.2015,,126,146.92, -31,1,02.02.2015,,101,146.92, -31,1,03.02.2015,,160,146.92, -31,1,04.02.2015,,135,146.92, -31,1,05.02.2015,,75,146.92, -31,1,06.02.2015,,138,146.92, -31,1,07.02.2015,,132,146.92, -31,1,08.02.2015,,110,146.92, -31,1,09.02.2015,,132,146.92, -31,1,10.02.2015,,123,146.92, -31,1,11.02.2015,,148,146.92, -31,1,12.02.2015,,157,146.92, -31,1,13.02.2015,,192,149.37, -31,1,14.02.2015,,113,149.37, -31,1,15.02.2015,,141,149.37, -31,1,16.02.2015,,173,149.37, -31,1,17.02.2015,1.0,242,149.37,143.8 -31,1,18.02.2015,1.0,123,149.37,135.64 -31,1,19.02.2015,1.0,368,149.37,135.64 -31,1,20.02.2015,1.0,327,149.37,135.64 -31,1,21.02.2015,1.0,317,149.37,135.64 -31,1,22.02.2015,1.0,192,149.37,135.64 -31,1,23.02.2015,1.0,239,149.37,135.64 -31,1,24.02.2015,,182,149.37, -31,1,25.02.2015,,173,149.37, -31,1,26.02.2015,,157,149.37, -31,1,27.02.2015,,148,143.93, -31,1,28.02.2015,,195,143.93, -31,1,01.03.2015,,232,143.93, -31,1,02.03.2015,,308,143.93, -31,1,03.03.2015,1.0,173,143.93,138.36 -31,1,04.03.2015,1.0,151,143.93,135.64 -31,1,05.03.2015,1.0,192,138.5,135.64 -31,1,06.03.2015,1.0,236,138.5,135.64 -31,1,07.03.2015,1.0,129,138.5,135.64 -31,1,08.03.2015,1.0,82,138.5,135.64 -31,1,09.03.2015,,135,140.67, -31,1,10.03.2015,,135,140.67, -31,1,11.03.2015,,179,140.67, -31,1,12.03.2015,,185,140.67, -31,1,13.03.2015,,192,140.67, -31,1,14.03.2015,,141,140.67, -31,1,15.03.2015,,185,140.67, -31,1,16.03.2015,,119,140.67, -31,1,17.03.2015,1.0,591,140.67,127.49 -31,1,18.03.2015,1.0,631,140.67,127.49 -31,1,19.03.2015,1.0,522,140.67,127.49 -31,1,20.03.2015,1.0,518,140.67,127.49 -31,1,21.03.2015,1.0,440,140.67,127.49 -31,1,22.03.2015,1.0,496,140.67,127.49 -31,1,23.03.2015,1.0,229,140.67,127.49 -31,1,24.03.2015,,113,140.67, -31,1,25.03.2015,,179,140.67, -31,1,26.03.2015,,248,131.97, -31,1,27.03.2015,,305,131.97, -31,1,28.03.2015,,157,131.97, -31,1,29.03.2015,,154,131.97, -31,1,30.03.2015,,192,131.97, -31,1,31.03.2015,,163,131.97, -31,1,01.04.2015,,292,133.33, -31,1,02.04.2015,,157,133.33, -31,1,03.04.2015,,157,133.33, -31,1,04.04.2015,,167,133.33, -31,1,05.04.2015,,192,133.33, -31,1,06.04.2015,,135,133.33, -31,1,07.04.2015,1.0,873,133.33,119.33 -31,1,08.04.2015,1.0,647,133.33,119.33 -31,1,09.04.2015,1.0,572,133.33,119.33 -31,1,10.04.2015,1.0,515,133.33,119.33 -31,1,11.04.2015,1.0,616,133.33,119.33 -31,1,12.04.2015,1.0,167,133.33,119.33 -31,1,13.04.2015,1.0,239,133.33,119.33 -31,1,14.04.2015,,126,130.34, -31,1,15.04.2015,,176,130.34, -31,1,16.04.2015,,110,130.34, -31,1,17.04.2015,,113,138.5, -31,1,18.04.2015,,113,138.5, -31,1,19.04.2015,,82,138.5, -31,1,20.04.2015,,107,138.5, -31,1,21.04.2015,,107,138.5, -31,1,22.04.2015,,126,128.98, -31,1,23.04.2015,,107,128.98, -31,1,24.04.2015,,182,128.98, -31,1,25.04.2015,,113,128.98, -31,1,26.04.2015,,135,128.98, -31,1,27.04.2015,,129,128.98, -31,1,28.04.2015,,129,128.98, -31,1,29.04.2015,,157,131.7, -31,1,30.04.2015,,141,131.7, -31,1,01.05.2015,,129,131.7, -31,1,02.05.2015,,129,131.7, -31,1,03.05.2015,,97,131.7, -31,1,04.05.2015,,160,131.7, -31,1,05.05.2015,,132,131.7, -31,1,06.05.2015,,145,131.7, -31,1,07.05.2015,,132,131.7, -31,1,08.05.2015,,157,131.7, -31,1,09.05.2015,,79,131.7, -31,1,10.05.2015,,126,131.7, -31,1,11.05.2015,,116,131.7, -31,1,12.05.2015,,160,131.7, -31,1,13.05.2015,,163,131.7, -31,1,14.05.2015,,126,131.7, -31,1,15.05.2015,,119,131.7, -31,1,16.05.2015,,151,131.7, -31,1,17.05.2015,,135,131.7, -31,1,18.05.2015,,132,131.7, -31,1,19.05.2015,1.0,302,131.7,119.33 -31,1,20.05.2015,1.0,358,131.7,119.33 -31,1,21.05.2015,1.0,346,131.7,119.33 -31,1,22.05.2015,1.0,226,131.7,119.33 -31,1,23.05.2015,1.0,405,131.7,119.33 -31,1,24.05.2015,1.0,163,131.7,119.33 -31,1,25.05.2015,1.0,251,131.7,119.33 -31,1,26.05.2015,,94,131.7, -31,1,27.05.2015,,107,129.25, -31,1,28.05.2015,,132,129.25, -31,1,29.05.2015,,167,129.25, -31,1,30.05.2015,,135,129.25, -31,1,31.05.2015,,163,129.25, -31,1,01.06.2015,,167,129.25, -31,1,02.06.2015,,104,129.25, -31,1,03.06.2015,,145,129.25, -31,1,04.06.2015,,129,129.25, -31,1,05.06.2015,,163,129.25, -31,1,06.06.2015,,129,131.7, -31,1,07.06.2015,,94,131.7, -31,1,08.06.2015,,132,131.7, -31,1,09.06.2015,1.0,531,131.7,119.33 -31,1,10.06.2015,1.0,594,131.7,119.33 -31,1,11.06.2015,1.0,594,131.7,119.33 -31,1,12.06.2015,1.0,298,131.7,119.33 -31,1,13.06.2015,1.0,254,131.7,119.33 -31,1,14.06.2015,1.0,220,131.7,119.33 -31,1,15.06.2015,1.0,283,131.7,119.33 -31,1,16.06.2015,,126,131.7, -31,1,17.06.2015,,126,131.7, -31,1,18.06.2015,,207,128.17, -31,1,19.06.2015,,214,128.17, -31,1,20.06.2015,,188,128.17, -31,1,21.06.2015,,226,128.17, -31,1,22.06.2015,,182,128.17, -31,1,23.06.2015,,292,128.17, -31,1,24.06.2015,,173,128.17, -31,1,25.06.2015,,258,128.17, -31,1,26.06.2015,,302,128.17, -31,1,27.06.2015,,204,128.17, -31,1,28.06.2015,,226,130.34, -31,1,29.06.2015,,220,130.34, -31,1,30.06.2015,1.0,437,130.34,119.33 -31,1,01.07.2015,1.0,478,130.34,119.33 -31,1,02.07.2015,1.0,446,130.34,119.33 -31,1,03.07.2015,1.0,308,130.34,119.33 -31,1,04.07.2015,1.0,352,130.34,119.33 -31,1,05.07.2015,1.0,396,130.34,119.33 -31,1,06.07.2015,1.0,412,130.34,119.33 -31,1,07.07.2015,,254,130.34, -31,1,08.07.2015,,182,130.34, -31,1,09.07.2015,,173,121.64, -31,1,10.07.2015,,220,121.64, -31,1,11.07.2015,,258,123.55, -31,1,12.07.2015,,276,123.55, -31,1,13.07.2015,,346,123.55, -31,1,14.07.2015,,324,123.55, -31,1,15.07.2015,,358,125.45, -31,1,16.07.2015,,214,125.45, -31,1,17.07.2015,,339,125.45, -31,1,18.07.2015,,522,125.45, -31,1,19.07.2015,,452,127.62, -31,1,20.07.2015,,481,127.62, -31,1,21.07.2015,,644,127.62, -31,1,22.07.2015,,333,127.62, -31,1,23.07.2015,,559,127.62, -31,1,24.07.2015,,478,127.62, -31,1,25.07.2015,,383,127.62, -31,1,26.07.2015,,506,130.07, -31,1,27.07.2015,,276,130.07, -31,1,28.07.2015,1.0,317,130.07,124.77 -31,1,29.07.2015,1.0,537,130.07,124.77 -31,1,30.07.2015,1.0,509,130.07,124.77 -31,1,31.07.2015,1.0,443,130.07,124.77 -31,1,01.08.2015,1.0,283,130.07,124.77 -31,1,02.08.2015,1.0,339,130.07,124.77 -31,1,03.08.2015,1.0,424,130.07,124.77 -31,1,04.08.2015,,286,138.5, -31,1,05.08.2015,,204,138.5, -31,1,06.08.2015,,251,138.5, -31,1,07.08.2015,,151,138.5, -31,1,08.08.2015,,214,141.21, -31,1,09.08.2015,,217,141.21, -31,1,10.08.2015,,198,141.21, -31,1,11.08.2015,,258,141.21, -31,1,12.08.2015,,217,142.85, -31,1,13.08.2015,,220,142.85, -31,1,14.08.2015,,138,142.85, -31,1,15.08.2015,,170,142.85, -31,1,16.08.2015,,267,142.85, -31,1,17.08.2015,,320,142.85, -31,1,18.08.2015,,261,142.85, -31,1,19.08.2015,,157,142.85, -31,1,20.08.2015,,110,142.85, -31,1,21.08.2015,,151,142.85, -31,1,22.08.2015,,179,142.85, -31,1,23.08.2015,,195,142.85, -31,1,24.08.2015,,198,142.85, -31,1,25.08.2015,,254,142.85, -31,1,26.08.2015,,185,142.85, -31,1,27.08.2015,,245,137.68, -31,1,28.08.2015,,145,137.68, -31,1,29.08.2015,,157,137.68, -31,1,30.08.2015,,170,137.68, -31,1,31.08.2015,,167,137.68, -31,1,01.09.2015,,182,137.68, -31,1,02.09.2015,,220,135.78, -31,1,03.09.2015,,204,135.78, -31,1,04.09.2015,,154,135.78, -31,1,05.09.2015,,210,133.6, -31,1,06.09.2015,,185,133.6, -31,1,07.09.2015,,280,133.6, -31,1,08.09.2015,,182,133.6, -31,1,09.09.2015,,286,135.51, -31,1,10.09.2015,,261,135.51, -31,1,11.09.2015,,456,135.51, -31,1,12.09.2015,,220,135.51, -31,1,13.09.2015,,179,135.51, -31,1,14.09.2015,,226,135.51, -31,1,15.09.2015,,195,135.51, -31,1,16.09.2015,,254,138.5, -31,1,17.09.2015,,135,138.5, -31,1,18.09.2015,,135,138.5, -31,1,19.09.2015,,138,138.5, -31,1,20.09.2015,,163,138.5, -31,1,21.09.2015,,207,138.5, -31,1,22.09.2015,,188,138.5, -31,1,23.09.2015,,182,141.21, -31,1,24.09.2015,,119,141.21, -31,1,25.09.2015,,160,141.21, -31,1,26.09.2015,,104,141.21, -31,1,27.09.2015,,123,141.21, -31,1,28.09.2015,,154,141.21, -31,1,29.09.2015,,173,141.21, -31,1,30.09.2015,,132,141.21, -31,1,01.10.2015,,170,141.21, -31,1,02.10.2015,,126,141.21, -31,1,03.10.2015,,157,141.21, -31,1,04.10.2015,,85,141.21, -31,1,05.10.2015,,91,141.21, -31,1,06.10.2015,1.0,386,141.21,132.92 -31,1,07.10.2015,1.0,267,141.21,130.21 -31,1,08.10.2015,1.0,185,141.11,130.21 -31,1,09.10.2015,1.0,195,141.21,130.21 -31,1,10.10.2015,1.0,239,141.21,130.21 -31,1,11.10.2015,1.0,239,141.21,130.21 -31,1,12.10.2015,1.0,503,141.21,130.21 -31,1,13.10.2015,,176,141.21, -31,1,14.10.2015,,135,141.21, -31,1,15.10.2015,,135,142.57, -31,1,16.10.2015,,88,142.57, -31,1,17.10.2015,,113,142.57, -31,1,18.10.2015,,85,142.57, -31,1,19.10.2015,,113,142.57, -31,1,20.10.2015,,138,142.57, -31,1,21.10.2015,,242,130.34, -31,1,22.10.2015,,167,130.34, -31,1,23.10.2015,,151,130.34, -31,1,24.10.2015,,195,130.34, -31,1,25.10.2015,,154,130.34, -31,1,26.10.2015,,160,130.34, -31,1,27.10.2015,1.0,295,130.34,122.32 -31,1,28.10.2015,1.0,223,130.34,124.77 -31,1,29.10.2015,1.0,267,130.34,124.77 -31,1,30.10.2015,1.0,286,130.34,124.77 -31,1,31.10.2015,1.0,242,130.34,124.77 -31,1,01.11.2015,1.0,188,130.34,124.77 -31,1,02.11.2015,1.0,188,130.34,124.77 -31,1,03.11.2015,,113,130.34, -31,1,04.11.2015,,119,134.69, -31,1,05.11.2015,,72,134.69, -31,1,06.11.2015,,97,134.69, -31,1,07.11.2015,,94,134.69, -31,1,08.11.2015,,123,134.69, -31,1,09.11.2015,,113,134.69, -31,1,10.11.2015,,141,134.69, -31,1,11.11.2015,,173,130.34, -31,1,12.11.2015,,151,130.34, -31,1,13.11.2015,,173,130.34, -31,1,14.11.2015,,135,130.34, -31,1,15.11.2015,,167,130.34, -31,1,16.11.2015,,104,130.34, -31,1,17.11.2015,,85,130.34, -31,1,18.11.2015,,104,130.34, -31,1,19.11.2015,,57,130.34, -31,1,20.11.2015,,141,130.34, -31,1,21.11.2015,,141,130.34, -31,1,22.11.2015,,154,130.34, -31,1,23.11.2015,,129,130.34, -31,1,24.11.2015,1.0,349,130.34,116.61 -31,1,25.11.2015,1.0,518,130.34,116.61 -31,1,26.11.2015,1.0,550,130.34,116.61 -31,1,27.11.2015,1.0,506,130.34,116.61 -31,1,28.11.2015,1.0,506,130.34,116.61 -31,1,29.11.2015,1.0,276,130.34,116.61 -31,1,30.11.2015,1.0,327,130.34,116.61 -31,1,01.12.2015,,123,130.34, -31,1,02.12.2015,,91,130.34, -31,1,03.12.2015,,79,130.34, -31,1,04.12.2015,,82,130.34, -31,1,05.12.2015,1.0,245,130.34,116.61 -31,1,06.12.2015,1.0,245,130.34,116.61 -31,1,07.12.2015,1.0,210,130.34,116.61 -31,1,08.12.2015,,69,130.34, -31,1,09.12.2015,,79,130.34, -31,1,10.12.2015,,88,130.34, -31,1,11.12.2015,,141,130.34, -31,1,12.12.2015,,60,130.34, -31,1,13.12.2015,,145,130.34, -31,1,14.12.2015,,119,130.34, -31,1,15.12.2015,,113,130.34, -31,1,16.12.2015,,148,130.34, -31,1,17.12.2015,,104,130.34, -31,1,18.12.2015,,85,130.34, -31,1,19.12.2015,,141,130.34, -31,1,20.12.2015,,97,130.34, -31,1,21.12.2015,,145,130.34, -31,1,22.12.2015,,148,130.34, -31,1,23.12.2015,,110,130.34, -31,1,24.12.2015,,129,126.54, -31,1,25.12.2015,,148,126.54, -31,1,26.12.2015,,154,126.54, -31,1,27.12.2015,,104,126.54, -31,1,28.12.2015,,129,126.54, -31,1,29.12.2015,,167,126.54, -31,1,30.12.2015,,192,126.54, -31,1,31.12.2015,,123,126.54, -31,1,01.01.2016,,47,126.54, -31,1,02.01.2016,,85,126.54, -31,1,03.01.2016,,123,126.54, -31,1,04.01.2016,,145,126.54, -31,1,05.01.2016,,107,126.54, -31,1,06.01.2016,,129,126.54, -31,1,07.01.2016,,145,126.54, -31,1,08.01.2016,,154,126.54, -31,1,09.01.2016,,145,126.54, -31,1,10.01.2016,,157,126.54, -31,1,11.01.2016,,280,126.54, -31,1,12.01.2016,,236,126.54, -31,1,13.01.2016,,119,126.54, -31,1,14.01.2016,,141,126.54, -31,1,15.01.2016,,141,126.54, -31,1,16.01.2016,,141,126.54, -31,1,17.01.2016,,141,126.54, -31,1,18.01.2016,,141,126.54, -31,1,19.01.2016,,141,126.54, -31,1,20.01.2016,,141,126.54, -31,1,21.01.2016,,141,127.9, -31,1,22.01.2016,,141,127.9, -31,1,23.01.2016,,141,127.9, -31,1,24.01.2016,,141,127.9, -31,1,25.01.2016,,141,127.9, -31,1,26.01.2016,,141,127.9, -31,1,27.01.2016,,141,127.9, -31,1,28.01.2016,,141,127.9, -31,1,29.01.2016,,283,127.9, -31,1,30.01.2016,,292,127.9, -31,1,31.01.2016,,223,127.9, -31,1,01.02.2016,,210,130.34, -31,1,02.02.2016,,151,130.34, -31,1,03.02.2016,,154,130.34, -31,1,04.02.2016,,157,133.06, -31,1,05.02.2016,,157,133.06, -31,1,06.02.2016,,163,133.06, -31,1,07.02.2016,,123,141.76, -31,1,08.02.2016,,116,141.76, -31,1,09.02.2016,1.0,110,141.76,130.75 -31,1,10.02.2016,1.0,148,141.76,130.75 -31,1,11.02.2016,1.0,358,141.76,130.75 -31,1,12.02.2016,1.0,270,141.76,130.75 -31,1,13.02.2016,1.0,185,141.76,130.75 -31,1,14.02.2016,1.0,214,141.76,130.75 -31,1,15.02.2016,1.0,145,141.76,130.75 -31,1,16.02.2016,1.0,226,141.76,130.75 -31,1,17.02.2016,1.0,273,141.76,130.75 -31,1,18.02.2016,,160,141.76, -31,1,19.02.2016,,97,141.76, -31,1,20.02.2016,,94,141.76, -31,1,21.02.2016,,104,141.76, -31,1,22.02.2016,,91,141.76, -31,1,23.02.2016,,110,141.76, -31,1,24.02.2016,,101,141.76, -31,1,25.02.2016,,173,141.76, -31,1,26.02.2016,,97,141.76, -31,1,27.02.2016,,129,141.76, -31,1,28.02.2016,,126,141.76, -31,1,29.02.2016,,126,141.76, -31,1,01.03.2016,,94,141.76, -31,1,02.03.2016,,91,141.76, -31,1,03.03.2016,,110,141.76, -31,1,04.03.2016,,132,141.76, -31,1,05.03.2016,,97,141.21, -31,1,06.03.2016,,135,141.21, -31,1,07.03.2016,,116,141.21, -31,1,08.03.2016,1.0,192,141.21,132.92 -31,1,09.03.2016,1.0,217,141.21,132.92 -31,1,10.03.2016,1.0,236,141.21,132.92 -31,1,11.03.2016,1.0,286,141.21,132.92 -31,1,12.03.2016,1.0,214,141.21,132.92 -31,1,13.03.2016,1.0,223,141.21,132.92 -31,1,14.03.2016,1.0,305,141.21,132.92 -31,1,15.03.2016,1.0,342,141.21,130.21 -31,1,16.03.2016,1.0,242,141.21,130.21 -31,1,17.03.2016,1.0,339,141.21,130.21 -31,1,18.03.2016,1.0,201,141.21,130.21 -31,1,19.03.2016,1.0,251,141.21,130.21 -31,1,20.03.2016,,113,141.21, -31,1,21.03.2016,,79,141.21, -31,1,22.03.2016,,157,141.21, -31,1,23.03.2016,,85,141.21, -31,1,24.03.2016,,132,141.21, -31,1,25.03.2016,,94,141.21, -31,1,26.03.2016,,75,141.21, -31,1,27.03.2016,,157,141.21, -31,1,28.03.2016,,113,141.21, -31,1,29.03.2016,,91,141.21, -31,1,30.03.2016,,126,141.21, -31,1,31.03.2016,,91,141.21, -31,1,01.04.2016,,101,141.21, -31,1,02.04.2016,,138,141.21, -31,1,03.04.2016,,157,141.21, -31,1,04.04.2016,,110,141.21, -31,1,05.04.2016,,119,141.21, -31,1,06.04.2016,,97,141.21, -31,1,07.04.2016,,110,141.21, -31,1,08.04.2016,,151,141.21, -31,1,09.04.2016,,91,141.21, -31,1,10.04.2016,,113,141.21, -31,1,11.04.2016,,154,141.21, -31,1,12.04.2016,,91,141.21, -31,1,13.04.2016,,104,141.21, -31,1,14.04.2016,,157,135.78, -31,1,15.04.2016,,195,135.78, -31,1,16.04.2016,,207,135.78, -31,1,17.04.2016,,182,135.78, -31,1,18.04.2016,,123,135.78, -31,1,19.04.2016,1.0,405,135.78,122.32 -31,1,20.04.2016,1.0,471,135.78,122.32 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-31,1,21.05.2016,1.0,223,128.98,119.6 -31,1,22.05.2016,1.0,371,128.98,119.6 -32,1,01.01.2015,,9,163.78, -32,1,02.01.2015,,31,163.78, -32,1,03.01.2015,,57,163.78, -32,1,04.01.2015,,47,163.78, -32,1,05.01.2015,,63,163.78, -32,1,06.01.2015,,57,163.78, -32,1,07.01.2015,,47,163.78, -32,1,08.01.2015,,135,163.78, -32,1,09.01.2015,,60,163.78, -32,1,10.01.2015,,75,163.78, -32,1,11.01.2015,,47,163.78, -32,1,12.01.2015,,94,163.78, -32,1,13.01.2015,,101,163.78, -32,1,14.01.2015,,57,168.4, -32,1,15.01.2015,,53,168.4, -32,1,16.01.2015,,38,168.4, -32,1,17.01.2015,,82,168.4, -32,1,18.01.2015,,47,168.4, -32,1,19.01.2015,,66,168.4, -32,1,20.01.2015,,72,168.4, -32,1,21.01.2015,,60,168.4, -32,1,22.01.2015,,47,168.4, -32,1,23.01.2015,,69,160.24, -32,1,24.01.2015,,75,160.24, -32,1,25.01.2015,,60,160.24, -32,1,26.01.2015,,138,160.24, -32,1,27.01.2015,,151,160.24, -32,1,28.01.2015,,182,146.92, -32,1,29.01.2015,,204,146.92, -32,1,30.01.2015,,204,146.92, -32,1,31.01.2015,,167,146.92, 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-32,1,02.05.2015,,101,131.7, -32,1,03.05.2015,,141,131.7, -32,1,04.05.2015,,210,131.7, -32,1,05.05.2015,,167,131.7, -32,1,06.05.2015,,226,131.7, -32,1,07.05.2015,,141,131.7, -32,1,08.05.2015,,217,131.7, -32,1,09.05.2015,,126,131.7, -32,1,10.05.2015,,132,131.7, -32,1,11.05.2015,,82,131.7, -32,1,12.05.2015,,148,131.7, -32,1,13.05.2015,,135,131.7, -32,1,14.05.2015,,151,131.7, -32,1,15.05.2015,,141,131.7, -32,1,16.05.2015,,264,131.7, -32,1,17.05.2015,,126,131.7, -32,1,18.05.2015,,232,131.7, -32,1,19.05.2015,1.0,977,131.7,119.33 -32,1,20.05.2015,1.0,1081,131.7,119.33 -32,1,21.05.2015,1.0,773,131.7,119.33 -32,1,22.05.2015,1.0,584,131.7,119.33 -32,1,23.05.2015,1.0,565,131.7,119.33 -32,1,24.05.2015,1.0,421,131.7,119.33 -32,1,25.05.2015,1.0,565,131.7,119.33 -32,1,26.05.2015,,79,131.7, -32,1,27.05.2015,,182,129.25, -32,1,28.05.2015,,148,129.25, -32,1,29.05.2015,,126,129.25, -32,1,30.05.2015,,101,129.25, -32,1,31.05.2015,,123,129.25, -32,1,01.06.2015,,132,129.25, -32,1,02.06.2015,,85,129.25, -32,1,03.06.2015,,116,129.25, -32,1,04.06.2015,,210,129.25, -32,1,05.06.2015,,141,129.25, -32,1,06.06.2015,,126,131.7, -32,1,07.06.2015,,113,131.7, -32,1,08.06.2015,,135,131.7, -32,1,09.06.2015,1.0,603,131.7,119.33 -32,1,10.06.2015,1.0,977,131.7,119.33 -32,1,11.06.2015,1.0,898,131.7,119.33 -32,1,12.06.2015,1.0,597,131.7,119.33 -32,1,13.06.2015,1.0,355,131.7,119.33 -32,1,14.06.2015,1.0,826,131.7,119.33 -32,1,15.06.2015,1.0,487,131.7,119.33 -32,1,16.06.2015,,226,131.7, -32,1,17.06.2015,,163,131.7, -32,1,18.06.2015,,214,128.17, -32,1,19.06.2015,,188,128.17, -32,1,20.06.2015,,160,128.17, -32,1,21.06.2015,,163,128.17, -32,1,22.06.2015,,258,128.17, -32,1,23.06.2015,,283,128.17, -32,1,24.06.2015,,289,128.17, -32,1,25.06.2015,,270,128.17, -32,1,26.06.2015,,239,128.17, -32,1,27.06.2015,,179,128.17, -32,1,28.06.2015,,273,130.34, -32,1,29.06.2015,,167,130.34, -32,1,30.06.2015,1.0,1379,130.34,119.33 -32,1,01.07.2015,1.0,898,130.34,119.33 -32,1,02.07.2015,1.0,685,130.34,119.33 -32,1,03.07.2015,1.0,999,130.34,119.33 -32,1,04.07.2015,1.0,440,130.34,119.33 -32,1,05.07.2015,1.0,487,130.34,119.33 -32,1,06.07.2015,1.0,553,130.34,119.33 -32,1,07.07.2015,,179,130.34, -32,1,08.07.2015,,192,130.34, -32,1,09.07.2015,,424,121.64, -32,1,10.07.2015,,490,121.64, -32,1,11.07.2015,,314,123.55, -32,1,12.07.2015,,267,123.55, -32,1,13.07.2015,,314,123.55, -32,1,14.07.2015,,314,123.55, -32,1,15.07.2015,,261,125.45, -32,1,16.07.2015,,437,125.45, -32,1,17.07.2015,,412,125.45, -32,1,18.07.2015,,377,127.62, -32,1,19.07.2015,,361,127.62, -32,1,20.07.2015,,534,127.62, -32,1,21.07.2015,,320,127.62, -32,1,22.07.2015,,537,127.62, -32,1,23.07.2015,,374,127.62, -32,1,24.07.2015,,704,127.62, -32,1,25.07.2015,,481,127.62, -32,1,26.07.2015,,320,130.07, -32,1,27.07.2015,,311,130.07, -32,1,28.07.2015,1.0,650,130.07,124.77 -32,1,29.07.2015,1.0,701,130.07,124.77 -32,1,30.07.2015,1.0,898,130.07,124.77 -32,1,31.07.2015,1.0,1213,130.07,124.77 -32,1,01.08.2015,1.0,2708,130.07,124.77 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-32,1,03.09.2015,,393,135.78, -32,1,04.09.2015,,308,135.78, -32,1,05.09.2015,,408,133.6, -32,1,06.09.2015,,440,133.6, -32,1,07.09.2015,,490,133.6, -32,1,08.09.2015,,578,133.6, -32,1,09.09.2015,,518,135.51, -32,1,10.09.2015,,311,135.51, -32,1,11.09.2015,,396,135.51, -32,1,12.09.2015,,697,135.51, -32,1,13.09.2015,,336,135.51, -32,1,14.09.2015,,465,135.51, -32,1,15.09.2015,,465,135.51, -32,1,16.09.2015,,201,138.5, -32,1,17.09.2015,,232,138.5, -32,1,18.09.2015,,236,138.5, -32,1,19.09.2015,,157,138.5, -32,1,20.09.2015,,160,138.5, -32,1,21.09.2015,,286,138.5, -32,1,22.09.2015,,185,138.5, -32,1,23.09.2015,,248,141.21, -32,1,24.09.2015,,192,141.21, -32,1,25.09.2015,,192,141.21, -32,1,26.09.2015,,229,141.21, -32,1,27.09.2015,,195,141.21, -32,1,28.09.2015,,210,141.21, -32,1,29.09.2015,,179,141.21, -32,1,30.09.2015,,226,141.21, -32,1,01.10.2015,,207,141.21, -32,1,02.10.2015,,160,141.21, -32,1,03.10.2015,,207,141.21, -32,1,04.10.2015,,176,141.21, -32,1,05.10.2015,,182,141.21, -32,1,06.10.2015,1.0,456,141.21,132.92 -32,1,07.10.2015,1.0,452,141.21,130.21 -32,1,08.10.2015,1.0,694,141.11,130.21 -32,1,09.10.2015,1.0,565,141.21,130.21 -32,1,10.10.2015,1.0,443,141.21,130.21 -32,1,11.10.2015,1.0,264,141.21,130.21 -32,1,12.10.2015,1.0,358,141.21,130.21 -32,1,13.10.2015,,195,141.21, -32,1,14.10.2015,,185,141.21, -32,1,15.10.2015,,104,142.57, -32,1,16.10.2015,,160,142.57, -32,1,17.10.2015,,116,142.57, -32,1,18.10.2015,,116,142.57, -32,1,19.10.2015,,101,142.57, -32,1,20.10.2015,,97,142.57, -32,1,21.10.2015,,361,130.34, -32,1,22.10.2015,,424,130.34, -32,1,23.10.2015,,330,130.34, -32,1,24.10.2015,,427,130.34, -32,1,25.10.2015,,289,130.34, -32,1,26.10.2015,,188,130.34, -32,1,27.10.2015,1.0,1213,130.34,122.32 -32,1,28.10.2015,1.0,1232,130.34,122.32 -32,1,29.10.2015,1.0,829,130.34,122.32 -32,1,30.10.2015,1.0,437,130.34,122.32 -32,1,31.10.2015,1.0,738,130.34,122.32 -32,1,01.11.2015,1.0,364,130.34,122.32 -32,1,02.11.2015,1.0,349,130.34,122.32 -32,1,03.11.2015,,239,130.34, -32,1,04.11.2015,,119,134.69, -32,1,05.11.2015,,110,134.69, -32,1,06.11.2015,,97,134.69, -32,1,07.11.2015,,113,134.69, -32,1,08.11.2015,,167,134.69, -32,1,09.11.2015,,132,134.69, -32,1,10.11.2015,,179,134.69, -32,1,11.11.2015,,132,130.34, -32,1,12.11.2015,,185,130.34, -32,1,13.11.2015,,210,130.34, -32,1,14.11.2015,,198,130.34, -32,1,15.11.2015,,182,130.34, -32,1,16.11.2015,,167,130.34, -32,1,17.11.2015,,182,130.34, -32,1,18.11.2015,,273,130.34, -32,1,19.11.2015,,317,130.34, -32,1,20.11.2015,,179,130.34, -32,1,21.11.2015,,264,130.34, -32,1,22.11.2015,,302,130.34, -32,1,23.11.2015,,145,130.34, -32,1,24.11.2015,1.0,3292,130.34,116.61 -32,1,25.11.2015,1.0,3396,130.34,116.61 -32,1,26.11.2015,1.0,946,130.34,116.61 -32,1,27.11.2015,1.0,993,130.34,116.61 -32,1,28.11.2015,1.0,738,130.34,116.61 -32,1,29.11.2015,1.0,1178,130.34,116.61 -32,1,30.11.2015,1.0,2209,130.34,116.61 -32,1,01.12.2015,,148,130.34, -32,1,02.12.2015,,119,130.34, -32,1,03.12.2015,,126,130.34, -32,1,04.12.2015,,110,130.34, 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-32,1,06.01.2016,,173,126.54, -32,1,07.01.2016,,123,126.54, -32,1,08.01.2016,,116,126.54, -32,1,09.01.2016,,151,126.54, -32,1,10.01.2016,,138,126.54, -32,1,11.01.2016,,195,126.54, -32,1,12.01.2016,,198,126.54, -32,1,13.01.2016,,145,126.54, -32,1,14.01.2016,,173,126.54, -32,1,15.01.2016,,292,126.54, -32,1,16.01.2016,,201,126.54, -32,1,17.01.2016,,138,126.54, -32,1,18.01.2016,,198,126.54, -32,1,19.01.2016,,173,126.54, -32,1,20.01.2016,,232,126.54, -32,1,21.01.2016,,242,127.9, -32,1,22.01.2016,,195,127.9, -32,1,23.01.2016,,324,127.9, -32,1,24.01.2016,,232,127.9, -32,1,25.01.2016,,223,127.9, -32,1,26.01.2016,,264,127.9, -32,1,27.01.2016,,173,127.9, -32,1,28.01.2016,,223,127.9, -32,1,29.01.2016,,361,127.9, -32,1,30.01.2016,,317,127.9, -32,1,31.01.2016,,195,127.9, -32,1,01.02.2016,,273,130.34, -32,1,02.02.2016,,173,130.34, -32,1,03.02.2016,,261,130.34, -32,1,04.02.2016,,123,133.06, -32,1,05.02.2016,,245,133.06, -32,1,06.02.2016,,424,133.06, -32,1,07.02.2016,,79,141.76, 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-34,1,05.08.2015,,776,138.5, -34,1,06.08.2015,,1065,138.5, -34,1,07.08.2015,,864,138.5, -34,1,08.08.2015,,977,141.21, -34,1,09.08.2015,,694,141.21, -34,1,10.08.2015,,974,141.21, -34,1,11.08.2015,,1194,141.21, -34,1,12.08.2015,,1257,142.85, -34,1,13.08.2015,,757,142.85, -34,1,14.08.2015,,1253,142.85, -34,1,15.08.2015,,625,142.85, -34,1,16.08.2015,,795,142.85, -34,1,17.08.2015,,704,142.85, -34,1,18.08.2015,,917,142.85, -34,1,19.08.2015,,760,142.85, -34,1,20.08.2015,,440,142.85, -34,1,21.08.2015,,1106,142.85, -34,1,22.08.2015,,763,142.85, -34,1,23.08.2015,,908,142.85, -34,1,24.08.2015,,801,142.85, -34,1,25.08.2015,,1524,142.85, -34,1,26.08.2015,,1134,142.85, -34,1,27.08.2015,,1332,137.68, -34,1,28.08.2015,,817,137.68, -34,1,29.08.2015,,889,137.68, -34,1,30.08.2015,,930,137.68, -34,1,31.08.2015,,1037,137.68, -34,1,01.09.2015,,1096,137.68, -34,1,02.09.2015,,1630,135.78, -34,1,03.09.2015,,1766,135.78, -34,1,04.09.2015,,1668,135.78, -34,1,05.09.2015,,757,133.6, -34,1,06.09.2015,,1854,133.6, -34,1,07.09.2015,,1106,133.6, -34,1,08.09.2015,,2183,133.6, -34,1,09.09.2015,,1279,135.51, -34,1,10.09.2015,,1357,135.51, -34,1,11.09.2015,,1505,135.51, -34,1,12.09.2015,,1866,135.51, -34,1,13.09.2015,,1505,135.51, -34,1,14.09.2015,,1517,135.51, -34,1,15.09.2015,,2303,135.51, -34,1,16.09.2015,,1232,138.5, -34,1,17.09.2015,,983,138.5, -34,1,18.09.2015,,1137,138.5, -34,1,19.09.2015,,795,138.5, -34,1,20.09.2015,,616,138.5, -34,1,21.09.2015,,738,138.5, -34,1,22.09.2015,,829,138.5, -34,1,23.09.2015,,738,141.21, -34,1,24.09.2015,,723,141.21, -34,1,25.09.2015,,1103,141.21, -34,1,26.09.2015,,939,141.21, -34,1,27.09.2015,,980,141.21, -34,1,28.09.2015,,773,141.21, -34,1,29.09.2015,,848,141.21, -34,1,30.09.2015,,666,141.21, -34,1,01.10.2015,,738,141.21, -34,1,02.10.2015,,1049,141.21, -34,1,03.10.2015,,487,141.21, -34,1,04.10.2015,,594,141.21, -34,1,05.10.2015,,657,141.21, -34,1,06.10.2015,1.0,2450,141.21,132.92 -34,1,07.10.2015,1.0,1989,141.21,130.21 -34,1,08.10.2015,1.0,2080,141.11,130.21 -34,1,09.10.2015,1.0,2435,141.21,130.21 -34,1,10.10.2015,1.0,1103,141.21,130.21 -34,1,11.10.2015,1.0,726,141.21,130.21 -34,1,12.10.2015,1.0,1514,141.21,130.21 -34,1,13.10.2015,,421,141.21, -34,1,14.10.2015,,478,141.21, -34,1,15.10.2015,,870,142.57, -34,1,16.10.2015,,1005,142.57, -34,1,17.10.2015,,440,142.57, -34,1,18.10.2015,,503,142.57, -34,1,19.10.2015,,459,142.57, -34,1,20.10.2015,,597,142.57, -34,1,21.10.2015,,1580,130.34, -34,1,22.10.2015,,2391,130.34, -34,1,23.10.2015,,1338,130.34, -34,1,24.10.2015,,361,130.34, -34,1,25.10.2015,,936,130.34, -34,1,26.10.2015,,1018,130.34, -34,1,27.10.2015,1.0,2695,130.34,122.32 -34,1,28.10.2015,1.0,3239,130.34,124.77 -34,1,29.10.2015,1.0,2309,130.34,124.77 -34,1,30.10.2015,1.0,1866,130.34,124.77 -34,1,31.10.2015,1.0,368,130.34,124.77 -34,1,01.11.2015,1.0,1630,130.34,124.77 -34,1,02.11.2015,1.0,239,130.34,124.77 -34,1,03.11.2015,,1153,130.34, -34,1,04.11.2015,,462,134.69, -34,1,05.11.2015,,437,134.69, -34,1,06.11.2015,,468,134.69, 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-34,1,07.12.2015,1.0,565,130.34,116.61 -34,1,08.12.2015,,248,130.34, -34,1,09.12.2015,,462,130.34, -34,1,10.12.2015,,650,130.34, -34,1,11.12.2015,,452,130.34, -34,1,12.12.2015,,408,130.34, -34,1,13.12.2015,,701,130.34, -34,1,14.12.2015,,361,130.34, -34,1,15.12.2015,,732,130.34, -34,1,16.12.2015,,688,130.34, -34,1,17.12.2015,,465,130.34, -34,1,18.12.2015,,437,130.34, -34,1,19.12.2015,,245,130.34, -34,1,20.12.2015,,641,130.34, -34,1,21.12.2015,,229,130.34, -34,1,22.12.2015,,424,130.34, -34,1,23.12.2015,,522,130.34, -34,1,24.12.2015,,371,126.54, -34,1,25.12.2015,,851,126.54, -34,1,26.12.2015,,782,126.54, -34,1,27.12.2015,,443,126.54, -34,1,28.12.2015,,707,126.54, -34,1,29.12.2015,,877,126.54, -34,1,30.12.2015,,559,126.54, -34,1,31.12.2015,,471,126.54, -34,1,01.01.2016,,31,126.54, -34,1,02.01.2016,,280,126.54, -34,1,03.01.2016,,302,126.54, -34,1,04.01.2016,,430,126.54, -34,1,05.01.2016,,484,126.54, -34,1,06.01.2016,,587,126.54, -34,1,07.01.2016,,434,126.54, -34,1,08.01.2016,,735,126.54, 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-36,1,16.09.2015,,474,138.5, -36,1,17.09.2015,,751,138.5, -36,1,18.09.2015,,434,138.5, -36,1,19.09.2015,,547,138.5, -36,1,20.09.2015,,1341,138.5, -36,1,21.09.2015,,292,138.5, -36,1,22.09.2015,,330,138.5, -36,1,23.09.2015,,245,141.21, -36,1,24.09.2015,,135,141.21, -36,1,25.09.2015,,207,141.21, -36,1,26.09.2015,,528,141.21, -36,1,27.09.2015,,418,141.21, -36,1,28.09.2015,,289,141.21, -36,1,29.09.2015,,254,141.21, -36,1,30.09.2015,,179,141.21, -36,1,01.10.2015,,336,141.21, -36,1,02.10.2015,,236,141.21, -36,1,03.10.2015,,264,141.21, -36,1,04.10.2015,,358,141.21, -36,1,05.10.2015,,173,141.21, -36,1,06.10.2015,1.0,792,141.21,132.92 -36,1,07.10.2015,1.0,694,141.21,130.21 -36,1,08.10.2015,1.0,754,141.11,130.21 -36,1,09.10.2015,1.0,305,141.21,130.21 -36,1,10.10.2015,1.0,587,141.21,130.21 -36,1,11.10.2015,1.0,503,141.21,130.21 -36,1,12.10.2015,1.0,333,141.21,130.21 -36,1,13.10.2015,,192,141.21, -36,1,14.10.2015,,192,141.21, -36,1,15.10.2015,,217,142.57, -36,1,16.10.2015,,210,142.57, 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-36,1,17.12.2015,,157,130.34, -36,1,18.12.2015,,339,130.34, -36,1,19.12.2015,,229,130.34, -36,1,20.12.2015,,452,130.34, -36,1,21.12.2015,,126,130.34, -36,1,22.12.2015,,452,130.34, -36,1,23.12.2015,,302,130.34, -36,1,24.12.2015,,374,126.54, -36,1,25.12.2015,,412,126.54, -36,1,26.12.2015,,487,126.54, -36,1,27.12.2015,,540,126.54, -36,1,28.12.2015,,437,126.54, -36,1,29.12.2015,,355,126.54, -36,1,30.12.2015,,330,126.54, -36,1,31.12.2015,,298,126.54, -36,1,01.01.2016,,31,126.54, -36,1,02.01.2016,,104,126.54, -36,1,03.01.2016,,113,126.54, -36,1,04.01.2016,,188,126.54, -36,1,05.01.2016,,176,126.54, -36,1,06.01.2016,,358,126.54, -36,1,07.01.2016,,396,126.54, -36,1,08.01.2016,,223,126.54, -36,1,09.01.2016,,220,126.54, -36,1,10.01.2016,,195,126.54, -36,1,11.01.2016,,239,126.54, -36,1,12.01.2016,,346,126.54, -36,1,13.01.2016,,210,126.54, -36,1,14.01.2016,,336,126.54, -36,1,15.01.2016,,295,126.54, -36,1,16.01.2016,,456,126.54, -36,1,17.01.2016,,298,126.54, -36,1,18.01.2016,,437,126.54, 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-37,1,31.03.2015,,236,131.97, -37,1,01.04.2015,,188,133.33, -37,1,02.04.2015,,170,133.33, -37,1,03.04.2015,,129,133.33, -37,1,04.04.2015,,258,133.33, -37,1,05.04.2015,,229,133.33, -37,1,06.04.2015,,223,133.33, -37,1,07.04.2015,1.0,2340,133.33,119.33 -37,1,08.04.2015,1.0,1360,133.33,119.33 -37,1,09.04.2015,1.0,2089,133.33,119.33 -37,1,10.04.2015,1.0,1181,133.33,119.33 -37,1,11.04.2015,1.0,773,133.33,119.33 -37,1,12.04.2015,1.0,1363,133.33,119.33 -37,1,13.04.2015,1.0,1351,133.33,119.33 -37,1,14.04.2015,,94,130.34, -37,1,15.04.2015,,126,130.34, -37,1,16.04.2015,,135,130.34, -37,1,17.04.2015,,151,138.5, -37,1,18.04.2015,,113,138.5, -37,1,19.04.2015,,148,138.5, -37,1,20.04.2015,,116,138.5, -37,1,21.04.2015,,129,138.5, -37,1,22.04.2015,,248,128.98, -37,1,23.04.2015,,198,128.98, -37,1,24.04.2015,,286,128.98, -37,1,25.04.2015,,324,128.98, -37,1,26.04.2015,,145,128.98, -37,1,27.04.2015,,258,128.98, -37,1,28.04.2015,,440,128.98, -37,1,29.04.2015,,141,131.7, -37,1,30.04.2015,,195,131.7, 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-37,1,01.06.2015,,145,129.25, -37,1,02.06.2015,,97,129.25, -37,1,03.06.2015,,104,129.25, -37,1,04.06.2015,,88,129.25, -37,1,05.06.2015,,126,129.25, -37,1,06.06.2015,,138,131.7, -37,1,07.06.2015,,97,131.7, -37,1,08.06.2015,,185,131.7, -37,1,09.06.2015,1.0,1297,131.7,119.33 -37,1,10.06.2015,1.0,1825,131.7,119.33 -37,1,11.06.2015,1.0,1288,131.7,119.33 -37,1,12.06.2015,1.0,851,131.7,119.33 -37,1,13.06.2015,1.0,603,131.7,119.33 -37,1,14.06.2015,1.0,892,131.7,119.33 -37,1,15.06.2015,1.0,167,131.7,119.33 -37,1,16.06.2015,,135,131.7, -37,1,17.06.2015,,198,131.7, -37,1,18.06.2015,,214,128.17, -37,1,19.06.2015,,355,128.17, -37,1,20.06.2015,,264,128.17, -37,1,21.06.2015,,298,128.17, -37,1,22.06.2015,,346,128.17, -37,1,23.06.2015,,264,128.17, -37,1,24.06.2015,,204,128.17, -37,1,25.06.2015,,352,128.17, -37,1,26.06.2015,,289,128.17, -37,1,27.06.2015,,261,128.17, -37,1,28.06.2015,,427,130.34, -37,1,29.06.2015,,270,130.34, -37,1,30.06.2015,1.0,500,130.34,119.33 -37,1,01.07.2015,1.0,1275,130.34,119.33 -37,1,02.07.2015,1.0,845,130.34,119.33 -37,1,03.07.2015,1.0,971,130.34,119.33 -37,1,04.07.2015,1.0,471,130.34,119.33 -37,1,05.07.2015,1.0,1389,130.34,119.33 -37,1,06.07.2015,1.0,1040,130.34,119.33 -37,1,07.07.2015,,135,130.34, -37,1,08.07.2015,,264,130.34, -37,1,09.07.2015,,47,121.64, -37,1,10.07.2015,,286,121.64, -37,1,11.07.2015,,493,123.55, -37,1,12.07.2015,,459,123.55, -37,1,13.07.2015,,217,123.55, -37,1,14.07.2015,,481,123.55, -37,1,15.07.2015,,286,125.45, -37,1,16.07.2015,,355,125.45, -37,1,17.07.2015,,663,125.45, -37,1,18.07.2015,,653,127.62, -37,1,19.07.2015,,452,127.62, -37,1,20.07.2015,,575,127.62, -37,1,21.07.2015,,986,127.62, -37,1,22.07.2015,,864,127.62, -37,1,23.07.2015,,801,127.62, -37,1,24.07.2015,,289,127.62, -37,1,25.07.2015,,540,127.62, -37,1,26.07.2015,,540,130.07, -37,1,27.07.2015,,540,130.07, -37,1,28.07.2015,1.0,902,130.07,124.77 -37,1,29.07.2015,1.0,902,130.07,124.77 -37,1,30.07.2015,1.0,902,130.07,124.77 -37,1,31.07.2015,1.0,902,130.07,124.77 -37,1,01.08.2015,1.0,902,130.07,124.77 -37,1,02.08.2015,1.0,902,130.07,124.77 -37,1,03.08.2015,1.0,902,130.07,124.77 -37,1,04.08.2015,,540,138.5, -37,1,05.08.2015,,540,138.5, -37,1,06.08.2015,,540,138.5, -37,1,07.08.2015,,540,138.5, -37,1,08.08.2015,,540,141.21, -37,1,09.08.2015,,540,141.21, -37,1,10.08.2015,,540,141.21, -37,1,11.08.2015,,540,141.21, -37,1,12.08.2015,,540,142.85, -37,1,13.08.2015,,540,142.85, -37,1,14.08.2015,,540,142.85, -37,1,15.08.2015,,540,142.85, -37,1,16.08.2015,,540,142.85, -37,1,17.08.2015,,540,142.85, -37,1,18.08.2015,,540,142.85, -37,1,19.08.2015,,540,142.85, -37,1,20.08.2015,,540,142.85, -37,1,21.08.2015,,342,142.85, -37,1,22.08.2015,,368,142.85, -37,1,23.08.2015,,308,142.85, -37,1,24.08.2015,,254,142.85, -37,1,25.08.2015,,434,142.85, -37,1,26.08.2015,,342,142.85, -37,1,27.08.2015,,333,137.68, -37,1,28.08.2015,,609,137.68, -37,1,29.08.2015,,374,137.68, -37,1,30.08.2015,,336,137.68, -37,1,31.08.2015,,292,137.68, -37,1,01.09.2015,,412,137.68, -37,1,02.09.2015,,430,135.78, -37,1,03.09.2015,,468,135.78, -37,1,04.09.2015,,399,135.78, -37,1,05.09.2015,,638,133.6, -37,1,06.09.2015,,506,133.6, -37,1,07.09.2015,,540,133.6, -37,1,08.09.2015,,823,133.6, -37,1,09.09.2015,,355,135.51, -37,1,10.09.2015,,807,135.51, -37,1,11.09.2015,,657,135.51, -37,1,12.09.2015,,522,135.51, -37,1,13.09.2015,,729,135.51, -37,1,14.09.2015,,342,135.51, -37,1,15.09.2015,,531,135.51, -37,1,16.09.2015,,289,138.5, -37,1,17.09.2015,,883,138.5, -37,1,18.09.2015,,386,138.5, -37,1,19.09.2015,,317,138.5, -37,1,20.09.2015,,242,138.5, -37,1,21.09.2015,,320,138.5, -37,1,22.09.2015,,185,138.5, -37,1,23.09.2015,,176,141.21, -37,1,24.09.2015,,245,141.21, -37,1,25.09.2015,,311,141.21, -37,1,26.09.2015,,251,141.21, -37,1,27.09.2015,,248,141.21, -37,1,28.09.2015,,154,141.21, -37,1,29.09.2015,,280,141.21, -37,1,30.09.2015,,223,141.21, -37,1,01.10.2015,,314,141.21, -37,1,02.10.2015,,192,141.21, -37,1,03.10.2015,,151,141.21, -37,1,04.10.2015,,138,141.21, -37,1,05.10.2015,,123,141.21, -37,1,06.10.2015,1.0,858,141.21,132.92 -37,1,07.10.2015,1.0,449,141.21,130.21 -37,1,08.10.2015,1.0,679,141.11,130.21 -37,1,09.10.2015,1.0,773,141.21,130.21 -37,1,10.10.2015,1.0,867,141.21,130.21 -37,1,11.10.2015,1.0,704,141.21,130.21 -37,1,12.10.2015,1.0,1068,141.21,130.21 -37,1,13.10.2015,,280,141.21, -37,1,14.10.2015,,141,141.21, -37,1,15.10.2015,,119,142.57, -37,1,16.10.2015,,170,142.57, -37,1,17.10.2015,,145,142.57, -37,1,18.10.2015,,167,142.57, -37,1,19.10.2015,,254,142.57, -37,1,20.10.2015,,195,142.57, -37,1,21.10.2015,,509,130.34, -37,1,22.10.2015,,1002,130.34, -37,1,23.10.2015,,999,130.34, -37,1,24.10.2015,,305,130.34, -37,1,25.10.2015,,446,130.34, -37,1,26.10.2015,,261,130.34, -37,1,27.10.2015,1.0,2893,130.34,122.32 -37,1,28.10.2015,1.0,738,130.34,122.32 -37,1,29.10.2015,1.0,1876,130.34,122.32 -37,1,30.10.2015,1.0,638,130.34,122.32 -37,1,31.10.2015,1.0,1005,130.34,122.32 -37,1,01.11.2015,1.0,795,130.34,122.32 -37,1,02.11.2015,1.0,936,130.34,122.32 -37,1,03.11.2015,,522,130.34, -37,1,04.11.2015,,163,134.69, -37,1,05.11.2015,,239,134.69, -37,1,06.11.2015,,434,134.69, -37,1,07.11.2015,,204,134.69, -37,1,08.11.2015,,163,134.69, -37,1,09.11.2015,,368,134.69, -37,1,10.11.2015,,148,134.69, -37,1,11.11.2015,,289,130.34, -37,1,12.11.2015,,283,130.34, -37,1,13.11.2015,,135,130.34, -37,1,14.11.2015,,361,130.34, -37,1,15.11.2015,,173,130.34, -37,1,16.11.2015,,173,130.34, -37,1,17.11.2015,,368,130.34, -37,1,18.11.2015,,141,130.34, -37,1,19.11.2015,,195,130.34, -37,1,20.11.2015,,399,130.34, -37,1,21.11.2015,,324,130.34, -37,1,22.11.2015,,273,130.34, -37,1,23.11.2015,,220,130.34, -37,1,24.11.2015,1.0,2900,130.34,116.61 -37,1,25.11.2015,1.0,2928,130.34,116.61 -37,1,26.11.2015,1.0,999,130.34,116.61 -37,1,27.11.2015,1.0,1310,130.34,116.61 -37,1,28.11.2015,1.0,1250,130.34,116.61 -37,1,29.11.2015,1.0,1461,130.34,116.61 -37,1,30.11.2015,1.0,1891,130.34,116.61 -37,1,01.12.2015,,286,130.34, -37,1,02.12.2015,,342,130.34, -37,1,03.12.2015,,236,130.34, -37,1,04.12.2015,,188,130.34, -37,1,05.12.2015,1.0,2051,130.34,116.61 -37,1,06.12.2015,1.0,980,130.34,116.61 -37,1,07.12.2015,1.0,1461,130.34,116.61 -37,1,08.12.2015,,298,130.34, -37,1,09.12.2015,,245,130.34, -37,1,10.12.2015,,232,130.34, -37,1,11.12.2015,,91,130.34, -37,1,12.12.2015,,75,130.34, -37,1,13.12.2015,,198,130.34, -37,1,14.12.2015,,170,130.34, -37,1,15.12.2015,,145,130.34, -37,1,16.12.2015,,126,130.34, -37,1,17.12.2015,,330,130.34, -37,1,18.12.2015,,424,130.34, -37,1,19.12.2015,,421,130.34, -37,1,20.12.2015,,336,130.34, -37,1,21.12.2015,,192,130.34, -37,1,22.12.2015,,167,130.34, -37,1,23.12.2015,,283,130.34, -37,1,24.12.2015,,232,126.54, -37,1,25.12.2015,,298,126.54, -37,1,26.12.2015,,192,126.54, -37,1,27.12.2015,,157,126.54, -37,1,28.12.2015,,320,126.54, -37,1,29.12.2015,,289,126.54, -37,1,30.12.2015,,286,126.54, -37,1,31.12.2015,,201,126.54, -37,1,01.01.2016,,19,126.54, -37,1,02.01.2016,,91,126.54, -37,1,03.01.2016,,123,126.54, -37,1,04.01.2016,,261,126.54, -37,1,05.01.2016,,490,126.54, -37,1,06.01.2016,,468,126.54, -37,1,07.01.2016,,201,126.54, -37,1,08.01.2016,,245,126.54, -37,1,09.01.2016,,333,126.54, -37,1,10.01.2016,,176,126.54, -37,1,11.01.2016,,368,126.54, -37,1,12.01.2016,,427,126.54, -37,1,13.01.2016,,352,126.54, -37,1,14.01.2016,,402,126.54, -37,1,15.01.2016,,399,126.54, -37,1,16.01.2016,,503,126.54, -37,1,17.01.2016,,302,126.54, -37,1,18.01.2016,,452,126.54, -37,1,19.01.2016,,518,126.54, -37,1,20.01.2016,,537,126.54, -37,1,21.01.2016,,427,127.9, -37,1,22.01.2016,,427,127.9, -37,1,23.01.2016,,462,127.9, -37,1,24.01.2016,,547,127.9, -37,1,25.01.2016,,440,127.9, -37,1,26.01.2016,,358,127.9, -37,1,27.01.2016,,817,127.9, -37,1,28.01.2016,,701,127.9, -37,1,29.01.2016,,836,127.9, -37,1,30.01.2016,,484,127.9, -37,1,31.01.2016,,364,127.9, -37,1,01.02.2016,,273,130.34, -37,1,02.02.2016,,336,130.34, -37,1,03.02.2016,,462,130.34, -37,1,04.02.2016,,547,133.06, -37,1,05.02.2016,,543,133.06, -37,1,06.02.2016,,377,133.06, 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-38,1,22.12.2015,,176,130.34, -38,1,23.12.2015,,298,130.34, -38,1,24.12.2015,,543,126.54, -38,1,25.12.2015,,506,126.54, -38,1,26.12.2015,,402,126.54, -38,1,27.12.2015,,311,126.54, -38,1,28.12.2015,,324,126.54, -38,1,29.12.2015,,358,126.54, -38,1,30.12.2015,,330,126.54, -38,1,31.12.2015,,170,126.54, -38,1,01.01.2016,,119,126.54, -38,1,02.01.2016,,163,126.54, -38,1,03.01.2016,,346,126.54, -38,1,04.01.2016,,232,126.54, -38,1,05.01.2016,,248,126.54, -38,1,06.01.2016,,320,126.54, -38,1,07.01.2016,,220,126.54, -38,1,08.01.2016,,302,126.54, -38,1,09.01.2016,,207,126.54, -38,1,10.01.2016,,493,126.54, -38,1,11.01.2016,,280,126.54, -38,1,12.01.2016,,305,126.54, -38,1,13.01.2016,,358,126.54, -38,1,14.01.2016,,229,126.54, -38,1,15.01.2016,,496,126.54, -38,1,16.01.2016,,336,126.54, -38,1,17.01.2016,,446,126.54, -38,1,18.01.2016,,443,126.54, -38,1,19.01.2016,,192,126.54, -38,1,20.01.2016,,292,126.54, -38,1,21.01.2016,,261,127.9, -38,1,22.01.2016,,496,127.9, -38,1,23.01.2016,,333,127.9, -38,1,24.01.2016,,1056,127.9, -38,1,25.01.2016,,386,127.9, -38,1,26.01.2016,,339,127.9, -38,1,27.01.2016,,424,127.9, -38,1,28.01.2016,,258,127.9, -38,1,29.01.2016,,462,127.9, -38,1,30.01.2016,,500,127.9, -38,1,31.01.2016,,534,127.9, -38,1,01.02.2016,,239,130.34, -38,1,02.02.2016,,242,130.34, -38,1,03.02.2016,,490,130.34, -38,1,04.02.2016,,358,133.06, -38,1,05.02.2016,,748,133.06, -38,1,06.02.2016,,437,133.06, -38,1,07.02.2016,,270,141.76, -38,1,08.02.2016,,170,141.76, -38,1,09.02.2016,1.0,1018,141.76,130.75 -38,1,10.02.2016,1.0,1012,141.76,130.75 -38,1,11.02.2016,1.0,547,141.76,130.75 -38,1,12.02.2016,1.0,719,141.76,130.75 -38,1,13.02.2016,1.0,763,141.76,130.75 -38,1,14.02.2016,1.0,914,141.76,130.75 -38,1,15.02.2016,1.0,449,141.76,130.75 -38,1,16.02.2016,1.0,836,141.76,130.75 -38,1,17.02.2016,1.0,440,141.76,130.75 -38,1,18.02.2016,,179,141.76, -38,1,19.02.2016,,201,141.76, -38,1,20.02.2016,,232,141.76, -38,1,21.02.2016,,207,141.76, -38,1,22.02.2016,,210,141.76, -38,1,23.02.2016,,160,141.76, -38,1,24.02.2016,,104,141.76, -38,1,25.02.2016,,176,141.76, -38,1,26.02.2016,,280,141.76, -38,1,27.02.2016,,192,141.76, -38,1,28.02.2016,,248,141.76, -38,1,29.02.2016,,185,141.76, -38,1,01.03.2016,,167,141.76, -38,1,02.03.2016,,352,141.76, -38,1,03.03.2016,,170,141.76, -38,1,04.03.2016,,286,141.76, -38,1,05.03.2016,,267,141.21, -38,1,06.03.2016,,496,141.21, -38,1,07.03.2016,,232,141.21, -38,1,08.03.2016,1.0,374,141.21,132.92 -38,1,09.03.2016,1.0,1068,141.21,132.92 -38,1,10.03.2016,1.0,713,141.21,132.92 -38,1,11.03.2016,1.0,691,141.21,132.92 -38,1,12.03.2016,1.0,763,141.21,132.92 -38,1,13.03.2016,1.0,440,141.21,132.92 -38,1,14.03.2016,1.0,408,141.21,132.92 -38,1,15.03.2016,1.0,1166,141.21,130.21 -38,1,16.03.2016,1.0,942,141.21,130.21 -38,1,17.03.2016,1.0,173,141.21,130.21 -38,1,18.03.2016,,207,141.21, -38,1,19.03.2016,,223,141.21, -38,1,20.03.2016,,160,141.21, -38,1,21.03.2016,,308,141.21, -38,1,22.03.2016,,217,141.21, -38,1,23.03.2016,,94,141.21, -38,1,24.03.2016,,132,141.21, -38,1,25.03.2016,,349,141.21, -38,1,26.03.2016,,179,141.21, -38,1,27.03.2016,,258,141.21, -38,1,28.03.2016,,107,141.21, -38,1,29.03.2016,,94,141.21, -38,1,30.03.2016,,72,141.21, -38,1,31.03.2016,,129,141.21, -38,1,01.04.2016,,214,141.21, -38,1,02.04.2016,,135,141.21, -38,1,03.04.2016,,374,141.21, -38,1,04.04.2016,,126,141.21, -38,1,05.04.2016,,210,141.21, -38,1,06.04.2016,,185,141.21, -38,1,07.04.2016,,185,141.21, -38,1,08.04.2016,,173,141.21, -38,1,09.04.2016,,273,141.21, -38,1,10.04.2016,,135,141.21, -38,1,11.04.2016,,119,141.21, -38,1,12.04.2016,,192,141.21, -38,1,13.04.2016,,226,141.21, -38,1,14.04.2016,,157,135.78, -38,1,15.04.2016,,188,135.78, -38,1,16.04.2016,,408,135.78, -38,1,17.04.2016,,324,135.78, -38,1,18.04.2016,,179,135.78, -38,1,19.04.2016,1.0,908,135.78,122.32 -38,1,20.04.2016,1.0,1483,135.78,122.32 -38,1,21.04.2016,1.0,1577,135.78,122.32 -38,1,22.04.2016,1.0,1684,135.78,122.32 -38,1,23.04.2016,1.0,547,135.78,122.32 -38,1,24.04.2016,1.0,826,135.78,122.32 -38,1,25.04.2016,1.0,581,135.78,122.32 -38,1,26.04.2016,,176,135.78, -38,1,27.04.2016,,188,135.78, -38,1,28.04.2016,,138,135.78, -38,1,29.04.2016,,258,131.16, -38,1,30.04.2016,,245,131.16, -38,1,01.05.2016,,75,131.16, -38,1,02.05.2016,,97,131.16, -38,1,03.05.2016,,157,131.16, -38,1,04.05.2016,,295,131.16, -38,1,05.05.2016,,123,131.16, -38,1,06.05.2016,,116,131.16, -38,1,07.05.2016,,210,131.16, -38,1,08.05.2016,,132,131.16, -38,1,09.05.2016,,160,131.16, -38,1,10.05.2016,,154,131.16, -38,1,11.05.2016,,198,131.16, -38,1,12.05.2016,,371,131.16, -38,1,13.05.2016,,405,128.98, -38,1,14.05.2016,,364,128.98, -38,1,15.05.2016,,380,128.98, -38,1,16.05.2016,,210,128.98, -38,1,17.05.2016,1.0,889,128.98,119.6 -38,1,18.05.2016,1.0,760,128.98,119.6 -38,1,19.05.2016,1.0,682,128.98,119.6 -38,1,20.05.2016,1.0,1445,128.98,119.6 -38,1,21.05.2016,1.0,1084,128.98,119.6 -38,1,22.05.2016,1.0,789,128.98,119.6 -39,1,01.01.2015,,6,163.78, -39,1,02.01.2015,,85,163.78, -39,1,03.01.2015,,119,163.78, -39,1,04.01.2015,,75,163.78, -39,1,05.01.2015,,119,163.78, -39,1,06.01.2015,,85,163.78, -39,1,07.01.2015,,44,163.78, -39,1,08.01.2015,,88,163.78, -39,1,09.01.2015,,104,163.78, -39,1,10.01.2015,,176,163.78, -39,1,11.01.2015,,66,163.78, -39,1,12.01.2015,,88,163.78, -39,1,13.01.2015,,82,163.78, -39,1,14.01.2015,,110,168.4, -39,1,15.01.2015,,176,168.4, -39,1,16.01.2015,,148,168.4, -39,1,17.01.2015,,170,168.4, -39,1,18.01.2015,,138,168.4, -39,1,19.01.2015,,88,168.4, -39,1,20.01.2015,,204,168.4, -39,1,21.01.2015,,97,168.4, -39,1,22.01.2015,,75,168.4, -39,1,23.01.2015,,79,160.24, -39,1,24.01.2015,,160,160.24, -39,1,25.01.2015,,94,160.24, -39,1,26.01.2015,,157,160.24, -39,1,27.01.2015,,154,160.24, -39,1,28.01.2015,,217,146.92, -39,1,29.01.2015,,380,146.92, -39,1,30.01.2015,,207,146.92, -39,1,31.01.2015,,248,146.92, -39,1,01.02.2015,,113,146.92, -39,1,02.02.2015,,195,146.92, -39,1,03.02.2015,,305,146.92, -39,1,04.02.2015,,317,146.92, -39,1,05.02.2015,,176,146.92, -39,1,06.02.2015,,182,146.92, -39,1,07.02.2015,,188,146.92, -39,1,08.02.2015,,270,146.92, -39,1,09.02.2015,,280,146.92, -39,1,10.02.2015,,176,146.92, -39,1,11.02.2015,,198,146.92, -39,1,12.02.2015,,226,146.92, -39,1,13.02.2015,,437,149.37, -39,1,14.02.2015,,383,149.37, -39,1,15.02.2015,,261,149.37, -39,1,16.02.2015,,270,149.37, -39,1,17.02.2015,1.0,556,149.37,143.8 -39,1,18.02.2015,1.0,952,149.37,135.64 -39,1,19.02.2015,1.0,2253,149.37,135.64 -39,1,20.02.2015,1.0,1963,149.37,135.64 -39,1,21.02.2015,1.0,1451,149.37,135.64 -39,1,22.02.2015,1.0,902,149.37,135.64 -39,1,23.02.2015,1.0,719,149.37,135.64 -39,1,24.02.2015,,154,149.37, -39,1,25.02.2015,,154,149.37, -39,1,26.02.2015,,314,149.37, -39,1,27.02.2015,,280,143.93, -39,1,28.02.2015,,280,143.93, -39,1,01.03.2015,,631,143.93, -39,1,02.03.2015,,361,143.93, -39,1,03.03.2015,1.0,748,143.93,138.36 -39,1,04.03.2015,1.0,346,143.93,135.64 -39,1,05.03.2015,1.0,672,138.5,135.64 -39,1,06.03.2015,1.0,490,138.5,135.64 -39,1,07.03.2015,1.0,311,138.5,135.64 -39,1,08.03.2015,1.0,873,138.5,135.64 -39,1,09.03.2015,,85,140.67, -39,1,10.03.2015,,192,140.67, -39,1,11.03.2015,,116,140.67, -39,1,12.03.2015,,226,140.67, -39,1,13.03.2015,,311,140.67, -39,1,14.03.2015,,258,140.67, -39,1,15.03.2015,,220,140.67, -39,1,16.03.2015,,132,140.67, -39,1,17.03.2015,1.0,3654,140.67,127.49 -39,1,18.03.2015,1.0,2391,140.67,127.49 -39,1,19.03.2015,1.0,1731,140.67,127.49 -39,1,20.03.2015,1.0,1395,140.67,127.49 -39,1,21.03.2015,1.0,723,140.67,127.49 -39,1,22.03.2015,1.0,688,140.67,127.49 -39,1,23.03.2015,1.0,732,140.67,127.49 -39,1,24.03.2015,,210,140.67, -39,1,25.03.2015,,157,140.67, -39,1,26.03.2015,,305,131.97, -39,1,27.03.2015,,408,131.97, -39,1,28.03.2015,,283,131.97, -39,1,29.03.2015,,242,131.97, -39,1,30.03.2015,,154,131.97, -39,1,31.03.2015,,258,131.97, -39,1,01.04.2015,,198,133.33, -39,1,02.04.2015,,258,133.33, -39,1,03.04.2015,,72,133.33, -39,1,04.04.2015,,97,133.33, -39,1,05.04.2015,,198,133.33, -39,1,06.04.2015,,66,133.33, -39,1,07.04.2015,1.0,1206,133.33,119.33 -39,1,08.04.2015,1.0,1734,133.33,119.33 -39,1,09.04.2015,1.0,1235,133.33,119.33 -39,1,10.04.2015,1.0,1100,133.33,119.33 -39,1,11.04.2015,1.0,964,133.33,119.33 -39,1,12.04.2015,1.0,254,133.33,119.33 -39,1,13.04.2015,1.0,261,133.33,119.33 -39,1,14.04.2015,,123,130.34, -39,1,15.04.2015,,195,130.34, -39,1,16.04.2015,,75,130.34, -39,1,17.04.2015,,85,138.5, -39,1,18.04.2015,,57,138.5, -39,1,19.04.2015,,44,138.5, -39,1,20.04.2015,,163,138.5, -39,1,21.04.2015,,110,138.5, -39,1,22.04.2015,,119,128.98, -39,1,23.04.2015,,101,128.98, -39,1,24.04.2015,,57,128.98, -39,1,25.04.2015,,151,128.98, -39,1,26.04.2015,,232,128.98, -39,1,27.04.2015,,210,128.98, -39,1,28.04.2015,,346,128.98, -39,1,29.04.2015,,201,131.7, -39,1,30.04.2015,,123,131.7, -39,1,01.05.2015,,160,131.7, -39,1,02.05.2015,,154,131.7, -39,1,03.05.2015,,148,131.7, -39,1,04.05.2015,,185,131.7, -39,1,05.05.2015,,170,131.7, -39,1,06.05.2015,,141,131.7, 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-39,1,09.01.2016,,207,126.54, -39,1,10.01.2016,,276,126.54, -39,1,11.01.2016,,339,126.54, -39,1,12.01.2016,,342,126.54, -39,1,13.01.2016,,320,126.54, -39,1,14.01.2016,,650,126.54, -39,1,15.01.2016,,496,126.54, -39,1,16.01.2016,,735,126.54, -39,1,17.01.2016,,462,126.54, -39,1,18.01.2016,,459,126.54, -39,1,19.01.2016,,371,126.54, -39,1,20.01.2016,,496,126.54, -39,1,21.01.2016,,374,127.9, -39,1,22.01.2016,,358,127.9, -39,1,23.01.2016,,553,127.9, -39,1,24.01.2016,,468,127.9, -39,1,25.01.2016,,346,127.9, -39,1,26.01.2016,,1087,127.9, -39,1,27.01.2016,,776,127.9, -39,1,28.01.2016,,619,127.9, -39,1,29.01.2016,,716,127.9, -39,1,30.01.2016,,446,127.9, -39,1,31.01.2016,,584,127.9, -39,1,01.02.2016,,346,130.34, -39,1,02.02.2016,,459,130.34, -39,1,03.02.2016,,386,130.34, -39,1,04.02.2016,,688,133.06, -39,1,05.02.2016,,484,133.06, -39,1,06.02.2016,,518,133.06, -39,1,07.02.2016,,220,141.76, -39,1,08.02.2016,,239,141.76, -39,1,09.02.2016,1.0,1109,141.76,130.75 -39,1,10.02.2016,1.0,1197,141.76,130.75 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-39,1,11.04.2016,,160,141.21, -39,1,12.04.2016,,220,141.21, -39,1,13.04.2016,,160,141.21, -39,1,14.04.2016,,298,135.78, -39,1,15.04.2016,,434,135.78, -39,1,16.04.2016,,430,135.78, -39,1,17.04.2016,,591,135.78, -39,1,18.04.2016,,462,135.78, -39,1,19.04.2016,1.0,7609,135.78,122.32 -39,1,20.04.2016,1.0,1998,135.78,122.32 -39,1,21.04.2016,1.0,1439,135.78,122.32 -39,1,22.04.2016,1.0,1376,135.78,122.32 -39,1,23.04.2016,1.0,1279,135.78,122.32 -39,1,24.04.2016,1.0,1213,135.78,122.32 -39,1,25.04.2016,1.0,1162,135.78,122.32 -39,1,26.04.2016,,44,135.78, -39,1,27.04.2016,,298,135.78, -39,1,28.04.2016,,298,135.78, -39,1,29.04.2016,,239,131.16, -39,1,30.04.2016,,490,131.16, -39,1,01.05.2016,,163,131.16, -39,1,02.05.2016,,229,131.16, -39,1,03.05.2016,,254,131.16, -39,1,04.05.2016,,167,131.16, -39,1,05.05.2016,,317,131.16, -39,1,06.05.2016,,273,131.16, -39,1,07.05.2016,,264,131.16, -39,1,08.05.2016,,311,131.16, -39,1,09.05.2016,,201,131.16, -39,1,10.05.2016,,217,131.16, -39,1,11.05.2016,,229,131.16, 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-40,1,26.12.2015,,236,126.54, -40,1,27.12.2015,,229,126.54, -40,1,28.12.2015,,336,126.54, -40,1,29.12.2015,,302,126.54, -40,1,30.12.2015,,305,126.54, -40,1,31.12.2015,,217,126.54, -40,1,01.01.2016,,28,126.54, -40,1,02.01.2016,,217,126.54, -40,1,03.01.2016,,126,126.54, -40,1,04.01.2016,,383,126.54, -40,1,05.01.2016,,204,126.54, -40,1,06.01.2016,,267,126.54, -40,1,07.01.2016,,311,126.54, -40,1,08.01.2016,,214,126.54, -40,1,09.01.2016,,276,126.54, -40,1,10.01.2016,,305,126.54, -40,1,11.01.2016,,405,126.54, -40,1,12.01.2016,,311,126.54, -40,1,13.01.2016,,330,126.54, -40,1,14.01.2016,,273,126.54, -40,1,15.01.2016,,569,126.54, -40,1,16.01.2016,,490,126.54, -40,1,17.01.2016,,660,126.54, -40,1,18.01.2016,,503,126.54, -40,1,19.01.2016,,1693,126.54, -40,1,20.01.2016,,380,126.54, -40,1,21.01.2016,,735,127.9, -40,1,22.01.2016,,487,127.9, -40,1,23.01.2016,,657,127.9, -40,1,24.01.2016,,503,127.9, -40,1,25.01.2016,,276,127.9, -40,1,26.01.2016,,327,127.9, -40,1,27.01.2016,,522,127.9, -40,1,28.01.2016,,851,127.9, -40,1,29.01.2016,,443,127.9, -40,1,30.01.2016,,719,127.9, -40,1,31.01.2016,,776,127.9, -40,1,01.02.2016,,302,130.34, -40,1,02.02.2016,,333,130.34, -40,1,03.02.2016,,496,130.34, -40,1,04.02.2016,,236,133.06, -40,1,05.02.2016,,286,133.06, -40,1,06.02.2016,,437,133.06, -40,1,07.02.2016,,239,141.76, -40,1,08.02.2016,,264,141.76, -40,1,09.02.2016,1.0,531,141.76,130.75 -40,1,10.02.2016,1.0,506,141.76,130.75 -40,1,11.02.2016,1.0,487,141.76,130.75 -40,1,12.02.2016,1.0,496,141.76,130.75 -40,1,13.02.2016,1.0,952,141.76,130.75 -40,1,14.02.2016,1.0,543,141.76,130.75 -40,1,15.02.2016,1.0,704,141.76,130.75 -40,1,16.02.2016,1.0,556,141.76,130.75 -40,1,17.02.2016,1.0,650,141.76,130.75 -40,1,18.02.2016,,311,141.76, -40,1,19.02.2016,,261,141.76, -40,1,20.02.2016,,151,141.76, -40,1,21.02.2016,,132,141.76, -40,1,22.02.2016,,214,141.76, -40,1,23.02.2016,,364,141.76, -40,1,24.02.2016,,192,141.76, -40,1,25.02.2016,,248,141.76, -40,1,26.02.2016,,245,141.76, 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-40,1,27.03.2016,,364,141.21, -40,1,28.03.2016,,160,141.21, -40,1,29.03.2016,,295,141.21, -40,1,30.03.2016,,214,141.21, -40,1,31.03.2016,,188,141.21, -40,1,01.04.2016,,214,141.21, -40,1,02.04.2016,,204,141.21, -40,1,03.04.2016,,182,141.21, -40,1,04.04.2016,,182,141.21, -40,1,05.04.2016,,320,141.21, -40,1,06.04.2016,,283,141.21, -40,1,07.04.2016,,201,141.21, -40,1,08.04.2016,,242,141.21, -40,1,09.04.2016,,160,141.21, -40,1,10.04.2016,,179,141.21, -40,1,11.04.2016,,201,141.21, -40,1,12.04.2016,,320,141.21, -40,1,13.04.2016,,258,141.21, -40,1,14.04.2016,,283,135.78, -40,1,15.04.2016,,449,135.78, -40,1,16.04.2016,,217,135.78, -40,1,17.04.2016,,192,135.78, -40,1,18.04.2016,,236,135.78, -40,1,19.04.2016,1.0,4423,135.78,122.32 -40,1,20.04.2016,1.0,2887,135.78,122.32 -40,1,21.04.2016,1.0,232,135.78,122.32 -40,1,22.04.2016,1.0,1194,135.78,122.32 -40,1,23.04.2016,1.0,1137,135.78,122.32 -40,1,24.04.2016,1.0,1052,135.78,122.32 -40,1,25.04.2016,1.0,685,135.78,122.32 -40,1,26.04.2016,,289,135.78, -40,1,27.04.2016,,311,135.78, -40,1,28.04.2016,,195,135.78, -40,1,29.04.2016,,330,131.16, -40,1,30.04.2016,,327,131.16, -40,1,01.05.2016,,135,131.16, -40,1,02.05.2016,,173,131.16, -40,1,03.05.2016,,182,131.16, -40,1,04.05.2016,,188,131.16, -40,1,05.05.2016,,148,131.16, -40,1,06.05.2016,,185,131.16, -40,1,07.05.2016,,198,131.16, -40,1,08.05.2016,,214,131.16, -40,1,09.05.2016,,145,131.16, -40,1,10.05.2016,,201,131.16, -40,1,11.05.2016,,204,131.16, -40,1,12.05.2016,,198,131.16, -40,1,13.05.2016,,302,128.98, -40,1,14.05.2016,,459,128.98, -40,1,15.05.2016,,440,128.98, -40,1,16.05.2016,,390,128.98, -40,1,17.05.2016,1.0,1395,128.98,119.6 -40,1,18.05.2016,1.0,3286,128.98,119.6 -40,1,19.05.2016,1.0,2174,128.98,119.6 -40,1,20.05.2016,1.0,1517,128.98,119.6 -40,1,21.05.2016,1.0,1018,128.98,119.6 -40,1,22.05.2016,1.0,254,128.98,119.6 -41,1,01.01.2015,,0,163.78, -41,1,02.01.2015,,28,163.78, -41,1,03.01.2015,,41,163.78, -41,1,04.01.2015,,25,163.78, -41,1,05.01.2015,,60,163.78, -41,1,06.01.2015,,19,163.78, -41,1,07.01.2015,,19,163.78, -41,1,08.01.2015,,57,163.78, -41,1,09.01.2015,,38,163.78, -41,1,10.01.2015,,35,163.78, -41,1,11.01.2015,,35,163.78, -41,1,12.01.2015,,38,163.78, -41,1,13.01.2015,,72,163.78, -41,1,14.01.2015,,38,168.4, -41,1,15.01.2015,,44,168.4, -41,1,16.01.2015,,63,168.4, -41,1,17.01.2015,,50,168.4, -41,1,18.01.2015,,41,168.4, -41,1,19.01.2015,,38,168.4, -41,1,20.01.2015,,69,168.4, -41,1,21.01.2015,,44,168.4, -41,1,22.01.2015,,88,168.4, -41,1,23.01.2015,,31,160.24, -41,1,24.01.2015,,66,160.24, -41,1,25.01.2015,,88,160.24, -41,1,26.01.2015,,57,160.24, -41,1,27.01.2015,,79,160.24, -41,1,28.01.2015,,273,146.92, -41,1,29.01.2015,,151,146.92, -41,1,30.01.2015,,214,146.92, -41,1,31.01.2015,,141,146.92, -41,1,01.02.2015,,138,146.92, -41,1,02.02.2015,,91,146.92, -41,1,03.02.2015,,138,146.92, -41,1,04.02.2015,,167,146.92, -41,1,05.02.2015,,119,146.92, -41,1,06.02.2015,,129,146.92, -41,1,07.02.2015,,173,146.92, -41,1,08.02.2015,,192,146.92, -41,1,09.02.2015,,217,146.92, -41,1,10.02.2015,,157,146.92, -41,1,11.02.2015,,182,146.92, -41,1,12.02.2015,,69,146.92, -41,1,13.02.2015,,179,149.37, -41,1,14.02.2015,,129,149.37, -41,1,15.02.2015,,173,149.37, -41,1,16.02.2015,,154,149.37, -41,1,17.02.2015,1.0,349,149.37,143.8 -41,1,18.02.2015,1.0,141,149.37,135.64 -41,1,19.02.2015,1.0,522,149.37,135.64 -41,1,20.02.2015,1.0,745,149.37,135.64 -41,1,21.02.2015,1.0,675,149.37,135.64 -41,1,22.02.2015,1.0,361,149.37,135.64 -41,1,23.02.2015,1.0,264,149.37,135.64 -41,1,24.02.2015,,97,149.37, -41,1,25.02.2015,,66,149.37, -41,1,26.02.2015,,57,149.37, -41,1,27.02.2015,,157,143.93, -41,1,28.02.2015,,289,143.93, -41,1,01.03.2015,,195,143.93, -41,1,02.03.2015,,811,143.93, -41,1,03.03.2015,1.0,333,143.93,138.36 -41,1,04.03.2015,1.0,145,143.93,135.64 -41,1,05.03.2015,1.0,163,138.5,135.64 -41,1,06.03.2015,1.0,239,138.5,135.64 -41,1,07.03.2015,1.0,129,138.5,135.64 -41,1,08.03.2015,1.0,311,138.5,135.64 -41,1,09.03.2015,,324,140.67, -41,1,10.03.2015,,204,140.67, -41,1,11.03.2015,,229,140.67, -41,1,12.03.2015,,302,140.67, -41,1,13.03.2015,,110,140.67, -41,1,14.03.2015,,145,140.67, -41,1,15.03.2015,,167,140.67, -41,1,16.03.2015,,132,140.67, -41,1,17.03.2015,1.0,1608,140.67,127.49 -41,1,18.03.2015,1.0,2045,140.67,127.49 -41,1,19.03.2015,1.0,515,140.67,127.49 -41,1,20.03.2015,1.0,481,140.67,127.49 -41,1,21.03.2015,1.0,427,140.67,127.49 -41,1,22.03.2015,1.0,380,140.67,127.49 -41,1,23.03.2015,1.0,185,140.67,127.49 -41,1,24.03.2015,,94,140.67, -41,1,25.03.2015,,110,140.67, -41,1,26.03.2015,,333,131.97, -41,1,27.03.2015,,239,131.97, -41,1,28.03.2015,,383,131.97, -41,1,29.03.2015,,377,131.97, -41,1,30.03.2015,,148,131.97, -41,1,31.03.2015,,229,131.97, -41,1,01.04.2015,,123,133.33, -41,1,02.04.2015,,160,133.33, -41,1,03.04.2015,,148,133.33, -41,1,04.04.2015,,210,133.33, -41,1,05.04.2015,,113,133.33, -41,1,06.04.2015,,182,133.33, -41,1,07.04.2015,1.0,3541,133.33,119.33 -41,1,08.04.2015,1.0,1323,133.33,119.33 -41,1,09.04.2015,1.0,663,133.33,119.33 -41,1,10.04.2015,1.0,650,133.33,119.33 -41,1,11.04.2015,1.0,635,133.33,119.33 -41,1,12.04.2015,1.0,628,133.33,119.33 -41,1,13.04.2015,1.0,625,133.33,119.33 -41,1,14.04.2015,,50,130.34, -41,1,15.04.2015,,258,130.34, -41,1,16.04.2015,,173,130.34, -41,1,17.04.2015,,50,138.5, -41,1,18.04.2015,,50,138.5, -41,1,19.04.2015,,69,138.5, -41,1,20.04.2015,,148,138.5, -41,1,21.04.2015,,72,138.5, -41,1,22.04.2015,,91,128.98, -41,1,23.04.2015,,210,128.98, -41,1,24.04.2015,,53,128.98, -41,1,25.04.2015,,138,128.98, -41,1,26.04.2015,,188,128.98, -41,1,27.04.2015,,135,128.98, -41,1,28.04.2015,,66,128.98, -41,1,29.04.2015,,53,131.7, -41,1,30.04.2015,,119,131.7, -41,1,01.05.2015,,75,131.7, -41,1,02.05.2015,,25,131.7, -41,1,03.05.2015,,85,131.7, -41,1,04.05.2015,,113,131.7, -41,1,05.05.2015,,66,131.7, -41,1,06.05.2015,,88,131.7, -41,1,07.05.2015,,179,131.7, -41,1,08.05.2015,,160,131.7, -41,1,09.05.2015,,60,131.7, -41,1,10.05.2015,,119,131.7, -41,1,11.05.2015,,47,131.7, -41,1,12.05.2015,,170,131.7, -41,1,13.05.2015,,72,131.7, -41,1,14.05.2015,,28,131.7, -41,1,15.05.2015,,107,131.7, -41,1,16.05.2015,,113,131.7, -41,1,17.05.2015,,185,131.7, -41,1,18.05.2015,,116,131.7, -41,1,19.05.2015,1.0,170,131.7,119.33 -41,1,20.05.2015,1.0,537,131.7,119.33 -41,1,21.05.2015,1.0,701,131.7,119.33 -41,1,22.05.2015,1.0,377,131.7,119.33 -41,1,23.05.2015,1.0,396,131.7,119.33 -41,1,24.05.2015,1.0,317,131.7,119.33 -41,1,25.05.2015,1.0,298,131.7,119.33 -41,1,26.05.2015,,104,131.7, -41,1,27.05.2015,,101,129.25, -41,1,28.05.2015,,63,129.25, -41,1,29.05.2015,,160,129.25, -41,1,30.05.2015,,119,129.25, -41,1,31.05.2015,,85,129.25, -41,1,01.06.2015,,44,129.25, -41,1,02.06.2015,,28,129.25, -41,1,03.06.2015,,119,129.25, -41,1,04.06.2015,,60,129.25, -41,1,05.06.2015,,60,129.25, -41,1,06.06.2015,,229,131.7, -41,1,07.06.2015,,126,131.7, -41,1,08.06.2015,,119,131.7, -41,1,09.06.2015,1.0,512,131.7,119.33 -41,1,10.06.2015,1.0,515,131.7,119.33 -41,1,11.06.2015,1.0,286,131.7,119.33 -41,1,12.06.2015,1.0,509,131.7,119.33 -41,1,13.06.2015,1.0,443,131.7,119.33 -41,1,14.06.2015,1.0,374,131.7,119.33 -41,1,15.06.2015,1.0,311,131.7,119.33 -41,1,16.06.2015,,97,131.7, -41,1,17.06.2015,,311,131.7, -41,1,18.06.2015,,123,128.17, -41,1,19.06.2015,,126,128.17, -41,1,20.06.2015,,66,128.17, -41,1,21.06.2015,,85,128.17, -41,1,22.06.2015,,110,128.17, -41,1,23.06.2015,,101,128.17, -41,1,24.06.2015,,173,128.17, -41,1,25.06.2015,,163,128.17, -41,1,26.06.2015,,88,128.17, -41,1,27.06.2015,,107,128.17, -41,1,28.06.2015,,129,130.34, -41,1,29.06.2015,,119,130.34, -41,1,30.06.2015,1.0,198,130.34,119.33 -41,1,01.07.2015,1.0,839,130.34,119.33 -41,1,02.07.2015,1.0,474,130.34,119.33 -41,1,03.07.2015,1.0,569,130.34,119.33 -41,1,04.07.2015,1.0,424,130.34,119.33 -41,1,05.07.2015,1.0,440,130.34,119.33 -41,1,06.07.2015,1.0,738,130.34,119.33 -41,1,07.07.2015,,113,130.34, -41,1,08.07.2015,,185,130.34, -41,1,09.07.2015,,110,121.64, -41,1,10.07.2015,,236,121.64, -41,1,11.07.2015,,160,123.55, -41,1,12.07.2015,,116,123.55, -41,1,13.07.2015,,254,123.55, -41,1,14.07.2015,,207,123.55, -41,1,15.07.2015,,254,125.45, -41,1,16.07.2015,,167,125.45, -41,1,17.07.2015,,151,125.45, -41,1,18.07.2015,,223,127.62, -41,1,19.07.2015,,170,127.62, -41,1,20.07.2015,,188,127.62, -41,1,21.07.2015,,292,127.62, -41,1,22.07.2015,,248,127.62, -41,1,23.07.2015,,408,127.62, -41,1,24.07.2015,,302,127.62, -41,1,25.07.2015,,377,127.62, -41,1,26.07.2015,,170,130.07, -41,1,27.07.2015,,374,130.07, -41,1,28.07.2015,1.0,427,130.07,124.77 -41,1,29.07.2015,1.0,342,130.07,124.77 -41,1,30.07.2015,1.0,537,130.07,124.77 -41,1,31.07.2015,1.0,330,130.07,124.77 -41,1,01.08.2015,1.0,591,130.07,124.77 -41,1,02.08.2015,1.0,547,130.07,124.77 -41,1,03.08.2015,1.0,314,130.07,124.77 -41,1,04.08.2015,,135,138.5, -41,1,05.08.2015,,232,138.5, -41,1,06.08.2015,,91,138.5, -41,1,07.08.2015,,85,138.5, -41,1,08.08.2015,,157,141.21, -41,1,09.08.2015,,38,141.21, -41,1,10.08.2015,,85,141.21, -41,1,11.08.2015,,107,141.21, 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-41,1,16.11.2015,,214,130.34, -41,1,17.11.2015,,236,130.34, -41,1,18.11.2015,,185,130.34, -41,1,19.11.2015,,129,130.34, -41,1,20.11.2015,,151,130.34, -41,1,21.11.2015,,188,130.34, -41,1,22.11.2015,,119,130.34, -41,1,23.11.2015,,91,130.34, -41,1,24.11.2015,1.0,2083,130.34,116.61 -41,1,25.11.2015,1.0,1926,130.34,116.61 -41,1,26.11.2015,1.0,920,130.34,116.61 -41,1,27.11.2015,1.0,625,130.34,116.61 -41,1,28.11.2015,1.0,606,130.34,116.61 -41,1,29.11.2015,1.0,283,130.34,116.61 -41,1,30.11.2015,1.0,660,130.34,116.61 -41,1,01.12.2015,,69,130.34, -41,1,02.12.2015,,79,130.34, -41,1,03.12.2015,,110,130.34, -41,1,04.12.2015,,145,130.34, -41,1,05.12.2015,1.0,437,130.34,116.61 -41,1,06.12.2015,1.0,1125,130.34,116.61 -41,1,07.12.2015,1.0,110,130.34,116.61 -41,1,08.12.2015,,119,130.34, -41,1,09.12.2015,,104,130.34, -41,1,10.12.2015,,135,130.34, -41,1,11.12.2015,,101,130.34, -41,1,12.12.2015,,91,130.34, -41,1,13.12.2015,,101,130.34, -41,1,14.12.2015,,57,130.34, -41,1,15.12.2015,,101,130.34, 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-42,1,04.11.2015,,204,134.69, -42,1,05.11.2015,,101,134.69, -42,1,06.11.2015,,148,134.69, -42,1,07.11.2015,,170,134.69, -42,1,08.11.2015,,188,134.69, -42,1,09.11.2015,,75,134.69, -42,1,10.11.2015,,113,134.69, -42,1,11.11.2015,,129,130.34, -42,1,12.11.2015,,298,130.34, -42,1,13.11.2015,,685,130.34, -42,1,14.11.2015,,383,130.34, -42,1,15.11.2015,,587,130.34, -42,1,16.11.2015,,295,130.34, -42,1,17.11.2015,,459,130.34, -42,1,18.11.2015,,276,130.34, -42,1,19.11.2015,,283,130.34, -42,1,20.11.2015,,192,130.34, -42,1,21.11.2015,,283,130.34, -42,1,22.11.2015,,355,130.34, -42,1,23.11.2015,,182,130.34, -42,1,24.11.2015,1.0,4326,130.34,116.61 -42,1,25.11.2015,1.0,2231,130.34,116.61 -42,1,26.11.2015,1.0,1722,130.34,116.61 -42,1,27.11.2015,1.0,1718,130.34,116.61 -42,1,28.11.2015,1.0,308,130.34,116.61 -42,1,29.11.2015,1.0,1103,130.34,116.61 -42,1,30.11.2015,1.0,1599,130.34,116.61 -42,1,01.12.2015,,478,130.34, -42,1,02.12.2015,,412,130.34, -42,1,03.12.2015,,496,130.34, -42,1,04.12.2015,,374,130.34, -42,1,05.12.2015,1.0,980,130.34,116.61 -42,1,06.12.2015,1.0,1630,130.34,116.61 -42,1,07.12.2015,1.0,1181,130.34,116.61 -42,1,08.12.2015,,336,130.34, -42,1,09.12.2015,,145,130.34, -42,1,10.12.2015,,330,130.34, -42,1,11.12.2015,,352,130.34, -42,1,12.12.2015,,443,130.34, -42,1,13.12.2015,,368,130.34, -42,1,14.12.2015,,267,130.34, -42,1,15.12.2015,,327,130.34, -42,1,16.12.2015,,273,130.34, -42,1,17.12.2015,,204,130.34, -42,1,18.12.2015,,245,130.34, -42,1,19.12.2015,,280,130.34, -42,1,20.12.2015,,295,130.34, -42,1,21.12.2015,,119,130.34, -42,1,22.12.2015,,399,130.34, -42,1,23.12.2015,,254,130.34, -42,1,24.12.2015,,317,126.54, -42,1,25.12.2015,,339,126.54, -42,1,26.12.2015,,402,126.54, -42,1,27.12.2015,,459,126.54, -42,1,28.12.2015,,126,126.54, -42,1,29.12.2015,,286,126.54, -42,1,30.12.2015,,333,126.54, -42,1,31.12.2015,,173,126.54, -42,1,01.01.2016,,50,126.54, -42,1,02.01.2016,,220,126.54, -42,1,03.01.2016,,129,126.54, -42,1,04.01.2016,,107,126.54, -42,1,05.01.2016,,91,126.54, -42,1,06.01.2016,,361,126.54, -42,1,07.01.2016,,107,126.54, -42,1,08.01.2016,,276,126.54, -42,1,09.01.2016,,195,126.54, -42,1,10.01.2016,,145,126.54, -42,1,11.01.2016,,267,126.54, -42,1,12.01.2016,,383,126.54, -42,1,13.01.2016,,374,126.54, -42,1,14.01.2016,,427,126.54, -42,1,15.01.2016,,616,126.54, -42,1,16.01.2016,,468,126.54, -42,1,17.01.2016,,443,126.54, -42,1,18.01.2016,,364,126.54, -42,1,19.01.2016,,390,126.54, -42,1,20.01.2016,,151,126.54, -42,1,21.01.2016,,443,127.9, -42,1,22.01.2016,,550,127.9, -42,1,23.01.2016,,1216,127.9, -42,1,24.01.2016,,1238,127.9, -42,1,25.01.2016,,88,127.9, -42,1,26.01.2016,,163,127.9, -42,1,27.01.2016,,713,127.9, -42,1,28.01.2016,,2579,127.9, -42,1,29.01.2016,,559,127.9, -42,1,30.01.2016,,594,127.9, -42,1,31.01.2016,,644,127.9, -42,1,01.02.2016,,396,130.34, -42,1,02.02.2016,,1122,130.34, -42,1,03.02.2016,,550,130.34, -42,1,04.02.2016,,305,133.06, -42,1,05.02.2016,,597,133.06, -42,1,06.02.2016,,826,133.06, -42,1,07.02.2016,,424,141.76, 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-43,1,27.04.2016,,110,135.78, -43,1,28.04.2016,,151,135.78, -43,1,29.04.2016,,317,131.16, -43,1,30.04.2016,,346,131.16, -43,1,01.05.2016,,97,131.16, -43,1,02.05.2016,,126,131.16, -43,1,03.05.2016,,75,131.16, -43,1,04.05.2016,,151,131.16, -43,1,05.05.2016,,188,131.16, -43,1,06.05.2016,,192,131.16, -43,1,07.05.2016,,273,131.16, -43,1,08.05.2016,,141,131.16, -43,1,09.05.2016,,160,131.16, -43,1,10.05.2016,,145,131.16, -43,1,11.05.2016,,478,131.16, -43,1,12.05.2016,,251,131.16, -43,1,13.05.2016,,267,128.98, -43,1,14.05.2016,,324,128.98, -43,1,15.05.2016,,232,128.98, -43,1,16.05.2016,,286,128.98, -43,1,17.05.2016,1.0,1275,128.98,119.6 -43,1,18.05.2016,1.0,1508,128.98,119.6 -43,1,19.05.2016,1.0,1275,128.98,119.6 -43,1,20.05.2016,1.0,1071,128.98,119.6 -43,1,21.05.2016,1.0,1565,128.98,119.6 -43,1,22.05.2016,1.0,1319,128.98,119.6 -44,1,01.01.2015,,3,163.78, -44,1,02.01.2015,,25,163.78, -44,1,03.01.2015,,38,163.78, -44,1,04.01.2015,,35,163.78, -44,1,05.01.2015,,63,163.78, -44,1,06.01.2015,,53,163.78, -44,1,07.01.2015,,72,163.78, -44,1,08.01.2015,,123,163.78, -44,1,09.01.2015,,53,163.78, -44,1,10.01.2015,,47,163.78, -44,1,11.01.2015,,38,163.78, -44,1,12.01.2015,,75,163.78, -44,1,13.01.2015,,91,163.78, -44,1,14.01.2015,,44,168.4, -44,1,15.01.2015,,75,168.4, -44,1,16.01.2015,,60,168.4, -44,1,17.01.2015,,53,168.4, -44,1,18.01.2015,,69,168.4, -44,1,19.01.2015,,72,168.4, -44,1,20.01.2015,,63,168.4, -44,1,21.01.2015,,123,168.4, -44,1,22.01.2015,,57,168.4, -44,1,23.01.2015,,57,160.24, -44,1,24.01.2015,,107,160.24, -44,1,25.01.2015,,79,160.24, -44,1,26.01.2015,,60,160.24, -44,1,27.01.2015,,91,160.24, -44,1,28.01.2015,,151,146.92, -44,1,29.01.2015,,135,146.92, -44,1,30.01.2015,,110,146.92, -44,1,31.01.2015,,97,146.92, -44,1,01.02.2015,,135,146.92, -44,1,02.02.2015,,119,146.92, -44,1,03.02.2015,,132,146.92, -44,1,04.02.2015,,170,146.92, -44,1,05.02.2015,,192,146.92, -44,1,06.02.2015,,148,146.92, -44,1,07.02.2015,,123,146.92, -44,1,08.02.2015,,132,146.92, 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-44,1,19.04.2016,1.0,1524,135.78,122.32 -44,1,20.04.2016,1.0,2419,135.78,122.32 -44,1,21.04.2016,1.0,1272,135.78,122.32 -44,1,22.04.2016,1.0,833,135.78,122.32 -44,1,23.04.2016,1.0,807,135.78,122.32 -44,1,24.04.2016,1.0,773,135.78,122.32 -44,1,25.04.2016,1.0,415,135.78,122.32 -44,1,26.04.2016,,198,135.78, -44,1,27.04.2016,,188,135.78, -44,1,28.04.2016,,176,135.78, -44,1,29.04.2016,,154,131.16, -44,1,30.04.2016,,141,131.16, -44,1,01.05.2016,,138,131.16, -44,1,02.05.2016,,88,131.16, -44,1,03.05.2016,,85,131.16, -44,1,04.05.2016,,126,131.16, -44,1,05.05.2016,,119,131.16, -44,1,06.05.2016,,145,131.16, -44,1,07.05.2016,,104,131.16, -44,1,08.05.2016,,69,131.16, -44,1,09.05.2016,,79,131.16, -44,1,10.05.2016,,63,131.16, -44,1,11.05.2016,,173,131.16, -44,1,12.05.2016,,207,131.16, -44,1,13.05.2016,,295,128.98, -44,1,14.05.2016,,126,128.98, -44,1,15.05.2016,,148,128.98, -44,1,16.05.2016,,151,128.98, -44,1,17.05.2016,1.0,952,128.98,119.6 -44,1,18.05.2016,1.0,1228,128.98,119.6 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-45,1,30.01.2015,,192,154.26, -45,1,31.01.2015,,182,154.26, -45,1,01.02.2015,,226,154.26, -45,1,02.02.2015,,85,154.26, -45,1,03.02.2015,,154,154.26, -45,1,04.02.2015,,248,154.26, -45,1,05.02.2015,,292,154.26, -45,1,06.02.2015,,251,154.26, -45,1,07.02.2015,,182,154.26, -45,1,08.02.2015,,232,154.26, -45,1,09.02.2015,,214,154.26, -45,1,10.02.2015,,239,154.26, -45,1,11.02.2015,,185,154.26, -45,1,12.02.2015,,182,154.26, -45,1,13.02.2015,,126,156.98, -45,1,14.02.2015,,349,156.98, -45,1,15.02.2015,,182,156.98, -45,1,16.02.2015,,126,156.98, -45,1,17.02.2015,1.0,635,156.98,143.8 -45,1,18.02.2015,1.0,446,156.98,135.64 -45,1,19.02.2015,1.0,826,156.98,135.64 -45,1,20.02.2015,1.0,842,156.98,135.64 -45,1,21.02.2015,1.0,858,156.98,135.64 -45,1,22.02.2015,1.0,462,156.98,135.64 -45,1,23.02.2015,1.0,496,156.98,135.64 -45,1,24.02.2015,,94,156.98, -45,1,25.02.2015,,245,156.98, -45,1,26.02.2015,,170,156.98, -45,1,27.02.2015,,176,151.27, -45,1,28.02.2015,,437,151.27, -45,1,01.03.2015,,412,151.27, 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-45,1,31.03.2015,,355,131.97, -45,1,01.04.2015,,311,133.33, -45,1,02.04.2015,,308,133.33, -45,1,03.04.2015,,273,133.33, -45,1,04.04.2015,,239,133.33, -45,1,05.04.2015,,449,133.33, -45,1,06.04.2015,,210,133.33, -45,1,07.04.2015,1.0,1568,133.33,119.33 -45,1,08.04.2015,1.0,1448,133.33,119.33 -45,1,09.04.2015,1.0,1250,133.33,119.33 -45,1,10.04.2015,1.0,1354,133.33,119.33 -45,1,11.04.2015,1.0,1539,133.33,119.33 -45,1,12.04.2015,1.0,261,133.33,119.33 -45,1,13.04.2015,1.0,534,133.33,119.33 -45,1,14.04.2015,,192,130.34, -45,1,15.04.2015,,201,130.34, -45,1,16.04.2015,,377,130.34, -45,1,17.04.2015,,176,138.5, -45,1,18.04.2015,,314,138.5, -45,1,19.04.2015,,214,138.5, -45,1,20.04.2015,,182,138.5, -45,1,21.04.2015,,239,138.5, -45,1,22.04.2015,,317,128.98, -45,1,23.04.2015,,462,128.98, -45,1,24.04.2015,,434,128.98, -45,1,25.04.2015,,295,128.98, -45,1,26.04.2015,,308,128.98, -45,1,27.04.2015,,342,128.98, -45,1,28.04.2015,,239,128.98, -45,1,29.04.2015,,286,131.7, -45,1,30.04.2015,,478,131.7, 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-45,1,01.06.2015,,126,129.25, -45,1,02.06.2015,,258,129.25, -45,1,03.06.2015,,349,129.25, -45,1,04.06.2015,,267,129.25, -45,1,05.06.2015,,305,129.25, -45,1,06.06.2015,,572,131.7, -45,1,07.06.2015,,405,131.7, -45,1,08.06.2015,,239,131.7, -45,1,09.06.2015,1.0,1665,131.7,119.33 -45,1,10.06.2015,1.0,1703,131.7,119.33 -45,1,11.06.2015,1.0,1093,131.7,119.33 -45,1,12.06.2015,1.0,1002,131.7,119.33 -45,1,13.06.2015,1.0,170,131.7,119.33 -45,1,14.06.2015,1.0,886,131.7,119.33 -45,1,15.06.2015,1.0,848,131.7,119.33 -45,1,16.06.2015,,308,131.7, -45,1,17.06.2015,,792,131.7, -45,1,18.06.2015,,522,128.17, -45,1,19.06.2015,,1093,128.17, -45,1,20.06.2015,,390,128.17, -45,1,21.06.2015,,374,128.17, -45,1,22.06.2015,,644,128.17, -45,1,23.06.2015,,814,128.17, -45,1,24.06.2015,,644,128.17, -45,1,25.06.2015,,609,128.17, -45,1,26.06.2015,,217,128.17, -45,1,27.06.2015,,616,128.17, -45,1,28.06.2015,,757,130.34, -45,1,29.06.2015,,270,130.34, -45,1,30.06.2015,1.0,2253,130.34,119.33 -45,1,01.07.2015,1.0,2259,130.34,119.33 -45,1,02.07.2015,1.0,1643,130.34,119.33 -45,1,03.07.2015,1.0,1860,130.34,119.33 -45,1,04.07.2015,1.0,666,130.34,119.33 -45,1,05.07.2015,1.0,902,130.34,119.33 -45,1,06.07.2015,1.0,506,130.34,119.33 -45,1,07.07.2015,,342,130.34, -45,1,08.07.2015,,481,130.34, -45,1,09.07.2015,,660,121.64, -45,1,10.07.2015,,789,121.64, -45,1,11.07.2015,,685,123.55, -45,1,12.07.2015,,440,123.55, -45,1,13.07.2015,,386,123.55, -45,1,14.07.2015,,644,123.55, -45,1,15.07.2015,,829,125.45, -45,1,16.07.2015,,490,125.45, -45,1,17.07.2015,,531,125.45, -45,1,18.07.2015,,990,127.62, -45,1,19.07.2015,,858,127.62, -45,1,20.07.2015,,1674,127.62, -45,1,21.07.2015,,1659,127.62, -45,1,22.07.2015,,1235,127.62, -45,1,23.07.2015,,801,127.62, -45,1,24.07.2015,,745,127.62, -45,1,25.07.2015,,694,127.62, -45,1,26.07.2015,,873,130.07, -45,1,27.07.2015,,1835,130.07, -45,1,28.07.2015,1.0,2325,130.07,124.77 -45,1,29.07.2015,1.0,839,130.07,124.77 -45,1,30.07.2015,1.0,839,130.07,124.77 -45,1,31.07.2015,1.0,836,130.07,124.77 -45,1,01.08.2015,1.0,823,130.07,124.77 -45,1,02.08.2015,1.0,823,130.07,124.77 -45,1,03.08.2015,1.0,2658,130.07,124.77 -45,1,04.08.2015,,807,138.5, -45,1,05.08.2015,,591,138.5, -45,1,06.08.2015,,452,138.5, -45,1,07.08.2015,,386,138.5, -45,1,08.08.2015,,242,141.21, -45,1,09.08.2015,,276,141.21, -45,1,10.08.2015,,236,141.21, -45,1,11.08.2015,,261,141.21, -45,1,12.08.2015,,459,142.85, -45,1,13.08.2015,,239,142.85, -45,1,14.08.2015,,116,142.85, -45,1,15.08.2015,,217,142.85, -45,1,16.08.2015,,518,142.85, -45,1,17.08.2015,,245,142.85, -45,1,18.08.2015,,267,142.85, -45,1,19.08.2015,,286,142.85, -45,1,20.08.2015,,349,142.85, -45,1,21.08.2015,,201,142.85, -45,1,22.08.2015,,173,142.85, -45,1,23.08.2015,,151,142.85, -45,1,24.08.2015,,214,142.85, -45,1,25.08.2015,,302,142.85, -45,1,26.08.2015,,349,142.85, -45,1,27.08.2015,,195,137.68, -45,1,28.08.2015,,603,137.68, -45,1,29.08.2015,,377,137.68, -45,1,30.08.2015,,418,137.68, -45,1,31.08.2015,,176,137.68, -45,1,01.09.2015,,333,137.68, -45,1,02.09.2015,,735,135.78, -45,1,03.09.2015,,456,135.78, -45,1,04.09.2015,,723,135.78, -45,1,05.09.2015,,562,133.6, -45,1,06.09.2015,,732,133.6, -45,1,07.09.2015,,424,133.6, -45,1,08.09.2015,,851,133.6, -45,1,09.09.2015,,559,135.51, -45,1,10.09.2015,,358,135.51, -45,1,11.09.2015,,242,135.51, -45,1,12.09.2015,,380,135.51, -45,1,13.09.2015,,471,135.51, -45,1,14.09.2015,,405,135.51, -45,1,15.09.2015,,1103,135.51, -45,1,16.09.2015,,616,138.5, -45,1,17.09.2015,,254,138.5, -45,1,18.09.2015,,666,138.5, -45,1,19.09.2015,,528,138.5, -45,1,20.09.2015,,550,138.5, -45,1,21.09.2015,,412,138.5, -45,1,22.09.2015,,553,138.5, -45,1,23.09.2015,,339,141.21, -45,1,24.09.2015,,443,141.21, -45,1,25.09.2015,,364,141.21, -45,1,26.09.2015,,434,141.21, -45,1,27.09.2015,,408,141.21, -45,1,28.09.2015,,305,141.21, -45,1,29.09.2015,,346,141.21, -45,1,30.09.2015,,270,141.21, -45,1,01.10.2015,,223,141.21, -45,1,02.10.2015,,490,141.21, -45,1,03.10.2015,,427,141.21, -45,1,04.10.2015,,512,141.21, -45,1,05.10.2015,,264,141.21, -45,1,06.10.2015,1.0,754,141.21,132.92 -45,1,07.10.2015,1.0,682,141.21,130.21 -45,1,08.10.2015,1.0,701,141.11,130.21 -45,1,09.10.2015,1.0,1087,141.21,130.21 -45,1,10.10.2015,1.0,723,141.21,130.21 -45,1,11.10.2015,1.0,1043,141.21,130.21 -45,1,12.10.2015,1.0,581,141.21,130.21 -45,1,13.10.2015,,430,141.21, -45,1,14.10.2015,,188,141.21, -45,1,15.10.2015,,160,142.57, -45,1,16.10.2015,,320,142.57, -45,1,17.10.2015,,358,142.57, -45,1,18.10.2015,,446,142.57, -45,1,19.10.2015,,314,142.57, -45,1,20.10.2015,,324,142.57, -45,1,21.10.2015,,877,130.34, -45,1,22.10.2015,,525,130.34, -45,1,23.10.2015,,572,130.34, -45,1,24.10.2015,,666,130.34, -45,1,25.10.2015,,836,130.34, -45,1,26.10.2015,,471,130.34, -45,1,27.10.2015,1.0,1772,130.34,122.32 -45,1,28.10.2015,1.0,1222,130.34,124.77 -45,1,29.10.2015,1.0,754,130.34,124.77 -45,1,30.10.2015,1.0,672,130.34,124.77 -45,1,31.10.2015,1.0,842,130.34,124.77 -45,1,01.11.2015,1.0,587,130.34,124.77 -45,1,02.11.2015,1.0,468,130.34,124.77 -45,1,03.11.2015,,584,130.34, -45,1,04.11.2015,,311,134.69, -45,1,05.11.2015,,342,134.69, -45,1,06.11.2015,,361,134.69, -45,1,07.11.2015,,342,134.69, -45,1,08.11.2015,,302,134.69, -45,1,09.11.2015,,242,134.69, -45,1,10.11.2015,,396,134.69, -45,1,11.11.2015,,346,130.34, -45,1,12.11.2015,,346,130.34, -45,1,13.11.2015,,446,130.34, -45,1,14.11.2015,,609,130.34, -45,1,15.11.2015,,597,130.34, -45,1,16.11.2015,,408,130.34, -45,1,17.11.2015,,650,130.34, -45,1,18.11.2015,,443,130.34, -45,1,19.11.2015,,600,130.34, -45,1,20.11.2015,,427,130.34, -45,1,21.11.2015,,317,130.34, -45,1,22.11.2015,,478,130.34, -45,1,23.11.2015,,352,130.34, -45,1,24.11.2015,1.0,2249,130.34,116.61 -45,1,25.11.2015,1.0,3968,130.34,116.61 -45,1,26.11.2015,1.0,452,130.34,116.61 -45,1,27.11.2015,1.0,1235,130.34,116.61 -45,1,28.11.2015,1.0,2746,130.34,116.61 -45,1,29.11.2015,1.0,3044,130.34,116.61 -45,1,30.11.2015,1.0,2849,130.34,116.61 -45,1,01.12.2015,,503,130.34, -45,1,02.12.2015,,273,130.34, -45,1,03.12.2015,,427,130.34, -45,1,04.12.2015,,402,130.34, -45,1,05.12.2015,1.0,880,130.34,116.61 -45,1,06.12.2015,1.0,892,130.34,116.61 -45,1,07.12.2015,1.0,487,130.34,116.61 -45,1,08.12.2015,,515,130.34, -45,1,09.12.2015,,368,130.34, -45,1,10.12.2015,,408,130.34, -45,1,11.12.2015,,440,130.34, -45,1,12.12.2015,,719,130.34, -45,1,13.12.2015,,361,130.34, -45,1,14.12.2015,,248,130.34, -45,1,15.12.2015,,437,130.34, -45,1,16.12.2015,,261,130.34, -45,1,17.12.2015,,248,130.34, -45,1,18.12.2015,,399,130.34, -45,1,19.12.2015,,452,130.34, -45,1,20.12.2015,,283,130.34, -45,1,21.12.2015,,245,130.34, -45,1,22.12.2015,,364,130.34, -45,1,23.12.2015,,468,130.34, -45,1,24.12.2015,,578,126.54, -45,1,25.12.2015,,716,126.54, -45,1,26.12.2015,,53,126.54, -45,1,27.12.2015,,245,126.54, -45,1,28.12.2015,,572,126.54, -45,1,29.12.2015,,619,126.54, -45,1,30.12.2015,,760,126.54, -45,1,31.12.2015,,716,126.54, -45,1,01.01.2016,,47,126.54, -45,1,02.01.2016,,245,126.54, -45,1,03.01.2016,,459,126.54, -45,1,04.01.2016,,214,126.54, -45,1,05.01.2016,,314,126.54, -45,1,06.01.2016,,349,126.54, -45,1,07.01.2016,,232,126.54, -45,1,08.01.2016,,242,126.54, -45,1,09.01.2016,,342,126.54, -45,1,10.01.2016,,258,126.54, -45,1,11.01.2016,,201,126.54, -45,1,12.01.2016,,449,126.54, -45,1,13.01.2016,,201,126.54, -45,1,14.01.2016,,210,126.54, -45,1,15.01.2016,,308,126.54, -45,1,16.01.2016,,462,126.54, -45,1,17.01.2016,,594,126.54, -45,1,18.01.2016,,572,126.54, -45,1,19.01.2016,,569,126.54, -45,1,20.01.2016,,418,126.54, -45,1,21.01.2016,,424,127.9, -45,1,22.01.2016,,462,127.9, -45,1,23.01.2016,,298,127.9, -45,1,24.01.2016,,591,127.9, -45,1,25.01.2016,,421,127.9, -45,1,26.01.2016,,217,127.9, -45,1,27.01.2016,,358,127.9, -45,1,28.01.2016,,415,127.9, -45,1,29.01.2016,,270,127.9, -45,1,30.01.2016,,515,127.9, -45,1,31.01.2016,,647,127.9, -45,1,01.02.2016,,207,130.34, -45,1,02.02.2016,,368,130.34, -45,1,03.02.2016,,600,130.34, -45,1,04.02.2016,,421,133.06, 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-47,1,07.01.2016,,210,126.54, -47,1,08.01.2016,,333,126.54, -47,1,09.01.2016,,327,126.54, -47,1,10.01.2016,,386,126.54, -47,1,11.01.2016,,556,126.54, -47,1,12.01.2016,,415,126.54, -47,1,13.01.2016,,446,126.54, -47,1,14.01.2016,,575,126.54, -47,1,15.01.2016,,660,126.54, -47,1,16.01.2016,,528,126.54, -47,1,17.01.2016,,587,126.54, -47,1,18.01.2016,,412,126.54, -47,1,19.01.2016,,864,126.54, -47,1,20.01.2016,,113,126.54, -47,1,21.01.2016,,383,127.9, -47,1,22.01.2016,,833,127.9, -47,1,23.01.2016,,754,127.9, -47,1,24.01.2016,,754,127.9, -47,1,25.01.2016,,547,127.9, -47,1,26.01.2016,,471,127.9, -47,1,27.01.2016,,635,127.9, -47,1,28.01.2016,,412,127.9, -47,1,29.01.2016,,490,127.9, -47,1,30.01.2016,,424,127.9, -47,1,31.01.2016,,528,127.9, -47,1,01.02.2016,,358,130.34, -47,1,02.02.2016,,443,130.34, -47,1,03.02.2016,,481,130.34, -47,1,04.02.2016,,487,133.06, -47,1,05.02.2016,,606,133.06, -47,1,06.02.2016,,710,133.06, -47,1,07.02.2016,,487,141.76, -47,1,08.02.2016,,292,141.76, 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-47,1,10.05.2016,,261,131.16, -47,1,11.05.2016,,217,131.16, -47,1,12.05.2016,,462,131.16, -47,1,13.05.2016,,547,128.98, -47,1,14.05.2016,,440,128.98, -47,1,15.05.2016,,478,128.98, -47,1,16.05.2016,,484,128.98, -47,1,17.05.2016,1.0,1414,128.98,119.6 -47,1,18.05.2016,1.0,1367,128.98,119.6 -47,1,19.05.2016,1.0,1137,128.98,119.6 -47,1,20.05.2016,1.0,1411,128.98,119.6 -47,1,21.05.2016,1.0,1722,128.98,119.6 -47,1,22.05.2016,1.0,2127,128.98,119.6 -48,1,01.01.2015,,6,163.78, -48,1,02.01.2015,,28,163.78, -48,1,03.01.2015,,9,163.78, -48,1,04.01.2015,,31,163.78, -48,1,05.01.2015,,35,163.78, -48,1,06.01.2015,,47,163.78, -48,1,07.01.2015,,35,163.78, -48,1,08.01.2015,,38,163.78, -48,1,09.01.2015,,57,163.78, -48,1,10.01.2015,,35,163.78, -48,1,11.01.2015,,25,163.78, -48,1,12.01.2015,,41,163.78, -48,1,13.01.2015,,57,163.78, -48,1,14.01.2015,,31,168.4, -48,1,15.01.2015,,50,168.4, -48,1,16.01.2015,,75,168.4, -48,1,17.01.2015,,60,168.4, -48,1,18.01.2015,,31,168.4, -48,1,19.01.2015,,53,168.4, 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-48,1,25.10.2015,,82,130.34, -48,1,26.10.2015,,223,130.34, -48,1,27.10.2015,1.0,1439,130.34,122.32 -48,1,28.10.2015,1.0,2432,130.34,124.77 -48,1,29.10.2015,1.0,864,130.34,124.77 -48,1,30.10.2015,1.0,908,130.34,124.77 -48,1,31.10.2015,1.0,682,130.34,124.77 -48,1,01.11.2015,1.0,339,130.34,124.77 -48,1,02.11.2015,1.0,462,130.34,124.77 -48,1,03.11.2015,,295,130.34, -48,1,04.11.2015,,167,134.69, -48,1,05.11.2015,,123,134.69, -48,1,06.11.2015,,176,134.69, -48,1,07.11.2015,,141,134.69, -48,1,08.11.2015,,207,134.69, -48,1,09.11.2015,,251,134.69, -48,1,10.11.2015,,163,134.69, -48,1,11.11.2015,,600,130.34, -48,1,12.11.2015,,412,130.34, -48,1,13.11.2015,,302,130.34, -48,1,14.11.2015,,245,130.34, -48,1,15.11.2015,,292,130.34, -48,1,16.11.2015,,179,130.34, -48,1,17.11.2015,,113,130.34, -48,1,18.11.2015,,295,130.34, -48,1,19.11.2015,,129,130.34, -48,1,20.11.2015,,245,130.34, -48,1,21.11.2015,,151,130.34, -48,1,22.11.2015,,176,130.34, -48,1,23.11.2015,,129,130.34, 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-48,1,24.12.2015,,314,126.54, -48,1,25.12.2015,,616,126.54, -48,1,26.12.2015,,232,126.54, -48,1,27.12.2015,,264,126.54, -48,1,28.12.2015,,320,126.54, -48,1,29.12.2015,,383,126.54, -48,1,30.12.2015,,361,126.54, -48,1,31.12.2015,,210,126.54, -48,1,01.01.2016,,35,126.54, -48,1,02.01.2016,,110,126.54, -48,1,03.01.2016,,85,126.54, -48,1,04.01.2016,,195,126.54, -48,1,05.01.2016,,368,126.54, -48,1,06.01.2016,,358,126.54, -48,1,07.01.2016,,232,126.54, -48,1,08.01.2016,,273,126.54, -48,1,09.01.2016,,188,126.54, -48,1,10.01.2016,,217,126.54, -48,1,11.01.2016,,214,126.54, -48,1,12.01.2016,,286,126.54, -48,1,13.01.2016,,232,126.54, -48,1,14.01.2016,,242,126.54, -48,1,15.01.2016,,317,126.54, -48,1,16.01.2016,,1992,126.54, -48,1,17.01.2016,,355,126.54, -48,1,18.01.2016,,487,126.54, -48,1,19.01.2016,,446,126.54, -48,1,20.01.2016,,537,126.54, -48,1,21.01.2016,,308,127.9, -48,1,22.01.2016,,738,127.9, -48,1,23.01.2016,,408,127.9, -48,1,24.01.2016,,418,127.9, -48,1,25.01.2016,,242,127.9, 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-48,1,25.04.2016,1.0,2249,135.78,122.32 -48,1,26.04.2016,,160,135.78, -48,1,27.04.2016,,393,135.78, -48,1,28.04.2016,,170,135.78, -48,1,29.04.2016,,518,131.16, -48,1,30.04.2016,,327,131.16, -48,1,01.05.2016,,311,131.16, -48,1,02.05.2016,,195,131.16, -48,1,03.05.2016,,107,131.16, -48,1,04.05.2016,,657,131.16, -48,1,05.05.2016,,302,131.16, -48,1,06.05.2016,,236,131.16, -48,1,07.05.2016,,267,131.16, -48,1,08.05.2016,,201,131.16, -48,1,09.05.2016,,85,131.16, -48,1,10.05.2016,,201,131.16, -48,1,11.05.2016,,286,131.16, -48,1,12.05.2016,,258,131.16, -48,1,13.05.2016,,584,128.98, -48,1,14.05.2016,,405,128.98, -48,1,15.05.2016,,578,128.98, -48,1,16.05.2016,,390,128.98, -48,1,17.05.2016,1.0,9686,128.98,119.6 -48,1,18.05.2016,1.0,1932,128.98,119.6 -48,1,19.05.2016,1.0,2064,128.98,119.6 -48,1,20.05.2016,1.0,2064,128.98,119.6 -48,1,21.05.2016,1.0,2064,128.98,119.6 -48,1,22.05.2016,1.0,2064,128.98,119.6 -49,1,01.01.2015,,6,163.78, -49,1,02.01.2015,,47,163.78, -49,1,03.01.2015,,31,163.78, 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-49,1,07.04.2015,1.0,430,133.33,119.33 -49,1,08.04.2015,1.0,801,133.33,119.33 -49,1,09.04.2015,1.0,547,133.33,119.33 -49,1,10.04.2015,1.0,716,133.33,119.33 -49,1,11.04.2015,1.0,679,133.33,119.33 -49,1,12.04.2015,1.0,163,133.33,119.33 -49,1,13.04.2015,1.0,440,133.33,119.33 -49,1,14.04.2015,,57,130.34, -49,1,15.04.2015,,119,130.34, -49,1,16.04.2015,,163,130.34, -49,1,17.04.2015,,91,138.5, -49,1,18.04.2015,,116,138.5, -49,1,19.04.2015,,97,138.5, -49,1,20.04.2015,,135,138.5, -49,1,21.04.2015,,132,138.5, -49,1,22.04.2015,,176,128.98, -49,1,23.04.2015,,135,128.98, -49,1,24.04.2015,,116,128.98, -49,1,25.04.2015,,135,128.98, -49,1,26.04.2015,,110,128.98, -49,1,27.04.2015,,138,128.98, -49,1,28.04.2015,,126,128.98, -49,1,29.04.2015,,85,131.7, -49,1,30.04.2015,,145,131.7, -49,1,01.05.2015,,91,131.7, -49,1,02.05.2015,,119,131.7, -49,1,03.05.2015,,50,131.7, -49,1,04.05.2015,,57,131.7, -49,1,05.05.2015,,97,131.7, -49,1,06.05.2015,,101,131.7, -49,1,07.05.2015,,138,131.7, -49,1,08.05.2015,,132,131.7, 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-49,1,09.07.2015,,170,121.64, -49,1,10.07.2015,,126,121.64, -49,1,11.07.2015,,79,123.55, -49,1,12.07.2015,,135,123.55, -49,1,13.07.2015,,126,123.55, -49,1,14.07.2015,,167,123.55, -49,1,15.07.2015,,129,125.45, -49,1,16.07.2015,,217,125.45, -49,1,17.07.2015,,248,125.45, -49,1,18.07.2015,,349,127.62, -49,1,19.07.2015,,412,127.62, -49,1,20.07.2015,,314,127.62, -49,1,21.07.2015,,248,127.62, -49,1,22.07.2015,,214,127.62, -49,1,23.07.2015,,270,127.62, -49,1,24.07.2015,,371,127.62, -49,1,25.07.2015,,245,127.62, -49,1,26.07.2015,,245,130.07, -49,1,27.07.2015,,320,130.07, -49,1,28.07.2015,1.0,280,130.07,124.77 -49,1,29.07.2015,1.0,346,130.07,124.77 -49,1,30.07.2015,1.0,358,130.07,124.77 -49,1,31.07.2015,1.0,386,130.07,124.77 -49,1,01.08.2015,1.0,468,130.07,124.77 -49,1,02.08.2015,1.0,619,130.07,124.77 -49,1,03.08.2015,1.0,358,130.07,124.77 -49,1,04.08.2015,,101,138.5, -49,1,05.08.2015,,132,138.5, -49,1,06.08.2015,,232,138.5, -49,1,07.08.2015,,97,138.5, -49,1,08.08.2015,,113,141.21, 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-49,1,16.04.2016,,129,135.78, -49,1,17.04.2016,,154,135.78, -49,1,18.04.2016,,179,135.78, -49,1,19.04.2016,1.0,726,135.78,122.32 -49,1,20.04.2016,1.0,1294,135.78,122.32 -49,1,21.04.2016,1.0,920,135.78,122.32 -49,1,22.04.2016,1.0,1197,135.78,122.32 -49,1,23.04.2016,1.0,1360,135.78,122.32 -49,1,24.04.2016,1.0,716,135.78,122.32 -49,1,25.04.2016,1.0,726,135.78,122.32 -49,1,26.04.2016,,132,135.78, -49,1,27.04.2016,,101,135.78, -49,1,28.04.2016,,239,135.78, -49,1,29.04.2016,,210,131.16, -49,1,30.04.2016,,160,131.16, -49,1,01.05.2016,,151,131.16, -49,1,02.05.2016,,116,131.16, -49,1,03.05.2016,,129,131.16, -49,1,04.05.2016,,113,131.16, -49,1,05.05.2016,,145,131.16, -49,1,06.05.2016,,132,131.16, -49,1,07.05.2016,,210,131.16, -49,1,08.05.2016,,110,131.16, -49,1,09.05.2016,,116,131.16, -49,1,10.05.2016,,126,131.16, -49,1,11.05.2016,,94,131.16, -49,1,12.05.2016,,160,131.16, -49,1,13.05.2016,,182,128.98, -49,1,14.05.2016,,182,128.98, -49,1,15.05.2016,,173,128.98, -49,1,16.05.2016,,210,128.98, 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-52,1,07.04.2015,1.0,1524,133.33,119.33 -52,1,08.04.2015,1.0,1492,133.33,119.33 -52,1,09.04.2015,1.0,895,133.33,119.33 -52,1,10.04.2015,1.0,543,133.33,119.33 -52,1,11.04.2015,1.0,421,133.33,119.33 -52,1,12.04.2015,1.0,770,133.33,119.33 -52,1,13.04.2015,1.0,251,133.33,119.33 -52,1,14.04.2015,,79,130.34, -52,1,15.04.2015,,110,130.34, -52,1,16.04.2015,,41,130.34, -52,1,17.04.2015,,91,138.5, -52,1,18.04.2015,,47,138.5, -52,1,19.04.2015,,53,138.5, -52,1,20.04.2015,,60,138.5, -52,1,21.04.2015,,31,138.5, -52,1,22.04.2015,,126,128.98, -52,1,23.04.2015,,79,128.98, -52,1,24.04.2015,,201,128.98, -52,1,25.04.2015,,119,128.98, -52,1,26.04.2015,,110,128.98, -52,1,27.04.2015,,107,128.98, -52,1,28.04.2015,,167,128.98, -52,1,29.04.2015,,185,131.7, -52,1,30.04.2015,,163,131.7, -52,1,01.05.2015,,82,131.7, -52,1,02.05.2015,,66,131.7, -52,1,03.05.2015,,50,131.7, -52,1,04.05.2015,,135,131.7, -52,1,05.05.2015,,185,131.7, -52,1,06.05.2015,,280,131.7, -52,1,07.05.2015,,217,131.7, -52,1,08.05.2015,,273,131.7, 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-53,1,25.01.2015,,85,160.24, -53,1,26.01.2015,,75,160.24, -53,1,27.01.2015,,69,160.24, -53,1,28.01.2015,,258,146.92, -53,1,29.01.2015,,198,146.92, -53,1,30.01.2015,,141,146.92, -53,1,31.01.2015,,148,146.92, -53,1,01.02.2015,,85,146.92, -53,1,02.02.2015,,141,146.92, -53,1,03.02.2015,,138,146.92, -53,1,04.02.2015,,167,146.92, -53,1,05.02.2015,,198,146.92, -53,1,06.02.2015,,320,146.92, -53,1,07.02.2015,,135,146.92, -53,1,08.02.2015,,129,146.92, -53,1,09.02.2015,,160,146.92, -53,1,10.02.2015,,214,146.92, -53,1,11.02.2015,,204,146.92, -53,1,12.02.2015,,123,146.92, -53,1,13.02.2015,,160,149.37, -53,1,14.02.2015,,179,149.37, -53,1,15.02.2015,,104,149.37, -53,1,16.02.2015,,123,149.37, -53,1,17.02.2015,1.0,232,149.37,143.8 -53,1,18.02.2015,1.0,320,149.37,135.64 -53,1,19.02.2015,1.0,443,149.37,135.64 -53,1,20.02.2015,1.0,707,149.37,135.64 -53,1,21.02.2015,1.0,434,149.37,135.64 -53,1,22.02.2015,1.0,346,149.37,135.64 -53,1,23.02.2015,1.0,443,149.37,135.64 -53,1,24.02.2015,,97,149.37, -53,1,25.02.2015,,188,149.37, -53,1,26.02.2015,,116,149.37, -53,1,27.02.2015,,129,143.93, -53,1,28.02.2015,,405,143.93, -53,1,01.03.2015,,185,143.93, -53,1,02.03.2015,,264,143.93, -53,1,03.03.2015,1.0,396,143.93,138.36 -53,1,04.03.2015,1.0,330,143.93,135.64 -53,1,05.03.2015,1.0,254,138.5,135.64 -53,1,06.03.2015,1.0,377,138.5,135.64 -53,1,07.03.2015,1.0,456,138.5,135.64 -53,1,08.03.2015,1.0,201,138.5,135.64 -53,1,09.03.2015,,129,140.67, -53,1,10.03.2015,,201,140.67, -53,1,11.03.2015,,141,140.67, -53,1,12.03.2015,,116,140.67, -53,1,13.03.2015,,179,140.67, -53,1,14.03.2015,,204,140.67, -53,1,15.03.2015,,198,140.67, -53,1,16.03.2015,,317,140.67, -53,1,17.03.2015,1.0,1816,140.67,127.49 -53,1,18.03.2015,1.0,1514,140.67,127.49 -53,1,19.03.2015,1.0,895,140.67,127.49 -53,1,20.03.2015,1.0,930,140.67,127.49 -53,1,21.03.2015,1.0,1002,140.67,127.49 -53,1,22.03.2015,1.0,628,140.67,127.49 -53,1,23.03.2015,1.0,641,140.67,127.49 -53,1,24.03.2015,,113,140.67, -53,1,25.03.2015,,148,140.67, -53,1,26.03.2015,,195,131.97, -53,1,27.03.2015,,198,131.97, -53,1,28.03.2015,,188,131.97, -53,1,29.03.2015,,179,131.97, -53,1,30.03.2015,,242,131.97, -53,1,31.03.2015,,173,131.97, -53,1,01.04.2015,,151,133.33, -53,1,02.04.2015,,88,133.33, -53,1,03.04.2015,,104,133.33, -53,1,04.04.2015,,141,133.33, -53,1,05.04.2015,,204,133.33, -53,1,06.04.2015,,223,133.33, -53,1,07.04.2015,1.0,1332,133.33,119.33 -53,1,08.04.2015,1.0,1024,133.33,119.33 -53,1,09.04.2015,1.0,1021,133.33,119.33 -53,1,10.04.2015,1.0,845,133.33,119.33 -53,1,11.04.2015,1.0,820,133.33,119.33 -53,1,12.04.2015,1.0,785,133.33,119.33 -53,1,13.04.2015,1.0,773,133.33,119.33 -53,1,14.04.2015,,163,130.34, -53,1,15.04.2015,,97,130.34, -53,1,16.04.2015,,116,130.34, -53,1,17.04.2015,,94,138.5, -53,1,18.04.2015,,57,138.5, -53,1,19.04.2015,,66,138.5, -53,1,20.04.2015,,72,138.5, -53,1,21.04.2015,,94,138.5, -53,1,22.04.2015,,75,128.98, -53,1,23.04.2015,,101,128.98, -53,1,24.04.2015,,79,128.98, -53,1,25.04.2015,,97,128.98, -53,1,26.04.2015,,85,128.98, -53,1,27.04.2015,,104,128.98, -53,1,28.04.2015,,232,128.98, -53,1,29.04.2015,,79,131.7, -53,1,30.04.2015,,107,131.7, -53,1,01.05.2015,,60,131.7, -53,1,02.05.2015,,91,131.7, -53,1,03.05.2015,,148,131.7, -53,1,04.05.2015,,88,131.7, -53,1,05.05.2015,,82,131.7, -53,1,06.05.2015,,85,131.7, -53,1,07.05.2015,,75,131.7, -53,1,08.05.2015,,151,131.7, -53,1,09.05.2015,,50,131.7, -53,1,10.05.2015,,72,131.7, -53,1,11.05.2015,,97,131.7, -53,1,12.05.2015,,60,131.7, -53,1,13.05.2015,,132,131.7, -53,1,14.05.2015,,88,131.7, -53,1,15.05.2015,,104,131.7, -53,1,16.05.2015,,198,131.7, -53,1,17.05.2015,,69,131.7, -53,1,18.05.2015,,123,131.7, -53,1,19.05.2015,1.0,525,131.7,119.33 -53,1,20.05.2015,1.0,336,131.7,119.33 -53,1,21.05.2015,1.0,204,131.7,119.33 -53,1,22.05.2015,1.0,452,131.7,119.33 -53,1,23.05.2015,1.0,207,131.7,119.33 -53,1,24.05.2015,1.0,214,131.7,119.33 -53,1,25.05.2015,1.0,236,131.7,119.33 -53,1,26.05.2015,,141,131.7, -53,1,27.05.2015,,66,129.25, -53,1,28.05.2015,,148,129.25, -53,1,29.05.2015,,104,129.25, -53,1,30.05.2015,,110,129.25, -53,1,31.05.2015,,50,129.25, -53,1,01.06.2015,,138,129.25, -53,1,02.06.2015,,119,129.25, -53,1,03.06.2015,,192,129.25, -53,1,04.06.2015,,94,129.25, -53,1,05.06.2015,,85,129.25, -53,1,06.06.2015,,116,131.7, -53,1,07.06.2015,,113,131.7, -53,1,08.06.2015,,214,131.7, -53,1,09.06.2015,1.0,531,131.7,119.33 -53,1,10.06.2015,1.0,437,131.7,119.33 -53,1,11.06.2015,1.0,402,131.7,119.33 -53,1,12.06.2015,1.0,412,131.7,119.33 -53,1,13.06.2015,1.0,569,131.7,119.33 -53,1,14.06.2015,1.0,346,131.7,119.33 -53,1,15.06.2015,1.0,823,131.7,119.33 -53,1,16.06.2015,,82,131.7, -53,1,17.06.2015,,104,131.7, -53,1,18.06.2015,,110,128.17, -53,1,19.06.2015,,151,128.17, -53,1,20.06.2015,,283,128.17, -53,1,21.06.2015,,116,128.17, -53,1,22.06.2015,,157,128.17, -53,1,23.06.2015,,145,128.17, -53,1,24.06.2015,,185,128.17, -53,1,25.06.2015,,210,128.17, -53,1,26.06.2015,,295,128.17, -53,1,27.06.2015,,207,128.17, -53,1,28.06.2015,,204,130.34, -53,1,29.06.2015,,97,130.34, -53,1,30.06.2015,1.0,468,130.34,119.33 -53,1,01.07.2015,1.0,767,130.34,119.33 -53,1,02.07.2015,1.0,324,130.34,119.33 -53,1,03.07.2015,1.0,518,130.34,119.33 -53,1,04.07.2015,1.0,377,130.34,119.33 -53,1,05.07.2015,1.0,261,130.34,119.33 -53,1,06.07.2015,1.0,396,130.34,119.33 -53,1,07.07.2015,,123,130.34, -53,1,08.07.2015,,97,130.34, -53,1,09.07.2015,,258,121.64, -53,1,10.07.2015,,452,121.64, -53,1,11.07.2015,,292,123.55, -53,1,12.07.2015,,220,123.55, -53,1,13.07.2015,,320,123.55, -53,1,14.07.2015,,223,123.55, -53,1,15.07.2015,,226,125.45, -53,1,16.07.2015,,525,125.45, -53,1,17.07.2015,,405,125.45, -53,1,18.07.2015,,415,127.62, -53,1,19.07.2015,,528,127.62, -53,1,20.07.2015,,327,127.62, -53,1,21.07.2015,,418,127.62, -53,1,22.07.2015,,449,127.62, -53,1,23.07.2015,,352,127.62, -53,1,24.07.2015,,474,127.62, -53,1,25.07.2015,,704,127.62, -53,1,26.07.2015,,421,130.07, -53,1,27.07.2015,,902,130.07, -53,1,28.07.2015,1.0,789,130.07,124.77 -53,1,29.07.2015,1.0,1549,130.07,124.77 -53,1,30.07.2015,1.0,682,130.07,124.77 -53,1,31.07.2015,1.0,770,130.07,124.77 -53,1,01.08.2015,1.0,993,130.07,124.77 -53,1,02.08.2015,1.0,547,130.07,124.77 -53,1,03.08.2015,1.0,79,130.07,124.77 -53,1,04.08.2015,,214,138.5, -53,1,05.08.2015,,295,138.5, -53,1,06.08.2015,,377,138.5, -53,1,07.08.2015,,490,138.5, -53,1,08.08.2015,,393,141.21, -53,1,09.08.2015,,305,141.21, -53,1,10.08.2015,,305,141.21, -53,1,11.08.2015,,248,141.21, -53,1,12.08.2015,,258,142.85, -53,1,13.08.2015,,314,142.85, -53,1,14.08.2015,,314,142.85, -53,1,15.08.2015,,553,142.85, -53,1,16.08.2015,,311,142.85, -53,1,17.08.2015,,430,142.85, -53,1,18.08.2015,,273,142.85, -53,1,19.08.2015,,236,142.85, -53,1,20.08.2015,,179,142.85, -53,1,21.08.2015,,280,142.85, -53,1,22.08.2015,,229,142.85, -53,1,23.08.2015,,192,142.85, -53,1,24.08.2015,,531,142.85, -53,1,25.08.2015,,251,142.85, -53,1,26.08.2015,,283,142.85, -53,1,27.08.2015,,239,137.68, -53,1,28.08.2015,,223,137.68, -53,1,29.08.2015,,195,137.68, -53,1,30.08.2015,,264,137.68, -53,1,31.08.2015,,295,137.68, -53,1,01.09.2015,,251,137.68, -53,1,02.09.2015,,242,135.78, -53,1,03.09.2015,,261,135.78, -53,1,04.09.2015,,346,135.78, -53,1,05.09.2015,,264,133.6, -53,1,06.09.2015,,176,133.6, -53,1,07.09.2015,,167,133.6, -53,1,08.09.2015,,236,133.6, -53,1,09.09.2015,,311,135.51, -53,1,10.09.2015,,207,135.51, -53,1,11.09.2015,,339,135.51, -53,1,12.09.2015,,226,135.51, -53,1,13.09.2015,,258,135.51, -53,1,14.09.2015,,163,135.51, -53,1,15.09.2015,,251,135.51, -53,1,16.09.2015,,160,138.5, -53,1,17.09.2015,,160,138.5, -53,1,18.09.2015,,170,138.5, -53,1,19.09.2015,,207,138.5, -53,1,20.09.2015,,173,138.5, -53,1,21.09.2015,,210,138.5, -53,1,22.09.2015,,173,138.5, -53,1,23.09.2015,,160,141.21, -53,1,24.09.2015,,110,141.21, -53,1,25.09.2015,,138,141.21, -53,1,26.09.2015,,126,141.21, -53,1,27.09.2015,,135,141.21, -53,1,28.09.2015,,185,141.21, -53,1,29.09.2015,,104,141.21, -53,1,30.09.2015,,148,141.21, -53,1,01.10.2015,,141,141.21, -53,1,02.10.2015,,151,141.21, -53,1,03.10.2015,,154,141.21, -53,1,04.10.2015,,101,141.21, -53,1,05.10.2015,,123,141.21, -53,1,06.10.2015,1.0,330,141.21,132.92 -53,1,07.10.2015,1.0,295,141.21,130.21 -53,1,08.10.2015,1.0,361,141.11,130.21 -53,1,09.10.2015,1.0,421,141.21,130.21 -53,1,10.10.2015,1.0,540,141.21,130.21 -53,1,11.10.2015,1.0,707,141.21,130.21 -53,1,12.10.2015,1.0,226,141.21,130.21 -53,1,13.10.2015,,160,141.21, -53,1,14.10.2015,,185,141.21, -53,1,15.10.2015,,167,142.57, -53,1,16.10.2015,,119,142.57, -53,1,17.10.2015,,110,142.57, -53,1,18.10.2015,,82,142.57, -53,1,19.10.2015,,113,142.57, -53,1,20.10.2015,,82,142.57, -53,1,21.10.2015,,79,130.34, -53,1,22.10.2015,,210,130.34, -53,1,23.10.2015,,217,130.34, -53,1,24.10.2015,,295,130.34, -53,1,25.10.2015,,167,130.34, -53,1,26.10.2015,,157,130.34, -53,1,27.10.2015,1.0,553,130.34,122.32 -53,1,28.10.2015,1.0,767,130.34,124.77 -53,1,29.10.2015,1.0,691,130.34,124.77 -53,1,30.10.2015,1.0,364,130.34,124.77 -53,1,31.10.2015,1.0,361,130.34,124.77 -53,1,01.11.2015,1.0,270,130.34,124.77 -53,1,02.11.2015,1.0,229,130.34,124.77 -53,1,03.11.2015,,141,130.34, -53,1,04.11.2015,,91,134.69, -53,1,05.11.2015,,135,134.69, -53,1,06.11.2015,,110,134.69, -53,1,07.11.2015,,311,134.69, -53,1,08.11.2015,,116,134.69, -53,1,09.11.2015,,107,134.69, -53,1,10.11.2015,,104,134.69, -53,1,11.11.2015,,123,130.34, -53,1,12.11.2015,,91,130.34, -53,1,13.11.2015,,223,130.34, -53,1,14.11.2015,,123,130.34, -53,1,15.11.2015,,119,130.34, -53,1,16.11.2015,,163,130.34, -53,1,17.11.2015,,97,130.34, -53,1,18.11.2015,,135,130.34, -53,1,19.11.2015,,135,130.34, -53,1,20.11.2015,,185,130.34, -53,1,21.11.2015,,79,130.34, -53,1,22.11.2015,,167,130.34, -53,1,23.11.2015,,85,130.34, -53,1,24.11.2015,1.0,1021,130.34,116.61 -53,1,25.11.2015,1.0,1587,130.34,116.61 -53,1,26.11.2015,1.0,776,130.34,116.61 -53,1,27.11.2015,1.0,958,130.34,116.61 -53,1,28.11.2015,1.0,1385,130.34,116.61 -53,1,29.11.2015,1.0,145,130.34,116.61 -53,1,30.11.2015,1.0,650,130.34,116.61 -53,1,01.12.2015,,63,130.34, -53,1,02.12.2015,,85,130.34, -53,1,03.12.2015,,57,130.34, -53,1,04.12.2015,,72,130.34, -53,1,05.12.2015,1.0,496,130.34,116.61 -53,1,06.12.2015,1.0,424,130.34,116.61 -53,1,07.12.2015,1.0,597,130.34,116.61 -53,1,08.12.2015,,119,130.34, -53,1,09.12.2015,,163,130.34, -53,1,10.12.2015,,66,130.34, -53,1,11.12.2015,,97,130.34, -53,1,12.12.2015,,60,130.34, -53,1,13.12.2015,,116,130.34, -53,1,14.12.2015,,132,130.34, -53,1,15.12.2015,,88,130.34, -53,1,16.12.2015,,97,130.34, -53,1,17.12.2015,,91,130.34, -53,1,18.12.2015,,151,130.34, -53,1,19.12.2015,,91,130.34, -53,1,20.12.2015,,57,130.34, -53,1,21.12.2015,,82,130.34, -53,1,22.12.2015,,129,130.34, -53,1,23.12.2015,,138,130.34, -53,1,24.12.2015,,88,126.54, -53,1,25.12.2015,,138,126.54, -53,1,26.12.2015,,94,126.54, -53,1,27.12.2015,,116,126.54, -53,1,28.12.2015,,104,126.54, -53,1,29.12.2015,,657,126.54, -53,1,30.12.2015,,154,126.54, -53,1,31.12.2015,,107,126.54, -53,1,01.01.2016,,25,126.54, -53,1,02.01.2016,,66,126.54, -53,1,03.01.2016,,110,126.54, -53,1,04.01.2016,,132,126.54, -53,1,05.01.2016,,145,126.54, -53,1,06.01.2016,,132,126.54, -53,1,07.01.2016,,88,126.54, -53,1,08.01.2016,,91,126.54, -53,1,09.01.2016,,157,126.54, -53,1,10.01.2016,,113,126.54, -53,1,11.01.2016,,104,126.54, -53,1,12.01.2016,,141,126.54, -53,1,13.01.2016,,126,126.54, -53,1,14.01.2016,,110,126.54, -53,1,15.01.2016,,66,126.54, -53,1,16.01.2016,,110,126.54, -53,1,17.01.2016,,82,126.54, -53,1,18.01.2016,,116,126.54, -53,1,19.01.2016,,163,126.54, -53,1,20.01.2016,,79,126.54, -53,1,21.01.2016,,94,127.9, -53,1,22.01.2016,,148,127.9, -53,1,23.01.2016,,135,127.9, -53,1,24.01.2016,,135,127.9, -53,1,25.01.2016,,185,127.9, -53,1,26.01.2016,,236,127.9, -53,1,27.01.2016,,176,127.9, -53,1,28.01.2016,,185,127.9, -53,1,29.01.2016,,179,127.9, -53,1,30.01.2016,,185,127.9, -53,1,31.01.2016,,154,127.9, -53,1,01.02.2016,,220,130.34, -53,1,02.02.2016,,314,130.34, -53,1,03.02.2016,,188,130.34, -53,1,04.02.2016,,116,133.06, -53,1,05.02.2016,,195,133.06, -53,1,06.02.2016,,286,133.06, -53,1,07.02.2016,,110,141.76, -53,1,08.02.2016,,72,141.76, -53,1,09.02.2016,1.0,192,141.76,130.75 -53,1,10.02.2016,1.0,528,141.76,130.75 -53,1,11.02.2016,1.0,377,141.76,130.75 -53,1,12.02.2016,1.0,173,141.76,130.75 -53,1,13.02.2016,1.0,399,141.76,130.75 -53,1,14.02.2016,1.0,204,141.76,130.75 -53,1,15.02.2016,1.0,280,141.76,130.75 -53,1,16.02.2016,1.0,270,141.76,130.75 -53,1,17.02.2016,1.0,157,141.76,130.75 -53,1,18.02.2016,,66,141.76, -53,1,19.02.2016,,110,141.76, -53,1,20.02.2016,,72,141.76, -53,1,21.02.2016,,69,141.76, -53,1,22.02.2016,,132,141.76, -53,1,23.02.2016,,69,141.76, -53,1,24.02.2016,,60,141.76, -53,1,25.02.2016,,85,141.76, -53,1,26.02.2016,,94,141.76, -53,1,27.02.2016,,123,141.76, -53,1,28.02.2016,,53,141.76, -53,1,29.02.2016,,88,141.76, -53,1,01.03.2016,,57,141.76, -53,1,02.03.2016,,79,141.76, -53,1,03.03.2016,,154,141.76, -53,1,04.03.2016,,79,141.76, -53,1,05.03.2016,,88,141.21, -53,1,06.03.2016,,110,141.21, -53,1,07.03.2016,,104,141.21, -53,1,08.03.2016,1.0,126,141.21,132.92 -53,1,09.03.2016,1.0,195,141.21,132.92 -53,1,10.03.2016,1.0,239,141.21,132.92 -53,1,11.03.2016,1.0,386,141.21,132.92 -53,1,12.03.2016,1.0,264,141.21,132.92 -53,1,13.03.2016,1.0,57,141.21,132.92 -53,1,14.03.2016,1.0,207,141.21,132.92 -53,1,15.03.2016,1.0,267,141.21,130.21 -53,1,16.03.2016,1.0,320,141.21,130.21 -53,1,17.03.2016,1.0,377,141.21,130.21 -53,1,18.03.2016,1.0,286,141.21,130.21 -53,1,19.03.2016,1.0,408,141.21,130.21 -53,1,20.03.2016,,85,141.21, -53,1,21.03.2016,,160,141.21, -53,1,22.03.2016,,107,141.21, -53,1,23.03.2016,,104,141.21, -53,1,24.03.2016,,69,141.21, -53,1,25.03.2016,,119,141.21, -53,1,26.03.2016,,69,141.21, -53,1,27.03.2016,,53,141.21, -53,1,28.03.2016,,69,141.21, -53,1,29.03.2016,,57,141.21, -53,1,30.03.2016,,254,141.21, -53,1,31.03.2016,,94,141.21, -53,1,01.04.2016,,69,141.21, -53,1,02.04.2016,,66,141.21, -53,1,03.04.2016,,41,141.21, -53,1,04.04.2016,,116,141.21, 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-56,1,28.05.2015,,97,129.25, -56,1,29.05.2015,,75,129.25, -56,1,30.05.2015,,75,129.25, -56,1,31.05.2015,,110,129.25, -56,1,01.06.2015,,126,129.25, -56,1,02.06.2015,,116,129.25, -56,1,03.06.2015,,97,129.25, -56,1,04.06.2015,,72,129.25, -56,1,05.06.2015,,66,129.25, -56,1,06.06.2015,,141,131.7, -56,1,07.06.2015,,57,131.7, -56,1,08.06.2015,,226,131.7, -56,1,09.06.2015,1.0,292,131.7,119.33 -56,1,10.06.2015,1.0,471,131.7,119.33 -56,1,11.06.2015,1.0,550,131.7,119.33 -56,1,12.06.2015,1.0,462,131.7,119.33 -56,1,13.06.2015,1.0,292,131.7,119.33 -56,1,14.06.2015,1.0,236,131.7,119.33 -56,1,15.06.2015,1.0,229,131.7,119.33 -56,1,16.06.2015,,88,131.7, -56,1,17.06.2015,,116,131.7, -56,1,18.06.2015,,185,128.17, -56,1,19.06.2015,,69,128.17, -56,1,20.06.2015,,116,128.17, -56,1,21.06.2015,,85,128.17, -56,1,22.06.2015,,113,128.17, -56,1,23.06.2015,,82,128.17, -56,1,24.06.2015,,101,128.17, -56,1,25.06.2015,,148,128.17, -56,1,26.06.2015,,185,128.17, -56,1,27.06.2015,,97,128.17, -56,1,28.06.2015,,126,130.34, 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-56,1,29.08.2015,,129,137.68, -56,1,30.08.2015,,229,137.68, -56,1,31.08.2015,,214,137.68, -56,1,01.09.2015,,132,137.68, -56,1,02.09.2015,,198,135.78, -56,1,03.09.2015,,223,135.78, -56,1,04.09.2015,,270,135.78, -56,1,05.09.2015,,173,133.6, -56,1,06.09.2015,,192,133.6, -56,1,07.09.2015,,320,133.6, -56,1,08.09.2015,,289,133.6, -56,1,09.09.2015,,185,135.51, -56,1,10.09.2015,,302,135.51, -56,1,11.09.2015,,170,135.51, -56,1,12.09.2015,,148,135.51, -56,1,13.09.2015,,232,135.51, -56,1,14.09.2015,,276,135.51, -56,1,15.09.2015,,182,135.51, -56,1,16.09.2015,,135,138.5, -56,1,17.09.2015,,185,138.5, -56,1,18.09.2015,,107,138.5, -56,1,19.09.2015,,141,138.5, -56,1,20.09.2015,,185,138.5, -56,1,21.09.2015,,192,138.5, -56,1,22.09.2015,,170,138.5, -56,1,23.09.2015,,141,141.21, -56,1,24.09.2015,,116,141.21, -56,1,25.09.2015,,210,141.21, -56,1,26.09.2015,,119,141.21, -56,1,27.09.2015,,157,141.21, -56,1,28.09.2015,,97,141.21, -56,1,29.09.2015,,151,141.21, -56,1,30.09.2015,,116,141.21, -56,1,01.10.2015,,101,141.21, -56,1,02.10.2015,,97,141.21, -56,1,03.10.2015,,132,141.21, -56,1,04.10.2015,,135,141.21, -56,1,05.10.2015,,173,141.21, -56,1,06.10.2015,1.0,298,141.21,132.92 -56,1,07.10.2015,1.0,229,141.21,130.21 -56,1,08.10.2015,1.0,361,141.11,130.21 -56,1,09.10.2015,1.0,427,141.21,130.21 -56,1,10.10.2015,1.0,446,141.21,130.21 -56,1,11.10.2015,1.0,336,141.21,130.21 -56,1,12.10.2015,1.0,500,141.21,130.21 -56,1,13.10.2015,,217,141.21, -56,1,14.10.2015,,97,141.21, -56,1,15.10.2015,,104,142.57, -56,1,16.10.2015,,94,142.57, -56,1,17.10.2015,,69,142.57, -56,1,18.10.2015,,91,142.57, -56,1,19.10.2015,,110,142.57, -56,1,20.10.2015,,101,142.57, -56,1,21.10.2015,,75,130.34, -56,1,22.10.2015,,91,130.34, -56,1,23.10.2015,,113,130.34, -56,1,24.10.2015,,79,130.34, -56,1,25.10.2015,,245,130.34, -56,1,26.10.2015,,383,130.34, -56,1,27.10.2015,1.0,760,130.34,122.32 -56,1,28.10.2015,1.0,814,130.34,124.77 -56,1,29.10.2015,1.0,647,130.34,124.77 -56,1,30.10.2015,1.0,462,130.34,124.77 -56,1,31.10.2015,1.0,220,130.34,124.77 -56,1,01.11.2015,1.0,358,130.34,124.77 -56,1,02.11.2015,1.0,374,130.34,124.77 -56,1,03.11.2015,,242,130.34, -56,1,04.11.2015,,138,134.69, -56,1,05.11.2015,,116,134.69, -56,1,06.11.2015,,69,134.69, -56,1,07.11.2015,,138,134.69, -56,1,08.11.2015,,88,134.69, -56,1,09.11.2015,,91,134.69, -56,1,10.11.2015,,79,134.69, -56,1,11.11.2015,,201,130.34, -56,1,12.11.2015,,123,130.34, -56,1,13.11.2015,,151,130.34, -56,1,14.11.2015,,123,130.34, -56,1,15.11.2015,,185,130.34, -56,1,16.11.2015,,101,130.34, -56,1,17.11.2015,,132,130.34, -56,1,18.11.2015,,135,130.34, -56,1,19.11.2015,,188,130.34, -56,1,20.11.2015,,145,130.34, -56,1,21.11.2015,,145,130.34, -56,1,22.11.2015,,148,130.34, -56,1,23.11.2015,,135,130.34, -56,1,24.11.2015,1.0,1533,130.34,116.61 -56,1,25.11.2015,1.0,1844,130.34,116.61 -56,1,26.11.2015,1.0,1316,130.34,116.61 -56,1,27.11.2015,1.0,757,130.34,116.61 -56,1,28.11.2015,1.0,713,130.34,116.61 -56,1,29.11.2015,1.0,688,130.34,116.61 -56,1,30.11.2015,1.0,1024,130.34,116.61 -56,1,01.12.2015,,163,130.34, -56,1,02.12.2015,,104,130.34, -56,1,03.12.2015,,116,130.34, -56,1,04.12.2015,,110,130.34, -56,1,05.12.2015,1.0,679,130.34,116.61 -56,1,06.12.2015,1.0,760,130.34,116.61 -56,1,07.12.2015,1.0,704,130.34,116.61 -56,1,08.12.2015,,135,130.34, -56,1,09.12.2015,,148,130.34, -56,1,10.12.2015,,129,130.34, -56,1,11.12.2015,,145,130.34, -56,1,12.12.2015,,126,130.34, -56,1,13.12.2015,,135,130.34, -56,1,14.12.2015,,129,130.34, -56,1,15.12.2015,,119,130.34, -56,1,16.12.2015,,126,130.34, -56,1,17.12.2015,,132,130.34, -56,1,18.12.2015,,154,130.34, -56,1,19.12.2015,,110,130.34, -56,1,20.12.2015,,160,130.34, -56,1,21.12.2015,,123,130.34, -56,1,22.12.2015,,148,130.34, -56,1,23.12.2015,,138,130.34, -56,1,24.12.2015,,104,126.54, -56,1,25.12.2015,,204,126.54, -56,1,26.12.2015,,179,126.54, -56,1,27.12.2015,,176,126.54, -56,1,28.12.2015,,182,126.54, -56,1,29.12.2015,,229,126.54, -56,1,30.12.2015,,223,126.54, -56,1,31.12.2015,,132,126.54, -56,1,01.01.2016,,19,126.54, -56,1,02.01.2016,,94,126.54, -56,1,03.01.2016,,116,126.54, -56,1,04.01.2016,,201,126.54, -56,1,05.01.2016,,160,126.54, -56,1,06.01.2016,,302,126.54, -56,1,07.01.2016,,167,126.54, -56,1,08.01.2016,,185,126.54, -56,1,09.01.2016,,148,126.54, -56,1,10.01.2016,,141,126.54, -56,1,11.01.2016,,173,126.54, -56,1,12.01.2016,,195,126.54, -56,1,13.01.2016,,210,126.54, -56,1,14.01.2016,,176,126.54, -56,1,15.01.2016,,283,126.54, -56,1,16.01.2016,,207,126.54, -56,1,17.01.2016,,195,126.54, -56,1,18.01.2016,,204,126.54, -56,1,19.01.2016,,132,126.54, -56,1,20.01.2016,,286,126.54, -56,1,21.01.2016,,188,127.9, -56,1,22.01.2016,,207,127.9, -56,1,23.01.2016,,276,127.9, -56,1,24.01.2016,,273,127.9, -56,1,25.01.2016,,210,127.9, -56,1,26.01.2016,,217,127.9, -56,1,27.01.2016,,311,127.9, -56,1,28.01.2016,,248,127.9, -56,1,29.01.2016,,185,127.9, -56,1,30.01.2016,,264,127.9, -56,1,31.01.2016,,110,127.9, -56,1,01.02.2016,,135,130.34, -56,1,02.02.2016,,182,130.34, -56,1,03.02.2016,,157,130.34, -56,1,04.02.2016,,148,133.06, -56,1,05.02.2016,,173,133.06, -56,1,06.02.2016,,198,133.06, -56,1,07.02.2016,,107,141.76, -56,1,08.02.2016,,163,141.76, -56,1,09.02.2016,1.0,396,141.76,130.75 -56,1,10.02.2016,1.0,327,141.76,130.75 -56,1,11.02.2016,1.0,358,141.76,130.75 -56,1,12.02.2016,1.0,346,141.76,130.75 -56,1,13.02.2016,1.0,314,141.76,130.75 -56,1,14.02.2016,1.0,383,141.76,130.75 -56,1,15.02.2016,1.0,258,141.76,130.75 -56,1,16.02.2016,1.0,352,141.76,130.75 -56,1,17.02.2016,1.0,383,141.76,130.75 -56,1,18.02.2016,,91,141.76, -56,1,19.02.2016,,113,141.76, -56,1,20.02.2016,,113,141.76, -56,1,21.02.2016,,126,141.76, -56,1,22.02.2016,,119,141.76, -56,1,23.02.2016,,135,141.76, -56,1,24.02.2016,,107,141.76, -56,1,25.02.2016,,101,141.76, -56,1,26.02.2016,,88,141.76, -56,1,27.02.2016,,126,141.76, -56,1,28.02.2016,,129,141.76, -56,1,29.02.2016,,104,141.76, -56,1,01.03.2016,,148,141.76, -56,1,02.03.2016,,94,141.76, -56,1,03.03.2016,,192,141.76, 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-57,1,17.03.2015,1.0,141,140.67,127.49 -57,1,18.03.2015,1.0,141,140.67,127.49 -57,1,19.03.2015,1.0,138,140.67,127.49 -57,1,20.03.2015,1.0,569,140.67,127.49 -57,1,21.03.2015,1.0,795,140.67,127.49 -57,1,22.03.2015,1.0,726,140.67,127.49 -57,1,23.03.2015,1.0,361,140.67,127.49 -57,1,24.03.2015,,132,140.67, -57,1,25.03.2015,,110,140.67, -57,1,26.03.2015,,210,131.97, -57,1,27.03.2015,,182,131.97, -57,1,28.03.2015,,289,131.97, -57,1,29.03.2015,,273,131.97, -57,1,30.03.2015,,223,131.97, -57,1,31.03.2015,,210,131.97, -57,1,01.04.2015,,302,133.33, -57,1,02.04.2015,,182,133.33, -57,1,03.04.2015,,176,133.33, -57,1,04.04.2015,,308,133.33, -57,1,05.04.2015,,242,133.33, -57,1,06.04.2015,,132,133.33, -57,1,07.04.2015,1.0,1084,133.33,119.33 -57,1,08.04.2015,1.0,902,133.33,119.33 -57,1,09.04.2015,1.0,713,133.33,119.33 -57,1,10.04.2015,1.0,1128,133.33,119.33 -57,1,11.04.2015,1.0,1112,133.33,119.33 -57,1,12.04.2015,1.0,405,133.33,119.33 -57,1,13.04.2015,1.0,418,133.33,119.33 -57,1,14.04.2015,,201,130.34, 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-57,1,17.06.2015,,163,131.7, -57,1,18.06.2015,,173,128.17, -57,1,19.06.2015,,135,128.17, -57,1,20.06.2015,,195,128.17, -57,1,21.06.2015,,182,128.17, -57,1,22.06.2015,,210,128.17, -57,1,23.06.2015,,308,128.17, -57,1,24.06.2015,,248,128.17, -57,1,25.06.2015,,295,128.17, -57,1,26.06.2015,,280,128.17, -57,1,27.06.2015,,320,128.17, -57,1,28.06.2015,,236,130.34, -57,1,29.06.2015,,192,130.34, -57,1,30.06.2015,1.0,427,130.34,119.33 -57,1,01.07.2015,1.0,553,130.34,119.33 -57,1,02.07.2015,1.0,729,130.34,119.33 -57,1,03.07.2015,1.0,1087,130.34,119.33 -57,1,04.07.2015,1.0,867,130.34,119.33 -57,1,05.07.2015,1.0,647,130.34,119.33 -57,1,06.07.2015,1.0,490,130.34,119.33 -57,1,07.07.2015,,198,130.34, -57,1,08.07.2015,,182,130.34, -57,1,09.07.2015,,160,121.64, -57,1,10.07.2015,,465,121.64, -57,1,11.07.2015,,333,123.55, -57,1,12.07.2015,,141,123.55, -57,1,13.07.2015,,339,123.55, -57,1,14.07.2015,,298,123.55, -57,1,15.07.2015,,286,125.45, -57,1,16.07.2015,,377,125.45, -57,1,17.07.2015,,421,125.45, -57,1,18.07.2015,,465,127.62, -57,1,19.07.2015,,377,127.62, -57,1,20.07.2015,,119,127.62, -57,1,21.07.2015,,374,127.62, -57,1,22.07.2015,,292,127.62, -57,1,23.07.2015,,352,127.62, -57,1,24.07.2015,,270,127.62, -57,1,25.07.2015,,456,127.62, -57,1,26.07.2015,,355,130.07, -57,1,27.07.2015,,434,130.07, -57,1,28.07.2015,1.0,393,130.07,124.77 -57,1,29.07.2015,1.0,449,130.07,124.77 -57,1,30.07.2015,1.0,490,130.07,124.77 -57,1,31.07.2015,1.0,390,130.07,124.77 -57,1,01.08.2015,1.0,383,130.07,124.77 -57,1,02.08.2015,1.0,603,130.07,124.77 -57,1,03.08.2015,1.0,653,130.07,124.77 -57,1,04.08.2015,,258,138.5, -57,1,05.08.2015,,182,138.5, -57,1,06.08.2015,,94,138.5, -57,1,07.08.2015,,163,138.5, -57,1,08.08.2015,,148,141.21, -57,1,09.08.2015,,280,141.21, -57,1,10.08.2015,,135,141.21, -57,1,11.08.2015,,107,141.21, -57,1,12.08.2015,,182,142.85, -57,1,13.08.2015,,135,142.85, -57,1,14.08.2015,,141,142.85, -57,1,15.08.2015,,123,142.85, -57,1,16.08.2015,,198,142.85, -57,1,17.08.2015,,214,142.85, 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-57,1,20.09.2015,,207,138.5, -57,1,21.09.2015,,160,138.5, -57,1,22.09.2015,,145,138.5, -57,1,23.09.2015,,151,141.21, -57,1,24.09.2015,,141,141.21, -57,1,25.09.2015,,220,141.21, -57,1,26.09.2015,,141,141.21, -57,1,27.09.2015,,201,141.21, -57,1,28.09.2015,,116,141.21, -57,1,29.09.2015,,119,141.21, -57,1,30.09.2015,,94,141.21, -57,1,01.10.2015,,135,141.21, -57,1,02.10.2015,,195,141.21, -57,1,03.10.2015,,214,141.21, -57,1,04.10.2015,,204,141.21, -57,1,05.10.2015,,132,141.21, -57,1,06.10.2015,1.0,550,141.21,122.32 -57,1,07.10.2015,1.0,525,141.21,122.32 -57,1,08.10.2015,1.0,443,141.11,122.32 -57,1,09.10.2015,1.0,531,141.21,122.32 -57,1,10.10.2015,1.0,587,141.21,122.32 -57,1,11.10.2015,1.0,358,141.21,122.32 -57,1,12.10.2015,1.0,471,141.21,122.32 -57,1,13.10.2015,,298,141.21, -57,1,14.10.2015,,192,141.21, -57,1,15.10.2015,,151,142.57, -57,1,16.10.2015,,101,142.57, -57,1,17.10.2015,,132,142.57, -57,1,18.10.2015,,94,142.57, -57,1,19.10.2015,,69,142.57, -57,1,20.10.2015,,63,142.57, -57,1,21.10.2015,,179,130.34, -57,1,22.10.2015,,204,130.34, -57,1,23.10.2015,,280,130.34, -57,1,24.10.2015,,418,130.34, -57,1,25.10.2015,,412,130.34, -57,1,26.10.2015,,195,130.34, -57,1,27.10.2015,1.0,1078,130.34,122.32 -57,1,28.10.2015,1.0,1429,130.34,122.32 -57,1,29.10.2015,1.0,883,130.34,122.32 -57,1,30.10.2015,1.0,613,130.34,122.32 -57,1,31.10.2015,1.0,443,130.34,122.32 -57,1,01.11.2015,1.0,355,130.34,122.32 -57,1,02.11.2015,1.0,305,130.34,122.32 -57,1,03.11.2015,,308,130.34, -57,1,04.11.2015,,276,134.69, -57,1,05.11.2015,,167,134.69, -57,1,06.11.2015,,101,134.69, -57,1,07.11.2015,,254,134.69, -57,1,08.11.2015,,97,134.69, -57,1,09.11.2015,,104,134.69, -57,1,10.11.2015,,258,134.69, -57,1,11.11.2015,,270,130.34, -57,1,12.11.2015,,179,130.34, -57,1,13.11.2015,,154,130.34, -57,1,14.11.2015,,239,130.34, -57,1,15.11.2015,,113,130.34, -57,1,16.11.2015,,72,130.34, -57,1,17.11.2015,,160,130.34, -57,1,18.11.2015,,185,130.34, -57,1,19.11.2015,,339,130.34, -57,1,20.11.2015,,151,130.34, -57,1,21.11.2015,,160,130.34, -57,1,22.11.2015,,179,130.34, -57,1,23.11.2015,,104,130.34, -57,1,24.11.2015,1.0,735,130.34,116.61 -57,1,25.11.2015,1.0,1294,130.34,116.61 -57,1,26.11.2015,1.0,1178,130.34,116.61 -57,1,27.11.2015,1.0,826,130.34,116.61 -57,1,28.11.2015,1.0,1037,130.34,116.61 -57,1,29.11.2015,1.0,515,130.34,116.61 -57,1,30.11.2015,1.0,767,130.34,116.61 -57,1,01.12.2015,,123,130.34, -57,1,02.12.2015,,107,130.34, -57,1,03.12.2015,,79,130.34, -57,1,04.12.2015,,201,130.34, -57,1,05.12.2015,1.0,983,130.34,116.61 -57,1,06.12.2015,1.0,657,130.34,116.61 -57,1,07.12.2015,1.0,691,130.34,116.61 -57,1,08.12.2015,,201,130.34, -57,1,09.12.2015,,94,130.34, -57,1,10.12.2015,,188,130.34, -57,1,11.12.2015,,126,130.34, -57,1,12.12.2015,,220,130.34, -57,1,13.12.2015,,176,130.34, -57,1,14.12.2015,,94,130.34, -57,1,15.12.2015,,97,130.34, -57,1,16.12.2015,,66,130.34, -57,1,17.12.2015,,170,130.34, -57,1,18.12.2015,,145,130.34, -57,1,19.12.2015,,104,130.34, -57,1,20.12.2015,,85,130.34, -57,1,21.12.2015,,113,130.34, -57,1,22.12.2015,,116,130.34, -57,1,23.12.2015,,160,130.34, -57,1,24.12.2015,,170,126.54, -57,1,25.12.2015,,154,126.54, -57,1,26.12.2015,,232,126.54, -57,1,27.12.2015,,232,126.54, -57,1,28.12.2015,,151,126.54, -57,1,29.12.2015,,185,126.54, -57,1,30.12.2015,,236,126.54, -57,1,31.12.2015,,283,126.54, -57,1,01.01.2016,,38,126.54, -57,1,02.01.2016,,75,126.54, -57,1,03.01.2016,,173,126.54, -57,1,04.01.2016,,113,126.54, -57,1,05.01.2016,,195,126.54, -57,1,06.01.2016,,157,126.54, -57,1,07.01.2016,,104,126.54, -57,1,08.01.2016,,94,126.54, -57,1,09.01.2016,,229,126.54, -57,1,10.01.2016,,192,126.54, -57,1,11.01.2016,,220,126.54, -57,1,12.01.2016,,223,126.54, -57,1,13.01.2016,,154,126.54, -57,1,14.01.2016,,151,126.54, -57,1,15.01.2016,,242,126.54, -57,1,16.01.2016,,280,126.54, -57,1,17.01.2016,,547,126.54, -57,1,18.01.2016,,232,126.54, -57,1,19.01.2016,,339,126.54, -57,1,20.01.2016,,217,126.54, -57,1,21.01.2016,,160,127.9, -57,1,22.01.2016,,361,127.9, -57,1,23.01.2016,,239,127.9, -57,1,24.01.2016,,254,127.9, -57,1,25.01.2016,,154,127.9, -57,1,26.01.2016,,314,127.9, -57,1,27.01.2016,,295,127.9, -57,1,28.01.2016,,245,127.9, -57,1,29.01.2016,,195,127.9, -57,1,30.01.2016,,402,127.9, -57,1,31.01.2016,,333,127.9, -57,1,01.02.2016,,302,130.34, -57,1,02.02.2016,,280,130.34, -57,1,03.02.2016,,185,130.34, -57,1,04.02.2016,,242,133.06, -57,1,05.02.2016,,295,133.06, -57,1,06.02.2016,,408,133.06, -57,1,07.02.2016,,167,141.76, -57,1,08.02.2016,,138,141.76, -57,1,09.02.2016,1.0,528,141.76,130.75 -57,1,10.02.2016,1.0,459,141.76,130.75 -57,1,11.02.2016,1.0,556,141.76,130.75 -57,1,12.02.2016,1.0,697,141.76,130.75 -57,1,13.02.2016,1.0,697,141.76,130.75 -57,1,14.02.2016,1.0,653,141.76,130.75 -57,1,15.02.2016,1.0,713,141.76,130.75 -57,1,16.02.2016,1.0,358,141.76,130.75 -57,1,17.02.2016,1.0,286,141.76,130.75 -57,1,18.02.2016,,157,141.76, -57,1,19.02.2016,,116,141.76, -57,1,20.02.2016,,251,141.76, -57,1,21.02.2016,,173,141.76, -57,1,22.02.2016,,185,141.76, -57,1,23.02.2016,,145,141.76, -57,1,24.02.2016,,113,141.76, -57,1,25.02.2016,,151,141.76, -57,1,26.02.2016,,160,141.76, -57,1,27.02.2016,,192,141.76, -57,1,28.02.2016,,210,141.76, -57,1,29.02.2016,,88,141.76, -57,1,01.03.2016,,116,141.76, -57,1,02.03.2016,,145,141.76, -57,1,03.03.2016,,204,141.76, -57,1,04.03.2016,,185,141.76, -57,1,05.03.2016,,276,141.21, -57,1,06.03.2016,,229,141.21, -57,1,07.03.2016,,232,141.21, -57,1,08.03.2016,1.0,305,141.21,132.92 -57,1,09.03.2016,1.0,559,141.21,132.92 -57,1,10.03.2016,1.0,543,141.21,132.92 -57,1,11.03.2016,1.0,443,141.21,132.92 -57,1,12.03.2016,1.0,543,141.21,132.92 -57,1,13.03.2016,1.0,308,141.21,132.92 -57,1,14.03.2016,1.0,248,141.21,132.92 -57,1,15.03.2016,1.0,452,141.21,130.21 -57,1,16.03.2016,1.0,565,141.21,130.21 -57,1,17.03.2016,1.0,386,141.21,130.21 -57,1,18.03.2016,1.0,613,141.21,130.21 -57,1,19.03.2016,1.0,581,141.21,130.21 -57,1,20.03.2016,1.0,163,141.21,130.21 -57,1,21.03.2016,,82,141.21, -57,1,22.03.2016,,94,141.21, -57,1,23.03.2016,,132,141.21, -57,1,24.03.2016,,138,141.21, -57,1,25.03.2016,,154,141.21, -57,1,26.03.2016,,198,141.21, -57,1,27.03.2016,,179,141.21, -57,1,28.03.2016,,79,141.21, -57,1,29.03.2016,,217,141.21, -57,1,30.03.2016,,129,141.21, -57,1,31.03.2016,,82,141.21, -57,1,01.04.2016,,141,141.21, -57,1,02.04.2016,,135,141.21, -57,1,03.04.2016,,110,141.21, -57,1,04.04.2016,,160,141.21, -57,1,05.04.2016,,182,141.21, -57,1,06.04.2016,,129,141.21, -57,1,07.04.2016,,107,141.21, -57,1,08.04.2016,,126,141.21, -57,1,09.04.2016,,173,141.21, -57,1,10.04.2016,,148,141.21, -57,1,11.04.2016,,270,141.21, -57,1,12.04.2016,,157,141.21, -57,1,13.04.2016,,82,141.21, -57,1,14.04.2016,,167,135.78, -57,1,15.04.2016,,198,135.78, -57,1,16.04.2016,,481,135.78, -57,1,17.04.2016,,157,135.78, -57,1,18.04.2016,,75,135.78, -57,1,19.04.2016,1.0,1216,135.78,122.32 -57,1,20.04.2016,1.0,1957,135.78,122.32 -57,1,21.04.2016,1.0,1555,135.78,122.32 -57,1,22.04.2016,1.0,955,135.78,122.32 -57,1,23.04.2016,1.0,270,135.78,122.32 -57,1,24.04.2016,1.0,701,135.78,122.32 -57,1,25.04.2016,1.0,701,135.78,122.32 -57,1,26.04.2016,,88,135.78, -57,1,27.04.2016,,154,135.78, -57,1,28.04.2016,,226,135.78, -57,1,29.04.2016,,214,131.16, -57,1,30.04.2016,,239,131.16, -57,1,01.05.2016,,132,131.16, -57,1,02.05.2016,,63,131.16, -57,1,03.05.2016,,188,131.16, -57,1,04.05.2016,,220,131.16, -57,1,05.05.2016,,195,131.16, -57,1,06.05.2016,,185,131.16, -57,1,07.05.2016,,245,131.16, -57,1,08.05.2016,,201,131.16, -57,1,09.05.2016,,110,131.16, -57,1,10.05.2016,,110,131.16, -57,1,11.05.2016,,210,131.16, -57,1,12.05.2016,,188,131.16, -57,1,13.05.2016,,330,128.98, -57,1,14.05.2016,,405,128.98, -57,1,15.05.2016,,267,128.98, -57,1,16.05.2016,,327,128.98, -57,1,17.05.2016,1.0,1052,128.98,119.6 -57,1,18.05.2016,1.0,1266,128.98,119.6 -57,1,19.05.2016,1.0,1797,128.98,119.6 -57,1,20.05.2016,1.0,880,128.98,119.6 -57,1,21.05.2016,1.0,616,128.98,119.6 -57,1,22.05.2016,1.0,927,128.98,119.6 -58,1,01.01.2015,,3,163.78, -58,1,02.01.2015,,28,163.78, -58,1,03.01.2015,,41,163.78, -58,1,04.01.2015,,60,163.78, -58,1,05.01.2015,,50,163.78, -58,1,06.01.2015,,75,163.78, -58,1,07.01.2015,,91,163.78, -58,1,08.01.2015,,22,163.78, -58,1,09.01.2015,,60,163.78, -58,1,10.01.2015,,28,163.78, -58,1,11.01.2015,,101,163.78, -58,1,12.01.2015,,91,163.78, -58,1,13.01.2015,,44,163.78, -58,1,14.01.2015,,72,168.4, -58,1,15.01.2015,,41,168.4, -58,1,16.01.2015,,50,168.4, -58,1,17.01.2015,,63,168.4, -58,1,18.01.2015,,41,168.4, -58,1,19.01.2015,,47,168.4, -58,1,20.01.2015,,60,168.4, -58,1,21.01.2015,,44,168.4, -58,1,22.01.2015,,31,168.4, -58,1,23.01.2015,,35,160.24, -58,1,24.01.2015,,85,160.24, -58,1,25.01.2015,,69,160.24, -58,1,26.01.2015,,63,160.24, -58,1,27.01.2015,,57,160.24, -58,1,28.01.2015,,258,146.92, -58,1,29.01.2015,,232,146.92, -58,1,30.01.2015,,242,146.92, -58,1,31.01.2015,,267,146.92, -58,1,01.02.2015,,204,146.92, -58,1,02.02.2015,,132,146.92, -58,1,03.02.2015,,145,146.92, -58,1,04.02.2015,,179,146.92, -58,1,05.02.2015,,239,146.92, -58,1,06.02.2015,,251,146.92, -58,1,07.02.2015,,333,146.92, -58,1,08.02.2015,,236,146.92, -58,1,09.02.2015,,393,146.92, -58,1,10.02.2015,,236,146.92, -58,1,11.02.2015,,349,146.92, -58,1,12.02.2015,,170,146.92, -58,1,13.02.2015,,327,149.37, -58,1,14.02.2015,,267,149.37, -58,1,15.02.2015,,239,149.37, -58,1,16.02.2015,,104,149.37, -58,1,17.02.2015,1.0,481,149.37,143.8 -58,1,18.02.2015,1.0,390,149.37,135.64 -58,1,19.02.2015,1.0,412,149.37,135.64 -58,1,20.02.2015,1.0,415,149.37,135.64 -58,1,21.02.2015,1.0,459,149.37,135.64 -58,1,22.02.2015,1.0,622,149.37,135.64 -58,1,23.02.2015,1.0,792,149.37,135.64 -58,1,24.02.2015,,135,149.37, -58,1,25.02.2015,,157,149.37, -58,1,26.02.2015,,179,149.37, -58,1,27.02.2015,,446,143.93, -58,1,28.02.2015,,251,143.93, -58,1,01.03.2015,,396,143.93, -58,1,02.03.2015,,525,143.93, -58,1,03.03.2015,1.0,358,143.93,138.36 -58,1,04.03.2015,1.0,512,143.93,135.64 -58,1,05.03.2015,1.0,547,138.5,135.64 -58,1,06.03.2015,1.0,949,138.5,135.64 -58,1,07.03.2015,1.0,1244,138.5,135.64 -58,1,08.03.2015,1.0,522,138.5,135.64 -58,1,09.03.2015,,195,140.67, -58,1,10.03.2015,,248,140.67, -58,1,11.03.2015,,214,140.67, -58,1,12.03.2015,,267,140.67, -58,1,13.03.2015,,383,140.67, -58,1,14.03.2015,,415,140.67, -58,1,15.03.2015,,399,140.67, -58,1,16.03.2015,,449,140.67, -58,1,17.03.2015,1.0,2271,140.67,127.49 -58,1,18.03.2015,1.0,1960,140.67,127.49 -58,1,19.03.2015,1.0,1766,140.67,127.49 -58,1,20.03.2015,1.0,1096,140.67,127.49 -58,1,21.03.2015,1.0,936,140.67,127.49 -58,1,22.03.2015,1.0,905,140.67,127.49 -58,1,23.03.2015,1.0,609,140.67,127.49 -58,1,24.03.2015,,210,140.67, -58,1,25.03.2015,,173,140.67, -58,1,26.03.2015,,324,131.97, -58,1,27.03.2015,,390,131.97, -58,1,28.03.2015,,613,131.97, -58,1,29.03.2015,,339,131.97, -58,1,30.03.2015,,317,131.97, -58,1,31.03.2015,,138,131.97, -58,1,01.04.2015,,229,133.33, -58,1,02.04.2015,,242,133.33, -58,1,03.04.2015,,308,133.33, -58,1,04.04.2015,,298,133.33, -58,1,05.04.2015,,264,133.33, -58,1,06.04.2015,,232,133.33, -58,1,07.04.2015,1.0,3211,133.33,119.33 -58,1,08.04.2015,1.0,2994,133.33,119.33 -58,1,09.04.2015,1.0,1235,133.33,119.33 -58,1,10.04.2015,1.0,1197,133.33,119.33 -58,1,11.04.2015,1.0,1131,133.33,119.33 -58,1,12.04.2015,1.0,1040,133.33,119.33 -58,1,13.04.2015,1.0,113,133.33,119.33 -58,1,14.04.2015,,217,130.34, -58,1,15.04.2015,,207,130.34, -58,1,16.04.2015,,415,130.34, -58,1,17.04.2015,,94,138.5, -58,1,18.04.2015,,104,138.5, -58,1,19.04.2015,,173,138.5, -58,1,20.04.2015,,72,138.5, -58,1,21.04.2015,,79,138.5, -58,1,22.04.2015,,75,128.98, -58,1,23.04.2015,,440,128.98, -58,1,24.04.2015,,289,128.98, -58,1,25.04.2015,,113,128.98, -58,1,26.04.2015,,232,128.98, -58,1,27.04.2015,,179,128.98, -58,1,28.04.2015,,273,128.98, -58,1,29.04.2015,,179,131.7, -58,1,30.04.2015,,214,131.7, -58,1,01.05.2015,,91,131.7, -58,1,02.05.2015,,148,131.7, -58,1,03.05.2015,,179,131.7, -58,1,04.05.2015,,123,131.7, -58,1,05.05.2015,,185,131.7, -58,1,06.05.2015,,236,131.7, -58,1,07.05.2015,,273,131.7, -58,1,08.05.2015,,286,131.7, -58,1,09.05.2015,,176,131.7, -58,1,10.05.2015,,107,131.7, -58,1,11.05.2015,,192,131.7, -58,1,12.05.2015,,254,131.7, -58,1,13.05.2015,,355,131.7, -58,1,14.05.2015,,167,131.7, -58,1,15.05.2015,,182,131.7, -58,1,16.05.2015,,248,131.7, -58,1,17.05.2015,,207,131.7, -58,1,18.05.2015,,236,131.7, -58,1,19.05.2015,1.0,1100,131.7,119.33 -58,1,20.05.2015,1.0,817,131.7,119.33 -58,1,21.05.2015,1.0,877,131.7,119.33 -58,1,22.05.2015,1.0,543,131.7,119.33 -58,1,23.05.2015,1.0,1018,131.7,119.33 -58,1,24.05.2015,1.0,1002,131.7,119.33 -58,1,25.05.2015,1.0,176,131.7,119.33 -58,1,26.05.2015,,123,131.7, -58,1,27.05.2015,,223,129.25, -58,1,28.05.2015,,167,129.25, -58,1,29.05.2015,,239,129.25, -58,1,30.05.2015,,226,129.25, -58,1,31.05.2015,,223,129.25, -58,1,01.06.2015,,170,129.25, -58,1,02.06.2015,,75,129.25, -58,1,03.06.2015,,157,129.25, -58,1,04.06.2015,,110,129.25, -58,1,05.06.2015,,317,129.25, -58,1,06.06.2015,,280,131.7, -58,1,07.06.2015,,393,131.7, -58,1,08.06.2015,,349,131.7, -58,1,09.06.2015,1.0,1326,131.7,119.33 -58,1,10.06.2015,1.0,2601,131.7,119.33 -58,1,11.06.2015,1.0,1599,131.7,119.33 -58,1,12.06.2015,1.0,1153,131.7,119.33 -58,1,13.06.2015,1.0,760,131.7,119.33 -58,1,14.06.2015,1.0,939,131.7,119.33 -58,1,15.06.2015,1.0,691,131.7,119.33 -58,1,16.06.2015,,412,131.7, -58,1,17.06.2015,,220,131.7, -58,1,18.06.2015,,176,128.17, -58,1,19.06.2015,,358,128.17, -58,1,20.06.2015,,402,128.17, -58,1,21.06.2015,,261,128.17, -58,1,22.06.2015,,258,128.17, -58,1,23.06.2015,,468,128.17, -58,1,24.06.2015,,471,128.17, -58,1,25.06.2015,,368,128.17, -58,1,26.06.2015,,446,128.17, -58,1,27.06.2015,,342,128.17, -58,1,28.06.2015,,701,130.34, -58,1,29.06.2015,,305,130.34, -58,1,30.06.2015,1.0,836,130.34,119.33 -58,1,01.07.2015,1.0,1769,130.34,119.33 -58,1,02.07.2015,1.0,2108,130.34,119.33 -58,1,03.07.2015,1.0,123,130.34,119.33 -58,1,04.07.2015,1.0,1018,130.34,119.33 -58,1,05.07.2015,1.0,949,130.34,119.33 -58,1,06.07.2015,1.0,936,130.34,119.33 -58,1,07.07.2015,,123,130.34, -58,1,08.07.2015,,311,130.34, -58,1,09.07.2015,,207,121.64, -58,1,10.07.2015,,267,121.64, -58,1,11.07.2015,,201,123.55, -58,1,12.07.2015,,408,123.55, -58,1,13.07.2015,,254,123.55, -58,1,14.07.2015,,157,123.55, -58,1,15.07.2015,,135,125.45, -58,1,16.07.2015,,210,125.45, -58,1,17.07.2015,,214,125.45, -58,1,18.07.2015,,129,127.62, -58,1,19.07.2015,,1040,127.62, -58,1,20.07.2015,,836,127.62, -58,1,21.07.2015,,798,127.62, -58,1,22.07.2015,,346,127.62, -58,1,23.07.2015,,1156,127.62, -58,1,24.07.2015,,1618,127.62, -58,1,25.07.2015,,537,127.62, -58,1,26.07.2015,,512,130.07, -58,1,27.07.2015,,1118,130.07, -58,1,28.07.2015,1.0,2168,130.07,124.77 -58,1,29.07.2015,1.0,2897,130.07,124.77 -58,1,30.07.2015,1.0,226,130.07,124.77 -58,1,31.07.2015,1.0,1753,130.07,124.77 -58,1,01.08.2015,1.0,855,130.07,124.77 -58,1,02.08.2015,1.0,964,130.07,124.77 -58,1,03.08.2015,1.0,917,130.07,124.77 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-58,1,06.09.2015,,669,133.6, -58,1,07.09.2015,,361,133.6, -58,1,08.09.2015,,660,133.6, -58,1,09.09.2015,,870,135.51, -58,1,10.09.2015,,650,135.51, -58,1,11.09.2015,,779,135.51, -58,1,12.09.2015,,631,135.51, -58,1,13.09.2015,,855,135.51, -58,1,14.09.2015,,644,135.51, -58,1,15.09.2015,,531,135.51, -58,1,16.09.2015,,506,138.5, -58,1,17.09.2015,,232,138.5, -58,1,18.09.2015,,346,138.5, -58,1,19.09.2015,,320,138.5, -58,1,20.09.2015,,217,138.5, -58,1,21.09.2015,,214,138.5, -58,1,22.09.2015,,327,138.5, -58,1,23.09.2015,,390,141.21, -58,1,24.09.2015,,352,141.21, -58,1,25.09.2015,,295,141.21, -58,1,26.09.2015,,154,141.21, -58,1,27.09.2015,,167,141.21, -58,1,28.09.2015,,220,141.21, -58,1,29.09.2015,,418,141.21, -58,1,30.09.2015,,188,141.21, -58,1,01.10.2015,,267,141.21, -58,1,02.10.2015,,399,141.21, -58,1,03.10.2015,,242,141.21, -58,1,04.10.2015,,210,141.21, -58,1,05.10.2015,,273,141.21, -58,1,06.10.2015,1.0,540,141.21,132.92 -58,1,07.10.2015,1.0,735,141.21,130.21 -58,1,08.10.2015,1.0,1228,141.11,130.21 -58,1,09.10.2015,1.0,1140,141.21,130.21 -58,1,10.10.2015,1.0,660,141.21,130.21 -58,1,11.10.2015,1.0,767,141.21,130.21 -58,1,12.10.2015,1.0,534,141.21,130.21 -58,1,13.10.2015,,167,141.21, -58,1,14.10.2015,,195,141.21, -58,1,15.10.2015,,176,142.57, -58,1,16.10.2015,,129,142.57, -58,1,17.10.2015,,129,142.57, -58,1,18.10.2015,,273,142.57, -58,1,19.10.2015,,185,142.57, -58,1,20.10.2015,,242,142.57, -58,1,21.10.2015,,355,130.34, -58,1,22.10.2015,,412,130.34, -58,1,23.10.2015,,631,130.34, -58,1,24.10.2015,,484,130.34, -58,1,25.10.2015,,509,130.34, -58,1,26.10.2015,,452,130.34, -58,1,27.10.2015,1.0,2284,130.34,122.32 -58,1,28.10.2015,1.0,1222,130.34,122.32 -58,1,29.10.2015,1.0,1115,130.34,122.32 -58,1,30.10.2015,1.0,763,130.34,122.32 -58,1,31.10.2015,1.0,1096,130.34,122.32 -58,1,01.11.2015,1.0,789,130.34,122.32 -58,1,02.11.2015,1.0,701,130.34,122.32 -58,1,03.11.2015,,773,130.34, -58,1,04.11.2015,,242,134.69, -58,1,05.11.2015,,336,134.69, -58,1,06.11.2015,,562,134.69, -58,1,07.11.2015,,336,134.69, -58,1,08.11.2015,,361,134.69, -58,1,09.11.2015,,333,134.69, -58,1,10.11.2015,,258,134.69, -58,1,11.11.2015,,188,130.34, -58,1,12.11.2015,,355,130.34, -58,1,13.11.2015,,452,130.34, -58,1,14.11.2015,,320,130.34, -58,1,15.11.2015,,311,130.34, -58,1,16.11.2015,,327,130.34, -58,1,17.11.2015,,371,130.34, -58,1,18.11.2015,,434,130.34, -58,1,19.11.2015,,368,130.34, -58,1,20.11.2015,,339,130.34, -58,1,21.11.2015,,232,130.34, -58,1,22.11.2015,,201,130.34, -58,1,23.11.2015,,311,130.34, -58,1,24.11.2015,1.0,5400,130.34,116.61 -58,1,25.11.2015,1.0,4317,130.34,116.61 -58,1,26.11.2015,1.0,1489,130.34,116.61 -58,1,27.11.2015,1.0,336,130.34,116.61 -58,1,28.11.2015,1.0,1385,130.34,116.61 -58,1,29.11.2015,1.0,2542,130.34,116.61 -58,1,30.11.2015,1.0,2328,130.34,116.61 -58,1,01.12.2015,,390,130.34, -58,1,02.12.2015,,336,130.34, -58,1,03.12.2015,,368,130.34, -58,1,04.12.2015,,490,130.34, -58,1,05.12.2015,1.0,1005,130.34,116.61 -58,1,06.12.2015,1.0,1715,130.34,116.61 -58,1,07.12.2015,1.0,2117,130.34,116.61 -58,1,08.12.2015,,999,130.34, -58,1,09.12.2015,,324,130.34, -58,1,10.12.2015,,346,130.34, -58,1,11.12.2015,,330,130.34, -58,1,12.12.2015,,217,130.34, -58,1,13.12.2015,,349,130.34, -58,1,14.12.2015,,324,130.34, -58,1,15.12.2015,,308,130.34, -58,1,16.12.2015,,295,130.34, -58,1,17.12.2015,,631,130.34, -58,1,18.12.2015,,254,130.34, -58,1,19.12.2015,,179,130.34, -58,1,20.12.2015,,308,130.34, -58,1,21.12.2015,,355,130.34, -58,1,22.12.2015,,311,130.34, -58,1,23.12.2015,,424,130.34, -58,1,24.12.2015,,336,126.54, -58,1,25.12.2015,,710,126.54, -58,1,26.12.2015,,405,126.54, -58,1,27.12.2015,,500,126.54, -58,1,28.12.2015,,503,126.54, -58,1,29.12.2015,,606,126.54, -58,1,30.12.2015,,685,126.54, -58,1,31.12.2015,,500,126.54, -58,1,01.01.2016,,79,126.54, -58,1,02.01.2016,,286,126.54, -58,1,03.01.2016,,239,126.54, -58,1,04.01.2016,,380,126.54, -58,1,05.01.2016,,434,126.54, -58,1,06.01.2016,,779,126.54, -58,1,07.01.2016,,371,126.54, -58,1,08.01.2016,,801,126.54, -58,1,09.01.2016,,336,126.54, -58,1,10.01.2016,,427,126.54, -58,1,11.01.2016,,540,126.54, -58,1,12.01.2016,,1049,126.54, -58,1,13.01.2016,,496,126.54, -58,1,14.01.2016,,826,126.54, -58,1,15.01.2016,,964,126.54, -58,1,16.01.2016,,584,126.54, -58,1,17.01.2016,,870,126.54, -58,1,18.01.2016,,1115,126.54, -58,1,19.01.2016,,487,126.54, -58,1,20.01.2016,,729,126.54, -58,1,21.01.2016,,528,127.9, -58,1,22.01.2016,,578,127.9, -58,1,23.01.2016,,710,127.9, -58,1,24.01.2016,,719,127.9, -58,1,25.01.2016,,710,127.9, -58,1,26.01.2016,,748,127.9, -58,1,27.01.2016,,713,127.9, -58,1,28.01.2016,,1103,127.9, -58,1,29.01.2016,,839,127.9, -58,1,30.01.2016,,1191,127.9, -58,1,31.01.2016,,968,127.9, -58,1,01.02.2016,,531,130.34, -58,1,02.02.2016,,1382,130.34, -58,1,03.02.2016,,974,130.34, -58,1,04.02.2016,,1131,133.06, -58,1,05.02.2016,,1275,133.06, -58,1,06.02.2016,,1291,133.06, -58,1,07.02.2016,,660,141.76, -58,1,08.02.2016,,647,141.76, -58,1,09.02.2016,1.0,4087,141.76,130.75 -58,1,10.02.2016,1.0,1521,141.76,130.75 -58,1,11.02.2016,1.0,1637,141.76,130.75 -58,1,12.02.2016,1.0,1923,141.76,130.75 -58,1,13.02.2016,1.0,1015,141.76,130.75 -58,1,14.02.2016,1.0,873,141.76,130.75 -58,1,15.02.2016,1.0,924,141.76,130.75 -58,1,16.02.2016,1.0,1131,141.76,130.75 -58,1,17.02.2016,1.0,958,141.76,130.75 -58,1,18.02.2016,,691,141.76, -58,1,19.02.2016,,452,141.76, -58,1,20.02.2016,,198,141.76, -58,1,21.02.2016,,185,141.76, -58,1,22.02.2016,,242,141.76, -58,1,23.02.2016,,236,141.76, -58,1,24.02.2016,,207,141.76, -58,1,25.02.2016,,383,141.76, -58,1,26.02.2016,,327,141.76, -58,1,27.02.2016,,217,141.76, -58,1,28.02.2016,,245,141.76, -58,1,29.02.2016,,204,141.76, -58,1,01.03.2016,,223,141.76, -58,1,02.03.2016,,107,141.76, -58,1,03.03.2016,,141,141.76, -58,1,04.03.2016,,239,141.76, -58,1,05.03.2016,,270,141.21, -58,1,06.03.2016,,132,141.21, -58,1,07.03.2016,,226,141.21, -58,1,08.03.2016,1.0,421,141.21,132.92 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-58,1,07.04.2016,,176,141.21, -58,1,08.04.2016,,207,141.21, -58,1,09.04.2016,,185,141.21, -58,1,10.04.2016,,141,141.21, -58,1,11.04.2016,,151,141.21, -58,1,12.04.2016,,157,141.21, -58,1,13.04.2016,,123,141.21, -58,1,14.04.2016,,132,135.78, -58,1,15.04.2016,,314,135.78, -58,1,16.04.2016,,154,135.78, -58,1,17.04.2016,,314,135.78, -58,1,18.04.2016,,361,135.78, -58,1,19.04.2016,1.0,3735,135.78,122.32 -58,1,20.04.2016,1.0,2689,135.78,122.32 -58,1,21.04.2016,1.0,1687,135.78,122.32 -58,1,22.04.2016,1.0,2482,135.78,122.32 -58,1,23.04.2016,1.0,2275,135.78,122.32 -58,1,24.04.2016,1.0,2538,135.78,122.32 -58,1,25.04.2016,1.0,364,135.78,122.32 -58,1,26.04.2016,,195,135.78, -58,1,27.04.2016,,179,135.78, -58,1,28.04.2016,,167,135.78, -58,1,29.04.2016,,500,131.16, -58,1,30.04.2016,,710,131.16, -58,1,01.05.2016,,220,131.16, -58,1,02.05.2016,,292,131.16, -58,1,03.05.2016,,273,131.16, -58,1,04.05.2016,,226,131.16, -58,1,05.05.2016,,289,131.16, -58,1,06.05.2016,,374,131.16, -58,1,07.05.2016,,254,131.16, -58,1,08.05.2016,,496,131.16, -58,1,09.05.2016,,220,131.16, -58,1,10.05.2016,,355,131.16, -58,1,11.05.2016,,735,131.16, -58,1,12.05.2016,,264,131.16, -58,1,13.05.2016,,562,128.98, -58,1,14.05.2016,,324,128.98, -58,1,15.05.2016,,789,128.98, -58,1,16.05.2016,,675,128.98, -58,1,17.05.2016,1.0,1684,128.98,119.6 -58,1,18.05.2016,1.0,2001,128.98,119.6 -58,1,19.05.2016,1.0,5328,128.98,119.6 -58,1,20.05.2016,1.0,3503,128.98,119.6 -58,1,21.05.2016,1.0,484,128.98,119.6 -58,1,22.05.2016,1.0,2073,128.98,119.6 -59,1,01.01.2015,,41,163.78, -59,1,02.01.2015,,50,163.78, -59,1,03.01.2015,,25,163.78, -59,1,04.01.2015,,107,163.78, -59,1,05.01.2015,,57,163.78, -59,1,06.01.2015,,88,163.78, -59,1,07.01.2015,,63,163.78, -59,1,08.01.2015,,63,163.78, -59,1,09.01.2015,,79,163.78, -59,1,10.01.2015,,38,163.78, -59,1,11.01.2015,,79,163.78, -59,1,12.01.2015,,88,163.78, -59,1,13.01.2015,,151,163.78, -59,1,14.01.2015,,60,168.4, -59,1,15.01.2015,,53,168.4, -59,1,16.01.2015,,91,168.4, -59,1,17.01.2015,,28,168.4, 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-59,1,20.06.2015,,867,128.17, -59,1,21.06.2015,,493,128.17, -59,1,22.06.2015,,701,128.17, -59,1,23.06.2015,,804,128.17, -59,1,24.06.2015,,327,128.17, -59,1,25.06.2015,,575,128.17, -59,1,26.06.2015,,669,128.17, -59,1,27.06.2015,,449,128.17, -59,1,28.06.2015,,383,130.34, -59,1,29.06.2015,,283,130.34, -59,1,30.06.2015,1.0,2893,130.34,119.33 -59,1,01.07.2015,1.0,1778,130.34,119.33 -59,1,02.07.2015,1.0,2077,130.34,119.33 -59,1,03.07.2015,1.0,2136,130.34,119.33 -59,1,04.07.2015,1.0,1118,130.34,119.33 -59,1,05.07.2015,1.0,669,130.34,119.33 -59,1,06.07.2015,1.0,506,130.34,119.33 -59,1,07.07.2015,,534,130.34, -59,1,08.07.2015,,308,130.34, -59,1,09.07.2015,,352,121.64, -59,1,10.07.2015,,600,121.64, -59,1,11.07.2015,,471,123.55, -59,1,12.07.2015,,864,123.55, -59,1,13.07.2015,,324,123.55, -59,1,14.07.2015,,1323,123.55, -59,1,15.07.2015,,242,125.45, -59,1,16.07.2015,,858,125.45, -59,1,17.07.2015,,1015,125.45, -59,1,18.07.2015,,889,127.62, -59,1,19.07.2015,,820,127.62, -59,1,20.07.2015,,418,127.62, -59,1,21.07.2015,,2271,127.62, -59,1,22.07.2015,,1332,127.62, -59,1,23.07.2015,,1257,127.62, -59,1,24.07.2015,,1797,127.62, -59,1,25.07.2015,,958,127.62, -59,1,26.07.2015,,1439,130.07, -59,1,27.07.2015,,1332,130.07, -59,1,28.07.2015,1.0,2168,130.07,124.77 -59,1,29.07.2015,1.0,1159,130.07,124.77 -59,1,30.07.2015,1.0,1034,130.07,124.77 -59,1,31.07.2015,1.0,958,130.07,124.77 -59,1,01.08.2015,1.0,110,130.07,124.77 -59,1,02.08.2015,1.0,961,130.07,124.77 -59,1,03.08.2015,1.0,1024,130.07,124.77 -59,1,04.08.2015,,1078,138.5, -59,1,05.08.2015,,719,138.5, -59,1,06.08.2015,,503,138.5, -59,1,07.08.2015,,270,138.5, -59,1,08.08.2015,,264,141.21, -59,1,09.08.2015,,223,141.21, -59,1,10.08.2015,,506,141.21, -59,1,11.08.2015,,465,141.21, -59,1,12.08.2015,,399,142.85, -59,1,13.08.2015,,330,142.85, -59,1,14.08.2015,,767,142.85, -59,1,15.08.2015,,386,142.85, -59,1,16.08.2015,,374,142.85, -59,1,17.08.2015,,396,142.85, -59,1,18.08.2015,,584,142.85, -59,1,19.08.2015,,289,142.85, -59,1,20.08.2015,,195,142.85, -59,1,21.08.2015,,330,142.85, -59,1,22.08.2015,,339,142.85, -59,1,23.08.2015,,355,142.85, -59,1,24.08.2015,,352,142.85, -59,1,25.08.2015,,858,142.85, -59,1,26.08.2015,,390,142.85, -59,1,27.08.2015,,330,137.68, -59,1,28.08.2015,,330,137.68, -59,1,29.08.2015,,484,137.68, -59,1,30.08.2015,,619,137.68, -59,1,31.08.2015,,760,137.68, -59,1,01.09.2015,,550,137.68, -59,1,02.09.2015,,694,135.78, -59,1,03.09.2015,,864,135.78, -59,1,04.09.2015,,892,135.78, -59,1,05.09.2015,,308,133.6, -59,1,06.09.2015,,254,133.6, -59,1,07.09.2015,,421,133.6, -59,1,08.09.2015,,1401,133.6, -59,1,09.09.2015,,474,135.51, -59,1,10.09.2015,,515,135.51, -59,1,11.09.2015,,603,135.51, -59,1,12.09.2015,,371,135.51, -59,1,13.09.2015,,685,135.51, -59,1,14.09.2015,,578,135.51, -59,1,15.09.2015,,1002,135.51, -59,1,16.09.2015,,672,138.5, -59,1,17.09.2015,,873,138.5, -59,1,18.09.2015,,861,138.5, -59,1,19.09.2015,,320,138.5, -59,1,20.09.2015,,386,138.5, -59,1,21.09.2015,,418,138.5, -59,1,22.09.2015,,650,138.5, -59,1,23.09.2015,,503,141.21, -59,1,24.09.2015,,613,141.21, -59,1,25.09.2015,,638,141.21, -59,1,26.09.2015,,223,141.21, -59,1,27.09.2015,,317,141.21, -59,1,28.09.2015,,258,141.21, -59,1,29.09.2015,,591,141.21, -59,1,30.09.2015,,424,141.21, -59,1,01.10.2015,,418,141.21, -59,1,02.10.2015,,434,141.21, -59,1,03.10.2015,,314,141.21, -59,1,04.10.2015,,490,141.21, -59,1,05.10.2015,,368,141.21, -59,1,06.10.2015,1.0,1263,141.21,122.32 -59,1,07.10.2015,1.0,895,141.21,122.32 -59,1,08.10.2015,1.0,1543,141.11,122.32 -59,1,09.10.2015,1.0,2403,141.21,122.32 -59,1,10.10.2015,1.0,358,141.21,122.32 -59,1,11.10.2015,1.0,735,141.21,122.32 -59,1,12.10.2015,1.0,522,141.21,122.32 -59,1,13.10.2015,,412,141.21, -59,1,14.10.2015,,330,141.21, -59,1,15.10.2015,,292,142.57, -59,1,16.10.2015,,556,142.57, -59,1,17.10.2015,,223,142.57, -59,1,18.10.2015,,430,142.57, -59,1,19.10.2015,,254,142.57, -59,1,20.10.2015,,506,142.57, -59,1,21.10.2015,,688,130.34, -59,1,22.10.2015,,939,130.34, -59,1,23.10.2015,,1056,130.34, -59,1,24.10.2015,,622,130.34, -59,1,25.10.2015,,779,130.34, -59,1,26.10.2015,,675,130.34, -59,1,27.10.2015,1.0,2523,130.34,122.32 -59,1,28.10.2015,1.0,1580,130.34,122.32 -59,1,29.10.2015,1.0,933,130.34,122.32 -59,1,30.10.2015,1.0,1530,130.34,122.32 -59,1,31.10.2015,1.0,1382,130.34,122.32 -59,1,01.11.2015,1.0,983,130.34,122.32 -59,1,02.11.2015,1.0,829,130.34,122.32 -59,1,03.11.2015,,701,130.34, -59,1,04.11.2015,,314,134.69, -59,1,05.11.2015,,267,134.69, -59,1,06.11.2015,,471,134.69, -59,1,07.11.2015,,324,134.69, -59,1,08.11.2015,,371,134.69, -59,1,09.11.2015,,151,134.69, -59,1,10.11.2015,,349,134.69, -59,1,11.11.2015,,339,130.34, -59,1,12.11.2015,,402,130.34, -59,1,13.11.2015,,437,130.34, -59,1,14.11.2015,,333,130.34, -59,1,15.11.2015,,298,130.34, -59,1,16.11.2015,,195,130.34, -59,1,17.11.2015,,547,130.34, -59,1,18.11.2015,,408,130.34, -59,1,19.11.2015,,503,130.34, -59,1,20.11.2015,,424,130.34, -59,1,21.11.2015,,236,130.34, -59,1,22.11.2015,,210,130.34, -59,1,23.11.2015,,204,130.34, -59,1,24.11.2015,1.0,5284,130.34,116.61 -59,1,25.11.2015,1.0,990,130.34,116.61 -59,1,26.11.2015,1.0,223,130.34,116.61 -59,1,27.11.2015,1.0,1863,130.34,116.61 -59,1,28.11.2015,1.0,1869,130.34,116.61 -59,1,29.11.2015,1.0,1882,130.34,116.61 -59,1,30.11.2015,1.0,1781,130.34,116.61 -59,1,01.12.2015,,254,130.34, -59,1,02.12.2015,,220,130.34, -59,1,03.12.2015,,217,130.34, -59,1,04.12.2015,,374,130.34, -59,1,05.12.2015,1.0,1188,130.34,116.61 -59,1,06.12.2015,1.0,1621,130.34,116.61 -59,1,07.12.2015,1.0,1194,130.34,116.61 -59,1,08.12.2015,,286,130.34, -59,1,09.12.2015,,163,130.34, -59,1,10.12.2015,,157,130.34, -59,1,11.12.2015,,119,130.34, -59,1,12.12.2015,,101,130.34, -59,1,13.12.2015,,308,130.34, -59,1,14.12.2015,,163,130.34, -59,1,15.12.2015,,443,130.34, -59,1,16.12.2015,,368,130.34, -59,1,17.12.2015,,264,130.34, -59,1,18.12.2015,,305,130.34, -59,1,19.12.2015,,207,130.34, -59,1,20.12.2015,,176,130.34, -59,1,21.12.2015,,160,130.34, -59,1,22.12.2015,,333,130.34, -59,1,23.12.2015,,141,130.34, -59,1,24.12.2015,,308,126.54, -59,1,25.12.2015,,415,126.54, -59,1,26.12.2015,,201,126.54, -59,1,27.12.2015,,305,126.54, -59,1,28.12.2015,,358,126.54, -59,1,29.12.2015,,898,126.54, -59,1,30.12.2015,,581,126.54, -59,1,31.12.2015,,214,126.54, -59,1,01.01.2016,,69,126.54, -59,1,02.01.2016,,236,126.54, -59,1,03.01.2016,,311,126.54, -59,1,04.01.2016,,393,126.54, -59,1,05.01.2016,,418,126.54, -59,1,06.01.2016,,305,126.54, -59,1,07.01.2016,,242,126.54, -59,1,08.01.2016,,292,126.54, -59,1,09.01.2016,,236,126.54, -59,1,10.01.2016,,302,126.54, -59,1,11.01.2016,,418,126.54, -59,1,12.01.2016,,760,126.54, -59,1,13.01.2016,,336,126.54, -59,1,14.01.2016,,446,126.54, -59,1,15.01.2016,,701,126.54, -59,1,16.01.2016,,456,126.54, -59,1,17.01.2016,,581,126.54, -59,1,18.01.2016,,333,126.54, -59,1,19.01.2016,,619,126.54, -59,1,20.01.2016,,597,126.54, -59,1,21.01.2016,,267,127.9, -59,1,22.01.2016,,474,127.9, -59,1,23.01.2016,,468,127.9, -59,1,24.01.2016,,603,127.9, -59,1,25.01.2016,,481,127.9, 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-61,1,06.01.2016,,151,126.54, -61,1,07.01.2016,,170,126.54, -61,1,08.01.2016,,201,126.54, -61,1,09.01.2016,,154,126.54, -61,1,10.01.2016,,160,126.54, -61,1,11.01.2016,,229,126.54, -61,1,12.01.2016,,223,126.54, -61,1,13.01.2016,,188,126.54, -61,1,14.01.2016,,264,126.54, -61,1,15.01.2016,,198,126.54, -61,1,16.01.2016,,173,126.54, -61,1,17.01.2016,,223,126.54, -61,1,18.01.2016,,251,126.54, -61,1,19.01.2016,,176,126.54, -61,1,20.01.2016,,280,126.54, -61,1,21.01.2016,,270,127.9, -61,1,22.01.2016,,229,127.9, -61,1,23.01.2016,,182,127.9, -61,1,24.01.2016,,210,127.9, -61,1,25.01.2016,,386,127.9, -61,1,26.01.2016,,289,127.9, -61,1,27.01.2016,,236,127.9, -61,1,28.01.2016,,204,127.9, -61,1,29.01.2016,,248,127.9, -61,1,30.01.2016,,151,127.9, -61,1,31.01.2016,,223,127.9, -61,1,01.02.2016,,258,130.34, -61,1,02.02.2016,,226,130.34, -61,1,03.02.2016,,182,130.34, -61,1,04.02.2016,,138,133.06, -61,1,05.02.2016,,198,133.06, -61,1,06.02.2016,,248,133.06, -61,1,07.02.2016,,123,141.76, 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-61,1,07.04.2016,,129,141.21, -61,1,08.04.2016,,101,141.21, -61,1,09.04.2016,,129,141.21, -61,1,10.04.2016,,75,141.21, -61,1,11.04.2016,,141,141.21, -61,1,12.04.2016,,88,141.21, -61,1,13.04.2016,,129,141.21, -61,1,14.04.2016,,91,135.78, -61,1,15.04.2016,,179,135.78, -61,1,16.04.2016,,132,135.78, -61,1,17.04.2016,,179,135.78, -61,1,18.04.2016,,163,135.78, -61,1,19.04.2016,1.0,955,135.78,122.32 -61,1,20.04.2016,1.0,1131,135.78,122.32 -61,1,21.04.2016,1.0,1247,135.78,122.32 -61,1,22.04.2016,1.0,330,135.78,122.32 -61,1,23.04.2016,1.0,569,135.78,122.32 -61,1,24.04.2016,1.0,534,135.78,122.32 -61,1,25.04.2016,1.0,631,135.78,122.32 -61,1,26.04.2016,,176,135.78, -61,1,27.04.2016,,163,135.78, -61,1,28.04.2016,,138,135.78, -61,1,29.04.2016,,217,131.16, -61,1,30.04.2016,,135,131.16, -61,1,01.05.2016,,72,131.16, -61,1,02.05.2016,,75,131.16, -61,1,03.05.2016,,107,131.16, -61,1,04.05.2016,,148,131.16, -61,1,05.05.2016,,154,131.16, -61,1,06.05.2016,,167,131.16, -61,1,07.05.2016,,141,131.16, 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-62,1,24.07.2015,,496,127.62, -62,1,25.07.2015,,427,127.62, -62,1,26.07.2015,,270,130.07, -62,1,27.07.2015,,339,130.07, -62,1,28.07.2015,1.0,594,130.07,124.77 -62,1,29.07.2015,1.0,396,130.07,124.77 -62,1,30.07.2015,1.0,591,130.07,124.77 -62,1,31.07.2015,1.0,474,130.07,124.77 -62,1,01.08.2015,1.0,619,130.07,124.77 -62,1,02.08.2015,1.0,528,130.07,124.77 -62,1,03.08.2015,1.0,1662,130.07,124.77 -62,1,04.08.2015,,148,138.5, -62,1,05.08.2015,,163,138.5, -62,1,06.08.2015,,138,138.5, -62,1,07.08.2015,,145,138.5, -62,1,08.08.2015,,148,141.21, -62,1,09.08.2015,,126,141.21, -62,1,10.08.2015,,173,141.21, -62,1,11.08.2015,,198,141.21, -62,1,12.08.2015,,236,142.85, -62,1,13.08.2015,,201,142.85, -62,1,14.08.2015,,232,142.85, -62,1,15.08.2015,,220,142.85, -62,1,16.08.2015,,270,142.85, -62,1,17.08.2015,,201,142.85, -62,1,18.08.2015,,254,142.85, -62,1,19.08.2015,,163,142.85, -62,1,20.08.2015,,258,142.85, -62,1,21.08.2015,,210,142.85, -62,1,22.08.2015,,210,142.85, -62,1,23.08.2015,,173,142.85, -62,1,24.08.2015,,182,142.85, -62,1,25.08.2015,,302,142.85, -62,1,26.08.2015,,182,142.85, -62,1,27.08.2015,,289,142.85, -62,1,28.08.2015,,298,137.68, -62,1,29.08.2015,,239,137.68, -62,1,30.08.2015,,305,137.68, -62,1,31.08.2015,,261,137.68, -62,1,01.09.2015,,188,137.68, -62,1,02.09.2015,,179,135.78, -62,1,03.09.2015,,204,135.78, -62,1,04.09.2015,,264,135.78, -62,1,05.09.2015,,188,133.6, -62,1,06.09.2015,,273,133.6, -62,1,07.09.2015,,302,133.6, -62,1,08.09.2015,,182,133.6, -62,1,09.09.2015,,173,135.51, -62,1,10.09.2015,,179,135.51, -62,1,11.09.2015,,182,135.51, -62,1,12.09.2015,,223,135.51, -62,1,13.09.2015,,226,135.51, -62,1,14.09.2015,,141,135.51, -62,1,15.09.2015,,251,135.51, -62,1,16.09.2015,,204,138.5, -62,1,17.09.2015,,198,138.5, -62,1,18.09.2015,,176,138.5, -62,1,19.09.2015,,132,138.5, -62,1,20.09.2015,,176,138.5, -62,1,21.09.2015,,141,138.5, -62,1,22.09.2015,,160,138.5, -62,1,23.09.2015,,258,141.21, -62,1,24.09.2015,,223,141.21, -62,1,25.09.2015,,239,141.21, -62,1,26.09.2015,,145,141.21, -62,1,27.09.2015,,276,141.21, -62,1,28.09.2015,,214,141.21, -62,1,29.09.2015,,151,141.21, -62,1,30.09.2015,,167,141.21, -62,1,01.10.2015,,267,141.21, -62,1,02.10.2015,,176,141.21, -62,1,03.10.2015,,123,141.21, -62,1,04.10.2015,,236,141.21, -62,1,05.10.2015,,226,141.21, -62,1,06.10.2015,1.0,688,141.21,132.92 -62,1,07.10.2015,1.0,572,141.21,130.21 -62,1,08.10.2015,1.0,462,141.11,130.21 -62,1,09.10.2015,1.0,415,141.21,130.21 -62,1,10.10.2015,1.0,474,141.21,130.21 -62,1,11.10.2015,1.0,493,141.21,130.21 -62,1,12.10.2015,1.0,386,141.21,130.21 -62,1,13.10.2015,,101,141.21, -62,1,14.10.2015,,173,141.21, -62,1,15.10.2015,,116,142.57, -62,1,16.10.2015,,160,142.57, -62,1,17.10.2015,,101,142.57, -62,1,18.10.2015,,123,142.57, -62,1,19.10.2015,,113,142.57, -62,1,20.10.2015,,129,142.57, -62,1,21.10.2015,,154,130.34, -62,1,22.10.2015,,374,130.34, -62,1,23.10.2015,,663,130.34, -62,1,24.10.2015,,346,130.34, -62,1,25.10.2015,,368,130.34, -62,1,26.10.2015,,261,130.34, -62,1,27.10.2015,1.0,870,130.34,122.32 -62,1,28.10.2015,1.0,814,130.34,122.32 -62,1,29.10.2015,1.0,515,130.34,122.32 -62,1,30.10.2015,1.0,474,130.34,122.32 -62,1,31.10.2015,1.0,500,130.34,122.32 -62,1,01.11.2015,1.0,267,130.34,122.32 -62,1,02.11.2015,1.0,314,130.34,122.32 -62,1,03.11.2015,,126,130.34, -62,1,04.11.2015,,167,134.69, -62,1,05.11.2015,,276,134.69, -62,1,06.11.2015,,157,134.69, -62,1,07.11.2015,,195,134.69, -62,1,08.11.2015,,173,134.69, -62,1,09.11.2015,,107,134.69, -62,1,10.11.2015,,129,134.69, -62,1,11.11.2015,,167,130.34, -62,1,12.11.2015,,276,130.34, -62,1,13.11.2015,,264,130.34, -62,1,14.11.2015,,75,130.34, -62,1,15.11.2015,,110,130.34, -62,1,16.11.2015,,75,130.34, -62,1,17.11.2015,,126,130.34, -62,1,18.11.2015,,163,130.34, -62,1,19.11.2015,,151,130.34, -62,1,20.11.2015,,154,130.34, -62,1,21.11.2015,,110,130.34, -62,1,22.11.2015,,107,130.34, -62,1,23.11.2015,,157,130.34, -62,1,24.11.2015,1.0,1470,130.34,116.61 -62,1,25.11.2015,1.0,1341,130.34,116.61 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-62,1,27.12.2015,,116,126.54, -62,1,28.12.2015,,85,126.54, -62,1,29.12.2015,,116,126.54, -62,1,30.12.2015,,151,126.54, -62,1,31.12.2015,,157,126.54, -62,1,01.01.2016,,41,126.54, -62,1,02.01.2016,,57,126.54, -62,1,03.01.2016,,151,126.54, -62,1,04.01.2016,,88,126.54, -62,1,05.01.2016,,154,126.54, -62,1,06.01.2016,,135,126.54, -62,1,07.01.2016,,104,126.54, -62,1,08.01.2016,,192,126.54, -62,1,09.01.2016,,116,126.54, -62,1,10.01.2016,,101,126.54, -62,1,11.01.2016,,138,126.54, -62,1,12.01.2016,,104,126.54, -62,1,13.01.2016,,79,126.54, -62,1,14.01.2016,,446,126.54, -62,1,15.01.2016,,157,126.54, -62,1,16.01.2016,,223,126.54, -62,1,17.01.2016,,170,126.54, -62,1,18.01.2016,,148,126.54, -62,1,19.01.2016,,173,126.54, -62,1,20.01.2016,,232,126.54, -62,1,21.01.2016,,207,127.9, -62,1,22.01.2016,,270,127.9, -62,1,23.01.2016,,119,127.9, -62,1,24.01.2016,,126,127.9, -62,1,25.01.2016,,176,127.9, -62,1,26.01.2016,,145,127.9, -62,1,27.01.2016,,119,127.9, -62,1,28.01.2016,,151,127.9, -62,1,29.01.2016,,129,127.9, -62,1,30.01.2016,,145,127.9, -62,1,31.01.2016,,126,127.9, -62,1,01.02.2016,,182,130.34, -62,1,02.02.2016,,145,130.34, -62,1,03.02.2016,,188,130.34, -62,1,04.02.2016,,145,133.06, -62,1,05.02.2016,,239,133.06, -62,1,06.02.2016,,232,133.06, -62,1,07.02.2016,,119,141.76, -62,1,08.02.2016,,119,141.76, -62,1,09.02.2016,1.0,232,141.76,130.75 -62,1,10.02.2016,1.0,236,141.76,130.75 -62,1,11.02.2016,1.0,176,141.76,130.75 -62,1,12.02.2016,1.0,141,141.76,130.75 -62,1,13.02.2016,1.0,223,141.76,130.75 -62,1,14.02.2016,1.0,349,141.76,130.75 -62,1,15.02.2016,1.0,107,141.76,130.75 -62,1,16.02.2016,1.0,264,141.76,130.75 -62,1,17.02.2016,1.0,148,141.76,130.75 -62,1,18.02.2016,,154,141.76, -62,1,19.02.2016,,141,141.76, -62,1,20.02.2016,,97,141.76, -62,1,21.02.2016,,88,141.76, -62,1,22.02.2016,,135,141.76, -62,1,23.02.2016,,88,141.76, -62,1,24.02.2016,,116,141.76, -62,1,25.02.2016,,119,141.76, -62,1,26.02.2016,,104,141.76, -62,1,27.02.2016,,151,141.76, -62,1,28.02.2016,,123,141.76, -62,1,29.02.2016,,107,141.76, -62,1,01.03.2016,,126,141.76, -62,1,02.03.2016,,129,141.76, -62,1,03.03.2016,,151,141.76, -62,1,04.03.2016,,94,141.76, -62,1,05.03.2016,,123,141.21, -62,1,06.03.2016,,107,141.21, -62,1,07.03.2016,,154,141.21, -62,1,08.03.2016,1.0,154,141.21,132.92 -62,1,09.03.2016,1.0,346,141.21,132.92 -62,1,10.03.2016,1.0,286,141.21,132.92 -62,1,11.03.2016,1.0,324,141.21,132.92 -62,1,12.03.2016,1.0,308,141.21,132.92 -62,1,13.03.2016,1.0,311,141.21,132.92 -62,1,14.03.2016,1.0,201,141.21,132.92 -62,1,15.03.2016,1.0,346,141.21,130.21 -62,1,16.03.2016,1.0,380,141.21,130.21 -62,1,17.03.2016,1.0,418,141.21,130.21 -62,1,18.03.2016,1.0,471,141.21,130.21 -62,1,19.03.2016,1.0,525,141.21,130.21 -62,1,20.03.2016,1.0,160,141.21,130.21 -62,1,21.03.2016,,119,141.21, -62,1,22.03.2016,,79,141.21, -62,1,23.03.2016,,145,141.21, -62,1,24.03.2016,,69,141.21, -62,1,25.03.2016,,116,141.21, -62,1,26.03.2016,,82,141.21, -62,1,27.03.2016,,119,141.21, -62,1,28.03.2016,,129,141.21, -62,1,29.03.2016,,66,141.21, -62,1,30.03.2016,,94,141.21, -62,1,31.03.2016,,91,141.21, -62,1,01.04.2016,,91,141.21, -62,1,02.04.2016,,116,141.21, -62,1,03.04.2016,,79,141.21, -62,1,04.04.2016,,91,141.21, -62,1,05.04.2016,,79,141.21, -62,1,06.04.2016,,72,141.21, -62,1,07.04.2016,,97,141.21, -62,1,08.04.2016,,97,141.21, -62,1,09.04.2016,,113,141.21, -62,1,10.04.2016,,91,141.21, -62,1,11.04.2016,,113,141.21, -62,1,12.04.2016,,104,141.21, -62,1,13.04.2016,,126,141.21, -62,1,14.04.2016,,119,135.78, -62,1,15.04.2016,,204,135.78, -62,1,16.04.2016,,217,135.78, -62,1,17.04.2016,,176,135.78, -62,1,18.04.2016,,129,135.78, -62,1,19.04.2016,1.0,1096,135.78,122.32 -62,1,20.04.2016,1.0,779,135.78,122.32 -62,1,21.04.2016,1.0,657,135.78,122.32 -62,1,22.04.2016,1.0,609,135.78,122.32 -62,1,23.04.2016,1.0,496,135.78,122.32 -62,1,24.04.2016,1.0,559,135.78,122.32 -62,1,25.04.2016,1.0,276,135.78,122.32 -62,1,26.04.2016,,135,135.78, -62,1,27.04.2016,,160,135.78, -62,1,28.04.2016,,91,135.78, -62,1,29.04.2016,,116,131.16, -62,1,30.04.2016,,94,131.16, -62,1,01.05.2016,,44,131.16, -62,1,02.05.2016,,38,131.16, -62,1,03.05.2016,,82,131.16, -62,1,04.05.2016,,85,131.16, -62,1,05.05.2016,,60,131.16, -62,1,06.05.2016,,101,131.16, -62,1,07.05.2016,,104,131.16, -62,1,08.05.2016,,53,131.16, -62,1,09.05.2016,,91,131.16, -62,1,10.05.2016,,97,131.16, -62,1,11.05.2016,,110,131.16, -62,1,12.05.2016,,123,131.16, -62,1,13.05.2016,,107,128.98, -62,1,14.05.2016,,91,128.98, -62,1,15.05.2016,,198,128.98, -62,1,16.05.2016,,289,128.98, -62,1,17.05.2016,1.0,726,128.98,119.6 -62,1,18.05.2016,1.0,691,128.98,119.6 -62,1,19.05.2016,1.0,440,128.98,119.6 -62,1,20.05.2016,1.0,603,128.98,119.6 -62,1,21.05.2016,1.0,418,128.98,119.6 -62,1,22.05.2016,1.0,396,128.98,119.6 -63,1,07.07.2015,,220,131.7, -63,1,08.07.2015,,72,131.7, -63,1,09.07.2015,,179,121.64, -63,1,10.07.2015,,160,121.64, -63,1,11.07.2015,,145,123.55, -63,1,12.07.2015,,160,123.55, -63,1,13.07.2015,,167,123.55, -63,1,14.07.2015,,415,123.55, -63,1,15.07.2015,,283,125.45, -63,1,16.07.2015,,440,125.45, -63,1,17.07.2015,,251,125.45, -63,1,18.07.2015,,245,127.62, -63,1,19.07.2015,,132,127.62, -63,1,20.07.2015,,270,127.62, -63,1,21.07.2015,,361,127.62, -63,1,22.07.2015,,559,127.62, -63,1,23.07.2015,,368,127.62, -63,1,24.07.2015,,540,127.62, -63,1,25.07.2015,,961,127.62, -63,1,26.07.2015,,660,130.07, -63,1,27.07.2015,,713,130.07, -63,1,28.07.2015,1.0,1144,130.07,124.77 -63,1,29.07.2015,1.0,1630,130.07,124.77 -63,1,30.07.2015,1.0,1583,130.07,124.77 -63,1,31.07.2015,1.0,1156,130.07,124.77 -63,1,01.08.2015,1.0,763,130.07,124.77 -63,1,02.08.2015,1.0,716,130.07,124.77 -63,1,03.08.2015,1.0,1112,130.07,124.77 -63,1,04.08.2015,,276,138.5, -63,1,05.08.2015,,167,138.5, -63,1,06.08.2015,,185,138.5, -63,1,07.08.2015,,201,138.5, -63,1,08.08.2015,,380,141.21, -63,1,09.08.2015,,220,141.21, -63,1,10.08.2015,,220,141.21, -63,1,11.08.2015,,311,141.21, -63,1,12.08.2015,,223,142.85, -63,1,13.08.2015,,380,142.85, -63,1,14.08.2015,,204,142.85, -63,1,15.08.2015,,163,142.85, -63,1,16.08.2015,,324,142.85, -63,1,17.08.2015,,550,142.85, -63,1,18.08.2015,,298,142.85, -63,1,19.08.2015,,270,142.85, -63,1,20.08.2015,,254,142.85, -63,1,21.08.2015,,305,142.85, -63,1,22.08.2015,,374,142.85, -63,1,23.08.2015,,229,142.85, -63,1,24.08.2015,,368,142.85, -63,1,25.08.2015,,421,142.85, -63,1,26.08.2015,,229,142.85, -63,1,27.08.2015,,339,137.68, -63,1,28.08.2015,,390,137.68, -63,1,29.08.2015,,195,137.68, -63,1,30.08.2015,,182,137.68, -63,1,31.08.2015,,129,137.68, -63,1,01.09.2015,,214,137.68, -63,1,02.09.2015,,110,135.78, -63,1,03.09.2015,,179,135.78, -63,1,04.09.2015,,148,135.78, -63,1,05.09.2015,,242,133.6, -63,1,06.09.2015,,273,133.6, -63,1,07.09.2015,,163,133.6, -63,1,08.09.2015,,207,133.6, -63,1,09.09.2015,,286,135.51, -63,1,10.09.2015,,179,135.51, -63,1,11.09.2015,,305,135.51, -63,1,12.09.2015,,302,135.51, -63,1,13.09.2015,,201,135.51, -63,1,14.09.2015,,135,135.51, -63,1,15.09.2015,,342,135.51, -63,1,16.09.2015,,195,138.5, -63,1,17.09.2015,,97,138.5, -63,1,18.09.2015,,116,138.5, -63,1,19.09.2015,,126,138.5, -63,1,20.09.2015,,123,138.5, -63,1,21.09.2015,,91,138.5, -63,1,22.09.2015,,107,138.5, -63,1,23.09.2015,,75,141.21, -63,1,24.09.2015,,110,141.21, -63,1,25.09.2015,,101,141.21, -63,1,26.09.2015,,176,141.21, -63,1,27.09.2015,,107,141.21, -63,1,28.09.2015,,113,141.21, -63,1,29.09.2015,,204,141.21, -63,1,30.09.2015,,119,141.21, -63,1,01.10.2015,,107,141.21, -63,1,02.10.2015,,135,141.21, -63,1,03.10.2015,,154,141.21, -63,1,04.10.2015,,63,141.21, -63,1,05.10.2015,,91,141.21, -63,1,06.10.2015,1.0,167,141.21,132.92 -63,1,07.10.2015,1.0,160,141.21,130.21 -63,1,08.10.2015,1.0,374,141.11,130.21 -63,1,09.10.2015,1.0,276,141.21,130.21 -63,1,10.10.2015,1.0,490,141.21,130.21 -63,1,11.10.2015,1.0,232,141.21,130.21 -63,1,12.10.2015,1.0,418,141.21,130.21 -63,1,13.10.2015,,72,141.21, -63,1,14.10.2015,,85,141.21, -63,1,15.10.2015,,63,142.57, -63,1,16.10.2015,,110,142.57, -63,1,17.10.2015,,85,142.57, -63,1,18.10.2015,,82,142.57, -63,1,19.10.2015,,126,142.57, -63,1,20.10.2015,,132,142.57, -63,1,21.10.2015,,204,130.34, -63,1,22.10.2015,,163,130.34, -63,1,23.10.2015,,107,130.34, -63,1,24.10.2015,,129,130.34, -63,1,25.10.2015,,94,130.34, -63,1,26.10.2015,,163,130.34, -63,1,27.10.2015,1.0,176,130.34,122.32 -63,1,28.10.2015,1.0,261,130.34,124.77 -63,1,29.10.2015,1.0,145,130.34,124.77 -63,1,30.10.2015,1.0,207,130.34,124.77 -63,1,31.10.2015,1.0,151,130.34,124.77 -63,1,01.11.2015,1.0,207,130.34,124.77 -63,1,02.11.2015,1.0,251,130.34,124.77 -63,1,03.11.2015,,72,130.34, -63,1,04.11.2015,,79,134.69, -63,1,05.11.2015,,85,134.69, -63,1,06.11.2015,,91,134.69, -63,1,07.11.2015,,116,134.69, -63,1,08.11.2015,,75,134.69, -63,1,09.11.2015,,97,134.69, -63,1,10.11.2015,,110,134.69, -63,1,11.11.2015,,110,130.34, -63,1,12.11.2015,,116,130.34, -63,1,13.11.2015,,135,130.34, -63,1,14.11.2015,,116,130.34, -63,1,15.11.2015,,94,130.34, -63,1,16.11.2015,,126,130.34, -63,1,17.11.2015,,160,130.34, -63,1,18.11.2015,,148,130.34, -63,1,19.11.2015,,104,130.34, -63,1,20.11.2015,,135,130.34, -63,1,21.11.2015,,94,130.34, -63,1,22.11.2015,,138,130.34, -63,1,23.11.2015,,188,130.34, -63,1,24.11.2015,1.0,898,130.34,116.61 -63,1,25.11.2015,1.0,883,130.34,116.61 -63,1,26.11.2015,1.0,625,130.34,116.61 -63,1,27.11.2015,1.0,641,130.34,116.61 -63,1,28.11.2015,1.0,672,130.34,116.61 -63,1,29.11.2015,1.0,119,130.34,116.61 -63,1,30.11.2015,1.0,374,130.34,116.61 -63,1,01.12.2015,,119,130.34, -63,1,02.12.2015,,204,130.34, -63,1,03.12.2015,,116,130.34, -63,1,04.12.2015,,167,130.34, -63,1,05.12.2015,1.0,531,130.34,116.61 -63,1,06.12.2015,1.0,635,130.34,116.61 -63,1,07.12.2015,1.0,534,130.34,116.61 -63,1,08.12.2015,,104,130.34, -63,1,09.12.2015,,185,130.34, -63,1,10.12.2015,,97,130.34, -63,1,11.12.2015,,261,130.34, -63,1,12.12.2015,,251,130.34, -63,1,13.12.2015,,188,130.34, -63,1,14.12.2015,,126,130.34, -63,1,15.12.2015,,163,130.34, -63,1,16.12.2015,,85,130.34, -63,1,17.12.2015,,116,130.34, -63,1,18.12.2015,,119,130.34, -63,1,19.12.2015,,170,130.34, -63,1,20.12.2015,,138,130.34, -63,1,21.12.2015,,154,130.34, -63,1,22.12.2015,,176,130.34, -63,1,23.12.2015,,157,130.34, -63,1,24.12.2015,,135,126.54, -63,1,25.12.2015,,116,126.54, -63,1,26.12.2015,,138,126.54, -63,1,27.12.2015,,204,126.54, -63,1,28.12.2015,,179,126.54, -63,1,29.12.2015,,214,126.54, -63,1,30.12.2015,,214,126.54, -63,1,31.12.2015,,154,126.54, -63,1,01.01.2016,,50,126.54, -63,1,02.01.2016,,97,126.54, -63,1,03.01.2016,,110,126.54, -63,1,04.01.2016,,119,126.54, -63,1,05.01.2016,,135,126.54, -63,1,06.01.2016,,135,126.54, -63,1,07.01.2016,,119,126.54, -63,1,08.01.2016,,151,126.54, -63,1,09.01.2016,,129,126.54, -63,1,10.01.2016,,173,126.54, -63,1,11.01.2016,,104,126.54, -63,1,12.01.2016,,167,126.54, -63,1,13.01.2016,,198,126.54, -63,1,14.01.2016,,151,126.54, -63,1,15.01.2016,,170,126.54, -63,1,16.01.2016,,207,126.54, -63,1,17.01.2016,,157,126.54, -63,1,18.01.2016,,273,126.54, -63,1,19.01.2016,,97,126.54, -63,1,20.01.2016,,151,126.54, -63,1,21.01.2016,,292,127.9, -63,1,22.01.2016,,383,127.9, -63,1,23.01.2016,,3063,127.9, -63,1,24.01.2016,,861,127.9, -63,1,25.01.2016,,298,127.9, -63,1,26.01.2016,,242,127.9, -63,1,27.01.2016,,239,127.9, -63,1,28.01.2016,,226,127.9, -63,1,29.01.2016,,195,127.9, -63,1,30.01.2016,,151,127.9, -63,1,31.01.2016,,207,127.9, -63,1,01.02.2016,,210,130.34, -63,1,02.02.2016,,119,130.34, -63,1,03.02.2016,,258,130.34, -63,1,04.02.2016,,173,133.06, -63,1,05.02.2016,,173,133.06, -63,1,06.02.2016,,195,133.06, -63,1,07.02.2016,,132,141.76, -63,1,08.02.2016,,94,141.76, -63,1,09.02.2016,1.0,264,141.76,130.75 -63,1,10.02.2016,1.0,273,141.76,130.75 -63,1,11.02.2016,1.0,314,141.76,130.75 -63,1,12.02.2016,1.0,405,141.76,130.75 -63,1,13.02.2016,1.0,572,141.76,130.75 -63,1,14.02.2016,1.0,600,141.76,130.75 -63,1,15.02.2016,1.0,380,141.76,130.75 -63,1,16.02.2016,1.0,606,141.76,130.75 -63,1,17.02.2016,1.0,487,141.76,130.75 -63,1,18.02.2016,,198,141.76, 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-63,1,20.05.2016,1.0,358,128.98,119.6 -63,1,21.05.2016,1.0,232,128.98,119.6 -63,1,22.05.2016,1.0,408,128.98,119.6 -64,1,01.01.2015,,3,163.78, -64,1,02.01.2015,,31,163.78, -64,1,03.01.2015,,28,163.78, -64,1,04.01.2015,,6,163.78, -64,1,05.01.2015,,41,163.78, -64,1,06.01.2015,,19,163.78, -64,1,07.01.2015,,41,163.78, -64,1,08.01.2015,,31,163.78, -64,1,09.01.2015,,19,163.78, -64,1,10.01.2015,,50,163.78, -64,1,11.01.2015,,53,163.78, -64,1,12.01.2015,,35,163.78, -64,1,13.01.2015,,38,163.78, -64,1,14.01.2015,,31,168.4, -64,1,15.01.2015,,31,168.4, -64,1,16.01.2015,,16,168.4, -64,1,17.01.2015,,25,168.4, -64,1,18.01.2015,,85,168.4, -64,1,19.01.2015,,63,168.4, -64,1,20.01.2015,,47,168.4, -64,1,21.01.2015,,38,168.4, -64,1,22.01.2015,,41,168.4, -64,1,23.01.2015,,22,160.24, -64,1,24.01.2015,,53,160.24, -64,1,25.01.2015,,53,160.24, -64,1,26.01.2015,,47,160.24, -64,1,27.01.2015,,53,160.24, -64,1,28.01.2015,,82,146.92, -64,1,29.01.2015,,66,146.92, -64,1,30.01.2015,,66,146.92, -64,1,31.01.2015,,75,146.92, -64,1,01.02.2015,,47,146.92, -64,1,02.02.2015,,57,146.92, -64,1,03.02.2015,,66,146.92, -64,1,04.02.2015,,63,146.92, -64,1,05.02.2015,,47,146.92, -64,1,06.02.2015,,53,146.92, -64,1,07.02.2015,,75,146.92, -64,1,08.02.2015,,116,146.92, -64,1,09.02.2015,,113,146.92, -64,1,10.02.2015,,66,146.92, -64,1,11.02.2015,,107,146.92, -64,1,12.02.2015,,116,146.92, -64,1,13.02.2015,,101,149.37, -64,1,14.02.2015,,85,149.37, -64,1,15.02.2015,,66,149.37, -64,1,16.02.2015,,63,149.37, -64,1,17.02.2015,1.0,160,149.37,143.8 -64,1,18.02.2015,1.0,280,149.37,135.64 -64,1,19.02.2015,1.0,314,149.37,135.64 -64,1,20.02.2015,1.0,333,149.37,135.64 -64,1,21.02.2015,1.0,368,149.37,135.64 -64,1,22.02.2015,1.0,302,149.37,135.64 -64,1,23.02.2015,1.0,192,149.37,135.64 -64,1,24.02.2015,,57,149.37, -64,1,25.02.2015,,63,149.37, -64,1,26.02.2015,,88,149.37, -64,1,27.02.2015,,94,143.93, -64,1,28.02.2015,,129,143.93, -64,1,01.03.2015,,113,143.93, -64,1,02.03.2015,,101,143.93, -64,1,03.03.2015,1.0,254,143.93,138.36 -64,1,04.03.2015,1.0,163,143.93,135.64 -64,1,05.03.2015,1.0,129,138.5,135.64 -64,1,06.03.2015,1.0,151,138.5,135.64 -64,1,07.03.2015,1.0,286,138.5,135.64 -64,1,08.03.2015,1.0,113,138.5,135.64 -64,1,09.03.2015,,63,140.67, -64,1,10.03.2015,,63,140.67, -64,1,11.03.2015,,50,140.67, -64,1,12.03.2015,,94,140.67, -64,1,13.03.2015,,63,140.67, -64,1,14.03.2015,,94,140.67, -64,1,15.03.2015,,94,140.67, -64,1,16.03.2015,,107,140.67, -64,1,17.03.2015,1.0,207,140.67,127.49 -64,1,18.03.2015,1.0,446,140.67,127.49 -64,1,19.03.2015,1.0,320,140.67,127.49 -64,1,20.03.2015,1.0,459,140.67,127.49 -64,1,21.03.2015,1.0,550,140.67,127.49 -64,1,22.03.2015,1.0,628,140.67,127.49 -64,1,23.03.2015,1.0,336,140.67,127.49 -64,1,24.03.2015,,173,140.67, -64,1,25.03.2015,,173,140.67, -64,1,26.03.2015,,135,131.97, -64,1,27.03.2015,,154,131.97, -64,1,28.03.2015,,163,131.97, -64,1,29.03.2015,,254,131.97, -64,1,30.03.2015,,116,131.97, -64,1,31.03.2015,,151,131.97, -64,1,01.04.2015,,192,133.33, -64,1,02.04.2015,,113,133.33, -64,1,03.04.2015,,101,133.33, -64,1,04.04.2015,,82,133.33, -64,1,05.04.2015,,160,133.33, -64,1,06.04.2015,,163,133.33, -64,1,07.04.2015,1.0,280,133.33,119.33 -64,1,08.04.2015,1.0,584,133.33,119.33 -64,1,09.04.2015,1.0,1147,133.33,119.33 -64,1,10.04.2015,1.0,697,133.33,119.33 -64,1,11.04.2015,1.0,587,133.33,119.33 -64,1,12.04.2015,1.0,188,133.33,119.33 -64,1,13.04.2015,1.0,113,133.33,119.33 -64,1,14.04.2015,,141,130.34, -64,1,15.04.2015,,79,130.34, -64,1,16.04.2015,,138,130.34, -64,1,17.04.2015,,63,138.5, -64,1,18.04.2015,,50,138.5, -64,1,19.04.2015,,63,138.5, -64,1,20.04.2015,,57,138.5, -64,1,21.04.2015,,60,138.5, -64,1,22.04.2015,,94,128.98, -64,1,23.04.2015,,57,128.98, -64,1,24.04.2015,,101,128.98, -64,1,25.04.2015,,94,128.98, -64,1,26.04.2015,,79,128.98, -64,1,27.04.2015,,69,128.98, -64,1,28.04.2015,,66,128.98, -64,1,29.04.2015,,75,131.7, -64,1,30.04.2015,,104,131.7, -64,1,01.05.2015,,57,131.7, -64,1,02.05.2015,,60,131.7, -64,1,03.05.2015,,60,131.7, -64,1,04.05.2015,,57,131.7, -64,1,05.05.2015,,50,131.7, -64,1,06.05.2015,,94,131.7, -64,1,07.05.2015,,63,131.7, -64,1,08.05.2015,,66,131.7, -64,1,09.05.2015,,41,131.7, -64,1,10.05.2015,,66,131.7, -64,1,11.05.2015,,50,131.7, -64,1,12.05.2015,,57,131.7, -64,1,13.05.2015,,41,131.7, -64,1,14.05.2015,,69,131.7, -64,1,15.05.2015,,63,131.7, -64,1,16.05.2015,,110,131.7, -64,1,17.05.2015,,63,131.7, -64,1,18.05.2015,,94,131.7, -64,1,19.05.2015,1.0,302,131.7,119.33 -64,1,20.05.2015,1.0,622,131.7,119.33 -64,1,21.05.2015,1.0,374,131.7,119.33 -64,1,22.05.2015,1.0,239,131.7,119.33 -64,1,23.05.2015,1.0,141,131.7,119.33 -64,1,24.05.2015,1.0,214,131.7,119.33 -64,1,25.05.2015,1.0,280,131.7,119.33 -64,1,26.05.2015,,79,131.7, -64,1,27.05.2015,,79,129.25, -64,1,28.05.2015,,113,129.25, -64,1,29.05.2015,,79,129.25, -64,1,30.05.2015,,75,129.25, -64,1,31.05.2015,,69,129.25, -64,1,01.06.2015,,85,129.25, -64,1,02.06.2015,,79,129.25, -64,1,03.06.2015,,88,129.25, -64,1,04.06.2015,,132,129.25, -64,1,05.06.2015,,57,129.25, -64,1,06.06.2015,,57,131.7, -64,1,07.06.2015,,63,131.7, -64,1,08.06.2015,,91,131.7, -64,1,09.06.2015,1.0,352,131.7,119.33 -64,1,10.06.2015,1.0,267,131.7,119.33 -64,1,11.06.2015,1.0,298,131.7,119.33 -64,1,12.06.2015,1.0,214,131.7,119.33 -64,1,13.06.2015,1.0,148,131.7,119.33 -64,1,14.06.2015,1.0,192,131.7,119.33 -64,1,15.06.2015,1.0,339,131.7,119.33 -64,1,16.06.2015,,35,131.7, -64,1,17.06.2015,,63,131.7, -64,1,18.06.2015,,72,128.17, -64,1,19.06.2015,,82,128.17, -64,1,20.06.2015,,53,128.17, -64,1,21.06.2015,,72,128.17, -64,1,22.06.2015,,79,128.17, -64,1,23.06.2015,,113,128.17, -64,1,24.06.2015,,91,128.17, -64,1,25.06.2015,,63,128.17, -64,1,26.06.2015,,119,128.17, -64,1,27.06.2015,,82,128.17, -64,1,28.06.2015,,129,130.34, -64,1,29.06.2015,,104,130.34, -64,1,30.06.2015,1.0,192,130.34,119.33 -64,1,01.07.2015,1.0,572,130.34,119.33 -64,1,02.07.2015,1.0,368,130.34,119.33 -64,1,03.07.2015,1.0,305,130.34,119.33 -64,1,04.07.2015,1.0,261,130.34,119.33 -64,1,05.07.2015,1.0,380,130.34,119.33 -64,1,06.07.2015,1.0,468,130.34,119.33 -64,1,07.07.2015,,110,130.34, -64,1,08.07.2015,,116,130.34, -64,1,09.07.2015,,232,121.64, -64,1,10.07.2015,,173,121.64, -64,1,11.07.2015,,154,123.55, -64,1,12.07.2015,,352,123.55, -64,1,13.07.2015,,195,123.55, -64,1,14.07.2015,,280,123.55, -64,1,15.07.2015,,195,125.45, -64,1,16.07.2015,,261,125.45, -64,1,17.07.2015,,264,125.45, -64,1,18.07.2015,,167,127.62, -64,1,19.07.2015,,292,127.62, -64,1,20.07.2015,,223,127.62, -64,1,21.07.2015,,292,127.62, -64,1,22.07.2015,,198,127.62, -64,1,23.07.2015,,270,127.62, -64,1,24.07.2015,,173,127.62, -64,1,25.07.2015,,201,127.62, -64,1,26.07.2015,,236,130.07, -64,1,27.07.2015,,267,130.07, -64,1,28.07.2015,1.0,371,130.07,124.77 -64,1,29.07.2015,1.0,412,130.07,124.77 -64,1,30.07.2015,1.0,528,130.07,124.77 -64,1,31.07.2015,1.0,302,130.07,124.77 -64,1,01.08.2015,1.0,427,130.07,124.77 -64,1,02.08.2015,1.0,493,130.07,124.77 -64,1,03.08.2015,1.0,591,130.07,124.77 -64,1,04.08.2015,,226,138.5, -64,1,05.08.2015,,160,138.5, -64,1,06.08.2015,,82,138.5, -64,1,07.08.2015,,126,138.5, -64,1,08.08.2015,,210,141.21, -64,1,09.08.2015,,167,141.21, -64,1,10.08.2015,,163,141.21, -64,1,11.08.2015,,192,141.21, -64,1,12.08.2015,,298,142.85, -64,1,13.08.2015,,116,142.85, -64,1,14.08.2015,,135,142.85, -64,1,15.08.2015,,141,142.85, -64,1,16.08.2015,,104,142.85, -64,1,17.08.2015,,182,142.85, -64,1,18.08.2015,,157,142.85, -64,1,19.08.2015,,145,142.85, -64,1,20.08.2015,,167,142.85, -64,1,21.08.2015,,336,142.85, -64,1,22.08.2015,,167,142.85, -64,1,23.08.2015,,104,142.85, -64,1,24.08.2015,,157,142.85, -64,1,25.08.2015,,154,142.85, -64,1,26.08.2015,,201,142.85, -64,1,27.08.2015,,192,137.68, -64,1,28.08.2015,,135,137.68, -64,1,29.08.2015,,138,137.68, -64,1,30.08.2015,,129,137.68, -64,1,31.08.2015,,132,137.68, -64,1,01.09.2015,,167,137.68, -64,1,02.09.2015,,261,135.78, -64,1,03.09.2015,,154,135.78, -64,1,04.09.2015,,302,135.78, -64,1,05.09.2015,,276,133.6, -64,1,06.09.2015,,154,133.6, -64,1,07.09.2015,,201,133.6, -64,1,08.09.2015,,132,133.6, -64,1,09.09.2015,,289,135.51, -64,1,10.09.2015,,280,135.51, -64,1,11.09.2015,,214,135.51, -64,1,12.09.2015,,232,135.51, -64,1,13.09.2015,,242,135.51, -64,1,14.09.2015,,223,135.51, -64,1,15.09.2015,,226,135.51, -64,1,16.09.2015,,107,138.5, -64,1,17.09.2015,,214,138.5, -64,1,18.09.2015,,352,138.5, -64,1,19.09.2015,,123,138.5, -64,1,20.09.2015,,119,138.5, -64,1,21.09.2015,,157,138.5, -64,1,22.09.2015,,188,138.5, -64,1,23.09.2015,,107,141.21, -64,1,24.09.2015,,163,141.21, -64,1,25.09.2015,,97,141.21, -64,1,26.09.2015,,85,141.21, -64,1,27.09.2015,,72,141.21, -64,1,28.09.2015,,79,141.21, -64,1,29.09.2015,,113,141.21, -64,1,30.09.2015,,79,141.21, -64,1,01.10.2015,,104,141.21, -64,1,02.10.2015,,126,141.21, -64,1,03.10.2015,,116,141.21, -64,1,04.10.2015,,91,141.21, -64,1,05.10.2015,,66,141.21, -64,1,06.10.2015,1.0,204,141.21,132.92 -64,1,07.10.2015,1.0,368,141.21,130.21 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-64,1,06.11.2015,,160,134.69, -64,1,07.11.2015,,123,134.69, -64,1,08.11.2015,,94,134.69, -64,1,09.11.2015,,91,134.69, -64,1,10.11.2015,,85,134.69, -64,1,11.11.2015,,135,130.34, -64,1,12.11.2015,,110,130.34, -64,1,13.11.2015,,157,130.34, -64,1,14.11.2015,,119,130.34, -64,1,15.11.2015,,119,130.34, -64,1,16.11.2015,,129,130.34, -64,1,17.11.2015,,210,130.34, -64,1,18.11.2015,,173,130.34, -64,1,19.11.2015,,91,130.34, -64,1,20.11.2015,,138,130.34, -64,1,21.11.2015,,135,130.34, -64,1,22.11.2015,,170,130.34, -64,1,23.11.2015,,217,130.34, -64,1,24.11.2015,1.0,942,130.34,116.61 -64,1,25.11.2015,1.0,1093,130.34,116.61 -64,1,26.11.2015,1.0,1338,130.34,116.61 -64,1,27.11.2015,1.0,625,130.34,116.61 -64,1,28.11.2015,1.0,534,130.34,116.61 -64,1,29.11.2015,1.0,518,130.34,116.61 -64,1,30.11.2015,1.0,490,130.34,116.61 -64,1,01.12.2015,,97,130.34, -64,1,02.12.2015,,91,130.34, -64,1,03.12.2015,,104,130.34, -64,1,04.12.2015,,113,130.34, -64,1,05.12.2015,1.0,531,130.34,116.61 -64,1,06.12.2015,1.0,776,130.34,116.61 -64,1,07.12.2015,1.0,883,130.34,116.61 -64,1,08.12.2015,,72,130.34, -64,1,09.12.2015,,107,130.34, -64,1,10.12.2015,,79,130.34, -64,1,11.12.2015,,132,130.34, -64,1,12.12.2015,,116,130.34, -64,1,13.12.2015,,97,130.34, -64,1,14.12.2015,,116,130.34, -64,1,15.12.2015,,88,130.34, -64,1,16.12.2015,,135,130.34, -64,1,17.12.2015,,79,130.34, -64,1,18.12.2015,,101,130.34, -64,1,19.12.2015,,101,130.34, -64,1,20.12.2015,,160,130.34, -64,1,21.12.2015,,60,130.34, -64,1,22.12.2015,,66,130.34, -64,1,23.12.2015,,66,130.34, -64,1,24.12.2015,,63,126.54, -64,1,25.12.2015,,69,126.54, -64,1,26.12.2015,,97,126.54, -64,1,27.12.2015,,94,126.54, -64,1,28.12.2015,,82,126.54, -64,1,29.12.2015,,97,126.54, -64,1,30.12.2015,,75,126.54, -64,1,31.12.2015,,94,126.54, -64,1,01.01.2016,,19,126.54, -64,1,02.01.2016,,129,126.54, -64,1,03.01.2016,,217,126.54, -64,1,04.01.2016,,207,126.54, -64,1,05.01.2016,,239,126.54, -64,1,06.01.2016,,188,126.54, -64,1,07.01.2016,,195,126.54, -64,1,08.01.2016,,232,126.54, -64,1,09.01.2016,,116,126.54, -64,1,10.01.2016,,173,126.54, -64,1,11.01.2016,,264,126.54, -64,1,12.01.2016,,179,126.54, -64,1,13.01.2016,,204,126.54, -64,1,14.01.2016,,330,126.54, -64,1,15.01.2016,,204,126.54, -64,1,16.01.2016,,258,126.54, -64,1,17.01.2016,,179,126.54, -64,1,18.01.2016,,214,126.54, -64,1,19.01.2016,,198,126.54, -64,1,20.01.2016,,192,126.54, -64,1,21.01.2016,,179,127.9, -64,1,22.01.2016,,217,127.9, -64,1,23.01.2016,,223,127.9, -64,1,24.01.2016,,232,127.9, -64,1,25.01.2016,,132,127.9, -64,1,26.01.2016,,154,127.9, -64,1,27.01.2016,,346,127.9, -64,1,28.01.2016,,229,127.9, -64,1,29.01.2016,,305,127.9, -64,1,30.01.2016,,226,127.9, -64,1,31.01.2016,,220,127.9, -64,1,01.02.2016,,214,130.34, -64,1,02.02.2016,,239,130.34, -64,1,03.02.2016,,176,130.34, -64,1,04.02.2016,,145,133.06, -64,1,05.02.2016,,154,133.06, -64,1,06.02.2016,,173,133.06, -64,1,07.02.2016,,145,141.76, -64,1,08.02.2016,,173,141.76, -64,1,09.02.2016,1.0,258,141.76,130.75 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-64,1,11.04.2016,,82,141.21, -64,1,12.04.2016,,82,141.21, -64,1,13.04.2016,,53,141.21, -64,1,14.04.2016,,75,135.78, -64,1,15.04.2016,,101,135.78, -64,1,16.04.2016,,66,135.78, -64,1,17.04.2016,,148,135.78, -64,1,18.04.2016,,138,135.78, -64,1,19.04.2016,1.0,807,135.78,122.32 -64,1,20.04.2016,1.0,845,135.78,122.32 -64,1,21.04.2016,1.0,606,135.78,122.32 -64,1,22.04.2016,1.0,792,135.78,122.32 -64,1,23.04.2016,1.0,553,135.78,122.32 -64,1,24.04.2016,1.0,383,135.78,122.32 -64,1,25.04.2016,1.0,462,135.78,122.32 -64,1,26.04.2016,,31,135.78, -64,1,27.04.2016,,151,135.78, -64,1,28.04.2016,,85,135.78, -64,1,29.04.2016,,97,131.16, -64,1,30.04.2016,,113,131.16, -64,1,01.05.2016,,57,131.16, -64,1,02.05.2016,,38,131.16, -64,1,03.05.2016,,79,131.16, -64,1,04.05.2016,,47,131.16, -64,1,05.05.2016,,63,131.16, -64,1,06.05.2016,,85,131.16, -64,1,07.05.2016,,50,131.16, -64,1,08.05.2016,,123,131.16, -64,1,09.05.2016,,88,131.16, -64,1,10.05.2016,,75,131.16, -64,1,11.05.2016,,97,131.16, 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-65,1,22.01.2015,,53,168.4, -65,1,23.01.2015,,9,160.24, -65,1,24.01.2015,,57,160.24, -65,1,25.01.2015,,104,160.24, -65,1,26.01.2015,,31,160.24, -65,1,27.01.2015,,47,160.24, -65,1,28.01.2015,,66,146.92, -65,1,29.01.2015,,69,146.92, -65,1,30.01.2015,,60,146.92, -65,1,31.01.2015,,135,146.92, -65,1,01.02.2015,,229,146.92, -65,1,02.02.2015,,31,146.92, -65,1,03.02.2015,,129,146.92, -65,1,04.02.2015,,167,146.92, -65,1,05.02.2015,,135,146.92, -65,1,06.02.2015,,119,146.92, -65,1,07.02.2015,,60,146.92, -65,1,08.02.2015,,179,146.92, -65,1,09.02.2015,,72,146.92, -65,1,10.02.2015,,126,146.92, -65,1,11.02.2015,,47,146.92, -65,1,12.02.2015,,167,146.92, -65,1,13.02.2015,,104,149.37, -65,1,14.02.2015,,50,149.37, -65,1,15.02.2015,,60,149.37, -65,1,16.02.2015,,47,149.37, -65,1,17.02.2015,1.0,942,149.37,143.8 -65,1,18.02.2015,1.0,553,149.37,135.64 -65,1,19.02.2015,1.0,732,149.37,135.64 -65,1,20.02.2015,1.0,807,149.37,135.64 -65,1,21.02.2015,1.0,355,149.37,135.64 -65,1,22.02.2015,1.0,336,149.37,135.64 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-66,1,11.01.2015,,16,163.78, -66,1,12.01.2015,,31,163.78, -66,1,13.01.2015,,16,163.78, -66,1,14.01.2015,,25,168.4, -66,1,15.01.2015,,47,168.4, -66,1,16.01.2015,,57,168.4, -66,1,17.01.2015,,119,168.4, -66,1,18.01.2015,,47,168.4, -66,1,19.01.2015,,35,168.4, -66,1,20.01.2015,,19,168.4, -66,1,21.01.2015,,22,168.4, -66,1,22.01.2015,,28,168.4, -66,1,23.01.2015,,22,160.24, -66,1,24.01.2015,,35,160.24, -66,1,25.01.2015,,22,160.24, -66,1,26.01.2015,,28,160.24, -66,1,27.01.2015,,19,160.24, -66,1,28.01.2015,,63,146.92, -66,1,29.01.2015,,35,146.92, -66,1,30.01.2015,,66,146.92, -66,1,31.01.2015,,53,146.92, -66,1,01.02.2015,,50,146.92, -66,1,02.02.2015,,35,146.92, -66,1,03.02.2015,,63,146.92, -66,1,04.02.2015,,50,146.92, -66,1,05.02.2015,,31,146.92, -66,1,06.02.2015,,41,146.92, -66,1,07.02.2015,,141,146.92, -66,1,08.02.2015,,151,146.92, -66,1,09.02.2015,,50,146.92, -66,1,10.02.2015,,119,146.92, -66,1,11.02.2015,,50,146.92, -66,1,12.02.2015,,66,146.92, -66,1,13.02.2015,,113,149.37, -66,1,14.02.2015,,94,149.37, -66,1,15.02.2015,,101,149.37, -66,1,16.02.2015,,57,149.37, -66,1,17.02.2015,1.0,66,149.37,143.8 -66,1,18.02.2015,1.0,188,149.37,135.64 -66,1,19.02.2015,1.0,239,149.37,135.64 -66,1,20.02.2015,1.0,540,149.37,135.64 -66,1,21.02.2015,1.0,214,149.37,135.64 -66,1,22.02.2015,1.0,336,149.37,135.64 -66,1,23.02.2015,1.0,107,149.37,135.64 -66,1,24.02.2015,,69,149.37, -66,1,25.02.2015,,25,149.37, -66,1,26.02.2015,,50,149.37, -66,1,27.02.2015,,25,143.93, -66,1,28.02.2015,,138,143.93, -66,1,01.03.2015,,192,143.93, -66,1,02.03.2015,,104,143.93, -66,1,03.03.2015,1.0,60,143.93,138.36 -66,1,04.03.2015,1.0,217,143.93,135.64 -66,1,05.03.2015,1.0,330,138.5,135.64 -66,1,06.03.2015,1.0,151,138.5,135.64 -66,1,07.03.2015,1.0,123,138.5,135.64 -66,1,08.03.2015,1.0,188,138.5,135.64 -66,1,09.03.2015,,85,140.67, -66,1,10.03.2015,,44,140.67, -66,1,11.03.2015,,160,140.67, -66,1,12.03.2015,,53,140.67, -66,1,13.03.2015,,88,140.67, -66,1,14.03.2015,,72,140.67, -66,1,15.03.2015,,63,140.67, -66,1,16.03.2015,,72,140.67, -66,1,17.03.2015,1.0,559,140.67,127.49 -66,1,18.03.2015,1.0,537,140.67,127.49 -66,1,19.03.2015,1.0,895,140.67,127.49 -66,1,20.03.2015,1.0,798,140.67,127.49 -66,1,21.03.2015,1.0,569,140.67,127.49 -66,1,22.03.2015,1.0,493,140.67,127.49 -66,1,23.03.2015,1.0,550,140.67,127.49 -66,1,24.03.2015,,72,140.67, -66,1,25.03.2015,,60,140.67, -66,1,26.03.2015,,145,131.97, -66,1,27.03.2015,,94,131.97, -66,1,28.03.2015,,91,131.97, -66,1,29.03.2015,,107,131.97, -66,1,30.03.2015,,38,131.97, -66,1,31.03.2015,,101,131.97, -66,1,01.04.2015,,88,133.33, -66,1,02.04.2015,,104,133.33, -66,1,03.04.2015,,123,133.33, -66,1,04.04.2015,,104,133.33, -66,1,05.04.2015,,138,133.33, -66,1,06.04.2015,,85,133.33, -66,1,07.04.2015,1.0,1015,133.33,119.33 -66,1,08.04.2015,1.0,1131,133.33,119.33 -66,1,09.04.2015,1.0,2086,133.33,119.33 -66,1,10.04.2015,1.0,1385,133.33,119.33 -66,1,11.04.2015,1.0,443,133.33,119.33 -66,1,12.04.2015,1.0,336,133.33,119.33 -66,1,13.04.2015,1.0,101,133.33,119.33 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-66,1,19.05.2015,1.0,867,131.7,119.33 -66,1,20.05.2015,1.0,553,131.7,119.33 -66,1,21.05.2015,1.0,286,131.7,119.33 -66,1,22.05.2015,1.0,594,131.7,119.33 -66,1,23.05.2015,1.0,449,131.7,119.33 -66,1,24.05.2015,1.0,361,131.7,119.33 -66,1,25.05.2015,1.0,355,131.7,119.33 -66,1,26.05.2015,,47,131.7, -66,1,27.05.2015,,41,129.25, -66,1,28.05.2015,,119,129.25, -66,1,29.05.2015,,31,129.25, -66,1,30.05.2015,,66,129.25, -66,1,31.05.2015,,72,129.25, -66,1,01.06.2015,,57,129.25, -66,1,02.06.2015,,63,129.25, -66,1,03.06.2015,,157,129.25, -66,1,04.06.2015,,97,129.25, -66,1,05.06.2015,,154,129.25, -66,1,06.06.2015,,94,131.7, -66,1,07.06.2015,,138,131.7, -66,1,08.06.2015,,47,131.7, -66,1,09.06.2015,1.0,383,131.7,119.33 -66,1,10.06.2015,1.0,792,131.7,119.33 -66,1,11.06.2015,1.0,895,131.7,119.33 -66,1,12.06.2015,1.0,814,131.7,119.33 -66,1,13.06.2015,1.0,594,131.7,119.33 -66,1,14.06.2015,1.0,789,131.7,119.33 -66,1,15.06.2015,1.0,609,131.7,119.33 -66,1,16.06.2015,,145,131.7, -66,1,17.06.2015,,69,131.7, -66,1,18.06.2015,,88,128.17, -66,1,19.06.2015,,251,128.17, -66,1,20.06.2015,,314,128.17, -66,1,21.06.2015,,104,128.17, -66,1,22.06.2015,,324,128.17, -66,1,23.06.2015,,107,128.17, -66,1,24.06.2015,,157,128.17, -66,1,25.06.2015,,163,128.17, -66,1,26.06.2015,,371,128.17, -66,1,27.06.2015,,141,128.17, -66,1,28.06.2015,,101,130.34, -66,1,29.06.2015,,113,130.34, -66,1,30.06.2015,1.0,220,130.34,119.33 -66,1,01.07.2015,1.0,427,130.34,119.33 -66,1,02.07.2015,1.0,801,130.34,119.33 -66,1,03.07.2015,1.0,543,130.34,119.33 -66,1,04.07.2015,1.0,587,130.34,119.33 -66,1,05.07.2015,1.0,440,130.34,119.33 -66,1,06.07.2015,1.0,782,130.34,119.33 -66,1,07.07.2015,,132,130.34, -66,1,08.07.2015,,88,130.34, -66,1,09.07.2015,,97,121.64, -66,1,10.07.2015,,509,121.64, -66,1,11.07.2015,,412,123.55, -66,1,12.07.2015,,537,123.55, -66,1,13.07.2015,,226,123.55, -66,1,14.07.2015,,352,123.55, -66,1,15.07.2015,,138,125.45, -66,1,16.07.2015,,481,125.45, -66,1,17.07.2015,,669,125.45, -66,1,18.07.2015,,210,127.62, -66,1,19.07.2015,,569,127.62, -66,1,20.07.2015,,572,127.62, -66,1,21.07.2015,,723,127.62, -66,1,22.07.2015,,468,127.62, -66,1,23.07.2015,,1982,127.62, -66,1,24.07.2015,,986,127.62, -66,1,25.07.2015,,795,127.62, -66,1,26.07.2015,,569,130.07, -66,1,27.07.2015,,1385,130.07, -66,1,28.07.2015,1.0,581,130.07,124.77 -66,1,29.07.2015,1.0,635,130.07,124.77 -66,1,30.07.2015,1.0,864,130.07,124.77 -66,1,31.07.2015,1.0,1634,130.07,124.77 -66,1,01.08.2015,1.0,192,130.07,124.77 -66,1,02.08.2015,1.0,606,130.07,124.77 -66,1,03.08.2015,1.0,1574,130.07,124.77 -66,1,04.08.2015,,248,138.5, -66,1,05.08.2015,,515,138.5, -66,1,06.08.2015,,232,138.5, -66,1,07.08.2015,,3138,138.5, -66,1,08.08.2015,,324,141.21, -66,1,09.08.2015,,289,141.21, -66,1,10.08.2015,,126,141.21, -66,1,11.08.2015,,226,141.21, -66,1,12.08.2015,,207,142.85, -66,1,13.08.2015,,173,142.85, -66,1,14.08.2015,,91,142.85, -66,1,15.08.2015,,342,142.85, -66,1,16.08.2015,,192,142.85, -66,1,17.08.2015,,267,142.85, -66,1,18.08.2015,,254,142.85, -66,1,19.08.2015,,179,142.85, -66,1,20.08.2015,,217,142.85, -66,1,21.08.2015,,188,142.85, -66,1,22.08.2015,,226,142.85, -66,1,23.08.2015,,330,142.85, -66,1,24.08.2015,,232,142.85, -66,1,25.08.2015,,434,142.85, -66,1,26.08.2015,,572,142.85, -66,1,27.08.2015,,173,137.68, -66,1,28.08.2015,,214,137.68, -66,1,29.08.2015,,352,137.68, -66,1,30.08.2015,,405,137.68, -66,1,31.08.2015,,471,137.68, -66,1,01.09.2015,,368,137.68, -66,1,02.09.2015,,280,135.78, -66,1,03.09.2015,,273,135.78, -66,1,04.09.2015,,587,135.78, -66,1,05.09.2015,,842,133.6, -66,1,06.09.2015,,490,133.6, -66,1,07.09.2015,,449,133.6, -66,1,08.09.2015,,317,133.6, -66,1,09.09.2015,,383,135.51, -66,1,10.09.2015,,273,135.51, -66,1,11.09.2015,,270,135.51, -66,1,12.09.2015,,132,135.51, -66,1,13.09.2015,,163,135.51, -66,1,14.09.2015,,145,135.51, -66,1,15.09.2015,,201,135.51, -66,1,16.09.2015,,85,138.5, -66,1,17.09.2015,,210,138.5, -66,1,18.09.2015,,229,138.5, -66,1,19.09.2015,,201,138.5, -66,1,20.09.2015,,176,138.5, -66,1,21.09.2015,,126,138.5, -66,1,22.09.2015,,94,138.5, -66,1,23.09.2015,,97,141.21, -66,1,24.09.2015,,104,141.21, -66,1,25.09.2015,,53,141.21, -66,1,26.09.2015,,132,141.21, -66,1,27.09.2015,,361,141.21, -66,1,28.09.2015,,138,141.21, -66,1,29.09.2015,,116,141.21, -66,1,30.09.2015,,88,141.21, -66,1,01.10.2015,,38,141.21, -66,1,02.10.2015,,132,141.21, -66,1,03.10.2015,,50,141.21, -66,1,04.10.2015,,167,141.21, -66,1,05.10.2015,,69,141.21, -66,1,06.10.2015,1.0,85,141.21,132.92 -66,1,07.10.2015,1.0,653,141.21,130.21 -66,1,08.10.2015,1.0,540,141.11,130.21 -66,1,09.10.2015,1.0,801,141.21,130.21 -66,1,10.10.2015,1.0,298,141.21,130.21 -66,1,11.10.2015,1.0,792,141.21,130.21 -66,1,12.10.2015,1.0,361,141.21,130.21 -66,1,13.10.2015,,72,141.21, -66,1,14.10.2015,,63,141.21, -66,1,15.10.2015,,31,142.57, -66,1,16.10.2015,,63,142.57, -66,1,17.10.2015,,101,142.57, -66,1,18.10.2015,,35,142.57, -66,1,19.10.2015,,79,142.57, -66,1,20.10.2015,,138,142.57, -66,1,21.10.2015,,123,130.34, -66,1,22.10.2015,,346,130.34, -66,1,23.10.2015,,220,130.34, -66,1,24.10.2015,,449,130.34, -66,1,25.10.2015,,679,130.34, -66,1,26.10.2015,,167,130.34, -66,1,27.10.2015,1.0,239,130.34,122.32 -66,1,28.10.2015,1.0,138,130.34,124.77 -66,1,29.10.2015,1.0,223,130.34,124.77 -66,1,30.10.2015,1.0,543,130.34,124.77 -66,1,31.10.2015,1.0,210,130.34,124.77 -66,1,01.11.2015,1.0,440,130.34,124.77 -66,1,02.11.2015,1.0,342,130.34,124.77 -66,1,03.11.2015,,157,130.34, -66,1,04.11.2015,,79,134.69, -66,1,05.11.2015,,88,134.69, -66,1,06.11.2015,,63,134.69, -66,1,07.11.2015,,47,134.69, -66,1,08.11.2015,,57,134.69, -66,1,09.11.2015,,60,134.69, -66,1,10.11.2015,,88,134.69, -66,1,11.11.2015,,72,130.34, -66,1,12.11.2015,,75,130.34, -66,1,13.11.2015,,104,130.34, -66,1,14.11.2015,,79,130.34, -66,1,15.11.2015,,82,130.34, -66,1,16.11.2015,,129,130.34, -66,1,17.11.2015,,44,130.34, -66,1,18.11.2015,,107,130.34, -66,1,19.11.2015,,94,130.34, -66,1,20.11.2015,,119,130.34, -66,1,21.11.2015,,97,130.34, -66,1,22.11.2015,,138,130.34, -66,1,23.11.2015,,188,130.34, -66,1,24.11.2015,1.0,609,130.34,116.61 -66,1,25.11.2015,1.0,1172,130.34,116.61 -66,1,26.11.2015,1.0,845,130.34,116.61 -66,1,27.11.2015,1.0,760,130.34,116.61 -66,1,28.11.2015,1.0,873,130.34,116.61 -66,1,29.11.2015,1.0,603,130.34,116.61 -66,1,30.11.2015,1.0,635,130.34,116.61 -66,1,01.12.2015,,25,130.34, -66,1,02.12.2015,,94,130.34, -66,1,03.12.2015,,28,130.34, -66,1,04.12.2015,,97,130.34, -66,1,05.12.2015,1.0,898,130.34,116.61 -66,1,06.12.2015,1.0,1561,130.34,116.61 -66,1,07.12.2015,1.0,946,130.34,116.61 -66,1,08.12.2015,,91,130.34, -66,1,09.12.2015,,145,130.34, -66,1,10.12.2015,,41,130.34, -66,1,11.12.2015,,151,130.34, -66,1,12.12.2015,,123,130.34, -66,1,13.12.2015,,173,130.34, -66,1,14.12.2015,,75,130.34, -66,1,15.12.2015,,85,130.34, -66,1,16.12.2015,,154,130.34, -66,1,17.12.2015,,60,130.34, -66,1,18.12.2015,,79,130.34, -66,1,19.12.2015,,204,130.34, -66,1,20.12.2015,,75,130.34, -66,1,21.12.2015,,47,130.34, 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-67,1,05.01.2015,,82,163.78, -67,1,06.01.2015,,66,163.78, -67,1,07.01.2015,,50,163.78, -67,1,08.01.2015,,53,163.78, -67,1,09.01.2015,,66,163.78, -67,1,10.01.2015,,69,163.78, -67,1,11.01.2015,,104,163.78, -67,1,12.01.2015,,85,163.78, -67,1,13.01.2015,,79,163.78, -67,1,14.01.2015,,69,168.4, -67,1,15.01.2015,,66,168.4, -67,1,16.01.2015,,151,168.4, -67,1,17.01.2015,,119,168.4, -67,1,18.01.2015,,47,168.4, -67,1,19.01.2015,,57,168.4, -67,1,20.01.2015,,31,168.4, -67,1,21.01.2015,,88,168.4, -67,1,22.01.2015,,63,168.4, -67,1,23.01.2015,,85,160.24, -67,1,24.01.2015,,38,160.24, -67,1,25.01.2015,,41,160.24, -67,1,26.01.2015,,167,160.24, -67,1,27.01.2015,,145,160.24, -67,1,28.01.2015,,185,146.92, -67,1,29.01.2015,,141,146.92, -67,1,30.01.2015,,185,146.92, -67,1,31.01.2015,,236,146.92, -67,1,01.02.2015,,179,146.92, -67,1,02.02.2015,,116,146.92, -67,1,03.02.2015,,239,146.92, -67,1,04.02.2015,,207,146.92, -67,1,05.02.2015,,119,146.92, -67,1,06.02.2015,,594,146.92, -67,1,07.02.2015,,160,146.92, -67,1,08.02.2015,,229,146.92, -67,1,09.02.2015,,160,146.92, -67,1,10.02.2015,,484,146.92, -67,1,11.02.2015,,195,146.92, -67,1,12.02.2015,,333,146.92, -67,1,13.02.2015,,232,149.37, -67,1,14.02.2015,,163,149.37, -67,1,15.02.2015,,264,149.37, -67,1,16.02.2015,,217,149.37, -67,1,17.02.2015,1.0,393,149.37,143.8 -67,1,18.02.2015,1.0,735,149.37,135.64 -67,1,19.02.2015,1.0,817,149.37,135.64 -67,1,20.02.2015,1.0,1178,149.37,135.64 -67,1,21.02.2015,1.0,757,149.37,135.64 -67,1,22.02.2015,1.0,559,149.37,135.64 -67,1,23.02.2015,1.0,308,149.37,135.64 -67,1,24.02.2015,,138,149.37, -67,1,25.02.2015,,110,149.37, -67,1,26.02.2015,,104,149.37, -67,1,27.02.2015,,188,143.93, -67,1,28.02.2015,,349,143.93, -67,1,01.03.2015,,468,143.93, -67,1,02.03.2015,,399,143.93, -67,1,03.03.2015,1.0,462,143.93,138.36 -67,1,04.03.2015,1.0,430,143.93,135.64 -67,1,05.03.2015,1.0,474,138.5,135.64 -67,1,06.03.2015,1.0,823,138.5,135.64 -67,1,07.03.2015,1.0,383,138.5,135.64 -67,1,08.03.2015,1.0,245,138.5,135.64 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-67,1,10.05.2015,,160,131.7, -67,1,11.05.2015,,148,131.7, -67,1,12.05.2015,,141,131.7, -67,1,13.05.2015,,157,131.7, -67,1,14.05.2015,,226,131.7, -67,1,15.05.2015,,305,131.7, -67,1,16.05.2015,,236,131.7, -67,1,17.05.2015,,248,131.7, -67,1,18.05.2015,,305,131.7, -67,1,19.05.2015,1.0,1027,131.7,119.33 -67,1,20.05.2015,1.0,1078,131.7,119.33 -67,1,21.05.2015,1.0,1090,131.7,119.33 -67,1,22.05.2015,1.0,993,131.7,119.33 -67,1,23.05.2015,1.0,412,131.7,119.33 -67,1,24.05.2015,1.0,569,131.7,119.33 -67,1,25.05.2015,1.0,280,131.7,119.33 -67,1,26.05.2015,,295,131.7, -67,1,27.05.2015,,198,129.25, -67,1,28.05.2015,,192,129.25, -67,1,29.05.2015,,182,129.25, -67,1,30.05.2015,,440,129.25, -67,1,31.05.2015,,317,129.25, -67,1,01.06.2015,,217,129.25, -67,1,02.06.2015,,320,129.25, -67,1,03.06.2015,,298,129.25, -67,1,04.06.2015,,251,129.25, -67,1,05.06.2015,,424,129.25, -67,1,06.06.2015,,465,131.7, -67,1,07.06.2015,,210,131.7, -67,1,08.06.2015,,217,131.7, -67,1,09.06.2015,1.0,807,131.7,119.33 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-68,1,27.03.2015,,198,131.97, -68,1,28.03.2015,,220,131.97, -68,1,29.03.2015,,418,131.97, -68,1,30.03.2015,,289,131.97, -68,1,31.03.2015,,201,131.97, -68,1,01.04.2015,,101,133.33, -68,1,02.04.2015,,119,133.33, -68,1,03.04.2015,,201,133.33, -68,1,04.04.2015,,229,133.33, -68,1,05.04.2015,,151,133.33, -68,1,06.04.2015,,179,133.33, -68,1,07.04.2015,1.0,1596,133.33,119.33 -68,1,08.04.2015,1.0,1232,133.33,119.33 -68,1,09.04.2015,1.0,1373,133.33,119.33 -68,1,10.04.2015,1.0,1225,133.33,119.33 -68,1,11.04.2015,1.0,898,133.33,119.33 -68,1,12.04.2015,1.0,402,133.33,119.33 -68,1,13.04.2015,1.0,396,133.33,119.33 -68,1,14.04.2015,,258,130.34, -68,1,15.04.2015,,198,130.34, -68,1,16.04.2015,,273,130.34, -68,1,17.04.2015,,116,138.5, -68,1,18.04.2015,,88,138.5, -68,1,19.04.2015,,113,138.5, -68,1,20.04.2015,,116,138.5, -68,1,21.04.2015,,176,138.5, -68,1,22.04.2015,,223,128.98, -68,1,23.04.2015,,314,128.98, -68,1,24.04.2015,,232,128.98, -68,1,25.04.2015,,283,128.98, -68,1,26.04.2015,,217,128.98, -68,1,27.04.2015,,242,128.98, -68,1,28.04.2015,,141,128.98, -68,1,29.04.2015,,110,131.7, -68,1,30.04.2015,,173,131.7, -68,1,01.05.2015,,88,131.7, -68,1,02.05.2015,,129,131.7, -68,1,03.05.2015,,173,131.7, -68,1,04.05.2015,,145,131.7, -68,1,05.05.2015,,198,131.7, -68,1,06.05.2015,,229,131.7, -68,1,07.05.2015,,214,131.7, -68,1,08.05.2015,,195,131.7, -68,1,09.05.2015,,104,131.7, -68,1,10.05.2015,,119,131.7, -68,1,11.05.2015,,154,131.7, -68,1,12.05.2015,,129,131.7, -68,1,13.05.2015,,110,131.7, -68,1,14.05.2015,,280,131.7, -68,1,15.05.2015,,204,131.7, -68,1,16.05.2015,,383,131.7, -68,1,17.05.2015,,248,131.7, -68,1,18.05.2015,,286,131.7, -68,1,19.05.2015,1.0,902,131.7,119.33 -68,1,20.05.2015,1.0,1345,131.7,119.33 -68,1,21.05.2015,1.0,823,131.7,119.33 -68,1,22.05.2015,1.0,729,131.7,119.33 -68,1,23.05.2015,1.0,339,131.7,119.33 -68,1,24.05.2015,1.0,625,131.7,119.33 -68,1,25.05.2015,1.0,270,131.7,119.33 -68,1,26.05.2015,,188,131.7, -68,1,27.05.2015,,163,129.25, -68,1,28.05.2015,,145,129.25, -68,1,29.05.2015,,198,129.25, -68,1,30.05.2015,,352,129.25, -68,1,31.05.2015,,145,129.25, -68,1,01.06.2015,,185,129.25, -68,1,02.06.2015,,132,129.25, -68,1,03.06.2015,,170,129.25, -68,1,04.06.2015,,298,129.25, -68,1,05.06.2015,,236,129.25, -68,1,06.06.2015,,267,131.7, -68,1,07.06.2015,,195,131.7, -68,1,08.06.2015,,258,131.7, -68,1,09.06.2015,1.0,587,131.7,119.33 -68,1,10.06.2015,1.0,478,131.7,119.33 -68,1,11.06.2015,1.0,1297,131.7,119.33 -68,1,12.06.2015,1.0,412,131.7,119.33 -68,1,13.06.2015,1.0,619,131.7,119.33 -68,1,14.06.2015,1.0,154,131.7,119.33 -68,1,15.06.2015,1.0,386,131.7,119.33 -68,1,16.06.2015,,270,131.7, -68,1,17.06.2015,,123,131.7, -68,1,18.06.2015,,292,128.17, -68,1,19.06.2015,,286,128.17, -68,1,20.06.2015,,182,128.17, -68,1,21.06.2015,,173,128.17, -68,1,22.06.2015,,163,128.17, -68,1,23.06.2015,,273,128.17, -68,1,24.06.2015,,295,128.17, -68,1,25.06.2015,,320,128.17, -68,1,26.06.2015,,371,128.17, -68,1,27.06.2015,,314,128.17, -68,1,28.06.2015,,383,130.34, -68,1,29.06.2015,,163,130.34, -68,1,30.06.2015,1.0,826,130.34,119.33 -68,1,01.07.2015,1.0,1291,130.34,119.33 -68,1,02.07.2015,1.0,1398,130.34,119.33 -68,1,03.07.2015,1.0,427,130.34,119.33 -68,1,04.07.2015,1.0,101,130.34,119.33 -68,1,05.07.2015,1.0,594,130.34,119.33 -68,1,06.07.2015,1.0,594,130.34,119.33 -68,1,07.07.2015,,157,130.34, -68,1,08.07.2015,,270,130.34, -68,1,09.07.2015,,223,121.64, -68,1,10.07.2015,,600,121.64, -68,1,11.07.2015,,616,123.55, -68,1,12.07.2015,,317,123.55, -68,1,13.07.2015,,182,123.55, -68,1,14.07.2015,,280,123.55, -68,1,15.07.2015,,333,125.45, -68,1,16.07.2015,,487,125.45, -68,1,17.07.2015,,311,125.45, -68,1,18.07.2015,,314,127.62, -68,1,19.07.2015,,320,127.62, -68,1,20.07.2015,,408,127.62, -68,1,21.07.2015,,776,127.62, -68,1,22.07.2015,,792,127.62, -68,1,23.07.2015,,625,127.62, -68,1,24.07.2015,,500,127.62, -68,1,25.07.2015,,776,127.62, -68,1,26.07.2015,,459,130.07, -68,1,27.07.2015,,1156,130.07, -68,1,28.07.2015,1.0,1612,130.07,124.77 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-68,1,29.08.2015,,308,137.68, -68,1,30.08.2015,,371,137.68, -68,1,31.08.2015,,302,137.68, -68,1,01.09.2015,,157,137.68, -68,1,02.09.2015,,418,135.78, -68,1,03.09.2015,,336,135.78, -68,1,04.09.2015,,858,135.78, -68,1,05.09.2015,,531,133.6, -68,1,06.09.2015,,468,133.6, -68,1,07.09.2015,,408,133.6, -68,1,08.09.2015,,682,133.6, -68,1,09.09.2015,,707,135.51, -68,1,10.09.2015,,562,135.51, -68,1,11.09.2015,,553,135.51, -68,1,12.09.2015,,405,135.51, -68,1,13.09.2015,,587,135.51, -68,1,14.09.2015,,380,135.51, -68,1,15.09.2015,,342,135.51, -68,1,16.09.2015,,283,138.5, -68,1,17.09.2015,,468,138.5, -68,1,18.09.2015,,311,138.5, -68,1,19.09.2015,,380,138.5, -68,1,20.09.2015,,377,138.5, -68,1,21.09.2015,,232,138.5, -68,1,22.09.2015,,188,138.5, -68,1,23.09.2015,,251,141.21, -68,1,24.09.2015,,160,141.21, -68,1,25.09.2015,,355,141.21, -68,1,26.09.2015,,478,141.21, -68,1,27.09.2015,,135,141.21, -68,1,28.09.2015,,336,141.21, -68,1,29.09.2015,,434,141.21, -68,1,30.09.2015,,305,141.21, -68,1,01.10.2015,,283,141.21, -68,1,02.10.2015,,358,141.21, -68,1,03.10.2015,,430,141.21, -68,1,04.10.2015,,280,141.21, -68,1,05.10.2015,,217,141.21, -68,1,06.10.2015,1.0,273,141.21,132.92 -68,1,07.10.2015,1.0,867,141.21,130.21 -68,1,08.10.2015,1.0,657,141.11,130.21 -68,1,09.10.2015,1.0,782,141.21,130.21 -68,1,10.10.2015,1.0,713,141.21,130.21 -68,1,11.10.2015,1.0,688,141.21,130.21 -68,1,12.10.2015,1.0,490,141.21,130.21 -68,1,13.10.2015,,201,141.21, -68,1,14.10.2015,,173,141.21, -68,1,15.10.2015,,101,142.57, -68,1,16.10.2015,,223,142.57, -68,1,17.10.2015,,226,142.57, -68,1,18.10.2015,,207,142.57, -68,1,19.10.2015,,160,142.57, -68,1,20.10.2015,,151,142.57, -68,1,21.10.2015,,320,130.34, -68,1,22.10.2015,,434,130.34, -68,1,23.10.2015,,537,130.34, -68,1,24.10.2015,,440,130.34, -68,1,25.10.2015,,188,130.34, -68,1,26.10.2015,,283,130.34, -68,1,27.10.2015,1.0,1100,130.34,122.32 -68,1,28.10.2015,1.0,613,130.34,124.77 -68,1,29.10.2015,1.0,126,130.34,124.77 -68,1,30.10.2015,1.0,132,130.34,124.77 -68,1,31.10.2015,1.0,217,130.34,124.77 -68,1,01.11.2015,1.0,478,130.34,124.77 -68,1,02.11.2015,1.0,484,130.34,124.77 -68,1,03.11.2015,,638,130.34, -68,1,04.11.2015,,217,134.69, -68,1,05.11.2015,,267,134.69, -68,1,06.11.2015,,484,134.69, -68,1,07.11.2015,,160,134.69, -68,1,08.11.2015,,223,134.69, -68,1,09.11.2015,,195,134.69, -68,1,10.11.2015,,267,134.69, -68,1,11.11.2015,,192,130.34, -68,1,12.11.2015,,232,130.34, -68,1,13.11.2015,,305,130.34, -68,1,14.11.2015,,245,130.34, -68,1,15.11.2015,,324,130.34, -68,1,16.11.2015,,201,130.34, -68,1,17.11.2015,,305,130.34, -68,1,18.11.2015,,160,130.34, -68,1,19.11.2015,,232,130.34, -68,1,20.11.2015,,264,130.34, -68,1,21.11.2015,,232,130.34, -68,1,22.11.2015,,141,130.34, -68,1,23.11.2015,,251,130.34, -68,1,24.11.2015,1.0,1967,130.34,116.61 -68,1,25.11.2015,1.0,1860,130.34,116.61 -68,1,26.11.2015,1.0,487,130.34,116.61 -68,1,27.11.2015,1.0,1005,130.34,116.61 -68,1,28.11.2015,1.0,707,130.34,116.61 -68,1,29.11.2015,1.0,657,130.34,116.61 -68,1,30.11.2015,1.0,1530,130.34,116.61 -68,1,01.12.2015,,198,130.34, -68,1,02.12.2015,,239,130.34, -68,1,03.12.2015,,167,130.34, -68,1,04.12.2015,,280,130.34, -68,1,05.12.2015,1.0,1558,130.34,116.61 -68,1,06.12.2015,1.0,1608,130.34,116.61 -68,1,07.12.2015,1.0,1511,130.34,116.61 -68,1,08.12.2015,,214,130.34, -68,1,09.12.2015,,192,130.34, -68,1,10.12.2015,,195,130.34, -68,1,11.12.2015,,195,130.34, -68,1,12.12.2015,,82,130.34, -68,1,13.12.2015,,160,130.34, -68,1,14.12.2015,,145,130.34, -68,1,15.12.2015,,292,130.34, -68,1,16.12.2015,,226,130.34, -68,1,17.12.2015,,232,130.34, -68,1,18.12.2015,,333,130.34, -68,1,19.12.2015,,305,130.34, -68,1,20.12.2015,,236,130.34, -68,1,21.12.2015,,201,130.34, -68,1,22.12.2015,,151,130.34, -68,1,23.12.2015,,308,130.34, -68,1,24.12.2015,,302,126.54, -68,1,25.12.2015,,226,126.54, -68,1,26.12.2015,,459,126.54, -68,1,27.12.2015,,481,126.54, -68,1,28.12.2015,,239,126.54, -68,1,29.12.2015,,383,126.54, -68,1,30.12.2015,,390,126.54, -68,1,31.12.2015,,402,126.54, -68,1,01.01.2016,,104,126.54, -68,1,02.01.2016,,198,126.54, -68,1,03.01.2016,,364,126.54, -68,1,04.01.2016,,217,126.54, -68,1,05.01.2016,,361,126.54, -68,1,06.01.2016,,258,126.54, -68,1,07.01.2016,,258,126.54, -68,1,08.01.2016,,245,126.54, -68,1,09.01.2016,,415,126.54, -68,1,10.01.2016,,248,126.54, -68,1,11.01.2016,,333,126.54, -68,1,12.01.2016,,456,126.54, -68,1,13.01.2016,,364,126.54, -68,1,14.01.2016,,349,126.54, -68,1,15.01.2016,,349,126.54, -68,1,16.01.2016,,390,126.54, -68,1,17.01.2016,,408,126.54, -68,1,18.01.2016,,358,126.54, -68,1,19.01.2016,,408,126.54, -68,1,20.01.2016,,587,126.54, -68,1,21.01.2016,,437,127.9, -68,1,22.01.2016,,443,127.9, -68,1,23.01.2016,,283,127.9, -68,1,24.01.2016,,503,127.9, -68,1,25.01.2016,,239,127.9, -68,1,26.01.2016,,405,127.9, -68,1,27.01.2016,,396,127.9, -68,1,28.01.2016,,408,127.9, -68,1,29.01.2016,,374,127.9, -68,1,30.01.2016,,465,127.9, -68,1,31.01.2016,,490,127.9, -68,1,01.02.2016,,421,130.34, -68,1,02.02.2016,,380,130.34, -68,1,03.02.2016,,333,130.34, -68,1,04.02.2016,,327,133.06, -68,1,05.02.2016,,594,133.06, -68,1,06.02.2016,,587,133.06, -68,1,07.02.2016,,330,141.76, -68,1,08.02.2016,,405,141.76, -68,1,09.02.2016,1.0,644,141.76,130.75 -68,1,10.02.2016,1.0,773,141.76,130.75 -68,1,11.02.2016,1.0,971,141.76,130.75 -68,1,12.02.2016,1.0,861,141.76,130.75 -68,1,13.02.2016,1.0,1210,141.76,130.75 -68,1,14.02.2016,1.0,650,141.76,130.75 -68,1,15.02.2016,1.0,663,141.76,130.75 -68,1,16.02.2016,1.0,509,141.76,130.75 -68,1,17.02.2016,1.0,443,141.76,130.75 -68,1,18.02.2016,,258,141.76, -68,1,19.02.2016,,201,141.76, -68,1,20.02.2016,,214,141.76, -68,1,21.02.2016,,280,141.76, -68,1,22.02.2016,,267,141.76, -68,1,23.02.2016,,182,141.76, -68,1,24.02.2016,,239,141.76, -68,1,25.02.2016,,226,141.76, -68,1,26.02.2016,,232,141.76, -68,1,27.02.2016,,276,141.76, -68,1,28.02.2016,,226,141.76, -68,1,29.02.2016,,267,141.76, -68,1,01.03.2016,,232,141.76, -68,1,02.03.2016,,185,141.76, -68,1,03.03.2016,,198,141.76, -68,1,04.03.2016,,317,141.76, -68,1,05.03.2016,,333,141.21, -68,1,06.03.2016,,176,141.21, -68,1,07.03.2016,,173,141.21, -68,1,08.03.2016,1.0,226,141.21,132.92 -68,1,09.03.2016,1.0,434,141.21,132.92 -68,1,10.03.2016,1.0,958,141.21,132.92 -68,1,11.03.2016,1.0,650,141.21,132.92 -68,1,12.03.2016,1.0,851,141.21,132.92 -68,1,13.03.2016,1.0,575,141.21,132.92 -68,1,14.03.2016,1.0,443,141.21,132.92 -68,1,15.03.2016,1.0,653,141.21,130.21 -68,1,16.03.2016,1.0,616,141.21,130.21 -68,1,17.03.2016,1.0,820,141.21,130.21 -68,1,18.03.2016,1.0,679,141.21,130.21 -68,1,19.03.2016,,201,141.21, -68,1,20.03.2016,,182,141.21, -68,1,21.03.2016,,289,141.21, -68,1,22.03.2016,,148,141.21, -68,1,23.03.2016,,167,141.21, -68,1,24.03.2016,,145,141.21, -68,1,25.03.2016,,364,141.21, -68,1,26.03.2016,,214,141.21, -68,1,27.03.2016,,210,141.21, -68,1,28.03.2016,,151,141.21, -68,1,29.03.2016,,173,141.21, -68,1,30.03.2016,,101,141.21, -68,1,31.03.2016,,245,141.21, -68,1,01.04.2016,,185,141.21, -68,1,02.04.2016,,138,141.21, 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-68,1,04.05.2016,,104,131.16, -68,1,05.05.2016,,195,131.16, -68,1,06.05.2016,,239,131.16, -68,1,07.05.2016,,148,131.16, -68,1,08.05.2016,,195,131.16, -68,1,09.05.2016,,292,131.16, -68,1,10.05.2016,,239,131.16, -68,1,11.05.2016,,226,131.16, -68,1,12.05.2016,,258,131.16, -68,1,13.05.2016,,430,128.98, -68,1,14.05.2016,,474,128.98, -68,1,15.05.2016,,276,128.98, -68,1,16.05.2016,,204,128.98, -68,1,17.05.2016,1.0,1602,128.98,119.6 -68,1,18.05.2016,1.0,1040,128.98,119.6 -68,1,19.05.2016,1.0,682,128.98,119.6 -68,1,20.05.2016,1.0,895,128.98,119.6 -68,1,21.05.2016,1.0,983,128.98,119.6 -68,1,22.05.2016,1.0,305,128.98,119.6 -69,1,01.01.2015,,16,163.78, -69,1,02.01.2015,,41,163.78, -69,1,03.01.2015,,35,163.78, -69,1,04.01.2015,,57,163.78, -69,1,05.01.2015,,82,163.78, -69,1,06.01.2015,,53,163.78, -69,1,07.01.2015,,85,163.78, -69,1,08.01.2015,,94,163.78, -69,1,09.01.2015,,72,163.78, -69,1,10.01.2015,,126,163.78, -69,1,11.01.2015,,113,163.78, -69,1,12.01.2015,,82,163.78, -69,1,13.01.2015,,79,163.78, 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-69,1,19.06.2015,,126,128.17, -69,1,20.06.2015,,88,128.17, -69,1,21.06.2015,,79,128.17, -69,1,22.06.2015,,63,128.17, -69,1,23.06.2015,,176,128.17, -69,1,24.06.2015,,126,128.17, -69,1,25.06.2015,,88,128.17, -69,1,26.06.2015,,148,128.17, -69,1,27.06.2015,,182,128.17, -69,1,28.06.2015,,280,130.34, -69,1,29.06.2015,,104,130.34, -69,1,30.06.2015,1.0,368,130.34,119.33 -69,1,01.07.2015,1.0,368,130.34,119.33 -69,1,02.07.2015,1.0,430,130.34,119.33 -69,1,03.07.2015,1.0,239,130.34,119.33 -69,1,04.07.2015,1.0,327,130.34,119.33 -69,1,05.07.2015,1.0,339,130.34,119.33 -69,1,06.07.2015,1.0,390,130.34,119.33 -69,1,07.07.2015,,69,130.34, -69,1,08.07.2015,,145,130.34, -69,1,09.07.2015,,157,121.64, -69,1,10.07.2015,,396,121.64, -69,1,11.07.2015,,185,123.55, -69,1,12.07.2015,,223,123.55, -69,1,13.07.2015,,327,123.55, -69,1,14.07.2015,,302,123.55, -69,1,15.07.2015,,286,125.45, -69,1,16.07.2015,,236,125.45, -69,1,17.07.2015,,160,125.45, -69,1,18.07.2015,,195,127.62, -69,1,19.07.2015,,195,127.62, -69,1,20.07.2015,,220,127.62, -69,1,21.07.2015,,280,127.62, -69,1,22.07.2015,,324,127.62, -69,1,23.07.2015,,251,127.62, -69,1,24.07.2015,,298,127.62, -69,1,25.07.2015,,361,127.62, -69,1,26.07.2015,,229,130.07, -69,1,27.07.2015,,346,130.07, -69,1,28.07.2015,1.0,701,130.07,124.77 -69,1,29.07.2015,1.0,361,130.07,124.77 -69,1,30.07.2015,1.0,434,130.07,124.77 -69,1,31.07.2015,1.0,528,130.07,124.77 -69,1,01.08.2015,1.0,390,130.07,124.77 -69,1,02.08.2015,1.0,562,130.07,124.77 -69,1,03.08.2015,1.0,704,130.07,124.77 -69,1,04.08.2015,,229,138.5, -69,1,05.08.2015,,151,138.5, -69,1,06.08.2015,,135,138.5, -69,1,07.08.2015,,201,138.5, -69,1,08.08.2015,,207,141.21, -69,1,09.08.2015,,135,141.21, -69,1,10.08.2015,,167,141.21, -69,1,11.08.2015,,170,141.21, -69,1,12.08.2015,,192,142.85, -69,1,13.08.2015,,324,142.85, -69,1,14.08.2015,,214,142.85, -69,1,15.08.2015,,145,142.85, -69,1,16.08.2015,,248,142.85, -69,1,17.08.2015,,154,142.85, -69,1,18.08.2015,,198,142.85, -69,1,19.08.2015,,160,142.85, -69,1,20.08.2015,,163,142.85, -69,1,21.08.2015,,148,142.85, -69,1,22.08.2015,,138,142.85, -69,1,23.08.2015,,286,142.85, -69,1,24.08.2015,,195,142.85, -69,1,25.08.2015,,204,142.85, -69,1,26.08.2015,,280,142.85, -69,1,27.08.2015,,198,137.68, -69,1,28.08.2015,,283,137.68, -69,1,29.08.2015,,207,137.68, -69,1,30.08.2015,,273,137.68, -69,1,31.08.2015,,154,137.68, -69,1,01.09.2015,,167,137.68, -69,1,02.09.2015,,264,135.78, -69,1,03.09.2015,,286,135.78, -69,1,04.09.2015,,229,135.78, -69,1,05.09.2015,,261,133.6, -69,1,06.09.2015,,311,133.6, -69,1,07.09.2015,,254,133.6, -69,1,08.09.2015,,402,133.6, -69,1,09.09.2015,,427,135.51, -69,1,10.09.2015,,336,135.51, -69,1,11.09.2015,,264,135.51, -69,1,12.09.2015,,229,135.51, -69,1,13.09.2015,,254,135.51, -69,1,14.09.2015,,167,135.51, -69,1,15.09.2015,,207,135.51, -69,1,16.09.2015,,217,138.5, -69,1,17.09.2015,,170,138.5, -69,1,18.09.2015,,141,138.5, -69,1,19.09.2015,,176,138.5, -69,1,20.09.2015,,261,138.5, -69,1,21.09.2015,,91,138.5, -69,1,22.09.2015,,151,138.5, -69,1,23.09.2015,,167,141.21, -69,1,24.09.2015,,236,141.21, -69,1,25.09.2015,,129,141.21, -69,1,26.09.2015,,116,141.21, -69,1,27.09.2015,,141,141.21, -69,1,28.09.2015,,258,141.21, -69,1,29.09.2015,,188,141.21, -69,1,30.09.2015,,110,141.21, -69,1,01.10.2015,,113,141.21, -69,1,02.10.2015,,113,141.21, -69,1,03.10.2015,,173,141.21, -69,1,04.10.2015,,151,141.21, -69,1,05.10.2015,,220,141.21, -69,1,06.10.2015,1.0,358,141.21,132.92 -69,1,07.10.2015,1.0,418,141.21,130.21 -69,1,08.10.2015,1.0,619,141.11,130.21 -69,1,09.10.2015,1.0,512,141.21,130.21 -69,1,10.10.2015,1.0,556,141.21,130.21 -69,1,11.10.2015,1.0,465,141.21,130.21 -69,1,12.10.2015,1.0,641,141.21,130.21 -69,1,13.10.2015,,91,141.21, -69,1,14.10.2015,,63,141.21, -69,1,15.10.2015,,104,142.57, -69,1,16.10.2015,,113,142.57, -69,1,17.10.2015,,129,142.57, -69,1,18.10.2015,,107,142.57, -69,1,19.10.2015,,129,142.57, -69,1,20.10.2015,,126,142.57, -69,1,21.10.2015,,358,130.34, -69,1,22.10.2015,,449,130.34, 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-69,1,25.04.2016,1.0,672,135.78,122.32 -69,1,26.04.2016,,138,135.78, -69,1,27.04.2016,,167,135.78, -69,1,28.04.2016,,145,135.78, -69,1,29.04.2016,,198,131.16, -69,1,30.04.2016,,160,131.16, -69,1,01.05.2016,,138,131.16, -69,1,02.05.2016,,79,131.16, -69,1,03.05.2016,,160,131.16, -69,1,04.05.2016,,141,131.16, -69,1,05.05.2016,,113,131.16, -69,1,06.05.2016,,104,131.16, -69,1,07.05.2016,,101,131.16, -69,1,08.05.2016,,82,131.16, -69,1,09.05.2016,,79,131.16, -69,1,10.05.2016,,123,131.16, -69,1,11.05.2016,,204,131.16, -69,1,12.05.2016,,179,131.16, -69,1,13.05.2016,,251,128.98, -69,1,14.05.2016,,192,128.98, -69,1,15.05.2016,,170,128.98, -69,1,16.05.2016,,157,128.98, -69,1,17.05.2016,1.0,653,128.98,119.6 -69,1,18.05.2016,1.0,908,128.98,119.6 -69,1,19.05.2016,1.0,754,128.98,119.6 -69,1,20.05.2016,1.0,751,128.98,119.6 -69,1,21.05.2016,1.0,735,128.98,119.6 -69,1,22.05.2016,1.0,638,128.98,119.6 -70,1,01.01.2015,,6,163.78, -70,1,02.01.2015,,16,163.78, -70,1,03.01.2015,,16,163.78, 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-71,1,26.02.2015,,63,149.37, -71,1,27.02.2015,,101,143.93, -71,1,28.02.2015,,157,143.93, -71,1,01.03.2015,,173,143.93, -71,1,02.03.2015,,173,143.93, -71,1,03.03.2015,1.0,176,143.93,138.36 -71,1,04.03.2015,1.0,204,143.93,135.64 -71,1,05.03.2015,1.0,302,138.5,135.64 -71,1,06.03.2015,1.0,468,138.5,135.64 -71,1,07.03.2015,1.0,276,138.5,135.64 -71,1,08.03.2015,1.0,217,138.5,135.64 -71,1,09.03.2015,,129,140.67, -71,1,10.03.2015,,107,140.67, -71,1,11.03.2015,,176,140.67, -71,1,12.03.2015,,138,140.67, -71,1,13.03.2015,,97,140.67, -71,1,14.03.2015,,94,140.67, -71,1,15.03.2015,,94,140.67, -71,1,16.03.2015,,94,140.67, -71,1,17.03.2015,1.0,1156,140.67,127.49 -71,1,18.03.2015,1.0,697,140.67,127.49 -71,1,19.03.2015,1.0,773,140.67,127.49 -71,1,20.03.2015,1.0,540,140.67,127.49 -71,1,21.03.2015,1.0,320,140.67,127.49 -71,1,22.03.2015,1.0,383,140.67,127.49 -71,1,23.03.2015,1.0,393,140.67,127.49 -71,1,24.03.2015,,66,140.67, -71,1,25.03.2015,,195,140.67, -71,1,26.03.2015,,176,131.97, -71,1,27.03.2015,,229,131.97, -71,1,28.03.2015,,173,131.97, -71,1,29.03.2015,,239,131.97, -71,1,30.03.2015,,88,131.97, -71,1,31.03.2015,,201,131.97, -71,1,01.04.2015,,129,133.33, -71,1,02.04.2015,,179,133.33, -71,1,03.04.2015,,176,133.33, -71,1,04.04.2015,,292,133.33, -71,1,05.04.2015,,88,133.33, -71,1,06.04.2015,,123,133.33, -71,1,07.04.2015,1.0,996,133.33,119.33 -71,1,08.04.2015,1.0,1335,133.33,119.33 -71,1,09.04.2015,1.0,732,133.33,119.33 -71,1,10.04.2015,1.0,694,133.33,119.33 -71,1,11.04.2015,1.0,496,133.33,119.33 -71,1,12.04.2015,1.0,132,133.33,119.33 -71,1,13.04.2015,1.0,210,133.33,119.33 -71,1,14.04.2015,,91,130.34, -71,1,15.04.2015,,176,130.34, -71,1,16.04.2015,,47,130.34, -71,1,17.04.2015,,50,138.5, -71,1,18.04.2015,,69,138.5, -71,1,19.04.2015,,85,138.5, -71,1,20.04.2015,,75,138.5, -71,1,21.04.2015,,69,138.5, -71,1,22.04.2015,,88,128.98, -71,1,23.04.2015,,170,128.98, -71,1,24.04.2015,,157,128.98, -71,1,25.04.2015,,148,128.98, -71,1,26.04.2015,,94,128.98, -71,1,27.04.2015,,104,128.98, -71,1,28.04.2015,,113,128.98, -71,1,29.04.2015,,88,131.7, -71,1,30.04.2015,,94,131.7, -71,1,01.05.2015,,101,131.7, -71,1,02.05.2015,,138,131.7, -71,1,03.05.2015,,41,131.7, -71,1,04.05.2015,,258,131.7, -71,1,05.05.2015,,101,131.7, -71,1,06.05.2015,,104,131.7, -71,1,07.05.2015,,104,131.7, -71,1,08.05.2015,,198,131.7, -71,1,09.05.2015,,72,131.7, -71,1,10.05.2015,,63,131.7, -71,1,11.05.2015,,66,131.7, -71,1,12.05.2015,,88,131.7, -71,1,13.05.2015,,44,131.7, -71,1,14.05.2015,,79,131.7, -71,1,15.05.2015,,75,131.7, -71,1,16.05.2015,,82,131.7, -71,1,17.05.2015,,60,131.7, -71,1,18.05.2015,,82,131.7, -71,1,19.05.2015,1.0,352,131.7,119.33 -71,1,20.05.2015,1.0,440,131.7,119.33 -71,1,21.05.2015,1.0,474,131.7,119.33 -71,1,22.05.2015,1.0,474,131.7,119.33 -71,1,23.05.2015,1.0,267,131.7,119.33 -71,1,24.05.2015,1.0,569,131.7,119.33 -71,1,25.05.2015,1.0,283,131.7,119.33 -71,1,26.05.2015,,66,131.7, -71,1,27.05.2015,,167,129.25, -71,1,28.05.2015,,88,129.25, -71,1,29.05.2015,,104,129.25, -71,1,30.05.2015,,82,129.25, -71,1,31.05.2015,,113,129.25, -71,1,01.06.2015,,104,129.25, -71,1,02.06.2015,,107,129.25, -71,1,03.06.2015,,72,129.25, -71,1,04.06.2015,,173,129.25, -71,1,05.06.2015,,182,129.25, -71,1,06.06.2015,,138,131.7, -71,1,07.06.2015,,141,131.7, -71,1,08.06.2015,,129,131.7, -71,1,09.06.2015,1.0,694,131.7,119.33 -71,1,10.06.2015,1.0,1489,131.7,119.33 -71,1,11.06.2015,1.0,801,131.7,119.33 -71,1,12.06.2015,1.0,449,131.7,119.33 -71,1,13.06.2015,1.0,578,131.7,119.33 -71,1,14.06.2015,1.0,377,131.7,119.33 -71,1,15.06.2015,1.0,390,131.7,119.33 -71,1,16.06.2015,,50,131.7, -71,1,17.06.2015,,119,131.7, -71,1,18.06.2015,,132,128.17, -71,1,19.06.2015,,132,128.17, -71,1,20.06.2015,,148,128.17, -71,1,21.06.2015,,104,128.17, -71,1,22.06.2015,,97,128.17, -71,1,23.06.2015,,57,128.17, -71,1,24.06.2015,,157,128.17, -71,1,25.06.2015,,145,128.17, -71,1,26.06.2015,,236,128.17, -71,1,27.06.2015,,424,128.17, -71,1,28.06.2015,,135,130.34, -71,1,29.06.2015,,185,130.34, -71,1,30.06.2015,1.0,1008,130.34,119.33 -71,1,01.07.2015,1.0,732,130.34,119.33 -71,1,02.07.2015,1.0,594,130.34,119.33 -71,1,03.07.2015,1.0,905,130.34,119.33 -71,1,04.07.2015,1.0,767,130.34,119.33 -71,1,05.07.2015,1.0,650,130.34,119.33 -71,1,06.07.2015,1.0,638,130.34,119.33 -71,1,07.07.2015,,214,130.34, -71,1,08.07.2015,,223,130.34, -71,1,09.07.2015,,198,121.64, -71,1,10.07.2015,,456,121.64, -71,1,11.07.2015,,349,123.55, -71,1,12.07.2015,,94,123.55, -71,1,13.07.2015,,223,123.55, -71,1,14.07.2015,,239,123.55, -71,1,15.07.2015,,302,125.45, -71,1,16.07.2015,,151,125.45, -71,1,17.07.2015,,248,125.45, -71,1,18.07.2015,,298,127.62, -71,1,19.07.2015,,214,127.62, -71,1,20.07.2015,,522,127.62, -71,1,21.07.2015,,364,127.62, -71,1,22.07.2015,,713,127.62, -71,1,23.07.2015,,691,127.62, -71,1,24.07.2015,,487,127.62, -71,1,25.07.2015,,509,127.62, -71,1,26.07.2015,,707,130.07, -71,1,27.07.2015,,515,130.07, -71,1,28.07.2015,1.0,968,130.07,124.77 -71,1,29.07.2015,1.0,889,130.07,124.77 -71,1,30.07.2015,1.0,691,130.07,124.77 -71,1,31.07.2015,1.0,1071,130.07,124.77 -71,1,01.08.2015,1.0,914,130.07,124.77 -71,1,02.08.2015,1.0,116,130.07,124.77 -71,1,03.08.2015,1.0,349,130.07,124.77 -71,1,04.08.2015,,236,138.5, -71,1,05.08.2015,,232,138.5, -71,1,06.08.2015,,232,138.5, -71,1,07.08.2015,,295,138.5, -71,1,08.08.2015,,239,141.21, -71,1,09.08.2015,,236,141.21, -71,1,10.08.2015,,201,141.21, -71,1,11.08.2015,,214,141.21, -71,1,12.08.2015,,207,142.85, -71,1,13.08.2015,,173,142.85, -71,1,14.08.2015,,91,142.85, -71,1,15.08.2015,,141,142.85, -71,1,16.08.2015,,123,142.85, -71,1,17.08.2015,,264,142.85, -71,1,18.08.2015,,179,142.85, -71,1,19.08.2015,,163,142.85, -71,1,20.08.2015,,163,142.85, -71,1,21.08.2015,,251,142.85, -71,1,22.08.2015,,163,142.85, -71,1,23.08.2015,,151,142.85, -71,1,24.08.2015,,129,142.85, -71,1,25.08.2015,,217,142.85, -71,1,26.08.2015,,270,142.85, -71,1,27.08.2015,,292,137.68, -71,1,28.08.2015,,198,137.68, -71,1,29.08.2015,,292,137.68, -71,1,30.08.2015,,110,137.68, -71,1,31.08.2015,,220,137.68, -71,1,01.09.2015,,220,137.68, -71,1,02.09.2015,,254,135.78, -71,1,03.09.2015,,405,135.78, -71,1,04.09.2015,,333,135.78, -71,1,05.09.2015,,204,133.6, -71,1,06.09.2015,,302,133.6, -71,1,07.09.2015,,236,133.6, -71,1,08.09.2015,,427,133.6, -71,1,09.09.2015,,434,135.51, -71,1,10.09.2015,,110,135.51, -71,1,11.09.2015,,239,135.51, -71,1,12.09.2015,,207,135.51, -71,1,13.09.2015,,295,135.51, -71,1,14.09.2015,,201,135.51, -71,1,15.09.2015,,267,135.51, -71,1,16.09.2015,,232,138.5, -71,1,17.09.2015,,145,138.5, -71,1,18.09.2015,,154,138.5, -71,1,19.09.2015,,232,138.5, -71,1,20.09.2015,,179,138.5, -71,1,21.09.2015,,220,138.5, -71,1,22.09.2015,,232,138.5, -71,1,23.09.2015,,107,141.21, -71,1,24.09.2015,,141,141.21, -71,1,25.09.2015,,198,141.21, -71,1,26.09.2015,,179,141.21, -71,1,27.09.2015,,126,141.21, -71,1,28.09.2015,,129,141.21, -71,1,29.09.2015,,123,141.21, -71,1,30.09.2015,,129,141.21, -71,1,01.10.2015,,201,141.21, -71,1,02.10.2015,,129,141.21, -71,1,03.10.2015,,201,141.21, -71,1,04.10.2015,,163,141.21, -71,1,05.10.2015,,110,141.21, -71,1,06.10.2015,1.0,628,141.21,132.92 -71,1,07.10.2015,1.0,553,141.21,130.21 -71,1,08.10.2015,1.0,415,141.11,130.21 -71,1,09.10.2015,1.0,434,141.21,130.21 -71,1,10.10.2015,1.0,346,141.21,130.21 -71,1,11.10.2015,1.0,276,141.21,130.21 -71,1,12.10.2015,1.0,286,141.21,130.21 -71,1,13.10.2015,,141,141.21, -71,1,14.10.2015,,101,141.21, -71,1,15.10.2015,,85,142.57, -71,1,16.10.2015,,123,142.57, -71,1,17.10.2015,,88,142.57, -71,1,18.10.2015,,97,142.57, -71,1,19.10.2015,,57,142.57, -71,1,20.10.2015,,123,142.57, -71,1,21.10.2015,,261,130.34, -71,1,22.10.2015,,311,130.34, -71,1,23.10.2015,,330,130.34, -71,1,24.10.2015,,229,130.34, -71,1,25.10.2015,,160,130.34, -71,1,26.10.2015,,79,130.34, -71,1,27.10.2015,1.0,336,130.34,122.32 -71,1,28.10.2015,1.0,443,130.34,124.77 -71,1,29.10.2015,1.0,443,130.34,124.77 -71,1,30.10.2015,1.0,292,130.34,124.77 -71,1,31.10.2015,1.0,371,130.34,124.77 -71,1,01.11.2015,1.0,151,130.34,124.77 -71,1,02.11.2015,1.0,289,130.34,124.77 -71,1,03.11.2015,,170,130.34, -71,1,04.11.2015,,104,134.69, -71,1,05.11.2015,,91,134.69, -71,1,06.11.2015,,94,134.69, -71,1,07.11.2015,,94,134.69, -71,1,08.11.2015,,116,134.69, -71,1,09.11.2015,,170,134.69, -71,1,10.11.2015,,85,134.69, -71,1,11.11.2015,,154,130.34, -71,1,12.11.2015,,91,130.34, -71,1,13.11.2015,,154,130.34, -71,1,14.11.2015,,242,130.34, -71,1,15.11.2015,,75,130.34, -71,1,16.11.2015,,82,130.34, -71,1,17.11.2015,,179,130.34, -71,1,18.11.2015,,107,130.34, -71,1,19.11.2015,,91,130.34, -71,1,20.11.2015,,126,130.34, -71,1,21.11.2015,,192,130.34, -71,1,22.11.2015,,129,130.34, -71,1,23.11.2015,,82,130.34, -71,1,24.11.2015,1.0,1825,130.34,116.61 -71,1,25.11.2015,1.0,886,130.34,116.61 -71,1,26.11.2015,1.0,971,130.34,116.61 -71,1,27.11.2015,1.0,817,130.34,116.61 -71,1,28.11.2015,1.0,531,130.34,116.61 -71,1,29.11.2015,1.0,509,130.34,116.61 -71,1,30.11.2015,1.0,490,130.34,116.61 -71,1,01.12.2015,,35,130.34, -71,1,02.12.2015,,85,130.34, -71,1,03.12.2015,,101,130.34, -71,1,04.12.2015,,138,130.34, -71,1,05.12.2015,1.0,185,130.34,116.61 -71,1,06.12.2015,1.0,478,130.34,116.61 -71,1,07.12.2015,1.0,1565,130.34,116.61 -71,1,08.12.2015,,82,130.34, -71,1,09.12.2015,,113,130.34, -71,1,10.12.2015,,66,130.34, -71,1,11.12.2015,,72,130.34, -71,1,12.12.2015,,88,130.34, -71,1,13.12.2015,,97,130.34, -71,1,14.12.2015,,82,130.34, -71,1,15.12.2015,,94,130.34, -71,1,16.12.2015,,79,130.34, -71,1,17.12.2015,,113,130.34, -71,1,18.12.2015,,110,130.34, -71,1,19.12.2015,,132,130.34, -71,1,20.12.2015,,116,130.34, -71,1,21.12.2015,,69,130.34, -71,1,22.12.2015,,182,130.34, -71,1,23.12.2015,,204,130.34, -71,1,24.12.2015,,182,126.54, -71,1,25.12.2015,,97,126.54, -71,1,26.12.2015,,163,126.54, -71,1,27.12.2015,,145,126.54, -71,1,28.12.2015,,232,126.54, -71,1,29.12.2015,,198,126.54, -71,1,30.12.2015,,151,126.54, -71,1,31.12.2015,,69,126.54, -71,1,01.01.2016,,25,126.54, -71,1,02.01.2016,,44,126.54, -71,1,03.01.2016,,119,126.54, -71,1,04.01.2016,,97,126.54, -71,1,05.01.2016,,148,126.54, -71,1,06.01.2016,,91,126.54, -71,1,07.01.2016,,229,126.54, -71,1,08.01.2016,,132,126.54, -71,1,09.01.2016,,173,126.54, -71,1,10.01.2016,,141,126.54, -71,1,11.01.2016,,148,126.54, -71,1,12.01.2016,,148,126.54, -71,1,13.01.2016,,88,126.54, -71,1,14.01.2016,,63,126.54, -71,1,15.01.2016,,188,126.54, -71,1,16.01.2016,,192,126.54, -71,1,17.01.2016,,214,126.54, -71,1,18.01.2016,,132,126.54, -71,1,19.01.2016,,308,126.54, -71,1,20.01.2016,,239,126.54, -71,1,21.01.2016,,408,127.9, -71,1,22.01.2016,,336,127.9, -71,1,23.01.2016,,311,127.9, -71,1,24.01.2016,,245,127.9, -71,1,25.01.2016,,138,127.9, -71,1,26.01.2016,,232,127.9, -71,1,27.01.2016,,236,127.9, -71,1,28.01.2016,,94,127.9, -71,1,29.01.2016,,116,127.9, -71,1,30.01.2016,,163,127.9, -71,1,31.01.2016,,258,127.9, -71,1,01.02.2016,,204,130.34, -71,1,02.02.2016,,119,130.34, -71,1,03.02.2016,,163,130.34, -71,1,04.02.2016,,286,133.06, -71,1,05.02.2016,,298,133.06, -71,1,06.02.2016,,459,133.06, -71,1,07.02.2016,,267,141.76, -71,1,08.02.2016,,132,141.76, -71,1,09.02.2016,1.0,581,141.76,130.75 -71,1,10.02.2016,1.0,512,141.76,130.75 -71,1,11.02.2016,1.0,380,141.76,130.75 -71,1,12.02.2016,1.0,214,141.76,130.75 -71,1,13.02.2016,1.0,232,141.76,130.75 -71,1,14.02.2016,1.0,176,141.76,130.75 -71,1,15.02.2016,1.0,220,141.76,130.75 -71,1,16.02.2016,1.0,210,141.76,130.75 -71,1,17.02.2016,1.0,220,141.76,130.75 -71,1,18.02.2016,,135,141.76, -71,1,19.02.2016,,82,141.76, -71,1,20.02.2016,,101,141.76, -71,1,21.02.2016,,63,141.76, -71,1,22.02.2016,,85,141.76, -71,1,23.02.2016,,50,141.76, -71,1,24.02.2016,,88,141.76, -71,1,25.02.2016,,91,141.76, -71,1,26.02.2016,,126,141.76, -71,1,27.02.2016,,101,141.76, -71,1,28.02.2016,,79,141.76, -71,1,29.02.2016,,151,141.76, -71,1,01.03.2016,,91,141.76, -71,1,02.03.2016,,75,141.76, -71,1,03.03.2016,,132,141.76, 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-75,1,30.05.2015,,94,129.25, -75,1,31.05.2015,,66,129.25, -75,1,01.06.2015,,63,129.25, -75,1,02.06.2015,,75,129.25, -75,1,03.06.2015,,50,129.25, -75,1,04.06.2015,,69,129.25, -75,1,05.06.2015,,94,129.25, -75,1,06.06.2015,,119,131.7, -75,1,07.06.2015,,97,131.7, -75,1,08.06.2015,,173,131.7, -75,1,09.06.2015,1.0,352,131.7,119.33 -75,1,10.06.2015,1.0,537,131.7,119.33 -75,1,11.06.2015,1.0,484,131.7,119.33 -75,1,12.06.2015,1.0,452,131.7,119.33 -75,1,13.06.2015,1.0,578,131.7,119.33 -75,1,14.06.2015,1.0,779,131.7,119.33 -75,1,15.06.2015,1.0,393,131.7,119.33 -75,1,16.06.2015,,173,131.7, -75,1,17.06.2015,,79,131.7, -75,1,18.06.2015,,185,128.17, -75,1,19.06.2015,,148,128.17, -75,1,20.06.2015,,273,128.17, -75,1,21.06.2015,,245,128.17, -75,1,22.06.2015,,204,128.17, -75,1,23.06.2015,,333,128.17, -75,1,24.06.2015,,129,128.17, -75,1,25.06.2015,,75,128.17, -75,1,26.06.2015,,126,128.17, -75,1,27.06.2015,,245,128.17, -75,1,28.06.2015,,383,130.34, -75,1,29.06.2015,,110,130.34, -75,1,30.06.2015,1.0,653,130.34,119.33 -75,1,01.07.2015,1.0,490,130.34,119.33 -75,1,02.07.2015,1.0,606,130.34,119.33 -75,1,03.07.2015,1.0,324,130.34,119.33 -75,1,04.07.2015,1.0,839,130.34,119.33 -75,1,05.07.2015,1.0,575,130.34,119.33 -75,1,06.07.2015,1.0,606,130.34,119.33 -75,1,07.07.2015,,217,130.34, -75,1,08.07.2015,,176,130.34, -75,1,09.07.2015,,336,121.64, -75,1,10.07.2015,,342,121.64, -75,1,11.07.2015,,760,123.55, -75,1,12.07.2015,,286,123.55, -75,1,13.07.2015,,223,123.55, -75,1,14.07.2015,,543,123.55, -75,1,15.07.2015,,361,125.45, -75,1,16.07.2015,,368,125.45, -75,1,17.07.2015,,113,125.45, -75,1,18.07.2015,,192,125.45, -75,1,19.07.2015,,104,127.62, -75,1,20.07.2015,,286,127.62, -75,1,21.07.2015,,371,127.62, -75,1,22.07.2015,,503,127.62, -75,1,23.07.2015,,418,127.62, -75,1,24.07.2015,,515,127.62, -75,1,25.07.2015,,955,127.62, -75,1,26.07.2015,,575,130.07, -75,1,27.07.2015,,522,130.07, -75,1,28.07.2015,1.0,848,130.07,124.77 -75,1,29.07.2015,1.0,641,130.07,124.77 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-75,1,31.08.2015,,97,137.68, -75,1,01.09.2015,,522,137.68, -75,1,02.09.2015,,176,135.78, -75,1,03.09.2015,,232,135.78, -75,1,04.09.2015,,390,135.78, -75,1,05.09.2015,,283,133.6, -75,1,06.09.2015,,160,133.6, -75,1,07.09.2015,,195,133.6, -75,1,08.09.2015,,107,133.6, -75,1,09.09.2015,,85,135.51, -75,1,10.09.2015,,305,135.51, -75,1,11.09.2015,,207,135.51, -75,1,12.09.2015,,210,135.51, -75,1,13.09.2015,,207,135.51, -75,1,14.09.2015,,148,135.51, -75,1,15.09.2015,,210,135.51, -75,1,16.09.2015,,126,138.5, -75,1,17.09.2015,,163,138.5, -75,1,18.09.2015,,167,138.5, -75,1,19.09.2015,,107,138.5, -75,1,20.09.2015,,91,138.5, -75,1,21.09.2015,,63,138.5, -75,1,22.09.2015,,116,138.5, -75,1,23.09.2015,,69,141.21, -75,1,24.09.2015,,85,141.21, -75,1,25.09.2015,,123,141.21, -75,1,26.09.2015,,79,141.21, -75,1,27.09.2015,,132,141.21, -75,1,28.09.2015,,91,141.21, -75,1,29.09.2015,,88,141.21, -75,1,30.09.2015,,82,141.21, -75,1,01.10.2015,,35,141.21, -75,1,02.10.2015,,107,141.21, -75,1,03.10.2015,,72,141.21, -75,1,04.10.2015,,85,141.21, -75,1,05.10.2015,,63,141.21, -75,1,06.10.2015,1.0,198,141.21,132.92 -75,1,07.10.2015,1.0,173,141.21,130.21 -75,1,08.10.2015,1.0,311,141.11,130.21 -75,1,09.10.2015,1.0,280,141.21,130.21 -75,1,10.10.2015,1.0,305,141.21,130.21 -75,1,11.10.2015,1.0,258,141.21,130.21 -75,1,12.10.2015,1.0,308,141.21,130.21 -75,1,13.10.2015,,145,141.21, -75,1,14.10.2015,,28,141.21, -75,1,15.10.2015,,35,142.57, -75,1,16.10.2015,,44,142.57, -75,1,17.10.2015,,75,142.57, -75,1,18.10.2015,,66,142.57, -75,1,19.10.2015,,28,142.57, -75,1,20.10.2015,,94,142.57, -75,1,21.10.2015,,123,130.34, -75,1,22.10.2015,,94,130.34, -75,1,23.10.2015,,207,130.34, -75,1,24.10.2015,,245,130.34, -75,1,25.10.2015,,119,130.34, -75,1,26.10.2015,,101,130.34, -75,1,27.10.2015,1.0,402,130.34,122.32 -75,1,28.10.2015,1.0,135,130.34,124.77 -75,1,29.10.2015,1.0,75,130.34,124.77 -75,1,30.10.2015,1.0,182,130.34,124.77 -75,1,31.10.2015,1.0,201,130.34,124.77 -75,1,01.11.2015,1.0,126,130.34,124.77 -75,1,02.11.2015,1.0,173,130.34,124.77 -75,1,03.11.2015,,145,130.34, -75,1,04.11.2015,,19,134.69, -75,1,05.11.2015,,75,134.69, -75,1,06.11.2015,,66,134.69, -75,1,07.11.2015,,44,134.69, -75,1,08.11.2015,,75,134.69, -75,1,09.11.2015,,53,134.69, -75,1,10.11.2015,,63,134.69, -75,1,11.11.2015,,57,130.34, -75,1,12.11.2015,,31,130.34, -75,1,13.11.2015,,88,130.34, -75,1,14.11.2015,,113,130.34, -75,1,15.11.2015,,104,130.34, -75,1,16.11.2015,,47,130.34, -75,1,17.11.2015,,104,130.34, -75,1,18.11.2015,,94,130.34, -75,1,19.11.2015,,101,130.34, -75,1,20.11.2015,,63,130.34, -75,1,21.11.2015,,154,130.34, -75,1,22.11.2015,,126,130.34, -75,1,23.11.2015,,126,130.34, -75,1,24.11.2015,1.0,597,130.34,116.61 -75,1,25.11.2015,1.0,669,130.34,116.61 -75,1,26.11.2015,1.0,619,130.34,116.61 -75,1,27.11.2015,1.0,716,130.34,116.61 -75,1,28.11.2015,1.0,886,130.34,116.61 -75,1,29.11.2015,1.0,949,130.34,116.61 -75,1,30.11.2015,1.0,773,130.34,116.61 -75,1,01.12.2015,,41,130.34, -75,1,02.12.2015,,72,130.34, -75,1,03.12.2015,,82,130.34, -75,1,04.12.2015,,72,130.34, -75,1,05.12.2015,1.0,528,130.34,116.61 -75,1,06.12.2015,1.0,675,130.34,116.61 -75,1,07.12.2015,1.0,512,130.34,116.61 -75,1,08.12.2015,,91,130.34, -75,1,09.12.2015,,85,130.34, -75,1,10.12.2015,,94,130.34, -75,1,11.12.2015,,91,130.34, -75,1,12.12.2015,,88,130.34, -75,1,13.12.2015,,110,130.34, -75,1,14.12.2015,,66,130.34, -75,1,15.12.2015,,145,130.34, -75,1,16.12.2015,,66,130.34, -75,1,17.12.2015,,104,130.34, -75,1,18.12.2015,,85,130.34, -75,1,19.12.2015,,126,130.34, -75,1,20.12.2015,,154,130.34, -75,1,21.12.2015,,57,130.34, -75,1,22.12.2015,,126,130.34, -75,1,23.12.2015,,91,130.34, -75,1,24.12.2015,,110,126.54, -75,1,25.12.2015,,88,126.54, -75,1,26.12.2015,,167,126.54, -75,1,27.12.2015,,107,126.54, -75,1,28.12.2015,,113,126.54, -75,1,29.12.2015,,113,126.54, -75,1,30.12.2015,,138,126.54, -75,1,31.12.2015,,57,126.54, -75,1,01.01.2016,,22,126.54, -75,1,02.01.2016,,66,126.54, -75,1,03.01.2016,,47,126.54, -75,1,04.01.2016,,107,126.54, -75,1,05.01.2016,,141,126.54, -75,1,06.01.2016,,188,126.54, -75,1,07.01.2016,,119,126.54, -75,1,08.01.2016,,123,126.54, -75,1,09.01.2016,,101,126.54, -75,1,10.01.2016,,129,126.54, -75,1,11.01.2016,,94,126.54, -75,1,12.01.2016,,82,126.54, -75,1,13.01.2016,,138,126.54, -75,1,14.01.2016,,104,126.54, -75,1,15.01.2016,,135,126.54, -75,1,16.01.2016,,176,126.54, -75,1,17.01.2016,,214,126.54, -75,1,18.01.2016,,151,126.54, -75,1,19.01.2016,,195,126.54, -75,1,20.01.2016,,104,126.54, -75,1,21.01.2016,,72,127.9, -75,1,22.01.2016,,185,127.9, -75,1,23.01.2016,,173,127.9, -75,1,24.01.2016,,126,127.9, -75,1,25.01.2016,,239,127.9, -75,1,26.01.2016,,129,127.9, -75,1,27.01.2016,,192,127.9, -75,1,28.01.2016,,154,127.9, -75,1,29.01.2016,,141,127.9, -75,1,30.01.2016,,236,127.9, -75,1,31.01.2016,,151,127.9, -75,1,01.02.2016,,88,130.34, -75,1,02.02.2016,,192,130.34, -75,1,03.02.2016,,176,130.34, -75,1,04.02.2016,,110,133.06, -75,1,05.02.2016,,141,133.06, -75,1,06.02.2016,,647,133.06, -75,1,07.02.2016,,75,141.76, -75,1,08.02.2016,,88,141.76, -75,1,09.02.2016,1.0,258,141.76,130.75 -75,1,10.02.2016,1.0,317,141.76,130.75 -75,1,11.02.2016,1.0,691,141.76,130.75 -75,1,12.02.2016,1.0,176,141.76,130.75 -75,1,13.02.2016,1.0,534,141.76,130.75 -75,1,14.02.2016,1.0,232,141.76,130.75 -75,1,15.02.2016,1.0,286,141.76,130.75 -75,1,16.02.2016,1.0,220,141.76,130.75 -75,1,17.02.2016,1.0,160,141.76,130.75 -75,1,18.02.2016,,94,141.76, -75,1,19.02.2016,,82,141.76, -75,1,20.02.2016,,119,141.76, -75,1,21.02.2016,,57,141.76, -75,1,22.02.2016,,242,141.76, -75,1,23.02.2016,,210,141.76, -75,1,24.02.2016,,66,141.76, -75,1,25.02.2016,,129,141.76, -75,1,26.02.2016,,69,141.76, -75,1,27.02.2016,,151,141.76, -75,1,28.02.2016,,110,141.76, -75,1,29.02.2016,,94,141.76, -75,1,01.03.2016,,119,141.76, -75,1,02.03.2016,,79,141.76, -75,1,03.03.2016,,101,141.76, -75,1,04.03.2016,,69,141.76, -75,1,05.03.2016,,167,141.21, -75,1,06.03.2016,,85,141.21, -75,1,07.03.2016,,251,141.21, -75,1,08.03.2016,1.0,298,141.21,132.92 -75,1,09.03.2016,1.0,207,141.21,132.92 -75,1,10.03.2016,1.0,578,141.21,132.92 -75,1,11.03.2016,1.0,302,141.21,132.92 -75,1,12.03.2016,1.0,251,141.21,132.92 -75,1,13.03.2016,1.0,276,141.21,132.92 -75,1,14.03.2016,1.0,160,141.21,132.92 -75,1,15.03.2016,1.0,719,141.21,130.21 -75,1,16.03.2016,1.0,440,141.21,130.21 -75,1,17.03.2016,1.0,207,141.21,130.21 -75,1,18.03.2016,1.0,682,141.21,130.21 -75,1,19.03.2016,1.0,399,141.21,130.21 -75,1,20.03.2016,,110,141.21, -75,1,21.03.2016,,97,141.21, -75,1,22.03.2016,,72,141.21, -75,1,23.03.2016,,63,141.21, -75,1,24.03.2016,,53,141.21, -75,1,25.03.2016,,204,141.21, -75,1,26.03.2016,,94,141.21, -75,1,27.03.2016,,57,141.21, -75,1,28.03.2016,,60,141.21, -75,1,29.03.2016,,119,141.21, -75,1,30.03.2016,,75,141.21, -75,1,31.03.2016,,123,141.21, -75,1,01.04.2016,,57,141.21, -75,1,02.04.2016,,69,141.21, -75,1,03.04.2016,,119,141.21, -75,1,04.04.2016,,101,141.21, -75,1,05.04.2016,,88,141.21, -75,1,06.04.2016,,72,141.21, -75,1,07.04.2016,,69,141.21, -75,1,08.04.2016,,119,141.21, -75,1,09.04.2016,,82,141.21, -75,1,10.04.2016,,47,141.21, -75,1,11.04.2016,,151,141.21, -75,1,12.04.2016,,88,141.21, -75,1,13.04.2016,,176,141.21, -75,1,14.04.2016,,110,135.78, -75,1,15.04.2016,,346,135.78, -75,1,16.04.2016,,443,135.78, -75,1,17.04.2016,,192,135.78, -75,1,18.04.2016,,160,135.78, -75,1,19.04.2016,1.0,911,135.78,122.32 -75,1,20.04.2016,1.0,1543,135.78,122.32 -75,1,21.04.2016,1.0,760,135.78,122.32 -75,1,22.04.2016,1.0,798,135.78,122.32 -75,1,23.04.2016,1.0,452,135.78,122.32 -75,1,24.04.2016,1.0,543,135.78,122.32 -75,1,25.04.2016,1.0,543,135.78,122.32 -75,1,26.04.2016,,82,135.78, -75,1,27.04.2016,,170,135.78, -75,1,28.04.2016,,160,135.78, -75,1,29.04.2016,,167,131.16, -75,1,30.04.2016,,148,131.16, -75,1,01.05.2016,,60,131.16, -75,1,02.05.2016,,66,131.16, -75,1,03.05.2016,,47,131.16, -75,1,04.05.2016,,47,131.16, -75,1,05.05.2016,,101,131.16, -75,1,06.05.2016,,88,131.16, -75,1,07.05.2016,,145,131.16, -75,1,08.05.2016,,129,131.16, -75,1,09.05.2016,,50,131.16, -75,1,10.05.2016,,116,131.16, -75,1,11.05.2016,,75,131.16, -75,1,12.05.2016,,135,131.16, -75,1,13.05.2016,,148,128.98, -75,1,14.05.2016,,126,128.98, -75,1,15.05.2016,,198,128.98, -75,1,16.05.2016,,157,128.98, -75,1,17.05.2016,1.0,729,128.98,119.6 -75,1,18.05.2016,1.0,889,128.98,119.6 -75,1,19.05.2016,1.0,650,128.98,119.6 -75,1,20.05.2016,1.0,1030,128.98,119.6 -75,1,21.05.2016,1.0,1106,128.98,119.6 -75,1,22.05.2016,1.0,484,128.98,119.6 -76,1,25.09.2015,,207,141.21, -76,1,26.09.2015,,85,141.21, -76,1,27.09.2015,,82,141.21, -76,1,28.09.2015,,66,141.21, -76,1,29.09.2015,,107,141.21, -76,1,30.09.2015,,101,141.21, -76,1,01.10.2015,,69,141.21, -76,1,02.10.2015,,88,141.21, -76,1,03.10.2015,,50,141.21, -76,1,04.10.2015,,35,141.21, -76,1,05.10.2015,,63,141.21, -76,1,06.10.2015,1.0,53,141.21,132.92 -76,1,07.10.2015,1.0,201,141.21,130.21 -76,1,08.10.2015,1.0,374,141.11,130.21 -76,1,09.10.2015,1.0,239,141.21,130.21 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-77,1,18.06.2015,,72,128.17, -77,1,19.06.2015,,69,128.17, -77,1,20.06.2015,,104,128.17, -77,1,21.06.2015,,72,128.17, -77,1,22.06.2015,,101,128.17, -77,1,23.06.2015,,85,128.17, -77,1,24.06.2015,,85,128.17, -77,1,25.06.2015,,72,128.17, -77,1,26.06.2015,,135,128.17, -77,1,27.06.2015,,79,128.17, -77,1,28.06.2015,,123,130.34, -77,1,29.06.2015,,75,130.34, -77,1,30.06.2015,1.0,342,130.34,119.33 -77,1,01.07.2015,1.0,276,130.34,119.33 -77,1,02.07.2015,1.0,217,130.34,119.33 -77,1,03.07.2015,1.0,364,130.34,119.33 -77,1,04.07.2015,1.0,207,130.34,119.33 -77,1,05.07.2015,1.0,386,130.34,119.33 -77,1,06.07.2015,1.0,295,130.34,119.33 -77,1,07.07.2015,,69,130.34, -77,1,08.07.2015,,101,130.34, -77,1,09.07.2015,,192,121.64, -77,1,10.07.2015,,154,121.64, -77,1,11.07.2015,,167,123.55, -77,1,12.07.2015,,110,123.55, -77,1,13.07.2015,,163,123.55, -77,1,14.07.2015,,176,123.55, -77,1,15.07.2015,,110,125.45, -77,1,16.07.2015,,110,125.45, -77,1,17.07.2015,,170,125.45, -77,1,18.07.2015,,236,125.45, -77,1,19.07.2015,,204,127.62, -77,1,20.07.2015,,254,127.62, -77,1,21.07.2015,,280,127.62, -77,1,22.07.2015,,242,127.62, -77,1,23.07.2015,,280,127.62, -77,1,24.07.2015,,145,127.62, -77,1,25.07.2015,,267,127.62, -77,1,26.07.2015,,188,130.07, -77,1,27.07.2015,,264,130.07, -77,1,28.07.2015,1.0,424,130.07,124.77 -77,1,29.07.2015,1.0,320,130.07,124.77 -77,1,30.07.2015,1.0,503,130.07,124.77 -77,1,31.07.2015,1.0,317,130.07,124.77 -77,1,01.08.2015,1.0,214,130.07,124.77 -77,1,02.08.2015,1.0,430,130.07,124.77 -77,1,03.08.2015,1.0,446,130.07,124.77 -77,1,04.08.2015,,141,138.5, -77,1,05.08.2015,,327,138.5, -77,1,06.08.2015,,154,138.5, -77,1,07.08.2015,,110,138.5, -77,1,08.08.2015,,104,141.21, -77,1,09.08.2015,,167,141.21, -77,1,10.08.2015,,145,141.21, -77,1,11.08.2015,,135,141.21, -77,1,12.08.2015,,82,142.85, -77,1,13.08.2015,,160,142.85, -77,1,14.08.2015,,119,142.85, -77,1,15.08.2015,,97,142.85, -77,1,16.08.2015,,145,142.85, -77,1,17.08.2015,,126,142.85, -77,1,18.08.2015,,160,142.85, -77,1,19.08.2015,,107,142.85, -77,1,20.08.2015,,123,142.85, -77,1,21.08.2015,,104,142.85, -77,1,22.08.2015,,85,142.85, -77,1,23.08.2015,,123,142.85, -77,1,24.08.2015,,157,142.85, -77,1,25.08.2015,,160,142.85, -77,1,26.08.2015,,132,142.85, -77,1,27.08.2015,,232,137.68, -77,1,28.08.2015,,104,137.68, -77,1,29.08.2015,,214,137.68, -77,1,30.08.2015,,160,137.68, -77,1,31.08.2015,,258,137.68, -77,1,01.09.2015,,148,137.68, -77,1,02.09.2015,,154,135.78, -77,1,03.09.2015,,192,135.78, -77,1,04.09.2015,,311,135.78, -77,1,05.09.2015,,163,133.6, -77,1,06.09.2015,,160,133.6, -77,1,07.09.2015,,258,133.6, -77,1,08.09.2015,,188,133.6, -77,1,09.09.2015,,254,135.51, -77,1,10.09.2015,,283,135.51, -77,1,11.09.2015,,449,135.51, -77,1,12.09.2015,,176,135.51, -77,1,13.09.2015,,182,135.51, -77,1,14.09.2015,,160,135.51, -77,1,15.09.2015,,201,135.51, -77,1,16.09.2015,,138,138.5, -77,1,17.09.2015,,85,138.5, -77,1,18.09.2015,,126,138.5, -77,1,19.09.2015,,88,138.5, -77,1,20.09.2015,,138,138.5, 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-77,1,22.11.2015,,104,130.34, -77,1,23.11.2015,,79,130.34, -77,1,24.11.2015,1.0,949,130.34,116.61 -77,1,25.11.2015,1.0,955,130.34,116.61 -77,1,26.11.2015,1.0,1426,130.34,116.61 -77,1,27.11.2015,1.0,836,130.34,116.61 -77,1,28.11.2015,1.0,484,130.34,116.61 -77,1,29.11.2015,1.0,148,130.34,116.61 -77,1,30.11.2015,1.0,729,130.34,116.61 -77,1,01.12.2015,,116,130.34, -77,1,02.12.2015,,101,130.34, -77,1,03.12.2015,,104,130.34, -77,1,04.12.2015,,119,130.34, -77,1,05.12.2015,1.0,522,130.34,116.61 -77,1,06.12.2015,1.0,980,130.34,116.61 -77,1,07.12.2015,1.0,767,130.34,116.61 -77,1,08.12.2015,,60,130.34, -77,1,09.12.2015,,123,130.34, -77,1,10.12.2015,,101,130.34, -77,1,11.12.2015,,113,130.34, -77,1,12.12.2015,,104,130.34, -77,1,13.12.2015,,113,130.34, -77,1,14.12.2015,,101,130.34, -77,1,15.12.2015,,82,130.34, -77,1,16.12.2015,,82,130.34, -77,1,17.12.2015,,97,130.34, -77,1,18.12.2015,,101,130.34, -77,1,19.12.2015,,79,130.34, -77,1,20.12.2015,,91,130.34, -77,1,21.12.2015,,63,130.34, -77,1,22.12.2015,,85,130.34, -77,1,23.12.2015,,82,130.34, -77,1,24.12.2015,,145,126.54, -77,1,25.12.2015,,185,126.54, -77,1,26.12.2015,,97,126.54, -77,1,27.12.2015,,88,126.54, -77,1,28.12.2015,,91,126.54, -77,1,29.12.2015,,157,126.54, -77,1,30.12.2015,,176,126.54, -77,1,31.12.2015,,298,126.54, -77,1,01.01.2016,,22,126.54, -77,1,02.01.2016,,104,126.54, -77,1,03.01.2016,,91,126.54, -77,1,04.01.2016,,129,126.54, -77,1,05.01.2016,,141,126.54, -77,1,06.01.2016,,129,126.54, -77,1,07.01.2016,,116,126.54, -77,1,08.01.2016,,132,126.54, -77,1,09.01.2016,,88,126.54, -77,1,10.01.2016,,160,126.54, -77,1,11.01.2016,,170,126.54, -77,1,12.01.2016,,110,126.54, -77,1,13.01.2016,,163,126.54, -77,1,14.01.2016,,88,126.54, -77,1,15.01.2016,,132,126.54, -77,1,16.01.2016,,113,126.54, -77,1,17.01.2016,,160,126.54, -77,1,18.01.2016,,104,126.54, -77,1,19.01.2016,,204,126.54, -77,1,20.01.2016,,232,126.54, -77,1,21.01.2016,,182,127.9, -77,1,22.01.2016,,280,127.9, -77,1,23.01.2016,,154,127.9, -77,1,24.01.2016,,217,127.9, -77,1,25.01.2016,,138,127.9, -77,1,26.01.2016,,145,127.9, -77,1,27.01.2016,,201,127.9, -77,1,28.01.2016,,176,127.9, -77,1,29.01.2016,,314,127.9, -77,1,30.01.2016,,135,127.9, -77,1,31.01.2016,,182,127.9, -77,1,01.02.2016,,195,130.34, -77,1,02.02.2016,,427,130.34, -77,1,03.02.2016,,292,130.34, -77,1,04.02.2016,,173,133.06, -77,1,05.02.2016,,151,133.06, -77,1,06.02.2016,,145,133.06, -77,1,07.02.2016,,157,141.76, -77,1,08.02.2016,,138,141.76, -77,1,09.02.2016,1.0,317,141.76,130.75 -77,1,10.02.2016,1.0,261,141.76,130.75 -77,1,11.02.2016,1.0,298,141.76,130.75 -77,1,12.02.2016,1.0,393,141.76,130.75 -77,1,13.02.2016,1.0,276,141.76,130.75 -77,1,14.02.2016,1.0,248,141.76,130.75 -77,1,15.02.2016,1.0,245,141.76,130.75 -77,1,16.02.2016,1.0,226,141.76,130.75 -77,1,17.02.2016,1.0,223,141.76,130.75 -77,1,18.02.2016,,101,141.76, -77,1,19.02.2016,,123,141.76, -77,1,20.02.2016,,110,141.76, -77,1,21.02.2016,,101,141.76, -77,1,22.02.2016,,113,141.76, -77,1,23.02.2016,,113,141.76, -77,1,24.02.2016,,138,141.76, -77,1,25.02.2016,,104,141.76, -77,1,26.02.2016,,123,141.76, -77,1,27.02.2016,,138,141.76, -77,1,28.02.2016,,104,141.76, -77,1,29.02.2016,,126,141.76, -77,1,01.03.2016,,119,141.76, -77,1,02.03.2016,,101,141.76, -77,1,03.03.2016,,220,141.76, -77,1,04.03.2016,,141,141.76, -77,1,05.03.2016,,145,141.21, -77,1,06.03.2016,,148,141.21, -77,1,07.03.2016,,176,141.21, -77,1,08.03.2016,1.0,214,141.21,132.92 -77,1,09.03.2016,1.0,217,141.21,132.92 -77,1,10.03.2016,1.0,311,141.21,132.92 -77,1,11.03.2016,1.0,295,141.21,132.92 -77,1,12.03.2016,1.0,339,141.21,132.92 -77,1,13.03.2016,1.0,232,141.21,132.92 -77,1,14.03.2016,1.0,402,141.21,132.92 -77,1,15.03.2016,1.0,456,141.21,130.21 -77,1,16.03.2016,1.0,672,141.21,130.21 -77,1,17.03.2016,1.0,399,141.21,130.21 -77,1,18.03.2016,1.0,308,141.21,130.21 -77,1,19.03.2016,1.0,342,141.21,130.21 -77,1,20.03.2016,,154,141.21, -77,1,21.03.2016,,167,141.21, -77,1,22.03.2016,,145,141.21, -77,1,23.03.2016,,173,141.21, -77,1,24.03.2016,,145,141.21, -77,1,25.03.2016,,101,141.21, -77,1,26.03.2016,,201,141.21, -77,1,27.03.2016,,123,141.21, -77,1,28.03.2016,,151,141.21, -77,1,29.03.2016,,79,141.21, -77,1,30.03.2016,,126,141.21, -77,1,31.03.2016,,82,141.21, -77,1,01.04.2016,,79,141.21, -77,1,02.04.2016,,110,141.21, -77,1,03.04.2016,,75,141.21, -77,1,04.04.2016,,110,141.21, -77,1,05.04.2016,,63,141.21, -77,1,06.04.2016,,91,141.21, -77,1,07.04.2016,,163,141.21, -77,1,08.04.2016,,107,141.21, -77,1,09.04.2016,,79,141.21, -77,1,10.04.2016,,75,141.21, -77,1,11.04.2016,,94,141.21, -77,1,12.04.2016,,79,141.21, -77,1,13.04.2016,,91,141.21, -77,1,14.04.2016,,72,135.78, -77,1,15.04.2016,,176,135.78, -77,1,16.04.2016,,50,135.78, -77,1,17.04.2016,,101,135.78, -77,1,18.04.2016,,123,135.78, -77,1,19.04.2016,1.0,380,135.78,122.32 -77,1,20.04.2016,1.0,826,135.78,122.32 -77,1,21.04.2016,1.0,380,135.78,122.32 -77,1,22.04.2016,1.0,606,135.78,122.32 -77,1,23.04.2016,1.0,619,135.78,122.32 -77,1,24.04.2016,1.0,575,135.78,122.32 -77,1,25.04.2016,1.0,493,135.78,122.32 -77,1,26.04.2016,,132,135.78, -77,1,27.04.2016,,101,135.78, -77,1,28.04.2016,,167,135.78, -77,1,29.04.2016,,163,131.16, -77,1,30.04.2016,,160,131.16, -77,1,01.05.2016,,75,131.16, -77,1,02.05.2016,,132,131.16, -77,1,03.05.2016,,151,131.16, -77,1,04.05.2016,,129,131.16, -77,1,05.05.2016,,192,131.16, -77,1,06.05.2016,,148,131.16, -77,1,07.05.2016,,167,131.16, -77,1,08.05.2016,,116,131.16, -77,1,09.05.2016,,94,131.16, -77,1,10.05.2016,,236,131.16, -77,1,11.05.2016,,192,131.16, -77,1,12.05.2016,,141,131.16, -77,1,13.05.2016,,236,128.98, -77,1,14.05.2016,,163,128.98, -77,1,15.05.2016,,145,128.98, -77,1,16.05.2016,,132,128.98, -77,1,17.05.2016,1.0,465,128.98,119.6 -77,1,18.05.2016,1.0,675,128.98,119.6 -77,1,19.05.2016,1.0,738,128.98,119.6 -77,1,20.05.2016,1.0,814,128.98,119.6 -77,1,21.05.2016,1.0,349,128.98,119.6 -77,1,22.05.2016,1.0,518,128.98,119.6 -78,1,09.04.2015,1.0,3717,133.33,119.33 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-78,1,12.05.2015,,126,131.7, -78,1,13.05.2015,,163,131.7, -78,1,14.05.2015,,126,131.7, -78,1,15.05.2015,,173,131.7, -78,1,16.05.2015,,145,131.7, -78,1,17.05.2015,,151,131.7, -78,1,18.05.2015,,188,131.7, -78,1,19.05.2015,1.0,408,131.7,119.33 -78,1,20.05.2015,1.0,512,131.7,119.33 -78,1,21.05.2015,1.0,515,131.7,119.33 -78,1,22.05.2015,1.0,522,131.7,119.33 -78,1,23.05.2015,1.0,273,131.7,119.33 -78,1,24.05.2015,1.0,735,131.7,119.33 -78,1,25.05.2015,1.0,380,131.7,119.33 -78,1,26.05.2015,,123,131.7, -78,1,27.05.2015,,151,129.25, -78,1,28.05.2015,,123,129.25, -78,1,29.05.2015,,129,129.25, -78,1,30.05.2015,,207,129.25, -78,1,31.05.2015,,232,129.25, -78,1,01.06.2015,,135,129.25, -78,1,02.06.2015,,119,129.25, -78,1,03.06.2015,,223,129.25, -78,1,04.06.2015,,135,129.25, -78,1,05.06.2015,,254,129.25, -78,1,06.06.2015,,123,131.7, -78,1,07.06.2015,,195,131.7, -78,1,08.06.2015,,135,131.7, -78,1,09.06.2015,1.0,528,131.7,119.33 -78,1,10.06.2015,1.0,820,131.7,119.33 -78,1,11.06.2015,1.0,1932,131.7,119.33 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-78,1,12.07.2015,,358,123.55, -78,1,13.07.2015,,286,123.55, -78,1,14.07.2015,,342,123.55, -78,1,15.07.2015,,210,125.45, -78,1,16.07.2015,,305,125.45, -78,1,17.07.2015,,305,125.45, -78,1,18.07.2015,,242,125.45, -78,1,19.07.2015,,179,127.62, -78,1,20.07.2015,,317,127.62, -78,1,21.07.2015,,1150,127.62, -78,1,22.07.2015,,811,127.62, -78,1,23.07.2015,,760,127.62, -78,1,24.07.2015,,1015,127.62, -78,1,25.07.2015,,1297,127.62, -78,1,26.07.2015,,804,130.07, -78,1,27.07.2015,,679,130.07, -78,1,28.07.2015,1.0,864,130.07,124.77 -78,1,29.07.2015,1.0,1074,130.07,124.77 -78,1,30.07.2015,1.0,1134,130.07,124.77 -78,1,31.07.2015,1.0,547,130.07,124.77 -78,1,01.08.2015,1.0,930,130.07,124.77 -78,1,02.08.2015,1.0,864,130.07,124.77 -78,1,03.08.2015,1.0,1096,130.07,124.77 -78,1,04.08.2015,,289,138.5, -78,1,05.08.2015,,214,138.5, -78,1,06.08.2015,,336,138.5, -78,1,07.08.2015,,292,138.5, -78,1,08.08.2015,,176,141.21, -78,1,09.08.2015,,201,141.21, -78,1,10.08.2015,,258,141.21, -78,1,11.08.2015,,270,141.21, -78,1,12.08.2015,,242,142.85, -78,1,13.08.2015,,314,142.85, -78,1,14.08.2015,,383,142.85, -78,1,15.08.2015,,292,142.85, -78,1,16.08.2015,,380,142.85, -78,1,17.08.2015,,198,142.85, -78,1,18.08.2015,,336,142.85, -78,1,19.08.2015,,192,142.85, -78,1,20.08.2015,,201,142.85, -78,1,21.08.2015,,217,142.85, -78,1,22.08.2015,,214,142.85, -78,1,23.08.2015,,267,142.85, -78,1,24.08.2015,,374,142.85, -78,1,25.08.2015,,459,142.85, -78,1,26.08.2015,,506,142.85, -78,1,27.08.2015,,308,137.68, -78,1,28.08.2015,,302,137.68, -78,1,29.08.2015,,314,137.68, -78,1,30.08.2015,,462,137.68, -78,1,31.08.2015,,320,137.68, -78,1,01.09.2015,,578,137.68, -78,1,02.09.2015,,298,135.78, -78,1,03.09.2015,,496,135.78, -78,1,04.09.2015,,368,135.78, -78,1,05.09.2015,,537,133.6, -78,1,06.09.2015,,364,133.6, -78,1,07.09.2015,,346,133.6, -78,1,08.09.2015,,622,133.6, -78,1,09.09.2015,,371,135.51, -78,1,10.09.2015,,625,135.51, -78,1,11.09.2015,,575,135.51, -78,1,12.09.2015,,320,135.51, -78,1,13.09.2015,,358,135.51, -78,1,14.09.2015,,280,135.51, -78,1,15.09.2015,,490,135.51, -78,1,16.09.2015,,440,138.5, -78,1,17.09.2015,,242,138.5, -78,1,18.09.2015,,361,138.5, -78,1,19.09.2015,,223,138.5, -78,1,20.09.2015,,232,138.5, -78,1,21.09.2015,,298,138.5, -78,1,22.09.2015,,408,138.5, -78,1,23.09.2015,,295,141.21, -78,1,24.09.2015,,198,141.21, -78,1,25.09.2015,,223,141.21, -78,1,26.09.2015,,129,141.21, -78,1,27.09.2015,,283,141.21, -78,1,28.09.2015,,223,141.21, -78,1,29.09.2015,,204,141.21, -78,1,30.09.2015,,151,141.21, -78,1,01.10.2015,,160,141.21, -78,1,02.10.2015,,204,141.21, -78,1,03.10.2015,,188,141.21, -78,1,04.10.2015,,214,141.21, -78,1,05.10.2015,,305,141.21, -78,1,06.10.2015,1.0,503,141.21,132.92 -78,1,07.10.2015,1.0,289,141.21,130.21 -78,1,08.10.2015,1.0,496,141.11,130.21 -78,1,09.10.2015,1.0,525,141.21,130.21 -78,1,10.10.2015,1.0,368,141.21,130.21 -78,1,11.10.2015,1.0,361,141.21,130.21 -78,1,12.10.2015,1.0,399,141.21,130.21 -78,1,13.10.2015,,220,141.21, -78,1,14.10.2015,,113,141.21, -78,1,15.10.2015,,245,142.57, -78,1,16.10.2015,,151,142.57, -78,1,17.10.2015,,116,142.57, -78,1,18.10.2015,,79,142.57, -78,1,19.10.2015,,198,142.57, -78,1,20.10.2015,,154,142.57, -78,1,21.10.2015,,239,130.34, -78,1,22.10.2015,,267,130.34, -78,1,23.10.2015,,188,130.34, -78,1,24.10.2015,,232,130.34, -78,1,25.10.2015,,229,130.34, -78,1,26.10.2015,,251,130.34, -78,1,27.10.2015,1.0,317,130.34,122.32 -78,1,28.10.2015,1.0,377,130.34,124.77 -78,1,29.10.2015,1.0,556,130.34,124.77 -78,1,30.10.2015,1.0,377,130.34,124.77 -78,1,31.10.2015,1.0,556,130.34,124.77 -78,1,01.11.2015,1.0,493,130.34,124.77 -78,1,02.11.2015,1.0,377,130.34,124.77 -78,1,03.11.2015,,242,130.34, -78,1,04.11.2015,,280,134.69, -78,1,05.11.2015,,317,134.69, -78,1,06.11.2015,,179,134.69, -78,1,07.11.2015,,207,134.69, -78,1,08.11.2015,,123,134.69, -78,1,09.11.2015,,163,134.69, -78,1,10.11.2015,,173,134.69, -78,1,11.11.2015,,126,130.34, -78,1,12.11.2015,,330,130.34, -78,1,13.11.2015,,245,130.34, -78,1,14.11.2015,,220,130.34, -78,1,15.11.2015,,276,130.34, -78,1,16.11.2015,,154,130.34, -78,1,17.11.2015,,179,130.34, -78,1,18.11.2015,,195,130.34, -78,1,19.11.2015,,245,130.34, -78,1,20.11.2015,,157,130.34, -78,1,21.11.2015,,167,130.34, -78,1,22.11.2015,,204,130.34, -78,1,23.11.2015,,154,130.34, -78,1,24.11.2015,1.0,820,130.34,116.61 -78,1,25.11.2015,1.0,1024,130.34,116.61 -78,1,26.11.2015,1.0,1269,130.34,116.61 -78,1,27.11.2015,1.0,845,130.34,116.61 -78,1,28.11.2015,1.0,556,130.34,116.61 -78,1,29.11.2015,1.0,556,130.34,116.61 -78,1,30.11.2015,1.0,556,130.34,116.61 -78,1,01.12.2015,,201,130.34, -78,1,02.12.2015,,182,130.34, -78,1,03.12.2015,,176,130.34, -78,1,04.12.2015,,236,130.34, -78,1,05.12.2015,1.0,653,130.34,116.61 -78,1,06.12.2015,1.0,741,130.34,116.61 -78,1,07.12.2015,1.0,657,130.34,116.61 -78,1,08.12.2015,,364,130.34, -78,1,09.12.2015,,145,130.34, -78,1,10.12.2015,,204,130.34, -78,1,11.12.2015,,393,130.34, -78,1,12.12.2015,,141,130.34, -78,1,13.12.2015,,195,130.34, -78,1,14.12.2015,,185,130.34, -78,1,15.12.2015,,176,130.34, -78,1,16.12.2015,,214,130.34, -78,1,17.12.2015,,270,130.34, -78,1,18.12.2015,,254,130.34, -78,1,19.12.2015,,198,130.34, -78,1,20.12.2015,,273,130.34, -78,1,21.12.2015,,305,130.34, -78,1,22.12.2015,,270,130.34, -78,1,23.12.2015,,176,130.34, -78,1,24.12.2015,,217,126.54, -78,1,25.12.2015,,314,126.54, -78,1,26.12.2015,,295,126.54, -78,1,27.12.2015,,188,126.54, -78,1,28.12.2015,,336,126.54, -78,1,29.12.2015,,342,126.54, -78,1,30.12.2015,,374,126.54, -78,1,31.12.2015,,270,126.54, -78,1,01.01.2016,,69,126.54, -78,1,02.01.2016,,132,126.54, -78,1,03.01.2016,,220,126.54, -78,1,04.01.2016,,188,126.54, -78,1,05.01.2016,,358,126.54, -78,1,06.01.2016,,286,126.54, -78,1,07.01.2016,,185,126.54, -78,1,08.01.2016,,214,126.54, -78,1,09.01.2016,,286,126.54, -78,1,10.01.2016,,276,126.54, -78,1,11.01.2016,,390,126.54, -78,1,12.01.2016,,305,126.54, -78,1,13.01.2016,,374,126.54, -78,1,14.01.2016,,374,126.54, -78,1,15.01.2016,,399,126.54, -78,1,16.01.2016,,339,126.54, -78,1,17.01.2016,,229,126.54, -78,1,18.01.2016,,258,126.54, -78,1,19.01.2016,,232,126.54, -78,1,20.01.2016,,459,126.54, -78,1,21.01.2016,,396,127.9, -78,1,22.01.2016,,264,127.9, -78,1,23.01.2016,,254,127.9, -78,1,24.01.2016,,280,127.9, -78,1,25.01.2016,,349,127.9, -78,1,26.01.2016,,232,127.9, -78,1,27.01.2016,,192,127.9, -78,1,28.01.2016,,324,127.9, -78,1,29.01.2016,,399,127.9, -78,1,30.01.2016,,185,127.9, -78,1,31.01.2016,,412,127.9, -78,1,01.02.2016,,311,130.34, -78,1,02.02.2016,,264,130.34, -78,1,03.02.2016,,314,130.34, -78,1,04.02.2016,,192,133.06, -78,1,05.02.2016,,408,133.06, -78,1,06.02.2016,,254,133.06, -78,1,07.02.2016,,188,141.76, -78,1,08.02.2016,,349,141.76, -78,1,09.02.2016,1.0,763,141.76,130.75 -78,1,10.02.2016,1.0,1065,141.76,130.75 -78,1,11.02.2016,1.0,741,141.76,130.75 -78,1,12.02.2016,1.0,484,141.76,130.75 -78,1,13.02.2016,1.0,543,141.76,130.75 -78,1,14.02.2016,1.0,669,141.76,130.75 -78,1,15.02.2016,1.0,675,141.76,130.75 -78,1,16.02.2016,1.0,559,141.76,130.75 -78,1,17.02.2016,1.0,503,141.76,130.75 -78,1,18.02.2016,,327,141.76, -78,1,19.02.2016,,160,141.76, -78,1,20.02.2016,,226,141.76, -78,1,21.02.2016,,314,141.76, -78,1,22.02.2016,,207,141.76, -78,1,23.02.2016,,141,141.76, -78,1,24.02.2016,,239,141.76, -78,1,25.02.2016,,371,141.76, -78,1,26.02.2016,,248,141.76, -78,1,27.02.2016,,163,141.76, -78,1,28.02.2016,,236,141.76, -78,1,29.02.2016,,248,141.76, -78,1,01.03.2016,,245,141.76, -78,1,02.03.2016,,229,141.76, -78,1,03.03.2016,,258,141.76, -78,1,04.03.2016,,195,141.76, -78,1,05.03.2016,,254,141.21, -78,1,06.03.2016,,201,141.21, -78,1,07.03.2016,,245,141.21, -78,1,08.03.2016,1.0,487,141.21,132.92 -78,1,09.03.2016,1.0,346,141.21,132.92 -78,1,10.03.2016,1.0,490,141.21,132.92 -78,1,11.03.2016,1.0,534,141.21,132.92 -78,1,12.03.2016,1.0,317,141.21,132.92 -78,1,13.03.2016,1.0,679,141.21,132.92 -78,1,14.03.2016,1.0,578,141.21,132.92 -78,1,15.03.2016,1.0,729,141.21,130.21 -78,1,16.03.2016,1.0,691,141.21,130.21 -78,1,17.03.2016,1.0,440,141.21,130.21 -78,1,18.03.2016,1.0,490,141.21,130.21 -78,1,19.03.2016,,245,141.21, -78,1,20.03.2016,,214,141.21, -78,1,21.03.2016,,176,141.21, -78,1,22.03.2016,,217,141.21, -78,1,23.03.2016,,239,141.21, -78,1,24.03.2016,,145,141.21, -78,1,25.03.2016,,173,141.21, -78,1,26.03.2016,,192,141.21, -78,1,27.03.2016,,116,141.21, -78,1,28.03.2016,,119,141.21, -78,1,29.03.2016,,82,141.21, -78,1,30.03.2016,,145,141.21, -78,1,31.03.2016,,110,141.21, -78,1,01.04.2016,,138,141.21, -78,1,02.04.2016,,157,141.21, -78,1,03.04.2016,,173,141.21, -78,1,04.04.2016,,195,141.21, -78,1,05.04.2016,,207,141.21, -78,1,06.04.2016,,113,141.21, -78,1,07.04.2016,,276,141.21, -78,1,08.04.2016,,251,141.21, -78,1,09.04.2016,,138,141.21, -78,1,10.04.2016,,182,141.21, -78,1,11.04.2016,,145,141.21, -78,1,12.04.2016,,207,141.21, -78,1,13.04.2016,,232,141.21, -78,1,14.04.2016,,179,135.78, -78,1,15.04.2016,,207,135.78, -78,1,16.04.2016,,160,135.78, -78,1,17.04.2016,,126,135.78, -78,1,18.04.2016,,173,135.78, -78,1,19.04.2016,1.0,1087,135.78,122.32 -78,1,20.04.2016,1.0,540,135.78,122.32 -78,1,21.04.2016,1.0,729,135.78,122.32 -78,1,22.04.2016,1.0,719,135.78,122.32 -78,1,23.04.2016,1.0,556,135.78,122.32 -78,1,24.04.2016,1.0,572,135.78,122.32 -78,1,25.04.2016,1.0,597,135.78,122.32 -78,1,26.04.2016,,135,135.78, -78,1,27.04.2016,,163,135.78, -78,1,28.04.2016,,239,135.78, -78,1,29.04.2016,,308,131.16, -78,1,30.04.2016,,270,131.16, -78,1,01.05.2016,,107,131.16, -78,1,02.05.2016,,88,131.16, -78,1,03.05.2016,,151,131.16, -78,1,04.05.2016,,217,131.16, -78,1,05.05.2016,,267,131.16, -78,1,06.05.2016,,245,131.16, -78,1,07.05.2016,,69,131.16, -78,1,08.05.2016,,192,131.16, -78,1,09.05.2016,,91,131.16, -78,1,10.05.2016,,204,131.16, -78,1,11.05.2016,,167,131.16, -78,1,12.05.2016,,176,131.16, -78,1,13.05.2016,,192,128.98, -78,1,14.05.2016,,188,128.98, -78,1,15.05.2016,,188,128.98, -78,1,16.05.2016,,283,128.98, -78,1,17.05.2016,1.0,550,128.98,119.6 -78,1,18.05.2016,1.0,591,128.98,119.6 -78,1,19.05.2016,1.0,540,128.98,119.6 -78,1,20.05.2016,1.0,496,128.98,119.6 -78,1,21.05.2016,1.0,679,128.98,119.6 -78,1,22.05.2016,1.0,858,128.98,119.6 -79,1,31.03.2015,,201,131.97, -79,1,01.04.2015,,204,133.33, -79,1,02.04.2015,,85,133.33, -79,1,03.04.2015,,101,133.33, -79,1,04.04.2015,,69,133.33, -79,1,05.04.2015,,72,133.33, -79,1,06.04.2015,,132,133.33, -79,1,07.04.2015,1.0,525,133.33,119.33 -79,1,08.04.2015,1.0,770,133.33,119.33 -79,1,09.04.2015,1.0,729,133.33,119.33 -79,1,10.04.2015,1.0,694,133.33,119.33 -79,1,11.04.2015,1.0,456,133.33,119.33 -79,1,12.04.2015,1.0,145,133.33,119.33 -79,1,13.04.2015,1.0,280,133.33,119.33 -79,1,14.04.2015,,69,130.34, -79,1,15.04.2015,,107,130.34, -79,1,16.04.2015,,72,130.34, -79,1,17.04.2015,,66,138.5, -79,1,18.04.2015,,53,138.5, -79,1,19.04.2015,,44,138.5, -79,1,20.04.2015,,60,138.5, -79,1,21.04.2015,,88,138.5, -79,1,22.04.2015,,85,128.98, -79,1,23.04.2015,,85,128.98, -79,1,24.04.2015,,126,128.98, -79,1,25.04.2015,,160,128.98, -79,1,26.04.2015,,79,128.98, -79,1,27.04.2015,,113,128.98, -79,1,28.04.2015,,82,128.98, -79,1,29.04.2015,,91,131.7, -79,1,30.04.2015,,94,131.7, -79,1,01.05.2015,,60,131.7, -79,1,02.05.2015,,75,131.7, -79,1,03.05.2015,,63,131.7, -79,1,04.05.2015,,91,131.7, -79,1,05.05.2015,,69,131.7, -79,1,06.05.2015,,91,131.7, -79,1,07.05.2015,,126,131.7, -79,1,08.05.2015,,69,131.7, -79,1,09.05.2015,,57,131.7, -79,1,10.05.2015,,53,131.7, -79,1,11.05.2015,,69,131.7, -79,1,12.05.2015,,66,131.7, -79,1,13.05.2015,,53,131.7, -79,1,14.05.2015,,72,131.7, -79,1,15.05.2015,,138,131.7, -79,1,16.05.2015,,138,131.7, -79,1,17.05.2015,,116,131.7, -79,1,18.05.2015,,94,131.7, -79,1,19.05.2015,1.0,276,131.7,119.33 -79,1,20.05.2015,1.0,434,131.7,119.33 -79,1,21.05.2015,1.0,295,131.7,119.33 -79,1,22.05.2015,1.0,248,131.7,119.33 -79,1,23.05.2015,1.0,289,131.7,119.33 -79,1,24.05.2015,1.0,280,131.7,119.33 -79,1,25.05.2015,1.0,204,131.7,119.33 -79,1,26.05.2015,,57,131.7, -79,1,27.05.2015,,60,129.25, -79,1,28.05.2015,,69,129.25, -79,1,29.05.2015,,116,129.25, -79,1,30.05.2015,,63,129.25, -79,1,31.05.2015,,79,129.25, -79,1,01.06.2015,,79,129.25, -79,1,02.06.2015,,79,129.25, -79,1,03.06.2015,,88,129.25, -79,1,04.06.2015,,116,129.25, -79,1,05.06.2015,,123,129.25, -79,1,06.06.2015,,141,131.7, -79,1,07.06.2015,,91,131.7, -79,1,08.06.2015,,116,131.7, -79,1,09.06.2015,1.0,327,131.7,119.33 -79,1,10.06.2015,1.0,478,131.7,119.33 -79,1,11.06.2015,1.0,496,131.7,119.33 -79,1,12.06.2015,1.0,273,131.7,119.33 -79,1,13.06.2015,1.0,232,131.7,119.33 -79,1,14.06.2015,1.0,210,131.7,119.33 -79,1,15.06.2015,1.0,286,131.7,119.33 -79,1,16.06.2015,,85,131.7, -79,1,17.06.2015,,135,131.7, -79,1,18.06.2015,,110,128.17, -79,1,19.06.2015,,85,128.17, -79,1,20.06.2015,,157,128.17, -79,1,21.06.2015,,151,128.17, -79,1,22.06.2015,,138,128.17, -79,1,23.06.2015,,110,128.17, -79,1,24.06.2015,,113,128.17, -79,1,25.06.2015,,179,128.17, -79,1,26.06.2015,,160,128.17, -79,1,27.06.2015,,123,128.17, 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-79,1,28.08.2015,,220,137.68, -79,1,29.08.2015,,390,137.68, -79,1,30.08.2015,,358,137.68, -79,1,31.08.2015,,210,137.68, -79,1,01.09.2015,,273,137.68, -79,1,02.09.2015,,273,135.78, -79,1,03.09.2015,,412,135.78, -79,1,04.09.2015,,396,135.78, -79,1,05.09.2015,,223,133.6, -79,1,06.09.2015,,248,133.6, -79,1,07.09.2015,,342,133.6, -79,1,08.09.2015,,314,133.6, -79,1,09.09.2015,,201,135.51, -79,1,10.09.2015,,289,135.51, -79,1,11.09.2015,,210,135.51, -79,1,12.09.2015,,305,135.51, -79,1,13.09.2015,,289,135.51, -79,1,14.09.2015,,339,135.51, -79,1,15.09.2015,,261,135.51, -79,1,16.09.2015,,163,138.5, -79,1,17.09.2015,,154,138.5, -79,1,18.09.2015,,223,138.5, -79,1,19.09.2015,,173,138.5, -79,1,20.09.2015,,198,138.5, -79,1,21.09.2015,,148,138.5, -79,1,22.09.2015,,204,138.5, -79,1,23.09.2015,,182,141.21, -79,1,24.09.2015,,167,141.21, -79,1,25.09.2015,,210,141.21, -79,1,26.09.2015,,167,141.21, -79,1,27.09.2015,,132,141.21, -79,1,28.09.2015,,148,141.21, -79,1,29.09.2015,,145,141.21, -79,1,30.09.2015,,176,141.21, -79,1,01.10.2015,,226,141.21, -79,1,02.10.2015,,141,141.21, -79,1,03.10.2015,,104,141.21, -79,1,04.10.2015,,119,141.21, -79,1,05.10.2015,,129,141.21, -79,1,06.10.2015,1.0,311,141.21,132.92 -79,1,07.10.2015,1.0,383,141.21,130.21 -79,1,08.10.2015,1.0,456,141.11,130.21 -79,1,09.10.2015,1.0,591,141.21,130.21 -79,1,10.10.2015,1.0,509,141.21,130.21 -79,1,11.10.2015,1.0,349,141.21,130.21 -79,1,12.10.2015,1.0,459,141.21,130.21 -79,1,13.10.2015,,151,141.21, -79,1,14.10.2015,,157,141.21, -79,1,15.10.2015,,119,142.57, -79,1,16.10.2015,,145,142.57, -79,1,17.10.2015,,129,142.57, -79,1,18.10.2015,,129,142.57, -79,1,19.10.2015,,110,142.57, -79,1,20.10.2015,,132,142.57, -79,1,21.10.2015,,305,130.34, -79,1,22.10.2015,,276,130.34, -79,1,23.10.2015,,283,130.34, -79,1,24.10.2015,,251,130.34, -79,1,25.10.2015,,276,130.34, -79,1,26.10.2015,,188,130.34, -79,1,27.10.2015,1.0,638,130.34,122.32 -79,1,28.10.2015,1.0,647,130.34,124.77 -79,1,29.10.2015,1.0,478,130.34,124.77 -79,1,30.10.2015,1.0,364,130.34,124.77 -79,1,31.10.2015,1.0,559,130.34,124.77 -79,1,01.11.2015,1.0,478,130.34,124.77 -79,1,02.11.2015,1.0,349,130.34,124.77 -79,1,03.11.2015,,123,130.34, -79,1,04.11.2015,,154,134.69, -79,1,05.11.2015,,104,134.69, -79,1,06.11.2015,,135,134.69, -79,1,07.11.2015,,179,134.69, -79,1,08.11.2015,,101,134.69, -79,1,09.11.2015,,154,134.69, -79,1,10.11.2015,,182,134.69, -79,1,11.11.2015,,207,130.34, -79,1,12.11.2015,,210,130.34, -79,1,13.11.2015,,185,130.34, -79,1,14.11.2015,,201,130.34, -79,1,15.11.2015,,207,130.34, -79,1,16.11.2015,,167,130.34, -79,1,17.11.2015,,173,130.34, -79,1,18.11.2015,,141,130.34, -79,1,19.11.2015,,179,130.34, -79,1,20.11.2015,,226,130.34, -79,1,21.11.2015,,188,130.34, -79,1,22.11.2015,,148,130.34, -79,1,23.11.2015,,264,130.34, -79,1,24.11.2015,1.0,1565,130.34,116.61 -79,1,25.11.2015,1.0,1737,130.34,116.61 -79,1,26.11.2015,1.0,829,130.34,116.61 -79,1,27.11.2015,1.0,644,130.34,116.61 -79,1,28.11.2015,1.0,644,130.34,116.61 -79,1,29.11.2015,1.0,644,130.34,116.61 -79,1,30.11.2015,1.0,1596,130.34,116.61 -79,1,01.12.2015,,258,130.34, -79,1,02.12.2015,,195,130.34, -79,1,03.12.2015,,188,130.34, -79,1,04.12.2015,,154,130.34, -79,1,05.12.2015,1.0,1012,130.34,116.61 -79,1,06.12.2015,1.0,776,130.34,116.61 -79,1,07.12.2015,1.0,968,130.34,116.61 -79,1,08.12.2015,,239,130.34, -79,1,09.12.2015,,160,130.34, -79,1,10.12.2015,,217,130.34, -79,1,11.12.2015,,148,130.34, -79,1,12.12.2015,,173,130.34, -79,1,13.12.2015,,126,130.34, -79,1,14.12.2015,,195,130.34, -79,1,15.12.2015,,151,130.34, -79,1,16.12.2015,,204,130.34, -79,1,17.12.2015,,163,130.34, -79,1,18.12.2015,,145,130.34, -79,1,19.12.2015,,270,130.34, -79,1,20.12.2015,,119,130.34, -79,1,21.12.2015,,204,130.34, -79,1,22.12.2015,,188,130.34, -79,1,23.12.2015,,110,130.34, -79,1,24.12.2015,,302,126.54, -79,1,25.12.2015,,443,126.54, -79,1,26.12.2015,,185,126.54, -79,1,27.12.2015,,201,126.54, -79,1,28.12.2015,,258,126.54, -79,1,29.12.2015,,210,126.54, -79,1,30.12.2015,,254,126.54, -79,1,31.12.2015,,151,126.54, -79,1,01.01.2016,,85,126.54, -79,1,02.01.2016,,88,126.54, -79,1,03.01.2016,,145,126.54, -79,1,04.01.2016,,129,126.54, -79,1,05.01.2016,,264,126.54, -79,1,06.01.2016,,289,126.54, -79,1,07.01.2016,,163,126.54, -79,1,08.01.2016,,176,126.54, -79,1,09.01.2016,,258,126.54, -79,1,10.01.2016,,198,126.54, -79,1,11.01.2016,,311,126.54, -79,1,12.01.2016,,346,126.54, -79,1,13.01.2016,,324,126.54, -79,1,14.01.2016,,226,126.54, -79,1,15.01.2016,,302,126.54, -79,1,16.01.2016,,267,126.54, -79,1,17.01.2016,,195,126.54, -79,1,18.01.2016,,418,126.54, -79,1,19.01.2016,,286,126.54, -79,1,20.01.2016,,349,126.54, -79,1,21.01.2016,,330,127.9, -79,1,22.01.2016,,302,127.9, -79,1,23.01.2016,,273,127.9, -79,1,24.01.2016,,251,127.9, -79,1,25.01.2016,,399,127.9, -79,1,26.01.2016,,346,127.9, -79,1,27.01.2016,,330,127.9, -79,1,28.01.2016,,565,127.9, -79,1,29.01.2016,,364,127.9, -79,1,30.01.2016,,311,127.9, -79,1,31.01.2016,,346,127.9, -79,1,01.02.2016,,204,130.34, -79,1,02.02.2016,,424,130.34, -79,1,03.02.2016,,258,130.34, -79,1,04.02.2016,,207,133.06, -79,1,05.02.2016,,232,133.06, -79,1,06.02.2016,,248,133.06, -79,1,07.02.2016,,188,141.76, -79,1,08.02.2016,,160,141.76, -79,1,09.02.2016,1.0,465,141.76,130.75 -79,1,10.02.2016,1.0,396,141.76,130.75 -79,1,11.02.2016,1.0,496,141.76,130.75 -79,1,12.02.2016,1.0,565,141.76,130.75 -79,1,13.02.2016,1.0,339,141.76,130.75 -79,1,14.02.2016,1.0,430,141.76,130.75 -79,1,15.02.2016,1.0,562,141.76,130.75 -79,1,16.02.2016,1.0,399,141.76,130.75 -79,1,17.02.2016,1.0,418,141.76,130.75 -79,1,18.02.2016,,198,141.76, -79,1,19.02.2016,,135,141.76, -79,1,20.02.2016,,192,141.76, -79,1,21.02.2016,,270,141.76, -79,1,22.02.2016,,110,141.76, -79,1,23.02.2016,,145,141.76, -79,1,24.02.2016,,138,141.76, -79,1,25.02.2016,,179,141.76, -79,1,26.02.2016,,167,141.76, -79,1,27.02.2016,,185,141.76, -79,1,28.02.2016,,179,141.76, -79,1,29.02.2016,,232,141.76, -79,1,01.03.2016,,248,141.76, -79,1,02.03.2016,,160,141.76, -79,1,03.03.2016,,192,141.76, -79,1,04.03.2016,,195,141.76, -79,1,05.03.2016,,308,141.21, -79,1,06.03.2016,,195,141.21, -79,1,07.03.2016,,182,141.21, -79,1,08.03.2016,1.0,346,141.21,132.92 -79,1,09.03.2016,1.0,723,141.21,132.92 -79,1,10.03.2016,1.0,685,141.21,132.92 -79,1,11.03.2016,1.0,1147,141.21,132.92 -79,1,12.03.2016,1.0,603,141.21,132.92 -79,1,13.03.2016,1.0,314,141.21,132.92 -79,1,14.03.2016,1.0,855,141.21,132.92 -79,1,15.03.2016,1.0,748,141.21,130.21 -79,1,16.03.2016,1.0,760,141.21,130.21 -79,1,17.03.2016,1.0,679,141.21,130.21 -79,1,18.03.2016,1.0,848,141.21,130.21 -79,1,19.03.2016,1.0,735,141.21,130.21 -79,1,20.03.2016,,179,141.21, -79,1,21.03.2016,,179,141.21, -79,1,22.03.2016,,192,141.21, -79,1,23.03.2016,,160,141.21, -79,1,24.03.2016,,119,141.21, -79,1,25.03.2016,,214,141.21, -79,1,26.03.2016,,151,141.21, -79,1,27.03.2016,,135,141.21, -79,1,28.03.2016,,126,141.21, -79,1,29.03.2016,,151,141.21, -79,1,30.03.2016,,163,141.21, -79,1,31.03.2016,,176,141.21, -79,1,01.04.2016,,198,141.21, -79,1,02.04.2016,,198,141.21, -79,1,03.04.2016,,119,141.21, -79,1,04.04.2016,,195,141.21, -79,1,05.04.2016,,195,141.21, -79,1,06.04.2016,,182,141.21, -79,1,07.04.2016,,201,141.21, -79,1,08.04.2016,,217,141.21, -79,1,09.04.2016,,145,141.21, -79,1,10.04.2016,,145,141.21, -79,1,11.04.2016,,185,141.21, -79,1,12.04.2016,,176,141.21, -79,1,13.04.2016,,145,141.21, -79,1,14.04.2016,,270,135.78, -79,1,15.04.2016,,261,135.78, -79,1,16.04.2016,,217,135.78, -79,1,17.04.2016,,242,135.78, -79,1,18.04.2016,,311,135.78, -79,1,19.04.2016,1.0,1549,135.78,122.32 -79,1,20.04.2016,1.0,2218,135.78,122.32 -79,1,21.04.2016,1.0,1612,135.78,122.32 -79,1,22.04.2016,1.0,1473,135.78,122.32 -79,1,23.04.2016,1.0,726,135.78,122.32 -79,1,24.04.2016,1.0,936,135.78,122.32 -79,1,25.04.2016,1.0,1860,135.78,122.32 -79,1,26.04.2016,,141,135.78, -79,1,27.04.2016,,210,135.78, -79,1,28.04.2016,,154,135.78, -79,1,29.04.2016,,160,131.16, -79,1,30.04.2016,,188,131.16, 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-81,1,26.12.2015,,113,126.54, -81,1,27.12.2015,,60,126.54, -81,1,28.12.2015,,69,126.54, -81,1,29.12.2015,,82,126.54, -81,1,30.12.2015,,82,126.54, -81,1,31.12.2015,,85,126.54, -81,1,01.01.2016,,22,126.54, -81,1,02.01.2016,,25,126.54, -81,1,03.01.2016,,85,126.54, -81,1,04.01.2016,,82,126.54, -81,1,05.01.2016,,57,126.54, -81,1,06.01.2016,,101,126.54, -81,1,07.01.2016,,82,126.54, -81,1,08.01.2016,,69,126.54, -81,1,09.01.2016,,148,126.54, -81,1,10.01.2016,,138,126.54, -81,1,11.01.2016,,176,126.54, -81,1,12.01.2016,,170,126.54, -81,1,13.01.2016,,170,126.54, -81,1,14.01.2016,,163,126.54, -81,1,15.01.2016,,185,126.54, -81,1,16.01.2016,,167,126.54, -81,1,17.01.2016,,201,126.54, -81,1,18.01.2016,,270,126.54, -81,1,19.01.2016,,151,126.54, -81,1,20.01.2016,,207,126.54, -81,1,21.01.2016,,254,127.9, -81,1,22.01.2016,,226,127.9, -81,1,23.01.2016,,119,127.9, -81,1,24.01.2016,,176,127.9, -81,1,25.01.2016,,173,127.9, -81,1,26.01.2016,,157,127.9, -81,1,27.01.2016,,151,127.9, -81,1,28.01.2016,,138,127.9, -81,1,29.01.2016,,101,127.9, -81,1,30.01.2016,,123,127.9, -81,1,31.01.2016,,157,127.9, -81,1,01.02.2016,,101,130.34, -81,1,02.02.2016,,107,130.34, -81,1,03.02.2016,,72,130.34, -81,1,04.02.2016,,119,133.06, -81,1,05.02.2016,,119,133.06, -81,1,06.02.2016,,132,133.06, -81,1,07.02.2016,,126,141.76, -81,1,08.02.2016,,119,141.76, -81,1,09.02.2016,1.0,135,141.76,130.75 -81,1,10.02.2016,1.0,195,141.76,130.75 -81,1,11.02.2016,1.0,192,141.76,130.75 -81,1,12.02.2016,1.0,176,141.76,130.75 -81,1,13.02.2016,1.0,220,141.76,130.75 -81,1,14.02.2016,1.0,179,141.76,130.75 -81,1,15.02.2016,1.0,264,141.76,130.75 -81,1,16.02.2016,1.0,154,141.76,130.75 -81,1,17.02.2016,1.0,129,141.76,130.75 -81,1,18.02.2016,,119,141.76, -81,1,19.02.2016,,72,141.76, -81,1,20.02.2016,,94,141.76, -81,1,21.02.2016,,101,141.76, -81,1,22.02.2016,,104,141.76, -81,1,23.02.2016,,47,141.76, -81,1,24.02.2016,,63,141.76, -81,1,25.02.2016,,91,141.76, -81,1,26.02.2016,,41,141.76, -81,1,27.02.2016,,97,141.76, -81,1,28.02.2016,,119,141.76, 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-81,1,30.04.2016,,135,131.16, -81,1,01.05.2016,,97,131.16, -81,1,02.05.2016,,104,131.16, -81,1,03.05.2016,,101,131.16, -81,1,04.05.2016,,69,131.16, -81,1,05.05.2016,,75,131.16, -81,1,06.05.2016,,97,131.16, -81,1,07.05.2016,,53,131.16, -81,1,08.05.2016,,82,131.16, -81,1,09.05.2016,,66,131.16, -81,1,10.05.2016,,113,131.16, -81,1,11.05.2016,,57,131.16, -81,1,12.05.2016,,107,131.16, -81,1,13.05.2016,,151,128.98, -81,1,14.05.2016,,101,128.98, -81,1,15.05.2016,,119,128.98, -81,1,16.05.2016,,91,128.98, -81,1,17.05.2016,1.0,569,128.98,119.6 -81,1,18.05.2016,1.0,704,128.98,119.6 -81,1,19.05.2016,1.0,578,128.98,119.6 -81,1,20.05.2016,1.0,443,128.98,119.6 -81,1,21.05.2016,1.0,399,128.98,119.6 -81,1,22.05.2016,1.0,210,128.98,119.6 -82,1,23.07.2015,,245,127.62, -82,1,24.07.2015,,308,127.62, -82,1,25.07.2015,,173,127.62, -82,1,26.07.2015,,251,130.07, -82,1,27.07.2015,,132,130.07, -82,1,28.07.2015,1.0,402,130.07,124.77 -82,1,29.07.2015,1.0,245,130.07,124.77 -82,1,30.07.2015,1.0,188,130.07,124.77 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-82,1,07.01.2016,,69,126.54, -82,1,08.01.2016,,91,126.54, -82,1,09.01.2016,,101,126.54, -82,1,10.01.2016,,97,126.54, -82,1,11.01.2016,,132,126.54, -82,1,12.01.2016,,167,126.54, -82,1,13.01.2016,,119,126.54, -82,1,14.01.2016,,160,126.54, -82,1,15.01.2016,,163,126.54, -82,1,16.01.2016,,163,126.54, -82,1,17.01.2016,,129,126.54, -82,1,18.01.2016,,132,126.54, -82,1,19.01.2016,,258,126.54, -82,1,20.01.2016,,317,126.54, -82,1,21.01.2016,,176,127.9, -82,1,22.01.2016,,182,127.9, -82,1,23.01.2016,,123,127.9, -82,1,24.01.2016,,138,127.9, -82,1,25.01.2016,,210,127.9, -82,1,26.01.2016,,141,127.9, -82,1,27.01.2016,,226,127.9, -82,1,28.01.2016,,192,127.9, -82,1,29.01.2016,,283,127.9, -82,1,30.01.2016,,217,127.9, -82,1,31.01.2016,,192,127.9, -82,1,01.02.2016,,267,130.34, -82,1,02.02.2016,,220,130.34, -82,1,03.02.2016,,154,130.34, -82,1,04.02.2016,,132,133.06, -82,1,05.02.2016,,148,133.06, -82,1,06.02.2016,,188,133.06, -82,1,07.02.2016,,113,141.76, -82,1,08.02.2016,,116,141.76, 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-82,1,09.04.2016,,88,141.21, -82,1,10.04.2016,,101,141.21, -82,1,11.04.2016,,91,141.21, -82,1,12.04.2016,,72,141.21, -82,1,13.04.2016,,75,141.21, -82,1,14.04.2016,,148,135.78, -82,1,15.04.2016,,126,135.78, -82,1,16.04.2016,,132,135.78, -82,1,17.04.2016,,154,135.78, -82,1,18.04.2016,,154,135.78, -82,1,19.04.2016,1.0,210,135.78,122.32 -82,1,20.04.2016,1.0,248,135.78,122.32 -82,1,21.04.2016,1.0,358,135.78,122.32 -82,1,22.04.2016,1.0,236,135.78,122.32 -82,1,23.04.2016,1.0,412,135.78,122.32 -82,1,24.04.2016,1.0,311,135.78,122.32 -82,1,25.04.2016,1.0,427,135.78,122.32 -82,1,26.04.2016,,47,135.78, -82,1,27.04.2016,,107,135.78, -82,1,28.04.2016,,94,135.78, -82,1,29.04.2016,,145,131.16, -82,1,30.04.2016,,97,131.16, -82,1,01.05.2016,,28,131.16, -82,1,02.05.2016,,63,131.16, -82,1,03.05.2016,,53,131.16, -82,1,04.05.2016,,66,131.16, -82,1,05.05.2016,,91,131.16, -82,1,06.05.2016,,69,131.16, -82,1,07.05.2016,,63,131.16, -82,1,08.05.2016,,79,131.16, -82,1,09.05.2016,,60,131.16, -82,1,10.05.2016,,75,131.16, -82,1,11.05.2016,,107,131.16, -82,1,12.05.2016,,119,131.16, -82,1,13.05.2016,,88,128.98, -82,1,14.05.2016,,79,128.98, -82,1,15.05.2016,,113,128.98, -82,1,16.05.2016,,132,128.98, -82,1,17.05.2016,1.0,248,128.98,119.6 -82,1,18.05.2016,1.0,355,128.98,119.6 -82,1,19.05.2016,1.0,223,128.98,119.6 -82,1,20.05.2016,1.0,176,128.98,119.6 -82,1,21.05.2016,1.0,236,128.98,119.6 -82,1,22.05.2016,1.0,148,128.98,119.6 -83,1,24.04.2015,,198,128.98, -83,1,25.04.2015,,261,128.98, -83,1,26.04.2015,,138,128.98, -83,1,27.04.2015,,126,128.98, -83,1,28.04.2015,,94,128.98, -83,1,29.04.2015,,75,131.7, -83,1,30.04.2015,,129,131.7, -83,1,01.05.2015,,72,131.7, -83,1,02.05.2015,,104,131.7, -83,1,03.05.2015,,66,131.7, -83,1,04.05.2015,,69,131.7, -83,1,05.05.2015,,126,131.7, -83,1,06.05.2015,,69,131.7, -83,1,07.05.2015,,182,131.7, -83,1,08.05.2015,,41,131.7, -83,1,09.05.2015,,22,131.7, -83,1,10.05.2015,,22,131.7, -83,1,11.05.2015,,38,131.7, -83,1,12.05.2015,,66,131.7, -83,1,13.05.2015,,35,131.7, -83,1,14.05.2015,,104,131.7, -83,1,15.05.2015,,135,131.7, -83,1,16.05.2015,,97,131.7, -83,1,17.05.2015,,47,131.7, -83,1,18.05.2015,,91,131.7, -83,1,19.05.2015,1.0,377,131.7,119.33 -83,1,20.05.2015,1.0,305,131.7,119.33 -83,1,21.05.2015,1.0,311,131.7,119.33 -83,1,22.05.2015,1.0,286,131.7,119.33 -83,1,23.05.2015,1.0,132,131.7,119.33 -83,1,24.05.2015,1.0,170,131.7,119.33 -83,1,25.05.2015,1.0,264,131.7,119.33 -83,1,26.05.2015,,63,131.7, -83,1,27.05.2015,,60,129.25, -83,1,28.05.2015,,97,129.25, -83,1,29.05.2015,,44,129.25, -83,1,30.05.2015,,41,129.25, -83,1,31.05.2015,,66,129.25, -83,1,01.06.2015,,57,129.25, -83,1,02.06.2015,,44,129.25, -83,1,03.06.2015,,63,129.25, -83,1,04.06.2015,,66,129.25, -83,1,05.06.2015,,47,129.25, -83,1,06.06.2015,,50,131.7, -83,1,07.06.2015,,53,131.7, -83,1,08.06.2015,,69,131.7, -83,1,09.06.2015,1.0,198,131.7,119.33 -83,1,10.06.2015,1.0,317,131.7,119.33 -83,1,11.06.2015,1.0,760,131.7,119.33 -83,1,12.06.2015,1.0,239,131.7,119.33 -83,1,13.06.2015,1.0,129,131.7,119.33 -83,1,14.06.2015,1.0,135,131.7,119.33 -83,1,15.06.2015,1.0,185,131.7,119.33 -83,1,16.06.2015,,69,131.7, -83,1,17.06.2015,,79,131.7, -83,1,18.06.2015,,132,128.17, -83,1,19.06.2015,,94,128.17, -83,1,20.06.2015,,79,128.17, -83,1,21.06.2015,,104,128.17, -83,1,22.06.2015,,107,128.17, -83,1,23.06.2015,,104,128.17, -83,1,24.06.2015,,160,128.17, -83,1,25.06.2015,,132,128.17, -83,1,26.06.2015,,179,128.17, -83,1,27.06.2015,,72,128.17, -83,1,28.06.2015,,135,130.34, -83,1,29.06.2015,,151,130.34, -83,1,30.06.2015,1.0,201,130.34,119.33 -83,1,01.07.2015,1.0,302,130.34,119.33 -83,1,02.07.2015,1.0,163,130.34,119.33 -83,1,03.07.2015,1.0,531,130.34,119.33 -83,1,04.07.2015,1.0,258,130.34,119.33 -83,1,05.07.2015,1.0,358,130.34,119.33 -83,1,06.07.2015,1.0,446,130.34,119.33 -83,1,07.07.2015,,129,130.34, -83,1,08.07.2015,,160,130.34, -83,1,09.07.2015,,138,121.64, -83,1,10.07.2015,,188,121.64, -83,1,11.07.2015,,141,123.55, -83,1,12.07.2015,,167,123.55, -83,1,13.07.2015,,204,123.55, -83,1,14.07.2015,,236,123.55, -83,1,15.07.2015,,232,125.45, -83,1,16.07.2015,,311,125.45, -83,1,17.07.2015,,236,125.45, -83,1,18.07.2015,,214,127.62, -83,1,19.07.2015,,245,127.62, -83,1,20.07.2015,,333,127.62, -83,1,21.07.2015,,342,127.62, -83,1,22.07.2015,,292,127.62, -83,1,23.07.2015,,264,127.62, -83,1,24.07.2015,,207,127.62, -83,1,25.07.2015,,314,127.62, -83,1,26.07.2015,,468,130.07, -83,1,27.07.2015,,295,130.07, -83,1,28.07.2015,1.0,553,130.07,124.77 -83,1,29.07.2015,1.0,493,130.07,124.77 -83,1,30.07.2015,1.0,298,130.07,124.77 -83,1,31.07.2015,1.0,421,130.07,124.77 -83,1,01.08.2015,1.0,380,130.07,124.77 -83,1,02.08.2015,1.0,380,130.07,124.77 -83,1,03.08.2015,1.0,509,130.07,124.77 -83,1,04.08.2015,,141,138.5, -83,1,05.08.2015,,201,138.5, -83,1,06.08.2015,,283,138.5, -83,1,07.08.2015,,201,138.5, -83,1,08.08.2015,,220,141.21, -83,1,09.08.2015,,176,141.21, -83,1,10.08.2015,,204,141.21, -83,1,11.08.2015,,192,141.21, -83,1,12.08.2015,,292,142.85, -83,1,13.08.2015,,141,142.85, -83,1,14.08.2015,,214,142.85, -83,1,15.08.2015,,179,142.85, -83,1,16.08.2015,,160,142.85, -83,1,17.08.2015,,295,142.85, -83,1,18.08.2015,,185,142.85, -83,1,19.08.2015,,148,142.85, -83,1,20.08.2015,,163,142.85, -83,1,21.08.2015,,176,142.85, -83,1,22.08.2015,,116,142.85, -83,1,23.08.2015,,167,142.85, -83,1,24.08.2015,,232,142.85, -83,1,25.08.2015,,248,142.85, -83,1,26.08.2015,,204,142.85, -83,1,27.08.2015,,201,137.68, -83,1,28.08.2015,,151,137.68, -83,1,29.08.2015,,220,137.68, -83,1,30.08.2015,,195,137.68, -83,1,31.08.2015,,160,137.68, -83,1,01.09.2015,,141,137.68, -83,1,02.09.2015,,251,135.78, -83,1,03.09.2015,,371,135.78, -83,1,04.09.2015,,229,135.78, -83,1,05.09.2015,,195,133.6, -83,1,06.09.2015,,173,133.6, -83,1,07.09.2015,,333,133.6, -83,1,08.09.2015,,302,133.6, -83,1,09.09.2015,,217,135.51, -83,1,10.09.2015,,295,135.51, -83,1,11.09.2015,,204,135.51, -83,1,12.09.2015,,185,135.51, -83,1,13.09.2015,,242,135.51, -83,1,14.09.2015,,192,135.51, -83,1,15.09.2015,,236,135.51, -83,1,16.09.2015,,157,138.5, -83,1,17.09.2015,,160,138.5, -83,1,18.09.2015,,207,138.5, -83,1,19.09.2015,,148,138.5, -83,1,20.09.2015,,217,138.5, -83,1,21.09.2015,,232,138.5, -83,1,22.09.2015,,195,138.5, -83,1,23.09.2015,,160,141.21, -83,1,24.09.2015,,182,141.21, -83,1,25.09.2015,,123,141.21, -83,1,26.09.2015,,104,141.21, -83,1,27.09.2015,,148,141.21, -83,1,28.09.2015,,157,141.21, -83,1,29.09.2015,,141,141.21, -83,1,30.09.2015,,179,141.21, -83,1,01.10.2015,,167,141.21, -83,1,02.10.2015,,110,141.21, -83,1,03.10.2015,,79,141.21, -83,1,04.10.2015,,135,141.21, -83,1,05.10.2015,,116,141.21, -83,1,06.10.2015,1.0,317,141.21,132.92 -83,1,07.10.2015,1.0,342,141.21,130.21 -83,1,08.10.2015,1.0,443,141.11,130.21 -83,1,09.10.2015,1.0,446,141.21,130.21 -83,1,10.10.2015,1.0,242,141.21,130.21 -83,1,11.10.2015,1.0,484,141.21,130.21 -83,1,12.10.2015,1.0,374,141.21,130.21 -83,1,13.10.2015,,129,141.21, -83,1,14.10.2015,,113,141.21, -83,1,15.10.2015,,141,142.57, -83,1,16.10.2015,,167,142.57, -83,1,17.10.2015,,104,142.57, -83,1,18.10.2015,,113,142.57, -83,1,19.10.2015,,116,142.57, -83,1,20.10.2015,,157,142.57, -83,1,21.10.2015,,138,130.34, -83,1,22.10.2015,,129,130.34, -83,1,23.10.2015,,412,130.34, -83,1,24.10.2015,,214,130.34, -83,1,25.10.2015,,358,130.34, -83,1,26.10.2015,,374,130.34, -83,1,27.10.2015,1.0,801,130.34,122.32 -83,1,28.10.2015,1.0,889,130.34,124.77 -83,1,29.10.2015,1.0,509,130.34,124.77 -83,1,30.10.2015,1.0,503,130.34,124.77 -83,1,31.10.2015,1.0,327,130.34,124.77 -83,1,01.11.2015,1.0,443,130.34,124.77 -83,1,02.11.2015,1.0,254,130.34,124.77 -83,1,03.11.2015,,217,130.34, -83,1,04.11.2015,,66,134.69, -83,1,05.11.2015,,126,134.69, -83,1,06.11.2015,,94,134.69, -83,1,07.11.2015,,72,134.69, -83,1,08.11.2015,,185,134.69, -83,1,09.11.2015,,107,134.69, -83,1,10.11.2015,,236,134.69, -83,1,11.11.2015,,167,130.34, -83,1,12.11.2015,,123,130.34, -83,1,13.11.2015,,223,130.34, -83,1,14.11.2015,,157,130.34, -83,1,15.11.2015,,123,130.34, -83,1,16.11.2015,,223,130.34, -83,1,17.11.2015,,188,130.34, -83,1,18.11.2015,,198,130.34, -83,1,19.11.2015,,151,130.34, -83,1,20.11.2015,,132,130.34, -83,1,21.11.2015,,154,130.34, -83,1,22.11.2015,,141,130.34, -83,1,23.11.2015,,160,130.34, -83,1,24.11.2015,1.0,1492,130.34,116.61 -83,1,25.11.2015,1.0,1301,130.34,116.61 -83,1,26.11.2015,1.0,1131,130.34,116.61 -83,1,27.11.2015,1.0,657,130.34,116.61 -83,1,28.11.2015,1.0,657,130.34,116.61 -83,1,29.11.2015,1.0,657,130.34,116.61 -83,1,30.11.2015,1.0,625,130.34,116.61 -83,1,01.12.2015,,85,130.34, -83,1,02.12.2015,,239,130.34, -83,1,03.12.2015,,154,130.34, -83,1,04.12.2015,,198,130.34, -83,1,05.12.2015,1.0,880,130.34,116.61 -83,1,06.12.2015,1.0,1128,130.34,116.61 -83,1,07.12.2015,1.0,911,130.34,116.61 -83,1,08.12.2015,,151,130.34, -83,1,09.12.2015,,188,130.34, -83,1,10.12.2015,,154,130.34, -83,1,11.12.2015,,148,130.34, -83,1,12.12.2015,,170,130.34, -83,1,13.12.2015,,198,130.34, -83,1,14.12.2015,,110,130.34, -83,1,15.12.2015,,160,130.34, -83,1,16.12.2015,,210,130.34, -83,1,17.12.2015,,179,130.34, -83,1,18.12.2015,,123,130.34, -83,1,19.12.2015,,151,130.34, -83,1,20.12.2015,,141,130.34, -83,1,21.12.2015,,126,130.34, -83,1,22.12.2015,,135,130.34, -83,1,23.12.2015,,138,130.34, -83,1,24.12.2015,,179,126.54, -83,1,25.12.2015,,179,126.54, -83,1,26.12.2015,,298,126.54, -83,1,27.12.2015,,154,126.54, -83,1,28.12.2015,,289,126.54, -83,1,29.12.2015,,167,126.54, -83,1,30.12.2015,,204,126.54, -83,1,31.12.2015,,254,126.54, -83,1,01.01.2016,,72,126.54, -83,1,02.01.2016,,141,126.54, -83,1,03.01.2016,,101,126.54, -83,1,04.01.2016,,173,126.54, -83,1,05.01.2016,,226,126.54, -83,1,06.01.2016,,226,126.54, -83,1,07.01.2016,,129,126.54, -83,1,08.01.2016,,286,126.54, -83,1,09.01.2016,,188,126.54, -83,1,10.01.2016,,182,126.54, -83,1,11.01.2016,,163,126.54, -83,1,12.01.2016,,270,126.54, -83,1,13.01.2016,,254,126.54, -83,1,14.01.2016,,289,126.54, -83,1,15.01.2016,,254,126.54, -83,1,16.01.2016,,280,126.54, -83,1,17.01.2016,,286,126.54, -83,1,18.01.2016,,330,126.54, -83,1,19.01.2016,,286,126.54, -83,1,20.01.2016,,327,126.54, -83,1,21.01.2016,,355,127.9, -83,1,22.01.2016,,210,127.9, -83,1,23.01.2016,,226,127.9, -83,1,24.01.2016,,267,127.9, -83,1,25.01.2016,,258,127.9, -83,1,26.01.2016,,308,127.9, -83,1,27.01.2016,,302,127.9, -83,1,28.01.2016,,245,127.9, -83,1,29.01.2016,,229,127.9, -83,1,30.01.2016,,204,127.9, -83,1,31.01.2016,,314,127.9, -83,1,01.02.2016,,173,130.34, -83,1,02.02.2016,,254,130.34, -83,1,03.02.2016,,198,130.34, -83,1,04.02.2016,,254,133.06, -83,1,05.02.2016,,232,133.06, -83,1,06.02.2016,,195,133.06, -83,1,07.02.2016,,160,141.76, -83,1,08.02.2016,,157,141.76, -83,1,09.02.2016,1.0,283,141.76,130.75 -83,1,10.02.2016,1.0,405,141.76,130.75 -83,1,11.02.2016,1.0,456,141.76,130.75 -83,1,12.02.2016,1.0,386,141.76,130.75 -83,1,13.02.2016,1.0,346,141.76,130.75 -83,1,14.02.2016,1.0,280,141.76,130.75 -83,1,15.02.2016,1.0,402,141.76,130.75 -83,1,16.02.2016,1.0,305,141.76,130.75 -83,1,17.02.2016,1.0,349,141.76,130.75 -83,1,18.02.2016,,204,141.76, -83,1,19.02.2016,,173,141.76, -83,1,20.02.2016,,107,141.76, -83,1,21.02.2016,,107,141.76, -83,1,22.02.2016,,145,141.76, -83,1,23.02.2016,,88,141.76, -83,1,24.02.2016,,69,141.76, -83,1,25.02.2016,,138,141.76, -83,1,26.02.2016,,167,141.76, -83,1,27.02.2016,,119,141.76, -83,1,28.02.2016,,157,141.76, -83,1,29.02.2016,,154,141.76, -83,1,01.03.2016,,138,141.76, -83,1,02.03.2016,,170,141.76, -83,1,03.03.2016,,160,141.76, -83,1,04.03.2016,,179,141.76, -83,1,05.03.2016,,214,141.21, -83,1,06.03.2016,,207,141.21, -83,1,07.03.2016,,173,141.21, -83,1,08.03.2016,1.0,248,141.21,132.92 -83,1,09.03.2016,1.0,500,141.21,132.92 -83,1,10.03.2016,1.0,528,141.21,132.92 -83,1,11.03.2016,1.0,534,141.21,132.92 -83,1,12.03.2016,1.0,660,141.21,132.92 -83,1,13.03.2016,1.0,434,141.21,132.92 -83,1,14.03.2016,1.0,446,141.21,132.92 -83,1,15.03.2016,1.0,584,141.21,130.21 -83,1,16.03.2016,1.0,669,141.21,130.21 -83,1,17.03.2016,1.0,600,141.21,130.21 -83,1,18.03.2016,1.0,471,141.21,130.21 -83,1,19.03.2016,1.0,484,141.21,130.21 -83,1,20.03.2016,,264,141.21, -83,1,21.03.2016,,223,141.21, -83,1,22.03.2016,,229,141.21, -83,1,23.03.2016,,126,141.21, -83,1,24.03.2016,,141,141.21, -83,1,25.03.2016,,129,141.21, -83,1,26.03.2016,,163,141.21, -83,1,27.03.2016,,163,141.21, -83,1,28.03.2016,,170,141.21, -83,1,29.03.2016,,88,141.21, -83,1,30.03.2016,,182,141.21, -83,1,31.03.2016,,107,141.21, -83,1,01.04.2016,,179,141.21, -83,1,02.04.2016,,94,141.21, -83,1,03.04.2016,,91,141.21, -83,1,04.04.2016,,126,141.21, -83,1,05.04.2016,,129,141.21, -83,1,06.04.2016,,107,141.21, -83,1,07.04.2016,,107,141.21, -83,1,08.04.2016,,132,141.21, -83,1,09.04.2016,,110,141.21, -83,1,10.04.2016,,119,141.21, -83,1,11.04.2016,,94,141.21, -83,1,12.04.2016,,148,141.21, -83,1,13.04.2016,,129,141.21, -83,1,14.04.2016,,239,135.78, -83,1,15.04.2016,,195,135.78, -83,1,16.04.2016,,173,135.78, -83,1,17.04.2016,,167,135.78, -83,1,18.04.2016,,195,135.78, -83,1,19.04.2016,1.0,971,135.78,122.32 -83,1,20.04.2016,1.0,1109,135.78,122.32 -83,1,21.04.2016,1.0,842,135.78,122.32 -83,1,22.04.2016,1.0,1008,135.78,122.32 -83,1,23.04.2016,1.0,170,135.78,122.32 -83,1,24.04.2016,1.0,603,135.78,122.32 -83,1,25.04.2016,1.0,556,135.78,122.32 -83,1,26.04.2016,,123,135.78, -83,1,27.04.2016,,163,135.78, -83,1,28.04.2016,,91,135.78, -83,1,29.04.2016,,129,131.16, -83,1,30.04.2016,,163,131.16, -83,1,01.05.2016,,79,131.16, -83,1,02.05.2016,,123,131.16, -83,1,03.05.2016,,160,131.16, -83,1,04.05.2016,,179,131.16, -83,1,05.05.2016,,145,131.16, -83,1,06.05.2016,,145,131.16, -83,1,07.05.2016,,113,131.16, -83,1,08.05.2016,,97,131.16, -83,1,09.05.2016,,107,131.16, -83,1,10.05.2016,,154,131.16, -83,1,11.05.2016,,141,131.16, -83,1,12.05.2016,,160,131.16, -83,1,13.05.2016,,336,128.98, -83,1,14.05.2016,,236,128.98, -83,1,15.05.2016,,201,128.98, -83,1,16.05.2016,,223,128.98, -83,1,17.05.2016,1.0,870,128.98,119.6 -83,1,18.05.2016,1.0,723,128.98,119.6 -83,1,19.05.2016,1.0,1090,128.98,119.6 -83,1,20.05.2016,1.0,773,128.98,119.6 -83,1,21.05.2016,1.0,694,128.98,119.6 -83,1,22.05.2016,1.0,952,128.98,119.6 -84,1,01.01.2015,,0,163.78, -84,1,02.01.2015,,6,163.78, -84,1,03.01.2015,,13,163.78, -84,1,04.01.2015,,35,163.78, -84,1,05.01.2015,,16,163.78, -84,1,06.01.2015,,9,163.78, -84,1,07.01.2015,,9,163.78, -84,1,08.01.2015,,13,163.78, -84,1,09.01.2015,,19,163.78, -84,1,10.01.2015,,9,163.78, -84,1,11.01.2015,,13,163.78, -84,1,12.01.2015,,19,163.78, -84,1,13.01.2015,,13,163.78, -84,1,14.01.2015,,16,168.4, -84,1,15.01.2015,,13,168.4, -84,1,16.01.2015,,9,168.4, -84,1,17.01.2015,,22,168.4, -84,1,18.01.2015,,9,168.4, -84,1,19.01.2015,,19,168.4, -84,1,20.01.2015,,13,168.4, -84,1,21.01.2015,,57,168.4, -84,1,22.01.2015,,13,168.4, -84,1,23.01.2015,,22,160.24, -84,1,24.01.2015,,31,160.24, -84,1,25.01.2015,,35,160.24, -84,1,26.01.2015,,6,160.24, -84,1,27.01.2015,,35,160.24, -84,1,28.01.2015,,38,146.92, -84,1,29.01.2015,,35,146.92, -84,1,30.01.2015,,35,146.92, -84,1,31.01.2015,,91,146.92, -84,1,01.02.2015,,25,146.92, -84,1,02.02.2015,,66,146.92, -84,1,03.02.2015,,41,146.92, -84,1,04.02.2015,,28,146.92, -84,1,05.02.2015,,19,146.92, -84,1,06.02.2015,,41,146.92, -84,1,07.02.2015,,31,146.92, -84,1,08.02.2015,,19,146.92, -84,1,09.02.2015,,38,146.92, -84,1,10.02.2015,,47,146.92, -84,1,11.02.2015,,53,146.92, -84,1,12.02.2015,,35,146.92, -84,1,13.02.2015,,38,149.37, -84,1,14.02.2015,,75,149.37, -84,1,15.02.2015,,63,149.37, -84,1,16.02.2015,,38,149.37, -84,1,17.02.2015,1.0,179,149.37,143.8 -84,1,18.02.2015,1.0,173,149.37,135.64 -84,1,19.02.2015,1.0,657,149.37,135.64 -84,1,20.02.2015,1.0,487,149.37,135.64 -84,1,21.02.2015,1.0,298,149.37,135.64 -84,1,22.02.2015,1.0,251,149.37,135.64 -84,1,23.02.2015,1.0,283,149.37,135.64 -84,1,24.02.2015,,72,149.37, -84,1,25.02.2015,,50,149.37, -84,1,26.02.2015,,157,149.37, -84,1,27.02.2015,,104,143.93, -84,1,28.02.2015,,154,143.93, -84,1,01.03.2015,,298,143.93, -84,1,02.03.2015,,151,143.93, -84,1,03.03.2015,1.0,88,143.93,138.36 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-84,1,08.09.2015,,85,133.6, -84,1,09.09.2015,,135,135.51, -84,1,10.09.2015,,292,135.51, -84,1,11.09.2015,,352,135.51, -84,1,12.09.2015,,283,135.51, -84,1,13.09.2015,,311,135.51, -84,1,14.09.2015,,305,135.51, -84,1,15.09.2015,,170,135.51, -84,1,16.09.2015,,198,138.5, -84,1,17.09.2015,,85,138.5, -84,1,18.09.2015,,349,138.5, -84,1,19.09.2015,,107,138.5, -84,1,20.09.2015,,270,138.5, -84,1,21.09.2015,,167,138.5, -84,1,22.09.2015,,104,138.5, -84,1,23.09.2015,,107,141.21, -84,1,24.09.2015,,82,141.21, -84,1,25.09.2015,,148,141.21, -84,1,26.09.2015,,88,141.21, -84,1,27.09.2015,,69,141.21, -84,1,28.09.2015,,110,141.21, -84,1,29.09.2015,,72,141.21, -84,1,30.09.2015,,119,141.21, -84,1,01.10.2015,,138,141.21, -84,1,02.10.2015,,79,141.21, -84,1,03.10.2015,,66,141.21, -84,1,04.10.2015,,50,141.21, -84,1,05.10.2015,,57,141.21, -84,1,06.10.2015,1.0,182,141.21,132.92 -84,1,07.10.2015,1.0,421,141.21,130.21 -84,1,08.10.2015,1.0,364,141.11,130.21 -84,1,09.10.2015,1.0,456,141.21,130.21 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-84,1,09.12.2015,,104,130.34, -84,1,10.12.2015,,50,130.34, -84,1,11.12.2015,,82,130.34, -84,1,12.12.2015,,110,130.34, -84,1,13.12.2015,,182,130.34, -84,1,14.12.2015,,50,130.34, -84,1,15.12.2015,,82,130.34, -84,1,16.12.2015,,135,130.34, -84,1,17.12.2015,,123,130.34, -84,1,18.12.2015,,75,130.34, -84,1,19.12.2015,,123,130.34, -84,1,20.12.2015,,91,130.34, -84,1,21.12.2015,,145,130.34, -84,1,22.12.2015,,123,130.34, -84,1,23.12.2015,,63,130.34, -84,1,24.12.2015,,101,126.54, -84,1,25.12.2015,,226,126.54, -84,1,26.12.2015,,170,126.54, -84,1,27.12.2015,,198,126.54, -84,1,28.12.2015,,101,126.54, -84,1,29.12.2015,,198,126.54, -84,1,30.12.2015,,289,126.54, -84,1,31.12.2015,,145,126.54, -84,1,01.01.2016,,35,126.54, -84,1,02.01.2016,,104,126.54, -84,1,03.01.2016,,138,126.54, -84,1,04.01.2016,,214,126.54, -84,1,05.01.2016,,110,126.54, -84,1,06.01.2016,,236,126.54, -84,1,07.01.2016,,63,126.54, -84,1,08.01.2016,,157,126.54, -84,1,09.01.2016,,207,126.54, -84,1,10.01.2016,,126,126.54, -84,1,11.01.2016,,273,126.54, -84,1,12.01.2016,,195,126.54, -84,1,13.01.2016,,226,126.54, -84,1,14.01.2016,,207,126.54, -84,1,15.01.2016,,330,126.54, -84,1,16.01.2016,,232,126.54, -84,1,17.01.2016,,280,126.54, -84,1,18.01.2016,,283,126.54, -84,1,19.01.2016,,217,126.54, -84,1,20.01.2016,,188,126.54, -84,1,21.01.2016,,188,127.9, -84,1,22.01.2016,,258,127.9, -84,1,23.01.2016,,361,127.9, -84,1,24.01.2016,,204,127.9, -84,1,25.01.2016,,336,127.9, -84,1,26.01.2016,,135,127.9, -84,1,27.01.2016,,261,127.9, -84,1,28.01.2016,,217,127.9, -84,1,29.01.2016,,364,127.9, -84,1,30.01.2016,,298,127.9, -84,1,31.01.2016,,302,127.9, -84,1,01.02.2016,,280,130.34, -84,1,02.02.2016,,261,130.34, -84,1,03.02.2016,,280,130.34, -84,1,04.02.2016,,339,133.06, -84,1,05.02.2016,,198,133.06, -84,1,06.02.2016,,223,133.06, -84,1,07.02.2016,,195,141.76, -84,1,08.02.2016,,176,141.76, -84,1,09.02.2016,1.0,434,141.76,130.75 -84,1,10.02.2016,1.0,729,141.76,130.75 -84,1,11.02.2016,1.0,481,141.76,130.75 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-85,1,24.12.2015,,119,126.54, -85,1,25.12.2015,,82,126.54, -85,1,26.12.2015,,79,126.54, -85,1,27.12.2015,,107,126.54, -85,1,28.12.2015,,75,126.54, -85,1,29.12.2015,,57,126.54, -85,1,30.12.2015,,110,126.54, -85,1,31.12.2015,,104,126.54, -85,1,01.01.2016,,50,126.54, -85,1,02.01.2016,,60,126.54, -85,1,03.01.2016,,72,126.54, -85,1,04.01.2016,,101,126.54, -85,1,05.01.2016,,101,126.54, -85,1,06.01.2016,,132,126.54, -85,1,07.01.2016,,163,126.54, -85,1,08.01.2016,,107,126.54, -85,1,09.01.2016,,104,126.54, -85,1,10.01.2016,,104,126.54, -85,1,11.01.2016,,145,126.54, -85,1,12.01.2016,,138,126.54, -85,1,13.01.2016,,126,126.54, -85,1,14.01.2016,,141,126.54, -85,1,15.01.2016,,123,126.54, -85,1,16.01.2016,,119,126.54, -85,1,17.01.2016,,217,126.54, -85,1,18.01.2016,,214,126.54, -85,1,19.01.2016,,167,126.54, -85,1,20.01.2016,,226,126.54, -85,1,21.01.2016,,170,127.9, -85,1,22.01.2016,,129,127.9, -85,1,23.01.2016,,204,127.9, -85,1,24.01.2016,,129,127.9, -85,1,25.01.2016,,251,127.9, 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-86,1,29.09.2015,,47,141.21, -86,1,30.09.2015,,66,141.21, -86,1,01.10.2015,,50,141.21, -86,1,02.10.2015,,50,141.21, -86,1,03.10.2015,,63,141.21, -86,1,04.10.2015,,41,141.21, -86,1,05.10.2015,,85,141.21, -86,1,06.10.2015,1.0,151,141.21,132.92 -86,1,07.10.2015,1.0,113,141.21,130.21 -86,1,08.10.2015,1.0,57,141.11,130.21 -86,1,09.10.2015,1.0,145,141.21,130.21 -86,1,10.10.2015,1.0,101,141.21,130.21 -86,1,11.10.2015,1.0,94,141.21,130.21 -86,1,12.10.2015,1.0,79,141.21,130.21 -86,1,13.10.2015,,91,141.21, -86,1,14.10.2015,,69,141.21, -86,1,15.10.2015,,75,142.57, -86,1,16.10.2015,,113,142.57, -86,1,17.10.2015,,50,142.57, -86,1,18.10.2015,,47,142.57, -86,1,19.10.2015,,91,142.57, -86,1,20.10.2015,,94,142.57, -86,1,21.10.2015,,94,130.34, -86,1,22.10.2015,,22,130.34, -86,1,23.10.2015,,50,130.34, -86,1,24.10.2015,,72,130.34, -86,1,25.10.2015,,53,130.34, -86,1,26.10.2015,,66,130.34, -86,1,27.10.2015,1.0,116,130.34,122.32 -86,1,28.10.2015,1.0,97,130.34,124.77 -86,1,29.10.2015,1.0,104,130.34,124.77 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-86,1,02.01.2016,,50,126.54, -86,1,03.01.2016,,53,126.54, -86,1,04.01.2016,,79,126.54, -86,1,05.01.2016,,123,126.54, -86,1,06.01.2016,,82,126.54, -86,1,07.01.2016,,47,126.54, -86,1,08.01.2016,,75,126.54, -86,1,09.01.2016,,123,126.54, -86,1,10.01.2016,,69,126.54, -86,1,11.01.2016,,97,126.54, -86,1,12.01.2016,,173,126.54, -86,1,13.01.2016,,132,126.54, -86,1,14.01.2016,,116,126.54, -86,1,15.01.2016,,66,126.54, -86,1,16.01.2016,,145,126.54, -86,1,17.01.2016,,176,126.54, -86,1,18.01.2016,,66,126.54, -86,1,19.01.2016,,119,126.54, -86,1,20.01.2016,,104,126.54, -86,1,21.01.2016,,126,127.9, -86,1,22.01.2016,,188,127.9, -86,1,23.01.2016,,82,127.9, -86,1,24.01.2016,,88,127.9, -86,1,25.01.2016,,148,127.9, -86,1,26.01.2016,,97,127.9, -86,1,27.01.2016,,145,127.9, -86,1,28.01.2016,,160,127.9, -86,1,29.01.2016,,214,127.9, -86,1,30.01.2016,,163,127.9, -86,1,31.01.2016,,154,127.9, -86,1,01.02.2016,,163,130.34, -86,1,02.02.2016,,201,130.34, -86,1,03.02.2016,,132,130.34, -86,1,04.02.2016,,119,133.06, -86,1,05.02.2016,,154,133.06, -86,1,06.02.2016,,123,133.06, -86,1,07.02.2016,,145,141.76, -86,1,08.02.2016,,138,141.76, -86,1,09.02.2016,1.0,201,141.76,130.75 -86,1,10.02.2016,1.0,336,141.76,130.75 -86,1,11.02.2016,1.0,154,141.76,130.75 -86,1,12.02.2016,1.0,179,141.76,130.75 -86,1,13.02.2016,1.0,355,141.76,130.75 -86,1,14.02.2016,1.0,148,141.76,130.75 -86,1,15.02.2016,1.0,160,141.76,130.75 -86,1,16.02.2016,1.0,192,141.76,130.75 -86,1,17.02.2016,1.0,148,141.76,130.75 -86,1,18.02.2016,,104,141.76, -86,1,19.02.2016,,91,141.76, -86,1,20.02.2016,,91,141.76, -86,1,21.02.2016,,85,141.76, -86,1,22.02.2016,,53,141.76, -86,1,23.02.2016,,38,141.76, -86,1,24.02.2016,,123,141.76, -86,1,25.02.2016,,69,141.76, -86,1,26.02.2016,,110,141.76, -86,1,27.02.2016,,57,141.76, -86,1,28.02.2016,,129,141.76, -86,1,29.02.2016,,60,141.76, -86,1,01.03.2016,,101,141.76, -86,1,02.03.2016,,107,141.76, -86,1,03.03.2016,,82,141.76, -86,1,04.03.2016,,88,141.76, -86,1,05.03.2016,,91,141.21, -86,1,06.03.2016,,107,141.21, -86,1,07.03.2016,,41,141.21, -86,1,08.03.2016,1.0,217,141.21,132.92 -86,1,09.03.2016,1.0,94,141.21,132.92 -86,1,10.03.2016,1.0,167,141.21,132.92 -86,1,11.03.2016,1.0,160,141.21,132.92 -86,1,12.03.2016,1.0,245,141.21,132.92 -86,1,13.03.2016,1.0,170,141.21,132.92 -86,1,14.03.2016,1.0,123,141.21,132.92 -86,1,15.03.2016,1.0,188,141.21,130.21 -86,1,16.03.2016,1.0,188,141.21,130.21 -86,1,17.03.2016,1.0,151,141.21,130.21 -86,1,18.03.2016,1.0,157,141.21,130.21 -86,1,19.03.2016,,82,141.21, -86,1,20.03.2016,,101,141.21, -86,1,21.03.2016,,104,141.21, -86,1,22.03.2016,,75,141.21, -86,1,23.03.2016,,69,141.21, -86,1,24.03.2016,,53,141.21, -86,1,25.03.2016,,129,141.21, -86,1,26.03.2016,,66,141.21, -86,1,27.03.2016,,82,141.21, -86,1,28.03.2016,,69,141.21, -86,1,29.03.2016,,66,141.21, -86,1,30.03.2016,,69,141.21, -86,1,31.03.2016,,57,141.21, -86,1,01.04.2016,,110,141.21, -86,1,02.04.2016,,60,141.21, -86,1,03.04.2016,,38,141.21, -86,1,04.04.2016,,94,141.21, -86,1,05.04.2016,,94,141.21, 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-1,2,09.08.2015,,13,150.73, -1,2,10.08.2015,,9,150.73, -1,2,11.08.2015,,9,150.73, -1,2,12.08.2015,,6,128.17, -1,2,13.08.2015,,16,128.17, -1,2,14.08.2015,,3,128.17, -1,2,15.08.2015,,6,128.17, -1,2,16.08.2015,,0,128.17, -1,2,17.08.2015,,25,128.17, -1,2,18.08.2015,1.0,157,128.17,100.3 -1,2,19.08.2015,1.0,35,128.17,100.3 -1,2,20.08.2015,1.0,173,128.17,100.3 -1,2,21.08.2015,1.0,170,128.17,100.3 -1,2,22.08.2015,1.0,119,128.17,100.3 -1,2,23.08.2015,1.0,116,128.17,100.3 -1,2,24.08.2015,1.0,217,128.17,100.3 -1,2,25.08.2015,,35,128.17, -1,2,26.08.2015,,35,128.17, -1,2,27.08.2015,,28,128.17, -1,2,28.08.2015,,28,128.17, -1,2,29.08.2015,,50,128.17, -1,2,30.08.2015,,44,128.17, -1,2,31.08.2015,,57,128.17, -1,2,01.09.2015,,13,128.17, -1,2,02.09.2015,,25,128.17, -1,2,03.09.2015,,22,128.17, -1,2,04.09.2015,,22,128.17, -1,2,05.09.2015,,22,128.17, -1,2,06.09.2015,,22,128.17, -1,2,07.09.2015,,22,128.17, -1,2,08.09.2015,,19,128.17, -1,2,09.09.2015,,16,128.17, -1,2,10.09.2015,,0,128.17, -1,2,11.09.2015,,0,128.17, -1,2,12.09.2015,,0,128.17, -1,2,13.09.2015,,0,128.17, -1,2,14.09.2015,,0,128.17, -1,2,15.09.2015,,0,128.17, -1,2,16.09.2015,,0,128.17, -1,2,17.09.2015,,0,128.17, -1,2,18.09.2015,,0,128.17, -1,2,19.09.2015,,0,128.17, -1,2,20.09.2015,,0,128.17, -1,2,21.09.2015,,0,128.17, -1,2,22.09.2015,,0,128.17, -1,2,23.09.2015,,0,128.17, -1,2,24.09.2015,,0,128.17, -1,2,25.09.2015,,0,128.17, -1,2,26.09.2015,,0,128.17, -1,2,27.09.2015,,0,128.17, -1,2,28.09.2015,,0,128.17, -1,2,29.09.2015,,0,128.17, -1,2,30.09.2015,,0,128.17, -1,2,01.10.2015,,0,162.96, -1,2,02.10.2015,,0,162.96, -1,2,03.10.2015,,0,162.96, -1,2,04.10.2015,,0,162.96, -1,2,05.10.2015,,0,162.96, -1,2,06.10.2015,1.0,104,143.39,122.05 -1,2,07.10.2015,1.0,38,143.39,122.05 -1,2,08.10.2015,1.0,94,143.39,122.05 -1,2,09.10.2015,1.0,85,143.39,122.05 -1,2,10.10.2015,1.0,79,143.39,122.05 -1,2,11.10.2015,1.0,75,143.39,122.05 -1,2,12.10.2015,1.0,72,143.39,122.05 -1,2,13.10.2015,1.0,63,143.93,122.05 -1,2,14.10.2015,1.0,60,143.93,122.05 -1,2,15.10.2015,1.0,47,143.93,122.05 -1,2,16.10.2015,1.0,35,143.93,122.05 -1,2,17.10.2015,1.0,25,162.96,122.05 -1,2,18.10.2015,1.0,19,162.96,122.05 -1,2,19.10.2015,,0,162.96, -1,2,20.10.2015,,0,162.96, -1,2,21.10.2015,,0,162.96, -1,2,22.10.2015,,0,162.96, -1,2,23.10.2015,,0,162.96, -1,2,24.10.2015,,0,162.96, -1,2,25.10.2015,,0,162.96, -1,2,26.10.2015,,0,162.96, -1,2,27.10.2015,,0,162.96, -1,2,28.10.2015,,0,162.96, -1,2,29.10.2015,,0,162.96, -1,2,30.10.2015,,0,162.96, -1,2,31.10.2015,,0,162.96, -1,2,01.11.2015,,0,162.96, -1,2,02.11.2015,,0,162.96, -1,2,03.11.2015,,0,162.96, -1,2,04.11.2015,,0,162.96, -1,2,05.11.2015,,0,162.96, -1,2,06.11.2015,,0,162.96, -1,2,07.11.2015,,0,162.96, -1,2,08.11.2015,,0,162.96, -1,2,09.11.2015,,0,162.96, -1,2,10.11.2015,,0,162.96, -1,2,11.11.2015,,0,138.36, -1,2,12.11.2015,,0,138.36, -1,2,13.11.2015,,0,138.36, -1,2,14.11.2015,,0,138.36, -1,2,15.11.2015,,0,138.36, -1,2,16.11.2015,,0,138.36, -1,2,17.11.2015,,0,138.36, -1,2,18.11.2015,,0,138.36, -1,2,19.11.2015,,0,138.36, -1,2,20.11.2015,,0,138.36, -1,2,21.11.2015,,0,138.36, -1,2,22.11.2015,,0,138.36, -1,2,23.11.2015,,0,138.36, -1,2,24.11.2015,,0,138.36, -1,2,25.11.2015,,0,138.36, -1,2,26.11.2015,,0,138.36, -1,2,27.11.2015,,0,138.36, -1,2,28.11.2015,,0,138.36, -1,2,29.11.2015,,0,138.36, -1,2,30.11.2015,,0,138.36, -1,2,01.12.2015,,0,162.96, -1,2,02.12.2015,,0,162.96, -1,2,03.12.2015,,0,138.5, -1,2,04.12.2015,,0,138.5, -1,2,05.12.2015,,0,138.5, -1,2,06.12.2015,,0,138.5, -1,2,07.12.2015,,0,138.5, -1,2,08.12.2015,,0,138.5, -1,2,09.12.2015,,0,138.5, -1,2,10.12.2015,,0,138.5, -1,2,11.12.2015,,0,142.57, -1,2,12.12.2015,,0,142.57, -1,2,13.12.2015,,0,142.57, -1,2,14.12.2015,,0,142.57, -1,2,15.12.2015,,0,142.57, -1,2,16.12.2015,,0,142.57, -1,2,17.12.2015,,0,142.57, -1,2,18.12.2015,,0,142.57, -1,2,19.12.2015,,0,142.57, -1,2,20.12.2015,,0,142.57, -1,2,21.12.2015,,0,142.57, -1,2,22.12.2015,1.0,94,142.57,103.02 -1,2,23.12.2015,1.0,0,142.57,103.02 -1,2,24.12.2015,1.0,154,142.57,103.02 -1,2,25.12.2015,1.0,167,142.57,103.02 -1,2,26.12.2015,1.0,28,142.57,103.02 -1,2,27.12.2015,1.0,22,142.57,103.02 -1,2,28.12.2015,1.0,214,142.57,103.02 -1,2,29.12.2015,,9,138.5, -1,2,30.12.2015,,13,138.5, -1,2,31.12.2015,,3,138.5, -1,2,01.01.2016,,0,138.5, -1,2,02.01.2016,,13,138.5, -1,2,03.01.2016,,13,138.5, -1,2,04.01.2016,,0,138.5, -1,2,05.01.2016,,3,138.5, -1,2,06.01.2016,,13,138.5, -1,2,07.01.2016,,25,138.5, -1,2,08.01.2016,,0,138.5, -1,2,09.01.2016,,9,138.5, -1,2,10.01.2016,,0,138.5, -1,2,11.01.2016,,0,138.5, -1,2,12.01.2016,,0,138.5, -1,2,13.01.2016,,0,138.5, -1,2,14.01.2016,,0,138.5, -1,2,15.01.2016,,0,138.5, -1,2,16.01.2016,,3,142.57, -1,2,17.01.2016,,3,142.57, -1,2,18.01.2016,,35,142.57, -1,2,19.01.2016,1.0,176,142.57,103.02 -1,2,20.01.2016,1.0,9,142.57,103.02 -1,2,21.01.2016,1.0,138,142.57,103.02 -1,2,22.01.2016,1.0,232,142.57,103.02 -1,2,23.01.2016,1.0,148,142.57,103.02 -1,2,24.01.2016,1.0,97,142.57,103.02 -1,2,25.01.2016,1.0,97,142.57,103.02 -1,2,26.01.2016,,0,142.57, -1,2,27.01.2016,,13,142.57, -1,2,28.01.2016,,6,142.57, -1,2,29.01.2016,,0,142.57, -1,2,30.01.2016,,6,142.57, -1,2,31.01.2016,,6,142.57, -1,2,01.02.2016,,3,142.57, -1,2,02.02.2016,,19,142.57, -1,2,03.02.2016,,0,142.57, -1,2,04.02.2016,,9,142.57, -1,2,05.02.2016,,6,142.57, -1,2,06.02.2016,,0,142.57, -1,2,07.02.2016,,0,142.57, -1,2,08.02.2016,,9,142.57, -1,2,09.02.2016,1.0,123,142.57,108.46 -1,2,10.02.2016,1.0,19,138.36,108.46 -1,2,11.02.2016,1.0,57,138.36,108.46 -1,2,12.02.2016,1.0,129,138.36,108.46 -1,2,13.02.2016,1.0,13,138.36,108.46 -1,2,14.02.2016,1.0,91,138.36,108.46 -1,2,15.02.2016,1.0,79,138.36,108.46 -1,2,16.02.2016,,0,138.36, -1,2,17.02.2016,,3,138.36, -1,2,18.02.2016,,9,138.36, -1,2,19.02.2016,,3,138.36, -1,2,20.02.2016,,0,138.36, -1,2,21.02.2016,,0,138.36, -1,2,22.02.2016,,3,138.36, -1,2,23.02.2016,,0,138.36, -1,2,24.02.2016,,3,138.36, -1,2,25.02.2016,,9,138.36, -1,2,26.02.2016,,3,138.36, -1,2,27.02.2016,,3,138.36, -1,2,28.02.2016,,0,138.36, -1,2,29.02.2016,,6,138.36, -1,2,01.03.2016,,0,138.36, -1,2,02.03.2016,,0,138.36, -1,2,03.03.2016,,0,138.36, -1,2,04.03.2016,,0,138.36, -1,2,05.03.2016,,0,138.36, -1,2,06.03.2016,,0,138.36, -1,2,07.03.2016,,0,138.36, -1,2,08.03.2016,,0,138.36, -1,2,09.03.2016,,0,138.36, -1,2,10.03.2016,,0,138.36, -1,2,11.03.2016,,0,138.36, -1,2,12.03.2016,,0,138.5, -1,2,13.03.2016,,0,138.5, -1,2,14.03.2016,,6,138.5, -1,2,15.03.2016,1.0,91,138.5,124.77 -1,2,16.03.2016,1.0,35,138.5,124.77 -1,2,17.03.2016,1.0,31,138.5,124.77 -1,2,18.03.2016,1.0,63,138.5,124.77 -1,2,19.03.2016,1.0,38,138.5,124.77 -1,2,20.03.2016,1.0,16,138.5,124.77 -1,2,21.03.2016,1.0,53,138.5,124.77 -1,2,22.03.2016,,6,138.36, -1,2,23.03.2016,,0,138.36, -1,2,24.03.2016,,3,138.36, -1,2,25.03.2016,,3,138.36, -1,2,26.03.2016,,6,138.36, -1,2,27.03.2016,,3,138.36, -1,2,28.03.2016,,3,138.36, -1,2,29.03.2016,,0,138.36, -1,2,30.03.2016,,3,138.36, -1,2,31.03.2016,,0,138.36, -1,2,01.04.2016,,9,138.36, -1,2,02.04.2016,,6,138.36, -1,2,03.04.2016,,0,138.36, -1,2,04.04.2016,,0,138.36, -1,2,05.04.2016,,6,138.36, -1,2,06.04.2016,,6,138.36, -1,2,07.04.2016,,3,138.36, -1,2,08.04.2016,,0,138.36, -1,2,09.04.2016,,9,138.5, -1,2,10.04.2016,,3,138.5, -1,2,11.04.2016,,0,138.5, -1,2,12.04.2016,,0,138.5, -1,2,13.04.2016,,3,138.5, -1,2,14.04.2016,,0,138.5, -1,2,15.04.2016,,3,138.5, -1,2,16.04.2016,,9,138.5, -1,2,17.04.2016,,3,138.5, -1,2,18.04.2016,,9,138.5, -1,2,19.04.2016,1.0,132,138.5,116.89 -1,2,20.04.2016,1.0,135,138.5,116.89 -1,2,21.04.2016,1.0,69,138.5,116.89 -1,2,22.04.2016,1.0,123,138.5,116.89 -1,2,23.04.2016,1.0,94,138.5,116.89 -1,2,24.04.2016,1.0,101,138.5,116.89 -1,2,25.04.2016,1.0,69,138.5,116.89 -1,2,26.04.2016,,0,138.5, -1,2,27.04.2016,,0,138.5, -1,2,28.04.2016,,0,138.5, -1,2,29.04.2016,,0,138.5, -1,2,30.04.2016,,0,138.5, -1,2,01.05.2016,,0,138.5, -1,2,02.05.2016,,13,138.5, -1,2,03.05.2016,1.0,195,138.5,106.01 -1,2,04.05.2016,1.0,148,138.5,106.01 -1,2,05.05.2016,1.0,182,138.5,106.01 -1,2,06.05.2016,1.0,132,138.5,106.01 -1,2,07.05.2016,1.0,35,138.5,106.01 -1,2,08.05.2016,1.0,85,138.5,106.01 -1,2,09.05.2016,1.0,126,138.5,106.01 -1,2,10.05.2016,,0,138.5, -1,2,11.05.2016,,3,138.5, -1,2,12.05.2016,,53,138.5, -1,2,13.05.2016,,6,138.5, -1,2,14.05.2016,,6,138.5, -1,2,15.05.2016,,6,138.5, -1,2,16.05.2016,,6,138.5, -1,2,17.05.2016,,6,138.5, -1,2,18.05.2016,,6,138.5, -1,2,19.05.2016,,9,138.5, -1,2,20.05.2016,,3,138.5, -1,2,21.05.2016,,3,138.5, -1,2,22.05.2016,,3,138.5, -2,2,01.01.2015,,0,140.94, -2,2,02.01.2015,,3,140.94, -2,2,03.01.2015,,6,140.94, -2,2,04.01.2015,,13,140.94, -2,2,05.01.2015,,13,140.94, -2,2,06.01.2015,,0,140.94, -2,2,07.01.2015,,0,140.94, -2,2,08.01.2015,,22,142.57, -2,2,09.01.2015,,3,142.57, -2,2,10.01.2015,,6,142.57, -2,2,11.01.2015,,3,142.57, -2,2,12.01.2015,,0,142.57, -2,2,13.01.2015,,0,142.57, -2,2,14.01.2015,,3,142.57, -2,2,15.01.2015,,3,142.57, -2,2,16.01.2015,,0,142.57, -2,2,17.01.2015,,0,142.57, -2,2,18.01.2015,,0,142.57, -2,2,19.01.2015,,6,142.57, -2,2,20.01.2015,,0,142.57, -2,2,21.01.2015,,0,142.57, -2,2,22.01.2015,,0,142.57, -2,2,23.01.2015,,0,142.57, -2,2,24.01.2015,,3,142.57, -2,2,25.01.2015,,3,142.57, -2,2,26.01.2015,,0,142.57, -2,2,27.01.2015,,0,142.57, -2,2,28.01.2015,,0,142.57, -2,2,29.01.2015,,3,142.57, -2,2,30.01.2015,,0,142.57, -2,2,31.01.2015,,0,142.57, -2,2,01.02.2015,,6,142.57, -2,2,02.02.2015,,3,142.57, -2,2,03.02.2015,,0,142.57, -2,2,04.02.2015,,3,142.57, -2,2,05.02.2015,,6,142.57, -2,2,06.02.2015,,6,142.57, -2,2,07.02.2015,,0,142.57, -2,2,08.02.2015,,0,142.57, -2,2,09.02.2015,,6,142.57, -2,2,10.02.2015,,0,142.57, -2,2,11.02.2015,,0,142.57, -2,2,12.02.2015,,0,142.57, -2,2,13.02.2015,,3,142.57, -2,2,14.02.2015,,0,142.57, -2,2,15.02.2015,,0,142.57, -2,2,16.02.2015,,0,142.57, -2,2,17.02.2015,,0,142.57, -2,2,18.02.2015,,0,142.57, -2,2,19.02.2015,,3,142.57, -2,2,20.02.2015,,3,142.57, -2,2,21.02.2015,,3,142.57, -2,2,22.02.2015,,0,142.57, -2,2,23.02.2015,,0,142.57, -2,2,24.02.2015,,0,142.57, -2,2,25.02.2015,,0,142.57, -2,2,26.02.2015,,9,142.57, -2,2,27.02.2015,,0,142.57, -2,2,28.02.2015,,6,142.57, -2,2,01.03.2015,,0,142.57, -2,2,02.03.2015,,0,142.57, -2,2,03.03.2015,,3,142.57, -2,2,04.03.2015,,3,142.57, -2,2,05.03.2015,,0,142.57, -2,2,06.03.2015,,0,142.57, -2,2,07.03.2015,,3,142.57, -2,2,08.03.2015,,0,153.72, -2,2,09.03.2015,,0,153.72, -2,2,10.03.2015,,0,153.72, -2,2,11.03.2015,,3,153.72, -2,2,12.03.2015,,0,153.72, -2,2,13.03.2015,,0,153.72, -2,2,14.03.2015,,0,153.72, -2,2,15.03.2015,,6,153.72, -2,2,16.03.2015,,0,153.72, -2,2,17.03.2015,,0,153.72, -2,2,18.03.2015,,0,153.72, -2,2,19.03.2015,,0,153.72, -2,2,20.03.2015,,0,153.72, -2,2,21.03.2015,,0,153.72, -2,2,22.03.2015,,3,153.72, -2,2,23.03.2015,,0,153.72, -2,2,24.03.2015,,0,153.72, -2,2,25.03.2015,,0,153.72, -2,2,26.03.2015,,6,153.72, -2,2,27.03.2015,,6,153.72, -2,2,28.03.2015,,0,153.72, -2,2,29.03.2015,,0,153.72, -2,2,30.03.2015,,0,153.72, -2,2,31.03.2015,,0,153.72, -2,2,01.04.2015,,0,153.72, -2,2,02.04.2015,,0,153.72, -2,2,03.04.2015,,0,153.72, -2,2,04.04.2015,,6,153.72, -2,2,05.04.2015,,0,153.72, -2,2,06.04.2015,,3,153.72, -2,2,07.04.2015,,0,153.72, -2,2,08.04.2015,,0,153.72, -2,2,09.04.2015,,9,153.72, -2,2,10.04.2015,,0,153.72, -2,2,11.04.2015,,0,153.72, -2,2,12.04.2015,,0,153.72, -2,2,13.04.2015,,0,153.72, -2,2,14.04.2015,,0,153.72, -2,2,15.04.2015,,0,153.72, -2,2,16.04.2015,,0,153.72, -2,2,17.04.2015,,0,153.72, -2,2,18.04.2015,,0,153.72, -2,2,19.04.2015,,6,153.72, -2,2,20.04.2015,,0,153.72, -2,2,21.04.2015,,6,153.72, -2,2,22.04.2015,,3,153.72, -2,2,23.04.2015,,0,153.72, -2,2,24.04.2015,,0,153.72, -2,2,25.04.2015,,0,153.72, -2,2,26.04.2015,,0,153.72, -2,2,27.04.2015,,3,153.72, -2,2,28.04.2015,,6,153.72, -2,2,29.04.2015,,0,153.72, -2,2,30.04.2015,,0,158.34, -2,2,01.05.2015,,0,158.34, -2,2,02.05.2015,,0,158.34, -2,2,03.05.2015,,3,158.34, -2,2,04.05.2015,,0,158.34, -2,2,05.05.2015,,0,158.34, -2,2,06.05.2015,,0,158.34, -2,2,07.05.2015,,3,158.34, -2,2,08.05.2015,,0,158.34, -2,2,09.05.2015,,3,158.34, -2,2,10.05.2015,,0,158.34, -2,2,11.05.2015,,0,158.34, -2,2,12.05.2015,,0,158.34, -2,2,13.05.2015,,3,158.34, -2,2,14.05.2015,,0,158.34, -2,2,15.05.2015,,0,158.34, -2,2,16.05.2015,,0,158.34, -2,2,17.05.2015,,0,158.34, -2,2,18.05.2015,,0,158.34, -2,2,19.05.2015,,0,158.34, -2,2,20.05.2015,,0,158.34, -2,2,21.05.2015,,0,158.34, -2,2,22.05.2015,,0,158.34, -2,2,23.05.2015,,0,158.34, -2,2,24.05.2015,,0,158.34, -2,2,25.05.2015,,0,158.34, -2,2,26.05.2015,,0,158.34, -2,2,27.05.2015,,0,158.34, -2,2,28.05.2015,,0,158.34, -2,2,29.05.2015,,0,158.34, -2,2,30.05.2015,,0,158.34, -2,2,31.05.2015,,0,158.34, -2,2,01.06.2015,,0,158.34, -2,2,02.06.2015,,0,158.34, -2,2,03.06.2015,,3,158.34, -2,2,04.06.2015,,9,158.34, -2,2,05.06.2015,,0,158.34, -2,2,06.06.2015,,0,158.34, -2,2,07.06.2015,,6,158.34, -2,2,08.06.2015,,0,158.34, -2,2,09.06.2015,,0,158.34, -2,2,10.06.2015,,0,158.34, -2,2,11.06.2015,,6,158.34, -2,2,12.06.2015,,0,158.34, -2,2,13.06.2015,,0,158.34, -2,2,14.06.2015,,3,150.73, -2,2,15.06.2015,,0,150.73, -2,2,16.06.2015,,0,150.73, -2,2,17.06.2015,,0,150.73, -2,2,18.06.2015,,0,150.73, -2,2,19.06.2015,,0,150.73, -2,2,20.06.2015,,0,150.73, -2,2,21.06.2015,,0,150.73, -2,2,22.06.2015,,0,150.73, -2,2,23.06.2015,,0,150.73, -2,2,24.06.2015,,0,150.73, -2,2,25.06.2015,,0,150.73, -2,2,26.06.2015,,0,150.73, -2,2,27.06.2015,,0,150.73, -2,2,28.06.2015,,0,150.73, -2,2,29.06.2015,,3,150.73, -2,2,30.06.2015,,0,150.73, -2,2,01.07.2015,,3,150.73, -2,2,02.07.2015,,0,150.73, -2,2,03.07.2015,,0,150.73, -2,2,04.07.2015,,0,150.73, -2,2,05.07.2015,,3,150.73, -2,2,06.07.2015,,3,150.73, -2,2,07.07.2015,,0,150.73, -2,2,08.07.2015,,0,150.73, -2,2,09.07.2015,,0,150.73, -2,2,10.07.2015,,0,150.73, -2,2,11.07.2015,,0,150.73, -2,2,12.07.2015,,0,150.73, -2,2,13.07.2015,,3,150.73, -2,2,14.07.2015,,0,150.73, -2,2,15.07.2015,,0,150.73, -2,2,16.07.2015,,3,150.73, -2,2,17.07.2015,,0,150.73, -2,2,18.07.2015,,0,150.73, -2,2,19.07.2015,,0,150.73, -2,2,20.07.2015,,3,150.73, -2,2,21.07.2015,,0,150.73, -2,2,22.07.2015,,0,150.73, -2,2,23.07.2015,,13,150.73, -2,2,24.07.2015,,6,150.73, -2,2,25.07.2015,,3,150.73, -2,2,26.07.2015,,3,150.73, -2,2,27.07.2015,,0,150.73, -2,2,28.07.2015,,0,150.73, -2,2,29.07.2015,,0,150.73, -2,2,30.07.2015,,0,150.73, -2,2,31.07.2015,,6,150.73, -2,2,01.08.2015,,19,150.73, -2,2,02.08.2015,,0,150.73, -2,2,03.08.2015,,0,150.73, -2,2,04.08.2015,,0,150.73, -2,2,05.08.2015,,19,150.73, -2,2,06.08.2015,,6,150.73, -2,2,07.08.2015,,0,150.73, -2,2,08.08.2015,,0,150.73, -2,2,09.08.2015,,0,150.73, -2,2,10.08.2015,,0,150.73, -2,2,11.08.2015,,3,150.73, -2,2,12.08.2015,,9,128.17, -2,2,13.08.2015,,0,128.17, -2,2,14.08.2015,,6,128.17, -2,2,15.08.2015,,3,128.17, -2,2,16.08.2015,,3,128.17, -2,2,17.08.2015,,60,128.17, -2,2,18.08.2015,1.0,204,128.17,100.3 -2,2,19.08.2015,1.0,148,128.17,100.3 -2,2,20.08.2015,1.0,195,128.17,100.3 -2,2,21.08.2015,1.0,132,128.17,100.3 -2,2,22.08.2015,1.0,148,128.17,100.3 -2,2,23.08.2015,1.0,226,128.17,100.3 -2,2,24.08.2015,1.0,69,128.17,100.3 -2,2,25.08.2015,,22,128.17, -2,2,26.08.2015,,13,128.17, -2,2,27.08.2015,,16,128.17, -2,2,28.08.2015,,0,128.17, -2,2,29.08.2015,,0,128.17, -2,2,30.08.2015,,0,128.17, -2,2,31.08.2015,,0,128.17, -2,2,01.09.2015,,6,128.17, -2,2,02.09.2015,,3,128.17, -2,2,03.09.2015,,6,128.17, -2,2,04.09.2015,,0,128.17, -2,2,05.09.2015,,0,128.17, -2,2,06.09.2015,,3,128.17, -2,2,07.09.2015,,9,128.17, -2,2,08.09.2015,,6,128.17, -2,2,09.09.2015,,9,128.17, -2,2,10.09.2015,,6,128.17, -2,2,11.09.2015,,3,162.96, -2,2,12.09.2015,,0,162.96, -2,2,13.09.2015,,3,162.96, -2,2,14.09.2015,,0,162.96, -2,2,15.09.2015,,0,162.96, -2,2,16.09.2015,,0,162.96, -2,2,17.09.2015,,13,162.96, -2,2,18.09.2015,,6,162.96, -2,2,19.09.2015,,16,162.96, -2,2,20.09.2015,,3,162.96, -2,2,21.09.2015,,0,162.96, -2,2,22.09.2015,,6,162.96, -2,2,23.09.2015,,0,162.96, -2,2,24.09.2015,,13,162.96, -2,2,25.09.2015,,3,162.96, -2,2,26.09.2015,,0,162.96, -2,2,27.09.2015,,16,162.96, -2,2,28.09.2015,,0,162.96, -2,2,29.09.2015,,19,162.96, -2,2,30.09.2015,,13,162.96, -2,2,01.10.2015,,3,162.96, -2,2,02.10.2015,,0,162.96, -2,2,03.10.2015,,6,162.96, -2,2,04.10.2015,,0,162.96, -2,2,05.10.2015,,0,162.96, -2,2,06.10.2015,1.0,126,143.39,122.05 -2,2,07.10.2015,1.0,110,143.39,122.05 -2,2,08.10.2015,1.0,97,143.39,122.05 -2,2,09.10.2015,1.0,97,143.39,122.05 -2,2,10.10.2015,1.0,97,143.39,122.05 -2,2,11.10.2015,1.0,82,143.39,122.05 -2,2,12.10.2015,1.0,60,143.39,122.05 -2,2,13.10.2015,1.0,113,143.93,122.05 -2,2,14.10.2015,1.0,104,143.93,122.05 -2,2,15.10.2015,1.0,88,143.93,122.05 -2,2,16.10.2015,1.0,79,143.93,122.05 -2,2,17.10.2015,1.0,69,162.96,122.05 -2,2,18.10.2015,1.0,53,162.96,122.05 -2,2,19.10.2015,,0,162.96, -2,2,20.10.2015,,6,162.96, -2,2,21.10.2015,,13,162.96, -2,2,22.10.2015,,41,162.96, -2,2,23.10.2015,,47,162.96, -2,2,24.10.2015,,13,162.96, -2,2,25.10.2015,,13,162.96, -2,2,26.10.2015,,13,162.96, -2,2,27.10.2015,,0,162.96, -2,2,28.10.2015,,0,162.96, -2,2,29.10.2015,,0,162.96, -2,2,30.10.2015,,0,162.96, -2,2,31.10.2015,,0,162.96, -2,2,01.11.2015,,3,162.96, -2,2,02.11.2015,,0,162.96, -2,2,03.11.2015,,3,162.96, -2,2,04.11.2015,,9,162.96, -2,2,05.11.2015,,9,162.96, -2,2,06.11.2015,,9,162.96, -2,2,07.11.2015,,3,162.96, -2,2,08.11.2015,,3,162.96, -2,2,09.11.2015,,0,162.96, -2,2,10.11.2015,,0,162.96, -2,2,11.11.2015,,3,138.36, -2,2,12.11.2015,,3,138.36, -2,2,13.11.2015,,0,138.36, -2,2,14.11.2015,,0,138.36, -2,2,15.11.2015,,3,138.36, -2,2,16.11.2015,,0,138.36, -2,2,17.11.2015,,3,138.36, -2,2,18.11.2015,,0,138.36, -2,2,19.11.2015,,3,138.36, -2,2,20.11.2015,,0,138.36, -2,2,21.11.2015,,3,138.36, -2,2,22.11.2015,,3,138.36, -2,2,23.11.2015,,3,138.36, -2,2,24.11.2015,,3,138.36, -2,2,25.11.2015,,3,138.36, -2,2,26.11.2015,,3,138.36, -2,2,27.11.2015,,3,138.36, -2,2,28.11.2015,,3,138.36, -2,2,29.11.2015,,3,138.36, -2,2,30.11.2015,,0,138.36, -2,2,01.12.2015,,3,162.96, -2,2,02.12.2015,,3,162.96, -2,2,03.12.2015,,0,138.5, -2,2,04.12.2015,,0,138.5, -2,2,05.12.2015,,0,138.5, -2,2,06.12.2015,,0,138.5, -2,2,07.12.2015,,0,138.5, -2,2,08.12.2015,,0,138.5, -2,2,09.12.2015,,0,138.5, -2,2,10.12.2015,,0,138.5, -2,2,11.12.2015,,0,142.57, -2,2,12.12.2015,,0,142.57, -2,2,13.12.2015,,0,142.57, -2,2,14.12.2015,,0,142.57, -2,2,15.12.2015,,0,142.57, -2,2,16.12.2015,,3,142.57, -2,2,17.12.2015,,6,142.57, -2,2,18.12.2015,,6,142.57, -2,2,19.12.2015,,3,142.57, -2,2,20.12.2015,,0,142.57, -2,2,21.12.2015,,3,142.57, -2,2,22.12.2015,1.0,173,142.57,103.02 -2,2,23.12.2015,1.0,113,142.57,103.02 -2,2,24.12.2015,1.0,173,142.57,103.02 -2,2,25.12.2015,1.0,107,142.57,103.02 -2,2,26.12.2015,1.0,91,142.57,103.02 -2,2,27.12.2015,1.0,66,142.57,103.02 -2,2,28.12.2015,1.0,75,142.57,103.02 -2,2,29.12.2015,,3,138.5, -2,2,30.12.2015,,9,138.5, -2,2,31.12.2015,,9,138.5, -2,2,01.01.2016,,3,138.5, -2,2,02.01.2016,,3,138.5, -2,2,03.01.2016,,3,138.5, -2,2,04.01.2016,,3,138.5, -2,2,05.01.2016,,19,138.5, -2,2,06.01.2016,,19,138.5, -2,2,07.01.2016,,6,138.5, -2,2,08.01.2016,,13,138.5, -2,2,09.01.2016,,9,138.5, -2,2,10.01.2016,,9,138.5, -2,2,11.01.2016,,9,138.5, -2,2,12.01.2016,,0,138.5, -2,2,13.01.2016,,13,138.5, -2,2,14.01.2016,,6,138.5, -2,2,15.01.2016,,9,138.5, -2,2,16.01.2016,,9,142.57, -2,2,17.01.2016,,9,142.57, -2,2,18.01.2016,,3,142.57, -2,2,19.01.2016,1.0,167,142.57,103.02 -2,2,20.01.2016,1.0,160,142.57,103.02 -2,2,21.01.2016,1.0,207,142.57,103.02 -2,2,22.01.2016,1.0,176,142.57,103.02 -2,2,23.01.2016,1.0,226,142.57,103.02 -2,2,24.01.2016,1.0,135,142.57,103.02 -2,2,25.01.2016,1.0,129,142.57,103.02 -2,2,26.01.2016,,9,142.57, -2,2,27.01.2016,,6,142.57, -2,2,28.01.2016,,16,142.57, -2,2,29.01.2016,,3,142.57, -2,2,30.01.2016,,9,142.57, -2,2,31.01.2016,,13,142.57, -2,2,01.02.2016,,9,142.57, -2,2,02.02.2016,,9,142.57, -2,2,03.02.2016,,22,142.57, -2,2,04.02.2016,,9,142.57, -2,2,05.02.2016,,22,142.57, -2,2,06.02.2016,,6,142.57, -2,2,07.02.2016,,22,142.57, -2,2,08.02.2016,,25,142.57, -2,2,09.02.2016,1.0,220,142.57,108.46 -2,2,10.02.2016,1.0,192,138.36,108.46 -2,2,11.02.2016,1.0,201,138.36,108.46 -2,2,12.02.2016,1.0,154,138.36,108.46 -2,2,13.02.2016,1.0,267,138.36,108.46 -2,2,14.02.2016,1.0,141,138.36,108.46 -2,2,15.02.2016,1.0,135,138.36,108.46 -2,2,16.02.2016,,3,138.36, -2,2,17.02.2016,,0,138.36, -2,2,18.02.2016,,13,138.36, -2,2,19.02.2016,,13,138.36, -2,2,20.02.2016,,0,138.36, -2,2,21.02.2016,,0,138.36, -2,2,22.02.2016,,0,138.36, -2,2,23.02.2016,,0,138.36, -2,2,24.02.2016,,3,138.36, -2,2,25.02.2016,,3,138.36, -2,2,26.02.2016,,0,138.36, -2,2,27.02.2016,,0,138.36, -2,2,28.02.2016,,3,138.36, -2,2,29.02.2016,,6,138.36, -2,2,01.03.2016,,6,138.36, -2,2,02.03.2016,,0,138.36, -2,2,03.03.2016,,9,138.36, -2,2,04.03.2016,,3,138.36, -2,2,05.03.2016,,3,138.36, -2,2,06.03.2016,,3,138.36, -2,2,07.03.2016,,9,138.36, -2,2,08.03.2016,,9,138.36, -2,2,09.03.2016,,0,138.36, -2,2,10.03.2016,,0,138.36, -2,2,11.03.2016,,0,138.36, -2,2,12.03.2016,,13,138.5, -2,2,13.03.2016,,3,138.5, -2,2,14.03.2016,,13,138.5, -2,2,15.03.2016,1.0,173,138.5,124.77 -2,2,16.03.2016,1.0,60,138.5,124.77 -2,2,17.03.2016,1.0,79,138.5,124.77 -2,2,18.03.2016,1.0,75,138.5,124.77 -2,2,19.03.2016,1.0,97,138.5,124.77 -2,2,20.03.2016,1.0,119,138.5,124.77 -2,2,21.03.2016,1.0,60,138.5,124.77 -2,2,22.03.2016,,6,138.36, -2,2,23.03.2016,,6,138.36, -2,2,24.03.2016,,0,138.36, -2,2,25.03.2016,,3,138.36, -2,2,26.03.2016,,9,138.36, -2,2,27.03.2016,,6,138.36, -2,2,28.03.2016,,0,138.36, -2,2,29.03.2016,,9,138.36, -2,2,30.03.2016,,0,138.36, -2,2,31.03.2016,,0,138.36, -2,2,01.04.2016,,0,138.36, -2,2,02.04.2016,,19,138.36, -2,2,03.04.2016,,9,138.36, -2,2,04.04.2016,,6,138.36, -2,2,05.04.2016,,6,138.36, -2,2,06.04.2016,,6,138.36, -2,2,07.04.2016,,6,138.36, -2,2,08.04.2016,,6,138.36, -2,2,09.04.2016,,6,138.5, -2,2,10.04.2016,,6,138.5, -2,2,11.04.2016,,3,138.5, -2,2,12.04.2016,,0,138.5, -2,2,13.04.2016,,3,138.5, -2,2,14.04.2016,,9,138.5, -2,2,15.04.2016,,6,138.5, -2,2,16.04.2016,,6,138.5, -2,2,17.04.2016,,6,138.5, -2,2,18.04.2016,,9,138.5, -2,2,19.04.2016,1.0,145,138.5,116.89 -2,2,20.04.2016,1.0,126,138.5,116.89 -2,2,21.04.2016,1.0,91,138.5,116.89 -2,2,22.04.2016,1.0,160,138.5,116.89 -2,2,23.04.2016,1.0,110,138.5,116.89 -2,2,24.04.2016,1.0,126,138.5,116.89 -2,2,25.04.2016,1.0,176,138.5,116.89 -2,2,26.04.2016,,22,138.5, -2,2,27.04.2016,,0,138.5, -2,2,28.04.2016,,0,138.5, -2,2,29.04.2016,,9,138.5, -2,2,30.04.2016,,9,138.5, -2,2,01.05.2016,,3,138.5, -2,2,02.05.2016,,19,138.5, -2,2,03.05.2016,1.0,123,138.5,106.01 -2,2,04.05.2016,1.0,185,138.5,106.01 -2,2,05.05.2016,1.0,138,138.5,106.01 -2,2,06.05.2016,1.0,132,138.5,106.01 -2,2,07.05.2016,1.0,195,138.5,106.01 -2,2,08.05.2016,1.0,167,138.5,106.01 -2,2,09.05.2016,1.0,88,138.5,106.01 -2,2,10.05.2016,,6,138.5, -2,2,11.05.2016,,6,138.5, -2,2,12.05.2016,,0,138.5, -2,2,13.05.2016,,9,138.5, -2,2,14.05.2016,,0,138.5, -2,2,15.05.2016,,3,138.5, -2,2,16.05.2016,,6,138.5, -2,2,17.05.2016,,6,138.5, -2,2,18.05.2016,,6,138.5, -2,2,19.05.2016,,0,138.5, -2,2,20.05.2016,,13,138.5, -2,2,21.05.2016,,9,138.5, -2,2,22.05.2016,,6,138.5, -3,2,01.01.2015,,3,135.78, -3,2,02.01.2015,,6,135.78, -3,2,03.01.2015,,25,135.78, -3,2,04.01.2015,,0,135.78, -3,2,05.01.2015,,9,135.78, -3,2,06.01.2015,,13,135.78, -3,2,07.01.2015,,3,135.78, -3,2,08.01.2015,,0,135.78, -3,2,09.01.2015,,6,135.78, -3,2,10.01.2015,,3,135.78, -3,2,11.01.2015,,6,135.78, -3,2,12.01.2015,,0,135.78, -3,2,13.01.2015,,6,135.78, -3,2,14.01.2015,,9,135.78, -3,2,15.01.2015,,13,135.78, -3,2,16.01.2015,,0,135.78, -3,2,17.01.2015,,6,135.78, -3,2,18.01.2015,,0,135.78, -3,2,19.01.2015,,6,135.78, -3,2,20.01.2015,,0,135.78, -3,2,21.01.2015,,6,135.78, -3,2,22.01.2015,,6,135.78, -3,2,23.01.2015,,6,135.78, -3,2,24.01.2015,,0,135.78, -3,2,25.01.2015,,0,135.78, -3,2,26.01.2015,,0,135.78, -3,2,27.01.2015,,0,135.78, -3,2,28.01.2015,,0,135.78, -3,2,29.01.2015,,0,135.78, -3,2,30.01.2015,,0,135.78, -3,2,31.01.2015,,0,135.78, -3,2,01.02.2015,,0,135.78, -3,2,02.02.2015,,0,135.78, -3,2,03.02.2015,,0,135.78, -3,2,04.02.2015,,0,135.78, -3,2,05.02.2015,,0,135.78, -3,2,06.02.2015,,0,135.78, -3,2,07.02.2015,,0,135.78, -3,2,08.02.2015,,0,135.78, -3,2,09.02.2015,,0,135.78, -3,2,10.02.2015,,13,135.78, -3,2,11.02.2015,,3,135.78, -3,2,12.02.2015,,0,135.78, -3,2,13.02.2015,,0,135.78, -3,2,14.02.2015,,9,135.78, -3,2,15.02.2015,,0,135.78, -3,2,16.02.2015,,0,135.78, -3,2,17.02.2015,,0,135.78, -3,2,18.02.2015,,0,135.78, -3,2,19.02.2015,,0,135.78, -3,2,20.02.2015,,0,135.78, -3,2,21.02.2015,,0,135.78, -3,2,22.02.2015,,3,135.78, -3,2,23.02.2015,,6,135.78, -3,2,24.02.2015,,3,135.78, -3,2,25.02.2015,,0,135.78, -3,2,26.02.2015,,0,135.78, -3,2,27.02.2015,,3,135.78, -3,2,28.02.2015,,0,135.78, -3,2,01.03.2015,,3,146.38, -3,2,02.03.2015,,6,146.38, -3,2,03.03.2015,,3,146.38, -3,2,04.03.2015,,0,146.38, -3,2,05.03.2015,,0,146.38, -3,2,06.03.2015,,0,146.38, -3,2,07.03.2015,,3,146.38, -3,2,08.03.2015,,0,146.38, -3,2,09.03.2015,,0,146.38, -3,2,10.03.2015,,0,146.38, -3,2,11.03.2015,,0,146.38, -3,2,12.03.2015,,0,146.38, -3,2,13.03.2015,,0,146.38, -3,2,14.03.2015,,0,146.38, -3,2,15.03.2015,,0,146.38, -3,2,16.03.2015,,0,146.38, -3,2,17.03.2015,,0,146.38, -3,2,18.03.2015,,0,146.38, -3,2,19.03.2015,,0,146.38, -3,2,20.03.2015,,0,146.38, -3,2,21.03.2015,,0,146.38, -3,2,22.03.2015,,0,146.38, -3,2,23.03.2015,,3,146.38, -3,2,24.03.2015,,3,146.38, -3,2,25.03.2015,,6,146.38, -3,2,26.03.2015,,3,146.38, -3,2,27.03.2015,,3,146.38, -3,2,28.03.2015,,0,146.38, -3,2,29.03.2015,,6,146.38, -3,2,30.03.2015,,0,146.38, -3,2,31.03.2015,,3,146.38, -3,2,01.04.2015,,3,146.38, -3,2,02.04.2015,,0,146.38, -3,2,03.04.2015,,0,146.38, -3,2,04.04.2015,,0,146.38, -3,2,05.04.2015,,3,146.38, -3,2,06.04.2015,,3,146.38, -3,2,07.04.2015,,0,146.38, -3,2,08.04.2015,,0,146.38, -3,2,09.04.2015,,0,146.38, -3,2,10.04.2015,,3,146.38, -3,2,11.04.2015,,0,146.38, -3,2,12.04.2015,,0,146.38, -3,2,13.04.2015,,3,146.38, -3,2,14.04.2015,,0,146.38, -3,2,15.04.2015,,0,146.38, -3,2,16.04.2015,,0,146.38, -3,2,17.04.2015,,3,146.38, -3,2,18.04.2015,,0,146.38, -3,2,19.04.2015,,0,146.38, -3,2,20.04.2015,,0,145.84, -3,2,21.04.2015,,0,145.84, -3,2,22.04.2015,,3,145.84, -3,2,23.04.2015,,6,145.84, -3,2,24.04.2015,,0,145.84, -3,2,25.04.2015,,0,145.84, -3,2,26.04.2015,,0,150.73, -3,2,27.04.2015,,0,150.73, -3,2,28.04.2015,,0,150.73, -3,2,29.04.2015,,0,150.73, -3,2,30.04.2015,,0,150.73, -3,2,01.05.2015,,0,150.73, -3,2,02.05.2015,,0,150.73, -3,2,03.05.2015,,0,150.73, -3,2,04.05.2015,,0,150.73, -3,2,05.05.2015,,0,150.73, -3,2,06.05.2015,,0,150.73, -3,2,07.05.2015,,0,150.73, -3,2,08.05.2015,,0,150.73, -3,2,09.05.2015,,0,150.73, -3,2,10.05.2015,,0,150.73, -3,2,11.05.2015,,3,150.73, -3,2,12.05.2015,,9,150.73, -3,2,13.05.2015,,6,150.73, -3,2,14.05.2015,,3,150.73, -3,2,15.05.2015,,0,150.73, -3,2,16.05.2015,,0,150.73, -3,2,17.05.2015,,3,150.73, -3,2,18.05.2015,,0,150.73, -3,2,19.05.2015,,0,150.73, -3,2,20.05.2015,,0,150.73, -3,2,21.05.2015,,0,150.73, -3,2,22.05.2015,,0,150.73, -3,2,23.05.2015,,0,150.73, -3,2,24.05.2015,,0,150.73, -3,2,25.05.2015,,0,150.73, -3,2,26.05.2015,,0,150.73, -3,2,27.05.2015,,0,150.73, -3,2,28.05.2015,,0,150.73, -3,2,29.05.2015,,0,150.73, -3,2,30.05.2015,,0,150.73, -3,2,31.05.2015,,0,150.73, -3,2,01.06.2015,,0,150.73, -3,2,02.06.2015,,0,150.73, -3,2,03.06.2015,,0,150.73, -3,2,04.06.2015,,0,150.73, -3,2,05.06.2015,,3,150.73, -3,2,06.06.2015,,6,150.73, -3,2,07.06.2015,,3,150.73, -3,2,08.06.2015,,6,150.73, -3,2,09.06.2015,,0,150.73, -3,2,10.06.2015,,0,150.73, -3,2,11.06.2015,,3,150.73, -3,2,12.06.2015,,0,150.73, -3,2,13.06.2015,,0,150.73, -3,2,14.06.2015,,0,150.73, -3,2,15.06.2015,,3,150.73, -3,2,16.06.2015,,0,150.73, -3,2,17.06.2015,,0,150.73, -3,2,18.06.2015,,3,150.73, -3,2,19.06.2015,,3,150.73, -3,2,20.06.2015,,0,150.73, -3,2,21.06.2015,,0,150.73, -3,2,22.06.2015,,0,150.73, -3,2,23.06.2015,,6,150.73, -3,2,24.06.2015,,0,150.73, -3,2,25.06.2015,,0,150.73, -3,2,26.06.2015,,0,150.73, -3,2,27.06.2015,,0,150.73, -3,2,28.06.2015,,0,150.73, -3,2,29.06.2015,,0,150.73, -3,2,30.06.2015,,0,150.73, -3,2,01.07.2015,,6,150.73, -3,2,02.07.2015,,3,150.73, -3,2,03.07.2015,,0,150.73, -3,2,04.07.2015,,0,150.73, -3,2,05.07.2015,,0,150.73, -3,2,06.07.2015,,0,150.73, -3,2,07.07.2015,,0,150.73, -3,2,08.07.2015,,0,150.73, -3,2,09.07.2015,,0,150.73, -3,2,10.07.2015,,0,150.73, -3,2,11.07.2015,,0,150.73, -3,2,12.07.2015,,3,150.73, -3,2,13.07.2015,,0,150.73, -3,2,14.07.2015,,0,150.73, -3,2,15.07.2015,,0,150.73, -3,2,16.07.2015,,0,150.73, -3,2,17.07.2015,,0,150.73, -3,2,18.07.2015,,0,150.73, -3,2,19.07.2015,,0,150.73, -3,2,20.07.2015,,25,150.73, -3,2,21.07.2015,,3,150.73, -3,2,22.07.2015,,3,150.73, -3,2,23.07.2015,,19,150.73, -3,2,24.07.2015,,19,150.73, -3,2,25.07.2015,,13,150.73, -3,2,26.07.2015,,0,150.73, -3,2,27.07.2015,,0,150.73, -3,2,28.07.2015,,0,150.73, -3,2,29.07.2015,,0,150.73, -3,2,30.07.2015,,0,150.73, -3,2,31.07.2015,,0,150.73, -3,2,01.08.2015,,0,150.73, -3,2,02.08.2015,,0,150.73, -3,2,03.08.2015,,0,150.73, -3,2,04.08.2015,,0,150.73, -3,2,05.08.2015,,0,150.73, -3,2,06.08.2015,,0,150.73, -3,2,07.08.2015,,0,150.73, -3,2,08.08.2015,,0,150.73, -3,2,09.08.2015,,0,150.73, -3,2,10.08.2015,,0,150.73, -3,2,11.08.2015,,0,150.73, -3,2,12.08.2015,,0,128.17, -3,2,13.08.2015,,0,128.17, -3,2,14.08.2015,,0,128.17, -3,2,15.08.2015,,0,128.17, -3,2,16.08.2015,,0,128.17, -3,2,17.08.2015,,35,128.17, -3,2,18.08.2015,1.0,167,128.17,100.3 -3,2,19.08.2015,1.0,50,128.17,100.3 -3,2,20.08.2015,1.0,107,128.17,100.3 -3,2,21.08.2015,1.0,132,128.17,100.3 -3,2,22.08.2015,1.0,63,128.17,100.3 -3,2,23.08.2015,1.0,69,128.17,100.3 -3,2,24.08.2015,1.0,50,128.17,100.3 -3,2,25.08.2015,,0,128.17, -3,2,26.08.2015,,0,128.17, -3,2,27.08.2015,,0,128.17, -3,2,28.08.2015,,22,128.17, -3,2,29.08.2015,,3,128.17, -3,2,30.08.2015,,0,128.17, -3,2,31.08.2015,,19,128.17, -3,2,01.09.2015,,0,128.17, -3,2,02.09.2015,,0,128.17, -3,2,03.09.2015,,3,128.17, -3,2,04.09.2015,,13,128.17, -3,2,05.09.2015,,3,128.17, -3,2,06.09.2015,,6,128.17, -3,2,07.09.2015,,19,128.17, -3,2,08.09.2015,,0,128.17, -3,2,09.09.2015,,0,128.17, -3,2,10.09.2015,,3,128.17, -3,2,11.09.2015,,16,128.17, -3,2,12.09.2015,,6,128.17, -3,2,13.09.2015,,6,128.17, -3,2,14.09.2015,,13,128.17, -3,2,15.09.2015,,6,128.17, -3,2,16.09.2015,,6,128.17, -3,2,17.09.2015,,0,128.17, -3,2,18.09.2015,,6,128.17, -3,2,19.09.2015,,19,128.17, -3,2,20.09.2015,,13,128.17, -3,2,21.09.2015,,9,128.17, -3,2,22.09.2015,,9,128.17, -3,2,23.09.2015,,0,128.17, -3,2,24.09.2015,,3,128.17, -3,2,25.09.2015,,0,128.17, -3,2,26.09.2015,,0,128.17, -3,2,27.09.2015,,16,128.17, -3,2,28.09.2015,,6,128.17, -3,2,29.09.2015,,16,128.17, -3,2,30.09.2015,,3,128.17, -3,2,01.10.2015,,0,162.96, -3,2,02.10.2015,,0,162.96, -3,2,03.10.2015,,3,162.96, -3,2,04.10.2015,,3,162.96, -3,2,05.10.2015,,0,162.96, -3,2,06.10.2015,1.0,57,143.39,122.05 -3,2,07.10.2015,1.0,50,143.39,122.05 -3,2,08.10.2015,1.0,41,143.39,122.05 -3,2,09.10.2015,1.0,16,143.39,122.05 -3,2,10.10.2015,1.0,57,143.39,122.05 -3,2,11.10.2015,1.0,57,143.39,122.05 -3,2,12.10.2015,1.0,19,143.39,122.05 -3,2,13.10.2015,,6,143.93, -3,2,14.10.2015,,0,143.93, -3,2,15.10.2015,,0,143.93, -3,2,16.10.2015,,3,143.93, -3,2,17.10.2015,,0,162.96, -3,2,18.10.2015,,0,162.96, -3,2,19.10.2015,,0,162.96, -3,2,20.10.2015,,0,162.96, -3,2,21.10.2015,,0,162.96, -3,2,22.10.2015,,3,162.96, -3,2,23.10.2015,,3,162.96, -3,2,24.10.2015,,0,162.96, -3,2,25.10.2015,,6,162.96, -3,2,26.10.2015,,0,162.96, -3,2,27.10.2015,,3,162.96, -3,2,28.10.2015,,0,162.96, -3,2,29.10.2015,,0,162.96, -3,2,30.10.2015,,0,162.96, -3,2,31.10.2015,,0,162.96, -3,2,01.11.2015,,0,162.96, -3,2,02.11.2015,,0,162.96, -3,2,03.11.2015,,0,162.96, -3,2,04.11.2015,,0,162.96, -3,2,05.11.2015,,0,162.96, -3,2,06.11.2015,,0,162.96, -3,2,07.11.2015,,0,162.96, -3,2,08.11.2015,,0,162.96, -3,2,09.11.2015,,0,162.96, -3,2,10.11.2015,,0,162.96, -3,2,11.11.2015,,0,138.36, -3,2,12.11.2015,,0,138.36, -3,2,13.11.2015,,0,138.36, -3,2,14.11.2015,,0,138.36, -3,2,15.11.2015,,0,138.36, -3,2,16.11.2015,,0,138.36, -3,2,17.11.2015,,0,138.36, -3,2,18.11.2015,,0,138.36, -3,2,19.11.2015,,0,138.36, -3,2,20.11.2015,,0,138.36, -3,2,21.11.2015,,0,138.36, -3,2,22.11.2015,,0,138.36, -3,2,23.11.2015,,0,138.36, -3,2,24.11.2015,,0,138.36, -3,2,25.11.2015,,0,138.36, -3,2,26.11.2015,,0,138.36, -3,2,27.11.2015,,0,138.36, -3,2,28.11.2015,,0,138.36, -3,2,29.11.2015,,0,138.36, -3,2,30.11.2015,,0,138.36, -3,2,01.12.2015,,0,162.96, -3,2,02.12.2015,,0,162.96, -3,2,03.12.2015,,0,138.5, -3,2,04.12.2015,,0,138.5, -3,2,05.12.2015,,0,138.5, -3,2,06.12.2015,,0,138.5, -3,2,07.12.2015,,0,138.5, -3,2,08.12.2015,,0,138.5, -3,2,09.12.2015,,0,138.5, -3,2,10.12.2015,,0,138.5, -3,2,11.12.2015,,0,142.57, -3,2,12.12.2015,,0,142.57, -3,2,13.12.2015,,0,142.57, -3,2,14.12.2015,,0,142.57, -3,2,15.12.2015,,0,142.57, -3,2,16.12.2015,,0,142.57, -3,2,17.12.2015,,0,142.57, -3,2,18.12.2015,,0,142.57, -3,2,19.12.2015,,0,142.57, -3,2,20.12.2015,,0,142.57, -3,2,21.12.2015,,6,142.57, -3,2,22.12.2015,1.0,91,142.57,103.02 -3,2,23.12.2015,1.0,16,142.57,103.02 -3,2,24.12.2015,1.0,107,142.57,103.02 -3,2,25.12.2015,1.0,44,142.57,103.02 -3,2,26.12.2015,1.0,50,142.57,103.02 -3,2,27.12.2015,1.0,41,142.57,103.02 -3,2,28.12.2015,1.0,35,142.57,103.02 -3,2,29.12.2015,,3,138.5, -3,2,30.12.2015,,0,138.5, -3,2,31.12.2015,,6,138.5, -3,2,01.01.2016,,0,138.5, -3,2,02.01.2016,,3,138.5, -3,2,03.01.2016,,6,138.5, -3,2,04.01.2016,,0,138.5, -3,2,05.01.2016,,0,138.5, -3,2,06.01.2016,,0,138.5, -3,2,07.01.2016,,0,138.5, -3,2,08.01.2016,,0,138.5, -3,2,09.01.2016,,3,138.5, -3,2,10.01.2016,,3,138.5, -3,2,11.01.2016,,3,138.5, -3,2,12.01.2016,,3,138.5, -3,2,13.01.2016,,3,138.5, -3,2,14.01.2016,,3,138.5, -3,2,15.01.2016,,3,138.5, -3,2,16.01.2016,,3,142.57, -3,2,17.01.2016,,3,142.57, -3,2,18.01.2016,,3,142.57, -3,2,19.01.2016,1.0,35,142.57,103.02 -3,2,20.01.2016,1.0,35,142.57,103.02 -3,2,21.01.2016,1.0,35,142.57,103.02 -3,2,22.01.2016,1.0,35,142.57,103.02 -3,2,23.01.2016,1.0,35,142.57,103.02 -3,2,24.01.2016,1.0,35,142.57,103.02 -3,2,25.01.2016,1.0,35,142.57,103.02 -3,2,26.01.2016,,3,142.57, -3,2,27.01.2016,,9,142.57, -3,2,28.01.2016,,9,142.57, -3,2,29.01.2016,,0,142.57, -3,2,30.01.2016,,3,142.57, -3,2,31.01.2016,,3,142.57, -3,2,01.02.2016,,0,142.57, -3,2,02.02.2016,,0,142.57, -3,2,03.02.2016,,0,142.57, -3,2,04.02.2016,,6,142.57, -3,2,05.02.2016,,9,142.57, -3,2,06.02.2016,,0,142.57, -3,2,07.02.2016,,3,142.57, -3,2,08.02.2016,,28,142.57, -3,2,09.02.2016,1.0,123,142.57,108.46 -3,2,10.02.2016,1.0,41,138.36,108.46 -3,2,11.02.2016,1.0,79,138.36,108.46 -3,2,12.02.2016,1.0,126,138.36,108.46 -3,2,13.02.2016,1.0,113,138.36,108.46 -3,2,14.02.2016,1.0,50,138.36,108.46 -3,2,15.02.2016,1.0,94,138.36,108.46 -3,2,16.02.2016,,0,138.36, -3,2,17.02.2016,,0,138.36, -3,2,18.02.2016,,0,138.36, -3,2,19.02.2016,,0,138.36, -3,2,20.02.2016,,0,138.36, -3,2,21.02.2016,,0,138.36, -3,2,22.02.2016,,0,138.36, -3,2,23.02.2016,,0,138.36, -3,2,24.02.2016,,0,138.36, -3,2,25.02.2016,,0,138.36, -3,2,26.02.2016,,0,138.36, -3,2,27.02.2016,,0,138.36, -3,2,28.02.2016,,0,138.36, -3,2,29.02.2016,,0,138.36, -3,2,01.03.2016,,0,138.36, -3,2,02.03.2016,,0,138.36, -3,2,03.03.2016,,0,138.36, -3,2,04.03.2016,,0,138.36, -3,2,05.03.2016,,0,138.36, -3,2,06.03.2016,,0,138.36, -3,2,07.03.2016,,0,138.36, -3,2,08.03.2016,,0,138.36, -3,2,09.03.2016,,0,138.36, -3,2,10.03.2016,,0,138.36, -3,2,11.03.2016,,0,138.36, -3,2,12.03.2016,,0,138.5, -3,2,13.03.2016,,0,138.5, -3,2,14.03.2016,,13,138.5, -3,2,15.03.2016,1.0,72,138.5,124.77 -3,2,16.03.2016,1.0,47,138.5,124.77 -3,2,17.03.2016,1.0,91,138.5,124.77 -3,2,18.03.2016,1.0,38,138.5,124.77 -3,2,19.03.2016,1.0,50,138.5,124.77 -3,2,20.03.2016,1.0,79,138.5,124.77 -3,2,21.03.2016,1.0,66,138.5,124.77 -3,2,22.03.2016,,0,138.36, -3,2,23.03.2016,,0,138.36, -3,2,24.03.2016,,6,138.36, -3,2,25.03.2016,,9,138.36, -3,2,26.03.2016,,0,138.36, -3,2,27.03.2016,,3,138.36, -3,2,28.03.2016,,0,138.36, -3,2,29.03.2016,,0,138.36, -3,2,30.03.2016,,0,138.36, -3,2,31.03.2016,,3,138.36, -3,2,01.04.2016,,0,138.36, -3,2,02.04.2016,,0,138.36, -3,2,03.04.2016,,3,138.36, -3,2,04.04.2016,,3,138.36, -3,2,05.04.2016,,0,138.36, -3,2,06.04.2016,,0,138.36, -3,2,07.04.2016,,0,138.36, -3,2,08.04.2016,,3,138.36, -3,2,09.04.2016,,0,138.5, -3,2,10.04.2016,,3,138.5, -3,2,11.04.2016,,6,138.5, -3,2,12.04.2016,,3,138.5, -3,2,13.04.2016,,0,138.5, -3,2,14.04.2016,,3,138.5, -3,2,15.04.2016,,0,138.5, -3,2,16.04.2016,,0,138.5, -3,2,17.04.2016,,0,138.5, -3,2,18.04.2016,,3,138.5, -3,2,19.04.2016,1.0,123,138.5,116.89 -3,2,20.04.2016,1.0,44,138.5,116.89 -3,2,21.04.2016,1.0,47,138.5,116.89 -3,2,22.04.2016,1.0,66,138.5,116.89 -3,2,23.04.2016,1.0,44,138.5,116.89 -3,2,24.04.2016,1.0,69,138.5,116.89 -3,2,25.04.2016,1.0,66,138.5,116.89 -3,2,26.04.2016,,0,138.5, -3,2,27.04.2016,,3,138.5, -3,2,28.04.2016,,3,138.5, -3,2,29.04.2016,,0,138.5, -3,2,30.04.2016,,6,138.5, -3,2,01.05.2016,,6,138.5, -3,2,02.05.2016,,9,138.5, -3,2,03.05.2016,1.0,110,138.5,106.01 -3,2,04.05.2016,1.0,82,138.5,106.01 -3,2,05.05.2016,1.0,135,138.5,106.01 -3,2,06.05.2016,1.0,88,138.5,106.01 -3,2,07.05.2016,1.0,138,138.5,106.01 -3,2,08.05.2016,1.0,88,138.5,106.01 -3,2,09.05.2016,1.0,94,138.5,106.01 -3,2,10.05.2016,,3,138.5, -3,2,11.05.2016,,6,138.5, -3,2,12.05.2016,,6,138.5, -3,2,13.05.2016,,9,138.5, -3,2,14.05.2016,,3,138.5, -3,2,15.05.2016,,0,138.5, -3,2,16.05.2016,,9,138.5, -3,2,17.05.2016,,0,138.5, -3,2,18.05.2016,,3,138.5, -3,2,19.05.2016,,6,138.5, -3,2,20.05.2016,,0,138.5, -3,2,21.05.2016,,0,138.5, -3,2,22.05.2016,,0,138.5, -4,2,01.01.2015,,0,134.72, -4,2,02.01.2015,,0,134.72, -4,2,03.01.2015,,0,134.72, -4,2,04.01.2015,,0,134.72, -4,2,05.01.2015,,0,134.72, -4,2,06.01.2015,,0,134.72, -4,2,07.01.2015,,0,134.72, -4,2,08.01.2015,,0,134.72, -4,2,09.01.2015,,0,134.72, -4,2,10.01.2015,,0,134.72, -4,2,11.01.2015,,0,134.72, -4,2,12.01.2015,,0,134.72, -4,2,13.01.2015,,0,134.72, -4,2,14.01.2015,,0,134.72, -4,2,15.01.2015,,0,134.72, -4,2,16.01.2015,,0,134.72, -4,2,17.01.2015,,0,134.72, -4,2,18.01.2015,,0,134.72, -4,2,19.01.2015,,0,134.72, -4,2,20.01.2015,,0,134.72, -4,2,21.01.2015,,0,134.72, -4,2,22.01.2015,,0,134.72, -4,2,23.01.2015,,0,134.72, -4,2,24.01.2015,,0,134.72, -4,2,25.01.2015,,0,134.72, -4,2,26.01.2015,,0,134.72, -4,2,27.01.2015,,0,134.72, -4,2,28.01.2015,,0,134.72, -4,2,29.01.2015,,0,134.72, -4,2,30.01.2015,,0,134.72, -4,2,31.01.2015,,0,134.72, -4,2,01.02.2015,,0,134.72, -4,2,02.02.2015,,0,134.72, -4,2,03.02.2015,,0,134.72, -4,2,04.02.2015,,0,134.72, -4,2,05.02.2015,,0,134.72, -4,2,06.02.2015,,0,134.72, -4,2,07.02.2015,,0,134.72, -4,2,08.02.2015,,0,134.72, -4,2,09.02.2015,,0,134.72, -4,2,10.02.2015,,0,134.72, -4,2,11.02.2015,,0,134.72, -4,2,12.02.2015,,0,134.72, -4,2,13.02.2015,,0,134.72, -4,2,14.02.2015,,0,134.72, -4,2,15.02.2015,,0,134.72, -4,2,16.02.2015,,0,134.72, -4,2,17.02.2015,,0,134.72, -4,2,18.02.2015,,0,134.72, -4,2,19.02.2015,,0,134.72, -4,2,20.02.2015,,0,134.72, -4,2,21.02.2015,,0,134.72, -4,2,22.02.2015,,0,134.72, -4,2,23.02.2015,,0,134.72, -4,2,24.02.2015,,0,134.72, -4,2,25.02.2015,,0,134.72, -4,2,26.02.2015,,0,134.72, -4,2,27.02.2015,,0,134.72, -4,2,28.02.2015,,0,134.72, -4,2,01.03.2015,,0,134.72, 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-4,2,08.04.2015,,0,134.72, -4,2,09.04.2015,,0,134.72, -4,2,10.04.2015,,0,134.72, -4,2,11.04.2015,,0,134.72, -4,2,12.04.2015,,0,134.72, -4,2,13.04.2015,,0,134.72, -4,2,14.04.2015,,0,134.72, -4,2,15.04.2015,,0,134.72, -4,2,16.04.2015,,0,134.72, -4,2,17.04.2015,,0,134.72, -4,2,18.04.2015,,0,134.72, -4,2,19.04.2015,,0,134.72, -4,2,20.04.2015,,0,134.72, -4,2,21.04.2015,,0,134.72, -4,2,22.04.2015,,0,134.72, -4,2,23.04.2015,,0,134.72, -4,2,24.04.2015,,0,134.72, -4,2,25.04.2015,,0,134.72, -4,2,26.04.2015,,0,134.72, -4,2,27.04.2015,,0,134.72, -4,2,28.04.2015,,0,134.72, -4,2,29.04.2015,,0,134.72, -4,2,30.04.2015,,0,134.72, -4,2,01.05.2015,,0,134.72, -4,2,02.05.2015,,0,134.72, -4,2,03.05.2015,,0,134.72, -4,2,04.05.2015,,0,134.72, -4,2,05.05.2015,,0,134.72, -4,2,06.05.2015,,0,134.72, -4,2,07.05.2015,,0,134.72, -4,2,08.05.2015,,0,134.72, -4,2,09.05.2015,,0,134.72, -4,2,10.05.2015,,0,134.72, -4,2,11.05.2015,,0,134.72, -4,2,12.05.2015,,0,134.72, -4,2,13.05.2015,,0,134.72, -4,2,14.05.2015,,0,134.72, 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-4,2,21.06.2015,,0,134.72, -4,2,22.06.2015,,0,134.72, -4,2,23.06.2015,,0,134.72, -4,2,24.06.2015,,0,134.72, -4,2,25.06.2015,,0,134.72, -4,2,26.06.2015,,0,134.72, -4,2,27.06.2015,,0,134.72, -4,2,28.06.2015,,0,134.72, -4,2,29.06.2015,,0,134.72, -4,2,30.06.2015,,0,134.72, -4,2,01.07.2015,,0,134.72, -4,2,02.07.2015,,0,134.72, -4,2,03.07.2015,,0,134.72, -4,2,04.07.2015,,0,134.72, -4,2,05.07.2015,,0,134.72, -4,2,06.07.2015,,0,134.72, -4,2,07.07.2015,,0,134.72, -4,2,08.07.2015,,0,134.72, -4,2,09.07.2015,,0,134.72, -4,2,10.07.2015,,0,134.72, -4,2,11.07.2015,,0,134.72, -4,2,12.07.2015,,0,134.72, -4,2,13.07.2015,,0,134.72, -4,2,14.07.2015,,0,134.72, -4,2,15.07.2015,,0,134.72, -4,2,16.07.2015,,0,134.72, -4,2,17.07.2015,,0,134.72, -4,2,18.07.2015,,0,134.72, -4,2,19.07.2015,,0,134.72, -4,2,20.07.2015,,0,134.72, -4,2,21.07.2015,,0,134.72, -4,2,22.07.2015,,0,134.72, -4,2,23.07.2015,,0,134.72, -4,2,24.07.2015,,0,134.72, -4,2,25.07.2015,,0,134.72, -4,2,26.07.2015,,0,134.72, -4,2,27.07.2015,,0,134.72, -4,2,28.07.2015,,0,134.72, -4,2,29.07.2015,,0,134.72, -4,2,30.07.2015,,0,134.72, -4,2,31.07.2015,,0,134.72, -4,2,01.08.2015,,0,134.72, -4,2,02.08.2015,,0,134.72, -4,2,03.08.2015,,0,134.72, -4,2,04.08.2015,,0,134.72, -4,2,05.08.2015,,0,134.72, -4,2,06.08.2015,,0,150.73, -4,2,07.08.2015,,0,150.73, -4,2,08.08.2015,,0,150.73, -4,2,09.08.2015,,0,150.73, -4,2,10.08.2015,,0,150.73, -4,2,11.08.2015,,9,150.73, -4,2,12.08.2015,,3,128.17, -4,2,13.08.2015,,0,128.17, -4,2,14.08.2015,,13,128.17, -4,2,15.08.2015,,3,128.17, -4,2,16.08.2015,,16,128.17, -4,2,17.08.2015,,25,128.17, -4,2,18.08.2015,1.0,116,128.17,100.3 -4,2,19.08.2015,1.0,91,128.17,100.3 -4,2,20.08.2015,1.0,110,128.17,100.3 -4,2,21.08.2015,1.0,41,128.17,100.3 -4,2,22.08.2015,1.0,79,128.17,100.3 -4,2,23.08.2015,1.0,119,128.17,100.3 -4,2,24.08.2015,1.0,104,128.17,100.3 -4,2,25.08.2015,,6,128.17, -4,2,26.08.2015,,6,128.17, -4,2,27.08.2015,,6,128.17, -4,2,28.08.2015,,6,128.17, -4,2,29.08.2015,,6,128.17, -4,2,30.08.2015,,6,128.17, -4,2,31.08.2015,,6,162.96, -4,2,01.09.2015,,6,162.96, -4,2,02.09.2015,,6,162.96, -4,2,03.09.2015,,6,162.96, -4,2,04.09.2015,,6,162.96, -4,2,05.09.2015,,6,162.96, -4,2,06.09.2015,,6,162.96, -4,2,07.09.2015,,6,162.96, -4,2,08.09.2015,,6,162.96, -4,2,09.09.2015,,6,162.96, -4,2,10.09.2015,,6,162.96, -4,2,11.09.2015,,6,162.96, -4,2,12.09.2015,,6,162.96, -4,2,13.09.2015,,6,162.96, -4,2,14.09.2015,,6,162.96, -4,2,15.09.2015,,6,162.96, -4,2,16.09.2015,,6,162.96, -4,2,17.09.2015,,0,162.96, -4,2,18.09.2015,,0,162.96, -4,2,19.09.2015,,0,162.96, -4,2,20.09.2015,,0,162.96, -4,2,21.09.2015,,0,162.96, -4,2,22.09.2015,,0,162.96, -4,2,23.09.2015,,0,162.96, -4,2,24.09.2015,,0,162.96, -4,2,25.09.2015,,0,162.96, -4,2,26.09.2015,,0,162.96, -4,2,27.09.2015,,0,162.96, -4,2,28.09.2015,,0,162.96, -4,2,29.09.2015,,0,162.96, -4,2,30.09.2015,,0,162.96, -4,2,01.10.2015,,0,162.96, -4,2,02.10.2015,,0,162.96, -4,2,03.10.2015,,0,162.96, -4,2,04.10.2015,,0,162.96, -4,2,05.10.2015,,0,162.96, -4,2,06.10.2015,1.0,94,143.39,122.05 -4,2,07.10.2015,1.0,94,143.39,122.05 -4,2,08.10.2015,1.0,79,143.39,122.05 -4,2,09.10.2015,1.0,69,143.39,122.05 -4,2,10.10.2015,1.0,82,143.39,122.05 -4,2,11.10.2015,1.0,79,143.39,122.05 -4,2,12.10.2015,1.0,79,143.39,122.05 -4,2,13.10.2015,,0,143.93, -4,2,14.10.2015,,3,143.93, -4,2,15.10.2015,,0,143.93, -4,2,16.10.2015,,0,143.93, -4,2,17.10.2015,,3,143.93, -4,2,18.10.2015,,6,143.93, -4,2,19.10.2015,,3,143.93, -4,2,20.10.2015,,0,143.93, -4,2,21.10.2015,,0,143.93, -4,2,22.10.2015,,0,143.93, -4,2,23.10.2015,,0,143.93, -4,2,24.10.2015,,0,143.93, -4,2,25.10.2015,,0,143.93, -4,2,26.10.2015,,0,143.93, -4,2,27.10.2015,,0,143.93, -4,2,28.10.2015,,0,143.93, -4,2,29.10.2015,,0,143.93, -4,2,30.10.2015,,0,143.93, -4,2,31.10.2015,,0,143.93, -4,2,01.11.2015,,0,143.93, -4,2,02.11.2015,,0,143.93, -4,2,03.11.2015,,0,143.93, -4,2,04.11.2015,,0,143.93, -4,2,05.11.2015,,0,143.93, -4,2,06.11.2015,,0,143.93, -4,2,07.11.2015,,0,143.93, -4,2,08.11.2015,,0,143.93, -4,2,09.11.2015,,0,143.93, -4,2,10.11.2015,,0,143.93, -4,2,11.11.2015,,0,143.93, -4,2,12.11.2015,,0,143.93, -4,2,13.11.2015,,0,143.93, -4,2,14.11.2015,,0,143.93, -4,2,15.11.2015,,0,143.93, -4,2,16.11.2015,,0,143.93, -4,2,17.11.2015,,0,143.93, -4,2,18.11.2015,,0,143.93, -4,2,19.11.2015,,0,143.93, -4,2,20.11.2015,,0,143.93, -4,2,21.11.2015,,0,143.93, -4,2,22.11.2015,,0,143.93, -4,2,23.11.2015,,0,143.93, -4,2,24.11.2015,,0,143.93, -4,2,25.11.2015,,0,143.93, -4,2,26.11.2015,,0,143.93, -4,2,27.11.2015,,0,143.93, -4,2,28.11.2015,,0,143.93, -4,2,29.11.2015,,0,143.93, -4,2,30.11.2015,,0,143.93, -4,2,01.12.2015,,0,143.93, -4,2,02.12.2015,,0,143.93, -4,2,03.12.2015,,0,143.93, -4,2,04.12.2015,,0,143.93, -4,2,05.12.2015,,0,143.93, -4,2,06.12.2015,,0,143.93, -4,2,07.12.2015,,0,143.93, -4,2,08.12.2015,,0,143.93, -4,2,09.12.2015,,0,138.5, -4,2,10.12.2015,,0,138.5, -4,2,11.12.2015,,0,142.57, -4,2,12.12.2015,,0,142.57, -4,2,13.12.2015,,0,142.57, -4,2,14.12.2015,,0,142.57, -4,2,15.12.2015,,0,142.57, -4,2,16.12.2015,,6,142.57, -4,2,17.12.2015,,9,142.57, -4,2,18.12.2015,,3,142.57, -4,2,19.12.2015,,0,142.57, -4,2,20.12.2015,,0,142.57, -4,2,21.12.2015,,9,142.57, -4,2,22.12.2015,1.0,85,142.57,103.02 -4,2,23.12.2015,1.0,79,142.57,103.02 -4,2,24.12.2015,1.0,47,142.57,103.02 -4,2,25.12.2015,1.0,107,142.57,103.02 -4,2,26.12.2015,1.0,16,142.57,103.02 -4,2,27.12.2015,1.0,69,142.57,103.02 -4,2,28.12.2015,1.0,69,142.57,103.02 -4,2,29.12.2015,,3,138.5, -4,2,30.12.2015,,3,138.5, -4,2,31.12.2015,,3,138.5, -4,2,01.01.2016,,3,138.5, -4,2,02.01.2016,,3,138.5, -4,2,03.01.2016,,3,138.5, -4,2,04.01.2016,,19,138.5, -4,2,05.01.2016,,3,138.5, -4,2,06.01.2016,,3,138.5, -4,2,07.01.2016,,3,138.5, -4,2,08.01.2016,,3,138.5, -4,2,09.01.2016,,0,138.5, -4,2,10.01.2016,,16,138.5, -4,2,11.01.2016,,3,138.5, -4,2,12.01.2016,,19,138.5, -4,2,13.01.2016,,6,138.5, -4,2,14.01.2016,,9,138.5, -4,2,15.01.2016,,3,138.5, -4,2,16.01.2016,,6,142.57, -4,2,17.01.2016,,6,142.57, -4,2,18.01.2016,,6,142.57, -4,2,19.01.2016,1.0,170,142.57,103.02 -4,2,20.01.2016,1.0,50,142.57,103.02 -4,2,21.01.2016,1.0,85,142.57,103.02 -4,2,22.01.2016,1.0,141,142.57,103.02 -4,2,23.01.2016,1.0,75,142.57,103.02 -4,2,24.01.2016,1.0,13,142.57,103.02 -4,2,25.01.2016,1.0,6,142.57,103.02 -4,2,26.01.2016,,9,142.57, -4,2,27.01.2016,,9,142.57, -4,2,28.01.2016,,9,142.57, -4,2,29.01.2016,,6,142.57, -4,2,30.01.2016,,0,142.57, -4,2,31.01.2016,,19,142.57, -4,2,01.02.2016,,3,142.57, -4,2,02.02.2016,,9,142.57, -4,2,03.02.2016,,9,142.57, -4,2,04.02.2016,,9,142.57, -4,2,05.02.2016,,9,142.57, -4,2,06.02.2016,,0,142.57, -4,2,07.02.2016,,0,142.57, -4,2,08.02.2016,,6,142.57, -4,2,09.02.2016,1.0,132,142.57,108.46 -4,2,10.02.2016,1.0,69,138.36,108.46 -4,2,11.02.2016,1.0,119,138.36,108.46 -4,2,12.02.2016,1.0,167,138.36,108.46 -4,2,13.02.2016,1.0,119,138.36,108.46 -4,2,14.02.2016,1.0,85,138.36,108.46 -4,2,15.02.2016,1.0,91,138.36,108.46 -4,2,16.02.2016,,6,138.36, -4,2,17.02.2016,,6,138.36, -4,2,18.02.2016,,6,138.36, -4,2,19.02.2016,,6,138.36, -4,2,20.02.2016,,6,138.36, -4,2,21.02.2016,,6,138.36, -4,2,22.02.2016,,6,138.36, -4,2,23.02.2016,,6,138.36, -4,2,24.02.2016,,6,138.36, -4,2,25.02.2016,,6,138.36, -4,2,26.02.2016,,6,138.36, -4,2,27.02.2016,,6,138.36, -4,2,28.02.2016,,6,138.36, -4,2,29.02.2016,,6,138.36, -4,2,01.03.2016,,6,138.36, -4,2,02.03.2016,,6,138.36, -4,2,03.03.2016,,6,138.36, -4,2,04.03.2016,,6,138.36, -4,2,05.03.2016,,6,138.36, -4,2,06.03.2016,,0,138.36, -4,2,07.03.2016,,0,138.36, -4,2,08.03.2016,,0,138.36, -4,2,09.03.2016,,0,138.36, -4,2,10.03.2016,,0,138.36, -4,2,11.03.2016,,0,138.36, -4,2,12.03.2016,,0,138.5, -4,2,13.03.2016,,0,138.5, -4,2,14.03.2016,,3,138.5, -4,2,15.03.2016,1.0,31,138.5,124.77 -4,2,16.03.2016,1.0,60,138.5,124.77 -4,2,17.03.2016,1.0,35,138.5,124.77 -4,2,18.03.2016,1.0,57,138.5,124.77 -4,2,19.03.2016,1.0,69,138.5,124.77 -4,2,20.03.2016,1.0,72,138.5,124.77 -4,2,21.03.2016,1.0,47,138.5,124.77 -4,2,22.03.2016,,3,138.36, -4,2,23.03.2016,,3,138.36, -4,2,24.03.2016,,3,138.36, -4,2,25.03.2016,,3,138.36, -4,2,26.03.2016,,6,138.36, -4,2,27.03.2016,,3,138.36, -4,2,28.03.2016,,3,138.36, -4,2,29.03.2016,,3,138.36, -4,2,30.03.2016,,3,138.36, -4,2,31.03.2016,,3,138.36, -4,2,01.04.2016,,3,138.36, -4,2,02.04.2016,,3,138.36, -4,2,03.04.2016,,3,138.36, -4,2,04.04.2016,,3,138.36, -4,2,05.04.2016,,3,138.36, -4,2,06.04.2016,,3,138.36, -4,2,07.04.2016,,3,138.36, -4,2,08.04.2016,,3,138.36, -4,2,09.04.2016,,3,138.5, -4,2,10.04.2016,,3,138.5, -4,2,11.04.2016,,3,138.5, -4,2,12.04.2016,,3,138.5, -4,2,13.04.2016,,3,138.5, -4,2,14.04.2016,,3,138.5, -4,2,15.04.2016,,3,138.5, -4,2,16.04.2016,,3,138.5, -4,2,17.04.2016,,3,138.5, -4,2,18.04.2016,,0,138.5, -4,2,19.04.2016,1.0,47,138.5,116.89 -4,2,20.04.2016,1.0,66,138.5,116.89 -4,2,21.04.2016,1.0,85,138.5,116.89 -4,2,22.04.2016,1.0,104,138.5,116.89 -4,2,23.04.2016,1.0,53,138.5,116.89 -4,2,24.04.2016,1.0,38,138.5,116.89 -4,2,25.04.2016,1.0,60,138.5,116.89 -4,2,26.04.2016,,3,138.5, -4,2,27.04.2016,,0,138.5, -4,2,28.04.2016,,0,138.5, -4,2,29.04.2016,,0,138.5, -4,2,30.04.2016,,0,138.5, -4,2,01.05.2016,,3,138.5, -4,2,02.05.2016,,16,138.5, -4,2,03.05.2016,1.0,126,138.5,106.01 -4,2,04.05.2016,1.0,188,138.5,106.01 -4,2,05.05.2016,1.0,192,138.5,106.01 -4,2,06.05.2016,1.0,154,138.5,106.01 -4,2,07.05.2016,1.0,141,138.5,106.01 -4,2,08.05.2016,1.0,94,138.5,106.01 -4,2,09.05.2016,1.0,94,138.5,106.01 -4,2,10.05.2016,,0,142.57, -4,2,11.05.2016,,0,142.57, -4,2,12.05.2016,,0,142.57, -4,2,13.05.2016,,0,142.57, -4,2,14.05.2016,,0,142.57, -4,2,15.05.2016,,0,142.57, -4,2,16.05.2016,,0,142.57, -4,2,17.05.2016,,0,142.57, -4,2,18.05.2016,,0,142.57, -4,2,19.05.2016,,0,142.57, -4,2,20.05.2016,,0,142.57, -4,2,21.05.2016,,0,142.57, -4,2,22.05.2016,,0,142.57, -5,2,01.01.2015,,0,135.78, -5,2,02.01.2015,,3,135.78, -5,2,03.01.2015,,3,135.78, -5,2,04.01.2015,,6,135.78, -5,2,05.01.2015,,0,135.78, -5,2,06.01.2015,,0,135.78, -5,2,07.01.2015,,0,135.78, -5,2,08.01.2015,,16,135.78, -5,2,09.01.2015,,0,135.78, -5,2,10.01.2015,,0,135.78, -5,2,11.01.2015,,9,135.78, -5,2,12.01.2015,,16,135.78, -5,2,13.01.2015,,3,135.78, -5,2,14.01.2015,,3,135.78, -5,2,15.01.2015,,3,135.78, -5,2,16.01.2015,,0,135.78, -5,2,17.01.2015,,0,135.78, -5,2,18.01.2015,,0,135.78, -5,2,19.01.2015,,0,135.78, -5,2,20.01.2015,,3,135.78, -5,2,21.01.2015,,0,135.78, -5,2,22.01.2015,,0,135.78, -5,2,23.01.2015,,3,135.78, -5,2,24.01.2015,,0,135.78, -5,2,25.01.2015,,0,135.78, -5,2,26.01.2015,,0,135.78, -5,2,27.01.2015,,0,135.78, -5,2,28.01.2015,,0,135.78, -5,2,29.01.2015,,0,135.78, -5,2,30.01.2015,,0,135.78, -5,2,31.01.2015,,0,135.78, -5,2,01.02.2015,,3,135.78, -5,2,02.02.2015,,3,135.78, -5,2,03.02.2015,,9,135.78, -5,2,04.02.2015,,3,135.78, -5,2,05.02.2015,,0,135.78, -5,2,06.02.2015,,3,135.78, -5,2,07.02.2015,,0,135.78, -5,2,08.02.2015,,6,135.78, -5,2,09.02.2015,,0,135.78, -5,2,10.02.2015,,6,135.78, -5,2,11.02.2015,,6,135.78, -5,2,12.02.2015,,0,135.78, -5,2,13.02.2015,,0,135.78, -5,2,14.02.2015,,0,135.78, -5,2,15.02.2015,,9,135.78, -5,2,16.02.2015,,0,135.78, -5,2,17.02.2015,,0,135.78, -5,2,18.02.2015,,9,135.78, -5,2,19.02.2015,,0,135.78, -5,2,20.02.2015,,3,135.78, -5,2,21.02.2015,,0,135.78, -5,2,22.02.2015,,3,135.78, -5,2,23.02.2015,,3,135.78, -5,2,24.02.2015,,0,135.78, -5,2,25.02.2015,,0,135.78, -5,2,26.02.2015,,3,135.78, -5,2,27.02.2015,,0,135.78, -5,2,28.02.2015,,9,135.78, -5,2,01.03.2015,,3,135.78, -5,2,02.03.2015,,3,135.78, -5,2,03.03.2015,,0,135.78, -5,2,04.03.2015,,3,135.78, -5,2,05.03.2015,,0,135.78, -5,2,06.03.2015,,3,135.78, -5,2,07.03.2015,,6,135.78, -5,2,08.03.2015,,3,146.38, -5,2,09.03.2015,,9,146.38, -5,2,10.03.2015,,0,146.38, -5,2,11.03.2015,,0,146.38, -5,2,12.03.2015,,3,146.38, -5,2,13.03.2015,,0,146.38, -5,2,14.03.2015,,3,146.38, -5,2,15.03.2015,,0,146.38, -5,2,16.03.2015,,6,146.38, -5,2,17.03.2015,,6,146.38, -5,2,18.03.2015,,0,146.38, -5,2,19.03.2015,,0,146.38, -5,2,20.03.2015,,0,146.38, -5,2,21.03.2015,,6,146.38, -5,2,22.03.2015,,0,146.38, -5,2,23.03.2015,,6,146.38, -5,2,24.03.2015,,3,146.38, -5,2,25.03.2015,,0,146.38, -5,2,26.03.2015,,0,146.38, -5,2,27.03.2015,,0,146.38, -5,2,28.03.2015,,0,146.38, -5,2,29.03.2015,,3,146.38, -5,2,30.03.2015,,0,146.38, -5,2,31.03.2015,,3,146.38, -5,2,01.04.2015,,0,146.38, -5,2,02.04.2015,,3,146.38, -5,2,03.04.2015,,0,146.38, -5,2,04.04.2015,,3,146.38, -5,2,05.04.2015,,0,146.38, -5,2,06.04.2015,,3,146.38, -5,2,07.04.2015,,0,146.38, -5,2,08.04.2015,,0,146.38, -5,2,09.04.2015,,0,146.38, -5,2,10.04.2015,,16,146.38, -5,2,11.04.2015,,0,146.38, -5,2,12.04.2015,,0,146.38, -5,2,13.04.2015,,0,146.38, -5,2,14.04.2015,,0,146.38, -5,2,15.04.2015,,3,146.38, -5,2,16.04.2015,,0,146.38, -5,2,17.04.2015,,3,146.38, -5,2,18.04.2015,,0,146.38, -5,2,19.04.2015,,0,146.38, -5,2,20.04.2015,,0,146.38, -5,2,21.04.2015,,0,146.38, -5,2,22.04.2015,,0,146.38, -5,2,23.04.2015,,0,146.38, -5,2,24.04.2015,,0,146.38, -5,2,25.04.2015,,0,146.38, -5,2,26.04.2015,,0,146.38, -5,2,27.04.2015,,0,150.73, -5,2,28.04.2015,,0,150.73, -5,2,29.04.2015,,3,150.73, -5,2,30.04.2015,,0,150.73, -5,2,01.05.2015,,0,150.73, -5,2,02.05.2015,,0,150.73, -5,2,03.05.2015,,0,150.73, -5,2,04.05.2015,,0,150.73, -5,2,05.05.2015,,0,150.73, -5,2,06.05.2015,,0,150.73, -5,2,07.05.2015,,0,150.73, -5,2,08.05.2015,,0,150.73, -5,2,09.05.2015,,0,150.73, -5,2,10.05.2015,,0,150.73, -5,2,11.05.2015,,0,150.73, -5,2,12.05.2015,,0,150.73, -5,2,13.05.2015,,9,150.73, -5,2,14.05.2015,,3,150.73, -5,2,15.05.2015,,0,150.73, -5,2,16.05.2015,,3,150.73, -5,2,17.05.2015,,0,150.73, -5,2,18.05.2015,,0,150.73, -5,2,19.05.2015,,6,150.73, -5,2,20.05.2015,,0,150.73, -5,2,21.05.2015,,0,150.73, -5,2,22.05.2015,,0,150.73, -5,2,23.05.2015,,3,150.73, -5,2,24.05.2015,,0,150.73, -5,2,25.05.2015,,0,150.73, -5,2,26.05.2015,,3,150.73, -5,2,27.05.2015,,0,150.73, -5,2,28.05.2015,,0,150.73, -5,2,29.05.2015,,0,150.73, -5,2,30.05.2015,,0,150.73, -5,2,31.05.2015,,0,150.73, -5,2,01.06.2015,,0,150.73, -5,2,02.06.2015,,0,150.73, -5,2,03.06.2015,,0,150.73, -5,2,04.06.2015,,0,150.73, -5,2,05.06.2015,,0,150.73, -5,2,06.06.2015,,0,150.73, -5,2,07.06.2015,,3,150.73, -5,2,08.06.2015,,0,150.73, -5,2,09.06.2015,,0,150.73, -5,2,10.06.2015,,3,150.73, -5,2,11.06.2015,,3,150.73, -5,2,12.06.2015,,3,150.73, -5,2,13.06.2015,,3,150.73, -5,2,14.06.2015,,0,150.73, -5,2,15.06.2015,,6,150.73, -5,2,16.06.2015,,3,150.73, -5,2,17.06.2015,,0,150.73, -5,2,18.06.2015,,0,150.73, -5,2,19.06.2015,,0,150.73, -5,2,20.06.2015,,0,150.73, -5,2,21.06.2015,,0,150.73, -5,2,22.06.2015,,0,150.73, -5,2,23.06.2015,,0,150.73, -5,2,24.06.2015,,3,150.73, -5,2,25.06.2015,,0,150.73, -5,2,26.06.2015,,0,150.73, -5,2,27.06.2015,,0,150.73, -5,2,28.06.2015,,0,150.73, -5,2,29.06.2015,,3,150.73, -5,2,30.06.2015,,0,150.73, -5,2,01.07.2015,,0,150.73, -5,2,02.07.2015,,0,150.73, -5,2,03.07.2015,,0,150.73, -5,2,04.07.2015,,0,150.73, -5,2,05.07.2015,,0,150.73, -5,2,06.07.2015,,0,150.73, -5,2,07.07.2015,,0,150.73, -5,2,08.07.2015,,0,150.73, -5,2,09.07.2015,,0,150.73, -5,2,10.07.2015,,0,150.73, -5,2,11.07.2015,,3,150.73, -5,2,12.07.2015,,6,150.73, -5,2,13.07.2015,,0,150.73, -5,2,14.07.2015,,0,150.73, -5,2,15.07.2015,,0,150.73, -5,2,16.07.2015,,0,150.73, -5,2,17.07.2015,,3,150.73, -5,2,18.07.2015,,0,150.73, -5,2,19.07.2015,,0,150.73, -5,2,20.07.2015,,0,150.73, -5,2,21.07.2015,,0,150.73, -5,2,22.07.2015,,13,150.73, -5,2,23.07.2015,,9,150.73, -5,2,24.07.2015,,3,150.73, -5,2,25.07.2015,,0,150.73, -5,2,26.07.2015,,0,150.73, -5,2,27.07.2015,,0,150.73, -5,2,28.07.2015,,0,150.73, -5,2,29.07.2015,,0,150.73, -5,2,30.07.2015,,0,150.73, -5,2,31.07.2015,,0,150.73, -5,2,01.08.2015,,0,150.73, -5,2,02.08.2015,,0,150.73, -5,2,03.08.2015,,0,150.73, -5,2,04.08.2015,,6,150.73, -5,2,05.08.2015,,9,150.73, -5,2,06.08.2015,,6,150.73, -5,2,07.08.2015,,13,150.73, -5,2,08.08.2015,,16,150.73, -5,2,09.08.2015,,0,150.73, -5,2,10.08.2015,,6,150.73, -5,2,11.08.2015,,3,150.73, -5,2,12.08.2015,,9,128.17, -5,2,13.08.2015,,3,128.17, -5,2,14.08.2015,,0,128.17, -5,2,15.08.2015,,3,128.17, -5,2,16.08.2015,,0,128.17, -5,2,17.08.2015,,19,128.17, -5,2,18.08.2015,1.0,182,128.17,100.3 -5,2,19.08.2015,1.0,69,128.17,100.3 -5,2,20.08.2015,1.0,129,128.17,100.3 -5,2,21.08.2015,1.0,148,128.17,100.3 -5,2,22.08.2015,1.0,75,128.17,100.3 -5,2,23.08.2015,1.0,69,128.17,100.3 -5,2,24.08.2015,1.0,66,128.17,100.3 -5,2,25.08.2015,,0,128.17, -5,2,26.08.2015,,0,128.17, -5,2,27.08.2015,,3,128.17, -5,2,28.08.2015,,22,128.17, -5,2,29.08.2015,,0,128.17, -5,2,30.08.2015,,0,128.17, -5,2,31.08.2015,,0,128.17, -5,2,01.09.2015,,13,128.17, -5,2,02.09.2015,,3,128.17, -5,2,03.09.2015,,0,128.17, -5,2,04.09.2015,,3,128.17, -5,2,05.09.2015,,13,128.17, -5,2,06.09.2015,,0,128.17, -5,2,07.09.2015,,6,128.17, -5,2,08.09.2015,,0,128.17, -5,2,09.09.2015,,3,128.17, -5,2,10.09.2015,,3,128.17, -5,2,11.09.2015,,0,162.96, -5,2,12.09.2015,,9,162.96, -5,2,13.09.2015,,9,162.96, -5,2,14.09.2015,,16,162.96, -5,2,15.09.2015,,0,162.96, -5,2,16.09.2015,,3,162.96, -5,2,17.09.2015,,0,162.96, -5,2,18.09.2015,,0,162.96, -5,2,19.09.2015,,0,162.96, -5,2,20.09.2015,,0,162.96, -5,2,21.09.2015,,0,162.96, -5,2,22.09.2015,,3,162.96, -5,2,23.09.2015,,16,162.96, -5,2,24.09.2015,,0,162.96, -5,2,25.09.2015,,3,162.96, -5,2,26.09.2015,,3,162.96, -5,2,27.09.2015,,13,162.96, -5,2,28.09.2015,,6,162.96, -5,2,29.09.2015,,13,162.96, -5,2,30.09.2015,,0,162.96, -5,2,01.10.2015,,0,162.96, -5,2,02.10.2015,,0,162.96, -5,2,03.10.2015,,3,162.96, -5,2,04.10.2015,,0,162.96, -5,2,05.10.2015,,3,162.96, -5,2,06.10.2015,1.0,101,143.39,122.05 -5,2,07.10.2015,1.0,82,143.39,122.05 -5,2,08.10.2015,1.0,104,143.39,122.05 -5,2,09.10.2015,1.0,88,143.39,122.05 -5,2,10.10.2015,1.0,129,143.39,122.05 -5,2,11.10.2015,1.0,22,143.39,122.05 -5,2,12.10.2015,1.0,69,143.39,122.05 -5,2,13.10.2015,1.0,57,143.93,122.05 -5,2,14.10.2015,1.0,57,143.93,122.05 -5,2,15.10.2015,1.0,47,143.93,122.05 -5,2,16.10.2015,1.0,38,143.93,122.05 -5,2,17.10.2015,1.0,38,162.96,122.05 -5,2,18.10.2015,1.0,38,162.96,122.05 -5,2,19.10.2015,,0,162.96, -5,2,20.10.2015,,0,162.96, -5,2,21.10.2015,,0,162.96, -5,2,22.10.2015,,0,162.96, -5,2,23.10.2015,,0,162.96, -5,2,24.10.2015,,0,162.96, -5,2,25.10.2015,,0,162.96, -5,2,26.10.2015,,0,162.96, -5,2,27.10.2015,,0,162.96, -5,2,28.10.2015,,0,162.96, -5,2,29.10.2015,,0,162.96, -5,2,30.10.2015,,0,162.96, -5,2,31.10.2015,,0,162.96, -5,2,01.11.2015,,6,162.96, -5,2,02.11.2015,,0,162.96, -5,2,03.11.2015,,0,162.96, -5,2,04.11.2015,,0,162.96, -5,2,05.11.2015,,3,162.96, -5,2,06.11.2015,,0,162.96, -5,2,07.11.2015,,0,162.96, -5,2,08.11.2015,,0,162.96, -5,2,09.11.2015,,0,162.96, -5,2,10.11.2015,,0,162.96, -5,2,11.11.2015,,0,138.36, -5,2,12.11.2015,,0,138.36, -5,2,13.11.2015,,3,138.36, -5,2,14.11.2015,,19,138.36, -5,2,15.11.2015,,16,138.36, -5,2,16.11.2015,,0,138.36, -5,2,17.11.2015,,0,138.36, -5,2,18.11.2015,,6,138.36, -5,2,19.11.2015,,6,138.36, -5,2,20.11.2015,,6,138.36, -5,2,21.11.2015,,0,138.36, -5,2,22.11.2015,,0,138.36, -5,2,23.11.2015,,0,138.36, -5,2,24.11.2015,,0,138.36, -5,2,25.11.2015,,0,138.36, -5,2,26.11.2015,,0,138.36, -5,2,27.11.2015,,0,138.36, -5,2,28.11.2015,,0,138.36, -5,2,29.11.2015,,0,138.36, -5,2,30.11.2015,,0,138.36, -5,2,01.12.2015,,0,162.96, -5,2,02.12.2015,,9,162.96, -5,2,03.12.2015,,3,138.5, -5,2,04.12.2015,,0,138.5, -5,2,05.12.2015,,3,138.5, -5,2,06.12.2015,,0,138.5, -5,2,07.12.2015,,0,138.5, -5,2,08.12.2015,,0,138.5, -5,2,09.12.2015,,0,138.5, -5,2,10.12.2015,,0,138.5, -5,2,11.12.2015,,0,142.57, -5,2,12.12.2015,,0,142.57, -5,2,13.12.2015,,0,142.57, -5,2,14.12.2015,,0,142.57, -5,2,15.12.2015,,0,142.57, -5,2,16.12.2015,,0,142.57, -5,2,17.12.2015,,0,142.57, -5,2,18.12.2015,,0,142.57, -5,2,19.12.2015,,0,142.57, -5,2,20.12.2015,,0,142.57, -5,2,21.12.2015,,3,142.57, -5,2,22.12.2015,1.0,270,142.57,103.02 -5,2,23.12.2015,1.0,66,142.57,103.02 -5,2,24.12.2015,1.0,129,142.57,103.02 -5,2,25.12.2015,1.0,261,142.57,103.02 -5,2,26.12.2015,1.0,138,142.57,103.02 -5,2,27.12.2015,1.0,53,142.57,103.02 -5,2,28.12.2015,1.0,28,142.57,103.02 -5,2,29.12.2015,,0,138.5, -5,2,30.12.2015,,0,138.5, -5,2,31.12.2015,,0,138.5, -5,2,01.01.2016,,6,138.5, -5,2,02.01.2016,,3,138.5, -5,2,03.01.2016,,9,138.5, -5,2,04.01.2016,,19,138.5, -5,2,05.01.2016,,16,138.5, -5,2,06.01.2016,,0,138.5, -5,2,07.01.2016,,9,138.5, -5,2,08.01.2016,,19,138.5, -5,2,09.01.2016,,0,138.5, -5,2,10.01.2016,,9,138.5, -5,2,11.01.2016,,0,138.5, -5,2,12.01.2016,,19,138.5, -5,2,13.01.2016,,9,138.5, -5,2,14.01.2016,,9,138.5, -5,2,15.01.2016,,9,138.5, -5,2,16.01.2016,,9,142.57, -5,2,17.01.2016,,25,142.57, -5,2,18.01.2016,,31,142.57, -5,2,19.01.2016,1.0,251,142.57,103.02 -5,2,20.01.2016,1.0,330,142.57,103.02 -5,2,21.01.2016,1.0,201,142.57,103.02 -5,2,22.01.2016,1.0,182,142.57,103.02 -5,2,23.01.2016,1.0,236,142.57,103.02 -5,2,24.01.2016,1.0,195,142.57,103.02 -5,2,25.01.2016,1.0,19,142.57,103.02 -5,2,26.01.2016,,13,142.57, -5,2,27.01.2016,,19,142.57, -5,2,28.01.2016,,19,142.57, -5,2,29.01.2016,,6,142.57, -5,2,30.01.2016,,16,142.57, -5,2,31.01.2016,,3,142.57, -5,2,01.02.2016,,6,142.57, -5,2,02.02.2016,,3,142.57, -5,2,03.02.2016,,9,142.57, -5,2,04.02.2016,,9,142.57, -5,2,05.02.2016,,22,142.57, -5,2,06.02.2016,,16,142.57, -5,2,07.02.2016,,22,142.57, -5,2,08.02.2016,,6,142.57, -5,2,09.02.2016,1.0,239,142.57,108.46 -5,2,10.02.2016,1.0,229,138.36,108.46 -5,2,11.02.2016,1.0,129,138.36,108.46 -5,2,12.02.2016,1.0,232,138.36,108.46 -5,2,13.02.2016,1.0,248,138.36,108.46 -5,2,14.02.2016,1.0,25,138.36,108.46 -5,2,15.02.2016,1.0,41,138.36,108.46 -5,2,16.02.2016,,13,138.36, -5,2,17.02.2016,,3,138.36, -5,2,18.02.2016,,3,138.36, -5,2,19.02.2016,,3,138.36, -5,2,20.02.2016,,9,138.36, -5,2,21.02.2016,,3,138.36, -5,2,22.02.2016,,3,138.36, -5,2,23.02.2016,,9,138.36, -5,2,24.02.2016,,9,138.36, -5,2,25.02.2016,,9,138.36, -5,2,26.02.2016,,9,138.36, -5,2,27.02.2016,,9,138.36, -5,2,28.02.2016,,9,138.36, -5,2,29.02.2016,,9,138.36, -5,2,01.03.2016,,9,138.36, -5,2,02.03.2016,,9,138.36, -5,2,03.03.2016,,9,138.36, -5,2,04.03.2016,,9,138.36, -5,2,05.03.2016,,0,138.36, -5,2,06.03.2016,,3,138.36, -5,2,07.03.2016,,0,138.36, -5,2,08.03.2016,,6,138.36, -5,2,09.03.2016,,9,138.36, -5,2,10.03.2016,,13,138.36, -5,2,11.03.2016,,0,138.36, -5,2,12.03.2016,,0,138.5, -5,2,13.03.2016,,6,138.5, -5,2,14.03.2016,,0,138.5, -5,2,15.03.2016,1.0,88,138.5,124.77 -5,2,16.03.2016,1.0,104,138.5,124.77 -5,2,17.03.2016,1.0,94,138.5,124.77 -5,2,18.03.2016,1.0,38,138.5,124.77 -5,2,19.03.2016,1.0,79,138.5,124.77 -5,2,20.03.2016,1.0,110,138.5,124.77 -5,2,21.03.2016,1.0,28,138.5,124.77 -5,2,22.03.2016,,6,138.36, -5,2,23.03.2016,,13,138.36, -5,2,24.03.2016,,3,138.36, -5,2,25.03.2016,,19,138.36, -5,2,26.03.2016,,0,138.36, -5,2,27.03.2016,,0,138.36, -5,2,28.03.2016,,6,138.36, -5,2,29.03.2016,,0,138.36, -5,2,30.03.2016,,0,138.36, -5,2,31.03.2016,,0,138.36, -5,2,01.04.2016,,3,138.36, -5,2,02.04.2016,,16,138.36, -5,2,03.04.2016,,9,138.36, -5,2,04.04.2016,,0,138.36, -5,2,05.04.2016,,9,138.36, -5,2,06.04.2016,,6,138.36, -5,2,07.04.2016,,0,138.36, -5,2,08.04.2016,,9,138.36, -5,2,09.04.2016,,9,138.5, -5,2,10.04.2016,,3,138.5, -5,2,11.04.2016,,0,138.5, -5,2,12.04.2016,,0,138.5, -5,2,13.04.2016,,0,138.5, -5,2,14.04.2016,,0,138.5, -5,2,15.04.2016,,0,138.5, -5,2,16.04.2016,,6,138.5, -5,2,17.04.2016,,6,138.5, -5,2,18.04.2016,,13,138.5, -5,2,19.04.2016,1.0,154,138.5,116.89 -5,2,20.04.2016,1.0,145,138.5,116.89 -5,2,21.04.2016,1.0,94,138.5,116.89 -5,2,22.04.2016,1.0,145,138.5,116.89 -5,2,23.04.2016,1.0,154,138.5,116.89 -5,2,24.04.2016,1.0,138,138.5,116.89 -5,2,25.04.2016,1.0,72,138.5,116.89 -5,2,26.04.2016,,0,138.5, -5,2,27.04.2016,,3,138.5, -5,2,28.04.2016,,0,138.5, -5,2,29.04.2016,,3,138.5, -5,2,30.04.2016,,16,138.5, -5,2,01.05.2016,,3,138.5, -5,2,02.05.2016,,13,138.5, -5,2,03.05.2016,1.0,261,138.5,106.01 -5,2,04.05.2016,1.0,245,138.5,106.01 -5,2,05.05.2016,1.0,163,138.5,106.01 -5,2,06.05.2016,1.0,229,138.5,106.01 -5,2,07.05.2016,1.0,188,138.5,106.01 -5,2,08.05.2016,1.0,135,138.5,106.01 -5,2,09.05.2016,1.0,113,138.5,106.01 -5,2,10.05.2016,,6,142.57, -5,2,11.05.2016,,3,142.57, -5,2,12.05.2016,,3,142.57, -5,2,13.05.2016,,6,142.57, -5,2,14.05.2016,,6,142.57, -5,2,15.05.2016,,25,142.57, -5,2,16.05.2016,,16,142.57, -5,2,17.05.2016,,0,142.57, -5,2,18.05.2016,,0,142.57, -5,2,19.05.2016,,0,142.57, -5,2,20.05.2016,,0,142.57, -5,2,21.05.2016,,3,142.57, -5,2,22.05.2016,,3,142.57, -6,2,01.01.2015,,9,135.78, -6,2,02.01.2015,,9,135.78, -6,2,03.01.2015,,0,135.78, -6,2,04.01.2015,,0,135.78, -6,2,05.01.2015,,0,135.78, -6,2,06.01.2015,,0,135.78, -6,2,07.01.2015,,0,135.78, -6,2,08.01.2015,,6,135.78, -6,2,09.01.2015,,6,135.78, -6,2,10.01.2015,,6,135.78, -6,2,11.01.2015,,6,135.78, -6,2,12.01.2015,,6,135.78, -6,2,13.01.2015,,6,135.78, -6,2,14.01.2015,,6,135.78, -6,2,15.01.2015,,6,135.78, -6,2,16.01.2015,,6,135.78, -6,2,17.01.2015,,6,135.78, -6,2,18.01.2015,,6,135.78, -6,2,19.01.2015,,6,135.78, -6,2,20.01.2015,,6,135.78, -6,2,21.01.2015,,6,135.78, -6,2,22.01.2015,,6,135.78, -6,2,23.01.2015,,19,135.78, -6,2,24.01.2015,,9,135.78, -6,2,25.01.2015,,16,135.78, -6,2,26.01.2015,,3,135.78, -6,2,27.01.2015,,3,135.78, -6,2,28.01.2015,,0,135.78, -6,2,29.01.2015,,3,135.78, -6,2,30.01.2015,,22,135.78, -6,2,31.01.2015,,19,135.78, -6,2,01.02.2015,,13,135.78, -6,2,02.02.2015,,19,135.78, -6,2,03.02.2015,,13,135.78, -6,2,04.02.2015,,38,135.78, -6,2,05.02.2015,,3,135.78, -6,2,06.02.2015,,25,135.78, -6,2,07.02.2015,,31,135.78, -6,2,08.02.2015,,6,135.78, -6,2,09.02.2015,,3,135.78, -6,2,10.02.2015,,0,135.78, -6,2,11.02.2015,,6,135.78, -6,2,12.02.2015,,3,135.78, -6,2,13.02.2015,,16,135.78, -6,2,14.02.2015,,0,135.78, -6,2,15.02.2015,,0,135.78, -6,2,16.02.2015,,19,135.78, -6,2,17.02.2015,,6,135.78, -6,2,18.02.2015,,9,135.78, -6,2,19.02.2015,,9,135.78, -6,2,20.02.2015,,6,135.78, -6,2,21.02.2015,,0,135.78, -6,2,22.02.2015,,0,135.78, -6,2,23.02.2015,,0,135.78, -6,2,24.02.2015,,0,135.78, -6,2,25.02.2015,,0,135.78, -6,2,26.02.2015,,0,135.78, -6,2,27.02.2015,,3,135.78, -6,2,28.02.2015,,0,135.78, -6,2,01.03.2015,,3,135.78, -6,2,02.03.2015,,3,135.78, -6,2,03.03.2015,,0,135.78, -6,2,04.03.2015,,0,135.78, -6,2,05.03.2015,,0,135.78, -6,2,06.03.2015,,13,135.78, -6,2,07.03.2015,,3,146.38, -6,2,08.03.2015,,0,146.38, -6,2,09.03.2015,,6,146.38, -6,2,10.03.2015,,0,146.38, -6,2,11.03.2015,,13,146.38, -6,2,12.03.2015,,19,146.38, -6,2,13.03.2015,,6,146.38, -6,2,14.03.2015,,60,146.38, -6,2,15.03.2015,,0,146.38, -6,2,16.03.2015,,0,146.38, -6,2,17.03.2015,,0,146.38, -6,2,18.03.2015,,25,146.38, -6,2,19.03.2015,,0,146.38, -6,2,20.03.2015,,3,146.38, -6,2,21.03.2015,,28,146.38, -6,2,22.03.2015,,0,146.38, -6,2,23.03.2015,,16,146.38, -6,2,24.03.2015,,3,146.38, -6,2,25.03.2015,,6,146.38, -6,2,26.03.2015,,6,146.38, -6,2,27.03.2015,,3,146.38, -6,2,28.03.2015,,6,146.38, -6,2,29.03.2015,,13,146.38, -6,2,30.03.2015,,0,146.38, -6,2,31.03.2015,,0,146.38, -6,2,01.04.2015,,0,146.38, -6,2,02.04.2015,,0,146.38, -6,2,03.04.2015,,6,146.38, -6,2,04.04.2015,,3,146.38, -6,2,05.04.2015,,19,146.38, -6,2,06.04.2015,,13,146.38, -6,2,07.04.2015,,6,146.38, -6,2,08.04.2015,,0,146.38, -6,2,09.04.2015,,0,146.38, -6,2,10.04.2015,,16,146.38, -6,2,11.04.2015,,3,146.38, -6,2,12.04.2015,,0,146.38, -6,2,13.04.2015,,0,146.38, -6,2,14.04.2015,,6,146.38, -6,2,15.04.2015,,6,146.38, -6,2,16.04.2015,,3,146.38, -6,2,17.04.2015,,6,146.38, -6,2,18.04.2015,,0,146.38, -6,2,19.04.2015,,3,146.38, -6,2,20.04.2015,,9,146.38, -6,2,21.04.2015,,0,146.38, -6,2,22.04.2015,,6,146.38, -6,2,23.04.2015,,3,146.38, -6,2,24.04.2015,,0,146.38, -6,2,25.04.2015,,3,146.38, -6,2,26.04.2015,,3,146.38, -6,2,27.04.2015,,0,150.73, -6,2,28.04.2015,,3,150.73, -6,2,29.04.2015,,6,150.73, -6,2,30.04.2015,,9,150.73, -6,2,01.05.2015,,3,150.73, -6,2,02.05.2015,,3,150.73, -6,2,03.05.2015,,16,150.73, -6,2,04.05.2015,,0,150.73, -6,2,05.05.2015,,6,150.73, -6,2,06.05.2015,,19,150.73, -6,2,07.05.2015,,3,150.73, -6,2,08.05.2015,,3,150.73, -6,2,09.05.2015,,0,150.73, -6,2,10.05.2015,,0,150.73, -6,2,11.05.2015,,3,150.73, -6,2,12.05.2015,,0,150.73, -6,2,13.05.2015,,13,150.73, -6,2,14.05.2015,,6,150.73, -6,2,15.05.2015,,0,150.73, -6,2,16.05.2015,,0,150.73, -6,2,17.05.2015,,0,150.73, -6,2,18.05.2015,,3,150.73, -6,2,19.05.2015,,3,150.73, -6,2,20.05.2015,,3,150.73, -6,2,21.05.2015,,3,150.73, -6,2,22.05.2015,,0,150.73, -6,2,23.05.2015,,0,150.73, -6,2,24.05.2015,,3,150.73, -6,2,25.05.2015,,0,150.73, -6,2,26.05.2015,,0,150.73, -6,2,27.05.2015,,0,150.73, -6,2,28.05.2015,,0,150.73, -6,2,29.05.2015,,0,150.73, -6,2,30.05.2015,,6,150.73, -6,2,31.05.2015,,13,150.73, -6,2,01.06.2015,,6,150.73, -6,2,02.06.2015,,0,150.73, -6,2,03.06.2015,,0,150.73, -6,2,04.06.2015,,0,150.73, -6,2,05.06.2015,,0,150.73, -6,2,06.06.2015,,6,150.73, -6,2,07.06.2015,,0,150.73, -6,2,08.06.2015,,6,150.73, -6,2,09.06.2015,,3,150.73, -6,2,10.06.2015,,0,150.73, -6,2,11.06.2015,,0,150.73, -6,2,12.06.2015,,0,150.73, -6,2,13.06.2015,,0,150.73, -6,2,14.06.2015,,0,150.73, -6,2,15.06.2015,,0,150.73, -6,2,16.06.2015,,6,150.73, -6,2,17.06.2015,,9,150.73, -6,2,18.06.2015,,6,150.73, -6,2,19.06.2015,,3,150.73, -6,2,20.06.2015,,16,150.73, -6,2,21.06.2015,,0,150.73, -6,2,22.06.2015,,3,150.73, -6,2,23.06.2015,,3,150.73, -6,2,24.06.2015,,3,150.73, -6,2,25.06.2015,,6,150.73, -6,2,26.06.2015,,0,150.73, -6,2,27.06.2015,,6,150.73, -6,2,28.06.2015,,0,150.73, -6,2,29.06.2015,,41,150.73, -6,2,30.06.2015,,22,150.73, -6,2,01.07.2015,,16,150.73, -6,2,02.07.2015,,3,150.73, -6,2,03.07.2015,,3,150.73, -6,2,04.07.2015,,6,150.73, -6,2,05.07.2015,,9,150.73, -6,2,06.07.2015,,13,150.73, -6,2,07.07.2015,,6,150.73, -6,2,08.07.2015,,3,150.73, -6,2,09.07.2015,,3,150.73, -6,2,10.07.2015,,6,150.73, -6,2,11.07.2015,,6,150.73, -6,2,12.07.2015,,0,150.73, -6,2,13.07.2015,,0,150.73, -6,2,14.07.2015,,25,150.73, -6,2,15.07.2015,,6,150.73, -6,2,16.07.2015,,0,150.73, -6,2,17.07.2015,,0,150.73, -6,2,18.07.2015,,19,150.73, -6,2,19.07.2015,,0,150.73, -6,2,20.07.2015,,6,150.73, -6,2,21.07.2015,,6,150.73, -6,2,22.07.2015,,6,150.73, -6,2,23.07.2015,,6,150.73, -6,2,24.07.2015,,16,150.73, -6,2,25.07.2015,,6,150.73, -6,2,26.07.2015,,3,150.73, -6,2,27.07.2015,,6,150.73, -6,2,28.07.2015,,9,150.73, -6,2,29.07.2015,,3,150.73, -6,2,30.07.2015,,6,150.73, -6,2,31.07.2015,,9,150.73, -6,2,01.08.2015,,13,150.73, -6,2,02.08.2015,,9,150.73, -6,2,03.08.2015,,6,150.73, -6,2,04.08.2015,,16,150.73, -6,2,05.08.2015,,9,150.73, -6,2,06.08.2015,,9,150.73, -6,2,07.08.2015,,9,150.73, -6,2,08.08.2015,,9,150.73, -6,2,09.08.2015,,9,150.73, -6,2,10.08.2015,,9,150.73, -6,2,11.08.2015,,9,150.73, -6,2,12.08.2015,,9,128.17, -6,2,13.08.2015,,9,128.17, -6,2,14.08.2015,,0,128.17, -6,2,15.08.2015,,13,128.17, -6,2,16.08.2015,,13,128.17, -6,2,17.08.2015,,9,128.17, -6,2,18.08.2015,1.0,126,128.17,100.3 -6,2,19.08.2015,1.0,104,128.17,100.3 -6,2,20.08.2015,1.0,145,128.17,100.3 -6,2,21.08.2015,1.0,97,128.17,100.3 -6,2,22.08.2015,1.0,57,128.17,100.3 -6,2,23.08.2015,1.0,97,128.17,100.3 -6,2,24.08.2015,1.0,97,128.17,100.3 -6,2,25.08.2015,,9,128.17, -6,2,26.08.2015,,16,128.17, -6,2,27.08.2015,,9,128.17, -6,2,28.08.2015,,16,128.17, -6,2,29.08.2015,,16,128.17, -6,2,30.08.2015,,16,128.17, -6,2,31.08.2015,,3,128.17, -6,2,01.09.2015,,19,128.17, -6,2,02.09.2015,,13,128.17, -6,2,03.09.2015,,13,128.17, -6,2,04.09.2015,,6,128.17, -6,2,05.09.2015,,35,128.17, -6,2,06.09.2015,,57,128.17, -6,2,07.09.2015,,16,128.17, -6,2,08.09.2015,,28,128.17, -6,2,09.09.2015,,22,128.17, -6,2,10.09.2015,,35,128.17, -6,2,11.09.2015,,9,128.17, -6,2,12.09.2015,,19,128.17, -6,2,13.09.2015,,16,128.17, -6,2,14.09.2015,,3,128.17, -6,2,15.09.2015,,22,128.17, -6,2,16.09.2015,,9,128.17, -6,2,17.09.2015,,28,128.17, -6,2,18.09.2015,,38,128.17, -6,2,19.09.2015,,16,128.17, -6,2,20.09.2015,,22,128.17, -6,2,21.09.2015,,22,128.17, -6,2,22.09.2015,,19,128.17, -6,2,23.09.2015,,6,128.17, -6,2,24.09.2015,,13,128.17, -6,2,25.09.2015,,9,128.17, -6,2,26.09.2015,,9,128.17, -6,2,27.09.2015,,0,128.17, -6,2,28.09.2015,,19,128.17, -6,2,29.09.2015,,16,128.17, -6,2,30.09.2015,,6,128.17, -6,2,01.10.2015,,13,162.96, -6,2,02.10.2015,,0,162.96, -6,2,03.10.2015,,6,162.96, -6,2,04.10.2015,,0,162.96, -6,2,05.10.2015,,16,162.96, -6,2,06.10.2015,1.0,110,143.39,122.05 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-6,2,08.11.2015,,0,162.96, -6,2,09.11.2015,,0,162.96, -6,2,10.11.2015,,3,162.96, -6,2,11.11.2015,,0,138.36, -6,2,12.11.2015,,6,138.36, -6,2,13.11.2015,,3,138.36, -6,2,14.11.2015,,0,138.36, -6,2,15.11.2015,,0,138.36, -6,2,16.11.2015,,6,138.36, -6,2,17.11.2015,,0,138.36, -6,2,18.11.2015,,6,138.36, -6,2,19.11.2015,,0,138.36, -6,2,20.11.2015,,0,138.36, -6,2,21.11.2015,,0,138.36, -6,2,22.11.2015,,0,138.36, -6,2,23.11.2015,,3,138.36, -6,2,24.11.2015,,0,138.36, -6,2,25.11.2015,,3,138.36, -6,2,26.11.2015,,0,138.36, -6,2,27.11.2015,,0,138.36, -6,2,28.11.2015,,0,138.36, -6,2,29.11.2015,,0,138.36, -6,2,30.11.2015,,0,138.36, -6,2,01.12.2015,,0,162.96, -6,2,02.12.2015,,0,162.96, -6,2,03.12.2015,,0,138.5, -6,2,04.12.2015,,0,138.5, -6,2,05.12.2015,,0,138.5, -6,2,06.12.2015,,0,138.5, -6,2,07.12.2015,,0,138.5, -6,2,08.12.2015,,3,138.5, -6,2,09.12.2015,,0,138.5, -6,2,10.12.2015,,13,138.5, -6,2,11.12.2015,,6,142.57, -6,2,12.12.2015,,0,142.57, -6,2,13.12.2015,,6,142.57, -6,2,14.12.2015,,22,142.57, -6,2,15.12.2015,,3,142.57, -6,2,16.12.2015,,3,142.57, -6,2,17.12.2015,,0,142.57, -6,2,18.12.2015,,6,142.57, -6,2,19.12.2015,,0,142.57, -6,2,20.12.2015,,3,142.57, -6,2,21.12.2015,,6,142.57, -6,2,22.12.2015,1.0,179,142.57,103.02 -6,2,23.12.2015,1.0,173,142.57,103.02 -6,2,24.12.2015,1.0,72,142.57,103.02 -6,2,25.12.2015,1.0,138,142.57,103.02 -6,2,26.12.2015,1.0,129,142.57,103.02 -6,2,27.12.2015,1.0,129,142.57,103.02 -6,2,28.12.2015,1.0,107,142.57,103.02 -6,2,29.12.2015,,6,138.5, -6,2,30.12.2015,,6,138.5, -6,2,31.12.2015,,6,138.5, -6,2,01.01.2016,,6,138.5, -6,2,02.01.2016,,6,138.5, -6,2,03.01.2016,,6,138.5, -6,2,04.01.2016,,6,138.5, -6,2,05.01.2016,,16,138.5, -6,2,06.01.2016,,22,138.5, -6,2,07.01.2016,,6,138.5, -6,2,08.01.2016,,6,138.5, -6,2,09.01.2016,,6,138.5, -6,2,10.01.2016,,6,138.5, -6,2,11.01.2016,,6,138.5, -6,2,12.01.2016,,9,138.5, -6,2,13.01.2016,,25,138.5, -6,2,14.01.2016,,6,138.5, -6,2,15.01.2016,,0,138.5, -6,2,16.01.2016,,6,142.57, -6,2,17.01.2016,,22,142.57, -6,2,18.01.2016,,6,142.57, -6,2,19.01.2016,1.0,151,142.57,103.02 -6,2,20.01.2016,1.0,324,142.57,103.02 -6,2,21.01.2016,1.0,91,142.57,103.02 -6,2,22.01.2016,1.0,101,142.57,103.02 -6,2,23.01.2016,1.0,119,142.57,103.02 -6,2,24.01.2016,1.0,185,142.57,103.02 -6,2,25.01.2016,1.0,173,142.57,103.02 -6,2,26.01.2016,,9,142.57, -6,2,27.01.2016,,6,142.57, -6,2,28.01.2016,,6,142.57, -6,2,29.01.2016,,3,142.57, -6,2,30.01.2016,,3,142.57, -6,2,31.01.2016,,9,142.57, -6,2,01.02.2016,,9,142.57, -6,2,02.02.2016,,6,142.57, -6,2,03.02.2016,,13,142.57, -6,2,04.02.2016,,9,142.57, -6,2,05.02.2016,,6,142.57, -6,2,06.02.2016,,6,142.57, -6,2,07.02.2016,,6,142.57, -6,2,08.02.2016,,9,142.57, -6,2,09.02.2016,1.0,75,142.57,108.46 -6,2,10.02.2016,1.0,138,138.36,108.46 -6,2,11.02.2016,1.0,138,138.36,108.46 -6,2,12.02.2016,1.0,151,138.36,108.46 -6,2,13.02.2016,1.0,135,138.36,108.46 -6,2,14.02.2016,1.0,94,138.36,108.46 -6,2,15.02.2016,1.0,132,138.36,108.46 -6,2,16.02.2016,,6,138.36, -6,2,17.02.2016,,3,138.36, -6,2,18.02.2016,,0,138.36, -6,2,19.02.2016,,0,138.36, -6,2,20.02.2016,,25,138.36, -6,2,21.02.2016,,6,138.36, -6,2,22.02.2016,,13,138.36, -6,2,23.02.2016,,13,138.36, -6,2,24.02.2016,,0,138.36, -6,2,25.02.2016,,0,138.36, -6,2,26.02.2016,,0,138.36, -6,2,27.02.2016,,3,138.36, -6,2,28.02.2016,,3,138.36, -6,2,29.02.2016,,9,138.36, -6,2,01.03.2016,,6,138.36, -6,2,02.03.2016,,6,138.36, -6,2,03.03.2016,,6,138.36, -6,2,04.03.2016,,3,138.36, -6,2,05.03.2016,,6,138.36, -6,2,06.03.2016,,3,138.36, -6,2,07.03.2016,,3,138.36, -6,2,08.03.2016,,13,138.36, -6,2,09.03.2016,,9,138.36, -6,2,10.03.2016,,13,138.36, -6,2,11.03.2016,,0,138.36, -6,2,12.03.2016,,0,138.5, -6,2,13.03.2016,,3,138.5, -6,2,14.03.2016,,3,138.5, -6,2,15.03.2016,1.0,25,138.5,124.77 -6,2,16.03.2016,1.0,50,138.5,124.77 -6,2,17.03.2016,1.0,47,138.5,124.77 -6,2,18.03.2016,1.0,50,138.5,124.77 -6,2,19.03.2016,1.0,35,138.5,124.77 -6,2,20.03.2016,1.0,72,138.5,124.77 -6,2,21.03.2016,1.0,138,138.5,124.77 -6,2,22.03.2016,,6,138.36, -6,2,23.03.2016,,6,138.36, -6,2,24.03.2016,,0,138.36, -6,2,25.03.2016,,0,138.36, -6,2,26.03.2016,,0,138.36, -6,2,27.03.2016,,0,138.36, -6,2,28.03.2016,,0,138.36, -6,2,29.03.2016,,6,138.36, -6,2,30.03.2016,,0,138.36, -6,2,31.03.2016,,0,138.36, -6,2,01.04.2016,,0,138.36, -6,2,02.04.2016,,0,138.36, -6,2,03.04.2016,,0,138.36, -6,2,04.04.2016,,0,138.36, -6,2,05.04.2016,,3,138.36, -6,2,06.04.2016,,9,138.36, -6,2,07.04.2016,,9,138.36, -6,2,08.04.2016,,6,138.36, -6,2,09.04.2016,,0,138.5, -6,2,10.04.2016,,6,138.5, -6,2,11.04.2016,,9,138.5, -6,2,12.04.2016,,16,138.5, -6,2,13.04.2016,,3,138.5, -6,2,14.04.2016,,13,138.5, -6,2,15.04.2016,,6,138.5, -6,2,16.04.2016,,3,138.5, -6,2,17.04.2016,,0,138.5, -6,2,18.04.2016,,9,138.5, -6,2,19.04.2016,1.0,116,138.5,116.89 -6,2,20.04.2016,1.0,151,138.5,116.89 -6,2,21.04.2016,1.0,104,138.5,116.89 -6,2,22.04.2016,1.0,135,138.5,116.89 -6,2,23.04.2016,1.0,129,138.5,116.89 -6,2,24.04.2016,1.0,135,138.5,116.89 -6,2,25.04.2016,1.0,19,138.5,116.89 -6,2,26.04.2016,,3,138.5, -6,2,27.04.2016,,3,138.5, -6,2,28.04.2016,,0,138.5, -6,2,29.04.2016,,0,138.5, -6,2,30.04.2016,,0,138.5, -6,2,01.05.2016,,3,138.5, -6,2,02.05.2016,,9,138.5, -6,2,03.05.2016,1.0,101,138.5,106.01 -6,2,04.05.2016,1.0,116,138.5,106.01 -6,2,05.05.2016,1.0,207,138.5,106.01 -6,2,06.05.2016,1.0,229,138.5,106.01 -6,2,07.05.2016,1.0,63,138.5,106.01 -6,2,08.05.2016,1.0,116,138.5,106.01 -6,2,09.05.2016,1.0,107,138.5,106.01 -6,2,10.05.2016,,3,138.5, -6,2,11.05.2016,,16,138.5, -6,2,12.05.2016,,0,138.5, -6,2,13.05.2016,,0,138.5, -6,2,14.05.2016,,0,138.5, -6,2,15.05.2016,,0,138.5, -6,2,16.05.2016,,0,138.5, -6,2,17.05.2016,,3,138.5, -6,2,18.05.2016,,9,138.5, -6,2,19.05.2016,,6,138.5, -6,2,20.05.2016,,3,138.5, -6,2,21.05.2016,,0,138.5, -6,2,22.05.2016,,3,138.5, -7,2,01.01.2015,,0,135.78, -7,2,02.01.2015,,0,135.78, -7,2,03.01.2015,,0,135.78, -7,2,04.01.2015,,0,135.78, -7,2,05.01.2015,,0,135.78, -7,2,06.01.2015,,0,135.78, -7,2,07.01.2015,,0,135.78, -7,2,08.01.2015,,0,135.78, -7,2,09.01.2015,,0,135.78, -7,2,10.01.2015,,0,135.78, -7,2,11.01.2015,,0,135.78, -7,2,12.01.2015,,0,135.78, -7,2,13.01.2015,,3,135.78, -7,2,14.01.2015,,0,135.78, -7,2,15.01.2015,,0,135.78, -7,2,16.01.2015,,16,135.78, -7,2,17.01.2015,,3,135.78, -7,2,18.01.2015,,3,135.78, -7,2,19.01.2015,,0,135.78, -7,2,20.01.2015,,0,135.78, -7,2,21.01.2015,,0,135.78, -7,2,22.01.2015,,0,135.78, -7,2,23.01.2015,,6,135.78, -7,2,24.01.2015,,0,135.78, -7,2,25.01.2015,,3,135.78, -7,2,26.01.2015,,0,135.78, -7,2,27.01.2015,,0,135.78, -7,2,28.01.2015,,0,135.78, -7,2,29.01.2015,,0,135.78, -7,2,30.01.2015,,0,135.78, -7,2,31.01.2015,,0,135.78, -7,2,01.02.2015,,0,135.78, -7,2,02.02.2015,,0,135.78, -7,2,03.02.2015,,0,135.78, -7,2,04.02.2015,,0,135.78, -7,2,05.02.2015,,0,135.78, -7,2,06.02.2015,,0,135.78, -7,2,07.02.2015,,0,135.78, -7,2,08.02.2015,,0,135.78, -7,2,09.02.2015,,0,135.78, -7,2,10.02.2015,,3,135.78, -7,2,11.02.2015,,3,135.78, -7,2,12.02.2015,,0,135.78, -7,2,13.02.2015,,0,135.78, -7,2,14.02.2015,,0,135.78, -7,2,15.02.2015,,0,135.78, -7,2,16.02.2015,,0,135.78, -7,2,17.02.2015,,0,135.78, -7,2,18.02.2015,,0,135.78, -7,2,19.02.2015,,6,135.78, -7,2,20.02.2015,,6,135.78, -7,2,21.02.2015,,0,135.78, -7,2,22.02.2015,,0,135.78, -7,2,23.02.2015,,0,135.78, -7,2,24.02.2015,,0,135.78, -7,2,25.02.2015,,0,135.78, -7,2,26.02.2015,,0,135.78, -7,2,27.02.2015,,3,135.78, -7,2,28.02.2015,,0,135.78, -7,2,01.03.2015,,0,146.38, -7,2,02.03.2015,,0,146.38, -7,2,03.03.2015,,0,146.38, -7,2,04.03.2015,,3,146.38, -7,2,05.03.2015,,0,146.38, -7,2,06.03.2015,,3,146.38, -7,2,07.03.2015,,0,146.38, -7,2,08.03.2015,,0,146.38, -7,2,09.03.2015,,6,146.38, -7,2,10.03.2015,,0,146.38, -7,2,11.03.2015,,0,146.38, -7,2,12.03.2015,,0,146.38, -7,2,13.03.2015,,0,146.38, -7,2,14.03.2015,,0,146.38, -7,2,15.03.2015,,0,146.38, -7,2,16.03.2015,,0,146.38, -7,2,17.03.2015,,0,146.38, -7,2,18.03.2015,,3,146.38, -7,2,19.03.2015,,0,146.38, -7,2,20.03.2015,,9,146.38, -7,2,21.03.2015,,0,146.38, -7,2,22.03.2015,,0,146.38, -7,2,23.03.2015,,0,146.38, -7,2,24.03.2015,,0,146.38, -7,2,25.03.2015,,0,146.38, -7,2,26.03.2015,,0,146.38, -7,2,27.03.2015,,0,146.38, -7,2,28.03.2015,,0,146.38, -7,2,29.03.2015,,0,146.38, -7,2,30.03.2015,,3,146.38, -7,2,31.03.2015,,0,146.38, -7,2,01.04.2015,,3,146.38, -7,2,02.04.2015,,0,146.38, -7,2,03.04.2015,,0,146.38, -7,2,04.04.2015,,3,146.38, -7,2,05.04.2015,,0,146.38, -7,2,06.04.2015,,0,146.38, -7,2,07.04.2015,,0,146.38, -7,2,08.04.2015,,0,146.38, -7,2,09.04.2015,,0,146.38, -7,2,10.04.2015,,0,146.38, -7,2,11.04.2015,,0,146.38, -7,2,12.04.2015,,0,146.38, -7,2,13.04.2015,,0,146.38, -7,2,14.04.2015,,0,146.38, -7,2,15.04.2015,,0,146.38, -7,2,16.04.2015,,0,146.38, -7,2,17.04.2015,,0,146.38, -7,2,18.04.2015,,0,146.38, -7,2,19.04.2015,,0,146.38, -7,2,20.04.2015,,0,145.84, -7,2,21.04.2015,,0,145.84, -7,2,22.04.2015,,0,145.84, -7,2,23.04.2015,,0,145.84, -7,2,24.04.2015,,3,145.84, -7,2,25.04.2015,,0,145.84, -7,2,26.04.2015,,0,150.73, -7,2,27.04.2015,,0,150.73, -7,2,28.04.2015,,0,150.73, -7,2,29.04.2015,,0,150.73, -7,2,30.04.2015,,0,150.73, -7,2,01.05.2015,,0,150.73, -7,2,02.05.2015,,0,150.73, -7,2,03.05.2015,,0,150.73, -7,2,04.05.2015,,0,150.73, -7,2,05.05.2015,,0,150.73, -7,2,06.05.2015,,0,150.73, -7,2,07.05.2015,,0,150.73, -7,2,08.05.2015,,0,150.73, -7,2,09.05.2015,,0,150.73, -7,2,10.05.2015,,0,150.73, -7,2,11.05.2015,,0,150.73, -7,2,12.05.2015,,0,150.73, -7,2,13.05.2015,,3,150.73, -7,2,14.05.2015,,0,150.73, -7,2,15.05.2015,,0,150.73, -7,2,16.05.2015,,3,150.73, -7,2,17.05.2015,,0,150.73, -7,2,18.05.2015,,0,150.73, -7,2,19.05.2015,,3,150.73, -7,2,20.05.2015,,0,150.73, -7,2,21.05.2015,,0,150.73, -7,2,22.05.2015,,3,150.73, -7,2,23.05.2015,,0,150.73, -7,2,24.05.2015,,0,150.73, -7,2,25.05.2015,,0,150.73, -7,2,26.05.2015,,0,150.73, -7,2,27.05.2015,,0,150.73, -7,2,28.05.2015,,0,150.73, -7,2,29.05.2015,,0,150.73, -7,2,30.05.2015,,0,150.73, -7,2,31.05.2015,,0,150.73, -7,2,01.06.2015,,0,150.73, -7,2,02.06.2015,,0,150.73, -7,2,03.06.2015,,3,150.73, -7,2,04.06.2015,,0,150.73, -7,2,05.06.2015,,0,150.73, -7,2,06.06.2015,,0,150.73, -7,2,07.06.2015,,0,150.73, -7,2,08.06.2015,,0,150.73, -7,2,09.06.2015,,0,150.73, -7,2,10.06.2015,,0,150.73, -7,2,11.06.2015,,0,150.73, -7,2,12.06.2015,,0,150.73, -7,2,13.06.2015,,0,150.73, -7,2,14.06.2015,,0,150.73, -7,2,15.06.2015,,0,150.73, -7,2,16.06.2015,,0,150.73, -7,2,17.06.2015,,0,150.73, -7,2,18.06.2015,,3,150.73, -7,2,19.06.2015,,0,150.73, -7,2,20.06.2015,,0,150.73, -7,2,21.06.2015,,0,150.73, -7,2,22.06.2015,,0,150.73, -7,2,23.06.2015,,0,150.73, -7,2,24.06.2015,,6,150.73, -7,2,25.06.2015,,0,150.73, -7,2,26.06.2015,,0,150.73, -7,2,27.06.2015,,0,150.73, -7,2,28.06.2015,,0,150.73, -7,2,29.06.2015,,3,150.73, -7,2,30.06.2015,,0,150.73, -7,2,01.07.2015,,0,150.73, -7,2,02.07.2015,,0,150.73, -7,2,03.07.2015,,3,150.73, -7,2,04.07.2015,,0,150.73, -7,2,05.07.2015,,0,150.73, -7,2,06.07.2015,,3,150.73, -7,2,07.07.2015,,0,150.73, -7,2,08.07.2015,,0,150.73, -7,2,09.07.2015,,0,150.73, -7,2,10.07.2015,,0,150.73, -7,2,11.07.2015,,0,150.73, -7,2,12.07.2015,,0,150.73, -7,2,13.07.2015,,0,150.73, -7,2,14.07.2015,,0,150.73, -7,2,15.07.2015,,0,150.73, -7,2,16.07.2015,,0,150.73, -7,2,17.07.2015,,0,150.73, -7,2,18.07.2015,,0,150.73, -7,2,19.07.2015,,0,150.73, -7,2,20.07.2015,,0,150.73, -7,2,21.07.2015,,0,150.73, -7,2,22.07.2015,,0,150.73, -7,2,23.07.2015,,0,150.73, -7,2,24.07.2015,,0,150.73, -7,2,25.07.2015,,0,150.73, -7,2,26.07.2015,,0,150.73, -7,2,27.07.2015,,0,150.73, -7,2,28.07.2015,,0,150.73, -7,2,29.07.2015,,0,150.73, -7,2,30.07.2015,,0,150.73, -7,2,31.07.2015,,0,150.73, -7,2,01.08.2015,,3,150.73, -7,2,02.08.2015,,3,150.73, -7,2,03.08.2015,,0,150.73, -7,2,04.08.2015,,16,150.73, -7,2,05.08.2015,,3,150.73, -7,2,06.08.2015,,3,150.73, -7,2,07.08.2015,,3,150.73, -7,2,08.08.2015,,3,150.73, -7,2,09.08.2015,,3,150.73, -7,2,10.08.2015,,3,150.73, -7,2,11.08.2015,,3,150.73, -7,2,12.08.2015,,3,128.17, -7,2,13.08.2015,,0,128.17, -7,2,14.08.2015,,0,128.17, -7,2,15.08.2015,,0,128.17, -7,2,16.08.2015,,6,128.17, -7,2,17.08.2015,,0,128.17, -7,2,18.08.2015,1.0,53,128.17,100.3 -7,2,19.08.2015,1.0,60,128.17,100.3 -7,2,20.08.2015,1.0,47,128.17,100.3 -7,2,21.08.2015,1.0,72,128.17,100.3 -7,2,22.08.2015,1.0,85,128.17,100.3 -7,2,23.08.2015,1.0,79,128.17,100.3 -7,2,24.08.2015,1.0,57,128.17,100.3 -7,2,25.08.2015,,3,128.17, -7,2,26.08.2015,,3,128.17, -7,2,27.08.2015,,0,128.17, -7,2,28.08.2015,,6,128.17, -7,2,29.08.2015,,9,128.17, -7,2,30.08.2015,,9,128.17, -7,2,31.08.2015,,3,128.17, -7,2,01.09.2015,,0,128.17, -7,2,02.09.2015,,6,128.17, -7,2,03.09.2015,,3,128.17, -7,2,04.09.2015,,6,128.17, -7,2,05.09.2015,,3,128.17, -7,2,06.09.2015,,3,128.17, -7,2,07.09.2015,,3,128.17, -7,2,08.09.2015,,3,128.17, -7,2,09.09.2015,,3,128.17, -7,2,10.09.2015,,3,128.17, -7,2,11.09.2015,,3,128.17, -7,2,12.09.2015,,3,128.17, -7,2,13.09.2015,,3,128.17, -7,2,14.09.2015,,3,128.17, -7,2,15.09.2015,,3,128.17, -7,2,16.09.2015,,3,128.17, -7,2,17.09.2015,,3,128.17, -7,2,18.09.2015,,3,128.17, -7,2,19.09.2015,,3,128.17, -7,2,20.09.2015,,3,128.17, -7,2,21.09.2015,,3,128.17, -7,2,22.09.2015,,3,128.17, -7,2,23.09.2015,,3,128.17, -7,2,24.09.2015,,3,128.17, -7,2,25.09.2015,,3,128.17, -7,2,26.09.2015,,3,128.17, -7,2,27.09.2015,,3,128.17, -7,2,28.09.2015,,3,128.17, -7,2,29.09.2015,,3,128.17, -7,2,30.09.2015,,0,128.17, -7,2,01.10.2015,,6,162.96, -7,2,02.10.2015,,3,162.96, -7,2,03.10.2015,,0,162.96, -7,2,04.10.2015,,0,162.96, -7,2,05.10.2015,,0,162.96, -7,2,06.10.2015,1.0,44,143.39,122.05 -7,2,07.10.2015,1.0,38,143.39,122.05 -7,2,08.10.2015,1.0,25,143.39,122.05 -7,2,09.10.2015,1.0,35,143.39,122.05 -7,2,10.10.2015,1.0,35,143.39,122.05 -7,2,11.10.2015,1.0,22,143.39,122.05 -7,2,12.10.2015,1.0,38,143.39,122.05 -7,2,13.10.2015,1.0,6,143.93,122.05 -7,2,14.10.2015,1.0,22,143.93,122.05 -7,2,15.10.2015,1.0,13,143.93,122.05 -7,2,16.10.2015,1.0,41,143.93,122.05 -7,2,17.10.2015,1.0,38,162.96,122.05 -7,2,18.10.2015,1.0,6,162.96,122.05 -7,2,19.10.2015,,3,162.96, -7,2,20.10.2015,,3,162.96, -7,2,21.10.2015,,3,162.96, -7,2,22.10.2015,,3,162.96, -7,2,23.10.2015,,3,162.96, -7,2,24.10.2015,,3,162.96, -7,2,25.10.2015,,3,162.96, -7,2,26.10.2015,,3,162.96, -7,2,27.10.2015,,3,162.96, -7,2,28.10.2015,,3,162.96, -7,2,29.10.2015,,3,162.96, -7,2,30.10.2015,,3,162.96, -7,2,31.10.2015,,3,162.96, -7,2,01.11.2015,,3,162.96, -7,2,02.11.2015,,3,162.96, -7,2,03.11.2015,,3,162.96, -7,2,04.11.2015,,3,162.96, -7,2,05.11.2015,,3,162.96, -7,2,06.11.2015,,3,162.96, -7,2,07.11.2015,,3,162.96, -7,2,08.11.2015,,0,162.96, -7,2,09.11.2015,,0,162.96, -7,2,10.11.2015,,0,162.96, -7,2,11.11.2015,,3,138.36, -7,2,12.11.2015,,0,138.36, -7,2,13.11.2015,,0,138.36, -7,2,14.11.2015,,6,138.36, -7,2,15.11.2015,,3,138.36, -7,2,16.11.2015,,3,138.36, -7,2,17.11.2015,,3,138.36, -7,2,18.11.2015,,3,138.36, -7,2,19.11.2015,,3,138.36, -7,2,20.11.2015,,3,138.36, -7,2,21.11.2015,,3,138.36, -7,2,22.11.2015,,3,138.36, -7,2,23.11.2015,,3,138.36, -7,2,24.11.2015,,3,138.36, -7,2,25.11.2015,,3,138.36, -7,2,26.11.2015,,3,138.36, -7,2,27.11.2015,,3,138.36, -7,2,28.11.2015,,3,138.36, -7,2,29.11.2015,,3,138.36, -7,2,30.11.2015,,3,138.36, -7,2,01.12.2015,,3,162.96, -7,2,02.12.2015,,3,162.96, -7,2,03.12.2015,,3,138.5, -7,2,04.12.2015,,3,138.5, -7,2,05.12.2015,,3,138.5, -7,2,06.12.2015,,3,138.5, -7,2,07.12.2015,,3,138.5, -7,2,08.12.2015,,0,138.5, -7,2,09.12.2015,,0,138.5, -7,2,10.12.2015,,9,138.5, -7,2,11.12.2015,,3,142.57, -7,2,12.12.2015,,3,142.57, -7,2,13.12.2015,,3,142.57, -7,2,14.12.2015,,0,142.57, -7,2,15.12.2015,,0,142.57, -7,2,16.12.2015,,0,142.57, -7,2,17.12.2015,,0,142.57, -7,2,18.12.2015,,0,142.57, -7,2,19.12.2015,,0,142.57, -7,2,20.12.2015,,0,142.57, -7,2,21.12.2015,,3,142.57, -7,2,22.12.2015,1.0,47,142.57,103.02 -7,2,23.12.2015,1.0,91,142.57,103.02 -7,2,24.12.2015,1.0,82,142.57,103.02 -7,2,25.12.2015,1.0,19,142.57,103.02 -7,2,26.12.2015,1.0,31,142.57,103.02 -7,2,27.12.2015,1.0,28,142.57,103.02 -7,2,28.12.2015,1.0,25,142.57,103.02 -7,2,29.12.2015,,0,138.5, -7,2,30.12.2015,,0,138.5, -7,2,31.12.2015,,0,138.5, -7,2,01.01.2016,,0,138.5, -7,2,02.01.2016,,0,138.5, -7,2,03.01.2016,,0,138.5, -7,2,04.01.2016,,0,138.5, -7,2,05.01.2016,,0,138.5, -7,2,06.01.2016,,0,138.5, -7,2,07.01.2016,,0,138.5, -7,2,08.01.2016,,0,138.5, -7,2,09.01.2016,,0,138.5, -7,2,10.01.2016,,0,138.5, -7,2,11.01.2016,,3,138.5, -7,2,12.01.2016,,9,138.5, -7,2,13.01.2016,,3,138.5, -7,2,14.01.2016,,3,138.5, -7,2,15.01.2016,,0,138.5, -7,2,16.01.2016,,0,142.57, -7,2,17.01.2016,,0,142.57, -7,2,18.01.2016,,3,142.57, -7,2,19.01.2016,1.0,157,142.57,103.02 -7,2,20.01.2016,1.0,9,142.57,103.02 -7,2,21.01.2016,1.0,85,142.57,103.02 -7,2,22.01.2016,1.0,66,142.57,103.02 -7,2,23.01.2016,1.0,53,142.57,103.02 -7,2,24.01.2016,1.0,60,142.57,103.02 -7,2,25.01.2016,1.0,47,142.57,103.02 -7,2,26.01.2016,,0,142.57, -7,2,27.01.2016,,0,142.57, -7,2,28.01.2016,,0,142.57, -7,2,29.01.2016,,0,142.57, -7,2,30.01.2016,,0,142.57, -7,2,31.01.2016,,0,142.57, -7,2,01.02.2016,,0,142.57, -7,2,02.02.2016,,0,142.57, -7,2,03.02.2016,,0,142.57, -7,2,04.02.2016,,0,142.57, -7,2,05.02.2016,,0,142.57, -7,2,06.02.2016,,0,142.57, -7,2,07.02.2016,,0,142.57, -7,2,08.02.2016,,3,142.57, -7,2,09.02.2016,1.0,94,142.57,108.46 -7,2,10.02.2016,1.0,35,138.36,108.46 -7,2,11.02.2016,1.0,41,138.36,108.46 -7,2,12.02.2016,1.0,53,138.36,108.46 -7,2,13.02.2016,1.0,47,138.36,108.46 -7,2,14.02.2016,1.0,47,138.36,108.46 -7,2,15.02.2016,1.0,47,138.36,108.46 -7,2,16.02.2016,,0,138.36, -7,2,17.02.2016,,0,138.36, -7,2,18.02.2016,,0,138.36, -7,2,19.02.2016,,0,138.36, -7,2,20.02.2016,,0,138.36, -7,2,21.02.2016,,0,138.36, -7,2,22.02.2016,,0,138.36, -7,2,23.02.2016,,0,138.36, -7,2,24.02.2016,,0,138.36, -7,2,25.02.2016,,0,138.36, -7,2,26.02.2016,,0,138.36, -7,2,27.02.2016,,3,138.36, -7,2,28.02.2016,,9,138.36, -7,2,29.02.2016,,6,138.36, -7,2,01.03.2016,,0,138.36, -7,2,02.03.2016,,0,138.36, -7,2,03.03.2016,,3,138.36, -7,2,04.03.2016,,6,138.36, -7,2,05.03.2016,,3,138.36, -7,2,06.03.2016,,0,138.36, -7,2,07.03.2016,,0,138.36, -7,2,08.03.2016,,0,138.36, -7,2,09.03.2016,,6,138.36, -7,2,10.03.2016,,6,138.36, -7,2,11.03.2016,,6,138.36, -7,2,12.03.2016,,0,138.5, -7,2,13.03.2016,,0,138.5, -7,2,14.03.2016,,6,138.5, -7,2,15.03.2016,1.0,19,138.5,124.77 -7,2,16.03.2016,1.0,13,138.5,124.77 -7,2,17.03.2016,1.0,19,138.5,124.77 -7,2,18.03.2016,1.0,19,138.5,124.77 -7,2,19.03.2016,1.0,16,138.5,124.77 -7,2,20.03.2016,1.0,9,138.5,124.77 -7,2,21.03.2016,1.0,28,138.5,124.77 -7,2,22.03.2016,,3,138.36, -7,2,23.03.2016,,0,138.36, -7,2,24.03.2016,,0,138.36, -7,2,25.03.2016,,3,138.36, -7,2,26.03.2016,,0,138.36, -7,2,27.03.2016,,6,138.36, -7,2,28.03.2016,,9,138.36, -7,2,29.03.2016,,16,138.36, -7,2,30.03.2016,,0,138.36, -7,2,31.03.2016,,0,138.36, -7,2,01.04.2016,,0,138.36, -7,2,02.04.2016,,0,138.36, -7,2,03.04.2016,,0,138.36, -7,2,04.04.2016,,0,138.36, -7,2,05.04.2016,,0,138.36, -7,2,06.04.2016,,0,138.36, -7,2,07.04.2016,,0,138.36, -7,2,08.04.2016,,0,138.36, -7,2,09.04.2016,,0,138.5, -7,2,10.04.2016,,0,138.5, -7,2,11.04.2016,,0,138.5, -7,2,12.04.2016,,0,138.5, -7,2,13.04.2016,,0,138.5, -7,2,14.04.2016,,0,138.5, -7,2,15.04.2016,,0,138.5, -7,2,16.04.2016,,0,138.5, -7,2,17.04.2016,,3,138.5, -7,2,18.04.2016,,0,138.5, -7,2,19.04.2016,1.0,195,138.5,116.89 -7,2,20.04.2016,1.0,47,138.5,116.89 -7,2,21.04.2016,1.0,38,138.5,116.89 -7,2,22.04.2016,1.0,38,138.5,116.89 -7,2,23.04.2016,1.0,22,138.5,116.89 -7,2,24.04.2016,1.0,41,138.5,116.89 -7,2,25.04.2016,1.0,9,138.5,116.89 -7,2,26.04.2016,,13,138.5, -7,2,27.04.2016,,0,138.5, -7,2,28.04.2016,,3,138.5, -7,2,29.04.2016,,0,138.5, -7,2,30.04.2016,,0,138.5, -7,2,01.05.2016,,13,138.5, -7,2,02.05.2016,,3,138.5, -7,2,03.05.2016,1.0,57,138.5,106.01 -7,2,04.05.2016,1.0,50,138.5,106.01 -7,2,05.05.2016,1.0,47,138.5,106.01 -7,2,06.05.2016,1.0,75,138.5,106.01 -7,2,07.05.2016,1.0,41,138.5,106.01 -7,2,08.05.2016,1.0,13,138.5,106.01 -7,2,09.05.2016,1.0,38,138.5,106.01 -7,2,10.05.2016,,6,138.5, -7,2,11.05.2016,,3,138.5, -7,2,12.05.2016,,3,138.5, -7,2,13.05.2016,,3,138.5, -7,2,14.05.2016,,3,138.5, -7,2,15.05.2016,,3,138.5, -7,2,16.05.2016,,3,138.5, -7,2,17.05.2016,,3,138.5, -7,2,18.05.2016,,3,138.5, -7,2,19.05.2016,,6,134.55, -7,2,20.05.2016,,3,138.5, -7,2,21.05.2016,,3,138.5, -7,2,22.05.2016,,3,138.5, -8,2,01.01.2015,,3,139.86, -8,2,02.01.2015,,0,139.86, -8,2,03.01.2015,,0,139.86, -8,2,04.01.2015,,13,139.86, -8,2,05.01.2015,,16,139.86, -8,2,06.01.2015,,19,139.86, -8,2,07.01.2015,,16,139.86, -8,2,08.01.2015,,0,139.86, -8,2,09.01.2015,,6,139.86, -8,2,10.01.2015,,16,139.86, -8,2,11.01.2015,,16,139.86, -8,2,12.01.2015,,3,139.86, -8,2,13.01.2015,,0,139.86, -8,2,14.01.2015,,0,139.86, -8,2,15.01.2015,,6,139.86, -8,2,16.01.2015,,19,139.86, -8,2,17.01.2015,,3,139.86, -8,2,18.01.2015,,3,139.86, -8,2,19.01.2015,,25,139.86, -8,2,20.01.2015,,6,139.86, -8,2,21.01.2015,,6,139.86, -8,2,22.01.2015,,6,139.86, -8,2,23.01.2015,,3,139.86, -8,2,24.01.2015,,6,139.86, -8,2,25.01.2015,,3,139.86, -8,2,26.01.2015,,3,139.86, -8,2,27.01.2015,,6,139.86, -8,2,28.01.2015,,9,139.86, -8,2,29.01.2015,,3,139.86, -8,2,30.01.2015,,6,139.86, -8,2,31.01.2015,,16,139.86, -8,2,01.02.2015,,0,139.86, -8,2,02.02.2015,,6,139.86, -8,2,03.02.2015,,6,139.86, -8,2,04.02.2015,,0,139.86, -8,2,05.02.2015,,3,139.86, -8,2,06.02.2015,,16,139.86, -8,2,07.02.2015,,3,139.86, -8,2,08.02.2015,,6,139.86, -8,2,09.02.2015,,6,139.86, -8,2,10.02.2015,,9,139.86, -8,2,11.02.2015,,0,139.86, -8,2,12.02.2015,,6,139.86, -8,2,13.02.2015,,6,139.86, -8,2,14.02.2015,,3,139.86, -8,2,15.02.2015,,9,139.86, -8,2,16.02.2015,,3,139.86, -8,2,17.02.2015,,0,139.86, -8,2,18.02.2015,,16,139.86, -8,2,19.02.2015,,0,139.86, -8,2,20.02.2015,,16,139.86, -8,2,21.02.2015,,0,139.86, -8,2,22.02.2015,,16,139.86, -8,2,23.02.2015,,3,139.86, -8,2,24.02.2015,,3,139.86, -8,2,25.02.2015,,0,139.86, -8,2,26.02.2015,,3,139.86, -8,2,27.02.2015,,3,139.86, -8,2,28.02.2015,,0,139.86, -8,2,01.03.2015,,0,139.86, -8,2,02.03.2015,,0,150.73, -8,2,03.03.2015,,3,150.73, -8,2,04.03.2015,,0,150.73, -8,2,05.03.2015,,6,150.73, -8,2,06.03.2015,,3,150.73, -8,2,07.03.2015,,6,150.73, -8,2,08.03.2015,,9,150.73, -8,2,09.03.2015,,9,150.73, -8,2,10.03.2015,,3,150.73, -8,2,11.03.2015,,3,150.73, -8,2,12.03.2015,,0,150.73, -8,2,13.03.2015,,0,150.73, -8,2,14.03.2015,,0,150.73, -8,2,15.03.2015,,9,150.73, -8,2,16.03.2015,,3,150.73, -8,2,17.03.2015,,3,150.73, -8,2,18.03.2015,,0,150.73, -8,2,19.03.2015,,6,150.73, -8,2,20.03.2015,,0,150.73, -8,2,21.03.2015,,3,150.73, -8,2,22.03.2015,,6,150.73, -8,2,23.03.2015,,9,150.73, -8,2,24.03.2015,,0,150.73, -8,2,25.03.2015,,3,150.73, -8,2,26.03.2015,,0,150.73, -8,2,27.03.2015,,0,150.73, -8,2,28.03.2015,,0,150.73, -8,2,29.03.2015,,13,150.73, -8,2,30.03.2015,,0,150.73, -8,2,31.03.2015,,3,150.73, -8,2,01.04.2015,,3,150.73, -8,2,02.04.2015,,3,150.73, -8,2,03.04.2015,,0,150.73, -8,2,04.04.2015,,9,150.73, -8,2,05.04.2015,,0,150.73, -8,2,06.04.2015,,3,150.73, -8,2,07.04.2015,,0,150.73, -8,2,08.04.2015,,0,150.73, -8,2,09.04.2015,,6,150.73, -8,2,10.04.2015,,0,150.73, -8,2,11.04.2015,,0,150.73, -8,2,12.04.2015,,0,150.73, -8,2,13.04.2015,,6,150.73, -8,2,14.04.2015,,13,150.73, -8,2,15.04.2015,,3,150.73, -8,2,16.04.2015,,3,150.73, -8,2,17.04.2015,,0,150.73, -8,2,18.04.2015,,0,150.73, -8,2,19.04.2015,,0,150.73, -8,2,20.04.2015,,6,150.73, -8,2,21.04.2015,,6,150.73, -8,2,22.04.2015,,9,150.73, -8,2,23.04.2015,,0,150.73, -8,2,24.04.2015,,0,150.73, -8,2,25.04.2015,,0,150.73, -8,2,26.04.2015,,0,155.35, -8,2,27.04.2015,,3,155.35, -8,2,28.04.2015,,0,155.35, -8,2,29.04.2015,,3,155.35, -8,2,30.04.2015,,0,155.35, -8,2,01.05.2015,,0,155.35, -8,2,02.05.2015,,3,155.35, -8,2,03.05.2015,,0,155.35, -8,2,04.05.2015,,0,155.35, -8,2,05.05.2015,,0,155.35, -8,2,06.05.2015,,0,155.35, -8,2,07.05.2015,,3,155.35, -8,2,08.05.2015,,9,155.35, -8,2,09.05.2015,,0,155.35, -8,2,10.05.2015,,0,155.35, -8,2,11.05.2015,,0,155.35, -8,2,12.05.2015,,3,155.35, -8,2,13.05.2015,,0,155.35, -8,2,14.05.2015,,0,155.35, -8,2,15.05.2015,,3,155.35, -8,2,16.05.2015,,0,155.35, -8,2,17.05.2015,,3,155.35, -8,2,18.05.2015,,0,155.35, -8,2,19.05.2015,,3,155.35, -8,2,20.05.2015,,6,155.35, -8,2,21.05.2015,,0,155.35, -8,2,22.05.2015,,6,155.35, -8,2,23.05.2015,,0,155.35, -8,2,24.05.2015,,0,155.35, -8,2,25.05.2015,,6,155.35, -8,2,26.05.2015,,0,155.35, -8,2,27.05.2015,,0,155.35, -8,2,28.05.2015,,3,155.35, -8,2,29.05.2015,,9,155.35, -8,2,30.05.2015,,0,155.35, -8,2,31.05.2015,,6,155.35, -8,2,01.06.2015,,9,155.35, -8,2,02.06.2015,,0,155.35, -8,2,03.06.2015,,0,155.35, -8,2,04.06.2015,,6,155.35, -8,2,05.06.2015,,13,155.35, -8,2,06.06.2015,,3,155.35, -8,2,07.06.2015,,3,155.35, -8,2,08.06.2015,,9,155.35, -8,2,09.06.2015,,3,155.35, -8,2,10.06.2015,,0,155.35, -8,2,11.06.2015,,0,155.35, -8,2,12.06.2015,,0,155.35, -8,2,13.06.2015,,3,155.35, -8,2,14.06.2015,,6,155.35, -8,2,15.06.2015,,3,155.35, -8,2,16.06.2015,,3,155.35, -8,2,17.06.2015,,13,155.35, -8,2,18.06.2015,,0,155.35, -8,2,19.06.2015,,0,155.35, -8,2,20.06.2015,,22,155.35, -8,2,21.06.2015,,3,155.35, -8,2,22.06.2015,,9,155.35, -8,2,23.06.2015,,3,155.35, -8,2,24.06.2015,,3,155.35, -8,2,25.06.2015,,0,155.35, -8,2,26.06.2015,,6,155.35, -8,2,27.06.2015,,3,155.35, -8,2,28.06.2015,,6,155.35, -8,2,29.06.2015,,6,155.35, -8,2,30.06.2015,,9,155.35, -8,2,01.07.2015,,6,155.35, -8,2,02.07.2015,,0,155.35, -8,2,03.07.2015,,6,155.35, -8,2,04.07.2015,,0,155.35, -8,2,05.07.2015,,3,155.35, -8,2,06.07.2015,,25,155.35, -8,2,07.07.2015,,0,155.35, -8,2,08.07.2015,,0,155.35, -8,2,09.07.2015,,0,155.35, -8,2,10.07.2015,,0,155.35, -8,2,11.07.2015,,9,155.35, -8,2,12.07.2015,,13,155.35, -8,2,13.07.2015,,3,155.35, -8,2,14.07.2015,,9,155.35, -8,2,15.07.2015,,0,155.35, -8,2,16.07.2015,,3,155.35, -8,2,17.07.2015,,0,155.35, -8,2,18.07.2015,,6,155.35, -8,2,19.07.2015,,13,155.35, -8,2,20.07.2015,,16,155.35, -8,2,21.07.2015,,3,155.35, -8,2,22.07.2015,,25,155.35, -8,2,23.07.2015,,28,155.35, -8,2,24.07.2015,,22,155.35, -8,2,25.07.2015,,25,155.35, -8,2,26.07.2015,,0,155.35, -8,2,27.07.2015,,31,155.35, -8,2,28.07.2015,,0,155.35, -8,2,29.07.2015,,57,155.35, -8,2,30.07.2015,,13,155.35, -8,2,31.07.2015,,28,155.35, -8,2,01.08.2015,,16,155.35, -8,2,02.08.2015,,19,155.35, -8,2,03.08.2015,,22,155.35, -8,2,04.08.2015,,19,155.35, -8,2,05.08.2015,,53,155.35, -8,2,06.08.2015,,31,155.35, -8,2,07.08.2015,,28,155.35, -8,2,08.08.2015,,22,155.35, -8,2,09.08.2015,,22,155.35, -8,2,10.08.2015,,19,155.35, -8,2,11.08.2015,,25,155.35, -8,2,12.08.2015,,16,128.17, -8,2,13.08.2015,,16,128.17, -8,2,14.08.2015,,41,128.17, -8,2,15.08.2015,,16,128.17, -8,2,16.08.2015,,6,128.17, -8,2,17.08.2015,,3,128.17, -8,2,18.08.2015,1.0,408,128.17,100.3 -8,2,19.08.2015,1.0,195,128.17,100.3 -8,2,20.08.2015,1.0,41,128.17,100.3 -8,2,21.08.2015,1.0,380,128.17,100.3 -8,2,22.08.2015,1.0,157,128.17,100.3 -8,2,23.08.2015,1.0,245,128.17,100.3 -8,2,24.08.2015,1.0,273,128.17,100.3 -8,2,25.08.2015,,104,128.17, -8,2,26.08.2015,,28,128.17, -8,2,27.08.2015,,35,128.17, -8,2,28.08.2015,,44,128.17, -8,2,29.08.2015,,50,128.17, -8,2,30.08.2015,,101,128.17, -8,2,31.08.2015,,35,128.17, -8,2,01.09.2015,,13,128.17, -8,2,02.09.2015,,22,128.17, -8,2,03.09.2015,,19,128.17, -8,2,04.09.2015,,28,128.17, -8,2,05.09.2015,,22,128.17, -8,2,06.09.2015,,60,128.17, -8,2,07.09.2015,,25,128.17, -8,2,08.09.2015,,41,128.17, -8,2,09.09.2015,,9,128.17, -8,2,10.09.2015,,25,128.17, -8,2,11.09.2015,,3,128.17, -8,2,12.09.2015,,31,128.17, -8,2,13.09.2015,,3,128.17, -8,2,14.09.2015,,16,128.17, -8,2,15.09.2015,,25,128.17, -8,2,16.09.2015,,6,128.17, -8,2,17.09.2015,,31,128.17, -8,2,18.09.2015,,19,128.17, -8,2,19.09.2015,,19,128.17, -8,2,20.09.2015,,19,128.17, -8,2,21.09.2015,,22,128.17, -8,2,22.09.2015,,22,128.17, -8,2,23.09.2015,,3,128.17, -8,2,24.09.2015,,16,128.17, -8,2,25.09.2015,,16,128.17, -8,2,26.09.2015,,28,128.17, -8,2,27.09.2015,,9,128.17, -8,2,28.09.2015,,19,128.17, -8,2,29.09.2015,,13,167.85, -8,2,30.09.2015,,38,167.85, -8,2,01.10.2015,,16,162.96, -8,2,02.10.2015,,6,162.96, -8,2,03.10.2015,,0,162.96, -8,2,04.10.2015,,9,162.96, -8,2,05.10.2015,,13,162.96, -8,2,06.10.2015,1.0,157,143.39,122.05 -8,2,07.10.2015,1.0,132,143.39,122.05 -8,2,08.10.2015,1.0,19,143.39,122.05 -8,2,09.10.2015,1.0,217,143.39,122.05 -8,2,10.10.2015,1.0,47,143.39,122.05 -8,2,11.10.2015,1.0,113,143.39,122.05 -8,2,12.10.2015,1.0,160,143.39,122.05 -8,2,13.10.2015,,0,143.93, -8,2,14.10.2015,,13,143.93, -8,2,15.10.2015,,13,143.93, -8,2,16.10.2015,,0,143.93, -8,2,17.10.2015,,3,162.96, -8,2,18.10.2015,,6,162.96, -8,2,19.10.2015,,0,162.96, -8,2,20.10.2015,,0,162.96, -8,2,21.10.2015,,3,162.96, -8,2,22.10.2015,,0,162.96, -8,2,23.10.2015,,0,162.96, -8,2,24.10.2015,,0,162.96, -8,2,25.10.2015,,0,162.96, -8,2,26.10.2015,,0,162.96, -8,2,27.10.2015,,0,162.96, -8,2,28.10.2015,,3,162.96, -8,2,29.10.2015,,0,162.96, -8,2,30.10.2015,,0,162.96, -8,2,31.10.2015,,0,162.96, -8,2,01.11.2015,,0,162.96, -8,2,02.11.2015,,0,162.96, -8,2,03.11.2015,,0,162.96, -8,2,04.11.2015,,0,162.96, -8,2,05.11.2015,,0,162.96, -8,2,06.11.2015,,3,162.96, -8,2,07.11.2015,,3,162.96, -8,2,08.11.2015,,6,162.96, -8,2,09.11.2015,,0,162.96, -8,2,10.11.2015,,0,162.96, -8,2,11.11.2015,,0,138.36, -8,2,12.11.2015,,0,138.36, -8,2,13.11.2015,,0,138.36, -8,2,14.11.2015,,3,138.36, -8,2,15.11.2015,,0,138.36, -8,2,16.11.2015,,13,138.36, -8,2,17.11.2015,,3,138.36, -8,2,18.11.2015,,0,138.36, -8,2,19.11.2015,,0,138.36, -8,2,20.11.2015,,0,138.36, -8,2,21.11.2015,,0,138.36, -8,2,22.11.2015,,0,138.36, -8,2,23.11.2015,,3,138.36, -8,2,24.11.2015,,3,138.36, -8,2,25.11.2015,,3,138.36, -8,2,26.11.2015,,0,138.36, -8,2,27.11.2015,,9,138.36, -8,2,28.11.2015,,0,138.36, -8,2,29.11.2015,,0,138.36, -8,2,30.11.2015,,9,138.36, -8,2,01.12.2015,,0,162.96, -8,2,02.12.2015,,0,162.96, -8,2,03.12.2015,,3,138.5, -8,2,04.12.2015,,0,138.5, -8,2,05.12.2015,,3,138.5, -8,2,06.12.2015,,0,138.5, -8,2,07.12.2015,,0,138.5, -8,2,08.12.2015,,3,138.5, -8,2,09.12.2015,,0,138.5, -8,2,10.12.2015,,9,138.5, -8,2,11.12.2015,,6,142.57, -8,2,12.12.2015,,13,142.57, -8,2,13.12.2015,,6,142.57, -8,2,14.12.2015,,16,142.57, -8,2,15.12.2015,,3,142.57, -8,2,16.12.2015,,16,142.57, -8,2,17.12.2015,,3,142.57, -8,2,18.12.2015,,6,142.57, -8,2,19.12.2015,,6,142.57, -8,2,20.12.2015,,0,142.57, -8,2,21.12.2015,,3,142.57, -8,2,22.12.2015,1.0,110,142.57,103.02 -8,2,23.12.2015,1.0,270,142.57,103.02 -8,2,24.12.2015,1.0,217,142.57,103.02 -8,2,25.12.2015,1.0,239,142.57,103.02 -8,2,26.12.2015,1.0,311,142.57,103.02 -8,2,27.12.2015,1.0,232,142.57,103.02 -8,2,28.12.2015,1.0,138,142.57,103.02 -8,2,29.12.2015,,6,138.5, -8,2,30.12.2015,,0,138.5, -8,2,31.12.2015,,3,138.5, -8,2,01.01.2016,,6,138.5, -8,2,02.01.2016,,9,138.5, -8,2,03.01.2016,,9,138.5, -8,2,04.01.2016,,9,138.5, -8,2,05.01.2016,,19,138.5, -8,2,06.01.2016,,16,138.5, -8,2,07.01.2016,,3,138.5, -8,2,08.01.2016,,6,138.5, -8,2,09.01.2016,,13,138.5, -8,2,10.01.2016,,25,138.5, -8,2,11.01.2016,,9,138.5, -8,2,12.01.2016,,9,138.5, -8,2,13.01.2016,,3,138.5, -8,2,14.01.2016,,6,138.5, -8,2,15.01.2016,,9,138.5, -8,2,16.01.2016,,0,142.57, -8,2,17.01.2016,,22,142.57, -8,2,18.01.2016,,16,142.57, -8,2,19.01.2016,1.0,415,142.57,103.02 -8,2,20.01.2016,1.0,339,142.57,103.02 -8,2,21.01.2016,1.0,110,142.57,103.02 -8,2,22.01.2016,1.0,251,142.57,103.02 -8,2,23.01.2016,1.0,232,142.57,103.02 -8,2,24.01.2016,1.0,236,142.57,103.02 -8,2,25.01.2016,1.0,214,142.57,103.02 -8,2,26.01.2016,,0,142.57, -8,2,27.01.2016,,0,142.57, -8,2,28.01.2016,,13,142.57, -8,2,29.01.2016,,3,142.57, -8,2,30.01.2016,,3,142.57, -8,2,31.01.2016,,3,142.57, -8,2,01.02.2016,,16,142.57, -8,2,02.02.2016,,9,142.57, -8,2,03.02.2016,,28,142.57, -8,2,04.02.2016,,9,142.57, -8,2,05.02.2016,,9,142.57, -8,2,06.02.2016,,9,142.57, -8,2,07.02.2016,,9,142.57, -8,2,08.02.2016,,60,142.57, -8,2,09.02.2016,1.0,342,142.57,108.46 -8,2,10.02.2016,1.0,31,138.36,108.46 -8,2,11.02.2016,1.0,264,138.36,108.46 -8,2,12.02.2016,1.0,195,138.36,108.46 -8,2,13.02.2016,1.0,88,138.36,108.46 -8,2,14.02.2016,1.0,198,138.36,108.46 -8,2,15.02.2016,1.0,198,138.36,108.46 -8,2,16.02.2016,,13,138.36, -8,2,17.02.2016,,0,138.36, -8,2,18.02.2016,,13,138.36, -8,2,19.02.2016,,16,138.36, -8,2,20.02.2016,,9,138.36, -8,2,21.02.2016,,13,138.36, -8,2,22.02.2016,,0,138.36, -8,2,23.02.2016,,0,138.36, -8,2,24.02.2016,,3,138.36, -8,2,25.02.2016,,0,138.36, -8,2,26.02.2016,,0,138.36, -8,2,27.02.2016,,3,138.36, -8,2,28.02.2016,,6,138.36, -8,2,29.02.2016,,3,138.36, -8,2,01.03.2016,,6,138.36, -8,2,02.03.2016,,0,138.36, -8,2,03.03.2016,,0,138.36, -8,2,04.03.2016,,0,138.36, -8,2,05.03.2016,,0,138.36, -8,2,06.03.2016,,0,138.36, -8,2,07.03.2016,,0,138.36, -8,2,08.03.2016,,0,138.36, -8,2,09.03.2016,,0,138.36, -8,2,10.03.2016,,0,138.36, -8,2,11.03.2016,,0,138.36, -8,2,12.03.2016,,0,138.5, -8,2,13.03.2016,,0,138.5, -8,2,14.03.2016,,6,138.5, -8,2,15.03.2016,1.0,145,138.5,124.77 -8,2,16.03.2016,1.0,69,138.5,124.77 -8,2,17.03.2016,1.0,126,138.5,124.77 -8,2,18.03.2016,1.0,163,138.5,124.77 -8,2,19.03.2016,1.0,88,138.5,124.77 -8,2,20.03.2016,1.0,19,138.5,124.77 -8,2,21.03.2016,1.0,129,138.5,124.77 -8,2,22.03.2016,,22,138.36, -8,2,23.03.2016,,22,138.36, -8,2,24.03.2016,,13,138.36, -8,2,25.03.2016,,9,138.36, -8,2,26.03.2016,,6,138.36, -8,2,27.03.2016,,0,138.36, -8,2,28.03.2016,,0,138.36, -8,2,29.03.2016,,3,138.36, -8,2,30.03.2016,,0,138.36, -8,2,31.03.2016,,0,138.36, -8,2,01.04.2016,,6,138.36, -8,2,02.04.2016,,3,138.36, -8,2,03.04.2016,,9,138.36, -8,2,04.04.2016,,9,138.36, -8,2,05.04.2016,,6,138.36, -8,2,06.04.2016,,3,138.36, -8,2,07.04.2016,,3,138.36, -8,2,08.04.2016,,3,138.36, -8,2,09.04.2016,,9,138.5, -8,2,10.04.2016,,13,138.5, -8,2,11.04.2016,,6,138.5, -8,2,12.04.2016,,3,138.5, -8,2,13.04.2016,,0,138.5, -8,2,14.04.2016,,0,138.5, -8,2,15.04.2016,,0,138.5, -8,2,16.04.2016,,0,138.5, -8,2,17.04.2016,,9,138.5, -8,2,18.04.2016,,13,138.5, -8,2,19.04.2016,1.0,173,138.5,116.89 -8,2,20.04.2016,1.0,192,138.5,116.89 -8,2,21.04.2016,1.0,132,138.5,116.89 -8,2,22.04.2016,1.0,236,138.5,116.89 -8,2,23.04.2016,1.0,157,138.5,116.89 -8,2,24.04.2016,1.0,170,138.5,116.89 -8,2,25.04.2016,1.0,173,138.5,116.89 -8,2,26.04.2016,,28,138.5, -8,2,27.04.2016,,57,138.5, -8,2,28.04.2016,,25,138.5, -8,2,29.04.2016,,44,138.5, -8,2,30.04.2016,,22,138.5, -8,2,01.05.2016,,38,138.5, -8,2,02.05.2016,,41,138.5, -8,2,03.05.2016,1.0,320,138.5,106.01 -8,2,04.05.2016,1.0,258,138.5,106.01 -8,2,05.05.2016,1.0,248,138.5,106.01 -8,2,06.05.2016,1.0,214,138.5,106.01 -8,2,07.05.2016,1.0,214,138.5,106.01 -8,2,08.05.2016,1.0,173,138.5,106.01 -8,2,09.05.2016,1.0,248,138.5,106.01 -8,2,10.05.2016,,19,138.5, -8,2,11.05.2016,,188,138.5, -8,2,12.05.2016,,75,138.5, -8,2,13.05.2016,,16,138.5, -8,2,14.05.2016,,13,138.5, -8,2,15.05.2016,,60,138.5, -8,2,16.05.2016,,41,138.5, -8,2,17.05.2016,,44,138.5, -8,2,18.05.2016,,41,138.5, -8,2,19.05.2016,,38,138.5, -8,2,20.05.2016,,35,138.5, -8,2,21.05.2016,,6,138.5, -8,2,22.05.2016,,31,138.5, -9,2,01.01.2015,,0,135.78, -9,2,02.01.2015,,0,135.78, -9,2,03.01.2015,,0,135.78, -9,2,04.01.2015,,0,135.78, -9,2,05.01.2015,,0,135.78, -9,2,06.01.2015,,0,135.78, -9,2,07.01.2015,,0,135.78, -9,2,08.01.2015,,0,135.78, -9,2,09.01.2015,,0,135.78, -9,2,10.01.2015,,0,135.78, -9,2,11.01.2015,,0,135.78, -9,2,12.01.2015,,0,135.78, -9,2,13.01.2015,,0,135.78, -9,2,14.01.2015,,6,135.78, -9,2,15.01.2015,,0,135.78, -9,2,16.01.2015,,0,135.78, -9,2,17.01.2015,,0,135.78, -9,2,18.01.2015,,0,135.78, -9,2,19.01.2015,,0,135.78, -9,2,20.01.2015,,6,135.78, -9,2,21.01.2015,,0,135.78, -9,2,22.01.2015,,3,135.78, -9,2,23.01.2015,,0,135.78, -9,2,24.01.2015,,0,135.78, -9,2,25.01.2015,,0,135.78, -9,2,26.01.2015,,0,135.78, -9,2,27.01.2015,,0,135.78, -9,2,28.01.2015,,0,135.78, -9,2,29.01.2015,,0,135.78, -9,2,30.01.2015,,0,135.78, -9,2,31.01.2015,,0,135.78, -9,2,01.02.2015,,0,135.78, -9,2,02.02.2015,,0,135.78, -9,2,03.02.2015,,0,135.78, -9,2,04.02.2015,,0,135.78, -9,2,05.02.2015,,0,135.78, -9,2,06.02.2015,,0,135.78, -9,2,07.02.2015,,0,135.78, -9,2,08.02.2015,,0,135.78, -9,2,09.02.2015,,0,135.78, -9,2,10.02.2015,,0,135.78, -9,2,11.02.2015,,0,135.78, -9,2,12.02.2015,,0,135.78, -9,2,13.02.2015,,0,135.78, -9,2,14.02.2015,,0,135.78, -9,2,15.02.2015,,0,135.78, -9,2,16.02.2015,,0,135.78, -9,2,17.02.2015,,0,135.78, -9,2,18.02.2015,,0,135.78, -9,2,19.02.2015,,0,135.78, -9,2,20.02.2015,,0,135.78, -9,2,21.02.2015,,3,135.78, -9,2,22.02.2015,,3,135.78, -9,2,23.02.2015,,3,135.78, -9,2,24.02.2015,,31,135.78, -9,2,25.02.2015,,6,135.78, -9,2,26.02.2015,,0,135.78, -9,2,27.02.2015,,0,135.78, -9,2,28.02.2015,,0,135.78, -9,2,01.03.2015,,3,135.78, -9,2,02.03.2015,,6,135.78, -9,2,03.03.2015,,0,135.78, -9,2,04.03.2015,,3,135.78, -9,2,05.03.2015,,0,135.78, -9,2,06.03.2015,,0,135.78, -9,2,07.03.2015,,0,135.78, -9,2,08.03.2015,,0,146.38, -9,2,09.03.2015,,0,146.38, -9,2,10.03.2015,,3,146.38, -9,2,11.03.2015,,6,146.38, -9,2,12.03.2015,,0,146.38, -9,2,13.03.2015,,0,146.38, -9,2,14.03.2015,,0,146.38, -9,2,15.03.2015,,6,146.38, -9,2,16.03.2015,,6,146.38, -9,2,17.03.2015,,0,146.38, -9,2,18.03.2015,,0,146.38, -9,2,19.03.2015,,0,146.38, -9,2,20.03.2015,,0,146.38, -9,2,21.03.2015,,6,146.38, -9,2,22.03.2015,,3,146.38, -9,2,23.03.2015,,0,146.38, -9,2,24.03.2015,,0,146.38, -9,2,25.03.2015,,0,146.38, -9,2,26.03.2015,,0,146.38, -9,2,27.03.2015,,0,146.38, -9,2,28.03.2015,,0,146.38, -9,2,29.03.2015,,0,146.38, -9,2,30.03.2015,,0,146.38, -9,2,31.03.2015,,0,146.38, -9,2,01.04.2015,,0,146.38, -9,2,02.04.2015,,0,146.38, -9,2,03.04.2015,,0,146.38, -9,2,04.04.2015,,0,146.38, -9,2,05.04.2015,,0,146.38, -9,2,06.04.2015,,0,146.38, -9,2,07.04.2015,,0,146.38, -9,2,08.04.2015,,0,146.38, -9,2,09.04.2015,,0,146.38, -9,2,10.04.2015,,0,146.38, -9,2,11.04.2015,,0,146.38, -9,2,12.04.2015,,0,146.38, -9,2,13.04.2015,,0,146.38, -9,2,14.04.2015,,0,146.38, -9,2,15.04.2015,,0,146.38, -9,2,16.04.2015,,0,146.38, -9,2,17.04.2015,,0,146.38, -9,2,18.04.2015,,0,146.38, -9,2,19.04.2015,,0,146.38, -9,2,20.04.2015,,3,146.38, -9,2,21.04.2015,,3,146.38, -9,2,22.04.2015,,0,146.38, -9,2,23.04.2015,,0,146.38, -9,2,24.04.2015,,0,146.38, -9,2,25.04.2015,,0,146.38, -9,2,26.04.2015,,3,146.38, -9,2,27.04.2015,,0,150.73, -9,2,28.04.2015,,6,150.73, -9,2,29.04.2015,,0,150.73, -9,2,30.04.2015,,0,150.73, -9,2,01.05.2015,,0,150.73, -9,2,02.05.2015,,6,150.73, -9,2,03.05.2015,,9,150.73, -9,2,04.05.2015,,3,150.73, -9,2,05.05.2015,,19,150.73, -9,2,06.05.2015,,3,150.73, -9,2,07.05.2015,,3,150.73, -9,2,08.05.2015,,0,150.73, -9,2,09.05.2015,,0,150.73, -9,2,10.05.2015,,0,150.73, -9,2,11.05.2015,,0,150.73, -9,2,12.05.2015,,0,150.73, -9,2,13.05.2015,,0,150.73, -9,2,14.05.2015,,0,150.73, -9,2,15.05.2015,,0,150.73, -9,2,16.05.2015,,0,150.73, -9,2,17.05.2015,,6,150.73, -9,2,18.05.2015,,0,150.73, -9,2,19.05.2015,,0,150.73, -9,2,20.05.2015,,0,150.73, -9,2,21.05.2015,,0,150.73, -9,2,22.05.2015,,0,150.73, -9,2,23.05.2015,,0,150.73, -9,2,24.05.2015,,0,150.73, -9,2,25.05.2015,,0,150.73, -9,2,26.05.2015,,0,150.73, -9,2,27.05.2015,,0,150.73, -9,2,28.05.2015,,0,150.73, -9,2,29.05.2015,,0,150.73, -9,2,30.05.2015,,0,150.73, -9,2,31.05.2015,,0,150.73, -9,2,01.06.2015,,3,150.73, -9,2,02.06.2015,,0,150.73, -9,2,03.06.2015,,0,150.73, -9,2,04.06.2015,,0,150.73, -9,2,05.06.2015,,0,150.73, -9,2,06.06.2015,,0,150.73, -9,2,07.06.2015,,0,150.73, -9,2,08.06.2015,,0,150.73, -9,2,09.06.2015,,0,150.73, -9,2,10.06.2015,,0,150.73, -9,2,11.06.2015,,0,150.73, -9,2,12.06.2015,,0,150.73, -9,2,13.06.2015,,0,150.73, -9,2,14.06.2015,,3,150.73, -9,2,15.06.2015,,0,150.73, -9,2,16.06.2015,,0,150.73, -9,2,17.06.2015,,0,150.73, -9,2,18.06.2015,,0,150.73, -9,2,19.06.2015,,0,150.73, -9,2,20.06.2015,,0,150.73, -9,2,21.06.2015,,0,150.73, -9,2,22.06.2015,,0,150.73, -9,2,23.06.2015,,0,150.73, -9,2,24.06.2015,,6,150.73, -9,2,25.06.2015,,0,150.73, -9,2,26.06.2015,,0,150.73, -9,2,27.06.2015,,0,150.73, -9,2,28.06.2015,,0,150.73, -9,2,29.06.2015,,0,150.73, -9,2,30.06.2015,,3,150.73, -9,2,01.07.2015,,6,150.73, -9,2,02.07.2015,,0,150.73, -9,2,03.07.2015,,0,150.73, -9,2,04.07.2015,,6,150.73, -9,2,05.07.2015,,0,150.73, -9,2,06.07.2015,,6,150.73, -9,2,07.07.2015,,0,150.73, -9,2,08.07.2015,,3,150.73, -9,2,09.07.2015,,0,150.73, -9,2,10.07.2015,,0,150.73, -9,2,11.07.2015,,0,150.73, -9,2,12.07.2015,,0,150.73, -9,2,13.07.2015,,0,150.73, -9,2,14.07.2015,,3,150.73, -9,2,15.07.2015,,0,150.73, -9,2,16.07.2015,,3,150.73, -9,2,17.07.2015,,3,150.73, -9,2,18.07.2015,,0,150.73, -9,2,19.07.2015,,0,150.73, -9,2,20.07.2015,,0,150.73, -9,2,21.07.2015,,6,150.73, -9,2,22.07.2015,,16,150.73, -9,2,23.07.2015,,3,150.73, -9,2,24.07.2015,,25,150.73, -9,2,25.07.2015,,3,150.73, -9,2,26.07.2015,,3,150.73, -9,2,27.07.2015,,16,150.73, -9,2,28.07.2015,,3,150.73, -9,2,29.07.2015,,6,150.73, -9,2,30.07.2015,,3,150.73, -9,2,31.07.2015,,16,150.73, -9,2,01.08.2015,,6,150.73, -9,2,02.08.2015,,6,150.73, -9,2,03.08.2015,,25,150.73, -9,2,04.08.2015,,9,150.73, -9,2,05.08.2015,,13,150.73, -9,2,06.08.2015,,19,150.73, -9,2,07.08.2015,,13,150.73, -9,2,08.08.2015,,6,150.73, -9,2,09.08.2015,,13,150.73, -9,2,10.08.2015,,22,150.73, -9,2,11.08.2015,,0,150.73, -9,2,12.08.2015,,13,128.17, -9,2,13.08.2015,,3,128.17, -9,2,14.08.2015,,9,128.17, -9,2,15.08.2015,,6,128.17, -9,2,16.08.2015,,13,128.17, -9,2,17.08.2015,,28,128.17, -9,2,18.08.2015,1.0,157,128.17,100.3 -9,2,19.08.2015,1.0,185,128.17,100.3 -9,2,20.08.2015,1.0,129,128.17,100.3 -9,2,21.08.2015,1.0,145,128.17,100.3 -9,2,22.08.2015,1.0,157,128.17,100.3 -9,2,23.08.2015,1.0,88,128.17,100.3 -9,2,24.08.2015,1.0,101,128.17,100.3 -9,2,25.08.2015,,9,128.17, -9,2,26.08.2015,,9,128.17, -9,2,27.08.2015,,6,128.17, -9,2,28.08.2015,,19,128.17, -9,2,29.08.2015,,6,128.17, -9,2,30.08.2015,,9,128.17, -9,2,31.08.2015,,19,128.17, -9,2,01.09.2015,,13,128.17, -9,2,02.09.2015,,3,128.17, -9,2,03.09.2015,,6,128.17, -9,2,04.09.2015,,3,128.17, -9,2,05.09.2015,,0,128.17, -9,2,06.09.2015,,0,128.17, -9,2,07.09.2015,,6,128.17, -9,2,08.09.2015,,9,128.17, -9,2,09.09.2015,,9,128.17, -9,2,10.09.2015,,6,128.17, -9,2,11.09.2015,,9,128.17, -9,2,12.09.2015,,19,128.17, -9,2,13.09.2015,,3,128.17, -9,2,14.09.2015,,6,128.17, -9,2,15.09.2015,,3,128.17, -9,2,16.09.2015,,3,128.17, -9,2,17.09.2015,,31,128.17, -9,2,18.09.2015,,9,128.17, -9,2,19.09.2015,,19,128.17, -9,2,20.09.2015,,9,128.17, -9,2,21.09.2015,,9,128.17, -9,2,22.09.2015,,19,128.17, -9,2,23.09.2015,,16,128.17, -9,2,24.09.2015,,3,128.17, -9,2,25.09.2015,,9,128.17, -9,2,26.09.2015,,9,128.17, -9,2,27.09.2015,,13,128.17, -9,2,28.09.2015,,16,128.17, -9,2,29.09.2015,,3,128.17, -9,2,30.09.2015,,9,128.17, -9,2,01.10.2015,,13,162.96, -9,2,02.10.2015,,13,162.96, -9,2,03.10.2015,,13,162.96, -9,2,04.10.2015,,13,162.96, -9,2,05.10.2015,,0,162.96, -9,2,06.10.2015,1.0,97,143.39,122.05 -9,2,07.10.2015,1.0,75,143.39,122.05 -9,2,08.10.2015,1.0,69,143.39,122.05 -9,2,09.10.2015,1.0,79,143.39,122.05 -9,2,10.10.2015,1.0,82,143.39,122.05 -9,2,11.10.2015,1.0,101,143.39,122.05 -9,2,12.10.2015,1.0,16,143.39,122.05 -9,2,13.10.2015,,3,143.93, -9,2,14.10.2015,,3,143.93, -9,2,15.10.2015,,3,143.93, -9,2,16.10.2015,,0,143.93, -9,2,17.10.2015,,0,162.96, -9,2,18.10.2015,,3,162.96, -9,2,19.10.2015,,0,162.96, -9,2,20.10.2015,,0,162.96, -9,2,21.10.2015,,0,162.96, -9,2,22.10.2015,,0,162.96, -9,2,23.10.2015,,0,162.96, -9,2,24.10.2015,,3,162.96, -9,2,25.10.2015,,0,162.96, -9,2,26.10.2015,,0,162.96, -9,2,27.10.2015,,0,162.96, -9,2,28.10.2015,,0,162.96, -9,2,29.10.2015,,0,162.96, -9,2,30.10.2015,,0,162.96, -9,2,31.10.2015,,0,162.96, -9,2,01.11.2015,,3,162.96, -9,2,02.11.2015,,3,162.96, -9,2,03.11.2015,,0,162.96, -9,2,04.11.2015,,0,162.96, -9,2,05.11.2015,,0,162.96, -9,2,06.11.2015,,0,162.96, -9,2,07.11.2015,,0,162.96, -9,2,08.11.2015,,0,162.96, -9,2,09.11.2015,,0,162.96, -9,2,10.11.2015,,0,162.96, -9,2,11.11.2015,,3,138.36, -9,2,12.11.2015,,0,138.36, -9,2,13.11.2015,,0,138.36, -9,2,14.11.2015,,3,138.36, -9,2,15.11.2015,,3,138.36, -9,2,16.11.2015,,6,138.36, -9,2,17.11.2015,,0,138.36, -9,2,18.11.2015,,3,138.36, -9,2,19.11.2015,,0,138.36, -9,2,20.11.2015,,3,138.36, -9,2,21.11.2015,,0,138.36, -9,2,22.11.2015,,6,138.36, -9,2,23.11.2015,,0,138.36, -9,2,24.11.2015,,3,138.36, -9,2,25.11.2015,,0,138.36, -9,2,26.11.2015,,9,138.36, -9,2,27.11.2015,,3,138.36, -9,2,28.11.2015,,0,138.36, -9,2,29.11.2015,,6,138.36, -9,2,30.11.2015,,3,138.36, -9,2,01.12.2015,,3,162.96, -9,2,02.12.2015,,3,162.96, -9,2,03.12.2015,,0,138.5, -9,2,04.12.2015,,3,138.5, -9,2,05.12.2015,,6,138.5, -9,2,06.12.2015,,9,138.5, -9,2,07.12.2015,,0,138.5, -9,2,08.12.2015,,0,138.5, -9,2,09.12.2015,,0,138.5, -9,2,10.12.2015,,9,138.5, -9,2,11.12.2015,,6,142.57, -9,2,12.12.2015,,0,142.57, -9,2,13.12.2015,,6,142.57, -9,2,14.12.2015,,3,142.57, -9,2,15.12.2015,,3,142.57, -9,2,16.12.2015,,3,142.57, -9,2,17.12.2015,,6,142.57, -9,2,18.12.2015,,13,142.57, -9,2,19.12.2015,,3,142.57, -9,2,20.12.2015,,3,142.57, -9,2,21.12.2015,,6,142.57, -9,2,22.12.2015,1.0,148,142.57,103.02 -9,2,23.12.2015,1.0,145,142.57,103.02 -9,2,24.12.2015,1.0,123,142.57,103.02 -9,2,25.12.2015,1.0,119,142.57,103.02 -9,2,26.12.2015,1.0,135,142.57,103.02 -9,2,27.12.2015,1.0,47,142.57,103.02 -9,2,28.12.2015,1.0,94,142.57,103.02 -9,2,29.12.2015,,6,138.5, -9,2,30.12.2015,,6,138.5, -9,2,31.12.2015,,6,138.5, -9,2,01.01.2016,,6,138.5, -9,2,02.01.2016,,6,138.5, -9,2,03.01.2016,,6,138.5, -9,2,04.01.2016,,6,138.5, -9,2,05.01.2016,,19,138.5, -9,2,06.01.2016,,19,138.5, -9,2,07.01.2016,,6,138.5, -9,2,08.01.2016,,6,138.5, -9,2,09.01.2016,,6,138.5, -9,2,10.01.2016,,6,138.5, -9,2,11.01.2016,,38,138.5, -9,2,12.01.2016,,19,138.5, -9,2,13.01.2016,,9,138.5, -9,2,14.01.2016,,6,138.5, -9,2,15.01.2016,,9,138.5, -9,2,16.01.2016,,3,142.57, -9,2,17.01.2016,,13,142.57, -9,2,18.01.2016,,3,142.57, -9,2,19.01.2016,1.0,123,142.57,103.02 -9,2,20.01.2016,1.0,289,142.57,103.02 -9,2,21.01.2016,1.0,179,142.57,103.02 -9,2,22.01.2016,1.0,192,142.57,103.02 -9,2,23.01.2016,1.0,160,142.57,103.02 -9,2,24.01.2016,1.0,223,142.57,103.02 -9,2,25.01.2016,1.0,151,142.57,103.02 -9,2,26.01.2016,,9,142.57, -9,2,27.01.2016,,19,142.57, -9,2,28.01.2016,,19,142.57, -9,2,29.01.2016,,13,142.57, -9,2,30.01.2016,,13,142.57, -9,2,31.01.2016,,13,142.57, -9,2,01.02.2016,,6,142.57, -9,2,02.02.2016,,3,142.57, -9,2,03.02.2016,,6,142.57, -9,2,04.02.2016,,3,142.57, -9,2,05.02.2016,,13,142.57, -9,2,06.02.2016,,3,142.57, -9,2,07.02.2016,,3,142.57, -9,2,08.02.2016,,6,142.57, -9,2,09.02.2016,1.0,182,142.57,108.46 -9,2,10.02.2016,1.0,207,138.36,108.46 -9,2,11.02.2016,1.0,154,138.36,108.46 -9,2,12.02.2016,1.0,226,138.36,108.46 -9,2,13.02.2016,1.0,217,138.36,108.46 -9,2,14.02.2016,1.0,160,138.36,108.46 -9,2,15.02.2016,1.0,132,138.36,108.46 -9,2,16.02.2016,,9,138.36, -9,2,17.02.2016,,3,138.36, -9,2,18.02.2016,,6,138.36, -9,2,19.02.2016,,3,138.36, -9,2,20.02.2016,,6,138.36, -9,2,21.02.2016,,6,138.36, -9,2,22.02.2016,,3,138.36, -9,2,23.02.2016,,6,138.36, -9,2,24.02.2016,,9,138.36, -9,2,25.02.2016,,6,138.36, -9,2,26.02.2016,,6,138.36, -9,2,27.02.2016,,3,138.36, -9,2,28.02.2016,,3,138.36, -9,2,29.02.2016,,3,138.36, -9,2,01.03.2016,,3,138.36, -9,2,02.03.2016,,3,138.36, -9,2,03.03.2016,,9,138.36, -9,2,04.03.2016,,3,138.36, -9,2,05.03.2016,,6,138.36, -9,2,06.03.2016,,6,138.36, -9,2,07.03.2016,,6,138.36, -9,2,08.03.2016,,6,138.36, -9,2,09.03.2016,,6,138.36, -9,2,10.03.2016,,19,138.36, -9,2,11.03.2016,,6,138.36, -9,2,12.03.2016,,6,138.5, -9,2,13.03.2016,,6,138.5, -9,2,14.03.2016,,47,138.5, -9,2,15.03.2016,1.0,182,138.5,124.77 -9,2,16.03.2016,1.0,173,138.5,124.77 -9,2,17.03.2016,1.0,141,138.5,124.77 -9,2,18.03.2016,1.0,85,138.5,124.77 -9,2,19.03.2016,1.0,126,138.5,124.77 -9,2,20.03.2016,1.0,85,138.5,124.77 -9,2,21.03.2016,1.0,72,138.5,124.77 -9,2,22.03.2016,,3,138.36, -9,2,23.03.2016,,13,138.36, -9,2,24.03.2016,,9,138.36, -9,2,25.03.2016,,3,138.36, -9,2,26.03.2016,,3,138.36, -9,2,27.03.2016,,9,138.36, -9,2,28.03.2016,,6,138.36, -9,2,29.03.2016,,6,138.36, -9,2,30.03.2016,,16,138.36, -9,2,31.03.2016,,9,138.36, -9,2,01.04.2016,,9,138.36, -9,2,02.04.2016,,9,138.36, -9,2,03.04.2016,,3,138.36, -9,2,04.04.2016,,19,138.36, -9,2,05.04.2016,,13,138.36, -9,2,06.04.2016,,6,138.36, -9,2,07.04.2016,,3,138.36, -9,2,08.04.2016,,9,138.36, -9,2,09.04.2016,,13,138.5, -9,2,10.04.2016,,13,138.5, -9,2,11.04.2016,,13,138.5, -9,2,12.04.2016,,3,138.5, -9,2,13.04.2016,,9,138.5, -9,2,14.04.2016,,9,138.5, -9,2,15.04.2016,,9,138.5, -9,2,16.04.2016,,3,138.5, -9,2,17.04.2016,,6,138.5, -9,2,18.04.2016,,16,138.5, -9,2,19.04.2016,1.0,210,138.5,116.89 -9,2,20.04.2016,1.0,113,138.5,116.89 -9,2,21.04.2016,1.0,138,138.5,116.89 -9,2,22.04.2016,1.0,145,138.5,116.89 -9,2,23.04.2016,1.0,151,138.5,116.89 -9,2,24.04.2016,1.0,138,138.5,116.89 -9,2,25.04.2016,1.0,63,138.5,116.89 -9,2,26.04.2016,,9,138.5, -9,2,27.04.2016,,9,138.5, -9,2,28.04.2016,,9,138.5, -9,2,29.04.2016,,9,138.5, -9,2,30.04.2016,,9,138.5, -9,2,01.05.2016,,3,138.5, -9,2,02.05.2016,,35,138.5, -9,2,03.05.2016,1.0,132,138.5,106.01 -9,2,04.05.2016,1.0,270,138.5,106.01 -9,2,05.05.2016,1.0,170,138.5,106.01 -9,2,06.05.2016,1.0,220,138.5,106.01 -9,2,07.05.2016,1.0,201,138.5,106.01 -9,2,08.05.2016,1.0,148,138.5,106.01 -9,2,09.05.2016,1.0,91,138.5,106.01 -9,2,10.05.2016,,31,138.5, -9,2,11.05.2016,,9,138.5, -9,2,12.05.2016,,75,138.5, -9,2,13.05.2016,,16,138.5, -9,2,14.05.2016,,16,138.5, -9,2,15.05.2016,,16,138.5, -9,2,16.05.2016,,16,138.5, -9,2,17.05.2016,,19,138.5, -9,2,18.05.2016,,6,138.5, -9,2,19.05.2016,,6,138.5, -9,2,20.05.2016,,6,138.5, -9,2,21.05.2016,,3,138.5, -9,2,22.05.2016,,16,138.5, -10,2,01.01.2015,,6,135.78, -10,2,02.01.2015,,3,135.78, -10,2,03.01.2015,,3,135.78, -10,2,04.01.2015,,13,135.78, -10,2,05.01.2015,,13,135.78, -10,2,06.01.2015,,6,135.78, -10,2,07.01.2015,,9,135.78, -10,2,08.01.2015,,22,135.78, -10,2,09.01.2015,,0,135.78, -10,2,10.01.2015,,0,135.78, -10,2,11.01.2015,,3,135.78, -10,2,12.01.2015,,3,135.78, -10,2,13.01.2015,,22,135.78, -10,2,14.01.2015,,13,135.78, -10,2,15.01.2015,,16,135.78, -10,2,16.01.2015,,13,135.78, -10,2,17.01.2015,,9,135.78, -10,2,18.01.2015,,6,135.78, -10,2,19.01.2015,,3,135.78, -10,2,20.01.2015,,6,135.78, -10,2,21.01.2015,,3,135.78, -10,2,22.01.2015,,3,135.78, -10,2,23.01.2015,,9,135.78, -10,2,24.01.2015,,0,135.78, -10,2,25.01.2015,,6,135.78, -10,2,26.01.2015,,6,135.78, -10,2,27.01.2015,,3,135.78, -10,2,28.01.2015,,0,135.78, -10,2,29.01.2015,,3,135.78, -10,2,30.01.2015,,3,135.78, -10,2,31.01.2015,,6,135.78, -10,2,01.02.2015,,3,135.78, -10,2,02.02.2015,,0,135.78, -10,2,03.02.2015,,6,135.78, -10,2,04.02.2015,,3,135.78, -10,2,05.02.2015,,3,135.78, -10,2,06.02.2015,,6,135.78, -10,2,07.02.2015,,6,135.78, -10,2,08.02.2015,,6,135.78, -10,2,09.02.2015,,9,135.78, -10,2,10.02.2015,,9,135.78, -10,2,11.02.2015,,6,135.78, -10,2,12.02.2015,,6,135.78, -10,2,13.02.2015,,6,135.78, -10,2,14.02.2015,,9,135.78, -10,2,15.02.2015,,13,135.78, -10,2,16.02.2015,,6,135.78, -10,2,17.02.2015,,16,135.78, -10,2,18.02.2015,,0,135.78, -10,2,19.02.2015,,9,135.78, -10,2,20.02.2015,,25,135.78, -10,2,21.02.2015,,9,135.78, -10,2,22.02.2015,,9,135.78, -10,2,23.02.2015,,9,135.78, -10,2,24.02.2015,,0,135.78, -10,2,25.02.2015,,0,135.78, -10,2,26.02.2015,,0,135.78, -10,2,27.02.2015,,3,135.78, -10,2,28.02.2015,,6,135.78, -10,2,01.03.2015,,0,135.78, -10,2,02.03.2015,,9,135.78, -10,2,03.03.2015,,13,135.78, -10,2,04.03.2015,,9,135.78, -10,2,05.03.2015,,3,135.78, -10,2,06.03.2015,,9,135.78, -10,2,07.03.2015,,3,135.78, -10,2,08.03.2015,,0,146.38, -10,2,09.03.2015,,16,146.38, -10,2,10.03.2015,,13,146.38, -10,2,11.03.2015,,25,146.38, -10,2,12.03.2015,,6,146.38, -10,2,13.03.2015,,0,146.38, -10,2,14.03.2015,,3,146.38, -10,2,15.03.2015,,3,146.38, -10,2,16.03.2015,,13,146.38, -10,2,17.03.2015,,3,146.38, -10,2,18.03.2015,,6,146.38, -10,2,19.03.2015,,9,146.38, -10,2,20.03.2015,,16,146.38, -10,2,21.03.2015,,0,146.38, -10,2,22.03.2015,,0,146.38, -10,2,23.03.2015,,13,146.38, -10,2,24.03.2015,,3,146.38, -10,2,25.03.2015,,6,146.38, -10,2,26.03.2015,,0,146.38, -10,2,27.03.2015,,3,146.38, -10,2,28.03.2015,,13,146.38, -10,2,29.03.2015,,0,146.38, -10,2,30.03.2015,,6,146.38, -10,2,31.03.2015,,6,146.38, -10,2,01.04.2015,,6,146.38, -10,2,02.04.2015,,3,146.38, -10,2,03.04.2015,,0,146.38, -10,2,04.04.2015,,9,146.38, -10,2,05.04.2015,,0,146.38, -10,2,06.04.2015,,0,146.38, -10,2,07.04.2015,,3,146.38, -10,2,08.04.2015,,0,146.38, -10,2,09.04.2015,,19,146.38, -10,2,10.04.2015,,3,146.38, -10,2,11.04.2015,,0,146.38, -10,2,12.04.2015,,3,146.38, -10,2,13.04.2015,,6,146.38, -10,2,14.04.2015,,0,146.38, -10,2,15.04.2015,,3,146.38, -10,2,16.04.2015,,0,146.38, -10,2,17.04.2015,,0,146.38, -10,2,18.04.2015,,6,146.38, -10,2,19.04.2015,,9,146.38, -10,2,20.04.2015,,9,146.38, -10,2,21.04.2015,,3,146.38, -10,2,22.04.2015,,6,146.38, -10,2,23.04.2015,,0,146.38, -10,2,24.04.2015,,3,146.38, -10,2,25.04.2015,,9,146.38, -10,2,26.04.2015,,3,146.38, -10,2,27.04.2015,,6,150.73, -10,2,28.04.2015,,6,150.73, -10,2,29.04.2015,,6,150.73, -10,2,30.04.2015,,0,150.73, -10,2,01.05.2015,,3,150.73, -10,2,02.05.2015,,0,150.73, -10,2,03.05.2015,,0,150.73, -10,2,04.05.2015,,6,150.73, -10,2,05.05.2015,,3,150.73, -10,2,06.05.2015,,0,150.73, -10,2,07.05.2015,,0,150.73, -10,2,08.05.2015,,0,150.73, -10,2,09.05.2015,,0,150.73, -10,2,10.05.2015,,0,150.73, -10,2,11.05.2015,,6,150.73, -10,2,12.05.2015,,3,150.73, -10,2,13.05.2015,,3,150.73, -10,2,14.05.2015,,9,150.73, -10,2,15.05.2015,,0,150.73, -10,2,16.05.2015,,3,150.73, -10,2,17.05.2015,,9,150.73, -10,2,18.05.2015,,6,150.73, -10,2,19.05.2015,,0,150.73, -10,2,20.05.2015,,3,150.73, -10,2,21.05.2015,,13,150.73, -10,2,22.05.2015,,6,150.73, -10,2,23.05.2015,,3,150.73, -10,2,24.05.2015,,0,150.73, -10,2,25.05.2015,,9,150.73, -10,2,26.05.2015,,6,150.73, -10,2,27.05.2015,,0,150.73, -10,2,28.05.2015,,3,150.73, -10,2,29.05.2015,,3,150.73, -10,2,30.05.2015,,3,150.73, -10,2,31.05.2015,,3,150.73, -10,2,01.06.2015,,0,150.73, -10,2,02.06.2015,,0,150.73, -10,2,03.06.2015,,13,150.73, -10,2,04.06.2015,,6,150.73, -10,2,05.06.2015,,3,150.73, -10,2,06.06.2015,,3,150.73, -10,2,07.06.2015,,0,150.73, -10,2,08.06.2015,,3,150.73, -10,2,09.06.2015,,0,150.73, -10,2,10.06.2015,,3,150.73, -10,2,11.06.2015,,3,150.73, -10,2,12.06.2015,,0,150.73, -10,2,13.06.2015,,0,150.73, -10,2,14.06.2015,,3,150.73, -10,2,15.06.2015,,0,150.73, -10,2,16.06.2015,,3,150.73, -10,2,17.06.2015,,16,150.73, -10,2,18.06.2015,,6,150.73, -10,2,19.06.2015,,6,150.73, -10,2,20.06.2015,,0,150.73, -10,2,21.06.2015,,6,150.73, 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-10,2,16.04.2016,,3,138.5, -10,2,17.04.2016,,6,138.5, -10,2,18.04.2016,,9,138.5, -10,2,19.04.2016,1.0,141,138.5,116.89 -10,2,20.04.2016,1.0,113,138.5,116.89 -10,2,21.04.2016,1.0,129,138.5,116.89 -10,2,22.04.2016,1.0,97,138.5,116.89 -10,2,23.04.2016,1.0,85,138.5,116.89 -10,2,24.04.2016,1.0,88,138.5,116.89 -10,2,25.04.2016,1.0,97,138.5,116.89 -10,2,26.04.2016,,19,138.5, -10,2,27.04.2016,,3,138.5, -10,2,28.04.2016,,6,138.5, -10,2,29.04.2016,,3,138.5, -10,2,30.04.2016,,3,138.5, -10,2,01.05.2016,,3,138.5, -10,2,02.05.2016,,19,138.5, -10,2,03.05.2016,1.0,97,138.5,106.01 -10,2,04.05.2016,1.0,248,138.5,106.01 -10,2,05.05.2016,1.0,207,138.5,106.01 -10,2,06.05.2016,1.0,163,138.5,106.01 -10,2,07.05.2016,1.0,138,138.5,106.01 -10,2,08.05.2016,1.0,123,138.5,106.01 -10,2,09.05.2016,1.0,60,138.5,106.01 -10,2,10.05.2016,,13,138.5, -10,2,11.05.2016,,19,138.5, -10,2,12.05.2016,,9,138.5, -10,2,13.05.2016,,6,138.5, -10,2,14.05.2016,,6,138.5, -10,2,15.05.2016,,16,138.5, -10,2,16.05.2016,,16,138.5, -10,2,17.05.2016,,9,138.5, -10,2,18.05.2016,,13,138.5, -10,2,19.05.2016,,13,138.5, -10,2,20.05.2016,,3,138.5, -10,2,21.05.2016,,3,138.5, -10,2,22.05.2016,,9,138.5, -11,2,01.01.2015,,6,135.78, -11,2,02.01.2015,,6,135.78, -11,2,03.01.2015,,0,135.78, -11,2,04.01.2015,,0,135.78, -11,2,05.01.2015,,9,135.78, -11,2,06.01.2015,,9,135.78, -11,2,07.01.2015,,3,135.78, -11,2,08.01.2015,,6,135.78, -11,2,09.01.2015,,0,135.78, -11,2,10.01.2015,,0,135.78, -11,2,11.01.2015,,19,135.78, -11,2,12.01.2015,,9,135.78, -11,2,13.01.2015,,13,135.78, -11,2,14.01.2015,,9,135.78, -11,2,15.01.2015,,9,135.78, -11,2,16.01.2015,,6,135.78, -11,2,17.01.2015,,3,135.78, -11,2,18.01.2015,,0,135.78, -11,2,19.01.2015,,9,135.78, -11,2,20.01.2015,,0,135.78, -11,2,21.01.2015,,3,135.78, -11,2,22.01.2015,,3,135.78, -11,2,23.01.2015,,0,135.78, -11,2,24.01.2015,,0,135.78, -11,2,25.01.2015,,0,135.78, -11,2,26.01.2015,,3,135.78, -11,2,27.01.2015,,0,135.78, -11,2,28.01.2015,,3,135.78, -11,2,29.01.2015,,0,135.78, -11,2,30.01.2015,,3,135.78, -11,2,31.01.2015,,6,135.78, -11,2,01.02.2015,,3,135.78, -11,2,02.02.2015,,0,135.78, -11,2,03.02.2015,,19,135.78, -11,2,04.02.2015,,9,135.78, -11,2,05.02.2015,,6,135.78, -11,2,06.02.2015,,3,135.78, -11,2,07.02.2015,,9,135.78, -11,2,08.02.2015,,6,135.78, -11,2,09.02.2015,,0,135.78, -11,2,10.02.2015,,0,135.78, -11,2,11.02.2015,,6,135.78, -11,2,12.02.2015,,9,135.78, -11,2,13.02.2015,,0,135.78, -11,2,14.02.2015,,0,135.78, -11,2,15.02.2015,,6,135.78, -11,2,16.02.2015,,3,135.78, -11,2,17.02.2015,,3,135.78, -11,2,18.02.2015,,3,135.78, -11,2,19.02.2015,,0,135.78, -11,2,20.02.2015,,0,135.78, -11,2,21.02.2015,,0,135.78, -11,2,22.02.2015,,0,135.78, -11,2,23.02.2015,,0,135.78, -11,2,24.02.2015,,0,135.78, -11,2,25.02.2015,,0,135.78, -11,2,26.02.2015,,6,135.78, -11,2,27.02.2015,,3,135.78, -11,2,28.02.2015,,3,135.78, -11,2,01.03.2015,,0,135.78, -11,2,02.03.2015,,0,135.78, -11,2,03.03.2015,,0,135.78, -11,2,04.03.2015,,0,135.78, -11,2,05.03.2015,,0,135.78, -11,2,06.03.2015,,6,135.78, -11,2,07.03.2015,,0,135.78, -11,2,08.03.2015,,0,146.38, -11,2,09.03.2015,,0,146.38, -11,2,10.03.2015,,3,146.38, -11,2,11.03.2015,,0,146.38, -11,2,12.03.2015,,0,146.38, -11,2,13.03.2015,,0,146.38, -11,2,14.03.2015,,0,146.38, -11,2,15.03.2015,,0,146.38, -11,2,16.03.2015,,0,146.38, -11,2,17.03.2015,,0,146.38, -11,2,18.03.2015,,0,146.38, -11,2,19.03.2015,,0,146.38, -11,2,20.03.2015,,0,146.38, -11,2,21.03.2015,,0,146.38, -11,2,22.03.2015,,0,146.38, -11,2,23.03.2015,,0,146.38, -11,2,24.03.2015,,0,146.38, -11,2,25.03.2015,,0,146.38, -11,2,26.03.2015,,0,146.38, -11,2,27.03.2015,,0,146.38, -11,2,28.03.2015,,0,146.38, -11,2,29.03.2015,,0,146.38, -11,2,30.03.2015,,0,146.38, -11,2,31.03.2015,,0,146.38, -11,2,01.04.2015,,0,146.38, -11,2,02.04.2015,,0,146.38, -11,2,03.04.2015,,0,146.38, -11,2,04.04.2015,,6,146.38, -11,2,05.04.2015,,0,146.38, -11,2,06.04.2015,,0,146.38, -11,2,07.04.2015,,0,146.38, -11,2,08.04.2015,,0,146.38, -11,2,09.04.2015,,0,146.38, -11,2,10.04.2015,,0,146.38, -11,2,11.04.2015,,3,146.38, -11,2,12.04.2015,,3,146.38, -11,2,13.04.2015,,0,146.38, -11,2,14.04.2015,,0,146.38, -11,2,15.04.2015,,0,146.38, -11,2,16.04.2015,,0,146.38, -11,2,17.04.2015,,3,146.38, -11,2,18.04.2015,,6,146.38, -11,2,19.04.2015,,3,146.38, -11,2,20.04.2015,,0,146.38, -11,2,21.04.2015,,0,146.38, -11,2,22.04.2015,,0,146.38, -11,2,23.04.2015,,0,146.38, -11,2,24.04.2015,,0,146.38, -11,2,25.04.2015,,0,146.38, -11,2,26.04.2015,,0,146.38, -11,2,27.04.2015,,0,150.73, -11,2,28.04.2015,,0,150.73, -11,2,29.04.2015,,3,150.73, -11,2,30.04.2015,,0,150.73, -11,2,01.05.2015,,0,150.73, -11,2,02.05.2015,,0,150.73, -11,2,03.05.2015,,6,150.73, -11,2,04.05.2015,,3,150.73, -11,2,05.05.2015,,0,150.73, -11,2,06.05.2015,,0,150.73, -11,2,07.05.2015,,0,150.73, -11,2,08.05.2015,,0,150.73, -11,2,09.05.2015,,0,150.73, -11,2,10.05.2015,,0,150.73, -11,2,11.05.2015,,9,150.73, -11,2,12.05.2015,,0,150.73, -11,2,13.05.2015,,0,150.73, -11,2,14.05.2015,,0,150.73, -11,2,15.05.2015,,3,150.73, -11,2,16.05.2015,,0,150.73, -11,2,17.05.2015,,0,150.73, -11,2,18.05.2015,,0,150.73, -11,2,19.05.2015,,0,150.73, -11,2,20.05.2015,,0,150.73, -11,2,21.05.2015,,0,150.73, -11,2,22.05.2015,,0,150.73, -11,2,23.05.2015,,0,150.73, -11,2,24.05.2015,,0,150.73, -11,2,25.05.2015,,6,150.73, -11,2,26.05.2015,,0,150.73, -11,2,27.05.2015,,0,150.73, -11,2,28.05.2015,,3,150.73, -11,2,29.05.2015,,0,150.73, -11,2,30.05.2015,,0,150.73, -11,2,31.05.2015,,0,150.73, -11,2,01.06.2015,,0,150.73, -11,2,02.06.2015,,0,150.73, -11,2,03.06.2015,,0,150.73, -11,2,04.06.2015,,0,150.73, -11,2,05.06.2015,,0,150.73, -11,2,06.06.2015,,0,150.73, -11,2,07.06.2015,,0,150.73, -11,2,08.06.2015,,0,150.73, -11,2,09.06.2015,,0,150.73, -11,2,10.06.2015,,0,150.73, -11,2,11.06.2015,,0,150.73, -11,2,12.06.2015,,0,150.73, -11,2,13.06.2015,,0,150.73, -11,2,14.06.2015,,0,150.73, -11,2,15.06.2015,,0,150.73, -11,2,16.06.2015,,0,150.73, -11,2,17.06.2015,,0,150.73, -11,2,18.06.2015,,0,150.73, -11,2,19.06.2015,,3,150.73, -11,2,20.06.2015,,0,150.73, -11,2,21.06.2015,,6,150.73, -11,2,22.06.2015,,0,150.73, -11,2,23.06.2015,,0,150.73, -11,2,24.06.2015,,0,150.73, -11,2,25.06.2015,,9,150.73, -11,2,26.06.2015,,0,150.73, -11,2,27.06.2015,,0,150.73, -11,2,28.06.2015,,3,150.73, -11,2,29.06.2015,,0,150.73, -11,2,30.06.2015,,0,150.73, -11,2,01.07.2015,,3,150.73, -11,2,02.07.2015,,0,150.73, -11,2,03.07.2015,,0,150.73, -11,2,04.07.2015,,3,150.73, -11,2,05.07.2015,,3,150.73, -11,2,06.07.2015,,0,150.73, -11,2,07.07.2015,,0,150.73, -11,2,08.07.2015,,0,150.73, -11,2,09.07.2015,,0,150.73, -11,2,10.07.2015,,0,150.73, -11,2,11.07.2015,,0,150.73, -11,2,12.07.2015,,0,150.73, -11,2,13.07.2015,,0,150.73, -11,2,14.07.2015,,0,150.73, -11,2,15.07.2015,,0,150.73, -11,2,16.07.2015,,0,150.73, -11,2,17.07.2015,,3,150.73, -11,2,18.07.2015,,0,150.73, -11,2,19.07.2015,,0,150.73, -11,2,20.07.2015,,3,150.73, -11,2,21.07.2015,,16,150.73, -11,2,22.07.2015,,3,150.73, -11,2,23.07.2015,,0,150.73, -11,2,24.07.2015,,0,150.73, -11,2,25.07.2015,,0,150.73, -11,2,26.07.2015,,0,150.73, -11,2,27.07.2015,,0,150.73, -11,2,28.07.2015,,0,150.73, -11,2,29.07.2015,,0,150.73, -11,2,30.07.2015,,0,150.73, -11,2,31.07.2015,,9,150.73, -11,2,01.08.2015,,13,150.73, -11,2,02.08.2015,,3,150.73, -11,2,03.08.2015,,0,150.73, -11,2,04.08.2015,,0,150.73, -11,2,05.08.2015,,0,150.73, -11,2,06.08.2015,,0,150.73, -11,2,07.08.2015,,0,150.73, -11,2,08.08.2015,,0,150.73, -11,2,09.08.2015,,0,150.73, -11,2,10.08.2015,,0,150.73, -11,2,11.08.2015,,0,150.73, -11,2,12.08.2015,,0,128.17, -11,2,13.08.2015,,0,128.17, -11,2,14.08.2015,,0,128.17, -11,2,15.08.2015,,0,128.17, -11,2,16.08.2015,,0,128.17, -11,2,17.08.2015,,16,128.17, -11,2,18.08.2015,1.0,207,128.17,100.3 -11,2,19.08.2015,1.0,28,128.17,100.3 -11,2,20.08.2015,1.0,13,128.17,100.3 -11,2,21.08.2015,1.0,163,128.17,100.3 -11,2,22.08.2015,1.0,85,128.17,100.3 -11,2,23.08.2015,1.0,75,128.17,100.3 -11,2,24.08.2015,1.0,85,128.17,100.3 -11,2,25.08.2015,,0,128.17, -11,2,26.08.2015,,0,128.17, -11,2,27.08.2015,,0,128.17, -11,2,28.08.2015,,0,128.17, -11,2,29.08.2015,,9,128.17, -11,2,30.08.2015,,9,128.17, -11,2,31.08.2015,,16,128.17, -11,2,01.09.2015,,3,128.17, -11,2,02.09.2015,,3,128.17, -11,2,03.09.2015,,3,128.17, -11,2,04.09.2015,,3,128.17, -11,2,05.09.2015,,3,128.17, -11,2,06.09.2015,,3,128.17, -11,2,07.09.2015,,3,128.17, -11,2,08.09.2015,,9,128.17, -11,2,09.09.2015,,3,128.17, -11,2,10.09.2015,,6,162.96, -11,2,11.09.2015,,6,162.96, -11,2,12.09.2015,,6,162.96, -11,2,13.09.2015,,6,162.96, -11,2,14.09.2015,,16,162.96, -11,2,15.09.2015,,6,162.96, -11,2,16.09.2015,,0,162.96, -11,2,17.09.2015,,6,162.96, -11,2,18.09.2015,,6,162.96, -11,2,19.09.2015,,0,162.96, -11,2,20.09.2015,,6,162.96, -11,2,21.09.2015,,19,162.96, -11,2,22.09.2015,,9,162.96, -11,2,23.09.2015,,3,162.96, -11,2,24.09.2015,,6,162.96, -11,2,25.09.2015,,3,162.96, -11,2,26.09.2015,,0,162.96, -11,2,27.09.2015,,9,162.96, -11,2,28.09.2015,,6,162.96, -11,2,29.09.2015,,19,162.96, -11,2,30.09.2015,,6,162.96, -11,2,01.10.2015,,6,162.96, -11,2,02.10.2015,,0,162.96, -11,2,03.10.2015,,0,162.96, -11,2,04.10.2015,,0,162.96, -11,2,05.10.2015,,0,162.96, -11,2,06.10.2015,1.0,170,143.39,122.05 -11,2,07.10.2015,1.0,91,143.39,122.05 -11,2,08.10.2015,1.0,19,143.39,122.05 -11,2,09.10.2015,1.0,53,143.39,122.05 -11,2,10.10.2015,1.0,75,143.39,122.05 -11,2,11.10.2015,1.0,97,143.39,122.05 -11,2,12.10.2015,1.0,107,143.39,122.05 -11,2,13.10.2015,1.0,57,143.93,122.05 -11,2,14.10.2015,1.0,35,143.93,122.05 -11,2,15.10.2015,1.0,60,143.93,122.05 -11,2,16.10.2015,1.0,69,143.93,122.05 -11,2,17.10.2015,1.0,66,162.96,122.05 -11,2,18.10.2015,1.0,66,162.96,122.05 -11,2,19.10.2015,,0,162.96, -11,2,20.10.2015,,0,162.96, -11,2,21.10.2015,,0,162.96, -11,2,22.10.2015,,0,162.96, -11,2,23.10.2015,,0,162.96, -11,2,24.10.2015,,0,162.96, -11,2,25.10.2015,,0,162.96, -11,2,26.10.2015,,3,162.96, -11,2,27.10.2015,,0,162.96, -11,2,28.10.2015,,0,162.96, -11,2,29.10.2015,,0,162.96, -11,2,30.10.2015,,0,162.96, -11,2,31.10.2015,,0,162.96, -11,2,01.11.2015,,0,162.96, -11,2,02.11.2015,,0,162.96, -11,2,03.11.2015,,0,162.96, -11,2,04.11.2015,,0,162.96, -11,2,05.11.2015,,0,162.96, -11,2,06.11.2015,,0,162.96, -11,2,07.11.2015,,0,162.96, -11,2,08.11.2015,,0,162.96, -11,2,09.11.2015,,0,162.96, -11,2,10.11.2015,,0,162.96, -11,2,11.11.2015,,0,138.36, -11,2,12.11.2015,,0,138.36, -11,2,13.11.2015,,0,138.36, -11,2,14.11.2015,,0,138.36, -11,2,15.11.2015,,0,138.36, -11,2,16.11.2015,,0,138.36, -11,2,17.11.2015,,0,138.36, -11,2,18.11.2015,,0,138.36, -11,2,19.11.2015,,0,138.36, -11,2,20.11.2015,,0,138.36, -11,2,21.11.2015,,0,138.36, -11,2,22.11.2015,,0,138.36, -11,2,23.11.2015,,0,138.36, -11,2,24.11.2015,,0,138.36, -11,2,25.11.2015,,0,138.36, -11,2,26.11.2015,,3,138.36, -11,2,27.11.2015,,0,138.36, -11,2,28.11.2015,,3,138.36, -11,2,29.11.2015,,0,138.36, -11,2,30.11.2015,,6,138.36, -11,2,01.12.2015,,3,162.96, -11,2,02.12.2015,,0,162.96, -11,2,03.12.2015,,0,138.5, -11,2,04.12.2015,,0,138.5, -11,2,05.12.2015,,0,138.5, -11,2,06.12.2015,,3,138.5, -11,2,07.12.2015,,0,138.5, -11,2,08.12.2015,,0,138.5, -11,2,09.12.2015,,0,138.5, -11,2,10.12.2015,,0,138.5, -11,2,11.12.2015,,0,142.57, -11,2,12.12.2015,,0,142.57, -11,2,13.12.2015,,0,142.57, -11,2,14.12.2015,,0,142.57, -11,2,15.12.2015,,0,142.57, -11,2,16.12.2015,,0,142.57, -11,2,17.12.2015,,0,142.57, -11,2,18.12.2015,,0,142.57, -11,2,19.12.2015,,0,142.57, -11,2,20.12.2015,,6,142.57, -11,2,21.12.2015,,3,142.57, -11,2,22.12.2015,1.0,251,142.57,103.02 -11,2,23.12.2015,1.0,170,142.57,103.02 -11,2,24.12.2015,1.0,123,142.57,103.02 -11,2,25.12.2015,1.0,145,142.57,103.02 -11,2,26.12.2015,1.0,204,142.57,103.02 -11,2,27.12.2015,1.0,220,142.57,103.02 -11,2,28.12.2015,1.0,176,142.57,103.02 -11,2,29.12.2015,,22,138.5, -11,2,30.12.2015,,6,138.5, -11,2,31.12.2015,,9,138.5, -11,2,01.01.2016,,3,138.5, -11,2,02.01.2016,,9,138.5, -11,2,03.01.2016,,3,138.5, -11,2,04.01.2016,,6,138.5, -11,2,05.01.2016,,3,138.5, -11,2,06.01.2016,,9,138.5, -11,2,07.01.2016,,0,138.5, -11,2,08.01.2016,,3,138.5, -11,2,09.01.2016,,3,138.5, -11,2,10.01.2016,,6,138.5, -11,2,11.01.2016,,0,138.5, -11,2,12.01.2016,,0,138.5, -11,2,13.01.2016,,0,138.5, -11,2,14.01.2016,,3,138.5, -11,2,15.01.2016,,3,138.5, -11,2,16.01.2016,,3,142.57, -11,2,17.01.2016,,3,142.57, -11,2,18.01.2016,,16,142.57, -11,2,19.01.2016,1.0,239,142.57,103.02 -11,2,20.01.2016,1.0,217,142.57,103.02 -11,2,21.01.2016,1.0,223,142.57,103.02 -11,2,22.01.2016,1.0,264,142.57,103.02 -11,2,23.01.2016,1.0,201,142.57,103.02 -11,2,24.01.2016,1.0,188,142.57,103.02 -11,2,25.01.2016,1.0,207,142.57,103.02 -11,2,26.01.2016,,3,142.57, -11,2,27.01.2016,,3,142.57, -11,2,28.01.2016,,16,142.57, -11,2,29.01.2016,,3,142.57, -11,2,30.01.2016,,6,142.57, -11,2,31.01.2016,,6,142.57, -11,2,01.02.2016,,6,142.57, -11,2,02.02.2016,,19,142.57, -11,2,03.02.2016,,6,142.57, -11,2,04.02.2016,,3,142.57, -11,2,05.02.2016,,3,142.57, -11,2,06.02.2016,,13,142.57, -11,2,07.02.2016,,6,142.57, -11,2,08.02.2016,,3,142.57, -11,2,09.02.2016,1.0,195,142.57,108.46 -11,2,10.02.2016,1.0,119,138.36,108.46 -11,2,11.02.2016,1.0,129,138.36,108.46 -11,2,12.02.2016,1.0,129,138.36,108.46 -11,2,13.02.2016,1.0,148,138.36,108.46 -11,2,14.02.2016,1.0,138,138.36,108.46 -11,2,15.02.2016,1.0,173,138.36,108.46 -11,2,16.02.2016,,6,138.36, -11,2,17.02.2016,,6,138.36, -11,2,18.02.2016,,6,138.36, -11,2,19.02.2016,,6,138.36, -11,2,20.02.2016,,3,138.36, -11,2,21.02.2016,,3,138.36, -11,2,22.02.2016,,6,138.36, -11,2,23.02.2016,,3,138.36, -11,2,24.02.2016,,6,138.36, -11,2,25.02.2016,,6,138.36, -11,2,26.02.2016,,6,138.36, -11,2,27.02.2016,,6,138.36, -11,2,28.02.2016,,6,138.36, -11,2,29.02.2016,,3,138.36, -11,2,01.03.2016,,6,138.36, -11,2,02.03.2016,,6,138.36, -11,2,03.03.2016,,6,138.36, -11,2,04.03.2016,,6,138.36, -11,2,05.03.2016,,6,138.36, -11,2,06.03.2016,,6,138.36, -11,2,07.03.2016,,6,138.36, -11,2,08.03.2016,,6,138.36, -11,2,09.03.2016,,6,138.36, -11,2,10.03.2016,,6,138.36, -11,2,11.03.2016,,6,138.36, -11,2,12.03.2016,,6,138.5, -11,2,13.03.2016,,6,138.5, -11,2,14.03.2016,,16,138.5, -11,2,15.03.2016,1.0,170,138.5,124.77 -11,2,16.03.2016,1.0,38,138.5,124.77 -11,2,17.03.2016,1.0,63,138.5,124.77 -11,2,18.03.2016,1.0,50,138.5,124.77 -11,2,19.03.2016,1.0,63,138.5,124.77 -11,2,20.03.2016,1.0,66,138.5,124.77 -11,2,21.03.2016,1.0,79,138.5,124.77 -11,2,22.03.2016,,3,138.36, -11,2,23.03.2016,,6,138.36, -11,2,24.03.2016,,6,138.36, -11,2,25.03.2016,,6,138.36, -11,2,26.03.2016,,6,138.36, -11,2,27.03.2016,,3,138.36, -11,2,28.03.2016,,6,138.36, -11,2,29.03.2016,,6,138.36, -11,2,30.03.2016,,16,138.36, -11,2,31.03.2016,,6,138.36, -11,2,01.04.2016,,6,138.36, -11,2,02.04.2016,,6,138.36, -11,2,03.04.2016,,6,138.36, -11,2,04.04.2016,,6,138.36, -11,2,05.04.2016,,6,138.36, -11,2,06.04.2016,,13,138.36, -11,2,07.04.2016,,6,138.36, -11,2,08.04.2016,,6,138.36, -11,2,09.04.2016,,6,138.5, -11,2,10.04.2016,,6,138.5, -11,2,11.04.2016,,6,138.5, -11,2,12.04.2016,,6,138.5, -11,2,13.04.2016,,0,138.5, -11,2,14.04.2016,,0,138.5, -11,2,15.04.2016,,9,138.5, -11,2,16.04.2016,,3,138.5, -11,2,17.04.2016,,6,138.5, -11,2,18.04.2016,,0,138.5, -11,2,19.04.2016,1.0,119,138.5,116.89 -11,2,20.04.2016,1.0,107,138.5,116.89 -11,2,21.04.2016,1.0,91,138.5,116.89 -11,2,22.04.2016,1.0,72,138.5,116.89 -11,2,23.04.2016,1.0,116,138.5,116.89 -11,2,24.04.2016,1.0,85,138.5,116.89 -11,2,25.04.2016,1.0,85,138.5,116.89 -11,2,26.04.2016,1.0,110,138.5,114.17 -11,2,27.04.2016,1.0,82,138.5,114.17 -11,2,28.04.2016,,3,138.5, -11,2,29.04.2016,,0,138.5, -11,2,30.04.2016,,13,138.5, -11,2,01.05.2016,,9,138.5, -11,2,02.05.2016,,25,138.5, -11,2,03.05.2016,1.0,163,138.5,106.01 -11,2,04.05.2016,1.0,145,138.5,106.01 -11,2,05.05.2016,1.0,210,138.5,106.01 -11,2,06.05.2016,1.0,283,138.5,106.01 -11,2,07.05.2016,1.0,141,138.5,106.01 -11,2,08.05.2016,1.0,101,138.5,106.01 -11,2,09.05.2016,1.0,91,138.5,106.01 -11,2,10.05.2016,,6,138.5, -11,2,11.05.2016,,6,138.5, -11,2,12.05.2016,,6,138.5, -11,2,13.05.2016,,3,138.5, -11,2,14.05.2016,,6,138.5, -11,2,15.05.2016,,9,138.5, -11,2,16.05.2016,,6,138.5, -11,2,17.05.2016,,6,138.5, -11,2,18.05.2016,,6,138.5, -11,2,19.05.2016,,6,138.5, -11,2,20.05.2016,,13,138.5, -11,2,21.05.2016,,6,138.5, -11,2,22.05.2016,,6,138.5, -12,2,01.01.2015,,16,142.57, -12,2,02.01.2015,,19,142.57, -12,2,03.01.2015,,16,142.57, -12,2,04.01.2015,,16,142.57, -12,2,05.01.2015,,0,142.57, -12,2,06.01.2015,,0,142.57, -12,2,07.01.2015,,0,142.57, -12,2,08.01.2015,,41,142.57, -12,2,09.01.2015,,28,142.57, -12,2,10.01.2015,,6,142.57, -12,2,11.01.2015,,22,142.57, -12,2,12.01.2015,,3,142.57, -12,2,13.01.2015,,13,142.57, -12,2,14.01.2015,,13,142.57, -12,2,15.01.2015,,28,142.57, -12,2,16.01.2015,,16,142.57, -12,2,17.01.2015,,19,142.57, -12,2,18.01.2015,,38,142.57, -12,2,19.01.2015,,16,142.57, -12,2,20.01.2015,,9,142.57, -12,2,21.01.2015,,9,142.57, -12,2,22.01.2015,,28,142.57, -12,2,23.01.2015,,9,142.57, -12,2,24.01.2015,,25,142.57, -12,2,25.01.2015,,38,142.57, -12,2,26.01.2015,,6,142.57, -12,2,27.01.2015,,13,142.57, -12,2,28.01.2015,,19,142.57, -12,2,29.01.2015,,22,142.57, -12,2,30.01.2015,,22,142.57, -12,2,31.01.2015,,13,142.57, -12,2,01.02.2015,,22,142.57, -12,2,02.02.2015,,19,142.57, -12,2,03.02.2015,,19,142.57, -12,2,04.02.2015,,28,142.57, -12,2,05.02.2015,,25,142.57, -12,2,06.02.2015,,22,142.57, -12,2,07.02.2015,,13,142.57, -12,2,08.02.2015,,9,142.57, -12,2,09.02.2015,,19,142.57, -12,2,10.02.2015,,25,142.57, -12,2,11.02.2015,,9,142.57, -12,2,12.02.2015,,3,142.57, -12,2,13.02.2015,,13,142.57, -12,2,14.02.2015,,19,142.57, -12,2,15.02.2015,,6,142.57, -12,2,16.02.2015,,22,142.57, -12,2,17.02.2015,,3,142.57, -12,2,18.02.2015,,6,142.57, -12,2,19.02.2015,,9,142.57, -12,2,20.02.2015,,13,142.57, -12,2,21.02.2015,,0,142.57, -12,2,22.02.2015,,31,142.57, -12,2,23.02.2015,,9,142.57, -12,2,24.02.2015,,0,142.57, -12,2,25.02.2015,,9,142.57, -12,2,26.02.2015,,16,142.57, -12,2,27.02.2015,,0,142.57, -12,2,28.02.2015,,9,142.57, -12,2,01.03.2015,,16,142.57, -12,2,02.03.2015,,6,153.72, -12,2,03.03.2015,,9,153.72, -12,2,04.03.2015,,19,153.72, -12,2,05.03.2015,,13,153.72, -12,2,06.03.2015,,3,153.72, -12,2,07.03.2015,,3,153.72, -12,2,08.03.2015,,9,153.72, -12,2,09.03.2015,,13,153.72, -12,2,10.03.2015,,16,153.72, -12,2,11.03.2015,,9,153.72, -12,2,12.03.2015,,9,153.72, -12,2,13.03.2015,,0,153.72, -12,2,14.03.2015,,13,153.72, -12,2,15.03.2015,,6,153.72, -12,2,16.03.2015,,6,153.72, -12,2,17.03.2015,,3,153.72, -12,2,18.03.2015,,6,153.72, -12,2,19.03.2015,,6,153.72, -12,2,20.03.2015,,22,153.72, -12,2,21.03.2015,,16,153.72, -12,2,22.03.2015,,16,153.72, -12,2,23.03.2015,,0,153.72, -12,2,24.03.2015,,9,153.72, -12,2,25.03.2015,,0,153.72, -12,2,26.03.2015,,9,153.72, -12,2,27.03.2015,,0,153.72, -12,2,28.03.2015,,13,153.72, -12,2,29.03.2015,,13,153.72, -12,2,30.03.2015,,3,153.72, -12,2,31.03.2015,,9,153.72, -12,2,01.04.2015,,3,153.72, -12,2,02.04.2015,,3,153.72, -12,2,03.04.2015,,9,153.72, -12,2,04.04.2015,,31,153.72, -12,2,05.04.2015,,13,153.72, -12,2,06.04.2015,,6,153.72, -12,2,07.04.2015,,13,153.72, -12,2,08.04.2015,,6,153.72, -12,2,09.04.2015,,0,153.72, -12,2,10.04.2015,,6,153.72, -12,2,11.04.2015,,19,153.72, -12,2,12.04.2015,,9,153.72, -12,2,13.04.2015,,13,153.72, -12,2,14.04.2015,,3,153.72, -12,2,15.04.2015,,9,153.72, -12,2,16.04.2015,,6,153.72, -12,2,17.04.2015,,3,153.72, -12,2,18.04.2015,,3,153.72, -12,2,19.04.2015,,6,153.72, -12,2,20.04.2015,,13,153.72, -12,2,21.04.2015,,16,153.72, -12,2,22.04.2015,,3,153.72, -12,2,23.04.2015,,6,153.72, -12,2,24.04.2015,,0,153.72, -12,2,25.04.2015,,3,153.72, -12,2,26.04.2015,,13,153.72, -12,2,27.04.2015,,6,158.34, -12,2,28.04.2015,,13,158.34, -12,2,29.04.2015,,3,158.34, -12,2,30.04.2015,,3,158.34, -12,2,01.05.2015,,3,158.34, -12,2,02.05.2015,,6,158.34, -12,2,03.05.2015,,9,158.34, -12,2,04.05.2015,,9,158.34, -12,2,05.05.2015,,13,158.34, -12,2,06.05.2015,,3,158.34, -12,2,07.05.2015,,6,158.34, -12,2,08.05.2015,,13,158.34, -12,2,09.05.2015,,3,158.34, -12,2,10.05.2015,,9,158.34, -12,2,11.05.2015,,16,158.34, -12,2,12.05.2015,,13,158.34, -12,2,13.05.2015,,0,158.34, -12,2,14.05.2015,,0,158.34, -12,2,15.05.2015,,25,158.34, -12,2,16.05.2015,,3,158.34, -12,2,17.05.2015,,19,158.34, -12,2,18.05.2015,,0,158.34, -12,2,19.05.2015,,0,158.34, -12,2,20.05.2015,,3,158.34, -12,2,21.05.2015,,13,158.34, -12,2,22.05.2015,,3,158.34, -12,2,23.05.2015,,0,158.34, -12,2,24.05.2015,,0,158.34, -12,2,25.05.2015,,9,158.34, -12,2,26.05.2015,,13,158.34, -12,2,27.05.2015,,9,158.34, -12,2,28.05.2015,,0,158.34, -12,2,29.05.2015,,22,158.34, -12,2,30.05.2015,,0,158.34, -12,2,31.05.2015,,13,158.34, -12,2,01.06.2015,,0,158.34, -12,2,02.06.2015,,6,158.34, -12,2,03.06.2015,,3,158.34, -12,2,04.06.2015,,0,158.34, -12,2,05.06.2015,,3,158.34, -12,2,06.06.2015,,6,158.34, -12,2,07.06.2015,,6,158.34, -12,2,08.06.2015,,0,158.34, -12,2,09.06.2015,,6,158.34, -12,2,10.06.2015,,6,158.34, -12,2,11.06.2015,,16,158.34, -12,2,12.06.2015,,0,158.34, -12,2,13.06.2015,,0,158.34, -12,2,14.06.2015,,0,158.34, -12,2,15.06.2015,,3,158.34, -12,2,16.06.2015,,0,158.34, -12,2,17.06.2015,,0,158.34, -12,2,18.06.2015,,6,158.34, -12,2,19.06.2015,,3,158.34, -12,2,20.06.2015,,0,158.34, -12,2,21.06.2015,,3,158.34, -12,2,22.06.2015,,6,158.34, -12,2,23.06.2015,,6,158.34, -12,2,24.06.2015,,0,158.34, -12,2,25.06.2015,,0,158.34, -12,2,26.06.2015,,0,158.34, -12,2,27.06.2015,,9,158.34, -12,2,28.06.2015,,9,158.34, -12,2,29.06.2015,,13,158.34, -12,2,30.06.2015,,3,158.34, -12,2,01.07.2015,,16,158.34, -12,2,02.07.2015,,28,158.34, -12,2,03.07.2015,,3,158.34, -12,2,04.07.2015,,6,158.34, -12,2,05.07.2015,,3,158.34, -12,2,06.07.2015,,3,158.34, -12,2,07.07.2015,,0,158.34, -12,2,08.07.2015,,3,158.34, -12,2,09.07.2015,,0,158.34, -12,2,10.07.2015,,13,158.34, -12,2,11.07.2015,,3,158.34, -12,2,12.07.2015,,25,158.34, -12,2,13.07.2015,,6,158.34, -12,2,14.07.2015,,3,158.34, -12,2,15.07.2015,,3,158.34, -12,2,16.07.2015,,3,158.34, -12,2,17.07.2015,,0,158.34, -12,2,18.07.2015,,6,158.34, -12,2,19.07.2015,,6,158.34, -12,2,20.07.2015,,60,158.34, -12,2,21.07.2015,,16,158.34, -12,2,22.07.2015,,31,158.34, -12,2,23.07.2015,,53,158.34, -12,2,24.07.2015,,75,158.34, -12,2,25.07.2015,,41,158.34, -12,2,26.07.2015,,41,158.34, -12,2,27.07.2015,,31,158.34, -12,2,28.07.2015,,53,158.34, -12,2,29.07.2015,,28,158.34, -12,2,30.07.2015,,66,158.34, -12,2,31.07.2015,,28,158.34, -12,2,01.08.2015,,47,158.34, -12,2,02.08.2015,,41,158.34, -12,2,03.08.2015,,50,158.34, -12,2,04.08.2015,,53,158.34, -12,2,05.08.2015,,44,158.34, -12,2,06.08.2015,,35,158.34, -12,2,07.08.2015,,60,158.34, -12,2,08.08.2015,,31,158.34, -12,2,09.08.2015,,41,158.34, -12,2,10.08.2015,,44,158.34, -12,2,11.08.2015,,16,158.34, -12,2,12.08.2015,,22,128.17, -12,2,13.08.2015,,28,128.17, -12,2,14.08.2015,,47,128.17, -12,2,15.08.2015,,22,128.17, -12,2,16.08.2015,,44,128.17, -12,2,17.08.2015,,22,128.17, -12,2,18.08.2015,1.0,214,128.17,100.3 -12,2,19.08.2015,1.0,176,128.17,100.3 -12,2,20.08.2015,1.0,123,128.17,100.3 -12,2,21.08.2015,1.0,126,128.17,100.3 -12,2,22.08.2015,1.0,167,128.17,100.3 -12,2,23.08.2015,1.0,141,128.17,100.3 -12,2,24.08.2015,1.0,60,128.17,100.3 -12,2,25.08.2015,,41,128.17, -12,2,26.08.2015,,19,128.17, -12,2,27.08.2015,,22,128.17, -12,2,28.08.2015,,44,128.17, -12,2,29.08.2015,,85,128.17, -12,2,30.08.2015,,31,128.17, -12,2,31.08.2015,,28,128.17, -12,2,01.09.2015,,28,128.17, -12,2,02.09.2015,,28,128.17, -12,2,03.09.2015,,28,128.17, -12,2,04.09.2015,,28,128.17, -12,2,05.09.2015,,25,128.17, -12,2,06.09.2015,,38,128.17, -12,2,07.09.2015,,57,128.17, -12,2,08.09.2015,,79,128.17, -12,2,09.09.2015,,9,128.17, -12,2,10.09.2015,,31,128.17, -12,2,11.09.2015,,31,128.17, -12,2,12.09.2015,,31,128.17, -12,2,13.09.2015,,31,128.17, -12,2,14.09.2015,,31,128.17, -12,2,15.09.2015,,16,128.17, -12,2,16.09.2015,,16,128.17, -12,2,17.09.2015,,41,128.17, -12,2,18.09.2015,,47,128.17, -12,2,19.09.2015,,31,128.17, -12,2,20.09.2015,,25,128.17, -12,2,21.09.2015,,22,128.17, -12,2,22.09.2015,,22,128.17, -12,2,23.09.2015,,53,128.17, -12,2,24.09.2015,,16,128.17, -12,2,25.09.2015,,38,128.17, -12,2,26.09.2015,,44,128.17, -12,2,27.09.2015,,13,128.17, -12,2,28.09.2015,,38,128.17, -12,2,29.09.2015,,13,128.17, -12,2,30.09.2015,,19,128.17, -12,2,01.10.2015,,13,162.96, -12,2,02.10.2015,,13,162.96, -12,2,03.10.2015,,25,162.96, -12,2,04.10.2015,,25,162.96, -12,2,05.10.2015,,31,162.96, -12,2,06.10.2015,1.0,145,143.39,122.05 -12,2,07.10.2015,1.0,123,143.39,122.05 -12,2,08.10.2015,1.0,116,143.39,122.05 -12,2,09.10.2015,1.0,135,143.39,122.05 -12,2,10.10.2015,1.0,97,143.39,122.05 -12,2,11.10.2015,1.0,94,143.39,122.05 -12,2,12.10.2015,1.0,85,143.39,122.05 -12,2,13.10.2015,1.0,116,143.93,122.05 -12,2,14.10.2015,1.0,28,143.93,122.05 -12,2,15.10.2015,1.0,22,143.93,122.05 -12,2,16.10.2015,1.0,88,143.93,122.05 -12,2,17.10.2015,1.0,79,162.96,122.05 -12,2,18.10.2015,1.0,66,162.96,122.05 -12,2,19.10.2015,,25,162.96, -12,2,20.10.2015,,22,162.96, -12,2,21.10.2015,,6,162.96, -12,2,22.10.2015,,3,162.96, -12,2,23.10.2015,,3,162.96, -12,2,24.10.2015,,3,162.96, -12,2,25.10.2015,,13,162.96, -12,2,26.10.2015,,6,162.96, -12,2,27.10.2015,,0,162.96, -12,2,28.10.2015,,0,162.96, -12,2,29.10.2015,,3,162.96, -12,2,30.10.2015,,3,162.96, -12,2,31.10.2015,,3,162.96, -12,2,01.11.2015,,0,162.96, -12,2,02.11.2015,,3,162.96, -12,2,03.11.2015,,3,162.96, -12,2,04.11.2015,,6,162.96, -12,2,05.11.2015,,3,162.96, -12,2,06.11.2015,,3,162.96, -12,2,07.11.2015,,19,162.96, -12,2,08.11.2015,,6,162.96, -12,2,09.11.2015,,9,162.96, -12,2,10.11.2015,,3,162.96, -12,2,11.11.2015,,16,138.36, -12,2,12.11.2015,,3,138.36, -12,2,13.11.2015,,31,138.36, -12,2,14.11.2015,,19,138.36, -12,2,15.11.2015,,9,138.36, -12,2,16.11.2015,,9,138.36, -12,2,17.11.2015,,3,138.36, -12,2,18.11.2015,,0,138.36, -12,2,19.11.2015,,9,138.36, -12,2,20.11.2015,,16,138.36, -12,2,21.11.2015,,13,138.36, -12,2,22.11.2015,,0,138.36, -12,2,23.11.2015,,22,138.36, -12,2,24.11.2015,,6,138.36, -12,2,25.11.2015,,3,138.36, -12,2,26.11.2015,,6,138.36, -12,2,27.11.2015,,16,138.36, -12,2,28.11.2015,,9,138.36, -12,2,29.11.2015,,3,138.36, -12,2,30.11.2015,,28,138.36, -12,2,01.12.2015,,9,162.96, -12,2,02.12.2015,,13,162.96, -12,2,03.12.2015,,9,138.5, -12,2,04.12.2015,,3,138.5, -12,2,05.12.2015,,3,138.5, -12,2,06.12.2015,,6,138.5, -12,2,07.12.2015,,9,138.5, -12,2,08.12.2015,,9,138.5, -12,2,09.12.2015,,9,138.5, -12,2,10.12.2015,,22,138.5, -12,2,11.12.2015,,13,142.57, -12,2,12.12.2015,,31,142.57, -12,2,13.12.2015,,3,142.57, -12,2,14.12.2015,,0,142.57, -12,2,15.12.2015,,0,142.57, -12,2,16.12.2015,,13,142.57, -12,2,17.12.2015,,16,142.57, -12,2,18.12.2015,,16,142.57, -12,2,19.12.2015,,31,142.57, -12,2,20.12.2015,,28,142.57, -12,2,21.12.2015,,3,142.57, -12,2,22.12.2015,1.0,138,142.57,103.02 -12,2,23.12.2015,1.0,79,142.57,103.02 -12,2,24.12.2015,1.0,116,142.57,103.02 -12,2,25.12.2015,1.0,170,142.57,103.02 -12,2,26.12.2015,1.0,182,142.57,103.02 -12,2,27.12.2015,1.0,188,142.57,103.02 -12,2,28.12.2015,1.0,104,142.57,103.02 -12,2,29.12.2015,,0,138.5, -12,2,30.12.2015,,0,138.5, -12,2,31.12.2015,,0,138.5, -12,2,01.01.2016,,0,138.5, -12,2,02.01.2016,,0,138.5, -12,2,03.01.2016,,19,138.5, -12,2,04.01.2016,,0,138.5, -12,2,05.01.2016,,13,138.5, -12,2,06.01.2016,,0,138.5, -12,2,07.01.2016,,19,138.5, -12,2,08.01.2016,,6,138.5, -12,2,09.01.2016,,16,138.5, -12,2,10.01.2016,,31,138.5, -12,2,11.01.2016,,9,138.5, -12,2,12.01.2016,,0,138.5, -12,2,13.01.2016,,41,138.5, -12,2,14.01.2016,,6,138.5, -12,2,15.01.2016,,44,138.5, -12,2,16.01.2016,,19,142.57, -12,2,17.01.2016,,22,142.57, -12,2,18.01.2016,,9,142.57, -12,2,19.01.2016,1.0,270,142.57,103.02 -12,2,20.01.2016,1.0,154,142.57,103.02 -12,2,21.01.2016,1.0,245,142.57,103.02 -12,2,22.01.2016,1.0,248,142.57,103.02 -12,2,23.01.2016,1.0,179,142.57,103.02 -12,2,24.01.2016,1.0,210,142.57,103.02 -12,2,25.01.2016,1.0,242,142.57,103.02 -12,2,26.01.2016,,31,142.57, -12,2,27.01.2016,,16,142.57, -12,2,28.01.2016,,31,142.57, -12,2,29.01.2016,,3,142.57, -12,2,30.01.2016,,9,142.57, -12,2,31.01.2016,,19,142.57, -12,2,01.02.2016,,6,142.57, -12,2,02.02.2016,,3,142.57, -12,2,03.02.2016,,6,142.57, -12,2,04.02.2016,,13,142.57, -12,2,05.02.2016,,19,142.57, -12,2,06.02.2016,,0,142.57, -12,2,07.02.2016,,0,142.57, -12,2,08.02.2016,,16,142.57, -12,2,09.02.2016,1.0,311,142.57,108.46 -12,2,10.02.2016,1.0,220,138.36,108.46 -12,2,11.02.2016,1.0,207,138.36,108.46 -12,2,12.02.2016,1.0,201,138.36,108.46 -12,2,13.02.2016,1.0,254,138.36,108.46 -12,2,14.02.2016,1.0,170,138.36,108.46 -12,2,15.02.2016,1.0,229,138.36,108.46 -12,2,16.02.2016,,6,138.36, -12,2,17.02.2016,,0,138.36, -12,2,18.02.2016,,3,138.36, -12,2,19.02.2016,,25,138.36, -12,2,20.02.2016,,3,138.36, -12,2,21.02.2016,,16,138.36, -12,2,22.02.2016,,13,138.36, -12,2,23.02.2016,,6,138.36, -12,2,24.02.2016,,0,138.36, -12,2,25.02.2016,,0,138.36, -12,2,26.02.2016,,0,138.36, -12,2,27.02.2016,,0,138.36, -12,2,28.02.2016,,0,138.36, -12,2,29.02.2016,,0,138.36, -12,2,01.03.2016,,0,138.36, -12,2,02.03.2016,,0,138.36, -12,2,03.03.2016,,0,138.36, -12,2,04.03.2016,,0,138.36, -12,2,05.03.2016,,0,138.36, -12,2,06.03.2016,,0,138.36, -12,2,07.03.2016,,0,138.36, -12,2,08.03.2016,,0,138.36, -12,2,09.03.2016,,0,138.36, -12,2,10.03.2016,,0,138.36, -12,2,11.03.2016,,0,138.36, -12,2,12.03.2016,,0,138.5, -12,2,13.03.2016,,0,138.5, -12,2,14.03.2016,,35,138.5, -12,2,15.03.2016,1.0,160,138.5,124.77 -12,2,16.03.2016,1.0,145,138.5,124.77 -12,2,17.03.2016,1.0,204,138.5,124.77 -12,2,18.03.2016,1.0,138,138.5,124.77 -12,2,19.03.2016,1.0,97,138.5,124.77 -12,2,20.03.2016,1.0,176,138.5,124.77 -12,2,21.03.2016,1.0,69,138.5,124.77 -12,2,22.03.2016,,0,138.36, -12,2,23.03.2016,,3,138.36, -12,2,24.03.2016,,6,138.36, -12,2,25.03.2016,,0,138.36, -12,2,26.03.2016,,0,138.36, -12,2,27.03.2016,,6,138.36, -12,2,28.03.2016,,3,138.36, -12,2,29.03.2016,,25,138.36, -12,2,30.03.2016,,13,138.36, -12,2,31.03.2016,,3,138.36, -12,2,01.04.2016,,13,138.36, -12,2,02.04.2016,,3,138.36, -12,2,03.04.2016,,3,138.36, -12,2,04.04.2016,,9,138.36, -12,2,05.04.2016,,3,138.36, -12,2,06.04.2016,,0,138.36, -12,2,07.04.2016,,0,138.36, -12,2,08.04.2016,,6,138.36, -12,2,09.04.2016,,9,138.5, -12,2,10.04.2016,,13,138.5, -12,2,11.04.2016,,13,138.5, -12,2,12.04.2016,,6,138.5, -12,2,13.04.2016,,0,138.5, -12,2,14.04.2016,,9,138.5, -12,2,15.04.2016,,6,138.5, -12,2,16.04.2016,,0,138.5, -12,2,17.04.2016,,9,138.5, -12,2,18.04.2016,,9,138.5, -12,2,19.04.2016,1.0,126,138.5,116.89 -12,2,20.04.2016,1.0,157,138.5,116.89 -12,2,21.04.2016,1.0,104,138.5,116.89 -12,2,22.04.2016,1.0,132,138.5,116.89 -12,2,23.04.2016,1.0,123,138.5,116.89 -12,2,24.04.2016,1.0,119,138.5,116.89 -12,2,25.04.2016,1.0,154,138.5,116.89 -12,2,26.04.2016,,9,138.5, -12,2,27.04.2016,,0,138.5, -12,2,28.04.2016,,0,138.5, -12,2,29.04.2016,,3,138.5, -12,2,30.04.2016,,0,138.5, -12,2,01.05.2016,,3,138.5, -12,2,02.05.2016,,0,138.5, -12,2,03.05.2016,1.0,195,138.5,106.01 -12,2,04.05.2016,1.0,232,138.5,106.01 -12,2,05.05.2016,1.0,192,138.5,106.01 -12,2,06.05.2016,1.0,173,138.5,106.01 -12,2,07.05.2016,1.0,148,138.5,106.01 -12,2,08.05.2016,1.0,170,138.5,106.01 -12,2,09.05.2016,1.0,145,138.5,106.01 -12,2,10.05.2016,,13,138.5, -12,2,11.05.2016,,16,138.5, -12,2,12.05.2016,,16,138.5, -12,2,13.05.2016,,16,138.5, -12,2,14.05.2016,,19,138.5, -12,2,15.05.2016,,9,138.5, -12,2,16.05.2016,,9,138.5, -12,2,17.05.2016,,3,138.5, -12,2,18.05.2016,,0,138.5, -12,2,19.05.2016,,6,138.5, -12,2,20.05.2016,,0,138.5, -12,2,21.05.2016,,0,138.5, -12,2,22.05.2016,,0,138.5, -13,2,01.01.2015,,0,134.15, -13,2,02.01.2015,,13,134.15, -13,2,03.01.2015,,3,134.15, -13,2,04.01.2015,,38,134.15, -13,2,05.01.2015,,19,134.15, -13,2,06.01.2015,,6,134.15, -13,2,07.01.2015,,25,135.78, -13,2,08.01.2015,,0,135.78, -13,2,09.01.2015,,6,135.78, -13,2,10.01.2015,,6,135.78, -13,2,11.01.2015,,3,135.78, -13,2,12.01.2015,,6,135.78, -13,2,13.01.2015,,0,135.78, -13,2,14.01.2015,,3,135.78, -13,2,15.01.2015,,0,135.78, -13,2,16.01.2015,,13,135.78, -13,2,17.01.2015,,0,135.78, -13,2,18.01.2015,,3,135.78, -13,2,19.01.2015,,25,135.78, -13,2,20.01.2015,,3,135.78, -13,2,21.01.2015,,0,135.78, -13,2,22.01.2015,,3,135.78, -13,2,23.01.2015,,0,135.78, -13,2,24.01.2015,,13,135.78, -13,2,25.01.2015,,0,135.78, -13,2,26.01.2015,,3,135.78, -13,2,27.01.2015,,0,135.78, -13,2,28.01.2015,,0,135.78, -13,2,29.01.2015,,0,135.78, -13,2,30.01.2015,,0,135.78, -13,2,31.01.2015,,0,135.78, -13,2,01.02.2015,,3,135.78, -13,2,02.02.2015,,0,135.78, -13,2,03.02.2015,,13,135.78, -13,2,04.02.2015,,0,135.78, -13,2,05.02.2015,,0,135.78, -13,2,06.02.2015,,13,135.78, -13,2,07.02.2015,,9,135.78, -13,2,08.02.2015,,6,135.78, -13,2,09.02.2015,,0,135.78, -13,2,10.02.2015,,9,135.78, -13,2,11.02.2015,,0,135.78, -13,2,12.02.2015,,0,135.78, -13,2,13.02.2015,,0,135.78, -13,2,14.02.2015,,9,135.78, -13,2,15.02.2015,,6,135.78, -13,2,16.02.2015,,6,135.78, -13,2,17.02.2015,,3,135.78, -13,2,18.02.2015,,0,135.78, -13,2,19.02.2015,,6,135.78, -13,2,20.02.2015,,3,135.78, -13,2,21.02.2015,,0,135.78, -13,2,22.02.2015,,0,135.78, -13,2,23.02.2015,,0,135.78, -13,2,24.02.2015,,0,135.78, -13,2,25.02.2015,,9,135.78, -13,2,26.02.2015,,0,135.78, -13,2,27.02.2015,,0,135.78, -13,2,28.02.2015,,0,135.78, -13,2,01.03.2015,,6,135.78, -13,2,02.03.2015,,6,135.78, -13,2,03.03.2015,,13,135.78, -13,2,04.03.2015,,0,135.78, -13,2,05.03.2015,,6,135.78, -13,2,06.03.2015,,16,135.78, -13,2,07.03.2015,,0,135.78, -13,2,08.03.2015,,3,146.38, -13,2,09.03.2015,,13,146.38, -13,2,10.03.2015,,9,146.38, -13,2,11.03.2015,,0,146.38, -13,2,12.03.2015,,9,146.38, -13,2,13.03.2015,,6,146.38, -13,2,14.03.2015,,0,146.38, -13,2,15.03.2015,,6,146.38, -13,2,16.03.2015,,0,146.38, -13,2,17.03.2015,,3,146.38, -13,2,18.03.2015,,3,146.38, -13,2,19.03.2015,,0,146.38, -13,2,20.03.2015,,9,146.38, -13,2,21.03.2015,,9,146.38, -13,2,22.03.2015,,3,146.38, -13,2,23.03.2015,,0,146.38, -13,2,24.03.2015,,3,146.38, -13,2,25.03.2015,,3,146.38, -13,2,26.03.2015,,3,146.38, -13,2,27.03.2015,,3,146.38, -13,2,28.03.2015,,3,146.38, -13,2,29.03.2015,,9,146.38, -13,2,30.03.2015,,0,146.38, -13,2,31.03.2015,,3,146.38, -13,2,01.04.2015,,0,146.38, -13,2,02.04.2015,,0,146.38, -13,2,03.04.2015,,0,146.38, -13,2,04.04.2015,,3,146.38, -13,2,05.04.2015,,0,146.38, -13,2,06.04.2015,,0,146.38, -13,2,07.04.2015,,3,146.38, -13,2,08.04.2015,,3,146.38, -13,2,09.04.2015,,0,146.38, -13,2,10.04.2015,,0,146.38, -13,2,11.04.2015,,0,146.38, -13,2,12.04.2015,,0,146.38, -13,2,13.04.2015,,0,146.38, -13,2,14.04.2015,,0,146.38, -13,2,15.04.2015,,0,146.38, -13,2,16.04.2015,,3,146.38, -13,2,17.04.2015,,3,146.38, -13,2,18.04.2015,,0,146.38, -13,2,19.04.2015,,19,146.38, -13,2,20.04.2015,,0,146.38, -13,2,21.04.2015,,3,146.38, -13,2,22.04.2015,,0,146.38, -13,2,23.04.2015,,0,146.38, -13,2,24.04.2015,,0,146.38, -13,2,25.04.2015,,0,146.38, -13,2,26.04.2015,,0,146.38, -13,2,27.04.2015,,0,150.73, -13,2,28.04.2015,,0,150.73, -13,2,29.04.2015,,0,150.73, -13,2,30.04.2015,,0,150.73, -13,2,01.05.2015,,0,150.73, -13,2,02.05.2015,,0,150.73, -13,2,03.05.2015,,0,150.73, -13,2,04.05.2015,,6,150.73, -13,2,05.05.2015,,0,150.73, -13,2,06.05.2015,,0,150.73, -13,2,07.05.2015,,0,150.73, -13,2,08.05.2015,,0,150.73, -13,2,09.05.2015,,0,150.73, -13,2,10.05.2015,,0,150.73, -13,2,11.05.2015,,0,150.73, -13,2,12.05.2015,,3,150.73, -13,2,13.05.2015,,6,150.73, -13,2,14.05.2015,,0,150.73, -13,2,15.05.2015,,0,150.73, -13,2,16.05.2015,,0,150.73, -13,2,17.05.2015,,0,150.73, -13,2,18.05.2015,,0,150.73, -13,2,19.05.2015,,0,150.73, -13,2,20.05.2015,,0,150.73, -13,2,21.05.2015,,0,150.73, -13,2,22.05.2015,,0,150.73, -13,2,23.05.2015,,0,150.73, -13,2,24.05.2015,,0,150.73, -13,2,25.05.2015,,0,150.73, -13,2,26.05.2015,,0,150.73, -13,2,27.05.2015,,0,150.73, -13,2,28.05.2015,,0,150.73, -13,2,29.05.2015,,6,150.73, -13,2,30.05.2015,,6,150.73, -13,2,31.05.2015,,3,150.73, -13,2,01.06.2015,,3,150.73, -13,2,02.06.2015,,0,150.73, -13,2,03.06.2015,,0,150.73, -13,2,04.06.2015,,0,150.73, -13,2,05.06.2015,,0,150.73, -13,2,06.06.2015,,0,150.73, -13,2,07.06.2015,,0,150.73, -13,2,08.06.2015,,3,150.73, -13,2,09.06.2015,,0,150.73, -13,2,10.06.2015,,3,150.73, -13,2,11.06.2015,,0,150.73, -13,2,12.06.2015,,0,150.73, -13,2,13.06.2015,,3,150.73, -13,2,14.06.2015,,9,150.73, -13,2,15.06.2015,,3,150.73, -13,2,16.06.2015,,0,150.73, -13,2,17.06.2015,,0,150.73, -13,2,18.06.2015,,0,150.73, -13,2,19.06.2015,,0,150.73, -13,2,20.06.2015,,0,150.73, -13,2,21.06.2015,,3,150.73, -13,2,22.06.2015,,0,150.73, -13,2,23.06.2015,,0,150.73, -13,2,24.06.2015,,3,150.73, -13,2,25.06.2015,,3,150.73, -13,2,26.06.2015,,0,150.73, -13,2,27.06.2015,,3,150.73, -13,2,28.06.2015,,0,150.73, -13,2,29.06.2015,,0,150.73, -13,2,30.06.2015,,3,150.73, -13,2,01.07.2015,,0,150.73, -13,2,02.07.2015,,0,150.73, -13,2,03.07.2015,,3,150.73, -13,2,04.07.2015,,3,150.73, -13,2,05.07.2015,,0,150.73, -13,2,06.07.2015,,6,150.73, -13,2,07.07.2015,,3,150.73, -13,2,08.07.2015,,0,150.73, -13,2,09.07.2015,,9,150.73, -13,2,10.07.2015,,3,150.73, -13,2,11.07.2015,,0,150.73, -13,2,12.07.2015,,0,150.73, -13,2,13.07.2015,,3,150.73, -13,2,14.07.2015,,3,150.73, -13,2,15.07.2015,,0,150.73, -13,2,16.07.2015,,3,150.73, -13,2,17.07.2015,,0,150.73, -13,2,18.07.2015,,0,150.73, -13,2,19.07.2015,,0,150.73, -13,2,20.07.2015,,16,150.73, -13,2,21.07.2015,,9,150.73, -13,2,22.07.2015,,3,150.73, -13,2,23.07.2015,,0,150.73, -13,2,24.07.2015,,0,150.73, -13,2,25.07.2015,,6,150.73, -13,2,26.07.2015,,38,150.73, -13,2,27.07.2015,,31,150.73, -13,2,28.07.2015,,9,150.73, -13,2,29.07.2015,,0,150.73, -13,2,30.07.2015,,25,150.73, -13,2,31.07.2015,,9,150.73, -13,2,01.08.2015,,19,150.73, -13,2,02.08.2015,,31,150.73, -13,2,03.08.2015,,13,150.73, -13,2,04.08.2015,,25,150.73, -13,2,05.08.2015,,16,150.73, -13,2,06.08.2015,,13,150.73, -13,2,07.08.2015,,28,150.73, -13,2,08.08.2015,,9,150.73, -13,2,09.08.2015,,16,150.73, -13,2,10.08.2015,,16,150.73, -13,2,11.08.2015,,9,150.73, -13,2,12.08.2015,,3,128.17, -13,2,13.08.2015,,9,128.17, -13,2,14.08.2015,,19,128.17, -13,2,15.08.2015,,9,128.17, -13,2,16.08.2015,,16,128.17, -13,2,17.08.2015,,72,128.17, -13,2,18.08.2015,1.0,324,128.17,100.3 -13,2,19.08.2015,1.0,157,128.17,100.3 -13,2,20.08.2015,1.0,258,128.17,100.3 -13,2,21.08.2015,1.0,119,128.17,100.3 -13,2,22.08.2015,1.0,182,128.17,100.3 -13,2,23.08.2015,1.0,167,128.17,100.3 -13,2,24.08.2015,1.0,101,128.17,100.3 -13,2,25.08.2015,,19,128.17, -13,2,26.08.2015,,16,128.17, -13,2,27.08.2015,,9,128.17, -13,2,28.08.2015,,19,128.17, -13,2,29.08.2015,,19,128.17, -13,2,30.08.2015,,19,128.17, -13,2,31.08.2015,,19,128.17, -13,2,01.09.2015,,19,128.17, -13,2,02.09.2015,,19,128.17, -13,2,03.09.2015,,19,128.17, -13,2,04.09.2015,,16,128.17, -13,2,05.09.2015,,16,128.17, -13,2,06.09.2015,,0,128.17, -13,2,07.09.2015,,22,128.17, -13,2,08.09.2015,,13,128.17, -13,2,09.09.2015,,22,128.17, -13,2,10.09.2015,,16,128.17, -13,2,11.09.2015,,9,128.17, -13,2,12.09.2015,,28,128.17, -13,2,13.09.2015,,16,128.17, -13,2,14.09.2015,,19,128.17, -13,2,15.09.2015,,19,128.17, -13,2,16.09.2015,,16,128.17, -13,2,17.09.2015,,22,128.17, -13,2,18.09.2015,,16,128.17, -13,2,19.09.2015,,16,128.17, -13,2,20.09.2015,,22,128.17, -13,2,21.09.2015,,16,128.17, -13,2,22.09.2015,,16,128.17, -13,2,23.09.2015,,16,128.17, -13,2,24.09.2015,,19,128.17, -13,2,25.09.2015,,3,128.17, -13,2,26.09.2015,,16,128.17, -13,2,27.09.2015,,16,128.17, -13,2,28.09.2015,,22,128.17, -13,2,29.09.2015,,16,128.17, -13,2,30.09.2015,,13,128.17, -13,2,01.10.2015,,16,162.96, -13,2,02.10.2015,,16,162.96, -13,2,03.10.2015,,6,162.96, -13,2,04.10.2015,,3,162.96, -13,2,05.10.2015,,0,162.96, -13,2,06.10.2015,1.0,113,143.39,122.05 -13,2,07.10.2015,1.0,123,143.39,122.05 -13,2,08.10.2015,1.0,154,143.39,122.05 -13,2,09.10.2015,1.0,173,143.39,122.05 -13,2,10.10.2015,1.0,132,143.39,122.05 -13,2,11.10.2015,1.0,75,143.39,122.05 -13,2,12.10.2015,1.0,104,143.39,122.05 -13,2,13.10.2015,,3,143.93, -13,2,14.10.2015,,3,143.93, -13,2,15.10.2015,,9,143.93, -13,2,16.10.2015,,0,143.93, -13,2,17.10.2015,,3,162.96, -13,2,18.10.2015,,3,162.96, -13,2,19.10.2015,,3,162.96, -13,2,20.10.2015,,3,162.96, -13,2,21.10.2015,,6,162.96, -13,2,22.10.2015,,6,162.96, -13,2,23.10.2015,,3,162.96, -13,2,24.10.2015,,9,162.96, -13,2,25.10.2015,,3,162.96, -13,2,26.10.2015,,3,162.96, -13,2,27.10.2015,,3,162.96, -13,2,28.10.2015,,3,162.96, -13,2,29.10.2015,,3,162.96, -13,2,30.10.2015,,3,162.96, -13,2,31.10.2015,,3,162.96, -13,2,01.11.2015,,3,162.96, -13,2,02.11.2015,,3,162.96, -13,2,03.11.2015,,3,162.96, -13,2,04.11.2015,,9,162.96, -13,2,05.11.2015,,3,162.96, -13,2,06.11.2015,,3,162.96, -13,2,07.11.2015,,3,162.96, -13,2,08.11.2015,,6,162.96, -13,2,09.11.2015,,6,162.96, -13,2,10.11.2015,,6,162.96, -13,2,11.11.2015,,6,138.36, -13,2,12.11.2015,,6,138.36, -13,2,13.11.2015,,6,138.36, -13,2,14.11.2015,,6,138.36, -13,2,15.11.2015,,9,138.36, -13,2,16.11.2015,,3,138.36, -13,2,17.11.2015,,6,138.36, -13,2,18.11.2015,,3,138.36, -13,2,19.11.2015,,6,138.36, -13,2,20.11.2015,,3,138.36, -13,2,21.11.2015,,6,138.36, -13,2,22.11.2015,,6,138.36, -13,2,23.11.2015,,3,138.36, -13,2,24.11.2015,,3,138.36, -13,2,25.11.2015,,6,138.36, 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-13,2,29.12.2015,,0,138.5, -13,2,30.12.2015,,16,138.5, -13,2,31.12.2015,,9,138.5, -13,2,01.01.2016,,9,138.5, -13,2,02.01.2016,,22,138.5, -13,2,03.01.2016,,13,138.5, -13,2,04.01.2016,,16,138.5, -13,2,05.01.2016,,3,138.5, -13,2,06.01.2016,,19,138.5, -13,2,07.01.2016,,13,138.5, -13,2,08.01.2016,,13,138.5, -13,2,09.01.2016,,13,138.5, -13,2,10.01.2016,,13,138.5, -13,2,11.01.2016,,38,138.5, -13,2,12.01.2016,,16,138.5, -13,2,13.01.2016,,16,138.5, -13,2,14.01.2016,,0,138.5, -13,2,15.01.2016,,9,138.5, -13,2,16.01.2016,,13,142.57, -13,2,17.01.2016,,22,142.57, -13,2,18.01.2016,,35,142.57, -13,2,19.01.2016,1.0,383,142.57,103.02 -13,2,20.01.2016,1.0,254,142.57,103.02 -13,2,21.01.2016,1.0,371,142.57,103.02 -13,2,22.01.2016,1.0,289,142.57,103.02 -13,2,23.01.2016,1.0,242,142.57,103.02 -13,2,24.01.2016,1.0,63,142.57,103.02 -13,2,25.01.2016,1.0,204,142.57,103.02 -13,2,26.01.2016,,16,142.57, -13,2,27.01.2016,,22,142.57, -13,2,28.01.2016,,9,142.57, -13,2,29.01.2016,,13,142.57, -13,2,30.01.2016,,13,142.57, -13,2,31.01.2016,,19,142.57, -13,2,01.02.2016,,38,142.57, -13,2,02.02.2016,,19,142.57, -13,2,03.02.2016,,19,142.57, -13,2,04.02.2016,,19,142.57, -13,2,05.02.2016,,13,142.57, -13,2,06.02.2016,,13,142.57, -13,2,07.02.2016,,28,142.57, -13,2,08.02.2016,,19,142.57, -13,2,09.02.2016,1.0,295,142.57,108.46 -13,2,10.02.2016,1.0,160,138.36,108.46 -13,2,11.02.2016,1.0,201,138.36,108.46 -13,2,12.02.2016,1.0,368,138.36,108.46 -13,2,13.02.2016,1.0,311,138.36,108.46 -13,2,14.02.2016,1.0,41,138.36,108.46 -13,2,15.02.2016,1.0,236,138.36,108.46 -13,2,16.02.2016,,3,138.36, -13,2,17.02.2016,,3,138.36, -13,2,18.02.2016,,6,138.36, -13,2,19.02.2016,,3,138.36, -13,2,20.02.2016,,6,138.36, -13,2,21.02.2016,,16,138.36, -13,2,22.02.2016,,3,138.36, -13,2,23.02.2016,,6,138.36, -13,2,24.02.2016,,13,138.36, -13,2,25.02.2016,,13,138.36, -13,2,26.02.2016,,0,138.36, -13,2,27.02.2016,,3,138.36, -13,2,28.02.2016,,13,138.36, -13,2,29.02.2016,,9,138.36, -13,2,01.03.2016,,9,138.36, -13,2,02.03.2016,,9,138.36, -13,2,03.03.2016,,3,138.36, -13,2,04.03.2016,,6,138.36, -13,2,05.03.2016,,13,138.36, -13,2,06.03.2016,,9,138.36, -13,2,07.03.2016,,9,138.36, -13,2,08.03.2016,,6,138.36, -13,2,09.03.2016,,13,138.36, -13,2,10.03.2016,,9,138.36, -13,2,11.03.2016,,6,138.36, -13,2,12.03.2016,,6,138.5, -13,2,13.03.2016,,6,138.5, -13,2,14.03.2016,,35,138.5, -13,2,15.03.2016,1.0,148,138.5,124.77 -13,2,16.03.2016,1.0,173,138.5,124.77 -13,2,17.03.2016,1.0,116,138.5,124.77 -13,2,18.03.2016,1.0,157,138.5,124.77 -13,2,19.03.2016,1.0,97,138.5,124.77 -13,2,20.03.2016,1.0,123,138.5,124.77 -13,2,21.03.2016,1.0,57,138.5,124.77 -13,2,22.03.2016,,9,138.36, -13,2,23.03.2016,,3,138.36, -13,2,24.03.2016,,0,138.36, -13,2,25.03.2016,,3,138.36, -13,2,26.03.2016,,9,138.36, -13,2,27.03.2016,,6,138.36, -13,2,28.03.2016,,9,138.36, -13,2,29.03.2016,,3,138.36, -13,2,30.03.2016,,9,138.36, -13,2,31.03.2016,,3,138.36, -13,2,01.04.2016,,13,138.36, -13,2,02.04.2016,,9,138.36, -13,2,03.04.2016,,3,138.36, -13,2,04.04.2016,,25,138.36, -13,2,05.04.2016,,16,138.36, -13,2,06.04.2016,,3,138.36, -13,2,07.04.2016,,6,138.36, -13,2,08.04.2016,,9,138.36, -13,2,09.04.2016,,3,138.5, -13,2,10.04.2016,,6,138.5, -13,2,11.04.2016,,9,138.5, -13,2,12.04.2016,,16,138.5, -13,2,13.04.2016,,9,138.5, -13,2,14.04.2016,,6,138.5, -13,2,15.04.2016,,6,138.5, -13,2,16.04.2016,,9,138.5, -13,2,17.04.2016,,3,138.5, -13,2,18.04.2016,,31,138.5, -13,2,19.04.2016,1.0,198,138.5,116.89 -13,2,20.04.2016,1.0,126,138.5,116.89 -13,2,21.04.2016,1.0,104,138.5,116.89 -13,2,22.04.2016,1.0,182,138.5,116.89 -13,2,23.04.2016,1.0,182,138.5,116.89 -13,2,24.04.2016,1.0,179,138.5,116.89 -13,2,25.04.2016,1.0,154,138.5,116.89 -13,2,26.04.2016,,9,138.5, -13,2,27.04.2016,,6,138.5, -13,2,28.04.2016,,6,138.5, -13,2,29.04.2016,,9,138.5, -13,2,30.04.2016,,6,138.5, -13,2,01.05.2016,,3,138.5, -13,2,02.05.2016,,0,138.5, -13,2,03.05.2016,1.0,201,138.5,106.01 -13,2,04.05.2016,1.0,412,138.5,106.01 -13,2,05.05.2016,1.0,368,138.5,106.01 -13,2,06.05.2016,1.0,245,138.5,106.01 -13,2,07.05.2016,1.0,229,138.5,106.01 -13,2,08.05.2016,1.0,195,138.5,106.01 -13,2,09.05.2016,1.0,192,138.5,106.01 -13,2,10.05.2016,,22,138.5, -13,2,11.05.2016,,13,138.5, -13,2,12.05.2016,,22,138.5, -13,2,13.05.2016,,50,138.5, -13,2,14.05.2016,,6,138.5, -13,2,15.05.2016,,13,138.5, -13,2,16.05.2016,,9,138.5, -13,2,17.05.2016,,9,138.5, -13,2,18.05.2016,,9,138.5, -13,2,19.05.2016,,9,138.5, -13,2,20.05.2016,,6,138.5, -13,2,21.05.2016,,13,138.5, -13,2,22.05.2016,,19,138.5, -14,2,01.01.2015,,0,135.78, -14,2,02.01.2015,,0,135.78, -14,2,03.01.2015,,0,135.78, -14,2,04.01.2015,,6,135.78, -14,2,05.01.2015,,0,135.78, -14,2,06.01.2015,,3,135.78, -14,2,07.01.2015,,0,135.78, -14,2,08.01.2015,,0,135.78, -14,2,09.01.2015,,3,135.78, -14,2,10.01.2015,,0,135.78, -14,2,11.01.2015,,0,135.78, -14,2,12.01.2015,,0,135.78, -14,2,13.01.2015,,0,135.78, -14,2,14.01.2015,,0,135.78, -14,2,15.01.2015,,0,135.78, -14,2,16.01.2015,,0,135.78, -14,2,17.01.2015,,0,135.78, -14,2,18.01.2015,,0,135.78, -14,2,19.01.2015,,0,135.78, -14,2,20.01.2015,,0,135.78, -14,2,21.01.2015,,0,135.78, -14,2,22.01.2015,,0,135.78, -14,2,23.01.2015,,0,135.78, -14,2,24.01.2015,,0,135.78, -14,2,25.01.2015,,3,135.78, -14,2,26.01.2015,,3,135.78, -14,2,27.01.2015,,0,135.78, -14,2,28.01.2015,,6,135.78, -14,2,29.01.2015,,6,135.78, -14,2,30.01.2015,,0,135.78, -14,2,31.01.2015,,3,135.78, -14,2,01.02.2015,,3,135.78, -14,2,02.02.2015,,3,135.78, -14,2,03.02.2015,,3,135.78, -14,2,04.02.2015,,6,135.78, -14,2,05.02.2015,,3,135.78, -14,2,06.02.2015,,0,135.78, -14,2,07.02.2015,,0,135.78, -14,2,08.02.2015,,3,135.78, -14,2,09.02.2015,,3,135.78, -14,2,10.02.2015,,3,135.78, -14,2,11.02.2015,,0,135.78, -14,2,12.02.2015,,0,135.78, -14,2,13.02.2015,,0,135.78, -14,2,14.02.2015,,0,135.78, -14,2,15.02.2015,,0,135.78, -14,2,16.02.2015,,3,135.78, -14,2,17.02.2015,,3,135.78, -14,2,18.02.2015,,16,135.78, -14,2,19.02.2015,,0,135.78, -14,2,20.02.2015,,3,135.78, -14,2,21.02.2015,,6,135.78, -14,2,22.02.2015,,9,135.78, -14,2,23.02.2015,,6,135.78, -14,2,24.02.2015,,0,135.78, -14,2,25.02.2015,,0,135.78, -14,2,26.02.2015,,3,135.78, -14,2,27.02.2015,,0,135.78, -14,2,28.02.2015,,0,135.78, -14,2,01.03.2015,,0,135.78, -14,2,02.03.2015,,0,135.78, -14,2,03.03.2015,,0,135.78, -14,2,04.03.2015,,0,135.78, -14,2,05.03.2015,,0,135.78, -14,2,06.03.2015,,0,135.78, -14,2,07.03.2015,,0,146.38, -14,2,08.03.2015,,3,146.38, -14,2,09.03.2015,,3,146.38, -14,2,10.03.2015,,0,146.38, -14,2,11.03.2015,,0,146.38, -14,2,12.03.2015,,9,146.38, -14,2,13.03.2015,,0,146.38, -14,2,14.03.2015,,0,146.38, -14,2,15.03.2015,,0,146.38, -14,2,16.03.2015,,0,146.38, -14,2,17.03.2015,,0,146.38, -14,2,18.03.2015,,0,146.38, -14,2,19.03.2015,,0,146.38, -14,2,20.03.2015,,0,146.38, -14,2,21.03.2015,,0,146.38, -14,2,22.03.2015,,3,146.38, -14,2,23.03.2015,,0,146.38, -14,2,24.03.2015,,0,146.38, -14,2,25.03.2015,,0,146.38, -14,2,26.03.2015,,0,146.38, -14,2,27.03.2015,,0,146.38, -14,2,28.03.2015,,0,146.38, -14,2,29.03.2015,,0,146.38, -14,2,30.03.2015,,0,146.38, -14,2,31.03.2015,,9,146.38, -14,2,01.04.2015,,0,146.38, -14,2,02.04.2015,,0,146.38, -14,2,03.04.2015,,0,146.38, -14,2,04.04.2015,,0,146.38, -14,2,05.04.2015,,0,146.38, -14,2,06.04.2015,,0,146.38, -14,2,07.04.2015,,0,146.38, -14,2,08.04.2015,,0,146.38, -14,2,09.04.2015,,0,146.38, -14,2,10.04.2015,,0,146.38, -14,2,11.04.2015,,0,146.38, -14,2,12.04.2015,,0,146.38, -14,2,13.04.2015,,0,146.38, -14,2,14.04.2015,,0,146.38, -14,2,15.04.2015,,0,146.38, -14,2,16.04.2015,,0,146.38, -14,2,17.04.2015,,0,146.38, -14,2,18.04.2015,,0,146.38, -14,2,19.04.2015,,0,146.38, -14,2,20.04.2015,,0,146.38, -14,2,21.04.2015,,13,146.38, -14,2,22.04.2015,,0,146.38, -14,2,23.04.2015,,0,146.38, -14,2,24.04.2015,,0,146.38, -14,2,25.04.2015,,0,146.38, -14,2,26.04.2015,,3,146.38, -14,2,27.04.2015,,3,150.73, -14,2,28.04.2015,,0,150.73, -14,2,29.04.2015,,0,150.73, -14,2,30.04.2015,,0,150.73, -14,2,01.05.2015,,0,150.73, -14,2,02.05.2015,,0,150.73, -14,2,03.05.2015,,0,150.73, -14,2,04.05.2015,,0,150.73, -14,2,05.05.2015,,0,150.73, -14,2,06.05.2015,,9,150.73, -14,2,07.05.2015,,0,150.73, -14,2,08.05.2015,,0,150.73, -14,2,09.05.2015,,0,150.73, -14,2,10.05.2015,,0,150.73, -14,2,11.05.2015,,0,150.73, -14,2,12.05.2015,,0,150.73, -14,2,13.05.2015,,3,150.73, -14,2,14.05.2015,,0,150.73, -14,2,15.05.2015,,0,150.73, -14,2,16.05.2015,,0,150.73, -14,2,17.05.2015,,0,150.73, -14,2,18.05.2015,,13,150.73, -14,2,19.05.2015,,0,150.73, -14,2,20.05.2015,,0,150.73, -14,2,21.05.2015,,0,150.73, -14,2,22.05.2015,,0,150.73, -14,2,23.05.2015,,0,150.73, -14,2,24.05.2015,,0,150.73, -14,2,25.05.2015,,0,150.73, -14,2,26.05.2015,,0,150.73, -14,2,27.05.2015,,0,150.73, -14,2,28.05.2015,,0,150.73, -14,2,29.05.2015,,0,150.73, -14,2,30.05.2015,,0,150.73, -14,2,31.05.2015,,0,150.73, -14,2,01.06.2015,,0,150.73, -14,2,02.06.2015,,0,150.73, -14,2,03.06.2015,,0,150.73, -14,2,04.06.2015,,3,150.73, -14,2,05.06.2015,,0,150.73, -14,2,06.06.2015,,0,150.73, -14,2,07.06.2015,,3,150.73, -14,2,08.06.2015,,3,150.73, -14,2,09.06.2015,,0,150.73, -14,2,10.06.2015,,0,150.73, -14,2,11.06.2015,,0,150.73, -14,2,12.06.2015,,3,150.73, -14,2,13.06.2015,,0,150.73, -14,2,14.06.2015,,0,150.73, -14,2,15.06.2015,,0,150.73, -14,2,16.06.2015,,0,150.73, -14,2,17.06.2015,,0,150.73, -14,2,18.06.2015,,0,150.73, -14,2,19.06.2015,,9,150.73, -14,2,20.06.2015,,0,150.73, -14,2,21.06.2015,,3,150.73, -14,2,22.06.2015,,0,150.73, -14,2,23.06.2015,,0,150.73, -14,2,24.06.2015,,0,150.73, -14,2,25.06.2015,,0,150.73, -14,2,26.06.2015,,0,150.73, -14,2,27.06.2015,,0,150.73, -14,2,28.06.2015,,3,150.73, -14,2,29.06.2015,,0,150.73, -14,2,30.06.2015,,0,150.73, -14,2,01.07.2015,,0,150.73, -14,2,02.07.2015,,0,150.73, -14,2,03.07.2015,,0,150.73, -14,2,04.07.2015,,0,150.73, -14,2,05.07.2015,,3,150.73, -14,2,06.07.2015,,0,150.73, -14,2,07.07.2015,,0,150.73, -14,2,08.07.2015,,0,150.73, -14,2,09.07.2015,,0,150.73, -14,2,10.07.2015,,0,150.73, -14,2,11.07.2015,,0,150.73, -14,2,12.07.2015,,3,150.73, -14,2,13.07.2015,,0,150.73, -14,2,14.07.2015,,0,150.73, -14,2,15.07.2015,,9,150.73, -14,2,16.07.2015,,0,150.73, -14,2,17.07.2015,,0,150.73, -14,2,18.07.2015,,0,150.73, -14,2,19.07.2015,,0,150.73, -14,2,20.07.2015,,3,150.73, -14,2,21.07.2015,,22,150.73, -14,2,22.07.2015,,0,150.73, -14,2,23.07.2015,,0,150.73, -14,2,24.07.2015,,0,150.73, -14,2,25.07.2015,,6,150.73, -14,2,26.07.2015,,3,150.73, -14,2,27.07.2015,,9,150.73, -14,2,28.07.2015,,3,150.73, -14,2,29.07.2015,,3,150.73, -14,2,30.07.2015,,0,150.73, -14,2,31.07.2015,,22,150.73, -14,2,01.08.2015,,3,150.73, -14,2,02.08.2015,,0,150.73, -14,2,03.08.2015,,0,150.73, -14,2,04.08.2015,,0,150.73, -14,2,05.08.2015,,0,150.73, -14,2,06.08.2015,,0,150.73, -14,2,07.08.2015,,0,150.73, -14,2,08.08.2015,,0,150.73, -14,2,09.08.2015,,0,150.73, -14,2,10.08.2015,,0,150.73, -14,2,11.08.2015,,0,150.73, -14,2,12.08.2015,,0,128.17, -14,2,13.08.2015,,0,128.17, -14,2,14.08.2015,,0,128.17, -14,2,15.08.2015,,0,128.17, -14,2,16.08.2015,,0,128.17, -14,2,17.08.2015,,13,128.17, -14,2,18.08.2015,1.0,132,128.17,100.3 -14,2,19.08.2015,1.0,57,128.17,100.3 -14,2,20.08.2015,1.0,44,128.17,100.3 -14,2,21.08.2015,1.0,110,128.17,100.3 -14,2,22.08.2015,1.0,154,128.17,100.3 -14,2,23.08.2015,1.0,69,128.17,100.3 -14,2,24.08.2015,1.0,97,128.17,100.3 -14,2,25.08.2015,,25,128.17, -14,2,26.08.2015,,9,128.17, -14,2,27.08.2015,,0,128.17, -14,2,28.08.2015,,6,128.17, -14,2,29.08.2015,,0,128.17, -14,2,30.08.2015,,6,128.17, -14,2,31.08.2015,,6,128.17, -14,2,01.09.2015,,0,128.17, -14,2,02.09.2015,,0,128.17, -14,2,03.09.2015,,0,128.17, -14,2,04.09.2015,,0,128.17, -14,2,05.09.2015,,0,128.17, -14,2,06.09.2015,,0,128.17, -14,2,07.09.2015,,6,128.17, -14,2,08.09.2015,,6,128.17, -14,2,09.09.2015,,6,128.17, -14,2,10.09.2015,,0,128.17, -14,2,11.09.2015,,0,128.17, -14,2,12.09.2015,,0,128.17, -14,2,13.09.2015,,3,128.17, -14,2,14.09.2015,,13,128.17, -14,2,15.09.2015,,0,128.17, -14,2,16.09.2015,,0,128.17, -14,2,17.09.2015,,3,128.17, -14,2,18.09.2015,,0,128.17, -14,2,19.09.2015,,0,128.17, -14,2,20.09.2015,,0,128.17, -14,2,21.09.2015,,0,128.17, -14,2,22.09.2015,,0,128.17, -14,2,23.09.2015,,0,128.17, -14,2,24.09.2015,,3,128.17, -14,2,25.09.2015,,0,128.17, -14,2,26.09.2015,,6,128.17, -14,2,27.09.2015,,0,128.17, -14,2,28.09.2015,,9,128.17, -14,2,29.09.2015,,3,128.17, -14,2,30.09.2015,,0,128.17, -14,2,01.10.2015,,0,162.96, -14,2,02.10.2015,,0,162.96, -14,2,03.10.2015,,0,162.96, -14,2,04.10.2015,,0,162.96, -14,2,05.10.2015,,0,162.96, -14,2,06.10.2015,1.0,47,143.39,122.05 -14,2,07.10.2015,1.0,25,143.39,122.05 -14,2,08.10.2015,1.0,41,143.39,122.05 -14,2,09.10.2015,1.0,13,143.39,122.05 -14,2,10.10.2015,1.0,88,143.39,122.05 -14,2,11.10.2015,1.0,41,143.39,122.05 -14,2,12.10.2015,1.0,19,143.39,122.05 -14,2,13.10.2015,1.0,13,143.93,122.05 -14,2,14.10.2015,1.0,31,143.93,122.05 -14,2,15.10.2015,1.0,50,143.93,122.05 -14,2,16.10.2015,1.0,47,143.93,122.05 -14,2,17.10.2015,1.0,41,162.96,122.05 -14,2,18.10.2015,1.0,28,162.96,122.05 -14,2,19.10.2015,,3,162.96, -14,2,20.10.2015,,0,162.96, -14,2,21.10.2015,,0,162.96, -14,2,22.10.2015,,0,162.96, -14,2,23.10.2015,,0,162.96, -14,2,24.10.2015,,6,162.96, -14,2,25.10.2015,,0,162.96, -14,2,26.10.2015,,0,162.96, -14,2,27.10.2015,,0,162.96, -14,2,28.10.2015,,0,162.96, -14,2,29.10.2015,,0,162.96, -14,2,30.10.2015,,0,162.96, -14,2,31.10.2015,,0,162.96, -14,2,01.11.2015,,0,162.96, -14,2,02.11.2015,,0,162.96, -14,2,03.11.2015,,0,162.96, -14,2,04.11.2015,,0,162.96, -14,2,05.11.2015,,0,162.96, -14,2,06.11.2015,,0,162.96, -14,2,07.11.2015,,0,162.96, -14,2,08.11.2015,,0,162.96, -14,2,09.11.2015,,0,162.96, -14,2,10.11.2015,,0,162.96, -14,2,11.11.2015,,0,138.36, -14,2,12.11.2015,,0,138.36, -14,2,13.11.2015,,0,138.36, -14,2,14.11.2015,,0,138.36, -14,2,15.11.2015,,0,138.36, -14,2,16.11.2015,,0,138.36, -14,2,17.11.2015,,0,138.36, -14,2,18.11.2015,,0,138.36, -14,2,19.11.2015,,0,138.36, -14,2,20.11.2015,,0,138.36, -14,2,21.11.2015,,3,138.36, -14,2,22.11.2015,,0,138.36, -14,2,23.11.2015,,3,138.36, -14,2,24.11.2015,,0,138.36, -14,2,25.11.2015,,0,138.36, -14,2,26.11.2015,,3,138.36, -14,2,27.11.2015,,3,138.36, -14,2,28.11.2015,,0,138.36, -14,2,29.11.2015,,0,138.36, -14,2,30.11.2015,,0,138.36, -14,2,01.12.2015,,0,162.96, -14,2,02.12.2015,,3,162.96, -14,2,03.12.2015,,0,138.5, -14,2,04.12.2015,,3,138.5, -14,2,05.12.2015,,0,138.5, -14,2,06.12.2015,,0,138.5, -14,2,07.12.2015,,0,138.5, -14,2,08.12.2015,,3,138.5, -14,2,09.12.2015,,0,138.5, -14,2,10.12.2015,,0,138.5, -14,2,11.12.2015,,0,142.57, -14,2,12.12.2015,,6,142.57, -14,2,13.12.2015,,0,142.57, -14,2,14.12.2015,,0,142.57, -14,2,15.12.2015,,0,142.57, -14,2,16.12.2015,,0,142.57, -14,2,17.12.2015,,0,142.57, -14,2,18.12.2015,,0,142.57, -14,2,19.12.2015,,0,142.57, -14,2,20.12.2015,,0,142.57, -14,2,21.12.2015,,0,142.57, -14,2,22.12.2015,1.0,188,142.57,103.02 -14,2,23.12.2015,1.0,38,142.57,103.02 -14,2,24.12.2015,1.0,138,142.57,103.02 -14,2,25.12.2015,1.0,123,142.57,103.02 -14,2,26.12.2015,1.0,154,142.57,103.02 -14,2,27.12.2015,1.0,123,142.57,103.02 -14,2,28.12.2015,1.0,9,142.57,103.02 -14,2,29.12.2015,,0,138.5, -14,2,30.12.2015,,0,138.5, -14,2,31.12.2015,,0,138.5, -14,2,01.01.2016,,0,138.5, -14,2,02.01.2016,,0,138.5, -14,2,03.01.2016,,0,138.5, -14,2,04.01.2016,,0,138.5, -14,2,05.01.2016,,0,138.5, -14,2,06.01.2016,,0,138.5, -14,2,07.01.2016,,0,138.5, -14,2,08.01.2016,,0,138.5, -14,2,09.01.2016,,0,138.5, -14,2,10.01.2016,,0,138.5, -14,2,11.01.2016,,0,138.5, -14,2,12.01.2016,,0,138.5, -14,2,13.01.2016,,0,138.5, -14,2,14.01.2016,,0,138.5, -14,2,15.01.2016,,0,138.5, -14,2,16.01.2016,,0,142.57, -14,2,17.01.2016,,0,142.57, -14,2,18.01.2016,,3,142.57, -14,2,19.01.2016,1.0,119,142.57,103.02 -14,2,20.01.2016,1.0,135,142.57,103.02 -14,2,21.01.2016,1.0,179,142.57,103.02 -14,2,22.01.2016,1.0,151,142.57,103.02 -14,2,23.01.2016,1.0,101,142.57,103.02 -14,2,24.01.2016,1.0,88,142.57,103.02 -14,2,25.01.2016,1.0,85,142.57,103.02 -14,2,26.01.2016,,6,142.57, -14,2,27.01.2016,,0,142.57, -14,2,28.01.2016,,0,142.57, -14,2,29.01.2016,,0,142.57, -14,2,30.01.2016,,0,142.57, -14,2,31.01.2016,,3,142.57, -14,2,01.02.2016,,0,142.57, -14,2,02.02.2016,,0,142.57, -14,2,03.02.2016,,6,142.57, -14,2,04.02.2016,,3,142.57, -14,2,05.02.2016,,3,142.57, -14,2,06.02.2016,,3,142.57, -14,2,07.02.2016,,3,142.57, -14,2,08.02.2016,,3,142.57, -14,2,09.02.2016,1.0,97,142.57,108.46 -14,2,10.02.2016,1.0,148,138.36,108.46 -14,2,11.02.2016,1.0,154,138.36,108.46 -14,2,12.02.2016,1.0,179,138.36,108.46 -14,2,13.02.2016,1.0,170,138.36,108.46 -14,2,14.02.2016,1.0,141,138.36,108.46 -14,2,15.02.2016,1.0,107,138.36,108.46 -14,2,16.02.2016,,3,138.36, -14,2,17.02.2016,,3,138.36, -14,2,18.02.2016,,3,138.36, -14,2,19.02.2016,,3,138.36, -14,2,20.02.2016,,3,138.36, -14,2,21.02.2016,,3,138.36, -14,2,22.02.2016,,3,138.36, -14,2,23.02.2016,,3,138.36, -14,2,24.02.2016,,0,138.36, -14,2,25.02.2016,,0,138.36, -14,2,26.02.2016,,6,138.36, -14,2,27.02.2016,,3,138.36, -14,2,28.02.2016,,0,138.36, -14,2,29.02.2016,,6,138.36, -14,2,01.03.2016,,0,138.36, -14,2,02.03.2016,,0,138.36, -14,2,03.03.2016,,3,138.36, -14,2,04.03.2016,,3,138.36, -14,2,05.03.2016,,0,138.36, -14,2,06.03.2016,,0,138.36, -14,2,07.03.2016,,9,138.36, -14,2,08.03.2016,,0,138.36, -14,2,09.03.2016,,0,138.36, -14,2,10.03.2016,,0,138.36, -14,2,11.03.2016,,0,138.36, -14,2,12.03.2016,,6,138.5, -14,2,13.03.2016,,0,138.5, -14,2,14.03.2016,,9,138.5, -14,2,15.03.2016,1.0,126,138.5,124.77 -14,2,16.03.2016,1.0,35,138.5,124.77 -14,2,17.03.2016,1.0,25,138.5,124.77 -14,2,18.03.2016,1.0,63,138.5,124.77 -14,2,19.03.2016,1.0,16,138.5,124.77 -14,2,20.03.2016,1.0,91,138.5,124.77 -14,2,21.03.2016,1.0,31,138.5,124.77 -14,2,22.03.2016,,3,138.36, -14,2,23.03.2016,,0,138.36, -14,2,24.03.2016,,0,138.36, -14,2,25.03.2016,,0,138.36, -14,2,26.03.2016,,0,138.36, -14,2,27.03.2016,,3,138.36, -14,2,28.03.2016,,6,138.36, -14,2,29.03.2016,,0,138.36, -14,2,30.03.2016,,0,138.36, -14,2,31.03.2016,,3,138.36, -14,2,01.04.2016,,0,138.36, -14,2,02.04.2016,,0,138.36, -14,2,03.04.2016,,0,138.36, -14,2,04.04.2016,,0,138.36, -14,2,05.04.2016,,0,138.36, -14,2,06.04.2016,,9,138.36, -14,2,07.04.2016,,3,138.36, -14,2,08.04.2016,,3,138.36, -14,2,09.04.2016,,3,138.5, -14,2,10.04.2016,,0,138.5, -14,2,11.04.2016,,0,138.5, -14,2,12.04.2016,,0,138.5, -14,2,13.04.2016,,0,138.5, -14,2,14.04.2016,,0,138.5, -14,2,15.04.2016,,0,138.5, -14,2,16.04.2016,,0,138.5, -14,2,17.04.2016,,0,138.5, -14,2,18.04.2016,,3,138.5, -14,2,19.04.2016,1.0,85,138.5,116.89 -14,2,20.04.2016,1.0,57,138.5,116.89 -14,2,21.04.2016,1.0,66,138.5,116.89 -14,2,22.04.2016,1.0,91,138.5,116.89 -14,2,23.04.2016,1.0,25,138.5,116.89 -14,2,24.04.2016,1.0,66,138.5,116.89 -14,2,25.04.2016,1.0,50,138.5,116.89 -14,2,26.04.2016,1.0,19,138.5,114.17 -14,2,27.04.2016,1.0,6,138.5,114.17 -14,2,28.04.2016,,0,138.5, -14,2,29.04.2016,,3,138.5, -14,2,30.04.2016,,3,138.5, -14,2,01.05.2016,,0,138.5, -14,2,02.05.2016,,3,138.5, -14,2,03.05.2016,1.0,57,138.5,106.01 -14,2,04.05.2016,1.0,50,138.5,106.01 -14,2,05.05.2016,1.0,85,138.5,106.01 -14,2,06.05.2016,1.0,28,138.5,106.01 -14,2,07.05.2016,1.0,63,138.5,106.01 -14,2,08.05.2016,1.0,53,138.5,106.01 -14,2,09.05.2016,1.0,44,138.5,106.01 -14,2,10.05.2016,,25,138.5, -14,2,11.05.2016,,44,138.5, -14,2,12.05.2016,,66,138.5, -14,2,13.05.2016,,47,138.5, -14,2,14.05.2016,,0,138.5, -14,2,15.05.2016,,13,138.5, -14,2,16.05.2016,,0,138.5, -14,2,17.05.2016,,0,138.5, -14,2,18.05.2016,,16,138.5, -14,2,19.05.2016,,16,138.5, -14,2,20.05.2016,,16,138.5, -14,2,21.05.2016,,16,138.5, -14,2,22.05.2016,,16,138.5, -15,2,01.01.2015,,0,135.78, -15,2,02.01.2015,,19,135.78, -15,2,03.01.2015,,0,135.78, -15,2,04.01.2015,,0,135.78, -15,2,05.01.2015,,13,135.78, -15,2,06.01.2015,,13,135.78, -15,2,07.01.2015,,0,135.78, -15,2,08.01.2015,,0,135.78, -15,2,09.01.2015,,0,135.78, -15,2,10.01.2015,,3,135.78, -15,2,11.01.2015,,0,135.78, -15,2,12.01.2015,,3,135.78, -15,2,13.01.2015,,9,135.78, -15,2,14.01.2015,,19,135.78, -15,2,15.01.2015,,0,135.78, -15,2,16.01.2015,,3,135.78, -15,2,17.01.2015,,0,135.78, -15,2,18.01.2015,,3,135.78, -15,2,19.01.2015,,0,135.78, -15,2,20.01.2015,,0,135.78, -15,2,21.01.2015,,0,135.78, -15,2,22.01.2015,,0,135.78, -15,2,23.01.2015,,0,135.78, -15,2,24.01.2015,,0,135.78, -15,2,25.01.2015,,6,135.78, -15,2,26.01.2015,,0,135.78, -15,2,27.01.2015,,0,135.78, -15,2,28.01.2015,,0,135.78, -15,2,29.01.2015,,0,135.78, -15,2,30.01.2015,,3,135.78, -15,2,31.01.2015,,0,135.78, -15,2,01.02.2015,,0,135.78, -15,2,02.02.2015,,0,135.78, -15,2,03.02.2015,,3,135.78, -15,2,04.02.2015,,3,135.78, -15,2,05.02.2015,,3,135.78, -15,2,06.02.2015,,6,135.78, -15,2,07.02.2015,,9,135.78, -15,2,08.02.2015,,0,135.78, -15,2,09.02.2015,,6,135.78, -15,2,10.02.2015,,0,135.78, -15,2,11.02.2015,,0,135.78, -15,2,12.02.2015,,6,135.78, -15,2,13.02.2015,,6,135.78, -15,2,14.02.2015,,0,135.78, -15,2,15.02.2015,,6,135.78, -15,2,16.02.2015,,3,135.78, -15,2,17.02.2015,,0,135.78, -15,2,18.02.2015,,0,135.78, -15,2,19.02.2015,,6,135.78, -15,2,20.02.2015,,0,135.78, -15,2,21.02.2015,,0,135.78, -15,2,22.02.2015,,3,135.78, -15,2,23.02.2015,,0,135.78, -15,2,24.02.2015,,0,135.78, -15,2,25.02.2015,,0,135.78, -15,2,26.02.2015,,3,135.78, -15,2,27.02.2015,,3,135.78, -15,2,28.02.2015,,0,135.78, -15,2,01.03.2015,,3,146.38, -15,2,02.03.2015,,3,146.38, -15,2,03.03.2015,,0,146.38, -15,2,04.03.2015,,0,146.38, -15,2,05.03.2015,,0,146.38, -15,2,06.03.2015,,3,146.38, -15,2,07.03.2015,,3,146.38, -15,2,08.03.2015,,6,146.38, -15,2,09.03.2015,,0,146.38, -15,2,10.03.2015,,9,146.38, -15,2,11.03.2015,,3,146.38, -15,2,12.03.2015,,3,146.38, -15,2,13.03.2015,,9,146.38, -15,2,14.03.2015,,13,146.38, -15,2,15.03.2015,,0,146.38, -15,2,16.03.2015,,3,146.38, -15,2,17.03.2015,,6,146.38, -15,2,18.03.2015,,3,146.38, -15,2,19.03.2015,,0,146.38, -15,2,20.03.2015,,0,146.38, -15,2,21.03.2015,,3,146.38, -15,2,22.03.2015,,13,146.38, -15,2,23.03.2015,,0,146.38, -15,2,24.03.2015,,0,146.38, -15,2,25.03.2015,,3,146.38, -15,2,26.03.2015,,13,146.38, -15,2,27.03.2015,,0,146.38, -15,2,28.03.2015,,3,146.38, -15,2,29.03.2015,,3,146.38, -15,2,30.03.2015,,3,146.38, -15,2,31.03.2015,,9,146.38, -15,2,01.04.2015,,0,146.38, -15,2,02.04.2015,,0,146.38, -15,2,03.04.2015,,3,146.38, -15,2,04.04.2015,,0,146.38, -15,2,05.04.2015,,0,146.38, -15,2,06.04.2015,,0,146.38, -15,2,07.04.2015,,3,146.38, -15,2,08.04.2015,,3,146.38, -15,2,09.04.2015,,3,146.38, -15,2,10.04.2015,,3,146.38, -15,2,11.04.2015,,3,146.38, -15,2,12.04.2015,,0,146.38, -15,2,13.04.2015,,0,145.84, -15,2,14.04.2015,,6,145.84, -15,2,15.04.2015,,0,145.84, -15,2,16.04.2015,,0,145.84, -15,2,17.04.2015,,3,145.84, -15,2,18.04.2015,,0,145.84, -15,2,19.04.2015,,9,145.84, -15,2,20.04.2015,,0,145.84, -15,2,21.04.2015,,0,145.84, -15,2,22.04.2015,,0,145.84, -15,2,23.04.2015,,6,145.84, -15,2,24.04.2015,,6,145.84, -15,2,25.04.2015,,0,145.84, -15,2,26.04.2015,,0,150.73, -15,2,27.04.2015,,0,150.73, -15,2,28.04.2015,,0,150.73, -15,2,29.04.2015,,0,150.73, -15,2,30.04.2015,,0,150.73, -15,2,01.05.2015,,0,150.73, -15,2,02.05.2015,,0,150.73, -15,2,03.05.2015,,0,150.73, -15,2,04.05.2015,,3,150.73, -15,2,05.05.2015,,0,150.73, -15,2,06.05.2015,,0,150.73, -15,2,07.05.2015,,3,150.73, -15,2,08.05.2015,,0,150.73, -15,2,09.05.2015,,0,150.73, -15,2,10.05.2015,,0,150.73, -15,2,11.05.2015,,0,150.73, -15,2,12.05.2015,,0,150.73, -15,2,13.05.2015,,0,150.73, -15,2,14.05.2015,,0,150.73, -15,2,15.05.2015,,0,150.73, -15,2,16.05.2015,,0,150.73, -15,2,17.05.2015,,0,150.73, -15,2,18.05.2015,,0,150.73, -15,2,19.05.2015,,3,150.73, -15,2,20.05.2015,,0,150.73, -15,2,21.05.2015,,0,150.73, -15,2,22.05.2015,,0,150.73, -15,2,23.05.2015,,3,150.73, -15,2,24.05.2015,,3,150.73, -15,2,25.05.2015,,0,150.73, -15,2,26.05.2015,,0,150.73, -15,2,27.05.2015,,0,150.73, -15,2,28.05.2015,,0,150.73, -15,2,29.05.2015,,0,150.73, -15,2,30.05.2015,,0,150.73, -15,2,31.05.2015,,3,150.73, -15,2,01.06.2015,,0,150.73, -15,2,02.06.2015,,0,150.73, -15,2,03.06.2015,,3,150.73, -15,2,04.06.2015,,0,150.73, -15,2,05.06.2015,,0,150.73, -15,2,06.06.2015,,9,150.73, -15,2,07.06.2015,,3,150.73, -15,2,08.06.2015,,0,150.73, -15,2,09.06.2015,,0,150.73, -15,2,10.06.2015,,0,150.73, -15,2,11.06.2015,,0,150.73, -15,2,12.06.2015,,0,150.73, -15,2,13.06.2015,,0,150.73, -15,2,14.06.2015,,0,150.73, -15,2,15.06.2015,,0,150.73, -15,2,16.06.2015,,0,150.73, -15,2,17.06.2015,,0,150.73, -15,2,18.06.2015,,0,150.73, -15,2,19.06.2015,,6,150.73, -15,2,20.06.2015,,0,150.73, -15,2,21.06.2015,,0,150.73, -15,2,22.06.2015,,0,150.73, -15,2,23.06.2015,,0,150.73, -15,2,24.06.2015,,3,150.73, -15,2,25.06.2015,,3,150.73, -15,2,26.06.2015,,3,150.73, -15,2,27.06.2015,,9,150.73, -15,2,28.06.2015,,0,150.73, -15,2,29.06.2015,,3,150.73, -15,2,30.06.2015,,0,150.73, -15,2,01.07.2015,,0,150.73, -15,2,02.07.2015,,3,150.73, -15,2,03.07.2015,,0,150.73, -15,2,04.07.2015,,3,150.73, -15,2,05.07.2015,,3,150.73, -15,2,06.07.2015,,0,150.73, -15,2,07.07.2015,,0,150.73, -15,2,08.07.2015,,0,150.73, -15,2,09.07.2015,,0,150.73, -15,2,10.07.2015,,0,150.73, -15,2,11.07.2015,,0,150.73, -15,2,12.07.2015,,0,150.73, -15,2,13.07.2015,,3,150.73, -15,2,14.07.2015,,3,150.73, -15,2,15.07.2015,,0,150.73, -15,2,16.07.2015,,3,150.73, -15,2,17.07.2015,,3,150.73, -15,2,18.07.2015,,0,150.73, -15,2,19.07.2015,,0,150.73, -15,2,20.07.2015,,19,150.73, -15,2,21.07.2015,,0,150.73, -15,2,22.07.2015,,19,150.73, -15,2,23.07.2015,,13,150.73, -15,2,24.07.2015,,9,150.73, -15,2,25.07.2015,,19,150.73, -15,2,26.07.2015,,13,150.73, -15,2,27.07.2015,,28,150.73, -15,2,28.07.2015,,3,150.73, -15,2,29.07.2015,,19,150.73, -15,2,30.07.2015,,44,150.73, -15,2,31.07.2015,,3,150.73, -15,2,01.08.2015,,6,150.73, -15,2,02.08.2015,,0,150.73, -15,2,03.08.2015,,3,150.73, -15,2,04.08.2015,,19,150.73, -15,2,05.08.2015,,3,150.73, -15,2,06.08.2015,,13,150.73, -15,2,07.08.2015,,13,150.73, -15,2,08.08.2015,,13,150.73, -15,2,09.08.2015,,13,150.73, -15,2,10.08.2015,,13,150.73, -15,2,11.08.2015,,13,150.73, -15,2,12.08.2015,,16,128.17, -15,2,13.08.2015,,13,128.17, -15,2,14.08.2015,,19,128.17, -15,2,15.08.2015,,0,128.17, -15,2,16.08.2015,,9,128.17, -15,2,17.08.2015,,9,128.17, -15,2,18.08.2015,1.0,207,128.17,100.3 -15,2,19.08.2015,1.0,210,128.17,100.3 -15,2,20.08.2015,1.0,248,128.17,100.3 -15,2,21.08.2015,1.0,179,128.17,100.3 -15,2,22.08.2015,1.0,160,128.17,100.3 -15,2,23.08.2015,1.0,201,128.17,100.3 -15,2,24.08.2015,1.0,123,128.17,100.3 -15,2,25.08.2015,,22,128.17, -15,2,26.08.2015,,16,128.17, -15,2,27.08.2015,,6,128.17, -15,2,28.08.2015,,19,128.17, -15,2,29.08.2015,,31,128.17, -15,2,30.08.2015,,13,128.17, -15,2,31.08.2015,,19,128.17, -15,2,01.09.2015,,19,128.17, -15,2,02.09.2015,,16,128.17, -15,2,03.09.2015,,3,128.17, -15,2,04.09.2015,,3,128.17, -15,2,05.09.2015,,9,128.17, -15,2,06.09.2015,,3,128.17, -15,2,07.09.2015,,13,128.17, -15,2,08.09.2015,,0,128.17, -15,2,09.09.2015,,0,128.17, -15,2,10.09.2015,,0,128.17, -15,2,11.09.2015,,9,128.17, -15,2,12.09.2015,,0,128.17, -15,2,13.09.2015,,3,128.17, -15,2,14.09.2015,,9,128.17, -15,2,15.09.2015,,0,128.17, -15,2,16.09.2015,,6,128.17, -15,2,17.09.2015,,19,128.17, -15,2,18.09.2015,,19,128.17, -15,2,19.09.2015,,28,128.17, -15,2,20.09.2015,,3,128.17, -15,2,21.09.2015,,9,128.17, -15,2,22.09.2015,,6,128.17, -15,2,23.09.2015,,3,128.17, -15,2,24.09.2015,,3,128.17, -15,2,25.09.2015,,0,128.17, -15,2,26.09.2015,,9,128.17, -15,2,27.09.2015,,3,128.17, -15,2,28.09.2015,,19,128.17, -15,2,29.09.2015,,13,128.17, -15,2,30.09.2015,,6,128.17, -15,2,01.10.2015,,3,162.96, -15,2,02.10.2015,,0,162.96, -15,2,03.10.2015,,0,162.96, -15,2,04.10.2015,,0,162.96, -15,2,05.10.2015,,0,162.96, -15,2,06.10.2015,1.0,104,143.39,122.05 -15,2,07.10.2015,1.0,63,143.39,122.05 -15,2,08.10.2015,1.0,75,143.39,122.05 -15,2,09.10.2015,1.0,50,143.39,122.05 -15,2,10.10.2015,1.0,75,143.39,122.05 -15,2,11.10.2015,1.0,57,143.39,122.05 -15,2,12.10.2015,1.0,88,143.39,122.05 -15,2,13.10.2015,1.0,47,143.93,122.05 -15,2,14.10.2015,1.0,38,143.93,122.05 -15,2,15.10.2015,1.0,107,143.93,122.05 -15,2,16.10.2015,1.0,107,143.93,122.05 -15,2,17.10.2015,1.0,91,162.96,122.05 -15,2,18.10.2015,1.0,79,162.96,122.05 -15,2,19.10.2015,,0,162.96, -15,2,20.10.2015,,3,162.96, -15,2,21.10.2015,,0,162.96, -15,2,22.10.2015,,0,162.96, -15,2,23.10.2015,,0,162.96, -15,2,24.10.2015,,0,162.96, -15,2,25.10.2015,,0,162.96, -15,2,26.10.2015,,3,162.96, -15,2,27.10.2015,,0,162.96, -15,2,28.10.2015,,0,162.96, -15,2,29.10.2015,,0,162.96, -15,2,30.10.2015,,0,162.96, -15,2,31.10.2015,,3,162.96, -15,2,01.11.2015,,0,162.96, -15,2,02.11.2015,,0,162.96, -15,2,03.11.2015,,6,162.96, -15,2,04.11.2015,,0,162.96, -15,2,05.11.2015,,0,162.96, -15,2,06.11.2015,,0,162.96, -15,2,07.11.2015,,0,162.96, -15,2,08.11.2015,,0,162.96, -15,2,09.11.2015,,0,162.96, -15,2,10.11.2015,,0,162.96, -15,2,11.11.2015,,3,138.36, -15,2,12.11.2015,,0,138.36, -15,2,13.11.2015,,3,138.36, -15,2,14.11.2015,,0,138.36, -15,2,15.11.2015,,3,138.36, -15,2,16.11.2015,,9,138.36, -15,2,17.11.2015,,3,138.36, -15,2,18.11.2015,,0,138.36, -15,2,19.11.2015,,3,138.36, -15,2,20.11.2015,,0,138.36, -15,2,21.11.2015,,0,138.36, -15,2,22.11.2015,,0,138.36, -15,2,23.11.2015,,0,138.36, -15,2,24.11.2015,,0,138.36, -15,2,25.11.2015,,0,138.36, -15,2,26.11.2015,,0,138.36, -15,2,27.11.2015,,0,138.36, -15,2,28.11.2015,,0,138.36, -15,2,29.11.2015,,0,138.36, -15,2,30.11.2015,,0,138.36, -15,2,01.12.2015,,0,162.96, -15,2,02.12.2015,,0,162.96, -15,2,03.12.2015,,3,138.5, -15,2,04.12.2015,,3,138.5, -15,2,05.12.2015,,0,138.5, -15,2,06.12.2015,,0,138.5, -15,2,07.12.2015,,0,138.5, -15,2,08.12.2015,,0,138.5, -15,2,09.12.2015,,0,138.5, -15,2,10.12.2015,,0,138.5, -15,2,11.12.2015,,0,142.57, -15,2,12.12.2015,,6,142.57, -15,2,13.12.2015,,0,142.57, -15,2,14.12.2015,,3,142.57, -15,2,15.12.2015,,0,142.57, -15,2,16.12.2015,,0,142.57, -15,2,17.12.2015,,9,142.57, -15,2,18.12.2015,,6,142.57, -15,2,19.12.2015,,3,142.57, -15,2,20.12.2015,,0,142.57, -15,2,21.12.2015,,9,142.57, -15,2,22.12.2015,1.0,154,142.57,103.02 -15,2,23.12.2015,1.0,132,142.57,103.02 -15,2,24.12.2015,1.0,138,142.57,103.02 -15,2,25.12.2015,1.0,185,142.57,103.02 -15,2,26.12.2015,1.0,141,142.57,103.02 -15,2,27.12.2015,1.0,28,142.57,103.02 -15,2,28.12.2015,1.0,113,142.57,103.02 -15,2,29.12.2015,,0,138.5, -15,2,30.12.2015,,3,138.5, -15,2,31.12.2015,,3,138.5, -15,2,01.01.2016,,0,138.5, -15,2,02.01.2016,,19,138.5, -15,2,03.01.2016,,0,138.5, -15,2,04.01.2016,,13,138.5, -15,2,05.01.2016,,0,138.5, -15,2,06.01.2016,,6,138.5, -15,2,07.01.2016,,3,138.5, -15,2,08.01.2016,,0,138.5, -15,2,09.01.2016,,3,138.5, -15,2,10.01.2016,,16,138.5, -15,2,11.01.2016,,3,138.5, -15,2,12.01.2016,,6,138.5, -15,2,13.01.2016,,9,138.5, -15,2,14.01.2016,,6,138.5, -15,2,15.01.2016,,6,138.5, -15,2,16.01.2016,,3,142.57, -15,2,17.01.2016,,9,142.57, -15,2,18.01.2016,,3,142.57, -15,2,19.01.2016,1.0,157,142.57,103.02 -15,2,20.01.2016,1.0,170,142.57,103.02 -15,2,21.01.2016,1.0,126,142.57,103.02 -15,2,22.01.2016,1.0,170,142.57,103.02 -15,2,23.01.2016,1.0,138,142.57,103.02 -15,2,24.01.2016,1.0,157,142.57,103.02 -15,2,25.01.2016,1.0,195,142.57,103.02 -15,2,26.01.2016,,0,142.57, -15,2,27.01.2016,,0,142.57, -15,2,28.01.2016,,9,142.57, -15,2,29.01.2016,,6,142.57, -15,2,30.01.2016,,0,142.57, -15,2,31.01.2016,,3,142.57, -15,2,01.02.2016,,0,142.57, -15,2,02.02.2016,,0,142.57, -15,2,03.02.2016,,3,142.57, -15,2,04.02.2016,,16,142.57, -15,2,05.02.2016,,0,142.57, -15,2,06.02.2016,,3,142.57, -15,2,07.02.2016,,3,142.57, -15,2,08.02.2016,,31,142.57, -15,2,09.02.2016,1.0,198,142.57,108.46 -15,2,10.02.2016,1.0,154,138.36,108.46 -15,2,11.02.2016,1.0,176,138.36,108.46 -15,2,12.02.2016,1.0,138,138.36,108.46 -15,2,13.02.2016,1.0,129,138.36,108.46 -15,2,14.02.2016,1.0,163,138.36,108.46 -15,2,15.02.2016,1.0,148,138.36,108.46 -15,2,16.02.2016,,3,138.36, -15,2,17.02.2016,,3,138.36, -15,2,18.02.2016,,3,138.36, -15,2,19.02.2016,,0,138.36, -15,2,20.02.2016,,0,138.36, -15,2,21.02.2016,,0,138.36, -15,2,22.02.2016,,3,138.36, -15,2,23.02.2016,,0,138.36, -15,2,24.02.2016,,6,138.36, -15,2,25.02.2016,,3,138.36, -15,2,26.02.2016,,9,138.36, -15,2,27.02.2016,,0,138.36, -15,2,28.02.2016,,3,138.36, -15,2,29.02.2016,,9,138.36, -15,2,01.03.2016,,3,138.36, -15,2,02.03.2016,,3,138.36, -15,2,03.03.2016,,3,138.36, -15,2,04.03.2016,,9,138.36, -15,2,05.03.2016,,0,138.36, -15,2,06.03.2016,,6,138.36, -15,2,07.03.2016,,6,138.36, -15,2,08.03.2016,,3,138.36, -15,2,09.03.2016,,3,138.36, -15,2,10.03.2016,,3,138.36, -15,2,11.03.2016,,3,138.36, -15,2,12.03.2016,,3,138.5, -15,2,13.03.2016,,3,138.5, -15,2,14.03.2016,,3,138.5, -15,2,15.03.2016,1.0,148,138.5,124.77 -15,2,16.03.2016,1.0,148,138.5,124.77 -15,2,17.03.2016,1.0,148,138.5,124.77 -15,2,18.03.2016,1.0,148,138.5,124.77 -15,2,19.03.2016,1.0,148,138.5,124.77 -15,2,20.03.2016,1.0,148,138.5,124.77 -15,2,21.03.2016,1.0,148,138.5,124.77 -15,2,22.03.2016,,3,138.36, -15,2,23.03.2016,,3,138.36, -15,2,24.03.2016,,3,138.36, -15,2,25.03.2016,,3,138.36, -15,2,26.03.2016,,3,138.36, -15,2,27.03.2016,,3,138.36, -15,2,28.03.2016,,3,138.36, -15,2,29.03.2016,,3,138.36, -15,2,30.03.2016,,0,138.36, -15,2,31.03.2016,,3,138.36, -15,2,01.04.2016,,0,138.36, -15,2,02.04.2016,,0,138.36, -15,2,03.04.2016,,6,138.36, -15,2,04.04.2016,,6,138.36, -15,2,05.04.2016,,3,138.36, -15,2,06.04.2016,,3,138.36, -15,2,07.04.2016,,3,138.36, -15,2,08.04.2016,,3,138.36, -15,2,09.04.2016,,3,138.5, -15,2,10.04.2016,,0,138.5, -15,2,11.04.2016,,3,138.5, -15,2,12.04.2016,,0,138.5, -15,2,13.04.2016,,0,138.5, -15,2,14.04.2016,,3,138.5, -15,2,15.04.2016,,3,138.5, -15,2,16.04.2016,,9,138.5, -15,2,17.04.2016,,6,138.5, -15,2,18.04.2016,,16,138.5, -15,2,19.04.2016,1.0,107,138.5,116.89 -15,2,20.04.2016,1.0,60,138.5,116.89 -15,2,21.04.2016,1.0,79,138.5,116.89 -15,2,22.04.2016,1.0,151,138.5,116.89 -15,2,23.04.2016,1.0,44,138.5,116.89 -15,2,24.04.2016,1.0,107,138.5,116.89 -15,2,25.04.2016,1.0,104,138.5,116.89 -15,2,26.04.2016,,3,138.5, -15,2,27.04.2016,,0,138.5, -15,2,28.04.2016,,0,138.5, -15,2,29.04.2016,,0,138.5, -15,2,30.04.2016,,3,138.5, -15,2,01.05.2016,,0,138.5, -15,2,02.05.2016,,0,138.5, -15,2,03.05.2016,1.0,154,138.5,106.01 -15,2,04.05.2016,1.0,148,138.5,106.01 -15,2,05.05.2016,1.0,163,138.5,106.01 -15,2,06.05.2016,1.0,176,138.5,106.01 -15,2,07.05.2016,1.0,141,138.5,106.01 -15,2,08.05.2016,1.0,123,138.5,106.01 -15,2,09.05.2016,1.0,113,138.5,106.01 -15,2,10.05.2016,,3,138.5, -15,2,11.05.2016,,19,138.5, -15,2,12.05.2016,,6,138.5, -15,2,13.05.2016,,3,138.5, -15,2,14.05.2016,,9,138.5, -15,2,15.05.2016,,9,138.5, -15,2,16.05.2016,,9,138.5, -15,2,17.05.2016,,0,138.5, -15,2,18.05.2016,,3,138.5, -15,2,19.05.2016,,6,138.5, -15,2,20.05.2016,,25,138.5, -15,2,21.05.2016,,3,138.5, -15,2,22.05.2016,,6,138.5, -16,2,01.01.2015,,6,135.78, -16,2,02.01.2015,,0,135.78, -16,2,03.01.2015,,0,135.78, -16,2,04.01.2015,,0,135.78, -16,2,05.01.2015,,0,135.78, -16,2,06.01.2015,,0,135.78, -16,2,07.01.2015,,50,135.78, -16,2,08.01.2015,,0,135.78, -16,2,09.01.2015,,50,135.78, -16,2,10.01.2015,,0,135.78, -16,2,11.01.2015,,0,135.78, -16,2,12.01.2015,,28,135.78, -16,2,13.01.2015,,13,135.78, -16,2,14.01.2015,,9,135.78, -16,2,15.01.2015,,0,135.78, -16,2,16.01.2015,,9,135.78, -16,2,17.01.2015,,16,135.78, -16,2,18.01.2015,,0,135.78, -16,2,19.01.2015,,9,135.78, -16,2,20.01.2015,,13,135.78, -16,2,21.01.2015,,6,135.78, -16,2,22.01.2015,,0,135.78, -16,2,23.01.2015,,13,135.78, -16,2,24.01.2015,,19,135.78, -16,2,25.01.2015,,13,135.78, -16,2,26.01.2015,,3,135.78, -16,2,27.01.2015,,0,135.78, -16,2,28.01.2015,,13,135.78, -16,2,29.01.2015,,0,135.78, -16,2,30.01.2015,,0,135.78, -16,2,31.01.2015,,9,135.78, -16,2,01.02.2015,,25,135.78, -16,2,02.02.2015,,9,135.78, -16,2,03.02.2015,,16,135.78, -16,2,04.02.2015,,9,135.78, -16,2,05.02.2015,,13,135.78, -16,2,06.02.2015,,13,135.78, -16,2,07.02.2015,,31,135.78, -16,2,08.02.2015,,22,135.78, -16,2,09.02.2015,,38,135.78, -16,2,10.02.2015,,3,135.78, -16,2,11.02.2015,,22,135.78, -16,2,12.02.2015,,19,135.78, -16,2,13.02.2015,,35,135.78, -16,2,14.02.2015,,16,135.78, -16,2,15.02.2015,,9,135.78, -16,2,16.02.2015,,25,135.78, -16,2,17.02.2015,,16,135.78, -16,2,18.02.2015,,6,135.78, -16,2,19.02.2015,,3,135.78, -16,2,20.02.2015,,28,135.78, -16,2,21.02.2015,,22,135.78, -16,2,22.02.2015,,19,135.78, -16,2,23.02.2015,,0,135.78, -16,2,24.02.2015,,0,135.78, -16,2,25.02.2015,,0,135.78, -16,2,26.02.2015,,0,135.78, -16,2,27.02.2015,,6,135.78, -16,2,28.02.2015,,16,135.78, -16,2,01.03.2015,,3,135.78, -16,2,02.03.2015,,3,146.38, -16,2,03.03.2015,,3,146.38, -16,2,04.03.2015,,6,146.38, -16,2,05.03.2015,,13,146.38, -16,2,06.03.2015,,3,146.38, -16,2,07.03.2015,,6,146.38, -16,2,08.03.2015,,0,146.38, -16,2,09.03.2015,,16,146.38, -16,2,10.03.2015,,3,146.38, -16,2,11.03.2015,,25,146.38, -16,2,12.03.2015,,6,146.38, -16,2,13.03.2015,,6,146.38, -16,2,14.03.2015,,6,146.38, -16,2,15.03.2015,,6,146.38, -16,2,16.03.2015,,6,146.38, -16,2,17.03.2015,,6,146.38, -16,2,18.03.2015,,6,146.38, -16,2,19.03.2015,,6,146.38, -16,2,20.03.2015,,6,146.38, -16,2,21.03.2015,,6,146.38, -16,2,22.03.2015,,6,146.38, -16,2,23.03.2015,,6,146.38, -16,2,24.03.2015,,6,146.38, -16,2,25.03.2015,,6,146.38, 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-16,2,09.07.2015,,0,146.38, -16,2,10.07.2015,,0,146.38, -16,2,11.07.2015,,0,146.38, -16,2,12.07.2015,,0,146.38, -16,2,13.07.2015,,0,146.38, -16,2,14.07.2015,,0,146.38, -16,2,15.07.2015,,0,146.38, -16,2,16.07.2015,,0,146.38, -16,2,17.07.2015,,0,146.38, -16,2,18.07.2015,,0,146.38, -16,2,19.07.2015,,0,146.38, -16,2,20.07.2015,,0,146.38, -16,2,21.07.2015,,0,146.38, -16,2,22.07.2015,,0,146.38, -16,2,23.07.2015,,0,146.38, -16,2,24.07.2015,,0,146.38, -16,2,25.07.2015,,0,146.38, -16,2,26.07.2015,,0,146.38, -16,2,27.07.2015,,0,146.38, -16,2,28.07.2015,,0,146.38, -16,2,29.07.2015,,0,146.38, -16,2,30.07.2015,,0,146.38, -16,2,31.07.2015,,0,146.38, -16,2,01.08.2015,,0,146.38, -16,2,02.08.2015,,0,146.38, -16,2,03.08.2015,,0,146.38, -16,2,04.08.2015,,0,146.38, -16,2,05.08.2015,,0,146.38, -16,2,06.08.2015,,0,150.73, -16,2,07.08.2015,,25,150.73, -16,2,08.08.2015,,3,150.73, -16,2,09.08.2015,,3,150.73, -16,2,10.08.2015,,3,150.73, -16,2,11.08.2015,,16,150.73, -16,2,12.08.2015,,3,128.17, -16,2,13.08.2015,,0,128.17, -16,2,14.08.2015,,38,128.17, -16,2,15.08.2015,,9,128.17, -16,2,16.08.2015,,22,128.17, -16,2,17.08.2015,,19,128.17, -16,2,18.08.2015,1.0,185,128.17,100.3 -16,2,19.08.2015,1.0,188,128.17,100.3 -16,2,20.08.2015,1.0,123,128.17,100.3 -16,2,21.08.2015,1.0,239,128.17,100.3 -16,2,22.08.2015,1.0,101,128.17,100.3 -16,2,23.08.2015,1.0,138,128.17,100.3 -16,2,24.08.2015,1.0,50,128.17,100.3 -16,2,25.08.2015,,9,128.17, -16,2,26.08.2015,,16,128.17, -16,2,27.08.2015,,13,128.17, -16,2,28.08.2015,,13,128.17, -16,2,29.08.2015,,13,128.17, -16,2,30.08.2015,,13,128.17, -16,2,31.08.2015,,13,128.17, -16,2,01.09.2015,,13,128.17, -16,2,02.09.2015,,13,128.17, -16,2,03.09.2015,,13,128.17, -16,2,04.09.2015,,13,128.17, -16,2,05.09.2015,,13,128.17, -16,2,06.09.2015,,13,128.17, -16,2,07.09.2015,,13,128.17, -16,2,08.09.2015,,13,128.17, -16,2,09.09.2015,,13,128.17, -16,2,10.09.2015,,13,128.17, -16,2,11.09.2015,,13,128.17, -16,2,12.09.2015,,13,128.17, -16,2,13.09.2015,,13,128.17, -16,2,14.09.2015,,13,128.17, -16,2,15.09.2015,,13,128.17, -16,2,16.09.2015,,13,128.17, -16,2,17.09.2015,,13,128.17, -16,2,18.09.2015,,13,128.17, -16,2,19.09.2015,,0,128.17, -16,2,20.09.2015,,0,128.17, -16,2,21.09.2015,,0,128.17, -16,2,22.09.2015,,0,128.17, -16,2,23.09.2015,,0,128.17, -16,2,24.09.2015,,0,128.17, -16,2,25.09.2015,,0,162.96, -16,2,26.09.2015,,0,162.96, -16,2,27.09.2015,,0,162.96, -16,2,28.09.2015,,0,162.96, -16,2,29.09.2015,,41,162.96, -16,2,30.09.2015,,16,162.96, -16,2,01.10.2015,,0,162.96, -16,2,02.10.2015,,9,162.96, -16,2,03.10.2015,,9,162.96, -16,2,04.10.2015,,6,162.96, -16,2,05.10.2015,,9,162.96, -16,2,06.10.2015,1.0,123,143.39,122.05 -16,2,07.10.2015,1.0,151,143.39,122.05 -16,2,08.10.2015,1.0,116,143.39,122.05 -16,2,09.10.2015,1.0,179,143.39,122.05 -16,2,10.10.2015,1.0,107,143.39,122.05 -16,2,11.10.2015,1.0,75,143.39,122.05 -16,2,12.10.2015,1.0,126,143.39,122.05 -16,2,13.10.2015,,3,143.93, -16,2,14.10.2015,,0,143.93, -16,2,15.10.2015,,6,143.93, -16,2,16.10.2015,,0,143.93, -16,2,17.10.2015,,0,143.93, -16,2,18.10.2015,,0,143.93, -16,2,19.10.2015,,0,143.93, -16,2,20.10.2015,,0,143.93, -16,2,21.10.2015,,0,143.93, -16,2,22.10.2015,,0,143.93, -16,2,23.10.2015,,0,143.93, -16,2,24.10.2015,,0,143.93, -16,2,25.10.2015,,0,143.93, -16,2,26.10.2015,,0,143.93, -16,2,27.10.2015,,0,143.93, -16,2,28.10.2015,,0,143.93, -16,2,29.10.2015,,0,143.93, -16,2,30.10.2015,,0,143.93, -16,2,31.10.2015,,0,143.93, -16,2,01.11.2015,,0,143.93, -16,2,02.11.2015,,0,143.93, -16,2,03.11.2015,,0,143.93, -16,2,04.11.2015,,0,143.93, -16,2,05.11.2015,,0,143.93, -16,2,06.11.2015,,0,143.93, -16,2,07.11.2015,,0,143.93, -16,2,08.11.2015,,0,143.93, -16,2,09.11.2015,,0,143.93, -16,2,10.11.2015,,0,143.93, -16,2,11.11.2015,,0,143.93, -16,2,12.11.2015,,0,143.93, -16,2,13.11.2015,,0,143.93, -16,2,14.11.2015,,0,143.93, -16,2,15.11.2015,,0,143.93, -16,2,16.11.2015,,0,143.93, -16,2,17.11.2015,,0,143.93, -16,2,18.11.2015,,0,143.93, -16,2,19.11.2015,,0,143.93, -16,2,20.11.2015,,0,143.93, -16,2,21.11.2015,,0,143.93, -16,2,22.11.2015,,0,143.93, -16,2,23.11.2015,,0,143.93, -16,2,24.11.2015,,0,143.93, -16,2,25.11.2015,,0,143.93, -16,2,26.11.2015,,0,143.93, -16,2,27.11.2015,,0,143.93, -16,2,28.11.2015,,0,143.93, -16,2,29.11.2015,,0,143.93, -16,2,30.11.2015,,0,143.93, -16,2,01.12.2015,,0,162.96, -16,2,02.12.2015,,0,162.96, -16,2,03.12.2015,,0,162.96, -16,2,04.12.2015,,0,162.96, -16,2,05.12.2015,,0,162.96, -16,2,06.12.2015,,0,162.96, -16,2,07.12.2015,,0,162.96, -16,2,08.12.2015,,0,162.96, -16,2,09.12.2015,,0,162.96, -16,2,10.12.2015,,0,162.96, -16,2,11.12.2015,,0,142.57, -16,2,12.12.2015,,0,142.57, -16,2,13.12.2015,,0,142.57, -16,2,14.12.2015,,0,142.57, -16,2,15.12.2015,,0,142.57, -16,2,16.12.2015,,6,142.57, -16,2,17.12.2015,,9,142.57, -16,2,18.12.2015,,3,142.57, -16,2,19.12.2015,,16,142.57, -16,2,20.12.2015,,13,142.57, -16,2,21.12.2015,,3,142.57, -16,2,22.12.2015,1.0,110,142.57,103.02 -16,2,23.12.2015,1.0,188,142.57,103.02 -16,2,24.12.2015,1.0,176,142.57,103.02 -16,2,25.12.2015,1.0,192,142.57,103.02 -16,2,26.12.2015,1.0,214,142.57,103.02 -16,2,27.12.2015,1.0,119,142.57,103.02 -16,2,28.12.2015,1.0,116,142.57,103.02 -16,2,29.12.2015,,0,138.5, -16,2,30.12.2015,,0,138.5, -16,2,31.12.2015,,0,138.5, -16,2,01.01.2016,,0,138.5, -16,2,02.01.2016,,0,138.5, -16,2,03.01.2016,,0,138.5, -16,2,04.01.2016,,0,138.5, -16,2,05.01.2016,,9,138.5, -16,2,06.01.2016,,101,138.5, -16,2,07.01.2016,,0,138.5, -16,2,08.01.2016,,0,138.5, -16,2,09.01.2016,,0,138.5, -16,2,10.01.2016,,0,138.5, -16,2,11.01.2016,,0,138.5, -16,2,12.01.2016,,0,138.5, -16,2,13.01.2016,,3,138.5, -16,2,14.01.2016,,13,138.5, -16,2,15.01.2016,,25,138.5, -16,2,16.01.2016,,28,142.57, -16,2,17.01.2016,,35,142.57, -16,2,18.01.2016,,13,142.57, -16,2,19.01.2016,1.0,110,142.57,103.02 -16,2,20.01.2016,1.0,141,142.57,103.02 -16,2,21.01.2016,1.0,179,142.57,103.02 -16,2,22.01.2016,1.0,141,142.57,103.02 -16,2,23.01.2016,1.0,170,142.57,103.02 -16,2,24.01.2016,1.0,267,142.57,103.02 -16,2,25.01.2016,1.0,60,142.57,103.02 -16,2,26.01.2016,,41,142.57, -16,2,27.01.2016,,31,142.57, -16,2,28.01.2016,,3,142.57, -16,2,29.01.2016,,6,142.57, -16,2,30.01.2016,,28,142.57, -16,2,31.01.2016,,63,142.57, -16,2,01.02.2016,,35,142.57, -16,2,02.02.2016,,22,142.57, -16,2,03.02.2016,,6,142.57, -16,2,04.02.2016,,6,142.57, -16,2,05.02.2016,,22,142.57, -16,2,06.02.2016,,19,142.57, -16,2,07.02.2016,,16,142.57, -16,2,08.02.2016,,16,142.57, -16,2,09.02.2016,1.0,154,142.57,108.46 -16,2,10.02.2016,1.0,148,138.36,108.46 -16,2,11.02.2016,1.0,132,138.36,108.46 -16,2,12.02.2016,1.0,229,138.36,108.46 -16,2,13.02.2016,1.0,138,138.36,108.46 -16,2,14.02.2016,1.0,160,138.36,108.46 -16,2,15.02.2016,1.0,145,138.36,108.46 -16,2,16.02.2016,,25,138.36, -16,2,17.02.2016,,6,138.36, -16,2,18.02.2016,,3,138.36, -16,2,19.02.2016,,9,138.36, -16,2,20.02.2016,,13,138.36, -16,2,21.02.2016,,9,138.36, -16,2,22.02.2016,,19,138.36, -16,2,23.02.2016,,9,138.36, -16,2,24.02.2016,,0,138.36, -16,2,25.02.2016,,13,138.36, -16,2,26.02.2016,,0,138.36, -16,2,27.02.2016,,0,138.36, -16,2,28.02.2016,,0,138.36, -16,2,29.02.2016,,0,138.36, -16,2,01.03.2016,,0,138.36, -16,2,02.03.2016,,0,138.36, -16,2,03.03.2016,,0,138.36, -16,2,04.03.2016,,3,138.36, -16,2,05.03.2016,,19,138.36, -16,2,06.03.2016,,0,138.36, -16,2,07.03.2016,,3,138.36, -16,2,08.03.2016,,9,138.36, -16,2,09.03.2016,,9,138.36, -16,2,10.03.2016,,0,138.36, -16,2,11.03.2016,,0,138.36, -16,2,12.03.2016,,13,138.5, -16,2,13.03.2016,,6,138.5, -16,2,14.03.2016,,9,138.5, -16,2,15.03.2016,1.0,44,138.5,124.77 -16,2,16.03.2016,1.0,44,138.5,124.77 -16,2,17.03.2016,1.0,82,138.5,124.77 -16,2,18.03.2016,1.0,135,138.5,124.77 -16,2,19.03.2016,1.0,113,138.5,124.77 -16,2,20.03.2016,1.0,97,138.5,124.77 -16,2,21.03.2016,1.0,119,138.5,124.77 -16,2,22.03.2016,,9,138.36, -16,2,23.03.2016,,3,138.36, -16,2,24.03.2016,,3,138.36, -16,2,25.03.2016,,3,138.36, -16,2,26.03.2016,,13,138.36, -16,2,27.03.2016,,25,138.36, -16,2,28.03.2016,,16,138.36, -16,2,29.03.2016,,3,138.36, -16,2,30.03.2016,,6,138.36, -16,2,31.03.2016,,38,138.36, -16,2,01.04.2016,,0,138.36, -16,2,02.04.2016,,22,138.36, -16,2,03.04.2016,,16,138.36, -16,2,04.04.2016,,0,138.36, -16,2,05.04.2016,,0,138.36, -16,2,06.04.2016,,0,138.36, -16,2,07.04.2016,,0,138.36, -16,2,08.04.2016,,0,138.36, -16,2,09.04.2016,,0,138.5, -16,2,10.04.2016,,0,138.5, -16,2,11.04.2016,,0,138.5, -16,2,12.04.2016,,0,138.5, -16,2,13.04.2016,,0,138.5, -16,2,14.04.2016,,0,138.5, -16,2,15.04.2016,,0,138.5, -16,2,16.04.2016,,0,138.5, -16,2,17.04.2016,,0,138.5, -16,2,18.04.2016,,0,138.5, -16,2,19.04.2016,1.0,31,138.5,116.89 -16,2,20.04.2016,1.0,72,138.5,116.89 -16,2,21.04.2016,1.0,91,138.5,116.89 -16,2,22.04.2016,1.0,113,138.5,116.89 -16,2,23.04.2016,1.0,138,138.5,116.89 -16,2,24.04.2016,1.0,113,138.5,116.89 -16,2,25.04.2016,1.0,97,138.5,116.89 -16,2,26.04.2016,1.0,53,138.5,114.17 -16,2,27.04.2016,1.0,44,138.5,114.17 -16,2,28.04.2016,,3,138.5, -16,2,29.04.2016,,3,138.5, -16,2,30.04.2016,,19,138.5, -16,2,01.05.2016,,6,138.5, -16,2,02.05.2016,,6,138.5, -16,2,03.05.2016,1.0,97,138.5,106.01 -16,2,04.05.2016,1.0,390,138.5,106.01 -16,2,05.05.2016,1.0,22,138.5,106.01 -16,2,06.05.2016,1.0,223,138.5,106.01 -16,2,07.05.2016,1.0,173,138.5,106.01 -16,2,08.05.2016,1.0,119,138.5,106.01 -16,2,09.05.2016,1.0,119,138.5,106.01 -16,2,10.05.2016,,3,138.5, -16,2,11.05.2016,,3,138.5, -16,2,12.05.2016,,3,138.5, -16,2,13.05.2016,,3,138.5, -16,2,14.05.2016,,3,138.5, -16,2,15.05.2016,,3,138.5, -16,2,16.05.2016,,3,138.5, -16,2,17.05.2016,,3,138.5, -16,2,18.05.2016,,3,138.5, -16,2,19.05.2016,,3,138.5, -16,2,20.05.2016,,3,138.5, -16,2,21.05.2016,,3,138.5, -16,2,22.05.2016,,3,138.5, -17,2,01.01.2015,,0,135.78, -17,2,02.01.2015,,0,135.78, -17,2,03.01.2015,,0,135.78, -17,2,04.01.2015,,0,135.78, -17,2,05.01.2015,,0,135.78, -17,2,06.01.2015,,0,135.78, -17,2,07.01.2015,,0,135.78, -17,2,08.01.2015,,3,135.78, -17,2,09.01.2015,,3,135.78, -17,2,10.01.2015,,3,135.78, 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-17,2,10.09.2015,,22,128.17, -17,2,11.09.2015,,22,128.17, -17,2,12.09.2015,,22,128.17, -17,2,13.09.2015,,22,128.17, -17,2,14.09.2015,,22,128.17, -17,2,15.09.2015,,22,128.17, -17,2,16.09.2015,,22,128.17, -17,2,17.09.2015,,22,128.17, -17,2,18.09.2015,,22,128.17, -17,2,19.09.2015,,22,128.17, -17,2,20.09.2015,,22,128.17, -17,2,21.09.2015,,22,128.17, -17,2,22.09.2015,,22,128.17, -17,2,23.09.2015,,0,128.17, -17,2,24.09.2015,,0,128.17, -17,2,25.09.2015,,0,162.96, -17,2,26.09.2015,,9,162.96, -17,2,27.09.2015,,3,162.96, -17,2,28.09.2015,,25,162.96, -17,2,29.09.2015,,0,162.96, -17,2,30.09.2015,,0,162.96, -17,2,01.10.2015,,3,162.96, -17,2,02.10.2015,,22,162.96, -17,2,03.10.2015,,3,162.96, -17,2,04.10.2015,,6,162.96, -17,2,05.10.2015,,9,162.96, -17,2,06.10.2015,1.0,138,143.39,122.05 -17,2,07.10.2015,1.0,97,143.39,122.05 -17,2,08.10.2015,1.0,94,143.39,122.05 -17,2,09.10.2015,1.0,97,143.39,122.05 -17,2,10.10.2015,1.0,91,143.39,122.05 -17,2,11.10.2015,1.0,66,143.39,122.05 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-17,2,19.12.2015,,13,142.57, -17,2,20.12.2015,,9,142.57, -17,2,21.12.2015,,13,142.57, -17,2,22.12.2015,1.0,141,142.57,103.02 -17,2,23.12.2015,1.0,132,142.57,103.02 -17,2,24.12.2015,1.0,101,142.57,103.02 -17,2,25.12.2015,1.0,151,142.57,103.02 -17,2,26.12.2015,1.0,110,142.57,103.02 -17,2,27.12.2015,1.0,123,142.57,103.02 -17,2,28.12.2015,1.0,72,142.57,103.02 -17,2,29.12.2015,,0,138.5, -17,2,30.12.2015,,0,138.5, -17,2,31.12.2015,,0,138.5, -17,2,01.01.2016,,0,138.5, -17,2,02.01.2016,,6,138.5, -17,2,03.01.2016,,9,138.5, -17,2,04.01.2016,,3,138.5, -17,2,05.01.2016,,19,138.5, -17,2,06.01.2016,,0,138.5, -17,2,07.01.2016,,6,138.5, -17,2,08.01.2016,,13,138.5, -17,2,09.01.2016,,3,138.5, -17,2,10.01.2016,,0,138.5, -17,2,11.01.2016,,16,138.5, -17,2,12.01.2016,,0,138.5, -17,2,13.01.2016,,0,138.5, -17,2,14.01.2016,,19,138.5, -17,2,15.01.2016,,0,138.5, -17,2,16.01.2016,,3,142.57, -17,2,17.01.2016,,13,142.57, -17,2,18.01.2016,,25,142.57, -17,2,19.01.2016,1.0,251,142.57,103.02 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-17,2,19.02.2016,,3,138.36, -17,2,20.02.2016,,6,138.36, -17,2,21.02.2016,,9,138.36, -17,2,22.02.2016,,6,138.36, -17,2,23.02.2016,,3,138.36, -17,2,24.02.2016,,3,138.36, -17,2,25.02.2016,,9,138.36, -17,2,26.02.2016,,9,138.36, -17,2,27.02.2016,,3,138.36, -17,2,28.02.2016,,6,138.36, -17,2,29.02.2016,,6,138.36, -17,2,01.03.2016,,6,138.36, -17,2,02.03.2016,,6,138.36, -17,2,03.03.2016,,3,138.36, -17,2,04.03.2016,,3,138.36, -17,2,05.03.2016,,16,138.36, -17,2,06.03.2016,,6,138.36, -17,2,07.03.2016,,6,138.36, -17,2,08.03.2016,,3,138.36, -17,2,09.03.2016,,6,138.36, -17,2,10.03.2016,,3,138.36, -17,2,11.03.2016,,3,138.36, -17,2,12.03.2016,,13,138.5, -17,2,13.03.2016,,0,138.5, -17,2,14.03.2016,,3,138.5, -17,2,15.03.2016,1.0,60,138.5,124.77 -17,2,16.03.2016,1.0,66,138.5,124.77 -17,2,17.03.2016,1.0,41,138.5,124.77 -17,2,18.03.2016,1.0,79,138.5,124.77 -17,2,19.03.2016,1.0,66,138.5,124.77 -17,2,20.03.2016,1.0,82,138.5,124.77 -17,2,21.03.2016,1.0,69,138.5,124.77 -17,2,22.03.2016,,9,138.36, 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-18,2,18.12.2015,,6,142.57, -18,2,19.12.2015,,13,142.57, -18,2,20.12.2015,,13,142.57, -18,2,21.12.2015,,25,142.57, -18,2,22.12.2015,1.0,82,142.57,103.02 -18,2,23.12.2015,1.0,123,142.57,103.02 -18,2,24.12.2015,1.0,79,142.57,103.02 -18,2,25.12.2015,1.0,113,142.57,103.02 -18,2,26.12.2015,1.0,119,142.57,103.02 -18,2,27.12.2015,1.0,79,142.57,103.02 -18,2,28.12.2015,1.0,75,142.57,103.02 -18,2,29.12.2015,,0,138.5, -18,2,30.12.2015,,3,138.5, -18,2,31.12.2015,,0,138.5, -18,2,01.01.2016,,3,138.5, -18,2,02.01.2016,,9,138.5, -18,2,03.01.2016,,3,138.5, -18,2,04.01.2016,,0,138.5, -18,2,05.01.2016,,16,138.5, -18,2,06.01.2016,,3,138.5, -18,2,07.01.2016,,3,138.5, -18,2,08.01.2016,,16,138.5, -18,2,09.01.2016,,0,138.5, -18,2,10.01.2016,,0,138.5, -18,2,11.01.2016,,0,138.5, -18,2,12.01.2016,,0,138.5, -18,2,13.01.2016,,0,138.5, -18,2,14.01.2016,,6,138.5, -18,2,15.01.2016,,13,138.5, -18,2,16.01.2016,,9,142.57, -18,2,17.01.2016,,6,142.57, -18,2,18.01.2016,,16,142.57, -18,2,19.01.2016,1.0,192,142.57,103.02 -18,2,20.01.2016,1.0,126,142.57,103.02 -18,2,21.01.2016,1.0,179,142.57,103.02 -18,2,22.01.2016,1.0,148,142.57,103.02 -18,2,23.01.2016,1.0,107,142.57,103.02 -18,2,24.01.2016,1.0,154,142.57,103.02 -18,2,25.01.2016,1.0,119,142.57,103.02 -18,2,26.01.2016,,6,142.57, -18,2,27.01.2016,,6,142.57, -18,2,28.01.2016,,6,142.57, -18,2,29.01.2016,,9,142.57, -18,2,30.01.2016,,3,142.57, -18,2,31.01.2016,,9,142.57, -18,2,01.02.2016,,16,142.57, -18,2,02.02.2016,,13,142.57, -18,2,03.02.2016,,9,142.57, -18,2,04.02.2016,,16,142.57, -18,2,05.02.2016,,9,142.57, -18,2,06.02.2016,,16,142.57, -18,2,07.02.2016,,16,142.57, -18,2,08.02.2016,,22,142.57, -18,2,09.02.2016,1.0,141,142.57,108.46 -18,2,10.02.2016,1.0,141,138.36,108.46 -18,2,11.02.2016,1.0,182,138.36,108.46 -18,2,12.02.2016,1.0,126,138.36,108.46 -18,2,13.02.2016,1.0,129,138.36,108.46 -18,2,14.02.2016,1.0,135,138.36,108.46 -18,2,15.02.2016,1.0,210,138.36,108.46 -18,2,16.02.2016,,3,138.36, -18,2,17.02.2016,,0,138.36, -18,2,18.02.2016,,6,138.36, -18,2,19.02.2016,,3,138.36, -18,2,20.02.2016,,9,138.36, -18,2,21.02.2016,,9,138.36, -18,2,22.02.2016,,9,138.36, -18,2,23.02.2016,,9,138.36, -18,2,24.02.2016,,9,138.36, -18,2,25.02.2016,,9,138.36, -18,2,26.02.2016,,9,138.36, -18,2,27.02.2016,,9,138.36, -18,2,28.02.2016,,9,138.36, -18,2,29.02.2016,,9,138.36, -18,2,01.03.2016,,9,138.36, -18,2,02.03.2016,,6,138.36, -18,2,03.03.2016,,3,138.36, -18,2,04.03.2016,,3,138.36, -18,2,05.03.2016,,3,138.36, -18,2,06.03.2016,,3,138.36, -18,2,07.03.2016,,0,138.36, -18,2,08.03.2016,,3,138.36, -18,2,09.03.2016,,6,138.36, -18,2,10.03.2016,,6,138.36, -18,2,11.03.2016,,9,138.36, -18,2,12.03.2016,,0,138.5, -18,2,13.03.2016,,9,138.5, -18,2,14.03.2016,,9,138.5, -18,2,15.03.2016,1.0,79,138.5,124.77 -18,2,16.03.2016,1.0,63,138.5,124.77 -18,2,17.03.2016,1.0,41,138.5,124.77 -18,2,18.03.2016,1.0,75,138.5,124.77 -18,2,19.03.2016,1.0,107,138.5,124.77 -18,2,20.03.2016,1.0,44,138.5,124.77 -18,2,21.03.2016,1.0,50,138.5,124.77 -18,2,22.03.2016,,0,138.36, 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-18,2,25.04.2016,1.0,75,138.5,116.89 -18,2,26.04.2016,,0,138.5, -18,2,27.04.2016,,0,138.5, -18,2,28.04.2016,,0,138.5, -18,2,29.04.2016,,0,138.5, -18,2,30.04.2016,,0,138.5, -18,2,01.05.2016,,0,138.5, -18,2,02.05.2016,,0,138.5, -18,2,03.05.2016,1.0,75,138.5,106.01 -18,2,04.05.2016,1.0,126,138.5,106.01 -18,2,05.05.2016,1.0,116,138.5,106.01 -18,2,06.05.2016,1.0,113,138.5,106.01 -18,2,07.05.2016,1.0,135,138.5,106.01 -18,2,08.05.2016,1.0,113,138.5,106.01 -18,2,09.05.2016,1.0,113,138.5,106.01 -18,2,10.05.2016,,3,138.5, -18,2,11.05.2016,,6,138.5, -18,2,12.05.2016,,38,138.5, -18,2,13.05.2016,,0,138.5, -18,2,14.05.2016,,0,138.5, -18,2,15.05.2016,,0,138.5, -18,2,16.05.2016,,0,138.5, -18,2,17.05.2016,,0,138.5, -18,2,18.05.2016,,0,138.5, -18,2,19.05.2016,,0,138.5, -18,2,20.05.2016,,0,138.5, -18,2,21.05.2016,,0,138.5, -18,2,22.05.2016,,0,138.5, -19,2,01.01.2015,,0,135.78, -19,2,02.01.2015,,6,135.78, -19,2,03.01.2015,,3,135.78, -19,2,04.01.2015,,3,135.78, -19,2,05.01.2015,,0,135.78, -19,2,06.01.2015,,0,135.78, -19,2,07.01.2015,,25,135.78, -19,2,08.01.2015,,0,135.78, -19,2,09.01.2015,,0,135.78, -19,2,10.01.2015,,3,135.78, -19,2,11.01.2015,,6,135.78, -19,2,12.01.2015,,0,135.78, -19,2,13.01.2015,,3,135.78, -19,2,14.01.2015,,0,135.78, -19,2,15.01.2015,,3,135.78, -19,2,16.01.2015,,3,135.78, -19,2,17.01.2015,,3,135.78, -19,2,18.01.2015,,9,135.78, -19,2,19.01.2015,,6,135.78, -19,2,20.01.2015,,3,135.78, -19,2,21.01.2015,,3,135.78, -19,2,22.01.2015,,0,135.78, -19,2,23.01.2015,,0,135.78, -19,2,24.01.2015,,3,135.78, -19,2,25.01.2015,,0,135.78, -19,2,26.01.2015,,13,135.78, -19,2,27.01.2015,,6,135.78, -19,2,28.01.2015,,3,135.78, -19,2,29.01.2015,,0,135.78, -19,2,30.01.2015,,0,135.78, -19,2,31.01.2015,,0,135.78, -19,2,01.02.2015,,6,135.78, -19,2,02.02.2015,,3,135.78, -19,2,03.02.2015,,13,135.78, -19,2,04.02.2015,,0,135.78, -19,2,05.02.2015,,3,135.78, -19,2,06.02.2015,,22,135.78, -19,2,07.02.2015,,9,135.78, -19,2,08.02.2015,,16,135.78, -19,2,09.02.2015,,0,135.78, -19,2,10.02.2015,,22,135.78, -19,2,11.02.2015,,13,135.78, -19,2,12.02.2015,,9,135.78, -19,2,13.02.2015,,6,135.78, -19,2,14.02.2015,,0,135.78, -19,2,15.02.2015,,25,135.78, -19,2,16.02.2015,,6,135.78, -19,2,17.02.2015,,6,135.78, -19,2,18.02.2015,,9,135.78, -19,2,19.02.2015,,3,135.78, -19,2,20.02.2015,,0,135.78, -19,2,21.02.2015,,13,135.78, -19,2,22.02.2015,,31,135.78, -19,2,23.02.2015,,3,135.78, -19,2,24.02.2015,,3,135.78, -19,2,25.02.2015,,0,135.78, -19,2,26.02.2015,,0,135.78, -19,2,27.02.2015,,0,135.78, -19,2,28.02.2015,,0,135.78, -19,2,01.03.2015,,3,135.78, -19,2,02.03.2015,,0,146.38, -19,2,03.03.2015,,3,146.38, -19,2,04.03.2015,,0,146.38, -19,2,05.03.2015,,13,146.38, -19,2,06.03.2015,,6,146.38, -19,2,07.03.2015,,9,146.38, -19,2,08.03.2015,,6,146.38, -19,2,09.03.2015,,13,146.38, -19,2,10.03.2015,,0,146.38, -19,2,11.03.2015,,0,146.38, -19,2,12.03.2015,,3,146.38, -19,2,13.03.2015,,0,146.38, -19,2,14.03.2015,,0,146.38, -19,2,15.03.2015,,6,146.38, -19,2,16.03.2015,,0,146.38, -19,2,17.03.2015,,6,146.38, -19,2,18.03.2015,,6,146.38, -19,2,19.03.2015,,0,146.38, -19,2,20.03.2015,,0,146.38, -19,2,21.03.2015,,0,146.38, -19,2,22.03.2015,,0,146.38, -19,2,23.03.2015,,0,146.38, -19,2,24.03.2015,,0,146.38, -19,2,25.03.2015,,0,146.38, -19,2,26.03.2015,,0,146.38, -19,2,27.03.2015,,0,146.38, -19,2,28.03.2015,,3,146.38, -19,2,29.03.2015,,0,146.38, -19,2,30.03.2015,,3,146.38, -19,2,31.03.2015,,3,146.38, -19,2,01.04.2015,,0,146.38, -19,2,02.04.2015,,0,146.38, -19,2,03.04.2015,,0,146.38, -19,2,04.04.2015,,0,146.38, -19,2,05.04.2015,,0,146.38, -19,2,06.04.2015,,0,146.38, -19,2,07.04.2015,,3,146.38, -19,2,08.04.2015,,0,146.38, -19,2,09.04.2015,,13,146.38, -19,2,10.04.2015,,3,146.38, -19,2,11.04.2015,,3,146.38, -19,2,12.04.2015,,3,146.38, -19,2,13.04.2015,,0,145.84, -19,2,14.04.2015,,13,145.84, -19,2,15.04.2015,,0,145.84, -19,2,16.04.2015,,3,145.84, -19,2,17.04.2015,,3,145.84, -19,2,18.04.2015,,3,145.84, -19,2,19.04.2015,,13,145.84, -19,2,20.04.2015,,9,145.84, -19,2,21.04.2015,,0,145.84, -19,2,22.04.2015,,0,145.84, -19,2,23.04.2015,,3,145.84, -19,2,24.04.2015,,3,145.84, -19,2,25.04.2015,,0,145.84, -19,2,26.04.2015,,0,150.73, -19,2,27.04.2015,,0,150.73, -19,2,28.04.2015,,0,150.73, -19,2,29.04.2015,,3,150.73, -19,2,30.04.2015,,3,150.73, -19,2,01.05.2015,,6,150.73, -19,2,02.05.2015,,0,150.73, -19,2,03.05.2015,,0,150.73, -19,2,04.05.2015,,0,150.73, -19,2,05.05.2015,,0,150.73, -19,2,06.05.2015,,0,150.73, -19,2,07.05.2015,,13,150.73, -19,2,08.05.2015,,9,150.73, -19,2,09.05.2015,,6,150.73, -19,2,10.05.2015,,6,150.73, -19,2,11.05.2015,,3,150.73, -19,2,12.05.2015,,6,150.73, -19,2,13.05.2015,,0,150.73, -19,2,14.05.2015,,0,150.73, -19,2,15.05.2015,,6,150.73, -19,2,16.05.2015,,0,150.73, -19,2,17.05.2015,,6,150.73, -19,2,18.05.2015,,0,150.73, -19,2,19.05.2015,,0,150.73, -19,2,20.05.2015,,3,150.73, -19,2,21.05.2015,,0,150.73, -19,2,22.05.2015,,0,150.73, -19,2,23.05.2015,,3,150.73, -19,2,24.05.2015,,6,150.73, -19,2,25.05.2015,,0,150.73, -19,2,26.05.2015,,0,150.73, -19,2,27.05.2015,,0,150.73, -19,2,28.05.2015,,0,150.73, -19,2,29.05.2015,,0,150.73, -19,2,30.05.2015,,0,150.73, -19,2,31.05.2015,,0,150.73, -19,2,01.06.2015,,0,150.73, -19,2,02.06.2015,,0,150.73, -19,2,03.06.2015,,0,150.73, -19,2,04.06.2015,,3,150.73, -19,2,05.06.2015,,6,150.73, -19,2,06.06.2015,,0,150.73, -19,2,07.06.2015,,6,150.73, -19,2,08.06.2015,,0,150.73, -19,2,09.06.2015,,0,150.73, -19,2,10.06.2015,,0,150.73, -19,2,11.06.2015,,3,150.73, -19,2,12.06.2015,,3,150.73, -19,2,13.06.2015,,3,150.73, -19,2,14.06.2015,,0,150.73, -19,2,15.06.2015,,0,150.73, -19,2,16.06.2015,,3,150.73, -19,2,17.06.2015,,3,150.73, -19,2,18.06.2015,,6,150.73, -19,2,19.06.2015,,0,150.73, -19,2,20.06.2015,,3,150.73, -19,2,21.06.2015,,3,150.73, -19,2,22.06.2015,,3,150.73, -19,2,23.06.2015,,0,150.73, -19,2,24.06.2015,,6,150.73, -19,2,25.06.2015,,0,150.73, -19,2,26.06.2015,,3,150.73, -19,2,27.06.2015,,0,150.73, -19,2,28.06.2015,,13,150.73, -19,2,29.06.2015,,0,150.73, -19,2,30.06.2015,,0,150.73, -19,2,01.07.2015,,0,150.73, -19,2,02.07.2015,,3,150.73, -19,2,03.07.2015,,3,150.73, -19,2,04.07.2015,,3,150.73, -19,2,05.07.2015,,3,150.73, -19,2,06.07.2015,,3,150.73, -19,2,07.07.2015,,3,150.73, -19,2,08.07.2015,,3,150.73, -19,2,09.07.2015,,3,150.73, -19,2,10.07.2015,,3,150.73, -19,2,11.07.2015,,3,150.73, -19,2,12.07.2015,,3,150.73, -19,2,13.07.2015,,3,150.73, -19,2,14.07.2015,,3,150.73, -19,2,15.07.2015,,3,150.73, -19,2,16.07.2015,,3,150.73, -19,2,17.07.2015,,3,150.73, -19,2,18.07.2015,,3,150.73, -19,2,19.07.2015,,3,150.73, -19,2,20.07.2015,,3,150.73, -19,2,21.07.2015,,3,150.73, -19,2,22.07.2015,,3,150.73, -19,2,23.07.2015,,3,150.73, -19,2,24.07.2015,,3,150.73, -19,2,25.07.2015,,0,150.73, -19,2,26.07.2015,,0,150.73, -19,2,27.07.2015,,0,150.73, -19,2,28.07.2015,,0,150.73, -19,2,29.07.2015,,0,150.73, -19,2,30.07.2015,,0,150.73, -19,2,31.07.2015,,0,150.73, -19,2,01.08.2015,,0,150.73, -19,2,02.08.2015,,0,150.73, -19,2,03.08.2015,,0,150.73, -19,2,04.08.2015,,0,150.73, -19,2,05.08.2015,,0,150.73, -19,2,06.08.2015,,0,150.73, -19,2,07.08.2015,,0,150.73, -19,2,08.08.2015,,0,150.73, -19,2,09.08.2015,,0,150.73, -19,2,10.08.2015,,0,150.73, -19,2,11.08.2015,,0,150.73, -19,2,12.08.2015,,0,128.17, -19,2,13.08.2015,,0,128.17, -19,2,14.08.2015,,0,128.17, -19,2,15.08.2015,,0,128.17, -19,2,16.08.2015,,0,128.17, -19,2,17.08.2015,,0,128.17, -19,2,18.08.2015,1.0,44,128.17,100.3 -19,2,19.08.2015,1.0,44,128.17,100.3 -19,2,20.08.2015,1.0,44,128.17,100.3 -19,2,21.08.2015,1.0,44,128.17,100.3 -19,2,22.08.2015,1.0,44,128.17,100.3 -19,2,23.08.2015,1.0,44,128.17,100.3 -19,2,24.08.2015,1.0,44,128.17,100.3 -19,2,25.08.2015,,0,128.17, -19,2,26.08.2015,,0,128.17, -19,2,27.08.2015,,0,128.17, -19,2,28.08.2015,,0,128.17, -19,2,29.08.2015,,0,128.17, -19,2,30.08.2015,,0,128.17, -19,2,31.08.2015,,0,128.17, -19,2,01.09.2015,,0,128.17, -19,2,02.09.2015,,0,128.17, -19,2,03.09.2015,,0,128.17, -19,2,04.09.2015,,0,128.17, -19,2,05.09.2015,,0,128.17, -19,2,06.09.2015,,0,128.17, -19,2,07.09.2015,,0,128.17, -19,2,08.09.2015,,0,128.17, -19,2,09.09.2015,,0,128.17, -19,2,10.09.2015,,0,128.17, -19,2,11.09.2015,,0,128.17, -19,2,12.09.2015,,0,128.17, -19,2,13.09.2015,,0,128.17, -19,2,14.09.2015,,0,128.17, -19,2,15.09.2015,,0,128.17, -19,2,16.09.2015,,0,128.17, -19,2,17.09.2015,,0,128.17, -19,2,18.09.2015,,0,128.17, -19,2,19.09.2015,,0,128.17, -19,2,20.09.2015,,0,128.17, -19,2,21.09.2015,,0,128.17, -19,2,22.09.2015,,0,128.17, -19,2,23.09.2015,,0,128.17, -19,2,24.09.2015,,0,128.17, -19,2,25.09.2015,,0,162.96, -19,2,26.09.2015,,0,162.96, -19,2,27.09.2015,,0,162.96, -19,2,28.09.2015,,0,162.96, -19,2,29.09.2015,,0,162.96, -19,2,30.09.2015,,0,162.96, -19,2,01.10.2015,,0,162.96, -19,2,02.10.2015,,0,162.96, -19,2,03.10.2015,,0,162.96, -19,2,04.10.2015,,0,162.96, -19,2,05.10.2015,,16,162.96, -19,2,06.10.2015,1.0,173,143.39,122.05 -19,2,07.10.2015,1.0,6,143.39,122.05 -19,2,08.10.2015,1.0,6,143.39,122.05 -19,2,09.10.2015,1.0,50,143.39,122.05 -19,2,10.10.2015,1.0,75,143.39,122.05 -19,2,11.10.2015,1.0,88,143.39,122.05 -19,2,12.10.2015,1.0,53,143.39,122.05 -19,2,13.10.2015,,0,143.93, -19,2,14.10.2015,,0,143.93, -19,2,15.10.2015,,0,143.93, -19,2,16.10.2015,,0,143.93, -19,2,17.10.2015,,0,143.93, -19,2,18.10.2015,,0,143.93, -19,2,19.10.2015,,0,143.93, -19,2,20.10.2015,,0,143.93, -19,2,21.10.2015,,0,143.93, -19,2,22.10.2015,,0,143.93, -19,2,23.10.2015,,0,143.93, -19,2,24.10.2015,,0,143.93, -19,2,25.10.2015,,0,143.93, -19,2,26.10.2015,,0,143.93, -19,2,27.10.2015,,0,143.93, -19,2,28.10.2015,,0,143.93, -19,2,29.10.2015,,0,143.93, -19,2,30.10.2015,,0,143.93, -19,2,31.10.2015,,0,143.93, -19,2,01.11.2015,,0,143.93, -19,2,02.11.2015,,0,143.93, -19,2,03.11.2015,,0,143.93, -19,2,04.11.2015,,0,143.93, -19,2,05.11.2015,,0,143.93, -19,2,06.11.2015,,0,143.93, -19,2,07.11.2015,,0,143.93, -19,2,08.11.2015,,0,143.93, -19,2,09.11.2015,,0,143.93, -19,2,10.11.2015,,0,143.93, -19,2,11.11.2015,,0,143.93, -19,2,12.11.2015,,0,143.93, -19,2,13.11.2015,,0,143.93, -19,2,14.11.2015,,0,143.93, -19,2,15.11.2015,,0,143.93, -19,2,16.11.2015,,0,143.93, -19,2,17.11.2015,,0,143.93, -19,2,18.11.2015,,0,143.93, -19,2,19.11.2015,,0,143.93, -19,2,20.11.2015,,0,143.93, -19,2,21.11.2015,,0,143.93, -19,2,22.11.2015,,0,143.93, -19,2,23.11.2015,,0,143.93, -19,2,24.11.2015,,0,143.93, -19,2,25.11.2015,,0,143.93, -19,2,26.11.2015,,0,143.93, -19,2,27.11.2015,,0,143.93, -19,2,28.11.2015,,0,143.93, -19,2,29.11.2015,,0,143.93, -19,2,30.11.2015,,0,143.93, -19,2,01.12.2015,,0,162.96, -19,2,02.12.2015,,0,162.96, -19,2,03.12.2015,,0,162.96, -19,2,04.12.2015,,0,162.96, -19,2,05.12.2015,,0,162.96, -19,2,06.12.2015,,0,162.96, -19,2,07.12.2015,,0,162.96, -19,2,08.12.2015,,0,162.96, -19,2,09.12.2015,,0,162.96, -19,2,10.12.2015,,0,162.96, -19,2,11.12.2015,,0,142.57, -19,2,12.12.2015,,0,142.57, -19,2,13.12.2015,,0,142.57, -19,2,14.12.2015,,0,142.57, -19,2,15.12.2015,,0,142.57, -19,2,16.12.2015,,0,142.57, -19,2,17.12.2015,,0,142.57, -19,2,18.12.2015,,0,142.57, -19,2,19.12.2015,,0,142.57, -19,2,20.12.2015,,0,142.57, -19,2,21.12.2015,,0,142.57, -19,2,22.12.2015,1.0,157,142.57,103.02 -19,2,23.12.2015,1.0,160,142.57,103.02 -19,2,24.12.2015,1.0,107,142.57,103.02 -19,2,25.12.2015,1.0,79,142.57,103.02 -19,2,26.12.2015,1.0,72,142.57,103.02 -19,2,27.12.2015,1.0,69,142.57,103.02 -19,2,28.12.2015,1.0,66,142.57,103.02 -19,2,29.12.2015,,0,138.5, -19,2,30.12.2015,,0,138.5, -19,2,31.12.2015,,6,138.5, -19,2,01.01.2016,,13,138.5, -19,2,02.01.2016,,22,138.5, -19,2,03.01.2016,,148,138.5, -19,2,04.01.2016,,0,138.5, -19,2,05.01.2016,,0,138.5, -19,2,06.01.2016,,13,138.5, -19,2,07.01.2016,,6,138.5, -19,2,08.01.2016,,16,138.5, -19,2,09.01.2016,,16,138.5, -19,2,10.01.2016,,16,138.5, -19,2,11.01.2016,,3,138.5, -19,2,12.01.2016,,3,138.5, -19,2,13.01.2016,,13,138.5, -19,2,14.01.2016,,16,138.5, -19,2,15.01.2016,,16,138.5, -19,2,16.01.2016,,16,142.57, -19,2,17.01.2016,,16,142.57, -19,2,18.01.2016,,16,142.57, -19,2,19.01.2016,1.0,242,142.57,103.02 -19,2,20.01.2016,1.0,170,142.57,103.02 -19,2,21.01.2016,1.0,25,142.57,103.02 -19,2,22.01.2016,1.0,132,142.57,103.02 -19,2,23.01.2016,1.0,107,142.57,103.02 -19,2,24.01.2016,1.0,97,142.57,103.02 -19,2,25.01.2016,1.0,97,142.57,103.02 -19,2,26.01.2016,,16,142.57, -19,2,27.01.2016,,16,142.57, -19,2,28.01.2016,,16,142.57, -19,2,29.01.2016,,38,142.57, -19,2,30.01.2016,,53,142.57, -19,2,31.01.2016,,94,142.57, -19,2,01.02.2016,,3,142.57, -19,2,02.02.2016,,28,142.57, -19,2,03.02.2016,,28,142.57, -19,2,04.02.2016,,28,142.57, -19,2,05.02.2016,,28,142.57, -19,2,06.02.2016,,28,142.57, -19,2,07.02.2016,,28,142.57, -19,2,08.02.2016,,19,142.57, -19,2,09.02.2016,1.0,185,142.57,108.46 -19,2,10.02.2016,1.0,97,138.36,108.46 -19,2,11.02.2016,1.0,104,138.36,108.46 -19,2,12.02.2016,1.0,85,138.36,108.46 -19,2,13.02.2016,1.0,94,138.36,108.46 -19,2,14.02.2016,1.0,94,138.36,108.46 -19,2,15.02.2016,1.0,94,138.36,108.46 -19,2,16.02.2016,,19,138.36, -19,2,17.02.2016,,19,138.36, -19,2,18.02.2016,,19,138.36, -19,2,19.02.2016,,19,138.36, -19,2,20.02.2016,,19,138.36, -19,2,21.02.2016,,19,138.36, -19,2,22.02.2016,,19,138.36, -19,2,23.02.2016,,19,138.36, -19,2,24.02.2016,,19,138.36, -19,2,25.02.2016,,19,138.36, -19,2,26.02.2016,,19,138.36, -19,2,27.02.2016,,19,138.36, -19,2,28.02.2016,,19,138.36, -19,2,29.02.2016,,19,138.36, -19,2,01.03.2016,,19,138.36, -19,2,02.03.2016,,19,138.36, -19,2,03.03.2016,,16,138.36, -19,2,04.03.2016,,16,138.36, -19,2,05.03.2016,,16,138.36, -19,2,06.03.2016,,16,138.36, -19,2,07.03.2016,,6,138.36, -19,2,08.03.2016,,3,138.36, -19,2,09.03.2016,,16,138.36, -19,2,10.03.2016,,13,138.36, -19,2,11.03.2016,,0,138.36, -19,2,12.03.2016,,0,138.5, -19,2,13.03.2016,,0,138.5, -19,2,14.03.2016,,3,138.5, -19,2,15.03.2016,1.0,75,138.5,124.77 -19,2,16.03.2016,1.0,88,138.5,124.77 -19,2,17.03.2016,1.0,66,138.5,124.77 -19,2,18.03.2016,1.0,75,138.5,124.77 -19,2,19.03.2016,1.0,101,138.5,124.77 -19,2,20.03.2016,1.0,94,138.5,124.77 -19,2,21.03.2016,1.0,82,138.5,124.77 -19,2,22.03.2016,,0,138.36, -19,2,23.03.2016,,0,138.36, -19,2,24.03.2016,,0,138.36, -19,2,25.03.2016,,0,138.36, -19,2,26.03.2016,,0,138.36, -19,2,27.03.2016,,0,138.36, -19,2,28.03.2016,,0,138.36, -19,2,29.03.2016,,0,138.36, -19,2,30.03.2016,,0,138.36, -19,2,31.03.2016,,0,138.36, -19,2,01.04.2016,,0,138.36, -19,2,02.04.2016,,0,138.36, -19,2,03.04.2016,,0,138.36, -19,2,04.04.2016,,0,138.36, -19,2,05.04.2016,,0,138.36, -19,2,06.04.2016,,0,138.36, -19,2,07.04.2016,,0,138.36, -19,2,08.04.2016,,0,138.36, -19,2,09.04.2016,,0,138.5, -19,2,10.04.2016,,0,138.5, -19,2,11.04.2016,,0,138.5, -19,2,12.04.2016,,6,138.5, -19,2,13.04.2016,,3,138.5, -19,2,14.04.2016,,9,138.5, -19,2,15.04.2016,,6,138.5, -19,2,16.04.2016,,9,138.5, -19,2,17.04.2016,,6,138.5, -19,2,18.04.2016,,6,138.5, -19,2,19.04.2016,1.0,107,138.5,116.89 -19,2,20.04.2016,1.0,129,138.5,116.89 -19,2,21.04.2016,1.0,145,138.5,116.89 -19,2,22.04.2016,1.0,132,138.5,116.89 -19,2,23.04.2016,1.0,101,138.5,116.89 -19,2,24.04.2016,1.0,88,138.5,116.89 -19,2,25.04.2016,1.0,88,138.5,116.89 -19,2,26.04.2016,,3,138.5, -19,2,27.04.2016,,3,138.5, -19,2,28.04.2016,,3,138.5, -19,2,29.04.2016,,3,138.5, -19,2,30.04.2016,,3,138.5, -19,2,01.05.2016,,3,138.5, -19,2,02.05.2016,,6,138.5, -19,2,03.05.2016,1.0,236,138.5,106.01 -19,2,04.05.2016,1.0,141,138.5,106.01 -19,2,05.05.2016,1.0,185,138.5,106.01 -19,2,06.05.2016,1.0,163,138.5,106.01 -19,2,07.05.2016,1.0,79,138.5,106.01 -19,2,08.05.2016,1.0,38,138.5,106.01 -19,2,09.05.2016,1.0,113,138.5,106.01 -19,2,10.05.2016,,13,138.5, -19,2,11.05.2016,,6,138.5, -19,2,12.05.2016,,6,138.5, -19,2,13.05.2016,,6,138.5, -19,2,14.05.2016,,6,138.5, -19,2,15.05.2016,,6,138.5, -19,2,16.05.2016,,6,138.5, -19,2,17.05.2016,,6,138.5, -19,2,18.05.2016,,6,138.5, -19,2,19.05.2016,,6,138.5, -19,2,20.05.2016,,6,138.5, -19,2,21.05.2016,,6,138.5, -19,2,22.05.2016,,6,138.5, -20,2,01.01.2015,,0,139.86, -20,2,02.01.2015,,9,139.86, -20,2,03.01.2015,,9,139.86, -20,2,04.01.2015,,0,139.86, -20,2,05.01.2015,,9,139.86, -20,2,06.01.2015,,6,139.86, -20,2,07.01.2015,,3,139.86, -20,2,08.01.2015,,3,139.86, -20,2,09.01.2015,,3,139.86, -20,2,10.01.2015,,0,139.86, -20,2,11.01.2015,,0,139.86, -20,2,12.01.2015,,6,139.86, -20,2,13.01.2015,,0,139.86, -20,2,14.01.2015,,0,139.86, -20,2,15.01.2015,,3,139.86, -20,2,16.01.2015,,3,139.86, -20,2,17.01.2015,,0,139.86, -20,2,18.01.2015,,6,139.86, -20,2,19.01.2015,,3,139.86, -20,2,20.01.2015,,3,139.86, -20,2,21.01.2015,,0,139.86, -20,2,22.01.2015,,3,139.86, -20,2,23.01.2015,,3,139.86, -20,2,24.01.2015,,3,139.86, -20,2,25.01.2015,,6,139.86, -20,2,26.01.2015,,3,139.86, -20,2,27.01.2015,,0,139.86, -20,2,28.01.2015,,6,139.86, -20,2,29.01.2015,,6,139.86, -20,2,30.01.2015,,0,139.86, -20,2,31.01.2015,,9,139.86, -20,2,01.02.2015,,6,139.86, -20,2,02.02.2015,,6,139.86, -20,2,03.02.2015,,3,139.86, -20,2,04.02.2015,,9,139.86, -20,2,05.02.2015,,3,139.86, -20,2,06.02.2015,,6,139.86, -20,2,07.02.2015,,3,139.86, -20,2,08.02.2015,,3,139.86, -20,2,09.02.2015,,0,139.86, -20,2,10.02.2015,,3,139.86, -20,2,11.02.2015,,9,139.86, -20,2,12.02.2015,,6,139.86, -20,2,13.02.2015,,0,139.86, -20,2,14.02.2015,,13,139.86, -20,2,15.02.2015,,0,139.86, -20,2,16.02.2015,,0,139.86, -20,2,17.02.2015,,3,139.86, -20,2,18.02.2015,,3,139.86, -20,2,19.02.2015,,6,139.86, -20,2,20.02.2015,,0,139.86, -20,2,21.02.2015,,3,139.86, -20,2,22.02.2015,,0,139.86, -20,2,23.02.2015,,6,139.86, -20,2,24.02.2015,,0,139.86, -20,2,25.02.2015,,3,139.86, -20,2,26.02.2015,,0,139.86, -20,2,27.02.2015,,0,139.86, -20,2,28.02.2015,,3,139.86, -20,2,01.03.2015,,0,139.86, -20,2,02.03.2015,,0,150.73, -20,2,03.03.2015,,0,150.73, -20,2,04.03.2015,,0,150.73, -20,2,05.03.2015,,3,150.73, -20,2,06.03.2015,,0,150.73, -20,2,07.03.2015,,0,150.73, -20,2,08.03.2015,,9,150.73, -20,2,09.03.2015,,0,150.73, -20,2,10.03.2015,,0,150.73, -20,2,11.03.2015,,3,150.73, -20,2,12.03.2015,,13,150.73, -20,2,13.03.2015,,0,150.73, -20,2,14.03.2015,,6,150.73, -20,2,15.03.2015,,3,150.73, -20,2,16.03.2015,,6,150.73, -20,2,17.03.2015,,3,150.73, -20,2,18.03.2015,,0,150.73, -20,2,19.03.2015,,3,150.73, -20,2,20.03.2015,,3,150.73, -20,2,21.03.2015,,0,150.73, -20,2,22.03.2015,,0,150.73, -20,2,23.03.2015,,0,150.73, -20,2,24.03.2015,,0,150.73, -20,2,25.03.2015,,0,150.73, -20,2,26.03.2015,,0,150.73, -20,2,27.03.2015,,6,150.73, -20,2,28.03.2015,,0,150.73, -20,2,29.03.2015,,0,150.73, -20,2,30.03.2015,,0,150.73, -20,2,31.03.2015,,0,150.73, -20,2,01.04.2015,,0,150.73, -20,2,02.04.2015,,3,150.73, -20,2,03.04.2015,,0,150.73, -20,2,04.04.2015,,13,150.73, -20,2,05.04.2015,,3,150.73, -20,2,06.04.2015,,0,150.73, -20,2,07.04.2015,,0,150.73, -20,2,08.04.2015,,0,150.73, -20,2,09.04.2015,,0,150.73, -20,2,10.04.2015,,3,150.73, -20,2,11.04.2015,,6,150.73, -20,2,12.04.2015,,3,150.73, -20,2,13.04.2015,,3,150.73, -20,2,14.04.2015,,6,150.73, -20,2,15.04.2015,,6,150.73, -20,2,16.04.2015,,6,150.73, -20,2,17.04.2015,,0,150.73, -20,2,18.04.2015,,0,150.73, -20,2,19.04.2015,,3,150.73, 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-20,2,03.08.2015,,22,150.73, -20,2,04.08.2015,,3,150.73, -20,2,05.08.2015,,0,150.73, -20,2,06.08.2015,,0,150.73, -20,2,07.08.2015,,35,150.73, -20,2,08.08.2015,,13,150.73, -20,2,09.08.2015,,25,150.73, -20,2,10.08.2015,,3,150.73, -20,2,11.08.2015,,9,150.73, -20,2,12.08.2015,,0,128.17, -20,2,13.08.2015,,3,128.17, -20,2,14.08.2015,,13,128.17, -20,2,15.08.2015,,9,128.17, -20,2,16.08.2015,,9,128.17, -20,2,17.08.2015,,3,128.17, -20,2,18.08.2015,1.0,207,128.17,100.3 -20,2,19.08.2015,1.0,185,128.17,100.3 -20,2,20.08.2015,1.0,192,128.17,100.3 -20,2,21.08.2015,1.0,160,128.17,100.3 -20,2,22.08.2015,1.0,94,128.17,100.3 -20,2,23.08.2015,1.0,135,128.17,100.3 -20,2,24.08.2015,1.0,123,128.17,100.3 -20,2,25.08.2015,,13,128.17, -20,2,26.08.2015,,16,128.17, -20,2,27.08.2015,,13,128.17, -20,2,28.08.2015,,25,128.17, -20,2,29.08.2015,,28,128.17, -20,2,30.08.2015,,35,128.17, -20,2,31.08.2015,,31,128.17, -20,2,01.09.2015,,16,128.17, -20,2,02.09.2015,,6,128.17, -20,2,03.09.2015,,13,128.17, 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-23,2,01.09.2015,,0,128.17, -23,2,02.09.2015,,0,128.17, -23,2,03.09.2015,,0,128.17, -23,2,04.09.2015,,0,128.17, -23,2,05.09.2015,,0,128.17, -23,2,06.09.2015,,0,128.17, -23,2,07.09.2015,,0,128.17, -23,2,08.09.2015,,0,128.17, -23,2,09.09.2015,,0,128.17, -23,2,10.09.2015,,0,128.17, -23,2,11.09.2015,,0,128.17, -23,2,12.09.2015,,0,128.17, -23,2,13.09.2015,,0,128.17, -23,2,14.09.2015,,0,128.17, -23,2,15.09.2015,,0,128.17, -23,2,16.09.2015,,0,128.17, -23,2,17.09.2015,,0,128.17, -23,2,18.09.2015,,0,128.17, -23,2,19.09.2015,,0,128.17, -23,2,20.09.2015,,0,128.17, -23,2,21.09.2015,,0,128.17, -23,2,22.09.2015,,0,128.17, -23,2,23.09.2015,,0,128.17, -23,2,24.09.2015,,0,128.17, -23,2,25.09.2015,,0,162.96, -23,2,26.09.2015,,0,162.96, -23,2,27.09.2015,,0,162.96, -23,2,28.09.2015,,0,162.96, -23,2,29.09.2015,,0,162.96, -23,2,30.09.2015,,0,162.96, -23,2,01.10.2015,,0,162.96, -23,2,02.10.2015,,0,162.96, -23,2,03.10.2015,,0,162.96, -23,2,04.10.2015,,0,162.96, -23,2,05.10.2015,,0,162.96, 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-23,2,15.01.2016,,0,138.5, -23,2,16.01.2016,,0,142.57, -23,2,17.01.2016,,0,142.57, -23,2,18.01.2016,,9,142.57, -23,2,19.01.2016,1.0,132,142.57,103.02 -23,2,20.01.2016,1.0,116,142.57,103.02 -23,2,21.01.2016,1.0,101,142.57,103.02 -23,2,22.01.2016,1.0,110,142.57,103.02 -23,2,23.01.2016,1.0,72,142.57,103.02 -23,2,24.01.2016,1.0,66,142.57,103.02 -23,2,25.01.2016,1.0,66,142.57,103.02 -23,2,26.01.2016,,0,142.57, -23,2,27.01.2016,,0,142.57, -23,2,28.01.2016,,0,142.57, -23,2,29.01.2016,,0,142.57, -23,2,30.01.2016,,0,142.57, -23,2,31.01.2016,,0,142.57, -23,2,01.02.2016,,0,142.57, -23,2,02.02.2016,,0,142.57, -23,2,03.02.2016,,0,142.57, -23,2,04.02.2016,,0,142.57, -23,2,05.02.2016,,0,142.57, -23,2,06.02.2016,,0,142.57, -23,2,07.02.2016,,0,142.57, -23,2,08.02.2016,,9,142.57, -23,2,09.02.2016,1.0,85,142.57,108.46 -23,2,10.02.2016,1.0,110,138.36,108.46 -23,2,11.02.2016,1.0,116,138.36,108.46 -23,2,12.02.2016,1.0,38,138.36,108.46 -23,2,13.02.2016,1.0,60,138.36,108.46 -23,2,14.02.2016,1.0,57,138.36,108.46 -23,2,15.02.2016,1.0,57,138.36,108.46 -23,2,16.02.2016,,0,138.36, -23,2,17.02.2016,,0,138.36, -23,2,18.02.2016,,0,138.36, -23,2,19.02.2016,,0,138.36, -23,2,20.02.2016,,0,138.36, -23,2,21.02.2016,,0,138.36, -23,2,22.02.2016,,0,138.36, -23,2,23.02.2016,,0,138.36, -23,2,24.02.2016,,0,138.36, -23,2,25.02.2016,,0,138.36, -23,2,26.02.2016,,0,138.36, -23,2,27.02.2016,,0,138.36, -23,2,28.02.2016,,0,138.36, -23,2,29.02.2016,,0,138.36, -23,2,01.03.2016,,0,138.36, -23,2,02.03.2016,,0,138.36, -23,2,03.03.2016,,0,138.36, -23,2,04.03.2016,,0,138.36, -23,2,05.03.2016,,0,138.36, -23,2,06.03.2016,,0,138.36, -23,2,07.03.2016,,0,138.36, -23,2,08.03.2016,,0,138.36, -23,2,09.03.2016,,0,138.36, -23,2,10.03.2016,,0,138.36, -23,2,11.03.2016,,0,138.36, -23,2,12.03.2016,,0,138.5, -23,2,13.03.2016,,0,138.5, -23,2,14.03.2016,,9,138.5, -23,2,15.03.2016,1.0,31,138.5,124.77 -23,2,16.03.2016,1.0,41,138.5,124.77 -23,2,17.03.2016,1.0,53,138.5,124.77 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-24,2,01.01.2015,,0,135.78, -24,2,02.01.2015,,9,135.78, -24,2,03.01.2015,,3,135.78, -24,2,04.01.2015,,0,135.78, -24,2,05.01.2015,,0,135.78, -24,2,06.01.2015,,0,135.78, -24,2,07.01.2015,,0,135.78, -24,2,08.01.2015,,0,135.78, -24,2,09.01.2015,,0,135.78, -24,2,10.01.2015,,0,135.78, -24,2,11.01.2015,,0,135.78, -24,2,12.01.2015,,0,135.78, -24,2,13.01.2015,,0,135.78, -24,2,14.01.2015,,0,135.78, -24,2,15.01.2015,,0,135.78, -24,2,16.01.2015,,0,135.78, -24,2,17.01.2015,,0,135.78, -24,2,18.01.2015,,0,135.78, -24,2,19.01.2015,,0,135.78, -24,2,20.01.2015,,0,135.78, -24,2,21.01.2015,,0,135.78, -24,2,22.01.2015,,0,135.78, -24,2,23.01.2015,,0,135.78, -24,2,24.01.2015,,0,135.78, -24,2,25.01.2015,,0,135.78, -24,2,26.01.2015,,0,135.78, -24,2,27.01.2015,,0,135.78, -24,2,28.01.2015,,0,135.78, -24,2,29.01.2015,,0,135.78, -24,2,30.01.2015,,0,135.78, -24,2,31.01.2015,,0,135.78, -24,2,01.02.2015,,0,135.78, -24,2,02.02.2015,,0,135.78, -24,2,03.02.2015,,0,135.78, -24,2,04.02.2015,,3,135.78, 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-24,2,25.06.2015,,3,150.73, -24,2,26.06.2015,,6,150.73, -24,2,27.06.2015,,0,150.73, -24,2,28.06.2015,,6,150.73, -24,2,29.06.2015,,3,150.73, -24,2,30.06.2015,,0,150.73, -24,2,01.07.2015,,6,150.73, -24,2,02.07.2015,,0,150.73, -24,2,03.07.2015,,0,150.73, -24,2,04.07.2015,,0,150.73, -24,2,05.07.2015,,16,150.73, -24,2,06.07.2015,,0,150.73, -24,2,07.07.2015,,3,150.73, -24,2,08.07.2015,,0,150.73, -24,2,09.07.2015,,3,150.73, -24,2,10.07.2015,,0,150.73, -24,2,11.07.2015,,0,150.73, -24,2,12.07.2015,,0,150.73, -24,2,13.07.2015,,3,150.73, -24,2,14.07.2015,,0,150.73, -24,2,15.07.2015,,3,150.73, -24,2,16.07.2015,,0,150.73, -24,2,17.07.2015,,0,150.73, -24,2,18.07.2015,,0,150.73, -24,2,19.07.2015,,0,150.73, -24,2,20.07.2015,,16,150.73, -24,2,21.07.2015,,19,150.73, -24,2,22.07.2015,,0,150.73, -24,2,23.07.2015,,25,150.73, -24,2,24.07.2015,,6,150.73, -24,2,25.07.2015,,9,150.73, -24,2,26.07.2015,,13,150.73, -24,2,27.07.2015,,22,150.73, -24,2,28.07.2015,,28,150.73, -24,2,29.07.2015,,13,150.73, -24,2,30.07.2015,,0,150.73, -24,2,31.07.2015,,6,150.73, -24,2,01.08.2015,,16,150.73, -24,2,02.08.2015,,13,150.73, -24,2,03.08.2015,,0,150.73, -24,2,04.08.2015,,3,150.73, -24,2,05.08.2015,,22,150.73, -24,2,06.08.2015,,0,150.73, -24,2,07.08.2015,,0,150.73, -24,2,08.08.2015,,0,150.73, -24,2,09.08.2015,,0,150.73, -24,2,10.08.2015,,13,150.73, -24,2,11.08.2015,,3,150.73, -24,2,12.08.2015,,13,128.17, -24,2,13.08.2015,,13,128.17, -24,2,14.08.2015,,3,128.17, -24,2,15.08.2015,,3,128.17, -24,2,16.08.2015,,13,128.17, -24,2,17.08.2015,,3,128.17, -24,2,18.08.2015,1.0,123,128.17,100.3 -24,2,19.08.2015,1.0,148,128.17,100.3 -24,2,20.08.2015,1.0,126,128.17,100.3 -24,2,21.08.2015,1.0,110,128.17,100.3 -24,2,22.08.2015,1.0,110,128.17,100.3 -24,2,23.08.2015,1.0,148,128.17,100.3 -24,2,24.08.2015,1.0,94,128.17,100.3 -24,2,25.08.2015,,0,128.17, -24,2,26.08.2015,,0,128.17, -24,2,27.08.2015,,0,128.17, -24,2,28.08.2015,,19,128.17, -24,2,29.08.2015,,13,128.17, -24,2,30.08.2015,,9,128.17, -24,2,31.08.2015,,13,128.17, -24,2,01.09.2015,,22,128.17, -24,2,02.09.2015,,6,128.17, -24,2,03.09.2015,,19,128.17, -24,2,04.09.2015,,9,128.17, -24,2,05.09.2015,,6,128.17, -24,2,06.09.2015,,13,128.17, -24,2,07.09.2015,,9,128.17, -24,2,08.09.2015,,13,128.17, -24,2,09.09.2015,,13,128.17, -24,2,10.09.2015,,13,128.17, -24,2,11.09.2015,,0,162.96, -24,2,12.09.2015,,0,162.96, -24,2,13.09.2015,,19,162.96, -24,2,14.09.2015,,0,162.96, -24,2,15.09.2015,,9,162.96, -24,2,16.09.2015,,19,162.96, -24,2,17.09.2015,,6,162.96, -24,2,18.09.2015,,3,162.96, -24,2,19.09.2015,,0,162.96, -24,2,20.09.2015,,0,162.96, -24,2,21.09.2015,,0,162.96, -24,2,22.09.2015,,13,162.96, -24,2,23.09.2015,,25,162.96, -24,2,24.09.2015,,0,162.96, -24,2,25.09.2015,,0,162.96, -24,2,26.09.2015,,3,162.96, -24,2,27.09.2015,,16,162.96, -24,2,28.09.2015,,16,162.96, -24,2,29.09.2015,,3,162.96, -24,2,30.09.2015,,0,162.96, -24,2,01.10.2015,,0,162.96, -24,2,02.10.2015,,3,162.96, -24,2,03.10.2015,,0,162.96, -24,2,04.10.2015,,0,162.96, -24,2,05.10.2015,,6,162.96, -24,2,06.10.2015,1.0,97,143.39,122.05 -24,2,07.10.2015,1.0,94,143.39,122.05 -24,2,08.10.2015,1.0,47,143.39,122.05 -24,2,09.10.2015,1.0,72,143.39,122.05 -24,2,10.10.2015,1.0,107,143.39,122.05 -24,2,11.10.2015,1.0,116,143.39,122.05 -24,2,12.10.2015,1.0,22,143.39,122.05 -24,2,13.10.2015,1.0,94,143.93,122.05 -24,2,14.10.2015,1.0,94,143.93,122.05 -24,2,15.10.2015,1.0,85,143.93,122.05 -24,2,16.10.2015,1.0,75,143.93,122.05 -24,2,17.10.2015,1.0,66,162.96,122.05 -24,2,18.10.2015,1.0,60,162.96,122.05 -24,2,19.10.2015,,0,162.96, -24,2,20.10.2015,,6,162.96, -24,2,21.10.2015,,0,162.96, -24,2,22.10.2015,,9,162.96, -24,2,23.10.2015,,0,162.96, -24,2,24.10.2015,,6,162.96, -24,2,25.10.2015,,3,162.96, -24,2,26.10.2015,,0,162.96, -24,2,27.10.2015,,6,162.96, -24,2,28.10.2015,,3,162.96, -24,2,29.10.2015,,6,162.96, -24,2,30.10.2015,,13,162.96, -24,2,31.10.2015,,3,162.96, -24,2,01.11.2015,,0,162.96, -24,2,02.11.2015,,0,162.96, -24,2,03.11.2015,,6,162.96, -24,2,04.11.2015,,3,162.96, -24,2,05.11.2015,,0,162.96, -24,2,06.11.2015,,6,162.96, -24,2,07.11.2015,,0,162.96, -24,2,08.11.2015,,9,162.96, -24,2,09.11.2015,,9,162.96, -24,2,10.11.2015,,0,162.96, -24,2,11.11.2015,,3,138.36, -24,2,12.11.2015,,6,138.36, -24,2,13.11.2015,,0,138.36, -24,2,14.11.2015,,3,138.36, -24,2,15.11.2015,,38,138.36, -24,2,16.11.2015,,0,138.36, -24,2,17.11.2015,,16,138.36, -24,2,18.11.2015,,6,138.36, -24,2,19.11.2015,,6,138.36, -24,2,20.11.2015,,3,138.36, -24,2,21.11.2015,,0,138.36, -24,2,22.11.2015,,0,138.36, -24,2,23.11.2015,,0,138.36, -24,2,24.11.2015,,13,138.36, -24,2,25.11.2015,,0,138.36, -24,2,26.11.2015,,3,138.36, -24,2,27.11.2015,,0,138.36, -24,2,28.11.2015,,6,138.36, -24,2,29.11.2015,,0,138.36, -24,2,30.11.2015,,0,138.36, -24,2,01.12.2015,,13,162.96, -24,2,02.12.2015,,0,162.96, -24,2,03.12.2015,,3,138.5, -24,2,04.12.2015,,6,138.5, -24,2,05.12.2015,,3,138.5, -24,2,06.12.2015,,6,138.5, -24,2,07.12.2015,,3,138.5, -24,2,08.12.2015,,3,138.5, -24,2,09.12.2015,,6,138.5, -24,2,10.12.2015,,6,138.5, -24,2,11.12.2015,,0,142.57, -24,2,12.12.2015,,0,142.57, -24,2,13.12.2015,,0,142.57, -24,2,14.12.2015,,0,142.57, -24,2,15.12.2015,,0,142.57, -24,2,16.12.2015,,0,142.57, -24,2,17.12.2015,,0,142.57, -24,2,18.12.2015,,3,142.57, -24,2,19.12.2015,,3,142.57, -24,2,20.12.2015,,9,142.57, -24,2,21.12.2015,,13,142.57, -24,2,22.12.2015,1.0,119,142.57,103.02 -24,2,23.12.2015,1.0,157,142.57,103.02 -24,2,24.12.2015,1.0,126,142.57,103.02 -24,2,25.12.2015,1.0,157,142.57,103.02 -24,2,26.12.2015,1.0,19,142.57,103.02 -24,2,27.12.2015,1.0,69,142.57,103.02 -24,2,28.12.2015,1.0,69,142.57,103.02 -24,2,29.12.2015,,3,138.5, -24,2,30.12.2015,,3,138.5, -24,2,31.12.2015,,3,138.5, -24,2,01.01.2016,,3,138.5, -24,2,02.01.2016,,3,138.5, -24,2,03.01.2016,,3,138.5, -24,2,04.01.2016,,19,138.5, -24,2,05.01.2016,,19,138.5, -24,2,06.01.2016,,6,138.5, -24,2,07.01.2016,,19,138.5, -24,2,08.01.2016,,6,138.5, -24,2,09.01.2016,,9,138.5, -24,2,10.01.2016,,9,138.5, -24,2,11.01.2016,,6,138.5, -24,2,12.01.2016,,3,138.5, -24,2,13.01.2016,,9,138.5, -24,2,14.01.2016,,9,138.5, -24,2,15.01.2016,,9,138.5, -24,2,16.01.2016,,3,142.57, -24,2,17.01.2016,,9,142.57, -24,2,18.01.2016,,9,142.57, -24,2,19.01.2016,1.0,261,142.57,103.02 -24,2,20.01.2016,1.0,195,142.57,103.02 -24,2,21.01.2016,1.0,185,142.57,103.02 -24,2,22.01.2016,1.0,167,142.57,103.02 -24,2,23.01.2016,1.0,195,142.57,103.02 -24,2,24.01.2016,1.0,163,142.57,103.02 -24,2,25.01.2016,1.0,126,142.57,103.02 -24,2,26.01.2016,,0,142.57, -24,2,27.01.2016,,9,142.57, -24,2,28.01.2016,,9,142.57, -24,2,29.01.2016,,9,142.57, -24,2,30.01.2016,,9,142.57, -24,2,31.01.2016,,9,142.57, -24,2,01.02.2016,,19,142.57, -24,2,02.02.2016,,9,142.57, -24,2,03.02.2016,,9,142.57, -24,2,04.02.2016,,6,142.57, -24,2,05.02.2016,,28,142.57, -24,2,06.02.2016,,3,142.57, -24,2,07.02.2016,,9,142.57, -24,2,08.02.2016,,9,142.57, -24,2,09.02.2016,1.0,232,142.57,108.46 -24,2,10.02.2016,1.0,63,138.36,108.46 -24,2,11.02.2016,1.0,135,138.36,108.46 -24,2,12.02.2016,1.0,245,138.36,108.46 -24,2,13.02.2016,1.0,151,138.36,108.46 -24,2,14.02.2016,1.0,53,138.36,108.46 -24,2,15.02.2016,1.0,148,138.36,108.46 -24,2,16.02.2016,,6,138.36, -24,2,17.02.2016,,3,138.36, -24,2,18.02.2016,,6,138.36, -24,2,19.02.2016,,9,138.36, -24,2,20.02.2016,,0,138.36, -24,2,21.02.2016,,13,138.36, -24,2,22.02.2016,,9,138.36, -24,2,23.02.2016,,0,138.36, -24,2,24.02.2016,,3,138.36, -24,2,25.02.2016,,3,138.36, -24,2,26.02.2016,,0,138.36, -24,2,27.02.2016,,0,138.36, -24,2,28.02.2016,,3,138.36, -24,2,29.02.2016,,3,138.36, -24,2,01.03.2016,,3,138.36, -24,2,02.03.2016,,3,138.36, -24,2,03.03.2016,,3,138.36, -24,2,04.03.2016,,3,138.36, -24,2,05.03.2016,,0,138.36, -24,2,06.03.2016,,3,138.36, -24,2,07.03.2016,,6,138.36, -24,2,08.03.2016,,9,138.36, -24,2,09.03.2016,,3,138.36, -24,2,10.03.2016,,0,138.36, -24,2,11.03.2016,,0,138.36, -24,2,12.03.2016,,9,138.5, -24,2,13.03.2016,,9,138.5, -24,2,14.03.2016,,16,138.5, -24,2,15.03.2016,1.0,41,138.5,124.77 -24,2,16.03.2016,1.0,50,138.5,124.77 -24,2,17.03.2016,1.0,41,138.5,124.77 -24,2,18.03.2016,1.0,60,138.5,124.77 -24,2,19.03.2016,1.0,66,138.5,124.77 -24,2,20.03.2016,1.0,35,138.5,124.77 -24,2,21.03.2016,1.0,66,138.5,124.77 -24,2,22.03.2016,,13,138.36, -24,2,23.03.2016,,3,138.36, -24,2,24.03.2016,,13,138.36, -24,2,25.03.2016,,0,138.36, -24,2,26.03.2016,,6,138.36, -24,2,27.03.2016,,9,138.36, -24,2,28.03.2016,,3,138.36, -24,2,29.03.2016,,6,138.36, -24,2,30.03.2016,,0,138.36, -24,2,31.03.2016,,0,138.36, -24,2,01.04.2016,,6,138.36, -24,2,02.04.2016,,3,138.36, -24,2,03.04.2016,,3,138.36, -24,2,04.04.2016,,0,138.36, -24,2,05.04.2016,,0,138.36, -24,2,06.04.2016,,0,138.36, -24,2,07.04.2016,,0,138.36, -24,2,08.04.2016,,0,138.36, -24,2,09.04.2016,,0,138.5, -24,2,10.04.2016,,0,138.5, -24,2,11.04.2016,,3,138.5, -24,2,12.04.2016,,3,138.5, -24,2,13.04.2016,,0,138.5, -24,2,14.04.2016,,9,138.5, -24,2,15.04.2016,,3,138.5, -24,2,16.04.2016,,3,138.5, -24,2,17.04.2016,,3,138.5, -24,2,18.04.2016,,19,138.5, -24,2,19.04.2016,1.0,123,138.5,116.89 -24,2,20.04.2016,1.0,113,138.5,116.89 -24,2,21.04.2016,1.0,157,138.5,116.89 -24,2,22.04.2016,1.0,163,138.5,116.89 -24,2,23.04.2016,1.0,104,138.5,116.89 -24,2,24.04.2016,1.0,107,138.5,116.89 -24,2,25.04.2016,1.0,88,138.5,116.89 -24,2,26.04.2016,,3,138.5, -24,2,27.04.2016,,3,138.5, -24,2,28.04.2016,,6,138.5, -24,2,29.04.2016,,13,138.5, -24,2,30.04.2016,,13,138.5, -24,2,01.05.2016,,6,138.5, -24,2,02.05.2016,,13,138.5, -24,2,03.05.2016,1.0,210,138.5,106.01 -24,2,04.05.2016,1.0,248,138.5,106.01 -24,2,05.05.2016,1.0,163,138.5,106.01 -24,2,06.05.2016,1.0,204,138.5,106.01 -24,2,07.05.2016,1.0,138,138.5,106.01 -24,2,08.05.2016,1.0,201,138.5,106.01 -24,2,09.05.2016,1.0,38,138.5,106.01 -24,2,10.05.2016,,6,142.57, -24,2,11.05.2016,,6,142.57, -24,2,12.05.2016,,6,142.57, -24,2,13.05.2016,,9,142.57, -24,2,14.05.2016,,6,142.57, -24,2,15.05.2016,,6,142.57, -24,2,16.05.2016,,6,142.57, -24,2,17.05.2016,,6,142.57, -24,2,18.05.2016,,3,142.57, -24,2,19.05.2016,,31,142.57, -24,2,20.05.2016,,9,142.57, -24,2,21.05.2016,,9,142.57, -24,2,22.05.2016,,9,142.57, -25,2,01.01.2015,,0,134.72, -25,2,02.01.2015,,0,134.72, -25,2,03.01.2015,,0,134.72, -25,2,04.01.2015,,0,134.72, -25,2,05.01.2015,,0,134.72, -25,2,06.01.2015,,0,134.72, -25,2,07.01.2015,,0,134.72, -25,2,08.01.2015,,0,134.72, -25,2,09.01.2015,,0,134.72, -25,2,10.01.2015,,0,134.72, -25,2,11.01.2015,,0,134.72, -25,2,12.01.2015,,0,134.72, -25,2,13.01.2015,,0,134.72, -25,2,14.01.2015,,0,134.72, -25,2,15.01.2015,,0,134.72, -25,2,16.01.2015,,0,134.72, -25,2,17.01.2015,,0,134.72, -25,2,18.01.2015,,0,134.72, -25,2,19.01.2015,,0,134.72, -25,2,20.01.2015,,0,134.72, -25,2,21.01.2015,,0,134.72, -25,2,22.01.2015,,0,134.72, -25,2,23.01.2015,,0,134.72, -25,2,24.01.2015,,0,134.72, -25,2,25.01.2015,,0,134.72, -25,2,26.01.2015,,0,134.72, -25,2,27.01.2015,,0,134.72, -25,2,28.01.2015,,0,134.72, -25,2,29.01.2015,,0,134.72, -25,2,30.01.2015,,0,134.72, -25,2,31.01.2015,,0,134.72, 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-25,2,02.10.2015,,0,162.96, -25,2,03.10.2015,,0,162.96, -25,2,04.10.2015,,0,162.96, -25,2,05.10.2015,,0,162.96, -25,2,06.10.2015,1.0,50,143.39,122.05 -25,2,07.10.2015,1.0,47,143.39,122.05 -25,2,08.10.2015,1.0,82,143.39,122.05 -25,2,09.10.2015,1.0,19,143.39,122.05 -25,2,10.10.2015,1.0,19,143.39,122.05 -25,2,11.10.2015,1.0,44,143.39,122.05 -25,2,12.10.2015,1.0,25,143.39,122.05 -25,2,13.10.2015,1.0,25,143.93,122.05 -25,2,14.10.2015,1.0,53,143.93,122.05 -25,2,15.10.2015,1.0,50,143.93,122.05 -25,2,16.10.2015,1.0,50,143.93,122.05 -25,2,17.10.2015,1.0,50,143.93,122.05 -25,2,18.10.2015,1.0,50,143.93,122.05 -25,2,19.10.2015,,13,143.93, -25,2,20.10.2015,,13,143.93, -25,2,21.10.2015,,13,143.93, -25,2,22.10.2015,,13,143.93, -25,2,23.10.2015,,13,143.93, -25,2,24.10.2015,,13,143.93, -25,2,25.10.2015,,13,143.93, -25,2,26.10.2015,,13,143.93, -25,2,27.10.2015,,13,143.93, -25,2,28.10.2015,,13,143.93, -25,2,29.10.2015,,13,143.93, -25,2,30.10.2015,,13,143.93, -25,2,31.10.2015,,13,143.93, -25,2,01.11.2015,,0,143.93, -25,2,02.11.2015,,13,143.93, -25,2,03.11.2015,,13,143.93, -25,2,04.11.2015,,13,143.93, -25,2,05.11.2015,,13,143.93, -25,2,06.11.2015,,13,143.93, -25,2,07.11.2015,,13,143.93, -25,2,08.11.2015,,13,143.93, -25,2,09.11.2015,,13,143.93, -25,2,10.11.2015,,13,143.93, -25,2,11.11.2015,,13,143.93, -25,2,12.11.2015,,0,143.93, -25,2,13.11.2015,,0,143.93, -25,2,14.11.2015,,0,143.93, -25,2,15.11.2015,,0,143.93, -25,2,16.11.2015,,0,143.93, -25,2,17.11.2015,,0,143.93, -25,2,18.11.2015,,0,143.93, -25,2,19.11.2015,,0,143.93, -25,2,20.11.2015,,0,143.93, -25,2,21.11.2015,,0,143.93, -25,2,22.11.2015,,0,143.93, -25,2,23.11.2015,,0,143.93, -25,2,24.11.2015,,0,143.93, -25,2,25.11.2015,,0,143.93, -25,2,26.11.2015,,0,143.93, -25,2,27.11.2015,,0,143.93, -25,2,28.11.2015,,0,143.93, -25,2,29.11.2015,,0,143.93, -25,2,30.11.2015,,0,143.93, -25,2,01.12.2015,,0,143.93, -25,2,02.12.2015,,0,143.93, -25,2,03.12.2015,,0,143.93, -25,2,04.12.2015,,0,143.93, -25,2,05.12.2015,,0,143.93, -25,2,06.12.2015,,0,143.93, -25,2,07.12.2015,,0,143.93, -25,2,08.12.2015,,0,143.93, -25,2,09.12.2015,,0,138.5, -25,2,10.12.2015,,3,138.5, -25,2,11.12.2015,,13,142.57, -25,2,12.12.2015,,3,142.57, -25,2,13.12.2015,,0,142.57, -25,2,14.12.2015,,0,142.57, -25,2,15.12.2015,,13,142.57, -25,2,16.12.2015,,6,142.57, -25,2,17.12.2015,,6,142.57, -25,2,18.12.2015,,9,142.57, -25,2,19.12.2015,,3,142.57, -25,2,20.12.2015,,9,142.57, -25,2,21.12.2015,,3,142.57, -25,2,22.12.2015,1.0,116,142.57,103.02 -25,2,23.12.2015,1.0,126,142.57,103.02 -25,2,24.12.2015,1.0,85,142.57,103.02 -25,2,25.12.2015,1.0,101,142.57,103.02 -25,2,26.12.2015,1.0,123,142.57,103.02 -25,2,27.12.2015,1.0,41,142.57,103.02 -25,2,28.12.2015,1.0,94,142.57,103.02 -25,2,29.12.2015,,6,138.5, -25,2,30.12.2015,,6,138.5, -25,2,31.12.2015,,6,138.5, -25,2,01.01.2016,,6,138.5, -25,2,02.01.2016,,6,138.5, -25,2,03.01.2016,,6,138.5, -25,2,04.01.2016,,19,138.5, -25,2,05.01.2016,,6,138.5, -25,2,06.01.2016,,9,138.5, -25,2,07.01.2016,,3,138.5, -25,2,08.01.2016,,0,138.5, -25,2,09.01.2016,,6,138.5, -25,2,10.01.2016,,13,138.5, -25,2,11.01.2016,,6,138.5, -25,2,12.01.2016,,3,138.5, -25,2,13.01.2016,,22,138.5, -25,2,14.01.2016,,6,138.5, -25,2,15.01.2016,,6,138.5, -25,2,16.01.2016,,3,142.57, -25,2,17.01.2016,,9,142.57, -25,2,18.01.2016,,22,142.57, -25,2,19.01.2016,1.0,163,142.57,103.02 -25,2,20.01.2016,1.0,82,142.57,103.02 -25,2,21.01.2016,1.0,129,142.57,103.02 -25,2,22.01.2016,1.0,101,142.57,103.02 -25,2,23.01.2016,1.0,104,142.57,103.02 -25,2,24.01.2016,1.0,123,142.57,103.02 -25,2,25.01.2016,1.0,160,142.57,103.02 -25,2,26.01.2016,,9,142.57, -25,2,27.01.2016,,16,142.57, -25,2,28.01.2016,,22,142.57, -25,2,29.01.2016,,6,142.57, -25,2,30.01.2016,,0,142.57, -25,2,31.01.2016,,3,142.57, -25,2,01.02.2016,,9,142.57, -25,2,02.02.2016,,13,142.57, -25,2,03.02.2016,,6,142.57, -25,2,04.02.2016,,19,142.57, -25,2,05.02.2016,,6,142.57, -25,2,06.02.2016,,19,142.57, -25,2,07.02.2016,,6,142.57, -25,2,08.02.2016,,9,142.57, -25,2,09.02.2016,1.0,167,142.57,108.46 -25,2,10.02.2016,1.0,94,138.36,108.46 -25,2,11.02.2016,1.0,116,138.36,108.46 -25,2,12.02.2016,1.0,119,138.36,108.46 -25,2,13.02.2016,1.0,66,138.36,108.46 -25,2,14.02.2016,1.0,85,138.36,108.46 -25,2,15.02.2016,1.0,123,138.36,108.46 -25,2,16.02.2016,,9,138.36, -25,2,17.02.2016,,9,138.36, -25,2,18.02.2016,,9,138.36, -25,2,19.02.2016,,9,138.36, -25,2,20.02.2016,,9,138.36, -25,2,21.02.2016,,9,138.36, -25,2,22.02.2016,,9,138.36, -25,2,23.02.2016,,9,138.36, -25,2,24.02.2016,,9,138.36, -25,2,25.02.2016,,9,138.36, -25,2,26.02.2016,,9,138.36, -25,2,27.02.2016,,9,138.36, -25,2,28.02.2016,,9,138.36, -25,2,29.02.2016,,9,138.36, -25,2,01.03.2016,,0,138.36, -25,2,02.03.2016,,0,138.36, -25,2,03.03.2016,,0,138.36, -25,2,04.03.2016,,0,138.36, -25,2,05.03.2016,,0,138.36, -25,2,06.03.2016,,0,138.36, -25,2,07.03.2016,,0,138.36, -25,2,08.03.2016,,0,138.36, -25,2,09.03.2016,,13,138.36, -25,2,10.03.2016,,6,138.36, -25,2,11.03.2016,,0,138.36, -25,2,12.03.2016,,0,138.5, -25,2,13.03.2016,,3,138.5, -25,2,14.03.2016,,0,138.5, -25,2,15.03.2016,1.0,107,138.5,124.77 -25,2,16.03.2016,1.0,25,138.5,124.77 -25,2,17.03.2016,1.0,41,138.5,124.77 -25,2,18.03.2016,1.0,47,138.5,124.77 -25,2,19.03.2016,1.0,28,138.5,124.77 -25,2,20.03.2016,1.0,66,138.5,124.77 -25,2,21.03.2016,1.0,31,138.5,124.77 -25,2,22.03.2016,,6,138.36, -25,2,23.03.2016,,0,138.36, -25,2,24.03.2016,,0,138.36, -25,2,25.03.2016,,0,138.36, -25,2,26.03.2016,,0,138.36, -25,2,27.03.2016,,0,138.36, -25,2,28.03.2016,,0,138.36, -25,2,29.03.2016,,0,138.36, -25,2,30.03.2016,,0,138.36, -25,2,31.03.2016,,0,138.36, -25,2,01.04.2016,,0,138.36, -25,2,02.04.2016,,0,138.36, -25,2,03.04.2016,,0,138.36, -25,2,04.04.2016,,0,138.36, -25,2,05.04.2016,,0,138.36, -25,2,06.04.2016,,0,138.36, -25,2,07.04.2016,,0,138.36, -25,2,08.04.2016,,3,138.36, -25,2,09.04.2016,,16,138.5, -25,2,10.04.2016,,3,138.5, -25,2,11.04.2016,,3,138.5, -25,2,12.04.2016,,0,138.5, -25,2,13.04.2016,,13,138.5, -25,2,14.04.2016,,0,138.5, -25,2,15.04.2016,,3,138.5, -25,2,16.04.2016,,0,138.5, -25,2,17.04.2016,,9,138.5, -25,2,18.04.2016,,0,138.5, -25,2,19.04.2016,1.0,63,138.5,116.89 -25,2,20.04.2016,1.0,66,138.5,116.89 -25,2,21.04.2016,1.0,41,138.5,116.89 -25,2,22.04.2016,1.0,50,138.5,116.89 -25,2,23.04.2016,1.0,60,138.5,116.89 -25,2,24.04.2016,1.0,44,138.5,116.89 -25,2,25.04.2016,1.0,57,138.5,116.89 -25,2,26.04.2016,1.0,31,138.5,114.17 -25,2,27.04.2016,1.0,31,138.5,114.17 -25,2,28.04.2016,,6,138.5, -25,2,29.04.2016,,0,138.5, -25,2,30.04.2016,,0,138.5, -25,2,01.05.2016,,6,138.5, -25,2,02.05.2016,,0,138.5, -25,2,03.05.2016,1.0,97,138.5,106.01 -25,2,04.05.2016,1.0,63,138.5,106.01 -25,2,05.05.2016,1.0,129,138.5,106.01 -25,2,06.05.2016,1.0,75,138.5,106.01 -25,2,07.05.2016,1.0,41,138.5,106.01 -25,2,08.05.2016,1.0,110,138.5,106.01 -25,2,09.05.2016,1.0,72,138.5,106.01 -25,2,10.05.2016,,22,138.5, -25,2,11.05.2016,,28,138.5, -25,2,12.05.2016,,22,138.5, -25,2,13.05.2016,,9,138.5, -25,2,14.05.2016,,9,138.5, -25,2,15.05.2016,,9,138.5, -25,2,16.05.2016,,9,138.5, -25,2,17.05.2016,,9,138.5, -25,2,18.05.2016,,9,138.5, -25,2,19.05.2016,,9,138.5, -25,2,20.05.2016,,9,138.5, -25,2,21.05.2016,,9,138.5, -25,2,22.05.2016,,9,138.5, -26,2,01.01.2015,,0,135.78, -26,2,02.01.2015,,3,135.78, -26,2,03.01.2015,,6,135.78, -26,2,04.01.2015,,0,135.78, -26,2,05.01.2015,,3,135.78, -26,2,06.01.2015,,6,135.78, -26,2,07.01.2015,,0,135.78, -26,2,08.01.2015,,0,135.78, -26,2,09.01.2015,,0,135.78, -26,2,10.01.2015,,0,135.78, -26,2,11.01.2015,,0,135.78, -26,2,12.01.2015,,3,135.78, -26,2,13.01.2015,,3,135.78, -26,2,14.01.2015,,3,135.78, -26,2,15.01.2015,,3,135.78, -26,2,16.01.2015,,3,135.78, -26,2,17.01.2015,,3,135.78, -26,2,18.01.2015,,3,135.78, -26,2,19.01.2015,,3,135.78, -26,2,20.01.2015,,3,135.78, -26,2,21.01.2015,,3,135.78, -26,2,22.01.2015,,3,135.78, -26,2,23.01.2015,,3,135.78, -26,2,24.01.2015,,3,135.78, -26,2,25.01.2015,,3,135.78, -26,2,26.01.2015,,3,135.78, -26,2,27.01.2015,,3,135.78, -26,2,28.01.2015,,3,135.78, -26,2,29.01.2015,,3,135.78, -26,2,30.01.2015,,3,135.78, -26,2,31.01.2015,,3,135.78, -26,2,01.02.2015,,3,135.78, -26,2,02.02.2015,,6,135.78, -26,2,03.02.2015,,3,135.78, -26,2,04.02.2015,,16,135.78, -26,2,05.02.2015,,0,135.78, -26,2,06.02.2015,,0,135.78, -26,2,07.02.2015,,0,135.78, -26,2,08.02.2015,,6,135.78, -26,2,09.02.2015,,6,135.78, -26,2,10.02.2015,,0,135.78, -26,2,11.02.2015,,0,135.78, -26,2,12.02.2015,,3,135.78, -26,2,13.02.2015,,0,135.78, -26,2,14.02.2015,,0,135.78, -26,2,15.02.2015,,19,135.78, -26,2,16.02.2015,,0,135.78, -26,2,17.02.2015,,0,135.78, -26,2,18.02.2015,,0,135.78, -26,2,19.02.2015,,0,135.78, -26,2,20.02.2015,,0,135.78, -26,2,21.02.2015,,0,135.78, -26,2,22.02.2015,,0,135.78, -26,2,23.02.2015,,0,135.78, -26,2,24.02.2015,,0,135.78, -26,2,25.02.2015,,0,135.78, -26,2,26.02.2015,,0,135.78, -26,2,27.02.2015,,3,135.78, -26,2,28.02.2015,,0,135.78, -26,2,01.03.2015,,0,135.78, -26,2,02.03.2015,,3,135.78, -26,2,03.03.2015,,9,135.78, -26,2,04.03.2015,,3,135.78, -26,2,05.03.2015,,0,135.78, -26,2,06.03.2015,,3,135.78, -26,2,07.03.2015,,0,135.78, -26,2,08.03.2015,,0,146.38, -26,2,09.03.2015,,0,146.38, -26,2,10.03.2015,,0,146.38, -26,2,11.03.2015,,0,146.38, -26,2,12.03.2015,,0,146.38, -26,2,13.03.2015,,0,146.38, -26,2,14.03.2015,,0,146.38, -26,2,15.03.2015,,0,146.38, -26,2,16.03.2015,,0,146.38, -26,2,17.03.2015,,13,146.38, -26,2,18.03.2015,,0,146.38, -26,2,19.03.2015,,0,146.38, -26,2,20.03.2015,,0,146.38, -26,2,21.03.2015,,0,146.38, -26,2,22.03.2015,,0,146.38, -26,2,23.03.2015,,0,146.38, -26,2,24.03.2015,,0,146.38, -26,2,25.03.2015,,0,146.38, -26,2,26.03.2015,,0,146.38, -26,2,27.03.2015,,0,146.38, -26,2,28.03.2015,,0,146.38, -26,2,29.03.2015,,0,146.38, -26,2,30.03.2015,,0,146.38, -26,2,31.03.2015,,0,146.38, -26,2,01.04.2015,,0,146.38, -26,2,02.04.2015,,0,146.38, -26,2,03.04.2015,,3,146.38, -26,2,04.04.2015,,0,146.38, -26,2,05.04.2015,,3,146.38, -26,2,06.04.2015,,3,146.38, -26,2,07.04.2015,,0,146.38, -26,2,08.04.2015,,0,146.38, -26,2,09.04.2015,,0,146.38, -26,2,10.04.2015,,0,146.38, -26,2,11.04.2015,,3,146.38, -26,2,12.04.2015,,0,146.38, -26,2,13.04.2015,,0,146.38, -26,2,14.04.2015,,0,146.38, -26,2,15.04.2015,,0,146.38, -26,2,16.04.2015,,0,146.38, -26,2,17.04.2015,,0,146.38, -26,2,18.04.2015,,0,146.38, -26,2,19.04.2015,,0,146.38, -26,2,20.04.2015,,0,146.38, -26,2,21.04.2015,,0,146.38, -26,2,22.04.2015,,0,146.38, -26,2,23.04.2015,,13,146.38, -26,2,24.04.2015,,0,146.38, -26,2,25.04.2015,,0,146.38, -26,2,26.04.2015,,0,146.38, -26,2,27.04.2015,,0,146.38, -26,2,28.04.2015,,0,146.38, -26,2,29.04.2015,,0,146.38, -26,2,30.04.2015,,0,150.73, -26,2,01.05.2015,,0,150.73, -26,2,02.05.2015,,0,150.73, -26,2,03.05.2015,,0,150.73, -26,2,04.05.2015,,0,150.73, -26,2,05.05.2015,,0,150.73, -26,2,06.05.2015,,0,150.73, -26,2,07.05.2015,,0,150.73, -26,2,08.05.2015,,0,150.73, -26,2,09.05.2015,,0,150.73, -26,2,10.05.2015,,0,150.73, -26,2,11.05.2015,,0,150.73, -26,2,12.05.2015,,0,150.73, -26,2,13.05.2015,,0,150.73, -26,2,14.05.2015,,0,150.73, -26,2,15.05.2015,,0,150.73, -26,2,16.05.2015,,6,150.73, -26,2,17.05.2015,,0,150.73, -26,2,18.05.2015,,0,150.73, -26,2,19.05.2015,,0,150.73, -26,2,20.05.2015,,0,150.73, -26,2,21.05.2015,,0,150.73, -26,2,22.05.2015,,0,150.73, -26,2,23.05.2015,,0,150.73, -26,2,24.05.2015,,0,150.73, -26,2,25.05.2015,,0,150.73, -26,2,26.05.2015,,0,150.73, -26,2,27.05.2015,,0,150.73, -26,2,28.05.2015,,0,150.73, -26,2,29.05.2015,,0,150.73, -26,2,30.05.2015,,0,150.73, -26,2,31.05.2015,,0,150.73, -26,2,01.06.2015,,0,150.73, -26,2,02.06.2015,,0,150.73, -26,2,03.06.2015,,0,150.73, -26,2,04.06.2015,,0,150.73, -26,2,05.06.2015,,6,150.73, -26,2,06.06.2015,,0,150.73, -26,2,07.06.2015,,3,150.73, -26,2,08.06.2015,,0,150.73, -26,2,09.06.2015,,0,150.73, -26,2,10.06.2015,,0,150.73, -26,2,11.06.2015,,0,150.73, -26,2,12.06.2015,,0,150.73, -26,2,13.06.2015,,0,150.73, -26,2,14.06.2015,,6,150.73, -26,2,15.06.2015,,0,150.73, -26,2,16.06.2015,,0,150.73, -26,2,17.06.2015,,0,150.73, -26,2,18.06.2015,,0,150.73, -26,2,19.06.2015,,0,150.73, -26,2,20.06.2015,,0,150.73, -26,2,21.06.2015,,0,150.73, -26,2,22.06.2015,,0,150.73, -26,2,23.06.2015,,0,150.73, -26,2,24.06.2015,,3,150.73, -26,2,25.06.2015,,0,150.73, -26,2,26.06.2015,,3,150.73, -26,2,27.06.2015,,0,150.73, -26,2,28.06.2015,,3,150.73, -26,2,29.06.2015,,0,150.73, -26,2,30.06.2015,,6,150.73, -26,2,01.07.2015,,0,150.73, -26,2,02.07.2015,,0,150.73, -26,2,03.07.2015,,0,150.73, -26,2,04.07.2015,,0,150.73, -26,2,05.07.2015,,0,150.73, -26,2,06.07.2015,,3,150.73, -26,2,07.07.2015,,3,150.73, -26,2,08.07.2015,,0,150.73, -26,2,09.07.2015,,0,150.73, -26,2,10.07.2015,,0,150.73, -26,2,11.07.2015,,0,150.73, -26,2,12.07.2015,,0,150.73, -26,2,13.07.2015,,0,150.73, -26,2,14.07.2015,,0,150.73, -26,2,15.07.2015,,0,150.73, -26,2,16.07.2015,,0,150.73, -26,2,17.07.2015,,6,150.73, -26,2,18.07.2015,,0,150.73, -26,2,19.07.2015,,0,150.73, -26,2,20.07.2015,,9,150.73, -26,2,21.07.2015,,3,150.73, -26,2,22.07.2015,,0,150.73, -26,2,23.07.2015,,13,150.73, -26,2,24.07.2015,,16,150.73, -26,2,25.07.2015,,6,150.73, -26,2,26.07.2015,,9,150.73, -26,2,27.07.2015,,6,150.73, -26,2,28.07.2015,,13,150.73, -26,2,29.07.2015,,6,150.73, -26,2,30.07.2015,,19,150.73, -26,2,31.07.2015,,3,150.73, -26,2,01.08.2015,,6,150.73, -26,2,02.08.2015,,9,150.73, -26,2,03.08.2015,,0,150.73, -26,2,04.08.2015,,0,150.73, -26,2,05.08.2015,,0,150.73, -26,2,06.08.2015,,0,150.73, -26,2,07.08.2015,,0,150.73, -26,2,08.08.2015,,0,150.73, -26,2,09.08.2015,,0,150.73, -26,2,10.08.2015,,0,150.73, -26,2,11.08.2015,,0,150.73, -26,2,12.08.2015,,6,128.17, -26,2,13.08.2015,,0,128.17, -26,2,14.08.2015,,3,128.17, -26,2,15.08.2015,,9,128.17, -26,2,16.08.2015,,0,128.17, -26,2,17.08.2015,,3,128.17, -26,2,18.08.2015,1.0,75,128.17,100.3 -26,2,19.08.2015,1.0,145,128.17,100.3 -26,2,20.08.2015,1.0,101,128.17,100.3 -26,2,21.08.2015,1.0,141,128.17,100.3 -26,2,22.08.2015,1.0,148,128.17,100.3 -26,2,23.08.2015,1.0,57,128.17,100.3 -26,2,24.08.2015,1.0,16,128.17,100.3 -26,2,25.08.2015,,3,128.17, -26,2,26.08.2015,,6,128.17, -26,2,27.08.2015,,9,128.17, -26,2,28.08.2015,,16,128.17, -26,2,29.08.2015,,6,128.17, -26,2,30.08.2015,,0,128.17, -26,2,31.08.2015,,6,128.17, -26,2,01.09.2015,,0,128.17, -26,2,02.09.2015,,0,128.17, -26,2,03.09.2015,,3,128.17, -26,2,04.09.2015,,3,128.17, -26,2,05.09.2015,,0,128.17, -26,2,06.09.2015,,13,128.17, -26,2,07.09.2015,,0,128.17, -26,2,08.09.2015,,0,128.17, -26,2,09.09.2015,,3,128.17, -26,2,10.09.2015,,0,128.17, -26,2,11.09.2015,,3,128.17, -26,2,12.09.2015,,0,128.17, -26,2,13.09.2015,,0,128.17, -26,2,14.09.2015,,0,128.17, -26,2,15.09.2015,,0,128.17, -26,2,16.09.2015,,0,128.17, -26,2,17.09.2015,,3,128.17, -26,2,18.09.2015,,6,128.17, -26,2,19.09.2015,,9,128.17, -26,2,20.09.2015,,19,128.17, -26,2,21.09.2015,,0,128.17, -26,2,22.09.2015,,0,128.17, -26,2,23.09.2015,,0,128.17, -26,2,24.09.2015,,0,128.17, -26,2,25.09.2015,,3,128.17, -26,2,26.09.2015,,3,128.17, -26,2,27.09.2015,,0,128.17, -26,2,28.09.2015,,3,128.17, -26,2,29.09.2015,,3,162.96, -26,2,30.09.2015,,3,162.96, -26,2,01.10.2015,,3,162.96, -26,2,02.10.2015,,0,162.96, -26,2,03.10.2015,,6,162.96, -26,2,04.10.2015,,0,162.96, -26,2,05.10.2015,,0,162.96, -26,2,06.10.2015,1.0,69,143.39,122.05 -26,2,07.10.2015,1.0,66,143.39,122.05 -26,2,08.10.2015,1.0,69,143.39,122.05 -26,2,09.10.2015,1.0,107,143.39,122.05 -26,2,10.10.2015,1.0,66,143.39,122.05 -26,2,11.10.2015,1.0,47,143.39,122.05 -26,2,12.10.2015,1.0,79,143.39,122.05 -26,2,13.10.2015,1.0,72,143.93,122.05 -26,2,14.10.2015,1.0,63,143.93,122.05 -26,2,15.10.2015,1.0,57,143.93,122.05 -26,2,16.10.2015,1.0,16,143.93,122.05 -26,2,17.10.2015,1.0,19,162.96,122.05 -26,2,18.10.2015,1.0,9,162.96,122.05 -26,2,19.10.2015,,6,162.96, -26,2,20.10.2015,,6,162.96, -26,2,21.10.2015,,9,162.96, -26,2,22.10.2015,,3,162.96, -26,2,23.10.2015,,3,162.96, -26,2,24.10.2015,,3,162.96, -26,2,25.10.2015,,0,162.96, -26,2,26.10.2015,,0,162.96, -26,2,27.10.2015,,3,162.96, -26,2,28.10.2015,,0,162.96, -26,2,29.10.2015,,0,162.96, -26,2,30.10.2015,,3,162.96, -26,2,31.10.2015,,0,162.96, -26,2,01.11.2015,,0,162.96, -26,2,02.11.2015,,0,162.96, -26,2,03.11.2015,,3,162.96, -26,2,04.11.2015,,0,162.96, -26,2,05.11.2015,,0,162.96, -26,2,06.11.2015,,0,162.96, -26,2,07.11.2015,,0,162.96, -26,2,08.11.2015,,0,162.96, -26,2,09.11.2015,,0,162.96, -26,2,10.11.2015,,3,162.96, -26,2,11.11.2015,,0,138.36, -26,2,12.11.2015,,3,138.36, -26,2,13.11.2015,,9,138.36, -26,2,14.11.2015,,3,138.36, -26,2,15.11.2015,,6,138.36, -26,2,16.11.2015,,0,138.36, -26,2,17.11.2015,,0,138.36, -26,2,18.11.2015,,0,138.36, -26,2,19.11.2015,,0,138.36, -26,2,20.11.2015,,0,138.36, -26,2,21.11.2015,,0,138.36, -26,2,22.11.2015,,0,138.36, -26,2,23.11.2015,,0,138.36, -26,2,24.11.2015,,9,138.36, -26,2,25.11.2015,,0,138.36, -26,2,26.11.2015,,0,138.36, -26,2,27.11.2015,,0,138.36, -26,2,28.11.2015,,3,138.36, -26,2,29.11.2015,,0,138.36, -26,2,30.11.2015,,9,138.36, -26,2,01.12.2015,,0,162.96, -26,2,02.12.2015,,0,162.96, -26,2,03.12.2015,,0,138.5, -26,2,04.12.2015,,9,138.5, -26,2,05.12.2015,,0,138.5, -26,2,06.12.2015,,3,138.5, -26,2,07.12.2015,,0,138.5, -26,2,08.12.2015,,3,138.5, -26,2,09.12.2015,,0,138.5, -26,2,10.12.2015,,9,138.5, -26,2,11.12.2015,,0,142.57, -26,2,12.12.2015,,0,142.57, -26,2,13.12.2015,,0,142.57, -26,2,14.12.2015,,0,142.57, -26,2,15.12.2015,,0,142.57, -26,2,16.12.2015,,0,142.57, -26,2,17.12.2015,,0,142.57, -26,2,18.12.2015,,0,142.57, -26,2,19.12.2015,,0,142.57, -26,2,20.12.2015,,0,142.57, -26,2,21.12.2015,,13,142.57, -26,2,22.12.2015,1.0,141,142.57,103.02 -26,2,23.12.2015,1.0,66,142.57,103.02 -26,2,24.12.2015,1.0,113,142.57,103.02 -26,2,25.12.2015,1.0,79,142.57,103.02 -26,2,26.12.2015,1.0,53,142.57,103.02 -26,2,27.12.2015,1.0,6,142.57,103.02 -26,2,28.12.2015,1.0,35,142.57,103.02 -26,2,29.12.2015,,0,138.5, -26,2,30.12.2015,,0,138.5, -26,2,31.12.2015,,0,138.5, -26,2,01.01.2016,,0,138.5, -26,2,02.01.2016,,0,138.5, -26,2,03.01.2016,,0,138.5, -26,2,04.01.2016,,19,138.5, -26,2,05.01.2016,,0,138.5, -26,2,06.01.2016,,0,138.5, -26,2,07.01.2016,,0,138.5, -26,2,08.01.2016,,0,138.5, -26,2,09.01.2016,,0,138.5, -26,2,10.01.2016,,0,138.5, -26,2,11.01.2016,,0,138.5, -26,2,12.01.2016,,0,138.5, -26,2,13.01.2016,,0,138.5, -26,2,14.01.2016,,0,138.5, -26,2,15.01.2016,,0,138.5, -26,2,16.01.2016,,0,142.57, -26,2,17.01.2016,,0,142.57, -26,2,18.01.2016,,22,142.57, -26,2,19.01.2016,1.0,151,142.57,103.02 -26,2,20.01.2016,1.0,119,142.57,103.02 -26,2,21.01.2016,1.0,94,142.57,103.02 -26,2,22.01.2016,1.0,173,142.57,103.02 -26,2,23.01.2016,1.0,69,142.57,103.02 -26,2,24.01.2016,1.0,110,142.57,103.02 -26,2,25.01.2016,1.0,9,142.57,103.02 -26,2,26.01.2016,,0,142.57, -26,2,27.01.2016,,9,142.57, -26,2,28.01.2016,,13,142.57, -26,2,29.01.2016,,9,142.57, -26,2,30.01.2016,,3,142.57, -26,2,31.01.2016,,3,142.57, -26,2,01.02.2016,,6,142.57, -26,2,02.02.2016,,6,142.57, -26,2,03.02.2016,,6,142.57, -26,2,04.02.2016,,3,142.57, -26,2,05.02.2016,,3,142.57, -26,2,06.02.2016,,6,142.57, -26,2,07.02.2016,,0,142.57, -26,2,08.02.2016,,13,142.57, -26,2,09.02.2016,1.0,129,142.57,108.46 -26,2,10.02.2016,1.0,107,138.36,108.46 -26,2,11.02.2016,1.0,104,138.36,108.46 -26,2,12.02.2016,1.0,151,138.36,108.46 -26,2,13.02.2016,1.0,113,138.36,108.46 -26,2,14.02.2016,1.0,94,138.36,108.46 -26,2,15.02.2016,1.0,94,138.36,108.46 -26,2,16.02.2016,,0,138.36, -26,2,17.02.2016,,0,138.36, -26,2,18.02.2016,,0,138.36, -26,2,19.02.2016,,3,138.36, -26,2,20.02.2016,,0,138.36, -26,2,21.02.2016,,0,138.36, -26,2,22.02.2016,,0,138.36, -26,2,23.02.2016,,0,138.36, -26,2,24.02.2016,,0,138.36, -26,2,25.02.2016,,0,138.36, -26,2,26.02.2016,,6,138.36, -26,2,27.02.2016,,0,138.36, -26,2,28.02.2016,,0,138.36, -26,2,29.02.2016,,0,138.36, -26,2,01.03.2016,,3,138.36, -26,2,02.03.2016,,3,138.36, -26,2,03.03.2016,,0,138.36, -26,2,04.03.2016,,0,138.36, -26,2,05.03.2016,,0,138.36, -26,2,06.03.2016,,0,138.36, -26,2,07.03.2016,,0,138.36, -26,2,08.03.2016,,0,138.36, -26,2,09.03.2016,,0,138.36, -26,2,10.03.2016,,0,138.36, -26,2,11.03.2016,,0,138.36, -26,2,12.03.2016,,0,138.5, -26,2,13.03.2016,,0,138.5, -26,2,14.03.2016,,25,138.5, -26,2,15.03.2016,1.0,47,138.5,124.77 -26,2,16.03.2016,1.0,53,138.5,124.77 -26,2,17.03.2016,1.0,88,138.5,124.77 -26,2,18.03.2016,1.0,22,138.5,124.77 -26,2,19.03.2016,1.0,35,138.5,124.77 -26,2,20.03.2016,1.0,44,138.5,124.77 -26,2,21.03.2016,1.0,41,138.5,124.77 -26,2,22.03.2016,,16,138.36, -26,2,23.03.2016,,0,138.36, -26,2,24.03.2016,,3,138.36, -26,2,25.03.2016,,0,138.36, -26,2,26.03.2016,,0,138.36, -26,2,27.03.2016,,3,138.36, -26,2,28.03.2016,,0,138.36, -26,2,29.03.2016,,6,138.36, -26,2,30.03.2016,,0,138.36, -26,2,31.03.2016,,3,138.36, -26,2,01.04.2016,,0,138.36, -26,2,02.04.2016,,3,138.36, -26,2,03.04.2016,,3,138.36, -26,2,04.04.2016,,25,138.36, -26,2,05.04.2016,,0,138.36, -26,2,06.04.2016,,0,138.36, -26,2,07.04.2016,,0,138.36, -26,2,08.04.2016,,0,138.36, -26,2,09.04.2016,,0,138.5, -26,2,10.04.2016,,3,138.5, -26,2,11.04.2016,,0,138.5, -26,2,12.04.2016,,0,138.5, -26,2,13.04.2016,,6,138.5, -26,2,14.04.2016,,0,138.5, -26,2,15.04.2016,,0,138.5, -26,2,16.04.2016,,0,138.5, -26,2,17.04.2016,,0,138.5, -26,2,18.04.2016,,0,138.5, -26,2,19.04.2016,1.0,57,138.5,116.89 -26,2,20.04.2016,1.0,22,138.5,116.89 -26,2,21.04.2016,1.0,22,138.5,116.89 -26,2,22.04.2016,1.0,57,138.5,116.89 -26,2,23.04.2016,1.0,41,138.5,116.89 -26,2,24.04.2016,1.0,60,138.5,116.89 -26,2,25.04.2016,1.0,25,138.5,116.89 -26,2,26.04.2016,1.0,44,138.5,114.17 -26,2,27.04.2016,1.0,13,138.5,114.17 -26,2,28.04.2016,,6,138.5, -26,2,29.04.2016,,0,138.5, -26,2,30.04.2016,,0,138.5, -26,2,01.05.2016,,6,138.5, -26,2,02.05.2016,,19,138.5, -26,2,03.05.2016,1.0,9,138.5,106.01 -26,2,04.05.2016,1.0,110,138.5,106.01 -26,2,05.05.2016,1.0,69,138.5,106.01 -26,2,06.05.2016,1.0,104,138.5,106.01 -26,2,07.05.2016,1.0,75,138.5,106.01 -26,2,08.05.2016,1.0,72,138.5,106.01 -26,2,09.05.2016,1.0,101,138.5,106.01 -26,2,10.05.2016,,13,138.5, -26,2,11.05.2016,,3,138.5, -26,2,12.05.2016,,0,138.5, -26,2,13.05.2016,,6,138.5, -26,2,14.05.2016,,3,138.5, -26,2,15.05.2016,,3,138.5, -26,2,16.05.2016,,3,138.5, -26,2,17.05.2016,,3,138.5, -26,2,18.05.2016,,3,138.5, -26,2,19.05.2016,,3,138.5, -26,2,20.05.2016,,3,138.5, -26,2,21.05.2016,,3,138.5, -26,2,22.05.2016,,3,138.5, -27,2,01.01.2015,,0,135.78, -27,2,02.01.2015,,0,135.78, -27,2,03.01.2015,,3,135.78, -27,2,04.01.2015,,0,135.78, -27,2,05.01.2015,,0,135.78, -27,2,06.01.2015,,0,135.78, -27,2,07.01.2015,,0,135.78, -27,2,08.01.2015,,0,135.78, -27,2,09.01.2015,,0,135.78, -27,2,10.01.2015,,0,135.78, -27,2,11.01.2015,,0,135.78, -27,2,12.01.2015,,0,135.78, -27,2,13.01.2015,,0,135.78, -27,2,14.01.2015,,0,135.78, -27,2,15.01.2015,,0,135.78, -27,2,16.01.2015,,0,135.78, -27,2,17.01.2015,,0,135.78, -27,2,18.01.2015,,3,135.78, -27,2,19.01.2015,,16,135.78, -27,2,20.01.2015,,0,135.78, -27,2,21.01.2015,,0,135.78, -27,2,22.01.2015,,0,135.78, -27,2,23.01.2015,,0,135.78, -27,2,24.01.2015,,0,135.78, -27,2,25.01.2015,,6,135.78, -27,2,26.01.2015,,0,135.78, -27,2,27.01.2015,,25,135.78, -27,2,28.01.2015,,0,135.78, -27,2,29.01.2015,,0,135.78, -27,2,30.01.2015,,0,135.78, -27,2,31.01.2015,,3,135.78, -27,2,01.02.2015,,9,135.78, -27,2,02.02.2015,,9,135.78, -27,2,03.02.2015,,3,135.78, -27,2,04.02.2015,,0,135.78, -27,2,05.02.2015,,9,135.78, -27,2,06.02.2015,,0,135.78, -27,2,07.02.2015,,3,135.78, -27,2,08.02.2015,,9,135.78, -27,2,09.02.2015,,3,135.78, -27,2,10.02.2015,,0,135.78, -27,2,11.02.2015,,16,135.78, -27,2,12.02.2015,,3,135.78, -27,2,13.02.2015,,6,135.78, -27,2,14.02.2015,,13,135.78, -27,2,15.02.2015,,0,135.78, -27,2,16.02.2015,,9,135.78, -27,2,17.02.2015,,0,135.78, -27,2,18.02.2015,,0,135.78, -27,2,19.02.2015,,0,135.78, -27,2,20.02.2015,,3,135.78, -27,2,21.02.2015,,9,135.78, -27,2,22.02.2015,,9,135.78, -27,2,23.02.2015,,6,135.78, -27,2,24.02.2015,,0,135.78, -27,2,25.02.2015,,0,135.78, -27,2,26.02.2015,,0,135.78, -27,2,27.02.2015,,0,135.78, -27,2,28.02.2015,,9,135.78, -27,2,01.03.2015,,6,135.78, -27,2,02.03.2015,,3,146.38, -27,2,03.03.2015,,0,146.38, -27,2,04.03.2015,,3,146.38, -27,2,05.03.2015,,0,146.38, -27,2,06.03.2015,,9,146.38, -27,2,07.03.2015,,3,146.38, -27,2,08.03.2015,,3,146.38, -27,2,09.03.2015,,3,146.38, -27,2,10.03.2015,,9,146.38, -27,2,11.03.2015,,3,146.38, -27,2,12.03.2015,,3,146.38, -27,2,13.03.2015,,9,146.38, -27,2,14.03.2015,,0,146.38, -27,2,15.03.2015,,0,146.38, -27,2,16.03.2015,,3,146.38, -27,2,17.03.2015,,0,146.38, -27,2,18.03.2015,,0,146.38, -27,2,19.03.2015,,0,146.38, -27,2,20.03.2015,,0,146.38, -27,2,21.03.2015,,0,146.38, -27,2,22.03.2015,,0,146.38, -27,2,23.03.2015,,0,146.38, -27,2,24.03.2015,,0,146.38, -27,2,25.03.2015,,0,146.38, -27,2,26.03.2015,,0,146.38, -27,2,27.03.2015,,3,146.38, -27,2,28.03.2015,,0,146.38, -27,2,29.03.2015,,3,146.38, -27,2,30.03.2015,,3,146.38, -27,2,31.03.2015,,3,146.38, -27,2,01.04.2015,,3,146.38, -27,2,02.04.2015,,0,146.38, -27,2,03.04.2015,,0,146.38, -27,2,04.04.2015,,0,146.38, -27,2,05.04.2015,,6,146.38, -27,2,06.04.2015,,0,146.38, -27,2,07.04.2015,,3,146.38, -27,2,08.04.2015,,3,146.38, -27,2,09.04.2015,,0,146.38, -27,2,10.04.2015,,0,146.38, -27,2,11.04.2015,,0,146.38, -27,2,12.04.2015,,3,146.38, -27,2,13.04.2015,,0,146.38, -27,2,14.04.2015,,3,146.38, -27,2,15.04.2015,,0,146.38, -27,2,16.04.2015,,9,146.38, -27,2,17.04.2015,,9,146.38, -27,2,18.04.2015,,3,146.38, -27,2,19.04.2015,,16,146.38, -27,2,20.04.2015,,13,145.84, -27,2,21.04.2015,,3,145.84, -27,2,22.04.2015,,0,145.84, -27,2,23.04.2015,,0,145.84, -27,2,24.04.2015,,0,145.84, -27,2,25.04.2015,,0,145.84, -27,2,26.04.2015,,0,145.84, -27,2,27.04.2015,,3,150.73, -27,2,28.04.2015,,16,150.73, -27,2,29.04.2015,,3,150.73, -27,2,30.04.2015,,0,150.73, -27,2,01.05.2015,,0,150.73, -27,2,02.05.2015,,3,150.73, -27,2,03.05.2015,,16,150.73, -27,2,04.05.2015,,19,150.73, -27,2,05.05.2015,,6,150.73, -27,2,06.05.2015,,0,150.73, -27,2,07.05.2015,,0,150.73, -27,2,08.05.2015,,3,150.73, -27,2,09.05.2015,,3,150.73, -27,2,10.05.2015,,3,150.73, -27,2,11.05.2015,,0,150.73, -27,2,12.05.2015,,3,150.73, -27,2,13.05.2015,,3,150.73, -27,2,14.05.2015,,0,150.73, -27,2,15.05.2015,,0,150.73, -27,2,16.05.2015,,3,150.73, -27,2,17.05.2015,,6,150.73, -27,2,18.05.2015,,0,150.73, -27,2,19.05.2015,,0,150.73, -27,2,20.05.2015,,0,150.73, -27,2,21.05.2015,,0,150.73, -27,2,22.05.2015,,0,150.73, -27,2,23.05.2015,,3,150.73, -27,2,24.05.2015,,0,150.73, -27,2,25.05.2015,,3,150.73, -27,2,26.05.2015,,3,150.73, -27,2,27.05.2015,,0,150.73, -27,2,28.05.2015,,0,150.73, -27,2,29.05.2015,,0,150.73, -27,2,30.05.2015,,0,150.73, -27,2,31.05.2015,,0,150.73, -27,2,01.06.2015,,0,150.73, -27,2,02.06.2015,,0,150.73, -27,2,03.06.2015,,0,150.73, -27,2,04.06.2015,,6,150.73, -27,2,05.06.2015,,6,150.73, -27,2,06.06.2015,,3,150.73, -27,2,07.06.2015,,0,150.73, -27,2,08.06.2015,,9,150.73, -27,2,09.06.2015,,0,150.73, -27,2,10.06.2015,,0,150.73, -27,2,11.06.2015,,0,150.73, -27,2,12.06.2015,,0,150.73, -27,2,13.06.2015,,0,150.73, -27,2,14.06.2015,,0,150.73, -27,2,15.06.2015,,13,150.73, -27,2,16.06.2015,,0,150.73, -27,2,17.06.2015,,0,150.73, -27,2,18.06.2015,,6,150.73, -27,2,19.06.2015,,0,150.73, -27,2,20.06.2015,,9,150.73, -27,2,21.06.2015,,9,150.73, -27,2,22.06.2015,,3,150.73, -27,2,23.06.2015,,3,150.73, -27,2,24.06.2015,,0,150.73, -27,2,25.06.2015,,0,150.73, -27,2,26.06.2015,,0,150.73, -27,2,27.06.2015,,0,150.73, -27,2,28.06.2015,,0,150.73, -27,2,29.06.2015,,0,150.73, -27,2,30.06.2015,,3,150.73, -27,2,01.07.2015,,0,150.73, -27,2,02.07.2015,,9,150.73, -27,2,03.07.2015,,13,150.73, -27,2,04.07.2015,,0,150.73, -27,2,05.07.2015,,0,150.73, -27,2,06.07.2015,,3,150.73, -27,2,07.07.2015,,3,150.73, -27,2,08.07.2015,,3,150.73, -27,2,09.07.2015,,3,150.73, -27,2,10.07.2015,,3,150.73, -27,2,11.07.2015,,3,150.73, -27,2,12.07.2015,,3,150.73, -27,2,13.07.2015,,3,150.73, -27,2,14.07.2015,,3,150.73, -27,2,15.07.2015,,3,150.73, -27,2,16.07.2015,,3,150.73, -27,2,17.07.2015,,3,150.73, -27,2,18.07.2015,,3,150.73, -27,2,19.07.2015,,3,150.73, -27,2,20.07.2015,,3,150.73, -27,2,21.07.2015,,3,150.73, -27,2,22.07.2015,,3,150.73, -27,2,23.07.2015,,3,150.73, -27,2,24.07.2015,,3,150.73, -27,2,25.07.2015,,3,150.73, -27,2,26.07.2015,,3,150.73, -27,2,27.07.2015,,3,150.73, -27,2,28.07.2015,,3,150.73, -27,2,29.07.2015,,0,150.73, -27,2,30.07.2015,,0,150.73, -27,2,31.07.2015,,0,150.73, -27,2,01.08.2015,,0,150.73, -27,2,02.08.2015,,0,150.73, -27,2,03.08.2015,,0,150.73, -27,2,04.08.2015,,0,150.73, -27,2,05.08.2015,,0,150.73, -27,2,06.08.2015,,0,150.73, -27,2,07.08.2015,,0,150.73, -27,2,08.08.2015,,0,150.73, -27,2,09.08.2015,,0,150.73, -27,2,10.08.2015,,0,150.73, -27,2,11.08.2015,,0,150.73, -27,2,12.08.2015,,0,128.17, -27,2,13.08.2015,,9,128.17, -27,2,14.08.2015,,6,128.17, -27,2,15.08.2015,,9,128.17, -27,2,16.08.2015,,0,128.17, -27,2,17.08.2015,,60,128.17, -27,2,18.08.2015,1.0,141,128.17,100.3 -27,2,19.08.2015,1.0,57,128.17,100.3 -27,2,20.08.2015,1.0,72,128.17,100.3 -27,2,21.08.2015,1.0,182,128.17,100.3 -27,2,22.08.2015,1.0,72,128.17,100.3 -27,2,23.08.2015,1.0,50,128.17,100.3 -27,2,24.08.2015,1.0,69,128.17,100.3 -27,2,25.08.2015,,6,128.17, -27,2,26.08.2015,,3,128.17, -27,2,27.08.2015,,0,128.17, -27,2,28.08.2015,,13,128.17, -27,2,29.08.2015,,3,128.17, -27,2,30.08.2015,,9,128.17, -27,2,31.08.2015,,9,128.17, -27,2,01.09.2015,,9,128.17, -27,2,02.09.2015,,9,128.17, -27,2,03.09.2015,,9,128.17, -27,2,04.09.2015,,9,128.17, -27,2,05.09.2015,,9,128.17, -27,2,06.09.2015,,9,128.17, -27,2,07.09.2015,,9,128.17, -27,2,08.09.2015,,9,128.17, -27,2,09.09.2015,,9,128.17, -27,2,10.09.2015,,9,128.17, -27,2,11.09.2015,,9,128.17, -27,2,12.09.2015,,9,128.17, -27,2,13.09.2015,,9,128.17, -27,2,14.09.2015,,9,128.17, -27,2,15.09.2015,,9,128.17, -27,2,16.09.2015,,9,128.17, -27,2,17.09.2015,,9,128.17, -27,2,18.09.2015,,9,128.17, -27,2,19.09.2015,,9,128.17, -27,2,20.09.2015,,9,128.17, -27,2,21.09.2015,,9,128.17, -27,2,22.09.2015,,0,128.17, -27,2,23.09.2015,,0,128.17, -27,2,24.09.2015,,0,128.17, -27,2,25.09.2015,,0,162.96, -27,2,26.09.2015,,0,162.96, -27,2,27.09.2015,,0,162.96, -27,2,28.09.2015,,0,162.96, -27,2,29.09.2015,,0,162.96, -27,2,30.09.2015,,0,162.96, -27,2,01.10.2015,,0,162.96, -27,2,02.10.2015,,0,162.96, -27,2,03.10.2015,,6,162.96, -27,2,04.10.2015,,9,162.96, -27,2,05.10.2015,,9,162.96, -27,2,06.10.2015,1.0,97,143.39,122.05 -27,2,07.10.2015,1.0,9,143.39,122.05 -27,2,08.10.2015,1.0,97,143.39,122.05 -27,2,09.10.2015,1.0,210,143.39,122.05 -27,2,10.10.2015,1.0,214,143.39,122.05 -27,2,11.10.2015,1.0,195,143.39,122.05 -27,2,12.10.2015,1.0,101,143.39,122.05 -27,2,13.10.2015,,9,143.93, -27,2,14.10.2015,,9,143.93, -27,2,15.10.2015,,9,143.93, -27,2,16.10.2015,,9,143.93, -27,2,17.10.2015,,9,143.93, -27,2,18.10.2015,,9,143.93, -27,2,19.10.2015,,9,143.93, -27,2,20.10.2015,,9,143.93, -27,2,21.10.2015,,9,143.93, -27,2,22.10.2015,,9,143.93, -27,2,23.10.2015,,9,143.93, -27,2,24.10.2015,,9,143.93, -27,2,25.10.2015,,9,143.93, -27,2,26.10.2015,,9,143.93, -27,2,27.10.2015,,9,143.93, -27,2,28.10.2015,,9,143.93, -27,2,29.10.2015,,9,143.93, -27,2,30.10.2015,,9,143.93, -27,2,31.10.2015,,9,143.93, -27,2,01.11.2015,,9,143.93, -27,2,02.11.2015,,9,143.93, -27,2,03.11.2015,,9,143.93, -27,2,04.11.2015,,9,143.93, -27,2,05.11.2015,,0,143.93, -27,2,06.11.2015,,0,143.93, -27,2,07.11.2015,,0,143.93, -27,2,08.11.2015,,0,143.93, -27,2,09.11.2015,,0,143.93, -27,2,10.11.2015,,0,143.93, -27,2,11.11.2015,,0,143.93, -27,2,12.11.2015,,0,143.93, -27,2,13.11.2015,,0,143.93, -27,2,14.11.2015,,0,143.93, -27,2,15.11.2015,,0,143.93, -27,2,16.11.2015,,0,143.93, -27,2,17.11.2015,,0,143.93, -27,2,18.11.2015,,0,143.93, -27,2,19.11.2015,,0,143.93, -27,2,20.11.2015,,0,143.93, -27,2,21.11.2015,,0,143.93, -27,2,22.11.2015,,0,143.93, -27,2,23.11.2015,,0,143.93, -27,2,24.11.2015,,0,143.93, -27,2,25.11.2015,,0,143.93, -27,2,26.11.2015,,0,143.93, -27,2,27.11.2015,,0,143.93, -27,2,28.11.2015,,0,143.93, -27,2,29.11.2015,,0,143.93, -27,2,30.11.2015,,0,143.93, -27,2,01.12.2015,,0,143.93, -27,2,02.12.2015,,0,143.93, -27,2,03.12.2015,,0,143.93, -27,2,04.12.2015,,0,143.93, -27,2,05.12.2015,,0,143.93, -27,2,06.12.2015,,0,143.93, -27,2,07.12.2015,,0,143.93, -27,2,08.12.2015,,0,143.93, -27,2,09.12.2015,,0,138.5, -27,2,10.12.2015,,0,138.5, -27,2,11.12.2015,,0,142.57, -27,2,12.12.2015,,0,142.57, -27,2,13.12.2015,,0,142.57, -27,2,14.12.2015,,0,142.57, -27,2,15.12.2015,,0,142.57, -27,2,16.12.2015,,0,142.57, -27,2,17.12.2015,,6,142.57, -27,2,18.12.2015,,0,142.57, -27,2,19.12.2015,,3,142.57, -27,2,20.12.2015,,3,142.57, -27,2,21.12.2015,,19,142.57, -27,2,22.12.2015,1.0,141,142.57,103.02 -27,2,23.12.2015,1.0,160,142.57,103.02 -27,2,24.12.2015,1.0,163,142.57,103.02 -27,2,25.12.2015,1.0,239,142.57,103.02 -27,2,26.12.2015,1.0,123,142.57,103.02 -27,2,27.12.2015,1.0,82,142.57,103.02 -27,2,28.12.2015,1.0,132,142.57,103.02 -27,2,29.12.2015,,6,138.5, -27,2,30.12.2015,,6,138.5, -27,2,31.12.2015,,9,138.5, -27,2,01.01.2016,,3,138.5, -27,2,02.01.2016,,19,138.5, -27,2,03.01.2016,,6,138.5, -27,2,04.01.2016,,6,138.5, -27,2,05.01.2016,,38,138.5, -27,2,06.01.2016,,9,138.5, -27,2,07.01.2016,,6,138.5, -27,2,08.01.2016,,9,138.5, -27,2,09.01.2016,,3,138.5, -27,2,10.01.2016,,9,138.5, -27,2,11.01.2016,,9,138.5, -27,2,12.01.2016,,3,138.5, -27,2,13.01.2016,,3,138.5, -27,2,14.01.2016,,9,138.5, -27,2,15.01.2016,,3,138.5, -27,2,16.01.2016,,6,142.57, -27,2,17.01.2016,,9,142.57, -27,2,18.01.2016,,6,142.57, -27,2,19.01.2016,1.0,119,142.57,103.02 -27,2,20.01.2016,1.0,145,142.57,103.02 -27,2,21.01.2016,1.0,132,142.57,103.02 -27,2,22.01.2016,1.0,173,142.57,103.02 -27,2,23.01.2016,1.0,157,142.57,103.02 -27,2,24.01.2016,1.0,101,142.57,103.02 -27,2,25.01.2016,1.0,135,142.57,103.02 -27,2,26.01.2016,1.0,135,142.57,103.02 -27,2,27.01.2016,1.0,28,142.57,103.02 -27,2,28.01.2016,1.0,19,142.57,103.02 -27,2,29.01.2016,,9,142.57, -27,2,30.01.2016,,9,142.57, -27,2,31.01.2016,,6,142.57, -27,2,01.02.2016,,9,142.57, -27,2,02.02.2016,,3,142.57, -27,2,03.02.2016,,16,142.57, -27,2,04.02.2016,,6,142.57, -27,2,05.02.2016,,6,142.57, -27,2,06.02.2016,,3,142.57, -27,2,07.02.2016,,9,142.57, -27,2,08.02.2016,,19,142.57, -27,2,09.02.2016,1.0,182,142.57,108.46 -27,2,10.02.2016,1.0,129,138.36,108.46 -27,2,11.02.2016,1.0,110,138.36,108.46 -27,2,12.02.2016,1.0,101,138.36,108.46 -27,2,13.02.2016,1.0,126,138.36,108.46 -27,2,14.02.2016,1.0,113,138.36,108.46 -27,2,15.02.2016,1.0,101,138.36,108.46 -27,2,16.02.2016,,6,138.36, -27,2,17.02.2016,,0,138.36, -27,2,18.02.2016,,0,138.36, -27,2,19.02.2016,,0,138.36, -27,2,20.02.2016,,0,138.36, -27,2,21.02.2016,,0,138.36, -27,2,22.02.2016,,0,138.36, -27,2,23.02.2016,,0,138.36, -27,2,24.02.2016,,0,138.36, -27,2,25.02.2016,,0,138.36, -27,2,26.02.2016,,0,138.36, -27,2,27.02.2016,,0,138.36, -27,2,28.02.2016,,0,138.36, -27,2,29.02.2016,,0,138.36, -27,2,01.03.2016,,0,138.36, -27,2,02.03.2016,,0,138.36, -27,2,03.03.2016,,0,138.36, -27,2,04.03.2016,,0,138.36, -27,2,05.03.2016,,0,138.36, -27,2,06.03.2016,,0,138.36, -27,2,07.03.2016,,0,138.36, -27,2,08.03.2016,,0,138.36, -27,2,09.03.2016,,0,138.36, -27,2,10.03.2016,,0,138.36, -27,2,11.03.2016,,0,138.36, -27,2,12.03.2016,,0,138.5, -27,2,13.03.2016,,0,138.5, -27,2,14.03.2016,,0,138.5, -27,2,15.03.2016,1.0,107,138.5,124.77 -27,2,16.03.2016,1.0,107,138.5,124.77 -27,2,17.03.2016,1.0,107,138.5,124.77 -27,2,18.03.2016,1.0,107,138.5,124.77 -27,2,19.03.2016,1.0,107,138.5,124.77 -27,2,20.03.2016,1.0,107,138.5,124.77 -27,2,21.03.2016,1.0,107,138.5,124.77 -27,2,22.03.2016,,0,138.36, -27,2,23.03.2016,,0,138.36, -27,2,24.03.2016,,0,138.36, -27,2,25.03.2016,,0,138.36, -27,2,26.03.2016,,0,138.36, -27,2,27.03.2016,,0,138.36, -27,2,28.03.2016,,0,138.36, -27,2,29.03.2016,,0,138.36, -27,2,30.03.2016,,0,138.36, -27,2,31.03.2016,,0,138.36, -27,2,01.04.2016,,0,138.36, -27,2,02.04.2016,,0,138.36, -27,2,03.04.2016,,0,138.36, -27,2,04.04.2016,,0,138.36, -27,2,05.04.2016,,0,138.36, -27,2,06.04.2016,,0,138.36, -27,2,07.04.2016,,3,138.36, -27,2,08.04.2016,,3,138.36, -27,2,09.04.2016,,3,138.5, -27,2,10.04.2016,,3,138.5, -27,2,11.04.2016,,0,138.5, -27,2,12.04.2016,,6,138.5, -27,2,13.04.2016,,0,138.5, -27,2,14.04.2016,,3,138.5, -27,2,15.04.2016,,0,138.5, -27,2,16.04.2016,,0,138.5, -27,2,17.04.2016,,6,138.5, -27,2,18.04.2016,,9,138.5, -27,2,19.04.2016,1.0,101,138.5,116.89 -27,2,20.04.2016,1.0,75,138.5,116.89 -27,2,21.04.2016,1.0,57,138.5,116.89 -27,2,22.04.2016,1.0,63,138.5,116.89 -27,2,23.04.2016,1.0,38,138.5,116.89 -27,2,24.04.2016,1.0,57,138.5,116.89 -27,2,25.04.2016,1.0,107,138.5,116.89 -27,2,26.04.2016,,9,138.5, -27,2,27.04.2016,,13,138.5, -27,2,28.04.2016,,13,138.5, -27,2,29.04.2016,,3,138.5, -27,2,30.04.2016,,3,138.5, -27,2,01.05.2016,,0,138.5, -27,2,02.05.2016,,6,138.5, -27,2,03.05.2016,1.0,113,138.5,106.01 -27,2,04.05.2016,1.0,141,138.5,106.01 -27,2,05.05.2016,1.0,154,138.5,106.01 -27,2,06.05.2016,1.0,229,138.5,106.01 -27,2,07.05.2016,1.0,53,138.5,106.01 -27,2,08.05.2016,1.0,91,138.5,106.01 -27,2,09.05.2016,1.0,85,138.5,106.01 -27,2,10.05.2016,,0,138.5, -27,2,11.05.2016,,6,138.5, -27,2,12.05.2016,,6,138.5, -27,2,13.05.2016,,6,138.5, -27,2,14.05.2016,,6,138.5, -27,2,15.05.2016,,6,138.5, -27,2,16.05.2016,,6,138.5, -27,2,17.05.2016,,6,138.5, -27,2,18.05.2016,,6,138.5, -27,2,19.05.2016,,6,138.5, -27,2,20.05.2016,,6,138.5, -27,2,21.05.2016,,6,138.5, -27,2,22.05.2016,,6,138.5, -28,2,01.01.2015,,0,135.78, -28,2,02.01.2015,,3,135.78, -28,2,03.01.2015,,9,135.78, -28,2,04.01.2015,,6,135.78, -28,2,05.01.2015,,0,135.78, -28,2,06.01.2015,,0,135.78, -28,2,07.01.2015,,0,135.78, -28,2,08.01.2015,,0,135.78, -28,2,09.01.2015,,0,135.78, -28,2,10.01.2015,,0,135.78, -28,2,11.01.2015,,0,135.78, -28,2,12.01.2015,,0,135.78, -28,2,13.01.2015,,0,135.78, -28,2,14.01.2015,,19,135.78, -28,2,15.01.2015,,16,135.78, -28,2,16.01.2015,,3,135.78, -28,2,17.01.2015,,3,135.78, -28,2,18.01.2015,,3,135.78, -28,2,19.01.2015,,0,135.78, -28,2,20.01.2015,,13,135.78, -28,2,21.01.2015,,6,135.78, -28,2,22.01.2015,,0,135.78, -28,2,23.01.2015,,9,135.78, -28,2,24.01.2015,,6,135.78, -28,2,25.01.2015,,3,135.78, -28,2,26.01.2015,,0,135.78, -28,2,27.01.2015,,0,135.78, -28,2,28.01.2015,,0,135.78, -28,2,29.01.2015,,0,135.78, -28,2,30.01.2015,,0,135.78, -28,2,31.01.2015,,0,135.78, -28,2,01.02.2015,,0,135.78, -28,2,02.02.2015,,0,135.78, -28,2,03.02.2015,,6,135.78, -28,2,04.02.2015,,9,135.78, -28,2,05.02.2015,,9,135.78, -28,2,06.02.2015,,0,135.78, -28,2,07.02.2015,,16,135.78, -28,2,08.02.2015,,9,135.78, -28,2,09.02.2015,,13,135.78, -28,2,10.02.2015,,13,135.78, -28,2,11.02.2015,,6,135.78, -28,2,12.02.2015,,9,135.78, -28,2,13.02.2015,,0,135.78, -28,2,14.02.2015,,3,135.78, -28,2,15.02.2015,,31,135.78, -28,2,16.02.2015,,0,135.78, -28,2,17.02.2015,,0,135.78, -28,2,18.02.2015,,0,135.78, -28,2,19.02.2015,,13,135.78, -28,2,20.02.2015,,0,135.78, -28,2,21.02.2015,,3,135.78, -28,2,22.02.2015,,25,135.78, -28,2,23.02.2015,,6,135.78, -28,2,24.02.2015,,3,135.78, -28,2,25.02.2015,,0,135.78, -28,2,26.02.2015,,0,135.78, -28,2,27.02.2015,,6,135.78, -28,2,28.02.2015,,0,135.78, -28,2,01.03.2015,,9,135.78, -28,2,02.03.2015,,0,146.38, -28,2,03.03.2015,,0,146.38, -28,2,04.03.2015,,9,146.38, -28,2,05.03.2015,,6,146.38, -28,2,06.03.2015,,0,146.38, -28,2,07.03.2015,,0,146.38, -28,2,08.03.2015,,3,146.38, -28,2,09.03.2015,,0,146.38, -28,2,10.03.2015,,0,146.38, -28,2,11.03.2015,,0,146.38, -28,2,12.03.2015,,3,146.38, -28,2,13.03.2015,,0,146.38, -28,2,14.03.2015,,9,146.38, -28,2,15.03.2015,,0,146.38, -28,2,16.03.2015,,3,146.38, -28,2,17.03.2015,,0,146.38, -28,2,18.03.2015,,0,146.38, -28,2,19.03.2015,,0,146.38, -28,2,20.03.2015,,0,146.38, -28,2,21.03.2015,,0,146.38, -28,2,22.03.2015,,0,146.38, -28,2,23.03.2015,,0,146.38, -28,2,24.03.2015,,0,146.38, -28,2,25.03.2015,,0,146.38, -28,2,26.03.2015,,0,146.38, -28,2,27.03.2015,,0,146.38, -28,2,28.03.2015,,0,146.38, -28,2,29.03.2015,,0,146.38, -28,2,30.03.2015,,0,146.38, -28,2,31.03.2015,,0,146.38, -28,2,01.04.2015,,0,146.38, -28,2,02.04.2015,,0,146.38, -28,2,03.04.2015,,0,146.38, -28,2,04.04.2015,,0,146.38, -28,2,05.04.2015,,0,146.38, -28,2,06.04.2015,,0,146.38, -28,2,07.04.2015,,0,146.38, -28,2,08.04.2015,,0,146.38, -28,2,09.04.2015,,0,146.38, -28,2,10.04.2015,,3,146.38, -28,2,11.04.2015,,3,146.38, -28,2,12.04.2015,,0,146.38, -28,2,13.04.2015,,0,146.38, -28,2,14.04.2015,,0,146.38, -28,2,15.04.2015,,0,146.38, -28,2,16.04.2015,,0,146.38, -28,2,17.04.2015,,0,146.38, -28,2,18.04.2015,,0,146.38, -28,2,19.04.2015,,0,146.38, -28,2,20.04.2015,,0,145.84, -28,2,21.04.2015,,0,145.84, -28,2,22.04.2015,,0,145.84, -28,2,23.04.2015,,0,145.84, -28,2,24.04.2015,,0,145.84, -28,2,25.04.2015,,0,145.84, -28,2,26.04.2015,,0,150.73, -28,2,27.04.2015,,0,150.73, -28,2,28.04.2015,,0,150.73, -28,2,29.04.2015,,0,150.73, -28,2,30.04.2015,,0,150.73, -28,2,01.05.2015,,3,150.73, -28,2,02.05.2015,,0,150.73, -28,2,03.05.2015,,9,150.73, -28,2,04.05.2015,,0,150.73, -28,2,05.05.2015,,0,150.73, -28,2,06.05.2015,,0,150.73, -28,2,07.05.2015,,0,150.73, -28,2,08.05.2015,,0,150.73, -28,2,09.05.2015,,0,150.73, -28,2,10.05.2015,,0,150.73, -28,2,11.05.2015,,0,150.73, -28,2,12.05.2015,,0,150.73, -28,2,13.05.2015,,0,150.73, -28,2,14.05.2015,,0,150.73, -28,2,15.05.2015,,6,150.73, -28,2,16.05.2015,,0,150.73, -28,2,17.05.2015,,0,150.73, -28,2,18.05.2015,,0,150.73, -28,2,19.05.2015,,0,150.73, -28,2,20.05.2015,,0,150.73, -28,2,21.05.2015,,0,150.73, -28,2,22.05.2015,,0,150.73, -28,2,23.05.2015,,3,150.73, -28,2,24.05.2015,,0,150.73, -28,2,25.05.2015,,0,150.73, -28,2,26.05.2015,,3,150.73, -28,2,27.05.2015,,0,150.73, -28,2,28.05.2015,,0,150.73, -28,2,29.05.2015,,0,150.73, -28,2,30.05.2015,,0,150.73, -28,2,31.05.2015,,0,150.73, -28,2,01.06.2015,,0,150.73, -28,2,02.06.2015,,0,150.73, -28,2,03.06.2015,,0,150.73, -28,2,04.06.2015,,0,150.73, -28,2,05.06.2015,,0,150.73, -28,2,06.06.2015,,0,150.73, -28,2,07.06.2015,,0,150.73, -28,2,08.06.2015,,3,150.73, -28,2,09.06.2015,,0,150.73, -28,2,10.06.2015,,0,150.73, -28,2,11.06.2015,,3,150.73, -28,2,12.06.2015,,3,150.73, -28,2,13.06.2015,,0,150.73, -28,2,14.06.2015,,0,150.73, -28,2,15.06.2015,,0,150.73, 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-28,2,21.07.2015,,0,150.73, -28,2,22.07.2015,,0,150.73, -28,2,23.07.2015,,0,150.73, -28,2,24.07.2015,,0,150.73, -28,2,25.07.2015,,0,150.73, -28,2,26.07.2015,,0,150.73, -28,2,27.07.2015,,0,150.73, -28,2,28.07.2015,,0,150.73, -28,2,29.07.2015,,0,150.73, -28,2,30.07.2015,,0,150.73, -28,2,31.07.2015,,0,150.73, -28,2,01.08.2015,,0,150.73, -28,2,02.08.2015,,0,150.73, -28,2,03.08.2015,,0,150.73, -28,2,04.08.2015,,0,150.73, -28,2,05.08.2015,,0,150.73, -28,2,06.08.2015,,0,150.73, -28,2,07.08.2015,,0,150.73, -28,2,08.08.2015,,0,150.73, -28,2,09.08.2015,,0,150.73, -28,2,10.08.2015,,0,150.73, -28,2,11.08.2015,,0,150.73, -28,2,12.08.2015,,0,128.17, -28,2,13.08.2015,,0,128.17, -28,2,14.08.2015,,19,128.17, -28,2,15.08.2015,,28,128.17, -28,2,16.08.2015,,3,128.17, -28,2,17.08.2015,,9,128.17, -28,2,18.08.2015,1.0,129,128.17,100.3 -28,2,19.08.2015,1.0,157,128.17,100.3 -28,2,20.08.2015,1.0,16,128.17,100.3 -28,2,21.08.2015,1.0,138,128.17,100.3 -28,2,22.08.2015,1.0,75,128.17,100.3 -28,2,23.08.2015,1.0,217,128.17,100.3 -28,2,24.08.2015,1.0,138,128.17,100.3 -28,2,25.08.2015,,13,128.17, -28,2,26.08.2015,,13,128.17, -28,2,27.08.2015,,6,128.17, -28,2,28.08.2015,,25,128.17, -28,2,29.08.2015,,9,128.17, -28,2,30.08.2015,,9,128.17, -28,2,31.08.2015,,19,128.17, -28,2,01.09.2015,,19,128.17, -28,2,02.09.2015,,9,128.17, -28,2,03.09.2015,,3,128.17, -28,2,04.09.2015,,16,128.17, -28,2,05.09.2015,,13,128.17, -28,2,06.09.2015,,13,128.17, -28,2,07.09.2015,,16,128.17, -28,2,08.09.2015,,3,128.17, -28,2,09.09.2015,,19,128.17, -28,2,10.09.2015,,16,128.17, -28,2,11.09.2015,,16,128.17, -28,2,12.09.2015,,3,128.17, -28,2,13.09.2015,,6,128.17, -28,2,14.09.2015,,3,128.17, -28,2,15.09.2015,,6,128.17, -28,2,16.09.2015,,19,128.17, -28,2,17.09.2015,,3,128.17, -28,2,18.09.2015,,25,128.17, -28,2,19.09.2015,,9,128.17, -28,2,20.09.2015,,9,128.17, -28,2,21.09.2015,,13,128.17, -28,2,22.09.2015,,6,128.17, -28,2,23.09.2015,,9,128.17, -28,2,24.09.2015,,9,128.17, -28,2,25.09.2015,,3,128.17, -28,2,26.09.2015,,6,128.17, -28,2,27.09.2015,,9,128.17, -28,2,28.09.2015,,9,128.17, -28,2,29.09.2015,,9,128.17, -28,2,30.09.2015,,6,128.17, -28,2,01.10.2015,,3,162.96, -28,2,02.10.2015,,0,162.96, -28,2,03.10.2015,,9,162.96, -28,2,04.10.2015,,16,162.96, -28,2,05.10.2015,,13,162.96, -28,2,06.10.2015,1.0,113,143.39,122.05 -28,2,07.10.2015,1.0,91,143.39,122.05 -28,2,08.10.2015,1.0,97,143.39,122.05 -28,2,09.10.2015,1.0,75,143.39,122.05 -28,2,10.10.2015,1.0,107,143.39,122.05 -28,2,11.10.2015,1.0,119,143.39,122.05 -28,2,12.10.2015,1.0,66,143.39,122.05 -28,2,13.10.2015,1.0,38,143.93,122.05 -28,2,14.10.2015,1.0,94,143.93,122.05 -28,2,15.10.2015,1.0,94,143.93,122.05 -28,2,16.10.2015,1.0,85,143.93,122.05 -28,2,17.10.2015,1.0,72,162.96,122.05 -28,2,18.10.2015,1.0,72,162.96,122.05 -28,2,19.10.2015,,0,162.96, -28,2,20.10.2015,,3,162.96, -28,2,21.10.2015,,0,162.96, -28,2,22.10.2015,,0,162.96, -28,2,23.10.2015,,0,162.96, -28,2,24.10.2015,,3,162.96, -28,2,25.10.2015,,6,162.96, -28,2,26.10.2015,,0,162.96, -28,2,27.10.2015,,0,162.96, -28,2,28.10.2015,,0,162.96, -28,2,29.10.2015,,0,162.96, -28,2,30.10.2015,,0,162.96, -28,2,31.10.2015,,0,162.96, -28,2,01.11.2015,,0,162.96, -28,2,02.11.2015,,0,162.96, -28,2,03.11.2015,,0,162.96, -28,2,04.11.2015,,0,162.96, -28,2,05.11.2015,,0,162.96, -28,2,06.11.2015,,0,162.96, -28,2,07.11.2015,,0,162.96, -28,2,08.11.2015,,6,162.96, -28,2,09.11.2015,,0,162.96, -28,2,10.11.2015,,3,162.96, -28,2,11.11.2015,,3,138.36, -28,2,12.11.2015,,3,138.36, -28,2,13.11.2015,,6,138.36, -28,2,14.11.2015,,6,138.36, -28,2,15.11.2015,,6,138.36, -28,2,16.11.2015,,3,138.36, -28,2,17.11.2015,,6,138.36, -28,2,18.11.2015,,0,138.36, -28,2,19.11.2015,,0,138.36, -28,2,20.11.2015,,0,138.36, -28,2,21.11.2015,,0,138.36, -28,2,22.11.2015,,0,138.36, -28,2,23.11.2015,,0,138.36, -28,2,24.11.2015,,0,138.36, -28,2,25.11.2015,,0,138.36, -28,2,26.11.2015,,6,138.36, -28,2,27.11.2015,,0,138.36, -28,2,28.11.2015,,0,138.36, -28,2,29.11.2015,,6,138.36, -28,2,30.11.2015,,0,138.36, -28,2,01.12.2015,,0,162.96, -28,2,02.12.2015,,0,162.96, -28,2,03.12.2015,,0,138.5, -28,2,04.12.2015,,0,138.5, -28,2,05.12.2015,,3,138.5, -28,2,06.12.2015,,0,138.5, -28,2,07.12.2015,,0,138.5, -28,2,08.12.2015,,0,138.5, -28,2,09.12.2015,,6,138.5, -28,2,10.12.2015,,3,138.5, -28,2,11.12.2015,,3,142.57, -28,2,12.12.2015,,0,142.57, -28,2,13.12.2015,,3,142.57, -28,2,14.12.2015,,3,142.57, -28,2,15.12.2015,,9,142.57, -28,2,16.12.2015,,3,142.57, -28,2,17.12.2015,,9,142.57, -28,2,18.12.2015,,6,142.57, -28,2,19.12.2015,,3,142.57, -28,2,20.12.2015,,6,142.57, -28,2,21.12.2015,,13,142.57, -28,2,22.12.2015,1.0,167,142.57,103.02 -28,2,23.12.2015,1.0,179,142.57,103.02 -28,2,24.12.2015,1.0,157,142.57,103.02 -28,2,25.12.2015,1.0,145,142.57,103.02 -28,2,26.12.2015,1.0,88,142.57,103.02 -28,2,27.12.2015,1.0,88,142.57,103.02 -28,2,28.12.2015,1.0,94,142.57,103.02 -28,2,29.12.2015,,6,138.5, -28,2,30.12.2015,,6,138.5, -28,2,31.12.2015,,6,138.5, -28,2,01.01.2016,,6,138.5, -28,2,02.01.2016,,6,138.5, -28,2,03.01.2016,,6,138.5, -28,2,04.01.2016,,6,138.5, -28,2,05.01.2016,,19,138.5, -28,2,06.01.2016,,6,138.5, -28,2,07.01.2016,,3,138.5, -28,2,08.01.2016,,13,138.5, -28,2,09.01.2016,,6,138.5, -28,2,10.01.2016,,3,138.5, -28,2,11.01.2016,,6,138.5, -28,2,12.01.2016,,6,138.5, -28,2,13.01.2016,,6,138.5, -28,2,14.01.2016,,13,138.5, -28,2,15.01.2016,,3,138.5, -28,2,16.01.2016,,19,142.57, -28,2,17.01.2016,,3,142.57, -28,2,18.01.2016,,22,142.57, -28,2,19.01.2016,1.0,91,142.57,103.02 -28,2,20.01.2016,1.0,214,142.57,103.02 -28,2,21.01.2016,1.0,151,142.57,103.02 -28,2,22.01.2016,1.0,135,142.57,103.02 -28,2,23.01.2016,1.0,232,142.57,103.02 -28,2,24.01.2016,1.0,129,142.57,103.02 -28,2,25.01.2016,1.0,25,142.57,103.02 -28,2,26.01.2016,,9,142.57, -28,2,27.01.2016,,6,142.57, -28,2,28.01.2016,,19,142.57, -28,2,29.01.2016,,9,142.57, -28,2,30.01.2016,,3,142.57, -28,2,31.01.2016,,9,142.57, -28,2,01.02.2016,,19,142.57, -28,2,02.02.2016,,9,142.57, -28,2,03.02.2016,,19,142.57, -28,2,04.02.2016,,19,142.57, -28,2,05.02.2016,,13,142.57, -28,2,06.02.2016,,13,142.57, -28,2,07.02.2016,,13,142.57, -28,2,08.02.2016,,22,142.57, -28,2,09.02.2016,1.0,176,142.57,108.46 -28,2,10.02.2016,1.0,160,138.36,108.46 -28,2,11.02.2016,1.0,160,138.36,108.46 -28,2,12.02.2016,1.0,192,138.36,108.46 -28,2,13.02.2016,1.0,173,138.36,108.46 -28,2,14.02.2016,1.0,41,138.36,108.46 -28,2,15.02.2016,1.0,57,138.36,108.46 -28,2,16.02.2016,,13,138.36, -28,2,17.02.2016,,19,138.36, -28,2,18.02.2016,,13,138.36, -28,2,19.02.2016,,13,138.36, -28,2,20.02.2016,,6,138.36, -28,2,21.02.2016,,13,138.36, -28,2,22.02.2016,,13,138.36, -28,2,23.02.2016,,13,138.36, -28,2,24.02.2016,,3,138.36, -28,2,25.02.2016,,3,138.36, -28,2,26.02.2016,,3,138.36, -28,2,27.02.2016,,6,138.36, -28,2,28.02.2016,,13,138.36, -28,2,29.02.2016,,13,138.36, -28,2,01.03.2016,,0,138.36, -28,2,02.03.2016,,6,138.36, -28,2,03.03.2016,,0,138.36, -28,2,04.03.2016,,0,138.36, -28,2,05.03.2016,,9,138.36, -28,2,06.03.2016,,0,138.36, -28,2,07.03.2016,,0,138.36, -28,2,08.03.2016,,13,138.36, -28,2,09.03.2016,,3,138.36, -28,2,10.03.2016,,0,138.36, -28,2,11.03.2016,,16,138.36, -28,2,12.03.2016,,3,138.5, -28,2,13.03.2016,,3,138.5, -28,2,14.03.2016,,13,138.5, -28,2,15.03.2016,1.0,97,138.5,124.77 -28,2,16.03.2016,1.0,35,138.5,124.77 -28,2,17.03.2016,1.0,79,138.5,124.77 -28,2,18.03.2016,1.0,22,138.5,124.77 -28,2,19.03.2016,1.0,44,138.5,124.77 -28,2,20.03.2016,1.0,41,138.5,124.77 -28,2,21.03.2016,1.0,63,138.5,124.77 -28,2,22.03.2016,,9,138.36, -28,2,23.03.2016,,0,138.36, -28,2,24.03.2016,,0,138.36, -28,2,25.03.2016,,16,138.36, -28,2,26.03.2016,,19,138.36, -28,2,27.03.2016,,6,138.36, -28,2,28.03.2016,,28,138.36, -28,2,29.03.2016,,16,138.36, -28,2,30.03.2016,,9,138.36, -28,2,31.03.2016,,9,138.36, -28,2,01.04.2016,,9,138.36, -28,2,02.04.2016,,9,138.36, -28,2,03.04.2016,,9,138.36, -28,2,04.04.2016,,9,138.36, -28,2,05.04.2016,,9,138.36, -28,2,06.04.2016,,9,138.36, -28,2,07.04.2016,,9,138.36, -28,2,08.04.2016,,9,138.36, -28,2,09.04.2016,,9,138.5, -28,2,10.04.2016,,9,138.5, -28,2,11.04.2016,,9,138.5, -28,2,12.04.2016,,9,138.5, -28,2,13.04.2016,,9,138.5, -28,2,14.04.2016,,9,138.5, -28,2,15.04.2016,,9,138.5, -28,2,16.04.2016,,9,138.5, -28,2,17.04.2016,,9,138.5, -28,2,18.04.2016,,6,138.5, -28,2,19.04.2016,1.0,88,138.5,116.89 -28,2,20.04.2016,1.0,69,138.5,116.89 -28,2,21.04.2016,1.0,47,138.5,116.89 -28,2,22.04.2016,1.0,88,138.5,116.89 -28,2,23.04.2016,1.0,88,138.5,116.89 -28,2,24.04.2016,1.0,66,138.5,116.89 -28,2,25.04.2016,1.0,41,138.5,116.89 -28,2,26.04.2016,,50,138.5, -28,2,27.04.2016,,9,138.5, -28,2,28.04.2016,,3,138.5, -28,2,29.04.2016,,0,138.5, -28,2,30.04.2016,,6,138.5, -28,2,01.05.2016,,0,138.5, -28,2,02.05.2016,,3,138.5, -28,2,03.05.2016,1.0,126,138.5,106.01 -28,2,04.05.2016,1.0,145,138.5,106.01 -28,2,05.05.2016,1.0,138,138.5,106.01 -28,2,06.05.2016,1.0,145,138.5,106.01 -28,2,07.05.2016,1.0,94,138.5,106.01 -28,2,08.05.2016,1.0,85,138.5,106.01 -28,2,09.05.2016,1.0,38,138.5,106.01 -28,2,10.05.2016,,13,138.5, -28,2,11.05.2016,,9,138.5, -28,2,12.05.2016,,0,138.5, -28,2,13.05.2016,,0,138.5, -28,2,14.05.2016,,3,138.5, -28,2,15.05.2016,,9,138.5, -28,2,16.05.2016,,16,138.5, -28,2,17.05.2016,,6,138.5, -28,2,18.05.2016,,6,138.5, -28,2,19.05.2016,,0,138.5, -28,2,20.05.2016,,6,138.5, -28,2,21.05.2016,,0,138.5, -28,2,22.05.2016,,0,138.5, -29,2,01.01.2015,,3,142.57, -29,2,02.01.2015,,19,142.57, -29,2,03.01.2015,,9,142.57, -29,2,04.01.2015,,0,142.57, -29,2,05.01.2015,,25,142.57, -29,2,06.01.2015,,0,142.57, -29,2,07.01.2015,,9,142.57, -29,2,08.01.2015,,3,142.57, -29,2,09.01.2015,,3,142.57, -29,2,10.01.2015,,16,142.57, -29,2,11.01.2015,,6,142.57, -29,2,12.01.2015,,16,142.57, -29,2,13.01.2015,,3,142.57, -29,2,14.01.2015,,3,142.57, -29,2,15.01.2015,,0,142.57, -29,2,16.01.2015,,0,142.57, -29,2,17.01.2015,,25,142.57, -29,2,18.01.2015,,16,142.57, -29,2,19.01.2015,,3,142.57, -29,2,20.01.2015,,3,142.57, -29,2,21.01.2015,,6,142.57, -29,2,22.01.2015,,16,142.57, -29,2,23.01.2015,,6,142.57, -29,2,24.01.2015,,3,142.57, -29,2,25.01.2015,,0,142.57, -29,2,26.01.2015,,3,142.57, -29,2,27.01.2015,,3,142.57, -29,2,28.01.2015,,3,142.57, -29,2,29.01.2015,,13,142.57, -29,2,30.01.2015,,0,142.57, -29,2,31.01.2015,,13,142.57, -29,2,01.02.2015,,6,142.57, -29,2,02.02.2015,,13,142.57, -29,2,03.02.2015,,25,142.57, -29,2,04.02.2015,,9,142.57, -29,2,05.02.2015,,6,142.57, -29,2,06.02.2015,,3,142.57, -29,2,07.02.2015,,13,142.57, -29,2,08.02.2015,,31,142.57, -29,2,09.02.2015,,35,142.57, -29,2,10.02.2015,,16,142.57, -29,2,11.02.2015,,16,142.57, -29,2,12.02.2015,,19,142.57, -29,2,13.02.2015,,3,142.57, -29,2,14.02.2015,,9,142.57, -29,2,15.02.2015,,16,142.57, -29,2,16.02.2015,,3,142.57, -29,2,17.02.2015,,16,142.57, -29,2,18.02.2015,,6,142.57, -29,2,19.02.2015,,6,142.57, -29,2,20.02.2015,,13,142.57, -29,2,21.02.2015,,16,142.57, -29,2,22.02.2015,,6,142.57, -29,2,23.02.2015,,6,142.57, -29,2,24.02.2015,,3,142.57, -29,2,25.02.2015,,6,142.57, -29,2,26.02.2015,,3,142.57, -29,2,27.02.2015,,0,142.57, -29,2,28.02.2015,,13,142.57, -29,2,01.03.2015,,13,142.57, -29,2,02.03.2015,,0,142.57, -29,2,03.03.2015,,9,142.57, -29,2,04.03.2015,,13,142.57, -29,2,05.03.2015,,3,142.57, -29,2,06.03.2015,,9,142.57, -29,2,07.03.2015,,16,142.57, -29,2,08.03.2015,,31,142.57, -29,2,09.03.2015,,6,153.72, -29,2,10.03.2015,,3,153.72, -29,2,11.03.2015,,0,153.72, -29,2,12.03.2015,,3,153.72, -29,2,13.03.2015,,9,153.72, -29,2,14.03.2015,,3,153.72, -29,2,15.03.2015,,9,153.72, -29,2,16.03.2015,,3,153.72, -29,2,17.03.2015,,3,153.72, -29,2,18.03.2015,,3,153.72, -29,2,19.03.2015,,3,153.72, -29,2,20.03.2015,,9,153.72, -29,2,21.03.2015,,0,153.72, -29,2,22.03.2015,,6,153.72, -29,2,23.03.2015,,3,153.72, -29,2,24.03.2015,,3,153.72, -29,2,25.03.2015,,3,153.72, -29,2,26.03.2015,,0,153.72, -29,2,27.03.2015,,3,153.72, -29,2,28.03.2015,,3,153.72, -29,2,29.03.2015,,6,153.72, -29,2,30.03.2015,,0,153.72, -29,2,31.03.2015,,0,153.72, -29,2,01.04.2015,,0,153.72, -29,2,02.04.2015,,0,153.72, -29,2,03.04.2015,,9,153.72, -29,2,04.04.2015,,0,153.72, -29,2,05.04.2015,,3,153.72, -29,2,06.04.2015,,0,153.72, -29,2,07.04.2015,,0,153.72, -29,2,08.04.2015,,3,153.72, -29,2,09.04.2015,,6,153.72, -29,2,10.04.2015,,9,153.72, -29,2,11.04.2015,,3,153.72, -29,2,12.04.2015,,0,153.72, -29,2,13.04.2015,,6,153.72, -29,2,14.04.2015,,0,153.72, -29,2,15.04.2015,,16,153.72, -29,2,16.04.2015,,6,153.72, -29,2,17.04.2015,,9,153.72, -29,2,18.04.2015,,9,153.72, -29,2,19.04.2015,,9,153.72, -29,2,20.04.2015,,0,153.72, -29,2,21.04.2015,,6,153.72, -29,2,22.04.2015,,3,153.72, -29,2,23.04.2015,,0,153.72, -29,2,24.04.2015,,3,153.72, -29,2,25.04.2015,,3,153.72, -29,2,26.04.2015,,16,153.72, -29,2,27.04.2015,,9,153.72, -29,2,28.04.2015,,3,153.72, -29,2,29.04.2015,,0,153.72, -29,2,30.04.2015,,3,153.72, -29,2,01.05.2015,,3,153.72, -29,2,02.05.2015,,9,153.72, -29,2,03.05.2015,,6,153.72, -29,2,04.05.2015,,6,153.72, -29,2,05.05.2015,,3,153.72, -29,2,06.05.2015,,0,153.72, -29,2,07.05.2015,,0,153.72, -29,2,08.05.2015,,0,153.72, -29,2,09.05.2015,,0,153.72, -29,2,10.05.2015,,0,153.72, -29,2,11.05.2015,,13,158.34, -29,2,12.05.2015,,3,158.34, -29,2,13.05.2015,,6,158.34, -29,2,14.05.2015,,9,158.34, -29,2,15.05.2015,,3,158.34, -29,2,16.05.2015,,9,158.34, -29,2,17.05.2015,,13,158.34, -29,2,18.05.2015,,0,158.34, -29,2,19.05.2015,,13,158.34, -29,2,20.05.2015,,3,158.34, -29,2,21.05.2015,,3,158.34, -29,2,22.05.2015,,6,158.34, -29,2,23.05.2015,,3,158.34, -29,2,24.05.2015,,6,158.34, -29,2,25.05.2015,,3,158.34, -29,2,26.05.2015,,6,158.34, -29,2,27.05.2015,,9,158.34, -29,2,28.05.2015,,6,158.34, -29,2,29.05.2015,,9,158.34, -29,2,30.05.2015,,0,158.34, -29,2,31.05.2015,,9,158.34, -29,2,01.06.2015,,3,158.34, -29,2,02.06.2015,,6,158.34, -29,2,03.06.2015,,0,158.34, -29,2,04.06.2015,,9,158.34, -29,2,05.06.2015,,6,158.34, -29,2,06.06.2015,,3,158.34, -29,2,07.06.2015,,6,158.34, -29,2,08.06.2015,,6,158.34, -29,2,09.06.2015,,3,158.34, -29,2,10.06.2015,,16,158.34, -29,2,11.06.2015,,3,158.34, -29,2,12.06.2015,,6,158.34, -29,2,13.06.2015,,0,158.34, -29,2,14.06.2015,,3,158.34, -29,2,15.06.2015,,13,158.34, -29,2,16.06.2015,,3,158.34, -29,2,17.06.2015,,0,158.34, -29,2,18.06.2015,,6,158.34, -29,2,19.06.2015,,3,158.34, -29,2,20.06.2015,,6,158.34, -29,2,21.06.2015,,6,158.34, -29,2,22.06.2015,,6,158.34, -29,2,23.06.2015,,9,158.34, -29,2,24.06.2015,,19,158.34, -29,2,25.06.2015,,9,158.34, -29,2,26.06.2015,,3,158.34, -29,2,27.06.2015,,0,158.34, -29,2,28.06.2015,,3,158.34, -29,2,29.06.2015,,6,158.34, -29,2,30.06.2015,,3,158.34, -29,2,01.07.2015,,3,158.34, -29,2,02.07.2015,,0,158.34, -29,2,03.07.2015,,6,158.34, -29,2,04.07.2015,,3,158.34, -29,2,05.07.2015,,3,158.34, -29,2,06.07.2015,,9,158.34, -29,2,07.07.2015,,3,158.34, -29,2,08.07.2015,,3,158.34, -29,2,09.07.2015,,0,158.34, -29,2,10.07.2015,,6,158.34, -29,2,11.07.2015,,0,158.34, -29,2,12.07.2015,,0,158.34, -29,2,13.07.2015,,3,158.34, -29,2,14.07.2015,,0,158.34, -29,2,15.07.2015,,0,158.34, -29,2,16.07.2015,,0,158.34, -29,2,17.07.2015,,0,158.34, -29,2,18.07.2015,,0,158.34, -29,2,19.07.2015,,3,158.34, -29,2,20.07.2015,,25,158.34, -29,2,21.07.2015,,3,158.34, -29,2,22.07.2015,,3,158.34, -29,2,23.07.2015,,3,158.34, -29,2,24.07.2015,,22,158.34, -29,2,25.07.2015,,25,158.34, -29,2,26.07.2015,,6,158.34, -29,2,27.07.2015,,25,158.34, -29,2,28.07.2015,,6,158.34, -29,2,29.07.2015,,31,158.34, -29,2,30.07.2015,,19,158.34, -29,2,31.07.2015,,0,158.34, -29,2,01.08.2015,,25,158.34, -29,2,02.08.2015,,13,158.34, -29,2,03.08.2015,,13,158.34, -29,2,04.08.2015,,25,158.34, -29,2,05.08.2015,,16,158.34, -29,2,06.08.2015,,25,158.34, -29,2,07.08.2015,,16,158.34, -29,2,08.08.2015,,16,158.34, -29,2,09.08.2015,,16,158.34, -29,2,10.08.2015,,16,158.34, -29,2,11.08.2015,,22,158.34, -29,2,12.08.2015,,3,128.17, -29,2,13.08.2015,,19,128.17, -29,2,14.08.2015,,19,128.17, -29,2,15.08.2015,,22,128.17, -29,2,16.08.2015,,3,128.17, -29,2,17.08.2015,,368,128.17, -29,2,18.08.2015,1.0,531,128.17,100.3 -29,2,19.08.2015,1.0,292,128.17,100.3 -29,2,20.08.2015,1.0,320,128.17,100.3 -29,2,21.08.2015,1.0,361,128.17,100.3 -29,2,22.08.2015,1.0,380,128.17,100.3 -29,2,23.08.2015,1.0,145,128.17,100.3 -29,2,24.08.2015,1.0,295,128.17,100.3 -29,2,25.08.2015,,25,128.17, -29,2,26.08.2015,,63,128.17, -29,2,27.08.2015,,13,128.17, -29,2,28.08.2015,,129,128.17, -29,2,29.08.2015,,123,128.17, -29,2,30.08.2015,,60,128.17, -29,2,31.08.2015,,60,128.17, -29,2,01.09.2015,,72,128.17, -29,2,02.09.2015,,97,128.17, -29,2,03.09.2015,,31,128.17, -29,2,04.09.2015,,44,128.17, -29,2,05.09.2015,,25,128.17, -29,2,06.09.2015,,44,128.17, -29,2,07.09.2015,,28,128.17, -29,2,08.09.2015,,31,128.17, -29,2,09.09.2015,,28,128.17, -29,2,10.09.2015,,6,128.17, -29,2,11.09.2015,,50,128.17, -29,2,12.09.2015,,41,128.17, -29,2,13.09.2015,,6,128.17, -29,2,14.09.2015,,38,128.17, -29,2,15.09.2015,,3,128.17, -29,2,16.09.2015,,57,128.17, -29,2,17.09.2015,,53,128.17, -29,2,18.09.2015,,75,128.17, -29,2,19.09.2015,,31,128.17, -29,2,20.09.2015,,31,128.17, -29,2,21.09.2015,,31,128.17, -29,2,22.09.2015,,31,128.17, -29,2,23.09.2015,,113,128.17, -29,2,24.09.2015,,38,128.17, -29,2,25.09.2015,,38,128.17, -29,2,26.09.2015,,38,171.12, -29,2,27.09.2015,,60,171.12, -29,2,28.09.2015,,53,171.12, -29,2,29.09.2015,,44,171.12, -29,2,30.09.2015,,75,171.12, -29,2,01.10.2015,,44,162.96, -29,2,02.10.2015,,41,162.96, -29,2,03.10.2015,,16,162.96, -29,2,04.10.2015,,41,162.96, -29,2,05.10.2015,,41,162.96, -29,2,06.10.2015,1.0,273,143.39,122.05 -29,2,07.10.2015,1.0,94,143.39,122.05 -29,2,08.10.2015,1.0,226,143.39,122.05 -29,2,09.10.2015,1.0,182,143.39,122.05 -29,2,10.10.2015,1.0,182,143.39,122.05 -29,2,11.10.2015,1.0,179,143.39,122.05 -29,2,12.10.2015,1.0,167,143.39,122.05 -29,2,13.10.2015,,41,143.93, -29,2,14.10.2015,,38,143.93, -29,2,15.10.2015,,35,143.93, -29,2,16.10.2015,,3,143.93, -29,2,17.10.2015,,22,162.96, -29,2,18.10.2015,,19,162.96, -29,2,19.10.2015,,0,162.96, -29,2,20.10.2015,,13,162.96, -29,2,21.10.2015,,3,162.96, -29,2,22.10.2015,,6,162.96, -29,2,23.10.2015,,3,162.96, -29,2,24.10.2015,,3,162.96, -29,2,25.10.2015,,6,162.96, -29,2,26.10.2015,,0,162.96, -29,2,27.10.2015,,0,162.96, -29,2,28.10.2015,,0,162.96, -29,2,29.10.2015,,3,162.96, -29,2,30.10.2015,,6,162.96, -29,2,31.10.2015,,28,162.96, -29,2,01.11.2015,,0,162.96, -29,2,02.11.2015,,0,162.96, -29,2,03.11.2015,,0,162.96, -29,2,04.11.2015,,0,162.96, -29,2,05.11.2015,,0,162.96, -29,2,06.11.2015,,0,162.96, -29,2,07.11.2015,,0,162.96, -29,2,08.11.2015,,0,162.96, -29,2,09.11.2015,,0,162.96, -29,2,10.11.2015,,0,162.96, -29,2,11.11.2015,,0,138.36, -29,2,12.11.2015,,0,138.36, -29,2,13.11.2015,,6,138.36, -29,2,14.11.2015,,19,138.36, -29,2,15.11.2015,,41,138.36, -29,2,16.11.2015,,16,138.36, -29,2,17.11.2015,,9,138.36, -29,2,18.11.2015,,13,138.36, -29,2,19.11.2015,,3,138.36, -29,2,20.11.2015,,0,138.36, -29,2,21.11.2015,,3,138.36, -29,2,22.11.2015,,0,138.36, -29,2,23.11.2015,,13,138.36, -29,2,24.11.2015,,3,138.36, -29,2,25.11.2015,,3,138.36, -29,2,26.11.2015,,9,138.36, -29,2,27.11.2015,,0,138.36, -29,2,28.11.2015,,13,138.36, -29,2,29.11.2015,,0,138.36, -29,2,30.11.2015,,0,138.36, -29,2,01.12.2015,,0,162.96, -29,2,02.12.2015,,0,162.96, -29,2,03.12.2015,,0,138.5, -29,2,04.12.2015,,3,138.5, -29,2,05.12.2015,,6,138.5, -29,2,06.12.2015,,6,138.5, -29,2,07.12.2015,,9,138.5, -29,2,08.12.2015,,13,138.5, -29,2,09.12.2015,,13,138.5, -29,2,10.12.2015,,19,138.5, -29,2,11.12.2015,,6,142.57, -29,2,12.12.2015,,0,142.57, -29,2,13.12.2015,,0,142.57, -29,2,14.12.2015,,0,142.57, -29,2,15.12.2015,,0,142.57, -29,2,16.12.2015,,31,142.57, -29,2,17.12.2015,,6,142.57, -29,2,18.12.2015,,16,142.57, -29,2,19.12.2015,,31,142.57, -29,2,20.12.2015,,35,142.57, -29,2,21.12.2015,,63,142.57, -29,2,22.12.2015,1.0,192,142.57,103.02 -29,2,23.12.2015,1.0,132,142.57,103.02 -29,2,24.12.2015,1.0,358,142.57,103.02 -29,2,25.12.2015,1.0,72,142.57,103.02 -29,2,26.12.2015,1.0,113,142.57,103.02 -29,2,27.12.2015,1.0,85,142.57,103.02 -29,2,28.12.2015,1.0,75,142.57,103.02 -29,2,29.12.2015,,16,138.5, -29,2,30.12.2015,,35,138.5, -29,2,31.12.2015,,69,138.5, -29,2,01.01.2016,,22,138.5, -29,2,02.01.2016,,60,138.5, -29,2,03.01.2016,,3,138.5, -29,2,04.01.2016,,25,138.5, -29,2,05.01.2016,,19,138.5, -29,2,06.01.2016,,19,138.5, -29,2,07.01.2016,,6,138.5, -29,2,08.01.2016,,6,138.5, -29,2,09.01.2016,,22,138.5, -29,2,10.01.2016,,22,138.5, -29,2,11.01.2016,,35,138.5, -29,2,12.01.2016,,22,138.5, -29,2,13.01.2016,,53,138.5, -29,2,14.01.2016,,3,138.5, -29,2,15.01.2016,,22,138.5, -29,2,16.01.2016,,13,142.57, -29,2,17.01.2016,,25,142.57, -29,2,18.01.2016,,35,142.57, -29,2,19.01.2016,1.0,386,142.57,103.02 -29,2,20.01.2016,1.0,380,142.57,103.02 -29,2,21.01.2016,1.0,339,142.57,103.02 -29,2,22.01.2016,1.0,415,142.57,103.02 -29,2,23.01.2016,1.0,355,142.57,103.02 -29,2,24.01.2016,1.0,380,142.57,103.02 -29,2,25.01.2016,1.0,157,142.57,103.02 -29,2,26.01.2016,,19,142.57, -29,2,27.01.2016,,22,142.57, -29,2,28.01.2016,,44,142.57, -29,2,29.01.2016,,19,142.57, -29,2,30.01.2016,,22,142.57, -29,2,31.01.2016,,9,142.57, -29,2,01.02.2016,,19,142.57, -29,2,02.02.2016,,19,142.57, -29,2,03.02.2016,,53,142.57, -29,2,04.02.2016,,38,142.57, -29,2,05.02.2016,,66,142.57, -29,2,06.02.2016,,50,142.57, -29,2,07.02.2016,,25,142.57, -29,2,08.02.2016,,31,142.57, -29,2,09.02.2016,1.0,320,142.57,108.46 -29,2,10.02.2016,1.0,289,138.36,108.46 -29,2,11.02.2016,1.0,371,138.36,108.46 -29,2,12.02.2016,1.0,396,138.36,108.46 -29,2,13.02.2016,1.0,217,138.36,108.46 -29,2,14.02.2016,1.0,104,138.36,108.46 -29,2,15.02.2016,1.0,226,138.36,108.46 -29,2,16.02.2016,,25,138.36, -29,2,17.02.2016,,25,138.36, -29,2,18.02.2016,,22,138.36, -29,2,19.02.2016,,6,138.36, -29,2,20.02.2016,,13,138.36, -29,2,21.02.2016,,22,138.36, -29,2,22.02.2016,,66,138.36, -29,2,23.02.2016,,25,138.36, -29,2,24.02.2016,,3,138.36, -29,2,25.02.2016,,3,138.36, -29,2,26.02.2016,,13,138.36, -29,2,27.02.2016,,3,138.36, -29,2,28.02.2016,,19,138.36, -29,2,29.02.2016,,0,138.36, -29,2,01.03.2016,,3,138.36, -29,2,02.03.2016,,9,138.36, -29,2,03.03.2016,,16,138.36, -29,2,04.03.2016,,6,138.36, -29,2,05.03.2016,,3,138.36, -29,2,06.03.2016,,13,138.36, -29,2,07.03.2016,,16,138.36, -29,2,08.03.2016,,9,138.36, -29,2,09.03.2016,,13,138.36, -29,2,10.03.2016,,3,138.36, -29,2,11.03.2016,,13,138.36, -29,2,12.03.2016,,6,138.5, -29,2,13.03.2016,,0,138.5, -29,2,14.03.2016,,19,138.5, -29,2,15.03.2016,1.0,132,138.5,124.77 -29,2,16.03.2016,1.0,97,138.5,124.77 -29,2,17.03.2016,1.0,135,138.5,124.77 -29,2,18.03.2016,1.0,119,138.5,124.77 -29,2,19.03.2016,1.0,154,138.5,124.77 -29,2,20.03.2016,1.0,116,138.5,124.77 -29,2,21.03.2016,1.0,192,138.5,124.77 -29,2,22.03.2016,,13,138.36, -29,2,23.03.2016,,19,138.36, -29,2,24.03.2016,,0,138.36, -29,2,25.03.2016,,3,138.36, -29,2,26.03.2016,,9,138.36, -29,2,27.03.2016,,0,138.36, -29,2,28.03.2016,,3,138.36, -29,2,29.03.2016,,19,138.36, -29,2,30.03.2016,,6,138.36, -29,2,31.03.2016,,0,138.36, -29,2,01.04.2016,,0,138.36, -29,2,02.04.2016,,0,138.36, -29,2,03.04.2016,,6,138.36, -29,2,04.04.2016,,6,138.36, -29,2,05.04.2016,,6,138.36, -29,2,06.04.2016,,3,138.36, -29,2,07.04.2016,,0,138.36, -29,2,08.04.2016,,13,138.36, -29,2,09.04.2016,,3,138.5, -29,2,10.04.2016,,19,138.5, -29,2,11.04.2016,,9,138.5, -29,2,12.04.2016,,9,138.5, -29,2,13.04.2016,,3,138.5, -29,2,14.04.2016,,3,138.5, -29,2,15.04.2016,,6,138.5, -29,2,16.04.2016,,6,138.5, -29,2,17.04.2016,,6,138.5, -29,2,18.04.2016,,9,138.5, -29,2,19.04.2016,1.0,229,138.5,116.89 -29,2,20.04.2016,1.0,229,138.5,116.89 -29,2,21.04.2016,1.0,198,138.5,116.89 -29,2,22.04.2016,1.0,198,138.5,116.89 -29,2,23.04.2016,1.0,220,138.5,116.89 -29,2,24.04.2016,1.0,229,138.5,116.89 -29,2,25.04.2016,1.0,182,138.5,116.89 -29,2,26.04.2016,,6,138.5, -29,2,27.04.2016,,0,138.5, -29,2,28.04.2016,,13,138.5, -29,2,29.04.2016,,9,138.5, -29,2,30.04.2016,,0,138.5, -29,2,01.05.2016,,6,138.5, -29,2,02.05.2016,,16,138.5, -29,2,03.05.2016,1.0,314,138.5,106.01 -29,2,04.05.2016,1.0,314,138.5,106.01 -29,2,05.05.2016,1.0,346,138.5,106.01 -29,2,06.05.2016,1.0,261,138.5,106.01 -29,2,07.05.2016,1.0,295,138.5,106.01 -29,2,08.05.2016,1.0,364,138.5,106.01 -29,2,09.05.2016,1.0,188,138.5,106.01 -29,2,10.05.2016,,13,138.5, -29,2,11.05.2016,,22,138.5, -29,2,12.05.2016,,25,138.5, -29,2,13.05.2016,,6,138.5, -29,2,14.05.2016,,6,138.5, -29,2,15.05.2016,,25,138.5, -29,2,16.05.2016,,16,138.5, -29,2,17.05.2016,,13,138.5, -29,2,18.05.2016,,3,138.5, -29,2,19.05.2016,,13,138.5, -29,2,20.05.2016,,9,138.5, -29,2,21.05.2016,,6,138.5, -29,2,22.05.2016,,0,138.5, -30,2,01.01.2015,,3,135.78, -30,2,02.01.2015,,6,135.78, -30,2,03.01.2015,,3,135.78, -30,2,04.01.2015,,6,135.78, -30,2,05.01.2015,,3,135.78, -30,2,06.01.2015,,0,135.78, -30,2,07.01.2015,,6,135.78, -30,2,08.01.2015,,3,135.78, -30,2,09.01.2015,,3,135.78, -30,2,10.01.2015,,6,135.78, -30,2,11.01.2015,,3,135.78, -30,2,12.01.2015,,9,135.78, -30,2,13.01.2015,,6,135.78, -30,2,14.01.2015,,9,135.78, -30,2,15.01.2015,,6,135.78, -30,2,16.01.2015,,9,135.78, -30,2,17.01.2015,,3,135.78, -30,2,18.01.2015,,6,135.78, -30,2,19.01.2015,,0,135.78, -30,2,20.01.2015,,6,135.78, -30,2,21.01.2015,,9,135.78, -30,2,22.01.2015,,0,135.78, -30,2,23.01.2015,,3,135.78, -30,2,24.01.2015,,0,135.78, -30,2,25.01.2015,,0,135.78, -30,2,26.01.2015,,6,135.78, -30,2,27.01.2015,,9,135.78, -30,2,28.01.2015,,0,135.78, -30,2,29.01.2015,,3,135.78, -30,2,30.01.2015,,3,135.78, -30,2,31.01.2015,,0,135.78, -30,2,01.02.2015,,0,135.78, -30,2,02.02.2015,,3,135.78, -30,2,03.02.2015,,6,135.78, -30,2,04.02.2015,,6,135.78, -30,2,05.02.2015,,3,135.78, -30,2,06.02.2015,,0,135.78, -30,2,07.02.2015,,3,135.78, -30,2,08.02.2015,,13,135.78, -30,2,09.02.2015,,6,135.78, -30,2,10.02.2015,,6,135.78, -30,2,11.02.2015,,0,135.78, -30,2,12.02.2015,,6,135.78, -30,2,13.02.2015,,9,135.78, -30,2,14.02.2015,,6,135.78, -30,2,15.02.2015,,3,135.78, -30,2,16.02.2015,,3,135.78, -30,2,17.02.2015,,3,135.78, -30,2,18.02.2015,,19,135.78, -30,2,19.02.2015,,0,135.78, -30,2,20.02.2015,,3,135.78, -30,2,21.02.2015,,9,135.78, -30,2,22.02.2015,,0,135.78, -30,2,23.02.2015,,3,135.78, -30,2,24.02.2015,,0,135.78, -30,2,25.02.2015,,6,135.78, -30,2,26.02.2015,,0,135.78, -30,2,27.02.2015,,0,135.78, -30,2,28.02.2015,,3,135.78, -30,2,01.03.2015,,3,135.78, -30,2,02.03.2015,,0,146.38, -30,2,03.03.2015,,0,146.38, -30,2,04.03.2015,,6,146.38, -30,2,05.03.2015,,6,146.38, -30,2,06.03.2015,,6,146.38, -30,2,07.03.2015,,9,146.38, -30,2,08.03.2015,,16,146.38, -30,2,09.03.2015,,3,146.38, -30,2,10.03.2015,,0,146.38, -30,2,11.03.2015,,0,146.38, -30,2,12.03.2015,,13,146.38, -30,2,13.03.2015,,9,146.38, -30,2,14.03.2015,,3,146.38, -30,2,15.03.2015,,13,146.38, -30,2,16.03.2015,,3,146.38, -30,2,17.03.2015,,3,146.38, -30,2,18.03.2015,,6,146.38, -30,2,19.03.2015,,6,146.38, -30,2,20.03.2015,,6,146.38, -30,2,21.03.2015,,19,146.38, -30,2,22.03.2015,,3,146.38, -30,2,23.03.2015,,13,146.38, -30,2,24.03.2015,,3,146.38, -30,2,25.03.2015,,3,146.38, -30,2,26.03.2015,,3,146.38, -30,2,27.03.2015,,6,146.38, -30,2,28.03.2015,,6,146.38, -30,2,29.03.2015,,3,146.38, -30,2,30.03.2015,,0,146.38, -30,2,31.03.2015,,9,146.38, -30,2,01.04.2015,,6,146.38, -30,2,02.04.2015,,3,146.38, -30,2,03.04.2015,,0,146.38, -30,2,04.04.2015,,9,146.38, -30,2,05.04.2015,,0,146.38, -30,2,06.04.2015,,0,146.38, -30,2,07.04.2015,,6,146.38, -30,2,08.04.2015,,0,146.38, -30,2,09.04.2015,,3,146.38, -30,2,10.04.2015,,3,146.38, -30,2,11.04.2015,,0,146.38, -30,2,12.04.2015,,3,146.38, -30,2,13.04.2015,,0,146.38, -30,2,14.04.2015,,9,146.38, -30,2,15.04.2015,,3,146.38, -30,2,16.04.2015,,0,146.38, -30,2,17.04.2015,,6,146.38, -30,2,18.04.2015,,6,146.38, -30,2,19.04.2015,,3,146.38, -30,2,20.04.2015,,6,145.84, -30,2,21.04.2015,,6,145.84, -30,2,22.04.2015,,0,145.84, -30,2,23.04.2015,,0,145.84, -30,2,24.04.2015,,3,145.84, -30,2,25.04.2015,,0,145.84, -30,2,26.04.2015,,0,145.84, -30,2,27.04.2015,,0,150.73, -30,2,28.04.2015,,0,150.73, -30,2,29.04.2015,,0,150.73, -30,2,30.04.2015,,6,150.73, -30,2,01.05.2015,,0,150.73, -30,2,02.05.2015,,0,150.73, -30,2,03.05.2015,,3,150.73, -30,2,04.05.2015,,3,150.73, -30,2,05.05.2015,,0,150.73, -30,2,06.05.2015,,0,150.73, -30,2,07.05.2015,,0,150.73, -30,2,08.05.2015,,0,150.73, -30,2,09.05.2015,,0,150.73, -30,2,10.05.2015,,0,150.73, -30,2,11.05.2015,,3,150.73, -30,2,12.05.2015,,6,150.73, -30,2,13.05.2015,,0,150.73, -30,2,14.05.2015,,0,150.73, -30,2,15.05.2015,,3,150.73, -30,2,16.05.2015,,0,150.73, -30,2,17.05.2015,,0,150.73, -30,2,18.05.2015,,0,150.73, -30,2,19.05.2015,,3,150.73, -30,2,20.05.2015,,3,150.73, -30,2,21.05.2015,,0,150.73, -30,2,22.05.2015,,0,150.73, -30,2,23.05.2015,,9,150.73, -30,2,24.05.2015,,6,150.73, -30,2,25.05.2015,,0,150.73, -30,2,26.05.2015,,3,150.73, -30,2,27.05.2015,,0,150.73, -30,2,28.05.2015,,0,150.73, -30,2,29.05.2015,,0,150.73, -30,2,30.05.2015,,0,150.73, -30,2,31.05.2015,,0,150.73, -30,2,01.06.2015,,0,150.73, -30,2,02.06.2015,,0,150.73, -30,2,03.06.2015,,0,150.73, -30,2,04.06.2015,,0,150.73, -30,2,05.06.2015,,0,150.73, -30,2,06.06.2015,,9,150.73, -30,2,07.06.2015,,6,150.73, -30,2,08.06.2015,,9,150.73, -30,2,09.06.2015,,0,150.73, -30,2,10.06.2015,,0,150.73, -30,2,11.06.2015,,0,150.73, -30,2,12.06.2015,,3,150.73, -30,2,13.06.2015,,0,150.73, -30,2,14.06.2015,,3,150.73, -30,2,15.06.2015,,9,150.73, -30,2,16.06.2015,,0,150.73, -30,2,17.06.2015,,3,150.73, -30,2,18.06.2015,,3,150.73, -30,2,19.06.2015,,0,150.73, -30,2,20.06.2015,,0,150.73, -30,2,21.06.2015,,0,150.73, -30,2,22.06.2015,,0,150.73, -30,2,23.06.2015,,0,150.73, -30,2,24.06.2015,,3,150.73, -30,2,25.06.2015,,0,150.73, -30,2,26.06.2015,,0,150.73, -30,2,27.06.2015,,0,150.73, -30,2,28.06.2015,,0,150.73, -30,2,29.06.2015,,0,150.73, -30,2,30.06.2015,,0,150.73, -30,2,01.07.2015,,0,150.73, -30,2,02.07.2015,,0,150.73, -30,2,03.07.2015,,6,150.73, -30,2,04.07.2015,,0,150.73, -30,2,05.07.2015,,3,150.73, -30,2,06.07.2015,,3,150.73, -30,2,07.07.2015,,6,150.73, -30,2,08.07.2015,,0,150.73, -30,2,09.07.2015,,3,150.73, -30,2,10.07.2015,,0,150.73, -30,2,11.07.2015,,3,150.73, -30,2,12.07.2015,,6,150.73, -30,2,13.07.2015,,3,150.73, -30,2,14.07.2015,,0,150.73, -30,2,15.07.2015,,3,150.73, -30,2,16.07.2015,,3,150.73, -30,2,17.07.2015,,3,150.73, -30,2,18.07.2015,,0,150.73, -30,2,19.07.2015,,3,150.73, -30,2,20.07.2015,,3,150.73, -30,2,21.07.2015,,0,150.73, -30,2,22.07.2015,,16,150.73, -30,2,23.07.2015,,9,150.73, -30,2,24.07.2015,,16,150.73, -30,2,25.07.2015,,16,150.73, -30,2,26.07.2015,,6,150.73, -30,2,27.07.2015,,6,150.73, -30,2,28.07.2015,,0,150.73, -30,2,29.07.2015,,6,150.73, -30,2,30.07.2015,,13,150.73, -30,2,31.07.2015,,0,150.73, -30,2,01.08.2015,,13,150.73, -30,2,02.08.2015,,6,150.73, -30,2,03.08.2015,,13,150.73, -30,2,04.08.2015,,0,150.73, -30,2,05.08.2015,,9,150.73, -30,2,06.08.2015,,19,150.73, -30,2,07.08.2015,,35,150.73, -30,2,08.08.2015,,35,150.73, -30,2,09.08.2015,,9,150.73, -30,2,10.08.2015,,0,150.73, -30,2,11.08.2015,,0,150.73, -30,2,12.08.2015,,3,128.17, -30,2,13.08.2015,,9,128.17, -30,2,14.08.2015,,3,128.17, -30,2,15.08.2015,,6,128.17, -30,2,16.08.2015,,3,128.17, -30,2,17.08.2015,,9,128.17, -30,2,18.08.2015,1.0,123,128.17,100.3 -30,2,19.08.2015,1.0,113,128.17,100.3 -30,2,20.08.2015,1.0,94,128.17,100.3 -30,2,21.08.2015,1.0,60,128.17,100.3 -30,2,22.08.2015,1.0,97,128.17,100.3 -30,2,23.08.2015,1.0,104,128.17,100.3 -30,2,24.08.2015,1.0,126,128.17,100.3 -30,2,25.08.2015,,3,128.17, -30,2,26.08.2015,,19,128.17, -30,2,27.08.2015,,3,128.17, -30,2,28.08.2015,,22,128.17, -30,2,29.08.2015,,31,128.17, -30,2,30.08.2015,,38,128.17, -30,2,31.08.2015,,6,128.17, -30,2,01.09.2015,,3,128.17, -30,2,02.09.2015,,0,128.17, -30,2,03.09.2015,,9,128.17, -30,2,04.09.2015,,6,128.17, -30,2,05.09.2015,,3,128.17, -30,2,06.09.2015,,0,128.17, -30,2,07.09.2015,,9,128.17, -30,2,08.09.2015,,0,128.17, -30,2,09.09.2015,,0,128.17, -30,2,10.09.2015,,13,128.17, -30,2,11.09.2015,,0,128.17, -30,2,12.09.2015,,3,128.17, -30,2,13.09.2015,,0,128.17, -30,2,14.09.2015,,3,128.17, -30,2,15.09.2015,,3,128.17, -30,2,16.09.2015,,0,128.17, -30,2,17.09.2015,,16,128.17, -30,2,18.09.2015,,6,128.17, -30,2,19.09.2015,,9,128.17, -30,2,20.09.2015,,31,128.17, -30,2,21.09.2015,,13,128.17, -30,2,22.09.2015,,0,128.17, -30,2,23.09.2015,,9,128.17, -30,2,24.09.2015,,9,128.17, -30,2,25.09.2015,,0,128.17, -30,2,26.09.2015,,0,128.17, -30,2,27.09.2015,,0,128.17, -30,2,28.09.2015,,0,128.17, -30,2,29.09.2015,,0,128.17, -30,2,30.09.2015,,0,128.17, -30,2,01.10.2015,,0,162.96, -30,2,02.10.2015,,0,162.96, -30,2,03.10.2015,,6,162.96, -30,2,04.10.2015,,6,162.96, -30,2,05.10.2015,,6,162.96, -30,2,06.10.2015,1.0,75,143.39,122.05 -30,2,07.10.2015,1.0,50,143.39,122.05 -30,2,08.10.2015,1.0,53,143.39,122.05 -30,2,09.10.2015,1.0,72,143.39,122.05 -30,2,10.10.2015,1.0,60,143.39,122.05 -30,2,11.10.2015,1.0,60,143.39,122.05 -30,2,12.10.2015,1.0,41,143.39,122.05 -30,2,13.10.2015,1.0,53,143.93,122.05 -30,2,14.10.2015,1.0,13,143.93,122.05 -30,2,15.10.2015,1.0,35,143.93,122.05 -30,2,16.10.2015,1.0,60,143.93,122.05 -30,2,17.10.2015,1.0,53,162.96,122.05 -30,2,18.10.2015,1.0,47,162.96,122.05 -30,2,19.10.2015,,0,162.96, -30,2,20.10.2015,,0,162.96, -30,2,21.10.2015,,3,162.96, -30,2,22.10.2015,,3,162.96, -30,2,23.10.2015,,0,162.96, -30,2,24.10.2015,,0,162.96, -30,2,25.10.2015,,3,162.96, -30,2,26.10.2015,,0,162.96, -30,2,27.10.2015,,3,162.96, -30,2,28.10.2015,,0,162.96, -30,2,29.10.2015,,0,162.96, -30,2,30.10.2015,,3,162.96, -30,2,31.10.2015,,3,162.96, -30,2,01.11.2015,,3,162.96, -30,2,02.11.2015,,3,162.96, -30,2,03.11.2015,,0,162.96, -30,2,04.11.2015,,3,162.96, -30,2,05.11.2015,,0,162.96, -30,2,06.11.2015,,0,162.96, -30,2,07.11.2015,,0,162.96, -30,2,08.11.2015,,0,162.96, -30,2,09.11.2015,,0,162.96, -30,2,10.11.2015,,0,162.96, -30,2,11.11.2015,,0,138.36, -30,2,12.11.2015,,0,138.36, -30,2,13.11.2015,,0,138.36, -30,2,14.11.2015,,0,138.36, -30,2,15.11.2015,,0,138.36, -30,2,16.11.2015,,0,138.36, -30,2,17.11.2015,,0,138.36, -30,2,18.11.2015,,3,138.36, -30,2,19.11.2015,,6,138.36, -30,2,20.11.2015,,0,138.36, -30,2,21.11.2015,,0,138.36, -30,2,22.11.2015,,0,138.36, -30,2,23.11.2015,,0,138.36, -30,2,24.11.2015,,0,138.36, -30,2,25.11.2015,,0,138.36, -30,2,26.11.2015,,3,138.36, -30,2,27.11.2015,,0,138.36, -30,2,28.11.2015,,13,138.36, -30,2,29.11.2015,,6,138.36, -30,2,30.11.2015,,0,138.36, -30,2,01.12.2015,,0,162.96, -30,2,02.12.2015,,0,162.96, -30,2,03.12.2015,,0,138.5, -30,2,04.12.2015,,0,138.5, -30,2,05.12.2015,,0,138.5, -30,2,06.12.2015,,0,138.5, -30,2,07.12.2015,,0,138.5, -30,2,08.12.2015,,0,138.5, -30,2,09.12.2015,,6,138.5, -30,2,10.12.2015,,3,138.5, -30,2,11.12.2015,,3,142.57, -30,2,12.12.2015,,3,142.57, -30,2,13.12.2015,,16,142.57, -30,2,14.12.2015,,0,142.57, -30,2,15.12.2015,,22,142.57, -30,2,16.12.2015,,3,142.57, -30,2,17.12.2015,,6,142.57, -30,2,18.12.2015,,6,142.57, -30,2,19.12.2015,,9,142.57, -30,2,20.12.2015,,6,142.57, -30,2,21.12.2015,,19,142.57, -30,2,22.12.2015,1.0,110,142.57,103.02 -30,2,23.12.2015,1.0,170,142.57,103.02 -30,2,24.12.2015,1.0,113,142.57,103.02 -30,2,25.12.2015,1.0,148,142.57,103.02 -30,2,26.12.2015,1.0,16,142.57,103.02 -30,2,27.12.2015,1.0,53,142.57,103.02 -30,2,28.12.2015,1.0,19,142.57,103.02 -30,2,29.12.2015,,0,138.5, -30,2,30.12.2015,,0,138.5, -30,2,31.12.2015,,3,138.5, -30,2,01.01.2016,,0,138.5, -30,2,02.01.2016,,6,138.5, -30,2,03.01.2016,,9,138.5, -30,2,04.01.2016,,0,138.5, -30,2,05.01.2016,,0,138.5, -30,2,06.01.2016,,0,138.5, -30,2,07.01.2016,,3,138.5, -30,2,08.01.2016,,3,138.5, -30,2,09.01.2016,,6,138.5, -30,2,10.01.2016,,9,138.5, -30,2,11.01.2016,,6,138.5, -30,2,12.01.2016,,13,138.5, -30,2,13.01.2016,,3,138.5, -30,2,14.01.2016,,6,138.5, -30,2,15.01.2016,,6,138.5, -30,2,16.01.2016,,0,142.57, -30,2,17.01.2016,,16,142.57, -30,2,18.01.2016,,9,142.57, -30,2,19.01.2016,1.0,107,142.57,103.02 -30,2,20.01.2016,1.0,123,142.57,103.02 -30,2,21.01.2016,1.0,97,142.57,103.02 -30,2,22.01.2016,1.0,135,142.57,103.02 -30,2,23.01.2016,1.0,201,142.57,103.02 -30,2,24.01.2016,1.0,123,142.57,103.02 -30,2,25.01.2016,1.0,97,142.57,103.02 -30,2,26.01.2016,,13,142.57, -30,2,27.01.2016,,13,142.57, -30,2,28.01.2016,,13,142.57, -30,2,29.01.2016,,9,142.57, -30,2,30.01.2016,,6,142.57, -30,2,31.01.2016,,6,142.57, -30,2,01.02.2016,,13,142.57, -30,2,02.02.2016,,6,142.57, -30,2,03.02.2016,,13,142.57, -30,2,04.02.2016,,19,142.57, -30,2,05.02.2016,,3,142.57, -30,2,06.02.2016,,6,142.57, -30,2,07.02.2016,,19,142.57, -30,2,08.02.2016,,28,142.57, -30,2,09.02.2016,1.0,151,142.57,108.46 -30,2,10.02.2016,1.0,75,138.36,108.46 -30,2,11.02.2016,1.0,116,138.36,108.46 -30,2,12.02.2016,1.0,132,138.36,108.46 -30,2,13.02.2016,1.0,101,138.36,108.46 -30,2,14.02.2016,1.0,119,138.36,108.46 -30,2,15.02.2016,1.0,79,138.36,108.46 -30,2,16.02.2016,,3,138.36, -30,2,17.02.2016,,0,138.36, -30,2,18.02.2016,,0,138.36, -30,2,19.02.2016,,6,138.36, -30,2,20.02.2016,,9,138.36, -30,2,21.02.2016,,19,138.36, -30,2,22.02.2016,,0,138.36, -30,2,23.02.2016,,3,138.36, -30,2,24.02.2016,,3,138.36, -30,2,25.02.2016,,3,138.36, -30,2,26.02.2016,,3,138.36, -30,2,27.02.2016,,3,138.36, -30,2,28.02.2016,,0,138.36, -30,2,29.02.2016,,6,138.36, -30,2,01.03.2016,,3,138.36, -30,2,02.03.2016,,9,138.36, -30,2,03.03.2016,,0,138.36, -30,2,04.03.2016,,6,138.36, -30,2,05.03.2016,,0,138.36, -30,2,06.03.2016,,3,138.36, -30,2,07.03.2016,,3,138.36, -30,2,08.03.2016,,3,138.36, -30,2,09.03.2016,,6,138.36, -30,2,10.03.2016,,3,138.36, -30,2,11.03.2016,,9,138.36, -30,2,12.03.2016,,6,138.5, -30,2,13.03.2016,,0,138.5, -30,2,14.03.2016,,3,138.5, -30,2,15.03.2016,1.0,53,138.5,124.77 -30,2,16.03.2016,1.0,63,138.5,124.77 -30,2,17.03.2016,1.0,25,138.5,124.77 -30,2,18.03.2016,1.0,53,138.5,124.77 -30,2,19.03.2016,1.0,35,138.5,124.77 -30,2,20.03.2016,1.0,69,138.5,124.77 -30,2,21.03.2016,1.0,38,138.5,124.77 -30,2,22.03.2016,,0,138.36, -30,2,23.03.2016,,0,138.36, -30,2,24.03.2016,,9,138.36, -30,2,25.03.2016,,13,138.36, -30,2,26.03.2016,,0,138.36, -30,2,27.03.2016,,3,138.36, -30,2,28.03.2016,,3,138.36, -30,2,29.03.2016,,6,138.36, -30,2,30.03.2016,,16,138.36, -30,2,31.03.2016,,3,138.36, -30,2,01.04.2016,,0,138.36, -30,2,02.04.2016,,9,138.36, -30,2,03.04.2016,,13,138.36, -30,2,04.04.2016,,3,138.36, -30,2,05.04.2016,,6,138.36, -30,2,06.04.2016,,19,138.36, -30,2,07.04.2016,,0,138.36, -30,2,08.04.2016,,9,138.36, -30,2,09.04.2016,,13,138.5, -30,2,10.04.2016,,22,138.5, -30,2,11.04.2016,,3,138.5, -30,2,12.04.2016,,3,138.5, -30,2,13.04.2016,,0,138.5, -30,2,14.04.2016,,6,138.5, -30,2,15.04.2016,,9,138.5, -30,2,16.04.2016,,3,138.5, -30,2,17.04.2016,,6,138.5, -30,2,18.04.2016,,16,138.5, -30,2,19.04.2016,1.0,94,138.5,116.89 -30,2,20.04.2016,1.0,85,138.5,116.89 -30,2,21.04.2016,1.0,91,138.5,116.89 -30,2,22.04.2016,1.0,85,138.5,116.89 -30,2,23.04.2016,1.0,85,138.5,116.89 -30,2,24.04.2016,1.0,69,138.5,116.89 -30,2,25.04.2016,1.0,69,138.5,116.89 -30,2,26.04.2016,,9,138.5, -30,2,27.04.2016,,0,138.5, -30,2,28.04.2016,,6,138.5, -30,2,29.04.2016,,0,138.5, -30,2,30.04.2016,,3,138.5, -30,2,01.05.2016,,0,138.5, -30,2,02.05.2016,,6,138.5, -30,2,03.05.2016,1.0,110,138.5,106.01 -30,2,04.05.2016,1.0,113,138.5,106.01 -30,2,05.05.2016,1.0,141,138.5,106.01 -30,2,06.05.2016,1.0,101,138.5,106.01 -30,2,07.05.2016,1.0,148,138.5,106.01 -30,2,08.05.2016,1.0,69,138.5,106.01 -30,2,09.05.2016,1.0,69,138.5,106.01 -30,2,10.05.2016,,0,138.5, -30,2,11.05.2016,,3,138.5, -30,2,12.05.2016,,60,138.5, -30,2,13.05.2016,,0,138.5, -30,2,14.05.2016,,6,138.5, -30,2,15.05.2016,,13,138.5, -30,2,16.05.2016,,0,138.5, -30,2,17.05.2016,,0,138.5, -30,2,18.05.2016,,0,138.5, -30,2,19.05.2016,,0,138.5, -30,2,20.05.2016,,0,138.5, -30,2,21.05.2016,,0,138.5, -30,2,22.05.2016,,0,138.5, -31,2,01.01.2015,,0,135.78, -31,2,02.01.2015,,3,135.78, -31,2,03.01.2015,,0,135.78, -31,2,04.01.2015,,9,135.78, -31,2,05.01.2015,,6,135.78, -31,2,06.01.2015,,3,135.78, -31,2,07.01.2015,,0,135.78, -31,2,08.01.2015,,0,135.78, -31,2,09.01.2015,,0,135.78, -31,2,10.01.2015,,0,135.78, -31,2,11.01.2015,,0,135.78, -31,2,12.01.2015,,0,135.78, -31,2,13.01.2015,,6,135.78, -31,2,14.01.2015,,0,135.78, -31,2,15.01.2015,,0,135.78, -31,2,16.01.2015,,9,135.78, -31,2,17.01.2015,,6,135.78, -31,2,18.01.2015,,3,135.78, -31,2,19.01.2015,,9,135.78, -31,2,20.01.2015,,3,135.78, -31,2,21.01.2015,,3,135.78, -31,2,22.01.2015,,3,135.78, -31,2,23.01.2015,,3,135.78, -31,2,24.01.2015,,0,135.78, -31,2,25.01.2015,,3,135.78, -31,2,26.01.2015,,3,135.78, -31,2,27.01.2015,,0,135.78, -31,2,28.01.2015,,9,135.78, -31,2,29.01.2015,,0,135.78, -31,2,30.01.2015,,6,135.78, -31,2,31.01.2015,,3,135.78, -31,2,01.02.2015,,13,135.78, -31,2,02.02.2015,,13,135.78, -31,2,03.02.2015,,16,135.78, -31,2,04.02.2015,,6,135.78, -31,2,05.02.2015,,16,135.78, -31,2,06.02.2015,,9,135.78, -31,2,07.02.2015,,3,135.78, -31,2,08.02.2015,,13,135.78, -31,2,09.02.2015,,9,135.78, -31,2,10.02.2015,,9,135.78, -31,2,11.02.2015,,9,135.78, -31,2,12.02.2015,,9,135.78, -31,2,13.02.2015,,0,135.78, -31,2,14.02.2015,,0,135.78, -31,2,15.02.2015,,6,135.78, -31,2,16.02.2015,,16,135.78, -31,2,17.02.2015,,6,135.78, -31,2,18.02.2015,,3,135.78, -31,2,19.02.2015,,3,135.78, -31,2,20.02.2015,,6,135.78, -31,2,21.02.2015,,6,135.78, -31,2,22.02.2015,,3,135.78, -31,2,23.02.2015,,0,135.78, -31,2,24.02.2015,,6,135.78, -31,2,25.02.2015,,3,135.78, -31,2,26.02.2015,,3,135.78, -31,2,27.02.2015,,3,135.78, -31,2,28.02.2015,,0,135.78, -31,2,01.03.2015,,0,135.78, -31,2,02.03.2015,,0,146.38, -31,2,03.03.2015,,6,146.38, -31,2,04.03.2015,,3,146.38, -31,2,05.03.2015,,3,146.38, -31,2,06.03.2015,,0,146.38, -31,2,07.03.2015,,3,146.38, -31,2,08.03.2015,,0,146.38, -31,2,09.03.2015,,0,146.38, -31,2,10.03.2015,,3,146.38, -31,2,11.03.2015,,6,146.38, -31,2,12.03.2015,,0,146.38, -31,2,13.03.2015,,0,146.38, -31,2,14.03.2015,,0,146.38, -31,2,15.03.2015,,3,146.38, -31,2,16.03.2015,,16,146.38, -31,2,17.03.2015,,0,146.38, -31,2,18.03.2015,,0,146.38, -31,2,19.03.2015,,6,146.38, -31,2,20.03.2015,,0,146.38, -31,2,21.03.2015,,0,146.38, -31,2,22.03.2015,,3,146.38, -31,2,23.03.2015,,0,146.38, -31,2,24.03.2015,,3,146.38, -31,2,25.03.2015,,9,146.38, -31,2,26.03.2015,,6,146.38, -31,2,27.03.2015,,3,146.38, -31,2,28.03.2015,,0,146.38, -31,2,29.03.2015,,3,146.38, -31,2,30.03.2015,,3,146.38, -31,2,31.03.2015,,0,146.38, -31,2,01.04.2015,,0,146.38, -31,2,02.04.2015,,0,146.38, -31,2,03.04.2015,,0,146.38, -31,2,04.04.2015,,0,146.38, -31,2,05.04.2015,,3,146.38, -31,2,06.04.2015,,0,146.38, -31,2,07.04.2015,,0,146.38, -31,2,08.04.2015,,9,146.38, -31,2,09.04.2015,,0,146.38, -31,2,10.04.2015,,0,146.38, -31,2,11.04.2015,,0,146.38, -31,2,12.04.2015,,0,146.38, -31,2,13.04.2015,,0,146.38, -31,2,14.04.2015,,0,146.38, -31,2,15.04.2015,,0,146.38, -31,2,16.04.2015,,3,146.38, -31,2,17.04.2015,,0,146.38, -31,2,18.04.2015,,0,146.38, -31,2,19.04.2015,,19,146.38, -31,2,20.04.2015,,0,145.84, -31,2,21.04.2015,,0,145.84, -31,2,22.04.2015,,0,145.84, -31,2,23.04.2015,,0,145.84, -31,2,24.04.2015,,0,145.84, -31,2,25.04.2015,,0,145.84, -31,2,26.04.2015,,0,150.73, -31,2,27.04.2015,,0,150.73, -31,2,28.04.2015,,0,150.73, -31,2,29.04.2015,,3,150.73, -31,2,30.04.2015,,0,150.73, -31,2,01.05.2015,,0,150.73, -31,2,02.05.2015,,0,150.73, -31,2,03.05.2015,,0,150.73, -31,2,04.05.2015,,0,150.73, -31,2,05.05.2015,,0,150.73, -31,2,06.05.2015,,0,150.73, -31,2,07.05.2015,,6,150.73, -31,2,08.05.2015,,0,150.73, -31,2,09.05.2015,,0,150.73, -31,2,10.05.2015,,3,150.73, -31,2,11.05.2015,,0,150.73, -31,2,12.05.2015,,0,150.73, -31,2,13.05.2015,,0,150.73, -31,2,14.05.2015,,0,150.73, -31,2,15.05.2015,,0,150.73, -31,2,16.05.2015,,0,150.73, -31,2,17.05.2015,,9,150.73, -31,2,18.05.2015,,3,150.73, -31,2,19.05.2015,,0,150.73, -31,2,20.05.2015,,3,150.73, -31,2,21.05.2015,,0,150.73, -31,2,22.05.2015,,0,150.73, -31,2,23.05.2015,,0,150.73, -31,2,24.05.2015,,0,150.73, -31,2,25.05.2015,,0,150.73, -31,2,26.05.2015,,0,150.73, -31,2,27.05.2015,,0,150.73, -31,2,28.05.2015,,0,150.73, -31,2,29.05.2015,,0,150.73, -31,2,30.05.2015,,0,150.73, -31,2,31.05.2015,,0,150.73, -31,2,01.06.2015,,0,150.73, -31,2,02.06.2015,,6,150.73, -31,2,03.06.2015,,0,150.73, -31,2,04.06.2015,,0,150.73, -31,2,05.06.2015,,0,150.73, -31,2,06.06.2015,,0,150.73, -31,2,07.06.2015,,0,150.73, -31,2,08.06.2015,,0,150.73, -31,2,09.06.2015,,3,150.73, -31,2,10.06.2015,,0,150.73, -31,2,11.06.2015,,0,150.73, -31,2,12.06.2015,,0,150.73, -31,2,13.06.2015,,3,150.73, -31,2,14.06.2015,,0,150.73, -31,2,15.06.2015,,0,150.73, -31,2,16.06.2015,,0,150.73, -31,2,17.06.2015,,0,150.73, -31,2,18.06.2015,,0,150.73, -31,2,19.06.2015,,0,150.73, -31,2,20.06.2015,,0,150.73, -31,2,21.06.2015,,0,150.73, -31,2,22.06.2015,,0,150.73, -31,2,23.06.2015,,0,150.73, -31,2,24.06.2015,,0,150.73, -31,2,25.06.2015,,0,150.73, -31,2,26.06.2015,,0,150.73, -31,2,27.06.2015,,0,150.73, -31,2,28.06.2015,,3,150.73, -31,2,29.06.2015,,6,150.73, -31,2,30.06.2015,,6,150.73, -31,2,01.07.2015,,0,150.73, -31,2,02.07.2015,,0,150.73, -31,2,03.07.2015,,0,150.73, -31,2,04.07.2015,,0,150.73, -31,2,05.07.2015,,0,150.73, -31,2,06.07.2015,,0,150.73, -31,2,07.07.2015,,6,150.73, -31,2,08.07.2015,,0,150.73, -31,2,09.07.2015,,0,150.73, -31,2,10.07.2015,,0,150.73, -31,2,11.07.2015,,0,150.73, -31,2,12.07.2015,,0,150.73, -31,2,13.07.2015,,0,150.73, -31,2,14.07.2015,,9,150.73, -31,2,15.07.2015,,3,150.73, -31,2,16.07.2015,,0,150.73, -31,2,17.07.2015,,0,150.73, -31,2,18.07.2015,,0,150.73, -31,2,19.07.2015,,9,150.73, -31,2,20.07.2015,,0,150.73, -31,2,21.07.2015,,0,150.73, -31,2,22.07.2015,,0,150.73, -31,2,23.07.2015,,13,150.73, -31,2,24.07.2015,,28,150.73, -31,2,25.07.2015,,13,150.73, -31,2,26.07.2015,,25,150.73, -31,2,27.07.2015,,35,150.73, -31,2,28.07.2015,,47,150.73, -31,2,29.07.2015,,31,150.73, -31,2,30.07.2015,,22,150.73, -31,2,31.07.2015,,25,150.73, -31,2,01.08.2015,,19,150.73, -31,2,02.08.2015,,16,150.73, -31,2,03.08.2015,,25,150.73, -31,2,04.08.2015,,16,150.73, -31,2,05.08.2015,,41,150.73, -31,2,06.08.2015,,13,150.73, -31,2,07.08.2015,,22,150.73, -31,2,08.08.2015,,9,150.73, -31,2,09.08.2015,,16,150.73, -31,2,10.08.2015,,25,150.73, -31,2,11.08.2015,,13,150.73, -31,2,12.08.2015,,13,128.17, -31,2,13.08.2015,,19,128.17, -31,2,14.08.2015,,3,128.17, -31,2,15.08.2015,,13,128.17, -31,2,16.08.2015,,19,128.17, -31,2,17.08.2015,,57,128.17, -31,2,18.08.2015,1.0,188,128.17,100.3 -31,2,19.08.2015,1.0,160,128.17,100.3 -31,2,20.08.2015,1.0,154,128.17,100.3 -31,2,21.08.2015,1.0,163,128.17,100.3 -31,2,22.08.2015,1.0,75,128.17,100.3 -31,2,23.08.2015,1.0,141,128.17,100.3 -31,2,24.08.2015,1.0,110,128.17,100.3 -31,2,25.08.2015,,19,128.17, -31,2,26.08.2015,,16,128.17, -31,2,27.08.2015,,16,128.17, -31,2,28.08.2015,,13,128.17, -31,2,29.08.2015,,19,128.17, -31,2,30.08.2015,,22,128.17, -31,2,31.08.2015,,6,128.17, -31,2,01.09.2015,,6,128.17, -31,2,02.09.2015,,3,128.17, -31,2,03.09.2015,,6,128.17, -31,2,04.09.2015,,3,128.17, -31,2,05.09.2015,,13,128.17, -31,2,06.09.2015,,3,128.17, -31,2,07.09.2015,,6,128.17, -31,2,08.09.2015,,22,128.17, -31,2,09.09.2015,,22,128.17, -31,2,10.09.2015,,6,128.17, -31,2,11.09.2015,,0,128.17, -31,2,12.09.2015,,0,128.17, -31,2,13.09.2015,,0,128.17, -31,2,14.09.2015,,0,128.17, -31,2,15.09.2015,,0,128.17, -31,2,16.09.2015,,6,128.17, -31,2,17.09.2015,,3,128.17, -31,2,18.09.2015,,0,128.17, -31,2,19.09.2015,,13,128.17, -31,2,20.09.2015,,9,128.17, -31,2,21.09.2015,,19,128.17, -31,2,22.09.2015,,3,128.17, -31,2,23.09.2015,,3,128.17, -31,2,24.09.2015,,0,128.17, -31,2,25.09.2015,,6,128.17, -31,2,26.09.2015,,0,128.17, -31,2,27.09.2015,,3,128.17, -31,2,28.09.2015,,0,128.17, -31,2,29.09.2015,,0,128.17, -31,2,30.09.2015,,6,128.17, -31,2,01.10.2015,,0,162.96, -31,2,02.10.2015,,0,162.96, -31,2,03.10.2015,,0,162.96, -31,2,04.10.2015,,0,162.96, -31,2,05.10.2015,,6,162.96, -31,2,06.10.2015,1.0,57,143.39,122.05 -31,2,07.10.2015,1.0,82,143.39,122.05 -31,2,08.10.2015,1.0,94,143.39,122.05 -31,2,09.10.2015,1.0,66,143.39,122.05 -31,2,10.10.2015,1.0,63,143.39,122.05 -31,2,11.10.2015,1.0,82,143.39,122.05 -31,2,12.10.2015,1.0,72,143.39,122.05 -31,2,13.10.2015,1.0,41,143.93,122.05 -31,2,14.10.2015,1.0,31,143.93,122.05 -31,2,15.10.2015,1.0,19,143.93,122.05 -31,2,16.10.2015,1.0,38,143.93,122.05 -31,2,17.10.2015,1.0,69,162.96,122.05 -31,2,18.10.2015,1.0,69,162.96,122.05 -31,2,19.10.2015,,3,162.96, -31,2,20.10.2015,,3,162.96, -31,2,21.10.2015,,3,162.96, -31,2,22.10.2015,,0,162.96, -31,2,23.10.2015,,0,162.96, -31,2,24.10.2015,,0,162.96, -31,2,25.10.2015,,0,162.96, -31,2,26.10.2015,,0,162.96, -31,2,27.10.2015,,0,162.96, -31,2,28.10.2015,,0,162.96, -31,2,29.10.2015,,3,162.96, -31,2,30.10.2015,,0,162.96, -31,2,31.10.2015,,0,162.96, -31,2,01.11.2015,,0,162.96, -31,2,02.11.2015,,0,162.96, -31,2,03.11.2015,,3,162.96, -31,2,04.11.2015,,0,162.96, -31,2,05.11.2015,,6,162.96, -31,2,06.11.2015,,3,162.96, -31,2,07.11.2015,,3,162.96, -31,2,08.11.2015,,16,162.96, -31,2,09.11.2015,,3,162.96, -31,2,10.11.2015,,9,162.96, -31,2,11.11.2015,,9,138.36, -31,2,12.11.2015,,9,138.36, -31,2,13.11.2015,,13,138.36, -31,2,14.11.2015,,6,138.36, -31,2,15.11.2015,,6,138.36, -31,2,16.11.2015,,6,138.36, -31,2,17.11.2015,,6,138.36, -31,2,18.11.2015,,9,138.36, -31,2,19.11.2015,,3,138.36, -31,2,20.11.2015,,6,138.36, -31,2,21.11.2015,,6,138.36, -31,2,22.11.2015,,6,138.36, -31,2,23.11.2015,,3,138.36, -31,2,24.11.2015,,6,138.36, -31,2,25.11.2015,,6,138.36, -31,2,26.11.2015,,6,138.36, -31,2,27.11.2015,,6,138.36, -31,2,28.11.2015,,6,138.36, -31,2,29.11.2015,,3,138.36, -31,2,30.11.2015,,6,138.36, -31,2,01.12.2015,,3,162.96, -31,2,02.12.2015,,6,162.96, -31,2,03.12.2015,,6,138.5, -31,2,04.12.2015,,9,138.5, -31,2,05.12.2015,,13,138.5, -31,2,06.12.2015,,3,138.5, -31,2,07.12.2015,,6,138.5, -31,2,08.12.2015,,3,138.5, -31,2,09.12.2015,,3,138.5, -31,2,10.12.2015,,6,138.5, -31,2,11.12.2015,,9,142.57, -31,2,12.12.2015,,9,142.57, -31,2,13.12.2015,,9,142.57, -31,2,14.12.2015,,6,142.57, -31,2,15.12.2015,,6,142.57, -31,2,16.12.2015,,13,142.57, -31,2,17.12.2015,,9,142.57, -31,2,18.12.2015,,3,142.57, -31,2,19.12.2015,,13,142.57, -31,2,20.12.2015,,9,142.57, -31,2,21.12.2015,,3,142.57, -31,2,22.12.2015,1.0,116,142.57,103.02 -31,2,23.12.2015,1.0,151,142.57,103.02 -31,2,24.12.2015,1.0,94,142.57,103.02 -31,2,25.12.2015,1.0,88,142.57,103.02 -31,2,26.12.2015,1.0,79,142.57,103.02 -31,2,27.12.2015,1.0,66,142.57,103.02 -31,2,28.12.2015,1.0,66,142.57,103.02 -31,2,29.12.2015,,6,138.5, -31,2,30.12.2015,,6,138.5, -31,2,31.12.2015,,6,138.5, -31,2,01.01.2016,,6,138.5, -31,2,02.01.2016,,6,138.5, -31,2,03.01.2016,,6,138.5, -31,2,04.01.2016,,6,138.5, -31,2,05.01.2016,,6,138.5, -31,2,06.01.2016,,9,138.5, -31,2,07.01.2016,,6,138.5, -31,2,08.01.2016,,6,138.5, -31,2,09.01.2016,,6,138.5, -31,2,10.01.2016,,6,138.5, -31,2,11.01.2016,,3,138.5, -31,2,12.01.2016,,6,138.5, -31,2,13.01.2016,,6,138.5, -31,2,14.01.2016,,6,138.5, -31,2,15.01.2016,,6,138.5, -31,2,16.01.2016,,6,142.57, -31,2,17.01.2016,,6,142.57, -31,2,18.01.2016,,6,142.57, -31,2,19.01.2016,1.0,66,142.57,103.02 -31,2,20.01.2016,1.0,66,142.57,103.02 -31,2,21.01.2016,1.0,66,142.57,103.02 -31,2,22.01.2016,1.0,66,142.57,103.02 -31,2,23.01.2016,1.0,66,142.57,103.02 -31,2,24.01.2016,1.0,66,142.57,103.02 -31,2,25.01.2016,1.0,66,142.57,103.02 -31,2,26.01.2016,,6,142.57, -31,2,27.01.2016,,6,142.57, -31,2,28.01.2016,,6,142.57, -31,2,29.01.2016,,3,142.57, -31,2,30.01.2016,,13,142.57, -31,2,31.01.2016,,3,142.57, -31,2,01.02.2016,,3,142.57, -31,2,02.02.2016,,6,142.57, -31,2,03.02.2016,,6,142.57, -31,2,04.02.2016,,3,142.57, -31,2,05.02.2016,,6,142.57, -31,2,06.02.2016,,6,142.57, -31,2,07.02.2016,,6,142.57, -31,2,08.02.2016,,9,142.57, -31,2,09.02.2016,1.0,110,142.57,108.46 -31,2,10.02.2016,1.0,82,138.36,108.46 -31,2,11.02.2016,1.0,132,138.36,108.46 -31,2,12.02.2016,1.0,119,138.36,108.46 -31,2,13.02.2016,1.0,116,138.36,108.46 -31,2,14.02.2016,1.0,88,138.36,108.46 -31,2,15.02.2016,1.0,60,138.36,108.46 -31,2,16.02.2016,,3,138.36, -31,2,17.02.2016,,3,138.36, -31,2,18.02.2016,,0,138.36, -31,2,19.02.2016,,0,138.36, -31,2,20.02.2016,,0,138.36, -31,2,21.02.2016,,13,138.36, -31,2,22.02.2016,,0,138.36, -31,2,23.02.2016,,3,138.36, -31,2,24.02.2016,,3,138.36, -31,2,25.02.2016,,22,138.36, -31,2,26.02.2016,,3,138.36, -31,2,27.02.2016,,3,138.36, -31,2,28.02.2016,,0,138.36, -31,2,29.02.2016,,0,138.36, -31,2,01.03.2016,,0,138.36, -31,2,02.03.2016,,0,138.36, -31,2,03.03.2016,,0,138.36, -31,2,04.03.2016,,0,138.36, -31,2,05.03.2016,,3,138.36, -31,2,06.03.2016,,0,138.36, -31,2,07.03.2016,,6,138.36, -31,2,08.03.2016,,3,138.36, -31,2,09.03.2016,,0,138.36, -31,2,10.03.2016,,0,138.36, -31,2,11.03.2016,,0,138.36, -31,2,12.03.2016,,0,138.5, -31,2,13.03.2016,,9,138.5, -31,2,14.03.2016,,9,138.5, -31,2,15.03.2016,1.0,60,138.5,124.77 -31,2,16.03.2016,1.0,82,138.5,124.77 -31,2,17.03.2016,1.0,60,138.5,124.77 -31,2,18.03.2016,1.0,63,138.5,124.77 -31,2,19.03.2016,1.0,31,138.5,124.77 -31,2,20.03.2016,1.0,47,138.5,124.77 -31,2,21.03.2016,1.0,50,138.5,124.77 -31,2,22.03.2016,,0,138.36, -31,2,23.03.2016,,0,138.36, -31,2,24.03.2016,,3,138.36, -31,2,25.03.2016,,3,138.36, -31,2,26.03.2016,,0,138.36, -31,2,27.03.2016,,0,138.36, -31,2,28.03.2016,,3,138.36, -31,2,29.03.2016,,6,138.36, -31,2,30.03.2016,,0,138.36, -31,2,31.03.2016,,0,138.36, -31,2,01.04.2016,,3,138.36, -31,2,02.04.2016,,3,138.36, -31,2,03.04.2016,,0,138.36, -31,2,04.04.2016,,3,138.36, -31,2,05.04.2016,,0,138.36, -31,2,06.04.2016,,0,138.36, -31,2,07.04.2016,,0,138.36, -31,2,08.04.2016,,6,138.36, -31,2,09.04.2016,,6,138.5, -31,2,10.04.2016,,6,138.5, -31,2,11.04.2016,,0,138.5, -31,2,12.04.2016,,6,138.5, -31,2,13.04.2016,,0,138.5, -31,2,14.04.2016,,6,138.5, -31,2,15.04.2016,,3,138.5, -31,2,16.04.2016,,0,138.5, -31,2,17.04.2016,,0,138.5, -31,2,18.04.2016,,22,138.5, -31,2,19.04.2016,1.0,107,138.5,116.89 -31,2,20.04.2016,1.0,88,138.5,116.89 -31,2,21.04.2016,1.0,79,138.5,116.89 -31,2,22.04.2016,1.0,145,138.5,116.89 -31,2,23.04.2016,1.0,132,138.5,116.89 -31,2,24.04.2016,1.0,91,138.5,116.89 -31,2,25.04.2016,1.0,19,138.5,116.89 -31,2,26.04.2016,,0,138.5, -31,2,27.04.2016,,0,138.5, -31,2,28.04.2016,,0,138.5, -31,2,29.04.2016,,0,138.5, -31,2,30.04.2016,,9,138.5, -31,2,01.05.2016,,0,138.5, -31,2,02.05.2016,,13,138.5, -31,2,03.05.2016,1.0,148,138.5,106.01 -31,2,04.05.2016,1.0,204,138.5,106.01 -31,2,05.05.2016,1.0,201,138.5,106.01 -31,2,06.05.2016,1.0,126,138.5,106.01 -31,2,07.05.2016,1.0,88,138.5,106.01 -31,2,08.05.2016,1.0,138,138.5,106.01 -31,2,09.05.2016,1.0,69,138.5,106.01 -31,2,10.05.2016,,6,138.5, -31,2,11.05.2016,,9,138.5, -31,2,12.05.2016,,3,138.5, -31,2,13.05.2016,,0,138.5, -31,2,14.05.2016,,0,138.5, -31,2,15.05.2016,,0,138.5, -31,2,16.05.2016,,0,138.5, -31,2,17.05.2016,,3,138.5, -31,2,18.05.2016,,0,138.5, -31,2,19.05.2016,,0,138.5, -31,2,20.05.2016,,0,138.5, -31,2,21.05.2016,,0,138.5, -31,2,22.05.2016,,0,138.5, -32,2,01.01.2015,,0,134.72, -32,2,02.01.2015,,0,134.72, -32,2,03.01.2015,,0,134.72, -32,2,04.01.2015,,0,134.72, -32,2,05.01.2015,,0,134.72, -32,2,06.01.2015,,0,134.72, -32,2,07.01.2015,,0,134.72, -32,2,08.01.2015,,0,134.72, -32,2,09.01.2015,,0,134.72, -32,2,10.01.2015,,0,134.72, -32,2,11.01.2015,,0,134.72, -32,2,12.01.2015,,0,134.72, -32,2,13.01.2015,,0,134.72, -32,2,14.01.2015,,0,134.72, -32,2,15.01.2015,,0,134.72, -32,2,16.01.2015,,0,134.72, -32,2,17.01.2015,,0,134.72, -32,2,18.01.2015,,0,134.72, -32,2,19.01.2015,,0,134.72, -32,2,20.01.2015,,0,134.72, -32,2,21.01.2015,,0,134.72, -32,2,22.01.2015,,0,134.72, -32,2,23.01.2015,,0,134.72, -32,2,24.01.2015,,0,134.72, -32,2,25.01.2015,,0,134.72, -32,2,26.01.2015,,0,134.72, -32,2,27.01.2015,,0,134.72, -32,2,28.01.2015,,0,134.72, -32,2,29.01.2015,,0,134.72, -32,2,30.01.2015,,0,134.72, -32,2,31.01.2015,,0,134.72, -32,2,01.02.2015,,0,134.72, -32,2,02.02.2015,,0,134.72, -32,2,03.02.2015,,0,134.72, -32,2,04.02.2015,,0,134.72, -32,2,05.02.2015,,0,134.72, -32,2,06.02.2015,,0,134.72, -32,2,07.02.2015,,0,134.72, -32,2,08.02.2015,,0,134.72, -32,2,09.02.2015,,0,134.72, -32,2,10.02.2015,,0,134.72, -32,2,11.02.2015,,0,134.72, -32,2,12.02.2015,,0,134.72, -32,2,13.02.2015,,0,134.72, -32,2,14.02.2015,,0,134.72, -32,2,15.02.2015,,0,134.72, -32,2,16.02.2015,,0,134.72, 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-32,2,13.09.2015,,0,128.17, -32,2,14.09.2015,,0,128.17, -32,2,15.09.2015,,0,128.17, -32,2,16.09.2015,,0,128.17, -32,2,17.09.2015,,0,128.17, -32,2,18.09.2015,,0,128.17, -32,2,19.09.2015,,0,128.17, -32,2,20.09.2015,,0,128.17, -32,2,21.09.2015,,0,128.17, -32,2,22.09.2015,,0,128.17, -32,2,23.09.2015,,0,128.17, -32,2,24.09.2015,,0,128.17, -32,2,25.09.2015,,0,128.17, -32,2,26.09.2015,,0,128.17, -32,2,27.09.2015,,19,128.17, -32,2,28.09.2015,,0,128.17, -32,2,29.09.2015,,0,162.96, -32,2,30.09.2015,,3,162.96, -32,2,01.10.2015,,0,162.96, -32,2,02.10.2015,,0,162.96, -32,2,03.10.2015,,0,162.96, -32,2,04.10.2015,,0,162.96, -32,2,05.10.2015,,6,162.96, -32,2,06.10.2015,1.0,113,143.39,122.05 -32,2,07.10.2015,1.0,60,143.39,122.05 -32,2,08.10.2015,1.0,72,143.39,122.05 -32,2,09.10.2015,1.0,63,143.39,122.05 -32,2,10.10.2015,1.0,94,143.39,122.05 -32,2,11.10.2015,1.0,41,143.39,122.05 -32,2,12.10.2015,1.0,53,143.39,122.05 -32,2,13.10.2015,1.0,60,143.93,122.05 -32,2,14.10.2015,1.0,57,143.93,122.05 -32,2,15.10.2015,1.0,6,143.93,122.05 -32,2,16.10.2015,1.0,47,143.93,122.05 -32,2,17.10.2015,1.0,82,143.93,122.05 -32,2,18.10.2015,1.0,50,143.93,122.05 -32,2,19.10.2015,,0,143.93, -32,2,20.10.2015,,0,143.93, -32,2,21.10.2015,,0,143.93, -32,2,22.10.2015,,0,143.93, -32,2,23.10.2015,,0,143.93, -32,2,24.10.2015,,0,143.93, -32,2,25.10.2015,,0,143.93, -32,2,26.10.2015,,0,143.93, -32,2,27.10.2015,,0,143.93, -32,2,28.10.2015,,0,143.93, -32,2,29.10.2015,,0,143.93, -32,2,30.10.2015,,0,143.93, -32,2,31.10.2015,,0,143.93, -32,2,01.11.2015,,0,143.93, -32,2,02.11.2015,,0,143.93, -32,2,03.11.2015,,0,143.93, -32,2,04.11.2015,,0,143.93, -32,2,05.11.2015,,0,143.93, -32,2,06.11.2015,,0,143.93, -32,2,07.11.2015,,0,143.93, -32,2,08.11.2015,,0,143.93, -32,2,09.11.2015,,0,143.93, -32,2,10.11.2015,,0,143.93, -32,2,11.11.2015,,0,143.93, -32,2,12.11.2015,,0,143.93, -32,2,13.11.2015,,0,143.93, -32,2,14.11.2015,,0,143.93, -32,2,15.11.2015,,0,143.93, -32,2,16.11.2015,,0,143.93, -32,2,17.11.2015,,0,143.93, -32,2,18.11.2015,,0,143.93, -32,2,19.11.2015,,0,143.93, -32,2,20.11.2015,,0,143.93, -32,2,21.11.2015,,0,143.93, -32,2,22.11.2015,,0,143.93, -32,2,23.11.2015,,0,143.93, -32,2,24.11.2015,,0,143.93, -32,2,25.11.2015,,0,143.93, -32,2,26.11.2015,,0,143.93, -32,2,27.11.2015,,0,143.93, -32,2,28.11.2015,,0,143.93, -32,2,29.11.2015,,0,143.93, -32,2,30.11.2015,,0,143.93, -32,2,01.12.2015,,0,143.93, -32,2,02.12.2015,,0,143.93, -32,2,03.12.2015,,0,143.93, -32,2,04.12.2015,,0,143.93, -32,2,05.12.2015,,0,143.93, -32,2,06.12.2015,,0,143.93, -32,2,07.12.2015,,0,143.93, -32,2,08.12.2015,,0,143.93, -32,2,09.12.2015,,0,138.5, -32,2,10.12.2015,,0,138.5, -32,2,11.12.2015,,0,142.57, -32,2,12.12.2015,,0,142.57, -32,2,13.12.2015,,0,142.57, -32,2,14.12.2015,,0,142.57, -32,2,15.12.2015,,0,142.57, -32,2,16.12.2015,,0,142.57, -32,2,17.12.2015,,9,142.57, -32,2,18.12.2015,,9,142.57, -32,2,19.12.2015,,0,142.57, -32,2,20.12.2015,,0,142.57, -32,2,21.12.2015,,16,142.57, -32,2,22.12.2015,1.0,101,142.57,103.02 -32,2,23.12.2015,1.0,72,142.57,103.02 -32,2,24.12.2015,1.0,88,142.57,103.02 -32,2,25.12.2015,1.0,82,142.57,103.02 -32,2,26.12.2015,1.0,47,142.57,103.02 -32,2,27.12.2015,1.0,41,142.57,103.02 -32,2,28.12.2015,1.0,6,142.57,103.02 -32,2,29.12.2015,,6,138.5, -32,2,30.12.2015,,0,138.5, -32,2,31.12.2015,,0,138.5, -32,2,01.01.2016,,6,138.5, -32,2,02.01.2016,,0,138.5, -32,2,03.01.2016,,0,138.5, -32,2,04.01.2016,,0,138.5, -32,2,05.01.2016,,0,138.5, -32,2,06.01.2016,,0,138.5, -32,2,07.01.2016,,0,138.5, -32,2,08.01.2016,,0,138.5, -32,2,09.01.2016,,0,138.5, -32,2,10.01.2016,,0,138.5, -32,2,11.01.2016,,0,138.5, -32,2,12.01.2016,,0,138.5, -32,2,13.01.2016,,0,138.5, -32,2,14.01.2016,,0,138.5, -32,2,15.01.2016,,0,138.5, -32,2,16.01.2016,,0,142.57, -32,2,17.01.2016,,0,142.57, -32,2,18.01.2016,,9,142.57, -32,2,19.01.2016,1.0,113,142.57,103.02 -32,2,20.01.2016,1.0,104,142.57,103.02 -32,2,21.01.2016,1.0,138,142.57,103.02 -32,2,22.01.2016,1.0,119,142.57,103.02 -32,2,23.01.2016,1.0,135,142.57,103.02 -32,2,24.01.2016,1.0,57,142.57,103.02 -32,2,25.01.2016,1.0,72,142.57,103.02 -32,2,26.01.2016,,6,142.57, -32,2,27.01.2016,,3,142.57, -32,2,28.01.2016,,9,142.57, -32,2,29.01.2016,,0,142.57, -32,2,30.01.2016,,0,142.57, -32,2,31.01.2016,,3,142.57, -32,2,01.02.2016,,16,142.57, -32,2,02.02.2016,,0,142.57, -32,2,03.02.2016,,0,142.57, -32,2,04.02.2016,,0,142.57, -32,2,05.02.2016,,0,142.57, -32,2,06.02.2016,,0,142.57, -32,2,07.02.2016,,0,142.57, -32,2,08.02.2016,,0,142.57, -32,2,09.02.2016,1.0,53,142.57,108.46 -32,2,10.02.2016,1.0,60,138.36,108.46 -32,2,11.02.2016,1.0,57,138.36,108.46 -32,2,12.02.2016,1.0,97,138.36,108.46 -32,2,13.02.2016,1.0,91,138.36,108.46 -32,2,14.02.2016,1.0,75,138.36,108.46 -32,2,15.02.2016,1.0,75,138.36,108.46 -32,2,16.02.2016,,0,138.36, -32,2,17.02.2016,,0,138.36, -32,2,18.02.2016,,0,138.36, -32,2,19.02.2016,,0,138.36, -32,2,20.02.2016,,0,138.36, -32,2,21.02.2016,,0,138.36, -32,2,22.02.2016,,0,138.36, -32,2,23.02.2016,,0,138.36, -32,2,24.02.2016,,0,138.36, -32,2,25.02.2016,,0,138.36, -32,2,26.02.2016,,0,138.36, -32,2,27.02.2016,,0,138.36, -32,2,28.02.2016,,0,138.36, -32,2,29.02.2016,,0,138.36, -32,2,01.03.2016,,0,138.36, -32,2,02.03.2016,,0,138.36, -32,2,03.03.2016,,0,138.36, -32,2,04.03.2016,,0,138.36, -32,2,05.03.2016,,0,138.36, -32,2,06.03.2016,,0,138.36, -32,2,07.03.2016,,0,138.36, -32,2,08.03.2016,,0,138.36, -32,2,09.03.2016,,0,138.36, -32,2,10.03.2016,,0,138.36, -32,2,11.03.2016,,0,138.36, -32,2,12.03.2016,,0,138.5, -32,2,13.03.2016,,0,138.5, -32,2,14.03.2016,,6,138.5, -32,2,15.03.2016,1.0,47,138.5,124.77 -32,2,16.03.2016,1.0,44,138.5,124.77 -32,2,17.03.2016,1.0,66,138.5,124.77 -32,2,18.03.2016,1.0,28,138.5,124.77 -32,2,19.03.2016,1.0,47,138.5,124.77 -32,2,20.03.2016,1.0,47,138.5,124.77 -32,2,21.03.2016,1.0,38,138.5,124.77 -32,2,22.03.2016,,3,138.36, -32,2,23.03.2016,,6,138.36, -32,2,24.03.2016,,0,138.36, -32,2,25.03.2016,,0,138.36, -32,2,26.03.2016,,3,138.36, -32,2,27.03.2016,,3,138.36, -32,2,28.03.2016,,9,138.36, -32,2,29.03.2016,,0,138.36, -32,2,30.03.2016,,3,138.36, -32,2,31.03.2016,,0,138.36, -32,2,01.04.2016,,0,138.36, -32,2,02.04.2016,,0,138.36, -32,2,03.04.2016,,0,138.36, -32,2,04.04.2016,,0,138.36, -32,2,05.04.2016,,0,138.36, -32,2,06.04.2016,,0,138.36, -32,2,07.04.2016,,3,138.36, -32,2,08.04.2016,,0,138.36, -32,2,09.04.2016,,3,138.5, -32,2,10.04.2016,,6,138.5, -32,2,11.04.2016,,6,138.5, -32,2,12.04.2016,,0,138.5, -32,2,13.04.2016,,0,138.5, -32,2,14.04.2016,,0,138.5, -32,2,15.04.2016,,0,138.5, -32,2,16.04.2016,,0,138.5, -32,2,17.04.2016,,0,138.5, -32,2,18.04.2016,,3,138.5, -32,2,19.04.2016,1.0,63,138.5,116.89 -32,2,20.04.2016,1.0,110,138.5,116.89 -32,2,21.04.2016,1.0,63,138.5,116.89 -32,2,22.04.2016,1.0,85,138.5,116.89 -32,2,23.04.2016,1.0,57,138.5,116.89 -32,2,24.04.2016,1.0,69,138.5,116.89 -32,2,25.04.2016,1.0,22,138.5,116.89 -32,2,26.04.2016,,0,138.5, -32,2,27.04.2016,,0,138.5, -32,2,28.04.2016,,0,138.5, -32,2,29.04.2016,,0,138.5, -32,2,30.04.2016,,0,138.5, -32,2,01.05.2016,,0,138.5, -32,2,02.05.2016,,22,138.5, -32,2,03.05.2016,1.0,107,138.5,106.01 -32,2,04.05.2016,1.0,85,138.5,106.01 -32,2,05.05.2016,1.0,119,138.5,106.01 -32,2,06.05.2016,1.0,82,138.5,106.01 -32,2,07.05.2016,1.0,60,138.5,106.01 -32,2,08.05.2016,1.0,50,138.5,106.01 -32,2,09.05.2016,1.0,60,138.5,106.01 -32,2,10.05.2016,,0,138.5, -32,2,11.05.2016,,0,138.5, -32,2,12.05.2016,,31,138.5, -32,2,13.05.2016,,0,138.5, -32,2,14.05.2016,,0,138.5, -32,2,15.05.2016,,0,138.5, -32,2,16.05.2016,,0,138.5, -32,2,17.05.2016,,3,138.5, -32,2,18.05.2016,,3,138.5, -32,2,19.05.2016,,3,138.5, -32,2,20.05.2016,,3,138.5, -32,2,21.05.2016,,3,138.5, -32,2,22.05.2016,,3,138.5, -33,2,01.01.2015,,0,135.78, -33,2,02.01.2015,,0,135.78, -33,2,03.01.2015,,0,135.78, -33,2,04.01.2015,,0,135.78, -33,2,05.01.2015,,0,135.78, -33,2,06.01.2015,,0,135.78, -33,2,07.01.2015,,0,135.78, -33,2,08.01.2015,,0,135.78, -33,2,09.01.2015,,0,135.78, -33,2,10.01.2015,,0,135.78, -33,2,11.01.2015,,0,135.78, -33,2,12.01.2015,,0,135.78, -33,2,13.01.2015,,0,135.78, -33,2,14.01.2015,,0,135.78, -33,2,15.01.2015,,0,135.78, -33,2,16.01.2015,,0,135.78, -33,2,17.01.2015,,0,135.78, -33,2,18.01.2015,,0,135.78, -33,2,19.01.2015,,0,135.78, -33,2,20.01.2015,,0,135.78, -33,2,21.01.2015,,0,135.78, -33,2,22.01.2015,,3,135.78, -33,2,23.01.2015,,3,135.78, -33,2,24.01.2015,,0,135.78, -33,2,25.01.2015,,0,135.78, -33,2,26.01.2015,,3,135.78, -33,2,27.01.2015,,0,135.78, -33,2,28.01.2015,,3,135.78, -33,2,29.01.2015,,0,135.78, -33,2,30.01.2015,,0,135.78, -33,2,31.01.2015,,6,135.78, -33,2,01.02.2015,,3,135.78, -33,2,02.02.2015,,0,135.78, -33,2,03.02.2015,,0,135.78, -33,2,04.02.2015,,0,135.78, -33,2,05.02.2015,,3,135.78, -33,2,06.02.2015,,0,135.78, -33,2,07.02.2015,,3,135.78, -33,2,08.02.2015,,16,135.78, -33,2,09.02.2015,,0,135.78, -33,2,10.02.2015,,0,135.78, -33,2,11.02.2015,,0,135.78, -33,2,12.02.2015,,3,135.78, -33,2,13.02.2015,,3,135.78, -33,2,14.02.2015,,0,135.78, -33,2,15.02.2015,,3,135.78, -33,2,16.02.2015,,9,135.78, -33,2,17.02.2015,,3,135.78, -33,2,18.02.2015,,0,135.78, -33,2,19.02.2015,,0,135.78, -33,2,20.02.2015,,3,135.78, -33,2,21.02.2015,,0,135.78, -33,2,22.02.2015,,0,135.78, -33,2,23.02.2015,,0,135.78, -33,2,24.02.2015,,0,135.78, -33,2,25.02.2015,,0,135.78, -33,2,26.02.2015,,0,146.38, -33,2,27.02.2015,,0,146.38, -33,2,28.02.2015,,0,146.38, -33,2,01.03.2015,,0,146.38, -33,2,02.03.2015,,0,146.38, -33,2,03.03.2015,,0,146.38, -33,2,04.03.2015,,0,146.38, -33,2,05.03.2015,,0,146.38, -33,2,06.03.2015,,0,146.38, -33,2,07.03.2015,,13,146.38, -33,2,08.03.2015,,0,146.38, -33,2,09.03.2015,,0,146.38, -33,2,10.03.2015,,0,146.38, -33,2,11.03.2015,,3,146.38, -33,2,12.03.2015,,6,146.38, -33,2,13.03.2015,,0,146.38, -33,2,14.03.2015,,0,146.38, -33,2,15.03.2015,,3,146.38, -33,2,16.03.2015,,6,146.38, -33,2,17.03.2015,,6,146.38, -33,2,18.03.2015,,0,146.38, -33,2,19.03.2015,,0,146.38, -33,2,20.03.2015,,0,146.38, -33,2,21.03.2015,,0,146.38, -33,2,22.03.2015,,6,146.38, -33,2,23.03.2015,,0,146.38, -33,2,24.03.2015,,0,146.38, -33,2,25.03.2015,,3,146.38, -33,2,26.03.2015,,3,146.38, -33,2,27.03.2015,,0,146.38, -33,2,28.03.2015,,3,146.38, -33,2,29.03.2015,,6,146.38, -33,2,30.03.2015,,0,146.38, -33,2,31.03.2015,,0,146.38, -33,2,01.04.2015,,0,146.38, -33,2,02.04.2015,,0,146.38, -33,2,03.04.2015,,3,146.38, -33,2,04.04.2015,,0,146.38, -33,2,05.04.2015,,0,146.38, -33,2,06.04.2015,,3,146.38, -33,2,07.04.2015,,9,146.38, -33,2,08.04.2015,,0,146.38, -33,2,09.04.2015,,0,146.38, -33,2,10.04.2015,,3,146.38, -33,2,11.04.2015,,6,146.38, -33,2,12.04.2015,,0,146.38, -33,2,13.04.2015,,13,145.84, -33,2,14.04.2015,,3,145.84, -33,2,15.04.2015,,0,145.84, -33,2,16.04.2015,,0,145.84, -33,2,17.04.2015,,6,145.84, -33,2,18.04.2015,,16,145.84, -33,2,19.04.2015,,3,145.84, -33,2,20.04.2015,,0,145.84, -33,2,21.04.2015,,0,145.84, -33,2,22.04.2015,,0,145.84, -33,2,23.04.2015,,3,145.84, -33,2,24.04.2015,,0,145.84, -33,2,25.04.2015,,0,145.84, -33,2,26.04.2015,,9,150.73, -33,2,27.04.2015,,3,150.73, -33,2,28.04.2015,,3,150.73, -33,2,29.04.2015,,0,150.73, -33,2,30.04.2015,,0,150.73, -33,2,01.05.2015,,3,150.73, -33,2,02.05.2015,,13,150.73, -33,2,03.05.2015,,3,150.73, -33,2,04.05.2015,,0,150.73, -33,2,05.05.2015,,3,150.73, -33,2,06.05.2015,,0,150.73, -33,2,07.05.2015,,0,150.73, -33,2,08.05.2015,,0,150.73, -33,2,09.05.2015,,0,150.73, -33,2,10.05.2015,,0,150.73, -33,2,11.05.2015,,6,150.73, -33,2,12.05.2015,,3,150.73, -33,2,13.05.2015,,0,150.73, -33,2,14.05.2015,,9,150.73, -33,2,15.05.2015,,0,150.73, -33,2,16.05.2015,,0,150.73, -33,2,17.05.2015,,0,150.73, -33,2,18.05.2015,,0,150.73, -33,2,19.05.2015,,0,150.73, -33,2,20.05.2015,,9,150.73, -33,2,21.05.2015,,0,150.73, -33,2,22.05.2015,,0,150.73, -33,2,23.05.2015,,0,150.73, -33,2,24.05.2015,,3,150.73, -33,2,25.05.2015,,0,150.73, -33,2,26.05.2015,,6,150.73, -33,2,27.05.2015,,0,150.73, -33,2,28.05.2015,,0,150.73, -33,2,29.05.2015,,0,150.73, -33,2,30.05.2015,,0,150.73, -33,2,31.05.2015,,6,150.73, -33,2,01.06.2015,,3,150.73, -33,2,02.06.2015,,0,150.73, -33,2,03.06.2015,,3,150.73, -33,2,04.06.2015,,6,150.73, -33,2,05.06.2015,,0,150.73, -33,2,06.06.2015,,0,150.73, -33,2,07.06.2015,,0,150.73, -33,2,08.06.2015,,0,150.73, -33,2,09.06.2015,,6,150.73, -33,2,10.06.2015,,9,150.73, -33,2,11.06.2015,,13,150.73, -33,2,12.06.2015,,0,150.73, -33,2,13.06.2015,,0,150.73, -33,2,14.06.2015,,3,150.73, -33,2,15.06.2015,,3,150.73, -33,2,16.06.2015,,9,150.73, -33,2,17.06.2015,,0,150.73, -33,2,18.06.2015,,9,150.73, -33,2,19.06.2015,,0,150.73, -33,2,20.06.2015,,3,150.73, -33,2,21.06.2015,,6,150.73, -33,2,22.06.2015,,6,150.73, -33,2,23.06.2015,,6,150.73, -33,2,24.06.2015,,0,150.73, -33,2,25.06.2015,,13,150.73, -33,2,26.06.2015,,0,150.73, -33,2,27.06.2015,,6,150.73, -33,2,28.06.2015,,0,150.73, -33,2,29.06.2015,,13,150.73, -33,2,30.06.2015,,9,150.73, -33,2,01.07.2015,,0,150.73, -33,2,02.07.2015,,3,150.73, -33,2,03.07.2015,,0,150.73, -33,2,04.07.2015,,0,150.73, -33,2,05.07.2015,,0,150.73, -33,2,06.07.2015,,9,150.73, -33,2,07.07.2015,,13,150.73, -33,2,08.07.2015,,0,150.73, -33,2,09.07.2015,,0,150.73, -33,2,10.07.2015,,0,150.73, -33,2,11.07.2015,,0,150.73, -33,2,12.07.2015,,3,150.73, -33,2,13.07.2015,,9,150.73, -33,2,14.07.2015,,0,150.73, -33,2,15.07.2015,,0,150.73, -33,2,16.07.2015,,0,150.73, -33,2,17.07.2015,,3,150.73, -33,2,18.07.2015,,3,150.73, -33,2,19.07.2015,,0,150.73, -33,2,20.07.2015,,13,150.73, -33,2,21.07.2015,,0,150.73, -33,2,22.07.2015,,0,150.73, -33,2,23.07.2015,,0,150.73, -33,2,24.07.2015,,13,150.73, -33,2,25.07.2015,,9,150.73, -33,2,26.07.2015,,3,150.73, -33,2,27.07.2015,,0,150.73, -33,2,28.07.2015,,0,150.73, -33,2,29.07.2015,,0,150.73, -33,2,30.07.2015,,9,150.73, -33,2,31.07.2015,,3,150.73, -33,2,01.08.2015,,6,150.73, -33,2,02.08.2015,,6,150.73, -33,2,03.08.2015,,0,150.73, -33,2,04.08.2015,,0,150.73, -33,2,05.08.2015,,3,150.73, -33,2,06.08.2015,,3,150.73, -33,2,07.08.2015,,6,150.73, -33,2,08.08.2015,,6,150.73, -33,2,09.08.2015,,3,150.73, -33,2,10.08.2015,,6,150.73, -33,2,11.08.2015,,3,150.73, -33,2,12.08.2015,,3,128.17, -33,2,13.08.2015,,3,128.17, -33,2,14.08.2015,,3,128.17, -33,2,15.08.2015,,3,128.17, -33,2,16.08.2015,,3,128.17, -33,2,17.08.2015,,3,128.17, -33,2,18.08.2015,1.0,116,128.17,100.3 -33,2,19.08.2015,1.0,116,128.17,100.3 -33,2,20.08.2015,1.0,116,128.17,100.3 -33,2,21.08.2015,1.0,116,128.17,100.3 -33,2,22.08.2015,1.0,116,128.17,100.3 -33,2,23.08.2015,1.0,116,128.17,100.3 -33,2,24.08.2015,1.0,116,128.17,100.3 -33,2,25.08.2015,,3,128.17, -33,2,26.08.2015,,3,128.17, -33,2,27.08.2015,,3,128.17, -33,2,28.08.2015,,16,128.17, -33,2,29.08.2015,,3,128.17, -33,2,30.08.2015,,0,128.17, -33,2,31.08.2015,,25,128.17, -33,2,01.09.2015,,3,128.17, -33,2,02.09.2015,,0,128.17, -33,2,03.09.2015,,3,128.17, -33,2,04.09.2015,,6,128.17, -33,2,05.09.2015,,9,128.17, -33,2,06.09.2015,,9,128.17, -33,2,07.09.2015,,9,128.17, -33,2,08.09.2015,,6,128.17, -33,2,09.09.2015,,6,128.17, -33,2,10.09.2015,,0,128.17, -33,2,11.09.2015,,3,128.17, -33,2,12.09.2015,,3,128.17, -33,2,13.09.2015,,9,128.17, -33,2,14.09.2015,,3,128.17, -33,2,15.09.2015,,6,128.17, -33,2,16.09.2015,,6,128.17, -33,2,17.09.2015,,13,128.17, -33,2,18.09.2015,,6,128.17, -33,2,19.09.2015,,6,128.17, -33,2,20.09.2015,,6,128.17, -33,2,21.09.2015,,0,128.17, -33,2,22.09.2015,,16,128.17, -33,2,23.09.2015,,3,128.17, -33,2,24.09.2015,,3,128.17, -33,2,25.09.2015,,13,128.17, -33,2,26.09.2015,,3,128.17, -33,2,27.09.2015,,6,128.17, -33,2,28.09.2015,,6,128.17, -33,2,29.09.2015,,6,128.17, -33,2,30.09.2015,,6,128.17, -33,2,01.10.2015,,6,162.96, -33,2,02.10.2015,,0,162.96, -33,2,03.10.2015,,0,162.96, -33,2,04.10.2015,,0,162.96, -33,2,05.10.2015,,0,162.96, -33,2,06.10.2015,1.0,66,143.39,122.05 -33,2,07.10.2015,1.0,63,143.39,122.05 -33,2,08.10.2015,1.0,79,143.39,122.05 -33,2,09.10.2015,1.0,88,143.39,122.05 -33,2,10.10.2015,1.0,119,143.39,122.05 -33,2,11.10.2015,1.0,132,143.39,122.05 -33,2,12.10.2015,1.0,31,143.39,122.05 -33,2,13.10.2015,,6,143.93, -33,2,14.10.2015,,6,143.93, -33,2,15.10.2015,,0,143.93, -33,2,16.10.2015,,0,143.93, -33,2,17.10.2015,,3,162.96, -33,2,18.10.2015,,3,162.96, -33,2,19.10.2015,,0,162.96, -33,2,20.10.2015,,0,162.96, -33,2,21.10.2015,,3,162.96, -33,2,22.10.2015,,0,162.96, -33,2,23.10.2015,,0,162.96, -33,2,24.10.2015,,0,162.96, -33,2,25.10.2015,,3,162.96, -33,2,26.10.2015,,0,162.96, -33,2,27.10.2015,,0,162.96, -33,2,28.10.2015,,0,162.96, -33,2,29.10.2015,,0,162.96, -33,2,30.10.2015,,0,162.96, -33,2,31.10.2015,,6,162.96, -33,2,01.11.2015,,3,162.96, -33,2,02.11.2015,,3,162.96, -33,2,03.11.2015,,3,162.96, -33,2,04.11.2015,,3,162.96, -33,2,05.11.2015,,0,162.96, -33,2,06.11.2015,,6,162.96, -33,2,07.11.2015,,6,162.96, -33,2,08.11.2015,,16,162.96, -33,2,09.11.2015,,3,162.96, -33,2,10.11.2015,,3,162.96, -33,2,11.11.2015,,3,138.36, -33,2,12.11.2015,,0,138.36, -33,2,13.11.2015,,6,138.36, -33,2,14.11.2015,,13,138.36, -33,2,15.11.2015,,6,138.36, -33,2,16.11.2015,,6,138.36, -33,2,17.11.2015,,3,138.36, -33,2,18.11.2015,,0,138.36, -33,2,19.11.2015,,0,138.36, -33,2,20.11.2015,,0,138.36, -33,2,21.11.2015,,3,138.36, -33,2,22.11.2015,,3,138.36, -33,2,23.11.2015,,3,138.36, -33,2,24.11.2015,,3,138.36, -33,2,25.11.2015,,0,138.36, -33,2,26.11.2015,,9,138.36, -33,2,27.11.2015,,6,138.36, -33,2,28.11.2015,,6,138.36, -33,2,29.11.2015,,0,138.36, -33,2,30.11.2015,,0,138.36, -33,2,01.12.2015,,0,162.96, -33,2,02.12.2015,,0,162.96, -33,2,03.12.2015,,0,138.5, -33,2,04.12.2015,,0,138.5, -33,2,05.12.2015,,0,138.5, -33,2,06.12.2015,,3,138.5, -33,2,07.12.2015,,0,138.5, -33,2,08.12.2015,,6,138.5, -33,2,09.12.2015,,3,138.5, -33,2,10.12.2015,,0,138.5, -33,2,11.12.2015,,0,142.57, -33,2,12.12.2015,,0,142.57, -33,2,13.12.2015,,0,142.57, -33,2,14.12.2015,,0,142.57, -33,2,15.12.2015,,3,142.57, -33,2,16.12.2015,,6,142.57, -33,2,17.12.2015,,9,142.57, -33,2,18.12.2015,,3,142.57, -33,2,19.12.2015,,3,142.57, -33,2,20.12.2015,,3,142.57, -33,2,21.12.2015,,0,142.57, -33,2,22.12.2015,1.0,145,142.57,103.02 -33,2,23.12.2015,1.0,157,142.57,103.02 -33,2,24.12.2015,1.0,41,142.57,103.02 -33,2,25.12.2015,1.0,101,142.57,103.02 -33,2,26.12.2015,1.0,123,142.57,103.02 -33,2,27.12.2015,1.0,145,142.57,103.02 -33,2,28.12.2015,1.0,25,142.57,103.02 -33,2,29.12.2015,,3,138.5, -33,2,30.12.2015,,3,138.5, -33,2,31.12.2015,,9,138.5, -33,2,01.01.2016,,0,138.5, -33,2,02.01.2016,,9,138.5, -33,2,03.01.2016,,19,138.5, -33,2,04.01.2016,,3,138.5, -33,2,05.01.2016,,3,138.5, -33,2,06.01.2016,,3,138.5, -33,2,07.01.2016,,3,138.5, -33,2,08.01.2016,,3,138.5, -33,2,09.01.2016,,16,138.5, -33,2,10.01.2016,,6,138.5, -33,2,11.01.2016,,6,138.5, -33,2,12.01.2016,,13,138.5, -33,2,13.01.2016,,6,138.5, -33,2,14.01.2016,,0,138.5, -33,2,15.01.2016,,22,138.5, -33,2,16.01.2016,,3,142.57, -33,2,17.01.2016,,13,142.57, -33,2,18.01.2016,,0,142.57, -33,2,19.01.2016,1.0,163,142.57,103.02 -33,2,20.01.2016,1.0,179,142.57,103.02 -33,2,21.01.2016,1.0,148,142.57,103.02 -33,2,22.01.2016,1.0,217,142.57,103.02 -33,2,23.01.2016,1.0,28,142.57,103.02 -33,2,24.01.2016,1.0,116,142.57,103.02 -33,2,25.01.2016,1.0,107,142.57,103.02 -33,2,26.01.2016,,0,142.57, -33,2,27.01.2016,,3,142.57, -33,2,28.01.2016,,13,142.57, -33,2,29.01.2016,,3,142.57, -33,2,30.01.2016,,9,142.57, -33,2,31.01.2016,,6,142.57, -33,2,01.02.2016,,0,142.57, -33,2,02.02.2016,,0,142.57, -33,2,03.02.2016,,9,142.57, -33,2,04.02.2016,,0,142.57, -33,2,05.02.2016,,6,142.57, -33,2,06.02.2016,,6,142.57, -33,2,07.02.2016,,6,142.57, -33,2,08.02.2016,,0,142.57, -33,2,09.02.2016,1.0,207,142.57,108.46 -33,2,10.02.2016,1.0,116,138.36,108.46 -33,2,11.02.2016,1.0,107,138.36,108.46 -33,2,12.02.2016,1.0,185,138.36,108.46 -33,2,13.02.2016,1.0,135,138.36,108.46 -33,2,14.02.2016,1.0,119,138.36,108.46 -33,2,15.02.2016,1.0,145,138.36,108.46 -33,2,16.02.2016,,3,138.36, -33,2,17.02.2016,,3,138.36, -33,2,18.02.2016,,3,138.36, -33,2,19.02.2016,,0,138.36, -33,2,20.02.2016,,0,138.36, -33,2,21.02.2016,,0,138.36, -33,2,22.02.2016,,57,138.36, -33,2,23.02.2016,,6,138.36, -33,2,24.02.2016,,6,138.36, -33,2,25.02.2016,,6,138.36, -33,2,26.02.2016,,6,138.36, -33,2,27.02.2016,,6,138.36, -33,2,28.02.2016,,13,138.36, -33,2,29.02.2016,,6,138.36, -33,2,01.03.2016,,6,138.36, -33,2,02.03.2016,,6,138.36, -33,2,03.03.2016,,6,138.36, -33,2,04.03.2016,,6,138.36, -33,2,05.03.2016,,6,138.36, -33,2,06.03.2016,,6,138.36, -33,2,07.03.2016,,6,138.36, -33,2,08.03.2016,,3,138.36, -33,2,09.03.2016,,6,138.36, -33,2,10.03.2016,,0,138.36, -33,2,11.03.2016,,3,138.36, -33,2,12.03.2016,,0,138.5, -33,2,13.03.2016,,16,138.5, -33,2,14.03.2016,,6,138.5, -33,2,15.03.2016,1.0,57,138.5,124.77 -33,2,16.03.2016,1.0,72,138.5,124.77 -33,2,17.03.2016,1.0,69,138.5,124.77 -33,2,18.03.2016,1.0,110,138.5,124.77 -33,2,19.03.2016,1.0,63,138.5,124.77 -33,2,20.03.2016,1.0,72,138.5,124.77 -33,2,21.03.2016,1.0,38,138.5,124.77 -33,2,22.03.2016,,9,138.36, -33,2,23.03.2016,,9,138.36, -33,2,24.03.2016,,0,138.36, -33,2,25.03.2016,,0,138.36, -33,2,26.03.2016,,9,138.36, -33,2,27.03.2016,,3,138.36, -33,2,28.03.2016,,3,138.36, -33,2,29.03.2016,,3,138.36, -33,2,30.03.2016,,3,138.36, -33,2,31.03.2016,,3,138.36, -33,2,01.04.2016,,3,138.36, -33,2,02.04.2016,,3,138.36, -33,2,03.04.2016,,3,138.36, -33,2,04.04.2016,,3,138.36, -33,2,05.04.2016,,3,138.36, -33,2,06.04.2016,,3,138.36, -33,2,07.04.2016,,3,138.36, -33,2,08.04.2016,,3,138.36, -33,2,09.04.2016,,6,138.5, -33,2,10.04.2016,,3,138.5, -33,2,11.04.2016,,3,138.5, -33,2,12.04.2016,,3,138.5, -33,2,13.04.2016,,3,138.5, -33,2,14.04.2016,,3,138.5, -33,2,15.04.2016,,3,138.5, -33,2,16.04.2016,,3,138.5, -33,2,17.04.2016,,6,138.5, -33,2,18.04.2016,,13,138.5, -33,2,19.04.2016,1.0,85,138.5,116.89 -33,2,20.04.2016,1.0,75,138.5,116.89 -33,2,21.04.2016,1.0,91,138.5,116.89 -33,2,22.04.2016,1.0,107,138.5,116.89 -33,2,23.04.2016,1.0,85,138.5,116.89 -33,2,24.04.2016,1.0,66,138.5,116.89 -33,2,25.04.2016,1.0,69,138.5,116.89 -33,2,26.04.2016,,3,138.5, -33,2,27.04.2016,,3,138.5, -33,2,28.04.2016,,9,138.5, -33,2,29.04.2016,,9,138.5, -33,2,30.04.2016,,16,138.5, -33,2,01.05.2016,,13,138.5, -33,2,02.05.2016,,19,138.5, -33,2,03.05.2016,1.0,242,138.5,106.01 -33,2,04.05.2016,1.0,179,138.5,106.01 -33,2,05.05.2016,1.0,135,138.5,106.01 -33,2,06.05.2016,1.0,182,138.5,106.01 -33,2,07.05.2016,1.0,138,138.5,106.01 -33,2,08.05.2016,1.0,113,138.5,106.01 -33,2,09.05.2016,1.0,113,138.5,106.01 -33,2,10.05.2016,,9,142.57, -33,2,11.05.2016,,9,142.57, -33,2,12.05.2016,,6,142.57, -33,2,13.05.2016,,3,142.57, -33,2,14.05.2016,,0,142.57, -33,2,15.05.2016,,6,142.57, -33,2,16.05.2016,,3,142.57, -33,2,17.05.2016,,0,142.57, -33,2,18.05.2016,,0,142.57, -33,2,19.05.2016,,0,142.57, -33,2,20.05.2016,,6,142.57, -33,2,21.05.2016,,6,142.57, -33,2,22.05.2016,,6,142.57, -34,2,01.01.2015,,0,135.78, -34,2,02.01.2015,,0,135.78, -34,2,03.01.2015,,19,135.78, -34,2,04.01.2015,,6,135.78, -34,2,05.01.2015,,0,135.78, -34,2,06.01.2015,,0,135.78, -34,2,07.01.2015,,0,135.78, -34,2,08.01.2015,,22,135.78, -34,2,09.01.2015,,3,135.78, -34,2,10.01.2015,,0,135.78, -34,2,11.01.2015,,0,135.78, -34,2,12.01.2015,,0,135.78, -34,2,13.01.2015,,3,135.78, -34,2,14.01.2015,,9,135.78, -34,2,15.01.2015,,0,135.78, -34,2,16.01.2015,,6,135.78, -34,2,17.01.2015,,6,135.78, -34,2,18.01.2015,,3,135.78, -34,2,19.01.2015,,16,135.78, -34,2,20.01.2015,,9,135.78, -34,2,21.01.2015,,0,135.78, -34,2,22.01.2015,,0,135.78, -34,2,23.01.2015,,19,135.78, -34,2,24.01.2015,,3,135.78, -34,2,25.01.2015,,0,135.78, -34,2,26.01.2015,,3,135.78, -34,2,27.01.2015,,3,135.78, -34,2,28.01.2015,,0,135.78, -34,2,29.01.2015,,3,135.78, -34,2,30.01.2015,,19,135.78, -34,2,31.01.2015,,3,135.78, -34,2,01.02.2015,,3,135.78, -34,2,02.02.2015,,6,135.78, -34,2,03.02.2015,,6,135.78, -34,2,04.02.2015,,13,135.78, -34,2,05.02.2015,,6,135.78, -34,2,06.02.2015,,0,135.78, -34,2,07.02.2015,,6,135.78, -34,2,08.02.2015,,9,135.78, -34,2,09.02.2015,,16,135.78, -34,2,10.02.2015,,6,135.78, -34,2,11.02.2015,,0,135.78, -34,2,12.02.2015,,0,135.78, -34,2,13.02.2015,,16,135.78, -34,2,14.02.2015,,3,135.78, -34,2,15.02.2015,,0,135.78, -34,2,16.02.2015,,6,135.78, -34,2,17.02.2015,,19,135.78, -34,2,18.02.2015,,16,135.78, -34,2,19.02.2015,,22,135.78, -34,2,20.02.2015,,25,135.78, -34,2,21.02.2015,,9,135.78, -34,2,22.02.2015,,19,135.78, -34,2,23.02.2015,,3,135.78, -34,2,24.02.2015,,0,135.78, -34,2,25.02.2015,,0,135.78, -34,2,26.02.2015,,0,146.38, -34,2,27.02.2015,,0,146.38, -34,2,28.02.2015,,13,146.38, -34,2,01.03.2015,,3,146.38, -34,2,02.03.2015,,0,146.38, -34,2,03.03.2015,,0,146.38, -34,2,04.03.2015,,0,146.38, -34,2,05.03.2015,,3,146.38, -34,2,06.03.2015,,6,146.38, -34,2,07.03.2015,,3,146.38, -34,2,08.03.2015,,3,146.38, -34,2,09.03.2015,,0,146.38, -34,2,10.03.2015,,0,146.38, -34,2,11.03.2015,,3,146.38, -34,2,12.03.2015,,0,146.38, -34,2,13.03.2015,,0,146.38, -34,2,14.03.2015,,0,146.38, -34,2,15.03.2015,,0,146.38, -34,2,16.03.2015,,0,146.38, -34,2,17.03.2015,,0,146.38, -34,2,18.03.2015,,9,146.38, -34,2,19.03.2015,,6,146.38, -34,2,20.03.2015,,6,146.38, -34,2,21.03.2015,,3,146.38, -34,2,22.03.2015,,0,146.38, -34,2,23.03.2015,,0,146.38, -34,2,24.03.2015,,0,146.38, -34,2,25.03.2015,,3,146.38, -34,2,26.03.2015,,3,146.38, -34,2,27.03.2015,,3,146.38, -34,2,28.03.2015,,3,146.38, -34,2,29.03.2015,,3,146.38, -34,2,30.03.2015,,3,146.38, -34,2,31.03.2015,,3,146.38, -34,2,01.04.2015,,3,146.38, -34,2,02.04.2015,,3,146.38, -34,2,03.04.2015,,0,146.38, -34,2,04.04.2015,,6,146.38, -34,2,05.04.2015,,0,146.38, -34,2,06.04.2015,,3,146.38, -34,2,07.04.2015,,0,146.38, -34,2,08.04.2015,,6,146.38, -34,2,09.04.2015,,0,146.38, -34,2,10.04.2015,,0,146.38, -34,2,11.04.2015,,0,146.38, -34,2,12.04.2015,,0,146.38, -34,2,13.04.2015,,0,145.84, -34,2,14.04.2015,,3,145.84, -34,2,15.04.2015,,0,145.84, -34,2,16.04.2015,,6,145.84, -34,2,17.04.2015,,9,145.84, -34,2,18.04.2015,,6,145.84, -34,2,19.04.2015,,0,145.84, -34,2,20.04.2015,,0,145.84, -34,2,21.04.2015,,0,145.84, -34,2,22.04.2015,,0,145.84, -34,2,23.04.2015,,3,145.84, -34,2,24.04.2015,,6,145.84, -34,2,25.04.2015,,0,145.84, -34,2,26.04.2015,,0,150.73, -34,2,27.04.2015,,0,150.73, -34,2,28.04.2015,,0,150.73, -34,2,29.04.2015,,6,150.73, -34,2,30.04.2015,,6,150.73, -34,2,01.05.2015,,0,150.73, -34,2,02.05.2015,,0,150.73, -34,2,03.05.2015,,28,150.73, -34,2,04.05.2015,,6,150.73, -34,2,05.05.2015,,6,150.73, -34,2,06.05.2015,,9,150.73, -34,2,07.05.2015,,6,150.73, -34,2,08.05.2015,,0,150.73, -34,2,09.05.2015,,0,150.73, -34,2,10.05.2015,,0,150.73, -34,2,11.05.2015,,0,150.73, -34,2,12.05.2015,,13,150.73, -34,2,13.05.2015,,6,150.73, -34,2,14.05.2015,,3,150.73, -34,2,15.05.2015,,0,150.73, -34,2,16.05.2015,,0,150.73, -34,2,17.05.2015,,0,150.73, -34,2,18.05.2015,,0,150.73, -34,2,19.05.2015,,0,150.73, -34,2,20.05.2015,,0,150.73, -34,2,21.05.2015,,0,150.73, -34,2,22.05.2015,,0,150.73, -34,2,23.05.2015,,6,150.73, -34,2,24.05.2015,,0,150.73, -34,2,25.05.2015,,3,150.73, -34,2,26.05.2015,,9,150.73, -34,2,27.05.2015,,3,150.73, -34,2,28.05.2015,,3,150.73, -34,2,29.05.2015,,0,150.73, -34,2,30.05.2015,,0,150.73, -34,2,31.05.2015,,0,150.73, -34,2,01.06.2015,,0,150.73, -34,2,02.06.2015,,0,150.73, -34,2,03.06.2015,,0,150.73, -34,2,04.06.2015,,0,150.73, -34,2,05.06.2015,,3,150.73, -34,2,06.06.2015,,3,150.73, -34,2,07.06.2015,,0,150.73, -34,2,08.06.2015,,3,150.73, -34,2,09.06.2015,,19,150.73, -34,2,10.06.2015,,6,150.73, -34,2,11.06.2015,,13,150.73, -34,2,12.06.2015,,3,150.73, -34,2,13.06.2015,,0,150.73, -34,2,14.06.2015,,0,150.73, -34,2,15.06.2015,,25,150.73, -34,2,16.06.2015,,3,150.73, -34,2,17.06.2015,,3,150.73, -34,2,18.06.2015,,3,150.73, -34,2,19.06.2015,,6,150.73, -34,2,20.06.2015,,0,150.73, -34,2,21.06.2015,,19,150.73, -34,2,22.06.2015,,9,150.73, -34,2,23.06.2015,,6,150.73, -34,2,24.06.2015,,6,150.73, -34,2,25.06.2015,,0,150.73, -34,2,26.06.2015,,9,150.73, -34,2,27.06.2015,,16,150.73, -34,2,28.06.2015,,22,150.73, -34,2,29.06.2015,,0,150.73, -34,2,30.06.2015,,19,150.73, -34,2,01.07.2015,,19,150.73, -34,2,02.07.2015,,0,150.73, -34,2,03.07.2015,,13,150.73, -34,2,04.07.2015,,9,150.73, -34,2,05.07.2015,,9,150.73, -34,2,06.07.2015,,9,150.73, -34,2,07.07.2015,,0,150.73, -34,2,08.07.2015,,0,150.73, -34,2,09.07.2015,,3,150.73, -34,2,10.07.2015,,3,150.73, -34,2,11.07.2015,,3,150.73, -34,2,12.07.2015,,19,150.73, -34,2,13.07.2015,,9,150.73, -34,2,14.07.2015,,3,150.73, -34,2,15.07.2015,,0,150.73, -34,2,16.07.2015,,3,150.73, -34,2,17.07.2015,,0,150.73, -34,2,18.07.2015,,9,150.73, -34,2,19.07.2015,,9,150.73, -34,2,20.07.2015,,6,150.73, -34,2,21.07.2015,,3,150.73, -34,2,22.07.2015,,6,150.73, -34,2,23.07.2015,,16,150.73, -34,2,24.07.2015,,6,150.73, -34,2,25.07.2015,,0,150.73, -34,2,26.07.2015,,6,150.73, -34,2,27.07.2015,,41,150.73, -34,2,28.07.2015,,22,150.73, -34,2,29.07.2015,,6,150.73, -34,2,30.07.2015,,9,150.73, -34,2,31.07.2015,,9,150.73, -34,2,01.08.2015,,9,150.73, -34,2,02.08.2015,,9,150.73, -34,2,03.08.2015,,9,150.73, -34,2,04.08.2015,,0,150.73, -34,2,05.08.2015,,25,150.73, -34,2,06.08.2015,,9,150.73, -34,2,07.08.2015,,9,150.73, -34,2,08.08.2015,,9,150.73, -34,2,09.08.2015,,9,150.73, -34,2,10.08.2015,,9,150.73, -34,2,11.08.2015,,0,150.73, -34,2,12.08.2015,,6,128.17, -34,2,13.08.2015,,3,128.17, -34,2,14.08.2015,,3,128.17, -34,2,15.08.2015,,13,128.17, -34,2,16.08.2015,,9,128.17, -34,2,17.08.2015,,9,128.17, -34,2,18.08.2015,1.0,79,128.17,100.3 -34,2,19.08.2015,1.0,116,128.17,100.3 -34,2,20.08.2015,1.0,170,128.17,100.3 -34,2,21.08.2015,1.0,135,128.17,100.3 -34,2,22.08.2015,1.0,110,128.17,100.3 -34,2,23.08.2015,1.0,116,128.17,100.3 -34,2,24.08.2015,1.0,104,128.17,100.3 -34,2,25.08.2015,,47,128.17, -34,2,26.08.2015,,22,128.17, -34,2,27.08.2015,,25,128.17, -34,2,28.08.2015,,41,128.17, -34,2,29.08.2015,,16,128.17, -34,2,30.08.2015,,16,128.17, -34,2,31.08.2015,,16,128.17, -34,2,01.09.2015,,0,128.17, -34,2,02.09.2015,,13,128.17, -34,2,03.09.2015,,3,128.17, -34,2,04.09.2015,,3,128.17, -34,2,05.09.2015,,13,128.17, -34,2,06.09.2015,,13,128.17, -34,2,07.09.2015,,13,128.17, -34,2,08.09.2015,,3,128.17, -34,2,09.09.2015,,3,128.17, -34,2,10.09.2015,,6,128.17, -34,2,11.09.2015,,3,128.17, -34,2,12.09.2015,,3,128.17, -34,2,13.09.2015,,31,128.17, -34,2,14.09.2015,,6,128.17, -34,2,15.09.2015,,13,128.17, -34,2,16.09.2015,,13,128.17, -34,2,17.09.2015,,19,128.17, -34,2,18.09.2015,,9,128.17, -34,2,19.09.2015,,0,128.17, -34,2,20.09.2015,,0,128.17, -34,2,21.09.2015,,19,128.17, -34,2,22.09.2015,,9,128.17, -34,2,23.09.2015,,9,128.17, -34,2,24.09.2015,,9,128.17, -34,2,25.09.2015,,9,128.17, -34,2,26.09.2015,,9,128.17, -34,2,27.09.2015,,9,128.17, -34,2,28.09.2015,,9,128.17, -34,2,29.09.2015,,13,128.17, -34,2,30.09.2015,,6,128.17, -34,2,01.10.2015,,9,162.96, -34,2,02.10.2015,,9,162.96, -34,2,03.10.2015,,0,162.96, -34,2,04.10.2015,,3,162.96, -34,2,05.10.2015,,0,162.96, -34,2,06.10.2015,1.0,88,143.39,122.05 -34,2,07.10.2015,1.0,119,143.39,122.05 -34,2,08.10.2015,1.0,110,143.39,122.05 -34,2,09.10.2015,1.0,119,143.39,122.05 -34,2,10.10.2015,1.0,91,143.39,122.05 -34,2,11.10.2015,1.0,31,143.39,122.05 -34,2,12.10.2015,1.0,101,143.39,122.05 -34,2,13.10.2015,,6,143.93, -34,2,14.10.2015,,3,143.93, -34,2,15.10.2015,,0,143.93, -34,2,16.10.2015,,3,143.93, -34,2,17.10.2015,,9,162.96, -34,2,18.10.2015,,3,162.96, -34,2,19.10.2015,,0,162.96, -34,2,20.10.2015,,0,162.96, -34,2,21.10.2015,,3,162.96, -34,2,22.10.2015,,0,162.96, -34,2,23.10.2015,,0,162.96, -34,2,24.10.2015,,0,162.96, -34,2,25.10.2015,,3,162.96, -34,2,26.10.2015,,3,162.96, -34,2,27.10.2015,,3,162.96, -34,2,28.10.2015,,6,162.96, -34,2,29.10.2015,,0,162.96, -34,2,30.10.2015,,0,162.96, -34,2,31.10.2015,,0,162.96, -34,2,01.11.2015,,0,162.96, -34,2,02.11.2015,,0,162.96, -34,2,03.11.2015,,0,162.96, -34,2,04.11.2015,,0,162.96, -34,2,05.11.2015,,0,162.96, -34,2,06.11.2015,,0,162.96, -34,2,07.11.2015,,0,162.96, -34,2,08.11.2015,,0,162.96, -34,2,09.11.2015,,0,162.96, -34,2,10.11.2015,,0,162.96, -34,2,11.11.2015,,0,138.36, -34,2,12.11.2015,,0,138.36, -34,2,13.11.2015,,0,138.36, -34,2,14.11.2015,,0,138.36, -34,2,15.11.2015,,0,138.36, -34,2,16.11.2015,,0,138.36, -34,2,17.11.2015,,0,138.36, -34,2,18.11.2015,,0,138.36, -34,2,19.11.2015,,0,138.36, -34,2,20.11.2015,,0,138.36, -34,2,21.11.2015,,0,138.36, -34,2,22.11.2015,,0,138.36, -34,2,23.11.2015,,0,138.36, -34,2,24.11.2015,,0,138.36, -34,2,25.11.2015,,0,138.36, -34,2,26.11.2015,,0,138.36, -34,2,27.11.2015,,0,138.36, -34,2,28.11.2015,,13,138.36, -34,2,29.11.2015,,6,138.36, -34,2,30.11.2015,,0,138.36, -34,2,01.12.2015,,0,162.96, -34,2,02.12.2015,,3,162.96, -34,2,03.12.2015,,6,138.5, -34,2,04.12.2015,,6,138.5, -34,2,05.12.2015,,0,138.5, -34,2,06.12.2015,,0,138.5, -34,2,07.12.2015,,0,138.5, -34,2,08.12.2015,,3,138.5, -34,2,09.12.2015,,6,138.5, -34,2,10.12.2015,,3,138.5, -34,2,11.12.2015,,9,142.57, -34,2,12.12.2015,,0,142.57, -34,2,13.12.2015,,6,142.57, -34,2,14.12.2015,,3,142.57, -34,2,15.12.2015,,6,142.57, -34,2,16.12.2015,,3,142.57, -34,2,17.12.2015,,0,142.57, -34,2,18.12.2015,,9,142.57, -34,2,19.12.2015,,0,142.57, -34,2,20.12.2015,,0,142.57, -34,2,21.12.2015,,3,142.57, -34,2,22.12.2015,1.0,163,142.57,103.02 -34,2,23.12.2015,1.0,129,142.57,103.02 -34,2,24.12.2015,1.0,123,142.57,103.02 -34,2,25.12.2015,1.0,132,142.57,103.02 -34,2,26.12.2015,1.0,72,142.57,103.02 -34,2,27.12.2015,1.0,91,142.57,103.02 -34,2,28.12.2015,1.0,85,142.57,103.02 -34,2,29.12.2015,,0,138.5, -34,2,30.12.2015,,0,138.5, -34,2,31.12.2015,,0,138.5, -34,2,01.01.2016,,0,138.5, -34,2,02.01.2016,,0,138.5, -34,2,03.01.2016,,0,138.5, -34,2,04.01.2016,,0,138.5, -34,2,05.01.2016,,19,138.5, -34,2,06.01.2016,,0,138.5, -34,2,07.01.2016,,19,138.5, -34,2,08.01.2016,,0,138.5, -34,2,09.01.2016,,3,138.5, -34,2,10.01.2016,,35,138.5, -34,2,11.01.2016,,0,138.5, -34,2,12.01.2016,,13,138.5, -34,2,13.01.2016,,6,138.5, -34,2,14.01.2016,,13,138.5, -34,2,15.01.2016,,0,138.5, -34,2,16.01.2016,,6,142.57, -34,2,17.01.2016,,0,142.57, -34,2,18.01.2016,,0,142.57, -34,2,19.01.2016,1.0,97,142.57,103.02 -34,2,20.01.2016,1.0,182,142.57,103.02 -34,2,21.01.2016,1.0,204,142.57,103.02 -34,2,22.01.2016,1.0,242,142.57,103.02 -34,2,23.01.2016,1.0,66,142.57,103.02 -34,2,24.01.2016,1.0,94,142.57,103.02 -34,2,25.01.2016,1.0,107,142.57,103.02 -34,2,26.01.2016,,0,142.57, -34,2,27.01.2016,,13,142.57, -34,2,28.01.2016,,6,142.57, -34,2,29.01.2016,,6,142.57, -34,2,30.01.2016,,6,142.57, -34,2,31.01.2016,,6,142.57, -34,2,01.02.2016,,6,142.57, -34,2,02.02.2016,,0,142.57, -34,2,03.02.2016,,0,142.57, -34,2,04.02.2016,,13,142.57, -34,2,05.02.2016,,6,142.57, -34,2,06.02.2016,,6,142.57, -34,2,07.02.2016,,6,142.57, -34,2,08.02.2016,,6,142.57, -34,2,09.02.2016,1.0,129,142.57,108.46 -34,2,10.02.2016,1.0,188,138.36,108.46 -34,2,11.02.2016,1.0,160,138.36,108.46 -34,2,12.02.2016,1.0,66,138.36,108.46 -34,2,13.02.2016,1.0,138,138.36,108.46 -34,2,14.02.2016,1.0,50,138.36,108.46 -34,2,15.02.2016,1.0,116,138.36,108.46 -34,2,16.02.2016,,3,138.36, -34,2,17.02.2016,,3,138.36, -34,2,18.02.2016,,6,138.36, -34,2,19.02.2016,,0,138.36, -34,2,20.02.2016,,3,138.36, -34,2,21.02.2016,,6,138.36, -34,2,22.02.2016,,3,138.36, -34,2,23.02.2016,,13,138.36, -34,2,24.02.2016,,3,138.36, -34,2,25.02.2016,,3,138.36, -34,2,26.02.2016,,3,138.36, -34,2,27.02.2016,,3,138.36, -34,2,28.02.2016,,3,138.36, -34,2,29.02.2016,,6,138.36, -34,2,01.03.2016,,0,138.36, -34,2,02.03.2016,,6,138.36, -34,2,03.03.2016,,0,138.36, -34,2,04.03.2016,,6,138.36, -34,2,05.03.2016,,0,138.36, -34,2,06.03.2016,,6,138.36, -34,2,07.03.2016,,6,138.36, -34,2,08.03.2016,,16,138.36, -34,2,09.03.2016,,3,138.36, -34,2,10.03.2016,,9,138.36, -34,2,11.03.2016,,19,138.36, -34,2,12.03.2016,,9,138.5, -34,2,13.03.2016,,6,138.5, -34,2,14.03.2016,,6,138.5, -34,2,15.03.2016,1.0,85,138.5,124.77 -34,2,16.03.2016,1.0,104,138.5,124.77 -34,2,17.03.2016,1.0,72,138.5,124.77 -34,2,18.03.2016,1.0,101,138.5,124.77 -34,2,19.03.2016,1.0,82,138.5,124.77 -34,2,20.03.2016,1.0,44,138.5,124.77 -34,2,21.03.2016,1.0,88,138.5,124.77 -34,2,22.03.2016,,0,138.36, -34,2,23.03.2016,,6,138.36, -34,2,24.03.2016,,3,138.36, -34,2,25.03.2016,,0,138.36, -34,2,26.03.2016,,16,138.36, -34,2,27.03.2016,,0,138.36, -34,2,28.03.2016,,3,138.36, -34,2,29.03.2016,,0,138.36, -34,2,30.03.2016,,3,138.36, -34,2,31.03.2016,,0,138.36, -34,2,01.04.2016,,0,138.36, -34,2,02.04.2016,,9,138.36, -34,2,03.04.2016,,6,138.36, -34,2,04.04.2016,,3,138.36, -34,2,05.04.2016,,0,138.36, -34,2,06.04.2016,,0,138.36, -34,2,07.04.2016,,6,138.36, -34,2,08.04.2016,,9,138.36, -34,2,09.04.2016,,0,138.5, -34,2,10.04.2016,,3,138.5, -34,2,11.04.2016,,0,138.5, -34,2,12.04.2016,,3,138.5, -34,2,13.04.2016,,6,138.5, -34,2,14.04.2016,,3,138.5, -34,2,15.04.2016,,16,138.5, -34,2,16.04.2016,,6,138.5, -34,2,17.04.2016,,3,138.5, -34,2,18.04.2016,,0,138.5, -34,2,19.04.2016,1.0,25,138.5,116.89 -34,2,20.04.2016,1.0,101,138.5,116.89 -34,2,21.04.2016,1.0,148,138.5,116.89 -34,2,22.04.2016,1.0,148,138.5,116.89 -34,2,23.04.2016,1.0,107,138.5,116.89 -34,2,24.04.2016,1.0,72,138.5,116.89 -34,2,25.04.2016,1.0,35,138.5,116.89 -34,2,26.04.2016,,3,138.5, -34,2,27.04.2016,,0,138.5, -34,2,28.04.2016,,3,138.5, -34,2,29.04.2016,,6,138.5, -34,2,30.04.2016,,13,138.5, -34,2,01.05.2016,,9,138.5, -34,2,02.05.2016,,0,138.5, -34,2,03.05.2016,1.0,107,138.5,106.01 -34,2,04.05.2016,1.0,179,138.5,106.01 -34,2,05.05.2016,1.0,97,138.5,106.01 -34,2,06.05.2016,1.0,160,138.5,106.01 -34,2,07.05.2016,1.0,160,138.5,106.01 -34,2,08.05.2016,1.0,57,138.5,106.01 -34,2,09.05.2016,1.0,16,138.5,106.01 -34,2,10.05.2016,,3,138.5, -34,2,11.05.2016,,3,138.5, -34,2,12.05.2016,,6,138.5, -34,2,13.05.2016,,19,138.5, -34,2,14.05.2016,,9,138.5, -34,2,15.05.2016,,22,138.5, -34,2,16.05.2016,,9,138.5, -34,2,17.05.2016,,3,138.5, -34,2,18.05.2016,,9,138.5, -34,2,19.05.2016,,6,138.5, -34,2,20.05.2016,,22,138.5, -34,2,21.05.2016,,6,138.5, -34,2,22.05.2016,,25,138.5, -35,2,01.01.2015,,0,134.72, -35,2,02.01.2015,,0,134.72, -35,2,03.01.2015,,0,134.72, -35,2,04.01.2015,,0,134.72, -35,2,05.01.2015,,0,134.72, -35,2,06.01.2015,,0,134.72, -35,2,07.01.2015,,0,134.72, -35,2,08.01.2015,,0,134.72, -35,2,09.01.2015,,0,134.72, -35,2,10.01.2015,,0,134.72, -35,2,11.01.2015,,0,134.72, 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-35,2,23.12.2015,1.0,72,142.57,103.02 -35,2,24.12.2015,1.0,35,142.57,103.02 -35,2,25.12.2015,1.0,72,142.57,103.02 -35,2,26.12.2015,1.0,75,142.57,103.02 -35,2,27.12.2015,1.0,69,142.57,103.02 -35,2,28.12.2015,1.0,63,142.57,103.02 -35,2,29.12.2015,,0,138.5, -35,2,30.12.2015,,0,138.5, -35,2,31.12.2015,,0,138.5, -35,2,01.01.2016,,0,138.5, -35,2,02.01.2016,,0,138.5, -35,2,03.01.2016,,0,138.5, -35,2,04.01.2016,,0,138.5, -35,2,05.01.2016,,0,138.5, -35,2,06.01.2016,,0,138.5, -35,2,07.01.2016,,0,138.5, -35,2,08.01.2016,,0,138.5, -35,2,09.01.2016,,0,138.5, -35,2,10.01.2016,,0,138.5, -35,2,11.01.2016,,0,138.5, -35,2,12.01.2016,,0,138.5, -35,2,13.01.2016,,6,138.5, -35,2,14.01.2016,,6,138.5, -35,2,15.01.2016,,16,138.5, -35,2,16.01.2016,,3,142.57, -35,2,17.01.2016,,3,142.57, -35,2,18.01.2016,,25,142.57, -35,2,19.01.2016,1.0,138,142.57,103.02 -35,2,20.01.2016,1.0,110,142.57,103.02 -35,2,21.01.2016,1.0,135,142.57,103.02 -35,2,22.01.2016,1.0,123,142.57,103.02 -35,2,23.01.2016,1.0,19,142.57,103.02 -35,2,24.01.2016,1.0,72,142.57,103.02 -35,2,25.01.2016,1.0,72,142.57,103.02 -35,2,26.01.2016,,3,142.57, -35,2,27.01.2016,,3,142.57, -35,2,28.01.2016,,16,142.57, -35,2,29.01.2016,,6,142.57, -35,2,30.01.2016,,16,142.57, -35,2,31.01.2016,,6,142.57, -35,2,01.02.2016,,6,142.57, -35,2,02.02.2016,,13,142.57, -35,2,03.02.2016,,0,142.57, -35,2,04.02.2016,,0,142.57, -35,2,05.02.2016,,22,142.57, -35,2,06.02.2016,,6,142.57, -35,2,07.02.2016,,13,142.57, -35,2,08.02.2016,,9,142.57, -35,2,09.02.2016,1.0,132,142.57,108.46 -35,2,10.02.2016,1.0,104,138.36,108.46 -35,2,11.02.2016,1.0,38,138.36,108.46 -35,2,12.02.2016,1.0,82,138.36,108.46 -35,2,13.02.2016,1.0,31,138.36,108.46 -35,2,14.02.2016,1.0,72,138.36,108.46 -35,2,15.02.2016,1.0,66,138.36,108.46 -35,2,16.02.2016,,6,138.36, -35,2,17.02.2016,,0,138.36, -35,2,18.02.2016,,0,138.36, -35,2,19.02.2016,,13,138.36, -35,2,20.02.2016,,0,138.36, -35,2,21.02.2016,,0,138.36, -35,2,22.02.2016,,0,138.36, -35,2,23.02.2016,,0,138.36, -35,2,24.02.2016,,0,138.36, 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-35,2,29.03.2016,,0,138.36, -35,2,30.03.2016,,0,138.36, -35,2,31.03.2016,,0,138.36, -35,2,01.04.2016,,0,138.36, -35,2,02.04.2016,,0,138.36, -35,2,03.04.2016,,0,138.36, -35,2,04.04.2016,,0,138.36, -35,2,05.04.2016,,0,138.36, -35,2,06.04.2016,,0,138.36, -35,2,07.04.2016,,0,138.36, -35,2,08.04.2016,,0,138.36, -35,2,09.04.2016,,0,138.5, -35,2,10.04.2016,,0,138.5, -35,2,11.04.2016,,0,138.5, -35,2,12.04.2016,,3,138.5, -35,2,13.04.2016,,13,138.5, -35,2,14.04.2016,,6,138.5, -35,2,15.04.2016,,0,138.5, -35,2,16.04.2016,,0,138.5, -35,2,17.04.2016,,3,138.5, -35,2,18.04.2016,,0,138.5, -35,2,19.04.2016,1.0,75,138.5,116.89 -35,2,20.04.2016,1.0,69,138.5,116.89 -35,2,21.04.2016,1.0,35,138.5,116.89 -35,2,22.04.2016,1.0,91,138.5,116.89 -35,2,23.04.2016,1.0,85,138.5,116.89 -35,2,24.04.2016,1.0,63,138.5,116.89 -35,2,25.04.2016,1.0,47,138.5,116.89 -35,2,26.04.2016,,3,138.5, -35,2,27.04.2016,,3,138.5, -35,2,28.04.2016,,3,138.5, -35,2,29.04.2016,,0,138.5, -35,2,30.04.2016,,6,138.5, -35,2,01.05.2016,,9,138.5, -35,2,02.05.2016,,9,138.5, -35,2,03.05.2016,1.0,173,138.5,106.01 -35,2,04.05.2016,1.0,57,138.5,106.01 -35,2,05.05.2016,1.0,151,138.5,106.01 -35,2,06.05.2016,1.0,104,138.5,106.01 -35,2,07.05.2016,1.0,63,138.5,106.01 -35,2,08.05.2016,1.0,75,138.5,106.01 -35,2,09.05.2016,1.0,72,138.5,106.01 -35,2,10.05.2016,,25,138.5, -35,2,11.05.2016,,9,138.5, -35,2,12.05.2016,,16,138.5, -35,2,13.05.2016,,3,138.5, -35,2,14.05.2016,,3,138.5, -35,2,15.05.2016,,0,138.5, -35,2,16.05.2016,,0,138.5, -35,2,17.05.2016,,0,138.5, -35,2,18.05.2016,,0,138.5, -35,2,19.05.2016,,0,138.5, -35,2,20.05.2016,,0,138.5, -35,2,21.05.2016,,0,138.5, -35,2,22.05.2016,,0,138.5, -36,2,01.01.2015,,0,135.78, -36,2,02.01.2015,,0,135.78, -36,2,03.01.2015,,0,135.78, -36,2,04.01.2015,,0,135.78, -36,2,05.01.2015,,0,135.78, -36,2,06.01.2015,,0,135.78, -36,2,07.01.2015,,0,135.78, -36,2,08.01.2015,,0,135.78, -36,2,09.01.2015,,0,135.78, -36,2,10.01.2015,,0,135.78, -36,2,11.01.2015,,0,135.78, -36,2,12.01.2015,,0,135.78, -36,2,13.01.2015,,0,135.78, -36,2,14.01.2015,,0,135.78, -36,2,15.01.2015,,0,135.78, -36,2,16.01.2015,,3,135.78, -36,2,17.01.2015,,0,135.78, -36,2,18.01.2015,,0,135.78, -36,2,19.01.2015,,0,135.78, -36,2,20.01.2015,,0,135.78, -36,2,21.01.2015,,0,135.78, -36,2,22.01.2015,,0,135.78, -36,2,23.01.2015,,3,135.78, -36,2,24.01.2015,,0,135.78, -36,2,25.01.2015,,0,135.78, -36,2,26.01.2015,,0,135.78, -36,2,27.01.2015,,0,135.78, -36,2,28.01.2015,,0,135.78, -36,2,29.01.2015,,0,135.78, -36,2,30.01.2015,,0,135.78, -36,2,31.01.2015,,0,135.78, -36,2,01.02.2015,,0,135.78, -36,2,02.02.2015,,0,135.78, -36,2,03.02.2015,,0,135.78, -36,2,04.02.2015,,0,135.78, -36,2,05.02.2015,,0,135.78, -36,2,06.02.2015,,6,135.78, -36,2,07.02.2015,,0,135.78, -36,2,08.02.2015,,3,135.78, -36,2,09.02.2015,,6,135.78, -36,2,10.02.2015,,0,135.78, -36,2,11.02.2015,,6,135.78, -36,2,12.02.2015,,0,135.78, -36,2,13.02.2015,,0,135.78, -36,2,14.02.2015,,3,135.78, -36,2,15.02.2015,,0,135.78, -36,2,16.02.2015,,3,135.78, -36,2,17.02.2015,,3,135.78, -36,2,18.02.2015,,0,135.78, -36,2,19.02.2015,,0,135.78, -36,2,20.02.2015,,3,135.78, -36,2,21.02.2015,,0,135.78, -36,2,22.02.2015,,0,135.78, -36,2,23.02.2015,,3,135.78, -36,2,24.02.2015,,0,135.78, -36,2,25.02.2015,,0,135.78, -36,2,26.02.2015,,0,146.38, -36,2,27.02.2015,,0,146.38, -36,2,28.02.2015,,0,146.38, -36,2,01.03.2015,,0,146.38, -36,2,02.03.2015,,0,146.38, -36,2,03.03.2015,,0,146.38, -36,2,04.03.2015,,0,146.38, -36,2,05.03.2015,,0,146.38, -36,2,06.03.2015,,0,146.38, -36,2,07.03.2015,,0,146.38, -36,2,08.03.2015,,0,146.38, -36,2,09.03.2015,,0,146.38, -36,2,10.03.2015,,0,146.38, -36,2,11.03.2015,,0,146.38, -36,2,12.03.2015,,0,146.38, -36,2,13.03.2015,,0,146.38, -36,2,14.03.2015,,0,146.38, -36,2,15.03.2015,,0,146.38, -36,2,16.03.2015,,0,146.38, -36,2,17.03.2015,,0,146.38, -36,2,18.03.2015,,0,146.38, -36,2,19.03.2015,,0,146.38, -36,2,20.03.2015,,0,146.38, -36,2,21.03.2015,,0,146.38, -36,2,22.03.2015,,0,146.38, -36,2,23.03.2015,,0,146.38, 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-36,2,07.07.2015,,0,150.73, -36,2,08.07.2015,,0,150.73, -36,2,09.07.2015,,0,150.73, -36,2,10.07.2015,,0,150.73, -36,2,11.07.2015,,0,150.73, -36,2,12.07.2015,,0,150.73, -36,2,13.07.2015,,3,150.73, -36,2,14.07.2015,,3,150.73, -36,2,15.07.2015,,0,150.73, -36,2,16.07.2015,,0,150.73, -36,2,17.07.2015,,0,150.73, -36,2,18.07.2015,,0,150.73, -36,2,19.07.2015,,0,150.73, -36,2,20.07.2015,,0,150.73, -36,2,21.07.2015,,0,150.73, -36,2,22.07.2015,,0,150.73, -36,2,23.07.2015,,0,150.73, -36,2,24.07.2015,,16,150.73, -36,2,25.07.2015,,3,150.73, -36,2,26.07.2015,,6,150.73, -36,2,27.07.2015,,0,150.73, -36,2,28.07.2015,,0,150.73, -36,2,29.07.2015,,0,150.73, -36,2,30.07.2015,,13,150.73, -36,2,31.07.2015,,3,150.73, -36,2,01.08.2015,,6,150.73, -36,2,02.08.2015,,3,150.73, -36,2,03.08.2015,,0,150.73, -36,2,04.08.2015,,0,150.73, -36,2,05.08.2015,,0,150.73, -36,2,06.08.2015,,0,150.73, -36,2,07.08.2015,,0,150.73, -36,2,08.08.2015,,16,150.73, -36,2,09.08.2015,,3,150.73, -36,2,10.08.2015,,3,150.73, -36,2,11.08.2015,,0,150.73, -36,2,12.08.2015,,0,128.17, -36,2,13.08.2015,,3,128.17, -36,2,14.08.2015,,3,128.17, -36,2,15.08.2015,,3,128.17, -36,2,16.08.2015,,6,128.17, -36,2,17.08.2015,,0,128.17, -36,2,18.08.2015,1.0,82,128.17,100.3 -36,2,19.08.2015,1.0,57,128.17,100.3 -36,2,20.08.2015,1.0,97,128.17,100.3 -36,2,21.08.2015,1.0,113,128.17,100.3 -36,2,22.08.2015,1.0,104,128.17,100.3 -36,2,23.08.2015,1.0,38,128.17,100.3 -36,2,24.08.2015,1.0,53,128.17,100.3 -36,2,25.08.2015,,0,128.17, -36,2,26.08.2015,,6,128.17, -36,2,27.08.2015,,13,128.17, -36,2,28.08.2015,,6,128.17, -36,2,29.08.2015,,3,128.17, -36,2,30.08.2015,,3,128.17, -36,2,31.08.2015,,3,128.17, -36,2,01.09.2015,,0,128.17, -36,2,02.09.2015,,3,128.17, -36,2,03.09.2015,,3,128.17, -36,2,04.09.2015,,0,128.17, -36,2,05.09.2015,,3,128.17, -36,2,06.09.2015,,9,128.17, -36,2,07.09.2015,,0,128.17, -36,2,08.09.2015,,3,128.17, -36,2,09.09.2015,,3,128.17, -36,2,10.09.2015,,0,128.17, -36,2,11.09.2015,,0,128.17, -36,2,12.09.2015,,0,128.17, -36,2,13.09.2015,,0,128.17, -36,2,14.09.2015,,0,128.17, -36,2,15.09.2015,,0,128.17, -36,2,16.09.2015,,0,128.17, -36,2,17.09.2015,,0,128.17, -36,2,18.09.2015,,9,128.17, -36,2,19.09.2015,,9,128.17, -36,2,20.09.2015,,6,128.17, -36,2,21.09.2015,,13,128.17, -36,2,22.09.2015,,3,128.17, -36,2,23.09.2015,,3,128.17, -36,2,24.09.2015,,19,128.17, -36,2,25.09.2015,,9,128.17, -36,2,26.09.2015,,3,128.17, -36,2,27.09.2015,,3,128.17, -36,2,28.09.2015,,0,128.17, -36,2,29.09.2015,,0,128.17, -36,2,30.09.2015,,19,128.17, -36,2,01.10.2015,,0,162.96, -36,2,02.10.2015,,0,162.96, -36,2,03.10.2015,,0,162.96, -36,2,04.10.2015,,0,162.96, -36,2,05.10.2015,,0,162.96, -36,2,06.10.2015,1.0,31,143.39,122.05 -36,2,07.10.2015,1.0,57,143.39,122.05 -36,2,08.10.2015,1.0,35,143.39,122.05 -36,2,09.10.2015,1.0,16,143.39,122.05 -36,2,10.10.2015,1.0,41,143.39,122.05 -36,2,11.10.2015,1.0,47,143.39,122.05 -36,2,12.10.2015,1.0,47,143.39,122.05 -36,2,13.10.2015,1.0,47,143.93,122.05 -36,2,14.10.2015,1.0,47,143.93,122.05 -36,2,15.10.2015,1.0,47,143.93,122.05 -36,2,16.10.2015,1.0,41,143.93,122.05 -36,2,17.10.2015,1.0,38,162.96,122.05 -36,2,18.10.2015,1.0,31,162.96,122.05 -36,2,19.10.2015,,0,162.96, -36,2,20.10.2015,,0,162.96, -36,2,21.10.2015,,0,162.96, -36,2,22.10.2015,,0,162.96, -36,2,23.10.2015,,0,162.96, -36,2,24.10.2015,,0,162.96, -36,2,25.10.2015,,3,162.96, -36,2,26.10.2015,,0,162.96, -36,2,27.10.2015,,0,162.96, -36,2,28.10.2015,,0,162.96, -36,2,29.10.2015,,0,162.96, -36,2,30.10.2015,,0,162.96, -36,2,31.10.2015,,0,162.96, -36,2,01.11.2015,,0,162.96, -36,2,02.11.2015,,0,162.96, -36,2,03.11.2015,,0,162.96, -36,2,04.11.2015,,0,162.96, -36,2,05.11.2015,,0,162.96, -36,2,06.11.2015,,0,162.96, -36,2,07.11.2015,,0,162.96, -36,2,08.11.2015,,0,162.96, -36,2,09.11.2015,,0,162.96, -36,2,10.11.2015,,0,162.96, -36,2,11.11.2015,,0,138.36, -36,2,12.11.2015,,0,138.36, -36,2,13.11.2015,,3,138.36, -36,2,14.11.2015,,0,138.36, -36,2,15.11.2015,,0,138.36, -36,2,16.11.2015,,0,138.36, -36,2,17.11.2015,,0,138.36, -36,2,18.11.2015,,0,138.36, -36,2,19.11.2015,,0,138.36, -36,2,20.11.2015,,0,138.36, -36,2,21.11.2015,,0,138.36, -36,2,22.11.2015,,0,138.36, -36,2,23.11.2015,,0,138.36, -36,2,24.11.2015,,3,138.36, -36,2,25.11.2015,,0,138.36, -36,2,26.11.2015,,0,138.36, -36,2,27.11.2015,,0,138.36, -36,2,28.11.2015,,0,138.36, -36,2,29.11.2015,,3,138.36, -36,2,30.11.2015,,3,138.36, -36,2,01.12.2015,,0,162.96, -36,2,02.12.2015,,0,162.96, -36,2,03.12.2015,,0,138.5, -36,2,04.12.2015,,0,138.5, -36,2,05.12.2015,,0,138.5, -36,2,06.12.2015,,0,138.5, -36,2,07.12.2015,,0,138.5, -36,2,08.12.2015,,0,138.5, -36,2,09.12.2015,,0,138.5, -36,2,10.12.2015,,0,138.5, -36,2,11.12.2015,,0,142.57, -36,2,12.12.2015,,0,142.57, -36,2,13.12.2015,,0,142.57, -36,2,14.12.2015,,0,142.57, -36,2,15.12.2015,,0,142.57, -36,2,16.12.2015,,0,142.57, -36,2,17.12.2015,,0,142.57, -36,2,18.12.2015,,3,142.57, -36,2,19.12.2015,,0,142.57, -36,2,20.12.2015,,0,142.57, -36,2,21.12.2015,,3,142.57, -36,2,22.12.2015,1.0,69,142.57,103.02 -36,2,23.12.2015,1.0,41,142.57,103.02 -36,2,24.12.2015,1.0,13,142.57,103.02 -36,2,25.12.2015,1.0,50,142.57,103.02 -36,2,26.12.2015,1.0,97,142.57,103.02 -36,2,27.12.2015,1.0,97,142.57,103.02 -36,2,28.12.2015,1.0,31,142.57,103.02 -36,2,29.12.2015,,0,138.5, -36,2,30.12.2015,,0,138.5, -36,2,31.12.2015,,0,138.5, -36,2,01.01.2016,,0,138.5, -36,2,02.01.2016,,0,138.5, -36,2,03.01.2016,,0,138.5, -36,2,04.01.2016,,0,138.5, -36,2,05.01.2016,,9,138.5, -36,2,06.01.2016,,9,138.5, -36,2,07.01.2016,,0,138.5, -36,2,08.01.2016,,0,138.5, -36,2,09.01.2016,,0,138.5, -36,2,10.01.2016,,0,138.5, -36,2,11.01.2016,,0,138.5, -36,2,12.01.2016,,0,138.5, -36,2,13.01.2016,,0,138.5, -36,2,14.01.2016,,0,138.5, -36,2,15.01.2016,,6,138.5, -36,2,16.01.2016,,6,142.57, -36,2,17.01.2016,,6,142.57, -36,2,18.01.2016,,0,142.57, -36,2,19.01.2016,1.0,85,142.57,103.02 -36,2,20.01.2016,1.0,107,142.57,103.02 -36,2,21.01.2016,1.0,129,142.57,103.02 -36,2,22.01.2016,1.0,129,142.57,103.02 -36,2,23.01.2016,1.0,116,142.57,103.02 -36,2,24.01.2016,1.0,75,142.57,103.02 -36,2,25.01.2016,1.0,72,142.57,103.02 -36,2,26.01.2016,,3,142.57, -36,2,27.01.2016,,0,142.57, -36,2,28.01.2016,,0,142.57, -36,2,29.01.2016,,0,142.57, -36,2,30.01.2016,,0,142.57, -36,2,31.01.2016,,3,142.57, -36,2,01.02.2016,,0,142.57, -36,2,02.02.2016,,3,142.57, -36,2,03.02.2016,,3,142.57, -36,2,04.02.2016,,3,142.57, -36,2,05.02.2016,,0,142.57, -36,2,06.02.2016,,3,142.57, -36,2,07.02.2016,,6,142.57, -36,2,08.02.2016,,13,142.57, -36,2,09.02.2016,1.0,47,142.57,108.46 -36,2,10.02.2016,1.0,60,138.36,108.46 -36,2,11.02.2016,1.0,66,138.36,108.46 -36,2,12.02.2016,1.0,88,138.36,108.46 -36,2,13.02.2016,1.0,63,138.36,108.46 -36,2,14.02.2016,1.0,47,138.36,108.46 -36,2,15.02.2016,1.0,72,138.36,108.46 -36,2,16.02.2016,,0,138.36, -36,2,17.02.2016,,0,138.36, -36,2,18.02.2016,,0,138.36, -36,2,19.02.2016,,3,138.36, -36,2,20.02.2016,,3,138.36, -36,2,21.02.2016,,0,138.36, -36,2,22.02.2016,,0,138.36, -36,2,23.02.2016,,0,138.36, -36,2,24.02.2016,,0,138.36, -36,2,25.02.2016,,0,138.36, -36,2,26.02.2016,,0,138.36, -36,2,27.02.2016,,0,138.36, -36,2,28.02.2016,,0,138.36, -36,2,29.02.2016,,0,138.36, -36,2,01.03.2016,,0,138.36, -36,2,02.03.2016,,0,138.36, -36,2,03.03.2016,,0,138.36, -36,2,04.03.2016,,0,138.36, -36,2,05.03.2016,,0,138.36, -36,2,06.03.2016,,0,138.36, -36,2,07.03.2016,,0,138.36, -36,2,08.03.2016,,0,138.36, -36,2,09.03.2016,,0,138.36, -36,2,10.03.2016,,0,138.36, -36,2,11.03.2016,,0,138.36, -36,2,12.03.2016,,0,138.5, -36,2,13.03.2016,,0,138.5, -36,2,14.03.2016,,0,138.5, -36,2,15.03.2016,1.0,66,138.5,124.77 -36,2,16.03.2016,1.0,19,138.5,124.77 -36,2,17.03.2016,1.0,9,138.5,124.77 -36,2,18.03.2016,1.0,44,138.5,124.77 -36,2,19.03.2016,1.0,6,138.5,124.77 -36,2,20.03.2016,1.0,6,138.5,124.77 -36,2,21.03.2016,1.0,22,138.5,124.77 -36,2,22.03.2016,,0,138.36, -36,2,23.03.2016,,0,138.36, -36,2,24.03.2016,,0,138.36, -36,2,25.03.2016,,0,138.36, -36,2,26.03.2016,,3,138.36, -36,2,27.03.2016,,0,138.36, -36,2,28.03.2016,,0,138.36, -36,2,29.03.2016,,0,138.36, -36,2,30.03.2016,,0,138.36, -36,2,31.03.2016,,0,138.36, -36,2,01.04.2016,,0,138.36, -36,2,02.04.2016,,0,138.36, -36,2,03.04.2016,,0,138.36, -36,2,04.04.2016,,3,138.36, -36,2,05.04.2016,,0,138.36, -36,2,06.04.2016,,3,138.36, -36,2,07.04.2016,,0,138.36, -36,2,08.04.2016,,0,138.36, -36,2,09.04.2016,,0,138.5, -36,2,10.04.2016,,3,138.5, -36,2,11.04.2016,,0,138.5, -36,2,12.04.2016,,3,138.5, -36,2,13.04.2016,,3,138.5, -36,2,14.04.2016,,0,138.5, -36,2,15.04.2016,,0,138.5, -36,2,16.04.2016,,0,138.5, -36,2,17.04.2016,,6,138.5, -36,2,18.04.2016,,0,138.5, -36,2,19.04.2016,1.0,19,138.5,116.89 -36,2,20.04.2016,1.0,28,138.5,116.89 -36,2,21.04.2016,1.0,19,138.5,116.89 -36,2,22.04.2016,1.0,35,138.5,116.89 -36,2,23.04.2016,1.0,57,138.5,116.89 -36,2,24.04.2016,1.0,19,138.5,116.89 -36,2,25.04.2016,1.0,19,138.5,116.89 -36,2,26.04.2016,1.0,19,138.5,114.17 -36,2,27.04.2016,1.0,3,138.5,114.17 -36,2,28.04.2016,,0,138.5, -36,2,29.04.2016,,6,138.5, -36,2,30.04.2016,,0,138.5, -36,2,01.05.2016,,0,138.5, -36,2,02.05.2016,,0,138.5, -36,2,03.05.2016,1.0,31,138.5,106.01 -36,2,04.05.2016,1.0,85,138.5,106.01 -36,2,05.05.2016,1.0,47,138.5,106.01 -36,2,06.05.2016,1.0,22,138.5,106.01 -36,2,07.05.2016,1.0,38,138.5,106.01 -36,2,08.05.2016,1.0,57,138.5,106.01 -36,2,09.05.2016,1.0,47,138.5,106.01 -36,2,10.05.2016,,0,138.5, -36,2,11.05.2016,,0,138.5, -36,2,12.05.2016,,0,138.5, -36,2,13.05.2016,,0,138.5, -36,2,14.05.2016,,3,138.5, -36,2,15.05.2016,,0,138.5, -36,2,16.05.2016,,0,138.5, -36,2,17.05.2016,,6,138.5, -36,2,18.05.2016,,0,138.5, -36,2,19.05.2016,,0,138.5, -36,2,20.05.2016,,0,138.5, -36,2,21.05.2016,,0,138.5, -36,2,22.05.2016,,0,138.5, -37,2,01.01.2015,,3,135.78, -37,2,02.01.2015,,0,135.78, -37,2,03.01.2015,,3,135.78, -37,2,04.01.2015,,13,135.78, -37,2,05.01.2015,,3,135.78, -37,2,06.01.2015,,0,135.78, -37,2,07.01.2015,,0,135.78, -37,2,08.01.2015,,0,135.78, -37,2,09.01.2015,,0,135.78, -37,2,10.01.2015,,0,135.78, -37,2,11.01.2015,,9,135.78, -37,2,12.01.2015,,3,135.78, -37,2,13.01.2015,,6,135.78, -37,2,14.01.2015,,0,135.78, -37,2,15.01.2015,,3,135.78, -37,2,16.01.2015,,3,135.78, -37,2,17.01.2015,,6,135.78, -37,2,18.01.2015,,9,135.78, -37,2,19.01.2015,,6,135.78, -37,2,20.01.2015,,3,135.78, -37,2,21.01.2015,,9,135.78, -37,2,22.01.2015,,0,135.78, -37,2,23.01.2015,,0,135.78, -37,2,24.01.2015,,3,135.78, -37,2,25.01.2015,,3,135.78, -37,2,26.01.2015,,3,135.78, -37,2,27.01.2015,,0,135.78, -37,2,28.01.2015,,6,135.78, -37,2,29.01.2015,,3,135.78, -37,2,30.01.2015,,6,135.78, -37,2,31.01.2015,,0,135.78, -37,2,01.02.2015,,6,135.78, -37,2,02.02.2015,,6,135.78, -37,2,03.02.2015,,3,135.78, -37,2,04.02.2015,,13,135.78, -37,2,05.02.2015,,13,135.78, -37,2,06.02.2015,,3,135.78, -37,2,07.02.2015,,22,135.78, -37,2,08.02.2015,,0,135.78, -37,2,09.02.2015,,3,135.78, -37,2,10.02.2015,,16,135.78, -37,2,11.02.2015,,6,135.78, -37,2,12.02.2015,,0,135.78, -37,2,13.02.2015,,0,135.78, -37,2,14.02.2015,,19,135.78, -37,2,15.02.2015,,3,135.78, -37,2,16.02.2015,,3,135.78, -37,2,17.02.2015,,22,135.78, -37,2,18.02.2015,,0,135.78, -37,2,19.02.2015,,0,135.78, -37,2,20.02.2015,,0,135.78, -37,2,21.02.2015,,0,135.78, -37,2,22.02.2015,,0,135.78, -37,2,23.02.2015,,6,135.78, -37,2,24.02.2015,,3,135.78, -37,2,25.02.2015,,0,135.78, -37,2,26.02.2015,,0,146.38, -37,2,27.02.2015,,0,146.38, -37,2,28.02.2015,,3,146.38, -37,2,01.03.2015,,3,146.38, -37,2,02.03.2015,,0,146.38, -37,2,03.03.2015,,0,146.38, -37,2,04.03.2015,,0,146.38, -37,2,05.03.2015,,0,146.38, -37,2,06.03.2015,,0,146.38, -37,2,07.03.2015,,16,146.38, -37,2,08.03.2015,,9,146.38, -37,2,09.03.2015,,0,146.38, -37,2,10.03.2015,,3,146.38, -37,2,11.03.2015,,0,146.38, -37,2,12.03.2015,,0,146.38, -37,2,13.03.2015,,0,146.38, -37,2,14.03.2015,,0,146.38, -37,2,15.03.2015,,0,146.38, -37,2,16.03.2015,,0,146.38, -37,2,17.03.2015,,0,146.38, -37,2,18.03.2015,,3,146.38, -37,2,19.03.2015,,13,146.38, -37,2,20.03.2015,,0,146.38, -37,2,21.03.2015,,3,146.38, -37,2,22.03.2015,,6,146.38, -37,2,23.03.2015,,3,146.38, -37,2,24.03.2015,,3,146.38, -37,2,25.03.2015,,9,146.38, -37,2,26.03.2015,,3,146.38, -37,2,27.03.2015,,3,146.38, -37,2,28.03.2015,,0,146.38, -37,2,29.03.2015,,6,146.38, -37,2,30.03.2015,,6,146.38, -37,2,31.03.2015,,0,146.38, -37,2,01.04.2015,,0,146.38, -37,2,02.04.2015,,0,146.38, -37,2,03.04.2015,,0,146.38, -37,2,04.04.2015,,6,146.38, -37,2,05.04.2015,,0,146.38, -37,2,06.04.2015,,6,146.38, -37,2,07.04.2015,,0,146.38, -37,2,08.04.2015,,3,146.38, -37,2,09.04.2015,,0,146.38, -37,2,10.04.2015,,9,146.38, -37,2,11.04.2015,,19,146.38, -37,2,12.04.2015,,0,146.38, -37,2,13.04.2015,,0,146.38, -37,2,14.04.2015,,6,146.38, -37,2,15.04.2015,,6,146.38, -37,2,16.04.2015,,3,146.38, -37,2,17.04.2015,,3,146.38, -37,2,18.04.2015,,3,146.38, -37,2,19.04.2015,,3,146.38, -37,2,20.04.2015,,3,145.84, -37,2,21.04.2015,,0,145.84, -37,2,22.04.2015,,0,145.84, -37,2,23.04.2015,,0,145.84, -37,2,24.04.2015,,0,145.84, -37,2,25.04.2015,,0,145.84, -37,2,26.04.2015,,0,150.73, -37,2,27.04.2015,,0,150.73, -37,2,28.04.2015,,0,150.73, -37,2,29.04.2015,,0,150.73, -37,2,30.04.2015,,0,150.73, -37,2,01.05.2015,,0,150.73, -37,2,02.05.2015,,0,150.73, -37,2,03.05.2015,,0,150.73, -37,2,04.05.2015,,0,150.73, -37,2,05.05.2015,,3,150.73, -37,2,06.05.2015,,0,150.73, -37,2,07.05.2015,,0,150.73, -37,2,08.05.2015,,3,150.73, -37,2,09.05.2015,,0,150.73, -37,2,10.05.2015,,6,150.73, -37,2,11.05.2015,,0,150.73, -37,2,12.05.2015,,0,150.73, -37,2,13.05.2015,,0,150.73, -37,2,14.05.2015,,9,150.73, -37,2,15.05.2015,,3,150.73, -37,2,16.05.2015,,0,150.73, -37,2,17.05.2015,,0,150.73, -37,2,18.05.2015,,0,150.73, -37,2,19.05.2015,,9,150.73, -37,2,20.05.2015,,0,150.73, -37,2,21.05.2015,,0,150.73, -37,2,22.05.2015,,6,150.73, -37,2,23.05.2015,,6,150.73, -37,2,24.05.2015,,0,150.73, -37,2,25.05.2015,,0,150.73, -37,2,26.05.2015,,0,150.73, -37,2,27.05.2015,,0,150.73, -37,2,28.05.2015,,3,150.73, -37,2,29.05.2015,,0,150.73, -37,2,30.05.2015,,6,150.73, -37,2,31.05.2015,,6,150.73, -37,2,01.06.2015,,0,150.73, -37,2,02.06.2015,,0,150.73, -37,2,03.06.2015,,3,150.73, -37,2,04.06.2015,,3,150.73, -37,2,05.06.2015,,3,150.73, -37,2,06.06.2015,,0,150.73, -37,2,07.06.2015,,0,150.73, -37,2,08.06.2015,,0,150.73, -37,2,09.06.2015,,0,150.73, -37,2,10.06.2015,,3,150.73, -37,2,11.06.2015,,0,150.73, -37,2,12.06.2015,,9,150.73, -37,2,13.06.2015,,3,150.73, -37,2,14.06.2015,,0,150.73, -37,2,15.06.2015,,0,150.73, -37,2,16.06.2015,,0,150.73, -37,2,17.06.2015,,3,150.73, -37,2,18.06.2015,,0,150.73, -37,2,19.06.2015,,13,150.73, -37,2,20.06.2015,,3,150.73, -37,2,21.06.2015,,6,150.73, -37,2,22.06.2015,,0,150.73, -37,2,23.06.2015,,0,150.73, -37,2,24.06.2015,,0,150.73, -37,2,25.06.2015,,0,150.73, -37,2,26.06.2015,,0,150.73, -37,2,27.06.2015,,0,150.73, -37,2,28.06.2015,,0,150.73, -37,2,29.06.2015,,0,150.73, -37,2,30.06.2015,,0,150.73, -37,2,01.07.2015,,3,150.73, -37,2,02.07.2015,,3,150.73, -37,2,03.07.2015,,13,150.73, -37,2,04.07.2015,,9,150.73, -37,2,05.07.2015,,3,150.73, -37,2,06.07.2015,,9,150.73, -37,2,07.07.2015,,13,150.73, -37,2,08.07.2015,,6,150.73, -37,2,09.07.2015,,3,150.73, -37,2,10.07.2015,,0,150.73, -37,2,11.07.2015,,9,150.73, -37,2,12.07.2015,,3,150.73, -37,2,13.07.2015,,3,150.73, -37,2,14.07.2015,,3,150.73, -37,2,15.07.2015,,0,150.73, -37,2,16.07.2015,,0,150.73, -37,2,17.07.2015,,9,150.73, -37,2,18.07.2015,,0,150.73, -37,2,19.07.2015,,9,150.73, -37,2,20.07.2015,,47,150.73, -37,2,21.07.2015,,25,150.73, -37,2,22.07.2015,,0,150.73, -37,2,23.07.2015,,0,150.73, -37,2,24.07.2015,,9,150.73, -37,2,25.07.2015,,16,150.73, -37,2,26.07.2015,,22,150.73, -37,2,27.07.2015,,3,150.73, -37,2,28.07.2015,,0,150.73, -37,2,29.07.2015,,9,150.73, -37,2,30.07.2015,,9,150.73, -37,2,31.07.2015,,9,150.73, -37,2,01.08.2015,,9,150.73, -37,2,02.08.2015,,9,150.73, -37,2,03.08.2015,,9,150.73, -37,2,04.08.2015,,9,150.73, -37,2,05.08.2015,,9,150.73, -37,2,06.08.2015,,9,150.73, -37,2,07.08.2015,,9,150.73, -37,2,08.08.2015,,9,150.73, -37,2,09.08.2015,,9,150.73, -37,2,10.08.2015,,9,150.73, -37,2,11.08.2015,,9,150.73, -37,2,12.08.2015,,9,128.17, -37,2,13.08.2015,,9,128.17, -37,2,14.08.2015,,9,128.17, -37,2,15.08.2015,,9,128.17, -37,2,16.08.2015,,9,128.17, -37,2,17.08.2015,,9,128.17, -37,2,18.08.2015,1.0,82,128.17,100.3 -37,2,19.08.2015,1.0,82,128.17,100.3 -37,2,20.08.2015,1.0,82,128.17,100.3 -37,2,21.08.2015,1.0,50,128.17,100.3 -37,2,22.08.2015,1.0,79,128.17,100.3 -37,2,23.08.2015,1.0,79,128.17,100.3 -37,2,24.08.2015,1.0,50,128.17,100.3 -37,2,25.08.2015,,38,128.17, -37,2,26.08.2015,,22,128.17, -37,2,27.08.2015,,22,128.17, -37,2,28.08.2015,,31,128.17, -37,2,29.08.2015,,13,128.17, -37,2,30.08.2015,,19,128.17, -37,2,31.08.2015,,3,128.17, -37,2,01.09.2015,,0,128.17, -37,2,02.09.2015,,9,128.17, -37,2,03.09.2015,,0,128.17, -37,2,04.09.2015,,19,128.17, -37,2,05.09.2015,,6,128.17, -37,2,06.09.2015,,19,128.17, -37,2,07.09.2015,,44,128.17, -37,2,08.09.2015,,9,128.17, -37,2,09.09.2015,,16,128.17, -37,2,10.09.2015,,3,128.17, -37,2,11.09.2015,,3,128.17, -37,2,12.09.2015,,9,128.17, -37,2,13.09.2015,,16,128.17, -37,2,14.09.2015,,3,128.17, -37,2,15.09.2015,,3,128.17, -37,2,16.09.2015,,25,128.17, -37,2,17.09.2015,,19,128.17, -37,2,18.09.2015,,13,128.17, -37,2,19.09.2015,,13,128.17, -37,2,20.09.2015,,13,128.17, -37,2,21.09.2015,,38,128.17, -37,2,22.09.2015,,13,128.17, -37,2,23.09.2015,,31,128.17, -37,2,24.09.2015,,16,128.17, -37,2,25.09.2015,,16,128.17, -37,2,26.09.2015,,0,128.17, -37,2,27.09.2015,,6,128.17, -37,2,28.09.2015,,13,128.17, -37,2,29.09.2015,,3,128.17, -37,2,30.09.2015,,9,128.17, -37,2,01.10.2015,,13,162.96, -37,2,02.10.2015,,13,162.96, -37,2,03.10.2015,,13,162.96, -37,2,04.10.2015,,3,162.96, -37,2,05.10.2015,,16,162.96, -37,2,06.10.2015,1.0,110,143.39,122.05 -37,2,07.10.2015,1.0,72,143.39,122.05 -37,2,08.10.2015,1.0,101,143.39,122.05 -37,2,09.10.2015,1.0,75,143.39,122.05 -37,2,10.10.2015,1.0,69,143.39,122.05 -37,2,11.10.2015,1.0,57,143.39,122.05 -37,2,12.10.2015,1.0,53,143.39,122.05 -37,2,13.10.2015,,0,143.93, -37,2,14.10.2015,,13,143.93, -37,2,15.10.2015,,13,143.93, -37,2,16.10.2015,,13,143.93, -37,2,17.10.2015,,13,162.96, -37,2,18.10.2015,,3,162.96, -37,2,19.10.2015,,0,162.96, -37,2,20.10.2015,,6,162.96, -37,2,21.10.2015,,0,162.96, -37,2,22.10.2015,,6,162.96, -37,2,23.10.2015,,3,162.96, -37,2,24.10.2015,,0,162.96, -37,2,25.10.2015,,0,162.96, -37,2,26.10.2015,,3,162.96, -37,2,27.10.2015,,3,162.96, -37,2,28.10.2015,,6,162.96, -37,2,29.10.2015,,0,162.96, -37,2,30.10.2015,,0,162.96, -37,2,31.10.2015,,6,162.96, -37,2,01.11.2015,,0,162.96, -37,2,02.11.2015,,0,162.96, -37,2,03.11.2015,,0,162.96, -37,2,04.11.2015,,0,162.96, -37,2,05.11.2015,,0,162.96, -37,2,06.11.2015,,0,162.96, -37,2,07.11.2015,,0,162.96, -37,2,08.11.2015,,0,162.96, -37,2,09.11.2015,,0,162.96, -37,2,10.11.2015,,0,162.96, -37,2,11.11.2015,,0,138.36, -37,2,12.11.2015,,0,138.36, -37,2,13.11.2015,,0,138.36, -37,2,14.11.2015,,0,138.36, -37,2,15.11.2015,,0,138.36, -37,2,16.11.2015,,0,138.36, -37,2,17.11.2015,,0,138.36, -37,2,18.11.2015,,0,138.36, -37,2,19.11.2015,,0,138.36, -37,2,20.11.2015,,3,138.36, -37,2,21.11.2015,,3,138.36, -37,2,22.11.2015,,3,138.36, -37,2,23.11.2015,,9,138.36, -37,2,24.11.2015,,0,138.36, -37,2,25.11.2015,,0,138.36, -37,2,26.11.2015,,0,138.36, -37,2,27.11.2015,,0,138.36, -37,2,28.11.2015,,0,138.36, -37,2,29.11.2015,,0,138.36, -37,2,30.11.2015,,0,138.36, -37,2,01.12.2015,,0,162.96, -37,2,02.12.2015,,0,162.96, -37,2,03.12.2015,,0,138.5, -37,2,04.12.2015,,3,138.5, -37,2,05.12.2015,,3,138.5, -37,2,06.12.2015,,0,138.5, -37,2,07.12.2015,,3,138.5, -37,2,08.12.2015,,6,138.5, -37,2,09.12.2015,,6,138.5, -37,2,10.12.2015,,6,138.5, -37,2,11.12.2015,,6,142.57, -37,2,12.12.2015,,3,142.57, -37,2,13.12.2015,,0,142.57, -37,2,14.12.2015,,3,142.57, -37,2,15.12.2015,,0,142.57, -37,2,16.12.2015,,0,142.57, -37,2,17.12.2015,,13,142.57, -37,2,18.12.2015,,3,142.57, -37,2,19.12.2015,,0,142.57, -37,2,20.12.2015,,0,142.57, -37,2,21.12.2015,,19,142.57, -37,2,22.12.2015,1.0,195,142.57,103.02 -37,2,23.12.2015,1.0,113,142.57,103.02 -37,2,24.12.2015,1.0,107,142.57,103.02 -37,2,25.12.2015,1.0,173,142.57,103.02 -37,2,26.12.2015,1.0,66,142.57,103.02 -37,2,27.12.2015,1.0,69,142.57,103.02 -37,2,28.12.2015,1.0,94,142.57,103.02 -37,2,29.12.2015,,0,138.5, -37,2,30.12.2015,,0,138.5, -37,2,31.12.2015,,0,138.5, -37,2,01.01.2016,,0,138.5, -37,2,02.01.2016,,0,138.5, -37,2,03.01.2016,,3,138.5, -37,2,04.01.2016,,6,138.5, -37,2,05.01.2016,,6,138.5, -37,2,06.01.2016,,0,138.5, -37,2,07.01.2016,,0,138.5, -37,2,08.01.2016,,0,138.5, -37,2,09.01.2016,,6,138.5, -37,2,10.01.2016,,3,138.5, -37,2,11.01.2016,,13,138.5, -37,2,12.01.2016,,16,138.5, -37,2,13.01.2016,,9,138.5, -37,2,14.01.2016,,9,138.5, -37,2,15.01.2016,,9,138.5, -37,2,16.01.2016,,6,142.57, -37,2,17.01.2016,,9,142.57, -37,2,18.01.2016,,31,142.57, -37,2,19.01.2016,1.0,217,142.57,103.02 -37,2,20.01.2016,1.0,179,142.57,103.02 -37,2,21.01.2016,1.0,88,142.57,103.02 -37,2,22.01.2016,1.0,295,142.57,103.02 -37,2,23.01.2016,1.0,163,142.57,103.02 -37,2,24.01.2016,1.0,50,142.57,103.02 -37,2,25.01.2016,1.0,129,142.57,103.02 -37,2,26.01.2016,,22,142.57, -37,2,27.01.2016,,0,142.57, -37,2,28.01.2016,,22,142.57, -37,2,29.01.2016,,16,142.57, -37,2,30.01.2016,,0,142.57, -37,2,31.01.2016,,0,142.57, -37,2,01.02.2016,,0,142.57, -37,2,02.02.2016,,0,142.57, -37,2,03.02.2016,,0,142.57, -37,2,04.02.2016,,0,142.57, -37,2,05.02.2016,,0,142.57, -37,2,06.02.2016,,0,142.57, -37,2,07.02.2016,,0,142.57, -37,2,08.02.2016,,16,142.57, -37,2,09.02.2016,1.0,273,142.57,108.46 -37,2,10.02.2016,1.0,188,138.36,108.46 -37,2,11.02.2016,1.0,226,138.36,108.46 -37,2,12.02.2016,1.0,210,138.36,108.46 -37,2,13.02.2016,1.0,151,138.36,108.46 -37,2,14.02.2016,1.0,148,138.36,108.46 -37,2,15.02.2016,1.0,223,138.36,108.46 -37,2,16.02.2016,,0,138.36, -37,2,17.02.2016,,9,138.36, -37,2,18.02.2016,,3,138.36, -37,2,19.02.2016,,6,138.36, -37,2,20.02.2016,,13,138.36, -37,2,21.02.2016,,13,138.36, -37,2,22.02.2016,,3,138.36, -37,2,23.02.2016,,0,138.36, -37,2,24.02.2016,,0,138.36, -37,2,25.02.2016,,6,138.36, -37,2,26.02.2016,,13,138.36, -37,2,27.02.2016,,0,138.36, -37,2,28.02.2016,,3,138.36, -37,2,29.02.2016,,0,138.36, -37,2,01.03.2016,,0,138.36, -37,2,02.03.2016,,9,138.36, -37,2,03.03.2016,,0,138.36, -37,2,04.03.2016,,0,138.36, -37,2,05.03.2016,,0,138.36, -37,2,06.03.2016,,0,138.36, -37,2,07.03.2016,,0,138.36, -37,2,08.03.2016,,0,138.36, -37,2,09.03.2016,,0,138.36, -37,2,10.03.2016,,0,138.36, -37,2,11.03.2016,,0,138.36, -37,2,12.03.2016,,0,138.5, -37,2,13.03.2016,,0,138.5, -37,2,14.03.2016,,3,138.5, -37,2,15.03.2016,1.0,82,138.5,124.77 -37,2,16.03.2016,1.0,66,138.5,124.77 -37,2,17.03.2016,1.0,97,138.5,124.77 -37,2,18.03.2016,1.0,113,138.5,124.77 -37,2,19.03.2016,1.0,53,138.5,124.77 -37,2,20.03.2016,1.0,145,138.5,124.77 -37,2,21.03.2016,1.0,16,138.5,124.77 -37,2,22.03.2016,,9,138.36, -37,2,23.03.2016,,0,138.36, -37,2,24.03.2016,,0,138.36, -37,2,25.03.2016,,3,138.36, -37,2,26.03.2016,,3,138.36, -37,2,27.03.2016,,6,138.36, -37,2,28.03.2016,,13,138.36, -37,2,29.03.2016,,6,138.36, -37,2,30.03.2016,,0,138.36, -37,2,31.03.2016,,0,138.36, -37,2,01.04.2016,,9,138.36, -37,2,02.04.2016,,6,138.36, -37,2,03.04.2016,,3,138.36, -37,2,04.04.2016,,0,138.36, -37,2,05.04.2016,,9,138.36, -37,2,06.04.2016,,3,138.36, -37,2,07.04.2016,,6,138.36, -37,2,08.04.2016,,9,138.36, -37,2,09.04.2016,,6,138.5, -37,2,10.04.2016,,0,138.5, -37,2,11.04.2016,,3,138.5, -37,2,12.04.2016,,3,138.5, -37,2,13.04.2016,,9,138.5, -37,2,14.04.2016,,0,138.5, -37,2,15.04.2016,,0,138.5, -37,2,16.04.2016,,6,138.5, -37,2,17.04.2016,,19,138.5, -37,2,18.04.2016,,50,138.5, -37,2,19.04.2016,1.0,160,138.5,116.89 -37,2,20.04.2016,1.0,138,138.5,116.89 -37,2,21.04.2016,1.0,88,138.5,116.89 -37,2,22.04.2016,1.0,182,138.5,116.89 -37,2,23.04.2016,1.0,110,138.5,116.89 -37,2,24.04.2016,1.0,57,138.5,116.89 -37,2,25.04.2016,1.0,13,138.5,116.89 -37,2,26.04.2016,,22,138.5, -37,2,27.04.2016,,25,138.5, -37,2,28.04.2016,,13,138.5, -37,2,29.04.2016,,22,138.5, -37,2,30.04.2016,,31,138.5, -37,2,01.05.2016,,19,138.5, -37,2,02.05.2016,,13,138.5, -37,2,03.05.2016,1.0,236,138.5,106.01 -37,2,04.05.2016,1.0,204,138.5,106.01 -37,2,05.05.2016,1.0,173,138.5,106.01 -37,2,06.05.2016,1.0,264,138.5,106.01 -37,2,07.05.2016,1.0,236,138.5,106.01 -37,2,08.05.2016,1.0,63,138.5,106.01 -37,2,09.05.2016,1.0,38,138.5,106.01 -37,2,10.05.2016,,3,142.57, -37,2,11.05.2016,,16,142.57, -37,2,12.05.2016,,94,142.57, -37,2,13.05.2016,,22,142.57, -37,2,14.05.2016,,3,142.57, -37,2,15.05.2016,,16,142.57, -37,2,16.05.2016,,9,142.57, -37,2,17.05.2016,,6,142.57, -37,2,18.05.2016,,3,142.57, -37,2,19.05.2016,,16,142.57, -37,2,20.05.2016,,6,142.57, -37,2,21.05.2016,,0,142.57, -37,2,22.05.2016,,6,142.57, -38,2,01.01.2015,,0,135.78, -38,2,02.01.2015,,3,135.78, -38,2,03.01.2015,,0,135.78, -38,2,04.01.2015,,0,135.78, -38,2,05.01.2015,,9,135.78, -38,2,06.01.2015,,6,135.78, -38,2,07.01.2015,,0,135.78, -38,2,08.01.2015,,0,135.78, -38,2,09.01.2015,,0,135.78, -38,2,10.01.2015,,0,135.78, -38,2,11.01.2015,,6,135.78, -38,2,12.01.2015,,6,135.78, -38,2,13.01.2015,,0,135.78, -38,2,14.01.2015,,6,135.78, -38,2,15.01.2015,,0,135.78, -38,2,16.01.2015,,0,135.78, -38,2,17.01.2015,,0,135.78, -38,2,18.01.2015,,0,135.78, -38,2,19.01.2015,,0,135.78, -38,2,20.01.2015,,0,135.78, -38,2,21.01.2015,,3,135.78, -38,2,22.01.2015,,0,135.78, -38,2,23.01.2015,,0,135.78, -38,2,24.01.2015,,0,135.78, -38,2,25.01.2015,,0,135.78, -38,2,26.01.2015,,3,135.78, -38,2,27.01.2015,,0,135.78, -38,2,28.01.2015,,0,135.78, -38,2,29.01.2015,,0,135.78, -38,2,30.01.2015,,0,135.78, -38,2,31.01.2015,,0,135.78, -38,2,01.02.2015,,3,135.78, -38,2,02.02.2015,,3,135.78, -38,2,03.02.2015,,0,135.78, -38,2,04.02.2015,,0,135.78, -38,2,05.02.2015,,0,135.78, -38,2,06.02.2015,,0,135.78, -38,2,07.02.2015,,0,135.78, -38,2,08.02.2015,,0,135.78, -38,2,09.02.2015,,0,135.78, -38,2,10.02.2015,,0,135.78, -38,2,11.02.2015,,0,135.78, -38,2,12.02.2015,,0,135.78, -38,2,13.02.2015,,0,135.78, -38,2,14.02.2015,,3,135.78, -38,2,15.02.2015,,0,135.78, -38,2,16.02.2015,,0,135.78, -38,2,17.02.2015,,0,135.78, -38,2,18.02.2015,,0,135.78, -38,2,19.02.2015,,0,135.78, -38,2,20.02.2015,,0,135.78, -38,2,21.02.2015,,0,135.78, -38,2,22.02.2015,,0,135.78, -38,2,23.02.2015,,0,135.78, -38,2,24.02.2015,,0,135.78, -38,2,25.02.2015,,0,135.78, -38,2,26.02.2015,,0,146.38, -38,2,27.02.2015,,9,146.38, -38,2,28.02.2015,,0,146.38, -38,2,01.03.2015,,0,146.38, -38,2,02.03.2015,,0,146.38, -38,2,03.03.2015,,0,146.38, -38,2,04.03.2015,,0,146.38, -38,2,05.03.2015,,0,146.38, -38,2,06.03.2015,,0,146.38, -38,2,07.03.2015,,0,146.38, -38,2,08.03.2015,,0,146.38, -38,2,09.03.2015,,0,146.38, -38,2,10.03.2015,,0,146.38, -38,2,11.03.2015,,0,146.38, -38,2,12.03.2015,,0,146.38, -38,2,13.03.2015,,0,146.38, -38,2,14.03.2015,,0,146.38, -38,2,15.03.2015,,0,146.38, -38,2,16.03.2015,,0,146.38, -38,2,17.03.2015,,3,146.38, -38,2,18.03.2015,,0,146.38, -38,2,19.03.2015,,0,146.38, -38,2,20.03.2015,,0,146.38, -38,2,21.03.2015,,0,146.38, -38,2,22.03.2015,,0,146.38, -38,2,23.03.2015,,0,146.38, -38,2,24.03.2015,,0,146.38, -38,2,25.03.2015,,0,146.38, -38,2,26.03.2015,,0,146.38, -38,2,27.03.2015,,3,146.38, -38,2,28.03.2015,,0,146.38, -38,2,29.03.2015,,0,146.38, -38,2,30.03.2015,,0,146.38, -38,2,31.03.2015,,0,146.38, -38,2,01.04.2015,,0,146.38, -38,2,02.04.2015,,0,146.38, -38,2,03.04.2015,,0,146.38, -38,2,04.04.2015,,0,146.38, -38,2,05.04.2015,,0,146.38, -38,2,06.04.2015,,0,146.38, -38,2,07.04.2015,,0,146.38, -38,2,08.04.2015,,0,146.38, -38,2,09.04.2015,,0,146.38, -38,2,10.04.2015,,0,146.38, -38,2,11.04.2015,,0,146.38, -38,2,12.04.2015,,0,146.38, -38,2,13.04.2015,,0,146.38, -38,2,14.04.2015,,0,146.38, -38,2,15.04.2015,,0,146.38, -38,2,16.04.2015,,0,146.38, -38,2,17.04.2015,,0,146.38, -38,2,18.04.2015,,0,146.38, -38,2,19.04.2015,,0,146.38, -38,2,20.04.2015,,0,146.38, -38,2,21.04.2015,,0,146.38, -38,2,22.04.2015,,0,146.38, -38,2,23.04.2015,,0,146.38, -38,2,24.04.2015,,0,146.38, -38,2,25.04.2015,,0,146.38, -38,2,26.04.2015,,0,158.34, -38,2,27.04.2015,,0,158.34, -38,2,28.04.2015,,0,158.34, -38,2,29.04.2015,,0,158.34, -38,2,30.04.2015,,0,158.34, -38,2,01.05.2015,,0,158.34, -38,2,02.05.2015,,0,158.34, -38,2,03.05.2015,,0,158.34, -38,2,04.05.2015,,0,158.34, -38,2,05.05.2015,,0,158.34, -38,2,06.05.2015,,0,158.34, -38,2,07.05.2015,,0,158.34, -38,2,08.05.2015,,0,158.34, -38,2,09.05.2015,,0,158.34, -38,2,10.05.2015,,0,158.34, -38,2,11.05.2015,,0,158.34, -38,2,12.05.2015,,0,158.34, -38,2,13.05.2015,,3,158.34, -38,2,14.05.2015,,0,158.34, -38,2,15.05.2015,,3,158.34, -38,2,16.05.2015,,0,158.34, -38,2,17.05.2015,,0,158.34, -38,2,18.05.2015,,0,158.34, -38,2,19.05.2015,,0,158.34, -38,2,20.05.2015,,0,158.34, -38,2,21.05.2015,,0,158.34, -38,2,22.05.2015,,0,158.34, -38,2,23.05.2015,,6,158.34, -38,2,24.05.2015,,0,158.34, -38,2,25.05.2015,,3,158.34, -38,2,26.05.2015,,0,158.34, -38,2,27.05.2015,,0,158.34, -38,2,28.05.2015,,0,158.34, -38,2,29.05.2015,,0,158.34, -38,2,30.05.2015,,0,158.34, -38,2,31.05.2015,,0,158.34, -38,2,01.06.2015,,0,158.34, -38,2,02.06.2015,,0,158.34, -38,2,03.06.2015,,0,158.34, -38,2,04.06.2015,,0,158.34, -38,2,05.06.2015,,3,158.34, -38,2,06.06.2015,,3,158.34, -38,2,07.06.2015,,0,158.34, -38,2,08.06.2015,,3,158.34, -38,2,09.06.2015,,0,158.34, -38,2,10.06.2015,,0,158.34, -38,2,11.06.2015,,0,158.34, -38,2,12.06.2015,,9,158.34, -38,2,13.06.2015,,3,158.34, -38,2,14.06.2015,,0,150.73, -38,2,15.06.2015,,0,150.73, -38,2,16.06.2015,,0,150.73, -38,2,17.06.2015,,0,150.73, -38,2,18.06.2015,,0,150.73, -38,2,19.06.2015,,0,150.73, -38,2,20.06.2015,,0,150.73, -38,2,21.06.2015,,0,150.73, -38,2,22.06.2015,,0,150.73, -38,2,23.06.2015,,0,150.73, -38,2,24.06.2015,,0,150.73, -38,2,25.06.2015,,0,150.73, -38,2,26.06.2015,,0,150.73, -38,2,27.06.2015,,0,150.73, -38,2,28.06.2015,,0,150.73, -38,2,29.06.2015,,0,150.73, -38,2,30.06.2015,,0,150.73, -38,2,01.07.2015,,0,150.73, -38,2,02.07.2015,,0,150.73, -38,2,03.07.2015,,3,150.73, -38,2,04.07.2015,,0,150.73, -38,2,05.07.2015,,0,150.73, -38,2,06.07.2015,,0,150.73, -38,2,07.07.2015,,0,150.73, -38,2,08.07.2015,,0,150.73, -38,2,09.07.2015,,0,150.73, -38,2,10.07.2015,,3,150.73, -38,2,11.07.2015,,0,150.73, -38,2,12.07.2015,,3,150.73, -38,2,13.07.2015,,0,150.73, -38,2,14.07.2015,,0,150.73, -38,2,15.07.2015,,0,150.73, -38,2,16.07.2015,,0,150.73, -38,2,17.07.2015,,0,150.73, -38,2,18.07.2015,,3,150.73, -38,2,19.07.2015,,9,150.73, -38,2,20.07.2015,,25,150.73, -38,2,21.07.2015,,6,150.73, -38,2,22.07.2015,,6,150.73, -38,2,23.07.2015,,3,150.73, -38,2,24.07.2015,,22,150.73, -38,2,25.07.2015,,6,150.73, -38,2,26.07.2015,,22,150.73, -38,2,27.07.2015,,6,150.73, -38,2,28.07.2015,,19,150.73, -38,2,29.07.2015,,6,150.73, -38,2,30.07.2015,,9,150.73, -38,2,31.07.2015,,9,150.73, -38,2,01.08.2015,,6,150.73, -38,2,02.08.2015,,16,150.73, -38,2,03.08.2015,,3,150.73, -38,2,04.08.2015,,3,150.73, -38,2,05.08.2015,,13,150.73, -38,2,06.08.2015,,13,150.73, -38,2,07.08.2015,,16,150.73, -38,2,08.08.2015,,3,150.73, -38,2,09.08.2015,,3,150.73, -38,2,10.08.2015,,9,150.73, -38,2,11.08.2015,,6,150.73, -38,2,12.08.2015,,16,128.17, -38,2,13.08.2015,,0,128.17, -38,2,14.08.2015,,0,128.17, -38,2,15.08.2015,,3,128.17, -38,2,16.08.2015,,6,128.17, -38,2,17.08.2015,,6,128.17, -38,2,18.08.2015,1.0,69,128.17,100.3 -38,2,19.08.2015,1.0,47,128.17,100.3 -38,2,20.08.2015,1.0,91,128.17,100.3 -38,2,21.08.2015,1.0,82,128.17,100.3 -38,2,22.08.2015,1.0,110,128.17,100.3 -38,2,23.08.2015,1.0,85,128.17,100.3 -38,2,24.08.2015,1.0,13,128.17,100.3 -38,2,25.08.2015,,13,128.17, -38,2,26.08.2015,,6,128.17, -38,2,27.08.2015,,6,128.17, -38,2,28.08.2015,,6,128.17, -38,2,29.08.2015,,6,128.17, -38,2,30.08.2015,,6,128.17, -38,2,31.08.2015,,6,128.17, -38,2,01.09.2015,,3,128.17, -38,2,02.09.2015,,3,128.17, -38,2,03.09.2015,,3,128.17, -38,2,04.09.2015,,6,128.17, -38,2,05.09.2015,,19,128.17, -38,2,06.09.2015,,3,128.17, -38,2,07.09.2015,,6,128.17, -38,2,08.09.2015,,6,128.17, -38,2,09.09.2015,,6,128.17, -38,2,10.09.2015,,3,128.17, -38,2,11.09.2015,,3,128.17, -38,2,12.09.2015,,3,128.17, -38,2,13.09.2015,,6,128.17, -38,2,14.09.2015,,9,128.17, -38,2,15.09.2015,,6,128.17, -38,2,16.09.2015,,13,128.17, -38,2,17.09.2015,,6,128.17, -38,2,18.09.2015,,38,128.17, -38,2,19.09.2015,,38,128.17, -38,2,20.09.2015,,19,128.17, -38,2,21.09.2015,,16,128.17, -38,2,22.09.2015,,3,128.17, -38,2,23.09.2015,,6,128.17, -38,2,24.09.2015,,9,128.17, -38,2,25.09.2015,,0,128.17, -38,2,26.09.2015,,0,128.17, -38,2,27.09.2015,,6,128.17, -38,2,28.09.2015,,6,128.17, -38,2,29.09.2015,,0,128.17, -38,2,30.09.2015,,13,128.17, -38,2,01.10.2015,,0,162.96, -38,2,02.10.2015,,0,162.96, -38,2,03.10.2015,,9,162.96, -38,2,04.10.2015,,0,162.96, -38,2,05.10.2015,,0,162.96, -38,2,06.10.2015,1.0,41,143.39,122.05 -38,2,07.10.2015,1.0,25,143.39,122.05 -38,2,08.10.2015,1.0,22,143.39,122.05 -38,2,09.10.2015,1.0,19,143.39,122.05 -38,2,10.10.2015,1.0,38,143.39,122.05 -38,2,11.10.2015,1.0,28,143.39,122.05 -38,2,12.10.2015,1.0,28,143.39,122.05 -38,2,13.10.2015,1.0,13,143.93,122.05 -38,2,14.10.2015,1.0,41,143.93,122.05 -38,2,15.10.2015,1.0,6,143.93,122.05 -38,2,16.10.2015,1.0,41,143.93,122.05 -38,2,17.10.2015,1.0,41,162.96,122.05 -38,2,18.10.2015,1.0,41,162.96,122.05 -38,2,19.10.2015,,3,162.96, -38,2,20.10.2015,,3,162.96, -38,2,21.10.2015,,3,162.96, -38,2,22.10.2015,,3,162.96, -38,2,23.10.2015,,3,162.96, -38,2,24.10.2015,,6,162.96, -38,2,25.10.2015,,0,162.96, -38,2,26.10.2015,,3,162.96, -38,2,27.10.2015,,0,162.96, -38,2,28.10.2015,,0,162.96, -38,2,29.10.2015,,0,162.96, -38,2,30.10.2015,,0,162.96, -38,2,31.10.2015,,0,162.96, -38,2,01.11.2015,,0,162.96, -38,2,02.11.2015,,0,162.96, -38,2,03.11.2015,,0,162.96, -38,2,04.11.2015,,0,162.96, -38,2,05.11.2015,,3,162.96, -38,2,06.11.2015,,0,162.96, -38,2,07.11.2015,,0,162.96, -38,2,08.11.2015,,0,162.96, -38,2,09.11.2015,,3,162.96, -38,2,10.11.2015,,0,162.96, -38,2,11.11.2015,,0,138.36, -38,2,12.11.2015,,0,138.36, -38,2,13.11.2015,,0,138.36, -38,2,14.11.2015,,3,138.36, -38,2,15.11.2015,,0,138.36, -38,2,16.11.2015,,0,138.36, -38,2,17.11.2015,,0,138.36, -38,2,18.11.2015,,0,138.36, -38,2,19.11.2015,,0,138.36, -38,2,20.11.2015,,0,138.36, -38,2,21.11.2015,,0,138.36, -38,2,22.11.2015,,0,138.36, -38,2,23.11.2015,,0,138.36, -38,2,24.11.2015,,3,138.36, -38,2,25.11.2015,,3,138.36, -38,2,26.11.2015,,3,138.36, -38,2,27.11.2015,,0,138.36, -38,2,28.11.2015,,3,138.36, -38,2,29.11.2015,,0,138.36, -38,2,30.11.2015,,3,138.36, -38,2,01.12.2015,,0,162.96, -38,2,02.12.2015,,0,162.96, -38,2,03.12.2015,,0,138.5, -38,2,04.12.2015,,3,138.5, -38,2,05.12.2015,,3,138.5, -38,2,06.12.2015,,6,138.5, -38,2,07.12.2015,,3,138.5, -38,2,08.12.2015,,6,138.5, -38,2,09.12.2015,,0,138.5, -38,2,10.12.2015,,0,138.5, -38,2,11.12.2015,,0,142.57, -38,2,12.12.2015,,3,142.57, -38,2,13.12.2015,,3,142.57, -38,2,14.12.2015,,3,142.57, -38,2,15.12.2015,,0,142.57, -38,2,16.12.2015,,3,142.57, -38,2,17.12.2015,,0,142.57, -38,2,18.12.2015,,3,142.57, -38,2,19.12.2015,,3,142.57, -38,2,20.12.2015,,0,142.57, -38,2,21.12.2015,,3,142.57, -38,2,22.12.2015,1.0,35,142.57,103.02 -38,2,23.12.2015,1.0,60,142.57,103.02 -38,2,24.12.2015,1.0,60,142.57,103.02 -38,2,25.12.2015,1.0,75,142.57,103.02 -38,2,26.12.2015,1.0,69,142.57,103.02 -38,2,27.12.2015,1.0,66,142.57,103.02 -38,2,28.12.2015,1.0,63,142.57,103.02 -38,2,29.12.2015,,0,138.5, -38,2,30.12.2015,,0,138.5, -38,2,31.12.2015,,0,138.5, -38,2,01.01.2016,,0,138.5, -38,2,02.01.2016,,0,138.5, -38,2,03.01.2016,,0,138.5, -38,2,04.01.2016,,0,138.5, -38,2,05.01.2016,,6,138.5, -38,2,06.01.2016,,13,138.5, -38,2,07.01.2016,,6,138.5, -38,2,08.01.2016,,6,138.5, -38,2,09.01.2016,,3,138.5, -38,2,10.01.2016,,3,138.5, -38,2,11.01.2016,,0,138.5, -38,2,12.01.2016,,3,138.5, -38,2,13.01.2016,,0,138.5, -38,2,14.01.2016,,0,138.5, -38,2,15.01.2016,,3,138.5, -38,2,16.01.2016,,3,142.57, -38,2,17.01.2016,,3,142.57, -38,2,18.01.2016,,9,142.57, -38,2,19.01.2016,1.0,88,142.57,103.02 -38,2,20.01.2016,1.0,66,142.57,103.02 -38,2,21.01.2016,1.0,9,142.57,103.02 -38,2,22.01.2016,1.0,31,142.57,103.02 -38,2,23.01.2016,1.0,63,142.57,103.02 -38,2,24.01.2016,1.0,57,142.57,103.02 -38,2,25.01.2016,1.0,50,142.57,103.02 -38,2,26.01.2016,,0,142.57, -38,2,27.01.2016,,0,142.57, -38,2,28.01.2016,,0,142.57, -38,2,29.01.2016,,0,142.57, -38,2,30.01.2016,,0,142.57, -38,2,31.01.2016,,6,142.57, -38,2,01.02.2016,,0,142.57, -38,2,02.02.2016,,0,142.57, -38,2,03.02.2016,,3,142.57, -38,2,04.02.2016,,6,142.57, -38,2,05.02.2016,,9,142.57, -38,2,06.02.2016,,13,142.57, -38,2,07.02.2016,,6,142.57, -38,2,08.02.2016,,3,142.57, -38,2,09.02.2016,1.0,94,142.57,108.46 -38,2,10.02.2016,1.0,66,138.36,108.46 -38,2,11.02.2016,1.0,16,138.36,108.46 -38,2,12.02.2016,1.0,44,138.36,108.46 -38,2,13.02.2016,1.0,72,138.36,108.46 -38,2,14.02.2016,1.0,16,138.36,108.46 -38,2,15.02.2016,1.0,44,138.36,108.46 -38,2,16.02.2016,,0,138.36, -38,2,17.02.2016,,3,138.36, -38,2,18.02.2016,,0,138.36, -38,2,19.02.2016,,3,138.36, -38,2,20.02.2016,,0,138.36, -38,2,21.02.2016,,0,138.36, -38,2,22.02.2016,,0,138.36, -38,2,23.02.2016,,3,138.36, -38,2,24.02.2016,,0,138.36, -38,2,25.02.2016,,0,138.36, -38,2,26.02.2016,,0,138.36, -38,2,27.02.2016,,0,138.36, -38,2,28.02.2016,,0,138.36, -38,2,29.02.2016,,0,138.36, -38,2,01.03.2016,,0,138.36, -38,2,02.03.2016,,3,138.36, -38,2,03.03.2016,,3,138.36, -38,2,04.03.2016,,0,138.36, -38,2,05.03.2016,,0,138.36, -38,2,06.03.2016,,0,138.36, -38,2,07.03.2016,,0,138.36, -38,2,08.03.2016,,0,138.36, -38,2,09.03.2016,,0,138.36, -38,2,10.03.2016,,0,138.36, -38,2,11.03.2016,,0,138.36, -38,2,12.03.2016,,0,138.5, -38,2,13.03.2016,,0,138.5, -38,2,14.03.2016,,0,138.5, -38,2,15.03.2016,1.0,38,138.5,124.77 -38,2,16.03.2016,1.0,9,138.5,124.77 -38,2,17.03.2016,1.0,16,138.5,124.77 -38,2,18.03.2016,1.0,9,138.5,124.77 -38,2,19.03.2016,1.0,6,138.5,124.77 -38,2,20.03.2016,1.0,6,138.5,124.77 -38,2,21.03.2016,1.0,60,138.5,124.77 -38,2,22.03.2016,,6,138.36, -38,2,23.03.2016,,6,138.36, -38,2,24.03.2016,,6,138.36, -38,2,25.03.2016,,0,138.36, -38,2,26.03.2016,,3,138.36, -38,2,27.03.2016,,3,138.36, -38,2,28.03.2016,,0,138.36, -38,2,29.03.2016,,0,138.36, -38,2,30.03.2016,,0,138.36, -38,2,31.03.2016,,0,138.36, -38,2,01.04.2016,,0,138.36, -38,2,02.04.2016,,3,138.36, -38,2,03.04.2016,,3,138.36, -38,2,04.04.2016,,0,138.36, -38,2,05.04.2016,,3,138.36, -38,2,06.04.2016,,3,138.36, -38,2,07.04.2016,,3,138.36, -38,2,08.04.2016,,0,138.36, -38,2,09.04.2016,,3,138.5, -38,2,10.04.2016,,0,138.5, -38,2,11.04.2016,,6,138.5, -38,2,12.04.2016,,0,138.5, -38,2,13.04.2016,,9,138.5, -38,2,14.04.2016,,0,138.5, -38,2,15.04.2016,,3,138.5, -38,2,16.04.2016,,0,138.5, -38,2,17.04.2016,,0,138.5, -38,2,18.04.2016,,0,138.5, -38,2,19.04.2016,1.0,31,138.5,116.89 -38,2,20.04.2016,1.0,22,138.5,116.89 -38,2,21.04.2016,1.0,44,138.5,116.89 -38,2,22.04.2016,1.0,25,138.5,116.89 -38,2,23.04.2016,1.0,22,138.5,116.89 -38,2,24.04.2016,1.0,19,138.5,116.89 -38,2,25.04.2016,1.0,13,138.5,116.89 -38,2,26.04.2016,1.0,13,138.5,114.17 -38,2,27.04.2016,1.0,3,138.5,114.17 -38,2,28.04.2016,,0,138.5, -38,2,29.04.2016,,0,138.5, -38,2,30.04.2016,,3,138.5, -38,2,01.05.2016,,3,138.5, -38,2,02.05.2016,,6,138.5, -38,2,03.05.2016,1.0,16,138.5,106.01 -38,2,04.05.2016,1.0,31,138.5,106.01 -38,2,05.05.2016,1.0,44,138.5,106.01 -38,2,06.05.2016,1.0,38,138.5,106.01 -38,2,07.05.2016,1.0,38,138.5,106.01 -38,2,08.05.2016,1.0,60,138.5,106.01 -38,2,09.05.2016,1.0,66,138.5,106.01 -38,2,10.05.2016,,3,138.5, -38,2,11.05.2016,,3,138.5, -38,2,12.05.2016,,35,138.5, -38,2,13.05.2016,,31,138.5, -38,2,14.05.2016,,16,138.5, -38,2,15.05.2016,,6,138.5, -38,2,16.05.2016,,0,138.5, -38,2,17.05.2016,,0,138.5, -38,2,18.05.2016,,3,138.5, -38,2,19.05.2016,,0,138.5, -38,2,20.05.2016,,0,138.5, -38,2,21.05.2016,,0,138.5, -38,2,22.05.2016,,0,138.5, -39,2,01.01.2015,,3,135.78, -39,2,02.01.2015,,6,135.78, -39,2,03.01.2015,,3,135.78, -39,2,04.01.2015,,0,135.78, -39,2,05.01.2015,,0,135.78, -39,2,06.01.2015,,0,135.78, -39,2,07.01.2015,,0,135.78, -39,2,08.01.2015,,0,135.78, -39,2,09.01.2015,,0,135.78, -39,2,10.01.2015,,0,135.78, -39,2,11.01.2015,,0,135.78, -39,2,12.01.2015,,0,135.78, -39,2,13.01.2015,,0,135.78, -39,2,14.01.2015,,3,135.78, -39,2,15.01.2015,,3,135.78, -39,2,16.01.2015,,9,135.78, -39,2,17.01.2015,,0,135.78, -39,2,18.01.2015,,6,135.78, -39,2,19.01.2015,,9,135.78, -39,2,20.01.2015,,0,135.78, -39,2,21.01.2015,,3,135.78, -39,2,22.01.2015,,6,135.78, -39,2,23.01.2015,,3,135.78, -39,2,24.01.2015,,6,135.78, -39,2,25.01.2015,,3,135.78, -39,2,26.01.2015,,0,135.78, -39,2,27.01.2015,,3,135.78, -39,2,28.01.2015,,0,135.78, -39,2,29.01.2015,,0,135.78, -39,2,30.01.2015,,0,135.78, -39,2,31.01.2015,,6,135.78, -39,2,01.02.2015,,0,135.78, -39,2,02.02.2015,,0,135.78, -39,2,03.02.2015,,0,135.78, -39,2,04.02.2015,,0,135.78, -39,2,05.02.2015,,0,135.78, -39,2,06.02.2015,,3,135.78, -39,2,07.02.2015,,0,135.78, -39,2,08.02.2015,,9,135.78, -39,2,09.02.2015,,9,135.78, -39,2,10.02.2015,,3,135.78, -39,2,11.02.2015,,0,135.78, -39,2,12.02.2015,,0,135.78, -39,2,13.02.2015,,3,135.78, -39,2,14.02.2015,,0,135.78, -39,2,15.02.2015,,22,135.78, -39,2,16.02.2015,,0,135.78, -39,2,17.02.2015,,0,135.78, -39,2,18.02.2015,,0,135.78, -39,2,19.02.2015,,0,135.78, -39,2,20.02.2015,,0,135.78, -39,2,21.02.2015,,0,135.78, -39,2,22.02.2015,,0,135.78, -39,2,23.02.2015,,0,135.78, -39,2,24.02.2015,,0,135.78, -39,2,25.02.2015,,0,135.78, -39,2,26.02.2015,,0,146.38, -39,2,27.02.2015,,0,146.38, -39,2,28.02.2015,,0,146.38, -39,2,01.03.2015,,0,146.38, -39,2,02.03.2015,,0,146.38, -39,2,03.03.2015,,0,146.38, -39,2,04.03.2015,,0,146.38, -39,2,05.03.2015,,0,146.38, -39,2,06.03.2015,,0,146.38, -39,2,07.03.2015,,0,146.38, -39,2,08.03.2015,,0,146.38, -39,2,09.03.2015,,0,146.38, -39,2,10.03.2015,,0,146.38, -39,2,11.03.2015,,3,146.38, -39,2,12.03.2015,,0,146.38, -39,2,13.03.2015,,0,146.38, -39,2,14.03.2015,,0,146.38, -39,2,15.03.2015,,0,146.38, -39,2,16.03.2015,,0,146.38, -39,2,17.03.2015,,0,146.38, -39,2,18.03.2015,,0,146.38, -39,2,19.03.2015,,6,146.38, -39,2,20.03.2015,,0,146.38, -39,2,21.03.2015,,3,146.38, -39,2,22.03.2015,,0,146.38, -39,2,23.03.2015,,0,146.38, -39,2,24.03.2015,,0,146.38, -39,2,25.03.2015,,0,146.38, -39,2,26.03.2015,,0,146.38, -39,2,27.03.2015,,0,146.38, -39,2,28.03.2015,,0,146.38, -39,2,29.03.2015,,0,146.38, -39,2,30.03.2015,,0,146.38, -39,2,31.03.2015,,0,146.38, -39,2,01.04.2015,,0,146.38, -39,2,02.04.2015,,0,146.38, -39,2,03.04.2015,,3,146.38, -39,2,04.04.2015,,0,146.38, -39,2,05.04.2015,,0,146.38, -39,2,06.04.2015,,0,146.38, -39,2,07.04.2015,,0,146.38, -39,2,08.04.2015,,0,146.38, -39,2,09.04.2015,,0,145.84, -39,2,10.04.2015,,0,145.84, -39,2,11.04.2015,,3,145.84, -39,2,12.04.2015,,3,145.84, -39,2,13.04.2015,,3,145.84, -39,2,14.04.2015,,0,145.84, -39,2,15.04.2015,,0,145.84, -39,2,16.04.2015,,0,145.84, -39,2,17.04.2015,,0,145.84, -39,2,18.04.2015,,6,145.84, -39,2,19.04.2015,,3,145.84, -39,2,20.04.2015,,0,145.84, -39,2,21.04.2015,,0,145.84, -39,2,22.04.2015,,3,145.84, -39,2,23.04.2015,,0,145.84, -39,2,24.04.2015,,0,145.84, -39,2,25.04.2015,,0,145.84, -39,2,26.04.2015,,0,150.73, -39,2,27.04.2015,,0,150.73, -39,2,28.04.2015,,0,150.73, -39,2,29.04.2015,,0,150.73, -39,2,30.04.2015,,0,150.73, -39,2,01.05.2015,,0,150.73, -39,2,02.05.2015,,0,150.73, -39,2,03.05.2015,,0,150.73, -39,2,04.05.2015,,0,150.73, -39,2,05.05.2015,,0,150.73, -39,2,06.05.2015,,0,150.73, -39,2,07.05.2015,,0,150.73, -39,2,08.05.2015,,0,150.73, -39,2,09.05.2015,,3,150.73, -39,2,10.05.2015,,3,150.73, -39,2,11.05.2015,,0,150.73, -39,2,12.05.2015,,6,150.73, -39,2,13.05.2015,,0,150.73, -39,2,14.05.2015,,0,150.73, -39,2,15.05.2015,,0,150.73, -39,2,16.05.2015,,0,150.73, -39,2,17.05.2015,,0,150.73, -39,2,18.05.2015,,0,150.73, -39,2,19.05.2015,,0,150.73, -39,2,20.05.2015,,0,150.73, -39,2,21.05.2015,,9,150.73, -39,2,22.05.2015,,3,150.73, -39,2,23.05.2015,,9,150.73, -39,2,24.05.2015,,3,150.73, -39,2,25.05.2015,,3,150.73, -39,2,26.05.2015,,3,150.73, -39,2,27.05.2015,,9,150.73, -39,2,28.05.2015,,3,150.73, -39,2,29.05.2015,,13,150.73, -39,2,30.05.2015,,3,150.73, -39,2,31.05.2015,,0,150.73, -39,2,01.06.2015,,0,150.73, -39,2,02.06.2015,,0,150.73, -39,2,03.06.2015,,3,150.73, -39,2,04.06.2015,,3,150.73, -39,2,05.06.2015,,3,150.73, -39,2,06.06.2015,,0,150.73, -39,2,07.06.2015,,3,150.73, -39,2,08.06.2015,,3,150.73, -39,2,09.06.2015,,3,150.73, -39,2,10.06.2015,,3,150.73, -39,2,11.06.2015,,0,150.73, -39,2,12.06.2015,,0,150.73, -39,2,13.06.2015,,6,150.73, -39,2,14.06.2015,,6,150.73, -39,2,15.06.2015,,3,150.73, -39,2,16.06.2015,,6,150.73, -39,2,17.06.2015,,3,150.73, -39,2,18.06.2015,,3,150.73, -39,2,19.06.2015,,0,150.73, -39,2,20.06.2015,,0,150.73, -39,2,21.06.2015,,6,150.73, -39,2,22.06.2015,,0,150.73, -39,2,23.06.2015,,3,150.73, -39,2,24.06.2015,,3,150.73, -39,2,25.06.2015,,3,150.73, -39,2,26.06.2015,,3,150.73, -39,2,27.06.2015,,0,150.73, -39,2,28.06.2015,,9,150.73, -39,2,29.06.2015,,0,150.73, -39,2,30.06.2015,,0,150.73, -39,2,01.07.2015,,3,150.73, -39,2,02.07.2015,,3,150.73, -39,2,03.07.2015,,9,150.73, -39,2,04.07.2015,,0,150.73, -39,2,05.07.2015,,13,150.73, -39,2,06.07.2015,,0,150.73, -39,2,07.07.2015,,3,150.73, -39,2,08.07.2015,,0,150.73, -39,2,09.07.2015,,0,150.73, -39,2,10.07.2015,,6,150.73, -39,2,11.07.2015,,13,150.73, -39,2,12.07.2015,,6,150.73, -39,2,13.07.2015,,0,150.73, -39,2,14.07.2015,,0,150.73, -39,2,15.07.2015,,0,150.73, -39,2,16.07.2015,,0,150.73, -39,2,17.07.2015,,0,150.73, -39,2,18.07.2015,,0,150.73, -39,2,19.07.2015,,9,150.73, -39,2,20.07.2015,,41,150.73, -39,2,21.07.2015,,6,150.73, -39,2,22.07.2015,,6,150.73, -39,2,23.07.2015,,3,150.73, -39,2,24.07.2015,,19,150.73, -39,2,25.07.2015,,9,150.73, -39,2,26.07.2015,,19,150.73, -39,2,27.07.2015,,9,150.73, -39,2,28.07.2015,,9,150.73, -39,2,29.07.2015,,9,150.73, -39,2,30.07.2015,,9,150.73, -39,2,31.07.2015,,9,150.73, -39,2,01.08.2015,,9,150.73, -39,2,02.08.2015,,9,150.73, -39,2,03.08.2015,,9,150.73, -39,2,04.08.2015,,9,150.73, -39,2,05.08.2015,,9,150.73, -39,2,06.08.2015,,9,150.73, -39,2,07.08.2015,,6,150.73, -39,2,08.08.2015,,3,150.73, -39,2,09.08.2015,,6,150.73, -39,2,10.08.2015,,6,150.73, -39,2,11.08.2015,,0,150.73, -39,2,12.08.2015,,3,128.17, -39,2,13.08.2015,,13,128.17, -39,2,14.08.2015,,9,128.17, -39,2,15.08.2015,,9,128.17, -39,2,16.08.2015,,9,128.17, -39,2,17.08.2015,,6,128.17, -39,2,18.08.2015,1.0,69,128.17,100.3 -39,2,19.08.2015,1.0,35,128.17,100.3 -39,2,20.08.2015,1.0,31,128.17,100.3 -39,2,21.08.2015,1.0,123,128.17,100.3 -39,2,22.08.2015,1.0,72,128.17,100.3 -39,2,23.08.2015,1.0,41,128.17,100.3 -39,2,24.08.2015,1.0,38,128.17,100.3 -39,2,25.08.2015,,0,128.17, -39,2,26.08.2015,,0,128.17, -39,2,27.08.2015,,0,128.17, -39,2,28.08.2015,,0,128.17, -39,2,29.08.2015,,0,128.17, -39,2,30.08.2015,,0,128.17, -39,2,31.08.2015,,0,128.17, -39,2,01.09.2015,,0,128.17, -39,2,02.09.2015,,0,128.17, -39,2,03.09.2015,,0,128.17, -39,2,04.09.2015,,0,128.17, -39,2,05.09.2015,,0,128.17, -39,2,06.09.2015,,0,128.17, -39,2,07.09.2015,,0,128.17, -39,2,08.09.2015,,0,128.17, -39,2,09.09.2015,,0,128.17, -39,2,10.09.2015,,3,128.17, -39,2,11.09.2015,,16,128.17, -39,2,12.09.2015,,0,128.17, -39,2,13.09.2015,,0,128.17, -39,2,14.09.2015,,0,128.17, -39,2,15.09.2015,,0,128.17, -39,2,16.09.2015,,0,128.17, -39,2,17.09.2015,,0,128.17, -39,2,18.09.2015,,0,128.17, -39,2,19.09.2015,,0,128.17, -39,2,20.09.2015,,0,128.17, -39,2,21.09.2015,,0,128.17, -39,2,22.09.2015,,0,128.17, -39,2,23.09.2015,,0,162.96, -39,2,24.09.2015,,6,162.96, -39,2,25.09.2015,,9,162.96, -39,2,26.09.2015,,3,162.96, -39,2,27.09.2015,,0,162.96, -39,2,28.09.2015,,0,162.96, -39,2,29.09.2015,,13,162.96, -39,2,30.09.2015,,6,162.96, -39,2,01.10.2015,,3,162.96, -39,2,02.10.2015,,6,162.96, -39,2,03.10.2015,,3,162.96, -39,2,04.10.2015,,6,162.58, -39,2,05.10.2015,,3,162.96, -39,2,06.10.2015,1.0,82,143.39,122.05 -39,2,07.10.2015,1.0,66,143.39,122.05 -39,2,08.10.2015,1.0,79,143.39,122.05 -39,2,09.10.2015,1.0,16,143.39,122.05 -39,2,10.10.2015,1.0,47,143.39,122.05 -39,2,11.10.2015,1.0,38,143.39,122.05 -39,2,12.10.2015,1.0,38,143.39,122.05 -39,2,13.10.2015,,0,143.93, -39,2,14.10.2015,,0,143.93, -39,2,15.10.2015,,0,143.93, -39,2,16.10.2015,,0,143.93, -39,2,17.10.2015,,0,162.96, -39,2,18.10.2015,,0,162.96, -39,2,19.10.2015,,0,162.96, -39,2,20.10.2015,,0,162.96, -39,2,21.10.2015,,0,162.96, -39,2,22.10.2015,,0,162.96, -39,2,23.10.2015,,0,162.96, -39,2,24.10.2015,,0,162.96, -39,2,25.10.2015,,6,162.96, -39,2,26.10.2015,,0,162.96, -39,2,27.10.2015,,0,162.96, -39,2,28.10.2015,,0,162.96, -39,2,29.10.2015,,0,162.96, -39,2,30.10.2015,,0,162.96, -39,2,31.10.2015,,0,162.96, -39,2,01.11.2015,,0,162.96, -39,2,02.11.2015,,0,162.96, -39,2,03.11.2015,,0,162.96, -39,2,04.11.2015,,0,162.96, -39,2,05.11.2015,,0,162.96, -39,2,06.11.2015,,0,162.96, -39,2,07.11.2015,,0,162.96, -39,2,08.11.2015,,0,162.96, -39,2,09.11.2015,,0,162.96, -39,2,10.11.2015,,0,162.96, -39,2,11.11.2015,,0,138.36, -39,2,12.11.2015,,0,138.36, -39,2,13.11.2015,,3,138.36, -39,2,14.11.2015,,9,138.36, -39,2,15.11.2015,,3,138.36, -39,2,16.11.2015,,0,138.36, -39,2,17.11.2015,,0,138.36, -39,2,18.11.2015,,0,138.36, -39,2,19.11.2015,,0,138.36, -39,2,20.11.2015,,13,138.36, -39,2,21.11.2015,,9,138.36, -39,2,22.11.2015,,0,138.36, -39,2,23.11.2015,,0,138.36, -39,2,24.11.2015,,0,138.36, -39,2,25.11.2015,,6,138.36, -39,2,26.11.2015,,3,138.36, -39,2,27.11.2015,,9,138.36, -39,2,28.11.2015,,0,138.36, -39,2,29.11.2015,,0,138.36, -39,2,30.11.2015,,0,138.36, -39,2,01.12.2015,,0,162.96, -39,2,02.12.2015,,0,162.96, -39,2,03.12.2015,,0,138.5, -39,2,04.12.2015,,0,138.5, -39,2,05.12.2015,,0,138.5, -39,2,06.12.2015,,0,138.5, -39,2,07.12.2015,,0,138.5, -39,2,08.12.2015,,0,138.5, -39,2,09.12.2015,,0,138.5, -39,2,10.12.2015,,0,138.5, -39,2,11.12.2015,,0,142.57, -39,2,12.12.2015,,0,142.57, -39,2,13.12.2015,,0,142.57, -39,2,14.12.2015,,0,142.57, -39,2,15.12.2015,,0,142.57, -39,2,16.12.2015,,0,142.57, -39,2,17.12.2015,,6,142.57, -39,2,18.12.2015,,0,142.57, -39,2,19.12.2015,,3,142.57, -39,2,20.12.2015,,3,142.57, -39,2,21.12.2015,,13,142.57, -39,2,22.12.2015,1.0,126,142.57,103.02 -39,2,23.12.2015,1.0,44,142.57,103.02 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-39,2,26.02.2016,,6,138.36, -39,2,27.02.2016,,0,138.36, -39,2,28.02.2016,,0,138.36, -39,2,29.02.2016,,0,138.36, -39,2,01.03.2016,,0,138.36, -39,2,02.03.2016,,0,138.36, -39,2,03.03.2016,,0,138.36, -39,2,04.03.2016,,0,138.36, -39,2,05.03.2016,,0,138.36, -39,2,06.03.2016,,3,138.36, -39,2,07.03.2016,,0,138.36, -39,2,08.03.2016,,0,138.36, -39,2,09.03.2016,,0,138.36, -39,2,10.03.2016,,0,138.36, -39,2,11.03.2016,,0,138.36, -39,2,12.03.2016,,0,138.5, -39,2,13.03.2016,,0,138.5, -39,2,14.03.2016,,0,138.5, -39,2,15.03.2016,1.0,57,138.5,124.77 -39,2,16.03.2016,1.0,31,138.5,124.77 -39,2,17.03.2016,1.0,75,138.5,124.77 -39,2,18.03.2016,1.0,63,138.5,124.77 -39,2,19.03.2016,1.0,6,138.5,124.77 -39,2,20.03.2016,1.0,57,138.5,124.77 -39,2,21.03.2016,1.0,57,138.5,124.77 -39,2,22.03.2016,,3,138.36, -39,2,23.03.2016,,22,138.36, -39,2,24.03.2016,,3,138.36, -39,2,25.03.2016,,9,138.36, -39,2,26.03.2016,,6,138.36, -39,2,27.03.2016,,0,138.36, -39,2,28.03.2016,,6,138.36, -39,2,29.03.2016,,0,138.36, -39,2,30.03.2016,,0,138.36, -39,2,31.03.2016,,0,138.36, -39,2,01.04.2016,,0,138.36, -39,2,02.04.2016,,0,138.36, -39,2,03.04.2016,,0,138.36, -39,2,04.04.2016,,0,138.36, -39,2,05.04.2016,,0,138.36, -39,2,06.04.2016,,0,138.36, -39,2,07.04.2016,,0,138.36, -39,2,08.04.2016,,0,138.36, -39,2,09.04.2016,,0,138.5, -39,2,10.04.2016,,0,138.5, -39,2,11.04.2016,,0,138.5, -39,2,12.04.2016,,0,138.5, -39,2,13.04.2016,,0,138.5, -39,2,14.04.2016,,0,138.5, -39,2,15.04.2016,,6,138.5, -39,2,16.04.2016,,3,138.5, -39,2,17.04.2016,,25,138.5, -39,2,18.04.2016,,19,138.5, -39,2,19.04.2016,1.0,126,138.5,116.89 -39,2,20.04.2016,1.0,82,138.5,116.89 -39,2,21.04.2016,1.0,69,138.5,116.89 -39,2,22.04.2016,1.0,66,138.5,116.89 -39,2,23.04.2016,1.0,82,138.5,116.89 -39,2,24.04.2016,1.0,85,138.5,116.89 -39,2,25.04.2016,1.0,50,138.5,116.89 -39,2,26.04.2016,,0,138.5, -39,2,27.04.2016,,6,138.5, -39,2,28.04.2016,,13,138.5, -39,2,29.04.2016,,3,138.5, -39,2,30.04.2016,,9,138.5, -39,2,01.05.2016,,3,138.5, -39,2,02.05.2016,,25,138.5, -39,2,03.05.2016,1.0,113,138.5,106.01 -39,2,04.05.2016,1.0,113,138.5,106.01 -39,2,05.05.2016,1.0,94,138.5,106.01 -39,2,06.05.2016,1.0,123,138.5,106.01 -39,2,07.05.2016,1.0,126,138.5,106.01 -39,2,08.05.2016,1.0,157,138.5,106.01 -39,2,09.05.2016,1.0,145,138.5,106.01 -39,2,10.05.2016,,0,142.57, -39,2,11.05.2016,,0,142.57, -39,2,12.05.2016,,0,142.57, -39,2,13.05.2016,,0,142.57, -39,2,14.05.2016,,0,142.57, -39,2,15.05.2016,,0,142.57, -39,2,16.05.2016,,0,142.57, -39,2,17.05.2016,,0,142.57, -39,2,18.05.2016,,0,142.57, -39,2,19.05.2016,,0,142.57, -39,2,20.05.2016,,0,142.57, -39,2,21.05.2016,,0,142.57, -39,2,22.05.2016,,0,142.57, -40,2,01.01.2015,,0,135.78, -40,2,02.01.2015,,0,135.78, -40,2,03.01.2015,,0,135.78, -40,2,04.01.2015,,0,135.78, -40,2,05.01.2015,,16,135.78, -40,2,06.01.2015,,9,135.78, -40,2,07.01.2015,,0,135.78, -40,2,08.01.2015,,0,135.78, -40,2,09.01.2015,,0,135.78, -40,2,10.01.2015,,0,135.78, -40,2,11.01.2015,,0,135.78, -40,2,12.01.2015,,0,135.78, 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-40,2,24.03.2015,,0,146.38, -40,2,25.03.2015,,0,146.38, -40,2,26.03.2015,,0,146.38, -40,2,27.03.2015,,0,146.38, -40,2,28.03.2015,,9,146.38, -40,2,29.03.2015,,0,146.38, -40,2,30.03.2015,,3,146.38, -40,2,31.03.2015,,3,146.38, -40,2,01.04.2015,,0,146.38, -40,2,02.04.2015,,0,146.38, -40,2,03.04.2015,,0,146.38, -40,2,04.04.2015,,3,146.38, -40,2,05.04.2015,,6,146.38, -40,2,06.04.2015,,3,146.38, -40,2,07.04.2015,,0,146.38, -40,2,08.04.2015,,0,146.38, -40,2,09.04.2015,,3,146.38, -40,2,10.04.2015,,0,146.38, -40,2,11.04.2015,,3,146.38, -40,2,12.04.2015,,0,146.38, -40,2,13.04.2015,,9,146.38, -40,2,14.04.2015,,0,146.38, -40,2,15.04.2015,,3,146.38, -40,2,16.04.2015,,3,146.38, -40,2,17.04.2015,,3,146.38, -40,2,18.04.2015,,6,146.38, -40,2,19.04.2015,,3,146.38, -40,2,20.04.2015,,6,145.84, -40,2,21.04.2015,,3,145.84, -40,2,22.04.2015,,0,145.84, -40,2,23.04.2015,,0,145.84, -40,2,24.04.2015,,0,145.84, -40,2,25.04.2015,,3,145.84, -40,2,26.04.2015,,0,150.73, -40,2,27.04.2015,,0,150.73, 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-40,2,02.06.2015,,0,150.73, -40,2,03.06.2015,,3,150.73, -40,2,04.06.2015,,0,150.73, -40,2,05.06.2015,,9,150.73, -40,2,06.06.2015,,0,150.73, -40,2,07.06.2015,,3,150.73, -40,2,08.06.2015,,3,150.73, -40,2,09.06.2015,,3,150.73, -40,2,10.06.2015,,0,150.73, -40,2,11.06.2015,,0,150.73, -40,2,12.06.2015,,0,150.73, -40,2,13.06.2015,,6,150.73, -40,2,14.06.2015,,3,150.73, -40,2,15.06.2015,,6,150.73, -40,2,16.06.2015,,3,150.73, -40,2,17.06.2015,,0,150.73, -40,2,18.06.2015,,0,150.73, -40,2,19.06.2015,,0,150.73, -40,2,20.06.2015,,0,150.73, -40,2,21.06.2015,,0,150.73, -40,2,22.06.2015,,0,150.73, -40,2,23.06.2015,,0,150.73, -40,2,24.06.2015,,0,150.73, -40,2,25.06.2015,,0,150.73, -40,2,26.06.2015,,0,150.73, -40,2,27.06.2015,,0,150.73, -40,2,28.06.2015,,0,150.73, -40,2,29.06.2015,,0,150.73, -40,2,30.06.2015,,0,150.73, -40,2,01.07.2015,,0,150.73, -40,2,02.07.2015,,0,150.73, -40,2,03.07.2015,,0,150.73, -40,2,04.07.2015,,0,150.73, -40,2,05.07.2015,,0,150.73, -40,2,06.07.2015,,9,150.73, -40,2,07.07.2015,,3,150.73, -40,2,08.07.2015,,13,150.73, -40,2,09.07.2015,,0,150.73, -40,2,10.07.2015,,0,150.73, -40,2,11.07.2015,,3,150.73, -40,2,12.07.2015,,0,150.73, -40,2,13.07.2015,,3,150.73, -40,2,14.07.2015,,0,150.73, -40,2,15.07.2015,,0,150.73, -40,2,16.07.2015,,0,150.73, -40,2,17.07.2015,,0,150.73, -40,2,18.07.2015,,0,150.73, -40,2,19.07.2015,,0,150.73, -40,2,20.07.2015,,0,150.73, -40,2,21.07.2015,,0,150.73, -40,2,22.07.2015,,0,150.73, -40,2,23.07.2015,,13,150.73, -40,2,24.07.2015,,28,150.73, -40,2,25.07.2015,,3,150.73, -40,2,26.07.2015,,6,150.73, -40,2,27.07.2015,,3,150.73, -40,2,28.07.2015,,16,150.73, -40,2,29.07.2015,,19,150.73, -40,2,30.07.2015,,6,150.73, -40,2,31.07.2015,,25,150.73, -40,2,01.08.2015,,3,150.73, -40,2,02.08.2015,,0,150.73, -40,2,03.08.2015,,13,150.73, -40,2,04.08.2015,,16,150.73, -40,2,05.08.2015,,0,150.73, -40,2,06.08.2015,,6,150.73, -40,2,07.08.2015,,6,150.73, -40,2,08.08.2015,,19,150.73, -40,2,09.08.2015,,16,150.73, -40,2,10.08.2015,,0,150.73, 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-40,2,13.09.2015,,0,128.17, -40,2,14.09.2015,,0,128.17, -40,2,15.09.2015,,0,128.17, -40,2,16.09.2015,,0,128.17, -40,2,17.09.2015,,22,128.17, -40,2,18.09.2015,,16,128.17, -40,2,19.09.2015,,0,128.17, -40,2,20.09.2015,,3,128.17, -40,2,21.09.2015,,3,128.17, -40,2,22.09.2015,,6,128.17, -40,2,23.09.2015,,0,128.17, -40,2,24.09.2015,,3,128.17, -40,2,25.09.2015,,6,128.17, -40,2,26.09.2015,,0,128.17, -40,2,27.09.2015,,9,128.17, -40,2,28.09.2015,,13,128.17, -40,2,29.09.2015,,13,128.17, -40,2,30.09.2015,,13,128.17, -40,2,01.10.2015,,3,162.96, -40,2,02.10.2015,,0,162.96, -40,2,03.10.2015,,3,162.96, -40,2,04.10.2015,,6,162.96, -40,2,05.10.2015,,3,162.96, -40,2,06.10.2015,1.0,57,143.39,122.05 -40,2,07.10.2015,1.0,31,143.39,122.05 -40,2,08.10.2015,1.0,38,143.39,122.05 -40,2,09.10.2015,1.0,79,143.39,122.05 -40,2,10.10.2015,1.0,72,143.39,122.05 -40,2,11.10.2015,1.0,110,143.39,122.05 -40,2,12.10.2015,1.0,97,143.39,122.05 -40,2,13.10.2015,,0,143.93, -40,2,14.10.2015,,3,143.93, -40,2,15.10.2015,,3,143.93, -40,2,16.10.2015,,0,143.93, -40,2,17.10.2015,,6,162.96, -40,2,18.10.2015,,9,162.96, -40,2,19.10.2015,,0,162.96, -40,2,20.10.2015,,0,162.96, -40,2,21.10.2015,,0,162.96, -40,2,22.10.2015,,0,162.96, -40,2,23.10.2015,,0,162.96, -40,2,24.10.2015,,0,162.96, -40,2,25.10.2015,,6,162.96, -40,2,26.10.2015,,3,162.96, -40,2,27.10.2015,,0,162.96, -40,2,28.10.2015,,0,162.96, -40,2,29.10.2015,,0,162.96, -40,2,30.10.2015,,0,162.96, -40,2,31.10.2015,,0,162.96, -40,2,01.11.2015,,0,162.96, -40,2,02.11.2015,,0,162.96, -40,2,03.11.2015,,0,162.96, -40,2,04.11.2015,,0,162.96, -40,2,05.11.2015,,0,162.96, -40,2,06.11.2015,,0,162.96, -40,2,07.11.2015,,0,162.96, -40,2,08.11.2015,,0,162.96, -40,2,09.11.2015,,3,162.96, -40,2,10.11.2015,,6,162.96, -40,2,11.11.2015,,0,138.36, -40,2,12.11.2015,,0,138.36, -40,2,13.11.2015,,3,138.36, -40,2,14.11.2015,,3,138.36, -40,2,15.11.2015,,3,138.36, -40,2,16.11.2015,,0,138.36, -40,2,17.11.2015,,0,138.36, -40,2,18.11.2015,,0,138.36, -40,2,19.11.2015,,0,138.36, -40,2,20.11.2015,,0,138.36, -40,2,21.11.2015,,0,138.36, -40,2,22.11.2015,,0,138.36, -40,2,23.11.2015,,0,138.36, -40,2,24.11.2015,,0,138.36, -40,2,25.11.2015,,0,138.36, -40,2,26.11.2015,,3,138.36, -40,2,27.11.2015,,16,138.36, -40,2,28.11.2015,,0,138.36, -40,2,29.11.2015,,0,138.36, -40,2,30.11.2015,,3,138.36, -40,2,01.12.2015,,0,162.96, -40,2,02.12.2015,,6,162.96, -40,2,03.12.2015,,3,138.5, -40,2,04.12.2015,,3,138.5, -40,2,05.12.2015,,0,138.5, -40,2,06.12.2015,,0,138.5, -40,2,07.12.2015,,3,138.5, -40,2,08.12.2015,,0,138.5, -40,2,09.12.2015,,0,138.5, -40,2,10.12.2015,,0,138.5, -40,2,11.12.2015,,0,142.57, -40,2,12.12.2015,,0,142.57, -40,2,13.12.2015,,0,142.57, -40,2,14.12.2015,,0,142.57, -40,2,15.12.2015,,3,142.57, -40,2,16.12.2015,,9,142.57, -40,2,17.12.2015,,0,142.57, -40,2,18.12.2015,,3,142.57, -40,2,19.12.2015,,3,142.57, -40,2,20.12.2015,,6,142.57, -40,2,21.12.2015,,16,142.57, -40,2,22.12.2015,1.0,107,142.57,103.02 -40,2,23.12.2015,1.0,151,142.57,103.02 -40,2,24.12.2015,1.0,107,142.57,103.02 -40,2,25.12.2015,1.0,66,142.57,103.02 -40,2,26.12.2015,1.0,72,142.57,103.02 -40,2,27.12.2015,1.0,129,142.57,103.02 -40,2,28.12.2015,1.0,63,142.57,103.02 -40,2,29.12.2015,,0,138.5, -40,2,30.12.2015,,0,138.5, -40,2,31.12.2015,,0,138.5, -40,2,01.01.2016,,0,138.5, -40,2,02.01.2016,,0,138.5, -40,2,03.01.2016,,0,138.5, -40,2,04.01.2016,,38,138.5, -40,2,05.01.2016,,0,138.5, -40,2,06.01.2016,,0,138.5, -40,2,07.01.2016,,0,138.5, -40,2,08.01.2016,,0,138.5, -40,2,09.01.2016,,0,138.5, -40,2,10.01.2016,,0,138.5, -40,2,11.01.2016,,6,138.5, -40,2,12.01.2016,,9,138.5, -40,2,13.01.2016,,3,138.5, -40,2,14.01.2016,,9,138.5, -40,2,15.01.2016,,6,138.5, -40,2,16.01.2016,,9,142.57, -40,2,17.01.2016,,0,142.57, -40,2,18.01.2016,,9,142.57, -40,2,19.01.2016,1.0,198,142.57,103.02 -40,2,20.01.2016,1.0,126,142.57,103.02 -40,2,21.01.2016,1.0,170,142.57,103.02 -40,2,22.01.2016,1.0,170,142.57,103.02 -40,2,23.01.2016,1.0,195,142.57,103.02 -40,2,24.01.2016,1.0,157,142.57,103.02 -40,2,25.01.2016,1.0,123,142.57,103.02 -40,2,26.01.2016,,0,142.57, -40,2,27.01.2016,,0,142.57, -40,2,28.01.2016,,0,142.57, -40,2,29.01.2016,,0,142.57, -40,2,30.01.2016,,6,142.57, -40,2,31.01.2016,,13,142.57, -40,2,01.02.2016,,0,142.57, -40,2,02.02.2016,,0,142.57, -40,2,03.02.2016,,0,142.57, -40,2,04.02.2016,,0,142.57, -40,2,05.02.2016,,3,142.57, -40,2,06.02.2016,,3,142.57, -40,2,07.02.2016,,3,142.57, -40,2,08.02.2016,,0,142.57, -40,2,09.02.2016,1.0,129,142.57,108.46 -40,2,10.02.2016,1.0,110,138.36,108.46 -40,2,11.02.2016,1.0,101,138.36,108.46 -40,2,12.02.2016,1.0,195,138.36,108.46 -40,2,13.02.2016,1.0,132,138.36,108.46 -40,2,14.02.2016,1.0,22,138.36,108.46 -40,2,15.02.2016,1.0,126,138.36,108.46 -40,2,16.02.2016,,3,138.36, -40,2,17.02.2016,,3,138.36, -40,2,18.02.2016,,3,138.36, -40,2,19.02.2016,,3,138.36, -40,2,20.02.2016,,3,138.36, -40,2,21.02.2016,,6,138.36, -40,2,22.02.2016,,3,138.36, -40,2,23.02.2016,,3,138.36, -40,2,24.02.2016,,3,138.36, -40,2,25.02.2016,,3,138.36, -40,2,26.02.2016,,3,138.36, -40,2,27.02.2016,,9,138.36, -40,2,28.02.2016,,22,138.36, -40,2,29.02.2016,,3,138.36, -40,2,01.03.2016,,6,138.36, -40,2,02.03.2016,,6,138.36, -40,2,03.03.2016,,6,138.36, -40,2,04.03.2016,,3,138.36, -40,2,05.03.2016,,3,138.36, -40,2,06.03.2016,,6,138.36, -40,2,07.03.2016,,6,138.36, -40,2,08.03.2016,,19,138.36, -40,2,09.03.2016,,6,138.36, -40,2,10.03.2016,,6,138.36, -40,2,11.03.2016,,6,138.36, -40,2,12.03.2016,,6,138.5, -40,2,13.03.2016,,6,138.5, -40,2,14.03.2016,,0,138.5, -40,2,15.03.2016,1.0,38,138.5,124.77 -40,2,16.03.2016,1.0,94,138.5,124.77 -40,2,17.03.2016,1.0,41,138.5,124.77 -40,2,18.03.2016,1.0,60,138.5,124.77 -40,2,19.03.2016,1.0,72,138.5,124.77 -40,2,20.03.2016,1.0,35,138.5,124.77 -40,2,21.03.2016,1.0,47,138.5,124.77 -40,2,22.03.2016,,6,138.36, -40,2,23.03.2016,,6,138.36, -40,2,24.03.2016,,3,138.36, -40,2,25.03.2016,,6,138.36, -40,2,26.03.2016,,3,138.36, -40,2,27.03.2016,,6,138.36, -40,2,28.03.2016,,6,138.36, -40,2,29.03.2016,,6,138.36, -40,2,30.03.2016,,3,138.36, -40,2,31.03.2016,,6,138.36, -40,2,01.04.2016,,6,138.36, -40,2,02.04.2016,,9,138.36, -40,2,03.04.2016,,6,138.36, -40,2,04.04.2016,,3,138.36, -40,2,05.04.2016,,6,138.36, -40,2,06.04.2016,,3,138.36, -40,2,07.04.2016,,6,138.36, -40,2,08.04.2016,,9,138.36, -40,2,09.04.2016,,16,138.5, -40,2,10.04.2016,,6,138.5, -40,2,11.04.2016,,6,138.5, -40,2,12.04.2016,,6,138.5, -40,2,13.04.2016,,6,138.5, -40,2,14.04.2016,,6,138.5, -40,2,15.04.2016,,3,138.5, -40,2,16.04.2016,,3,138.5, -40,2,17.04.2016,,9,138.5, -40,2,18.04.2016,,6,138.5, -40,2,19.04.2016,1.0,85,138.5,116.89 -40,2,20.04.2016,1.0,53,138.5,116.89 -40,2,21.04.2016,1.0,79,138.5,116.89 -40,2,22.04.2016,1.0,104,138.5,116.89 -40,2,23.04.2016,1.0,113,138.5,116.89 -40,2,24.04.2016,1.0,91,138.5,116.89 -40,2,25.04.2016,1.0,101,138.5,116.89 -40,2,26.04.2016,,9,138.5, -40,2,27.04.2016,,6,138.5, -40,2,28.04.2016,,6,138.5, -40,2,29.04.2016,,6,138.5, -40,2,30.04.2016,,44,138.5, -40,2,01.05.2016,,16,138.5, -40,2,02.05.2016,,6,138.5, -40,2,03.05.2016,1.0,126,138.5,106.01 -40,2,04.05.2016,1.0,157,138.5,106.01 -40,2,05.05.2016,1.0,176,138.5,106.01 -40,2,06.05.2016,1.0,207,138.5,106.01 -40,2,07.05.2016,1.0,195,138.5,106.01 -40,2,08.05.2016,1.0,138,138.5,106.01 -40,2,09.05.2016,1.0,119,138.5,106.01 -40,2,10.05.2016,,9,142.57, -40,2,11.05.2016,,3,142.57, -40,2,12.05.2016,,9,142.57, -40,2,13.05.2016,,3,142.57, -40,2,14.05.2016,,16,142.57, -40,2,15.05.2016,,3,142.57, -40,2,16.05.2016,,6,142.57, -40,2,17.05.2016,,6,142.57, -40,2,18.05.2016,,6,142.57, -40,2,19.05.2016,,16,142.57, -40,2,20.05.2016,,9,142.57, -40,2,21.05.2016,,9,142.57, -40,2,22.05.2016,,9,142.57, -41,2,01.01.2015,,0,135.78, -41,2,02.01.2015,,0,135.78, -41,2,03.01.2015,,0,135.78, -41,2,04.01.2015,,0,135.78, -41,2,05.01.2015,,0,135.78, -41,2,06.01.2015,,0,135.78, -41,2,07.01.2015,,0,135.78, -41,2,08.01.2015,,0,135.78, -41,2,09.01.2015,,0,135.78, -41,2,10.01.2015,,0,135.78, -41,2,11.01.2015,,0,135.78, -41,2,12.01.2015,,0,135.78, -41,2,13.01.2015,,0,135.78, -41,2,14.01.2015,,0,135.78, -41,2,15.01.2015,,0,135.78, -41,2,16.01.2015,,0,135.78, -41,2,17.01.2015,,0,135.78, -41,2,18.01.2015,,0,135.78, -41,2,19.01.2015,,0,135.78, -41,2,20.01.2015,,0,135.78, -41,2,21.01.2015,,0,135.78, -41,2,22.01.2015,,0,135.78, -41,2,23.01.2015,,0,135.78, -41,2,24.01.2015,,0,135.78, -41,2,25.01.2015,,0,135.78, -41,2,26.01.2015,,0,135.78, -41,2,27.01.2015,,0,135.78, -41,2,28.01.2015,,0,135.78, -41,2,29.01.2015,,0,135.78, -41,2,30.01.2015,,0,135.78, -41,2,31.01.2015,,0,135.78, -41,2,01.02.2015,,0,135.78, -41,2,02.02.2015,,0,135.78, -41,2,03.02.2015,,0,135.78, -41,2,04.02.2015,,0,135.78, -41,2,05.02.2015,,0,135.78, -41,2,06.02.2015,,0,135.78, -41,2,07.02.2015,,0,135.78, -41,2,08.02.2015,,0,135.78, -41,2,09.02.2015,,0,135.78, -41,2,10.02.2015,,0,135.78, -41,2,11.02.2015,,0,135.78, -41,2,12.02.2015,,0,135.78, -41,2,13.02.2015,,3,135.78, -41,2,14.02.2015,,3,135.78, -41,2,15.02.2015,,9,135.78, -41,2,16.02.2015,,3,135.78, -41,2,17.02.2015,,0,135.78, -41,2,18.02.2015,,6,135.78, -41,2,19.02.2015,,9,135.78, -41,2,20.02.2015,,6,135.78, -41,2,21.02.2015,,3,135.78, -41,2,22.02.2015,,3,135.78, -41,2,23.02.2015,,0,135.78, -41,2,24.02.2015,,0,135.78, -41,2,25.02.2015,,0,135.78, -41,2,26.02.2015,,0,135.78, -41,2,27.02.2015,,0,135.78, -41,2,28.02.2015,,3,135.78, -41,2,01.03.2015,,0,146.38, -41,2,02.03.2015,,3,146.38, -41,2,03.03.2015,,0,146.38, -41,2,04.03.2015,,0,146.38, -41,2,05.03.2015,,3,146.38, -41,2,06.03.2015,,0,146.38, -41,2,07.03.2015,,0,146.38, -41,2,08.03.2015,,3,146.38, -41,2,09.03.2015,,3,146.38, -41,2,10.03.2015,,0,146.38, -41,2,11.03.2015,,6,146.38, -41,2,12.03.2015,,0,146.38, -41,2,13.03.2015,,0,146.38, -41,2,14.03.2015,,0,146.38, -41,2,15.03.2015,,0,146.38, -41,2,16.03.2015,,0,146.38, -41,2,17.03.2015,,0,146.38, -41,2,18.03.2015,,0,146.38, -41,2,19.03.2015,,0,146.38, -41,2,20.03.2015,,0,146.38, -41,2,21.03.2015,,0,146.38, -41,2,22.03.2015,,0,146.38, -41,2,23.03.2015,,0,146.38, -41,2,24.03.2015,,0,146.38, -41,2,25.03.2015,,0,146.38, -41,2,26.03.2015,,0,146.38, -41,2,27.03.2015,,0,146.38, -41,2,28.03.2015,,0,146.38, -41,2,29.03.2015,,0,146.38, -41,2,30.03.2015,,0,146.38, -41,2,31.03.2015,,0,146.38, -41,2,01.04.2015,,0,146.38, -41,2,02.04.2015,,0,146.38, -41,2,03.04.2015,,0,146.38, -41,2,04.04.2015,,0,146.38, -41,2,05.04.2015,,0,146.38, -41,2,06.04.2015,,0,146.38, -41,2,07.04.2015,,0,146.38, -41,2,08.04.2015,,0,146.38, -41,2,09.04.2015,,0,146.38, -41,2,10.04.2015,,0,146.38, -41,2,11.04.2015,,0,146.38, -41,2,12.04.2015,,3,146.38, -41,2,13.04.2015,,0,146.38, -41,2,14.04.2015,,0,146.38, -41,2,15.04.2015,,0,146.38, -41,2,16.04.2015,,0,146.38, -41,2,17.04.2015,,0,146.38, -41,2,18.04.2015,,0,146.38, -41,2,19.04.2015,,0,146.38, -41,2,20.04.2015,,6,145.84, -41,2,21.04.2015,,0,145.84, -41,2,22.04.2015,,0,145.84, -41,2,23.04.2015,,0,145.84, -41,2,24.04.2015,,0,145.84, -41,2,25.04.2015,,0,145.84, -41,2,26.04.2015,,0,150.73, -41,2,27.04.2015,,0,150.73, -41,2,28.04.2015,,0,150.73, -41,2,29.04.2015,,0,150.73, -41,2,30.04.2015,,0,150.73, -41,2,01.05.2015,,0,150.73, -41,2,02.05.2015,,0,150.73, -41,2,03.05.2015,,0,150.73, -41,2,04.05.2015,,0,150.73, -41,2,05.05.2015,,0,150.73, -41,2,06.05.2015,,0,150.73, -41,2,07.05.2015,,0,150.73, -41,2,08.05.2015,,0,150.73, -41,2,09.05.2015,,0,150.73, -41,2,10.05.2015,,0,150.73, -41,2,11.05.2015,,0,150.73, -41,2,12.05.2015,,0,150.73, -41,2,13.05.2015,,0,150.73, -41,2,14.05.2015,,0,150.73, -41,2,15.05.2015,,0,150.73, -41,2,16.05.2015,,0,150.73, -41,2,17.05.2015,,0,150.73, -41,2,18.05.2015,,0,150.73, -41,2,19.05.2015,,0,150.73, -41,2,20.05.2015,,0,150.73, -41,2,21.05.2015,,0,150.73, -41,2,22.05.2015,,0,150.73, -41,2,23.05.2015,,3,150.73, -41,2,24.05.2015,,3,150.73, -41,2,25.05.2015,,0,150.73, -41,2,26.05.2015,,0,150.73, -41,2,27.05.2015,,0,150.73, -41,2,28.05.2015,,0,150.73, -41,2,29.05.2015,,0,150.73, -41,2,30.05.2015,,0,150.73, -41,2,31.05.2015,,0,150.73, -41,2,01.06.2015,,0,150.73, -41,2,02.06.2015,,0,150.73, -41,2,03.06.2015,,0,150.73, -41,2,04.06.2015,,0,150.73, -41,2,05.06.2015,,0,150.73, -41,2,06.06.2015,,0,150.73, -41,2,07.06.2015,,0,150.73, -41,2,08.06.2015,,0,150.73, -41,2,09.06.2015,,0,150.73, -41,2,10.06.2015,,0,150.73, -41,2,11.06.2015,,0,150.73, -41,2,12.06.2015,,0,150.73, -41,2,13.06.2015,,0,150.73, -41,2,14.06.2015,,0,150.73, -41,2,15.06.2015,,3,150.73, -41,2,16.06.2015,,0,150.73, -41,2,17.06.2015,,0,150.73, -41,2,18.06.2015,,0,150.73, -41,2,19.06.2015,,0,150.73, -41,2,20.06.2015,,0,150.73, -41,2,21.06.2015,,0,150.73, -41,2,22.06.2015,,0,150.73, -41,2,23.06.2015,,0,150.73, -41,2,24.06.2015,,0,150.73, -41,2,25.06.2015,,0,150.73, -41,2,26.06.2015,,0,150.73, -41,2,27.06.2015,,0,150.73, -41,2,28.06.2015,,0,150.73, -41,2,29.06.2015,,0,150.73, -41,2,30.06.2015,,0,150.73, -41,2,01.07.2015,,0,150.73, -41,2,02.07.2015,,0,150.73, -41,2,03.07.2015,,0,150.73, -41,2,04.07.2015,,0,150.73, -41,2,05.07.2015,,0,150.73, -41,2,06.07.2015,,0,150.73, -41,2,07.07.2015,,0,150.73, -41,2,08.07.2015,,0,150.73, -41,2,09.07.2015,,0,150.73, -41,2,10.07.2015,,0,150.73, -41,2,11.07.2015,,0,150.73, -41,2,12.07.2015,,0,150.73, -41,2,13.07.2015,,0,150.73, -41,2,14.07.2015,,0,150.73, -41,2,15.07.2015,,0,150.73, -41,2,16.07.2015,,0,150.73, -41,2,17.07.2015,,0,150.73, -41,2,18.07.2015,,0,150.73, -41,2,19.07.2015,,0,150.73, -41,2,20.07.2015,,0,150.73, -41,2,21.07.2015,,0,150.73, -41,2,22.07.2015,,0,150.73, -41,2,23.07.2015,,0,150.73, -41,2,24.07.2015,,0,150.73, -41,2,25.07.2015,,0,150.73, -41,2,26.07.2015,,0,150.73, -41,2,27.07.2015,,0,150.73, -41,2,28.07.2015,,0,150.73, -41,2,29.07.2015,,0,150.73, -41,2,30.07.2015,,0,150.73, -41,2,31.07.2015,,0,150.73, -41,2,01.08.2015,,0,150.73, -41,2,02.08.2015,,0,150.73, -41,2,03.08.2015,,0,150.73, -41,2,04.08.2015,,0,150.73, -41,2,05.08.2015,,0,150.73, -41,2,06.08.2015,,0,150.73, -41,2,07.08.2015,,0,150.73, -41,2,08.08.2015,,0,150.73, -41,2,09.08.2015,,0,150.73, -41,2,10.08.2015,,0,150.73, -41,2,11.08.2015,,0,150.73, -41,2,12.08.2015,,0,128.17, -41,2,13.08.2015,,0,128.17, -41,2,14.08.2015,,0,128.17, -41,2,15.08.2015,,0,128.17, -41,2,16.08.2015,,0,128.17, -41,2,17.08.2015,,41,128.17, -41,2,18.08.2015,1.0,113,128.17,100.3 -41,2,19.08.2015,1.0,22,128.17,100.3 -41,2,20.08.2015,1.0,63,128.17,100.3 -41,2,21.08.2015,1.0,60,128.17,100.3 -41,2,22.08.2015,1.0,53,128.17,100.3 -41,2,23.08.2015,1.0,53,128.17,100.3 -41,2,24.08.2015,1.0,50,128.17,100.3 -41,2,25.08.2015,,0,128.17, -41,2,26.08.2015,,3,128.17, -41,2,27.08.2015,,6,128.17, -41,2,28.08.2015,,0,128.17, -41,2,29.08.2015,,0,128.17, -41,2,30.08.2015,,3,128.17, -41,2,31.08.2015,,3,128.17, -41,2,01.09.2015,,3,128.17, -41,2,02.09.2015,,0,128.17, -41,2,03.09.2015,,0,128.17, -41,2,04.09.2015,,0,128.17, -41,2,05.09.2015,,0,128.17, -41,2,06.09.2015,,0,128.17, -41,2,07.09.2015,,0,128.17, -41,2,08.09.2015,,0,128.17, -41,2,09.09.2015,,0,128.17, -41,2,10.09.2015,,0,128.17, -41,2,11.09.2015,,0,128.17, -41,2,12.09.2015,,0,128.17, -41,2,13.09.2015,,0,128.17, -41,2,14.09.2015,,0,128.17, -41,2,15.09.2015,,0,128.17, -41,2,16.09.2015,,0,128.17, -41,2,17.09.2015,,0,128.17, -41,2,18.09.2015,,0,128.17, -41,2,19.09.2015,,0,128.17, -41,2,20.09.2015,,0,128.17, -41,2,21.09.2015,,0,128.17, -41,2,22.09.2015,,0,128.17, -41,2,23.09.2015,,0,128.17, -41,2,24.09.2015,,0,128.17, -41,2,25.09.2015,,0,128.17, -41,2,26.09.2015,,0,128.17, -41,2,27.09.2015,,0,128.17, -41,2,28.09.2015,,0,128.17, -41,2,29.09.2015,,0,128.17, -41,2,30.09.2015,,0,128.17, -41,2,01.10.2015,,0,162.96, -41,2,02.10.2015,,0,162.96, -41,2,03.10.2015,,0,162.96, -41,2,04.10.2015,,0,162.96, -41,2,05.10.2015,,0,162.96, -41,2,06.10.2015,1.0,28,143.39,122.05 -41,2,07.10.2015,1.0,66,143.39,122.05 -41,2,08.10.2015,1.0,25,143.39,122.05 -41,2,09.10.2015,1.0,28,143.39,122.05 -41,2,10.10.2015,1.0,38,143.39,122.05 -41,2,11.10.2015,1.0,44,143.39,122.05 -41,2,12.10.2015,1.0,44,143.39,122.05 -41,2,13.10.2015,,0,143.93, -41,2,14.10.2015,,0,143.93, -41,2,15.10.2015,,0,143.93, -41,2,16.10.2015,,0,143.93, -41,2,17.10.2015,,0,143.93, -41,2,18.10.2015,,0,143.93, -41,2,19.10.2015,,0,143.93, -41,2,20.10.2015,,0,143.93, -41,2,21.10.2015,,0,143.93, -41,2,22.10.2015,,0,143.93, -41,2,23.10.2015,,0,143.93, -41,2,24.10.2015,,0,143.93, -41,2,25.10.2015,,0,143.93, -41,2,26.10.2015,,0,143.93, -41,2,27.10.2015,,0,143.93, -41,2,28.10.2015,,0,143.93, -41,2,29.10.2015,,0,143.93, -41,2,30.10.2015,,0,143.93, -41,2,31.10.2015,,0,143.93, -41,2,01.11.2015,,0,143.93, -41,2,02.11.2015,,0,143.93, -41,2,03.11.2015,,0,143.93, -41,2,04.11.2015,,0,143.93, -41,2,05.11.2015,,0,143.93, -41,2,06.11.2015,,0,143.93, -41,2,07.11.2015,,0,143.93, -41,2,08.11.2015,,0,143.93, -41,2,09.11.2015,,0,143.93, -41,2,10.11.2015,,0,143.93, -41,2,11.11.2015,,0,143.93, -41,2,12.11.2015,,0,143.93, -41,2,13.11.2015,,0,143.93, -41,2,14.11.2015,,0,143.93, -41,2,15.11.2015,,0,143.93, -41,2,16.11.2015,,0,143.93, -41,2,17.11.2015,,0,143.93, -41,2,18.11.2015,,0,143.93, -41,2,19.11.2015,,0,143.93, -41,2,20.11.2015,,0,143.93, -41,2,21.11.2015,,0,143.93, -41,2,22.11.2015,,0,143.93, -41,2,23.11.2015,,0,143.93, -41,2,24.11.2015,,0,143.93, -41,2,25.11.2015,,0,143.93, -41,2,26.11.2015,,0,143.93, -41,2,27.11.2015,,0,143.93, -41,2,28.11.2015,,0,143.93, -41,2,29.11.2015,,0,143.93, -41,2,30.11.2015,,0,143.93, -41,2,01.12.2015,,0,143.93, -41,2,02.12.2015,,0,143.93, -41,2,03.12.2015,,0,143.93, -41,2,04.12.2015,,0,143.93, -41,2,05.12.2015,,0,143.93, -41,2,06.12.2015,,0,143.93, -41,2,07.12.2015,,0,143.93, -41,2,08.12.2015,,0,143.93, -41,2,09.12.2015,,0,138.5, -41,2,10.12.2015,,0,138.5, -41,2,11.12.2015,,0,142.57, -41,2,12.12.2015,,0,142.57, -41,2,13.12.2015,,0,142.57, -41,2,14.12.2015,,0,142.57, -41,2,15.12.2015,,0,142.57, -41,2,16.12.2015,,0,142.57, -41,2,17.12.2015,,0,142.57, -41,2,18.12.2015,,0,142.57, -41,2,19.12.2015,,0,142.57, -41,2,20.12.2015,,0,142.57, -41,2,21.12.2015,,0,142.57, -41,2,22.12.2015,1.0,94,142.57,103.02 -41,2,23.12.2015,1.0,19,142.57,103.02 -41,2,24.12.2015,1.0,41,142.57,103.02 -41,2,25.12.2015,1.0,53,142.57,103.02 -41,2,26.12.2015,1.0,28,142.57,103.02 -41,2,27.12.2015,1.0,47,142.57,103.02 -41,2,28.12.2015,1.0,38,142.57,103.02 -41,2,29.12.2015,,0,138.5, -41,2,30.12.2015,,0,138.5, -41,2,31.12.2015,,0,138.5, -41,2,01.01.2016,,0,138.5, -41,2,02.01.2016,,0,138.5, -41,2,03.01.2016,,0,138.5, -41,2,04.01.2016,,3,138.5, -41,2,05.01.2016,,0,138.5, -41,2,06.01.2016,,6,138.5, -41,2,07.01.2016,,6,138.5, -41,2,08.01.2016,,6,138.5, -41,2,09.01.2016,,3,138.5, -41,2,10.01.2016,,6,138.5, -41,2,11.01.2016,,0,138.5, -41,2,12.01.2016,,0,138.5, -41,2,13.01.2016,,0,138.5, -41,2,14.01.2016,,0,138.5, -41,2,15.01.2016,,0,138.5, -41,2,16.01.2016,,0,142.57, -41,2,17.01.2016,,0,142.57, -41,2,18.01.2016,,0,142.57, -41,2,19.01.2016,1.0,50,142.57,103.02 -41,2,20.01.2016,1.0,44,142.57,103.02 -41,2,21.01.2016,1.0,126,142.57,103.02 -41,2,22.01.2016,1.0,53,142.57,103.02 -41,2,23.01.2016,1.0,66,142.57,103.02 -41,2,24.01.2016,1.0,47,142.57,103.02 -41,2,25.01.2016,1.0,47,142.57,103.02 -41,2,26.01.2016,,3,142.57, -41,2,27.01.2016,,3,142.57, -41,2,28.01.2016,,3,142.57, -41,2,29.01.2016,,3,142.57, -41,2,30.01.2016,,3,142.57, -41,2,31.01.2016,,3,142.57, -41,2,01.02.2016,,3,142.57, -41,2,02.02.2016,,3,142.57, -41,2,03.02.2016,,3,142.57, -41,2,04.02.2016,,3,142.57, -41,2,05.02.2016,,3,142.57, -41,2,06.02.2016,,3,142.57, -41,2,07.02.2016,,3,142.57, -41,2,08.02.2016,,16,142.57, -41,2,09.02.2016,1.0,79,142.57,108.46 -41,2,10.02.2016,1.0,38,138.36,108.46 -41,2,11.02.2016,1.0,41,138.36,108.46 -41,2,12.02.2016,1.0,53,138.36,108.46 -41,2,13.02.2016,1.0,60,138.36,108.46 -41,2,14.02.2016,1.0,35,138.36,108.46 -41,2,15.02.2016,1.0,6,138.36,108.46 -41,2,16.02.2016,,3,138.36, -41,2,17.02.2016,,3,138.36, -41,2,18.02.2016,,3,138.36, -41,2,19.02.2016,,3,138.36, -41,2,20.02.2016,,3,138.36, -41,2,21.02.2016,,3,138.36, -41,2,22.02.2016,,3,138.36, -41,2,23.02.2016,,3,138.36, -41,2,24.02.2016,,3,138.36, -41,2,25.02.2016,,3,138.36, -41,2,26.02.2016,,3,138.36, -41,2,27.02.2016,,0,138.36, -41,2,28.02.2016,,0,138.36, -41,2,29.02.2016,,0,138.36, -41,2,01.03.2016,,0,138.36, -41,2,02.03.2016,,0,138.36, -41,2,03.03.2016,,0,138.36, -41,2,04.03.2016,,0,138.36, -41,2,05.03.2016,,0,138.36, -41,2,06.03.2016,,0,138.36, -41,2,07.03.2016,,0,138.36, -41,2,08.03.2016,,0,138.36, -41,2,09.03.2016,,0,138.36, -41,2,10.03.2016,,0,138.36, -41,2,11.03.2016,,0,138.36, -41,2,12.03.2016,,0,138.5, -41,2,13.03.2016,,0,138.5, -41,2,14.03.2016,,0,138.5, -41,2,15.03.2016,1.0,28,138.5,124.77 -41,2,16.03.2016,1.0,13,138.5,124.77 -41,2,17.03.2016,1.0,25,138.5,124.77 -41,2,18.03.2016,1.0,13,138.5,124.77 -41,2,19.03.2016,1.0,19,138.5,124.77 -41,2,20.03.2016,1.0,31,138.5,124.77 -41,2,21.03.2016,1.0,25,138.5,124.77 -41,2,22.03.2016,,6,138.36, -41,2,23.03.2016,,0,138.36, -41,2,24.03.2016,,0,138.36, -41,2,25.03.2016,,0,138.36, -41,2,26.03.2016,,0,138.36, -41,2,27.03.2016,,0,138.36, -41,2,28.03.2016,,3,138.36, -41,2,29.03.2016,,0,138.36, 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-41,2,02.05.2016,,16,138.5, -41,2,03.05.2016,1.0,63,138.5,106.01 -41,2,04.05.2016,1.0,72,138.5,106.01 -41,2,05.05.2016,1.0,28,138.5,106.01 -41,2,06.05.2016,1.0,38,138.5,106.01 -41,2,07.05.2016,1.0,47,138.5,106.01 -41,2,08.05.2016,1.0,19,138.5,106.01 -41,2,09.05.2016,1.0,35,138.5,106.01 -41,2,10.05.2016,,0,138.5, -41,2,11.05.2016,,0,138.5, -41,2,12.05.2016,,0,138.5, -41,2,13.05.2016,,0,138.5, -41,2,14.05.2016,,0,138.5, -41,2,15.05.2016,,0,138.5, -41,2,16.05.2016,,0,138.5, -41,2,17.05.2016,,0,138.5, -41,2,18.05.2016,,0,138.5, -41,2,19.05.2016,,0,138.5, -41,2,20.05.2016,,0,138.5, -41,2,21.05.2016,,0,138.5, -41,2,22.05.2016,,0,138.5, -42,2,01.01.2015,,0,135.78, -42,2,02.01.2015,,0,135.78, -42,2,03.01.2015,,3,135.78, -42,2,04.01.2015,,0,135.78, -42,2,05.01.2015,,0,135.78, -42,2,06.01.2015,,0,135.78, -42,2,07.01.2015,,0,135.78, -42,2,08.01.2015,,0,135.78, -42,2,09.01.2015,,0,135.78, -42,2,10.01.2015,,0,135.78, -42,2,11.01.2015,,0,135.78, -42,2,12.01.2015,,0,135.78, 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-42,2,13.09.2015,,3,128.17, -42,2,14.09.2015,,3,128.17, -42,2,15.09.2015,,3,128.17, -42,2,16.09.2015,,3,128.17, -42,2,17.09.2015,,3,128.17, -42,2,18.09.2015,,3,128.17, -42,2,19.09.2015,,3,128.17, -42,2,20.09.2015,,3,128.17, -42,2,21.09.2015,,3,128.17, -42,2,22.09.2015,,3,128.17, -42,2,23.09.2015,,3,128.17, -42,2,24.09.2015,,0,128.17, -42,2,25.09.2015,,0,128.17, -42,2,26.09.2015,,0,128.17, -42,2,27.09.2015,,0,128.17, -42,2,28.09.2015,,0,128.17, -42,2,29.09.2015,,0,128.17, -42,2,30.09.2015,,0,128.17, -42,2,01.10.2015,,0,162.96, -42,2,02.10.2015,,0,162.96, -42,2,03.10.2015,,0,162.96, -42,2,04.10.2015,,0,162.96, -42,2,05.10.2015,,0,162.96, -42,2,06.10.2015,1.0,22,143.39,122.05 -42,2,07.10.2015,1.0,31,143.39,122.05 -42,2,08.10.2015,1.0,13,143.39,122.05 -42,2,09.10.2015,1.0,19,143.39,122.05 -42,2,10.10.2015,1.0,22,143.39,122.05 -42,2,11.10.2015,1.0,44,143.39,122.05 -42,2,12.10.2015,1.0,28,143.39,122.05 -42,2,13.10.2015,,0,143.93, -42,2,14.10.2015,,0,143.93, -42,2,15.10.2015,,0,143.93, -42,2,16.10.2015,,6,143.93, -42,2,17.10.2015,,3,162.96, -42,2,18.10.2015,,3,162.96, -42,2,19.10.2015,,3,162.96, -42,2,20.10.2015,,3,162.96, -42,2,21.10.2015,,3,162.96, -42,2,22.10.2015,,3,162.96, -42,2,23.10.2015,,3,162.96, -42,2,24.10.2015,,3,162.96, -42,2,25.10.2015,,3,162.96, -42,2,26.10.2015,,3,162.96, -42,2,27.10.2015,,3,162.96, -42,2,28.10.2015,,0,162.96, -42,2,29.10.2015,,0,162.96, -42,2,30.10.2015,,0,162.96, -42,2,31.10.2015,,3,162.96, -42,2,01.11.2015,,0,162.96, -42,2,02.11.2015,,3,162.96, -42,2,03.11.2015,,0,162.96, -42,2,04.11.2015,,0,162.96, -42,2,05.11.2015,,0,162.96, -42,2,06.11.2015,,0,162.96, -42,2,07.11.2015,,0,162.96, -42,2,08.11.2015,,0,162.96, -42,2,09.11.2015,,0,162.96, -42,2,10.11.2015,,0,162.96, -42,2,11.11.2015,,0,138.36, -42,2,12.11.2015,,0,138.36, -42,2,13.11.2015,,0,138.36, -42,2,14.11.2015,,0,138.36, -42,2,15.11.2015,,0,138.36, -42,2,16.11.2015,,0,138.36, -42,2,17.11.2015,,0,138.36, -42,2,18.11.2015,,0,138.36, -42,2,19.11.2015,,0,138.36, -42,2,20.11.2015,,0,138.36, -42,2,21.11.2015,,0,138.36, -42,2,22.11.2015,,19,138.36, -42,2,23.11.2015,,0,138.36, -42,2,24.11.2015,,0,138.36, -42,2,25.11.2015,,0,138.36, -42,2,26.11.2015,,0,138.36, -42,2,27.11.2015,,0,138.36, -42,2,28.11.2015,,0,138.36, -42,2,29.11.2015,,0,138.36, -42,2,30.11.2015,,0,138.36, -42,2,01.12.2015,,0,162.96, -42,2,02.12.2015,,0,162.96, -42,2,03.12.2015,,0,138.5, -42,2,04.12.2015,,0,138.5, -42,2,05.12.2015,,0,138.5, -42,2,06.12.2015,,0,138.5, -42,2,07.12.2015,,0,138.5, -42,2,08.12.2015,,0,138.5, -42,2,09.12.2015,,0,138.5, -42,2,10.12.2015,,0,138.5, -42,2,11.12.2015,,0,142.57, -42,2,12.12.2015,,0,142.57, -42,2,13.12.2015,,0,142.57, -42,2,14.12.2015,,0,142.57, -42,2,15.12.2015,,0,142.57, -42,2,16.12.2015,,0,142.57, -42,2,17.12.2015,,0,142.57, -42,2,18.12.2015,,0,142.57, -42,2,19.12.2015,,0,142.57, -42,2,20.12.2015,,0,142.57, -42,2,21.12.2015,,3,142.57, -42,2,22.12.2015,1.0,9,142.57,103.02 -42,2,23.12.2015,1.0,44,142.57,103.02 -42,2,24.12.2015,1.0,41,142.57,103.02 -42,2,25.12.2015,1.0,47,142.57,103.02 -42,2,26.12.2015,1.0,25,142.57,103.02 -42,2,27.12.2015,1.0,16,142.57,103.02 -42,2,28.12.2015,1.0,28,142.57,103.02 -42,2,29.12.2015,,0,138.5, -42,2,30.12.2015,,0,138.5, -42,2,31.12.2015,,0,138.5, -42,2,01.01.2016,,0,138.5, -42,2,02.01.2016,,0,138.5, -42,2,03.01.2016,,0,138.5, -42,2,04.01.2016,,16,138.5, -42,2,05.01.2016,,3,138.5, -42,2,06.01.2016,,0,138.5, -42,2,07.01.2016,,0,138.5, -42,2,08.01.2016,,0,138.5, -42,2,09.01.2016,,0,138.5, -42,2,10.01.2016,,0,138.5, -42,2,11.01.2016,,3,138.5, -42,2,12.01.2016,,0,138.5, -42,2,13.01.2016,,6,138.5, -42,2,14.01.2016,,3,138.5, -42,2,15.01.2016,,3,138.5, -42,2,16.01.2016,,3,142.57, -42,2,17.01.2016,,3,142.57, -42,2,18.01.2016,,3,142.57, -42,2,19.01.2016,1.0,41,142.57,103.02 -42,2,20.01.2016,1.0,16,142.57,103.02 -42,2,21.01.2016,1.0,31,142.57,103.02 -42,2,22.01.2016,1.0,50,142.57,103.02 -42,2,23.01.2016,1.0,19,142.57,103.02 -42,2,24.01.2016,1.0,47,142.57,103.02 -42,2,25.01.2016,1.0,25,142.57,103.02 -42,2,26.01.2016,,3,142.57, -42,2,27.01.2016,,3,142.57, -42,2,28.01.2016,,3,142.57, -42,2,29.01.2016,,3,142.57, -42,2,30.01.2016,,3,142.57, -42,2,31.01.2016,,3,142.57, -42,2,01.02.2016,,3,142.57, -42,2,02.02.2016,,3,142.57, -42,2,03.02.2016,,3,142.57, -42,2,04.02.2016,,3,142.57, -42,2,05.02.2016,,3,142.57, -42,2,06.02.2016,,3,142.57, -42,2,07.02.2016,,3,142.57, -42,2,08.02.2016,,9,142.57, -42,2,09.02.2016,1.0,53,142.57,108.46 -42,2,10.02.2016,1.0,31,138.36,108.46 -42,2,11.02.2016,1.0,31,138.36,108.46 -42,2,12.02.2016,1.0,28,138.36,108.46 -42,2,13.02.2016,1.0,25,138.36,108.46 -42,2,14.02.2016,1.0,25,138.36,108.46 -42,2,15.02.2016,1.0,22,138.36,108.46 -42,2,16.02.2016,,3,138.36, -42,2,17.02.2016,,0,138.36, -42,2,18.02.2016,,0,138.36, -42,2,19.02.2016,,0,138.36, -42,2,20.02.2016,,0,138.36, -42,2,21.02.2016,,19,138.36, -42,2,22.02.2016,,28,138.36, -42,2,23.02.2016,,0,138.36, -42,2,24.02.2016,,0,138.36, -42,2,25.02.2016,,0,138.36, 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-42,2,30.03.2016,,3,138.36, -42,2,31.03.2016,,3,138.36, -42,2,01.04.2016,,3,138.36, -42,2,02.04.2016,,3,138.36, -42,2,03.04.2016,,3,138.36, -42,2,04.04.2016,,0,138.36, -42,2,05.04.2016,,0,138.36, -42,2,06.04.2016,,0,138.36, -42,2,07.04.2016,,3,138.36, -42,2,08.04.2016,,0,138.36, -42,2,09.04.2016,,0,138.5, -42,2,10.04.2016,,3,138.5, -42,2,11.04.2016,,0,138.5, -42,2,12.04.2016,,0,138.5, -42,2,13.04.2016,,3,138.5, -42,2,14.04.2016,,3,138.5, -42,2,15.04.2016,,3,138.5, -42,2,16.04.2016,,3,138.5, -42,2,17.04.2016,,3,138.5, -42,2,18.04.2016,,3,138.5, -42,2,19.04.2016,1.0,19,138.5,116.89 -42,2,20.04.2016,1.0,35,138.5,116.89 -42,2,21.04.2016,1.0,19,138.5,116.89 -42,2,22.04.2016,1.0,22,138.5,116.89 -42,2,23.04.2016,1.0,25,138.5,116.89 -42,2,24.04.2016,1.0,41,138.5,116.89 -42,2,25.04.2016,1.0,22,138.5,116.89 -42,2,26.04.2016,,0,138.5, -42,2,27.04.2016,,0,138.5, -42,2,28.04.2016,,0,138.5, -42,2,29.04.2016,,3,138.5, -42,2,30.04.2016,,3,138.5, -42,2,01.05.2016,,6,138.5, -42,2,02.05.2016,,9,138.5, -42,2,03.05.2016,1.0,66,138.5,106.01 -42,2,04.05.2016,1.0,85,138.5,106.01 -42,2,05.05.2016,1.0,28,138.5,106.01 -42,2,06.05.2016,1.0,88,138.5,106.01 -42,2,07.05.2016,1.0,19,138.5,106.01 -42,2,08.05.2016,1.0,31,138.5,106.01 -42,2,09.05.2016,1.0,31,138.5,106.01 -42,2,10.05.2016,,3,142.57, -42,2,11.05.2016,,3,142.57, -42,2,12.05.2016,,0,142.57, -42,2,13.05.2016,,9,142.57, -42,2,14.05.2016,,0,142.57, -42,2,15.05.2016,,9,142.57, -42,2,16.05.2016,,9,142.57, -42,2,17.05.2016,,9,142.57, -42,2,18.05.2016,,3,142.57, -42,2,19.05.2016,,0,142.57, -42,2,20.05.2016,,0,142.57, -42,2,21.05.2016,,3,142.57, -42,2,22.05.2016,,3,142.57, -43,2,01.01.2015,,0,135.78, -43,2,02.01.2015,,0,135.78, -43,2,03.01.2015,,0,135.78, -43,2,04.01.2015,,0,135.78, -43,2,05.01.2015,,19,135.78, -43,2,06.01.2015,,6,135.78, -43,2,07.01.2015,,3,135.78, -43,2,08.01.2015,,13,135.78, -43,2,09.01.2015,,0,135.78, -43,2,10.01.2015,,0,135.78, -43,2,11.01.2015,,0,135.78, -43,2,12.01.2015,,0,135.78, -43,2,13.01.2015,,0,135.78, -43,2,14.01.2015,,3,135.78, -43,2,15.01.2015,,0,135.78, -43,2,16.01.2015,,0,135.78, -43,2,17.01.2015,,0,135.78, -43,2,18.01.2015,,0,135.78, -43,2,19.01.2015,,9,135.78, -43,2,20.01.2015,,0,135.78, -43,2,21.01.2015,,0,135.78, -43,2,22.01.2015,,0,135.78, -43,2,23.01.2015,,3,135.78, -43,2,24.01.2015,,0,135.78, -43,2,25.01.2015,,0,135.78, -43,2,26.01.2015,,0,135.78, -43,2,27.01.2015,,0,135.78, -43,2,28.01.2015,,0,135.78, -43,2,29.01.2015,,0,135.78, -43,2,30.01.2015,,0,135.78, -43,2,31.01.2015,,0,135.78, -43,2,01.02.2015,,0,135.78, -43,2,02.02.2015,,0,135.78, -43,2,03.02.2015,,0,135.78, -43,2,04.02.2015,,0,135.78, -43,2,05.02.2015,,0,135.78, -43,2,06.02.2015,,0,135.78, -43,2,07.02.2015,,0,135.78, -43,2,08.02.2015,,9,135.78, -43,2,09.02.2015,,0,135.78, -43,2,10.02.2015,,0,135.78, -43,2,11.02.2015,,0,135.78, -43,2,12.02.2015,,0,135.78, -43,2,13.02.2015,,0,135.78, -43,2,14.02.2015,,0,135.78, -43,2,15.02.2015,,0,135.78, -43,2,16.02.2015,,0,135.78, -43,2,17.02.2015,,0,135.78, -43,2,18.02.2015,,0,135.78, -43,2,19.02.2015,,0,135.78, -43,2,20.02.2015,,0,135.78, -43,2,21.02.2015,,3,135.78, -43,2,22.02.2015,,9,135.78, -43,2,23.02.2015,,0,135.78, -43,2,24.02.2015,,0,135.78, -43,2,25.02.2015,,0,135.78, -43,2,26.02.2015,,0,135.78, -43,2,27.02.2015,,0,135.78, -43,2,28.02.2015,,0,135.78, -43,2,01.03.2015,,0,135.78, -43,2,02.03.2015,,0,146.38, -43,2,03.03.2015,,0,146.38, -43,2,04.03.2015,,0,146.38, -43,2,05.03.2015,,0,146.38, -43,2,06.03.2015,,0,146.38, -43,2,07.03.2015,,0,146.38, -43,2,08.03.2015,,0,146.38, -43,2,09.03.2015,,0,146.38, -43,2,10.03.2015,,0,146.38, -43,2,11.03.2015,,0,146.38, -43,2,12.03.2015,,0,146.38, -43,2,13.03.2015,,0,146.38, -43,2,14.03.2015,,3,146.38, -43,2,15.03.2015,,3,146.38, -43,2,16.03.2015,,6,146.38, -43,2,17.03.2015,,0,146.38, -43,2,18.03.2015,,0,146.38, -43,2,19.03.2015,,0,146.38, -43,2,20.03.2015,,0,146.38, -43,2,21.03.2015,,6,146.38, -43,2,22.03.2015,,0,146.38, -43,2,23.03.2015,,0,146.38, -43,2,24.03.2015,,0,146.38, 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-43,2,29.04.2015,,0,150.73, -43,2,30.04.2015,,3,150.73, -43,2,01.05.2015,,0,150.73, -43,2,02.05.2015,,0,150.73, -43,2,03.05.2015,,0,150.73, -43,2,04.05.2015,,0,150.73, -43,2,05.05.2015,,9,150.73, -43,2,06.05.2015,,0,150.73, -43,2,07.05.2015,,3,150.73, -43,2,08.05.2015,,0,150.73, -43,2,09.05.2015,,3,150.73, -43,2,10.05.2015,,0,150.73, -43,2,11.05.2015,,0,150.73, -43,2,12.05.2015,,0,150.73, -43,2,13.05.2015,,0,150.73, -43,2,14.05.2015,,0,150.73, -43,2,15.05.2015,,0,150.73, -43,2,16.05.2015,,0,150.73, -43,2,17.05.2015,,6,150.73, -43,2,18.05.2015,,6,150.73, -43,2,19.05.2015,,0,150.73, -43,2,20.05.2015,,0,150.73, -43,2,21.05.2015,,0,150.73, -43,2,22.05.2015,,0,150.73, -43,2,23.05.2015,,0,150.73, -43,2,24.05.2015,,0,150.73, -43,2,25.05.2015,,0,150.73, -43,2,26.05.2015,,0,150.73, -43,2,27.05.2015,,3,150.73, -43,2,28.05.2015,,3,150.73, -43,2,29.05.2015,,0,150.73, -43,2,30.05.2015,,0,150.73, -43,2,31.05.2015,,3,150.73, -43,2,01.06.2015,,0,150.73, -43,2,02.06.2015,,0,150.73, -43,2,03.06.2015,,0,150.73, -43,2,04.06.2015,,0,150.73, -43,2,05.06.2015,,0,150.73, -43,2,06.06.2015,,0,150.73, -43,2,07.06.2015,,0,150.73, -43,2,08.06.2015,,0,150.73, -43,2,09.06.2015,,0,150.73, -43,2,10.06.2015,,0,150.73, -43,2,11.06.2015,,0,150.73, -43,2,12.06.2015,,0,150.73, -43,2,13.06.2015,,0,150.73, -43,2,14.06.2015,,0,150.73, -43,2,15.06.2015,,0,150.73, -43,2,16.06.2015,,0,150.73, -43,2,17.06.2015,,0,150.73, -43,2,18.06.2015,,0,150.73, -43,2,19.06.2015,,0,150.73, -43,2,20.06.2015,,0,150.73, -43,2,21.06.2015,,3,150.73, -43,2,22.06.2015,,3,150.73, -43,2,23.06.2015,,19,150.73, -43,2,24.06.2015,,0,150.73, -43,2,25.06.2015,,0,150.73, -43,2,26.06.2015,,13,150.73, -43,2,27.06.2015,,3,150.73, -43,2,28.06.2015,,9,150.73, -43,2,29.06.2015,,0,150.73, -43,2,30.06.2015,,3,150.73, -43,2,01.07.2015,,0,150.73, -43,2,02.07.2015,,3,150.73, -43,2,03.07.2015,,3,150.73, -43,2,04.07.2015,,3,150.73, -43,2,05.07.2015,,0,150.73, -43,2,06.07.2015,,0,150.73, -43,2,07.07.2015,,3,150.73, -43,2,08.07.2015,,0,150.73, -43,2,09.07.2015,,0,150.73, -43,2,10.07.2015,,0,150.73, -43,2,11.07.2015,,0,150.73, -43,2,12.07.2015,,0,150.73, -43,2,13.07.2015,,0,150.73, -43,2,14.07.2015,,0,150.73, -43,2,15.07.2015,,0,150.73, -43,2,16.07.2015,,0,150.73, -43,2,17.07.2015,,0,150.73, -43,2,18.07.2015,,0,150.73, -43,2,19.07.2015,,0,150.73, -43,2,20.07.2015,,0,150.73, -43,2,21.07.2015,,0,150.73, -43,2,22.07.2015,,0,150.73, -43,2,23.07.2015,,0,150.73, -43,2,24.07.2015,,0,150.73, -43,2,25.07.2015,,0,150.73, -43,2,26.07.2015,,0,150.73, -43,2,27.07.2015,,0,150.73, -43,2,28.07.2015,,0,150.73, -43,2,29.07.2015,,0,150.73, -43,2,30.07.2015,,0,150.73, -43,2,31.07.2015,,0,150.73, -43,2,01.08.2015,,0,150.73, -43,2,02.08.2015,,0,150.73, -43,2,03.08.2015,,0,150.73, -43,2,04.08.2015,,0,150.73, -43,2,05.08.2015,,0,150.73, -43,2,06.08.2015,,0,150.73, -43,2,07.08.2015,,0,150.73, -43,2,08.08.2015,,0,150.73, -43,2,09.08.2015,,0,150.73, -43,2,10.08.2015,,0,150.73, -43,2,11.08.2015,,0,150.73, -43,2,12.08.2015,,0,128.17, -43,2,13.08.2015,,0,128.17, -43,2,14.08.2015,,0,128.17, -43,2,15.08.2015,,0,128.17, -43,2,16.08.2015,,0,128.17, -43,2,17.08.2015,,0,128.17, -43,2,18.08.2015,1.0,38,128.17,100.3 -43,2,19.08.2015,1.0,25,128.17,100.3 -43,2,20.08.2015,1.0,85,128.17,100.3 -43,2,21.08.2015,1.0,63,128.17,100.3 -43,2,22.08.2015,1.0,60,128.17,100.3 -43,2,23.08.2015,1.0,28,128.17,100.3 -43,2,24.08.2015,1.0,38,128.17,100.3 -43,2,25.08.2015,,3,128.17, -43,2,26.08.2015,,0,128.17, -43,2,27.08.2015,,0,128.17, -43,2,28.08.2015,,0,128.17, -43,2,29.08.2015,,0,128.17, -43,2,30.08.2015,,0,128.17, -43,2,31.08.2015,,0,128.17, -43,2,01.09.2015,,0,162.96, -43,2,02.09.2015,,0,162.96, -43,2,03.09.2015,,0,162.96, -43,2,04.09.2015,,0,162.96, -43,2,05.09.2015,,0,162.96, -43,2,06.09.2015,,0,162.96, -43,2,07.09.2015,,0,162.96, -43,2,08.09.2015,,0,162.96, -43,2,09.09.2015,,0,162.96, -43,2,10.09.2015,,0,162.96, -43,2,11.09.2015,,0,162.96, -43,2,12.09.2015,,0,162.96, -43,2,13.09.2015,,0,162.96, -43,2,14.09.2015,,0,162.96, -43,2,15.09.2015,,0,162.96, -43,2,16.09.2015,,0,162.96, -43,2,17.09.2015,,0,162.96, -43,2,18.09.2015,,0,162.96, -43,2,19.09.2015,,0,162.96, -43,2,20.09.2015,,0,162.96, -43,2,21.09.2015,,0,162.96, -43,2,22.09.2015,,0,162.96, -43,2,23.09.2015,,0,162.96, -43,2,24.09.2015,,0,162.96, -43,2,25.09.2015,,0,162.96, -43,2,26.09.2015,,0,162.96, -43,2,27.09.2015,,0,162.96, -43,2,28.09.2015,,0,162.96, -43,2,29.09.2015,,6,162.96, -43,2,30.09.2015,,6,162.96, -43,2,01.10.2015,,0,162.96, -43,2,02.10.2015,,0,162.96, -43,2,03.10.2015,,0,162.96, -43,2,04.10.2015,,3,162.96, -43,2,05.10.2015,,0,162.96, -43,2,06.10.2015,1.0,63,143.39,122.05 -43,2,07.10.2015,1.0,31,143.39,122.05 -43,2,08.10.2015,1.0,25,143.39,122.05 -43,2,09.10.2015,1.0,25,143.39,122.05 -43,2,10.10.2015,1.0,38,143.39,122.05 -43,2,11.10.2015,1.0,50,143.39,122.05 -43,2,12.10.2015,1.0,50,143.39,122.05 -43,2,13.10.2015,,0,143.93, -43,2,14.10.2015,,0,143.93, -43,2,15.10.2015,,9,143.93, -43,2,16.10.2015,,3,143.93, -43,2,17.10.2015,,3,143.93, -43,2,18.10.2015,,3,143.93, -43,2,19.10.2015,,3,143.93, -43,2,20.10.2015,,3,143.93, -43,2,21.10.2015,,3,143.93, -43,2,22.10.2015,,3,143.93, -43,2,23.10.2015,,3,143.93, -43,2,24.10.2015,,3,143.93, -43,2,25.10.2015,,3,143.93, -43,2,26.10.2015,,3,143.93, -43,2,27.10.2015,,3,143.93, -43,2,28.10.2015,,3,143.93, -43,2,29.10.2015,,3,143.93, -43,2,30.10.2015,,3,143.93, -43,2,31.10.2015,,3,143.93, -43,2,01.11.2015,,0,143.93, -43,2,02.11.2015,,0,143.93, -43,2,03.11.2015,,0,143.93, -43,2,04.11.2015,,0,143.93, -43,2,05.11.2015,,0,143.93, -43,2,06.11.2015,,0,143.93, -43,2,07.11.2015,,0,143.93, -43,2,08.11.2015,,0,143.93, -43,2,09.11.2015,,0,143.93, -43,2,10.11.2015,,0,143.93, -43,2,11.11.2015,,0,143.93, -43,2,12.11.2015,,0,143.93, -43,2,13.11.2015,,0,143.93, -43,2,14.11.2015,,0,143.93, -43,2,15.11.2015,,0,143.93, -43,2,16.11.2015,,0,143.93, -43,2,17.11.2015,,0,143.93, -43,2,18.11.2015,,0,143.93, -43,2,19.11.2015,,0,143.93, -43,2,20.11.2015,,0,143.93, -43,2,21.11.2015,,0,143.93, -43,2,22.11.2015,,0,143.93, -43,2,23.11.2015,,0,143.93, -43,2,24.11.2015,,0,143.93, -43,2,25.11.2015,,0,143.93, -43,2,26.11.2015,,0,143.93, -43,2,27.11.2015,,0,143.93, -43,2,28.11.2015,,0,143.93, -43,2,29.11.2015,,0,143.93, -43,2,30.11.2015,,0,143.93, -43,2,01.12.2015,,0,143.93, -43,2,02.12.2015,,0,143.93, -43,2,03.12.2015,,0,143.93, -43,2,04.12.2015,,0,143.93, -43,2,05.12.2015,,0,143.93, -43,2,06.12.2015,,0,143.93, -43,2,07.12.2015,,0,143.93, -43,2,08.12.2015,,0,143.93, -43,2,09.12.2015,,0,138.5, -43,2,10.12.2015,,0,138.5, -43,2,11.12.2015,,3,142.57, -43,2,12.12.2015,,3,142.57, -43,2,13.12.2015,,0,142.57, -43,2,14.12.2015,,0,142.57, -43,2,15.12.2015,,9,142.57, -43,2,16.12.2015,,0,142.57, -43,2,17.12.2015,,3,142.57, -43,2,18.12.2015,,3,142.57, -43,2,19.12.2015,,0,142.57, -43,2,20.12.2015,,6,142.57, -43,2,21.12.2015,,9,142.57, -43,2,22.12.2015,1.0,66,142.57,103.02 -43,2,23.12.2015,1.0,41,142.57,103.02 -43,2,24.12.2015,1.0,41,142.57,103.02 -43,2,25.12.2015,1.0,60,142.57,103.02 -43,2,26.12.2015,1.0,75,142.57,103.02 -43,2,27.12.2015,1.0,44,142.57,103.02 -43,2,28.12.2015,1.0,44,142.57,103.02 -43,2,29.12.2015,,3,138.5, -43,2,30.12.2015,,3,138.5, -43,2,31.12.2015,,3,138.5, -43,2,01.01.2016,,3,138.5, -43,2,02.01.2016,,3,138.5, -43,2,03.01.2016,,3,138.5, -43,2,04.01.2016,,6,138.5, -43,2,05.01.2016,,13,138.5, -43,2,06.01.2016,,3,138.5, -43,2,07.01.2016,,3,138.5, -43,2,08.01.2016,,3,138.5, -43,2,09.01.2016,,3,138.5, -43,2,10.01.2016,,3,138.5, -43,2,11.01.2016,,9,138.5, -43,2,12.01.2016,,6,138.5, -43,2,13.01.2016,,3,138.5, -43,2,14.01.2016,,6,138.5, -43,2,15.01.2016,,6,138.5, -43,2,16.01.2016,,6,142.57, -43,2,17.01.2016,,6,142.57, -43,2,18.01.2016,,6,142.57, -43,2,19.01.2016,1.0,44,142.57,103.02 -43,2,20.01.2016,1.0,44,142.57,103.02 -43,2,21.01.2016,1.0,44,142.57,103.02 -43,2,22.01.2016,1.0,44,142.57,103.02 -43,2,23.01.2016,1.0,44,142.57,103.02 -43,2,24.01.2016,1.0,44,142.57,103.02 -43,2,25.01.2016,1.0,44,142.57,103.02 -43,2,26.01.2016,,6,142.57, -43,2,27.01.2016,,6,142.57, -43,2,28.01.2016,,0,142.57, -43,2,29.01.2016,,0,142.57, -43,2,30.01.2016,,9,142.57, -43,2,31.01.2016,,13,142.57, -43,2,01.02.2016,,6,142.57, -43,2,02.02.2016,,6,142.57, -43,2,03.02.2016,,6,142.57, -43,2,04.02.2016,,6,142.57, -43,2,05.02.2016,,6,142.57, -43,2,06.02.2016,,6,142.57, -43,2,07.02.2016,,6,142.57, -43,2,08.02.2016,,22,142.57, -43,2,09.02.2016,1.0,119,142.57,108.46 -43,2,10.02.2016,1.0,28,138.36,108.46 -43,2,11.02.2016,1.0,104,138.36,108.46 -43,2,12.02.2016,1.0,50,138.36,108.46 -43,2,13.02.2016,1.0,19,138.36,108.46 -43,2,14.02.2016,1.0,53,138.36,108.46 -43,2,15.02.2016,1.0,19,138.36,108.46 -43,2,16.02.2016,,6,138.36, -43,2,17.02.2016,,6,138.36, -43,2,18.02.2016,,6,138.36, -43,2,19.02.2016,,6,138.36, -43,2,20.02.2016,,6,138.36, -43,2,21.02.2016,,6,138.36, -43,2,22.02.2016,,6,138.36, -43,2,23.02.2016,,6,138.36, -43,2,24.02.2016,,6,138.36, -43,2,25.02.2016,,6,138.36, -43,2,26.02.2016,,6,138.36, -43,2,27.02.2016,,6,138.36, -43,2,28.02.2016,,6,138.36, -43,2,29.02.2016,,6,138.36, -43,2,01.03.2016,,6,138.36, -43,2,02.03.2016,,6,138.36, -43,2,03.03.2016,,0,138.36, -43,2,04.03.2016,,0,138.36, -43,2,05.03.2016,,0,138.36, -43,2,06.03.2016,,0,138.36, -43,2,07.03.2016,,3,138.36, -43,2,08.03.2016,,0,138.36, -43,2,09.03.2016,,3,138.36, -43,2,10.03.2016,,0,138.36, -43,2,11.03.2016,,0,138.36, -43,2,12.03.2016,,3,138.5, -43,2,13.03.2016,,0,138.5, -43,2,14.03.2016,,0,138.5, -43,2,15.03.2016,1.0,35,138.5,124.77 -43,2,16.03.2016,1.0,25,138.5,124.77 -43,2,17.03.2016,1.0,25,138.5,124.77 -43,2,18.03.2016,1.0,38,138.5,124.77 -43,2,19.03.2016,1.0,31,138.5,124.77 -43,2,20.03.2016,1.0,41,138.5,124.77 -43,2,21.03.2016,1.0,38,138.5,124.77 -43,2,22.03.2016,,0,138.36, -43,2,23.03.2016,,0,138.36, -43,2,24.03.2016,,0,138.36, -43,2,25.03.2016,,0,138.36, -43,2,26.03.2016,,0,138.36, -43,2,27.03.2016,,0,138.36, -43,2,28.03.2016,,0,138.36, -43,2,29.03.2016,,0,138.36, -43,2,30.03.2016,,0,138.36, -43,2,31.03.2016,,0,138.36, -43,2,01.04.2016,,0,138.36, -43,2,02.04.2016,,0,138.36, -43,2,03.04.2016,,0,138.36, -43,2,04.04.2016,,0,138.36, -43,2,05.04.2016,,0,138.36, -43,2,06.04.2016,,0,138.36, -43,2,07.04.2016,,3,138.36, -43,2,08.04.2016,,6,138.36, -43,2,09.04.2016,,6,138.5, -43,2,10.04.2016,,3,138.5, -43,2,11.04.2016,,3,138.5, -43,2,12.04.2016,,3,138.5, -43,2,13.04.2016,,3,138.5, -43,2,14.04.2016,,0,138.5, -43,2,15.04.2016,,0,138.5, -43,2,16.04.2016,,3,138.5, -43,2,17.04.2016,,3,138.5, -43,2,18.04.2016,,6,138.5, -43,2,19.04.2016,1.0,53,138.5,116.89 -43,2,20.04.2016,1.0,28,138.5,116.89 -43,2,21.04.2016,1.0,16,138.5,116.89 -43,2,22.04.2016,1.0,41,138.5,116.89 -43,2,23.04.2016,1.0,41,138.5,116.89 -43,2,24.04.2016,1.0,31,138.5,116.89 -43,2,25.04.2016,1.0,6,138.5,116.89 -43,2,26.04.2016,,0,138.5, -43,2,27.04.2016,,0,138.5, -43,2,28.04.2016,,6,138.5, -43,2,29.04.2016,,0,138.5, -43,2,30.04.2016,,3,138.5, -43,2,01.05.2016,,3,138.5, -43,2,02.05.2016,,38,138.5, -43,2,03.05.2016,1.0,91,138.5,106.01 -43,2,04.05.2016,1.0,63,138.5,106.01 -43,2,05.05.2016,1.0,107,138.5,106.01 -43,2,06.05.2016,1.0,6,138.5,106.01 -43,2,07.05.2016,1.0,41,138.5,106.01 -43,2,08.05.2016,1.0,41,138.5,106.01 -43,2,09.05.2016,1.0,41,138.5,106.01 -43,2,10.05.2016,,6,138.5, -43,2,11.05.2016,,6,138.5, -43,2,12.05.2016,,6,138.5, -43,2,13.05.2016,,6,138.5, -43,2,14.05.2016,,6,138.5, -43,2,15.05.2016,,6,138.5, -43,2,16.05.2016,,6,138.5, -43,2,17.05.2016,,6,138.5, -43,2,18.05.2016,,6,138.5, -43,2,19.05.2016,,6,138.5, -43,2,20.05.2016,,6,138.5, -43,2,21.05.2016,,6,138.5, -43,2,22.05.2016,,6,138.5, -44,2,01.01.2015,,3,135.78, -44,2,02.01.2015,,0,135.78, -44,2,03.01.2015,,0,135.78, -44,2,04.01.2015,,0,135.78, -44,2,05.01.2015,,0,135.78, -44,2,06.01.2015,,0,135.78, -44,2,07.01.2015,,0,135.78, -44,2,08.01.2015,,0,135.78, -44,2,09.01.2015,,0,135.78, -44,2,10.01.2015,,0,135.78, -44,2,11.01.2015,,0,135.78, -44,2,12.01.2015,,6,135.78, -44,2,13.01.2015,,0,135.78, -44,2,14.01.2015,,0,135.78, -44,2,15.01.2015,,0,135.78, -44,2,16.01.2015,,0,135.78, -44,2,17.01.2015,,0,135.78, -44,2,18.01.2015,,0,135.78, -44,2,19.01.2015,,0,135.78, -44,2,20.01.2015,,0,135.78, -44,2,21.01.2015,,3,135.78, -44,2,22.01.2015,,0,135.78, -44,2,23.01.2015,,9,135.78, -44,2,24.01.2015,,3,135.78, -44,2,25.01.2015,,6,135.78, -44,2,26.01.2015,,0,135.78, -44,2,27.01.2015,,0,135.78, -44,2,28.01.2015,,0,135.78, -44,2,29.01.2015,,3,135.78, -44,2,30.01.2015,,0,135.78, -44,2,31.01.2015,,0,135.78, -44,2,01.02.2015,,0,135.78, -44,2,02.02.2015,,0,135.78, -44,2,03.02.2015,,0,135.78, -44,2,04.02.2015,,0,135.78, -44,2,05.02.2015,,0,135.78, -44,2,06.02.2015,,0,135.78, -44,2,07.02.2015,,0,135.78, -44,2,08.02.2015,,0,135.78, -44,2,09.02.2015,,0,135.78, -44,2,10.02.2015,,0,135.78, -44,2,11.02.2015,,3,135.78, -44,2,12.02.2015,,3,135.78, -44,2,13.02.2015,,0,135.78, -44,2,14.02.2015,,3,135.78, -44,2,15.02.2015,,0,135.78, -44,2,16.02.2015,,0,135.78, -44,2,17.02.2015,,0,135.78, -44,2,18.02.2015,,0,135.78, -44,2,19.02.2015,,19,135.78, -44,2,20.02.2015,,6,135.78, -44,2,21.02.2015,,0,135.78, -44,2,22.02.2015,,3,135.78, -44,2,23.02.2015,,6,135.78, -44,2,24.02.2015,,0,135.78, -44,2,25.02.2015,,6,135.78, -44,2,26.02.2015,,0,135.78, -44,2,27.02.2015,,0,135.78, -44,2,28.02.2015,,0,135.78, -44,2,01.03.2015,,3,135.78, -44,2,02.03.2015,,0,135.78, -44,2,03.03.2015,,0,135.78, -44,2,04.03.2015,,0,135.78, -44,2,05.03.2015,,3,135.78, -44,2,06.03.2015,,0,135.78, -44,2,07.03.2015,,0,135.78, -44,2,08.03.2015,,0,146.38, -44,2,09.03.2015,,0,146.38, -44,2,10.03.2015,,0,146.38, -44,2,11.03.2015,,0,146.38, -44,2,12.03.2015,,0,146.38, -44,2,13.03.2015,,0,146.38, -44,2,14.03.2015,,0,146.38, -44,2,15.03.2015,,0,146.38, -44,2,16.03.2015,,0,146.38, -44,2,17.03.2015,,0,146.38, -44,2,18.03.2015,,0,146.38, -44,2,19.03.2015,,0,146.38, -44,2,20.03.2015,,0,146.38, -44,2,21.03.2015,,0,146.38, -44,2,22.03.2015,,0,146.38, -44,2,23.03.2015,,0,146.38, -44,2,24.03.2015,,0,146.38, -44,2,25.03.2015,,0,146.38, -44,2,26.03.2015,,0,146.38, -44,2,27.03.2015,,0,146.38, -44,2,28.03.2015,,0,146.38, -44,2,29.03.2015,,0,146.38, -44,2,30.03.2015,,0,146.38, -44,2,31.03.2015,,3,146.38, -44,2,01.04.2015,,6,146.38, -44,2,02.04.2015,,0,146.38, -44,2,03.04.2015,,0,146.38, -44,2,04.04.2015,,3,146.38, -44,2,05.04.2015,,0,146.38, -44,2,06.04.2015,,0,146.38, -44,2,07.04.2015,,0,146.38, -44,2,08.04.2015,,0,146.38, -44,2,09.04.2015,,0,146.38, -44,2,10.04.2015,,3,146.38, -44,2,11.04.2015,,0,146.38, -44,2,12.04.2015,,0,146.38, -44,2,13.04.2015,,3,146.38, -44,2,14.04.2015,,0,146.38, -44,2,15.04.2015,,0,146.38, -44,2,16.04.2015,,0,146.38, -44,2,17.04.2015,,3,146.38, -44,2,18.04.2015,,0,146.38, -44,2,19.04.2015,,0,146.38, -44,2,20.04.2015,,0,146.38, -44,2,21.04.2015,,0,146.38, -44,2,22.04.2015,,0,146.38, -44,2,23.04.2015,,0,146.38, -44,2,24.04.2015,,0,146.38, -44,2,25.04.2015,,0,146.38, -44,2,26.04.2015,,0,146.38, -44,2,27.04.2015,,0,150.73, -44,2,28.04.2015,,0,150.73, -44,2,29.04.2015,,0,150.73, -44,2,30.04.2015,,0,150.73, -44,2,01.05.2015,,0,150.73, -44,2,02.05.2015,,0,150.73, -44,2,03.05.2015,,0,150.73, -44,2,04.05.2015,,0,150.73, -44,2,05.05.2015,,0,150.73, -44,2,06.05.2015,,0,150.73, -44,2,07.05.2015,,0,150.73, -44,2,08.05.2015,,0,150.73, -44,2,09.05.2015,,0,150.73, -44,2,10.05.2015,,0,150.73, -44,2,11.05.2015,,0,150.73, -44,2,12.05.2015,,3,150.73, -44,2,13.05.2015,,3,150.73, -44,2,14.05.2015,,0,150.73, -44,2,15.05.2015,,0,150.73, -44,2,16.05.2015,,3,150.73, -44,2,17.05.2015,,0,150.73, -44,2,18.05.2015,,3,150.73, -44,2,19.05.2015,,0,150.73, -44,2,20.05.2015,,0,150.73, -44,2,21.05.2015,,0,150.73, -44,2,22.05.2015,,0,150.73, -44,2,23.05.2015,,0,150.73, -44,2,24.05.2015,,0,150.73, -44,2,25.05.2015,,0,150.73, -44,2,26.05.2015,,0,150.73, -44,2,27.05.2015,,6,150.73, -44,2,28.05.2015,,3,150.73, -44,2,29.05.2015,,3,150.73, -44,2,30.05.2015,,3,150.73, -44,2,31.05.2015,,3,150.73, -44,2,01.06.2015,,0,150.73, -44,2,02.06.2015,,0,150.73, -44,2,03.06.2015,,0,150.73, -44,2,04.06.2015,,0,150.73, -44,2,05.06.2015,,0,150.73, -44,2,06.06.2015,,0,150.73, -44,2,07.06.2015,,3,150.73, -44,2,08.06.2015,,0,150.73, -44,2,09.06.2015,,3,150.73, -44,2,10.06.2015,,0,150.73, -44,2,11.06.2015,,0,150.73, -44,2,12.06.2015,,0,150.73, -44,2,13.06.2015,,0,150.73, -44,2,14.06.2015,,0,150.73, -44,2,15.06.2015,,0,150.73, -44,2,16.06.2015,,0,150.73, -44,2,17.06.2015,,0,150.73, -44,2,18.06.2015,,3,150.73, -44,2,19.06.2015,,0,150.73, -44,2,20.06.2015,,0,150.73, -44,2,21.06.2015,,0,150.73, -44,2,22.06.2015,,0,150.73, -44,2,23.06.2015,,0,150.73, -44,2,24.06.2015,,0,150.73, -44,2,25.06.2015,,0,150.73, -44,2,26.06.2015,,0,150.73, -44,2,27.06.2015,,0,150.73, -44,2,28.06.2015,,0,150.73, -44,2,29.06.2015,,0,150.73, -44,2,30.06.2015,,0,150.73, -44,2,01.07.2015,,0,150.73, -44,2,02.07.2015,,0,150.73, -44,2,03.07.2015,,0,150.73, -44,2,04.07.2015,,0,150.73, -44,2,05.07.2015,,0,150.73, -44,2,06.07.2015,,0,150.73, -44,2,07.07.2015,,0,150.73, -44,2,08.07.2015,,0,150.73, -44,2,09.07.2015,,0,150.73, -44,2,10.07.2015,,0,150.73, -44,2,11.07.2015,,3,150.73, -44,2,12.07.2015,,6,150.73, -44,2,13.07.2015,,0,150.73, -44,2,14.07.2015,,0,150.73, -44,2,15.07.2015,,0,150.73, -44,2,16.07.2015,,0,150.73, -44,2,17.07.2015,,0,150.73, -44,2,18.07.2015,,9,150.73, -44,2,19.07.2015,,0,150.73, -44,2,20.07.2015,,0,150.73, -44,2,21.07.2015,,0,150.73, -44,2,22.07.2015,,0,150.73, -44,2,23.07.2015,,0,150.73, -44,2,24.07.2015,,9,150.73, -44,2,25.07.2015,,13,150.73, -44,2,26.07.2015,,19,150.73, -44,2,27.07.2015,,22,150.73, -44,2,28.07.2015,,6,150.73, -44,2,29.07.2015,,16,150.73, -44,2,30.07.2015,,19,150.73, -44,2,31.07.2015,,28,150.73, -44,2,01.08.2015,,9,150.73, -44,2,02.08.2015,,0,150.73, -44,2,03.08.2015,,6,150.73, -44,2,04.08.2015,,0,150.73, -44,2,05.08.2015,,16,150.73, -44,2,06.08.2015,,9,150.73, -44,2,07.08.2015,,13,150.73, -44,2,08.08.2015,,13,150.73, -44,2,09.08.2015,,13,150.73, -44,2,10.08.2015,,13,150.73, -44,2,11.08.2015,,13,150.73, -44,2,12.08.2015,,13,128.17, -44,2,13.08.2015,,13,128.17, -44,2,14.08.2015,,13,128.17, -44,2,15.08.2015,,6,128.17, -44,2,16.08.2015,,6,128.17, -44,2,17.08.2015,,25,128.17, -44,2,18.08.2015,1.0,141,128.17,100.3 -44,2,19.08.2015,1.0,101,128.17,100.3 -44,2,20.08.2015,1.0,151,128.17,100.3 -44,2,21.08.2015,1.0,123,128.17,100.3 -44,2,22.08.2015,1.0,79,128.17,100.3 -44,2,23.08.2015,1.0,129,128.17,100.3 -44,2,24.08.2015,1.0,16,128.17,100.3 -44,2,25.08.2015,,0,128.17, -44,2,26.08.2015,,6,128.17, -44,2,27.08.2015,,9,128.17, -44,2,28.08.2015,,19,128.17, -44,2,29.08.2015,,19,128.17, -44,2,30.08.2015,,22,128.17, -44,2,31.08.2015,,47,128.17, -44,2,01.09.2015,,22,128.17, -44,2,02.09.2015,,3,128.17, -44,2,03.09.2015,,6,128.17, -44,2,04.09.2015,,3,128.17, -44,2,05.09.2015,,0,128.17, -44,2,06.09.2015,,0,128.17, -44,2,07.09.2015,,0,128.17, -44,2,08.09.2015,,0,128.17, -44,2,09.09.2015,,0,128.17, -44,2,10.09.2015,,0,128.17, -44,2,11.09.2015,,0,128.17, -44,2,12.09.2015,,0,128.17, -44,2,13.09.2015,,0,128.17, -44,2,14.09.2015,,0,128.17, -44,2,15.09.2015,,0,128.17, -44,2,16.09.2015,,0,128.17, -44,2,17.09.2015,,0,128.17, -44,2,18.09.2015,,0,128.17, -44,2,19.09.2015,,6,128.17, -44,2,20.09.2015,,9,128.17, -44,2,21.09.2015,,3,128.17, -44,2,22.09.2015,,0,128.17, -44,2,23.09.2015,,9,128.17, -44,2,24.09.2015,,9,128.17, -44,2,25.09.2015,,9,128.17, -44,2,26.09.2015,,16,128.17, -44,2,27.09.2015,,13,128.17, -44,2,28.09.2015,,13,128.17, -44,2,29.09.2015,,13,128.17, -44,2,30.09.2015,,3,128.17, -44,2,01.10.2015,,0,162.96, -44,2,02.10.2015,,0,162.96, -44,2,03.10.2015,,0,162.96, -44,2,04.10.2015,,0,162.96, -44,2,05.10.2015,,0,162.96, -44,2,06.10.2015,1.0,35,143.39,122.05 -44,2,07.10.2015,1.0,47,143.39,122.05 -44,2,08.10.2015,1.0,57,143.39,122.05 -44,2,09.10.2015,1.0,53,143.39,122.05 -44,2,10.10.2015,1.0,63,143.39,122.05 -44,2,11.10.2015,1.0,9,143.39,122.05 -44,2,12.10.2015,1.0,28,143.39,122.05 -44,2,13.10.2015,1.0,28,143.93,122.05 -44,2,14.10.2015,1.0,38,143.93,122.05 -44,2,15.10.2015,1.0,57,143.93,122.05 -44,2,16.10.2015,1.0,44,143.93,122.05 -44,2,17.10.2015,1.0,35,162.96,122.05 -44,2,18.10.2015,1.0,6,162.96,122.05 -44,2,19.10.2015,,3,162.96, -44,2,20.10.2015,,0,162.96, -44,2,21.10.2015,,6,162.96, -44,2,22.10.2015,,0,162.96, -44,2,23.10.2015,,0,162.96, -44,2,24.10.2015,,0,162.96, -44,2,25.10.2015,,3,162.96, -44,2,26.10.2015,,0,162.96, -44,2,27.10.2015,,0,162.96, -44,2,28.10.2015,,0,162.96, -44,2,29.10.2015,,0,162.96, -44,2,30.10.2015,,0,162.96, -44,2,31.10.2015,,0,162.96, -44,2,01.11.2015,,0,162.96, -44,2,02.11.2015,,0,162.96, -44,2,03.11.2015,,0,162.96, -44,2,04.11.2015,,0,162.96, -44,2,05.11.2015,,0,162.96, -44,2,06.11.2015,,0,162.96, -44,2,07.11.2015,,0,162.96, -44,2,08.11.2015,,0,162.96, -44,2,09.11.2015,,0,162.96, -44,2,10.11.2015,,0,162.96, -44,2,11.11.2015,,0,138.36, -44,2,12.11.2015,,0,138.36, -44,2,13.11.2015,,0,138.36, -44,2,14.11.2015,,0,138.36, -44,2,15.11.2015,,9,138.36, -44,2,16.11.2015,,0,138.36, -44,2,17.11.2015,,3,138.36, -44,2,18.11.2015,,3,138.36, -44,2,19.11.2015,,0,138.36, -44,2,20.11.2015,,3,138.36, -44,2,21.11.2015,,3,138.36, -44,2,22.11.2015,,0,138.36, -44,2,23.11.2015,,0,138.36, -44,2,24.11.2015,,0,138.36, -44,2,25.11.2015,,0,138.36, -44,2,26.11.2015,,0,138.36, -44,2,27.11.2015,,6,138.36, -44,2,28.11.2015,,0,138.36, -44,2,29.11.2015,,0,138.36, -44,2,30.11.2015,,3,138.36, -44,2,01.12.2015,,0,162.96, -44,2,02.12.2015,,0,162.96, -44,2,03.12.2015,,0,138.5, -44,2,04.12.2015,,3,138.5, -44,2,05.12.2015,,0,138.5, -44,2,06.12.2015,,0,138.5, -44,2,07.12.2015,,0,138.5, -44,2,08.12.2015,,0,138.5, -44,2,09.12.2015,,0,138.5, -44,2,10.12.2015,,0,138.5, -44,2,11.12.2015,,0,142.57, -44,2,12.12.2015,,0,142.57, -44,2,13.12.2015,,0,142.57, -44,2,14.12.2015,,0,142.57, -44,2,15.12.2015,,0,142.57, -44,2,16.12.2015,,0,142.57, -44,2,17.12.2015,,0,142.57, -44,2,18.12.2015,,0,142.57, -44,2,19.12.2015,,0,142.57, -44,2,20.12.2015,,0,142.57, -44,2,21.12.2015,,3,142.57, -44,2,22.12.2015,1.0,135,142.57,103.02 -44,2,23.12.2015,1.0,126,142.57,103.02 -44,2,24.12.2015,1.0,104,142.57,103.02 -44,2,25.12.2015,1.0,132,142.57,103.02 -44,2,26.12.2015,1.0,47,142.57,103.02 -44,2,27.12.2015,1.0,44,142.57,103.02 -44,2,28.12.2015,1.0,9,142.57,103.02 -44,2,29.12.2015,,0,138.5, -44,2,30.12.2015,,0,138.5, -44,2,31.12.2015,,0,138.5, -44,2,01.01.2016,,0,138.5, -44,2,02.01.2016,,0,138.5, -44,2,03.01.2016,,0,138.5, -44,2,04.01.2016,,0,138.5, -44,2,05.01.2016,,0,138.5, -44,2,06.01.2016,,0,138.5, -44,2,07.01.2016,,0,138.5, -44,2,08.01.2016,,0,138.5, -44,2,09.01.2016,,0,138.5, -44,2,10.01.2016,,9,138.5, -44,2,11.01.2016,,0,138.5, -44,2,12.01.2016,,6,138.5, -44,2,13.01.2016,,6,138.5, -44,2,14.01.2016,,0,138.5, -44,2,15.01.2016,,0,138.5, -44,2,16.01.2016,,9,142.57, -44,2,17.01.2016,,9,142.57, -44,2,18.01.2016,,25,142.57, -44,2,19.01.2016,1.0,116,142.57,103.02 -44,2,20.01.2016,1.0,132,142.57,103.02 -44,2,21.01.2016,1.0,119,142.57,103.02 -44,2,22.01.2016,1.0,195,142.57,103.02 -44,2,23.01.2016,1.0,145,142.57,103.02 -44,2,24.01.2016,1.0,173,142.57,103.02 -44,2,25.01.2016,1.0,160,142.57,103.02 -44,2,26.01.2016,,3,142.57, -44,2,27.01.2016,,6,142.57, -44,2,28.01.2016,,6,142.57, -44,2,29.01.2016,,9,142.57, -44,2,30.01.2016,,0,142.57, -44,2,31.01.2016,,6,142.57, -44,2,01.02.2016,,13,142.57, -44,2,02.02.2016,,16,142.57, -44,2,03.02.2016,,16,142.57, -44,2,04.02.2016,,9,142.57, -44,2,05.02.2016,,9,142.57, -44,2,06.02.2016,,13,142.57, -44,2,07.02.2016,,0,142.57, -44,2,08.02.2016,,9,142.57, -44,2,09.02.2016,1.0,104,142.57,108.46 -44,2,10.02.2016,1.0,123,138.36,108.46 -44,2,11.02.2016,1.0,188,138.36,108.46 -44,2,12.02.2016,1.0,141,138.36,108.46 -44,2,13.02.2016,1.0,13,138.36,108.46 -44,2,14.02.2016,1.0,185,138.36,108.46 -44,2,15.02.2016,1.0,160,138.36,108.46 -44,2,16.02.2016,,0,138.36, -44,2,17.02.2016,,6,138.36, -44,2,18.02.2016,,9,138.36, -44,2,19.02.2016,,9,138.36, -44,2,20.02.2016,,3,138.36, -44,2,21.02.2016,,6,138.36, -44,2,22.02.2016,,3,138.36, -44,2,23.02.2016,,6,138.36, -44,2,24.02.2016,,6,138.36, -44,2,25.02.2016,,6,138.36, -44,2,26.02.2016,,6,138.36, -44,2,27.02.2016,,6,138.36, -44,2,28.02.2016,,6,138.36, -44,2,29.02.2016,,6,138.36, -44,2,01.03.2016,,6,138.36, -44,2,02.03.2016,,6,138.36, -44,2,03.03.2016,,9,138.36, -44,2,04.03.2016,,3,138.36, -44,2,05.03.2016,,6,138.36, -44,2,06.03.2016,,6,138.36, -44,2,07.03.2016,,6,138.36, -44,2,08.03.2016,,6,138.36, -44,2,09.03.2016,,6,138.36, -44,2,10.03.2016,,3,138.36, -44,2,11.03.2016,,3,138.36, -44,2,12.03.2016,,6,138.5, -44,2,13.03.2016,,6,138.5, -44,2,14.03.2016,,13,138.5, -44,2,15.03.2016,1.0,50,138.5,124.77 -44,2,16.03.2016,1.0,41,138.5,124.77 -44,2,17.03.2016,1.0,41,138.5,124.77 -44,2,18.03.2016,1.0,28,138.5,124.77 -44,2,19.03.2016,1.0,85,138.5,124.77 -44,2,20.03.2016,1.0,57,138.5,124.77 -44,2,21.03.2016,1.0,110,138.5,124.77 -44,2,22.03.2016,,123,138.36, -44,2,23.03.2016,,19,138.36, -44,2,24.03.2016,,9,138.36, -44,2,25.03.2016,,16,138.36, -44,2,26.03.2016,,3,138.36, -44,2,27.03.2016,,16,138.36, -44,2,28.03.2016,,9,138.36, -44,2,29.03.2016,,6,138.36, -44,2,30.03.2016,,16,138.36, -44,2,31.03.2016,,16,138.36, -44,2,01.04.2016,,6,138.36, -44,2,02.04.2016,,6,138.36, -44,2,03.04.2016,,6,138.36, -44,2,04.04.2016,,6,138.36, -44,2,05.04.2016,,3,138.36, -44,2,06.04.2016,,6,138.36, -44,2,07.04.2016,,13,138.36, -44,2,08.04.2016,,6,138.36, -44,2,09.04.2016,,16,138.5, -44,2,10.04.2016,,3,138.5, -44,2,11.04.2016,,6,138.5, -44,2,12.04.2016,,6,138.5, -44,2,13.04.2016,,3,138.5, -44,2,14.04.2016,,3,138.5, -44,2,15.04.2016,,3,138.5, -44,2,16.04.2016,,6,138.5, -44,2,17.04.2016,,6,138.5, -44,2,18.04.2016,,3,138.5, -44,2,19.04.2016,1.0,101,138.5,116.89 -44,2,20.04.2016,1.0,75,138.5,116.89 -44,2,21.04.2016,1.0,47,138.5,116.89 -44,2,22.04.2016,1.0,79,138.5,116.89 -44,2,23.04.2016,1.0,82,138.5,116.89 -44,2,24.04.2016,1.0,113,138.5,116.89 -44,2,25.04.2016,1.0,94,138.5,116.89 -44,2,26.04.2016,,16,138.5, -44,2,27.04.2016,,13,138.5, -44,2,28.04.2016,,6,138.5, -44,2,29.04.2016,,3,138.5, -44,2,30.04.2016,,3,138.5, -44,2,01.05.2016,,6,138.5, -44,2,02.05.2016,,0,138.5, -44,2,03.05.2016,1.0,176,138.5,106.01 -44,2,04.05.2016,1.0,163,138.5,106.01 -44,2,05.05.2016,1.0,245,138.5,106.01 -44,2,06.05.2016,1.0,163,138.5,106.01 -44,2,07.05.2016,1.0,195,138.5,106.01 -44,2,08.05.2016,1.0,220,138.5,106.01 -44,2,09.05.2016,1.0,160,138.5,106.01 -44,2,10.05.2016,,35,138.5, -44,2,11.05.2016,,94,138.5, -44,2,12.05.2016,,13,138.5, -44,2,13.05.2016,,19,138.5, -44,2,14.05.2016,,9,138.5, -44,2,15.05.2016,,6,138.5, -44,2,16.05.2016,,3,138.5, -44,2,17.05.2016,,9,138.5, -44,2,18.05.2016,,3,138.5, -44,2,19.05.2016,,6,138.5, -44,2,20.05.2016,,3,138.5, -44,2,21.05.2016,,3,138.5, -44,2,22.05.2016,,3,138.5, -45,2,01.01.2015,,0,142.57, -45,2,02.01.2015,,3,142.57, -45,2,03.01.2015,,19,142.57, -45,2,04.01.2015,,0,142.57, -45,2,05.01.2015,,0,142.57, -45,2,06.01.2015,,0,142.57, -45,2,07.01.2015,,0,142.57, -45,2,08.01.2015,,0,142.57, -45,2,09.01.2015,,0,142.57, -45,2,10.01.2015,,0,142.57, -45,2,11.01.2015,,0,142.57, -45,2,12.01.2015,,0,142.57, -45,2,13.01.2015,,0,142.57, -45,2,14.01.2015,,0,142.57, -45,2,15.01.2015,,0,142.57, -45,2,16.01.2015,,0,142.57, -45,2,17.01.2015,,0,142.57, -45,2,18.01.2015,,0,142.57, -45,2,19.01.2015,,3,142.57, -45,2,20.01.2015,,0,142.57, -45,2,21.01.2015,,0,142.57, -45,2,22.01.2015,,0,142.57, -45,2,23.01.2015,,0,142.57, -45,2,24.01.2015,,0,142.57, -45,2,25.01.2015,,0,142.57, -45,2,26.01.2015,,0,142.57, -45,2,27.01.2015,,0,142.57, -45,2,28.01.2015,,0,142.57, -45,2,29.01.2015,,3,142.57, -45,2,30.01.2015,,0,142.57, -45,2,31.01.2015,,0,142.57, -45,2,01.02.2015,,0,142.57, -45,2,02.02.2015,,0,142.57, -45,2,03.02.2015,,0,142.57, -45,2,04.02.2015,,0,142.57, -45,2,05.02.2015,,0,142.57, -45,2,06.02.2015,,0,142.57, -45,2,07.02.2015,,0,142.57, -45,2,08.02.2015,,0,142.57, -45,2,09.02.2015,,0,142.57, -45,2,10.02.2015,,0,142.57, -45,2,11.02.2015,,0,142.57, -45,2,12.02.2015,,0,142.57, -45,2,13.02.2015,,0,142.57, -45,2,14.02.2015,,0,142.57, -45,2,15.02.2015,,0,142.57, -45,2,16.02.2015,,0,142.57, -45,2,17.02.2015,,0,142.57, -45,2,18.02.2015,,0,142.57, -45,2,19.02.2015,,0,142.57, -45,2,20.02.2015,,0,142.57, -45,2,21.02.2015,,0,142.57, -45,2,22.02.2015,,0,142.57, -45,2,23.02.2015,,0,142.57, -45,2,24.02.2015,,0,142.57, -45,2,25.02.2015,,0,142.57, -45,2,26.02.2015,,0,142.57, -45,2,27.02.2015,,0,142.57, -45,2,28.02.2015,,0,142.57, -45,2,01.03.2015,,0,142.57, -45,2,02.03.2015,,0,153.72, -45,2,03.03.2015,,0,153.72, -45,2,04.03.2015,,0,153.72, -45,2,05.03.2015,,0,153.72, -45,2,06.03.2015,,0,153.72, -45,2,07.03.2015,,0,153.72, -45,2,08.03.2015,,0,153.72, -45,2,09.03.2015,,0,153.72, -45,2,10.03.2015,,0,153.72, -45,2,11.03.2015,,0,153.72, -45,2,12.03.2015,,0,153.72, -45,2,13.03.2015,,0,153.72, -45,2,14.03.2015,,0,153.72, -45,2,15.03.2015,,0,153.72, -45,2,16.03.2015,,0,153.72, -45,2,17.03.2015,,0,153.72, -45,2,18.03.2015,,0,153.72, -45,2,19.03.2015,,0,153.72, -45,2,20.03.2015,,0,153.72, -45,2,21.03.2015,,0,153.72, -45,2,22.03.2015,,0,153.72, -45,2,23.03.2015,,0,153.72, -45,2,24.03.2015,,0,153.72, -45,2,25.03.2015,,0,153.72, -45,2,26.03.2015,,0,153.72, -45,2,27.03.2015,,3,153.72, -45,2,28.03.2015,,3,153.72, -45,2,29.03.2015,,0,153.72, -45,2,30.03.2015,,0,153.72, -45,2,31.03.2015,,3,153.72, -45,2,01.04.2015,,0,153.72, -45,2,02.04.2015,,0,153.72, -45,2,03.04.2015,,0,153.72, -45,2,04.04.2015,,0,153.72, -45,2,05.04.2015,,0,153.72, -45,2,06.04.2015,,0,153.72, -45,2,07.04.2015,,0,153.72, -45,2,08.04.2015,,0,153.72, -45,2,09.04.2015,,0,153.72, -45,2,10.04.2015,,0,153.72, -45,2,11.04.2015,,0,153.72, -45,2,12.04.2015,,0,153.72, -45,2,13.04.2015,,0,153.72, -45,2,14.04.2015,,0,153.72, -45,2,15.04.2015,,0,153.72, -45,2,16.04.2015,,0,153.72, -45,2,17.04.2015,,0,153.72, -45,2,18.04.2015,,0,153.72, -45,2,19.04.2015,,0,153.72, -45,2,20.04.2015,,0,153.72, -45,2,21.04.2015,,0,153.72, -45,2,22.04.2015,,0,153.72, -45,2,23.04.2015,,0,153.72, -45,2,24.04.2015,,0,153.72, -45,2,25.04.2015,,0,153.72, -45,2,26.04.2015,,0,153.72, -45,2,27.04.2015,,0,158.34, -45,2,28.04.2015,,0,158.34, -45,2,29.04.2015,,0,158.34, -45,2,30.04.2015,,0,158.34, -45,2,01.05.2015,,0,158.34, -45,2,02.05.2015,,0,158.34, -45,2,03.05.2015,,0,158.34, -45,2,04.05.2015,,0,158.34, -45,2,05.05.2015,,0,158.34, -45,2,06.05.2015,,0,158.34, -45,2,07.05.2015,,0,158.34, -45,2,08.05.2015,,0,158.34, -45,2,09.05.2015,,0,158.34, -45,2,10.05.2015,,0,158.34, -45,2,11.05.2015,,0,158.34, -45,2,12.05.2015,,0,158.34, -45,2,13.05.2015,,0,158.34, -45,2,14.05.2015,,0,158.34, -45,2,15.05.2015,,0,158.34, -45,2,16.05.2015,,0,158.34, -45,2,17.05.2015,,0,158.34, -45,2,18.05.2015,,0,158.34, -45,2,19.05.2015,,0,158.34, -45,2,20.05.2015,,0,158.34, -45,2,21.05.2015,,0,158.34, -45,2,22.05.2015,,0,158.34, -45,2,23.05.2015,,0,158.34, -45,2,24.05.2015,,0,158.34, -45,2,25.05.2015,,0,158.34, -45,2,26.05.2015,,0,158.34, -45,2,27.05.2015,,0,158.34, -45,2,28.05.2015,,0,158.34, -45,2,29.05.2015,,0,158.34, -45,2,30.05.2015,,0,158.34, -45,2,31.05.2015,,0,158.34, -45,2,01.06.2015,,0,158.34, -45,2,02.06.2015,,0,158.34, -45,2,03.06.2015,,0,158.34, -45,2,04.06.2015,,0,158.34, -45,2,05.06.2015,,0,158.34, -45,2,06.06.2015,,0,158.34, -45,2,07.06.2015,,0,158.34, -45,2,08.06.2015,,0,158.34, -45,2,09.06.2015,,0,158.34, -45,2,10.06.2015,,0,158.34, -45,2,11.06.2015,,0,158.34, -45,2,12.06.2015,,0,158.34, -45,2,13.06.2015,,0,158.34, -45,2,14.06.2015,,0,158.34, -45,2,15.06.2015,,0,158.34, -45,2,16.06.2015,,0,158.34, -45,2,17.06.2015,,0,158.34, -45,2,18.06.2015,,0,158.34, -45,2,19.06.2015,,0,158.34, -45,2,20.06.2015,,0,158.34, -45,2,21.06.2015,,0,158.34, -45,2,22.06.2015,,0,158.34, -45,2,23.06.2015,,0,158.34, -45,2,24.06.2015,,0,158.34, -45,2,25.06.2015,,0,158.34, -45,2,26.06.2015,,0,158.34, -45,2,27.06.2015,,0,158.34, -45,2,28.06.2015,,0,158.34, -45,2,29.06.2015,,0,158.34, -45,2,30.06.2015,,0,158.34, -45,2,01.07.2015,,0,158.34, -45,2,02.07.2015,,0,158.34, -45,2,03.07.2015,,0,158.34, -45,2,04.07.2015,,0,158.34, -45,2,05.07.2015,,0,158.34, -45,2,06.07.2015,,0,158.34, -45,2,07.07.2015,,0,158.34, -45,2,08.07.2015,,0,158.34, -45,2,09.07.2015,,0,158.34, -45,2,10.07.2015,,0,158.34, -45,2,11.07.2015,,0,158.34, -45,2,12.07.2015,,0,158.34, -45,2,13.07.2015,,0,158.34, -45,2,14.07.2015,,0,158.34, -45,2,15.07.2015,,0,158.34, -45,2,16.07.2015,,0,158.34, -45,2,17.07.2015,,0,158.34, -45,2,18.07.2015,,0,158.34, -45,2,19.07.2015,,0,158.34, -45,2,20.07.2015,,0,158.34, -45,2,21.07.2015,,0,158.34, -45,2,22.07.2015,,0,158.34, -45,2,23.07.2015,,0,158.34, -45,2,24.07.2015,,0,158.34, -45,2,25.07.2015,,0,158.34, -45,2,26.07.2015,,0,158.34, -45,2,27.07.2015,,0,158.34, -45,2,28.07.2015,,0,158.34, -45,2,29.07.2015,,0,158.34, -45,2,30.07.2015,,0,158.34, -45,2,31.07.2015,,0,158.34, -45,2,01.08.2015,,0,158.34, -45,2,02.08.2015,,0,158.34, -45,2,03.08.2015,,0,158.34, -45,2,04.08.2015,,0,158.34, -45,2,05.08.2015,,0,158.34, -45,2,06.08.2015,,0,158.34, -45,2,07.08.2015,,0,158.34, -45,2,08.08.2015,,0,158.34, -45,2,09.08.2015,,0,158.34, -45,2,10.08.2015,,0,158.34, -45,2,11.08.2015,,13,158.34, -45,2,12.08.2015,,0,128.17, -45,2,13.08.2015,,3,128.17, -45,2,14.08.2015,,3,128.17, -45,2,15.08.2015,,3,128.17, -45,2,16.08.2015,,3,128.17, -45,2,17.08.2015,,3,128.17, -45,2,18.08.2015,1.0,44,128.17,100.3 -45,2,19.08.2015,1.0,31,128.17,100.3 -45,2,20.08.2015,1.0,31,128.17,100.3 -45,2,21.08.2015,1.0,38,128.17,100.3 -45,2,22.08.2015,1.0,9,128.17,100.3 -45,2,23.08.2015,1.0,47,128.17,100.3 -45,2,24.08.2015,1.0,6,128.17,100.3 -45,2,25.08.2015,,9,128.17, -45,2,26.08.2015,,3,128.17, -45,2,27.08.2015,,28,128.17, -45,2,28.08.2015,,6,128.17, -45,2,29.08.2015,,13,128.17, -45,2,30.08.2015,,3,128.17, -45,2,31.08.2015,,0,128.17, -45,2,01.09.2015,,0,128.17, -45,2,02.09.2015,,0,128.17, -45,2,03.09.2015,,0,128.17, -45,2,04.09.2015,,6,128.17, -45,2,05.09.2015,,6,128.17, -45,2,06.09.2015,,6,128.17, -45,2,07.09.2015,,6,128.17, -45,2,08.09.2015,,3,128.17, -45,2,09.09.2015,,0,128.17, -45,2,10.09.2015,,0,128.17, -45,2,11.09.2015,,0,128.17, -45,2,12.09.2015,,0,128.17, -45,2,13.09.2015,,0,128.17, -45,2,14.09.2015,,3,128.17, -45,2,15.09.2015,,13,128.17, -45,2,16.09.2015,,3,128.17, -45,2,17.09.2015,,3,128.17, -45,2,18.09.2015,,3,128.17, -45,2,19.09.2015,,3,128.17, -45,2,20.09.2015,,3,128.17, -45,2,21.09.2015,,3,128.17, -45,2,22.09.2015,,9,128.17, -45,2,23.09.2015,,9,128.17, -45,2,24.09.2015,,3,128.17, -45,2,25.09.2015,,3,128.17, -45,2,26.09.2015,,3,128.17, -45,2,27.09.2015,,3,128.17, -45,2,28.09.2015,,3,128.17, -45,2,29.09.2015,,3,128.17, -45,2,30.09.2015,,16,128.17, -45,2,01.10.2015,,3,162.96, -45,2,02.10.2015,,3,162.96, -45,2,03.10.2015,,3,162.96, -45,2,04.10.2015,,3,162.96, -45,2,05.10.2015,,3,162.96, -45,2,06.10.2015,1.0,47,143.39,122.05 -45,2,07.10.2015,1.0,9,143.39,122.05 -45,2,08.10.2015,1.0,19,143.39,122.05 -45,2,09.10.2015,1.0,44,143.39,122.05 -45,2,10.10.2015,1.0,25,143.39,122.05 -45,2,11.10.2015,1.0,47,143.39,122.05 -45,2,12.10.2015,1.0,35,143.39,122.05 -45,2,13.10.2015,1.0,69,143.93,122.05 -45,2,14.10.2015,1.0,6,143.93,122.05 -45,2,15.10.2015,1.0,28,143.93,122.05 -45,2,16.10.2015,1.0,28,143.93,122.05 -45,2,17.10.2015,1.0,28,162.96,122.05 -45,2,18.10.2015,1.0,28,162.96,122.05 -45,2,19.10.2015,,3,162.96, -45,2,20.10.2015,,0,162.96, -45,2,21.10.2015,,0,162.96, -45,2,22.10.2015,,3,162.96, -45,2,23.10.2015,,0,162.96, -45,2,24.10.2015,,0,162.96, -45,2,25.10.2015,,0,162.96, -45,2,26.10.2015,,0,162.96, -45,2,27.10.2015,,6,162.96, -45,2,28.10.2015,,3,162.96, -45,2,29.10.2015,,3,162.96, -45,2,30.10.2015,,3,162.96, -45,2,31.10.2015,,3,162.96, -45,2,01.11.2015,,3,162.96, -45,2,02.11.2015,,3,162.96, -45,2,03.11.2015,,3,162.96, -45,2,04.11.2015,,0,162.96, -45,2,05.11.2015,,0,162.96, -45,2,06.11.2015,,0,162.96, -45,2,07.11.2015,,0,162.96, -45,2,08.11.2015,,0,162.96, -45,2,09.11.2015,,0,162.96, -45,2,10.11.2015,,0,162.96, -45,2,11.11.2015,,0,138.36, -45,2,12.11.2015,,0,138.36, -45,2,13.11.2015,,0,138.36, -45,2,14.11.2015,,0,138.36, -45,2,15.11.2015,,0,138.36, -45,2,16.11.2015,,0,138.36, -45,2,17.11.2015,,0,138.36, -45,2,18.11.2015,,0,138.36, -45,2,19.11.2015,,0,138.36, -45,2,20.11.2015,,0,138.36, -45,2,21.11.2015,,0,138.36, -45,2,22.11.2015,,0,138.36, -45,2,23.11.2015,,0,138.36, -45,2,24.11.2015,,0,138.36, -45,2,25.11.2015,,0,138.36, -45,2,26.11.2015,,0,138.36, -45,2,27.11.2015,,0,138.36, -45,2,28.11.2015,,0,138.36, -45,2,29.11.2015,,0,138.36, -45,2,30.11.2015,,0,138.36, -45,2,01.12.2015,,0,171.12, -45,2,02.12.2015,,0,171.12, -45,2,03.12.2015,,0,145.56, -45,2,04.12.2015,,0,145.56, -45,2,05.12.2015,,0,145.56, -45,2,06.12.2015,,0,145.56, -45,2,07.12.2015,,0,145.56, -45,2,08.12.2015,,0,145.56, -45,2,09.12.2015,,0,145.56, -45,2,10.12.2015,,0,145.56, -45,2,11.12.2015,,0,142.57, -45,2,12.12.2015,,0,142.57, -45,2,13.12.2015,,0,142.57, -45,2,14.12.2015,,0,142.57, -45,2,15.12.2015,,0,142.57, -45,2,16.12.2015,,0,142.57, -45,2,17.12.2015,,0,142.57, -45,2,18.12.2015,,0,142.57, -45,2,19.12.2015,,0,142.57, -45,2,20.12.2015,,0,142.57, -45,2,21.12.2015,,0,142.57, -45,2,22.12.2015,1.0,82,142.57,103.02 -45,2,23.12.2015,1.0,69,142.57,103.02 -45,2,24.12.2015,1.0,35,142.57,103.02 -45,2,25.12.2015,1.0,79,142.57,103.02 -45,2,26.12.2015,1.0,145,142.57,103.02 -45,2,27.12.2015,1.0,41,142.57,103.02 -45,2,28.12.2015,1.0,50,142.57,103.02 -45,2,29.12.2015,,0,145.56, -45,2,30.12.2015,,0,145.56, -45,2,31.12.2015,,0,145.56, -45,2,01.01.2016,,0,145.56, -45,2,02.01.2016,,0,145.56, -45,2,03.01.2016,,0,145.56, -45,2,04.01.2016,,0,145.56, -45,2,05.01.2016,,19,145.56, -45,2,06.01.2016,,0,145.56, -45,2,07.01.2016,,0,145.56, -45,2,08.01.2016,,0,145.56, -45,2,09.01.2016,,0,145.56, -45,2,10.01.2016,,0,145.56, -45,2,11.01.2016,,0,145.56, -45,2,12.01.2016,,0,145.56, -45,2,13.01.2016,,3,145.56, -45,2,14.01.2016,,3,145.56, -45,2,15.01.2016,,0,145.56, -45,2,16.01.2016,,6,142.57, -45,2,17.01.2016,,9,142.57, -45,2,18.01.2016,,9,142.57, -45,2,19.01.2016,1.0,119,142.57,103.02 -45,2,20.01.2016,1.0,72,142.57,103.02 -45,2,21.01.2016,1.0,60,142.57,103.02 -45,2,22.01.2016,1.0,104,142.57,103.02 -45,2,23.01.2016,1.0,132,142.57,103.02 -45,2,24.01.2016,1.0,135,142.57,103.02 -45,2,25.01.2016,1.0,79,142.57,103.02 -45,2,26.01.2016,,19,142.57, -45,2,27.01.2016,,28,142.57, -45,2,28.01.2016,,31,142.57, -45,2,29.01.2016,,9,142.57, -45,2,30.01.2016,,9,142.57, -45,2,31.01.2016,,9,142.57, -45,2,01.02.2016,,9,142.57, -45,2,02.02.2016,,9,142.57, -45,2,03.02.2016,,28,142.57, -45,2,04.02.2016,,3,142.57, -45,2,05.02.2016,,0,142.57, -45,2,06.02.2016,,6,142.57, -45,2,07.02.2016,,16,142.57, -45,2,08.02.2016,,3,142.57, -45,2,09.02.2016,1.0,163,142.57,108.46 -45,2,10.02.2016,1.0,97,138.36,108.46 -45,2,11.02.2016,1.0,126,138.36,108.46 -45,2,12.02.2016,1.0,104,138.36,108.46 -45,2,13.02.2016,1.0,101,138.36,108.46 -45,2,14.02.2016,1.0,94,138.36,108.46 -45,2,15.02.2016,1.0,157,138.36,108.46 -45,2,16.02.2016,,13,138.36, -45,2,17.02.2016,,3,138.36, -45,2,18.02.2016,,13,138.36, -45,2,19.02.2016,,13,138.36, -45,2,20.02.2016,,13,138.36, -45,2,21.02.2016,,13,138.36, -45,2,22.02.2016,,13,138.36, -45,2,23.02.2016,,13,138.36, -45,2,24.02.2016,,13,138.36, -45,2,25.02.2016,,13,138.36, -45,2,26.02.2016,,13,138.36, -45,2,27.02.2016,,13,138.36, -45,2,28.02.2016,,13,138.36, -45,2,29.02.2016,,13,138.36, -45,2,01.03.2016,,13,138.36, -45,2,02.03.2016,,13,138.36, -45,2,03.03.2016,,13,138.36, -45,2,04.03.2016,,13,138.36, -45,2,05.03.2016,,13,138.36, -45,2,06.03.2016,,13,138.36, -45,2,07.03.2016,,13,138.36, -45,2,08.03.2016,,13,138.36, -45,2,09.03.2016,,13,138.36, -45,2,10.03.2016,,13,138.36, -45,2,11.03.2016,,13,138.36, -45,2,12.03.2016,,0,138.5, -45,2,13.03.2016,,0,138.5, -45,2,14.03.2016,,0,138.5, -45,2,15.03.2016,1.0,110,138.5,124.77 -45,2,16.03.2016,1.0,107,138.5,124.77 -45,2,17.03.2016,1.0,97,138.5,124.77 -45,2,18.03.2016,1.0,94,138.5,124.77 -45,2,19.03.2016,1.0,44,138.5,124.77 -45,2,20.03.2016,1.0,88,138.5,124.77 -45,2,21.03.2016,1.0,16,138.5,124.77 -45,2,22.03.2016,,0,138.36, -45,2,23.03.2016,,3,138.36, -45,2,24.03.2016,,0,138.36, -45,2,25.03.2016,,0,138.36, -45,2,26.03.2016,,3,138.36, -45,2,27.03.2016,,0,138.36, -45,2,28.03.2016,,0,138.36, -45,2,29.03.2016,,0,138.36, -45,2,30.03.2016,,3,138.36, -45,2,31.03.2016,,3,138.36, -45,2,01.04.2016,,3,138.36, -45,2,02.04.2016,,3,138.36, -45,2,03.04.2016,,3,138.36, -45,2,04.04.2016,,3,138.36, -45,2,05.04.2016,,3,138.36, -45,2,06.04.2016,,6,138.36, -45,2,07.04.2016,,3,138.36, -45,2,08.04.2016,,0,138.36, -45,2,09.04.2016,,6,138.5, -45,2,10.04.2016,,3,138.5, -45,2,11.04.2016,,3,138.5, -45,2,12.04.2016,,6,138.5, -45,2,13.04.2016,,13,138.5, -45,2,14.04.2016,,19,138.5, -45,2,15.04.2016,,31,138.5, -45,2,16.04.2016,,6,138.5, -45,2,17.04.2016,,6,138.5, -45,2,18.04.2016,,6,138.5, -45,2,19.04.2016,1.0,31,138.5,116.89 -45,2,20.04.2016,1.0,88,138.5,116.89 -45,2,21.04.2016,1.0,38,138.5,116.89 -45,2,22.04.2016,1.0,57,138.5,116.89 -45,2,23.04.2016,1.0,57,138.5,116.89 -45,2,24.04.2016,1.0,57,138.5,116.89 -45,2,25.04.2016,1.0,53,138.5,116.89 -45,2,26.04.2016,1.0,47,138.5,114.17 -45,2,27.04.2016,1.0,28,138.5,114.17 -45,2,28.04.2016,,3,138.5, -45,2,29.04.2016,,0,138.5, -45,2,30.04.2016,,66,138.5, -45,2,01.05.2016,,13,138.5, -45,2,02.05.2016,,0,138.5, -45,2,03.05.2016,1.0,63,138.5,106.01 -45,2,04.05.2016,1.0,123,138.5,106.01 -45,2,05.05.2016,1.0,82,138.5,106.01 -45,2,06.05.2016,1.0,97,138.5,106.01 -45,2,07.05.2016,1.0,113,138.5,106.01 -45,2,08.05.2016,1.0,119,138.5,106.01 -45,2,09.05.2016,1.0,60,138.5,106.01 -45,2,10.05.2016,,0,145.56, -45,2,11.05.2016,,6,145.56, -45,2,12.05.2016,,63,145.56, -45,2,13.05.2016,,47,145.56, -45,2,14.05.2016,,19,145.56, -45,2,15.05.2016,,6,145.56, -45,2,16.05.2016,,19,145.56, -45,2,17.05.2016,,0,145.56, -45,2,18.05.2016,,3,145.56, -45,2,19.05.2016,,3,145.56, -45,2,20.05.2016,,3,145.56, -45,2,21.05.2016,,6,145.56, -45,2,22.05.2016,,3,145.56, -46,2,01.01.2015,,0,135.78, -46,2,02.01.2015,,6,135.78, -46,2,03.01.2015,,3,135.78, -46,2,04.01.2015,,0,135.78, -46,2,05.01.2015,,0,135.78, -46,2,06.01.2015,,3,135.78, -46,2,07.01.2015,,3,135.78, -46,2,08.01.2015,,0,135.78, -46,2,09.01.2015,,0,135.78, -46,2,10.01.2015,,6,135.78, -46,2,11.01.2015,,6,135.78, -46,2,12.01.2015,,3,135.78, -46,2,13.01.2015,,6,135.78, -46,2,14.01.2015,,6,135.78, -46,2,15.01.2015,,3,135.78, -46,2,16.01.2015,,19,135.78, -46,2,17.01.2015,,28,135.78, -46,2,18.01.2015,,0,135.78, -46,2,19.01.2015,,6,135.78, -46,2,20.01.2015,,3,135.78, -46,2,21.01.2015,,0,135.78, -46,2,22.01.2015,,9,135.78, -46,2,23.01.2015,,6,135.78, -46,2,24.01.2015,,0,135.78, -46,2,25.01.2015,,0,135.78, -46,2,26.01.2015,,6,135.78, -46,2,27.01.2015,,19,135.78, -46,2,28.01.2015,,0,135.78, -46,2,29.01.2015,,0,135.78, -46,2,30.01.2015,,0,135.78, -46,2,31.01.2015,,0,135.78, -46,2,01.02.2015,,6,135.78, -46,2,02.02.2015,,6,135.78, -46,2,03.02.2015,,9,135.78, -46,2,04.02.2015,,0,135.78, -46,2,05.02.2015,,6,135.78, -46,2,06.02.2015,,3,135.78, -46,2,07.02.2015,,6,135.78, -46,2,08.02.2015,,3,135.78, -46,2,09.02.2015,,3,135.78, -46,2,10.02.2015,,0,135.78, -46,2,11.02.2015,,3,135.78, -46,2,12.02.2015,,3,135.78, -46,2,13.02.2015,,6,135.78, -46,2,14.02.2015,,6,135.78, -46,2,15.02.2015,,6,135.78, -46,2,16.02.2015,,6,135.78, -46,2,17.02.2015,,6,135.78, -46,2,18.02.2015,,3,135.78, -46,2,19.02.2015,,6,135.78, -46,2,20.02.2015,,3,135.78, -46,2,21.02.2015,,13,135.78, -46,2,22.02.2015,,19,135.78, -46,2,23.02.2015,,0,135.78, -46,2,24.02.2015,,0,135.78, -46,2,25.02.2015,,0,135.78, -46,2,26.02.2015,,0,135.78, -46,2,27.02.2015,,0,135.78, -46,2,28.02.2015,,3,135.78, -46,2,01.03.2015,,6,135.78, -46,2,02.03.2015,,3,135.78, -46,2,03.03.2015,,0,135.78, -46,2,04.03.2015,,9,135.78, -46,2,05.03.2015,,0,135.78, -46,2,06.03.2015,,3,135.78, -46,2,07.03.2015,,6,135.78, -46,2,08.03.2015,,0,146.38, -46,2,09.03.2015,,3,146.38, -46,2,10.03.2015,,3,146.38, -46,2,11.03.2015,,6,146.38, -46,2,12.03.2015,,0,146.38, -46,2,13.03.2015,,0,146.38, -46,2,14.03.2015,,0,146.38, -46,2,15.03.2015,,16,146.38, -46,2,16.03.2015,,0,146.38, -46,2,17.03.2015,,3,146.38, -46,2,18.03.2015,,0,146.38, -46,2,19.03.2015,,0,146.38, -46,2,20.03.2015,,0,146.38, 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-46,2,10.09.2015,,0,128.17, -46,2,11.09.2015,,0,128.17, -46,2,12.09.2015,,0,128.17, -46,2,13.09.2015,,0,128.17, -46,2,14.09.2015,,0,128.17, -46,2,15.09.2015,,13,128.17, -46,2,16.09.2015,,6,128.17, -46,2,17.09.2015,,6,128.17, -46,2,18.09.2015,,13,167.85, -46,2,19.09.2015,,19,167.85, -46,2,20.09.2015,,6,167.85, -46,2,21.09.2015,,6,167.85, -46,2,22.09.2015,,6,167.85, -46,2,23.09.2015,,6,167.85, -46,2,24.09.2015,,6,167.85, -46,2,25.09.2015,,6,167.85, -46,2,26.09.2015,,6,167.85, -46,2,27.09.2015,,6,167.85, -46,2,28.09.2015,,6,167.85, -46,2,29.09.2015,,6,167.85, -46,2,30.09.2015,,6,167.85, -46,2,01.10.2015,,0,162.96, -46,2,02.10.2015,,0,162.96, -46,2,03.10.2015,,0,162.96, -46,2,04.10.2015,,0,162.96, -46,2,05.10.2015,,0,162.96, -46,2,06.10.2015,1.0,135,143.39,122.05 -46,2,07.10.2015,1.0,53,143.39,122.05 -46,2,08.10.2015,1.0,94,143.39,122.05 -46,2,09.10.2015,1.0,104,143.39,122.05 -46,2,10.10.2015,1.0,19,143.39,122.05 -46,2,11.10.2015,1.0,104,143.39,122.05 -46,2,12.10.2015,1.0,104,143.39,122.05 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-46,2,17.11.2015,,0,138.36, -46,2,18.11.2015,,0,138.36, -46,2,19.11.2015,,0,138.36, -46,2,20.11.2015,,0,138.36, -46,2,21.11.2015,,0,138.36, -46,2,22.11.2015,,0,138.36, -46,2,23.11.2015,,0,138.36, -46,2,24.11.2015,,0,138.36, -46,2,25.11.2015,,0,138.36, -46,2,26.11.2015,,0,138.36, -46,2,27.11.2015,,0,138.36, -46,2,28.11.2015,,0,138.36, -46,2,29.11.2015,,0,138.36, -46,2,30.11.2015,,0,138.36, -46,2,01.12.2015,,0,162.96, -46,2,02.12.2015,,0,162.96, -46,2,03.12.2015,,0,138.5, -46,2,04.12.2015,,0,138.5, -46,2,05.12.2015,,0,138.5, -46,2,06.12.2015,,0,138.5, -46,2,07.12.2015,,0,138.5, -46,2,08.12.2015,,0,138.5, -46,2,09.12.2015,,0,138.5, -46,2,10.12.2015,,0,138.5, -46,2,11.12.2015,,0,142.57, -46,2,12.12.2015,,0,142.57, -46,2,13.12.2015,,0,142.57, -46,2,14.12.2015,,0,142.57, -46,2,15.12.2015,,0,142.57, -46,2,16.12.2015,,0,142.57, -46,2,17.12.2015,,6,142.57, -46,2,18.12.2015,,3,142.57, -46,2,19.12.2015,,25,142.57, -46,2,20.12.2015,,3,142.57, -46,2,21.12.2015,,13,142.57, -46,2,22.12.2015,1.0,314,142.57,103.02 -46,2,23.12.2015,1.0,141,142.57,103.02 -46,2,24.12.2015,1.0,119,142.57,103.02 -46,2,25.12.2015,1.0,160,142.57,103.02 -46,2,26.12.2015,1.0,126,142.57,103.02 -46,2,27.12.2015,1.0,88,142.57,103.02 -46,2,28.12.2015,1.0,94,142.57,103.02 -46,2,29.12.2015,,0,138.5, -46,2,30.12.2015,,0,138.5, -46,2,31.12.2015,,0,138.5, -46,2,01.01.2016,,0,138.5, -46,2,02.01.2016,,9,138.5, -46,2,03.01.2016,,9,138.5, -46,2,04.01.2016,,0,138.5, -46,2,05.01.2016,,0,138.5, -46,2,06.01.2016,,9,138.5, -46,2,07.01.2016,,9,138.5, -46,2,08.01.2016,,0,138.5, -46,2,09.01.2016,,3,138.5, -46,2,10.01.2016,,6,138.5, -46,2,11.01.2016,,6,138.5, -46,2,12.01.2016,,3,138.5, -46,2,13.01.2016,,0,138.5, -46,2,14.01.2016,,0,138.5, -46,2,15.01.2016,,0,138.5, -46,2,16.01.2016,,3,142.57, -46,2,17.01.2016,,6,142.57, -46,2,18.01.2016,,28,142.57, -46,2,19.01.2016,1.0,210,142.57,103.02 -46,2,20.01.2016,1.0,198,142.57,103.02 -46,2,21.01.2016,1.0,188,142.57,103.02 -46,2,22.01.2016,1.0,154,142.57,103.02 -46,2,23.01.2016,1.0,160,142.57,103.02 -46,2,24.01.2016,1.0,173,142.57,103.02 -46,2,25.01.2016,1.0,28,142.57,103.02 -46,2,26.01.2016,,0,142.57, -46,2,27.01.2016,,0,142.57, -46,2,28.01.2016,,13,142.57, -46,2,29.01.2016,,6,142.57, -46,2,30.01.2016,,3,142.57, -46,2,31.01.2016,,3,142.57, -46,2,01.02.2016,,3,142.57, -46,2,02.02.2016,,9,142.57, -46,2,03.02.2016,,0,142.57, -46,2,04.02.2016,,0,142.57, -46,2,05.02.2016,,0,142.57, -46,2,06.02.2016,,9,142.57, -46,2,07.02.2016,,3,142.57, -46,2,08.02.2016,,19,142.57, -46,2,09.02.2016,1.0,195,142.57,108.46 -46,2,10.02.2016,1.0,132,138.36,108.46 -46,2,11.02.2016,1.0,201,138.36,108.46 -46,2,12.02.2016,1.0,157,138.36,108.46 -46,2,13.02.2016,1.0,94,138.36,108.46 -46,2,14.02.2016,1.0,116,138.36,108.46 -46,2,15.02.2016,1.0,72,138.36,108.46 -46,2,16.02.2016,,3,138.36, -46,2,17.02.2016,,6,138.36, -46,2,18.02.2016,,0,138.36, -46,2,19.02.2016,,0,138.36, -46,2,20.02.2016,,22,138.36, -46,2,21.02.2016,,3,138.36, -46,2,22.02.2016,,0,138.36, -46,2,23.02.2016,,3,138.36, -46,2,24.02.2016,,0,138.36, -46,2,25.02.2016,,0,138.36, -46,2,26.02.2016,,3,138.36, -46,2,27.02.2016,,3,138.36, -46,2,28.02.2016,,3,138.36, -46,2,29.02.2016,,3,138.36, -46,2,01.03.2016,,3,138.36, -46,2,02.03.2016,,6,138.36, -46,2,03.03.2016,,0,138.36, -46,2,04.03.2016,,0,138.36, -46,2,05.03.2016,,3,138.36, -46,2,06.03.2016,,3,138.36, -46,2,07.03.2016,,3,138.36, -46,2,08.03.2016,,3,138.36, -46,2,09.03.2016,,9,138.36, -46,2,10.03.2016,,3,138.36, -46,2,11.03.2016,,3,138.36, -46,2,12.03.2016,,3,138.5, -46,2,13.03.2016,,3,138.5, -46,2,14.03.2016,,22,138.5, -46,2,15.03.2016,1.0,110,138.5,124.77 -46,2,16.03.2016,1.0,60,138.5,124.77 -46,2,17.03.2016,1.0,72,138.5,124.77 -46,2,18.03.2016,1.0,53,138.5,124.77 -46,2,19.03.2016,1.0,79,138.5,124.77 -46,2,20.03.2016,1.0,44,138.5,124.77 -46,2,21.03.2016,1.0,31,138.5,124.77 -46,2,22.03.2016,,6,138.36, -46,2,23.03.2016,,6,138.36, -46,2,24.03.2016,,3,138.36, -46,2,25.03.2016,,3,138.36, -46,2,26.03.2016,,9,138.36, -46,2,27.03.2016,,3,138.36, -46,2,28.03.2016,,6,138.36, -46,2,29.03.2016,,3,138.36, -46,2,30.03.2016,,3,138.36, -46,2,31.03.2016,,6,138.36, -46,2,01.04.2016,,6,138.36, -46,2,02.04.2016,,3,138.36, -46,2,03.04.2016,,6,138.36, -46,2,04.04.2016,,6,138.36, -46,2,05.04.2016,,6,138.36, -46,2,06.04.2016,,6,138.36, -46,2,07.04.2016,,3,138.36, -46,2,08.04.2016,,6,138.36, -46,2,09.04.2016,,6,138.5, -46,2,10.04.2016,,3,138.5, -46,2,11.04.2016,,6,138.5, -46,2,12.04.2016,,6,138.5, -46,2,13.04.2016,,6,138.5, -46,2,14.04.2016,,6,138.5, -46,2,15.04.2016,,6,138.5, -46,2,16.04.2016,,6,138.5, -46,2,17.04.2016,,6,138.5, -46,2,18.04.2016,,35,138.5, -46,2,19.04.2016,1.0,151,138.5,116.89 -46,2,20.04.2016,1.0,113,138.5,116.89 -46,2,21.04.2016,1.0,107,138.5,116.89 -46,2,22.04.2016,1.0,163,138.5,116.89 -46,2,23.04.2016,1.0,116,138.5,116.89 -46,2,24.04.2016,1.0,88,138.5,116.89 -46,2,25.04.2016,1.0,57,138.5,116.89 -46,2,26.04.2016,,9,138.5, -46,2,27.04.2016,,9,138.5, -46,2,28.04.2016,,0,138.5, -46,2,29.04.2016,,9,138.5, -46,2,30.04.2016,,0,138.5, -46,2,01.05.2016,,6,138.5, -46,2,02.05.2016,,25,138.5, -46,2,03.05.2016,1.0,201,138.5,106.01 -46,2,04.05.2016,1.0,179,138.5,106.01 -46,2,05.05.2016,1.0,195,138.5,106.01 -46,2,06.05.2016,1.0,195,138.5,106.01 -46,2,07.05.2016,1.0,28,138.5,106.01 -46,2,08.05.2016,1.0,132,138.5,106.01 -46,2,09.05.2016,1.0,129,138.5,106.01 -46,2,10.05.2016,,28,138.5, -46,2,11.05.2016,,0,138.5, -46,2,12.05.2016,,22,138.5, -46,2,13.05.2016,,9,138.5, -46,2,14.05.2016,,9,138.5, -46,2,15.05.2016,,9,138.5, -46,2,16.05.2016,,9,138.5, -46,2,17.05.2016,,6,138.5, -46,2,18.05.2016,,0,138.5, -46,2,19.05.2016,,6,138.5, -46,2,20.05.2016,,3,138.5, -46,2,21.05.2016,,3,138.5, -46,2,22.05.2016,,6,138.5, -47,2,01.01.2015,,0,142.57, -47,2,02.01.2015,,3,142.57, -47,2,03.01.2015,,6,142.57, -47,2,04.01.2015,,35,142.57, -47,2,05.01.2015,,9,142.57, -47,2,06.01.2015,,0,142.57, -47,2,07.01.2015,,0,142.57, -47,2,08.01.2015,,0,142.57, -47,2,09.01.2015,,0,142.57, -47,2,10.01.2015,,16,142.57, -47,2,11.01.2015,,9,142.57, -47,2,12.01.2015,,0,142.57, -47,2,13.01.2015,,0,142.57, -47,2,14.01.2015,,0,142.57, -47,2,15.01.2015,,0,142.57, -47,2,16.01.2015,,0,142.57, -47,2,17.01.2015,,0,142.57, -47,2,18.01.2015,,6,142.57, -47,2,19.01.2015,,0,142.57, -47,2,20.01.2015,,0,142.57, -47,2,21.01.2015,,0,142.57, -47,2,22.01.2015,,6,142.57, -47,2,23.01.2015,,0,142.57, -47,2,24.01.2015,,6,142.57, -47,2,25.01.2015,,0,142.57, -47,2,26.01.2015,,0,142.57, -47,2,27.01.2015,,6,142.57, -47,2,28.01.2015,,0,142.57, -47,2,29.01.2015,,3,142.57, -47,2,30.01.2015,,35,142.57, -47,2,31.01.2015,,0,142.57, -47,2,01.02.2015,,0,142.57, -47,2,02.02.2015,,0,142.57, -47,2,03.02.2015,,0,142.57, -47,2,04.02.2015,,9,142.57, -47,2,05.02.2015,,0,142.57, -47,2,06.02.2015,,0,142.57, -47,2,07.02.2015,,0,142.57, -47,2,08.02.2015,,6,142.57, -47,2,09.02.2015,,0,142.57, -47,2,10.02.2015,,0,142.57, -47,2,11.02.2015,,0,142.57, -47,2,12.02.2015,,3,142.57, -47,2,13.02.2015,,0,142.57, 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-47,2,04.07.2015,,0,158.34, -47,2,05.07.2015,,0,158.34, -47,2,06.07.2015,,0,158.34, -47,2,07.07.2015,,3,158.34, -47,2,08.07.2015,,0,158.34, -47,2,09.07.2015,,0,158.34, -47,2,10.07.2015,,0,158.34, -47,2,11.07.2015,,0,158.34, -47,2,12.07.2015,,0,158.34, -47,2,13.07.2015,,0,158.34, -47,2,14.07.2015,,0,158.34, -47,2,15.07.2015,,0,158.34, -47,2,16.07.2015,,3,158.34, -47,2,17.07.2015,,0,158.34, -47,2,18.07.2015,,0,158.34, -47,2,19.07.2015,,0,158.34, -47,2,20.07.2015,,0,158.34, -47,2,21.07.2015,,0,158.34, -47,2,22.07.2015,,25,158.34, -47,2,23.07.2015,,0,158.34, -47,2,24.07.2015,,0,158.34, -47,2,25.07.2015,,0,158.34, -47,2,26.07.2015,,25,158.34, -47,2,27.07.2015,,0,158.34, -47,2,28.07.2015,,0,158.34, -47,2,29.07.2015,,13,158.34, -47,2,30.07.2015,,13,158.34, -47,2,31.07.2015,,6,158.34, -47,2,01.08.2015,,6,158.34, -47,2,02.08.2015,,6,158.34, -47,2,03.08.2015,,6,158.34, -47,2,04.08.2015,,6,158.34, -47,2,05.08.2015,,6,158.34, -47,2,06.08.2015,,6,158.34, -47,2,07.08.2015,,0,158.34, -47,2,08.08.2015,,25,158.34, -47,2,09.08.2015,,6,158.34, -47,2,10.08.2015,,6,158.34, -47,2,11.08.2015,,3,158.34, -47,2,12.08.2015,,3,128.17, -47,2,13.08.2015,,6,128.17, -47,2,14.08.2015,,6,128.17, -47,2,15.08.2015,,9,128.17, -47,2,16.08.2015,,9,128.17, -47,2,17.08.2015,,13,128.17, -47,2,18.08.2015,1.0,6,128.17,100.3 -47,2,19.08.2015,1.0,9,128.17,100.3 -47,2,20.08.2015,1.0,101,128.17,100.3 -47,2,21.08.2015,1.0,129,128.17,100.3 -47,2,22.08.2015,1.0,132,128.17,100.3 -47,2,23.08.2015,1.0,126,128.17,100.3 -47,2,24.08.2015,1.0,69,128.17,100.3 -47,2,25.08.2015,,22,128.17, -47,2,26.08.2015,,25,128.17, -47,2,27.08.2015,,28,128.17, -47,2,28.08.2015,,72,128.17, -47,2,29.08.2015,,25,128.17, -47,2,30.08.2015,,31,128.17, -47,2,31.08.2015,,25,128.17, -47,2,01.09.2015,,3,128.17, -47,2,02.09.2015,,19,128.17, -47,2,03.09.2015,,19,128.17, -47,2,04.09.2015,,3,128.17, -47,2,05.09.2015,,6,128.17, -47,2,06.09.2015,,9,128.17, -47,2,07.09.2015,,19,128.17, -47,2,08.09.2015,,3,128.17, -47,2,09.09.2015,,9,128.17, -47,2,10.09.2015,,6,128.17, -47,2,11.09.2015,,38,128.17, -47,2,12.09.2015,,38,128.17, -47,2,13.09.2015,,38,128.17, -47,2,14.09.2015,,22,128.17, -47,2,15.09.2015,,13,128.17, -47,2,16.09.2015,,13,128.17, -47,2,17.09.2015,,3,128.17, -47,2,18.09.2015,,28,128.17, -47,2,19.09.2015,,16,128.17, -47,2,20.09.2015,,22,128.17, -47,2,21.09.2015,,25,128.17, -47,2,22.09.2015,,35,128.17, -47,2,23.09.2015,,19,128.17, -47,2,24.09.2015,,35,128.17, -47,2,25.09.2015,,31,128.17, -47,2,26.09.2015,,25,128.17, -47,2,27.09.2015,,25,128.17, -47,2,28.09.2015,,25,128.17, -47,2,29.09.2015,,3,128.17, -47,2,30.09.2015,,3,128.17, -47,2,01.10.2015,,19,162.96, -47,2,02.10.2015,,16,162.96, -47,2,03.10.2015,,6,162.96, -47,2,04.10.2015,,3,162.96, -47,2,05.10.2015,,6,162.96, -47,2,06.10.2015,1.0,107,143.39,122.05 -47,2,07.10.2015,1.0,63,143.39,122.05 -47,2,08.10.2015,1.0,41,143.39,122.05 -47,2,09.10.2015,1.0,72,143.39,122.05 -47,2,10.10.2015,1.0,60,143.39,122.05 -47,2,11.10.2015,1.0,41,143.39,122.05 -47,2,12.10.2015,1.0,66,143.39,122.05 -47,2,13.10.2015,1.0,9,143.93,122.05 -47,2,14.10.2015,1.0,25,143.93,122.05 -47,2,15.10.2015,1.0,47,143.93,122.05 -47,2,16.10.2015,1.0,44,143.93,122.05 -47,2,17.10.2015,1.0,22,162.96,122.05 -47,2,18.10.2015,1.0,60,162.96,122.05 -47,2,19.10.2015,,16,162.96, -47,2,20.10.2015,,3,162.96, -47,2,21.10.2015,,0,162.96, -47,2,22.10.2015,,3,162.96, -47,2,23.10.2015,,0,162.96, -47,2,24.10.2015,,0,162.96, -47,2,25.10.2015,,0,162.96, -47,2,26.10.2015,,0,162.96, -47,2,27.10.2015,,0,162.96, -47,2,28.10.2015,,0,162.96, -47,2,29.10.2015,,0,162.96, -47,2,30.10.2015,,0,162.96, -47,2,31.10.2015,,0,162.96, -47,2,01.11.2015,,0,162.96, -47,2,02.11.2015,,0,162.96, -47,2,03.11.2015,,0,162.96, -47,2,04.11.2015,,0,162.96, -47,2,05.11.2015,,0,162.96, -47,2,06.11.2015,,0,162.96, -47,2,07.11.2015,,16,162.96, -47,2,08.11.2015,,3,162.96, -47,2,09.11.2015,,0,162.96, -47,2,10.11.2015,,0,162.96, -47,2,11.11.2015,,0,138.36, -47,2,12.11.2015,,0,138.36, -47,2,13.11.2015,,3,138.36, -47,2,14.11.2015,,0,138.36, -47,2,15.11.2015,,3,138.36, -47,2,16.11.2015,,3,138.36, -47,2,17.11.2015,,0,138.36, -47,2,18.11.2015,,0,138.36, -47,2,19.11.2015,,0,138.36, -47,2,20.11.2015,,0,138.36, -47,2,21.11.2015,,0,138.36, -47,2,22.11.2015,,0,138.36, -47,2,23.11.2015,,0,138.36, -47,2,24.11.2015,,0,138.36, -47,2,25.11.2015,,6,138.36, -47,2,26.11.2015,,3,138.36, -47,2,27.11.2015,,3,138.36, -47,2,28.11.2015,,0,138.36, -47,2,29.11.2015,,3,138.36, -47,2,30.11.2015,,3,138.36, -47,2,01.12.2015,,0,162.96, -47,2,02.12.2015,,0,162.96, -47,2,03.12.2015,,0,138.5, -47,2,04.12.2015,,0,138.5, -47,2,05.12.2015,,0,138.5, -47,2,06.12.2015,,0,138.5, -47,2,07.12.2015,,0,138.5, -47,2,08.12.2015,,0,138.5, -47,2,09.12.2015,,0,138.5, -47,2,10.12.2015,,0,138.5, -47,2,11.12.2015,,0,142.57, -47,2,12.12.2015,,0,142.57, -47,2,13.12.2015,,0,142.57, -47,2,14.12.2015,,0,142.57, -47,2,15.12.2015,,0,142.57, -47,2,16.12.2015,,0,142.57, -47,2,17.12.2015,,0,142.57, -47,2,18.12.2015,,0,142.57, -47,2,19.12.2015,,6,142.57, -47,2,20.12.2015,,0,142.57, -47,2,21.12.2015,,6,142.57, -47,2,22.12.2015,1.0,63,142.57,103.02 -47,2,23.12.2015,1.0,119,142.57,103.02 -47,2,24.12.2015,1.0,104,142.57,103.02 -47,2,25.12.2015,1.0,72,142.57,103.02 -47,2,26.12.2015,1.0,69,142.57,103.02 -47,2,27.12.2015,1.0,60,142.57,103.02 -47,2,28.12.2015,1.0,57,142.57,103.02 -47,2,29.12.2015,,3,138.5, -47,2,30.12.2015,,9,138.5, -47,2,31.12.2015,,3,138.5, -47,2,01.01.2016,,3,138.5, -47,2,02.01.2016,,9,138.5, -47,2,03.01.2016,,0,138.5, -47,2,04.01.2016,,16,138.5, -47,2,05.01.2016,,28,138.5, -47,2,06.01.2016,,9,138.5, -47,2,07.01.2016,,0,138.5, -47,2,08.01.2016,,0,138.5, -47,2,09.01.2016,,0,138.5, -47,2,10.01.2016,,0,138.5, -47,2,11.01.2016,,0,138.5, -47,2,12.01.2016,,6,138.5, -47,2,13.01.2016,,3,138.5, -47,2,14.01.2016,,0,138.5, -47,2,15.01.2016,,0,138.5, -47,2,16.01.2016,,0,142.57, -47,2,17.01.2016,,9,142.57, -47,2,18.01.2016,,3,142.57, -47,2,19.01.2016,1.0,126,142.57,103.02 -47,2,20.01.2016,1.0,72,142.57,103.02 -47,2,21.01.2016,1.0,126,142.57,103.02 -47,2,22.01.2016,1.0,160,142.57,103.02 -47,2,23.01.2016,1.0,79,142.57,103.02 -47,2,24.01.2016,1.0,82,142.57,103.02 -47,2,25.01.2016,1.0,79,142.57,103.02 -47,2,26.01.2016,,0,142.57, -47,2,27.01.2016,,9,142.57, -47,2,28.01.2016,,6,142.57, -47,2,29.01.2016,,0,142.57, -47,2,30.01.2016,,13,142.57, -47,2,31.01.2016,,0,142.57, -47,2,01.02.2016,,6,142.57, -47,2,02.02.2016,,0,142.57, -47,2,03.02.2016,,0,142.57, -47,2,04.02.2016,,3,142.57, -47,2,05.02.2016,,0,142.57, -47,2,06.02.2016,,6,142.57, -47,2,07.02.2016,,9,142.57, -47,2,08.02.2016,,13,142.57, -47,2,09.02.2016,1.0,75,142.57,108.46 -47,2,10.02.2016,1.0,97,138.36,108.46 -47,2,11.02.2016,1.0,138,138.36,108.46 -47,2,12.02.2016,1.0,151,138.36,108.46 -47,2,13.02.2016,1.0,104,138.36,108.46 -47,2,14.02.2016,1.0,151,138.36,108.46 -47,2,15.02.2016,1.0,85,138.36,108.46 -47,2,16.02.2016,,0,138.36, -47,2,17.02.2016,,0,138.36, -47,2,18.02.2016,,0,138.36, -47,2,19.02.2016,,0,138.36, -47,2,20.02.2016,,0,138.36, -47,2,21.02.2016,,0,138.36, -47,2,22.02.2016,,0,138.36, -47,2,23.02.2016,,3,138.36, -47,2,24.02.2016,,0,138.36, -47,2,25.02.2016,,0,138.36, -47,2,26.02.2016,,0,138.36, -47,2,27.02.2016,,0,138.36, -47,2,28.02.2016,,0,138.36, -47,2,29.02.2016,,0,138.36, -47,2,01.03.2016,,0,138.36, -47,2,02.03.2016,,0,138.36, -47,2,03.03.2016,,0,138.36, -47,2,04.03.2016,,0,138.36, -47,2,05.03.2016,,0,138.36, -47,2,06.03.2016,,0,138.36, -47,2,07.03.2016,,0,138.36, -47,2,08.03.2016,,0,138.36, -47,2,09.03.2016,,0,138.36, -47,2,10.03.2016,,0,138.36, -47,2,11.03.2016,,0,138.36, -47,2,12.03.2016,,0,138.5, -47,2,13.03.2016,,0,138.5, -47,2,14.03.2016,,0,138.5, -47,2,15.03.2016,1.0,13,138.5,124.77 -47,2,16.03.2016,1.0,28,138.5,124.77 -47,2,17.03.2016,1.0,94,138.5,124.77 -47,2,18.03.2016,1.0,25,138.5,124.77 -47,2,19.03.2016,1.0,44,138.5,124.77 -47,2,20.03.2016,1.0,66,138.5,124.77 -47,2,21.03.2016,1.0,107,138.5,124.77 -47,2,22.03.2016,,0,138.36, -47,2,23.03.2016,,0,138.36, -47,2,24.03.2016,,0,138.36, -47,2,25.03.2016,,0,138.36, -47,2,26.03.2016,,0,138.36, -47,2,27.03.2016,,0,138.36, -47,2,28.03.2016,,0,138.36, -47,2,29.03.2016,,0,138.36, -47,2,30.03.2016,,0,138.36, -47,2,31.03.2016,,0,138.36, -47,2,01.04.2016,,0,138.36, -47,2,02.04.2016,,3,138.36, -47,2,03.04.2016,,0,138.36, -47,2,04.04.2016,,0,138.36, -47,2,05.04.2016,,0,138.36, -47,2,06.04.2016,,0,138.36, -47,2,07.04.2016,,0,138.36, -47,2,08.04.2016,,0,138.36, -47,2,09.04.2016,,9,138.5, -47,2,10.04.2016,,6,138.5, -47,2,11.04.2016,,0,138.5, -47,2,12.04.2016,,0,138.5, -47,2,13.04.2016,,3,138.5, -47,2,14.04.2016,,6,138.5, -47,2,15.04.2016,,3,138.5, -47,2,16.04.2016,,3,138.5, -47,2,17.04.2016,,0,138.5, -47,2,18.04.2016,,3,138.5, -47,2,19.04.2016,1.0,41,138.5,116.89 -47,2,20.04.2016,1.0,110,138.5,116.89 -47,2,21.04.2016,1.0,69,138.5,116.89 -47,2,22.04.2016,1.0,75,138.5,116.89 -47,2,23.04.2016,1.0,44,138.5,116.89 -47,2,24.04.2016,1.0,75,138.5,116.89 -47,2,25.04.2016,1.0,38,138.5,116.89 -47,2,26.04.2016,,0,138.5, -47,2,27.04.2016,,0,138.5, -47,2,28.04.2016,,0,138.5, -47,2,29.04.2016,,3,138.5, -47,2,30.04.2016,,0,138.5, -47,2,01.05.2016,,0,138.5, -47,2,02.05.2016,,0,138.5, -47,2,03.05.2016,1.0,9,138.5,106.01 -47,2,04.05.2016,1.0,97,138.5,106.01 -47,2,05.05.2016,1.0,188,138.5,106.01 -47,2,06.05.2016,1.0,113,138.5,106.01 -47,2,07.05.2016,1.0,107,138.5,106.01 -47,2,08.05.2016,1.0,192,138.5,106.01 -47,2,09.05.2016,1.0,47,138.5,106.01 -47,2,10.05.2016,,0,138.5, -47,2,11.05.2016,,3,138.5, -47,2,12.05.2016,,3,138.5, -47,2,13.05.2016,,3,138.5, -47,2,14.05.2016,,3,138.5, -47,2,15.05.2016,,3,138.5, -47,2,16.05.2016,,3,138.5, -47,2,17.05.2016,,3,138.5, -47,2,18.05.2016,,3,138.5, -47,2,19.05.2016,,3,138.5, -47,2,20.05.2016,,3,138.5, -47,2,21.05.2016,,3,138.5, -47,2,22.05.2016,,3,138.5, -48,2,01.01.2015,,0,135.78, -48,2,02.01.2015,,28,135.78, -48,2,03.01.2015,,50,135.78, -48,2,04.01.2015,,85,135.78, -48,2,05.01.2015,,19,135.78, -48,2,06.01.2015,,6,135.78, -48,2,07.01.2015,,19,135.78, -48,2,08.01.2015,,6,135.78, -48,2,09.01.2015,,44,135.78, -48,2,10.01.2015,,3,135.78, -48,2,11.01.2015,,25,135.78, -48,2,12.01.2015,,22,135.78, -48,2,13.01.2015,,19,135.78, -48,2,14.01.2015,,6,135.78, -48,2,15.01.2015,,3,135.78, -48,2,16.01.2015,,3,135.78, -48,2,17.01.2015,,16,135.78, -48,2,18.01.2015,,9,135.78, -48,2,19.01.2015,,0,135.78, -48,2,20.01.2015,,19,135.78, -48,2,21.01.2015,,22,135.78, -48,2,22.01.2015,,6,135.78, -48,2,23.01.2015,,3,135.78, -48,2,24.01.2015,,0,135.78, -48,2,25.01.2015,,0,135.78, -48,2,26.01.2015,,19,135.78, -48,2,27.01.2015,,0,135.78, -48,2,28.01.2015,,0,135.78, -48,2,29.01.2015,,3,135.78, -48,2,30.01.2015,,0,135.78, -48,2,31.01.2015,,6,135.78, -48,2,01.02.2015,,9,135.78, -48,2,02.02.2015,,31,135.78, -48,2,03.02.2015,,25,135.78, -48,2,04.02.2015,,9,135.78, -48,2,05.02.2015,,16,135.78, -48,2,06.02.2015,,9,135.78, -48,2,07.02.2015,,13,135.78, -48,2,08.02.2015,,50,135.78, -48,2,09.02.2015,,22,135.78, -48,2,10.02.2015,,31,135.78, -48,2,11.02.2015,,16,135.78, -48,2,12.02.2015,,16,135.78, -48,2,13.02.2015,,9,135.78, -48,2,14.02.2015,,6,135.78, -48,2,15.02.2015,,3,135.78, -48,2,16.02.2015,,16,135.78, -48,2,17.02.2015,,13,135.78, -48,2,18.02.2015,,13,135.78, -48,2,19.02.2015,,9,135.78, -48,2,20.02.2015,,6,135.78, -48,2,21.02.2015,,6,135.78, -48,2,22.02.2015,,6,135.78, -48,2,23.02.2015,,0,135.78, -48,2,24.02.2015,,0,135.78, -48,2,25.02.2015,,0,135.78, -48,2,26.02.2015,,3,146.38, -48,2,27.02.2015,,3,146.38, -48,2,28.02.2015,,0,146.38, -48,2,01.03.2015,,0,146.38, -48,2,02.03.2015,,9,146.38, -48,2,03.03.2015,,3,146.38, -48,2,04.03.2015,,0,146.38, -48,2,05.03.2015,,0,146.38, -48,2,06.03.2015,,6,146.38, -48,2,07.03.2015,,3,146.38, -48,2,08.03.2015,,0,146.38, -48,2,09.03.2015,,3,146.38, -48,2,10.03.2015,,0,146.38, -48,2,11.03.2015,,0,146.38, -48,2,12.03.2015,,0,146.38, -48,2,13.03.2015,,0,146.38, -48,2,14.03.2015,,0,146.38, -48,2,15.03.2015,,0,146.38, -48,2,16.03.2015,,0,146.38, -48,2,17.03.2015,,6,146.38, -48,2,18.03.2015,,0,146.38, -48,2,19.03.2015,,3,146.38, -48,2,20.03.2015,,0,146.38, -48,2,21.03.2015,,0,146.38, -48,2,22.03.2015,,0,146.38, -48,2,23.03.2015,,0,146.38, -48,2,24.03.2015,,0,146.38, -48,2,25.03.2015,,0,146.38, -48,2,26.03.2015,,0,146.38, -48,2,27.03.2015,,0,146.38, -48,2,28.03.2015,,0,146.38, -48,2,29.03.2015,,0,146.38, -48,2,30.03.2015,,0,146.38, -48,2,31.03.2015,,0,146.38, -48,2,01.04.2015,,0,146.38, -48,2,02.04.2015,,0,146.38, -48,2,03.04.2015,,0,146.38, -48,2,04.04.2015,,0,146.38, -48,2,05.04.2015,,0,146.38, -48,2,06.04.2015,,0,146.38, -48,2,07.04.2015,,0,146.38, -48,2,08.04.2015,,0,146.38, -48,2,09.04.2015,,0,146.38, -48,2,10.04.2015,,0,146.38, -48,2,11.04.2015,,0,146.38, -48,2,12.04.2015,,3,146.38, -48,2,13.04.2015,,9,145.84, -48,2,14.04.2015,,0,145.84, -48,2,15.04.2015,,0,145.84, -48,2,16.04.2015,,6,145.84, -48,2,17.04.2015,,0,145.84, -48,2,18.04.2015,,0,145.84, -48,2,19.04.2015,,0,145.84, -48,2,20.04.2015,,0,145.84, -48,2,21.04.2015,,0,145.84, -48,2,22.04.2015,,0,145.84, -48,2,23.04.2015,,0,145.84, -48,2,24.04.2015,,0,145.84, -48,2,25.04.2015,,6,145.84, -48,2,26.04.2015,,3,150.73, -48,2,27.04.2015,,3,150.73, -48,2,28.04.2015,,0,150.73, -48,2,29.04.2015,,0,150.73, -48,2,30.04.2015,,6,150.73, -48,2,01.05.2015,,0,150.73, -48,2,02.05.2015,,0,150.73, -48,2,03.05.2015,,13,150.73, -48,2,04.05.2015,,6,150.73, -48,2,05.05.2015,,0,150.73, -48,2,06.05.2015,,0,150.73, -48,2,07.05.2015,,3,150.73, -48,2,08.05.2015,,3,150.73, -48,2,09.05.2015,,0,150.73, -48,2,10.05.2015,,0,150.73, -48,2,11.05.2015,,9,150.73, -48,2,12.05.2015,,6,150.73, -48,2,13.05.2015,,0,150.73, -48,2,14.05.2015,,0,150.73, -48,2,15.05.2015,,0,150.73, -48,2,16.05.2015,,0,150.73, -48,2,17.05.2015,,0,150.73, -48,2,18.05.2015,,3,150.73, -48,2,19.05.2015,,0,150.73, -48,2,20.05.2015,,0,150.73, -48,2,21.05.2015,,3,150.73, -48,2,22.05.2015,,3,150.73, -48,2,23.05.2015,,0,150.73, -48,2,24.05.2015,,0,150.73, -48,2,25.05.2015,,3,150.73, -48,2,26.05.2015,,3,150.73, -48,2,27.05.2015,,3,150.73, -48,2,28.05.2015,,3,150.73, -48,2,29.05.2015,,3,150.73, -48,2,30.05.2015,,0,150.73, -48,2,31.05.2015,,0,150.73, -48,2,01.06.2015,,0,150.73, -48,2,02.06.2015,,3,150.73, -48,2,03.06.2015,,3,150.73, -48,2,04.06.2015,,0,150.73, -48,2,05.06.2015,,0,150.73, -48,2,06.06.2015,,3,150.73, -48,2,07.06.2015,,3,150.73, -48,2,08.06.2015,,6,150.73, -48,2,09.06.2015,,3,150.73, -48,2,10.06.2015,,3,150.73, -48,2,11.06.2015,,0,150.73, -48,2,12.06.2015,,0,150.73, -48,2,13.06.2015,,3,150.73, -48,2,14.06.2015,,0,150.73, -48,2,15.06.2015,,0,150.73, -48,2,16.06.2015,,0,150.73, -48,2,17.06.2015,,3,150.73, -48,2,18.06.2015,,3,150.73, -48,2,19.06.2015,,0,150.73, -48,2,20.06.2015,,0,150.73, -48,2,21.06.2015,,0,150.73, -48,2,22.06.2015,,3,150.73, -48,2,23.06.2015,,13,150.73, -48,2,24.06.2015,,13,150.73, -48,2,25.06.2015,,0,150.73, -48,2,26.06.2015,,0,150.73, -48,2,27.06.2015,,0,150.73, -48,2,28.06.2015,,0,150.73, -48,2,29.06.2015,,0,150.73, -48,2,30.06.2015,,0,150.73, -48,2,01.07.2015,,0,150.73, -48,2,02.07.2015,,0,150.73, -48,2,03.07.2015,,0,150.73, -48,2,04.07.2015,,0,150.73, -48,2,05.07.2015,,6,150.73, -48,2,06.07.2015,,13,150.73, -48,2,07.07.2015,,0,150.73, -48,2,08.07.2015,,0,150.73, -48,2,09.07.2015,,0,150.73, -48,2,10.07.2015,,3,150.73, -48,2,11.07.2015,,0,150.73, -48,2,12.07.2015,,0,150.73, -48,2,13.07.2015,,3,150.73, -48,2,14.07.2015,,3,150.73, -48,2,15.07.2015,,6,150.73, -48,2,16.07.2015,,0,150.73, -48,2,17.07.2015,,3,150.73, -48,2,18.07.2015,,3,150.73, -48,2,19.07.2015,,9,150.73, -48,2,20.07.2015,,3,150.73, -48,2,21.07.2015,,0,150.73, -48,2,22.07.2015,,9,150.73, -48,2,23.07.2015,,9,150.73, -48,2,24.07.2015,,6,150.73, -48,2,25.07.2015,,9,150.73, -48,2,26.07.2015,,6,150.73, -48,2,27.07.2015,,0,150.73, -48,2,28.07.2015,,25,150.73, -48,2,29.07.2015,,0,150.73, -48,2,30.07.2015,,13,150.73, -48,2,31.07.2015,,13,150.73, -48,2,01.08.2015,,0,150.73, -48,2,02.08.2015,,0,150.73, -48,2,03.08.2015,,0,150.73, -48,2,04.08.2015,,25,150.73, -48,2,05.08.2015,,0,150.73, -48,2,06.08.2015,,0,150.73, -48,2,07.08.2015,,22,150.73, -48,2,08.08.2015,,3,150.73, -48,2,09.08.2015,,0,150.73, -48,2,10.08.2015,,3,150.73, -48,2,11.08.2015,,3,150.73, -48,2,12.08.2015,,9,128.17, -48,2,13.08.2015,,25,128.17, -48,2,14.08.2015,,9,128.17, -48,2,15.08.2015,,0,128.17, -48,2,16.08.2015,,0,128.17, -48,2,17.08.2015,,13,128.17, -48,2,18.08.2015,1.0,198,128.17,100.3 -48,2,19.08.2015,1.0,132,128.17,100.3 -48,2,20.08.2015,1.0,75,128.17,100.3 -48,2,21.08.2015,1.0,113,128.17,100.3 -48,2,22.08.2015,1.0,107,128.17,100.3 -48,2,23.08.2015,1.0,16,128.17,100.3 -48,2,24.08.2015,1.0,72,128.17,100.3 -48,2,25.08.2015,,0,128.17, -48,2,26.08.2015,,0,128.17, -48,2,27.08.2015,,3,128.17, -48,2,28.08.2015,,22,128.17, -48,2,29.08.2015,,0,128.17, -48,2,30.08.2015,,0,128.17, -48,2,31.08.2015,,0,128.17, -48,2,01.09.2015,,6,128.17, -48,2,02.09.2015,,6,128.17, -48,2,03.09.2015,,6,128.17, -48,2,04.09.2015,,13,128.17, -48,2,05.09.2015,,6,128.17, -48,2,06.09.2015,,6,128.17, -48,2,07.09.2015,,6,128.17, -48,2,08.09.2015,,6,128.17, -48,2,09.09.2015,,6,128.17, -48,2,10.09.2015,,3,128.17, -48,2,11.09.2015,,16,128.17, -48,2,12.09.2015,,6,128.17, -48,2,13.09.2015,,6,128.17, -48,2,14.09.2015,,6,128.17, -48,2,15.09.2015,,6,128.17, -48,2,16.09.2015,,3,128.17, -48,2,17.09.2015,,3,128.17, -48,2,18.09.2015,,3,128.17, -48,2,19.09.2015,,6,128.17, -48,2,20.09.2015,,6,128.17, -48,2,21.09.2015,,6,128.17, -48,2,22.09.2015,,13,128.17, -48,2,23.09.2015,,9,128.17, -48,2,24.09.2015,,6,128.17, -48,2,25.09.2015,,6,128.17, -48,2,26.09.2015,,6,128.17, -48,2,27.09.2015,,6,128.17, -48,2,28.09.2015,,6,128.17, -48,2,29.09.2015,,9,128.17, -48,2,30.09.2015,,3,128.17, -48,2,01.10.2015,,6,162.96, -48,2,02.10.2015,,3,162.96, -48,2,03.10.2015,,6,162.96, -48,2,04.10.2015,,3,162.96, -48,2,05.10.2015,,0,162.96, -48,2,06.10.2015,1.0,57,143.39,122.05 -48,2,07.10.2015,1.0,75,143.39,122.05 -48,2,08.10.2015,1.0,85,143.39,122.05 -48,2,09.10.2015,1.0,57,143.39,122.05 -48,2,10.10.2015,1.0,25,143.39,122.05 -48,2,11.10.2015,1.0,57,143.39,122.05 -48,2,12.10.2015,1.0,50,143.39,122.05 -48,2,13.10.2015,,3,143.93, -48,2,14.10.2015,,6,143.93, -48,2,15.10.2015,,6,143.93, -48,2,16.10.2015,,6,143.93, -48,2,17.10.2015,,3,162.96, -48,2,18.10.2015,,3,162.96, -48,2,19.10.2015,,9,162.96, -48,2,20.10.2015,,3,162.96, -48,2,21.10.2015,,0,162.96, -48,2,22.10.2015,,3,162.96, -48,2,23.10.2015,,0,162.96, -48,2,24.10.2015,,13,162.96, -48,2,25.10.2015,,13,162.96, -48,2,26.10.2015,,0,162.96, -48,2,27.10.2015,,3,162.96, -48,2,28.10.2015,,3,162.96, -48,2,29.10.2015,,0,162.96, -48,2,30.10.2015,,0,162.96, -48,2,31.10.2015,,6,162.96, -48,2,01.11.2015,,3,162.96, -48,2,02.11.2015,,3,162.96, -48,2,03.11.2015,,0,162.96, -48,2,04.11.2015,,25,162.96, -48,2,05.11.2015,,3,162.96, -48,2,06.11.2015,,3,162.96, -48,2,07.11.2015,,0,162.96, -48,2,08.11.2015,,0,162.96, -48,2,09.11.2015,,6,162.96, -48,2,10.11.2015,,0,162.96, -48,2,11.11.2015,,9,138.36, -48,2,12.11.2015,,9,138.36, -48,2,13.11.2015,,0,138.36, -48,2,14.11.2015,,6,138.36, -48,2,15.11.2015,,3,138.36, -48,2,16.11.2015,,3,138.36, -48,2,17.11.2015,,3,138.36, -48,2,18.11.2015,,6,138.36, -48,2,19.11.2015,,0,138.36, -48,2,20.11.2015,,0,138.36, -48,2,21.11.2015,,0,138.36, -48,2,22.11.2015,,3,138.36, -48,2,23.11.2015,,3,138.36, -48,2,24.11.2015,,0,138.36, -48,2,25.11.2015,,3,138.36, -48,2,26.11.2015,,3,138.36, -48,2,27.11.2015,,16,138.36, -48,2,28.11.2015,,6,138.36, -48,2,29.11.2015,,6,138.36, -48,2,30.11.2015,,0,138.36, -48,2,01.12.2015,,0,162.96, -48,2,02.12.2015,,0,162.96, -48,2,03.12.2015,,6,138.5, -48,2,04.12.2015,,0,138.5, -48,2,05.12.2015,,0,138.5, -48,2,06.12.2015,,13,138.5, -48,2,07.12.2015,,3,138.5, -48,2,08.12.2015,,0,138.5, -48,2,09.12.2015,,9,138.5, -48,2,10.12.2015,,13,138.5, -48,2,11.12.2015,,13,142.57, -48,2,12.12.2015,,6,142.57, -48,2,13.12.2015,,6,142.57, -48,2,14.12.2015,,0,142.57, -48,2,15.12.2015,,9,142.57, -48,2,16.12.2015,,9,142.57, -48,2,17.12.2015,,0,142.57, -48,2,18.12.2015,,0,142.57, -48,2,19.12.2015,,6,142.57, -48,2,20.12.2015,,13,142.57, -48,2,21.12.2015,,0,142.57, -48,2,22.12.2015,1.0,176,142.57,103.02 -48,2,23.12.2015,1.0,69,142.57,103.02 -48,2,24.12.2015,1.0,28,142.57,103.02 -48,2,25.12.2015,1.0,85,142.57,103.02 -48,2,26.12.2015,1.0,82,142.57,103.02 -48,2,27.12.2015,1.0,50,142.57,103.02 -48,2,28.12.2015,1.0,63,142.57,103.02 -48,2,29.12.2015,,6,138.5, -48,2,30.12.2015,,6,138.5, -48,2,31.12.2015,,6,138.5, -48,2,01.01.2016,,6,138.5, -48,2,02.01.2016,,6,138.5, -48,2,03.01.2016,,6,138.5, -48,2,04.01.2016,,19,138.5, -48,2,05.01.2016,,6,138.5, -48,2,06.01.2016,,6,138.5, -48,2,07.01.2016,,6,138.5, -48,2,08.01.2016,,13,138.5, -48,2,09.01.2016,,9,138.5, -48,2,10.01.2016,,9,138.5, -48,2,11.01.2016,,19,138.5, -48,2,12.01.2016,,9,138.5, -48,2,13.01.2016,,9,138.5, -48,2,14.01.2016,,3,138.5, -48,2,15.01.2016,,6,138.5, -48,2,16.01.2016,,6,142.57, -48,2,17.01.2016,,3,142.57, -48,2,18.01.2016,,6,142.57, -48,2,19.01.2016,1.0,104,142.57,103.02 -48,2,20.01.2016,1.0,151,142.57,103.02 -48,2,21.01.2016,1.0,82,142.57,103.02 -48,2,22.01.2016,1.0,110,142.57,103.02 -48,2,23.01.2016,1.0,57,142.57,103.02 -48,2,24.01.2016,1.0,82,142.57,103.02 -48,2,25.01.2016,1.0,13,142.57,103.02 -48,2,26.01.2016,,3,142.57, -48,2,27.01.2016,,3,142.57, -48,2,28.01.2016,,3,142.57, -48,2,29.01.2016,,3,142.57, -48,2,30.01.2016,,6,142.57, -48,2,31.01.2016,,6,142.57, -48,2,01.02.2016,,13,142.57, -48,2,02.02.2016,,6,142.57, -48,2,03.02.2016,,6,142.57, -48,2,04.02.2016,,6,142.57, -48,2,05.02.2016,,13,142.57, -48,2,06.02.2016,,3,142.57, -48,2,07.02.2016,,3,142.57, -48,2,08.02.2016,,3,142.57, -48,2,09.02.2016,1.0,66,142.57,108.46 -48,2,10.02.2016,1.0,82,138.36,108.46 -48,2,11.02.2016,1.0,94,138.36,108.46 -48,2,12.02.2016,1.0,44,138.36,108.46 -48,2,13.02.2016,1.0,82,138.36,108.46 -48,2,14.02.2016,1.0,72,138.36,108.46 -48,2,15.02.2016,1.0,13,138.36,108.46 -48,2,16.02.2016,,3,138.36, -48,2,17.02.2016,,13,138.36, -48,2,18.02.2016,,16,138.36, -48,2,19.02.2016,,9,138.36, -48,2,20.02.2016,,3,138.36, -48,2,21.02.2016,,19,138.36, -48,2,22.02.2016,,9,138.36, -48,2,23.02.2016,,9,138.36, -48,2,24.02.2016,,9,138.36, -48,2,25.02.2016,,9,138.36, -48,2,26.02.2016,,9,138.36, -48,2,27.02.2016,,9,138.36, -48,2,28.02.2016,,9,138.36, -48,2,29.02.2016,,9,138.36, -48,2,01.03.2016,,9,138.36, -48,2,02.03.2016,,3,138.36, -48,2,03.03.2016,,9,138.36, -48,2,04.03.2016,,6,138.36, -48,2,05.03.2016,,9,138.36, -48,2,06.03.2016,,3,138.36, -48,2,07.03.2016,,6,138.36, -48,2,08.03.2016,,6,138.36, -48,2,09.03.2016,,6,138.36, -48,2,10.03.2016,,6,138.36, -48,2,11.03.2016,,6,138.36, -48,2,12.03.2016,,6,138.5, -48,2,13.03.2016,,6,138.5, -48,2,14.03.2016,,16,138.5, -48,2,15.03.2016,1.0,82,138.5,124.77 -48,2,16.03.2016,1.0,63,138.5,124.77 -48,2,17.03.2016,1.0,82,138.5,124.77 -48,2,18.03.2016,1.0,60,138.5,124.77 -48,2,19.03.2016,1.0,50,138.5,124.77 -48,2,20.03.2016,1.0,13,138.5,124.77 -48,2,21.03.2016,1.0,53,138.5,124.77 -48,2,22.03.2016,,9,138.36, -48,2,23.03.2016,,6,138.36, -48,2,24.03.2016,,28,138.36, -48,2,25.03.2016,,3,138.36, -48,2,26.03.2016,,6,138.36, -48,2,27.03.2016,,0,138.36, -48,2,28.03.2016,,3,138.36, -48,2,29.03.2016,,16,138.36, -48,2,30.03.2016,,19,138.36, -48,2,31.03.2016,,0,138.36, -48,2,01.04.2016,,0,138.36, -48,2,02.04.2016,,3,138.36, -48,2,03.04.2016,,9,138.36, -48,2,04.04.2016,,0,138.36, -48,2,05.04.2016,,6,138.36, -48,2,06.04.2016,,0,138.36, -48,2,07.04.2016,,6,138.36, -48,2,08.04.2016,,0,138.36, -48,2,09.04.2016,,3,138.5, -48,2,10.04.2016,,3,138.5, -48,2,11.04.2016,,0,138.5, -48,2,12.04.2016,,6,138.5, -48,2,13.04.2016,,9,138.5, -48,2,14.04.2016,,3,138.5, -48,2,15.04.2016,,3,138.5, -48,2,16.04.2016,,3,138.5, -48,2,17.04.2016,,3,138.5, -48,2,18.04.2016,,6,138.5, -48,2,19.04.2016,1.0,88,138.5,116.89 -48,2,20.04.2016,1.0,60,138.5,116.89 -48,2,21.04.2016,1.0,69,138.5,116.89 -48,2,22.04.2016,1.0,88,138.5,116.89 -48,2,23.04.2016,1.0,72,138.5,116.89 -48,2,24.04.2016,1.0,16,138.5,116.89 -48,2,25.04.2016,1.0,19,138.5,116.89 -48,2,26.04.2016,,6,138.5, -48,2,27.04.2016,,3,138.5, -48,2,28.04.2016,,3,138.5, -48,2,29.04.2016,,3,138.5, -48,2,30.04.2016,,3,138.5, -48,2,01.05.2016,,3,138.5, -48,2,02.05.2016,,9,138.5, -48,2,03.05.2016,1.0,276,138.5,106.01 -48,2,04.05.2016,1.0,69,138.5,106.01 -48,2,05.05.2016,1.0,50,138.5,106.01 -48,2,06.05.2016,1.0,145,138.5,106.01 -48,2,07.05.2016,1.0,160,138.5,106.01 -48,2,08.05.2016,1.0,79,138.5,106.01 -48,2,09.05.2016,1.0,16,138.5,106.01 -48,2,10.05.2016,,0,142.57, -48,2,11.05.2016,,16,142.57, -48,2,12.05.2016,,0,142.57, -48,2,13.05.2016,,0,142.57, -48,2,14.05.2016,,6,142.57, -48,2,15.05.2016,,3,142.57, -48,2,16.05.2016,,6,142.57, -48,2,17.05.2016,,6,142.57, -48,2,18.05.2016,,6,142.57, -48,2,19.05.2016,,6,142.57, -48,2,20.05.2016,,6,142.57, -48,2,21.05.2016,,6,142.57, -48,2,22.05.2016,,6,142.57, -49,2,01.01.2015,,0,135.78, -49,2,02.01.2015,,13,135.78, -49,2,03.01.2015,,9,135.78, -49,2,04.01.2015,,0,135.78, -49,2,05.01.2015,,9,135.78, -49,2,06.01.2015,,9,135.78, -49,2,07.01.2015,,0,135.78, -49,2,08.01.2015,,16,135.78, -49,2,09.01.2015,,6,135.78, -49,2,10.01.2015,,9,135.78, -49,2,11.01.2015,,3,135.78, -49,2,12.01.2015,,6,135.78, -49,2,13.01.2015,,0,135.78, -49,2,14.01.2015,,0,135.78, -49,2,15.01.2015,,9,135.78, -49,2,16.01.2015,,6,135.78, -49,2,17.01.2015,,13,135.78, -49,2,18.01.2015,,6,135.78, -49,2,19.01.2015,,3,135.78, -49,2,20.01.2015,,6,135.78, -49,2,21.01.2015,,0,135.78, -49,2,22.01.2015,,3,135.78, -49,2,23.01.2015,,3,135.78, -49,2,24.01.2015,,0,135.78, -49,2,25.01.2015,,13,135.78, -49,2,26.01.2015,,3,135.78, -49,2,27.01.2015,,0,135.78, -49,2,28.01.2015,,0,135.78, -49,2,29.01.2015,,3,135.78, -49,2,30.01.2015,,0,135.78, -49,2,31.01.2015,,3,135.78, -49,2,01.02.2015,,0,135.78, -49,2,02.02.2015,,0,135.78, -49,2,03.02.2015,,3,135.78, -49,2,04.02.2015,,0,135.78, -49,2,05.02.2015,,0,135.78, -49,2,06.02.2015,,0,135.78, -49,2,07.02.2015,,3,135.78, -49,2,08.02.2015,,3,135.78, -49,2,09.02.2015,,6,135.78, -49,2,10.02.2015,,6,135.78, -49,2,11.02.2015,,0,135.78, -49,2,12.02.2015,,13,135.78, -49,2,13.02.2015,,3,135.78, -49,2,14.02.2015,,0,135.78, -49,2,15.02.2015,,13,135.78, -49,2,16.02.2015,,3,135.78, -49,2,17.02.2015,,6,135.78, -49,2,18.02.2015,,6,135.78, -49,2,19.02.2015,,6,135.78, -49,2,20.02.2015,,6,135.78, -49,2,21.02.2015,,0,135.78, -49,2,22.02.2015,,0,135.78, -49,2,23.02.2015,,0,135.78, -49,2,24.02.2015,,0,135.78, -49,2,25.02.2015,,0,135.78, -49,2,26.02.2015,,0,135.78, -49,2,27.02.2015,,13,135.78, -49,2,28.02.2015,,9,135.78, -49,2,01.03.2015,,9,135.78, -49,2,02.03.2015,,0,146.38, -49,2,03.03.2015,,3,146.38, -49,2,04.03.2015,,6,146.38, -49,2,05.03.2015,,6,146.38, -49,2,06.03.2015,,6,146.38, -49,2,07.03.2015,,0,146.38, -49,2,08.03.2015,,0,146.38, -49,2,09.03.2015,,0,146.38, -49,2,10.03.2015,,6,146.38, -49,2,11.03.2015,,6,146.38, -49,2,12.03.2015,,0,146.38, -49,2,13.03.2015,,3,146.38, -49,2,14.03.2015,,3,146.38, -49,2,15.03.2015,,3,146.38, -49,2,16.03.2015,,0,146.38, -49,2,17.03.2015,,0,146.38, -49,2,18.03.2015,,3,146.38, -49,2,19.03.2015,,6,146.38, -49,2,20.03.2015,,6,146.38, -49,2,21.03.2015,,6,146.38, -49,2,22.03.2015,,3,146.38, -49,2,23.03.2015,,0,146.38, -49,2,24.03.2015,,0,146.38, -49,2,25.03.2015,,0,146.38, -49,2,26.03.2015,,6,146.38, -49,2,27.03.2015,,0,146.38, -49,2,28.03.2015,,0,146.38, -49,2,29.03.2015,,3,146.38, -49,2,30.03.2015,,0,146.38, -49,2,31.03.2015,,0,146.38, -49,2,01.04.2015,,3,146.38, -49,2,02.04.2015,,3,146.38, -49,2,03.04.2015,,3,146.38, -49,2,04.04.2015,,6,146.38, -49,2,05.04.2015,,3,146.38, -49,2,06.04.2015,,3,146.38, -49,2,07.04.2015,,0,146.38, -49,2,08.04.2015,,0,146.38, -49,2,09.04.2015,,0,146.38, -49,2,10.04.2015,,6,146.38, -49,2,11.04.2015,,3,146.38, -49,2,12.04.2015,,0,146.38, -49,2,13.04.2015,,3,146.38, -49,2,14.04.2015,,6,146.38, -49,2,15.04.2015,,0,146.38, -49,2,16.04.2015,,3,146.38, -49,2,17.04.2015,,0,146.38, -49,2,18.04.2015,,0,146.38, -49,2,19.04.2015,,6,146.38, -49,2,20.04.2015,,6,145.84, -49,2,21.04.2015,,3,145.84, -49,2,22.04.2015,,0,145.84, -49,2,23.04.2015,,0,145.84, -49,2,24.04.2015,,0,145.84, -49,2,25.04.2015,,0,145.84, -49,2,26.04.2015,,0,150.73, -49,2,27.04.2015,,0,150.73, -49,2,28.04.2015,,0,150.73, -49,2,29.04.2015,,0,150.73, -49,2,30.04.2015,,0,150.73, -49,2,01.05.2015,,0,150.73, -49,2,02.05.2015,,0,150.73, -49,2,03.05.2015,,0,150.73, -49,2,04.05.2015,,6,150.73, -49,2,05.05.2015,,0,150.73, -49,2,06.05.2015,,0,150.73, -49,2,07.05.2015,,3,150.73, -49,2,08.05.2015,,0,150.73, -49,2,09.05.2015,,6,150.73, -49,2,10.05.2015,,0,150.73, -49,2,11.05.2015,,0,150.73, -49,2,12.05.2015,,3,150.73, -49,2,13.05.2015,,0,150.73, -49,2,14.05.2015,,0,150.73, -49,2,15.05.2015,,6,150.73, -49,2,16.05.2015,,0,150.73, -49,2,17.05.2015,,0,150.73, -49,2,18.05.2015,,0,150.73, -49,2,19.05.2015,,0,150.73, -49,2,20.05.2015,,0,150.73, -49,2,21.05.2015,,0,150.73, -49,2,22.05.2015,,0,150.73, -49,2,23.05.2015,,0,150.73, -49,2,24.05.2015,,0,150.73, -49,2,25.05.2015,,0,150.73, -49,2,26.05.2015,,0,150.73, -49,2,27.05.2015,,3,150.73, -49,2,28.05.2015,,0,150.73, -49,2,29.05.2015,,3,150.73, -49,2,30.05.2015,,3,150.73, -49,2,31.05.2015,,3,150.73, -49,2,01.06.2015,,3,150.73, -49,2,02.06.2015,,0,150.73, -49,2,03.06.2015,,0,150.73, -49,2,04.06.2015,,0,150.73, -49,2,05.06.2015,,0,150.73, -49,2,06.06.2015,,0,150.73, -49,2,07.06.2015,,6,150.73, -49,2,08.06.2015,,0,150.73, -49,2,09.06.2015,,0,150.73, -49,2,10.06.2015,,0,150.73, -49,2,11.06.2015,,3,150.73, -49,2,12.06.2015,,0,150.73, -49,2,13.06.2015,,0,150.73, -49,2,14.06.2015,,3,150.73, -49,2,15.06.2015,,0,150.73, -49,2,16.06.2015,,16,150.73, -49,2,17.06.2015,,0,150.73, -49,2,18.06.2015,,0,150.73, -49,2,19.06.2015,,3,150.73, -49,2,20.06.2015,,9,150.73, -49,2,21.06.2015,,9,150.73, -49,2,22.06.2015,,3,150.73, -49,2,23.06.2015,,0,150.73, -49,2,24.06.2015,,0,150.73, -49,2,25.06.2015,,0,150.73, -49,2,26.06.2015,,0,150.73, -49,2,27.06.2015,,0,150.73, -49,2,28.06.2015,,0,150.73, -49,2,29.06.2015,,0,150.73, -49,2,30.06.2015,,3,150.73, -49,2,01.07.2015,,0,150.73, -49,2,02.07.2015,,0,150.73, -49,2,03.07.2015,,0,150.73, -49,2,04.07.2015,,0,150.73, -49,2,05.07.2015,,6,150.73, -49,2,06.07.2015,,0,150.73, -49,2,07.07.2015,,9,150.73, -49,2,08.07.2015,,6,150.73, -49,2,09.07.2015,,3,150.73, -49,2,10.07.2015,,3,150.73, -49,2,11.07.2015,,6,150.73, -49,2,12.07.2015,,9,150.73, -49,2,13.07.2015,,0,150.73, -49,2,14.07.2015,,0,150.73, -49,2,15.07.2015,,0,150.73, -49,2,16.07.2015,,0,150.73, -49,2,17.07.2015,,0,150.73, -49,2,18.07.2015,,0,150.73, -49,2,19.07.2015,,3,150.73, -49,2,20.07.2015,,0,150.73, -49,2,21.07.2015,,0,150.73, -49,2,22.07.2015,,0,150.73, -49,2,23.07.2015,,0,150.73, -49,2,24.07.2015,,9,150.73, -49,2,25.07.2015,,13,150.73, -49,2,26.07.2015,,25,150.73, -49,2,27.07.2015,,25,150.73, -49,2,28.07.2015,,41,150.73, -49,2,29.07.2015,,13,150.73, -49,2,30.07.2015,,0,150.73, -49,2,31.07.2015,,0,150.73, -49,2,01.08.2015,,0,150.73, -49,2,02.08.2015,,35,150.73, -49,2,03.08.2015,,41,150.73, -49,2,04.08.2015,,0,150.73, -49,2,05.08.2015,,0,150.73, -49,2,06.08.2015,,16,150.73, -49,2,07.08.2015,,16,150.73, -49,2,08.08.2015,,16,150.73, -49,2,09.08.2015,,3,150.73, -49,2,10.08.2015,,13,150.73, -49,2,11.08.2015,,6,150.73, -49,2,12.08.2015,,16,128.17, -49,2,13.08.2015,,6,128.17, -49,2,14.08.2015,,6,128.17, -49,2,15.08.2015,,19,128.17, -49,2,16.08.2015,,9,128.17, -49,2,17.08.2015,,53,128.17, -49,2,18.08.2015,1.0,148,128.17,100.3 -49,2,19.08.2015,1.0,57,128.17,100.3 -49,2,20.08.2015,1.0,132,128.17,100.3 -49,2,21.08.2015,1.0,88,128.17,100.3 -49,2,22.08.2015,1.0,44,128.17,100.3 -49,2,23.08.2015,1.0,13,128.17,100.3 -49,2,24.08.2015,1.0,63,128.17,100.3 -49,2,25.08.2015,,25,128.17, -49,2,26.08.2015,,25,128.17, -49,2,27.08.2015,,19,128.17, -49,2,28.08.2015,,25,128.17, -49,2,29.08.2015,,16,128.17, -49,2,30.08.2015,,16,128.17, -49,2,31.08.2015,,16,128.17, -49,2,01.09.2015,,0,128.17, -49,2,02.09.2015,,19,128.17, -49,2,03.09.2015,,16,128.17, -49,2,04.09.2015,,16,128.17, -49,2,05.09.2015,,16,128.17, -49,2,06.09.2015,,16,128.17, -49,2,07.09.2015,,6,128.17, -49,2,08.09.2015,,6,128.17, -49,2,09.09.2015,,6,128.17, -49,2,10.09.2015,,16,128.17, -49,2,11.09.2015,,3,128.17, -49,2,12.09.2015,,16,128.17, -49,2,13.09.2015,,0,128.17, -49,2,14.09.2015,,19,128.17, -49,2,15.09.2015,,16,128.17, -49,2,16.09.2015,,16,128.17, -49,2,17.09.2015,,16,128.17, -49,2,18.09.2015,,16,128.17, -49,2,19.09.2015,,16,128.17, -49,2,20.09.2015,,16,128.17, -49,2,21.09.2015,,0,128.17, -49,2,22.09.2015,,9,128.17, -49,2,23.09.2015,,9,128.17, -49,2,24.09.2015,,9,128.17, -49,2,25.09.2015,,9,128.17, -49,2,26.09.2015,,16,128.17, -49,2,27.09.2015,,3,128.17, -49,2,28.09.2015,,9,128.17, -49,2,29.09.2015,,16,128.17, -49,2,30.09.2015,,3,128.17, -49,2,01.10.2015,,0,162.96, -49,2,02.10.2015,,0,162.96, -49,2,03.10.2015,,0,162.96, -49,2,04.10.2015,,6,162.96, -49,2,05.10.2015,,6,162.96, -49,2,06.10.2015,1.0,101,143.39,122.05 -49,2,07.10.2015,1.0,50,143.39,122.05 -49,2,08.10.2015,1.0,60,143.39,122.05 -49,2,09.10.2015,1.0,53,143.39,122.05 -49,2,10.10.2015,1.0,28,143.39,122.05 -49,2,11.10.2015,1.0,16,143.39,122.05 -49,2,12.10.2015,1.0,57,143.39,122.05 -49,2,13.10.2015,1.0,57,143.93,122.05 -49,2,14.10.2015,1.0,57,143.93,122.05 -49,2,15.10.2015,1.0,6,143.93,122.05 -49,2,16.10.2015,1.0,9,143.93,122.05 -49,2,17.10.2015,1.0,57,162.96,122.05 -49,2,18.10.2015,1.0,47,162.96,122.05 -49,2,19.10.2015,,3,162.96, -49,2,20.10.2015,,6,162.96, -49,2,21.10.2015,,0,162.96, -49,2,22.10.2015,,0,162.96, -49,2,23.10.2015,,6,162.96, -49,2,24.10.2015,,0,162.96, -49,2,25.10.2015,,0,162.96, -49,2,26.10.2015,,0,162.96, -49,2,27.10.2015,,3,162.96, -49,2,28.10.2015,,3,162.96, -49,2,29.10.2015,,3,162.96, -49,2,30.10.2015,,0,162.96, -49,2,31.10.2015,,0,162.96, -49,2,01.11.2015,,0,162.96, -49,2,02.11.2015,,0,162.96, -49,2,03.11.2015,,0,162.96, -49,2,04.11.2015,,0,162.96, -49,2,05.11.2015,,0,162.96, -49,2,06.11.2015,,3,162.96, -49,2,07.11.2015,,6,162.96, -49,2,08.11.2015,,0,162.96, -49,2,09.11.2015,,0,162.96, -49,2,10.11.2015,,0,162.96, -49,2,11.11.2015,,0,138.36, -49,2,12.11.2015,,0,138.36, -49,2,13.11.2015,,0,138.36, -49,2,14.11.2015,,0,138.36, -49,2,15.11.2015,,0,138.36, -49,2,16.11.2015,,0,138.36, -49,2,17.11.2015,,0,138.36, -49,2,18.11.2015,,0,138.36, -49,2,19.11.2015,,0,138.36, -49,2,20.11.2015,,0,138.36, -49,2,21.11.2015,,0,138.36, -49,2,22.11.2015,,0,138.36, -49,2,23.11.2015,,0,138.36, -49,2,24.11.2015,,0,138.36, -49,2,25.11.2015,,0,138.36, -49,2,26.11.2015,,0,138.36, -49,2,27.11.2015,,0,138.36, -49,2,28.11.2015,,0,138.36, -49,2,29.11.2015,,0,138.36, -49,2,30.11.2015,,0,138.36, -49,2,01.12.2015,,0,162.96, -49,2,02.12.2015,,0,162.96, -49,2,03.12.2015,,0,138.5, -49,2,04.12.2015,,0,138.5, -49,2,05.12.2015,,3,138.5, -49,2,06.12.2015,,3,138.5, -49,2,07.12.2015,,3,138.5, -49,2,08.12.2015,,0,138.5, -49,2,09.12.2015,,0,138.5, -49,2,10.12.2015,,9,138.5, -49,2,11.12.2015,,3,142.57, -49,2,12.12.2015,,3,142.57, -49,2,13.12.2015,,3,142.57, -49,2,14.12.2015,,3,142.57, -49,2,15.12.2015,,3,142.57, -49,2,16.12.2015,,6,142.57, -49,2,17.12.2015,,6,142.57, -49,2,18.12.2015,,6,142.57, -49,2,19.12.2015,,3,142.57, -49,2,20.12.2015,,3,142.57, -49,2,21.12.2015,,6,142.57, -49,2,22.12.2015,1.0,85,142.57,103.02 -49,2,23.12.2015,1.0,79,142.57,103.02 -49,2,24.12.2015,1.0,154,142.57,103.02 -49,2,25.12.2015,1.0,167,142.57,103.02 -49,2,26.12.2015,1.0,72,142.57,103.02 -49,2,27.12.2015,1.0,154,142.57,103.02 -49,2,28.12.2015,1.0,75,142.57,103.02 -49,2,29.12.2015,,3,138.5, -49,2,30.12.2015,,3,138.5, -49,2,31.12.2015,,3,138.5, -49,2,01.01.2016,,3,138.5, -49,2,02.01.2016,,3,138.5, -49,2,03.01.2016,,3,138.5, -49,2,04.01.2016,,0,138.5, -49,2,05.01.2016,,19,138.5, -49,2,06.01.2016,,6,138.5, -49,2,07.01.2016,,6,138.5, -49,2,08.01.2016,,6,138.5, -49,2,09.01.2016,,6,138.5, -49,2,10.01.2016,,6,138.5, -49,2,11.01.2016,,9,138.5, -49,2,12.01.2016,,3,138.5, -49,2,13.01.2016,,6,138.5, -49,2,14.01.2016,,6,138.5, -49,2,15.01.2016,,3,138.5, -49,2,16.01.2016,,3,142.57, -49,2,17.01.2016,,3,142.57, -49,2,18.01.2016,,22,142.57, -49,2,19.01.2016,1.0,198,142.57,103.02 -49,2,20.01.2016,1.0,119,142.57,103.02 -49,2,21.01.2016,1.0,101,142.57,103.02 -49,2,22.01.2016,1.0,154,142.57,103.02 -49,2,23.01.2016,1.0,60,142.57,103.02 -49,2,24.01.2016,1.0,35,142.57,103.02 -49,2,25.01.2016,1.0,104,142.57,103.02 -49,2,26.01.2016,,0,142.57, -49,2,27.01.2016,,16,142.57, -49,2,28.01.2016,,3,142.57, -49,2,29.01.2016,,6,142.57, -49,2,30.01.2016,,0,142.57, -49,2,31.01.2016,,6,142.57, -49,2,01.02.2016,,9,142.57, -49,2,02.02.2016,,6,142.57, -49,2,03.02.2016,,13,142.57, -49,2,04.02.2016,,6,142.57, -49,2,05.02.2016,,0,142.57, -49,2,06.02.2016,,9,142.57, -49,2,07.02.2016,,3,142.57, -49,2,08.02.2016,,16,142.57, -49,2,09.02.2016,1.0,173,142.57,108.46 -49,2,10.02.2016,1.0,110,138.36,108.46 -49,2,11.02.2016,1.0,267,138.36,108.46 -49,2,12.02.2016,1.0,157,138.36,108.46 -49,2,13.02.2016,1.0,123,138.36,108.46 -49,2,14.02.2016,1.0,242,138.36,108.46 -49,2,15.02.2016,1.0,132,138.36,108.46 -49,2,16.02.2016,,6,138.36, -49,2,17.02.2016,,0,138.36, -49,2,18.02.2016,,0,138.36, -49,2,19.02.2016,,3,138.36, -49,2,20.02.2016,,3,138.36, -49,2,21.02.2016,,3,138.36, -49,2,22.02.2016,,0,138.36, -49,2,23.02.2016,,0,138.36, -49,2,24.02.2016,,6,138.36, -49,2,25.02.2016,,9,138.36, -49,2,26.02.2016,,0,138.36, -49,2,27.02.2016,,6,138.36, -49,2,28.02.2016,,6,138.36, -49,2,29.02.2016,,0,138.36, -49,2,01.03.2016,,6,138.36, -49,2,02.03.2016,,3,138.36, -49,2,03.03.2016,,13,138.36, -49,2,04.03.2016,,0,138.36, -49,2,05.03.2016,,3,138.36, -49,2,06.03.2016,,0,138.36, -49,2,07.03.2016,,0,138.36, -49,2,08.03.2016,,9,138.36, -49,2,09.03.2016,,0,138.36, -49,2,10.03.2016,,13,138.36, -49,2,11.03.2016,,9,138.36, -49,2,12.03.2016,,6,138.5, -49,2,13.03.2016,,6,138.5, -49,2,14.03.2016,,9,138.5, -49,2,15.03.2016,1.0,47,138.5,124.77 -49,2,16.03.2016,1.0,28,138.5,124.77 -49,2,17.03.2016,1.0,72,138.5,124.77 -49,2,18.03.2016,1.0,57,138.5,124.77 -49,2,19.03.2016,1.0,69,138.5,124.77 -49,2,20.03.2016,1.0,19,138.5,124.77 -49,2,21.03.2016,1.0,101,138.5,124.77 -49,2,22.03.2016,,6,138.36, -49,2,23.03.2016,,6,138.36, -49,2,24.03.2016,,6,138.36, -49,2,25.03.2016,,6,138.36, -49,2,26.03.2016,,6,138.36, -49,2,27.03.2016,,6,138.36, -49,2,28.03.2016,,3,138.36, -49,2,29.03.2016,,3,138.36, -49,2,30.03.2016,,0,138.36, -49,2,31.03.2016,,0,138.36, -49,2,01.04.2016,,0,138.36, -49,2,02.04.2016,,3,138.36, -49,2,03.04.2016,,3,138.36, -49,2,04.04.2016,,3,138.36, -49,2,05.04.2016,,3,138.36, -49,2,06.04.2016,,0,138.36, -49,2,07.04.2016,,9,138.36, -49,2,08.04.2016,,3,138.36, -49,2,09.04.2016,,3,138.5, -49,2,10.04.2016,,3,138.5, -49,2,11.04.2016,,9,138.5, -49,2,12.04.2016,,9,138.5, -49,2,13.04.2016,,6,138.5, -49,2,14.04.2016,,0,138.5, -49,2,15.04.2016,,3,138.5, -49,2,16.04.2016,,13,138.5, -49,2,17.04.2016,,3,138.5, -49,2,18.04.2016,,6,138.5, -49,2,19.04.2016,1.0,151,138.5,116.89 -49,2,20.04.2016,1.0,135,138.5,116.89 -49,2,21.04.2016,1.0,69,138.5,116.89 -49,2,22.04.2016,1.0,195,138.5,116.89 -49,2,23.04.2016,1.0,126,138.5,116.89 -49,2,24.04.2016,1.0,28,138.5,116.89 -49,2,25.04.2016,1.0,88,138.5,116.89 -49,2,26.04.2016,,0,138.5, -49,2,27.04.2016,,6,138.5, -49,2,28.04.2016,,3,138.5, -49,2,29.04.2016,,3,138.5, -49,2,30.04.2016,,0,138.5, -49,2,01.05.2016,,3,138.5, -49,2,02.05.2016,,3,138.5, -49,2,03.05.2016,1.0,204,138.5,106.01 -49,2,04.05.2016,1.0,176,138.5,106.01 -49,2,05.05.2016,1.0,242,138.5,106.01 -49,2,06.05.2016,1.0,201,138.5,106.01 -49,2,07.05.2016,1.0,157,138.5,106.01 -49,2,08.05.2016,1.0,123,138.5,106.01 -49,2,09.05.2016,1.0,85,138.5,106.01 -49,2,10.05.2016,,3,138.5, -49,2,11.05.2016,,0,138.5, -49,2,12.05.2016,,35,138.5, -49,2,13.05.2016,,6,138.5, -49,2,14.05.2016,,0,138.5, -49,2,15.05.2016,,13,138.5, -49,2,16.05.2016,,3,138.5, -49,2,17.05.2016,,3,138.5, -49,2,18.05.2016,,0,138.5, -49,2,19.05.2016,,0,138.5, -49,2,20.05.2016,,0,138.5, -49,2,21.05.2016,,3,138.5, -49,2,22.05.2016,,6,138.5, -50,2,01.01.2015,,0,135.78, -50,2,02.01.2015,,0,135.78, -50,2,03.01.2015,,3,135.78, -50,2,04.01.2015,,0,135.78, -50,2,05.01.2015,,6,135.78, -50,2,06.01.2015,,0,135.78, -50,2,07.01.2015,,0,135.78, -50,2,08.01.2015,,6,135.78, -50,2,09.01.2015,,0,135.78, -50,2,10.01.2015,,3,135.78, -50,2,11.01.2015,,3,135.78, -50,2,12.01.2015,,0,135.78, -50,2,13.01.2015,,6,135.78, -50,2,14.01.2015,,0,135.78, -50,2,15.01.2015,,9,135.78, -50,2,16.01.2015,,6,135.78, -50,2,17.01.2015,,6,135.78, -50,2,18.01.2015,,0,135.78, -50,2,19.01.2015,,0,135.78, -50,2,20.01.2015,,0,135.78, -50,2,21.01.2015,,0,135.78, -50,2,22.01.2015,,0,135.78, -50,2,23.01.2015,,0,135.78, -50,2,24.01.2015,,0,135.78, -50,2,25.01.2015,,3,135.78, -50,2,26.01.2015,,3,135.78, -50,2,27.01.2015,,0,135.78, -50,2,28.01.2015,,0,135.78, -50,2,29.01.2015,,0,135.78, -50,2,30.01.2015,,0,135.78, -50,2,31.01.2015,,0,135.78, -50,2,01.02.2015,,0,135.78, -50,2,02.02.2015,,0,135.78, -50,2,03.02.2015,,0,135.78, -50,2,04.02.2015,,3,135.78, -50,2,05.02.2015,,0,135.78, -50,2,06.02.2015,,0,135.78, 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-50,2,18.01.2016,,3,142.57, -50,2,19.01.2016,1.0,50,142.57,103.02 -50,2,20.01.2016,1.0,50,142.57,103.02 -50,2,21.01.2016,1.0,35,142.57,103.02 -50,2,22.01.2016,1.0,53,142.57,103.02 -50,2,23.01.2016,1.0,38,142.57,103.02 -50,2,24.01.2016,1.0,19,142.57,103.02 -50,2,25.01.2016,1.0,35,142.57,103.02 -50,2,26.01.2016,,3,142.57, -50,2,27.01.2016,,3,142.57, -50,2,28.01.2016,,3,142.57, -50,2,29.01.2016,,3,142.57, -50,2,30.01.2016,,3,142.57, -50,2,31.01.2016,,3,142.57, -50,2,01.02.2016,,0,142.57, -50,2,02.02.2016,,0,142.57, -50,2,03.02.2016,,3,142.57, -50,2,04.02.2016,,0,142.57, -50,2,05.02.2016,,13,142.57, -50,2,06.02.2016,,0,142.57, -50,2,07.02.2016,,0,142.57, -50,2,08.02.2016,,0,142.57, -50,2,09.02.2016,1.0,41,142.57,108.46 -50,2,10.02.2016,1.0,50,138.36,108.46 -50,2,11.02.2016,1.0,41,138.36,108.46 -50,2,12.02.2016,1.0,28,138.36,108.46 -50,2,13.02.2016,1.0,38,138.36,108.46 -50,2,14.02.2016,1.0,13,138.36,108.46 -50,2,15.02.2016,1.0,38,138.36,108.46 -50,2,16.02.2016,,3,138.36, 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-51,2,05.01.2015,,13,135.78, -51,2,06.01.2015,,3,135.78, -51,2,07.01.2015,,0,135.78, -51,2,08.01.2015,,0,135.78, -51,2,09.01.2015,,0,135.78, -51,2,10.01.2015,,25,135.78, -51,2,11.01.2015,,0,135.78, -51,2,12.01.2015,,0,135.78, -51,2,13.01.2015,,6,135.78, -51,2,14.01.2015,,3,135.78, -51,2,15.01.2015,,0,135.78, -51,2,16.01.2015,,0,135.78, -51,2,17.01.2015,,0,135.78, -51,2,18.01.2015,,3,135.78, -51,2,19.01.2015,,6,135.78, -51,2,20.01.2015,,3,135.78, -51,2,21.01.2015,,6,135.78, -51,2,22.01.2015,,0,135.78, -51,2,23.01.2015,,0,135.78, -51,2,24.01.2015,,6,135.78, -51,2,25.01.2015,,3,135.78, -51,2,26.01.2015,,3,135.78, -51,2,27.01.2015,,6,135.78, -51,2,28.01.2015,,0,135.78, -51,2,29.01.2015,,0,135.78, -51,2,30.01.2015,,0,135.78, -51,2,31.01.2015,,0,135.78, -51,2,01.02.2015,,0,135.78, -51,2,02.02.2015,,0,135.78, -51,2,03.02.2015,,0,135.78, -51,2,04.02.2015,,0,135.78, -51,2,05.02.2015,,0,135.78, -51,2,06.02.2015,,16,135.78, -51,2,07.02.2015,,9,135.78, -51,2,08.02.2015,,0,135.78, 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-51,2,03.08.2015,,25,150.73, -51,2,04.08.2015,,35,150.73, -51,2,05.08.2015,,19,150.73, -51,2,06.08.2015,,19,150.73, -51,2,07.08.2015,,38,150.73, -51,2,08.08.2015,,53,150.73, -51,2,09.08.2015,,60,150.73, -51,2,10.08.2015,,28,150.73, -51,2,11.08.2015,,28,150.73, -51,2,12.08.2015,,44,128.17, -51,2,13.08.2015,,31,128.17, -51,2,14.08.2015,,25,128.17, -51,2,15.08.2015,,22,128.17, -51,2,16.08.2015,,38,128.17, -51,2,17.08.2015,,19,128.17, -51,2,18.08.2015,1.0,113,128.17,100.3 -51,2,19.08.2015,1.0,135,128.17,100.3 -51,2,20.08.2015,1.0,116,128.17,100.3 -51,2,21.08.2015,1.0,185,128.17,100.3 -51,2,22.08.2015,1.0,72,128.17,100.3 -51,2,23.08.2015,1.0,119,128.17,100.3 -51,2,24.08.2015,1.0,151,128.17,100.3 -51,2,25.08.2015,,75,128.17, -51,2,26.08.2015,,35,128.17, -51,2,27.08.2015,,50,128.17, -51,2,28.08.2015,,50,128.17, -51,2,29.08.2015,,69,128.17, -51,2,30.08.2015,,47,128.17, -51,2,31.08.2015,,44,128.17, -51,2,01.09.2015,,41,128.17, -51,2,02.09.2015,,38,128.17, -51,2,03.09.2015,,35,128.17, -51,2,04.09.2015,,31,128.17, -51,2,05.09.2015,,3,128.17, -51,2,06.09.2015,,6,128.17, -51,2,07.09.2015,,16,128.17, -51,2,08.09.2015,,50,128.17, -51,2,09.09.2015,,22,128.17, -51,2,10.09.2015,,3,128.17, -51,2,11.09.2015,,47,128.17, -51,2,12.09.2015,,25,128.17, -51,2,13.09.2015,,35,128.17, -51,2,14.09.2015,,13,128.17, -51,2,15.09.2015,,35,128.17, -51,2,16.09.2015,,6,128.17, -51,2,17.09.2015,,19,128.17, -51,2,18.09.2015,,53,128.17, -51,2,19.09.2015,,75,128.17, -51,2,20.09.2015,,16,128.17, -51,2,21.09.2015,,28,128.17, -51,2,22.09.2015,,25,128.17, -51,2,23.09.2015,,22,128.17, -51,2,24.09.2015,,22,128.17, -51,2,25.09.2015,,3,128.17, -51,2,26.09.2015,,9,128.17, -51,2,27.09.2015,,16,128.17, -51,2,28.09.2015,,41,128.17, -51,2,29.09.2015,,75,128.17, -51,2,30.09.2015,,25,128.17, -51,2,01.10.2015,,25,162.96, -51,2,02.10.2015,,3,162.96, -51,2,03.10.2015,,22,162.96, -51,2,04.10.2015,,19,162.96, -51,2,05.10.2015,,6,162.96, -51,2,06.10.2015,1.0,97,143.39,122.05 -51,2,07.10.2015,1.0,104,143.39,122.05 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-51,2,14.12.2015,,3,142.57, -51,2,15.12.2015,,6,142.57, -51,2,16.12.2015,,6,142.57, -51,2,17.12.2015,,3,142.57, -51,2,18.12.2015,,3,142.57, -51,2,19.12.2015,,3,142.57, -51,2,20.12.2015,,3,142.57, -51,2,21.12.2015,,3,142.57, -51,2,22.12.2015,1.0,35,142.57,103.02 -51,2,23.12.2015,1.0,107,142.57,103.02 -51,2,24.12.2015,1.0,97,142.57,103.02 -51,2,25.12.2015,1.0,132,142.57,103.02 -51,2,26.12.2015,1.0,126,142.57,103.02 -51,2,27.12.2015,1.0,113,142.57,103.02 -51,2,28.12.2015,1.0,47,142.57,103.02 -51,2,29.12.2015,,3,138.5, -51,2,30.12.2015,,3,138.5, -51,2,31.12.2015,,3,138.5, -51,2,01.01.2016,,3,138.5, -51,2,02.01.2016,,3,138.5, -51,2,03.01.2016,,3,138.5, -51,2,04.01.2016,,3,138.5, -51,2,05.01.2016,,9,138.5, -51,2,06.01.2016,,9,138.5, -51,2,07.01.2016,,3,138.5, -51,2,08.01.2016,,3,138.5, -51,2,09.01.2016,,3,138.5, -51,2,10.01.2016,,3,138.5, -51,2,11.01.2016,,3,138.5, -51,2,12.01.2016,,6,138.5, -51,2,13.01.2016,,13,138.5, -51,2,14.01.2016,,6,138.5, -51,2,15.01.2016,,6,138.5, -51,2,16.01.2016,,6,142.57, -51,2,17.01.2016,,9,142.57, -51,2,18.01.2016,,35,142.57, -51,2,19.01.2016,1.0,327,142.57,103.02 -51,2,20.01.2016,1.0,239,142.57,103.02 -51,2,21.01.2016,1.0,210,142.57,103.02 -51,2,22.01.2016,1.0,13,142.57,103.02 -51,2,23.01.2016,1.0,97,142.57,103.02 -51,2,24.01.2016,1.0,22,142.57,103.02 -51,2,25.01.2016,1.0,236,142.57,103.02 -51,2,26.01.2016,,13,142.57, -51,2,27.01.2016,,9,142.57, -51,2,28.01.2016,,9,142.57, -51,2,29.01.2016,,3,142.57, -51,2,30.01.2016,,13,142.57, -51,2,31.01.2016,,6,142.57, -51,2,01.02.2016,,13,142.57, -51,2,02.02.2016,,16,142.57, -51,2,03.02.2016,,38,142.57, -51,2,04.02.2016,,22,142.57, -51,2,05.02.2016,,0,142.57, -51,2,06.02.2016,,0,142.57, -51,2,07.02.2016,,0,142.57, -51,2,08.02.2016,,0,142.57, -51,2,09.02.2016,1.0,104,142.57,108.46 -51,2,10.02.2016,1.0,273,138.36,108.46 -51,2,11.02.2016,1.0,19,138.36,108.46 -51,2,12.02.2016,1.0,107,138.36,108.46 -51,2,13.02.2016,1.0,135,138.36,108.46 -51,2,14.02.2016,1.0,119,138.36,108.46 -51,2,15.02.2016,1.0,119,138.36,108.46 -51,2,16.02.2016,,0,138.36, -51,2,17.02.2016,,3,138.36, -51,2,18.02.2016,,0,138.36, -51,2,19.02.2016,,3,138.36, -51,2,20.02.2016,,3,138.36, -51,2,21.02.2016,,6,138.36, -51,2,22.02.2016,,3,138.36, -51,2,23.02.2016,,0,138.36, -51,2,24.02.2016,,6,138.36, -51,2,25.02.2016,,6,138.36, -51,2,26.02.2016,,6,138.36, -51,2,27.02.2016,,6,138.36, -51,2,28.02.2016,,6,138.36, -51,2,29.02.2016,,6,138.36, -51,2,01.03.2016,,6,138.36, -51,2,02.03.2016,,6,138.36, -51,2,03.03.2016,,6,138.36, -51,2,04.03.2016,,6,138.36, -51,2,05.03.2016,,6,138.36, -51,2,06.03.2016,,6,138.36, -51,2,07.03.2016,,6,138.36, -51,2,08.03.2016,,6,138.36, -51,2,09.03.2016,,6,138.36, -51,2,10.03.2016,,6,138.36, -51,2,11.03.2016,,6,138.36, -51,2,12.03.2016,,6,138.5, -51,2,13.03.2016,,6,138.5, -51,2,14.03.2016,,6,138.5, -51,2,15.03.2016,1.0,28,138.5,124.77 -51,2,16.03.2016,1.0,69,138.5,124.77 -51,2,17.03.2016,1.0,91,138.5,124.77 -51,2,18.03.2016,1.0,101,138.5,124.77 -51,2,19.03.2016,1.0,72,138.5,124.77 -51,2,20.03.2016,1.0,97,138.5,124.77 -51,2,21.03.2016,1.0,69,138.5,124.77 -51,2,22.03.2016,,6,138.36, -51,2,23.03.2016,,6,138.36, -51,2,24.03.2016,,6,138.36, -51,2,25.03.2016,,0,138.36, -51,2,26.03.2016,,0,138.36, -51,2,27.03.2016,,0,138.36, -51,2,28.03.2016,,0,138.36, -51,2,29.03.2016,,0,138.36, -51,2,30.03.2016,,0,138.36, -51,2,31.03.2016,,0,138.36, -51,2,01.04.2016,,0,138.36, -51,2,02.04.2016,,0,138.36, -51,2,03.04.2016,,0,138.36, -51,2,04.04.2016,,0,138.36, -51,2,05.04.2016,,0,138.36, -51,2,06.04.2016,,0,138.36, -51,2,07.04.2016,,0,138.36, -51,2,08.04.2016,,13,138.36, -51,2,09.04.2016,,0,138.5, -51,2,10.04.2016,,0,138.5, -51,2,11.04.2016,,0,138.5, -51,2,12.04.2016,,0,138.5, -51,2,13.04.2016,,0,138.5, -51,2,14.04.2016,,3,138.5, -51,2,15.04.2016,,0,138.5, -51,2,16.04.2016,,0,138.5, -51,2,17.04.2016,,0,138.5, -51,2,18.04.2016,,0,138.5, -51,2,19.04.2016,1.0,72,138.5,116.89 -51,2,20.04.2016,1.0,126,138.5,116.89 -51,2,21.04.2016,1.0,129,138.5,116.89 -51,2,22.04.2016,1.0,132,138.5,116.89 -51,2,23.04.2016,1.0,126,138.5,116.89 -51,2,24.04.2016,1.0,101,138.5,116.89 -51,2,25.04.2016,1.0,179,138.5,116.89 -51,2,26.04.2016,,3,138.5, -51,2,27.04.2016,,13,138.5, -51,2,28.04.2016,,6,138.5, -51,2,29.04.2016,,19,138.5, -51,2,30.04.2016,,13,138.5, -51,2,01.05.2016,,31,138.5, -51,2,02.05.2016,,9,138.5, -51,2,03.05.2016,1.0,104,138.5,106.01 -51,2,04.05.2016,1.0,248,138.5,106.01 -51,2,05.05.2016,1.0,163,138.5,106.01 -51,2,06.05.2016,1.0,119,138.5,106.01 -51,2,07.05.2016,1.0,214,138.5,106.01 -51,2,08.05.2016,1.0,129,138.5,106.01 -51,2,09.05.2016,1.0,129,138.5,106.01 -51,2,10.05.2016,,3,142.57, -51,2,11.05.2016,,3,142.57, -51,2,12.05.2016,,31,142.57, -51,2,13.05.2016,,9,142.57, -51,2,14.05.2016,,9,142.57, -51,2,15.05.2016,,9,142.57, -51,2,16.05.2016,,9,142.57, -51,2,17.05.2016,,9,142.57, -51,2,18.05.2016,,0,142.57, -51,2,19.05.2016,,3,142.57, -51,2,20.05.2016,,3,142.57, -51,2,21.05.2016,,0,142.57, -51,2,22.05.2016,,0,142.57, -52,2,01.01.2015,,0,142.57, -52,2,02.01.2015,,0,142.57, -52,2,03.01.2015,,22,142.57, -52,2,04.01.2015,,13,142.57, -52,2,05.01.2015,,3,142.57, -52,2,06.01.2015,,0,142.57, -52,2,07.01.2015,,3,142.57, -52,2,08.01.2015,,0,142.57, -52,2,09.01.2015,,3,142.57, -52,2,10.01.2015,,22,142.57, -52,2,11.01.2015,,0,142.57, -52,2,12.01.2015,,0,142.57, -52,2,13.01.2015,,0,142.57, -52,2,14.01.2015,,0,142.57, -52,2,15.01.2015,,0,142.57, -52,2,16.01.2015,,0,142.57, -52,2,17.01.2015,,0,142.57, -52,2,18.01.2015,,3,142.57, -52,2,19.01.2015,,3,142.57, -52,2,20.01.2015,,3,142.57, -52,2,21.01.2015,,0,142.57, -52,2,22.01.2015,,3,142.57, -52,2,23.01.2015,,0,142.57, -52,2,24.01.2015,,0,142.57, -52,2,25.01.2015,,0,142.57, -52,2,26.01.2015,,0,142.57, -52,2,27.01.2015,,0,142.57, -52,2,28.01.2015,,3,142.57, -52,2,29.01.2015,,6,142.57, -52,2,30.01.2015,,3,142.57, -52,2,31.01.2015,,0,142.57, -52,2,01.02.2015,,0,142.57, -52,2,02.02.2015,,0,142.57, -52,2,03.02.2015,,0,142.57, -52,2,04.02.2015,,6,142.57, -52,2,05.02.2015,,3,142.57, -52,2,06.02.2015,,0,142.57, -52,2,07.02.2015,,0,142.57, -52,2,08.02.2015,,0,142.57, -52,2,09.02.2015,,0,142.57, -52,2,10.02.2015,,0,142.57, -52,2,11.02.2015,,6,142.57, -52,2,12.02.2015,,0,142.57, -52,2,13.02.2015,,0,142.57, -52,2,14.02.2015,,0,142.57, -52,2,15.02.2015,,3,142.57, -52,2,16.02.2015,,0,142.57, -52,2,17.02.2015,,0,142.57, -52,2,18.02.2015,,0,142.57, -52,2,19.02.2015,,0,142.57, -52,2,20.02.2015,,0,142.57, -52,2,21.02.2015,,0,142.57, -52,2,22.02.2015,,0,142.57, -52,2,23.02.2015,,0,142.57, -52,2,24.02.2015,,0,142.57, -52,2,25.02.2015,,0,142.57, -52,2,26.02.2015,,0,153.72, -52,2,27.02.2015,,0,153.72, -52,2,28.02.2015,,0,153.72, -52,2,01.03.2015,,0,153.72, -52,2,02.03.2015,,3,153.72, -52,2,03.03.2015,,0,153.72, -52,2,04.03.2015,,6,153.72, -52,2,05.03.2015,,0,153.72, -52,2,06.03.2015,,0,153.72, -52,2,07.03.2015,,0,153.72, -52,2,08.03.2015,,0,153.72, -52,2,09.03.2015,,0,153.72, -52,2,10.03.2015,,0,153.72, -52,2,11.03.2015,,13,153.72, -52,2,12.03.2015,,0,153.72, -52,2,13.03.2015,,0,153.72, -52,2,14.03.2015,,0,153.72, -52,2,15.03.2015,,0,153.72, -52,2,16.03.2015,,0,153.72, -52,2,17.03.2015,,0,153.72, -52,2,18.03.2015,,6,153.72, -52,2,19.03.2015,,0,153.72, -52,2,20.03.2015,,0,153.72, -52,2,21.03.2015,,0,153.72, -52,2,22.03.2015,,0,153.72, -52,2,23.03.2015,,0,153.72, -52,2,24.03.2015,,0,153.72, -52,2,25.03.2015,,3,153.72, -52,2,26.03.2015,,0,153.72, -52,2,27.03.2015,,0,153.72, -52,2,28.03.2015,,6,153.72, -52,2,29.03.2015,,0,153.72, -52,2,30.03.2015,,3,153.72, -52,2,31.03.2015,,0,153.72, -52,2,01.04.2015,,3,153.72, -52,2,02.04.2015,,0,153.72, -52,2,03.04.2015,,0,153.72, -52,2,04.04.2015,,0,153.72, -52,2,05.04.2015,,3,153.72, -52,2,06.04.2015,,0,153.72, -52,2,07.04.2015,,0,153.72, -52,2,08.04.2015,,0,153.72, -52,2,09.04.2015,,0,153.72, -52,2,10.04.2015,,0,153.72, -52,2,11.04.2015,,0,153.72, -52,2,12.04.2015,,0,153.72, -52,2,13.04.2015,,0,153.72, -52,2,14.04.2015,,0,153.72, -52,2,15.04.2015,,0,153.72, -52,2,16.04.2015,,0,153.72, -52,2,17.04.2015,,0,153.72, -52,2,18.04.2015,,0,153.72, -52,2,19.04.2015,,0,153.72, -52,2,20.04.2015,,3,153.72, -52,2,21.04.2015,,0,153.72, -52,2,22.04.2015,,0,153.72, -52,2,23.04.2015,,0,153.72, -52,2,24.04.2015,,0,153.72, -52,2,25.04.2015,,0,153.72, -52,2,26.04.2015,,0,158.34, -52,2,27.04.2015,,0,158.34, -52,2,28.04.2015,,0,158.34, -52,2,29.04.2015,,0,158.34, -52,2,30.04.2015,,0,158.34, -52,2,01.05.2015,,0,158.34, -52,2,02.05.2015,,0,158.34, -52,2,03.05.2015,,0,158.34, -52,2,04.05.2015,,0,158.34, -52,2,05.05.2015,,0,158.34, -52,2,06.05.2015,,0,158.34, -52,2,07.05.2015,,0,158.34, -52,2,08.05.2015,,0,158.34, -52,2,09.05.2015,,0,158.34, -52,2,10.05.2015,,0,158.34, -52,2,11.05.2015,,0,158.34, -52,2,12.05.2015,,6,158.34, -52,2,13.05.2015,,0,158.34, -52,2,14.05.2015,,0,158.34, -52,2,15.05.2015,,0,158.34, -52,2,16.05.2015,,0,158.34, -52,2,17.05.2015,,0,158.34, -52,2,18.05.2015,,0,158.34, -52,2,19.05.2015,,0,158.34, -52,2,20.05.2015,,0,158.34, -52,2,21.05.2015,,0,158.34, -52,2,22.05.2015,,0,158.34, -52,2,23.05.2015,,0,158.34, -52,2,24.05.2015,,0,158.34, -52,2,25.05.2015,,0,158.34, -52,2,26.05.2015,,3,158.34, -52,2,27.05.2015,,0,158.34, -52,2,28.05.2015,,0,158.34, -52,2,29.05.2015,,0,158.34, -52,2,30.05.2015,,16,158.34, -52,2,31.05.2015,,6,150.73, -52,2,01.06.2015,,0,150.73, -52,2,02.06.2015,,0,150.73, -52,2,03.06.2015,,0,150.73, -52,2,04.06.2015,,13,150.73, -52,2,05.06.2015,,0,150.73, -52,2,06.06.2015,,3,150.73, -52,2,07.06.2015,,6,150.73, -52,2,08.06.2015,,0,150.73, -52,2,09.06.2015,,0,150.73, -52,2,10.06.2015,,3,150.73, -52,2,11.06.2015,,3,150.73, -52,2,12.06.2015,,3,150.73, -52,2,13.06.2015,,0,150.73, -52,2,14.06.2015,,0,150.73, -52,2,15.06.2015,,0,150.73, -52,2,16.06.2015,,0,150.73, -52,2,17.06.2015,,0,150.73, -52,2,18.06.2015,,6,150.73, -52,2,19.06.2015,,6,150.73, -52,2,20.06.2015,,9,150.73, -52,2,21.06.2015,,9,150.73, -52,2,22.06.2015,,0,150.73, -52,2,23.06.2015,,9,150.73, -52,2,24.06.2015,,13,150.73, -52,2,25.06.2015,,3,150.73, -52,2,26.06.2015,,3,150.73, -52,2,27.06.2015,,0,150.73, -52,2,28.06.2015,,13,150.73, -52,2,29.06.2015,,0,150.73, -52,2,30.06.2015,,6,150.73, -52,2,01.07.2015,,0,150.73, -52,2,02.07.2015,,6,150.73, -52,2,03.07.2015,,22,150.73, -52,2,04.07.2015,,0,150.73, -52,2,05.07.2015,,0,150.73, -52,2,06.07.2015,,0,150.73, -52,2,07.07.2015,,3,150.73, -52,2,08.07.2015,,3,150.73, -52,2,09.07.2015,,0,150.73, -52,2,10.07.2015,,0,150.73, -52,2,11.07.2015,,0,150.73, -52,2,12.07.2015,,0,150.73, -52,2,13.07.2015,,6,150.73, -52,2,14.07.2015,,0,150.73, -52,2,15.07.2015,,0,150.73, -52,2,16.07.2015,,0,150.73, -52,2,17.07.2015,,3,150.73, -52,2,18.07.2015,,3,150.73, -52,2,19.07.2015,,0,150.73, -52,2,20.07.2015,,0,150.73, -52,2,21.07.2015,,3,150.73, -52,2,22.07.2015,,19,150.73, -52,2,23.07.2015,,3,150.73, -52,2,24.07.2015,,0,150.73, -52,2,25.07.2015,,25,150.73, -52,2,26.07.2015,,3,150.73, -52,2,27.07.2015,,3,150.73, -52,2,28.07.2015,,6,150.73, -52,2,29.07.2015,,16,150.73, -52,2,30.07.2015,,3,150.73, -52,2,31.07.2015,,6,150.73, -52,2,01.08.2015,,13,150.73, -52,2,02.08.2015,,6,150.73, -52,2,03.08.2015,,6,150.73, -52,2,04.08.2015,,13,150.73, -52,2,05.08.2015,,13,150.73, -52,2,06.08.2015,,9,150.73, -52,2,07.08.2015,,9,150.73, -52,2,08.08.2015,,9,150.73, -52,2,09.08.2015,,9,150.73, -52,2,10.08.2015,,9,150.73, -52,2,11.08.2015,,0,150.73, -52,2,12.08.2015,,3,128.17, -52,2,13.08.2015,,0,128.17, -52,2,14.08.2015,,6,128.17, -52,2,15.08.2015,,6,128.17, -52,2,16.08.2015,,6,128.17, -52,2,17.08.2015,,3,128.17, -52,2,18.08.2015,1.0,104,128.17,100.3 -52,2,19.08.2015,1.0,182,128.17,100.3 -52,2,20.08.2015,1.0,16,128.17,100.3 -52,2,21.08.2015,1.0,50,128.17,100.3 -52,2,22.08.2015,1.0,85,128.17,100.3 -52,2,23.08.2015,1.0,113,128.17,100.3 -52,2,24.08.2015,1.0,28,128.17,100.3 -52,2,25.08.2015,,6,128.17, -52,2,26.08.2015,,6,128.17, -52,2,27.08.2015,,6,128.17, -52,2,28.08.2015,,6,128.17, -52,2,29.08.2015,,6,128.17, -52,2,30.08.2015,,6,128.17, -52,2,31.08.2015,,6,128.17, -52,2,01.09.2015,,6,128.17, -52,2,02.09.2015,,13,128.17, -52,2,03.09.2015,,6,128.17, -52,2,04.09.2015,,0,128.17, -52,2,05.09.2015,,19,128.17, -52,2,06.09.2015,,6,128.17, -52,2,07.09.2015,,6,128.17, -52,2,08.09.2015,,6,128.17, -52,2,09.09.2015,,6,128.17, -52,2,10.09.2015,,6,128.17, -52,2,11.09.2015,,0,128.17, -52,2,12.09.2015,,6,128.17, -52,2,13.09.2015,,6,128.17, -52,2,14.09.2015,,6,128.17, -52,2,15.09.2015,,6,128.17, -52,2,16.09.2015,,6,128.17, -52,2,17.09.2015,,6,128.17, -52,2,18.09.2015,,6,128.17, -52,2,19.09.2015,,6,128.17, -52,2,20.09.2015,,6,128.17, -52,2,21.09.2015,,6,128.17, -52,2,22.09.2015,,9,128.17, -52,2,23.09.2015,,6,128.17, -52,2,24.09.2015,,3,128.17, -52,2,25.09.2015,,6,128.17, -52,2,26.09.2015,,6,128.17, -52,2,27.09.2015,,6,128.17, -52,2,28.09.2015,,6,128.17, -52,2,29.09.2015,,19,128.17, -52,2,30.09.2015,,6,128.17, -52,2,01.10.2015,,6,162.96, -52,2,02.10.2015,,6,162.96, -52,2,03.10.2015,,6,162.96, -52,2,04.10.2015,,6,162.96, -52,2,05.10.2015,,6,162.96, -52,2,06.10.2015,1.0,13,143.39,122.05 -52,2,07.10.2015,1.0,13,143.39,122.05 -52,2,08.10.2015,1.0,44,143.39,122.05 -52,2,09.10.2015,1.0,35,143.39,122.05 -52,2,10.10.2015,1.0,47,143.39,122.05 -52,2,11.10.2015,1.0,31,143.39,122.05 -52,2,12.10.2015,1.0,38,143.39,122.05 -52,2,13.10.2015,1.0,31,143.93,122.05 -52,2,14.10.2015,1.0,13,143.93,122.05 -52,2,15.10.2015,1.0,41,143.93,122.05 -52,2,16.10.2015,1.0,41,143.93,122.05 -52,2,17.10.2015,1.0,41,162.96,122.05 -52,2,18.10.2015,1.0,41,162.96,122.05 -52,2,19.10.2015,,6,162.96, -52,2,20.10.2015,,6,162.96, -52,2,21.10.2015,,6,162.96, -52,2,22.10.2015,,6,162.96, -52,2,23.10.2015,,0,162.96, -52,2,24.10.2015,,0,162.96, -52,2,25.10.2015,,0,162.96, -52,2,26.10.2015,,0,162.96, -52,2,27.10.2015,,0,162.96, -52,2,28.10.2015,,0,162.96, -52,2,29.10.2015,,0,162.96, -52,2,30.10.2015,,0,162.96, -52,2,31.10.2015,,6,162.96, -52,2,01.11.2015,,3,162.96, -52,2,02.11.2015,,3,162.96, -52,2,03.11.2015,,3,162.96, -52,2,04.11.2015,,3,162.96, -52,2,05.11.2015,,3,162.96, -52,2,06.11.2015,,0,162.96, -52,2,07.11.2015,,0,162.96, -52,2,08.11.2015,,0,162.96, -52,2,09.11.2015,,0,162.96, -52,2,10.11.2015,,0,162.96, -52,2,11.11.2015,,3,138.36, -52,2,12.11.2015,,0,138.36, -52,2,13.11.2015,,0,138.36, -52,2,14.11.2015,,3,138.36, -52,2,15.11.2015,,0,138.36, -52,2,16.11.2015,,0,138.36, -52,2,17.11.2015,,0,138.36, -52,2,18.11.2015,,0,138.36, -52,2,19.11.2015,,0,138.36, -52,2,20.11.2015,,0,138.36, -52,2,21.11.2015,,0,138.36, -52,2,22.11.2015,,0,138.36, -52,2,23.11.2015,,0,138.36, -52,2,24.11.2015,,0,138.36, -52,2,25.11.2015,,0,138.36, -52,2,26.11.2015,,0,138.36, -52,2,27.11.2015,,0,138.36, -52,2,28.11.2015,,0,138.36, -52,2,29.11.2015,,0,138.36, -52,2,30.11.2015,,0,138.36, -52,2,01.12.2015,,0,162.96, -52,2,02.12.2015,,0,162.96, -52,2,03.12.2015,,0,138.5, -52,2,04.12.2015,,0,138.5, -52,2,05.12.2015,,0,138.5, -52,2,06.12.2015,,0,138.5, -52,2,07.12.2015,,0,138.5, -52,2,08.12.2015,,0,138.5, -52,2,09.12.2015,,0,138.5, -52,2,10.12.2015,,0,138.5, -52,2,11.12.2015,,0,142.57, -52,2,12.12.2015,,0,142.57, -52,2,13.12.2015,,0,142.57, -52,2,14.12.2015,,0,142.57, -52,2,15.12.2015,,0,142.57, -52,2,16.12.2015,,0,142.57, -52,2,17.12.2015,,0,142.57, -52,2,18.12.2015,,0,142.57, -52,2,19.12.2015,,0,142.57, -52,2,20.12.2015,,0,142.57, -52,2,21.12.2015,,0,142.57, -52,2,22.12.2015,1.0,35,142.57,103.02 -52,2,23.12.2015,1.0,66,142.57,103.02 -52,2,24.12.2015,1.0,13,142.57,103.02 -52,2,25.12.2015,1.0,57,142.57,103.02 -52,2,26.12.2015,1.0,57,142.57,103.02 -52,2,27.12.2015,1.0,35,142.57,103.02 -52,2,28.12.2015,1.0,35,142.57,103.02 -52,2,29.12.2015,,0,138.5, -52,2,30.12.2015,,0,138.5, -52,2,31.12.2015,,0,138.5, -52,2,01.01.2016,,0,138.5, -52,2,02.01.2016,,0,138.5, -52,2,03.01.2016,,0,138.5, -52,2,04.01.2016,,0,138.5, -52,2,05.01.2016,,0,138.5, -52,2,06.01.2016,,0,138.5, -52,2,07.01.2016,,0,138.5, -52,2,08.01.2016,,0,138.5, -52,2,09.01.2016,,0,138.5, -52,2,10.01.2016,,0,138.5, -52,2,11.01.2016,,0,138.5, -52,2,12.01.2016,,0,138.5, -52,2,13.01.2016,,0,138.5, -52,2,14.01.2016,,0,138.5, -52,2,15.01.2016,,0,138.5, -52,2,16.01.2016,,0,142.57, -52,2,17.01.2016,,0,142.57, -52,2,18.01.2016,,0,142.57, -52,2,19.01.2016,1.0,35,142.57,103.02 -52,2,20.01.2016,1.0,79,142.57,103.02 -52,2,21.01.2016,1.0,38,142.57,103.02 -52,2,22.01.2016,1.0,91,142.57,103.02 -52,2,23.01.2016,1.0,22,142.57,103.02 -52,2,24.01.2016,1.0,44,142.57,103.02 -52,2,25.01.2016,1.0,44,142.57,103.02 -52,2,26.01.2016,,0,142.57, -52,2,27.01.2016,,0,142.57, -52,2,28.01.2016,,0,142.57, -52,2,29.01.2016,,6,142.57, -52,2,30.01.2016,,0,142.57, -52,2,31.01.2016,,0,142.57, -52,2,01.02.2016,,3,142.57, -52,2,02.02.2016,,0,142.57, -52,2,03.02.2016,,3,142.57, -52,2,04.02.2016,,0,142.57, -52,2,05.02.2016,,0,142.57, -52,2,06.02.2016,,0,142.57, -52,2,07.02.2016,,0,142.57, -52,2,08.02.2016,,0,142.57, -52,2,09.02.2016,1.0,16,142.57,108.46 -52,2,10.02.2016,1.0,31,138.36,108.46 -52,2,11.02.2016,1.0,41,138.36,108.46 -52,2,12.02.2016,1.0,50,138.36,108.46 -52,2,13.02.2016,1.0,119,138.36,108.46 -52,2,14.02.2016,1.0,9,138.36,108.46 -52,2,15.02.2016,1.0,44,138.36,108.46 -52,2,16.02.2016,,3,138.36, -52,2,17.02.2016,,3,138.36, -52,2,18.02.2016,,0,138.36, -52,2,19.02.2016,,3,138.36, -52,2,20.02.2016,,9,138.36, -52,2,21.02.2016,,0,138.36, -52,2,22.02.2016,,9,138.36, -52,2,23.02.2016,,6,138.36, -52,2,24.02.2016,,16,138.36, -52,2,25.02.2016,,3,138.36, -52,2,26.02.2016,,3,138.36, -52,2,27.02.2016,,3,138.36, -52,2,28.02.2016,,3,138.36, -52,2,29.02.2016,,3,138.36, -52,2,01.03.2016,,3,138.36, -52,2,02.03.2016,,3,138.36, -52,2,03.03.2016,,3,138.36, -52,2,04.03.2016,,0,138.36, -52,2,05.03.2016,,3,138.36, -52,2,06.03.2016,,22,138.36, -52,2,07.03.2016,,0,138.36, -52,2,08.03.2016,,0,138.36, -52,2,09.03.2016,,0,138.36, -52,2,10.03.2016,,3,138.36, -52,2,11.03.2016,,0,138.36, -52,2,12.03.2016,,0,138.5, -52,2,13.03.2016,,13,138.5, -52,2,14.03.2016,,0,138.5, -52,2,15.03.2016,1.0,9,138.5,124.77 -52,2,16.03.2016,1.0,38,138.5,124.77 -52,2,17.03.2016,1.0,6,138.5,124.77 -52,2,18.03.2016,1.0,38,138.5,124.77 -52,2,19.03.2016,1.0,3,138.5,124.77 -52,2,20.03.2016,1.0,44,138.5,124.77 -52,2,21.03.2016,1.0,38,138.5,124.77 -52,2,22.03.2016,,3,138.36, -52,2,23.03.2016,,0,138.36, -52,2,24.03.2016,,3,138.36, -52,2,25.03.2016,,3,138.36, -52,2,26.03.2016,,0,138.36, -52,2,27.03.2016,,0,138.36, -52,2,28.03.2016,,0,138.36, -52,2,29.03.2016,,0,138.36, -52,2,30.03.2016,,0,138.36, -52,2,31.03.2016,,0,138.36, -52,2,01.04.2016,,0,138.36, -52,2,02.04.2016,,0,138.36, -52,2,03.04.2016,,0,138.36, -52,2,04.04.2016,,0,138.36, -52,2,05.04.2016,,0,138.36, -52,2,06.04.2016,,0,138.36, -52,2,07.04.2016,,0,138.36, -52,2,08.04.2016,,0,138.36, -52,2,09.04.2016,,3,138.5, -52,2,10.04.2016,,3,138.5, -52,2,11.04.2016,,0,138.5, -52,2,12.04.2016,,6,138.5, -52,2,13.04.2016,,6,138.5, -52,2,14.04.2016,,0,138.5, -52,2,15.04.2016,,3,138.5, -52,2,16.04.2016,,0,138.5, -52,2,17.04.2016,,3,138.5, -52,2,18.04.2016,,3,138.5, -52,2,19.04.2016,1.0,9,138.5,116.89 -52,2,20.04.2016,1.0,69,138.5,116.89 -52,2,21.04.2016,1.0,47,138.5,116.89 -52,2,22.04.2016,1.0,47,138.5,116.89 -52,2,23.04.2016,1.0,35,138.5,116.89 -52,2,24.04.2016,1.0,22,138.5,116.89 -52,2,25.04.2016,1.0,60,138.5,116.89 -52,2,26.04.2016,,0,138.5, -52,2,27.04.2016,,0,138.5, -52,2,28.04.2016,,0,138.5, -52,2,29.04.2016,,0,138.5, -52,2,30.04.2016,,0,138.5, -52,2,01.05.2016,,3,138.5, -52,2,02.05.2016,,3,138.5, -52,2,03.05.2016,1.0,60,138.5,106.01 -52,2,04.05.2016,1.0,167,138.5,106.01 -52,2,05.05.2016,1.0,72,138.5,106.01 -52,2,06.05.2016,1.0,66,138.5,106.01 -52,2,07.05.2016,1.0,113,138.5,106.01 -52,2,08.05.2016,1.0,57,138.5,106.01 -52,2,09.05.2016,1.0,53,138.5,106.01 -52,2,10.05.2016,,0,142.57, -52,2,11.05.2016,,0,142.57, -52,2,12.05.2016,,0,142.57, -52,2,13.05.2016,,0,142.57, -52,2,14.05.2016,,0,142.57, -52,2,15.05.2016,,6,142.57, -52,2,16.05.2016,,3,142.57, -52,2,17.05.2016,,9,142.57, -52,2,18.05.2016,,0,142.57, -52,2,19.05.2016,,0,142.57, -52,2,20.05.2016,,0,142.57, -52,2,21.05.2016,,0,142.57, -52,2,22.05.2016,,0,142.57, -53,2,01.01.2015,,3,135.78, -53,2,02.01.2015,,13,135.78, -53,2,03.01.2015,,9,135.78, -53,2,04.01.2015,,0,135.78, -53,2,05.01.2015,,13,135.78, -53,2,06.01.2015,,47,135.78, -53,2,07.01.2015,,0,135.78, -53,2,08.01.2015,,13,135.78, -53,2,09.01.2015,,9,135.78, -53,2,10.01.2015,,9,135.78, -53,2,11.01.2015,,3,135.78, -53,2,12.01.2015,,9,135.78, -53,2,13.01.2015,,13,135.78, -53,2,14.01.2015,,6,135.78, -53,2,15.01.2015,,6,135.78, -53,2,16.01.2015,,19,135.78, -53,2,17.01.2015,,16,135.78, -53,2,18.01.2015,,13,135.78, -53,2,19.01.2015,,3,135.78, -53,2,20.01.2015,,3,135.78, -53,2,21.01.2015,,0,135.78, -53,2,22.01.2015,,3,135.78, -53,2,23.01.2015,,13,135.78, -53,2,24.01.2015,,3,135.78, -53,2,25.01.2015,,3,135.78, -53,2,26.01.2015,,3,135.78, -53,2,27.01.2015,,6,135.78, -53,2,28.01.2015,,6,135.78, -53,2,29.01.2015,,9,135.78, -53,2,30.01.2015,,0,135.78, -53,2,31.01.2015,,3,135.78, -53,2,01.02.2015,,9,135.78, -53,2,02.02.2015,,19,135.78, -53,2,03.02.2015,,0,135.78, -53,2,04.02.2015,,9,135.78, -53,2,05.02.2015,,0,135.78, -53,2,06.02.2015,,13,135.78, -53,2,07.02.2015,,9,135.78, -53,2,08.02.2015,,3,135.78, -53,2,09.02.2015,,6,135.78, -53,2,10.02.2015,,0,135.78, -53,2,11.02.2015,,6,135.78, -53,2,12.02.2015,,0,135.78, -53,2,13.02.2015,,6,135.78, -53,2,14.02.2015,,16,135.78, -53,2,15.02.2015,,13,135.78, -53,2,16.02.2015,,3,135.78, -53,2,17.02.2015,,3,135.78, -53,2,18.02.2015,,16,135.78, -53,2,19.02.2015,,6,135.78, -53,2,20.02.2015,,16,135.78, -53,2,21.02.2015,,19,135.78, -53,2,22.02.2015,,22,135.78, -53,2,23.02.2015,,0,135.78, -53,2,24.02.2015,,0,135.78, -53,2,25.02.2015,,0,135.78, -53,2,26.02.2015,,0,135.78, -53,2,27.02.2015,,0,135.78, -53,2,28.02.2015,,0,135.78, -53,2,01.03.2015,,3,135.78, -53,2,02.03.2015,,6,146.38, -53,2,03.03.2015,,3,146.38, -53,2,04.03.2015,,6,146.38, -53,2,05.03.2015,,0,146.38, -53,2,06.03.2015,,0,146.38, -53,2,07.03.2015,,0,146.38, -53,2,08.03.2015,,0,146.38, -53,2,09.03.2015,,3,146.38, -53,2,10.03.2015,,9,146.38, -53,2,11.03.2015,,9,146.38, -53,2,12.03.2015,,16,146.38, -53,2,13.03.2015,,0,146.38, -53,2,14.03.2015,,9,146.38, -53,2,15.03.2015,,9,146.38, -53,2,16.03.2015,,6,146.38, -53,2,17.03.2015,,0,146.38, -53,2,18.03.2015,,0,146.38, -53,2,19.03.2015,,3,146.38, -53,2,20.03.2015,,6,146.38, -53,2,21.03.2015,,6,146.38, -53,2,22.03.2015,,3,146.38, -53,2,23.03.2015,,0,146.38, -53,2,24.03.2015,,3,146.38, -53,2,25.03.2015,,0,146.38, -53,2,26.03.2015,,6,146.38, -53,2,27.03.2015,,0,146.38, -53,2,28.03.2015,,3,146.38, -53,2,29.03.2015,,0,146.38, -53,2,30.03.2015,,0,146.38, -53,2,31.03.2015,,9,146.38, -53,2,01.04.2015,,13,146.38, -53,2,02.04.2015,,3,146.38, -53,2,03.04.2015,,0,146.38, -53,2,04.04.2015,,3,146.38, -53,2,05.04.2015,,3,146.38, -53,2,06.04.2015,,9,146.38, -53,2,07.04.2015,,3,146.38, -53,2,08.04.2015,,0,146.38, -53,2,09.04.2015,,6,146.38, -53,2,10.04.2015,,0,146.38, -53,2,11.04.2015,,13,146.38, -53,2,12.04.2015,,3,146.38, -53,2,13.04.2015,,0,146.38, -53,2,14.04.2015,,6,146.38, -53,2,15.04.2015,,3,146.38, -53,2,16.04.2015,,3,146.38, -53,2,17.04.2015,,0,146.38, -53,2,18.04.2015,,9,146.38, -53,2,19.04.2015,,22,146.38, -53,2,20.04.2015,,16,145.84, -53,2,21.04.2015,,3,145.84, -53,2,22.04.2015,,0,145.84, -53,2,23.04.2015,,6,145.84, -53,2,24.04.2015,,16,145.84, -53,2,25.04.2015,,9,145.84, -53,2,26.04.2015,,6,150.73, -53,2,27.04.2015,,9,150.73, -53,2,28.04.2015,,3,150.73, -53,2,29.04.2015,,0,150.73, -53,2,30.04.2015,,0,150.73, -53,2,01.05.2015,,0,150.73, -53,2,02.05.2015,,0,150.73, -53,2,03.05.2015,,6,150.73, -53,2,04.05.2015,,9,150.73, -53,2,05.05.2015,,3,150.73, -53,2,06.05.2015,,6,150.73, -53,2,07.05.2015,,0,150.73, -53,2,08.05.2015,,0,150.73, -53,2,09.05.2015,,6,150.73, -53,2,10.05.2015,,6,150.73, -53,2,11.05.2015,,0,150.73, -53,2,12.05.2015,,9,150.73, -53,2,13.05.2015,,16,150.73, -53,2,14.05.2015,,0,150.73, -53,2,15.05.2015,,16,150.73, -53,2,16.05.2015,,3,150.73, -53,2,17.05.2015,,6,150.73, -53,2,18.05.2015,,0,150.73, -53,2,19.05.2015,,3,150.73, -53,2,20.05.2015,,0,150.73, -53,2,21.05.2015,,0,150.73, -53,2,22.05.2015,,3,150.73, -53,2,23.05.2015,,0,150.73, -53,2,24.05.2015,,3,150.73, -53,2,25.05.2015,,6,150.73, -53,2,26.05.2015,,0,150.73, -53,2,27.05.2015,,3,150.73, -53,2,28.05.2015,,3,150.73, -53,2,29.05.2015,,6,150.73, -53,2,30.05.2015,,0,150.73, -53,2,31.05.2015,,0,150.73, -53,2,01.06.2015,,0,150.73, -53,2,02.06.2015,,0,150.73, -53,2,03.06.2015,,0,150.73, -53,2,04.06.2015,,3,150.73, -53,2,05.06.2015,,9,150.73, -53,2,06.06.2015,,0,150.73, -53,2,07.06.2015,,3,150.73, -53,2,08.06.2015,,3,150.73, -53,2,09.06.2015,,0,150.73, -53,2,10.06.2015,,13,150.73, -53,2,11.06.2015,,3,150.73, -53,2,12.06.2015,,0,150.73, -53,2,13.06.2015,,13,150.73, -53,2,14.06.2015,,9,150.73, -53,2,15.06.2015,,6,150.73, -53,2,16.06.2015,,6,150.73, -53,2,17.06.2015,,0,150.73, -53,2,18.06.2015,,9,150.73, -53,2,19.06.2015,,6,150.73, -53,2,20.06.2015,,0,150.73, -53,2,21.06.2015,,0,150.73, -53,2,22.06.2015,,13,150.73, -53,2,23.06.2015,,3,150.73, -53,2,24.06.2015,,0,150.73, -53,2,25.06.2015,,0,150.73, -53,2,26.06.2015,,0,150.73, -53,2,27.06.2015,,9,150.73, -53,2,28.06.2015,,16,150.73, -53,2,29.06.2015,,0,150.73, -53,2,30.06.2015,,3,150.73, -53,2,01.07.2015,,13,150.73, -53,2,02.07.2015,,0,150.73, -53,2,03.07.2015,,9,150.73, -53,2,04.07.2015,,6,150.73, -53,2,05.07.2015,,6,150.73, -53,2,06.07.2015,,6,150.73, -53,2,07.07.2015,,6,150.73, -53,2,08.07.2015,,6,150.73, -53,2,09.07.2015,,6,150.73, -53,2,10.07.2015,,6,150.73, -53,2,11.07.2015,,6,150.73, -53,2,12.07.2015,,6,150.73, -53,2,13.07.2015,,6,150.73, -53,2,14.07.2015,,6,150.73, -53,2,15.07.2015,,6,150.73, -53,2,16.07.2015,,6,150.73, -53,2,17.07.2015,,6,150.73, -53,2,18.07.2015,,6,150.73, -53,2,19.07.2015,,6,150.73, -53,2,20.07.2015,,6,150.73, -53,2,21.07.2015,,6,150.73, -53,2,22.07.2015,,6,150.73, -53,2,23.07.2015,,0,150.73, -53,2,24.07.2015,,6,150.73, -53,2,25.07.2015,,6,150.73, -53,2,26.07.2015,,6,150.73, -53,2,27.07.2015,,6,150.73, -53,2,28.07.2015,,0,150.73, -53,2,29.07.2015,,0,150.73, -53,2,30.07.2015,,0,150.73, -53,2,31.07.2015,,0,150.73, -53,2,01.08.2015,,0,150.73, -53,2,02.08.2015,,0,150.73, -53,2,03.08.2015,,0,150.73, -53,2,04.08.2015,,0,150.73, -53,2,05.08.2015,,0,150.73, -53,2,06.08.2015,,0,150.73, -53,2,07.08.2015,,0,150.73, -53,2,08.08.2015,,0,150.73, -53,2,09.08.2015,,0,150.73, -53,2,10.08.2015,,19,150.73, -53,2,11.08.2015,,3,150.73, -53,2,12.08.2015,,3,128.17, -53,2,13.08.2015,,3,128.17, -53,2,14.08.2015,,13,128.17, -53,2,15.08.2015,,6,128.17, -53,2,16.08.2015,,22,128.17, -53,2,17.08.2015,,13,128.17, -53,2,18.08.2015,1.0,207,128.17,100.3 -53,2,19.08.2015,1.0,88,128.17,100.3 -53,2,20.08.2015,1.0,135,128.17,100.3 -53,2,21.08.2015,1.0,182,128.17,100.3 -53,2,22.08.2015,1.0,157,128.17,100.3 -53,2,23.08.2015,1.0,145,128.17,100.3 -53,2,24.08.2015,1.0,63,128.17,100.3 -53,2,25.08.2015,,22,128.17, -53,2,26.08.2015,,9,128.17, -53,2,27.08.2015,,9,128.17, -53,2,28.08.2015,,9,128.17, -53,2,29.08.2015,,9,128.17, -53,2,30.08.2015,,9,128.17, -53,2,31.08.2015,,9,162.96, -53,2,01.09.2015,,9,162.96, -53,2,02.09.2015,,9,162.96, -53,2,03.09.2015,,9,162.96, -53,2,04.09.2015,,9,162.96, -53,2,05.09.2015,,9,162.96, -53,2,06.09.2015,,9,162.96, -53,2,07.09.2015,,9,162.96, -53,2,08.09.2015,,9,162.96, -53,2,09.09.2015,,9,162.96, -53,2,10.09.2015,,9,162.96, -53,2,11.09.2015,,9,162.96, -53,2,12.09.2015,,9,162.96, -53,2,13.09.2015,,9,162.96, -53,2,14.09.2015,,9,162.96, -53,2,15.09.2015,,9,162.96, -53,2,16.09.2015,,9,162.96, -53,2,17.09.2015,,9,162.96, -53,2,18.09.2015,,0,162.96, -53,2,19.09.2015,,0,162.96, -53,2,20.09.2015,,0,162.96, -53,2,21.09.2015,,0,162.96, -53,2,22.09.2015,,0,162.96, -53,2,23.09.2015,,0,162.96, -53,2,24.09.2015,,0,162.96, -53,2,25.09.2015,,0,162.96, -53,2,26.09.2015,,0,162.96, -53,2,27.09.2015,,0,162.96, -53,2,28.09.2015,,19,162.96, -53,2,29.09.2015,,0,162.96, -53,2,30.09.2015,,0,162.96, -53,2,01.10.2015,,0,162.96, -53,2,02.10.2015,,0,162.96, -53,2,03.10.2015,,3,162.96, -53,2,04.10.2015,,3,162.96, -53,2,05.10.2015,,6,162.96, -53,2,06.10.2015,1.0,173,143.39,122.05 -53,2,07.10.2015,1.0,79,143.39,122.05 -53,2,08.10.2015,1.0,69,143.39,122.05 -53,2,09.10.2015,1.0,79,143.39,122.05 -53,2,10.10.2015,1.0,94,143.39,122.05 -53,2,11.10.2015,1.0,119,143.39,122.05 -53,2,12.10.2015,1.0,91,143.39,122.05 -53,2,13.10.2015,1.0,57,143.93,122.05 -53,2,14.10.2015,1.0,110,143.93,122.05 -53,2,15.10.2015,1.0,110,143.93,122.05 -53,2,16.10.2015,1.0,110,143.93,122.05 -53,2,17.10.2015,1.0,110,143.93,122.05 -53,2,18.10.2015,1.0,110,143.93,122.05 -53,2,19.10.2015,,9,143.93, -53,2,20.10.2015,,9,143.93, -53,2,21.10.2015,,9,143.93, -53,2,22.10.2015,,9,143.93, -53,2,23.10.2015,,9,143.93, -53,2,24.10.2015,,9,143.93, -53,2,25.10.2015,,9,143.93, -53,2,26.10.2015,,9,143.93, -53,2,27.10.2015,,9,143.93, -53,2,28.10.2015,,9,143.93, -53,2,29.10.2015,,9,143.93, -53,2,30.10.2015,,9,143.93, -53,2,31.10.2015,,9,143.93, -53,2,01.11.2015,,0,143.93, -53,2,02.11.2015,,0,143.93, -53,2,03.11.2015,,0,143.93, -53,2,04.11.2015,,0,143.93, -53,2,05.11.2015,,0,143.93, -53,2,06.11.2015,,0,143.93, -53,2,07.11.2015,,0,143.93, -53,2,08.11.2015,,0,143.93, -53,2,09.11.2015,,0,143.93, -53,2,10.11.2015,,0,143.93, -53,2,11.11.2015,,0,143.93, -53,2,12.11.2015,,0,143.93, -53,2,13.11.2015,,0,143.93, -53,2,14.11.2015,,0,143.93, -53,2,15.11.2015,,0,143.93, -53,2,16.11.2015,,0,143.93, -53,2,17.11.2015,,0,143.93, -53,2,18.11.2015,,0,143.93, -53,2,19.11.2015,,0,143.93, -53,2,20.11.2015,,0,143.93, -53,2,21.11.2015,,0,143.93, -53,2,22.11.2015,,0,143.93, -53,2,23.11.2015,,0,143.93, -53,2,24.11.2015,,0,143.93, -53,2,25.11.2015,,0,143.93, -53,2,26.11.2015,,0,143.93, -53,2,27.11.2015,,0,143.93, -53,2,28.11.2015,,0,143.93, -53,2,29.11.2015,,0,143.93, -53,2,30.11.2015,,0,143.93, -53,2,01.12.2015,,0,143.93, -53,2,02.12.2015,,0,143.93, -53,2,03.12.2015,,0,143.93, -53,2,04.12.2015,,0,143.93, -53,2,05.12.2015,,0,143.93, -53,2,06.12.2015,,0,143.93, -53,2,07.12.2015,,0,143.93, -53,2,08.12.2015,,0,143.93, -53,2,09.12.2015,,0,138.5, -53,2,10.12.2015,,0,138.5, -53,2,11.12.2015,,0,142.57, -53,2,12.12.2015,,0,142.57, -53,2,13.12.2015,,19,142.57, -53,2,14.12.2015,,0,142.57, -53,2,15.12.2015,,6,142.57, -53,2,16.12.2015,,9,142.57, -53,2,17.12.2015,,3,142.57, -53,2,18.12.2015,,3,142.57, -53,2,19.12.2015,,3,142.57, -53,2,20.12.2015,,3,142.57, -53,2,21.12.2015,,16,142.57, -53,2,22.12.2015,1.0,104,142.57,103.02 -53,2,23.12.2015,1.0,126,142.57,103.02 -53,2,24.12.2015,1.0,126,142.57,103.02 -53,2,25.12.2015,1.0,91,142.57,103.02 -53,2,26.12.2015,1.0,110,142.57,103.02 -53,2,27.12.2015,1.0,88,142.57,103.02 -53,2,28.12.2015,1.0,97,142.57,103.02 -53,2,29.12.2015,,3,138.5, -53,2,30.12.2015,,0,138.5, -53,2,31.12.2015,,0,138.5, -53,2,01.01.2016,,6,138.5, -53,2,02.01.2016,,0,138.5, -53,2,03.01.2016,,0,138.5, -53,2,04.01.2016,,25,138.5, -53,2,05.01.2016,,3,138.5, -53,2,06.01.2016,,6,138.5, -53,2,07.01.2016,,3,138.5, -53,2,08.01.2016,,3,138.5, -53,2,09.01.2016,,3,138.5, -53,2,10.01.2016,,0,138.5, -53,2,11.01.2016,,0,138.5, -53,2,12.01.2016,,9,138.5, -53,2,13.01.2016,,0,138.5, -53,2,14.01.2016,,3,138.5, -53,2,15.01.2016,,0,138.5, -53,2,16.01.2016,,3,142.57, -53,2,17.01.2016,,0,142.57, -53,2,18.01.2016,,3,142.57, -53,2,19.01.2016,1.0,113,142.57,103.02 -53,2,20.01.2016,1.0,101,142.57,103.02 -53,2,21.01.2016,1.0,101,142.57,103.02 -53,2,22.01.2016,1.0,220,142.57,103.02 -53,2,23.01.2016,1.0,157,142.57,103.02 -53,2,24.01.2016,1.0,88,142.57,103.02 -53,2,25.01.2016,1.0,176,142.57,103.02 -53,2,26.01.2016,1.0,97,142.57,103.02 -53,2,27.01.2016,1.0,119,142.57,103.02 -53,2,28.01.2016,1.0,41,142.57,103.02 -53,2,29.01.2016,,3,142.57, -53,2,30.01.2016,,0,142.57, -53,2,31.01.2016,,3,142.57, -53,2,01.02.2016,,6,142.57, -53,2,02.02.2016,,3,142.57, -53,2,03.02.2016,,16,142.57, -53,2,04.02.2016,,9,142.57, -53,2,05.02.2016,,19,142.57, -53,2,06.02.2016,,41,142.57, -53,2,07.02.2016,,3,142.57, -53,2,08.02.2016,,6,142.57, -53,2,09.02.2016,1.0,179,142.57,108.46 -53,2,10.02.2016,1.0,192,138.36,108.46 -53,2,11.02.2016,1.0,104,138.36,108.46 -53,2,12.02.2016,1.0,123,138.36,108.46 -53,2,13.02.2016,1.0,151,138.36,108.46 -53,2,14.02.2016,1.0,135,138.36,108.46 -53,2,15.02.2016,1.0,123,138.36,108.46 -53,2,16.02.2016,,3,138.36, -53,2,17.02.2016,,0,138.36, -53,2,18.02.2016,,13,138.36, -53,2,19.02.2016,,0,138.36, -53,2,20.02.2016,,9,138.36, -53,2,21.02.2016,,13,138.36, -53,2,22.02.2016,,3,138.36, -53,2,23.02.2016,,9,138.36, -53,2,24.02.2016,,3,138.36, -53,2,25.02.2016,,9,138.36, -53,2,26.02.2016,,9,138.36, -53,2,27.02.2016,,9,138.36, -53,2,28.02.2016,,9,138.36, -53,2,29.02.2016,,9,138.36, -53,2,01.03.2016,,9,138.36, -53,2,02.03.2016,,9,138.36, -53,2,03.03.2016,,3,138.36, -53,2,04.03.2016,,0,138.36, -53,2,05.03.2016,,9,138.36, -53,2,06.03.2016,,0,138.36, -53,2,07.03.2016,,3,138.36, -53,2,08.03.2016,,6,138.36, -53,2,09.03.2016,,3,138.36, -53,2,10.03.2016,,6,138.36, -53,2,11.03.2016,,6,138.36, -53,2,12.03.2016,,16,138.5, -53,2,13.03.2016,,6,138.5, -53,2,14.03.2016,,19,138.5, -53,2,15.03.2016,1.0,79,138.5,124.77 -53,2,16.03.2016,1.0,88,138.5,124.77 -53,2,17.03.2016,1.0,69,138.5,124.77 -53,2,18.03.2016,1.0,97,138.5,124.77 -53,2,19.03.2016,1.0,66,138.5,124.77 -53,2,20.03.2016,1.0,63,138.5,124.77 -53,2,21.03.2016,1.0,50,138.5,124.77 -53,2,22.03.2016,,6,138.36, -53,2,23.03.2016,,0,138.36, -53,2,24.03.2016,,9,138.36, -53,2,25.03.2016,,0,138.36, -53,2,26.03.2016,,9,138.36, -53,2,27.03.2016,,16,138.36, -53,2,28.03.2016,,9,138.36, -53,2,29.03.2016,,6,138.36, -53,2,30.03.2016,,3,138.36, -53,2,31.03.2016,,0,138.36, -53,2,01.04.2016,,6,138.36, -53,2,02.04.2016,,0,138.36, -53,2,03.04.2016,,0,138.36, -53,2,04.04.2016,,0,138.36, -53,2,05.04.2016,,0,138.36, -53,2,06.04.2016,,0,138.36, -53,2,07.04.2016,,6,138.36, -53,2,08.04.2016,,6,138.36, -53,2,09.04.2016,,0,138.5, -53,2,10.04.2016,,6,138.5, -53,2,11.04.2016,,0,138.5, -53,2,12.04.2016,,6,138.5, -53,2,13.04.2016,,13,138.5, -53,2,14.04.2016,,0,138.5, -53,2,15.04.2016,,0,138.5, -53,2,16.04.2016,,0,138.5, -53,2,17.04.2016,,0,138.5, -53,2,18.04.2016,,0,138.5, -53,2,19.04.2016,1.0,85,138.5,116.89 -53,2,20.04.2016,1.0,85,138.5,116.89 -53,2,21.04.2016,1.0,94,138.5,116.89 -53,2,22.04.2016,1.0,91,138.5,116.89 -53,2,23.04.2016,1.0,85,138.5,116.89 -53,2,24.04.2016,1.0,75,138.5,116.89 -53,2,25.04.2016,1.0,75,138.5,116.89 -53,2,26.04.2016,,16,138.5, -53,2,27.04.2016,,3,138.5, -53,2,28.04.2016,,0,138.5, -53,2,29.04.2016,,6,138.5, -53,2,30.04.2016,,9,138.5, -53,2,01.05.2016,,6,138.5, -53,2,02.05.2016,,6,138.5, -53,2,03.05.2016,1.0,179,138.5,106.01 -53,2,04.05.2016,1.0,145,138.5,106.01 -53,2,05.05.2016,1.0,179,138.5,106.01 -53,2,06.05.2016,1.0,157,138.5,106.01 -53,2,07.05.2016,1.0,148,138.5,106.01 -53,2,08.05.2016,1.0,41,138.5,106.01 -53,2,09.05.2016,1.0,66,138.5,106.01 -53,2,10.05.2016,,19,138.5, -53,2,11.05.2016,,16,138.5, -53,2,12.05.2016,,31,138.5, -53,2,13.05.2016,,0,138.5, -53,2,14.05.2016,,0,138.5, -53,2,15.05.2016,,0,138.5, -53,2,16.05.2016,,0,138.5, -53,2,17.05.2016,,9,138.5, -53,2,18.05.2016,,9,138.5, -53,2,19.05.2016,,9,138.5, -53,2,20.05.2016,,9,138.5, -53,2,21.05.2016,,9,138.5, -53,2,22.05.2016,,9,138.5, -54,2,01.01.2015,,0,135.78, -54,2,02.01.2015,,0,135.78, -54,2,03.01.2015,,0,135.78, -54,2,04.01.2015,,16,135.78, -54,2,05.01.2015,,9,135.78, -54,2,06.01.2015,,0,135.78, -54,2,07.01.2015,,0,135.78, -54,2,08.01.2015,,0,135.78, -54,2,09.01.2015,,0,135.78, -54,2,10.01.2015,,0,135.78, -54,2,11.01.2015,,6,135.78, -54,2,12.01.2015,,6,135.78, -54,2,13.01.2015,,0,135.78, -54,2,14.01.2015,,0,135.78, -54,2,15.01.2015,,0,135.78, -54,2,16.01.2015,,6,135.78, -54,2,17.01.2015,,0,135.78, -54,2,18.01.2015,,6,135.78, -54,2,19.01.2015,,3,135.78, -54,2,20.01.2015,,0,135.78, -54,2,21.01.2015,,3,135.78, -54,2,22.01.2015,,0,135.78, -54,2,23.01.2015,,0,135.78, -54,2,24.01.2015,,0,135.78, -54,2,25.01.2015,,3,135.78, -54,2,26.01.2015,,0,135.78, -54,2,27.01.2015,,0,135.78, -54,2,28.01.2015,,0,135.78, -54,2,29.01.2015,,3,135.78, -54,2,30.01.2015,,0,135.78, -54,2,31.01.2015,,0,135.78, -54,2,01.02.2015,,0,135.78, -54,2,02.02.2015,,0,135.78, -54,2,03.02.2015,,0,135.78, -54,2,04.02.2015,,13,135.78, -54,2,05.02.2015,,0,135.78, -54,2,06.02.2015,,0,135.78, -54,2,07.02.2015,,0,135.78, -54,2,08.02.2015,,6,135.78, -54,2,09.02.2015,,6,135.78, -54,2,10.02.2015,,0,135.78, -54,2,11.02.2015,,0,135.78, -54,2,12.02.2015,,3,135.78, -54,2,13.02.2015,,0,135.78, -54,2,14.02.2015,,0,135.78, -54,2,15.02.2015,,6,135.78, -54,2,16.02.2015,,9,135.78, -54,2,17.02.2015,,0,135.78, -54,2,18.02.2015,,0,135.78, -54,2,19.02.2015,,3,135.78, -54,2,20.02.2015,,0,135.78, -54,2,21.02.2015,,9,135.78, -54,2,22.02.2015,,0,135.78, -54,2,23.02.2015,,0,135.78, -54,2,24.02.2015,,6,135.78, -54,2,25.02.2015,,0,135.78, -54,2,26.02.2015,,0,146.38, -54,2,27.02.2015,,0,146.38, -54,2,28.02.2015,,0,146.38, -54,2,01.03.2015,,0,146.38, -54,2,02.03.2015,,3,146.38, -54,2,03.03.2015,,0,146.38, -54,2,04.03.2015,,0,146.38, -54,2,05.03.2015,,0,146.38, -54,2,06.03.2015,,9,146.38, -54,2,07.03.2015,,3,146.38, -54,2,08.03.2015,,0,146.38, -54,2,09.03.2015,,3,146.38, -54,2,10.03.2015,,0,146.38, -54,2,11.03.2015,,0,146.38, -54,2,12.03.2015,,0,146.38, -54,2,13.03.2015,,0,146.38, -54,2,14.03.2015,,0,146.38, -54,2,15.03.2015,,0,146.38, -54,2,16.03.2015,,0,146.38, -54,2,17.03.2015,,0,146.38, -54,2,18.03.2015,,0,146.38, -54,2,19.03.2015,,0,146.38, -54,2,20.03.2015,,3,146.38, -54,2,21.03.2015,,0,146.38, -54,2,22.03.2015,,0,146.38, -54,2,23.03.2015,,6,146.38, -54,2,24.03.2015,,3,146.38, -54,2,25.03.2015,,13,146.38, -54,2,26.03.2015,,0,146.38, -54,2,27.03.2015,,3,146.38, -54,2,28.03.2015,,0,146.38, -54,2,29.03.2015,,0,146.38, -54,2,30.03.2015,,0,146.38, -54,2,31.03.2015,,0,146.38, -54,2,01.04.2015,,0,146.38, -54,2,02.04.2015,,6,146.38, -54,2,03.04.2015,,0,146.38, -54,2,04.04.2015,,0,146.38, -54,2,05.04.2015,,0,146.38, -54,2,06.04.2015,,0,146.38, -54,2,07.04.2015,,0,146.38, -54,2,08.04.2015,,0,146.38, -54,2,09.04.2015,,0,146.38, -54,2,10.04.2015,,0,146.38, 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-54,2,25.07.2015,,0,150.73, -54,2,26.07.2015,,6,150.73, -54,2,27.07.2015,,3,150.73, -54,2,28.07.2015,,6,150.73, -54,2,29.07.2015,,3,150.73, -54,2,30.07.2015,,3,150.73, -54,2,31.07.2015,,0,150.73, -54,2,01.08.2015,,19,150.73, -54,2,02.08.2015,,3,150.73, -54,2,03.08.2015,,22,150.73, -54,2,04.08.2015,,9,150.73, -54,2,05.08.2015,,22,150.73, -54,2,06.08.2015,,0,150.73, -54,2,07.08.2015,,16,150.73, -54,2,08.08.2015,,9,150.73, -54,2,09.08.2015,,6,150.73, -54,2,10.08.2015,,9,150.73, -54,2,11.08.2015,,25,150.73, -54,2,12.08.2015,,0,128.17, -54,2,13.08.2015,,3,128.17, -54,2,14.08.2015,,3,128.17, -54,2,15.08.2015,,3,128.17, -54,2,16.08.2015,,0,128.17, -54,2,17.08.2015,,9,128.17, -54,2,18.08.2015,1.0,44,128.17,100.3 -54,2,19.08.2015,1.0,85,128.17,100.3 -54,2,20.08.2015,1.0,47,128.17,100.3 -54,2,21.08.2015,1.0,25,128.17,100.3 -54,2,22.08.2015,1.0,72,128.17,100.3 -54,2,23.08.2015,1.0,60,128.17,100.3 -54,2,24.08.2015,1.0,63,128.17,100.3 -54,2,25.08.2015,,13,128.17, -54,2,26.08.2015,,16,128.17, -54,2,27.08.2015,,9,128.17, -54,2,28.08.2015,,31,128.17, -54,2,29.08.2015,,9,128.17, -54,2,30.08.2015,,25,128.17, -54,2,31.08.2015,,13,128.17, -54,2,01.09.2015,,3,128.17, -54,2,02.09.2015,,0,128.17, -54,2,03.09.2015,,0,128.17, -54,2,04.09.2015,,0,128.17, -54,2,05.09.2015,,0,128.17, -54,2,06.09.2015,,0,128.17, -54,2,07.09.2015,,0,128.17, -54,2,08.09.2015,,0,128.17, -54,2,09.09.2015,,0,128.17, -54,2,10.09.2015,,0,128.17, -54,2,11.09.2015,,0,128.17, -54,2,12.09.2015,,0,128.17, -54,2,13.09.2015,,0,128.17, -54,2,14.09.2015,,0,128.17, -54,2,15.09.2015,,0,128.17, -54,2,16.09.2015,,0,128.17, -54,2,17.09.2015,,0,128.17, -54,2,18.09.2015,,0,128.17, -54,2,19.09.2015,,0,128.17, -54,2,20.09.2015,,0,128.17, -54,2,21.09.2015,,0,128.17, -54,2,22.09.2015,,0,128.17, -54,2,23.09.2015,,0,128.17, -54,2,24.09.2015,,0,128.17, -54,2,25.09.2015,,0,128.17, -54,2,26.09.2015,,0,128.17, -54,2,27.09.2015,,0,128.17, -54,2,28.09.2015,,0,128.17, -54,2,29.09.2015,,0,128.17, -54,2,30.09.2015,,0,128.17, -54,2,01.10.2015,,0,162.96, -54,2,02.10.2015,,0,162.96, -54,2,03.10.2015,,0,162.96, -54,2,04.10.2015,,0,162.96, -54,2,05.10.2015,,0,162.96, -54,2,06.10.2015,1.0,44,143.39,122.05 -54,2,07.10.2015,1.0,41,143.39,122.05 -54,2,08.10.2015,1.0,31,143.39,122.05 -54,2,09.10.2015,1.0,41,143.39,122.05 -54,2,10.10.2015,1.0,35,143.39,122.05 -54,2,11.10.2015,1.0,31,143.39,122.05 -54,2,12.10.2015,1.0,31,143.39,122.05 -54,2,13.10.2015,1.0,13,143.93,122.05 -54,2,14.10.2015,1.0,13,143.93,122.05 -54,2,15.10.2015,1.0,9,143.93,122.05 -54,2,16.10.2015,1.0,22,143.93,122.05 -54,2,17.10.2015,1.0,22,162.96,122.05 -54,2,18.10.2015,1.0,22,162.96,122.05 -54,2,19.10.2015,,0,162.96, -54,2,20.10.2015,,0,162.96, -54,2,21.10.2015,,0,162.96, -54,2,22.10.2015,,0,162.96, -54,2,23.10.2015,,3,162.96, -54,2,24.10.2015,,0,162.96, -54,2,25.10.2015,,0,162.96, -54,2,26.10.2015,,0,162.96, -54,2,27.10.2015,,0,162.96, -54,2,28.10.2015,,0,162.96, -54,2,29.10.2015,,0,162.96, -54,2,30.10.2015,,0,162.96, -54,2,31.10.2015,,0,162.96, 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-54,2,08.01.2016,,0,138.5, -54,2,09.01.2016,,0,138.5, -54,2,10.01.2016,,0,138.5, -54,2,11.01.2016,,0,138.5, -54,2,12.01.2016,,0,138.5, -54,2,13.01.2016,,0,138.5, -54,2,14.01.2016,,3,138.5, -54,2,15.01.2016,,9,138.5, -54,2,16.01.2016,,6,142.57, -54,2,17.01.2016,,0,142.57, -54,2,18.01.2016,,3,142.57, -54,2,19.01.2016,1.0,63,142.57,103.02 -54,2,20.01.2016,1.0,57,142.57,103.02 -54,2,21.01.2016,1.0,60,142.57,103.02 -54,2,22.01.2016,1.0,94,142.57,103.02 -54,2,23.01.2016,1.0,16,142.57,103.02 -54,2,24.01.2016,1.0,57,142.57,103.02 -54,2,25.01.2016,1.0,9,142.57,103.02 -54,2,26.01.2016,,25,142.57, -54,2,27.01.2016,,3,142.57, -54,2,28.01.2016,,13,142.57, -54,2,29.01.2016,,19,142.57, -54,2,30.01.2016,,3,142.57, -54,2,31.01.2016,,3,142.57, -54,2,01.02.2016,,0,142.57, -54,2,02.02.2016,,0,142.57, -54,2,03.02.2016,,3,142.57, -54,2,04.02.2016,,6,142.57, -54,2,05.02.2016,,13,142.57, -54,2,06.02.2016,,0,142.57, -54,2,07.02.2016,,16,142.57, -54,2,08.02.2016,,6,142.57, -54,2,09.02.2016,1.0,6,142.57,108.46 -54,2,10.02.2016,1.0,44,138.36,108.46 -54,2,11.02.2016,1.0,28,138.36,108.46 -54,2,12.02.2016,1.0,82,138.36,108.46 -54,2,13.02.2016,1.0,69,138.36,108.46 -54,2,14.02.2016,1.0,91,138.36,108.46 -54,2,15.02.2016,1.0,91,138.36,108.46 -54,2,16.02.2016,,0,138.36, -54,2,17.02.2016,,0,138.36, -54,2,18.02.2016,,0,138.36, -54,2,19.02.2016,,0,138.36, -54,2,20.02.2016,,9,138.36, -54,2,21.02.2016,,22,138.36, -54,2,22.02.2016,,13,138.36, -54,2,23.02.2016,,3,138.36, -54,2,24.02.2016,,0,138.36, -54,2,25.02.2016,,3,138.36, -54,2,26.02.2016,,0,138.36, -54,2,27.02.2016,,0,138.36, -54,2,28.02.2016,,0,138.36, -54,2,29.02.2016,,0,138.36, -54,2,01.03.2016,,3,138.36, -54,2,02.03.2016,,9,138.36, -54,2,03.03.2016,,0,138.36, -54,2,04.03.2016,,0,138.36, -54,2,05.03.2016,,0,138.36, -54,2,06.03.2016,,0,138.36, -54,2,07.03.2016,,0,138.36, -54,2,08.03.2016,,0,138.36, -54,2,09.03.2016,,9,138.36, -54,2,10.03.2016,,0,138.36, -54,2,11.03.2016,,0,138.36, -54,2,12.03.2016,,0,138.5, -54,2,13.03.2016,,6,138.5, -54,2,14.03.2016,,3,138.5, -54,2,15.03.2016,1.0,19,138.5,124.77 -54,2,16.03.2016,1.0,35,138.5,124.77 -54,2,17.03.2016,1.0,16,138.5,124.77 -54,2,18.03.2016,1.0,25,138.5,124.77 -54,2,19.03.2016,1.0,6,138.5,124.77 -54,2,20.03.2016,1.0,9,138.5,124.77 -54,2,21.03.2016,1.0,9,138.5,124.77 -54,2,22.03.2016,,0,138.36, -54,2,23.03.2016,,6,138.36, -54,2,24.03.2016,,0,138.36, -54,2,25.03.2016,,3,138.36, -54,2,26.03.2016,,0,138.36, -54,2,27.03.2016,,0,138.36, -54,2,28.03.2016,,0,138.36, -54,2,29.03.2016,,0,138.36, -54,2,30.03.2016,,0,138.36, -54,2,31.03.2016,,0,138.36, -54,2,01.04.2016,,0,138.36, -54,2,02.04.2016,,0,138.36, -54,2,03.04.2016,,0,138.36, -54,2,04.04.2016,,0,138.36, -54,2,05.04.2016,,0,138.36, -54,2,06.04.2016,,0,138.36, -54,2,07.04.2016,,0,138.36, -54,2,08.04.2016,,6,138.36, -54,2,09.04.2016,,13,138.5, -54,2,10.04.2016,,0,138.5, -54,2,11.04.2016,,0,138.5, -54,2,12.04.2016,,0,138.5, -54,2,13.04.2016,,0,138.5, -54,2,14.04.2016,,0,138.5, -54,2,15.04.2016,,0,138.5, 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-55,2,06.12.2015,,0,162.96, -55,2,07.12.2015,,0,162.96, -55,2,08.12.2015,,0,162.96, -55,2,09.12.2015,,0,138.5, -55,2,10.12.2015,,0,138.5, -55,2,11.12.2015,,9,142.57, -55,2,12.12.2015,,6,142.57, -55,2,13.12.2015,,6,142.57, -55,2,14.12.2015,,9,142.57, -55,2,15.12.2015,,6,142.57, -55,2,16.12.2015,,3,142.57, -55,2,17.12.2015,,16,142.57, -55,2,18.12.2015,,3,142.57, -55,2,19.12.2015,,3,142.57, -55,2,20.12.2015,,3,142.57, -55,2,21.12.2015,,6,142.57, -55,2,22.12.2015,1.0,167,142.57,103.02 -55,2,23.12.2015,1.0,185,142.57,103.02 -55,2,24.12.2015,1.0,69,142.57,103.02 -55,2,25.12.2015,1.0,188,142.57,103.02 -55,2,26.12.2015,1.0,217,142.57,103.02 -55,2,27.12.2015,1.0,116,142.57,103.02 -55,2,28.12.2015,1.0,69,142.57,103.02 -55,2,29.12.2015,,3,138.5, -55,2,30.12.2015,,9,138.5, -55,2,31.12.2015,,3,138.5, -55,2,01.01.2016,,6,138.5, -55,2,02.01.2016,,6,138.5, -55,2,03.01.2016,,6,138.5, -55,2,04.01.2016,,6,138.5, -55,2,05.01.2016,,19,138.5, -55,2,06.01.2016,,6,138.5, -55,2,07.01.2016,,13,138.5, -55,2,08.01.2016,,9,138.5, -55,2,09.01.2016,,13,138.5, -55,2,10.01.2016,,35,138.5, -55,2,11.01.2016,,9,138.5, -55,2,12.01.2016,,16,138.5, -55,2,13.01.2016,,0,138.5, -55,2,14.01.2016,,3,138.5, -55,2,15.01.2016,,13,138.5, -55,2,16.01.2016,,6,142.57, -55,2,17.01.2016,,19,142.57, -55,2,18.01.2016,,13,142.57, -55,2,19.01.2016,1.0,245,142.57,103.02 -55,2,20.01.2016,1.0,170,142.57,103.02 -55,2,21.01.2016,1.0,104,142.57,103.02 -55,2,22.01.2016,1.0,286,142.57,103.02 -55,2,23.01.2016,1.0,201,142.57,103.02 -55,2,24.01.2016,1.0,19,142.57,103.02 -55,2,25.01.2016,1.0,170,142.57,103.02 -55,2,26.01.2016,,13,142.57, -55,2,27.01.2016,,13,142.57, -55,2,28.01.2016,,9,142.57, -55,2,29.01.2016,,9,142.57, -55,2,30.01.2016,,3,142.57, -55,2,31.01.2016,,6,142.57, -55,2,01.02.2016,,25,142.57, -55,2,02.02.2016,,3,142.57, -55,2,03.02.2016,,3,142.57, -55,2,04.02.2016,,9,142.57, -55,2,05.02.2016,,6,142.57, -55,2,06.02.2016,,13,142.57, -55,2,07.02.2016,,6,142.57, -55,2,08.02.2016,,0,142.57, -55,2,09.02.2016,1.0,292,142.57,108.46 -55,2,10.02.2016,1.0,421,138.36,108.46 -55,2,11.02.2016,1.0,462,138.36,108.46 -55,2,12.02.2016,1.0,276,138.36,108.46 -55,2,13.02.2016,1.0,176,138.36,108.46 -55,2,14.02.2016,1.0,346,138.36,108.46 -55,2,15.02.2016,1.0,540,138.36,108.46 -55,2,16.02.2016,,41,138.36, -55,2,17.02.2016,,19,138.36, -55,2,18.02.2016,,0,138.36, -55,2,19.02.2016,,3,138.36, -55,2,20.02.2016,,16,138.36, -55,2,21.02.2016,,69,138.36, -55,2,22.02.2016,,6,138.36, -55,2,23.02.2016,,25,138.36, -55,2,24.02.2016,,35,138.36, -55,2,25.02.2016,,16,138.36, -55,2,26.02.2016,,16,138.36, -55,2,27.02.2016,,16,138.36, -55,2,28.02.2016,,16,138.36, -55,2,29.02.2016,,16,138.36, -55,2,01.03.2016,,16,138.36, -55,2,02.03.2016,,16,138.36, -55,2,03.03.2016,,16,138.36, -55,2,04.03.2016,,16,138.36, -55,2,05.03.2016,,9,138.36, -55,2,06.03.2016,,9,138.36, -55,2,07.03.2016,,13,138.36, -55,2,08.03.2016,,19,138.36, -55,2,09.03.2016,,3,138.36, -55,2,10.03.2016,,3,138.36, -55,2,11.03.2016,,0,138.36, -55,2,12.03.2016,,6,138.5, -55,2,13.03.2016,,28,138.5, -55,2,14.03.2016,,3,138.5, -55,2,15.03.2016,1.0,148,138.5,124.77 -55,2,16.03.2016,1.0,94,138.5,124.77 -55,2,17.03.2016,1.0,85,138.5,124.77 -55,2,18.03.2016,1.0,101,138.5,124.77 -55,2,19.03.2016,1.0,63,138.5,124.77 -55,2,20.03.2016,1.0,28,138.5,124.77 -55,2,21.03.2016,1.0,217,138.5,124.77 -55,2,22.03.2016,,3,138.36, -55,2,23.03.2016,,6,138.36, -55,2,24.03.2016,,3,138.36, -55,2,25.03.2016,,6,138.36, -55,2,26.03.2016,,9,138.36, -55,2,27.03.2016,,9,138.36, -55,2,28.03.2016,,9,138.36, -55,2,29.03.2016,,9,138.36, -55,2,30.03.2016,,9,138.36, -55,2,31.03.2016,,9,138.36, -55,2,01.04.2016,,9,138.36, -55,2,02.04.2016,,9,138.36, -55,2,03.04.2016,,9,138.36, -55,2,04.04.2016,,9,138.36, -55,2,05.04.2016,,9,138.36, -55,2,06.04.2016,,9,138.36, -55,2,07.04.2016,,9,138.36, -55,2,08.04.2016,,3,138.36, -55,2,09.04.2016,,3,138.5, -55,2,10.04.2016,,19,138.5, -55,2,11.04.2016,,3,138.5, -55,2,12.04.2016,,6,138.5, -55,2,13.04.2016,,3,138.5, -55,2,14.04.2016,,9,138.5, -55,2,15.04.2016,,3,138.5, -55,2,16.04.2016,,6,138.5, -55,2,17.04.2016,,16,138.5, -55,2,18.04.2016,,19,138.5, -55,2,19.04.2016,1.0,195,138.5,116.89 -55,2,20.04.2016,1.0,148,138.5,116.89 -55,2,21.04.2016,1.0,135,138.5,116.89 -55,2,22.04.2016,1.0,151,138.5,116.89 -55,2,23.04.2016,1.0,210,138.5,116.89 -55,2,24.04.2016,1.0,154,138.5,116.89 -55,2,25.04.2016,1.0,167,138.5,116.89 -55,2,26.04.2016,,0,138.5, -55,2,27.04.2016,,3,138.5, -55,2,28.04.2016,,0,138.5, -55,2,29.04.2016,,13,138.5, -55,2,30.04.2016,,0,138.5, -55,2,01.05.2016,,0,138.5, -55,2,02.05.2016,,13,138.5, -55,2,03.05.2016,1.0,226,138.5,106.01 -55,2,04.05.2016,1.0,210,138.5,106.01 -55,2,05.05.2016,1.0,267,138.5,106.01 -55,2,06.05.2016,1.0,258,138.5,106.01 -55,2,07.05.2016,1.0,245,138.5,106.01 -55,2,08.05.2016,1.0,107,138.5,106.01 -55,2,09.05.2016,1.0,132,138.5,106.01 -55,2,10.05.2016,,6,138.5, -55,2,11.05.2016,,6,138.5, -55,2,12.05.2016,,6,138.5, -55,2,13.05.2016,,6,138.5, -55,2,14.05.2016,,6,138.5, -55,2,15.05.2016,,6,138.5, -55,2,16.05.2016,,6,138.5, -55,2,17.05.2016,,6,138.5, -55,2,18.05.2016,,6,138.5, -55,2,19.05.2016,,6,138.5, -55,2,20.05.2016,,6,138.5, -55,2,21.05.2016,,6,138.5, -55,2,22.05.2016,,6,138.5, -56,2,01.01.2015,,0,139.86, -56,2,02.01.2015,,0,139.86, -56,2,03.01.2015,,0,139.86, -56,2,04.01.2015,,0,139.86, -56,2,05.01.2015,,6,139.86, -56,2,06.01.2015,,19,139.86, -56,2,07.01.2015,,0,139.86, -56,2,08.01.2015,,0,139.86, -56,2,09.01.2015,,0,139.86, -56,2,10.01.2015,,0,139.86, -56,2,11.01.2015,,0,139.86, -56,2,12.01.2015,,0,139.86, -56,2,13.01.2015,,3,139.86, -56,2,14.01.2015,,0,139.86, -56,2,15.01.2015,,3,139.86, -56,2,16.01.2015,,3,139.86, -56,2,17.01.2015,,0,139.86, -56,2,18.01.2015,,0,139.86, -56,2,19.01.2015,,0,139.86, -56,2,20.01.2015,,0,139.86, -56,2,21.01.2015,,0,139.86, -56,2,22.01.2015,,0,139.86, -56,2,23.01.2015,,0,139.86, -56,2,24.01.2015,,0,139.86, -56,2,25.01.2015,,0,139.86, -56,2,26.01.2015,,0,139.86, -56,2,27.01.2015,,0,139.86, -56,2,28.01.2015,,0,139.86, -56,2,29.01.2015,,0,139.86, -56,2,30.01.2015,,0,139.86, -56,2,31.01.2015,,0,139.86, -56,2,01.02.2015,,0,139.86, -56,2,02.02.2015,,0,139.86, -56,2,03.02.2015,,0,139.86, -56,2,04.02.2015,,0,139.86, -56,2,05.02.2015,,6,139.86, -56,2,06.02.2015,,0,139.86, -56,2,07.02.2015,,3,139.86, -56,2,08.02.2015,,3,139.86, -56,2,09.02.2015,,6,139.86, -56,2,10.02.2015,,0,139.86, -56,2,11.02.2015,,0,139.86, -56,2,12.02.2015,,0,139.86, -56,2,13.02.2015,,0,139.86, -56,2,14.02.2015,,0,139.86, -56,2,15.02.2015,,0,139.86, -56,2,16.02.2015,,0,139.86, -56,2,17.02.2015,,0,139.86, -56,2,18.02.2015,,0,139.86, -56,2,19.02.2015,,0,139.86, -56,2,20.02.2015,,0,139.86, -56,2,21.02.2015,,0,139.86, -56,2,22.02.2015,,0,139.86, -56,2,23.02.2015,,0,139.86, -56,2,24.02.2015,,0,139.86, -56,2,25.02.2015,,0,139.86, -56,2,26.02.2015,,0,139.86, -56,2,27.02.2015,,0,139.86, -56,2,28.02.2015,,0,139.86, -56,2,01.03.2015,,0,139.86, -56,2,02.03.2015,,0,150.73, -56,2,03.03.2015,,0,150.73, -56,2,04.03.2015,,0,150.73, -56,2,05.03.2015,,0,150.73, -56,2,06.03.2015,,0,150.73, -56,2,07.03.2015,,0,150.73, -56,2,08.03.2015,,0,150.73, -56,2,09.03.2015,,3,150.73, -56,2,10.03.2015,,0,150.73, -56,2,11.03.2015,,0,150.73, -56,2,12.03.2015,,0,150.73, -56,2,13.03.2015,,3,150.73, -56,2,14.03.2015,,0,150.73, -56,2,15.03.2015,,0,150.73, -56,2,16.03.2015,,0,150.73, -56,2,17.03.2015,,0,150.73, -56,2,18.03.2015,,0,150.73, -56,2,19.03.2015,,3,150.73, -56,2,20.03.2015,,0,150.73, -56,2,21.03.2015,,0,150.73, -56,2,22.03.2015,,0,150.73, -56,2,23.03.2015,,0,150.73, -56,2,24.03.2015,,0,150.73, -56,2,25.03.2015,,0,150.73, -56,2,26.03.2015,,0,150.73, -56,2,27.03.2015,,0,150.73, -56,2,28.03.2015,,0,150.73, -56,2,29.03.2015,,0,150.73, -56,2,30.03.2015,,0,150.73, -56,2,31.03.2015,,0,150.73, -56,2,01.04.2015,,0,150.73, -56,2,02.04.2015,,0,150.73, -56,2,03.04.2015,,3,150.73, -56,2,04.04.2015,,0,150.73, -56,2,05.04.2015,,3,150.73, -56,2,06.04.2015,,0,150.73, -56,2,07.04.2015,,0,150.73, -56,2,08.04.2015,,0,150.73, -56,2,09.04.2015,,3,150.73, -56,2,10.04.2015,,0,150.73, -56,2,11.04.2015,,0,150.73, -56,2,12.04.2015,,0,150.73, -56,2,13.04.2015,,0,150.73, -56,2,14.04.2015,,13,150.73, -56,2,15.04.2015,,3,150.73, -56,2,16.04.2015,,0,150.73, -56,2,17.04.2015,,0,150.73, -56,2,18.04.2015,,0,150.73, -56,2,19.04.2015,,0,150.73, -56,2,20.04.2015,,0,150.73, -56,2,21.04.2015,,0,150.73, -56,2,22.04.2015,,0,150.73, -56,2,23.04.2015,,0,150.73, -56,2,24.04.2015,,0,150.73, -56,2,25.04.2015,,0,150.73, -56,2,26.04.2015,,0,155.35, -56,2,27.04.2015,,0,155.35, -56,2,28.04.2015,,0,155.35, -56,2,29.04.2015,,3,155.35, -56,2,30.04.2015,,0,155.35, -56,2,01.05.2015,,0,155.35, -56,2,02.05.2015,,0,155.35, -56,2,03.05.2015,,0,155.35, -56,2,04.05.2015,,0,155.35, -56,2,05.05.2015,,0,155.35, -56,2,06.05.2015,,0,155.35, -56,2,07.05.2015,,0,155.35, -56,2,08.05.2015,,0,155.35, -56,2,09.05.2015,,0,155.35, -56,2,10.05.2015,,0,155.35, -56,2,11.05.2015,,0,155.35, -56,2,12.05.2015,,0,155.35, -56,2,13.05.2015,,6,155.35, -56,2,14.05.2015,,0,155.35, -56,2,15.05.2015,,0,155.35, -56,2,16.05.2015,,0,155.35, -56,2,17.05.2015,,0,155.35, -56,2,18.05.2015,,0,155.35, -56,2,19.05.2015,,3,155.35, -56,2,20.05.2015,,0,155.35, -56,2,21.05.2015,,3,155.35, -56,2,22.05.2015,,0,155.35, -56,2,23.05.2015,,0,155.35, -56,2,24.05.2015,,6,155.35, -56,2,25.05.2015,,0,155.35, -56,2,26.05.2015,,0,155.35, -56,2,27.05.2015,,0,155.35, -56,2,28.05.2015,,0,155.35, -56,2,29.05.2015,,0,155.35, -56,2,30.05.2015,,0,155.35, -56,2,31.05.2015,,0,155.35, -56,2,01.06.2015,,6,155.35, -56,2,02.06.2015,,0,155.35, -56,2,03.06.2015,,3,155.35, -56,2,04.06.2015,,0,155.35, -56,2,05.06.2015,,0,155.35, -56,2,06.06.2015,,0,155.35, -56,2,07.06.2015,,0,155.35, -56,2,08.06.2015,,0,155.35, -56,2,09.06.2015,,0,155.35, -56,2,10.06.2015,,0,155.35, -56,2,11.06.2015,,0,155.35, -56,2,12.06.2015,,0,155.35, -56,2,13.06.2015,,0,155.35, -56,2,14.06.2015,,0,155.35, -56,2,15.06.2015,,3,155.35, -56,2,16.06.2015,,0,155.35, -56,2,17.06.2015,,0,155.35, -56,2,18.06.2015,,0,155.35, -56,2,19.06.2015,,0,155.35, -56,2,20.06.2015,,0,155.35, -56,2,21.06.2015,,0,155.35, -56,2,22.06.2015,,0,155.35, -56,2,23.06.2015,,0,155.35, -56,2,24.06.2015,,0,155.35, -56,2,25.06.2015,,0,155.35, -56,2,26.06.2015,,0,155.35, -56,2,27.06.2015,,0,155.35, -56,2,28.06.2015,,0,155.35, -56,2,29.06.2015,,0,155.35, -56,2,30.06.2015,,0,155.35, -56,2,01.07.2015,,0,155.35, -56,2,02.07.2015,,0,155.35, -56,2,03.07.2015,,0,155.35, -56,2,04.07.2015,,0,155.35, -56,2,05.07.2015,,0,155.35, -56,2,06.07.2015,,0,155.35, -56,2,07.07.2015,,0,155.35, -56,2,08.07.2015,,0,155.35, -56,2,09.07.2015,,0,155.35, -56,2,10.07.2015,,9,155.35, -56,2,11.07.2015,,3,155.35, -56,2,12.07.2015,,9,155.35, -56,2,13.07.2015,,6,155.35, -56,2,14.07.2015,,3,155.35, -56,2,15.07.2015,,3,155.35, -56,2,16.07.2015,,3,155.35, -56,2,17.07.2015,,0,155.35, -56,2,18.07.2015,,0,155.35, -56,2,19.07.2015,,6,155.35, -56,2,20.07.2015,,9,155.35, -56,2,21.07.2015,,0,155.35, -56,2,22.07.2015,,0,155.35, -56,2,23.07.2015,,0,155.35, -56,2,24.07.2015,,3,155.35, -56,2,25.07.2015,,6,155.35, -56,2,26.07.2015,,0,155.35, -56,2,27.07.2015,,0,155.35, -56,2,28.07.2015,,3,155.35, -56,2,29.07.2015,,0,155.35, -56,2,30.07.2015,,22,155.35, -56,2,31.07.2015,,3,155.35, -56,2,01.08.2015,,6,155.35, -56,2,02.08.2015,,19,155.35, -56,2,03.08.2015,,9,155.35, -56,2,04.08.2015,,16,155.35, -56,2,05.08.2015,,6,155.35, -56,2,06.08.2015,,3,155.35, -56,2,07.08.2015,,6,155.35, -56,2,08.08.2015,,6,155.35, -56,2,09.08.2015,,6,155.35, -56,2,10.08.2015,,3,155.35, -56,2,11.08.2015,,6,155.35, -56,2,12.08.2015,,19,128.17, -56,2,13.08.2015,,9,128.17, -56,2,14.08.2015,,9,128.17, -56,2,15.08.2015,,0,128.17, -56,2,16.08.2015,,25,128.17, -56,2,17.08.2015,,19,128.17, -56,2,18.08.2015,1.0,217,128.17,100.3 -56,2,19.08.2015,1.0,41,128.17,100.3 -56,2,20.08.2015,1.0,167,128.17,100.3 -56,2,21.08.2015,1.0,185,128.17,100.3 -56,2,22.08.2015,1.0,75,128.17,100.3 -56,2,23.08.2015,1.0,25,128.17,100.3 -56,2,24.08.2015,1.0,25,128.17,100.3 -56,2,25.08.2015,,9,128.17, -56,2,26.08.2015,,9,128.17, -56,2,27.08.2015,,9,128.17, -56,2,28.08.2015,,9,128.17, -56,2,29.08.2015,,9,128.17, -56,2,30.08.2015,,9,128.17, -56,2,31.08.2015,,13,128.17, -56,2,01.09.2015,,6,128.17, -56,2,02.09.2015,,0,128.17, -56,2,03.09.2015,,6,128.17, -56,2,04.09.2015,,3,128.17, -56,2,05.09.2015,,9,128.17, -56,2,06.09.2015,,9,128.17, -56,2,07.09.2015,,9,128.17, -56,2,08.09.2015,,0,128.17, -56,2,09.09.2015,,16,128.17, -56,2,10.09.2015,,3,128.17, -56,2,11.09.2015,,0,128.17, -56,2,12.09.2015,,3,128.17, -56,2,13.09.2015,,16,128.17, -56,2,14.09.2015,,16,128.17, -56,2,15.09.2015,,0,128.17, -56,2,16.09.2015,,3,128.17, -56,2,17.09.2015,,19,128.17, -56,2,18.09.2015,,6,128.17, -56,2,19.09.2015,,6,128.17, -56,2,20.09.2015,,6,128.17, -56,2,21.09.2015,,19,128.17, -56,2,22.09.2015,,9,128.17, -56,2,23.09.2015,,9,128.17, -56,2,24.09.2015,,0,128.17, -56,2,25.09.2015,,0,128.17, -56,2,26.09.2015,,6,128.17, -56,2,27.09.2015,,13,128.17, -56,2,28.09.2015,,9,128.17, -56,2,29.09.2015,,6,128.17, -56,2,30.09.2015,,13,128.17, -56,2,01.10.2015,,3,162.96, -56,2,02.10.2015,,0,162.96, -56,2,03.10.2015,,0,162.96, -56,2,04.10.2015,,0,162.96, -56,2,05.10.2015,,28,162.96, -56,2,06.10.2015,1.0,176,143.39,122.05 -56,2,07.10.2015,1.0,57,143.39,122.05 -56,2,08.10.2015,1.0,72,143.39,122.05 -56,2,09.10.2015,1.0,104,143.39,122.05 -56,2,10.10.2015,1.0,123,143.39,122.05 -56,2,11.10.2015,1.0,79,143.39,122.05 -56,2,12.10.2015,1.0,19,143.39,122.05 -56,2,13.10.2015,,9,143.93, -56,2,14.10.2015,,9,143.93, -56,2,15.10.2015,,9,143.93, -56,2,16.10.2015,,3,143.93, -56,2,17.10.2015,,0,162.96, -56,2,18.10.2015,,3,162.96, -56,2,19.10.2015,,0,162.96, -56,2,20.10.2015,,0,162.96, -56,2,21.10.2015,,0,162.96, -56,2,22.10.2015,,0,162.96, -56,2,23.10.2015,,0,162.96, -56,2,24.10.2015,,3,162.96, -56,2,25.10.2015,,0,162.96, -56,2,26.10.2015,,3,162.96, -56,2,27.10.2015,,0,162.96, -56,2,28.10.2015,,0,162.96, -56,2,29.10.2015,,3,162.96, -56,2,30.10.2015,,3,162.96, -56,2,31.10.2015,,3,162.96, -56,2,01.11.2015,,3,162.96, -56,2,02.11.2015,,3,162.96, -56,2,03.11.2015,,3,162.96, -56,2,04.11.2015,,3,162.96, -56,2,05.11.2015,,3,162.96, -56,2,06.11.2015,,3,162.96, -56,2,07.11.2015,,3,162.96, -56,2,08.11.2015,,3,162.96, -56,2,09.11.2015,,3,162.96, -56,2,10.11.2015,,3,162.96, -56,2,11.11.2015,,3,138.36, -56,2,12.11.2015,,3,138.36, -56,2,13.11.2015,,3,138.36, -56,2,14.11.2015,,3,138.36, -56,2,15.11.2015,,3,138.36, -56,2,16.11.2015,,3,138.36, -56,2,17.11.2015,,3,138.36, -56,2,18.11.2015,,3,138.36, -56,2,19.11.2015,,3,138.36, -56,2,20.11.2015,,3,138.36, -56,2,21.11.2015,,0,138.36, -56,2,22.11.2015,,0,138.36, -56,2,23.11.2015,,0,138.36, -56,2,24.11.2015,,0,138.36, -56,2,25.11.2015,,0,138.36, -56,2,26.11.2015,,0,138.36, -56,2,27.11.2015,,3,138.36, -56,2,28.11.2015,,9,138.36, -56,2,29.11.2015,,0,138.36, -56,2,30.11.2015,,13,138.36, -56,2,01.12.2015,,0,162.96, -56,2,02.12.2015,,0,162.96, -56,2,03.12.2015,,0,138.5, -56,2,04.12.2015,,0,138.5, -56,2,05.12.2015,,3,138.5, -56,2,06.12.2015,,3,138.5, -56,2,07.12.2015,,0,138.5, -56,2,08.12.2015,,0,138.5, -56,2,09.12.2015,,0,138.5, -56,2,10.12.2015,,3,138.5, -56,2,11.12.2015,,6,142.57, -56,2,12.12.2015,,3,142.57, -56,2,13.12.2015,,0,142.57, -56,2,14.12.2015,,3,142.57, -56,2,15.12.2015,,0,142.57, -56,2,16.12.2015,,0,142.57, -56,2,17.12.2015,,13,142.57, -56,2,18.12.2015,,0,142.57, -56,2,19.12.2015,,6,142.57, -56,2,20.12.2015,,3,142.57, -56,2,21.12.2015,,0,142.57, -56,2,22.12.2015,1.0,145,142.57,103.02 -56,2,23.12.2015,1.0,148,142.57,103.02 -56,2,24.12.2015,1.0,119,142.57,103.02 -56,2,25.12.2015,1.0,154,142.57,103.02 -56,2,26.12.2015,1.0,160,142.57,103.02 -56,2,27.12.2015,1.0,69,142.57,103.02 -56,2,28.12.2015,1.0,104,142.57,103.02 -56,2,29.12.2015,,3,138.5, -56,2,30.12.2015,,3,138.5, -56,2,31.12.2015,,6,138.5, -56,2,01.01.2016,,6,138.5, -56,2,02.01.2016,,9,138.5, -56,2,03.01.2016,,3,138.5, -56,2,04.01.2016,,6,138.5, -56,2,05.01.2016,,9,138.5, -56,2,06.01.2016,,6,138.5, -56,2,07.01.2016,,6,138.5, -56,2,08.01.2016,,6,138.5, -56,2,09.01.2016,,6,138.5, -56,2,10.01.2016,,6,138.5, -56,2,11.01.2016,,6,138.5, -56,2,12.01.2016,,6,138.5, -56,2,13.01.2016,,6,138.5, -56,2,14.01.2016,,6,138.5, -56,2,15.01.2016,,6,138.5, -56,2,16.01.2016,,6,142.57, -56,2,17.01.2016,,6,142.57, -56,2,18.01.2016,,6,142.57, -56,2,19.01.2016,1.0,280,142.57,103.02 -56,2,20.01.2016,1.0,298,142.57,103.02 -56,2,21.01.2016,1.0,176,142.57,103.02 -56,2,22.01.2016,1.0,242,142.57,103.02 -56,2,23.01.2016,1.0,204,142.57,103.02 -56,2,24.01.2016,1.0,135,142.57,103.02 -56,2,25.01.2016,1.0,57,142.57,103.02 -56,2,26.01.2016,,6,142.57, -56,2,27.01.2016,,6,142.57, -56,2,28.01.2016,,3,142.57, -56,2,29.01.2016,,6,142.57, -56,2,30.01.2016,,9,142.57, -56,2,31.01.2016,,6,142.57, -56,2,01.02.2016,,6,142.57, -56,2,02.02.2016,,6,142.57, -56,2,03.02.2016,,6,142.57, -56,2,04.02.2016,,6,142.57, -56,2,05.02.2016,,6,142.57, -56,2,06.02.2016,,6,142.57, -56,2,07.02.2016,,3,142.57, -56,2,08.02.2016,,22,142.57, -56,2,09.02.2016,1.0,201,142.57,108.46 -56,2,10.02.2016,1.0,119,138.36,108.46 -56,2,11.02.2016,1.0,79,138.36,108.46 -56,2,12.02.2016,1.0,145,138.36,108.46 -56,2,13.02.2016,1.0,91,138.36,108.46 -56,2,14.02.2016,1.0,126,138.36,108.46 -56,2,15.02.2016,1.0,116,138.36,108.46 -56,2,16.02.2016,,6,138.36, -56,2,17.02.2016,,3,138.36, -56,2,18.02.2016,,3,138.36, -56,2,19.02.2016,,3,138.36, -56,2,20.02.2016,,6,138.36, -56,2,21.02.2016,,6,138.36, -56,2,22.02.2016,,6,138.36, -56,2,23.02.2016,,13,138.36, -56,2,24.02.2016,,6,138.36, -56,2,25.02.2016,,6,138.36, -56,2,26.02.2016,,6,138.36, -56,2,27.02.2016,,6,138.36, -56,2,28.02.2016,,6,138.36, -56,2,29.02.2016,,6,138.36, -56,2,01.03.2016,,6,138.36, -56,2,02.03.2016,,6,138.36, -56,2,03.03.2016,,6,138.36, -56,2,04.03.2016,,3,138.36, -56,2,05.03.2016,,6,138.36, -56,2,06.03.2016,,6,138.36, -56,2,07.03.2016,,6,138.36, -56,2,08.03.2016,,13,138.36, -56,2,09.03.2016,,6,138.36, -56,2,10.03.2016,,6,138.36, -56,2,11.03.2016,,6,138.36, -56,2,12.03.2016,,6,138.5, -56,2,13.03.2016,,6,138.5, -56,2,14.03.2016,,13,138.5, -56,2,15.03.2016,1.0,173,138.5,124.77 -56,2,16.03.2016,1.0,47,138.5,124.77 -56,2,17.03.2016,1.0,79,138.5,124.77 -56,2,18.03.2016,1.0,82,138.5,124.77 -56,2,19.03.2016,1.0,69,138.5,124.77 -56,2,20.03.2016,1.0,47,138.5,124.77 -56,2,21.03.2016,1.0,60,138.5,124.77 -56,2,22.03.2016,,3,138.36, -56,2,23.03.2016,,6,138.36, -56,2,24.03.2016,,6,138.36, -56,2,25.03.2016,,3,138.36, -56,2,26.03.2016,,6,138.36, -56,2,27.03.2016,,13,138.36, -56,2,28.03.2016,,6,138.36, -56,2,29.03.2016,,3,138.36, -56,2,30.03.2016,,6,138.36, -56,2,31.03.2016,,9,138.36, -56,2,01.04.2016,,9,138.36, -56,2,02.04.2016,,6,138.36, -56,2,03.04.2016,,6,138.36, -56,2,04.04.2016,,6,138.36, -56,2,05.04.2016,,6,138.36, -56,2,06.04.2016,,6,138.36, -56,2,07.04.2016,,6,138.36, -56,2,08.04.2016,,6,138.36, -56,2,09.04.2016,,6,138.5, -56,2,10.04.2016,,9,138.5, -56,2,11.04.2016,,3,138.5, -56,2,12.04.2016,,6,138.5, -56,2,13.04.2016,,0,138.5, -56,2,14.04.2016,,0,138.5, -56,2,15.04.2016,,0,138.5, -56,2,16.04.2016,,6,138.5, -56,2,17.04.2016,,9,138.5, -56,2,18.04.2016,,6,138.5, -56,2,19.04.2016,1.0,138,138.5,116.89 -56,2,20.04.2016,1.0,138,138.5,116.89 -56,2,21.04.2016,1.0,135,138.5,116.89 -56,2,22.04.2016,1.0,148,138.5,116.89 -56,2,23.04.2016,1.0,138,138.5,116.89 -56,2,24.04.2016,1.0,148,138.5,116.89 -56,2,25.04.2016,1.0,101,138.5,116.89 -56,2,26.04.2016,,0,138.5, -56,2,27.04.2016,,0,138.5, -56,2,28.04.2016,,0,138.5, -56,2,29.04.2016,,0,138.5, -56,2,30.04.2016,,0,138.5, -56,2,01.05.2016,,0,138.5, -56,2,02.05.2016,,0,138.5, -56,2,03.05.2016,1.0,273,138.5,106.01 -56,2,04.05.2016,1.0,154,138.5,106.01 -56,2,05.05.2016,1.0,188,138.5,106.01 -56,2,06.05.2016,1.0,123,138.5,106.01 -56,2,07.05.2016,1.0,129,138.5,106.01 -56,2,08.05.2016,1.0,88,138.5,106.01 -56,2,09.05.2016,1.0,31,138.5,106.01 -56,2,10.05.2016,,3,138.5, -56,2,11.05.2016,,0,138.5, -56,2,12.05.2016,,6,138.5, -56,2,13.05.2016,,0,138.5, -56,2,14.05.2016,,9,138.5, -56,2,15.05.2016,,3,138.5, -56,2,16.05.2016,,6,138.5, -56,2,17.05.2016,,3,138.5, -56,2,18.05.2016,,3,138.5, -56,2,19.05.2016,,3,138.5, -56,2,20.05.2016,,3,138.5, -56,2,21.05.2016,,3,138.5, -56,2,22.05.2016,,3,138.5, -57,2,01.01.2015,,0,135.78, -57,2,02.01.2015,,0,135.78, -57,2,03.01.2015,,9,135.78, -57,2,04.01.2015,,3,135.78, -57,2,05.01.2015,,0,135.78, -57,2,06.01.2015,,0,135.78, -57,2,07.01.2015,,0,135.78, -57,2,08.01.2015,,9,135.78, -57,2,09.01.2015,,16,135.78, -57,2,10.01.2015,,0,135.78, -57,2,11.01.2015,,0,135.78, -57,2,12.01.2015,,0,135.78, -57,2,13.01.2015,,0,135.78, -57,2,14.01.2015,,13,135.78, -57,2,15.01.2015,,9,135.78, -57,2,16.01.2015,,6,135.78, -57,2,17.01.2015,,13,135.78, -57,2,18.01.2015,,9,135.78, -57,2,19.01.2015,,0,135.78, -57,2,20.01.2015,,0,135.78, -57,2,21.01.2015,,0,135.78, -57,2,22.01.2015,,0,135.78, -57,2,23.01.2015,,0,135.78, -57,2,24.01.2015,,0,135.78, -57,2,25.01.2015,,6,135.78, -57,2,26.01.2015,,0,135.78, -57,2,27.01.2015,,0,135.78, -57,2,28.01.2015,,0,135.78, -57,2,29.01.2015,,0,135.78, -57,2,30.01.2015,,3,135.78, -57,2,31.01.2015,,6,135.78, -57,2,01.02.2015,,6,135.78, -57,2,02.02.2015,,0,135.78, -57,2,03.02.2015,,0,135.78, -57,2,04.02.2015,,0,135.78, -57,2,05.02.2015,,0,135.78, -57,2,06.02.2015,,0,135.78, -57,2,07.02.2015,,3,135.78, -57,2,08.02.2015,,3,135.78, -57,2,09.02.2015,,0,135.78, -57,2,10.02.2015,,0,135.78, -57,2,11.02.2015,,9,135.78, -57,2,12.02.2015,,3,135.78, -57,2,13.02.2015,,6,135.78, -57,2,14.02.2015,,0,135.78, -57,2,15.02.2015,,9,135.78, -57,2,16.02.2015,,3,135.78, -57,2,17.02.2015,,13,135.78, -57,2,18.02.2015,,0,135.78, -57,2,19.02.2015,,0,135.78, -57,2,20.02.2015,,0,135.78, -57,2,21.02.2015,,0,135.78, -57,2,22.02.2015,,0,135.78, -57,2,23.02.2015,,0,135.78, -57,2,24.02.2015,,0,135.78, -57,2,25.02.2015,,0,135.78, -57,2,26.02.2015,,0,135.78, -57,2,27.02.2015,,16,135.78, -57,2,28.02.2015,,9,135.78, -57,2,01.03.2015,,0,146.38, -57,2,02.03.2015,,3,146.38, -57,2,03.03.2015,,6,146.38, -57,2,04.03.2015,,13,146.38, -57,2,05.03.2015,,3,146.38, -57,2,06.03.2015,,0,146.38, -57,2,07.03.2015,,0,146.38, -57,2,08.03.2015,,0,146.38, -57,2,09.03.2015,,0,146.38, -57,2,10.03.2015,,0,146.38, -57,2,11.03.2015,,0,146.38, -57,2,12.03.2015,,0,146.38, -57,2,13.03.2015,,0,146.38, -57,2,14.03.2015,,0,146.38, -57,2,15.03.2015,,0,146.38, -57,2,16.03.2015,,0,146.38, -57,2,17.03.2015,,0,146.38, -57,2,18.03.2015,,0,146.38, -57,2,19.03.2015,,0,146.38, -57,2,20.03.2015,,3,146.38, -57,2,21.03.2015,,0,146.38, -57,2,22.03.2015,,0,146.38, -57,2,23.03.2015,,6,146.38, -57,2,24.03.2015,,0,146.38, -57,2,25.03.2015,,0,146.38, -57,2,26.03.2015,,0,146.38, -57,2,27.03.2015,,6,146.38, -57,2,28.03.2015,,3,146.38, -57,2,29.03.2015,,0,146.38, -57,2,30.03.2015,,25,146.38, -57,2,31.03.2015,,0,146.38, -57,2,01.04.2015,,6,146.38, -57,2,02.04.2015,,3,146.38, -57,2,03.04.2015,,0,146.38, -57,2,04.04.2015,,3,146.38, -57,2,05.04.2015,,6,146.38, -57,2,06.04.2015,,3,146.38, -57,2,07.04.2015,,3,146.38, -57,2,08.04.2015,,0,146.38, -57,2,09.04.2015,,3,146.38, -57,2,10.04.2015,,0,146.38, -57,2,11.04.2015,,3,146.38, -57,2,12.04.2015,,0,146.38, -57,2,13.04.2015,,9,145.84, -57,2,14.04.2015,,0,145.84, -57,2,15.04.2015,,0,145.84, -57,2,16.04.2015,,0,145.84, -57,2,17.04.2015,,0,145.84, -57,2,18.04.2015,,0,145.84, -57,2,19.04.2015,,3,145.84, -57,2,20.04.2015,,0,145.84, -57,2,21.04.2015,,0,145.84, -57,2,22.04.2015,,0,145.84, -57,2,23.04.2015,,0,145.84, -57,2,24.04.2015,,0,145.84, -57,2,25.04.2015,,0,145.84, -57,2,26.04.2015,,3,150.73, -57,2,27.04.2015,,0,150.73, -57,2,28.04.2015,,0,150.73, -57,2,29.04.2015,,0,150.73, -57,2,30.04.2015,,6,150.73, -57,2,01.05.2015,,9,150.73, -57,2,02.05.2015,,6,150.73, -57,2,03.05.2015,,3,150.73, -57,2,04.05.2015,,19,150.73, -57,2,05.05.2015,,3,150.73, -57,2,06.05.2015,,3,150.73, -57,2,07.05.2015,,3,150.73, -57,2,08.05.2015,,3,150.73, -57,2,09.05.2015,,3,150.73, -57,2,10.05.2015,,0,150.73, -57,2,11.05.2015,,3,150.73, -57,2,12.05.2015,,3,150.73, -57,2,13.05.2015,,3,150.73, -57,2,14.05.2015,,6,150.73, -57,2,15.05.2015,,22,150.73, -57,2,16.05.2015,,13,150.73, -57,2,17.05.2015,,9,150.73, -57,2,18.05.2015,,3,150.73, -57,2,19.05.2015,,0,150.73, -57,2,20.05.2015,,3,150.73, -57,2,21.05.2015,,3,150.73, -57,2,22.05.2015,,9,150.73, -57,2,23.05.2015,,3,150.73, -57,2,24.05.2015,,16,150.73, -57,2,25.05.2015,,9,150.73, -57,2,26.05.2015,,0,150.73, -57,2,27.05.2015,,3,150.73, -57,2,28.05.2015,,0,150.73, -57,2,29.05.2015,,9,150.73, -57,2,30.05.2015,,0,150.73, -57,2,31.05.2015,,3,150.73, -57,2,01.06.2015,,3,150.73, -57,2,02.06.2015,,3,150.73, -57,2,03.06.2015,,0,150.73, -57,2,04.06.2015,,6,150.73, -57,2,05.06.2015,,9,150.73, -57,2,06.06.2015,,0,150.73, -57,2,07.06.2015,,3,150.73, -57,2,08.06.2015,,9,150.73, -57,2,09.06.2015,,0,150.73, -57,2,10.06.2015,,0,150.73, -57,2,11.06.2015,,0,150.73, -57,2,12.06.2015,,0,150.73, -57,2,13.06.2015,,0,150.73, -57,2,14.06.2015,,0,150.73, -57,2,15.06.2015,,3,150.73, -57,2,16.06.2015,,0,150.73, -57,2,17.06.2015,,0,150.73, -57,2,18.06.2015,,0,150.73, -57,2,19.06.2015,,0,150.73, -57,2,20.06.2015,,6,150.73, -57,2,21.06.2015,,3,150.73, -57,2,22.06.2015,,0,150.73, -57,2,23.06.2015,,0,150.73, -57,2,24.06.2015,,0,150.73, -57,2,25.06.2015,,0,150.73, -57,2,26.06.2015,,0,150.73, -57,2,27.06.2015,,0,150.73, -57,2,28.06.2015,,0,150.73, -57,2,29.06.2015,,6,150.73, -57,2,30.06.2015,,0,150.73, -57,2,01.07.2015,,0,150.73, -57,2,02.07.2015,,3,150.73, -57,2,03.07.2015,,0,150.73, -57,2,04.07.2015,,13,150.73, -57,2,05.07.2015,,0,150.73, -57,2,06.07.2015,,6,150.73, -57,2,07.07.2015,,3,150.73, -57,2,08.07.2015,,0,150.73, -57,2,09.07.2015,,0,150.73, -57,2,10.07.2015,,0,150.73, -57,2,11.07.2015,,16,150.73, -57,2,12.07.2015,,25,150.73, -57,2,13.07.2015,,6,150.73, -57,2,14.07.2015,,6,150.73, -57,2,15.07.2015,,6,150.73, -57,2,16.07.2015,,3,150.73, -57,2,17.07.2015,,0,150.73, -57,2,18.07.2015,,3,150.73, -57,2,19.07.2015,,3,150.73, -57,2,20.07.2015,,25,150.73, -57,2,21.07.2015,,16,150.73, -57,2,22.07.2015,,6,150.73, -57,2,23.07.2015,,9,150.73, -57,2,24.07.2015,,16,150.73, -57,2,25.07.2015,,9,150.73, -57,2,26.07.2015,,9,150.73, -57,2,27.07.2015,,6,150.73, -57,2,28.07.2015,,6,150.73, -57,2,29.07.2015,,13,150.73, -57,2,30.07.2015,,0,150.73, -57,2,31.07.2015,,9,150.73, -57,2,01.08.2015,,13,150.73, -57,2,02.08.2015,,3,150.73, -57,2,03.08.2015,,9,150.73, -57,2,04.08.2015,,9,150.73, -57,2,05.08.2015,,9,150.73, -57,2,06.08.2015,,0,150.73, -57,2,07.08.2015,,28,150.73, -57,2,08.08.2015,,22,150.73, -57,2,09.08.2015,,35,150.73, -57,2,10.08.2015,,25,150.73, -57,2,11.08.2015,,6,150.73, -57,2,12.08.2015,,6,128.17, -57,2,13.08.2015,,9,128.17, -57,2,14.08.2015,,19,128.17, -57,2,15.08.2015,,13,128.17, -57,2,16.08.2015,,13,128.17, -57,2,17.08.2015,,28,128.17, -57,2,18.08.2015,1.0,119,128.17,100.3 -57,2,19.08.2015,1.0,88,128.17,100.3 -57,2,20.08.2015,1.0,104,128.17,100.3 -57,2,21.08.2015,1.0,123,128.17,100.3 -57,2,22.08.2015,1.0,107,128.17,100.3 -57,2,23.08.2015,1.0,104,128.17,100.3 -57,2,24.08.2015,1.0,82,128.17,100.3 -57,2,25.08.2015,,16,128.17, -57,2,26.08.2015,,16,128.17, -57,2,27.08.2015,,3,128.17, -57,2,28.08.2015,,28,128.17, -57,2,29.08.2015,,16,128.17, -57,2,30.08.2015,,25,128.17, -57,2,31.08.2015,,3,128.17, -57,2,01.09.2015,,6,128.17, -57,2,02.09.2015,,19,128.17, -57,2,03.09.2015,,0,128.17, -57,2,04.09.2015,,0,128.17, -57,2,05.09.2015,,0,128.17, -57,2,06.09.2015,,0,128.17, -57,2,07.09.2015,,0,128.17, -57,2,08.09.2015,,0,128.17, -57,2,09.09.2015,,0,128.17, -57,2,10.09.2015,,0,128.17, -57,2,11.09.2015,,0,128.17, -57,2,12.09.2015,,0,128.17, -57,2,13.09.2015,,0,128.17, -57,2,14.09.2015,,0,128.17, -57,2,15.09.2015,,0,128.17, -57,2,16.09.2015,,0,128.17, -57,2,17.09.2015,,0,128.17, -57,2,18.09.2015,,0,128.17, -57,2,19.09.2015,,0,128.17, -57,2,20.09.2015,,0,128.17, -57,2,21.09.2015,,13,128.17, -57,2,22.09.2015,,9,128.17, -57,2,23.09.2015,,3,128.17, -57,2,24.09.2015,,16,128.17, -57,2,25.09.2015,,38,128.17, -57,2,26.09.2015,,22,162.96, -57,2,27.09.2015,,3,162.96, -57,2,28.09.2015,,41,162.96, -57,2,29.09.2015,,0,162.96, -57,2,30.09.2015,,6,162.96, -57,2,01.10.2015,,9,162.96, -57,2,02.10.2015,,6,162.96, -57,2,03.10.2015,,13,162.96, -57,2,04.10.2015,,35,162.96, -57,2,05.10.2015,,28,162.96, -57,2,06.10.2015,1.0,113,143.39,122.05 -57,2,07.10.2015,1.0,101,143.39,122.05 -57,2,08.10.2015,1.0,91,143.39,122.05 -57,2,09.10.2015,1.0,104,143.39,122.05 -57,2,10.10.2015,1.0,94,143.39,122.05 -57,2,11.10.2015,1.0,94,143.39,122.05 -57,2,12.10.2015,1.0,94,143.39,122.05 -57,2,13.10.2015,1.0,94,143.93,122.05 -57,2,14.10.2015,1.0,94,143.93,122.05 -57,2,15.10.2015,1.0,88,143.93,122.05 -57,2,16.10.2015,1.0,82,143.93,122.05 -57,2,17.10.2015,1.0,72,162.96,122.05 -57,2,18.10.2015,1.0,25,162.96,122.05 -57,2,19.10.2015,,3,162.96, -57,2,20.10.2015,,16,162.96, -57,2,21.10.2015,,6,162.96, -57,2,22.10.2015,,0,162.96, -57,2,23.10.2015,,3,162.96, -57,2,24.10.2015,,0,162.96, -57,2,25.10.2015,,13,162.96, -57,2,26.10.2015,,0,162.96, -57,2,27.10.2015,,0,162.96, -57,2,28.10.2015,,0,162.96, -57,2,29.10.2015,,3,162.96, -57,2,30.10.2015,,3,162.96, -57,2,31.10.2015,,3,162.96, -57,2,01.11.2015,,0,162.96, -57,2,02.11.2015,,0,162.96, -57,2,03.11.2015,,0,162.96, -57,2,04.11.2015,,0,162.96, -57,2,05.11.2015,,9,162.96, -57,2,06.11.2015,,6,162.96, -57,2,07.11.2015,,3,162.96, -57,2,08.11.2015,,3,162.96, -57,2,09.11.2015,,9,162.96, -57,2,10.11.2015,,6,162.96, -57,2,11.11.2015,,0,138.36, -57,2,12.11.2015,,0,138.36, -57,2,13.11.2015,,6,138.36, -57,2,14.11.2015,,6,138.36, -57,2,15.11.2015,,3,138.36, -57,2,16.11.2015,,0,138.36, -57,2,17.11.2015,,3,138.36, -57,2,18.11.2015,,3,138.36, -57,2,19.11.2015,,0,138.36, -57,2,20.11.2015,,3,138.36, -57,2,21.11.2015,,9,138.36, -57,2,22.11.2015,,0,138.36, -57,2,23.11.2015,,6,138.36, -57,2,24.11.2015,,0,138.36, -57,2,25.11.2015,,0,138.36, -57,2,26.11.2015,,6,138.36, -57,2,27.11.2015,,0,138.36, -57,2,28.11.2015,,3,138.36, -57,2,29.11.2015,,6,138.36, -57,2,30.11.2015,,0,138.36, -57,2,01.12.2015,,0,162.96, -57,2,02.12.2015,,0,162.96, -57,2,03.12.2015,,3,138.5, -57,2,04.12.2015,,0,138.5, -57,2,05.12.2015,,0,138.5, -57,2,06.12.2015,,19,138.5, -57,2,07.12.2015,,9,138.5, -57,2,08.12.2015,,3,138.5, -57,2,09.12.2015,,3,138.5, -57,2,10.12.2015,,3,138.5, -57,2,11.12.2015,,3,142.57, -57,2,12.12.2015,,3,142.57, -57,2,13.12.2015,,3,142.57, -57,2,14.12.2015,,9,142.57, -57,2,15.12.2015,,6,142.57, -57,2,16.12.2015,,13,142.57, -57,2,17.12.2015,,6,142.57, -57,2,18.12.2015,,19,142.57, -57,2,19.12.2015,,3,142.57, -57,2,20.12.2015,,6,142.57, -57,2,21.12.2015,,0,142.57, -57,2,22.12.2015,1.0,72,142.57,103.02 -57,2,23.12.2015,1.0,135,142.57,103.02 -57,2,24.12.2015,1.0,31,142.57,103.02 -57,2,25.12.2015,1.0,116,142.57,103.02 -57,2,26.12.2015,1.0,6,142.57,103.02 -57,2,27.12.2015,1.0,13,142.57,103.02 -57,2,28.12.2015,1.0,13,142.57,103.02 -57,2,29.12.2015,,0,138.5, -57,2,30.12.2015,,6,138.5, -57,2,31.12.2015,,6,138.5, -57,2,01.01.2016,,3,138.5, -57,2,02.01.2016,,3,138.5, -57,2,03.01.2016,,3,138.5, -57,2,04.01.2016,,3,138.5, -57,2,05.01.2016,,19,138.5, -57,2,06.01.2016,,3,138.5, -57,2,07.01.2016,,6,138.5, -57,2,08.01.2016,,13,138.5, -57,2,09.01.2016,,6,138.5, -57,2,10.01.2016,,6,138.5, -57,2,11.01.2016,,6,138.5, -57,2,12.01.2016,,13,138.5, -57,2,13.01.2016,,6,138.5, -57,2,14.01.2016,,0,138.5, -57,2,15.01.2016,,0,138.5, -57,2,16.01.2016,,19,142.57, -57,2,17.01.2016,,19,142.57, -57,2,18.01.2016,,6,142.57, -57,2,19.01.2016,1.0,163,142.57,103.02 -57,2,20.01.2016,1.0,185,142.57,103.02 -57,2,21.01.2016,1.0,154,142.57,103.02 -57,2,22.01.2016,1.0,170,142.57,103.02 -57,2,23.01.2016,1.0,239,142.57,103.02 -57,2,24.01.2016,1.0,116,142.57,103.02 -57,2,25.01.2016,1.0,101,142.57,103.02 -57,2,26.01.2016,,0,142.57, -57,2,27.01.2016,,0,142.57, -57,2,28.01.2016,,9,142.57, -57,2,29.01.2016,,0,142.57, -57,2,30.01.2016,,25,142.57, -57,2,31.01.2016,,3,142.57, -57,2,01.02.2016,,0,142.57, -57,2,02.02.2016,,0,142.57, -57,2,03.02.2016,,0,142.57, -57,2,04.02.2016,,6,142.57, -57,2,05.02.2016,,22,142.57, -57,2,06.02.2016,,44,142.57, -57,2,07.02.2016,,19,142.57, -57,2,08.02.2016,,28,142.57, -57,2,09.02.2016,1.0,210,142.57,108.46 -57,2,10.02.2016,1.0,192,138.36,108.46 -57,2,11.02.2016,1.0,192,138.36,108.46 -57,2,12.02.2016,1.0,258,138.36,108.46 -57,2,13.02.2016,1.0,176,138.36,108.46 -57,2,14.02.2016,1.0,19,138.36,108.46 -57,2,15.02.2016,1.0,148,138.36,108.46 -57,2,16.02.2016,,6,138.36, -57,2,17.02.2016,,9,138.36, -57,2,18.02.2016,,3,138.36, -57,2,19.02.2016,,9,138.36, -57,2,20.02.2016,,13,138.36, -57,2,21.02.2016,,25,138.36, -57,2,22.02.2016,,3,138.36, -57,2,23.02.2016,,3,138.36, -57,2,24.02.2016,,3,138.36, -57,2,25.02.2016,,6,138.36, -57,2,26.02.2016,,9,138.36, -57,2,27.02.2016,,6,138.36, -57,2,28.02.2016,,3,138.36, -57,2,29.02.2016,,0,138.36, -57,2,01.03.2016,,0,138.36, -57,2,02.03.2016,,0,138.36, -57,2,03.03.2016,,0,138.36, -57,2,04.03.2016,,0,138.36, -57,2,05.03.2016,,0,138.36, -57,2,06.03.2016,,0,138.36, -57,2,07.03.2016,,6,138.36, -57,2,08.03.2016,,0,138.36, -57,2,09.03.2016,,9,138.36, -57,2,10.03.2016,,3,138.36, -57,2,11.03.2016,,0,138.36, -57,2,12.03.2016,,0,138.5, -57,2,13.03.2016,,0,138.5, -57,2,14.03.2016,,0,138.5, -57,2,15.03.2016,1.0,132,138.5,124.77 -57,2,16.03.2016,1.0,72,138.5,124.77 -57,2,17.03.2016,1.0,129,138.5,124.77 -57,2,18.03.2016,1.0,60,138.5,124.77 -57,2,19.03.2016,1.0,79,138.5,124.77 -57,2,20.03.2016,1.0,167,138.5,124.77 -57,2,21.03.2016,1.0,88,138.5,124.77 -57,2,22.03.2016,,6,138.36, -57,2,23.03.2016,,3,138.36, -57,2,24.03.2016,,0,138.36, -57,2,25.03.2016,,3,138.36, -57,2,26.03.2016,,3,138.36, -57,2,27.03.2016,,13,138.36, -57,2,28.03.2016,,16,138.36, -57,2,29.03.2016,,9,138.36, -57,2,30.03.2016,,3,138.36, -57,2,31.03.2016,,3,138.36, -57,2,01.04.2016,,16,138.36, -57,2,02.04.2016,,16,138.36, -57,2,03.04.2016,,9,138.36, -57,2,04.04.2016,,0,138.36, -57,2,05.04.2016,,13,138.36, -57,2,06.04.2016,,6,138.36, -57,2,07.04.2016,,9,138.36, -57,2,08.04.2016,,6,138.36, -57,2,09.04.2016,,3,138.5, -57,2,10.04.2016,,13,138.5, -57,2,11.04.2016,,25,138.5, -57,2,12.04.2016,,3,138.5, -57,2,13.04.2016,,3,138.5, -57,2,14.04.2016,,3,138.5, -57,2,15.04.2016,,3,138.5, -57,2,16.04.2016,,9,138.5, -57,2,17.04.2016,,6,138.5, -57,2,18.04.2016,,19,138.5, -57,2,19.04.2016,1.0,110,138.5,116.89 -57,2,20.04.2016,1.0,88,138.5,116.89 -57,2,21.04.2016,1.0,69,138.5,116.89 -57,2,22.04.2016,1.0,132,138.5,116.89 -57,2,23.04.2016,1.0,145,138.5,116.89 -57,2,24.04.2016,1.0,107,138.5,116.89 -57,2,25.04.2016,1.0,110,138.5,116.89 -57,2,26.04.2016,,6,138.5, -57,2,27.04.2016,,0,138.5, -57,2,28.04.2016,,0,138.5, -57,2,29.04.2016,,6,138.5, -57,2,30.04.2016,,28,138.5, -57,2,01.05.2016,,0,138.5, -57,2,02.05.2016,,28,138.5, -57,2,03.05.2016,1.0,254,138.5,106.01 -57,2,04.05.2016,1.0,198,138.5,106.01 -57,2,05.05.2016,1.0,195,138.5,106.01 -57,2,06.05.2016,1.0,129,138.5,106.01 -57,2,07.05.2016,1.0,170,138.5,106.01 -57,2,08.05.2016,1.0,176,138.5,106.01 -57,2,09.05.2016,1.0,145,138.5,106.01 -57,2,10.05.2016,,3,142.57, -57,2,11.05.2016,,69,142.57, -57,2,12.05.2016,,53,142.57, -57,2,13.05.2016,,16,142.57, -57,2,14.05.2016,,16,142.57, -57,2,15.05.2016,,16,142.57, -57,2,16.05.2016,,16,142.57, -57,2,17.05.2016,,3,142.57, -57,2,18.05.2016,,0,142.57, -57,2,19.05.2016,,3,142.57, -57,2,20.05.2016,,0,142.57, -57,2,21.05.2016,,6,142.57, -57,2,22.05.2016,,3,142.57, -58,2,01.01.2015,,0,135.78, -58,2,02.01.2015,,0,135.78, -58,2,03.01.2015,,0,135.78, -58,2,04.01.2015,,0,135.78, -58,2,05.01.2015,,0,135.78, -58,2,06.01.2015,,0,135.78, -58,2,07.01.2015,,0,135.78, -58,2,08.01.2015,,13,135.78, -58,2,09.01.2015,,13,135.78, -58,2,10.01.2015,,0,135.78, -58,2,11.01.2015,,0,135.78, -58,2,12.01.2015,,0,135.78, -58,2,13.01.2015,,0,135.78, -58,2,14.01.2015,,3,135.78, -58,2,15.01.2015,,3,135.78, -58,2,16.01.2015,,3,135.78, -58,2,17.01.2015,,0,135.78, -58,2,18.01.2015,,38,135.78, -58,2,19.01.2015,,3,135.78, -58,2,20.01.2015,,0,135.78, -58,2,21.01.2015,,3,135.78, -58,2,22.01.2015,,0,135.78, -58,2,23.01.2015,,3,135.78, -58,2,24.01.2015,,3,135.78, -58,2,25.01.2015,,9,135.78, -58,2,26.01.2015,,6,135.78, -58,2,27.01.2015,,6,135.78, -58,2,28.01.2015,,13,135.78, -58,2,29.01.2015,,0,135.78, -58,2,30.01.2015,,3,135.78, -58,2,31.01.2015,,16,135.78, -58,2,01.02.2015,,13,135.78, -58,2,02.02.2015,,0,135.78, -58,2,03.02.2015,,6,135.78, -58,2,04.02.2015,,13,135.78, -58,2,05.02.2015,,3,135.78, -58,2,06.02.2015,,3,135.78, -58,2,07.02.2015,,0,135.78, -58,2,08.02.2015,,0,135.78, -58,2,09.02.2015,,0,135.78, -58,2,10.02.2015,,0,135.78, -58,2,11.02.2015,,3,135.78, -58,2,12.02.2015,,3,135.78, -58,2,13.02.2015,,9,135.78, -58,2,14.02.2015,,0,135.78, -58,2,15.02.2015,,3,135.78, -58,2,16.02.2015,,0,135.78, -58,2,17.02.2015,,0,135.78, -58,2,18.02.2015,,13,135.78, -58,2,19.02.2015,,9,135.78, -58,2,20.02.2015,,9,135.78, -58,2,21.02.2015,,0,135.78, -58,2,22.02.2015,,0,135.78, -58,2,23.02.2015,,0,135.78, -58,2,24.02.2015,,0,135.78, -58,2,25.02.2015,,0,135.78, -58,2,26.02.2015,,6,146.38, -58,2,27.02.2015,,3,146.38, -58,2,28.02.2015,,13,146.38, -58,2,01.03.2015,,3,146.38, -58,2,02.03.2015,,0,146.38, -58,2,03.03.2015,,0,146.38, -58,2,04.03.2015,,0,146.38, -58,2,05.03.2015,,0,146.38, -58,2,06.03.2015,,0,146.38, -58,2,07.03.2015,,0,146.38, -58,2,08.03.2015,,6,146.38, -58,2,09.03.2015,,0,146.38, -58,2,10.03.2015,,16,146.38, -58,2,11.03.2015,,0,146.38, -58,2,12.03.2015,,3,146.38, -58,2,13.03.2015,,13,146.38, -58,2,14.03.2015,,0,146.38, -58,2,15.03.2015,,0,146.38, -58,2,16.03.2015,,3,146.38, -58,2,17.03.2015,,0,146.38, -58,2,18.03.2015,,9,146.38, -58,2,19.03.2015,,0,146.38, -58,2,20.03.2015,,0,146.38, -58,2,21.03.2015,,0,146.38, -58,2,22.03.2015,,0,146.38, -58,2,23.03.2015,,0,146.38, -58,2,24.03.2015,,3,146.38, -58,2,25.03.2015,,3,146.38, -58,2,26.03.2015,,3,146.38, -58,2,27.03.2015,,0,146.38, -58,2,28.03.2015,,3,146.38, -58,2,29.03.2015,,3,146.38, -58,2,30.03.2015,,0,146.38, -58,2,31.03.2015,,3,146.38, -58,2,01.04.2015,,0,146.38, -58,2,02.04.2015,,0,146.38, -58,2,03.04.2015,,3,146.38, -58,2,04.04.2015,,0,146.38, -58,2,05.04.2015,,13,146.38, -58,2,06.04.2015,,3,146.38, -58,2,07.04.2015,,0,146.38, -58,2,08.04.2015,,0,146.38, -58,2,09.04.2015,,3,146.38, -58,2,10.04.2015,,16,146.38, -58,2,11.04.2015,,6,146.38, -58,2,12.04.2015,,0,146.38, -58,2,13.04.2015,,0,145.84, -58,2,14.04.2015,,3,145.84, -58,2,15.04.2015,,0,145.84, -58,2,16.04.2015,,3,145.84, -58,2,17.04.2015,,3,145.84, -58,2,18.04.2015,,3,145.84, -58,2,19.04.2015,,9,145.84, -58,2,20.04.2015,,0,145.84, -58,2,21.04.2015,,0,145.84, -58,2,22.04.2015,,0,145.84, -58,2,23.04.2015,,3,145.84, -58,2,24.04.2015,,0,145.84, -58,2,25.04.2015,,0,145.84, -58,2,26.04.2015,,6,150.73, -58,2,27.04.2015,,0,150.73, -58,2,28.04.2015,,0,150.73, -58,2,29.04.2015,,0,150.73, -58,2,30.04.2015,,3,150.73, -58,2,01.05.2015,,0,150.73, -58,2,02.05.2015,,0,150.73, -58,2,03.05.2015,,6,150.73, -58,2,04.05.2015,,3,150.73, -58,2,05.05.2015,,0,150.73, -58,2,06.05.2015,,0,150.73, -58,2,07.05.2015,,0,150.73, -58,2,08.05.2015,,13,150.73, -58,2,09.05.2015,,3,150.73, -58,2,10.05.2015,,9,150.73, -58,2,11.05.2015,,0,150.73, -58,2,12.05.2015,,0,150.73, -58,2,13.05.2015,,0,150.73, -58,2,14.05.2015,,0,150.73, -58,2,15.05.2015,,6,150.73, -58,2,16.05.2015,,0,150.73, -58,2,17.05.2015,,0,150.73, -58,2,18.05.2015,,0,150.73, -58,2,19.05.2015,,3,150.73, -58,2,20.05.2015,,0,150.73, -58,2,21.05.2015,,0,150.73, -58,2,22.05.2015,,13,150.73, -58,2,23.05.2015,,0,150.73, -58,2,24.05.2015,,0,150.73, -58,2,25.05.2015,,0,150.73, -58,2,26.05.2015,,0,150.73, -58,2,27.05.2015,,0,150.73, -58,2,28.05.2015,,3,150.73, -58,2,29.05.2015,,0,150.73, -58,2,30.05.2015,,0,150.73, -58,2,31.05.2015,,0,150.73, -58,2,01.06.2015,,0,150.73, -58,2,02.06.2015,,0,150.73, -58,2,03.06.2015,,3,150.73, -58,2,04.06.2015,,0,150.73, -58,2,05.06.2015,,0,150.73, -58,2,06.06.2015,,13,150.73, -58,2,07.06.2015,,6,150.73, -58,2,08.06.2015,,3,155.35, -58,2,09.06.2015,,0,155.35, -58,2,10.06.2015,,3,155.35, -58,2,11.06.2015,,0,155.35, -58,2,12.06.2015,,0,155.35, -58,2,13.06.2015,,0,155.35, -58,2,14.06.2015,,3,155.35, -58,2,15.06.2015,,0,155.35, -58,2,16.06.2015,,0,155.35, -58,2,17.06.2015,,0,155.35, -58,2,18.06.2015,,0,155.35, -58,2,19.06.2015,,9,155.35, -58,2,20.06.2015,,0,155.35, -58,2,21.06.2015,,0,155.35, -58,2,22.06.2015,,0,155.35, -58,2,23.06.2015,,3,155.35, -58,2,24.06.2015,,13,155.35, -58,2,25.06.2015,,0,155.35, -58,2,26.06.2015,,9,155.35, -58,2,27.06.2015,,0,155.35, -58,2,28.06.2015,,0,155.35, -58,2,29.06.2015,,0,155.35, -58,2,30.06.2015,,0,155.35, -58,2,01.07.2015,,3,155.35, -58,2,02.07.2015,,0,155.35, -58,2,03.07.2015,,13,155.35, -58,2,04.07.2015,,6,155.35, -58,2,05.07.2015,,6,155.35, -58,2,06.07.2015,,3,155.35, -58,2,07.07.2015,,6,155.35, -58,2,08.07.2015,,0,155.35, -58,2,09.07.2015,,0,155.35, -58,2,10.07.2015,,0,155.35, -58,2,11.07.2015,,0,155.35, -58,2,12.07.2015,,44,155.35, -58,2,13.07.2015,,6,155.35, -58,2,14.07.2015,,0,155.35, -58,2,15.07.2015,,0,155.35, -58,2,16.07.2015,,6,155.35, -58,2,17.07.2015,,9,155.35, -58,2,18.07.2015,,0,155.35, -58,2,19.07.2015,,0,155.35, -58,2,20.07.2015,,22,155.35, -58,2,21.07.2015,,0,155.35, -58,2,22.07.2015,,0,155.35, -58,2,23.07.2015,,0,155.35, -58,2,24.07.2015,,0,155.35, -58,2,25.07.2015,,3,155.35, -58,2,26.07.2015,,0,155.35, -58,2,27.07.2015,,0,155.35, -58,2,28.07.2015,,0,155.35, -58,2,29.07.2015,,25,155.35, -58,2,30.07.2015,,0,155.35, -58,2,31.07.2015,,0,155.35, -58,2,01.08.2015,,0,155.35, -58,2,02.08.2015,,0,155.35, -58,2,03.08.2015,,0,155.35, -58,2,04.08.2015,,0,155.35, -58,2,05.08.2015,,0,155.35, -58,2,06.08.2015,,9,155.35, -58,2,07.08.2015,,6,155.35, -58,2,08.08.2015,,0,155.35, -58,2,09.08.2015,,0,155.35, -58,2,10.08.2015,,0,155.35, -58,2,11.08.2015,,0,155.35, -58,2,12.08.2015,,3,128.17, -58,2,13.08.2015,,3,128.17, -58,2,14.08.2015,,9,128.17, -58,2,15.08.2015,,0,128.17, -58,2,16.08.2015,,0,128.17, -58,2,17.08.2015,,9,128.17, -58,2,18.08.2015,1.0,182,128.17,100.3 -58,2,19.08.2015,1.0,104,128.17,100.3 -58,2,20.08.2015,1.0,157,128.17,100.3 -58,2,21.08.2015,1.0,195,128.17,100.3 -58,2,22.08.2015,1.0,123,128.17,100.3 -58,2,23.08.2015,1.0,79,128.17,100.3 -58,2,24.08.2015,1.0,72,128.17,100.3 -58,2,25.08.2015,,16,128.17, -58,2,26.08.2015,,9,128.17, -58,2,27.08.2015,,0,128.17, -58,2,28.08.2015,,0,128.17, -58,2,29.08.2015,,0,128.17, -58,2,30.08.2015,,0,128.17, -58,2,31.08.2015,,0,128.17, -58,2,01.09.2015,,0,128.17, -58,2,02.09.2015,,0,128.17, -58,2,03.09.2015,,0,128.17, -58,2,04.09.2015,,0,128.17, -58,2,05.09.2015,,0,128.17, -58,2,06.09.2015,,0,128.17, -58,2,07.09.2015,,0,128.17, -58,2,08.09.2015,,0,128.17, -58,2,09.09.2015,,0,128.17, -58,2,10.09.2015,,0,128.17, -58,2,11.09.2015,,0,128.17, -58,2,12.09.2015,,0,128.17, -58,2,13.09.2015,,13,128.17, -58,2,14.09.2015,,6,128.17, -58,2,15.09.2015,,0,128.17, -58,2,16.09.2015,,0,128.17, -58,2,17.09.2015,,0,128.17, -58,2,18.09.2015,,19,128.17, -58,2,19.09.2015,,0,128.17, -58,2,20.09.2015,,0,128.17, -58,2,21.09.2015,,0,128.17, -58,2,22.09.2015,,0,128.17, -58,2,23.09.2015,,0,128.17, -58,2,24.09.2015,,0,128.17, -58,2,25.09.2015,,0,128.17, -58,2,26.09.2015,,0,128.17, -58,2,27.09.2015,,0,128.17, -58,2,28.09.2015,,0,128.17, -58,2,29.09.2015,,6,128.17, -58,2,30.09.2015,,0,128.17, -58,2,01.10.2015,,0,162.96, -58,2,02.10.2015,,0,162.96, -58,2,03.10.2015,,0,162.96, -58,2,04.10.2015,,0,162.96, -58,2,05.10.2015,,0,162.96, -58,2,06.10.2015,1.0,113,143.39,122.05 -58,2,07.10.2015,1.0,79,143.39,122.05 -58,2,08.10.2015,1.0,57,143.39,122.05 -58,2,09.10.2015,1.0,9,143.39,122.05 -58,2,10.10.2015,1.0,72,143.39,122.05 -58,2,11.10.2015,1.0,72,143.39,122.05 -58,2,12.10.2015,1.0,72,143.39,122.05 -58,2,13.10.2015,,0,143.93, -58,2,14.10.2015,,0,143.93, -58,2,15.10.2015,,0,143.93, -58,2,16.10.2015,,0,143.93, -58,2,17.10.2015,,0,162.96, -58,2,18.10.2015,,0,162.96, -58,2,19.10.2015,,0,162.96, -58,2,20.10.2015,,0,162.96, -58,2,21.10.2015,,0,162.96, -58,2,22.10.2015,,3,162.96, -58,2,23.10.2015,,0,162.96, -58,2,24.10.2015,,0,162.96, -58,2,25.10.2015,,0,162.96, -58,2,26.10.2015,,3,162.96, -58,2,27.10.2015,,0,162.96, -58,2,28.10.2015,,0,162.96, -58,2,29.10.2015,,0,162.96, -58,2,30.10.2015,,0,162.96, -58,2,31.10.2015,,6,162.96, -58,2,01.11.2015,,0,162.96, -58,2,02.11.2015,,0,162.96, -58,2,03.11.2015,,0,162.96, -58,2,04.11.2015,,0,162.96, -58,2,05.11.2015,,0,162.96, -58,2,06.11.2015,,0,162.96, -58,2,07.11.2015,,9,162.96, -58,2,08.11.2015,,3,162.96, -58,2,09.11.2015,,3,162.96, -58,2,10.11.2015,,3,162.96, -58,2,11.11.2015,,0,138.36, -58,2,12.11.2015,,0,138.36, -58,2,13.11.2015,,0,138.36, -58,2,14.11.2015,,9,138.36, -58,2,15.11.2015,,0,138.36, -58,2,16.11.2015,,6,138.36, -58,2,17.11.2015,,0,138.36, -58,2,18.11.2015,,0,138.36, -58,2,19.11.2015,,0,138.36, -58,2,20.11.2015,,0,138.36, -58,2,21.11.2015,,22,138.36, -58,2,22.11.2015,,3,138.36, -58,2,23.11.2015,,3,138.36, -58,2,24.11.2015,,0,138.36, -58,2,25.11.2015,,13,138.36, -58,2,26.11.2015,,0,138.36, -58,2,27.11.2015,,3,138.36, -58,2,28.11.2015,,0,138.36, -58,2,29.11.2015,,0,138.36, -58,2,30.11.2015,,0,138.36, -58,2,01.12.2015,,0,162.96, -58,2,02.12.2015,,0,162.96, -58,2,03.12.2015,,3,138.5, -58,2,04.12.2015,,0,138.5, -58,2,05.12.2015,,13,138.5, -58,2,06.12.2015,,0,138.5, -58,2,07.12.2015,,3,138.5, -58,2,08.12.2015,,3,138.5, -58,2,09.12.2015,,9,138.5, -58,2,10.12.2015,,0,138.5, -58,2,11.12.2015,,0,142.57, -58,2,12.12.2015,,0,142.57, -58,2,13.12.2015,,0,142.57, -58,2,14.12.2015,,0,142.57, -58,2,15.12.2015,,6,142.57, -58,2,16.12.2015,,3,142.57, -58,2,17.12.2015,,9,142.57, -58,2,18.12.2015,,6,142.57, -58,2,19.12.2015,,6,142.57, -58,2,20.12.2015,,3,142.57, -58,2,21.12.2015,,0,142.57, -58,2,22.12.2015,1.0,88,142.57,103.02 -58,2,23.12.2015,1.0,154,142.57,103.02 -58,2,24.12.2015,1.0,75,142.57,103.02 -58,2,25.12.2015,1.0,167,142.57,103.02 -58,2,26.12.2015,1.0,151,142.57,103.02 -58,2,27.12.2015,1.0,138,142.57,103.02 -58,2,28.12.2015,1.0,19,142.57,103.02 -58,2,29.12.2015,,0,138.5, -58,2,30.12.2015,,0,138.5, -58,2,31.12.2015,,0,138.5, -58,2,01.01.2016,,3,138.5, -58,2,02.01.2016,,0,138.5, -58,2,03.01.2016,,35,138.5, -58,2,04.01.2016,,0,138.5, -58,2,05.01.2016,,16,138.5, -58,2,06.01.2016,,3,138.5, -58,2,07.01.2016,,6,138.5, -58,2,08.01.2016,,9,138.5, -58,2,09.01.2016,,0,138.5, -58,2,10.01.2016,,0,138.5, -58,2,11.01.2016,,0,138.5, -58,2,12.01.2016,,13,138.5, -58,2,13.01.2016,,0,138.5, -58,2,14.01.2016,,0,138.5, -58,2,15.01.2016,,6,138.5, -58,2,16.01.2016,,0,142.57, -58,2,17.01.2016,,19,142.57, -58,2,18.01.2016,,0,142.57, -58,2,19.01.2016,1.0,179,142.57,103.02 -58,2,20.01.2016,1.0,138,142.57,103.02 -58,2,21.01.2016,1.0,72,142.57,103.02 -58,2,22.01.2016,1.0,135,142.57,103.02 -58,2,23.01.2016,1.0,94,142.57,103.02 -58,2,24.01.2016,1.0,94,142.57,103.02 -58,2,25.01.2016,1.0,94,142.57,103.02 -58,2,26.01.2016,,0,142.57, -58,2,27.01.2016,,0,142.57, -58,2,28.01.2016,,0,142.57, -58,2,29.01.2016,,0,142.57, -58,2,30.01.2016,,0,142.57, -58,2,31.01.2016,,0,142.57, -58,2,01.02.2016,,0,142.57, -58,2,02.02.2016,,0,142.57, -58,2,03.02.2016,,0,142.57, -58,2,04.02.2016,,0,142.57, -58,2,05.02.2016,,0,142.57, -58,2,06.02.2016,,0,142.57, -58,2,07.02.2016,,0,142.57, -58,2,08.02.2016,,0,142.57, -58,2,09.02.2016,1.0,41,142.57,108.46 -58,2,10.02.2016,1.0,160,138.36,108.46 -58,2,11.02.2016,1.0,145,138.36,108.46 -58,2,12.02.2016,1.0,69,138.36,108.46 -58,2,13.02.2016,1.0,79,138.36,108.46 -58,2,14.02.2016,1.0,69,138.36,108.46 -58,2,15.02.2016,1.0,66,138.36,108.46 -58,2,16.02.2016,,0,138.36, -58,2,17.02.2016,,0,138.36, -58,2,18.02.2016,,0,138.36, -58,2,19.02.2016,,0,138.36, -58,2,20.02.2016,,6,138.36, -58,2,21.02.2016,,9,138.36, -58,2,22.02.2016,,0,138.36, -58,2,23.02.2016,,0,138.36, -58,2,24.02.2016,,6,138.36, -58,2,25.02.2016,,0,138.36, -58,2,26.02.2016,,0,138.36, -58,2,27.02.2016,,3,138.36, -58,2,28.02.2016,,0,138.36, -58,2,29.02.2016,,0,138.36, -58,2,01.03.2016,,0,138.36, -58,2,02.03.2016,,6,138.36, -58,2,03.03.2016,,3,138.36, -58,2,04.03.2016,,0,138.36, -58,2,05.03.2016,,0,138.36, -58,2,06.03.2016,,0,138.36, -58,2,07.03.2016,,0,138.36, -58,2,08.03.2016,,0,138.36, -58,2,09.03.2016,,0,138.36, -58,2,10.03.2016,,0,138.36, -58,2,11.03.2016,,3,138.36, -58,2,12.03.2016,,3,138.5, -58,2,13.03.2016,,6,138.5, -58,2,14.03.2016,,3,138.5, -58,2,15.03.2016,1.0,72,138.5,124.77 -58,2,16.03.2016,1.0,101,138.5,124.77 -58,2,17.03.2016,1.0,28,138.5,124.77 -58,2,18.03.2016,1.0,116,138.5,124.77 -58,2,19.03.2016,1.0,19,138.5,124.77 -58,2,20.03.2016,1.0,53,138.5,124.77 -58,2,21.03.2016,1.0,53,138.5,124.77 -58,2,22.03.2016,,3,138.36, -58,2,23.03.2016,,3,138.36, -58,2,24.03.2016,,0,138.36, -58,2,25.03.2016,,0,138.36, -58,2,26.03.2016,,0,138.36, -58,2,27.03.2016,,0,138.36, -58,2,28.03.2016,,0,138.36, -58,2,29.03.2016,,9,138.36, -58,2,30.03.2016,,3,138.36, -58,2,31.03.2016,,3,138.36, -58,2,01.04.2016,,9,138.36, -58,2,02.04.2016,,6,138.36, -58,2,03.04.2016,,0,138.36, -58,2,04.04.2016,,0,138.36, -58,2,05.04.2016,,0,138.36, -58,2,06.04.2016,,0,138.36, -58,2,07.04.2016,,0,138.36, -58,2,08.04.2016,,6,138.36, -58,2,09.04.2016,,3,138.5, -58,2,10.04.2016,,0,138.5, -58,2,11.04.2016,,6,138.5, -58,2,12.04.2016,,0,138.5, -58,2,13.04.2016,,22,138.5, -58,2,14.04.2016,,0,138.5, -58,2,15.04.2016,,0,138.5, -58,2,16.04.2016,,0,138.5, -58,2,17.04.2016,,0,138.5, -58,2,18.04.2016,,0,138.5, -58,2,19.04.2016,1.0,16,138.5,116.89 -58,2,20.04.2016,1.0,104,138.5,116.89 -58,2,21.04.2016,1.0,107,138.5,116.89 -58,2,22.04.2016,1.0,132,138.5,116.89 -58,2,23.04.2016,1.0,82,138.5,116.89 -58,2,24.04.2016,1.0,13,138.5,116.89 -58,2,25.04.2016,1.0,57,138.5,116.89 -58,2,26.04.2016,,0,138.5, -58,2,27.04.2016,,0,138.5, -58,2,28.04.2016,,0,138.5, -58,2,29.04.2016,,0,138.5, -58,2,30.04.2016,,0,138.5, -58,2,01.05.2016,,6,138.5, -58,2,02.05.2016,,3,138.5, -58,2,03.05.2016,1.0,135,138.5,106.01 -58,2,04.05.2016,1.0,148,138.5,106.01 -58,2,05.05.2016,1.0,179,138.5,106.01 -58,2,06.05.2016,1.0,151,138.5,106.01 -58,2,07.05.2016,1.0,226,138.5,106.01 -58,2,08.05.2016,1.0,57,138.5,106.01 -58,2,09.05.2016,1.0,97,138.5,106.01 -58,2,10.05.2016,,3,142.57, -58,2,11.05.2016,,9,142.57, -58,2,12.05.2016,,0,142.57, -58,2,13.05.2016,,6,142.57, -58,2,14.05.2016,,3,142.57, -58,2,15.05.2016,,0,142.57, -58,2,16.05.2016,,0,142.57, -58,2,17.05.2016,,3,142.57, -58,2,18.05.2016,,0,142.57, -58,2,19.05.2016,,0,142.57, -58,2,20.05.2016,,3,142.57, -58,2,21.05.2016,,3,142.57, -58,2,22.05.2016,,3,142.57, -59,2,01.01.2015,,0,134.72, -59,2,02.01.2015,,0,134.72, -59,2,03.01.2015,,0,134.72, -59,2,04.01.2015,,0,134.72, -59,2,05.01.2015,,0,134.72, -59,2,06.01.2015,,0,134.72, -59,2,07.01.2015,,0,134.72, -59,2,08.01.2015,,0,134.72, -59,2,09.01.2015,,0,134.72, -59,2,10.01.2015,,0,134.72, -59,2,11.01.2015,,0,134.72, -59,2,12.01.2015,,0,134.72, -59,2,13.01.2015,,0,134.72, -59,2,14.01.2015,,0,134.72, -59,2,15.01.2015,,0,134.72, -59,2,16.01.2015,,0,134.72, -59,2,17.01.2015,,0,134.72, -59,2,18.01.2015,,0,134.72, -59,2,19.01.2015,,0,134.72, -59,2,20.01.2015,,0,134.72, -59,2,21.01.2015,,0,134.72, -59,2,22.01.2015,,0,134.72, -59,2,23.01.2015,,0,134.72, -59,2,24.01.2015,,0,134.72, -59,2,25.01.2015,,0,134.72, -59,2,26.01.2015,,0,134.72, -59,2,27.01.2015,,0,134.72, -59,2,28.01.2015,,0,134.72, -59,2,29.01.2015,,0,134.72, -59,2,30.01.2015,,0,134.72, -59,2,31.01.2015,,0,134.72, -59,2,01.02.2015,,0,134.72, -59,2,02.02.2015,,0,134.72, -59,2,03.02.2015,,0,134.72, -59,2,04.02.2015,,0,134.72, -59,2,05.02.2015,,0,134.72, -59,2,06.02.2015,,0,134.72, -59,2,07.02.2015,,0,134.72, -59,2,08.02.2015,,0,134.72, -59,2,09.02.2015,,0,134.72, -59,2,10.02.2015,,0,134.72, -59,2,11.02.2015,,0,134.72, -59,2,12.02.2015,,0,134.72, -59,2,13.02.2015,,0,134.72, -59,2,14.02.2015,,0,134.72, -59,2,15.02.2015,,0,134.72, -59,2,16.02.2015,,0,134.72, -59,2,17.02.2015,,0,134.72, -59,2,18.02.2015,,0,134.72, -59,2,19.02.2015,,0,134.72, -59,2,20.02.2015,,0,134.72, -59,2,21.02.2015,,0,134.72, -59,2,22.02.2015,,0,134.72, -59,2,23.02.2015,,0,134.72, -59,2,24.02.2015,,0,134.72, -59,2,25.02.2015,,0,134.72, -59,2,26.02.2015,,0,134.72, -59,2,27.02.2015,,0,134.72, -59,2,28.02.2015,,0,134.72, -59,2,01.03.2015,,0,134.72, 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-59,2,23.08.2015,1.0,47,128.17,100.3 -59,2,24.08.2015,1.0,38,128.17,100.3 -59,2,25.08.2015,,6,128.17, -59,2,26.08.2015,,6,128.17, -59,2,27.08.2015,,9,128.17, -59,2,28.08.2015,,3,128.17, -59,2,29.08.2015,,3,128.17, -59,2,30.08.2015,,3,128.17, -59,2,31.08.2015,,3,128.17, -59,2,01.09.2015,,3,128.17, -59,2,02.09.2015,,3,128.17, -59,2,03.09.2015,,0,128.17, -59,2,04.09.2015,,0,128.17, -59,2,05.09.2015,,0,128.17, -59,2,06.09.2015,,0,128.17, -59,2,07.09.2015,,0,128.17, -59,2,08.09.2015,,0,128.17, -59,2,09.09.2015,,0,128.17, -59,2,10.09.2015,,0,128.17, -59,2,11.09.2015,,0,128.17, -59,2,12.09.2015,,0,128.17, -59,2,13.09.2015,,0,128.17, -59,2,14.09.2015,,0,128.17, -59,2,15.09.2015,,0,128.17, -59,2,16.09.2015,,0,128.17, -59,2,17.09.2015,,0,128.17, -59,2,18.09.2015,,0,162.96, -59,2,19.09.2015,,0,162.96, -59,2,20.09.2015,,0,162.96, -59,2,21.09.2015,,0,162.96, -59,2,22.09.2015,,0,162.96, -59,2,23.09.2015,,0,162.96, -59,2,24.09.2015,,0,162.96, -59,2,25.09.2015,,0,162.96, -59,2,26.09.2015,,0,162.96, -59,2,27.09.2015,,0,162.96, -59,2,28.09.2015,,0,162.96, -59,2,29.09.2015,,0,162.96, -59,2,30.09.2015,,0,162.96, -59,2,01.10.2015,,0,162.96, -59,2,02.10.2015,,3,162.96, -59,2,03.10.2015,,6,162.96, -59,2,04.10.2015,,9,162.96, -59,2,05.10.2015,,0,162.96, -59,2,06.10.2015,1.0,44,143.39,122.05 -59,2,07.10.2015,1.0,44,143.39,122.05 -59,2,08.10.2015,1.0,44,143.39,122.05 -59,2,09.10.2015,1.0,85,143.39,122.05 -59,2,10.10.2015,1.0,57,143.39,122.05 -59,2,11.10.2015,1.0,69,143.39,122.05 -59,2,12.10.2015,1.0,50,143.39,122.05 -59,2,13.10.2015,,0,143.93, -59,2,14.10.2015,,9,143.93, -59,2,15.10.2015,,0,143.93, -59,2,16.10.2015,,0,143.93, -59,2,17.10.2015,,0,143.93, -59,2,18.10.2015,,16,143.93, -59,2,19.10.2015,,0,143.93, -59,2,20.10.2015,,0,143.93, -59,2,21.10.2015,,0,143.93, -59,2,22.10.2015,,0,143.93, -59,2,23.10.2015,,0,143.93, -59,2,24.10.2015,,0,143.93, -59,2,25.10.2015,,0,143.93, -59,2,26.10.2015,,0,143.93, -59,2,27.10.2015,,0,143.93, -59,2,28.10.2015,,0,143.93, -59,2,29.10.2015,,0,143.93, -59,2,30.10.2015,,0,143.93, -59,2,31.10.2015,,0,143.93, -59,2,01.11.2015,,0,143.93, -59,2,02.11.2015,,0,143.93, -59,2,03.11.2015,,0,143.93, -59,2,04.11.2015,,0,143.93, -59,2,05.11.2015,,0,143.93, -59,2,06.11.2015,,0,143.93, -59,2,07.11.2015,,0,143.93, -59,2,08.11.2015,,0,143.93, -59,2,09.11.2015,,0,143.93, -59,2,10.11.2015,,0,143.93, -59,2,11.11.2015,,0,143.93, -59,2,12.11.2015,,0,143.93, -59,2,13.11.2015,,0,143.93, -59,2,14.11.2015,,0,143.93, -59,2,15.11.2015,,0,143.93, -59,2,16.11.2015,,0,143.93, -59,2,17.11.2015,,0,143.93, -59,2,18.11.2015,,0,143.93, -59,2,19.11.2015,,0,143.93, -59,2,20.11.2015,,0,143.93, -59,2,21.11.2015,,0,143.93, -59,2,22.11.2015,,0,143.93, -59,2,23.11.2015,,0,143.93, -59,2,24.11.2015,,0,143.93, -59,2,25.11.2015,,0,143.93, -59,2,26.11.2015,,0,143.93, -59,2,27.11.2015,,0,143.93, -59,2,28.11.2015,,0,143.93, -59,2,29.11.2015,,0,143.93, -59,2,30.11.2015,,0,143.93, -59,2,01.12.2015,,0,143.93, -59,2,02.12.2015,,0,143.93, -59,2,03.12.2015,,0,143.93, -59,2,04.12.2015,,0,143.93, -59,2,05.12.2015,,0,143.93, -59,2,06.12.2015,,0,143.93, -59,2,07.12.2015,,0,143.93, -59,2,08.12.2015,,0,143.93, -59,2,09.12.2015,,0,138.5, -59,2,10.12.2015,,0,138.5, -59,2,11.12.2015,,13,142.57, -59,2,12.12.2015,,6,142.57, -59,2,13.12.2015,,3,142.57, -59,2,14.12.2015,,3,142.57, -59,2,15.12.2015,,13,142.57, -59,2,16.12.2015,,3,142.57, -59,2,17.12.2015,,3,142.57, -59,2,18.12.2015,,0,142.57, -59,2,19.12.2015,,0,142.57, -59,2,20.12.2015,,0,142.57, -59,2,21.12.2015,,0,142.57, -59,2,22.12.2015,1.0,75,142.57,103.02 -59,2,23.12.2015,1.0,57,142.57,103.02 -59,2,24.12.2015,1.0,63,142.57,103.02 -59,2,25.12.2015,1.0,79,142.57,103.02 -59,2,26.12.2015,1.0,47,142.57,103.02 -59,2,27.12.2015,1.0,63,142.57,103.02 -59,2,28.12.2015,1.0,63,142.57,103.02 -59,2,29.12.2015,,3,138.5, -59,2,30.12.2015,,3,138.5, -59,2,31.12.2015,,3,138.5, -59,2,01.01.2016,,3,138.5, -59,2,02.01.2016,,3,138.5, -59,2,03.01.2016,,3,138.5, -59,2,04.01.2016,,3,138.5, -59,2,05.01.2016,,94,138.5, -59,2,06.01.2016,,9,138.5, -59,2,07.01.2016,,9,138.5, -59,2,08.01.2016,,9,138.5, -59,2,09.01.2016,,9,138.5, -59,2,10.01.2016,,9,138.5, -59,2,11.01.2016,,9,138.5, -59,2,12.01.2016,,6,138.5, -59,2,13.01.2016,,16,138.5, -59,2,14.01.2016,,6,138.5, -59,2,15.01.2016,,3,138.5, -59,2,16.01.2016,,3,142.57, -59,2,17.01.2016,,3,142.57, -59,2,18.01.2016,,9,142.57, -59,2,19.01.2016,1.0,38,142.57,103.02 -59,2,20.01.2016,1.0,38,142.57,103.02 -59,2,21.01.2016,1.0,79,142.57,103.02 -59,2,22.01.2016,1.0,145,142.57,103.02 -59,2,23.01.2016,1.0,113,142.57,103.02 -59,2,24.01.2016,1.0,47,142.57,103.02 -59,2,25.01.2016,1.0,69,142.57,103.02 -59,2,26.01.2016,,3,142.57, -59,2,27.01.2016,,3,142.57, -59,2,28.01.2016,,6,142.57, -59,2,29.01.2016,,9,142.57, -59,2,30.01.2016,,0,142.57, -59,2,31.01.2016,,38,142.57, -59,2,01.02.2016,,19,142.57, -59,2,02.02.2016,,3,142.57, -59,2,03.02.2016,,13,142.57, -59,2,04.02.2016,,6,142.57, -59,2,05.02.2016,,9,142.57, -59,2,06.02.2016,,28,142.57, -59,2,07.02.2016,,9,142.57, -59,2,08.02.2016,,9,142.57, -59,2,09.02.2016,1.0,101,142.57,108.46 -59,2,10.02.2016,1.0,157,138.36,108.46 -59,2,11.02.2016,1.0,69,138.36,108.46 -59,2,12.02.2016,1.0,25,138.36,108.46 -59,2,13.02.2016,1.0,132,138.36,108.46 -59,2,14.02.2016,1.0,85,138.36,108.46 -59,2,15.02.2016,1.0,75,138.36,108.46 -59,2,16.02.2016,,9,138.36, -59,2,17.02.2016,,3,138.36, -59,2,18.02.2016,,6,138.36, -59,2,19.02.2016,,16,138.36, -59,2,20.02.2016,,3,138.36, -59,2,21.02.2016,,6,138.36, -59,2,22.02.2016,,3,138.36, -59,2,23.02.2016,,13,138.36, -59,2,24.02.2016,,13,138.36, -59,2,25.02.2016,,13,138.36, -59,2,26.02.2016,,13,138.36, -59,2,27.02.2016,,13,138.36, -59,2,28.02.2016,,13,138.36, -59,2,29.02.2016,,13,138.36, -59,2,01.03.2016,,13,138.36, -59,2,02.03.2016,,13,138.36, -59,2,03.03.2016,,13,138.36, -59,2,04.03.2016,,3,138.36, -59,2,05.03.2016,,3,138.36, -59,2,06.03.2016,,9,138.36, -59,2,07.03.2016,,6,138.36, -59,2,08.03.2016,,6,138.36, -59,2,09.03.2016,,9,138.36, -59,2,10.03.2016,,6,138.36, -59,2,11.03.2016,,3,138.36, -59,2,12.03.2016,,3,138.5, -59,2,13.03.2016,,6,138.5, -59,2,14.03.2016,,6,138.5, -59,2,15.03.2016,1.0,75,138.5,124.77 -59,2,16.03.2016,1.0,25,138.5,124.77 -59,2,17.03.2016,1.0,25,138.5,124.77 -59,2,18.03.2016,1.0,35,138.5,124.77 -59,2,19.03.2016,1.0,22,138.5,124.77 -59,2,20.03.2016,1.0,9,138.5,124.77 -59,2,21.03.2016,1.0,22,138.5,124.77 -59,2,22.03.2016,,13,138.36, -59,2,23.03.2016,,3,138.36, -59,2,24.03.2016,,9,138.36, -59,2,25.03.2016,,0,138.36, -59,2,26.03.2016,,0,138.36, -59,2,27.03.2016,,3,138.36, -59,2,28.03.2016,,0,138.36, -59,2,29.03.2016,,3,138.36, -59,2,30.03.2016,,3,138.36, -59,2,31.03.2016,,3,138.36, -59,2,01.04.2016,,3,138.36, -59,2,02.04.2016,,3,138.36, -59,2,03.04.2016,,3,138.36, -59,2,04.04.2016,,3,138.36, -59,2,05.04.2016,,3,138.36, -59,2,06.04.2016,,3,138.36, -59,2,07.04.2016,,3,138.36, -59,2,08.04.2016,,0,138.36, -59,2,09.04.2016,,0,138.5, -59,2,10.04.2016,,3,138.5, -59,2,11.04.2016,,3,138.5, -59,2,12.04.2016,,0,138.5, -59,2,13.04.2016,,3,138.5, -59,2,14.04.2016,,0,138.5, -59,2,15.04.2016,,0,138.5, -59,2,16.04.2016,,0,138.5, -59,2,17.04.2016,,0,138.5, -59,2,18.04.2016,,3,138.5, -59,2,19.04.2016,1.0,57,138.5,116.89 -59,2,20.04.2016,1.0,60,138.5,116.89 -59,2,21.04.2016,1.0,19,138.5,116.89 -59,2,22.04.2016,1.0,28,138.5,116.89 -59,2,23.04.2016,1.0,31,138.5,116.89 -59,2,24.04.2016,1.0,38,138.5,116.89 -59,2,25.04.2016,1.0,16,138.5,116.89 -59,2,26.04.2016,1.0,38,138.5,114.17 -59,2,27.04.2016,1.0,9,138.5,114.17 -59,2,28.04.2016,,6,138.5, -59,2,29.04.2016,,3,138.5, -59,2,30.04.2016,,6,138.5, -59,2,01.05.2016,,0,138.5, -59,2,02.05.2016,,0,138.5, -59,2,03.05.2016,1.0,44,138.5,106.01 -59,2,04.05.2016,1.0,25,138.5,106.01 -59,2,05.05.2016,1.0,44,138.5,106.01 -59,2,06.05.2016,1.0,41,138.5,106.01 -59,2,07.05.2016,1.0,28,138.5,106.01 -59,2,08.05.2016,1.0,69,138.5,106.01 -59,2,09.05.2016,1.0,19,138.5,106.01 -59,2,10.05.2016,,19,138.5, -59,2,11.05.2016,,6,138.5, -59,2,12.05.2016,,25,138.5, -59,2,13.05.2016,,16,138.5, -59,2,14.05.2016,,13,138.5, -59,2,15.05.2016,,3,138.5, -59,2,16.05.2016,,9,138.5, -59,2,17.05.2016,,0,138.5, -59,2,18.05.2016,,9,138.5, -59,2,19.05.2016,,9,138.5, -59,2,20.05.2016,,9,138.5, -59,2,21.05.2016,,9,138.5, -59,2,22.05.2016,,9,138.5, -60,2,01.01.2015,,0,135.78, -60,2,02.01.2015,,0,135.78, -60,2,03.01.2015,,3,135.78, -60,2,04.01.2015,,19,135.78, -60,2,05.01.2015,,0,135.78, -60,2,06.01.2015,,0,135.78, -60,2,07.01.2015,,0,135.78, -60,2,08.01.2015,,0,135.78, -60,2,09.01.2015,,0,135.78, -60,2,10.01.2015,,0,135.78, -60,2,11.01.2015,,0,135.78, -60,2,12.01.2015,,0,135.78, -60,2,13.01.2015,,0,135.78, -60,2,14.01.2015,,0,135.78, -60,2,15.01.2015,,0,135.78, -60,2,16.01.2015,,0,135.78, -60,2,17.01.2015,,0,135.78, -60,2,18.01.2015,,13,135.78, -60,2,19.01.2015,,0,135.78, -60,2,20.01.2015,,0,135.78, -60,2,21.01.2015,,0,135.78, -60,2,22.01.2015,,0,135.78, -60,2,23.01.2015,,0,135.78, -60,2,24.01.2015,,0,135.78, -60,2,25.01.2015,,3,135.78, -60,2,26.01.2015,,0,135.78, 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-60,2,23.08.2015,1.0,3,128.17,100.3 -60,2,24.08.2015,1.0,16,128.17,100.3 -60,2,25.08.2015,,0,128.17, -60,2,26.08.2015,,0,128.17, -60,2,27.08.2015,,0,128.17, -60,2,28.08.2015,,0,128.17, -60,2,29.08.2015,,0,128.17, -60,2,30.08.2015,,0,128.17, -60,2,31.08.2015,,0,128.17, -60,2,01.09.2015,,0,128.17, -60,2,02.09.2015,,0,128.17, -60,2,03.09.2015,,0,128.17, -60,2,04.09.2015,,0,128.17, -60,2,05.09.2015,,0,128.17, -60,2,06.09.2015,,0,128.17, -60,2,07.09.2015,,0,128.17, -60,2,08.09.2015,,0,128.17, -60,2,09.09.2015,,0,128.17, -60,2,10.09.2015,,0,128.17, -60,2,11.09.2015,,0,128.17, -60,2,12.09.2015,,0,128.17, -60,2,13.09.2015,,0,128.17, -60,2,14.09.2015,,0,128.17, -60,2,15.09.2015,,0,128.17, -60,2,16.09.2015,,0,128.17, -60,2,17.09.2015,,0,128.17, -60,2,18.09.2015,,0,128.17, -60,2,19.09.2015,,0,128.17, -60,2,20.09.2015,,0,128.17, -60,2,21.09.2015,,0,128.17, -60,2,22.09.2015,,0,128.17, -60,2,23.09.2015,,0,128.17, -60,2,24.09.2015,,0,128.17, -60,2,25.09.2015,,0,162.96, -60,2,26.09.2015,,0,162.96, 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-60,2,04.12.2015,,0,143.93, -60,2,05.12.2015,,0,143.93, -60,2,06.12.2015,,0,143.93, -60,2,07.12.2015,,0,143.93, -60,2,08.12.2015,,0,143.93, -60,2,09.12.2015,,0,138.5, -60,2,10.12.2015,,0,138.5, -60,2,11.12.2015,,0,142.57, -60,2,12.12.2015,,0,142.57, -60,2,13.12.2015,,0,142.57, -60,2,14.12.2015,,0,142.57, -60,2,15.12.2015,,0,142.57, -60,2,16.12.2015,,0,142.57, -60,2,17.12.2015,,0,142.57, -60,2,18.12.2015,,0,142.57, -60,2,19.12.2015,,0,142.57, -60,2,20.12.2015,,3,142.57, -60,2,21.12.2015,,6,142.57, -60,2,22.12.2015,1.0,31,142.57,103.02 -60,2,23.12.2015,1.0,38,142.57,103.02 -60,2,24.12.2015,1.0,9,142.57,103.02 -60,2,25.12.2015,1.0,6,142.57,103.02 -60,2,26.12.2015,1.0,16,142.57,103.02 -60,2,27.12.2015,1.0,16,142.57,103.02 -60,2,28.12.2015,1.0,16,142.57,103.02 -60,2,29.12.2015,,0,138.5, -60,2,30.12.2015,,0,138.5, -60,2,31.12.2015,,0,138.5, -60,2,01.01.2016,,0,138.5, -60,2,02.01.2016,,0,138.5, -60,2,03.01.2016,,0,138.5, -60,2,04.01.2016,,0,138.5, -60,2,05.01.2016,,0,138.5, 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-61,2,28.01.2015,,0,135.78, -61,2,29.01.2015,,3,135.78, -61,2,30.01.2015,,3,135.78, -61,2,31.01.2015,,3,135.78, -61,2,01.02.2015,,6,135.78, -61,2,02.02.2015,,0,135.78, -61,2,03.02.2015,,0,135.78, -61,2,04.02.2015,,0,135.78, -61,2,05.02.2015,,3,135.78, -61,2,06.02.2015,,3,135.78, -61,2,07.02.2015,,3,135.78, -61,2,08.02.2015,,6,135.78, -61,2,09.02.2015,,3,135.78, -61,2,10.02.2015,,3,135.78, -61,2,11.02.2015,,9,135.78, -61,2,12.02.2015,,16,135.78, -61,2,13.02.2015,,0,135.78, -61,2,14.02.2015,,0,135.78, -61,2,15.02.2015,,3,135.78, -61,2,16.02.2015,,3,135.78, -61,2,17.02.2015,,3,135.78, -61,2,18.02.2015,,6,135.78, -61,2,19.02.2015,,0,135.78, -61,2,20.02.2015,,9,135.78, -61,2,21.02.2015,,0,135.78, -61,2,22.02.2015,,0,135.78, -61,2,23.02.2015,,3,135.78, -61,2,24.02.2015,,9,135.78, -61,2,25.02.2015,,3,135.78, -61,2,26.02.2015,,3,135.78, -61,2,27.02.2015,,3,135.78, -61,2,28.02.2015,,19,135.78, -61,2,01.03.2015,,3,135.78, -61,2,02.03.2015,,13,146.38, -61,2,03.03.2015,,6,146.38, 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-61,2,22.07.2015,,0,150.73, -61,2,23.07.2015,,0,150.73, -61,2,24.07.2015,,3,150.73, -61,2,25.07.2015,,9,150.73, -61,2,26.07.2015,,13,150.73, -61,2,27.07.2015,,0,150.73, -61,2,28.07.2015,,0,150.73, -61,2,29.07.2015,,16,150.73, -61,2,30.07.2015,,9,150.73, -61,2,31.07.2015,,3,150.73, -61,2,01.08.2015,,9,150.73, -61,2,02.08.2015,,13,150.73, -61,2,03.08.2015,,6,150.73, -61,2,04.08.2015,,6,150.73, -61,2,05.08.2015,,6,150.73, -61,2,06.08.2015,,6,150.73, -61,2,07.08.2015,,0,150.73, -61,2,08.08.2015,,25,150.73, -61,2,09.08.2015,,6,150.73, -61,2,10.08.2015,,16,150.73, -61,2,11.08.2015,,9,150.73, -61,2,12.08.2015,,9,128.17, -61,2,13.08.2015,,9,128.17, -61,2,14.08.2015,,0,128.17, -61,2,15.08.2015,,0,128.17, -61,2,16.08.2015,,0,128.17, -61,2,17.08.2015,,31,128.17, -61,2,18.08.2015,1.0,188,128.17,100.3 -61,2,19.08.2015,1.0,31,128.17,100.3 -61,2,20.08.2015,1.0,69,128.17,100.3 -61,2,21.08.2015,1.0,107,128.17,100.3 -61,2,22.08.2015,1.0,44,128.17,100.3 -61,2,23.08.2015,1.0,129,128.17,100.3 -61,2,24.08.2015,1.0,28,128.17,100.3 -61,2,25.08.2015,,9,128.17, -61,2,26.08.2015,,9,128.17, -61,2,27.08.2015,,25,128.17, -61,2,28.08.2015,,13,128.17, -61,2,29.08.2015,,13,128.17, -61,2,30.08.2015,,13,128.17, -61,2,31.08.2015,,13,128.17, -61,2,01.09.2015,,6,128.17, -61,2,02.09.2015,,3,128.17, -61,2,03.09.2015,,0,128.17, -61,2,04.09.2015,,0,128.17, -61,2,05.09.2015,,6,128.17, -61,2,06.09.2015,,9,128.17, -61,2,07.09.2015,,9,128.17, -61,2,08.09.2015,,9,128.17, -61,2,09.09.2015,,9,128.17, -61,2,10.09.2015,,9,128.17, -61,2,11.09.2015,,16,128.17, -61,2,12.09.2015,,3,128.17, -61,2,13.09.2015,,9,128.17, -61,2,14.09.2015,,3,128.17, -61,2,15.09.2015,,3,128.17, -61,2,16.09.2015,,13,128.17, -61,2,17.09.2015,,9,128.17, -61,2,18.09.2015,,9,128.17, -61,2,19.09.2015,,6,128.17, -61,2,20.09.2015,,6,128.17, -61,2,21.09.2015,,0,128.17, -61,2,22.09.2015,,19,128.17, -61,2,23.09.2015,,6,128.17, -61,2,24.09.2015,,6,128.17, -61,2,25.09.2015,,6,128.17, -61,2,26.09.2015,,6,128.17, -61,2,27.09.2015,,6,128.17, -61,2,28.09.2015,,0,128.17, -61,2,29.09.2015,,9,128.17, -61,2,30.09.2015,,9,128.17, -61,2,01.10.2015,,9,162.96, -61,2,02.10.2015,,9,162.96, -61,2,03.10.2015,,9,162.96, -61,2,04.10.2015,,9,162.96, -61,2,05.10.2015,,3,162.96, -61,2,06.10.2015,1.0,66,143.39,122.05 -61,2,07.10.2015,1.0,101,143.39,122.05 -61,2,08.10.2015,1.0,16,143.39,122.05 -61,2,09.10.2015,1.0,110,143.39,122.05 -61,2,10.10.2015,1.0,44,143.39,122.05 -61,2,11.10.2015,1.0,91,143.39,122.05 -61,2,12.10.2015,1.0,72,143.39,122.05 -61,2,13.10.2015,1.0,63,143.93,122.05 -61,2,14.10.2015,1.0,69,143.93,122.05 -61,2,15.10.2015,1.0,69,143.93,122.05 -61,2,16.10.2015,1.0,66,143.93,122.05 -61,2,17.10.2015,1.0,63,162.96,122.05 -61,2,18.10.2015,1.0,6,162.96,122.05 -61,2,19.10.2015,,0,162.96, -61,2,20.10.2015,,0,162.96, -61,2,21.10.2015,,0,162.96, -61,2,22.10.2015,,0,162.96, -61,2,23.10.2015,,0,162.96, -61,2,24.10.2015,,0,162.96, -61,2,25.10.2015,,3,162.96, -61,2,26.10.2015,,0,162.96, -61,2,27.10.2015,,0,162.96, -61,2,28.10.2015,,3,162.96, -61,2,29.10.2015,,3,162.96, -61,2,30.10.2015,,0,162.96, -61,2,31.10.2015,,0,162.96, -61,2,01.11.2015,,0,162.96, -61,2,02.11.2015,,6,162.96, -61,2,03.11.2015,,3,162.96, -61,2,04.11.2015,,3,162.96, -61,2,05.11.2015,,0,162.96, -61,2,06.11.2015,,0,162.96, -61,2,07.11.2015,,0,162.96, -61,2,08.11.2015,,0,162.96, -61,2,09.11.2015,,0,162.96, -61,2,10.11.2015,,0,162.96, -61,2,11.11.2015,,0,138.36, -61,2,12.11.2015,,0,138.36, -61,2,13.11.2015,,0,138.36, -61,2,14.11.2015,,0,138.36, -61,2,15.11.2015,,0,138.36, -61,2,16.11.2015,,0,138.36, -61,2,17.11.2015,,0,138.36, -61,2,18.11.2015,,0,138.36, -61,2,19.11.2015,,0,138.36, -61,2,20.11.2015,,0,138.36, -61,2,21.11.2015,,0,138.36, -61,2,22.11.2015,,0,138.36, -61,2,23.11.2015,,0,138.36, -61,2,24.11.2015,,0,138.36, -61,2,25.11.2015,,0,138.36, -61,2,26.11.2015,,3,138.36, -61,2,27.11.2015,,0,138.36, -61,2,28.11.2015,,6,138.36, -61,2,29.11.2015,,0,138.36, -61,2,30.11.2015,,6,138.36, -61,2,01.12.2015,,0,162.96, -61,2,02.12.2015,,0,162.96, -61,2,03.12.2015,,0,138.5, -61,2,04.12.2015,,0,138.5, -61,2,05.12.2015,,0,138.5, -61,2,06.12.2015,,0,138.5, -61,2,07.12.2015,,0,138.5, -61,2,08.12.2015,,0,138.5, -61,2,09.12.2015,,0,138.5, -61,2,10.12.2015,,6,138.5, -61,2,11.12.2015,,3,142.57, -61,2,12.12.2015,,3,142.57, -61,2,13.12.2015,,6,142.57, -61,2,14.12.2015,,3,142.57, -61,2,15.12.2015,,3,142.57, -61,2,16.12.2015,,3,142.57, -61,2,17.12.2015,,3,142.57, -61,2,18.12.2015,,3,142.57, -61,2,19.12.2015,,3,142.57, -61,2,20.12.2015,,3,142.57, -61,2,21.12.2015,,3,142.57, -61,2,22.12.2015,1.0,204,142.57,103.02 -61,2,23.12.2015,1.0,94,142.57,103.02 -61,2,24.12.2015,1.0,145,142.57,103.02 -61,2,25.12.2015,1.0,44,142.57,103.02 -61,2,26.12.2015,1.0,31,142.57,103.02 -61,2,27.12.2015,1.0,157,142.57,103.02 -61,2,28.12.2015,1.0,38,142.57,103.02 -61,2,29.12.2015,,3,138.5, -61,2,30.12.2015,,0,138.5, -61,2,31.12.2015,,0,138.5, -61,2,01.01.2016,,3,138.5, -61,2,02.01.2016,,13,138.5, -61,2,03.01.2016,,3,138.5, -61,2,04.01.2016,,9,138.5, -61,2,05.01.2016,,9,138.5, -61,2,06.01.2016,,3,138.5, -61,2,07.01.2016,,0,138.5, -61,2,08.01.2016,,0,138.5, -61,2,09.01.2016,,0,138.5, -61,2,10.01.2016,,0,138.5, -61,2,11.01.2016,,0,138.5, -61,2,12.01.2016,,0,138.5, -61,2,13.01.2016,,0,138.5, -61,2,14.01.2016,,0,138.5, -61,2,15.01.2016,,0,138.5, -61,2,16.01.2016,,0,142.57, -61,2,17.01.2016,,0,142.57, -61,2,18.01.2016,,9,142.57, -61,2,19.01.2016,1.0,170,142.57,103.02 -61,2,20.01.2016,1.0,179,142.57,103.02 -61,2,21.01.2016,1.0,210,142.57,103.02 -61,2,22.01.2016,1.0,72,142.57,103.02 -61,2,23.01.2016,1.0,28,142.57,103.02 -61,2,24.01.2016,1.0,85,142.57,103.02 -61,2,25.01.2016,1.0,57,142.57,103.02 -61,2,26.01.2016,,0,142.57, -61,2,27.01.2016,,3,142.57, -61,2,28.01.2016,,6,142.57, -61,2,29.01.2016,,0,142.57, -61,2,30.01.2016,,0,142.57, -61,2,31.01.2016,,0,142.57, -61,2,01.02.2016,,0,142.57, -61,2,02.02.2016,,0,142.57, -61,2,03.02.2016,,0,142.57, -61,2,04.02.2016,,0,142.57, -61,2,05.02.2016,,0,142.57, -61,2,06.02.2016,,0,142.57, -61,2,07.02.2016,,0,142.57, -61,2,08.02.2016,,3,142.57, -61,2,09.02.2016,1.0,182,142.57,108.46 -61,2,10.02.2016,1.0,116,138.36,108.46 -61,2,11.02.2016,1.0,173,138.36,108.46 -61,2,12.02.2016,1.0,201,138.36,108.46 -61,2,13.02.2016,1.0,145,138.36,108.46 -61,2,14.02.2016,1.0,163,138.36,108.46 -61,2,15.02.2016,1.0,88,138.36,108.46 -61,2,16.02.2016,,0,138.36, -61,2,17.02.2016,,0,138.36, -61,2,18.02.2016,,0,138.36, -61,2,19.02.2016,,0,138.36, -61,2,20.02.2016,,6,138.36, -61,2,21.02.2016,,13,138.36, -61,2,22.02.2016,,0,138.36, -61,2,23.02.2016,,0,138.36, -61,2,24.02.2016,,0,138.36, -61,2,25.02.2016,,0,138.36, -61,2,26.02.2016,,0,138.36, -61,2,27.02.2016,,0,138.36, -61,2,28.02.2016,,16,138.36, -61,2,29.02.2016,,0,138.36, -61,2,01.03.2016,,0,138.36, -61,2,02.03.2016,,16,138.36, -61,2,03.03.2016,,3,138.36, -61,2,04.03.2016,,3,138.36, -61,2,05.03.2016,,0,138.36, -61,2,06.03.2016,,9,138.36, -61,2,07.03.2016,,9,138.36, -61,2,08.03.2016,,3,138.36, -61,2,09.03.2016,,0,138.36, -61,2,10.03.2016,,13,138.36, -61,2,11.03.2016,,0,138.36, -61,2,12.03.2016,,0,138.5, -61,2,13.03.2016,,0,138.5, -61,2,14.03.2016,,22,138.5, -61,2,15.03.2016,1.0,104,138.5,124.77 -61,2,16.03.2016,1.0,75,138.5,124.77 -61,2,17.03.2016,1.0,88,138.5,124.77 -61,2,18.03.2016,1.0,110,138.5,124.77 -61,2,19.03.2016,1.0,16,138.5,124.77 -61,2,20.03.2016,1.0,31,138.5,124.77 -61,2,21.03.2016,1.0,44,138.5,124.77 -61,2,22.03.2016,,6,138.36, -61,2,23.03.2016,,3,138.36, -61,2,24.03.2016,,0,138.36, -61,2,25.03.2016,,6,138.36, -61,2,26.03.2016,,6,138.36, -61,2,27.03.2016,,3,138.36, -61,2,28.03.2016,,9,138.36, -61,2,29.03.2016,,0,138.36, -61,2,30.03.2016,,3,138.36, -61,2,31.03.2016,,0,138.36, -61,2,01.04.2016,,3,138.36, -61,2,02.04.2016,,0,138.36, -61,2,03.04.2016,,0,138.36, -61,2,04.04.2016,,16,138.36, -61,2,05.04.2016,,0,138.36, -61,2,06.04.2016,,3,138.36, -61,2,07.04.2016,,25,138.36, -61,2,08.04.2016,,9,138.36, -61,2,09.04.2016,,6,138.5, -61,2,10.04.2016,,3,138.5, -61,2,11.04.2016,,3,138.5, -61,2,12.04.2016,,0,138.5, -61,2,13.04.2016,,0,138.5, -61,2,14.04.2016,,0,138.5, -61,2,15.04.2016,,0,138.5, -61,2,16.04.2016,,0,138.5, -61,2,17.04.2016,,6,138.5, -61,2,18.04.2016,,19,138.5, -61,2,19.04.2016,1.0,145,138.5,116.89 -61,2,20.04.2016,1.0,170,138.5,116.89 -61,2,21.04.2016,1.0,129,138.5,116.89 -61,2,22.04.2016,1.0,135,138.5,116.89 -61,2,23.04.2016,1.0,94,138.5,116.89 -61,2,24.04.2016,1.0,79,138.5,116.89 -61,2,25.04.2016,1.0,88,138.5,116.89 -61,2,26.04.2016,,0,138.5, -61,2,27.04.2016,,0,138.5, -61,2,28.04.2016,,0,138.5, -61,2,29.04.2016,,0,138.5, -61,2,30.04.2016,,0,138.5, -61,2,01.05.2016,,0,138.5, -61,2,02.05.2016,,0,138.5, -61,2,03.05.2016,1.0,113,138.5,106.01 -61,2,04.05.2016,1.0,214,138.5,106.01 -61,2,05.05.2016,1.0,151,138.5,106.01 -61,2,06.05.2016,1.0,104,138.5,106.01 -61,2,07.05.2016,1.0,85,138.5,106.01 -61,2,08.05.2016,1.0,129,138.5,106.01 -61,2,09.05.2016,1.0,72,138.5,106.01 -61,2,10.05.2016,,3,138.5, -61,2,11.05.2016,,13,138.5, -61,2,12.05.2016,,6,138.5, -61,2,13.05.2016,,3,138.5, -61,2,14.05.2016,,0,138.5, -61,2,15.05.2016,,19,138.5, -61,2,16.05.2016,,19,138.5, -61,2,17.05.2016,,13,138.5, -61,2,18.05.2016,,6,138.5, -61,2,19.05.2016,,6,138.5, -61,2,20.05.2016,,9,138.5, -61,2,21.05.2016,,3,138.5, -61,2,22.05.2016,,3,138.5, -62,2,01.01.2015,,0,134.72, -62,2,02.01.2015,,0,134.72, -62,2,03.01.2015,,0,134.72, -62,2,04.01.2015,,0,134.72, -62,2,05.01.2015,,0,134.72, -62,2,06.01.2015,,0,134.72, -62,2,07.01.2015,,0,134.72, -62,2,08.01.2015,,0,134.72, -62,2,09.01.2015,,0,134.72, -62,2,10.01.2015,,0,134.72, -62,2,11.01.2015,,0,134.72, -62,2,12.01.2015,,0,134.72, -62,2,13.01.2015,,0,134.72, -62,2,14.01.2015,,0,134.72, -62,2,15.01.2015,,0,134.72, -62,2,16.01.2015,,0,134.72, -62,2,17.01.2015,,0,134.72, -62,2,18.01.2015,,0,134.72, -62,2,19.01.2015,,0,134.72, -62,2,20.01.2015,,0,134.72, -62,2,21.01.2015,,0,134.72, -62,2,22.01.2015,,0,134.72, -62,2,23.01.2015,,0,134.72, -62,2,24.01.2015,,0,134.72, -62,2,25.01.2015,,0,134.72, -62,2,26.01.2015,,0,134.72, -62,2,27.01.2015,,0,134.72, -62,2,28.01.2015,,0,134.72, -62,2,29.01.2015,,0,134.72, -62,2,30.01.2015,,0,134.72, -62,2,31.01.2015,,0,134.72, -62,2,01.02.2015,,0,134.72, -62,2,02.02.2015,,0,134.72, -62,2,03.02.2015,,0,134.72, -62,2,04.02.2015,,0,134.72, -62,2,05.02.2015,,0,134.72, -62,2,06.02.2015,,0,134.72, -62,2,07.02.2015,,0,134.72, -62,2,08.02.2015,,0,134.72, -62,2,09.02.2015,,0,134.72, -62,2,10.02.2015,,0,134.72, -62,2,11.02.2015,,0,134.72, -62,2,12.02.2015,,0,134.72, -62,2,13.02.2015,,0,134.72, -62,2,14.02.2015,,0,134.72, -62,2,15.02.2015,,0,134.72, -62,2,16.02.2015,,0,134.72, -62,2,17.02.2015,,0,134.72, -62,2,18.02.2015,,0,134.72, -62,2,19.02.2015,,0,134.72, -62,2,20.02.2015,,0,134.72, -62,2,21.02.2015,,0,134.72, -62,2,22.02.2015,,0,134.72, -62,2,23.02.2015,,0,134.72, -62,2,24.02.2015,,0,134.72, -62,2,25.02.2015,,0,134.72, -62,2,26.02.2015,,0,134.72, -62,2,27.02.2015,,0,134.72, -62,2,28.02.2015,,0,134.72, -62,2,01.03.2015,,0,134.72, -62,2,02.03.2015,,0,134.72, -62,2,03.03.2015,,0,134.72, -62,2,04.03.2015,,0,134.72, -62,2,05.03.2015,,0,134.72, -62,2,06.03.2015,,0,134.72, -62,2,07.03.2015,,0,134.72, -62,2,08.03.2015,,0,134.72, -62,2,09.03.2015,,0,134.72, -62,2,10.03.2015,,0,134.72, -62,2,11.03.2015,,0,134.72, -62,2,12.03.2015,,0,134.72, -62,2,13.03.2015,,0,134.72, -62,2,14.03.2015,,0,134.72, -62,2,15.03.2015,,0,134.72, -62,2,16.03.2015,,0,134.72, -62,2,17.03.2015,,0,134.72, -62,2,18.03.2015,,0,134.72, -62,2,19.03.2015,,0,134.72, -62,2,20.03.2015,,0,134.72, -62,2,21.03.2015,,0,134.72, -62,2,22.03.2015,,0,134.72, -62,2,23.03.2015,,0,134.72, -62,2,24.03.2015,,0,134.72, -62,2,25.03.2015,,0,134.72, -62,2,26.03.2015,,0,134.72, -62,2,27.03.2015,,0,134.72, -62,2,28.03.2015,,0,134.72, -62,2,29.03.2015,,0,134.72, -62,2,30.03.2015,,0,134.72, -62,2,31.03.2015,,0,134.72, -62,2,01.04.2015,,0,134.72, -62,2,02.04.2015,,0,134.72, -62,2,03.04.2015,,0,134.72, -62,2,04.04.2015,,0,134.72, -62,2,05.04.2015,,0,134.72, -62,2,06.04.2015,,0,134.72, 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-62,2,23.08.2015,1.0,50,128.17,100.3 -62,2,24.08.2015,1.0,50,128.17,100.3 -62,2,25.08.2015,,3,128.17, -62,2,26.08.2015,,3,128.17, -62,2,27.08.2015,,3,128.17, -62,2,28.08.2015,,3,128.17, -62,2,29.08.2015,,3,128.17, -62,2,30.08.2015,,3,128.17, -62,2,31.08.2015,,3,128.17, -62,2,01.09.2015,,0,128.17, -62,2,02.09.2015,,0,128.17, -62,2,03.09.2015,,0,128.17, -62,2,04.09.2015,,0,128.17, -62,2,05.09.2015,,0,128.17, -62,2,06.09.2015,,0,128.17, -62,2,07.09.2015,,0,128.17, -62,2,08.09.2015,,0,128.17, -62,2,09.09.2015,,0,128.17, -62,2,10.09.2015,,0,128.17, -62,2,11.09.2015,,0,128.17, -62,2,12.09.2015,,0,128.17, -62,2,13.09.2015,,0,128.17, -62,2,14.09.2015,,0,128.17, -62,2,15.09.2015,,0,128.17, -62,2,16.09.2015,,0,128.17, -62,2,17.09.2015,,0,128.17, -62,2,18.09.2015,,0,128.17, -62,2,19.09.2015,,0,128.17, -62,2,20.09.2015,,0,128.17, -62,2,21.09.2015,,0,128.17, -62,2,22.09.2015,,0,128.17, -62,2,23.09.2015,,0,128.17, -62,2,24.09.2015,,0,128.17, -62,2,25.09.2015,,0,167.85, -62,2,26.09.2015,,19,167.85, -62,2,27.09.2015,,0,167.85, -62,2,28.09.2015,,0,167.85, -62,2,29.09.2015,,0,162.96, -62,2,30.09.2015,,13,162.96, -62,2,01.10.2015,,0,162.96, -62,2,02.10.2015,,0,162.96, -62,2,03.10.2015,,0,162.96, -62,2,04.10.2015,,3,162.96, -62,2,05.10.2015,,0,162.96, -62,2,06.10.2015,1.0,47,143.39,122.05 -62,2,07.10.2015,1.0,44,143.39,122.05 -62,2,08.10.2015,1.0,41,143.39,122.05 -62,2,09.10.2015,1.0,35,143.39,122.05 -62,2,10.10.2015,1.0,31,143.39,122.05 -62,2,11.10.2015,1.0,28,143.39,122.05 -62,2,12.10.2015,1.0,25,143.39,122.05 -62,2,13.10.2015,1.0,25,143.93,122.05 -62,2,14.10.2015,1.0,25,143.93,122.05 -62,2,15.10.2015,1.0,25,143.93,122.05 -62,2,16.10.2015,1.0,25,143.93,122.05 -62,2,17.10.2015,1.0,25,143.93,122.05 -62,2,18.10.2015,1.0,25,143.93,122.05 -62,2,19.10.2015,,6,143.93, -62,2,20.10.2015,,6,143.93, -62,2,21.10.2015,,6,143.93, -62,2,22.10.2015,,6,143.93, -62,2,23.10.2015,,6,143.93, -62,2,24.10.2015,,6,143.93, -62,2,25.10.2015,,6,143.93, -62,2,26.10.2015,,6,143.93, -62,2,27.10.2015,,6,143.93, -62,2,28.10.2015,,6,143.93, -62,2,29.10.2015,,6,143.93, -62,2,30.10.2015,,6,143.93, -62,2,31.10.2015,,6,143.93, -62,2,01.11.2015,,0,143.93, -62,2,02.11.2015,,0,143.93, -62,2,03.11.2015,,0,143.93, -62,2,04.11.2015,,0,143.93, -62,2,05.11.2015,,0,143.93, -62,2,06.11.2015,,0,143.93, -62,2,07.11.2015,,0,143.93, -62,2,08.11.2015,,0,143.93, -62,2,09.11.2015,,0,143.93, -62,2,10.11.2015,,0,143.93, -62,2,11.11.2015,,0,143.93, -62,2,12.11.2015,,0,143.93, -62,2,13.11.2015,,0,143.93, -62,2,14.11.2015,,0,143.93, -62,2,15.11.2015,,0,143.93, -62,2,16.11.2015,,0,143.93, -62,2,17.11.2015,,0,143.93, -62,2,18.11.2015,,0,143.93, -62,2,19.11.2015,,0,143.93, -62,2,20.11.2015,,0,143.93, -62,2,21.11.2015,,0,143.93, -62,2,22.11.2015,,0,143.93, -62,2,23.11.2015,,0,143.93, -62,2,24.11.2015,,0,143.93, -62,2,25.11.2015,,0,143.93, -62,2,26.11.2015,,0,143.93, -62,2,27.11.2015,,0,143.93, -62,2,28.11.2015,,0,143.93, -62,2,29.11.2015,,0,143.93, -62,2,30.11.2015,,0,143.93, -62,2,01.12.2015,,0,143.93, -62,2,02.12.2015,,0,143.93, -62,2,03.12.2015,,0,143.93, -62,2,04.12.2015,,0,143.93, -62,2,05.12.2015,,0,143.93, -62,2,06.12.2015,,0,143.93, -62,2,07.12.2015,,0,143.93, -62,2,08.12.2015,,0,143.93, -62,2,09.12.2015,,0,142.57, -62,2,10.12.2015,,0,142.57, -62,2,11.12.2015,,0,142.57, -62,2,12.12.2015,,0,142.57, -62,2,13.12.2015,,0,142.57, -62,2,14.12.2015,,0,142.57, -62,2,15.12.2015,,0,142.57, -62,2,16.12.2015,,0,142.57, -62,2,17.12.2015,,0,142.57, -62,2,18.12.2015,,0,142.57, -62,2,19.12.2015,,0,142.57, -62,2,20.12.2015,,0,142.57, -62,2,21.12.2015,,0,142.57, -62,2,22.12.2015,1.0,94,142.57,103.02 -62,2,23.12.2015,1.0,31,142.57,103.02 -62,2,24.12.2015,1.0,31,142.57,103.02 -62,2,25.12.2015,1.0,31,142.57,103.02 -62,2,26.12.2015,1.0,31,142.57,103.02 -62,2,27.12.2015,1.0,31,142.57,103.02 -62,2,28.12.2015,1.0,31,142.57,103.02 -62,2,29.12.2015,,0,138.5, -62,2,30.12.2015,,0,138.5, -62,2,31.12.2015,,0,138.5, -62,2,01.01.2016,,0,138.5, -62,2,02.01.2016,,0,138.5, -62,2,03.01.2016,,0,138.5, -62,2,04.01.2016,,0,138.5, -62,2,05.01.2016,,3,138.5, -62,2,06.01.2016,,6,138.5, -62,2,07.01.2016,,9,138.5, -62,2,08.01.2016,,0,138.5, -62,2,09.01.2016,,0,138.5, -62,2,10.01.2016,,0,138.5, -62,2,11.01.2016,,0,138.5, -62,2,12.01.2016,,0,138.5, -62,2,13.01.2016,,0,138.5, -62,2,14.01.2016,,0,138.5, -62,2,15.01.2016,,0,138.5, -62,2,16.01.2016,,0,142.57, -62,2,17.01.2016,,0,142.57, -62,2,18.01.2016,,3,142.57, -62,2,19.01.2016,1.0,97,142.57,103.02 -62,2,20.01.2016,1.0,13,142.57,103.02 -62,2,21.01.2016,1.0,75,142.57,103.02 -62,2,22.01.2016,1.0,28,142.57,103.02 -62,2,23.01.2016,1.0,28,142.57,103.02 -62,2,24.01.2016,1.0,28,142.57,103.02 -62,2,25.01.2016,1.0,28,142.57,103.02 -62,2,26.01.2016,,3,142.57, -62,2,27.01.2016,,3,142.57, -62,2,28.01.2016,,3,142.57, -62,2,29.01.2016,,3,142.57, -62,2,30.01.2016,,3,142.57, -62,2,31.01.2016,,3,142.57, -62,2,01.02.2016,,0,142.57, -62,2,02.02.2016,,0,142.57, -62,2,03.02.2016,,0,142.57, -62,2,04.02.2016,,0,142.57, -62,2,05.02.2016,,0,142.57, -62,2,06.02.2016,,3,142.57, -62,2,07.02.2016,,6,142.57, -62,2,08.02.2016,,3,142.57, -62,2,09.02.2016,1.0,104,142.57,108.46 -62,2,10.02.2016,1.0,53,138.36,108.46 -62,2,11.02.2016,1.0,69,138.36,108.46 -62,2,12.02.2016,1.0,6,138.36,108.46 -62,2,13.02.2016,1.0,38,138.36,108.46 -62,2,14.02.2016,1.0,38,138.36,108.46 -62,2,15.02.2016,1.0,38,138.36,108.46 -62,2,16.02.2016,,3,138.36, -62,2,17.02.2016,,3,138.36, -62,2,18.02.2016,,0,138.36, -62,2,19.02.2016,,0,138.36, -62,2,20.02.2016,,0,138.36, -62,2,21.02.2016,,0,138.36, -62,2,22.02.2016,,0,138.36, -62,2,23.02.2016,,0,138.36, -62,2,24.02.2016,,0,138.36, -62,2,25.02.2016,,0,138.36, -62,2,26.02.2016,,0,138.36, -62,2,27.02.2016,,0,138.36, -62,2,28.02.2016,,0,138.36, -62,2,29.02.2016,,0,138.36, -62,2,01.03.2016,,0,138.36, -62,2,02.03.2016,,0,138.36, -62,2,03.03.2016,,0,138.36, -62,2,04.03.2016,,0,138.36, -62,2,05.03.2016,,0,138.36, -62,2,06.03.2016,,0,138.36, -62,2,07.03.2016,,0,138.36, -62,2,08.03.2016,,0,138.36, -62,2,09.03.2016,,0,138.36, -62,2,10.03.2016,,0,138.36, -62,2,11.03.2016,,0,138.36, -62,2,12.03.2016,,0,138.5, -62,2,13.03.2016,,0,138.5, -62,2,14.03.2016,,16,138.5, -62,2,15.03.2016,1.0,53,138.5,124.77 -62,2,16.03.2016,1.0,25,138.5,124.77 -62,2,17.03.2016,1.0,47,138.5,124.77 -62,2,18.03.2016,1.0,9,138.5,124.77 -62,2,19.03.2016,1.0,47,138.5,124.77 -62,2,20.03.2016,1.0,47,138.5,124.77 -62,2,21.03.2016,1.0,41,138.5,124.77 -62,2,22.03.2016,,0,138.36, -62,2,23.03.2016,,0,138.36, -62,2,24.03.2016,,0,138.36, -62,2,25.03.2016,,0,138.36, -62,2,26.03.2016,,0,138.36, -62,2,27.03.2016,,0,138.36, -62,2,28.03.2016,,0,138.36, -62,2,29.03.2016,,0,138.36, -62,2,30.03.2016,,0,138.36, -62,2,31.03.2016,,0,138.36, -62,2,01.04.2016,,0,138.36, -62,2,02.04.2016,,0,138.36, -62,2,03.04.2016,,0,138.36, -62,2,04.04.2016,,0,138.36, -62,2,05.04.2016,,0,138.36, -62,2,06.04.2016,,0,138.36, -62,2,07.04.2016,,0,138.36, -62,2,08.04.2016,,0,138.36, -62,2,09.04.2016,,0,138.5, -62,2,10.04.2016,,0,138.5, -62,2,11.04.2016,,0,138.5, -62,2,12.04.2016,,0,138.5, -62,2,13.04.2016,,0,138.5, -62,2,14.04.2016,,0,138.5, -62,2,15.04.2016,,0,138.5, -62,2,16.04.2016,,0,138.5, -62,2,17.04.2016,,0,138.5, -62,2,18.04.2016,,6,138.5, -62,2,19.04.2016,1.0,53,138.5,116.89 -62,2,20.04.2016,1.0,75,138.5,116.89 -62,2,21.04.2016,1.0,50,138.5,116.89 -62,2,22.04.2016,1.0,79,138.5,116.89 -62,2,23.04.2016,1.0,35,138.5,116.89 -62,2,24.04.2016,1.0,35,138.5,116.89 -62,2,25.04.2016,1.0,35,138.5,116.89 -62,2,26.04.2016,,3,138.5, -62,2,27.04.2016,,3,138.5, -62,2,28.04.2016,,3,138.5, -62,2,29.04.2016,,3,138.5, -62,2,30.04.2016,,3,138.5, -62,2,01.05.2016,,3,138.5, -62,2,02.05.2016,,3,138.5, -62,2,03.05.2016,1.0,107,138.5,106.01 -62,2,04.05.2016,1.0,129,138.5,106.01 -62,2,05.05.2016,1.0,119,138.5,106.01 -62,2,06.05.2016,1.0,53,138.5,106.01 -62,2,07.05.2016,1.0,53,138.5,106.01 -62,2,08.05.2016,1.0,53,138.5,106.01 -62,2,09.05.2016,1.0,53,138.5,106.01 -62,2,10.05.2016,,0,138.5, -62,2,11.05.2016,,0,138.5, -62,2,12.05.2016,,0,138.5, -62,2,13.05.2016,,0,138.5, -62,2,14.05.2016,,0,138.5, -62,2,15.05.2016,,0,138.5, -62,2,16.05.2016,,0,138.5, -62,2,17.05.2016,,0,138.5, -62,2,18.05.2016,,0,138.5, -62,2,19.05.2016,,0,138.5, -62,2,20.05.2016,,0,138.5, -62,2,21.05.2016,,0,138.5, -62,2,22.05.2016,,0,138.5, -63,2,07.07.2015,,0,150.73, -63,2,08.07.2015,,0,150.73, -63,2,09.07.2015,,0,150.73, -63,2,10.07.2015,,6,150.73, -63,2,11.07.2015,,3,150.73, -63,2,12.07.2015,,0,150.73, -63,2,13.07.2015,,3,150.73, -63,2,14.07.2015,,3,150.73, -63,2,15.07.2015,,3,150.73, -63,2,16.07.2015,,0,150.73, -63,2,17.07.2015,,0,150.73, -63,2,18.07.2015,,0,150.73, -63,2,19.07.2015,,0,150.73, -63,2,20.07.2015,,6,150.73, -63,2,21.07.2015,,0,150.73, -63,2,22.07.2015,,0,150.73, -63,2,23.07.2015,,0,150.73, -63,2,24.07.2015,,6,150.73, -63,2,25.07.2015,,9,150.73, -63,2,26.07.2015,,3,150.73, -63,2,27.07.2015,,3,150.73, -63,2,28.07.2015,,3,150.73, -63,2,29.07.2015,,3,150.73, -63,2,30.07.2015,,3,150.73, -63,2,31.07.2015,,13,150.73, -63,2,01.08.2015,,9,150.73, -63,2,02.08.2015,,3,150.73, -63,2,03.08.2015,,3,150.73, -63,2,04.08.2015,,3,150.73, -63,2,05.08.2015,,3,150.73, -63,2,06.08.2015,,9,150.73, -63,2,07.08.2015,,13,150.73, -63,2,08.08.2015,,3,150.73, -63,2,09.08.2015,,6,150.73, -63,2,10.08.2015,,6,150.73, -63,2,11.08.2015,,6,150.73, -63,2,12.08.2015,,6,128.17, -63,2,13.08.2015,,6,128.17, -63,2,14.08.2015,,6,128.17, -63,2,15.08.2015,,6,128.17, -63,2,16.08.2015,,6,128.17, -63,2,17.08.2015,,19,128.17, -63,2,18.08.2015,1.0,245,128.17,100.3 -63,2,19.08.2015,1.0,38,128.17,100.3 -63,2,20.08.2015,1.0,88,128.17,100.3 -63,2,21.08.2015,1.0,135,128.17,100.3 -63,2,22.08.2015,1.0,110,128.17,100.3 -63,2,23.08.2015,1.0,94,128.17,100.3 -63,2,24.08.2015,1.0,119,128.17,100.3 -63,2,25.08.2015,,6,128.17, -63,2,26.08.2015,,6,128.17, -63,2,27.08.2015,,13,128.17, -63,2,28.08.2015,,13,128.17, -63,2,29.08.2015,,9,128.17, -63,2,30.08.2015,,9,128.17, -63,2,31.08.2015,,9,128.17, -63,2,01.09.2015,,9,128.17, -63,2,02.09.2015,,9,128.17, -63,2,03.09.2015,,9,128.17, -63,2,04.09.2015,,9,128.17, -63,2,05.09.2015,,13,128.17, -63,2,06.09.2015,,6,128.17, -63,2,07.09.2015,,9,128.17, -63,2,08.09.2015,,9,128.17, -63,2,09.09.2015,,9,128.17, -63,2,10.09.2015,,9,128.17, -63,2,11.09.2015,,9,128.17, -63,2,12.09.2015,,9,128.17, -63,2,13.09.2015,,9,128.17, -63,2,14.09.2015,,9,128.17, -63,2,15.09.2015,,9,128.17, -63,2,16.09.2015,,9,128.17, -63,2,17.09.2015,,19,128.17, -63,2,18.09.2015,,9,128.17, -63,2,19.09.2015,,9,128.17, -63,2,20.09.2015,,9,128.17, -63,2,21.09.2015,,16,128.17, -63,2,22.09.2015,,3,128.17, -63,2,23.09.2015,,13,128.17, -63,2,24.09.2015,,3,128.17, -63,2,25.09.2015,,6,128.17, -63,2,26.09.2015,,9,128.17, -63,2,27.09.2015,,9,128.17, -63,2,28.09.2015,,9,128.17, -63,2,29.09.2015,,9,128.17, -63,2,30.09.2015,,9,128.17, -63,2,01.10.2015,,9,162.96, -63,2,02.10.2015,,9,162.96, -63,2,03.10.2015,,3,162.96, -63,2,04.10.2015,,9,162.96, -63,2,05.10.2015,,0,162.96, -63,2,06.10.2015,1.0,44,143.39,122.05 -63,2,07.10.2015,1.0,66,143.39,122.05 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-63,2,10.11.2015,,3,162.96, -63,2,11.11.2015,,3,138.36, -63,2,12.11.2015,,0,138.36, -63,2,13.11.2015,,3,138.36, -63,2,14.11.2015,,0,138.36, -63,2,15.11.2015,,0,138.36, -63,2,16.11.2015,,0,138.36, -63,2,17.11.2015,,0,138.36, -63,2,18.11.2015,,0,138.36, -63,2,19.11.2015,,0,138.36, -63,2,20.11.2015,,0,138.36, -63,2,21.11.2015,,0,138.36, -63,2,22.11.2015,,0,138.36, -63,2,23.11.2015,,6,138.36, -63,2,24.11.2015,,0,138.36, -63,2,25.11.2015,,0,138.36, -63,2,26.11.2015,,0,138.36, -63,2,27.11.2015,,0,138.36, -63,2,28.11.2015,,6,138.36, -63,2,29.11.2015,,3,138.36, -63,2,30.11.2015,,0,138.36, -63,2,01.12.2015,,0,162.96, -63,2,02.12.2015,,0,162.96, -63,2,03.12.2015,,0,138.5, -63,2,04.12.2015,,3,138.5, -63,2,05.12.2015,,3,138.5, -63,2,06.12.2015,,3,138.5, -63,2,07.12.2015,,0,138.5, -63,2,08.12.2015,,0,138.5, -63,2,09.12.2015,,0,138.5, -63,2,10.12.2015,,0,138.5, -63,2,11.12.2015,,0,142.57, -63,2,12.12.2015,,6,142.57, -63,2,13.12.2015,,3,142.57, -63,2,14.12.2015,,3,142.57, -63,2,15.12.2015,,3,142.57, -63,2,16.12.2015,,3,142.57, -63,2,17.12.2015,,3,142.57, -63,2,18.12.2015,,3,142.57, -63,2,19.12.2015,,0,142.57, -63,2,20.12.2015,,3,142.57, -63,2,21.12.2015,,9,142.57, -63,2,22.12.2015,1.0,91,142.57,103.02 -63,2,23.12.2015,1.0,126,142.57,103.02 -63,2,24.12.2015,1.0,35,142.57,103.02 -63,2,25.12.2015,1.0,91,142.57,103.02 -63,2,26.12.2015,1.0,97,142.57,103.02 -63,2,27.12.2015,1.0,94,142.57,103.02 -63,2,28.12.2015,1.0,31,142.57,103.02 -63,2,29.12.2015,,3,138.5, -63,2,30.12.2015,,3,138.5, -63,2,31.12.2015,,0,138.5, -63,2,01.01.2016,,0,138.5, -63,2,02.01.2016,,19,138.5, -63,2,03.01.2016,,3,138.5, -63,2,04.01.2016,,3,138.5, -63,2,05.01.2016,,3,138.5, -63,2,06.01.2016,,3,138.5, -63,2,07.01.2016,,3,138.5, -63,2,08.01.2016,,16,138.5, -63,2,09.01.2016,,3,138.5, -63,2,10.01.2016,,3,138.5, -63,2,11.01.2016,,3,138.5, -63,2,12.01.2016,,3,138.5, -63,2,13.01.2016,,3,138.5, -63,2,14.01.2016,,3,138.5, -63,2,15.01.2016,,3,138.5, -63,2,16.01.2016,,3,142.57, -63,2,17.01.2016,,3,142.57, -63,2,18.01.2016,,0,142.57, -63,2,19.01.2016,1.0,69,142.57,103.02 -63,2,20.01.2016,1.0,129,142.57,103.02 -63,2,21.01.2016,1.0,116,142.57,103.02 -63,2,22.01.2016,1.0,167,142.57,103.02 -63,2,23.01.2016,1.0,79,142.57,103.02 -63,2,24.01.2016,1.0,22,142.57,103.02 -63,2,25.01.2016,1.0,13,142.57,103.02 -63,2,26.01.2016,,3,142.57, -63,2,27.01.2016,,3,142.57, -63,2,28.01.2016,,0,142.57, -63,2,29.01.2016,,28,142.57, -63,2,30.01.2016,,9,142.57, -63,2,31.01.2016,,6,142.57, -63,2,01.02.2016,,6,142.57, -63,2,02.02.2016,,6,142.57, -63,2,03.02.2016,,6,142.57, -63,2,04.02.2016,,3,142.57, -63,2,05.02.2016,,6,142.57, -63,2,06.02.2016,,3,142.57, -63,2,07.02.2016,,6,142.57, -63,2,08.02.2016,,0,142.57, -63,2,09.02.2016,1.0,79,142.57,108.46 -63,2,10.02.2016,1.0,57,138.36,108.46 -63,2,11.02.2016,1.0,85,138.36,108.46 -63,2,12.02.2016,1.0,91,138.36,108.46 -63,2,13.02.2016,1.0,47,138.36,108.46 -63,2,14.02.2016,1.0,72,138.36,108.46 -63,2,15.02.2016,1.0,72,138.36,108.46 -63,2,16.02.2016,,6,138.36, -63,2,17.02.2016,,6,138.36, -63,2,18.02.2016,,3,138.36, -63,2,19.02.2016,,6,138.36, -63,2,20.02.2016,,3,138.36, -63,2,21.02.2016,,13,138.36, -63,2,22.02.2016,,6,138.36, -63,2,23.02.2016,,6,138.36, -63,2,24.02.2016,,6,138.36, -63,2,25.02.2016,,6,138.36, -63,2,26.02.2016,,6,138.36, -63,2,27.02.2016,,3,138.36, -63,2,28.02.2016,,6,138.36, -63,2,29.02.2016,,6,138.36, -63,2,01.03.2016,,6,138.36, -63,2,02.03.2016,,3,138.36, -63,2,03.03.2016,,6,138.36, -63,2,04.03.2016,,3,138.36, -63,2,05.03.2016,,6,138.36, -63,2,06.03.2016,,3,138.36, -63,2,07.03.2016,,3,138.36, -63,2,08.03.2016,,3,138.36, -63,2,09.03.2016,,3,138.36, -63,2,10.03.2016,,3,138.36, -63,2,11.03.2016,,3,138.36, -63,2,12.03.2016,,3,138.5, -63,2,13.03.2016,,3,138.5, -63,2,14.03.2016,,0,138.5, -63,2,15.03.2016,1.0,31,138.5,124.77 -63,2,16.03.2016,1.0,44,138.5,124.77 -63,2,17.03.2016,1.0,44,138.5,124.77 -63,2,18.03.2016,1.0,57,138.5,124.77 -63,2,19.03.2016,1.0,16,138.5,124.77 -63,2,20.03.2016,1.0,44,138.5,124.77 -63,2,21.03.2016,1.0,50,138.5,124.77 -63,2,22.03.2016,,3,138.36, -63,2,23.03.2016,,0,138.36, -63,2,24.03.2016,,0,138.36, -63,2,25.03.2016,,0,138.36, -63,2,26.03.2016,,3,138.36, -63,2,27.03.2016,,9,138.36, -63,2,28.03.2016,,25,138.36, -63,2,29.03.2016,,3,138.36, -63,2,30.03.2016,,3,138.36, -63,2,31.03.2016,,3,138.36, -63,2,01.04.2016,,3,138.36, -63,2,02.04.2016,,3,138.36, -63,2,03.04.2016,,0,138.36, -63,2,04.04.2016,,0,138.36, -63,2,05.04.2016,,3,138.36, -63,2,06.04.2016,,0,138.36, -63,2,07.04.2016,,0,138.36, -63,2,08.04.2016,,0,138.36, -63,2,09.04.2016,,6,138.5, -63,2,10.04.2016,,3,138.5, -63,2,11.04.2016,,3,138.5, -63,2,12.04.2016,,3,138.5, -63,2,13.04.2016,,3,138.5, -63,2,14.04.2016,,3,138.5, -63,2,15.04.2016,,3,138.5, -63,2,16.04.2016,,3,138.5, -63,2,17.04.2016,,3,138.5, -63,2,18.04.2016,,0,138.5, -63,2,19.04.2016,1.0,47,138.5,116.89 -63,2,20.04.2016,1.0,88,138.5,116.89 -63,2,21.04.2016,1.0,57,138.5,116.89 -63,2,22.04.2016,1.0,82,138.5,116.89 -63,2,23.04.2016,1.0,53,138.5,116.89 -63,2,24.04.2016,1.0,28,138.5,116.89 -63,2,25.04.2016,1.0,44,138.5,116.89 -63,2,26.04.2016,,3,138.5, -63,2,27.04.2016,,9,138.5, -63,2,28.04.2016,,0,138.5, -63,2,29.04.2016,,0,138.5, -63,2,30.04.2016,,6,138.5, -63,2,01.05.2016,,3,138.5, -63,2,02.05.2016,,3,138.5, -63,2,03.05.2016,1.0,35,138.5,106.01 -63,2,04.05.2016,1.0,97,138.5,106.01 -63,2,05.05.2016,1.0,75,138.5,106.01 -63,2,06.05.2016,1.0,104,138.5,106.01 -63,2,07.05.2016,1.0,107,138.5,106.01 -63,2,08.05.2016,1.0,66,138.5,106.01 -63,2,09.05.2016,1.0,66,138.5,106.01 -63,2,10.05.2016,,3,142.57, -63,2,11.05.2016,,3,142.57, -63,2,12.05.2016,,3,142.57, -63,2,13.05.2016,,0,142.57, -63,2,14.05.2016,,0,142.57, -63,2,15.05.2016,,0,142.57, -63,2,16.05.2016,,0,142.57, -63,2,17.05.2016,,3,142.57, -63,2,18.05.2016,,0,142.57, -63,2,19.05.2016,,3,142.57, -63,2,20.05.2016,,22,142.57, -63,2,21.05.2016,,0,142.57, -63,2,22.05.2016,,0,142.57, -64,2,01.01.2015,,0,134.72, -64,2,02.01.2015,,0,134.72, -64,2,03.01.2015,,0,134.72, -64,2,04.01.2015,,0,134.72, -64,2,05.01.2015,,0,134.72, -64,2,06.01.2015,,0,134.72, -64,2,07.01.2015,,0,134.72, -64,2,08.01.2015,,0,134.72, -64,2,09.01.2015,,0,134.72, -64,2,10.01.2015,,0,134.72, -64,2,11.01.2015,,0,134.72, -64,2,12.01.2015,,0,134.72, -64,2,13.01.2015,,0,134.72, -64,2,14.01.2015,,0,134.72, -64,2,15.01.2015,,0,134.72, -64,2,16.01.2015,,0,134.72, -64,2,17.01.2015,,0,134.72, -64,2,18.01.2015,,0,134.72, -64,2,19.01.2015,,0,134.72, -64,2,20.01.2015,,0,134.72, -64,2,21.01.2015,,0,134.72, -64,2,22.01.2015,,0,134.72, -64,2,23.01.2015,,0,134.72, -64,2,24.01.2015,,0,134.72, -64,2,25.01.2015,,0,134.72, -64,2,26.01.2015,,0,134.72, -64,2,27.01.2015,,0,134.72, -64,2,28.01.2015,,0,134.72, -64,2,29.01.2015,,0,134.72, -64,2,30.01.2015,,0,134.72, -64,2,31.01.2015,,0,134.72, -64,2,01.02.2015,,0,134.72, -64,2,02.02.2015,,0,134.72, -64,2,03.02.2015,,0,134.72, -64,2,04.02.2015,,0,134.72, -64,2,05.02.2015,,0,134.72, -64,2,06.02.2015,,0,134.72, -64,2,07.02.2015,,0,134.72, -64,2,08.02.2015,,0,134.72, -64,2,09.02.2015,,0,134.72, -64,2,10.02.2015,,0,134.72, -64,2,11.02.2015,,0,134.72, -64,2,12.02.2015,,0,134.72, -64,2,13.02.2015,,0,134.72, -64,2,14.02.2015,,0,134.72, -64,2,15.02.2015,,0,134.72, -64,2,16.02.2015,,0,134.72, -64,2,17.02.2015,,0,134.72, -64,2,18.02.2015,,0,134.72, -64,2,19.02.2015,,0,134.72, -64,2,20.02.2015,,0,134.72, -64,2,21.02.2015,,0,134.72, -64,2,22.02.2015,,0,134.72, -64,2,23.02.2015,,0,134.72, -64,2,24.02.2015,,0,134.72, -64,2,25.02.2015,,0,134.72, -64,2,26.02.2015,,0,134.72, -64,2,27.02.2015,,0,134.72, -64,2,28.02.2015,,0,134.72, -64,2,01.03.2015,,0,134.72, -64,2,02.03.2015,,0,134.72, -64,2,03.03.2015,,0,134.72, -64,2,04.03.2015,,0,134.72, -64,2,05.03.2015,,0,134.72, -64,2,06.03.2015,,0,134.72, -64,2,07.03.2015,,0,134.72, -64,2,08.03.2015,,0,134.72, -64,2,09.03.2015,,0,134.72, -64,2,10.03.2015,,0,134.72, -64,2,11.03.2015,,0,134.72, -64,2,12.03.2015,,0,134.72, -64,2,13.03.2015,,0,134.72, -64,2,14.03.2015,,0,134.72, -64,2,15.03.2015,,0,134.72, -64,2,16.03.2015,,0,134.72, -64,2,17.03.2015,,0,134.72, -64,2,18.03.2015,,0,134.72, -64,2,19.03.2015,,0,134.72, -64,2,20.03.2015,,0,134.72, -64,2,21.03.2015,,0,134.72, -64,2,22.03.2015,,0,134.72, -64,2,23.03.2015,,0,134.72, -64,2,24.03.2015,,0,134.72, -64,2,25.03.2015,,0,134.72, -64,2,26.03.2015,,0,134.72, -64,2,27.03.2015,,0,134.72, -64,2,28.03.2015,,0,134.72, -64,2,29.03.2015,,0,134.72, -64,2,30.03.2015,,0,134.72, -64,2,31.03.2015,,0,134.72, -64,2,01.04.2015,,0,134.72, -64,2,02.04.2015,,0,134.72, -64,2,03.04.2015,,0,134.72, -64,2,04.04.2015,,0,134.72, -64,2,05.04.2015,,0,134.72, -64,2,06.04.2015,,0,134.72, -64,2,07.04.2015,,0,134.72, -64,2,08.04.2015,,0,134.72, -64,2,09.04.2015,,0,134.72, -64,2,10.04.2015,,0,134.72, -64,2,11.04.2015,,0,134.72, -64,2,12.04.2015,,0,134.72, -64,2,13.04.2015,,0,134.72, -64,2,14.04.2015,,0,134.72, -64,2,15.04.2015,,0,134.72, -64,2,16.04.2015,,0,134.72, -64,2,17.04.2015,,0,134.72, -64,2,18.04.2015,,0,134.72, -64,2,19.04.2015,,0,134.72, -64,2,20.04.2015,,0,134.72, -64,2,21.04.2015,,0,134.72, -64,2,22.04.2015,,0,134.72, -64,2,23.04.2015,,0,134.72, -64,2,24.04.2015,,0,134.72, -64,2,25.04.2015,,0,134.72, -64,2,26.04.2015,,0,134.72, -64,2,27.04.2015,,0,134.72, -64,2,28.04.2015,,0,134.72, -64,2,29.04.2015,,0,134.72, -64,2,30.04.2015,,0,134.72, -64,2,01.05.2015,,0,134.72, -64,2,02.05.2015,,0,134.72, -64,2,03.05.2015,,0,134.72, -64,2,04.05.2015,,0,134.72, -64,2,05.05.2015,,0,134.72, -64,2,06.05.2015,,0,134.72, -64,2,07.05.2015,,0,134.72, -64,2,08.05.2015,,0,134.72, -64,2,09.05.2015,,0,134.72, -64,2,10.05.2015,,0,134.72, -64,2,11.05.2015,,0,134.72, -64,2,12.05.2015,,0,134.72, -64,2,13.05.2015,,0,134.72, -64,2,14.05.2015,,0,134.72, -64,2,15.05.2015,,0,134.72, -64,2,16.05.2015,,0,134.72, -64,2,17.05.2015,,0,134.72, -64,2,18.05.2015,,0,134.72, -64,2,19.05.2015,,0,134.72, -64,2,20.05.2015,,0,134.72, -64,2,21.05.2015,,0,134.72, -64,2,22.05.2015,,0,134.72, -64,2,23.05.2015,,0,134.72, -64,2,24.05.2015,,0,134.72, -64,2,25.05.2015,,0,134.72, -64,2,26.05.2015,,0,134.72, -64,2,27.05.2015,,0,134.72, -64,2,28.05.2015,,0,134.72, -64,2,29.05.2015,,0,134.72, -64,2,30.05.2015,,0,134.72, -64,2,31.05.2015,,0,134.72, -64,2,01.06.2015,,0,134.72, -64,2,02.06.2015,,0,134.72, -64,2,03.06.2015,,0,134.72, -64,2,04.06.2015,,0,134.72, -64,2,05.06.2015,,0,134.72, -64,2,06.06.2015,,0,134.72, -64,2,07.06.2015,,0,134.72, -64,2,08.06.2015,,0,134.72, -64,2,09.06.2015,,0,134.72, -64,2,10.06.2015,,0,134.72, -64,2,11.06.2015,,0,134.72, -64,2,12.06.2015,,0,134.72, -64,2,13.06.2015,,0,134.72, -64,2,14.06.2015,,0,134.72, -64,2,15.06.2015,,0,134.72, -64,2,16.06.2015,,0,134.72, -64,2,17.06.2015,,0,134.72, -64,2,18.06.2015,,0,134.72, -64,2,19.06.2015,,0,134.72, -64,2,20.06.2015,,0,134.72, -64,2,21.06.2015,,0,134.72, -64,2,22.06.2015,,0,134.72, -64,2,23.06.2015,,0,134.72, -64,2,24.06.2015,,0,134.72, -64,2,25.06.2015,,0,134.72, -64,2,26.06.2015,,0,134.72, -64,2,27.06.2015,,0,134.72, -64,2,28.06.2015,,0,134.72, -64,2,29.06.2015,,0,134.72, -64,2,30.06.2015,,0,134.72, -64,2,01.07.2015,,0,134.72, -64,2,02.07.2015,,0,134.72, -64,2,03.07.2015,,0,134.72, -64,2,04.07.2015,,0,134.72, -64,2,05.07.2015,,0,134.72, -64,2,06.07.2015,,0,134.72, -64,2,07.07.2015,,0,134.72, -64,2,08.07.2015,,0,134.72, -64,2,09.07.2015,,0,134.72, -64,2,10.07.2015,,0,134.72, -64,2,11.07.2015,,0,134.72, -64,2,12.07.2015,,0,134.72, -64,2,13.07.2015,,0,134.72, -64,2,14.07.2015,,0,134.72, -64,2,15.07.2015,,0,134.72, -64,2,16.07.2015,,0,134.72, -64,2,17.07.2015,,0,134.72, -64,2,18.07.2015,,0,134.72, -64,2,19.07.2015,,0,134.72, -64,2,20.07.2015,,0,134.72, -64,2,21.07.2015,,0,134.72, -64,2,22.07.2015,,0,134.72, -64,2,23.07.2015,,0,134.72, -64,2,24.07.2015,,0,134.72, -64,2,25.07.2015,,0,134.72, -64,2,26.07.2015,,0,134.72, -64,2,27.07.2015,,0,134.72, -64,2,28.07.2015,,0,134.72, -64,2,29.07.2015,,0,134.72, -64,2,30.07.2015,,0,134.72, -64,2,31.07.2015,,0,134.72, -64,2,01.08.2015,,0,134.72, -64,2,02.08.2015,,0,134.72, -64,2,03.08.2015,,0,134.72, -64,2,04.08.2015,,0,134.72, -64,2,05.08.2015,,0,134.72, -64,2,06.08.2015,,0,150.73, -64,2,07.08.2015,,0,150.73, -64,2,08.08.2015,,0,150.73, -64,2,09.08.2015,,6,150.73, -64,2,10.08.2015,,13,150.73, -64,2,11.08.2015,,0,150.73, -64,2,12.08.2015,,6,128.17, -64,2,13.08.2015,,0,128.17, -64,2,14.08.2015,,3,128.17, -64,2,15.08.2015,,22,128.17, -64,2,16.08.2015,,9,128.17, -64,2,17.08.2015,,19,128.17, -64,2,18.08.2015,1.0,119,128.17,100.3 -64,2,19.08.2015,1.0,53,128.17,100.3 -64,2,20.08.2015,1.0,72,128.17,100.3 -64,2,21.08.2015,1.0,75,128.17,100.3 -64,2,22.08.2015,1.0,53,128.17,100.3 -64,2,23.08.2015,1.0,101,128.17,100.3 -64,2,24.08.2015,1.0,91,128.17,100.3 -64,2,25.08.2015,,9,128.17, -64,2,26.08.2015,,6,128.17, -64,2,27.08.2015,,6,128.17, -64,2,28.08.2015,,6,128.17, -64,2,29.08.2015,,6,128.17, -64,2,30.08.2015,,6,128.17, -64,2,31.08.2015,,6,128.17, -64,2,01.09.2015,,6,128.17, -64,2,02.09.2015,,6,128.17, -64,2,03.09.2015,,6,128.17, -64,2,04.09.2015,,6,128.17, -64,2,05.09.2015,,6,128.17, -64,2,06.09.2015,,6,128.17, -64,2,07.09.2015,,6,128.17, -64,2,08.09.2015,,6,128.17, -64,2,09.09.2015,,6,128.17, -64,2,10.09.2015,,6,128.17, -64,2,11.09.2015,,6,128.17, -64,2,12.09.2015,,6,128.17, -64,2,13.09.2015,,6,128.17, -64,2,14.09.2015,,6,128.17, -64,2,15.09.2015,,6,128.17, -64,2,16.09.2015,,6,128.17, -64,2,17.09.2015,,6,128.17, -64,2,18.09.2015,,0,128.17, -64,2,19.09.2015,,0,128.17, -64,2,20.09.2015,,0,128.17, -64,2,21.09.2015,,0,128.17, -64,2,22.09.2015,,0,128.17, -64,2,23.09.2015,,0,128.17, -64,2,24.09.2015,,0,128.17, -64,2,25.09.2015,,0,162.96, -64,2,26.09.2015,,0,162.96, -64,2,27.09.2015,,19,162.96, -64,2,28.09.2015,,0,162.96, -64,2,29.09.2015,,0,162.96, -64,2,30.09.2015,,19,162.96, -64,2,01.10.2015,,0,162.96, -64,2,02.10.2015,,0,162.96, -64,2,03.10.2015,,3,162.96, -64,2,04.10.2015,,3,162.96, -64,2,05.10.2015,,0,162.96, -64,2,06.10.2015,1.0,82,143.39,122.05 -64,2,07.10.2015,1.0,63,143.39,122.05 -64,2,08.10.2015,1.0,66,143.39,122.05 -64,2,09.10.2015,1.0,104,143.39,122.05 -64,2,10.10.2015,1.0,35,143.39,122.05 -64,2,11.10.2015,1.0,53,143.39,122.05 -64,2,12.10.2015,1.0,63,143.39,122.05 -64,2,13.10.2015,1.0,41,143.93,122.05 -64,2,14.10.2015,1.0,35,143.93,122.05 -64,2,15.10.2015,1.0,66,143.93,122.05 -64,2,16.10.2015,1.0,66,143.93,122.05 -64,2,17.10.2015,1.0,66,143.93,122.05 -64,2,18.10.2015,1.0,66,143.93,122.05 -64,2,19.10.2015,,9,143.93, -64,2,20.10.2015,,9,143.93, -64,2,21.10.2015,,9,143.93, -64,2,22.10.2015,,9,143.93, -64,2,23.10.2015,,9,143.93, -64,2,24.10.2015,,9,143.93, -64,2,25.10.2015,,9,143.93, -64,2,26.10.2015,,9,143.93, -64,2,27.10.2015,,9,143.93, -64,2,28.10.2015,,9,143.93, -64,2,29.10.2015,,9,143.93, -64,2,30.10.2015,,9,143.93, -64,2,31.10.2015,,9,143.93, -64,2,01.11.2015,,9,143.93, -64,2,02.11.2015,,9,143.93, -64,2,03.11.2015,,9,143.93, -64,2,04.11.2015,,9,143.93, -64,2,05.11.2015,,9,143.93, -64,2,06.11.2015,,9,143.93, -64,2,07.11.2015,,0,143.93, -64,2,08.11.2015,,0,143.93, -64,2,09.11.2015,,0,143.93, -64,2,10.11.2015,,0,143.93, -64,2,11.11.2015,,0,143.93, -64,2,12.11.2015,,0,143.93, -64,2,13.11.2015,,0,143.93, -64,2,14.11.2015,,0,143.93, -64,2,15.11.2015,,0,143.93, -64,2,16.11.2015,,0,143.93, -64,2,17.11.2015,,0,143.93, -64,2,18.11.2015,,0,143.93, -64,2,19.11.2015,,0,143.93, -64,2,20.11.2015,,0,143.93, -64,2,21.11.2015,,0,143.93, -64,2,22.11.2015,,0,143.93, -64,2,23.11.2015,,0,143.93, -64,2,24.11.2015,,0,143.93, -64,2,25.11.2015,,0,143.93, -64,2,26.11.2015,,0,143.93, -64,2,27.11.2015,,0,143.93, -64,2,28.11.2015,,0,143.93, -64,2,29.11.2015,,0,143.93, -64,2,30.11.2015,,0,143.93, -64,2,01.12.2015,,0,143.93, -64,2,02.12.2015,,0,143.93, -64,2,03.12.2015,,0,143.93, -64,2,04.12.2015,,0,143.93, -64,2,05.12.2015,,0,143.93, -64,2,06.12.2015,,0,143.93, -64,2,07.12.2015,,0,143.93, -64,2,08.12.2015,,0,143.93, -64,2,09.12.2015,,0,138.5, -64,2,10.12.2015,,6,138.5, -64,2,11.12.2015,,3,142.57, -64,2,12.12.2015,,6,142.57, -64,2,13.12.2015,,6,142.57, -64,2,14.12.2015,,0,142.57, -64,2,15.12.2015,,0,142.57, -64,2,16.12.2015,,0,142.57, -64,2,17.12.2015,,6,142.57, -64,2,18.12.2015,,9,142.57, -64,2,19.12.2015,,9,142.57, -64,2,20.12.2015,,6,142.57, -64,2,21.12.2015,,13,142.57, -64,2,22.12.2015,1.0,135,142.57,103.02 -64,2,23.12.2015,1.0,79,142.57,103.02 -64,2,24.12.2015,1.0,107,142.57,103.02 -64,2,25.12.2015,1.0,104,142.57,103.02 -64,2,26.12.2015,1.0,110,142.57,103.02 -64,2,27.12.2015,1.0,113,142.57,103.02 -64,2,28.12.2015,1.0,50,142.57,103.02 -64,2,29.12.2015,,9,138.5, -64,2,30.12.2015,,9,138.5, -64,2,31.12.2015,,6,138.5, -64,2,01.01.2016,,3,138.5, -64,2,02.01.2016,,9,138.5, -64,2,03.01.2016,,9,138.5, -64,2,04.01.2016,,9,138.5, -64,2,05.01.2016,,6,138.5, -64,2,06.01.2016,,19,138.5, -64,2,07.01.2016,,9,138.5, -64,2,08.01.2016,,9,138.5, -64,2,09.01.2016,,9,138.5, -64,2,10.01.2016,,9,138.5, -64,2,11.01.2016,,9,138.5, -64,2,12.01.2016,,6,138.5, -64,2,13.01.2016,,13,138.5, -64,2,14.01.2016,,9,138.5, -64,2,15.01.2016,,9,138.5, -64,2,16.01.2016,,9,142.57, -64,2,17.01.2016,,6,142.57, -64,2,18.01.2016,,13,142.57, -64,2,19.01.2016,1.0,119,142.57,103.02 -64,2,20.01.2016,1.0,148,142.57,103.02 -64,2,21.01.2016,1.0,135,142.57,103.02 -64,2,22.01.2016,1.0,132,142.57,103.02 -64,2,23.01.2016,1.0,176,142.57,103.02 -64,2,24.01.2016,1.0,170,142.57,103.02 -64,2,25.01.2016,1.0,119,142.57,103.02 -64,2,26.01.2016,,9,142.57, -64,2,27.01.2016,,9,142.57, -64,2,28.01.2016,,16,142.57, -64,2,29.01.2016,,3,142.57, -64,2,30.01.2016,,16,142.57, -64,2,31.01.2016,,9,142.57, -64,2,01.02.2016,,3,142.57, -64,2,02.02.2016,,6,142.57, -64,2,03.02.2016,,13,142.57, -64,2,04.02.2016,,13,142.57, -64,2,05.02.2016,,6,142.57, -64,2,06.02.2016,,9,142.57, -64,2,07.02.2016,,9,142.57, -64,2,08.02.2016,,57,142.57, -64,2,09.02.2016,1.0,182,142.57,108.46 -64,2,10.02.2016,1.0,119,138.36,108.46 -64,2,11.02.2016,1.0,94,138.36,108.46 -64,2,12.02.2016,1.0,154,138.36,108.46 -64,2,13.02.2016,1.0,69,138.36,108.46 -64,2,14.02.2016,1.0,113,138.36,108.46 -64,2,15.02.2016,1.0,126,138.36,108.46 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-65,2,11.11.2015,,0,143.93, -65,2,12.11.2015,,0,143.93, -65,2,13.11.2015,,0,143.93, -65,2,14.11.2015,,0,143.93, -65,2,15.11.2015,,0,143.93, -65,2,16.11.2015,,0,143.93, -65,2,17.11.2015,,0,143.93, -65,2,18.11.2015,,0,143.93, -65,2,19.11.2015,,0,143.93, -65,2,20.11.2015,,0,143.93, -65,2,21.11.2015,,0,143.93, -65,2,22.11.2015,,0,143.93, -65,2,23.11.2015,,0,143.93, -65,2,24.11.2015,,0,143.93, -65,2,25.11.2015,,0,143.93, -65,2,26.11.2015,,0,143.93, -65,2,27.11.2015,,0,143.93, -65,2,28.11.2015,,0,143.93, -65,2,29.11.2015,,0,143.93, -65,2,30.11.2015,,0,143.93, -65,2,01.12.2015,,0,143.93, -65,2,02.12.2015,,0,143.93, -65,2,03.12.2015,,0,143.93, -65,2,04.12.2015,,0,143.93, -65,2,05.12.2015,,0,143.93, -65,2,06.12.2015,,0,143.93, -65,2,07.12.2015,,0,143.93, -65,2,08.12.2015,,0,143.93, -65,2,09.12.2015,,0,138.5, -65,2,10.12.2015,,0,138.5, -65,2,11.12.2015,,0,142.57, -65,2,12.12.2015,,0,142.57, -65,2,13.12.2015,,0,142.57, -65,2,14.12.2015,,0,142.57, -65,2,15.12.2015,,0,142.57, -65,2,16.12.2015,,0,142.57, -65,2,17.12.2015,,0,142.57, -65,2,18.12.2015,,0,142.57, -65,2,19.12.2015,,0,142.57, -65,2,20.12.2015,,0,142.57, -65,2,21.12.2015,,0,142.57, -65,2,22.12.2015,1.0,35,142.57,103.02 -65,2,23.12.2015,1.0,3,142.57,103.02 -65,2,24.12.2015,1.0,6,142.57,103.02 -65,2,25.12.2015,1.0,57,142.57,103.02 -65,2,26.12.2015,1.0,28,142.57,103.02 -65,2,27.12.2015,1.0,38,142.57,103.02 -65,2,28.12.2015,1.0,19,142.57,103.02 -65,2,29.12.2015,,0,138.5, -65,2,30.12.2015,,0,138.5, -65,2,31.12.2015,,0,138.5, -65,2,01.01.2016,,0,138.5, -65,2,02.01.2016,,0,138.5, -65,2,03.01.2016,,0,138.5, -65,2,04.01.2016,,0,138.5, -65,2,05.01.2016,,13,138.5, -65,2,06.01.2016,,6,138.5, -65,2,07.01.2016,,0,138.5, -65,2,08.01.2016,,0,138.5, -65,2,09.01.2016,,0,138.5, -65,2,10.01.2016,,0,138.5, -65,2,11.01.2016,,0,138.5, -65,2,12.01.2016,,0,138.5, -65,2,13.01.2016,,6,138.5, -65,2,14.01.2016,,0,138.5, -65,2,15.01.2016,,13,138.5, -65,2,16.01.2016,,0,142.57, -65,2,17.01.2016,,3,142.57, -65,2,18.01.2016,,3,142.57, -65,2,19.01.2016,1.0,44,142.57,103.02 -65,2,20.01.2016,1.0,53,142.57,103.02 -65,2,21.01.2016,1.0,16,142.57,103.02 -65,2,22.01.2016,1.0,44,142.57,103.02 -65,2,23.01.2016,1.0,31,142.57,103.02 -65,2,24.01.2016,1.0,50,142.57,103.02 -65,2,25.01.2016,1.0,31,142.57,103.02 -65,2,26.01.2016,,3,142.57, -65,2,27.01.2016,,3,142.57, -65,2,28.01.2016,,3,142.57, -65,2,29.01.2016,,3,142.57, -65,2,30.01.2016,,3,142.57, -65,2,31.01.2016,,3,142.57, -65,2,01.02.2016,,3,142.57, -65,2,02.02.2016,,3,142.57, -65,2,03.02.2016,,3,142.57, -65,2,04.02.2016,,3,142.57, -65,2,05.02.2016,,3,142.57, -65,2,06.02.2016,,3,142.57, -65,2,07.02.2016,,3,142.57, -65,2,08.02.2016,,3,142.57, -65,2,09.02.2016,1.0,9,142.57,108.46 -65,2,10.02.2016,1.0,28,138.36,108.46 -65,2,11.02.2016,1.0,44,138.36,108.46 -65,2,12.02.2016,1.0,66,138.36,108.46 -65,2,13.02.2016,1.0,69,138.36,108.46 -65,2,14.02.2016,1.0,41,138.36,108.46 -65,2,15.02.2016,1.0,41,138.36,108.46 -65,2,16.02.2016,,3,138.36, -65,2,17.02.2016,,3,138.36, -65,2,18.02.2016,,3,138.36, -65,2,19.02.2016,,3,138.36, -65,2,20.02.2016,,3,138.36, -65,2,21.02.2016,,3,138.36, -65,2,22.02.2016,,3,138.36, -65,2,23.02.2016,,3,138.36, -65,2,24.02.2016,,3,138.36, -65,2,25.02.2016,,0,138.36, -65,2,26.02.2016,,0,138.36, -65,2,27.02.2016,,0,138.36, -65,2,28.02.2016,,0,138.36, -65,2,29.02.2016,,0,138.36, -65,2,01.03.2016,,0,138.36, -65,2,02.03.2016,,0,138.36, -65,2,03.03.2016,,0,138.36, -65,2,04.03.2016,,0,138.36, -65,2,05.03.2016,,0,138.36, -65,2,06.03.2016,,0,138.36, -65,2,07.03.2016,,0,138.36, -65,2,08.03.2016,,0,138.36, -65,2,09.03.2016,,0,138.36, -65,2,10.03.2016,,0,138.36, -65,2,11.03.2016,,0,138.36, -65,2,12.03.2016,,0,138.5, -65,2,13.03.2016,,0,138.5, -65,2,14.03.2016,,0,138.5, -65,2,15.03.2016,1.0,35,138.5,124.77 -65,2,16.03.2016,1.0,35,138.5,124.77 -65,2,17.03.2016,1.0,6,138.5,124.77 -65,2,18.03.2016,1.0,19,138.5,124.77 -65,2,19.03.2016,1.0,35,138.5,124.77 -65,2,20.03.2016,1.0,57,138.5,124.77 -65,2,21.03.2016,1.0,16,138.5,124.77 -65,2,22.03.2016,,16,138.36, -65,2,23.03.2016,,0,138.36, -65,2,24.03.2016,,0,138.36, -65,2,25.03.2016,,9,138.36, -65,2,26.03.2016,,0,138.36, -65,2,27.03.2016,,0,138.36, -65,2,28.03.2016,,0,138.36, -65,2,29.03.2016,,0,138.36, -65,2,30.03.2016,,3,138.36, -65,2,31.03.2016,,3,138.36, -65,2,01.04.2016,,3,138.36, -65,2,02.04.2016,,3,138.36, -65,2,03.04.2016,,3,138.36, -65,2,04.04.2016,,3,138.36, -65,2,05.04.2016,,3,138.36, -65,2,06.04.2016,,3,138.36, -65,2,07.04.2016,,3,138.36, -65,2,08.04.2016,,3,138.36, -65,2,09.04.2016,,3,138.5, -65,2,10.04.2016,,3,138.5, -65,2,11.04.2016,,3,138.5, -65,2,12.04.2016,,3,138.5, -65,2,13.04.2016,,3,138.5, -65,2,14.04.2016,,3,138.5, -65,2,15.04.2016,,3,138.5, -65,2,16.04.2016,,3,138.5, -65,2,17.04.2016,,3,138.5, -65,2,18.04.2016,,3,138.5, -65,2,19.04.2016,1.0,13,138.5,116.89 -65,2,20.04.2016,1.0,41,138.5,116.89 -65,2,21.04.2016,1.0,16,138.5,116.89 -65,2,22.04.2016,1.0,35,138.5,116.89 -65,2,23.04.2016,1.0,35,138.5,116.89 -65,2,24.04.2016,1.0,22,138.5,116.89 -65,2,25.04.2016,1.0,9,138.5,116.89 -65,2,26.04.2016,,3,138.5, -65,2,27.04.2016,,3,138.5, -65,2,28.04.2016,,3,138.5, -65,2,29.04.2016,,0,138.5, -65,2,30.04.2016,,3,138.5, -65,2,01.05.2016,,0,138.5, -65,2,02.05.2016,,3,138.5, -65,2,03.05.2016,1.0,16,138.5,106.01 -65,2,04.05.2016,1.0,75,138.5,106.01 -65,2,05.05.2016,1.0,50,138.5,106.01 -65,2,06.05.2016,1.0,63,138.5,106.01 -65,2,07.05.2016,1.0,44,138.5,106.01 -65,2,08.05.2016,1.0,44,138.5,106.01 -65,2,09.05.2016,1.0,35,138.5,106.01 -65,2,10.05.2016,,0,142.57, -65,2,11.05.2016,,0,142.57, -65,2,12.05.2016,,0,142.57, -65,2,13.05.2016,,0,142.57, -65,2,14.05.2016,,0,142.57, -65,2,15.05.2016,,0,142.57, -65,2,16.05.2016,,0,142.57, -65,2,17.05.2016,,0,142.57, -65,2,18.05.2016,,0,142.57, -65,2,19.05.2016,,0,142.57, -65,2,20.05.2016,,0,142.57, -65,2,21.05.2016,,0,142.57, -65,2,22.05.2016,,0,142.57, -66,2,01.08.2015,,0,150.73, -66,2,02.08.2015,,0,150.73, -66,2,03.08.2015,,0,150.73, -66,2,04.08.2015,,0,150.73, 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-66,2,07.09.2015,,3,128.17, -66,2,08.09.2015,,3,128.17, -66,2,09.09.2015,,3,128.17, -66,2,10.09.2015,,3,128.17, -66,2,11.09.2015,,3,128.17, -66,2,12.09.2015,,3,128.17, -66,2,13.09.2015,,3,128.17, -66,2,14.09.2015,,3,128.17, -66,2,15.09.2015,,3,128.17, -66,2,16.09.2015,,3,128.17, -66,2,17.09.2015,,0,128.17, -66,2,18.09.2015,,0,128.17, -66,2,19.09.2015,,0,128.17, -66,2,20.09.2015,,0,128.17, -66,2,21.09.2015,,0,128.17, -66,2,22.09.2015,,0,128.17, -66,2,23.09.2015,,0,128.17, -66,2,24.09.2015,,0,128.17, -66,2,25.09.2015,,0,162.96, -66,2,26.09.2015,,0,162.96, -66,2,27.09.2015,,0,162.96, -66,2,28.09.2015,,0,162.96, -66,2,29.09.2015,,3,162.96, -66,2,30.09.2015,,9,162.96, -66,2,01.10.2015,,0,162.96, -66,2,02.10.2015,,3,162.96, -66,2,03.10.2015,,0,162.96, -66,2,04.10.2015,,0,162.96, -66,2,05.10.2015,,3,162.96, -66,2,06.10.2015,1.0,9,143.39,122.05 -66,2,07.10.2015,1.0,38,143.39,122.05 -66,2,08.10.2015,1.0,41,143.39,122.05 -66,2,09.10.2015,1.0,31,143.39,122.05 -66,2,10.10.2015,1.0,28,143.39,122.05 -66,2,11.10.2015,1.0,31,143.39,122.05 -66,2,12.10.2015,1.0,13,143.39,122.05 -66,2,13.10.2015,,0,143.93, -66,2,14.10.2015,,0,143.93, -66,2,15.10.2015,,3,143.93, -66,2,16.10.2015,,0,143.93, -66,2,17.10.2015,,0,143.93, -66,2,18.10.2015,,0,143.93, -66,2,19.10.2015,,0,143.93, -66,2,20.10.2015,,0,143.93, -66,2,21.10.2015,,0,143.93, -66,2,22.10.2015,,0,143.93, -66,2,23.10.2015,,0,143.93, -66,2,24.10.2015,,0,143.93, -66,2,25.10.2015,,0,143.93, -66,2,26.10.2015,,0,143.93, -66,2,27.10.2015,,0,143.93, -66,2,28.10.2015,,0,143.93, -66,2,29.10.2015,,0,143.93, -66,2,30.10.2015,,0,143.93, -66,2,31.10.2015,,0,143.93, -66,2,01.11.2015,,0,143.93, -66,2,02.11.2015,,0,143.93, -66,2,03.11.2015,,0,143.93, -66,2,04.11.2015,,0,143.93, -66,2,05.11.2015,,0,143.93, -66,2,06.11.2015,,0,143.93, -66,2,07.11.2015,,0,143.93, -66,2,08.11.2015,,0,143.93, -66,2,09.11.2015,,0,143.93, -66,2,10.11.2015,,0,143.93, -66,2,11.11.2015,,0,143.93, -66,2,12.11.2015,,0,143.93, -66,2,13.11.2015,,0,143.93, -66,2,14.11.2015,,0,143.93, -66,2,15.11.2015,,0,143.93, -66,2,16.11.2015,,0,143.93, -66,2,17.11.2015,,0,143.93, -66,2,18.11.2015,,0,143.93, -66,2,19.11.2015,,0,143.93, -66,2,20.11.2015,,0,143.93, -66,2,21.11.2015,,0,143.93, -66,2,22.11.2015,,0,143.93, -66,2,23.11.2015,,0,143.93, -66,2,24.11.2015,,0,143.93, -66,2,25.11.2015,,0,143.93, -66,2,26.11.2015,,0,143.93, -66,2,27.11.2015,,0,143.93, -66,2,28.11.2015,,0,143.93, -66,2,29.11.2015,,0,143.93, -66,2,30.11.2015,,0,143.93, -66,2,01.12.2015,,0,143.93, -66,2,02.12.2015,,0,143.93, -66,2,03.12.2015,,0,143.93, -66,2,04.12.2015,,0,143.93, -66,2,05.12.2015,,0,143.93, -66,2,06.12.2015,,0,143.93, -66,2,07.12.2015,,0,143.93, -66,2,08.12.2015,,0,143.93, -66,2,09.12.2015,,0,138.5, -66,2,10.12.2015,,0,138.5, -66,2,11.12.2015,,0,142.57, -66,2,12.12.2015,,0,142.57, -66,2,13.12.2015,,0,142.57, -66,2,14.12.2015,,0,142.57, -66,2,15.12.2015,,0,142.57, -66,2,16.12.2015,,0,142.57, -66,2,17.12.2015,,3,142.57, -66,2,18.12.2015,,13,142.57, -66,2,19.12.2015,,0,142.57, -66,2,20.12.2015,,3,142.57, -66,2,21.12.2015,,0,142.57, -66,2,22.12.2015,1.0,13,142.57,103.02 -66,2,23.12.2015,1.0,75,142.57,103.02 -66,2,24.12.2015,1.0,38,142.57,103.02 -66,2,25.12.2015,1.0,53,142.57,103.02 -66,2,26.12.2015,1.0,28,142.57,103.02 -66,2,27.12.2015,1.0,31,142.57,103.02 -66,2,28.12.2015,1.0,28,142.57,103.02 -66,2,29.12.2015,,0,138.5, -66,2,30.12.2015,,0,138.5, -66,2,31.12.2015,,0,138.5, -66,2,01.01.2016,,0,138.5, -66,2,02.01.2016,,0,138.5, -66,2,03.01.2016,,0,138.5, -66,2,04.01.2016,,0,138.5, -66,2,05.01.2016,,19,138.5, -66,2,06.01.2016,,16,138.5, -66,2,07.01.2016,,0,138.5, -66,2,08.01.2016,,3,138.5, -66,2,09.01.2016,,3,138.5, -66,2,10.01.2016,,3,138.5, -66,2,11.01.2016,,3,138.5, -66,2,12.01.2016,,3,138.5, -66,2,13.01.2016,,0,138.5, -66,2,14.01.2016,,0,138.5, -66,2,15.01.2016,,3,138.5, -66,2,16.01.2016,,6,142.57, -66,2,17.01.2016,,13,142.57, -66,2,18.01.2016,,6,142.57, -66,2,19.01.2016,1.0,35,142.57,103.02 -66,2,20.01.2016,1.0,66,142.57,103.02 -66,2,21.01.2016,1.0,41,142.57,103.02 -66,2,22.01.2016,1.0,88,142.57,103.02 -66,2,23.01.2016,1.0,97,142.57,103.02 -66,2,24.01.2016,1.0,66,142.57,103.02 -66,2,25.01.2016,1.0,16,142.57,103.02 -66,2,26.01.2016,,0,142.57, -66,2,27.01.2016,,3,142.57, -66,2,28.01.2016,,0,142.57, -66,2,29.01.2016,,6,142.57, -66,2,30.01.2016,,0,142.57, -66,2,31.01.2016,,9,142.57, -66,2,01.02.2016,,3,142.57, -66,2,02.02.2016,,6,142.57, -66,2,03.02.2016,,3,142.57, -66,2,04.02.2016,,3,142.57, -66,2,05.02.2016,,16,142.57, -66,2,06.02.2016,,3,142.57, -66,2,07.02.2016,,6,142.57, -66,2,08.02.2016,,6,142.57, -66,2,09.02.2016,1.0,35,142.57,108.46 -66,2,10.02.2016,1.0,66,138.36,108.46 -66,2,11.02.2016,1.0,53,138.36,108.46 -66,2,12.02.2016,1.0,79,138.36,108.46 -66,2,13.02.2016,1.0,38,138.36,108.46 -66,2,14.02.2016,1.0,82,138.36,108.46 -66,2,15.02.2016,1.0,35,138.36,108.46 -66,2,16.02.2016,,0,138.36, -66,2,17.02.2016,,0,138.36, -66,2,18.02.2016,,3,138.36, -66,2,19.02.2016,,0,138.36, -66,2,20.02.2016,,3,138.36, -66,2,21.02.2016,,6,138.36, -66,2,22.02.2016,,3,138.36, -66,2,23.02.2016,,3,138.36, -66,2,24.02.2016,,3,138.36, -66,2,25.02.2016,,3,138.36, -66,2,26.02.2016,,3,138.36, -66,2,27.02.2016,,3,138.36, -66,2,28.02.2016,,3,138.36, -66,2,29.02.2016,,3,138.36, -66,2,01.03.2016,,3,138.36, -66,2,02.03.2016,,3,138.36, -66,2,03.03.2016,,3,138.36, -66,2,04.03.2016,,3,138.36, -66,2,05.03.2016,,3,138.36, -66,2,06.03.2016,,3,138.36, -66,2,07.03.2016,,3,138.36, -66,2,08.03.2016,,3,138.36, -66,2,09.03.2016,,3,138.36, -66,2,10.03.2016,,3,138.36, -66,2,11.03.2016,,3,138.36, -66,2,12.03.2016,,3,138.5, -66,2,13.03.2016,,3,138.5, -66,2,14.03.2016,,3,138.5, -66,2,15.03.2016,1.0,31,138.5,124.77 -66,2,16.03.2016,1.0,16,138.5,124.77 -66,2,17.03.2016,1.0,22,138.5,124.77 -66,2,18.03.2016,1.0,22,138.5,124.77 -66,2,19.03.2016,1.0,22,138.5,124.77 -66,2,20.03.2016,1.0,38,138.5,124.77 -66,2,21.03.2016,1.0,19,138.5,124.77 -66,2,22.03.2016,,3,138.36, -66,2,23.03.2016,,0,138.36, -66,2,24.03.2016,,0,138.36, -66,2,25.03.2016,,0,138.36, -66,2,26.03.2016,,0,138.36, -66,2,27.03.2016,,0,138.36, -66,2,28.03.2016,,0,138.36, -66,2,29.03.2016,,0,138.36, -66,2,30.03.2016,,0,138.36, -66,2,31.03.2016,,0,138.36, -66,2,01.04.2016,,0,138.36, -66,2,02.04.2016,,0,138.36, -66,2,03.04.2016,,0,138.36, -66,2,04.04.2016,,0,138.36, -66,2,05.04.2016,,0,138.36, -66,2,06.04.2016,,0,138.36, -66,2,07.04.2016,,0,138.36, -66,2,08.04.2016,,3,138.36, -66,2,09.04.2016,,13,138.5, -66,2,10.04.2016,,0,138.5, -66,2,11.04.2016,,3,138.5, -66,2,12.04.2016,,3,138.5, -66,2,13.04.2016,,6,138.5, -66,2,14.04.2016,,3,138.5, -66,2,15.04.2016,,6,138.5, -66,2,16.04.2016,,6,138.5, -66,2,17.04.2016,,3,138.5, -66,2,18.04.2016,,3,138.5, -66,2,19.04.2016,1.0,25,138.5,116.89 -66,2,20.04.2016,1.0,47,138.5,116.89 -66,2,21.04.2016,1.0,53,138.5,116.89 -66,2,22.04.2016,1.0,57,138.5,116.89 -66,2,23.04.2016,1.0,53,138.5,116.89 -66,2,24.04.2016,1.0,41,138.5,116.89 -66,2,25.04.2016,1.0,6,138.5,116.89 -66,2,26.04.2016,,0,138.5, -66,2,27.04.2016,,0,138.5, -66,2,28.04.2016,,3,138.5, -66,2,29.04.2016,,6,138.5, -66,2,30.04.2016,,9,138.5, -66,2,01.05.2016,,0,138.5, -66,2,02.05.2016,,0,138.5, -66,2,03.05.2016,1.0,47,138.5,106.01 -66,2,04.05.2016,1.0,104,138.5,106.01 -66,2,05.05.2016,1.0,38,138.5,106.01 -66,2,06.05.2016,1.0,69,138.5,106.01 -66,2,07.05.2016,1.0,88,138.5,106.01 -66,2,08.05.2016,1.0,129,138.5,106.01 -66,2,09.05.2016,1.0,38,138.5,106.01 -66,2,10.05.2016,,3,138.5, -66,2,11.05.2016,,3,138.5, -66,2,12.05.2016,,3,138.5, -66,2,13.05.2016,,3,138.5, -66,2,14.05.2016,,3,138.5, -66,2,15.05.2016,,3,138.5, -66,2,16.05.2016,,3,138.5, -66,2,17.05.2016,,3,138.5, -66,2,18.05.2016,,3,138.5, -66,2,19.05.2016,,3,138.5, -66,2,20.05.2016,,3,138.5, -66,2,21.05.2016,,3,138.5, -66,2,22.05.2016,,3,138.5, -67,2,01.01.2015,,0,135.78, -67,2,02.01.2015,,0,135.78, -67,2,03.01.2015,,0,135.78, -67,2,04.01.2015,,0,135.78, -67,2,05.01.2015,,0,135.78, -67,2,06.01.2015,,0,135.78, -67,2,07.01.2015,,0,135.78, -67,2,08.01.2015,,0,135.78, -67,2,09.01.2015,,0,135.78, -67,2,10.01.2015,,9,135.78, -67,2,11.01.2015,,16,135.78, -67,2,12.01.2015,,0,135.78, -67,2,13.01.2015,,0,135.78, -67,2,14.01.2015,,0,135.78, -67,2,15.01.2015,,0,135.78, -67,2,16.01.2015,,0,135.78, -67,2,17.01.2015,,6,135.78, -67,2,18.01.2015,,6,135.78, -67,2,19.01.2015,,6,135.78, -67,2,20.01.2015,,3,135.78, -67,2,21.01.2015,,3,135.78, -67,2,22.01.2015,,0,135.78, -67,2,23.01.2015,,3,135.78, -67,2,24.01.2015,,0,135.78, -67,2,25.01.2015,,0,135.78, -67,2,26.01.2015,,16,135.78, -67,2,27.01.2015,,6,135.78, -67,2,28.01.2015,,0,135.78, -67,2,29.01.2015,,0,135.78, -67,2,30.01.2015,,0,135.78, -67,2,31.01.2015,,0,135.78, -67,2,01.02.2015,,0,135.78, -67,2,02.02.2015,,0,135.78, -67,2,03.02.2015,,0,135.78, -67,2,04.02.2015,,0,135.78, -67,2,05.02.2015,,0,135.78, -67,2,06.02.2015,,0,135.78, -67,2,07.02.2015,,0,135.78, -67,2,08.02.2015,,3,135.78, -67,2,09.02.2015,,6,135.78, -67,2,10.02.2015,,3,135.78, -67,2,11.02.2015,,0,135.78, -67,2,12.02.2015,,9,135.78, -67,2,13.02.2015,,0,135.78, -67,2,14.02.2015,,3,135.78, -67,2,15.02.2015,,0,135.78, -67,2,16.02.2015,,3,135.78, -67,2,17.02.2015,,0,135.78, -67,2,18.02.2015,,6,135.78, -67,2,19.02.2015,,0,135.78, -67,2,20.02.2015,,6,135.78, -67,2,21.02.2015,,0,135.78, -67,2,22.02.2015,,3,135.78, -67,2,23.02.2015,,3,135.78, -67,2,24.02.2015,,0,135.78, -67,2,25.02.2015,,0,135.78, -67,2,26.02.2015,,0,146.38, -67,2,27.02.2015,,0,146.38, -67,2,28.02.2015,,0,146.38, -67,2,01.03.2015,,3,146.38, -67,2,02.03.2015,,0,146.38, -67,2,03.03.2015,,3,146.38, -67,2,04.03.2015,,3,146.38, -67,2,05.03.2015,,0,146.38, -67,2,06.03.2015,,6,146.38, -67,2,07.03.2015,,3,146.38, -67,2,08.03.2015,,0,146.38, -67,2,09.03.2015,,3,146.38, -67,2,10.03.2015,,0,146.38, -67,2,11.03.2015,,0,146.38, -67,2,12.03.2015,,0,146.38, -67,2,13.03.2015,,0,146.38, -67,2,14.03.2015,,0,146.38, -67,2,15.03.2015,,0,146.38, -67,2,16.03.2015,,0,146.38, -67,2,17.03.2015,,0,146.38, -67,2,18.03.2015,,0,146.38, -67,2,19.03.2015,,0,146.38, -67,2,20.03.2015,,0,146.38, -67,2,21.03.2015,,6,146.38, -67,2,22.03.2015,,13,146.38, -67,2,23.03.2015,,0,146.38, -67,2,24.03.2015,,0,146.38, -67,2,25.03.2015,,0,146.38, -67,2,26.03.2015,,0,146.38, -67,2,27.03.2015,,3,146.38, -67,2,28.03.2015,,0,146.38, -67,2,29.03.2015,,6,146.38, -67,2,30.03.2015,,0,146.38, -67,2,31.03.2015,,3,146.38, -67,2,01.04.2015,,0,146.38, -67,2,02.04.2015,,3,146.38, -67,2,03.04.2015,,0,146.38, -67,2,04.04.2015,,0,146.38, -67,2,05.04.2015,,3,146.38, -67,2,06.04.2015,,0,145.84, -67,2,07.04.2015,,0,145.84, -67,2,08.04.2015,,0,145.84, -67,2,09.04.2015,,0,145.84, -67,2,10.04.2015,,0,145.84, -67,2,11.04.2015,,0,145.84, -67,2,12.04.2015,,0,145.84, -67,2,13.04.2015,,0,145.84, -67,2,14.04.2015,,0,145.84, -67,2,15.04.2015,,0,145.84, -67,2,16.04.2015,,3,145.84, -67,2,17.04.2015,,3,145.84, -67,2,18.04.2015,,6,145.84, -67,2,19.04.2015,,9,145.84, -67,2,20.04.2015,,3,145.84, -67,2,21.04.2015,,0,145.84, -67,2,22.04.2015,,0,145.84, -67,2,23.04.2015,,0,145.84, -67,2,24.04.2015,,0,145.84, -67,2,25.04.2015,,0,145.84, -67,2,26.04.2015,,0,150.73, -67,2,27.04.2015,,0,150.73, -67,2,28.04.2015,,0,150.73, -67,2,29.04.2015,,0,150.73, -67,2,30.04.2015,,0,150.73, -67,2,01.05.2015,,0,150.73, -67,2,02.05.2015,,0,150.73, -67,2,03.05.2015,,0,150.73, -67,2,04.05.2015,,0,150.73, -67,2,05.05.2015,,0,150.73, -67,2,06.05.2015,,0,150.73, -67,2,07.05.2015,,0,150.73, -67,2,08.05.2015,,0,150.73, -67,2,09.05.2015,,0,150.73, -67,2,10.05.2015,,0,150.73, -67,2,11.05.2015,,0,150.73, -67,2,12.05.2015,,0,150.73, -67,2,13.05.2015,,0,150.73, -67,2,14.05.2015,,0,150.73, -67,2,15.05.2015,,0,150.73, -67,2,16.05.2015,,0,150.73, -67,2,17.05.2015,,3,150.73, -67,2,18.05.2015,,0,150.73, -67,2,19.05.2015,,0,150.73, -67,2,20.05.2015,,0,150.73, -67,2,21.05.2015,,0,150.73, -67,2,22.05.2015,,3,150.73, -67,2,23.05.2015,,0,150.73, -67,2,24.05.2015,,0,150.73, -67,2,25.05.2015,,3,150.73, -67,2,26.05.2015,,6,150.73, -67,2,27.05.2015,,0,150.73, -67,2,28.05.2015,,0,150.73, -67,2,29.05.2015,,0,150.73, -67,2,30.05.2015,,0,150.73, -67,2,31.05.2015,,0,158.34, -67,2,01.06.2015,,0,158.34, -67,2,02.06.2015,,9,158.34, -67,2,03.06.2015,,13,158.34, -67,2,04.06.2015,,0,158.34, -67,2,05.06.2015,,0,158.34, -67,2,06.06.2015,,0,158.34, -67,2,07.06.2015,,3,158.34, -67,2,08.06.2015,,0,158.34, -67,2,09.06.2015,,3,158.34, -67,2,10.06.2015,,13,158.34, -67,2,11.06.2015,,0,158.34, -67,2,12.06.2015,,3,158.34, -67,2,13.06.2015,,0,158.34, -67,2,14.06.2015,,3,158.34, -67,2,15.06.2015,,0,158.34, -67,2,16.06.2015,,0,158.34, -67,2,17.06.2015,,3,158.34, -67,2,18.06.2015,,0,158.34, -67,2,19.06.2015,,0,158.34, -67,2,20.06.2015,,0,158.34, -67,2,21.06.2015,,0,158.34, -67,2,22.06.2015,,0,158.34, -67,2,23.06.2015,,0,158.34, -67,2,24.06.2015,,0,158.34, -67,2,25.06.2015,,0,158.34, -67,2,26.06.2015,,0,158.34, -67,2,27.06.2015,,0,158.34, -67,2,28.06.2015,,0,158.34, -67,2,29.06.2015,,0,158.34, -67,2,30.06.2015,,0,158.34, -67,2,01.07.2015,,0,158.34, -67,2,02.07.2015,,0,158.34, -67,2,03.07.2015,,0,158.34, -67,2,04.07.2015,,0,158.34, -67,2,05.07.2015,,0,158.34, -67,2,06.07.2015,,0,158.34, -67,2,07.07.2015,,0,158.34, -67,2,08.07.2015,,0,158.34, -67,2,09.07.2015,,0,158.34, -67,2,10.07.2015,,0,158.34, -67,2,11.07.2015,,0,158.34, -67,2,12.07.2015,,0,158.34, -67,2,13.07.2015,,0,158.34, -67,2,14.07.2015,,0,158.34, -67,2,15.07.2015,,0,158.34, -67,2,16.07.2015,,0,158.34, -67,2,17.07.2015,,0,158.34, -67,2,18.07.2015,,0,158.34, -67,2,19.07.2015,,0,158.34, -67,2,20.07.2015,,0,158.34, -67,2,21.07.2015,,0,158.34, -67,2,22.07.2015,,0,158.34, -67,2,23.07.2015,,0,158.34, -67,2,24.07.2015,,6,158.34, -67,2,25.07.2015,,19,158.34, -67,2,26.07.2015,,0,158.34, -67,2,27.07.2015,,0,158.34, -67,2,28.07.2015,,0,158.34, -67,2,29.07.2015,,0,158.34, -67,2,30.07.2015,,0,158.34, -67,2,31.07.2015,,0,158.34, -67,2,01.08.2015,,0,158.34, -67,2,02.08.2015,,13,158.34, -67,2,03.08.2015,,13,158.34, -67,2,04.08.2015,,3,158.34, -67,2,05.08.2015,,3,158.34, -67,2,06.08.2015,,3,158.34, -67,2,07.08.2015,,6,158.34, -67,2,08.08.2015,,16,158.34, -67,2,09.08.2015,,3,158.34, -67,2,10.08.2015,,6,158.34, -67,2,11.08.2015,,6,158.34, -67,2,12.08.2015,,6,128.17, -67,2,13.08.2015,,6,128.17, -67,2,14.08.2015,,6,128.17, -67,2,15.08.2015,,0,128.17, -67,2,16.08.2015,,3,128.17, -67,2,17.08.2015,,3,128.17, -67,2,18.08.2015,1.0,6,128.17,100.3 -67,2,19.08.2015,1.0,13,128.17,100.3 -67,2,20.08.2015,1.0,0,128.17,100.3 -67,2,21.08.2015,1.0,0,128.17,100.3 -67,2,22.08.2015,1.0,60,128.17,100.3 -67,2,23.08.2015,1.0,104,128.17,100.3 -67,2,24.08.2015,1.0,53,128.17,100.3 -67,2,25.08.2015,,13,128.17, -67,2,26.08.2015,,28,128.17, -67,2,27.08.2015,,22,128.17, -67,2,28.08.2015,,0,128.17, -67,2,29.08.2015,,9,128.17, -67,2,30.08.2015,,13,128.17, -67,2,31.08.2015,,9,128.17, -67,2,01.09.2015,,9,128.17, -67,2,02.09.2015,,9,128.17, -67,2,03.09.2015,,9,128.17, -67,2,04.09.2015,,16,128.17, -67,2,05.09.2015,,3,128.17, -67,2,06.09.2015,,9,128.17, -67,2,07.09.2015,,9,128.17, -67,2,08.09.2015,,9,128.17, -67,2,09.09.2015,,9,128.17, -67,2,10.09.2015,,9,128.17, -67,2,11.09.2015,,9,128.17, -67,2,12.09.2015,,3,128.17, -67,2,13.09.2015,,9,128.17, -67,2,14.09.2015,,6,128.17, -67,2,15.09.2015,,9,128.17, -67,2,16.09.2015,,9,128.17, -67,2,17.09.2015,,9,128.17, -67,2,18.09.2015,,9,128.17, -67,2,19.09.2015,,9,128.17, -67,2,20.09.2015,,9,128.17, -67,2,21.09.2015,,9,128.17, -67,2,22.09.2015,,9,128.17, -67,2,23.09.2015,,9,128.17, -67,2,24.09.2015,,9,128.17, -67,2,25.09.2015,,3,128.17, -67,2,26.09.2015,,9,171.12, -67,2,27.09.2015,,6,171.12, -67,2,28.09.2015,,9,171.12, -67,2,29.09.2015,,9,171.12, -67,2,30.09.2015,,9,171.12, -67,2,01.10.2015,,9,162.96, -67,2,02.10.2015,,9,162.96, -67,2,03.10.2015,,9,162.96, -67,2,04.10.2015,,9,162.96, -67,2,05.10.2015,,9,162.96, -67,2,06.10.2015,1.0,41,143.39,122.05 -67,2,07.10.2015,1.0,41,143.39,122.05 -67,2,08.10.2015,1.0,41,143.39,122.05 -67,2,09.10.2015,1.0,19,143.39,122.05 -67,2,10.10.2015,1.0,38,143.39,122.05 -67,2,11.10.2015,1.0,38,143.39,122.05 -67,2,12.10.2015,1.0,38,143.39,122.05 -67,2,13.10.2015,,9,143.93, -67,2,14.10.2015,,9,143.93, -67,2,15.10.2015,,9,143.93, -67,2,16.10.2015,,3,143.93, -67,2,17.10.2015,,0,162.96, -67,2,18.10.2015,,0,162.96, -67,2,19.10.2015,,0,162.96, -67,2,20.10.2015,,0,162.96, -67,2,21.10.2015,,0,162.96, -67,2,22.10.2015,,3,162.96, -67,2,23.10.2015,,0,162.96, -67,2,24.10.2015,,13,162.96, -67,2,25.10.2015,,3,162.96, -67,2,26.10.2015,,3,162.96, -67,2,27.10.2015,,3,162.96, -67,2,28.10.2015,,3,162.96, -67,2,29.10.2015,,3,162.96, -67,2,30.10.2015,,3,162.96, -67,2,31.10.2015,,3,162.96, -67,2,01.11.2015,,9,162.96, -67,2,02.11.2015,,0,162.96, -67,2,03.11.2015,,0,162.96, -67,2,04.11.2015,,0,162.96, -67,2,05.11.2015,,0,162.96, -67,2,06.11.2015,,6,162.96, -67,2,07.11.2015,,0,162.96, -67,2,08.11.2015,,3,162.96, -67,2,09.11.2015,,3,162.96, -67,2,10.11.2015,,0,162.96, -67,2,11.11.2015,,6,138.36, -67,2,12.11.2015,,0,138.36, -67,2,13.11.2015,,0,138.36, -67,2,14.11.2015,,16,138.36, -67,2,15.11.2015,,0,138.36, -67,2,16.11.2015,,13,138.36, -67,2,17.11.2015,,0,138.36, -67,2,18.11.2015,,9,138.36, -67,2,19.11.2015,,3,138.36, -67,2,20.11.2015,,0,138.36, -67,2,21.11.2015,,0,138.36, -67,2,22.11.2015,,0,138.36, -67,2,23.11.2015,,6,138.36, -67,2,24.11.2015,,0,138.36, -67,2,25.11.2015,,0,138.36, -67,2,26.11.2015,,0,138.36, -67,2,27.11.2015,,3,138.36, -67,2,28.11.2015,,0,138.36, -67,2,29.11.2015,,0,138.36, -67,2,30.11.2015,,3,138.36, -67,2,01.12.2015,,3,162.96, -67,2,02.12.2015,,3,162.96, -67,2,03.12.2015,,3,138.5, -67,2,04.12.2015,,3,138.5, -67,2,05.12.2015,,3,138.5, -67,2,06.12.2015,,3,138.5, -67,2,07.12.2015,,3,138.5, -67,2,08.12.2015,,3,138.5, -67,2,09.12.2015,,3,138.5, -67,2,10.12.2015,,3,138.5, -67,2,11.12.2015,,3,142.57, -67,2,12.12.2015,,3,142.57, -67,2,13.12.2015,,3,142.57, -67,2,14.12.2015,,3,142.57, -67,2,15.12.2015,,3,142.57, -67,2,16.12.2015,,3,142.57, -67,2,17.12.2015,,3,142.57, -67,2,18.12.2015,,3,142.57, -67,2,19.12.2015,,3,142.57, -67,2,20.12.2015,,3,142.57, -67,2,21.12.2015,,3,142.57, -67,2,22.12.2015,1.0,38,142.57,103.02 -67,2,23.12.2015,1.0,38,142.57,103.02 -67,2,24.12.2015,1.0,38,142.57,103.02 -67,2,25.12.2015,1.0,31,142.57,103.02 -67,2,26.12.2015,1.0,63,142.57,103.02 -67,2,27.12.2015,1.0,41,142.57,103.02 -67,2,28.12.2015,1.0,9,142.57,103.02 -67,2,29.12.2015,,3,138.5, -67,2,30.12.2015,,0,138.5, -67,2,31.12.2015,,0,138.5, -67,2,01.01.2016,,0,138.5, -67,2,02.01.2016,,0,138.5, -67,2,03.01.2016,,0,138.5, -67,2,04.01.2016,,0,138.5, -67,2,05.01.2016,,0,138.5, -67,2,06.01.2016,,0,138.5, -67,2,07.01.2016,,0,138.5, -67,2,08.01.2016,,0,138.5, -67,2,09.01.2016,,0,138.5, -67,2,10.01.2016,,0,138.5, -67,2,11.01.2016,,0,138.5, -67,2,12.01.2016,,0,138.5, -67,2,13.01.2016,,0,138.5, -67,2,14.01.2016,,0,138.5, -67,2,15.01.2016,,0,138.5, -67,2,16.01.2016,,9,142.57, -67,2,17.01.2016,,3,142.57, -67,2,18.01.2016,,0,142.57, -67,2,19.01.2016,1.0,25,142.57,103.02 -67,2,20.01.2016,1.0,31,142.57,103.02 -67,2,21.01.2016,1.0,31,142.57,103.02 -67,2,22.01.2016,1.0,31,142.57,103.02 -67,2,23.01.2016,1.0,3,142.57,103.02 -67,2,24.01.2016,1.0,6,142.57,103.02 -67,2,25.01.2016,1.0,38,142.57,103.02 -67,2,26.01.2016,,3,142.57, -67,2,27.01.2016,,9,142.57, -67,2,28.01.2016,,3,142.57, -67,2,29.01.2016,,6,142.57, -67,2,30.01.2016,,13,142.57, -67,2,31.01.2016,,9,142.57, -67,2,01.02.2016,,0,142.57, -67,2,02.02.2016,,0,142.57, -67,2,03.02.2016,,3,142.57, -67,2,04.02.2016,,3,142.57, -67,2,05.02.2016,,0,142.57, -67,2,06.02.2016,,0,142.57, -67,2,07.02.2016,,13,142.57, -67,2,08.02.2016,,6,142.57, -67,2,09.02.2016,1.0,31,142.57,108.46 -67,2,10.02.2016,1.0,31,138.36,108.46 -67,2,11.02.2016,1.0,31,138.36,108.46 -67,2,12.02.2016,1.0,25,138.36,108.46 -67,2,13.02.2016,1.0,47,138.36,108.46 -67,2,14.02.2016,1.0,22,138.36,108.46 -67,2,15.02.2016,1.0,25,138.36,108.46 -67,2,16.02.2016,,6,138.36, -67,2,17.02.2016,,6,138.36, -67,2,18.02.2016,,6,138.36, -67,2,19.02.2016,,3,138.36, -67,2,20.02.2016,,6,138.36, -67,2,21.02.2016,,13,138.36, -67,2,22.02.2016,,0,138.36, -67,2,23.02.2016,,3,138.36, -67,2,24.02.2016,,0,138.36, -67,2,25.02.2016,,6,138.36, -67,2,26.02.2016,,0,138.36, -67,2,27.02.2016,,3,138.36, -67,2,28.02.2016,,3,138.36, -67,2,29.02.2016,,3,138.36, -67,2,01.03.2016,,3,138.36, -67,2,02.03.2016,,3,138.36, -67,2,03.03.2016,,3,138.36, -67,2,04.03.2016,,0,138.36, -67,2,05.03.2016,,3,138.36, -67,2,06.03.2016,,3,138.36, -67,2,07.03.2016,,0,138.36, -67,2,08.03.2016,,6,138.36, -67,2,09.03.2016,,0,138.36, -67,2,10.03.2016,,3,138.36, -67,2,11.03.2016,,3,138.36, -67,2,12.03.2016,,3,138.5, -67,2,13.03.2016,,3,138.5, -67,2,14.03.2016,,3,138.5, -67,2,15.03.2016,1.0,25,138.5,124.77 -67,2,16.03.2016,1.0,25,138.5,124.77 -67,2,17.03.2016,1.0,25,138.5,124.77 -67,2,18.03.2016,1.0,22,138.5,124.77 -67,2,19.03.2016,1.0,16,138.5,124.77 -67,2,20.03.2016,1.0,22,138.5,124.77 -67,2,21.03.2016,1.0,9,138.5,124.77 -67,2,22.03.2016,,0,138.36, -67,2,23.03.2016,,13,138.36, -67,2,24.03.2016,,3,138.36, -67,2,25.03.2016,,0,138.36, -67,2,26.03.2016,,13,138.36, -67,2,27.03.2016,,0,138.36, -67,2,28.03.2016,,0,138.36, -67,2,29.03.2016,,0,138.36, -67,2,30.03.2016,,0,138.36, -67,2,31.03.2016,,0,138.36, -67,2,01.04.2016,,0,138.36, -67,2,02.04.2016,,0,138.36, -67,2,03.04.2016,,0,138.36, -67,2,04.04.2016,,0,138.36, -67,2,05.04.2016,,0,138.36, -67,2,06.04.2016,,0,138.36, -67,2,07.04.2016,,0,138.36, -67,2,08.04.2016,,3,138.36, -67,2,09.04.2016,,3,138.5, -67,2,10.04.2016,,6,138.5, -67,2,11.04.2016,,3,138.5, -67,2,12.04.2016,,3,138.5, -67,2,13.04.2016,,0,138.5, -67,2,14.04.2016,,0,138.5, -67,2,15.04.2016,,0,138.5, -67,2,16.04.2016,,0,138.5, -67,2,17.04.2016,,0,138.5, -67,2,18.04.2016,,0,138.5, -67,2,19.04.2016,1.0,16,138.5,116.89 -67,2,20.04.2016,1.0,0,138.5,116.89 -67,2,21.04.2016,1.0,0,138.5,116.89 -67,2,22.04.2016,1.0,22,138.5,116.89 -67,2,23.04.2016,1.0,19,138.5,116.89 -67,2,24.04.2016,1.0,25,138.5,116.89 -67,2,25.04.2016,1.0,9,138.5,116.89 -67,2,26.04.2016,1.0,6,138.5,114.17 -67,2,27.04.2016,1.0,6,138.5,114.17 -67,2,28.04.2016,,0,138.5, -67,2,29.04.2016,,0,138.5, -67,2,30.04.2016,,3,138.5, -67,2,01.05.2016,,0,138.5, -67,2,02.05.2016,,0,138.5, -67,2,03.05.2016,1.0,25,138.5,106.01 -67,2,04.05.2016,1.0,3,138.5,106.01 -67,2,05.05.2016,1.0,0,138.5,106.01 -67,2,06.05.2016,1.0,0,138.5,106.01 -67,2,07.05.2016,1.0,0,138.5,106.01 -67,2,08.05.2016,1.0,0,138.5,106.01 -67,2,09.05.2016,1.0,0,138.5,106.01 -67,2,10.05.2016,,0,138.5, -67,2,11.05.2016,,0,138.5, -67,2,12.05.2016,,0,138.5, -67,2,13.05.2016,,0,138.5, -67,2,14.05.2016,,3,138.5, -67,2,15.05.2016,,3,138.5, -67,2,16.05.2016,,6,138.5, -67,2,17.05.2016,,0,138.5, -67,2,18.05.2016,,0,138.5, -67,2,19.05.2016,,3,138.5, -67,2,20.05.2016,,0,138.5, -67,2,21.05.2016,,0,138.5, -67,2,22.05.2016,,0,138.5, -68,2,01.01.2015,,0,135.78, -68,2,02.01.2015,,0,135.78, -68,2,03.01.2015,,0,135.78, -68,2,04.01.2015,,0,135.78, -68,2,05.01.2015,,47,135.78, -68,2,06.01.2015,,3,135.78, -68,2,07.01.2015,,0,135.78, -68,2,08.01.2015,,3,135.78, -68,2,09.01.2015,,9,135.78, -68,2,10.01.2015,,6,135.78, -68,2,11.01.2015,,6,135.78, -68,2,12.01.2015,,3,135.78, -68,2,13.01.2015,,0,135.78, -68,2,14.01.2015,,6,135.78, -68,2,15.01.2015,,3,135.78, -68,2,16.01.2015,,13,135.78, -68,2,17.01.2015,,0,135.78, -68,2,18.01.2015,,9,135.78, -68,2,19.01.2015,,6,135.78, -68,2,20.01.2015,,3,135.78, -68,2,21.01.2015,,6,135.78, -68,2,22.01.2015,,0,135.78, -68,2,23.01.2015,,0,135.78, -68,2,24.01.2015,,0,135.78, -68,2,25.01.2015,,3,135.78, -68,2,26.01.2015,,6,135.78, -68,2,27.01.2015,,3,135.78, -68,2,28.01.2015,,6,135.78, -68,2,29.01.2015,,6,135.78, -68,2,30.01.2015,,3,135.78, -68,2,31.01.2015,,3,135.78, -68,2,01.02.2015,,0,135.78, -68,2,02.02.2015,,0,135.78, -68,2,03.02.2015,,6,135.78, -68,2,04.02.2015,,3,135.78, -68,2,05.02.2015,,0,135.78, -68,2,06.02.2015,,0,135.78, -68,2,07.02.2015,,0,135.78, -68,2,08.02.2015,,0,135.78, -68,2,09.02.2015,,3,135.78, -68,2,10.02.2015,,0,135.78, -68,2,11.02.2015,,0,135.78, -68,2,12.02.2015,,0,135.78, -68,2,13.02.2015,,0,135.78, -68,2,14.02.2015,,0,135.78, -68,2,15.02.2015,,0,135.78, -68,2,16.02.2015,,0,135.78, -68,2,17.02.2015,,0,135.78, -68,2,18.02.2015,,0,135.78, -68,2,19.02.2015,,0,135.78, -68,2,20.02.2015,,3,135.78, -68,2,21.02.2015,,19,135.78, -68,2,22.02.2015,,3,135.78, -68,2,23.02.2015,,0,135.78, -68,2,24.02.2015,,0,135.78, -68,2,25.02.2015,,0,135.78, -68,2,26.02.2015,,0,135.78, -68,2,27.02.2015,,3,135.78, -68,2,28.02.2015,,3,135.78, -68,2,01.03.2015,,3,135.78, -68,2,02.03.2015,,6,135.78, -68,2,03.03.2015,,3,135.78, -68,2,04.03.2015,,3,135.78, -68,2,05.03.2015,,0,135.78, -68,2,06.03.2015,,3,135.78, -68,2,07.03.2015,,6,146.38, -68,2,08.03.2015,,0,146.38, -68,2,09.03.2015,,0,146.38, -68,2,10.03.2015,,0,146.38, -68,2,11.03.2015,,3,146.38, -68,2,12.03.2015,,3,146.38, -68,2,13.03.2015,,3,146.38, -68,2,14.03.2015,,6,146.38, -68,2,15.03.2015,,6,146.38, -68,2,16.03.2015,,0,146.38, -68,2,17.03.2015,,3,146.38, -68,2,18.03.2015,,6,146.38, -68,2,19.03.2015,,0,146.38, -68,2,20.03.2015,,0,146.38, -68,2,21.03.2015,,0,146.38, -68,2,22.03.2015,,3,146.38, -68,2,23.03.2015,,0,146.38, -68,2,24.03.2015,,3,146.38, -68,2,25.03.2015,,0,146.38, -68,2,26.03.2015,,3,146.38, -68,2,27.03.2015,,0,146.38, -68,2,28.03.2015,,0,146.38, -68,2,29.03.2015,,3,146.38, -68,2,30.03.2015,,0,146.38, -68,2,31.03.2015,,0,146.38, -68,2,01.04.2015,,0,146.38, -68,2,02.04.2015,,3,146.38, -68,2,03.04.2015,,3,146.38, -68,2,04.04.2015,,6,146.38, -68,2,05.04.2015,,0,146.38, -68,2,06.04.2015,,3,146.38, -68,2,07.04.2015,,3,146.38, -68,2,08.04.2015,,0,146.38, -68,2,09.04.2015,,0,146.38, -68,2,10.04.2015,,0,146.38, -68,2,11.04.2015,,0,146.38, -68,2,12.04.2015,,0,146.38, -68,2,13.04.2015,,0,146.38, -68,2,14.04.2015,,0,146.38, -68,2,15.04.2015,,0,146.38, -68,2,16.04.2015,,3,146.38, -68,2,17.04.2015,,3,146.38, -68,2,18.04.2015,,22,146.38, -68,2,19.04.2015,,13,146.38, -68,2,20.04.2015,,0,146.38, -68,2,21.04.2015,,0,146.38, -68,2,22.04.2015,,0,146.38, -68,2,23.04.2015,,0,146.38, -68,2,24.04.2015,,0,146.38, -68,2,25.04.2015,,0,146.38, -68,2,26.04.2015,,0,146.38, -68,2,27.04.2015,,0,150.73, -68,2,28.04.2015,,0,150.73, -68,2,29.04.2015,,3,150.73, -68,2,30.04.2015,,0,150.73, -68,2,01.05.2015,,3,150.73, -68,2,02.05.2015,,0,150.73, -68,2,03.05.2015,,0,150.73, -68,2,04.05.2015,,13,150.73, -68,2,05.05.2015,,0,150.73, -68,2,06.05.2015,,3,150.73, -68,2,07.05.2015,,6,150.73, -68,2,08.05.2015,,0,150.73, -68,2,09.05.2015,,0,150.73, -68,2,10.05.2015,,13,150.73, -68,2,11.05.2015,,0,150.73, -68,2,12.05.2015,,0,150.73, -68,2,13.05.2015,,0,150.73, -68,2,14.05.2015,,6,150.73, -68,2,15.05.2015,,3,150.73, -68,2,16.05.2015,,16,150.73, -68,2,17.05.2015,,13,150.73, -68,2,18.05.2015,,0,150.73, -68,2,19.05.2015,,0,150.73, -68,2,20.05.2015,,6,150.73, -68,2,21.05.2015,,0,150.73, -68,2,22.05.2015,,6,150.73, -68,2,23.05.2015,,0,150.73, -68,2,24.05.2015,,9,150.73, -68,2,25.05.2015,,9,150.73, -68,2,26.05.2015,,3,150.73, -68,2,27.05.2015,,6,150.73, -68,2,28.05.2015,,3,150.73, -68,2,29.05.2015,,9,150.73, -68,2,30.05.2015,,6,150.73, -68,2,31.05.2015,,0,150.73, -68,2,01.06.2015,,0,150.73, -68,2,02.06.2015,,0,150.73, -68,2,03.06.2015,,0,150.73, -68,2,04.06.2015,,3,150.73, -68,2,05.06.2015,,0,150.73, -68,2,06.06.2015,,0,150.73, -68,2,07.06.2015,,0,150.73, -68,2,08.06.2015,,3,150.73, -68,2,09.06.2015,,0,150.73, -68,2,10.06.2015,,3,150.73, -68,2,11.06.2015,,6,150.73, -68,2,12.06.2015,,0,150.73, -68,2,13.06.2015,,3,150.73, -68,2,14.06.2015,,0,150.73, -68,2,15.06.2015,,13,155.35, -68,2,16.06.2015,,0,155.35, -68,2,17.06.2015,,0,155.35, -68,2,18.06.2015,,0,155.35, -68,2,19.06.2015,,0,155.35, -68,2,20.06.2015,,0,155.35, -68,2,21.06.2015,,6,155.35, -68,2,22.06.2015,,0,155.35, -68,2,23.06.2015,,0,155.35, -68,2,24.06.2015,,3,155.35, -68,2,25.06.2015,,3,155.35, -68,2,26.06.2015,,3,155.35, -68,2,27.06.2015,,0,155.35, -68,2,28.06.2015,,9,155.35, -68,2,29.06.2015,,0,155.35, -68,2,30.06.2015,,0,155.35, -68,2,01.07.2015,,0,155.35, -68,2,02.07.2015,,0,155.35, -68,2,03.07.2015,,0,155.35, -68,2,04.07.2015,,0,155.35, -68,2,05.07.2015,,3,155.35, -68,2,06.07.2015,,0,155.35, -68,2,07.07.2015,,3,155.35, -68,2,08.07.2015,,0,155.35, -68,2,09.07.2015,,0,155.35, -68,2,10.07.2015,,0,155.35, -68,2,11.07.2015,,0,155.35, -68,2,12.07.2015,,0,155.35, -68,2,13.07.2015,,0,155.35, -68,2,14.07.2015,,3,155.35, -68,2,15.07.2015,,0,155.35, -68,2,16.07.2015,,0,155.35, -68,2,17.07.2015,,6,155.35, -68,2,18.07.2015,,0,155.35, -68,2,19.07.2015,,6,155.35, -68,2,20.07.2015,,0,155.35, -68,2,21.07.2015,,16,155.35, -68,2,22.07.2015,,47,155.35, -68,2,23.07.2015,,57,155.35, -68,2,24.07.2015,,31,155.35, -68,2,25.07.2015,,19,155.35, -68,2,26.07.2015,,0,155.35, -68,2,27.07.2015,,0,155.35, -68,2,28.07.2015,,0,155.35, -68,2,29.07.2015,,0,155.35, -68,2,30.07.2015,,0,155.35, -68,2,31.07.2015,,0,155.35, -68,2,01.08.2015,,0,155.35, -68,2,02.08.2015,,0,155.35, -68,2,03.08.2015,,0,155.35, -68,2,04.08.2015,,0,155.35, -68,2,05.08.2015,,0,155.35, -68,2,06.08.2015,,0,155.35, -68,2,07.08.2015,,0,155.35, -68,2,08.08.2015,,0,155.35, -68,2,09.08.2015,,0,155.35, -68,2,10.08.2015,,0,155.35, -68,2,11.08.2015,,0,155.35, -68,2,12.08.2015,,0,128.17, -68,2,13.08.2015,,0,128.17, -68,2,14.08.2015,,0,128.17, -68,2,15.08.2015,,0,128.17, -68,2,16.08.2015,,0,128.17, -68,2,17.08.2015,,145,128.17, -68,2,18.08.2015,1.0,308,128.17,100.3 -68,2,19.08.2015,1.0,101,128.17,100.3 -68,2,20.08.2015,1.0,364,128.17,100.3 -68,2,21.08.2015,1.0,239,128.17,100.3 -68,2,22.08.2015,1.0,201,128.17,100.3 -68,2,23.08.2015,1.0,170,128.17,100.3 -68,2,24.08.2015,1.0,145,128.17,100.3 -68,2,25.08.2015,,0,128.17, -68,2,26.08.2015,,6,128.17, -68,2,27.08.2015,,38,128.17, -68,2,28.08.2015,,38,128.17, -68,2,29.08.2015,,79,128.17, -68,2,30.08.2015,,88,128.17, -68,2,31.08.2015,,44,128.17, -68,2,01.09.2015,,31,128.17, -68,2,02.09.2015,,35,128.17, -68,2,03.09.2015,,35,128.17, -68,2,04.09.2015,,35,128.17, -68,2,05.09.2015,,35,128.17, -68,2,06.09.2015,,35,128.17, -68,2,07.09.2015,,22,128.17, -68,2,08.09.2015,,22,128.17, -68,2,09.09.2015,,22,128.17, -68,2,10.09.2015,,19,128.17, -68,2,11.09.2015,,19,128.17, -68,2,12.09.2015,,0,128.17, -68,2,13.09.2015,,0,128.17, -68,2,14.09.2015,,0,128.17, -68,2,15.09.2015,,0,128.17, -68,2,16.09.2015,,0,128.17, -68,2,17.09.2015,,0,128.17, -68,2,18.09.2015,,0,128.17, -68,2,19.09.2015,,0,128.17, -68,2,20.09.2015,,0,128.17, -68,2,21.09.2015,,0,128.17, -68,2,22.09.2015,,0,128.17, -68,2,23.09.2015,,6,128.17, -68,2,24.09.2015,,3,128.17, -68,2,25.09.2015,,0,128.17, -68,2,26.09.2015,,9,128.17, -68,2,27.09.2015,,47,128.17, -68,2,28.09.2015,,63,128.17, -68,2,29.09.2015,,16,128.17, -68,2,30.09.2015,,25,128.17, -68,2,01.10.2015,,0,162.96, -68,2,02.10.2015,,0,162.96, -68,2,03.10.2015,,0,162.96, -68,2,04.10.2015,,0,162.96, -68,2,05.10.2015,,13,162.96, -68,2,06.10.2015,1.0,135,143.39,122.05 -68,2,07.10.2015,1.0,160,143.39,122.05 -68,2,08.10.2015,1.0,135,143.39,122.05 -68,2,09.10.2015,1.0,119,143.39,122.05 -68,2,10.10.2015,1.0,97,143.39,122.05 -68,2,11.10.2015,1.0,19,143.39,122.05 -68,2,12.10.2015,1.0,119,143.39,122.05 -68,2,13.10.2015,1.0,97,143.93,122.05 -68,2,14.10.2015,1.0,91,143.93,122.05 -68,2,15.10.2015,1.0,66,143.93,122.05 -68,2,16.10.2015,1.0,47,143.93,122.05 -68,2,17.10.2015,1.0,47,162.96,122.05 -68,2,18.10.2015,1.0,47,162.96,122.05 -68,2,19.10.2015,,0,162.96, -68,2,20.10.2015,,0,162.96, -68,2,21.10.2015,,0,162.96, -68,2,22.10.2015,,6,162.96, -68,2,23.10.2015,,9,162.96, -68,2,24.10.2015,,6,162.96, -68,2,25.10.2015,,6,162.96, -68,2,26.10.2015,,3,162.96, -68,2,27.10.2015,,0,162.96, -68,2,28.10.2015,,0,162.96, -68,2,29.10.2015,,0,162.96, -68,2,30.10.2015,,0,162.96, -68,2,31.10.2015,,0,162.96, -68,2,01.11.2015,,0,162.96, -68,2,02.11.2015,,0,162.96, -68,2,03.11.2015,,0,162.96, -68,2,04.11.2015,,3,162.96, -68,2,05.11.2015,,6,162.96, -68,2,06.11.2015,,16,162.96, -68,2,07.11.2015,,13,162.96, -68,2,08.11.2015,,0,162.96, -68,2,09.11.2015,,0,162.96, -68,2,10.11.2015,,0,162.96, -68,2,11.11.2015,,9,138.36, -68,2,12.11.2015,,3,138.36, -68,2,13.11.2015,,22,138.36, -68,2,14.11.2015,,3,138.36, -68,2,15.11.2015,,0,138.36, -68,2,16.11.2015,,0,138.36, -68,2,17.11.2015,,0,138.36, -68,2,18.11.2015,,0,138.36, -68,2,19.11.2015,,0,138.36, -68,2,20.11.2015,,0,138.36, -68,2,21.11.2015,,0,138.36, -68,2,22.11.2015,,0,138.36, -68,2,23.11.2015,,0,138.36, -68,2,24.11.2015,,0,138.36, -68,2,25.11.2015,,0,138.36, -68,2,26.11.2015,,0,138.36, -68,2,27.11.2015,,0,138.36, -68,2,28.11.2015,,0,138.36, -68,2,29.11.2015,,0,138.36, -68,2,30.11.2015,,0,138.36, -68,2,01.12.2015,,0,162.96, -68,2,02.12.2015,,3,162.96, -68,2,03.12.2015,,3,138.5, -68,2,04.12.2015,,19,138.5, -68,2,05.12.2015,,3,138.5, -68,2,06.12.2015,,6,138.5, -68,2,07.12.2015,,9,138.5, -68,2,08.12.2015,,3,138.5, -68,2,09.12.2015,,0,138.5, -68,2,10.12.2015,,13,138.5, -68,2,11.12.2015,,9,142.57, -68,2,12.12.2015,,16,142.57, -68,2,13.12.2015,,19,142.57, -68,2,14.12.2015,,6,142.57, -68,2,15.12.2015,,0,142.57, -68,2,16.12.2015,,13,142.57, -68,2,17.12.2015,,6,142.57, -68,2,18.12.2015,,9,142.57, -68,2,19.12.2015,,28,142.57, -68,2,20.12.2015,,0,142.57, -68,2,21.12.2015,,3,142.57, -68,2,22.12.2015,1.0,248,142.57,103.02 -68,2,23.12.2015,1.0,289,142.57,103.02 -68,2,24.12.2015,1.0,229,142.57,103.02 -68,2,25.12.2015,1.0,289,142.57,103.02 -68,2,26.12.2015,1.0,195,142.57,103.02 -68,2,27.12.2015,1.0,311,142.57,103.02 -68,2,28.12.2015,1.0,201,142.57,103.02 -68,2,29.12.2015,,13,138.5, -68,2,30.12.2015,,19,138.5, -68,2,31.12.2015,,3,138.5, -68,2,01.01.2016,,6,138.5, -68,2,02.01.2016,,3,138.5, -68,2,03.01.2016,,28,138.5, -68,2,04.01.2016,,13,138.5, -68,2,05.01.2016,,28,138.5, -68,2,06.01.2016,,3,138.5, -68,2,07.01.2016,,6,138.5, -68,2,08.01.2016,,41,138.5, -68,2,09.01.2016,,9,138.5, -68,2,10.01.2016,,94,138.5, -68,2,11.01.2016,,28,138.5, -68,2,12.01.2016,,38,138.5, -68,2,13.01.2016,,28,138.5, -68,2,14.01.2016,,9,138.5, -68,2,15.01.2016,,19,138.5, -68,2,16.01.2016,,16,142.57, -68,2,17.01.2016,,16,142.57, -68,2,18.01.2016,,22,142.57, -68,2,19.01.2016,1.0,298,142.57,103.02 -68,2,20.01.2016,1.0,270,142.57,103.02 -68,2,21.01.2016,1.0,289,142.57,103.02 -68,2,22.01.2016,1.0,273,142.57,103.02 -68,2,23.01.2016,1.0,254,142.57,103.02 -68,2,24.01.2016,1.0,305,142.57,103.02 -68,2,25.01.2016,1.0,320,142.57,103.02 -68,2,26.01.2016,,6,142.57, -68,2,27.01.2016,,19,142.57, -68,2,28.01.2016,,3,142.57, -68,2,29.01.2016,,28,142.57, -68,2,30.01.2016,,44,142.57, -68,2,31.01.2016,,19,142.57, -68,2,01.02.2016,,19,142.57, -68,2,02.02.2016,,0,142.57, -68,2,03.02.2016,,44,142.57, -68,2,04.02.2016,,31,142.57, -68,2,05.02.2016,,28,142.57, -68,2,06.02.2016,,19,142.57, -68,2,07.02.2016,,25,142.57, -68,2,08.02.2016,,47,142.57, -68,2,09.02.2016,1.0,349,142.57,108.46 -68,2,10.02.2016,1.0,358,138.36,108.46 -68,2,11.02.2016,1.0,311,138.36,108.46 -68,2,12.02.2016,1.0,386,138.36,108.46 -68,2,13.02.2016,1.0,289,138.36,108.46 -68,2,14.02.2016,1.0,220,138.36,108.46 -68,2,15.02.2016,1.0,283,138.36,108.46 -68,2,16.02.2016,,22,138.36, -68,2,17.02.2016,,19,138.36, -68,2,18.02.2016,,19,138.36, -68,2,19.02.2016,,3,138.36, -68,2,20.02.2016,,13,138.36, -68,2,21.02.2016,,13,138.36, -68,2,22.02.2016,,25,138.36, -68,2,23.02.2016,,6,138.36, -68,2,24.02.2016,,3,138.36, -68,2,25.02.2016,,3,138.36, -68,2,26.02.2016,,6,138.36, -68,2,27.02.2016,,0,138.36, -68,2,28.02.2016,,0,138.36, -68,2,29.02.2016,,0,138.36, -68,2,01.03.2016,,0,138.36, -68,2,02.03.2016,,0,138.36, -68,2,03.03.2016,,0,138.36, -68,2,04.03.2016,,0,138.36, -68,2,05.03.2016,,0,138.36, -68,2,06.03.2016,,0,138.36, -68,2,07.03.2016,,0,138.36, -68,2,08.03.2016,,0,138.36, -68,2,09.03.2016,,0,138.36, -68,2,10.03.2016,,0,138.36, -68,2,11.03.2016,,0,138.36, -68,2,12.03.2016,,0,138.5, -68,2,13.03.2016,,0,138.5, -68,2,14.03.2016,,13,138.5, -68,2,15.03.2016,1.0,126,138.5,124.77 -68,2,16.03.2016,1.0,88,138.5,124.77 -68,2,17.03.2016,1.0,85,138.5,124.77 -68,2,18.03.2016,1.0,97,138.5,124.77 -68,2,19.03.2016,1.0,101,138.5,124.77 -68,2,20.03.2016,1.0,132,138.5,124.77 -68,2,21.03.2016,1.0,185,138.5,124.77 -68,2,22.03.2016,,3,138.36, -68,2,23.03.2016,,0,138.36, -68,2,24.03.2016,,0,138.36, -68,2,25.03.2016,,0,138.36, -68,2,26.03.2016,,0,138.36, -68,2,27.03.2016,,0,138.36, -68,2,28.03.2016,,0,138.36, -68,2,29.03.2016,,0,138.36, -68,2,30.03.2016,,0,138.36, -68,2,31.03.2016,,0,138.36, -68,2,01.04.2016,,0,138.36, -68,2,02.04.2016,,0,138.36, -68,2,03.04.2016,,0,138.36, -68,2,04.04.2016,,0,138.36, -68,2,05.04.2016,,0,138.36, -68,2,06.04.2016,,0,138.36, -68,2,07.04.2016,,0,138.36, -68,2,08.04.2016,,16,138.36, -68,2,09.04.2016,,9,138.5, -68,2,10.04.2016,,0,138.5, -68,2,11.04.2016,,3,138.5, -68,2,12.04.2016,,0,138.5, -68,2,13.04.2016,,6,138.5, -68,2,14.04.2016,,3,138.5, -68,2,15.04.2016,,3,138.5, -68,2,16.04.2016,,0,138.5, -68,2,17.04.2016,,6,138.5, -68,2,18.04.2016,,6,138.5, -68,2,19.04.2016,1.0,79,138.5,116.89 -68,2,20.04.2016,1.0,179,138.5,116.89 -68,2,21.04.2016,1.0,132,138.5,116.89 -68,2,22.04.2016,1.0,148,138.5,116.89 -68,2,23.04.2016,1.0,119,138.5,116.89 -68,2,24.04.2016,1.0,129,138.5,116.89 -68,2,25.04.2016,1.0,113,138.5,116.89 -68,2,26.04.2016,1.0,75,138.5,114.17 -68,2,27.04.2016,1.0,41,138.5,114.17 -68,2,28.04.2016,,13,138.5, -68,2,29.04.2016,,3,138.5, -68,2,30.04.2016,,13,138.5, -68,2,01.05.2016,,3,138.5, -68,2,02.05.2016,,0,138.5, -68,2,03.05.2016,1.0,185,138.5,106.01 -68,2,04.05.2016,1.0,327,138.5,106.01 -68,2,05.05.2016,1.0,355,138.5,106.01 -68,2,06.05.2016,1.0,217,138.5,106.01 -68,2,07.05.2016,1.0,314,138.5,106.01 -68,2,08.05.2016,1.0,270,138.5,106.01 -68,2,09.05.2016,1.0,31,138.5,106.01 -68,2,10.05.2016,,0,138.5, -68,2,11.05.2016,,0,138.5, -68,2,12.05.2016,,0,138.5, -68,2,13.05.2016,,0,138.5, -68,2,14.05.2016,,0,138.5, -68,2,15.05.2016,,0,138.5, -68,2,16.05.2016,,0,138.5, -68,2,17.05.2016,,0,138.5, -68,2,18.05.2016,,0,138.5, -68,2,19.05.2016,,3,138.5, -68,2,20.05.2016,,3,138.5, -68,2,21.05.2016,,3,138.5, -68,2,22.05.2016,,3,138.5, -69,2,01.01.2015,,0,135.78, -69,2,02.01.2015,,3,135.78, -69,2,03.01.2015,,0,135.78, -69,2,04.01.2015,,0,135.78, -69,2,05.01.2015,,0,135.78, -69,2,06.01.2015,,9,135.78, -69,2,07.01.2015,,13,135.78, -69,2,08.01.2015,,0,135.78, -69,2,09.01.2015,,6,135.78, -69,2,10.01.2015,,6,135.78, -69,2,11.01.2015,,6,135.78, -69,2,12.01.2015,,0,135.78, -69,2,13.01.2015,,0,135.78, -69,2,14.01.2015,,0,135.78, -69,2,15.01.2015,,6,135.78, -69,2,16.01.2015,,3,135.78, -69,2,17.01.2015,,0,135.78, -69,2,18.01.2015,,0,135.78, -69,2,19.01.2015,,0,135.78, -69,2,20.01.2015,,0,135.78, -69,2,21.01.2015,,0,135.78, -69,2,22.01.2015,,0,135.78, -69,2,23.01.2015,,0,135.78, -69,2,24.01.2015,,0,135.78, -69,2,25.01.2015,,0,135.78, -69,2,26.01.2015,,0,135.78, -69,2,27.01.2015,,0,135.78, -69,2,28.01.2015,,0,135.78, -69,2,29.01.2015,,0,135.78, -69,2,30.01.2015,,0,135.78, -69,2,31.01.2015,,0,135.78, -69,2,01.02.2015,,3,135.78, -69,2,02.02.2015,,9,135.78, -69,2,03.02.2015,,0,135.78, -69,2,04.02.2015,,3,135.78, -69,2,05.02.2015,,0,135.78, -69,2,06.02.2015,,3,135.78, -69,2,07.02.2015,,3,135.78, -69,2,08.02.2015,,3,135.78, -69,2,09.02.2015,,0,135.78, -69,2,10.02.2015,,6,135.78, -69,2,11.02.2015,,0,135.78, -69,2,12.02.2015,,0,135.78, -69,2,13.02.2015,,0,135.78, -69,2,14.02.2015,,0,135.78, -69,2,15.02.2015,,3,135.78, -69,2,16.02.2015,,3,135.78, -69,2,17.02.2015,,6,135.78, -69,2,18.02.2015,,0,135.78, -69,2,19.02.2015,,6,135.78, -69,2,20.02.2015,,3,135.78, -69,2,21.02.2015,,9,135.78, -69,2,22.02.2015,,3,135.78, -69,2,23.02.2015,,9,135.78, -69,2,24.02.2015,,0,135.78, -69,2,25.02.2015,,3,135.78, -69,2,26.02.2015,,0,135.78, -69,2,27.02.2015,,0,135.78, -69,2,28.02.2015,,0,135.78, -69,2,01.03.2015,,3,135.78, -69,2,02.03.2015,,9,135.78, -69,2,03.03.2015,,0,135.78, -69,2,04.03.2015,,0,135.78, -69,2,05.03.2015,,0,135.78, -69,2,06.03.2015,,0,135.78, -69,2,07.03.2015,,3,135.78, -69,2,08.03.2015,,0,146.38, -69,2,09.03.2015,,0,146.38, -69,2,10.03.2015,,3,146.38, -69,2,11.03.2015,,0,146.38, -69,2,12.03.2015,,0,146.38, -69,2,13.03.2015,,3,146.38, -69,2,14.03.2015,,0,146.38, -69,2,15.03.2015,,13,146.38, -69,2,16.03.2015,,0,146.38, -69,2,17.03.2015,,0,146.38, -69,2,18.03.2015,,0,146.38, -69,2,19.03.2015,,3,146.38, -69,2,20.03.2015,,0,146.38, -69,2,21.03.2015,,0,146.38, -69,2,22.03.2015,,3,146.38, -69,2,23.03.2015,,3,146.38, -69,2,24.03.2015,,0,146.38, -69,2,25.03.2015,,0,146.38, -69,2,26.03.2015,,0,146.38, -69,2,27.03.2015,,0,146.38, -69,2,28.03.2015,,0,146.38, -69,2,29.03.2015,,19,146.38, -69,2,30.03.2015,,6,146.38, -69,2,31.03.2015,,0,146.38, -69,2,01.04.2015,,0,146.38, -69,2,02.04.2015,,6,146.38, -69,2,03.04.2015,,9,146.38, -69,2,04.04.2015,,0,146.38, -69,2,05.04.2015,,0,146.38, -69,2,06.04.2015,,0,146.38, -69,2,07.04.2015,,3,146.38, -69,2,08.04.2015,,0,146.38, -69,2,09.04.2015,,3,146.38, -69,2,10.04.2015,,0,146.38, -69,2,11.04.2015,,6,146.38, -69,2,12.04.2015,,6,146.38, -69,2,13.04.2015,,0,146.38, -69,2,14.04.2015,,0,146.38, -69,2,15.04.2015,,0,146.38, -69,2,16.04.2015,,0,146.38, -69,2,17.04.2015,,0,146.38, -69,2,18.04.2015,,0,146.38, -69,2,19.04.2015,,0,146.38, -69,2,20.04.2015,,0,146.38, -69,2,21.04.2015,,0,146.38, -69,2,22.04.2015,,0,146.38, -69,2,23.04.2015,,0,146.38, -69,2,24.04.2015,,0,146.38, -69,2,25.04.2015,,0,146.38, -69,2,26.04.2015,,0,146.38, -69,2,27.04.2015,,0,150.73, -69,2,28.04.2015,,0,150.73, -69,2,29.04.2015,,0,150.73, -69,2,30.04.2015,,0,150.73, -69,2,01.05.2015,,0,150.73, -69,2,02.05.2015,,3,150.73, -69,2,03.05.2015,,0,150.73, -69,2,04.05.2015,,6,150.73, -69,2,05.05.2015,,0,150.73, -69,2,06.05.2015,,0,150.73, -69,2,07.05.2015,,0,150.73, -69,2,08.05.2015,,0,150.73, -69,2,09.05.2015,,0,150.73, -69,2,10.05.2015,,0,150.73, -69,2,11.05.2015,,3,150.73, -69,2,12.05.2015,,0,150.73, -69,2,13.05.2015,,0,150.73, -69,2,14.05.2015,,0,150.73, -69,2,15.05.2015,,0,150.73, -69,2,16.05.2015,,0,150.73, -69,2,17.05.2015,,0,150.73, -69,2,18.05.2015,,0,150.73, -69,2,19.05.2015,,0,150.73, -69,2,20.05.2015,,0,150.73, -69,2,21.05.2015,,0,150.73, -69,2,22.05.2015,,0,150.73, -69,2,23.05.2015,,0,150.73, -69,2,24.05.2015,,0,150.73, -69,2,25.05.2015,,0,150.73, -69,2,26.05.2015,,0,150.73, -69,2,27.05.2015,,0,150.73, -69,2,28.05.2015,,0,150.73, -69,2,29.05.2015,,0,150.73, -69,2,30.05.2015,,0,150.73, -69,2,31.05.2015,,0,150.73, -69,2,01.06.2015,,0,150.73, -69,2,02.06.2015,,0,150.73, -69,2,03.06.2015,,0,150.73, -69,2,04.06.2015,,0,150.73, -69,2,05.06.2015,,6,150.73, -69,2,06.06.2015,,0,150.73, -69,2,07.06.2015,,0,150.73, -69,2,08.06.2015,,0,150.73, -69,2,09.06.2015,,0,150.73, -69,2,10.06.2015,,0,150.73, -69,2,11.06.2015,,3,150.73, -69,2,12.06.2015,,0,150.73, -69,2,13.06.2015,,0,150.73, -69,2,14.06.2015,,0,150.73, -69,2,15.06.2015,,0,150.73, -69,2,16.06.2015,,0,150.73, -69,2,17.06.2015,,0,150.73, -69,2,18.06.2015,,0,150.73, -69,2,19.06.2015,,0,150.73, -69,2,20.06.2015,,0,150.73, -69,2,21.06.2015,,6,150.73, -69,2,22.06.2015,,0,150.73, -69,2,23.06.2015,,3,150.73, -69,2,24.06.2015,,0,150.73, -69,2,25.06.2015,,0,150.73, -69,2,26.06.2015,,0,150.73, -69,2,27.06.2015,,0,150.73, -69,2,28.06.2015,,0,150.73, -69,2,29.06.2015,,0,150.73, -69,2,30.06.2015,,0,150.73, -69,2,01.07.2015,,0,150.73, -69,2,02.07.2015,,0,150.73, -69,2,03.07.2015,,0,150.73, -69,2,04.07.2015,,0,150.73, -69,2,05.07.2015,,9,150.73, -69,2,06.07.2015,,3,150.73, -69,2,07.07.2015,,3,150.73, -69,2,08.07.2015,,0,150.73, -69,2,09.07.2015,,0,150.73, -69,2,10.07.2015,,0,150.73, -69,2,11.07.2015,,6,150.73, -69,2,12.07.2015,,6,150.73, -69,2,13.07.2015,,0,150.73, -69,2,14.07.2015,,0,150.73, -69,2,15.07.2015,,0,150.73, -69,2,16.07.2015,,0,150.73, -69,2,17.07.2015,,0,150.73, -69,2,18.07.2015,,0,150.73, -69,2,19.07.2015,,0,150.73, -69,2,20.07.2015,,0,150.73, -69,2,21.07.2015,,0,150.73, -69,2,22.07.2015,,0,150.73, -69,2,23.07.2015,,0,150.73, -69,2,24.07.2015,,16,150.73, -69,2,25.07.2015,,9,150.73, -69,2,26.07.2015,,3,150.73, -69,2,27.07.2015,,0,150.73, -69,2,28.07.2015,,6,150.73, -69,2,29.07.2015,,19,150.73, -69,2,30.07.2015,,13,150.73, -69,2,31.07.2015,,13,150.73, -69,2,01.08.2015,,0,150.73, -69,2,02.08.2015,,0,150.73, -69,2,03.08.2015,,0,150.73, -69,2,04.08.2015,,13,150.73, -69,2,05.08.2015,,13,150.73, -69,2,06.08.2015,,0,150.73, -69,2,07.08.2015,,0,150.73, -69,2,08.08.2015,,0,150.73, -69,2,09.08.2015,,0,150.73, -69,2,10.08.2015,,3,150.73, -69,2,11.08.2015,,0,150.73, -69,2,12.08.2015,,3,128.17, -69,2,13.08.2015,,0,128.17, -69,2,14.08.2015,,6,128.17, -69,2,15.08.2015,,3,128.17, -69,2,16.08.2015,,6,128.17, -69,2,17.08.2015,,38,128.17, -69,2,18.08.2015,1.0,192,128.17,100.3 -69,2,19.08.2015,1.0,116,128.17,100.3 -69,2,20.08.2015,1.0,129,128.17,100.3 -69,2,21.08.2015,1.0,107,128.17,100.3 -69,2,22.08.2015,1.0,110,128.17,100.3 -69,2,23.08.2015,1.0,110,128.17,100.3 -69,2,24.08.2015,1.0,94,128.17,100.3 -69,2,25.08.2015,,16,128.17, -69,2,26.08.2015,,3,128.17, -69,2,27.08.2015,,19,128.17, -69,2,28.08.2015,,6,128.17, -69,2,29.08.2015,,6,128.17, -69,2,30.08.2015,,6,128.17, -69,2,31.08.2015,,6,128.17, -69,2,01.09.2015,,3,128.17, -69,2,02.09.2015,,3,128.17, -69,2,03.09.2015,,0,128.17, -69,2,04.09.2015,,13,128.17, -69,2,05.09.2015,,9,128.17, -69,2,06.09.2015,,9,128.17, -69,2,07.09.2015,,9,128.17, -69,2,08.09.2015,,16,128.17, -69,2,09.09.2015,,3,128.17, -69,2,10.09.2015,,0,128.17, -69,2,11.09.2015,,6,128.17, -69,2,12.09.2015,,13,128.17, -69,2,13.09.2015,,9,128.17, -69,2,14.09.2015,,9,128.17, -69,2,15.09.2015,,0,128.17, -69,2,16.09.2015,,19,128.17, -69,2,17.09.2015,,6,128.17, -69,2,18.09.2015,,6,128.17, -69,2,19.09.2015,,9,128.17, -69,2,20.09.2015,,28,128.17, -69,2,21.09.2015,,9,128.17, -69,2,22.09.2015,,9,128.17, -69,2,23.09.2015,,0,128.17, -69,2,24.09.2015,,3,128.17, -69,2,25.09.2015,,13,128.17, -69,2,26.09.2015,,6,128.17, -69,2,27.09.2015,,6,128.17, -69,2,28.09.2015,,9,128.17, -69,2,29.09.2015,,9,128.17, -69,2,30.09.2015,,9,128.17, -69,2,01.10.2015,,9,162.96, -69,2,02.10.2015,,9,162.96, -69,2,03.10.2015,,9,162.96, -69,2,04.10.2015,,6,162.96, -69,2,05.10.2015,,3,162.96, -69,2,06.10.2015,1.0,132,143.39,122.05 -69,2,07.10.2015,1.0,85,143.39,122.05 -69,2,08.10.2015,1.0,157,143.39,122.05 -69,2,09.10.2015,1.0,79,143.39,122.05 -69,2,10.10.2015,1.0,97,143.39,122.05 -69,2,11.10.2015,1.0,107,143.39,122.05 -69,2,12.10.2015,1.0,107,143.39,122.05 -69,2,13.10.2015,1.0,47,143.93,122.05 -69,2,14.10.2015,1.0,97,143.93,122.05 -69,2,15.10.2015,1.0,97,143.93,122.05 -69,2,16.10.2015,1.0,9,143.93,122.05 -69,2,17.10.2015,1.0,85,162.96,122.05 -69,2,18.10.2015,1.0,79,162.96,122.05 -69,2,19.10.2015,,3,162.96, -69,2,20.10.2015,,9,162.96, -69,2,21.10.2015,,9,162.96, -69,2,22.10.2015,,3,162.96, -69,2,23.10.2015,,9,162.96, -69,2,24.10.2015,,9,162.96, -69,2,25.10.2015,,3,162.96, -69,2,26.10.2015,,6,162.96, -69,2,27.10.2015,,6,162.96, -69,2,28.10.2015,,6,162.96, -69,2,29.10.2015,,6,162.96, -69,2,30.10.2015,,6,162.96, -69,2,31.10.2015,,6,162.96, -69,2,01.11.2015,,6,162.96, -69,2,02.11.2015,,6,162.96, -69,2,03.11.2015,,6,162.96, -69,2,04.11.2015,,6,162.96, -69,2,05.11.2015,,6,162.96, -69,2,06.11.2015,,3,162.96, -69,2,07.11.2015,,6,162.96, -69,2,08.11.2015,,13,162.96, -69,2,09.11.2015,,9,162.96, -69,2,10.11.2015,,3,162.96, -69,2,11.11.2015,,6,138.36, -69,2,12.11.2015,,6,138.36, -69,2,13.11.2015,,3,138.36, -69,2,14.11.2015,,6,138.36, -69,2,15.11.2015,,16,138.36, -69,2,16.11.2015,,9,138.36, -69,2,17.11.2015,,3,138.36, -69,2,18.11.2015,,6,138.36, -69,2,19.11.2015,,3,138.36, -69,2,20.11.2015,,6,138.36, -69,2,21.11.2015,,6,138.36, -69,2,22.11.2015,,6,138.36, -69,2,23.11.2015,,6,138.36, -69,2,24.11.2015,,0,138.36, -69,2,25.11.2015,,3,138.36, -69,2,26.11.2015,,0,138.36, -69,2,27.11.2015,,0,138.36, -69,2,28.11.2015,,0,138.36, -69,2,29.11.2015,,0,138.36, -69,2,30.11.2015,,3,138.36, -69,2,01.12.2015,,0,162.96, -69,2,02.12.2015,,3,162.96, -69,2,03.12.2015,,3,138.5, -69,2,04.12.2015,,3,138.5, -69,2,05.12.2015,,3,138.5, -69,2,06.12.2015,,3,138.5, -69,2,07.12.2015,,0,138.5, -69,2,08.12.2015,,3,138.5, -69,2,09.12.2015,,3,138.5, -69,2,10.12.2015,,6,138.5, -69,2,11.12.2015,,3,142.57, -69,2,12.12.2015,,3,142.57, -69,2,13.12.2015,,16,142.57, -69,2,14.12.2015,,3,142.57, -69,2,15.12.2015,,3,142.57, -69,2,16.12.2015,,3,142.57, -69,2,17.12.2015,,6,142.57, -69,2,18.12.2015,,9,142.57, -69,2,19.12.2015,,13,142.57, -69,2,20.12.2015,,9,142.57, -69,2,21.12.2015,,6,142.57, -69,2,22.12.2015,1.0,210,142.57,103.02 -69,2,23.12.2015,1.0,145,142.57,103.02 -69,2,24.12.2015,1.0,163,142.57,103.02 -69,2,25.12.2015,1.0,188,142.57,103.02 -69,2,26.12.2015,1.0,173,142.57,103.02 -69,2,27.12.2015,1.0,129,142.57,103.02 -69,2,28.12.2015,1.0,22,142.57,103.02 -69,2,29.12.2015,,6,138.5, -69,2,30.12.2015,,6,138.5, -69,2,31.12.2015,,6,138.5, -69,2,01.01.2016,,6,138.5, -69,2,02.01.2016,,9,138.5, -69,2,03.01.2016,,16,138.5, -69,2,04.01.2016,,9,138.5, -69,2,05.01.2016,,9,138.5, -69,2,06.01.2016,,13,138.5, -69,2,07.01.2016,,19,138.5, -69,2,08.01.2016,,9,138.5, -69,2,09.01.2016,,9,138.5, -69,2,10.01.2016,,9,138.5, -69,2,11.01.2016,,16,138.5, -69,2,12.01.2016,,22,138.5, -69,2,13.01.2016,,9,138.5, -69,2,14.01.2016,,6,138.5, -69,2,15.01.2016,,3,138.5, -69,2,16.01.2016,,3,142.57, -69,2,17.01.2016,,16,142.57, -69,2,18.01.2016,,16,142.57, -69,2,19.01.2016,1.0,368,142.57,103.02 -69,2,20.01.2016,1.0,204,142.57,103.02 -69,2,21.01.2016,1.0,232,142.57,103.02 -69,2,22.01.2016,1.0,295,142.57,103.02 -69,2,23.01.2016,1.0,157,142.57,103.02 -69,2,24.01.2016,1.0,85,142.57,103.02 -69,2,25.01.2016,1.0,141,142.57,103.02 -69,2,26.01.2016,,9,142.57, -69,2,27.01.2016,,9,142.57, -69,2,28.01.2016,,6,142.57, -69,2,29.01.2016,,6,142.57, -69,2,30.01.2016,,13,142.57, -69,2,31.01.2016,,13,142.57, -69,2,01.02.2016,,19,142.57, -69,2,02.02.2016,,13,142.57, -69,2,03.02.2016,,19,142.57, -69,2,04.02.2016,,13,142.57, -69,2,05.02.2016,,9,142.57, -69,2,06.02.2016,,6,142.57, -69,2,07.02.2016,,9,142.57, -69,2,08.02.2016,,22,142.57, -69,2,09.02.2016,1.0,254,142.57,108.46 -69,2,10.02.2016,1.0,151,138.36,108.46 -69,2,11.02.2016,1.0,217,138.36,108.46 -69,2,12.02.2016,1.0,292,138.36,108.46 -69,2,13.02.2016,1.0,25,138.36,108.46 -69,2,14.02.2016,1.0,31,138.36,108.46 -69,2,15.02.2016,1.0,176,138.36,108.46 -69,2,16.02.2016,,13,138.36, -69,2,17.02.2016,,13,138.36, -69,2,18.02.2016,,13,138.36, -69,2,19.02.2016,,3,138.36, -69,2,20.02.2016,,13,138.36, -69,2,21.02.2016,,3,138.36, -69,2,22.02.2016,,3,138.36, -69,2,23.02.2016,,9,138.36, -69,2,24.02.2016,,6,138.36, -69,2,25.02.2016,,0,138.36, -69,2,26.02.2016,,0,138.36, -69,2,27.02.2016,,13,138.36, -69,2,28.02.2016,,9,138.36, -69,2,29.02.2016,,0,138.36, -69,2,01.03.2016,,0,138.36, -69,2,02.03.2016,,0,138.36, -69,2,03.03.2016,,0,138.36, -69,2,04.03.2016,,6,138.36, -69,2,05.03.2016,,0,138.36, -69,2,06.03.2016,,3,138.36, -69,2,07.03.2016,,0,138.36, -69,2,08.03.2016,,3,138.36, -69,2,09.03.2016,,3,138.36, -69,2,10.03.2016,,3,138.36, -69,2,11.03.2016,,3,138.36, -69,2,12.03.2016,,0,138.5, -69,2,13.03.2016,,16,138.5, -69,2,14.03.2016,,0,138.5, -69,2,15.03.2016,1.0,88,138.5,124.77 -69,2,16.03.2016,1.0,66,138.5,124.77 -69,2,17.03.2016,1.0,91,138.5,124.77 -69,2,18.03.2016,1.0,119,138.5,124.77 -69,2,19.03.2016,1.0,88,138.5,124.77 -69,2,20.03.2016,1.0,116,138.5,124.77 -69,2,21.03.2016,1.0,57,138.5,124.77 -69,2,22.03.2016,,6,138.36, -69,2,23.03.2016,,9,138.36, -69,2,24.03.2016,,3,138.36, -69,2,25.03.2016,,3,138.36, -69,2,26.03.2016,,0,138.36, -69,2,27.03.2016,,3,138.36, -69,2,28.03.2016,,3,138.36, -69,2,29.03.2016,,3,138.36, -69,2,30.03.2016,,3,138.36, -69,2,31.03.2016,,3,138.36, -69,2,01.04.2016,,3,138.36, -69,2,02.04.2016,,3,138.36, -69,2,03.04.2016,,3,138.36, -69,2,04.04.2016,,19,138.36, -69,2,05.04.2016,,6,138.36, -69,2,06.04.2016,,6,138.36, -69,2,07.04.2016,,6,138.36, -69,2,08.04.2016,,6,138.36, -69,2,09.04.2016,,6,138.5, -69,2,10.04.2016,,6,138.5, -69,2,11.04.2016,,3,138.5, -69,2,12.04.2016,,3,138.5, -69,2,13.04.2016,,6,138.5, -69,2,14.04.2016,,6,138.5, -69,2,15.04.2016,,6,138.5, -69,2,16.04.2016,,3,138.5, -69,2,17.04.2016,,6,138.5, -69,2,18.04.2016,,13,138.5, -69,2,19.04.2016,1.0,38,138.5,116.89 -69,2,20.04.2016,1.0,145,138.5,116.89 -69,2,21.04.2016,1.0,163,138.5,116.89 -69,2,22.04.2016,1.0,163,138.5,116.89 -69,2,23.04.2016,1.0,163,138.5,116.89 -69,2,24.04.2016,1.0,132,138.5,116.89 -69,2,25.04.2016,1.0,91,138.5,116.89 -69,2,26.04.2016,,6,138.5, -69,2,27.04.2016,,6,138.5, -69,2,28.04.2016,,6,138.5, -69,2,29.04.2016,,0,138.5, -69,2,30.04.2016,,9,138.5, -69,2,01.05.2016,,16,138.5, -69,2,02.05.2016,,6,138.5, -69,2,03.05.2016,1.0,336,138.5,106.01 -69,2,04.05.2016,1.0,176,138.5,106.01 -69,2,05.05.2016,1.0,295,138.5,106.01 -69,2,06.05.2016,1.0,179,138.5,106.01 -69,2,07.05.2016,1.0,94,138.5,106.01 -69,2,08.05.2016,1.0,154,138.5,106.01 -69,2,09.05.2016,1.0,154,138.5,106.01 -69,2,10.05.2016,,0,138.5, -69,2,11.05.2016,,0,138.5, -69,2,12.05.2016,,9,138.5, -69,2,13.05.2016,,3,138.5, -69,2,14.05.2016,,3,138.5, -69,2,15.05.2016,,3,138.5, -69,2,16.05.2016,,19,138.5, -69,2,17.05.2016,,0,138.5, -69,2,18.05.2016,,6,138.5, -69,2,19.05.2016,,3,138.5, -69,2,20.05.2016,,6,138.5, -69,2,21.05.2016,,0,138.5, -69,2,22.05.2016,,6,138.5, -70,2,01.01.2015,,0,135.78, -70,2,02.01.2015,,0,135.78, -70,2,03.01.2015,,6,135.78, -70,2,04.01.2015,,3,135.78, -70,2,05.01.2015,,3,135.78, -70,2,06.01.2015,,3,135.78, -70,2,07.01.2015,,0,135.78, -70,2,08.01.2015,,0,135.78, -70,2,09.01.2015,,0,135.78, -70,2,10.01.2015,,0,135.78, -70,2,11.01.2015,,0,135.78, -70,2,12.01.2015,,0,135.78, -70,2,13.01.2015,,0,135.78, -70,2,14.01.2015,,0,135.78, -70,2,15.01.2015,,0,135.78, -70,2,16.01.2015,,0,135.78, -70,2,17.01.2015,,0,135.78, -70,2,18.01.2015,,0,135.78, -70,2,19.01.2015,,0,135.78, -70,2,20.01.2015,,3,135.78, -70,2,21.01.2015,,0,135.78, -70,2,22.01.2015,,0,135.78, -70,2,23.01.2015,,0,135.78, -70,2,24.01.2015,,3,135.78, -70,2,25.01.2015,,0,135.78, -70,2,26.01.2015,,0,135.78, -70,2,27.01.2015,,6,135.78, -70,2,28.01.2015,,3,135.78, -70,2,29.01.2015,,3,135.78, -70,2,30.01.2015,,0,135.78, -70,2,31.01.2015,,3,135.78, -70,2,01.02.2015,,9,135.78, -70,2,02.02.2015,,0,135.78, -70,2,03.02.2015,,0,135.78, -70,2,04.02.2015,,0,135.78, -70,2,05.02.2015,,0,135.78, -70,2,06.02.2015,,0,135.78, 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-70,2,14.03.2015,,0,146.38, -70,2,15.03.2015,,0,146.38, -70,2,16.03.2015,,0,146.38, -70,2,17.03.2015,,0,146.38, -70,2,18.03.2015,,0,146.38, -70,2,19.03.2015,,3,146.38, -70,2,20.03.2015,,3,146.38, -70,2,21.03.2015,,0,146.38, -70,2,22.03.2015,,0,146.38, -70,2,23.03.2015,,0,146.38, -70,2,24.03.2015,,0,146.38, -70,2,25.03.2015,,0,146.38, -70,2,26.03.2015,,0,146.38, -70,2,27.03.2015,,6,146.38, -70,2,28.03.2015,,0,146.38, -70,2,29.03.2015,,6,146.38, -70,2,30.03.2015,,0,146.38, -70,2,31.03.2015,,3,146.38, -70,2,01.04.2015,,0,146.38, -70,2,02.04.2015,,0,146.38, -70,2,03.04.2015,,16,146.38, -70,2,04.04.2015,,0,146.38, -70,2,05.04.2015,,0,146.38, -70,2,06.04.2015,,0,146.38, -70,2,07.04.2015,,0,146.38, -70,2,08.04.2015,,0,146.38, -70,2,09.04.2015,,0,145.84, -70,2,10.04.2015,,0,145.84, -70,2,11.04.2015,,0,145.84, -70,2,12.04.2015,,0,145.84, -70,2,13.04.2015,,0,145.84, -70,2,14.04.2015,,0,145.84, -70,2,15.04.2015,,0,145.84, -70,2,16.04.2015,,0,145.84, -70,2,17.04.2015,,3,145.84, -70,2,18.04.2015,,0,145.84, -70,2,19.04.2015,,3,145.84, -70,2,20.04.2015,,6,145.84, -70,2,21.04.2015,,0,145.84, -70,2,22.04.2015,,0,145.84, -70,2,23.04.2015,,0,150.73, -70,2,24.04.2015,,0,150.73, -70,2,25.04.2015,,3,150.73, -70,2,26.04.2015,,0,150.73, -70,2,27.04.2015,,0,150.73, -70,2,28.04.2015,,6,150.73, -70,2,29.04.2015,,0,150.73, -70,2,30.04.2015,,6,150.73, -70,2,01.05.2015,,3,150.73, -70,2,02.05.2015,,3,150.73, -70,2,03.05.2015,,0,150.73, -70,2,04.05.2015,,3,150.73, -70,2,05.05.2015,,3,150.73, -70,2,06.05.2015,,0,150.73, -70,2,07.05.2015,,0,150.73, -70,2,08.05.2015,,0,150.73, -70,2,09.05.2015,,0,150.73, -70,2,10.05.2015,,6,146.65, -70,2,11.05.2015,,6,150.73, -70,2,12.05.2015,,0,150.73, -70,2,13.05.2015,,9,150.73, -70,2,14.05.2015,,0,150.73, -70,2,15.05.2015,,0,150.73, -70,2,16.05.2015,,3,150.73, -70,2,17.05.2015,,3,150.73, -70,2,18.05.2015,,0,150.73, -70,2,19.05.2015,,0,150.73, -70,2,20.05.2015,,0,150.73, -70,2,21.05.2015,,0,150.73, -70,2,22.05.2015,,0,150.73, -70,2,23.05.2015,,0,150.73, -70,2,24.05.2015,,0,150.73, -70,2,25.05.2015,,3,150.73, -70,2,26.05.2015,,0,150.73, -70,2,27.05.2015,,0,150.73, -70,2,28.05.2015,,3,150.73, -70,2,29.05.2015,,3,150.73, -70,2,30.05.2015,,3,150.73, -70,2,31.05.2015,,3,150.73, -70,2,01.06.2015,,0,150.73, -70,2,02.06.2015,,3,150.73, -70,2,03.06.2015,,3,150.73, -70,2,04.06.2015,,3,150.73, -70,2,05.06.2015,,3,150.73, -70,2,06.06.2015,,3,150.73, -70,2,07.06.2015,,0,150.73, -70,2,08.06.2015,,0,150.73, -70,2,09.06.2015,,3,150.73, -70,2,10.06.2015,,9,150.73, -70,2,11.06.2015,,0,150.73, -70,2,12.06.2015,,0,150.73, -70,2,13.06.2015,,0,150.73, -70,2,14.06.2015,,0,150.73, -70,2,15.06.2015,,3,150.73, -70,2,16.06.2015,,0,150.73, -70,2,17.06.2015,,0,150.73, -70,2,18.06.2015,,0,150.73, -70,2,19.06.2015,,0,150.73, -70,2,20.06.2015,,3,150.73, -70,2,21.06.2015,,0,150.73, -70,2,22.06.2015,,3,150.73, -70,2,23.06.2015,,0,150.73, -70,2,24.06.2015,,6,150.73, -70,2,25.06.2015,,0,150.73, -70,2,26.06.2015,,0,150.73, -70,2,27.06.2015,,0,150.73, -70,2,28.06.2015,,0,150.73, -70,2,29.06.2015,,0,150.73, -70,2,30.06.2015,,0,150.73, -70,2,01.07.2015,,6,150.73, -70,2,02.07.2015,,3,150.73, -70,2,03.07.2015,,0,150.73, -70,2,04.07.2015,,0,150.73, -70,2,05.07.2015,,0,150.73, -70,2,06.07.2015,,0,150.73, -70,2,07.07.2015,,0,150.73, -70,2,08.07.2015,,6,150.73, -70,2,09.07.2015,,0,150.73, -70,2,10.07.2015,,0,150.73, -70,2,11.07.2015,,0,150.73, -70,2,12.07.2015,,22,150.73, -70,2,13.07.2015,,0,150.73, -70,2,14.07.2015,,0,150.73, -70,2,15.07.2015,,0,150.73, -70,2,16.07.2015,,0,150.73, -70,2,17.07.2015,,0,150.73, -70,2,18.07.2015,,0,150.73, -70,2,19.07.2015,,0,150.73, -70,2,20.07.2015,,0,150.73, -70,2,21.07.2015,,0,150.73, -70,2,22.07.2015,,0,150.73, -70,2,23.07.2015,,0,150.73, -70,2,24.07.2015,,0,150.73, -70,2,25.07.2015,,0,150.73, -70,2,26.07.2015,,0,150.73, -70,2,27.07.2015,,0,150.73, -70,2,28.07.2015,,0,150.73, -70,2,29.07.2015,,0,150.73, -70,2,30.07.2015,,0,150.73, -70,2,31.07.2015,,0,150.73, -70,2,01.08.2015,,0,150.73, -70,2,02.08.2015,,0,150.73, -70,2,03.08.2015,,0,150.73, -70,2,04.08.2015,,0,150.73, -70,2,05.08.2015,,6,150.73, -70,2,06.08.2015,,3,150.73, -70,2,07.08.2015,,9,150.73, -70,2,08.08.2015,,16,150.73, -70,2,09.08.2015,,9,150.73, -70,2,10.08.2015,,6,150.73, -70,2,11.08.2015,,3,150.73, -70,2,12.08.2015,,3,128.17, -70,2,13.08.2015,,3,128.17, -70,2,14.08.2015,,6,128.17, -70,2,15.08.2015,,13,128.17, -70,2,16.08.2015,,0,128.17, -70,2,17.08.2015,,6,128.17, -70,2,18.08.2015,1.0,69,128.17,100.3 -70,2,19.08.2015,1.0,50,128.17,100.3 -70,2,20.08.2015,1.0,97,128.17,100.3 -70,2,21.08.2015,1.0,85,128.17,100.3 -70,2,22.08.2015,1.0,44,128.17,100.3 -70,2,23.08.2015,1.0,60,128.17,100.3 -70,2,24.08.2015,1.0,75,128.17,100.3 -70,2,25.08.2015,,6,128.17, -70,2,26.08.2015,,13,128.17, -70,2,27.08.2015,,6,128.17, -70,2,28.08.2015,,13,128.17, -70,2,29.08.2015,,3,128.17, -70,2,30.08.2015,,6,128.17, -70,2,31.08.2015,,3,128.17, -70,2,01.09.2015,,9,128.17, -70,2,02.09.2015,,28,128.17, -70,2,03.09.2015,,13,128.17, -70,2,04.09.2015,,6,128.17, -70,2,05.09.2015,,0,128.17, -70,2,06.09.2015,,6,128.17, -70,2,07.09.2015,,0,128.17, -70,2,08.09.2015,,0,128.17, -70,2,09.09.2015,,13,128.17, -70,2,10.09.2015,,9,128.17, -70,2,11.09.2015,,3,128.17, -70,2,12.09.2015,,0,128.17, -70,2,13.09.2015,,0,128.17, -70,2,14.09.2015,,0,128.17, -70,2,15.09.2015,,0,128.17, -70,2,16.09.2015,,0,128.17, -70,2,17.09.2015,,16,128.17, -70,2,18.09.2015,,16,128.17, -70,2,19.09.2015,,28,128.17, -70,2,20.09.2015,,13,128.17, -70,2,21.09.2015,,13,128.17, -70,2,22.09.2015,,0,128.17, -70,2,23.09.2015,,3,128.17, -70,2,24.09.2015,,9,128.17, -70,2,25.09.2015,,13,128.17, -70,2,26.09.2015,,3,128.17, -70,2,27.09.2015,,3,128.17, -70,2,28.09.2015,,9,128.17, -70,2,29.09.2015,,0,128.17, -70,2,30.09.2015,,3,128.17, -70,2,01.10.2015,,3,162.96, -70,2,02.10.2015,,0,162.96, -70,2,03.10.2015,,6,162.96, -70,2,04.10.2015,,0,162.96, -70,2,05.10.2015,,3,162.96, -70,2,06.10.2015,1.0,41,143.39,122.05 -70,2,07.10.2015,1.0,50,143.39,122.05 -70,2,08.10.2015,1.0,41,143.39,122.05 -70,2,09.10.2015,1.0,44,143.39,122.05 -70,2,10.10.2015,1.0,57,143.39,122.05 -70,2,11.10.2015,1.0,44,143.39,122.05 -70,2,12.10.2015,1.0,28,143.39,122.05 -70,2,13.10.2015,1.0,9,143.93,122.05 -70,2,14.10.2015,1.0,13,143.93,122.05 -70,2,15.10.2015,1.0,38,143.93,122.05 -70,2,16.10.2015,1.0,31,143.93,122.05 -70,2,17.10.2015,1.0,28,162.96,122.05 -70,2,18.10.2015,1.0,28,162.96,122.05 -70,2,19.10.2015,,3,162.96, -70,2,20.10.2015,,3,162.96, -70,2,21.10.2015,,0,162.96, -70,2,22.10.2015,,0,162.96, -70,2,23.10.2015,,0,162.96, -70,2,24.10.2015,,0,162.96, -70,2,25.10.2015,,3,162.96, -70,2,26.10.2015,,3,162.96, -70,2,27.10.2015,,0,162.96, -70,2,28.10.2015,,0,162.96, -70,2,29.10.2015,,0,162.96, -70,2,30.10.2015,,3,162.96, -70,2,31.10.2015,,13,162.96, -70,2,01.11.2015,,0,162.96, -70,2,02.11.2015,,0,162.96, -70,2,03.11.2015,,0,162.96, -70,2,04.11.2015,,3,162.96, -70,2,05.11.2015,,0,162.96, -70,2,06.11.2015,,3,162.96, -70,2,07.11.2015,,0,162.96, -70,2,08.11.2015,,0,162.96, -70,2,09.11.2015,,0,162.96, -70,2,10.11.2015,,0,162.96, -70,2,11.11.2015,,0,138.36, -70,2,12.11.2015,,0,138.36, -70,2,13.11.2015,,6,138.36, -70,2,14.11.2015,,3,138.36, -70,2,15.11.2015,,0,138.36, -70,2,16.11.2015,,9,138.36, -70,2,17.11.2015,,6,138.36, -70,2,18.11.2015,,0,138.36, -70,2,19.11.2015,,0,138.36, -70,2,20.11.2015,,3,138.36, -70,2,21.11.2015,,0,138.36, -70,2,22.11.2015,,6,138.36, -70,2,23.11.2015,,0,138.36, -70,2,24.11.2015,,3,138.36, -70,2,25.11.2015,,0,138.36, -70,2,26.11.2015,,0,138.36, -70,2,27.11.2015,,13,138.36, -70,2,28.11.2015,,0,138.36, -70,2,29.11.2015,,3,138.36, -70,2,30.11.2015,,3,138.36, -70,2,01.12.2015,,0,162.96, -70,2,02.12.2015,,0,162.96, -70,2,03.12.2015,,0,138.5, -70,2,04.12.2015,,0,138.5, -70,2,05.12.2015,,6,138.5, -70,2,06.12.2015,,0,138.5, -70,2,07.12.2015,,0,138.5, -70,2,08.12.2015,,0,138.5, -70,2,09.12.2015,,0,138.5, -70,2,10.12.2015,,3,138.5, -70,2,11.12.2015,,3,142.57, -70,2,12.12.2015,,13,142.57, -70,2,13.12.2015,,0,142.57, -70,2,14.12.2015,,3,142.57, -70,2,15.12.2015,,16,142.57, -70,2,16.12.2015,,0,142.57, -70,2,17.12.2015,,9,142.57, -70,2,18.12.2015,,6,142.57, -70,2,19.12.2015,,13,142.57, -70,2,20.12.2015,,9,142.57, -70,2,21.12.2015,,16,142.57, -70,2,22.12.2015,1.0,82,142.57,103.02 -70,2,23.12.2015,1.0,63,142.57,103.02 -70,2,24.12.2015,1.0,97,142.57,103.02 -70,2,25.12.2015,1.0,82,142.57,103.02 -70,2,26.12.2015,1.0,35,142.57,103.02 -70,2,27.12.2015,1.0,31,142.57,103.02 -70,2,28.12.2015,1.0,28,142.57,103.02 -70,2,29.12.2015,,3,138.5, -70,2,30.12.2015,,0,138.5, -70,2,31.12.2015,,3,138.5, -70,2,01.01.2016,,3,138.5, -70,2,02.01.2016,,13,138.5, -70,2,03.01.2016,,9,138.5, -70,2,04.01.2016,,16,138.5, -70,2,05.01.2016,,13,138.5, -70,2,06.01.2016,,0,138.5, -70,2,07.01.2016,,22,138.5, -70,2,08.01.2016,,13,138.5, -70,2,09.01.2016,,13,138.5, -70,2,10.01.2016,,6,138.5, -70,2,11.01.2016,,0,138.5, -70,2,12.01.2016,,13,138.5, -70,2,13.01.2016,,3,138.5, -70,2,14.01.2016,,9,138.5, -70,2,15.01.2016,,13,138.5, -70,2,16.01.2016,,6,142.57, -70,2,17.01.2016,,6,142.57, -70,2,18.01.2016,,3,142.57, -70,2,19.01.2016,1.0,85,142.57,103.02 -70,2,20.01.2016,1.0,97,142.57,103.02 -70,2,21.01.2016,1.0,123,142.57,103.02 -70,2,22.01.2016,1.0,91,142.57,103.02 -70,2,23.01.2016,1.0,79,142.57,103.02 -70,2,24.01.2016,1.0,28,142.57,103.02 -70,2,25.01.2016,1.0,85,142.57,103.02 -70,2,26.01.2016,,9,142.57, -70,2,27.01.2016,,6,142.57, -70,2,28.01.2016,,19,142.57, -70,2,29.01.2016,,19,142.57, -70,2,30.01.2016,,25,142.57, -70,2,31.01.2016,,22,142.57, -70,2,01.02.2016,,6,142.57, -70,2,02.02.2016,,3,142.57, -70,2,03.02.2016,,19,142.57, -70,2,04.02.2016,,3,142.57, -70,2,05.02.2016,,13,142.57, -70,2,06.02.2016,,3,142.57, -70,2,07.02.2016,,0,142.57, -70,2,08.02.2016,,9,142.57, -70,2,09.02.2016,1.0,119,142.57,108.46 -70,2,10.02.2016,1.0,60,138.36,108.46 -70,2,11.02.2016,1.0,75,138.36,108.46 -70,2,12.02.2016,1.0,113,138.36,108.46 -70,2,13.02.2016,1.0,154,138.36,108.46 -70,2,14.02.2016,1.0,79,138.36,108.46 -70,2,15.02.2016,1.0,88,138.36,108.46 -70,2,16.02.2016,,0,138.36, -70,2,17.02.2016,,0,138.36, -70,2,18.02.2016,,3,138.36, -70,2,19.02.2016,,3,138.36, -70,2,20.02.2016,,6,138.36, -70,2,21.02.2016,,9,138.36, -70,2,22.02.2016,,0,138.36, -70,2,23.02.2016,,0,138.36, -70,2,24.02.2016,,0,138.36, -70,2,25.02.2016,,0,138.36, -70,2,26.02.2016,,0,138.36, -70,2,27.02.2016,,0,138.36, -70,2,28.02.2016,,0,138.36, -70,2,29.02.2016,,0,138.36, -70,2,01.03.2016,,0,138.36, -70,2,02.03.2016,,0,138.36, -70,2,03.03.2016,,0,138.36, -70,2,04.03.2016,,0,138.36, -70,2,05.03.2016,,0,138.36, -70,2,06.03.2016,,0,138.36, -70,2,07.03.2016,,0,138.36, -70,2,08.03.2016,,0,138.36, -70,2,09.03.2016,,0,138.36, -70,2,10.03.2016,,0,138.36, -70,2,11.03.2016,,0,138.36, -70,2,12.03.2016,,0,138.5, -70,2,13.03.2016,,0,138.5, -70,2,14.03.2016,,6,138.5, -70,2,15.03.2016,1.0,28,138.5,124.77 -70,2,16.03.2016,1.0,47,138.5,124.77 -70,2,17.03.2016,1.0,38,138.5,124.77 -70,2,18.03.2016,1.0,53,138.5,124.77 -70,2,19.03.2016,1.0,41,138.5,124.77 -70,2,20.03.2016,1.0,28,138.5,124.77 -70,2,21.03.2016,1.0,66,138.5,124.77 -70,2,22.03.2016,,3,138.36, -70,2,23.03.2016,,3,138.36, -70,2,24.03.2016,,3,138.36, -70,2,25.03.2016,,0,138.36, -70,2,26.03.2016,,9,138.36, -70,2,27.03.2016,,0,138.36, -70,2,28.03.2016,,6,138.36, -70,2,29.03.2016,,0,138.36, -70,2,30.03.2016,,0,138.36, -70,2,31.03.2016,,3,138.36, -70,2,01.04.2016,,3,138.36, -70,2,02.04.2016,,3,138.36, -70,2,03.04.2016,,3,138.36, -70,2,04.04.2016,,0,138.36, -70,2,05.04.2016,,3,138.36, -70,2,06.04.2016,,9,138.36, -70,2,07.04.2016,,0,138.36, -70,2,08.04.2016,,6,138.36, -70,2,09.04.2016,,3,138.5, -70,2,10.04.2016,,3,138.5, -70,2,11.04.2016,,16,138.5, -70,2,12.04.2016,,0,138.5, -70,2,13.04.2016,,3,138.5, -70,2,14.04.2016,,0,138.5, -70,2,15.04.2016,,0,138.5, -70,2,16.04.2016,,6,138.5, -70,2,17.04.2016,,3,138.5, -70,2,18.04.2016,,35,138.5, -70,2,19.04.2016,1.0,82,138.5,116.89 -70,2,20.04.2016,1.0,69,138.5,116.89 -70,2,21.04.2016,1.0,50,138.5,116.89 -70,2,22.04.2016,1.0,75,138.5,116.89 -70,2,23.04.2016,1.0,75,138.5,116.89 -70,2,24.04.2016,1.0,85,138.5,116.89 -70,2,25.04.2016,1.0,28,138.5,116.89 -70,2,26.04.2016,,6,138.5, -70,2,27.04.2016,,6,138.5, -70,2,28.04.2016,,9,138.5, -70,2,29.04.2016,,0,138.5, -70,2,30.04.2016,,3,138.5, -70,2,01.05.2016,,0,138.5, -70,2,02.05.2016,,22,138.5, -70,2,03.05.2016,1.0,91,138.5,106.01 -70,2,04.05.2016,1.0,88,138.5,106.01 -70,2,05.05.2016,1.0,113,138.5,106.01 -70,2,06.05.2016,1.0,145,138.5,106.01 -70,2,07.05.2016,1.0,160,138.5,106.01 -70,2,08.05.2016,1.0,85,138.5,106.01 -70,2,09.05.2016,1.0,38,138.5,106.01 -70,2,10.05.2016,,6,142.57, -70,2,11.05.2016,,6,142.57, -70,2,12.05.2016,,0,142.57, -70,2,13.05.2016,,3,142.57, -70,2,14.05.2016,,6,142.57, -70,2,15.05.2016,,3,142.57, -70,2,16.05.2016,,19,142.57, -70,2,17.05.2016,,3,142.57, -70,2,18.05.2016,,3,142.57, -70,2,19.05.2016,,0,142.57, -70,2,20.05.2016,,0,142.57, -70,2,21.05.2016,,0,142.57, -70,2,22.05.2016,,6,142.57, -71,2,01.01.2015,,0,135.78, -71,2,02.01.2015,,0,135.78, -71,2,03.01.2015,,6,135.78, -71,2,04.01.2015,,9,135.78, -71,2,05.01.2015,,0,135.78, -71,2,06.01.2015,,0,135.78, -71,2,07.01.2015,,0,135.78, -71,2,08.01.2015,,0,135.78, -71,2,09.01.2015,,0,135.78, -71,2,10.01.2015,,0,135.78, -71,2,11.01.2015,,0,135.78, -71,2,12.01.2015,,0,135.78, -71,2,13.01.2015,,0,135.78, -71,2,14.01.2015,,0,135.78, -71,2,15.01.2015,,0,135.78, -71,2,16.01.2015,,0,135.78, -71,2,17.01.2015,,0,135.78, -71,2,18.01.2015,,0,135.78, -71,2,19.01.2015,,0,135.78, -71,2,20.01.2015,,0,135.78, -71,2,21.01.2015,,0,135.78, -71,2,22.01.2015,,0,135.78, -71,2,23.01.2015,,0,135.78, -71,2,24.01.2015,,0,135.78, -71,2,25.01.2015,,0,135.78, -71,2,26.01.2015,,0,135.78, -71,2,27.01.2015,,13,135.78, -71,2,28.01.2015,,13,135.78, -71,2,29.01.2015,,0,135.78, -71,2,30.01.2015,,0,135.78, -71,2,31.01.2015,,0,135.78, -71,2,01.02.2015,,0,135.78, -71,2,02.02.2015,,0,135.78, -71,2,03.02.2015,,0,135.78, -71,2,04.02.2015,,0,135.78, -71,2,05.02.2015,,0,135.78, -71,2,06.02.2015,,0,135.78, -71,2,07.02.2015,,0,135.78, -71,2,08.02.2015,,0,135.78, -71,2,09.02.2015,,0,135.78, -71,2,10.02.2015,,0,135.78, -71,2,11.02.2015,,0,135.78, -71,2,12.02.2015,,0,135.78, -71,2,13.02.2015,,0,135.78, -71,2,14.02.2015,,0,135.78, -71,2,15.02.2015,,0,135.78, -71,2,16.02.2015,,0,135.78, -71,2,17.02.2015,,3,135.78, -71,2,18.02.2015,,13,135.78, -71,2,19.02.2015,,3,135.78, -71,2,20.02.2015,,13,135.78, -71,2,21.02.2015,,16,135.78, -71,2,22.02.2015,,3,135.78, -71,2,23.02.2015,,0,135.78, -71,2,24.02.2015,,0,135.78, -71,2,25.02.2015,,3,135.78, -71,2,26.02.2015,,0,146.38, -71,2,27.02.2015,,0,146.38, -71,2,28.02.2015,,3,146.38, -71,2,01.03.2015,,3,146.38, -71,2,02.03.2015,,0,146.38, -71,2,03.03.2015,,0,146.38, -71,2,04.03.2015,,3,146.38, -71,2,05.03.2015,,3,146.38, -71,2,06.03.2015,,3,146.38, -71,2,07.03.2015,,0,146.38, -71,2,08.03.2015,,0,146.38, -71,2,09.03.2015,,0,146.38, -71,2,10.03.2015,,0,146.38, -71,2,11.03.2015,,0,146.38, -71,2,12.03.2015,,0,146.38, -71,2,13.03.2015,,0,146.38, -71,2,14.03.2015,,0,146.38, -71,2,15.03.2015,,0,146.38, -71,2,16.03.2015,,0,146.38, -71,2,17.03.2015,,0,146.38, -71,2,18.03.2015,,0,146.38, -71,2,19.03.2015,,0,146.38, -71,2,20.03.2015,,0,146.38, -71,2,21.03.2015,,0,146.38, -71,2,22.03.2015,,0,146.38, -71,2,23.03.2015,,0,146.38, -71,2,24.03.2015,,0,146.38, -71,2,25.03.2015,,0,146.38, -71,2,26.03.2015,,0,146.38, -71,2,27.03.2015,,0,146.38, -71,2,28.03.2015,,0,146.38, -71,2,29.03.2015,,0,146.38, -71,2,30.03.2015,,0,146.38, -71,2,31.03.2015,,0,146.38, -71,2,01.04.2015,,0,146.38, -71,2,02.04.2015,,0,146.38, -71,2,03.04.2015,,0,146.38, -71,2,04.04.2015,,0,146.38, -71,2,05.04.2015,,3,146.38, -71,2,06.04.2015,,3,146.38, -71,2,07.04.2015,,0,146.38, -71,2,08.04.2015,,6,146.38, -71,2,09.04.2015,,0,145.84, -71,2,10.04.2015,,0,145.84, -71,2,11.04.2015,,0,145.84, -71,2,12.04.2015,,6,145.84, -71,2,13.04.2015,,0,145.84, -71,2,14.04.2015,,0,145.84, -71,2,15.04.2015,,0,145.84, -71,2,16.04.2015,,0,145.84, -71,2,17.04.2015,,0,145.84, -71,2,18.04.2015,,0,145.84, -71,2,19.04.2015,,0,145.84, -71,2,20.04.2015,,0,145.84, -71,2,21.04.2015,,0,145.84, -71,2,22.04.2015,,0,145.84, -71,2,23.04.2015,,0,145.84, -71,2,24.04.2015,,0,145.84, -71,2,25.04.2015,,0,145.84, -71,2,26.04.2015,,0,150.73, -71,2,27.04.2015,,0,150.73, -71,2,28.04.2015,,0,150.73, -71,2,29.04.2015,,0,150.73, -71,2,30.04.2015,,0,150.73, -71,2,01.05.2015,,0,150.73, -71,2,02.05.2015,,0,150.73, -71,2,03.05.2015,,0,150.73, -71,2,04.05.2015,,0,150.73, -71,2,05.05.2015,,0,150.73, -71,2,06.05.2015,,0,150.73, -71,2,07.05.2015,,0,150.73, -71,2,08.05.2015,,0,150.73, -71,2,09.05.2015,,0,150.73, -71,2,10.05.2015,,0,150.73, -71,2,11.05.2015,,0,150.73, -71,2,12.05.2015,,0,150.73, -71,2,13.05.2015,,0,150.73, -71,2,14.05.2015,,0,150.73, -71,2,15.05.2015,,0,150.73, -71,2,16.05.2015,,0,150.73, -71,2,17.05.2015,,0,150.73, -71,2,18.05.2015,,0,150.73, -71,2,19.05.2015,,0,150.73, -71,2,20.05.2015,,0,150.73, -71,2,21.05.2015,,0,150.73, -71,2,22.05.2015,,0,150.73, -71,2,23.05.2015,,0,150.73, -71,2,24.05.2015,,0,150.73, -71,2,25.05.2015,,0,150.73, -71,2,26.05.2015,,0,150.73, -71,2,27.05.2015,,0,150.73, -71,2,28.05.2015,,0,150.73, -71,2,29.05.2015,,0,150.73, -71,2,30.05.2015,,0,150.73, -71,2,31.05.2015,,0,150.73, -71,2,01.06.2015,,0,150.73, -71,2,02.06.2015,,0,150.73, -71,2,03.06.2015,,0,150.73, -71,2,04.06.2015,,6,150.73, -71,2,05.06.2015,,19,150.73, -71,2,06.06.2015,,0,150.73, -71,2,07.06.2015,,0,150.73, -71,2,08.06.2015,,0,150.73, -71,2,09.06.2015,,0,150.73, -71,2,10.06.2015,,0,150.73, -71,2,11.06.2015,,0,150.73, -71,2,12.06.2015,,0,150.73, -71,2,13.06.2015,,16,150.73, -71,2,14.06.2015,,9,150.73, -71,2,15.06.2015,,0,150.73, -71,2,16.06.2015,,0,150.73, -71,2,17.06.2015,,0,150.73, -71,2,18.06.2015,,0,150.73, -71,2,19.06.2015,,0,150.73, -71,2,20.06.2015,,0,150.73, -71,2,21.06.2015,,0,150.73, -71,2,22.06.2015,,0,150.73, -71,2,23.06.2015,,0,150.73, -71,2,24.06.2015,,0,150.73, -71,2,25.06.2015,,0,150.73, -71,2,26.06.2015,,3,150.73, -71,2,27.06.2015,,3,150.73, -71,2,28.06.2015,,9,150.73, -71,2,29.06.2015,,0,150.73, -71,2,30.06.2015,,0,150.73, -71,2,01.07.2015,,0,150.73, -71,2,02.07.2015,,0,150.73, -71,2,03.07.2015,,16,150.73, -71,2,04.07.2015,,0,150.73, -71,2,05.07.2015,,3,150.73, -71,2,06.07.2015,,0,150.73, -71,2,07.07.2015,,3,150.73, -71,2,08.07.2015,,3,150.73, -71,2,09.07.2015,,0,150.73, -71,2,10.07.2015,,0,150.73, -71,2,11.07.2015,,0,150.73, -71,2,12.07.2015,,0,150.73, -71,2,13.07.2015,,0,150.73, -71,2,14.07.2015,,0,150.73, -71,2,15.07.2015,,0,150.73, -71,2,16.07.2015,,0,150.73, -71,2,17.07.2015,,0,150.73, -71,2,18.07.2015,,6,150.73, -71,2,19.07.2015,,3,150.73, -71,2,20.07.2015,,3,150.73, -71,2,21.07.2015,,0,150.73, -71,2,22.07.2015,,22,150.73, -71,2,23.07.2015,,3,150.73, -71,2,24.07.2015,,3,150.73, -71,2,25.07.2015,,3,150.73, -71,2,26.07.2015,,3,150.73, -71,2,27.07.2015,,3,150.73, -71,2,28.07.2015,,3,150.73, -71,2,29.07.2015,,13,150.73, -71,2,30.07.2015,,9,150.73, -71,2,31.07.2015,,3,150.73, -71,2,01.08.2015,,3,150.73, -71,2,02.08.2015,,3,150.73, -71,2,03.08.2015,,3,150.73, -71,2,04.08.2015,,3,150.73, -71,2,05.08.2015,,3,150.73, -71,2,06.08.2015,,3,150.73, -71,2,07.08.2015,,13,150.73, -71,2,08.08.2015,,3,150.73, -71,2,09.08.2015,,6,150.73, -71,2,10.08.2015,,6,150.73, -71,2,11.08.2015,,6,150.73, -71,2,12.08.2015,,0,128.17, -71,2,13.08.2015,,6,128.17, -71,2,14.08.2015,,0,128.17, -71,2,15.08.2015,,3,128.17, -71,2,16.08.2015,,3,128.17, -71,2,17.08.2015,,0,128.17, -71,2,18.08.2015,1.0,25,128.17,100.3 -71,2,19.08.2015,1.0,72,128.17,100.3 -71,2,20.08.2015,1.0,88,128.17,100.3 -71,2,21.08.2015,1.0,28,128.17,100.3 -71,2,22.08.2015,1.0,0,128.17,100.3 -71,2,23.08.2015,1.0,0,128.17,100.3 -71,2,24.08.2015,1.0,0,128.17,100.3 -71,2,25.08.2015,,9,128.17, -71,2,26.08.2015,,9,128.17, -71,2,27.08.2015,,6,128.17, -71,2,28.08.2015,,3,128.17, -71,2,29.08.2015,,3,128.17, -71,2,30.08.2015,,3,128.17, -71,2,31.08.2015,,3,128.17, -71,2,01.09.2015,,0,128.17, -71,2,02.09.2015,,3,128.17, -71,2,03.09.2015,,0,128.17, -71,2,04.09.2015,,3,128.17, -71,2,05.09.2015,,6,128.17, -71,2,06.09.2015,,6,128.17, -71,2,07.09.2015,,3,128.17, -71,2,08.09.2015,,3,128.17, -71,2,09.09.2015,,3,128.17, -71,2,10.09.2015,,3,128.17, -71,2,11.09.2015,,3,128.17, -71,2,12.09.2015,,3,128.17, -71,2,13.09.2015,,3,128.17, -71,2,14.09.2015,,3,128.17, -71,2,15.09.2015,,0,128.17, -71,2,16.09.2015,,6,128.17, -71,2,17.09.2015,,3,128.17, -71,2,18.09.2015,,9,128.17, -71,2,19.09.2015,,3,128.17, -71,2,20.09.2015,,3,128.17, -71,2,21.09.2015,,3,128.17, -71,2,22.09.2015,,6,128.17, -71,2,23.09.2015,,13,128.17, -71,2,24.09.2015,,6,128.17, -71,2,25.09.2015,,6,128.17, -71,2,26.09.2015,,6,128.17, -71,2,27.09.2015,,6,128.17, -71,2,28.09.2015,,6,128.17, -71,2,29.09.2015,,0,128.17, -71,2,30.09.2015,,13,128.17, -71,2,01.10.2015,,6,162.96, -71,2,02.10.2015,,6,162.96, -71,2,03.10.2015,,6,162.96, -71,2,04.10.2015,,6,162.96, -71,2,05.10.2015,,6,162.96, -71,2,06.10.2015,1.0,44,143.39,122.05 -71,2,07.10.2015,1.0,44,143.39,122.05 -71,2,08.10.2015,1.0,44,143.39,122.05 -71,2,09.10.2015,1.0,50,143.39,122.05 -71,2,10.10.2015,1.0,47,143.39,122.05 -71,2,11.10.2015,1.0,22,143.39,122.05 -71,2,12.10.2015,1.0,41,143.39,122.05 -71,2,13.10.2015,1.0,25,143.93,122.05 -71,2,14.10.2015,1.0,25,143.93,122.05 -71,2,15.10.2015,1.0,41,143.93,122.05 -71,2,16.10.2015,1.0,41,143.93,122.05 -71,2,17.10.2015,1.0,41,162.96,122.05 -71,2,18.10.2015,1.0,41,162.96,122.05 -71,2,19.10.2015,,6,162.96, -71,2,20.10.2015,,6,162.96, -71,2,21.10.2015,,6,162.96, -71,2,22.10.2015,,0,162.96, -71,2,23.10.2015,,0,162.96, -71,2,24.10.2015,,0,162.96, -71,2,25.10.2015,,0,162.96, -71,2,26.10.2015,,0,162.96, -71,2,27.10.2015,,9,162.96, -71,2,28.10.2015,,0,162.96, -71,2,29.10.2015,,0,162.96, -71,2,30.10.2015,,0,162.96, -71,2,31.10.2015,,0,162.96, -71,2,01.11.2015,,0,162.96, -71,2,02.11.2015,,0,162.96, -71,2,03.11.2015,,0,162.96, -71,2,04.11.2015,,0,162.96, -71,2,05.11.2015,,0,162.96, -71,2,06.11.2015,,0,162.96, -71,2,07.11.2015,,3,162.96, -71,2,08.11.2015,,0,162.96, -71,2,09.11.2015,,0,162.96, -71,2,10.11.2015,,0,162.96, -71,2,11.11.2015,,0,138.36, -71,2,12.11.2015,,0,138.36, -71,2,13.11.2015,,0,138.36, -71,2,14.11.2015,,0,138.36, -71,2,15.11.2015,,0,138.36, -71,2,16.11.2015,,0,138.36, -71,2,17.11.2015,,0,138.36, -71,2,18.11.2015,,0,138.36, -71,2,19.11.2015,,0,138.36, -71,2,20.11.2015,,0,138.36, -71,2,21.11.2015,,0,138.36, -71,2,22.11.2015,,0,138.36, -71,2,23.11.2015,,0,138.36, -71,2,24.11.2015,,0,138.36, -71,2,25.11.2015,,0,138.36, -71,2,26.11.2015,,0,138.36, -71,2,27.11.2015,,0,138.36, -71,2,28.11.2015,,0,138.36, -71,2,29.11.2015,,0,138.36, -71,2,30.11.2015,,0,138.36, -71,2,01.12.2015,,0,162.96, -71,2,02.12.2015,,0,162.96, -71,2,03.12.2015,,0,138.5, -71,2,04.12.2015,,0,138.5, -71,2,05.12.2015,,0,138.5, -71,2,06.12.2015,,0,138.5, -71,2,07.12.2015,,0,138.5, -71,2,08.12.2015,,0,138.5, -71,2,09.12.2015,,6,138.5, -71,2,10.12.2015,,0,138.5, -71,2,11.12.2015,,3,142.57, -71,2,12.12.2015,,6,142.57, -71,2,13.12.2015,,0,142.57, -71,2,14.12.2015,,3,142.57, -71,2,15.12.2015,,0,142.57, -71,2,16.12.2015,,0,142.57, -71,2,17.12.2015,,9,142.57, -71,2,18.12.2015,,3,142.57, -71,2,19.12.2015,,3,142.57, -71,2,20.12.2015,,3,142.57, -71,2,21.12.2015,,3,142.57, -71,2,22.12.2015,1.0,38,142.57,103.02 -71,2,23.12.2015,1.0,22,142.57,103.02 -71,2,24.12.2015,1.0,13,142.57,103.02 -71,2,25.12.2015,1.0,13,142.57,103.02 -71,2,26.12.2015,1.0,57,142.57,103.02 -71,2,27.12.2015,1.0,82,142.57,103.02 -71,2,28.12.2015,1.0,72,142.57,103.02 -71,2,29.12.2015,,0,138.5, -71,2,30.12.2015,,13,138.5, -71,2,31.12.2015,,3,138.5, -71,2,01.01.2016,,0,138.5, -71,2,02.01.2016,,3,138.5, -71,2,03.01.2016,,0,138.5, -71,2,04.01.2016,,0,138.5, -71,2,05.01.2016,,0,138.5, -71,2,06.01.2016,,0,138.5, -71,2,07.01.2016,,0,138.5, -71,2,08.01.2016,,3,138.5, -71,2,09.01.2016,,3,138.5, -71,2,10.01.2016,,3,138.5, -71,2,11.01.2016,,3,138.5, -71,2,12.01.2016,,0,138.5, -71,2,13.01.2016,,3,138.5, -71,2,14.01.2016,,0,138.5, -71,2,15.01.2016,,3,138.5, -71,2,16.01.2016,,6,142.57, -71,2,17.01.2016,,3,142.57, -71,2,18.01.2016,,3,142.57, -71,2,19.01.2016,1.0,35,142.57,103.02 -71,2,20.01.2016,1.0,75,142.57,103.02 -71,2,21.01.2016,1.0,107,142.57,103.02 -71,2,22.01.2016,1.0,44,142.57,103.02 -71,2,23.01.2016,1.0,41,142.57,103.02 -71,2,24.01.2016,1.0,41,142.57,103.02 -71,2,25.01.2016,1.0,41,142.57,103.02 -71,2,26.01.2016,,3,142.57, -71,2,27.01.2016,,6,142.57, -71,2,28.01.2016,,3,142.57, -71,2,29.01.2016,,13,142.57, -71,2,30.01.2016,,13,142.57, -71,2,31.01.2016,,6,142.57, -71,2,01.02.2016,,0,142.57, -71,2,02.02.2016,,13,142.57, -71,2,03.02.2016,,3,142.57, -71,2,04.02.2016,,6,142.57, -71,2,05.02.2016,,6,142.57, -71,2,06.02.2016,,6,142.57, -71,2,07.02.2016,,6,142.57, -71,2,08.02.2016,,6,142.57, -71,2,09.02.2016,1.0,38,142.57,108.46 -71,2,10.02.2016,1.0,44,138.36,108.46 -71,2,11.02.2016,1.0,60,138.36,108.46 -71,2,12.02.2016,1.0,57,138.36,108.46 -71,2,13.02.2016,1.0,6,138.36,108.46 -71,2,14.02.2016,1.0,44,138.36,108.46 -71,2,15.02.2016,1.0,44,138.36,108.46 -71,2,16.02.2016,,0,138.36, -71,2,17.02.2016,,3,138.36, -71,2,18.02.2016,,3,138.36, -71,2,19.02.2016,,0,138.36, -71,2,20.02.2016,,6,138.36, -71,2,21.02.2016,,6,138.36, -71,2,22.02.2016,,6,138.36, -71,2,23.02.2016,,3,138.36, -71,2,24.02.2016,,0,138.36, -71,2,25.02.2016,,3,138.36, -71,2,26.02.2016,,0,138.36, -71,2,27.02.2016,,0,138.36, -71,2,28.02.2016,,3,138.36, -71,2,29.02.2016,,3,138.36, -71,2,01.03.2016,,3,138.36, -71,2,02.03.2016,,3,138.36, -71,2,03.03.2016,,6,138.36, -71,2,04.03.2016,,0,138.36, -71,2,05.03.2016,,3,138.36, -71,2,06.03.2016,,3,138.36, -71,2,07.03.2016,,3,138.36, -71,2,08.03.2016,,3,138.36, -71,2,09.03.2016,,19,138.36, -71,2,10.03.2016,,6,138.36, -71,2,11.03.2016,,9,138.36, -71,2,12.03.2016,,3,138.5, -71,2,13.03.2016,,3,138.5, -71,2,14.03.2016,,3,138.5, -71,2,15.03.2016,1.0,44,138.5,124.77 -71,2,16.03.2016,1.0,6,138.5,124.77 -71,2,17.03.2016,1.0,16,138.5,124.77 -71,2,18.03.2016,1.0,19,138.5,124.77 -71,2,19.03.2016,1.0,13,138.5,124.77 -71,2,20.03.2016,1.0,44,138.5,124.77 -71,2,21.03.2016,1.0,44,138.5,124.77 -71,2,22.03.2016,,0,138.36, -71,2,23.03.2016,,9,138.36, -71,2,24.03.2016,,0,138.36, -71,2,25.03.2016,,6,138.36, -71,2,26.03.2016,,3,138.36, -71,2,27.03.2016,,16,138.36, -71,2,28.03.2016,,9,138.36, -71,2,29.03.2016,,9,138.36, -71,2,30.03.2016,,6,138.36, -71,2,31.03.2016,,0,138.36, -71,2,01.04.2016,,0,138.36, -71,2,02.04.2016,,6,138.36, -71,2,03.04.2016,,6,138.36, -71,2,04.04.2016,,6,138.36, -71,2,05.04.2016,,0,138.36, -71,2,06.04.2016,,0,138.36, -71,2,07.04.2016,,6,138.36, -71,2,08.04.2016,,6,138.36, -71,2,09.04.2016,,3,138.5, -71,2,10.04.2016,,6,138.5, -71,2,11.04.2016,,6,138.5, -71,2,12.04.2016,,0,138.5, -71,2,13.04.2016,,6,138.5, -71,2,14.04.2016,,0,138.5, -71,2,15.04.2016,,9,138.5, -71,2,16.04.2016,,6,138.5, -71,2,17.04.2016,,3,138.5, -71,2,18.04.2016,,3,138.5, -71,2,19.04.2016,1.0,9,138.5,116.89 -71,2,20.04.2016,1.0,35,138.5,116.89 -71,2,21.04.2016,1.0,35,138.5,116.89 -71,2,22.04.2016,1.0,66,138.5,116.89 -71,2,23.04.2016,1.0,53,138.5,116.89 -71,2,24.04.2016,1.0,47,138.5,116.89 -71,2,25.04.2016,1.0,38,138.5,116.89 -71,2,26.04.2016,,3,138.5, -71,2,27.04.2016,,3,138.5, -71,2,28.04.2016,,3,138.5, -71,2,29.04.2016,,3,138.5, -71,2,30.04.2016,,3,138.5, -71,2,01.05.2016,,3,138.5, -71,2,02.05.2016,,3,138.5, -71,2,03.05.2016,1.0,31,138.5,106.01 -71,2,04.05.2016,1.0,19,138.5,106.01 -71,2,05.05.2016,1.0,13,138.5,106.01 -71,2,06.05.2016,1.0,35,138.5,106.01 -71,2,07.05.2016,1.0,63,138.5,106.01 -71,2,08.05.2016,1.0,94,138.5,106.01 -71,2,09.05.2016,1.0,60,138.5,106.01 -71,2,10.05.2016,,0,142.57, -71,2,11.05.2016,,0,142.57, -71,2,12.05.2016,,0,142.57, -71,2,13.05.2016,,3,142.57, -71,2,14.05.2016,,0,142.57, -71,2,15.05.2016,,0,142.57, -71,2,16.05.2016,,3,142.57, -71,2,17.05.2016,,3,142.57, -71,2,18.05.2016,,0,142.57, -71,2,19.05.2016,,3,142.57, -71,2,20.05.2016,,0,142.57, -71,2,21.05.2016,,0,142.57, -71,2,22.05.2016,,0,142.57, -72,2,01.01.2015,,0,135.78, -72,2,02.01.2015,,0,135.78, -72,2,03.01.2015,,3,135.78, -72,2,04.01.2015,,6,135.78, -72,2,05.01.2015,,3,135.78, -72,2,06.01.2015,,6,135.78, -72,2,07.01.2015,,0,135.78, -72,2,08.01.2015,,0,135.78, -72,2,09.01.2015,,0,135.78, -72,2,10.01.2015,,0,135.78, -72,2,11.01.2015,,0,135.78, -72,2,12.01.2015,,0,135.78, -72,2,13.01.2015,,0,135.78, -72,2,14.01.2015,,0,135.78, -72,2,15.01.2015,,3,135.78, -72,2,16.01.2015,,3,135.78, -72,2,17.01.2015,,6,135.78, -72,2,18.01.2015,,13,135.78, -72,2,19.01.2015,,0,135.78, -72,2,20.01.2015,,0,135.78, -72,2,21.01.2015,,0,135.78, -72,2,22.01.2015,,6,135.78, -72,2,23.01.2015,,3,135.78, -72,2,24.01.2015,,0,135.78, -72,2,25.01.2015,,3,135.78, -72,2,26.01.2015,,0,135.78, -72,2,27.01.2015,,3,135.78, -72,2,28.01.2015,,3,135.78, -72,2,29.01.2015,,3,135.78, -72,2,30.01.2015,,3,135.78, -72,2,31.01.2015,,0,135.78, -72,2,01.02.2015,,6,135.78, -72,2,02.02.2015,,9,135.78, 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-72,2,23.06.2015,,9,150.73, -72,2,24.06.2015,,6,150.73, -72,2,25.06.2015,,9,150.73, -72,2,26.06.2015,,9,150.73, -72,2,27.06.2015,,3,150.73, -72,2,28.06.2015,,0,150.73, -72,2,29.06.2015,,3,150.73, -72,2,30.06.2015,,6,150.73, -72,2,01.07.2015,,9,150.73, -72,2,02.07.2015,,9,150.73, -72,2,03.07.2015,,3,150.73, -72,2,04.07.2015,,3,150.73, -72,2,05.07.2015,,3,150.73, -72,2,06.07.2015,,6,150.73, -72,2,07.07.2015,,9,150.73, -72,2,08.07.2015,,0,150.73, -72,2,09.07.2015,,6,150.73, -72,2,10.07.2015,,9,150.73, -72,2,11.07.2015,,3,150.73, -72,2,12.07.2015,,13,150.73, -72,2,13.07.2015,,16,150.73, -72,2,14.07.2015,,3,150.73, -72,2,15.07.2015,,3,150.73, -72,2,16.07.2015,,3,150.73, -72,2,17.07.2015,,3,150.73, -72,2,18.07.2015,,3,150.73, -72,2,19.07.2015,,0,150.73, -72,2,20.07.2015,,13,150.73, -72,2,21.07.2015,,41,150.73, -72,2,22.07.2015,,3,150.73, -72,2,23.07.2015,,3,150.73, -72,2,24.07.2015,,22,150.73, -72,2,25.07.2015,,13,150.73, -72,2,26.07.2015,,13,150.73, -72,2,27.07.2015,,25,150.73, -72,2,28.07.2015,,0,150.73, -72,2,29.07.2015,,25,150.73, -72,2,30.07.2015,,13,150.73, -72,2,31.07.2015,,13,150.73, -72,2,01.08.2015,,3,150.73, -72,2,02.08.2015,,3,150.73, -72,2,03.08.2015,,25,150.73, -72,2,04.08.2015,,22,150.73, -72,2,05.08.2015,,19,150.73, -72,2,06.08.2015,,3,150.73, -72,2,07.08.2015,,16,150.73, -72,2,08.08.2015,,16,150.73, -72,2,09.08.2015,,16,150.73, -72,2,10.08.2015,,19,150.73, -72,2,11.08.2015,,6,150.73, -72,2,12.08.2015,,13,128.17, -72,2,13.08.2015,,0,128.17, -72,2,14.08.2015,,6,128.17, -72,2,15.08.2015,,16,128.17, -72,2,16.08.2015,,28,128.17, -72,2,17.08.2015,,63,128.17, -72,2,18.08.2015,1.0,198,128.17,100.3 -72,2,19.08.2015,1.0,167,128.17,100.3 -72,2,20.08.2015,1.0,170,128.17,100.3 -72,2,21.08.2015,1.0,151,128.17,100.3 -72,2,22.08.2015,1.0,210,128.17,100.3 -72,2,23.08.2015,1.0,198,128.17,100.3 -72,2,24.08.2015,1.0,182,128.17,100.3 -72,2,25.08.2015,,22,128.17, -72,2,26.08.2015,,19,128.17, -72,2,27.08.2015,,28,128.17, -72,2,28.08.2015,,31,128.17, -72,2,29.08.2015,,19,128.17, -72,2,30.08.2015,,19,128.17, -72,2,31.08.2015,,19,128.17, -72,2,01.09.2015,,6,128.17, -72,2,02.09.2015,,13,128.17, -72,2,03.09.2015,,3,128.17, -72,2,04.09.2015,,9,128.17, -72,2,05.09.2015,,25,128.17, -72,2,06.09.2015,,22,128.17, -72,2,07.09.2015,,6,128.17, -72,2,08.09.2015,,13,128.17, -72,2,09.09.2015,,22,128.17, -72,2,10.09.2015,,13,128.17, -72,2,11.09.2015,,3,128.17, -72,2,12.09.2015,,22,128.17, -72,2,13.09.2015,,16,128.17, -72,2,14.09.2015,,16,128.17, -72,2,15.09.2015,,16,128.17, -72,2,16.09.2015,,3,128.17, -72,2,17.09.2015,,6,128.17, -72,2,18.09.2015,,13,128.17, -72,2,19.09.2015,,3,128.17, -72,2,20.09.2015,,35,128.17, -72,2,21.09.2015,,13,128.17, -72,2,22.09.2015,,9,128.17, -72,2,23.09.2015,,9,128.17, -72,2,24.09.2015,,6,128.17, -72,2,25.09.2015,,0,128.17, -72,2,26.09.2015,,13,128.17, -72,2,27.09.2015,,9,128.17, -72,2,28.09.2015,,3,128.17, -72,2,29.09.2015,,13,128.17, -72,2,30.09.2015,,3,128.17, -72,2,01.10.2015,,3,162.96, -72,2,02.10.2015,,6,162.96, -72,2,03.10.2015,,6,162.96, -72,2,04.10.2015,,3,162.96, -72,2,05.10.2015,,9,162.96, -72,2,06.10.2015,1.0,145,143.39,122.05 -72,2,07.10.2015,1.0,97,143.39,122.05 -72,2,08.10.2015,1.0,123,143.39,122.05 -72,2,09.10.2015,1.0,88,143.39,122.05 -72,2,10.10.2015,1.0,132,143.39,122.05 -72,2,11.10.2015,1.0,47,143.39,122.05 -72,2,12.10.2015,1.0,145,143.39,122.05 -72,2,13.10.2015,,3,143.93, -72,2,14.10.2015,,3,143.93, -72,2,15.10.2015,,3,143.93, -72,2,16.10.2015,,3,143.93, -72,2,17.10.2015,,6,162.96, -72,2,18.10.2015,,6,162.96, -72,2,19.10.2015,,6,162.96, -72,2,20.10.2015,,6,162.96, -72,2,21.10.2015,,3,162.96, -72,2,22.10.2015,,6,162.96, -72,2,23.10.2015,,6,162.96, -72,2,24.10.2015,,3,162.96, -72,2,25.10.2015,,3,162.96, -72,2,26.10.2015,,3,162.96, -72,2,27.10.2015,,3,162.96, -72,2,28.10.2015,,3,162.96, -72,2,29.10.2015,,6,162.96, -72,2,30.10.2015,,3,162.96, -72,2,31.10.2015,,16,162.96, -72,2,01.11.2015,,6,162.96, -72,2,02.11.2015,,6,162.96, -72,2,03.11.2015,,6,162.96, -72,2,04.11.2015,,6,162.96, -72,2,05.11.2015,,6,162.96, -72,2,06.11.2015,,6,162.96, -72,2,07.11.2015,,6,162.96, -72,2,08.11.2015,,9,162.96, -72,2,09.11.2015,,6,162.96, -72,2,10.11.2015,,6,162.96, -72,2,11.11.2015,,6,138.36, -72,2,12.11.2015,,6,138.36, -72,2,13.11.2015,,6,138.36, -72,2,14.11.2015,,6,138.36, -72,2,15.11.2015,,6,138.36, -72,2,16.11.2015,,6,138.36, -72,2,17.11.2015,,6,138.36, -72,2,18.11.2015,,3,138.36, -72,2,19.11.2015,,3,138.36, -72,2,20.11.2015,,16,138.36, -72,2,21.11.2015,,9,138.36, -72,2,22.11.2015,,6,138.36, -72,2,23.11.2015,,6,138.36, -72,2,24.11.2015,,6,138.36, -72,2,25.11.2015,,6,138.36, -72,2,26.11.2015,,3,138.36, -72,2,27.11.2015,,9,138.36, -72,2,28.11.2015,,6,138.36, -72,2,29.11.2015,,9,138.36, -72,2,30.11.2015,,9,138.36, -72,2,01.12.2015,,6,162.96, -72,2,02.12.2015,,3,162.96, -72,2,03.12.2015,,6,138.5, -72,2,04.12.2015,,9,138.5, -72,2,05.12.2015,,6,138.5, -72,2,06.12.2015,,6,138.5, -72,2,07.12.2015,,13,138.5, -72,2,08.12.2015,,6,138.5, -72,2,09.12.2015,,6,138.5, -72,2,10.12.2015,,6,138.5, -72,2,11.12.2015,,6,142.57, -72,2,12.12.2015,,6,142.57, -72,2,13.12.2015,,19,142.57, -72,2,14.12.2015,,13,142.57, -72,2,15.12.2015,,6,142.57, -72,2,16.12.2015,,6,142.57, -72,2,17.12.2015,,13,142.57, -72,2,18.12.2015,,9,142.57, -72,2,19.12.2015,,3,142.57, -72,2,20.12.2015,,13,142.57, -72,2,21.12.2015,,25,142.57, -72,2,22.12.2015,1.0,223,142.57,103.02 -72,2,23.12.2015,1.0,163,142.57,103.02 -72,2,24.12.2015,1.0,141,142.57,103.02 -72,2,25.12.2015,1.0,141,142.57,103.02 -72,2,26.12.2015,1.0,185,142.57,103.02 -72,2,27.12.2015,1.0,151,142.57,103.02 -72,2,28.12.2015,1.0,104,142.57,103.02 -72,2,29.12.2015,,9,138.5, -72,2,30.12.2015,,9,138.5, -72,2,31.12.2015,,9,138.5, -72,2,01.01.2016,,9,138.5, -72,2,02.01.2016,,9,138.5, -72,2,03.01.2016,,9,138.5, -72,2,04.01.2016,,19,138.5, -72,2,05.01.2016,,19,138.5, -72,2,06.01.2016,,13,138.5, -72,2,07.01.2016,,16,138.5, -72,2,08.01.2016,,3,138.5, -72,2,09.01.2016,,13,138.5, -72,2,10.01.2016,,19,138.5, -72,2,11.01.2016,,13,138.5, -72,2,12.01.2016,,6,138.5, -72,2,13.01.2016,,6,138.5, -72,2,14.01.2016,,6,138.5, -72,2,15.01.2016,,22,138.5, -72,2,16.01.2016,,16,142.57, -72,2,17.01.2016,,16,142.57, -72,2,18.01.2016,,38,142.57, -72,2,19.01.2016,1.0,157,142.57,103.02 -72,2,20.01.2016,1.0,163,142.57,103.02 -72,2,21.01.2016,1.0,198,142.57,103.02 -72,2,22.01.2016,1.0,160,142.57,103.02 -72,2,23.01.2016,1.0,176,142.57,103.02 -72,2,24.01.2016,1.0,94,142.57,103.02 -72,2,25.01.2016,1.0,151,142.57,103.02 -72,2,26.01.2016,,9,142.57, -72,2,27.01.2016,,3,142.57, -72,2,28.01.2016,,3,142.57, -72,2,29.01.2016,,22,142.57, -72,2,30.01.2016,,6,142.57, -72,2,31.01.2016,,3,142.57, -72,2,01.02.2016,,9,142.57, -72,2,02.02.2016,,13,142.57, -72,2,03.02.2016,,13,142.57, -72,2,04.02.2016,,13,142.57, -72,2,05.02.2016,,13,142.57, -72,2,06.02.2016,,6,142.57, -72,2,07.02.2016,,13,142.57, -72,2,08.02.2016,,25,142.57, -72,2,09.02.2016,1.0,229,142.57,108.46 -72,2,10.02.2016,1.0,179,138.36,108.46 -72,2,11.02.2016,1.0,220,138.36,108.46 -72,2,12.02.2016,1.0,157,138.36,108.46 -72,2,13.02.2016,1.0,138,138.36,108.46 -72,2,14.02.2016,1.0,145,138.36,108.46 -72,2,15.02.2016,1.0,135,138.36,108.46 -72,2,16.02.2016,,6,138.36, -72,2,17.02.2016,,13,138.36, -72,2,18.02.2016,,0,138.36, -72,2,19.02.2016,,3,138.36, -72,2,20.02.2016,,0,138.36, -72,2,21.02.2016,,16,138.36, -72,2,22.02.2016,,9,138.36, -72,2,23.02.2016,,3,138.36, -72,2,24.02.2016,,0,138.36, -72,2,25.02.2016,,0,138.36, -72,2,26.02.2016,,6,138.36, -72,2,27.02.2016,,3,138.36, -72,2,28.02.2016,,9,138.36, -72,2,29.02.2016,,13,138.36, -72,2,01.03.2016,,6,138.36, -72,2,02.03.2016,,3,138.36, -72,2,03.03.2016,,6,138.36, -72,2,04.03.2016,,6,138.36, -72,2,05.03.2016,,3,138.36, -72,2,06.03.2016,,6,138.36, -72,2,07.03.2016,,3,138.36, -72,2,08.03.2016,,6,138.36, -72,2,09.03.2016,,3,138.36, -72,2,10.03.2016,,3,138.36, -72,2,11.03.2016,,6,138.36, -72,2,12.03.2016,,3,138.5, -72,2,13.03.2016,,3,138.5, -72,2,14.03.2016,,19,138.5, -72,2,15.03.2016,1.0,94,138.5,124.77 -72,2,16.03.2016,1.0,123,138.5,124.77 -72,2,17.03.2016,1.0,119,138.5,124.77 -72,2,18.03.2016,1.0,101,138.5,124.77 -72,2,19.03.2016,1.0,85,138.5,124.77 -72,2,20.03.2016,1.0,25,138.5,124.77 -72,2,21.03.2016,1.0,66,138.5,124.77 -72,2,22.03.2016,,3,138.36, -72,2,23.03.2016,,6,138.36, -72,2,24.03.2016,,6,138.36, -72,2,25.03.2016,,6,138.36, -72,2,26.03.2016,,6,138.36, -72,2,27.03.2016,,6,138.36, -72,2,28.03.2016,,6,138.36, -72,2,29.03.2016,,6,138.36, -72,2,30.03.2016,,6,138.36, -72,2,31.03.2016,,6,138.36, -72,2,01.04.2016,,3,138.36, -72,2,02.04.2016,,6,138.36, -72,2,03.04.2016,,13,138.36, -72,2,04.04.2016,,13,138.36, -72,2,05.04.2016,,6,138.36, -72,2,06.04.2016,,3,138.36, -72,2,07.04.2016,,3,138.36, -72,2,08.04.2016,,3,138.36, -72,2,09.04.2016,,3,138.5, -72,2,10.04.2016,,3,138.5, -72,2,11.04.2016,,6,138.5, -72,2,12.04.2016,,6,138.5, -72,2,13.04.2016,,6,138.5, -72,2,14.04.2016,,6,138.5, -72,2,15.04.2016,,6,138.5, -72,2,16.04.2016,,6,138.5, -72,2,17.04.2016,,3,138.5, -72,2,18.04.2016,,3,138.5, -72,2,19.04.2016,1.0,72,138.5,116.89 -72,2,20.04.2016,1.0,75,138.5,116.89 -72,2,21.04.2016,1.0,66,138.5,116.89 -72,2,22.04.2016,1.0,129,138.5,116.89 -72,2,23.04.2016,1.0,123,138.5,116.89 -72,2,24.04.2016,1.0,88,138.5,116.89 -72,2,25.04.2016,1.0,110,138.5,116.89 -72,2,26.04.2016,,6,138.5, -72,2,27.04.2016,,6,138.5, -72,2,28.04.2016,,6,138.5, -72,2,29.04.2016,,6,138.5, -72,2,30.04.2016,,6,138.5, -72,2,01.05.2016,,9,138.5, -72,2,02.05.2016,,6,138.5, -72,2,03.05.2016,1.0,223,138.5,106.01 -72,2,04.05.2016,1.0,192,138.5,106.01 -72,2,05.05.2016,1.0,220,138.5,106.01 -72,2,06.05.2016,1.0,223,138.5,106.01 -72,2,07.05.2016,1.0,229,138.5,106.01 -72,2,08.05.2016,1.0,129,138.5,106.01 -72,2,09.05.2016,1.0,44,138.5,106.01 -72,2,10.05.2016,,31,138.5, -72,2,11.05.2016,,6,138.5, -72,2,12.05.2016,,3,138.5, -72,2,13.05.2016,,9,138.5, -72,2,14.05.2016,,6,138.5, -72,2,15.05.2016,,38,138.5, -72,2,16.05.2016,,9,138.5, -72,2,17.05.2016,,9,138.5, -72,2,18.05.2016,,3,138.5, -72,2,19.05.2016,,9,138.5, -72,2,20.05.2016,,3,138.5, -72,2,21.05.2016,,3,138.5, -72,2,22.05.2016,,9,138.5, -73,2,01.01.2015,,0,135.78, -73,2,02.01.2015,,9,135.78, -73,2,03.01.2015,,0,135.78, -73,2,04.01.2015,,6,135.78, -73,2,05.01.2015,,3,135.78, -73,2,06.01.2015,,3,135.78, -73,2,07.01.2015,,0,135.78, -73,2,08.01.2015,,0,135.78, -73,2,09.01.2015,,0,135.78, -73,2,10.01.2015,,0,135.78, -73,2,11.01.2015,,0,135.78, -73,2,12.01.2015,,0,135.78, -73,2,13.01.2015,,0,135.78, -73,2,14.01.2015,,3,135.78, -73,2,15.01.2015,,3,135.78, -73,2,16.01.2015,,3,135.78, -73,2,17.01.2015,,3,135.78, -73,2,18.01.2015,,9,135.78, -73,2,19.01.2015,,9,135.78, -73,2,20.01.2015,,6,135.78, -73,2,21.01.2015,,3,135.78, -73,2,22.01.2015,,3,135.78, -73,2,23.01.2015,,13,135.78, -73,2,24.01.2015,,3,135.78, -73,2,25.01.2015,,3,135.78, -73,2,26.01.2015,,9,135.78, -73,2,27.01.2015,,6,135.78, -73,2,28.01.2015,,13,135.78, -73,2,29.01.2015,,9,135.78, -73,2,30.01.2015,,3,135.78, -73,2,31.01.2015,,3,135.78, -73,2,01.02.2015,,3,135.78, -73,2,02.02.2015,,3,135.78, -73,2,03.02.2015,,3,135.78, -73,2,04.02.2015,,6,135.78, -73,2,05.02.2015,,0,135.78, -73,2,06.02.2015,,6,135.78, -73,2,07.02.2015,,9,135.78, -73,2,08.02.2015,,3,135.78, -73,2,09.02.2015,,0,135.78, -73,2,10.02.2015,,13,135.78, -73,2,11.02.2015,,3,135.78, -73,2,12.02.2015,,6,135.78, -73,2,13.02.2015,,3,135.78, -73,2,14.02.2015,,0,135.78, -73,2,15.02.2015,,6,135.78, -73,2,16.02.2015,,3,135.78, -73,2,17.02.2015,,6,135.78, -73,2,18.02.2015,,6,135.78, -73,2,19.02.2015,,0,135.78, -73,2,20.02.2015,,16,135.78, -73,2,21.02.2015,,13,135.78, -73,2,22.02.2015,,0,135.78, -73,2,23.02.2015,,0,135.78, -73,2,24.02.2015,,0,135.78, -73,2,25.02.2015,,0,135.78, -73,2,26.02.2015,,0,135.78, -73,2,27.02.2015,,0,135.78, -73,2,28.02.2015,,0,135.78, -73,2,01.03.2015,,0,146.38, -73,2,02.03.2015,,0,146.38, -73,2,03.03.2015,,0,146.38, -73,2,04.03.2015,,13,146.38, -73,2,05.03.2015,,0,146.38, -73,2,06.03.2015,,0,146.38, -73,2,07.03.2015,,3,146.38, -73,2,08.03.2015,,3,146.38, -73,2,09.03.2015,,6,146.38, -73,2,10.03.2015,,3,146.38, -73,2,11.03.2015,,3,146.38, -73,2,12.03.2015,,0,146.38, -73,2,13.03.2015,,0,146.38, -73,2,14.03.2015,,6,146.38, -73,2,15.03.2015,,6,146.38, -73,2,16.03.2015,,6,146.38, -73,2,17.03.2015,,6,146.38, -73,2,18.03.2015,,0,146.38, -73,2,19.03.2015,,0,146.38, -73,2,20.03.2015,,3,146.38, -73,2,21.03.2015,,3,146.38, -73,2,22.03.2015,,6,146.38, -73,2,23.03.2015,,9,146.38, -73,2,24.03.2015,,6,146.38, -73,2,25.03.2015,,3,146.38, -73,2,26.03.2015,,0,146.38, -73,2,27.03.2015,,3,146.38, -73,2,28.03.2015,,3,146.38, -73,2,29.03.2015,,3,146.38, -73,2,30.03.2015,,6,146.38, -73,2,31.03.2015,,3,146.38, -73,2,01.04.2015,,9,146.38, -73,2,02.04.2015,,3,146.38, -73,2,03.04.2015,,3,146.38, -73,2,04.04.2015,,3,146.38, -73,2,05.04.2015,,3,146.38, -73,2,06.04.2015,,3,146.38, -73,2,07.04.2015,,3,146.38, -73,2,08.04.2015,,0,146.38, -73,2,09.04.2015,,3,146.38, -73,2,10.04.2015,,0,146.38, -73,2,11.04.2015,,6,146.38, -73,2,12.04.2015,,3,146.38, -73,2,13.04.2015,,0,146.38, -73,2,14.04.2015,,3,146.38, -73,2,15.04.2015,,0,146.38, -73,2,16.04.2015,,3,146.38, -73,2,17.04.2015,,0,146.38, -73,2,18.04.2015,,3,146.38, -73,2,19.04.2015,,9,146.38, -73,2,20.04.2015,,0,146.38, -73,2,21.04.2015,,19,146.38, -73,2,22.04.2015,,3,146.38, -73,2,23.04.2015,,6,146.38, -73,2,24.04.2015,,0,146.38, -73,2,25.04.2015,,3,146.38, -73,2,26.04.2015,,0,146.38, -73,2,27.04.2015,,9,146.38, -73,2,28.04.2015,,3,150.73, -73,2,29.04.2015,,6,150.73, -73,2,30.04.2015,,3,150.73, -73,2,01.05.2015,,9,150.73, -73,2,02.05.2015,,6,150.73, -73,2,03.05.2015,,6,150.73, -73,2,04.05.2015,,6,150.73, -73,2,05.05.2015,,0,150.73, -73,2,06.05.2015,,6,150.73, -73,2,07.05.2015,,3,150.73, -73,2,08.05.2015,,6,150.73, -73,2,09.05.2015,,3,150.73, -73,2,10.05.2015,,3,150.73, -73,2,11.05.2015,,0,150.73, -73,2,12.05.2015,,0,150.73, -73,2,13.05.2015,,3,150.73, -73,2,14.05.2015,,19,150.73, -73,2,15.05.2015,,13,150.73, -73,2,16.05.2015,,3,150.73, -73,2,17.05.2015,,0,150.73, 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-73,2,27.07.2015,,19,155.35, -73,2,28.07.2015,,38,155.35, -73,2,29.07.2015,,31,155.35, -73,2,30.07.2015,,25,155.35, -73,2,31.07.2015,,19,155.35, -73,2,01.08.2015,,22,155.35, -73,2,02.08.2015,,19,155.35, -73,2,03.08.2015,,69,155.35, -73,2,04.08.2015,,31,155.35, -73,2,05.08.2015,,38,155.35, -73,2,06.08.2015,,28,155.35, -73,2,07.08.2015,,28,155.35, -73,2,08.08.2015,,6,155.35, -73,2,09.08.2015,,9,155.35, -73,2,10.08.2015,,28,155.35, -73,2,11.08.2015,,6,155.35, -73,2,12.08.2015,,3,128.17, -73,2,13.08.2015,,6,128.17, -73,2,14.08.2015,,9,128.17, -73,2,15.08.2015,,19,128.17, -73,2,16.08.2015,,16,128.17, -73,2,17.08.2015,,13,128.17, -73,2,18.08.2015,1.0,157,128.17,100.3 -73,2,19.08.2015,1.0,104,128.17,100.3 -73,2,20.08.2015,1.0,132,128.17,100.3 -73,2,21.08.2015,1.0,148,128.17,100.3 -73,2,22.08.2015,1.0,116,128.17,100.3 -73,2,23.08.2015,1.0,129,128.17,100.3 -73,2,24.08.2015,1.0,141,128.17,100.3 -73,2,25.08.2015,,6,128.17, -73,2,26.08.2015,,9,128.17, -73,2,27.08.2015,,19,128.17, -73,2,28.08.2015,,53,128.17, -73,2,29.08.2015,,31,128.17, -73,2,30.08.2015,,16,128.17, -73,2,31.08.2015,,13,128.17, -73,2,01.09.2015,,6,128.17, -73,2,02.09.2015,,13,128.17, -73,2,03.09.2015,,9,128.17, -73,2,04.09.2015,,19,128.17, -73,2,05.09.2015,,6,128.17, -73,2,06.09.2015,,9,128.17, -73,2,07.09.2015,,16,128.17, -73,2,08.09.2015,,22,128.17, -73,2,09.09.2015,,16,128.17, -73,2,10.09.2015,,16,128.17, -73,2,11.09.2015,,9,128.17, -73,2,12.09.2015,,16,128.17, -73,2,13.09.2015,,9,128.17, -73,2,14.09.2015,,13,128.17, -73,2,15.09.2015,,13,128.17, -73,2,16.09.2015,,13,128.17, -73,2,17.09.2015,,47,128.17, -73,2,18.09.2015,,28,128.17, -73,2,19.09.2015,,3,128.17, -73,2,20.09.2015,,19,128.17, -73,2,21.09.2015,,16,128.17, -73,2,22.09.2015,,16,128.17, -73,2,23.09.2015,,6,128.17, -73,2,24.09.2015,,9,128.17, -73,2,25.09.2015,,22,128.17, -73,2,26.09.2015,,16,128.17, -73,2,27.09.2015,,13,128.17, -73,2,28.09.2015,,9,128.17, -73,2,29.09.2015,,16,128.17, -73,2,30.09.2015,,13,128.17, -73,2,01.10.2015,,3,162.96, -73,2,02.10.2015,,9,162.96, -73,2,03.10.2015,,6,162.96, -73,2,04.10.2015,,19,162.96, -73,2,05.10.2015,,3,162.96, -73,2,06.10.2015,1.0,85,143.39,122.05 -73,2,07.10.2015,1.0,57,143.39,122.05 -73,2,08.10.2015,1.0,79,143.39,122.05 -73,2,09.10.2015,1.0,57,143.39,122.05 -73,2,10.10.2015,1.0,41,143.39,122.05 -73,2,11.10.2015,1.0,82,143.39,122.05 -73,2,12.10.2015,1.0,28,143.39,122.05 -73,2,13.10.2015,1.0,22,143.93,122.05 -73,2,14.10.2015,1.0,28,143.93,122.05 -73,2,15.10.2015,1.0,9,143.93,122.05 -73,2,16.10.2015,1.0,63,143.93,122.05 -73,2,17.10.2015,1.0,53,162.96,122.05 -73,2,18.10.2015,1.0,44,162.96,122.05 -73,2,19.10.2015,,0,162.96, -73,2,20.10.2015,,3,162.96, -73,2,21.10.2015,,0,162.96, -73,2,22.10.2015,,9,162.96, -73,2,23.10.2015,,0,162.96, -73,2,24.10.2015,,0,162.96, -73,2,25.10.2015,,3,162.96, -73,2,26.10.2015,,3,162.96, -73,2,27.10.2015,,0,162.96, -73,2,28.10.2015,,9,162.96, -73,2,29.10.2015,,0,162.96, -73,2,30.10.2015,,6,162.96, -73,2,31.10.2015,,3,162.96, 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-73,2,08.01.2016,,16,138.5, -73,2,09.01.2016,,6,138.5, -73,2,10.01.2016,,3,138.5, -73,2,11.01.2016,,3,138.5, -73,2,12.01.2016,,19,138.5, -73,2,13.01.2016,,3,138.5, -73,2,14.01.2016,,9,138.5, -73,2,15.01.2016,,25,138.5, -73,2,16.01.2016,,3,142.57, -73,2,17.01.2016,,6,142.57, -73,2,18.01.2016,,13,142.57, -73,2,19.01.2016,1.0,119,142.57,103.02 -73,2,20.01.2016,1.0,132,142.57,103.02 -73,2,21.01.2016,1.0,148,142.57,103.02 -73,2,22.01.2016,1.0,170,142.57,103.02 -73,2,23.01.2016,1.0,154,142.57,103.02 -73,2,24.01.2016,1.0,195,142.57,103.02 -73,2,25.01.2016,1.0,210,142.57,103.02 -73,2,26.01.2016,,3,142.57, -73,2,27.01.2016,,13,142.57, -73,2,28.01.2016,,22,142.57, -73,2,29.01.2016,,3,142.57, -73,2,30.01.2016,,9,142.57, -73,2,31.01.2016,,13,142.57, -73,2,01.02.2016,,9,142.57, -73,2,02.02.2016,,9,142.57, -73,2,03.02.2016,,9,142.57, -73,2,04.02.2016,,3,142.57, -73,2,05.02.2016,,19,142.57, -73,2,06.02.2016,,9,142.57, -73,2,07.02.2016,,9,142.57, -73,2,08.02.2016,,28,142.57, -73,2,09.02.2016,1.0,226,142.57,108.46 -73,2,10.02.2016,1.0,163,138.36,108.46 -73,2,11.02.2016,1.0,207,138.36,108.46 -73,2,12.02.2016,1.0,163,138.36,108.46 -73,2,13.02.2016,1.0,173,138.36,108.46 -73,2,14.02.2016,1.0,154,138.36,108.46 -73,2,15.02.2016,1.0,160,138.36,108.46 -73,2,16.02.2016,,19,138.36, -73,2,17.02.2016,,13,138.36, -73,2,18.02.2016,,9,138.36, -73,2,19.02.2016,,6,138.36, -73,2,20.02.2016,,3,138.36, -73,2,21.02.2016,,19,138.36, -73,2,22.02.2016,,13,138.36, -73,2,23.02.2016,,3,138.36, -73,2,24.02.2016,,13,138.36, -73,2,25.02.2016,,3,138.36, -73,2,26.02.2016,,3,138.36, -73,2,27.02.2016,,9,138.36, -73,2,28.02.2016,,13,138.36, -73,2,29.02.2016,,9,138.36, -73,2,01.03.2016,,3,138.36, -73,2,02.03.2016,,9,138.36, -73,2,03.03.2016,,3,138.36, -73,2,04.03.2016,,6,138.36, -73,2,05.03.2016,,9,138.36, -73,2,06.03.2016,,3,138.36, -73,2,07.03.2016,,9,138.36, -73,2,08.03.2016,,9,138.36, -73,2,09.03.2016,,9,138.36, -73,2,10.03.2016,,6,138.36, -73,2,11.03.2016,,6,138.36, -73,2,12.03.2016,,6,138.5, -73,2,13.03.2016,,9,138.5, -73,2,14.03.2016,,3,138.5, -73,2,15.03.2016,1.0,167,138.5,124.77 -73,2,16.03.2016,1.0,160,138.5,124.77 -73,2,17.03.2016,1.0,69,138.5,124.77 -73,2,18.03.2016,1.0,47,138.5,124.77 -73,2,19.03.2016,1.0,104,138.5,124.77 -73,2,20.03.2016,1.0,28,138.5,124.77 -73,2,21.03.2016,1.0,132,138.5,124.77 -73,2,22.03.2016,,6,138.36, -73,2,23.03.2016,,6,138.36, -73,2,24.03.2016,,3,138.36, -73,2,25.03.2016,,6,138.36, -73,2,26.03.2016,,3,138.36, -73,2,27.03.2016,,16,138.36, -73,2,28.03.2016,,9,138.36, -73,2,29.03.2016,,6,138.36, -73,2,30.03.2016,,6,138.36, -73,2,31.03.2016,,3,138.36, -73,2,01.04.2016,,6,138.36, -73,2,02.04.2016,,6,138.36, -73,2,03.04.2016,,3,138.36, -73,2,04.04.2016,,6,138.36, -73,2,05.04.2016,,6,138.36, -73,2,06.04.2016,,3,138.36, -73,2,07.04.2016,,0,138.36, -73,2,08.04.2016,,0,138.36, -73,2,09.04.2016,,9,138.5, -73,2,10.04.2016,,9,138.5, -73,2,11.04.2016,,0,138.5, -73,2,12.04.2016,,0,138.5, -73,2,13.04.2016,,13,138.5, -73,2,14.04.2016,,0,138.5, -73,2,15.04.2016,,3,138.5, -73,2,16.04.2016,,3,138.5, -73,2,17.04.2016,,0,138.5, -73,2,18.04.2016,,13,138.5, -73,2,19.04.2016,1.0,82,138.5,116.89 -73,2,20.04.2016,1.0,63,138.5,116.89 -73,2,21.04.2016,1.0,97,138.5,116.89 -73,2,22.04.2016,1.0,35,138.5,116.89 -73,2,23.04.2016,1.0,38,138.5,116.89 -73,2,24.04.2016,1.0,107,138.5,116.89 -73,2,25.04.2016,1.0,129,138.5,116.89 -73,2,26.04.2016,1.0,41,138.5,114.17 -73,2,27.04.2016,1.0,25,138.5,114.17 -73,2,28.04.2016,,6,138.5, -73,2,29.04.2016,,0,138.5, -73,2,30.04.2016,,6,138.5, -73,2,01.05.2016,,3,138.5, -73,2,02.05.2016,,3,138.5, -73,2,03.05.2016,1.0,157,138.5,106.01 -73,2,04.05.2016,1.0,170,138.5,106.01 -73,2,05.05.2016,1.0,173,138.5,106.01 -73,2,06.05.2016,1.0,132,138.5,106.01 -73,2,07.05.2016,1.0,101,138.5,106.01 -73,2,08.05.2016,1.0,97,138.5,106.01 -73,2,09.05.2016,1.0,132,138.5,106.01 -73,2,10.05.2016,,16,138.5, -73,2,11.05.2016,,3,138.5, -73,2,12.05.2016,,13,138.5, -73,2,13.05.2016,,13,138.5, -73,2,14.05.2016,,6,138.5, -73,2,15.05.2016,,3,138.5, -73,2,16.05.2016,,0,138.5, -73,2,17.05.2016,,3,138.5, -73,2,18.05.2016,,13,138.5, -73,2,19.05.2016,,9,138.5, -73,2,20.05.2016,,3,138.5, -73,2,21.05.2016,,9,138.5, -73,2,22.05.2016,,0,138.5, -74,2,28.08.2015,,9,128.17, -74,2,29.08.2015,,13,128.17, -74,2,30.08.2015,,0,128.17, -74,2,31.08.2015,,19,128.17, -74,2,01.09.2015,,0,128.17, -74,2,02.09.2015,,6,128.17, -74,2,03.09.2015,,9,128.17, -74,2,04.09.2015,,6,128.17, -74,2,05.09.2015,,13,128.17, -74,2,06.09.2015,,22,128.17, -74,2,07.09.2015,,6,128.17, -74,2,08.09.2015,,25,128.17, -74,2,09.09.2015,,13,128.17, -74,2,10.09.2015,,16,128.17, -74,2,11.09.2015,,9,128.17, -74,2,12.09.2015,,13,128.17, -74,2,13.09.2015,,25,128.17, -74,2,14.09.2015,,0,128.17, -74,2,15.09.2015,,0,128.17, -74,2,16.09.2015,,28,128.17, -74,2,17.09.2015,,6,128.17, -74,2,18.09.2015,,22,128.17, -74,2,19.09.2015,,19,128.17, -74,2,20.09.2015,,31,128.17, -74,2,21.09.2015,,13,128.17, -74,2,22.09.2015,,6,128.17, -74,2,23.09.2015,,16,128.17, -74,2,24.09.2015,,9,128.17, -74,2,25.09.2015,,16,128.17, -74,2,26.09.2015,,9,128.17, -74,2,27.09.2015,,6,128.17, -74,2,28.09.2015,,19,128.17, -74,2,29.09.2015,,0,128.17, -74,2,30.09.2015,,25,128.17, -74,2,01.10.2015,,0,162.96, -74,2,02.10.2015,,0,162.96, -74,2,03.10.2015,,6,162.96, -74,2,04.10.2015,,13,162.96, -74,2,05.10.2015,,9,162.96, -74,2,06.10.2015,1.0,63,143.39,122.05 -74,2,07.10.2015,1.0,31,143.39,122.05 -74,2,08.10.2015,1.0,47,143.39,122.05 -74,2,09.10.2015,1.0,60,143.39,122.05 -74,2,10.10.2015,1.0,28,143.39,122.05 -74,2,11.10.2015,1.0,38,143.39,122.05 -74,2,12.10.2015,1.0,35,143.39,122.05 -74,2,13.10.2015,1.0,35,143.93,122.05 -74,2,14.10.2015,1.0,28,143.93,122.05 -74,2,15.10.2015,1.0,28,143.93,122.05 -74,2,16.10.2015,1.0,25,143.93,122.05 -74,2,17.10.2015,1.0,22,162.96,122.05 -74,2,18.10.2015,1.0,22,162.96,122.05 -74,2,19.10.2015,,0,162.96, -74,2,20.10.2015,,0,162.96, -74,2,21.10.2015,,0,162.96, -74,2,22.10.2015,,3,162.96, -74,2,23.10.2015,,0,162.96, -74,2,24.10.2015,,0,162.96, -74,2,25.10.2015,,3,162.96, -74,2,26.10.2015,,0,162.96, -74,2,27.10.2015,,0,162.96, -74,2,28.10.2015,,0,162.96, -74,2,29.10.2015,,6,162.96, -74,2,30.10.2015,,3,162.96, -74,2,31.10.2015,,6,162.96, -74,2,01.11.2015,,3,162.96, -74,2,02.11.2015,,0,162.96, -74,2,03.11.2015,,0,162.96, -74,2,04.11.2015,,0,162.96, -74,2,05.11.2015,,6,162.96, -74,2,06.11.2015,,0,162.96, -74,2,07.11.2015,,0,162.96, -74,2,08.11.2015,,3,162.96, -74,2,09.11.2015,,0,162.96, -74,2,10.11.2015,,3,162.96, -74,2,11.11.2015,,6,138.36, -74,2,12.11.2015,,9,138.36, -74,2,13.11.2015,,3,138.36, -74,2,14.11.2015,,0,138.36, -74,2,15.11.2015,,0,138.36, -74,2,16.11.2015,,3,138.36, -74,2,17.11.2015,,19,138.36, -74,2,18.11.2015,,3,138.36, -74,2,19.11.2015,,3,138.36, -74,2,20.11.2015,,0,138.36, -74,2,21.11.2015,,3,138.36, -74,2,22.11.2015,,0,138.36, -74,2,23.11.2015,,0,138.36, -74,2,24.11.2015,,0,138.36, -74,2,25.11.2015,,0,138.36, -74,2,26.11.2015,,3,138.36, -74,2,27.11.2015,,0,138.36, -74,2,28.11.2015,,0,138.36, -74,2,29.11.2015,,3,138.36, -74,2,30.11.2015,,0,138.36, -74,2,01.12.2015,,0,162.96, -74,2,02.12.2015,,6,162.96, -74,2,03.12.2015,,0,138.5, -74,2,04.12.2015,,0,138.5, -74,2,05.12.2015,,0,138.5, -74,2,06.12.2015,,0,138.5, -74,2,07.12.2015,,6,138.5, -74,2,08.12.2015,,0,138.5, -74,2,09.12.2015,,0,138.5, -74,2,10.12.2015,,6,138.5, -74,2,11.12.2015,,3,142.57, -74,2,12.12.2015,,0,142.57, -74,2,13.12.2015,,0,142.57, -74,2,14.12.2015,,0,142.57, -74,2,15.12.2015,,0,142.57, -74,2,16.12.2015,,3,142.57, -74,2,17.12.2015,,0,142.57, -74,2,18.12.2015,,13,142.57, -74,2,19.12.2015,,3,142.57, -74,2,20.12.2015,,3,142.57, -74,2,21.12.2015,,25,142.57, -74,2,22.12.2015,1.0,88,142.57,103.02 -74,2,23.12.2015,1.0,79,142.57,103.02 -74,2,24.12.2015,1.0,79,142.57,103.02 -74,2,25.12.2015,1.0,60,142.57,103.02 -74,2,26.12.2015,1.0,97,142.57,103.02 -74,2,27.12.2015,1.0,22,142.57,103.02 -74,2,28.12.2015,1.0,31,142.57,103.02 -74,2,29.12.2015,,6,138.5, -74,2,30.12.2015,,0,138.5, -74,2,31.12.2015,,0,138.5, -74,2,01.01.2016,,0,138.5, -74,2,02.01.2016,,0,138.5, -74,2,03.01.2016,,0,138.5, -74,2,04.01.2016,,13,138.5, -74,2,05.01.2016,,0,138.5, -74,2,06.01.2016,,0,138.5, -74,2,07.01.2016,,0,138.5, -74,2,08.01.2016,,0,138.5, -74,2,09.01.2016,,6,138.5, -74,2,10.01.2016,,0,138.5, -74,2,11.01.2016,,6,138.5, -74,2,12.01.2016,,0,138.5, -74,2,13.01.2016,,9,138.5, -74,2,14.01.2016,,3,138.5, -74,2,15.01.2016,,9,138.5, -74,2,16.01.2016,,6,142.57, -74,2,17.01.2016,,3,142.57, -74,2,18.01.2016,,25,142.57, -74,2,19.01.2016,1.0,198,142.57,103.02 -74,2,20.01.2016,1.0,91,142.57,103.02 -74,2,21.01.2016,1.0,119,142.57,103.02 -74,2,22.01.2016,1.0,104,142.57,103.02 -74,2,23.01.2016,1.0,104,142.57,103.02 -74,2,24.01.2016,1.0,110,142.57,103.02 -74,2,25.01.2016,1.0,97,142.57,103.02 -74,2,26.01.2016,,22,142.57, -74,2,27.01.2016,,22,142.57, -74,2,28.01.2016,,31,142.57, -74,2,29.01.2016,,6,142.57, -74,2,30.01.2016,,9,142.57, -74,2,31.01.2016,,3,142.57, 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-74,2,09.05.2016,1.0,50,138.5,106.01 -74,2,10.05.2016,,35,138.5, -74,2,11.05.2016,,9,138.5, -74,2,12.05.2016,,35,138.5, -74,2,13.05.2016,,13,138.5, -74,2,14.05.2016,,0,138.5, -74,2,15.05.2016,,6,138.5, -74,2,16.05.2016,,6,138.5, -74,2,17.05.2016,,9,138.5, -74,2,18.05.2016,,0,138.5, -74,2,19.05.2016,,3,138.5, -74,2,20.05.2016,,0,138.5, -74,2,21.05.2016,,6,138.5, -74,2,22.05.2016,,0,138.5, -75,2,01.08.2015,,0,150.73, -75,2,02.08.2015,,0,150.73, -75,2,03.08.2015,,0,150.73, -75,2,04.08.2015,,0,150.73, -75,2,05.08.2015,,0,150.73, -75,2,06.08.2015,,0,150.73, -75,2,07.08.2015,,0,150.73, -75,2,08.08.2015,,0,150.73, -75,2,09.08.2015,,0,150.73, -75,2,10.08.2015,,3,150.73, -75,2,11.08.2015,,6,150.73, -75,2,12.08.2015,,3,128.17, -75,2,13.08.2015,,6,128.17, -75,2,14.08.2015,,6,128.17, -75,2,15.08.2015,,6,128.17, -75,2,16.08.2015,,6,128.17, -75,2,17.08.2015,,6,128.17, -75,2,18.08.2015,1.0,38,128.17,100.3 -75,2,19.08.2015,1.0,50,128.17,100.3 -75,2,20.08.2015,1.0,47,128.17,100.3 -75,2,21.08.2015,1.0,116,128.17,100.3 -75,2,22.08.2015,1.0,110,128.17,100.3 -75,2,23.08.2015,1.0,60,128.17,100.3 -75,2,24.08.2015,1.0,69,128.17,100.3 -75,2,25.08.2015,,6,128.17, -75,2,26.08.2015,,6,128.17, -75,2,27.08.2015,,6,128.17, -75,2,28.08.2015,,6,128.17, -75,2,29.08.2015,,6,128.17, -75,2,30.08.2015,,6,128.17, -75,2,31.08.2015,,6,128.17, -75,2,01.09.2015,,6,128.17, -75,2,02.09.2015,,6,128.17, -75,2,03.09.2015,,6,128.17, -75,2,04.09.2015,,6,128.17, -75,2,05.09.2015,,6,128.17, -75,2,06.09.2015,,6,128.17, -75,2,07.09.2015,,6,128.17, -75,2,08.09.2015,,6,128.17, -75,2,09.09.2015,,6,128.17, -75,2,10.09.2015,,6,128.17, -75,2,11.09.2015,,6,128.17, -75,2,12.09.2015,,6,128.17, -75,2,13.09.2015,,6,128.17, -75,2,14.09.2015,,6,128.17, -75,2,15.09.2015,,0,128.17, -75,2,16.09.2015,,0,128.17, -75,2,17.09.2015,,0,128.17, -75,2,18.09.2015,,0,128.17, -75,2,19.09.2015,,0,128.17, -75,2,20.09.2015,,0,128.17, -75,2,21.09.2015,,0,128.17, -75,2,22.09.2015,,0,128.17, -75,2,23.09.2015,,0,128.17, -75,2,24.09.2015,,0,128.17, -75,2,25.09.2015,,0,128.17, -75,2,26.09.2015,,0,128.17, -75,2,27.09.2015,,0,128.17, -75,2,28.09.2015,,0,128.17, -75,2,29.09.2015,,0,128.17, -75,2,30.09.2015,,9,128.17, -75,2,01.10.2015,,0,162.96, -75,2,02.10.2015,,9,162.96, -75,2,03.10.2015,,3,162.96, -75,2,04.10.2015,,3,162.96, -75,2,05.10.2015,,9,162.96, -75,2,06.10.2015,1.0,69,143.39,122.05 -75,2,07.10.2015,1.0,41,143.39,122.05 -75,2,08.10.2015,1.0,82,143.39,122.05 -75,2,09.10.2015,1.0,28,143.39,122.05 -75,2,10.10.2015,1.0,53,143.39,122.05 -75,2,11.10.2015,1.0,63,143.39,122.05 -75,2,12.10.2015,1.0,63,143.39,122.05 -75,2,13.10.2015,,6,143.93, -75,2,14.10.2015,,6,143.93, -75,2,15.10.2015,,6,143.93, -75,2,16.10.2015,,6,143.93, -75,2,17.10.2015,,6,143.93, -75,2,18.10.2015,,6,143.93, -75,2,19.10.2015,,6,143.93, -75,2,20.10.2015,,6,143.93, -75,2,21.10.2015,,6,143.93, -75,2,22.10.2015,,6,143.93, -75,2,23.10.2015,,6,143.93, -75,2,24.10.2015,,6,143.93, -75,2,25.10.2015,,6,143.93, -75,2,26.10.2015,,6,143.93, -75,2,27.10.2015,,6,143.93, -75,2,28.10.2015,,6,143.93, -75,2,29.10.2015,,6,143.93, -75,2,30.10.2015,,6,143.93, -75,2,31.10.2015,,6,143.93, -75,2,01.11.2015,,0,143.93, -75,2,02.11.2015,,0,143.93, -75,2,03.11.2015,,0,143.93, -75,2,04.11.2015,,0,143.93, -75,2,05.11.2015,,0,143.93, -75,2,06.11.2015,,0,143.93, -75,2,07.11.2015,,0,143.93, -75,2,08.11.2015,,0,143.93, -75,2,09.11.2015,,0,143.93, -75,2,10.11.2015,,0,143.93, -75,2,11.11.2015,,0,143.93, -75,2,12.11.2015,,0,143.93, -75,2,13.11.2015,,0,143.93, -75,2,14.11.2015,,0,143.93, -75,2,15.11.2015,,0,143.93, -75,2,16.11.2015,,0,143.93, -75,2,17.11.2015,,0,143.93, -75,2,18.11.2015,,0,143.93, -75,2,19.11.2015,,0,143.93, -75,2,20.11.2015,,0,143.93, -75,2,21.11.2015,,0,143.93, -75,2,22.11.2015,,0,143.93, -75,2,23.11.2015,,0,143.93, -75,2,24.11.2015,,0,143.93, -75,2,25.11.2015,,0,143.93, -75,2,26.11.2015,,0,143.93, -75,2,27.11.2015,,0,143.93, -75,2,28.11.2015,,0,143.93, -75,2,29.11.2015,,0,143.93, -75,2,30.11.2015,,0,143.93, -75,2,01.12.2015,,0,143.93, -75,2,02.12.2015,,0,143.93, -75,2,03.12.2015,,0,143.93, -75,2,04.12.2015,,0,143.93, -75,2,05.12.2015,,0,143.93, -75,2,06.12.2015,,0,143.93, -75,2,07.12.2015,,0,143.93, -75,2,08.12.2015,,0,143.93, -75,2,09.12.2015,,0,138.5, -75,2,10.12.2015,,0,138.5, -75,2,11.12.2015,,0,142.57, -75,2,12.12.2015,,0,142.57, -75,2,13.12.2015,,0,142.57, -75,2,14.12.2015,,0,142.57, -75,2,15.12.2015,,3,142.57, -75,2,16.12.2015,,13,142.57, -75,2,17.12.2015,,3,142.57, -75,2,18.12.2015,,0,142.57, -75,2,19.12.2015,,0,142.57, -75,2,20.12.2015,,0,142.57, -75,2,21.12.2015,,6,142.57, -75,2,22.12.2015,1.0,104,142.57,103.02 -75,2,23.12.2015,1.0,75,142.57,103.02 -75,2,24.12.2015,1.0,66,142.57,103.02 -75,2,25.12.2015,1.0,31,142.57,103.02 -75,2,26.12.2015,1.0,19,142.57,103.02 -75,2,27.12.2015,1.0,66,142.57,103.02 -75,2,28.12.2015,1.0,66,142.57,103.02 -75,2,29.12.2015,,3,138.5, -75,2,30.12.2015,,3,138.5, -75,2,31.12.2015,,3,138.5, -75,2,01.01.2016,,3,138.5, -75,2,02.01.2016,,3,138.5, -75,2,03.01.2016,,3,138.5, -75,2,04.01.2016,,19,138.5, -75,2,05.01.2016,,19,138.5, -75,2,06.01.2016,,3,138.5, -75,2,07.01.2016,,3,138.5, -75,2,08.01.2016,,3,138.5, -75,2,09.01.2016,,3,138.5, -75,2,10.01.2016,,3,138.5, -75,2,11.01.2016,,16,138.5, -75,2,12.01.2016,,22,138.5, -75,2,13.01.2016,,6,138.5, -75,2,14.01.2016,,6,138.5, -75,2,15.01.2016,,6,138.5, -75,2,16.01.2016,,6,142.57, -75,2,17.01.2016,,6,142.57, -75,2,18.01.2016,,0,142.57, -75,2,19.01.2016,1.0,97,142.57,103.02 -75,2,20.01.2016,1.0,69,142.57,103.02 -75,2,21.01.2016,1.0,66,142.57,103.02 -75,2,22.01.2016,1.0,50,142.57,103.02 -75,2,23.01.2016,1.0,75,142.57,103.02 -75,2,24.01.2016,1.0,94,142.57,103.02 -75,2,25.01.2016,1.0,66,142.57,103.02 -75,2,26.01.2016,,6,142.57, -75,2,27.01.2016,,6,142.57, -75,2,28.01.2016,,6,142.57, -75,2,29.01.2016,,6,142.57, -75,2,30.01.2016,,6,142.57, -75,2,31.01.2016,,6,142.57, -75,2,01.02.2016,,6,142.57, -75,2,02.02.2016,,6,142.57, -75,2,03.02.2016,,13,142.57, -75,2,04.02.2016,,9,142.57, -75,2,05.02.2016,,9,142.57, -75,2,06.02.2016,,9,142.57, -75,2,07.02.2016,,9,142.57, -75,2,08.02.2016,,6,142.57, -75,2,09.02.2016,1.0,75,142.57,108.46 -75,2,10.02.2016,1.0,97,138.36,108.46 -75,2,11.02.2016,1.0,107,138.36,108.46 -75,2,12.02.2016,1.0,69,138.36,108.46 -75,2,13.02.2016,1.0,72,138.36,108.46 -75,2,14.02.2016,1.0,72,138.36,108.46 -75,2,15.02.2016,1.0,72,138.36,108.46 -75,2,16.02.2016,,9,138.36, -75,2,17.02.2016,,9,138.36, -75,2,18.02.2016,,9,138.36, -75,2,19.02.2016,,9,138.36, -75,2,20.02.2016,,9,138.36, -75,2,21.02.2016,,9,138.36, -75,2,22.02.2016,,9,138.36, -75,2,23.02.2016,,0,138.36, -75,2,24.02.2016,,0,138.36, -75,2,25.02.2016,,0,138.36, -75,2,26.02.2016,,0,138.36, -75,2,27.02.2016,,0,138.36, -75,2,28.02.2016,,0,138.36, -75,2,29.02.2016,,0,138.36, -75,2,01.03.2016,,0,138.36, -75,2,02.03.2016,,0,138.36, -75,2,03.03.2016,,0,138.36, -75,2,04.03.2016,,0,138.36, -75,2,05.03.2016,,6,138.36, -75,2,06.03.2016,,6,138.36, -75,2,07.03.2016,,9,138.36, -75,2,08.03.2016,,3,138.36, -75,2,09.03.2016,,6,138.36, -75,2,10.03.2016,,9,138.36, -75,2,11.03.2016,,9,138.36, -75,2,12.03.2016,,6,138.5, -75,2,13.03.2016,,6,138.5, -75,2,14.03.2016,,0,138.5, -75,2,15.03.2016,1.0,41,138.5,124.77 -75,2,16.03.2016,1.0,31,138.5,124.77 -75,2,17.03.2016,1.0,44,138.5,124.77 -75,2,18.03.2016,1.0,35,138.5,124.77 -75,2,19.03.2016,1.0,50,138.5,124.77 -75,2,20.03.2016,1.0,35,138.5,124.77 -75,2,21.03.2016,1.0,57,138.5,124.77 -75,2,22.03.2016,,9,138.36, -75,2,23.03.2016,,3,138.36, -75,2,24.03.2016,,6,138.36, -75,2,25.03.2016,,6,138.36, -75,2,26.03.2016,,6,138.36, -75,2,27.03.2016,,6,138.36, -75,2,28.03.2016,,6,138.36, -75,2,29.03.2016,,6,138.36, -75,2,30.03.2016,,6,138.36, -75,2,31.03.2016,,6,138.36, -75,2,01.04.2016,,6,138.36, -75,2,02.04.2016,,6,138.36, -75,2,03.04.2016,,6,138.36, -75,2,04.04.2016,,6,138.36, -75,2,05.04.2016,,6,138.36, -75,2,06.04.2016,,6,138.36, -75,2,07.04.2016,,6,138.36, -75,2,08.04.2016,,6,138.36, -75,2,09.04.2016,,6,138.5, -75,2,10.04.2016,,6,138.5, -75,2,11.04.2016,,6,138.5, -75,2,12.04.2016,,6,138.5, -75,2,13.04.2016,,6,138.5, -75,2,14.04.2016,,6,138.5, -75,2,15.04.2016,,6,138.5, -75,2,16.04.2016,,6,138.5, -75,2,17.04.2016,,6,138.5, -75,2,18.04.2016,,3,138.5, -75,2,19.04.2016,1.0,110,138.5,116.89 -75,2,20.04.2016,1.0,60,138.5,116.89 -75,2,21.04.2016,1.0,22,138.5,116.89 -75,2,22.04.2016,1.0,66,138.5,116.89 -75,2,23.04.2016,1.0,22,138.5,116.89 -75,2,24.04.2016,1.0,19,138.5,116.89 -75,2,25.04.2016,1.0,47,138.5,116.89 -75,2,26.04.2016,,0,138.5, -75,2,27.04.2016,,0,138.5, -75,2,28.04.2016,,3,138.5, -75,2,29.04.2016,,9,138.5, -75,2,30.04.2016,,0,138.5, -75,2,01.05.2016,,6,138.5, -75,2,02.05.2016,,3,138.5, -75,2,03.05.2016,1.0,113,138.5,106.01 -75,2,04.05.2016,1.0,107,138.5,106.01 -75,2,05.05.2016,1.0,41,138.5,106.01 -75,2,06.05.2016,1.0,69,138.5,106.01 -75,2,07.05.2016,1.0,63,138.5,106.01 -75,2,08.05.2016,1.0,60,138.5,106.01 -75,2,09.05.2016,1.0,60,138.5,106.01 -75,2,10.05.2016,,6,142.57, -75,2,11.05.2016,,6,142.57, -75,2,12.05.2016,,6,142.57, -75,2,13.05.2016,,6,142.57, -75,2,14.05.2016,,6,142.57, -75,2,15.05.2016,,6,142.57, -75,2,16.05.2016,,6,142.57, -75,2,17.05.2016,,6,142.57, -75,2,18.05.2016,,6,142.57, -75,2,19.05.2016,,6,142.57, -75,2,20.05.2016,,6,142.57, -75,2,21.05.2016,,6,142.57, -75,2,22.05.2016,,6,142.57, -76,2,25.09.2015,,9,162.96, -76,2,26.09.2015,,9,162.96, -76,2,27.09.2015,,0,162.96, -76,2,28.09.2015,,0,162.96, -76,2,29.09.2015,,0,162.96, -76,2,30.09.2015,,3,162.96, -76,2,01.10.2015,,0,162.96, -76,2,02.10.2015,,0,162.96, -76,2,03.10.2015,,0,162.96, -76,2,04.10.2015,,0,162.96, -76,2,05.10.2015,,0,162.96, -76,2,06.10.2015,1.0,38,143.39,122.05 -76,2,07.10.2015,1.0,69,143.39,122.05 -76,2,08.10.2015,1.0,3,143.39,122.05 -76,2,09.10.2015,1.0,0,143.39,122.05 -76,2,10.10.2015,1.0,3,143.39,122.05 -76,2,11.10.2015,1.0,19,143.39,122.05 -76,2,12.10.2015,1.0,3,143.39,122.05 -76,2,13.10.2015,1.0,31,143.93,122.05 -76,2,14.10.2015,1.0,19,143.93,122.05 -76,2,15.10.2015,1.0,19,143.93,122.05 -76,2,16.10.2015,1.0,19,143.93,122.05 -76,2,17.10.2015,1.0,16,162.96,122.05 -76,2,18.10.2015,1.0,0,162.96,122.05 -76,2,19.10.2015,,0,162.96, -76,2,20.10.2015,,0,162.96, -76,2,21.10.2015,,0,162.96, -76,2,22.10.2015,,0,162.96, -76,2,23.10.2015,,0,162.96, -76,2,24.10.2015,,9,162.96, -76,2,25.10.2015,,0,162.96, -76,2,26.10.2015,,0,162.96, -76,2,27.10.2015,,0,162.96, -76,2,28.10.2015,,3,162.96, -76,2,29.10.2015,,3,162.96, -76,2,30.10.2015,,0,162.96, -76,2,31.10.2015,,0,162.96, -76,2,01.11.2015,,9,162.96, -76,2,02.11.2015,,6,162.96, -76,2,03.11.2015,,3,162.96, -76,2,04.11.2015,,3,162.96, -76,2,05.11.2015,,3,162.96, -76,2,06.11.2015,,0,162.96, -76,2,07.11.2015,,0,162.96, -76,2,08.11.2015,,0,162.96, -76,2,09.11.2015,,0,162.96, -76,2,10.11.2015,,0,162.96, -76,2,11.11.2015,,0,138.36, -76,2,12.11.2015,,0,138.36, -76,2,13.11.2015,,0,138.36, -76,2,14.11.2015,,0,138.36, -76,2,15.11.2015,,0,138.36, -76,2,16.11.2015,,0,138.36, -76,2,17.11.2015,,0,138.36, -76,2,18.11.2015,,0,138.36, -76,2,19.11.2015,,0,138.36, -76,2,20.11.2015,,0,138.36, -76,2,21.11.2015,,3,138.36, -76,2,22.11.2015,,13,138.36, -76,2,23.11.2015,,6,138.36, -76,2,24.11.2015,,3,138.36, -76,2,25.11.2015,,0,138.36, -76,2,26.11.2015,,0,138.36, -76,2,27.11.2015,,3,138.36, -76,2,28.11.2015,,0,138.36, -76,2,29.11.2015,,0,138.36, -76,2,30.11.2015,,0,138.36, -76,2,01.12.2015,,0,162.96, -76,2,02.12.2015,,13,162.96, -76,2,03.12.2015,,0,138.5, -76,2,04.12.2015,,0,138.5, -76,2,05.12.2015,,0,138.5, -76,2,06.12.2015,,0,138.5, -76,2,07.12.2015,,0,138.5, -76,2,08.12.2015,,0,138.5, -76,2,09.12.2015,,0,138.5, -76,2,10.12.2015,,0,138.5, -76,2,11.12.2015,,0,142.57, -76,2,12.12.2015,,0,142.57, -76,2,13.12.2015,,0,142.57, -76,2,14.12.2015,,0,142.57, -76,2,15.12.2015,,6,142.57, -76,2,16.12.2015,,3,142.57, -76,2,17.12.2015,,6,142.57, -76,2,18.12.2015,,0,142.57, -76,2,19.12.2015,,0,142.57, -76,2,20.12.2015,,9,142.57, -76,2,21.12.2015,,6,142.57, -76,2,22.12.2015,1.0,107,142.57,103.02 -76,2,23.12.2015,1.0,13,142.57,103.02 -76,2,24.12.2015,1.0,22,142.57,103.02 -76,2,25.12.2015,1.0,35,142.57,103.02 -76,2,26.12.2015,1.0,50,142.57,103.02 -76,2,27.12.2015,1.0,28,142.57,103.02 -76,2,28.12.2015,1.0,47,142.57,103.02 -76,2,29.12.2015,,3,138.5, -76,2,30.12.2015,,6,138.5, -76,2,31.12.2015,,3,138.5, -76,2,01.01.2016,,0,138.5, -76,2,02.01.2016,,0,138.5, -76,2,03.01.2016,,13,138.5, -76,2,04.01.2016,,6,138.5, -76,2,05.01.2016,,0,138.5, -76,2,06.01.2016,,3,138.5, -76,2,07.01.2016,,16,138.5, -76,2,08.01.2016,,0,138.5, -76,2,09.01.2016,,0,138.5, -76,2,10.01.2016,,0,138.5, -76,2,11.01.2016,,0,138.5, -76,2,12.01.2016,,6,138.5, -76,2,13.01.2016,,3,138.5, -76,2,14.01.2016,,3,138.5, -76,2,15.01.2016,,3,138.5, -76,2,16.01.2016,,16,142.57, -76,2,17.01.2016,,6,142.57, -76,2,18.01.2016,,3,142.57, -76,2,19.01.2016,1.0,97,142.57,103.02 -76,2,20.01.2016,1.0,72,142.57,103.02 -76,2,21.01.2016,1.0,101,142.57,103.02 -76,2,22.01.2016,1.0,13,142.57,103.02 -76,2,23.01.2016,1.0,38,142.57,103.02 -76,2,24.01.2016,1.0,94,142.57,103.02 -76,2,25.01.2016,1.0,19,142.57,103.02 -76,2,26.01.2016,,3,142.57, -76,2,27.01.2016,,3,142.57, -76,2,28.01.2016,,3,142.57, -76,2,29.01.2016,,3,142.57, -76,2,30.01.2016,,0,142.57, -76,2,31.01.2016,,9,142.57, -76,2,01.02.2016,,9,142.57, -76,2,02.02.2016,,6,142.57, -76,2,03.02.2016,,6,142.57, -76,2,04.02.2016,,6,142.57, -76,2,05.02.2016,,3,142.57, -76,2,06.02.2016,,3,142.57, -76,2,07.02.2016,,6,142.57, -76,2,08.02.2016,,9,142.57, -76,2,09.02.2016,1.0,85,142.57,108.46 -76,2,10.02.2016,1.0,41,138.36,108.46 -76,2,11.02.2016,1.0,50,138.36,108.46 -76,2,12.02.2016,1.0,25,138.36,108.46 -76,2,13.02.2016,1.0,9,138.36,108.46 -76,2,14.02.2016,1.0,47,138.36,108.46 -76,2,15.02.2016,1.0,47,138.36,108.46 -76,2,16.02.2016,,6,138.36, -76,2,17.02.2016,,6,138.36, -76,2,18.02.2016,,6,138.36, -76,2,19.02.2016,,6,138.36, -76,2,20.02.2016,,6,138.36, -76,2,21.02.2016,,6,138.36, -76,2,22.02.2016,,6,138.36, -76,2,23.02.2016,,6,138.36, -76,2,24.02.2016,,6,138.36, -76,2,25.02.2016,,6,138.36, -76,2,26.02.2016,,6,138.36, -76,2,27.02.2016,,6,138.36, -76,2,28.02.2016,,6,138.36, -76,2,29.02.2016,,6,138.36, -76,2,01.03.2016,,6,138.36, -76,2,02.03.2016,,3,138.36, -76,2,03.03.2016,,3,138.36, -76,2,04.03.2016,,6,138.36, -76,2,05.03.2016,,6,138.36, -76,2,06.03.2016,,6,138.36, -76,2,07.03.2016,,6,138.36, -76,2,08.03.2016,,3,138.36, -76,2,09.03.2016,,6,138.36, -76,2,10.03.2016,,3,138.36, -76,2,11.03.2016,,3,138.36, -76,2,12.03.2016,,6,138.5, -76,2,13.03.2016,,9,138.5, -76,2,14.03.2016,,6,138.5, -76,2,15.03.2016,1.0,44,138.5,124.77 -76,2,16.03.2016,1.0,47,138.5,124.77 -76,2,17.03.2016,1.0,28,138.5,124.77 -76,2,18.03.2016,1.0,28,138.5,124.77 -76,2,19.03.2016,1.0,57,138.5,124.77 -76,2,20.03.2016,1.0,19,138.5,124.77 -76,2,21.03.2016,1.0,35,138.5,124.77 -76,2,22.03.2016,,6,138.36, -76,2,23.03.2016,,3,138.36, -76,2,24.03.2016,,6,138.36, -76,2,25.03.2016,,3,138.36, -76,2,26.03.2016,,6,138.36, -76,2,27.03.2016,,6,138.36, -76,2,28.03.2016,,9,138.36, -76,2,29.03.2016,,3,138.36, -76,2,30.03.2016,,6,138.36, -76,2,31.03.2016,,6,138.36, -76,2,01.04.2016,,9,138.36, -76,2,02.04.2016,,6,138.36, -76,2,03.04.2016,,6,138.36, -76,2,04.04.2016,,6,138.36, -76,2,05.04.2016,,3,138.36, -76,2,06.04.2016,,13,138.36, -76,2,07.04.2016,,13,138.36, -76,2,08.04.2016,,3,138.36, -76,2,09.04.2016,,6,138.5, -76,2,10.04.2016,,6,138.5, -76,2,11.04.2016,,6,138.5, -76,2,12.04.2016,,3,138.5, -76,2,13.04.2016,,6,138.5, -76,2,14.04.2016,,9,138.5, -76,2,15.04.2016,,6,138.5, -76,2,16.04.2016,,3,138.5, -76,2,17.04.2016,,6,138.5, -76,2,18.04.2016,,3,138.5, -76,2,19.04.2016,1.0,57,138.5,116.89 -76,2,20.04.2016,1.0,69,138.5,116.89 -76,2,21.04.2016,1.0,50,138.5,116.89 -76,2,22.04.2016,1.0,79,138.5,116.89 -76,2,23.04.2016,1.0,35,138.5,116.89 -76,2,24.04.2016,1.0,50,138.5,116.89 -76,2,25.04.2016,1.0,13,138.5,116.89 -76,2,26.04.2016,,6,138.5, -76,2,27.04.2016,,6,138.5, -76,2,28.04.2016,,6,138.5, -76,2,29.04.2016,,6,138.5, -76,2,30.04.2016,,6,138.5, -76,2,01.05.2016,,6,138.5, -76,2,02.05.2016,,6,138.5, -76,2,03.05.2016,1.0,47,138.5,106.01 -76,2,04.05.2016,1.0,85,138.5,106.01 -76,2,05.05.2016,1.0,104,138.5,106.01 -76,2,06.05.2016,1.0,60,138.5,106.01 -76,2,07.05.2016,1.0,41,138.5,106.01 -76,2,08.05.2016,1.0,35,138.5,106.01 -76,2,09.05.2016,1.0,35,138.5,106.01 -76,2,10.05.2016,,16,138.5, -76,2,11.05.2016,,6,138.5, -76,2,12.05.2016,,63,138.5, -76,2,13.05.2016,,38,138.5, -76,2,14.05.2016,,13,138.5, -76,2,15.05.2016,,13,138.5, -76,2,16.05.2016,,13,138.5, -76,2,17.05.2016,,13,138.5, -76,2,18.05.2016,,13,138.5, -76,2,19.05.2016,,13,138.5, -76,2,20.05.2016,,13,138.5, -76,2,21.05.2016,,0,138.5, -76,2,22.05.2016,,0,138.5, -77,2,01.08.2015,,0,150.73, -77,2,02.08.2015,,0,150.73, -77,2,03.08.2015,,0,150.73, -77,2,04.08.2015,,0,150.73, -77,2,05.08.2015,,0,150.73, -77,2,06.08.2015,,0,150.73, -77,2,07.08.2015,,0,150.73, -77,2,08.08.2015,,0,150.73, -77,2,09.08.2015,,0,150.73, -77,2,10.08.2015,,0,150.73, -77,2,11.08.2015,,0,150.73, -77,2,12.08.2015,,0,128.17, -77,2,13.08.2015,,0,128.17, -77,2,14.08.2015,,9,128.17, -77,2,15.08.2015,,9,128.17, -77,2,16.08.2015,,6,128.17, -77,2,17.08.2015,,47,128.17, -77,2,18.08.2015,1.0,154,128.17,100.3 -77,2,19.08.2015,1.0,210,128.17,100.3 -77,2,20.08.2015,1.0,182,128.17,100.3 -77,2,21.08.2015,1.0,163,128.17,100.3 -77,2,22.08.2015,1.0,157,128.17,100.3 -77,2,23.08.2015,1.0,41,128.17,100.3 -77,2,24.08.2015,1.0,151,128.17,100.3 -77,2,25.08.2015,,16,128.17, -77,2,26.08.2015,,16,128.17, -77,2,27.08.2015,,16,128.17, -77,2,28.08.2015,,16,128.17, -77,2,29.08.2015,,16,128.17, -77,2,30.08.2015,,16,128.17, -77,2,31.08.2015,,16,128.17, -77,2,01.09.2015,,16,128.17, -77,2,02.09.2015,,16,128.17, -77,2,03.09.2015,,16,128.17, -77,2,04.09.2015,,16,128.17, -77,2,05.09.2015,,16,128.17, -77,2,06.09.2015,,16,128.17, -77,2,07.09.2015,,16,128.17, -77,2,08.09.2015,,16,128.17, -77,2,09.09.2015,,16,128.17, -77,2,10.09.2015,,16,128.17, -77,2,11.09.2015,,16,128.17, -77,2,12.09.2015,,16,128.17, -77,2,13.09.2015,,16,128.17, -77,2,14.09.2015,,16,128.17, -77,2,15.09.2015,,16,128.17, -77,2,16.09.2015,,16,128.17, -77,2,17.09.2015,,0,128.17, -77,2,18.09.2015,,0,128.17, -77,2,19.09.2015,,0,128.17, -77,2,20.09.2015,,0,128.17, -77,2,21.09.2015,,0,128.17, -77,2,22.09.2015,,0,128.17, -77,2,23.09.2015,,0,128.17, -77,2,24.09.2015,,0,128.17, -77,2,25.09.2015,,0,128.17, -77,2,26.09.2015,,0,128.17, -77,2,27.09.2015,,0,128.17, -77,2,28.09.2015,,0,128.17, -77,2,29.09.2015,,3,128.17, -77,2,30.09.2015,,9,128.17, -77,2,01.10.2015,,6,162.96, -77,2,02.10.2015,,0,162.96, -77,2,03.10.2015,,0,162.96, -77,2,04.10.2015,,0,162.96, -77,2,05.10.2015,,13,162.96, -77,2,06.10.2015,1.0,132,143.39,122.05 -77,2,07.10.2015,1.0,141,143.39,122.05 -77,2,08.10.2015,1.0,123,143.39,122.05 -77,2,09.10.2015,1.0,101,143.39,122.05 -77,2,10.10.2015,1.0,141,143.39,122.05 -77,2,11.10.2015,1.0,141,143.39,122.05 -77,2,12.10.2015,1.0,141,143.39,122.05 -77,2,13.10.2015,,0,143.93, -77,2,14.10.2015,,0,143.93, -77,2,15.10.2015,,0,143.93, -77,2,16.10.2015,,0,143.93, -77,2,17.10.2015,,0,143.93, -77,2,18.10.2015,,0,143.93, -77,2,19.10.2015,,0,143.93, -77,2,20.10.2015,,0,143.93, -77,2,21.10.2015,,0,143.93, -77,2,22.10.2015,,0,143.93, -77,2,23.10.2015,,0,143.93, -77,2,24.10.2015,,0,143.93, -77,2,25.10.2015,,0,143.93, -77,2,26.10.2015,,0,143.93, -77,2,27.10.2015,,0,143.93, -77,2,28.10.2015,,0,143.93, -77,2,29.10.2015,,0,143.93, -77,2,30.10.2015,,0,143.93, -77,2,31.10.2015,,0,143.93, -77,2,01.11.2015,,0,143.93, -77,2,02.11.2015,,0,143.93, -77,2,03.11.2015,,0,143.93, -77,2,04.11.2015,,0,143.93, -77,2,05.11.2015,,0,143.93, -77,2,06.11.2015,,0,143.93, -77,2,07.11.2015,,0,143.93, -77,2,08.11.2015,,0,143.93, -77,2,09.11.2015,,0,143.93, -77,2,10.11.2015,,0,143.93, -77,2,11.11.2015,,0,143.93, -77,2,12.11.2015,,0,143.93, -77,2,13.11.2015,,0,143.93, -77,2,14.11.2015,,0,143.93, -77,2,15.11.2015,,0,143.93, -77,2,16.11.2015,,0,143.93, -77,2,17.11.2015,,0,143.93, -77,2,18.11.2015,,0,143.93, -77,2,19.11.2015,,0,143.93, -77,2,20.11.2015,,0,143.93, -77,2,21.11.2015,,0,143.93, -77,2,22.11.2015,,0,143.93, -77,2,23.11.2015,,0,143.93, -77,2,24.11.2015,,0,143.93, -77,2,25.11.2015,,0,143.93, -77,2,26.11.2015,,0,143.93, -77,2,27.11.2015,,0,143.93, -77,2,28.11.2015,,0,143.93, -77,2,29.11.2015,,0,143.93, -77,2,30.11.2015,,0,143.93, -77,2,01.12.2015,,0,143.93, -77,2,02.12.2015,,0,143.93, -77,2,03.12.2015,,0,143.93, -77,2,04.12.2015,,0,143.93, -77,2,05.12.2015,,0,143.93, -77,2,06.12.2015,,0,143.93, -77,2,07.12.2015,,0,143.93, -77,2,08.12.2015,,0,143.93, -77,2,09.12.2015,,0,138.5, -77,2,10.12.2015,,0,138.5, -77,2,11.12.2015,,0,142.57, -77,2,12.12.2015,,0,142.57, -77,2,13.12.2015,,0,142.57, -77,2,14.12.2015,,0,142.57, -77,2,15.12.2015,,0,142.57, -77,2,16.12.2015,,0,142.57, -77,2,17.12.2015,,0,142.57, -77,2,18.12.2015,,0,142.57, -77,2,19.12.2015,,0,142.57, -77,2,20.12.2015,,0,142.57, -77,2,21.12.2015,,0,142.57, -77,2,22.12.2015,1.0,214,142.57,103.02 -77,2,23.12.2015,1.0,110,142.57,103.02 -77,2,24.12.2015,1.0,91,142.57,103.02 -77,2,25.12.2015,1.0,163,142.57,103.02 -77,2,26.12.2015,1.0,132,142.57,103.02 -77,2,27.12.2015,1.0,116,142.57,103.02 -77,2,28.12.2015,1.0,104,142.57,103.02 -77,2,29.12.2015,,0,138.5, -77,2,30.12.2015,,0,138.5, -77,2,31.12.2015,,0,138.5, -77,2,01.01.2016,,0,138.5, -77,2,02.01.2016,,0,138.5, -77,2,03.01.2016,,3,138.5, -77,2,04.01.2016,,6,138.5, -77,2,05.01.2016,,6,138.5, -77,2,06.01.2016,,0,138.5, -77,2,07.01.2016,,3,138.5, -77,2,08.01.2016,,0,138.5, -77,2,09.01.2016,,0,138.5, -77,2,10.01.2016,,0,138.5, -77,2,11.01.2016,,16,138.5, -77,2,12.01.2016,,3,138.5, -77,2,13.01.2016,,3,138.5, -77,2,14.01.2016,,3,138.5, -77,2,15.01.2016,,3,138.5, -77,2,16.01.2016,,3,142.57, -77,2,17.01.2016,,3,142.57, -77,2,18.01.2016,,16,142.57, -77,2,19.01.2016,1.0,220,142.57,103.02 -77,2,20.01.2016,1.0,198,142.57,103.02 -77,2,21.01.2016,1.0,148,142.57,103.02 -77,2,22.01.2016,1.0,154,142.57,103.02 -77,2,23.01.2016,1.0,119,142.57,103.02 -77,2,24.01.2016,1.0,119,142.57,103.02 -77,2,25.01.2016,1.0,119,142.57,103.02 -77,2,26.01.2016,,3,142.57, -77,2,27.01.2016,,3,142.57, -77,2,28.01.2016,,3,142.57, -77,2,29.01.2016,,3,142.57, -77,2,30.01.2016,,3,142.57, -77,2,31.01.2016,,3,142.57, -77,2,01.02.2016,,3,142.57, -77,2,02.02.2016,,3,142.57, -77,2,03.02.2016,,3,142.57, -77,2,04.02.2016,,3,142.57, -77,2,05.02.2016,,3,142.57, -77,2,06.02.2016,,3,142.57, -77,2,07.02.2016,,3,142.57, -77,2,08.02.2016,,16,142.57, -77,2,09.02.2016,1.0,170,142.57,108.46 -77,2,10.02.2016,1.0,135,138.36,108.46 -77,2,11.02.2016,1.0,154,138.36,108.46 -77,2,12.02.2016,1.0,217,138.36,108.46 -77,2,13.02.2016,1.0,148,138.36,108.46 -77,2,14.02.2016,1.0,66,138.36,108.46 -77,2,15.02.2016,1.0,116,138.36,108.46 -77,2,16.02.2016,,0,138.36, -77,2,17.02.2016,,9,138.36, -77,2,18.02.2016,,0,138.36, -77,2,19.02.2016,,3,138.36, -77,2,20.02.2016,,16,138.36, -77,2,21.02.2016,,6,138.36, -77,2,22.02.2016,,6,138.36, -77,2,23.02.2016,,6,138.36, -77,2,24.02.2016,,6,138.36, -77,2,25.02.2016,,6,138.36, -77,2,26.02.2016,,6,138.36, -77,2,27.02.2016,,6,138.36, -77,2,28.02.2016,,6,138.36, -77,2,29.02.2016,,6,138.36, -77,2,01.03.2016,,0,138.36, -77,2,02.03.2016,,0,138.36, -77,2,03.03.2016,,0,138.36, -77,2,04.03.2016,,0,138.36, -77,2,05.03.2016,,0,138.36, -77,2,06.03.2016,,0,138.36, -77,2,07.03.2016,,0,138.36, -77,2,08.03.2016,,0,138.36, -77,2,09.03.2016,,0,138.36, -77,2,10.03.2016,,0,138.36, -77,2,11.03.2016,,0,138.36, -77,2,12.03.2016,,0,138.5, -77,2,13.03.2016,,0,138.5, -77,2,14.03.2016,,6,138.5, -77,2,15.03.2016,1.0,63,138.5,124.77 -77,2,16.03.2016,1.0,57,138.5,124.77 -77,2,17.03.2016,1.0,16,138.5,124.77 -77,2,18.03.2016,1.0,69,138.5,124.77 -77,2,19.03.2016,1.0,82,138.5,124.77 -77,2,20.03.2016,1.0,79,138.5,124.77 -77,2,21.03.2016,1.0,116,138.5,124.77 -77,2,22.03.2016,,3,138.36, -77,2,23.03.2016,,0,138.36, -77,2,24.03.2016,,0,138.36, -77,2,25.03.2016,,0,138.36, -77,2,26.03.2016,,0,138.36, -77,2,27.03.2016,,0,138.36, -77,2,28.03.2016,,0,138.36, -77,2,29.03.2016,,0,138.36, -77,2,30.03.2016,,0,138.36, -77,2,31.03.2016,,0,138.36, -77,2,01.04.2016,,0,138.36, -77,2,02.04.2016,,0,138.36, -77,2,03.04.2016,,0,138.36, -77,2,04.04.2016,,0,138.36, -77,2,05.04.2016,,0,138.36, -77,2,06.04.2016,,0,138.36, -77,2,07.04.2016,,0,138.36, -77,2,08.04.2016,,0,138.36, -77,2,09.04.2016,,0,138.5, -77,2,10.04.2016,,0,138.5, -77,2,11.04.2016,,0,138.5, -77,2,12.04.2016,,0,138.5, -77,2,13.04.2016,,0,138.5, -77,2,14.04.2016,,0,138.5, -77,2,15.04.2016,,0,138.5, -77,2,16.04.2016,,0,138.5, -77,2,17.04.2016,,0,138.5, -77,2,18.04.2016,,0,138.5, -77,2,19.04.2016,1.0,57,138.5,116.89 -77,2,20.04.2016,1.0,123,138.5,116.89 -77,2,21.04.2016,1.0,119,138.5,116.89 -77,2,22.04.2016,1.0,157,138.5,116.89 -77,2,23.04.2016,1.0,66,138.5,116.89 -77,2,24.04.2016,1.0,85,138.5,116.89 -77,2,25.04.2016,1.0,16,138.5,116.89 -77,2,26.04.2016,,0,138.5, -77,2,27.04.2016,,0,138.5, -77,2,28.04.2016,,0,138.5, -77,2,29.04.2016,,0,138.5, -77,2,30.04.2016,,0,138.5, -77,2,01.05.2016,,0,138.5, -77,2,02.05.2016,,6,138.5, -77,2,03.05.2016,1.0,173,138.5,106.01 -77,2,04.05.2016,1.0,229,138.5,106.01 -77,2,05.05.2016,1.0,160,138.5,106.01 -77,2,06.05.2016,1.0,154,138.5,106.01 -77,2,07.05.2016,1.0,148,138.5,106.01 -77,2,08.05.2016,1.0,185,138.5,106.01 -77,2,09.05.2016,1.0,19,138.5,106.01 -77,2,10.05.2016,,6,138.5, -77,2,11.05.2016,,6,138.5, -77,2,12.05.2016,,3,138.5, -77,2,13.05.2016,,3,138.5, -77,2,14.05.2016,,3,138.5, -77,2,15.05.2016,,3,138.5, -77,2,16.05.2016,,3,138.5, -77,2,17.05.2016,,3,138.5, -77,2,18.05.2016,,3,138.5, -77,2,19.05.2016,,3,138.5, -77,2,20.05.2016,,3,138.5, -77,2,21.05.2016,,3,138.5, -77,2,22.05.2016,,3,138.5, -78,2,10.04.2015,,6,146.38, -78,2,11.04.2015,,9,146.38, -78,2,12.04.2015,,6,146.38, -78,2,13.04.2015,,0,146.38, -78,2,14.04.2015,,3,146.38, -78,2,15.04.2015,,3,146.38, -78,2,16.04.2015,,0,146.38, -78,2,17.04.2015,,0,146.38, -78,2,18.04.2015,,0,146.38, -78,2,19.04.2015,,3,146.38, -78,2,20.04.2015,,6,146.38, -78,2,21.04.2015,,3,146.38, -78,2,22.04.2015,,6,146.38, -78,2,23.04.2015,,0,146.38, -78,2,24.04.2015,,0,146.38, -78,2,25.04.2015,,0,146.38, -78,2,26.04.2015,,0,146.38, -78,2,27.04.2015,,6,150.73, -78,2,28.04.2015,,0,150.73, -78,2,29.04.2015,,0,150.73, -78,2,30.04.2015,,3,150.73, -78,2,01.05.2015,,0,150.73, -78,2,02.05.2015,,3,150.73, -78,2,03.05.2015,,0,150.73, -78,2,04.05.2015,,3,150.73, -78,2,05.05.2015,,9,150.73, -78,2,06.05.2015,,9,150.73, -78,2,07.05.2015,,0,150.73, -78,2,08.05.2015,,0,150.73, -78,2,09.05.2015,,0,150.73, -78,2,10.05.2015,,0,150.73, -78,2,11.05.2015,,0,150.73, -78,2,12.05.2015,,3,150.73, -78,2,13.05.2015,,0,150.73, -78,2,14.05.2015,,0,150.73, -78,2,15.05.2015,,6,150.73, -78,2,16.05.2015,,9,150.73, -78,2,17.05.2015,,0,150.73, -78,2,18.05.2015,,0,150.73, -78,2,19.05.2015,,0,150.73, -78,2,20.05.2015,,0,150.73, -78,2,21.05.2015,,0,150.73, -78,2,22.05.2015,,0,150.73, -78,2,23.05.2015,,3,150.73, -78,2,24.05.2015,,0,150.73, -78,2,25.05.2015,,0,150.73, -78,2,26.05.2015,,0,150.73, -78,2,27.05.2015,,3,150.73, -78,2,28.05.2015,,0,150.73, -78,2,29.05.2015,,0,150.73, -78,2,30.05.2015,,0,150.73, -78,2,31.05.2015,,0,150.73, -78,2,01.06.2015,,0,150.73, -78,2,02.06.2015,,0,150.73, -78,2,03.06.2015,,0,150.73, -78,2,04.06.2015,,6,150.73, -78,2,05.06.2015,,0,150.73, -78,2,06.06.2015,,6,150.73, -78,2,07.06.2015,,0,150.73, -78,2,08.06.2015,,3,150.73, -78,2,09.06.2015,,3,150.73, -78,2,10.06.2015,,0,150.73, -78,2,11.06.2015,,0,150.73, -78,2,12.06.2015,,9,150.73, -78,2,13.06.2015,,0,150.73, -78,2,14.06.2015,,0,150.73, -78,2,15.06.2015,,16,150.73, -78,2,16.06.2015,,3,150.73, -78,2,17.06.2015,,3,150.73, -78,2,18.06.2015,,0,150.73, -78,2,19.06.2015,,6,150.73, -78,2,20.06.2015,,16,150.73, -78,2,21.06.2015,,13,150.73, -78,2,22.06.2015,,9,150.73, -78,2,23.06.2015,,0,150.73, -78,2,24.06.2015,,0,150.73, -78,2,25.06.2015,,6,150.73, -78,2,26.06.2015,,6,150.73, -78,2,27.06.2015,,0,150.73, -78,2,28.06.2015,,3,150.73, -78,2,29.06.2015,,16,150.73, -78,2,30.06.2015,,0,150.73, -78,2,01.07.2015,,9,150.73, -78,2,02.07.2015,,9,150.73, -78,2,03.07.2015,,6,150.73, -78,2,04.07.2015,,6,150.73, -78,2,05.07.2015,,6,150.73, -78,2,06.07.2015,,6,150.73, -78,2,07.07.2015,,9,150.73, -78,2,08.07.2015,,0,150.73, -78,2,09.07.2015,,3,150.73, -78,2,10.07.2015,,0,150.73, -78,2,11.07.2015,,0,150.73, -78,2,12.07.2015,,6,150.73, -78,2,13.07.2015,,6,150.73, -78,2,14.07.2015,,0,150.73, -78,2,15.07.2015,,0,150.73, -78,2,16.07.2015,,0,150.73, -78,2,17.07.2015,,0,150.73, -78,2,18.07.2015,,0,150.73, -78,2,19.07.2015,,0,150.73, -78,2,20.07.2015,,9,150.73, -78,2,21.07.2015,,0,150.73, -78,2,22.07.2015,,0,150.73, -78,2,23.07.2015,,0,150.73, -78,2,24.07.2015,,0,150.73, -78,2,25.07.2015,,13,150.73, -78,2,26.07.2015,,3,150.73, -78,2,27.07.2015,,35,150.73, -78,2,28.07.2015,,0,150.73, -78,2,29.07.2015,,0,150.73, -78,2,30.07.2015,,3,150.73, -78,2,31.07.2015,,3,150.73, -78,2,01.08.2015,,3,150.73, -78,2,02.08.2015,,3,150.73, -78,2,03.08.2015,,3,150.73, -78,2,04.08.2015,,6,150.73, -78,2,05.08.2015,,13,150.73, -78,2,06.08.2015,,6,150.73, -78,2,07.08.2015,,13,150.73, -78,2,08.08.2015,,13,150.73, -78,2,09.08.2015,,6,150.73, -78,2,10.08.2015,,6,150.73, -78,2,11.08.2015,,0,150.73, -78,2,12.08.2015,,9,128.17, -78,2,13.08.2015,,3,128.17, -78,2,14.08.2015,,0,128.17, -78,2,15.08.2015,,22,128.17, -78,2,16.08.2015,,16,128.17, -78,2,17.08.2015,,9,128.17, -78,2,18.08.2015,1.0,79,128.17,100.3 -78,2,19.08.2015,1.0,57,128.17,100.3 -78,2,20.08.2015,1.0,113,128.17,100.3 -78,2,21.08.2015,1.0,113,128.17,100.3 -78,2,22.08.2015,1.0,104,128.17,100.3 -78,2,23.08.2015,1.0,138,128.17,100.3 -78,2,24.08.2015,1.0,101,128.17,100.3 -78,2,25.08.2015,,9,128.17, -78,2,26.08.2015,,9,128.17, -78,2,27.08.2015,,9,128.17, -78,2,28.08.2015,,0,128.17, -78,2,29.08.2015,,3,128.17, -78,2,30.08.2015,,22,128.17, -78,2,31.08.2015,,9,128.17, -78,2,01.09.2015,,38,128.17, -78,2,02.09.2015,,13,128.17, -78,2,03.09.2015,,13,128.17, -78,2,04.09.2015,,6,128.17, -78,2,05.09.2015,,13,128.17, -78,2,06.09.2015,,6,128.17, -78,2,07.09.2015,,16,128.17, -78,2,08.09.2015,,16,128.17, -78,2,09.09.2015,,3,128.17, -78,2,10.09.2015,,6,128.17, -78,2,11.09.2015,,22,128.17, -78,2,12.09.2015,,25,128.17, -78,2,13.09.2015,,13,128.17, -78,2,14.09.2015,,13,128.17, -78,2,15.09.2015,,9,128.17, -78,2,16.09.2015,,28,128.17, -78,2,17.09.2015,,16,128.17, -78,2,18.09.2015,,13,128.17, -78,2,19.09.2015,,6,128.17, -78,2,20.09.2015,,16,128.17, -78,2,21.09.2015,,16,128.17, -78,2,22.09.2015,,9,128.17, -78,2,23.09.2015,,9,128.17, -78,2,24.09.2015,,28,128.17, -78,2,25.09.2015,,31,128.17, -78,2,26.09.2015,,16,128.17, -78,2,27.09.2015,,16,128.17, -78,2,28.09.2015,,16,128.17, -78,2,29.09.2015,,13,128.17, -78,2,30.09.2015,,22,128.17, -78,2,01.10.2015,,3,162.96, -78,2,02.10.2015,,6,162.96, -78,2,03.10.2015,,9,162.96, -78,2,04.10.2015,,0,162.96, -78,2,05.10.2015,,6,162.96, -78,2,06.10.2015,1.0,85,143.39,122.05 -78,2,07.10.2015,1.0,79,143.39,122.05 -78,2,08.10.2015,1.0,91,143.39,122.05 -78,2,09.10.2015,1.0,126,143.39,122.05 -78,2,10.10.2015,1.0,97,143.39,122.05 -78,2,11.10.2015,1.0,82,143.39,122.05 -78,2,12.10.2015,1.0,60,143.39,122.05 -78,2,13.10.2015,,13,143.93, -78,2,14.10.2015,,13,143.93, -78,2,15.10.2015,,3,143.93, -78,2,16.10.2015,,6,143.93, -78,2,17.10.2015,,13,162.96, -78,2,18.10.2015,,13,162.96, -78,2,19.10.2015,,9,162.96, -78,2,20.10.2015,,3,162.96, -78,2,21.10.2015,,13,162.96, -78,2,22.10.2015,,3,162.96, -78,2,23.10.2015,,3,162.96, -78,2,24.10.2015,,6,162.96, -78,2,25.10.2015,,6,162.96, -78,2,26.10.2015,,6,162.96, -78,2,27.10.2015,,3,162.96, -78,2,28.10.2015,,6,162.96, -78,2,29.10.2015,,6,162.96, -78,2,30.10.2015,,6,162.96, -78,2,31.10.2015,,3,162.96, -78,2,01.11.2015,,6,162.96, -78,2,02.11.2015,,6,162.96, -78,2,03.11.2015,,6,162.96, -78,2,04.11.2015,,3,162.96, -78,2,05.11.2015,,3,162.96, -78,2,06.11.2015,,3,162.96, -78,2,07.11.2015,,3,162.96, -78,2,08.11.2015,,3,162.96, -78,2,09.11.2015,,3,162.96, -78,2,10.11.2015,,3,162.96, -78,2,11.11.2015,,3,138.36, -78,2,12.11.2015,,3,138.36, -78,2,13.11.2015,,3,138.36, -78,2,14.11.2015,,3,138.36, -78,2,15.11.2015,,3,138.36, -78,2,16.11.2015,,3,138.36, -78,2,17.11.2015,,3,138.36, -78,2,18.11.2015,,3,138.36, -78,2,19.11.2015,,3,138.36, -78,2,20.11.2015,,3,138.36, -78,2,21.11.2015,,3,138.36, -78,2,22.11.2015,,3,138.36, -78,2,23.11.2015,,3,138.36, -78,2,24.11.2015,,3,138.36, -78,2,25.11.2015,,3,138.36, -78,2,26.11.2015,,3,138.36, -78,2,27.11.2015,,3,138.36, -78,2,28.11.2015,,3,138.36, -78,2,29.11.2015,,3,138.36, -78,2,30.11.2015,,3,138.36, -78,2,01.12.2015,,3,162.96, -78,2,02.12.2015,,3,162.96, -78,2,03.12.2015,,0,138.5, -78,2,04.12.2015,,0,138.5, -78,2,05.12.2015,,6,138.5, -78,2,06.12.2015,,9,138.5, -78,2,07.12.2015,,3,138.5, -78,2,08.12.2015,,0,138.5, -78,2,09.12.2015,,0,138.5, -78,2,10.12.2015,,6,138.5, -78,2,11.12.2015,,9,142.57, -78,2,12.12.2015,,13,142.57, -78,2,13.12.2015,,9,142.57, -78,2,14.12.2015,,6,142.57, -78,2,15.12.2015,,0,142.57, -78,2,16.12.2015,,6,142.57, -78,2,17.12.2015,,6,142.57, -78,2,18.12.2015,,9,142.57, -78,2,19.12.2015,,13,142.57, -78,2,20.12.2015,,13,142.57, -78,2,21.12.2015,,3,142.57, -78,2,22.12.2015,1.0,91,142.57,103.02 -78,2,23.12.2015,1.0,104,142.57,103.02 -78,2,24.12.2015,1.0,91,142.57,103.02 -78,2,25.12.2015,1.0,154,142.57,103.02 -78,2,26.12.2015,1.0,75,142.57,103.02 -78,2,27.12.2015,1.0,132,142.57,103.02 -78,2,28.12.2015,1.0,145,142.57,103.02 -78,2,29.12.2015,,13,138.5, -78,2,30.12.2015,,6,138.5, -78,2,31.12.2015,,19,138.5, -78,2,01.01.2016,,9,138.5, -78,2,02.01.2016,,22,138.5, -78,2,03.01.2016,,9,138.5, -78,2,04.01.2016,,9,138.5, -78,2,05.01.2016,,75,138.5, -78,2,06.01.2016,,16,138.5, -78,2,07.01.2016,,16,138.5, -78,2,08.01.2016,,16,138.5, -78,2,09.01.2016,,16,138.5, -78,2,10.01.2016,,16,138.5, -78,2,11.01.2016,,16,138.5, -78,2,12.01.2016,,16,138.5, -78,2,13.01.2016,,9,138.5, -78,2,14.01.2016,,13,138.5, -78,2,15.01.2016,,16,138.5, -78,2,16.01.2016,,13,142.57, -78,2,17.01.2016,,6,142.57, -78,2,18.01.2016,,16,142.57, -78,2,19.01.2016,1.0,88,142.57,103.02 -78,2,20.01.2016,1.0,116,142.57,103.02 -78,2,21.01.2016,1.0,154,142.57,103.02 -78,2,22.01.2016,1.0,101,142.57,103.02 -78,2,23.01.2016,1.0,135,142.57,103.02 -78,2,24.01.2016,1.0,223,142.57,103.02 -78,2,25.01.2016,1.0,119,142.57,103.02 -78,2,26.01.2016,,16,142.57, -78,2,27.01.2016,,16,142.57, -78,2,28.01.2016,,16,142.57, -78,2,29.01.2016,,6,142.57, -78,2,30.01.2016,,13,142.57, -78,2,31.01.2016,,16,142.57, -78,2,01.02.2016,,13,142.57, -78,2,02.02.2016,,6,142.57, -78,2,03.02.2016,,6,142.57, -78,2,04.02.2016,,25,142.57, -78,2,05.02.2016,,19,142.57, -78,2,06.02.2016,,6,142.57, -78,2,07.02.2016,,6,142.57, -78,2,08.02.2016,,16,142.57, -78,2,09.02.2016,1.0,101,142.57,108.46 -78,2,10.02.2016,1.0,132,138.36,108.46 -78,2,11.02.2016,1.0,88,138.36,108.46 -78,2,12.02.2016,1.0,148,138.36,108.46 -78,2,13.02.2016,1.0,97,138.36,108.46 -78,2,14.02.2016,1.0,167,138.36,108.46 -78,2,15.02.2016,1.0,123,138.36,108.46 -78,2,16.02.2016,,0,138.36, -78,2,17.02.2016,,3,138.36, -78,2,18.02.2016,,3,138.36, -78,2,19.02.2016,,6,138.36, -78,2,20.02.2016,,6,138.36, -78,2,21.02.2016,,9,138.36, -78,2,22.02.2016,,9,138.36, -78,2,23.02.2016,,6,138.36, -78,2,24.02.2016,,3,138.36, -78,2,25.02.2016,,6,138.36, -78,2,26.02.2016,,6,138.36, -78,2,27.02.2016,,3,138.36, -78,2,28.02.2016,,6,138.36, -78,2,29.02.2016,,3,138.36, -78,2,01.03.2016,,3,138.36, -78,2,02.03.2016,,3,138.36, -78,2,03.03.2016,,6,138.36, -78,2,04.03.2016,,9,138.36, -78,2,05.03.2016,,6,138.36, -78,2,06.03.2016,,3,138.36, -78,2,07.03.2016,,3,138.36, -78,2,08.03.2016,,3,138.36, -78,2,09.03.2016,,6,138.36, -78,2,10.03.2016,,6,138.36, -78,2,11.03.2016,,6,138.36, -78,2,12.03.2016,,9,138.5, -78,2,13.03.2016,,3,138.5, -78,2,14.03.2016,,6,138.5, -78,2,15.03.2016,1.0,16,138.5,124.77 -78,2,16.03.2016,1.0,57,138.5,124.77 -78,2,17.03.2016,1.0,44,138.5,124.77 -78,2,18.03.2016,1.0,57,138.5,124.77 -78,2,19.03.2016,1.0,50,138.5,124.77 -78,2,20.03.2016,1.0,53,138.5,124.77 -78,2,21.03.2016,1.0,57,138.5,124.77 -78,2,22.03.2016,,0,138.36, -78,2,23.03.2016,,6,138.36, -78,2,24.03.2016,,25,138.36, -78,2,25.03.2016,,0,138.36, -78,2,26.03.2016,,6,138.36, -78,2,27.03.2016,,0,138.36, -78,2,28.03.2016,,13,138.36, -78,2,29.03.2016,,3,138.36, -78,2,30.03.2016,,0,138.36, -78,2,31.03.2016,,0,138.36, -78,2,01.04.2016,,0,138.36, -78,2,02.04.2016,,0,138.36, -78,2,03.04.2016,,0,138.36, -78,2,04.04.2016,,0,138.36, -78,2,05.04.2016,,0,138.36, -78,2,06.04.2016,,0,138.36, -78,2,07.04.2016,,3,138.36, -78,2,08.04.2016,,0,138.36, -78,2,09.04.2016,,3,138.5, -78,2,10.04.2016,,16,138.5, -78,2,11.04.2016,,13,138.5, -78,2,12.04.2016,,3,138.5, -78,2,13.04.2016,,6,138.5, -78,2,14.04.2016,,3,138.5, -78,2,15.04.2016,,3,138.5, -78,2,16.04.2016,,3,138.5, -78,2,17.04.2016,,6,138.5, -78,2,18.04.2016,,22,138.5, -78,2,19.04.2016,1.0,28,138.5,116.89 -78,2,20.04.2016,1.0,72,138.5,116.89 -78,2,21.04.2016,1.0,72,138.5,116.89 -78,2,22.04.2016,1.0,75,138.5,116.89 -78,2,23.04.2016,1.0,104,138.5,116.89 -78,2,24.04.2016,1.0,104,138.5,116.89 -78,2,25.04.2016,1.0,110,138.5,116.89 -78,2,26.04.2016,,9,138.5, -78,2,27.04.2016,,9,138.5, -78,2,28.04.2016,,6,138.5, -78,2,29.04.2016,,9,138.5, -78,2,30.04.2016,,3,138.5, -78,2,01.05.2016,,6,138.5, -78,2,02.05.2016,,6,138.5, -78,2,03.05.2016,1.0,38,138.5,106.01 -78,2,04.05.2016,1.0,154,138.5,106.01 -78,2,05.05.2016,1.0,101,138.5,106.01 -78,2,06.05.2016,1.0,185,138.5,106.01 -78,2,07.05.2016,1.0,163,138.5,106.01 -78,2,08.05.2016,1.0,47,138.5,106.01 -78,2,09.05.2016,1.0,88,138.5,106.01 -78,2,10.05.2016,,9,138.5, -78,2,11.05.2016,,16,138.5, -78,2,12.05.2016,,19,138.5, -78,2,13.05.2016,,3,138.5, -78,2,14.05.2016,,6,138.5, -78,2,15.05.2016,,9,138.5, -78,2,16.05.2016,,9,138.5, -78,2,17.05.2016,,3,138.5, -78,2,18.05.2016,,9,138.5, -78,2,19.05.2016,,9,138.5, -78,2,20.05.2016,,3,138.5, -78,2,21.05.2016,,9,138.5, -78,2,22.05.2016,,9,138.5, -79,2,04.04.2015,,3,145.84, -79,2,05.04.2015,,0,145.84, -79,2,06.04.2015,,0,145.84, -79,2,07.04.2015,,0,145.84, -79,2,08.04.2015,,0,145.84, -79,2,09.04.2015,,0,145.84, -79,2,10.04.2015,,0,145.84, -79,2,11.04.2015,,0,145.84, -79,2,12.04.2015,,0,145.84, -79,2,13.04.2015,,0,145.84, -79,2,14.04.2015,,0,145.84, -79,2,15.04.2015,,3,145.84, -79,2,16.04.2015,,0,145.84, -79,2,17.04.2015,,0,145.84, -79,2,18.04.2015,,6,145.84, -79,2,19.04.2015,,0,145.84, -79,2,20.04.2015,,6,145.84, -79,2,21.04.2015,,3,145.84, -79,2,22.04.2015,,0,145.84, -79,2,23.04.2015,,0,145.84, -79,2,24.04.2015,,0,145.84, -79,2,25.04.2015,,0,145.84, -79,2,26.04.2015,,0,150.73, -79,2,27.04.2015,,0,150.73, -79,2,28.04.2015,,0,150.73, -79,2,29.04.2015,,0,150.73, -79,2,30.04.2015,,0,150.73, -79,2,01.05.2015,,0,150.73, -79,2,02.05.2015,,0,150.73, -79,2,03.05.2015,,0,150.73, -79,2,04.05.2015,,0,150.73, -79,2,05.05.2015,,0,150.73, -79,2,06.05.2015,,0,150.73, -79,2,07.05.2015,,0,150.73, -79,2,08.05.2015,,0,150.73, -79,2,09.05.2015,,0,150.73, -79,2,10.05.2015,,0,150.73, -79,2,11.05.2015,,0,150.73, -79,2,12.05.2015,,3,150.73, -79,2,13.05.2015,,0,150.73, -79,2,14.05.2015,,0,150.73, -79,2,15.05.2015,,6,150.73, -79,2,16.05.2015,,0,150.73, -79,2,17.05.2015,,0,150.73, -79,2,18.05.2015,,3,150.73, -79,2,19.05.2015,,0,150.73, -79,2,20.05.2015,,0,150.73, -79,2,21.05.2015,,0,150.73, -79,2,22.05.2015,,6,150.73, -79,2,23.05.2015,,0,150.73, -79,2,24.05.2015,,0,150.73, -79,2,25.05.2015,,0,150.73, -79,2,26.05.2015,,0,150.73, -79,2,27.05.2015,,3,150.73, -79,2,28.05.2015,,0,150.73, -79,2,29.05.2015,,0,150.73, -79,2,30.05.2015,,0,150.73, -79,2,31.05.2015,,0,150.73, -79,2,01.06.2015,,3,150.73, -79,2,02.06.2015,,3,150.73, -79,2,03.06.2015,,0,150.73, -79,2,04.06.2015,,0,150.73, -79,2,05.06.2015,,0,150.73, -79,2,06.06.2015,,0,150.73, -79,2,07.06.2015,,0,150.73, -79,2,08.06.2015,,0,150.73, -79,2,09.06.2015,,0,150.73, -79,2,10.06.2015,,0,150.73, -79,2,11.06.2015,,0,150.73, -79,2,12.06.2015,,0,150.73, -79,2,13.06.2015,,0,150.73, -79,2,14.06.2015,,0,150.73, -79,2,15.06.2015,,3,150.73, -79,2,16.06.2015,,0,150.73, -79,2,17.06.2015,,6,150.73, -79,2,18.06.2015,,0,150.73, -79,2,19.06.2015,,0,150.73, -79,2,20.06.2015,,0,150.73, -79,2,21.06.2015,,0,150.73, -79,2,22.06.2015,,0,150.73, -79,2,23.06.2015,,0,150.73, -79,2,24.06.2015,,6,150.73, -79,2,25.06.2015,,3,150.73, -79,2,26.06.2015,,0,150.73, -79,2,27.06.2015,,0,150.73, -79,2,28.06.2015,,3,150.73, -79,2,29.06.2015,,0,150.73, -79,2,30.06.2015,,0,150.73, -79,2,01.07.2015,,3,150.73, -79,2,02.07.2015,,3,150.73, -79,2,03.07.2015,,0,150.73, -79,2,04.07.2015,,0,150.73, -79,2,05.07.2015,,3,150.73, -79,2,06.07.2015,,0,150.73, -79,2,07.07.2015,,3,150.73, -79,2,08.07.2015,,3,150.73, -79,2,09.07.2015,,3,150.73, -79,2,10.07.2015,,3,150.73, -79,2,11.07.2015,,3,150.73, -79,2,12.07.2015,,3,150.73, -79,2,13.07.2015,,3,150.73, -79,2,14.07.2015,,3,150.73, -79,2,15.07.2015,,3,150.73, -79,2,16.07.2015,,3,150.73, -79,2,17.07.2015,,3,150.73, -79,2,18.07.2015,,3,150.73, -79,2,19.07.2015,,0,150.73, -79,2,20.07.2015,,28,150.73, -79,2,21.07.2015,,13,150.73, -79,2,22.07.2015,,3,150.73, -79,2,23.07.2015,,3,150.73, -79,2,24.07.2015,,3,150.73, -79,2,25.07.2015,,0,150.73, -79,2,26.07.2015,,19,150.73, -79,2,27.07.2015,,6,150.73, -79,2,28.07.2015,,6,150.73, -79,2,29.07.2015,,0,150.73, -79,2,30.07.2015,,16,150.73, -79,2,31.07.2015,,9,150.73, -79,2,01.08.2015,,6,150.73, -79,2,02.08.2015,,25,150.73, -79,2,03.08.2015,,9,150.73, -79,2,04.08.2015,,3,150.73, -79,2,05.08.2015,,16,150.73, -79,2,06.08.2015,,6,150.73, -79,2,07.08.2015,,16,150.73, -79,2,08.08.2015,,9,150.73, -79,2,09.08.2015,,13,150.73, -79,2,10.08.2015,,6,150.73, -79,2,11.08.2015,,3,150.73, -79,2,12.08.2015,,3,128.17, -79,2,13.08.2015,,9,128.17, -79,2,14.08.2015,,9,128.17, -79,2,15.08.2015,,13,128.17, -79,2,16.08.2015,,9,128.17, -79,2,17.08.2015,,25,128.17, -79,2,18.08.2015,1.0,157,128.17,100.3 -79,2,19.08.2015,1.0,160,128.17,100.3 -79,2,20.08.2015,1.0,185,128.17,100.3 -79,2,21.08.2015,1.0,245,128.17,100.3 -79,2,22.08.2015,1.0,145,128.17,100.3 -79,2,23.08.2015,1.0,167,128.17,100.3 -79,2,24.08.2015,1.0,110,128.17,100.3 -79,2,25.08.2015,,9,128.17, -79,2,26.08.2015,,9,128.17, -79,2,27.08.2015,,9,128.17, -79,2,28.08.2015,,16,128.17, -79,2,29.08.2015,,22,128.17, -79,2,30.08.2015,,13,128.17, -79,2,31.08.2015,,13,128.17, -79,2,01.09.2015,,13,128.17, -79,2,02.09.2015,,13,128.17, -79,2,03.09.2015,,13,128.17, -79,2,04.09.2015,,13,128.17, -79,2,05.09.2015,,13,128.17, -79,2,06.09.2015,,13,128.17, -79,2,07.09.2015,,13,128.17, -79,2,08.09.2015,,13,128.17, -79,2,09.09.2015,,6,128.17, -79,2,10.09.2015,,9,128.17, -79,2,11.09.2015,,9,128.17, -79,2,12.09.2015,,13,128.17, -79,2,13.09.2015,,13,128.17, -79,2,14.09.2015,,9,128.17, -79,2,15.09.2015,,9,128.17, -79,2,16.09.2015,,13,128.17, -79,2,17.09.2015,,13,128.17, -79,2,18.09.2015,,13,128.17, -79,2,19.09.2015,,13,128.17, -79,2,20.09.2015,,9,128.17, -79,2,21.09.2015,,9,128.17, -79,2,22.09.2015,,13,128.17, -79,2,23.09.2015,,3,128.17, -79,2,24.09.2015,,3,128.17, -79,2,25.09.2015,,13,128.17, -79,2,26.09.2015,,13,128.17, -79,2,27.09.2015,,3,128.17, -79,2,28.09.2015,,16,128.17, -79,2,29.09.2015,,9,128.17, -79,2,30.09.2015,,6,128.17, -79,2,01.10.2015,,9,162.96, -79,2,02.10.2015,,9,162.96, -79,2,03.10.2015,,9,162.96, -79,2,04.10.2015,,9,162.96, -79,2,05.10.2015,,9,162.96, -79,2,06.10.2015,1.0,160,143.39,122.05 -79,2,07.10.2015,1.0,50,143.39,122.05 -79,2,08.10.2015,1.0,75,143.39,122.05 -79,2,09.10.2015,1.0,88,143.39,122.05 -79,2,10.10.2015,1.0,79,143.39,122.05 -79,2,11.10.2015,1.0,88,143.39,122.05 -79,2,12.10.2015,1.0,28,143.39,122.05 -79,2,13.10.2015,,9,143.93, -79,2,14.10.2015,,9,143.93, -79,2,15.10.2015,,9,143.93, -79,2,16.10.2015,,9,143.93, -79,2,17.10.2015,,9,162.96, -79,2,18.10.2015,,9,162.96, -79,2,19.10.2015,,9,162.96, -79,2,20.10.2015,,3,162.96, -79,2,21.10.2015,,9,162.96, -79,2,22.10.2015,,9,162.96, -79,2,23.10.2015,,9,162.96, -79,2,24.10.2015,,9,162.96, -79,2,25.10.2015,,9,162.96, -79,2,26.10.2015,,9,162.96, -79,2,27.10.2015,,9,162.96, -79,2,28.10.2015,,9,162.96, -79,2,29.10.2015,,9,162.96, -79,2,30.10.2015,,9,162.96, -79,2,31.10.2015,,3,162.96, -79,2,01.11.2015,,6,162.96, -79,2,02.11.2015,,6,162.96, -79,2,03.11.2015,,6,162.96, -79,2,04.11.2015,,9,162.96, -79,2,05.11.2015,,6,162.96, -79,2,06.11.2015,,6,162.96, -79,2,07.11.2015,,6,162.96, -79,2,08.11.2015,,6,162.96, -79,2,09.11.2015,,0,162.96, -79,2,10.11.2015,,0,162.96, -79,2,11.11.2015,,9,138.36, -79,2,12.11.2015,,3,138.36, -79,2,13.11.2015,,6,138.36, -79,2,14.11.2015,,6,138.36, -79,2,15.11.2015,,6,138.36, -79,2,16.11.2015,,6,138.36, -79,2,17.11.2015,,6,138.36, -79,2,18.11.2015,,3,138.36, -79,2,19.11.2015,,6,138.36, -79,2,20.11.2015,,3,138.36, -79,2,21.11.2015,,6,138.36, -79,2,22.11.2015,,3,138.36, -79,2,23.11.2015,,6,138.36, -79,2,24.11.2015,,6,138.36, -79,2,25.11.2015,,13,138.36, -79,2,26.11.2015,,6,138.36, -79,2,27.11.2015,,0,138.36, -79,2,28.11.2015,,3,138.36, -79,2,29.11.2015,,0,138.36, -79,2,30.11.2015,,3,138.36, -79,2,01.12.2015,,0,162.96, -79,2,02.12.2015,,0,162.96, -79,2,03.12.2015,,0,138.5, -79,2,04.12.2015,,3,138.5, -79,2,05.12.2015,,0,138.5, -79,2,06.12.2015,,3,138.5, -79,2,07.12.2015,,3,138.5, -79,2,08.12.2015,,3,138.5, -79,2,09.12.2015,,3,138.5, -79,2,10.12.2015,,3,138.5, -79,2,11.12.2015,,6,142.57, -79,2,12.12.2015,,6,142.57, -79,2,13.12.2015,,3,142.57, -79,2,14.12.2015,,3,142.57, -79,2,15.12.2015,,6,142.57, -79,2,16.12.2015,,6,142.57, -79,2,17.12.2015,,6,142.57, -79,2,18.12.2015,,3,142.57, -79,2,19.12.2015,,3,142.57, -79,2,20.12.2015,,3,142.57, -79,2,21.12.2015,,6,142.57, -79,2,22.12.2015,1.0,157,142.57,103.02 -79,2,23.12.2015,1.0,101,142.57,103.02 -79,2,24.12.2015,1.0,145,142.57,103.02 -79,2,25.12.2015,1.0,160,142.57,103.02 -79,2,26.12.2015,1.0,110,142.57,103.02 -79,2,27.12.2015,1.0,110,142.57,103.02 -79,2,28.12.2015,1.0,88,142.57,103.02 -79,2,29.12.2015,,6,138.5, -79,2,30.12.2015,,3,138.5, -79,2,31.12.2015,,3,138.5, -79,2,01.01.2016,,6,138.5, -79,2,02.01.2016,,3,138.5, -79,2,03.01.2016,,6,138.5, -79,2,04.01.2016,,3,138.5, -79,2,05.01.2016,,6,138.5, -79,2,06.01.2016,,6,138.5, -79,2,07.01.2016,,6,138.5, -79,2,08.01.2016,,6,138.5, -79,2,09.01.2016,,6,138.5, -79,2,10.01.2016,,13,138.5, -79,2,11.01.2016,,6,138.5, -79,2,12.01.2016,,6,138.5, -79,2,13.01.2016,,3,138.5, -79,2,14.01.2016,,13,138.5, -79,2,15.01.2016,,3,138.5, -79,2,16.01.2016,,6,142.57, -79,2,17.01.2016,,6,142.57, -79,2,18.01.2016,,16,142.57, -79,2,19.01.2016,1.0,201,142.57,103.02 -79,2,20.01.2016,1.0,226,142.57,103.02 -79,2,21.01.2016,1.0,236,142.57,103.02 -79,2,22.01.2016,1.0,192,142.57,103.02 -79,2,23.01.2016,1.0,267,142.57,103.02 -79,2,24.01.2016,1.0,163,142.57,103.02 -79,2,25.01.2016,1.0,151,142.57,103.02 -79,2,26.01.2016,,3,142.57, -79,2,27.01.2016,,9,142.57, -79,2,28.01.2016,,3,142.57, -79,2,29.01.2016,,6,142.57, -79,2,30.01.2016,,3,142.57, -79,2,31.01.2016,,6,142.57, -79,2,01.02.2016,,6,142.57, -79,2,02.02.2016,,6,142.57, -79,2,03.02.2016,,16,142.57, -79,2,04.02.2016,,3,142.57, -79,2,05.02.2016,,6,142.57, -79,2,06.02.2016,,6,142.57, -79,2,07.02.2016,,3,142.57, -79,2,08.02.2016,,6,142.57, -79,2,09.02.2016,1.0,283,142.57,108.46 -79,2,10.02.2016,1.0,302,138.36,108.46 -79,2,11.02.2016,1.0,276,138.36,108.46 -79,2,12.02.2016,1.0,289,138.36,108.46 -79,2,13.02.2016,1.0,176,138.36,108.46 -79,2,14.02.2016,1.0,214,138.36,108.46 -79,2,15.02.2016,1.0,226,138.36,108.46 -79,2,16.02.2016,,6,138.36, -79,2,17.02.2016,,6,138.36, -79,2,18.02.2016,,0,138.36, -79,2,19.02.2016,,3,138.36, -79,2,20.02.2016,,0,138.36, -79,2,21.02.2016,,6,138.36, -79,2,22.02.2016,,3,138.36, -79,2,23.02.2016,,3,138.36, -79,2,24.02.2016,,3,138.36, -79,2,25.02.2016,,6,138.36, -79,2,26.02.2016,,3,138.36, -79,2,27.02.2016,,0,138.36, -79,2,28.02.2016,,0,138.36, -79,2,29.02.2016,,3,138.36, -79,2,01.03.2016,,0,138.36, -79,2,02.03.2016,,0,138.36, -79,2,03.03.2016,,3,138.36, -79,2,04.03.2016,,9,138.36, -79,2,05.03.2016,,0,138.36, -79,2,06.03.2016,,0,138.36, -79,2,07.03.2016,,0,138.36, -79,2,08.03.2016,,16,138.36, -79,2,09.03.2016,,6,138.36, -79,2,10.03.2016,,3,138.36, -79,2,11.03.2016,,13,138.36, -79,2,12.03.2016,,3,138.5, -79,2,13.03.2016,,16,138.5, -79,2,14.03.2016,,6,138.5, -79,2,15.03.2016,1.0,223,138.5,124.77 -79,2,16.03.2016,1.0,72,138.5,124.77 -79,2,17.03.2016,1.0,66,138.5,124.77 -79,2,18.03.2016,1.0,44,138.5,124.77 -79,2,19.03.2016,1.0,82,138.5,124.77 -79,2,20.03.2016,1.0,88,138.5,124.77 -79,2,21.03.2016,1.0,60,138.5,124.77 -79,2,22.03.2016,,6,138.36, -79,2,23.03.2016,,13,138.36, -79,2,24.03.2016,,6,138.36, -79,2,25.03.2016,,0,138.36, -79,2,26.03.2016,,0,138.36, -79,2,27.03.2016,,13,138.36, -79,2,28.03.2016,,3,138.36, -79,2,29.03.2016,,9,138.36, -79,2,30.03.2016,,9,138.36, -79,2,31.03.2016,,9,138.36, -79,2,01.04.2016,,16,138.36, -79,2,02.04.2016,,3,138.36, -79,2,03.04.2016,,3,138.36, -79,2,04.04.2016,,3,138.36, -79,2,05.04.2016,,9,138.36, -79,2,06.04.2016,,0,138.36, -79,2,07.04.2016,,0,138.36, -79,2,08.04.2016,,13,138.36, -79,2,09.04.2016,,9,138.5, -79,2,10.04.2016,,13,138.5, -79,2,11.04.2016,,6,138.5, -79,2,12.04.2016,,0,138.5, -79,2,13.04.2016,,9,138.5, -79,2,14.04.2016,,3,138.5, -79,2,15.04.2016,,0,138.5, -79,2,16.04.2016,,0,138.5, -79,2,17.04.2016,,0,138.5, -79,2,18.04.2016,,9,138.5, -79,2,19.04.2016,1.0,204,138.5,116.89 -79,2,20.04.2016,1.0,135,138.5,116.89 -79,2,21.04.2016,1.0,176,138.5,116.89 -79,2,22.04.2016,1.0,176,138.5,116.89 -79,2,23.04.2016,1.0,107,138.5,116.89 -79,2,24.04.2016,1.0,132,138.5,116.89 -79,2,25.04.2016,1.0,85,138.5,116.89 -79,2,26.04.2016,,3,138.5, -79,2,27.04.2016,,6,138.5, -79,2,28.04.2016,,6,138.5, -79,2,29.04.2016,,6,138.5, -79,2,30.04.2016,,6,138.5, -79,2,01.05.2016,,3,138.5, -79,2,02.05.2016,,9,138.5, -79,2,03.05.2016,1.0,214,138.5,106.01 -79,2,04.05.2016,1.0,214,138.5,106.01 -79,2,05.05.2016,1.0,267,138.5,106.01 -79,2,06.05.2016,1.0,157,138.5,106.01 -79,2,07.05.2016,1.0,210,138.5,106.01 -79,2,08.05.2016,1.0,223,138.5,106.01 -79,2,09.05.2016,1.0,185,138.5,106.01 -79,2,10.05.2016,,19,138.5, -79,2,11.05.2016,,16,138.5, -79,2,12.05.2016,,226,138.5, -79,2,13.05.2016,,207,138.5, -79,2,14.05.2016,,38,138.5, -79,2,15.05.2016,,38,138.5, -79,2,16.05.2016,,13,138.5, -79,2,17.05.2016,,13,138.5, -79,2,18.05.2016,,38,138.5, -79,2,19.05.2016,,38,138.5, -79,2,20.05.2016,,16,138.5, -79,2,21.05.2016,,38,138.5, -79,2,22.05.2016,,6,138.5, -80,2,01.05.2015,,3,150.73, -80,2,02.05.2015,,3,150.73, -80,2,03.05.2015,,0,150.73, -80,2,04.05.2015,,3,150.73, -80,2,05.05.2015,,0,150.73, -80,2,06.05.2015,,0,150.73, -80,2,07.05.2015,,0,150.73, -80,2,08.05.2015,,0,150.73, -80,2,09.05.2015,,0,150.73, -80,2,10.05.2015,,0,150.73, -80,2,11.05.2015,,0,150.73, -80,2,12.05.2015,,0,150.73, -80,2,13.05.2015,,0,150.73, -80,2,14.05.2015,,0,150.73, -80,2,15.05.2015,,0,150.73, -80,2,16.05.2015,,0,150.73, -80,2,17.05.2015,,0,150.73, -80,2,18.05.2015,,0,150.73, -80,2,19.05.2015,,0,150.73, -80,2,20.05.2015,,0,150.73, -80,2,21.05.2015,,0,150.73, -80,2,22.05.2015,,0,150.73, -80,2,23.05.2015,,0,150.73, -80,2,24.05.2015,,0,150.73, -80,2,25.05.2015,,0,150.73, -80,2,26.05.2015,,0,150.73, -80,2,27.05.2015,,0,150.73, -80,2,28.05.2015,,0,150.73, -80,2,29.05.2015,,0,150.73, -80,2,30.05.2015,,0,150.73, -80,2,31.05.2015,,0,150.73, -80,2,01.06.2015,,0,150.73, -80,2,02.06.2015,,0,150.73, -80,2,03.06.2015,,0,150.73, -80,2,04.06.2015,,0,150.73, -80,2,05.06.2015,,0,150.73, -80,2,06.06.2015,,0,150.73, -80,2,07.06.2015,,0,150.73, -80,2,08.06.2015,,0,150.73, -80,2,09.06.2015,,0,150.73, -80,2,10.06.2015,,0,150.73, -80,2,11.06.2015,,0,150.73, -80,2,12.06.2015,,0,150.73, -80,2,13.06.2015,,0,150.73, -80,2,14.06.2015,,0,150.73, -80,2,15.06.2015,,0,150.73, -80,2,16.06.2015,,6,150.73, -80,2,17.06.2015,,0,150.73, -80,2,18.06.2015,,0,150.73, -80,2,19.06.2015,,0,150.73, -80,2,20.06.2015,,0,150.73, -80,2,21.06.2015,,0,150.73, -80,2,22.06.2015,,0,150.73, -80,2,23.06.2015,,3,150.73, -80,2,24.06.2015,,0,150.73, -80,2,25.06.2015,,0,150.73, -80,2,26.06.2015,,0,150.73, -80,2,27.06.2015,,0,150.73, -80,2,28.06.2015,,0,150.73, -80,2,29.06.2015,,0,150.73, -80,2,30.06.2015,,0,150.73, -80,2,01.07.2015,,0,150.73, -80,2,02.07.2015,,0,150.73, -80,2,03.07.2015,,0,150.73, -80,2,04.07.2015,,0,150.73, -80,2,05.07.2015,,0,150.73, -80,2,06.07.2015,,0,150.73, -80,2,07.07.2015,,0,150.73, -80,2,08.07.2015,,0,150.73, -80,2,09.07.2015,,0,150.73, -80,2,10.07.2015,,0,150.73, -80,2,11.07.2015,,0,150.73, -80,2,12.07.2015,,0,150.73, -80,2,13.07.2015,,0,150.73, -80,2,14.07.2015,,3,150.73, -80,2,15.07.2015,,0,150.73, -80,2,16.07.2015,,0,150.73, -80,2,17.07.2015,,0,150.73, -80,2,18.07.2015,,6,150.73, -80,2,19.07.2015,,0,150.73, -80,2,20.07.2015,,13,150.73, -80,2,21.07.2015,,0,150.73, -80,2,22.07.2015,,6,150.73, -80,2,23.07.2015,,3,150.73, -80,2,24.07.2015,,3,150.73, -80,2,25.07.2015,,0,150.73, -80,2,26.07.2015,,0,150.73, -80,2,27.07.2015,,0,150.73, -80,2,28.07.2015,,0,150.73, -80,2,29.07.2015,,0,150.73, -80,2,30.07.2015,,0,150.73, -80,2,31.07.2015,,0,150.73, -80,2,01.08.2015,,0,150.73, -80,2,02.08.2015,,0,150.73, -80,2,03.08.2015,,0,150.73, -80,2,04.08.2015,,0,150.73, -80,2,05.08.2015,,9,150.73, -80,2,06.08.2015,,3,150.73, -80,2,07.08.2015,,13,150.73, -80,2,08.08.2015,,0,150.73, -80,2,09.08.2015,,0,150.73, -80,2,10.08.2015,,0,150.73, -80,2,11.08.2015,,0,150.73, -80,2,12.08.2015,,6,128.17, -80,2,13.08.2015,,0,128.17, -80,2,14.08.2015,,6,128.17, -80,2,15.08.2015,,3,128.17, -80,2,16.08.2015,,6,128.17, -80,2,17.08.2015,,3,128.17, -80,2,18.08.2015,1.0,13,128.17,100.3 -80,2,19.08.2015,1.0,91,128.17,100.3 -80,2,20.08.2015,1.0,79,128.17,100.3 -80,2,21.08.2015,1.0,38,128.17,100.3 -80,2,22.08.2015,1.0,50,128.17,100.3 -80,2,23.08.2015,1.0,91,128.17,100.3 -80,2,24.08.2015,1.0,53,128.17,100.3 -80,2,25.08.2015,,13,128.17, -80,2,26.08.2015,,16,128.17, -80,2,27.08.2015,,6,128.17, -80,2,28.08.2015,,44,128.17, -80,2,29.08.2015,,13,128.17, -80,2,30.08.2015,,28,128.17, -80,2,31.08.2015,,28,128.17, -80,2,01.09.2015,,6,128.17, -80,2,02.09.2015,,3,128.17, -80,2,03.09.2015,,0,128.17, -80,2,04.09.2015,,3,128.17, -80,2,05.09.2015,,6,128.17, -80,2,06.09.2015,,3,128.17, -80,2,07.09.2015,,9,128.17, -80,2,08.09.2015,,9,128.17, -80,2,09.09.2015,,3,128.17, -80,2,10.09.2015,,9,128.17, -80,2,11.09.2015,,3,128.17, -80,2,12.09.2015,,3,128.17, -80,2,13.09.2015,,0,128.17, -80,2,14.09.2015,,16,128.17, -80,2,15.09.2015,,0,128.17, -80,2,16.09.2015,,0,128.17, -80,2,17.09.2015,,0,128.17, -80,2,18.09.2015,,0,128.17, -80,2,19.09.2015,,0,128.17, -80,2,20.09.2015,,0,128.17, -80,2,21.09.2015,,0,128.17, -80,2,22.09.2015,,0,128.17, -80,2,23.09.2015,,0,128.17, -80,2,24.09.2015,,0,128.17, -80,2,25.09.2015,,0,128.17, -80,2,26.09.2015,,0,128.17, -80,2,27.09.2015,,0,128.17, -80,2,28.09.2015,,0,128.17, -80,2,29.09.2015,,0,128.17, -80,2,30.09.2015,,0,128.17, -80,2,01.10.2015,,0,162.96, -80,2,02.10.2015,,0,162.96, -80,2,03.10.2015,,0,162.96, -80,2,04.10.2015,,0,162.96, -80,2,05.10.2015,,0,162.96, -80,2,06.10.2015,1.0,13,143.39,122.05 -80,2,07.10.2015,1.0,19,143.39,122.05 -80,2,08.10.2015,1.0,6,143.39,122.05 -80,2,09.10.2015,1.0,22,143.39,122.05 -80,2,10.10.2015,1.0,19,143.39,122.05 -80,2,11.10.2015,1.0,22,143.39,122.05 -80,2,12.10.2015,1.0,25,143.39,122.05 -80,2,13.10.2015,1.0,38,143.93,122.05 -80,2,14.10.2015,1.0,16,143.93,122.05 -80,2,15.10.2015,1.0,13,143.93,122.05 -80,2,16.10.2015,1.0,28,143.93,122.05 -80,2,17.10.2015,1.0,25,162.96,122.05 -80,2,18.10.2015,1.0,13,162.96,122.05 -80,2,19.10.2015,,0,162.96, -80,2,20.10.2015,,0,162.96, -80,2,21.10.2015,,0,162.96, -80,2,22.10.2015,,0,162.96, -80,2,23.10.2015,,0,162.96, -80,2,24.10.2015,,0,162.96, -80,2,25.10.2015,,0,162.96, -80,2,26.10.2015,,0,162.96, -80,2,27.10.2015,,0,162.96, -80,2,28.10.2015,,16,162.96, -80,2,29.10.2015,,0,162.96, -80,2,30.10.2015,,0,162.96, -80,2,31.10.2015,,0,162.96, -80,2,01.11.2015,,0,162.96, -80,2,02.11.2015,,0,162.96, -80,2,03.11.2015,,0,162.96, -80,2,04.11.2015,,0,162.96, -80,2,05.11.2015,,0,162.96, -80,2,06.11.2015,,0,162.96, -80,2,07.11.2015,,0,162.96, -80,2,08.11.2015,,0,162.96, -80,2,09.11.2015,,0,162.96, -80,2,10.11.2015,,3,162.96, -80,2,11.11.2015,,0,138.36, -80,2,12.11.2015,,0,138.36, -80,2,13.11.2015,,0,138.36, -80,2,14.11.2015,,0,138.36, -80,2,15.11.2015,,0,138.36, -80,2,16.11.2015,,0,138.36, -80,2,17.11.2015,,0,138.36, -80,2,18.11.2015,,0,138.36, -80,2,19.11.2015,,0,138.36, -80,2,20.11.2015,,0,138.36, -80,2,21.11.2015,,0,138.36, -80,2,22.11.2015,,0,138.36, -80,2,23.11.2015,,0,138.36, -80,2,24.11.2015,,0,138.36, -80,2,25.11.2015,,0,138.36, -80,2,26.11.2015,,0,138.36, -80,2,27.11.2015,,0,138.36, -80,2,28.11.2015,,0,138.36, -80,2,29.11.2015,,0,138.36, -80,2,30.11.2015,,0,138.36, -80,2,01.12.2015,,0,162.96, -80,2,02.12.2015,,0,162.96, -80,2,03.12.2015,,0,138.5, -80,2,04.12.2015,,0,138.5, -80,2,05.12.2015,,0,138.5, -80,2,06.12.2015,,0,138.5, -80,2,07.12.2015,,0,138.5, -80,2,08.12.2015,,0,138.5, -80,2,09.12.2015,,0,138.5, -80,2,10.12.2015,,0,138.5, -80,2,11.12.2015,,0,142.57, -80,2,12.12.2015,,0,142.57, -80,2,13.12.2015,,0,142.57, -80,2,14.12.2015,,0,142.57, -80,2,15.12.2015,,0,142.57, -80,2,16.12.2015,,0,142.57, -80,2,17.12.2015,,0,142.57, -80,2,18.12.2015,,0,142.57, -80,2,19.12.2015,,0,142.57, -80,2,20.12.2015,,0,142.57, -80,2,21.12.2015,,3,142.57, -80,2,22.12.2015,1.0,47,142.57,103.02 -80,2,23.12.2015,1.0,66,142.57,103.02 -80,2,24.12.2015,1.0,53,142.57,103.02 -80,2,25.12.2015,1.0,25,142.57,103.02 -80,2,26.12.2015,1.0,3,142.57,103.02 -80,2,27.12.2015,1.0,53,142.57,103.02 -80,2,28.12.2015,1.0,38,142.57,103.02 -80,2,29.12.2015,,0,138.5, -80,2,30.12.2015,,0,138.5, -80,2,31.12.2015,,0,138.5, -80,2,01.01.2016,,0,138.5, -80,2,02.01.2016,,0,138.5, -80,2,03.01.2016,,0,138.5, -80,2,04.01.2016,,0,138.5, -80,2,05.01.2016,,9,138.5, -80,2,06.01.2016,,0,138.5, -80,2,07.01.2016,,3,138.5, -80,2,08.01.2016,,0,138.5, -80,2,09.01.2016,,6,138.5, -80,2,10.01.2016,,0,138.5, -80,2,11.01.2016,,0,138.5, -80,2,12.01.2016,,3,138.5, -80,2,13.01.2016,,9,138.5, -80,2,14.01.2016,,9,138.5, -80,2,15.01.2016,,0,138.5, -80,2,16.01.2016,,6,142.57, -80,2,17.01.2016,,6,142.57, -80,2,18.01.2016,,3,142.57, 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-81,2,08.09.2015,,0,128.17, -81,2,09.09.2015,,0,128.17, -81,2,10.09.2015,,0,128.17, -81,2,11.09.2015,,0,128.17, -81,2,12.09.2015,,0,128.17, -81,2,13.09.2015,,0,128.17, -81,2,14.09.2015,,0,128.17, -81,2,15.09.2015,,0,128.17, -81,2,16.09.2015,,0,128.17, -81,2,17.09.2015,,0,128.17, -81,2,18.09.2015,,0,128.17, -81,2,19.09.2015,,0,128.17, -81,2,20.09.2015,,0,128.17, -81,2,21.09.2015,,0,128.17, -81,2,22.09.2015,,0,128.17, -81,2,23.09.2015,,0,128.17, -81,2,24.09.2015,,0,128.17, -81,2,25.09.2015,,0,128.17, -81,2,26.09.2015,,0,128.17, -81,2,27.09.2015,,16,128.17, -81,2,28.09.2015,,6,128.17, -81,2,29.09.2015,,35,128.17, -81,2,30.09.2015,,35,128.17, -81,2,01.10.2015,,19,162.96, -81,2,02.10.2015,,0,162.96, -81,2,03.10.2015,,0,162.96, -81,2,04.10.2015,,0,162.96, -81,2,05.10.2015,,0,162.96, -81,2,06.10.2015,1.0,79,143.39,122.05 -81,2,07.10.2015,1.0,60,143.39,122.05 -81,2,08.10.2015,1.0,13,143.39,122.05 -81,2,09.10.2015,1.0,57,143.39,122.05 -81,2,10.10.2015,1.0,57,143.39,122.05 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-81,2,20.12.2015,,9,142.57, -81,2,21.12.2015,,9,142.57, -81,2,22.12.2015,1.0,135,142.57,103.02 -81,2,23.12.2015,1.0,19,142.57,103.02 -81,2,24.12.2015,1.0,94,142.57,103.02 -81,2,25.12.2015,1.0,19,142.57,103.02 -81,2,26.12.2015,1.0,57,142.57,103.02 -81,2,27.12.2015,1.0,44,142.57,103.02 -81,2,28.12.2015,1.0,107,142.57,103.02 -81,2,29.12.2015,,0,138.5, -81,2,30.12.2015,,6,138.5, -81,2,31.12.2015,,3,138.5, -81,2,01.01.2016,,0,138.5, -81,2,02.01.2016,,3,138.5, -81,2,03.01.2016,,31,138.5, -81,2,04.01.2016,,25,138.5, -81,2,05.01.2016,,22,138.5, -81,2,06.01.2016,,38,138.5, -81,2,07.01.2016,,25,138.5, -81,2,08.01.2016,,19,138.5, -81,2,09.01.2016,,6,138.5, -81,2,10.01.2016,,0,138.5, -81,2,11.01.2016,,0,138.5, -81,2,12.01.2016,,0,138.5, -81,2,13.01.2016,,0,138.5, -81,2,14.01.2016,,9,138.5, -81,2,15.01.2016,,6,138.5, -81,2,16.01.2016,,0,142.57, -81,2,17.01.2016,,9,142.57, -81,2,18.01.2016,,3,142.57, -81,2,19.01.2016,1.0,119,142.57,103.02 -81,2,20.01.2016,1.0,151,142.57,103.02 -81,2,21.01.2016,1.0,141,142.57,103.02 -81,2,22.01.2016,1.0,85,142.57,103.02 -81,2,23.01.2016,1.0,69,142.57,103.02 -81,2,24.01.2016,1.0,66,142.57,103.02 -81,2,25.01.2016,1.0,66,142.57,103.02 -81,2,26.01.2016,,0,142.57, -81,2,27.01.2016,,0,142.57, -81,2,28.01.2016,,0,142.57, -81,2,29.01.2016,,0,142.57, -81,2,30.01.2016,,0,142.57, -81,2,31.01.2016,,0,142.57, -81,2,01.02.2016,,0,142.57, -81,2,02.02.2016,,0,142.57, -81,2,03.02.2016,,0,142.57, -81,2,04.02.2016,,0,142.57, -81,2,05.02.2016,,0,142.57, -81,2,06.02.2016,,0,142.57, -81,2,07.02.2016,,0,142.57, -81,2,08.02.2016,,0,142.57, -81,2,09.02.2016,1.0,88,142.57,108.46 -81,2,10.02.2016,1.0,148,138.36,108.46 -81,2,11.02.2016,1.0,148,138.36,108.46 -81,2,12.02.2016,1.0,148,138.36,108.46 -81,2,13.02.2016,1.0,129,138.36,108.46 -81,2,14.02.2016,1.0,201,138.36,108.46 -81,2,15.02.2016,1.0,123,138.36,108.46 -81,2,16.02.2016,,0,138.36, -81,2,17.02.2016,,0,138.36, -81,2,18.02.2016,,0,138.36, -81,2,19.02.2016,,0,138.36, -81,2,20.02.2016,,0,138.36, 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-81,2,25.03.2016,,0,138.36, -81,2,26.03.2016,,3,138.36, -81,2,27.03.2016,,0,138.36, -81,2,28.03.2016,,0,138.36, -81,2,29.03.2016,,3,138.36, -81,2,30.03.2016,,0,138.36, -81,2,31.03.2016,,0,138.36, -81,2,01.04.2016,,0,138.36, -81,2,02.04.2016,,0,138.36, -81,2,03.04.2016,,0,138.36, -81,2,04.04.2016,,0,138.36, -81,2,05.04.2016,,0,138.36, -81,2,06.04.2016,,0,138.36, -81,2,07.04.2016,,0,138.36, -81,2,08.04.2016,,0,138.36, -81,2,09.04.2016,,0,138.5, -81,2,10.04.2016,,0,138.5, -81,2,11.04.2016,,0,138.5, -81,2,12.04.2016,,0,138.5, -81,2,13.04.2016,,0,138.5, -81,2,14.04.2016,,0,138.5, -81,2,15.04.2016,,0,138.5, -81,2,16.04.2016,,0,138.5, -81,2,17.04.2016,,0,138.5, -81,2,18.04.2016,,9,138.5, -81,2,19.04.2016,1.0,53,138.5,116.89 -81,2,20.04.2016,1.0,110,138.5,116.89 -81,2,21.04.2016,1.0,82,138.5,116.89 -81,2,22.04.2016,1.0,126,138.5,116.89 -81,2,23.04.2016,1.0,60,138.5,116.89 -81,2,24.04.2016,1.0,79,138.5,116.89 -81,2,25.04.2016,1.0,53,138.5,116.89 -81,2,26.04.2016,,0,138.5, -81,2,27.04.2016,,0,138.5, -81,2,28.04.2016,,0,138.5, -81,2,29.04.2016,,6,138.5, -81,2,30.04.2016,,3,138.5, -81,2,01.05.2016,,13,138.5, -81,2,02.05.2016,,6,138.5, -81,2,03.05.2016,1.0,79,138.5,106.01 -81,2,04.05.2016,1.0,107,138.5,106.01 -81,2,05.05.2016,1.0,97,138.5,106.01 -81,2,06.05.2016,1.0,182,138.5,106.01 -81,2,07.05.2016,1.0,151,138.5,106.01 -81,2,08.05.2016,1.0,79,138.5,106.01 -81,2,09.05.2016,1.0,163,138.5,106.01 -81,2,10.05.2016,,3,138.5, -81,2,11.05.2016,,3,138.5, -81,2,12.05.2016,,0,138.5, -81,2,13.05.2016,,0,138.5, -81,2,14.05.2016,,0,138.5, -81,2,15.05.2016,,0,138.5, -81,2,16.05.2016,,0,138.5, -81,2,17.05.2016,,0,138.5, -81,2,18.05.2016,,0,138.5, -81,2,19.05.2016,,0,138.5, -81,2,20.05.2016,,0,138.5, -81,2,21.05.2016,,0,138.5, -81,2,22.05.2016,,0,138.5, -82,2,23.07.2015,,13,150.73, -82,2,24.07.2015,,16,150.73, -82,2,25.07.2015,,9,150.73, -82,2,26.07.2015,,31,150.73, -82,2,27.07.2015,,38,150.73, -82,2,28.07.2015,,9,150.73, -82,2,29.07.2015,,22,150.73, -82,2,30.07.2015,,16,150.73, -82,2,31.07.2015,,22,150.73, -82,2,01.08.2015,,3,150.73, -82,2,02.08.2015,,38,150.73, -82,2,03.08.2015,,9,150.73, -82,2,04.08.2015,,19,150.73, -82,2,05.08.2015,,13,150.73, -82,2,06.08.2015,,19,150.73, -82,2,07.08.2015,,3,150.73, -82,2,08.08.2015,,13,150.73, -82,2,09.08.2015,,13,150.73, -82,2,10.08.2015,,13,150.73, -82,2,11.08.2015,,13,150.73, -82,2,12.08.2015,,0,128.17, -82,2,13.08.2015,,0,128.17, -82,2,14.08.2015,,6,128.17, -82,2,15.08.2015,,0,128.17, -82,2,16.08.2015,,9,128.17, -82,2,17.08.2015,,35,128.17, -82,2,18.08.2015,1.0,28,128.17,100.3 -82,2,19.08.2015,1.0,119,128.17,100.3 -82,2,20.08.2015,1.0,123,128.17,100.3 -82,2,21.08.2015,1.0,113,128.17,100.3 -82,2,22.08.2015,1.0,72,128.17,100.3 -82,2,23.08.2015,1.0,110,128.17,100.3 -82,2,24.08.2015,1.0,97,128.17,100.3 -82,2,25.08.2015,,25,128.17, -82,2,26.08.2015,,13,128.17, -82,2,27.08.2015,,9,128.17, -82,2,28.08.2015,,9,128.17, -82,2,29.08.2015,,22,128.17, -82,2,30.08.2015,,22,128.17, -82,2,31.08.2015,,13,128.17, -82,2,01.09.2015,,6,128.17, -82,2,02.09.2015,,19,128.17, -82,2,03.09.2015,,6,128.17, -82,2,04.09.2015,,13,128.17, -82,2,05.09.2015,,13,128.17, -82,2,06.09.2015,,31,128.17, -82,2,07.09.2015,,38,128.17, -82,2,08.09.2015,,13,128.17, -82,2,09.09.2015,,13,128.17, -82,2,10.09.2015,,6,128.17, -82,2,11.09.2015,,9,128.17, -82,2,12.09.2015,,6,128.17, -82,2,13.09.2015,,31,128.17, -82,2,14.09.2015,,19,128.17, -82,2,15.09.2015,,31,128.17, -82,2,16.09.2015,,6,128.17, -82,2,17.09.2015,,13,128.17, -82,2,18.09.2015,,35,128.17, -82,2,19.09.2015,,16,128.17, -82,2,20.09.2015,,9,128.17, -82,2,21.09.2015,,28,128.17, -82,2,22.09.2015,,13,128.17, -82,2,23.09.2015,,9,128.17, -82,2,24.09.2015,,22,128.17, -82,2,25.09.2015,,16,128.17, -82,2,26.09.2015,,19,128.17, -82,2,27.09.2015,,22,128.17, -82,2,28.09.2015,,19,128.17, -82,2,29.09.2015,,9,128.17, -82,2,30.09.2015,,28,128.17, -82,2,01.10.2015,,9,162.96, -82,2,02.10.2015,,9,162.96, -82,2,03.10.2015,,3,162.96, -82,2,04.10.2015,,38,162.96, -82,2,05.10.2015,,6,162.96, -82,2,06.10.2015,1.0,94,143.39,122.05 -82,2,07.10.2015,1.0,69,143.39,122.05 -82,2,08.10.2015,1.0,60,143.39,122.05 -82,2,09.10.2015,1.0,69,143.39,122.05 -82,2,10.10.2015,1.0,63,143.39,122.05 -82,2,11.10.2015,1.0,66,143.39,122.05 -82,2,12.10.2015,1.0,75,143.39,122.05 -82,2,13.10.2015,,0,143.93, -82,2,14.10.2015,,6,143.93, -82,2,15.10.2015,,6,143.93, -82,2,16.10.2015,,0,143.93, -82,2,17.10.2015,,0,162.96, -82,2,18.10.2015,,0,162.96, -82,2,19.10.2015,,0,162.96, -82,2,20.10.2015,,0,162.96, -82,2,21.10.2015,,0,162.96, -82,2,22.10.2015,,3,162.96, -82,2,23.10.2015,,0,162.96, -82,2,24.10.2015,,0,162.96, -82,2,25.10.2015,,3,162.96, -82,2,26.10.2015,,0,162.96, -82,2,27.10.2015,,0,162.96, -82,2,28.10.2015,,0,162.96, -82,2,29.10.2015,,0,162.96, -82,2,30.10.2015,,0,162.96, -82,2,31.10.2015,,0,162.96, -82,2,01.11.2015,,0,162.96, -82,2,02.11.2015,,0,162.96, -82,2,03.11.2015,,0,162.96, -82,2,04.11.2015,,0,162.96, -82,2,05.11.2015,,0,162.96, -82,2,06.11.2015,,0,162.96, -82,2,07.11.2015,,6,162.96, -82,2,08.11.2015,,9,162.96, -82,2,09.11.2015,,0,162.96, -82,2,10.11.2015,,3,162.96, -82,2,11.11.2015,,0,138.36, -82,2,12.11.2015,,0,138.36, -82,2,13.11.2015,,0,138.36, -82,2,14.11.2015,,0,138.36, -82,2,15.11.2015,,0,138.36, -82,2,16.11.2015,,0,138.36, -82,2,17.11.2015,,3,138.36, -82,2,18.11.2015,,0,138.36, -82,2,19.11.2015,,3,138.36, -82,2,20.11.2015,,0,138.36, -82,2,21.11.2015,,3,138.36, -82,2,22.11.2015,,0,138.36, -82,2,23.11.2015,,0,138.36, -82,2,24.11.2015,,0,138.36, -82,2,25.11.2015,,0,138.36, -82,2,26.11.2015,,9,138.36, -82,2,27.11.2015,,0,138.36, -82,2,28.11.2015,,0,138.36, -82,2,29.11.2015,,3,138.36, -82,2,30.11.2015,,9,138.36, -82,2,01.12.2015,,0,162.96, -82,2,02.12.2015,,6,162.96, -82,2,03.12.2015,,0,138.5, -82,2,04.12.2015,,0,138.5, -82,2,05.12.2015,,3,138.5, -82,2,06.12.2015,,6,138.5, -82,2,07.12.2015,,0,138.5, -82,2,08.12.2015,,6,138.5, -82,2,09.12.2015,,3,138.5, -82,2,10.12.2015,,6,138.5, -82,2,11.12.2015,,3,142.57, -82,2,12.12.2015,,3,142.57, -82,2,13.12.2015,,13,142.57, -82,2,14.12.2015,,6,142.57, -82,2,15.12.2015,,6,142.57, -82,2,16.12.2015,,6,142.57, -82,2,17.12.2015,,6,142.57, -82,2,18.12.2015,,13,142.57, -82,2,19.12.2015,,16,142.57, -82,2,20.12.2015,,6,142.57, -82,2,21.12.2015,,6,142.57, -82,2,22.12.2015,1.0,160,142.57,103.02 -82,2,23.12.2015,1.0,88,142.57,103.02 -82,2,24.12.2015,1.0,97,142.57,103.02 -82,2,25.12.2015,1.0,97,142.57,103.02 -82,2,26.12.2015,1.0,104,142.57,103.02 -82,2,27.12.2015,1.0,129,142.57,103.02 -82,2,28.12.2015,1.0,123,142.57,103.02 -82,2,29.12.2015,,0,138.5, -82,2,30.12.2015,,0,138.5, -82,2,31.12.2015,,0,138.5, -82,2,01.01.2016,,6,138.5, -82,2,02.01.2016,,6,138.5, -82,2,03.01.2016,,0,138.5, -82,2,04.01.2016,,6,138.5, -82,2,05.01.2016,,9,138.5, -82,2,06.01.2016,,16,138.5, -82,2,07.01.2016,,41,138.5, -82,2,08.01.2016,,0,138.5, -82,2,09.01.2016,,0,138.5, -82,2,10.01.2016,,9,138.5, -82,2,11.01.2016,,9,138.5, -82,2,12.01.2016,,38,138.5, -82,2,13.01.2016,,0,138.5, -82,2,14.01.2016,,0,138.5, -82,2,15.01.2016,,13,138.5, -82,2,16.01.2016,,16,142.57, -82,2,17.01.2016,,25,142.57, -82,2,18.01.2016,,9,142.57, -82,2,19.01.2016,1.0,198,142.57,103.02 -82,2,20.01.2016,1.0,192,142.57,103.02 -82,2,21.01.2016,1.0,154,142.57,103.02 -82,2,22.01.2016,1.0,192,142.57,103.02 -82,2,23.01.2016,1.0,201,142.57,103.02 -82,2,24.01.2016,1.0,91,142.57,103.02 -82,2,25.01.2016,1.0,60,142.57,103.02 -82,2,26.01.2016,,0,142.57, -82,2,27.01.2016,,3,142.57, -82,2,28.01.2016,,13,142.57, -82,2,29.01.2016,,3,142.57, -82,2,30.01.2016,,3,142.57, -82,2,31.01.2016,,9,142.57, -82,2,01.02.2016,,6,142.57, -82,2,02.02.2016,,3,142.57, -82,2,03.02.2016,,0,142.57, -82,2,04.02.2016,,9,142.57, -82,2,05.02.2016,,0,142.57, -82,2,06.02.2016,,0,142.57, -82,2,07.02.2016,,0,142.57, -82,2,08.02.2016,,13,142.57, -82,2,09.02.2016,1.0,245,142.57,108.46 -82,2,10.02.2016,1.0,163,138.36,108.46 -82,2,11.02.2016,1.0,145,138.36,108.46 -82,2,12.02.2016,1.0,145,138.36,108.46 -82,2,13.02.2016,1.0,126,138.36,108.46 -82,2,14.02.2016,1.0,82,138.36,108.46 -82,2,15.02.2016,1.0,91,138.36,108.46 -82,2,16.02.2016,,0,138.36, -82,2,17.02.2016,,19,138.36, -82,2,18.02.2016,,0,138.36, -82,2,19.02.2016,,3,138.36, -82,2,20.02.2016,,6,138.36, -82,2,21.02.2016,,16,138.36, -82,2,22.02.2016,,6,138.36, -82,2,23.02.2016,,0,138.36, -82,2,24.02.2016,,3,138.36, -82,2,25.02.2016,,13,138.36, -82,2,26.02.2016,,6,138.36, -82,2,27.02.2016,,9,138.36, -82,2,28.02.2016,,0,138.36, -82,2,29.02.2016,,0,138.36, -82,2,01.03.2016,,0,138.36, -82,2,02.03.2016,,0,138.36, -82,2,03.03.2016,,0,138.36, -82,2,04.03.2016,,0,138.36, -82,2,05.03.2016,,0,138.36, -82,2,06.03.2016,,0,138.36, -82,2,07.03.2016,,3,138.36, -82,2,08.03.2016,,3,138.36, -82,2,09.03.2016,,13,138.36, -82,2,10.03.2016,,13,138.36, -82,2,11.03.2016,,3,138.36, -82,2,12.03.2016,,3,138.5, -82,2,13.03.2016,,3,138.5, -82,2,14.03.2016,,9,138.5, -82,2,15.03.2016,1.0,107,138.5,124.77 -82,2,16.03.2016,1.0,63,138.5,124.77 -82,2,17.03.2016,1.0,79,138.5,124.77 -82,2,18.03.2016,1.0,63,138.5,124.77 -82,2,19.03.2016,1.0,72,138.5,124.77 -82,2,20.03.2016,1.0,72,138.5,124.77 -82,2,21.03.2016,1.0,25,138.5,124.77 -82,2,22.03.2016,,0,138.36, -82,2,23.03.2016,,6,138.36, -82,2,24.03.2016,,3,138.36, -82,2,25.03.2016,,3,138.36, -82,2,26.03.2016,,0,138.36, -82,2,27.03.2016,,3,138.36, -82,2,28.03.2016,,3,138.36, -82,2,29.03.2016,,0,138.36, -82,2,30.03.2016,,9,138.36, -82,2,31.03.2016,,6,138.36, -82,2,01.04.2016,,0,138.36, -82,2,02.04.2016,,0,138.36, -82,2,03.04.2016,,0,138.36, -82,2,04.04.2016,,6,138.36, -82,2,05.04.2016,,13,138.36, -82,2,06.04.2016,,3,138.36, -82,2,07.04.2016,,6,138.36, -82,2,08.04.2016,,9,138.36, -82,2,09.04.2016,,9,138.5, -82,2,10.04.2016,,0,138.5, -82,2,11.04.2016,,3,138.5, -82,2,12.04.2016,,3,138.5, -82,2,13.04.2016,,3,138.5, -82,2,14.04.2016,,0,138.5, -82,2,15.04.2016,,0,138.5, -82,2,16.04.2016,,0,138.5, -82,2,17.04.2016,,9,138.5, -82,2,18.04.2016,,9,138.5, -82,2,19.04.2016,1.0,94,138.5,116.89 -82,2,20.04.2016,1.0,88,138.5,116.89 -82,2,21.04.2016,1.0,129,138.5,116.89 -82,2,22.04.2016,1.0,119,138.5,116.89 -82,2,23.04.2016,1.0,138,138.5,116.89 -82,2,24.04.2016,1.0,91,138.5,116.89 -82,2,25.04.2016,1.0,72,138.5,116.89 -82,2,26.04.2016,,0,138.5, -82,2,27.04.2016,,0,138.5, -82,2,28.04.2016,,0,138.5, -82,2,29.04.2016,,0,138.5, -82,2,30.04.2016,,3,138.5, -82,2,01.05.2016,,9,138.5, -82,2,02.05.2016,,28,138.5, -82,2,03.05.2016,1.0,151,138.5,106.01 -82,2,04.05.2016,1.0,207,138.5,106.01 -82,2,05.05.2016,1.0,236,138.5,106.01 -82,2,06.05.2016,1.0,145,138.5,106.01 -82,2,07.05.2016,1.0,119,138.5,106.01 -82,2,08.05.2016,1.0,167,138.5,106.01 -82,2,09.05.2016,1.0,145,138.5,106.01 -82,2,10.05.2016,,22,138.5, -82,2,11.05.2016,,3,138.5, -82,2,12.05.2016,,47,138.5, -82,2,13.05.2016,,0,138.5, -82,2,14.05.2016,,0,138.5, -82,2,15.05.2016,,0,138.5, -82,2,16.05.2016,,0,138.5, -82,2,17.05.2016,,0,138.5, -82,2,18.05.2016,,6,138.5, -82,2,19.05.2016,,0,138.5, -82,2,20.05.2016,,6,138.5, -82,2,21.05.2016,,13,138.5, -82,2,22.05.2016,,3,138.5, -83,2,27.04.2015,,9,150.73, -83,2,28.04.2015,,3,150.73, -83,2,29.04.2015,,0,150.73, -83,2,30.04.2015,,3,150.73, -83,2,01.05.2015,,0,150.73, -83,2,02.05.2015,,0,150.73, -83,2,03.05.2015,,3,150.73, -83,2,04.05.2015,,0,150.73, -83,2,05.05.2015,,3,150.73, -83,2,06.05.2015,,6,150.73, -83,2,07.05.2015,,13,150.73, -83,2,08.05.2015,,0,150.73, -83,2,09.05.2015,,0,150.73, -83,2,10.05.2015,,3,150.73, -83,2,11.05.2015,,0,150.73, -83,2,12.05.2015,,6,150.73, -83,2,13.05.2015,,3,150.73, -83,2,14.05.2015,,6,150.73, -83,2,15.05.2015,,0,150.73, -83,2,16.05.2015,,3,150.73, -83,2,17.05.2015,,13,150.73, -83,2,18.05.2015,,13,150.73, -83,2,19.05.2015,,0,150.73, -83,2,20.05.2015,,6,150.73, -83,2,21.05.2015,,0,150.73, -83,2,22.05.2015,,0,150.73, -83,2,23.05.2015,,0,150.73, -83,2,24.05.2015,,0,150.73, -83,2,25.05.2015,,0,150.73, -83,2,26.05.2015,,0,150.73, -83,2,27.05.2015,,0,150.73, -83,2,28.05.2015,,0,150.73, -83,2,29.05.2015,,0,150.73, -83,2,30.05.2015,,0,150.73, -83,2,31.05.2015,,0,150.73, -83,2,01.06.2015,,0,150.73, -83,2,02.06.2015,,0,150.73, -83,2,03.06.2015,,0,150.73, -83,2,04.06.2015,,0,150.73, -83,2,05.06.2015,,0,150.73, -83,2,06.06.2015,,0,150.73, -83,2,07.06.2015,,0,150.73, -83,2,08.06.2015,,0,150.73, -83,2,09.06.2015,,0,150.73, -83,2,10.06.2015,,0,150.73, -83,2,11.06.2015,,0,150.73, -83,2,12.06.2015,,0,150.73, -83,2,13.06.2015,,0,150.73, -83,2,14.06.2015,,0,150.73, -83,2,15.06.2015,,0,150.73, -83,2,16.06.2015,,0,150.73, -83,2,17.06.2015,,3,150.73, -83,2,18.06.2015,,6,150.73, -83,2,19.06.2015,,6,150.73, -83,2,20.06.2015,,0,150.73, -83,2,21.06.2015,,3,150.73, -83,2,22.06.2015,,0,150.73, -83,2,23.06.2015,,0,150.73, -83,2,24.06.2015,,3,150.73, -83,2,25.06.2015,,0,150.73, -83,2,26.06.2015,,0,150.73, -83,2,27.06.2015,,0,150.73, -83,2,28.06.2015,,0,150.73, -83,2,29.06.2015,,0,150.73, -83,2,30.06.2015,,0,150.73, -83,2,01.07.2015,,0,150.73, -83,2,02.07.2015,,0,150.73, -83,2,03.07.2015,,0,150.73, -83,2,04.07.2015,,0,150.73, -83,2,05.07.2015,,13,150.73, -83,2,06.07.2015,,0,150.73, -83,2,07.07.2015,,3,150.73, -83,2,08.07.2015,,16,150.73, -83,2,09.07.2015,,0,150.73, -83,2,10.07.2015,,0,150.73, -83,2,11.07.2015,,6,150.73, -83,2,12.07.2015,,25,150.73, -83,2,13.07.2015,,3,150.73, -83,2,14.07.2015,,0,150.73, -83,2,15.07.2015,,0,150.73, -83,2,16.07.2015,,0,150.73, -83,2,17.07.2015,,0,150.73, -83,2,18.07.2015,,0,150.73, -83,2,19.07.2015,,3,150.73, -83,2,20.07.2015,,6,150.73, -83,2,21.07.2015,,6,150.73, -83,2,22.07.2015,,0,150.73, -83,2,23.07.2015,,9,150.73, -83,2,24.07.2015,,0,150.73, -83,2,25.07.2015,,3,150.73, -83,2,26.07.2015,,0,150.73, -83,2,27.07.2015,,3,150.73, -83,2,28.07.2015,,9,150.73, -83,2,29.07.2015,,19,150.73, -83,2,30.07.2015,,19,150.73, -83,2,31.07.2015,,13,150.73, -83,2,01.08.2015,,13,150.73, -83,2,02.08.2015,,13,150.73, -83,2,03.08.2015,,0,150.73, -83,2,04.08.2015,,3,150.73, -83,2,05.08.2015,,6,150.73, -83,2,06.08.2015,,6,150.73, -83,2,07.08.2015,,3,150.73, -83,2,08.08.2015,,6,150.73, -83,2,09.08.2015,,22,150.73, -83,2,10.08.2015,,3,150.73, -83,2,11.08.2015,,6,150.73, -83,2,12.08.2015,,0,128.17, -83,2,13.08.2015,,9,128.17, -83,2,14.08.2015,,9,128.17, -83,2,15.08.2015,,0,128.17, -83,2,16.08.2015,,0,128.17, -83,2,17.08.2015,,47,128.17, -83,2,18.08.2015,1.0,104,128.17,100.3 -83,2,19.08.2015,1.0,126,128.17,100.3 -83,2,20.08.2015,1.0,195,128.17,100.3 -83,2,21.08.2015,1.0,119,128.17,100.3 -83,2,22.08.2015,1.0,113,128.17,100.3 -83,2,23.08.2015,1.0,53,128.17,100.3 -83,2,24.08.2015,1.0,47,128.17,100.3 -83,2,25.08.2015,,3,128.17, -83,2,26.08.2015,,3,128.17, -83,2,27.08.2015,,25,128.17, -83,2,28.08.2015,,28,128.17, -83,2,29.08.2015,,16,128.17, -83,2,30.08.2015,,25,128.17, -83,2,31.08.2015,,0,128.17, -83,2,01.09.2015,,0,128.17, -83,2,02.09.2015,,6,128.17, -83,2,03.09.2015,,3,128.17, -83,2,04.09.2015,,13,128.17, -83,2,05.09.2015,,9,128.17, -83,2,06.09.2015,,6,128.17, -83,2,07.09.2015,,9,128.17, -83,2,08.09.2015,,3,128.17, -83,2,09.09.2015,,3,128.17, -83,2,10.09.2015,,0,128.17, -83,2,11.09.2015,,0,128.17, -83,2,12.09.2015,,3,128.17, -83,2,13.09.2015,,0,128.17, -83,2,14.09.2015,,3,128.17, -83,2,15.09.2015,,6,128.17, -83,2,16.09.2015,,6,128.17, -83,2,17.09.2015,,9,128.17, -83,2,18.09.2015,,25,128.17, -83,2,19.09.2015,,25,128.17, -83,2,20.09.2015,,44,128.17, -83,2,21.09.2015,,19,128.17, -83,2,22.09.2015,,3,128.17, -83,2,23.09.2015,,0,128.17, -83,2,24.09.2015,,0,128.17, -83,2,25.09.2015,,3,128.17, -83,2,26.09.2015,,28,128.17, -83,2,27.09.2015,,6,128.17, -83,2,28.09.2015,,0,128.17, -83,2,29.09.2015,,19,128.17, -83,2,30.09.2015,,16,128.17, -83,2,01.10.2015,,3,162.96, -83,2,02.10.2015,,3,162.96, -83,2,03.10.2015,,3,162.96, -83,2,04.10.2015,,19,162.96, -83,2,05.10.2015,,13,162.96, -83,2,06.10.2015,1.0,119,143.39,122.05 -83,2,07.10.2015,1.0,69,143.39,122.05 -83,2,08.10.2015,1.0,129,143.39,122.05 -83,2,09.10.2015,1.0,91,143.39,122.05 -83,2,10.10.2015,1.0,63,143.39,122.05 -83,2,11.10.2015,1.0,85,143.39,122.05 -83,2,12.10.2015,1.0,28,143.39,122.05 -83,2,13.10.2015,,9,143.93, -83,2,14.10.2015,,9,143.93, -83,2,15.10.2015,,9,143.93, -83,2,16.10.2015,,25,143.93, -83,2,17.10.2015,,0,162.96, -83,2,18.10.2015,,0,162.96, -83,2,19.10.2015,,6,162.96, -83,2,20.10.2015,,0,162.96, -83,2,21.10.2015,,9,162.96, -83,2,22.10.2015,,0,162.96, -83,2,23.10.2015,,0,162.96, -83,2,24.10.2015,,3,162.96, -83,2,25.10.2015,,3,162.96, -83,2,26.10.2015,,0,162.96, -83,2,27.10.2015,,3,162.96, -83,2,28.10.2015,,3,162.96, -83,2,29.10.2015,,0,162.96, -83,2,30.10.2015,,0,162.96, -83,2,31.10.2015,,0,162.96, -83,2,01.11.2015,,3,162.96, -83,2,02.11.2015,,3,162.96, -83,2,03.11.2015,,3,162.96, -83,2,04.11.2015,,3,162.96, -83,2,05.11.2015,,0,162.96, -83,2,06.11.2015,,3,162.96, -83,2,07.11.2015,,3,162.96, -83,2,08.11.2015,,3,162.96, -83,2,09.11.2015,,6,162.96, -83,2,10.11.2015,,0,162.96, -83,2,11.11.2015,,9,138.36, -83,2,12.11.2015,,0,138.36, -83,2,13.11.2015,,6,138.36, -83,2,14.11.2015,,0,138.36, -83,2,15.11.2015,,0,138.36, -83,2,16.11.2015,,0,138.36, -83,2,17.11.2015,,0,138.36, -83,2,18.11.2015,,3,138.36, -83,2,19.11.2015,,0,138.36, -83,2,20.11.2015,,0,138.36, -83,2,21.11.2015,,3,138.36, -83,2,22.11.2015,,0,138.36, -83,2,23.11.2015,,0,138.36, -83,2,24.11.2015,,3,138.36, -83,2,25.11.2015,,3,138.36, -83,2,26.11.2015,,0,138.36, -83,2,27.11.2015,,0,138.36, -83,2,28.11.2015,,0,138.36, -83,2,29.11.2015,,0,138.36, -83,2,30.11.2015,,0,138.36, -83,2,01.12.2015,,0,162.96, -83,2,02.12.2015,,0,162.96, -83,2,03.12.2015,,0,138.5, -83,2,04.12.2015,,0,138.5, -83,2,05.12.2015,,0,138.5, -83,2,06.12.2015,,0,138.5, -83,2,07.12.2015,,0,138.5, -83,2,08.12.2015,,0,138.5, -83,2,09.12.2015,,6,138.5, -83,2,10.12.2015,,13,138.5, -83,2,11.12.2015,,3,142.57, -83,2,12.12.2015,,0,142.57, -83,2,13.12.2015,,6,142.57, -83,2,14.12.2015,,0,142.57, -83,2,15.12.2015,,9,142.57, -83,2,16.12.2015,,3,142.57, -83,2,17.12.2015,,9,142.57, -83,2,18.12.2015,,9,142.57, -83,2,19.12.2015,,3,142.57, -83,2,20.12.2015,,13,142.57, -83,2,21.12.2015,,19,142.57, -83,2,22.12.2015,1.0,245,142.57,103.02 -83,2,23.12.2015,1.0,85,142.57,103.02 -83,2,24.12.2015,1.0,104,142.57,103.02 -83,2,25.12.2015,1.0,217,142.57,103.02 -83,2,26.12.2015,1.0,163,142.57,103.02 -83,2,27.12.2015,1.0,57,142.57,103.02 -83,2,28.12.2015,1.0,107,142.57,103.02 -83,2,29.12.2015,,6,138.5, -83,2,30.12.2015,,6,138.5, -83,2,31.12.2015,,19,138.5, -83,2,01.01.2016,,6,138.5, -83,2,02.01.2016,,6,138.5, -83,2,03.01.2016,,6,138.5, -83,2,04.01.2016,,6,138.5, -83,2,05.01.2016,,6,138.5, -83,2,06.01.2016,,6,138.5, -83,2,07.01.2016,,6,138.5, -83,2,08.01.2016,,6,138.5, -83,2,09.01.2016,,6,138.5, -83,2,10.01.2016,,6,138.5, -83,2,11.01.2016,,6,138.5, -83,2,12.01.2016,,6,138.5, -83,2,13.01.2016,,6,138.5, -83,2,14.01.2016,,6,138.5, -83,2,15.01.2016,,6,138.5, -83,2,16.01.2016,,6,142.57, -83,2,17.01.2016,,6,142.57, -83,2,18.01.2016,,9,142.57, -83,2,19.01.2016,1.0,226,142.57,103.02 -83,2,20.01.2016,1.0,198,142.57,103.02 -83,2,21.01.2016,1.0,157,142.57,103.02 -83,2,22.01.2016,1.0,280,142.57,103.02 -83,2,23.01.2016,1.0,223,142.57,103.02 -83,2,24.01.2016,1.0,19,142.57,103.02 -83,2,25.01.2016,1.0,148,142.57,103.02 -83,2,26.01.2016,,9,142.57, -83,2,27.01.2016,,9,142.57, -83,2,28.01.2016,,19,142.57, -83,2,29.01.2016,,9,142.57, -83,2,30.01.2016,,9,142.57, -83,2,31.01.2016,,0,142.57, -83,2,01.02.2016,,6,142.57, -83,2,02.02.2016,,13,142.57, -83,2,03.02.2016,,9,142.57, -83,2,04.02.2016,,6,142.57, -83,2,05.02.2016,,13,142.57, -83,2,06.02.2016,,9,142.57, -83,2,07.02.2016,,9,142.57, -83,2,08.02.2016,,22,142.57, -83,2,09.02.2016,1.0,185,142.57,108.46 -83,2,10.02.2016,1.0,258,138.36,108.46 -83,2,11.02.2016,1.0,207,138.36,108.46 -83,2,12.02.2016,1.0,182,138.36,108.46 -83,2,13.02.2016,1.0,141,138.36,108.46 -83,2,14.02.2016,1.0,229,138.36,108.46 -83,2,15.02.2016,1.0,41,138.36,108.46 -83,2,16.02.2016,,13,138.36, -83,2,17.02.2016,,3,138.36, -83,2,18.02.2016,,13,138.36, -83,2,19.02.2016,,13,138.36, -83,2,20.02.2016,,13,138.36, -83,2,21.02.2016,,13,138.36, -83,2,22.02.2016,,13,138.36, -83,2,23.02.2016,,3,138.36, -83,2,24.02.2016,,13,138.36, -83,2,25.02.2016,,13,138.36, -83,2,26.02.2016,,9,138.36, -83,2,27.02.2016,,3,138.36, -83,2,28.02.2016,,6,138.36, -83,2,29.02.2016,,9,138.36, -83,2,01.03.2016,,0,138.36, -83,2,02.03.2016,,9,138.36, -83,2,03.03.2016,,3,138.36, -83,2,04.03.2016,,13,138.36, -83,2,05.03.2016,,6,138.36, -83,2,06.03.2016,,6,138.36, -83,2,07.03.2016,,0,138.36, -83,2,08.03.2016,,9,138.36, -83,2,09.03.2016,,0,138.36, -83,2,10.03.2016,,0,138.36, -83,2,11.03.2016,,0,138.36, -83,2,12.03.2016,,0,138.5, -83,2,13.03.2016,,19,138.5, -83,2,14.03.2016,,9,138.5, -83,2,15.03.2016,1.0,69,138.5,124.77 -83,2,16.03.2016,1.0,66,138.5,124.77 -83,2,17.03.2016,1.0,148,138.5,124.77 -83,2,18.03.2016,1.0,66,138.5,124.77 -83,2,19.03.2016,1.0,79,138.5,124.77 -83,2,20.03.2016,1.0,88,138.5,124.77 -83,2,21.03.2016,1.0,69,138.5,124.77 -83,2,22.03.2016,,3,138.36, -83,2,23.03.2016,,13,138.36, -83,2,24.03.2016,,3,138.36, -83,2,25.03.2016,,0,138.36, -83,2,26.03.2016,,0,138.36, -83,2,27.03.2016,,3,138.36, -83,2,28.03.2016,,0,138.36, -83,2,29.03.2016,,0,138.36, -83,2,30.03.2016,,0,138.36, -83,2,31.03.2016,,3,138.36, -83,2,01.04.2016,,0,138.36, -83,2,02.04.2016,,6,138.36, -83,2,03.04.2016,,3,138.36, -83,2,04.04.2016,,3,138.36, -83,2,05.04.2016,,3,138.36, -83,2,06.04.2016,,6,138.36, -83,2,07.04.2016,,6,138.36, -83,2,08.04.2016,,0,138.36, -83,2,09.04.2016,,3,138.5, -83,2,10.04.2016,,3,138.5, -83,2,11.04.2016,,3,138.5, -83,2,12.04.2016,,6,138.5, -83,2,13.04.2016,,6,138.5, -83,2,14.04.2016,,3,138.5, -83,2,15.04.2016,,0,138.5, -83,2,16.04.2016,,3,138.5, -83,2,17.04.2016,,6,138.5, -83,2,18.04.2016,,19,138.5, -83,2,19.04.2016,1.0,151,138.5,116.89 -83,2,20.04.2016,1.0,167,138.5,116.89 -83,2,21.04.2016,1.0,113,138.5,116.89 -83,2,22.04.2016,1.0,107,138.5,116.89 -83,2,23.04.2016,1.0,116,138.5,116.89 -83,2,24.04.2016,1.0,129,138.5,116.89 -83,2,25.04.2016,1.0,88,138.5,116.89 -83,2,26.04.2016,,6,138.5, -83,2,27.04.2016,,6,138.5, -83,2,28.04.2016,,6,138.5, -83,2,29.04.2016,,6,138.5, -83,2,30.04.2016,,3,138.5, -83,2,01.05.2016,,6,138.5, -83,2,02.05.2016,,3,138.5, -83,2,03.05.2016,1.0,207,138.5,106.01 -83,2,04.05.2016,1.0,223,138.5,106.01 -83,2,05.05.2016,1.0,176,138.5,106.01 -83,2,06.05.2016,1.0,217,138.5,106.01 -83,2,07.05.2016,1.0,195,138.5,106.01 -83,2,08.05.2016,1.0,47,138.5,106.01 -83,2,09.05.2016,1.0,138,138.5,106.01 -83,2,10.05.2016,,6,138.5, -83,2,11.05.2016,,25,138.5, -83,2,12.05.2016,,9,138.5, -83,2,13.05.2016,,0,138.5, -83,2,14.05.2016,,3,138.5, -83,2,15.05.2016,,13,138.5, -83,2,16.05.2016,,13,138.5, -83,2,17.05.2016,,3,138.5, -83,2,18.05.2016,,3,138.5, -83,2,19.05.2016,,6,138.5, -83,2,20.05.2016,,6,138.5, -83,2,21.05.2016,,6,138.5, -83,2,22.05.2016,,6,138.5, -84,2,01.01.2015,,0,135.78, -84,2,02.01.2015,,0,135.78, -84,2,03.01.2015,,0,135.78, -84,2,04.01.2015,,0,135.78, -84,2,05.01.2015,,0,135.78, -84,2,06.01.2015,,0,135.78, -84,2,07.01.2015,,6,135.78, -84,2,08.01.2015,,0,135.78, -84,2,09.01.2015,,0,135.78, -84,2,10.01.2015,,3,135.78, -84,2,11.01.2015,,0,135.78, -84,2,12.01.2015,,0,135.78, -84,2,13.01.2015,,0,135.78, -84,2,14.01.2015,,6,135.78, -84,2,15.01.2015,,0,135.78, -84,2,16.01.2015,,3,135.78, -84,2,17.01.2015,,0,135.78, -84,2,18.01.2015,,3,135.78, -84,2,19.01.2015,,0,135.78, -84,2,20.01.2015,,0,135.78, -84,2,21.01.2015,,3,135.78, -84,2,22.01.2015,,0,135.78, -84,2,23.01.2015,,0,135.78, -84,2,24.01.2015,,0,135.78, -84,2,25.01.2015,,3,135.78, -84,2,26.01.2015,,0,135.78, -84,2,27.01.2015,,0,135.78, -84,2,28.01.2015,,0,135.78, -84,2,29.01.2015,,0,135.78, -84,2,30.01.2015,,0,135.78, -84,2,31.01.2015,,0,135.78, -84,2,01.02.2015,,16,135.78, -84,2,02.02.2015,,3,135.78, -84,2,03.02.2015,,3,135.78, -84,2,04.02.2015,,13,135.78, -84,2,05.02.2015,,3,135.78, -84,2,06.02.2015,,0,135.78, -84,2,07.02.2015,,0,135.78, -84,2,08.02.2015,,6,135.78, -84,2,09.02.2015,,6,135.78, -84,2,10.02.2015,,0,135.78, -84,2,11.02.2015,,6,135.78, -84,2,12.02.2015,,0,135.78, -84,2,13.02.2015,,0,135.78, -84,2,14.02.2015,,0,135.78, -84,2,15.02.2015,,3,135.78, -84,2,16.02.2015,,0,135.78, -84,2,17.02.2015,,0,135.78, -84,2,18.02.2015,,6,135.78, -84,2,19.02.2015,,0,135.78, -84,2,20.02.2015,,0,135.78, -84,2,21.02.2015,,0,135.78, -84,2,22.02.2015,,13,135.78, -84,2,23.02.2015,,13,146.38, -84,2,24.02.2015,,0,146.38, -84,2,25.02.2015,,0,146.38, -84,2,26.02.2015,,0,146.38, -84,2,27.02.2015,,0,146.38, -84,2,28.02.2015,,3,146.38, -84,2,01.03.2015,,0,146.38, -84,2,02.03.2015,,3,146.38, -84,2,03.03.2015,,0,146.38, -84,2,04.03.2015,,0,146.38, -84,2,05.03.2015,,0,146.38, -84,2,06.03.2015,,0,146.38, -84,2,07.03.2015,,0,146.38, -84,2,08.03.2015,,0,146.38, -84,2,09.03.2015,,3,146.38, -84,2,10.03.2015,,0,146.38, -84,2,11.03.2015,,0,146.38, -84,2,12.03.2015,,6,146.38, -84,2,13.03.2015,,0,146.38, -84,2,14.03.2015,,6,146.38, -84,2,15.03.2015,,0,146.38, -84,2,16.03.2015,,0,146.38, -84,2,17.03.2015,,0,146.38, -84,2,18.03.2015,,0,146.38, -84,2,19.03.2015,,0,146.38, -84,2,20.03.2015,,0,146.38, -84,2,21.03.2015,,0,146.38, -84,2,22.03.2015,,9,146.38, -84,2,23.03.2015,,0,146.38, -84,2,24.03.2015,,0,146.38, -84,2,25.03.2015,,3,146.38, -84,2,26.03.2015,,0,146.38, -84,2,27.03.2015,,0,146.38, -84,2,28.03.2015,,0,146.38, -84,2,29.03.2015,,0,146.38, -84,2,30.03.2015,,9,146.38, -84,2,31.03.2015,,0,146.38, -84,2,01.04.2015,,0,146.38, -84,2,02.04.2015,,0,146.38, -84,2,03.04.2015,,0,146.38, -84,2,04.04.2015,,0,146.38, -84,2,05.04.2015,,0,146.38, -84,2,06.04.2015,,0,146.38, -84,2,07.04.2015,,0,146.38, -84,2,08.04.2015,,6,146.38, -84,2,09.04.2015,,0,145.84, -84,2,10.04.2015,,0,145.84, -84,2,11.04.2015,,0,145.84, -84,2,12.04.2015,,0,145.84, -84,2,13.04.2015,,0,145.84, -84,2,14.04.2015,,3,145.84, -84,2,15.04.2015,,3,145.84, -84,2,16.04.2015,,0,145.84, -84,2,17.04.2015,,0,145.84, -84,2,18.04.2015,,0,145.84, -84,2,19.04.2015,,0,145.84, -84,2,20.04.2015,,0,145.84, -84,2,21.04.2015,,6,145.84, -84,2,22.04.2015,,9,145.84, -84,2,23.04.2015,,0,145.84, -84,2,24.04.2015,,0,145.84, -84,2,25.04.2015,,0,145.84, -84,2,26.04.2015,,0,150.73, -84,2,27.04.2015,,0,150.73, -84,2,28.04.2015,,0,150.73, -84,2,29.04.2015,,0,150.73, -84,2,30.04.2015,,6,150.73, -84,2,01.05.2015,,0,150.73, -84,2,02.05.2015,,0,150.73, -84,2,03.05.2015,,0,150.73, -84,2,04.05.2015,,6,150.73, -84,2,05.05.2015,,0,150.73, -84,2,06.05.2015,,0,150.73, -84,2,07.05.2015,,0,150.73, -84,2,08.05.2015,,13,150.73, -84,2,09.05.2015,,3,150.73, -84,2,10.05.2015,,0,150.73, -84,2,11.05.2015,,0,155.35, -84,2,12.05.2015,,0,155.35, -84,2,13.05.2015,,0,155.35, -84,2,14.05.2015,,0,155.35, -84,2,15.05.2015,,6,155.35, -84,2,16.05.2015,,3,155.35, -84,2,17.05.2015,,3,155.35, -84,2,18.05.2015,,0,155.35, -84,2,19.05.2015,,0,155.35, -84,2,20.05.2015,,3,155.35, -84,2,21.05.2015,,0,155.35, -84,2,22.05.2015,,9,155.35, -84,2,23.05.2015,,0,155.35, -84,2,24.05.2015,,0,155.35, -84,2,25.05.2015,,0,155.35, -84,2,26.05.2015,,3,155.35, -84,2,27.05.2015,,3,155.35, -84,2,28.05.2015,,0,155.35, -84,2,29.05.2015,,6,155.35, -84,2,30.05.2015,,0,155.35, -84,2,31.05.2015,,0,155.35, -84,2,01.06.2015,,0,155.35, -84,2,02.06.2015,,0,155.35, -84,2,03.06.2015,,0,155.35, -84,2,04.06.2015,,6,155.35, -84,2,05.06.2015,,0,155.35, -84,2,06.06.2015,,0,155.35, -84,2,07.06.2015,,0,155.35, -84,2,08.06.2015,,0,155.35, -84,2,09.06.2015,,0,155.35, -84,2,10.06.2015,,0,155.35, -84,2,11.06.2015,,9,155.35, -84,2,12.06.2015,,0,155.35, -84,2,13.06.2015,,3,155.35, -84,2,14.06.2015,,3,155.35, -84,2,15.06.2015,,6,155.35, -84,2,16.06.2015,,0,155.35, -84,2,17.06.2015,,0,155.35, -84,2,18.06.2015,,0,155.35, -84,2,19.06.2015,,9,155.35, -84,2,20.06.2015,,0,155.35, -84,2,21.06.2015,,0,155.35, -84,2,22.06.2015,,0,155.35, -84,2,23.06.2015,,3,155.35, -84,2,24.06.2015,,38,155.35, -84,2,25.06.2015,,0,155.35, -84,2,26.06.2015,,0,155.35, -84,2,27.06.2015,,0,155.35, -84,2,28.06.2015,,16,155.35, -84,2,29.06.2015,,9,155.35, -84,2,30.06.2015,,16,155.35, -84,2,01.07.2015,,9,155.35, -84,2,02.07.2015,,16,155.35, -84,2,03.07.2015,,0,155.35, -84,2,04.07.2015,,0,155.35, -84,2,05.07.2015,,0,155.35, -84,2,06.07.2015,,0,155.35, -84,2,07.07.2015,,0,155.35, -84,2,08.07.2015,,0,155.35, -84,2,09.07.2015,,0,155.35, -84,2,10.07.2015,,13,155.35, -84,2,11.07.2015,,0,155.35, -84,2,12.07.2015,,0,155.35, -84,2,13.07.2015,,0,155.35, -84,2,14.07.2015,,0,155.35, -84,2,15.07.2015,,0,155.35, -84,2,16.07.2015,,3,155.35, -84,2,17.07.2015,,0,155.35, -84,2,18.07.2015,,6,155.35, -84,2,19.07.2015,,3,155.35, -84,2,20.07.2015,,13,155.35, -84,2,21.07.2015,,31,155.35, -84,2,22.07.2015,,9,155.35, -84,2,23.07.2015,,0,155.35, -84,2,24.07.2015,,0,155.35, -84,2,25.07.2015,,0,155.35, -84,2,26.07.2015,,0,155.35, -84,2,27.07.2015,,0,155.35, -84,2,28.07.2015,,0,155.35, -84,2,29.07.2015,,0,155.35, -84,2,30.07.2015,,3,155.35, -84,2,31.07.2015,,13,155.35, -84,2,01.08.2015,,9,155.35, -84,2,02.08.2015,,0,155.35, -84,2,03.08.2015,,6,155.35, -84,2,04.08.2015,,19,155.35, -84,2,05.08.2015,,25,155.35, -84,2,06.08.2015,,22,155.35, -84,2,07.08.2015,,3,155.35, -84,2,08.08.2015,,0,155.35, -84,2,09.08.2015,,0,155.35, -84,2,10.08.2015,,9,155.35, -84,2,11.08.2015,,13,155.35, -84,2,12.08.2015,,22,128.17, -84,2,13.08.2015,,6,128.17, -84,2,14.08.2015,,0,128.17, -84,2,15.08.2015,,19,128.17, -84,2,16.08.2015,,0,128.17, -84,2,17.08.2015,,28,128.17, -84,2,18.08.2015,1.0,267,128.17,100.3 -84,2,19.08.2015,1.0,88,128.17,100.3 -84,2,20.08.2015,1.0,60,128.17,100.3 -84,2,21.08.2015,1.0,94,128.17,100.3 -84,2,22.08.2015,1.0,63,128.17,100.3 -84,2,23.08.2015,1.0,66,128.17,100.3 -84,2,24.08.2015,1.0,75,128.17,100.3 -84,2,25.08.2015,,38,128.17, -84,2,26.08.2015,,16,128.17, -84,2,27.08.2015,,38,128.17, -84,2,28.08.2015,,19,128.17, -84,2,29.08.2015,,19,128.17, -84,2,30.08.2015,,13,128.17, -84,2,31.08.2015,,16,128.17, -84,2,01.09.2015,,22,128.17, -84,2,02.09.2015,,3,128.17, -84,2,03.09.2015,,6,128.17, -84,2,04.09.2015,,16,128.17, -84,2,05.09.2015,,0,128.17, -84,2,06.09.2015,,0,128.17, -84,2,07.09.2015,,0,128.17, -84,2,08.09.2015,,0,128.17, -84,2,09.09.2015,,0,128.17, -84,2,10.09.2015,,0,128.17, -84,2,11.09.2015,,0,128.17, -84,2,12.09.2015,,0,128.17, -84,2,13.09.2015,,0,128.17, -84,2,14.09.2015,,0,128.17, -84,2,15.09.2015,,0,128.17, -84,2,16.09.2015,,0,128.17, -84,2,17.09.2015,,0,128.17, -84,2,18.09.2015,,0,128.17, -84,2,19.09.2015,,0,128.17, -84,2,20.09.2015,,0,128.17, -84,2,21.09.2015,,13,128.17, -84,2,22.09.2015,,3,128.17, -84,2,23.09.2015,,35,128.17, -84,2,24.09.2015,,6,128.17, -84,2,25.09.2015,,0,128.17, -84,2,26.09.2015,,0,128.17, -84,2,27.09.2015,,0,128.17, -84,2,28.09.2015,,0,128.17, -84,2,29.09.2015,,19,128.17, -84,2,30.09.2015,,0,128.17, -84,2,01.10.2015,,0,162.96, -84,2,02.10.2015,,0,162.96, -84,2,03.10.2015,,0,162.96, -84,2,04.10.2015,,3,162.96, -84,2,05.10.2015,,0,162.96, -84,2,06.10.2015,1.0,66,143.39,122.05 -84,2,07.10.2015,1.0,35,143.39,122.05 -84,2,08.10.2015,1.0,63,143.39,122.05 -84,2,09.10.2015,1.0,47,143.39,122.05 -84,2,10.10.2015,1.0,44,143.39,122.05 -84,2,11.10.2015,1.0,60,143.39,122.05 -84,2,12.10.2015,1.0,72,143.39,122.05 -84,2,13.10.2015,1.0,53,143.93,122.05 -84,2,14.10.2015,1.0,47,143.93,122.05 -84,2,15.10.2015,1.0,44,143.93,122.05 -84,2,16.10.2015,1.0,38,143.93,122.05 -84,2,17.10.2015,1.0,31,162.96,122.05 -84,2,18.10.2015,1.0,28,162.96,122.05 -84,2,19.10.2015,,0,162.96, -84,2,20.10.2015,,0,162.96, -84,2,21.10.2015,,0,162.96, -84,2,22.10.2015,,6,162.96, -84,2,23.10.2015,,3,162.96, -84,2,24.10.2015,,3,162.96, -84,2,25.10.2015,,3,162.96, -84,2,26.10.2015,,0,162.96, -84,2,27.10.2015,,6,162.96, -84,2,28.10.2015,,0,162.96, -84,2,29.10.2015,,0,162.96, -84,2,30.10.2015,,0,162.96, -84,2,31.10.2015,,0,162.96, -84,2,01.11.2015,,0,162.96, -84,2,02.11.2015,,0,162.96, -84,2,03.11.2015,,0,162.96, -84,2,04.11.2015,,0,162.96, -84,2,05.11.2015,,0,162.96, -84,2,06.11.2015,,0,162.96, -84,2,07.11.2015,,16,162.96, -84,2,08.11.2015,,0,162.96, -84,2,09.11.2015,,0,162.96, -84,2,10.11.2015,,3,162.96, -84,2,11.11.2015,,0,138.36, -84,2,12.11.2015,,3,138.36, -84,2,13.11.2015,,0,138.36, -84,2,14.11.2015,,0,138.36, -84,2,15.11.2015,,0,138.36, -84,2,16.11.2015,,0,138.36, -84,2,17.11.2015,,6,138.36, -84,2,18.11.2015,,0,138.36, -84,2,19.11.2015,,0,138.36, -84,2,20.11.2015,,0,138.36, -84,2,21.11.2015,,0,138.36, -84,2,22.11.2015,,9,138.36, -84,2,23.11.2015,,9,138.36, -84,2,24.11.2015,,0,138.36, -84,2,25.11.2015,,0,138.36, -84,2,26.11.2015,,0,138.36, -84,2,27.11.2015,,3,138.36, -84,2,28.11.2015,,3,138.36, -84,2,29.11.2015,,0,138.36, -84,2,30.11.2015,,6,138.36, -84,2,01.12.2015,,0,162.96, -84,2,02.12.2015,,0,162.96, -84,2,03.12.2015,,3,138.5, -84,2,04.12.2015,,0,138.5, -84,2,05.12.2015,,0,138.5, -84,2,06.12.2015,,0,138.5, -84,2,07.12.2015,,0,138.5, -84,2,08.12.2015,,0,138.5, -84,2,09.12.2015,,6,138.5, -84,2,10.12.2015,,0,138.5, -84,2,11.12.2015,,0,142.57, -84,2,12.12.2015,,0,142.57, -84,2,13.12.2015,,0,142.57, -84,2,14.12.2015,,0,142.57, -84,2,15.12.2015,,0,142.57, -84,2,16.12.2015,,0,142.57, -84,2,17.12.2015,,0,142.57, -84,2,18.12.2015,,0,142.57, -84,2,19.12.2015,,0,142.57, -84,2,20.12.2015,,0,142.57, -84,2,21.12.2015,,9,142.57, -84,2,22.12.2015,1.0,101,142.57,103.02 -84,2,23.12.2015,1.0,94,142.57,103.02 -84,2,24.12.2015,1.0,60,142.57,103.02 -84,2,25.12.2015,1.0,69,142.57,103.02 -84,2,26.12.2015,1.0,25,142.57,103.02 -84,2,27.12.2015,1.0,28,142.57,103.02 -84,2,28.12.2015,1.0,25,142.57,103.02 -84,2,29.12.2015,,0,138.5, -84,2,30.12.2015,,0,138.5, -84,2,31.12.2015,,0,138.5, -84,2,01.01.2016,,0,138.5, -84,2,02.01.2016,,0,138.5, -84,2,03.01.2016,,0,138.5, -84,2,04.01.2016,,13,138.5, -84,2,05.01.2016,,6,138.5, -84,2,06.01.2016,,0,138.5, -84,2,07.01.2016,,0,138.5, -84,2,08.01.2016,,0,138.5, -84,2,09.01.2016,,19,138.5, -84,2,10.01.2016,,0,138.5, -84,2,11.01.2016,,6,138.5, -84,2,12.01.2016,,13,138.5, -84,2,13.01.2016,,0,138.5, -84,2,14.01.2016,,0,138.5, -84,2,15.01.2016,,0,138.5, -84,2,16.01.2016,,0,142.57, -84,2,17.01.2016,,0,142.57, -84,2,18.01.2016,,13,142.57, -84,2,19.01.2016,1.0,160,142.57,103.02 -84,2,20.01.2016,1.0,157,142.57,103.02 -84,2,21.01.2016,1.0,47,142.57,103.02 -84,2,22.01.2016,1.0,176,142.57,103.02 -84,2,23.01.2016,1.0,145,142.57,103.02 -84,2,24.01.2016,1.0,38,142.57,103.02 -84,2,25.01.2016,1.0,75,142.57,103.02 -84,2,26.01.2016,,0,142.57, -84,2,27.01.2016,,0,142.57, -84,2,28.01.2016,,0,142.57, -84,2,29.01.2016,,0,142.57, -84,2,30.01.2016,,3,142.57, -84,2,31.01.2016,,3,142.57, -84,2,01.02.2016,,13,142.57, -84,2,02.02.2016,,13,142.57, -84,2,03.02.2016,,6,142.57, -84,2,04.02.2016,,3,142.57, -84,2,05.02.2016,,6,142.57, -84,2,06.02.2016,,13,142.57, -84,2,07.02.2016,,6,142.57, -84,2,08.02.2016,,16,142.57, -84,2,09.02.2016,1.0,167,142.57,108.46 -84,2,10.02.2016,1.0,16,138.36,108.46 -84,2,11.02.2016,1.0,163,138.36,108.46 -84,2,12.02.2016,1.0,157,138.36,108.46 -84,2,13.02.2016,1.0,38,138.36,108.46 -84,2,14.02.2016,1.0,91,138.36,108.46 -84,2,15.02.2016,1.0,132,138.36,108.46 -84,2,16.02.2016,,0,138.36, -84,2,17.02.2016,,0,138.36, -84,2,18.02.2016,,0,138.36, -84,2,19.02.2016,,6,138.36, -84,2,20.02.2016,,0,138.36, -84,2,21.02.2016,,13,138.36, -84,2,22.02.2016,,0,138.36, -84,2,23.02.2016,,0,138.36, -84,2,24.02.2016,,0,138.36, -84,2,25.02.2016,,0,138.36, -84,2,26.02.2016,,0,138.36, -84,2,27.02.2016,,6,138.36, -84,2,28.02.2016,,3,138.36, -84,2,29.02.2016,,6,138.36, -84,2,01.03.2016,,0,138.36, -84,2,02.03.2016,,0,138.36, -84,2,03.03.2016,,6,138.36, -84,2,04.03.2016,,3,138.36, -84,2,05.03.2016,,0,138.36, -84,2,06.03.2016,,0,138.36, -84,2,07.03.2016,,3,138.36, -84,2,08.03.2016,,6,138.36, -84,2,09.03.2016,,3,138.36, -84,2,10.03.2016,,0,138.36, -84,2,11.03.2016,,0,138.36, -84,2,12.03.2016,,0,138.5, -84,2,13.03.2016,,3,138.5, -84,2,14.03.2016,,19,138.5, -84,2,15.03.2016,1.0,72,138.5,124.77 -84,2,16.03.2016,1.0,66,138.5,124.77 -84,2,17.03.2016,1.0,72,138.5,124.77 -84,2,18.03.2016,1.0,63,138.5,124.77 -84,2,19.03.2016,1.0,13,138.5,124.77 -84,2,20.03.2016,1.0,60,138.5,124.77 -84,2,21.03.2016,1.0,41,138.5,124.77 -84,2,22.03.2016,,0,138.36, -84,2,23.03.2016,,0,138.36, -84,2,24.03.2016,,0,138.36, -84,2,25.03.2016,,0,138.36, -84,2,26.03.2016,,0,138.36, -84,2,27.03.2016,,0,138.36, -84,2,28.03.2016,,9,138.36, -84,2,29.03.2016,,3,138.36, -84,2,30.03.2016,,0,138.36, -84,2,31.03.2016,,6,138.36, -84,2,01.04.2016,,3,138.36, -84,2,02.04.2016,,3,138.36, -84,2,03.04.2016,,9,138.36, -84,2,04.04.2016,,3,138.36, -84,2,05.04.2016,,6,138.36, -84,2,06.04.2016,,3,138.36, -84,2,07.04.2016,,0,138.36, -84,2,08.04.2016,,3,138.36, -84,2,09.04.2016,,0,138.5, -84,2,10.04.2016,,0,138.5, -84,2,11.04.2016,,3,138.5, -84,2,12.04.2016,,0,138.5, -84,2,13.04.2016,,0,138.5, -84,2,14.04.2016,,3,138.5, -84,2,15.04.2016,,0,138.5, -84,2,16.04.2016,,0,138.5, -84,2,17.04.2016,,9,138.5, -84,2,18.04.2016,,0,138.5, -84,2,19.04.2016,1.0,85,138.5,116.89 -84,2,20.04.2016,1.0,107,138.5,116.89 -84,2,21.04.2016,1.0,69,138.5,116.89 -84,2,22.04.2016,1.0,138,138.5,116.89 -84,2,23.04.2016,1.0,72,138.5,116.89 -84,2,24.04.2016,1.0,72,138.5,116.89 -84,2,25.04.2016,1.0,6,138.5,116.89 -84,2,26.04.2016,,6,138.5, -84,2,27.04.2016,,3,138.5, -84,2,28.04.2016,,0,138.5, -84,2,29.04.2016,,3,138.5, -84,2,30.04.2016,,6,138.5, -84,2,01.05.2016,,16,138.5, -84,2,02.05.2016,,16,138.5, -84,2,03.05.2016,1.0,6,138.5,106.01 -84,2,04.05.2016,1.0,28,138.5,106.01 -84,2,05.05.2016,1.0,79,138.5,106.01 -84,2,06.05.2016,1.0,110,138.5,106.01 -84,2,07.05.2016,1.0,138,138.5,106.01 -84,2,08.05.2016,1.0,157,138.5,106.01 -84,2,09.05.2016,1.0,129,138.5,106.01 -84,2,10.05.2016,,0,142.57, -84,2,11.05.2016,,0,142.57, -84,2,12.05.2016,,0,142.57, -84,2,13.05.2016,,0,142.57, -84,2,14.05.2016,,9,142.57, -84,2,15.05.2016,,6,142.57, -84,2,16.05.2016,,6,142.57, -84,2,17.05.2016,,6,142.57, -84,2,18.05.2016,,19,142.57, -84,2,19.05.2016,,0,142.57, -84,2,20.05.2016,,47,142.57, -84,2,21.05.2016,,0,142.57, -84,2,22.05.2016,,0,142.57, -85,2,29.05.2015,,3,150.73, -85,2,30.05.2015,,6,150.73, -85,2,31.05.2015,,9,150.73, -85,2,01.06.2015,,6,150.73, -85,2,02.06.2015,,9,150.73, -85,2,03.06.2015,,13,150.73, -85,2,04.06.2015,,3,150.73, -85,2,05.06.2015,,9,150.73, -85,2,06.06.2015,,3,150.73, -85,2,07.06.2015,,6,150.73, -85,2,08.06.2015,,16,150.73, -85,2,09.06.2015,,9,150.73, -85,2,10.06.2015,,0,150.73, -85,2,11.06.2015,,6,150.73, -85,2,12.06.2015,,6,150.73, -85,2,13.06.2015,,6,150.73, -85,2,14.06.2015,,0,150.73, -85,2,15.06.2015,,3,150.73, -85,2,16.06.2015,,0,150.73, -85,2,17.06.2015,,0,150.73, -85,2,18.06.2015,,0,150.73, -85,2,19.06.2015,,0,150.73, -85,2,20.06.2015,,0,150.73, -85,2,21.06.2015,,0,150.73, -85,2,22.06.2015,,0,150.73, -85,2,23.06.2015,,0,150.73, -85,2,24.06.2015,,0,150.73, -85,2,25.06.2015,,0,150.73, -85,2,26.06.2015,,0,150.73, -85,2,27.06.2015,,0,150.73, -85,2,28.06.2015,,0,150.73, -85,2,29.06.2015,,0,150.73, -85,2,30.06.2015,,0,150.73, -85,2,01.07.2015,,0,150.73, -85,2,02.07.2015,,0,150.73, -85,2,03.07.2015,,0,150.73, -85,2,04.07.2015,,3,150.73, -85,2,05.07.2015,,9,150.73, -85,2,06.07.2015,,0,150.73, -85,2,07.07.2015,,0,150.73, -85,2,08.07.2015,,0,150.73, -85,2,09.07.2015,,0,150.73, -85,2,10.07.2015,,0,150.73, -85,2,11.07.2015,,9,150.73, -85,2,12.07.2015,,13,150.73, -85,2,13.07.2015,,3,150.73, -85,2,14.07.2015,,0,150.73, -85,2,15.07.2015,,0,150.73, -85,2,16.07.2015,,3,150.73, -85,2,17.07.2015,,0,150.73, -85,2,18.07.2015,,0,150.73, -85,2,19.07.2015,,0,150.73, -85,2,20.07.2015,,6,150.73, -85,2,21.07.2015,,16,150.73, -85,2,22.07.2015,,0,150.73, -85,2,23.07.2015,,3,150.73, -85,2,24.07.2015,,0,150.73, -85,2,25.07.2015,,0,150.73, -85,2,26.07.2015,,0,150.73, -85,2,27.07.2015,,0,150.73, -85,2,28.07.2015,,0,150.73, -85,2,29.07.2015,,6,150.73, -85,2,30.07.2015,,3,150.73, -85,2,31.07.2015,,0,150.73, -85,2,01.08.2015,,0,150.73, -85,2,02.08.2015,,0,150.73, -85,2,03.08.2015,,0,150.73, -85,2,04.08.2015,,0,150.73, -85,2,05.08.2015,,0,150.73, -85,2,06.08.2015,,0,150.73, -85,2,07.08.2015,,0,150.73, -85,2,08.08.2015,,0,150.73, -85,2,09.08.2015,,0,150.73, -85,2,10.08.2015,,0,150.73, -85,2,11.08.2015,,0,150.73, -85,2,12.08.2015,,0,128.17, -85,2,13.08.2015,,0,128.17, -85,2,14.08.2015,,0,128.17, -85,2,15.08.2015,,0,128.17, -85,2,16.08.2015,,0,128.17, -85,2,17.08.2015,,6,128.17, -85,2,18.08.2015,1.0,88,128.17,100.3 -85,2,19.08.2015,1.0,88,128.17,100.3 -85,2,20.08.2015,1.0,97,128.17,100.3 -85,2,21.08.2015,1.0,126,128.17,100.3 -85,2,22.08.2015,1.0,72,128.17,100.3 -85,2,23.08.2015,1.0,85,128.17,100.3 -85,2,24.08.2015,1.0,107,128.17,100.3 -85,2,25.08.2015,,9,128.17, -85,2,26.08.2015,,13,128.17, -85,2,27.08.2015,,13,128.17, -85,2,28.08.2015,,3,128.17, -85,2,29.08.2015,,3,128.17, -85,2,30.08.2015,,3,128.17, -85,2,31.08.2015,,3,128.17, -85,2,01.09.2015,,3,128.17, -85,2,02.09.2015,,3,128.17, -85,2,03.09.2015,,3,128.17, -85,2,04.09.2015,,6,128.17, -85,2,05.09.2015,,3,128.17, -85,2,06.09.2015,,3,128.17, -85,2,07.09.2015,,19,128.17, -85,2,08.09.2015,,6,128.17, -85,2,09.09.2015,,6,128.17, -85,2,10.09.2015,,6,128.17, -85,2,11.09.2015,,9,128.17, -85,2,12.09.2015,,9,128.17, -85,2,13.09.2015,,6,128.17, -85,2,14.09.2015,,6,128.17, -85,2,15.09.2015,,16,128.17, -85,2,16.09.2015,,3,128.17, -85,2,17.09.2015,,9,128.17, -85,2,18.09.2015,,3,128.17, -85,2,19.09.2015,,9,128.17, -85,2,20.09.2015,,6,128.17, -85,2,21.09.2015,,9,128.17, -85,2,22.09.2015,,9,128.17, -85,2,23.09.2015,,9,128.17, -85,2,24.09.2015,,3,128.17, -85,2,25.09.2015,,6,128.17, -85,2,26.09.2015,,6,128.17, -85,2,27.09.2015,,13,128.17, -85,2,28.09.2015,,6,128.17, -85,2,29.09.2015,,6,128.17, -85,2,30.09.2015,,6,128.17, -85,2,01.10.2015,,6,162.96, -85,2,02.10.2015,,0,162.96, -85,2,03.10.2015,,6,162.96, -85,2,04.10.2015,,57,162.96, -85,2,05.10.2015,,0,162.96, -85,2,06.10.2015,1.0,94,143.39,122.05 -85,2,07.10.2015,1.0,75,143.39,122.05 -85,2,08.10.2015,1.0,79,143.39,122.05 -85,2,09.10.2015,1.0,85,143.39,122.05 -85,2,10.10.2015,1.0,25,143.39,122.05 -85,2,11.10.2015,1.0,57,143.39,122.05 -85,2,12.10.2015,1.0,66,143.39,122.05 -85,2,13.10.2015,,9,143.93, -85,2,14.10.2015,,3,143.93, -85,2,15.10.2015,,9,143.93, -85,2,16.10.2015,,9,143.93, -85,2,17.10.2015,,9,162.96, -85,2,18.10.2015,,9,162.96, -85,2,19.10.2015,,13,162.96, -85,2,20.10.2015,,3,162.96, -85,2,21.10.2015,,13,162.96, -85,2,22.10.2015,,9,162.96, -85,2,23.10.2015,,3,162.96, -85,2,24.10.2015,,6,162.96, -85,2,25.10.2015,,9,162.96, -85,2,26.10.2015,,9,162.96, -85,2,27.10.2015,,9,162.96, -85,2,28.10.2015,,3,162.96, -85,2,29.10.2015,,3,162.96, -85,2,30.10.2015,,9,162.96, -85,2,31.10.2015,,9,162.96, -85,2,01.11.2015,,6,162.96, -85,2,02.11.2015,,9,162.96, -85,2,03.11.2015,,9,162.96, -85,2,04.11.2015,,9,162.96, -85,2,05.11.2015,,6,162.96, -85,2,06.11.2015,,6,162.96, -85,2,07.11.2015,,6,162.96, -85,2,08.11.2015,,6,162.96, -85,2,09.11.2015,,6,162.96, -85,2,10.11.2015,,6,162.96, -85,2,11.11.2015,,3,138.36, -85,2,12.11.2015,,6,138.36, -85,2,13.11.2015,,6,138.36, -85,2,14.11.2015,,3,138.36, -85,2,15.11.2015,,13,138.36, -85,2,16.11.2015,,6,138.36, -85,2,17.11.2015,,3,138.36, -85,2,18.11.2015,,3,138.36, -85,2,19.11.2015,,6,138.36, -85,2,20.11.2015,,6,138.36, -85,2,21.11.2015,,6,138.36, -85,2,22.11.2015,,6,138.36, -85,2,23.11.2015,,6,138.36, -85,2,24.11.2015,,9,138.36, -85,2,25.11.2015,,6,138.36, -85,2,26.11.2015,,6,138.36, -85,2,27.11.2015,,3,138.36, -85,2,28.11.2015,,3,138.36, -85,2,29.11.2015,,6,138.36, -85,2,30.11.2015,,16,138.36, -85,2,01.12.2015,,6,162.96, -85,2,02.12.2015,,6,162.96, -85,2,03.12.2015,,6,138.5, -85,2,04.12.2015,,6,138.5, -85,2,05.12.2015,,6,138.5, -85,2,06.12.2015,,6,138.5, -85,2,07.12.2015,,6,138.5, -85,2,08.12.2015,,6,138.5, -85,2,09.12.2015,,6,138.5, -85,2,10.12.2015,,6,138.5, -85,2,11.12.2015,,6,142.57, -85,2,12.12.2015,,6,142.57, -85,2,13.12.2015,,6,142.57, -85,2,14.12.2015,,6,142.57, -85,2,15.12.2015,,6,142.57, -85,2,16.12.2015,,6,142.57, -85,2,17.12.2015,,0,142.57, -85,2,18.12.2015,,0,142.57, -85,2,19.12.2015,,3,142.57, -85,2,20.12.2015,,6,142.57, -85,2,21.12.2015,,6,142.57, -85,2,22.12.2015,1.0,214,142.57,103.02 -85,2,23.12.2015,1.0,104,142.57,103.02 -85,2,24.12.2015,1.0,104,142.57,103.02 -85,2,25.12.2015,1.0,138,142.57,103.02 -85,2,26.12.2015,1.0,154,142.57,103.02 -85,2,27.12.2015,1.0,157,142.57,103.02 -85,2,28.12.2015,1.0,129,142.57,103.02 -85,2,29.12.2015,,6,138.5, -85,2,30.12.2015,,6,138.5, -85,2,31.12.2015,,13,138.5, -85,2,01.01.2016,,6,138.5, -85,2,02.01.2016,,3,138.5, -85,2,03.01.2016,,16,138.5, -85,2,04.01.2016,,126,138.5, -85,2,05.01.2016,,9,138.5, -85,2,06.01.2016,,16,138.5, -85,2,07.01.2016,,16,138.5, -85,2,08.01.2016,,16,138.5, -85,2,09.01.2016,,9,138.5, -85,2,10.01.2016,,38,138.5, -85,2,11.01.2016,,19,138.5, -85,2,12.01.2016,,19,138.5, -85,2,13.01.2016,,3,138.5, -85,2,14.01.2016,,6,138.5, -85,2,15.01.2016,,6,138.5, -85,2,16.01.2016,,9,142.57, -85,2,17.01.2016,,3,142.57, -85,2,18.01.2016,,9,142.57, -85,2,19.01.2016,1.0,154,142.57,103.02 -85,2,20.01.2016,1.0,167,142.57,103.02 -85,2,21.01.2016,1.0,170,142.57,103.02 -85,2,22.01.2016,1.0,170,142.57,103.02 -85,2,23.01.2016,1.0,104,142.57,103.02 -85,2,24.01.2016,1.0,132,142.57,103.02 -85,2,25.01.2016,1.0,38,142.57,103.02 -85,2,26.01.2016,,19,142.57, -85,2,27.01.2016,,19,142.57, -85,2,28.01.2016,,19,142.57, -85,2,29.01.2016,,3,142.57, -85,2,30.01.2016,,6,142.57, -85,2,31.01.2016,,6,142.57, -85,2,01.02.2016,,16,142.57, -85,2,02.02.2016,,16,142.57, -85,2,03.02.2016,,16,142.57, -85,2,04.02.2016,,3,142.57, -85,2,05.02.2016,,0,142.57, -85,2,06.02.2016,,3,142.57, -85,2,07.02.2016,,0,142.57, -85,2,08.02.2016,,16,142.57, -85,2,09.02.2016,1.0,94,142.57,108.46 -85,2,10.02.2016,1.0,173,138.36,108.46 -85,2,11.02.2016,1.0,145,138.36,108.46 -85,2,12.02.2016,1.0,154,138.36,108.46 -85,2,13.02.2016,1.0,135,138.36,108.46 -85,2,14.02.2016,1.0,154,138.36,108.46 -85,2,15.02.2016,1.0,113,138.36,108.46 -85,2,16.02.2016,,0,138.36, -85,2,17.02.2016,,0,138.36, -85,2,18.02.2016,,3,138.36, -85,2,19.02.2016,,3,138.36, -85,2,20.02.2016,,3,138.36, -85,2,21.02.2016,,0,138.36, -85,2,22.02.2016,,0,138.36, -85,2,23.02.2016,,0,138.36, -85,2,24.02.2016,,0,138.36, -85,2,25.02.2016,,3,138.36, -85,2,26.02.2016,,0,138.36, -85,2,27.02.2016,,6,138.36, -85,2,28.02.2016,,0,138.36, -85,2,29.02.2016,,0,138.36, -85,2,01.03.2016,,6,138.36, -85,2,02.03.2016,,0,138.36, -85,2,03.03.2016,,0,138.36, -85,2,04.03.2016,,0,138.36, -85,2,05.03.2016,,0,138.36, -85,2,06.03.2016,,0,138.36, -85,2,07.03.2016,,0,138.36, -85,2,08.03.2016,,0,138.36, -85,2,09.03.2016,,6,138.36, -85,2,10.03.2016,,3,138.36, -85,2,11.03.2016,,0,138.36, -85,2,12.03.2016,,3,138.5, -85,2,13.03.2016,,6,138.5, -85,2,14.03.2016,,16,138.5, -85,2,15.03.2016,1.0,41,138.5,124.77 -85,2,16.03.2016,1.0,57,138.5,124.77 -85,2,17.03.2016,1.0,16,138.5,124.77 -85,2,18.03.2016,1.0,44,138.5,124.77 -85,2,19.03.2016,1.0,41,138.5,124.77 -85,2,20.03.2016,1.0,63,138.5,124.77 -85,2,21.03.2016,1.0,63,138.5,124.77 -85,2,22.03.2016,,0,138.36, -85,2,23.03.2016,,3,138.36, -85,2,24.03.2016,,0,138.36, -85,2,25.03.2016,,3,138.36, -85,2,26.03.2016,,0,138.36, -85,2,27.03.2016,,13,138.36, -85,2,28.03.2016,,6,138.36, -85,2,29.03.2016,,6,138.36, -85,2,30.03.2016,,0,138.36, -85,2,31.03.2016,,0,138.36, -85,2,01.04.2016,,0,138.36, -85,2,02.04.2016,,0,138.36, -85,2,03.04.2016,,0,138.36, -85,2,04.04.2016,,0,138.36, -85,2,05.04.2016,,0,138.36, -85,2,06.04.2016,,3,138.36, -85,2,07.04.2016,,9,138.36, -85,2,08.04.2016,,6,138.36, -85,2,09.04.2016,,0,138.5, -85,2,10.04.2016,,19,138.5, -85,2,11.04.2016,,0,138.5, -85,2,12.04.2016,,0,138.5, -85,2,13.04.2016,,0,138.5, -85,2,14.04.2016,,0,138.5, -85,2,15.04.2016,,0,138.5, -85,2,16.04.2016,,0,138.5, -85,2,17.04.2016,,0,138.5, -85,2,18.04.2016,,9,138.5, -85,2,19.04.2016,1.0,82,138.5,116.89 -85,2,20.04.2016,1.0,104,138.5,116.89 -85,2,21.04.2016,1.0,69,138.5,116.89 -85,2,22.04.2016,1.0,85,138.5,116.89 -85,2,23.04.2016,1.0,79,138.5,116.89 -85,2,24.04.2016,1.0,123,138.5,116.89 -85,2,25.04.2016,1.0,16,138.5,116.89 -85,2,26.04.2016,,0,138.5, -85,2,27.04.2016,,0,138.5, -85,2,28.04.2016,,3,138.5, -85,2,29.04.2016,,3,138.5, -85,2,30.04.2016,,13,138.5, -85,2,01.05.2016,,6,138.5, -85,2,02.05.2016,,19,138.5, -85,2,03.05.2016,1.0,126,138.5,106.01 -85,2,04.05.2016,1.0,157,138.5,106.01 -85,2,05.05.2016,1.0,97,138.5,106.01 -85,2,06.05.2016,1.0,148,138.5,106.01 -85,2,07.05.2016,1.0,88,138.5,106.01 -85,2,08.05.2016,1.0,119,138.5,106.01 -85,2,09.05.2016,1.0,123,138.5,106.01 -85,2,10.05.2016,,0,138.5, -85,2,11.05.2016,,3,138.5, -85,2,12.05.2016,,0,138.5, -85,2,13.05.2016,,9,138.5, -85,2,14.05.2016,,9,138.5, -85,2,15.05.2016,,19,138.5, -85,2,16.05.2016,,13,138.5, -85,2,17.05.2016,,0,138.5, -85,2,18.05.2016,,0,138.5, -85,2,19.05.2016,,0,138.5, -85,2,20.05.2016,,3,138.5, -85,2,21.05.2016,,3,138.5, -85,2,22.05.2016,,6,138.5, -86,2,26.06.2015,,3,150.73, -86,2,27.06.2015,,0,150.73, -86,2,28.06.2015,,0,150.73, -86,2,29.06.2015,,0,150.73, -86,2,30.06.2015,,0,150.73, -86,2,01.07.2015,,6,150.73, -86,2,02.07.2015,,3,150.73, -86,2,03.07.2015,,6,150.73, -86,2,04.07.2015,,0,150.73, -86,2,05.07.2015,,0,150.73, -86,2,06.07.2015,,0,150.73, -86,2,07.07.2015,,3,150.73, -86,2,08.07.2015,,3,150.73, -86,2,09.07.2015,,3,150.73, -86,2,10.07.2015,,0,150.73, -86,2,11.07.2015,,0,150.73, -86,2,12.07.2015,,0,150.73, -86,2,13.07.2015,,0,150.73, -86,2,14.07.2015,,0,150.73, -86,2,15.07.2015,,0,150.73, -86,2,16.07.2015,,3,150.73, -86,2,17.07.2015,,0,150.73, -86,2,18.07.2015,,3,150.73, -86,2,19.07.2015,,0,150.73, -86,2,20.07.2015,,0,150.73, -86,2,21.07.2015,,0,150.73, -86,2,22.07.2015,,0,150.73, -86,2,23.07.2015,,0,150.73, -86,2,24.07.2015,,22,150.73, -86,2,25.07.2015,,9,150.73, -86,2,26.07.2015,,6,150.73, -86,2,27.07.2015,,6,150.73, -86,2,28.07.2015,,9,150.73, -86,2,29.07.2015,,0,150.73, -86,2,30.07.2015,,6,150.73, -86,2,31.07.2015,,6,150.73, -86,2,01.08.2015,,3,150.73, -86,2,02.08.2015,,6,150.73, -86,2,03.08.2015,,0,150.73, -86,2,04.08.2015,,0,150.73, -86,2,05.08.2015,,6,150.73, -86,2,06.08.2015,,6,150.73, -86,2,07.08.2015,,6,150.73, -86,2,08.08.2015,,6,150.73, -86,2,09.08.2015,,6,150.73, -86,2,10.08.2015,,3,150.73, -86,2,11.08.2015,,0,150.73, -86,2,12.08.2015,,9,128.17, -86,2,13.08.2015,,22,128.17, -86,2,14.08.2015,,0,128.17, -86,2,15.08.2015,,0,128.17, -86,2,16.08.2015,,6,128.17, -86,2,17.08.2015,,9,128.17, -86,2,18.08.2015,1.0,66,128.17,100.3 -86,2,19.08.2015,1.0,47,128.17,100.3 -86,2,20.08.2015,1.0,53,128.17,100.3 -86,2,21.08.2015,1.0,47,128.17,100.3 -86,2,22.08.2015,1.0,53,128.17,100.3 -86,2,23.08.2015,1.0,53,128.17,100.3 -86,2,24.08.2015,1.0,44,128.17,100.3 -86,2,25.08.2015,,13,128.17, -86,2,26.08.2015,,0,128.17, -86,2,27.08.2015,,0,128.17, -86,2,28.08.2015,,3,128.17, -86,2,29.08.2015,,3,128.17, -86,2,30.08.2015,,0,128.17, -86,2,31.08.2015,,3,128.17, -86,2,01.09.2015,,0,128.17, -86,2,02.09.2015,,0,128.17, -86,2,03.09.2015,,3,128.17, -86,2,04.09.2015,,0,128.17, -86,2,05.09.2015,,3,128.17, -86,2,06.09.2015,,0,128.17, -86,2,07.09.2015,,3,128.17, -86,2,08.09.2015,,0,128.17, -86,2,09.09.2015,,3,128.17, -86,2,10.09.2015,,3,128.17, -86,2,11.09.2015,,0,128.17, -86,2,12.09.2015,,6,128.17, -86,2,13.09.2015,,6,128.17, -86,2,14.09.2015,,0,128.17, -86,2,15.09.2015,,0,128.17, -86,2,16.09.2015,,6,128.17, -86,2,17.09.2015,,16,128.17, -86,2,18.09.2015,,31,128.17, -86,2,19.09.2015,,16,128.17, -86,2,20.09.2015,,22,128.17, -86,2,21.09.2015,,3,128.17, -86,2,22.09.2015,,9,128.17, -86,2,23.09.2015,,3,128.17, -86,2,24.09.2015,,16,128.17, -86,2,25.09.2015,,0,128.17, -86,2,26.09.2015,,3,128.17, -86,2,27.09.2015,,13,128.17, -86,2,28.09.2015,,19,128.17, -86,2,29.09.2015,,25,128.17, -86,2,30.09.2015,,9,128.17, -86,2,01.10.2015,,0,162.96, -86,2,02.10.2015,,0,162.96, -86,2,03.10.2015,,0,162.96, -86,2,04.10.2015,,0,162.96, -86,2,05.10.2015,,3,162.96, -86,2,06.10.2015,1.0,50,143.39,122.05 -86,2,07.10.2015,1.0,35,143.39,122.05 -86,2,08.10.2015,1.0,38,143.39,122.05 -86,2,09.10.2015,1.0,19,143.39,122.05 -86,2,10.10.2015,1.0,22,143.39,122.05 -86,2,11.10.2015,1.0,16,143.39,122.05 -86,2,12.10.2015,1.0,47,143.39,122.05 -86,2,13.10.2015,1.0,13,143.93,122.05 -86,2,14.10.2015,1.0,6,143.93,122.05 -86,2,15.10.2015,1.0,6,143.93,122.05 -86,2,16.10.2015,1.0,6,143.93,122.05 -86,2,17.10.2015,1.0,28,162.96,122.05 -86,2,18.10.2015,1.0,28,162.96,122.05 -86,2,19.10.2015,,6,162.96, -86,2,20.10.2015,,6,162.96, -86,2,21.10.2015,,6,162.96, -86,2,22.10.2015,,6,162.96, -86,2,23.10.2015,,6,162.96, -86,2,24.10.2015,,6,162.96, -86,2,25.10.2015,,6,162.96, -86,2,26.10.2015,,6,162.96, -86,2,27.10.2015,,6,162.96, -86,2,28.10.2015,,6,162.96, -86,2,29.10.2015,,6,162.96, -86,2,30.10.2015,,0,162.96, -86,2,31.10.2015,,0,162.96, -86,2,01.11.2015,,0,162.96, -86,2,02.11.2015,,0,162.96, -86,2,03.11.2015,,0,162.96, -86,2,04.11.2015,,0,162.96, -86,2,05.11.2015,,0,162.96, -86,2,06.11.2015,,3,162.96, -86,2,07.11.2015,,3,162.96, -86,2,08.11.2015,,0,162.96, -86,2,09.11.2015,,3,162.96, -86,2,10.11.2015,,3,162.96, -86,2,11.11.2015,,0,138.36, -86,2,12.11.2015,,3,138.36, -86,2,13.11.2015,,3,138.36, -86,2,14.11.2015,,3,138.36, -86,2,15.11.2015,,3,138.36, -86,2,16.11.2015,,3,138.36, -86,2,17.11.2015,,3,138.36, -86,2,18.11.2015,,0,138.36, -86,2,19.11.2015,,0,138.36, -86,2,20.11.2015,,0,138.36, -86,2,21.11.2015,,0,138.36, -86,2,22.11.2015,,0,138.36, -86,2,23.11.2015,,0,138.36, -86,2,24.11.2015,,0,138.36, -86,2,25.11.2015,,0,138.36, -86,2,26.11.2015,,0,138.36, -86,2,27.11.2015,,0,138.36, -86,2,28.11.2015,,0,138.36, -86,2,29.11.2015,,0,138.36, -86,2,30.11.2015,,6,138.36, -86,2,01.12.2015,,0,162.96, -86,2,02.12.2015,,0,162.96, -86,2,03.12.2015,,0,138.5, -86,2,04.12.2015,,0,138.5, -86,2,05.12.2015,,0,138.5, -86,2,06.12.2015,,6,138.5, -86,2,07.12.2015,,0,138.5, -86,2,08.12.2015,,3,138.5, -86,2,09.12.2015,,0,138.5, -86,2,10.12.2015,,0,138.5, -86,2,11.12.2015,,3,142.57, -86,2,12.12.2015,,0,142.57, -86,2,13.12.2015,,0,142.57, -86,2,14.12.2015,,0,142.57, -86,2,15.12.2015,,9,142.57, -86,2,16.12.2015,,6,142.57, -86,2,17.12.2015,,3,142.57, -86,2,18.12.2015,,3,142.57, -86,2,19.12.2015,,0,142.57, -86,2,20.12.2015,,0,142.57, -86,2,21.12.2015,,13,142.57, -86,2,22.12.2015,1.0,66,142.57,103.02 -86,2,23.12.2015,1.0,75,142.57,103.02 -86,2,24.12.2015,1.0,9,142.57,103.02 -86,2,25.12.2015,1.0,25,142.57,103.02 -86,2,26.12.2015,1.0,22,142.57,103.02 -86,2,27.12.2015,1.0,22,142.57,103.02 -86,2,28.12.2015,1.0,19,142.57,103.02 -86,2,29.12.2015,,0,138.5, -86,2,30.12.2015,,0,138.5, -86,2,31.12.2015,,0,138.5, -86,2,01.01.2016,,0,138.5, -86,2,02.01.2016,,0,138.5, -86,2,03.01.2016,,0,138.5, -86,2,04.01.2016,,0,138.5, -86,2,05.01.2016,,0,138.5, -86,2,06.01.2016,,0,138.5, -86,2,07.01.2016,,0,138.5, -86,2,08.01.2016,,0,138.5, -86,2,09.01.2016,,0,138.5, -86,2,10.01.2016,,0,138.5, -86,2,11.01.2016,,0,138.5, -86,2,12.01.2016,,0,138.5, -86,2,13.01.2016,,0,138.5, -86,2,14.01.2016,,3,138.5, -86,2,15.01.2016,,3,138.5, -86,2,16.01.2016,,3,142.57, -86,2,17.01.2016,,9,142.57, -86,2,18.01.2016,,6,142.57, -86,2,19.01.2016,1.0,72,142.57,103.02 -86,2,20.01.2016,1.0,66,142.57,103.02 -86,2,21.01.2016,1.0,75,142.57,103.02 -86,2,22.01.2016,1.0,91,142.57,103.02 -86,2,23.01.2016,1.0,79,142.57,103.02 -86,2,24.01.2016,1.0,6,142.57,103.02 -86,2,25.01.2016,1.0,57,142.57,103.02 -86,2,26.01.2016,,3,142.57, -86,2,27.01.2016,,0,142.57, -86,2,28.01.2016,,0,142.57, -86,2,29.01.2016,,3,142.57, -86,2,30.01.2016,,13,142.57, -86,2,31.01.2016,,6,142.57, -86,2,01.02.2016,,6,142.57, -86,2,02.02.2016,,0,142.57, -86,2,03.02.2016,,0,142.57, -86,2,04.02.2016,,0,142.57, -86,2,05.02.2016,,0,142.57, -86,2,06.02.2016,,0,142.57, -86,2,07.02.2016,,0,142.57, -86,2,08.02.2016,,6,142.57, -86,2,09.02.2016,1.0,104,142.57,108.46 -86,2,10.02.2016,1.0,60,138.36,108.46 -86,2,11.02.2016,1.0,53,138.36,108.46 -86,2,12.02.2016,1.0,60,138.36,108.46 -86,2,13.02.2016,1.0,50,138.36,108.46 -86,2,14.02.2016,1.0,6,138.36,108.46 -86,2,15.02.2016,1.0,13,138.36,108.46 -86,2,16.02.2016,,0,138.36, -86,2,17.02.2016,,0,138.36, -86,2,18.02.2016,,0,138.36, -86,2,19.02.2016,,0,138.36, -86,2,20.02.2016,,0,138.36, -86,2,21.02.2016,,0,138.36, -86,2,22.02.2016,,0,138.36, -86,2,23.02.2016,,0,138.36, -86,2,24.02.2016,,0,138.36, -86,2,25.02.2016,,0,138.36, -86,2,26.02.2016,,0,138.36, -86,2,27.02.2016,,0,138.36, -86,2,28.02.2016,,0,138.36, 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-86,2,02.04.2016,,0,138.36, -86,2,03.04.2016,,0,138.36, -86,2,04.04.2016,,3,138.36, -86,2,05.04.2016,,0,138.36, -86,2,06.04.2016,,6,138.36, -86,2,07.04.2016,,16,138.36, -86,2,08.04.2016,,16,138.36, -86,2,09.04.2016,,6,138.5, -86,2,10.04.2016,,6,138.5, -86,2,11.04.2016,,16,138.5, -86,2,12.04.2016,,9,138.5, -86,2,13.04.2016,,16,138.5, -86,2,14.04.2016,,9,138.5, -86,2,15.04.2016,,0,138.5, -86,2,16.04.2016,,3,138.5, -86,2,17.04.2016,,16,138.5, -86,2,18.04.2016,,16,138.5, -86,2,19.04.2016,1.0,85,138.5,116.89 -86,2,20.04.2016,1.0,57,138.5,116.89 -86,2,21.04.2016,1.0,60,138.5,116.89 -86,2,22.04.2016,1.0,66,138.5,116.89 -86,2,23.04.2016,1.0,44,138.5,116.89 -86,2,24.04.2016,1.0,66,138.5,116.89 -86,2,25.04.2016,1.0,47,138.5,116.89 -86,2,26.04.2016,,13,138.5, -86,2,27.04.2016,,16,138.5, -86,2,28.04.2016,,9,138.5, -86,2,29.04.2016,,13,138.5, -86,2,30.04.2016,,16,138.5, -86,2,01.05.2016,,6,138.5, -86,2,02.05.2016,,9,138.5, -86,2,03.05.2016,1.0,66,138.5,106.01 -86,2,04.05.2016,1.0,101,138.5,106.01 -86,2,05.05.2016,1.0,116,138.5,106.01 -86,2,06.05.2016,1.0,75,138.5,106.01 -86,2,07.05.2016,1.0,88,138.5,106.01 -86,2,08.05.2016,1.0,88,138.5,106.01 -86,2,09.05.2016,1.0,47,138.5,106.01 -86,2,10.05.2016,,13,138.5, -86,2,11.05.2016,,16,138.5, -86,2,12.05.2016,,44,138.5, -86,2,13.05.2016,,13,138.5, -86,2,14.05.2016,,0,138.5, -86,2,15.05.2016,,3,138.5, -86,2,16.05.2016,,13,138.5, -86,2,17.05.2016,,19,138.5, -86,2,18.05.2016,,3,138.5, -86,2,19.05.2016,,3,138.5, -86,2,20.05.2016,,6,138.5, -86,2,21.05.2016,,0,138.5, -86,2,22.05.2016,,3,138.5, -87,2,18.09.2015,,28,162.96, -87,2,19.09.2015,,9,162.96, -87,2,20.09.2015,,0,162.96, -87,2,21.09.2015,,9,162.96, -87,2,22.09.2015,,13,162.96, -87,2,23.09.2015,,6,162.96, -87,2,24.09.2015,,6,162.96, -87,2,25.09.2015,,3,162.96, -87,2,26.09.2015,,0,162.96, -87,2,27.09.2015,,3,162.96, -87,2,28.09.2015,,6,162.96, -87,2,29.09.2015,,13,162.96, -87,2,30.09.2015,,9,162.96, -87,2,01.10.2015,,0,162.96, -87,2,02.10.2015,,0,162.96, -87,2,03.10.2015,,3,162.96, -87,2,04.10.2015,,0,162.96, -87,2,05.10.2015,,6,162.96, -87,2,06.10.2015,1.0,41,143.39,122.05 -87,2,07.10.2015,1.0,75,143.39,122.05 -87,2,08.10.2015,1.0,63,143.39,122.05 -87,2,09.10.2015,1.0,60,143.39,122.05 -87,2,10.10.2015,1.0,50,143.39,122.05 -87,2,11.10.2015,1.0,82,143.39,122.05 -87,2,12.10.2015,1.0,53,143.39,122.05 -87,2,13.10.2015,1.0,60,143.93,122.05 -87,2,14.10.2015,1.0,60,143.93,122.05 -87,2,15.10.2015,1.0,53,143.93,122.05 -87,2,16.10.2015,1.0,47,143.93,122.05 -87,2,17.10.2015,1.0,44,162.96,122.05 -87,2,18.10.2015,1.0,38,162.96,122.05 -87,2,19.10.2015,,3,162.96, -87,2,20.10.2015,,0,162.96, -87,2,21.10.2015,,0,162.96, -87,2,22.10.2015,,0,162.96, -87,2,23.10.2015,,28,162.96, -87,2,24.10.2015,,0,162.96, -87,2,25.10.2015,,0,162.96, -87,2,26.10.2015,,0,162.96, -87,2,27.10.2015,,0,162.96, -87,2,28.10.2015,,3,162.96, -87,2,29.10.2015,,0,162.96, -87,2,30.10.2015,,0,162.96, -87,2,31.10.2015,,0,162.96, -87,2,01.11.2015,,0,162.96, -87,2,02.11.2015,,0,162.96, -87,2,03.11.2015,,0,162.96, -87,2,04.11.2015,,6,162.96, -87,2,05.11.2015,,3,162.96, -87,2,06.11.2015,,0,162.96, -87,2,07.11.2015,,0,162.96, -87,2,08.11.2015,,0,162.96, -87,2,09.11.2015,,0,162.96, -87,2,10.11.2015,,0,162.96, -87,2,11.11.2015,,3,138.36, -87,2,12.11.2015,,0,138.36, -87,2,13.11.2015,,3,138.36, -87,2,14.11.2015,,3,138.36, -87,2,15.11.2015,,0,138.36, -87,2,16.11.2015,,0,138.36, -87,2,17.11.2015,,3,138.36, -87,2,18.11.2015,,3,138.36, -87,2,19.11.2015,,0,138.36, -87,2,20.11.2015,,0,138.36, -87,2,21.11.2015,,0,138.36, -87,2,22.11.2015,,0,138.36, -87,2,23.11.2015,,0,138.36, -87,2,24.11.2015,,0,138.36, -87,2,25.11.2015,,16,138.36, -87,2,26.11.2015,,3,138.36, -87,2,27.11.2015,,6,138.36, -87,2,28.11.2015,,6,138.36, -87,2,29.11.2015,,3,138.36, -87,2,30.11.2015,,0,138.36, -87,2,01.12.2015,,3,162.96, -87,2,02.12.2015,,3,162.96, -87,2,03.12.2015,,3,138.5, -87,2,04.12.2015,,0,138.5, -87,2,05.12.2015,,3,138.5, -87,2,06.12.2015,,3,138.5, -87,2,07.12.2015,,3,138.5, -87,2,08.12.2015,,0,138.5, -87,2,09.12.2015,,6,138.5, -87,2,10.12.2015,,0,138.5, -87,2,11.12.2015,,0,142.57, -87,2,12.12.2015,,0,142.57, -87,2,13.12.2015,,3,142.57, -87,2,14.12.2015,,6,142.57, -87,2,15.12.2015,,13,142.57, -87,2,16.12.2015,,6,142.57, -87,2,17.12.2015,,13,142.57, -87,2,18.12.2015,,3,142.57, -87,2,19.12.2015,,3,142.57, -87,2,20.12.2015,,13,142.57, -87,2,21.12.2015,,19,142.57, -87,2,22.12.2015,1.0,119,142.57,103.02 -87,2,23.12.2015,1.0,91,142.57,103.02 -87,2,24.12.2015,1.0,107,142.57,103.02 -87,2,25.12.2015,1.0,82,142.57,103.02 -87,2,26.12.2015,1.0,57,142.57,103.02 -87,2,27.12.2015,1.0,57,142.57,103.02 -87,2,28.12.2015,1.0,94,142.57,103.02 -87,2,29.12.2015,,6,138.5, -87,2,30.12.2015,,3,138.5, -87,2,31.12.2015,,6,138.5, -87,2,01.01.2016,,3,138.5, -87,2,02.01.2016,,6,138.5, -87,2,03.01.2016,,19,138.5, -87,2,04.01.2016,,19,138.5, -87,2,05.01.2016,,9,138.5, -87,2,06.01.2016,,16,138.5, -87,2,07.01.2016,,3,138.5, -87,2,08.01.2016,,9,138.5, -87,2,09.01.2016,,9,138.5, -87,2,10.01.2016,,6,138.5, -87,2,11.01.2016,,3,138.5, -87,2,12.01.2016,,16,138.5, -87,2,13.01.2016,,3,138.5, -87,2,14.01.2016,,13,138.5, -87,2,15.01.2016,,9,138.5, -87,2,16.01.2016,,13,142.57, -87,2,17.01.2016,,38,142.57, -87,2,18.01.2016,,16,142.57, -87,2,19.01.2016,1.0,116,142.57,103.02 -87,2,20.01.2016,1.0,116,142.57,103.02 -87,2,21.01.2016,1.0,135,142.57,103.02 -87,2,22.01.2016,1.0,148,142.57,103.02 -87,2,23.01.2016,1.0,167,142.57,103.02 -87,2,24.01.2016,1.0,60,142.57,103.02 -87,2,25.01.2016,1.0,94,142.57,103.02 -87,2,26.01.2016,,13,142.57, -87,2,27.01.2016,,19,142.57, -87,2,28.01.2016,,13,142.57, -87,2,29.01.2016,,3,142.57, -87,2,30.01.2016,,0,142.57, -87,2,31.01.2016,,13,142.57, -87,2,01.02.2016,,0,142.57, -87,2,02.02.2016,,0,142.57, -87,2,03.02.2016,,9,142.57, -87,2,04.02.2016,,13,142.57, -87,2,05.02.2016,,13,142.57, -87,2,06.02.2016,,6,142.57, -87,2,07.02.2016,,9,142.57, -87,2,08.02.2016,,35,142.57, -87,2,09.02.2016,1.0,94,142.57,108.46 -87,2,10.02.2016,1.0,97,138.36,108.46 -87,2,11.02.2016,1.0,151,138.36,108.46 -87,2,12.02.2016,1.0,91,138.36,108.46 -87,2,13.02.2016,1.0,110,138.36,108.46 -87,2,14.02.2016,1.0,60,138.36,108.46 -87,2,15.02.2016,1.0,110,138.36,108.46 -87,2,16.02.2016,,6,138.36, -87,2,17.02.2016,,6,138.36, -87,2,18.02.2016,,0,138.36, -87,2,19.02.2016,,3,138.36, -87,2,20.02.2016,,0,138.36, -87,2,21.02.2016,,6,138.36, -87,2,22.02.2016,,6,138.36, -87,2,23.02.2016,,0,138.36, -87,2,24.02.2016,,0,138.36, -87,2,25.02.2016,,0,138.36, -87,2,26.02.2016,,3,138.36, -87,2,27.02.2016,,0,138.36, -87,2,28.02.2016,,9,138.36, -87,2,29.02.2016,,3,138.36, -87,2,01.03.2016,,6,138.36, -87,2,02.03.2016,,0,138.36, -87,2,03.03.2016,,0,138.36, -87,2,04.03.2016,,0,138.36, -87,2,05.03.2016,,6,138.36, -87,2,06.03.2016,,0,138.36, -87,2,07.03.2016,,3,138.36, -87,2,08.03.2016,,9,138.36, -87,2,09.03.2016,,6,138.36, -87,2,10.03.2016,,3,138.36, -87,2,11.03.2016,,19,138.36, -87,2,12.03.2016,,6,138.5, -87,2,13.03.2016,,6,138.5, -87,2,14.03.2016,,22,138.5, -87,2,15.03.2016,1.0,129,138.5,124.77 -87,2,16.03.2016,1.0,57,138.5,124.77 -87,2,17.03.2016,1.0,69,138.5,124.77 -87,2,18.03.2016,1.0,69,138.5,124.77 -87,2,19.03.2016,1.0,85,138.5,124.77 -87,2,20.03.2016,1.0,72,138.5,124.77 -87,2,21.03.2016,1.0,60,138.5,124.77 -87,2,22.03.2016,,0,138.36, -87,2,23.03.2016,,13,138.36, -87,2,24.03.2016,,6,138.36, -87,2,25.03.2016,,3,138.36, -87,2,26.03.2016,,3,138.36, -87,2,27.03.2016,,6,138.36, -87,2,28.03.2016,,3,138.36, -87,2,29.03.2016,,0,138.36, -87,2,30.03.2016,,6,138.36, -87,2,31.03.2016,,6,138.36, -87,2,01.04.2016,,6,138.36, -87,2,02.04.2016,,6,138.36, -87,2,03.04.2016,,6,138.36, -87,2,04.04.2016,,3,138.36, -87,2,05.04.2016,,3,138.36, -87,2,06.04.2016,,6,138.36, -87,2,07.04.2016,,0,138.36, -87,2,08.04.2016,,3,138.36, -87,2,09.04.2016,,0,138.5, -87,2,10.04.2016,,6,138.5, -87,2,11.04.2016,,6,138.5, -87,2,12.04.2016,,3,138.5, -87,2,13.04.2016,,3,138.5, -87,2,14.04.2016,,0,138.5, -87,2,15.04.2016,,3,138.5, -87,2,16.04.2016,,3,138.5, -87,2,17.04.2016,,0,138.5, -87,2,18.04.2016,,0,138.5, -87,2,19.04.2016,1.0,123,138.5,116.89 -87,2,20.04.2016,1.0,85,138.5,116.89 -87,2,21.04.2016,1.0,138,138.5,116.89 -87,2,22.04.2016,1.0,116,138.5,116.89 -87,2,23.04.2016,1.0,94,138.5,116.89 -87,2,24.04.2016,1.0,44,138.5,116.89 -87,2,25.04.2016,1.0,57,138.5,116.89 -87,2,26.04.2016,,9,138.5, -87,2,27.04.2016,,3,138.5, -87,2,28.04.2016,,3,138.5, -87,2,29.04.2016,,0,138.5, -87,2,30.04.2016,,0,138.5, -87,2,01.05.2016,,0,138.5, -87,2,02.05.2016,,3,138.5, -87,2,03.05.2016,1.0,135,138.5,106.01 -87,2,04.05.2016,1.0,97,138.5,106.01 -87,2,05.05.2016,1.0,91,138.5,106.01 -87,2,06.05.2016,1.0,107,138.5,106.01 -87,2,07.05.2016,1.0,82,138.5,106.01 -87,2,08.05.2016,1.0,72,138.5,106.01 -87,2,09.05.2016,1.0,94,138.5,106.01 -87,2,10.05.2016,,3,138.5, -87,2,11.05.2016,,3,138.5, -87,2,12.05.2016,,3,138.5, -87,2,13.05.2016,,6,138.5, -87,2,14.05.2016,,0,138.5, -87,2,15.05.2016,,6,138.5, -87,2,16.05.2016,,13,138.5, -87,2,17.05.2016,,3,138.5, 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-89,2,26.03.2016,,3,138.36, -89,2,27.03.2016,,19,138.36, -89,2,28.03.2016,,22,138.36, -89,2,29.03.2016,,19,138.36, -89,2,30.03.2016,,0,138.36, -89,2,31.03.2016,,9,138.36, -89,2,01.04.2016,,9,138.36, -89,2,02.04.2016,,9,138.36, -89,2,03.04.2016,,9,138.36, -89,2,04.04.2016,,9,138.36, -89,2,05.04.2016,,9,138.36, -89,2,06.04.2016,,9,138.36, -89,2,07.04.2016,,0,138.36, -89,2,08.04.2016,,0,138.36, -89,2,09.04.2016,,3,138.5, -89,2,10.04.2016,,0,138.5, -89,2,11.04.2016,,3,138.5, -89,2,12.04.2016,,3,138.5, -89,2,13.04.2016,,6,138.5, -89,2,14.04.2016,,3,138.5, -89,2,15.04.2016,,13,138.5, -89,2,16.04.2016,,6,138.5, -89,2,17.04.2016,,6,138.5, -89,2,18.04.2016,,19,138.5, -89,2,19.04.2016,1.0,79,138.5,116.89 -89,2,20.04.2016,1.0,79,138.5,116.89 -89,2,21.04.2016,1.0,41,138.5,116.89 -89,2,22.04.2016,1.0,60,138.5,116.89 -89,2,23.04.2016,1.0,50,138.5,116.89 -89,2,24.04.2016,1.0,60,138.5,116.89 -89,2,25.04.2016,1.0,75,138.5,116.89 -89,2,26.04.2016,1.0,69,138.5,114.17 -89,2,27.04.2016,1.0,16,138.5,114.17 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-90,2,06.05.2016,1.0,38,138.5,106.01 -90,2,07.05.2016,1.0,9,138.5,106.01 -90,2,08.05.2016,1.0,19,138.5,106.01 -90,2,09.05.2016,1.0,16,138.5,106.01 -90,2,10.05.2016,,16,142.57, -90,2,11.05.2016,,0,142.57, -90,2,12.05.2016,,63,142.57, -90,2,13.05.2016,,38,142.57, -90,2,14.05.2016,,0,142.57, -90,2,15.05.2016,,0,142.57, -90,2,16.05.2016,,0,142.57, -90,2,17.05.2016,,0,142.57, -90,2,18.05.2016,,0,142.57, -90,2,19.05.2016,,0,142.57, -90,2,20.05.2016,,0,142.57, -90,2,21.05.2016,,3,142.57, -90,2,22.05.2016,,0,142.57, -91,2,13.11.2015,,0,138.36, -91,2,14.11.2015,,6,138.36, -91,2,15.11.2015,,25,138.36, -91,2,16.11.2015,,25,138.36, -91,2,17.11.2015,,0,138.36, -91,2,18.11.2015,,3,138.36, -91,2,19.11.2015,,6,138.36, -91,2,20.11.2015,,0,138.36, -91,2,21.11.2015,,3,138.36, -91,2,22.11.2015,,3,138.36, -91,2,23.11.2015,,0,138.36, -91,2,24.11.2015,,9,138.36, -91,2,25.11.2015,,3,138.36, -91,2,26.11.2015,,3,138.36, -91,2,27.11.2015,,13,138.36, -91,2,28.11.2015,,0,138.36, -91,2,29.11.2015,,0,138.36, -91,2,30.11.2015,,0,138.36, -91,2,01.12.2015,,6,162.96, -91,2,02.12.2015,,3,162.96, -91,2,03.12.2015,,3,138.5, -91,2,04.12.2015,,0,138.5, -91,2,05.12.2015,,6,138.5, -91,2,06.12.2015,,0,138.5, -91,2,07.12.2015,,9,138.5, -91,2,08.12.2015,,0,138.5, -91,2,09.12.2015,,0,138.5, -91,2,10.12.2015,,16,138.5, -91,2,11.12.2015,,0,142.57, -91,2,12.12.2015,,6,142.57, -91,2,13.12.2015,,9,142.57, -91,2,14.12.2015,,0,142.57, -91,2,15.12.2015,,0,142.57, -91,2,16.12.2015,,0,142.57, -91,2,17.12.2015,,0,142.57, -91,2,18.12.2015,,0,142.57, -91,2,19.12.2015,,6,142.57, -91,2,20.12.2015,,6,142.57, -91,2,21.12.2015,,0,142.57, -91,2,22.12.2015,1.0,9,142.57,103.02 -91,2,23.12.2015,1.0,25,142.57,103.02 -91,2,24.12.2015,1.0,53,142.57,103.02 -91,2,25.12.2015,1.0,69,142.57,103.02 -91,2,26.12.2015,1.0,60,142.57,103.02 -91,2,27.12.2015,1.0,22,142.57,103.02 -91,2,28.12.2015,1.0,35,142.57,103.02 -91,2,29.12.2015,,0,138.5, -91,2,30.12.2015,,3,138.5, -91,2,31.12.2015,,0,138.5, -91,2,01.01.2016,,0,138.5, 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Language :: Python :: 3.10", - "Operating System :: OS Independent", - "Intended Audience :: Science/Research", - "Development Status :: 3 - Alpha", - "Environment :: Console", - "Natural Language :: Russian", - "Topic :: Scientific/Engineering :: Artificial Intelligence", -] - -[tool.poetry.dependencies] -python = "^3.9" -scikit-learn = "^1.3.2" -numpy = "^1.26.3" -pandas = "^2.1.4" -holidays = "^0.40" -catboost = "^1.2.0" -scipy = "^1.11.4" -torch = "^2.3.0" -tqdm = "^4.66.4" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[project.urls] -Homepage = "https://github.com/sb-ai-lab/tsururu" -Issues = "https://github.com/sb-ai-lab/tsururu/issues" +[tool.poetry] +name = "tsururu" +version = "0.1.0" +description = "Python tool for time series forecasting" +authors = ["Alina Kostromina ", "Dmitry Simakov "] +license = "Apache License 2.0" +readme = "README.md" +repository = "https://github.com/sb-ai-lab/tsururu" +classifiers = [ + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Operating System :: OS Independent", + "Intended Audience :: Science/Research", + "Development Status :: 3 - Alpha", + "Environment :: Console", + "Natural Language :: Russian", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] + +[tool.poetry.dependencies] +python = ">=3.9, <3.12" +scikit-learn = "^1.3.2" +numpy = "^1.26.3" +pandas = "^2.1.4" +holidays = "^0.40" +scipy = "^1.11.4" +catboost = { version = ">=1.2.0", optional = true } +torch = { version = ">=1.9.0, <=2.0.0", optional = true } + +[tool.poetry.extras] +catboost = ["catboost"] +torch = ["torch"] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[project.urls] +Homepage = "https://github.com/sb-ai-lab/tsururu" +Issues = "https://github.com/sb-ai-lab/tsururu/issues" diff --git a/tsururu/__init__.py b/tsururu/__init__.py index 156cc84..77b2c24 100644 --- a/tsururu/__init__.py +++ b/tsururu/__init__.py @@ -1 +1 @@ -__all__ = ["dataset", "transformers", "models", "strategies", "validation", "torch_based"] +__all__ = ["dataset", "transformers", "models", "strategies", "validation", "torch_based"] diff --git a/tsururu/dataset/__init__.py b/tsururu/dataset/__init__.py index 22334db..ee29819 100644 --- a/tsururu/dataset/__init__.py +++ b/tsururu/dataset/__init__.py @@ -1,7 +1,7 @@ -"""Tools for data manipulation.""" - -from .dataset import TSDataset -from .pipeline import Pipeline -from .slice import IndexSlicer - -__all__ = ["TSDataset", "IndexSlicer", "Pipeline"] +"""Tools for data manipulation.""" + +from .dataset import TSDataset +from .pipeline import Pipeline +from .slice import IndexSlicer + +__all__ = ["TSDataset", "IndexSlicer", "Pipeline"] diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index 96e9fca..6898e06 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -1,248 +1,252 @@ -"""Module for initializing and manipulating time series data.""" - -from typing import Optional, Sequence, Union - -import numpy as np -import pandas as pd - -from .slice import IndexSlicer - -slicer = IndexSlicer() - - -class TSDataset: - """Class for initializing data from pandas DataFrame. - - Args: - data: source dataframe. - columns_params: columns' roles and types: - for example: - { - "target": { - "columns": ["value"], - "type": "continious", - }, - "date": {...}, - ... - }. - delta: the pd.DateOffset class. Usually generated - automatically, but can be externally specified. Needs to - create datetime features and new values. - - Notes: - 1. If the printed information about freq and period values is - not correct for user's task, user should redefine `delta` - parameter. - - """ - - def _check_single_column(self): - """Check that `target`, `id`, `date` columns contains only one - column. - - Raises: - AssertionError: if the `columns` container for one of the - roles from `target`, `date`, `id` contains more than - one column. - - """ - for role in ["target", "date", "id"]: - assert ( - len(self.columns_params[role]["columns"]) == 1 - ), f"the `columns` container for role {role} should contain only one column" - - def _print_freq_period_info(self): - """Print the frequency and period information for data for - validation purposes. - - """ - info = slicer.timedelta( - self.data[self.date_column], delta=self.delta, return_freq_period_info=True - )[2] - - print(info) - - def __init__( - self, - data: pd.DataFrame, - columns_params: dict, - delta: pd.DateOffset = None, - print_freq_period_info: bool = False, - ): - # Columns typing - for _, role_dict in columns_params.items(): - column_name = role_dict["columns"][0] - column_type = role_dict["type"] - if column_type == "continious": - data[column_name] = data[column_name].astype("float") - elif column_type == "datetime": - data[column_name] = pd.to_datetime(data[column_name]) - - self.data = data - self.columns_params = columns_params - self.delta = delta - - self._check_single_column() - - self.id_column = columns_params["id"]["columns"][0] - self.target_column = columns_params["target"]["columns"][0] - self.date_column = columns_params["date"]["columns"][0] - - self.data = data.sort_values([self.id_column, self.date_column]) - - if print_freq_period_info: - self._print_freq_period_info() - - def _crop_segment( - self, - segment: np.ndarray, - test_last: bool, - horizon: int, - history: int, - ) -> np.ndarray: - """Crop a segment of data based on the history and horizon. - - Args: - segment: the input segment of data to crop. - test_last: if True, return the last history elements of the - segment. Otherwise, return the elements between history - and history + horizon. - - Returns: - the cropped segment of data. - - """ - if test_last: - return segment[-history:] - - return segment[-history - horizon : -horizon] - - @staticmethod - def _pad_segment( - segment: np.ndarray, - horizon: int, - time_delta: Union[pd.Timedelta, pd.DateOffset], - date_col_id: int, - id_col_id: Sequence[int], - ) -> np.ndarray: - """Pad a segment of data with new rows based on the horizon. - - Args: - segment: the input segment of data to pad. - horizon: the number of new rows to add to the segment. - time_delta: the time_delta to use for the new date values. - date_col_id: the index of the date column in the segment. - id_col_id: the index of the id columns in the segment. - - Returns: - padded segment of data. - - """ - result = np.full((horizon, segment.shape[1]), np.nan, dtype=object) - - last_date = segment[-1, date_col_id] - new_dates = pd.date_range(last_date + time_delta, periods=horizon, freq=time_delta) - result[:, date_col_id] = new_dates - - if isinstance(id_col_id, np.ndarray): - for i in range(len(id_col_id)): - result[:, id_col_id[i]] = np.repeat(segment[0, id_col_id[i]], horizon) - else: - result[:, id_col_id] = np.repeat(segment[0, id_col_id], horizon) - - return result - - def make_padded_test( - self, - horizon: int, - history: int, - test_last: bool = True, - test_all: bool = False, - model_horizon: Optional[int] = None, - id_column_name: Optional[Union[str, Sequence[str]]] = None, - ): - """Generate a test dataframe with new rows with NaN targets. - - Args: - horizon: number of points to prediction - (number of new rows to add to each segment). - history: number of previous for feature generating. - test_last: if True, return generated test data built by - the last point. - test_all: if True, return generated test data for all - points (like rolling forecast). - model_horizon: the number of points to predict in one step. - Needs for test_all=True. - id_column_name: name of the column(s) by which the data is - split (in some cases it is different from the original - id column(s)). - - Notes: - 1. The new rows are filled with NaN target values, - generated datetimes values (based on the time_delta) - and the same id values as the last row of the segment. - - Returns: - the padded test dataset. - - """ - if test_all: - current_test_ids = slicer.create_idx_data( - self.data, - horizon, - history, - model_horizon, - date_column=self.date_column, - ) - extended_data = slicer.get_slice(self.data, (current_test_ids, None)) - extended_data = pd.DataFrame( - extended_data.reshape(-1, extended_data.shape[-1]), - columns=self.data.columns, - ) - extended_data_nrows = extended_data.shape[0] - - extended_data["segment_col"] = np.repeat( - np.arange(extended_data_nrows // history), history - ) - id_column_name = ["segment_col", self.id_column] - else: - extended_data = self.data - - columns = self.data.columns - date_col_id = slicer.get_cols_idx(extended_data, self.date_column) - if id_column_name is None: - id_column_name = self.id_column - id_col_id = slicer.get_cols_idx(extended_data, id_column_name) - - # Find indices for segments - ids, time_delta = slicer.ids_from_date( - extended_data, self.date_column, delta=self.delta, return_delta=True - ) - - if test_all: - ids = list(np.unique(extended_data.segment_col, return_index=True)[1])[1:] - - data = extended_data.to_numpy() - - segments = np.split(data, ids) - segments = [ - self._crop_segment(segment, test_last, horizon, history) for segment in segments - ] - - # Find padded parts for each segment - padded_segments_results = [ - self._pad_segment(segment, horizon, time_delta, date_col_id, id_col_id) - for segment in segments - ] - - # Concatenate together - result = np.vstack(np.concatenate((segments, padded_segments_results), axis=1)) - if test_all: - result = pd.DataFrame(result, columns=list(columns) + ["segment_col"]) - else: - result = pd.DataFrame(result, columns=columns) - result[self.date_column] = pd.to_datetime(result[self.date_column]) - other = [col for col in columns if col not in [self.id_column, self.date_column]] - result[other] = result[other].astype("float") - - return result +"""Module for initializing and manipulating time series data.""" + +from typing import Optional, Sequence, Union + +import numpy as np +import pandas as pd + +from .slice import IndexSlicer + +slicer = IndexSlicer() + + +class TSDataset: + """Class for initializing data from pandas DataFrame. + + Args: + data: source dataframe. + columns_params: columns' roles and types: + for example: + { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": {...}, + "id": {...}, + "exog_1": {...}, + "exog_2": {...}, + ..., + }. + delta: the pd.DateOffset class. Usually generated + automatically, but can be externally specified. Needs to + create datetime features and new values. + + Notes: + 1. If the printed information about freq and period values is + not correct for user's task, user should redefine `delta` + parameter. + + """ + + def _check_single_column(self): + """Check that `target`, `id`, `date` columns contains only one + column. + + Raises: + AssertionError: if the `columns` container for one of the + roles from `target`, `date`, `id` contains more than + one column. + + """ + for role in ["target", "date", "id"]: + assert ( + len(self.columns_params[role]["columns"]) == 1 + ), f"the `columns` container for role {role} should contain only one column" + + def _print_freq_period_info(self): + """Print the frequency and period information for data for + validation purposes. + + """ + info = slicer.timedelta( + self.data[self.date_column], delta=self.delta, return_freq_period_info=True + )[2] + + print(info) + + def __init__( + self, + data: pd.DataFrame, + columns_params: dict, + delta: pd.DateOffset = None, + print_freq_period_info: bool = False, + ): + # Columns typing + for _, role_dict in columns_params.items(): + column_name = role_dict["columns"][0] + column_type = role_dict["type"] + if column_type == "continious": + data[column_name] = data[column_name].astype("float") + elif column_type == "datetime": + data[column_name] = pd.to_datetime(data[column_name]) + + self.data = data + self.columns_params = columns_params + self.delta = delta + + self._check_single_column() + + self.id_column = columns_params["id"]["columns"][0] + self.target_column = columns_params["target"]["columns"][0] + self.date_column = columns_params["date"]["columns"][0] + + self.data = data.sort_values([self.id_column, self.date_column]) + + if print_freq_period_info: + self._print_freq_period_info() + + def _crop_segment( + self, + segment: np.ndarray, + test_last: bool, + horizon: int, + history: int, + ) -> np.ndarray: + """Crop a segment of data based on the history and horizon. + + Args: + segment: the input segment of data to crop. + test_last: if True, return the last history elements of the + segment. Otherwise, return the elements between history + and history + horizon. + + Returns: + the cropped segment of data. + + """ + if test_last: + return segment[-history:] + + return segment[-history - horizon : -horizon] + + @staticmethod + def _pad_segment( + segment: np.ndarray, + horizon: int, + time_delta: Union[pd.Timedelta, pd.DateOffset], + date_col_id: int, + id_col_id: Sequence[int], + ) -> np.ndarray: + """Pad a segment of data with new rows based on the horizon. + + Args: + segment: the input segment of data to pad. + horizon: the number of new rows to add to the segment. + time_delta: the time_delta to use for the new date values. + date_col_id: the index of the date column in the segment. + id_col_id: the index of the id columns in the segment. + + Returns: + padded segment of data. + + """ + result = np.full((horizon, segment.shape[1]), np.nan, dtype=object) + + last_date = segment[-1, date_col_id] + new_dates = pd.date_range(last_date + time_delta, periods=horizon, freq=time_delta) + result[:, date_col_id] = new_dates + + if isinstance(id_col_id, np.ndarray): + for i in range(len(id_col_id)): + result[:, id_col_id[i]] = np.repeat(segment[0, id_col_id[i]], horizon) + else: + result[:, id_col_id] = np.repeat(segment[0, id_col_id], horizon) + + return result + + def make_padded_test( + self, + horizon: int, + history: int, + test_last: bool = True, + test_all: bool = False, + step: Optional[int] = None, + id_column_name: Optional[Union[str, Sequence[str]]] = None, + ): + """Generate a test dataframe with new rows with NaN targets. + + Args: + horizon: number of points to prediction + (number of new rows to add to each segment). + history: number of previous for feature generating. + test_last: if True, return generated test data built by + the last point. + test_all: if True, return generated test data for all + points (like rolling forecast). + step: in how many points to take the next observation while + making samples' matrix. + Needs for test_all=True. + id_column_name: name of the column(s) by which the data is + split (in some cases it is different from the originalß + id column(s)). + + Notes: + 1. The new rows are filled with NaN target values, + generated datetimes values (based on the time_delta) + and the same id values as the last row of the segment. + + Returns: + the padded test dataset. + + """ + if test_all: + current_test_ids = slicer.create_idx_data( + self.data, + horizon, + history, + step, + date_column=self.date_column, + ) + extended_data = slicer.get_slice(self.data, (current_test_ids, None)) + extended_data = pd.DataFrame( + extended_data.reshape(-1, extended_data.shape[-1]), + columns=self.data.columns, + ) + extended_data_nrows = extended_data.shape[0] + + extended_data["segment_col"] = np.repeat( + np.arange(extended_data_nrows // history), history + ) + id_column_name = ["segment_col", self.id_column] + else: + extended_data = self.data + + columns = self.data.columns + date_col_id = slicer.get_cols_idx(extended_data, self.date_column) + if id_column_name is None: + id_column_name = self.id_column + id_col_id = slicer.get_cols_idx(extended_data, id_column_name) + + # Find indices for segments + ids, time_delta = slicer.ids_from_date( + extended_data, self.date_column, delta=self.delta, return_delta=True + ) + + if test_all: + ids = list(np.unique(extended_data.segment_col, return_index=True)[1])[1:] + + data = extended_data.to_numpy() + + segments = np.split(data, ids) + segments = [ + self._crop_segment(segment, test_last, horizon, history) for segment in segments + ] + + # Find padded parts for each segment + padded_segments_results = [ + self._pad_segment(segment, horizon, time_delta, date_col_id, id_col_id) + for segment in segments + ] + + # Concatenate together + result = np.vstack(np.concatenate((segments, padded_segments_results), axis=1)) + if test_all: + result = pd.DataFrame(result, columns=list(columns) + ["segment_col"]) + else: + result = pd.DataFrame(result, columns=columns) + result[self.date_column] = pd.to_datetime(result[self.date_column]) + other = [col for col in columns if col not in [self.id_column, self.date_column]] + result[other] = result[other].astype("float") + + return result diff --git a/tsururu/dataset/pipeline.py b/tsururu/dataset/pipeline.py index 30296f1..f7b8069 100644 --- a/tsururu/dataset/pipeline.py +++ b/tsururu/dataset/pipeline.py @@ -1,533 +1,537 @@ -"""Module for Pipeline class, which is a wrapper for the transformers.""" - -from itertools import product -from typing import Tuple - -import numpy as np -import pandas as pd - -from ..transformers import ( - SequentialTransformer, - TargetGenerator, - Transformer, - TransformersFactory, - UnionTransformer, -) -from .dataset import TSDataset -from .slice import IndexSlicer - -transormers_factory = TransformersFactory() -index_slicer = IndexSlicer() - - -class Pipeline: - """Class for creating and applying a pipeline of transformers. - - Args: - transformers: an outer transformer to be applied. - multivariate: whether to apply the pipeline to get - multivariate data. - - """ - - def __init__(self, transformers: Transformer, multivariate: bool = False): - self.transformers = transformers - self.multivariate = multivariate - - self.is_fitted = False - self.strategy_name = None - self.output_features = None - self.y_original_shape = None - - @classmethod - def from_dict(cls, columns_params: dict, multivariate: bool) -> "Pipeline": - """Create a pipeline from a dict of column parameters. - - Args: - columns_params: a dictionary containing the parameters - for each column. - multivariate: whether the pipeline is multivariate. - - Returns: - the created pipeline. - - """ - # Resulting pipeline is a Union transformer with Sequential transformers - result_union_transformers_list = [] - - # For each column create a list of transformers for resulting Sequential transformer - for role, columns_params in columns_params.items(): - current_sequential_transformers_list = [] - - transformers_dict = columns_params["features"] - for transformer_name, transformer_params in transformers_dict.items(): - if transformer_name == "LagTransformer" and role == "target": - features_transformer = transormers_factory.create_transformer( - transformer_name, transformer_params - ) - target_transformer = TargetGenerator() - transformer = UnionTransformer( - transformers_list=[features_transformer, target_transformer] - ) - else: - transformer = transormers_factory.create_transformer( - transformer_name, transformer_params - ) - current_sequential_transformers_list.append(transformer) - - result_union_transformers_list.append( - SequentialTransformer( - transformers_list=current_sequential_transformers_list, - input_features=columns_params["columns"], - ) - ) - - union = UnionTransformer(transformers_list=result_union_transformers_list) - - return cls(union, multivariate) - - @classmethod - def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> "Pipeline": - """Create a pipeline semi-automatically from a dict of columns roles - and a dict of small description of pipeline. - - Args: - roles: columns' roles and types. - pipeline_params: a dictionary containing the parameters - for pipeline. - multivariate: whether the pipeline is multivariate. - - Returns: - the created pipeline. - - """ - # Resulting pipeline is a Union transformer with Sequential transformers - result_union_transformers_list = [] - - # For each column create a list of transformers for resulting Sequential transformer - for role, columns_params in roles.items(): - # Some checks for params' correctness - if pipeline_params["target_normalizer"] in ["standard_scaler", "none"]: - assert ( - pipeline_params["normalizer_regime"] == "none" - ), "normalizer_regime MUST BE `none` for this normalizer" - else: - assert ( - pipeline_params["normalizer_regime"] != "none" - ), "normalizer_regime MUST BE NOT `none` for this normalizer" - assert pipeline_params["normalizer_regime"] in [ - "none", - "delta", - "ratio", - ], "there is no such normalizer_regime!" - - current_sequential_transformers_list = [] - if role == "target": - target_lag = transormers_factory.create_transformer( - "LagTransformer", {"lags": pipeline_params["target_lags"]} - ) - target_generator = TargetGenerator() - target_union = UnionTransformer(transformers_list=[target_lag, target_generator]) - - if pipeline_params["normalizer_transform_regime"] == "features": - transform_features = True - transform_target = False - elif pipeline_params["normalizer_transform_regime"] == "target": - transform_features = False - transform_target = True - elif pipeline_params["normalizer_transform_regime"] == "features_target": - transform_features = True - transform_target = True - else: - assert ValueError("there is no such normalizer_transform_regime!") - - if pipeline_params["target_normalizer"] == "standard_scaler": - target_normalizer = transormers_factory.create_transformer( - "StandardScalerTransformer", - { - "transform_features": transform_features, - "transform_target": transform_target, - }, - ) - current_sequential_transformers_list.append(target_normalizer) - current_sequential_transformers_list.append(target_union) - - elif pipeline_params["target_normalizer"] == "difference_normalizer": - target_normalizer = transormers_factory.create_transformer( - "DifferenceNormalizer", - { - "transform_features": transform_features, - "transform_target": transform_target, - "regime": pipeline_params["normalizer_regime"], - }, - ) - current_sequential_transformers_list.append(target_normalizer) - current_sequential_transformers_list.append(target_union) - - elif pipeline_params["target_normalizer"] == "last_known_normalizer": - target_normalizer = transormers_factory.create_transformer( - "LastKnownNormalizer", - { - "transform_features": transform_features, - "transform_target": transform_target, - "regime": pipeline_params["normalizer_regime"], - }, - ) - current_sequential_transformers_list.append(target_union) - current_sequential_transformers_list.append(target_normalizer) - - else: - assert ( - pipeline_params["target_normalizer"] == "none" - ), "there is no such target_normalizer!" - - elif role == "date": - date_season = transormers_factory.create_transformer( - "DateSeasonsGenerator", - { - "seasonalities": ["y", "m", "d", "doy", "wd"], - "from_target_date": True, - }, - ) - date_lag = transormers_factory.create_transformer( - "LagTransformer", {"lags": pipeline_params["date_lags"]} - ) - current_sequential_transformers_list.append(date_season) - current_sequential_transformers_list.append(date_lag) - - elif role == "id": - id_lag = transormers_factory.create_transformer("LagTransformer", {"lags": 1}) - current_sequential_transformers_list.append(id_lag) - - else: - exog_lag = transormers_factory.create_transformer( - "LagTransformer", {pipeline_params["exog_lags"]} - ) - current_sequential_transformers_list.append(id_lag) - - result_union_transformers_list.append( - SequentialTransformer( - transformers_list=current_sequential_transformers_list, - input_features=columns_params["columns"], - ) - ) - - union = UnionTransformer(transformers_list=result_union_transformers_list) - - return cls(union, multivariate) - - def create_data_dict_for_pipeline( - self, dataset: TSDataset, features_idx: np.ndarray, target_idx: np.ndarray - ) -> dict: - """Create a data dictionary for the pipeline. - - Args: - dataset: the input time series dataset. - features_idx: the indices of the features in the dataset. - target_idx: the indices of the target in the dataset. - - Returns: - the created data dictionary. - - """ - data = {} - data["raw_ts_X"] = dataset.data.copy() - data["raw_ts_y"] = dataset.data.copy() - data["X"] = np.array([]) - data["y"] = np.array([]) - data["id_column_name"] = dataset.id_column - data["date_column_name"] = dataset.date_column - data["target_column_name"] = dataset.target_column - data["idx_X"] = features_idx - data["idx_y"] = target_idx - - return data - - def fit_transform(self, data: dict, strategy_name: str) -> dict: - """Fit the transformers to the data and transform - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - strategy_name: the name of the strategy to use. - - Returns: - the transformed data dictionary. - - """ - self.strategy_name = strategy_name - - data = self.transformers.fit_transform(data) - self.is_fitted = True - - return data - - def transform(self, data: dict) -> dict: - """Transforms the input data using the transformers. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - the transformed data dictionary. - - """ - data = self.transformers.transform(data) - - return data - - def from_mimo_to_flatwidemimo(self, data: dict) -> dict: - """ - Converts the input data from MIMO to FlatWideMIMO format. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - the dictionary containing the converted data and the - column names of the converted data. - - """ - X = pd.DataFrame(data["X"], columns=self.transformers.output_features) - - date_features_mask = X.columns.str.contains(data["date_column_name"]) - id_features_mask = X.columns.str.contains(data["id_column_name"]) - - horizon = data["idx_y"].shape[1] - fh_array = np.arange(1, horizon + 1) - - direct_lag_index_dict = {} - - if sum(id_features_mask) > 0: - id_count = len(X.loc[:, id_features_mask].value_counts()) - else: - id_count = 1 - direct_lag_index_dict["ID"] = np.repeat( - np.arange(id_count), - repeats=(len(X) / id_count * len(fh_array)), - ) - direct_lag_index_dict["FH"] = np.tile(fh_array, len(X.index)) - direct_lag_index_df = pd.DataFrame(direct_lag_index_dict) - - # get date features for each horizon (unfolding MIMO lags over time) - new_date_features = np.empty( - (X.shape[0] * horizon, X.loc[:, date_features_mask].shape[1] // horizon) - ) - try: - for i in range(horizon): - new_date_features[i::horizon, :] = X.loc[:, date_features_mask].values[ - :, i::horizon - ] - except ValueError: - raise ValueError( - "Something is wrong while making FlatWideMIMO strategy's X. Check that you use number of lags equal to horizon for datetime features!" - ) - - # get unique date feature names without lag suffix - date_feature_names = ( - X.columns[date_features_mask].str.replace("__lag_\d+$", "", regex=True).unique() - ) - - features_df = pd.DataFrame( - np.repeat(X.loc[:, ~id_features_mask & ~date_features_mask].values, horizon, axis=0), - columns=X.loc[:, ~id_features_mask & ~date_features_mask].columns, - ) - - X = pd.concat( - [ - direct_lag_index_df, - pd.DataFrame(new_date_features, columns=date_feature_names), - features_df, - ], - axis=1, - ) - - data["X"] = X.values - - data["y"] = data["y"].reshape(-1, 1) - - return data, X.columns - - def _make_multivariate_X_y(self, data: dict) -> dict: - """Converts the input data dictionary into a multivariate - X and y arrays. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - the updated data dictionary and the columns of the X. - - """ - X = pd.DataFrame(data["X"], columns=self.output_features) - - date_features_colname = X.columns[X.columns.str.contains(data["date_column_name"])].values - id_features_colname = X.columns[X.columns.str.contains(data["id_column_name"])].values - - if id_features_colname.size == 0: - # add temporary column with id for make multivariate merging - id_idx = index_slicer.get_cols_idx(data["raw_ts_X"], data["id_column_name"]) - X["temp_ID"] = index_slicer.get_slice(data["raw_ts_X"], (data["idx_X"][:, 0], id_idx)) - id_features_colname = np.array(["temp_ID"]) - - other_features_colname = np.setdiff1d( - X.columns.values, - np.hstack((id_features_colname, date_features_colname)), - ) - - date_features_idx = index_slicer.get_cols_idx(X, date_features_colname) - other_features_idx = index_slicer.get_cols_idx(X, other_features_colname) - - segments_ids = np.append(np.unique(X[id_features_colname], return_index=1)[1], len(X)) - segments_ids_array = np.array( - [ - np.arange(segments_ids[segment_id - 1], segments_ids[segment_id]) - for segment_id in range(1, len(segments_ids)) - ] - ).T - - date_features_array = index_slicer.get_slice( - X, (segments_ids_array[:, 0], date_features_idx) - ).reshape(len(segments_ids_array), len(date_features_colname)) - other_features_array = index_slicer.get_slice( - X, (segments_ids_array, other_features_idx) - ).reshape( - len(segments_ids_array), - len(other_features_colname) * (len(segments_ids) - 1), - ) - - final_other_features_colname = [ - f"{feat}__{i}" - for i, feat in product(range(len(segments_ids) - 1), other_features_colname) - ] - - data["X"] = np.hstack((date_features_array, other_features_array)) - new_columns = np.hstack((date_features_colname, final_other_features_colname)) - - if data["y"] is not None: - data["y"] = index_slicer.get_slice(data["y"], (segments_ids_array, None)).reshape( - len(segments_ids_array), -1 - ) - - return data, new_columns - - def _make_multivariate_X_y_flatwidemimo(self, data: dict) -> dict: - """Converts the input data dictionary into a multivariate - X and y arrays for FlatWideMIMO strategy. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - the updated data dictionary and the columns of the X. - - """ - X = pd.DataFrame(data["X"], columns=self.output_features) - - id_feature_colname = np.array(["ID"]) - fh_feature_colname = np.array(["FH"]) - date_features_colname = X.columns[X.columns.str.contains(data["date_column_name"])].values - other_features_colname = np.setdiff1d( - X.columns.values, - np.hstack([id_feature_colname, date_features_colname, fh_feature_colname]), - ) - - date_features_idx = index_slicer.get_cols_idx(X, date_features_colname) - other_features_idx = index_slicer.get_cols_idx(X, other_features_colname) - fh_feature_idx = index_slicer.get_cols_idx(X, fh_feature_colname) - - segments_ids = np.append(np.unique(X[id_feature_colname], return_index=1)[1], len(X)) - segments_ids_array = np.array( - [ - np.arange(segments_ids[segment_id - 1], segments_ids[segment_id]) - for segment_id in range(1, len(segments_ids)) - ] - ).T - - date_features_array = index_slicer.get_slice( - X, (segments_ids_array[:, 0], date_features_idx) - ).reshape(len(segments_ids_array), len(date_features_colname)) - fh_feature_array = index_slicer.get_slice( - X, (segments_ids_array[:, 0], fh_feature_idx) - ).reshape(len(segments_ids_array), -1) - other_features_array = index_slicer.get_slice( - X, (segments_ids_array, other_features_idx) - ).reshape( - len(segments_ids_array), - len(other_features_colname) * (len(segments_ids) - 1), - ) - - final_other_features_colname = [ - f"{feat}__{i}" - for i, feat in product(range(len(segments_ids) - 1), other_features_colname) - ] - - data["X"] = np.hstack((fh_feature_array, date_features_array, other_features_array)) - new_columns = np.hstack( - ( - fh_feature_colname, - date_features_colname, - final_other_features_colname, - ) - ) - - if data["y"] is not None: - data["y"] = index_slicer.get_slice(data["y"], (segments_ids_array, None)).reshape( - len(segments_ids_array), -1 - ) - - return data, new_columns - - def generate(self, data: dict) -> Tuple[np.ndarray]: - """Generate the X and y arrays based on the provided data. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - tuple containing the generated dataset's input features (X) - and targets (y). - """ - data = self.transformers.generate(data) - self.y_original_shape = data["y"].shape - - if self.strategy_name == "FlatWideMIMOStrategy": - data, new_output_features = self.from_mimo_to_flatwidemimo(data) - self.output_features = new_output_features - else: - self.output_features = self.transformers.output_features - - if self.multivariate: - if self.strategy_name == "FlatWideMIMOStrategy": - data, new_output_features = self._make_multivariate_X_y_flatwidemimo(data) - else: - data, new_output_features = self._make_multivariate_X_y(data) - self.output_features = new_output_features - - return data["X"], data["y"] - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Applies the inverse transformation to the target variable. - - Args: - y: the target variable to be transformed. - - Returns: - the inverse transformed target variable. - - """ - y = y.reshape(self.y_original_shape) - y = self.transformers.inverse_transform_y(y) - - return y.reshape(-1) +"""Module for Pipeline class, which is a wrapper for the transformers.""" + +from itertools import product +from typing import Tuple + +import numpy as np +import pandas as pd + +from ..transformers import ( + SequentialTransformer, + TargetGenerator, + Transformer, + TransformersFactory, + UnionTransformer, +) +from .dataset import TSDataset +from .slice import IndexSlicer + +transormers_factory = TransformersFactory() +index_slicer = IndexSlicer() + + +class Pipeline: + """Class for creating and applying a pipeline of transformers. + + Args: + transformers: an outer transformer to be applied. + multivariate: whether to apply the pipeline to get + multivariate data. + + """ + + def __init__(self, transformers: Transformer, multivariate: bool = False): + self.transformers = transformers + self.multivariate = multivariate + + self.is_fitted = False + self.strategy_name = None + self.output_features = None + self.y_original_shape = None + + @classmethod + def from_dict(cls, columns_params: dict, multivariate: bool) -> "Pipeline": + """Create a pipeline from a dict of column parameters. + + Args: + columns_params: a dictionary containing the parameters + for each column. + multivariate: whether the pipeline is multivariate. + + Returns: + the created pipeline. + + """ + # Resulting pipeline is a Union transformer with Sequential transformers + result_union_transformers_list = [] + + # For each column create a list of transformers for resulting Sequential transformer + for role, columns_params in columns_params.items(): + current_sequential_transformers_list = [] + + transformers_dict = columns_params["features"] + for transformer_name, transformer_params in transformers_dict.items(): + if transformer_name == "LagTransformer" and role == "target": + features_transformer = transormers_factory.create_transformer( + transformer_name, transformer_params + ) + target_transformer = TargetGenerator() + transformer = UnionTransformer( + transformers_list=[features_transformer, target_transformer] + ) + else: + transformer = transormers_factory.create_transformer( + transformer_name, transformer_params + ) + current_sequential_transformers_list.append(transformer) + + result_union_transformers_list.append( + SequentialTransformer( + transformers_list=current_sequential_transformers_list, + input_features=columns_params["columns"], + ) + ) + + union = UnionTransformer(transformers_list=result_union_transformers_list) + + return cls(union, multivariate) + + @classmethod + def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> "Pipeline": + """Create a pipeline semi-automatically from a dict of columns roles + and a dict of small description of pipeline. + + Args: + roles: columns' roles and types. + pipeline_params: a dictionary containing the parameters + for pipeline. + multivariate: whether the pipeline is multivariate. + + Returns: + the created pipeline. + + """ + # Resulting pipeline is a Union transformer with Sequential transformers + result_union_transformers_list = [] + + # For each column create a list of transformers for resulting Sequential transformer + for role, columns_params in roles.items(): + # Some checks for params' correctness + if pipeline_params["target_normalizer"] in ["standard_scaler", "none"]: + assert ( + pipeline_params["normalizer_regime"] == "none" + ), "normalizer_regime MUST BE `none` for this normalizer" + else: + assert ( + pipeline_params["normalizer_regime"] != "none" + ), "normalizer_regime MUST BE NOT `none` for this normalizer" + assert pipeline_params["normalizer_regime"] in [ + "none", + "delta", + "ratio", + ], "there is no such normalizer_regime!" + + current_sequential_transformers_list = [] + if role == "target": + target_lag = transormers_factory.create_transformer( + "LagTransformer", {"lags": pipeline_params["target_lags"]} + ) + target_generator = TargetGenerator() + target_union = UnionTransformer(transformers_list=[target_lag, target_generator]) + + if pipeline_params["normalizer_transform_regime"] == "features": + transform_features = True + transform_target = False + elif pipeline_params["normalizer_transform_regime"] == "target": + transform_features = False + transform_target = True + elif pipeline_params["normalizer_transform_regime"] == "features_target": + transform_features = True + transform_target = True + else: + assert ValueError("there is no such normalizer_transform_regime!") + + if pipeline_params["target_normalizer"] == "standard_scaler": + target_normalizer = transormers_factory.create_transformer( + "StandardScalerTransformer", + { + "transform_features": transform_features, + "transform_target": transform_target, + }, + ) + current_sequential_transformers_list.append(target_normalizer) + current_sequential_transformers_list.append(target_union) + + elif pipeline_params["target_normalizer"] == "difference_normalizer": + target_normalizer = transormers_factory.create_transformer( + "DifferenceNormalizer", + { + "transform_features": transform_features, + "transform_target": transform_target, + "regime": pipeline_params["normalizer_regime"], + }, + ) + current_sequential_transformers_list.append(target_normalizer) + current_sequential_transformers_list.append(target_union) + + elif pipeline_params["target_normalizer"] == "last_known_normalizer": + target_normalizer = transormers_factory.create_transformer( + "LastKnownNormalizer", + { + "transform_features": transform_features, + "transform_target": transform_target, + "regime": pipeline_params["normalizer_regime"], + }, + ) + current_sequential_transformers_list.append(target_union) + current_sequential_transformers_list.append(target_normalizer) + + else: + assert ( + pipeline_params["target_normalizer"] == "none" + ), "there is no such target_normalizer!" + + elif role == "date": + date_season = transormers_factory.create_transformer( + "DateSeasonsGenerator", + { + "seasonalities": ["y", "m", "d", "doy", "wd"], + "from_target_date": True, + }, + ) + date_lag = transormers_factory.create_transformer( + "LagTransformer", {"lags": pipeline_params["date_lags"]} + ) + current_sequential_transformers_list.append(date_season) + current_sequential_transformers_list.append(date_lag) + + elif role == "id": + id_lag = transormers_factory.create_transformer("LagTransformer", {"lags": 1}) + current_sequential_transformers_list.append(id_lag) + + else: + exog_lag = transormers_factory.create_transformer( + "LagTransformer", {pipeline_params["exog_lags"]} + ) + current_sequential_transformers_list.append(exog_lag) + + result_union_transformers_list.append( + SequentialTransformer( + transformers_list=current_sequential_transformers_list, + input_features=columns_params["columns"], + ) + ) + + union = UnionTransformer(transformers_list=result_union_transformers_list) + + return cls(union, multivariate) + + def create_data_dict_for_pipeline( + self, dataset: TSDataset, features_idx: np.ndarray, target_idx: np.ndarray + ) -> dict: + """Create a data dictionary for the pipeline. + + Args: + dataset: the input time series dataset. + features_idx: the indices of the features in the dataset. + target_idx: the indices of the target in the dataset. + + Returns: + the created data dictionary. + + """ + data = {} + data["raw_ts_X"] = dataset.data.copy() + data["raw_ts_y"] = dataset.data.copy() + data["X"] = np.array([]) + data["y"] = np.array([]) + data["id_column_name"] = dataset.id_column + data["date_column_name"] = dataset.date_column + data["target_column_name"] = dataset.target_column + data["idx_X"] = features_idx + data["idx_y"] = target_idx + + return data + + def fit_transform(self, data: dict, strategy_name: str) -> dict: + """Fit the transformers to the data and transform + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + strategy_name: the name of the strategy to use. + + Returns: + the transformed data dictionary. + + """ + self.strategy_name = strategy_name + + data = self.transformers.fit_transform(data) + self.is_fitted = True + + return data + + def transform(self, data: dict) -> dict: + """Transforms the input data using the transformers. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + the transformed data dictionary. + + """ + data = self.transformers.transform(data) + + return data + + def from_mimo_to_flatwidemimo(self, data: dict) -> dict: + """ + Converts the input data from MIMO to FlatWideMIMO format. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + the dictionary containing the converted data and the + column names of the converted data. + + """ + X = pd.DataFrame(data["X"], columns=self.transformers.output_features) + + date_features_mask = X.columns.str.contains(data["date_column_name"]) + id_features_mask = X.columns.str.contains(data["id_column_name"]) + + horizon = data["idx_y"].shape[-1] + fh_array = np.arange(1, horizon + 1) + + direct_lag_index_dict = {} + + # TODO: Can we use only else? + if sum(id_features_mask) > 0: + id_count = len(X.loc[:, id_features_mask].value_counts()) + else: + id_count = len( + data["raw_ts_X"].iloc[data["idx_X"][:, 0]][data["id_column_name"]].value_counts() + ) + direct_lag_index_dict["ID"] = np.repeat( + np.arange(id_count), + repeats=(len(X) / id_count * len(fh_array)), + ) + direct_lag_index_dict["FH"] = np.tile(fh_array, len(X.index)) + direct_lag_index_df = pd.DataFrame(direct_lag_index_dict) + + # get date features for each horizon (unfolding MIMO lags over time) + new_date_features = np.empty( + (X.shape[0] * horizon, X.loc[:, date_features_mask].shape[1] // horizon) + ) + try: + for i in range(horizon): + new_date_features[i::horizon, :] = X.loc[:, date_features_mask].values[ + :, i::horizon + ] + except ValueError: + raise ValueError( + "Something is wrong while making FlatWideMIMO strategy's X. Check that you use number of lags equal to horizon for datetime features!" + ) + + # get unique date feature names without lag suffix + date_feature_names = ( + X.columns[date_features_mask].str.replace("__lag_\d+$", "", regex=True).unique() + ) + + features_df = pd.DataFrame( + np.repeat(X.loc[:, ~id_features_mask & ~date_features_mask].values, horizon, axis=0), + columns=X.loc[:, ~id_features_mask & ~date_features_mask].columns, + ) + + X = pd.concat( + [ + direct_lag_index_df, + pd.DataFrame(new_date_features, columns=date_feature_names), + features_df, + ], + axis=1, + ) + + data["X"] = X.values + + data["y"] = data["y"].reshape(-1, 1) + + return data, X.columns + + def _make_multivariate_X_y(self, data: dict) -> dict: + """Converts the input data dictionary into a multivariate + X and y arrays. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + the updated data dictionary and the columns of the X. + + """ + X = pd.DataFrame(data["X"], columns=self.output_features) + + date_features_colname = X.columns[X.columns.str.contains(data["date_column_name"])].values + id_features_colname = X.columns[X.columns.str.contains(data["id_column_name"])].values + + if id_features_colname.size == 0: + # add temporary column with id for make multivariate merging + id_idx = index_slicer.get_cols_idx(data["raw_ts_X"], data["id_column_name"]) + X["temp_ID"] = index_slicer.get_slice(data["raw_ts_X"], (data["idx_X"][:, 0], id_idx)) + id_features_colname = np.array(["temp_ID"]) + + other_features_colname = X.columns.difference( + np.hstack((id_features_colname, date_features_colname)), sort=False + ) + + date_features_idx = index_slicer.get_cols_idx(X, date_features_colname) + other_features_idx = index_slicer.get_cols_idx(X, other_features_colname) + + segments_ids = np.append(np.unique(X[id_features_colname], return_index=1)[1], len(X)) + segments_ids_array = np.array( + [ + np.arange(segments_ids[segment_id - 1], segments_ids[segment_id]) + for segment_id in range(1, len(segments_ids)) + ] + ).T + + date_features_array = index_slicer.get_slice( + X, (segments_ids_array[:, 0], date_features_idx) + ).reshape(len(segments_ids_array), len(date_features_colname)) + other_features_array = index_slicer.get_slice( + X, (segments_ids_array, other_features_idx) + ).reshape( + len(segments_ids_array), + len(other_features_colname) * (len(segments_ids) - 1), + ) + + final_other_features_colname = [ + f"{feat}__{i}" + for i, feat in product(range(len(segments_ids) - 1), other_features_colname) + ] + + data["X"] = np.hstack((date_features_array, other_features_array)) + new_columns = np.hstack((date_features_colname, final_other_features_colname)) + + if data["y"] is not None: + data["y"] = index_slicer.get_slice(data["y"], (segments_ids_array, None)).reshape( + len(segments_ids_array), -1 + ) + + return data, new_columns + + def _make_multivariate_X_y_flatwidemimo(self, data: dict) -> dict: + """Converts the input data dictionary into a multivariate + X and y arrays for FlatWideMIMO strategy. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + the updated data dictionary and the columns of the X. + + """ + X = pd.DataFrame(data["X"], columns=self.output_features) + + id_feature_colname = np.array(["ID"]) + fh_feature_colname = np.array(["FH"]) + date_features_colname = X.columns[X.columns.str.contains(data["date_column_name"])].values + other_features_colname = [ + col + for col in X.columns.values + if col + not in np.hstack([id_feature_colname, date_features_colname, fh_feature_colname]) + ] + + date_features_idx = index_slicer.get_cols_idx(X, date_features_colname) + other_features_idx = index_slicer.get_cols_idx(X, other_features_colname) + fh_feature_idx = index_slicer.get_cols_idx(X, fh_feature_colname) + + segments_ids = np.append(np.unique(X[id_feature_colname], return_index=1)[1], len(X)) + segments_ids_array = np.array( + [ + np.arange(segments_ids[segment_id - 1], segments_ids[segment_id]) + for segment_id in range(1, len(segments_ids)) + ] + ).T + + date_features_array = index_slicer.get_slice( + X, (segments_ids_array[:, 0], date_features_idx) + ).reshape(len(segments_ids_array), len(date_features_colname)) + fh_feature_array = index_slicer.get_slice( + X, (segments_ids_array[:, 0], fh_feature_idx) + ).reshape(len(segments_ids_array), -1) + other_features_array = index_slicer.get_slice( + X, (segments_ids_array, other_features_idx) + ).reshape( + len(segments_ids_array), + len(other_features_colname) * (len(segments_ids) - 1), + ) + + final_other_features_colname = [ + f"{feat}__{i}" + for i, feat in product(range(len(segments_ids) - 1), other_features_colname) + ] + + data["X"] = np.hstack((fh_feature_array, date_features_array, other_features_array)) + new_columns = np.hstack( + ( + fh_feature_colname, + date_features_colname, + final_other_features_colname, + ) + ) + + if data["y"] is not None: + data["y"] = index_slicer.get_slice(data["y"], (segments_ids_array, None)).reshape( + len(segments_ids_array), -1 + ) + + return data, new_columns + + def generate(self, data: dict) -> Tuple[np.ndarray]: + """Generate the X and y arrays based on the provided data. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + tuple containing the generated dataset's input features (X) + and targets (y). + """ + data = self.transformers.generate(data) + self.y_original_shape = data["y"].shape + + if self.strategy_name == "FlatWideMIMOStrategy": + data, new_output_features = self.from_mimo_to_flatwidemimo(data) + self.output_features = new_output_features + else: + self.output_features = self.transformers.output_features + + if self.multivariate: + if self.strategy_name == "FlatWideMIMOStrategy": + data, new_output_features = self._make_multivariate_X_y_flatwidemimo(data) + else: + data, new_output_features = self._make_multivariate_X_y(data) + self.output_features = new_output_features + + return data["X"], data["y"] + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Applies the inverse transformation to the target variable. + + Args: + y: the target variable to be transformed. + + Returns: + the inverse transformed target variable. + + """ + y = y.reshape(self.y_original_shape) + y = self.transformers.inverse_transform_y(y) + + return y.reshape(-1) diff --git a/tsururu/dataset/slice.py b/tsururu/dataset/slice.py index 32a58df..0f63a1d 100644 --- a/tsururu/dataset/slice.py +++ b/tsururu/dataset/slice.py @@ -1,464 +1,464 @@ -"""Module for creating indexes and manipulating data.""" - -import re -from typing import List, Optional, Sequence, Tuple, Union - -import numpy as np -import pandas as pd -from pandas.api.types import is_datetime64_any_dtype as is_datetime -from pandas.tseries.offsets import MonthEnd -from scipy import stats as st - - -class IndexSlicer: - """Combines ways to create indexes and manipulate data.""" - - @staticmethod - def _timedelta_above_daily_freq( - d_multiplier: int, - check_end_regex: str, - d_from_series: int, - freq_name: str, - inferred_freq: str, - ) -> Tuple[Union[pd.DateOffset, MonthEnd], str]: - """Calculate the timedelta based on the given parameters for - the frequencies above daily. - - Args: - d_multiplier: the multiplier for the number of months. - check_end_regex: the regular expression to check if the - inferred frequency is an end frequency. - d_from_series: the number of periods from the series. - freq_name: the name of the frequency. - inferred_freq: the inferred frequency. - - Returns: - the calculated timedelta and the information about the - frequency and period. - - """ - if inferred_freq and re.match(check_end_regex, inferred_freq): - delta = MonthEnd(d_multiplier * d_from_series) - freq_period_info = f"freq: {freq_name}End; period: {d_from_series}" - else: - delta = pd.DateOffset(months=d_multiplier * d_from_series) - freq_period_info = f"freq: {freq_name}; period: {d_from_series}" - - return delta, freq_period_info - - def timedelta( - self, - x: np.ndarray, - delta: Optional[pd.DateOffset] = None, - return_freq_period_info: bool = False, - ) -> Union[Tuple[np.ndarray, pd.DateOffset], Tuple[np.ndarray, pd.DateOffset, str]]: - """Returns the difference between neighboring observations in - the array in terms of delta and the delta itself. - - Args: - x: array with datetime points. - delta: custom offset if needed. - return_freq_period_info: either to return information about - inferred frequency and period. - - Returns: - difference between neighboring observations and the delta - itself; if return_freq_period_info is True, return - information about inferred frequency and period. - - Raises: - AssertionError: if the frequency and period are failed - to be defined. - - Notes: - 1. It is used to correctly generate indexes for - observations without mixing observations - with different IDs. - - """ - - if not is_datetime(x): - x = pd.to_datetime(x) - - if delta is None: - inferred_freq = pd.infer_freq(x[-3:]) # Need at least 3 dates to infer frequency - delta = x.diff().iloc[-1] - - # N Years - if delta > pd.Timedelta(days=360) and (delta.days % 365 == 0 or delta.days % 366 == 0): - delta, freq_period_info = self._timedelta_above_daily_freq( - d_multiplier=12, - check_end_regex=r"\b\d*A-|\b\d*YE-", - d_from_series=x.dt.year.diff().values[-1], - freq_name="Year", - inferred_freq=inferred_freq, - ) - - # N Quarters and Months - elif delta > pd.Timedelta(days=27): - if delta > pd.Timedelta(days=88): - check_end_regex = r"\b\d*Q-|\b\d*QE-" - else: - check_end_regex = r"\b\d*M\b|\b\d*ME\b" - delta, freq_period_info = self._timedelta_above_daily_freq( - d_multiplier=1, - check_end_regex=check_end_regex, - d_from_series=st.mode(x.dt.month.diff())[0], - freq_name="Month", - inferred_freq=inferred_freq, - ) - - # N Days - elif delta >= pd.Timedelta(days=1): - freq_period_info = f"freq: Day; period: {delta.days}" - - # N Hours; Min; Sec; etc - elif delta <= pd.Timedelta(days=1): - freq_period_info = f"freq: less then Day (Hour, Min, Sec, etc); period: {delta.total_seconds()} seconds" - else: - freq_period_info = f"Custom OffSet: {delta}" - - assert delta, "either or both frequency and period are failed to be defined." - - if return_freq_period_info: - return x.diff().fillna(delta).values, delta, freq_period_info - - return x.diff().fillna(delta).values, delta - - @staticmethod - def get_cols_idx( - data: pd.DataFrame, columns: Union[str, Sequence[str]] - ) -> Union[int, np.ndarray]: - """Get numeric index of columns by column names. - - Args: - data: source dataframe. - columns: sequence of columns or single column. - - Returns: - sequence of int indexes or single int. - - """ - if type(columns) is str: - idx = data.columns.get_loc(columns) - else: - idx = data.columns.get_indexer(columns) - - return idx - - @staticmethod - def get_slice(data: pd.DataFrame, k: Tuple[np.ndarray]) -> np.ndarray: - """Get 3d slice. - - Args: - data: source dataframe. - k: tuple of integer sequences. - - Returns: - slice. - - """ - rows, cols = k - if cols is None: - if isinstance(data, np.ndarray): - new_data = data[rows, :] - else: - # new_data = data.iloc[:, :].values[rows] - new_data = data.values[rows] - else: - if isinstance(data, np.ndarray): - new_data = data[rows, cols] - else: - new_data = data.iloc[:, cols].values[rows] - - if len(new_data.shape) == 2: - return np.expand_dims(new_data, axis=0) - return new_data - - def ids_from_date( - self, - data: pd.DataFrame, - date_column: str, - delta: Optional[bool] = None, - return_delta: bool = False, - ) -> List[int]: - """Find indexes by which the dataset can be divided into - segments that are "identical" in terms of time stamps, but - different in terms of some identifier. - - Args: - data: source dataframe. - date_column: date column name in source dataframe. - delta: custom offset if needed. - return_delta: whether to return value of delta. - - Returns: - indexes of the ends of segments; if return_delta is True, - return value of delta. - - """ - _, time_delta = self.timedelta(data[date_column], delta=delta) - ids = ( - np.argwhere( - data[date_column][1:].values != (data[date_column] + time_delta)[:-1].values - ) - + 1 - ) - if return_delta: - return list(ids.reshape(-1)), time_delta - - return list(ids.reshape(-1)) - - def _rolling_window( - self, - a: np.ndarray, - window: int, - step: int, - from_last: bool = True, - ) -> np.ndarray: - """Generate a rolling window view of a numpy array. - - Args: - a: the input array. - window: the size of the window. - step: the step size between windows. - from_last: whether to start the window from last element. - - Returns: - the rolling window view of the input array. - - """ - sliding_window = np.lib.stride_tricks.sliding_window_view(a, window) - - return sliding_window[(len(a) - window) % step if from_last else 0 :][::step] - - def _create_idx_data( - self, data: np.ndarray, horizon: int, history: int, step: int, *_ - ) -> np.ndarray: - """Create index data for train observations' windows. - - Args: - data: the input data array. - horizon: the number of steps to predict into the future. - history: the number of past steps to consider. - step: the step size between each window. - - Returns: - the index data array for train observations' windows. - - """ - return self._rolling_window(np.arange(len(data))[:-horizon], history, step) - - def _create_idx_target( - self, - data: np.ndarray, - horizon: int, - history: int, - step: int, - n_last_horizon: Optional[int], - ) -> np.ndarray: - """Create index data for targets. - - Args: - data: the input data array. - horizon: the number of steps to predict into the future. - history: the number of past steps to consider. - step: the step size between each window. - n_last_horizon: how many last points we wish to leave. - - Returns: - the index data array for targets. - - """ - return self._rolling_window(np.arange(len(data))[history:], horizon, step)[ - :, -n_last_horizon: - ] - - def _create_idx_test( - self, data: np.ndarray, horizon: int, history: int, step: int, *_ - ) -> np.ndarray: - """Create index data for test observations' windows. - - Args: - data: the input data array. - horizon: the number of steps to predict into the future. - history: the number of past steps to consider. - step: the step size between each window. - - Returns: - the index data array for test observations' windows. - - """ - return self._rolling_window(np.arange(len(data)), history, step)[-(horizon + 1) : -horizon] - - def _get_ids( - self, - func, - data: np.ndarray, - horizon: int, - history: int, - step: int, - ids: np.ndarray, - cond: int = 0, - n_last_horizon: Optional[int] = None, - ) -> np.ndarray: - """Get indices for creating windows of data. - - Args: - func: the function to create index data. - data: the input data array. - horizon: the number of steps to predict into the future. - history: the number of past steps to consider. - step: the step size between each window. - ids: indexes of the ends of segments. - cond: the condition for segment length. - n_last_horizon: how many last points to leave. - - Returns: - the index data array. - - """ - prev = 0 - inds = [] - for i, split in enumerate(ids + [len(data)]): - if isinstance(data, np.ndarray): - segment = data[prev:split] - else: - segment = data.iloc[prev:split] - if len(segment) >= cond: - ind = func(segment, horizon, history, step, n_last_horizon) + prev - inds.append(ind) - prev = split - inds = np.vstack(inds) - - return inds - - def create_idx_data( - self, - data: pd.DataFrame, - horizon: int, - history: int, - step: int, - ids: Optional[np.ndarray] = None, - date_column: Optional[str] = None, - delta: Optional[pd.DateOffset] = None, - ): - """Find indices that, when applied to the original dataset, - can be used to obtain windows for building - train observations' features. - - Args: - data: source dataframe. - horizon: the number of steps to predict into the future. - history: the number of past steps to consider. - step: number of points to take the next observation. - ids: indexes of the ends of segments. - date_column: date column name in source dataframe, - needs in the absence of ids. - - Returns: - indices of train observations' windows. - - """ - if ids is None: - ids = self.ids_from_date(data, date_column, delta=delta) - - seq_idx_data = self._get_ids( - self._create_idx_data, - data, - horizon, - history, - step, - ids, - history + horizon, - ) - - return seq_idx_data - - def create_idx_test( - self, - data: pd.DataFrame, - horizon: int, - history: int, - step: int, - ids: Optional[np.ndarray] = None, - date_column: Optional[str] = None, - delta: Optional[pd.DateOffset] = None, - ): - """Find indices that, when applied to the original dataset, - can be used to obtain windows for building - test observations' features. - - Arguments: - data: source dataframe. - horizon: the number of steps to predict into the future. - history: the number of past steps to consider. - step: number of points to take the next observation. - ids: indexes of the ends of segments. - date_column: date column name in source dataframe, - needs in the absence of ids. - - Returns: - indices of test observations' windows. - - """ - if ids is None: - ids = self.ids_from_date(data, date_column, delta=delta) - - seq_idx_test = self._get_ids( - self._create_idx_test, - data, - horizon, - history, - step, - ids, - history, - ) - - return seq_idx_test - - def create_idx_target( - self, - data: pd.DataFrame, - horizon: int, - history: int, - step: int, - ids: Optional[np.ndarray] = None, - date_column: Optional[str] = None, - delta: Optional[pd.DateOffset] = None, - n_last_horizon: Optional[int] = None, - ): - """Find indices that, when applied to the original dataset, - can be used to obtain targets. - - Arguments: - data: source dataframe. - horizon: number of points to prediction. - history: number of points to use for prediction. - step: number of points to take the next observation. - ids: indexes of the ends of segments. - date_column: date column name in source dataframe, - needs in the absence of ids. - n_last_horizon: how many last points we wish to leave. - - Returns: - indices of targets. - """ - if ids is None: - ids = self.ids_from_date(data, date_column, delta=delta) - - if n_last_horizon is None: - n_last_horizon = horizon - - seq_idx_target = self._get_ids( - self._create_idx_target, - data, - horizon, - history, - step, - ids, - history + horizon, - n_last_horizon, - ) - - return seq_idx_target +"""Module for creating indexes and manipulating data.""" + +import re +from typing import List, Optional, Sequence, Tuple, Union + +import numpy as np +import pandas as pd +from pandas.api.types import is_datetime64_any_dtype as is_datetime +from pandas.tseries.offsets import MonthEnd +from scipy import stats as st + + +class IndexSlicer: + """Combines ways to create indexes and manipulate data.""" + + @staticmethod + def _timedelta_above_daily_freq( + d_multiplier: int, + check_end_regex: str, + d_from_series: int, + freq_name: str, + inferred_freq: str, + ) -> Tuple[Union[pd.DateOffset, MonthEnd], str]: + """Calculate the timedelta based on the given parameters for + the frequencies above daily. + + Args: + d_multiplier: the multiplier for the number of months. + check_end_regex: the regular expression to check if the + inferred frequency is an end frequency. + d_from_series: the number of periods from the series. + freq_name: the name of the frequency. + inferred_freq: the inferred frequency. + + Returns: + the calculated timedelta and the information about the + frequency and period. + + """ + if inferred_freq and re.match(check_end_regex, inferred_freq): + delta = MonthEnd(d_multiplier * d_from_series) + freq_period_info = f"freq: {freq_name}End; period: {d_from_series}" + else: + delta = pd.DateOffset(months=d_multiplier * d_from_series) + freq_period_info = f"freq: {freq_name}; period: {d_from_series}" + + return delta, freq_period_info + + def timedelta( + self, + x: np.ndarray, + delta: Optional[pd.DateOffset] = None, + return_freq_period_info: bool = False, + ) -> Union[Tuple[np.ndarray, pd.DateOffset], Tuple[np.ndarray, pd.DateOffset, str]]: + """Returns the difference between neighboring observations in + the array in terms of delta and the delta itself. + + Args: + x: array with datetime points. + delta: custom offset if needed. + return_freq_period_info: either to return information about + inferred frequency and period. + + Returns: + difference between neighboring observations and the delta + itself; if return_freq_period_info is True, return + information about inferred frequency and period. + + Raises: + AssertionError: if the frequency and period are failed + to be defined. + + Notes: + 1. It is used to correctly generate indexes for + observations without mixing observations + with different IDs. + + """ + + if not is_datetime(x): + x = pd.to_datetime(x) + + if delta is None: + inferred_freq = pd.infer_freq(x[-3:]) # Need at least 3 dates to infer frequency + delta = x.diff().iloc[-1] + + # N Years + if delta > pd.Timedelta(days=360) and (delta.days % 365 == 0 or delta.days % 366 == 0): + delta, freq_period_info = self._timedelta_above_daily_freq( + d_multiplier=12, + check_end_regex=r"\b\d*A-|\b\d*YE-", + d_from_series=x.dt.year.diff().values[-1], + freq_name="Year", + inferred_freq=inferred_freq, + ) + + # N Quarters and Months + elif delta > pd.Timedelta(days=27): + if delta > pd.Timedelta(days=88): + check_end_regex = r"\b\d*Q-|\b\d*QE-" + else: + check_end_regex = r"\b\d*M\b|\b\d*ME\b" + delta, freq_period_info = self._timedelta_above_daily_freq( + d_multiplier=1, + check_end_regex=check_end_regex, + d_from_series=st.mode(x.dt.month.diff())[0], + freq_name="Month", + inferred_freq=inferred_freq, + ) + + # N Days + elif delta >= pd.Timedelta(days=1): + freq_period_info = f"freq: Day; period: {delta.days}" + + # N Hours; Min; Sec; etc + elif delta <= pd.Timedelta(days=1): + freq_period_info = f"freq: less then Day (Hour, Min, Sec, etc); period: {delta.total_seconds()} seconds" + else: + freq_period_info = f"Custom OffSet: {delta}" + + assert delta, "either or both frequency and period are failed to be defined." + + if return_freq_period_info: + return x.diff().fillna(delta).values, delta, freq_period_info + + return x.diff().fillna(delta).values, delta + + @staticmethod + def get_cols_idx( + data: pd.DataFrame, columns: Union[str, Sequence[str]] + ) -> Union[int, np.ndarray]: + """Get numeric index of columns by column names. + + Args: + data: source dataframe. + columns: sequence of columns or single column. + + Returns: + sequence of int indexes or single int. + + """ + if type(columns) is str: + idx = data.columns.get_loc(columns) + else: + idx = data.columns.get_indexer(columns) + + return idx + + @staticmethod + def get_slice(data: pd.DataFrame, k: Tuple[np.ndarray]) -> np.ndarray: + """Get 3d slice. + + Args: + data: source dataframe. + k: tuple of integer sequences. + + Returns: + slice. + + """ + rows, cols = k + if cols is None: + if isinstance(data, np.ndarray): + new_data = data[rows, :] + else: + # new_data = data.iloc[:, :].values[rows] + new_data = data.values[rows] + else: + if isinstance(data, np.ndarray): + new_data = data[rows, cols] + else: + new_data = data.iloc[:, cols].values[rows] + + if len(new_data.shape) == 2: + return np.expand_dims(new_data, axis=0) + return new_data + + def ids_from_date( + self, + data: pd.DataFrame, + date_column: str, + delta: Optional[bool] = None, + return_delta: bool = False, + ) -> List[int]: + """Find indexes by which the dataset can be divided into + segments that are "identical" in terms of time stamps, but + different in terms of some identifier. + + Args: + data: source dataframe. + date_column: date column name in source dataframe. + delta: custom offset if needed. + return_delta: whether to return value of delta. + + Returns: + indexes of the ends of segments; if return_delta is True, + return value of delta. + + """ + _, time_delta = self.timedelta(data[date_column], delta=delta) + ids = ( + np.argwhere( + data[date_column][1:].values != (data[date_column] + time_delta)[:-1].values + ) + + 1 + ) + if return_delta: + return list(ids.reshape(-1)), time_delta + + return list(ids.reshape(-1)) + + def _rolling_window( + self, + a: np.ndarray, + window: int, + step: int, + from_last: bool = True, + ) -> np.ndarray: + """Generate a rolling window view of a numpy array. + + Args: + a: the input array. + window: the size of the window. + step: the step size between windows. + from_last: whether to start the window from last element. + + Returns: + the rolling window view of the input array. + + """ + sliding_window = np.lib.stride_tricks.sliding_window_view(a, window) + + return sliding_window[(len(a) - window) % step if from_last else 0 :][::step] + + def _create_idx_data( + self, data: np.ndarray, horizon: int, history: int, step: int, *_ + ) -> np.ndarray: + """Create index data for train observations' windows. + + Args: + data: the input data array. + horizon: the number of steps to predict into the future. + history: the number of past steps to consider. + step: the step size between each window. + + Returns: + the index data array for train observations' windows. + + """ + return self._rolling_window(np.arange(len(data))[:-horizon], history, step) + + def _create_idx_target( + self, + data: np.ndarray, + horizon: int, + history: int, + step: int, + n_last_horizon: Optional[int], + ) -> np.ndarray: + """Create index data for targets. + + Args: + data: the input data array. + horizon: the number of steps to predict into the future. + history: the number of past steps to consider. + step: the step size between each window. + n_last_horizon: how many last points we wish to leave. + + Returns: + the index data array for targets. + + """ + return self._rolling_window(np.arange(len(data))[history:], horizon, step)[ + :, -n_last_horizon: + ] + + def _create_idx_test( + self, data: np.ndarray, horizon: int, history: int, step: int, *_ + ) -> np.ndarray: + """Create index data for test observations' windows. + + Args: + data: the input data array. + horizon: the number of steps to predict into the future. + history: the number of past steps to consider. + step: the step size between each window. + + Returns: + the index data array for test observations' windows. + + """ + return self._rolling_window(np.arange(len(data)), history, step)[-(horizon + 1) : -horizon] + + def _get_ids( + self, + func, + data: np.ndarray, + horizon: int, + history: int, + step: int, + ids: np.ndarray, + cond: int = 0, + n_last_horizon: Optional[int] = None, + ) -> np.ndarray: + """Get indices for creating windows of data. + + Args: + func: the function to create index data. + data: the input data array. + horizon: the number of steps to predict into the future. + history: the number of past steps to consider. + step: the step size between each window. + ids: indexes of the ends of segments. + cond: the condition for segment length. + n_last_horizon: how many last points to leave. + + Returns: + the index data array. + + """ + prev = 0 + inds = [] + for i, split in enumerate(ids + [len(data)]): + if isinstance(data, np.ndarray): + segment = data[prev:split] + else: + segment = data.iloc[prev:split] + if len(segment) >= cond: + ind = func(segment, horizon, history, step, n_last_horizon) + prev + inds.append(ind) + prev = split + inds = np.vstack(inds) + + return inds + + def create_idx_data( + self, + data: pd.DataFrame, + horizon: int, + history: int, + step: int, + ids: Optional[np.ndarray] = None, + date_column: Optional[str] = None, + delta: Optional[pd.DateOffset] = None, + ): + """Find indices that, when applied to the original dataset, + can be used to obtain windows for building + train observations' features. + + Args: + data: source dataframe. + horizon: the number of steps to predict into the future. + history: the number of past steps to consider. + step: number of points to take the next observation. + ids: indexes of the ends of segments. + date_column: date column name in source dataframe, + needs in the absence of ids. + + Returns: + indices of train observations' windows. + + """ + if ids is None: + ids = self.ids_from_date(data, date_column, delta=delta) + + seq_idx_data = self._get_ids( + self._create_idx_data, + data, + horizon, + history, + step, + ids, + history + horizon, + ) + + return seq_idx_data + + def create_idx_test( + self, + data: pd.DataFrame, + horizon: int, + history: int, + step: int, + ids: Optional[np.ndarray] = None, + date_column: Optional[str] = None, + delta: Optional[pd.DateOffset] = None, + ): + """Find indices that, when applied to the original dataset, + can be used to obtain windows for building + test observations' features. + + Arguments: + data: source dataframe. + horizon: the number of steps to predict into the future. + history: the number of past steps to consider. + step: number of points to take the next observation. + ids: indexes of the ends of segments. + date_column: date column name in source dataframe, + needs in the absence of ids. + + Returns: + indices of test observations' windows. + + """ + if ids is None: + ids = self.ids_from_date(data, date_column, delta=delta) + + seq_idx_test = self._get_ids( + self._create_idx_test, + data, + horizon, + history, + step, + ids, + history, + ) + + return seq_idx_test + + def create_idx_target( + self, + data: pd.DataFrame, + horizon: int, + history: int, + step: int, + ids: Optional[np.ndarray] = None, + date_column: Optional[str] = None, + delta: Optional[pd.DateOffset] = None, + n_last_horizon: Optional[int] = None, + ): + """Find indices that, when applied to the original dataset, + can be used to obtain targets. + + Arguments: + data: source dataframe. + horizon: number of points to prediction. + history: number of points to use for prediction. + step: number of points to take the next observation. + ids: indexes of the ends of segments. + date_column: date column name in source dataframe, + needs in the absence of ids. + n_last_horizon: how many last points we wish to leave. + + Returns: + indices of targets. + """ + if ids is None: + ids = self.ids_from_date(data, date_column, delta=delta) + + if n_last_horizon is None: + n_last_horizon = horizon + + seq_idx_target = self._get_ids( + self._create_idx_target, + data, + horizon, + history, + step, + ids, + history + horizon, + n_last_horizon, + ) + + return seq_idx_target diff --git a/tsururu/model_training/torch_based/__init__.py b/tsururu/model_training/torch_based/__init__.py index 1305abf..d003746 100644 --- a/tsururu/model_training/torch_based/__init__.py +++ b/tsururu/model_training/torch_based/__init__.py @@ -1,3 +1,3 @@ -from .data_provider import nnDataset - -__all__ = ["nnDataset"] +from .data_provider import Dataset_NN + +__all__ = ["Dataset_NN"] diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index 19fb810..db3930a 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -1,109 +1,327 @@ -import heapq -import os - -import torch - - -class Callback: - def on_epoch_begin(self, epoch, logs=None): - pass - - def on_epoch_end(self, epoch, logs=None): - pass - - def on_batch_begin(self, logs=None): - pass - - def on_batch_end(self, logs=None): - pass - - def on_train_begin(self, logs=None): - pass - - def on_train_end(self, logs=None): - pass - - -class ES_Checkpoints_Manager(Callback): - def __init__( - self, monitor="val_loss", verbose=1, save_best_only=True, k=5, patience=5, mode="min" - ): - self.monitor = monitor - self.verbose = verbose - self.save_best_only = save_best_only - self.k = k - self.patience = patience - self.mode = mode - self.best_snapshots = [] - self.best_score = None - self.early_stopping_counter = 0 - self.stop_training = False - - def _is_improvement(self, current, best): - if self.mode == "min": - return current < best - else: - return current > best - - def _should_save_checkpoint(self, current_score): - if len(self.best_snapshots) < self.k: - return True - if self.mode == "min": - return current_score < -self.best_snapshots[0][0] - else: - return current_score > self.best_snapshots[0][0] - - def _update_worst_best_score(self): - if self.mode == "min": - self.worst_best_score = -self.best_snapshots[0][0] - else: - self.worst_best_score = self.best_snapshots[0][0] - - def on_epoch_end(self, epoch, logs=None): - current_score = logs.get(self.monitor) - if current_score is None: - return - - model_path = logs.get("filepath") / f"model_{epoch}.pth" - model_path.parent.mkdir(parents=True, exist_ok=True) - - if self.save_best_only: - if self._should_save_checkpoint(current_score): - if len(self.best_snapshots) == self.k: - worst_snapshot = heapq.heappop(self.best_snapshots) - os.remove(worst_snapshot[1]) - if self.verbose: - print(f"Removing worst model snapshot: {worst_snapshot[1]}") - - if self.mode == "min": - heapq.heappush(self.best_snapshots, (-current_score, model_path)) - else: - heapq.heappush(self.best_snapshots, (current_score, model_path)) - - self._update_worst_best_score() - torch.save(logs["model_state_dict"], model_path) - if self.verbose: - print(f"Model saved to {model_path}") - else: - torch.save(logs["model_state_dict"], model_path) - if self.verbose: - print(f"Model saved to {model_path}") - - # Early stopping logic - if self.best_score is None or self._is_improvement(current_score, self.best_score): - self.best_score = current_score - self.early_stopping_counter = 0 - else: - self.early_stopping_counter += 1 - if self.early_stopping_counter >= self.patience: - if self.verbose: - print("Early stopping triggered") - self.stop_training = True - - def get_best_snapshots(self): - return [ - snapshot[1] - for snapshot in sorted( - self.best_snapshots, key=lambda x: -x[0] if self.mode == "min" else x[0] - ) - ] +"""Module for callbacks used in training process.""" + +import heapq +import os +from pathlib import Path +from typing import Optional + +try: + import torch +except ImportError: + torch = None + + +class Callback: + """Base class for callbacks, that are used in training process.""" + + def on_epoch_begin(self, epoch, logs=None): + pass + + def on_epoch_end(self, epoch, logs=None): + pass + + def on_batch_begin(self, logs=None): + pass + + def on_batch_end(self, logs=None): + pass + + def on_train_begin(self, logs=None): + pass + + def on_train_end(self, logs=None): + pass + + +class ES_Checkpoints_Manager(Callback): + """Manager for early stopping and checkpointing during training. + + Args: + monitor: metric to monitor for early stopping and checkpointing. + verbose: verbosity mode, 0 or 1. + save_k_best: number of best checkpoints to keep. + early_stopping_patience: number of epochs to wait for an improvement before stopping. + mode: mode for monitoring, either 'min' or 'max'. + save_to_dir: whether to save checkpoints to a directory. + averaging_snapshots: whether to average weights of saved checkpoints at the end of training. + + """ + + def __init__( + self, + monitor: str = "val_loss", + verbose: int = 1, + save_k_best: int = 1, + early_stopping_patience: int = 5, + mode: str = "min", + save_to_dir: bool = True, + averaging_snapshots: bool = False, + ): + self.monitor = monitor + self.verbose = verbose + self.save_k_best = save_k_best + self.early_stopping_patience = early_stopping_patience + self.mode = mode + self.save_to_dir = save_to_dir + self.averaging_snapshots = averaging_snapshots + + self.best_snapshots = [] + self.best_score = None + self.early_stopping_counter = 0 + self.stop_training = False + + @staticmethod + def _safe_remove(file_path: str): + """Safely removes a file if it exists. + + Args: + file_path: path to the file to be removed. + + """ + if os.path.exists(file_path): + os.remove(file_path) + + @staticmethod + def _safe_rename(src_path: str, dst_path: str): + """Safely renames a file if it exists. + + Args: + src_path: source name of the file. + dst_path: new name of the file. + + """ + if os.path.exists(src_path): + os.rename(src_path, dst_path) + + def _del_inner_params(self): + """Deletes internal parameters to reset the manager.""" + self.best_snapshots = [] + self.best_score = None + self.early_stopping_counter = 0 + self.stop_training = False + + def _is_improvement(self, current: float, best: float) -> bool: + """Checks if the current score is an improvement. + + Args: + current: current score. + best: best score. + + Returns: + whether the current score is an improvement. + + """ + if self.mode == "min": + return current < best + else: + return current > best + + def _should_save_checkpoint(self, current_score: float) -> bool: + """Checks if the current checkpoint should be saved. + + Args: + current_score: current score. + + Returns: + whether the checkpoint should be saved. + + """ + if len(self.best_snapshots) < self.save_k_best: + return True + + worst_best_score = ( + -self.best_snapshots[0][0] if self.mode == "min" else self.best_snapshots[0][0] + ) + return self._is_improvement(current_score, worst_best_score) + + def _update_worst_best_score(self): + """Updates the worst best score from the saved checkpoints.""" + if self.mode == "min": + self.worst_best_score = -self.best_snapshots[0][0] + else: + self.worst_best_score = self.best_snapshots[0][0] + + def _rename_checkpoints(self): + """Renames checkpoints based on their ranking. + + Notes: is called at the end of training to reflect the ranking of the checkpoints. + + """ + for i, (_, snapshot_info) in enumerate( + sorted( + self.best_snapshots, + key=lambda x: -x[0] if self.mode == "min" else x[0], + reverse=True, + ) + ): + epoch = snapshot_info["epoch"] + if self.save_to_dir: + new_model_path = snapshot_info["model"].with_name( + f"model_epoch_{epoch}_top_{i+1}.pth" + ) + new_opt_path = snapshot_info["optimizer"].with_name( + f"opt_epoch_{epoch}_top_{i+1}.pth" + ) + new_sch_path = snapshot_info["scheduler"].with_name( + f"sch_epoch_{epoch}_top_{i+1}.pth" + ) + + self._safe_rename(snapshot_info["model"], new_model_path) + snapshot_info["model"] = new_model_path + + self._safe_rename(snapshot_info["optimizer"], new_opt_path) + snapshot_info["optimizer"] = new_opt_path + + self._safe_rename(snapshot_info["scheduler"], new_sch_path) + snapshot_info["scheduler"] = new_sch_path + + def get_best_snapshot(self) -> dict: + """Returns the best saved snapshot. + + Returns: + the best model snapshot. + + """ + best_snapshot = [ + snapshot[1]["model"] + for snapshot in sorted( + self.best_snapshots, key=lambda x: -x[0] if self.mode == "min" else x[0] + ) + ][-1] + + if self.save_to_dir: + return torch.load(best_snapshot) + return best_snapshot + + def get_average_snapshot(self) -> dict: + """Returns the average snapshot. + + Returns: + the average model snapshot. + + Notes: + - Use simple averaging to combine the weights of the saved checkpoints. + - Is called by trainer at the end of training if averaging_snapshots is True. + + """ + average_snapshot = None + for snapshot in self.best_snapshots: + if average_snapshot is None: + average_snapshot = torch.load(snapshot[1]["model"]) + else: + average_snapshot += torch.load(snapshot[1]["model"]) + average_snapshot /= len(self.best_snapshots) + return average_snapshot + + def on_train_begin(self, logs: Optional[dict] = None): + """Called at the beginning of training. + + Args: + logs: dictionary of logs. + + Notes: + - Resets internal parameters at the beginning of training. + + """ + self._del_inner_params() + + def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): + """Called at the end of each epoch. + + Args: + epoch: the current epoch. + logs: dictionary of logs. + + Notes: + - Saves model checkpoints based on the current score. + - Triggers early stopping if the score does not improve for a number of epochs. + + """ + current_score = logs.get(self.monitor) + if current_score is None: + return + + model_state = logs["model_state_dict"] + optimizer_state = logs["optimizer_state_dict"] + scheduler_state = logs.get("scheduler_state_dict", None) + + if self.save_to_dir: + model_path = Path(logs.get("filepath")) / f"model_{epoch}.pth" + opt_path = Path(logs.get("filepath")) / f"opt_{epoch}.pth" + sch_path = Path(logs.get("filepath")) / f"sch_{epoch}.pth" + model_path.parent.mkdir(parents=True, exist_ok=True) + + if self.save_k_best > 0: + if self._should_save_checkpoint(current_score): + if len(self.best_snapshots) == self.save_k_best: + worst_snapshot = heapq.heappop(self.best_snapshots) + if self.save_to_dir: + self._safe_remove(worst_snapshot[1]["model"]) + self._safe_remove(worst_snapshot[1]["optimizer"]) + self._safe_remove(worst_snapshot[1]["scheduler"]) + if self.verbose: + print( + f"Removing worst model snapshot: from epoch {worst_snapshot[1]['epoch']}" + ) + + snapshot_info = { + "model": model_path if self.save_to_dir else model_state, + "optimizer": opt_path if self.save_to_dir else optimizer_state, + "scheduler": sch_path if self.save_to_dir else scheduler_state, + "epoch": epoch, + } + + if self.mode == "min": + heapq.heappush(self.best_snapshots, (-current_score, snapshot_info)) + else: + heapq.heappush(self.best_snapshots, (current_score, snapshot_info)) + + self._update_worst_best_score() + if self.save_to_dir: + torch.save(model_state, model_path) + torch.save(optimizer_state, opt_path) + if scheduler_state: + torch.save(scheduler_state, sch_path) + if self.verbose: + print(f"Model saved to {model_path}") + print(f"Optimizer saved to {opt_path}") + if scheduler_state: + print(f"Scheduler saved to {sch_path}") + + else: + if self.save_to_dir: + torch.save(model_state, model_path) + torch.save(optimizer_state, opt_path) + if scheduler_state: + torch.save(scheduler_state, sch_path) + if self.verbose: + print(f"Model saved to {model_path}") + print(f"Optimizer saved to {opt_path}") + if scheduler_state: + print(f"Scheduler saved to {sch_path}") + + # Early stopping logic + if self.early_stopping_patience > 0: + if self.best_score is None or self._is_improvement(current_score, self.best_score): + self.best_score = current_score + self.early_stopping_counter = 0 + else: + self.early_stopping_counter += 1 + print(f"Early stopping counter: {self.early_stopping_counter}") + if self.early_stopping_counter >= self.early_stopping_patience: + if self.verbose: + print("Early stopping triggered") + self.stop_training = True + + def on_train_end(self, logs: Optional[dict] = None): + """Called at the end of training. + + Args: + logs: dictionary of logs. + + Notes: + - Renames checkpoints to reflect their current ranking at the end of training. + + """ + if self.save_to_dir: + self._rename_checkpoints() diff --git a/tsururu/model_training/torch_based/data_provider.py b/tsururu/model_training/torch_based/data_provider.py index f9fb10f..6fd0d55 100644 --- a/tsururu/model_training/torch_based/data_provider.py +++ b/tsururu/model_training/torch_based/data_provider.py @@ -1,49 +1,134 @@ +"""Module for creating a custom dataset for neural networks.""" + import numpy as np -import torch from pandas import to_datetime -from torch.utils.data import Dataset +from ...dataset import Pipeline + +try: + import torch + from torch.utils.data import Dataset +except ImportError: + torch = None + Dataset = None + + +class Dataset_NN(Dataset): + """Custom Dataset for neural networks. -class nnDataset(Dataset): - def __init__(self, data, pipeline): + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + pipeline: pipeline object for creating and applying a pipeline of transformers. + + """ + + def __init__(self, data: dict, pipeline: Pipeline): self.data = data self.pipeline = pipeline self.idx_X = self.data["idx_X"] self.idx_y = self.data["idx_y"] - if pipeline.multivariate: - self.indices = np.arange( - len(self.data["idx_X"]) // self.data["raw_ts_X"]["id"].nunique() - ) - unique_dates, inverse_indices = np.unique( - self.data["raw_ts_X"][self.data["date_column_name"]], return_inverse=True - ) - unique_dates = to_datetime(unique_dates) - self.date_indices = { - date: np.where(inverse_indices == idx)[0] for idx, date in enumerate(unique_dates) - } + self.indices = self._create_indices() - else: - self.indices = np.arange(len(self.data["idx_X"])) + def _create_date_indices(self): + """Creates indices for each unique date for multivariate data.""" + unique_dates, inverse_indices = np.unique( + self.data["raw_ts_X"][self.data["date_column_name"]], return_inverse=True + ) + unique_dates = to_datetime(unique_dates) + self.date_indices = { + date: np.where(inverse_indices == idx)[0] for idx, date in enumerate(unique_dates) + } + + def _create_indices(self) -> np.ndarray: + """Creates indices for the dataset based on the pipeline configuration. + + Returns: + array of indices. - def __getitem__(self, index): + """ + # Create indices for the dataset + # If multivariate, we need to create indices for each unique date + # (we use a bunch of rows in the idx_X for each date) if self.pipeline.multivariate: - current_date = self.data["raw_ts_X"][self.data["date_column_name"]].iloc[self.idx_X[index][0]] - current_date = to_datetime(current_date) - first_idx = self.date_indices[current_date] - idx_X = self.idx_X[np.isin(self.idx_X[:, 0], first_idx)] - idx_y = self.idx_y[np.isin(self.idx_X[:, 0], first_idx)] + arange_value = len(self.data["idx_X"]) // self.data["raw_ts_X"]["id"].nunique() + self._create_date_indices() else: - idx_X = self.idx_X[index] - idx_y = self.idx_y[index] + # If global, we need to create indices for each row in the idx_X + arange_value = len(self.data["idx_X"]) + + if self.pipeline.strategy_name == "FlatWideMIMOStrategy": + horizon = self.idx_y.shape[1] + arange_value *= horizon + + return np.arange(arange_value) + def _adjust_fvm_indices(self, index: int) -> tuple: + """Adjusts indices for the FlatWideMIMO strategy. + + Args: + index: index to adjust. + + Returns: + adjusted index and sample index for the horizon. + + Notes: If the strategy is FlatWideMIMO, we need to adjust the index to get firstly get the + correct index for the MIMO sample and then the correct index for the sample in the horizon. + + """ + horizon = self.idx_y.shape[1] + index_of_sample = index % horizon + index = index // horizon + return index, index_of_sample + + def _adjust_multivariate_indices(self, index: int) -> tuple: + """Adjusts indices for the current date in multivariate data. + + Args: + index: index to get the date indices for. + + Returns: + indices for features and targets. + + Notes: If the data is multivariate, we need to get the indices for the current date and + then get the correct indices for the sample. + + """ + current_date = self.data["raw_ts_X"][self.data["date_column_name"]].iloc[ + self.idx_X[index][0] + ] + current_date = to_datetime(current_date) + first_idx = self.date_indices[current_date] + idx_X = self.idx_X[np.isin(self.idx_X[:, 0], first_idx)] + idx_y = self.idx_y[np.isin(self.idx_X[:, 0], first_idx)] + return idx_X, idx_y + + def _get_adjusted_data(self, idx_X: np.ndarray, idx_y: np.ndarray) -> dict: + """Adjusts raw time series data based on indices. + + Args: + idx_X: indices for features. + idx_y: indices for targets. + + Returns: + adjusted data and indices. + + Notes: We want to get only the time series points that are necessary for the current sample + + """ raw_ts_X_adjusted = self.data["raw_ts_X"].iloc[idx_X.flatten()].reset_index(drop=True) raw_ts_y_adjusted = self.data["raw_ts_y"].iloc[idx_y.flatten()].reset_index(drop=True) idx_X_adjusted = np.arange(np.size(idx_X)).reshape(idx_X.shape) idx_y_adjusted = np.arange(np.size(idx_y)).reshape(idx_y.shape) - data = { + if self.pipeline.strategy_name == "FlatWideMIMOStrategy": + idx_X_adjusted = idx_X_adjusted.reshape(-1, idx_X_adjusted.shape[-1]) + idx_y_adjusted = idx_y_adjusted.reshape(-1, idx_y_adjusted.shape[-1]) + + return { "raw_ts_X": raw_ts_X_adjusted, "raw_ts_y": raw_ts_y_adjusted, "X": np.array([]), @@ -55,17 +140,89 @@ def __getitem__(self, index): "id_column_name": self.data["id_column_name"], } - X, y = self.pipeline.generate(data) + def _reshape_tensors( + self, X_tensor: torch.Tensor, y_tensor: torch.Tensor, index_of_sample: int + ) -> tuple: + """Reshapes and transposes tensors to fit the neural network input. - X_tensor = torch.from_numpy(X).float() - y_tensor = torch.from_numpy(y).float() + Args: + X_tensor: feature tensor. + y_tensor: target tensor. + index_of_sample: index of the sample in the horizon. + + Returns: + reshaped feature and target tensors. + """ + if self.pipeline.strategy_name == "FlatWideMIMOStrategy": + X_tensor = X_tensor[index_of_sample, :].reshape(-1, X_tensor.shape[1]) + y_tensor = y_tensor[index_of_sample, :].reshape(-1, y_tensor.shape[1]) if self.pipeline.multivariate: - num_series = raw_ts_X_adjusted["id"].nunique() - X_tensor = X_tensor.view(num_series, -1).T.flip(0) + other_columns_idx_list = [] + target_column_idx_list = [] + for i, feature in enumerate(self.pipeline.output_features): + if feature.split("__")[0] != self.data["target_column_name"]: + other_columns_idx_list.append(i) + else: + target_column_idx_list.append(i) + + num_series = self.data["raw_ts_X"]["id"].nunique() + + X_tensor_target_column = X_tensor[:, target_column_idx_list] + X_tensor_other_column = X_tensor[:, other_columns_idx_list] + + X_tensor_target_column = X_tensor_target_column.view(num_series, -1).T + X_tensor_other_column = torch.repeat_interleave( + X_tensor_other_column, + X_tensor_target_column.size(0) // X_tensor_other_column.size(0), + dim=0, + ) + X_tensor = torch.cat([X_tensor_target_column, X_tensor_other_column], dim=1) + y_tensor = y_tensor.view(num_series, -1).T + else: + X_tensor = X_tensor.reshape(-1, 1) + y_tensor = y_tensor.reshape(-1, 1) + return X_tensor, y_tensor - def __len__(self): + def __getitem__(self, index: int) -> tuple: + """Gets a data sample for the given index. + + Args: + index: index of the data sample to retrieve. + + Returns: + a tuple containing the feature tensor and target tensor. + + """ + if self.pipeline.strategy_name == "FlatWideMIMOStrategy": + index, index_of_sample = self._adjust_fvm_indices(index) + else: + index_of_sample = None + + if self.pipeline.multivariate: + idx_X, idx_y = self._adjust_multivariate_indices(index) + else: + idx_X = self.idx_X[index] + idx_y = self.idx_y[index] + + data = self._get_adjusted_data(idx_X, idx_y) + + X, y = self.pipeline.generate(data) + + X_tensor = torch.from_numpy(X).float() + y_tensor = torch.from_numpy(y).float() + + # Reshape and transpose tensors to fit the neaural network input + return self._reshape_tensors(X_tensor, y_tensor, index_of_sample) + + def __len__(self) -> int: + """Returns the number of samples in the dataset. + + Returns: + number of samples in the dataset. + + """ return len(self.indices) diff --git a/tsururu/model_training/torch_based/metrics.py b/tsururu/model_training/torch_based/metrics.py index 359e571..04e70bf 100644 --- a/tsururu/model_training/torch_based/metrics.py +++ b/tsururu/model_training/torch_based/metrics.py @@ -1,12 +1,31 @@ -from torch import nn - - -class NegativeMSEMetric(nn.Module): - def __init__(self): - super(NegativeMSEMetric, self).__init__() - self.mse_loss = nn.MSELoss() - - def forward(self, input, target): - loss = self.mse_loss(input, target) - negative_loss = -1 * loss - return negative_loss +"""Module for creating custom metrics for neural networks.""" + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None + + +class NegativeMSEMetric(nn.Module): + """Custom metric that returns the negative of the Mean Squared Error (MSE).""" + + def __init__(self): + super(NegativeMSEMetric, self).__init__() + self.mse_loss = nn.MSELoss() + + def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Computes the negative mean squared error between input and target. + + Args: + input: predicted tensor. + target: ground truth tensor. + + Returns: + the negative mean squared error. + + """ + loss = self.mse_loss(input, target) + negative_loss = -1 * loss + return negative_loss diff --git a/tsururu/model_training/torch_based/scheduler.py b/tsururu/model_training/torch_based/scheduler.py deleted file mode 100644 index 17bd3c0..0000000 --- a/tsururu/model_training/torch_based/scheduler.py +++ /dev/null @@ -1,30 +0,0 @@ -import torch -from torch.optim.lr_scheduler import _LRScheduler - - -class LRAdjScheduler(_LRScheduler): - def __init__(self, optimizer, lr_schedule_type, args, last_epoch=-1): - self.lr_schedule_type = lr_schedule_type - self.args = args - super(LRAdjScheduler, self).__init__(optimizer, last_epoch) - - def get_lr(self): - epoch = self.last_epoch + 1 - - if self.lr_schedule_type == "type1": - lr = self.args.learning_rate * (0.5 ** ((epoch - 1) // 1)) - elif self.lr_schedule_type == "type2": - lr_adjust = {2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6, 10: 5e-7, 15: 1e-7, 20: 5e-8} - lr = lr_adjust.get(epoch, self.base_lrs[0]) - elif self.lr_schedule_type == "3": - lr = self.args.learning_rate if epoch < 10 else self.args.learning_rate * 0.1 - elif self.lr_schedule_type == "4": - lr = self.args.learning_rate if epoch < 15 else self.args.learning_rate * 0.1 - elif self.lr_schedule_type == "5": - lr = self.args.learning_rate if epoch < 25 else self.args.learning_rate * 0.1 - elif self.lr_schedule_type == "6": - lr = self.args.learning_rate if epoch < 5 else self.args.learning_rate * 0.1 - else: - raise ValueError(f"Unknown lr_schedule_type: {self.lr_schedule_type}") - - return [lr for _ in self.optimizer.param_groups] diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 18dbd8d..82f5215 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -1,18 +1,27 @@ +"""Module for training and predicting using models and validation strategies.""" + +import time from pathlib import Path -from typing import Callable, Dict, List, Optional, Union +from typing import Callable, Dict, List, Optional, Tuple, Union import numpy as np -import torch -from torch.utils.data import Subset -from tqdm.notebook import tqdm from ..dataset.pipeline import Pipeline from ..models.base import Estimator from .torch_based.callbacks import ES_Checkpoints_Manager -from .torch_based.data_provider import nnDataset +from .torch_based.data_provider import Dataset_NN from .torch_based.metrics import NegativeMSEMetric from .validator import Validator +try: + import torch + import torch.nn as nn + from torch.utils.data import Subset +except ImportError: + torch = None + Subset = None + nn = None + class MLTrainer: """Class for training and predicting using a model and a validation strategy. @@ -107,6 +116,47 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: class DLTrainer: + """Class for training and predicting using a deep learning model and a validation strategy. + + Args: + model: the model estimator to be used for training. + model_params: the parameters for the model. + validator: the validation strategy to be used for training. + validation_params: the parameters for the validation strategy. + n_epochs: the number of epochs to train the model. + batch_size: size of batches during training. + drop_last: whether to drop the last incomplete batch. + device: device to run the training on. + device_ids: list of device IDs for data parallelism. + num_workers: number of workers for data loading. + metric: metric function to evaluate the model. + criterion: loss function for training the model. + optimizer: optimizer for training the model. + optimizer_params: parameters for the optimizer. + scheduler: learning rate scheduler. + scheduler_params: parameters for the scheduler. + scheduler_after_epoch: whether to step the scheduler after each epoch. + pretrained_paths: dict (!) of paths for each fold to load pretrained checkpoints from. + format: { + fold_num: { + 'model': path_to_checkpoint, + 'optimizer': path_to_checkpoint, + 'scheduler': path_to_checkpoint + } + }. + best_by_metric: whether to select the best model by metric instead of loss. + early_stopping_patience: number of epochs to wait for improvement before early stopping. + 0 for early stopping disable. + save_k_best: number of best checkpoints to save. + 0 for none, `n_epochs` for all. + averaging_snapshots: whether to average weights of saved checkpoints at the end of training. + save_to_dir: whether to save checkpoints to a directory. + checkpoint_path: path to save checkpoints. + train_shuffle: whether to shuffle the training data. + verbose: verbosity level. + + """ + def __init__( self, model: Estimator, @@ -122,65 +172,81 @@ def __init__( metric: Callable = NegativeMSEMetric(), criterion: torch.nn.Module = torch.nn.MSELoss(), optimizer: Optional[torch.optim.Optimizer] = torch.optim.Adam, - optinizer_params: Dict = {}, + optimizer_params: Dict = {}, scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, scheduler_params: Dict = {}, scheduler_after_epoch: bool = True, + pretrained_paths: Optional[dict] = None, + # es_checkpoint_manager params + best_by_metric: bool = False, + early_stopping_patience: int = 5, + save_k_best: Union[int] = 5, + averaging_snapshots: bool = False, + save_to_dir: bool = True, + checkpoint_path: Union[bool, Path] = "checkpoints/", + train_shuffle: bool = True, verbose: int = 1, - early_stopping: bool = True, - stop_by_metric: bool = False, - patience: int = 5, - pretrained_path: Optional[Union[str, Path]] = None, - checkpoint_path: Union[str, Path] = "checkpoints/", ): self.model_base = model self.model_params = model_params self.validator_base = validator self.validation_params = validation_params + self.n_epochs = n_epochs self.batch_size = batch_size self.drop_last = drop_last self.device = device self.device_ids = device_ids self.num_workers = num_workers + self.metric = metric self.criterion = criterion + self.optimizer_base = optimizer - self.optinizer_params = optinizer_params + self.optimizer_params = optimizer_params self.scheduler_base = scheduler self.scheduler_params = scheduler_params self.scheduler_after_epoch = scheduler_after_epoch - self.verbose = verbose - self.early_stopping = early_stopping - # either by loss or metric - self.stop_by_metric = stop_by_metric - # how many epochs to wait for improvement before early stopping - self.patience = patience - self.pretrained_path = pretrained_path + + self.pretrained_paths = pretrained_paths + + self.train_shuffle = train_shuffle + + self.es = ES_Checkpoints_Manager( + monitor="val_metric" if best_by_metric else "val_loss", + verbose=verbose, + save_k_best=save_k_best, + early_stopping_patience=early_stopping_patience, + mode="max" if best_by_metric else "min", + save_to_dir=save_to_dir, + averaging_snapshots=averaging_snapshots, + ) if isinstance(checkpoint_path, str): checkpoint_path = Path(checkpoint_path) self.checkpoint_path = checkpoint_path - self.callbacks = [ - ES_Checkpoints_Manager( - monitor="val_metric" if stop_by_metric else "val_loss", - verbose=verbose, - save_best_only=True, - k=5, - patience=patience, - mode="max" if stop_by_metric else "min" - ) - ] # Provide by strategy if needed + self.callbacks = [self.es] self.history = None self.horizon = None + self.target_len = None + self.models = [] self.optimizers = [] self.schedulers = [] self.scores = [] - def init_trainer_one_fold(self): - self.model_params["seq_len"] = self.history + def init_trainer_one_fold(self, num_features: int): + """Initializes the model, optimizer, and scheduler for one fold. + + Args: + num_features: Number of features in the input data. + + Returns: + Initialized model, optimizer, and scheduler. + + """ + self.model_params["seq_len"] = num_features self.model_params["pred_len"] = self.horizon model = self.model_base(**self.model_params) @@ -189,20 +255,77 @@ def init_trainer_one_fold(self): else: model.to(self.device) - optimizer = self.optimizer_base(model.parameters(), **self.optinizer_params) + optimizer = self.optimizer_base(model.parameters(), **self.optimizer_params) if self.scheduler_base is not None: scheduler = self.scheduler_base(optimizer, **self.scheduler_params) else: scheduler = None + + return model, optimizer, scheduler + + def load_trainer_one_fold( + self, + fold_i: int, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler], + ): + """Loads pretrained model, optimizer, and scheduler states for one fold. + + Args: + fold_i: fold index. + model: model to load the state into. + optimizer: optimizer to load the state into. + scheduler: scheduler to load the state into (if exists). + + Returns: + model, optimizer, and scheduler with loaded states. + + """ + model_path = self.pretrained_paths[fold_i]["model"] + optimizer_path = self.pretrained_paths[fold_i]["optimizer"] + if scheduler: + scheduler_path = self.pretrained_paths[fold_i]["scheduler"] + + model.load_state_dict(torch.load(model_path)) + optimizer.load_state_dict(torch.load(optimizer_path)) + if scheduler: + scheduler.load_state_dict(torch.load(scheduler_path)) + return model, optimizer, scheduler - def train_model(self, train_loader, val_loader, model, optimizer, scheduler): + def train_model( + self, + train_loader: torch.utils.data.DataLoader, + val_loader: torch.utils.data.DataLoader, + model: nn.Module, + optimizer: torch.optim.Optimizer, + scheduler: Optional[torch.optim.lr_scheduler._LRScheduler], + ) -> Tuple[ + nn.Module, torch.optim.Optimizer, Optional[torch.optim.lr_scheduler._LRScheduler], float + ]: + """Trains the model for all epochs. + + Args: + train_loader: dataLoader for the training data. + val_loader: dataLoader for the validation data. + model: model to be trained. + optimizer: optimizer for training. + scheduler: learning rate scheduler (if exists). + + Returns: + trained model, optimizer, scheduler, and validation metric. + """ + for cb in self.callbacks: + cb.on_train_begin() + for epoch in range(self.n_epochs): for callback in self.callbacks: callback.on_epoch_begin(epoch) model.train() running_loss = 0.0 + start_time = time.time() for inputs, targets in train_loader: for callback in self.callbacks: @@ -212,6 +335,9 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): optimizer.zero_grad() outputs = model(inputs) + if self.target_len is None: + self.target_len = targets.shape[2] + outputs = outputs[:, :, : self.target_len] loss = self.criterion(outputs, targets) loss.backward() optimizer.step() @@ -223,15 +349,19 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): if not self.scheduler_after_epoch and scheduler is not None: scheduler.step() + print(f"Updating learning rate to {scheduler.get_last_lr()[0]:.6f}.") epoch_loss = running_loss / len(train_loader.dataset) - print(f"Epoch {epoch+1}/{self.n_epochs}, Loss: {epoch_loss:.4f}") + epoch_time = time.time() - start_time + print(f"Epoch {epoch+1}/{self.n_epochs}, cost time: {epoch_time:.2f}s") + print(f"train loss: {epoch_loss:.4f}") val_loss, val_metric = self.validate_model(val_loader, model) - print(f"Validation, Loss: {val_loss:.4f}, Metric: {val_metric:.4f}") + print(f"val loss: {val_loss:.4f}") if self.scheduler_after_epoch and scheduler is not None: - scheduler.step(val_loss) + scheduler.step() + print(f"Updating learning rate to {scheduler.get_last_lr()[0]:.6f}.") # Сохранение модели и проверка early stopping logs = { @@ -241,8 +371,12 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): "val_loss": val_loss, "val_metric": val_metric, "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), } + if scheduler: + logs["scheduler_state_dict"] = scheduler.state_dict() + for cb in self.callbacks: cb.on_epoch_end(epoch, logs) if getattr(cb, "stop_training", False): @@ -253,9 +387,35 @@ def train_model(self, train_loader, val_loader, model, optimizer, scheduler): for cb in self.callbacks: cb.on_train_end() + # return best_model or average_model + if self.es.averaging_snapshots: + model.load_state_dict(self.es.get_average_snapshot()) + else: + model.load_state_dict(self.es.get_best_snapshot()) + return model, optimizer, scheduler, val_metric - def validate_model(self, val_loader, model, return_outputs=False): + def validate_model( + self, + val_loader: torch.utils.data.DataLoader, + model: nn.Module, + return_outputs: bool = False, + ) -> Union[float, Tuple[float, float], Tuple[float, float, torch.Tensor, torch.Tensor]]: + """Validates the model on the validation data. + + Args: + val_loader: data loader for the validation data. + model: model to be validated. + return_outputs: whether to return the outputs and targets. + + Returns: + validation loss, metric, and optionally the outputs and targets. + + Note: + The same method for both validation and make predictions on test data. + The are NaN values in metric if test data is used. + + """ model.eval() all_outputs = [] @@ -265,6 +425,7 @@ def validate_model(self, val_loader, model, return_outputs=False): for inputs, targets in val_loader: inputs, targets = inputs.to(self.device), targets.to(self.device) outputs = model(inputs) + outputs = outputs[:, :, : self.target_len] all_outputs.append(outputs) all_targets.append(targets) @@ -295,10 +456,10 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - the fitted models. """ - train_dataset = nnDataset(data, pipeline) + train_dataset = Dataset_NN(data, pipeline) train_dataset_idx = np.arange(len(train_dataset)) if val_data: - val_dataset = nnDataset(val_data, pipeline) + val_dataset = Dataset_NN(val_data, pipeline) val_dataset_all_idx = np.arange(len(val_dataset)) else: val_dataset = None @@ -311,6 +472,7 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - ): checkpoint_path = self.checkpoint_path self.checkpoint_path /= f"fold_{fold_i}" + train_subset = Subset(train_dataset, train_dataset_idx) if val_dataset is not None: val_subset = Subset(val_dataset, val_dataset_idx) @@ -319,7 +481,7 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - train_loader = torch.utils.data.DataLoader( train_subset, batch_size=self.batch_size, - shuffle=True, + shuffle=self.train_shuffle, drop_last=self.drop_last, num_workers=self.num_workers, ) @@ -331,7 +493,17 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - num_workers=self.num_workers, ) - model, optimizer, scheduler = self.init_trainer_one_fold() + print(f"length of train dataset: {len(train_subset)}") + print(f"length of val dataset: {len(val_subset)}") + + num_features = train_dataset[0][0].shape[0] + + # load or initialize model, optimizer, scheduler + model, optimizer, scheduler = self.init_trainer_one_fold(num_features) + if self.pretrained_paths: + model, optimizer, scheduler = self.load_trainer_one_fold( + fold_i, model, optimizer, scheduler + ) model, optimizer, scheduler, score = self.train_model( train_loader, val_loader, model, optimizer, scheduler ) @@ -360,7 +532,7 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: array of predicted values. """ - test_dataset = nnDataset(data, pipeline) + test_dataset = Dataset_NN(data, pipeline) test_loader = torch.utils.data.DataLoader( test_dataset, batch_size=self.batch_size, @@ -369,15 +541,30 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: num_workers=self.num_workers, ) - models_preds = [self.validate_model(test_loader, model, return_outputs=True)[2] for model in self.models] - + print(f"length of test dataset: {len(test_dataset)}") + + models_preds = [ + self.validate_model(test_loader, model, return_outputs=True)[2].cpu() + for model in self.models + ] + y_pred = np.mean(models_preds, axis=0) y_pred = torch.tensor(y_pred) - + + if pipeline.strategy_name == "FlatWideMIMOStrategy" and pipeline.multivariate is False: + full_horizon = pipeline.y_original_shape[1] + num_series = y_pred.shape[0] // full_horizon + y_pred = y_pred.reshape(num_series, full_horizon, 1) + + if pipeline.strategy_name == "FlatWideMIMOStrategy" and pipeline.multivariate: + y_pred = y_pred.permute(1, 0, 2) + + pipeline.y_original_shape = list(pipeline.y_original_shape) + pipeline.y_original_shape[0] *= y_pred.shape[0] + + if pipeline.multivariate: + y_pred = y_pred.permute(2, 0, 1) if pipeline.multivariate: - pipeline.y_original_shape = list(pipeline.y_original_shape) - pipeline.y_original_shape[0] *= y_pred.shape[0] - y_pred = y_pred.permute(0, 2, 1) y_pred = y_pred.reshape(-1, y_pred.shape[2]) return y_pred.numpy() diff --git a/tsururu/model_training/validator.py b/tsururu/model_training/validator.py index 210f3f4..503aec2 100644 --- a/tsururu/model_training/validator.py +++ b/tsururu/model_training/validator.py @@ -1,79 +1,135 @@ -from typing import Dict, Iterator, Optional, Tuple, Union - -import numpy as np -from sklearn.model_selection import KFold, TimeSeriesSplit - - -class Validator: - """Base class for validation strategy.""" - - def __init__(self, validation_params: Optional[Dict[str, Union[str, int]]] = None): - self.validation_params = validation_params - - def get_split( - self, - X: np.ndarray, - y: Optional[np.ndarray] = None, - X_val: Optional[np.ndarray] = None, - y_val: Optional[np.ndarray] = None, - ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: - raise NotImplementedError() - - -class KFoldCrossValidator(Validator): - def __init__(self, n_splits: int = 3, shuffle: bool = True, random_state: int = 42): - self.n_splits = n_splits - self.shuffle = shuffle - self.random_state = random_state - - def get_split( - self, - X: np.ndarray, - y: Optional[np.ndarray] = None, - X_val: Optional[np.ndarray] = None, - y_val: Optional[np.ndarray] = None, - ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: - cv = KFold(n_splits=self.n_splits, shuffle=self.shuffle, random_state=self.random_state) - for X_train_idx, X_val_idx in cv.split(X): - if y is None: - yield X[X_train_idx], None, X[X_val_idx], None - else: - yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] - - -class TimeSeriesValidator(Validator): - def __init__(self, n_splits: int = 3): - self.n_splits = n_splits - - def get_split( - self, - X: np.ndarray, - y: Optional[np.ndarray] = None, - X_val: Optional[np.ndarray] = None, - y_val: Optional[np.ndarray] = None, - ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: - cv = TimeSeriesSplit(n_splits=self.n_splits) - for X_train_idx, X_val_idx in cv.split(X): - if y is None: - yield X[X_train_idx], None, X[X_val_idx], None - else: - yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] - - -class HoldOutValidator(Validator): - def __init__(self, validation_data: Dict[str, np.ndarray]): - self.validation_data = validation_data - - def get_split( - self, - X: np.ndarray, - y: Optional[np.ndarray] = None, - X_val: Optional[np.ndarray] = None, - y_val: Optional[np.ndarray] = None, - ) -> Iterator[Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]]: - train_idx = np.arange(len(X)) - val_idx = np.arange(len(X_val)) - if y is None: - yield (X[train_idx], None, X_val[val_idx], None) - else: - yield (X[train_idx], y[train_idx], X_val[val_idx], y_val[val_idx]) +"""Module for validation strategy.""" + +from typing import Dict, Iterator, Optional, Tuple, Union + +import numpy as np +from sklearn.model_selection import KFold, TimeSeriesSplit + +from ..dataset import TSDataset + + +class Validator: + """Base class for validation strategy. + + Args: + validation_params: optional dictionary with validation parameters. + unique for each validation strategy. + + """ + + def __init__(self, validation_params: Optional[Dict[str, Union[str, int]]] = None): + self.validation_params = validation_params + + def get_split( + self, + X: np.ndarray, + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, + ) -> Iterator[ + Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]] + ]: + """Get splits for training and validation data. + + Args: + X: feature array. + y: target array. + X_val: validation feature array. + y_val: validation target array. + + Returns: + iterator over tuples of training and validation data splits. + + Notes: + For some purposes, the validation data is not needed, so it is optional. + + """ + raise NotImplementedError() + + +class KFoldCrossValidator(Validator): + """K-Fold Cross-Validation strategy. + + Args: + n_splits: number of folds. Must be at least 2. + shuffle: whether to shuffle the data before splitting into batches. + random_state: when shuffle is True, random_state affects the ordering of the indices. + + """ + + def __init__(self, n_splits: int = 3, shuffle: bool = True, random_state: int = 42): + self.n_splits = n_splits + self.shuffle = shuffle + self.random_state = random_state + + def get_split( + self, + X: np.ndarray, + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, + ) -> Iterator[ + Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]] + ]: + cv = KFold(n_splits=self.n_splits, shuffle=self.shuffle, random_state=self.random_state) + for X_train_idx, X_val_idx in cv.split(X): + if y is None: + yield X[X_train_idx], None, X[X_val_idx], None + else: + yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] + + +class TimeSeriesValidator(Validator): + """Time Series Cross-Validation strategy. + + Args: + n_splits: number of folds. Must be at least 2. + + """ + + def __init__(self, n_splits: int = 3): + self.n_splits = n_splits + + def get_split( + self, + X: np.ndarray, + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, + ) -> Iterator[ + Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]] + ]: + cv = TimeSeriesSplit(n_splits=self.n_splits) + for X_train_idx, X_val_idx in cv.split(X): + if y is None: + yield X[X_train_idx], None, X[X_val_idx], None + else: + yield X[X_train_idx], y[X_train_idx], X[X_val_idx], y[X_val_idx] + + +class HoldOutValidator(Validator): + """Hold-Out Validation strategy. + + Args: + validation_data: validation dataset. + + """ + + def __init__(self, validation_data: TSDataset): + self.validation_data = validation_data + + def get_split( + self, + X: np.ndarray, + y: Optional[np.ndarray] = None, + X_val: Optional[np.ndarray] = None, + y_val: Optional[np.ndarray] = None, + ) -> Iterator[ + Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]] + ]: + train_idx = np.arange(len(X)) + val_idx = np.arange(len(X_val)) + if y is None: + yield (X[train_idx], None, X_val[val_idx], None) + else: + yield (X[train_idx], y[train_idx], X_val[val_idx], y_val[val_idx]) diff --git a/tsururu/models/__init__.py b/tsururu/models/__init__.py index 17cda87..cfd6123 100644 --- a/tsururu/models/__init__.py +++ b/tsururu/models/__init__.py @@ -1,27 +1,27 @@ -"""Algorithms for time series forecasting.""" - -from .base import Estimator -from .boost import CatBoost - - -# Factory Object -class ModelsFactory: - def __init__(self): - self.models = { - "CatBoost": CatBoost, - } - - def get_allowed(self): - return sorted(list(self.models.keys())) - - def __getitem__(self, params): - return self.models[params["model_name"]]( - params["validation_params"], - params["model_params"], - ) - - def create_model(self, model_name, model_params): - return self.models[model_name](**model_params) - - -__all__ = ["CatBoost", "Estimator", "ModelsFactory"] +"""Algorithms for time series forecasting.""" + +from .base import Estimator +from .boost import CatBoost + + +# Factory Object +class ModelsFactory: + def __init__(self): + self.models = { + "CatBoost": CatBoost, + } + + def get_allowed(self): + return sorted(list(self.models.keys())) + + def __getitem__(self, params): + return self.models[params["model_name"]]( + params["validation_params"], + params["model_params"], + ) + + def create_model(self, model_name, model_params): + return self.models[model_name](**model_params) + + +__all__ = ["CatBoost", "Estimator", "ModelsFactory"] diff --git a/tsururu/models/base.py b/tsururu/models/base.py index 515cb2c..b439a9c 100644 --- a/tsururu/models/base.py +++ b/tsururu/models/base.py @@ -1,52 +1,52 @@ -from typing import Dict, Optional, Union - -import numpy as np - - -class Estimator: - """Base class for all models. - - Args: - model_params: parameters for the model. - Individually defined for each model. - - """ - - def __init__(self, model_params: Dict[str, Union[str, int]]): - self.model_params = model_params - - self.model = None - self.score = None - - def fit_one_fold( - self, - X_train: np.ndarray, - y_train: np.ndarray, - X_val: Optional[np.ndarray], - y_val: Optional[np.ndarray], - ) -> "Estimator": - """Fits the model on one fold using the input data. - - Args: - X_train: features array. - y_train: target array. - X_val: validation features array. - y_val: validation target array. - - Returns: - the fitted model. - - """ - raise NotImplementedError() - - def predict(self, X: np.ndarray) -> np.ndarray: - """Generates predictions using the trained model. - - Args: - X: features array. - - Returns: - array of predicted values. - - """ - return self.model.predict(X) +from typing import Dict, Any, Optional + +import numpy as np + + +class Estimator: + """Base class for all models. + + Args: + model_params: parameters for the model. + Individually defined for each model. + + """ + + def __init__(self, model_params: Dict[str, Any]): + self.model_params = model_params + + self.model = None + self.score = None + + def fit_one_fold( + self, + X_train: np.ndarray, + y_train: np.ndarray, + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> "Estimator": + """Fits the model on one fold using the input data. + + Args: + X_train: features array. + y_train: target array. + X_val: validation features array. + y_val: validation target array. + + Returns: + the fitted model. + + """ + raise NotImplementedError() + + def predict(self, X: np.ndarray) -> np.ndarray: + """Generates predictions using the trained model. + + Args: + X: features array. + + Returns: + array of predicted values. + + """ + return self.model.predict(X) diff --git a/tsururu/models/boost.py b/tsururu/models/boost.py index f470ef8..41b84be 100644 --- a/tsururu/models/boost.py +++ b/tsururu/models/boost.py @@ -1,64 +1,64 @@ -from typing import Dict, Optional, Union - -import numpy as np - -try: - from catboost import CatBoostRegressor, Pool -except ImportError: - Pool = None - CatBoostRegressor = None -from .base import Estimator - - -class CatBoost(Estimator): - """CatBoost is a class that performs cross-validation - using CatBoostRegressor. - - Args: - model_params: parameters for the model, - for example: { - "loss_function": "MultiRMSE", - "early_stopping_rounds": 100, - }. - - """ - - def __init__(self, model_params: Dict[str, Union[str, int]]): - super().__init__(model_params) - self.trainer_type = "MLTrainer" - - def fit_one_fold( - self, - X_train: np.ndarray, - y_train: np.ndarray, - X_val: Optional[np.ndarray], - y_val: Optional[np.ndarray], - ) -> "CatBoost": - train_dataset = Pool(data=X_train, label=y_train) - eval_dataset = Pool(data=X_val, label=y_val) - - # Set default params if params are None - for param, default_value in [ - ("loss_function", "MultiRMSE"), - ("thread_count", -1), - ("random_state", 42), - ("early_stopping_rounds", 100), - ]: - if self.model_params.get(param) is None: - self.model_params[param] = default_value - - self.model = CatBoostRegressor(**self.model_params) - - self.model.fit( - train_dataset, - eval_set=eval_dataset, - use_best_model=True, - plot=False, - ) - - self.score = self.model.best_score_["validation"][f"{self.model_params['loss_function']}"] - - return self - - def predict(self, X: np.ndarray) -> np.ndarray: - return self.model.predict(X) +from typing import Dict, Optional, Union + +import numpy as np + +try: + from catboost import CatBoostRegressor, Pool +except ImportError: + Pool = None + CatBoostRegressor = None +from .base import Estimator + + +class CatBoost(Estimator): + """CatBoost is a class that performs cross-validation + using CatBoostRegressor. + + Args: + model_params: parameters for the model, + for example: { + "loss_function": "MultiRMSE", + "early_stopping_rounds": 100, + }. + + """ + + def __init__(self, model_params: Dict[str, Union[str, int]]): + super().__init__(model_params) + self.trainer_type = "MLTrainer" + + def fit_one_fold( + self, + X_train: np.ndarray, + y_train: np.ndarray, + X_val: Optional[np.ndarray], + y_val: Optional[np.ndarray], + ) -> "CatBoost": + train_dataset = Pool(data=X_train, label=y_train) + eval_dataset = Pool(data=X_val, label=y_val) + + # Set default params if params are None + for param, default_value in [ + ("loss_function", "MultiRMSE"), + ("thread_count", -1), + ("random_state", 42), + ("early_stopping_rounds", 100), + ]: + if self.model_params.get(param) is None: + self.model_params[param] = default_value + + self.model = CatBoostRegressor(**self.model_params) + + self.model.fit( + train_dataset, + eval_set=eval_dataset, + use_best_model=True, + plot=False, + ) + + self.score = self.model.best_score_["validation"][f"{self.model_params['loss_function']}"] + + return self + + def predict(self, X: np.ndarray) -> np.ndarray: + return self.model.predict(X) diff --git a/tsururu/models/torch_based/TimesNet.py b/tsururu/models/torch_based/TimesNet.py deleted file mode 100644 index 9db6c91..0000000 --- a/tsururu/models/torch_based/TimesNet.py +++ /dev/null @@ -1,102 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.fft -from tsururu.models.torch_based.layers.Embed import DataEmbedding -from tsururu.models.torch_based.layers.Conv_Block import Inception_Block_V1 - - -def FFT_for_Period(x, k=2): - # [B, T, C] - xf = torch.fft.rfft(x, dim=1) - # find period by amplitudes - frequency_list = abs(xf).mean(0).mean(-1) - frequency_list[0] = 0 - _, top_list = torch.topk(frequency_list, k) - top_list = top_list.detach().cpu().numpy() - period = x.shape[1] // top_list - return period, abs(xf).mean(-1)[:, top_list] - - -class TimesBlock(nn.Module): - def __init__(self, seq_len, pred_len, top_k, d_model, d_ff, num_kernels): - super(TimesBlock, self).__init__() - - self.seq_len = seq_len - self.pred_len = pred_len - self.k = top_k - - self.conv = nn.Sequential( - Inception_Block_V1(d_model, d_ff, num_kernels=num_kernels), - nn.GELU(), - Inception_Block_V1(d_ff, d_model, num_kernels=num_kernels) - ) - - def forward(self, x): - B, T, N = x.size() - period_list, period_weight = FFT_for_Period(x, self.k) - - res = [] - for i in range(self.k): - period = period_list[i] - if (self.seq_len + self.pred_len) % period != 0: - length = (((self.seq_len + self.pred_len) // period) + 1) * period - padding = torch.zeros([B, (length - (self.seq_len + self.pred_len)), N]).to(x.device) - out = torch.cat([x, padding], dim=1) - else: - length = self.seq_len + self.pred_len - out = x - out = out.reshape(B, length // period, period, N).permute(0, 3, 1, 2).contiguous() - out = self.conv(out) - out = out.permute(0, 2, 3, 1).reshape(B, -1, N) - res.append(out[:, :(self.seq_len + self.pred_len), :]) - res = torch.stack(res, dim=-1) - period_weight = F.softmax(period_weight, dim=1) - period_weight = period_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1) - res = torch.sum(res * period_weight, -1) - res = res + x - return res - - -class TimesNet_NN(nn.Module): - def __init__(self, seq_len, pred_len, e_layers, enc_in, d_model, d_ff, num_kernels, top_k, c_out, dropout, embed, freq): - super(TimesNet_NN, self).__init__() - - self.seq_len = seq_len - self.pred_len = pred_len - self.e_layers = e_layers - self.d_model = d_model - self.d_ff = d_ff - self.num_kernels = num_kernels - self.top_k = top_k - self.c_out = c_out - self.dropout = dropout - self.embed = embed - self.freq = freq - - self.model = nn.ModuleList([TimesBlock(seq_len, pred_len, top_k, d_model, d_ff, num_kernels) - for _ in range(self.e_layers)]) - - self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout) - self.layer_norm = nn.LayerNorm(d_model) - self.predict_linear = nn.Linear(seq_len, pred_len + seq_len) - self.projection = nn.Linear(d_model, c_out, bias=True) - - def forward(self, x_enc, x_mark_enc): - means = x_enc.mean(1, keepdim=True).detach() - x_enc = x_enc - means - stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) - x_enc /= stdev - - enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] - enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(0, 2, 1) - - for i in range(self.e_layers): - enc_out = self.layer_norm(self.model) - - dec_out = self.projection(enc_out) - - dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)) - dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1)) - - return dec_out[:, -self.pred_len:, :] # [B, L, D] diff --git a/tsururu/models/torch_based/dlinear.py b/tsururu/models/torch_based/dlinear.py index 0b2e3fb..4807de1 100644 --- a/tsururu/models/torch_based/dlinear.py +++ b/tsururu/models/torch_based/dlinear.py @@ -1,30 +1,44 @@ -import torch -import torch.nn as nn +"""DLinear model for time series forecasting.""" + +from .layers.decomposition import series_decomp + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None class DLinear_NN(nn.Module): """DLInear model from the paper https://arxiv.org/pdf/2205.13504.pdf. Args: - model_params: dict with parameters for the model: - - seq_len: int, the length of the input sequence. - - pred_len: int, the length of the output sequence. - - moving_avg: int, the size of the moving average window. - - individual: bool, whether shared model among different variates. - true may be better for time series with different trend and seasonal patterns. - - enc_in: int, the number of input time series. - Needs for individual=True. + - seq_len: int, the length of the input sequence. + - pred_len: int, the length of the output sequence. + - moving_avg: int, the size of the moving average window. + - individual: bool, whether shared model among different variates. + true may be better for time series with different trend and seasonal patterns. + - enc_in: int, the number of input time series. + Needs for individual=True. """ - def __init__(self, seq_len, pred_len, moving_avg=7, individual=False, enc_in=1): + def __init__( + self, + seq_len: int, + pred_len: int, + moving_avg: int = 7, + individual: bool = False, + enc_in: int = 1, + ): super(DLinear_NN, self).__init__() # Params from model_params self.seq_len = seq_len self.pred_len = pred_len # Decomposition - self.decompsition = self.series_decomp(moving_avg) + self.decompsition = series_decomp(moving_avg) self.individual = individual self.channels = enc_in @@ -55,7 +69,16 @@ def __init__(self, seq_len, pred_len, moving_avg=7, individual=False, enc_in=1): self.trainer_type = "DLTrainer" - def encoder(self, x): + def encoder(self, x: torch.Tensor) -> torch.Tensor: + """Encode the input sequence by decomposing it into seasonal and trend components. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + + Returns: + Encoded tensor of shape (batch_size, pred_len, num_features). + + """ seasonal_init, trend_init = self.decompsition(x) seasonal_init, trend_init = seasonal_init.permute(0, 2, 1), trend_init.permute(0, 2, 1) if self.individual: @@ -73,48 +96,31 @@ def encoder(self, x): seasonal_output = self.Linear_Seasonal(seasonal_init) trend_output = self.Linear_Trend(trend_init) x = seasonal_output + trend_output - return x.permute(0, 2, 1) - - def forecast(self, x_enc): - return self.encoder(x_enc) - def forward(self, x_enc): - dec_out = self.forecast(x_enc) - return dec_out[:, -self.pred_len :, :] - - @staticmethod - def my_Layernorm(channels): - layernorm = nn.LayerNorm(channels) + return x.permute(0, 2, 1) - def forward(x): - x_hat = layernorm(x) - bias = torch.mean(x_hat, dim=1).unsqueeze(1).repeat(1, x.shape[1], 1) - return x_hat - bias + def forecast(self, x_enc: torch.Tensor) -> torch.Tensor: + """Forecast the output sequence. - return forward + Args: + x_enc: input tensor of shape (batch_size, seq_len, num_features). - @staticmethod - def moving_avg(kernel_size, stride): - avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) + Returns: + Forecasted tensor of shape (batch_size, pred_len, num_features). - def forward(x): - front = x[:, 0:1, :].repeat(1, (kernel_size - 1) // 2, 1) - end = x[:, -1:, :].repeat(1, (kernel_size - 1) // 2, 1) - x = torch.cat([front, x, end], dim=1) - x = avg(x.permute(0, 2, 1)) - x = x.permute(0, 2, 1) - return x + """ + return self.encoder(x_enc) - return forward + def forward(self, x_enc: torch.Tensor) -> torch.Tensor: + """Forward pass of the model. - @staticmethod - def series_decomp(kernel_size): - moving_avg = DLinear_NN.moving_avg(kernel_size, stride=1) + Args: + x_enc: input tensor of shape (batch_size, seq_len, num_features). - def forward(x): - moving_mean = moving_avg(x) - res = x - moving_mean - return res, moving_mean + Returns: + Output tensor of shape (batch_size, pred_len, num_features). - return forward + """ + dec_out = self.forecast(x_enc) + return dec_out[:, -self.pred_len :, :] diff --git a/tsururu/models/torch_based/layers/Conv_Block.py b/tsururu/models/torch_based/layers/Conv_Block.py deleted file mode 100644 index b98f8ed..0000000 --- a/tsururu/models/torch_based/layers/Conv_Block.py +++ /dev/null @@ -1,30 +0,0 @@ -import torch -import torch.nn as nn - - -class Inception_Block_V1(nn.Module): - def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True): - super(Inception_Block_V1, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.num_kernels = num_kernels - kernels = [] - for i in range(self.num_kernels): - kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i)) - self.kernels = nn.ModuleList(kernels) - if init_weight: - self._initialize_weights() - - def _initialize_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def forward(self, x): - res_list = [] - for i in range(self.num_kernels): - res_list.append(self.kernels[i](x)) - res = torch.stack(res_list, dim=-1).mean(-1) - return res \ No newline at end of file diff --git a/tsururu/models/torch_based/layers/Embed.py b/tsururu/models/torch_based/layers/Embed.py deleted file mode 100644 index 4a8d40f..0000000 --- a/tsururu/models/torch_based/layers/Embed.py +++ /dev/null @@ -1,126 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.utils import weight_norm -import math - - -class TokenEmbedding(nn.Module): - def __init__(self, c_in, d_model): - super(TokenEmbedding, self).__init__() - padding = 1 if torch.__version__ >= '1.5.0' else 2 - self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model, - kernel_size=3, padding=padding, padding_mode='circular', bias=False) - for m in self.modules(): - if isinstance(m, nn.Conv1d): - nn.init.kaiming_normal_( - m.weight, mode='fan_in', nonlinearity='leaky_relu') - - def forward(self, x): - x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2) - return x - - -class PositionalEmbedding(nn.Module): - def __init__(self, d_model, max_len=5000): - super(PositionalEmbedding, self).__init__() - # Compute the positional encodings once in log space. - pe = torch.zeros(max_len, d_model).float() - pe.require_grad = False - - position = torch.arange(0, max_len).float().unsqueeze(1) - div_term = (torch.arange(0, d_model, 2).float() - * -(math.log(10000.0) / d_model)).exp() - - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - - pe = pe.unsqueeze(0) - self.register_buffer('pe', pe) - - def forward(self, x): - return self.pe[:, :x.size(1)] - - -class FixedEmbedding(nn.Module): - def __init__(self, c_in, d_model): - super(FixedEmbedding, self).__init__() - - w = torch.zeros(c_in, d_model).float() - w.require_grad = False - - position = torch.arange(0, c_in).float().unsqueeze(1) - div_term = (torch.arange(0, d_model, 2).float() - * -(math.log(10000.0) / d_model)).exp() - - w[:, 0::2] = torch.sin(position * div_term) - w[:, 1::2] = torch.cos(position * div_term) - - self.emb = nn.Embedding(c_in, d_model) - self.emb.weight = nn.Parameter(w, requires_grad=False) - - def forward(self, x): - return self.emb(x).detach() - - -class TemporalEmbedding(nn.Module): - def __init__(self, d_model, embed_type='fixed', freq='h'): - super(TemporalEmbedding, self).__init__() - - minute_size = 4 - hour_size = 24 - weekday_size = 7 - day_size = 32 - month_size = 13 - - Embed = FixedEmbedding if embed_type == 'fixed' else nn.Embedding - if freq == 't': - self.minute_embed = Embed(minute_size, d_model) - self.hour_embed = Embed(hour_size, d_model) - self.weekday_embed = Embed(weekday_size, d_model) - self.day_embed = Embed(day_size, d_model) - self.month_embed = Embed(month_size, d_model) - - def forward(self, x): - x = x.long() - minute_x = self.minute_embed(x[:, :, 4]) if hasattr( - self, 'minute_embed') else 0. - hour_x = self.hour_embed(x[:, :, 3]) - weekday_x = self.weekday_embed(x[:, :, 2]) - day_x = self.day_embed(x[:, :, 1]) - month_x = self.month_embed(x[:, :, 0]) - - return hour_x + weekday_x + day_x + month_x + minute_x - - -class TimeFeatureEmbedding(nn.Module): - def __init__(self, d_model, embed_type='timeF', freq='h'): - super(TimeFeatureEmbedding, self).__init__() - - freq_map = {'h': 4, 't': 5, 's': 6, - 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3} - d_inp = freq_map[freq] - self.embed = nn.Linear(d_inp, d_model, bias=False) - - def forward(self, x): - return self.embed(x) - - -class DataEmbedding(nn.Module): - def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1): - super(DataEmbedding, self).__init__() - - self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) - self.position_embedding = PositionalEmbedding(d_model=d_model) - self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, - freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding( - d_model=d_model, embed_type=embed_type, freq=freq) - self.dropout = nn.Dropout(p=dropout) - - def forward(self, x, x_mark): - if x_mark is None: - x = self.value_embedding(x) + self.position_embedding(x) - else: - x = self.value_embedding( - x) + self.temporal_embedding(x_mark) + self.position_embedding(x) - return self.dropout(x) diff --git a/tsururu/models/torch_based/layers/RevIN.py b/tsururu/models/torch_based/layers/RevIN.py deleted file mode 100644 index f7fed81..0000000 --- a/tsururu/models/torch_based/layers/RevIN.py +++ /dev/null @@ -1,63 +0,0 @@ -# code from https://github.com/ts-kim/RevIN, with minor modifications - -import torch -import torch.nn as nn - -class RevIN(nn.Module): - def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False): - """ - :param num_features: the number of features or channels - :param eps: a value added for numerical stability - :param affine: if True, RevIN has learnable affine parameters - """ - super(RevIN, self).__init__() - self.num_features = num_features - self.eps = eps - self.affine = affine - self.subtract_last = subtract_last - if self.affine: - self._init_params() - - def forward(self, x, mode:str): - if mode == 'norm': - self._get_statistics(x) - x = self._normalize(x) - elif mode == 'denorm': - x = self._denormalize(x) - else: raise NotImplementedError - return x - - def _init_params(self): - # initialize RevIN params: (C,) - self.affine_weight = nn.Parameter(torch.ones(self.num_features)) - self.affine_bias = nn.Parameter(torch.zeros(self.num_features)) - - def _get_statistics(self, x): - dim2reduce = tuple(range(1, x.ndim-1)) - if self.subtract_last: - self.last = x[:,-1,:].unsqueeze(1) - else: - self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach() - self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach() - - def _normalize(self, x): - if self.subtract_last: - x = x - self.last - else: - x = x - self.mean - x = x / self.stdev - if self.affine: - x = x * self.affine_weight - x = x + self.affine_bias - return x - - def _denormalize(self, x): - if self.affine: - x = x - self.affine_bias - x = x / (self.affine_weight + self.eps*self.eps) - x = x * self.stdev - if self.subtract_last: - x = x + self.last - else: - x = x + self.mean - return x diff --git a/tsururu/models/torch_based/layers/convolution.py b/tsururu/models/torch_based/layers/convolution.py new file mode 100644 index 0000000..4cc8c05 --- /dev/null +++ b/tsururu/models/torch_based/layers/convolution.py @@ -0,0 +1,64 @@ +"""Module for convolution layers.""" + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None + + +class Inception_Block_V1(nn.Module): + """Inception Block Version 1. + + Args: + in_channels: number of input channels. + out_channels: number of output channels. + num_kernels: number of convolutional kernels. + init_weight: whether to initialize weights. + + """ + + def __init__( + self, in_channels: int, out_channels: int, num_kernels: int = 6, init_weight: bool = True + ): + super(Inception_Block_V1, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.num_kernels = num_kernels + kernels = [] + + # Create a list of convolutional layers with varying kernel sizes + for i in range(self.num_kernels): + kernels.append(nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i)) + self.kernels = nn.ModuleList(kernels) + + # Initialize weights if specified + if init_weight: + self._initialize_weights() + + def _initialize_weights(self): + """Initialize weights of the convolutional layers.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the Inception block. + + Args: + x: input tensor of shape (batch_size, in_channels, height, width). + + Returns: + output tensor after applying Inception block. + + """ + # Apply each convolutional kernel to the input and collect results + res_list = [kernel(x) for kernel in self.kernels] + + # Stack the results along a new dimension and take the mean along that dimension + res = torch.stack(res_list, dim=-1).mean(-1) + + return res diff --git a/tsururu/models/torch_based/layers/decomposition.py b/tsururu/models/torch_based/layers/decomposition.py new file mode 100644 index 0000000..f6db4c2 --- /dev/null +++ b/tsururu/models/torch_based/layers/decomposition.py @@ -0,0 +1,70 @@ +"""Module for decomposition layers.""" + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None + + +class moving_avg(nn.Module): + """Moving average block to highlight the trend of time series. + + Args: + kernel_size: size of the kernel. + stride: stride for the moving average. + + """ + + def __init__(self, kernel_size: int, stride: int): + super(moving_avg, self).__init__() + self.kernel_size = kernel_size + self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass for computing the moving average. + + Args: + x: input tensor. + + Returns: + tensor with the moving average applied. + + """ + # padding on the both ends of time series + front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1) + end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1) + x = torch.cat([front, x, end], dim=1) + x = self.avg(x.permute(0, 2, 1)) + x = x.permute(0, 2, 1) + + return x + + +class series_decomp(nn.Module): + """Series decomposition block. + + Args: + kernel_size: size of the kernel for the moving average. + + """ + + def __init__(self, kernel_size: int): + super(series_decomp, self).__init__() + self.moving_avg = moving_avg(kernel_size, stride=1) + + def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Forward pass for decomposing the series into trend and remainder. + + Args: + x: input tensor. + + Returns: + tuple of tensors (remainder, trend). + + """ + moving_mean = self.moving_avg(x) + res = x - moving_mean + + return res, moving_mean diff --git a/tsururu/models/torch_based/layers/embedding.py b/tsururu/models/torch_based/layers/embedding.py new file mode 100644 index 0000000..bdf8cae --- /dev/null +++ b/tsururu/models/torch_based/layers/embedding.py @@ -0,0 +1,254 @@ +"""Module for embedding layers.""" + +import math +from typing import Optional + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None + + +class TokenEmbedding(nn.Module): + """Token embedding layer using 1D convolution. + + Args: + c_in: number of input channels. + d_model: dimension of the model. + + """ + + def __init__(self, c_in: int, d_model: int): + super(TokenEmbedding, self).__init__() + padding = 1 if torch.__version__ >= "1.5.0" else 2 + self.tokenConv = nn.Conv1d( + in_channels=c_in, + out_channels=d_model, + kernel_size=3, + padding=padding, + padding_mode="circular", + bias=False, + ) + for m in self.modules(): + if isinstance(m, nn.Conv1d): + nn.init.kaiming_normal_(m.weight, mode="fan_in", nonlinearity="leaky_relu") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the token embedding. + + Args: + x: input tensor of shape (batch_size, c_in, seq_len). + + Returns: + tensor of shape (batch_size, seq_len, d_model). + """ + x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2) + + return x + + +class PositionalEmbedding(nn.Module): + """Positional encoding using sine and cosine functions. + + Args: + d_model: dimension of the model. + max_len: maximum length of the sequence. + + """ + + def __init__(self, d_model: int, max_len: int = 5000): + super(PositionalEmbedding, self).__init__() + # Compute the positional encodings once in log space. + pe = torch.zeros(max_len, d_model).float() + pe.require_grad = False + + position = torch.arange(0, max_len).float().unsqueeze(1) + div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() + + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + + pe = pe.unsqueeze(0) + self.register_buffer("pe", pe) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the positional embedding. + + Args: + x: input tensor of shape (batch_size, seq_len, d_model). + + Returns: + positional encoding tensor of shape (1, seq_len, d_model). + + """ + return self.pe[:, : x.size(1)] + + +class FixedEmbedding(nn.Module): + """Fixed embedding layer using precomputed sine and cosine values. + + Args: + c_in: number of input channels. + d_model: dimension of the model. + + """ + + def __init__(self, c_in: int, d_model: int): + super(FixedEmbedding, self).__init__() + + w = torch.zeros(c_in, d_model).float() + w.require_grad = False + + position = torch.arange(0, c_in).float().unsqueeze(1) + div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() + + w[:, 0::2] = torch.sin(position * div_term) + w[:, 1::2] = torch.cos(position * div_term) + + self.emb = nn.Embedding(c_in, d_model) + self.emb.weight = nn.Parameter(w, requires_grad=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the fixed embedding. + + Args: + x: input tensor of shape (batch_size, seq_len). + + Returns: + embedding tensor of shape (batch_size, seq_len, d_model). + + """ + return self.emb(x).detach() + + +class TemporalEmbedding(nn.Module): + """Temporal embedding layer for time-related features. + + Args: + d_model: dimension of the model. + embed_type: type of embedding ('fixed' or 'learned'). + freq: frequency of the time features ('h', 't', etc.). + + """ + + def __init__(self, d_model: int, embed_type: str = "fixed", freq: str = "h"): + super(TemporalEmbedding, self).__init__() + + minute_size = 4 + hour_size = 24 + weekday_size = 7 + day_size = 32 + month_size = 13 + + Embed = FixedEmbedding if embed_type == "fixed" else nn.Embedding + if freq == "t": + self.minute_embed = Embed(minute_size, d_model) + self.hour_embed = Embed(hour_size, d_model) + self.weekday_embed = Embed(weekday_size, d_model) + self.day_embed = Embed(day_size, d_model) + self.month_embed = Embed(month_size, d_model) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the temporal embedding. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + + Returns: + embedding tensor of shape (batch_size, seq_len, d_model). + + """ + x = x.long() + minute_x = self.minute_embed(x[:, :, 4]) if hasattr(self, "minute_embed") else 0.0 + hour_x = self.hour_embed(x[:, :, 3]) + weekday_x = self.weekday_embed(x[:, :, 2]) + day_x = self.day_embed(x[:, :, 1]) + month_x = self.month_embed(x[:, :, 0]) + + return hour_x + weekday_x + day_x + month_x + minute_x + + +class TimeFeatureEmbedding(nn.Module): + """Time feature embedding layer using linear transformation. + + Args: + d_model: dimension of the model. + embed_type: type of embedding (default is 'timeF'). + freq: frequency of the time features ('h', 't', etc.). + + """ + + def __init__(self, d_model: int, embed_type: str = "timeF", freq: str = "h"): + super(TimeFeatureEmbedding, self).__init__() + + freq_map = {"h": 4, "t": 5, "s": 6, "m": 1, "a": 1, "w": 2, "d": 3, "b": 3} + d_inp = freq_map[freq] + self.embed = nn.Linear(d_inp, d_model, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass of the time feature embedding. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + + Returns: + embedding tensor of shape (batch_size, seq_len, d_model). + + """ + return self.embed(x) + + +class DataEmbedding(nn.Module): + """Data embedding layer combining token, positional, and temporal embeddings. + + Args: + c_in: number of input channels. + d_model: dimension of the model. + embed_type: type of temporal embedding ('fixed', 'learned', or 'timeF'). + freq: frequency of the time features ('h', 't', etc.). + dropout: dropout rate. + + """ + + def __init__( + self, + c_in: int, + d_model: int, + embed_type: str = "fixed", + freq: str = "h", + dropout: float = 0.1, + ): + super(DataEmbedding, self).__init__() + + self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) + self.position_embedding = PositionalEmbedding(d_model=d_model) + self.temporal_embedding = ( + TemporalEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) + if embed_type != "timeF" + else TimeFeatureEmbedding(d_model=d_model, embed_type=embed_type, freq=freq) + ) + self.dropout = nn.Dropout(p=dropout) + + def forward(self, x: torch.Tensor, x_mark: Optional[torch.Tensor] = None) -> torch.Tensor: + """Forward pass of the data embedding. + + Args: + x: input tensor of shape (batch_size, seq_len, c_in). + x_mark: optional tensor for temporal features. + + Returns: + embedding tensor of shape (batch_size, seq_len, d_model). + + """ + if x_mark is None: + x = self.value_embedding(x) + self.position_embedding(x) + else: + x = ( + self.value_embedding(x) + + self.temporal_embedding(x_mark) + + self.position_embedding(x) + ) + + return self.dropout(x) diff --git a/tsururu/models/torch_based/layers/PatchTST_backbone.py b/tsururu/models/torch_based/layers/patch_tst.py similarity index 53% rename from tsururu/models/torch_based/layers/PatchTST_backbone.py rename to tsururu/models/torch_based/layers/patch_tst.py index 8debb8e..7d88f64 100644 --- a/tsururu/models/torch_based/layers/PatchTST_backbone.py +++ b/tsururu/models/torch_based/layers/patch_tst.py @@ -1,605 +1,861 @@ -__all__ = ["PatchTST_backbone"] - -# Cell -from typing import Optional -import torch -from torch import nn -from torch import Tensor -import torch.nn.functional as F -import numpy as np - -# from collections import OrderedDict -from tsururu.models.torch_based.layers.PatchTST_layers import Transpose, positional_encoding, get_activation_fn -from tsururu.models.torch_based.layers.RevIN import RevIN - - -# Cell -class PatchTST_backbone(nn.Module): - def __init__( - self, - c_in: int, - context_window: int, - target_window: int, - patch_len: int, - stride: int, - max_seq_len: Optional[int] = 1024, - n_layers: int = 3, - d_model=128, - n_heads=16, - d_k: Optional[int] = None, - d_v: Optional[int] = None, - d_ff: int = 256, - norm: str = "BatchNorm", - attn_dropout: float = 0.0, - dropout: float = 0.0, - act: str = "gelu", - key_padding_mask: bool = "auto", - padding_var: Optional[int] = None, - attn_mask: Optional[Tensor] = None, - res_attention: bool = True, - pre_norm: bool = False, - store_attn: bool = False, - pe: str = "zeros", - learn_pe: bool = True, - fc_dropout: float = 0.0, - head_dropout=0, - padding_patch=None, - pretrain_head: bool = False, - head_type="flatten", - individual=False, - revin=True, - affine=True, - subtract_last=False, - verbose: bool = False, - **kwargs, - ): - - super().__init__() - - # RevIn - self.revin = revin - if self.revin: - self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last) - - # Patching - self.patch_len = patch_len - self.stride = stride - self.padding_patch = padding_patch - patch_num = int((context_window - patch_len) / stride + 1) - if padding_patch == "end": # can be modified to general case - self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) - patch_num += 1 - - # Backbone - self.backbone = TSTiEncoder( - c_in, - patch_num=patch_num, - patch_len=patch_len, - max_seq_len=max_seq_len, - n_layers=n_layers, - d_model=d_model, - n_heads=n_heads, - d_k=d_k, - d_v=d_v, - d_ff=d_ff, - attn_dropout=attn_dropout, - dropout=dropout, - act=act, - key_padding_mask=key_padding_mask, - padding_var=padding_var, - attn_mask=attn_mask, - res_attention=res_attention, - pre_norm=pre_norm, - store_attn=store_attn, - pe=pe, - learn_pe=learn_pe, - verbose=verbose, - **kwargs, - ) - - # Head - self.head_nf = d_model * patch_num - self.n_vars = c_in - self.pretrain_head = pretrain_head - self.head_type = head_type - self.individual = individual - - if self.pretrain_head: - self.head = self.create_pretrain_head( - self.head_nf, c_in, fc_dropout - ) # custom head passed as a partial func with all its kwargs - elif head_type == "flatten": - self.head = Flatten_Head( - self.individual, - self.n_vars, - self.head_nf, - target_window, - head_dropout=head_dropout, - ) - - def forward(self, z): # z: [bs x nvars x seq_len] - # norm - if self.revin: - z = z.permute(0, 2, 1) - z = self.revin_layer(z, "norm") - z = z.permute(0, 2, 1) - - # do patching - if self.padding_patch == "end": - z = self.padding_patch_layer(z) - z = z.unfold( - dimension=-1, size=self.patch_len, step=self.stride - ) # z: [bs x nvars x patch_num x patch_len] - z = z.permute(0, 1, 3, 2) # z: [bs x nvars x patch_len x patch_num] - - # model - z = self.backbone(z) # z: [bs x nvars x d_model x patch_num] - z = self.head(z) # z: [bs x nvars x target_window] - - # denorm - if self.revin: - z = z.permute(0, 2, 1) - z = self.revin_layer(z, "denorm") - z = z.permute(0, 2, 1) - return z - - def create_pretrain_head(self, head_nf, vars, dropout): - return nn.Sequential(nn.Dropout(dropout), nn.Conv1d(head_nf, vars, 1)) - - -class Flatten_Head(nn.Module): - def __init__(self, individual, n_vars, nf, target_window, head_dropout=0): - super().__init__() - - self.individual = individual - self.n_vars = n_vars - - if self.individual: - self.linears = nn.ModuleList() - self.dropouts = nn.ModuleList() - self.flattens = nn.ModuleList() - for i in range(self.n_vars): - self.flattens.append(nn.Flatten(start_dim=-2)) - self.linears.append(nn.Linear(nf, target_window)) - self.dropouts.append(nn.Dropout(head_dropout)) - else: - self.flatten = nn.Flatten(start_dim=-2) - self.linear = nn.Linear(nf, target_window) - self.dropout = nn.Dropout(head_dropout) - - def forward(self, x): # x: [bs x nvars x d_model x patch_num] - if self.individual: - x_out = [] - for i in range(self.n_vars): - z = self.flattens[i](x[:, i, :, :]) # z: [bs x d_model * patch_num] - z = self.linears[i](z) # z: [bs x target_window] - z = self.dropouts[i](z) - x_out.append(z) - x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window] - else: - x = self.flatten(x) - x = self.linear(x) - x = self.dropout(x) - return x - - -class TSTiEncoder(nn.Module): # i means channel-independent - def __init__( - self, - c_in, - patch_num, - patch_len, - max_seq_len=1024, - n_layers=3, - d_model=128, - n_heads=16, - d_k=None, - d_v=None, - d_ff=256, - norm="BatchNorm", - attn_dropout=0.0, - dropout=0.0, - act="gelu", - store_attn=False, - key_padding_mask="auto", - padding_var=None, - attn_mask=None, - res_attention=True, - pre_norm=False, - pe="zeros", - learn_pe=True, - verbose=False, - **kwargs, - ): - - super().__init__() - - self.patch_num = patch_num - self.patch_len = patch_len - - # Input encoding - q_len = patch_num - self.W_P = nn.Linear( - patch_len, d_model - ) # Eq 1: projection of feature vectors onto a d-dim vector space - self.seq_len = q_len - - # Positional encoding - self.W_pos = positional_encoding(pe, learn_pe, q_len, d_model) - - # Residual dropout - self.dropout = nn.Dropout(dropout) - - # Encoder - self.encoder = TSTEncoder( - q_len, - d_model, - n_heads, - d_k=d_k, - d_v=d_v, - d_ff=d_ff, - norm=norm, - attn_dropout=attn_dropout, - dropout=dropout, - pre_norm=pre_norm, - activation=act, - res_attention=res_attention, - n_layers=n_layers, - store_attn=store_attn, - ) - - def forward(self, x) -> Tensor: # x: [bs x nvars x patch_len x patch_num] - - n_vars = x.shape[1] - # Input encoding - x = x.permute(0, 1, 3, 2) # x: [bs x nvars x patch_num x patch_len] - x = self.W_P(x) # x: [bs x nvars x patch_num x d_model] - - u = torch.reshape( - x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]) - ) # u: [bs * nvars x patch_num x d_model] - u = self.dropout(u + self.W_pos) # u: [bs * nvars x patch_num x d_model] - - # Encoder - z = self.encoder(u) # z: [bs * nvars x patch_num x d_model] - z = torch.reshape( - z, (-1, n_vars, z.shape[-2], z.shape[-1]) - ) # z: [bs x nvars x patch_num x d_model] - z = z.permute(0, 1, 3, 2) # z: [bs x nvars x d_model x patch_num] - - return z - - -# Cell -class TSTEncoder(nn.Module): - def __init__( - self, - q_len, - d_model, - n_heads, - d_k=None, - d_v=None, - d_ff=None, - norm="BatchNorm", - attn_dropout=0.0, - dropout=0.0, - activation="gelu", - res_attention=False, - n_layers=1, - pre_norm=False, - store_attn=False, - ): - super().__init__() - - self.layers = nn.ModuleList( - [ - TSTEncoderLayer( - q_len, - d_model, - n_heads=n_heads, - d_k=d_k, - d_v=d_v, - d_ff=d_ff, - norm=norm, - attn_dropout=attn_dropout, - dropout=dropout, - activation=activation, - res_attention=res_attention, - pre_norm=pre_norm, - store_attn=store_attn, - ) - for i in range(n_layers) - ] - ) - self.res_attention = res_attention - - def forward( - self, - src: Tensor, - key_padding_mask: Optional[Tensor] = None, - attn_mask: Optional[Tensor] = None, - ): - output = src - scores = None - if self.res_attention: - for mod in self.layers: - output, scores = mod( - output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - return output - else: - for mod in self.layers: - output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask) - return output - - -class TSTEncoderLayer(nn.Module): - def __init__( - self, - q_len, - d_model, - n_heads, - d_k=None, - d_v=None, - d_ff=256, - store_attn=False, - norm="BatchNorm", - attn_dropout=0, - dropout=0.0, - bias=True, - activation="gelu", - res_attention=False, - pre_norm=False, - ): - super().__init__() - assert ( - not d_model % n_heads - ), f"d_model ({d_model}) must be divisible by n_heads ({n_heads})" - d_k = d_model // n_heads if d_k is None else d_k - d_v = d_model // n_heads if d_v is None else d_v - - # Multi-Head attention - self.res_attention = res_attention - self.self_attn = _MultiheadAttention( - d_model, - n_heads, - d_k, - d_v, - attn_dropout=attn_dropout, - proj_dropout=dropout, - res_attention=res_attention, - ) - - # Add & Norm - self.dropout_attn = nn.Dropout(dropout) - if "batch" in norm.lower(): - self.norm_attn = nn.Sequential( - Transpose(1, 2), nn.BatchNorm1d(d_model), Transpose(1, 2) - ) - else: - self.norm_attn = nn.LayerNorm(d_model) - - # Position-wise Feed-Forward - self.ff = nn.Sequential( - nn.Linear(d_model, d_ff, bias=bias), - get_activation_fn(activation), - nn.Dropout(dropout), - nn.Linear(d_ff, d_model, bias=bias), - ) - - # Add & Norm - self.dropout_ffn = nn.Dropout(dropout) - if "batch" in norm.lower(): - self.norm_ffn = nn.Sequential( - Transpose(1, 2), nn.BatchNorm1d(d_model), Transpose(1, 2) - ) - else: - self.norm_ffn = nn.LayerNorm(d_model) - - self.pre_norm = pre_norm - self.store_attn = store_attn - - def forward( - self, - src: Tensor, - prev: Optional[Tensor] = None, - key_padding_mask: Optional[Tensor] = None, - attn_mask: Optional[Tensor] = None, - ) -> Tensor: - - # Multi-Head attention sublayer - if self.pre_norm: - src = self.norm_attn(src) - # Multi-Head attention - if self.res_attention: - src2, attn, scores = self.self_attn( - src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - else: - src2, attn = self.self_attn( - src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - if self.store_attn: - self.attn = attn - # Add & Norm - src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout - if not self.pre_norm: - src = self.norm_attn(src) - - # Feed-forward sublayer - if self.pre_norm: - src = self.norm_ffn(src) - # Position-wise Feed-Forward - src2 = self.ff(src) - # Add & Norm - src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout - if not self.pre_norm: - src = self.norm_ffn(src) - - if self.res_attention: - return src, scores - else: - return src - - -class _MultiheadAttention(nn.Module): - def __init__( - self, - d_model, - n_heads, - d_k=None, - d_v=None, - res_attention=False, - attn_dropout=0.0, - proj_dropout=0.0, - qkv_bias=True, - lsa=False, - ): - """Multi Head Attention Layer - Input shape: - Q: [batch_size (bs) x max_q_len x d_model] - K, V: [batch_size (bs) x q_len x d_model] - mask: [q_len x q_len] - """ - super().__init__() - d_k = d_model // n_heads if d_k is None else d_k - d_v = d_model // n_heads if d_v is None else d_v - - self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v - - self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias) - self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias) - self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias) - - # Scaled Dot-Product Attention (multiple heads) - self.res_attention = res_attention - self.sdp_attn = _ScaledDotProductAttention( - d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa - ) - - # Poject output - self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout)) - - def forward( - self, - Q: Tensor, - K: Optional[Tensor] = None, - V: Optional[Tensor] = None, - prev: Optional[Tensor] = None, - key_padding_mask: Optional[Tensor] = None, - attn_mask: Optional[Tensor] = None, - ): - - bs = Q.size(0) - if K is None: - K = Q - if V is None: - V = Q - - # Linear (+ split in multiple heads) - q_s = ( - self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1, 2) - ) # q_s : [bs x n_heads x max_q_len x d_k] - k_s = ( - self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0, 2, 3, 1) - ) # k_s : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3) - v_s = ( - self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1, 2) - ) # v_s : [bs x n_heads x q_len x d_v] - - # Apply Scaled Dot-Product Attention (multiple heads) - if self.res_attention: - output, attn_weights, attn_scores = self.sdp_attn( - q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - else: - output, attn_weights = self.sdp_attn( - q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask - ) - # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len] - - # back to the original inputs dimensions - output = ( - output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) - ) # output: [bs x q_len x n_heads * d_v] - output = self.to_out(output) - - if self.res_attention: - return output, attn_weights, attn_scores - else: - return output, attn_weights - - -class _ScaledDotProductAttention(nn.Module): - r"""Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) with optional residual attention from previous layer - (Realformer: Transformer likes residual attention by He et al, 2020) and locality self sttention (Vision Transformer for Small-Size Datasets - by Lee et al, 2021)""" - - def __init__(self, d_model, n_heads, attn_dropout=0.0, res_attention=False, lsa=False): - super().__init__() - self.attn_dropout = nn.Dropout(attn_dropout) - self.res_attention = res_attention - head_dim = d_model // n_heads - self.scale = nn.Parameter(torch.tensor(head_dim**-0.5), requires_grad=lsa) - self.lsa = lsa - - def forward( - self, - q: Tensor, - k: Tensor, - v: Tensor, - prev: Optional[Tensor] = None, - key_padding_mask: Optional[Tensor] = None, - attn_mask: Optional[Tensor] = None, - ): - """ - Input shape: - q : [bs x n_heads x max_q_len x d_k] - k : [bs x n_heads x d_k x seq_len] - v : [bs x n_heads x seq_len x d_v] - prev : [bs x n_heads x q_len x seq_len] - key_padding_mask: [bs x seq_len] - attn_mask : [1 x seq_len x seq_len] - Output shape: - output: [bs x n_heads x q_len x d_v] - attn : [bs x n_heads x q_len x seq_len] - scores : [bs x n_heads x q_len x seq_len] - """ - - # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence - attn_scores = ( - torch.matmul(q, k) * self.scale - ) # attn_scores : [bs x n_heads x max_q_len x q_len] - - # Add pre-softmax attention scores from the previous layer (optional) - if prev is not None: - attn_scores = attn_scores + prev - - # Attention mask (optional) - if ( - attn_mask is not None - ): # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len - if attn_mask.dtype == torch.bool: - attn_scores.masked_fill_(attn_mask, -np.inf) - else: - attn_scores += attn_mask - - # Key padding mask (optional) - if ( - key_padding_mask is not None - ): # mask with shape [bs x q_len] (only when max_w_len == q_len) - attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf) - - # normalize the attention weights - attn_weights = F.softmax( - attn_scores, dim=-1 - ) # attn_weights : [bs x n_heads x max_q_len x q_len] - attn_weights = self.attn_dropout(attn_weights) - - # compute the new values given the attention weights - output = torch.matmul(attn_weights, v) # output: [bs x n_heads x max_q_len x d_v] - - if self.res_attention: - return output, attn_weights, attn_scores - else: - return output, attn_weights +"""Module for PatchTST layers.""" + +from typing import Optional, Tuple, Union + +import numpy as np + +from .positional_encoding import positional_encoding +from .rev_in import RevIN +from .utils import Transpose, get_activation_fn + +try: + import torch + import torch.nn as nn + import torch.nn.functional as F + from torch import Tensor +except ImportError: + torch = None + nn = None + Tensor = None + F = None + + +# Cell +class PatchTST_backbone(nn.Module): + """Backbone for Patch Time Series Transformer. + + Args: + c_in: number of input channels. + context_window: length of the context window. + target_window: length of the target window. + patch_len: length of each patch. + stride: stride between patches. + max_seq_len: maximum sequence length. + n_layers: number of layers in the encoder. + d_model: dimension of the model. + n_heads: number of attention heads. + d_k: dimension of the key vectors. + d_v: dimension of the value vectors. + d_ff: dimension of the feed-forward network. + norm: type of normalization. + attn_dropout: dropout rate for attention. + dropout: dropout rate. + act: activation function. + key_padding_mask: use key padding mask. + padding_var: padding variable. + attn_mask: attention mask. + res_attention: use residual attention. + pre_norm: use pre-normalization. + store_attn: store attention weights. + pe: type of positional encoding. + learn_pe: learn positional encoding. + fc_dropout: dropout rate for fully connected layer. + head_dropout: dropout rate for head. + padding_patch: padding for patches. + pretrain_head: use pretrain head. + head_type: type of head. + individual: use individual head. + revin: use RevIN layer. + affine: use affine transformation in RevIN. + subtract_last: subtract last value in RevIN. + verbose: print additional information. + **kwargs: additional arguments. + """ + + def __init__( + self, + c_in: int, + context_window: int, + target_window: int, + patch_len: int, + stride: int, + max_seq_len: Optional[int] = 1024, + n_layers: int = 3, + d_model: int = 128, + n_heads: int = 16, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + d_ff: int = 256, + norm: str = "BatchNorm", + attn_dropout: float = 0.0, + dropout: float = 0.0, + act: str = "gelu", + key_padding_mask: Union[bool, str] = "auto", + padding_var: Optional[int] = None, + attn_mask: Optional[Tensor] = None, + res_attention: bool = True, + pre_norm: bool = False, + store_attn: bool = False, + pe: str = "zeros", + learn_pe: bool = True, + fc_dropout: float = 0.0, + head_dropout: float = 0, + padding_patch: Optional[str] = None, + pretrain_head: bool = False, + head_type: str = "flatten", + individual: bool = False, + revin: bool = True, + affine: bool = True, + subtract_last: bool = False, + verbose: bool = False, + **kwargs, + ): + + super().__init__() + + # RevIn + self.revin = revin + if self.revin: + self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last) + + # Patching + self.patch_len = patch_len + self.stride = stride + self.padding_patch = padding_patch + patch_num = int((context_window - patch_len) / stride + 1) + if padding_patch == "end": # can be modified to general case + self.padding_patch_layer = nn.ReplicationPad1d((0, stride)) + patch_num += 1 + + # Backbone + self.backbone = TSTiEncoder( + c_in, + patch_num=patch_num, + patch_len=patch_len, + max_seq_len=max_seq_len, + n_layers=n_layers, + d_model=d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + attn_dropout=attn_dropout, + dropout=dropout, + act=act, + key_padding_mask=key_padding_mask, + padding_var=padding_var, + attn_mask=attn_mask, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + pe=pe, + learn_pe=learn_pe, + verbose=verbose, + **kwargs, + ) + + # Head + self.head_nf = d_model * patch_num + self.n_vars = c_in + self.pretrain_head = pretrain_head + self.head_type = head_type + self.individual = individual + + if self.pretrain_head: + self.head = self.create_pretrain_head( + self.head_nf, c_in, fc_dropout + ) # custom head passed as a partial func with all its kwargs + elif head_type == "flatten": + self.head = Flatten_Head( + self.individual, + self.n_vars, + self.head_nf, + target_window, + head_dropout=head_dropout, + ) + + def forward(self, z: Tensor) -> Tensor: + """Forward pass of the PatchTST backbone. + + Args: + z: input tensor of shape (batch_size, nvars, seq_len). + + Returns: + output tensor of shape (batch_size, nvars, target_window). + + """ + # norm + if self.revin: + z = z.permute(0, 2, 1) + z = self.revin_layer(z, "norm") + z = z.permute(0, 2, 1) + + # do patching + if self.padding_patch == "end": + z = self.padding_patch_layer(z) + z = z.unfold( + dimension=-1, size=self.patch_len, step=self.stride + ) # z: [bs x nvars x patch_num x patch_len] + z = z.permute(0, 1, 3, 2) # z: [bs x nvars x patch_len x patch_num] + + # model + z = self.backbone(z) # z: [bs x nvars x d_model x patch_num] + z = self.head(z) # z: [bs x nvars x target_window] + + # denorm + if self.revin: + z = z.permute(0, 2, 1) + z = self.revin_layer(z, "denorm") + z = z.permute(0, 2, 1) + + return z + + def create_pretrain_head(self, head_nf, vars, dropout): + """Create pretrain head. + + Args: + head_nf: number of features in the head. + vars: number of variables. + dropout: dropout rate. + + Returns: + pretrain head module. + + """ + return nn.Sequential(nn.Dropout(dropout), nn.Conv1d(head_nf, vars, 1)) + + +class Flatten_Head(nn.Module): + """Flatten head for PatchTST. + + Args: + individual: whether to use individual heads for each variable. + n_vars: number of variables. + nf: number of features. + target_window: length of the target window. + head_dropout: dropout rate for the head. Default is 0. + + """ + + def __init__(self, individual, n_vars, nf, target_window, head_dropout=0): + super().__init__() + + self.individual = individual + self.n_vars = n_vars + + if self.individual: + self.linears = nn.ModuleList() + self.dropouts = nn.ModuleList() + self.flattens = nn.ModuleList() + for i in range(self.n_vars): + self.flattens.append(nn.Flatten(start_dim=-2)) + self.linears.append(nn.Linear(nf, target_window)) + self.dropouts.append(nn.Dropout(head_dropout)) + else: + self.flatten = nn.Flatten(start_dim=-2) + self.linear = nn.Linear(nf, target_window) + self.dropout = nn.Dropout(head_dropout) + + def forward(self, x): + """Forward pass of the flatten head. + + Args: + x: input tensor of shape (batch_size, nvars, d_model, patch_num). + + Returns: + output tensor of shape (batch_size, nvars, target_window). + + """ + if self.individual: + x_out = [] + for i in range(self.n_vars): + z = self.flattens[i](x[:, i, :, :]) # z: [bs x d_model * patch_num] + z = self.linears[i](z) # z: [bs x target_window] + z = self.dropouts[i](z) + x_out.append(z) + x = torch.stack(x_out, dim=1) # x: [bs x nvars x target_window] + else: + x = self.flatten(x) + x = self.linear(x) + x = self.dropout(x) + return x + + +class TSTiEncoder(nn.Module): + """Time Series Transformer independent encoder. + + Args: + c_in: number of input channels. + patch_num: number of patches. + patch_len: length of each patch. + max_seq_len: maximum sequence length. Default is 1024. + n_layers: number of layers in the encoder. Default is 3. + d_model: dimension of the model. Default is 128. + n_heads: number of attention heads. Default is 16. + d_k: dimension of the key vectors. Default is None. + d_v: dimension of the value vectors. Default is None. + d_ff: dimension of the feed-forward network. Default is 256. + norm: type of normalization. Default is "BatchNorm". + attn_dropout: dropout rate for attention. Default is 0.0. + dropout: dropout rate. Default is 0.0. + act: activation function. Default is "gelu". + store_attn: store attention weights. Default is False. + key_padding_mask: use key padding mask. Default is "auto". + padding_var: padding variable. Default is None. + attn_mask: attention mask. Default is None. + res_attention: use residual attention. Default is True. + pre_norm: use pre-normalization. Default is False. + pe: type of positional encoding. Default is "zeros". + learn_pe: learn positional encoding. Default is True. + verbose: print additional information. Default is False. + **kwargs: additional arguments. + + """ + + def __init__( + self, + c_in: int, + patch_num: int, + patch_len: int, + max_seq_len: int = 1024, + n_layers: int = 3, + d_model: int = 128, + n_heads: int = 16, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + d_ff: int = 256, + norm: str = "BatchNorm", + attn_dropout: float = 0.0, + dropout: float = 0.0, + act: str = "gelu", + store_attn: bool = False, + key_padding_mask: Union[bool, str] = "auto", + padding_var: Optional[int] = None, + attn_mask: Optional[Tensor] = None, + res_attention: bool = True, + pre_norm: bool = False, + pe: str = "zeros", + learn_pe: bool = True, + verbose: bool = False, + **kwargs, + ): + + super().__init__() + + self.patch_num = patch_num + self.patch_len = patch_len + + # Input encoding + q_len = patch_num + self.W_P = nn.Linear( + patch_len, d_model + ) # Eq 1: projection of feature vectors onto a d-dim vector space + self.seq_len = q_len + + # Positional encoding + self.W_pos = positional_encoding(pe, learn_pe, q_len, d_model) + + # Residual dropout + self.dropout = nn.Dropout(dropout) + + # Encoder + self.encoder = TSTEncoder( + q_len, + d_model, + n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + pre_norm=pre_norm, + activation=act, + res_attention=res_attention, + n_layers=n_layers, + store_attn=store_attn, + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward pass of the TSTi encoder. + + Args: + x: input tensor of shape (batch_size, nvars, patch_len, patch_num). + + Returns: + output tensor of shape (batch_size, nvars, d_model, patch_num). + + """ + + n_vars = x.shape[1] + # Input encoding + x = x.permute(0, 1, 3, 2) # x: [bs x nvars x patch_num x patch_len] + x = self.W_P(x) # x: [bs x nvars x patch_num x d_model] + + u = torch.reshape( + x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]) + ) # u: [bs * nvars x patch_num x d_model] + u = self.dropout(u + self.W_pos) # u: [bs * nvars x patch_num x d_model] + + # Encoder + z = self.encoder(u) # z: [bs * nvars x patch_num x d_model] + z = torch.reshape( + z, (-1, n_vars, z.shape[-2], z.shape[-1]) + ) # z: [bs x nvars x patch_num x d_model] + z = z.permute(0, 1, 3, 2) # z: [bs x nvars x d_model x patch_num] + + return z + + +# Cell +class TSTEncoder(nn.Module): + """Time Series Transformer encoder. + + Args: + q_len: length of the query sequence. + d_model: dimension of the model. + n_heads: number of attention heads. + d_k: dimension of the key vectors. Default is None. + d_v: dimension of the value vectors. Default is None. + d_ff: dimension of the feed-forward network. Default is None. + norm: type of normalization. Default is "BatchNorm". + attn_dropout: dropout rate for attention. Default is 0.0. + dropout: dropout rate. Default is 0.0. + activation: activation function. Default is "gelu". + res_attention: use residual attention. Default is False. + n_layers: number of layers in the encoder. Default is 1. + pre_norm: use pre-normalization. Default is False. + store_attn: store attention weights. Default is False. + + """ + + def __init__( + self, + q_len: int, + d_model: int, + n_heads: int, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + d_ff: Optional[int] = None, + norm: str = "BatchNorm", + attn_dropout: float = 0.0, + dropout: float = 0.0, + activation: str = "gelu", + res_attention: bool = False, + n_layers: int = 1, + pre_norm: bool = False, + store_attn: bool = False, + ): + super().__init__() + + self.layers = nn.ModuleList( + [ + TSTEncoderLayer( + q_len, + d_model, + n_heads=n_heads, + d_k=d_k, + d_v=d_v, + d_ff=d_ff, + norm=norm, + attn_dropout=attn_dropout, + dropout=dropout, + activation=activation, + res_attention=res_attention, + pre_norm=pre_norm, + store_attn=store_attn, + ) + for i in range(n_layers) + ] + ) + self.res_attention = res_attention + + def forward( + self, + src: Tensor, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ) -> Tensor: + """Forward pass of the TST encoder. + + Args: + src: input tensor of shape (batch_size, seq_len, d_model). + key_padding_mask: optional tensor for key padding mask. + attn_mask: optional tensor for attention mask. + + Returns: + output tensor of shape (batch_size, seq_len, d_model). + + """ + output = src + scores = None + if self.res_attention: + for mod in self.layers: + output, scores = mod( + output, prev=scores, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + + return output + else: + for mod in self.layers: + output = mod(output, key_padding_mask=key_padding_mask, attn_mask=attn_mask) + + return output + + +class TSTEncoderLayer(nn.Module): + """Encoder layer for the Time Series Transformer. + + Args: + q_len: length of the query sequence. + d_model: dimension of the model. + n_heads: number of attention heads. + d_k: dimension of the key vectors. Default is None. + d_v: dimension of the value vectors. Default is None. + d_ff: dimension of the feed-forward network. Default is 256. + store_attn: store attention weights. Default is False. + norm: type of normalization. Default is "BatchNorm". + attn_dropout: dropout rate for attention. Default is 0.0. + dropout: dropout rate. Default is 0.0. + bias: use bias in linear layers. Default is True. + activation: activation function. Default is "gelu". + res_attention: use residual attention. Default is False. + pre_norm: use pre-normalization. Default is False. + + """ + + def __init__( + self, + q_len: int, + d_model: int, + n_heads: int, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + d_ff: int = 256, + store_attn: bool = False, + norm: str = "BatchNorm", + attn_dropout: float = 0.0, + dropout: float = 0.0, + bias: bool = True, + activation: str = "gelu", + res_attention: bool = False, + pre_norm: bool = False, + ): + super().__init__() + assert ( + not d_model % n_heads + ), f"d_model ({d_model}) must be divisible by n_heads ({n_heads})" + d_k = d_model // n_heads if d_k is None else d_k + d_v = d_model // n_heads if d_v is None else d_v + + # Multi-Head attention + self.res_attention = res_attention + self.self_attn = _MultiheadAttention( + d_model, + n_heads, + d_k, + d_v, + attn_dropout=attn_dropout, + proj_dropout=dropout, + res_attention=res_attention, + ) + + # Add & Norm + self.dropout_attn = nn.Dropout(dropout) + if "batch" in norm.lower(): + self.norm_attn = nn.Sequential( + Transpose(1, 2), nn.BatchNorm1d(d_model), Transpose(1, 2) + ) + else: + self.norm_attn = nn.LayerNorm(d_model) + + # Position-wise Feed-Forward + self.ff = nn.Sequential( + nn.Linear(d_model, d_ff, bias=bias), + get_activation_fn(activation), + nn.Dropout(dropout), + nn.Linear(d_ff, d_model, bias=bias), + ) + + # Add & Norm + self.dropout_ffn = nn.Dropout(dropout) + if "batch" in norm.lower(): + self.norm_ffn = nn.Sequential( + Transpose(1, 2), nn.BatchNorm1d(d_model), Transpose(1, 2) + ) + else: + self.norm_ffn = nn.LayerNorm(d_model) + + self.pre_norm = pre_norm + self.store_attn = store_attn + + def forward( + self, + src: Tensor, + prev: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ) -> Union[Tensor, Tuple[Tensor, Tensor]]: + """Forward pass of the TST encoder layer. + + Args: + src: input tensor of shape (batch_size, seq_len, d_model). + prev: previous attention scores for residual attention. + key_padding_mask: optional tensor for key padding mask. + attn_mask: optional tensor for attention mask. + + Returns: + output tensor of shape (batch_size, seq_len, d_model). + If res_attention is True, also returns attention scores. + + """ + # Multi-Head attention sublayer + if self.pre_norm: + src = self.norm_attn(src) + # Multi-Head attention + if self.res_attention: + src2, attn, scores = self.self_attn( + src, src, src, prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + else: + src2, attn = self.self_attn( + src, src, src, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + if self.store_attn: + self.attn = attn + # Add & Norm + src = src + self.dropout_attn(src2) # Add: residual connection with residual dropout + if not self.pre_norm: + src = self.norm_attn(src) + + # Feed-forward sublayer + if self.pre_norm: + src = self.norm_ffn(src) + # Position-wise Feed-Forward + src2 = self.ff(src) + # Add & Norm + src = src + self.dropout_ffn(src2) # Add: residual connection with residual dropout + if not self.pre_norm: + src = self.norm_ffn(src) + + if self.res_attention: + return src, scores + else: + return src + + +class _MultiheadAttention(nn.Module): + """Multi-Head Attention Layer. + + Args: + d_model: dimension of the model. + n_heads: number of attention heads. + d_k: dimension of the key vectors. Default is None. + d_v: dimension of the value vectors. Default is None. + res_attention: use residual attention. Default is False. + attn_dropout: dropout rate for attention. Default is 0.0. + proj_dropout: dropout rate for projection. Default is 0.0. + qkv_bias: use bias in linear layers. Default is True. + lsa: use locality-sensitive attention. Default is False. + + Notes: + Input shape: + Q: [batch_size (bs) x max_q_len x d_model] + K, V: [batch_size (bs) x q_len x d_model] + mask: [q_len x q_len] + + """ + + def __init__( + self, + d_model: int, + n_heads: int, + d_k: Optional[int] = None, + d_v: Optional[int] = None, + res_attention: bool = False, + attn_dropout: float = 0.0, + proj_dropout: float = 0.0, + qkv_bias: bool = True, + lsa: bool = False, + ): + super().__init__() + d_k = d_model // n_heads if d_k is None else d_k + d_v = d_model // n_heads if d_v is None else d_v + + self.n_heads, self.d_k, self.d_v = n_heads, d_k, d_v + + self.W_Q = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias) + self.W_K = nn.Linear(d_model, d_k * n_heads, bias=qkv_bias) + self.W_V = nn.Linear(d_model, d_v * n_heads, bias=qkv_bias) + + # Scaled Dot-Product Attention (multiple heads) + self.res_attention = res_attention + self.sdp_attn = _ScaledDotProductAttention( + d_model, n_heads, attn_dropout=attn_dropout, res_attention=self.res_attention, lsa=lsa + ) + + # Poject output + self.to_out = nn.Sequential(nn.Linear(n_heads * d_v, d_model), nn.Dropout(proj_dropout)) + + def forward( + self, + Q: Tensor, + K: Optional[Tensor] = None, + V: Optional[Tensor] = None, + prev: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ) -> Union[Tensor, Tuple[Tensor, Tensor]]: + """Forward pass of the multi-head attention layer. + + Args: + Q: query tensor of shape (batch_size, max_q_len, d_model). + K: key tensor of shape (batch_size, seq_len, d_model). Default is None. + V: value tensor of shape (batch_size, seq_len, d_model). Default is None. + prev: previous attention scores for residual attention. Default is None. + key_padding_mask: optional tensor for key padding mask. + attn_mask: optional tensor for attention mask. + + Returns: + output tensor of shape (batch_size, q_len, d_model). + If res_attention is True, also returns attention scores. + + """ + + bs = Q.size(0) + if K is None: + K = Q + if V is None: + V = Q + + # Linear (+ split in multiple heads) + q_s = ( + self.W_Q(Q).view(bs, -1, self.n_heads, self.d_k).transpose(1, 2) + ) # q_s : [bs x n_heads x max_q_len x d_k] + k_s = ( + self.W_K(K).view(bs, -1, self.n_heads, self.d_k).permute(0, 2, 3, 1) + ) # k_s : [bs x n_heads x d_k x q_len] - transpose(1,2) + transpose(2,3) + v_s = ( + self.W_V(V).view(bs, -1, self.n_heads, self.d_v).transpose(1, 2) + ) # v_s : [bs x n_heads x q_len x d_v] + + # Apply Scaled Dot-Product Attention (multiple heads) + if self.res_attention: + output, attn_weights, attn_scores = self.sdp_attn( + q_s, k_s, v_s, prev=prev, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + else: + output, attn_weights = self.sdp_attn( + q_s, k_s, v_s, key_padding_mask=key_padding_mask, attn_mask=attn_mask + ) + # output: [bs x n_heads x q_len x d_v], attn: [bs x n_heads x q_len x q_len], scores: [bs x n_heads x max_q_len x q_len] + + # back to the original inputs dimensions + output = ( + output.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * self.d_v) + ) # output: [bs x q_len x n_heads * d_v] + output = self.to_out(output) + + if self.res_attention: + return output, attn_weights, attn_scores + else: + return output, attn_weights + + +class _ScaledDotProductAttention(nn.Module): + """Scaled Dot-Product Attention module. + + Args: + d_model: dimension of the model. + n_heads: number of attention heads. + attn_dropout: dropout rate for attention. Default is 0.0. + res_attention: use residual attention. Default is False. + lsa: use locality-sensitive attention. Default is False. + + Notes: + Scaled Dot-Product Attention module (Attention is all you need by Vaswani et al., 2017) + with optional residual attention from previous layer (Realformer: Transformer likes + residual attention by He et al, 2020) and locality self sttention (Vision Transformer for + Small-Size Datasets by Lee et al, 2021) + + """ + + def __init__( + self, + d_model: int, + n_heads: int, + attn_dropout: float = 0.0, + res_attention: bool = False, + lsa: bool = False, + ): + super().__init__() + self.attn_dropout = nn.Dropout(attn_dropout) + self.res_attention = res_attention + head_dim = d_model // n_heads + self.scale = nn.Parameter(torch.tensor(head_dim**-0.5), requires_grad=lsa) + self.lsa = lsa + + def forward( + self, + q: Tensor, + k: Tensor, + v: Tensor, + prev: Optional[Tensor] = None, + key_padding_mask: Optional[Tensor] = None, + attn_mask: Optional[Tensor] = None, + ) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor]]: + """Forward pass of the scaled dot-product attention. + + Args: + q: query tensor of shape (batch_size, n_heads, max_q_len, d_k). + k: key tensor of shape (batch_size, n_heads, d_k, seq_len). + v: value tensor of shape (batch_size, n_heads, seq_len, d_v). + prev: previous attention scores for residual attention. Default is None. + key_padding_mask: optional tensor for key padding mask. + attn_mask: optional tensor for attention mask. + + Returns: + output tensor of shape (batch_size, n_heads, max_q_len, d_v). + If res_attention is True, also returns attention weights and scores. + + Notes: + Input shape: + q : [bs x n_heads x max_q_len x d_k] + k : [bs x n_heads x d_k x seq_len] + v : [bs x n_heads x seq_len x d_v] + prev : [bs x n_heads x q_len x seq_len] + key_padding_mask: [bs x seq_len] + attn_mask : [1 x seq_len x seq_len] + Output shape: + output: [bs x n_heads x q_len x d_v] + attn : [bs x n_heads x q_len x seq_len] + scores : [bs x n_heads x q_len x seq_len] + """ + + # Scaled MatMul (q, k) - similarity scores for all pairs of positions in an input sequence + attn_scores = ( + torch.matmul(q, k) * self.scale + ) # attn_scores : [bs x n_heads x max_q_len x q_len] + + # Add pre-softmax attention scores from the previous layer (optional) + if prev is not None: + attn_scores = attn_scores + prev + + # Attention mask (optional) + if ( + attn_mask is not None + ): # attn_mask with shape [q_len x seq_len] - only used when q_len == seq_len + if attn_mask.dtype == torch.bool: + attn_scores.masked_fill_(attn_mask, -np.inf) + else: + attn_scores += attn_mask + + # Key padding mask (optional) + if ( + key_padding_mask is not None + ): # mask with shape [bs x q_len] (only when max_w_len == q_len) + attn_scores.masked_fill_(key_padding_mask.unsqueeze(1).unsqueeze(2), -np.inf) + + # normalize the attention weights + attn_weights = F.softmax( + attn_scores, dim=-1 + ) # attn_weights : [bs x n_heads x max_q_len x q_len] + attn_weights = self.attn_dropout(attn_weights) + + # compute the new values given the attention weights + output = torch.matmul(attn_weights, v) # output: [bs x n_heads x max_q_len x d_v] + + if self.res_attention: + return output, attn_weights, attn_scores + else: + return output, attn_weights diff --git a/tsururu/models/torch_based/layers/PatchTST_layers.py b/tsururu/models/torch_based/layers/positional_encoding.py similarity index 54% rename from tsururu/models/torch_based/layers/PatchTST_layers.py rename to tsururu/models/torch_based/layers/positional_encoding.py index fd9fc91..a81698e 100644 --- a/tsururu/models/torch_based/layers/PatchTST_layers.py +++ b/tsururu/models/torch_based/layers/positional_encoding.py @@ -1,82 +1,30 @@ -__all__ = [ - "Transpose", - "get_activation_fn", - "moving_avg", - "series_decomp", - "PositionalEncoding", - "SinCosPosEncoding", - "Coord2dPosEncoding", - "Coord1dPosEncoding", - "positional_encoding", -] - -import torch -from torch import nn -import math - - -class Transpose(nn.Module): - def __init__(self, *dims, contiguous=False): - super().__init__() - self.dims, self.contiguous = dims, contiguous - - def forward(self, x): - if self.contiguous: - return x.transpose(*self.dims).contiguous() - else: - return x.transpose(*self.dims) - - -def get_activation_fn(activation): - if callable(activation): - return activation() - elif activation.lower() == "relu": - return nn.ReLU() - elif activation.lower() == "gelu": - return nn.GELU() - raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') +"""Positional encoding for transformers.""" +import math +from typing import Optional -# decomposition +from .utils import pv +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None -class moving_avg(nn.Module): - """ - Moving average block to highlight the trend of time series - """ - def __init__(self, kernel_size, stride): - super(moving_avg, self).__init__() - self.kernel_size = kernel_size - self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) +def PositionalEncoding(q_len: int, d_model: int, normalize: bool = True) -> torch.Tensor: + """Generate positional encoding. - def forward(self, x): - # padding on the both ends of time series - front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1) - end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1) - x = torch.cat([front, x, end], dim=1) - x = self.avg(x.permute(0, 2, 1)) - x = x.permute(0, 2, 1) - return x + Args: + q_len: length of the query. + d_model: dimension of the model. + normalize: whether to normalize the positional encoding. + Returns: + positional encoding tensor. -class series_decomp(nn.Module): - """ - Series decomposition block """ - - def __init__(self, kernel_size): - super(series_decomp, self).__init__() - self.moving_avg = moving_avg(kernel_size, stride=1) - - def forward(self, x): - moving_mean = self.moving_avg(x) - res = x - moving_mean - return res, moving_mean - - -# pos_encoding -def PositionalEncoding(q_len, d_model, normalize=True): pe = torch.zeros(q_len, d_model) position = torch.arange(0, q_len).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model)) @@ -85,13 +33,32 @@ def PositionalEncoding(q_len, d_model, normalize=True): if normalize: pe = pe - pe.mean() pe = pe / (pe.std() * 10) + return pe -SinCosPosEncoding = PositionalEncoding +def Coord2dPosEncoding( + q_len: int, + d_model: int, + exponential: bool = False, + normalize: bool = True, + eps: float = 1e-3, + verbose: bool = False, +) -> torch.Tensor: + """Generate 2D coordinate positional encoding. + Args: + q_len: length of the query. + d_model: dimension of the model. + exponential: whether to use exponential scaling. + normalize: whether to normalize the positional encoding. + eps: tolerance for the mean value. + verbose: whether to print intermediate values. -def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e-3, verbose=False): + Returns: + positional encoding tensor. + + """ x = 0.5 if exponential else 1 i = 0 for i in range(100): @@ -112,19 +79,47 @@ def Coord2dPosEncoding(q_len, d_model, exponential=False, normalize=True, eps=1e if normalize: cpe = cpe - cpe.mean() cpe = cpe / (cpe.std() * 10) + return cpe -def Coord1dPosEncoding(q_len, exponential=False, normalize=True): +def Coord1dPosEncoding( + q_len: int, exponential: bool = False, normalize: bool = True +) -> torch.Tensor: + """Generate 1D coordinate positional encoding. + + Args: + q_len: length of the query. + exponential: whether to use exponential scaling. + normalize: whether to normalize the positional encoding. + + Returns: + positional encoding tensor. + + """ cpe = 2 * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** (0.5 if exponential else 1)) - 1 if normalize: cpe = cpe - cpe.mean() cpe = cpe / (cpe.std() * 10) + return cpe -def positional_encoding(pe, learn_pe, q_len, d_model): - # Positional encoding +def positional_encoding( + pe: Optional[str], learn_pe: bool, q_len: int, d_model: int +) -> nn.Parameter: + """Initialize positional encoding. + + Args: + pe: type of positional encoding. + learn_pe: whether the positional encoding is learnable. + q_len: length of the query. + d_model: dimension of the model. + + Returns: + positional encoding parameter. + + """ if pe is None: W_pos = torch.empty( (q_len, d_model) diff --git a/tsururu/models/torch_based/layers/rev_in.py b/tsururu/models/torch_based/layers/rev_in.py new file mode 100644 index 0000000..2f58df5 --- /dev/null +++ b/tsururu/models/torch_based/layers/rev_in.py @@ -0,0 +1,115 @@ +"""Reversible Instance Normalization (RevIN) module.""" + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None + + +class RevIN(nn.Module): + def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False): + """Reversible Instance Normalization (RevIN) module. + + Args: + num_features: number of features or channels. + eps: a value added for numerical stability. Default is 1e-5. + affine: if True, RevIN has learnable affine parameters. Default is True. + subtract_last: if True, subtracts the last value for normalization. Default is False. + + Notes: + code from https://github.com/ts-kim/RevIN, with minor modifications + + """ + super(RevIN, self).__init__() + self.num_features = num_features + self.eps = eps + self.affine = affine + self.subtract_last = subtract_last + if self.affine: + self._init_params() + + def forward(self, x, mode: str): + """Forward pass for RevIN. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + mode: mode of operation, either "norm" for normalization or "denorm" for denormalization. + + Returns: + normalized or denormalized tensor. + + """ + if mode == "norm": + self._get_statistics(x) + x = self._normalize(x) + elif mode == "denorm": + x = self._denormalize(x) + else: + raise NotImplementedError + return x + + def _init_params(self): + """Initialize learnable affine parameters.""" + # initialize RevIN params: (C,) + self.affine_weight = nn.Parameter(torch.ones(self.num_features)) + self.affine_bias = nn.Parameter(torch.zeros(self.num_features)) + + def _get_statistics(self, x: torch.Tensor): + """Compute mean and standard deviation for normalization. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + + """ + dim2reduce = tuple(range(1, x.ndim - 1)) + if self.subtract_last: + self.last = x[:, -1, :].unsqueeze(1) + else: + self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach() + self.stdev = torch.sqrt( + torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps + ).detach() + + def _normalize(self, x: torch.Tensor) -> torch.Tensor: + """Normalize the input tensor. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + + Returns: + normalized tensor. + + """ + if self.subtract_last: + x = x - self.last + else: + x = x - self.mean + x = x / self.stdev + if self.affine: + x = x * self.affine_weight + x = x + self.affine_bias + + return x + + def _denormalize(self, x: torch.Tensor) -> torch.Tensor: + """Denormalize the input tensor. + + Args: + x: input tensor of shape (batch_size, seq_len, num_features). + + Returns: + denormalized tensor. + + """ + if self.affine: + x = x - self.affine_bias + x = x / (self.affine_weight + self.eps * self.eps) + x = x * self.stdev + if self.subtract_last: + x = x + self.last + else: + x = x + self.mean + + return x diff --git a/tsururu/models/torch_based/layers/utils.py b/tsururu/models/torch_based/layers/utils.py new file mode 100644 index 0000000..8eaeb22 --- /dev/null +++ b/tsururu/models/torch_based/layers/utils.py @@ -0,0 +1,68 @@ +"""Module containing utility functions and classes.""" + +from typing import Callable, Union + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None + + +# https://github.com/fastai/fastai/blob/85625d621ff1a068f114a3318cc31b927019de05/fastai/imports.py +def pv(text, verbose): + if verbose: + print(text) + + +def get_activation_fn(activation: Union[str, Callable[[], nn.Module]]) -> nn.Module: + """Get the activation function based on the provided name or callable. + + Args: + activation: activation function name ('relu', 'gelu') or a callable. + + Returns: + activation function instance. + + Raises: + ValueError: if the activation function is not available. + + """ + if callable(activation): + return activation() + elif activation.lower() == "relu": + return nn.ReLU() + elif activation.lower() == "gelu": + return nn.GELU() + raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') + + +class Transpose(nn.Module): + """Module for transposing tensors with optional contiguous memory layout. + + Args: + dims: dimensions to transpose. + contiguous: whether to make the tensor contiguous in memory after transposing. + + """ + + def __init__(self, *dims: int, contiguous: bool = False): + super().__init__() + self.dims = dims + self.contiguous = contiguous + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass for transposing the tensor. + + Args: + x: input tensor. + + Returns: + transposed tensor. + + """ + if self.contiguous: + return x.transpose(*self.dims).contiguous() + else: + return x.transpose(*self.dims) diff --git a/tsururu/models/torch_based/patchTST.py b/tsururu/models/torch_based/patch_tst.py similarity index 58% rename from tsururu/models/torch_based/patchTST.py rename to tsururu/models/torch_based/patch_tst.py index 373b110..0ee987e 100644 --- a/tsururu/models/torch_based/patchTST.py +++ b/tsururu/models/torch_based/patch_tst.py @@ -1,43 +1,91 @@ -from typing import Optional +"""PatchTST model for time series forecasting.""" -from torch import Tensor, nn +from typing import Optional, Union -from tsururu.models.torch_based.layers.PatchTST_backbone import \ - PatchTST_backbone -from tsururu.models.torch_based.layers.PatchTST_layers import series_decomp +from .layers.decomposition import series_decomp +from .layers.patch_tst import PatchTST_backbone + +try: + import torch + import torch.nn as nn +except ImportError: + torch = None + nn = None class PatchTST_NN(nn.Module): + """PatchTST_NN model from the paper https://arxiv.org/abs/2211.14730. + + Args: + seq_len: input sequence length + pred_len: prediction sequence length + enc_in: encoder input size + e_layers: number of encoder layers + n_heads: number of attention heads + d_model: dimension of model + d_ff: dimension of feedforward network + dropout: dropout rate + fc_dropout: fully connected dropout rate + head_dropout: head dropout rate + individual: individual head flag + patch_len: patch length + stride: stride length + padding_patch: padding type (None or "end") + revin: RevIN flag + affine: RevIN-affine flag + subtract_last: subtract last flag (0: subtract mean; 1: subtract last) + decomposition: decomposition flag + kernel_size: decomposition kernel size + max_seq_len: maximum sequence length + d_k: dimension of key + d_v: dimension of value + norm: normalization type + attn_dropout: attention dropout rate + act: activation function + key_padding_mask: key padding mask + padding_var: padding variable + attn_mask: attention mask + res_attention: residual attention flag + pre_norm: pre-norm flag + store_attn: store attention flag + pe: positional encoding type + learn_pe: learn positional encoding flag + pretrain_head: pretrain head flag + head_type: head type + verbose: verbose flag + + """ + def __init__( self, - c_in, - context_window, - target_window, - n_layers, - n_heads, - d_model, - d_ff, - dropout, - fc_dropout, - head_dropout, - individual, - patch_len, - stride, - padding_patch, - revin, - affine, - subtract_last, - decomposition, - kernel_size, + seq_len: int, + pred_len: int, + enc_in: int, + e_layers: int = 3, + n_heads: int = 4, + d_model: int = 16, + d_ff: int = 128, + dropout: float = 0.05, + fc_dropout: float = 0.05, + head_dropout: float = 0.0, + individual: int = 0, + patch_len: int = 16, + stride: int = 8, + padding_patch: str = "end", + revin: int = 1, + affine: int = 0, + subtract_last: int = 0, + decomposition: int = 0, + kernel_size: int = 25, max_seq_len: Optional[int] = 1024, d_k: Optional[int] = None, d_v: Optional[int] = None, norm: str = "BatchNorm", attn_dropout: float = 0.0, act: str = "gelu", - key_padding_mask: bool = "auto", + key_padding_mask: Union[str, bool] = "auto", padding_var: Optional[int] = None, - attn_mask: Optional[Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, res_attention: bool = True, pre_norm: bool = False, store_attn: bool = False, @@ -52,30 +100,30 @@ def __init__( super().__init__() # load parameters - self.c_in = c_in - self.context_window = context_window - self.target_window = target_window + c_in = enc_in + context_window = seq_len + target_window = pred_len - self.n_layers = n_layers - self.n_heads = n_heads - self.d_model = d_model - self.d_ff = d_ff - self.dropout = dropout - self.fc_dropout = fc_dropout - self.head_dropout = head_dropout + n_layers = e_layers + n_heads = n_heads + d_model = d_model + d_ff = d_ff + dropout = dropout + fc_dropout = fc_dropout + head_dropout = head_dropout - self.individual = individual + individual = individual - self.patch_len = patch_len - self.stride = stride - self.padding_patch = padding_patch + patch_len = patch_len + stride = stride + padding_patch = padding_patch - self.revin = revin - self.affine = affine - self.subtract_last = subtract_last + revin = revin + affine = affine + subtract_last = subtract_last - self.decomposition = decomposition - self.kernel_size = kernel_size + decomposition = decomposition + kernel_size = kernel_size # model self.decomposition = decomposition @@ -194,18 +242,26 @@ def __init__( **kwargs ) - def forward(self, x): # x: [Batch, Input length, Channel] + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass for the PatchTST_NN model. + + Args: + x: input tensor of shape [Batch, Input length, Channel]. + + Returns: + Output tensor of shape [Batch, Input length, Channel]. + + """ if self.decomposition: res_init, trend_init = self.decomp_module(x) - res_init, trend_init = res_init.permute(0, 2, 1), trend_init.permute( - 0, 2, 1 - ) # x: [Batch, Channel, Input length] + res_init, trend_init = res_init.permute(0, 2, 1), trend_init.permute(0, 2, 1) res = self.model_res(res_init) trend = self.model_trend(trend_init) x = res + trend - x = x.permute(0, 2, 1) # x: [Batch, Input length, Channel] + x = x.permute(0, 2, 1) else: - x = x.permute(0, 2, 1) # x: [Batch, Channel, Input length] + x = x.permute(0, 2, 1) x = self.model(x) - x = x.permute(0, 2, 1) # x: [Batch, Input length, Channel] + x = x.permute(0, 2, 1) + return x diff --git a/tsururu/models/torch_based/times_net.py b/tsururu/models/torch_based/times_net.py new file mode 100644 index 0000000..851e365 --- /dev/null +++ b/tsururu/models/torch_based/times_net.py @@ -0,0 +1,205 @@ +"""TimesNet neural network for time series forecasting.""" + +from typing import Tuple + +import numpy as np + + +from .layers.convolution import Inception_Block_V1 +from .layers.embedding import DataEmbedding + +try: + import torch + import torch.fft + import torch.nn as nn + import torch.nn.functional as F +except ImportError: + torch = None + nn = None + torch.fft = None + F = None + + +def FFT_for_Period(x: torch.Tensor, k: int = 2) -> Tuple[np.ndarray, torch.Tensor]: + """Compute the FFT for the input tensor and find the top-k periods. + + Args: + x: input tensor of shape [B, T, C]. + k: number of top periods to return. Default is 2. + + Returns: + Tuple containing: + - array of top-k periods + - tensor of top-k period weights + + """ + # [B, T, C] + xf = torch.fft.rfft(x, dim=1) + # find period by amplitudes + frequency_list = abs(xf).mean(0).mean(-1) + frequency_list[0] = 0 + _, top_list = torch.topk(frequency_list, k) + top_list = top_list.detach().cpu().numpy() + period = x.shape[1] // top_list + + return period, abs(xf).mean(-1)[:, top_list] + + +class TimesBlock(nn.Module): + """TimesBlock module for time series forecasting. + + Args: + seq_len: length of the input sequence. + pred_len: length of the prediction sequence. + top_k: number of top periods to consider. + d_model: dimension of the model. + d_ff: dimension of the feed-forward network. + num_kernels: number of kernels for convolutional layers. + + """ + + def __init__( + self, seq_len: int, pred_len: int, top_k: int, d_model: int, d_ff: int, num_kernels: int + ): + super(TimesBlock, self).__init__() + + self.seq_len = seq_len + self.pred_len = pred_len + self.k = top_k + + self.conv = nn.Sequential( + Inception_Block_V1(d_model, d_ff, num_kernels=num_kernels), + nn.GELU(), + Inception_Block_V1(d_ff, d_model, num_kernels=num_kernels), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """Forward pass for the TimesBlock module. + + Args: + x: input tensor of shape [B, T, N]. + + Returns: + Output tensor of shape [B, T, N]. + + """ + B, T, N = x.size() + period_list, period_weight = FFT_for_Period(x, self.k) + + res = [] + for i in range(self.k): + period = period_list[i] + if (self.seq_len + self.pred_len) % period != 0: + length = (((self.seq_len + self.pred_len) // period) + 1) * period + padding = torch.zeros([B, (length - (self.seq_len + self.pred_len)), N]).to( + x.device + ) + out = torch.cat([x, padding], dim=1) + else: + length = self.seq_len + self.pred_len + out = x + out = out.reshape(B, length // period, period, N).permute(0, 3, 1, 2).contiguous() + out = self.conv(out) + out = out.permute(0, 2, 3, 1).reshape(B, -1, N) + res.append(out[:, : (self.seq_len + self.pred_len), :]) + res = torch.stack(res, dim=-1) + period_weight = F.softmax(period_weight, dim=1) + period_weight = period_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1) + res = torch.sum(res * period_weight, -1) + res = res + x + + return res + + +class TimesNet_NN(nn.Module): + """TimesNet neural network for time series forecasting. + + Args: + seq_len: length of the input sequence. + pred_len: length of the prediction sequence. + e_layers: number of encoder layers. Default is 2. + enc_in: number of input channels. Default is 7. + d_model: dimension of the model. Default is 512. + d_ff: dimension of the feed-forward network. Default is 2048. + num_kernels: number of kernels for convolutional layers. Default is 6. + top_k: number of top periods to consider. Default is 5. + c_out: number of output channels. Default is 7. + dropout: dropout rate. Default is 0.1. + embed: type of embedding. Default is "timeF". + freq: frequency of the data. Default is "h". + + """ + + def __init__( + self, + seq_len: int, + pred_len: int, + e_layers: int = 2, + enc_in: int = 7, + d_model: int = 512, + d_ff: int = 2048, + num_kernels: int = 6, + top_k: int = 5, + c_out: int = 7, + dropout: float = 0.1, + embed: str = "timeF", + freq: str = "h", + ): + super(TimesNet_NN, self).__init__() + + self.seq_len = seq_len + self.pred_len = pred_len + self.e_layers = e_layers + self.d_model = d_model + self.d_ff = d_ff + self.num_kernels = num_kernels + self.top_k = top_k + self.c_out = c_out + self.dropout = dropout + self.embed = embed + self.freq = freq + + self.model = nn.ModuleList( + [ + TimesBlock(seq_len, pred_len, top_k, d_model, d_ff, num_kernels) + for _ in range(self.e_layers) + ] + ) + + self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout) + self.layer_norm = nn.LayerNorm(d_model) + self.predict_linear = nn.Linear(seq_len, pred_len + seq_len) + self.projection = nn.Linear(d_model, c_out, bias=True) + + def forward(self, x_enc: torch.Tensor, x_mark_enc: torch.Tensor) -> torch.Tensor: + """Forward pass for the TimesNet neural network. + + Args: + x_enc: input tensor of shape [B, T, C]. + x_mark_enc: time features tensor of shape [B, T, F]. + + Returns: + Output tensor of shape [B, pred_len, C]. + + """ + means = x_enc.mean(1, keepdim=True).detach() + x_enc = x_enc - means + stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) + x_enc /= stdev + + enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] + enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(0, 2, 1) + + for i in range(self.e_layers): + enc_out = self.layer_norm(self.model) + + dec_out = self.projection(enc_out) + + dec_out = dec_out * ( + stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1) + ) + dec_out = dec_out + ( + means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1) + ) + + return dec_out[:, -self.pred_len :, :] # [B, L, D] diff --git a/tsururu/strategies/__init__.py b/tsururu/strategies/__init__.py index 63a3aa6..b108fc2 100644 --- a/tsururu/strategies/__init__.py +++ b/tsururu/strategies/__init__.py @@ -1,35 +1,35 @@ -"""Time series forecasting strategies.""" - -from .direct import DirectStrategy -from .flat_wide_mimo import FlatWideMIMOStrategy -from .mimo import MIMOStrategy -from .recursive import RecursiveStrategy - - -# Factory Object -class StrategiesFactory: - def __init__(self): - self.models = { - "RecursiveStrategy": RecursiveStrategy, - "DirectStrategy": DirectStrategy, - "MIMOStrategy": MIMOStrategy, - "FlatWideMIMOStrategy": FlatWideMIMOStrategy, - } - - def get_allowed(self): - return sorted(list(self.models.keys())) - - def __getitem__(self, params): - return self.models[params["strategy_name"]](**params["strategy_params"]) - - def create_strategy(self, strategy_name, strategy_params): - return self.models[strategy_name](**strategy_params) - - -__all__ = [ - "RecursiveStrategy", - "DirectStrategy", - "MIMOStrategy", - "FlatWideMIMOStrategy", - "StrategiesFactory", -] +"""Time series forecasting strategies.""" + +from .direct import DirectStrategy +from .flat_wide_mimo import FlatWideMIMOStrategy +from .mimo import MIMOStrategy +from .recursive import RecursiveStrategy + + +# Factory Object +class StrategiesFactory: + def __init__(self): + self.models = { + "RecursiveStrategy": RecursiveStrategy, + "DirectStrategy": DirectStrategy, + "MIMOStrategy": MIMOStrategy, + "FlatWideMIMOStrategy": FlatWideMIMOStrategy, + } + + def get_allowed(self): + return sorted(list(self.models.keys())) + + def __getitem__(self, params): + return self.models[params["strategy_name"]](**params["strategy_params"]) + + def create_strategy(self, strategy_name, strategy_params): + return self.models[strategy_name](**strategy_params) + + +__all__ = [ + "RecursiveStrategy", + "DirectStrategy", + "MIMOStrategy", + "FlatWideMIMOStrategy", + "StrategiesFactory", +] diff --git a/tsururu/strategies/base.py b/tsururu/strategies/base.py index 8d4fe9d..60b4409 100644 --- a/tsururu/strategies/base.py +++ b/tsururu/strategies/base.py @@ -1,248 +1,248 @@ -from typing import List, Optional, Union - -import numpy as np -import pandas as pd - -from ..dataset.dataset import TSDataset -from ..dataset.pipeline import Pipeline -from ..dataset.slice import IndexSlicer -from ..model_training.trainer import DLTrainer, MLTrainer -from .utils import timing_decorator - - -class Strategy: - """Base class for strategies, that are needed for fitting and - inference of base models. - - Args: - horizon: forecast horizon. - history: number of previous for feature generating - (i.e., features for observation y_t are counted from - observations (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while - making samples' matrix. - trainer: trainer with model params and validation params. - pipeline: pipeline for feature and target generation. - - Notes: - 1. A type of strategy defines what features and targets will be - built for subsequent training and inference of the base model. - 2. Now the `step` param should be 1. It will be changed in the - future. - - """ - - @staticmethod - def check_step_param(step: int): - """Check if the given step parameter is valid. - - Args: - step: the step parameter to be checked. - - Raises: - AssertionError: if the step parameter is not equal to 1. - - """ - assert step == 1, "Step should be 1. It will be changed in the future." - - def __init__( - self, - horizon: int, - history: int, - step: int, - trainer: Union[MLTrainer, DLTrainer], - pipeline: Pipeline, - ): - self.check_step_param(step) - - self.horizon = horizon - self.history = history - self.step = step - self.trainer = trainer - self.pipeline = pipeline - - self.trainers = [] - - @staticmethod - def _make_preds_df( - dataset: TSDataset, horizon: int, history: int, id_column_name: Optional[str] = None - ) -> pd.DataFrame: - """Create a DataFrame with predictions based on the given - dataset. - - Args: - dataset: the input time series dataset. - horizon: forecast horizon. - history: number of previous for feature generating - (i.e., features for observation y_t are counted from - observations (y_{t-history}, ..., y_{t-1}). - historical data. - id_column_name: the name of the column containing the IDs. - If not provided, the ID column name from the dataset - will be used. Defaults to None. - - Returns: - A DataFrame with the predicted values, - including the ID, date, and target columns. - - """ - if id_column_name is None: - id_column_name = dataset.id_column - - columns_list = [id_column_name, dataset.date_column, dataset.target_column] - - index_slicer = IndexSlicer() - # Get dataframe with predictions only - target_ids = index_slicer.create_idx_target( - data=dataset.data, - horizon=horizon, - history=history, - step=horizon + history, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - columns_ids = index_slicer.get_cols_idx(dataset.data, columns_list) - data = index_slicer.get_slice(dataset.data, (target_ids, columns_ids)) - pred_df = pd.DataFrame(np.vstack(data), columns=columns_list) - - return pred_df - - @staticmethod - def _backtest_generator(dataset: TSDataset, cv: int, horizon: int): - """Generate train-test splits for cross-validation. - - Args: - dataset: the time series dataset. - cv: the number of cross-validation folds. - horizon: the forecast horizon. - - Yields: - a tuple containing the train and test indices for each fold. - - """ - index_slicer = IndexSlicer() - segments_ids = index_slicer.ids_from_date( - dataset.data, dataset.date_column, delta=dataset.delta - ) - segments_ids = [0] + segments_ids + [len(dataset.data)] - - for val_idx in range(cv): - full_train = np.array([]) - full_test = np.array([]) - - for i in range(len(segments_ids) - 1): - if len(full_train) > 0: - full_train = np.vstack( - ( - full_train, - np.arange( - segments_ids[i], - segments_ids[i + 1] - horizon * (val_idx + 1), - ), - ) - ) - full_test = np.vstack( - ( - full_test, - np.arange( - segments_ids[i + 1] - horizon * (val_idx + 1), - segments_ids[i + 1] - horizon * (val_idx), - ), - ) - ) - else: - full_train = np.arange( - segments_ids[i], segments_ids[i + 1] - horizon * (val_idx + 1) - ) - full_test = np.arange( - segments_ids[i + 1] - horizon * (val_idx + 1), - segments_ids[i + 1] - horizon * (val_idx), - ) - - yield (full_train, full_test) - - def make_step(self, dataset: TSDataset): - """Make a step in the strategy. - - Args: - step: the step number. - dataset: the dataset to make the step on. - - Returns: - the updated dataset. - - """ - raise NotImplementedError() - - @timing_decorator - def fit( - self, - dataset: TSDataset, - ): - """Fits the strategy to the given dataset. - - Args: - dataset: The dataset to fit the strategy on. - - Returns: - self. - - """ - raise NotImplementedError() - - def back_test(self, dataset: TSDataset, cv: int) -> Union[List, np.ndarray]: - """Perform backtesting on the given dataset using - cross-validation. - - Args: - dataset: the dataset to perform backtesting on. - cv: the number of cross-validation folds. - - Returns: - a tuple containing the following lists: - ids_list: IDs of the predictions. - test_list: actual test values. - preds_list: predicted values. - fit_time_list: fit times for each fold. - forecast_time_list: forecast times for each fold. - - """ - ids_list = [] - test_list = [] - preds_list = [] - fit_time_list = [] - forecast_time_list = [] - - for train_idx, test_idx in self._backtest_generator(dataset, cv, self.horizon): - current_train = dataset.data.iloc[train_idx.reshape(-1)] - current_test = dataset.data.iloc[test_idx.reshape(-1)] - current_dataset = TSDataset(current_train, dataset.columns_params, dataset.delta) - - fit_time, _ = self.fit(current_dataset) - forecast_time, current_pred = self.predict(current_dataset) - - test_list.append(np.asarray(current_test[dataset.target_column].values)) - preds_list.append(np.asarray(current_pred[dataset.target_column].values)) - fit_time_list.append(fit_time) - forecast_time_list.append(forecast_time) - - if dataset.data[dataset.id_column].nunique() > 1: - ids_list.append(np.asarray(current_pred[dataset.id_column].values)) - - return (ids_list, test_list, preds_list, fit_time_list, forecast_time_list) - - @timing_decorator - def predict(self, dataset: TSDataset, test_all: bool = False) -> np.ndarray: - """Predicts the target values for the given dataset. - - Args: - dataset: the dataset to make predictions on. - test_all: whether to predict all the target values - (like rolling forecast) or only the last one. - - Returns: - a pandas DataFrame containing the predicted target values. - - """ - raise NotImplementedError() +from typing import List, Optional, Union + +import numpy as np +import pandas as pd + +from ..dataset.dataset import TSDataset +from ..dataset.pipeline import Pipeline +from ..dataset.slice import IndexSlicer +from ..model_training.trainer import DLTrainer, MLTrainer +from .utils import timing_decorator + + +class Strategy: + """Base class for strategies, that are needed for fitting and + inference of base models. + + Args: + horizon: forecast horizon. + history: number of previous for feature generating + (i.e., features for observation y_t are counted from + observations (y_{t-history}, ..., y_{t-1}). + step: in how many points to take the next observation while + making samples' matrix. + trainer: trainer with model params and validation params. + pipeline: pipeline for feature and target generation. + + Notes: + 1. A type of strategy defines what features and targets will be + built for subsequent training and inference of the base model. + 2. Now the `step` param should be 1. It will be changed in the + future. + + """ + + @staticmethod + def check_step_param(step: int): + """Check if the given step parameter is valid. + + Args: + step: the step parameter to be checked. + + Raises: + AssertionError: if the step parameter is not equal to 1. + + """ + assert step == 1, "Step should be 1. It will be changed in the future." + + def __init__( + self, + horizon: int, + history: int, + step: int, + trainer: Union[MLTrainer, DLTrainer], + pipeline: Pipeline, + ): + self.check_step_param(step) + + self.horizon = horizon + self.history = history + self.step = step + self.trainer = trainer + self.pipeline = pipeline + + self.trainers = [] + + @staticmethod + def _make_preds_df( + dataset: TSDataset, horizon: int, history: int, id_column_name: Optional[str] = None + ) -> pd.DataFrame: + """Create a DataFrame with predictions based on the given + dataset. + + Args: + dataset: the input time series dataset. + horizon: forecast horizon. + history: number of previous for feature generating + (i.e., features for observation y_t are counted from + observations (y_{t-history}, ..., y_{t-1}). + historical data. + id_column_name: the name of the column containing the IDs. + If not provided, the ID column name from the dataset + will be used. Defaults to None. + + Returns: + A DataFrame with the predicted values, + including the ID, date, and target columns. + + """ + if id_column_name is None: + id_column_name = dataset.id_column + + columns_list = [id_column_name, dataset.date_column, dataset.target_column] + + index_slicer = IndexSlicer() + # Get dataframe with predictions only + target_ids = index_slicer.create_idx_target( + data=dataset.data, + horizon=horizon, + history=history, + step=horizon + history, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + columns_ids = index_slicer.get_cols_idx(dataset.data, columns_list) + data = index_slicer.get_slice(dataset.data, (target_ids, columns_ids)) + pred_df = pd.DataFrame(np.vstack(data), columns=columns_list) + + return pred_df + + @staticmethod + def _backtest_generator(dataset: TSDataset, cv: int, horizon: int): + """Generate train-test splits for cross-validation. + + Args: + dataset: the time series dataset. + cv: the number of cross-validation folds. + horizon: the forecast horizon. + + Yields: + a tuple containing the train and test indices for each fold. + + """ + index_slicer = IndexSlicer() + segments_ids = index_slicer.ids_from_date( + dataset.data, dataset.date_column, delta=dataset.delta + ) + segments_ids = [0] + segments_ids + [len(dataset.data)] + + for val_idx in range(cv): + full_train = np.array([]) + full_test = np.array([]) + + for i in range(len(segments_ids) - 1): + if len(full_train) > 0: + full_train = np.vstack( + ( + full_train, + np.arange( + segments_ids[i], + segments_ids[i + 1] - horizon * (val_idx + 1), + ), + ) + ) + full_test = np.vstack( + ( + full_test, + np.arange( + segments_ids[i + 1] - horizon * (val_idx + 1), + segments_ids[i + 1] - horizon * (val_idx), + ), + ) + ) + else: + full_train = np.arange( + segments_ids[i], segments_ids[i + 1] - horizon * (val_idx + 1) + ) + full_test = np.arange( + segments_ids[i + 1] - horizon * (val_idx + 1), + segments_ids[i + 1] - horizon * (val_idx), + ) + + yield (full_train, full_test) + + def make_step(self, dataset: TSDataset): + """Make a step in the strategy. + + Args: + step: the step number. + dataset: the dataset to make the step on. + + Returns: + the updated dataset. + + """ + raise NotImplementedError() + + @timing_decorator + def fit( + self, + dataset: TSDataset, + ): + """Fits the strategy to the given dataset. + + Args: + dataset: The dataset to fit the strategy on. + + Returns: + self. + + """ + raise NotImplementedError() + + def back_test(self, dataset: TSDataset, cv: int) -> Union[List, np.ndarray]: + """Perform backtesting on the given dataset using + cross-validation. + + Args: + dataset: the dataset to perform backtesting on. + cv: the number of cross-validation folds. + + Returns: + a tuple containing the following lists: + ids_list: IDs of the predictions. + test_list: actual test values. + preds_list: predicted values. + fit_time_list: fit times for each fold. + forecast_time_list: forecast times for each fold. + + """ + ids_list = [] + test_list = [] + preds_list = [] + fit_time_list = [] + forecast_time_list = [] + + for train_idx, test_idx in self._backtest_generator(dataset, cv, self.horizon): + current_train = dataset.data.iloc[train_idx.reshape(-1)] + current_test = dataset.data.iloc[test_idx.reshape(-1)] + current_dataset = TSDataset(current_train, dataset.columns_params, dataset.delta) + + fit_time, _ = self.fit(current_dataset) + forecast_time, current_pred = self.predict(current_dataset) + + test_list.append(np.asarray(current_test[dataset.target_column].values)) + preds_list.append(np.asarray(current_pred[dataset.target_column].values)) + fit_time_list.append(fit_time) + forecast_time_list.append(forecast_time) + + if dataset.data[dataset.id_column].nunique() > 1: + ids_list.append(np.asarray(current_pred[dataset.id_column].values)) + + return (ids_list, test_list, preds_list, fit_time_list, forecast_time_list) + + @timing_decorator + def predict(self, dataset: TSDataset, test_all: bool = False) -> np.ndarray: + """Predicts the target values for the given dataset. + + Args: + dataset: the dataset to make predictions on. + test_all: whether to predict all the target values + (like rolling forecast) or only the last one. + + Returns: + a pandas DataFrame containing the predicted target values. + + """ + raise NotImplementedError() diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index 6e93388..ce1aefe 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -1,256 +1,256 @@ -from copy import deepcopy -from typing import Optional, Union - -from ..dataset.dataset import TSDataset -from ..dataset.pipeline import Pipeline -from ..dataset.slice import IndexSlicer -from ..model_training.trainer import DLTrainer, MLTrainer -from .recursive import RecursiveStrategy -from .utils import timing_decorator - -index_slicer = IndexSlicer() - - -class DirectStrategy(RecursiveStrategy): - """A strategy that uses an individual model for each point in the - forecast horizon. - - Args: - horizon: forecast horizon. - history: number of previous for feature generating - (i.e., features for observation y_t are counted from observations - (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while making - samples' matrix. - trainer: trainer with model params and validation params. - pipeline: pipeline for feature and target generation. - model_horizon: how many points to predict at a time, - if model_horizon > 1, then it's an intermediate strategy between - RecursiveStrategy and MIMOStrategy. - equal_train_size: if true, all models are trained with the same - training sample (which is equal to the training sample - of the last model if equal_train_size=false). - - Notes: - 1. Fit: the models is fitted to predict certain point in the - forecasting horizon (number of models = horizon). - 2. Inference: each model predict one point. - - """ - - def __init__( - self, - horizon: int, - history: int, - step: int, - trainer: Union[MLTrainer, DLTrainer], - pipeline: Pipeline, - model_horizon: int = 1, - equal_train_size: bool = False, - ): - super().__init__(horizon, history, step, trainer, pipeline, model_horizon) - self.equal_train_size = equal_train_size - self.strategy_name = "direct" - - @timing_decorator - def fit( - self, - dataset: TSDataset, - ) -> "DirectStrategy": - """Fits the direct strategy to the given dataset. - - Args: - dataset: The dataset to fit the strategy on. - - Returns: - self. - - """ - self.trainers = [] - - if self.equal_train_size: - features_idx = index_slicer.create_idx_data( - dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - target_idx = index_slicer.create_idx_target( - dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) - data = self.pipeline.fit_transform(data, self.strategy_name) - - val_dataset = self.trainer.validation_params.get("validation_data") - - if val_dataset: - val_features_idx = index_slicer.create_idx_data( - val_dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=val_dataset.date_column, - delta=val_dataset.delta, - ) - - val_target_idx = index_slicer.create_idx_target( - val_dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=val_dataset.date_column, - delta=val_dataset.delta, - ) - - val_data = self.pipeline.create_data_dict_for_pipeline( - val_dataset, val_features_idx, val_target_idx - ) - val_data = self.pipeline.transform(val_data) - else: - val_data = None - - for horizon in range(1, self.horizon // self.model_horizon + 1): - target_idx = index_slicer.create_idx_target( - dataset.data, - self.horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon] - - data["target_idx"] = target_idx - - if val_dataset: - val_target_idx = index_slicer.create_idx_target( - val_dataset.data, - self.horizon, - self.history, - self.step, - date_column=val_dataset.date_column, - delta=val_dataset.delta, - )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon] - - val_data["target_idx"] = val_target_idx - - if isinstance(self.trainer, DLTrainer): - self.trainer.horizon = self.model_horizon - self.trainer.history = self.history - - current_trainer = deepcopy(self.trainer) - current_trainer.fit(data, self.pipeline, val_data) - - self.trainers.append(current_trainer) - - else: - for horizon in range(1, self.horizon // self.model_horizon + 1): - features_idx = index_slicer.create_idx_data( - dataset.data, - self.model_horizon * horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - target_idx = index_slicer.create_idx_target( - dataset.data, - self.model_horizon * horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - n_last_horizon=self.model_horizon, - ) - - data = self.pipeline.create_data_dict_for_pipeline( - dataset, features_idx, target_idx - ) - data = self.pipeline.fit_transform(data, self.strategy_name) - - val_dataset = self.trainer.validation_params.get("validation_data") - - if val_dataset: - val_features_idx = index_slicer.create_idx_data( - val_dataset.data, - self.model_horizon * horizon, - self.history, - self.step, - date_column=val_dataset.date_column, - delta=val_dataset.delta, - ) - - val_target_idx = index_slicer.create_idx_target( - val_dataset.data, - self.model_horizon * horizon, - self.history, - self.step, - date_column=val_dataset.date_column, - delta=val_dataset.delta, - n_last_horizon=self.model_horizon, - ) - - val_data = self.pipeline.create_data_dict_for_pipeline( - val_dataset, val_features_idx, val_target_idx - ) - val_data = self.pipeline.transform(val_data) - else: - val_data = None - - if isinstance(self.trainer, DLTrainer): - self.trainer.horizon = self.model_horizon - self.trainer.history = self.history - - current_trainer = deepcopy(self.trainer) - current_trainer.fit(data, self.pipeline, val_data) - - self.trainers.append(current_trainer) - - return self - - def make_step(self, step, dataset): - """Make a step in the direct strategy. - - Args: - step: the step number. - dataset: the dataset to make the step on. - - Returns: - the updated dataset. - - """ - test_idx = index_slicer.create_idx_test( - dataset.data, - self.horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - target_idx = index_slicer.create_idx_target( - dataset.data, - self.horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - )[:, self.model_horizon * step : self.model_horizon * (step + 1)] - - data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx) - data = self.pipeline.transform(data) - - pred = self.trainers[step].predict(data, self.pipeline) - pred = self.pipeline.inverse_transform_y(pred) - - dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1) - - return dataset +from copy import deepcopy +from typing import Optional, Union + +from ..dataset.dataset import TSDataset +from ..dataset.pipeline import Pipeline +from ..dataset.slice import IndexSlicer +from ..model_training.trainer import DLTrainer, MLTrainer +from .recursive import RecursiveStrategy +from .utils import timing_decorator + +index_slicer = IndexSlicer() + + +class DirectStrategy(RecursiveStrategy): + """A strategy that uses an individual model for each point in the + forecast horizon. + + Args: + horizon: forecast horizon. + history: number of previous for feature generating + (i.e., features for observation y_t are counted from observations + (y_{t-history}, ..., y_{t-1}). + step: in how many points to take the next observation while making + samples' matrix. + trainer: trainer with model params and validation params. + pipeline: pipeline for feature and target generation. + model_horizon: how many points to predict at a time, + if model_horizon > 1, then it's an intermediate strategy between + RecursiveStrategy and MIMOStrategy. + equal_train_size: if true, all models are trained with the same + training sample (which is equal to the training sample + of the last model if equal_train_size=false). + + Notes: + 1. Fit: the models is fitted to predict certain point in the + forecasting horizon (number of models = horizon). + 2. Inference: each model predict one point. + + """ + + def __init__( + self, + horizon: int, + history: int, + step: int, + trainer: Union[MLTrainer, DLTrainer], + pipeline: Pipeline, + model_horizon: int = 1, + equal_train_size: bool = False, + ): + super().__init__(horizon, history, step, trainer, pipeline, model_horizon) + self.equal_train_size = equal_train_size + self.strategy_name = "direct" + + @timing_decorator + def fit( + self, + dataset: TSDataset, + ) -> "DirectStrategy": + """Fits the direct strategy to the given dataset. + + Args: + dataset: The dataset to fit the strategy on. + + Returns: + self. + + """ + self.trainers = [] + + if self.equal_train_size: + features_idx = index_slicer.create_idx_data( + dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = self.pipeline.fit_transform(data, self.strategy_name) + + val_dataset = self.trainer.validation_params.get("validation_data") + + if val_dataset: + val_features_idx = index_slicer.create_idx_data( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + ) + + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + ) + + val_data = self.pipeline.create_data_dict_for_pipeline( + val_dataset, val_features_idx, val_target_idx + ) + val_data = self.pipeline.transform(val_data) + else: + val_data = None + + for horizon in range(1, self.horizon // self.model_horizon + 1): + target_idx = index_slicer.create_idx_target( + dataset.data, + self.horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon] + + data["target_idx"] = target_idx + + if val_dataset: + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon] + + val_data["target_idx"] = val_target_idx + + if isinstance(self.trainer, DLTrainer): + self.trainer.horizon = self.model_horizon + self.trainer.history = self.history + + current_trainer = deepcopy(self.trainer) + current_trainer.fit(data, self.pipeline, val_data) + + self.trainers.append(current_trainer) + + else: + for horizon in range(1, self.horizon // self.model_horizon + 1): + features_idx = index_slicer.create_idx_data( + dataset.data, + self.model_horizon * horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + self.model_horizon * horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + n_last_horizon=self.model_horizon, + ) + + data = self.pipeline.create_data_dict_for_pipeline( + dataset, features_idx, target_idx + ) + data = self.pipeline.fit_transform(data, self.strategy_name) + + val_dataset = self.trainer.validation_params.get("validation_data") + + if val_dataset: + val_features_idx = index_slicer.create_idx_data( + val_dataset.data, + self.model_horizon * horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + ) + + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.model_horizon * horizon, + self.history, + self.step, + date_column=val_dataset.date_column, + delta=val_dataset.delta, + n_last_horizon=self.model_horizon, + ) + + val_data = self.pipeline.create_data_dict_for_pipeline( + val_dataset, val_features_idx, val_target_idx + ) + val_data = self.pipeline.transform(val_data) + else: + val_data = None + + if isinstance(self.trainer, DLTrainer): + self.trainer.horizon = self.model_horizon + self.trainer.history = self.history + + current_trainer = deepcopy(self.trainer) + current_trainer.fit(data, self.pipeline, val_data) + + self.trainers.append(current_trainer) + + return self + + def make_step(self, step, dataset): + """Make a step in the direct strategy. + + Args: + step: the step number. + dataset: the dataset to make the step on. + + Returns: + the updated dataset. + + """ + test_idx = index_slicer.create_idx_test( + dataset.data, + self.horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + target_idx = index_slicer.create_idx_target( + dataset.data, + self.horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + )[:, self.model_horizon * step : self.model_horizon * (step + 1)] + + data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx) + data = self.pipeline.transform(data) + + pred = self.trainers[step].predict(data, self.pipeline) + pred = self.pipeline.inverse_transform_y(pred) + + dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1) + + return dataset diff --git a/tsururu/strategies/flat_wide_mimo.py b/tsururu/strategies/flat_wide_mimo.py index e3d168c..3d322ba 100644 --- a/tsururu/strategies/flat_wide_mimo.py +++ b/tsururu/strategies/flat_wide_mimo.py @@ -1,37 +1,37 @@ -from typing import Union - -from ..dataset.pipeline import Pipeline -from ..model_training.trainer import DLTrainer, MLTrainer -from .mimo import MIMOStrategy - - -class FlatWideMIMOStrategy(MIMOStrategy): - """A strategy that uses a single model for all points - in the prediction horizon. - - Arguments: - horizon: forecast horizon. - history: number of previous for feature generating - (i.e., features for observation y_t are counted from observations - (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while making - samples' matrix. - trainer: trainer with model params and validation params. - - Notes: - 1. Fit: mixture of DirectStrategy and MIMOStrategy, fit one - model, but uses deployed over horizon DirectStrategy's features. - 2. Inference: similarly. - - """ - - def __init__( - self, - horizon: int, - history: int, - step: int, - trainer: Union[MLTrainer, DLTrainer], - pipeline: Pipeline, - ): - super().__init__(horizon, history, step, trainer, pipeline) - self.strategy_name = "FlatWideMIMOStrategy" +from typing import Union + +from ..dataset.pipeline import Pipeline +from ..model_training.trainer import DLTrainer, MLTrainer +from .mimo import MIMOStrategy + + +class FlatWideMIMOStrategy(MIMOStrategy): + """A strategy that uses a single model for all points + in the prediction horizon. + + Arguments: + horizon: forecast horizon. + history: number of previous for feature generating + (i.e., features for observation y_t are counted from observations + (y_{t-history}, ..., y_{t-1}). + step: in how many points to take the next observation while making + samples' matrix. + trainer: trainer with model params and validation params. + + Notes: + 1. Fit: mixture of DirectStrategy and MIMOStrategy, fit one + model, but uses deployed over horizon DirectStrategy's features. + 2. Inference: similarly. + + """ + + def __init__( + self, + horizon: int, + history: int, + step: int, + trainer: Union[MLTrainer, DLTrainer], + pipeline: Pipeline, + ): + super().__init__(horizon, history, step, trainer, pipeline) + self.strategy_name = "FlatWideMIMOStrategy" diff --git a/tsururu/strategies/mimo.py b/tsururu/strategies/mimo.py index 43f54fe..e5f6114 100644 --- a/tsururu/strategies/mimo.py +++ b/tsururu/strategies/mimo.py @@ -1,42 +1,42 @@ -from typing import Union - -from ..dataset.pipeline import Pipeline -from ..model_training.trainer import DLTrainer, MLTrainer -from .recursive import RecursiveStrategy - - -class MIMOStrategy(RecursiveStrategy): - """A strategy that uses one model that learns to predict - the entire prediction horizon. - - Arguments: - horizon: forecast horizon. - history: number of previous for feature generating - (i.e., features for observation y_t are counted from observations - (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while making - samples' matrix. - trainer: trainer with model params and validation params. - pipeline: pipeline for feature and target generation. - - Notes: - 1. Technically, `MIMOStrategy` is a `RecursiveStrategy` or - `DirectStrategy` for which the horizon of the individual model - (`model_horizon`) coincides with the full prediction horizon - (`horizon`). - 2. Fit: the model is fitted to predict a vector which length is equal - to the length of the prediction horizon. - 3. Inference: the model makes a vector of predictions. - - """ - - def __init__( - self, - horizon: int, - history: int, - step: int, - trainer: Union[MLTrainer, DLTrainer], - pipeline: Pipeline, - ): - super().__init__(horizon, history, step, trainer, pipeline, model_horizon=horizon) - self.strategy_name = "MIMOStrategy" +from typing import Union + +from ..dataset.pipeline import Pipeline +from ..model_training.trainer import DLTrainer, MLTrainer +from .recursive import RecursiveStrategy + + +class MIMOStrategy(RecursiveStrategy): + """A strategy that uses one model that learns to predict + the entire prediction horizon. + + Arguments: + horizon: forecast horizon. + history: number of previous for feature generating + (i.e., features for observation y_t are counted from observations + (y_{t-history}, ..., y_{t-1}). + step: in how many points to take the next observation while making + samples' matrix. + trainer: trainer with model params and validation params. + pipeline: pipeline for feature and target generation. + + Notes: + 1. Technically, `MIMOStrategy` is a `RecursiveStrategy` or + `DirectStrategy` for which the horizon of the individual model + (`model_horizon`) coincides with the full prediction horizon + (`horizon`). + 2. Fit: the model is fitted to predict a vector which length is equal + to the length of the prediction horizon. + 3. Inference: the model makes a vector of predictions. + + """ + + def __init__( + self, + horizon: int, + history: int, + step: int, + trainer: Union[MLTrainer, DLTrainer], + pipeline: Pipeline, + ): + super().__init__(horizon, history, step, trainer, pipeline, model_horizon=horizon) + self.strategy_name = "MIMOStrategy" diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 87f0d5b..1442385 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -1,225 +1,228 @@ -from copy import deepcopy -from typing import Optional, Union - -import pandas as pd - -from ..dataset import IndexSlicer, Pipeline, TSDataset -from ..model_training.trainer import DLTrainer, MLTrainer -from .base import Strategy -from .utils import timing_decorator - -index_slicer = IndexSlicer() - - -class RecursiveStrategy(Strategy): - """Strategy that uses a single model to predict all points in the - forecast horizon. - - Arguments: - horizon: forecast horizon. - history: number of previous for feature generating - (i.e., features for observation y_t are counted from - observations (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while - making samples' matrix. - trainer: trainer with model params and validation params. - pipeline: pipeline for feature and target generation. - model_horizon: how many points to predict at a time, - if model_horizon > 1, then it's an intermediate strategy - between RecursiveStrategy and MIMOStrategy. - reduced: whether to form features for all test observations at - once, in this case, unavailable values are replaced by NaN. - - Notes: - 1. Fit: the model is fitted to predict one point ahead. - 2. Inference: the model iteratively predicts the next point and - - use this prediction to build further features - (`reduced` == False); - - use NaN instead of prediction (`reduced` == True). - - """ - - def __init__( - self, - horizon: int, - history: int, - step: int, - trainer: Union[MLTrainer, DLTrainer], - pipeline: Pipeline, - model_horizon: int = 1, - reduced: bool = False, - ): - super().__init__(horizon, history, step, trainer, pipeline) - self.model_horizon = model_horizon - self.reduced = reduced - self.strategy_name = "recursive" - - @timing_decorator - def fit( - self, - dataset: TSDataset, - ) -> "RecursiveStrategy": - """Fits the recursive strategy to the given dataset. - - Args: - dataset: The dataset to fit the strategy on. - - Returns: - self. - - """ - features_idx = index_slicer.create_idx_data( - dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - target_idx = index_slicer.create_idx_target( - dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) - data = self.pipeline.fit_transform(data, self.strategy_name) - - val_dataset = self.trainer.validation_params.get("validation_data") - - if val_dataset: - val_features_idx = index_slicer.create_idx_data( - val_dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - val_target_idx = index_slicer.create_idx_target( - val_dataset.data, - self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - val_data = self.pipeline.create_data_dict_for_pipeline( - val_dataset, val_features_idx, val_target_idx - ) - val_data = self.pipeline.transform(val_data) - else: - val_data = None - - if isinstance(self.trainer, DLTrainer): - self.trainer.horizon = self.model_horizon - self.trainer.history = self.history - - current_trainer = deepcopy(self.trainer) - current_trainer.fit(data, self.pipeline, val_data) - - self.trainers.append(current_trainer) - return self - - def make_step(self, step: int, dataset: TSDataset) -> TSDataset: - """Make a step in the recursive strategy. - - Args: - step: the step number. - dataset: the dataset to make the step on. - - Returns: - the updated dataset. - - """ - test_idx = index_slicer.create_idx_test( - dataset.data, - self.horizon - step * self.model_horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - target_idx = index_slicer.create_idx_target( - dataset.data, - self.horizon, - self.history, - self.step, - date_column=dataset.date_column, - delta=dataset.delta, - )[:, self.model_horizon * step : self.model_horizon * (step + 1)] - - data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx) - data = self.pipeline.transform(data) - - pred = self.trainers[0].predict(data, self.pipeline) - pred = self.pipeline.inverse_transform_y(pred) - - dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1) - - return dataset - - @timing_decorator - def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame: - """Predicts the target values for the given dataset. - - Args: - dataset: the dataset to make predictions on. - - Returns: - a pandas DataFrame containing the predicted target values. - - """ - new_data = dataset.make_padded_test( - self.horizon, self.history, test_all=test_all, model_horizon=self.model_horizon - ) - new_dataset = TSDataset(new_data, dataset.columns_params, dataset.delta) - - if test_all: - new_dataset.data = new_dataset.data.sort_values( - [dataset.id_column, "segment_col", dataset.date_column] - ) - - if self.reduced: - current_test_ids = index_slicer.create_idx_data( - new_dataset.data, - self.model_horizon, - self.history, - step=self.model_horizon, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - target_ids = index_slicer.create_idx_target( - new_dataset.data, - self.horizon, - self.history, - step=self.model_horizon, - date_column=dataset.date_column, - delta=dataset.delta, - ) - - data = self.pipeline.create_data_dict_for_pipeline( - new_dataset, current_test_ids, target_ids - ) - data = self.pipeline.transform(data) - - pred = self.trainers[0].predict(data, self.pipeline) - pred = self.pipeline.inverse_transform_y(pred) - - new_dataset.data.loc[target_ids.reshape(-1), dataset.target_column] = pred.reshape(-1) - - else: - for step in range(self.horizon // self.model_horizon): - new_dataset = self.make_step(step, new_dataset) - - # Get dataframe with predictions only - pred_df = self._make_preds_df(new_dataset, self.horizon, self.history) - return pred_df +from copy import deepcopy +from typing import Union + +import pandas as pd + +from ..dataset import IndexSlicer, Pipeline, TSDataset +from ..model_training.trainer import DLTrainer, MLTrainer +from .base import Strategy +from .utils import timing_decorator + +index_slicer = IndexSlicer() + + +class RecursiveStrategy(Strategy): + """Strategy that uses a single model to predict all points in the + forecast horizon. + + Arguments: + horizon: forecast horizon. + history: number of previous for feature generating + (i.e., features for observation y_t are counted from + observations (y_{t-history}, ..., y_{t-1}). + step: in how many points to take the next observation while + making samples' matrix. + trainer: trainer with model params and validation params. + pipeline: pipeline for feature and target generation. + model_horizon: how many points to predict at a time, + if model_horizon > 1, then it's an intermediate strategy + between RecursiveStrategy and MIMOStrategy. + reduced: whether to form features for all test observations at + once, in this case, unavailable values are replaced by NaN. + + Notes: + 1. Fit: the model is fitted to predict one point ahead. + 2. Inference: the model iteratively predicts the next point and + - use this prediction to build further features + (`reduced` == False); + - use NaN instead of prediction (`reduced` == True). + + """ + + def __init__( + self, + horizon: int, + history: int, + step: int, + trainer: Union[MLTrainer, DLTrainer], + pipeline: Pipeline, + model_horizon: int = 1, + reduced: bool = False, + ): + super().__init__(horizon, history, step, trainer, pipeline) + self.model_horizon = model_horizon + self.reduced = reduced + self.strategy_name = "recursive" + + @timing_decorator + def fit( + self, + dataset: TSDataset, + ) -> "RecursiveStrategy": + """Fits the recursive strategy to the given dataset. + + Args: + dataset: The dataset to fit the strategy on. + + Returns: + self. + + """ + features_idx = index_slicer.create_idx_data( + dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = self.pipeline.fit_transform(data, self.strategy_name) + + val_dataset = self.trainer.validation_params.get("validation_data") + + if val_dataset: + val_features_idx = index_slicer.create_idx_data( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + val_target_idx = index_slicer.create_idx_target( + val_dataset.data, + self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + val_data = self.pipeline.create_data_dict_for_pipeline( + val_dataset, val_features_idx, val_target_idx + ) + val_data = self.pipeline.transform(val_data) + else: + val_data = None + + if isinstance(self.trainer, DLTrainer): + if self.strategy_name == "FlatWideMIMOStrategy": + self.trainer.horizon = 1 + else: + self.trainer.horizon = self.model_horizon + self.trainer.history = self.history + + current_trainer = deepcopy(self.trainer) + current_trainer.fit(data, self.pipeline, val_data) + + self.trainers.append(current_trainer) + return self + + def make_step(self, step: int, dataset: TSDataset) -> TSDataset: + """Make a step in the recursive strategy. + + Args: + step: the step number. + dataset: the dataset to make the step on. + + Returns: + the updated dataset. + + """ + test_idx = index_slicer.create_idx_test( + dataset.data, + self.horizon - step * self.model_horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + self.horizon, + self.history, + self.step, + date_column=dataset.date_column, + delta=dataset.delta, + )[:, self.model_horizon * step : self.model_horizon * (step + 1)] + + data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx) + data = self.pipeline.transform(data) + + pred = self.trainers[0].predict(data, self.pipeline) + pred = self.pipeline.inverse_transform_y(pred) + + dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1) + + return dataset + + @timing_decorator + def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame: + """Predicts the target values for the given dataset. + + Args: + dataset: the dataset to make predictions on. + + Returns: + a pandas DataFrame containing the predicted target values. + + """ + new_data = dataset.make_padded_test( + self.horizon, self.history, test_all=test_all, step=self.step + ) + new_dataset = TSDataset(new_data, dataset.columns_params, dataset.delta) + + if test_all: + new_dataset.data = new_dataset.data.sort_values( + [dataset.id_column, "segment_col", dataset.date_column] + ) + + if self.reduced: + current_test_ids = index_slicer.create_idx_data( + new_dataset.data, + self.model_horizon, + self.history, + step=self.model_horizon, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_ids = index_slicer.create_idx_target( + new_dataset.data, + self.horizon, + self.history, + step=self.model_horizon, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + data = self.pipeline.create_data_dict_for_pipeline( + new_dataset, current_test_ids, target_ids + ) + data = self.pipeline.transform(data) + + pred = self.trainers[0].predict(data, self.pipeline) + pred = self.pipeline.inverse_transform_y(pred) + + new_dataset.data.loc[target_ids.reshape(-1), dataset.target_column] = pred.reshape(-1) + + else: + for step in range(self.horizon // self.model_horizon): + new_dataset = self.make_step(step, new_dataset) + + # Get dataframe with predictions only + pred_df = self._make_preds_df(new_dataset, self.horizon, self.history) + return pred_df diff --git a/tsururu/strategies/utils.py b/tsururu/strategies/utils.py index 4b1050c..82cec5d 100644 --- a/tsururu/strategies/utils.py +++ b/tsururu/strategies/utils.py @@ -1,13 +1,13 @@ -import time -from typing import Callable - - -def timing_decorator(func: Callable) -> Callable: - def wrapper(*args, **kwargs): - start_time = time.time() - result = func(*args, **kwargs) - end_time = time.time() - execution_time = end_time - start_time - return execution_time, result - - return wrapper +import time +from typing import Callable + + +def timing_decorator(func: Callable) -> Callable: + def wrapper(*args, **kwargs): + start_time = time.time() + result = func(*args, **kwargs) + end_time = time.time() + execution_time = end_time - start_time + return execution_time, result + + return wrapper diff --git a/tsururu/transformers/__init__.py b/tsururu/transformers/__init__.py index 2d72e57..cb7df91 100644 --- a/tsururu/transformers/__init__.py +++ b/tsururu/transformers/__init__.py @@ -1,58 +1,61 @@ -"""Algorithms for time series forecasting.""" - -from .base import SequentialTransformer, UnionTransformer, Transformer -from .categorical import LabelEncodingTransformer, OneHotEncodingTransformer -from .datetime import DateSeasonsGenerator, TimeToNumGenerator -from .numeric import ( - DifferenceNormalizer, - LastKnownNormalizer, - StandardScalerTransformer, -) -from .seq import LagTransformer, TargetGenerator - - -# Factory Object -class TransformersFactory: - def __init__(self): - self.transformers = { - "Transformer": Transformer, # "base" transformer - "UnionTransformer": UnionTransformer, - "SequentialTransformer": SequentialTransformer, - "StandardScalerTransformer": StandardScalerTransformer, - "LastKnownNormalizer": LastKnownNormalizer, - "DifferenceNormalizer": DifferenceNormalizer, - "LabelEncodingTransformer": LabelEncodingTransformer, - "OneHotEncodingTransformer": OneHotEncodingTransformer, - "TimeToNumGenerator": TimeToNumGenerator, - "DateSeasonsGenerator": DateSeasonsGenerator, - "LagTransformer": LagTransformer, - "TargetGenerator": TargetGenerator, - } - - def get_allowed(self): - return sorted(list(self.transformers.keys())) - - def __getitem__(self, params): - return self.transformers[params["transformer_name"]]( - **params["transformer_params"] - ) - - def create_transformer(self, transformer_name, transformer_params): - return self.transformers[transformer_name](**transformer_params) - - -__all__ = [ - "Transformer" - "UnionTransformer", - "SequentialTransformer", - "StandardScalerTransformer", - "LastKnownNormalizer", - "DifferenceNormalizer", - "LabelEncodingTransformer", - "OneHotEncodingTransformer", - "TimeToNumGenerator", - "DateSeasonsGenerator", - "LagTransformer", - "TargetGenerator", - "TransformersFactory", -] +"""Algorithms for time series forecasting.""" + +from .base import SequentialTransformer, UnionTransformer, Transformer +from .categorical import LabelEncodingTransformer, OneHotEncodingTransformer +from .datetime import DateSeasonsGenerator, TimeToNumGenerator +from .numeric import ( + DifferenceNormalizer, + LastKnownNormalizer, + StandardScalerTransformer, +) +from .seq import LagTransformer, TargetGenerator +from .impute import MissingValuesImputer + + +# Factory Object +class TransformersFactory: + def __init__(self): + self.transformers = { + "Transformer": Transformer, # "base" transformer + "UnionTransformer": UnionTransformer, + "SequentialTransformer": SequentialTransformer, + "StandardScalerTransformer": StandardScalerTransformer, + "LastKnownNormalizer": LastKnownNormalizer, + "DifferenceNormalizer": DifferenceNormalizer, + "LabelEncodingTransformer": LabelEncodingTransformer, + "OneHotEncodingTransformer": OneHotEncodingTransformer, + "TimeToNumGenerator": TimeToNumGenerator, + "DateSeasonsGenerator": DateSeasonsGenerator, + "LagTransformer": LagTransformer, + "TargetGenerator": TargetGenerator, + "MissingValueImputer": MissingValuesImputer, + } + + def get_allowed(self): + return sorted(list(self.transformers.keys())) + + def __getitem__(self, params): + return self.transformers[params["transformer_name"]]( + **params["transformer_params"] + ) + + def create_transformer(self, transformer_name, transformer_params): + return self.transformers[transformer_name](**transformer_params) + + +__all__ = [ + "Transformer" + "UnionTransformer", + "SequentialTransformer", + "StandardScalerTransformer", + "LastKnownNormalizer", + "DifferenceNormalizer", + "LabelEncodingTransformer", + "OneHotEncodingTransformer", + "TimeToNumGenerator", + "DateSeasonsGenerator", + "LagTransformer", + "TargetGenerator", + "TransformersFactory", + "MissingValuesImputer", +] diff --git a/tsururu/transformers/base.py b/tsururu/transformers/base.py index 1c7ed8d..05665f1 100644 --- a/tsururu/transformers/base.py +++ b/tsururu/transformers/base.py @@ -1,600 +1,600 @@ -"""Base classes for transformers, that are needed for feature generating.""" - -from copy import deepcopy -from typing import Optional, Sequence - -import numpy as np - - -class Transformer: - """Base class for transformers, that are needed for feature generating. - - Args: - input_features: array with names of columns to transform. - - Note: there are two categories of transformers: - - 1. Transformers that are used to collect pipelines: - - "Union" transformers; - - "Sequential" transformers. - - 2. Transformers that are used to transform raw rows: - and generate features and targets: - - "FeaturesGenerator" transformers; - - "SeriesToSeries" transformers; - - "SeriesToFeatures" transformers; - - "FeaturesToFeatures" transformers. - - 3. In methods `fit`, `transform`, `fit_transform` and `generate`, - all transformers take as input and pass as output the dictionary - named `data`, which contains 7 objects: - - `raw_ts_X` and `raw_ts_y`: pd.DataFrame - "elongated series"; - - `X` and `y`: np.ndarray - arrays with features and targets; - - `id_column_name`: str - name of id column; - - `idx_X` and `idx_y`: np.ndarray - arrays with indices of - time series' points for features and targets generating. - - Though each method uses and modifies only part of them: - 1. `fit` is trained on `raw_ts_X`; - 2. `transform` changes `raw_ts_X` and `raw_ts_y` (depending on the - flags `transform_features`, `transform_targets`); - 3. `generate` uses `raw_ts_X` and `raw_ts_y` and - modifies `X` and `y`. - 4. `transform` and `generate` can use `idx_X` and `idx_y` to update - transformer params and generate features and targets. - 5. all methods can use `id_column_name`. - - """ - - def __init__(self, input_features: Optional[Sequence[str]] = None): - self.input_features = input_features - self.output_features = None # array with names of resulting columns - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "Transformer": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - if input_features is not None: - self.input_features = input_features - - return self - - def transform(self, data: dict) -> dict: - """Transform "elongated series" for feautures' and targets' further - generation and update self.params if needed. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - raise NotImplementedError() - - def fit_transform( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> dict: - """Default implementation of fit_transform - fit and then transform. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - current states of `data` dictionary. - - """ - self.fit(data, input_features) - - return self.transform(data) - - def generate(self, data: dict) -> dict: - """Generate or transform features and targets in X, y arrays. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - raise NotImplementedError() - - -class SequentialTransformer(Transformer): - """Transformer that contains the sequence of transformers - and apply them one by one sequentially. - - Args: - transformers_list: Sequence of transformers. - input_features: array with names of columns to transform. - - Notes: - 1. In this transformer, the names of the input columns should be - provided at initialisation rather than at fitting. - - """ - - def __init__( - self, transformers_list: Sequence[Transformer], input_features: Sequence[str] - ): - super().__init__(input_features=input_features) - self.transformers_list = transformers_list - self.inverse_transformers_list = [] - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "SequentialTransformer": - """Fit not supported. Needs output to fit next transformer. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Raises: - NotImplementedError: raised if called. - - """ - raise NotImplementedError( - "Sequential supports only fit_transform since needs output" - "to fit next transformer." - ) - - def transform(self, data: dict) -> dict: - """Apply the sequence of transformers to data containers - one after the other and transform "elongated series". - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - self.inverse_transformers_list = [] - - for trf in self.transformers_list: - data = trf.transform(data) - if hasattr(trf, "transform_target") and trf.transform_target: - self.inverse_transformers_list.append(trf) - - self.inverse_transformers_list = self.inverse_transformers_list[::-1] - - return data - - def fit_transform( - self, data, input_features: Optional[Sequence[str]] = None - ) -> dict: - """Fit and apply the sequence of transformers to data containers - one after the other and transform "elongated series". - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - current states of `data` dictionary. - - """ - self.inverse_transformers_list = [] - - if input_features is not None: - self.input_features = input_features - - current_input_features = self.input_features - - for trf in self.transformers_list: - data = trf.fit_transform(data, current_input_features) - current_input_features = trf.output_features - if hasattr(trf, "transform_target") and trf.transform_target: - # Check that transform_target corresponding to transformer for target column - assert self.input_features == [data["target_column_name"]], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" - self.inverse_transformers_list.append(trf) - elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: - self.inverse_transformers_list.extend(trf.inverse_transformers_list) - - self.inverse_transformers_list = self.inverse_transformers_list[::-1] - self.output_features = current_input_features - - return data - - def generate(self, data: dict) -> dict: - """Apply the sequence of transformers to containers with data - one after the other and generate or transform features and - targets in X, y arrays. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - for trf in self.transformers_list: - data = trf.generate(data) - - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - for trf in self.inverse_transformers_list: - y = trf.inverse_transform_y(y) - - return y - - -class UnionTransformer(Transformer): - """Transformer that contains the sequence of transformers - and apply them `in parallel` and concatenate the result. - - Args: - transformer_list: Sequence of transformers. - - Notes: - 1. There is no true parallelism, but the idea is to apply all - transformers to the same dataset and concatenate the results. - - """ - - def __init__(self, transformers_list: Sequence[Transformer]): - super().__init__() - self.transformers_list = transformers_list - self.inverse_transformers_list = [] - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "UnionTransformer": - """Fit transformers on "elongated series" in parallel and return - their instances. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - for trf in self.transformers_list: - trf.fit(data, input_features) - - return self - - def transform(self, data: dict) -> dict: - """Apply the sequence of transformers to data containers in parallel - and transform "elongated series". - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - self.inverse_transformers_list = [] - - for trf in self.transformers_list: - data = trf.transform(data) - if hasattr(trf, "transform_target") and trf.transform_target: - self.inverse_transformers_list.append(trf) - elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: - self.inverse_transformers_list.extend(trf.inverse_transformers_list) - - return data - - def fit_transform( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> dict: - """Fit and apply the sequence of transformers to data containers - in parallel and transform "elongated series". - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - current states of `data` dictionary. - - """ - self.inverse_transformers_list = [] - output_features_list = [] - - for trf in self.transformers_list: - data = trf.fit_transform(data, input_features) - if trf.output_features is not None: - output_features_list.append(trf.output_features) - if hasattr(trf, "transform_target") and trf.transform_target: - # Check that transform_target corresponding to transformer for target column - assert self.input_features == [data["target_column_name"]], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" - self.inverse_transformers_list.append(trf) - elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: - self.inverse_transformers_list.extend(trf.inverse_transformers_list) - - self.output_features = np.concatenate(output_features_list) - - return data - - def generate(self, data: dict) -> dict: - """Apply the sequence of transformers to containers with data - in parallel and generate or transform features and targets - in X, y arrays - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - for trf in self.transformers_list: - copy_X = data["X"] - data["X"] = np.array([]) - - data = trf.generate(data) - - if copy_X.shape != (0,) and data["X"].shape != (0,): - data["X"] = np.hstack((copy_X, data["X"])) - elif data["X"].shape == (0,): - data["X"] = copy_X - - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - for trf in self.inverse_transformers_list: - y = trf.inverse_transform_y(y) - - return y - - -class FeaturesGenerator(Transformer): - """A transformer that is trained on the "elongated series" - and uses them to generate new columns. - - Notes: - 1. For this transformer, the active method is `transform`, which - changes the state of raw_ts_X, raw_ts_y; `generate` does nothing - and just passes data through it. - - """ - - def generate(self, data: dict) -> dict: - """For FeaturesGenerator `generate` does nothing and just - passes data through it. - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - return data - - -class SeriesToSeriesTransformer(Transformer): - """A transformer that is trained on the "elongated series" - and applied to them. - - Args: - transform_features: whether to transform features. - transform_target: whether to transform targets. - - Notes: - 1. For this transformer, the active method is `transform`, which - changes the state of raw_ts_X, raw_ts_y; `generate` does nothing - and just passes data through it. - - 2. This transformer has flags `transform_features`, `transform_target`. - - 3. This transformer has inverse_transform_y method. - - """ - - def __init__(self, transform_features: bool, transform_target: bool): - super().__init__() - self.transform_features = transform_features - self.transform_target = transform_target - self.params = {} - - def _transform(self, data: dict, data_key: str) -> dict: - """A method to transform the data based on the given data key - (`raw_ts_X` or `raw_ts_y`). - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - data_key: A string representing the key in the data dictionary: - either `raw_ts_X` or `raw_ts_y`. - - Returns: - A dictionary with the transformed data. - - """ - data[data_key] = ( - data[data_key] - .groupby(data["id_column_name"], sort=False) - .apply(self._transform_segment, data["id_column_name"]) - .reset_index(level=data["id_column_name"], drop=True) - ) - - return data - - def transform(self, data: dict) -> dict: - """Transform "elongated series" for feautures' and targets' further - generation and update self.params if needed. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - if self.transform_features: - self._transform(data, "raw_ts_X") - else: - for i, column_name in enumerate(self.input_features): - data["raw_ts_X"].loc[:, self.output_features[i]] = data["raw_ts_X"].loc[:, column_name] - if self.transform_target: - self._transform(data, "raw_ts_y") - else: - for i, column_name in enumerate(self.input_features): - data["raw_ts_y"].loc[:, self.output_features[i]] = data["raw_ts_y"].loc[:, column_name] - - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - assert NotImplementedError() - - def generate(self, data: dict) -> dict: - """For SeriesToSeriesTransformer `generate` does nothing and just - passes data through it. - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - return data - - -class SeriesToFeaturesTransformer(Transformer): - """Transformer that is trained on the "elongated series" and applied - to them to generate or transform features and targets in X, y arrays. - - Notes: - 1. For this transformer, the active method is `generate`, which - changes the state of X, y arrays; `transform` does nothing and - just passes data through it. - - """ - - def transform(self, data: dict) -> dict: - """For SeriesToFeaturesTransformer transform does nothing and just - passes data through it. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - return data - - -class FeaturesToFeaturesTransformer(Transformer): - """A transformer that is trained on generated features in X, y arrays - and applied to them to transform features and targets. - - Notes: - 1. For this transformer, the active method is `generate`, which - changes the state of X, y arrays; `transform` does nothing and - just passes data through it. - - 2. This transformer has flags `transform_features`, `transform_target`. - - 3. This transformer has inverse_transform_y method. - - """ - - def __init__(self, transform_features: bool, transform_target: bool): - super().__init__() - self.transform_features = transform_features - self.transform_target = transform_target - - def transform(self, data: dict) -> dict: - """For FeaturesToFeaturesTransformer `transform` update - self.params if needed. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - assert NotImplementedError() +"""Base classes for transformers, that are needed for feature generating.""" + +from copy import deepcopy +from typing import Optional, Sequence + +import numpy as np + + +class Transformer: + """Base class for transformers, that are needed for feature generating. + + Args: + input_features: array with names of columns to transform. + + Note: there are two categories of transformers: + + 1. Transformers that are used to collect pipelines: + - "Union" transformers; + - "Sequential" transformers. + + 2. Transformers that are used to transform raw rows: + and generate features and targets: + - "FeaturesGenerator" transformers; + - "SeriesToSeries" transformers; + - "SeriesToFeatures" transformers; + - "FeaturesToFeatures" transformers. + + 3. In methods `fit`, `transform`, `fit_transform` and `generate`, + all transformers take as input and pass as output the dictionary + named `data`, which contains 7 objects: + - `raw_ts_X` and `raw_ts_y`: pd.DataFrame - "elongated series"; + - `X` and `y`: np.ndarray - arrays with features and targets; + - `id_column_name`: str - name of id column; + - `idx_X` and `idx_y`: np.ndarray - arrays with indices of + time series' points for features and targets generating. + + Though each method uses and modifies only part of them: + 1. `fit` is trained on `raw_ts_X`; + 2. `transform` changes `raw_ts_X` and `raw_ts_y` (depending on the + flags `transform_features`, `transform_targets`); + 3. `generate` uses `raw_ts_X` and `raw_ts_y` and + modifies `X` and `y`. + 4. `transform` and `generate` can use `idx_X` and `idx_y` to update + transformer params and generate features and targets. + 5. all methods can use `id_column_name`. + + """ + + def __init__(self, input_features: Optional[Sequence[str]] = None): + self.input_features = input_features + self.output_features = None # array with names of resulting columns + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "Transformer": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + if input_features is not None: + self.input_features = input_features + + return self + + def transform(self, data: dict) -> dict: + """Transform "elongated series" for feautures' and targets' further + generation and update self.params if needed. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + raise NotImplementedError() + + def fit_transform( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> dict: + """Default implementation of fit_transform - fit and then transform. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + current states of `data` dictionary. + + """ + self.fit(data, input_features) + + return self.transform(data) + + def generate(self, data: dict) -> dict: + """Generate or transform features and targets in X, y arrays. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + raise NotImplementedError() + + +class SequentialTransformer(Transformer): + """Transformer that contains the sequence of transformers + and apply them one by one sequentially. + + Args: + transformers_list: Sequence of transformers. + input_features: array with names of columns to transform. + + Notes: + 1. In this transformer, the names of the input columns should be + provided at initialisation rather than at fitting. + + """ + + def __init__( + self, transformers_list: Sequence[Transformer], input_features: Sequence[str] + ): + super().__init__(input_features=input_features) + self.transformers_list = transformers_list + self.inverse_transformers_list = [] + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "SequentialTransformer": + """Fit not supported. Needs output to fit next transformer. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Raises: + NotImplementedError: raised if called. + + """ + raise NotImplementedError( + "Sequential supports only fit_transform since needs output" + "to fit next transformer." + ) + + def transform(self, data: dict) -> dict: + """Apply the sequence of transformers to data containers + one after the other and transform "elongated series". + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + self.inverse_transformers_list = [] + + for trf in self.transformers_list: + data = trf.transform(data) + if hasattr(trf, "transform_target") and trf.transform_target: + self.inverse_transformers_list.append(trf) + + self.inverse_transformers_list = self.inverse_transformers_list[::-1] + + return data + + def fit_transform( + self, data, input_features: Optional[Sequence[str]] = None + ) -> dict: + """Fit and apply the sequence of transformers to data containers + one after the other and transform "elongated series". + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + current states of `data` dictionary. + + """ + self.inverse_transformers_list = [] + + if input_features is not None: + self.input_features = input_features + + current_input_features = self.input_features + + for trf in self.transformers_list: + data = trf.fit_transform(data, current_input_features) + current_input_features = trf.output_features + if hasattr(trf, "transform_target") and trf.transform_target: + # Check that transform_target corresponding to transformer for target column + assert self.input_features == [data["target_column_name"]], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" + self.inverse_transformers_list.append(trf) + elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: + self.inverse_transformers_list.extend(trf.inverse_transformers_list) + + self.inverse_transformers_list = self.inverse_transformers_list[::-1] + self.output_features = current_input_features + + return data + + def generate(self, data: dict) -> dict: + """Apply the sequence of transformers to containers with data + one after the other and generate or transform features and + targets in X, y arrays. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + for trf in self.transformers_list: + data = trf.generate(data) + + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + for trf in self.inverse_transformers_list: + y = trf.inverse_transform_y(y) + + return y + + +class UnionTransformer(Transformer): + """Transformer that contains the sequence of transformers + and apply them `in parallel` and concatenate the result. + + Args: + transformer_list: Sequence of transformers. + + Notes: + 1. There is no true parallelism, but the idea is to apply all + transformers to the same dataset and concatenate the results. + + """ + + def __init__(self, transformers_list: Sequence[Transformer]): + super().__init__() + self.transformers_list = transformers_list + self.inverse_transformers_list = [] + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "UnionTransformer": + """Fit transformers on "elongated series" in parallel and return + their instances. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + for trf in self.transformers_list: + trf.fit(data, input_features) + + return self + + def transform(self, data: dict) -> dict: + """Apply the sequence of transformers to data containers in parallel + and transform "elongated series". + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + self.inverse_transformers_list = [] + + for trf in self.transformers_list: + data = trf.transform(data) + if hasattr(trf, "transform_target") and trf.transform_target: + self.inverse_transformers_list.append(trf) + elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: + self.inverse_transformers_list.extend(trf.inverse_transformers_list) + + return data + + def fit_transform( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> dict: + """Fit and apply the sequence of transformers to data containers + in parallel and transform "elongated series". + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + current states of `data` dictionary. + + """ + self.inverse_transformers_list = [] + output_features_list = [] + + for trf in self.transformers_list: + data = trf.fit_transform(data, input_features) + if trf.output_features is not None: + output_features_list.append(trf.output_features) + if hasattr(trf, "transform_target") and trf.transform_target: + # Check that transform_target corresponding to transformer for target column + assert self.input_features == [data["target_column_name"]], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" + self.inverse_transformers_list.append(trf) + elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: + self.inverse_transformers_list.extend(trf.inverse_transformers_list) + + self.output_features = np.concatenate(output_features_list) + + return data + + def generate(self, data: dict) -> dict: + """Apply the sequence of transformers to containers with data + in parallel and generate or transform features and targets + in X, y arrays + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + for trf in self.transformers_list: + copy_X = data["X"] + data["X"] = np.array([]) + + data = trf.generate(data) + + if copy_X.shape != (0,) and data["X"].shape != (0,): + data["X"] = np.hstack((copy_X, data["X"])) + elif data["X"].shape == (0,): + data["X"] = copy_X + + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + for trf in self.inverse_transformers_list: + y = trf.inverse_transform_y(y) + + return y + + +class FeaturesGenerator(Transformer): + """A transformer that is trained on the "elongated series" + and uses them to generate new columns. + + Notes: + 1. For this transformer, the active method is `transform`, which + changes the state of raw_ts_X, raw_ts_y; `generate` does nothing + and just passes data through it. + + """ + + def generate(self, data: dict) -> dict: + """For FeaturesGenerator `generate` does nothing and just + passes data through it. + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + return data + + +class SeriesToSeriesTransformer(Transformer): + """A transformer that is trained on the "elongated series" + and applied to them. + + Args: + transform_features: whether to transform features. + transform_target: whether to transform targets. + + Notes: + 1. For this transformer, the active method is `transform`, which + changes the state of raw_ts_X, raw_ts_y; `generate` does nothing + and just passes data through it. + + 2. This transformer has flags `transform_features`, `transform_target`. + + 3. This transformer has inverse_transform_y method. + + """ + + def __init__(self, transform_features: bool, transform_target: bool): + super().__init__() + self.transform_features = transform_features + self.transform_target = transform_target + self.params = {} + + def _transform(self, data: dict, data_key: str) -> dict: + """A method to transform the data based on the given data key + (`raw_ts_X` or `raw_ts_y`). + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + data_key: A string representing the key in the data dictionary: + either `raw_ts_X` or `raw_ts_y`. + + Returns: + A dictionary with the transformed data. + + """ + data[data_key] = ( + data[data_key] + .groupby(data["id_column_name"], sort=False) + .apply(self._transform_segment, data["id_column_name"]) + .reset_index(level=data["id_column_name"], drop=True) + ) + + return data + + def transform(self, data: dict) -> dict: + """Transform "elongated series" for feautures' and targets' further + generation and update self.params if needed. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + if self.transform_features: + self._transform(data, "raw_ts_X") + else: + for i, column_name in enumerate(self.input_features): + data["raw_ts_X"].loc[:, self.output_features[i]] = data["raw_ts_X"].loc[:, column_name] + if self.transform_target: + self._transform(data, "raw_ts_y") + else: + for i, column_name in enumerate(self.input_features): + data["raw_ts_y"].loc[:, self.output_features[i]] = data["raw_ts_y"].loc[:, column_name] + + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + assert NotImplementedError() + + def generate(self, data: dict) -> dict: + """For SeriesToSeriesTransformer `generate` does nothing and just + passes data through it. + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + return data + + +class SeriesToFeaturesTransformer(Transformer): + """Transformer that is trained on the "elongated series" and applied + to them to generate or transform features and targets in X, y arrays. + + Notes: + 1. For this transformer, the active method is `generate`, which + changes the state of X, y arrays; `transform` does nothing and + just passes data through it. + + """ + + def transform(self, data: dict) -> dict: + """For SeriesToFeaturesTransformer transform does nothing and just + passes data through it. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + return data + + +class FeaturesToFeaturesTransformer(Transformer): + """A transformer that is trained on generated features in X, y arrays + and applied to them to transform features and targets. + + Notes: + 1. For this transformer, the active method is `generate`, which + changes the state of X, y arrays; `transform` does nothing and + just passes data through it. + + 2. This transformer has flags `transform_features`, `transform_target`. + + 3. This transformer has inverse_transform_y method. + + """ + + def __init__(self, transform_features: bool, transform_target: bool): + super().__init__() + self.transform_features = transform_features + self.transform_target = transform_target + + def transform(self, data: dict) -> dict: + """For FeaturesToFeaturesTransformer `transform` update + self.params if needed. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + assert NotImplementedError() diff --git a/tsururu/transformers/categorical.py b/tsururu/transformers/categorical.py index cd88820..bcddda2 100644 --- a/tsururu/transformers/categorical.py +++ b/tsururu/transformers/categorical.py @@ -1,142 +1,142 @@ -"""Module for transformers for categorical features.""" - -from typing import Optional, Sequence - -import numpy as np -from sklearn.preprocessing import LabelEncoder, OneHotEncoder - -from .base import FeaturesGenerator - - -class LabelEncodingTransformer(FeaturesGenerator): - """A transformer that encodes categorical features into integer values.""" - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "LabelEncodingTransformer": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - - self.output_features = [f"{column_name}__label" for column_name in self.input_features] - - return self - - def transform(self, data: dict) -> dict: - """Generate features in `raw_ts_X`. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - new_arr = np.empty((len(data["raw_ts_X"]), len(self.output_features)), np.int32) - for i, column_name in enumerate(self.input_features): - new_arr[:, i] = LabelEncoder().fit_transform(data["raw_ts_X"][column_name]) - data["raw_ts_X"][self.output_features] = new_arr - - return data - - -class OneHotEncodingTransformer(FeaturesGenerator): - """A transformer that encodes categorical features as a one-hot - numeric array. - - Args: - drop: one from ['first', 'if_binary', None] or - array-list of shape (n_features, ): - 1. if None: retain all features. - 2. if "first": drop the first category in each feature. - 3. if "if_binary": drop the first category in each feature with - two categories. - 4. if `array`: drop[i] is the category in feature X[:, i] that - should be dropped. - - """ - - def __init__(self, drop: str = None): - super().__init__() - self.drop = drop - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "LabelEncodingTransformer": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - self.output_features = [] - - if self.drop == "first": - for column_name in self.input_features: - for id_name in data["raw_ts_X"][column_name].unique()[1:]: - self.output_features.append(f"{column_name}__{id_name}_ohe") - - elif self.drop == "is_binary": - for column_name in self.input_features: - if data["raw_ts_X"][column_name].nunique() == 2: - for id_name in data["raw_ts_X"][column_name].unique()[1:]: - self.output_features.append(f"{column_name}__{id_name}_ohe") - else: - for id_name in data["raw_ts_X"][column_name].unique(): - self.output_features.append(f"{column_name}__{id_name}_ohe") - - elif isinstance(self.drop, np.ndarray): - for column_i, column_name in enumerate(self.input_features): - for id_name in np.delete( - data["raw_ts_X"][column_name].unique(), - np.where(data["raw_ts_X"][column_name].unique() == self.drop[column_i]), - ): - self.output_features.append(f"{column_name}__{id_name}_ohe") - - else: - for column_i, column_name in enumerate(self.input_features): - for id_name in data["raw_ts_X"][column_name].unique(): - self.output_features.append(f"{column_name}__{id_name}_ohe") - - return self - - def transform(self, data: dict) -> dict: - """Generate features in `raw_ts_X`. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - result_data = [ - OneHotEncoder(drop=self.drop) - .fit_transform(data["raw_ts_X"][column_name].values.reshape(-1, 1)) - .todense() - for column_name in self.input_features - ] - data["raw_ts_X"][self.output_features] = np.hstack(result_data) - - return data +"""Module for transformers for categorical features.""" + +from typing import Optional, Sequence + +import numpy as np +from sklearn.preprocessing import LabelEncoder, OneHotEncoder + +from .base import FeaturesGenerator + + +class LabelEncodingTransformer(FeaturesGenerator): + """A transformer that encodes categorical features into integer values.""" + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "LabelEncodingTransformer": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + + self.output_features = [f"{column_name}__label" for column_name in self.input_features] + + return self + + def transform(self, data: dict) -> dict: + """Generate features in `raw_ts_X`. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + new_arr = np.empty((len(data["raw_ts_X"]), len(self.output_features)), np.int32) + for i, column_name in enumerate(self.input_features): + new_arr[:, i] = LabelEncoder().fit_transform(data["raw_ts_X"][column_name]) + data["raw_ts_X"][self.output_features] = new_arr + + return data + + +class OneHotEncodingTransformer(FeaturesGenerator): + """A transformer that encodes categorical features as a one-hot + numeric array. + + Args: + drop: one from ['first', 'if_binary', None] or + array-list of shape (n_features, ): + 1. if None: retain all features. + 2. if "first": drop the first category in each feature. + 3. if "if_binary": drop the first category in each feature with + two categories. + 4. if `array`: drop[i] is the category in feature X[:, i] that + should be dropped. + + """ + + def __init__(self, drop: str = None): + super().__init__() + self.drop = drop + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "LabelEncodingTransformer": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + self.output_features = [] + + if self.drop == "first": + for column_name in self.input_features: + for id_name in data["raw_ts_X"][column_name].unique()[1:]: + self.output_features.append(f"{column_name}__{id_name}_ohe") + + elif self.drop == "is_binary": + for column_name in self.input_features: + if data["raw_ts_X"][column_name].nunique() == 2: + for id_name in data["raw_ts_X"][column_name].unique()[1:]: + self.output_features.append(f"{column_name}__{id_name}_ohe") + else: + for id_name in data["raw_ts_X"][column_name].unique(): + self.output_features.append(f"{column_name}__{id_name}_ohe") + + elif isinstance(self.drop, np.ndarray): + for column_i, column_name in enumerate(self.input_features): + for id_name in np.delete( + data["raw_ts_X"][column_name].unique(), + np.where(data["raw_ts_X"][column_name].unique() == self.drop[column_i]), + ): + self.output_features.append(f"{column_name}__{id_name}_ohe") + + else: + for column_i, column_name in enumerate(self.input_features): + for id_name in data["raw_ts_X"][column_name].unique(): + self.output_features.append(f"{column_name}__{id_name}_ohe") + + return self + + def transform(self, data: dict) -> dict: + """Generate features in `raw_ts_X`. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + result_data = [ + OneHotEncoder(drop=self.drop) + .fit_transform(data["raw_ts_X"][column_name].values.reshape(-1, 1)) + .todense() + for column_name in self.input_features + ] + data["raw_ts_X"][self.output_features] = np.hstack(result_data) + + return data diff --git a/tsururu/transformers/datetime.py b/tsururu/transformers/datetime.py index 66d1e0e..bfec70c 100644 --- a/tsururu/transformers/datetime.py +++ b/tsururu/transformers/datetime.py @@ -1,204 +1,204 @@ -"""Module for transformers for datetime features.""" - -from typing import List, Optional, Sequence - -import holidays -import numpy as np -import pandas as pd - -from ..dataset.slice import IndexSlicer -from .base import FeaturesGenerator -from .utils import date_attrs - -index_slicer = IndexSlicer() - - -class TimeToNumGenerator(FeaturesGenerator): - """A transformer that converts datetime to difference with basic_date. - - Args: - basic_date: date relating to which normalization takes place. - from_target_date: if True, features are built from the targets' dates; - otherwise, features are built from the last training dates. - horizon: forecast horizon. - delta: frequency of the time series. - - """ - - def __init__( - self, - basic_date: Optional[str] = "2020-01-01", - from_target_date: Optional[bool] = False, - delta: Optional[pd.DateOffset] = None, - ): - super().__init__() - self.basic_date = basic_date - self.from_target_date = from_target_date - self.delta = delta - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "TimeToNumGenerator": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - - self.output_features = [ - f"{column_name}__time_to_num" for column_name in self.input_features - ] - return self - - def transform(self, data: dict) -> dict: - """Generate features in `raw_ts_X`. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - result_data = [] - for column_name in self.input_features: - time_col = data["raw_ts_X"][column_name] - - _, time_delta = index_slicer.timedelta(time_col, delta=self.delta) - if self.from_target_date: - horizon = data["target_idx"][0, -1] - data["features_idx"][0, -1] - time_col = time_col + horizon * time_delta - - data = pd.to_datetime(time_col.to_numpy().reshape(-1), origin="unix") - data_transformed = ( - (data - np.datetime64(self.basic_date)) / np.timedelta64(1, self.delta) - ).values.astype(np.float32) - - result_data.append(data_transformed) - - data["raw_ts_X"][:, self.output_features] = result_data - - return data - - -class DateSeasonsGenerator(FeaturesGenerator): - """A transformer that generates features that reflect seasonality. - - Args: - seasonalities: features to build. - from_target_date: features are built from the targets' dates. - horizon: forecast horizon. - country: country code ("RU" for Russia). - prov: province inside country. - state: state inside country. - - Notes: - 1. In case when country is provided (it is possible to specify prov - and state) indicator 'there is a holiday at that moment' will be - generated. - - """ - - def __init__( - self, - seasonalities: Optional[List[str]] = ["doy", "m", "wd", "d", "y"], - from_target_date: Optional[bool] = False, - country: Optional[str] = None, - prov: Optional[str] = None, - state: Optional[str] = None, - delta: Optional[pd.DateOffset] = None, - ): - super().__init__() - self.seasonalities = seasonalities - self.from_target_date = from_target_date - self._country = country - self._prov = prov - self._state = state - self.delta = delta - - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "DateSeasonsGenerator": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - - self.output_features = [] - - for s in self.seasonalities: - self.output_features.extend( - [f"{column_name}__season_{s}" for column_name in self.input_features] - ) - if self._country is not None: - self.output_features.extend( - [f"{column_name}__season_hol" for column_name in self.input_features] - ) - - return self - - def transform(self, data: dict) -> dict: - """Generate features in `raw_ts_X`. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - result_data = [] - - for column_name in self.input_features: - time_col = data["raw_ts_X"][column_name] - - _, time_delta = index_slicer.timedelta(time_col, delta=self.delta) - if self.from_target_date: - horizon = data["idx_y"][0, -1] - data["idx_X"][0, -1] - time_col = time_col + horizon * time_delta - time_col = pd.to_datetime(time_col.to_numpy(), origin="unix") - - new_arr = np.empty((time_col.shape[0], len(self.output_features)), np.int32) - - n = 0 - for seas in self.seasonalities: - new_arr[:, n] = getattr(time_col, date_attrs[seas]) - n += 1 - - if self._country is not None: - # get years - years = np.unique(time_col.year) - hol = holidays.CountryHoliday( - self._country, - years=years, - prov=self._prov, - state=self._state, - ) - new_arr[:, n] = time_col.date.isin(hol) - n += 1 - result_data.append(new_arr) - - data["raw_ts_X"][self.output_features] = np.hstack(result_data) - - return data +"""Module for transformers for datetime features.""" + +from typing import List, Optional, Sequence + +import holidays +import numpy as np +import pandas as pd + +from ..dataset.slice import IndexSlicer +from .base import FeaturesGenerator +from .utils import date_attrs + +index_slicer = IndexSlicer() + + +class TimeToNumGenerator(FeaturesGenerator): + """A transformer that converts datetime to difference with basic_date. + + Args: + basic_date: date relating to which normalization takes place. + from_target_date: if True, features are built from the targets' dates; + otherwise, features are built from the last training dates. + horizon: forecast horizon. + delta: frequency of the time series. + + """ + + def __init__( + self, + basic_date: Optional[str] = "2020-01-01", + from_target_date: Optional[bool] = False, + delta: Optional[pd.DateOffset] = None, + ): + super().__init__() + self.basic_date = basic_date + self.from_target_date = from_target_date + self.delta = delta + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "TimeToNumGenerator": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + + self.output_features = [ + f"{column_name}__time_to_num" for column_name in self.input_features + ] + return self + + def transform(self, data: dict) -> dict: + """Generate features in `raw_ts_X`. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + result_data = [] + for column_name in self.input_features: + time_col = data["raw_ts_X"][column_name] + + _, time_delta = index_slicer.timedelta(time_col, delta=self.delta) + if self.from_target_date: + horizon = data["target_idx"][0, -1] - data["features_idx"][0, -1] + time_col = time_col + horizon * time_delta + + data = pd.to_datetime(time_col.to_numpy().reshape(-1), origin="unix") + data_transformed = ( + (data - np.datetime64(self.basic_date)) / np.timedelta64(1, self.delta) + ).values.astype(np.float32) + + result_data.append(data_transformed) + + data["raw_ts_X"][:, self.output_features] = result_data + + return data + + +class DateSeasonsGenerator(FeaturesGenerator): + """A transformer that generates features that reflect seasonality. + + Args: + seasonalities: features to build. + from_target_date: features are built from the targets' dates. + horizon: forecast horizon. + country: country code ("RU" for Russia). + prov: province inside country. + state: state inside country. + + Notes: + 1. In case when country is provided (it is possible to specify prov + and state) indicator 'there is a holiday at that moment' will be + generated. + + """ + + def __init__( + self, + seasonalities: Optional[List[str]] = ["doy", "m", "wd", "d", "y"], + from_target_date: Optional[bool] = False, + country: Optional[str] = None, + prov: Optional[str] = None, + state: Optional[str] = None, + delta: Optional[pd.DateOffset] = None, + ): + super().__init__() + self.seasonalities = seasonalities + self.from_target_date = from_target_date + self._country = country + self._prov = prov + self._state = state + self.delta = delta + + def fit( + self, data: dict, input_features: Optional[Sequence[str]] = None + ) -> "DateSeasonsGenerator": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + + self.output_features = [] + + for s in self.seasonalities: + self.output_features.extend( + [f"{column_name}__season_{s}" for column_name in self.input_features] + ) + if self._country is not None: + self.output_features.extend( + [f"{column_name}__season_hol" for column_name in self.input_features] + ) + + return self + + def transform(self, data: dict) -> dict: + """Generate features in `raw_ts_X`. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + result_data = [] + + for column_name in self.input_features: + time_col = data["raw_ts_X"][column_name] + + _, time_delta = index_slicer.timedelta(time_col, delta=self.delta) + if self.from_target_date: + horizon = data["idx_y"][0, -1] - data["idx_X"][0, -1] + time_col = time_col + horizon * time_delta + time_col = pd.to_datetime(time_col.to_numpy(), origin="unix") + + new_arr = np.empty((time_col.shape[0], len(self.output_features)), np.int32) + + n = 0 + for seas in self.seasonalities: + new_arr[:, n] = getattr(time_col, date_attrs[seas]) + n += 1 + + if self._country is not None: + # get years + years = np.unique(time_col.year) + hol = holidays.CountryHoliday( + self._country, + years=years, + prov=self._prov, + state=self._state, + ) + new_arr[:, n] = time_col.date.isin(hol) + n += 1 + result_data.append(new_arr) + + data["raw_ts_X"][self.output_features] = np.hstack(result_data) + + return data diff --git a/tsururu/transformers/impute.py b/tsururu/transformers/impute.py new file mode 100644 index 0000000..20b3f0f --- /dev/null +++ b/tsururu/transformers/impute.py @@ -0,0 +1,169 @@ +import pandas as pd +import numpy as np +from typing import Sequence, Optional +from .base import SeriesToSeriesTransformer + + +class MissingValuesImputer(SeriesToSeriesTransformer): + """Imputes missing values in time series data using various strategies. + + Args: + regime: the strategy to use for imputation. Options are 'mean' or 'lag'. + if None, the transformer will fill missing values with a constant value. + constant_value: the constant value to fill remaining missing values + after applying the chosen regime. + transform_features: whether to transform features. + transform_target: whether to transform target. + window: the size of the window for the mean imputation strategy. + if -1, the window size is the length of the series. + if window size is bigger than the length of the series, the window size + is the length of the series. + weighted_alpha: the alpha value for weighting in the mean imputation strategy. + the bigger the alpha, the more recent values are weighted. + lag: the lag value for the lag imputation strategy. + + """ + + def __init__( + self, + regime: Optional[str] = None, + constant_value: Optional[float] = None, + transform_features: bool = True, + transform_target: bool = True, + window: Optional[int] = None, + weighted_alpha: Optional[float] = None, + lag: Optional[int] = None, + ): + super().__init__(transform_features=transform_features, transform_target=transform_target) + self.regime = regime + self.constant_value = constant_value + self.window = window if window is not None else -1 + self.weighted_alpha = weighted_alpha if weighted_alpha is not None else 0 + self.lag = lag if lag is not None else 1 + + def fit(self, data: dict, input_features: Sequence[str]) -> SeriesToSeriesTransformer: + """Fit transformer on 'elongated series' and return its instance. + + Args: + data: dictionary with current states of 'elongated series', + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + """ + super().fit(data, input_features) + self.output_features = [f"{column}__imputed" for column in self.input_features] + return self + + def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Series: + """Transform segment (points with similar id) of 'elongated series' + for features' and targets' further generation. + + Args: + segment: segment of 'elongated series' to transform. + id_column_name: name of id column. + + Returns: + transformed segment of 'elongated series'. + """ + for column_name in self.input_features: + if self.regime == "mean": + segment[column_name] = self._fill_mean(segment[column_name]) + elif self.regime == "lag": + segment[column_name] = self._fill_lag(segment[column_name]) + # Fill remaining missing values with constant value + segment[column_name].fillna(self.constant_value, inplace=True) + + return segment + + def _fill_mean(self, series: pd.Series) -> pd.Series: + """Fill missing values using mean with optional weighting. + + Args: + series: series with missing values to be filled. + + Returns: + series with missing values filled using mean. + + """ + filled_series = series.copy() + if self.window == -1: + window_size = len(series) + else: + window_size = self.window + + for idx in series[series.isnull()].index: + if idx >= window_size: + window = series[idx - window_size : idx] + else: + window = series[:idx] + + try: + if self.weighted_alpha > 0: + weights = np.exp(np.linspace(-self.weighted_alpha, 0, len(window))) + mean_value = np.average( + window.dropna(), weights=weights[: len(window.dropna())] + ) + else: + mean_value = window.mean() + except: + mean_value = series[idx] + + filled_series.at[idx] = mean_value + + return filled_series + + def _fill_lag(self, series: pd.Series) -> pd.Series: + """Fill missing values using lagged values. + + Args: + series: series with missing values to be filled. + + Returns: + series with missing values filled using lagged values. + + """ + filled_series = series.copy() + + for idx in series[series.isnull()].index: + try: + current_lag = series[idx - self.lag] + except: + current_lag = series[idx] + + filled_series.at[idx] = current_lag + + return filled_series + + def transform(self, data: dict) -> dict: + """Transform 'elongated series' for features' and targets' further + generation and update self.params. + + Args: + data: dictionary with current states of 'elongated series', + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + """ + data = super().transform(data) + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + Notes: + This method does not perform any transformation on the target variable + as it is not needed for this transformer. + + """ + return y diff --git a/tsururu/transformers/numeric.py b/tsururu/transformers/numeric.py index 96ed9ac..f251c34 100644 --- a/tsururu/transformers/numeric.py +++ b/tsururu/transformers/numeric.py @@ -1,416 +1,427 @@ -"""Module for transformers for numeric features.""" - -import re -from typing import Sequence - -import numpy as np -import pandas as pd - -from ..dataset.slice import IndexSlicer -from .base import FeaturesToFeaturesTransformer, SeriesToSeriesTransformer - -index_slicer = IndexSlicer() - - -class StandardScalerTransformer(SeriesToSeriesTransformer): - """Transformer that standardizes features by removing the mean and scaling. - - Args: - transform_features: whether to transform features. - transform_target: whether to transform target. - - Notes: - 1. Transformer has a parameter self.fitted_params = { - id_1: { - (colname_1, 'mean'): mean, (colname_2, 'std'): std, ... - }, - id_2: { - (colname_1, 'mean'): mean, (colname_2, 'std'): std, ... - }, - ... - }. - 2. self.params: np.ndarray len(idx_y) x 2. - - """ - - def __init__(self, transform_features: bool, transform_target: bool): - super().__init__( - transform_features=transform_features, - transform_target=transform_target, - ) - - self.fitted_params = {} - - def fit(self, data: dict, input_features: Sequence[str]) -> SeriesToSeriesTransformer: - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - stat_df = ( - data["raw_ts_X"] - .groupby(data["id_column_name"])[self.input_features] - .agg(["mean", "std"]) - ) - self.fitted_params = stat_df.to_dict(orient="index") - self.output_features = [f"{column}__standard_scaler" for column in self.input_features] - - return self - - def _get_mask_mean_std(self, segment, column_name, current_id): - """ - Calculate the column_mask, mean and standard deviation of a segment - for a given column name and current id. - - Args: - segment: segment of "elongated series" to transform to calculate - the mean and standard deviation for. - column_name: the name of the column to calculate the mean and - standard deviation for. - current_id: the name of id column of the segment. - - Returns: - column mask, mean, and standard deviation. - - """ - column_mask = [segment.columns.str.contains(column_name)][0] - mean = self.fitted_params[current_id][(column_name, "mean")] - std = self.fitted_params[current_id][(column_name, "std")] - - return column_mask, mean, std - - def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Series: - """Transform segment (points with similar id) of "elongated series" - for feautures' and targets' further generation. - - Args: - segment: segment of "elongated series" to transform. - id_column_name: name of id column. - - Returns: - transformed segment of "elongated series". - - """ - current_id = segment[id_column_name].values[0] - - for i, column_name in enumerate(self.input_features): - column_mask, mean, std = self._get_mask_mean_std( - segment=segment, - column_name=column_name, - current_id=current_id, - ) - segment.loc[:, self.output_features[i]] = (segment.loc[:, column_mask] - mean) / std - - return segment - - def transform(self, data: dict) -> dict: - """Transform "elongated series" for feautures' and targets' further - generation and update self.params. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - data = super().transform(data) - - # Update the params if self.transform_target is True - if self.transform_target: - id_from_target_idx = index_slicer.get_slice( - data["raw_ts_y"][data["id_column_name"]], (data["idx_y"][:, 0], None) - ) - self.params = [ - list(self.fitted_params[current_id].values()) for current_id in id_from_target_idx - ] - self.params = np.array(self.params) - - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - if self.transform_target: - if len(y.shape) == 1 or y.shape[0] == 1: - y = y * self.params[:, 1] + self.params[:, 0] - else: - y = y * self.params[:, np.newaxis, 1] + self.params[:, np.newaxis, 0] - - return y - - -class DifferenceNormalizer(SeriesToSeriesTransformer): - """Transformer that normalizes values by the previous value. - - Args: - regime: "delta" to take the difference or "ratio" to take the ratio - between the current and the previous value. - transform_features: whether to transform features. - transform_target: whether to transform target. - - Notes: - 1. self.params: np.ndarray len(idx_y) x 1. - - """ - - def __init__(self, regime: str, transform_features: bool, transform_target: bool): - super().__init__( - transform_features=transform_features, - transform_target=transform_target, - ) - self.regime = regime - - def fit(self, data: dict, input_features: Sequence[str]) -> SeriesToSeriesTransformer: - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - last_values_df = ( - data["raw_ts_X"].groupby(data["id_column_name"])[self.input_features].last() - ) - self.params = last_values_df.to_dict(orient="index") - self.output_features = [f"{column}__diff_norm" for column in self.input_features] - - return self - - def _transform_segment(self, segment: pd.Series, *_): - """Transform segment (points with similar id) of "elongated series" - for feautures' and targets' further generation. - - Args: - segment: segment of "elongated series" to transform. - - Returns: - transformed segment of "elongated series". - - """ - for i, column_name in enumerate(self.input_features): - if self.regime == "delta": - segment.loc[:, self.output_features[i]] = segment.loc[ - :, column_name - ] - segment.loc[:, column_name].shift(1) - elif self.regime == "ratio": - segment.loc[:, self.output_features[i]] = segment.loc[ - :, column_name - ] / segment.loc[:, column_name].shift(1) - - return segment - - def transform(self, data: dict) -> dict: - """Transform "elongated series" for feautures' and targets' further - generation and update self.params. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id column and - arrays with indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - data = super().transform(data) - - # Update the params if self.transform_target is True - if self.transform_target: - self.params = index_slicer.get_slice( - data["raw_ts_y"][self.input_features], (data["idx_y"][:, 0] - 1, None) - ) - - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - if len(y.shape) == 1: - y = y.reshape(-1, 1) - - if len(self.params.shape) == 3: - self.params = self.params[0] - - if self.transform_target: - if self.regime == "delta": - y = np.cumsum(np.hstack((self.params, y)), axis=1)[:, 1:] - elif self.regime == "ratio": - y = np.cumprod(np.hstack((self.params, y)), axis=1)[:, 1:] - - return y - - -class LastKnownNormalizer(FeaturesToFeaturesTransformer): - """Transformer that normalizes values by the last known value. - - Args: - regime: "delta" to take the difference or "ratio" -- the ratio - between the current and the last known value in the future. - last_lag_substring: a substring that is included in the name - of any columns in the feature table and denotes - the last known (nearest) lag features. - - Notes: - 1. self.params: np.ndarray len(idx_y) x 1. - - """ - - def __init__( - self, - regime: str, - transform_features: bool, - transform_target: bool, - last_lag_substring: str = "lag_0", - ): - super().__init__( - transform_features=transform_features, - transform_target=transform_target, - ) - self.regime = regime - self.last_lag_substring = last_lag_substring - - def fit(self, data: dict, input_features: Sequence[str]) -> FeaturesToFeaturesTransformer: - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - self.output_features = [f"{column}__last_known_norm" for column in self.input_features] - - return self - - def transform(self, data: dict) -> dict: - """Update self.params. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - # Update the params if self.transform_target is True - if self.transform_target: - try: - feature = re.compile("^(.*)__(lag_\d+)$").findall(self.input_features[0])[0][0] - except IndexError: - raise ValueError( - "There is no lags in data['raw_ts_X']! Make sure that you initialize LastKnownNormalizer AFTER LagTransformer!" - ) - self.params = index_slicer.get_slice( - data["raw_ts_X"][feature], (data["idx_X"][:, -1], None) - ) - - return data - - def generate(self, data: dict) -> dict: - """Generate or transform features and targets in X, y arrays. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - """ - assert ( - len(data["X"]) != 0 - ), "X is empty! Make sure that you initialize LastKnownNormalizer AFTER LagTransformer!" - - last_lag_idx_by_feature = {} - feature_by_idx = {} - for i, column in enumerate(self.input_features): - feature, lag_suffix = re.compile("^(.*)__(lag_\d+)$").findall(column)[0] - feature_by_idx[i] = feature - if lag_suffix == self.last_lag_substring: - last_lag_idx_by_feature[feature] = i - - if self.transform_target: - feature = feature_by_idx[0] - last_lag_idx = last_lag_idx_by_feature[feature] - - if self.regime == "delta": - data["y"] = data["y"] - data["X"][:, last_lag_idx].reshape(-1, 1) - elif self.regime == "ratio": - data["y"] = data["y"] / data["X"][:, last_lag_idx].reshape(-1, 1) - - for i, column in enumerate(self.input_features): - feature = feature_by_idx[i] - last_lag_idx = last_lag_idx_by_feature[feature] - - if self.regime == "delta": - if self.transform_features: - data["X"][:, i] = data["X"][:, i] - data["X"][:, last_lag_idx] - elif self.regime == "ratio": - if self.transform_features: - data["X"][:, i] = data["X"][:, i] / data["X"][:, last_lag_idx] - - return data - - def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: - """Inverse transforms on the target variable y. - - Args: - y: the target variable to be inversed. - - Returns: - the inversed target variable. - - """ - if len(y.shape) == 1: - y = y.reshape(-1, 1) - if len(self.params.shape) == 1: - self.params = self.params.reshape(-1, 1) - elif len(self.params.shape) == 3: - self.params = self.params[0] - - if self.transform_target: - if self.regime == "delta": - y = y + self.params - elif self.regime == "ratio": - y = y * self.params - - return y +"""Module for transformers for numeric features.""" + +import re +from typing import Sequence + +import numpy as np +import pandas as pd + +from ..dataset.slice import IndexSlicer +from .base import FeaturesToFeaturesTransformer, SeriesToSeriesTransformer + +index_slicer = IndexSlicer() + + +class StandardScalerTransformer(SeriesToSeriesTransformer): + """Transformer that standardizes features by removing the mean and scaling. + + Args: + transform_features: whether to transform features. + transform_target: whether to transform target. + agg_by_id: whether to aggregate statistics by id or over the entire dataset. + + Notes: + 1. Transformer has a parameter self.fitted_params = { + id_1: { + (colname_1, 'mean'): mean, (colname_2, 'std'): std, ... + }, + id_2: { + (colname_1, 'mean'): mean, (colname_2, 'std'): std, ... + }, + ... + }. + 2. self.params: np.ndarray len(idx_y) x 2. + + """ + + def __init__(self, transform_features: bool, transform_target: bool, agg_by_id: bool = True): + super().__init__( + transform_features=transform_features, + transform_target=transform_target, + ) + self.agg_by_id = agg_by_id + self.fitted_params = {} + + def fit(self, data: dict, input_features: Sequence[str]) -> SeriesToSeriesTransformer: + """Fit transformer on "elongated series" and return its instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + + if self.agg_by_id: + stat_df = ( + data["raw_ts_X"] + .groupby(data["id_column_name"])[self.input_features] + .agg(["mean", "std"]) + ) + else: + overall_mean_std = data["raw_ts_X"][self.input_features].agg(["mean", "std"]) + ids = data["raw_ts_X"][data["id_column_name"]].unique() + stat_df = pd.DataFrame(index=ids, columns=pd.MultiIndex.from_product([self.input_features, ["mean", "std"]])) + for column in self.input_features: + stat_df[(column, "mean")] = overall_mean_std.loc["mean", column] + stat_df[(column, "std")] = overall_mean_std.loc["std", column] + + self.fitted_params = stat_df.to_dict(orient="index") + self.output_features = [f"{column}__standard_scaler" for column in self.input_features] + + return self + + def _get_mask_mean_std(self, segment, column_name, current_id): + """ + Calculate the column_mask, mean and standard deviation of a segment + for a given column name and current id. + + Args: + segment: segment of "elongated series" to transform to calculate + the mean and standard deviation for. + column_name: the name of the column to calculate the mean and + standard deviation for. + current_id: the name of id column of the segment. + + Returns: + column mask, mean, and standard deviation. + + """ + column_mask = [segment.columns.str.contains(column_name)][0] + mean = self.fitted_params[current_id][(column_name, "mean")] + std = self.fitted_params[current_id][(column_name, "std")] + + return column_mask, mean, std + + def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Series: + """Transform segment (points with similar id) of "elongated series" + for features' and targets' further generation. + + Args: + segment: segment of "elongated series" to transform. + id_column_name: name of id column. + + Returns: + transformed segment of "elongated series". + + """ + current_id = segment[id_column_name].values[0] + + for i, column_name in enumerate(self.input_features): + column_mask, mean, std = self._get_mask_mean_std( + segment=segment, + column_name=column_name, + current_id=current_id, + ) + segment.loc[:, self.output_features[i]] = (segment.loc[:, column_mask] - mean) / std + + return segment + + def transform(self, data: dict) -> dict: + """Transform "elongated series" for features' and targets' further + generation and update self.params. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + data = super().transform(data) + + # Update the params if self.transform_target is True + if self.transform_target: + id_from_target_idx = index_slicer.get_slice( + data["raw_ts_y"][data["id_column_name"]], (data["idx_y"][:, 0], None) + ) + self.params = [ + list(self.fitted_params[current_id].values()) for current_id in id_from_target_idx + ] + self.params = np.array(self.params) + + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + if self.transform_target: + if len(y.shape) == 1 or y.shape[0] == 1: + y = y * self.params[:, 1] + self.params[:, 0] + else: + y = y * self.params[:, np.newaxis, 1] + self.params[:, np.newaxis, 0] + + return y + + +class DifferenceNormalizer(SeriesToSeriesTransformer): + """Transformer that normalizes values by the previous value. + + Args: + regime: "delta" to take the difference or "ratio" to take the ratio + between the current and the previous value. + transform_features: whether to transform features. + transform_target: whether to transform target. + + Notes: + 1. self.params: np.ndarray len(idx_y) x 1. + + """ + + def __init__(self, regime: str, transform_features: bool, transform_target: bool): + super().__init__( + transform_features=transform_features, + transform_target=transform_target, + ) + self.regime = regime + + def fit(self, data: dict, input_features: Sequence[str]) -> SeriesToSeriesTransformer: + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + last_values_df = ( + data["raw_ts_X"].groupby(data["id_column_name"])[self.input_features].last() + ) + self.params = last_values_df.to_dict(orient="index") + self.output_features = [f"{column}__diff_norm" for column in self.input_features] + + return self + + def _transform_segment(self, segment: pd.Series, *_): + """Transform segment (points with similar id) of "elongated series" + for feautures' and targets' further generation. + + Args: + segment: segment of "elongated series" to transform. + + Returns: + transformed segment of "elongated series". + + """ + for i, column_name in enumerate(self.input_features): + if self.regime == "delta": + segment.loc[:, self.output_features[i]] = segment.loc[ + :, column_name + ] - segment.loc[:, column_name].shift(1) + elif self.regime == "ratio": + segment.loc[:, self.output_features[i]] = segment.loc[ + :, column_name + ] / segment.loc[:, column_name].shift(1) + + return segment + + def transform(self, data: dict) -> dict: + """Transform "elongated series" for feautures' and targets' further + generation and update self.params. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id column and + arrays with indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + data = super().transform(data) + + # Update the params if self.transform_target is True + if self.transform_target: + self.params = index_slicer.get_slice( + data["raw_ts_y"][self.input_features], (data["idx_y"][:, 0] - 1, None) + ) + + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + if len(y.shape) == 1: + y = y.reshape(-1, 1) + + if len(self.params.shape) == 3: + self.params = self.params[0] + + if self.transform_target: + if self.regime == "delta": + y = np.cumsum(np.hstack((self.params, y)), axis=1)[:, 1:] + elif self.regime == "ratio": + y = np.cumprod(np.hstack((self.params, y)), axis=1)[:, 1:] + + return y + + +class LastKnownNormalizer(FeaturesToFeaturesTransformer): + """Transformer that normalizes values by the last known value. + + Args: + regime: "delta" to take the difference or "ratio" -- the ratio + between the current and the last known value in the future. + last_lag_substring: a substring that is included in the name + of any columns in the feature table and denotes + the last known (nearest) lag features. + + Notes: + 1. self.params: np.ndarray len(idx_y) x 1. + + """ + + def __init__( + self, + regime: str, + transform_features: bool, + transform_target: bool, + last_lag_substring: str = "lag_0", + ): + super().__init__( + transform_features=transform_features, + transform_target=transform_target, + ) + self.regime = regime + self.last_lag_substring = last_lag_substring + + def fit(self, data: dict, input_features: Sequence[str]) -> FeaturesToFeaturesTransformer: + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + self.output_features = [f"{column}__last_known_norm" for column in self.input_features] + + return self + + def transform(self, data: dict) -> dict: + """Update self.params. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + # Update the params if self.transform_target is True + if self.transform_target: + try: + feature = re.compile("^(.*)__(lag_\d+)$").findall(self.input_features[0])[0][0] + except IndexError: + raise ValueError( + "There is no lags in data['raw_ts_X']! Make sure that you initialize LastKnownNormalizer AFTER LagTransformer!" + ) + self.params = index_slicer.get_slice( + data["raw_ts_X"][feature], (data["idx_X"][:, -1], None) + ) + + return data + + def generate(self, data: dict) -> dict: + """Generate or transform features and targets in X, y arrays. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + """ + assert ( + len(data["X"]) != 0 + ), "X is empty! Make sure that you initialize LastKnownNormalizer AFTER LagTransformer!" + + last_lag_idx_by_feature = {} + feature_by_idx = {} + for i, column in enumerate(self.input_features): + feature, lag_suffix = re.compile("^(.*)__(lag_\d+)$").findall(column)[0] + feature_by_idx[i] = feature + if lag_suffix == self.last_lag_substring: + last_lag_idx_by_feature[feature] = i + + if self.transform_target: + feature = feature_by_idx[0] + last_lag_idx = last_lag_idx_by_feature[feature] + + if self.regime == "delta": + data["y"] = data["y"] - data["X"][:, last_lag_idx].reshape(-1, 1) + elif self.regime == "ratio": + data["y"] = data["y"] / data["X"][:, last_lag_idx].reshape(-1, 1) + + for i, column in enumerate(self.input_features): + feature = feature_by_idx[i] + last_lag_idx = last_lag_idx_by_feature[feature] + + if self.regime == "delta": + if self.transform_features: + data["X"][:, i] = data["X"][:, i] - data["X"][:, last_lag_idx] + elif self.regime == "ratio": + if self.transform_features: + data["X"][:, i] = data["X"][:, i] / data["X"][:, last_lag_idx] + + return data + + def inverse_transform_y(self, y: np.ndarray) -> np.ndarray: + """Inverse transforms on the target variable y. + + Args: + y: the target variable to be inversed. + + Returns: + the inversed target variable. + + """ + if len(y.shape) == 1: + y = y.reshape(-1, 1) + if len(self.params.shape) == 1: + self.params = self.params.reshape(-1, 1) + elif len(self.params.shape) == 3: + self.params = self.params[0] + + if self.transform_target: + if self.regime == "delta": + y = y + self.params + elif self.regime == "ratio": + y = y * self.params + + return y diff --git a/tsururu/transformers/seq.py b/tsururu/transformers/seq.py index 640ee0b..3266e3a 100644 --- a/tsururu/transformers/seq.py +++ b/tsururu/transformers/seq.py @@ -1,160 +1,160 @@ -"""Module for transformers which generate features and targets from "elongated" series.""" - -from itertools import product -from typing import Sequence, Union - -import numpy as np -import pandas as pd - -from ..dataset.slice import IndexSlicer -from .base import SeriesToFeaturesTransformer -from .utils import _seq_mult_ts - -index_slicer = IndexSlicer() - - -class LagTransformer(SeriesToFeaturesTransformer): - """A transformer that generates lag features. - - Args: - lags: lags features to build. - - Notes: - 1. Lags can be represented either as an integer value or as a sequnece - with specific values. - - 2. Maximum lag must be less than history, otherwise it is impossible - to generate features. - - """ - - def __init__(self, lags: Union[int, Sequence[int]]): - super().__init__() - if isinstance(lags, Sequence): - self.lags = np.array(lags) - if isinstance(lags, int): - self.lags = np.arange(lags) - - def fit(self, data: dict, input_features: Sequence[str]) -> "LagTransformer": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - - self.output_features = [ - f"{column}__lag_{lag}" for column, lag in product(self.input_features, self.lags[::-1]) - ] - - return self - - def _check_lags_less_than_history( - self, - data: pd.DataFrame, - idx: np.ndarray, - input_features_idx: np.ndarray, - ) -> None: - """Check if the maximum value of the lags is less than the history. - - Args: - data: the source "elongated series" raw_ts_X. - idx: the indices to take one observation. - input_features_idx: the indices of the input features. - - Raises: - AssertionError: If the maximum value of the lags is not less - than the number of columns in the sample data (history). - """ - sample_data = index_slicer.get_slice(data, (idx, input_features_idx)) - assert self.lags.max() < sample_data.shape[1], "lags must be less than history" - - def generate(self, data: dict) -> dict: - """Generate lag features in X array. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - Notes: - 1. Either both idx_X or idx_y must be specified, - LagTransformer uses only idx_X. - - """ - input_features_idx = index_slicer.get_cols_idx(data["raw_ts_X"], self.input_features) - - if len(data["idx_X"].shape) == 3: - self._check_lags_less_than_history( - data["raw_ts_X"], data["idx_X"][0], input_features_idx - ) - X = _seq_mult_ts(data["raw_ts_X"], data["idx_X"], input_features_idx) - else: - self._check_lags_less_than_history(data["raw_ts_X"], data["idx_X"], input_features_idx) - X = index_slicer.get_slice(data["raw_ts_X"], (data["idx_X"], input_features_idx)) - - X = X[:, (X.shape[1] - 1) - self.lags[::-1], :] - X = np.moveaxis(X, 1, 2).reshape(len(X), -1) - - if data["X"].shape == (0,): - data["X"] = X - else: - data["X"] = np.hstack((data["X"], X)) - - return data - - -class TargetGenerator(SeriesToFeaturesTransformer): - """A transformer that selects specific indices from "elongated" raw_ts_y - and generates values for y array. - """ - - def fit(self, data: dict, input_features: Sequence[str]) -> "LagTransformer": - """Fit transformer on "elongated series" and return it's instance. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - input_features: array with names of columns to transform. - - Returns: - self. - - """ - super().fit(data, input_features) - - self.output_features = None - - def generate(self, data: dict) -> dict: - """Generate features in y array. - - Args: - data: dictionary with current states of "elongated series", - arrays with features and targets, name of id, date and target - columns and indices for features and targets. - - Returns: - current states of `data` dictionary. - - Notes: - 1. Either both idx_X or idx_y must be specified, - TargetGenerator uses only idx_y. - - """ - input_features_idx = index_slicer.get_cols_idx(data["raw_ts_y"], self.input_features) - data["y"] = index_slicer.get_slice( - data["raw_ts_y"], (data["idx_y"], input_features_idx) - ).squeeze(-1) - - return data +"""Module for transformers which generate features and targets from "elongated" series.""" + +from itertools import product +from typing import Sequence, Union + +import numpy as np +import pandas as pd + +from ..dataset.slice import IndexSlicer +from .base import SeriesToFeaturesTransformer +from .utils import _seq_mult_ts + +index_slicer = IndexSlicer() + + +class LagTransformer(SeriesToFeaturesTransformer): + """A transformer that generates lag features. + + Args: + lags: lags features to build. + + Notes: + 1. Lags can be represented either as an integer value or as a sequnece + with specific values. + + 2. Maximum lag must be less than history, otherwise it is impossible + to generate features. + + """ + + def __init__(self, lags: Union[int, Sequence[int]]): + super().__init__() + if isinstance(lags, Sequence): + self.lags = np.array(lags) + if isinstance(lags, int): + self.lags = np.arange(lags) + + def fit(self, data: dict, input_features: Sequence[str]) -> "LagTransformer": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + + self.output_features = [ + f"{column}__lag_{lag}" for column, lag in product(self.input_features, self.lags[::-1]) + ] + + return self + + def _check_lags_less_than_history( + self, + data: pd.DataFrame, + idx: np.ndarray, + input_features_idx: np.ndarray, + ) -> None: + """Check if the maximum value of the lags is less than the history. + + Args: + data: the source "elongated series" raw_ts_X. + idx: the indices to take one observation. + input_features_idx: the indices of the input features. + + Raises: + AssertionError: If the maximum value of the lags is not less + than the number of columns in the sample data (history). + """ + sample_data = index_slicer.get_slice(data, (idx, input_features_idx)) + assert self.lags.max() < sample_data.shape[1], "lags must be less than history" + + def generate(self, data: dict) -> dict: + """Generate lag features in X array. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + Notes: + 1. Either both idx_X or idx_y must be specified, + LagTransformer uses only idx_X. + + """ + input_features_idx = index_slicer.get_cols_idx(data["raw_ts_X"], self.input_features) + + if len(data["idx_X"].shape) == 3: + self._check_lags_less_than_history( + data["raw_ts_X"], data["idx_X"][0], input_features_idx + ) + X = _seq_mult_ts(data["raw_ts_X"], data["idx_X"], input_features_idx) + else: + self._check_lags_less_than_history(data["raw_ts_X"], data["idx_X"], input_features_idx) + X = index_slicer.get_slice(data["raw_ts_X"], (data["idx_X"], input_features_idx)) + + X = X[:, (X.shape[1] - 1) - self.lags[::-1], :] + X = np.moveaxis(X, 1, 2).reshape(len(X), -1) + + if data["X"].shape == (0,): + data["X"] = X + else: + data["X"] = np.hstack((data["X"], X)) + + return data + + +class TargetGenerator(SeriesToFeaturesTransformer): + """A transformer that selects specific indices from "elongated" raw_ts_y + and generates values for y array. + """ + + def fit(self, data: dict, input_features: Sequence[str]) -> "LagTransformer": + """Fit transformer on "elongated series" and return it's instance. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + input_features: array with names of columns to transform. + + Returns: + self. + + """ + super().fit(data, input_features) + + self.output_features = None + + def generate(self, data: dict) -> dict: + """Generate features in y array. + + Args: + data: dictionary with current states of "elongated series", + arrays with features and targets, name of id, date and target + columns and indices for features and targets. + + Returns: + current states of `data` dictionary. + + Notes: + 1. Either both idx_X or idx_y must be specified, + TargetGenerator uses only idx_y. + + """ + input_features_idx = index_slicer.get_cols_idx(data["raw_ts_y"], self.input_features) + data["y"] = index_slicer.get_slice( + data["raw_ts_y"], (data["idx_y"], input_features_idx) + ).squeeze(-1) + + return data diff --git a/tsururu/transformers/utils.py b/tsururu/transformers/utils.py index 272556d..b48a89a 100644 --- a/tsururu/transformers/utils.py +++ b/tsururu/transformers/utils.py @@ -1,38 +1,38 @@ -import numpy as np - -from ..dataset.slice import IndexSlicer - -transformers_masks = { - "raw": r"^", - "LAG": r"lag_\d+__", - "SEASON": r"season_\w+__", - "TIMETONUM": r"time_to_num__", - "LABEL": r"label_encoder__", - "OHE": r"ohe_encoder_\S+__", -} - -date_attrs = { - "y": "year", - "m": "month", - "d": "day", - "wd": "weekday", - "doy": "dayofyear", - "hour": "hour", - "min": "minute", - "sec": "second", - "ms": "microsecond", - "ns": "nanosecond", -} - - -def _seq_mult_ts(data, idx_data): - index_slicer = IndexSlicer() - data_seq = np.array([]) - - for idx in range(len(data)): - current_data_seq = index_slicer.get_slice(data[idx], (idx_data[idx], None)) - if data_seq.shape[0] == 0: - data_seq = current_data_seq - else: - data_seq = np.hstack((data_seq, current_data_seq)) - return data_seq +import numpy as np + +from ..dataset.slice import IndexSlicer + +transformers_masks = { + "raw": r"^", + "LAG": r"lag_\d+__", + "SEASON": r"season_\w+__", + "TIMETONUM": r"time_to_num__", + "LABEL": r"label_encoder__", + "OHE": r"ohe_encoder_\S+__", +} + +date_attrs = { + "y": "year", + "m": "month", + "d": "day", + "wd": "weekday", + "doy": "dayofyear", + "hour": "hour", + "min": "minute", + "sec": "second", + "ms": "microsecond", + "ns": "nanosecond", +} + + +def _seq_mult_ts(data, idx_data): + index_slicer = IndexSlicer() + data_seq = np.array([]) + + for idx in range(len(data)): + current_data_seq = index_slicer.get_slice(data[idx], (idx_data[idx], None)) + if data_seq.shape[0] == 0: + data_seq = current_data_seq + else: + data_seq = np.hstack((data_seq, current_data_seq)) + return data_seq From d4d82125e8501d0393775b13cb836c9d89fc9150 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Wed, 18 Sep 2024 13:24:25 +0300 Subject: [PATCH 17/50] bug fixes + update nn_tutorial --- Tutorial_4_Neural_Networks.ipynb | 3614 +++++++++++++++++ tsururu/dataset/pipeline.py | 1 - .../model_training/torch_based/callbacks.py | 226 +- tsururu/model_training/trainer.py | 74 +- tsururu/strategies/base.py | 6 +- tsururu/strategies/direct.py | 42 +- tsururu/strategies/flat_wide_mimo.py | 7 +- tsururu/strategies/mimo.py | 8 +- tsururu/strategies/recursive.py | 18 +- tsururu/transformers/base.py | 46 +- 10 files changed, 3840 insertions(+), 202 deletions(-) create mode 100644 Tutorial_4_Neural_Networks.ipynb diff --git a/Tutorial_4_Neural_Networks.ipynb b/Tutorial_4_Neural_Networks.ipynb new file mode 100644 index 0000000..0e64341 --- /dev/null +++ b/Tutorial_4_Neural_Networks.ipynb @@ -0,0 +1,3614 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import importlib\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from pathlib import Path\n", + "from typing import List, Optional, Union\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from tsururu.dataset import Pipeline, TSDataset\n", + "from tsururu.model_training.trainer import DLTrainer\n", + "from tsururu.model_training.validator import KFoldCrossValidator\n", + "from tsururu.models.torch_based.dlinear import DLinear_NN\n", + "from tsururu.strategies import (\n", + " RecursiveStrategy,\n", + " MIMOStrategy,\n", + " DirectStrategy,\n", + " FlatWideMIMOStrategy,\n", + ")\n", + "from tsururu.transformers import (\n", + " LagTransformer,\n", + " SequentialTransformer,\n", + " TargetGenerator,\n", + " UnionTransformer,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_results(\n", + " cv: int,\n", + " regime: str,\n", + " y_true: Optional[List[np.ndarray]] = None,\n", + " y_pred: Optional[List[np.ndarray]] = None,\n", + " ids: Optional[List[Union[float, str]]] = None,\n", + ") -> pd.DataFrame:\n", + " def _get_fold_value(\n", + " value: Optional[Union[float, np.ndarray]], idx: int\n", + " ) -> List[Optional[Union[float, np.ndarray]]]:\n", + " if value is None:\n", + " return [None]\n", + " if isinstance(value[idx], float):\n", + " return value[idx]\n", + " if isinstance(value[idx], np.ndarray):\n", + " return value[idx].reshape(-1)\n", + " raise TypeError(f\"Unexpected value type. Value: {value}\")\n", + "\n", + " df_res_dict = {}\n", + "\n", + " for idx_fold in range(cv):\n", + " # Fill df_res_dict\n", + " for name, value in [(\"y_true\", y_true), (\"y_pred\", y_pred)]:\n", + " df_res_dict[f\"{name}_{idx_fold+1}\"] = _get_fold_value(value, idx_fold)\n", + " if regime != \"local\":\n", + " df_res_dict[f\"id_{idx_fold+1}\"] = _get_fold_value(ids, idx_fold)\n", + "\n", + " # Save datasets to specified directory\n", + " df_res = pd.DataFrame(df_res_dict)\n", + " return df_res" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initialize TSDataset, Pipeline, Model, Validator, Strategy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The initialization of the main components is exactly the same as when using ML models. The only difference is that `DLTrainer` allows you to pass many more parameters compared to `MLTrainer`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TSDataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_path = Path(\"datasets/global/simulated_data_to_check.csv\")\n", + "\n", + "dataset_params = {\n", + " \"target\": {\n", + " \"columns\": [\"value\"],\n", + " \"type\": \"continious\",\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"date\"],\n", + " \"type\": \"datetime\",\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"type\": \"categorical\",\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + } + ], + "source": [ + "dataset = TSDataset(\n", + " data=pd.read_csv(df_path),\n", + " columns_params=dataset_params,\n", + " print_freq_period_info=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "lag = LagTransformer(lags=7)\n", + "target_generator = TargetGenerator()\n", + "\n", + "union_1 = UnionTransformer(transformers_list=[lag, target_generator])\n", + "seq_1 = SequentialTransformer(transformers_list=[union_1], input_features=[\"value\"])\n", + "union = UnionTransformer(transformers_list=[seq_1])\n", + "\n", + "pipeline = Pipeline(union, multivariate=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trainer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure the model parameters\n", + "model = DLinear_NN\n", + "model_params = {\"moving_avg\": 7, \"individual\": False, \"enc_in\": None}\n", + "\n", + "# Configure the validation parameters\n", + "validation = KFoldCrossValidator\n", + "validation_params = {\n", + " \"n_splits\": 2,\n", + "}\n", + "\n", + "trainer_params = {\n", + " \"device\": \"cpu\",\n", + " \"num_workers\": 0,\n", + " \"best_by_metric\": True,\n", + " \"save_to_dir\": False,\n", + "}\n", + "\n", + "trainer = DLTrainer(\n", + " model, \n", + " model_params, \n", + " validation, \n", + " validation_params, \n", + " **trainer_params\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Strategy" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "horizon = 7\n", + "model_horizon = 7\n", + "history = 7" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "strategy = RecursiveStrategy(\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " horizon=horizon,\n", + " history=history,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of train dataset: 496\n", + "length of val dataset: 497\n", + "Epoch 1/10, cost time: 0.71s\n", + "train loss: 397.4165\n", + "Validation, Loss: 162.1953, Metric: -162.1953\n", + "val loss: 162.1953\n", + "Epoch 2/10, cost time: 0.55s\n", + "train loss: 64.1882\n", + "Validation, Loss: 47.4241, Metric: -47.4241\n", + "val loss: 47.4241\n", + "Epoch 3/10, cost time: 0.54s\n", + "train loss: 17.5566\n", + "Validation, Loss: 8.8561, Metric: -8.8561\n", + "val loss: 8.8561\n", + "Epoch 4/10, cost time: 0.54s\n", + "train loss: 6.4756\n", + "Validation, Loss: 4.4630, Metric: -4.4630\n", + "val loss: 4.4630\n", + "Epoch 5/10, cost time: 0.54s\n", + "train loss: 4.3727\n", + "Validation, Loss: 3.9053, Metric: -3.9053\n", + "val loss: 3.9053\n", + "Epoch 6/10, cost time: 0.56s\n", + "train loss: 3.8692\n", + "Validation, Loss: 3.8264, Metric: -3.8264\n", + "val loss: 3.8264\n", + "Removing worst model snapshot: from epoch 0\n", + "Epoch 7/10, cost time: 0.55s\n", + "train loss: 3.7675\n", + "Validation, Loss: 3.7656, Metric: -3.7656\n", + "val loss: 3.7656\n", + "Removing worst model snapshot: from epoch 1\n", + "Epoch 8/10, cost time: 0.61s\n", + "train loss: 3.7314\n", + "Validation, Loss: 3.7414, Metric: -3.7414\n", + "val loss: 3.7414\n", + "Removing worst model snapshot: from epoch 2\n", + "Epoch 9/10, cost time: 0.55s\n", + "train loss: 3.7106\n", + "Validation, Loss: 3.7250, Metric: -3.7250\n", + "val loss: 3.7250\n", + "Removing worst model snapshot: from epoch 3\n", + "Epoch 10/10, cost time: 0.55s\n", + "train loss: 3.6937\n", + "Validation, Loss: 3.7062, Metric: -3.7062\n", + "val loss: 3.7062\n", + "Removing worst model snapshot: from epoch 4\n", + "Fold 0. Score: -3.706167459487915\n", + "length of train dataset: 497\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.54s\n", + "train loss: 403.0955\n", + "Validation, Loss: 162.1690, Metric: -162.1690\n", + "val loss: 162.1690\n", + "Epoch 2/10, cost time: 0.54s\n", + "train loss: 63.4995\n", + "Validation, Loss: 48.7841, Metric: -48.7841\n", + "val loss: 48.7841\n", + "Epoch 3/10, cost time: 0.60s\n", + "train loss: 16.5093\n", + "Validation, Loss: 8.7141, Metric: -8.7141\n", + "val loss: 8.7141\n", + "Epoch 4/10, cost time: 0.54s\n", + "train loss: 5.3522\n", + "Validation, Loss: 2.9066, Metric: -2.9066\n", + "val loss: 2.9066\n", + "Epoch 5/10, cost time: 0.55s\n", + "train loss: 3.2029\n", + "Validation, Loss: 2.8261, Metric: -2.8261\n", + "val loss: 2.8261\n", + "Epoch 6/10, cost time: 0.69s\n", + "train loss: 2.7355\n", + "Validation, Loss: 2.6951, Metric: -2.6951\n", + "val loss: 2.6951\n", + "Removing worst model snapshot: from epoch 0\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 2.6526\n", + "Validation, Loss: 2.6279, Metric: -2.6279\n", + "val loss: 2.6279\n", + "Removing worst model snapshot: from epoch 1\n", + "Epoch 8/10, cost time: 0.56s\n", + "train loss: 2.6296\n", + "Validation, Loss: 2.6028, Metric: -2.6028\n", + "val loss: 2.6028\n", + "Removing worst model snapshot: from epoch 2\n", + "Epoch 9/10, cost time: 0.57s\n", + "train loss: 2.6118\n", + "Validation, Loss: 2.5885, Metric: -2.5885\n", + "val loss: 2.5885\n", + "Removing worst model snapshot: from epoch 3\n", + "Epoch 10/10, cost time: 0.56s\n", + "train loss: 2.5993\n", + "Validation, Loss: 2.5757, Metric: -2.5757\n", + "val loss: 2.5757\n", + "Removing worst model snapshot: from epoch 4\n", + "Fold 1. Score: -2.5756940841674805\n", + "Mean score: -3.1409\n", + "Std: 0.5652\n" + ] + }, + { + "data": { + "text/plain": [ + "(22.96847891807556,\n", + " )" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strategy.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n", + "Validation, Loss: nan, Metric: nan\n", + "Validation, Loss: nan, Metric: nan\n" + ] + } + ], + "source": [ + "forecast_time, current_pred = strategy.predict(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date value\n", + "0 0 2022-09-27 1997.029297\n", + "1 0 2022-09-28 1997.616943\n", + "2 0 2022-09-29 1998.106445\n", + "3 0 2022-09-30 1998.513306\n", + "4 0 2022-10-01 1998.86499\n", + ".. .. ... ...\n", + "65 9 2022-09-29 11004.607422\n", + "66 9 2022-09-30 11006.004883\n", + "67 9 2022-10-01 11007.439453\n", + "68 9 2022-10-02 11008.916016\n", + "69 9 2022-10-03 11010.459961\n", + "\n", + "[70 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Saving and loading checkpoints is an essential practice in training DL models. \n", + "\n", + "Let's explore how to save checkpoints to disk, what structure the saved files have, and how to restore the model from a checkpoint for either fine-tuning or inference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save and load checkpoints" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let’s consider working with checkpoints using the Direct strategy as an example." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "trainer_params = {\n", + " \"device\": \"cpu\",\n", + " \"num_workers\": 0,\n", + " \"best_by_metric\": True,\n", + " # Let's enable save_to_dir (by the way, default value is True)\n", + " \"save_to_dir\": True,\n", + " \"checkpoint_path\": \"checkpoints/\",\n", + " # Save checkpoints for 3 best model\n", + " \"save_k_best\": 3,\n", + " # Average checkpoints for the final model\n", + " \"average_snapshots\": True,\n", + "}\n", + "\n", + "trainer = DLTrainer(\n", + " model, \n", + " model_params, \n", + " validation, \n", + " validation_params, \n", + " **trainer_params\n", + ")\n", + "\n", + "strategy = DirectStrategy(\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " horizon=horizon,\n", + " history=history,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of train dataset: 496\n", + "length of val dataset: 497\n", + "Epoch 1/10, cost time: 0.67s\n", + "train loss: 410.0893\n", + "Validation, Loss: 175.5516, Metric: -175.5516\n", + "val loss: 175.5516\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 66.1765\n", + "Validation, Loss: 52.3162, Metric: -52.3162\n", + "val loss: 52.3162\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.56s\n", + "train loss: 17.1039\n", + "Validation, Loss: 9.6560, Metric: -9.6560\n", + "val loss: 9.6560\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.56s\n", + "train loss: 5.2014\n", + "Validation, Loss: 2.4881, Metric: -2.4881\n", + "val loss: 2.4881\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 2.9388\n", + "Validation, Loss: 2.6873, Metric: -2.6873\n", + "val loss: 2.6873\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_4.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.56s\n", + "train loss: 2.4510\n", + "Validation, Loss: 2.4363, Metric: -2.4363\n", + "val loss: 2.4363\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.56s\n", + "train loss: 2.3589\n", + "Validation, Loss: 2.3555, Metric: -2.3555\n", + "val loss: 2.3555\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.56s\n", + "train loss: 2.3237\n", + "Validation, Loss: 2.3299, Metric: -2.3299\n", + "val loss: 2.3299\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.56s\n", + "train loss: 2.3057\n", + "Validation, Loss: 2.3130, Metric: -2.3130\n", + "val loss: 2.3130\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 2.2946\n", + "Validation, Loss: 2.3030, Metric: -2.3030\n", + "val loss: 2.3030\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -2.3029584884643555\n", + "length of train dataset: 497\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.56s\n", + "train loss: 402.0421\n", + "Validation, Loss: 175.4913, Metric: -175.4913\n", + "val loss: 175.4913\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.56s\n", + "train loss: 66.1697\n", + "Validation, Loss: 51.3572, Metric: -51.3572\n", + "val loss: 51.3572\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.61s\n", + "train loss: 17.4254\n", + "Validation, Loss: 10.0762, Metric: -10.0762\n", + "val loss: 10.0762\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.56s\n", + "train loss: 6.2863\n", + "Validation, Loss: 3.7394, Metric: -3.7394\n", + "val loss: 3.7394\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 4.1120\n", + "Validation, Loss: 3.7602, Metric: -3.7602\n", + "val loss: 3.7602\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_4.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 3.5857\n", + "Validation, Loss: 3.5395, Metric: -3.5395\n", + "val loss: 3.5395\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 3.5012\n", + "Validation, Loss: 3.4661, Metric: -3.4661\n", + "val loss: 3.4661\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.57s\n", + "train loss: 3.4733\n", + "Validation, Loss: 3.4416, Metric: -3.4416\n", + "val loss: 3.4416\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.57s\n", + "train loss: 3.4547\n", + "Validation, Loss: 3.4227, Metric: -3.4227\n", + "val loss: 3.4227\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 3.4392\n", + "Validation, Loss: 3.4071, Metric: -3.4071\n", + "val loss: 3.4071\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -3.407080888748169\n", + "Mean score: -2.855\n", + "Std: 0.5521\n", + "length of train dataset: 496\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 378.3954\n", + "Validation, Loss: 118.6615, Metric: -118.6615\n", + "val loss: 118.6615\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.57s\n", + "train loss: 61.5287\n", + "Validation, Loss: 34.4297, Metric: -34.4297\n", + "val loss: 34.4297\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 17.6670\n", + "Validation, Loss: 6.5823, Metric: -6.5823\n", + "val loss: 6.5823\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.57s\n", + "train loss: 8.1401\n", + "Validation, Loss: 7.6014, Metric: -7.6014\n", + "val loss: 7.6014\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_3.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.57s\n", + "train loss: 6.4557\n", + "Validation, Loss: 5.9087, Metric: -5.9087\n", + "val loss: 5.9087\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 5.9378\n", + "Validation, Loss: 5.8568, Metric: -5.8568\n", + "val loss: 5.8568\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 5.8375\n", + "Validation, Loss: 5.8387, Metric: -5.8387\n", + "val loss: 5.8387\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 5.7944\n", + "Validation, Loss: 5.8086, Metric: -5.8086\n", + "val loss: 5.8086\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.64s\n", + "train loss: 5.7653\n", + "Validation, Loss: 5.7895, Metric: -5.7895\n", + "val loss: 5.7895\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 5.7400\n", + "Validation, Loss: 5.7563, Metric: -5.7563\n", + "val loss: 5.7563\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -5.756300449371338\n", + "length of train dataset: 496\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 378.4389\n", + "Validation, Loss: 122.0446, Metric: -122.0446\n", + "val loss: 122.0446\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 61.1700\n", + "Validation, Loss: 35.1154, Metric: -35.1154\n", + "val loss: 35.1154\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 17.2550\n", + "Validation, Loss: 6.4812, Metric: -6.4812\n", + "val loss: 6.4812\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.84s\n", + "train loss: 7.8464\n", + "Validation, Loss: 7.0618, Metric: -7.0618\n", + "val loss: 7.0618\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_3.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.67s\n", + "train loss: 6.1514\n", + "Validation, Loss: 5.5988, Metric: -5.5988\n", + "val loss: 5.5988\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 5.6928\n", + "Validation, Loss: 5.5747, Metric: -5.5747\n", + "val loss: 5.5747\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 5.6151\n", + "Validation, Loss: 5.5705, Metric: -5.5705\n", + "val loss: 5.5705\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 5.5965\n", + "Validation, Loss: 5.5548, Metric: -5.5548\n", + "val loss: 5.5548\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 5.5527\n", + "Validation, Loss: 5.5025, Metric: -5.5025\n", + "val loss: 5.5025\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.59s\n", + "train loss: 5.5238\n", + "Validation, Loss: 5.4796, Metric: -5.4796\n", + "val loss: 5.4796\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -5.479559421539307\n", + "Mean score: -5.6179\n", + "Std: 0.1384\n", + "length of train dataset: 495\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 368.7471\n", + "Validation, Loss: 98.2119, Metric: -98.2119\n", + "val loss: 98.2119\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.63s\n", + "train loss: 62.4588\n", + "Validation, Loss: 28.2995, Metric: -28.2995\n", + "val loss: 28.2995\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.62s\n", + "train loss: 19.1735\n", + "Validation, Loss: 7.3410, Metric: -7.3410\n", + "val loss: 7.3410\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 9.5496\n", + "Validation, Loss: 9.0898, Metric: -9.0898\n", + "val loss: 9.0898\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_3.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.61s\n", + "train loss: 7.7423\n", + "Validation, Loss: 7.3874, Metric: -7.3874\n", + "val loss: 7.3874\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_4.pth\n", + "Early stopping counter: 2\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.60s\n", + "train loss: 7.3339\n", + "Validation, Loss: 7.2613, Metric: -7.2613\n", + "val loss: 7.2613\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.61s\n", + "train loss: 7.2308\n", + "Validation, Loss: 7.2357, Metric: -7.2357\n", + "val loss: 7.2357\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 7.1769\n", + "Validation, Loss: 7.1993, Metric: -7.1993\n", + "val loss: 7.1993\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.61s\n", + "train loss: 7.1335\n", + "Validation, Loss: 7.1545, Metric: -7.1545\n", + "val loss: 7.1545\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.67s\n", + "train loss: 7.1028\n", + "Validation, Loss: 7.1250, Metric: -7.1250\n", + "val loss: 7.1250\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -7.124959945678711\n", + "length of train dataset: 496\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.60s\n", + "train loss: 385.4868\n", + "Validation, Loss: 140.1279, Metric: -140.1279\n", + "val loss: 140.1279\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 64.4549\n", + "Validation, Loss: 41.3462, Metric: -41.3462\n", + "val loss: 41.3462\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.62s\n", + "train loss: 17.9917\n", + "Validation, Loss: 8.0459, Metric: -8.0459\n", + "val loss: 8.0459\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 8.3273\n", + "Validation, Loss: 7.1672, Metric: -7.1672\n", + "val loss: 7.1672\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 6.5713\n", + "Validation, Loss: 5.9262, Metric: -5.9262\n", + "val loss: 5.9262\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.66s\n", + "train loss: 6.0394\n", + "Validation, Loss: 5.9408, Metric: -5.9408\n", + "val loss: 5.9408\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_5.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 5.9436\n", + "Validation, Loss: 5.8866, Metric: -5.8866\n", + "val loss: 5.8866\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.87s\n", + "train loss: 5.8999\n", + "Validation, Loss: 5.8550, Metric: -5.8550\n", + "val loss: 5.8550\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.66s\n", + "train loss: 5.8734\n", + "Validation, Loss: 5.8239, Metric: -5.8239\n", + "val loss: 5.8239\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.81s\n", + "train loss: 5.8509\n", + "Validation, Loss: 5.7968, Metric: -5.7968\n", + "val loss: 5.7968\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -5.796763896942139\n", + "Mean score: -6.4609\n", + "Std: 0.6641\n", + "length of train dataset: 495\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 378.0090\n", + "Validation, Loss: 114.3918, Metric: -114.3918\n", + "val loss: 114.3918\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 64.8576\n", + "Validation, Loss: 35.0846, Metric: -35.0846\n", + "val loss: 35.0846\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 19.9201\n", + "Validation, Loss: 8.2947, Metric: -8.2947\n", + "val loss: 8.2947\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 9.9949\n", + "Validation, Loss: 9.4797, Metric: -9.4797\n", + "val loss: 9.4797\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_3.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 8.1923\n", + "Validation, Loss: 7.6396, Metric: -7.6396\n", + "val loss: 7.6396\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.60s\n", + "train loss: 7.6727\n", + "Validation, Loss: 7.5948, Metric: -7.5948\n", + "val loss: 7.5948\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 7.5681\n", + "Validation, Loss: 7.5673, Metric: -7.5673\n", + "val loss: 7.5673\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 7.5178\n", + "Validation, Loss: 7.5433, Metric: -7.5433\n", + "val loss: 7.5433\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 7.4774\n", + "Validation, Loss: 7.5010, Metric: -7.5010\n", + "val loss: 7.5010\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.70s\n", + "train loss: 7.4423\n", + "Validation, Loss: 7.4665, Metric: -7.4665\n", + "val loss: 7.4665\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -7.466495513916016\n", + "length of train dataset: 495\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.67s\n", + "train loss: 350.8603\n", + "Validation, Loss: 72.0800, Metric: -72.0800\n", + "val loss: 72.0800\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 61.0789\n", + "Validation, Loss: 20.1079, Metric: -20.1079\n", + "val loss: 20.1079\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.60s\n", + "train loss: 20.8031\n", + "Validation, Loss: 11.5243, Metric: -11.5243\n", + "val loss: 11.5243\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.74s\n", + "train loss: 12.7361\n", + "Validation, Loss: 11.4595, Metric: -11.4595\n", + "val loss: 11.4595\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 10.8085\n", + "Validation, Loss: 10.5288, Metric: -10.5288\n", + "val loss: 10.5288\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.64s\n", + "train loss: 10.3708\n", + "Validation, Loss: 10.2898, Metric: -10.2898\n", + "val loss: 10.2898\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 10.2825\n", + "Validation, Loss: 10.1843, Metric: -10.1843\n", + "val loss: 10.1843\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 10.2214\n", + "Validation, Loss: 10.1343, Metric: -10.1343\n", + "val loss: 10.1343\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 10.1778\n", + "Validation, Loss: 10.0894, Metric: -10.0894\n", + "val loss: 10.0894\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.58s\n", + "train loss: 10.1299\n", + "Validation, Loss: 10.0382, Metric: -10.0382\n", + "val loss: 10.0382\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -10.038150787353516\n", + "Mean score: -8.7523\n", + "Std: 1.2858\n", + "length of train dataset: 494\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.60s\n", + "train loss: 353.2743\n", + "Validation, Loss: 79.3986, Metric: -79.3986\n", + "val loss: 79.3986\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.65s\n", + "train loss: 61.6696\n", + "Validation, Loss: 21.9620, Metric: -21.9620\n", + "val loss: 21.9620\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.60s\n", + "train loss: 21.5628\n", + "Validation, Loss: 12.1240, Metric: -12.1240\n", + "val loss: 12.1240\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 13.6004\n", + "Validation, Loss: 12.7638, Metric: -12.7638\n", + "val loss: 12.7638\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_3.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.59s\n", + "train loss: 11.6205\n", + "Validation, Loss: 11.5344, Metric: -11.5344\n", + "val loss: 11.5344\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 11.1920\n", + "Validation, Loss: 11.1878, Metric: -11.1878\n", + "val loss: 11.1878\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 11.0455\n", + "Validation, Loss: 11.0981, Metric: -11.0981\n", + "val loss: 11.0981\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 10.9868\n", + "Validation, Loss: 11.0153, Metric: -11.0153\n", + "val loss: 11.0153\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 10.9333\n", + "Validation, Loss: 10.9771, Metric: -10.9771\n", + "val loss: 10.9771\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 10.8852\n", + "Validation, Loss: 10.9085, Metric: -10.9085\n", + "val loss: 10.9085\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -10.908503532409668\n", + "length of train dataset: 495\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 331.1463\n", + "Validation, Loss: 51.0125, Metric: -51.0125\n", + "val loss: 51.0125\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 60.7976\n", + "Validation, Loss: 15.0375, Metric: -15.0375\n", + "val loss: 15.0375\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 23.2117\n", + "Validation, Loss: 18.2942, Metric: -18.2942\n", + "val loss: 18.2942\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_2.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 15.7120\n", + "Validation, Loss: 13.5160, Metric: -13.5160\n", + "val loss: 13.5160\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.59s\n", + "train loss: 13.6714\n", + "Validation, Loss: 13.4925, Metric: -13.4925\n", + "val loss: 13.4925\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 13.3623\n", + "Validation, Loss: 13.2415, Metric: -13.2415\n", + "val loss: 13.2415\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 13.2645\n", + "Validation, Loss: 13.1447, Metric: -13.1447\n", + "val loss: 13.1447\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.57s\n", + "train loss: 13.1919\n", + "Validation, Loss: 13.0827, Metric: -13.0827\n", + "val loss: 13.0827\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 13.1265\n", + "Validation, Loss: 13.0275, Metric: -13.0275\n", + "val loss: 13.0275\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 13.0687\n", + "Validation, Loss: 12.9526, Metric: -12.9526\n", + "val loss: 12.9526\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -12.95260238647461\n", + "Mean score: -11.9306\n", + "Std: 1.022\n", + "length of train dataset: 494\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 341.2509\n", + "Validation, Loss: 63.2621, Metric: -63.2621\n", + "val loss: 63.2621\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 62.6343\n", + "Validation, Loss: 18.4068, Metric: -18.4068\n", + "val loss: 18.4068\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 24.6361\n", + "Validation, Loss: 18.0282, Metric: -18.0282\n", + "val loss: 18.0282\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 16.9802\n", + "Validation, Loss: 15.1966, Metric: -15.1966\n", + "val loss: 15.1966\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.59s\n", + "train loss: 14.9043\n", + "Validation, Loss: 14.8648, Metric: -14.8648\n", + "val loss: 14.8648\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 14.5332\n", + "Validation, Loss: 14.5774, Metric: -14.5774\n", + "val loss: 14.5774\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 14.4015\n", + "Validation, Loss: 14.4559, Metric: -14.4559\n", + "val loss: 14.4559\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 14.3272\n", + "Validation, Loss: 14.4021, Metric: -14.4021\n", + "val loss: 14.4021\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 14.2652\n", + "Validation, Loss: 14.3438, Metric: -14.3438\n", + "val loss: 14.3438\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.63s\n", + "train loss: 14.1977\n", + "Validation, Loss: 14.2568, Metric: -14.2568\n", + "val loss: 14.2568\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -14.256837844848633\n", + "length of train dataset: 494\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 329.8946\n", + "Validation, Loss: 49.9958, Metric: -49.9958\n", + "val loss: 49.9958\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 63.0375\n", + "Validation, Loss: 16.5045, Metric: -16.5045\n", + "val loss: 16.5045\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 25.1321\n", + "Validation, Loss: 21.0100, Metric: -21.0100\n", + "val loss: 21.0100\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_2.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 18.0811\n", + "Validation, Loss: 15.7223, Metric: -15.7223\n", + "val loss: 15.7223\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 16.0435\n", + "Validation, Loss: 15.6346, Metric: -15.6346\n", + "val loss: 15.6346\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.65s\n", + "train loss: 15.7121\n", + "Validation, Loss: 15.5237, Metric: -15.5237\n", + "val loss: 15.5237\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 15.6051\n", + "Validation, Loss: 15.4598, Metric: -15.4598\n", + "val loss: 15.4598\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 15.5376\n", + "Validation, Loss: 15.3857, Metric: -15.3857\n", + "val loss: 15.3857\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 15.4622\n", + "Validation, Loss: 15.3102, Metric: -15.3102\n", + "val loss: 15.3102\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.58s\n", + "train loss: 15.3827\n", + "Validation, Loss: 15.2329, Metric: -15.2329\n", + "val loss: 15.2329\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -15.232909202575684\n", + "Mean score: -14.7449\n", + "Std: 0.488\n", + "length of train dataset: 493\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.58s\n", + "train loss: 324.1140\n", + "Validation, Loss: 48.4809, Metric: -48.4809\n", + "val loss: 48.4809\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 63.8364\n", + "Validation, Loss: 19.9738, Metric: -19.9738\n", + "val loss: 19.9738\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 29.0590\n", + "Validation, Loss: 25.5278, Metric: -25.5278\n", + "val loss: 25.5278\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_2.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 21.7934\n", + "Validation, Loss: 19.8395, Metric: -19.8395\n", + "val loss: 19.8395\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 20.0393\n", + "Validation, Loss: 19.9126, Metric: -19.9126\n", + "val loss: 19.9126\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_4.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 19.6949\n", + "Validation, Loss: 19.6825, Metric: -19.6825\n", + "val loss: 19.6825\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 19.5491\n", + "Validation, Loss: 19.5981, Metric: -19.5981\n", + "val loss: 19.5981\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 19.4574\n", + "Validation, Loss: 19.5097, Metric: -19.5097\n", + "val loss: 19.5097\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.75s\n", + "train loss: 19.3638\n", + "Validation, Loss: 19.4166, Metric: -19.4166\n", + "val loss: 19.4166\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 19.2709\n", + "Validation, Loss: 19.3352, Metric: -19.3352\n", + "val loss: 19.3352\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -19.33523941040039\n", + "length of train dataset: 494\n", + "length of val dataset: 493\n", + "Epoch 1/10, cost time: 0.58s\n", + "train loss: 318.1459\n", + "Validation, Loss: 35.2745, Metric: -35.2745\n", + "val loss: 35.2745\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 64.2229\n", + "Validation, Loss: 18.6586, Metric: -18.6586\n", + "val loss: 18.6586\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.59s\n", + "train loss: 29.0946\n", + "Validation, Loss: 26.2358, Metric: -26.2358\n", + "val loss: 26.2358\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_2.pth\n", + "Early stopping counter: 1\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 20.7452\n", + "Validation, Loss: 18.3582, Metric: -18.3582\n", + "val loss: 18.3582\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.63s\n", + "train loss: 18.7621\n", + "Validation, Loss: 18.2058, Metric: -18.2058\n", + "val loss: 18.2058\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 18.4086\n", + "Validation, Loss: 18.1512, Metric: -18.1512\n", + "val loss: 18.1512\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.61s\n", + "train loss: 18.2357\n", + "Validation, Loss: 18.1464, Metric: -18.1464\n", + "val loss: 18.1464\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 18.1361\n", + "Validation, Loss: 17.9715, Metric: -17.9715\n", + "val loss: 17.9715\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 18.0310\n", + "Validation, Loss: 17.8863, Metric: -17.8863\n", + "val loss: 17.8863\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.59s\n", + "train loss: 17.9445\n", + "Validation, Loss: 17.7950, Metric: -17.7950\n", + "val loss: 17.7950\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -17.7950382232666\n", + "Mean score: -18.5651\n", + "Std: 0.7701\n" + ] + }, + { + "data": { + "text/plain": [ + "(166.6328408718109, )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strategy.fit(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load checkpoint for finetune" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once we have the saved checkpoints, we can continue training by passing the pretrained path and another checkpoint path to the trainer’s parameters. All other parameters remain the same." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "trainer_params = {\n", + " \"device\": \"cpu\",\n", + " \"num_workers\": 0,\n", + " \"best_by_metric\": True,\n", + " # Let's enable save_to_dir (by the way, default value is True)\n", + " \"save_to_dir\": True,\n", + " \"pretrained_path\": \"checkpoints/\",\n", + " \"checkpoint_path\": \"checkpoints_finetuned/\",\n", + " # Save checkpoints for 3 best model\n", + " \"save_k_best\": 3,\n", + " # Average checkpoints for the final model\n", + " \"average_snapshots\": True,\n", + "}\n", + "\n", + "trainer = DLTrainer(\n", + " model, \n", + " model_params, \n", + " validation, \n", + " validation_params, \n", + " **trainer_params\n", + ")\n", + "\n", + "strategy = DirectStrategy(\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " horizon=horizon,\n", + " history=history,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of train dataset: 496\n", + "length of val dataset: 497\n", + "Epoch 1/10, cost time: 0.69s\n", + "train loss: 2.2838\n", + "Validation, Loss: 2.2947, Metric: -2.2947\n", + "val loss: 2.2947\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.56s\n", + "train loss: 2.2745\n", + "Validation, Loss: 2.2801, Metric: -2.2801\n", + "val loss: 2.2801\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.61s\n", + "train loss: 2.2617\n", + "Validation, Loss: 2.2676, Metric: -2.2676\n", + "val loss: 2.2676\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.56s\n", + "train loss: 2.2480\n", + "Validation, Loss: 2.2553, Metric: -2.2553\n", + "val loss: 2.2553\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 2.2353\n", + "Validation, Loss: 2.2415, Metric: -2.2415\n", + "val loss: 2.2415\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.56s\n", + "train loss: 2.2222\n", + "Validation, Loss: 2.2277, Metric: -2.2277\n", + "val loss: 2.2277\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 2.2081\n", + "Validation, Loss: 2.2178, Metric: -2.2178\n", + "val loss: 2.2178\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.56s\n", + "train loss: 2.1964\n", + "Validation, Loss: 2.2056, Metric: -2.2056\n", + "val loss: 2.2056\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.56s\n", + "train loss: 2.1829\n", + "Validation, Loss: 2.1850, Metric: -2.1850\n", + "val loss: 2.1850\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.63s\n", + "train loss: 2.1646\n", + "Validation, Loss: 2.1708, Metric: -2.1708\n", + "val loss: 2.1708\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -2.170836925506592\n", + "length of train dataset: 497\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 3.4237\n", + "Validation, Loss: 3.3921, Metric: -3.3921\n", + "val loss: 3.3921\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 3.4045\n", + "Validation, Loss: 3.3732, Metric: -3.3732\n", + "val loss: 3.3732\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.63s\n", + "train loss: 3.3861\n", + "Validation, Loss: 3.3596, Metric: -3.3596\n", + "val loss: 3.3596\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 3.3722\n", + "Validation, Loss: 3.3366, Metric: -3.3366\n", + "val loss: 3.3366\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 3.3471\n", + "Validation, Loss: 3.3226, Metric: -3.3226\n", + "val loss: 3.3226\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.57s\n", + "train loss: 3.3297\n", + "Validation, Loss: 3.2950, Metric: -3.2950\n", + "val loss: 3.2950\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.56s\n", + "train loss: 3.3066\n", + "Validation, Loss: 3.2747, Metric: -3.2747\n", + "val loss: 3.2747\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.66s\n", + "train loss: 3.2855\n", + "Validation, Loss: 3.2533, Metric: -3.2533\n", + "val loss: 3.2533\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.56s\n", + "train loss: 3.2639\n", + "Validation, Loss: 3.2312, Metric: -3.2312\n", + "val loss: 3.2312\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 3.2411\n", + "Validation, Loss: 3.2079, Metric: -3.2079\n", + "val loss: 3.2079\n", + "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -3.2078869342803955\n", + "Mean score: -2.6894\n", + "Std: 0.5185\n", + "length of train dataset: 496\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 5.7111\n", + "Validation, Loss: 5.7242, Metric: -5.7242\n", + "val loss: 5.7242\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.56s\n", + "train loss: 5.6807\n", + "Validation, Loss: 5.7036, Metric: -5.7036\n", + "val loss: 5.7036\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.56s\n", + "train loss: 5.6523\n", + "Validation, Loss: 5.6615, Metric: -5.6615\n", + "val loss: 5.6615\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.56s\n", + "train loss: 5.6213\n", + "Validation, Loss: 5.6304, Metric: -5.6304\n", + "val loss: 5.6304\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 5.5915\n", + "Validation, Loss: 5.5990, Metric: -5.5990\n", + "val loss: 5.5990\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.56s\n", + "train loss: 5.5491\n", + "Validation, Loss: 5.5591, Metric: -5.5591\n", + "val loss: 5.5591\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 5.5151\n", + "Validation, Loss: 5.5252, Metric: -5.5252\n", + "val loss: 5.5252\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.57s\n", + "train loss: 5.4780\n", + "Validation, Loss: 5.4859, Metric: -5.4859\n", + "val loss: 5.4859\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.57s\n", + "train loss: 5.4402\n", + "Validation, Loss: 5.4482, Metric: -5.4482\n", + "val loss: 5.4482\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 5.4027\n", + "Validation, Loss: 5.4177, Metric: -5.4177\n", + "val loss: 5.4177\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -5.417725563049316\n", + "length of train dataset: 496\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.56s\n", + "train loss: 5.4984\n", + "Validation, Loss: 5.4640, Metric: -5.4640\n", + "val loss: 5.4640\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.56s\n", + "train loss: 5.4695\n", + "Validation, Loss: 5.4185, Metric: -5.4185\n", + "val loss: 5.4185\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.56s\n", + "train loss: 5.4359\n", + "Validation, Loss: 5.3877, Metric: -5.3877\n", + "val loss: 5.3877\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 5.4047\n", + "Validation, Loss: 5.3566, Metric: -5.3566\n", + "val loss: 5.3566\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 5.3721\n", + "Validation, Loss: 5.3328, Metric: -5.3328\n", + "val loss: 5.3328\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.65s\n", + "train loss: 5.3419\n", + "Validation, Loss: 5.2894, Metric: -5.2894\n", + "val loss: 5.2894\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 5.3049\n", + "Validation, Loss: 5.2565, Metric: -5.2565\n", + "val loss: 5.2565\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.55s\n", + "train loss: 5.2688\n", + "Validation, Loss: 5.2247, Metric: -5.2247\n", + "val loss: 5.2247\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.55s\n", + "train loss: 5.2404\n", + "Validation, Loss: 5.2022, Metric: -5.2022\n", + "val loss: 5.2022\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.61s\n", + "train loss: 5.2012\n", + "Validation, Loss: 5.1468, Metric: -5.1468\n", + "val loss: 5.1468\n", + "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -5.146778106689453\n", + "Mean score: -5.2823\n", + "Std: 0.1355\n", + "length of train dataset: 495\n", + "length of val dataset: 496\n", + "Epoch 1/10, cost time: 0.75s\n", + "train loss: 7.0662\n", + "Validation, Loss: 7.0849, Metric: -7.0849\n", + "val loss: 7.0849\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 7.0261\n", + "Validation, Loss: 7.0457, Metric: -7.0457\n", + "val loss: 7.0457\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.66s\n", + "train loss: 6.9895\n", + "Validation, Loss: 7.0050, Metric: -7.0050\n", + "val loss: 7.0050\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.65s\n", + "train loss: 6.9573\n", + "Validation, Loss: 6.9712, Metric: -6.9712\n", + "val loss: 6.9712\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.59s\n", + "train loss: 6.9107\n", + "Validation, Loss: 6.9318, Metric: -6.9318\n", + "val loss: 6.9318\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.64s\n", + "train loss: 6.8639\n", + "Validation, Loss: 6.8873, Metric: -6.8873\n", + "val loss: 6.8873\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 6.8236\n", + "Validation, Loss: 6.8324, Metric: -6.8324\n", + "val loss: 6.8324\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.57s\n", + "train loss: 6.7763\n", + "Validation, Loss: 6.7876, Metric: -6.7876\n", + "val loss: 6.7876\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.57s\n", + "train loss: 6.7298\n", + "Validation, Loss: 6.7403, Metric: -6.7403\n", + "val loss: 6.7403\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 6.6830\n", + "Validation, Loss: 6.6920, Metric: -6.6920\n", + "val loss: 6.6920\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -6.691991329193115\n", + "length of train dataset: 496\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 5.8217\n", + "Validation, Loss: 5.7710, Metric: -5.7710\n", + "val loss: 5.7710\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 5.7866\n", + "Validation, Loss: 5.7493, Metric: -5.7493\n", + "val loss: 5.7493\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.57s\n", + "train loss: 5.7567\n", + "Validation, Loss: 5.7043, Metric: -5.7043\n", + "val loss: 5.7043\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.57s\n", + "train loss: 5.7222\n", + "Validation, Loss: 5.6819, Metric: -5.6819\n", + "val loss: 5.6819\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.68s\n", + "train loss: 5.6983\n", + "Validation, Loss: 5.6439, Metric: -5.6439\n", + "val loss: 5.6439\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.62s\n", + "train loss: 5.6609\n", + "Validation, Loss: 5.6346, Metric: -5.6346\n", + "val loss: 5.6346\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 5.6246\n", + "Validation, Loss: 5.5833, Metric: -5.5833\n", + "val loss: 5.5833\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 5.5855\n", + "Validation, Loss: 5.5368, Metric: -5.5368\n", + "val loss: 5.5368\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.64s\n", + "train loss: 5.5482\n", + "Validation, Loss: 5.4926, Metric: -5.4926\n", + "val loss: 5.4926\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 5.5088\n", + "Validation, Loss: 5.4605, Metric: -5.4605\n", + "val loss: 5.4605\n", + "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -5.460498809814453\n", + "Mean score: -6.0762\n", + "Std: 0.6157\n", + "length of train dataset: 495\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 7.4068\n", + "Validation, Loss: 7.4242, Metric: -7.4242\n", + "val loss: 7.4242\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.57s\n", + "train loss: 7.3655\n", + "Validation, Loss: 7.3973, Metric: -7.3973\n", + "val loss: 7.3973\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.62s\n", + "train loss: 7.3383\n", + "Validation, Loss: 7.3738, Metric: -7.3738\n", + "val loss: 7.3738\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.57s\n", + "train loss: 7.2943\n", + "Validation, Loss: 7.3143, Metric: -7.3143\n", + "val loss: 7.3143\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.57s\n", + "train loss: 7.2483\n", + "Validation, Loss: 7.2718, Metric: -7.2718\n", + "val loss: 7.2718\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.57s\n", + "train loss: 7.1983\n", + "Validation, Loss: 7.2133, Metric: -7.2133\n", + "val loss: 7.2133\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 7.1556\n", + "Validation, Loss: 7.1657, Metric: -7.1657\n", + "val loss: 7.1657\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 7.1041\n", + "Validation, Loss: 7.1188, Metric: -7.1188\n", + "val loss: 7.1188\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.57s\n", + "train loss: 7.0549\n", + "Validation, Loss: 7.0729, Metric: -7.0729\n", + "val loss: 7.0729\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.57s\n", + "train loss: 7.0070\n", + "Validation, Loss: 7.0559, Metric: -7.0559\n", + "val loss: 7.0559\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -7.055933475494385\n", + "length of train dataset: 495\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.57s\n", + "train loss: 10.0828\n", + "Validation, Loss: 9.9944, Metric: -9.9944\n", + "val loss: 9.9944\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.62s\n", + "train loss: 10.0214\n", + "Validation, Loss: 9.9403, Metric: -9.9403\n", + "val loss: 9.9403\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.67s\n", + "train loss: 9.9638\n", + "Validation, Loss: 9.8732, Metric: -9.8732\n", + "val loss: 9.8732\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 9.9057\n", + "Validation, Loss: 9.8199, Metric: -9.8199\n", + "val loss: 9.8199\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.60s\n", + "train loss: 9.8503\n", + "Validation, Loss: 9.7647, Metric: -9.7647\n", + "val loss: 9.7647\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 9.7833\n", + "Validation, Loss: 9.6908, Metric: -9.6908\n", + "val loss: 9.6908\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 9.7204\n", + "Validation, Loss: 9.6201, Metric: -9.6201\n", + "val loss: 9.6201\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 9.6498\n", + "Validation, Loss: 9.5648, Metric: -9.5648\n", + "val loss: 9.5648\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 9.5808\n", + "Validation, Loss: 9.5249, Metric: -9.5249\n", + "val loss: 9.5249\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.62s\n", + "train loss: 9.5221\n", + "Validation, Loss: 9.4169, Metric: -9.4169\n", + "val loss: 9.4169\n", + "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -9.416933059692383\n", + "Mean score: -8.2364\n", + "Std: 1.1805\n", + "length of train dataset: 494\n", + "length of val dataset: 495\n", + "Epoch 1/10, cost time: 0.63s\n", + "train loss: 10.8233\n", + "Validation, Loss: 10.8490, Metric: -10.8490\n", + "val loss: 10.8490\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.61s\n", + "train loss: 10.7649\n", + "Validation, Loss: 10.8014, Metric: -10.8014\n", + "val loss: 10.8014\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 10.7058\n", + "Validation, Loss: 10.7308, Metric: -10.7308\n", + "val loss: 10.7308\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 10.6538\n", + "Validation, Loss: 10.6665, Metric: -10.6665\n", + "val loss: 10.6665\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.60s\n", + "train loss: 10.5789\n", + "Validation, Loss: 10.6223, Metric: -10.6223\n", + "val loss: 10.6223\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 10.5161\n", + "Validation, Loss: 10.5411, Metric: -10.5411\n", + "val loss: 10.5411\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 10.4431\n", + "Validation, Loss: 10.4628, Metric: -10.4628\n", + "val loss: 10.4628\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 10.3851\n", + "Validation, Loss: 10.3943, Metric: -10.3943\n", + "val loss: 10.3943\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 10.3052\n", + "Validation, Loss: 10.3331, Metric: -10.3331\n", + "val loss: 10.3331\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.58s\n", + "train loss: 10.2399\n", + "Validation, Loss: 10.2538, Metric: -10.2538\n", + "val loss: 10.2538\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -10.253775596618652\n", + "length of train dataset: 495\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.69s\n", + "train loss: 12.9979\n", + "Validation, Loss: 12.8790, Metric: -12.8790\n", + "val loss: 12.8790\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.61s\n", + "train loss: 12.9284\n", + "Validation, Loss: 12.8534, Metric: -12.8534\n", + "val loss: 12.8534\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.59s\n", + "train loss: 12.8511\n", + "Validation, Loss: 12.7317, Metric: -12.7317\n", + "val loss: 12.7317\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 12.7672\n", + "Validation, Loss: 12.6531, Metric: -12.6531\n", + "val loss: 12.6531\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 12.6920\n", + "Validation, Loss: 12.5797, Metric: -12.5797\n", + "val loss: 12.5797\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 12.6101\n", + "Validation, Loss: 12.4994, Metric: -12.4994\n", + "val loss: 12.4994\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 12.5235\n", + "Validation, Loss: 12.4034, Metric: -12.4034\n", + "val loss: 12.4034\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 12.4380\n", + "Validation, Loss: 12.3317, Metric: -12.3317\n", + "val loss: 12.3317\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 12.3537\n", + "Validation, Loss: 12.2280, Metric: -12.2280\n", + "val loss: 12.2280\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.58s\n", + "train loss: 12.2643\n", + "Validation, Loss: 12.1419, Metric: -12.1419\n", + "val loss: 12.1419\n", + "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -12.141925811767578\n", + "Mean score: -11.1979\n", + "Std: 0.9441\n", + "length of train dataset: 494\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.58s\n", + "train loss: 14.1279\n", + "Validation, Loss: 14.2159, Metric: -14.2159\n", + "val loss: 14.2159\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 14.0677\n", + "Validation, Loss: 14.1562, Metric: -14.1562\n", + "val loss: 14.1562\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 13.9850\n", + "Validation, Loss: 14.0829, Metric: -14.0829\n", + "val loss: 14.0829\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.62s\n", + "train loss: 13.9074\n", + "Validation, Loss: 13.9388, Metric: -13.9388\n", + "val loss: 13.9388\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.61s\n", + "train loss: 13.7966\n", + "Validation, Loss: 13.8638, Metric: -13.8638\n", + "val loss: 13.8638\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.71s\n", + "train loss: 13.7157\n", + "Validation, Loss: 13.7623, Metric: -13.7623\n", + "val loss: 13.7623\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 13.6267\n", + "Validation, Loss: 13.6874, Metric: -13.6874\n", + "val loss: 13.6874\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 13.5322\n", + "Validation, Loss: 13.5936, Metric: -13.5936\n", + "val loss: 13.5936\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 13.4324\n", + "Validation, Loss: 13.4838, Metric: -13.4838\n", + "val loss: 13.4838\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.59s\n", + "train loss: 13.3434\n", + "Validation, Loss: 13.3737, Metric: -13.3737\n", + "val loss: 13.3737\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -13.373733520507812\n", + "length of train dataset: 494\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.66s\n", + "train loss: 15.3095\n", + "Validation, Loss: 15.1644, Metric: -15.1644\n", + "val loss: 15.1644\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.66s\n", + "train loss: 15.2335\n", + "Validation, Loss: 15.0621, Metric: -15.0621\n", + "val loss: 15.0621\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.69s\n", + "train loss: 15.1290\n", + "Validation, Loss: 14.9738, Metric: -14.9738\n", + "val loss: 14.9738\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.64s\n", + "train loss: 15.0387\n", + "Validation, Loss: 14.9120, Metric: -14.9120\n", + "val loss: 14.9120\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 14.9545\n", + "Validation, Loss: 14.7915, Metric: -14.7915\n", + "val loss: 14.7915\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 14.8635\n", + "Validation, Loss: 14.7089, Metric: -14.7089\n", + "val loss: 14.7089\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 14.7660\n", + "Validation, Loss: 14.5910, Metric: -14.5910\n", + "val loss: 14.5910\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.58s\n", + "train loss: 14.6518\n", + "Validation, Loss: 14.4894, Metric: -14.4894\n", + "val loss: 14.4894\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 14.5501\n", + "Validation, Loss: 14.4009, Metric: -14.4009\n", + "val loss: 14.4009\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.58s\n", + "train loss: 14.4531\n", + "Validation, Loss: 14.2984, Metric: -14.2984\n", + "val loss: 14.2984\n", + "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -14.298404693603516\n", + "Mean score: -13.8361\n", + "Std: 0.4623\n", + "length of train dataset: 493\n", + "length of val dataset: 494\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 19.1676\n", + "Validation, Loss: 19.2452, Metric: -19.2452\n", + "val loss: 19.2452\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 19.0695\n", + "Validation, Loss: 19.0989, Metric: -19.0989\n", + "val loss: 19.0989\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.61s\n", + "train loss: 18.9595\n", + "Validation, Loss: 18.9872, Metric: -18.9872\n", + "val loss: 18.9872\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.58s\n", + "train loss: 18.8743\n", + "Validation, Loss: 18.8940, Metric: -18.8940\n", + "val loss: 18.8940\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.59s\n", + "train loss: 18.7763\n", + "Validation, Loss: 18.8358, Metric: -18.8358\n", + "val loss: 18.8358\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 18.6196\n", + "Validation, Loss: 18.6316, Metric: -18.6316\n", + "val loss: 18.6316\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 18.4809\n", + "Validation, Loss: 18.5037, Metric: -18.5037\n", + "val loss: 18.5037\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.63s\n", + "train loss: 18.3591\n", + "Validation, Loss: 18.4324, Metric: -18.4324\n", + "val loss: 18.4324\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 18.2561\n", + "Validation, Loss: 18.3160, Metric: -18.3160\n", + "val loss: 18.3160\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.59s\n", + "train loss: 18.1243\n", + "Validation, Loss: 18.2251, Metric: -18.2251\n", + "val loss: 18.2251\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 0. Score: -18.225141525268555\n", + "length of train dataset: 494\n", + "length of val dataset: 493\n", + "Epoch 1/10, cost time: 0.59s\n", + "train loss: 17.8511\n", + "Validation, Loss: 17.6984, Metric: -17.6984\n", + "val loss: 17.6984\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 17.7412\n", + "Validation, Loss: 17.5978, Metric: -17.5978\n", + "val loss: 17.5978\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_1.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_1.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_1.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 3/10, cost time: 0.59s\n", + "train loss: 17.6428\n", + "Validation, Loss: 17.4923, Metric: -17.4923\n", + "val loss: 17.4923\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_2.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_2.pth\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_2.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 17.5319\n", + "Validation, Loss: 17.3855, Metric: -17.3855\n", + "val loss: 17.3855\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_3.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_3.pth\n", + "Removing worst model snapshot: from epoch 0\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_3.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 5/10, cost time: 0.62s\n", + "train loss: 17.4258\n", + "Validation, Loss: 17.2621, Metric: -17.2621\n", + "val loss: 17.2621\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_4.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_4.pth\n", + "Removing worst model snapshot: from epoch 1\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_4.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 17.3086\n", + "Validation, Loss: 17.1568, Metric: -17.1568\n", + "val loss: 17.1568\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_5.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_5.pth\n", + "Removing worst model snapshot: from epoch 2\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_5.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 17.1922\n", + "Validation, Loss: 17.0323, Metric: -17.0323\n", + "val loss: 17.0323\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_6.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_6.pth\n", + "Removing worst model snapshot: from epoch 3\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_6.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 17.0769\n", + "Validation, Loss: 16.9577, Metric: -16.9577\n", + "val loss: 16.9577\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_7.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_7.pth\n", + "Removing worst model snapshot: from epoch 4\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_7.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 9/10, cost time: 0.67s\n", + "train loss: 16.9738\n", + "Validation, Loss: 16.8426, Metric: -16.8426\n", + "val loss: 16.8426\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_8.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_8.pth\n", + "Removing worst model snapshot: from epoch 5\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_8.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 16.8403\n", + "Validation, Loss: 16.7033, Metric: -16.7033\n", + "val loss: 16.7033\n", + "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_9.pth\n", + "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_9.pth\n", + "Removing worst model snapshot: from epoch 6\n", + "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_9.pth\n", + "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", + "Training finished.\n", + "Fold 1. Score: -16.703344345092773\n", + "Mean score: -17.4642\n", + "Std: 0.7609\n" + ] + }, + { + "data": { + "text/plain": [ + "(165.66884803771973, )" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "strategy.fit(dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load checkpoint for inference" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "trainer_params = {\n", + " \"device\": \"cpu\",\n", + " \"num_workers\": 0,\n", + " \"n_epochs\": 0,\n", + " \"pretrained_path\": \"checkpoints_finetuned/\",\n", + " # Average checkpoints for the final model\n", + " \"average_snapshots\": True,\n", + "}\n", + "\n", + "trainer = DLTrainer(\n", + " model, \n", + " model_params, \n", + " validation, \n", + " validation_params, \n", + " **trainer_params\n", + ")\n", + "\n", + "strategy = DirectStrategy(\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " horizon=horizon,\n", + " history=history,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "length of train dataset: 496\n", + "length of val dataset: 497\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 497\n", + "length of val dataset: 496\n", + "Training finished.\n", + "Fold 1. Score: nan\n", + "Mean score: nan\n", + "Std: nan\n", + "length of train dataset: 496\n", + "length of val dataset: 496\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 496\n", + "length of val dataset: 496\n", + "Training finished.\n", + "Fold 1. Score: nan\n", + "Mean score: nan\n", + "Std: nan\n", + "length of train dataset: 495\n", + "length of val dataset: 496\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 496\n", + "length of val dataset: 495\n", + "Training finished.\n", + "Fold 1. Score: nan\n", + "Mean score: nan\n", + "Std: nan\n", + "length of train dataset: 495\n", + "length of val dataset: 495\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 495\n", + "length of val dataset: 495\n", + "Training finished.\n", + "Fold 1. Score: nan\n", + "Mean score: nan\n", + "Std: nan\n", + "length of train dataset: 494\n", + "length of val dataset: 495\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 495\n", + "length of val dataset: 494\n", + "Training finished.\n", + "Fold 1. Score: nan\n", + "Mean score: nan\n", + "Std: nan\n", + "length of train dataset: 494\n", + "length of val dataset: 494\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 494\n", + "length of val dataset: 494\n", + "Training finished.\n", + "Fold 1. Score: nan\n", + "Mean score: nan\n", + "Std: nan\n", + "length of train dataset: 493\n", + "length of val dataset: 494\n", + "Training finished.\n", + "Fold 0. Score: nan\n", + "length of train dataset: 494\n", + "length of val dataset: 493\n", + "Training finished.\n", + "Fold 1. 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""" - y = y.reshape(self.y_original_shape) y = self.transformers.inverse_transform_y(y) return y.reshape(-1) diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index db3930a..cdd9023 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -43,8 +43,6 @@ class ES_Checkpoints_Manager(Callback): early_stopping_patience: number of epochs to wait for an improvement before stopping. mode: mode for monitoring, either 'min' or 'max'. save_to_dir: whether to save checkpoints to a directory. - averaging_snapshots: whether to average weights of saved checkpoints at the end of training. - """ def __init__( @@ -55,7 +53,6 @@ def __init__( early_stopping_patience: int = 5, mode: str = "min", save_to_dir: bool = True, - averaging_snapshots: bool = False, ): self.monitor = monitor self.verbose = verbose @@ -63,13 +60,14 @@ def __init__( self.early_stopping_patience = early_stopping_patience self.mode = mode self.save_to_dir = save_to_dir - self.averaging_snapshots = averaging_snapshots self.best_snapshots = [] self.best_score = None self.early_stopping_counter = 0 self.stop_training = False + self.last_snapshot = None # Для сохранения последней эпохи + @staticmethod def _safe_remove(file_path: str): """Safely removes a file if it exists. @@ -81,24 +79,13 @@ def _safe_remove(file_path: str): if os.path.exists(file_path): os.remove(file_path) - @staticmethod - def _safe_rename(src_path: str, dst_path: str): - """Safely renames a file if it exists. - - Args: - src_path: source name of the file. - dst_path: new name of the file. - - """ - if os.path.exists(src_path): - os.rename(src_path, dst_path) - def _del_inner_params(self): """Deletes internal parameters to reset the manager.""" self.best_snapshots = [] self.best_score = None self.early_stopping_counter = 0 self.stop_training = False + self.last_snapshot = None def _is_improvement(self, current: float, best: float) -> bool: """Checks if the current score is an improvement. @@ -141,40 +128,6 @@ def _update_worst_best_score(self): else: self.worst_best_score = self.best_snapshots[0][0] - def _rename_checkpoints(self): - """Renames checkpoints based on their ranking. - - Notes: is called at the end of training to reflect the ranking of the checkpoints. - - """ - for i, (_, snapshot_info) in enumerate( - sorted( - self.best_snapshots, - key=lambda x: -x[0] if self.mode == "min" else x[0], - reverse=True, - ) - ): - epoch = snapshot_info["epoch"] - if self.save_to_dir: - new_model_path = snapshot_info["model"].with_name( - f"model_epoch_{epoch}_top_{i+1}.pth" - ) - new_opt_path = snapshot_info["optimizer"].with_name( - f"opt_epoch_{epoch}_top_{i+1}.pth" - ) - new_sch_path = snapshot_info["scheduler"].with_name( - f"sch_epoch_{epoch}_top_{i+1}.pth" - ) - - self._safe_rename(snapshot_info["model"], new_model_path) - snapshot_info["model"] = new_model_path - - self._safe_rename(snapshot_info["optimizer"], new_opt_path) - snapshot_info["optimizer"] = new_opt_path - - self._safe_rename(snapshot_info["scheduler"], new_sch_path) - snapshot_info["scheduler"] = new_sch_path - def get_best_snapshot(self) -> dict: """Returns the best saved snapshot. @@ -205,38 +158,69 @@ def get_average_snapshot(self) -> dict: """ average_snapshot = None + num_snapshots = len(self.best_snapshots) + for snapshot in self.best_snapshots: + model_state = torch.load(snapshot[1]["model"]) + if average_snapshot is None: - average_snapshot = torch.load(snapshot[1]["model"]) + average_snapshot = {} + for key, value in model_state.items(): + average_snapshot[key] = value.clone() else: - average_snapshot += torch.load(snapshot[1]["model"]) - average_snapshot /= len(self.best_snapshots) + for key, value in model_state.items(): + average_snapshot[key] += value + + for key in average_snapshot: + average_snapshot[key] /= num_snapshots + return average_snapshot - def on_train_begin(self, logs: Optional[dict] = None): - """Called at the beginning of training. + def get_last_snapshot(self, full_state: bool = False) -> dict: + """Returns the last saved snapshot. Args: - logs: dictionary of logs. - - Notes: - - Resets internal parameters at the beginning of training. + full_state: Whether to return the full state (model, optimizer, scheduler) or only the model. + Returns: + dict: The last saved snapshot with model, optimizer, and scheduler if full_state is True. + Otherwise, only the model state. """ - self._del_inner_params() + if self.last_snapshot is None: + raise ValueError("No last snapshot saved.") - def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): - """Called at the end of each epoch. + if self.save_to_dir: + model_state = torch.load(self.last_snapshot["model"]) + if full_state: + optimizer_state = torch.load(self.last_snapshot["optimizer"]) + scheduler_state = ( + torch.load(self.last_snapshot["scheduler"]) + if self.last_snapshot["scheduler"] + and os.path.exists(self.last_snapshot["scheduler"]) + else None + ) + return { + "model": model_state, + "optimizer": optimizer_state, + "scheduler": scheduler_state, + } + return model_state - Args: - epoch: the current epoch. - logs: dictionary of logs. + # Если мы сохраняем без файловой системы + if full_state: + return { + "model": self.last_snapshot["model"], + "optimizer": self.last_snapshot["optimizer"], + "scheduler": self.last_snapshot["scheduler"], + } + return self.last_snapshot["model"] - Notes: - - Saves model checkpoints based on the current score. - - Triggers early stopping if the score does not improve for a number of epochs. + def on_train_begin(self, logs: Optional[dict] = None): + """Called at the beginning of training.""" + self._del_inner_params() - """ + def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): + """Called at the end of each epoch.""" current_score = logs.get(self.monitor) if current_score is None: return @@ -251,54 +235,54 @@ def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): sch_path = Path(logs.get("filepath")) / f"sch_{epoch}.pth" model_path.parent.mkdir(parents=True, exist_ok=True) - if self.save_k_best > 0: - if self._should_save_checkpoint(current_score): - if len(self.best_snapshots) == self.save_k_best: - worst_snapshot = heapq.heappop(self.best_snapshots) - if self.save_to_dir: - self._safe_remove(worst_snapshot[1]["model"]) - self._safe_remove(worst_snapshot[1]["optimizer"]) - self._safe_remove(worst_snapshot[1]["scheduler"]) - if self.verbose: - print( - f"Removing worst model snapshot: from epoch {worst_snapshot[1]['epoch']}" - ) - - snapshot_info = { - "model": model_path if self.save_to_dir else model_state, - "optimizer": opt_path if self.save_to_dir else optimizer_state, - "scheduler": sch_path if self.save_to_dir else scheduler_state, - "epoch": epoch, - } + # Save the last epoch's snapshot for resuming training + self.last_snapshot = { + "model": model_path if self.save_to_dir else model_state, + "optimizer": opt_path if self.save_to_dir else optimizer_state, + "scheduler": sch_path if self.save_to_dir else scheduler_state, + "epoch": epoch, + } - if self.mode == "min": - heapq.heappush(self.best_snapshots, (-current_score, snapshot_info)) - else: - heapq.heappush(self.best_snapshots, (current_score, snapshot_info)) + if self.save_to_dir: + torch.save(model_state, model_path) + torch.save(optimizer_state, opt_path) + if scheduler_state: + torch.save(scheduler_state, sch_path) + if self.verbose: + print(f"Last epoch model saved to {model_path}") + print(f"Last epoch optimizer saved to {opt_path}") + if scheduler_state: + print(f"Last epoch scheduler saved to {sch_path}") - self._update_worst_best_score() + # Save top-k best snapshots + if self.save_k_best > 0 and self._should_save_checkpoint(current_score): + if len(self.best_snapshots) == self.save_k_best: + worst_snapshot = heapq.heappop(self.best_snapshots) if self.save_to_dir: - torch.save(model_state, model_path) - torch.save(optimizer_state, opt_path) - if scheduler_state: - torch.save(scheduler_state, sch_path) - if self.verbose: - print(f"Model saved to {model_path}") - print(f"Optimizer saved to {opt_path}") - if scheduler_state: - print(f"Scheduler saved to {sch_path}") + self._safe_remove(worst_snapshot[1]["model"]) + self._safe_remove(worst_snapshot[1]["optimizer"]) + self._safe_remove(worst_snapshot[1]["scheduler"]) + if self.verbose: + print( + f"Removing worst model snapshot: from epoch {worst_snapshot[1]['epoch']}" + ) + + snapshot_info = { + "model": model_path if self.save_to_dir else model_state, + "optimizer": opt_path if self.save_to_dir else optimizer_state, + "scheduler": sch_path if self.save_to_dir else scheduler_state, + "epoch": epoch, + } + + if self.mode == "min": + heapq.heappush(self.best_snapshots, (-current_score, snapshot_info)) + else: + heapq.heappush(self.best_snapshots, (current_score, snapshot_info)) - else: + self._update_worst_best_score() if self.save_to_dir: - torch.save(model_state, model_path) - torch.save(optimizer_state, opt_path) - if scheduler_state: - torch.save(scheduler_state, sch_path) if self.verbose: - print(f"Model saved to {model_path}") - print(f"Optimizer saved to {opt_path}") - if scheduler_state: - print(f"Scheduler saved to {sch_path}") + print(f"Best model snapshot saved to {model_path}") # Early stopping logic if self.early_stopping_patience > 0: @@ -313,15 +297,13 @@ def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): print("Early stopping triggered") self.stop_training = True - def on_train_end(self, logs: Optional[dict] = None): - """Called at the end of training. - - Args: - logs: dictionary of logs. - - Notes: - - Renames checkpoints to reflect their current ranking at the end of training. - - """ if self.save_to_dir: - self._rename_checkpoints() + manager_path = Path(logs.get("filepath")) / "es_checkpoint_manager.pth" + torch.save(self, manager_path) + if self.verbose: + print(f"Checkpoint manager saved to {manager_path}") + + def on_train_end(self, logs: Optional[dict] = None): + """Called at the end of training.""" + if self.verbose: + print("Training finished.") diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 82f5215..334048a 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -136,14 +136,7 @@ class DLTrainer: scheduler: learning rate scheduler. scheduler_params: parameters for the scheduler. scheduler_after_epoch: whether to step the scheduler after each epoch. - pretrained_paths: dict (!) of paths for each fold to load pretrained checkpoints from. - format: { - fold_num: { - 'model': path_to_checkpoint, - 'optimizer': path_to_checkpoint, - 'scheduler': path_to_checkpoint - } - }. + pretrained_path: path to the pretrained checkpoints. best_by_metric: whether to select the best model by metric instead of loss. early_stopping_patience: number of epochs to wait for improvement before early stopping. 0 for early stopping disable. @@ -176,14 +169,14 @@ def __init__( scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, scheduler_params: Dict = {}, scheduler_after_epoch: bool = True, - pretrained_paths: Optional[dict] = None, + pretrained_path: Optional[Union[Path, str]] = None, # es_checkpoint_manager params best_by_metric: bool = False, early_stopping_patience: int = 5, save_k_best: Union[int] = 5, - averaging_snapshots: bool = False, + average_snapshots: bool = False, save_to_dir: bool = True, - checkpoint_path: Union[bool, Path] = "checkpoints/", + checkpoint_path: Union[Path, str] = "checkpoints/", train_shuffle: bool = True, verbose: int = 1, ): @@ -208,7 +201,7 @@ def __init__( self.scheduler_params = scheduler_params self.scheduler_after_epoch = scheduler_after_epoch - self.pretrained_paths = pretrained_paths + self.pretrained_path = pretrained_path self.train_shuffle = train_shuffle @@ -219,12 +212,17 @@ def __init__( early_stopping_patience=early_stopping_patience, mode="max" if best_by_metric else "min", save_to_dir=save_to_dir, - averaging_snapshots=averaging_snapshots, ) if isinstance(checkpoint_path, str): checkpoint_path = Path(checkpoint_path) self.checkpoint_path = checkpoint_path + if isinstance(pretrained_path, str): + pretrained_path = Path(pretrained_path) + self.pretrained_path = pretrained_path + + self.average_snapshots = average_snapshots + # Provide by strategy if needed self.callbacks = [self.es] self.history = None @@ -282,15 +280,13 @@ def load_trainer_one_fold( model, optimizer, and scheduler with loaded states. """ - model_path = self.pretrained_paths[fold_i]["model"] - optimizer_path = self.pretrained_paths[fold_i]["optimizer"] - if scheduler: - scheduler_path = self.pretrained_paths[fold_i]["scheduler"] + self.es = torch.load(self.pretrained_path / "es_checkpoint_manager.pth") + pretrained_weights = self.es.get_last_snapshot(full_state=True) - model.load_state_dict(torch.load(model_path)) - optimizer.load_state_dict(torch.load(optimizer_path)) + model.load_state_dict(pretrained_weights["model"]) + optimizer.load_state_dict(pretrained_weights["optimizer"]) if scheduler: - scheduler.load_state_dict(torch.load(scheduler_path)) + scheduler.load_state_dict(pretrained_weights["scheduler"]) return model, optimizer, scheduler @@ -385,13 +381,16 @@ def train_model( return model, optimizer, scheduler, val_metric for cb in self.callbacks: - cb.on_train_end() - - # return best_model or average_model - if self.es.averaging_snapshots: - model.load_state_dict(self.es.get_average_snapshot()) + cb.on_train_end({"filepath": self.checkpoint_path}) + + # return best_model or average_model if `n_epochs` = 0 + if self.n_epochs > 0: + if self.average_snapshots: + model.load_state_dict(self.es.get_average_snapshot()) + else: + model.load_state_dict(self.es.get_best_snapshot()) else: - model.load_state_dict(self.es.get_best_snapshot()) + val_metric = np.nan return model, optimizer, scheduler, val_metric @@ -471,7 +470,11 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - ) ): checkpoint_path = self.checkpoint_path + pretrained_path = self.pretrained_path + self.checkpoint_path /= f"fold_{fold_i}" + if pretrained_path: + self.pretrained_path /= f"fold_{fold_i}" train_subset = Subset(train_dataset, train_dataset_idx) if val_dataset is not None: @@ -500,7 +503,7 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - # load or initialize model, optimizer, scheduler model, optimizer, scheduler = self.init_trainer_one_fold(num_features) - if self.pretrained_paths: + if self.pretrained_path: model, optimizer, scheduler = self.load_trainer_one_fold( fold_i, model, optimizer, scheduler ) @@ -515,6 +518,7 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - print(f"Fold {fold_i}. Score: {score}") self.checkpoint_path = checkpoint_path + self.pretrained_path = pretrained_path print(f"Mean score: {np.mean(self.scores).round(4)}") print(f"Std: {np.std(self.scores).round(4)}") @@ -551,20 +555,18 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: y_pred = np.mean(models_preds, axis=0) y_pred = torch.tensor(y_pred) - if pipeline.strategy_name == "FlatWideMIMOStrategy" and pipeline.multivariate is False: + if pipeline.strategy_name == "FlatWideMIMOStrategy": full_horizon = pipeline.y_original_shape[1] num_series = y_pred.shape[0] // full_horizon - y_pred = y_pred.reshape(num_series, full_horizon, 1) - - if pipeline.strategy_name == "FlatWideMIMOStrategy" and pipeline.multivariate: - y_pred = y_pred.permute(1, 0, 2) - - pipeline.y_original_shape = list(pipeline.y_original_shape) - pipeline.y_original_shape[0] *= y_pred.shape[0] + if pipeline.multivariate: + y_pred = y_pred.reshape(num_series, full_horizon, -1) + else: + y_pred = y_pred.reshape(num_series, full_horizon, 1) if pipeline.multivariate: y_pred = y_pred.permute(2, 0, 1) - if pipeline.multivariate: y_pred = y_pred.reshape(-1, y_pred.shape[2]) + else: + y_pred = y_pred.squeeze(-1) return y_pred.numpy() diff --git a/tsururu/strategies/base.py b/tsururu/strategies/base.py index 60b4409..94b9471 100644 --- a/tsururu/strategies/base.py +++ b/tsururu/strategies/base.py @@ -19,10 +19,10 @@ class Strategy: history: number of previous for feature generating (i.e., features for observation y_t are counted from observations (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while - making samples' matrix. trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. + step: in how many points to take the next observation while + making samples' matrix. Notes: 1. A type of strategy defines what features and targets will be @@ -49,9 +49,9 @@ def __init__( self, horizon: int, history: int, - step: int, trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, + step: int = 1, ): self.check_step_param(step) diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index ce1aefe..8feca05 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -1,5 +1,5 @@ from copy import deepcopy -from typing import Optional, Union +from typing import Union from ..dataset.dataset import TSDataset from ..dataset.pipeline import Pipeline @@ -20,10 +20,10 @@ class DirectStrategy(RecursiveStrategy): history: number of previous for feature generating (i.e., features for observation y_t are counted from observations (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while making - samples' matrix. trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. + step: in how many points to take the next observation while making + samples' matrix. model_horizon: how many points to predict at a time, if model_horizon > 1, then it's an intermediate strategy between RecursiveStrategy and MIMOStrategy. @@ -42,13 +42,13 @@ def __init__( self, horizon: int, history: int, - step: int, trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, + step: int = 1, model_horizon: int = 1, equal_train_size: bool = False, ): - super().__init__(horizon, history, step, trainer, pipeline, model_horizon) + super().__init__(horizon, history, trainer, pipeline, step, model_horizon) self.equal_train_size = equal_train_size self.strategy_name = "direct" @@ -118,7 +118,7 @@ def fit( else: val_data = None - for horizon in range(1, self.horizon // self.model_horizon + 1): + for model_i, horizon in enumerate(range(1, self.horizon // self.model_horizon + 1)): target_idx = index_slicer.create_idx_target( dataset.data, self.horizon, @@ -147,12 +147,26 @@ def fit( self.trainer.history = self.history current_trainer = deepcopy(self.trainer) + + # In Direct strategy, we train several models, one for each model_horizon + if isinstance(current_trainer, DLTrainer): + checkpoint_path = current_trainer.checkpoint_path + pretrained_path = current_trainer.pretrained_path + + current_trainer.checkpoint_path /= f"trainer_{model_i}" + if pretrained_path: + current_trainer.pretrained_path /= f"trainer_{model_i}" + current_trainer.fit(data, self.pipeline, val_data) + if isinstance(current_trainer, DLTrainer): + current_trainer.checkpoint_path = checkpoint_path + current_trainer.pretrained_path = pretrained_path + self.trainers.append(current_trainer) else: - for horizon in range(1, self.horizon // self.model_horizon + 1): + for model_i, horizon in enumerate(range(1, self.horizon // self.model_horizon + 1)): features_idx = index_slicer.create_idx_data( dataset.data, self.model_horizon * horizon, @@ -211,8 +225,22 @@ def fit( self.trainer.history = self.history current_trainer = deepcopy(self.trainer) + + # In Direct strategy, we train several models, one for each model_horizon + if isinstance(current_trainer, DLTrainer): + checkpoint_path = current_trainer.checkpoint_path + pretrained_path = current_trainer.pretrained_path + + current_trainer.checkpoint_path /= f"trainer_{model_i}" + if pretrained_path: + current_trainer.pretrained_path /= f"trainer_{model_i}" + current_trainer.fit(data, self.pipeline, val_data) + if isinstance(current_trainer, DLTrainer): + current_trainer.checkpoint_path = checkpoint_path + current_trainer.pretrained_path = pretrained_path + self.trainers.append(current_trainer) return self diff --git a/tsururu/strategies/flat_wide_mimo.py b/tsururu/strategies/flat_wide_mimo.py index 3d322ba..4a2ac5b 100644 --- a/tsururu/strategies/flat_wide_mimo.py +++ b/tsururu/strategies/flat_wide_mimo.py @@ -14,9 +14,10 @@ class FlatWideMIMOStrategy(MIMOStrategy): history: number of previous for feature generating (i.e., features for observation y_t are counted from observations (y_{t-history}, ..., y_{t-1}). + trainer: trainer with model params and validation params. + pipeline: pipeline for feature and target generation. step: in how many points to take the next observation while making samples' matrix. - trainer: trainer with model params and validation params. Notes: 1. Fit: mixture of DirectStrategy and MIMOStrategy, fit one @@ -29,9 +30,9 @@ def __init__( self, horizon: int, history: int, - step: int, trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, + step: int = 1, ): - super().__init__(horizon, history, step, trainer, pipeline) + super().__init__(horizon, history, trainer, pipeline, step) self.strategy_name = "FlatWideMIMOStrategy" diff --git a/tsururu/strategies/mimo.py b/tsururu/strategies/mimo.py index e5f6114..9eb14dc 100644 --- a/tsururu/strategies/mimo.py +++ b/tsururu/strategies/mimo.py @@ -14,10 +14,10 @@ class MIMOStrategy(RecursiveStrategy): history: number of previous for feature generating (i.e., features for observation y_t are counted from observations (y_{t-history}, ..., y_{t-1}). - step: in how many points to take the next observation while making - samples' matrix. trainer: trainer with model params and validation params. pipeline: pipeline for feature and target generation. + step: in how many points to take the next observation while making + samples' matrix. Notes: 1. Technically, `MIMOStrategy` is a `RecursiveStrategy` or @@ -34,9 +34,9 @@ def __init__( self, horizon: int, history: int, - step: int, trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, + step: int = 1, ): - super().__init__(horizon, history, step, trainer, pipeline, model_horizon=horizon) + super().__init__(horizon, history, trainer, pipeline, step, model_horizon=horizon) self.strategy_name = "MIMOStrategy" diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 1442385..5f1eb17 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -43,13 +43,13 @@ def __init__( self, horizon: int, history: int, - step: int, trainer: Union[MLTrainer, DLTrainer], pipeline: Pipeline, + step: int = 1, model_horizon: int = 1, reduced: bool = False, ): - super().__init__(horizon, history, step, trainer, pipeline) + super().__init__(horizon, history, trainer, pipeline, step) self.model_horizon = model_horizon self.reduced = reduced self.strategy_name = "recursive" @@ -125,8 +125,22 @@ def fit( self.trainer.history = self.history current_trainer = deepcopy(self.trainer) + + # In Recursive strategy, we train the individual model + if isinstance(current_trainer, DLTrainer): + checkpoint_path = current_trainer.checkpoint_path + pretrained_path = current_trainer.pretrained_path + + current_trainer.checkpoint_path /= "trainer_0" + if pretrained_path: + current_trainer.pretrained_path /= "trainer_0" + current_trainer.fit(data, self.pipeline, val_data) + if isinstance(current_trainer, DLTrainer): + current_trainer.checkpoint_path = checkpoint_path + current_trainer.pretrained_path = pretrained_path + self.trainers.append(current_trainer) return self diff --git a/tsururu/transformers/base.py b/tsururu/transformers/base.py index 05665f1..909696d 100644 --- a/tsururu/transformers/base.py +++ b/tsururu/transformers/base.py @@ -1,6 +1,5 @@ """Base classes for transformers, that are needed for feature generating.""" -from copy import deepcopy from typing import Optional, Sequence import numpy as np @@ -50,9 +49,7 @@ def __init__(self, input_features: Optional[Sequence[str]] = None): self.input_features = input_features self.output_features = None # array with names of resulting columns - def fit( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> "Transformer": + def fit(self, data: dict, input_features: Optional[Sequence[str]] = None) -> "Transformer": """Fit transformer on "elongated series" and return it's instance. Args: @@ -85,9 +82,7 @@ def transform(self, data: dict) -> dict: """ raise NotImplementedError() - def fit_transform( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> dict: + def fit_transform(self, data: dict, input_features: Optional[Sequence[str]] = None) -> dict: """Default implementation of fit_transform - fit and then transform. Args: @@ -133,9 +128,7 @@ class SequentialTransformer(Transformer): """ - def __init__( - self, transformers_list: Sequence[Transformer], input_features: Sequence[str] - ): + def __init__(self, transformers_list: Sequence[Transformer], input_features: Sequence[str]): super().__init__(input_features=input_features) self.transformers_list = transformers_list self.inverse_transformers_list = [] @@ -156,8 +149,7 @@ def fit( """ raise NotImplementedError( - "Sequential supports only fit_transform since needs output" - "to fit next transformer." + "Sequential supports only fit_transform since needs output" "to fit next transformer." ) def transform(self, data: dict) -> dict: @@ -184,9 +176,7 @@ def transform(self, data: dict) -> dict: return data - def fit_transform( - self, data, input_features: Optional[Sequence[str]] = None - ) -> dict: + def fit_transform(self, data, input_features: Optional[Sequence[str]] = None) -> dict: """Fit and apply the sequence of transformers to data containers one after the other and transform "elongated series". @@ -212,7 +202,9 @@ def fit_transform( current_input_features = trf.output_features if hasattr(trf, "transform_target") and trf.transform_target: # Check that transform_target corresponding to transformer for target column - assert self.input_features == [data["target_column_name"]], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" + assert self.input_features == [ + data["target_column_name"] + ], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" self.inverse_transformers_list.append(trf) elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: self.inverse_transformers_list.extend(trf.inverse_transformers_list) @@ -320,9 +312,7 @@ def transform(self, data: dict) -> dict: return data - def fit_transform( - self, data: dict, input_features: Optional[Sequence[str]] = None - ) -> dict: + def fit_transform(self, data: dict, input_features: Optional[Sequence[str]] = None) -> dict: """Fit and apply the sequence of transformers to data containers in parallel and transform "elongated series". @@ -345,7 +335,10 @@ def fit_transform( output_features_list.append(trf.output_features) if hasattr(trf, "transform_target") and trf.transform_target: # Check that transform_target corresponding to transformer for target column - assert self.input_features == [data["target_column_name"]], f"`transform_target` can't be used with exogenous features. You try use it on {self.input_features}, while target column is `{data['target_column_name']}`" + assert self.input_features == [ + data["target_column_name"] + ], "`transform_target` can't be used with exogenous features. You try use it on" + f"{self.input_features}, while target column is `{data['target_column_name']}`" self.inverse_transformers_list.append(trf) elif hasattr(trf, "inverse_transformers_list") and trf.inverse_transformers_list: self.inverse_transformers_list.extend(trf.inverse_transformers_list) @@ -371,9 +364,9 @@ def generate(self, data: dict) -> dict: for trf in self.transformers_list: copy_X = data["X"] data["X"] = np.array([]) - + data = trf.generate(data) - + if copy_X.shape != (0,) and data["X"].shape != (0,): data["X"] = np.hstack((copy_X, data["X"])) elif data["X"].shape == (0,): @@ -489,12 +482,17 @@ def transform(self, data: dict) -> dict: self._transform(data, "raw_ts_X") else: for i, column_name in enumerate(self.input_features): - data["raw_ts_X"].loc[:, self.output_features[i]] = data["raw_ts_X"].loc[:, column_name] + data["raw_ts_X"].loc[:, self.output_features[i]] = data["raw_ts_X"].loc[ + :, column_name + ] if self.transform_target: self._transform(data, "raw_ts_y") else: for i, column_name in enumerate(self.input_features): - data["raw_ts_y"].loc[:, self.output_features[i]] = data["raw_ts_y"].loc[:, column_name] + if column_name.split("__")[0] == data["target_column_name"]: + data["raw_ts_y"].loc[:, self.output_features[i]] = data["raw_ts_y"].loc[ + :, column_name + ] return data From f9a04fbbbbb4d41181d2986e1b230a1494397fbc Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 19 Sep 2024 22:51:10 +0300 Subject: [PATCH 18/50] change column_mask logic for standard_scaler --- tsururu/transformers/numeric.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tsururu/transformers/numeric.py b/tsururu/transformers/numeric.py index f251c34..14bc818 100644 --- a/tsururu/transformers/numeric.py +++ b/tsururu/transformers/numeric.py @@ -92,7 +92,7 @@ def _get_mask_mean_std(self, segment, column_name, current_id): column mask, mean, and standard deviation. """ - column_mask = [segment.columns.str.contains(column_name)][0] + column_mask = [column for column in segment.columns if column == column_name] mean = self.fitted_params[current_id][(column_name, "mean")] std = self.fitted_params[current_id][(column_name, "std")] From 20c150e4f2f1156c8c6c45505cd9c879929cb5e1 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 19 Sep 2024 22:51:43 +0300 Subject: [PATCH 19/50] fix imputer --- tsururu/transformers/impute.py | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/tsururu/transformers/impute.py b/tsururu/transformers/impute.py index 20b3f0f..adea523 100644 --- a/tsururu/transformers/impute.py +++ b/tsururu/transformers/impute.py @@ -1,6 +1,8 @@ -import pandas as pd +from typing import Optional, Sequence + import numpy as np -from typing import Sequence, Optional +import pandas as pd + from .base import SeriesToSeriesTransformer @@ -68,13 +70,21 @@ def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Seri Returns: transformed segment of 'elongated series'. """ - for column_name in self.input_features: + for i, column_name in enumerate(self.input_features): if self.regime == "mean": - segment[column_name] = self._fill_mean(segment[column_name]) + segment.loc[:, self.output_features[i]] = segment.loc[:, column_name].copy() + segment.loc[:, self.output_features[i]] = self._fill_mean( + segment.loc[:, self.output_features[i]] + ) elif self.regime == "lag": - segment[column_name] = self._fill_lag(segment[column_name]) + segment.loc[:, self.output_features[i]] = segment.loc[:, column_name].copy() + segment.loc[:, self.output_features[i]] = self._fill_lag( + segment.loc[:, self.output_features[i]] + ) # Fill remaining missing values with constant value - segment[column_name].fillna(self.constant_value, inplace=True) + segment.loc[:, self.output_features[i]] = segment.loc[:, self.output_features[i]].fillna( + self.constant_value + ) return segment @@ -96,9 +106,9 @@ def _fill_mean(self, series: pd.Series) -> pd.Series: for idx in series[series.isnull()].index: if idx >= window_size: - window = series[idx - window_size : idx] + window = series.loc[idx - window_size : idx] else: - window = series[:idx] + window = series.loc[:idx] try: if self.weighted_alpha > 0: @@ -109,7 +119,7 @@ def _fill_mean(self, series: pd.Series) -> pd.Series: else: mean_value = window.mean() except: - mean_value = series[idx] + mean_value = series.loc[idx] filled_series.at[idx] = mean_value @@ -129,9 +139,9 @@ def _fill_lag(self, series: pd.Series) -> pd.Series: for idx in series[series.isnull()].index: try: - current_lag = series[idx - self.lag] + current_lag = series.loc[idx - self.lag] except: - current_lag = series[idx] + current_lag = series.loc[idx] filled_series.at[idx] = current_lag From f6d01b8839b350608637a71e0b57f47155b6f82a Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 19 Sep 2024 22:52:06 +0300 Subject: [PATCH 20/50] fix time_to_num --- tsururu/transformers/datetime.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/tsururu/transformers/datetime.py b/tsururu/transformers/datetime.py index bfec70c..7a0b05b 100644 --- a/tsururu/transformers/datetime.py +++ b/tsururu/transformers/datetime.py @@ -79,14 +79,18 @@ def transform(self, data: dict) -> dict: horizon = data["target_idx"][0, -1] - data["features_idx"][0, -1] time_col = time_col + horizon * time_delta - data = pd.to_datetime(time_col.to_numpy().reshape(-1), origin="unix") + new_arr = pd.to_datetime(time_col.to_numpy().reshape(-1), origin="unix") data_transformed = ( - (data - np.datetime64(self.basic_date)) / np.timedelta64(1, self.delta) + (new_arr - np.datetime64(self.basic_date)) / np.timedelta64(1, self.delta) ).values.astype(np.float32) result_data.append(data_transformed) - data["raw_ts_X"][:, self.output_features] = result_data + result_data = np.hstack(result_data) + if result_data.ndim == 1: + result_data = result_data.reshape(-1, 1) + + data["raw_ts_X"][self.output_features] = result_data return data From 15be4ce3db659832f1b91ccb43d8fc20fc7bd680 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 19 Sep 2024 22:52:31 +0300 Subject: [PATCH 21/50] change pipeline easy_setup logic --- tsururu/dataset/pipeline.py | 114 +++++++++++++++++++++++++----------- 1 file changed, 80 insertions(+), 34 deletions(-) diff --git a/tsururu/dataset/pipeline.py b/tsururu/dataset/pipeline.py index f3b6f33..e2df515 100644 --- a/tsururu/dataset/pipeline.py +++ b/tsururu/dataset/pipeline.py @@ -100,27 +100,57 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " Returns: the created pipeline. + Notes: pipeline_params is a dictionary with the following keys: + - target_lags (necessary): list of lags for target + - date_lags (optional, default False): list of lags for date + - exog_lags (optional, deafult False): list of lags for exogenous features + - target_normalizer (optional, default none): type of target normalizer + (none, standard_scaler, difference_normalizer, last_known_normalizer) + - target_normalizer_regime (optional, default none): regime of target normalizer + (none, delta, ratio) + """ + # Check if all necessary keys are in pipeline_params + assert "target_lags" in pipeline_params, "target_lags MUST BE in pipeline_params!" + + # Add default values for pipeline_params if they are not provided + if "date_lags" not in pipeline_params: + pipeline_params["date_lags"] = False + if "exog_lags" not in pipeline_params: + pipeline_params["exog_lags"] = False + if "target_normalizer" not in pipeline_params: + pipeline_params["target_normalizer"] = "none" + if "target_normalizer_regime" not in pipeline_params: + pipeline_params["target_normalizer_regime"] = "none" + + # Check some params' correctness + assert pipeline_params["target_normalizer"] in [ + "none", + "standard_scaler", + "difference_normalizer", + "last_known_normalizer", + ], "there is no such target_normalizer!" + + assert pipeline_params["target_normalizer_regime"] in [ + "none", + "delta", + "ratio", + ], "there is no such target_normalizer_regime!" + + if pipeline_params["target_normalizer"] in ["standard_scaler", "none"]: + assert ( + pipeline_params["target_normalizer_regime"] == "none" + ), "target_normalizer_regime MUST BE `none` for this normalizer" + else: + assert ( + pipeline_params["target_normalizer_regime"] != "none" + ), "target_normalizer_regime MUST BE NOT `none` for this normalizer" + # Resulting pipeline is a Union transformer with Sequential transformers result_union_transformers_list = [] # For each column create a list of transformers for resulting Sequential transformer for role, columns_params in roles.items(): - # Some checks for params' correctness - if pipeline_params["target_normalizer"] in ["standard_scaler", "none"]: - assert ( - pipeline_params["normalizer_regime"] == "none" - ), "normalizer_regime MUST BE `none` for this normalizer" - else: - assert ( - pipeline_params["normalizer_regime"] != "none" - ), "normalizer_regime MUST BE NOT `none` for this normalizer" - assert pipeline_params["normalizer_regime"] in [ - "none", - "delta", - "ratio", - ], "there is no such normalizer_regime!" - current_sequential_transformers_list = [] if role == "target": target_lag = transormers_factory.create_transformer( @@ -129,24 +159,12 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " target_generator = TargetGenerator() target_union = UnionTransformer(transformers_list=[target_lag, target_generator]) - if pipeline_params["normalizer_transform_regime"] == "features": - transform_features = True - transform_target = False - elif pipeline_params["normalizer_transform_regime"] == "target": - transform_features = False - transform_target = True - elif pipeline_params["normalizer_transform_regime"] == "features_target": - transform_features = True - transform_target = True - else: - assert ValueError("there is no such normalizer_transform_regime!") - if pipeline_params["target_normalizer"] == "standard_scaler": target_normalizer = transormers_factory.create_transformer( "StandardScalerTransformer", { - "transform_features": transform_features, - "transform_target": transform_target, + "transform_features": True, + "transform_target": True, }, ) current_sequential_transformers_list.append(target_normalizer) @@ -156,8 +174,8 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " target_normalizer = transormers_factory.create_transformer( "DifferenceNormalizer", { - "transform_features": transform_features, - "transform_target": transform_target, + "transform_features": True, + "transform_target": True, "regime": pipeline_params["normalizer_regime"], }, ) @@ -168,8 +186,8 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " target_normalizer = transormers_factory.create_transformer( "LastKnownNormalizer", { - "transform_features": transform_features, - "transform_target": transform_target, + "transform_features": True, + "transform_target": True, "regime": pipeline_params["normalizer_regime"], }, ) @@ -189,20 +207,47 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " "from_target_date": True, }, ) + date_scaler = transormers_factory.create_transformer( + "StandardScalerTransformer", + { + "transform_features": True, + "transform_target": False, + }, + ) date_lag = transormers_factory.create_transformer( "LagTransformer", {"lags": pipeline_params["date_lags"]} ) + current_sequential_transformers_list.append(date_season) + current_sequential_transformers_list.append(date_scaler) current_sequential_transformers_list.append(date_lag) elif role == "id": + id_encoder = transormers_factory.create_transformer("LabelEncodingTransformer", {}) + id_scaler = transormers_factory.create_transformer( + "StandardScalerTransformer", + { + "transform_features": True, + "transform_target": False, + }, + ) id_lag = transormers_factory.create_transformer("LagTransformer", {"lags": 1}) + current_sequential_transformers_list.append(id_encoder) + current_sequential_transformers_list.append(id_scaler) current_sequential_transformers_list.append(id_lag) else: + exog_scaler = transormers_factory.create_transformer( + "StandardScalerTransformer", + { + "transform_features": True, + "transform_target": False, + }, + ) exog_lag = transormers_factory.create_transformer( "LagTransformer", {pipeline_params["exog_lags"]} ) + current_sequential_transformers_list.append(exog_scaler) current_sequential_transformers_list.append(exog_lag) result_union_transformers_list.append( @@ -216,8 +261,9 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " return cls(union, multivariate) + @staticmethod def create_data_dict_for_pipeline( - self, dataset: TSDataset, features_idx: np.ndarray, target_idx: np.ndarray + dataset: TSDataset, features_idx: np.ndarray, target_idx: np.ndarray ) -> dict: """Create a data dictionary for the pipeline. From 5c4551351b177dec9d108333ba124c17e93a0b45 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 19 Sep 2024 22:55:53 +0300 Subject: [PATCH 22/50] change readme + first version of tutorials --- README.md | 34 +- Tutorial_1_Quick_start.ipynb | 569 ++--- Tutorial_2_Neural_Networks.ipynb | 471 ---- Tutorial_2_Strategies.ipynb | 944 +++++++ Tutorial_3_Transformers_and_Pipeline.ipynb | 2665 +++++++++++++++++++ Tutorial_4_Neural_Networks.ipynb | 2686 ++++++++++---------- 6 files changed, 5186 insertions(+), 2183 deletions(-) delete mode 100644 Tutorial_2_Neural_Networks.ipynb create mode 100644 Tutorial_2_Strategies.ipynb create mode 100644 Tutorial_3_Transformers_and_Pipeline.ipynb diff --git a/README.md b/README.md index 180e6ac..7b8c0e4 100644 --- a/README.md +++ b/README.md @@ -10,30 +10,32 @@ Also tsururu provides various preprocessing techniques. ## Multi-series prediction strategies: - _Local-modelling_: - - Individual model for each time series. + - An individual model for each time series. + - Each time series as independent from others. - _Global-modelling_: - - One model for all time series; - - features made up of individual series do not overlap. + - A single model for all time series. + - Features created from each series do not overlap with other series. Series are related but modeled separately. - _Multivariate-modelling_: - - One model for all time series; - - features made up of individual series corresponding to the same time point are concatenated for all time series. + - A single model for all time series. + - Features created from each series are concatenated at each time step. Try to capture dependencies between the series at the same time point. ## Multi-point-ahead prediction strategies: - _Recursive:_ - - one model for all points of the forecast horizon; - - *training*: the model is trained to predict one point ahead; - - *prediction*: a prediction is iteratively made one point ahead, and then this prediction is used to further shape the features in the test data. -- _Recursive-reduced:_ - - one model for all points in the prediction horizon; - - *training*: the model is trained to predict one point ahead; - - *prediction*: features are generated for all test observations at once, unavailable values are replaced by NaN. + - One model is used for the entire forecast horizon. + - training: The model is trained to predict one point ahead. + - prediction: The model iteratively predicts each point, using previous predictions to update the features in the test data. + - Note 1: There is an option to use a “reduced” version, where features are generated for all test observations at once, and unavailable values are filled with NaN. + - Note 2: Recursive can also be combined with the MIMO strategy, allowing the model to predict model_horizon points ahead at each step. - _Direct:_ - - individual models for each point in the prediction horizon. + - Individual models are trained for each point in the forecast horizon. + - Note 1: There is an option to use "equal_train_size" option, where all models can be trained on the same X_train set, formed for the last model predicting h point. Only the target variable (y) is updated for each model, reducing the time spent generating new training sets. + - Note 2: Direct can also be combined with MIMO, where each individual model predicts model_horizon points ahead. - _MultiOutput (MIMO - Multi-input-multi-output):_ - - one model that learns to predict the entire prediction horizon. - - __Also, this strategy supports the presence of exogenous features (only for local- or global-modelling).__ + - One model is trained and used for the entire forecast horizon at once. + - Note 1: This strategy can also accommodate exogenous features (for local- or global-modelling strategies). - _FlatWideMIMO:_. - - mixture of Direct and MIMO, fit one model, but uses deployed over horizon Direct's features. + - A hybrid of Direct and MIMO. One model is trained, but Direct’s features are deployed across the forecast horizon. + - Note 1: To use FlatWideMIMO with date-related features, h lags of them must be included (with help of LagTransformer). ## Installation diff --git a/Tutorial_1_Quick_start.ipynb b/Tutorial_1_Quick_start.ipynb index b5b77c0..cd29998 100644 --- a/Tutorial_1_Quick_start.ipynb +++ b/Tutorial_1_Quick_start.ipynb @@ -1,5 +1,26 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we will explore a basic example of forecasting multiple time series and go over the key components of the forecasting pipeline provided by the `Tsururu` library." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's import everything we will need." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -15,26 +36,11 @@ "import numpy as np\n", "import pandas as pd\n", "\n", - "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", + "from tsururu.dataset import Pipeline, TSDataset\n", "from tsururu.model_training.trainer import MLTrainer\n", "from tsururu.model_training.validator import KFoldCrossValidator\n", "from tsururu.models.boost import CatBoost\n", - "from tsururu.strategies import (\n", - " DirectStrategy,\n", - " FlatWideMIMOStrategy,\n", - " MIMOStrategy,\n", - " RecursiveStrategy,\n", - ")\n", - "from tsururu.transformers import (\n", - " DateSeasonsGenerator,\n", - " DifferenceNormalizer,\n", - " LagTransformer,\n", - " LastKnownNormalizer,\n", - " SequentialTransformer,\n", - " StandardScalerTransformer,\n", - " TargetGenerator,\n", - " UnionTransformer,\n", - ")" + "from tsururu.strategies import RecursiveStrategy" ] }, { @@ -88,6 +94,13 @@ "4) `Model`." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with Data" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -165,233 +178,90 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What kind of transformers are there?" + "The Pipeline class is designed to create and apply a sequence of transformations (transformers) to time series data. It is used for data preprocessing, feature and target generation, as well as performing transformations required for forecasting models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Special attention should be paid to the `Transformer` class: the elements of the pipeline that are responsible for transforming the values of a series and generating features. `Pipeline` class is a wrapper over transformers which is needed to provide some additional methods and functions above them.\n", - "\n", - "There are two types of transformers that are used to collect pipelines:\n", - "- `Union` transformers;\n", - "- `Sequential` transformers.\n", - "\n", - "Below is a list of available Transformers: \n", - "- `StandardScalerTransformer` *(Series4Series)*.\n", - "- `DifferenceNormalizer` *(Series4Series)*: subtract the previous value or divide by it.\n", - "- `LastKnownNormalizer` *(Features4Features)*: normalize all lags by the last known one: divide by it or subtract.\n", - "\n", - "This three transformers provide flags `transform_features` / `transform_target`, that allow you to manipulate traits and targets separately and get different results from them.\n", - "\n", - "Besides, __DifferenceNormalizer__ and __LastKnownNormalizer__ can be applied in two regimes: `delta` and `ratio`: in the first case, normalisation means subtracting the target value from the current value, and in the second, dividing by it.\n", - "\n", - "- `LabelEncodingTransformer` and `OneHotEncodingTransformer` *(Series4Series)* - encoders for categorical features.\n", - "- `TimeToNumGenerator` and `DateSeasonsGenerator` *(Series4Series)* - generator for seasonal features by dates.\n", - "- `LagTransformer` *(Series4Features) - generator for lags. \n", - "\n", - "__!!!The lag transformer must necessarily be present in the sequential transformer, otherwise the features will not be generated.!!!__\n", - "\n", - "Finally, to generate targets, you need to use `TargetGenerator`." + "In this tutorial, we will cover a simplified approach to initializing the Pipeline. For detailed information on the available transformers and methods for building a pipeline, refer to Tutorial 3 (Tutorial_3_Pipeline.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Transformers must be assembled in order!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The __SeriesToSeries__ transformers should come first, followed by the LagTransformer and TargetGenerator (__SeriesToFeatures__), and then the __FeaturesToFeatures__ transformers.\n", - "\n", - "!!!Thus, `StandardScalerNormalizer` and `DifferenceNormalizer` should be before `LagTransformer` and `LastKnownNormalizer` after it!!!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How to build a Pipeline?" + "#### What kind of transformers are there?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "So, there are two ways to build a pipline from transformers: initialise the transformers of interest by hand or use a config in the form of a dictionary. Let's look at both ways." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "standard_scaler = StandardScalerTransformer(\n", - " transform_features=True,\n", - " transform_target=True\n", - ")\n", - "\n", - "lag = LagTransformer(lags=3)\n", - "date_lag = LagTransformer(lags=3)\n", - "id_lag = LagTransformer(lags=1)\n", + "Special attention should be paid to the `Transformer` class: the elements of the pipeline that are responsible for transforming the values of a series and generating features. `Pipeline` class is a wrapper over transformers which is needed to provide some additional methods and functions above them.\n", "\n", - "target_generator = TargetGenerator()\n", + "There are two types of transformers that are used to collect pipelines:\n", + "- `Union` transformers;\n", + "- `Sequential` transformers.\n", "\n", - "date_seasons = DateSeasonsGenerator(\n", - " seasonalities=[\"doy\", \"m\", \"wd\"],\n", - " from_target_date=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "union_1 = UnionTransformer(transformers_list=[lag, target_generator])\n", + "Below is a list of available Transformers: \n", + "- **StandardScalerTransformer** *(Series2Series)*.\n", + "- **DifferenceNormalizer** *(Series2Series)*: subtract the previous value or divide by it.\n", + "- **TimeToNumGenerator** and **DateSeasonsGenerator** *(Series2Series)* - generator for seasonal features by dates.\n", + "- **LabelEncodingTransformer** and **OneHotEncodingTransformer** *(Series2Series)* - encoders for categorical features.\n", + "- **MissingValuesImputer** *(Series2Series)*.\n", + "- **LagTransformer** *(Series2Features)* - generator for lags. \n", + "- **LastKnownNormalizer** *(Features2Features)*: normalize all lags by the last known one: divide by it or subtract.\n", "\n", - "seq_1 = SequentialTransformer(transformers_list=[standard_scaler, union_1], input_features=[\"value\"])\n", - "seq_2 = SequentialTransformer(transformers_list=[date_seasons, date_lag], input_features=[\"date\"])\n", - "seq_3 = SequentialTransformer(transformers_list=[id_lag], input_features=[\"id\"])\n", + "!!!The lag transformer must necessarily be present in the sequential transformer, otherwise the features will not be generated.!!!\n", "\n", - "union = UnionTransformer(transformers_list=[seq_1, seq_2, seq_3])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_1 = Pipeline(union, multivariate=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'transformers': ,\n", - " 'multivariate': False,\n", - " 'is_fitted': False,\n", - " 'strategy_name': None,\n", - " 'output_features': None,\n", - " 'y_original_shape': None}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline_1.__dict__" + "Finally, to generate targets, you need to use **TargetGenerator**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_params = {\n", - " \"target\": {\n", - " \"columns\": [\"value\"],\n", - " \"features\": {\n", - " \"StandardScalerTransformer\":\n", - " {\n", - " \"transform_target\": True, \n", - " \"transform_features\": True\n", - " },\n", - " \"LagTransformer\": {\"lags\": 7},\n", - " },\n", - " },\n", - " \"date\": {\n", - " \"columns\": [\"date\"],\n", - " \"features\": {\n", - " \"DateSeasonsGenerator\": {\n", - " # Use seasonality features from the date column as \n", - " # features with datetime lags\n", - " # Possible values: [\n", - " # \"y\": year, \"m\": month, \"d\": day, \n", - " # \"wd\": weekday, \"doy\": dayofyear,\n", - " # \"hour\": hour, \"min\": minute, \"sec\": second, \n", - " # \"ms\": microsecond, \"ns\": nanosecond\n", - " # ]\n", - " \"seasonalities\": ['doy', 'm', 'wd'], \n", - " # Use date from target point to make datetime features\n", - " \"from_target_date\": True,\n", - " },\n", - " \"LagTransformer\": {\"lags\": 3}\n", - " },\n", - " },\n", - " \"id\": {\n", - " \"columns\": [\"id\"],\n", - " \"features\": {\n", - " \"LagTransformer\": {\"lags\": 1},\n", - " },\n", - " }\n", - "}" + "#### Transformers must be assembled in order!" ] }, { - "cell_type": "code", - "execution_count": 10, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "pipeline = Pipeline.from_dict(pipeline_params, multivariate=False)" + "The __SeriesToSeries__ transformers should come first, followed by the LagTransformer and TargetGenerator (__SeriesToFeatures__), and then the __FeaturesToFeatures__ transformers.\n", + "\n", + "!!!Thus, **StandardScalerNormalizer** and **DifferenceNormalizer** should be before **LagTransformer** and **LastKnownNormalizer** after it!!!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or:" + "#### How to build a Pipeline?" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "pipeline_easy_params = {\n", + " \"target_lags\": 3,\n", + " \"date_lags\": 1,\n", + "# \"exog_lags\": 1, # Uncomment this line if you have exogenous features\n", " # One from [\"none\", \"standard_scaler\", \"difference_normalizer\", \"last_known_normalizer\"]\n", " \"target_normalizer\": \"standard_scaler\",\n", - "\n", " # One from [\"none\", \"delta\", \"ratio\"] (MUST BE \"none\" for \"standard_scaler\" and NOT \"none\" for others)\n", - " \"normalizer_regime\": \"none\",\n", - "\n", - " # One from [\"features\", \"target\", \"features_target\"]\n", - " \"normalizer_transform_regime\": \"features_target\",\n", - "\n", - " \"target_lags\": 3,\n", - " \"date_lags\": 1,\n", - "# \"exog_lags\": 1,\n", + " \"target_normalizer_regime\": \"none\",\n", "}" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -416,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -463,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -522,51 +392,50 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "horizon = 3\n", - "history = 7\n", - "step = 1" + "history = 7" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "strategy = RecursiveStrategy(horizon, history, step, trainer, pipeline)" + "strategy = RecursiveStrategy(horizon, history, trainer, pipeline)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 56.5ms\tremaining: 56.4s\n", - "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 420ms\tremaining: 418ms\n", - "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 763ms\tremaining: 0us\n", + "0:\tlearn: 0.9605897\ttest: 0.9668517\tbest: 0.9668517 (0)\ttotal: 60.8ms\tremaining: 1m\n", + "500:\tlearn: 0.0040519\ttest: 0.0042506\tbest: 0.0042506 (500)\ttotal: 385ms\tremaining: 384ms\n", + "999:\tlearn: 0.0024776\ttest: 0.0026944\tbest: 0.0026944 (999)\ttotal: 707ms\tremaining: 0us\n", "\n", - "bestTest = 0.003047062205\n", + "bestTest = 0.002694363765\n", "bestIteration = 999\n", "\n", - "Fold 0. Score: 0.003047062205400275\n", - "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 1.65ms\tremaining: 1.65s\n", - "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 342ms\tremaining: 340ms\n", - "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 705ms\tremaining: 0us\n", + "Fold 0. Score: 0.0026943637647905937\n", + "0:\tlearn: 0.9659422\ttest: 0.9613653\tbest: 0.9613653 (0)\ttotal: 740us\tremaining: 739ms\n", + "500:\tlearn: 0.0040759\ttest: 0.0041558\tbest: 0.0041558 (500)\ttotal: 335ms\tremaining: 334ms\n", + "999:\tlearn: 0.0025120\ttest: 0.0026138\tbest: 0.0026138 (999)\ttotal: 669ms\tremaining: 0us\n", "\n", - "bestTest = 0.002916854444\n", + "bestTest = 0.00261378843\n", "bestIteration = 999\n", "\n", - "Fold 1. Score: 0.002916854443892958\n", - "Mean score: 0.003\n", - "Std: 0.0001\n" + "Fold 1. Score: 0.0026137884302204206\n", + "Mean score: 0.0027\n", + "Std: 0.0\n" ] } ], @@ -576,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -585,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -619,181 +488,181 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1997.088714\n", + " 1994.783481\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1997.191979\n", + " 1994.440142\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1995.496356\n", + " 1993.71401\n", " \n", " \n", " 3\n", " 1\n", " 2022-09-27\n", - " 2997.475196\n", + " 2994.783481\n", " \n", " \n", " 4\n", " 1\n", " 2022-09-28\n", - " 2997.553282\n", + " 2994.440142\n", " \n", " \n", " 5\n", " 1\n", " 2022-09-29\n", - " 2995.826568\n", + " 2993.71401\n", " \n", " \n", " 6\n", " 2\n", " 2022-09-27\n", - " 3997.392796\n", + " 3994.783481\n", " \n", " \n", " 7\n", " 2\n", " 2022-09-28\n", - 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" 7997.648131\n", + " 7994.440142\n", " \n", " \n", " 20\n", " 6\n", " 2022-09-29\n", - " 7995.923171\n", + " 7993.71401\n", " \n", " \n", " 21\n", " 7\n", " 2022-09-27\n", - " 8997.53187\n", + " 8994.783481\n", " \n", " \n", " 22\n", " 7\n", " 2022-09-28\n", - " 8997.559264\n", + " 8994.440142\n", " \n", " \n", " 23\n", " 7\n", " 2022-09-29\n", - " 8995.845535\n", + " 8993.71401\n", " \n", " \n", " 24\n", " 8\n", " 2022-09-27\n", - " 9997.5219\n", + " 9994.783481\n", " \n", " \n", " 25\n", " 8\n", " 2022-09-28\n", - " 9997.546915\n", + " 9994.440142\n", " \n", " \n", " 26\n", " 8\n", " 2022-09-29\n", - " 9995.828639\n", + " 9993.71401\n", " \n", " \n", " 27\n", " 9\n", " 2022-09-27\n", - " 10997.380235\n", + " 10994.783481\n", " \n", " \n", " 28\n", " 9\n", " 2022-09-28\n", - " 10997.403622\n", + " 10994.440142\n", " \n", " \n", " 29\n", " 9\n", " 2022-09-29\n", - " 10995.683994\n", + " 10993.71401\n", " \n", " \n", "\n", @@ -801,39 +670,39 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1997.088714\n", - "1 0 2022-09-28 1997.191979\n", - "2 0 2022-09-29 1995.496356\n", - "3 1 2022-09-27 2997.475196\n", - "4 1 2022-09-28 2997.553282\n", - "5 1 2022-09-29 2995.826568\n", - "6 2 2022-09-27 3997.392796\n", - "7 2 2022-09-28 3997.46765\n", - "8 2 2022-09-29 3995.743526\n", - "9 3 2022-09-27 4997.548721\n", - "10 3 2022-09-28 4997.572455\n", - "11 3 2022-09-29 4995.842271\n", - "12 4 2022-09-27 5997.700033\n", - "13 4 2022-09-28 5997.725905\n", - "14 4 2022-09-29 5995.998695\n", - "15 5 2022-09-27 6997.694582\n", - "16 5 2022-09-28 6997.722867\n", - "17 5 2022-09-29 6995.997412\n", - "18 6 2022-09-27 7997.620737\n", - "19 6 2022-09-28 7997.648131\n", - "20 6 2022-09-29 7995.923171\n", - "21 7 2022-09-27 8997.53187\n", - "22 7 2022-09-28 8997.559264\n", - "23 7 2022-09-29 8995.845535\n", - "24 8 2022-09-27 9997.5219\n", - "25 8 2022-09-28 9997.546915\n", - "26 8 2022-09-29 9995.828639\n", - "27 9 2022-09-27 10997.380235\n", - "28 9 2022-09-28 10997.403622\n", - "29 9 2022-09-29 10995.683994" + "0 0 2022-09-27 1994.783481\n", + "1 0 2022-09-28 1994.440142\n", + "2 0 2022-09-29 1993.71401\n", + "3 1 2022-09-27 2994.783481\n", + "4 1 2022-09-28 2994.440142\n", + "5 1 2022-09-29 2993.71401\n", + "6 2 2022-09-27 3994.783481\n", + "7 2 2022-09-28 3994.440142\n", + "8 2 2022-09-29 3993.71401\n", + "9 3 2022-09-27 4994.783481\n", + "10 3 2022-09-28 4994.440142\n", + "11 3 2022-09-29 4993.71401\n", + "12 4 2022-09-27 5994.783481\n", + "13 4 2022-09-28 5994.440142\n", + "14 4 2022-09-29 5993.71401\n", + "15 5 2022-09-27 6994.783481\n", + "16 5 2022-09-28 6994.440142\n", + "17 5 2022-09-29 6993.71401\n", + "18 6 2022-09-27 7994.783481\n", + "19 6 2022-09-28 7994.440142\n", + "20 6 2022-09-29 7993.71401\n", + "21 7 2022-09-27 8994.783481\n", + "22 7 2022-09-28 8994.440142\n", + "23 7 2022-09-29 8993.71401\n", + "24 8 2022-09-27 9994.783481\n", + "25 8 2022-09-28 9994.440142\n", + "26 8 2022-09-29 9993.71401\n", + "27 9 2022-09-27 10994.783481\n", + "28 9 2022-09-28 10994.440142\n", + "29 9 2022-09-29 10993.71401" ] }, - "execution_count": 19, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -851,31 +720,31 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 1.06ms\tremaining: 1.06s\n", - "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 371ms\tremaining: 370ms\n", - "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 747ms\tremaining: 0us\n", + "0:\tlearn: 0.9617997\ttest: 0.9655311\tbest: 0.9655311 (0)\ttotal: 978us\tremaining: 977ms\n", + "500:\tlearn: 0.0039542\ttest: 0.0041461\tbest: 0.0041461 (500)\ttotal: 390ms\tremaining: 388ms\n", + "999:\tlearn: 0.0024434\ttest: 0.0026360\tbest: 0.0026360 (999)\ttotal: 844ms\tremaining: 0us\n", "\n", - "bestTest = 0.002972644218\n", + "bestTest = 0.002636039164\n", "bestIteration = 999\n", "\n", - "Fold 0. Score: 0.0029726442175072565\n", - "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 1.12ms\tremaining: 1.12s\n", - "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 386ms\tremaining: 384ms\n", - "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 748ms\tremaining: 0us\n", + "Fold 0. Score: 0.0026360391639954407\n", + "0:\tlearn: 0.9647074\ttest: 0.9625819\tbest: 0.9625819 (0)\ttotal: 817us\tremaining: 817ms\n", + "500:\tlearn: 0.0044702\ttest: 0.0046456\tbest: 0.0046456 (500)\ttotal: 346ms\tremaining: 345ms\n", + "999:\tlearn: 0.0025553\ttest: 0.0027601\tbest: 0.0027601 (999)\ttotal: 671ms\tremaining: 0us\n", "\n", - "bestTest = 0.002971764957\n", + "bestTest = 0.002760124255\n", "bestIteration = 999\n", "\n", - "Fold 1. Score: 0.002971764957214351\n", - "Mean score: 0.003\n", - "Std: 0.0\n" + "Fold 1. Score: 0.002760124255276898\n", + "Mean score: 0.0027\n", + "Std: 0.0001\n" ] } ], @@ -885,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -918,181 +787,181 @@ " \n", " 0\n", " 1997.0\n", - " 1993.68704\n", + " 1994.048102\n", " 0\n", " \n", " \n", " 1\n", " 1998.0\n", - " 1994.202883\n", + " 1994.405436\n", " 0\n", " \n", " \n", " 2\n", " 1999.0\n", - " 1994.669445\n", + " 1995.303323\n", " 0\n", " \n", " \n", " 3\n", " 2997.0\n", - " 2993.991901\n", + " 2994.048102\n", " 1\n", " \n", " \n", " 4\n", " 2998.0\n", - " 2994.586288\n", + " 2994.405436\n", " 1\n", " \n", " \n", " 5\n", " 2999.0\n", - " 2995.082926\n", + " 2995.303323\n", " 1\n", " \n", " \n", " 6\n", " 3997.0\n", - " 3993.914717\n", + " 3994.048102\n", " 2\n", " \n", " \n", " 7\n", " 3998.0\n", - " 3994.509697\n", + " 3994.405436\n", " 2\n", " \n", " \n", " 8\n", " 3999.0\n", - " 3995.015013\n", + " 3995.303323\n", " 2\n", " \n", " \n", " 9\n", " 4997.0\n", - " 4994.022114\n", + " 4994.048102\n", " 3\n", " \n", " \n", " 10\n", " 4998.0\n", - " 4994.615917\n", + " 4994.405436\n", " 3\n", " \n", " \n", " 11\n", " 4999.0\n", - " 4995.170471\n", + " 4995.303323\n", " 3\n", " \n", " \n", " 12\n", " 5997.0\n", - " 5994.175996\n", + " 5994.048102\n", " 4\n", " \n", " \n", " 13\n", " 5998.0\n", - " 5994.779734\n", + " 5994.405436\n", " 4\n", " \n", " \n", " 14\n", " 5999.0\n", - " 5995.319329\n", + " 5995.303323\n", " 4\n", " \n", " \n", " 15\n", " 6997.0\n", - " 6994.179421\n", + " 6994.048102\n", " 5\n", " \n", " \n", " 16\n", " 6998.0\n", - " 6994.784167\n", + " 6994.405436\n", " 5\n", " \n", " \n", " 17\n", " 6999.0\n", - " 6995.312331\n", + " 6995.303323\n", " 5\n", " \n", " \n", " 18\n", " 7997.0\n", - " 7994.113358\n", + " 7994.048102\n", " 6\n", " \n", " \n", " 19\n", " 7998.0\n", - " 7994.705541\n", + " 7994.405436\n", " 6\n", " \n", " \n", " 20\n", " 7999.0\n", - " 7995.236341\n", + " 7995.303323\n", " 6\n", " \n", " \n", " 21\n", " 8997.0\n", - " 8993.839156\n", + " 8994.048102\n", " 7\n", " \n", " \n", " 22\n", " 8998.0\n", - " 8994.431338\n", + " 8994.405436\n", " 7\n", " \n", " \n", " 23\n", " 8999.0\n", - " 8995.147741\n", + " 8995.303323\n", " 7\n", " \n", " \n", " 24\n", " 9997.0\n", - " 9993.838821\n", + " 9994.048102\n", " 8\n", " \n", " \n", " 25\n", " 9998.0\n", - " 9994.4268\n", + " 9994.405436\n", " 8\n", " \n", " \n", " 26\n", " 9999.0\n", - " 9995.1378\n", + " 9995.303323\n", " 8\n", " \n", " \n", " 27\n", " 10997.0\n", - " 10993.687397\n", + " 10994.048102\n", " 9\n", " \n", " \n", " 28\n", " 10998.0\n", - " 10994.270971\n", + " 10994.405436\n", " 9\n", " \n", " \n", " 29\n", " 10999.0\n", - " 10994.990313\n", + " 10995.303323\n", " 9\n", " \n", " \n", @@ -1101,39 +970,39 @@ ], "text/plain": [ " y_true_1 y_pred_1 id_1\n", - "0 1997.0 1993.68704 0\n", - "1 1998.0 1994.202883 0\n", - "2 1999.0 1994.669445 0\n", - "3 2997.0 2993.991901 1\n", - "4 2998.0 2994.586288 1\n", - "5 2999.0 2995.082926 1\n", - "6 3997.0 3993.914717 2\n", - "7 3998.0 3994.509697 2\n", - "8 3999.0 3995.015013 2\n", - "9 4997.0 4994.022114 3\n", - "10 4998.0 4994.615917 3\n", - "11 4999.0 4995.170471 3\n", - "12 5997.0 5994.175996 4\n", - "13 5998.0 5994.779734 4\n", - "14 5999.0 5995.319329 4\n", - "15 6997.0 6994.179421 5\n", - "16 6998.0 6994.784167 5\n", - "17 6999.0 6995.312331 5\n", - "18 7997.0 7994.113358 6\n", - "19 7998.0 7994.705541 6\n", - "20 7999.0 7995.236341 6\n", - "21 8997.0 8993.839156 7\n", - "22 8998.0 8994.431338 7\n", - "23 8999.0 8995.147741 7\n", - "24 9997.0 9993.838821 8\n", - "25 9998.0 9994.4268 8\n", - "26 9999.0 9995.1378 8\n", - "27 10997.0 10993.687397 9\n", - "28 10998.0 10994.270971 9\n", - "29 10999.0 10994.990313 9" + "0 1997.0 1994.048102 0\n", + "1 1998.0 1994.405436 0\n", + "2 1999.0 1995.303323 0\n", + "3 2997.0 2994.048102 1\n", + "4 2998.0 2994.405436 1\n", + "5 2999.0 2995.303323 1\n", + "6 3997.0 3994.048102 2\n", + "7 3998.0 3994.405436 2\n", + "8 3999.0 3995.303323 2\n", + "9 4997.0 4994.048102 3\n", + "10 4998.0 4994.405436 3\n", + "11 4999.0 4995.303323 3\n", + "12 5997.0 5994.048102 4\n", + "13 5998.0 5994.405436 4\n", + "14 5999.0 5995.303323 4\n", + "15 6997.0 6994.048102 5\n", + "16 6998.0 6994.405436 5\n", + "17 6999.0 6995.303323 5\n", + "18 7997.0 7994.048102 6\n", + "19 7998.0 7994.405436 6\n", + "20 7999.0 7995.303323 6\n", + "21 8997.0 8994.048102 7\n", + "22 8998.0 8994.405436 7\n", + "23 8999.0 8995.303323 7\n", + "24 9997.0 9994.048102 8\n", + "25 9998.0 9994.405436 8\n", + "26 9999.0 9995.303323 8\n", + "27 10997.0 10994.048102 9\n", + "28 10998.0 10994.405436 9\n", + "29 10999.0 10995.303323 9" ] }, - "execution_count": 21, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1181,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1206,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1234,7 +1103,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "metadata": {}, "outputs": [ { diff --git a/Tutorial_2_Neural_Networks.ipynb b/Tutorial_2_Neural_Networks.ipynb deleted file mode 100644 index 0f0e861..0000000 --- a/Tutorial_2_Neural_Networks.ipynb +++ /dev/null @@ -1,471 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import importlib\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "from pathlib import Path\n", - "from typing import List, Optional, Union\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from tsururu.dataset import Pipeline, TSDataset\n", - "from tsururu.model_training.trainer import DLTrainer\n", - "from tsururu.model_training.validator import HoldOutValidator\n", - "from tsururu.models.torch_based.dlinear import DLinear_NN\n", - "from tsururu.strategies import MIMOStrategy\n", - "from tsururu.transformers import (\n", - " LagTransformer,\n", - " SequentialTransformer,\n", - " TargetGenerator,\n", - " UnionTransformer,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def get_results(\n", - " cv: int,\n", - " regime: str,\n", - " y_true: Optional[List[np.ndarray]] = None,\n", - " y_pred: Optional[List[np.ndarray]] = None,\n", - " ids: Optional[List[Union[float, str]]] = None,\n", - ") -> pd.DataFrame:\n", - " def _get_fold_value(\n", - " value: Optional[Union[float, np.ndarray]], idx: int\n", - " ) -> List[Optional[Union[float, np.ndarray]]]:\n", - " if value is None:\n", - " return [None]\n", - " if isinstance(value[idx], float):\n", - " return value[idx]\n", - " if isinstance(value[idx], np.ndarray):\n", - " return value[idx].reshape(-1)\n", - " raise TypeError(f\"Unexpected value type. Value: {value}\")\n", - "\n", - " df_res_dict = {}\n", - "\n", - " for idx_fold in range(cv):\n", - " # Fill df_res_dict\n", - " for name, value in [(\"y_true\", y_true), (\"y_pred\", y_pred)]:\n", - " df_res_dict[f\"{name}_{idx_fold+1}\"] = _get_fold_value(value, idx_fold)\n", - " if regime != \"local\":\n", - " df_res_dict[f\"id_{idx_fold+1}\"] = _get_fold_value(ids, idx_fold)\n", - "\n", - " # Save datasets to specified directory\n", - " df_res = pd.DataFrame(df_res_dict)\n", - " return df_res" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def expand_val_with_train(train_data, val_data, id_column, date_column, history):\n", - " L_split_data = train_data[date_column].values[(len(train_data) - history)]\n", - " L_last_train_data = train_data[train_data[date_column] >= L_split_data]\n", - " val_data_expanded = pd.concat((L_last_train_data, val_data))\n", - " val_data_expanded = val_data_expanded.sort_values([id_column, date_column]).reset_index(\n", - " drop=True\n", - " )\n", - " return val_data_expanded\n", - "\n", - "\n", - "def expand_test_with_val_and_train(\n", - " train_data, val_data, test_data, id_column, date_column, history\n", - "):\n", - " unqiue_id_cnt = val_data[id_column].nunique()\n", - " L_split_data = val_data[date_column].values[\n", - " (\n", - " (len(val_data) - history)\n", - " if (len(val_data) // val_data[id_column].nunique() - history) > 0\n", - " else 0\n", - " )\n", - " ]\n", - " L_last_val_data = val_data[val_data[date_column] >= L_split_data]\n", - " if len(val_data) // unqiue_id_cnt - history < 0:\n", - " if (len(train_data) - (history - len(L_last_val_data) / unqiue_id_cnt)) > 0:\n", - " L_split_data = train_data[date_column].values[\n", - " (\n", - " len(train_data) // unqiue_id_cnt\n", - " - (history - len(L_last_val_data) // unqiue_id_cnt)\n", - " )\n", - " ]\n", - " else:\n", - " L_split_data = 0\n", - " L_last_train_data = train_data[train_data[date_column] >= L_split_data]\n", - " test_data_expanded = pd.concat((L_last_train_data, L_last_val_data, test_data))\n", - " else:\n", - " test_data_expanded = pd.concat((L_last_val_data, test_data))\n", - " test_data_expanded = test_data_expanded.sort_values([id_column, date_column]).reset_index(\n", - " drop=True\n", - " )\n", - " return test_data_expanded\n", - "\n", - "\n", - "def get_train_val_test_datasets(dataset_path, columns_params, train_size, test_size, history):\n", - " data = pd.read_csv(dataset_path)\n", - "\n", - " date_column = columns_params[\"date\"][\"columns\"][0]\n", - " id_column = columns_params[\"id\"][\"columns\"][0]\n", - "\n", - " if dataset_path.parts[-1] in [\"ETTh1.csv\", \"ETTh2.csv\", \"ETTm1.csv\", \"ETTm2.csv\"]:\n", - " train_val_split_data = \"2017-06-25 23:00:00\"\n", - " val_test_slit_data = \"2017-10-23 23:00:00\"\n", - " else:\n", - " train_val_split_data = data[date_column].values[\n", - " int(data[date_column].nunique() * train_size)\n", - " ]\n", - " val_test_slit_data = data[date_column].values[\n", - " int(data[date_column].nunique() * (1 - test_size))\n", - " ]\n", - "\n", - " train_data = data[data[date_column] <= train_val_split_data]\n", - " val_data = data[\n", - " (data[date_column] > train_val_split_data) & (data[date_column] <= val_test_slit_data)\n", - " ]\n", - " test_data = data[data[date_column] > val_test_slit_data]\n", - " val_data = expand_val_with_train(train_data, val_data, id_column, date_column, history)\n", - " test_data_expanded = expand_test_with_val_and_train(\n", - " train_data, val_data, test_data, id_column, date_column, history\n", - " )\n", - "\n", - " # train, val and test TSDataset initialization\n", - " train_dataset = TSDataset(\n", - " data=train_data,\n", - " columns_params=columns_params,\n", - " )\n", - "\n", - " val_dataset = TSDataset(\n", - " data=val_data,\n", - " columns_params=columns_params,\n", - " )\n", - "\n", - " test_dataset = TSDataset(\n", - " data=test_data_expanded,\n", - " columns_params=columns_params,\n", - " )\n", - "\n", - " return train_dataset, val_dataset, test_dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Initialize TSDataset, Pipeline, Model, Validator, Strategy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TSDataset" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "TRAIN_SIZE = 0.7\n", - "TEST_SIZE = 0.2\n", - "history = 7\n", - "\n", - "df_path = Path(\"datasets/global/simulated_data_to_check.csv\")\n", - "\n", - "columns_params = {\n", - " \"target\": {\n", - " \"columns\": [\"value\"],\n", - " \"type\": \"continious\",\n", - " },\n", - " \"date\": {\n", - " \"columns\": [\"date\"],\n", - " \"type\": \"datetime\",\n", - " },\n", - " \"id\": {\n", - " \"columns\": [\"id\"],\n", - " \"type\": \"categorical\",\n", - " }\n", - "}\n", - "\n", - "train_dataset, val_dataset, test_dataset = get_train_val_test_datasets(\n", - " df_path, columns_params, TRAIN_SIZE, TEST_SIZE, history\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "lag = LagTransformer(lags=7)\n", - "target_generator = TargetGenerator()\n", - "\n", - "union_1 = UnionTransformer(transformers_list=[lag, target_generator])\n", - "seq_1 = SequentialTransformer(transformers_list=[union_1], input_features=[\"value\"])\n", - "union = UnionTransformer(transformers_list=[seq_1])\n", - "\n", - "pipeline = Pipeline(union, multivariate=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Trainer" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Configure the model parameters\n", - "model = DLinear_NN\n", - "model_params = {\"moving_avg\": 7, \"individual\": False, \"enc_in\": None}\n", - "\n", - "validation = HoldOutValidator\n", - "validation_params = {\"validation_data\": val_dataset}\n", - "\n", - "trainer_params = {\n", - " \"device\": \"cpu\",\n", - " \"num_workers\": 0,\n", - " \"best_by_metric\": True,\n", - "}\n", - "\n", - "trainer = DLTrainer(\n", - " model, \n", - " model_params, \n", - " validation, \n", - " validation_params, \n", - " **trainer_params\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Strategy" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "horizon = 7\n", - "model_horizon = 7\n", - "history = 7\n", - "step = 1" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "strategy = MIMOStrategy(\n", - " pipeline=pipeline,\n", - " trainer=trainer,\n", - " horizon=horizon,\n", - " history=history,\n", - " step=step,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "length of train dataset: 688\n", - "length of val dataset: 94\n", - "Epoch 1/10, cost time: 0.77s\n", - "train loss: 274.1573\n", - "Validation, Loss: 30.1381, Metric: -30.1381\n", - "val loss: 30.1381\n", - "Model saved to checkpoints/fold_0/model_0.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_0.pth\n", - "Epoch 2/10, cost time: 0.72s\n", - "train loss: 32.8188\n", - "Validation, Loss: 14.3539, Metric: -14.3539\n", - "val loss: 14.3539\n", - "Model saved to checkpoints/fold_0/model_1.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_1.pth\n", - "Epoch 3/10, cost time: 0.72s\n", - "train loss: 12.8709\n", - "Validation, Loss: 10.0110, Metric: -10.0110\n", - "val loss: 10.0110\n", - "Model saved to checkpoints/fold_0/model_2.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_2.pth\n", - "Epoch 4/10, cost time: 0.73s\n", - "train loss: 10.2632\n", - "Validation, Loss: 8.9626, Metric: -8.9626\n", - "val loss: 8.9626\n", - "Model saved to checkpoints/fold_0/model_3.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_3.pth\n", - "Epoch 5/10, cost time: 0.72s\n", - "train loss: 9.9284\n", - "Validation, Loss: 8.8743, Metric: -8.8743\n", - "val loss: 8.8743\n", - "Model saved to checkpoints/fold_0/model_4.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_4.pth\n", - "Epoch 6/10, cost time: 0.72s\n", - "train loss: 9.8400\n", - "Validation, Loss: 8.8730, Metric: -8.8730\n", - "val loss: 8.8730\n", - "Removing worst model snapshot: from epoch 0\n", - "Model saved to checkpoints/fold_0/model_5.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_5.pth\n", - "Epoch 7/10, cost time: 0.72s\n", - "train loss: 9.7701\n", - "Validation, Loss: 8.8641, Metric: -8.8641\n", - "val loss: 8.8641\n", - "Removing worst model snapshot: from epoch 1\n", - "Model saved to checkpoints/fold_0/model_6.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_6.pth\n", - "Epoch 8/10, cost time: 0.72s\n", - "train loss: 9.7031\n", - "Validation, Loss: 8.6850, Metric: -8.6850\n", - "val loss: 8.6850\n", - "Removing worst model snapshot: from epoch 2\n", - "Model saved to checkpoints/fold_0/model_7.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_7.pth\n", - "Epoch 9/10, cost time: 0.75s\n", - "train loss: 9.6208\n", - "Validation, Loss: 8.6171, Metric: -8.6171\n", - "val loss: 8.6171\n", - "Removing worst model snapshot: from epoch 3\n", - "Model saved to checkpoints/fold_0/model_8.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_8.pth\n", - "Epoch 10/10, cost time: 0.83s\n", - "train loss: 9.5413\n", - "Validation, Loss: 8.5182, Metric: -8.5182\n", - "val loss: 8.5182\n", - "Removing worst model snapshot: from epoch 4\n", - "Model saved to checkpoints/fold_0/model_9.pth\n", - "Optimizer saved to checkpoints/fold_0/opt_9.pth\n", - "Fold 0. Score: -8.518207550048828\n", - "Mean score: -8.5182\n", - "Std: 0.0\n" - ] - }, - { - "data": { - "text/plain": [ - "(8.324044942855835, )" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy.fit(train_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "length of test dataset: 193\n", - "Validation, Loss: nan, Metric: nan\n" - ] - }, - { - "data": { - "text/plain": [ - "(0.6019949913024902,\n", - " id date value\n", - " 0 0 2022-03-12 1798.098267\n", - " 1 0 2022-03-13 1798.139038\n", - " 2 0 2022-03-14 1798.520996\n", - " 3 0 2022-03-15 1798.796387\n", - " 4 0 2022-03-16 1799.245605\n", - " ... .. ... ...\n", - " 13505 9 2022-09-22 10997.950195\n", - " 13506 9 2022-09-23 10999.390625\n", - " 13507 9 2022-09-24 11000.709961\n", - " 13508 9 2022-09-25 11002.036133\n", - " 13509 9 2022-09-26 11003.500977\n", - " \n", - " [13510 rows x 3 columns])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "strategy.predict(test_dataset, test_all=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Tutorial_2_Strategies.ipynb b/Tutorial_2_Strategies.ipynb new file mode 100644 index 0000000..43a1ebe --- /dev/null +++ b/Tutorial_2_Strategies.ipynb @@ -0,0 +1,944 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we will take a closer look at strategies in the context of both multi-step forecasting and simultaneous forecasting of multiple time series.\n", + "\n", + "We will explore how strategies affect the feature generation process used for model training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's import everything we need and define functions to obtain the results." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from copy import deepcopy\n", + "from typing import List, Optional, Union\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", + "from tsururu.model_training.trainer import DLTrainer, MLTrainer\n", + "from tsururu.model_training.validator import KFoldCrossValidator\n", + "from tsururu.models.boost import CatBoost\n", + "from tsururu.strategies import (\n", + " DirectStrategy,\n", + " FlatWideMIMOStrategy,\n", + " MIMOStrategy,\n", + " RecursiveStrategy,\n", + ")\n", + "from tsururu.strategies.base import Strategy\n", + "from tsururu.strategies.utils import timing_decorator\n", + "from tsururu.transformers import (\n", + " DateSeasonsGenerator,\n", + " DifferenceNormalizer,\n", + " LagTransformer,\n", + " LastKnownNormalizer,\n", + " SequentialTransformer,\n", + " StandardScalerTransformer,\n", + " TargetGenerator,\n", + " UnionTransformer,\n", + ")\n", + "\n", + "index_slicer = IndexSlicer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's transfer the necessary code into the notebook so that we can output the values of variables." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "class RecursiveStrategy(Strategy):\n", + " def __init__(\n", + " self,\n", + " horizon: int,\n", + " history: int,\n", + " trainer: Union[MLTrainer, DLTrainer],\n", + " pipeline: Pipeline,\n", + " step: int = 1,\n", + " model_horizon: int = 1,\n", + " reduced: bool = False,\n", + " ):\n", + " super().__init__(horizon, history, trainer, pipeline, step)\n", + " self.model_horizon = model_horizon\n", + " self.reduced = reduced\n", + " self.strategy_name = \"recursive\"\n", + "\n", + " @timing_decorator\n", + " def fit(\n", + " self,\n", + " dataset: TSDataset,\n", + " ) -> \"RecursiveStrategy\":\n", + " features_idx = index_slicer.create_idx_data(\n", + " dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)\n", + " data = self.pipeline.fit_transform(data, self.strategy_name)\n", + "\n", + " val_dataset = self.trainer.validation_params.get(\"validation_data\")\n", + "\n", + " if val_dataset:\n", + " val_features_idx = index_slicer.create_idx_data(\n", + " val_dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " val_target_idx = index_slicer.create_idx_target(\n", + " val_dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " val_data = self.pipeline.create_data_dict_for_pipeline(\n", + " val_dataset, val_features_idx, val_target_idx\n", + " )\n", + " val_data = self.pipeline.transform(val_data)\n", + " else:\n", + " val_data = None\n", + "\n", + " if isinstance(self.trainer, DLTrainer):\n", + " if self.strategy_name == \"FlatWideMIMOStrategy\":\n", + " self.trainer.horizon = 1\n", + " else:\n", + " self.trainer.horizon = self.model_horizon\n", + " self.trainer.history = self.history\n", + "\n", + " current_trainer = deepcopy(self.trainer)\n", + "\n", + " # In Recursive strategy, we train the individual model\n", + " if isinstance(current_trainer, DLTrainer):\n", + " checkpoint_path = current_trainer.checkpoint_path\n", + " pretrained_path = current_trainer.pretrained_path\n", + "\n", + " current_trainer.checkpoint_path /= \"trainer_0\"\n", + " if pretrained_path:\n", + " current_trainer.pretrained_path /= \"trainer_0\"\n", + "\n", + " current_trainer.fit(data, self.pipeline, val_data)\n", + "\n", + " if isinstance(current_trainer, DLTrainer):\n", + " current_trainer.checkpoint_path = checkpoint_path\n", + " current_trainer.pretrained_path = pretrained_path\n", + "\n", + " self.trainers.append(current_trainer)\n", + " return self\n", + "\n", + " def make_step(self, step: int, dataset: TSDataset) -> TSDataset:\n", + " test_idx = index_slicer.create_idx_test(\n", + " dataset.data,\n", + " self.horizon - step * self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " self.horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )[:, self.model_horizon * step : self.model_horizon * (step + 1)]\n", + "\n", + " data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx)\n", + " data = self.pipeline.transform(data)\n", + "\n", + " pred = self.trainers[0].predict(data, self.pipeline)\n", + " pred = self.pipeline.inverse_transform_y(pred)\n", + "\n", + " dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1)\n", + "\n", + " return dataset\n", + "\n", + " @timing_decorator\n", + " def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame:\n", + " new_data = dataset.make_padded_test(\n", + " self.horizon, self.history, test_all=test_all, step=self.step\n", + " )\n", + " new_dataset = TSDataset(new_data, dataset.columns_params, dataset.delta)\n", + "\n", + " if test_all:\n", + " new_dataset.data = new_dataset.data.sort_values(\n", + " [dataset.id_column, \"segment_col\", dataset.date_column]\n", + " )\n", + "\n", + " if self.reduced:\n", + " current_test_ids = index_slicer.create_idx_data(\n", + " new_dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " step=self.model_horizon,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " target_ids = index_slicer.create_idx_target(\n", + " new_dataset.data,\n", + " self.horizon,\n", + " self.history,\n", + " step=self.model_horizon,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " data = self.pipeline.create_data_dict_for_pipeline(\n", + " new_dataset, current_test_ids, target_ids\n", + " )\n", + " data = self.pipeline.transform(data)\n", + "\n", + " pred = self.trainers[0].predict(data, self.pipeline)\n", + " pred = self.pipeline.inverse_transform_y(pred)\n", + "\n", + " new_dataset.data.loc[target_ids.reshape(-1), dataset.target_column] = pred.reshape(-1)\n", + "\n", + " else:\n", + " for step in range(self.horizon // self.model_horizon):\n", + " new_dataset = self.make_step(step, new_dataset)\n", + "\n", + " # Get dataframe with predictions only\n", + " pred_df = self._make_preds_df(new_dataset, self.horizon, self.history)\n", + " return pred_df\n", + "\n", + "class MIMOStrategy(RecursiveStrategy):\n", + " def __init__(\n", + " self,\n", + " horizon: int,\n", + " history: int,\n", + " trainer: Union[MLTrainer, DLTrainer],\n", + " pipeline: Pipeline,\n", + " step: int = 1,\n", + " ):\n", + " super().__init__(horizon, history, trainer, pipeline, step, model_horizon=horizon)\n", + " self.strategy_name = \"MIMOStrategy\"\n", + "\n", + "class DirectStrategy(RecursiveStrategy):\n", + " def __init__(\n", + " self,\n", + " horizon: int,\n", + " history: int,\n", + " trainer: Union[MLTrainer, DLTrainer],\n", + " pipeline: Pipeline,\n", + " step: int = 1,\n", + " model_horizon: int = 1,\n", + " equal_train_size: bool = False,\n", + " ):\n", + " super().__init__(horizon, history, trainer, pipeline, step, model_horizon)\n", + " self.equal_train_size = equal_train_size\n", + " self.strategy_name = \"direct\"\n", + "\n", + " @timing_decorator\n", + " def fit(\n", + " self,\n", + " dataset: TSDataset,\n", + " ) -> \"DirectStrategy\":\n", + " self.trainers = []\n", + "\n", + " if self.equal_train_size:\n", + " features_idx = index_slicer.create_idx_data(\n", + " dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)\n", + " data = self.pipeline.fit_transform(data, self.strategy_name)\n", + "\n", + " val_dataset = self.trainer.validation_params.get(\"validation_data\")\n", + "\n", + " if val_dataset:\n", + " val_features_idx = index_slicer.create_idx_data(\n", + " val_dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=val_dataset.date_column,\n", + " delta=val_dataset.delta,\n", + " )\n", + "\n", + " val_target_idx = index_slicer.create_idx_target(\n", + " val_dataset.data,\n", + " self.model_horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=val_dataset.date_column,\n", + " delta=val_dataset.delta,\n", + " )\n", + "\n", + " val_data = self.pipeline.create_data_dict_for_pipeline(\n", + " val_dataset, val_features_idx, val_target_idx\n", + " )\n", + " val_data = self.pipeline.transform(val_data)\n", + " else:\n", + " val_data = None\n", + "\n", + " for model_i, horizon in enumerate(range(1, self.horizon // self.model_horizon + 1)):\n", + " target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " self.horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon]\n", + "\n", + " data[\"target_idx\"] = target_idx\n", + "\n", + " if val_dataset:\n", + " val_target_idx = index_slicer.create_idx_target(\n", + " val_dataset.data,\n", + " self.horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=val_dataset.date_column,\n", + " delta=val_dataset.delta,\n", + " )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon]\n", + "\n", + " val_data[\"target_idx\"] = val_target_idx\n", + "\n", + " if isinstance(self.trainer, DLTrainer):\n", + " self.trainer.horizon = self.model_horizon\n", + " self.trainer.history = self.history\n", + "\n", + " current_trainer = deepcopy(self.trainer)\n", + "\n", + " # In Direct strategy, we train several models, one for each model_horizon\n", + " if isinstance(current_trainer, DLTrainer):\n", + " checkpoint_path = current_trainer.checkpoint_path\n", + " pretrained_path = current_trainer.pretrained_path\n", + "\n", + " current_trainer.checkpoint_path /= f\"trainer_{model_i}\"\n", + " if pretrained_path:\n", + " current_trainer.pretrained_path /= f\"trainer_{model_i}\"\n", + "\n", + " current_trainer.fit(data, self.pipeline, val_data)\n", + "\n", + " if isinstance(current_trainer, DLTrainer):\n", + " current_trainer.checkpoint_path = checkpoint_path\n", + " current_trainer.pretrained_path = pretrained_path\n", + "\n", + " self.trainers.append(current_trainer)\n", + "\n", + " else:\n", + " for model_i, horizon in enumerate(range(1, self.horizon // self.model_horizon + 1)):\n", + " features_idx = index_slicer.create_idx_data(\n", + " dataset.data,\n", + " self.model_horizon * horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + "\n", + " target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " self.model_horizon * horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " n_last_horizon=self.model_horizon,\n", + " )\n", + "\n", + " data = self.pipeline.create_data_dict_for_pipeline(\n", + " dataset, features_idx, target_idx\n", + " )\n", + " data = self.pipeline.fit_transform(data, self.strategy_name)\n", + "\n", + " val_dataset = self.trainer.validation_params.get(\"validation_data\")\n", + "\n", + " if val_dataset:\n", + " val_features_idx = index_slicer.create_idx_data(\n", + " val_dataset.data,\n", + " self.model_horizon * horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=val_dataset.date_column,\n", + " delta=val_dataset.delta,\n", + " )\n", + "\n", + " val_target_idx = index_slicer.create_idx_target(\n", + " val_dataset.data,\n", + " self.model_horizon * horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=val_dataset.date_column,\n", + " delta=val_dataset.delta,\n", + " n_last_horizon=self.model_horizon,\n", + " )\n", + "\n", + " val_data = self.pipeline.create_data_dict_for_pipeline(\n", + " val_dataset, val_features_idx, val_target_idx\n", + " )\n", + " val_data = self.pipeline.transform(val_data)\n", + " else:\n", + " val_data = None\n", + "\n", + " if isinstance(self.trainer, DLTrainer):\n", + " self.trainer.horizon = self.model_horizon\n", + " self.trainer.history = self.history\n", + "\n", + " current_trainer = deepcopy(self.trainer)\n", + "\n", + " # In Direct strategy, we train several models, one for each model_horizon\n", + " if isinstance(current_trainer, DLTrainer):\n", + " checkpoint_path = current_trainer.checkpoint_path\n", + " pretrained_path = current_trainer.pretrained_path\n", + "\n", + " current_trainer.checkpoint_path /= f\"trainer_{model_i}\"\n", + " if pretrained_path:\n", + " current_trainer.pretrained_path /= f\"trainer_{model_i}\"\n", + "\n", + " current_trainer.fit(data, self.pipeline, val_data)\n", + "\n", + " if isinstance(current_trainer, DLTrainer):\n", + " current_trainer.checkpoint_path = checkpoint_path\n", + " current_trainer.pretrained_path = pretrained_path\n", + "\n", + " self.trainers.append(current_trainer)\n", + "\n", + " return self\n", + "\n", + " def make_step(self, step, dataset):\n", + " test_idx = index_slicer.create_idx_test(\n", + " dataset.data,\n", + " self.horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )\n", + " target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " self.horizon,\n", + " self.history,\n", + " self.step,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + " )[:, self.model_horizon * step : self.model_horizon * (step + 1)]\n", + "\n", + " data = self.pipeline.create_data_dict_for_pipeline(dataset, test_idx, target_idx)\n", + " data = self.pipeline.transform(data)\n", + "\n", + " pred = self.trainers[step].predict(data, self.pipeline)\n", + " pred = self.pipeline.inverse_transform_y(pred)\n", + "\n", + " dataset.data.loc[target_idx.reshape(-1), dataset.target_column] = pred.reshape(-1)\n", + "\n", + " return dataset\n", + "\n", + "class FlatWideMIMOStrategy(MIMOStrategy):\n", + " def __init__(\n", + " self,\n", + " horizon: int,\n", + " history: int,\n", + " trainer: Union[MLTrainer, DLTrainer],\n", + " pipeline: Pipeline,\n", + " step: int = 1,\n", + " ):\n", + " super().__init__(horizon, history, trainer, pipeline, step)\n", + " self.strategy_name = \"FlatWideMIMOStrategy\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make it easier to track and understand how features and targets are generated depending on the strategy, we will use a synthetic series with the following structure:\n", + "- 10 series, each with 1000 points.\n", + "- The value in the `value` column is represented as `{id - 1}{point}`, meaning it is a concatenation of the series ID and the time point number within that series. For example, the value `5234` corresponds to the 234th point in the series with `id=4`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " value date id\n", + "2000 3000 2020-01-01 2\n", + "2001 3001 2020-01-02 2\n", + "2002 3002 2020-01-03 2\n", + "2003 3003 2020-01-04 2\n", + "2004 3004 2020-01-05 2\n", + "2005 3005 2020-01-06 2\n", + "2006 3006 2020-01-07 2\n", + "2007 3007 2020-01-08 2\n", + "2008 3008 2020-01-09 2\n", + "2009 3009 2020-01-10 2" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv(\"./datasets/global/simulated_data_to_check.csv\")\n", + "display(df.iloc[:10])\n", + "display(df.iloc[2000:2010])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "horizon = 3\n", + "history = 7" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + } + ], + "source": [ + "dataset_params = {\n", + " \"target\": {\n", + " \"columns\": [\"value\"],\n", + " \"type\": \"continious\",\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"date\"],\n", + " \"type\": \"datetime\",\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"type\": \"categorical\",\n", + " }\n", + "}\n", + "\n", + "dataset = TSDataset(\n", + " data=df,\n", + " columns_params=dataset_params,\n", + " print_freq_period_info=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_easy_params = {\n", + " \"target_lags\": 7,\n", + " \"date_lags\": 3,\n", + "}\n", + "\n", + "# Configure the model parameters\n", + "model = CatBoost\n", + "model_params = {\n", + " \"loss_function\": \"MultiRMSE\",\n", + " \"early_stopping_rounds\": 100,\n", + " \"verbose\": 500,\n", + "}\n", + "\n", + "# Configure the validation parameters\n", + "validation = KFoldCrossValidator\n", + "validation_params = {\n", + " \"n_splits\": 2,\n", + "}\n", + "\n", + "trainer_params = {}\n", + "trainer = MLTrainer(\n", + " model,\n", + " model_params,\n", + " validation,\n", + " validation_params,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Description of Strategies" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Multi-series prediction strategies:__\n", + "\n", + "- **Local-modelling**:\n", + " - An individual model for each time series. \n", + " - Each time series as independent from others.\n", + "\n", + "- **Global-modelling**:\n", + " - A single model for all time series.\n", + " - Features created from each series do not overlap with other series. Series are related but modeled separately.\n", + "\n", + "- **Multivariate-modelling**:\n", + " - A single model for all time series. \n", + " - Features created from each series are concatenated at each time step. Try to capture dependencies between the series at the same time point." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Multi-point-ahead prediction strategies:**\n", + "\n", + "- **Recursive**:\n", + "\t- One model is used for the entire forecast horizon. \n", + "\t- training: The model is trained to predict one point ahead.\n", + "\t- prediction: The model iteratively predicts each point, using previous predictions to update the features in the test data.\n", + "\t- Note 1: There is an option to use a “reduced” version, where features are generated for all test observations at once, and unavailable values are filled with NaN.\n", + "\t- Note 2: Recursive can also be combined with the MIMO strategy, allowing the model to predict model_horizon points ahead at each step.\n", + "\n", + "- **Direct**:\n", + "\t- Individual models are trained for each point in the forecast horizon.\n", + "\t- Note 1: There is an option to use \"equal_train_size\" option, where all models can be trained on the same X_train set, formed for the last model predicting h point. Only the target variable (y) is updated for each model, reducing the time spent generating new training sets.\n", + "\t- Note 2: Direct can also be combined with MIMO, where each individual model predicts model_horizon points ahead.\n", + "\n", + "- **MIMO (Multi-input-multi-output)**:\n", + " \t- One model is trained and used for the entire forecast horizon at once. \n", + "\t- Note 1: This strategy can also accommodate exogenous features (for local- or global-modelling strategies).\n", + "\n", + "- **FlatWideMIMO**:\n", + "\t- A hybrid of Direct and MIMO. One model is trained, but Direct’s features are deployed across the forecast horizon.\n", + "\t- Note 1: To use FlatWideMIMO with date-related features, h lags of them must be included (with help of LagTransformer)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MIMO — global" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take the `MIMO-global` strategy as the base strategy, and from there, we will explore all the others." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline.easy_setup(dataset_params, pipeline_easy_params, multivariate=False)\n", + "\n", + "strategy = MIMOStrategy(\n", + " horizon=horizon,\n", + " history=history,\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py_3_10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Tutorial_3_Transformers_and_Pipeline.ipynb b/Tutorial_3_Transformers_and_Pipeline.ipynb new file mode 100644 index 0000000..244160f --- /dev/null +++ b/Tutorial_3_Transformers_and_Pipeline.ipynb @@ -0,0 +1,2665 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial, we will take a closer look at the transformers available in the tsururu library and delve into how to initialize the `Pipeline` class.\n", + "We will explore how transformers affect the feature generation process used for model training." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from copy import deepcopy\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from tsururu.dataset import IndexSlicer, Pipeline, TSDataset\n", + "from tsururu.transformers import (\n", + " DateSeasonsGenerator,\n", + " DifferenceNormalizer,\n", + " LabelEncodingTransformer,\n", + " LagTransformer,\n", + " LastKnownNormalizer,\n", + " MissingValuesImputer,\n", + " OneHotEncodingTransformer,\n", + " SequentialTransformer,\n", + " StandardScalerTransformer,\n", + " TargetGenerator,\n", + " TimeToNumGenerator,\n", + " UnionTransformer,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "index_slicer = IndexSlicer() # Class to create indices for the dataset\n", + "\n", + "horizon = 3\n", + "history = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Working with Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To illustrate, we will use a simple setup with two time series and progressively explore the available transformers, observing their impact on the data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " date value id\n", + "0 2013-01-01 48 0\n", + "1 2013-01-02 44 0\n", + "2 2013-01-03 60 0\n", + "3 2013-01-04 56 0\n", + "4 2013-01-05 50 0\n", + "... ... ... ..\n", + "3647 2017-12-27 14 1\n", + "3648 2017-12-28 19 1\n", + "3649 2017-12-29 15 1\n", + "3650 2017-12-30 27 1\n", + "3651 2017-12-31 23 1\n", + "\n", + "[3652 rows x 3 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(\"datasets/global/demand_forecasting_kernels.csv\")\n", + "df = df[(df[\"id\"] == 0) | (df[\"id\"] == 1)]\n", + "\n", + "dataset_params = {\n", + " \"target\": {\n", + " \"columns\": [\"value\"],\n", + " \"type\": \"continious\",\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"date\"],\n", + " \"type\": \"datetime\",\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"type\": \"categorical\",\n", + " }\n", + "}\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + } + ], + "source": [ + "dataset = TSDataset(\n", + " data=df,\n", + " columns_params=dataset_params,\n", + " print_freq_period_info=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transformers work with data presented in a dictionary format, which contains:\n", + "\n", + "**raw_ts_X** and **raw_ts_y**:\n", + "- These are the original \"long\" time series copied from the dataset. They contain all available data, including columns like `id`, `date`, `target`, as well as exogenous and generated features.\n", + "\n", + "**X** and **y**:\n", + "- These are arrays that hold the generated features (X) and targets (y). Initially, they are empty but get populated as transformers are applied.\n", + "\n", + "**id_column_name**, **date_column_name**, **target_column_name**:\n", + "- These are simply the column names.\n", + "\n", + "**idx_X** and **idx_y**:\n", + "- These are indices that point to the available data for generating features and targets at each time point.\n", + "- Indices are external to the pipeline and are set or managed by the strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "features_idx = index_slicer.create_idx_data(\n", + " dataset.data,\n", + " horizon,\n", + " history,\n", + " step=1,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + ")\n", + "\n", + "target_idx = index_slicer.create_idx_target(\n", + " dataset.data,\n", + " horizon,\n", + " history,\n", + " step=1,\n", + " date_column=dataset.date_column,\n", + " delta=dataset.delta,\n", + ")\n", + "\n", + "data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw_ts_X: " + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " date value id value__standard_scaler\n", + "0 2013-01-01 48.0 0 48.0\n", + "1 2013-01-02 44.0 0 44.0\n", + "2 2013-01-03 60.0 0 60.0\n", + "3 2013-01-04 56.0 0 56.0\n", + "4 2013-01-05 50.0 0 50.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)\n", + "\n", + "scaler = StandardScalerTransformer(\n", + " transform_features=True,\n", + " transform_target=False,\n", + " agg_by_id=True,\n", + ")\n", + "\n", + "print(\"raw_ts_X before scaling:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "print(\"raw_ts_y before scaling:\")\n", + "display(data[\"raw_ts_y\"].head())\n", + "\n", + "data = scaler.fit_transform(data, input_features=[dataset.target_column])\n", + "\n", + "print(\"raw_ts_X after scaling:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "print(\"raw_ts_y after scaling:\")\n", + "display(data[\"raw_ts_y\"].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XEoRVVbWSpNWbaiNuSbwsWb85a1m6kLnFlFctuTvNwXQKN1vJKLoeyN6jwfRWAAAAAAAANvwWcnW1ryRHmBIsI8iX8bP7E9IY4Em06ntB9hGrmgSZ+2+9wn7tVqOIrAtQ529JIWcQ4hol1dRnJwObj4uTmj4Z7zuHPc3N55D/Qub5Ezd+2lIoiUp68L4CAAAAAABBBd5KHERFChnkI6DoX5CH0MsoHyeFjJ0Ksj/ki4s3N5uxcsGyO4dWSQ/LRAjXhNixnJrMqpC5zbnzMurCbz9wlTRhpAcAAAAAAPGV5C/rKAwn06EVsh/RZ6NnVefC1fN9F/61bksYjBy/FQvPd6qHfDisEmqWtRiUPxFVyERVIUe+xVWtxbRvzUel0cMojqD5T5AEt60okfQAAAAAAABFz2oKGEfTwhR0pEeCI0wJZntHtpeRHkFObxXyUA/z5q2D8K23PwYVsLbbjvV0YlYjPVrvOYgrq5EezQnSJlM+JIjritHif89cFULPSHv43XNkYp/04M0NAAAAAAD8shrVUeKokDlxidbEHIfycu7Nzwnyjutcy4LQ3NZC1ocohKjfvnb7z1fQPv18JysV20mLOevprbb9nznSw1rYI0Ask2dunl8kH3mxT3oAAAAAAOBUxtQwxfLNHaFxEiosZNKDBEsAPBxC+4LDHrZle3d3MFNlBXtdy07wFFsXNL8cN6mBsI+D9bktfDvgnlUh8+0jPYJLepq34TuB6nED/vfr7/l+JCrpwRsdAAAAAAAExkEUtKA1PYh7RMIuIOg3CeXl2RlFhAPsEPnq11iOKmnF/THsl25ZtNxm3XxtKWhNj1bcJ5rVNmSP9GiWOYIn/8EK43habdPhwKJv1s0/WiUJEpX0AAAAAAAgl4yAYXTNQAw1NGXfneuopEcha3oUbldFy+/UMX4Lmftlbn+u6a3qG7P7s+t9WSRYiqUPNh8f73e4+z8S9Y1Nttuxqv3itM5H87adLHOqurbB83OLXcvjajXSo7Zh27k2Fzl3UtOjIWNkSPYTmgxDtQ2Nrq9rDY1NGdNwubmWZV4XnD3Hru+1fLphGIFcu5wg6QEAAAAAKBoZAcNiid7BgvuTW9+Y/ZxUzObCZ0q2eHFzd3RQnAQcX56yTPv+ZbjenbXS8XbzdfXt01slvw9+vmyj9v/rcD3w/peekzh+j0JVTb2OvnOU7n7vC8vHrfqWdTH57GVTl2zQfn8dnrHs1jdn66CBI7Rw7WbXbX149Jc6+KZ39fd35+ZdN/m9w51Hxnyl/f46XFOXbEgvsxrpcf3Ln+vsR8er//NT08vsEg3mxdOWbjStn73uBU9M1P5/HaG7Rsxz3ObahiYdf8849bltpOU+88kcq5L/ife+N0/7/3W4Zi+vyrvub579VH0HjdbmAiTZYp/0aG1vJgAAAAAAUBgl8cp5EAOJiF2iwW8CwPfzbXrEdS9NlyT9z7Apjrdl1dUzgptFVE/iljdnq8mQ7nvfOuFQCCNmrNT6zXX2K/io1/KX12ZmnJtUSnrm44WqbzT08OivXLZU6cTMI2Pmu35usfv7u/NkGNLA12emlzXYZEPNCQzJ/fsnqITjqqoaLVm/RZvrvI70cNeOB0d/pSZDGjR8Tt51R81drbXVtRo7b42rfXgR+6QHAAAAAACOZUxvldCIHQomX52DQktqkLlYRTHSwyzsRIR5W82vtRi6oPld7fV4eStiv/1Jueo+SNbTWwXRjrA/94phJJBfTg+BbY0WuxEg3pqTxSrB4SrpYf7ZRaPcjJysaBt+SiJRSQ/+YAUAAAAAOMZXSOQRs9mtCCgGwFOwOqOOxvafg6zp4amoeWB7z9/X0zU9iqwLFjKWaE6SmWs7WK/rrJC5VftbXicykrdFdv7iwnyMnV6n3Y/0cLe+HadTpzlph5sm5fs4bTI1orxNqYste5OopAcAAAAAALkQ74EbsavpEXUDWiknBYc9bdfLcwKcXivvvvLUQErqzcdRva0bTUHdfEkPp6N43I/0QNicHmO794/tCJCAzp7VdcPdtSR3cXWXjUn/aH5PlDPSAwAAAAAAbwj+IJ94pTy8BdljlreJnLeRHna/FF6+RETo+y2yC2chj2GTTYDXDyfNN18DGC0WIxGN9LDajteRHm7k+yyqqd8+5Vt5G5IeRTesDgAAAAAQHruCxCguQZ3buCUMvNzpSz/3L7RAsae6EME3Q7KuX2Nd0yP5HcquVk/Y7/fMkR65a3o45bZvhn32uN7I8UG2r+nhbrlbQdb0cMPq7WXe1lZT0qOkAB++sU96AAAAAABaN+5cRVhil/Sgq0ciroc97P6QL0mc2P4Y1fRWpgNWV8iRHub1k3rOYi7loWyKfcFyd8vdsk56OH++15tH8k0XaR7pUQiJSnrwxgUAAAAA5GJXkBiwYndHeFSIexRORkDSY+HesAV5DcuX4GsOlBZbHyzo9FYuano45TYZFfpIj1i9Q6LhuJC56+26b4sVqwSHm5tHgvw7ynzZqanf/p4oxPsyUUkPAAAAAEDr4/XLcbEF7xC8knjlPBAAT1OEhdCObdv1V5/Dzd3ZXmQGNzP/T7KMkQ8FfEUN5qRHfTBJDyfNj7ymRzF0Ghecj/RwudxTa6y27296qyZT13U10sOqLaafa0xTvhXifUnSAwAAAABQNPJ9QV+zqVYjZq5UQ2NAAakCaWhs0oiZK7S2ujbqpjiysrJG781amXHnc9jqGpo0fMYKbdhc5/xJNre/b61r1DszVmhTTX3eTTQ1GXpv1kqtqNzqfL922/IQsIzbFF1+BHkszQzD0MjZqzK2G/a8+l6Zd1/IALbVvlpZLNs38/VuxtcbA9nmpIXr8q6zudYcTEbYnL4tbWt62G43mLP31erqrGVNLv7k8ZqszVvIvM7UT03bdfN560YCkh68XQEAAACgNfP6rdDqeafc/4H+919T9OyExX6aVHBPfbRQ//uvqTrzoY+iboojP/jbaP122BS99tnXBdvnQ6O/1O+em6qfPzHB8XPsYjR//e9M/f65qer//Gd5t/HG9OX67bAp+t7g0Y73ayfqgHvU3vzc/7G0OoZvTF+uK/75qfoOst5urOoGhTTVllVAMqOQeVPz9FYxOhYBKOTLMdf0+GJVduDZi98Om6IVlTXp3w3DyLpL/ta3Zm//JYrTV0SJVzteXmIhi9Cb97VhS3bywFUhc88NyT5K5m2Zp3wz76L58/b3z031umNLCUh6AAAAAADgTL67pNd9Mwpg1JxVBWpRMN6dtVKSMoJfcdY8zctHX60t2D7fmL5ckrtgo92dqa9MXSZJ+uCLNXm38cGX29YJIrja2ufL/+jLbf0l6ED1xxb90LD9ObjRNl5eh98psfyKaqRJkOyLTocbnQ/rcH29YUvGPnLX9Aj3nFnuO5ndxDOnx9h+RIfNch8DUPP1Pc/ThHp7mqXGJvO1bfvPzZ+3H34Z7N8LiUp6JPRaCwAAAADwIamBtyC1ghtpfWto9BCsDmC/QRZDb+1dPYipurwcQr/HvUNZG38bsFXA6a0i3XtxCOt4pUxvDCPPflr7NSQ05nPguKiHuwf8JKzyPbPR40gPN39/WY4mM7XMPNqkEN00UUkPAAAAAAByMX9Bz/VdPWl1EJIaxwrrMFsdDy/1MEoC6AhB9qWknuckyrhWRDy6wk6wbck99YzVsjgdCzfMicio66IUWthNsNx+wj5P/XJ6iN1+Jvk5d/n25Wp6K3lLTuTrBoV+d5D0AAAAAADEWvRhJCRBo4ei6UEkLIKM98UhaBqlIEfNuOH3sNudN9/b9fd0d/symvdpN/FXcthPbxWuQrx9810jgpyezbFkdpPQ2SUabKe38rGvIKe3MjxeAqz6kV0CshDvldgnPVr55z0AAAAAwIVi/Q7Zym6k9cRT0iNmR7ZY+69ThRyBlVm7onD7zSeskRb5pp7J1xbEg6HciQ8v58zN244uIccHwe1Hkp+kd/73spuRHs63a2b1eWo/ii78nhT7pAcAAAAAoHXzXICT6Eyr0hDVSA+mtwpMIDU9YjS6wlN9kYyfI6jpYVgsS5iMkR4FfBGFGemR53EP20wlbb7HCJiPkONC5nYjPezWd9ekFvvK/bi7mh7hdGTzRzQjPVoo5MUeAAAAAJBsfIeMgQLG0poiSnoEqbVPb1XIDhPkoQ40UVLAKWCsChbHdQSMZ6bXEPb7PazPHDcB99BHehRFp/DH6SGwncbK5gEvdamcampyvq7Xa4DlaLKMJCqFzAEAAAAASCN5wZ24Tri5k7VZENNbBTlFVmvv6cF0c/dHMTPREJ+zEGQQ1MuhTeq1N6m1YZzuI19VD4TLbyFz2+f7OHX5+p6rQuYeR3vlu34z0qMF3qoAAAAAAKfCmg8/anEKxMZVMUxv5WW0CrwJ8q5jt3d0+91uUKw2XwzX0MxC5hFMERYwN9cYTyM93Gzf/eaLjtP3td3lPJRC5gGOAPI62ixfsrHQf8fEPukBAAAAAGjdPNf0CLYZsTH+q7X6/XNTtHpTTdRNiZWoprey2saWugb94d+f6Z0ZK1xtq1j7rFMlPs7H/e9/obtGzPX03JaB/lvfnK3Hx813/Pyt9Y2e9uuXm/779cat+t2/pmjK4vXpZeYg5O1vz9E1L07TTW/MND0eSDMjZX4Nz3y0MOvxmV9X6sKnJqr/81O1tc7febznvXm69715oQZ3n/5ooRas2Wz7uKeaHqZg9e+fm6Lx89d62Epx8/JZMe6LNZbL7frHPe/Nc7+Tb8xZsSnn47mSIkM/Xqi/vDZDdQ1NuvY/0/TK1GXpxxqbmnTdS9P1n8lLPbct3QaL6fTC1Cb0PQAAAAAAUCBJnY4lH/P0Vr98apKkbXN0/+PXfaJqUux4md6qJJBhGtnbeOrDhXpj+nK9MX25Fg0+3fmmirP7OuZ1WqJNNfW6//0vJUlH9NrB0XPs5pqfs6JKz3ycHRwvFDfz6bvp8mPnbQvADp+50rZPvvbZ1y3aUlwdctLC9VnLXpm6TB9/tU6SdMFRPfXDfbt53v5bn29Lcl72g708b8OK+X3x93dzB8Y9BZNNb7t3ZqzUOzPs+0iRdQlPwjoE//l0Wf6VbPzs8Qk5H891T8DNb86WJG2ta9SrLa4Bb0xfrsmLNujlKcv0s6N65m5E3umtqOlhqxgyzAAAAACAwsgV/IlqzvcgLa/cGnUT8irkcY5TzGBdda2n58XoJSSK3zv0zX2nqqbBZ2tM2/XZlrCTDvm2Hqf3lFf5XkJD4/Y1vEyRZ6W+MboD522kB9zw+74Io3c05um7Tmp6LNuQ/TfF2uo6x22w6kd20+VR0wMAAAAAABe8FuBEsgQ1NUYQBeKtNlFa4i3c0tprt3g9HbUNTemf/Y7eaWxqyr9SiNyM9AiynkyxyveeykgsBfT2i3KEjJdLSDAj3uBYFN3DwT6tukGDi+thvs/TzJEe4R+E2Cc9WvnnPQAAAAC0enwvtL6DkjCVf35qSOTSttTbhlt7V/d6Omobto/0cHJHc0vmZzQGmPOI4x3hGdsPcPqsOHGTzAzjNUZ53DyN9ODDJK+kHyK766I5KVhq8YHY4HPUkmHzcyE+7GKf9AAAAAAAwCk3d0knidVLKaKXF5lwKnpIJR6zKcXUZ73wOvKmpn57psLpMbSbasXNnc2hMN8NHXqHcDEKIkHMvSjvFF4ZPwfzeiNNeoS886T2iTiJ00AP86xYVpdfN1O15bt6GxkjPcJH0gMAAAAAEGvegyw5anok/bbNhIj7cQ5reqs2XpMeBBQ9MU9v5e0Ibn9WlPUYWoq6JcWQhHMzmiWo15u093HML9NFJ4ppDO12Wd+Ye2pAN9P95fs4LfTLJukBAAAAACgaxVoTgemtwhHMSI/srVhNE+JEQHWUE8t7TQ9TIXOH1wBzYNr8lGBrejhsi+3UM7mf57f/5k0I+Nx+VNz1I+t+4Efg72MXr8fLa4h6OrDWJopDaFfovMG03Crp0eCiM+frRU0hJBhzIekBAAAAAIg1r1+OCc4gn7BGongd6eElGkbyS6o1TW/lJeBsfoqbIF9QMkYbZD5S4JZkKoYkcr5RF+HU9IiwkLmHPuPmGpL8HuFNEKMC46jRNLLNspB5gCPfMhLNFDIvjgssAAAAAMA7N98KbYtmolXKF1MIa3qr0hJv4RYvgaBi6udWdxo7sbXefSHzzGmNtv/SGMH0Vna1iPLlX/z2Xzf1LpIk46i4md4qoP0HHcp0lZTwVMk85O0jcnafhfVN+aa3cjHSw+L55v0y0gMAAAAAAI/MX6Rr65tUXdvgehs19Y2xuwHPMraZgDtPw2qhk7Ozta4xf9A4gLZYbcNzTY8W7d1a12i9okN+n19oXs9HTUbSY/vyhsYmbdhcZ/kcuwSpl2uGnYYmI3PqrZZtMIys643dtFtRMAxDW+sKfz0Ms9+2fC3m491c3yBp7xszt6dqa12jo/ddcz8txB36zf0uToL8LNsSwWuz+yw0H2erhHGDm5oe2vYeqmuwfk5GcrkA55ikBwAAAAAg1rwG3M58+CMdfNO7+strMxw/Z/WmGh1w4whd9MwnnvaJTFHlZWZ+XakDB47Qn1/Nfe6DaJ/V3a1ea3qYe/odb8/WgQNH6NNF6z1ta/iMFTpw4Ag98cF8T89PkhrT9Fbm68VPHvlYR97xvj74Ym3O55svMY9/sCCwdl35/Gc6/JaRtsG9i575RAfcOEKrNtXatCvcAHO+zc9ZsUkHDhyh61/+PNR2mE1ZvEEHDhyhm9+Y5Xkb5vfkiFkrMx5rGfw1H4PfPzc1fe3ww+loI6fcXKfcJCUeeP9LHThwhKpq8if6mj8XC5H/+v1zU3XgwBFasm5L+DtrJez65A/vGpP+2WpUh9vZ/o69a4yOuuN9y8fMTbh0yGQdOHCE3py+3N0OXEhU0iPqDDcAAAAAIO6yvzg+N2mJ42e/NX2FJOnDL3MHSRFvD4z6UpL04qdLc65nVYRc8p8MaVPqtZD59v775IcLJUl/GzHX07au+c80SdKd73h7fhS8Hne74uOzllepscnQyqqarMcyRleEGHDaWt+oaUs3Wj7WfJ3572dfWz4edhgs3+t+bOy2hNlLU5aF3JLt7nlvniRp6PhFnrdh7kYDX89MnmSP9Mh0y5veky3N3EwJFDQ3Xfm+979wte0Pv1xr2SeDfrXDZ25LVD33yeKAt9x6OUnE+b0M1jY2aUVljSq31jtuw9UvTvO30xwSlfQAAAAAALQ+hQwfeQ1WI16iDDpKmSM93LTFKuhU0bY0iCYlgtcaFXY1Mbw8Pwy5prjKJeqbf+M2zV8Qco30CGsfhRT6rouwT7QGDQ46Zdifm4XuOrFPevBWAgAAAAA45fdLtddpiRAvfoM3Vr3ArmtYxenNNT3czIlupVUlPQLYhuNC5ra/BK/WZo779O4zRp2YlofcsGItZJ5Ly2OaNfIjgBcddLLIbkSa9c4D3bWjzbeGT80ElNDKqaHRQdLDZ7/Nd4iCnvYtn9gnPQAAAAAArVshvyd7LUANa66CdQFymvRwE8hyMwqhtGR7uMVJsCmX8ja5QzdF1WM9vhjzNSJfYM0qIB3+SI/cSQ9zd81oXuQjPaLdv1e53qotX1PLlxjES452pEfh79ZPaDdpVeob8yffoxjZFeY+SXoAAAAAAIqG36/P5mB1nFglD4oq2O1SvjiJ75EeFlFTu+NtdW4yRnr4THq0rpEe/nt1vr7R3DUKGd+rrc89vVXm6A7rn634PVr5jkHYAXTLfQayS/sjk5X0yBrp4b8BQU8T5OY8hN2vi3HKs9bASZ8MI1mXLyEdZm+K519zNqK42AIAAAAAIsZID7jkN+hoFdizu3vcanlJoNNbJSp0E4mM6aHyrGsZeAs5kJt/pIf1/kMPYOc5WkmNb+ca6dHyWIcz0iPCQuaR7Rlx5qSmh99+m3e6PGp6AAAAAADgDTU9IHlLNOSbvcpueqt8PcZJsCmXijbeRnokMWAdxLz5+RIY6cCezeiKMOSt6WG7PNqTmMAulFdWkiPku9sLvb2wE3jF2CeciGqqxkJq8jvUw+Eou4ynhNihYp/0SOKHNAAAAAAgOGEE/uwC2OaRHkzj4V9UxV/DmN7KTT7M3HeczKWeS+ua3sobN1NCWeXDwn6r1zbknt7KrgGhX4LyTW9VhNfAvCM9AnjJfgtCt+Rma6F3meLrEvhG0P22pUIncWOf9AAAAAAAwCm/X6pLM6YlIrqTVF7OXb6Au92dvvkSO34TMK1peqsgkmT5pmhpftx8rQg7GFdb73ykhznRkO+1hJ1UTGqAO9dhMVqcijASO4FPb+VqpEewu87afribR4R85udjN11e6/nkBAAAAAAkUiG/KLcp3R4uC7oYLcLx8pRlqtxSn7HM6bmz61tWQVP7mh65I8/1BShkPndlld6dtTL9u2EYGVMqjZqzSjO/rvTVjkLwOoVMRrHcPIG7z5dlH4fwR3p4q+mxbMNW3fzGLI2dt9rTfqtq6nM+nncOfk97jU5Tk6HXp32tJeu32K7TMjAbRk2Pd2euzL9SSKYt3agtdQ2Wj1XXNujlKcu0ZN0WvTxlmaftJ2H0z4bNdXp5yjLb4wBrc1ZUOV63pr5Rr0xZptWbatLL3p+TfZ0yv998T5/lUpuC7s2nBLyvAAAAAAAR8l/TY/u9gfWNTfGZWsgiFhzV1FFxc91L09V3750yljlNeowwJQryJS/cHG5zP/RbyLysTf77VU+5/0NJ0qu//56+02tHjWkRJL/82U8lSYsGn+6rLWErRE2PXzw5UYsGn17QGFNd3qTH9p/N7frrf2dKkoaOX+Rpv9e8MM3T87a3JVmBuJenLNP1r3yec52sS0MI81s9/sEC39swczsS6dY3Z2vwuYdmLb/hlc/19ucrgmpWbF0y5BNNX1apCfPX6Z6fHRbMRvm8zXDfyC/0+AcLtMeO7dLL8l3nCn01YaQHAAAAACDWCvlF2VzTo8HnHfoonAkL1mX87r+mh9Uy54XMM6ZO8tmNSlxkAuau2CRJmrY0/qM6rBSipke+54chqmlfRs3NPUIk336TdgWc2OI6YKVlIiffyI8kenP6csvlQSQ8kpAHm/7NaK63Prc+DvBv+DejmZZt2JpzvYxReAXuPLFPehS6yAkAAAAAILn8fqc2x5ep6eFfWKNR8t2B7qUga96aHnYrhF1XIdzNx0sAHcZpYM2w/aXwzP25kHGwuM3B75eT5ra8rCftNToRZkFq4rSFcWCPzlE3IScvNxZQ0wMAAAAAAJOCTrES4LREiE4Yo3Tscx7Zj5i7bDEGVcPieaSHOWng4XiHHcjNV6vE69691kBxKorprfycCyftzRrZkYAkiNs2hVmPqpDHJ+z+HWclMX/p3pIejPQAAAAAAMATv8FL87OZ3iq5vARk8g0yyFfzwywj6eE3oB7CqJVi5vRo+U2UBCmq2hnFNr2VE1lJjqzpreL3qt22KMxRioU8On7PRZAjDQt9TY17zS4vo4mo6ZFD/C47AAAAAICwRfVdkOmtksvvubO6w9juzlur4FRGjYkIulHcA2Z2ClHI3PI5/nfri10h88jFqS0OOGluEkZ2tOS2T4f5mpJwvIqBm1pOUfByYwE1PQAAAAAAsBHUHPR24QTz8xsa4zO9lVV74x0SaRZNKxs9TE2WbyoVJyM9orpjv1iYz4HXY+k0FpeZmAp5eqs8XSdzZFDh5NtX0nqzk9PYMvDa8ilxfAvHKf9eyJEwcZreqtA5iPi8cmtO/z6K8gaA+Cc9YvTGBgAAAAAUXmFLemzfWT3TWyWW7zntLSJOTuqYN/fVIIPorakXmgOLbg5hZg0VL3cgu35KoDwHkn1GRvMdq6Ql8RyN9Gj5e4sFUfcFK3FqUlSfx61OzEd6eHmfFPq9Ff+kBwAAAAAA3yhkwCXMYrAIl6dzl7emR/7lzXeRR3V3a9KDhOZD7HUqFMcjPWJ0qPwmbcISn5Y44+TYNWV1kJaFzeP3quPYJoQr3ikPqcHDaEoKmQMAAAAAYFLIQK75O3m9hy/1iIcw6rHYTW+VMSWTxeN++2+QcaK4B08zRnq4eJ75GMfxNeYLYEZWyDzf4/E7lL7lq+kRx9ccxzYVgt/prYKcHqvQU23FfKCHvPx5RCHzHOL4wQUAAAAAbn3wwQc688wztdtuuymVSum///1v1E2Kja11jbrin5/qyQ8W6LKhk/XOjBUZjwcVpBv3xRr95tnJ2lRTb7tOQ57prZZv3KpLhnyiD75Y42yn8MUwDP3ff6brsbHz867rZaRHXUOTRs5eZft4StLI2at06ZBP9PRHC/XTRz7W2HmrLUd6mDvq/e9/qQGvfl6QmEbzLuwCdHEfvGROLJlHejz14QL94d+fOTqvVTUNedfZ/6/DNWziYm+NDMBrny3Tb56dnP7d/Krmr9nsaZuPjv1K//ef6YH2s631jYFtqxDe+nxF3nVaJiGzx33E703iddTTOzNW6LKhk7VxS51ufXN2IG3J1b8eHv2lrn852D4YFxMWrEsfy0KIeyFzNyM9Ji9ar4ue+UQL1lTnXfeqf3+mZz5aaPv47W8578dtHK8JAAAAAAjE5s2bddhhh+myyy7TOeecE3VzYmXo+EUaOXtVOvg8eu5qffLnE0LZ1/tzVuvJDxbo2pP3Ty8zh2ryBVhveOVzffjlWo2dt0aLBp8eShuTLsi4zcQF6/XK1GWSpM4VucMZjR6Dblf881Pbc5lKbXtcksbM25bouumNWTrrsN3S62zPeWzf/+i5qyVJl3xvL+3fvZOndrlld9ybDEOlsZ84ZRvzKbz97TmSpNMO6a5TDu6Rc10nahua9OCoL/00z5drXpye8bvXGLH5TN41Yp4k6edH9dR39+rqbANFFJteuNZZsqjlZb1lgD6OiUGv/eP3z02VJP2/V2ZoxKyVobWl+Rje/d4XkqRfHt1bh/fcIZD9xcnouav14KivCrKvuF+lnU8jaOj8f0xwvN03py/Xm9OX67If7JX12OJ1m/XC5KWOtxX7pEcMrzUAAAAA4Mupp56qU089NepmxNK66tqcj+cL/ri9S3dTbeZd4eYAWL5tra7K3dYgWQWx7aZbKlZb67efq7Cn5bE6slZ33n69YatNIXOLNnmMcLi5azpfl/B6x3ih5CtkvmGL/cisJAvyvGypyz/SJc68Hoq6Bmd3nrd8P7XcXRzfI37btGT9loBa4ixOW5OwEUJuVOUYHRqkOI70MAwj0r876hvdzakV+6QHAAAAALR2tbW1qq3dHmCvqqqKsDXhsqrFEGYIqrxNqefnxnEaFITHNtSTMtf0sO8TfvqaU/lio0kqU2N1LGttgqlxfyfma19UowuK6RrmNBabPdKjxQrFc0jSahqCS0JYXWNaBsLjF64PTnmbAlWKiOFBNAz3ozeDfTu523mianoAAAAAQGs0aNAgdenSJf2vZ8+eUTcpNPmmlMoXpHN7Q2zLAIZh+wtaE6cjawxlhmGau69V12lT4i2KFWQ3jONd7GbmWiRWl4Jah3fzx03+wx7ceXFVAD7e3cEV5++u1jfSo7Y+uPeN1Wdwy9EzcRmFGEYzCpG8lmKZ84j8TyK3H6GJSnpEfXABAAAAIAoDBgxQZWVl+t/Spc7nNE4ay5EeIX4ZLG/b4mtxTL94WhWmjmNQpKUktNEpJwG05uCf9bz3ATfIgzgGdM0yp7eyGOlhk/SI+cvKm6xN0gicuPI+0qN4a3o0CzJZGPf3Wtiy/mYISUzyRhmiLlDvdsovprcCAAAAgJgrLy9XeXl51M0oiMY80b/8NT3cyXXXZpxiO5Z310bQjtbMLt5iXp4rYFrIhINdaCjuwXWrUTNmtQFO01NIQdciauY3MNoaA9hhnYsw+W1RkO+b+B2dwqoo0EiPWNb0iHj/bg9J7Ed6tMYLMAAAAAC0VtY1PZx/MXR7J2JZ1vRWfAlNhAhOk9Vomyzp6a2CS1Ll69JWU8nYBYeSNNLD6oDZTdOT9PdtzE9LQjiLiLZ8D7Q89nE8F37vsA9yeiu7A2RuY1Dx+hjG/Vv5SI9o9+/oM9jE1ZkaNGiQjjrqKHXq1Em77LKLfvrTn2revHkZ69TU1Kh///7aaaed1LFjR5177rlatWqVq0YBAAAAQDGrrq7WtGnTNG3aNEnSwoULNW3aNC1ZsiTahsVAQ2P2t2qrZUHJqulhWP8ctaROb5VUlsfbbqRHRh0K++mtCpFwyF8wO0ad2oLVsTRL6vRW+Y57oFMqudhWzA+bK86nt2pZ0yN3EiQO/DaprjHImh7WwpgWLI7nomCFzGPI0+dHgOcw1JEe48aNU//+/TVx4kSNHDlS9fX1Ovnkk7V58+b0Otdcc43efPNNvfTSSxo3bpyWL1+uc845x12rAAAAAKCIffrppzriiCN0xBFHSJKuvfZaHXHEERo4cGDELYueVSFz89Qc+e54dfv9uqzU/mtx0u8ej4M43q3qldV0I4ZhtJje6pukh8XzvQbwguyFjXGMIppYHUuzuiItZF7oa03Uc/OHwemlJt/IjjgmBuPUJLu2mI9bXC77YbSjcIXM43IU46PEZSVzVzU9RowYkfH70KFDtcsuu2jKlCk69thjVVlZqaefflrPP/+8jj/+eEnSkCFDdOCBB2rixIk65phjXDWupTi9yQEAAADAq+OOO64og07NNm6p0w7tyzw9t8Gi6MDWusxlLbdf39ikmvpGdapoa7tdwzBUubU+a3lVTb0amwyVfvNl2s1p2bgle3vIZBe4aT4fXvtJ2LbWZ8+BbxduMS+vaWjSisqtlus1v+f9vD/8StJlx6qpNd8kQA3D0LINW7X7Du1cB8KisNHi2mNW73E0W/P7y/x5UtvQqC11DWpfZh/yq6ppUJd2bUP9HKr55j1U0bZAQWKHGVbzS964pS7rPbG5tiHAVgWjPsCRGn7V2xQG2rC5Lv1zlNNb+enTTq7NNRafDa1FxAM9XKeBfI3JqayslCR17dpVkjRlyhTV19frxBNPTK9zwAEHqFevXpowYYKfXQEAAAAAEuDVqct0+K0jde/ILzw932qkx5a67UGoIR8v0uG3jtQzHy1ML+t33wc65Ob3tK661vZL+R1vz9Hht47MWj7w9Vm64Int31fNT8/1Bf+xsfO1elOt/QoBswr+JHkUxa1vzdbht47Uu7NWRt2ULF+trrZ+wOJ4G8rsM2c8+KH6Dhqt9yxeV5MhvfDJEh1+60g9PPrLQNrqVhzvYjczN89yeqtvahM8OOor/fCuMbrmP9MK1DJ/3py+3Nfj+ZgP1f/+a6oOGvhuzuLVh93yzfXS117tNTQ26eCb3tVBA0eowWXA3mubHI/0+GYP//7mvThhwbqMx6tq4pf0WLbBOpEapndmrLBc/vi4BVnLXpy8VN+9c5RpSTAfTl4uV7/711RP+3rts2/+dnlvXs71bn1rtqftF4OoR7+63bvnpEdTU5Ouvvpqff/739fBBx8sSVq5cqXKysq0ww47ZKy76667auVK6z9kamtrVVVVlfHPLOoDCgAAAABw7s+vzZAkPTjKW1DXqpC5ORGy7pu7Sc2BhwVrt025/NFXa223+5QpSdLS5EUbXLfzbyPmun6OH1bBn5jHr3Ma8vEiSdLfhhf2ODrxn0+XWi63mt5KyjwPG74Z/TOxRSBV2hbf+H+vbnt/3P2e86SgqzuXv1nX7q53q6RinGTEgHLURRkyftv7+fVp25IFxTxyzgmrV7+ysibnc8bPz+6jQdm4tV4NTYaajO3vibhofgsM+Oa9CGu3vDnLcvlOHbJHQsQpUTTCYyL9xv9ue70Pjv4qyOZ4FsebGqK+zLq9zntOevTv318zZ87UCy+84HUTkrYVR+/SpUv6X8+ePX1tDwAAAACQXFZFy53WIdgW6PX3rdz8pbp1h1ELJIaBnQqbQrV2TbW6WdNqWi+bWWEKKuqglRtW+ZnmRU0xT94UmtWoGLskXbPyNiWJ6g9B8ZIgq2jb+opXl9lcBwvZZQoZ+I/qo6hdgaZ/C0LSLhee3rVXXnml3nrrLY0ZM0Z77LFHenn37t1VV1enjRs3Zqy/atUqde/e3XJbAwYMUGVlZfrf0qXWd1QAAAAAAIqfVU2PQt6dnjm9VXy+4id1eqt8bYzjSyi3CULZvRan3SQOM1kkaXqrXMer5UiWmL+s0Fm9/tI8tU7sgtpJ5bQLePk4KU3CxTZgbUtskh4O3myJPFwRtTlJxyrqv4nc7t7VFc4wDF155ZV67bXXNHr0aO21114Zj/fp00dt27bVqFHb53GbN2+elixZor59+1pus7y8XJ07d874l6MFbpoLAAAAAEgYqwSH00BtSgQ/W4swT3O5TTDY6s55DzNPRSr+01ttF/OmxkJzl7S6RuZLepS3KVUh4myFCuo6D8i6f835Rs3YaZPnHITJb4C6bWn0Iz0Kyes59itBOQ9P5z7KREkbNyv3799fzz//vF5//XV16tQpXaejS5cuateunbp06aLLL79c1157rbp27arOnTvrqquuUt++fXXMMceE8gIAAAAAAPHh9/utVU0Pp9MCBRGzyLzTHGGzqz0RJbukhx3LfmJV9DwGHSpJiYRcwbKW3SZBL6tg8r2zykKc3sq83UK9w8Mc6eH1MlVakrL8TCuEeoupIt1o2yZ/DSM7cbmqu/l8ieqjyK6NcfxsjPozzO3+XSU9HnvsMUnScccdl7F8yJAhuuSSSyRJ9913n0pKSnTuueeqtrZW/fr106OPPuquVSZRH1AAAAAAQOH4qumhVADBz3h+CbWc3io2oaVMbu7sjOMrsJveypbD1+t1ailXo0nybiue/btZRtIxR1Pj2G+iZNW38p3pMGOqUUzl5niaOQ9NK/E4YqNNSUq1np7pX01Do6/nt/E1vVXy3qFRtThRhyreHx9ZXCU9nHTsiooKPfLII3rkkUc8NwoAAAAA0DpZj/RwWsg84MYk7At+XERxl3eQghjpYfW649CdnCYQo2IOllsG8r9ZRE2PTFaXyHxJNsMoTGqicAHw8JKPnqe3Ki2R5C/54FVNnb/9ti31PtIjLslVN2ctqkSNXd+K42enlytGkD3B7f6Lq2oRAAAAACDRGq0KmRewpkdMYjWJ5uYQxvEuV9dJD4sXbPW68gVb7ZJ7XgJNdsfV6VRxcdB8uKwCqDHsNpFoPg5WxyjftSzUWZciuI46vXZ7S3q4fook+8RBIdTU+3uz+6npEZePUTftiKr8Shw/A+1E/fdRqIXMoxb1wQUAAAAAhMtqpIfT4suB1PTI+JkvoV64m94qjhEfd21y2k/yHZYgR2HYHVevU2wVirl5zW11klRq7e9Vq0tkviPS1GQUJM7m+h3usU2On+ZleiuPHy75ismHye/0VrZJDwedJuaXGRvxKmQex2RI1KfV7f4TlfQAAAAAAMSb3y/FVgkON4Far9NqvDNjhYZNWKQNW+pM29r++LyVm/TImK+0onKrp+2bVW6t17AJi7SuOqrZ3sPV8gyMmbtaH365xvHzl67foucmLVZtjqBdmNOnbKqpt1w+d+Umm7ZkL7O6y3r60o0592uX3HPzUj9ZuF7DZ6ywfTyuSY+PvlyrUXNWZSwzJM1eXqWXpiy1eMb2iODkRes1YubKcBsYU5vrGjVm3mrLC+8/JyzK+dyHx3ylxes3h9KuQvaymvpGPfPRQo2eu9rR+l5GuHhNekRUw1yStNXn9Farqmoslzsa6RHydWbk7FUa+vHCnJ8R0rarxPSlG/X6tK8zllfVbPsMXrNp+2ew+RQ/Nna+pizeoNnLq4JstnUb45jdsBGXacucclXTIwrJOpwAAAAAAD8sC5k7nqXDe/Dg989Nzfn4ZUMn6+uNWzVt6UY9edGRnvcjSde+OE2j5q7WK1O/1n/7f9/XtuLIHBepqmnQpUMnS5K+uP1UlbWYOsoq3vPju8eqocnQmk21uvrE/az3EVhrs/35tRmhbPfWt2bnfDyIhMRbn6/QW5+v0Pl99rDZh+9dBK6+sUm/enqSJOm3x+6dXt7UZOi0Bz9ssXb2Czj/HxPCbF7sXTpksj68/sdZyx8ftyDn8z78cq0+/HJtWM1KCzumO2LmyrzvLbMgp4vLxxxUL7Saen9JDzdJ3qx1fO05t8qt9brin59KknbtXKFTD+mRc/2fPPKxJGmPHdurT+8dJUk3vPy5hs9cqec/Warhf/yhpMzprf42Yq7alKQsR54GzXakR+h7ds/L0fD6sWYYhkXtJmp6AAAAAAASyurOTTeFzAMtmmna2Ncbt43wmLp4g+/tjvrmjuRpee78N7OcriiOURFlBhWrtm4fNeF0mrLmQNP4+evs9xFiLKreIvGWS1BNcXp8nFi8bovl8jiO9NhiuiN9S11D+udc030l6ObogthU05B/pQIqZDer3Go9MstOIUd6SNITv+6jY/bu6vn5XtU0hFPAx8k1JMzzb/5McdPvF67dPqppxKxtI8PmrNg+kqPlOS5EwkNK1rWskO9rq30xvRUAAAAAILGspgXKFfw03/lXiNhBkgIUUbGqyyC5P3a57uqMUw2HoAJBdkXGvWzebtoXpwnEQjK31VxHwGrUF6zF6f0Qd4Weoufkb3fXH0+wHrEWJr/TW9lxcvjCTK5uNY1g8drvLesEeW2Qb9Z79jvt1eU/2MvX860U8jpj1YeKu5B51A0AAAAAAOTm84ub1ZQcuQIo5hhuKpUK9E5E602R9XAj3+nIdQd1rvh8nAYsBBUICrKQeZ1NwiCGOQ/VmhKd5kNQn2NeO96FmeL0fpAKGxx1O0IqbseqpaCKn+erd+FV1CM9zH8jBLmfqGprhFXrPpTtFnKkRwDbSFTSAwAAAABQ3KymlMh1d3oTIz1iJ3Okx/afrY5druMZx6mYLAXUTK+FzK0OYZ3dSI8YHlO74GyuYDbvw0wxPK1pYbfNbZ/2kpDxOzrETX8tDahz+63pYcdRIfMQo+Pm0aB59+LiUEZ1TQlrvyUhZD0KeZmxfssVWU2PpFWGBwAAAAAEK1fw02gRVA8y2GL1fZRYa37mc2A+hm6/3ucc6eG2USEKqi1eExJWz6q1mc8/jkkPcxDT3D6r2irND1vWuGnFWvP0Vq6vK+GUughMSUCRWqupIoOQ1JEe+a4YkSU97Ka38rldP3Vo7DQfbzexeq/XplY3vRUAAAAAoPXJNbV/mEFcqy3H6Q7zGDUlg6tAVK6RHjmTXcUX5A2ykLlt0iOGAV/zHP3m93NDHBsbU3F+O4TdNPcjPdzz+xrcXKvbBJT12BrWSA8HByOo/mh13IKo6WG9L6a3yqf5eEc1TSKFzAEAAAAARSV38Hv7z6mUQo+wRRUYiVOyJR/zKcg/NVOumh7OznvUgkrA2E5v5aFT19oEPOM50sP6zm2rqe6ahRUoTKq4nVc/zXHb393WwvFyrAp5eIPq22FNb+VEUMkIq62EVdMjqmuKXS0Rv5/5YY70CDJBn29f+ZblkqikR8yu4QAAAACAFsKYZiVXUCuzpkewX/Ktdpuk5ENUMqa08tEfEjO9Vcg1PbxI6vRW5tY15BjiFVXR4biKY4H6ZmGPynK7eS/t8fu55qa/BlXIPKzprZwIsz/Wuqjp4eZIFts1JZSkxzf/u/kc8fr2D+KzKlFJDwAAAABA65Pry2/GY6nwg+HFFRYJh91ID7dJpFzByThNbxVUS+ySe54KmTcmJ+mRMV2NeXori9cQv9bHRbyOTCFbk2skoBUvb4FCvm1KA5reKtKRHgEdMKtrW02D6XUFeGKi+my3/wz016JwRnpsO94FGelhuazYCplH3QAAAAAAgK23Pl+unz8+Qauranxtp76xSRc/84nlY3eNmGe5vM9tI3X4rSPTv186ZLIufGqSr3ZksihkHtLdoHNWVOn8f4zXpAXrQtl+oWzcUqdfPjkx/fv4+blfT66jGccAvZWgmuk2eJuLXZuiKpOxZlOtfv74BL0+7eusx9xMb1Xf2KTLhk7W1xu3htLOpIrzWyXsprmf3iqkhuTg5mOjNKBI7dDxi4LZkAcDX5+lAa9+nne9O96erWtenKaqmnpd+NREvfDJkozHzadq6fot+tnjE/TW9BWWjzuxeN1m/ewfE6wfjCjrEUZyQgquH5k1v9UmLQz/7xSrxJnb61ybgNoCAAAAAGiFrnz+M0nSHe/M0QMXHOF5OyNnr9K4L9a4es66zXWe9+dEIQOJlw6ZrJVVNfr5ExO1aPDphdtxwB4a/ZVmfl1l+ZjbuzQTM71VQK0pRDA2qkTSoHfmaNLC9Zq0cL1+cvjuGY/VmabiylfI/MMv14bXyASL2/RWhRyJ5fa1u02SSIW93pQWwTRLS9Zv0ZJPtuiak/bTLp0qbNd78sOFkqQtdQ36+Kt1+virdbrgu70s173hlc/1ycL1Gcvcnso/vfy5Plm03vKxsJIP+djt1m9zwpyu67Khnzpe1/v0Vv63FfuRHgAAAACA+Nu4pV6S9y+4uYoWx0lAM49kWbXJ/UiZOMbGqrbWu3tCjheRlELmQQmjHk72PqKxYYt9gtJ8ns2XgfocNT2QKU7TvUn5p7ULdl8uC597GO7k9/i6uVS3CeMW/YjkOmzmkW0btlh/bpiP23qLmxzcnpeNOa5DkU1v5fF5N5xyQM7H3SRxOlc4GxPhbWo4j++dAK4biXonxe0iDgAAAADYxm8AvrxN/L6eWn0DDbpYemuX62gmJQQQVDuT8nq9yPXSzPPDm49BIeaNLxat+Ui57Sdekml+35tuPh/bBFDIvCwmiZNcr8Q84sbJS7Y6B3kLmbc48LnqpUR1E4HdiIx8zcnXT9x0o1KHK3tJzHu9jFvtq+hqerTqKzcAAAAAtBJxTHpYiePoiqRwGzhMSk2PoNi93CBvAI3qkOYKfGUmPXIXMoe1uL1VMkZ6hBzYcz29lYcobEGntwog6RHWiETXcrwU83nwOrWUm36fSuWucxHZ9FYen1eSp5+46UdOp8Lycp3x+jnO9FYAAAAAgKJQ0bY06iZksfqCHVZYxEtcII6jTtzGjXKtn5SkR1BJiUJMbxXVnaW5jlHm9Fbmmh7JOP9xELeZUfz0Zbcvxe1r95JM8398nV8Yg0h6xKUuSK7PqCbDXdLD+s5/5wwj90iPyHis6VGa53E3NT2crunlXeB5pEcA17QYnm0AAAAAQFJ5/ZpalpiRHvEIJkmFCpIHx3q6MHsept6PRFBnIWZx64IxJzfMh6CBmh6OxS0/lNGXQ26b2+Sol2Ra4kZ6xORzKlczzCM9vDbXbWA8V6IgaSM98vWTfEmRjDY4HunhZWo4b++eIN5zyfirEgAAAACQCHG749gPq6RCPEJJrUNyRnokZ/tRHVKnBY0zCpknJesVA40xe68UsjVuB254SaYlraZHvqmPCiVnTY8IprdqE8OaHnavPd9Iznzn2E0fcPravSRXvU9vZTGyp5int4rXJRwAAAAA0MxvvCBmMTtJNm2KRyxJUkynt8rRJquEWK47TJOS9AhKIV5uVEc016gkc9DafM4bGenhWNzeK+b3etgtc/vavSTTCpnMbxNAEfKY5DxyXt8zkx75t2VdyDz3eWm5+1yjI6Ia6eF1v/mmMAtjeisv72bPo9A8nO+WYp/0SNpwXQAAAACAF8n47hdVLClO02oFKef0VsnoEoHFLeyCtwk5DDnlijNnjFLIGOlRDK+8MOI2wq6QrXH72qNIprm5egdS0yMmWY9c58bt6CSrtd12+1zHJaqPWK/7zTvSw9X0Vs7W81TI3ON1PIjzHfukBwAAAAAg/lZW1Tpab/nGrZaBkJjF7CRt+9K9YXOdttY1ppcVKvmwsrLGc7AgrqxeTZNhaFVVjfX633SKuoYmrdnkrH9FwW/frW1o1NrqWs+BYrfFfKOQKzG0tnr7uc0oZO6h4HRrVaiZwNZsqlVdg7udmfvcppp6VdXU51x/RaX19aClxqZt146vN2511Z6VVTWat3KTq+cU8m0TyPRWMUmSG9p2HV9RmX2OzH02rGvfppqGjN+tkh7NI07iccS2y3cK851jN33A6ahRL+dpU21D/pUsWE5v5XIbJD0AAAAAAL7NWVGlJz6Yn/NL6RMfzNf3Bo/WA6O+zHosjuH9yi11OuK2kfrObSPTywoRGHlnxgodM2iUrv3PtJzrxSSulcFtm6Yu2aij7xylD75Yk/VYc87nzIc+0lF3vK+Fa7cE0MLg+e27J937gY68/X0tWe/t9bm50z2q2TTsmrh6U42en7TEcj0vBadbq0LU9FiwplpH3fG+Tn/ww7zrWjWnqcnQITe/p0Nvfk+1DY3ZK0h6bOx8x0mM//3XFB195yi9P2e1o/WbPTdpifrd/4Gr5/iv6eH8wlhcIz2kB0d9pb6DRuvRsV9lPOZ6pIeHmyUaW1xDrBJKlwz5RFJ0oym7diizXL5hS13O5+WbBS3f9FdmTruLYUgffpn9WR2GIC5pJD0AAAAAAIG48525jh6///3spEccTV9WKUnaWm8e6RH+fh/8Jin032nLc64Xx9ExXo/P0x8tzFrWfKfnvFXb7sp+b9ZKz+0K4u5pO37PQ3OyY9ScVaFsPw7sXsKkBetbrGcqak7Sw7FCTG/1zowVkqQvV1c7WNtc0+ObEVumkTurbUYG/m1E7s8Qs5Gzrd8vcXLfzw9z/ZxiGukhSfe9/4Uk6a4R8zKWm9/fXruv2ySuVTLowy/XSoruJoK/nXuo5fJ8I57yjvRwVcjc6UgPQ89YfFaHwXp6qyKr6WFWDB/0AAAAAIBscfy+ZxV0LUTx8DgeiyDkel2WU1+1vEu31PuxdxMAcitu9RRyiaypNvttGeA1n/LkHNXoFSI/5GbkTb5+lrRRPF7f42cfsYcktzU9AihkHpNob66khHkUhpOj67fGQyqV+zMkipzH/T8/XD27trd8zK7GU7N8o3nC+MgzjMKNiLH6+6voprdK0N8OAAAAAIAc2uYIOMQxcGwVdIjRDbSxast23hrlZOoSP8HAMEd65AtO+ZXvbmZXNT38NcUzu2PUMnBn7gcxvCTEVth9UMqeKigX85pWTWssVBGSgNi98lyfaWZurtVBXKvcTG0UqhxdpjHjve6gb/ns4oaR+zMkiumtcu0y3yExn2OrBIib0T5OP1qjviZTyBwAAAAAEJlcX0rL25TaPy+Etvhl1aao5v2OSQjLH5cnueXqfoKBYc5xn7Cb1iNhd4ha3nltvn4UIpBfLNwkJAqxD6tTZ16WtBr1dl2xIsdnmlelPka0NQtzZJsbuXqM3fR1rmoUubxG5Dq0MTlkaflemvkcW32+uflTxXkh88Jdk6npAQAAAABIjPI29l9B4xjftIrJFCIuElWx6bhpbDJUb4qO+klchDrSI6CAs+d57V08L6oRVXb7bXk3MokObwpx2NyN9LCammb7sobEjfSwfu1tc3ymmbmZFrGYRnrk6peNNqO67J4TxPRWOUd6JOzWAvO10+p8uxnp4XRVwyjcDRjWnwVFXNMDAAAAAJBcOZMeMQz0x72QckziWo65PcdNhqEaUxF5P4G8IObJt9MYcsS5GPIAdm+lNi3OS0ZNj2J44QVSiGSR15oezT+an56wnIdt/y0rDf66EsSotDBHtrmR65rf0Jhd7H7bz9vlu+S77fU5E0rxOGRp+d7T5tdi9brc9IE4Fb5v5jfJJSUs6RHHP4IBAAAAAM6Ut80xFUgMv+5ZBR0KUSDW6ot9DGMSloJsp2FINfXbo6N+th3mSI9CTC2UdHZHKKumh+nn1nZY/XTRZExvtX1h2InCQmnbJp41PeISxM51mptsRnrYPd9J3ad8ck37FZM8UZqr6a0s5u1yNdIjoDYFyfJ8u9xG7JMeRXIdBAAAAIBWL/dIj/ixTD4U4HbQIO5wjCO3r6HlSA83d5q3FG5Nj4Cmt3K53NM+oupHXgqZx/KqEJ6Wo17ciPP0Vs3n1PzspBUyt+uKbUMZ6eF/m3EZ6ZGLuT/lS27YcXuNyJVQitv0Vvk+V8wvxXp6Kxc7czq9lYyC3YARRB439kkPAAAAAEBxKEtYTQ+ru5FjcgNt0bE6/1lJDx/Vj1sWzA5SkuK3USUSnI70cHL3d7Hy00fjPL2V1TLz1EZJYHd8w5jeKpiRHs6WhS1nIXObBKfd9FZB3AyQKxkUxWd7rvbne2nmRIfVCBY3Iz2crlvYa3IAI3sCagkAAAAAoEg1Nhka8vFCzfy60tXz7n1vnsbPX6uqmno9Pm6+1lXX2a4bx7u6re44zRca2FrXqCc/WKCFazd73u9Xq6uz9xtxsuWr1Zv01IcLVNvQmHM9r8386Ku1enXqMv393bnpZU2G9M6Mlenf630ESsO883njVvt+7cbYeastl9fWN+qpDxdo/ppqNTYZeuYj9+/FqNkFq7JHemz/ubVNb+Wnj/5r0uIAW2LNa42j9Dk1Pd3r9Fabaxv0xAfz9fmyjZ6eH7RciXyvgrhWWQXBoxj90fIz9PFx81VVUy9JMuew7brD5EUb9PykJbbrNC+atnSjbntrtp74YH7O9tgF9w3D0Pj563I+t9DyjXgpyVPTw82AIac9Y/2WOr0/x/pzKmiGIb0+7euMz0W3dZ7aBN0oAAAAAEBxeXnKUt3y5mxJ0qLBpzt+3oOjv9KDo7/STw7fTa9PW55z3Tje1W0Z48uTfbjnvXl66qOFGjR8jhYMcn6s4u7Eez+QJG2pa9QfTtjX0zbSsU+bk33tf6ZnLbvv/S/SP9f5GOlRU5c7WePHxAXrA9nOppoGy+WPf7BAknT723N059mH6Na3ZnveR1TvM7s75Vv2hcz1YnhRCJGfO/xnfl0VYEus+R7pYTqfXmuQDBo+R/+auMTTc/04vOcO+nTxhqzle+/cQZ8vy5+AdJO0DiTpYbHDbdM3FfY91bIfDBo+V7NXVOmBC47InN7K5jmfLFyvTxau1547t8+5g58+8nHetqSUUlub0VTDZ660XB625vfEAd07ae7KTZmP5TlV5n5i1WdSbmp6OFz1j//+zPE2/Vq2cav++MI0Sdv/7iy6mh4ZWtfnHQAAAADEwuzl/gJq475Yk3edOH7dswrU5osNTFy47pvnBtsWq/1GMQf5FIvAn1tej42f6a326tbB83PjZNpSf8c/qqSHbaHiHOvFMRGaz8+O3MPzc9uEMFVSkNxMoWU1cs/8dK9JjwkR3Y1/wXd76cf7d8ta/ttjv6XrT9lfj174HfX/8bf0/X12crXdt676gV753fd0y1nfTi8LIulhVePBz8fFxX17e39yCx99uVZSjkSoRd9ZvG6LdZ9ysV9Dhm1dsS9WbbJcHrT2ZaX6o8VNA8MuP1q3nPVtnXFoj/SyfK8to6aH1cieEKa3qrJJzIdhfY6RwU7F+4qqeP7hCwAAAABwzsm0KG6nLSgEq2bniw0kba56t/IFK3Mdn+Zz7LX+gNfprXbpVO5qfvM4q2tIUAERE9si7Yb974WoUxG0c7/jPenRNgbFp7t1Krd9zM1IDyvm8+k16RGV0hLpF9/tlbV8xw5t9fvj9tFph/TQn/odoOd+c4w6VWRPqmNOUO++Q7v0zwfv3kV9eu+onx6+u2ld/wKohZ7h2pP291S/xOot3HwpzugDeZKddsfE7SUiqF7XpV1bT88ra1Oiy36wV9bybp3KdfH39lRn03atrn/dO1ekfzZ/plkmPWJwPQlK89+Q1PQAAAAAAMSKdQFSI+86UfNS0yOJgVo3GvJU7XYy+sR70sNbwD+VSuaoASu1PpMeUR0G+6Rmy+tAsguZu5lSpqU4jPTI1frGPO99M6sRO+bTmbSkR0opy3PrNJlqXs3qKeYkRRBHJuiAd6pEwWRjtm1NUmZdF681vdw8L6VUYNeUoA5vy/aYt+tmeiurqfHcXIr8XLcKobmvuO0n0V9RAQAAAABFwe57s9VIj6ygVwxjYJbTW+UJDvi9Gzru/AQrt9f08PZ8r8c2pZTnoFrc1NT7q00S1Ygqp7vNCJaH05RQ+Ykd+qnpEZRcQXw3733rRPf2n5N2nUyl7KYYtJDnpVkmPUwLgxjNFfTINq/bs7ruNm/K/HdBvuuDXeLa/UiPYPqd16SSYeS+RphvGsh3c4D5nFidHzfnLAaXnizm5qevPYz0AAAAAABEwe57c6PFl/ckBL2sgnz5YgNJu4PZrXznzUmcpdAjPaRkjhqw4nekR1Tsgo0tz4u5byRx1JSf2GHbGIz0yBX8dJX0sLiL39wH/LyXo2JZJsPLSA+LXmIOUAfxHg8+6eGtb1u9hZv7WIODQub5thXVFcJzEsgwMo5jy9eUsVnL17t9obkNbSwKtLtpY8wHeqSvPUVdyDx5H3cAAAAAAKtaDC2D53G8E99LTY9iT3r4GunxzVO9FzIv7mPrhN+7wKOb3spmeYvfM/pGAk+3r5EeFoHLQssVxHeTqLZc07QwadfJVCplM0IjmO2bp7eqbfA3mksKYXorWb/+fKzOcnPSJ3OkR55kuk3KxfVIj4C6nZ/ja36PtWxOnpyHbRusipa7aaKTaSmjZHXjjBOxT3rEsZgdAAAAACCbXcDMKsDV0OJO31h+9bMsqJo7OBBWMM/q2EZxd2YQiQevd/DXMdJDNX4DohEdB9uKHjlmuSuSU+ZYHGp65CqA7W6kR/bP5mcXSwLT8UgP0+dGEqe32ja9l/ttWsV0m4PxmTU9TM+x/uC13r7Lq0RQvc77dF+5R8xkJETyJYJMGyqxyHBYLbMTx+mtzMeYQuYAAAAAgFA4D+w4lzXSI4YxMMu7C/O8yCRM2+VHvsCnkz5geIzp+ZkSJ4lTJVmprU/etECS/fFvGdgzEj69lZ8JrtrGIPIYVE0Pq/Cy+XzWuyiKHgcp2U1Llb1uvqNkdYSDTnoEnT8rsRnpko/lSI9vNtRoU9PD1ds+oJEebhM6/kZ6mNtj/wLyvd3MLbCqB+QqMRPz+a22T2/l7oST9AAAAAAABMLN9+aWd/rGMbxpWcg833OKPOnR4CNY2Ryw8BrM9nN3eNKm07HjZ7SLFOE0co6nt3Je3DiOkj69Va5AqavprcxBbItlSXs/prZlPbKXBzQtkDlmHURNj6CntwqypkezJtuRHtlSsk4QuOlFqVRw1z/P73Mjs89kTW9lToi4aKvV+baa8spO9FeebOb+0XwDCiM9AAAAAAAZ7n//C13wxATPc4Wbgw2nPvChfvPsZMvgvpsAUMvgeRynNraKy+VrpZeRHlU19a6fI0kffrlWkxas8/Rcs5r6Rv3sHxO05/97W9e/PD29/KkPF+jcx8Zrk6l9dsHKLXUNOuTmd/XshMV59+c16VFd2+DpealUPEfg/OjvY3Tve/NcPcfvSI+o3maOp7eyCJYnSdILmedqv/maP2Xxep3+4If6ZOH69LJpSzfq9Ac/1Pj5ay3PnXmZVZ0nK+c+Nl7jv1qb3m5UUjafbikPp8x6qkJzIXP/NT2CL2SecjziM5+vN27VmQ99pEVrt2xfmOfCdMubsy2Xu/m7obahSY+PW2D52H3vf+F4O9v262r1DLkOY8aUTi72YZX0cHO6YjDILIv59Xu91yL6K6oLMfwbGAAAAABi7/73v9TEBev19ucrfG9rzooqvT9ntZZXbs1+sMhGenhJxHgZ6TH040Wun9Ps509M9PzcZi9NWaZPFm0LXv7n02Wqqd8WdLv97TmasniDnv5oYXpdu4Kiz01cok01eZIS3zy10PmHlJwnWq49ab9wG2OyeN0WPTj6K1fP8TPFV5Rsp7dq8c7PCHTFMAh0yff2zPm4n8Bw0Hfne5FrtIn5vX/ePyZo1vIq/ezxCellv3hiomYtr9Ivn5zUYroiI+N/SWp0GMWcsniDfvnUpPR248bLGbN7TqeKNpKkAace6Lk9zQIvZO55c9bv4RlfV+qBUV9armX1uVtd22CdSHNxiXgrgL9//MpqbosFFxzVU5LUd++dXP1RZJXkcpP4Ml+3CjXT1S+P7pXzcfPfUumRHi73EfukR/w+4gAAAAAgmbzOFW4VyPM7PUn2SA9fmwuF5WvM004vIwo2exzBEJR8/WJr3fY7j+2mmHIzCiOKUT1Op8Y67ZAe6Z8P6N4p57onHrirrzZ54ffIRfU2sz3lWSM94jvU49ELv6Obz/q2pt54ku06fuKFbXJUET/zsN18bNm5dm1LHa1ndT631jeaHreYishr3YYYSKWsPweDHFEx+S8natYt/XRYzx00beBJ6tN7R8/bCr6QubeJvJyeZydve6ttuelGdQGMoNm+X+8dONep2XfXTpo28CQ995ujXSV9LWt6uIj4m5/t9Brg1R47ttPUG0/S7370rZzrmZOs2wuZU9MDAAAAAGAhyDiT1fQkfgqZxy7CKbvprXK3M6y56sO8+TLfTcHmV2SX1HESoGleI4qZppyeF3NAKt9d++3Lwg0OWfGbMIpueiu7kR4tfs8IfsbrmtClXVtJUtcOZbbr+Ik1t80xymLH9m29b9iFdgH16Xw1GuJ1ZvOzKenh7XzbPKeibak6lG8b7bFD+zJXNRlaCjrpIcnTh5DT8+z1ve7mehaHRJthGC1qemQ3aof2ZSopSbka2VJqkeFw03/M/SXsz7XSkpS6dijL+97JqOmRLmTuDkkPAAAAAIBrVgWtfRUyj0FAoiUvAWa76Z/irOVUKC0TGIbFHZctuRnhEsUxclqA3U1cL4oaDMnrXdvYnfKWy5sy+lqIDQqJn8LWbXL0p1CC2BZy3eXtN8CccW4Tdp3cNtIje3mY58XPpi0T2T4PeZgjPZw8xypBELfEqBMZxcpzNN/q7w+7PmF16XA11Z5p1fI2ISc9vmlXvvZZTW/lFkkPAAAAAGglgowzWU0X5Cbg17I2QRxDF5bTaeRpaFgjPcLUMvjQ8iWYf7cd6eHgdTcfOy91T/xyel7MQcx8I2DK2hS+BoPf93BUQUKngUxzMDxuAU0nZzuskR4FS3oENtLDdB6NzP9b/pxkVqclqOn7/Jxyq5oeft9PXurVON1nxiFL2OgNt5weRcuRpqZl5tNhNTWem7ouGdNbhTzSo8Rhu8yf2emfXZ7vRCU94vaBBwAAAABJEuR3Ki+1K8xaBqHjGLywuhs5hs30reU0GC2DdlbTTLTkJtlT6HOdSqUc91c3cb2yKEZ6JHV6K4c7zoh9xu3NFnLeoW2OSfgL1dUqcoz08Jt3yTy3cTu5+aQsk/pOE/1ePnv9JLqcBpbdCDPv5nVaOzf9KMge53VElyFvySPLbZlekFWCw81ezH0t7Joe6ZEeedYzf2Rvn96qyGp6JO46CAAAAACtQGNTU1bAwc13+ZY1QeJ4k1sSp6ryomVAtWV+wHwY7KaJcpMEi2JqG6ejS9wEs6KY3iqp7I5+y65g/j2Bg6Z8BYbb5BrpEXAQ266dQU1vlXnCvwlYZkxv5WJbMWA/vZWHbTlcz0/Sw089kCDl6jMZ0zx53JabbhTkEQnq75Wg3gaWSQ83s1uZ1g076dF8LfNU06OYR3oAAAAAQLF45JFHtOeee6qiokJHH320Pvnkk9D3GWSsub7RyApcuStk3mJ6qxgGwayntwq+oVG/9JZ3nuZ6jXYjOpwVMjccrxu0ek+FzHOv27ZN8mp6RNXX7Ke3armex3luCsBJQsxXTY8cEfSgp7ey21pQAU+rMxfrUTx52Bcyt5pGKs+2HJ7LoKe38ivomh7mkU3m973Ta0W+7ceRYQSXfDH3D6vz7eaaYV61IvRC5s7WM/+t0fw3A0kPAAAAAIi5F198Uddee61uuukmTZ06VYcddpj69eun1atXR900xxoajazgtZtpG/xOj1UIhQrO19Y35l0nzBt3WwZHsmt6bF9gd97cTG8Vxan3UmslXwA7kkLmvrMeEdX0sAkFt0ywxTkw7uQ96G+kR47prYJOethsL7CaHhb1OzJH8cTs5OaRSqUso9Uh5BYCEUYNmKBrepTZJI3dDSiKZnorP5wWMnfD7/Uhc3qrcD/Xtk9vlaeQucX0Vm6R9AAAAACAArv33nt1xRVX6NJLL9VBBx2kf/zjH2rfvr2eeeaZUPfr5GtjQ2OTFq7dnH+9piZtrm3IWOZqpEeL6a0WOdhnoS1etyVrmSFp5teVWctrLBIXW+saVV3boBWVW7Vo7WYtXrdZW+oaLJ5rPWWU3Xa9WlG5VdW12fvPnt7KvqZH9nREhuavqXYUlNge/Cx8+Mlp0MRdTY+YRjx9WFVVo8qt9Y7WNQxDX62udnQ+nd69vbl2e38PKjAeVPw37LNd0OmtbJaHUcg8vSyjSL03C2L2OeE0EZBRgNrhtn3V9AihswY90sP8WeQkIWa1eP7qzYmqD2PI8FXTwy4pXGpx7fB6zWhf1sbT85xyOr1VfeP2v4vSIz1c7ivcVxKwBPVjAAAAALBUV1enKVOmaMCAAellJSUlOvHEEzVhwgTL59TW1qq2tjb9e1VVlbedO/hSdcU/P9WYeWv0wAWH6yeH7267XkOjoaPvHOWtHdqWXGk2Zu5q3TPyC8/bKqTPlmzUGQ99lLX8xHvH6aMbjs9YdtnQyZq8aH3G6IidOpRlPbemwT6xccU/P9Wwy4/20eJtVlRuVd9Bo1VWWqIv7jg147HskR4t7r5v0W021zaoQ/m2cMLg4XP1+AcLXLUlipEeu3YudxTMNwek8k5vlcRC5jke27C5Lv2eXjT49Lzbuue9L/TwmK/0++O+petPOSDnurbnvMXytdW1dg95VpJKFaw+j58ES65C5kEHsT3V9HCx/czC1NnP99qPo4oLBnn4nfaRnTuWe95HKNNbedjkR1+tdbSeOUl2x9tzbNdqacKCdbrnPWd/OwRa08NjP8y6acDlVa5jeRut2bTtGmkeKWM10sPN6I/mbUrBJT7t5JrGz+yZjxemf27+c9HtdSMBIz3IdAAAAAAoHmvXrlVjY6N23XXXjOW77rqrVq5cafmcQYMGqUuXLul/PXv29LRvJ9+uxsxbI0l65uNFOddraGpSbUOLEQouogrmIOhzk5Y4f2JMLduwNWvZhAXrsqaDWre5Lmu9lqNezD780lnQKJ/JizZIkuoas0eVZNf0yHy8yZB236Fd+vf1ptfgJuHRvNkoprZ57Fd9HK1nPhL5mhlGYDEfvwmjXK9pzgp3ydSHx3wlSXp07Hwne7ZZat+goLpJUKfJ/D558bfH6If77qzTD+mRuY6P0GpJSUpnH2GdaC7U9Fa5pthyI1/9haTd1Ozm8Fu9toxaQQ77yIDTcicSc8k3muCwPbp42arrZ7w4eamj9czH7LXPvs67jlnzdSjvPhyt5YzX/tvytLjdzrH77qyzDttNA049QL136qBfHdNLvzvuWxmjxH6478767bF7u6rPM3flpvTPXduX6VfH9HLXMEk/3r+bTjxwFx29V9ec65Wkp7fKrb2p/elC5i7blICkBwAAAAC0bgMGDFBlZWX639KlzgIJvuT5Nm5V28FNSMQc7CyPoCB0nLiZGsqrXIHflgHV7H0Z6moaoeK3LYVIenTvXJH+OZWSvtWto6PnuQluRpH0CFOYZ8V2eqscO23ZT/bftVPWOp0q8k9g4mc6mcztbP/56L130rDLj1avndrbruNWaSql60/Z3/KxllPVlPlMTtg1021yxe49YFWY2nzNT15ND/l6g3h5uX5GeuS7NP3xxH0tl39240l6/9ofWT7mpGvsvXOH/CtZcHJ4ktZnrPittVJSktKDvzhC//Ojb0mSbv/pIbrhlAMytnvLWd/Wn0870PPQllRq23Yf+sURrp435NLv6qmLj9IzlxyVc730NSNP+8yj87ye+0RNbwUAAAAASbfzzjurtLRUq1atyli+atUqde/e3fI55eXlKi/3HgBpFmTMwGp0gpvgojnOb1fQtLUoxNQ7uYItLQNkWTU9mjIDlm6n5Gip0LErN3EmN3fqh1EsOB//x97++WEGFd3M02/HatoVJ2cg6FESZi1fl589lZbY97+Wfa20JCX5KPdjd0hy5lIsTpZdcD3vSI8cuyl2hbhs5Muj2/WzVMrfyKjskQzOzrSjukBeGhQSr9OztTy0brdif30w/+ysZob9PuTv+Xme5/RmAfOg1PRID5cHrHX/ZQkAAAAABVZWVqY+ffpo1Kjt9TCampo0atQo9e3bN9R9B1nw02+BbXNb/N61nHSORnr43EeuOEPLu8izanrIaFFo1lsbms95Ie7YDSJgky/BEHRxaSd8T2+V6zGX23Y6N3uu/bppj9V0LU4SrYFNb2WxrKnFCfE10qOkxLatLS+Rbo69FafJlbzbsVvf4sQ6KVYdV36mLYtCXcupJx1KKWXbBxwlGFv0S6dn2cl6hewyYSWmso6tyxdl1y7zZ1HzOvney2G9xnzvle3TW+Vez/w34vYbQ9wdr0SN9EjWJREAAAAArF177bW6+OKLdeSRR+q73/2u7r//fm3evFmXXnpp1E1Ly/f9q7q2IWuZ13nPW/1ID0fTW/n7RpwrOJxvequWzfPblkIUMvcaz3HzvDBHEETB7Wkpb1OihjpnyU/76a2cjzzpUG6V9Mi/76BG5FhtJrsve99XaYn9+7TlazDP4e+F3SFxu12794DVyDDz+UxYziPYKtgFUNeY532ZKwlul/Tw8F4L8jwXMlHWrm2ptuS4tnltid9Lkd3Tzcfdac2MklTKepSpw6SEHacjPfKtZ25bY6O3kR6xT3ok7kIIAAAAAHn8/Oc/15o1azRw4ECtXLlShx9+uEaMGJFV3DxoQX692lRjkfRw8XxzAKO1Jz0KEczJFaAvaXH4s0Z6GMFMTdO8jcKM9PA5t4cDkZT0CLGQudtkVnnbUm12nPSwmd4q53Myf29Xlh3CcnIKghuRk72drOmtfOyqJJWyv5M7a3qrcGp65EoQWZ0r2+mt8nSlIEcdFoLPkh4ZxyOoGjO51NR7G+mhlP8+bOZ4Oj5HQz3ct8fMzcuqyJf08NiWluc+qHeB+X3YvIt859Fuhrywp7dymoQ23wzidQrQ2Cc9AAAAAKAYXXnllbryyiujboZnViM93GCkx3YFmd4qxyFuGYixGtlhtPjdjzgHPM13t+ZrZhQ1PfwmjHImGVxuy820dLbTW+XYactAV7u22fsr5PRWVlq2319ND/uphVpOG+R7eiuH+8nHrr0ZSVIje1khRnsFKZVKJeqm7Np801vleC12SUIn5yxreqv45DxcPb8iz98kXmsrtTy0bvuUbS0e0wMphyM1UjapvHTSxF3TWmw3x+MOt59xvWiu6eGyLa37L0sAAAAAaEWCDNpUW430cFXInJoezZxNb+VvH+Zz0zLpkD0lSebjTYaRscxvWwod8HQzTYebPEZ9k8e7qePK5Xkp///t3Xd83OT9B/CPbvi890xsJ3a2s/cmCQSSsFfKhpRVKJRVoFCgrFIo4wdltNBCWYVCwy5QNoQVVkhCgOy9h5PYcRzv+/3h3FnSaZ6kO539efeVYt/ppOek53TW96vn+ygkIVRXHcUxl/fD1GhHejhY3kr+2bVyF3970kP5OfnjlstbqbXB9Jweyo8rHW5JyasESiCYpRcQj0WqtFkh6WFkn2tNZC6fv0ZJtLk4QxOZx7DTJCvMHyRm10gPu14vfjx0DPQ2ZfX5aF/X0T7tBZVGenAicyIiIiIiInJcXZO18lbia9euPtIjFuWexIFfvSRLxEgPaP9ulpEkj53MBG/M9OFoJwu2wvLdzibm0NATMPG5Vd+u+jYjy1tFOaeHTUM9lNYSUd7Kwvo9gqA+wbjNIz3UGqq1r5QOodLIELW5mhN7IvPE0tSqfW5SS8wIUE8SGzlmEeWbbBzpEcuvjYBe0iPK9Tr0sZV8vxtN8qtPWO9MQtXs+iVzeoRHepjb8wn1l6Wbh8ASERERERG5ndNzelTvbzLelmAQry7chDcWb+k0Iz2M3Akr9/hna/Dtuj26y1kubyWKM+jVx5Zfe8sXf33RZry6cJPpNgSDwKY99fi/91eYfm2sSEfEaC8bj6SHUwmjV75v/yyGBINBvL5oM15buBkAsLXmAO5/fwV27GsILxPwdQQGv1m7G4/OWx3+DGzcXY/731+B6rrG9vXJtldd14j731+BTXsOqLZJHmRNVQxExre8lfxwWLmR2+sRIKicCuUjMMyWoZKzb6SHgH98ugZfrt4VfswjCIrxO6Weu3jjXjz80Uo06wTp483qYCFxsDYWVfH0zk1ag9RUR3pEUd7KqPXV9brLWC7tZ+L1egnd6JviULJSiPxZb4Sb2qHqeFl0bdUbwWG8vFXHTt7X0IIHPliBf3213lRbXD+nB9McREREREREsad3UV9vcU6PPfXNuOt/ywAAVx3e19K63KIlioD0H99aamg5qzcBigMg8oBXZDkryH4PSvrDIx+vBgAc2q/IdDtOeewrbN6rHuh2UlqS8sTbgtDR382EeUqyU+xpWJzVHGjGVf9ZLHmsvqkVl7+wCABw6IBCnPn411i9cz++WLULL108AQDgF5VY+sVj8wEA3bNTcMzQbvjFY/OxtaYBCzfuxTPnjok4n1zx4iJ8tnIXtLTaNdLDtvJWkesZ1SMH//5mAwBgUu98S+vPSfWrtlX+uN9iolh1Tg+TZbN272/CHW9Lz2EeQRrLCwX8xeeZUAD7uEe+ANA+Sf15kypMbTuWBAiG7zLXO1XHIukxrCwbn6/q+Hwl+z1oEX2g1JooCIJq34imvJWbbl430xQzo9gAID89Cbvq9G/6sLp/jIwEC8/poVtmylhywiy9l3kMtk+c4L/jraU40Kw+sbzqtky/goiIiIiIiBKSnQEIvfIZesRzgsTjjnkntLh4jgdxgEE+0kPeK+R31Kp1m2iCEGoJD70bhHvkpZreltz7V03BLcdUaS4jD8S8dslE3HDkgIjlnjhnFCry0iy3KVZCCQqlY9mi8FkWT4Tc0NSK1Tv3AwC+W98xKkkpOLp6Zx0AYGtN+4iQr1ZXt29X1su+PPi4Fvn5SqkMnpG4nNIyA0oyNV+jWLZJYbkThnfH7ccPwiXTeuGR00coBiUfPXME3r5sMu4+aYjq9u48cTAO7V+kXr5G1h69O+oFAXj49OGazyvRKptlNOivNum3OGYuf37l9n2G1q0mFiUSrXx9Wi0ZZIQ4CXnssG64/fhBOHNcOeZM6ImPfjtVsqzW3wJqXUBvhCCgXxrRilimTwQBGF6erbFER2vy05NwzviehtervBbj7VIiHimVfHCuJfGiiucCtZEe2k/rsmuuEHFfiuZvDYBJDyIiIiIiItKhdJFqNVFhJICSaJrlt6a7iLk5PeRJkaDpWtpKtNZRlque1MgI+HD4APOjSuS6ZadgzsQK9JQlUATJz6LyVmi/Y3pa/4KIdR02oCgmd2zbJTQxr9Ix8Hm0Q0NmBjAZLfFmbOJi6e/Rjm5QSs70LkzHySNLVV+jFPxXOt4ej4CzxvXANTP6IyvVr7jMzEElqOqWiX7FGarbO21MOZJ8Ho2RHvrtE5s9shRHVBWrPq+2Hatls9rXDYjDuR3HMXKkR4jVCZ4Hd8+y9Hol/Yo6jpet5a0cSoCIPx8eAThrXA/88fjBuOXYgegmG5WmOtID6n3DyEhGownzaFi9acNseaxRPXI02tLx84kjSg2PkLJr1Jmc+O+x0Lle/JkqzEiOeI1aS8Llp6Jsq355q4MjPWKQCGTSg4iIiIiIqIuwMwBhtQa7OPDeWRIgTk7QbXUXSctbad+OK99WMKi8fTsSISGawSDB2mTU8ldqriuBEhlmpGhMzKt3HNWChUpBSKOfZSNLyT9PSkkPKzFErZdanihcaXsGVqm2jPzzYSQ5ofORUqSXADPCozLSQ/yY/Gmru9uJeVvE+8/O1TuVLDXTZ7U+pmrnYiPfb/LvFju/I2I5kTmgHbwXN0WA8QC+fCmz3+tqWxEnPULnSUkyX+GFau+vIynhDItThpiSUEmPzvFnMBERERERUXxEG4BQujC3Wt5KkvSIdTTDIUplgtxCHN+Q37Er7xfy490WtCd0pRXg0QvQ2hkolAf1xMEf5e2oBYfsa5PTQvNh6JUdClGafwHQD7ar3Q2ulEjTI0+2KJe3ij74r5VoUy5v5fwBV2uS/HGfgVEvWq1V247W8TUaoBWgHL+TzPMhW5nVO+CduIM+KI9su5y5El/KB1MQAEFlNYaSHg5+lVueyNzMskHt87uk/5roG/JEg13lrZT+HtNL2sXr+8uJBKXqtmK3qeh0kht+iIiIiIiIOpXmFmsXa+IASouLy0KZ0ezkSA+LaQfxtbVe8MhoiRI7365W0FIQBEsBZ3mgyStPeqj8rFdOJRZBcLukhMtbRVJ6n2rzL8j3XcTrHPwMJCmUkDESQFO8y1nl8RClpILVIKGR/mK07JTfQDkd7USA8nNG1mtku3ojPeTTH1kf6eHsZ7F9InO71uUMM+Xf1E5t1s60+gn0eDI/qsLoSA/jk9xbHUil1ialkbe6Zab0tuVQRzU60bodXJ/0ICIiIiIiInuYuejXu4i3Wt5KHFhvdfEE4Ga0Opi8sRo8Ckr2t/7IDunvKoFxkwFuraX17kq38+5QebBFckesQiTG6N33bpasWd5K4bEoR3rYOdjJWHkrAyM9VEfqmBvpYYTVPqE+p4e8vJWBkR5ao6eiGOlhlCBIvz9C3Ufcp+TfL1bK1wH2tNtJ0lEjzrTVZyJhpXUutpJAkn/+7fxGtPwdaHJ5o7tBEMyMgpKN9DD5ptTa1BjFHGv6c2+YXqWx7cr+6yQmPYiIiIiIiEiT4kTmNpa3MjJBaiJodnHyRryH9ZMesteqlLeysyyZ3eWttJaPKG8lCr+Y2UxiJT3awz96d+CHiA+t+Ge9eQPUEpjR9BR58i3aiczVmqw50iNec3qoPG52IvP27WmMnlJ5XGtOD6PxWY9HeaSHWp8CbChv5XDSoz2wbc98Na4e6SFYOxbyfeSmkR5mG6O1F6KsbmU5ea/28qiSHiqPh46hUyMZo50gPRpMehAREREREXURdsYf7BzpYbVWt1tY3SdarI/06PhZb39HBK5UljM7Ab1W0FArwCIIgulAnOZEvRqREDObSczyVpE7RumxVrWRHjp3k5vtE1rkq4o26aEWZNMKQCr1t1jE6uycyDya7WjO6WFw3V5BWgoq1L+URn+EOBUMtksifNKTzCQ9NI6mqXOgbNnI7xb3fLfbOdJD/F1mbn9Z7egq5a10kh5Rbdax8lah/zr/qUqspId7PitERERERESdml7sstliKafOOKdHUxR3WxpleU4P0esjRnpELCvVFgwqXo/bOX+DZhAO1uIv8tdGBLTF5a0UtqS27UQa6ZGa5FN/UufYigN84hEGyqN/5KsO1TYy0kpZGyImMo8uEaF6/DR6lVKpoNhMZG6svJXVkShq70VrvUZHOgiCoLysZJ4Ye8tbOfENIi97Z9c2nPq2M1XeSispbOLEJp/jR28UYTw5OaeH4XXKFrVr/0Qz8jZe318sbyVi9Q87IiIiIiKirmL3/ibMfOBT/P3T1eHHJHXUTV5efbh0Ow699xP8sKkm4jmrpY3EJa3sLJMUT797eYkj651410f4as1uQ8v+66v1OPz/5mHz3gOoOdCMmQ98ioc/WimJ0oSCyc2tbZj96Je45Y2fJOt4+st1OPz/5oV//2T5TqzZtT9iW2bv6v/XVxskvxu9W716f5Ppu0K15zOQl7cy9rqI9ZhqUXwFNMpbKX38xAmH57/eGP5ZLyD67282oOd1b4V/b24N4uJ/LZAsI35ey7rqesnvSqWXDCU9oihvFf2cHs70CvlbV5po3YxoRnos27YvqnUf+/AX+HjZDkl075WFm1Hf1BL+/bF5a/B/768wtH4lZudGMLZO21fpKK3SZHLac3oY36b8fODmfbZkc+TfMVo094O4vFUMvzNUy1s16yU9lF6pvLbQMXTq+81qKTtT24rZloiIiIiIiMhRf/tkFZZt24c/vb0s/JiVIMR5T3+HNbv2Y8H6PTa0Tko8usPMnB7ds1Nsb4tdlm6tdWS9m/ceMLzsja/9iJU76vCnt5fi6S/XYdm2fbj3vRWyOT3a//vZyp34dt2eiPW/+cNWrNxRp7sts8mqf36xVvL7kNIsJPk8qMxPwx+OHqj5WitxEnkrr5/V39C6w8EflQUSZaRH9+wU7Tv4FUKg4kMrPm7RjDD434/bLM8BlJPqR0pS5GTsRu6yVitVdc6EnorLXzqtt+L7dKgyjcHXSl98RFWRtbaoPG7H3BgeIfJ755dPfRvx2NzvNkl+f/DDlVFv0+i56LLD+ig+/ruZ/SMeu+qIvuGflY7drEHFyhtRaErvwnQEfB50z07BdQe3dd6kCv0Gm5Dk86A4Mxkpfi/Kc9Minm9RGb0lpjSnx+Q++arblO+Xw2X90sU5EE1BQPMDKx3pYby0ZUSSSGMP3XJMFQDgvtlDO7al0qSrZ/QDAMyRndN65qXC7xVQVZIZ8RrV773w807N6aG9fTtpjG8kIiIiIiKiRKJUckqptrobiINURkcM3H3yEJwwvDv63PA/AMCAkkzHEg2JrrG5TRbk6ngutO+tlhWzOm97WpIPP9x8BHweAT6vBz/dOgNj7vgA+5taI5aVB4t65qVGjARQIw/wjavMw2fXTsPkuz+OWNZcIMb9WY+hZdl4+aLxuOHVH1WXUR79odw39Ob0cMq8a6ehuq4p4nH58brp6CrMHlWKIbe8p7oM0P6eexWk4+fbZqDqD+8CaA/uPnrmSKQFfPhw2Q7dbSlxYsQBIO3/X//+MKzeqZ+U1BLNPCdGqd3JLf/+sXMOJLX+evfJQ3Ds0G7wegQ0t7YhNcmHi6f0giAAizfuxSl//woAMKVvAXbVNeKJz9sTfD/fNgMHROch+Tv69obpyEtLMty+ZL8Xi0Xnup9vm6Fdci4KKX4vPvvdNLQFg0jyGb/HPdnvQcPBkQIChIh+/sApw1RfKz7WNx41QKG8lXv+5jDL8EdBEAxPJG7m+2XOxAr8YnQZUpN8+O3cxQfbpLyCkT1y8NOtM5AWkPapD66agpa2IJL9kQljvc+6U2f60LknFuUCmfQgIiIiIiLqJJSDe9GXt3KSOODVYjD4FfB5JJMZB0wEdroiv2T+hciJqa12B6uTVgsCJMGYtIBPfVSF7HczJTKUWpkuCg6JkzdW6rO7USjIGmqrUhBSaf+oBSvFNfxjeT5J8nqUR1/IfvcIQKoswKfVV8SB57QkXzhoaLF6lCIrQT7xflcKYNrFjtIzHkHQHT0E2HsnudpAj9Qkb3h/hb47QiOG/KLvD0FoTxp0vM4nSXoAkHxQCjICptuYLFu/3ZL90u9HLeLPblaKHw3NjeHfI8r/aRwncTm01CQf9je2SJ530Z8c5gRNTGQOoKE5MlGvxOw5QN5PtNokT3gA7WXwfCZPFx0jHM29zig7EquGtxW7TUVH/EF0011JREREREREbqMUsHLrVVQ0c3pEBmNsbVKnI74rX7yLzZQT09JqcaiHmXkTIsrumDj2SsF58b4RJ2+UmqRaCigBOqD8WCvOL63woFoeMtq5LuygOLm4Q8fAqzR/SBxH9kR2f2ttUZv+wY7dKSiUtwIi+5mde1O1XJPGVsTPKJdAM16KKJrl7GYmGSZuo3guEKXjr/WRFz/nEZybqDsejH7GBAHhkTJGlhUzP7m685zuv7Esb+X6pAcREREREREZoxSckNxIZuIK2+lghR0TmSdyQCUW/KJglvjYh/a31ZiD1eo0ioFGlWXli5ppu1IQR3znvLj/mQmiuz/l0ZGY0r5rWel1yh8uM5Ml20kQVCYyV/jdzJ3qaqKd00NrW3bN6WFHsNCrshI7EjseQVBOesi3ZeMHKJrvAvE+1S31kwAf9mQTt/SL95deIlOrT4hfq3jTRQJ/SWueM8XLQUBji7GRHpFzetjXJrP0kvZOJXljmTxm0oOIiIiIiKiTUJqE1q0j5sUlrYyOPJC/O3e+M/cQ3xkvnr9Drf69WdEmq0LMDBqQB0p0AzaipxVHeqhsXOlRtU0lQiC042PW3ljlUlaRj6n1EY+kZFps+Q3MJ6KUdDB6mMTnSqX+Ec/DLW+PXt/TCyw6OaeHIKj0DXl5K+ubClMrtae1n8RP6SfG3P9hD5XtMkK8u8QJPsV3qTnSQ5T08AgJMfrNKK13It5/gtA+h5YRccoZK1J7fyxvRURERERERK6jdPeudKRHDBujI7ryVk61pvNpvzO+Y4cdENUct5qsCLGaPFEMNBpMMOj1Bb1+rxackyRLdML68Sx3ZJS8BJnyHfgK83yovHWlERCxIEBQTkQY6EKKy+i8DSfep5Xzl7g5Rlaj33fVtmPXSA+luWNk5a1iMKeH0S2YKWvnVgG/sRBvktcjORLiz1V4kmlxfxMilwvxSEZ6KJRvMtQid1K6iSRE3JcFRD+nh+nyVjZ+ZtTWFWqSU/1fqY85hUkPIiIiIiKiTiKR7rKMbqRHJyoYHgMelaRHm21zetg/0sNoD7aacFAd6WGmvFUCfNxCny2z5a3UElrxGjkmCFCcpNnIPD/RHCejCZZYiXiflleo/LAd3yFqIz2cPF1HU0ZJ/FaNjBxz+9eN0fJWAZ9Hsr/0RjWFflZKBHpk+zBiCZfvMzVmznOCADS2GBzpkQDfGWEOtzUWNw24Pukh7mZuP8EQERERERHFk94FtZsuqWyZ08OuxnRS4v16oEk00sOu8lYW12Nk8uAQeRBet7yP6HmlAL6V0lqJRD5/i+KoDoXXqSU9xGXSYk1xng2F5eR9w2ggX3yclZNiBsprGVy/WWYnkNctb6X2uA1RQq9K1sOmXKsitf6qXd5KPEpBcG0pSKOMTmQe8Hsl71RcBjH0k3QOmfaflZKOEeWtZP00kfeo0USxIAiGR3rIV2q2z9mZeFVdl8PB91gmj32x2xQRERERERE5SSlgHO31689bay22RluzKHhqeKQHB3oo+nbdbowsz5HOtxAEFm3cG/5dPNJj+bZ98Hk8+GFTjaXt7qxttPR65aSH8rJ2H2sjIzr0tpkQIz1kSYrNew5ELKN0l/yG3fWK62uyOnt9lAQYC/xHl6qIFK8yXmok5a0EwXKZG/U5PWwqb6UQzF2yWXq+2VrTYHlbIW1RdEulEk5qzwPu/75JNljeKuDzSLIRXp2JJsIjPRTm1JEkPYTIz1pdY4uhNrmRVuJQ3hUaDE9kLluP2fJWMUzAO7WtUJ9heSsiIiIiIiIyTClgJQ4+uSloI55rwGi5Jfm7S/LxkhYAZj86Hy9+t1Hy2AdLt+OV7zeHfxeP9PjjW0tx2j++wsMfr7K03Wtf/sHS65ViKuo3n0Y/H0C0/b5bdorm83bWV3dK9xzpe5i7YFNEIFJp91z+wiLF9QUMltCxm1qgvzgrWXFZ2QMRyxSkBzS3V5IVeeyNHG6lu+HNvF6N2WRE9HN6mNqM8rpVSkE9+OFKye+PzlttfWMH5aT51Vpj6PWCAOSmqfcJAUBawNh94/H6njU60qOyIA2ZKR3vRTKR+cEfJeWtDv5SmZ8Wsa4S0efPIwiJkQk2oDgrBbmqfQooz00N/ywIQHFm5HlIid53ip5YjPQI6jxvebuy/zqJfyESERERERF1EkoBKzclOsRabBjpUZyZjF9NqcRRQ0pwRFWR7uvvOnEwThze3VQ7E8VSnZE5LdHcCm2DcydWIDctSfG5gImklZV+bHbC9X9fMA4zBxbjzycNAaAe/HFreO+kEaUozkzG5D75uG/2UADSxMb2Wukd9mbmQxhWliV+oZVmmhLa17+b2V/y+LUz+kuDsUqjhxTWd9n0Pprbu/qIfjhycDHuPnmIqXYWZATw66m9TL0mdIy0RDsC45ZjqnDamLKIx9X7tF0jPWLrF6PKcNywbhH7UiuJI5/T44yx5Th2aDfcf4ry8RhXmYvTxpTjD0dX2dFk2+mNTvr3BeMwY2AR7jl5KA6vKsbskaX44/GDlMvGiUfBHOwTfzl1OI6oKsJLF43HP84ehaMGl+CqI/qGl/MIQmLNWaHhD0dX4cQRpRGPv37JRMwYWIR/nD0q/JgAAbcfPwgzBxbjrhMHK67v2fPGYNagYtxyzEBL7bJz96qdU0KndacOZSzzYkx6EBERERERdRLyetqAbJ5EB0NR/YszTC3fLArCt0YbkBeA62cNwCOnj8DfRUEINaeOKcf/nTIsum25nF7pn3jMxdCvKAN/OKZKNXiidGey+pwe0t/NBNfMvvPxvfLw6FkjFUcRiOkFb84cV25yy/Y4emgJvvr9YXj2vLEoO3hHslZ+wkzuIt5J1Iun9sIbl04M/56d6sdfzxyh+RqlJmcmR97FLT4/ZqX68dczRmL6AP1kqty1M/vDr1AKSK27nDQyMrgqJ65AJEA/URUKVM+ZWIE7T4xM3KglN+wISNo16ffIHjmGl03yevCXU4dH7Eut86J8To9kvxcPnjYcJwwvjXgeaD833XniYJw7qcJwu5x20ZSOBJved8D4Xnl47KxRKM5Khtcj4J7ZQ3HmuB6S14XOv+L3HuoTZbmp+PvZozCqZy4OryrCI2eMQFZKx+eovbxVYmQ9pvYrCP88pW+B5Lm7ThyMgowA/F4PehVIR7cMLcvGY2eNQoUo0SoI7SPDHj1rJE4do3zOn9ynAH87cyQKMqSjicwknGNNOq9LdOsY0zM34rGO8lbKK738MO2EtBmuT3qIO4CL+wIREREREVHcKZa3cuk1VWsUIz3kYcPECK/Eht5dvsb3sX0adeqcpyglPVSWlSfszARhnOr3bg3wmS+FZGLZOJ1DlO48DxG/X8V3bqHRbjnC8mNq9eOs1kWcnNPDLK+JtqgtanQCeL3F3FrKTtwss5Pda77O4KrkiSOX7iZN8vcfVPlZTSxHt9i5f1VHeii866jPC4pz5Wivy45zUHhdtq2JiIiIiIiI4kqxvFXsm2FIsyhq1xpleSvqoDTKR8zoPrZTY0v7CB6146Y08a7RiczNBEacupvWrf1RuSt07AP506ZGeojnCDLVKmu07jrWOwzGU6raa7J6uK3N6SFdj1N92o4ArsemkR4682vLKDdcc6SHZJ/ad+zj9Z0bbdJDaR4apTk9lEhKhHlimwCwQtxMzTYbOJhWkt/xnMhc9QaDUHkrSfk3+7ahNG+MmJ19iEkPIiIiIiKiTkJ5pIfo5xi2RY84CG+09FKCxFPiQn+kR+zn9Ghq0d5msuKk2Gp1xoMGllLmVL/XDbbHa1SETsvkzTJzV74bRosJsgSAXhkWK212S2JLHpS3PtJD+Y3Zcpe1TTtNqS1KiVItWqNFdIO6Ljn2WsRNNDMyRkwpWaI1skptOY8guHb0mxathJeRj1kszxEx3ZbkZ/vK4YW6m+poMxuzHkx6EBERERERdRKKF5GSWg0uiFge1NwqntPD6EiPxAuoxIpesDKeIz3UNq08p4fysvHsuaq7Vqc7xqvNiiO+7JrTw3xzbCedb0C/pI6TcxmZE/35Sx7QbnPz6KWgPXtc6ZyWmuRTXDaaAKp4F9pZUideog0W6yXMtXaNeL95BCEhEkWA9G8Jrbfv5jk3LNMt6ab8s5he31HebPT9zSwmPYiIiIiIiDoJcQAidLEer3I0eiQjPeIQkO9s9GIz8ZjIPDSnR0ur8oiP5CTjc3pEBHl1y9FEfhbs5tq7mnWSHvKnzQTQ4xEDlB9q+aTe0jk97D0m0a7P7v0kf49Wkx6qeTwbIo5BwJYdoBTEV5oHCFB/P0ZLPim97Wh3RSwD5ZI5Pewc6SFOLGptX/SzRxASJnkkb7eY2dGxVj4zcU3mqzyuVAhR7bhGc7zD5a1UXhttP1aSUEkP/hlMRERERESkThy7CCUV3HqjojgI32qw9FJihFPiQ+/e6niM9Gg+eIzVtp3sMx6SkK9CL5Yp3h9OlQJya3zP9ETmUc7pESvydyNPRNhVZkzvvVlNCFh5ufi1QReNXVESDNoTv/Mq7C+18lbRlOuSJAL1kqgu/ayLRTunh9Ld+kbnO5HP6ZEAuymC0T6iJpbv2c7RrmrrUprTw9aRHjH8rCVU0oOIiIiIiIjUiYMereGRHh3clAARzzER7UTmLHfVQXekRxxH0zSrJT1MlLeS39lu5sg7N9JDb7uObFaXcpU7mxrjgnOIPBgnGQVhsrRXohCPemgLurvsTlswaMs+VwriK50zzK4jRHxO0YvdunZUl0i05a28CjPGCyo/R5KOBkjEr2StNhs5b1p5z+YnMrePmXWpjvSIojSa3nbtHC3EpAcREREREVEn8cK3G8M/h3IK8uBYTX0z7nx7KZZurY1l0yKI4+C1DS2GXpOIAZVYaWkL4t53l6s+H4+RHnrbVipVoxpcjKhuZaK8lXbzoqafdIvPPlcKRIlPA3f+bxlW7ajDgvV78Od3lqGhudXwuluDQfzfe8vx2cqdMUsmaI3yEWBkTg8Loi1xZN+qAEhLvrQFgzA4OC4u1lfX4463l1pej1Lw03R5K4NfGkrbivZ4xTIZLz7PRVsWSHmkh3YiUem5hCpvJWu3mKQkqMMjPcwmo+3cverzZ7W3STCwrO58MAp7R+892NmHlGcAIiIiIiIiooRSXdeIn7Z0JDIUR3ogiFvf/AmvfL8Zj326BuvuOirGrSQ7dM9Owea9BySP/fubDdinkTxqiUOUdNagYgDqSY8khfJWRkd6TO6TjwXr96hu+6ghJfjbJ6sBqAeuKvPTsGbXfoypyMU3a3errkt1/gPVVxhfwgl6W33/5+34bOVONDS394mV2/cZXvfri7aEfx7YLTOa5tlAGpCVjPxQWNroqAjdCXYNrcW6yoI0rNm5X/JYZkpH+C7J67E8p8eMgcX42aHEd12jsSS2nvTkyJBlQLW8lfI6FAYxhDk1kXm8RuFovVctvQrTAEgD2NKAt0Z5K9HPXk9i3pigPZG5/uvl+ycn1Y899c2Gtj2iPMfQciFDSrNMLW/U4VVFeP/n7e2/hMtb6c/poTRKSGxIWRbmr6mWPDayh/Z7trMPMelBRERERETUCdQ3Se/WVgo0B4PAT5vjO8LDikQoMWKHtCQv7j9lGC58dkH4sV9NqcThA4qwvbYRggD8+rnvJa/RSngAsZ/IfHD3LNwze6jmMmZq0IuDT/fNHooxFbl44IOVAIBD+xfio2U7AACH9C3AqaPLMH1AUTjpoebJX47Gl6urMWtQMX7eUou89IDh9gD6wZmAiTlL7KQUpJQf/VDCAwCWm0h6SNbpYJcqy03Bxt0HFJ+LZZm7eARxn5zT3i+vf2VJ+LHUJB9e+fUECGgv8aS367Xa/dczRuDwqiLc/8EKxec//900fLVmN66eu9h84200a1AJXvl+s+Qxn0qQVfx+bz9uIG56/ScA2ucY8V32esfZrcF8OyYyP2tcT+SkJqEqiiSmtLScS3eSDqsJL/nL373yECxYtwd3vbMM66vrFV/z6TXTsHLHPhzSt8DQNj6+eirWVe/HyB65ltoqJv576v5ThmHQze/Knu+g9jHyqnzFfXL1VKzdtR9BBPHYvDUAgPevPAQbdtdjch/t99ylyluJv0TdXLOQiIiIiIjITZQmMo9jhSN7GLwW7l2Y7mw7HDa+Vx6GlmVLHjukTwFG9czFUUNKVCfz1RLrOT2OGlKC9ID2fZZm5l8QN/+kkaWSEk5lOSnhn/PSknDk4BLJKBK1WEKPvDScNqYc2alJmNA7H/2KMzTbG9F+nQ4Zr6SHUoBKK5wS7SAgq6MNtEzqrR4YkwRaISt35eK4q9GgcKhfyo0oz8Hwg3eGRxsf83oEHDm4BH61aCWA0pxUHDGwKKr120npPOdXmt1cpm9Rx+dYq/yO3kgPtwXx9XLEPgP7RklKkhezR5VhYDfzowgStbyVdLSYVh8xMKeH7PfCjGTMGlyimXArz0vFYQOMf8Yq8tMwrV+h4eWNEL9t8Xd1UOF5teOqloTsmZ+Gaf0LJZ8xo+852rlplHCkBxERERERUScUTnqI61O7YRZiC+SXwmqXxp3hhrmI9yoJ7JoPCsR6Tg8jLVQKpKj1Ua2+61EpyxJi9a2r7u4EGumhJdq+4dTHLMnn0QzwCrJfPDoHwko7ow2/OX0O0jtkneAUqDhyQS2QLKgEsY0G4e2M1cdy10vvxo99wkG83z2Cu5OOauRdyvRnR+VNJ+CuAKB87lI7rmZGaxodqWtjzsP9Iz2IiIiIiIjIvPBd2JLR8/Fpi13kwVz1iTg7Adl78wragX09sZ7Tw0jwy0yQTqvvStajNMrBoR6hW95KZdJlpymO9NDYB9GO2HBqpEeyz6PZN6SHWxpKUwqsGT3+estZDegaKRdjlNPncjcEbJWSdz61ESoqo320y1t1SJwRCurMBKDtIh8NkCglKLX6SFDlZ9V1qW7D3ftCr31qiUQxvYnMxYwuGtfyVp9++imOOeYYdOvWDYIg4LXXXpM8HwwG8Yc//AElJSVISUnB9OnTsXLlSrvaS0RERERERAZ0jPTo0BlGQBjSCd+mZDSDYikW7dfHfqSHfuDCTBkmed9VCyArBr2tjvRQeS967zBuIz2UMz+qok1eNLc6k0hL8nk1A2TSeQScntPDmXVbDVDrHTOXx1sNUdpHftWRHso/awVQxftQabUJsQujGNXiFI8gJGS/0y5vZeT1yo/HIQdlilr7Qu9ZmtBSXlbvPCbef0bPpXEd6bF//34MHToUjzzyiOLzd999Nx588EE8+uij+Prrr5GWloYZM2agoaHBcmOJiIiIiIjImM44p4fRa+EEf5sIBiMD19LAfiS/Sm3tkFjP6WEkvmEmoKzVemlCyNxrrdBrf/zKW5lbPtquUd/UGt0LdQR8HlN9Q1CLeB9kNKdj513qSps0UiPfKCfnU3ELpeCnV3Uic+VzgOZIDxMB2UQI5sd9pIfH/YF+JRFtFnUMI6PE1JPiCbgzoHzuUjtfmStvZXA5Gz9spuf0mDVrFmbNmqX4XDAYxAMPPIAbb7wRxx13HADgmWeeQVFREV577TWceuqpphsorT9LRERERERERoSCYpJrqmBiz+th9Fq4M4xokb9X+d3tcj6vAK0YdKxHehhhJkAmD/JKApt6pb8ceuu6Iz3iVN7KbOIn2r5xoNmZpEey36P5WZdXM5MEyRTeipXD71TY0mqAOtpTXDwCj9FSmtDYyETm4rZr72fjO9FMADuWXz/iVsUj6SH+Xmo/D8e/3xhhdDSQlZEeLvgIadJrn3QklA1JD4OL2rnbbL3tYO3atdi2bRumT58efiwrKwtjx47F/PnzFV/T2NiI2tpayT8iIiIiIiKyRnmkh/sC32YYDTwl9rtsJ3+nkqSHwn7Qu3O8pTXWIz3MBSdD1Fqp1XXFgRczk6MbFW1QK14jPRT3gcYObIs26eHQSI/28lbGPuuCIP00KB1rtyRBpZM+c6SHHqV95FNJekRT3kpvF7o9aC0Xl4nMxQnIBCpvZaR0E9DJ5/RQaXnocyH+Woh2InPx/jNe3sq+/WbrN/C2bdsAAEVFRZLHi4qKws/J3XnnncjKygr/Kysrs7NJREREREREXVLHSI+upzPEA+UBAnEgQCnOoHe3fuzn9NBnZk4PrebrjYJxqj/oJeESpbxVa5Q7yKmSaQGfRzMQKSllBOnxV3orlkZ6RBl/09ul1uf0sPRyXW4I1yrtIp9qeauOn8W7xuhE5onKaCkvx7YP6fdSvOcViYZTc3ok3p6QEieLo016RMPOdcbnG1jk+uuvR01NTfjfxo0b490kIiIiIiKihBeaY1h84doWDCoGah/5eBUm3/0R3v1J+WY1t5BfeCd6UEFLxEgPj8aT0E9qLN++z3KbzDAS+zIXIJNPZK5817zSKq3eFa9+J6/26wK++JS3Uh7tos6puTmi1Z70MNE3tKtb2Zb0slqj384AtVtGrzhJQOTIAZ/qRObixFfHvvFaCGgn2pwMWu/VKfI+nSh7TNxS+WcxqPGb3rokj7t8Z6hOZI7IG2bUzsdqn0cr7NxvtiY9iouLAQDbt2+XPL59+/bwc3KBQACZmZmSf0RERERERGSNUnmr5lAmROaed5dj4+4D+NWzC2LRtKgZvRZO5HlL1OiVt2ppUz628WLkWJkJblw0pRd8HgFnjesR8Zy05I25gL9dSrKSAQCDu2eFHxtXmYf89AAm9MpTfI3SezFrUu/8iMeUdmsixciTdCYylxxtQRq8U3qfekmvCw+pBABceXhfhW05E7m0ekf8EQOVY2xm3HXiYAgCMLQs2/K6nCLfSz6vfhhTnP9VGRgCABhaloWCjABGlGfrt8PE4fr7WSMBtO9fOz121igAwA1HDkBGwIcpfQukyd843NYuLyXm9kC/Eq24/f2nDFN97rD+hUhL8mLGIOXP4m3HDQIA/FbhvBIPoX4JALlpSRjZI1dz+aCBOT3OHt8TSV4PZo8s1V2HUa4tb1VRUYHi4mJ8+OGH4cdqa2vx9ddfY/z48VGtU7x/EulLmoiIiIiIKJ6UAv8Nze4KjJtm8Fo4FvH/u08eYvo1Nx9TZWi5IMxPZO7CecoVifeBcnCj442U5aaEf+6Rl4alt8/E7ccPiniF+G7TWJa3Erf/2GHdsOKPs3DcsG7hx1KSvJh//aF47vyx4cdG9sgJ/zy2UjvoZMT4Xnk4dbS0TLjba8krOW1Mefhnr0fQKW8l+hmCtLyV4pwe2tv+/ZEDsPKOWehXnKG5nJ271UDsXlNWih8r75hlaR2njinH8ttnYWrfgojn3NCFBIVySaojPUQPi+eoUSuHBbSPxJp/3aF4+eIJhtpi1BEDi7Hyjlk4VdSn7XB4VRFW3jELFxxSie//cDie+uVoyfPxKG8l/k5WOl5OOXdihW3r0mrz5D4FmDOhp+Jzj58zCotuPgJZKX7F50f2yMGKP87Cbw7rY0czLQv1y7V3Homvf38YAn7lz0bofCk+b6rtopKsZPx46wzcM3uobe20sw/5zL6grq4Oq1atCv++du1aLFq0CLm5uSgvL8cVV1yBP/7xj+jTpw8qKipw0003oVu3bjj++ONtazQRERERERFpU7pwbWh2Vxkbs9xUPCOasg5mXiN/r+IgqXv2gjq1wHtqUkfJJ73ghl8WsPSLdoL4pZLHzTTSKLWa7bLge5LCHB5+r/w9iMqq2BTckW9Dca4UW7bkHEmZHDN3jAuy+RyiGOkBRO5Dp9kR2IumzfLNJvk8ru4b7ftJlMQwMJG50ZEe7etTX8DKIXKqP4XWq7T+eCQ9pHNNCTH7clLrB1GRrUp+uvCr9TlBUH0uROk7IZ46+o/+/pN8jjQ+DFrvMZoEvJ3d2HTS47vvvsO0adPCv1911VUAgHPOOQdPPfUUrr32Wuzfvx8XXngh9u7di0mTJuGdd95BcnKyfa0mIiIiIiIiQ8R3Pjc0t3bK0k/xEM3E4B4zV/Py+UskIz3cn/ZQaqIgSNtuV3DD71NOhjjN6qZM9Qe1NijuZ4USXwlUOqN9pIdWeSvpc9KRHpHc8tbjPem0KoUd5IYEsyAg4kOmmkwQJ75EvSAe81zEUrzfq7zUXKxaYDVpKH653rrccv6wm94ebJOUt4puG9F879j5943ppMfUqVM1Gy0IAm677TbcdtttlhpGRERERERE0QtdsEpHeiR2eSuj18KxCPBGswmjIz2CwaDp8lZuo9REeQxTtxSVVokj0c9+0X51oryK+kS15o+JZAJdG9qq1DY3xdONEjfZ6xG05/TQen8KH0w3JnycTnqY6Vru2zvt2kuXSR9T22/Sicz1l4+mLW7U5sB7NUNyDoR2stJOdk6grbcqt34+nBJU+Nsxlv3fzm7srnE2REREREREFBV5XE8pzncg4ctbyX5XCbDEIkjR6vAEnfIlxYGAhAhqq7xXvRErBnMeEo6Xt1IRzbbsvttfcaRHnCZzt0Jy57XOnB5yeiM93DLfjXiuIaeDw2ZOT0rlv9yQWG0fOSBtiJFgt/j9JMKoOCvE85fYMXLMCo8Qu35j9b1ypIc+8SiiWI4Qdu1E5k6Qdq4u2tOIiIiIiIh0yC9Kg7L/Au3lrdx6x6oR8gCW2juJRZDCyDwBckaD3IIQeae7NBDg/mOo1kLx43rBDaMBS3F991gGOaVzephnS3krRAYadeaHdyX5CBjDga+gPXN6xEKLKOvhpsSlS3ZPhPaJsaWPqZW3kkxk7sD7cWvuRNy341HeKigdDhCzvy/sfK/yc418ZFhnLQmqtgvDfzva8LajWYXePDym1mXfqoiIiIiIiChe5BeoHeWtOp5obEns8lZGxSJIEU1gzWjSQ6kcjzhA7tYAnJhaG6V32EY+L37vmm9T9KTTk1CrvxfzB0IcYLMjcKeUHHB7/1Cb7yXEqzPSQ/xUEEHpSA+l8lZRtFG8fruI5wHy2RnZs8gtI2HkBEQmf9XLW3VwS5IrFsQjDuNR3ko6Ms/cCC0r7JzIPFYludxG7WMSelz8fCw/UnbeuOCesywRERERERFFTX5NGr5wFT12oCnBy1sZvfk7FiM9opnI3KbyVokQolG647d9BEvH7/ojPYxtS1LeKk47x/CcHpIyTs5sV2m/uuluZcVEjehnvTk95F1LmgSJ5JY5PVpiWIqoM8RxBYVySX6VYLegk/iKdvtuJ36r8QjeSwZ6CIjZl5PVBI/4+0l3Tg93nD5iLl7JQzv7semJzImIiIiIiMgdGppbkXnwZ3mgZ9OeemQm+yTBoIaWVmzb3hD+vbUtGJe7Q6NltKWxuFSPJiBgKukhW1Q8KiAR6tSrNdHMhOxapVLEz0nKWyVESqidXSVa5GvRnSA+zpTetbhPe2XJMS1BQ+WtzLXPKeKRHk6fds0cbzclxOTku8lrIFPoRF9361lF3KfiM9JDOjIvYcpbSZLPsvJW1tacMNTLW7XvAfHfONHuk2g+i5zInIiIiIiIiHD13MXhn+XXlpe/sAiH3/8pqusaw4/trW+WLPPovNVONs92bhrpkZ3qN/0aoxfzQUQGjyQTgJvecuwpBrZlvyuPSDAvSTTSQ7yPMwLt93n2yEuNYq0djOxvo8E+SXDflonMI9fh9nIteu1rn8hcfZmA1xv+2euVjgpR6j+9CtJMtzHcFtG6A35rIbRk0et75kXfJiuU+mlBeiByORd0IQGRAWn1kR4dP2ckmz83J6rUpI7PQjzuXxCfe/0+T8zaYPXcWZSRHP7ZDX3dTUJ/Pxn5HKUFtMdSZKVE83dSFxrpIf7CctOdCURERERERPH2yfKd4Z/VrpfWVe9Xff2/vlqPS6b1trtZunrmpWJddb3ksUum9cKijXuxv7EVizbutbiFIO6bPRSLN+3FM/PXW1yXsmn9CjFnQk98vmoX/F4Plm6t1X1Nks940FR+3S8pb5UAQRq19yrojPSIKJdigE9lvpOXfz0Bf/14FS6f3tfYimJA/JY8HgGvXTIRxz/yhal1DC3NwuJNNeH1yRMfrh/pIQAnjSjFy99vCj8mvWNdO4CblerHNTP6AQAyZYG50Ii3Sb3z8fmqXQCAB04djnvfXY5XF2423dZkvxfXzeqPxuY2FIoCpdHoXZiBX0/thYKMAI4Z2g0BnwenjS23tE41Zs4RZ43vgZXb6zCtf6EjbZE7anAJmlvbsK56P1Zsr1Ndrn0ic+kbUZsLRbzU6J45OG9SBXoXpltqZyKMGjtldBlWbq/DqJ45to4ANHq6yE5NwtVH9IUgCMhM9sdsFGK0SY/HzhqJj5buwAWHVOCfX6w19Bq3lMeLtRHl2bhgcgV65qfhn58r76sBJZmKj4eMq8w1/VnU60K/nNgTtz1gbF2uT3oQERERERGREcoX5i0atV2iCRuMrcjF12t3R/HKdlkpfjx8+ggc/dDnkscHlGTimhn90doWRK/fv63yao1i/jInjSzFSSNLbUt6BHweyUTwHo+AW44dCAB458dtuOhfCwAAv57aC3/9RHkETYrfq/i4EeIgj9vv5AfU36sk6G/hfYhf6pPM6dHxRN+iDDxw6vCot6G0TiPtMcrnETCkNBtJPg+aRH1Ly/jKPMnkxYqTgit8MNxUwsgjCDh+eDdJ0kNcSsXn8ej2Db1k7eljy8NJj+7ZKbj/lGFRJT0A4KIpvaJ6nZJrZ/YP//x/pwyzbb1WBHxe/PnkIZLHnAz4l+el4ncH98PvX12C57/eoLJkZCtUJzKXJIUF3HR0lfWGqqzfTUpzUvHoWSPj2oZLD+0T/jlW+ynaOXFmDCzGjIHFqG9qCT/W3KJ9bnTPmdNeep9xQRBww1HtnyOlpMcJw7vrbyOKz6L43D+5Tz4+W7lL8vzMgcW4zei6TG2ZiIiIiIiIXEntZsRWraRHFBEKqwEAQVAOXIVqdGu1KJ7lrSJHXihPhCoOwMsFLCQ9EmEeD7Fkhfcqn5hYeUSCOKBv7D2LS6zEay8Z3a6ZidzVeCV9T6m8VVSrjRmPEBlwE5+mPILOROYauuhN2QlF3H+1jlf7+cJYeSsnPvkJdsq1VbRvPVbnHp/FDSX7Or6fmlpbJc91lXOIWiJc6VHFxxzaUXqjeMx8Lpn0ICIiIiIi6gTULj+bW+29MLV6oStA+aLWyJ2bRq91nbgUlweX1UYsaAVjkk3MCZDo5a3U5j8QB7v1gv6aCTDRz+KJzN1ekUbcvGhLtHhVynl1PKYw0sNFgTyPwkTl4vOKXnkrLW4a0ULKpOd67ZGI8n6ilVR2lstPLK7h7vJWIeI+qDfKzk3nzliI9/vVO7JMehAREREREbnUHXfcgQkTJiA1NRXZ2dm2rVd9pIf6BX00wXOrF8SCoDxJcegxrTYZvfvbiTsQ5W2WjPQQXVlrBWOURj+okd8JLwl0J0AATnGkBwRTIx20nhYfYb84EOrEKB9DCxk7JnZMZO6R9AWF55VG0ES1JYcIke1ukyQ99MtbqQmtJt6Bu0TnZGLVa+KzIu/LaknlREgEdwWxOg5G+5ARjQZLC3Y2pv6OcPh8Ki9Pp/RziJnvBtcnPcR/rPI7i4iIiIiIEl1TUxNmz56Niy++2Nb1qt3hrDWnR3TbsUZtpIedN/A6ce0ov85Wu0jXGulhdE6PYFC7nFYiBPjU3qs4GK0XnDf6PsUlb+zu73ZTHCFkssmSCj8KoybcnhQTwv/XoVUUd/R6ou/j7j76BEjP9ZrlrRDZl1WTHja0Kxbr7OxiNd+UT7XMmXnykR7yLtn1Ro9Fvl+jJa+ipVYuVOkom+linMiciIiIiIgohm699VYAwFNPPWXrelVHemiUt4pupIfF8laC8l2aHSM91Bslf0YtuOvEXd4R5a1URizYN9JD9nuClbdSfK+CNIBkZZ4S8TEWl7zRmsMmWkaaafSdiNdlS3krpW0oJBDdNPJBEISIz66kvJUNc3okwmfEzZzcfeKRSnpzekSM9FDJjjs95xH7kzGx2k12Jlf0Rnq46dwZC/F4vx4BaA3/rH1szRx7Jj2IiIiIiIhcrrGxEY2NjeHfa2trI5ZRu1DVuvNdgGA6iWE9pixIykGFGLmQ1RptIeZEeSt5gFo6N0XH4/bN6SErb6UzebXbqL1XvZEe4ttHtUYsiJMn4pEebQkUoYq2RIveqB+VVGBU23KCfEJ7QKm8VXTr7np3ZSceyUTmOsdLfh60OoF1tNx/xnWHmJW3srEf6M7pYduW3MXMsXJq0vKQ9s955DaMf78pc315KyIiIiIioq7uzjvvRFZWVvhfWVlZxDJqwSO9O9/N3hlv9dLXI6iVtzKQ9DB4uevMRObqv4vb5dWo05Xsi36khyTQbXgt8WOklJfihNuS541tyyfKosWrvJXxAJIoeRVFRCaIoOSzojU/jlsJUJrTo+Pn9onMrY30SKDclys5OXLCa3SkByJLt6mVNXJ3j+86YnXusTPp0VXn9FD77Bk9d9p5jlU7nIoPd6Y5PYiIiIiIiNzuuuuuay/ZovFv2bJlUa//+uuvR01NTfjfxo0bI5ZRuwBdvGmv6noFAViza7+5xjhc3soWjgQ85eWtlEd6aN2977EQqHGqvJVTMSrlicylh0ZvdxguGSX6uc2J8lYmQqp6Hw87ylvpTWSudEzdlATwKJSvEo/08HgiJ7CmzkN8rtfqlkojgvyq5a1saJiMiz4yJGPniJ+mVtmcHrKTpZvOnbGgdAON07tA7e8/peSrmc+665Me4h3b1ToaERERERElht/+9rdYunSp5r/Kysqo1x8IBJCZmSn5F6JXduCzlbtUnxMAnPH416baMrQsW/Hx44Z1BwD0KkjTfL0AQTH47zEQ1A89nprUHlCf2q9AcbmYj/QQT2SucidyblqSqe1FlvISP2BfwMcfzXADA5JUgpPi/qqX6DpmaDcAQKVenxKAtIN9oldBuplm2sZoYkS8VChBdvrYcsPbCQaliTVBiNy20n51UzhFKZg9oKTjnDa2IjfqkQahpNeAkoyo22eH08a0j8abPqAo6nWcPLIUADBrULGh5Sf3yY96W7FkdKQHENmX1RKFZhKTRok/Z/kZAdvX72anHuy/E3rlmXpdrEZ6WLmBIKQkKxkAMGOg9DM6okeO5Pcpfdv/zvDbOHm6G8gPVagk5ZS+hTFvi/izlpLUccNEZnLkrBx5acY/i5zTg4iIiIiIyKKCggIUFCgH4J3W2haEzytEdZOYIAjYua9Rf0GRqpJMdM9Owea9BySPXzC5Av1LMjCsNBvDb39fY5vKd2mauet93jXTsGxbLSb1jl2QL3Iic+WRHkleD9678hAccf+n4cceOm04BnXPMrytoGz9JwzvLtu24VXp8nkFNLXqL2eE3yvglYsnIuD3KAaljMzJIk6K/HJiBXoVpmO4UqJN1t9fv3QSlm2rxeFV0QeZ7WDm2IT6/PVH9seIHjm47N8LdV8ThPTzoxTsdXl1q4MTmXe4+oi+OG9SBY4f3h0bqusxskcu1u2qj2rdoW5RWZCOly8ej/z0+ASrbz5mIA6vKsK4SnNBY7E/Hj8IRw0uMbyOv505EoNufvfgb9Y6gdKrp/QtwL6GZny/Ya+ldUuSHhrpuPaJzKUtcSpJq8TjEfDelYegqaUNmcn+mG3XDa6Z0R8TeuVjdEWuqdepJf3tZsdIj7cum4wfNu3FIX0KcMOrPwJov2ljRLk06TFjYBGePW8M+hXHN5HqtM+uPVT17yrHByHI/oZ654rJaG0L4l9fbZAs9tczRiArxfhnkUkPIiIiIiKiGNqwYQN2796NDRs2oLW1FYsWLQIA9O7dG+np5u9Sbw0G4UN0E/iqhQ2m9ivAJ8t3Kr9GAEb2yIlIevi8HkzrVygJWgtC5MWyAOW7NOUle5TeTSj+VZARQEGGepLJiUk3tWIs4rb7vAL6FkmDI6ERC9HqXSjtF3aGlewsE9IWBAaXGk/u6N0V7PUImNZP+a5TydwfENC7MD1iP9nGwC4KvRW9riedsLv9RQGfF9MHGL+7VvL5URg1oZQIcXoiWjMESNt8zNBuSPZ70T07Bd2zUwBEX/pL/DZH9jAXsLVTst+LQ/tbS8Al+72Y1t9YvyjKDCA9IA7x2X+8p/YrwJs/bLW8Hsmx1ZrTQ4jsyapzejgUa5efy7uKJJ/HcN8Ti9VE81plJOUq89MUy3jmpiVhquz7ZVhZTsRygiBgcp/43NQSS3p/VzlJMmeZAPQvzlRcblC3LAAthtfLpAcREREREVEM/eEPf8DTTz8d/n348OEAgI8//hhTp041vb62g+Woo4ppqsQN9AIKrRobE49Q8AoCWmTLCoIQ9ZweRkuYODGXtVa5HXGcxxeDO5HtnGRYrUZ+NIwE1sWL2FYKxQUjG4w2Qdw3xckLw/tCXt5KYRGluKN7Uh4h2qNVoi1fE03yt0sxuFuVuqPPprlWxIk/zTk9FNoRq6A6RcfO7xMtZs4PbSb+OGposWnYYwIw80ly+rzq1Rm9GH7O5Mff9XN6EBERERERdSZPPfUUgsFgxL9oEh5ARwLCxpyH7l3WRu8aV1qPICg/bubOTTsZ3axWLkMyp0cMgnJ2bsLOciRGkk3i4EliBOfNBVr0lhV/dsR93ujIhiCC0lFRCnfD25kUc0L7ROYdvys1N9rzgYsGtMSUQi+wfRtej8eWuTOaWzomjtb6Lmmf+0W6PV8MJzIn86IdoWWWmYS5mZsgGpvb9BfqJKyeKu081TrVbdyf9AiKf+yi315EREREREQqWg9e0dtZvkYroCBACG9Tj1ICQKlOOyBNKqgFbY3fDO9EeSutfdLBiZrmRsoXRSsWI1NC5O1WOs6JHrTWa3+r6HlxgNBokL8tCIjjvkrBIsVkkov2a/vk69Lf5aK9YdxFbzOmYhH0tyuh29xqbKQHENmXVctbuWG4F8VsTg8zXdHo3ysA0NiFRnqY4fT3h/hvAem5zNqG3Z/0ICIiIiIiIlVtbRZGekSZXGg1eDOk4kgPCIqPGypvZTTpYWJnGI2daLVP/FwsynvYPZF5LNkVPHFTEN8McXJS3G+MlmsJBoPS8lYKL1NMJploo9PaR3pIR6soLROVRO0YtrO2H5SOidcj2HLuaWoVj/TQaMPB/4mxvJW7xSqJbqYfmrkhpCuN9HDTJ8nox5rlrYiIiIiIiLqQcHmrKGJcatePeheWRmtkK5UiaR/pEbmskbIYRu/mdSLsqbVlcYDWjvIesZx0OpZBRCMBC6OjdMTLOX2Xu5HVG22D0kTmZgQhTZAIsCcQHWviJkd7PlDClIdzfF6bkh7i8lYay7WXt5K1QSWonoifgc4odkl0h8pbcaSHIqU/SZwaXaz1WTZbupFJDyIiIiIiogTWFr6it+8CVPPCUjBeLkJ5pIfNd3YrcWQic2PP+WMQ9LFzV8Vq4lmg/dg7kc9JpHhnm+hG4mji+kH5ROaGRz+5Jx0gD2bbOpG5e95mTCmfae1l1138zQaHCip9V6glaZn0cAe/C0d6tJoZ6dHSdUZ6uInRv//MfsyZ9CAiIiIiIkpgLQcTEGbuZgwRBOUgvd6FpeGRHopzeiivXbyoWoDWiTk9jN45aLS8lTww6EQwzs6JqmNe3squ9cQwuG1kfxs9JuLPTrTHUZ5MdPvE5XLtc3poJ26insicYz0c4/UItiSnm1uNTWQOhVGBaskwzunhDrGayNzMVkyVt+pKSQ8XfW9IkuCiX6x+z/usvdx54i+srpqxJyIiIiIiUjPhro8wpW8B5q3Yafq1AgT4PB40t0pLOugFUY0mPRRHeqisWrysWgLH6CV6zOf0EOU5IgLSxptimJ3rjOVE5oB+ACqqMm0uCt7oMfrZURNR3srge3dTPEVekkvpHURd3spF7zO+7N8RPpvm9DA6kbkAATv3NVrfIMVMrMolmjnnm5nIvKkrJT0ssvMMIylvpbEc5/QgIiIiIiLqYqJJeADtF5CHDiiMeFwrbiHAeBBBcaSHyrJ2TGT+l1OHISPZh6fPHRN+7K9njEBGsg/PiB4zs04jy2lNZG5mBM51s/ojO9WPm4+pkm5bttfsLAU2ojzHtnUZobc7nvrlGGQk+3D/KUM1lyvOTMawsmyM6ZmLtCSvfQ004C+nDlN97hejy1CcmYzTx5ZLHj9/UgXy0wN46LQR6FOYjsP6R37uZg0qRn56QHPbwWBQ0sf8HgEXTK5ETqofAHCownqB2I+AyD7YHiUR5a1UJs0OqSrJNLzdrpbzuOvEwchM9uHhM0YAAC6d1ht5aUn4zaF9bN1ORX4aJvXJ100qFWcm667rV1MqO34RrS/gixwlt0OU9JCPSvRKkn+6m6UY0CtLN61fATKSfThqSIm17QjAkYOLUZGfprpMSVYyThtTZujvlbtPGoKMZJ/mub2zmTOhJwoyApgzoafusg+dPhyZyT7cdeJgR9qi1m3k5xuzI7pcP9KDiIiIiIiInCMPNAEGylsZvBlSeaSHSnkrlave9688BIff/6mhlh03rDuOGdJNsq4jB5dg5sDiqOcICNG681z8jJVyURdN6YULJ1dGtFW+y+wI8N1+/CCcOroMry3cbH1lBhm5O3d8rzws/sMRusfL4xHw6q8nGF6vFeK1z7tmKnrkRQbaQk3ISvHjy+sOjWj/jUdX4fdHDoDHI+DdKw5RPIZ/PWMEahtaMPTW91TbEgwCKf6OJE9ykhcFGQEsuPFwSTus6F+cgWXb9kX9+oxkH76/8XD0uuFtxSC5RxD0y1uJ9t+dJgJtXW2kx6ljyvGLUWXh/nb1jH646vC+GmWgzDtuWDfc/4th8HgE3ZFKlx7aGyeNKIXPK6DPDf+LeP5vZ4xASVZK+HdxMu6b30/H0Ns6+r68rf84e5Tkd69HCAe0mfNIDGMr8/DEOaMxb8VOvPXD1qjXI0DAX88Yiba2ICp//3bE89fN6h/+Ln1zsf52fjG6DCePLLX8d0IiyU1LwtfXH2boPY8oz8Gig9/L172yxPa2GJ/I3Nx6mfQgIiIiIiLqogRBiOrWaKMTgyqVTlK7ZlWr4W+2zI3SBbwdgQztC3HxnB7WtmWkrXaEZdKSvPB7Pa4sDWX0eMWj7UZG2ai1P/S4akBaEHQnNw8iiGR/x+cq2efVXGf4dSY+51br8gsH2+PzCJJSRuLntX4HjAfB5LrinB7yY2934DZ0PAH9kWuCAKRojLySf2bF/dIrSxjLl1Wa1LxJtF1yP4/Q3pfEn1NBMJ+sDB1vreRe6Dmjf690pYRHiJn3HLGsjadaSclGjb9wzB4h15e36mpZeiIiIiIiolhSunNXK5gsCILh8lZKF9Rqq1abWiLa4KdRRsslaE9k3vGzzxuDy2wb9kPywdECsYzzCEBC1h8y0u9iNZFyMNhx7ABIEiB6rzPKctLj4A5T+8y051o7GqS0nLgNpsq5JWD/cjvx90Gbzrlf73MgP5Tifmm224kTzIwdJgalz7Kd5RpDxKu0Oo8SOU/cA8THzmoS2/VJDzF2UyIiIiIiIvsEg8qXlHohCL3JqEOU5/RQuQtereyVwQkuo2ZwpVqJIHEbYzGRqx0B9lCJpFjfIZ2Id+KLu7vT+0tv9Iq8vJXWXfXRshqEDL1cLXkSeQd/5DJqI7/0JF7vSix6AWS9wyZ/WnLHv+xZvRFB4gRzi5nJk8hVovnK1O9nokQdu4brGf7OMdlXEirpQURERERERPYJBlUCAjoXlobLWynMb6F2basW5NSb8Ngqo2vUnNxdPNIjFkkPGzYRmsvFibtstST6TbdqfTCWu1GpvJUeM8kmq4cotCtUkx4RvyuVpBP9bGLnGk3IknHiva9b3kpvXRrlrfTmLpL/Lj7XGh19SO4TzXeQmRFFeqOTKP6k5a3Umb3hg0kPIiIiIiKiLiqIoGKQUC8I0Wp0InOF9ZidyNwttdq19ok44BaL8lZ2JCr8ChPYO05IzDvxxW2Od3cMAghIylsZTHqY2PHNLQY/4CpCn3H1kR6yYLdCVxS/1lR1q0TsYDEU1anDRKkgvXNT5EgP9ddGjvyInNMjhEmPxKB0nKJKeph4CctbuZ/kq8LgDSaG1htVa4iIiIiIiCjhtQWVg4Tad9oZv5taKeiptm61ARKxHomgRmsAhziO48RID70yL9HweznSwyhx0Extd9m1F42UlvOLhkEYndPDjGajWU0V4ZEeaqNidMoYyV9raqSH4SXJKGmpIItDPWQ05/TQWZd44nOWt0oMSqNEHSlvJZ6Hhl3D9dTO8Vb/XnB90oN9k4iIiIiIyDlKZW/0AgpG76r1K4x6UC1vFaeRHoZLSWssKA4Ehkp6qSU/7Hg/dqzDf7Cdscx5uCN9ZU2sJixXIw8CGR7pYWIbVgPI+nN6yH9XKm/FkR5uIS0VpLOsiXW1U5/QXq+8lXh5jvRIDKHPpzTZ5UB5K9NrJLPsnJ9L/FWhdWzNHldfdM2JD9ZmJCIiIiIiss+qHXWKd1lqXnQKxgNMSiWr1OIbRiYyjyetVrS0duyP0F34fq8HLW2tiuuxPmeC9X2SFKeRHonIyETmdu1GvfXIA00BB8qUNVksbxX6tBidyFxpMelID+NbtjMQR5H0JzI3PtcCoDOnh/y1Gutt0cvGkCso/e0QzblTf6SH+XVS/IjPG1rHzuy8bq4f6UFERERERETOWbG9LuKxnLQk1eVz05LQtyhD8lhakvLd5kqjHfoUZigsqR58d3ykh8EEQkV+mupz6YGO+wlDiZ6hZVkAHJrY3IZVZiT7AURXWsSKgoxAbDdoA/ExVEsyFGUmx6QtwSCQneoP/244CGQiF9BksbxVSFVJpuLjkcHsyPcgndPDeCftnp2i+lyPvFQAQElWbI5VZyHe+7pJD911SZeQzJcTMdJDJwsikhHwqz+ZwDIOfrcMUPksJZpQ0kN8aNXm87JCvMb+xe1/c2SldM4+0hlIR3qo69QjPYiIiIiIiMh5I8qzcdmhvfHl6mp8t36P5LkpfQswqHsW8tKTcEjfAsxbsRPnTuypuB4BwO3HD0JdQwvGVebipQWbcPUR/QAAT5wzCuc9/V14WdW7wh0uVKEUT737pCFYtGkvzhhbjtoDLfjfj1tx0ZReeOHbjYrrKM9LxTUz+iEntSNZ9JdTh+MvH67EWeN6qG77rHE9cOEhlXh03mqcOrrcUpuNumByBfLTAygOB37Nrey8SRVoamnDs1+tlzx+xthynDyyVPf1h/UvxK8OqcSQ0mxT242ntIAPNx9Thda2ILJTpQnBx84aiW/X7sbRQ7rFpC1BAIO6Z+GK6X3QTSPAH/k6abC6X1EGlm/fp7ysxSobof5510lDcP8HK7B6Rx2+Xrs7/LzHo7y8mCTpYWCbT587Bh8v24Gzxqt/3p49dyz+Nm81Ljyk0sAaKURS3kpvSg/9rIfx7RpY5t7ZQ7Fhdz0Gl2YZX3ECefWSCXji83W49NDe8W6KYU/9cjTmPPmt4nOhc4taeasXLhyHU//+lant/e/yybjvvRXYua8BizfVAJAmzP5x9ij89ZPVOH9yhan1kjY7izEZHXFq9m8fJj2IiIiIiIi6mIdPH45Ln1+o+rzXI+CqI/rhKgDDb3sPe+qbAQCjeuRAEATkpwdwxwmDAQAzBharricISIL+w8tzwj8fNqAIFx5Sib9/ugaA1kTmHT/HalBCaW4KfjG6LPz7+F55qDm4D9RcMk0alCrKTMafDu4jMUEQwtGC248fBADhfWmUlf1ww1FVkt/N3mR73az+8Hs9EUkPI+9BEAR4PAKuP3KAuY26wC8nKgfMZgws1vwMmKWX5AsFDa+Y3tfSds6e0AM3vPqj4nPiwHaS12N65EfoHRRkBPCnEwbj7neWSZIeEROZ6yQ9jATEpvQtwJS+BZrLlOel4s4TzX3WSDaRuU7WQ+9YyZ81k2BT+mwYSbQmst6FGQnXZ6f2K8ScCT3x1JfrIp5TmshcfFTHVeYZ2ob4NQNKMvH4OaOwc18jRt/xQfvzogXKcvm5dzvx8RInrKzmVVxf3orTeBAREREREdlLLzDlkdRXtu8CVItaCRun55xQWrvRyaHt2p7uazQm8LXKbI1slkqPr2g/g/LYilbZNb0SRnrkXSrg80Y8L5nLQaFXSc9BlppDFon3v1LQWm1Z5efVy1vprUtrPhBKDDZVztPvZ/ZshuJAu7wV5/QgIiIiIiIiDXrBIrWAgtlgqNUyOUB8JtpOcTLpYcPbsXOXmB3pYTZJIn1t1C/tMnT3UZQfKfnLtD5XVj+28sBUwO+RPS9bXmekB7mH5YSY7Het1UWMCLK0ZYoltfOL0t8E0X0vRL5Ish5+2TjOzqSj4b8rTB5WJj2IiIiIiIi6mGad2y0ld1mLHjd7kau3uJGkiBCHq1alkR7yORHiyc55TiQTyqqs1q74EcNQ1tnVC7WSCuISRtH0e3l/SZZP/i5bQClA6hWPMHPPR69LkszpoXOnvl7wMmK0hollKXGonV7aFOb0sOubQVD5mdxPUsZU4+CZPScw6UFERERERNTFNLa0aj4vqFyAmh25YUew0unghVKQLtkfealsV+DVjoSFncFA8fs3MqqGwaT4inb0lPx1WkkPvRJGeuRrDsiSiB5BmkxRaol4snOrowvIPnrHQn8ec1l5Kx7bTkntq0TpfgvbkurMknUK4nOE1dMDkx5ERERERERdTFNrdJPR6sxhG8HucJYTMQ3FOT18SiM9Oifx+1c77qwa4h5Rz+kh+10rwdVi9oMuIw8+BnzWylt11s9e4hBNZG55Tg8TWzUxKoTcRf1vCHuOop39jKJj5+hXSX/RGulhdr1RtSaG3DSEmIiIiIiIqDNoatGuUSINGIiDj/ZenxmJfzh+96bC6lOSFJIe9g31sL4KW+f0MDdhNO+mjS+7uqHh8lY2bC9yInNB83dA2i85GiC+JOWtdA6F3mgxK3N6cMRP4lD7nlA6hnZ9o/CbKXEZnbvN7N8frk96iPH8RkREREREZJ1e0kMtEN5Vrsnkd6a7jVNzeqjFE+xKdDBhYl20iUf5Z9fZkR7S3+Xl4gSF9shJRnp0kfOOW4kPZ5tO39D9hEeM3lBfn7wfyecT4U3S7qVe3sqmkR4mtknOsPPvEMl6u9JIDyIiIiIiIrKXqaSH6HHT5a1siFbGI46hFJy3K7wWzfuRBxc0btI3TXysjd5tSc7ROwTRfqTkL9Ma6WHVnv1Nkt8jR3ror0M8kblNcVJCdIFK6UgPi+WtIub0MN4Oq3PNUOyoT2Qe+Vg0Xzt6CXSnAvLUwc6ko8HqVqYx6UFERERERNTFTOqTp/m8V3SleNa4HuGfZ48stbUdRi6ZnY7Dz5nQEwDQvzhDc7n0gC/8s5V48dFDugEAxlXmRr0OO0dMiFellvTQuzu3ODPZ2LYMt4rURBv3PXlEd8nvVd0ywz/LR2IYkZHsU31uf1Or5Pe+RemS3wVBQFluqub6PaIPmdH+Rc47e3xPnSV0gtHykR6a5a2AoWXZ4d9Z3ipxTOlbqPj44VXtj/cv6fi+PXNs+98Yk3rnW9omEx2JJ+ngH5tT+hYoPi9PrJj900f9W4qIiIiIiIg6Ha9HwMge2gF38Z3Zv57WGwO7Z8IjCDikj/KFqRqrMapp/aTbcyKocflhfTCxdz6GlWVjzc79yE71Ky6X7Pfio99OwaKNezG1n3JAx4jbjhuIIwYWYUxPC0mPqF+ptC79OT2OGdoN/128RXUd715xiI0t6tqcCtydMbYHbnr9p/Dv3bNT8PZlk5GV6keyz4Pfzl2MT5bvNLSuyw7rg5cXbMK+hhZDyxdmJuOdKyZj5gOfAWjvv7lpSXjvykOQ4o+cPyfk02umobGlFVkqn0mKDXGf/M2hvfHaos1YX12vvKzJ7isPat47eyiunrv44LoE/Ou8MRh8y3vtyzLpkTDGVOTi+ln9cef/lgEAnj53DNIDXowozwEAlOak4u3LJiM71Y+izGSMrsjF0NLsiPW8eOE4nPL3rwAAST5PeJSqYjdjziPhzL/+UKzfXQ+/pyPxrnVTh9nvR9cnPXhOIyIiIiIisk/ozjpxAEEuWRSI9HoEHNq/KKptWS1/UJ6bKrnIdaKGu8/rwbjK9pEv4rvflVQWpKOyIF1zGT1pAR9mDCy2tA57JzIX/6y84rKcFM11MCgdO9EGfj0eAUleD5paOz7z4v4+tDTbcNKjd2E6PCYHh/Qv7thWqJv1LdIeXVWepz0ahGLP5/VgbEWuetJD5/V6E5mLR/UIkI6wa9WuykguM/xgggMAclOTMLg0S/K8+PwT+g6WGyt6fHD3LCxYv0d1e6zOGFt2xOvz0gPISw/gh017w49pHUazxzihyltxkiIiIiIiIiJrAgZK2aQkqd99bYbeRbHe84IgMJChwN7yVuI5PZSXkc/JEP22bFlNl+FTOCBWoiJNGlFjM+v1CIDPbNZD8np2hERi5nDpHVv5uUur3wmCdHmWt0os4tOXHR95M6vgKSZx2XpTh32rIiIiIiIiIrcL+NovA7WuK5N97rhUlF/8sma3/aSBKeX9G82cDxQd8SFIVij95FjcV2XFSo8KECzNa8OAZGKxM9is97z4ZueIBImsjzIH4m6CzUkPtXWHH7N3E+QGFj/j/MuFiIiIiIioC0kykNBQCrbGA+8Ib+fkbpBOZN7xc0DUT+zrDzyeZiglm9xQAcMjtJe9ixaTl4nFzMgy3aSH/AET3bkt/l2fTBGPIrT3M690DrFzBCTps/PjKE5gan0/dOryVkRERERERGRNaE4PLXYFuXXLW+lcNjOE4TxxoEj8s7jEWcAlI3+6AnGfVyor5tTd7WZWKwjWgpiMTXZeegkt+bGXfwdo9e9WZj0SijShbnPSw2xyjRKG1rE1mzB3/V8uPKURERERERHZR29+hiSvx9Jd3GJW70pncNR54l0sPuyposSXW0b+dDVK8+/EuLqVIkEQbDtHUCej2y3kJauMr5pzeiQWte+WqNfHREeX16lHevD8RkREREREZI1eeSsjE50bZfUajuUqnCe+A1f8c7Ik6ZFQoYNOIzmmIz2Mr1iAtfJWLFuXWMwcLr1FI0d6GN8WY4KJRW0UYbT0jj9PK7Fl5+dRbVVWN8G/XIiIiIiIiLoQvaSHnXf1612wmg1iMKhhP7USJOJ+oDc6KJptkTJxcFA5AWk90qRU4k7tsyifPBpo7ycsb0VK9PqF/Fkzk5OzvFVi8QjKPztFXPqI55jEJT528vOD2cPKpAcREREREVEXkpakHcC2865+n06kQ+95TnjczskRL2pByrSAdnkrBpWcDXbZfAAAQp1JREFU59RID/GxDa9XYTlBAPwKCRLB8kTmFC/RfG7l52GtY69bgki2gM9j/PtG3kf1vj8ovqRJCOvHysw5h387OM/A9HBR0Z7IvJPN6UFERERERETKHj1rpOFlfzezP4ozk3HzMQM1l2tobrPaLNx+3EAUZybjtuMGaS73qym9UJqTgssP66P4vEdon0R7Wr8CjKnIRWlOiuW2JZLzJlWgIj8NvxhVasv6zhxXHvGYZKSHKEJw8shSVBakYc6EnhjdMwdDSrNw9JASS9tnGEqf1yPgsP6FGN0zB5UFaRHPW8l5PHjacBRkBPD4OaMj1yta8WNnjURBRgDPnz8Oz5w7BoUZAfzl1GHoU5iO4sxkDC7NgtdCELOVN+wnFPmhvmJ6X3TPTsHVR/SNXNbkp/yOEwahODMZfzxe/bvizHHl6FWQhqMGt59/7ps9FIUZATx02ghT26LYko4ijH49fzi6CiVZyfjzSUMwtV8BxlUq/y3ARHxs3HjUAJRkJeOGI6tivm2zh9jnSCvsxKJ9REREREREiib1zscDpwzDFS8u0l32/MkVuGhKpe6dchdP6WW5XWeN74kzx/XQ3VZBRgCfXTtNdTlBaL+z78lfjkEwGOxyc3zcdHQVbjxqgG3v+1eHRB5bcZBSPOojPeDHh1dNCW/79UsmWm5HFzt8UXtizmgEg0Hc9PqPEc9Zmcz52KHdcMyQEsXjKJ7TY8bAYhxRVRRe7uvfHwZBEHDs0G4A2j+TJm7Qj9DY3Br9iynm5L2lKDMZn/+u/bx973srpMvqfMbl5Wr6FGVg/vWHhvuaUu/+4/GDJef/k0aW4sQR3bvc90Eis1IO79xJFfjlxJ4QBAFPddG/Bdzk/MmVOG9Sha3HQHxe0Fqt2U26P+khwvwHERERERGRlM9r7CrQKwiGLlL9Btenx+gFsdZy4kBJVw1yOP2+xcFr6f7WnohWgB2zS5Aap4676nqD6suFfhY/ZqW8VWOL9dFkFJ1ojppSl1FNVEe1fv1XRZx/uuj3QSIRHyKrh8v0pOjsHo5y8vMnXjMnMiciIiIiIurCjNZE9xgMUvqcKtQcBcYt7KUYvFSZ/JX73p2cuhnU7Gq9FoZ6NDHp0XnpjfTQebl8JAglLrVRhI5tj19anR7n9CAiIiIiIupC7J7M1cod3LZjFMNWSgED8eHmyBr3cyoobHa9VgaENbUy6ZFIzJwL9Ob0YE6j61AbRegUTl6euMSnBTv/9mDSg4iIiIiIKIEZLW9llF3lrezgpvxLZ6C0O9VGdzgRo2JQyjqnYsZmg9HWyltxTo/OSr9bMOvRVaiNInRse/x66RTsPIyuT3rwdEhERERERKTOaHkro6yUrbEbg+T20qvNLxnpEYsGkSbFRIRLyltZuXOb5a0Si5lDzRFiFGLnnB6mtx3bzZGNxH3F6sgw9/w1awATIERERERERFK2j/Rw0fAKFzWl01Ib3eFEORLGQ61rc6y8lbnlrYz0YNIjsZhJPut9xlnequsQd4XYlLeiROXUeSGhkh5EREREREQkZXc5KldNZM4ohq2UgpcetZEeOvs+mju6eTitc6y8lck1eyyVt2LSo7PS6xW6E5nb1RCKO/GxjM1E5vyG6Qw4pwcREREREREBsL8cld0To1vBIIa9lHanJOnh0V6WKMRroYMw6ZFYzJW3cq4dlFjEd++76M8K6kKY9CAiIiIiIkpgdicp7C6XZYVTAbSy3BQAQP/iDGc24FJ6E5lLR3q4px90VeI7pdMDPgDA0NJsR7bVMy/N1PJ9i9JNb6MgIwAAGF6Wbfq1ZE1akhcAMNTxfa993tArY5OfFrCxLeQWsfg+kZZq5PcXAb54N0AP6/0RERERERGp89tcjsruidGtcGoi8+fPH4envlyHcydVOLL+WLhgcgX+8dlacy9SnMi842fxPA16e54hpdh649KJeP7rDbhwSqUj6z99bDm21TbgkD4FhpY/f3Il9jW2YPqAIsx+dL7kuRuPGqD4mpcuGo9n5q/HBZOdeQ+k7o3fTMK/o+w/Zj7rVnPwg0uzcN2s/ijNSbG2InKVWOQgmOdIZMrBf6spAff8NWtAkBkQIiIiIiIiCa0Jhbtnmw8cdY2RHqm46eiqqPaPW8wYWBz++eKpvcI/9ypQv2NfKYkkviNWnEBz4k5Z3n0bvcqCdNx4dBUKM5IdWb/f68HvZvbH+F55hpZP9ntx/awBGN0zN+K5cZXK6+iRl4abjq5CcZYz74HU9XK4/4QofcaLMjtGbxiJ6100pReOHtLN1nZR7InnCeKcHqSFE5kTERERERFRBK2JzLUSIna+xikuaorriGMERudXUJ7To+NncV/ivo+/RL3v003nELLOTMBaaclYBLzJfeI5pwd7HAFMehARERERESU0n0Z5q2iCj24KUDlV3qozkASUDB5nxTk9RI+KS5tx31O03HQOIeusTmQufihB83gUBWnSg+cEij0mPYiIiIiIiBKY1kTm0SU9rLTGXoyTGCM+ZlolPpSeUxvpwX1P0XLTOYRiS6+EHnUd4vJW7AKkRS0ZanWaC9cnPTiPBxERERERkTqtpIfWc2rcdEcmg2XqxNfK4vJWWntM8TlJ0kM8p0f0bSO7JGY8hJ/bzsXM0dQ79AzxdU1u+ruCug7XJz3EeG4kIiIiIiKS0ipvFU2gwU3BCfe0xH3E18eGy1spzukhKm/lNV7eKppu4qKuRQ7inB5dl2J5K9FjQUb2uqRYnxH4XUNAgiU9iIiIiIiISEprNIcniiu+aF7jFMZO1UVTL12x9IzoZ3FfYtCIosXPbSdjaiJzpfJWol+Y8+gyOKcHxZuL/pwlIiIiIiIis3xe9WBCW5v59XldFJxgmRx14jumNQb7SOmM9BDfoa8XpIpmonMezq6BAc7OxWp5K/G5gjmPromnBNLiVNk7Jj2IiIiIiIgSmF9jaEY015FuSjTwjnFjDI/00CtvxZEeZAOj5dYoMZg5F+iVt6KuQxzIjvXfFexznYPVXIjrkx7MAhMREREREanTCjAGNW6fu3hqL8XHi7OSLbfJqlDAYlxlXnwb4mYqAaXZo0oBAN2MHke1icx1XnbRwf5z/LBuxrZDpiXKpM9HDS6R/M6cR+eiNarr2KHSz7942Sl9CwAA54zvGX6sT1G6vY0j1yrPTY3qdSeO6A4AuGSa8t8o1Pn0KkhTfPz4Yd0trddn6dWxliBf+ERERERERErWrVuH22+/HR999BG2bduGbt264cwzz8QNN9yApKSkqNf79e8Pw9g/fQgASEvy4r+/mYSMZD/OeuJr1ddcdmgfHNq/EIUZyahvakFqkg/NrW1ID8T/MnHhTYdj575G9CnKiHdTXEt8eewXlTg7cnAJJvTKR356AOPu/FDyGqXQpThAnRrwdiyrE7i+4rA+mD6gEANKMk20mjqj+08ZhuOHd8cFz3wHgOWtupL7fjEUhw0oxOUvLAIgPW/8/eyRWLGtDoO6Z+KYod1Q19iCwoz4J9UpNrJS/fj0mmkI+M3db//nk4bgnPE9Mah7lkMtI7fJSw/gk6unSv4GAYDpAwpx3qQKPPH52qjWG/+/ZomIiIiIiLqIZcuWoa2tDY899hh69+6NH3/8ERdccAH279+Pe++9N+r1FmV2BJKGlGajsqD9blqtO8U9HgFDSrOj3qaTslOTkJ0afRKoq/GJSpwJgoBB3bNQc6A5YjmlEiPix9KTOkIErTrzwUTTf6KZB4TcL8nnwdDSjgAlkx6di9bh9Hs9kvOAOIka8Hkx+GC/KMgIoCAj4FALya3K88yP9vB7PRhalm1/Y8jVeuZHjvYQBAFVFm6sYNKDiIiIiIgoRmbOnImZM2eGf6+srMTy5cvxt7/9zVLSQ0w8sXmQw+U7LXFCSzIXh8Zr9Ed6dIQIWtp0sh5EKljeqmsRNH4jigcm2DsPKzl018/pQURERERE1JnV1NQgNzfXtvWJ77JOlDkByDxxQkuc6AoffoVjrzjJsCg4lJbUUVrCiZwHBwCYk0ifX3FTOdKjc9E7muLDzUNPRHayck5x/UiPRPqSJyIiIiIiMmPVqlV46KGHdEd5NDY2orGxMfx7bW2t6rJe0W3Wbbyg6rTEh9YrGemhHiFQek4cUEgTjfRodaDvMB7aeYnPNR4O9ehUzAQdeeSJyC0SaqQHh2YTEREREZEbXXfddRAEQfPfsmXLJK/ZvHkzZs6cidmzZ+OCCy7QXP+dd96JrKys8L+ysjLVZcUBcF5BdQ1+b8elvVa8WXGkh+ixZH/HSI9WlrciE8Q5MuY8OheluYAkz0M80owHn+KP3bDzsFKqzPUjPYiIiIiIiNzut7/9LebMmaO5TGVlZfjnLVu2YNq0aZgwYQL+/ve/667/+uuvx1VXXRX+vba2VjXx4RVf7TPr0WmJD614Tg+z8QFxKaKAryN5ojeROTkvkW78lIz0YMSxSxGiP/0QEWnq1OWtiIiIiIiI3K6goAAFBQWGlt28eTOmTZuGkSNH4sknn4THoz8APxAIIBAIGFo/R3p0DcGgypweWuWtFJ4SB6iTJEkP9h4yTjzSgzmProsJLyJyCyY9iIiIiIiIYmTz5s2YOnUqevTogXvvvRc7d+4MP1dcXGzLNjinR9fjEyXOtGKOinN6iH4OOJz0YOmbrsFKORJyH1NzevDQE5GNrPzd4PqkB/9EJyIiIiKizuL999/HqlWrsGrVKpSWlkqeC9qUoBAnPVJEczRQ5yI+zj7JRObyHzrozemRkuTVXJZiSzzyJpGw73QtPN7kNkywdx5+C5NEJdQ3KG9SIiIiIiKiRDZnzhwEg0HFf3YRlxe5/5Rh6JmXigdPG27b+skdxlfmYVSPHJwxtlySAAkFe7JS/JgxsAgFGR1l0ZRCB4Ig4Nih3TC5Tz76FmbglxN7YmhZNg4bUGh7mxmGMufyw/qid2E6bjxqQLyboqs0JwVT+xXgqCElSGaytVMxM3KHsWYistNhA4owrCwbcyb0NP1a14/0ICIiIiIiIuPEd/0PKMnEJ9dMi2NryCk+rwcvXTwBAPDdut3hx8U3RT521igs2rgXxz/yBQD1u1/FSbGbjxnoQGspGgUZAXxw1ZR4N8MQQRDw1C/HxLsZ5AC9RIb4vMI77InITkk+D167ZGJUr02okR5ERERERESkzWOhFAAlJo+kvJXGROaxaAwRdSp65w1B5WeieGE/JIBJDyIiIiIiok7Fy6u8Lkc8usfV0R43t42IFOmP9DC+LBFRrLj+z2E7a9sSERERERF1dj6P6y/zyGbSOT2kz0nuwmZAkogc5OFJhohcIqH+Gmb6g4iIiIiISBuDTl2PJOmhsVy86+2zZxIlHr2JzMXP8zNORG6RUEkPIiIiIiIi0sbyVl2Pz8OJhIkoPgRO6kEuw69BApj0ICIiIiIi6lQ4kXnXIx7dw8NPRHYyE0DWGxVCRBQrTHoQERERERF1Ij5GvbscaXkrHn8iso/e6DHOG0REbsSkBxERERERUSfiZdSpyxEnPYIung2TpbeIOjd+wskNmPwnIMGSHkH3/u1GRERERETkCr0K0+PdBIqxjIA//LN8Ivuc1KRYN4eIOpHy3FTN58WhOvn5hygeirMC8W4CuYAv3g0gothpawuyxjNFZdOeengEAd2yU+LdFIqxL1ftAgBM6J0f55ZQZ1Db0IwdtY3oXZiOppY27N7fhOKsZNQ3tcDrERDweVHb0IxUvxc+zsRMZNqLF47Dd+v34Jgh3eLdFIqxrFQ/7ps9FD6vgGS/V/JceV4q/nTCYOSk+lVeHTu8EiFKHM+dPxZLNtdg+oBCzeXENygz50Hx9OQvR2Ptzv0Y2SM33k0hF2DSg6iL2Lz3AI5+8DOcOKIUNx1dFe/mUIw1t7bB7/UgePAvUrXSAk0tbXhj8RZM61eAHfsa0doWRGVBGib9+WMAwKo7ZoUDkZv3HkBhRgB+BiZdYePuerzz4zacPrYcaQHtr/cd+xqwo7YRg7pnYV9DM5L9XsXjuK+hGac//jUA4OfbZiA1qX29byzegr9+vAoPnTYcP22pxdaaBlw8tVf4df/33nIs3LgX980eiua2INbv2o/KgnRc9sJC9ClMxx0nDLbxnZMbfL2mGt2yU1AmuhPwQFMrahuaUZSZjNcXbUZ2ahKenb8eHyzdjr+cOgyXv7AIQPsF9Q2vLkFzaxB/O3MEZj86HycM745fjC5Dit+LfkUZeGb+OozskYs+Ren49XPfY2SPHFwyrXd4Ww3Nrag50L4toq5sbGUexlbmxbsZFCcnjSxVfe70seUxbIk6BkSJEsfE3vmYaPLGJ5YVonia1q8Q0/rFuxXkFo4lPR555BHcc8892LZtG4YOHYqHHnoIY8aMMb0elrSSamxpRXNrEOk6AS1yDzOB4da2oKQeL9B+zJO8HgiCgJr6ZmSm+LCnvhnrqvejZ14a/vHZGpwwvDv6FmVErG9HbQM+WbETE3vn45Y3fsKe+mY88fla3HR0FYLBILbUNOCdH7fhjLHlCPg8eG3RZpTlpGJUT2bF46WtLQhB0K933NoWxK+e/Q7pAR8eOHV4eBRPMBhEW1Ba1/mJz9fiz/9bhhuOGoCb3/gJAHDdrP6463/LMLlPPp6cMxqPf74WPfNS8d5P2/HKws0YX5mH79bvRnNrENP6FYTXtfdAM4JB4IdNe3He09/h0mm9cfUM5b8qdtU14kBTK8pyU101ymhvfRN+2lKL8ZV5rmmTGcFgELe9+TOS/V5cfUQ/1DW0ICvVj+Mf+QLV+5tQvb8JRw4uRu2BFvQpSseJf/0SMwcV46IpvfDs/HU4e0JPHHbvPOxrbMG/zhuLy19YiMqCNMy9aIJkO21tQayvrg//vmbnfjz39QYMKMnAH15v70eXvbAIS7fWAgDqGpvxxuItOKRPAZ77egMA4L73VuDF7zYCAHrkpWJ9dT2+Wbsb502qwF3/W4YjBhbjs5U7ceEhldhW04CG5jb0K85AdV0jXlqwCdfM7IfCDP0gtlL/eu7r9chK8eNoE3c7X/yvBdhS04C5vxqPJF/iJPNaWtuwrbYBpTnKpQf2N7bg23W7Ma4yL+LuX6C9T/20pRa9C9OxvroeizbuwYkjSnHHW0vRFgzi7PE98Nu5P+CIqiLMW74TZ4wrx7T+hbhm7mIk+70oy0nFwx+vAgDMGFiEspxU/Hpab1z+wkLMX12Nu04agqvnLpZsM5TwAIAzDibWAODYh78AALzw7Ua88O1GJPk8uPGoAbjlvz9LXv/Rsh1oaQ3ilYWbcOuxA/Hs/PX4ZMVOvHvFZPQubP8+/Gbtbny8fAeunN4XW/YeQHluKvbUN+HnrbWY0Ctfcp78ZPkO/PGtpfjzSYMld4Y1tbTB7xUUz8mNLa145fvNmDGwGLlpLB1DREREXZOb5xEioq5LCAbtTyu8+OKLOPvss/Hoo49i7NixeOCBBzB37lwsX74chYXaw+Jqa2uRlZWFmpoaZGZm4h+frsEdby8FAPzphMGuuUPFjP2N7SUblAINZp31xNf4YVMN3r/yEOSnB9AWbP96+c3zC5GfkYTLD+uLm177EWeO64FJfViKJB621TRg/ppdGNUjFz9tqcFF//oev57aC9fO7B9e5tt1u9HU0ia5a+KFbzbguleW4Mk5o/HTlhoUZiZjfGUeTvjrFxhRnoPjh3fHr5/7HrcfNxCPzluDzXsPICfVjz31zQCAa2b0w7zlO3HM0BJ8unIXRpTn4LWFm7F8+76INh45uBgfL9uJtmAQjS1tmDGwCLlpAfz7mw1I8nmw4o+zwss+9OFKvPT9Jlw0pRfWV9fjiul9sHzbPny4bAcumFwBr0fA4o01GN0zB89+tR6T+xSgMj8Ne+qbkJfeXkdx575G5KcnmZq48Lmv18MrCDh1THk4oPnxsh14+ONVuHJ6X/zh9R9x8qhS/Hpqb/2VuVhLaxvmLtiE8ZV56JmfhoueXYBv1+3Gh7+dgsxkfzgBUl3XiNy0JHywdAe+XL0Lxw7thhP++iUA4G9njMB1ryzBRVPa77S/973lePniCXj8szXYVdeIr9bs1mxD/+IMLNsW2U+UzBxYjHd+2iZ5bOUds/DJ8p3Ysa8BI3vk4LmvNuA3h/XGcQ9/ga01DfjNob3x2KdrcPSQEtx90pDwSJGaA+1JuOOHdcPPW2uR4vfi0P6FWLK5Bv2KMxDwaZ8zg8Eg3lqyFb0K0jGgJFNxmU9X7ERGsg+lOanYsLt9mOsxD32OJZtr8MjpI3DUkBJD79sN/vXVenTPTgEE4JdPfgsAOHF4d7z5w1Y8d8FYzH50PgBgYLdM/LSlVnU9eWlJqN7fFPH4SSNK8c26aswcWIwnv1iHlrYgThzeHa8s3OzMGzJgYLdMnD62HPsaWvCrQyrx1pKtqK5rwvHDu+OiZxdgTEUulmyuwZLNNbj6iL5Ysb0OQ8uy0a8oAzMe+BQA8M0Nh+GOt5Zi9c46bKtpwOWH9cHpY3uEA94L1u/Gkk01OGxAESbf3T6i6bGzRmLzngPYWnMA188agKbWNiR5Pa5Nkt3635/w5Bfr8LczRqA8LxUDu7WP4Lngme8wpW8h3l6yFUs21+Ds8T0wc1AxgkFgfGUe5i7YiA276/HIx6tNbzPefUOsICOAnfsaAbSfz/LTA7hoSi+c+cTXkuVG9cjB3gPNWLWjDpP75OPxc0Zh8cb2883QW98D0H4H8to7jwIA/PmdZfjbJ6sxqkcO6hpbsGzbPjx//lj8+9uN+H79HhxeVYSnvlyHASWZOGZoCXbua8ScCT3x6cpdeOSjVThxRHdM7lOA8b2M3XUv/xuYSA/7DCWSnte9BQDoW5SO966cEufWEJGdttYcwPg7PwIALL75CGSlxL+UHhF1Tmb+/nUk6TF27FiMHj0aDz/8MACgra0NZWVl+M1vfoPrrrtO87VaSY87ThiEM8b2sLu5qhqaWxHweSSB2le+34StNQ349dRe+HFzLfIzklCUkQyPR8DG3fXweIT2oBTaA3LV+5tw2H3zkOTz4D+/Go/H5q1GZoofswYV49WFmzGhVz48AvDovNU4eWQZctOS8I/P1mBirzws2LAHRw/phhOGd8f66nrs3t+E0/7xFQDg2pn98M6P27BqRx3SAz7sOHixL/bw6cOxvbYRR1QV4bpXfkDPvDTcdHQVVu2oQ1VJJppa25Ds9+KnLTW48sVFGNUzF7cfNwj/+W4jeualISPZh5U79uG4od3x8veb8OYPW/HQ6cMxf3U1kv1eTOlbELFNufqmFizcsBejeuZgf2MrclL9aGptQ1NLGzKS/Xh90WaUZKVgSGkWPli6HZN65yPbRRPtBYNBBIOAxyPgf0u2Yv3uesyZ0BOXv7AQo3vm4vzJleFlaw40Y0dtAw6//1PFdX1/0+HY39iChubW8DLnT6rA6wdLCf3nu00Rr+menYLNew848+Y03H/KUDw2bw165KXi3Z+2S54bUJKJYDCIZdv24cjBxXh7SXsAPD89Cbvq2gOp/YoysHz7Ptx90hDMW7ETby3Zit/N7I/zJlXgQFMrBA9w+39/xvDyHAwty8Jv/7MYVx7eFzMGFqO5tQ3Pzl+P296U3tV72WF98OCHKyPaevfJQ3DPu8tx8zFVGF+Zh5cWbMKJI0pRkBGbiauCwSD21DeH7/JduGEPkv1eDCjJDI/c+XTFTvzrq/W488TB+N3LP6CxpQ1PzhmNbbUN+HjZDtx08K750H4DgKqSTGzeewATe+dhfK983PTajzF5P9HISvGj5kCzoWUHdsvEtpoG/G5mf1z78g8Rz588shQvLdiEZL8Hp40pR31jKwZ1z8RzX2/AhYdUorUtiEfnrcZVh/fDht31+PM7y9C3KB3XzOiPb9ZWY1TPXFz54iKMq8zDnvomLNywFwAwvDwbCzfsxd0nD8G1L7Vv9+ghJXj49BG27Qcrttc24IEPVmJ4eTa+XbsbG/fU4y+nDsc7P27D01+uw0kjS3HPu8vj3cy4EidrSnNSsGlP9OfGB04Zhsl98vHWkq3hUSt6DulbgGfOjRyxur+xBXWNLeGySkojA1rbghAAU0mT7bUN2LL3AEpzUlGQEQifT6rrGuERBPz3hy34YOkOXDqtN37x2HzJa+8+aQjW7NqPR+eZT2Z0RZUFaVizc79j619755GS/tDWFsSSzTUY1D1LMtrkrQWrcfSo3gxgk2FMelAiYdKDqPNi0oOIYiWuSY+mpiakpqbipZdewvHHHx9+/JxzzsHevXvx+uuvS5ZvbGxEY2NHwL62thZlZWVRJz0+XbETFflpkprSIUu31uLJL9ZiVI9czB7VXu9UEAQ8//UGvPDtBtx8zEDc++5yVHXLxFFDSnDa37/CkNIsLNywF0WZyTh7fA/c+b9lAIBbjx0YLhMDAIUZAUniYfqAInywdDsOryrC+z9Lg8Zu4BGAtiCQkezDvoYWxWUyAj7sa1R+DgDeuHQiLn1+Ic4e3wMvf78Z+elJuGhKL7z5w1ZM6VuAVxduigiY33R0FW4/GMy+b/ZQ/FZW7mJMz1wk+TwYW5GL3xzWR/d9tLS2wSMIpgJJwWAQ89dUY0hpNrbVNKDmQDNGlGeHAxLBYBCb9hxAfnoAp/7jK2yvacD4Xnl4VeGO1om987C/sRVHDS4J91PSJgjOla0LJYkumFyBa2f2x4HmVizZVIPirGSkJfmQk+bHtpoGlGSlSErX7D4YSBWXJ6mpb8bbP27FCcO7o3p/E/bsb0IwCKzcsQ/Dy3NQXdeIG179Eb0K0/D2km24b/ZQTOqTj7F/+hBAe99YsH4PbjhyQDipQe5y3qSK8Pw2G3fXw+vpmKg9GAxCEATM/W4jVmzfh0un9cGZT3yNkT1ycPMxVRAEAfVNLdhT34zCjPZRd7MfnY/s1CQ8/cvRWLNrPw40teKrNdVITfLhtDFl+HlrLRpb2jCiPCe8jXd/2oZNew7gqS/XWQriu8WQ0iz8sKkm3s1wzO3HD8JXq6sRRBCH9CnAtP6FuOS57/Hd+j0ozkzGX88cgVMf+woXTanEjn2N8HgE3HH8IMx+dD6272vAe1dMwTfrdqMyPw3dslOwZHMNynJS8H/vr8DUfoU4vKoIu+oakZOahBP/+gUWH9yXHgFITfLhodOH47f/WRw+ZyUqtdFGIW9cOjFc5qoz6F+cgT+dOBhXvLAIMwYW4YnP16It2H4OGt0zF0WZATzx+Vq88e1qbHzgFwxgk2FMelAiCSU9+hVl4N0rD4lza4jITlv2HsCEu9qTHj/ccgQyk5n0ICJnxDXpsWXLFnTv3h1ffvklxo8fH3782muvxbx58/D119JSA7fccgtuvfXWiPXs3bsXmZmZePyzteFg8pwJPVGWm4qzx/fAroN3OqYmefGf7zahe3YKGppbccWLizC0NAvnTqrAzoOT8N75v2U4YXh3vPnDFjS3drzdnFQ/LjusD26V1Ykmd/jiukPx/fo9OKRPATwe4JPlOzGhVx6yUvzwCAJue/NnPPXlOhRmBFCSnYLFG/fi9LHlOGlEKXJS/ajITwsnMrbXNuCal35An8J05KYl4Z53lyMz2YdaUcJnaGkWjhxcgtZgEHe/sxx9CtOxckddvN6+o4aVZWPRxr3xbkbc+DwCpvYrQM+8NPxqSi8c9eBnCAJ4/OxR+HJ1NSb0ysNxj7QH3K4+oi8e+GAlWtrcV6d0ZI8cLFi/x9RrThtTjn9/syHicfEok0fPHIHCzGS8uXgr/vnFWslyAZ8H3XNSsHVvA04eWYpnv1of/RuIs8l98vGLUWX4eNkOvLJwM7JS/Lhv9lDc9c4yrJJ99kOJYgDITw9gV13k6LoQpRJgYqN75uDbdeaOm90q8tOwdtd+DC/PRktr+13nR1QV4b2DSXrxzzceNQAHmlpRmpuCO95ail11TfjlxJ548ot1AICXLhqPe99bjutnDcCg7llYX70f22obcPo/2r/v/3bGCNzw2o+4aEolvlm7Bx8slSbD/3B0FW5782dUlWTipYvH4/VFW3D9K0titzPg/J3+cmW5Kdi4OzLJleT1oKm1LWbtUGqP1yOg9WBnv+24geiZl4aizGR8taYa367bjTuOH4zb3/oZ7/60TXLTxJwJPbFl7wGcOqYM5z71HYD28+f+pla0BYP45YQKCAIkE41/snwH5jz5LfoVZaAkOxmfLN+Js8f3wG3HDQqPZH114Wbc8+4y9MxLw5pd7cfovEkV2Li7Hk2tbRhbkYcNu+txwsESkLMGFeOLVbvCyzphQElmeC4bO7U11jPpQaYw6UGJhEkPos5r894DmHgw6bHkliOQwaQHETkkoZIeaiM9yq74DzwB5Qk5u4q/nzUSl72wED3z0jCiRw7mLd+Ji6b2whOfrcG6g5PLjq3IxfHDuyM1yYuXFmxCwOfFgeYWfLGqWrKunnmp4dckoiSfB00t7YGg9IAPdRojUMQGdc9EbloAn67Y6WTzVF12aG88+FH75K4BnwcXTK7ElpoD2Lq3AfPXSI/RYf0LkeTzoCQrBdfO7IePl+3Agx+twtKttUj2e5Ae8KNfcTq+WFWNib3zkOL34vzJlUjyefDaws24/LA+mHLPJ6hrbMEJw7uHR6Y8f/5YPPH5Wpw7qQJbaxrw4+YaXDerP37/6hIM7JaFXXWN+NsnqzG1XwE+WS7dT5P75OPcSRXh+QPEI5dC80AYOR7i9pg1uU8+1lfXY8PuesncE6eMKgtPkNyZXDm9L37YtBdej4ANu+sj5tr46dYZePn7TZg+oAi5aUnYuLse3bJTcPMbP2HHvkbccOQAHP3QZziiqhg79zXim3W7keL34r5fDEWvgnT0LkzH717+Af2KMjCqZw4ueOY73HLsQPQvzsTlLyzEWeN64NQx7XMn1Rxoxp1vL8UJw7vj4+U78ei81fjbGSMwtjIPjS2t2N/Ygun/116q7fGzR+Hp+evQqyAdK7bvw5erq3HU4BL8sHkvNu4+gIum9IootRPweTB9QBGCCGJoaXZ4JF205kzoiY+W7cDQsmz8d/EWAMAZY8uxY18jxlbk4uu1u1FzoBmTeufj/95fYWlbThDPxXFI3wLd81avgjRcemhvVNc14cVvN+K6Wf3x5g9bcUjffJwwvBRXvbgIb/6wFQ+eNhxv/rAFZ4ztgR55qUhN8iI7NQkbqutRkp2Mjbvr8d36PTh2aDdMvvtjCGhPOm/acwDpAZ+kVNyPm2uwe38TDulbgO/W7UZpTiqKs5QnGl+6tRZZKf7w6BkAqK5rxPJt+zChdz6e/3oDggjijLE9sGXvARRkBOA/ON+L+G6x//xqPL5cvQsDu2Whf3EGfjt3MQZ2ywwnXcZV5iIvLYD6phZ0z0nBv75qT+qN6ZkbHlURCoD/+aTB+N3L0oTKDUcOwJyJPdHQ3IrquiZMvfcTdMtKxvVHDsBv/r0QMwcW45wJPXHH2z/jx832B7qdlp+ehF9OrAiXR1tw43T8/dM1OH1sOX7aUotXvt+Mu08egm/W7kb1/kacPLIUNfXNONDcivLcVM25mC59/nu8+cNWBHweLBfNBbV6Zx1Sk7woyUpRfS3QPtrp+w170bsgHS1tbfh4+U4cPaQkYg60usYWpPi9+Nsnq/Dj5lrcM3uI5sV0W1sQtQ3N+G7dHny4bDtWbK/DRVN6oW9ROvLSA3jhmw3441tLceeJgyUJtpKsZGytaZCM3p3StwDzVuyU/B310kXjEQQwqFsW3v1pG/76ySo8euZI/HfxVqyr3o/LD+uDhRv34MoXFys1T73dTHqQSUx6UCIJJT36F2fgnSuY9CDqTJj0IKJYSajyVnKhxrsh6VFVkomfZXfyvXjhOHy5uhpLt9aG74JND/iQl56E9dX1eOXXE/C/JVvx/Ya96J6dgjcWb8GsQcWormvCok178dm10/Dqws1Yt2s/hpVl4553l+OOEwZhQEkm1u7aj/z0AI5+6HPkpwfw3Y3T0XywfJO45nNtQzN+2lyLoWVZSE3yqba/pbUNf/9sDcZW5GFgt0x8tnIXijIDuP/9FVi7az9eu2Qiht32PgDg9uMG4p53l0tGPhw5uBhnjesJAHjnx614ev56FGUGcONRVfjNvxeiKDOA7bWRdztfeEgl/vn5WswYVAwEgbeWbMWoHjn4bv0ejKvMRWVBOp7/uj0o9YtRpWhsacOJI0rxf++vwOKNe5Hs96ChOfZ3uuoRB9zfuHQi7vrfMiT7vUhJ8qIyPw39ijNw6fMLcVj/QjwxZ3T4df/6aj0amltx3qSKcPCoubUNS7fWok9hBnbua8Se+iYMLcu23MZFG/dif2MLxlbk4sEPV2JSnwKMqcjVfE1TSxv+9+NWHDagCMu37cOXq3bh4qm9wpNNB4NBLNq4F/2LM9EWDOKHTTUYU5Eb7pN1jS04/+lvUXugBX86cTCWbNqLk0aWYn9jK3bua8SAkgwIgoBVO+rwr6/W49mv1uOGIwfgrSVbw+WfQqO5njhnFM57+jtM61eAq2f0wwvfbMTVR/TDgeZW7G9qQY/cVNz8xk8Y3ysPRw4qwaqddehTmI6K69+2vO/sdOeJg/Hpip3434/td/sf2r8Qn6/chaOHlmBYWXZ4DoFjhnbDmJ45OGJgMd5YtAX9ijNwiGiunO21DVi6tRYTe+dj5fY6BPwe9CpI193+nv1NSA14UV3XhLnfbcKZ48rDk8pHq60tiK21DeE5i0LW7tqPosyA5FxU19iCV77fhJmDilGY0R4QDwaD+Osnq5Ee8IVLA/7m0N747RH9wq+58dUlmNArHxv31OOhj9qDiKU5KdhW04DDBhSivqkVMx74NFwGqiAjgNPGlOPbtbvxwKnDJHMq/Oe7jTiiqgiFmZEB+WXbajHzgc8s7Y+Q3LQkzVJDanf0h4QmYB5Rno1/XzgObx+cmH1IaTa21hxAc0sQLy3YiAUb9uC+2cNw7lPfYl9jM96/ckpEYFiurS2IuqYWU8PLa+qbIXjgiiHpby/ZivXV9bhoSqVi4P3rNdX4bv0eXDylV7jEYX1TCx78cBVOHlmKgM+D+aurcdLIUjzx+RrUNbbiyul9sPNg+aiW1iDmr9mFKX0LJd+xK7fvQ3ZqEvLTk7BjXyMKMwIQBAHBYBBfrq7GGY9/HdGWEHGC3im3Hz8IN732I7JT/ehXlIGft9Ti9uMH4fVFm9EjLw1T+hbglv/+hGtn9MdRQ0rQ0toGQRBw+QsL0b84A5ceql860qjttQ345xdrcd7ECsXPmlu1tQWxs64RRZnJeOuHrVi6tRZXTO8T/t470NSKuQs24pgh3ZAjKnt477vLkez3GN6Ht/3354jRckD7yKJeBekR5U/njC7CrSePZgCbDGPSgxIJkx5EnReTHkQUK66YyHzMmDF46KGHALRPZF5eXo5LL73U8ETmTic9fjezP0pzUrC+ej+OHtINtQ3NyEj248fNNWg8GLA4vKooPAFTQ3MrfB4hfEEMtAfWag40oyAjgMaWVmzcfQC9C6UByba2oOZ8E6G68WKhOQicmog5dMgFQcA3a3fjm7XV+PXU3thT34Qknwd765uxakcdpvUvlLyupbUt/P6XbKpBeW4qNuyux7+/3YDTRpdj6bZaDC/LRp+iDM3t1zW24Okv12H2yFJJkGR/YwuWbK5BYUYAMx/4TFLiIzXJi/qmVgDtd/5/vXZ3RGCpICOAirw0DCnNwuOfRwYZ7jpxMN79aRty0pLwyvftow5evng8bnj1R5w2phwpfi+uffkHZKf6kZXix5DSbMweWYqft9ZiSGkWhpfl4L2ft2HGwGLFYGP7Hat7MKh7FgI+7WBkVxaajHdXXSNW76jD2Mo8rNlZB69HQI+8NPywaS8qC9KRHlBP6Mnd//4K/OXDlehfnIGLp/bCkk01yEj24+GPV+KowSXYU9+M79fvwYWHVGJASSa+XF2NJJ8Hq3fWITc1CbvqGvHhsh0A2sucLZbNSXDUkBK89cNWTOydh2OGdMM/v1iLe04eis9X7cLOfY0YXp6N+95bgdQkLx45Y0Q4MXH9K0tQXdeI+08ZBq9HgN/rgdfTPheEAAHJfo/mXdSd1Q+b9uLjZTvx62m9wnf2i7W1BbGttkEyQiCksaUVPo9HEqSOxp79TVi6tRZbaxrwzPx1uPvkoSjLTcHmPQdQnJWMv32yGoO7Z2Fa/0J8sWoXJvbOx9pd+/GPz9ZgSt8C/OntpchLC+CtyyZh/ppq/O2T1ehblIG3l2zFk78cjYxkP1pa29AjLw37GpqR4vfi/Z+346bXf8SVh/fFxF752F3fhBEH54dJTfIhJUn/vNHU0gZBgOJ+o9h458et8Ho8mNavAHMXbMKUvgXITPHjk+U7MLpnLvLSkrC1pgGpSV7c8t+fMbpnDo4b2h2vLdqMusYWjKvMw2X/XgifV8BVh/dF/+JMJPk8OPrBz1CWm4rbjx+EJZtqcM6Enrju5R8wd8Em/OXUYXjl+804bUw5Zg4qjvcuIIOCwSCemb8ed7y1FA+eNhwzBxVj05565KcHJH9HfLWmGn6vB31yvAxgkylMelAiYdKDqPPatKcek/78MQAmPYjIWXFPerz44os455xz8Nhjj2HMmDF44IEH8J///AfLli1DUVGR5mtDjf/f96tx0YtLcdSQEtw3eyi+WlONqm6Z+GTZTuysa8SZY3vg8c/XYOe+Rpw/uQL/W7IN0/oXondhOj5atgNrdtYhJy0JJVnJmNavEJ+s2Iln56/HRVN6YcX2fThtTLnloBk5Z+e+RjS2tCIzxY8F6/ZgSt8CrKvej7rGFgwpzcamPfVYvLEGHy3bge21Dbj5mKpwsiUYDOLhj1ahLDcVswYX483FW3Fo/0LJ3ZorD85doJegocSmlFRUUtfYgu/X78Gk3vmoOdCM575ejzPG9sDa6v0oyUpGSVZKOFlDBAD7Gprh9Qiao+2IiID20Z1GEpUMYJNZ7DOUSM576lt8uGwH7jl5CGaPKot3c4jIRg3Nreh/0zsAgFV3zJLcLExEZKe4Jz0A4OGHH8Y999yDbdu2YdiwYXjwwQcxduxY3deJG7+n2YuizGTdEh5ERERERImMAWwyi32GEklLaxvWVddHVEYgos5h575GeARYLqtMRKTFFUmPaPGPdyIiIiLqavg3MJnFPkNEREREXYmZv3855oyIiIiIiIiIiIiIiDoFJj2IiIiIiIiIiIiIiKhTYNKDiIiIiIiIiIiIiIg6BSY9iIiIiIiIiIiIiIioU2DSg4iIiIiIiIiIiIiIOgUmPYiIiIiIiIiIiIiIqFNg0oOIiIiIiIiIiIiIiDoFJj2IiIiIiIiIiIiIiKhTYNKDiIiIiIiIiIiIiIg6BSY9iIiIiIiIiIiIiIioU2DSg4iIiIiIiIiIiIiIOgUmPYiIiIiIiIiIiIiIqFNg0oOIiIiIiIiIiIiIiDoFJj2IiIiIiIiIiIiIiKhTYNKDiIiIiIiIiIiIiIg6BSY9iIiIiIiIiIiIiIioU2DSg4iIiIiIiIiIiIiIOgUmPYiIiIiIiIiIiIiIqFNg0oOIiIiIiIiIiIiIiDoFJj2IiIiIiIiIiIiIiKhTYNKDiIiIiIiIiIiIiIg6BV+8GyAXDAYBALW1tXFuCRERERFRbIT+9g39LUykh9dNRERERNSVmLlmcl3So7q6GgBQVlYW55YQEREREcVWdXU1srKy4t0MSgC8biIiIiKirsjINZPrkh65ubkAgA0bNrjigm/06NH49ttv490MAO5qC+Ce9tTW1qKsrAwbN25EZmZmvJvjmv0CuKstgHvawz6jzk1tAdzTHvYZdW5qC+Ce9rDPqHNTWwD3tKempgbl5eXhv4WJ9PC6SR3bosxN301u2i+Au9rjprawzyhzU1sAd7XHTX0GcNe+YVuUsc+oY1simblmcl3Sw+Npn2YkKyvLFZ3d6/W6oh2Au9oCuK89mZmZrmiPm/aLm9oCuK897DOR3NQWwH3tYZ+J5Ka2AO5rD/tMJDe1BXBfe0J/CxPp4XWTOrZFmxu+m9y2X9zUHje1JYR9RspNbQHc1x7AHX0GcNe+YVu0sc9EYlvUGblm4lWVjksuuSTeTQhzU1sA97XHLdy0X9zUFsB97XELN+0XN7UFcF973MJN+8VNbQHc1x63cNN+cVNbAPe1hyhRuemzxLa4n9v2i5va46a2uImb9oub2gK4rz1u4qZ9w7YkBjftG7bFGiHostkSa2trkZWVhZqaGldlkMi92GfILPYZMot9hsxinyGz2GfILPYZMot9hsxinyGz2GfILPYZMsNMf3HdSI9AIICbb74ZgUAg3k2hBME+Q2axz5BZ7DNkFvsMmcU+Q2axz5BZ7DNkFvsMmcU+Q2axz5AZZvqL60Z6EBERERERERERERERRcN1Iz2IiIiIiIiIiIiIiIiiwaQHERERERERERERERF1Ckx6EBERERERERERERFRp8CkBxERERERERERERERdQqOJD3uvPNOjB49GhkZGSgsLMTxxx+P5cuXS5ZpaGjAJZdcgry8PKSnp+Okk07C9u3bJctcdtllGDlyJAKBAIYNGxaxneXLl2PatGkoKipCcnIyKisrceONN6K5udmJt0UOilWfEVu1ahUyMjKQnZ1t87uhWIhVn1m3bh0EQYj499VXXzn59shmsTzHBINB3Hvvvejbty8CgQC6d++OO+64w6m3Rg6KVb+55ZZbFM8zaWlpTr49IoozXjORWbxmIrN4zURm8bqJzOI1E7mVI0mPefPm4ZJLLsFXX32F999/H83NzTjiiCOwf//+8DJXXnkl/vvf/2Lu3LmYN28etmzZghNPPDFiXeeeey5OOeUUxe34/X6cffbZeO+997B8+XI88MAD+Mc//oGbb77ZibdFDopVnwlpbm7GaaedhsmTJ9v+Xig2Yt1nPvjgA2zdujX8b+TIkba/J3JOLPvL5Zdfjscffxz33nsvli1bhjfeeANjxoxx5H2Rs2LVb66++mrJ+WXr1q2oqqrC7NmzHXtvRBR/vGYis3jNRGbxmonM4nUTmcVrJnKtYAzs2LEjCCA4b968YDAYDO7duzfo9/uDc+fODS+zdOnSIIDg/PnzI15/8803B4cOHWpoW1deeWVw0qRJtrSb4sfpPnPttdcGzzzzzOCTTz4ZzMrKsrv5FAdO9Zm1a9cGAQQXLlzoVNMpDpzqLz///HPQ5/MFly1b5ljbKX5i9ffMokWLggCCn376qW1tJyL34zUTmcVrJjKL10xkFq+byCxeM5FbxGROj5qaGgBAbm4uAGDBggVobm7G9OnTw8v0798f5eXlmD9/ftTbWbVqFd555x1MmTLFWoMp7pzsMx999BHmzp2LRx55xL4GU9w5fZ459thjUVhYiEmTJuGNN96wp9EUN071l//+97+orKzEm2++iYqKCvTs2RPnn38+du/ebe8boLiI1d8zjz/+OPr27cs7a4m6GF4zkVm8ZiKzeM1EZvG6icziNRO5heNJj7a2NlxxxRWYOHEiBg0aBADYtm0bkpKSIuqCFhUVYdu2baa3MWHCBCQnJ6NPnz6YPHkybrvtNjuaTnHiZJ+prq7GnDlz8NRTTyEzM9POZlMcOdln0tPTcd9992Hu3Ll46623MGnSJBx//PH8Iz6BOdlf1qxZg/Xr12Pu3Ll45pln8NRTT2HBggU4+eST7XwLFAex+HsGaK93+9xzz+G8886z2mQiSiC8ZiKzeM1EZvGaiczidROZxWsmchOf0xu45JJL8OOPP+Lzzz93bBsvvvgi9u3bh8WLF+Oaa67Bvffei2uvvdax7ZGznOwzF1xwAU4//XQccsghtq+b4sfJPpOfn4+rrroq/Pvo0aOxZcsW3HPPPTj22GNt3x45z8n+0tbWhsbGRjzzzDPo27cvAOCJJ57AyJEjsXz5cvTr18/2bVJsxOLvGQB49dVXsW/fPpxzzjmOboeI3IXXTGQWr5nILF4zkVm8biKzeM1EbuLoSI9LL70Ub775Jj7++GOUlpaGHy8uLkZTUxP27t0rWX779u0oLi42vZ2ysjJUVVXhtNNOw1133YVbbrkFra2tVptPceB0n/noo49w7733wufzwefz4bzzzkNNTQ18Ph/++c9/2vU2KIZidZ4RGzt2LFatWmVpHRQfTveXkpIS+Hy+8B/uADBgwAAAwIYNG6w1nuImlueZxx9/HEcffTSKioqsNJmIEgivmcgsXjORWbxmIrN43URm8ZqJ3MaRpEcwGMSll16KV199FR999BEqKiokz48cORJ+vx8ffvhh+LHly5djw4YNGD9+vKVtt7W1obm5GW1tbZbWQ7EVqz4zf/58LFq0KPzvtttuQ0ZGBhYtWoQTTjjBtvdDzovneWbRokUoKSmxtA6KrVj1l4kTJ6KlpQWrV68OP7ZixQoAQI8ePSy+C4q1WJ9n1q5di48//pjDtIm6CF4zkVm8ZiKzeM1EZvG6icziNRO5lSPlrS655BI8//zzeP3115GRkRGu0ZaVlYWUlBRkZWXhvPPOw1VXXYXc3FxkZmbiN7/5DcaPH49x48aF17Nq1SrU1dVh27ZtOHDgABYtWgQAqKqqQlJSEp577jn4/X4MHjwYgUAA3333Ha6//nqccsop8Pv9Trw1ckis+kzozoGQ7777Dh6PJ1xrkBJHrPrM008/jaSkJAwfPhwA8Morr+Cf//wnHn/88Zi/Z4perPrL9OnTMWLECJx77rl44IEH0NbWhksuuQSHH3645C4mSgyx6jch//znP1FSUoJZs2bF9H0SUXzwmonM4jUTmcVrJjKL101kFq+ZyLWCDgCg+O/JJ58ML3PgwIHgr3/962BOTk4wNTU1eMIJJwS3bt0qWc+UKVMU17N27dpgMBgMvvDCC8ERI0YE09PTg2lpacGqqqrgn/70p+CBAweceFvkoFj1Gbknn3wymJWV5dwbI8fEqs889dRTwQEDBgRTU1ODmZmZwTFjxgTnzp0bw3dKdojlOWbz5s3BE088MZienh4sKioKzpkzJ1hdXR2jd0p2imW/aW1tDZaWlgZ///vfx+jdEVG88ZqJzOI1E5nFayYyi9dNZBavmcithGAwGFRKhhARERERERERERERESUSRycyJyIiIiIiIiIiIiIiihUmPYiIiIiIiIiIiIiIqFNg0oOIiIiIiIiIiIiIiDoFJj2IiIiIiIiIiIiIiKhTYNKDiIiIiIiIiIiIiIg6BSY9iIiIiIiIiIiIiIioU2DSg4iIiIiIiIiIiIiIOgUmPYiIupCpU6fiiiuuiHcziIiIiIiIXInXTEREiY9JDyIiUvTJJ59AEATs3bs33k0hIiIiIiJyHV4zERG5E5MeRERERERERERERETUKTDpQUTUSe3fvx9nn3020tPTUVJSgvvuu0/y/LPPPotRo0YhIyMDxcXFOP3007Fjxw4AwLp16zBt2jQAQE5ODgRBwJw5cwAAbW1tuPPOO1FRUYGUlBQMHToUL730UkzfGxERERERkVW8ZiIi6pyY9CAi6qSuueYazJs3D6+//jree+89fPLJJ/j+++/Dzzc3N+P222/H4sWL8dprr2HdunXhP9LLysrw8ssvAwCWL1+OrVu34i9/+QsA4M4778QzzzyDRx99FD/99BOuvPJKnHnmmZg3b17M3yMREREREVG0eM1ERNQ5CcFgMBjvRhARkb3q6uqQl5eHf/3rX5g9ezYAYPfu3SgtLcWFF16IBx54IOI13333HUaPHo19+/YhPT0dn3zyCaZNm4Y9e/YgOzsbANDY2Ijc3Fx88MEHGD9+fPi1559/Purr6/H888/H4u0RERERERFZwmsmIqLOyxfvBhARkf1Wr16NpqYmjB07NvxYbm4u+vXrF/59wYIFuOWWW7B48WLs2bMHbW1tAIANGzagqqpKcb2rVq1CfX09Dj/8cMnjTU1NGD58uAPvhIiIiIiIyH68ZiIi6ryY9CAi6oL279+PGTNmYMaMGXjuuedQUFCADRs2YMaMGWhqalJ9XV1dHQDgrbfeQvfu3SXPBQIBR9tMREREREQUK7xmIiJKXEx6EBF1Qr169YLf78fXX3+N8vJyAMCePXuwYsUKTJkyBcuWLUN1dTXuuusulJWVAWgfqi2WlJQEAGhtbQ0/VlVVhUAggA0bNmDKlCkxejdERERERET24jUTEVHnxaQHEVEnlJ6ejvPOOw/XXHMN8vLyUFhYiBtuuAEejwcAUF5ejqSkJDz00EO46KKL8OOPP+L222+XrKNHjx4QBAFvvvkmjjzySKSkpCAjIwNXX301rrzySrS1tWHSpEmoqanBF198gczMTJxzzjnxeLtERERERESm8JqJiKjz8sS7AURE5Ix77rkHkydPxjHHHIPp06dj0qRJGDlyJACgoKAATz31FObOnYuqqircdddduPfeeyWv7969O2699VZcd911KCoqwqWXXgoAuP3223HTTTfhzjvvxIABAzBz5ky89dZ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datevalueidvalue_1value__diff_norm
02013-01-0148.0048.0NaN
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" + ], + "text/plain": [ + " date value id value_1 value__diff_norm\n", + "0 2013-01-01 48.0 0 48.0 NaN\n", + "1 2013-01-02 44.0 0 44.0 -4.0\n", + "2 2013-01-03 60.0 0 60.0 16.0\n", + "3 2013-01-04 56.0 0 56.0 -4.0\n", + "4 2013-01-05 50.0 0 50.0 -6.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show both the delta and ratio regimes -> create new column with the same values\n", + "# For data[dataset.target_column] use the delta regime and for data[f\"{dataset.target_column}_1\"] use the ratio regime\n", + "\n", + "dataset_copy = deepcopy(dataset)\n", + "dataset_copy.data[f\"{dataset.target_column}_1\"] = dataset_copy.data[dataset.target_column]\n", + "\n", + "data = Pipeline.create_data_dict_for_pipeline(dataset_copy, features_idx, target_idx)\n", + "\n", + "diff_delta = DifferenceNormalizer(\n", + " transform_features=True,\n", + " transform_target=True, # Now let's transform the target too\n", + " regime=\"delta\"\n", + ")\n", + "\n", + "diff_ratio = DifferenceNormalizer(\n", + " transform_features=True,\n", + " transform_target=False,\n", + " regime=\"ratio\"\n", + ")\n", + "\n", + "print(\"raw_ts_X before differencing:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "print(\"raw_ts_y before differencing:\")\n", + "display(data[\"raw_ts_y\"].head())\n", + "\n", + "data = diff_delta.fit_transform(data, input_features=[dataset.target_column])\n", + "data = diff_ratio.fit_transform(data, input_features=[f\"{dataset.target_column}_1\"])\n", + "\n", + "print(\"raw_ts_X after differencing:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "print(\"raw_ts_y after differencing:\")\n", + "display(data[\"raw_ts_y\"].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that after applying `DifferenceNormalizer`, missing values are generated. Therefore, if the base model cannot handle missing values, it is necessary to apply `MissingValuesImputer`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(20, 7))\n", + "\n", + "data[\"raw_ts_X\"][data[\"raw_ts_X\"][\"id\"] == 0].plot(\n", + " x=dataset.date_column, y=f\"{dataset.target_column}__diff_norm\", ax=ax[0]\n", + ")\n", + "data[\"raw_ts_X\"][data[\"raw_ts_X\"][\"id\"] == 1].plot(\n", + " x=dataset.date_column, y=f\"{dataset.target_column}__diff_norm\", ax=ax[1]\n", + ")\n", + "\n", + "plt.suptitle(\"Delta Difference Normalized Data\")\n", + "ax[0].set_title(\"ID 0\")\n", + "ax[1].set_title(\"ID 1\")\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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datevalueiddate__time_to_numdate__season_ddate__season_m
02013-01-0148.00-2556.011
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22013-01-0360.00-2554.031
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" + ], + "text/plain": [ + " date value id date__time_to_num date__season_d date__season_m\n", + "0 2013-01-01 48.0 0 -2556.0 1 1\n", + "1 2013-01-02 44.0 0 -2555.0 2 1\n", + "2 2013-01-03 60.0 0 -2554.0 3 1\n", + "3 2013-01-04 56.0 0 -2553.0 4 1\n", + "4 2013-01-05 50.0 0 -2552.0 5 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)\n", + "\n", + "time_to_num = TimeToNumGenerator(delta=\"D\")\n", + "date_seasons = DateSeasonsGenerator(seasonalities=[\"d\", \"m\"])\n", + "\n", + "print(\"raw_ts_X before scaling:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "data = time_to_num.fit_transform(data, input_features=[dataset.date_column])\n", + "data = date_seasons.fit_transform(data, input_features=[dataset.date_column])\n", + "\n", + "print(\"raw_ts_X after scaling:\")\n", + "display(data[\"raw_ts_X\"].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`LabelEncodingTransformer`, `OneHotEncodingTransformer`: feature generation based on categorical columns.\n", + "\n", + "- The `OneHotEncodingTransformer` has a **drop** parameter: one from ['first', 'if_binary', None] or an array-list of shape (n_features, ):\n", + " - if None: retain all features.\n", + " - if \"first\": drop the first category in each feature.\n", + " - if \"if_binary\": drop the first category in each feature with two categories.\n", + " - if array: drop[i] is the category in feature X[:, i] that should be dropped." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw_ts_X before scaling:\n" + ] + }, + { + "data": { + "text/html": [ + "
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datevalueid
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datevalueidid__labelid__0_oheid__1_ohe
02013-01-0148.0001.00.0
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22013-01-0360.0001.00.0
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" + ], + "text/plain": [ + " date value id id__label id__0_ohe id__1_ohe\n", + "0 2013-01-01 48.0 0 0 1.0 0.0\n", + "1 2013-01-02 44.0 0 0 1.0 0.0\n", + "2 2013-01-03 60.0 0 0 1.0 0.0\n", + "3 2013-01-04 56.0 0 0 1.0 0.0\n", + "4 2013-01-05 50.0 0 0 1.0 0.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)\n", + "\n", + "le = LabelEncodingTransformer()\n", + "ohe = OneHotEncodingTransformer()\n", + "\n", + "print(\"raw_ts_X before scaling:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "data = le.fit_transform(data, input_features=[dataset.id_column])\n", + "data = ohe.fit_transform(data, input_features=[dataset.id_column])\n", + "\n", + "print(\"raw_ts_X after scaling:\")\n", + "display(data[\"raw_ts_X\"].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`MissingValuesImputer`: filling in missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- **regime** can be 'mean' or 'lag'. If None, the transformer will fill missing values with a constant value.\n", + "- **constant_value**: the constant value to fill remaining missing values after applying the chosen regime.\n", + "- The **transform_features** and **transform_target** parameters allow independent control over the normalization of features and targets. Sometimes, we may need to fill in missing values only for one case.\n", + "- **window**: the size of the window for the mean imputation strategy.\n", + "- **weighted_alpha**: the alpha value for weighting in the mean imputation strategy.\n", + "- **lag**: the lag value for the lag imputation strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw_ts_X before scaling:\n" + ] + }, + { + "data": { + "text/html": [ + "
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datevalueid
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datevalueidvalue__imputed
02013-01-01NaN00.000000
12013-01-0244.0044.000000
22013-01-0360.0060.000000
32013-01-04NaN052.665128
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" + ], + "text/plain": [ + " date value id value__imputed\n", + "0 2013-01-01 NaN 0 0.000000\n", + "1 2013-01-02 44.0 0 44.000000\n", + "2 2013-01-03 60.0 0 60.000000\n", + "3 2013-01-04 NaN 0 52.665128\n", + "4 2013-01-05 50.0 0 50.000000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset_copy = deepcopy(dataset)\n", + "\n", + "# Make some values missing for id=0 and id=1 independently\n", + "idx_start_id_1 = dataset_copy.data[dataset_copy.data[\"id\"] == 1].index[0]\n", + "\n", + "dataset_copy.data.loc[:idx_start_id_1:3, dataset_copy.target_column] = np.nan\n", + "dataset_copy.data.loc[idx_start_id_1::3, dataset_copy.target_column] = np.nan\n", + "\n", + "data = Pipeline.create_data_dict_for_pipeline(dataset_copy, features_idx, target_idx)\n", + "\n", + "imputer = MissingValuesImputer(\n", + " regime=\"mean\",\n", + " constant_value=0,\n", + " transform_features=True,\n", + " transform_target=True,\n", + " window=3,\n", + " weighted_alpha=0.5,\n", + ")\n", + "\n", + "print(\"raw_ts_X before scaling:\")\n", + "display(data[\"raw_ts_X\"].head())\n", + "\n", + "data = imputer.fit_transform(data, input_features=[dataset.target_column])\n", + "\n", + "print(\"raw_ts_X after scaling:\")\n", + "display(data[\"raw_ts_X\"].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2, figsize=(20, 10))\n", + "\n", + "# Before imputation\n", + "data[\"raw_ts_X\"][data[\"raw_ts_X\"][\"id\"] == 0].plot(\n", + " x=dataset.date_column, y=dataset.target_column, ax=ax[0, 0]\n", + ")\n", + "data[\"raw_ts_X\"][data[\"raw_ts_X\"][\"id\"] == 1].plot(\n", + " x=dataset.date_column, y=dataset.target_column, ax=ax[0, 1]\n", + ")\n", + "\n", + "# After imputation\n", + "data[\"raw_ts_X\"][data[\"raw_ts_X\"][\"id\"] == 0].plot(\n", + " x=dataset.date_column, y=f\"{dataset.target_column}__imputed\", ax=ax[1, 0]\n", + ")\n", + "data[\"raw_ts_X\"][data[\"raw_ts_X\"][\"id\"] == 1].plot(\n", + " x=dataset.date_column, y=f\"{dataset.target_column}__imputed\", ax=ax[1, 1]\n", + ")\n", + "\n", + "plt.suptitle(\"Missing Values Imputed Data\")\n", + "ax[0, 0].set_title(\"ID 0 - Before Imputation\")\n", + "ax[0, 1].set_title(\"ID 1 - Before Imputation\")\n", + "ax[1, 0].set_title(\"ID 0 - After Imputation\")\n", + "ax[1, 1].set_title(\"ID 1 - After Imputation\")\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Series2Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- These transformers are responsible for creating the feature matrix for the base model.\n", + "- During the feature matrix generation stage, they take the transformed data from the long series and convert it into specific features for the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`LagTransformer`: generates lags, which create time-shifted features.\n", + "- The lags in the `lags` parameter can be represented as an integer value (which generates a range(k)), or as a sequence of numbers. Lags that exceed the available history will be truncated (i.e., if the history length is 7, lags beyond 7 will not be generated).\n", + "\n", + "For the target variable, it is important to apply the `TargetGenerator`, which generates targets for the model." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw_ts_X:\n", + " date value id\n", + "0 2013-01-01 48.0 0\n", + "1 2013-01-02 44.0 0\n", + "2 2013-01-03 60.0 0\n", + "3 2013-01-04 56.0 0\n", + "4 2013-01-05 50.0 0\n", + "5 2013-01-06 63.0 0\n", + "6 2013-01-07 27.0 0\n", + "7 2013-01-08 54.0 0\n", + "8 2013-01-09 55.0 0\n", + "9 2013-01-10 45.0 0\n", + "10 2013-01-11 53.0 0\n", + "11 2013-01-12 60.0 0\n", + "12 2013-01-13 7888.0 0\n", + "13 2013-01-14 44.0 0\n", + "14 2013-01-15 41.0 0\n", + "X after generating lags:\n", + "[[ 54. 55. 45.]\n", + " [ 55. 45. 53.]\n", + " [ 45. 53. 60.]\n", + " [ 53. 60. 7888.]\n", + " [ 60. 7888. 44.]]\n", + "y after generating lags:\n", + "[[ 53. 60. 7888.]\n", + " [ 60. 7888. 44.]\n", + " [7888. 44. 41.]\n", + " [ 44. 41. 54.]\n", + " [ 41. 54. 51.]]\n" + ] + } + ], + "source": [ + "dataset_copy = deepcopy(dataset)\n", + "data = Pipeline.create_data_dict_for_pipeline(dataset_copy, features_idx, target_idx)\n", + "\n", + "lag_transformer = LagTransformer(lags=[0, 1, 2])\n", + "target_generator = TargetGenerator()\n", + "\n", + "data = lag_transformer.fit_transform(data, input_features=[dataset.target_column])\n", + "data = target_generator.fit_transform(data, input_features=[dataset.target_column])\n", + "\n", + "# Now let's create features and target matrix \n", + "data = lag_transformer.generate(data)\n", + "data = target_generator.generate(data)\n", + "\n", + "print(\"raw_ts_X:\")\n", + "print(data[\"raw_ts_X\"][:15])\n", + "\n", + "print(\"X after generating lags:\")\n", + "print(data[\"X\"][:5])\n", + "\n", + "print(\"y after generating lags:\")\n", + "print(data[\"y\"][:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is important to emphasize again that we set the history length to 10 and the forecasting horizon to 3. Therefore, the first row in X starts from the 7th point of the original time series." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Features2Features" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- These transformers work directly with the feature matrix generated by the previous transformers. They can make changes to the already existing feature matrix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`LastKnownNormalizer`: normalization by subtracting or dividing lags based on the last known values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The **transform_features** and **transform_target** parameters allow independent control over the normalization of features and targets. It may be beneficial, in terms of model performance, to normalize only the features while keeping the targets in their original scale, or vice versa.\n", + "- The **regime** parameter defines the method of normalization (either we divide by the last known lag — **ratio**, or subtract the last known lag from all lags — **delta**)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X before last known normalization:\n", + "[[ 54. 55. 45.]\n", + " [ 55. 45. 53.]\n", + " [ 45. 53. 60.]\n", + " [ 53. 60. 7888.]\n", + " [ 60. 7888. 44.]]\n", + "y before last known normalization:\n", + "[[ 53. 60. 7888.]\n", + " [ 60. 7888. 44.]\n", + " [7888. 44. 41.]\n", + " [ 44. 41. 54.]\n", + " [ 41. 54. 51.]]\n", + "X after delta last known normalization:\n", + "[[ 9.000e+00 1.000e+01 0.000e+00]\n", + " [ 2.000e+00 -8.000e+00 0.000e+00]\n", + " [-1.500e+01 -7.000e+00 0.000e+00]\n", + " [-7.835e+03 -7.828e+03 0.000e+00]\n", + " [ 1.600e+01 7.844e+03 0.000e+00]]\n", + "y after delta last known normalization:\n", + "[[ 8.000e+00 1.500e+01 7.843e+03]\n", + " [ 7.000e+00 7.835e+03 -9.000e+00]\n", + " [ 7.828e+03 -1.600e+01 -1.900e+01]\n", + " [-7.844e+03 -7.847e+03 -7.834e+03]\n", + " [-3.000e+00 1.000e+01 7.000e+00]]\n", + "X after ratio last known normalization:\n", + "[[1.20000000e+00 1.22222222e+00 1.00000000e+00]\n", + " [1.03773585e+00 8.49056604e-01 1.00000000e+00]\n", + " [7.50000000e-01 8.83333333e-01 1.00000000e+00]\n", + " [6.71906694e-03 7.60649087e-03 1.00000000e+00]\n", + " [1.36363636e+00 1.79272727e+02 1.00000000e+00]]\n" + ] + } + ], + "source": [ + "last_known_delta = LastKnownNormalizer(\n", + " transform_features=True,\n", + " transform_target=True,\n", + " regime=\"delta\",\n", + ")\n", + "\n", + "last_known_ratio = LastKnownNormalizer(\n", + " transform_features=True,\n", + " transform_target=False,\n", + " regime=\"ratio\",\n", + ")\n", + "\n", + "data_lkn_delta = deepcopy(data)\n", + "data_lkn_ratio = deepcopy(data)\n", + "\n", + "data_lkn_delta = last_known_delta.fit_transform(data_lkn_delta, input_features=lag_transformer.output_features)\n", + "data_lkn_ratio = last_known_ratio.fit_transform(data_lkn_ratio, input_features=lag_transformer.output_features)\n", + "\n", + "print(\"X before last known normalization:\")\n", + "print(data[\"X\"][:5])\n", + "\n", + "print(\"y before last known normalization:\")\n", + "print(data[\"y\"][:5])\n", + "\n", + "data_lkn_delta = last_known_delta.generate(data_lkn_delta)\n", + "data_lkn_ratio = last_known_ratio.generate(data_lkn_ratio)\n", + "\n", + "print(\"X after delta last known normalization:\")\n", + "print(data_lkn_delta[\"X\"][:5])\n", + "\n", + "print(\"y after delta last known normalization:\")\n", + "print(data_lkn_delta[\"y\"][:5])\n", + "\n", + "print(\"X after ratio last known normalization:\")\n", + "print(data_lkn_ratio[\"X\"][:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sequential & Union" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`SequentialTransformer`:\n", + "- This transformer allows you to apply a sequence of transformers one after another. Each transformer in the sequence receives the output of the previous one as input.\n", + "- For example, you can first normalize the data using `StandardScalerTransformer`, and then generate lags using `LagTransformer`.\n", + "\n", + "`UnionTransformer`:\n", + "- This transformer allows you to apply several transformers in parallel, after which their results, presented as a feature matrix (not a long series!), are combined.\n", + "- For example, you can apply several transformers at once, such as `DateSeasonsGenerator` for generating seasonal features and `OneHotEncodingTransformer` for encoding categorical features, and combine their results into a single table." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After exploring the different types of transformers, we can combine them into a single chain using the `Pipeline` class. The `Pipeline` acts as a wrapper around the transformers and makes it easy to manage sequential or parallel data transformations using `SequentialTransformer` and `UnionTransformer`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several ways to initialize the `Pipeline` class — let's go through them, from simple but less functional, to complex but more flexible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, there are two ways to build a pipline from transformers: initialise the transformers of interest by hand or use a config in the form of a dictionary. Let's look at both ways." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "standard_scaler = StandardScalerTransformer(\n", + " transform_features=True,\n", + " transform_target=True\n", + ")\n", + "\n", + "lag = LagTransformer(lags=3)\n", + "date_lag = LagTransformer(lags=3)\n", + "id_lag = LagTransformer(lags=1)\n", + "\n", + "target_generator = TargetGenerator()\n", + "\n", + "date_seasons = DateSeasonsGenerator(\n", + " seasonalities=[\"doy\", \"m\", \"wd\"],\n", + " from_target_date=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "union_1 = UnionTransformer(transformers_list=[lag, target_generator])\n", + "\n", + "seq_1 = SequentialTransformer(transformers_list=[standard_scaler, union_1], input_features=[\"value\"])\n", + "seq_2 = SequentialTransformer(transformers_list=[date_seasons, date_lag], input_features=[\"date\"])\n", + "seq_3 = SequentialTransformer(transformers_list=[id_lag], input_features=[\"id\"])\n", + "\n", + "union = UnionTransformer(transformers_list=[seq_1, seq_2, seq_3])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_1 = Pipeline(union, multivariate=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'transformers': ,\n", + " 'multivariate': False,\n", + " 'is_fitted': False,\n", + " 'strategy_name': None,\n", + " 'output_features': None,\n", + " 'y_original_shape': None}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline_1.__dict__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_params = {\n", + " \"target\": {\n", + " \"columns\": [\"value\"],\n", + " \"features\": {\n", + " \"StandardScalerTransformer\":\n", + " {\n", + " \"transform_target\": True, \n", + " \"transform_features\": True\n", + " },\n", + " \"LagTransformer\": {\"lags\": 7},\n", + " },\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"date\"],\n", + " \"features\": {\n", + " \"DateSeasonsGenerator\": {\n", + " # Use seasonality features from the date column as \n", + " # features with datetime lags\n", + " # Possible values: [\n", + " # \"y\": year, \"m\": month, \"d\": day, \n", + " # \"wd\": weekday, \"doy\": dayofyear,\n", + " # \"hour\": hour, \"min\": minute, \"sec\": second, \n", + " # \"ms\": microsecond, \"ns\": nanosecond\n", + " # ]\n", + " \"seasonalities\": ['doy', 'm', 'wd'], \n", + " # Use date from target point to make datetime features\n", + " \"from_target_date\": True,\n", + " },\n", + " \"LagTransformer\": {\"lags\": 3}\n", + " },\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"features\": {\n", + " \"LagTransformer\": {\"lags\": 1},\n", + " },\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline.from_dict(pipeline_params, multivariate=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_easy_params = {\n", + " \"target_lags\": 3,\n", + " \"date_lags\": 1,\n", + "# \"exog_lags\": 1, # Uncomment this line if you have exogenous features\n", + " # One from [\"none\", \"standard_scaler\", \"difference_normalizer\", \"last_known_normalizer\"]\n", + " \"target_normalizer\": \"standard_scaler\",\n", + " # One from [\"none\", \"delta\", \"ratio\"] (MUST BE \"none\" for \"standard_scaler\" and NOT \"none\" for others)\n", + " \"target_normalizer_regime\": \"none\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline.easy_setup(dataset_params, pipeline_easy_params, multivariate=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Can I use exogenous variables in the pipeline?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Yes! Exogenous variables can also be specified here. Just include them in your pipeline.\n", + "\n", + "However, their operation is currently tested only for the `MIMOStrategy` in global-modelling. For other strategies support of additional variables is under development." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# pipeline_params[\"exog_group_1\"] = {\n", + "# \"columns\": [\"value\"],\n", + "# \"features\": {\n", + "# \"StandardScalerTransformer\":\n", + "# {\n", + "# \"transform_target\": False, \n", + "# \"transform_features\": True\n", + "# },\n", + "# \"LagTransformer\": {\"lags\": 7},\n", + "# },\n", + "# }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Make sure you have the transform_target = False flag for exogenous features!__" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py_3_10", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Tutorial_4_Neural_Networks.ipynb b/Tutorial_4_Neural_Networks.ipynb index 0e64341..d9f353e 100644 --- a/Tutorial_4_Neural_Networks.ipynb +++ b/Tutorial_4_Neural_Networks.ipynb @@ -6,7 +6,6 @@ "metadata": {}, "outputs": [], "source": [ - "import importlib\n", "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", @@ -21,12 +20,7 @@ "from tsururu.model_training.trainer import DLTrainer\n", "from tsururu.model_training.validator import KFoldCrossValidator\n", "from tsururu.models.torch_based.dlinear import DLinear_NN\n", - "from tsururu.strategies import (\n", - " RecursiveStrategy,\n", - " MIMOStrategy,\n", - " DirectStrategy,\n", - " FlatWideMIMOStrategy,\n", - ")\n", + "from tsururu.strategies import DirectStrategy, RecursiveStrategy\n", "from tsururu.transformers import (\n", " LagTransformer,\n", " SequentialTransformer,\n", @@ -169,6 +163,13 @@ "### Trainer" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Currently, the available architectures are `DLinear` and `PatchTST`. However, adding your own architecture is quite simple if you follow the logic of the base model class." + ] + }, { "cell_type": "code", "execution_count": 6, @@ -176,7 +177,7 @@ "outputs": [], "source": [ "# Configure the model parameters\n", - "model = DLinear_NN\n", + "model = DLinear_NN # DLinear_NN or PatchTST\n", "model_params = {\"moving_avg\": 7, \"individual\": False, \"enc_in\": None}\n", "\n", "# Configure the validation parameters\n", @@ -244,109 +245,113 @@ "text": [ "length of train dataset: 496\n", "length of val dataset: 497\n", - "Epoch 1/10, cost time: 0.71s\n", - "train loss: 397.4165\n", - "Validation, Loss: 162.1953, Metric: -162.1953\n", - "val loss: 162.1953\n", - "Epoch 2/10, cost time: 0.55s\n", - "train loss: 64.1882\n", - "Validation, Loss: 47.4241, Metric: -47.4241\n", - "val loss: 47.4241\n", - "Epoch 3/10, cost time: 0.54s\n", - "train loss: 17.5566\n", - "Validation, Loss: 8.8561, Metric: -8.8561\n", - "val loss: 8.8561\n", - "Epoch 4/10, cost time: 0.54s\n", - "train loss: 6.4756\n", - "Validation, Loss: 4.4630, Metric: -4.4630\n", - "val loss: 4.4630\n", - "Epoch 5/10, cost time: 0.54s\n", - "train loss: 4.3727\n", - "Validation, Loss: 3.9053, Metric: -3.9053\n", - "val loss: 3.9053\n", - "Epoch 6/10, cost time: 0.56s\n", - "train loss: 3.8692\n", - "Validation, Loss: 3.8264, Metric: -3.8264\n", - "val loss: 3.8264\n", + "Epoch 1/10, cost time: 0.69s\n", + "train loss: 414.3740\n", + "Validation, Loss: 194.2519, Metric: -194.2519\n", + "val loss: 194.2519\n", + "Epoch 2/10, cost time: 0.66s\n", + "train loss: 66.8766\n", + "Validation, Loss: 54.1316, Metric: -54.1316\n", + "val loss: 54.1316\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 17.5994\n", + "Validation, Loss: 11.6868, Metric: -11.6868\n", + "val loss: 11.6868\n", + "Epoch 4/10, cost time: 0.57s\n", + "train loss: 5.7403\n", + "Validation, Loss: 2.8006, Metric: -2.8006\n", + "val loss: 2.8006\n", + "Epoch 5/10, cost time: 0.95s\n", + "train loss: 3.3647\n", + "Validation, Loss: 3.2068, Metric: -3.2068\n", + "val loss: 3.2068\n", + "Early stopping counter: 1\n", + "Epoch 6/10, cost time: 0.66s\n", + "train loss: 2.8860\n", + "Validation, Loss: 2.8542, Metric: -2.8542\n", + "val loss: 2.8542\n", "Removing worst model snapshot: from epoch 0\n", - "Epoch 7/10, cost time: 0.55s\n", - "train loss: 3.7675\n", - "Validation, Loss: 3.7656, Metric: -3.7656\n", - "val loss: 3.7656\n", + "Early stopping counter: 2\n", + "Epoch 7/10, cost time: 0.71s\n", + "train loss: 2.7676\n", + "Validation, Loss: 2.7613, Metric: -2.7613\n", + "val loss: 2.7613\n", "Removing worst model snapshot: from epoch 1\n", - "Epoch 8/10, cost time: 0.61s\n", - "train loss: 3.7314\n", - "Validation, Loss: 3.7414, Metric: -3.7414\n", - "val loss: 3.7414\n", + "Epoch 8/10, cost time: 0.74s\n", + "train loss: 2.7374\n", + "Validation, Loss: 2.7461, Metric: -2.7461\n", + "val loss: 2.7461\n", "Removing worst model snapshot: from epoch 2\n", - "Epoch 9/10, cost time: 0.55s\n", - "train loss: 3.7106\n", - "Validation, Loss: 3.7250, Metric: -3.7250\n", - "val loss: 3.7250\n", - "Removing worst model snapshot: from epoch 3\n", - "Epoch 10/10, cost time: 0.55s\n", - "train loss: 3.6937\n", - "Validation, Loss: 3.7062, Metric: -3.7062\n", - "val loss: 3.7062\n", + "Epoch 9/10, cost time: 0.80s\n", + "train loss: 2.7226\n", + "Validation, Loss: 2.7328, Metric: -2.7328\n", + "val loss: 2.7328\n", "Removing worst model snapshot: from epoch 4\n", - "Fold 0. Score: -3.706167459487915\n", + "Epoch 10/10, cost time: 0.55s\n", + "train loss: 2.7105\n", + "Validation, Loss: 2.7210, Metric: -2.7210\n", + "val loss: 2.7210\n", + "Removing worst model snapshot: from epoch 5\n", + "Training finished.\n", + "Fold 0. Score: -2.721017599105835\n", "length of train dataset: 497\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.54s\n", - "train loss: 403.0955\n", - "Validation, Loss: 162.1690, Metric: -162.1690\n", - "val loss: 162.1690\n", - "Epoch 2/10, cost time: 0.54s\n", - "train loss: 63.4995\n", - "Validation, Loss: 48.7841, Metric: -48.7841\n", - "val loss: 48.7841\n", - "Epoch 3/10, cost time: 0.60s\n", - "train loss: 16.5093\n", - "Validation, Loss: 8.7141, Metric: -8.7141\n", - "val loss: 8.7141\n", - "Epoch 4/10, cost time: 0.54s\n", - "train loss: 5.3522\n", - "Validation, Loss: 2.9066, Metric: -2.9066\n", - "val loss: 2.9066\n", - "Epoch 5/10, cost time: 0.55s\n", - "train loss: 3.2029\n", - "Validation, Loss: 2.8261, Metric: -2.8261\n", - "val loss: 2.8261\n", - "Epoch 6/10, cost time: 0.69s\n", - "train loss: 2.7355\n", - "Validation, Loss: 2.6951, Metric: -2.6951\n", - "val loss: 2.6951\n", + "Epoch 1/10, cost time: 0.82s\n", + "train loss: 392.4304\n", + "Validation, Loss: 145.1556, Metric: -145.1556\n", + "val loss: 145.1556\n", + "Epoch 2/10, cost time: 0.58s\n", + "train loss: 63.3577\n", + "Validation, Loss: 42.9906, Metric: -42.9906\n", + "val loss: 42.9906\n", + "Epoch 3/10, cost time: 0.63s\n", + "train loss: 16.7435\n", + "Validation, Loss: 6.9562, Metric: -6.9562\n", + "val loss: 6.9562\n", + "Epoch 4/10, cost time: 0.56s\n", + "train loss: 6.0819\n", + "Validation, Loss: 4.4336, Metric: -4.4336\n", + "val loss: 4.4336\n", + "Epoch 5/10, cost time: 0.56s\n", + "train loss: 4.2536\n", + "Validation, Loss: 3.7486, Metric: -3.7486\n", + "val loss: 3.7486\n", + "Epoch 6/10, cost time: 0.56s\n", + "train loss: 3.7922\n", + "Validation, Loss: 3.7375, Metric: -3.7375\n", + "val loss: 3.7375\n", "Removing worst model snapshot: from epoch 0\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 2.6526\n", - "Validation, Loss: 2.6279, Metric: -2.6279\n", - "val loss: 2.6279\n", + "Epoch 7/10, cost time: 0.56s\n", + "train loss: 3.7092\n", + "Validation, Loss: 3.6670, Metric: -3.6670\n", + "val loss: 3.6670\n", "Removing worst model snapshot: from epoch 1\n", "Epoch 8/10, cost time: 0.56s\n", - "train loss: 2.6296\n", - "Validation, Loss: 2.6028, Metric: -2.6028\n", - "val loss: 2.6028\n", + "train loss: 3.6805\n", + "Validation, Loss: 3.6503, Metric: -3.6503\n", + "val loss: 3.6503\n", "Removing worst model snapshot: from epoch 2\n", - "Epoch 9/10, cost time: 0.57s\n", - "train loss: 2.6118\n", - "Validation, Loss: 2.5885, Metric: -2.5885\n", - "val loss: 2.5885\n", + "Epoch 9/10, cost time: 0.56s\n", + "train loss: 3.6643\n", + "Validation, Loss: 3.6290, Metric: -3.6290\n", + "val loss: 3.6290\n", "Removing worst model snapshot: from epoch 3\n", - "Epoch 10/10, cost time: 0.56s\n", - "train loss: 2.5993\n", - "Validation, Loss: 2.5757, Metric: -2.5757\n", - "val loss: 2.5757\n", + "Epoch 10/10, cost time: 0.55s\n", + "train loss: 3.6448\n", + "Validation, Loss: 3.6109, Metric: -3.6109\n", + "val loss: 3.6109\n", "Removing worst model snapshot: from epoch 4\n", - "Fold 1. Score: -2.5756940841674805\n", - "Mean score: -3.1409\n", - "Std: 0.5652\n" + "Training finished.\n", + "Fold 1. Score: -3.6109354496002197\n", + "Mean score: -3.166\n", + "Std: 0.445\n" ] }, { "data": { "text/plain": [ - "(22.96847891807556,\n", - " )" + "(25.945719003677368,\n", + " )" ] }, "execution_count": 9, @@ -431,31 +436,31 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1997.029297\n", + " 1997.017334\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1997.616943\n", + " 1997.603149\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1998.106445\n", + " 1998.090576\n", " \n", " \n", " 3\n", " 0\n", " 2022-09-30\n", - " 1998.513306\n", + " 1998.494995\n", " \n", " \n", " 4\n", " 0\n", " 2022-10-01\n", - " 1998.86499\n", + " 1998.843994\n", " \n", " \n", " ...\n", @@ -467,31 +472,31 @@ " 65\n", " 9\n", " 2022-09-29\n", - " 11004.607422\n", + " 11004.65918\n", " \n", " \n", " 66\n", " 9\n", " 2022-09-30\n", - " 11006.004883\n", + " 11006.064453\n", " \n", " \n", " 67\n", " 9\n", " 2022-10-01\n", - " 11007.439453\n", + " 11007.509766\n", " \n", " \n", " 68\n", " 9\n", " 2022-10-02\n", - " 11008.916016\n", + " 11008.994141\n", " \n", " \n", " 69\n", " 9\n", " 2022-10-03\n", - " 11010.459961\n", + " 11010.549805\n", " \n", " \n", "\n", @@ -500,17 +505,17 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1997.029297\n", - "1 0 2022-09-28 1997.616943\n", - "2 0 2022-09-29 1998.106445\n", - "3 0 2022-09-30 1998.513306\n", - "4 0 2022-10-01 1998.86499\n", + "0 0 2022-09-27 1997.017334\n", + "1 0 2022-09-28 1997.603149\n", + "2 0 2022-09-29 1998.090576\n", + "3 0 2022-09-30 1998.494995\n", + "4 0 2022-10-01 1998.843994\n", ".. .. ... ...\n", - "65 9 2022-09-29 11004.607422\n", - "66 9 2022-09-30 11006.004883\n", - "67 9 2022-10-01 11007.439453\n", - "68 9 2022-10-02 11008.916016\n", - "69 9 2022-10-03 11010.459961\n", + "65 9 2022-09-29 11004.65918\n", + "66 9 2022-09-30 11006.064453\n", + "67 9 2022-10-01 11007.509766\n", + "68 9 2022-10-02 11008.994141\n", + "69 9 2022-10-03 11010.549805\n", "\n", "[70 rows x 3 columns]" ] @@ -549,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -591,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -600,778 +605,775 @@ "text": [ "length of train dataset: 496\n", "length of val dataset: 497\n", - "Epoch 1/10, cost time: 0.67s\n", - "train loss: 410.0893\n", - "Validation, Loss: 175.5516, Metric: -175.5516\n", - "val loss: 175.5516\n", + "Epoch 1/10, cost time: 0.64s\n", + "train loss: 408.1764\n", + "Validation, Loss: 177.2360, Metric: -177.2360\n", + "val loss: 177.2360\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 66.1765\n", - "Validation, Loss: 52.3162, Metric: -52.3162\n", - "val loss: 52.3162\n", + "Epoch 2/10, cost time: 0.69s\n", + "train loss: 65.3214\n", + "Validation, Loss: 51.6638, Metric: -51.6638\n", + "val loss: 51.6638\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.56s\n", - "train loss: 17.1039\n", - "Validation, Loss: 9.6560, Metric: -9.6560\n", - "val loss: 9.6560\n", + "Epoch 3/10, cost time: 0.65s\n", + "train loss: 16.8678\n", + "Validation, Loss: 9.6748, Metric: -9.6748\n", + "val loss: 9.6748\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.56s\n", - "train loss: 5.2014\n", - "Validation, Loss: 2.4881, Metric: -2.4881\n", - "val loss: 2.4881\n", + "Epoch 4/10, cost time: 0.66s\n", + "train loss: 5.3270\n", + "Validation, Loss: 2.7434, Metric: -2.7434\n", + "val loss: 2.7434\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 2.9388\n", - "Validation, Loss: 2.6873, Metric: -2.6873\n", - "val loss: 2.6873\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 3.1594\n", + "Validation, Loss: 2.9166, Metric: -2.9166\n", + "val loss: 2.9166\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_4.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.56s\n", - "train loss: 2.4510\n", - "Validation, Loss: 2.4363, Metric: -2.4363\n", - "val loss: 2.4363\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 2.7221\n", + "Validation, Loss: 2.7164, Metric: -2.7164\n", + "val loss: 2.7164\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.56s\n", - "train loss: 2.3589\n", - "Validation, Loss: 2.3555, Metric: -2.3555\n", - "val loss: 2.3555\n", + "Epoch 7/10, cost time: 0.57s\n", + "train loss: 2.6359\n", + "Validation, Loss: 2.6274, Metric: -2.6274\n", + "val loss: 2.6274\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", "Epoch 8/10, cost time: 0.56s\n", - "train loss: 2.3237\n", - "Validation, Loss: 2.3299, Metric: -2.3299\n", - "val loss: 2.3299\n", + "train loss: 2.6042\n", + "Validation, Loss: 2.6130, Metric: -2.6130\n", + "val loss: 2.6130\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.56s\n", - "train loss: 2.3057\n", - "Validation, Loss: 2.3130, Metric: -2.3130\n", - "val loss: 2.3130\n", + "Epoch 9/10, cost time: 0.57s\n", + "train loss: 2.5933\n", + "Validation, Loss: 2.6077, Metric: -2.6077\n", + "val loss: 2.6077\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 2.2946\n", - "Validation, Loss: 2.3030, Metric: -2.3030\n", - "val loss: 2.3030\n", + "Epoch 10/10, cost time: 0.66s\n", + "train loss: 2.5827\n", + "Validation, Loss: 2.5904, Metric: -2.5904\n", + "val loss: 2.5904\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -2.3029584884643555\n", + "Fold 0. Score: -2.590420961380005\n", "length of train dataset: 497\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.56s\n", - "train loss: 402.0421\n", - "Validation, Loss: 175.4913, Metric: -175.4913\n", - "val loss: 175.4913\n", + "Epoch 1/10, cost time: 0.69s\n", + "train loss: 405.7398\n", + "Validation, Loss: 172.1121, Metric: -172.1121\n", + "val loss: 172.1121\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.56s\n", - "train loss: 66.1697\n", - "Validation, Loss: 51.3572, Metric: -51.3572\n", - "val loss: 51.3572\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 67.4312\n", + "Validation, Loss: 52.2455, Metric: -52.2455\n", + "val loss: 52.2455\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.61s\n", - "train loss: 17.4254\n", - "Validation, Loss: 10.0762, Metric: -10.0762\n", - "val loss: 10.0762\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 17.4579\n", + "Validation, Loss: 10.0573, Metric: -10.0573\n", + "val loss: 10.0573\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.56s\n", - "train loss: 6.2863\n", - "Validation, Loss: 3.7394, Metric: -3.7394\n", - "val loss: 3.7394\n", + "Epoch 4/10, cost time: 0.85s\n", + "train loss: 6.1413\n", + "Validation, Loss: 3.6228, Metric: -3.6228\n", + "val loss: 3.6228\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 4.1120\n", - "Validation, Loss: 3.7602, Metric: -3.7602\n", - "val loss: 3.7602\n", + "Epoch 5/10, cost time: 0.67s\n", + "train loss: 4.0260\n", + "Validation, Loss: 3.7045, Metric: -3.7045\n", + "val loss: 3.7045\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_4.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 3.5857\n", - "Validation, Loss: 3.5395, Metric: -3.5395\n", - "val loss: 3.5395\n", + "Epoch 6/10, cost time: 0.61s\n", + "train loss: 3.5357\n", + "Validation, Loss: 3.4776, Metric: -3.4776\n", + "val loss: 3.4776\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 3.5012\n", - "Validation, Loss: 3.4661, Metric: -3.4661\n", - "val loss: 3.4661\n", + "Epoch 7/10, cost time: 0.61s\n", + "train loss: 3.4319\n", + "Validation, Loss: 3.3974, Metric: -3.3974\n", + "val loss: 3.3974\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.57s\n", - "train loss: 3.4733\n", - "Validation, Loss: 3.4416, Metric: -3.4416\n", - "val loss: 3.4416\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 3.4033\n", + "Validation, Loss: 3.3723, Metric: -3.3723\n", + "val loss: 3.3723\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.57s\n", - "train loss: 3.4547\n", - "Validation, Loss: 3.4227, Metric: -3.4227\n", - "val loss: 3.4227\n", + "Epoch 9/10, cost time: 0.61s\n", + "train loss: 3.3884\n", + "Validation, Loss: 3.3558, Metric: -3.3558\n", + "val loss: 3.3558\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 3.4392\n", - "Validation, Loss: 3.4071, Metric: -3.4071\n", - "val loss: 3.4071\n", + "Epoch 10/10, cost time: 0.61s\n", + "train loss: 3.3702\n", + "Validation, Loss: 3.3397, Metric: -3.3397\n", + "val loss: 3.3397\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -3.407080888748169\n", - "Mean score: -2.855\n", - "Std: 0.5521\n", + "Fold 1. Score: -3.3396642208099365\n", + "Mean score: -2.965\n", + "Std: 0.3746\n", "length of train dataset: 496\n", "length of val dataset: 496\n", "Epoch 1/10, cost time: 0.57s\n", - "train loss: 378.3954\n", - "Validation, Loss: 118.6615, Metric: -118.6615\n", - "val loss: 118.6615\n", + "train loss: 394.5096\n", + "Validation, Loss: 150.7563, Metric: -150.7563\n", + "val loss: 150.7563\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.57s\n", - "train loss: 61.5287\n", - "Validation, Loss: 34.4297, Metric: -34.4297\n", - "val loss: 34.4297\n", + "Epoch 2/10, cost time: 0.64s\n", + "train loss: 63.1882\n", + "Validation, Loss: 44.5514, Metric: -44.5514\n", + "val loss: 44.5514\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 17.6670\n", - "Validation, Loss: 6.5823, Metric: -6.5823\n", - "val loss: 6.5823\n", + "Epoch 3/10, cost time: 0.62s\n", + "train loss: 17.2996\n", + "Validation, Loss: 7.9756, Metric: -7.9756\n", + "val loss: 7.9756\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.57s\n", - "train loss: 8.1401\n", - "Validation, Loss: 7.6014, Metric: -7.6014\n", - "val loss: 7.6014\n", + "Epoch 4/10, cost time: 0.69s\n", + "train loss: 6.7543\n", + "Validation, Loss: 5.0634, Metric: -5.0634\n", + "val loss: 5.0634\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_3.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.57s\n", - "train loss: 6.4557\n", - "Validation, Loss: 5.9087, Metric: -5.9087\n", - "val loss: 5.9087\n", + "Epoch 5/10, cost time: 0.58s\n", + "train loss: 4.9157\n", + "Validation, Loss: 4.4250, Metric: -4.4250\n", + "val loss: 4.4250\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", "Epoch 6/10, cost time: 0.58s\n", - "train loss: 5.9378\n", - "Validation, Loss: 5.8568, Metric: -5.8568\n", - "val loss: 5.8568\n", + "train loss: 4.4208\n", + "Validation, Loss: 4.3880, Metric: -4.3880\n", + "val loss: 4.3880\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_5.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 5.8375\n", - "Validation, Loss: 5.8387, Metric: -5.8387\n", - "val loss: 5.8387\n", + "Epoch 7/10, cost time: 0.63s\n", + "train loss: 4.3378\n", + "Validation, Loss: 4.3386, Metric: -4.3386\n", + "val loss: 4.3386\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 5.7944\n", - "Validation, Loss: 5.8086, Metric: -5.8086\n", - "val loss: 5.8086\n", + "Epoch 8/10, cost time: 0.68s\n", + "train loss: 4.3075\n", + "Validation, Loss: 4.3144, Metric: -4.3144\n", + "val loss: 4.3144\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.64s\n", - "train loss: 5.7653\n", - "Validation, Loss: 5.7895, Metric: -5.7895\n", - "val loss: 5.7895\n", + "Epoch 9/10, cost time: 0.61s\n", + "train loss: 4.2823\n", + "Validation, Loss: 4.2954, Metric: -4.2954\n", + "val loss: 4.2954\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 5.7400\n", - "Validation, Loss: 5.7563, Metric: -5.7563\n", - "val loss: 5.7563\n", + "Epoch 10/10, cost time: 0.64s\n", + "train loss: 4.2616\n", + "Validation, Loss: 4.2742, Metric: -4.2742\n", + "val loss: 4.2742\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -5.756300449371338\n", + "Fold 0. Score: -4.274166107177734\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 378.4389\n", - "Validation, Loss: 122.0446, Metric: -122.0446\n", - "val loss: 122.0446\n", + "Epoch 1/10, cost time: 0.58s\n", + "train loss: 390.7677\n", + "Validation, Loss: 135.1639, Metric: -135.1639\n", + "val loss: 135.1639\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 61.1700\n", - "Validation, Loss: 35.1154, Metric: -35.1154\n", - "val loss: 35.1154\n", + "Epoch 2/10, cost time: 0.65s\n", + "train loss: 64.3006\n", + "Validation, Loss: 43.0715, Metric: -43.0715\n", + "val loss: 43.0715\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 17.2550\n", - "Validation, Loss: 6.4812, Metric: -6.4812\n", - "val loss: 6.4812\n", + "Epoch 3/10, cost time: 0.64s\n", + "train loss: 17.2403\n", + "Validation, Loss: 7.0201, Metric: -7.0201\n", + "val loss: 7.0201\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.84s\n", - "train loss: 7.8464\n", - "Validation, Loss: 7.0618, Metric: -7.0618\n", - "val loss: 7.0618\n", + "Epoch 4/10, cost time: 0.65s\n", + "train loss: 6.5804\n", + "Validation, Loss: 5.0567, Metric: -5.0567\n", + "val loss: 5.0567\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_3.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.67s\n", - "train loss: 6.1514\n", - "Validation, Loss: 5.5988, Metric: -5.5988\n", - "val loss: 5.5988\n", + "Epoch 5/10, cost time: 0.68s\n", + "train loss: 4.7934\n", + "Validation, Loss: 4.2618, Metric: -4.2618\n", + "val loss: 4.2618\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 5.6928\n", - "Validation, Loss: 5.5747, Metric: -5.5747\n", - "val loss: 5.5747\n", + "Epoch 6/10, cost time: 0.86s\n", + "train loss: 4.3314\n", + "Validation, Loss: 4.2607, Metric: -4.2607\n", + "val loss: 4.2607\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_5.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 5.6151\n", - "Validation, Loss: 5.5705, Metric: -5.5705\n", - "val loss: 5.5705\n", + "Epoch 7/10, cost time: 0.70s\n", + "train loss: 4.2526\n", + "Validation, Loss: 4.2089, Metric: -4.2089\n", + "val loss: 4.2089\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_6.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 5.5965\n", - "Validation, Loss: 5.5548, Metric: -5.5548\n", - "val loss: 5.5548\n", + "Epoch 8/10, cost time: 0.85s\n", + "train loss: 4.2181\n", + "Validation, Loss: 4.1819, Metric: -4.1819\n", + "val loss: 4.1819\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 5.5527\n", - "Validation, Loss: 5.5025, Metric: -5.5025\n", - "val loss: 5.5025\n", + "Epoch 9/10, cost time: 0.62s\n", + "train loss: 4.1955\n", + "Validation, Loss: 4.1586, Metric: -4.1586\n", + "val loss: 4.1586\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.59s\n", - "train loss: 5.5238\n", - "Validation, Loss: 5.4796, Metric: -5.4796\n", - "val loss: 5.4796\n", + "Epoch 10/10, cost time: 0.67s\n", + "train loss: 4.1770\n", + "Validation, Loss: 4.1423, Metric: -4.1423\n", + "val loss: 4.1423\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -5.479559421539307\n", - "Mean score: -5.6179\n", - "Std: 0.1384\n", + "Fold 1. Score: -4.142251491546631\n", + "Mean score: -4.2082\n", + "Std: 0.066\n", "length of train dataset: 495\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 368.7471\n", - "Validation, Loss: 98.2119, Metric: -98.2119\n", - "val loss: 98.2119\n", + "Epoch 1/10, cost time: 0.84s\n", + "train loss: 370.6101\n", + "Validation, Loss: 105.8064, Metric: -105.8064\n", + "val loss: 105.8064\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.63s\n", - "train loss: 62.4588\n", - "Validation, Loss: 28.2995, Metric: -28.2995\n", - "val loss: 28.2995\n", + "Epoch 2/10, cost time: 0.78s\n", + "train loss: 64.1122\n", + "Validation, Loss: 30.7795, Metric: -30.7795\n", + "val loss: 30.7795\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.62s\n", - "train loss: 19.1735\n", - "Validation, Loss: 7.3410, Metric: -7.3410\n", - "val loss: 7.3410\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 19.6595\n", + "Validation, Loss: 7.5525, Metric: -7.5525\n", + "val loss: 7.5525\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 9.5496\n", - "Validation, Loss: 9.0898, Metric: -9.0898\n", - "val loss: 9.0898\n", + "Epoch 4/10, cost time: 0.67s\n", + "train loss: 9.9237\n", + "Validation, Loss: 9.5275, Metric: -9.5275\n", + "val loss: 9.5275\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_3.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.61s\n", - "train loss: 7.7423\n", - "Validation, Loss: 7.3874, Metric: -7.3874\n", - "val loss: 7.3874\n", + "Epoch 5/10, cost time: 0.62s\n", + "train loss: 8.0550\n", + "Validation, Loss: 7.6360, Metric: -7.6360\n", + "val loss: 7.6360\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_4.pth\n", "Early stopping counter: 2\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.60s\n", - "train loss: 7.3339\n", - "Validation, Loss: 7.2613, Metric: -7.2613\n", - "val loss: 7.2613\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 7.4960\n", + "Validation, Loss: 7.4307, Metric: -7.4307\n", + "val loss: 7.4307\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.61s\n", - "train loss: 7.2308\n", - "Validation, Loss: 7.2357, Metric: -7.2357\n", - "val loss: 7.2357\n", + "Epoch 7/10, cost time: 0.91s\n", + "train loss: 7.3759\n", + "Validation, Loss: 7.3835, Metric: -7.3835\n", + "val loss: 7.3835\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 7.1769\n", - "Validation, Loss: 7.1993, Metric: -7.1993\n", - "val loss: 7.1993\n", + "Epoch 8/10, cost time: 0.67s\n", + "train loss: 7.3245\n", + "Validation, Loss: 7.3472, Metric: -7.3472\n", + "val loss: 7.3472\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.61s\n", - "train loss: 7.1335\n", - "Validation, Loss: 7.1545, Metric: -7.1545\n", - "val loss: 7.1545\n", + "Epoch 9/10, cost time: 0.58s\n", + "train loss: 7.2910\n", + "Validation, Loss: 7.3193, Metric: -7.3193\n", + "val loss: 7.3193\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.67s\n", - "train loss: 7.1028\n", - "Validation, Loss: 7.1250, Metric: -7.1250\n", - "val loss: 7.1250\n", + "Epoch 10/10, cost time: 0.66s\n", + "train loss: 7.2569\n", + "Validation, Loss: 7.2778, Metric: -7.2778\n", + "val loss: 7.2778\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -7.124959945678711\n", + "Fold 0. Score: -7.27780294418335\n", "length of train dataset: 496\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.60s\n", - "train loss: 385.4868\n", - "Validation, Loss: 140.1279, Metric: -140.1279\n", - "val loss: 140.1279\n", + "Epoch 1/10, cost time: 0.65s\n", + "train loss: 380.2661\n", + "Validation, Loss: 135.4580, Metric: -135.4580\n", + "val loss: 135.4580\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 64.4549\n", - "Validation, Loss: 41.3462, Metric: -41.3462\n", - "val loss: 41.3462\n", + "Epoch 2/10, cost time: 0.61s\n", + "train loss: 63.1142\n", + "Validation, Loss: 39.2277, Metric: -39.2277\n", + "val loss: 39.2277\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.62s\n", - "train loss: 17.9917\n", - "Validation, Loss: 8.0459, Metric: -8.0459\n", - "val loss: 8.0459\n", + "Epoch 3/10, cost time: 0.74s\n", + "train loss: 18.4396\n", + "Validation, Loss: 7.9900, Metric: -7.9900\n", + "val loss: 7.9900\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 8.3273\n", - "Validation, Loss: 7.1672, Metric: -7.1672\n", - "val loss: 7.1672\n", + "Epoch 4/10, cost time: 0.68s\n", + "train loss: 8.6784\n", + "Validation, Loss: 7.8326, Metric: -7.8326\n", + "val loss: 7.8326\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 6.5713\n", - "Validation, Loss: 5.9262, Metric: -5.9262\n", - "val loss: 5.9262\n", + "Epoch 5/10, cost time: 0.71s\n", + "train loss: 7.0502\n", + "Validation, Loss: 6.3975, Metric: -6.3975\n", + "val loss: 6.3975\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.66s\n", - "train loss: 6.0394\n", - "Validation, Loss: 5.9408, Metric: -5.9408\n", - "val loss: 5.9408\n", + "Epoch 6/10, cost time: 0.69s\n", + "train loss: 6.5540\n", + "Validation, Loss: 6.3960, Metric: -6.3960\n", + "val loss: 6.3960\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_5.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 5.9436\n", - "Validation, Loss: 5.8866, Metric: -5.8866\n", - "val loss: 5.8866\n", + "Epoch 7/10, cost time: 0.58s\n", + "train loss: 6.4277\n", + "Validation, Loss: 6.3432, Metric: -6.3432\n", + "val loss: 6.3432\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.87s\n", - "train loss: 5.8999\n", - "Validation, Loss: 5.8550, Metric: -5.8550\n", - "val loss: 5.8550\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 6.3731\n", + "Validation, Loss: 6.3182, Metric: -6.3182\n", + "val loss: 6.3182\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_7.pth\n", - "Removing worst model snapshot: from epoch 5\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.66s\n", - "train loss: 5.8734\n", - "Validation, Loss: 5.8239, Metric: -5.8239\n", - "val loss: 5.8239\n", + "Epoch 9/10, cost time: 0.71s\n", + "train loss: 6.3430\n", + "Validation, Loss: 6.3168, Metric: -6.3168\n", + "val loss: 6.3168\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_8.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.81s\n", - "train loss: 5.8509\n", - "Validation, Loss: 5.7968, Metric: -5.7968\n", - "val loss: 5.7968\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 6.3195\n", + "Validation, Loss: 6.2573, Metric: -6.2573\n", + "val loss: 6.2573\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -5.796763896942139\n", - "Mean score: -6.4609\n", - "Std: 0.6641\n", + "Fold 1. Score: -6.257255554199219\n", + "Mean score: -6.7675\n", + "Std: 0.5103\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 378.0090\n", - "Validation, Loss: 114.3918, Metric: -114.3918\n", - "val loss: 114.3918\n", + "Epoch 1/10, cost time: 0.69s\n", + "train loss: 346.9920\n", + "Validation, Loss: 65.9282, Metric: -65.9282\n", + "val loss: 65.9282\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 64.8576\n", - "Validation, Loss: 35.0846, Metric: -35.0846\n", - "val loss: 35.0846\n", + "Epoch 2/10, cost time: 0.65s\n", + "train loss: 58.3577\n", + "Validation, Loss: 17.2433, Metric: -17.2433\n", + "val loss: 17.2433\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 19.9201\n", - "Validation, Loss: 8.2947, Metric: -8.2947\n", - "val loss: 8.2947\n", + "Epoch 3/10, cost time: 0.69s\n", + "train loss: 19.6333\n", + "Validation, Loss: 11.0935, Metric: -11.0935\n", + "val loss: 11.0935\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 9.9949\n", - "Validation, Loss: 9.4797, Metric: -9.4797\n", - "val loss: 9.4797\n", + "Epoch 4/10, cost time: 0.63s\n", + "train loss: 11.6597\n", + "Validation, Loss: 10.3450, Metric: -10.3450\n", + "val loss: 10.3450\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_3.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 8.1923\n", - "Validation, Loss: 7.6396, Metric: -7.6396\n", - "val loss: 7.6396\n", + "Epoch 5/10, cost time: 0.74s\n", + "train loss: 9.7085\n", + "Validation, Loss: 9.7430, Metric: -9.7430\n", + "val loss: 9.7430\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.60s\n", - "train loss: 7.6727\n", - "Validation, Loss: 7.5948, Metric: -7.5948\n", - "val loss: 7.5948\n", + "Epoch 6/10, cost time: 0.70s\n", + "train loss: 9.3758\n", + "Validation, Loss: 9.3782, Metric: -9.3782\n", + "val loss: 9.3782\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_5.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 7.5681\n", - "Validation, Loss: 7.5673, Metric: -7.5673\n", - "val loss: 7.5673\n", + "Epoch 7/10, cost time: 0.61s\n", + "train loss: 9.2463\n", + "Validation, Loss: 9.2625, Metric: -9.2625\n", + "val loss: 9.2625\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 7.5178\n", - "Validation, Loss: 7.5433, Metric: -7.5433\n", - "val loss: 7.5433\n", + "Epoch 8/10, cost time: 0.62s\n", + "train loss: 9.1890\n", + "Validation, Loss: 9.2153, Metric: -9.2153\n", + "val loss: 9.2153\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 7.4774\n", - "Validation, Loss: 7.5010, Metric: -7.5010\n", - "val loss: 7.5010\n", + "Epoch 9/10, cost time: 0.62s\n", + "train loss: 9.1442\n", + "Validation, Loss: 9.1684, Metric: -9.1684\n", + "val loss: 9.1684\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.70s\n", - "train loss: 7.4423\n", - "Validation, Loss: 7.4665, Metric: -7.4665\n", - "val loss: 7.4665\n", + "Epoch 10/10, cost time: 0.69s\n", + "train loss: 9.0973\n", + "Validation, Loss: 9.1324, Metric: -9.1324\n", + "val loss: 9.1324\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -7.466495513916016\n", + "Fold 0. Score: -9.13241958618164\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.67s\n", - "train loss: 350.8603\n", - "Validation, Loss: 72.0800, Metric: -72.0800\n", - "val loss: 72.0800\n", + "Epoch 1/10, cost time: 0.58s\n", + "train loss: 368.7106\n", + "Validation, Loss: 111.0458, Metric: -111.0458\n", + "val loss: 111.0458\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 61.0789\n", - "Validation, Loss: 20.1079, Metric: -20.1079\n", - "val loss: 20.1079\n", + "Epoch 2/10, cost time: 0.57s\n", + "train loss: 59.9375\n", + "Validation, Loss: 30.0969, Metric: -30.0969\n", + "val loss: 30.0969\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.60s\n", - "train loss: 20.8031\n", - "Validation, Loss: 11.5243, Metric: -11.5243\n", - "val loss: 11.5243\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 18.3879\n", + "Validation, Loss: 7.9343, Metric: -7.9343\n", + "val loss: 7.9343\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.74s\n", - "train loss: 12.7361\n", - "Validation, Loss: 11.4595, Metric: -11.4595\n", - "val loss: 11.4595\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 10.0047\n", + "Validation, Loss: 9.6751, Metric: -9.6751\n", + "val loss: 9.6751\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_3.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 10.8085\n", - "Validation, Loss: 10.5288, Metric: -10.5288\n", - "val loss: 10.5288\n", + "Epoch 5/10, cost time: 0.67s\n", + "train loss: 8.4017\n", + "Validation, Loss: 7.9002, Metric: -7.9002\n", + "val loss: 7.9002\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.64s\n", - "train loss: 10.3708\n", - "Validation, Loss: 10.2898, Metric: -10.2898\n", - "val loss: 10.2898\n", + "Epoch 6/10, cost time: 0.58s\n", + "train loss: 7.9538\n", + "Validation, Loss: 7.7806, Metric: -7.7806\n", + "val loss: 7.7806\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_5.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 10.2825\n", - "Validation, Loss: 10.1843, Metric: -10.1843\n", - "val loss: 10.1843\n", + "Epoch 7/10, cost time: 0.90s\n", + "train loss: 7.8327\n", + "Validation, Loss: 7.7473, Metric: -7.7473\n", + "val loss: 7.7473\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_6.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 10.2214\n", - "Validation, Loss: 10.1343, Metric: -10.1343\n", - "val loss: 10.1343\n", + "Epoch 8/10, cost time: 0.62s\n", + "train loss: 7.7787\n", + "Validation, Loss: 7.7155, Metric: -7.7155\n", + "val loss: 7.7155\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 10.1778\n", - "Validation, Loss: 10.0894, Metric: -10.0894\n", - "val loss: 10.0894\n", + "Epoch 9/10, cost time: 0.59s\n", + "train loss: 7.7438\n", + "Validation, Loss: 7.6956, Metric: -7.6956\n", + "val loss: 7.6956\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.58s\n", - "train loss: 10.1299\n", - "Validation, Loss: 10.0382, Metric: -10.0382\n", - "val loss: 10.0382\n", + "Epoch 10/10, cost time: 0.65s\n", + "train loss: 7.7080\n", + "Validation, Loss: 7.6409, Metric: -7.6409\n", + "val loss: 7.6409\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -10.038150787353516\n", - "Mean score: -8.7523\n", - "Std: 1.2858\n", + "Fold 1. Score: -7.64092493057251\n", + "Mean score: -8.3867\n", + "Std: 0.7457\n", "length of train dataset: 494\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.60s\n", - "train loss: 353.2743\n", - "Validation, Loss: 79.3986, Metric: -79.3986\n", - "val loss: 79.3986\n", + "Epoch 1/10, cost time: 0.67s\n", + "train loss: 348.4213\n", + "Validation, Loss: 73.2793, Metric: -73.2793\n", + "val loss: 73.2793\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.65s\n", - "train loss: 61.6696\n", - "Validation, Loss: 21.9620, Metric: -21.9620\n", - "val loss: 21.9620\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 61.8928\n", + "Validation, Loss: 20.4437, Metric: -20.4437\n", + "val loss: 20.4437\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.60s\n", - "train loss: 21.5628\n", - "Validation, Loss: 12.1240, Metric: -12.1240\n", - "val loss: 12.1240\n", + "Epoch 3/10, cost time: 0.58s\n", + "train loss: 21.6333\n", + "Validation, Loss: 13.2486, Metric: -13.2486\n", + "val loss: 13.2486\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 13.6004\n", - "Validation, Loss: 12.7638, Metric: -12.7638\n", - "val loss: 12.7638\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 14.0897\n", + "Validation, Loss: 13.4205, Metric: -13.4205\n", + "val loss: 13.4205\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", @@ -1379,536 +1381,533 @@ "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", "Epoch 5/10, cost time: 0.59s\n", - "train loss: 11.6205\n", - "Validation, Loss: 11.5344, Metric: -11.5344\n", - "val loss: 11.5344\n", + "train loss: 12.3617\n", + "Validation, Loss: 12.3097, Metric: -12.3097\n", + "val loss: 12.3097\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 11.1920\n", - "Validation, Loss: 11.1878, Metric: -11.1878\n", - "val loss: 11.1878\n", + "Epoch 6/10, cost time: 0.60s\n", + "train loss: 11.9895\n", + "Validation, Loss: 12.0575, Metric: -12.0575\n", + "val loss: 12.0575\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 11.0455\n", - "Validation, Loss: 11.0981, Metric: -11.0981\n", - "val loss: 11.0981\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 11.8794\n", + "Validation, Loss: 11.9171, Metric: -11.9171\n", + "val loss: 11.9171\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 10.9868\n", - "Validation, Loss: 11.0153, Metric: -11.0153\n", - "val loss: 11.0153\n", + "Epoch 8/10, cost time: 0.82s\n", + "train loss: 11.7933\n", + "Validation, Loss: 11.8261, Metric: -11.8261\n", + "val loss: 11.8261\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 10.9333\n", - "Validation, Loss: 10.9771, Metric: -10.9771\n", - "val loss: 10.9771\n", + "Epoch 9/10, cost time: 0.96s\n", + "train loss: 11.7350\n", + "Validation, Loss: 11.7775, Metric: -11.7775\n", + "val loss: 11.7775\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 10.8852\n", - "Validation, Loss: 10.9085, Metric: -10.9085\n", - "val loss: 10.9085\n", + "Epoch 10/10, cost time: 0.70s\n", + "train loss: 11.6735\n", + "Validation, Loss: 11.7246, Metric: -11.7246\n", + "val loss: 11.7246\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -10.908503532409668\n", + "Fold 0. Score: -11.724552154541016\n", "length of train dataset: 495\n", "length of val dataset: 494\n", "Epoch 1/10, cost time: 0.59s\n", - "train loss: 331.1463\n", - "Validation, Loss: 51.0125, Metric: -51.0125\n", - "val loss: 51.0125\n", + "train loss: 347.5624\n", + "Validation, Loss: 76.3863, Metric: -76.3863\n", + "val loss: 76.3863\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 60.7976\n", - "Validation, Loss: 15.0375, Metric: -15.0375\n", - "val loss: 15.0375\n", + "Epoch 2/10, cost time: 0.67s\n", + "train loss: 61.9291\n", + "Validation, Loss: 21.8872, Metric: -21.8872\n", + "val loss: 21.8872\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 23.2117\n", - "Validation, Loss: 18.2942, Metric: -18.2942\n", - "val loss: 18.2942\n", + "Epoch 3/10, cost time: 0.64s\n", + "train loss: 22.6591\n", + "Validation, Loss: 14.1557, Metric: -14.1557\n", + "val loss: 14.1557\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_2.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 15.7120\n", - "Validation, Loss: 13.5160, Metric: -13.5160\n", - "val loss: 13.5160\n", + "Epoch 4/10, cost time: 0.62s\n", + "train loss: 14.8450\n", + "Validation, Loss: 13.3702, Metric: -13.3702\n", + "val loss: 13.3702\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.59s\n", - "train loss: 13.6714\n", - "Validation, Loss: 13.4925, Metric: -13.4925\n", - "val loss: 13.4925\n", + "Epoch 5/10, cost time: 0.67s\n", + "train loss: 12.8992\n", + "Validation, Loss: 12.6735, Metric: -12.6735\n", + "val loss: 12.6735\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_4.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 13.3623\n", - "Validation, Loss: 13.2415, Metric: -13.2415\n", - "val loss: 13.2415\n", + "Epoch 6/10, cost time: 0.68s\n", + "train loss: 12.5828\n", + "Validation, Loss: 12.5003, Metric: -12.5003\n", + "val loss: 12.5003\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_5.pth\n", - "Removing worst model snapshot: from epoch 1\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 13.2645\n", - "Validation, Loss: 13.1447, Metric: -13.1447\n", - "val loss: 13.1447\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 12.4983\n", + "Validation, Loss: 12.3865, Metric: -12.3865\n", + "val loss: 12.3865\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.57s\n", - "train loss: 13.1919\n", - "Validation, Loss: 13.0827, Metric: -13.0827\n", - "val loss: 13.0827\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 12.4266\n", + "Validation, Loss: 12.2916, Metric: -12.2916\n", + "val loss: 12.2916\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 13.1265\n", - "Validation, Loss: 13.0275, Metric: -13.0275\n", - "val loss: 13.0275\n", + "Epoch 9/10, cost time: 0.66s\n", + "train loss: 12.3375\n", + "Validation, Loss: 12.2354, Metric: -12.2354\n", + "val loss: 12.2354\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 13.0687\n", - "Validation, Loss: 12.9526, Metric: -12.9526\n", - "val loss: 12.9526\n", + "Epoch 10/10, cost time: 0.64s\n", + "train loss: 12.2759\n", + "Validation, Loss: 12.1871, Metric: -12.1871\n", + "val loss: 12.1871\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -12.95260238647461\n", - "Mean score: -11.9306\n", - "Std: 1.022\n", + "Fold 1. Score: -12.187055587768555\n", + "Mean score: -11.9558\n", + "Std: 0.2313\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 341.2509\n", - "Validation, Loss: 63.2621, Metric: -63.2621\n", - "val loss: 63.2621\n", + "Epoch 1/10, cost time: 0.67s\n", + "train loss: 341.5293\n", + "Validation, Loss: 68.1881, Metric: -68.1881\n", + "val loss: 68.1881\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 62.6343\n", - "Validation, Loss: 18.4068, Metric: -18.4068\n", - "val loss: 18.4068\n", + "Epoch 2/10, cost time: 0.69s\n", + "train loss: 62.9321\n", + "Validation, Loss: 19.6805, Metric: -19.6805\n", + "val loss: 19.6805\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 24.6361\n", - "Validation, Loss: 18.0282, Metric: -18.0282\n", - "val loss: 18.0282\n", + "Epoch 3/10, cost time: 0.67s\n", + "train loss: 24.2058\n", + "Validation, Loss: 17.5941, Metric: -17.5941\n", + "val loss: 17.5941\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 16.9802\n", - "Validation, Loss: 15.1966, Metric: -15.1966\n", - "val loss: 15.1966\n", + "Epoch 4/10, cost time: 0.68s\n", + "train loss: 17.0483\n", + "Validation, Loss: 15.7751, Metric: -15.7751\n", + "val loss: 15.7751\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.59s\n", - "train loss: 14.9043\n", - "Validation, Loss: 14.8648, Metric: -14.8648\n", - "val loss: 14.8648\n", + "Epoch 5/10, cost time: 0.63s\n", + "train loss: 15.3673\n", + "Validation, Loss: 15.2840, Metric: -15.2840\n", + "val loss: 15.2840\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 14.5332\n", - "Validation, Loss: 14.5774, Metric: -14.5774\n", - "val loss: 14.5774\n", + "Epoch 6/10, cost time: 0.66s\n", + "train loss: 15.0072\n", + "Validation, Loss: 15.0363, Metric: -15.0363\n", + "val loss: 15.0363\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 14.4015\n", - "Validation, Loss: 14.4559, Metric: -14.4559\n", - "val loss: 14.4559\n", + "Epoch 7/10, cost time: 0.67s\n", + "train loss: 14.8720\n", + "Validation, Loss: 14.9547, Metric: -14.9547\n", + "val loss: 14.9547\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 14.3272\n", - "Validation, Loss: 14.4021, Metric: -14.4021\n", - "val loss: 14.4021\n", + "Epoch 8/10, cost time: 0.69s\n", + "train loss: 14.8059\n", + "Validation, Loss: 14.8659, Metric: -14.8659\n", + "val loss: 14.8659\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 14.2652\n", - "Validation, Loss: 14.3438, Metric: -14.3438\n", - "val loss: 14.3438\n", + "Epoch 9/10, cost time: 0.71s\n", + "train loss: 14.7318\n", + "Validation, Loss: 14.7924, Metric: -14.7924\n", + "val loss: 14.7924\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.63s\n", - "train loss: 14.1977\n", - "Validation, Loss: 14.2568, Metric: -14.2568\n", - "val loss: 14.2568\n", + "Epoch 10/10, cost time: 0.61s\n", + "train loss: 14.6650\n", + "Validation, Loss: 14.7389, Metric: -14.7389\n", + "val loss: 14.7389\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -14.256837844848633\n", + "Fold 0. Score: -14.738923072814941\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 329.8946\n", - "Validation, Loss: 49.9958, Metric: -49.9958\n", - "val loss: 49.9958\n", + "Epoch 1/10, cost time: 0.61s\n", + "train loss: 339.8815\n", + "Validation, Loss: 63.6576, Metric: -63.6576\n", + "val loss: 63.6576\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 63.0375\n", - "Validation, Loss: 16.5045, Metric: -16.5045\n", - "val loss: 16.5045\n", + "Epoch 2/10, cost time: 0.62s\n", + "train loss: 62.5787\n", + "Validation, Loss: 18.3003, Metric: -18.3003\n", + "val loss: 18.3003\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 25.1321\n", - "Validation, Loss: 21.0100, Metric: -21.0100\n", - "val loss: 21.0100\n", + "Epoch 3/10, cost time: 0.59s\n", + "train loss: 24.4187\n", + "Validation, Loss: 17.4426, Metric: -17.4426\n", + "val loss: 17.4426\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_2.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 18.0811\n", - "Validation, Loss: 15.7223, Metric: -15.7223\n", - "val loss: 15.7223\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 16.7605\n", + "Validation, Loss: 14.8631, Metric: -14.8631\n", + "val loss: 14.8631\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 16.0435\n", - "Validation, Loss: 15.6346, Metric: -15.6346\n", - "val loss: 15.6346\n", + "Epoch 5/10, cost time: 0.59s\n", + "train loss: 14.8361\n", + "Validation, Loss: 14.5053, Metric: -14.5053\n", + "val loss: 14.5053\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_4.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.65s\n", - "train loss: 15.7121\n", - "Validation, Loss: 15.5237, Metric: -15.5237\n", - "val loss: 15.5237\n", + "Epoch 6/10, cost time: 0.59s\n", + "train loss: 14.4907\n", + "Validation, Loss: 14.3412, Metric: -14.3412\n", + "val loss: 14.3412\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_5.pth\n", - "Removing worst model snapshot: from epoch 1\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", "Epoch 7/10, cost time: 0.59s\n", - "train loss: 15.6051\n", - "Validation, Loss: 15.4598, Metric: -15.4598\n", - "val loss: 15.4598\n", + "train loss: 14.4017\n", + "Validation, Loss: 14.2828, Metric: -14.2828\n", + "val loss: 14.2828\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 15.5376\n", - "Validation, Loss: 15.3857, Metric: -15.3857\n", - "val loss: 15.3857\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 14.3322\n", + "Validation, Loss: 14.2065, Metric: -14.2065\n", + "val loss: 14.2065\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 15.4622\n", - "Validation, Loss: 15.3102, Metric: -15.3102\n", - "val loss: 15.3102\n", + "Epoch 9/10, cost time: 0.65s\n", + "train loss: 14.2699\n", + "Validation, Loss: 14.1218, Metric: -14.1218\n", + "val loss: 14.1218\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.58s\n", - "train loss: 15.3827\n", - "Validation, Loss: 15.2329, Metric: -15.2329\n", - "val loss: 15.2329\n", + "Epoch 10/10, cost time: 0.59s\n", + "train loss: 14.1933\n", + "Validation, Loss: 14.0482, Metric: -14.0482\n", + "val loss: 14.0482\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -15.232909202575684\n", - "Mean score: -14.7449\n", - "Std: 0.488\n", + "Fold 1. Score: -14.048187255859375\n", + "Mean score: -14.3936\n", + "Std: 0.3454\n", "length of train dataset: 493\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.58s\n", - "train loss: 324.1140\n", - "Validation, Loss: 48.4809, Metric: -48.4809\n", - "val loss: 48.4809\n", + "Epoch 1/10, cost time: 0.63s\n", + "train loss: 326.5137\n", + "Validation, Loss: 44.3770, Metric: -44.3770\n", + "val loss: 44.3770\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 63.8364\n", - "Validation, Loss: 19.9738, Metric: -19.9738\n", - "val loss: 19.9738\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 64.2163\n", + "Validation, Loss: 17.0292, Metric: -17.0292\n", + "val loss: 17.0292\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 29.0590\n", - "Validation, Loss: 25.5278, Metric: -25.5278\n", - "val loss: 25.5278\n", + "Epoch 3/10, cost time: 0.59s\n", + "train loss: 26.8710\n", + "Validation, Loss: 23.8799, Metric: -23.8799\n", + "val loss: 23.8799\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_2.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 21.7934\n", - "Validation, Loss: 19.8395, Metric: -19.8395\n", - "val loss: 19.8395\n", + "Epoch 4/10, cost time: 0.59s\n", + "train loss: 19.4378\n", + "Validation, Loss: 16.9384, Metric: -16.9384\n", + "val loss: 16.9384\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 20.0393\n", - "Validation, Loss: 19.9126, Metric: -19.9126\n", - "val loss: 19.9126\n", + "Epoch 5/10, cost time: 0.60s\n", + "train loss: 17.2355\n", + "Validation, Loss: 16.8656, Metric: -16.8656\n", + "val loss: 16.8656\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_4.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 19.6949\n", - "Validation, Loss: 19.6825, Metric: -19.6825\n", - "val loss: 19.6825\n", + "Epoch 6/10, cost time: 0.61s\n", + "train loss: 16.8619\n", + "Validation, Loss: 16.8061, Metric: -16.8061\n", + "val loss: 16.8061\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 19.5491\n", - "Validation, Loss: 19.5981, Metric: -19.5981\n", - "val loss: 19.5981\n", + "Epoch 7/10, cost time: 0.59s\n", + "train loss: 16.6913\n", + "Validation, Loss: 16.7279, Metric: -16.7279\n", + "val loss: 16.7279\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 19.4574\n", - "Validation, Loss: 19.5097, Metric: -19.5097\n", - "val loss: 19.5097\n", + "Epoch 8/10, cost time: 0.59s\n", + "train loss: 16.6198\n", + "Validation, Loss: 16.6510, Metric: -16.6510\n", + "val loss: 16.6510\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_7.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.75s\n", - "train loss: 19.3638\n", - "Validation, Loss: 19.4166, Metric: -19.4166\n", - "val loss: 19.4166\n", + "Epoch 9/10, cost time: 0.60s\n", + "train loss: 16.5340\n", + "Validation, Loss: 16.5857, Metric: -16.5857\n", + "val loss: 16.5857\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", "Epoch 10/10, cost time: 0.60s\n", - "train loss: 19.2709\n", - "Validation, Loss: 19.3352, Metric: -19.3352\n", - "val loss: 19.3352\n", + "train loss: 16.4650\n", + "Validation, Loss: 16.4845, Metric: -16.4845\n", + "val loss: 16.4845\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -19.33523941040039\n", + "Fold 0. Score: -16.484466552734375\n", "length of train dataset: 494\n", "length of val dataset: 493\n", - "Epoch 1/10, cost time: 0.58s\n", - "train loss: 318.1459\n", - "Validation, Loss: 35.2745, Metric: -35.2745\n", - "val loss: 35.2745\n", + "Epoch 1/10, cost time: 0.74s\n", + "train loss: 320.8678\n", + "Validation, Loss: 44.4788, Metric: -44.4788\n", + "val loss: 44.4788\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 64.2229\n", - "Validation, Loss: 18.6586, Metric: -18.6586\n", - "val loss: 18.6586\n", + "Epoch 2/10, cost time: 0.76s\n", + "train loss: 61.1539\n", + "Validation, Loss: 17.5799, Metric: -17.5799\n", + "val loss: 17.5799\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.59s\n", - "train loss: 29.0946\n", - "Validation, Loss: 26.2358, Metric: -26.2358\n", - "val loss: 26.2358\n", + "Epoch 3/10, cost time: 0.73s\n", + "train loss: 27.0784\n", + "Validation, Loss: 25.1590, Metric: -25.1590\n", + "val loss: 25.1590\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_2.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 20.7452\n", - "Validation, Loss: 18.3582, Metric: -18.3582\n", - "val loss: 18.3582\n", + "Epoch 4/10, cost time: 0.64s\n", + "train loss: 19.9317\n", + "Validation, Loss: 17.5198, Metric: -17.5198\n", + "val loss: 17.5198\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.63s\n", - "train loss: 18.7621\n", - "Validation, Loss: 18.2058, Metric: -18.2058\n", - "val loss: 18.2058\n", + "Epoch 5/10, cost time: 0.60s\n", + "train loss: 17.9184\n", + "Validation, Loss: 17.4039, Metric: -17.4039\n", + "val loss: 17.4039\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Epoch 6/10, cost time: 0.59s\n", - "train loss: 18.4086\n", - "Validation, Loss: 18.1512, Metric: -18.1512\n", - "val loss: 18.1512\n", + "train loss: 17.5429\n", + "Validation, Loss: 17.3371, Metric: -17.3371\n", + "val loss: 17.3371\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.61s\n", - "train loss: 18.2357\n", - "Validation, Loss: 18.1464, Metric: -18.1464\n", - "val loss: 18.1464\n", + "Epoch 7/10, cost time: 0.63s\n", + "train loss: 17.3981\n", + "Validation, Loss: 17.2645, Metric: -17.2645\n", + "val loss: 17.2645\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 18.1361\n", - "Validation, Loss: 17.9715, Metric: -17.9715\n", - "val loss: 17.9715\n", + "Epoch 8/10, cost time: 0.68s\n", + "train loss: 17.3247\n", + "Validation, Loss: 17.2119, Metric: -17.2119\n", + "val loss: 17.2119\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Epoch 9/10, cost time: 0.59s\n", - "train loss: 18.0310\n", - "Validation, Loss: 17.8863, Metric: -17.8863\n", - "val loss: 17.8863\n", + "train loss: 17.2632\n", + "Validation, Loss: 17.1624, Metric: -17.1624\n", + "val loss: 17.1624\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.59s\n", - "train loss: 17.9445\n", - "Validation, Loss: 17.7950, Metric: -17.7950\n", - "val loss: 17.7950\n", + "Epoch 10/10, cost time: 0.58s\n", + "train loss: 17.1594\n", + "Validation, Loss: 17.0018, Metric: -17.0018\n", + "val loss: 17.0018\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -17.7950382232666\n", - "Mean score: -18.5651\n", - "Std: 0.7701\n" + "Fold 1. Score: -17.001792907714844\n", + "Mean score: -16.7431\n", + "Std: 0.2587\n" ] }, { "data": { "text/plain": [ - "(166.6328408718109, )" + "(179.15967321395874, )" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1933,7 +1932,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1969,7 +1968,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1978,1301 +1977,1302 @@ "text": [ "length of train dataset: 496\n", "length of val dataset: 497\n", - "Epoch 1/10, cost time: 0.69s\n", - "train loss: 2.2838\n", - "Validation, Loss: 2.2947, Metric: -2.2947\n", - "val loss: 2.2947\n", + "Epoch 1/10, cost time: 0.60s\n", + "train loss: 2.5679\n", + "Validation, Loss: 2.5761, Metric: -2.5761\n", + "val loss: 2.5761\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.56s\n", - "train loss: 2.2745\n", - "Validation, Loss: 2.2801, Metric: -2.2801\n", - "val loss: 2.2801\n", + "Epoch 2/10, cost time: 0.59s\n", + "train loss: 2.5570\n", + "Validation, Loss: 2.5764, Metric: -2.5764\n", + "val loss: 2.5764\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_1.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.61s\n", - "train loss: 2.2617\n", - "Validation, Loss: 2.2676, Metric: -2.2676\n", - "val loss: 2.2676\n", + "Epoch 3/10, cost time: 0.74s\n", + "train loss: 2.5461\n", + "Validation, Loss: 2.5519, Metric: -2.5519\n", + "val loss: 2.5519\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.56s\n", - "train loss: 2.2480\n", - "Validation, Loss: 2.2553, Metric: -2.2553\n", - "val loss: 2.2553\n", + "Epoch 4/10, cost time: 0.66s\n", + "train loss: 2.5275\n", + "Validation, Loss: 2.5394, Metric: -2.5394\n", + "val loss: 2.5394\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_3.pth\n", - "Removing worst model snapshot: from epoch 0\n", + "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 2.2353\n", - "Validation, Loss: 2.2415, Metric: -2.2415\n", - "val loss: 2.2415\n", + "Epoch 5/10, cost time: 0.66s\n", + "train loss: 2.5136\n", + "Validation, Loss: 2.5217, Metric: -2.5217\n", + "val loss: 2.5217\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_4.pth\n", - "Removing worst model snapshot: from epoch 1\n", + "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.56s\n", - "train loss: 2.2222\n", - "Validation, Loss: 2.2277, Metric: -2.2277\n", - "val loss: 2.2277\n", + "Epoch 6/10, cost time: 0.67s\n", + "train loss: 2.5016\n", + "Validation, Loss: 2.5050, Metric: -2.5050\n", + "val loss: 2.5050\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 2.2081\n", - "Validation, Loss: 2.2178, Metric: -2.2178\n", - "val loss: 2.2178\n", + "Epoch 7/10, cost time: 0.66s\n", + "train loss: 2.4832\n", + "Validation, Loss: 2.4893, Metric: -2.4893\n", + "val loss: 2.4893\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.56s\n", - "train loss: 2.1964\n", - "Validation, Loss: 2.2056, Metric: -2.2056\n", - "val loss: 2.2056\n", + "Epoch 8/10, cost time: 0.65s\n", + "train loss: 2.4666\n", + "Validation, Loss: 2.4733, Metric: -2.4733\n", + "val loss: 2.4733\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.56s\n", - "train loss: 2.1829\n", - "Validation, Loss: 2.1850, Metric: -2.1850\n", - "val loss: 2.1850\n", + "Epoch 9/10, cost time: 0.60s\n", + "train loss: 2.4503\n", + "Validation, Loss: 2.4580, Metric: -2.4580\n", + "val loss: 2.4580\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.63s\n", - "train loss: 2.1646\n", - "Validation, Loss: 2.1708, Metric: -2.1708\n", - "val loss: 2.1708\n", + "Epoch 10/10, cost time: 0.60s\n", + "train loss: 2.4348\n", + "Validation, Loss: 2.4401, Metric: -2.4401\n", + "val loss: 2.4401\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -2.170836925506592\n", + "Fold 0. Score: -2.4401051998138428\n", "length of train dataset: 497\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 3.4237\n", - "Validation, Loss: 3.3921, Metric: -3.3921\n", - "val loss: 3.3921\n", + "Epoch 1/10, cost time: 0.60s\n", + "train loss: 3.3538\n", + "Validation, Loss: 3.3247, Metric: -3.3247\n", + "val loss: 3.3247\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 3.4045\n", - "Validation, Loss: 3.3732, Metric: -3.3732\n", - "val loss: 3.3732\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 3.3385\n", + "Validation, Loss: 3.3076, Metric: -3.3076\n", + "val loss: 3.3076\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.63s\n", - "train loss: 3.3861\n", - "Validation, Loss: 3.3596, Metric: -3.3596\n", - "val loss: 3.3596\n", + "Epoch 3/10, cost time: 0.60s\n", + "train loss: 3.3193\n", + "Validation, Loss: 3.2881, Metric: -3.2881\n", + "val loss: 3.2881\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 3.3722\n", - "Validation, Loss: 3.3366, Metric: -3.3366\n", - "val loss: 3.3366\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 3.3005\n", + "Validation, Loss: 3.2691, Metric: -3.2691\n", + "val loss: 3.2691\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 3.3471\n", - "Validation, Loss: 3.3226, Metric: -3.3226\n", - "val loss: 3.3226\n", + "Epoch 5/10, cost time: 0.62s\n", + "train loss: 3.2825\n", + "Validation, Loss: 3.2580, Metric: -3.2580\n", + "val loss: 3.2580\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.57s\n", - "train loss: 3.3297\n", - "Validation, Loss: 3.2950, Metric: -3.2950\n", - "val loss: 3.2950\n", + "Epoch 6/10, cost time: 0.64s\n", + "train loss: 3.2627\n", + "Validation, Loss: 3.2354, Metric: -3.2354\n", + "val loss: 3.2354\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.56s\n", - "train loss: 3.3066\n", - "Validation, Loss: 3.2747, Metric: -3.2747\n", - "val loss: 3.2747\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 3.2434\n", + "Validation, Loss: 3.2111, Metric: -3.2111\n", + "val loss: 3.2111\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.66s\n", - "train loss: 3.2855\n", - "Validation, Loss: 3.2533, Metric: -3.2533\n", - "val loss: 3.2533\n", + "Epoch 8/10, cost time: 0.63s\n", + "train loss: 3.2211\n", + "Validation, Loss: 3.1884, Metric: -3.1884\n", + "val loss: 3.1884\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.56s\n", - "train loss: 3.2639\n", - "Validation, Loss: 3.2312, Metric: -3.2312\n", - "val loss: 3.2312\n", + "Epoch 9/10, cost time: 0.63s\n", + "train loss: 3.1999\n", + "Validation, Loss: 3.1671, Metric: -3.1671\n", + "val loss: 3.1671\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 3.2411\n", - "Validation, Loss: 3.2079, Metric: -3.2079\n", - "val loss: 3.2079\n", + "Epoch 10/10, cost time: 0.62s\n", + "train loss: 3.1782\n", + "Validation, Loss: 3.1450, Metric: -3.1450\n", + "val loss: 3.1450\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -3.2078869342803955\n", - "Mean score: -2.6894\n", - "Std: 0.5185\n", + "Fold 1. Score: -3.145007610321045\n", + "Mean score: -2.7926\n", + "Std: 0.3525\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 5.7111\n", - "Validation, Loss: 5.7242, Metric: -5.7242\n", - "val loss: 5.7242\n", + "Epoch 1/10, cost time: 0.60s\n", + "train loss: 4.2404\n", + "Validation, Loss: 4.2515, Metric: -4.2515\n", + "val loss: 4.2515\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.56s\n", - "train loss: 5.6807\n", - "Validation, Loss: 5.7036, Metric: -5.7036\n", - "val loss: 5.7036\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 4.2187\n", + "Validation, Loss: 4.2322, Metric: -4.2322\n", + "val loss: 4.2322\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.56s\n", - "train loss: 5.6523\n", - "Validation, Loss: 5.6615, Metric: -5.6615\n", - "val loss: 5.6615\n", + "Epoch 3/10, cost time: 0.60s\n", + "train loss: 4.1967\n", + "Validation, Loss: 4.2061, Metric: -4.2061\n", + "val loss: 4.2061\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.56s\n", - "train loss: 5.6213\n", - "Validation, Loss: 5.6304, Metric: -5.6304\n", - "val loss: 5.6304\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 4.1759\n", + "Validation, Loss: 4.1910, Metric: -4.1910\n", + "val loss: 4.1910\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 5.5915\n", - "Validation, Loss: 5.5990, Metric: -5.5990\n", - "val loss: 5.5990\n", + "Epoch 5/10, cost time: 0.67s\n", + "train loss: 4.1521\n", + "Validation, Loss: 4.1573, Metric: -4.1573\n", + "val loss: 4.1573\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.56s\n", - "train loss: 5.5491\n", - "Validation, Loss: 5.5591, Metric: -5.5591\n", - "val loss: 5.5591\n", + "Epoch 6/10, cost time: 0.66s\n", + "train loss: 4.1233\n", + "Validation, Loss: 4.1314, Metric: -4.1314\n", + "val loss: 4.1314\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 5.5151\n", - "Validation, Loss: 5.5252, Metric: -5.5252\n", - "val loss: 5.5252\n", + "Epoch 7/10, cost time: 0.69s\n", + "train loss: 4.0995\n", + "Validation, Loss: 4.1062, Metric: -4.1062\n", + "val loss: 4.1062\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.57s\n", - "train loss: 5.4780\n", - "Validation, Loss: 5.4859, Metric: -5.4859\n", - "val loss: 5.4859\n", + "Epoch 8/10, cost time: 0.68s\n", + "train loss: 4.0710\n", + "Validation, Loss: 4.0786, Metric: -4.0786\n", + "val loss: 4.0786\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.57s\n", - "train loss: 5.4402\n", - "Validation, Loss: 5.4482, Metric: -5.4482\n", - "val loss: 5.4482\n", + "Epoch 9/10, cost time: 0.64s\n", + "train loss: 4.0439\n", + "Validation, Loss: 4.0503, Metric: -4.0503\n", + "val loss: 4.0503\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 5.4027\n", - "Validation, Loss: 5.4177, Metric: -5.4177\n", - "val loss: 5.4177\n", + "Epoch 10/10, cost time: 0.61s\n", + "train loss: 4.0158\n", + "Validation, Loss: 4.0274, Metric: -4.0274\n", + "val loss: 4.0274\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -5.417725563049316\n", + "Fold 0. Score: -4.027385711669922\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.56s\n", - "train loss: 5.4984\n", - "Validation, Loss: 5.4640, Metric: -5.4640\n", - "val loss: 5.4640\n", + "Epoch 1/10, cost time: 0.61s\n", + "train loss: 4.1529\n", + "Validation, Loss: 4.1180, Metric: -4.1180\n", + "val loss: 4.1180\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.56s\n", - "train loss: 5.4695\n", - "Validation, Loss: 5.4185, Metric: -5.4185\n", - "val loss: 5.4185\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 4.1307\n", + "Validation, Loss: 4.0954, Metric: -4.0954\n", + "val loss: 4.0954\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.56s\n", - "train loss: 5.4359\n", - "Validation, Loss: 5.3877, Metric: -5.3877\n", - "val loss: 5.3877\n", + "Epoch 3/10, cost time: 0.62s\n", + "train loss: 4.1106\n", + "Validation, Loss: 4.0734, Metric: -4.0734\n", + "val loss: 4.0734\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 5.4047\n", - "Validation, Loss: 5.3566, Metric: -5.3566\n", - "val loss: 5.3566\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 4.0899\n", + "Validation, Loss: 4.0512, Metric: -4.0512\n", + "val loss: 4.0512\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 5.3721\n", - "Validation, Loss: 5.3328, Metric: -5.3328\n", - "val loss: 5.3328\n", + "Epoch 5/10, cost time: 0.63s\n", + "train loss: 4.0623\n", + "Validation, Loss: 4.0256, Metric: -4.0256\n", + "val loss: 4.0256\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.65s\n", - "train loss: 5.3419\n", - "Validation, Loss: 5.2894, Metric: -5.2894\n", - "val loss: 5.2894\n", + "Epoch 6/10, cost time: 0.89s\n", + "train loss: 4.0407\n", + "Validation, Loss: 3.9998, Metric: -3.9998\n", + "val loss: 3.9998\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 5.3049\n", - "Validation, Loss: 5.2565, Metric: -5.2565\n", - "val loss: 5.2565\n", + "Epoch 7/10, cost time: 0.62s\n", + "train loss: 4.0147\n", + "Validation, Loss: 3.9760, Metric: -3.9760\n", + "val loss: 3.9760\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.55s\n", - "train loss: 5.2688\n", - "Validation, Loss: 5.2247, Metric: -5.2247\n", - "val loss: 5.2247\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 3.9848\n", + "Validation, Loss: 3.9473, Metric: -3.9473\n", + "val loss: 3.9473\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.55s\n", - "train loss: 5.2404\n", - "Validation, Loss: 5.2022, Metric: -5.2022\n", - "val loss: 5.2022\n", + "Epoch 9/10, cost time: 0.61s\n", + "train loss: 3.9589\n", + "Validation, Loss: 3.9208, Metric: -3.9208\n", + "val loss: 3.9208\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.61s\n", - "train loss: 5.2012\n", - "Validation, Loss: 5.1468, Metric: -5.1468\n", - "val loss: 5.1468\n", + "Epoch 10/10, cost time: 0.69s\n", + "train loss: 3.9308\n", + "Validation, Loss: 3.8949, Metric: -3.8949\n", + "val loss: 3.8949\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -5.146778106689453\n", - "Mean score: -5.2823\n", - "Std: 0.1355\n", + "Fold 1. Score: -3.894927740097046\n", + "Mean score: -3.9612\n", + "Std: 0.0662\n", "length of train dataset: 495\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.75s\n", - "train loss: 7.0662\n", - "Validation, Loss: 7.0849, Metric: -7.0849\n", - "val loss: 7.0849\n", + "Epoch 1/10, cost time: 0.66s\n", + "train loss: 7.2257\n", + "Validation, Loss: 7.2556, Metric: -7.2556\n", + "val loss: 7.2556\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 7.0261\n", - "Validation, Loss: 7.0457, Metric: -7.0457\n", - "val loss: 7.0457\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 7.1865\n", + "Validation, Loss: 7.2081, Metric: -7.2081\n", + "val loss: 7.2081\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.66s\n", - "train loss: 6.9895\n", - "Validation, Loss: 7.0050, Metric: -7.0050\n", - "val loss: 7.0050\n", + "Epoch 3/10, cost time: 0.78s\n", + "train loss: 7.1567\n", + "Validation, Loss: 7.1722, Metric: -7.1722\n", + "val loss: 7.1722\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.65s\n", - "train loss: 6.9573\n", - "Validation, Loss: 6.9712, Metric: -6.9712\n", - "val loss: 6.9712\n", + "Epoch 4/10, cost time: 0.80s\n", + "train loss: 7.1052\n", + "Validation, Loss: 7.1191, Metric: -7.1191\n", + "val loss: 7.1191\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.59s\n", - "train loss: 6.9107\n", - "Validation, Loss: 6.9318, Metric: -6.9318\n", - "val loss: 6.9318\n", + "Epoch 5/10, cost time: 0.71s\n", + "train loss: 7.0628\n", + "Validation, Loss: 7.0766, Metric: -7.0766\n", + "val loss: 7.0766\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.64s\n", - "train loss: 6.8639\n", - "Validation, Loss: 6.8873, Metric: -6.8873\n", - "val loss: 6.8873\n", + "Epoch 6/10, cost time: 0.61s\n", + "train loss: 7.0216\n", + "Validation, Loss: 7.0315, Metric: -7.0315\n", + "val loss: 7.0315\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", "Epoch 7/10, cost time: 0.60s\n", - "train loss: 6.8236\n", - "Validation, Loss: 6.8324, Metric: -6.8324\n", - "val loss: 6.8324\n", + "train loss: 6.9806\n", + "Validation, Loss: 6.9873, Metric: -6.9873\n", + "val loss: 6.9873\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.57s\n", - "train loss: 6.7763\n", - "Validation, Loss: 6.7876, Metric: -6.7876\n", - "val loss: 6.7876\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 6.9270\n", + "Validation, Loss: 6.9384, Metric: -6.9384\n", + "val loss: 6.9384\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.57s\n", - "train loss: 6.7298\n", - "Validation, Loss: 6.7403, Metric: -6.7403\n", - "val loss: 6.7403\n", + "Epoch 9/10, cost time: 0.70s\n", + "train loss: 6.8798\n", + "Validation, Loss: 6.8904, Metric: -6.8904\n", + "val loss: 6.8904\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 6.6830\n", - "Validation, Loss: 6.6920, Metric: -6.6920\n", - "val loss: 6.6920\n", + "Epoch 10/10, cost time: 0.61s\n", + "train loss: 6.8301\n", + "Validation, Loss: 6.8432, Metric: -6.8432\n", + "val loss: 6.8432\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -6.691991329193115\n", + "Fold 0. Score: -6.843226432800293\n", "length of train dataset: 496\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 5.8217\n", - "Validation, Loss: 5.7710, Metric: -5.7710\n", - "val loss: 5.7710\n", + "Epoch 1/10, cost time: 0.61s\n", + "train loss: 6.2791\n", + "Validation, Loss: 6.2261, Metric: -6.2261\n", + "val loss: 6.2261\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 5.7866\n", - "Validation, Loss: 5.7493, Metric: -5.7493\n", - "val loss: 5.7493\n", + "Epoch 2/10, cost time: 0.68s\n", + "train loss: 6.2464\n", + "Validation, Loss: 6.1926, Metric: -6.1926\n", + "val loss: 6.1926\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.57s\n", - "train loss: 5.7567\n", - "Validation, Loss: 5.7043, Metric: -5.7043\n", - "val loss: 5.7043\n", + "Epoch 3/10, cost time: 0.64s\n", + "train loss: 6.2126\n", + "Validation, Loss: 6.1581, Metric: -6.1581\n", + "val loss: 6.1581\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.57s\n", - "train loss: 5.7222\n", - "Validation, Loss: 5.6819, Metric: -5.6819\n", - "val loss: 5.6819\n", + "Epoch 4/10, cost time: 0.65s\n", + "train loss: 6.1788\n", + "Validation, Loss: 6.1254, Metric: -6.1254\n", + "val loss: 6.1254\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.68s\n", - "train loss: 5.6983\n", - "Validation, Loss: 5.6439, Metric: -5.6439\n", - "val loss: 5.6439\n", + "Epoch 5/10, cost time: 1.08s\n", + "train loss: 6.1437\n", + "Validation, Loss: 6.0878, Metric: -6.0878\n", + "val loss: 6.0878\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.62s\n", - "train loss: 5.6609\n", - "Validation, Loss: 5.6346, Metric: -5.6346\n", - "val loss: 5.6346\n", + "Epoch 6/10, cost time: 0.76s\n", + "train loss: 6.1126\n", + "Validation, Loss: 6.0715, Metric: -6.0715\n", + "val loss: 6.0715\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 5.6246\n", - "Validation, Loss: 5.5833, Metric: -5.5833\n", - "val loss: 5.5833\n", + "Epoch 7/10, cost time: 0.63s\n", + "train loss: 6.0738\n", + "Validation, Loss: 6.0168, Metric: -6.0168\n", + "val loss: 6.0168\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 5.5855\n", - "Validation, Loss: 5.5368, Metric: -5.5368\n", - "val loss: 5.5368\n", + "Epoch 8/10, cost time: 0.64s\n", + "train loss: 6.0275\n", + "Validation, Loss: 5.9694, Metric: -5.9694\n", + "val loss: 5.9694\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.64s\n", - "train loss: 5.5482\n", - "Validation, Loss: 5.4926, Metric: -5.4926\n", - "val loss: 5.4926\n", + "Epoch 9/10, cost time: 0.66s\n", + "train loss: 5.9834\n", + "Validation, Loss: 5.9285, Metric: -5.9285\n", + "val loss: 5.9285\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 5.5088\n", - "Validation, Loss: 5.4605, Metric: -5.4605\n", - "val loss: 5.4605\n", + "Epoch 10/10, cost time: 0.67s\n", + "train loss: 5.9429\n", + "Validation, Loss: 5.8968, Metric: -5.8968\n", + "val loss: 5.8968\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -5.460498809814453\n", - "Mean score: -6.0762\n", - "Std: 0.6157\n", + "Fold 1. Score: -5.896804332733154\n", + "Mean score: -6.37\n", + "Std: 0.4732\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 7.4068\n", - "Validation, Loss: 7.4242, Metric: -7.4242\n", - "val loss: 7.4242\n", + "Epoch 1/10, cost time: 0.84s\n", + "train loss: 9.0494\n", + "Validation, Loss: 9.0744, Metric: -9.0744\n", + "val loss: 9.0744\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.57s\n", - "train loss: 7.3655\n", - "Validation, Loss: 7.3973, Metric: -7.3973\n", - "val loss: 7.3973\n", + "Epoch 2/10, cost time: 0.69s\n", + "train loss: 9.0002\n", + "Validation, Loss: 9.0227, Metric: -9.0227\n", + "val loss: 9.0227\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.62s\n", - "train loss: 7.3383\n", - "Validation, Loss: 7.3738, Metric: -7.3738\n", - "val loss: 7.3738\n", + "Epoch 3/10, cost time: 0.70s\n", + "train loss: 8.9587\n", + "Validation, Loss: 8.9764, Metric: -8.9764\n", + "val loss: 8.9764\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.57s\n", - "train loss: 7.2943\n", - "Validation, Loss: 7.3143, Metric: -7.3143\n", - "val loss: 7.3143\n", + "Epoch 4/10, cost time: 0.72s\n", + "train loss: 8.8969\n", + "Validation, Loss: 8.9240, Metric: -8.9240\n", + "val loss: 8.9240\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.57s\n", - "train loss: 7.2483\n", - "Validation, Loss: 7.2718, Metric: -7.2718\n", - "val loss: 7.2718\n", + "Epoch 5/10, cost time: 0.69s\n", + "train loss: 8.8431\n", + "Validation, Loss: 8.8636, Metric: -8.8636\n", + "val loss: 8.8636\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.57s\n", - "train loss: 7.1983\n", - "Validation, Loss: 7.2133, Metric: -7.2133\n", - "val loss: 7.2133\n", + "Epoch 6/10, cost time: 0.73s\n", + "train loss: 8.7906\n", + "Validation, Loss: 8.8040, Metric: -8.8040\n", + "val loss: 8.8040\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 7.1556\n", - "Validation, Loss: 7.1657, Metric: -7.1657\n", - "val loss: 7.1657\n", + "Epoch 7/10, cost time: 0.69s\n", + "train loss: 8.7315\n", + "Validation, Loss: 8.7580, Metric: -8.7580\n", + "val loss: 8.7580\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 7.1041\n", - "Validation, Loss: 7.1188, Metric: -7.1188\n", - "val loss: 7.1188\n", + "Epoch 8/10, cost time: 0.69s\n", + "train loss: 8.6697\n", + "Validation, Loss: 8.6826, Metric: -8.6826\n", + "val loss: 8.6826\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.57s\n", - "train loss: 7.0549\n", - "Validation, Loss: 7.0729, Metric: -7.0729\n", - "val loss: 7.0729\n", + "Epoch 9/10, cost time: 0.64s\n", + "train loss: 8.6085\n", + "Validation, Loss: 8.6196, Metric: -8.6196\n", + "val loss: 8.6196\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.57s\n", - "train loss: 7.0070\n", - "Validation, Loss: 7.0559, Metric: -7.0559\n", - "val loss: 7.0559\n", + "Epoch 10/10, cost time: 0.70s\n", + "train loss: 8.5446\n", + "Validation, Loss: 8.5844, Metric: -8.5844\n", + "val loss: 8.5844\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -7.055933475494385\n", + "Fold 0. Score: -8.584444999694824\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 10.0828\n", - "Validation, Loss: 9.9944, Metric: -9.9944\n", - "val loss: 9.9944\n", + "Epoch 1/10, cost time: 0.77s\n", + "train loss: 7.6699\n", + "Validation, Loss: 7.5988, Metric: -7.5988\n", + "val loss: 7.5988\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.62s\n", - "train loss: 10.0214\n", - "Validation, Loss: 9.9403, Metric: -9.9403\n", - "val loss: 9.9403\n", + "Epoch 2/10, cost time: 0.68s\n", + "train loss: 7.6315\n", + "Validation, Loss: 7.5741, Metric: -7.5741\n", + "val loss: 7.5741\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.67s\n", - "train loss: 9.9638\n", - "Validation, Loss: 9.8732, Metric: -9.8732\n", - "val loss: 9.8732\n", + "Epoch 3/10, cost time: 0.64s\n", + "train loss: 7.5926\n", + "Validation, Loss: 7.5289, Metric: -7.5289\n", + "val loss: 7.5289\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 9.9057\n", - "Validation, Loss: 9.8199, Metric: -9.8199\n", - "val loss: 9.8199\n", + "Epoch 4/10, cost time: 0.68s\n", + "train loss: 7.5421\n", + "Validation, Loss: 7.4752, Metric: -7.4752\n", + "val loss: 7.4752\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.60s\n", - "train loss: 9.8503\n", - "Validation, Loss: 9.7647, Metric: -9.7647\n", - "val loss: 9.7647\n", + "Epoch 5/10, cost time: 0.70s\n", + "train loss: 7.4971\n", + "Validation, Loss: 7.4366, Metric: -7.4366\n", + "val loss: 7.4366\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 9.7833\n", - "Validation, Loss: 9.6908, Metric: -9.6908\n", - "val loss: 9.6908\n", + "Epoch 6/10, cost time: 0.75s\n", + "train loss: 7.4532\n", + "Validation, Loss: 7.3766, Metric: -7.3766\n", + "val loss: 7.3766\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 9.7204\n", - "Validation, Loss: 9.6201, Metric: -9.6201\n", - "val loss: 9.6201\n", + "Epoch 7/10, cost time: 0.65s\n", + "train loss: 7.3993\n", + "Validation, Loss: 7.3409, Metric: -7.3409\n", + "val loss: 7.3409\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 9.6498\n", - "Validation, Loss: 9.5648, Metric: -9.5648\n", - "val loss: 9.5648\n", + "Epoch 8/10, cost time: 0.61s\n", + "train loss: 7.3549\n", + "Validation, Loss: 7.2776, Metric: -7.2776\n", + "val loss: 7.2776\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 9.5808\n", - "Validation, Loss: 9.5249, Metric: -9.5249\n", - "val loss: 9.5249\n", + "Epoch 9/10, cost time: 0.63s\n", + "train loss: 7.3006\n", + "Validation, Loss: 7.2303, Metric: -7.2303\n", + "val loss: 7.2303\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.62s\n", - "train loss: 9.5221\n", - "Validation, Loss: 9.4169, Metric: -9.4169\n", - "val loss: 9.4169\n", + "Epoch 10/10, cost time: 0.61s\n", + "train loss: 7.2614\n", + "Validation, Loss: 7.1951, Metric: -7.1951\n", + "val loss: 7.1951\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -9.416933059692383\n", - "Mean score: -8.2364\n", - "Std: 1.1805\n", + "Fold 1. Score: -7.195066928863525\n", + "Mean score: -7.8898\n", + "Std: 0.6947\n", "length of train dataset: 494\n", "length of val dataset: 495\n", "Epoch 1/10, cost time: 0.63s\n", - "train loss: 10.8233\n", - "Validation, Loss: 10.8490, Metric: -10.8490\n", - "val loss: 10.8490\n", + "train loss: 11.6259\n", + "Validation, Loss: 11.6559, Metric: -11.6559\n", + "val loss: 11.6559\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.61s\n", - "train loss: 10.7649\n", - "Validation, Loss: 10.8014, Metric: -10.8014\n", - "val loss: 10.8014\n", + "Epoch 2/10, cost time: 0.60s\n", + "train loss: 11.5693\n", + "Validation, Loss: 11.5940, Metric: -11.5940\n", + "val loss: 11.5940\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 10.7058\n", - "Validation, Loss: 10.7308, Metric: -10.7308\n", - "val loss: 10.7308\n", + "Epoch 3/10, cost time: 0.67s\n", + "train loss: 11.5005\n", + "Validation, Loss: 11.5175, Metric: -11.5175\n", + "val loss: 11.5175\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 10.6538\n", - "Validation, Loss: 10.6665, Metric: -10.6665\n", - "val loss: 10.6665\n", + "Epoch 4/10, cost time: 0.74s\n", + "train loss: 11.4334\n", + "Validation, Loss: 11.4457, Metric: -11.4457\n", + "val loss: 11.4457\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.60s\n", - "train loss: 10.5789\n", - "Validation, Loss: 10.6223, Metric: -10.6223\n", - "val loss: 10.6223\n", + "Epoch 5/10, cost time: 0.69s\n", + "train loss: 11.3574\n", + "Validation, Loss: 11.3815, Metric: -11.3815\n", + "val loss: 11.3815\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 10.5161\n", - "Validation, Loss: 10.5411, Metric: -10.5411\n", - "val loss: 10.5411\n", + "Epoch 6/10, cost time: 0.68s\n", + "train loss: 11.2892\n", + "Validation, Loss: 11.2992, Metric: -11.2992\n", + "val loss: 11.2992\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 10.4431\n", - "Validation, Loss: 10.4628, Metric: -10.4628\n", - "val loss: 10.4628\n", + "Epoch 7/10, cost time: 0.69s\n", + "train loss: 11.2189\n", + "Validation, Loss: 11.2282, Metric: -11.2282\n", + "val loss: 11.2282\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 10.3851\n", - "Validation, Loss: 10.3943, Metric: -10.3943\n", - "val loss: 10.3943\n", + "Epoch 8/10, cost time: 0.74s\n", + "train loss: 11.1360\n", + "Validation, Loss: 11.1575, Metric: -11.1575\n", + "val loss: 11.1575\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 10.3052\n", - "Validation, Loss: 10.3331, Metric: -10.3331\n", - "val loss: 10.3331\n", + "Epoch 9/10, cost time: 0.71s\n", + "train loss: 11.0524\n", + "Validation, Loss: 11.0737, Metric: -11.0737\n", + "val loss: 11.0737\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.58s\n", - "train loss: 10.2399\n", - "Validation, Loss: 10.2538, Metric: -10.2538\n", - "val loss: 10.2538\n", + "Epoch 10/10, cost time: 0.89s\n", + "train loss: 10.9718\n", + "Validation, Loss: 10.9886, Metric: -10.9886\n", + "val loss: 10.9886\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -10.253775596618652\n", + "Fold 0. Score: -10.988609313964844\n", "length of train dataset: 495\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.69s\n", - "train loss: 12.9979\n", - "Validation, Loss: 12.8790, Metric: -12.8790\n", - "val loss: 12.8790\n", + "Epoch 1/10, cost time: 0.71s\n", + "train loss: 12.2164\n", + "Validation, Loss: 12.1089, Metric: -12.1089\n", + "val loss: 12.1089\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.61s\n", - "train loss: 12.9284\n", - "Validation, Loss: 12.8534, Metric: -12.8534\n", - "val loss: 12.8534\n", + "Epoch 2/10, cost time: 0.63s\n", + "train loss: 12.1554\n", + "Validation, Loss: 12.0410, Metric: -12.0410\n", + "val loss: 12.0410\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.59s\n", - "train loss: 12.8511\n", - "Validation, Loss: 12.7317, Metric: -12.7317\n", - "val loss: 12.7317\n", + "Epoch 3/10, cost time: 0.65s\n", + "train loss: 12.0889\n", + "Validation, Loss: 12.0101, Metric: -12.0101\n", + "val loss: 12.0101\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 12.7672\n", - "Validation, Loss: 12.6531, Metric: -12.6531\n", - "val loss: 12.6531\n", + "Epoch 4/10, cost time: 0.73s\n", + "train loss: 12.0114\n", + "Validation, Loss: 11.8983, Metric: -11.8983\n", + "val loss: 11.8983\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 12.6920\n", - "Validation, Loss: 12.5797, Metric: -12.5797\n", - "val loss: 12.5797\n", + "Epoch 5/10, cost time: 0.77s\n", + "train loss: 11.9531\n", + "Validation, Loss: 11.8235, Metric: -11.8235\n", + "val loss: 11.8235\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 12.6101\n", - "Validation, Loss: 12.4994, Metric: -12.4994\n", - "val loss: 12.4994\n", + "Epoch 6/10, cost time: 0.80s\n", + "train loss: 11.8625\n", + "Validation, Loss: 11.7573, Metric: -11.7573\n", + "val loss: 11.7573\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 12.5235\n", - "Validation, Loss: 12.4034, Metric: -12.4034\n", - "val loss: 12.4034\n", + "Epoch 7/10, cost time: 0.74s\n", + "train loss: 11.7842\n", + "Validation, Loss: 11.6797, Metric: -11.6797\n", + "val loss: 11.6797\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 12.4380\n", - "Validation, Loss: 12.3317, Metric: -12.3317\n", - "val loss: 12.3317\n", + "Epoch 8/10, cost time: 0.88s\n", + "train loss: 11.7024\n", + "Validation, Loss: 11.5906, Metric: -11.5906\n", + "val loss: 11.5906\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 12.3537\n", - "Validation, Loss: 12.2280, Metric: -12.2280\n", - "val loss: 12.2280\n", + "Epoch 9/10, cost time: 0.65s\n", + "train loss: 11.6254\n", + "Validation, Loss: 11.5071, Metric: -11.5071\n", + "val loss: 11.5071\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.58s\n", - "train loss: 12.2643\n", - "Validation, Loss: 12.1419, Metric: -12.1419\n", - "val loss: 12.1419\n", + "Epoch 10/10, cost time: 0.72s\n", + "train loss: 11.5450\n", + "Validation, Loss: 11.4422, Metric: -11.4422\n", + "val loss: 11.4422\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -12.141925811767578\n", - "Mean score: -11.1979\n", - "Std: 0.9441\n", + "Fold 1. Score: -11.442185401916504\n", + "Mean score: -11.2154\n", + "Std: 0.2268\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.58s\n", - "train loss: 14.1279\n", - "Validation, Loss: 14.2159, Metric: -14.2159\n", - "val loss: 14.2159\n", + "Epoch 1/10, cost time: 0.70s\n", + "train loss: 14.5929\n", + "Validation, Loss: 14.6382, Metric: -14.6382\n", + "val loss: 14.6382\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 14.0677\n", - "Validation, Loss: 14.1562, Metric: -14.1562\n", - "val loss: 14.1562\n", + "Epoch 2/10, cost time: 0.78s\n", + "train loss: 14.5299\n", + "Validation, Loss: 14.5703, Metric: -14.5703\n", + "val loss: 14.5703\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 13.9850\n", - "Validation, Loss: 14.0829, Metric: -14.0829\n", - "val loss: 14.0829\n", + "Epoch 3/10, cost time: 0.62s\n", + "train loss: 14.4353\n", + "Validation, Loss: 14.4728, Metric: -14.4728\n", + "val loss: 14.4728\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.62s\n", - "train loss: 13.9074\n", - "Validation, Loss: 13.9388, Metric: -13.9388\n", - "val loss: 13.9388\n", + "Epoch 4/10, cost time: 0.70s\n", + "train loss: 14.3435\n", + "Validation, Loss: 14.4078, Metric: -14.4078\n", + "val loss: 14.4078\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.61s\n", - "train loss: 13.7966\n", - "Validation, Loss: 13.8638, Metric: -13.8638\n", - "val loss: 13.8638\n", + "Epoch 5/10, cost time: 0.78s\n", + "train loss: 14.2574\n", + "Validation, Loss: 14.3771, Metric: -14.3771\n", + "val loss: 14.3771\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.71s\n", - "train loss: 13.7157\n", - "Validation, Loss: 13.7623, Metric: -13.7623\n", - "val loss: 13.7623\n", + "Epoch 6/10, cost time: 0.66s\n", + "train loss: 14.2107\n", + "Validation, Loss: 14.2036, Metric: -14.2036\n", + "val loss: 14.2036\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 13.6267\n", - "Validation, Loss: 13.6874, Metric: -13.6874\n", - "val loss: 13.6874\n", + "Epoch 7/10, cost time: 0.63s\n", + "train loss: 14.1034\n", + "Validation, Loss: 14.1513, Metric: -14.1513\n", + "val loss: 14.1513\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 13.5322\n", - "Validation, Loss: 13.5936, Metric: -13.5936\n", - "val loss: 13.5936\n", + "Epoch 8/10, cost time: 0.77s\n", + "train loss: 13.9854\n", + "Validation, Loss: 14.0232, Metric: -14.0232\n", + "val loss: 14.0232\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 13.4324\n", - "Validation, Loss: 13.4838, Metric: -13.4838\n", - "val loss: 13.4838\n", + "Epoch 9/10, cost time: 0.71s\n", + "train loss: 13.8830\n", + "Validation, Loss: 13.9311, Metric: -13.9311\n", + "val loss: 13.9311\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.59s\n", - "train loss: 13.3434\n", - "Validation, Loss: 13.3737, Metric: -13.3737\n", - "val loss: 13.3737\n", + "Epoch 10/10, cost time: 0.65s\n", + "train loss: 13.7840\n", + "Validation, Loss: 13.8271, Metric: -13.8271\n", + "val loss: 13.8271\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -13.373733520507812\n", + "Fold 0. Score: -13.82713508605957\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.66s\n", - "train loss: 15.3095\n", - "Validation, Loss: 15.1644, Metric: -15.1644\n", - "val loss: 15.1644\n", + "Epoch 1/10, cost time: 0.61s\n", + "train loss: 14.1206\n", + "Validation, Loss: 13.9856, Metric: -13.9856\n", + "val loss: 13.9856\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.66s\n", - "train loss: 15.2335\n", - "Validation, Loss: 15.0621, Metric: -15.0621\n", - "val loss: 15.0621\n", + "Epoch 2/10, cost time: 0.62s\n", + "train loss: 14.0371\n", + "Validation, Loss: 13.8985, Metric: -13.8985\n", + "val loss: 13.8985\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.69s\n", - "train loss: 15.1290\n", - "Validation, Loss: 14.9738, Metric: -14.9738\n", - "val loss: 14.9738\n", + "Epoch 3/10, cost time: 0.64s\n", + "train loss: 13.9648\n", + "Validation, Loss: 13.8339, Metric: -13.8339\n", + "val loss: 13.8339\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.64s\n", - "train loss: 15.0387\n", - "Validation, Loss: 14.9120, Metric: -14.9120\n", - "val loss: 14.9120\n", + "Epoch 4/10, cost time: 0.60s\n", + "train loss: 13.8822\n", + "Validation, Loss: 13.7395, Metric: -13.7395\n", + "val loss: 13.7395\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 14.9545\n", - "Validation, Loss: 14.7915, Metric: -14.7915\n", - "val loss: 14.7915\n", + "Epoch 5/10, cost time: 0.60s\n", + "train loss: 13.7903\n", + "Validation, Loss: 13.6687, Metric: -13.6687\n", + "val loss: 13.6687\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 14.8635\n", - "Validation, Loss: 14.7089, Metric: -14.7089\n", - "val loss: 14.7089\n", + "Epoch 6/10, cost time: 0.61s\n", + "train loss: 13.7069\n", + "Validation, Loss: 13.5543, Metric: -13.5543\n", + "val loss: 13.5543\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 14.7660\n", - "Validation, Loss: 14.5910, Metric: -14.5910\n", - "val loss: 14.5910\n", + "Epoch 7/10, cost time: 0.60s\n", + "train loss: 13.6136\n", + "Validation, Loss: 13.4649, Metric: -13.4649\n", + "val loss: 13.4649\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.58s\n", - "train loss: 14.6518\n", - "Validation, Loss: 14.4894, Metric: -14.4894\n", - "val loss: 14.4894\n", + "Epoch 8/10, cost time: 0.60s\n", + "train loss: 13.5222\n", + "Validation, Loss: 13.3781, Metric: -13.3781\n", + "val loss: 13.3781\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 14.5501\n", - "Validation, Loss: 14.4009, Metric: -14.4009\n", - "val loss: 14.4009\n", + "Epoch 9/10, cost time: 0.60s\n", + "train loss: 13.4350\n", + "Validation, Loss: 13.2843, Metric: -13.2843\n", + "val loss: 13.2843\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.58s\n", - "train loss: 14.4531\n", - "Validation, Loss: 14.2984, Metric: -14.2984\n", - "val loss: 14.2984\n", + "Epoch 10/10, cost time: 0.59s\n", + "train loss: 13.3280\n", + "Validation, Loss: 13.1827, Metric: -13.1827\n", + "val loss: 13.1827\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -14.298404693603516\n", - "Mean score: -13.8361\n", - "Std: 0.4623\n", + "Fold 1. Score: -13.182726860046387\n", + "Mean score: -13.5049\n", + "Std: 0.3222\n", "length of train dataset: 493\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 19.1676\n", - "Validation, Loss: 19.2452, Metric: -19.2452\n", - "val loss: 19.2452\n", + "Epoch 1/10, cost time: 0.60s\n", + "train loss: 16.3563\n", + "Validation, Loss: 16.3956, Metric: -16.3956\n", + "val loss: 16.3956\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 19.0695\n", - "Validation, Loss: 19.0989, Metric: -19.0989\n", - "val loss: 19.0989\n", + "Epoch 2/10, cost time: 0.64s\n", + "train loss: 16.2662\n", + "Validation, Loss: 16.3085, Metric: -16.3085\n", + "val loss: 16.3085\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.61s\n", - "train loss: 18.9595\n", - "Validation, Loss: 18.9872, Metric: -18.9872\n", - "val loss: 18.9872\n", + "Epoch 3/10, cost time: 0.71s\n", + "train loss: 16.1773\n", + "Validation, Loss: 16.2451, Metric: -16.2451\n", + "val loss: 16.2451\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.58s\n", - "train loss: 18.8743\n", - "Validation, Loss: 18.8940, Metric: -18.8940\n", - "val loss: 18.8940\n", + "Epoch 4/10, cost time: 0.62s\n", + "train loss: 16.0996\n", + "Validation, Loss: 16.1621, Metric: -16.1621\n", + "val loss: 16.1621\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.59s\n", - "train loss: 18.7763\n", - "Validation, Loss: 18.8358, Metric: -18.8358\n", - "val loss: 18.8358\n", + "Epoch 5/10, cost time: 0.61s\n", + "train loss: 16.0046\n", + "Validation, Loss: 16.0087, Metric: -16.0087\n", + "val loss: 16.0087\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 18.6196\n", - "Validation, Loss: 18.6316, Metric: -18.6316\n", - "val loss: 18.6316\n", + "Epoch 6/10, cost time: 0.62s\n", + "train loss: 15.8729\n", + "Validation, Loss: 15.9024, Metric: -15.9024\n", + "val loss: 15.9024\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 18.4809\n", - "Validation, Loss: 18.5037, Metric: -18.5037\n", - "val loss: 18.5037\n", + "Epoch 7/10, cost time: 0.61s\n", + "train loss: 15.7679\n", + "Validation, Loss: 15.8665, Metric: -15.8665\n", + "val loss: 15.8665\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.63s\n", - "train loss: 18.3591\n", - "Validation, Loss: 18.4324, Metric: -18.4324\n", - "val loss: 18.4324\n", + "Epoch 8/10, cost time: 0.61s\n", + "train loss: 15.6997\n", + "Validation, Loss: 15.6833, Metric: -15.6833\n", + "val loss: 15.6833\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 18.2561\n", - "Validation, Loss: 18.3160, Metric: -18.3160\n", - "val loss: 18.3160\n", + "Epoch 9/10, cost time: 0.65s\n", + "train loss: 15.5581\n", + "Validation, Loss: 15.5959, Metric: -15.5959\n", + "val loss: 15.5959\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.59s\n", - "train loss: 18.1243\n", - "Validation, Loss: 18.2251, Metric: -18.2251\n", - "val loss: 18.2251\n", + "Epoch 10/10, cost time: 0.62s\n", + "train loss: 15.4514\n", + "Validation, Loss: 15.4734, Metric: -15.4734\n", + "val loss: 15.4734\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -18.225141525268555\n", + "Fold 0. Score: -15.473418235778809\n", "length of train dataset: 494\n", "length of val dataset: 493\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 17.8511\n", - "Validation, Loss: 17.6984, Metric: -17.6984\n", - "val loss: 17.6984\n", + "Epoch 1/10, cost time: 0.66s\n", + "train loss: 17.0522\n", + "Validation, Loss: 16.9092, Metric: -16.9092\n", + "val loss: 16.9092\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 17.7412\n", - "Validation, Loss: 17.5978, Metric: -17.5978\n", - "val loss: 17.5978\n", + "Epoch 2/10, cost time: 0.68s\n", + "train loss: 16.9659\n", + "Validation, Loss: 16.8336, Metric: -16.8336\n", + "val loss: 16.8336\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.59s\n", - "train loss: 17.6428\n", - "Validation, Loss: 17.4923, Metric: -17.4923\n", - "val loss: 17.4923\n", + "Epoch 3/10, cost time: 0.63s\n", + "train loss: 16.8774\n", + "Validation, Loss: 16.7134, Metric: -16.7134\n", + "val loss: 16.7134\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 17.5319\n", - "Validation, Loss: 17.3855, Metric: -17.3855\n", - "val loss: 17.3855\n", + "Epoch 4/10, cost time: 0.68s\n", + "train loss: 16.7588\n", + "Validation, Loss: 16.6066, Metric: -16.6066\n", + "val loss: 16.6066\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.62s\n", - "train loss: 17.4258\n", - "Validation, Loss: 17.2621, Metric: -17.2621\n", - "val loss: 17.2621\n", + "Epoch 5/10, cost time: 0.69s\n", + "train loss: 16.6589\n", + "Validation, Loss: 16.5006, Metric: -16.5006\n", + "val loss: 16.5006\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 17.3086\n", - "Validation, Loss: 17.1568, Metric: -17.1568\n", - "val loss: 17.1568\n", + "Epoch 6/10, cost time: 0.65s\n", + "train loss: 16.5478\n", + "Validation, Loss: 16.4862, Metric: -16.4862\n", + "val loss: 16.4862\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 17.1922\n", - "Validation, Loss: 17.0323, Metric: -17.0323\n", - "val loss: 17.0323\n", + "Epoch 7/10, cost time: 0.94s\n", + "train loss: 16.4392\n", + "Validation, Loss: 16.2885, Metric: -16.2885\n", + "val loss: 16.2885\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 17.0769\n", - "Validation, Loss: 16.9577, Metric: -16.9577\n", - "val loss: 16.9577\n", + "Epoch 8/10, cost time: 0.77s\n", + "train loss: 16.3296\n", + "Validation, Loss: 16.1659, Metric: -16.1659\n", + "val loss: 16.1659\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.67s\n", - "train loss: 16.9738\n", - "Validation, Loss: 16.8426, Metric: -16.8426\n", - "val loss: 16.8426\n", + "Epoch 9/10, cost time: 0.66s\n", + "train loss: 16.2050\n", + "Validation, Loss: 16.1123, Metric: -16.1123\n", + "val loss: 16.1123\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 16.8403\n", - "Validation, Loss: 16.7033, Metric: -16.7033\n", - "val loss: 16.7033\n", + "Epoch 10/10, cost time: 0.66s\n", + "train loss: 16.0964\n", + "Validation, Loss: 15.9350, Metric: -15.9350\n", + "val loss: 15.9350\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -16.703344345092773\n", - "Mean score: -17.4642\n", - "Std: 0.7609\n" + "Fold 1. Score: -15.935033798217773\n", + "Mean score: -15.7042\n", + "Std: 0.2308\n" ] }, { "data": { "text/plain": [ - "(165.66884803771973, )" + "(185.71449208259583, )" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -3290,7 +3290,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -3321,7 +3321,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -3403,10 +3403,11 @@ { "data": { "text/plain": [ - "(0.1153402328491211, )" + "(0.10583090782165527,\n", + " )" ] }, - "execution_count": 12, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -3417,7 +3418,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -3454,7 +3455,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -3488,31 +3489,31 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1997.256836\n", + " 1997.192505\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1997.116211\n", + " 1997.647217\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1997.849365\n", + " 1997.732788\n", " \n", " \n", " 3\n", " 0\n", " 2022-09-30\n", - " 1998.221558\n", + " 1998.238525\n", " \n", " \n", " 4\n", " 0\n", " 2022-10-01\n", - " 1998.371094\n", + " 1998.347656\n", " \n", " \n", " ...\n", @@ -3524,31 +3525,31 @@ " 65\n", " 9\n", " 2022-09-29\n", - " 11004.716797\n", + " 11004.697266\n", " \n", " \n", " 66\n", " 9\n", " 2022-09-30\n", - " 11006.404297\n", + " 11005.898438\n", " \n", " \n", " 67\n", " 9\n", " 2022-10-01\n", - " 11007.738281\n", + " 11007.662109\n", " \n", " \n", " 68\n", " 9\n", " 2022-10-02\n", - " 11009.052734\n", + " 11009.277344\n", " \n", " \n", " 69\n", " 9\n", " 2022-10-03\n", - " 11010.818359\n", + " 11010.523438\n", " \n", " \n", "\n", @@ -3557,22 +3558,22 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1997.256836\n", - "1 0 2022-09-28 1997.116211\n", - "2 0 2022-09-29 1997.849365\n", - "3 0 2022-09-30 1998.221558\n", - "4 0 2022-10-01 1998.371094\n", + "0 0 2022-09-27 1997.192505\n", + "1 0 2022-09-28 1997.647217\n", + "2 0 2022-09-29 1997.732788\n", + "3 0 2022-09-30 1998.238525\n", + "4 0 2022-10-01 1998.347656\n", ".. .. ... ...\n", - "65 9 2022-09-29 11004.716797\n", - "66 9 2022-09-30 11006.404297\n", - "67 9 2022-10-01 11007.738281\n", - "68 9 2022-10-02 11009.052734\n", - "69 9 2022-10-03 11010.818359\n", + "65 9 2022-09-29 11004.697266\n", + "66 9 2022-09-30 11005.898438\n", + "67 9 2022-10-01 11007.662109\n", + "68 9 2022-10-02 11009.277344\n", + "69 9 2022-10-03 11010.523438\n", "\n", "[70 rows x 3 columns]" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -3580,13 +3581,6 @@ "source": [ "current_pred" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From d755c4beb9e8ffee523ddeeb79b63d159cf63b87 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 20 Sep 2024 15:49:15 +0300 Subject: [PATCH 23/50] bug fixes + strategies_tutorial --- README.md | 2 +- Tutorial_2_Strategies.ipynb | 22881 +++++++++++++++++++- imgs/direct.png | Bin 0 -> 321773 bytes imgs/flatwidemimo.png | Bin 0 -> 221151 bytes imgs/global.png | Bin 0 -> 45609 bytes imgs/local.png | Bin 0 -> 50149 bytes imgs/mimo.png | Bin 0 -> 95452 bytes imgs/multivariate.png | Bin 0 -> 47164 bytes imgs/recursive.png | Bin 0 -> 200803 bytes imgs/time_series.png | Bin 0 -> 25366 bytes imgs/time_series_example.png | Bin 0 -> 27130 bytes tsururu_logo.png => imgs/tsururu_logo.png | Bin tsururu/dataset/pipeline.py | 10 +- tsururu/model_training/trainer.py | 2 + 14 files changed, 22844 insertions(+), 51 deletions(-) create mode 100644 imgs/direct.png create mode 100644 imgs/flatwidemimo.png create mode 100644 imgs/global.png create mode 100644 imgs/local.png create mode 100644 imgs/mimo.png create mode 100644 imgs/multivariate.png create mode 100644 imgs/recursive.png create mode 100644 imgs/time_series.png create mode 100644 imgs/time_series_example.png rename tsururu_logo.png => imgs/tsururu_logo.png (100%) diff --git a/README.md b/README.md index 7b8c0e4..4188dd9 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -![tsururu_logo](tsururu_logo.png) +![tsururu_logo](imgs/tsururu_logo.png) # Tsururu – a time series forecasting strategies framework diff --git a/Tutorial_2_Strategies.ipynb b/Tutorial_2_Strategies.ipynb index 43a1ebe..496f78c 100644 --- a/Tutorial_2_Strategies.ipynb +++ b/Tutorial_2_Strategies.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +62,25 @@ " UnionTransformer,\n", ")\n", "\n", - "index_slicer = IndexSlicer()" + "index_slicer = IndexSlicer()\n", + "\n", + "import time\n", + "from pathlib import Path\n", + "from typing import Callable, Dict, List, Optional, Tuple, Union\n", + "\n", + "import numpy as np\n", + "\n", + "from tsururu.models.base import Estimator\n", + "from tsururu.model_training.validator import Validator\n", + "\n", + "try:\n", + " import torch\n", + " import torch.nn as nn\n", + " from torch.utils.data import Subset\n", + "except ImportError:\n", + " torch = None\n", + " Subset = None\n", + " nn = None" ] }, { @@ -74,10 +92,76 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ + "class MLTrainer:\n", + " def __init__(\n", + " self,\n", + " model: Estimator,\n", + " model_params: Dict,\n", + " validator: Optional[Validator] = None,\n", + " validation_params: Dict = {},\n", + " ):\n", + " self.model = model\n", + " self.model_params = model_params\n", + " self.validator = validator\n", + " self.validation_params = validation_params\n", + "\n", + " # Provide by strategy if needed\n", + " self.history = None\n", + " self.horizon = None\n", + " self.models: List[Estimator] = []\n", + " self.scores: List[float] = []\n", + " self.columns: List[str] = []\n", + "\n", + " def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) -> \"MLTrainer\":\n", + " X, y = pipeline.generate(data)\n", + " \n", + " print(\"X for training: \", X.shape)\n", + " display(pd.DataFrame(X, columns=pipeline.output_features).head())\n", + " print(\"y for training: \", y.shape)\n", + " print(y[:5], \"\\n\")\n", + "\n", + " if val_data:\n", + " X_val, y_val = pipeline.generate(val_data)\n", + " else:\n", + " X_val, y_val = None, None\n", + "\n", + " # Initialize columns' order and reorder columns\n", + " self.features_argsort = np.argsort(pipeline.output_features)\n", + " X = X[:, self.features_argsort]\n", + "\n", + " for fold_i, (X_train, y_train, X_val, y_val) in enumerate(\n", + " self.validator(**self.validation_params).get_split(X, y, X_val, y_val)\n", + " ):\n", + " model = self.model(self.model_params)\n", + " model.fit_one_fold(X_train, y_train, X_val, y_val)\n", + " self.models.append(model)\n", + " self.scores.append(model.score)\n", + "\n", + " print(f\"Fold {fold_i}. Score: {model.score}\")\n", + "\n", + " print(f\"Mean score: {np.mean(self.scores).round(4)}\")\n", + " print(f\"Std: {np.std(self.scores).round(4)}\")\n", + "\n", + " def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray:\n", + " X, _ = pipeline.generate(data)\n", + "\n", + " print(\"X for testing: \", X.shape)\n", + " display(pd.DataFrame(X, columns=pipeline.output_features).head())\n", + "\n", + " # Reorder columns\n", + " X = X[:, self.features_argsort]\n", + "\n", + " models_preds = [model.predict(X) for model in self.models]\n", + " y_pred = np.mean(models_preds, axis=0)\n", + "\n", + " y_pred = y_pred.reshape(pipeline.y_original_shape)\n", + "\n", + " return y_pred\n", + "\n", "class RecursiveStrategy(Strategy):\n", " def __init__(\n", " self,\n", @@ -119,6 +203,9 @@ "\n", " data = self.pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx)\n", " data = self.pipeline.fit_transform(data, self.strategy_name)\n", + " \n", + " print(\"raw_ts_X_for_training: \", data[\"raw_ts_X\"].shape)\n", + " display(data[\"raw_ts_X\"].head())\n", "\n", " val_dataset = self.trainer.validation_params.get(\"validation_data\")\n", "\n", @@ -240,6 +327,9 @@ " )\n", " data = self.pipeline.transform(data)\n", "\n", + " print(f\"raw_ts_X on recursive reduced\", new_dataset.data[dataset.target_column].shape)\n", + " display(new_dataset.data[new_dataset.data[dataset.id_column] == 0].tail(10))\n", + "\n", " pred = self.trainers[0].predict(data, self.pipeline)\n", " pred = self.pipeline.inverse_transform_y(pred)\n", "\n", @@ -247,6 +337,8 @@ "\n", " else:\n", " for step in range(self.horizon // self.model_horizon):\n", + " print(f\"raw_ts_X on step: {step}\", new_dataset.data[dataset.target_column].shape)\n", + " display(new_dataset.data[new_dataset.data[dataset.id_column] == 0].tail(10))\n", " new_dataset = self.make_step(step, new_dataset)\n", "\n", " # Get dataframe with predictions only\n", @@ -290,7 +382,7 @@ " if self.equal_train_size:\n", " features_idx = index_slicer.create_idx_data(\n", " dataset.data,\n", - " self.model_horizon,\n", + " self.horizon,\n", " self.history,\n", " self.step,\n", " date_column=dataset.date_column,\n", @@ -299,7 +391,7 @@ "\n", " target_idx = index_slicer.create_idx_target(\n", " dataset.data,\n", - " self.model_horizon,\n", + " self.horizon,\n", " self.history,\n", " self.step,\n", " date_column=dataset.date_column,\n", @@ -314,7 +406,7 @@ " if val_dataset:\n", " val_features_idx = index_slicer.create_idx_data(\n", " val_dataset.data,\n", - " self.model_horizon,\n", + " self.horizon,\n", " self.history,\n", " self.step,\n", " date_column=val_dataset.date_column,\n", @@ -323,7 +415,7 @@ "\n", " val_target_idx = index_slicer.create_idx_target(\n", " val_dataset.data,\n", - " self.model_horizon,\n", + " self.horizon,\n", " self.history,\n", " self.step,\n", " date_column=val_dataset.date_column,\n", @@ -347,7 +439,7 @@ " delta=dataset.delta,\n", " )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon]\n", "\n", - " data[\"target_idx\"] = target_idx\n", + " data[\"idx_y\"] = target_idx\n", "\n", " if val_dataset:\n", " val_target_idx = index_slicer.create_idx_target(\n", @@ -359,7 +451,7 @@ " delta=val_dataset.delta,\n", " )[:, (horizon - 1) * self.model_horizon : horizon * self.model_horizon]\n", "\n", - " val_data[\"target_idx\"] = val_target_idx\n", + " val_data[\"idx_y\"] = val_target_idx\n", "\n", " if isinstance(self.trainer, DLTrainer):\n", " self.trainer.horizon = self.model_horizon\n", @@ -376,6 +468,13 @@ " if pretrained_path:\n", " current_trainer.pretrained_path /= f\"trainer_{model_i}\"\n", "\n", + " print(f\"Training model for horizon {horizon}\".center(100, \"-\"))\n", + " \n", + " print(\"raw_ts_X_for_training: \", data[\"raw_ts_X\"].shape)\n", + "\n", + " data[\"X\"] = np.array([])\n", + " data[\"y\"] = np.array([])\n", + "\n", " current_trainer.fit(data, self.pipeline, val_data)\n", "\n", " if isinstance(current_trainer, DLTrainer):\n", @@ -454,6 +553,10 @@ " if pretrained_path:\n", " current_trainer.pretrained_path /= f\"trainer_{model_i}\"\n", "\n", + " print(f\"Training model for horizon {horizon}\".center(100, \"-\"))\n", + " \n", + " print(\"raw_ts_X_for_training: \", data[\"raw_ts_X\"].shape)\n", + "\n", " current_trainer.fit(data, self.pipeline, val_data)\n", "\n", " if isinstance(current_trainer, DLTrainer):\n", @@ -523,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -751,17 +854,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "horizon = 3\n", + "horizon = 4\n", "history = 7" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -797,13 +900,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "pipeline_easy_params = {\n", " \"target_lags\": 7,\n", - " \"date_lags\": 3,\n", + " \"date_lags\": 4,\n", "}\n", "\n", "# Configure the model parameters\n", @@ -840,17 +943,53 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "__Multi-series prediction strategies:__\n", - "\n", - "- **Local-modelling**:\n", + "## Multi-series prediction strategies:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will examine how the feature and target matrices look later using the MIMO strategy as an example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Local-modelling**:\n", " - An individual model for each time series. \n", - " - Each time series as independent from others.\n", - "\n", - "- **Global-modelling**:\n", + " - Each time series as independent from others." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"local" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Global-modelling**:\n", " - A single model for all time series.\n", - " - Features created from each series do not overlap with other series. Series are related but modeled separately.\n", - "\n", - "- **Multivariate-modelling**:\n", + " - Features created from each series do not overlap with other series. Series are related but modeled separately." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"global" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Multivariate-modelling**:\n", " - A single model for all time series. \n", " - Features created from each series are concatenated at each time step. Try to capture dependencies between the series at the same time point." ] @@ -859,46 +998,82 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Multi-point-ahead prediction strategies:**\n", - "\n", - "- **Recursive**:\n", - "\t- One model is used for the entire forecast horizon. \n", - "\t- training: The model is trained to predict one point ahead.\n", - "\t- prediction: The model iteratively predicts each point, using previous predictions to update the features in the test data.\n", - "\t- Note 1: There is an option to use a “reduced” version, where features are generated for all test observations at once, and unavailable values are filled with NaN.\n", - "\t- Note 2: Recursive can also be combined with the MIMO strategy, allowing the model to predict model_horizon points ahead at each step.\n", + "\"multivariate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multi-point-ahead prediction strategies:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Представим, что у нас есть временной ряд:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Пусть на картинках у нас будет пример для следующих параметров (он не будет совпадать с примерами кода!):\n", "\n", - "- **Direct**:\n", - "\t- Individual models are trained for each point in the forecast horizon.\n", - "\t- Note 1: There is an option to use \"equal_train_size\" option, where all models can be trained on the same X_train set, formed for the last model predicting h point. Only the target variable (y) is updated for each model, reducing the time spent generating new training sets.\n", - "\t- Note 2: Direct can also be combined with MIMO, where each individual model predicts model_horizon points ahead.\n", + "$N=11$ (всего видим 11 точек)\n", "\n", - "- **MIMO (Multi-input-multi-output)**:\n", - " \t- One model is trained and used for the entire forecast horizon at once. \n", - "\t- Note 1: This strategy can also accommodate exogenous features (for local- or global-modelling strategies).\n", + "$history=7$ (для предсказания следующей точки используем 7 предыдущих)\n", "\n", - "- **FlatWideMIMO**:\n", - "\t- A hybrid of Direct and MIMO. One model is trained, but Direct’s features are deployed across the forecast horizon.\n", - "\t- Note 1: To use FlatWideMIMO with date-related features, h lags of them must be included (with help of LagTransformer)." + "$horizon=3$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**MIMO (Multi-input-multi-output)**:\n", + "- One model is trained and used for the entire forecast horizon at once. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"mimo\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## MIMO — global" + "- Note 1: This strategy can also accommodate exogenous features (for local- or global-modelling strategies). For more details on how to set exogenous features, refer to Tutorial 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's take the `MIMO-global` strategy as the base strategy, and from there, we will explore all the others." + "__global-modelling__" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -914,10 +1089,22620 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw_ts_X_for_training: (10000, 16)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date value\n", + "0 0 2022-09-27 1996.549771\n", + "1 0 2022-09-28 1994.363237\n", + "2 0 2022-09-29 1994.84152\n", + "3 0 2022-09-30 1991.156717\n", + "4 1 2022-09-27 2996.551933\n", + "5 1 2022-09-28 2994.365946\n", + "6 1 2022-09-29 2994.840157\n", + "7 1 2022-09-30 2991.131408\n", + "8 2 2022-09-27 3996.579405\n", + "9 2 2022-09-28 3994.364637" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_pred.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**- Note 1: There is an option to use \"equal_train_size\" option, where all models can be trained on the same X_train set, formed for the last model predicting h point. Only the target variable (y) is updated for each model, reducing the time spent generating new training sets.**" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline.easy_setup(dataset_params, pipeline_easy_params, multivariate=False)\n", + "\n", + "strategy = DirectStrategy(\n", + " horizon=horizon,\n", + " history=history,\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " equal_train_size=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------Training model for horizon 1------------------------------------\n", + "raw_ts_X_for_training: (10000, 16)\n", + "X for training: (9900, 28)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " id date value\n", + "0 0 2022-09-27 1993.583571\n", + "1 0 2022-09-28 1991.03587\n", + "2 0 2022-09-29 1991.952463\n", + "3 0 2022-09-30 1991.156717\n", + "4 1 2022-09-27 2993.558036\n", + "5 1 2022-09-28 2991.00616\n", + "6 1 2022-09-29 2991.926037\n", + "7 1 2022-09-30 2991.131408\n", + "8 2 2022-09-27 3993.548053\n", + "9 2 2022-09-28 3990.99943" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_pred.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**- Note 2: Direct can also be combined with MIMO, where each individual model predicts model_horizon points ahead.**" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline.easy_setup(dataset_params, pipeline_easy_params, multivariate=False)\n", + "\n", + "strategy = DirectStrategy(\n", + " horizon=horizon,\n", + " history=history,\n", + " pipeline=pipeline,\n", + " trainer=trainer,\n", + " model_horizon=2,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------Training model for horizon 1------------------------------------\n", + "raw_ts_X_for_training: (10000, 16)\n", + "X for training: (9920, 28)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ID FH date__season_y__standard_scaler date__season_m__standard_scaler \\\n", + "0 0.0 1.0 1.375134 0.870384 \n", + "1 0.0 2.0 1.375134 0.870384 \n", + "2 0.0 3.0 1.375134 0.870384 \n", + "3 0.0 4.0 1.375134 0.870384 \n", + "4 1.0 1.0 1.375134 0.870384 \n", + "\n", + " date__season_d__standard_scaler date__season_doy__standard_scaler \\\n", + "0 1.279197 0.979405 \n", + "1 1.393167 0.989333 \n", + "2 1.507137 0.999261 \n", + "3 1.621107 1.009188 \n", + "4 1.279197 0.979405 \n", + "\n", + " date__season_wd__standard_scaler value__standard_scaler__lag_6 \\\n", + "0 -0.998951 1.70868 \n", + "1 -0.498975 1.70868 \n", + "2 0.001000 1.70868 \n", + "3 0.500975 1.70868 \n", + "4 -0.998951 1.70868 \n", + "\n", + " value__standard_scaler__lag_5 value__standard_scaler__lag_4 \\\n", + "0 1.712142 1.715605 \n", + "1 1.712142 1.715605 \n", + "2 1.712142 1.715605 \n", + "3 1.712142 1.715605 \n", + "4 1.712142 1.715605 \n", + "\n", + " value__standard_scaler__lag_3 value__standard_scaler__lag_2 \\\n", + "0 1.719067 1.72253 \n", + "1 1.719067 1.72253 \n", + "2 1.719067 1.72253 \n", + "3 1.719067 1.72253 \n", + "4 1.719067 1.72253 \n", + "\n", + " value__standard_scaler__lag_1 value__standard_scaler__lag_0 \n", + "0 1.725992 1.729454 \n", + "1 1.725992 1.729454 \n", + "2 1.725992 1.729454 \n", + "3 1.725992 1.729454 \n", + "4 1.725992 1.729454 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "forecast_time, current_pred = strategy.predict(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatevalue
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102022-09-281996.039777
202022-09-291996.568991
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" + ], + "text/plain": [ + " id date value\n", + "0 0 2022-09-27 1995.401964\n", + "1 0 2022-09-28 1996.039777\n", + "2 0 2022-09-29 1996.568991\n", + "3 0 2022-09-30 1992.505841\n", + "4 1 2022-09-27 2995.402058\n", + "5 1 2022-09-28 2996.03987\n", + "6 1 2022-09-29 2996.569085\n", + "7 1 2022-09-30 2992.505934\n", + "8 2 2022-09-27 3995.404626\n", + "9 2 2022-09-28 3996.042439" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_pred.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that an additional column, `FH`, appears in the feature matrix, indicating how many steps ahead the model needs to predict." + ] } ], "metadata": { diff --git a/imgs/direct.png b/imgs/direct.png new file mode 100644 index 0000000000000000000000000000000000000000..a146b6a4b51160c3fc9fbb800f3131e52b18eb80 GIT binary patch literal 321773 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date_lags (optional, default False): list of lags for date - exog_lags (optional, deafult False): list of lags for exogenous features - - target_normalizer (optional, default none): type of target normalizer + - target_normalizer (optional, default standard_scaler): type of target normalizer (none, standard_scaler, difference_normalizer, last_known_normalizer) - target_normalizer_regime (optional, default none): regime of target normalizer (none, delta, ratio) @@ -119,7 +119,7 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " if "exog_lags" not in pipeline_params: pipeline_params["exog_lags"] = False if "target_normalizer" not in pipeline_params: - pipeline_params["target_normalizer"] = "none" + pipeline_params["target_normalizer"] = "standard_scaler" if "target_normalizer_regime" not in pipeline_params: pipeline_params["target_normalizer_regime"] = "none" @@ -194,6 +194,10 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " current_sequential_transformers_list.append(target_union) current_sequential_transformers_list.append(target_normalizer) + elif pipeline_params["target_normalizer"] == "none": + current_sequential_transformers_list.append(target_union) + current_sequential_transformers_list.append(target_lag) + else: assert ( pipeline_params["target_normalizer"] == "none" @@ -212,6 +216,7 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " { "transform_features": True, "transform_target": False, + "agg_by_id": False, }, ) date_lag = transormers_factory.create_transformer( @@ -229,6 +234,7 @@ def easy_setup(cls, roles: dict, pipeline_params: dict, multivariate: bool) -> " { "transform_features": True, "transform_target": False, + "agg_by_id": False, }, ) id_lag = transormers_factory.create_transformer("LagTransformer", {"lags": 1}) diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 334048a..4bc4ac8 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -112,6 +112,8 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: models_preds = [model.predict(X) for model in self.models] y_pred = np.mean(models_preds, axis=0) + y_pred = y_pred.reshape(pipeline.y_original_shape) + return y_pred From a853e81cf08aec16b31c35609539de630a43c1e4 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 18 Oct 2024 15:47:11 +0300 Subject: [PATCH 24/50] bug fixes: imputer, flatwidemimo_mult --- tsururu/dataset/pipeline.py | 29 ++++++++++++++++++++++----- tsururu/models/torch_based/dlinear.py | 2 +- tsururu/transformers/impute.py | 11 +++++++--- 3 files changed, 33 insertions(+), 9 deletions(-) diff --git a/tsururu/dataset/pipeline.py b/tsururu/dataset/pipeline.py index 992db04..f60f34e 100644 --- a/tsururu/dataset/pipeline.py +++ b/tsururu/dataset/pipeline.py @@ -362,10 +362,22 @@ def from_mimo_to_flatwidemimo(self, data: dict) -> dict: id_count = len( data["raw_ts_X"].iloc[data["idx_X"][:, 0]][data["id_column_name"]].value_counts() ) - direct_lag_index_dict["ID"] = np.repeat( - np.arange(id_count), - repeats=(len(X) / id_count * len(fh_array)), - ) + + if self.multivariate: + direct_lag_index_dict["ID"] = np.repeat( + np.arange(id_count), + repeats=(len(X) / id_count * len(fh_array)), + ) + else: + unique_id = np.unique([tuple(x) for x in X.loc[:, id_features_mask].values], axis=0, return_index=1) + sort_unique_id = unique_id[0][np.argsort(unique_id[1])] + + for id_idx, id_feature in enumerate(X.loc[:, id_features_mask].columns): + direct_lag_index_dict[id_feature] = np.repeat( + sort_unique_id[:, id_idx], + repeats=(len(X) / id_count * len(fh_array)), + ) + direct_lag_index_dict["FH"] = np.tile(fh_array, len(X.index)) direct_lag_index_df = pd.DataFrame(direct_lag_index_dict) @@ -439,7 +451,13 @@ def _make_multivariate_X_y(self, data: dict) -> dict: date_features_idx = index_slicer.get_cols_idx(X, date_features_colname) other_features_idx = index_slicer.get_cols_idx(X, other_features_colname) - segments_ids = np.append(np.unique(X[id_features_colname], return_index=1)[1], len(X)) + segments_ids = np.append( + np.unique([tuple(x) for x in X[id_features_colname].values], axis=0, return_index=1)[ + 1 + ], + len(X), + ) + segments_ids = np.sort(segments_ids) segments_ids_array = np.array( [ np.arange(segments_ids[segment_id - 1], segments_ids[segment_id]) @@ -502,6 +520,7 @@ def _make_multivariate_X_y_flatwidemimo(self, data: dict) -> dict: fh_feature_idx = index_slicer.get_cols_idx(X, fh_feature_colname) segments_ids = np.append(np.unique(X[id_feature_colname], return_index=1)[1], len(X)) + segments_ids = np.sort(segments_ids) segments_ids_array = np.array( [ np.arange(segments_ids[segment_id - 1], segments_ids[segment_id]) diff --git a/tsururu/models/torch_based/dlinear.py b/tsururu/models/torch_based/dlinear.py index 4807de1..3292d83 100644 --- a/tsururu/models/torch_based/dlinear.py +++ b/tsururu/models/torch_based/dlinear.py @@ -28,7 +28,7 @@ def __init__( self, seq_len: int, pred_len: int, - moving_avg: int = 7, + moving_avg: int = 25, individual: bool = False, enc_in: int = 1, ): diff --git a/tsururu/transformers/impute.py b/tsururu/transformers/impute.py index adea523..1e1d0ef 100644 --- a/tsururu/transformers/impute.py +++ b/tsururu/transformers/impute.py @@ -82,9 +82,14 @@ def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Seri segment.loc[:, self.output_features[i]] ) # Fill remaining missing values with constant value - segment.loc[:, self.output_features[i]] = segment.loc[:, self.output_features[i]].fillna( - self.constant_value - ) + if self.output_features[i] in segment.columns: + segment.loc[:, self.output_features[i]] = segment.loc[:, self.output_features[i]].fillna( + self.constant_value + ) + else: + segment.loc[:, self.output_features[i]] = segment.loc[:, column_name].fillna( + self.constant_value + ) return segment From 6de5b4e4ddb4cab96a8159121daad368753db7b3 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 18 Oct 2024 16:00:33 +0300 Subject: [PATCH 25/50] add all_configurations example --- .gitignore | 3 +- README.md | 9 +- examples/Example_1_All_configurations.py | 533 ++++++++++++++++++ .../Tutorial_1_Quick_start.ipynb | 0 .../Tutorial_2_Strategies.ipynb | 12 +- ...Tutorial_3_Transformers_and_Pipeline.ipynb | 0 .../Tutorial_4_Neural_Networks.ipynb | 0 7 files changed, 548 insertions(+), 9 deletions(-) create mode 100644 examples/Example_1_All_configurations.py rename Tutorial_1_Quick_start.ipynb => examples/Tutorial_1_Quick_start.ipynb (100%) rename Tutorial_2_Strategies.ipynb => examples/Tutorial_2_Strategies.ipynb (99%) rename Tutorial_3_Transformers_and_Pipeline.ipynb => examples/Tutorial_3_Transformers_and_Pipeline.ipynb (100%) rename Tutorial_4_Neural_Networks.ipynb => examples/Tutorial_4_Neural_Networks.ipynb (100%) diff --git a/.gitignore b/.gitignore index 82ab42a..9c3f8d1 100644 --- a/.gitignore +++ b/.gitignore @@ -152,4 +152,5 @@ poetry.lock *check*.ipynb *temp* *test* -*checkpoints* \ No newline at end of file +*checkpoints* +*logs* diff --git a/README.md b/README.md index 4188dd9..fe8ed21 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,11 @@ To install tsururu via pip you can use: For usage example please see: -* [Tutorial_1_Quick_Start](https://github.com/sb-ai-lab/tsururu/blob/main/Tutorial_1_Quick_start.ipynb) for simple usage examples +* [Tutorial_1_Quick_Start](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_1_Quick_start.ipynb) for simple usage examples -More examples are coming soon. +## Other tutorials and examples + +* [Tutorial_2_Strategies](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_2_Strategies.ipynb) covers forecasting strategies. +* [Tutorial_3_Transformers_and_Pipeline](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_3_Transformers_and_Pipeline.ipynb) provides a description of available data preprocessing techniques. +* [Tutorial_4_Neural_Networks](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_4_Neural_Networks.ipynb) demonstrates working with neural networks. +* [Example_1_All_configurations](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Example_1_All_configurations.py) script for benchmarking multiple configurations from available strategies, models and preprocessing methods on a dataset. diff --git a/examples/Example_1_All_configurations.py b/examples/Example_1_All_configurations.py new file mode 100644 index 0000000..74f56df --- /dev/null +++ b/examples/Example_1_All_configurations.py @@ -0,0 +1,533 @@ +import argparse +import os +import glob +import random +import re +import sys +import warnings +from itertools import product +from pathlib import Path + +import numpy as np +import pandas as pd +import torch +import torch.optim.lr_scheduler as lr_scheduler +from sklearn.metrics import ( + mean_absolute_error, + mean_absolute_percentage_error, + root_mean_squared_error, +) +from tqdm import tqdm + +from tsururu.dataset import Pipeline, TSDataset +from tsururu.model_training.trainer import DLTrainer, MLTrainer +from tsururu.model_training.validator import KFoldCrossValidator +from tsururu.models.boost import CatBoost +from tsururu.models.torch_based.dlinear import DLinear_NN +from tsururu.strategies import ( + DirectStrategy, + FlatWideMIMOStrategy, + MIMOStrategy, + RecursiveStrategy, +) + +warnings.filterwarnings("ignore") + +################################# +# Constants +################################# + +CURRENT_DIR = Path(__file__).parent + +# Data +DATE_COLUMN = "date" +ID_COLUMN = "id" +TARGET_COLUMN = "value" + +# Forecasting task +HISTORY = 52 +HORIZON = 4 +STEP = 1 + +VALIDATION = KFoldCrossValidator +VALIDATION_PARAMS = {"n_splits": 3} + +# Optimization +BATCH_SIZE = 32 +LEARNING_RATE = 0.05 + +# Model, Strategy, Preprocessing +MODELS = [ + (DLinear_NN, {"moving_avg": 25, "individual": False, "enc_in": 7}, DLTrainer), + (CatBoost, {}, MLTrainer), +] + +STRATEGIES_OVER_TIME = [ +# "RecursiveStrategy", +# "DirectStrategy", +# "MIMOStrategy", + "FlatWideMIMOStrategy", +] +MULTIVARIATE = [ +# True, + False +] + +DATE_FEATURES = [ + "with_normalization", +# "with_normalization_over_all", +# "with_normalization_by_id", +# False, +] + +ID_FEATURES = [ + "with_ohe", +# "with_le_normalization_over_all", +# False, +] + +INDIVIDUAL_MODEL_HORIZON = [None, 1, 2] + +TRANSFORMERS = { + None, +# "StandardScalerTransformer", +# "DifferenceNormalizer", +# "LastKnownNormalizer", +} +TRANSFORMERS_REGIMES = [None, "delta", "ratio"] +TRANSFORM_TARGET = [True, False] +TRANSFORM_FEATURES = [True, False] + + +# Training on GPU / CPU +cuda_device_available = torch.cuda.is_available() +cuda_device_number = 0 +if cuda_device_available: + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" + os.environ["CUDA_VISIBLE_DEVICES"] = str(cuda_device_number) + device = torch.device(f"cuda:{cuda_device_number}") +else: + device = torch.device("cpu") + + +def lradj(epoch): + if epoch < 3: + return 1 + else: + return 0.9 ** ((epoch - 3) // 1) + + +def get_metrics(x): + res_dict = { + "mae": mean_absolute_error(x[TARGET_COLUMN], x["y_pred"]), + "rmse": root_mean_squared_error(x[TARGET_COLUMN], x["y_pred"]), + "mape": mean_absolute_percentage_error(x[TARGET_COLUMN], x["y_pred"]), + } + return pd.Series(res_dict) + + +def seed_everything(seed=42): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def main(): + seed_everything() + + ################################# + # 0. CLI parameters + ################################# + + parser = argparse.ArgumentParser( + description="Check all strategies, regimes and preprocessings." + ) + + parser.add_argument( + "--df_path", + type=Path, + default=Path("datasets/global/simulated_data_to_check.csv"), + help="Path to the dataframe CSV file.", + ) + parser.add_argument( + "--log_path", + type=Path, + default=Path("./logs/all_configuraion.txt"), + help="Path to the log file.", + ) + parser.add_argument( + "--results_path", + type=Path, + default=Path("./results/"), + help="Path to the results file.", + ) + + args = parser.parse_args() + + args.df_path.parent.mkdir(parents=True, exist_ok=True) + args.log_path.parent.mkdir(parents=True, exist_ok=True) + args.results_path.parent.mkdir(parents=True, exist_ok=True) + + ################################# + # 1. dataset, pipeline, model, validation -> trainer + ################################# + + df = pd.read_csv(args.df_path) + df[DATE_COLUMN] = pd.to_datetime(df[DATE_COLUMN]) + + train_test_date_split = df.loc[df[ID_COLUMN] == 0, DATE_COLUMN].values[-HORIZON] + train = df.loc[df[DATE_COLUMN] < train_test_date_split] + test = df.loc[df[DATE_COLUMN] >= train_test_date_split] + + dataset_params = { + "target": { + "columns": [TARGET_COLUMN], + "type": "continious", + }, + "date": { + "columns": [DATE_COLUMN], + "type": "datetime", + }, + "id": { + "columns": [ID_COLUMN], + "type": "categorical", + }, + } + + dataset = TSDataset( + data=train, + columns_params=dataset_params, + ) + + pipeline_params = { + "target": { + "columns": [TARGET_COLUMN], + "features": {}, + } + } + + optimizer_params = { + "lr": LEARNING_RATE, + } + + sch, sch_params = lr_scheduler.LambdaLR, {"lr_lambda": lradj} + + dl_trainer_params = { + "device": device, + "num_workers": 0, + "best_by_metric": True, + "batch_size": BATCH_SIZE, + "optimizer_params": optimizer_params, + "scheduler": sch, + "scheduler_params": sch_params, + "save_k_best": 1, + "save_to_dir": False, + } + ml_trainer_params = {} + + ################################# + # 2. Loop through model, strategies, regimes, preprocessings + ################################# + + df_list = [] + + sys.stdout = open(args.log_path, "a") + + for ( + model, + strategy_over_time, + multivariate, + date_features, + id_features, + model_horizon, + transformer, + transformer_regime, + transform_target, + transform_features, + ) in tqdm( + product( + MODELS, + STRATEGIES_OVER_TIME, + MULTIVARIATE, + DATE_FEATURES, + ID_FEATURES, + INDIVIDUAL_MODEL_HORIZON, + TRANSFORMERS, + TRANSFORMERS_REGIMES, + TRANSFORM_TARGET, + TRANSFORM_FEATURES, + ), + total=( + len(MODELS) + * len(STRATEGIES_OVER_TIME) + * len(MULTIVARIATE) + * len(DATE_FEATURES) + * len(ID_FEATURES) + * len(INDIVIDUAL_MODEL_HORIZON) + * len(TRANSFORMERS) + * len(TRANSFORMERS_REGIMES) + * len(TRANSFORM_TARGET) + * len(TRANSFORM_FEATURES) + ), + ): + # Cases when we skip the combination + if ( + strategy_over_time == "MIMOStrategy" or strategy_over_time == "FlatWideMIMOStrategy" + ) and model_horizon is not None: + continue + + if ( + strategy_over_time == "RecursiveStrategy" or strategy_over_time == "DirectStrategy" + ) and model_horizon is None: + continue + + if strategy_over_time == "FlatWideMIMOStrategy" and date_features is False: + continue + + if multivariate is True and id_features is not False: + continue + + if transformer == "StandardScalerTransformer" or transformer is None: + if ( + transformer_regime is not None + or transform_target is not False + or transform_features is not False + ): + continue + else: + if ( + transformer_regime is None + or transform_target is False + and transform_features is False + ): + continue + + print( + f""" + Model: {model[0].__name__} + Strategy: {strategy_over_time} + Multivariate: {multivariate} + Date features: {date_features} + ID features: {id_features} + Model horizon: {model_horizon} + Transformer: {transformer} + Transformer regime: {transformer_regime} + Transform target: {transform_target} + Transform features: {transform_features} + """ + ) + + model_name = ( + f"model_{model[0].__name__}/strategy_time_{strategy_over_time}__mult_{multivariate}/" + f"dataset_{args.df_path.stem}__hist_{HISTORY}__hor_{HORIZON}__model_hor_{model_horizon}/" + f"datetime_{date_features}__id_{id_features}__" + f"transformer_{transformer}__regime_{transformer_regime}__" + f"tr_target_{transform_target}__tr_features_{transform_features}" + ) + + ################################# + # 4. pipeline -> trainer + ################################# + pipeline_params = { + "target": { + "columns": ["value"], + "features": {}, + } + } + + # Change pipeline params and initialize pipeline + # Target + if transformer == "StandardScalerTransformer": + pipeline_params["target"]["features"] = { + transformer: { + "transform_features": transform_features, + "transform_target": transform_target, + }, + "LagTransformer": {"lags": HISTORY}, + } + elif transformer == "DifferenceNormalizer": + pipeline_params["target"]["features"] = { + transformer: { + "transform_features": transform_features, + "transform_target": transform_target, + "regime": transformer_regime, + }, + "MissingValueImputer": { + "constant_value": 0, + "transform_features": transform_features, + "transform_target": transform_target, + }, + "LagTransformer": {"lags": HISTORY}, + } + elif transformer == "LastKnownNormalizer": + pipeline_params["target"]["features"] = { + "LagTransformer": {"lags": HISTORY}, + transformer: { + "transform_features": transform_features, + "transform_target": transform_target, + "regime": transformer_regime, + }, + } + else: + pipeline_params["target"]["features"] = {"LagTransformer": {"lags": HISTORY}} + + # Date + date_features_dict = { + "DateSeasonsGenerator": { + "seasonalities": ["doy", "m", "wd", "hour"], + "from_target_date": True, + }, + "MissingValueImputer": { + "constant_value": 0, + "transform_features": True, + "transform_target": False, + }, + } + + if date_features == "without_normalization": + scaler = None + elif date_features == "with_normalization_over_all": + scaler = { + "StandardScalerTransformer": { + "transform_features": True, + "transform_target": False, + "agg_by_id": False, + } + } + else: + pipeline_params.pop("date", None) + scaler = None + + if scaler is not None: + lags_value = HORIZON if strategy_over_time == "FlatWideMIMOStrategy" else 1 + date_features = { + **date_features_dict, + **scaler, + "LagTransformer": {"lags": lags_value}, + } + pipeline_params["date"] = {"columns": ["date"], "features": date_features} + + # ID + if id_features == "with_ohe": + pipeline_params["id"] = { + "columns": ["id"], + "features": { + "OneHotEncodingTransformer": { + "drop": "first", + }, + "LagTransformer": {"lags": 1}, + }, + } + elif id_features == "with_le_normalization_over_all": + pipeline_params["id"] = { + "columns": ["id"], + "features": { + "LabelEncodingTransformer": {}, + "StandardScalerTransformer": { + "transform_features": True, + "transform_target": False, + "agg_by_id": False, + }, + "LagTransformer": {"lags": 1}, + }, + } + elif id_features == "with_le_normalization_by_id": + pipeline_params["id"] = { + "columns": ["id"], + "features": { + "LabelEncodingTransformer": {}, + "StandardScalerTransformer": { + "transform_features": True, + "transform_target": False, + "agg_by_id": True, + }, + "LagTransformer": {"lags": 1}, + }, + } + else: + if "id" in pipeline_params: + del pipeline_params["id"] + + pipeline = Pipeline.from_dict(pipeline_params, multivariate=multivariate) + + model, model_params, trainer = model + + if trainer == MLTrainer: + trainer = trainer(model, model_params, VALIDATION, VALIDATION_PARAMS, **ml_trainer_params) + else: + trainer = trainer(model, model_params, VALIDATION, VALIDATION_PARAMS, **dl_trainer_params) + + try: + if strategy_over_time == "RecursiveStrategy": + strategy = RecursiveStrategy( + HORIZON, HISTORY, trainer, pipeline, STEP, model_horizon=model_horizon + ) + elif strategy_over_time == "DirectStrategy": + strategy = DirectStrategy( + HORIZON, HISTORY, trainer, pipeline, STEP, model_horizon=model_horizon + ) + elif strategy_over_time == "MIMOStrategy": + strategy = MIMOStrategy(HORIZON, HISTORY, trainer, pipeline, STEP) + elif strategy_over_time == "FlatWideMIMOStrategy": + strategy = FlatWideMIMOStrategy(HORIZON, HISTORY, trainer, pipeline, STEP) + + fit_time, _ = strategy.fit(dataset) + forecast_time, current_pred = strategy.predict(dataset) + current_pred = current_pred.rename(columns={TARGET_COLUMN: "y_pred"}) + current_pred = current_pred.merge(test, on=[DATE_COLUMN, ID_COLUMN]) + + (args.results_path / model_name).parent.mkdir(parents=True, exist_ok=True) + + current_pred.to_csv( + os.path.join(args.results_path / f"{model_name}_pred.csv"), index=False + ) + + metrics = current_pred.groupby(ID_COLUMN).apply(lambda x: get_metrics(x)).reset_index() + metrics.loc["mean"] = metrics.mean() + metrics.loc["fit_time"] = fit_time + metrics.loc["forecast_time"] = forecast_time + + metrics = metrics.reset_index() + metrics = metrics.drop(columns=[ID_COLUMN]) + metrics = metrics.rename(columns={"index": ID_COLUMN}) + + metrics.to_csv( + os.path.join(args.results_path / f"{model_name}_metrics.csv"), index=False + ) + print("Success!") + + except: + print("Fail!") + + # Aggregate results + print("Aggregating results...") + for file in glob.glob(str(args.results_path / "*/*/*/*.csv")): + if file.endswith("_metrics.csv"): + current_df = pd.read_csv(file) + current_df["model"] = re.search(r"model_(.*?)\/", file).group(1) + current_df["strategy_over_time"] = re.search(r"strategy_time_(.*?)__", file).group(1) + current_df["multivariate"] = re.search( + r"mult_(.*?)\/", file + ).group(1) + current_df["date_features"] = re.search(r"datetime_(.*?)__", file).group(1) + current_df["id_features"] = re.search(r"id_(.*?)__", file).group(1) + current_df["model_horizon"] = re.search(r"model_hor_(.*?)\/", file).group(1) + current_df["transformer"] = re.search(r"transformer_(.*?)__", file).group(1) + current_df["transformer_regime"] = re.search(r"regime_(.*?)__", file).group(1) + current_df["transform_target"] = re.search(r"tr_target_(.*?)__", file).group(1) + current_df["transform_features"] = re.search(r"tr_features_(.*?)_", file).group(1) + df_list.append(current_df) + + df = pd.concat(df_list) + df.to_csv(args.results_path / "all_results.csv", index=False) + + sys.stdout = sys.__stdout__ + + +if __name__ == "__main__": + main() diff --git a/Tutorial_1_Quick_start.ipynb b/examples/Tutorial_1_Quick_start.ipynb similarity index 100% rename from Tutorial_1_Quick_start.ipynb rename to examples/Tutorial_1_Quick_start.ipynb diff --git a/Tutorial_2_Strategies.ipynb b/examples/Tutorial_2_Strategies.ipynb similarity index 99% rename from Tutorial_2_Strategies.ipynb rename to examples/Tutorial_2_Strategies.ipynb index 496f78c..880d671 100644 --- a/Tutorial_2_Strategies.ipynb +++ b/examples/Tutorial_2_Strategies.ipynb @@ -966,7 +966,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"local" + "\"local" ] }, { @@ -982,7 +982,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"global" + "\"global" ] }, { @@ -998,7 +998,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"multivariate" + "\"multivariate" ] }, { @@ -1019,7 +1019,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"time" + "\"time" ] }, { @@ -1039,7 +1039,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"time" + "\"time" ] }, { @@ -1054,7 +1054,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"mimo\"" + "\"mimo\"" ] }, { diff --git a/Tutorial_3_Transformers_and_Pipeline.ipynb b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb similarity index 100% rename from Tutorial_3_Transformers_and_Pipeline.ipynb rename to examples/Tutorial_3_Transformers_and_Pipeline.ipynb diff --git a/Tutorial_4_Neural_Networks.ipynb b/examples/Tutorial_4_Neural_Networks.ipynb similarity index 100% rename from Tutorial_4_Neural_Networks.ipynb rename to examples/Tutorial_4_Neural_Networks.ipynb From 79e944ef626fd47945d2e9f89ee84033d5856e88 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Wed, 27 Nov 2024 21:54:41 +0300 Subject: [PATCH 26/50] Small changes in __init__ files --- tsururu/__init__.py | 2 +- tsururu/model_training/__init__.py | 5 +++++ tsururu/model_training/torch_based/__init__.py | 3 --- tsururu/models/__init__.py | 8 +++++++- tsururu/models/torch_based/__init__.py | 5 ----- 5 files changed, 13 insertions(+), 10 deletions(-) create mode 100644 tsururu/model_training/__init__.py diff --git a/tsururu/__init__.py b/tsururu/__init__.py index 77b2c24..8da31f5 100644 --- a/tsururu/__init__.py +++ b/tsururu/__init__.py @@ -1 +1 @@ -__all__ = ["dataset", "transformers", "models", "strategies", "validation", "torch_based"] +__all__ = ["dataset", "transformers", "models", "strategies", "model_training"] diff --git a/tsururu/model_training/__init__.py b/tsururu/model_training/__init__.py new file mode 100644 index 0000000..f66766d --- /dev/null +++ b/tsururu/model_training/__init__.py @@ -0,0 +1,5 @@ +from .torch_based import metrics +from .torch_based.data_provider import Dataset_NN +from .torch_based import callbacks + +__all__ = ["Dataset_NN", "metrics", "callbacks"] \ No newline at end of file diff --git a/tsururu/model_training/torch_based/__init__.py b/tsururu/model_training/torch_based/__init__.py index d003746..e69de29 100644 --- a/tsururu/model_training/torch_based/__init__.py +++ b/tsururu/model_training/torch_based/__init__.py @@ -1,3 +0,0 @@ -from .data_provider import Dataset_NN - -__all__ = ["Dataset_NN"] diff --git a/tsururu/models/__init__.py b/tsururu/models/__init__.py index cfd6123..bf03385 100644 --- a/tsururu/models/__init__.py +++ b/tsururu/models/__init__.py @@ -2,6 +2,9 @@ from .base import Estimator from .boost import CatBoost +from .torch_based.dlinear import DLinear_NN +from .torch_based.patch_tst import PatchTST_NN +from .torch_based.times_net import TimesNet_NN # Factory Object @@ -9,6 +12,9 @@ class ModelsFactory: def __init__(self): self.models = { "CatBoost": CatBoost, + "DLinear_NN": DLinear_NN, + "PatchTST_NN": PatchTST_NN, + "TimesNet_NN": TimesNet_NN, } def get_allowed(self): @@ -24,4 +30,4 @@ def create_model(self, model_name, model_params): return self.models[model_name](**model_params) -__all__ = ["CatBoost", "Estimator", "ModelsFactory"] +__all__ = ["CatBoost", "Estimator", "ModelsFactory", "DLinear_NN", "PatchTST_NN", "TimesNet_NN"] diff --git a/tsururu/models/torch_based/__init__.py b/tsururu/models/torch_based/__init__.py index dd826bc..e69de29 100644 --- a/tsururu/models/torch_based/__init__.py +++ b/tsururu/models/torch_based/__init__.py @@ -1,5 +0,0 @@ -"""NN algorithms for time series forecasting.""" - -from .dlinear import DLinear_NN - -__all__ = ["Dlinear_NN"] From 8e9e38dfe6a3199c0fee76571aca2634332c21bd Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 13 Dec 2024 11:45:37 +0300 Subject: [PATCH 27/50] logging + new asserts --- tsururu/__init__.py | 13 ++ tsururu/dataset/dataset.py | 51 ++++- tsururu/dataset/pipeline.py | 56 ++--- .../model_training/torch_based/callbacks.py | 23 ++- tsururu/model_training/trainer.py | 61 ++++-- tsururu/models/boost.py | 7 + .../torch_based/layers/positional_encoding.py | 3 - tsururu/models/torch_based/layers/utils.py | 6 - tsururu/strategies/base.py | 22 ++ tsururu/strategies/direct.py | 2 + tsururu/strategies/recursive.py | 6 + tsururu/utils/__init__.py | 1 + tsururu/utils/logging.py | 193 ++++++++++++++++++ 13 files changed, 377 insertions(+), 67 deletions(-) create mode 100644 tsururu/utils/__init__.py create mode 100644 tsururu/utils/logging.py diff --git a/tsururu/__init__.py b/tsururu/__init__.py index 8da31f5..43e2605 100644 --- a/tsururu/__init__.py +++ b/tsururu/__init__.py @@ -1 +1,14 @@ +import logging +import sys + +_root_logger = logging.getLogger() +_logger = logging.getLogger(__name__) +_logger.setLevel("DEBUG") + +# if root logger has handlers, propagate messages up and let root logger process them +if not _root_logger.hasHandlers(): + _logger.addHandler(logging.StreamHandler(sys.stdout)) + _logger.propagate = False + + __all__ = ["dataset", "transformers", "models", "strategies", "model_training"] diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index 6898e06..b7523b6 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -9,6 +9,10 @@ slicer = IndexSlicer() +import logging + +logger = logging.getLogger(__name__) + class TSDataset: """Class for initializing data from pandas DataFrame. @@ -54,23 +58,51 @@ def _check_single_column(self): len(self.columns_params[role]["columns"]) == 1 ), f"the `columns` container for role {role} should contain only one column" - def _print_freq_period_info(self): - """Print the frequency and period information for data for - validation purposes. + def _check_regular(self, print_freq_period_info): + """Check that the data is regular. + + Raises: + AssertionError: if the data is not regular. """ - info = slicer.timedelta( - self.data[self.date_column], delta=self.delta, return_freq_period_info=True - )[2] + ts_count = self.data.loc[:, self.id_column].nunique() + + _, delta, info = slicer.timedelta( + self.data[self.date_column], self.delta, return_freq_period_info=True + ) - print(info) + if print_freq_period_info: + logger.info(info) + + # Try to reconstruct regular data + min_data = self.data.min() + max_data = self.data.max() + + reconstructed_data = pd.date_range( + start=min_data[self.date_column], + end=max_data[self.date_column], + freq=delta, + ) + reconstructed_data = np.tile(reconstructed_data, ts_count) + + if ( + reconstructed_data.shape[0] != self.data.shape[0] + or not np.all(reconstructed_data == self.data[self.date_column].values) + ): + logger.warning( + f""" + It seems that the data is not regular. Please, check the data and the frequency info. + For multivariate regime it is critical to have regular data. + For global regime each regular part of time series will be processed as separate time series. + """ + ) def __init__( self, data: pd.DataFrame, columns_params: dict, delta: pd.DateOffset = None, - print_freq_period_info: bool = False, + print_freq_period_info: bool = True, ): # Columns typing for _, role_dict in columns_params.items(): @@ -93,8 +125,7 @@ def __init__( self.data = data.sort_values([self.id_column, self.date_column]) - if print_freq_period_info: - self._print_freq_period_info() + self._check_regular(print_freq_period_info) def _crop_segment( self, diff --git a/tsururu/dataset/pipeline.py b/tsururu/dataset/pipeline.py index f60f34e..8862fe5 100644 --- a/tsururu/dataset/pipeline.py +++ b/tsururu/dataset/pipeline.py @@ -61,6 +61,10 @@ def from_dict(cls, columns_params: dict, multivariate: bool) -> "Pipeline": transformers_dict = columns_params["features"] for transformer_name, transformer_params in transformers_dict.items(): + assert ( + role != "target" and transformer_params.get("transform_target", False) + ) is False, "It is not possible to use transform_target=True with transformers for exogenous variables" + if transformer_name == "LagTransformer" and role == "target": features_transformer = transormers_factory.create_transformer( transformer_name, transformer_params @@ -73,6 +77,7 @@ def from_dict(cls, columns_params: dict, multivariate: bool) -> "Pipeline": transformer = transormers_factory.create_transformer( transformer_name, transformer_params ) + current_sequential_transformers_list.append(transformer) result_union_transformers_list.append( @@ -362,16 +367,18 @@ def from_mimo_to_flatwidemimo(self, data: dict) -> dict: id_count = len( data["raw_ts_X"].iloc[data["idx_X"][:, 0]][data["id_column_name"]].value_counts() ) - + if self.multivariate: direct_lag_index_dict["ID"] = np.repeat( np.arange(id_count), repeats=(len(X) / id_count * len(fh_array)), ) - else: - unique_id = np.unique([tuple(x) for x in X.loc[:, id_features_mask].values], axis=0, return_index=1) + else: + unique_id = np.unique( + [tuple(x) for x in X.loc[:, id_features_mask].values], axis=0, return_index=1 + ) sort_unique_id = unique_id[0][np.argsort(unique_id[1])] - + for id_idx, id_feature in enumerate(X.loc[:, id_features_mask].columns): direct_lag_index_dict[id_feature] = np.repeat( sort_unique_id[:, id_idx], @@ -390,29 +397,32 @@ def from_mimo_to_flatwidemimo(self, data: dict) -> dict: new_date_features[i::horizon, :] = X.loc[:, date_features_mask].values[ :, i::horizon ] - except ValueError: - raise ValueError( - "Something is wrong while making FlatWideMIMO strategy's X. Check that you use number of lags equal to horizon for datetime features!" + + # get unique date feature names without lag suffix + date_feature_names = ( + X.columns[date_features_mask].str.replace("__lag_\d+$", "", regex=True).unique() ) - # get unique date feature names without lag suffix - date_feature_names = ( - X.columns[date_features_mask].str.replace("__lag_\d+$", "", regex=True).unique() - ) + features_df = pd.DataFrame( + np.repeat( + X.loc[:, ~id_features_mask & ~date_features_mask].values, horizon, axis=0 + ), + columns=X.loc[:, ~id_features_mask & ~date_features_mask].columns, + ) - features_df = pd.DataFrame( - np.repeat(X.loc[:, ~id_features_mask & ~date_features_mask].values, horizon, axis=0), - columns=X.loc[:, ~id_features_mask & ~date_features_mask].columns, - ) + X = pd.concat( + [ + direct_lag_index_df, + pd.DataFrame(new_date_features, columns=date_feature_names), + features_df, + ], + axis=1, + ) - X = pd.concat( - [ - direct_lag_index_df, - pd.DataFrame(new_date_features, columns=date_feature_names), - features_df, - ], - axis=1, - ) + except ValueError: + raise ValueError( + "Something is wrong while making FlatWideMIMO strategy's X. Check that you use number of lags equal to horizon for datetime features!" + ) data["X"] = X.values diff --git a/tsururu/model_training/torch_based/callbacks.py b/tsururu/model_training/torch_based/callbacks.py index cdd9023..5d84724 100644 --- a/tsururu/model_training/torch_based/callbacks.py +++ b/tsururu/model_training/torch_based/callbacks.py @@ -1,6 +1,7 @@ """Module for callbacks used in training process.""" import heapq +import logging import os from pathlib import Path from typing import Optional @@ -11,6 +12,9 @@ torch = None +logger = logging.getLogger(__name__) + + class Callback: """Base class for callbacks, that are used in training process.""" @@ -249,10 +253,11 @@ def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): if scheduler_state: torch.save(scheduler_state, sch_path) if self.verbose: - print(f"Last epoch model saved to {model_path}") - print(f"Last epoch optimizer saved to {opt_path}") + logger.info(f"Last epoch model saved to {model_path}") + logger.info(f"Last epoch optimizer saved to {opt_path}") + if scheduler_state: - print(f"Last epoch scheduler saved to {sch_path}") + logger.info(f"Last epoch scheduler saved to {sch_path}") # Save top-k best snapshots if self.save_k_best > 0 and self._should_save_checkpoint(current_score): @@ -263,7 +268,7 @@ def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): self._safe_remove(worst_snapshot[1]["optimizer"]) self._safe_remove(worst_snapshot[1]["scheduler"]) if self.verbose: - print( + logger.info( f"Removing worst model snapshot: from epoch {worst_snapshot[1]['epoch']}" ) @@ -282,7 +287,7 @@ def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): self._update_worst_best_score() if self.save_to_dir: if self.verbose: - print(f"Best model snapshot saved to {model_path}") + logger.info(f"Best model snapshot saved to {model_path}") # Early stopping logic if self.early_stopping_patience > 0: @@ -291,19 +296,19 @@ def on_epoch_end(self, epoch: int, logs: Optional[dict] = None): self.early_stopping_counter = 0 else: self.early_stopping_counter += 1 - print(f"Early stopping counter: {self.early_stopping_counter}") + logger.info(f"Early stopping counter: {self.early_stopping_counter}") if self.early_stopping_counter >= self.early_stopping_patience: if self.verbose: - print("Early stopping triggered") + logger.info("Early stopping triggered") self.stop_training = True if self.save_to_dir: manager_path = Path(logs.get("filepath")) / "es_checkpoint_manager.pth" torch.save(self, manager_path) if self.verbose: - print(f"Checkpoint manager saved to {manager_path}") + logger.info(f"Checkpoint manager saved to {manager_path}") def on_train_end(self, logs: Optional[dict] = None): """Called at the end of training.""" if self.verbose: - print("Training finished.") + logger.info("Training finished.") diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 4bc4ac8..f4b0a89 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -22,6 +22,10 @@ Subset = None nn = None +import logging + +logger = logging.getLogger(__name__) + class MLTrainer: """Class for training and predicting using a model and a validation strategy. @@ -74,6 +78,21 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - else: X_val, y_val = None, None + if ( + np.isnan(X).sum() != 0 + or (X_val is not None and np.isnan(X_val).sum() != 0) + or np.isnan(y).sum() != 0 + or (y_val is not None and np.isnan(y_val).sum() != 0) + ): + if np.isnan(X).sum() != 0 or np.isnan(X_val).sum() != 0: + logger.warning("It seems that there are NaN values in the input data.") + else: + logger.warning("It seems that there are NaN values in the target data.") + logger.warning( + "Try to check pipeline configuration (normalization part, especially)." + "NaN values can be caused by division by zero in DifferenceNormalizer or LastKnownNormalizer." + ) + # Initialize columns' order and reorder columns self.features_argsort = np.argsort(pipeline.output_features) X = X[:, self.features_argsort] @@ -85,11 +104,10 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - model.fit_one_fold(X_train, y_train, X_val, y_val) self.models.append(model) self.scores.append(model.score) + logger.info(f"Fold {fold_i}. Score: {model.score}") - print(f"Fold {fold_i}. Score: {model.score}") - - print(f"Mean score: {np.mean(self.scores).round(4)}") - print(f"Std: {np.std(self.scores).round(4)}") + logger.info(f"Mean score: {np.mean(self.scores).round(4)}") + logger.info(f"Std: {np.std(self.scores).round(4)}") def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: """Generates predictions using the trained model. @@ -332,6 +350,17 @@ def train_model( inputs, targets = inputs.to(self.device), targets.to(self.device) optimizer.zero_grad() + + if torch.isnan(inputs).sum() != 0 or torch.isnan(targets).sum() != 0: + if torch.isnan(inputs).sum() != 0: + logger.warning("It seems that there are NaN values in the input data.") + else: + logger.warning("It seems that there are NaN values in the target data.") + logger.warning( + "Try to check pipeline configuration (normalization part, especially)." + "NaN values can be caused by division by zero in DifferenceNormalizer or LastKnownNormalizer." + ) + outputs = model(inputs) if self.target_len is None: self.target_len = targets.shape[2] @@ -347,19 +376,19 @@ def train_model( if not self.scheduler_after_epoch and scheduler is not None: scheduler.step() - print(f"Updating learning rate to {scheduler.get_last_lr()[0]:.6f}.") + logger.info(f"Updating learning rate to {scheduler.get_last_lr()[0]:.6f}.") epoch_loss = running_loss / len(train_loader.dataset) epoch_time = time.time() - start_time - print(f"Epoch {epoch+1}/{self.n_epochs}, cost time: {epoch_time:.2f}s") - print(f"train loss: {epoch_loss:.4f}") + logger.info(f"Epoch {epoch+1}/{self.n_epochs}, cost time: {epoch_time:.2f}s") + logger.info(f"train loss: {epoch_loss:.4f}") val_loss, val_metric = self.validate_model(val_loader, model) - print(f"val loss: {val_loss:.4f}") + logger.info(f"val loss: {val_loss:.4f}, val metric: {val_metric:.4f}") if self.scheduler_after_epoch and scheduler is not None: scheduler.step() - print(f"Updating learning rate to {scheduler.get_last_lr()[0]:.6f}.") + logger.info(f"Updating learning rate to {scheduler.get_last_lr()[0]:.6f}.") # Сохранение модели и проверка early stopping logs = { @@ -435,7 +464,7 @@ def validate_model( loss = self.criterion(all_outputs, all_targets).item() metric = self.metric(all_outputs, all_targets).item() if self.metric else 0.0 - print(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") + logger.info(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") if return_outputs: return loss, metric, all_outputs, all_targets @@ -498,8 +527,8 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - num_workers=self.num_workers, ) - print(f"length of train dataset: {len(train_subset)}") - print(f"length of val dataset: {len(val_subset)}") + logger.info(f"length of train dataset: {len(train_subset)}") + logger.info(f"length of val dataset: {len(val_subset)}") num_features = train_dataset[0][0].shape[0] @@ -518,12 +547,12 @@ def fit(self, data: dict, pipeline: Pipeline, val_data: Optional[dict] = None) - self.schedulers.append(scheduler) self.scores.append(score) - print(f"Fold {fold_i}. Score: {score}") + logger.info(f"Fold {fold_i}. Score: {score}") self.checkpoint_path = checkpoint_path self.pretrained_path = pretrained_path - print(f"Mean score: {np.mean(self.scores).round(4)}") - print(f"Std: {np.std(self.scores).round(4)}") + logger.info(f"Mean score: {np.mean(self.scores).round(4)}") + logger.info(f"Std: {np.std(self.scores).round(4)}") def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: """Generates predictions using the trained model. @@ -547,7 +576,7 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: num_workers=self.num_workers, ) - print(f"length of test dataset: {len(test_dataset)}") + logger.info(f"length of test dataset: {len(test_dataset)}") models_preds = [ self.validate_model(test_loader, model, return_outputs=True)[2].cpu() diff --git a/tsururu/models/boost.py b/tsururu/models/boost.py index 41b84be..680aecd 100644 --- a/tsururu/models/boost.py +++ b/tsururu/models/boost.py @@ -1,7 +1,10 @@ +import logging from typing import Dict, Optional, Union import numpy as np +from ..utils.logging import LoggerStream + try: from catboost import CatBoostRegressor, Pool except ImportError: @@ -9,6 +12,8 @@ CatBoostRegressor = None from .base import Estimator +logger = logging.getLogger(__name__) + class CatBoost(Estimator): """CatBoost is a class that performs cross-validation @@ -43,6 +48,7 @@ def fit_one_fold( ("thread_count", -1), ("random_state", 42), ("early_stopping_rounds", 100), + ("verbose", 100), ]: if self.model_params.get(param) is None: self.model_params[param] = default_value @@ -54,6 +60,7 @@ def fit_one_fold( eval_set=eval_dataset, use_best_model=True, plot=False, + log_cout=LoggerStream(logger, verbose_eval=self.model_params["verbose"]), ) self.score = self.model.best_score_["validation"][f"{self.model_params['loss_function']}"] diff --git a/tsururu/models/torch_based/layers/positional_encoding.py b/tsururu/models/torch_based/layers/positional_encoding.py index a81698e..a8777ae 100644 --- a/tsururu/models/torch_based/layers/positional_encoding.py +++ b/tsururu/models/torch_based/layers/positional_encoding.py @@ -3,8 +3,6 @@ import math from typing import Optional -from .utils import pv - try: import torch import torch.nn as nn @@ -68,7 +66,6 @@ def Coord2dPosEncoding( * (torch.linspace(0, 1, d_model).reshape(1, -1) ** x) - 1 ) - pv(f"{i:4.0f} {x:5.3f} {cpe.mean():+6.3f}", verbose) if abs(cpe.mean()) <= eps: break elif cpe.mean() > eps: diff --git a/tsururu/models/torch_based/layers/utils.py b/tsururu/models/torch_based/layers/utils.py index 8eaeb22..56627ba 100644 --- a/tsururu/models/torch_based/layers/utils.py +++ b/tsururu/models/torch_based/layers/utils.py @@ -10,12 +10,6 @@ nn = None -# https://github.com/fastai/fastai/blob/85625d621ff1a068f114a3318cc31b927019de05/fastai/imports.py -def pv(text, verbose): - if verbose: - print(text) - - def get_activation_fn(activation: Union[str, Callable[[], nn.Module]]) -> nn.Module: """Get the activation function based on the provided name or callable. diff --git a/tsururu/strategies/base.py b/tsururu/strategies/base.py index 94b9471..849a4fc 100644 --- a/tsururu/strategies/base.py +++ b/tsururu/strategies/base.py @@ -1,3 +1,4 @@ +import logging from typing import List, Optional, Union import numpy as np @@ -7,8 +8,11 @@ from ..dataset.pipeline import Pipeline from ..dataset.slice import IndexSlicer from ..model_training.trainer import DLTrainer, MLTrainer +from ..utils.logging import set_stdout_level, verbosity_to_loglevel from .utils import timing_decorator +logger = logging.getLogger(__name__) + class Strategy: """Base class for strategies, that are needed for fitting and @@ -32,6 +36,23 @@ class Strategy: """ + @staticmethod + def set_verbosity_level(verbose): + """Verbosity level setter. + + Args: + verbose: Controls the verbosity: the higher, the more messages. + <1 : messages are not displayed; + >=1 : the common information about training and testing is displayed; + >=2 : the information about folds processing is also displayed; + >=3 : the training process for every algorithm is displayed; + >=4 : the debug information is displayed; + """ + level = verbosity_to_loglevel(verbose) + set_stdout_level(level) + + logger.info(f"Stdout logging level is {logging._levelToName[level]}.") + @staticmethod def check_step_param(step: int): """Check if the given step parameter is valid. @@ -62,6 +83,7 @@ def __init__( self.pipeline = pipeline self.trainers = [] + self.is_fitted = False @staticmethod def _make_preds_df( diff --git a/tsururu/strategies/direct.py b/tsururu/strategies/direct.py index 8feca05..e31aa2f 100644 --- a/tsururu/strategies/direct.py +++ b/tsururu/strategies/direct.py @@ -243,6 +243,8 @@ def fit( self.trainers.append(current_trainer) + self.is_fitted = True + return self def make_step(self, step, dataset): diff --git a/tsururu/strategies/recursive.py b/tsururu/strategies/recursive.py index 5f1eb17..0b8d892 100644 --- a/tsururu/strategies/recursive.py +++ b/tsururu/strategies/recursive.py @@ -142,6 +142,9 @@ def fit( current_trainer.pretrained_path = pretrained_path self.trainers.append(current_trainer) + + self.is_fitted = True + return self def make_step(self, step: int, dataset: TSDataset) -> TSDataset: @@ -194,6 +197,9 @@ def predict(self, dataset: TSDataset, test_all: bool = False) -> pd.DataFrame: a pandas DataFrame containing the predicted target values. """ + if not self.is_fitted: + raise ValueError("The strategy is not fitted yet.") + new_data = dataset.make_padded_test( self.horizon, self.history, test_all=test_all, step=self.step ) diff --git a/tsururu/utils/__init__.py b/tsururu/utils/__init__.py new file mode 100644 index 0000000..7c94be0 --- /dev/null +++ b/tsururu/utils/__init__.py @@ -0,0 +1 @@ +"""Common util tools.""" \ No newline at end of file diff --git a/tsururu/utils/logging.py b/tsururu/utils/logging.py new file mode 100644 index 0000000..52c4c9f --- /dev/null +++ b/tsururu/utils/logging.py @@ -0,0 +1,193 @@ +"""Utils for logging.""" + +import io +import logging +import os +import sys + +from .. import _logger + +formatter_debug = logging.Formatter( + "%(asctime)s\t[%(levelname)s]\t%(pathname)s.%(funcName)s:%(lineno)d\t%(message)s" +) +formatter_default = logging.Formatter("[%(asctime)s] %(message)s", "%H:%M:%S") + +INFO2 = 17 +INFO3 = 13 + + +def add_logging_level(levelName, levelNum, methodName=None): + """Comprehensively adds a new logging level to the `logging` module and the currently configured logging class. + + `levelName` becomes an attribute of the `logging` module with the value + `levelNum`. `methodName` becomes a convenience method for both `logging` + itself and the class returned by `logging.getLoggerClass()` (usually just + `logging.Logger`). If `methodName` is not specified, `levelName.lower()` is + used. + + To avoid accidental clobberings of existing attributes, this method will + raise an `AttributeError` if the level name is already an attribute of the + `logging` module or if the method name is already present + + Example: + ------- + >>> addLoggingLevel('TRACE', logging.DEBUG - 5) + >>> logging.getLogger(__name__).setLevel("TRACE") + >>> logging.getLogger(__name__).trace('that worked') + >>> logging.trace('so did this') + >>> logging.TRACE + 5 + + + Args: + levelName: Level name. + levelNum: Level number. + methodName: Method name. + + """ + assert (levelNum > 0) and (levelNum < 50) + if not methodName: + methodName = levelName.lower() + + if hasattr(logging, levelName): + raise AttributeError("{} already defined in logging module".format(levelName)) + if hasattr(logging, methodName): + raise AttributeError("{} already defined in logging module".format(methodName)) + if hasattr(logging.getLoggerClass(), methodName): + raise AttributeError("{} already defined in logger class".format(methodName)) + + def logForLevel(self, message, *args, **kwargs): + if self.isEnabledFor(levelNum): + self._log(levelNum, message, args, **kwargs) + + def logToRoot(message, *args, **kwargs): + logging.log(levelNum, message, *args, **kwargs) + + logging.addLevelName(levelNum, levelName) + setattr(logging, levelName, levelNum) + setattr(logging.getLoggerClass(), methodName, logForLevel) + setattr(logging, methodName, logToRoot) + + +add_logging_level("INFO2", INFO2) +add_logging_level("INFO3", INFO3) + + +class LoggerStream(io.IOBase): + def __init__(self, logger, verbose_eval=100) -> None: + super().__init__() + self.logger = logger + self.verbose_eval = verbose_eval + self.counter = 1 + + def write(self, message): + if message == "\n": + return + iter_num = message.split("\t")[0] + if ( + (iter_num == "[1]") + or (iter_num == "0:") + or ((iter_num[-1] != "]") and (iter_num[-1] != ":")) + ): + self.logger.info3(message.rstrip()) + return + + if self.counter < self.verbose_eval - 1: + self.logger.debug(message.rstrip()) + self.counter += 1 + else: + self.logger.info3(message.rstrip()) + self.counter = 0 + + +def verbosity_to_loglevel(verbosity: int, extended=True): + if extended: + if verbosity <= 0: + log_level = logging.ERROR + elif verbosity == 1: + log_level = logging.INFO + elif verbosity == 2: + log_level = logging.INFO2 + elif verbosity == 3: + log_level = logging.INFO3 + else: + log_level = logging.DEBUG + else: + if verbosity <= 0: + log_level = logging.ERROR + elif verbosity == 1: + log_level = logging.INFO + else: + log_level = logging.DEBUG + + return log_level + + +def get_stdout_level(): + for handler in _logger.handlers: + if type(handler) == logging.StreamHandler: + return handler.level + return _logger.getEffectiveLevel() + + +def set_stdout_level(level): + _logger.setLevel(logging.DEBUG) + + has_console_handler = False + + for handler in _logger.handlers: + if type(handler) == logging.StreamHandler: + if handler.level == level: + has_console_handler = True + else: + _logger.handlers.remove(handler) + + if not has_console_handler: + handler = logging.StreamHandler(sys.stdout) + handler.setFormatter(formatter_default) + handler.setLevel(level) + + _logger.addHandler(handler) + + +def add_filehandler(filename: str, level=logging.DEBUG): + if filename: + has_file_handler = False + + for handler in _logger.handlers: + if type(handler) == logging.FileHandler: + if handler.baseFilename == filename or handler.baseFilename == os.path.join( + os.getcwd(), filename + ): + has_file_handler = True + else: + _logger.handlers.remove(handler) + + if not has_file_handler: + file_handler = logging.FileHandler(filename, mode="w") + + if level == logging.DEBUG: + file_handler.setFormatter(formatter_debug) + else: + file_handler.setFormatter(formatter_default) + + file_handler.setLevel(level) + + # if handler_filter: + # file_handler.addFilter(handler_filter) + + _logger.addHandler(file_handler) + else: + for handler in _logger.handlers: + if type(handler) == logging.FileHandler: + _logger.handlers.remove(handler) + + +class DuplicateFilter(object): + def __init__(self): + self.msgs = set() + + def filter(self, record): + rv = record.msg not in self.msgs + self.msgs.add(record.msg) + return rv From b936a1709395f784f0a0b97a7465c4878232dc99 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 13 Dec 2024 11:46:01 +0300 Subject: [PATCH 28/50] modify imputer: impute infs --- tsururu/transformers/impute.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/tsururu/transformers/impute.py b/tsururu/transformers/impute.py index 1e1d0ef..788f21a 100644 --- a/tsururu/transformers/impute.py +++ b/tsururu/transformers/impute.py @@ -10,6 +10,7 @@ class MissingValuesImputer(SeriesToSeriesTransformer): """Imputes missing values in time series data using various strategies. Args: + impute_inf: whether to impute infinite values additionally to missing values. regime: the strategy to use for imputation. Options are 'mean' or 'lag'. if None, the transformer will fill missing values with a constant value. constant_value: the constant value to fill remaining missing values @@ -28,6 +29,7 @@ class MissingValuesImputer(SeriesToSeriesTransformer): def __init__( self, + impute_inf: bool = False, regime: Optional[str] = None, constant_value: Optional[float] = None, transform_features: bool = True, @@ -37,6 +39,7 @@ def __init__( lag: Optional[int] = None, ): super().__init__(transform_features=transform_features, transform_target=transform_target) + self.impute_inf = impute_inf self.regime = regime self.constant_value = constant_value self.window = window if window is not None else -1 @@ -86,10 +89,18 @@ def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Seri segment.loc[:, self.output_features[i]] = segment.loc[:, self.output_features[i]].fillna( self.constant_value ) + if self.impute_inf: + segment.loc[:, self.output_features[i]] = segment.loc[:, self.output_features[i]].replace( + [np.inf, -np.inf], self.constant_value + ) else: segment.loc[:, self.output_features[i]] = segment.loc[:, column_name].fillna( self.constant_value ) + if self.impute_inf: + segment.loc[:, self.output_features[i]] = segment.loc[:, self.output_features[i]].replace( + [np.inf, -np.inf], self.constant_value + ) return segment @@ -108,8 +119,12 @@ def _fill_mean(self, series: pd.Series) -> pd.Series: window_size = len(series) else: window_size = self.window + + idx_list = series[series.isnull()].index + if self.impute_inf: + idx_list = idx_list.union(series.index[~np.isfinite(series)]) - for idx in series[series.isnull()].index: + for idx in idx_list: if idx >= window_size: window = series.loc[idx - window_size : idx] else: @@ -142,7 +157,11 @@ def _fill_lag(self, series: pd.Series) -> pd.Series: """ filled_series = series.copy() - for idx in series[series.isnull()].index: + idx_list = series[series.isnull()].index + if self.impute_inf: + idx_list = idx_list.union(series.index[~np.isfinite(series)]) + + for idx in idx_list: try: current_lag = series.loc[idx - self.lag] except: From 08f383197b3b906cfed2f1b21a17816dcc1cc14f Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 13 Dec 2024 11:47:03 +0300 Subject: [PATCH 29/50] add tests --- .gitignore | 1 - examples/Example_1_All_configurations.py | 37 +- .../simulated_data_to_check_15min.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_1min.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_1ms.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_1s.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_28D.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_30min.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_32s.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_3D.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_5min.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_5ms.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_D.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_H.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_M.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_MS.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_Q.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_QS.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_W.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_Y.csv | 1001 +++++++++++++++++ .../simulated_data_to_check_YS.csv | 1001 +++++++++++++++++ tests/test_dataset/test_different_freqs.py | 388 +++++++ tests/test_model_training/test_ml.py | 95 ++ tests/test_model_training/test_nn.py | 94 ++ tests/test_pipeline/conftest.py | 62 + .../test_X_with_from_target_date.py | 73 ++ .../test_categorical_encoding.py | 126 +++ tests/test_pipeline/test_output_features.py | 226 ++++ tests/test_strategies/test_fwm.py | 91 ++ 29 files changed, 20190 insertions(+), 22 deletions(-) create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_15min.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_1min.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_1ms.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_1s.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_28D.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_30min.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_32s.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_3D.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_5min.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_5ms.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_D.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_H.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_M.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_MS.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_Q.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_QS.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_W.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_Y.csv create mode 100644 tests/test_dataset/different_freqs_datasets/simulated_data_to_check_YS.csv create mode 100644 tests/test_dataset/test_different_freqs.py create mode 100644 tests/test_model_training/test_ml.py create mode 100644 tests/test_model_training/test_nn.py create mode 100644 tests/test_pipeline/conftest.py create mode 100644 tests/test_pipeline/test_X_with_from_target_date.py create mode 100644 tests/test_pipeline/test_categorical_encoding.py create mode 100644 tests/test_pipeline/test_output_features.py create mode 100644 tests/test_strategies/test_fwm.py diff --git a/.gitignore b/.gitignore index 9c3f8d1..c8bf195 100644 --- a/.gitignore +++ b/.gitignore @@ -151,6 +151,5 @@ catboost_info/ poetry.lock *check*.ipynb *temp* -*test* *checkpoints* *logs* diff --git a/examples/Example_1_All_configurations.py b/examples/Example_1_All_configurations.py index 74f56df..a9cc994 100644 --- a/examples/Example_1_All_configurations.py +++ b/examples/Example_1_All_configurations.py @@ -63,36 +63,36 @@ ] STRATEGIES_OVER_TIME = [ -# "RecursiveStrategy", -# "DirectStrategy", -# "MIMOStrategy", + "RecursiveStrategy", + "DirectStrategy", + "MIMOStrategy", "FlatWideMIMOStrategy", ] MULTIVARIATE = [ -# True, + True, False ] DATE_FEATURES = [ - "with_normalization", -# "with_normalization_over_all", -# "with_normalization_by_id", -# False, + "without_normalization", + "with_normalization_over_all", + False, ] ID_FEATURES = [ "with_ohe", -# "with_le_normalization_over_all", -# False, + "with_le_without_normalization", + "with_le_normalization_over_all", + False, ] INDIVIDUAL_MODEL_HORIZON = [None, 1, 2] TRANSFORMERS = { None, -# "StandardScalerTransformer", -# "DifferenceNormalizer", -# "LastKnownNormalizer", + "StandardScalerTransformer", + "DifferenceNormalizer", + "LastKnownNormalizer", } TRANSFORMERS_REGIMES = [None, "delta", "ratio"] TRANSFORM_TARGET = [True, False] @@ -423,20 +423,15 @@ def main(): "LagTransformer": {"lags": 1}, }, } - elif id_features == "with_le_normalization_over_all": + elif id_features == "with_le_without_normalization": pipeline_params["id"] = { "columns": ["id"], "features": { "LabelEncodingTransformer": {}, - "StandardScalerTransformer": { - "transform_features": True, - "transform_target": False, - "agg_by_id": False, - }, "LagTransformer": {"lags": 1}, }, } - elif id_features == "with_le_normalization_by_id": + elif id_features == "with_le_normalization_over_all": pipeline_params["id"] = { "columns": ["id"], "features": { @@ -444,7 +439,7 @@ def main(): "StandardScalerTransformer": { "transform_features": True, "transform_target": False, - "agg_by_id": True, + "agg_by_id": False, }, "LagTransformer": {"lags": 1}, }, diff --git a/tests/test_dataset/different_freqs_datasets/simulated_data_to_check_15min.csv b/tests/test_dataset/different_freqs_datasets/simulated_data_to_check_15min.csv new file mode 100644 index 0000000..5b4d176 --- /dev/null +++ b/tests/test_dataset/different_freqs_datasets/simulated_data_to_check_15min.csv @@ -0,0 +1,1001 @@ +value,date,id +1000,2020-01-01 00:00:00,0 +1001,2020-01-01 00:15:00,0 +1002,2020-01-01 00:30:00,0 +1003,2020-01-01 00:45:00,0 +1004,2020-01-01 01:00:00,0 +1005,2020-01-01 01:15:00,0 +1006,2020-01-01 01:30:00,0 +1007,2020-01-01 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0000000..ba5b3a4 --- /dev/null +++ b/tests/test_dataset/test_different_freqs.py @@ -0,0 +1,388 @@ +import numpy as np +import pandas as pd +import pytest + +from tsururu.dataset import IndexSlicer, Pipeline, TSDataset + +index_slicer = IndexSlicer() + +HORIZON = 3 +HISTORY = 7 + +DATASET_PARAMS = { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": { + "columns": ["date"], + "type": "datetime", + }, + "id": { + "columns": ["id"], + "type": "categorical", + }, +} + +PIPELINE_PARAMS = { + "target": { + "columns": ["value"], + "features": { + "LagTransformer": {"lags": 3}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["y", "m", "d", "hour", "min", "sec", "ms"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 3}, + }, + }, + "id": { + "columns": ["id"], + "features": {"LagTransformer": {"lags": 1}}, + }, +} + +BASE_PATH = "./tests/test_dataset/different_freqs_datasets/" + +DF_PATH_YS = f"{BASE_PATH}simulated_data_to_check_YS.csv" +DF_PATH_Y = f"{BASE_PATH}simulated_data_to_check_Y.csv" +DF_PATH_QS = f"{BASE_PATH}simulated_data_to_check_QS.csv" +DF_PATH_Q = f"{BASE_PATH}simulated_data_to_check_Q.csv" +DF_PATH_MS = f"{BASE_PATH}simulated_data_to_check_MS.csv" +DF_PATH_M = f"{BASE_PATH}simulated_data_to_check_M.csv" +DF_PATH_W = f"{BASE_PATH}simulated_data_to_check_W.csv" +DF_PATH_3D = f"{BASE_PATH}simulated_data_to_check_3D.csv" +DF_PATH_D = f"{BASE_PATH}simulated_data_to_check_D.csv" +DF_PATH_H = f"{BASE_PATH}simulated_data_to_check_H.csv" +DF_PATH_30MIN = f"{BASE_PATH}simulated_data_to_check_30min.csv" +DF_PATH_15MIN = f"{BASE_PATH}simulated_data_to_check_15min.csv" +DF_PATH_5MIN = f"{BASE_PATH}simulated_data_to_check_5min.csv" +DF_PATH_MIN = f"{BASE_PATH}simulated_data_to_check_1min.csv" +DF_PATH_32S = f"{BASE_PATH}simulated_data_to_check_32s.csv" +DF_PATH_S = f"{BASE_PATH}simulated_data_to_check_1s.csv" +DF_PATH_1MS = f"{BASE_PATH}simulated_data_to_check_1ms.csv" + +DF_PATH_28D = f"{BASE_PATH}simulated_data_to_check_28D.csv" # wrong without pd.DateOffset + + +@pytest.mark.parametrize( + "df_path, result_lag_2__season_y__date, \ + result_lag_2__season_m__date, result_lag_2__season_d__date, \ + result_lag_2__season_h__date, result_lag_2__season_min__date, \ + result_lag_2__season_sec__date, result_lag_2__season_ms__date", + [ + ( + DF_PATH_YS, + np.array([2027, 2028, 2029, 2030, 2031]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_Y, + np.array([2027, 2028, 2029, 2030, 2031]), + np.array([12, 12, 12, 12, 12]), + np.array([31, 31, 31, 31, 31]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_QS, + np.array([2021, 2022, 2022, 2022, 2022]), + np.array([10, 1, 4, 7, 10]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_Q, + np.array([2021, 2022, 2022, 2022, 2022]), + np.array([12, 3, 6, 9, 12]), + np.array([31, 31, 30, 30, 31]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_MS, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([8, 9, 10, 11, 12]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_M, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([8, 9, 10, 11, 12]), + np.array([31, 30, 31, 30, 31]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_W, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([2, 3, 3, 3, 3]), + np.array([23, 1, 8, 15, 22]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_3D, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 2]), + np.array([22, 25, 28, 31, 3]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_D, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([8, 9, 10, 11, 12]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_H, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([7, 8, 9, 10, 11]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_30MIN, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([3, 4, 4, 5, 5]), + np.array([30, 0, 30, 0, 30]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_15MIN, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 2, 2, 2, 2]), + np.array([45, 0, 15, 30, 45]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_5MIN, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([35, 40, 45, 50, 55]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_MIN, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([7, 8, 9, 10, 11]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_32S, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([3, 4, 4, 5, 5]), + np.array([44, 16, 48, 20, 52]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_S, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([7, 8, 9, 10, 11]), + np.array([0, 0, 0, 0, 0]), + ), + ( + DF_PATH_1MS, + np.array([2020, 2020, 2020, 2020, 2020]), + np.array([1, 1, 1, 1, 1]), + np.array([1, 1, 1, 1, 1]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([0, 0, 0, 0, 0]), + np.array([7000, 8000, 9000, 10000, 11000]), + ), + ], +) +def test_date_features( + df_path, + result_lag_2__season_y__date, + result_lag_2__season_m__date, + result_lag_2__season_d__date, + result_lag_2__season_h__date, + result_lag_2__season_min__date, + result_lag_2__season_sec__date, + result_lag_2__season_ms__date, +): + df = pd.read_csv(df_path) + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + features_idx = index_slicer.create_idx_data( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = pipeline.fit_transform(data, strategy_name="MIMOStrategy") + X, _ = pipeline.generate(data) + + result_lag_2__season_y__date_idx = pipeline.output_features == "date__season_y__lag_2" + result_lag_2__season_m__date_idx = pipeline.output_features == "date__season_m__lag_2" + result_lag_2__season_d__date_idx = pipeline.output_features == "date__season_d__lag_2" + result_lag_2__season_h__date_idx = pipeline.output_features == "date__season_hour__lag_2" + result_lag_2__season_min__date_idx = pipeline.output_features == "date__season_min__lag_2" + result_lag_2__season_sec__date_idx = pipeline.output_features == "date__season_sec__lag_2" + result_lag_2__season_ms__date_idx = pipeline.output_features == "date__season_ms__lag_2" + + assert np.array_equal( + X[:, result_lag_2__season_y__date_idx][:5].flatten(), result_lag_2__season_y__date + ) + assert np.array_equal( + X[:, result_lag_2__season_m__date_idx][:5].flatten(), result_lag_2__season_m__date + ) + assert np.array_equal( + X[:, result_lag_2__season_d__date_idx][:5].flatten(), result_lag_2__season_d__date + ) + assert np.array_equal( + X[:, result_lag_2__season_h__date_idx][:5].flatten(), result_lag_2__season_h__date + ) + assert np.array_equal( + X[:, result_lag_2__season_min__date_idx][:5].flatten(), result_lag_2__season_min__date + ) + assert np.array_equal( + X[:, result_lag_2__season_sec__date_idx][:5].flatten(), result_lag_2__season_sec__date + ) + assert np.array_equal( + X[:, result_lag_2__season_ms__date_idx][:5].flatten(), result_lag_2__season_ms__date + ) + assert index_slicer.ids_from_date(dataset.data, "date") == [ + 100, + 200, + 300, + 400, + 500, + 600, + 700, + 800, + 900, + ] + + +def test_custom_offset_28days(): + df = pd.read_csv(DF_PATH_28D) + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + delta=pd.DateOffset(days=28), + ) + + PIPELINE_PARAMS["date"]["features"]["DateSeasonsGenerator"]["delta"] = pd.DateOffset(days=28) + + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + features_idx = index_slicer.create_idx_data( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = pipeline.fit_transform(data, strategy_name="MIMOStrategy") + X, _ = pipeline.generate(data) + + result_lag_2__season_y__date_idx = pipeline.output_features == "date__season_y__lag_2" + result_lag_2__season_m__date_idx = pipeline.output_features == "date__season_m__lag_2" + result_lag_2__season_d__date_idx = pipeline.output_features == "date__season_d__lag_2" + + assert np.array_equal( + X[:, result_lag_2__season_y__date_idx][:5].flatten(), [2020, 2020, 2020, 2020, 2020] + ) + assert np.array_equal(X[:, result_lag_2__season_m__date_idx][:5].flatten(), [7, 8, 9, 10, 11]) + assert np.array_equal(X[:, result_lag_2__season_d__date_idx][:5].flatten(), [15, 12, 9, 7, 4]) + + assert index_slicer.ids_from_date(dataset.data, "date", delta=pd.DateOffset(days=28)) == [ + 100, + 200, + 300, + 400, + 500, + 600, + 700, + 800, + 900, + ] diff --git a/tests/test_model_training/test_ml.py b/tests/test_model_training/test_ml.py new file mode 100644 index 0000000..ed7ede4 --- /dev/null +++ b/tests/test_model_training/test_ml.py @@ -0,0 +1,95 @@ +import logging + +import pandas as pd + +from tsururu.dataset import IndexSlicer, Pipeline, TSDataset +from tsururu.model_training.trainer import MLTrainer +from tsururu.model_training.validator import KFoldCrossValidator +from tsururu.models import CatBoost +from tsururu.strategies import MIMOStrategy + +index_slicer = IndexSlicer() + + +HORIZON = 3 +HISTORY = 7 + +DATASET_PARAMS = { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": { + "columns": ["date"], + "type": "datetime", + }, + "id": { + "columns": ["id"], + "type": "categorical", + }, +} + +PIPELINE_PARAMS = { + "target": { + "columns": ["value"], + "features": { + "DifferenceNormalizer": { + "regime": "delta", + "transform_features": True, + "transform_target": False, + }, + "LagTransformer": {"lags": 7}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["doy", "m", "wd"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 1}, + }, + }, +} + + +def test_assert_for_nans_in_nn_X(caplog): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + # Configure the model parameters + model = CatBoost + model_params = { + "loss_function": "MultiRMSE", + "early_stopping_rounds": 100, + "n_estimators": 100, + "verbose": 1000, + } + + # Configure the validation parameters + validation = KFoldCrossValidator + validation_params = { + "n_splits": 2, + } + + trainer = MLTrainer( + model, + model_params, + validation, + validation_params, + ) + + strategy = MIMOStrategy(HORIZON, HISTORY, trainer, pipeline) + + with caplog.at_level(logging.WARNING): + _, _ = strategy.fit(dataset) + assert "It seems that there are NaN values in the input data." in caplog.text + assert "Try to check pipeline configuration" in caplog.text + assert "NaN values can be caused by division by zero" in caplog.text diff --git a/tests/test_model_training/test_nn.py b/tests/test_model_training/test_nn.py new file mode 100644 index 0000000..41089d5 --- /dev/null +++ b/tests/test_model_training/test_nn.py @@ -0,0 +1,94 @@ +import logging + +import numpy as np +import pandas as pd + +from tsururu.dataset import IndexSlicer, Pipeline, TSDataset +from tsururu.model_training.trainer import DLTrainer +from tsururu.model_training.validator import KFoldCrossValidator +from tsururu.models.torch_based.dlinear import DLinear_NN +from tsururu.strategies import MIMOStrategy + +index_slicer = IndexSlicer() + + +HORIZON = 3 +HISTORY = 7 + +DATASET_PARAMS = { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": { + "columns": ["date"], + "type": "datetime", + }, + "id": { + "columns": ["id"], + "type": "categorical", + }, +} + +PIPELINE_PARAMS = { + "target": { + "columns": ["value"], + "features": { + "DifferenceNormalizer": { + "regime": "delta", + "transform_features": True, + "transform_target": False, + }, + "LagTransformer": {"lags": 7}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["doy", "m", "wd"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 1}, + }, + }, +} + + +def test_assert_for_nans_in_nn_X(caplog): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + # Configure the model parameters + model = DLinear_NN + model_params = {"moving_avg": 7, "individual": False, "enc_in": None} + + # Configure the validation parameters + validation = KFoldCrossValidator + validation_params = { + "n_splits": 2, + } + + trainer_params = { + "device": "cpu", + "num_workers": 0, + "best_by_metric": True, + "save_to_dir": False, + "n_epochs": 1, + } + + trainer = DLTrainer(model, model_params, validation, validation_params, **trainer_params) + + strategy = MIMOStrategy(HORIZON, HISTORY, trainer, pipeline) + + with caplog.at_level(logging.WARNING): + _, _ = strategy.fit(dataset) + assert "It seems that there are NaN values in the input data." in caplog.text + assert "Try to check pipeline configuration" in caplog.text + assert "NaN values can be caused by division by zero" in caplog.text diff --git a/tests/test_pipeline/conftest.py b/tests/test_pipeline/conftest.py new file mode 100644 index 0000000..b342855 --- /dev/null +++ b/tests/test_pipeline/conftest.py @@ -0,0 +1,62 @@ +import pandas as pd +import pytest + +from tsururu.dataset import TSDataset + + +@pytest.fixture(scope="package") +def get_dataset(): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + + # Add some features with different names + for i, i_name in enumerate( + [ + "Значение времени (ч)...проверка@123__", + "кол-во_клиентов#сегмент_A__тест?", + "прибыль_на_АКцию (%)-@финансы", + "объем Продаж! (Q3_2023)...анализ#данных", + "показатель/успешности_доход@услуги__OK?", + "add_feature_0", + "add_feature_1", + "add_feature_2", + ] + ): + df[f"{i_name}"] = df["value"] + 1000 * (i + 1) + + dataset_params = { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": { + "columns": ["date"], + "type": "datetime", + }, + "id": { + "columns": ["id"], + "type": "categorical", + }, + "exog_1": { + "columns": [ + "Значение времени (ч)...проверка@123__", + "прибыль_на_АКцию (%)-@финансы", + "feature_0", + "feature_2", + ], + "type": "continious", + }, + "exog_2": { + "columns": [ + "кол-во_клиентов#сегмент_A__тест?", + "объем Продаж! (Q3_2023)...анализ#данных", + "feature_1", + ], + "type": "continious", + }, + } + + return TSDataset( + data=df, + columns_params=dataset_params, + print_freq_period_info=False, + ) diff --git a/tests/test_pipeline/test_X_with_from_target_date.py b/tests/test_pipeline/test_X_with_from_target_date.py new file mode 100644 index 0000000..6ef5839 --- /dev/null +++ b/tests/test_pipeline/test_X_with_from_target_date.py @@ -0,0 +1,73 @@ +from copy import deepcopy + +import numpy as np +import pytest + +from tsururu.dataset import IndexSlicer, Pipeline + +index_slicer = IndexSlicer() + +HORIZON = 3 +HISTORY = 3 + +PIPELINE_FROM_TARGET_DATE_TRUE = { + "target": { + "columns": ["value"], + "features": { + "LagTransformer": {"lags": 1}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["d"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 1}, + }, + }, +} + +PIPELINE_FROM_TARGET_DATE_FALSE = deepcopy(PIPELINE_FROM_TARGET_DATE_TRUE) +PIPELINE_FROM_TARGET_DATE_FALSE["date"]["features"]["DateSeasonsGenerator"][ + "from_target_date" +] = False + + +@pytest.mark.parametrize( + "pipeline_params, result", + [ + (PIPELINE_FROM_TARGET_DATE_TRUE, np.array([6, 7, 8, 9, 10])), + (PIPELINE_FROM_TARGET_DATE_FALSE, np.array([3, 4, 5, 6, 7])), + ], +) +def test_features_names(get_dataset, pipeline_params, result): + dataset = get_dataset + pipeline = Pipeline.from_dict(pipeline_params, multivariate=False) + + features_idx = index_slicer.create_idx_data( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = pipeline.fit_transform(data, strategy_name="MIMOStrategy") + X, _ = pipeline.generate(data) + + day_column_name = pipeline.output_features == "date__season_d__lag_0" + day_column = X[:, day_column_name] + + assert np.array_equal(day_column[:5].flatten(), result) diff --git a/tests/test_pipeline/test_categorical_encoding.py b/tests/test_pipeline/test_categorical_encoding.py new file mode 100644 index 0000000..30f2108 --- /dev/null +++ b/tests/test_pipeline/test_categorical_encoding.py @@ -0,0 +1,126 @@ +import numpy as np +import pandas as pd + +from tsururu.dataset import IndexSlicer, Pipeline, TSDataset + +index_slicer = IndexSlicer() + + +HORIZON = 3 +HISTORY = 7 + +DATASET_PARAMS = { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": { + "columns": ["date"], + "type": "datetime", + }, + "id": { + "columns": ["id"], + "type": "categorical", + }, + "exog_1": { + "columns": ["id2"], + "type": "categorical", + }, +} + +PIPELINE_PARAMS = { + "target": { + "columns": ["value"], + "features": { + "LagTransformer": {"lags": 1}, + }, + }, + "id": { + "columns": ["id"], + "features": {"LabelEncodingTransformer": {}, "LagTransformer": {"lags": 1}}, + }, + "exog_1": { + "columns": ["id2"], + "features": { + "OneHotEncodingTransformer": {"drop": np.array(["k"])}, + "LagTransformer": {"lags": 1}, + }, + }, +} + + +def int_to_str(x): + str_map_arr = "abcdefghijklmn" + return str_map_arr[x] + + +def test_categorical_encoding(): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + df["id2"] = df["id"] + 2 + df["id"] = df["id"].apply(int_to_str) + df["id2"] = df["id2"].apply(int_to_str) + + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + features_idx = index_slicer.create_idx_data( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = pipeline.fit_transform(data, strategy_name="MIMOStrategy") + X, _ = pipeline.generate(data) + + id__label__lag_0_idx = pipeline.output_features == "id__label__lag_0" + assert np.all( + np.unique(X[:, id__label__lag_0_idx], return_counts=True)[0] + == np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]) + ) + assert np.all( + np.unique(X[:, id__label__lag_0_idx], return_counts=True)[1] + == np.array([991, 991, 991, 991, 991, 991, 991, 991, 991, 991]) + ) + + id2__g_ohe__lag_0_idx = pipeline.output_features == "id2__g_ohe__lag_0" + assert np.all( + np.unique(X[:, id2__g_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0, 1.0]) + ) + assert np.all( + np.unique(X[:, id2__g_ohe__lag_0_idx], return_counts=True)[1] == np.array([8919, 991]) + ) + + assert np.all( + pipeline.output_features + == np.array( + [ + "value__lag_0", + "id__label__lag_0", + "id2__c_ohe__lag_0", + "id2__d_ohe__lag_0", + "id2__e_ohe__lag_0", + "id2__f_ohe__lag_0", + "id2__g_ohe__lag_0", + "id2__h_ohe__lag_0", + "id2__i_ohe__lag_0", + "id2__j_ohe__lag_0", + "id2__l_ohe__lag_0", + ], + ) + ) diff --git a/tests/test_pipeline/test_output_features.py b/tests/test_pipeline/test_output_features.py new file mode 100644 index 0000000..0782322 --- /dev/null +++ b/tests/test_pipeline/test_output_features.py @@ -0,0 +1,226 @@ +from copy import deepcopy + +import numpy as np +import pytest + +from tsururu.dataset import IndexSlicer, Pipeline + +index_slicer = IndexSlicer() + +HORIZON = 3 +HISTORY = 7 + +PIPELINE_PARAMS_STANDARD = { + "target": { + "columns": ["value"], + "features": { + "LagTransformer": {"lags": 3}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["doy", "m", "wd"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 1}, + }, + }, + "id": { + "columns": ["id"], + "features": {"LagTransformer": {"lags": 1}}, + }, +} + +PIPELINE_PARAMS_WITH_ALL_EXOGS = deepcopy(PIPELINE_PARAMS_STANDARD) +PIPELINE_PARAMS_WITH_ALL_EXOGS["exog_1"] = { + "columns": [ + "Значение времени (ч)...проверка@123__", + "прибыль_на_АКцию (%)-@финансы", + "объем Продаж! (Q3_2023)...анализ#данных", + "add_feature_1", + ], + "features": {"LagTransformer": {"lags": 2}}, +} +PIPELINE_PARAMS_WITH_ALL_EXOGS["exog_2"] = { + "columns": [ + "кол-во_клиентов#сегмент_A__тест?", + "показатель/успешности_доход@услуги__OK?", + "add_feature_0", + "add_feature_2", + ], + "features": {"LagTransformer": {"lags": 1}}, +} + +PIPELINE_PARAMS_WITH_SOME_EXOGS = deepcopy(PIPELINE_PARAMS_STANDARD) +PIPELINE_PARAMS_WITH_SOME_EXOGS["exog_1"] = { + "columns": [ + "Значение времени (ч)...проверка@123__", + ], + "features": {"LagTransformer": {"lags": 2}}, +} +PIPELINE_PARAMS_WITH_SOME_EXOGS["exog_2"] = { + "columns": [ + "add_feature_0", + "add_feature_2", + ], + "features": {"LagTransformer": {"lags": 1}}, +} + +PIPELINE_WITH_VALUE_ONLY = { + "target": { + "columns": ["value"], + "features": { + "LagTransformer": {"lags": 3}, + }, + }, +} + +PIPELINE_WITH_VALUE_AND_DATE = deepcopy(PIPELINE_PARAMS_STANDARD) +PIPELINE_WITH_VALUE_AND_DATE.pop("id") + +PIPELINE_WITH_VALUE_AND_ID = deepcopy(PIPELINE_PARAMS_STANDARD) +PIPELINE_WITH_VALUE_AND_ID.pop("date") + +PIPELINE_PARAMS_WITH_WRONG_EXOG_TRANSFORMER = deepcopy(PIPELINE_PARAMS_STANDARD) +PIPELINE_PARAMS_WITH_WRONG_EXOG_TRANSFORMER["exog_1"] = { + "columns": [ + "add_feature_1", + ], + "features": { + "StandardScalerTransformer": {"transform_features": True, "transform_target": True}, + "LagTransformer": {"lags": 2}, + }, +} + + +@pytest.mark.parametrize( + "pipeline_params, result", + [ + ( + PIPELINE_PARAMS_STANDARD, + np.array( + [ + "value__lag_2", + "value__lag_1", + "value__lag_0", + "date__season_doy__lag_0", + "date__season_m__lag_0", + "date__season_wd__lag_0", + "id__lag_0", + ] + ), + ), + ( + PIPELINE_PARAMS_WITH_ALL_EXOGS, + np.array( + [ + "value__lag_2", + "value__lag_1", + "value__lag_0", + "date__season_doy__lag_0", + "date__season_m__lag_0", + "date__season_wd__lag_0", + "id__lag_0", + "Значение времени (ч)...проверка@123____lag_1", + "Значение времени (ч)...проверка@123____lag_0", + "прибыль_на_АКцию (%)-@финансы__lag_1", + "прибыль_на_АКцию (%)-@финансы__lag_0", + "объем Продаж! (Q3_2023)...анализ#данных__lag_1", + "объем Продаж! (Q3_2023)...анализ#данных__lag_0", + "add_feature_1__lag_1", + "add_feature_1__lag_0", + "кол-во_клиентов#сегмент_A__тест?__lag_0", + "показатель/успешности_доход@услуги__OK?__lag_0", + "add_feature_0__lag_0", + "add_feature_2__lag_0", + ] + ), + ), + ( + PIPELINE_PARAMS_WITH_SOME_EXOGS, + np.array( + [ + "value__lag_2", + "value__lag_1", + "value__lag_0", + "date__season_doy__lag_0", + "date__season_m__lag_0", + "date__season_wd__lag_0", + "id__lag_0", + "Значение времени (ч)...проверка@123____lag_1", + "Значение времени (ч)...проверка@123____lag_0", + "add_feature_0__lag_0", + "add_feature_2__lag_0", + ] + ), + ), + ( + PIPELINE_WITH_VALUE_ONLY, + np.array( + [ + "value__lag_2", + "value__lag_1", + "value__lag_0", + ] + ), + ), + ( + PIPELINE_WITH_VALUE_AND_DATE, + np.array( + [ + "value__lag_2", + "value__lag_1", + "value__lag_0", + "date__season_doy__lag_0", + "date__season_m__lag_0", + "date__season_wd__lag_0", + ] + ), + ), + ( + PIPELINE_WITH_VALUE_AND_ID, + np.array( + [ + "value__lag_2", + "value__lag_1", + "value__lag_0", + "id__lag_0", + ] + ), + ), + ], +) +def test_features_names(get_dataset, pipeline_params, result): + dataset = get_dataset + pipeline = Pipeline.from_dict(pipeline_params, multivariate=False) + + features_idx = index_slicer.create_idx_data( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = pipeline.fit_transform(data, strategy_name="MIMOStrategy") + X, _ = pipeline.generate(data) + + assert np.array_equal(pipeline.output_features, result) + assert X.shape[1] == len(result) + + +def test_wrong_exog_transformer(get_dataset): + with pytest.raises(AssertionError): + _ = Pipeline.from_dict(PIPELINE_PARAMS_WITH_WRONG_EXOG_TRANSFORMER, multivariate=False) diff --git a/tests/test_strategies/test_fwm.py b/tests/test_strategies/test_fwm.py new file mode 100644 index 0000000..b4dcc11 --- /dev/null +++ b/tests/test_strategies/test_fwm.py @@ -0,0 +1,91 @@ +import pandas as pd +import pytest + +from tsururu.dataset import IndexSlicer, Pipeline, TSDataset +from tsururu.model_training.trainer import MLTrainer +from tsururu.model_training.validator import KFoldCrossValidator +from tsururu.models import CatBoost +from tsururu.strategies import FlatWideMIMOStrategy + +index_slicer = IndexSlicer() + + +HORIZON = 3 +HISTORY = 7 + +DATASET_PARAMS = { + "target": { + "columns": ["value"], + "type": "continious", + }, + "date": { + "columns": ["date"], + "type": "datetime", + }, + "id": { + "columns": ["id"], + "type": "categorical", + }, +} + +PIPELINE_PARAMS_WRONG_DATE_LAGS = { + "target": { + "columns": ["value"], + "features": { + "DifferenceNormalizer": { + "regime": "delta", + "transform_features": True, + "transform_target": False, + }, + "LagTransformer": {"lags": 7}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["doy", "m", "wd"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 1}, + }, + }, +} + + +def test_assert_date_lags_less_than_history(): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + pipeline = Pipeline.from_dict(PIPELINE_PARAMS_WRONG_DATE_LAGS, multivariate=False) + + # Configure the model parameters + model = CatBoost + model_params = { + "loss_function": "MultiRMSE", + "early_stopping_rounds": 100, + "n_estimators": 100, + "verbose": 1000, + } + + # Configure the validation parameters + validation = KFoldCrossValidator + validation_params = { + "n_splits": 2, + } + + trainer = MLTrainer( + model, + model_params, + validation, + validation_params, + ) + + strategy = FlatWideMIMOStrategy(HORIZON, HISTORY, trainer, pipeline) + + with pytest.raises(ValueError): + _, _ = strategy.fit(dataset) From 4e88f90cb79d34b0463fd9e86e05b4244d404a99 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Fri, 20 Dec 2024 13:30:55 +0300 Subject: [PATCH 30/50] fix series2series transformers used to the same column --- tsururu/transformers/numeric.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/tsururu/transformers/numeric.py b/tsururu/transformers/numeric.py index 14bc818..6512d2d 100644 --- a/tsururu/transformers/numeric.py +++ b/tsururu/transformers/numeric.py @@ -66,7 +66,10 @@ def fit(self, data: dict, input_features: Sequence[str]) -> SeriesToSeriesTransf else: overall_mean_std = data["raw_ts_X"][self.input_features].agg(["mean", "std"]) ids = data["raw_ts_X"][data["id_column_name"]].unique() - stat_df = pd.DataFrame(index=ids, columns=pd.MultiIndex.from_product([self.input_features, ["mean", "std"]])) + stat_df = pd.DataFrame( + index=ids, + columns=pd.MultiIndex.from_product([self.input_features, ["mean", "std"]]), + ) for column in self.input_features: stat_df[(column, "mean")] = overall_mean_std.loc["mean", column] stat_df[(column, "std")] = overall_mean_std.loc["std", column] @@ -118,7 +121,9 @@ def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Seri column_name=column_name, current_id=current_id, ) - segment.loc[:, self.output_features[i]] = (segment.loc[:, column_mask] - mean) / std + segment.loc[:, self.output_features[i]] = ( + (segment.loc[:, column_mask] - mean) / std + ).values return segment @@ -224,13 +229,13 @@ def _transform_segment(self, segment: pd.Series, *_): """ for i, column_name in enumerate(self.input_features): if self.regime == "delta": - segment.loc[:, self.output_features[i]] = segment.loc[ - :, column_name - ] - segment.loc[:, column_name].shift(1) + segment.loc[:, self.output_features[i]] = ( + segment.loc[:, column_name] - segment.loc[:, column_name].shift(1) + ).values elif self.regime == "ratio": - segment.loc[:, self.output_features[i]] = segment.loc[ - :, column_name - ] / segment.loc[:, column_name].shift(1) + segment.loc[:, self.output_features[i]] = ( + segment.loc[:, column_name] / segment.loc[:, column_name].shift(1) + ).values return segment From d1c240ce98e82b613e1a53f8099ada2e8fa46f0d Mon Sep 17 00:00:00 2001 From: Aleksandr Yugay Date: Mon, 23 Dec 2024 14:38:10 +0300 Subject: [PATCH 31/50] fix holidays --- tsururu/transformers/datetime.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tsururu/transformers/datetime.py b/tsururu/transformers/datetime.py index 7a0b05b..f44fb57 100644 --- a/tsururu/transformers/datetime.py +++ b/tsururu/transformers/datetime.py @@ -199,7 +199,8 @@ def transform(self, data: dict) -> dict: prov=self._prov, state=self._state, ) - new_arr[:, n] = time_col.date.isin(hol) + dates, names = zip(*hol.items()) + new_arr[:, n] = np.isin(time_col.date, dates) n += 1 result_data.append(new_arr) From fcb87bd37c07b289686aea1f3af70c8c83836aab Mon Sep 17 00:00:00 2001 From: aayugay99 <108714729+aayugay99@users.noreply.github.com> Date: Fri, 27 Dec 2024 17:20:40 +0300 Subject: [PATCH 32/50] MissingValuesImputer bug fix --- tsururu/transformers/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tsururu/transformers/__init__.py b/tsururu/transformers/__init__.py index cb7df91..e001ca6 100644 --- a/tsururu/transformers/__init__.py +++ b/tsururu/transformers/__init__.py @@ -28,7 +28,7 @@ def __init__(self): "DateSeasonsGenerator": DateSeasonsGenerator, "LagTransformer": LagTransformer, "TargetGenerator": TargetGenerator, - "MissingValueImputer": MissingValuesImputer, + "MissingValuesImputer": MissingValuesImputer, } def get_allowed(self): From 1dea44ade4469623eb10b687572da6a86eda342b Mon Sep 17 00:00:00 2001 From: aayugay99 <108714729+aayugay99@users.noreply.github.com> Date: Fri, 27 Dec 2024 17:23:20 +0300 Subject: [PATCH 33/50] update version in pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4a279bf..5cbaf8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "tsururu" -version = "0.1.0" +version = "1.0.0" description = "Python tool for time series forecasting" authors = ["Alina Kostromina ", "Dmitry Simakov "] license = "Apache License 2.0" From 76e527e25c0295e34e863ef96a406babf2bd7b2e Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Sat, 28 Dec 2024 12:32:45 +0300 Subject: [PATCH 34/50] add new test: flatwidemimo strategy with different preprocessing --- ...different_transformers_for_flatwidemimo.py | 203 ++++++++++++++++++ 1 file changed, 203 insertions(+) create mode 100644 tests/test_pipeline/test_X_y_with_different_transformers_for_flatwidemimo.py diff --git a/tests/test_pipeline/test_X_y_with_different_transformers_for_flatwidemimo.py b/tests/test_pipeline/test_X_y_with_different_transformers_for_flatwidemimo.py new file mode 100644 index 0000000..05064a9 --- /dev/null +++ b/tests/test_pipeline/test_X_y_with_different_transformers_for_flatwidemimo.py @@ -0,0 +1,203 @@ +from copy import deepcopy +from itertools import product + +import numpy as np +import pandas as pd +import pytest + +from tsururu.dataset import IndexSlicer, Pipeline + +index_slicer = IndexSlicer() + +HORIZON = 3 +HISTORY = 3 + +PIPELINE_PARAMS_STANDARD = { + "target": { + "columns": ["value"], + "features": { + "LagTransformer": {"lags": 3}, + }, + }, + "date": { + "columns": ["date"], + "features": { + "DateSeasonsGenerator": { + "seasonalities": ["doy", "m", "wd"], + "from_target_date": True, + }, + "LagTransformer": {"lags": 3}, + }, + }, + "id": { + "columns": ["id"], + "features": {"LagTransformer": {"lags": 1}}, + }, +} + +transformers = ["StandardScalerTransformer", "DifferenceNormalizer", "LastKnownNormalizer"] +regimes = ["ratio", "delta"] +transform_features_list = [True, False] +transform_target_list = [True, False] + +PIPELINE_CONFIGURATIONS = {} + +for transformer, regime, transform_features, transform_target in product( + transformers, regimes, transform_features_list, transform_target_list +): + current_config = deepcopy(PIPELINE_PARAMS_STANDARD) + + if transform_target == False and transform_features == False: + continue + + if transformer == "StandardScalerTransformer": + if regime == "ratio": + current_config["target"]["features"] = { + transformer: { + "transform_features": transform_features, + "transform_target": transform_target, + }, + "LagTransformer": {"lags": 3}, + } + else: + continue + elif transformer == "LastKnownNormalizer": + current_config["target"]["features"] = { + "LagTransformer": {"lags": 3}, + transformer: { + "regime": regime, + "transform_features": transform_features, + "transform_target": transform_target, + }, + } + elif transformer == "DifferenceNormalizer": + current_config["target"]["features"] = { + transformer: { + "regime": regime, + "transform_features": transform_features, + "transform_target": transform_target, + }, + "LagTransformer": {"lags": 3}, + } + PIPELINE_CONFIGURATIONS[f"{transformer}_{regime}_{transform_features}_{transform_target}"] = ( + current_config + ) + + +@pytest.mark.parametrize( + "pipeline_params, result_lag_2__value, result_y", + [ + ( + PIPELINE_CONFIGURATIONS["StandardScalerTransformer_ratio_True_True"], + np.array([-1.729454, -1.729454, -1.729454, -1.725992, -1.725992]), + np.array([[-1.71906713], [-1.71560476], [-1.71214239]]), + ), + ( + PIPELINE_CONFIGURATIONS["StandardScalerTransformer_ratio_True_False"], + np.array([-1.729454, -1.729454, -1.729454, -1.725992, -1.725992]), + np.array([[1003], [1004], [1005]]), + ), + ( + PIPELINE_CONFIGURATIONS["StandardScalerTransformer_ratio_False_True"], + np.array([1000, 1000, 1000, 1001, 1001]), + np.array([[-1.71906713], [-1.71560476], [-1.71214239]]), + ), + ( + PIPELINE_CONFIGURATIONS["DifferenceNormalizer_ratio_True_True"], + np.array([np.nan, np.nan, np.nan, 1.001000, 1.001000]), + np.array([[1.000998], [1.00099701], [1.00099602]]), + ), + ( + PIPELINE_CONFIGURATIONS["DifferenceNormalizer_ratio_True_False"], + np.array([np.nan, np.nan, np.nan, 1.001000, 1.001000]), + np.array([[1003], [1004], [1005]]), + ), + ( + PIPELINE_CONFIGURATIONS["DifferenceNormalizer_ratio_False_True"], + np.array([1000, 1000, 1000, 1001, 1001]), + np.array([[1.000998], [1.00099701], [1.00099602]]), + ), + ( + PIPELINE_CONFIGURATIONS["DifferenceNormalizer_delta_True_True"], + np.array([np.nan, np.nan, np.nan, 1.0, 1.0]), + np.array([[1.0], [1.0], [1.0]]), + ), + ( + PIPELINE_CONFIGURATIONS["DifferenceNormalizer_delta_True_False"], + np.array([np.nan, np.nan, np.nan, 1.0, 1.0]), + np.array([[1003], [1004], [1005]]), + ), + ( + PIPELINE_CONFIGURATIONS["DifferenceNormalizer_delta_False_True"], + np.array([1000, 1000, 1000, 1001, 1001]), + np.array([[1.0], [1.0], [1.0]]), + ), + ( + PIPELINE_CONFIGURATIONS["LastKnownNormalizer_ratio_True_True"], + np.array([0.998004, 0.998004, 0.998004, 0.998006, 0.998006]), + np.array([[1.000998], [1.00199601], [1.00299401]]), + ), + ( + PIPELINE_CONFIGURATIONS["LastKnownNormalizer_ratio_True_False"], + np.array([0.998004, 0.998004, 0.998004, 0.998006, 0.998006]), + np.array([[1003], [1004], [1005]]), + ), + ( + PIPELINE_CONFIGURATIONS["LastKnownNormalizer_ratio_False_True"], + np.array([1000, 1000, 1000, 1001, 1001]), + np.array([[1.000998], [1.00199601], [1.00299401]]), + ), + ( + PIPELINE_CONFIGURATIONS["LastKnownNormalizer_delta_True_True"], + np.array([-2.0, -2.0, -2.0, -2.0, -2.0]), + np.array([[1.0], [2.0], [3.0]]), + ), + ( + PIPELINE_CONFIGURATIONS["LastKnownNormalizer_delta_True_False"], + np.array([-2.0, -2.0, -2.0, -2.0, -2.0]), + np.array([[1003], [1004], [1005]]), + ), + ( + PIPELINE_CONFIGURATIONS["LastKnownNormalizer_delta_False_True"], + np.array([1000, 1000, 1000, 1001, 1001]), + np.array([[1.0], [2.0], [3.0]]), + ), + ], +) +def test_features_names(get_dataset, pipeline_params, result_lag_2__value, result_y): + dataset = get_dataset + pipeline = Pipeline.from_dict(pipeline_params, multivariate=False) + + features_idx = index_slicer.create_idx_data( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + + target_idx = index_slicer.create_idx_target( + dataset.data, + HORIZON, + HISTORY, + step=1, + date_column=dataset.date_column, + delta=dataset.delta, + ) + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) + data = pipeline.fit_transform(data, strategy_name="FlatWideMIMOStrategy") + X, y = pipeline.generate(data) + + if pipeline_params["target"]["features"].get("StandardScalerTransformer"): + result_lag_2__value_idx = pipeline.output_features == "value__standard_scaler__lag_2" + elif pipeline_params["target"]["features"].get("DifferenceNormalizer"): + result_lag_2__value_idx = pipeline.output_features == "value__diff_norm__lag_2" + elif pipeline_params["target"]["features"].get("LastKnownNormalizer"): + result_lag_2__value_idx = pipeline.output_features == "value__lag_2__last_known_norm" + + + lag_2__value = X[:, result_lag_2__value_idx] + + assert np.allclose(lag_2__value[:5].flatten(), result_lag_2__value, equal_nan=True) + assert np.allclose(y[:3], result_y) From e1baa41bf346ef7fa28dd72e3f2424d2097292d0 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Sat, 28 Dec 2024 12:37:28 +0300 Subject: [PATCH 35/50] data["num_series"] instead of .nunique() in data_provider --- tsururu/dataset/pipeline.py | 1 + tsururu/model_training/torch_based/data_provider.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/tsururu/dataset/pipeline.py b/tsururu/dataset/pipeline.py index 8862fe5..da1b7e1 100644 --- a/tsururu/dataset/pipeline.py +++ b/tsururu/dataset/pipeline.py @@ -295,6 +295,7 @@ def create_data_dict_for_pipeline( data["id_column_name"] = dataset.id_column data["date_column_name"] = dataset.date_column data["target_column_name"] = dataset.target_column + data["num_series"] = dataset.data[dataset.id_column].nunique() data["idx_X"] = features_idx data["idx_y"] = target_idx diff --git a/tsururu/model_training/torch_based/data_provider.py b/tsururu/model_training/torch_based/data_provider.py index 6fd0d55..3e5e163 100644 --- a/tsururu/model_training/torch_based/data_provider.py +++ b/tsururu/model_training/torch_based/data_provider.py @@ -166,7 +166,7 @@ def _reshape_tensors( else: target_column_idx_list.append(i) - num_series = self.data["raw_ts_X"]["id"].nunique() + num_series = self.data["num_series"] X_tensor_target_column = X_tensor[:, target_column_idx_list] X_tensor_other_column = X_tensor[:, other_columns_idx_list] From d7636a8fbf94fc081571ccaf9e5debb9d7ea2de1 Mon Sep 17 00:00:00 2001 From: Aleksandr Yugay Date: Mon, 20 Jan 2025 15:43:54 +0300 Subject: [PATCH 36/50] fix notebooks --- examples/Tutorial_1_Quick_start.ipynb | 335 +- examples/Tutorial_2_Strategies.ipynb | 1211 ++++--- ...Tutorial_3_Transformers_and_Pipeline.ipynb | 119 +- examples/Tutorial_4_Neural_Networks.ipynb | 2943 +++++++++-------- 4 files changed, 2342 insertions(+), 2266 deletions(-) diff --git a/examples/Tutorial_1_Quick_start.ipynb b/examples/Tutorial_1_Quick_start.ipynb index cd29998..775e651 100644 --- a/examples/Tutorial_1_Quick_start.ipynb +++ b/examples/Tutorial_1_Quick_start.ipynb @@ -128,7 +128,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_path = \"datasets/global/simulated_data_to_check.csv\"\n", + "df_path = \"../datasets/global/simulated_data_to_check.csv\"\n", "\n", "dataset_params = {\n", " \"target\": {\n", @@ -418,24 +418,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9605897\ttest: 0.9668517\tbest: 0.9668517 (0)\ttotal: 60.8ms\tremaining: 1m\n", - "500:\tlearn: 0.0040519\ttest: 0.0042506\tbest: 0.0042506 (500)\ttotal: 385ms\tremaining: 384ms\n", - "999:\tlearn: 0.0024776\ttest: 0.0026944\tbest: 0.0026944 (999)\ttotal: 707ms\tremaining: 0us\n", - "\n", - "bestTest = 0.002694363765\n", + "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 53.5ms\tremaining: 53.5s\n", + "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 482ms\tremaining: 481ms\n", + "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 910ms\tremaining: 0us\n", + "bestTest = 0.003047062205\n", "bestIteration = 999\n", - "\n", - "Fold 0. Score: 0.0026943637647905937\n", - "0:\tlearn: 0.9659422\ttest: 0.9613653\tbest: 0.9613653 (0)\ttotal: 740us\tremaining: 739ms\n", - "500:\tlearn: 0.0040759\ttest: 0.0041558\tbest: 0.0041558 (500)\ttotal: 335ms\tremaining: 334ms\n", - "999:\tlearn: 0.0025120\ttest: 0.0026138\tbest: 0.0026138 (999)\ttotal: 669ms\tremaining: 0us\n", - "\n", - "bestTest = 0.00261378843\n", + "Fold 0. Score: 0.003047062205400275\n", + "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 1.22ms\tremaining: 1.22s\n", + "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 430ms\tremaining: 428ms\n", + "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 973ms\tremaining: 0us\n", + "bestTest = 0.002916854444\n", "bestIteration = 999\n", - "\n", - "Fold 1. Score: 0.0026137884302204206\n", - "Mean score: 0.0027\n", - "Std: 0.0\n" + "Fold 1. Score: 0.002916854443892958\n", + "Mean score: 0.003\n", + "Std: 0.0001\n" ] } ], @@ -447,7 +443,15 @@ "cell_type": "code", "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + } + ], "source": [ "forecast_time, current_pred = strategy.predict(dataset)" ] @@ -488,181 +492,181 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1994.783481\n", + " 1997.088714\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1994.440142\n", + " 1997.191979\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1993.71401\n", + " 1995.496356\n", " \n", " \n", " 3\n", " 1\n", " 2022-09-27\n", - " 2994.783481\n", + " 2997.475196\n", " \n", " \n", " 4\n", " 1\n", " 2022-09-28\n", - " 2994.440142\n", + " 2997.553282\n", " \n", " \n", " 5\n", " 1\n", " 2022-09-29\n", - " 2993.71401\n", + " 2995.826568\n", " \n", " \n", " 6\n", " 2\n", " 2022-09-27\n", - " 3994.783481\n", + " 3997.392796\n", " \n", " \n", " 7\n", " 2\n", " 2022-09-28\n", - 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Score: 0.002971764957214351\n", + "Mean score: 0.003\n", + "Std: 0.0\n", + "freq: Day; period: 1\n" ] } ], @@ -787,181 +795,181 @@ " \n", " 0\n", " 1997.0\n", - " 1994.048102\n", + " 1993.68704\n", " 0\n", " \n", " \n", " 1\n", " 1998.0\n", - " 1994.405436\n", + " 1994.202883\n", " 0\n", " \n", " \n", " 2\n", " 1999.0\n", - " 1995.303323\n", + " 1994.669445\n", " 0\n", " \n", " \n", " 3\n", " 2997.0\n", - " 2994.048102\n", + " 2993.991901\n", " 1\n", " \n", " \n", " 4\n", " 2998.0\n", - " 2994.405436\n", + " 2994.586288\n", " 1\n", " \n", " \n", " 5\n", " 2999.0\n", - " 2995.303323\n", + " 2995.082926\n", " 1\n", " \n", " \n", " 6\n", " 3997.0\n", - " 3994.048102\n", + " 3993.914717\n", " 2\n", " \n", " \n", " 7\n", " 3998.0\n", - " 3994.405436\n", + " 3994.509697\n", " 2\n", " \n", " \n", " 8\n", " 3999.0\n", - " 3995.303323\n", + " 3995.015013\n", " 2\n", " \n", " \n", " 9\n", " 4997.0\n", - " 4994.048102\n", + " 4994.022114\n", " 3\n", " \n", " \n", " 10\n", " 4998.0\n", - " 4994.405436\n", + " 4994.615917\n", " 3\n", " \n", " \n", " 11\n", " 4999.0\n", - 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" 8995.303323\n", + " 8995.147741\n", " 7\n", " \n", " \n", " 24\n", " 9997.0\n", - " 9994.048102\n", + " 9993.838821\n", " 8\n", " \n", " \n", " 25\n", " 9998.0\n", - " 9994.405436\n", + " 9994.4268\n", " 8\n", " \n", " \n", " 26\n", " 9999.0\n", - " 9995.303323\n", + " 9995.1378\n", " 8\n", " \n", " \n", " 27\n", " 10997.0\n", - " 10994.048102\n", + " 10993.687397\n", " 9\n", " \n", " \n", " 28\n", " 10998.0\n", - " 10994.405436\n", + " 10994.270971\n", " 9\n", " \n", " \n", " 29\n", " 10999.0\n", - " 10995.303323\n", + " 10994.990313\n", " 9\n", " \n", " \n", @@ -970,36 +978,36 @@ ], "text/plain": [ " y_true_1 y_pred_1 id_1\n", - "0 1997.0 1994.048102 0\n", - "1 1998.0 1994.405436 0\n", - "2 1999.0 1995.303323 0\n", - "3 2997.0 2994.048102 1\n", - "4 2998.0 2994.405436 1\n", - "5 2999.0 2995.303323 1\n", - "6 3997.0 3994.048102 2\n", - "7 3998.0 3994.405436 2\n", - "8 3999.0 3995.303323 2\n", - "9 4997.0 4994.048102 3\n", - "10 4998.0 4994.405436 3\n", - "11 4999.0 4995.303323 3\n", - "12 5997.0 5994.048102 4\n", - "13 5998.0 5994.405436 4\n", - "14 5999.0 5995.303323 4\n", - "15 6997.0 6994.048102 5\n", - "16 6998.0 6994.405436 5\n", - "17 6999.0 6995.303323 5\n", - "18 7997.0 7994.048102 6\n", - "19 7998.0 7994.405436 6\n", - "20 7999.0 7995.303323 6\n", - "21 8997.0 8994.048102 7\n", - "22 8998.0 8994.405436 7\n", - "23 8999.0 8995.303323 7\n", - "24 9997.0 9994.048102 8\n", - "25 9998.0 9994.405436 8\n", - "26 9999.0 9995.303323 8\n", - "27 10997.0 10994.048102 9\n", - "28 10998.0 10994.405436 9\n", - "29 10999.0 10995.303323 9" + "0 1997.0 1993.68704 0\n", + "1 1998.0 1994.202883 0\n", + "2 1999.0 1994.669445 0\n", + "3 2997.0 2993.991901 1\n", + "4 2998.0 2994.586288 1\n", + "5 2999.0 2995.082926 1\n", + "6 3997.0 3993.914717 2\n", + "7 3998.0 3994.509697 2\n", + "8 3999.0 3995.015013 2\n", + "9 4997.0 4994.022114 3\n", + "10 4998.0 4994.615917 3\n", + "11 4999.0 4995.170471 3\n", + "12 5997.0 5994.175996 4\n", + "13 5998.0 5994.779734 4\n", + "14 5999.0 5995.319329 4\n", + "15 6997.0 6994.179421 5\n", + "16 6998.0 6994.784167 5\n", + "17 6999.0 6995.312331 5\n", + "18 7997.0 7994.113358 6\n", + "19 7998.0 7994.705541 6\n", + "20 7999.0 7995.236341 6\n", + "21 8997.0 8993.839156 7\n", + "22 8998.0 8994.431338 7\n", + "23 8999.0 8995.147741 7\n", + "24 9997.0 9993.838821 8\n", + "25 9998.0 9994.4268 8\n", + "26 9999.0 9995.1378 8\n", + "27 10997.0 10993.687397 9\n", + "28 10998.0 10994.270971 9\n", + "29 10999.0 10994.990313 9" ] }, "execution_count": 15, @@ -1050,11 +1058,11 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "df_path_2 = \"datasets/global/simulated_data_to_check_28D.csv\"\n", + "df_path_2 = \"../datasets/global/simulated_data_to_check_28D.csv\"\n", "\n", "# Configure the features settings\n", "dataset_params_2 = {\n", @@ -1075,14 +1083,19 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "freq: Month; period: 1.0\n" + "freq: Month; period: 1.0\n", + "\n", + " It seems that the data is not regular. Please, check the data and the frequency info. \n", + " For multivariate regime it is critical to have regular data.\n", + " For global regime each regular part of time series will be processed as separate time series. \n", + " \n" ] } ], @@ -1103,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1133,7 +1146,7 @@ ], "metadata": { "kernelspec": { - "display_name": "tsururu", + "display_name": "tsururu_test", "language": "python", "name": "python3" }, @@ -1147,7 +1160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.13" }, "orig_nbformat": 4 }, diff --git a/examples/Tutorial_2_Strategies.ipynb b/examples/Tutorial_2_Strategies.ipynb index 880d671..b7310aa 100644 --- a/examples/Tutorial_2_Strategies.ipynb +++ b/examples/Tutorial_2_Strategies.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +626,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -847,14 +847,14 @@ } ], "source": [ - "df = pd.read_csv(\"./datasets/global/simulated_data_to_check.csv\")\n", + "df = pd.read_csv(\"../datasets/global/simulated_data_to_check.csv\")\n", "display(df.iloc[:10])\n", "display(df.iloc[2000:2010])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -864,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -900,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1073,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1089,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1120,9 +1120,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -1141,9 +1141,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -1160,9 +1160,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -1179,9 +1179,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -1198,9 +1198,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -1217,9 +1217,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -1239,12 +1239,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 5 5 6 \n", @@ -1578,21 +1578,17 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9179675\ttest: 1.9252979\tbest: 1.9252979 (0)\ttotal: 58.9ms\tremaining: 58.8s\n", - "500:\tlearn: 0.0071597\ttest: 0.0072534\tbest: 0.0072534 (500)\ttotal: 1.59s\tremaining: 1.58s\n", - "999:\tlearn: 0.0039772\ttest: 0.0042419\tbest: 0.0042419 (999)\ttotal: 2.95s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9179675\ttest: 1.9252979\tbest: 1.9252979 (0)\ttotal: 51ms\tremaining: 50.9s\n", + "500:\tlearn: 0.0071597\ttest: 0.0072534\tbest: 0.0072534 (500)\ttotal: 1.13s\tremaining: 1.12s\n", + "999:\tlearn: 0.0039772\ttest: 0.0042419\tbest: 0.0042419 (999)\ttotal: 2.31s\tremaining: 0us\n", "bestTest = 0.004241889806\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.004241889806475644\n", - "0:\tlearn: 1.9239154\ttest: 1.9197469\tbest: 1.9197469 (0)\ttotal: 3.04ms\tremaining: 3.03s\n", - "500:\tlearn: 0.0072229\ttest: 0.0074385\tbest: 0.0074385 (500)\ttotal: 1.35s\tremaining: 1.34s\n", - "999:\tlearn: 0.0040616\ttest: 0.0043572\tbest: 0.0043572 (999)\ttotal: 2.53s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9239154\ttest: 1.9197469\tbest: 1.9197469 (0)\ttotal: 5.12ms\tremaining: 5.11s\n", + "500:\tlearn: 0.0072229\ttest: 0.0074385\tbest: 0.0074385 (500)\ttotal: 1.07s\tremaining: 1.06s\n", + "999:\tlearn: 0.0040616\ttest: 0.0043572\tbest: 0.0043572 (999)\ttotal: 2.4s\tremaining: 0us\n", "bestTest = 0.004357247947\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.004357247946880891\n", "Mean score: 0.0043\n", "Std: 0.0001\n" @@ -1601,10 +1597,10 @@ { "data": { "text/plain": [ - "(5.744672060012817, <__main__.MIMOStrategy at 0x310fdf9a0>)" + "(5.169183015823364, <__main__.MIMOStrategy at 0x7f2e26015e40>)" ] }, - "execution_count": 8, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1615,13 +1611,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -2029,7 +2026,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -2137,7 +2134,7 @@ "9 2 2022-09-28 3989.124942" ] }, - "execution_count": 10, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -2155,7 +2152,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -2171,7 +2168,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -2202,9 +2199,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -2223,9 +2220,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -2242,9 +2239,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -2261,9 +2258,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -2280,9 +2277,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -2299,9 +2296,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -2321,12 +2318,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 5 5 6 \n", @@ -2690,21 +2687,17 @@ " -1.68444342 -1.68098105 -1.69136817 -1.6879058 -1.68444342 -1.68098105\n", " -1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 6.1091341\ttest: 6.0736765\tbest: 6.0736765 (0)\ttotal: 75.4ms\tremaining: 1m 15s\n", - "500:\tlearn: 0.0448467\ttest: 0.0596515\tbest: 0.0596515 (500)\ttotal: 31s\tremaining: 30.9s\n", - "999:\tlearn: 0.0252180\ttest: 0.0510570\tbest: 0.0510570 (999)\ttotal: 1m 6s\tremaining: 0us\n", - "\n", + "0:\tlearn: 6.1091341\ttest: 6.0736765\tbest: 6.0736765 (0)\ttotal: 66.5ms\tremaining: 1m 6s\n", + "500:\tlearn: 0.0448467\ttest: 0.0596515\tbest: 0.0596515 (500)\ttotal: 20.9s\tremaining: 20.8s\n", + "999:\tlearn: 0.0252180\ttest: 0.0510570\tbest: 0.0510570 (999)\ttotal: 42s\tremaining: 0us\n", "bestTest = 0.0510569861\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.05105698609713615\n", - "0:\tlearn: 6.0551918\ttest: 6.1223679\tbest: 6.1223679 (0)\ttotal: 65ms\tremaining: 1m 4s\n", - "500:\tlearn: 0.0435543\ttest: 0.0595089\tbest: 0.0595087 (499)\ttotal: 36.4s\tremaining: 36.3s\n", - "999:\tlearn: 0.0252648\ttest: 0.0521669\tbest: 0.0521625 (997)\ttotal: 1m 11s\tremaining: 0us\n", - "\n", + "0:\tlearn: 6.0551918\ttest: 6.1223679\tbest: 6.1223679 (0)\ttotal: 43.4ms\tremaining: 43.3s\n", + "500:\tlearn: 0.0435543\ttest: 0.0595089\tbest: 0.0595087 (499)\ttotal: 20.6s\tremaining: 20.5s\n", + "999:\tlearn: 0.0252648\ttest: 0.0521669\tbest: 0.0521625 (997)\ttotal: 41.1s\tremaining: 0us\n", "bestTest = 0.05216248184\n", "bestIteration = 997\n", - "\n", "Shrink model to first 998 iterations.\n", "Fold 1. Score: 0.052162481838623376\n", "Mean score: 0.0516\n", @@ -2714,10 +2707,10 @@ { "data": { "text/plain": [ - "(137.88563323020935, <__main__.MIMOStrategy at 0x310fdfc10>)" + "(83.68235731124878, <__main__.MIMOStrategy at 0x7f2e2616f400>)" ] }, - "execution_count": 12, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -2728,13 +2721,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -2986,7 +2980,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 52, "metadata": {}, "outputs": [ { @@ -3094,7 +3088,7 @@ "9 2 2022-09-28 3992.037829" ] }, - "execution_count": 14, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -3126,7 +3120,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -3160,9 +3154,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -3181,9 +3175,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -3200,9 +3194,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -3219,9 +3213,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -3238,9 +3232,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -3257,9 +3251,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -3279,12 +3273,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 5 5 6 \n", @@ -3618,25 +3612,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 5.22ms\tremaining: 5.21s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 802ms\tremaining: 798ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.61s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 2.43ms\tremaining: 2.43s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 925ms\tremaining: 921ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.76s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.64ms\tremaining: 1.63s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 772ms\tremaining: 769ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.66s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 2.11ms\tremaining: 2.11s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 658ms\tremaining: 655ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.31s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -3912,9 +3903,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -3933,9 +3924,9 @@ " \n", " \n", " 1000\n", + " 1\n", " 2000.0\n", " 2020-01-01\n", - " 1\n", " -1.729454\n", " 2020\n", " 1\n", @@ -3952,9 +3943,9 @@ " \n", " \n", " 1001\n", + " 1\n", " 2001.0\n", " 2020-01-02\n", - " 1\n", " -1.725992\n", " 2020\n", " 1\n", @@ -3971,9 +3962,9 @@ " \n", " \n", " 1002\n", + " 1\n", " 2002.0\n", " 2020-01-03\n", - " 1\n", " -1.722530\n", " 2020\n", " 1\n", @@ -3990,9 +3981,9 @@ " \n", " \n", " 1003\n", + " 1\n", " 2003.0\n", " 2020-01-04\n", - " 1\n", " -1.719067\n", " 2020\n", " 1\n", @@ -4009,9 +4000,9 @@ " \n", " \n", " 1004\n", + " 1\n", " 2004.0\n", " 2020-01-05\n", - " 1\n", " -1.715605\n", " 2020\n", " 1\n", @@ -4031,12 +4022,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "1000 2000.0 2020-01-01 1 -1.729454 2020 \n", - "1001 2001.0 2020-01-02 1 -1.725992 2020 \n", - "1002 2002.0 2020-01-03 1 -1.722530 2020 \n", - "1003 2003.0 2020-01-04 1 -1.719067 2020 \n", - "1004 2004.0 2020-01-05 1 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "1000 1 2000.0 2020-01-01 -1.729454 2020 \n", + "1001 1 2001.0 2020-01-02 -1.725992 2020 \n", + "1002 1 2002.0 2020-01-03 -1.722530 2020 \n", + "1003 1 2003.0 2020-01-04 -1.719067 2020 \n", + "1004 1 2004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "1000 1 5 5 6 \n", @@ -4371,24 +4362,21 @@ " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.82ms\tremaining: 1.82s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 846ms\tremaining: 843ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.63s\tremaining: 0us\n", - "\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 702ms\tremaining: 699ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.36s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.69ms\tremaining: 1.69s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 747ms\tremaining: 744ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.52s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 2.04ms\tremaining: 2.03s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 650ms\tremaining: 648ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.32s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -4596,9 +4584,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -4617,9 +4605,9 @@ " \n", " \n", " 2000\n", + " 2\n", " 3000.0\n", " 2020-01-01\n", - " 2\n", " -1.729454\n", " 2020\n", " 1\n", @@ -4636,9 +4624,9 @@ " \n", " \n", " 2001\n", + " 2\n", " 3001.0\n", " 2020-01-02\n", - " 2\n", " -1.725992\n", " 2020\n", " 1\n", @@ -4655,9 +4643,9 @@ " \n", " \n", " 2002\n", + " 2\n", " 3002.0\n", " 2020-01-03\n", - " 2\n", " -1.722530\n", " 2020\n", " 1\n", @@ -4674,9 +4662,9 @@ " \n", " \n", " 2003\n", + " 2\n", " 3003.0\n", " 2020-01-04\n", - " 2\n", " -1.719067\n", " 2020\n", " 1\n", @@ -4693,9 +4681,9 @@ " \n", " \n", " 2004\n", + " 2\n", " 3004.0\n", " 2020-01-05\n", - " 2\n", " -1.715605\n", " 2020\n", " 1\n", @@ -4715,12 +4703,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "2000 3000.0 2020-01-01 2 -1.729454 2020 \n", - "2001 3001.0 2020-01-02 2 -1.725992 2020 \n", - "2002 3002.0 2020-01-03 2 -1.722530 2020 \n", - "2003 3003.0 2020-01-04 2 -1.719067 2020 \n", - "2004 3004.0 2020-01-05 2 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "2000 2 3000.0 2020-01-01 -1.729454 2020 \n", + "2001 2 3001.0 2020-01-02 -1.725992 2020 \n", + "2002 2 3002.0 2020-01-03 -1.722530 2020 \n", + "2003 2 3003.0 2020-01-04 -1.719067 2020 \n", + "2004 2 3004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "2000 1 5 5 6 \n", @@ -5054,25 +5042,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.51ms\tremaining: 1.51s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 780ms\tremaining: 777ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.6s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.64ms\tremaining: 1.64s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 648ms\tremaining: 646ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.27s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.81ms\tremaining: 1.81s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 807ms\tremaining: 803ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.56s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.62ms\tremaining: 1.62s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 627ms\tremaining: 625ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.25s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -5280,9 +5265,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -5301,9 +5286,9 @@ " \n", " \n", " 3000\n", + " 3\n", " 4000.0\n", " 2020-01-01\n", - " 3\n", " -1.729454\n", " 2020\n", " 1\n", @@ -5320,9 +5305,9 @@ " \n", " \n", " 3001\n", + " 3\n", " 4001.0\n", " 2020-01-02\n", - " 3\n", " -1.725992\n", " 2020\n", " 1\n", @@ -5339,9 +5324,9 @@ " \n", " \n", " 3002\n", + " 3\n", " 4002.0\n", " 2020-01-03\n", - " 3\n", " -1.722530\n", " 2020\n", " 1\n", @@ -5358,9 +5343,9 @@ " \n", " \n", " 3003\n", + " 3\n", " 4003.0\n", " 2020-01-04\n", - " 3\n", " -1.719067\n", " 2020\n", " 1\n", @@ -5377,9 +5362,9 @@ " \n", " \n", " 3004\n", + " 3\n", " 4004.0\n", " 2020-01-05\n", - " 3\n", " -1.715605\n", " 2020\n", " 1\n", @@ -5399,12 +5384,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "3000 4000.0 2020-01-01 3 -1.729454 2020 \n", - "3001 4001.0 2020-01-02 3 -1.725992 2020 \n", - "3002 4002.0 2020-01-03 3 -1.722530 2020 \n", - "3003 4003.0 2020-01-04 3 -1.719067 2020 \n", - "3004 4004.0 2020-01-05 3 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "3000 3 4000.0 2020-01-01 -1.729454 2020 \n", + "3001 3 4001.0 2020-01-02 -1.725992 2020 \n", + "3002 3 4002.0 2020-01-03 -1.722530 2020 \n", + "3003 3 4003.0 2020-01-04 -1.719067 2020 \n", + "3004 3 4004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "3000 1 5 5 6 \n", @@ -5738,25 +5723,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.51ms\tremaining: 1.51s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 756ms\tremaining: 753ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.52s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 2.11ms\tremaining: 2.1s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 666ms\tremaining: 663ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.28s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.94ms\tremaining: 1.94s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 752ms\tremaining: 749ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.52s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.45ms\tremaining: 1.45s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 640ms\tremaining: 637ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.27s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -5964,9 +5946,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -5985,9 +5967,9 @@ " \n", " \n", " 4000\n", + " 4\n", " 5000.0\n", " 2020-01-01\n", - " 4\n", " -1.729454\n", " 2020\n", " 1\n", @@ -6004,9 +5986,9 @@ " \n", " \n", " 4001\n", + " 4\n", " 5001.0\n", " 2020-01-02\n", - " 4\n", " -1.725992\n", " 2020\n", " 1\n", @@ -6023,9 +6005,9 @@ " \n", " \n", " 4002\n", + " 4\n", " 5002.0\n", " 2020-01-03\n", - " 4\n", " -1.722530\n", " 2020\n", " 1\n", @@ -6042,9 +6024,9 @@ " \n", " \n", " 4003\n", + " 4\n", " 5003.0\n", " 2020-01-04\n", - " 4\n", " -1.719067\n", " 2020\n", " 1\n", @@ -6061,9 +6043,9 @@ " \n", " \n", " 4004\n", + " 4\n", " 5004.0\n", " 2020-01-05\n", - " 4\n", " -1.715605\n", " 2020\n", " 1\n", @@ -6083,12 +6065,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "4000 5000.0 2020-01-01 4 -1.729454 2020 \n", - "4001 5001.0 2020-01-02 4 -1.725992 2020 \n", - "4002 5002.0 2020-01-03 4 -1.722530 2020 \n", - "4003 5003.0 2020-01-04 4 -1.719067 2020 \n", - "4004 5004.0 2020-01-05 4 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "4000 4 5000.0 2020-01-01 -1.729454 2020 \n", + "4001 4 5001.0 2020-01-02 -1.725992 2020 \n", + "4002 4 5002.0 2020-01-03 -1.722530 2020 \n", + "4003 4 5003.0 2020-01-04 -1.719067 2020 \n", + "4004 4 5004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "4000 1 5 5 6 \n", @@ -6422,25 +6404,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.57ms\tremaining: 1.57s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 781ms\tremaining: 778ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.63s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.55ms\tremaining: 1.55s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 620ms\tremaining: 618ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.25s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 2.49ms\tremaining: 2.49s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 847ms\tremaining: 844ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.62s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.46ms\tremaining: 1.45s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 613ms\tremaining: 611ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.25s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -6648,9 +6627,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -6669,9 +6648,9 @@ " \n", " \n", " 5000\n", + " 5\n", " 6000.0\n", " 2020-01-01\n", - " 5\n", " -1.729454\n", " 2020\n", " 1\n", @@ -6688,9 +6667,9 @@ " \n", " \n", " 5001\n", + " 5\n", " 6001.0\n", " 2020-01-02\n", - " 5\n", " -1.725992\n", " 2020\n", " 1\n", @@ -6707,9 +6686,9 @@ " \n", " \n", " 5002\n", + " 5\n", " 6002.0\n", " 2020-01-03\n", - " 5\n", " -1.722530\n", " 2020\n", " 1\n", @@ -6726,9 +6705,9 @@ " \n", " \n", " 5003\n", + " 5\n", " 6003.0\n", " 2020-01-04\n", - " 5\n", " -1.719067\n", " 2020\n", " 1\n", @@ -6745,9 +6724,9 @@ " \n", " \n", " 5004\n", + " 5\n", " 6004.0\n", " 2020-01-05\n", - " 5\n", " -1.715605\n", " 2020\n", " 1\n", @@ -6767,12 +6746,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "5000 6000.0 2020-01-01 5 -1.729454 2020 \n", - "5001 6001.0 2020-01-02 5 -1.725992 2020 \n", - "5002 6002.0 2020-01-03 5 -1.722530 2020 \n", - "5003 6003.0 2020-01-04 5 -1.719067 2020 \n", - "5004 6004.0 2020-01-05 5 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "5000 5 6000.0 2020-01-01 -1.729454 2020 \n", + "5001 5 6001.0 2020-01-02 -1.725992 2020 \n", + "5002 5 6002.0 2020-01-03 -1.722530 2020 \n", + "5003 5 6003.0 2020-01-04 -1.719067 2020 \n", + "5004 5 6004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "5000 1 5 5 6 \n", @@ -7106,25 +7085,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.6ms\tremaining: 1.59s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 758ms\tremaining: 755ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.57s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.55ms\tremaining: 1.55s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 627ms\tremaining: 624ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.25s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.57ms\tremaining: 1.56s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 763ms\tremaining: 760ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.51s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.5ms\tremaining: 1.5s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 636ms\tremaining: 633ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.26s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -7332,9 +7308,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -7353,9 +7329,9 @@ " \n", " \n", " 6000\n", + " 6\n", " 7000.0\n", " 2020-01-01\n", - " 6\n", " -1.729454\n", " 2020\n", " 1\n", @@ -7372,9 +7348,9 @@ " \n", " \n", " 6001\n", + " 6\n", " 7001.0\n", " 2020-01-02\n", - " 6\n", " -1.725992\n", " 2020\n", " 1\n", @@ -7391,9 +7367,9 @@ " \n", " \n", " 6002\n", + " 6\n", " 7002.0\n", " 2020-01-03\n", - " 6\n", " -1.722530\n", " 2020\n", " 1\n", @@ -7410,9 +7386,9 @@ " \n", " \n", " 6003\n", + " 6\n", " 7003.0\n", " 2020-01-04\n", - " 6\n", " -1.719067\n", " 2020\n", " 1\n", @@ -7429,9 +7405,9 @@ " \n", " \n", " 6004\n", + " 6\n", " 7004.0\n", " 2020-01-05\n", - " 6\n", " -1.715605\n", " 2020\n", " 1\n", @@ -7451,12 +7427,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "6000 7000.0 2020-01-01 6 -1.729454 2020 \n", - "6001 7001.0 2020-01-02 6 -1.725992 2020 \n", - "6002 7002.0 2020-01-03 6 -1.722530 2020 \n", - "6003 7003.0 2020-01-04 6 -1.719067 2020 \n", - "6004 7004.0 2020-01-05 6 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "6000 6 7000.0 2020-01-01 -1.729454 2020 \n", + "6001 6 7001.0 2020-01-02 -1.725992 2020 \n", + "6002 6 7002.0 2020-01-03 -1.722530 2020 \n", + "6003 6 7003.0 2020-01-04 -1.719067 2020 \n", + "6004 6 7004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "6000 1 5 5 6 \n", @@ -7790,25 +7766,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.56ms\tremaining: 1.56s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 778ms\tremaining: 775ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.55s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.42ms\tremaining: 1.42s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 612ms\tremaining: 609ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.22s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.67ms\tremaining: 1.67s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 760ms\tremaining: 757ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.53s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.46ms\tremaining: 1.46s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 623ms\tremaining: 620ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.25s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -8016,9 +7989,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -8037,9 +8010,9 @@ " \n", " \n", " 7000\n", + " 7\n", " 8000.0\n", " 2020-01-01\n", - " 7\n", " -1.729454\n", " 2020\n", " 1\n", @@ -8056,9 +8029,9 @@ " \n", " \n", " 7001\n", + " 7\n", " 8001.0\n", " 2020-01-02\n", - " 7\n", " -1.725992\n", " 2020\n", " 1\n", @@ -8075,9 +8048,9 @@ " \n", " \n", " 7002\n", + " 7\n", " 8002.0\n", " 2020-01-03\n", - " 7\n", " -1.722530\n", " 2020\n", " 1\n", @@ -8094,9 +8067,9 @@ " \n", " \n", " 7003\n", + " 7\n", " 8003.0\n", " 2020-01-04\n", - " 7\n", " -1.719067\n", " 2020\n", " 1\n", @@ -8113,9 +8086,9 @@ " \n", " \n", " 7004\n", + " 7\n", " 8004.0\n", " 2020-01-05\n", - " 7\n", " -1.715605\n", " 2020\n", " 1\n", @@ -8135,12 +8108,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "7000 8000.0 2020-01-01 7 -1.729454 2020 \n", - "7001 8001.0 2020-01-02 7 -1.725992 2020 \n", - "7002 8002.0 2020-01-03 7 -1.722530 2020 \n", - "7003 8003.0 2020-01-04 7 -1.719067 2020 \n", - "7004 8004.0 2020-01-05 7 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "7000 7 8000.0 2020-01-01 -1.729454 2020 \n", + "7001 7 8001.0 2020-01-02 -1.725992 2020 \n", + "7002 7 8002.0 2020-01-03 -1.722530 2020 \n", + "7003 7 8003.0 2020-01-04 -1.719067 2020 \n", + "7004 7 8004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "7000 1 5 5 6 \n", @@ -8474,25 +8447,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.72ms\tremaining: 1.72s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 745ms\tremaining: 742ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.51s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.4ms\tremaining: 1.4s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 607ms\tremaining: 605ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.23s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.72ms\tremaining: 1.72s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 763ms\tremaining: 760ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.52s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 8.48ms\tremaining: 8.47s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 678ms\tremaining: 675ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.32s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -8700,9 +8670,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -8721,9 +8691,9 @@ " \n", " \n", " 8000\n", + " 8\n", " 9000.0\n", " 2020-01-01\n", - " 8\n", " -1.729454\n", " 2020\n", " 1\n", @@ -8740,9 +8710,9 @@ " \n", " \n", " 8001\n", + " 8\n", " 9001.0\n", " 2020-01-02\n", - " 8\n", " -1.725992\n", " 2020\n", " 1\n", @@ -8759,9 +8729,9 @@ " \n", " \n", " 8002\n", + " 8\n", " 9002.0\n", " 2020-01-03\n", - " 8\n", " -1.722530\n", " 2020\n", " 1\n", @@ -8778,9 +8748,9 @@ " \n", " \n", " 8003\n", + " 8\n", " 9003.0\n", " 2020-01-04\n", - " 8\n", " -1.719067\n", " 2020\n", " 1\n", @@ -8797,9 +8767,9 @@ " \n", " \n", " 8004\n", + " 8\n", " 9004.0\n", " 2020-01-05\n", - " 8\n", " -1.715605\n", " 2020\n", " 1\n", @@ -8819,12 +8789,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "8000 9000.0 2020-01-01 8 -1.729454 2020 \n", - "8001 9001.0 2020-01-02 8 -1.725992 2020 \n", - "8002 9002.0 2020-01-03 8 -1.722530 2020 \n", - "8003 9003.0 2020-01-04 8 -1.719067 2020 \n", - "8004 9004.0 2020-01-05 8 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "8000 8 9000.0 2020-01-01 -1.729454 2020 \n", + "8001 8 9001.0 2020-01-02 -1.725992 2020 \n", + "8002 8 9002.0 2020-01-03 -1.722530 2020 \n", + "8003 8 9003.0 2020-01-04 -1.719067 2020 \n", + "8004 8 9004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "8000 1 5 5 6 \n", @@ -9158,25 +9128,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 2.38ms\tremaining: 2.38s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 751ms\tremaining: 748ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.51s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.7ms\tremaining: 1.7s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 631ms\tremaining: 629ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.28s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 2.57ms\tremaining: 2.57s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 762ms\tremaining: 759ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.51s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.95ms\tremaining: 1.95s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 609ms\tremaining: 607ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.23s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -9384,9 +9351,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -9405,9 +9372,9 @@ " \n", " \n", " 9000\n", + " 9\n", " 10000.0\n", " 2020-01-01\n", - " 9\n", " -1.729454\n", " 2020\n", " 1\n", @@ -9424,9 +9391,9 @@ " \n", " \n", " 9001\n", + " 9\n", " 10001.0\n", " 2020-01-02\n", - " 9\n", " -1.725992\n", " 2020\n", " 1\n", @@ -9443,9 +9410,9 @@ " \n", " \n", " 9002\n", + " 9\n", " 10002.0\n", " 2020-01-03\n", - " 9\n", " -1.722530\n", " 2020\n", " 1\n", @@ -9462,9 +9429,9 @@ " \n", " \n", " 9003\n", + " 9\n", " 10003.0\n", " 2020-01-04\n", - " 9\n", " -1.719067\n", " 2020\n", " 1\n", @@ -9481,9 +9448,9 @@ " \n", " \n", " 9004\n", + " 9\n", " 10004.0\n", " 2020-01-05\n", - " 9\n", " -1.715605\n", " 2020\n", " 1\n", @@ -9503,12 +9470,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "9000 10000.0 2020-01-01 9 -1.729454 2020 \n", - "9001 10001.0 2020-01-02 9 -1.725992 2020 \n", - "9002 10002.0 2020-01-03 9 -1.722530 2020 \n", - "9003 10003.0 2020-01-04 9 -1.719067 2020 \n", - "9004 10004.0 2020-01-05 9 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "9000 9 10000.0 2020-01-01 -1.729454 2020 \n", + "9001 9 10001.0 2020-01-02 -1.725992 2020 \n", + "9002 9 10002.0 2020-01-03 -1.722530 2020 \n", + "9003 9 10003.0 2020-01-04 -1.719067 2020 \n", + "9004 9 10004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "9000 1 5 5 6 \n", @@ -9842,25 +9809,22 @@ " [-1.69483054 -1.69136817 -1.6879058 -1.68444342]\n", " [-1.69136817 -1.6879058 -1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 2.97ms\tremaining: 2.96s\n", - "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 742ms\tremaining: 739ms\n", - "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.49s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9332358\ttest: 1.9222850\tbest: 1.9222850 (0)\ttotal: 1.93ms\tremaining: 1.93s\n", + "500:\tlearn: 0.0135313\ttest: 0.0312388\tbest: 0.0312388 (500)\ttotal: 645ms\tremaining: 642ms\n", + "999:\tlearn: 0.0074311\ttest: 0.0291400\tbest: 0.0291400 (999)\ttotal: 1.27s\tremaining: 0us\n", "bestTest = 0.02914004013\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.029140040129593817\n", - "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.72ms\tremaining: 1.71s\n", - "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 767ms\tremaining: 764ms\n", - "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.51s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.9164336\ttest: 1.9384335\tbest: 1.9384335 (0)\ttotal: 1.8ms\tremaining: 1.8s\n", + "500:\tlearn: 0.0132088\ttest: 0.0234053\tbest: 0.0234053 (500)\ttotal: 633ms\tremaining: 631ms\n", + "999:\tlearn: 0.0069993\ttest: 0.0205025\tbest: 0.0205012 (998)\ttotal: 1.28s\tremaining: 0us\n", "bestTest = 0.02050117144\n", "bestIteration = 998\n", - "\n", "Shrink model to first 999 iterations.\n", "Fold 1. Score: 0.02050117143812392\n", "Mean score: 0.0248\n", "Std: 0.0043\n", + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (11,)\n" ] }, @@ -10066,7 +10030,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -10174,7 +10138,7 @@ "1 2 2022-09-28 3983.814539" ] }, - "execution_count": 16, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -10202,7 +10166,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -10218,7 +10182,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -10249,9 +10213,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -10270,9 +10234,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -10289,9 +10253,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -10308,9 +10272,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -10327,9 +10291,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -10346,9 +10310,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -10368,12 +10332,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 2 2 3 \n", @@ -10707,21 +10671,17 @@ " [-1.69483054]\n", " [-1.69136817]] \n", "\n", - "0:\tlearn: 0.9606043\ttest: 0.9668573\tbest: 0.9668573 (0)\ttotal: 1.39ms\tremaining: 1.38s\n", - "500:\tlearn: 0.0037506\ttest: 0.0038327\tbest: 0.0038327 (500)\ttotal: 600ms\tremaining: 597ms\n", - "999:\tlearn: 0.0021159\ttest: 0.0022825\tbest: 0.0022825 (999)\ttotal: 1.19s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9606043\ttest: 0.9668573\tbest: 0.9668573 (0)\ttotal: 1.36ms\tremaining: 1.36s\n", + "500:\tlearn: 0.0037506\ttest: 0.0038327\tbest: 0.0038327 (500)\ttotal: 506ms\tremaining: 504ms\n", + "999:\tlearn: 0.0021159\ttest: 0.0022825\tbest: 0.0022825 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002282486309\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.002282486308604412\n", - "0:\tlearn: 0.9660711\ttest: 0.9614294\tbest: 0.9614294 (0)\ttotal: 1.21ms\tremaining: 1.21s\n", - "500:\tlearn: 0.0038068\ttest: 0.0039266\tbest: 0.0039266 (500)\ttotal: 588ms\tremaining: 585ms\n", - "999:\tlearn: 0.0020537\ttest: 0.0022117\tbest: 0.0022117 (999)\ttotal: 1.18s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9660711\ttest: 0.9614294\tbest: 0.9614294 (0)\ttotal: 1.32ms\tremaining: 1.32s\n", + "500:\tlearn: 0.0038068\ttest: 0.0039266\tbest: 0.0039266 (500)\ttotal: 515ms\tremaining: 513ms\n", + "999:\tlearn: 0.0020537\ttest: 0.0022117\tbest: 0.0022117 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002211714388\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.002211714388200288\n", "Mean score: 0.0022\n", "Std: 0.0\n" @@ -10730,10 +10690,10 @@ { "data": { "text/plain": [ - "(2.5806527137756348, <__main__.RecursiveStrategy at 0x311004610>)" + "(2.397395133972168, <__main__.RecursiveStrategy at 0x7f2e2697cd00>)" ] }, - "execution_count": 18, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -10744,13 +10704,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -12370,7 +12331,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -12478,7 +12439,7 @@ "9 2 2022-09-28 3994.303798" ] }, - "execution_count": 20, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -12503,7 +12464,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -12520,7 +12481,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -12551,9 +12512,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -12572,9 +12533,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -12591,9 +12552,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -12610,9 +12571,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -12629,9 +12590,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -12648,9 +12609,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -12670,12 +12631,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 2 2 3 \n", @@ -13009,21 +12970,17 @@ " [-1.69483054]\n", " [-1.69136817]] \n", "\n", - "0:\tlearn: 0.9606043\ttest: 0.9668573\tbest: 0.9668573 (0)\ttotal: 1.17ms\tremaining: 1.17s\n", - "500:\tlearn: 0.0037506\ttest: 0.0038327\tbest: 0.0038327 (500)\ttotal: 598ms\tremaining: 596ms\n", - "999:\tlearn: 0.0021159\ttest: 0.0022825\tbest: 0.0022825 (999)\ttotal: 1.21s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9606043\ttest: 0.9668573\tbest: 0.9668573 (0)\ttotal: 1.41ms\tremaining: 1.41s\n", + "500:\tlearn: 0.0037506\ttest: 0.0038327\tbest: 0.0038327 (500)\ttotal: 521ms\tremaining: 519ms\n", + "999:\tlearn: 0.0021159\ttest: 0.0022825\tbest: 0.0022825 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.002282486309\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.002282486308604412\n", - "0:\tlearn: 0.9660711\ttest: 0.9614294\tbest: 0.9614294 (0)\ttotal: 1.13ms\tremaining: 1.12s\n", - "500:\tlearn: 0.0038068\ttest: 0.0039266\tbest: 0.0039266 (500)\ttotal: 610ms\tremaining: 608ms\n", - "999:\tlearn: 0.0020537\ttest: 0.0022117\tbest: 0.0022117 (999)\ttotal: 1.21s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9660711\ttest: 0.9614294\tbest: 0.9614294 (0)\ttotal: 1.37ms\tremaining: 1.37s\n", + "500:\tlearn: 0.0038068\ttest: 0.0039266\tbest: 0.0039266 (500)\ttotal: 513ms\tremaining: 511ms\n", + "999:\tlearn: 0.0020537\ttest: 0.0022117\tbest: 0.0022117 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002211714388\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.002211714388200288\n", "Mean score: 0.0022\n", "Std: 0.0\n" @@ -13032,10 +12989,10 @@ { "data": { "text/plain": [ - "(2.6219847202301025, <__main__.RecursiveStrategy at 0x311004d60>)" + "(2.5824122428894043, <__main__.RecursiveStrategy at 0x7f2e260080a0>)" ] }, - "execution_count": 22, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -13046,9 +13003,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 61, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -13460,7 +13424,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -13568,7 +13532,7 @@ "9 2 2022-09-28 3826.647477" ] }, - "execution_count": 24, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -13586,7 +13550,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -13603,7 +13567,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -13634,9 +13598,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -13655,9 +13619,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -13674,9 +13638,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -13693,9 +13657,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -13712,9 +13676,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -13731,9 +13695,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -13753,12 +13717,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 3 3 4 \n", @@ -14092,21 +14056,17 @@ " [-1.69483054 -1.69136817]\n", " [-1.69136817 -1.6879058 ]] \n", "\n", - "0:\tlearn: 1.3609687\ttest: 1.3619154\tbest: 1.3619154 (0)\ttotal: 1.94ms\tremaining: 1.93s\n", - "500:\tlearn: 0.0052110\ttest: 0.0055076\tbest: 0.0055076 (500)\ttotal: 771ms\tremaining: 768ms\n", - "999:\tlearn: 0.0029246\ttest: 0.0032282\tbest: 0.0032282 (999)\ttotal: 1.55s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.3609687\ttest: 1.3619154\tbest: 1.3619154 (0)\ttotal: 1.76ms\tremaining: 1.76s\n", + "500:\tlearn: 0.0052110\ttest: 0.0055076\tbest: 0.0055076 (500)\ttotal: 676ms\tremaining: 674ms\n", + "999:\tlearn: 0.0029246\ttest: 0.0032282\tbest: 0.0032282 (999)\ttotal: 1.34s\tremaining: 0us\n", "bestTest = 0.003228249025\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0032282490248902312\n", - "0:\tlearn: 1.3611017\ttest: 1.3621758\tbest: 1.3621758 (0)\ttotal: 1.64ms\tremaining: 1.64s\n", - "500:\tlearn: 0.0049621\ttest: 0.0052201\tbest: 0.0052201 (500)\ttotal: 777ms\tremaining: 774ms\n", - "999:\tlearn: 0.0027790\ttest: 0.0030534\tbest: 0.0030534 (999)\ttotal: 1.54s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.3611017\ttest: 1.3621758\tbest: 1.3621758 (0)\ttotal: 1.79ms\tremaining: 1.79s\n", + "500:\tlearn: 0.0049621\ttest: 0.0052201\tbest: 0.0052201 (500)\ttotal: 678ms\tremaining: 675ms\n", + "999:\tlearn: 0.0027790\ttest: 0.0030534\tbest: 0.0030534 (999)\ttotal: 1.35s\tremaining: 0us\n", "bestTest = 0.00305343819\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0030534381902755756\n", "Mean score: 0.0031\n", "Std: 0.0001\n" @@ -14115,10 +14075,10 @@ { "data": { "text/plain": [ - "(3.2906980514526367, <__main__.RecursiveStrategy at 0x311038a60>)" + "(3.052319049835205, <__main__.RecursiveStrategy at 0x7f2e26bcbf70>)" ] }, - "execution_count": 26, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -14129,13 +14089,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -14947,7 +14908,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -15055,7 +15016,7 @@ "9 2 2022-09-28 3994.364637" ] }, - "execution_count": 28, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -15088,7 +15049,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -15104,7 +15065,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -15409,21 +15370,17 @@ " [-1.69483054]\n", " [-1.69136817]] \n", "\n", - "0:\tlearn: 0.9606043\ttest: 0.9668573\tbest: 0.9668573 (0)\ttotal: 1.42ms\tremaining: 1.42s\n", - "500:\tlearn: 0.0037506\ttest: 0.0038327\tbest: 0.0038327 (500)\ttotal: 596ms\tremaining: 593ms\n", - "999:\tlearn: 0.0021159\ttest: 0.0022825\tbest: 0.0022825 (999)\ttotal: 1.18s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9606043\ttest: 0.9668573\tbest: 0.9668573 (0)\ttotal: 1.74ms\tremaining: 1.74s\n", + "500:\tlearn: 0.0037506\ttest: 0.0038327\tbest: 0.0038327 (500)\ttotal: 515ms\tremaining: 513ms\n", + "999:\tlearn: 0.0021159\ttest: 0.0022825\tbest: 0.0022825 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002282486309\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.002282486308604412\n", - "0:\tlearn: 0.9660711\ttest: 0.9614294\tbest: 0.9614294 (0)\ttotal: 1.25ms\tremaining: 1.25s\n", - "500:\tlearn: 0.0038068\ttest: 0.0039266\tbest: 0.0039266 (500)\ttotal: 602ms\tremaining: 599ms\n", - "999:\tlearn: 0.0020537\ttest: 0.0022117\tbest: 0.0022117 (999)\ttotal: 1.19s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9660711\ttest: 0.9614294\tbest: 0.9614294 (0)\ttotal: 1.67ms\tremaining: 1.67s\n", + "500:\tlearn: 0.0038068\ttest: 0.0039266\tbest: 0.0039266 (500)\ttotal: 511ms\tremaining: 509ms\n", + "999:\tlearn: 0.0020537\ttest: 0.0022117\tbest: 0.0022117 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002211714388\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.002211714388200288\n", "Mean score: 0.0022\n", "Std: 0.0\n", @@ -15725,21 +15682,17 @@ " [-1.69136817]\n", " [-1.6879058 ]] \n", "\n", - "0:\tlearn: 0.9623502\ttest: 0.9630196\tbest: 0.9630196 (0)\ttotal: 1.22ms\tremaining: 1.22s\n", - "500:\tlearn: 0.0036847\ttest: 0.0038944\tbest: 0.0038944 (500)\ttotal: 601ms\tremaining: 599ms\n", - "999:\tlearn: 0.0020680\ttest: 0.0022827\tbest: 0.0022827 (999)\ttotal: 1.2s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9623502\ttest: 0.9630196\tbest: 0.9630196 (0)\ttotal: 1.78ms\tremaining: 1.78s\n", + "500:\tlearn: 0.0036847\ttest: 0.0038944\tbest: 0.0038944 (500)\ttotal: 517ms\tremaining: 515ms\n", + "999:\tlearn: 0.0020680\ttest: 0.0022827\tbest: 0.0022827 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002282716871\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.002282716871130022\n", - "0:\tlearn: 0.9624442\ttest: 0.9632037\tbest: 0.9632037 (0)\ttotal: 1.25ms\tremaining: 1.25s\n", - "500:\tlearn: 0.0035087\ttest: 0.0036912\tbest: 0.0036912 (500)\ttotal: 600ms\tremaining: 598ms\n", - "999:\tlearn: 0.0019650\ttest: 0.0021591\tbest: 0.0021591 (999)\ttotal: 1.19s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9624442\ttest: 0.9632037\tbest: 0.9632037 (0)\ttotal: 1.69ms\tremaining: 1.68s\n", + "500:\tlearn: 0.0035087\ttest: 0.0036912\tbest: 0.0036912 (500)\ttotal: 529ms\tremaining: 527ms\n", + "999:\tlearn: 0.0019650\ttest: 0.0021591\tbest: 0.0021591 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.00215910794\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0021591079397916724\n", "Mean score: 0.0022\n", "Std: 0.0001\n", @@ -16041,21 +15994,17 @@ " [-1.6879058 ]\n", " [-1.68444342]] \n", "\n", - "0:\tlearn: 0.9600707\ttest: 0.9634615\tbest: 0.9634615 (0)\ttotal: 1.35ms\tremaining: 1.35s\n", - "500:\tlearn: 0.0035364\ttest: 0.0036527\tbest: 0.0036527 (500)\ttotal: 596ms\tremaining: 593ms\n", - "999:\tlearn: 0.0019679\ttest: 0.0021071\tbest: 0.0021071 (999)\ttotal: 1.19s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9600707\ttest: 0.9634615\tbest: 0.9634615 (0)\ttotal: 1.45ms\tremaining: 1.44s\n", + "500:\tlearn: 0.0035364\ttest: 0.0036527\tbest: 0.0036527 (500)\ttotal: 507ms\tremaining: 505ms\n", + "999:\tlearn: 0.0019679\ttest: 0.0021071\tbest: 0.0021071 (999)\ttotal: 1.01s\tremaining: 0us\n", "bestTest = 0.002107126777\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0021071267765355958\n", - "0:\tlearn: 0.9626636\ttest: 0.9611762\tbest: 0.9611762 (0)\ttotal: 1.2ms\tremaining: 1.2s\n", - "500:\tlearn: 0.0035232\ttest: 0.0036120\tbest: 0.0036120 (500)\ttotal: 603ms\tremaining: 600ms\n", - "999:\tlearn: 0.0019648\ttest: 0.0021312\tbest: 0.0021312 (999)\ttotal: 1.2s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9626636\ttest: 0.9611762\tbest: 0.9611762 (0)\ttotal: 1.57ms\tremaining: 1.57s\n", + "500:\tlearn: 0.0035232\ttest: 0.0036120\tbest: 0.0036120 (500)\ttotal: 522ms\tremaining: 520ms\n", + "999:\tlearn: 0.0019648\ttest: 0.0021312\tbest: 0.0021312 (999)\ttotal: 1.25s\tremaining: 0us\n", "bestTest = 0.002131204446\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0021312044459677773\n", "Mean score: 0.0021\n", "Std: 0.0\n", @@ -16357,21 +16306,17 @@ " [-1.68444342]\n", " [-1.68098105]] \n", "\n", - "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.14ms\tremaining: 1.14s\n", - "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 597ms\tremaining: 595ms\n", - "999:\tlearn: 0.0019922\ttest: 0.0021238\tbest: 0.0021238 (999)\ttotal: 1.19s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 3.93ms\tremaining: 3.93s\n", + "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 544ms\tremaining: 541ms\n", + "999:\tlearn: 0.0019922\ttest: 0.0021238\tbest: 0.0021238 (999)\ttotal: 1.12s\tremaining: 0us\n", "bestTest = 0.002123800997\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0021238009972551165\n", - "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.24ms\tremaining: 1.24s\n", - "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 597ms\tremaining: 594ms\n", - "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.19s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.39ms\tremaining: 1.39s\n", + "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 514ms\tremaining: 512ms\n", + "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.02s\tremaining: 0us\n", "bestTest = 0.002178623094\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0021786230943171268\n", "Mean score: 0.0022\n", "Std: 0.0\n" @@ -16380,10 +16325,10 @@ { "data": { "text/plain": [ - "(10.28906512260437, <__main__.DirectStrategy at 0x311004df0>)" + "(9.866692543029785, <__main__.DirectStrategy at 0x7f2e268d6ec0>)" ] }, - "execution_count": 30, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -16401,13 +16346,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -18027,7 +17973,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -18135,7 +18081,7 @@ "9 2 2022-09-28 3994.364637" ] }, - "execution_count": 32, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -18153,7 +18099,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -18170,7 +18116,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -18475,21 +18421,17 @@ " [-1.69483054]\n", " [-1.69136817]] \n", "\n", - "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 60.1ms\tremaining: 1m\n", - "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 655ms\tremaining: 652ms\n", - "999:\tlearn: 0.0019886\ttest: 0.0021209\tbest: 0.0021209 (999)\ttotal: 1.27s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.54ms\tremaining: 1.54s\n", + "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 526ms\tremaining: 524ms\n", + "999:\tlearn: 0.0019886\ttest: 0.0021209\tbest: 0.0021209 (999)\ttotal: 1.06s\tremaining: 0us\n", "bestTest = 0.002120945034\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.002120945033678485\n", - "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 2ms\tremaining: 2s\n", - "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 605ms\tremaining: 603ms\n", - "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.21s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 3.01ms\tremaining: 3.01s\n", + "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 518ms\tremaining: 516ms\n", + "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.002178625077\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.002178625076936698\n", "Mean score: 0.0021\n", "Std: 0.0\n", @@ -18791,21 +18733,17 @@ " [-1.69136817]\n", " [-1.6879058 ]] \n", "\n", - "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 2.07ms\tremaining: 2.07s\n", - "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 611ms\tremaining: 609ms\n", - "999:\tlearn: 0.0019922\ttest: 0.0021238\tbest: 0.0021238 (999)\ttotal: 1.21s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.49ms\tremaining: 1.49s\n", + "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 523ms\tremaining: 521ms\n", + "999:\tlearn: 0.0019922\ttest: 0.0021238\tbest: 0.0021238 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.002123801462\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0021238014616155863\n", - "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.47ms\tremaining: 1.47s\n", - "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 590ms\tremaining: 588ms\n", - "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.18s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.3ms\tremaining: 1.3s\n", + "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 508ms\tremaining: 506ms\n", + "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.002178623015\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.002178623015287188\n", "Mean score: 0.0022\n", "Std: 0.0\n", @@ -19107,21 +19045,17 @@ " [-1.6879058 ]\n", " [-1.68444342]] \n", "\n", - "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.62ms\tremaining: 1.61s\n", - "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 607ms\tremaining: 604ms\n", - "999:\tlearn: 0.0019886\ttest: 0.0021209\tbest: 0.0021209 (999)\ttotal: 1.2s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.33ms\tremaining: 1.33s\n", + "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 527ms\tremaining: 525ms\n", + "999:\tlearn: 0.0019886\ttest: 0.0021209\tbest: 0.0021209 (999)\ttotal: 1.04s\tremaining: 0us\n", "bestTest = 0.002120944768\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0021209447682686666\n", - "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.38ms\tremaining: 1.38s\n", - "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 603ms\tremaining: 601ms\n", - "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.2s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.57ms\tremaining: 1.57s\n", + "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 519ms\tremaining: 517ms\n", + "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.002178624707\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.002178624707219995\n", "Mean score: 0.0021\n", "Std: 0.0\n", @@ -19423,21 +19357,17 @@ " [-1.68444342]\n", " [-1.68098105]] \n", "\n", - "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.52ms\tremaining: 1.52s\n", - "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 655ms\tremaining: 653ms\n", - "999:\tlearn: 0.0019922\ttest: 0.0021238\tbest: 0.0021238 (999)\ttotal: 1.31s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9589837\ttest: 0.9626489\tbest: 0.9626489 (0)\ttotal: 1.73ms\tremaining: 1.73s\n", + "500:\tlearn: 0.0035799\ttest: 0.0036267\tbest: 0.0036267 (500)\ttotal: 508ms\tremaining: 506ms\n", + "999:\tlearn: 0.0019922\ttest: 0.0021238\tbest: 0.0021238 (999)\ttotal: 1.03s\tremaining: 0us\n", "bestTest = 0.002123800997\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0021238009972551165\n", - "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.28ms\tremaining: 1.28s\n", - "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 613ms\tremaining: 610ms\n", - "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.23s\tremaining: 0us\n", - "\n", + "0:\tlearn: 0.9619577\ttest: 0.9598735\tbest: 0.9598735 (0)\ttotal: 1.29ms\tremaining: 1.29s\n", + "500:\tlearn: 0.0036115\ttest: 0.0037193\tbest: 0.0037193 (500)\ttotal: 529ms\tremaining: 527ms\n", + "999:\tlearn: 0.0020308\ttest: 0.0021786\tbest: 0.0021786 (999)\ttotal: 1.06s\tremaining: 0us\n", "bestTest = 0.002178623094\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0021786230943171268\n", "Mean score: 0.0022\n", "Std: 0.0\n" @@ -19446,10 +19376,10 @@ { "data": { "text/plain": [ - "(10.621765851974487, <__main__.DirectStrategy at 0x103d95930>)" + "(9.481624364852905, <__main__.DirectStrategy at 0x7f2e264fce80>)" ] }, - "execution_count": 14, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -19460,13 +19390,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -21086,7 +21017,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -21194,7 +21125,7 @@ "9 2 2022-09-28 3990.99943" ] }, - "execution_count": 16, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -21212,7 +21143,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -21229,7 +21160,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -21534,21 +21465,17 @@ " [-1.69483054 -1.69136817]\n", " [-1.69136817 -1.6879058 ]] \n", "\n", - "0:\tlearn: 1.3609687\ttest: 1.3619154\tbest: 1.3619154 (0)\ttotal: 2.31ms\tremaining: 2.31s\n", - "500:\tlearn: 0.0052110\ttest: 0.0055076\tbest: 0.0055076 (500)\ttotal: 809ms\tremaining: 806ms\n", - "999:\tlearn: 0.0029246\ttest: 0.0032282\tbest: 0.0032282 (999)\ttotal: 1.58s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.3609687\ttest: 1.3619154\tbest: 1.3619154 (0)\ttotal: 3.6ms\tremaining: 3.59s\n", + "500:\tlearn: 0.0052110\ttest: 0.0055076\tbest: 0.0055076 (500)\ttotal: 732ms\tremaining: 729ms\n", + "999:\tlearn: 0.0029246\ttest: 0.0032282\tbest: 0.0032282 (999)\ttotal: 1.42s\tremaining: 0us\n", "bestTest = 0.003228249025\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.0032282490248902312\n", - "0:\tlearn: 1.3611017\ttest: 1.3621758\tbest: 1.3621758 (0)\ttotal: 1.91ms\tremaining: 1.91s\n", - "500:\tlearn: 0.0049621\ttest: 0.0052201\tbest: 0.0052201 (500)\ttotal: 809ms\tremaining: 806ms\n", - "999:\tlearn: 0.0027790\ttest: 0.0030534\tbest: 0.0030534 (999)\ttotal: 1.61s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.3611017\ttest: 1.3621758\tbest: 1.3621758 (0)\ttotal: 1.88ms\tremaining: 1.87s\n", + "500:\tlearn: 0.0049621\ttest: 0.0052201\tbest: 0.0052201 (500)\ttotal: 696ms\tremaining: 693ms\n", + "999:\tlearn: 0.0027790\ttest: 0.0030534\tbest: 0.0030534 (999)\ttotal: 1.36s\tremaining: 0us\n", "bestTest = 0.00305343819\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0030534381902755756\n", "Mean score: 0.0031\n", "Std: 0.0001\n", @@ -21850,21 +21777,17 @@ " [-1.6879058 -1.68444342]\n", " [-1.68444342 -1.68098105]] \n", "\n", - "0:\tlearn: 1.3562078\ttest: 1.3613912\tbest: 1.3613912 (0)\ttotal: 2.71ms\tremaining: 2.71s\n", - "500:\tlearn: 0.0050627\ttest: 0.0051289\tbest: 0.0051289 (500)\ttotal: 793ms\tremaining: 790ms\n", - "999:\tlearn: 0.0028123\ttest: 0.0029995\tbest: 0.0029995 (999)\ttotal: 1.64s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.3562078\ttest: 1.3613912\tbest: 1.3613912 (0)\ttotal: 1.83ms\tremaining: 1.83s\n", + "500:\tlearn: 0.0050627\ttest: 0.0051289\tbest: 0.0051289 (500)\ttotal: 708ms\tremaining: 705ms\n", + "999:\tlearn: 0.0028123\ttest: 0.0029995\tbest: 0.0029995 (999)\ttotal: 1.73s\tremaining: 0us\n", "bestTest = 0.002999468753\n", "bestIteration = 999\n", - "\n", "Fold 0. Score: 0.002999468752666121\n", - "0:\tlearn: 1.3604136\ttest: 1.3574661\tbest: 1.3574661 (0)\ttotal: 2.21ms\tremaining: 2.21s\n", - "500:\tlearn: 0.0051074\ttest: 0.0052598\tbest: 0.0052598 (500)\ttotal: 786ms\tremaining: 783ms\n", - "999:\tlearn: 0.0028720\ttest: 0.0030810\tbest: 0.0030810 (999)\ttotal: 1.59s\tremaining: 0us\n", - "\n", + "0:\tlearn: 1.3604136\ttest: 1.3574661\tbest: 1.3574661 (0)\ttotal: 4.17ms\tremaining: 4.17s\n", + "500:\tlearn: 0.0051074\ttest: 0.0052598\tbest: 0.0052598 (500)\ttotal: 724ms\tremaining: 722ms\n", + "999:\tlearn: 0.0028720\ttest: 0.0030810\tbest: 0.0030810 (999)\ttotal: 1.38s\tremaining: 0us\n", "bestTest = 0.003081039468\n", "bestIteration = 999\n", - "\n", "Fold 1. Score: 0.0030810394677772894\n", "Mean score: 0.003\n", "Std: 0.0\n" @@ -21873,10 +21796,10 @@ { "data": { "text/plain": [ - "(6.974285125732422, <__main__.DirectStrategy at 0x103d958a0>)" + "(6.788178205490112, <__main__.DirectStrategy at 0x7f2e26033070>)" ] }, - "execution_count": 18, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -21887,13 +21810,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -22705,7 +22629,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -22813,7 +22737,7 @@ "9 2 2022-09-28 3994.364637" ] }, - "execution_count": 20, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -22840,7 +22764,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -22856,7 +22780,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -22887,9 +22811,9 @@ " \n", " \n", " \n", + " id\n", " value\n", " date\n", - " id\n", " value__standard_scaler\n", " date__season_y\n", " date__season_m\n", @@ -22908,9 +22832,9 @@ " \n", " \n", " 0\n", + " 0\n", " 1000.0\n", " 2020-01-01\n", - " 0\n", " -1.729454\n", " 2020\n", " 1\n", @@ -22927,9 +22851,9 @@ " \n", " \n", " 1\n", + " 0\n", " 1001.0\n", " 2020-01-02\n", - " 0\n", " -1.725992\n", " 2020\n", " 1\n", @@ -22946,9 +22870,9 @@ " \n", " \n", " 2\n", + " 0\n", " 1002.0\n", " 2020-01-03\n", - " 0\n", " -1.722530\n", " 2020\n", " 1\n", @@ -22965,9 +22889,9 @@ " \n", " \n", " 3\n", + " 0\n", " 1003.0\n", " 2020-01-04\n", - " 0\n", " -1.719067\n", " 2020\n", " 1\n", @@ -22984,9 +22908,9 @@ " \n", " \n", " 4\n", + " 0\n", " 1004.0\n", " 2020-01-05\n", - " 0\n", " -1.715605\n", " 2020\n", " 1\n", @@ -23006,12 +22930,12 @@ "" ], "text/plain": [ - " value date id value__standard_scaler date__season_y \\\n", - "0 1000.0 2020-01-01 0 -1.729454 2020 \n", - "1 1001.0 2020-01-02 0 -1.725992 2020 \n", - "2 1002.0 2020-01-03 0 -1.722530 2020 \n", - "3 1003.0 2020-01-04 0 -1.719067 2020 \n", - "4 1004.0 2020-01-05 0 -1.715605 2020 \n", + " id value date value__standard_scaler date__season_y \\\n", + "0 0 1000.0 2020-01-01 -1.729454 2020 \n", + "1 0 1001.0 2020-01-02 -1.725992 2020 \n", + "2 0 1002.0 2020-01-03 -1.722530 2020 \n", + "3 0 1003.0 2020-01-04 -1.719067 2020 \n", + "4 0 1004.0 2020-01-05 -1.715605 2020 \n", "\n", " date__season_m date__season_d date__season_doy date__season_wd \\\n", "0 1 5 5 6 \n", @@ -23073,7 +22997,7 @@ " \n", " \n", " \n", - " ID\n", + " id__label__standard_scaler__lag_0\n", " FH\n", " date__season_y__standard_scaler\n", " date__season_m__standard_scaler\n", @@ -23092,7 +23016,7 @@ " \n", " \n", " 0\n", - " 0.0\n", + " -1.566621\n", " 1.0\n", " -1.150365\n", " -1.557466\n", @@ -23109,7 +23033,7 @@ " \n", " \n", " 1\n", - " 0.0\n", + " -1.566621\n", " 2.0\n", " -1.150365\n", " -1.557466\n", @@ -23126,7 +23050,7 @@ " \n", " \n", " 2\n", - " 0.0\n", + " -1.566621\n", " 3.0\n", " -1.150365\n", " -1.557466\n", @@ -23143,7 +23067,7 @@ " \n", " \n", " 3\n", - " 0.0\n", + " -1.566621\n", " 4.0\n", " -1.150365\n", " -1.557466\n", @@ -23160,7 +23084,7 @@ " \n", " \n", " 4\n", - " 0.0\n", + " -1.566621\n", " 1.0\n", " -1.150365\n", " -1.557466\n", @@ -23180,47 +23104,54 @@ "" ], "text/plain": [ - " ID FH date__season_y__standard_scaler date__season_m__standard_scaler \\\n", - "0 0.0 1.0 -1.150365 -1.557466 \n", - "1 0.0 2.0 -1.150365 -1.557466 \n", - "2 0.0 3.0 -1.150365 -1.557466 \n", - "3 0.0 4.0 -1.150365 -1.557466 \n", - "4 0.0 1.0 -1.150365 -1.557466 \n", - "\n", - " date__season_d__standard_scaler date__season_doy__standard_scaler \\\n", - "0 -0.886229 -1.621673 \n", - "1 -0.772259 -1.611745 \n", - "2 -0.658290 -1.601817 \n", - "3 -0.544320 -1.591889 \n", - "4 -0.772259 -1.611745 \n", - "\n", - " date__season_wd__standard_scaler value__standard_scaler__lag_6 \\\n", - "0 -0.498975 -1.729454 \n", - "1 0.001000 -1.729454 \n", - "2 0.500975 -1.729454 \n", - "3 1.000950 -1.729454 \n", - "4 0.001000 -1.725992 \n", + " id__label__standard_scaler__lag_0 FH date__season_y__standard_scaler \\\n", + "0 -1.566621 1.0 -1.150365 \n", + "1 -1.566621 2.0 -1.150365 \n", + "2 -1.566621 3.0 -1.150365 \n", + "3 -1.566621 4.0 -1.150365 \n", + "4 -1.566621 1.0 -1.150365 \n", + "\n", + " date__season_m__standard_scaler date__season_d__standard_scaler \\\n", + "0 -1.557466 -0.886229 \n", + "1 -1.557466 -0.772259 \n", + "2 -1.557466 -0.658290 \n", + "3 -1.557466 -0.544320 \n", + "4 -1.557466 -0.772259 \n", + "\n", + " date__season_doy__standard_scaler date__season_wd__standard_scaler \\\n", + "0 -1.621673 -0.498975 \n", + "1 -1.611745 0.001000 \n", + "2 -1.601817 0.500975 \n", + "3 -1.591889 1.000950 \n", + "4 -1.611745 0.001000 \n", "\n", - " value__standard_scaler__lag_5 value__standard_scaler__lag_4 \\\n", - "0 -1.725992 -1.722530 \n", - "1 -1.725992 -1.722530 \n", - "2 -1.725992 -1.722530 \n", - "3 -1.725992 -1.722530 \n", - "4 -1.722530 -1.719067 \n", + " value__standard_scaler__lag_6 value__standard_scaler__lag_5 \\\n", + "0 -1.729454 -1.725992 \n", + "1 -1.729454 -1.725992 \n", + "2 -1.729454 -1.725992 \n", + "3 -1.729454 -1.725992 \n", + "4 -1.725992 -1.722530 \n", "\n", - " value__standard_scaler__lag_3 value__standard_scaler__lag_2 \\\n", - "0 -1.719067 -1.715605 \n", - "1 -1.719067 -1.715605 \n", - "2 -1.719067 -1.715605 \n", - "3 -1.719067 -1.715605 \n", - "4 -1.715605 -1.712142 \n", + " value__standard_scaler__lag_4 value__standard_scaler__lag_3 \\\n", + "0 -1.722530 -1.719067 \n", + "1 -1.722530 -1.719067 \n", + "2 -1.722530 -1.719067 \n", + "3 -1.722530 -1.719067 \n", + "4 -1.719067 -1.715605 \n", "\n", - " value__standard_scaler__lag_1 value__standard_scaler__lag_0 \n", - "0 -1.712142 -1.708680 \n", - "1 -1.712142 -1.708680 \n", - "2 -1.712142 -1.708680 \n", - "3 -1.712142 -1.708680 \n", - "4 -1.708680 -1.705218 " + " value__standard_scaler__lag_2 value__standard_scaler__lag_1 \\\n", + "0 -1.715605 -1.712142 \n", + "1 -1.715605 -1.712142 \n", + "2 -1.715605 -1.712142 \n", + "3 -1.715605 -1.712142 \n", + "4 -1.712142 -1.708680 \n", + "\n", + " value__standard_scaler__lag_0 \n", + "0 -1.708680 \n", + "1 -1.708680 \n", + "2 -1.708680 \n", + "3 -1.708680 \n", + "4 -1.705218 " ] }, "metadata": {}, @@ -23237,22 +23168,18 @@ " [-1.69483054]\n", " [-1.70175528]] \n", "\n", - "0:\tlearn: 0.9586446\ttest: 0.9618324\tbest: 0.9618324 (0)\ttotal: 2.07ms\tremaining: 2.07s\n", - "500:\tlearn: 0.0041781\ttest: 0.0042034\tbest: 0.0042034 (500)\ttotal: 834ms\tremaining: 831ms\n", - "999:\tlearn: 0.0029084\ttest: 0.0029512\tbest: 0.0029512 (999)\ttotal: 1.68s\tremaining: 0us\n", - "\n", - "bestTest = 0.002951171897\n", + "0:\tlearn: 0.9586734\ttest: 0.9618613\tbest: 0.9618613 (0)\ttotal: 3.87ms\tremaining: 3.86s\n", + "500:\tlearn: 0.0042219\ttest: 0.0042646\tbest: 0.0042646 (500)\ttotal: 770ms\tremaining: 767ms\n", + "999:\tlearn: 0.0029351\ttest: 0.0030153\tbest: 0.0030153 (999)\ttotal: 1.53s\tremaining: 0us\n", + "bestTest = 0.00301528326\n", "bestIteration = 999\n", - "\n", - "Fold 0. Score: 0.002951171896897575\n", - "0:\tlearn: 0.9617331\ttest: 0.9587321\tbest: 0.9587321 (0)\ttotal: 2.69ms\tremaining: 2.69s\n", - "500:\tlearn: 0.0043870\ttest: 0.0044545\tbest: 0.0044545 (500)\ttotal: 873ms\tremaining: 869ms\n", - "999:\tlearn: 0.0031491\ttest: 0.0032166\tbest: 0.0032166 (999)\ttotal: 1.75s\tremaining: 0us\n", - "\n", - "bestTest = 0.003216634081\n", + "Fold 0. Score: 0.003015283259845395\n", + "0:\tlearn: 0.9617591\ttest: 0.9587677\tbest: 0.9587677 (0)\ttotal: 2.08ms\tremaining: 2.08s\n", + "500:\tlearn: 0.0044254\ttest: 0.0044378\tbest: 0.0044378 (500)\ttotal: 761ms\tremaining: 758ms\n", + "999:\tlearn: 0.0031824\ttest: 0.0032430\tbest: 0.0032430 (999)\ttotal: 1.55s\tremaining: 0us\n", + "bestTest = 0.003242976857\n", "bestIteration = 999\n", - "\n", - "Fold 1. Score: 0.0032166340805951004\n", + "Fold 1. Score: 0.0032429768568341395\n", "Mean score: 0.0031\n", "Std: 0.0001\n" ] @@ -23260,10 +23187,10 @@ { "data": { "text/plain": [ - "(3.7139110565185547, <__main__.FlatWideMIMOStrategy at 0x16e457b80>)" + "(3.6014578342437744, <__main__.FlatWideMIMOStrategy at 0x7f2e260182e0>)" ] }, - "execution_count": 22, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -23274,13 +23201,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "raw_ts_X on step: 0 (110,)\n" ] }, @@ -23420,7 +23348,7 @@ " \n", " \n", " \n", - " ID\n", + " id__label__standard_scaler__lag_0\n", " FH\n", " date__season_y__standard_scaler\n", " date__season_m__standard_scaler\n", @@ -23439,7 +23367,7 @@ " \n", " \n", " 0\n", - " 0.0\n", + " -1.566621\n", " 1.0\n", " 1.375134\n", " 0.870384\n", @@ -23456,7 +23384,7 @@ " \n", " \n", " 1\n", - " 0.0\n", + " -1.566621\n", " 2.0\n", " 1.375134\n", " 0.870384\n", @@ -23473,7 +23401,7 @@ " \n", " \n", " 2\n", - " 0.0\n", + " -1.566621\n", " 3.0\n", " 1.375134\n", " 0.870384\n", @@ -23490,7 +23418,7 @@ " \n", " \n", " 3\n", - " 0.0\n", + " -1.566621\n", " 4.0\n", " 1.375134\n", " 0.870384\n", @@ -23507,7 +23435,7 @@ " \n", " \n", " 4\n", - " 1.0\n", + " -1.218483\n", " 1.0\n", " 1.375134\n", " 0.870384\n", @@ -23527,47 +23455,54 @@ "" ], "text/plain": [ - " ID FH date__season_y__standard_scaler date__season_m__standard_scaler \\\n", - "0 0.0 1.0 1.375134 0.870384 \n", - "1 0.0 2.0 1.375134 0.870384 \n", - "2 0.0 3.0 1.375134 0.870384 \n", - "3 0.0 4.0 1.375134 0.870384 \n", - "4 1.0 1.0 1.375134 0.870384 \n", + " id__label__standard_scaler__lag_0 FH date__season_y__standard_scaler \\\n", + "0 -1.566621 1.0 1.375134 \n", + "1 -1.566621 2.0 1.375134 \n", + "2 -1.566621 3.0 1.375134 \n", + "3 -1.566621 4.0 1.375134 \n", + "4 -1.218483 1.0 1.375134 \n", + "\n", + " date__season_m__standard_scaler date__season_d__standard_scaler \\\n", + "0 0.870384 1.279197 \n", + "1 0.870384 1.393167 \n", + "2 0.870384 1.507137 \n", + "3 0.870384 1.621107 \n", + "4 0.870384 1.279197 \n", + "\n", + " date__season_doy__standard_scaler date__season_wd__standard_scaler \\\n", + "0 0.979405 -0.998951 \n", + "1 0.989333 -0.498975 \n", + "2 0.999261 0.001000 \n", + "3 1.009188 0.500975 \n", + "4 0.979405 -0.998951 \n", "\n", - " date__season_d__standard_scaler date__season_doy__standard_scaler \\\n", - "0 1.279197 0.979405 \n", - "1 1.393167 0.989333 \n", - "2 1.507137 0.999261 \n", - "3 1.621107 1.009188 \n", - "4 1.279197 0.979405 \n", - "\n", - " date__season_wd__standard_scaler value__standard_scaler__lag_6 \\\n", - "0 -0.998951 1.70868 \n", - "1 -0.498975 1.70868 \n", - "2 0.001000 1.70868 \n", - "3 0.500975 1.70868 \n", - "4 -0.998951 1.70868 \n", - "\n", - " value__standard_scaler__lag_5 value__standard_scaler__lag_4 \\\n", - "0 1.712142 1.715605 \n", - "1 1.712142 1.715605 \n", - "2 1.712142 1.715605 \n", - "3 1.712142 1.715605 \n", - "4 1.712142 1.715605 \n", + " value__standard_scaler__lag_6 value__standard_scaler__lag_5 \\\n", + "0 1.70868 1.712142 \n", + "1 1.70868 1.712142 \n", + "2 1.70868 1.712142 \n", + "3 1.70868 1.712142 \n", + "4 1.70868 1.712142 \n", "\n", - " value__standard_scaler__lag_3 value__standard_scaler__lag_2 \\\n", - "0 1.719067 1.72253 \n", - "1 1.719067 1.72253 \n", - "2 1.719067 1.72253 \n", - "3 1.719067 1.72253 \n", - "4 1.719067 1.72253 \n", + " value__standard_scaler__lag_4 value__standard_scaler__lag_3 \\\n", + "0 1.715605 1.719067 \n", + "1 1.715605 1.719067 \n", + "2 1.715605 1.719067 \n", + "3 1.715605 1.719067 \n", + "4 1.715605 1.719067 \n", + "\n", + " value__standard_scaler__lag_2 value__standard_scaler__lag_1 \\\n", + "0 1.72253 1.725992 \n", + "1 1.72253 1.725992 \n", + "2 1.72253 1.725992 \n", + "3 1.72253 1.725992 \n", + "4 1.72253 1.725992 \n", "\n", - " value__standard_scaler__lag_1 value__standard_scaler__lag_0 \n", - "0 1.725992 1.729454 \n", - "1 1.725992 1.729454 \n", - "2 1.725992 1.729454 \n", - "3 1.725992 1.729454 \n", - "4 1.725992 1.729454 " + " value__standard_scaler__lag_0 \n", + "0 1.729454 \n", + "1 1.729454 \n", + "2 1.729454 \n", + "3 1.729454 \n", + "4 1.729454 " ] }, "metadata": {}, @@ -23580,7 +23515,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -23614,61 +23549,61 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1995.401964\n", + " 1996.132986\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1996.039777\n", + " 1996.605203\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1996.568991\n", + " 1996.949984\n", " \n", " \n", " 3\n", " 0\n", " 2022-09-30\n", - " 1992.505841\n", + " 1996.912386\n", " \n", " \n", " 4\n", " 1\n", " 2022-09-27\n", - " 2995.402058\n", + " 2996.133539\n", " \n", " \n", " 5\n", " 1\n", " 2022-09-28\n", - " 2996.03987\n", + " 2996.605755\n", " \n", " \n", " 6\n", " 1\n", " 2022-09-29\n", - " 2996.569085\n", + " 2996.950536\n", " \n", " \n", " 7\n", " 1\n", " 2022-09-30\n", - " 2992.505934\n", + " 2996.912938\n", " \n", " \n", " 8\n", " 2\n", " 2022-09-27\n", - " 3995.404626\n", + " 3996.16268\n", " \n", " \n", " 9\n", " 2\n", " 2022-09-28\n", - " 3996.042439\n", + " 3996.634896\n", " \n", " \n", "\n", @@ -23676,19 +23611,19 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1995.401964\n", - "1 0 2022-09-28 1996.039777\n", - "2 0 2022-09-29 1996.568991\n", - "3 0 2022-09-30 1992.505841\n", - "4 1 2022-09-27 2995.402058\n", - "5 1 2022-09-28 2996.03987\n", - "6 1 2022-09-29 2996.569085\n", - "7 1 2022-09-30 2992.505934\n", - "8 2 2022-09-27 3995.404626\n", - "9 2 2022-09-28 3996.042439" + "0 0 2022-09-27 1996.132986\n", + "1 0 2022-09-28 1996.605203\n", + "2 0 2022-09-29 1996.949984\n", + "3 0 2022-09-30 1996.912386\n", + "4 1 2022-09-27 2996.133539\n", + "5 1 2022-09-28 2996.605755\n", + "6 1 2022-09-29 2996.950536\n", + "7 1 2022-09-30 2996.912938\n", + "8 2 2022-09-27 3996.16268\n", + "9 2 2022-09-28 3996.634896" ] }, - "execution_count": 24, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -23707,7 +23642,7 @@ ], "metadata": { "kernelspec": { - "display_name": "py_3_10", + "display_name": "tsururu_test", "language": "python", "name": "python3" }, @@ -23721,7 +23656,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/Tutorial_3_Transformers_and_Pipeline.ipynb b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb index 244160f..505cc7a 100644 --- a/examples/Tutorial_3_Transformers_and_Pipeline.ipynb +++ b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb @@ -200,7 +200,7 @@ } ], "source": [ - "df = pd.read_csv(\"datasets/global/demand_forecasting_kernels.csv\")\n", + "df = pd.read_csv(\"../datasets/global/demand_forecasting_kernels.csv\")\n", "df = df[(df[\"id\"] == 0) | (df[\"id\"] == 1)]\n", "\n", "dataset_params = {\n", @@ -463,6 +463,7 @@ "id_column_name: id\n", "date_column_name: date\n", "target_column_name: value\n", + "num_series: 2\n", "idx_X: [[ 0 1 2 3 4 5 6 7 8 9]\n", " [ 1 2 3 4 5 6 7 8 9 10]\n", " [ 2 3 4 5 6 7 8 9 10 11]\n", @@ -481,8 +482,10 @@ " print(key, end=\": \")\n", " if isinstance(value, pd.DataFrame):\n", " display(value.head())\n", + " elif isinstance(value, np.ndarray):\n", + " print(value[:5])\n", " else:\n", - " print(value[:5])" + " print(value)" ] }, { @@ -492,7 +495,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -754,46 +757,46 @@ " \n", " \n", " \n", + " id\n", " date\n", " value\n", - " id\n", " value__standard_scaler\n", " \n", " \n", " \n", " \n", " 0\n", + " 0\n", " 2013-01-01\n", " 48.0\n", - " 0\n", " -0.254108\n", " \n", " \n", " 1\n", + " 0\n", " 2013-01-02\n", " 44.0\n", - " 0\n", " -0.275840\n", " \n", " \n", " 2\n", + " 0\n", " 2013-01-03\n", " 60.0\n", - " 0\n", " -0.188914\n", " \n", " \n", " 3\n", + " 0\n", " 2013-01-04\n", " 56.0\n", - " 0\n", " -0.210645\n", " \n", " \n", " 4\n", + " 0\n", " 2013-01-05\n", " 50.0\n", - " 0\n", " -0.243242\n", " \n", " \n", @@ -801,12 +804,12 @@ "" ], "text/plain": [ - " date value id value__standard_scaler\n", - "0 2013-01-01 48.0 0 -0.254108\n", - "1 2013-01-02 44.0 0 -0.275840\n", - "2 2013-01-03 60.0 0 -0.188914\n", - "3 2013-01-04 56.0 0 -0.210645\n", - "4 2013-01-05 50.0 0 -0.243242" + " id date value value__standard_scaler\n", + "0 0 2013-01-01 48.0 -0.254108\n", + "1 0 2013-01-02 44.0 -0.275840\n", + "2 0 2013-01-03 60.0 -0.188914\n", + "3 0 2013-01-04 56.0 -0.210645\n", + "4 0 2013-01-05 50.0 -0.243242" ] }, "metadata": {}, @@ -930,7 +933,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1184,9 +1187,9 @@ " \n", " \n", " \n", + " id\n", " date\n", " value\n", - " id\n", " value_1\n", " value__diff_norm\n", " value_1__diff_norm\n", @@ -1195,45 +1198,45 @@ " \n", " \n", " 0\n", + " 0\n", " 2013-01-01\n", " 48.0\n", - " 0\n", " 48.0\n", " NaN\n", " NaN\n", " \n", " \n", " 1\n", + " 0\n", " 2013-01-02\n", " 44.0\n", - " 0\n", " 44.0\n", " -4.0\n", " 0.916667\n", " \n", " \n", " 2\n", + " 0\n", " 2013-01-03\n", " 60.0\n", - " 0\n", " 60.0\n", " 16.0\n", " 1.363636\n", " \n", " \n", " 3\n", + " 0\n", " 2013-01-04\n", " 56.0\n", - " 0\n", " 56.0\n", " -4.0\n", " 0.933333\n", " \n", " \n", " 4\n", + " 0\n", " 2013-01-05\n", " 50.0\n", - " 0\n", " 50.0\n", " -6.0\n", " 0.892857\n", @@ -1243,12 +1246,12 @@ "" ], "text/plain": [ - " date value id value_1 value__diff_norm value_1__diff_norm\n", - "0 2013-01-01 48.0 0 48.0 NaN NaN\n", - "1 2013-01-02 44.0 0 44.0 -4.0 0.916667\n", - "2 2013-01-03 60.0 0 60.0 16.0 1.363636\n", - "3 2013-01-04 56.0 0 56.0 -4.0 0.933333\n", - "4 2013-01-05 50.0 0 50.0 -6.0 0.892857" + " id date value value_1 value__diff_norm value_1__diff_norm\n", + "0 0 2013-01-01 48.0 48.0 NaN NaN\n", + "1 0 2013-01-02 44.0 44.0 -4.0 0.916667\n", + "2 0 2013-01-03 60.0 60.0 16.0 1.363636\n", + "3 0 2013-01-04 56.0 56.0 -4.0 0.933333\n", + "4 0 2013-01-05 50.0 50.0 -6.0 0.892857" ] }, "metadata": {}, @@ -1282,9 +1285,9 @@ " \n", " \n", " \n", + " id\n", " date\n", " value\n", - " id\n", " value_1\n", " value__diff_norm\n", " \n", @@ -1292,41 +1295,41 @@ " \n", " \n", " 0\n", + " 0\n", " 2013-01-01\n", " 48.0\n", - " 0\n", " 48.0\n", " NaN\n", " \n", " \n", " 1\n", + " 0\n", " 2013-01-02\n", " 44.0\n", - " 0\n", " 44.0\n", " -4.0\n", " \n", " \n", " 2\n", + " 0\n", " 2013-01-03\n", " 60.0\n", - " 0\n", " 60.0\n", " 16.0\n", " \n", " \n", " 3\n", + " 0\n", " 2013-01-04\n", " 56.0\n", - " 0\n", " 56.0\n", " -4.0\n", " \n", " \n", " 4\n", + " 0\n", " 2013-01-05\n", " 50.0\n", - " 0\n", " 50.0\n", " -6.0\n", " \n", @@ -1335,12 +1338,12 @@ "" ], "text/plain": [ - " date value id value_1 value__diff_norm\n", - "0 2013-01-01 48.0 0 48.0 NaN\n", - "1 2013-01-02 44.0 0 44.0 -4.0\n", - "2 2013-01-03 60.0 0 60.0 16.0\n", - "3 2013-01-04 56.0 0 56.0 -4.0\n", - "4 2013-01-05 50.0 0 50.0 -6.0" + " id date value value_1 value__diff_norm\n", + "0 0 2013-01-01 48.0 48.0 NaN\n", + "1 0 2013-01-02 44.0 44.0 -4.0\n", + "2 0 2013-01-03 60.0 60.0 16.0\n", + "3 0 2013-01-04 56.0 56.0 -4.0\n", + "4 0 2013-01-05 50.0 50.0 -6.0" ] }, "metadata": {}, @@ -1398,7 +1401,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -1432,7 +1435,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2044,46 +2047,46 @@ " \n", " \n", " \n", + " id\n", " date\n", " value\n", - " id\n", " value__imputed\n", " \n", " \n", " \n", " \n", " 0\n", + " 0\n", " 2013-01-01\n", " NaN\n", - " 0\n", " 0.000000\n", " \n", " \n", " 1\n", + " 0\n", " 2013-01-02\n", " 44.0\n", - " 0\n", " 44.000000\n", " \n", " \n", " 2\n", + " 0\n", " 2013-01-03\n", " 60.0\n", - " 0\n", " 60.000000\n", " \n", " \n", " 3\n", + " 0\n", " 2013-01-04\n", " NaN\n", - " 0\n", " 52.665128\n", " \n", " \n", " 4\n", + " 0\n", " 2013-01-05\n", " 50.0\n", - " 0\n", " 50.000000\n", " \n", " \n", @@ -2091,12 +2094,12 @@ "" ], "text/plain": [ - " date value id value__imputed\n", - "0 2013-01-01 NaN 0 0.000000\n", - "1 2013-01-02 44.0 0 44.000000\n", - "2 2013-01-03 60.0 0 60.000000\n", - "3 2013-01-04 NaN 0 52.665128\n", - "4 2013-01-05 50.0 0 50.000000" + " id date value value__imputed\n", + "0 0 2013-01-01 NaN 0.000000\n", + "1 0 2013-01-02 44.0 44.000000\n", + "2 0 2013-01-03 60.0 60.000000\n", + "3 0 2013-01-04 NaN 52.665128\n", + "4 0 2013-01-05 50.0 50.000000" ] }, "metadata": {}, @@ -2139,7 +2142,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -2486,7 +2489,7 @@ { "data": { "text/plain": [ - "{'transformers': ,\n", + "{'transformers': ,\n", " 'multivariate': False,\n", " 'is_fitted': False,\n", " 'strategy_name': None,\n", @@ -2643,7 +2646,7 @@ ], "metadata": { "kernelspec": { - "display_name": "py_3_10", + "display_name": "tsururu_test", "language": "python", "name": "python3" }, @@ -2657,7 +2660,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/examples/Tutorial_4_Neural_Networks.ipynb b/examples/Tutorial_4_Neural_Networks.ipynb index d9f353e..8344634 100644 --- a/examples/Tutorial_4_Neural_Networks.ipynb +++ b/examples/Tutorial_4_Neural_Networks.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_path = Path(\"datasets/global/simulated_data_to_check.csv\")\n", + "df_path = Path(\"../datasets/global/simulated_data_to_check.csv\")\n", "\n", "dataset_params = {\n", " \"target\": {\n", @@ -244,114 +244,119 @@ "output_type": "stream", "text": [ "length of train dataset: 496\n", - "length of val dataset: 497\n", - "Epoch 1/10, cost time: 0.69s\n", - "train loss: 414.3740\n", - "Validation, Loss: 194.2519, Metric: -194.2519\n", - "val loss: 194.2519\n", - "Epoch 2/10, cost time: 0.66s\n", - "train loss: 66.8766\n", - "Validation, Loss: 54.1316, Metric: -54.1316\n", - "val loss: 54.1316\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 17.5994\n", - "Validation, Loss: 11.6868, Metric: -11.6868\n", - "val loss: 11.6868\n", - "Epoch 4/10, cost time: 0.57s\n", - "train loss: 5.7403\n", - "Validation, Loss: 2.8006, Metric: -2.8006\n", - "val loss: 2.8006\n", - "Epoch 5/10, cost time: 0.95s\n", - "train loss: 3.3647\n", - "Validation, Loss: 3.2068, Metric: -3.2068\n", - "val loss: 3.2068\n", - "Early stopping counter: 1\n", - "Epoch 6/10, cost time: 0.66s\n", - "train loss: 2.8860\n", - "Validation, Loss: 2.8542, Metric: -2.8542\n", - "val loss: 2.8542\n", + "length of val dataset: 497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10, cost time: 2.32s\n", + "train loss: 394.8396\n", + "Validation, Loss: 152.7835, Metric: -152.7835\n", + "val loss: 152.7835, val metric: -152.7835\n", + "Epoch 2/10, cost time: 2.05s\n", + "train loss: 63.7407\n", + "Validation, Loss: 45.0496, Metric: -45.0496\n", + "val loss: 45.0496, val metric: -45.0496\n", + "Epoch 3/10, cost time: 2.10s\n", + "train loss: 16.8944\n", + "Validation, Loss: 7.9484, Metric: -7.9484\n", + "val loss: 7.9484, val metric: -7.9484\n", + "Epoch 4/10, cost time: 2.14s\n", + "train loss: 6.3505\n", + "Validation, Loss: 4.6323, Metric: -4.6323\n", + "val loss: 4.6323, val metric: -4.6323\n", + "Epoch 5/10, cost time: 2.09s\n", + "train loss: 4.5366\n", + "Validation, Loss: 3.9856, Metric: -3.9856\n", + "val loss: 3.9856, val metric: -3.9856\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 4.0089\n", + "Validation, Loss: 3.9551, Metric: -3.9551\n", + "val loss: 3.9551, val metric: -3.9551\n", "Removing worst model snapshot: from epoch 0\n", - "Early stopping counter: 2\n", - "Epoch 7/10, cost time: 0.71s\n", - "train loss: 2.7676\n", - "Validation, Loss: 2.7613, Metric: -2.7613\n", - "val loss: 2.7613\n", + "Epoch 7/10, cost time: 2.10s\n", + "train loss: 3.8999\n", + "Validation, Loss: 3.9088, Metric: -3.9088\n", + "val loss: 3.9088, val metric: -3.9088\n", "Removing worst model snapshot: from epoch 1\n", - "Epoch 8/10, cost time: 0.74s\n", - "train loss: 2.7374\n", - "Validation, Loss: 2.7461, Metric: -2.7461\n", - "val loss: 2.7461\n", + "Epoch 8/10, cost time: 2.06s\n", + "train loss: 3.8734\n", + "Validation, Loss: 3.8822, Metric: -3.8822\n", + "val loss: 3.8822, val metric: -3.8822\n", "Removing worst model snapshot: from epoch 2\n", - "Epoch 9/10, cost time: 0.80s\n", - "train loss: 2.7226\n", - "Validation, Loss: 2.7328, Metric: -2.7328\n", - "val loss: 2.7328\n", + "Epoch 9/10, cost time: 2.05s\n", + "train loss: 3.8514\n", + "Validation, Loss: 3.8659, Metric: -3.8659\n", + "val loss: 3.8659, val metric: -3.8659\n", + "Removing worst model snapshot: from epoch 3\n", + "Epoch 10/10, cost time: 2.09s\n", + "train loss: 3.8319\n", + "Validation, Loss: 3.8463, Metric: -3.8463\n", + "val loss: 3.8463, val metric: -3.8463\n", "Removing worst model snapshot: from epoch 4\n", - "Epoch 10/10, cost time: 0.55s\n", - "train loss: 2.7105\n", - "Validation, Loss: 2.7210, Metric: -2.7210\n", - "val loss: 2.7210\n", - "Removing worst model snapshot: from epoch 5\n", "Training finished.\n", - "Fold 0. Score: -2.721017599105835\n", + "Fold 0. Score: -3.846290349960327\n", "length of train dataset: 497\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.82s\n", - "train loss: 392.4304\n", - "Validation, Loss: 145.1556, Metric: -145.1556\n", - "val loss: 145.1556\n", - "Epoch 2/10, cost time: 0.58s\n", - "train loss: 63.3577\n", - "Validation, Loss: 42.9906, Metric: -42.9906\n", - "val loss: 42.9906\n", - "Epoch 3/10, cost time: 0.63s\n", - "train loss: 16.7435\n", - "Validation, Loss: 6.9562, Metric: -6.9562\n", - "val loss: 6.9562\n", - "Epoch 4/10, cost time: 0.56s\n", - "train loss: 6.0819\n", - "Validation, Loss: 4.4336, Metric: -4.4336\n", - "val loss: 4.4336\n", - "Epoch 5/10, cost time: 0.56s\n", - "train loss: 4.2536\n", - "Validation, Loss: 3.7486, Metric: -3.7486\n", - "val loss: 3.7486\n", - "Epoch 6/10, cost time: 0.56s\n", - "train loss: 3.7922\n", - "Validation, Loss: 3.7375, Metric: -3.7375\n", - "val loss: 3.7375\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 407.0723\n", + "Validation, Loss: 173.8472, Metric: -173.8472\n", + "val loss: 173.8472, val metric: -173.8472\n", + "Epoch 2/10, cost time: 2.10s\n", + "train loss: 67.2569\n", + "Validation, Loss: 54.6508, Metric: -54.6508\n", + "val loss: 54.6508, val metric: -54.6508\n", + "Epoch 3/10, cost time: 2.08s\n", + "train loss: 17.9681\n", + "Validation, Loss: 10.7160, Metric: -10.7160\n", + "val loss: 10.7160, val metric: -10.7160\n", + "Epoch 4/10, cost time: 2.07s\n", + "train loss: 5.9077\n", + "Validation, Loss: 3.0399, Metric: -3.0399\n", + "val loss: 3.0399, val metric: -3.0399\n", + "Epoch 5/10, cost time: 2.06s\n", + "train loss: 3.5744\n", + "Validation, Loss: 3.3291, Metric: -3.3291\n", + "val loss: 3.3291, val metric: -3.3291\n", + "Early stopping counter: 1\n", + "Epoch 6/10, cost time: 2.04s\n", + "train loss: 3.0904\n", + "Validation, Loss: 3.0306, Metric: -3.0306\n", + "val loss: 3.0306, val metric: -3.0306\n", "Removing worst model snapshot: from epoch 0\n", - "Epoch 7/10, cost time: 0.56s\n", - "train loss: 3.7092\n", - "Validation, Loss: 3.6670, Metric: -3.6670\n", - "val loss: 3.6670\n", + "Epoch 7/10, cost time: 2.10s\n", + "train loss: 2.9839\n", + "Validation, Loss: 2.9571, Metric: -2.9571\n", + "val loss: 2.9571, val metric: -2.9571\n", "Removing worst model snapshot: from epoch 1\n", - "Epoch 8/10, cost time: 0.56s\n", - "train loss: 3.6805\n", - "Validation, Loss: 3.6503, Metric: -3.6503\n", - "val loss: 3.6503\n", + "Epoch 8/10, cost time: 2.11s\n", + "train loss: 2.9575\n", + "Validation, Loss: 2.9269, Metric: -2.9269\n", + "val loss: 2.9269, val metric: -2.9269\n", "Removing worst model snapshot: from epoch 2\n", - "Epoch 9/10, cost time: 0.56s\n", - "train loss: 3.6643\n", - "Validation, Loss: 3.6290, Metric: -3.6290\n", - "val loss: 3.6290\n", - "Removing worst model snapshot: from epoch 3\n", - "Epoch 10/10, cost time: 0.55s\n", - "train loss: 3.6448\n", - "Validation, Loss: 3.6109, Metric: -3.6109\n", - "val loss: 3.6109\n", + "Epoch 9/10, cost time: 2.10s\n", + "train loss: 2.9374\n", + "Validation, Loss: 2.9119, Metric: -2.9119\n", + "val loss: 2.9119, val metric: -2.9119\n", "Removing worst model snapshot: from epoch 4\n", + "Epoch 10/10, cost time: 2.09s\n", + "train loss: 2.9242\n", + "Validation, Loss: 2.8975, Metric: -2.8975\n", + "val loss: 2.8975, val metric: -2.8975\n", + "Removing worst model snapshot: from epoch 3\n", "Training finished.\n", - "Fold 1. Score: -3.6109354496002197\n", - "Mean score: -3.166\n", - "Std: 0.445\n" + "Fold 1. Score: -2.8974790573120117\n", + "Mean score: -3.3719\n", + "Std: 0.4744\n" ] }, { "data": { "text/plain": [ - "(25.945719003677368,\n", - " )" + "(77.95777916908264,\n", + " )" ] }, "execution_count": 9, @@ -372,27 +377,20 @@ "name": "stdout", "output_type": "stream", "text": [ + "freq: Day; period: 1\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", - "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", - "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", + "length of test dataset: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n" + "length of test dataset: 1\n" ] } ], @@ -436,31 +434,31 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1997.017334\n", + " 1996.918457\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1997.603149\n", + " 1997.491211\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1998.090576\n", + " 1997.960571\n", " \n", " \n", " 3\n", " 0\n", " 2022-09-30\n", - " 1998.494995\n", + " 1998.343872\n", " \n", " \n", " 4\n", " 0\n", " 2022-10-01\n", - " 1998.843994\n", + " 1998.670898\n", " \n", " \n", " ...\n", @@ -472,31 +470,31 @@ " 65\n", " 9\n", " 2022-09-29\n", - " 11004.65918\n", + " 11004.71582\n", " \n", " \n", " 66\n", " 9\n", " 2022-09-30\n", - " 11006.064453\n", + " 11006.132812\n", " \n", " \n", " 67\n", " 9\n", " 2022-10-01\n", - " 11007.509766\n", + " 11007.585938\n", " \n", " \n", " 68\n", " 9\n", " 2022-10-02\n", - " 11008.994141\n", + " 11009.080078\n", " \n", " \n", " 69\n", " 9\n", " 2022-10-03\n", - " 11010.549805\n", + " 11010.646484\n", " \n", " \n", "\n", @@ -505,17 +503,17 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1997.017334\n", - "1 0 2022-09-28 1997.603149\n", - "2 0 2022-09-29 1998.090576\n", - "3 0 2022-09-30 1998.494995\n", - "4 0 2022-10-01 1998.843994\n", + "0 0 2022-09-27 1996.918457\n", + "1 0 2022-09-28 1997.491211\n", + "2 0 2022-09-29 1997.960571\n", + "3 0 2022-09-30 1998.343872\n", + "4 0 2022-10-01 1998.670898\n", ".. .. ... ...\n", - "65 9 2022-09-29 11004.65918\n", - "66 9 2022-09-30 11006.064453\n", - "67 9 2022-10-01 11007.509766\n", - "68 9 2022-10-02 11008.994141\n", - "69 9 2022-10-03 11010.549805\n", + "65 9 2022-09-29 11004.71582\n", + "66 9 2022-09-30 11006.132812\n", + "67 9 2022-10-01 11007.585938\n", + "68 9 2022-10-02 11009.080078\n", + "69 9 2022-10-03 11010.646484\n", "\n", "[70 rows x 3 columns]" ] @@ -604,1307 +602,1317 @@ "output_type": "stream", "text": [ "length of train dataset: 496\n", - "length of val dataset: 497\n", - "Epoch 1/10, cost time: 0.64s\n", - "train loss: 408.1764\n", - "Validation, Loss: 177.2360, Metric: -177.2360\n", - "val loss: 177.2360\n", + "length of val dataset: 497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10, cost time: 2.05s\n", + "train loss: 403.3343\n", + "Validation, Loss: 167.4490, Metric: -167.4490\n", + "val loss: 167.4490, val metric: -167.4490\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.69s\n", - "train loss: 65.3214\n", - "Validation, Loss: 51.6638, Metric: -51.6638\n", - "val loss: 51.6638\n", + "Epoch 2/10, cost time: 2.05s\n", + "train loss: 64.2173\n", + "Validation, Loss: 47.8190, Metric: -47.8190\n", + "val loss: 47.8190, val metric: -47.8190\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.65s\n", - "train loss: 16.8678\n", - "Validation, Loss: 9.6748, Metric: -9.6748\n", - "val loss: 9.6748\n", + "Epoch 3/10, cost time: 2.07s\n", + "train loss: 16.2793\n", + "Validation, Loss: 8.0289, Metric: -8.0289\n", + "val loss: 8.0289, val metric: -8.0289\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.66s\n", - "train loss: 5.3270\n", - "Validation, Loss: 2.7434, Metric: -2.7434\n", - "val loss: 2.7434\n", + "Epoch 4/10, cost time: 2.07s\n", + "train loss: 5.2114\n", + "Validation, Loss: 3.0257, Metric: -3.0257\n", + "val loss: 3.0257, val metric: -3.0257\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 3.1594\n", - "Validation, Loss: 2.9166, Metric: -2.9166\n", - "val loss: 2.9166\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 3.1989\n", + "Validation, Loss: 2.8681, Metric: -2.8681\n", + "val loss: 2.8681, val metric: -2.8681\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_4.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 2.7221\n", - "Validation, Loss: 2.7164, Metric: -2.7164\n", - "val loss: 2.7164\n", + "Epoch 6/10, cost time: 2.05s\n", + "train loss: 2.6938\n", + "Validation, Loss: 2.6690, Metric: -2.6690\n", + "val loss: 2.6690, val metric: -2.6690\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.57s\n", - "train loss: 2.6359\n", - "Validation, Loss: 2.6274, Metric: -2.6274\n", - "val loss: 2.6274\n", + "Epoch 7/10, cost time: 2.07s\n", + "train loss: 2.5845\n", + "Validation, Loss: 2.5779, Metric: -2.5779\n", + "val loss: 2.5779, val metric: -2.5779\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.56s\n", - "train loss: 2.6042\n", - "Validation, Loss: 2.6130, Metric: -2.6130\n", - "val loss: 2.6130\n", + "Epoch 8/10, cost time: 2.11s\n", + "train loss: 2.5551\n", + "Validation, Loss: 2.5615, Metric: -2.5615\n", + "val loss: 2.5615, val metric: -2.5615\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_7.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.57s\n", - "train loss: 2.5933\n", - "Validation, Loss: 2.6077, Metric: -2.6077\n", - "val loss: 2.6077\n", + "Epoch 9/10, cost time: 2.08s\n", + "train loss: 2.5402\n", + "Validation, Loss: 2.5501, Metric: -2.5501\n", + "val loss: 2.5501, val metric: -2.5501\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.66s\n", - "train loss: 2.5827\n", - "Validation, Loss: 2.5904, Metric: -2.5904\n", - "val loss: 2.5904\n", + "Epoch 10/10, cost time: 2.05s\n", + "train loss: 2.5287\n", + "Validation, Loss: 2.5374, Metric: -2.5374\n", + "val loss: 2.5374, val metric: -2.5374\n", "Last epoch model saved to checkpoints/trainer_0/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -2.590420961380005\n", + "Fold 0. Score: -2.537403106689453\n", "length of train dataset: 497\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.69s\n", - "train loss: 405.7398\n", - "Validation, Loss: 172.1121, Metric: -172.1121\n", - "val loss: 172.1121\n", + "Epoch 1/10, cost time: 2.06s\n", + "train loss: 395.2313\n", + "Validation, Loss: 157.7264, Metric: -157.7264\n", + "val loss: 157.7264, val metric: -157.7264\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 67.4312\n", - "Validation, Loss: 52.2455, Metric: -52.2455\n", - "val loss: 52.2455\n", + "Epoch 2/10, cost time: 2.07s\n", + "train loss: 63.4828\n", + "Validation, Loss: 46.7708, Metric: -46.7708\n", + "val loss: 46.7708, val metric: -46.7708\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 17.4579\n", - "Validation, Loss: 10.0573, Metric: -10.0573\n", - "val loss: 10.0573\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 16.9178\n", + "Validation, Loss: 8.1191, Metric: -8.1191\n", + "val loss: 8.1191, val metric: -8.1191\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.85s\n", - "train loss: 6.1413\n", - "Validation, Loss: 3.6228, Metric: -3.6228\n", - "val loss: 3.6228\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 6.1647\n", + "Validation, Loss: 4.2231, Metric: -4.2231\n", + "val loss: 4.2231, val metric: -4.2231\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.67s\n", - "train loss: 4.0260\n", - "Validation, Loss: 3.7045, Metric: -3.7045\n", - "val loss: 3.7045\n", + "Epoch 5/10, cost time: 2.07s\n", + "train loss: 4.2604\n", + "Validation, Loss: 3.8231, Metric: -3.8231\n", + "val loss: 3.8231, val metric: -3.8231\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_4.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.61s\n", - "train loss: 3.5357\n", - "Validation, Loss: 3.4776, Metric: -3.4776\n", - "val loss: 3.4776\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 3.7906\n", + "Validation, Loss: 3.7506, Metric: -3.7506\n", + "val loss: 3.7506, val metric: -3.7506\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.61s\n", - "train loss: 3.4319\n", - "Validation, Loss: 3.3974, Metric: -3.3974\n", - "val loss: 3.3974\n", + "Epoch 7/10, cost time: 2.05s\n", + "train loss: 3.7271\n", + "Validation, Loss: 3.6950, Metric: -3.6950\n", + "val loss: 3.6950, val metric: -3.6950\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_6.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 3.4033\n", - "Validation, Loss: 3.3723, Metric: -3.3723\n", - "val loss: 3.3723\n", + "Epoch 8/10, cost time: 2.04s\n", + "train loss: 3.6824\n", + "Validation, Loss: 3.6457, Metric: -3.6457\n", + "val loss: 3.6457, val metric: -3.6457\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_7.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.61s\n", - "train loss: 3.3884\n", - "Validation, Loss: 3.3558, Metric: -3.3558\n", - "val loss: 3.3558\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 3.6621\n", + "Validation, Loss: 3.6291, Metric: -3.6291\n", + "val loss: 3.6291, val metric: -3.6291\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.61s\n", - "train loss: 3.3702\n", - "Validation, Loss: 3.3397, Metric: -3.3397\n", - "val loss: 3.3397\n", + "Epoch 10/10, cost time: 2.07s\n", + "train loss: 3.6445\n", + "Validation, Loss: 3.6110, Metric: -3.6110\n", + "val loss: 3.6110, val metric: -3.6110\n", "Last epoch model saved to checkpoints/trainer_0/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -3.3396642208099365\n", - "Mean score: -2.965\n", - "Std: 0.3746\n", + "Fold 1. Score: -3.610971450805664\n", + "Mean score: -3.0742\n", + "Std: 0.5368\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.57s\n", - "train loss: 394.5096\n", - "Validation, Loss: 150.7563, Metric: -150.7563\n", - "val loss: 150.7563\n", + "Epoch 1/10, cost time: 2.09s\n", + "train loss: 391.3144\n", + "Validation, Loss: 151.6951, Metric: -151.6951\n", + "val loss: 151.6951, val metric: -151.6951\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.64s\n", - "train loss: 63.1882\n", - "Validation, Loss: 44.5514, Metric: -44.5514\n", - "val loss: 44.5514\n", + "Epoch 2/10, cost time: 2.06s\n", + "train loss: 65.5355\n", + "Validation, Loss: 45.3878, Metric: -45.3878\n", + "val loss: 45.3878, val metric: -45.3878\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.62s\n", - "train loss: 17.2996\n", - "Validation, Loss: 7.9756, Metric: -7.9756\n", - "val loss: 7.9756\n", + "Epoch 3/10, cost time: 2.04s\n", + "train loss: 18.1223\n", + "Validation, Loss: 7.9664, Metric: -7.9664\n", + "val loss: 7.9664, val metric: -7.9664\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.69s\n", - "train loss: 6.7543\n", - "Validation, Loss: 5.0634, Metric: -5.0634\n", - "val loss: 5.0634\n", + "Epoch 4/10, cost time: 2.08s\n", + "train loss: 7.2747\n", + "Validation, Loss: 5.7947, Metric: -5.7947\n", + "val loss: 5.7947, val metric: -5.7947\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.58s\n", - "train loss: 4.9157\n", - "Validation, Loss: 4.4250, Metric: -4.4250\n", - "val loss: 4.4250\n", + "Epoch 5/10, cost time: 2.07s\n", + "train loss: 5.4642\n", + "Validation, Loss: 4.9576, Metric: -4.9576\n", + "val loss: 4.9576, val metric: -4.9576\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 4.4208\n", - "Validation, Loss: 4.3880, Metric: -4.3880\n", - "val loss: 4.3880\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 4.9428\n", + "Validation, Loss: 4.9103, Metric: -4.9103\n", + "val loss: 4.9103, val metric: -4.9103\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.63s\n", - "train loss: 4.3378\n", - "Validation, Loss: 4.3386, Metric: -4.3386\n", - "val loss: 4.3386\n", + "Epoch 7/10, cost time: 2.06s\n", + "train loss: 4.8478\n", + "Validation, Loss: 4.8553, Metric: -4.8553\n", + "val loss: 4.8553, val metric: -4.8553\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.68s\n", - "train loss: 4.3075\n", - "Validation, Loss: 4.3144, Metric: -4.3144\n", - "val loss: 4.3144\n", + "Epoch 8/10, cost time: 2.07s\n", + "train loss: 4.8201\n", + "Validation, Loss: 4.8361, Metric: -4.8361\n", + "val loss: 4.8361, val metric: -4.8361\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.61s\n", - "train loss: 4.2823\n", - "Validation, Loss: 4.2954, Metric: -4.2954\n", - "val loss: 4.2954\n", + "Epoch 9/10, cost time: 2.04s\n", + "train loss: 4.7991\n", + "Validation, Loss: 4.8162, Metric: -4.8162\n", + "val loss: 4.8162, val metric: -4.8162\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.64s\n", - "train loss: 4.2616\n", - "Validation, Loss: 4.2742, Metric: -4.2742\n", - "val loss: 4.2742\n", + "Epoch 10/10, cost time: 2.07s\n", + "train loss: 4.7769\n", + "Validation, Loss: 4.7948, Metric: -4.7948\n", + "val loss: 4.7948, val metric: -4.7948\n", "Last epoch model saved to checkpoints/trainer_1/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -4.274166107177734\n", + "Fold 0. Score: -4.794838905334473\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.58s\n", - "train loss: 390.7677\n", - "Validation, Loss: 135.1639, Metric: -135.1639\n", - "val loss: 135.1639\n", + "Epoch 1/10, cost time: 2.06s\n", + "train loss: 387.6476\n", + "Validation, Loss: 132.7601, Metric: -132.7601\n", + "val loss: 132.7601, val metric: -132.7601\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.65s\n", - "train loss: 64.3006\n", - "Validation, Loss: 43.0715, Metric: -43.0715\n", - "val loss: 43.0715\n", + "Epoch 2/10, cost time: 2.15s\n", + "train loss: 62.6459\n", + "Validation, Loss: 38.6651, Metric: -38.6651\n", + "val loss: 38.6651, val metric: -38.6651\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.64s\n", - "train loss: 17.2403\n", - "Validation, Loss: 7.0201, Metric: -7.0201\n", - "val loss: 7.0201\n", + "Epoch 3/10, cost time: 2.10s\n", + "train loss: 16.5981\n", + "Validation, Loss: 6.0093, Metric: -6.0093\n", + "val loss: 6.0093, val metric: -6.0093\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.65s\n", - "train loss: 6.5804\n", - "Validation, Loss: 5.0567, Metric: -5.0567\n", - "val loss: 5.0567\n", + "Epoch 4/10, cost time: 2.11s\n", + "train loss: 6.3818\n", + "Validation, Loss: 5.1434, Metric: -5.1434\n", + "val loss: 5.1434, val metric: -5.1434\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.68s\n", - "train loss: 4.7934\n", - "Validation, Loss: 4.2618, Metric: -4.2618\n", - "val loss: 4.2618\n", + "Epoch 5/10, cost time: 2.09s\n", + "train loss: 4.6630\n", + "Validation, Loss: 4.1301, Metric: -4.1301\n", + "val loss: 4.1301, val metric: -4.1301\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.86s\n", - "train loss: 4.3314\n", - "Validation, Loss: 4.2607, Metric: -4.2607\n", - "val loss: 4.2607\n", + "Epoch 6/10, cost time: 2.08s\n", + "train loss: 4.2250\n", + "Validation, Loss: 4.1226, Metric: -4.1226\n", + "val loss: 4.1226, val metric: -4.1226\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.70s\n", - "train loss: 4.2526\n", - "Validation, Loss: 4.2089, Metric: -4.2089\n", - "val loss: 4.2089\n", + "Epoch 7/10, cost time: 2.09s\n", + "train loss: 4.1473\n", + "Validation, Loss: 4.1007, Metric: -4.1007\n", + "val loss: 4.1007, val metric: -4.1007\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.85s\n", - "train loss: 4.2181\n", - "Validation, Loss: 4.1819, Metric: -4.1819\n", - "val loss: 4.1819\n", + "Epoch 8/10, cost time: 2.04s\n", + "train loss: 4.1182\n", + "Validation, Loss: 4.0836, Metric: -4.0836\n", + "val loss: 4.0836, val metric: -4.0836\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.62s\n", - "train loss: 4.1955\n", - "Validation, Loss: 4.1586, Metric: -4.1586\n", - "val loss: 4.1586\n", + "Epoch 9/10, cost time: 2.05s\n", + "train loss: 4.0968\n", + "Validation, Loss: 4.0635, Metric: -4.0635\n", + "val loss: 4.0635, val metric: -4.0635\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.67s\n", - "train loss: 4.1770\n", - "Validation, Loss: 4.1423, Metric: -4.1423\n", - "val loss: 4.1423\n", + "Epoch 10/10, cost time: 2.05s\n", + "train loss: 4.0820\n", + "Validation, Loss: 4.0494, Metric: -4.0494\n", + "val loss: 4.0494, val metric: -4.0494\n", "Last epoch model saved to checkpoints/trainer_1/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -4.142251491546631\n", - "Mean score: -4.2082\n", - "Std: 0.066\n", + "Fold 1. Score: -4.049437999725342\n", + "Mean score: -4.4221\n", + "Std: 0.3727\n", "length of train dataset: 495\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.84s\n", - "train loss: 370.6101\n", - "Validation, Loss: 105.8064, Metric: -105.8064\n", - "val loss: 105.8064\n", + "Epoch 1/10, cost time: 2.05s\n", + "train loss: 377.4181\n", + "Validation, Loss: 119.6817, Metric: -119.6817\n", + "val loss: 119.6817, val metric: -119.6817\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.78s\n", - "train loss: 64.1122\n", - "Validation, Loss: 30.7795, Metric: -30.7795\n", - "val loss: 30.7795\n", + "Epoch 2/10, cost time: 2.07s\n", + "train loss: 62.3697\n", + "Validation, Loss: 33.8677, Metric: -33.8677\n", + "val loss: 33.8677, val metric: -33.8677\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 19.6595\n", - "Validation, Loss: 7.5525, Metric: -7.5525\n", - "val loss: 7.5525\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 18.4027\n", + "Validation, Loss: 7.1072, Metric: -7.1072\n", + "val loss: 7.1072, val metric: -7.1072\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.67s\n", - "train loss: 9.9237\n", - "Validation, Loss: 9.5275, Metric: -9.5275\n", - "val loss: 9.5275\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 8.6548\n", + "Validation, Loss: 8.0592, Metric: -8.0592\n", + "val loss: 8.0592, val metric: -8.0592\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_3.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.62s\n", - "train loss: 8.0550\n", - "Validation, Loss: 7.6360, Metric: -7.6360\n", - "val loss: 7.6360\n", + "Epoch 5/10, cost time: 2.05s\n", + "train loss: 6.9929\n", + "Validation, Loss: 6.5605, Metric: -6.5605\n", + "val loss: 6.5605, val metric: -6.5605\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_4.pth\n", - "Early stopping counter: 2\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 7.4960\n", - "Validation, Loss: 7.4307, Metric: -7.4307\n", - "val loss: 7.4307\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 6.6101\n", + "Validation, Loss: 6.5029, Metric: -6.5029\n", + "val loss: 6.5029, val metric: -6.5029\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.91s\n", - "train loss: 7.3759\n", - "Validation, Loss: 7.3835, Metric: -7.3835\n", - "val loss: 7.3835\n", + "Epoch 7/10, cost time: 2.04s\n", + "train loss: 6.4681\n", + "Validation, Loss: 6.4772, Metric: -6.4772\n", + "val loss: 6.4772, val metric: -6.4772\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.67s\n", - "train loss: 7.3245\n", - "Validation, Loss: 7.3472, Metric: -7.3472\n", - "val loss: 7.3472\n", + "Epoch 8/10, cost time: 2.06s\n", + "train loss: 6.4321\n", + "Validation, Loss: 6.4568, Metric: -6.4568\n", + "val loss: 6.4568, val metric: -6.4568\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_7.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.58s\n", - "train loss: 7.2910\n", - "Validation, Loss: 7.3193, Metric: -7.3193\n", - "val loss: 7.3193\n", + "Epoch 9/10, cost time: 2.03s\n", + "train loss: 6.4064\n", + "Validation, Loss: 6.4224, Metric: -6.4224\n", + "val loss: 6.4224, val metric: -6.4224\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.66s\n", - "train loss: 7.2569\n", - "Validation, Loss: 7.2778, Metric: -7.2778\n", - "val loss: 7.2778\n", + "Epoch 10/10, cost time: 2.04s\n", + "train loss: 6.3731\n", + "Validation, Loss: 6.3895, Metric: -6.3895\n", + "val loss: 6.3895, val metric: -6.3895\n", "Last epoch model saved to checkpoints/trainer_2/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -7.27780294418335\n", + "Fold 0. Score: -6.389479160308838\n", "length of train dataset: 496\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.65s\n", - "train loss: 380.2661\n", - "Validation, Loss: 135.4580, Metric: -135.4580\n", - "val loss: 135.4580\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 367.3132\n", + "Validation, Loss: 104.9462, Metric: -104.9462\n", + "val loss: 104.9462, val metric: -104.9462\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.61s\n", - "train loss: 63.1142\n", - "Validation, Loss: 39.2277, Metric: -39.2277\n", - "val loss: 39.2277\n", + "Epoch 2/10, cost time: 2.08s\n", + "train loss: 59.8073\n", + "Validation, Loss: 29.2100, Metric: -29.2100\n", + "val loss: 29.2100, val metric: -29.2100\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.74s\n", - "train loss: 18.4396\n", - "Validation, Loss: 7.9900, Metric: -7.9900\n", - "val loss: 7.9900\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 17.8022\n", + "Validation, Loss: 6.8590, Metric: -6.8590\n", + "val loss: 6.8590, val metric: -6.8590\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.68s\n", - "train loss: 8.6784\n", - "Validation, Loss: 7.8326, Metric: -7.8326\n", - "val loss: 7.8326\n", + "Epoch 4/10, cost time: 2.07s\n", + "train loss: 9.0700\n", + "Validation, Loss: 8.6057, Metric: -8.6057\n", + "val loss: 8.6057, val metric: -8.6057\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_3.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.71s\n", - "train loss: 7.0502\n", - "Validation, Loss: 6.3975, Metric: -6.3975\n", - "val loss: 6.3975\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 7.3478\n", + "Validation, Loss: 6.8900, Metric: -6.8900\n", + "val loss: 6.8900, val metric: -6.8900\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_4.pth\n", + "Early stopping counter: 2\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.69s\n", - "train loss: 6.5540\n", - "Validation, Loss: 6.3960, Metric: -6.3960\n", - "val loss: 6.3960\n", + "Epoch 6/10, cost time: 2.05s\n", + "train loss: 6.8923\n", + "Validation, Loss: 6.7494, Metric: -6.7494\n", + "val loss: 6.7494, val metric: -6.7494\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_5.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.58s\n", - "train loss: 6.4277\n", - "Validation, Loss: 6.3432, Metric: -6.3432\n", - "val loss: 6.3432\n", + "Epoch 7/10, cost time: 2.05s\n", + "train loss: 6.7842\n", + "Validation, Loss: 6.7169, Metric: -6.7169\n", + "val loss: 6.7169, val metric: -6.7169\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_6.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 6.3731\n", - "Validation, Loss: 6.3182, Metric: -6.3182\n", - "val loss: 6.3182\n", + "Epoch 8/10, cost time: 2.09s\n", + "train loss: 6.7418\n", + "Validation, Loss: 6.6857, Metric: -6.6857\n", + "val loss: 6.6857, val metric: -6.6857\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_7.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.71s\n", - "train loss: 6.3430\n", - "Validation, Loss: 6.3168, Metric: -6.3168\n", - "val loss: 6.3168\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 6.7103\n", + "Validation, Loss: 6.6524, Metric: -6.6524\n", + "val loss: 6.6524, val metric: -6.6524\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 6.3195\n", - "Validation, Loss: 6.2573, Metric: -6.2573\n", - "val loss: 6.2573\n", + "Epoch 10/10, cost time: 2.11s\n", + "train loss: 6.6812\n", + "Validation, Loss: 6.6214, Metric: -6.6214\n", + "val loss: 6.6214, val metric: -6.6214\n", "Last epoch model saved to checkpoints/trainer_2/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -6.257255554199219\n", - "Mean score: -6.7675\n", - "Std: 0.5103\n", + "Fold 1. Score: -6.621443271636963\n", + "Mean score: -6.5055\n", + "Std: 0.116\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.69s\n", - "train loss: 346.9920\n", - "Validation, Loss: 65.9282, Metric: -65.9282\n", - "val loss: 65.9282\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 363.6992\n", + "Validation, Loss: 84.1472, Metric: -84.1472\n", + "val loss: 84.1472, val metric: -84.1472\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.65s\n", - "train loss: 58.3577\n", - "Validation, Loss: 17.2433, Metric: -17.2433\n", - "val loss: 17.2433\n", + "Epoch 2/10, cost time: 2.06s\n", + "train loss: 61.5839\n", + "Validation, Loss: 22.4284, Metric: -22.4284\n", + "val loss: 22.4284, val metric: -22.4284\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.69s\n", - "train loss: 19.6333\n", - "Validation, Loss: 11.0935, Metric: -11.0935\n", - "val loss: 11.0935\n", + "Epoch 3/10, cost time: 2.09s\n", + "train loss: 19.7095\n", + "Validation, Loss: 8.4264, Metric: -8.4264\n", + "val loss: 8.4264, val metric: -8.4264\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.63s\n", - "train loss: 11.6597\n", - "Validation, Loss: 10.3450, Metric: -10.3450\n", - "val loss: 10.3450\n", + "Epoch 4/10, cost time: 2.04s\n", + "train loss: 10.5336\n", + "Validation, Loss: 9.9348, Metric: -9.9348\n", + "val loss: 9.9348, val metric: -9.9348\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_3.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.74s\n", - "train loss: 9.7085\n", - "Validation, Loss: 9.7430, Metric: -9.7430\n", - "val loss: 9.7430\n", + "Epoch 5/10, cost time: 2.04s\n", + "train loss: 8.6030\n", + "Validation, Loss: 8.3369, Metric: -8.3369\n", + "val loss: 8.3369, val metric: -8.3369\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.70s\n", - "train loss: 9.3758\n", - "Validation, Loss: 9.3782, Metric: -9.3782\n", - "val loss: 9.3782\n", + "Epoch 6/10, cost time: 2.08s\n", + "train loss: 8.1817\n", + "Validation, Loss: 8.1613, Metric: -8.1613\n", + "val loss: 8.1613, val metric: -8.1613\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_5.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.61s\n", - "train loss: 9.2463\n", - "Validation, Loss: 9.2625, Metric: -9.2625\n", - "val loss: 9.2625\n", + "Epoch 7/10, cost time: 2.06s\n", + "train loss: 8.0705\n", + "Validation, Loss: 8.0940, Metric: -8.0940\n", + "val loss: 8.0940, val metric: -8.0940\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.62s\n", - "train loss: 9.1890\n", - "Validation, Loss: 9.2153, Metric: -9.2153\n", - "val loss: 9.2153\n", + "Epoch 8/10, cost time: 2.04s\n", + "train loss: 8.0227\n", + "Validation, Loss: 8.0545, Metric: -8.0545\n", + "val loss: 8.0545, val metric: -8.0545\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.62s\n", - "train loss: 9.1442\n", - "Validation, Loss: 9.1684, Metric: -9.1684\n", - "val loss: 9.1684\n", + "Epoch 9/10, cost time: 2.04s\n", + "train loss: 7.9855\n", + "Validation, Loss: 8.0059, Metric: -8.0059\n", + "val loss: 8.0059, val metric: -8.0059\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.69s\n", - "train loss: 9.0973\n", - "Validation, Loss: 9.1324, Metric: -9.1324\n", - "val loss: 9.1324\n", + "Epoch 10/10, cost time: 2.05s\n", + "train loss: 7.9414\n", + "Validation, Loss: 7.9646, Metric: -7.9646\n", + "val loss: 7.9646, val metric: -7.9646\n", "Last epoch model saved to checkpoints/trainer_3/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -9.13241958618164\n", + "Fold 0. Score: -7.964632987976074\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.58s\n", - "train loss: 368.7106\n", - "Validation, Loss: 111.0458, Metric: -111.0458\n", - "val loss: 111.0458\n", + "Epoch 1/10, cost time: 2.05s\n", + "train loss: 351.5906\n", + "Validation, Loss: 77.0033, Metric: -77.0033\n", + "val loss: 77.0033, val metric: -77.0033\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.57s\n", - "train loss: 59.9375\n", - "Validation, Loss: 30.0969, Metric: -30.0969\n", - "val loss: 30.0969\n", + "Epoch 2/10, cost time: 2.05s\n", + "train loss: 61.6245\n", + "Validation, Loss: 21.3528, Metric: -21.3528\n", + "val loss: 21.3528, val metric: -21.3528\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 18.3879\n", - "Validation, Loss: 7.9343, Metric: -7.9343\n", - "val loss: 7.9343\n", + "Epoch 3/10, cost time: 2.05s\n", + "train loss: 20.8268\n", + "Validation, Loss: 10.9678, Metric: -10.9678\n", + "val loss: 10.9678, val metric: -10.9678\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 10.0047\n", - "Validation, Loss: 9.6751, Metric: -9.6751\n", - "val loss: 9.6751\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 12.5175\n", + "Validation, Loss: 11.5429, Metric: -11.5429\n", + "val loss: 11.5429, val metric: -11.5429\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_3.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.67s\n", - "train loss: 8.4017\n", - "Validation, Loss: 7.9002, Metric: -7.9002\n", - "val loss: 7.9002\n", + "Epoch 5/10, cost time: 2.05s\n", + "train loss: 10.6625\n", + "Validation, Loss: 10.4605, Metric: -10.4605\n", + "val loss: 10.4605, val metric: -10.4605\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.58s\n", - "train loss: 7.9538\n", - "Validation, Loss: 7.7806, Metric: -7.7806\n", - "val loss: 7.7806\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 10.2767\n", + "Validation, Loss: 10.1229, Metric: -10.1229\n", + "val loss: 10.1229, val metric: -10.1229\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.90s\n", - "train loss: 7.8327\n", - "Validation, Loss: 7.7473, Metric: -7.7473\n", - "val loss: 7.7473\n", + "Epoch 7/10, cost time: 2.05s\n", + "train loss: 10.1318\n", + "Validation, Loss: 10.0544, Metric: -10.0544\n", + "val loss: 10.0544, val metric: -10.0544\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.62s\n", - "train loss: 7.7787\n", - "Validation, Loss: 7.7155, Metric: -7.7155\n", - "val loss: 7.7155\n", + "Epoch 8/10, cost time: 2.07s\n", + "train loss: 10.0754\n", + "Validation, Loss: 9.9893, Metric: -9.9893\n", + "val loss: 9.9893, val metric: -9.9893\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 7.7438\n", - "Validation, Loss: 7.6956, Metric: -7.6956\n", - "val loss: 7.6956\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 10.0335\n", + "Validation, Loss: 9.9531, Metric: -9.9531\n", + "val loss: 9.9531, val metric: -9.9531\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.65s\n", - "train loss: 7.7080\n", - "Validation, Loss: 7.6409, Metric: -7.6409\n", - "val loss: 7.6409\n", + "Epoch 10/10, cost time: 2.08s\n", + "train loss: 9.9840\n", + "Validation, Loss: 9.8895, Metric: -9.8895\n", + "val loss: 9.8895, val metric: -9.8895\n", "Last epoch model saved to checkpoints/trainer_3/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -7.64092493057251\n", - "Mean score: -8.3867\n", - "Std: 0.7457\n", + "Fold 1. Score: -9.889528274536133\n", + "Mean score: -8.9271\n", + "Std: 0.9624\n", "length of train dataset: 494\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.67s\n", - "train loss: 348.4213\n", - "Validation, Loss: 73.2793, Metric: -73.2793\n", - "val loss: 73.2793\n", + "Epoch 1/10, cost time: 2.08s\n", + "train loss: 343.4748\n", + "Validation, Loss: 67.4538, Metric: -67.4538\n", + "val loss: 67.4538, val metric: -67.4538\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 61.8928\n", - "Validation, Loss: 20.4437, Metric: -20.4437\n", - "val loss: 20.4437\n", + "Epoch 2/10, cost time: 2.06s\n", + "train loss: 59.9123\n", + "Validation, Loss: 18.4222, Metric: -18.4222\n", + "val loss: 18.4222, val metric: -18.4222\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.58s\n", - "train loss: 21.6333\n", - "Validation, Loss: 13.2486, Metric: -13.2486\n", - "val loss: 13.2486\n", + "Epoch 3/10, cost time: 2.07s\n", + "train loss: 22.0047\n", + "Validation, Loss: 14.5544, Metric: -14.5544\n", + "val loss: 14.5544, val metric: -14.5544\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 14.0897\n", - "Validation, Loss: 13.4205, Metric: -13.4205\n", - "val loss: 13.4205\n", + "Epoch 4/10, cost time: 2.03s\n", + "train loss: 14.4075\n", + "Validation, Loss: 13.0656, Metric: -13.0656\n", + "val loss: 13.0656, val metric: -13.0656\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_3.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.59s\n", - "train loss: 12.3617\n", - "Validation, Loss: 12.3097, Metric: -12.3097\n", - "val loss: 12.3097\n", + "Epoch 5/10, cost time: 2.06s\n", + "train loss: 12.5399\n", + "Validation, Loss: 12.4322, Metric: -12.4322\n", + "val loss: 12.4322, val metric: -12.4322\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.60s\n", - "train loss: 11.9895\n", - "Validation, Loss: 12.0575, Metric: -12.0575\n", - "val loss: 12.0575\n", + "Epoch 6/10, cost time: 2.07s\n", + "train loss: 12.2428\n", + "Validation, Loss: 12.2647, Metric: -12.2647\n", + "val loss: 12.2647, val metric: -12.2647\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_5.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 11.8794\n", - "Validation, Loss: 11.9171, Metric: -11.9171\n", - "val loss: 11.9171\n", + "Epoch 7/10, cost time: 2.10s\n", + "train loss: 12.1589\n", + "Validation, Loss: 12.1953, Metric: -12.1953\n", + "val loss: 12.1953, val metric: -12.1953\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.82s\n", - "train loss: 11.7933\n", - "Validation, Loss: 11.8261, Metric: -11.8261\n", - "val loss: 11.8261\n", + "Epoch 8/10, cost time: 2.08s\n", + "train loss: 12.0992\n", + "Validation, Loss: 12.1414, Metric: -12.1414\n", + "val loss: 12.1414, val metric: -12.1414\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.96s\n", - "train loss: 11.7350\n", - "Validation, Loss: 11.7775, Metric: -11.7775\n", - "val loss: 11.7775\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 12.0343\n", + "Validation, Loss: 12.0809, Metric: -12.0809\n", + "val loss: 12.0809, val metric: -12.0809\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.70s\n", - "train loss: 11.6735\n", - "Validation, Loss: 11.7246, Metric: -11.7246\n", - "val loss: 11.7246\n", + "Epoch 10/10, cost time: 2.10s\n", + "train loss: 11.9855\n", + "Validation, Loss: 12.0072, Metric: -12.0072\n", + "val loss: 12.0072, val metric: -12.0072\n", "Last epoch model saved to checkpoints/trainer_4/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -11.724552154541016\n", + "Fold 0. Score: -12.007180213928223\n", "length of train dataset: 495\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.59s\n", - "train loss: 347.5624\n", - "Validation, Loss: 76.3863, Metric: -76.3863\n", - "val loss: 76.3863\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 349.2330\n", + "Validation, Loss: 77.6863, Metric: -77.6863\n", + "val loss: 77.6863, val metric: -77.6863\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.67s\n", - "train loss: 61.9291\n", - "Validation, Loss: 21.8872, Metric: -21.8872\n", - "val loss: 21.8872\n", + "Epoch 2/10, cost time: 2.11s\n", + "train loss: 64.0899\n", + "Validation, Loss: 20.9255, Metric: -20.9255\n", + "val loss: 20.9255, val metric: -20.9255\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.64s\n", - "train loss: 22.6591\n", - "Validation, Loss: 14.1557, Metric: -14.1557\n", - "val loss: 14.1557\n", + "Epoch 3/10, cost time: 2.12s\n", + "train loss: 22.2888\n", + "Validation, Loss: 12.9935, Metric: -12.9935\n", + "val loss: 12.9935, val metric: -12.9935\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.62s\n", - "train loss: 14.8450\n", - "Validation, Loss: 13.3702, Metric: -13.3702\n", - "val loss: 13.3702\n", + "Epoch 4/10, cost time: 2.07s\n", + "train loss: 13.9388\n", + "Validation, Loss: 12.7992, Metric: -12.7992\n", + "val loss: 12.7992, val metric: -12.7992\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.67s\n", - "train loss: 12.8992\n", - "Validation, Loss: 12.6735, Metric: -12.6735\n", - "val loss: 12.6735\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 12.1594\n", + "Validation, Loss: 12.0069, Metric: -12.0069\n", + "val loss: 12.0069, val metric: -12.0069\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.68s\n", - "train loss: 12.5828\n", - "Validation, Loss: 12.5003, Metric: -12.5003\n", - "val loss: 12.5003\n", + "Epoch 6/10, cost time: 2.08s\n", + "train loss: 11.8229\n", + "Validation, Loss: 11.7637, Metric: -11.7637\n", + "val loss: 11.7637, val metric: -11.7637\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 12.4983\n", - "Validation, Loss: 12.3865, Metric: -12.3865\n", - "val loss: 12.3865\n", + "Epoch 7/10, cost time: 2.11s\n", + "train loss: 11.7082\n", + "Validation, Loss: 11.5804, Metric: -11.5804\n", + "val loss: 11.5804, val metric: -11.5804\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 12.4266\n", - "Validation, Loss: 12.2916, Metric: -12.2916\n", - "val loss: 12.2916\n", + "Epoch 8/10, cost time: 2.07s\n", + "train loss: 11.6116\n", + "Validation, Loss: 11.5076, Metric: -11.5076\n", + "val loss: 11.5076, val metric: -11.5076\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.66s\n", - "train loss: 12.3375\n", - "Validation, Loss: 12.2354, Metric: -12.2354\n", - "val loss: 12.2354\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 11.5472\n", + "Validation, Loss: 11.4507, Metric: -11.4507\n", + "val loss: 11.4507, val metric: -11.4507\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.64s\n", - "train loss: 12.2759\n", - "Validation, Loss: 12.1871, Metric: -12.1871\n", - "val loss: 12.1871\n", + "Epoch 10/10, cost time: 2.09s\n", + "train loss: 11.4889\n", + "Validation, Loss: 11.4103, Metric: -11.4103\n", + "val loss: 11.4103, val metric: -11.4103\n", "Last epoch model saved to checkpoints/trainer_4/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -12.187055587768555\n", - "Mean score: -11.9558\n", - "Std: 0.2313\n", + "Fold 1. Score: -11.410284042358398\n", + "Mean score: -11.7087\n", + "Std: 0.2984\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.67s\n", - "train loss: 341.5293\n", - "Validation, Loss: 68.1881, Metric: -68.1881\n", - "val loss: 68.1881\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 334.8741\n", + "Validation, Loss: 50.5561, Metric: -50.5561\n", + "val loss: 50.5561, val metric: -50.5561\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.69s\n", - "train loss: 62.9321\n", - "Validation, Loss: 19.6805, Metric: -19.6805\n", - "val loss: 19.6805\n", + "Epoch 2/10, cost time: 2.06s\n", + "train loss: 62.8467\n", + "Validation, Loss: 16.8597, Metric: -16.8597\n", + "val loss: 16.8597, val metric: -16.8597\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.67s\n", - "train loss: 24.2058\n", - "Validation, Loss: 17.5941, Metric: -17.5941\n", - "val loss: 17.5941\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 25.3732\n", + "Validation, Loss: 20.4002, Metric: -20.4002\n", + "val loss: 20.4002, val metric: -20.4002\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_2.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.68s\n", - "train loss: 17.0483\n", - "Validation, Loss: 15.7751, Metric: -15.7751\n", - "val loss: 15.7751\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 18.1269\n", + "Validation, Loss: 15.9976, Metric: -15.9976\n", + "val loss: 15.9976, val metric: -15.9976\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.63s\n", - "train loss: 15.3673\n", - "Validation, Loss: 15.2840, Metric: -15.2840\n", - "val loss: 15.2840\n", + "Epoch 5/10, cost time: 2.03s\n", + "train loss: 16.0774\n", + "Validation, Loss: 15.8232, Metric: -15.8232\n", + "val loss: 15.8232, val metric: -15.8232\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_4.pth\n", - "Removing worst model snapshot: from epoch 1\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.66s\n", - "train loss: 15.0072\n", - "Validation, Loss: 15.0363, Metric: -15.0363\n", - "val loss: 15.0363\n", + "Epoch 6/10, cost time: 2.04s\n", + "train loss: 15.6645\n", + "Validation, Loss: 15.6549, Metric: -15.6549\n", + "val loss: 15.6549, val metric: -15.6549\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_5.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.67s\n", - "train loss: 14.8720\n", - "Validation, Loss: 14.9547, Metric: -14.9547\n", - "val loss: 14.9547\n", + "Epoch 7/10, cost time: 2.05s\n", + "train loss: 15.5125\n", + "Validation, Loss: 15.5761, Metric: -15.5761\n", + "val loss: 15.5761, val metric: -15.5761\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.69s\n", - "train loss: 14.8059\n", - "Validation, Loss: 14.8659, Metric: -14.8659\n", - "val loss: 14.8659\n", + "Epoch 8/10, cost time: 2.06s\n", + "train loss: 15.4324\n", + "Validation, Loss: 15.4982, Metric: -15.4982\n", + "val loss: 15.4982, val metric: -15.4982\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.71s\n", - "train loss: 14.7318\n", - "Validation, Loss: 14.7924, Metric: -14.7924\n", - "val loss: 14.7924\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 15.3632\n", + "Validation, Loss: 15.4235, Metric: -15.4235\n", + "val loss: 15.4235, val metric: -15.4235\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.61s\n", - "train loss: 14.6650\n", - "Validation, Loss: 14.7389, Metric: -14.7389\n", - "val loss: 14.7389\n", + "Epoch 10/10, cost time: 2.15s\n", + "train loss: 15.2883\n", + "Validation, Loss: 15.3461, Metric: -15.3461\n", + "val loss: 15.3461, val metric: -15.3461\n", "Last epoch model saved to checkpoints/trainer_5/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -14.738923072814941\n", + "Fold 0. Score: -15.34605598449707\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.61s\n", - "train loss: 339.8815\n", - "Validation, Loss: 63.6576, Metric: -63.6576\n", - "val loss: 63.6576\n", + "Epoch 1/10, cost time: 2.08s\n", + "train loss: 329.7118\n", + "Validation, Loss: 47.4063, Metric: -47.4063\n", + "val loss: 47.4063, val metric: -47.4063\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.62s\n", - "train loss: 62.5787\n", - "Validation, Loss: 18.3003, Metric: -18.3003\n", - "val loss: 18.3003\n", + "Epoch 2/10, cost time: 2.08s\n", + "train loss: 62.1197\n", + "Validation, Loss: 16.4023, Metric: -16.4023\n", + "val loss: 16.4023, val metric: -16.4023\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.59s\n", - "train loss: 24.4187\n", - "Validation, Loss: 17.4426, Metric: -17.4426\n", - "val loss: 17.4426\n", + "Epoch 3/10, cost time: 2.07s\n", + "train loss: 25.6771\n", + "Validation, Loss: 22.1170, Metric: -22.1170\n", + "val loss: 22.1170, val metric: -22.1170\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_2.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 16.7605\n", - "Validation, Loss: 14.8631, Metric: -14.8631\n", - "val loss: 14.8631\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 18.5193\n", + "Validation, Loss: 15.7406, Metric: -15.7406\n", + "val loss: 15.7406, val metric: -15.7406\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.59s\n", - "train loss: 14.8361\n", - "Validation, Loss: 14.5053, Metric: -14.5053\n", - "val loss: 14.5053\n", + "Epoch 5/10, cost time: 2.07s\n", + "train loss: 16.0920\n", + "Validation, Loss: 15.8028, Metric: -15.8028\n", + "val loss: 15.8028, val metric: -15.8028\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_4.pth\n", - "Removing worst model snapshot: from epoch 1\n", + "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_4.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 14.4907\n", - "Validation, Loss: 14.3412, Metric: -14.3412\n", - "val loss: 14.3412\n", + "Epoch 6/10, cost time: 2.11s\n", + "train loss: 15.7545\n", + "Validation, Loss: 15.5723, Metric: -15.5723\n", + "val loss: 15.5723, val metric: -15.5723\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_5.pth\n", - "Removing worst model snapshot: from epoch 2\n", + "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 14.4017\n", - "Validation, Loss: 14.2828, Metric: -14.2828\n", - "val loss: 14.2828\n", + "Epoch 7/10, cost time: 2.15s\n", + "train loss: 15.6393\n", + "Validation, Loss: 15.4866, Metric: -15.4866\n", + "val loss: 15.4866, val metric: -15.4866\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_6.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 14.3322\n", - "Validation, Loss: 14.2065, Metric: -14.2065\n", - "val loss: 14.2065\n", + "Epoch 8/10, cost time: 2.10s\n", + "train loss: 15.5835\n", + "Validation, Loss: 15.4511, Metric: -15.4511\n", + "val loss: 15.4511, val metric: -15.4511\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_7.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.65s\n", - "train loss: 14.2699\n", - "Validation, Loss: 14.1218, Metric: -14.1218\n", - "val loss: 14.1218\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 15.4962\n", + "Validation, Loss: 15.3410, Metric: -15.3410\n", + "val loss: 15.3410, val metric: -15.3410\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.59s\n", - "train loss: 14.1933\n", - "Validation, Loss: 14.0482, Metric: -14.0482\n", - "val loss: 14.0482\n", + "Epoch 10/10, cost time: 2.14s\n", + "train loss: 15.4146\n", + "Validation, Loss: 15.2659, Metric: -15.2659\n", + "val loss: 15.2659, val metric: -15.2659\n", "Last epoch model saved to checkpoints/trainer_5/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -14.048187255859375\n", - "Mean score: -14.3936\n", - "Std: 0.3454\n", + "Fold 1. Score: -15.265893936157227\n", + "Mean score: -15.306\n", + "Std: 0.0401\n", "length of train dataset: 493\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.63s\n", - "train loss: 326.5137\n", - "Validation, Loss: 44.3770, Metric: -44.3770\n", - "val loss: 44.3770\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 328.3206\n", + "Validation, Loss: 45.7254, Metric: -45.7254\n", + "val loss: 45.7254, val metric: -45.7254\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 64.2163\n", - "Validation, Loss: 17.0292, Metric: -17.0292\n", - "val loss: 17.0292\n", + "Epoch 2/10, cost time: 2.09s\n", + "train loss: 62.0865\n", + "Validation, Loss: 17.5388, Metric: -17.5388\n", + "val loss: 17.5388, val metric: -17.5388\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.59s\n", - "train loss: 26.8710\n", - "Validation, Loss: 23.8799, Metric: -23.8799\n", - "val loss: 23.8799\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 25.6690\n", + "Validation, Loss: 22.5810, Metric: -22.5810\n", + "val loss: 22.5810, val metric: -22.5810\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_2.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.59s\n", - "train loss: 19.4378\n", - "Validation, Loss: 16.9384, Metric: -16.9384\n", - "val loss: 16.9384\n", + "Epoch 4/10, cost time: 2.09s\n", + "train loss: 19.2950\n", + "Validation, Loss: 17.2979, Metric: -17.2979\n", + "val loss: 17.2979, val metric: -17.2979\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.60s\n", - "train loss: 17.2355\n", - "Validation, Loss: 16.8656, Metric: -16.8656\n", - "val loss: 16.8656\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 17.5411\n", + "Validation, Loss: 17.3080, Metric: -17.3080\n", + "val loss: 17.3080, val metric: -17.3080\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_4.pth\n", + "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.61s\n", - "train loss: 16.8619\n", - "Validation, Loss: 16.8061, Metric: -16.8061\n", - "val loss: 16.8061\n", + "Epoch 6/10, cost time: 2.07s\n", + "train loss: 17.1868\n", + "Validation, Loss: 17.1823, Metric: -17.1823\n", + "val loss: 17.1823, val metric: -17.1823\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.59s\n", - "train loss: 16.6913\n", - "Validation, Loss: 16.7279, Metric: -16.7279\n", - "val loss: 16.7279\n", + "Epoch 7/10, cost time: 2.10s\n", + "train loss: 17.0586\n", + "Validation, Loss: 17.0973, Metric: -17.0973\n", + "val loss: 17.0973, val metric: -17.0973\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_6.pth\n", - "Removing worst model snapshot: from epoch 3\n", + "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.59s\n", - "train loss: 16.6198\n", - "Validation, Loss: 16.6510, Metric: -16.6510\n", - "val loss: 16.6510\n", + "Epoch 8/10, cost time: 2.10s\n", + "train loss: 16.9728\n", + "Validation, Loss: 17.0155, Metric: -17.0155\n", + "val loss: 17.0155, val metric: -17.0155\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_7.pth\n", - "Removing worst model snapshot: from epoch 4\n", + "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.60s\n", - "train loss: 16.5340\n", - "Validation, Loss: 16.5857, Metric: -16.5857\n", - "val loss: 16.5857\n", + "Epoch 9/10, cost time: 2.15s\n", + "train loss: 16.8982\n", + "Validation, Loss: 16.9292, Metric: -16.9292\n", + "val loss: 16.9292, val metric: -16.9292\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 16.4650\n", - "Validation, Loss: 16.4845, Metric: -16.4845\n", - "val loss: 16.4845\n", + "Epoch 10/10, cost time: 2.14s\n", + "train loss: 16.8072\n", + "Validation, Loss: 16.8437, Metric: -16.8437\n", + "val loss: 16.8437, val metric: -16.8437\n", "Last epoch model saved to checkpoints/trainer_6/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -16.484466552734375\n", + "Fold 0. Score: -16.843671798706055\n", "length of train dataset: 494\n", "length of val dataset: 493\n", - "Epoch 1/10, cost time: 0.74s\n", - "train loss: 320.8678\n", - "Validation, Loss: 44.4788, Metric: -44.4788\n", - "val loss: 44.4788\n", + "Epoch 1/10, cost time: 2.09s\n", + "train loss: 326.5489\n", + "Validation, Loss: 46.3208, Metric: -46.3208\n", + "val loss: 46.3208, val metric: -46.3208\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.76s\n", - "train loss: 61.1539\n", - "Validation, Loss: 17.5799, Metric: -17.5799\n", - "val loss: 17.5799\n", + "Epoch 2/10, cost time: 2.10s\n", + "train loss: 61.9651\n", + "Validation, Loss: 17.5716, Metric: -17.5716\n", + "val loss: 17.5716, val metric: -17.5716\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.73s\n", - "train loss: 27.0784\n", - "Validation, Loss: 25.1590, Metric: -25.1590\n", - "val loss: 25.1590\n", + "Epoch 3/10, cost time: 2.13s\n", + "train loss: 26.2689\n", + "Validation, Loss: 22.3116, Metric: -22.3116\n", + "val loss: 22.3116, val metric: -22.3116\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_2.pth\n", "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.64s\n", - "train loss: 19.9317\n", - "Validation, Loss: 17.5198, Metric: -17.5198\n", - "val loss: 17.5198\n", + "Epoch 4/10, cost time: 2.12s\n", + "train loss: 19.3589\n", + "Validation, Loss: 17.0157, Metric: -17.0157\n", + "val loss: 17.0157, val metric: -17.0157\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.60s\n", - "train loss: 17.9184\n", - "Validation, Loss: 17.4039, Metric: -17.4039\n", - "val loss: 17.4039\n", + "Epoch 5/10, cost time: 2.14s\n", + "train loss: 17.4819\n", + "Validation, Loss: 17.0029, Metric: -17.0029\n", + "val loss: 17.0029, val metric: -17.0029\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.59s\n", - "train loss: 17.5429\n", - "Validation, Loss: 17.3371, Metric: -17.3371\n", - "val loss: 17.3371\n", + "Epoch 6/10, cost time: 2.07s\n", + "train loss: 17.0901\n", + "Validation, Loss: 16.8844, Metric: -16.8844\n", + "val loss: 16.8844, val metric: -16.8844\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.63s\n", - "train loss: 17.3981\n", - "Validation, Loss: 17.2645, Metric: -17.2645\n", - "val loss: 17.2645\n", + "Epoch 7/10, cost time: 2.06s\n", + "train loss: 16.9454\n", + "Validation, Loss: 16.8404, Metric: -16.8404\n", + "val loss: 16.8404, val metric: -16.8404\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.68s\n", - "train loss: 17.3247\n", - "Validation, Loss: 17.2119, Metric: -17.2119\n", - "val loss: 17.2119\n", + "Epoch 8/10, cost time: 2.09s\n", + "train loss: 16.8865\n", + "Validation, Loss: 16.7347, Metric: -16.7347\n", + "val loss: 16.7347, val metric: -16.7347\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.59s\n", - "train loss: 17.2632\n", - "Validation, Loss: 17.1624, Metric: -17.1624\n", - "val loss: 17.1624\n", + "Epoch 9/10, cost time: 2.09s\n", + "train loss: 16.7872\n", + "Validation, Loss: 16.6494, Metric: -16.6494\n", + "val loss: 16.6494, val metric: -16.6494\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.58s\n", - "train loss: 17.1594\n", - "Validation, Loss: 17.0018, Metric: -17.0018\n", - "val loss: 17.0018\n", + "Epoch 10/10, cost time: 2.07s\n", + "train loss: 16.7089\n", + "Validation, Loss: 16.5723, Metric: -16.5723\n", + "val loss: 16.5723, val metric: -16.5723\n", "Last epoch model saved to checkpoints/trainer_6/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -17.001792907714844\n", - "Mean score: -16.7431\n", - "Std: 0.2587\n" + "Fold 1. Score: -16.572263717651367\n", + "Mean score: -16.708\n", + "Std: 0.1357\n" ] }, { "data": { "text/plain": [ - "(179.15967321395874, )" + "(540.7743926048279,\n", + " )" ] }, "execution_count": 13, @@ -1976,1300 +1984,1306 @@ "output_type": "stream", "text": [ "length of train dataset: 496\n", - "length of val dataset: 497\n", - "Epoch 1/10, cost time: 0.60s\n", - "train loss: 2.5679\n", - "Validation, Loss: 2.5761, Metric: -2.5761\n", - "val loss: 2.5761\n", + "length of val dataset: 497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10, cost time: 2.10s\n", + "train loss: 2.5178\n", + "Validation, Loss: 2.5265, Metric: -2.5265\n", + "val loss: 2.5265, val metric: -2.5265\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.59s\n", - "train loss: 2.5570\n", - "Validation, Loss: 2.5764, Metric: -2.5764\n", - "val loss: 2.5764\n", + "Epoch 2/10, cost time: 2.10s\n", + "train loss: 2.5056\n", + "Validation, Loss: 2.5124, Metric: -2.5124\n", + "val loss: 2.5124, val metric: -2.5124\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_1.pth\n", - "Early stopping counter: 1\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.74s\n", - "train loss: 2.5461\n", - "Validation, Loss: 2.5519, Metric: -2.5519\n", - "val loss: 2.5519\n", + "Epoch 3/10, cost time: 2.09s\n", + "train loss: 2.4927\n", + "Validation, Loss: 2.5028, Metric: -2.5028\n", + "val loss: 2.5028, val metric: -2.5028\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.66s\n", - "train loss: 2.5275\n", - "Validation, Loss: 2.5394, Metric: -2.5394\n", - "val loss: 2.5394\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 2.4779\n", + "Validation, Loss: 2.4877, Metric: -2.4877\n", + "val loss: 2.4877, val metric: -2.4877\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_3.pth\n", - "Removing worst model snapshot: from epoch 1\n", + "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.66s\n", - "train loss: 2.5136\n", - "Validation, Loss: 2.5217, Metric: -2.5217\n", - "val loss: 2.5217\n", + "Epoch 5/10, cost time: 2.06s\n", + "train loss: 2.4636\n", + "Validation, Loss: 2.4696, Metric: -2.4696\n", + "val loss: 2.4696, val metric: -2.4696\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_4.pth\n", - "Removing worst model snapshot: from epoch 0\n", + "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.67s\n", - "train loss: 2.5016\n", - "Validation, Loss: 2.5050, Metric: -2.5050\n", - "val loss: 2.5050\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 2.4489\n", + "Validation, Loss: 2.4583, Metric: -2.4583\n", + "val loss: 2.4583, val metric: -2.4583\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.66s\n", - "train loss: 2.4832\n", - "Validation, Loss: 2.4893, Metric: -2.4893\n", - "val loss: 2.4893\n", + "Epoch 7/10, cost time: 2.06s\n", + "train loss: 2.4357\n", + "Validation, Loss: 2.4437, Metric: -2.4437\n", + "val loss: 2.4437, val metric: -2.4437\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.65s\n", - "train loss: 2.4666\n", - "Validation, Loss: 2.4733, Metric: -2.4733\n", - "val loss: 2.4733\n", + "Epoch 8/10, cost time: 2.07s\n", + "train loss: 2.4170\n", + "Validation, Loss: 2.4262, Metric: -2.4262\n", + "val loss: 2.4262, val metric: -2.4262\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.60s\n", - "train loss: 2.4503\n", - "Validation, Loss: 2.4580, Metric: -2.4580\n", - "val loss: 2.4580\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 2.4010\n", + "Validation, Loss: 2.4078, Metric: -2.4078\n", + "val loss: 2.4078, val metric: -2.4078\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.60s\n", - "train loss: 2.4348\n", - "Validation, Loss: 2.4401, Metric: -2.4401\n", - "val loss: 2.4401\n", + "Epoch 10/10, cost time: 2.06s\n", + "train loss: 2.3851\n", + "Validation, Loss: 2.3914, Metric: -2.3914\n", + "val loss: 2.3914, val metric: -2.3914\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -2.4401051998138428\n", + "Fold 0. Score: -2.391385793685913\n", "length of train dataset: 497\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.60s\n", - "train loss: 3.3538\n", - "Validation, Loss: 3.3247, Metric: -3.3247\n", - "val loss: 3.3247\n", + "Epoch 1/10, cost time: 2.09s\n", + "train loss: 3.6277\n", + "Validation, Loss: 3.5926, Metric: -3.5926\n", + "val loss: 3.5926, val metric: -3.5926\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 3.3385\n", - "Validation, Loss: 3.3076, Metric: -3.3076\n", - "val loss: 3.3076\n", + "Epoch 2/10, cost time: 2.09s\n", + "train loss: 3.6093\n", + "Validation, Loss: 3.5750, Metric: -3.5750\n", + "val loss: 3.5750, val metric: -3.5750\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.60s\n", - "train loss: 3.3193\n", - "Validation, Loss: 3.2881, Metric: -3.2881\n", - "val loss: 3.2881\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 3.5897\n", + "Validation, Loss: 3.5550, Metric: -3.5550\n", + "val loss: 3.5550, val metric: -3.5550\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 3.3005\n", - "Validation, Loss: 3.2691, Metric: -3.2691\n", - "val loss: 3.2691\n", + "Epoch 4/10, cost time: 2.10s\n", + "train loss: 3.5697\n", + "Validation, Loss: 3.5332, Metric: -3.5332\n", + "val loss: 3.5332, val metric: -3.5332\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.62s\n", - "train loss: 3.2825\n", - "Validation, Loss: 3.2580, Metric: -3.2580\n", - "val loss: 3.2580\n", + "Epoch 5/10, cost time: 2.10s\n", + "train loss: 3.5480\n", + "Validation, Loss: 3.5120, Metric: -3.5120\n", + "val loss: 3.5120, val metric: -3.5120\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.64s\n", - "train loss: 3.2627\n", - "Validation, Loss: 3.2354, Metric: -3.2354\n", - "val loss: 3.2354\n", + "Epoch 6/10, cost time: 2.10s\n", + "train loss: 3.5249\n", + "Validation, Loss: 3.4949, Metric: -3.4949\n", + "val loss: 3.4949, val metric: -3.4949\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 3.2434\n", - "Validation, Loss: 3.2111, Metric: -3.2111\n", - "val loss: 3.2111\n", + "Epoch 7/10, cost time: 2.09s\n", + "train loss: 3.5059\n", + "Validation, Loss: 3.4804, Metric: -3.4804\n", + "val loss: 3.4804, val metric: -3.4804\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.63s\n", - "train loss: 3.2211\n", - "Validation, Loss: 3.1884, Metric: -3.1884\n", - "val loss: 3.1884\n", + "Epoch 8/10, cost time: 2.07s\n", + "train loss: 3.4847\n", + "Validation, Loss: 3.4453, Metric: -3.4453\n", + "val loss: 3.4453, val metric: -3.4453\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.63s\n", - "train loss: 3.1999\n", - "Validation, Loss: 3.1671, Metric: -3.1671\n", - "val loss: 3.1671\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 3.4572\n", + "Validation, Loss: 3.4251, Metric: -3.4251\n", + "val loss: 3.4251, val metric: -3.4251\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.62s\n", - "train loss: 3.1782\n", - "Validation, Loss: 3.1450, Metric: -3.1450\n", - "val loss: 3.1450\n", + "Epoch 10/10, cost time: 2.18s\n", + "train loss: 3.4331\n", + "Validation, Loss: 3.3984, Metric: -3.3984\n", + "val loss: 3.3984, val metric: -3.3984\n", "Last epoch model saved to checkpoints_finetuned/trainer_0/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_0/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_0/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_0/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -3.145007610321045\n", - "Mean score: -2.7926\n", - "Std: 0.3525\n", + "Fold 1. Score: -3.398355484008789\n", + "Mean score: -2.8949\n", + "Std: 0.5035\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.60s\n", - "train loss: 4.2404\n", - "Validation, Loss: 4.2515, Metric: -4.2515\n", - "val loss: 4.2515\n", + "Epoch 1/10, cost time: 2.12s\n", + "train loss: 4.7531\n", + "Validation, Loss: 4.7663, Metric: -4.7663\n", + "val loss: 4.7663, val metric: -4.7663\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 4.2187\n", - "Validation, Loss: 4.2322, Metric: -4.2322\n", - "val loss: 4.2322\n", + "Epoch 2/10, cost time: 2.11s\n", + "train loss: 4.7284\n", + "Validation, Loss: 4.7421, Metric: -4.7421\n", + "val loss: 4.7421, val metric: -4.7421\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.60s\n", - "train loss: 4.1967\n", - "Validation, Loss: 4.2061, Metric: -4.2061\n", - "val loss: 4.2061\n", + "Epoch 3/10, cost time: 2.09s\n", + "train loss: 4.7040\n", + "Validation, Loss: 4.7146, Metric: -4.7146\n", + "val loss: 4.7146, val metric: -4.7146\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 4.1759\n", - "Validation, Loss: 4.1910, Metric: -4.1910\n", - "val loss: 4.1910\n", + "Epoch 4/10, cost time: 2.07s\n", + "train loss: 4.6771\n", + "Validation, Loss: 4.6898, Metric: -4.6898\n", + "val loss: 4.6898, val metric: -4.6898\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.67s\n", - "train loss: 4.1521\n", - "Validation, Loss: 4.1573, Metric: -4.1573\n", - "val loss: 4.1573\n", + "Epoch 5/10, cost time: 2.09s\n", + "train loss: 4.6512\n", + "Validation, Loss: 4.6613, Metric: -4.6613\n", + "val loss: 4.6613, val metric: -4.6613\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.66s\n", - "train loss: 4.1233\n", - "Validation, Loss: 4.1314, Metric: -4.1314\n", - "val loss: 4.1314\n", + "Epoch 6/10, cost time: 2.11s\n", + "train loss: 4.6248\n", + "Validation, Loss: 4.6329, Metric: -4.6329\n", + "val loss: 4.6329, val metric: -4.6329\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.69s\n", - "train loss: 4.0995\n", - "Validation, Loss: 4.1062, Metric: -4.1062\n", - "val loss: 4.1062\n", + "Epoch 7/10, cost time: 2.13s\n", + "train loss: 4.6016\n", + "Validation, Loss: 4.6325, Metric: -4.6325\n", + "val loss: 4.6325, val metric: -4.6325\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.68s\n", - "train loss: 4.0710\n", - "Validation, Loss: 4.0786, Metric: -4.0786\n", - "val loss: 4.0786\n", + "Epoch 8/10, cost time: 2.08s\n", + "train loss: 4.5703\n", + "Validation, Loss: 4.5739, Metric: -4.5739\n", + "val loss: 4.5739, val metric: -4.5739\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.64s\n", - "train loss: 4.0439\n", - "Validation, Loss: 4.0503, Metric: -4.0503\n", - "val loss: 4.0503\n", + "Epoch 9/10, cost time: 2.08s\n", + "train loss: 4.5375\n", + "Validation, Loss: 4.5417, Metric: -4.5417\n", + "val loss: 4.5417, val metric: -4.5417\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.61s\n", - "train loss: 4.0158\n", - "Validation, Loss: 4.0274, Metric: -4.0274\n", - "val loss: 4.0274\n", + "Epoch 10/10, cost time: 2.07s\n", + "train loss: 4.5054\n", + "Validation, Loss: 4.5085, Metric: -4.5085\n", + "val loss: 4.5085, val metric: -4.5085\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -4.027385711669922\n", + "Fold 0. Score: -4.508482933044434\n", "length of train dataset: 496\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.61s\n", - "train loss: 4.1529\n", - "Validation, Loss: 4.1180, Metric: -4.1180\n", - "val loss: 4.1180\n", + "Epoch 1/10, cost time: 2.07s\n", + "train loss: 4.0669\n", + "Validation, Loss: 4.0352, Metric: -4.0352\n", + "val loss: 4.0352, val metric: -4.0352\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 4.1307\n", - "Validation, Loss: 4.0954, Metric: -4.0954\n", - "val loss: 4.0954\n", + "Epoch 2/10, cost time: 2.07s\n", + "train loss: 4.0393\n", + "Validation, Loss: 4.0020, Metric: -4.0020\n", + "val loss: 4.0020, val metric: -4.0020\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.62s\n", - "train loss: 4.1106\n", - "Validation, Loss: 4.0734, Metric: -4.0734\n", - "val loss: 4.0734\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 4.0161\n", + "Validation, Loss: 3.9786, Metric: -3.9786\n", + "val loss: 3.9786, val metric: -3.9786\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 4.0899\n", - "Validation, Loss: 4.0512, Metric: -4.0512\n", - "val loss: 4.0512\n", + "Epoch 4/10, cost time: 2.08s\n", + "train loss: 3.9943\n", + "Validation, Loss: 3.9572, Metric: -3.9572\n", + "val loss: 3.9572, val metric: -3.9572\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.63s\n", - "train loss: 4.0623\n", - "Validation, Loss: 4.0256, Metric: -4.0256\n", - "val loss: 4.0256\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 3.9670\n", + "Validation, Loss: 3.9327, Metric: -3.9327\n", + "val loss: 3.9327, val metric: -3.9327\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.89s\n", - "train loss: 4.0407\n", - "Validation, Loss: 3.9998, Metric: -3.9998\n", - "val loss: 3.9998\n", + "Epoch 6/10, cost time: 2.06s\n", + "train loss: 3.9431\n", + "Validation, Loss: 3.9104, Metric: -3.9104\n", + "val loss: 3.9104, val metric: -3.9104\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.62s\n", - "train loss: 4.0147\n", - "Validation, Loss: 3.9760, Metric: -3.9760\n", - "val loss: 3.9760\n", + "Epoch 7/10, cost time: 2.10s\n", + "train loss: 3.9219\n", + "Validation, Loss: 3.8877, Metric: -3.8877\n", + "val loss: 3.8877, val metric: -3.8877\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 3.9848\n", - "Validation, Loss: 3.9473, Metric: -3.9473\n", - "val loss: 3.9473\n", + "Epoch 8/10, cost time: 2.08s\n", + "train loss: 3.8949\n", + "Validation, Loss: 3.8550, Metric: -3.8550\n", + "val loss: 3.8550, val metric: -3.8550\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.61s\n", - "train loss: 3.9589\n", - "Validation, Loss: 3.9208, Metric: -3.9208\n", - "val loss: 3.9208\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 3.8650\n", + "Validation, Loss: 3.8338, Metric: -3.8338\n", + "val loss: 3.8338, val metric: -3.8338\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.69s\n", - "train loss: 3.9308\n", - "Validation, Loss: 3.8949, Metric: -3.8949\n", - "val loss: 3.8949\n", + "Epoch 10/10, cost time: 2.06s\n", + "train loss: 3.8393\n", + "Validation, Loss: 3.8033, Metric: -3.8033\n", + "val loss: 3.8033, val metric: -3.8033\n", "Last epoch model saved to checkpoints_finetuned/trainer_1/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_1/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_1/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_1/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -3.894927740097046\n", - "Mean score: -3.9612\n", - "Std: 0.0662\n", + "Fold 1. Score: -3.8033361434936523\n", + "Mean score: -4.1559\n", + "Std: 0.3526\n", "length of train dataset: 495\n", "length of val dataset: 496\n", - "Epoch 1/10, cost time: 0.66s\n", - "train loss: 7.2257\n", - "Validation, Loss: 7.2556, Metric: -7.2556\n", - "val loss: 7.2556\n", + "Epoch 1/10, cost time: 2.08s\n", + "train loss: 6.3392\n", + "Validation, Loss: 6.3581, Metric: -6.3581\n", + "val loss: 6.3581, val metric: -6.3581\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 7.1865\n", - "Validation, Loss: 7.2081, Metric: -7.2081\n", - "val loss: 7.2081\n", + "Epoch 2/10, cost time: 2.08s\n", + "train loss: 6.3061\n", + "Validation, Loss: 6.3240, Metric: -6.3240\n", + "val loss: 6.3240, val metric: -6.3240\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.78s\n", - "train loss: 7.1567\n", - "Validation, Loss: 7.1722, Metric: -7.1722\n", - "val loss: 7.1722\n", + "Epoch 3/10, cost time: 2.10s\n", + "train loss: 6.2755\n", + "Validation, Loss: 6.3038, Metric: -6.3038\n", + "val loss: 6.3038, val metric: -6.3038\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.80s\n", - "train loss: 7.1052\n", - "Validation, Loss: 7.1191, Metric: -7.1191\n", - "val loss: 7.1191\n", + "Epoch 4/10, cost time: 2.09s\n", + "train loss: 6.2438\n", + "Validation, Loss: 6.2510, Metric: -6.2510\n", + "val loss: 6.2510, val metric: -6.2510\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.71s\n", - "train loss: 7.0628\n", - "Validation, Loss: 7.0766, Metric: -7.0766\n", - "val loss: 7.0766\n", + "Epoch 5/10, cost time: 2.12s\n", + "train loss: 6.2017\n", + "Validation, Loss: 6.2133, Metric: -6.2133\n", + "val loss: 6.2133, val metric: -6.2133\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.61s\n", - "train loss: 7.0216\n", - "Validation, Loss: 7.0315, Metric: -7.0315\n", - "val loss: 7.0315\n", + "Epoch 6/10, cost time: 2.13s\n", + "train loss: 6.1669\n", + "Validation, Loss: 6.1831, Metric: -6.1831\n", + "val loss: 6.1831, val metric: -6.1831\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 6.9806\n", - "Validation, Loss: 6.9873, Metric: -6.9873\n", - "val loss: 6.9873\n", + "Epoch 7/10, cost time: 2.06s\n", + "train loss: 6.1289\n", + "Validation, Loss: 6.1347, Metric: -6.1347\n", + "val loss: 6.1347, val metric: -6.1347\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 6.9270\n", - "Validation, Loss: 6.9384, Metric: -6.9384\n", - "val loss: 6.9384\n", + "Epoch 8/10, cost time: 2.06s\n", + "train loss: 6.0827\n", + "Validation, Loss: 6.0932, Metric: -6.0932\n", + "val loss: 6.0932, val metric: -6.0932\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.70s\n", - "train loss: 6.8798\n", - "Validation, Loss: 6.8904, Metric: -6.8904\n", - "val loss: 6.8904\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 6.0425\n", + "Validation, Loss: 6.0505, Metric: -6.0505\n", + "val loss: 6.0505, val metric: -6.0505\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.61s\n", - "train loss: 6.8301\n", - "Validation, Loss: 6.8432, Metric: -6.8432\n", - "val loss: 6.8432\n", + "Epoch 10/10, cost time: 2.06s\n", + "train loss: 5.9992\n", + "Validation, Loss: 6.0118, Metric: -6.0118\n", + "val loss: 6.0118, val metric: -6.0118\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -6.843226432800293\n", + "Fold 0. Score: -6.011759281158447\n", "length of train dataset: 496\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.61s\n", - "train loss: 6.2791\n", - "Validation, Loss: 6.2261, Metric: -6.2261\n", - "val loss: 6.2261\n", + "Epoch 1/10, cost time: 2.08s\n", + "train loss: 6.6428\n", + "Validation, Loss: 6.5922, Metric: -6.5922\n", + "val loss: 6.5922, val metric: -6.5922\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.68s\n", - "train loss: 6.2464\n", - "Validation, Loss: 6.1926, Metric: -6.1926\n", - "val loss: 6.1926\n", + "Epoch 2/10, cost time: 2.10s\n", + "train loss: 6.6067\n", + "Validation, Loss: 6.5492, Metric: -6.5492\n", + "val loss: 6.5492, val metric: -6.5492\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.64s\n", - "train loss: 6.2126\n", - "Validation, Loss: 6.1581, Metric: -6.1581\n", - "val loss: 6.1581\n", + "Epoch 3/10, cost time: 2.08s\n", + "train loss: 6.5707\n", + "Validation, Loss: 6.5151, Metric: -6.5151\n", + "val loss: 6.5151, val metric: -6.5151\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.65s\n", - "train loss: 6.1788\n", - "Validation, Loss: 6.1254, Metric: -6.1254\n", - "val loss: 6.1254\n", + "Epoch 4/10, cost time: 2.09s\n", + "train loss: 6.5316\n", + "Validation, Loss: 6.4720, Metric: -6.4720\n", + "val loss: 6.4720, val metric: -6.4720\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 1.08s\n", - "train loss: 6.1437\n", - "Validation, Loss: 6.0878, Metric: -6.0878\n", - "val loss: 6.0878\n", + "Epoch 5/10, cost time: 2.06s\n", + "train loss: 6.4917\n", + "Validation, Loss: 6.4422, Metric: -6.4422\n", + "val loss: 6.4422, val metric: -6.4422\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.76s\n", - "train loss: 6.1126\n", - "Validation, Loss: 6.0715, Metric: -6.0715\n", - "val loss: 6.0715\n", + "Epoch 6/10, cost time: 2.07s\n", + "train loss: 6.4620\n", + "Validation, Loss: 6.3913, Metric: -6.3913\n", + "val loss: 6.3913, val metric: -6.3913\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.63s\n", - "train loss: 6.0738\n", - "Validation, Loss: 6.0168, Metric: -6.0168\n", - "val loss: 6.0168\n", + "Epoch 7/10, cost time: 2.08s\n", + "train loss: 6.4110\n", + "Validation, Loss: 6.3547, Metric: -6.3547\n", + "val loss: 6.3547, val metric: -6.3547\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.64s\n", - "train loss: 6.0275\n", - "Validation, Loss: 5.9694, Metric: -5.9694\n", - "val loss: 5.9694\n", + "Epoch 8/10, cost time: 2.13s\n", + "train loss: 6.3662\n", + "Validation, Loss: 6.3058, Metric: -6.3058\n", + "val loss: 6.3058, val metric: -6.3058\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.66s\n", - "train loss: 5.9834\n", - "Validation, Loss: 5.9285, Metric: -5.9285\n", - "val loss: 5.9285\n", + "Epoch 9/10, cost time: 2.11s\n", + "train loss: 6.3216\n", + "Validation, Loss: 6.2664, Metric: -6.2664\n", + "val loss: 6.2664, val metric: -6.2664\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.67s\n", - "train loss: 5.9429\n", - "Validation, Loss: 5.8968, Metric: -5.8968\n", - "val loss: 5.8968\n", + "Epoch 10/10, cost time: 2.09s\n", + "train loss: 6.2787\n", + "Validation, Loss: 6.2173, Metric: -6.2173\n", + "val loss: 6.2173, val metric: -6.2173\n", "Last epoch model saved to checkpoints_finetuned/trainer_2/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_2/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_2/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_2/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -5.896804332733154\n", - "Mean score: -6.37\n", - "Std: 0.4732\n", + "Fold 1. Score: -6.217341423034668\n", + "Mean score: -6.1146\n", + "Std: 0.1028\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.84s\n", - "train loss: 9.0494\n", - "Validation, Loss: 9.0744, Metric: -9.0744\n", - "val loss: 9.0744\n", + "Epoch 1/10, cost time: 2.08s\n", + "train loss: 7.9035\n", + "Validation, Loss: 7.9314, Metric: -7.9314\n", + "val loss: 7.9314, val metric: -7.9314\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.69s\n", - "train loss: 9.0002\n", - "Validation, Loss: 9.0227, Metric: -9.0227\n", - "val loss: 9.0227\n", + "Epoch 2/10, cost time: 2.10s\n", + "train loss: 7.8635\n", + "Validation, Loss: 7.9143, Metric: -7.9143\n", + "val loss: 7.9143, val metric: -7.9143\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.70s\n", - "train loss: 8.9587\n", - "Validation, Loss: 8.9764, Metric: -8.9764\n", - "val loss: 8.9764\n", + "Epoch 3/10, cost time: 2.09s\n", + "train loss: 7.8207\n", + "Validation, Loss: 7.8357, Metric: -7.8357\n", + "val loss: 7.8357, val metric: -7.8357\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.72s\n", - "train loss: 8.8969\n", - "Validation, Loss: 8.9240, Metric: -8.9240\n", - "val loss: 8.9240\n", + "Epoch 4/10, cost time: 2.08s\n", + "train loss: 7.7694\n", + "Validation, Loss: 7.7896, Metric: -7.7896\n", + "val loss: 7.7896, val metric: -7.7896\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.69s\n", - "train loss: 8.8431\n", - "Validation, Loss: 8.8636, Metric: -8.8636\n", - "val loss: 8.8636\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 7.7235\n", + "Validation, Loss: 7.7403, Metric: -7.7403\n", + "val loss: 7.7403, val metric: -7.7403\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.73s\n", - "train loss: 8.7906\n", - "Validation, Loss: 8.8040, Metric: -8.8040\n", - "val loss: 8.8040\n", + "Epoch 6/10, cost time: 2.09s\n", + "train loss: 7.6790\n", + "Validation, Loss: 7.7013, Metric: -7.7013\n", + "val loss: 7.7013, val metric: -7.7013\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.69s\n", - "train loss: 8.7315\n", - "Validation, Loss: 8.7580, Metric: -8.7580\n", - "val loss: 8.7580\n", + "Epoch 7/10, cost time: 2.08s\n", + "train loss: 7.6283\n", + "Validation, Loss: 7.6454, Metric: -7.6454\n", + "val loss: 7.6454, val metric: -7.6454\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.69s\n", - "train loss: 8.6697\n", - "Validation, Loss: 8.6826, Metric: -8.6826\n", - "val loss: 8.6826\n", + "Epoch 8/10, cost time: 2.05s\n", + "train loss: 7.5799\n", + "Validation, Loss: 7.5969, Metric: -7.5969\n", + "val loss: 7.5969, val metric: -7.5969\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.64s\n", - "train loss: 8.6085\n", - "Validation, Loss: 8.6196, Metric: -8.6196\n", - "val loss: 8.6196\n", + "Epoch 9/10, cost time: 2.06s\n", + "train loss: 7.5252\n", + "Validation, Loss: 7.5361, Metric: -7.5361\n", + "val loss: 7.5361, val metric: -7.5361\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.70s\n", - "train loss: 8.5446\n", - "Validation, Loss: 8.5844, Metric: -8.5844\n", - "val loss: 8.5844\n", + "Epoch 10/10, cost time: 2.06s\n", + "train loss: 7.4698\n", + "Validation, Loss: 7.4840, Metric: -7.4840\n", + "val loss: 7.4840, val metric: -7.4840\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -8.584444999694824\n", + "Fold 0. Score: -7.483982563018799\n", "length of train dataset: 495\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.77s\n", - "train loss: 7.6699\n", - "Validation, Loss: 7.5988, Metric: -7.5988\n", - "val loss: 7.5988\n", + "Epoch 1/10, cost time: 2.09s\n", + "train loss: 9.9277\n", + "Validation, Loss: 9.8368, Metric: -9.8368\n", + "val loss: 9.8368, val metric: -9.8368\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.68s\n", - "train loss: 7.6315\n", - "Validation, Loss: 7.5741, Metric: -7.5741\n", - "val loss: 7.5741\n", + "Epoch 2/10, cost time: 2.13s\n", + "train loss: 9.8731\n", + "Validation, Loss: 9.7839, Metric: -9.7839\n", + "val loss: 9.7839, val metric: -9.7839\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.64s\n", - "train loss: 7.5926\n", - "Validation, Loss: 7.5289, Metric: -7.5289\n", - "val loss: 7.5289\n", + "Epoch 3/10, cost time: 2.03s\n", + "train loss: 9.8162\n", + "Validation, Loss: 9.7257, Metric: -9.7257\n", + "val loss: 9.7257, val metric: -9.7257\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.68s\n", - "train loss: 7.5421\n", - "Validation, Loss: 7.4752, Metric: -7.4752\n", - "val loss: 7.4752\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 9.7617\n", + "Validation, Loss: 9.6715, Metric: -9.6715\n", + "val loss: 9.6715, val metric: -9.6715\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.70s\n", - "train loss: 7.4971\n", - "Validation, Loss: 7.4366, Metric: -7.4366\n", - "val loss: 7.4366\n", + "Epoch 5/10, cost time: 2.05s\n", + "train loss: 9.7013\n", + "Validation, Loss: 9.6068, Metric: -9.6068\n", + "val loss: 9.6068, val metric: -9.6068\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.75s\n", - "train loss: 7.4532\n", - "Validation, Loss: 7.3766, Metric: -7.3766\n", - "val loss: 7.3766\n", + "Epoch 6/10, cost time: 2.08s\n", + "train loss: 9.6514\n", + "Validation, Loss: 9.5582, Metric: -9.5582\n", + "val loss: 9.5582, val metric: -9.5582\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.65s\n", - "train loss: 7.3993\n", - "Validation, Loss: 7.3409, Metric: -7.3409\n", - "val loss: 7.3409\n", + "Epoch 7/10, cost time: 2.09s\n", + "train loss: 9.5825\n", + "Validation, Loss: 9.4844, Metric: -9.4844\n", + "val loss: 9.4844, val metric: -9.4844\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.61s\n", - "train loss: 7.3549\n", - "Validation, Loss: 7.2776, Metric: -7.2776\n", - "val loss: 7.2776\n", + "Epoch 8/10, cost time: 2.08s\n", + "train loss: 9.5127\n", + "Validation, Loss: 9.4167, Metric: -9.4167\n", + "val loss: 9.4167, val metric: -9.4167\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.63s\n", - "train loss: 7.3006\n", - "Validation, Loss: 7.2303, Metric: -7.2303\n", - "val loss: 7.2303\n", + "Epoch 9/10, cost time: 2.10s\n", + "train loss: 9.4419\n", + "Validation, Loss: 9.3539, Metric: -9.3539\n", + "val loss: 9.3539, val metric: -9.3539\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.61s\n", - "train loss: 7.2614\n", - "Validation, Loss: 7.1951, Metric: -7.1951\n", - "val loss: 7.1951\n", + "Epoch 10/10, cost time: 2.07s\n", + "train loss: 9.3832\n", + "Validation, Loss: 9.2822, Metric: -9.2822\n", + "val loss: 9.2822, val metric: -9.2822\n", "Last epoch model saved to checkpoints_finetuned/trainer_3/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_3/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_3/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_3/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -7.195066928863525\n", - "Mean score: -7.8898\n", - "Std: 0.6947\n", + "Fold 1. Score: -9.282210350036621\n", + "Mean score: -8.3831\n", + "Std: 0.8991\n", "length of train dataset: 494\n", "length of val dataset: 495\n", - "Epoch 1/10, cost time: 0.63s\n", - "train loss: 11.6259\n", - "Validation, Loss: 11.6559, Metric: -11.6559\n", - "val loss: 11.6559\n", + "Epoch 1/10, cost time: 2.06s\n", + "train loss: 11.9388\n", + "Validation, Loss: 12.0070, Metric: -12.0070\n", + "val loss: 12.0070, val metric: -12.0070\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.60s\n", - "train loss: 11.5693\n", - "Validation, Loss: 11.5940, Metric: -11.5940\n", - "val loss: 11.5940\n", + "Epoch 2/10, cost time: 2.06s\n", + "train loss: 11.8565\n", + "Validation, Loss: 11.8742, Metric: -11.8742\n", + "val loss: 11.8742, val metric: -11.8742\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.67s\n", - "train loss: 11.5005\n", - "Validation, Loss: 11.5175, Metric: -11.5175\n", - "val loss: 11.5175\n", + "Epoch 3/10, cost time: 2.04s\n", + "train loss: 11.7842\n", + "Validation, Loss: 11.8067, Metric: -11.8067\n", + "val loss: 11.8067, val metric: -11.8067\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.74s\n", - "train loss: 11.4334\n", - "Validation, Loss: 11.4457, Metric: -11.4457\n", - "val loss: 11.4457\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 11.7100\n", + "Validation, Loss: 11.7324, Metric: -11.7324\n", + "val loss: 11.7324, val metric: -11.7324\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.69s\n", - "train loss: 11.3574\n", - "Validation, Loss: 11.3815, Metric: -11.3815\n", - "val loss: 11.3815\n", + "Epoch 5/10, cost time: 2.07s\n", + "train loss: 11.6352\n", + "Validation, Loss: 11.6606, Metric: -11.6606\n", + "val loss: 11.6606, val metric: -11.6606\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.68s\n", - "train loss: 11.2892\n", - "Validation, Loss: 11.2992, Metric: -11.2992\n", - "val loss: 11.2992\n", + "Epoch 6/10, cost time: 2.07s\n", + "train loss: 11.5656\n", + "Validation, Loss: 11.5994, Metric: -11.5994\n", + "val loss: 11.5994, val metric: -11.5994\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.69s\n", - "train loss: 11.2189\n", - "Validation, Loss: 11.2282, Metric: -11.2282\n", - "val loss: 11.2282\n", + "Epoch 7/10, cost time: 2.07s\n", + "train loss: 11.4948\n", + "Validation, Loss: 11.5051, Metric: -11.5051\n", + "val loss: 11.5051, val metric: -11.5051\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.74s\n", - "train loss: 11.1360\n", - "Validation, Loss: 11.1575, Metric: -11.1575\n", - "val loss: 11.1575\n", + "Epoch 8/10, cost time: 2.06s\n", + "train loss: 11.4072\n", + "Validation, Loss: 11.4263, Metric: -11.4263\n", + "val loss: 11.4263, val metric: -11.4263\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.71s\n", - "train loss: 11.0524\n", - "Validation, Loss: 11.0737, Metric: -11.0737\n", - "val loss: 11.0737\n", + "Epoch 9/10, cost time: 2.11s\n", + "train loss: 11.3300\n", + "Validation, Loss: 11.3804, Metric: -11.3804\n", + "val loss: 11.3804, val metric: -11.3804\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.89s\n", - "train loss: 10.9718\n", - "Validation, Loss: 10.9886, Metric: -10.9886\n", - "val loss: 10.9886\n", + "Epoch 10/10, cost time: 2.12s\n", + "train loss: 11.2633\n", + "Validation, Loss: 11.2711, Metric: -11.2711\n", + "val loss: 11.2711, val metric: -11.2711\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -10.988609313964844\n", + "Fold 0. Score: -11.271098136901855\n", "length of train dataset: 495\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.71s\n", - "train loss: 12.2164\n", - "Validation, Loss: 12.1089, Metric: -12.1089\n", - "val loss: 12.1089\n", + "Epoch 1/10, cost time: 2.08s\n", + "train loss: 11.4316\n", + "Validation, Loss: 11.3298, Metric: -11.3298\n", + "val loss: 11.3298, val metric: -11.3298\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.63s\n", - "train loss: 12.1554\n", - "Validation, Loss: 12.0410, Metric: -12.0410\n", - "val loss: 12.0410\n", + "Epoch 2/10, cost time: 2.09s\n", + "train loss: 11.3667\n", + "Validation, Loss: 11.2669, Metric: -11.2669\n", + "val loss: 11.2669, val metric: -11.2669\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.65s\n", - "train loss: 12.0889\n", - "Validation, Loss: 12.0101, Metric: -12.0101\n", - "val loss: 12.0101\n", + "Epoch 3/10, cost time: 2.08s\n", + "train loss: 11.3088\n", + "Validation, Loss: 11.2020, Metric: -11.2020\n", + "val loss: 11.2020, val metric: -11.2020\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.73s\n", - "train loss: 12.0114\n", - "Validation, Loss: 11.8983, Metric: -11.8983\n", - "val loss: 11.8983\n", + "Epoch 4/10, cost time: 2.09s\n", + "train loss: 11.2484\n", + "Validation, Loss: 11.1441, Metric: -11.1441\n", + "val loss: 11.1441, val metric: -11.1441\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.77s\n", - "train loss: 11.9531\n", - "Validation, Loss: 11.8235, Metric: -11.8235\n", - "val loss: 11.8235\n", + "Epoch 5/10, cost time: 2.09s\n", + "train loss: 11.1701\n", + "Validation, Loss: 11.0645, Metric: -11.0645\n", + "val loss: 11.0645, val metric: -11.0645\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.80s\n", - "train loss: 11.8625\n", - "Validation, Loss: 11.7573, Metric: -11.7573\n", - "val loss: 11.7573\n", + "Epoch 6/10, cost time: 2.08s\n", + "train loss: 11.0986\n", + "Validation, Loss: 10.9954, Metric: -10.9954\n", + "val loss: 10.9954, val metric: -10.9954\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.74s\n", - "train loss: 11.7842\n", - "Validation, Loss: 11.6797, Metric: -11.6797\n", - "val loss: 11.6797\n", + "Epoch 7/10, cost time: 2.09s\n", + "train loss: 11.0328\n", + "Validation, Loss: 10.9285, Metric: -10.9285\n", + "val loss: 10.9285, val metric: -10.9285\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.88s\n", - "train loss: 11.7024\n", - "Validation, Loss: 11.5906, Metric: -11.5906\n", - "val loss: 11.5906\n", + "Epoch 8/10, cost time: 2.08s\n", + "train loss: 10.9546\n", + "Validation, Loss: 10.9217, Metric: -10.9217\n", + "val loss: 10.9217, val metric: -10.9217\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.65s\n", - "train loss: 11.6254\n", - "Validation, Loss: 11.5071, Metric: -11.5071\n", - "val loss: 11.5071\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 10.8847\n", + "Validation, Loss: 10.7685, Metric: -10.7685\n", + "val loss: 10.7685, val metric: -10.7685\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.72s\n", - "train loss: 11.5450\n", - "Validation, Loss: 11.4422, Metric: -11.4422\n", - "val loss: 11.4422\n", + "Epoch 10/10, cost time: 2.10s\n", + "train loss: 10.8031\n", + "Validation, Loss: 10.6983, Metric: -10.6983\n", + "val loss: 10.6983, val metric: -10.6983\n", "Last epoch model saved to checkpoints_finetuned/trainer_4/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_4/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_4/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_4/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -11.442185401916504\n", - "Mean score: -11.2154\n", - "Std: 0.2268\n", + "Fold 1. Score: -10.69830322265625\n", + "Mean score: -10.9847\n", + "Std: 0.2864\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.70s\n", - "train loss: 14.5929\n", - "Validation, Loss: 14.6382, Metric: -14.6382\n", - "val loss: 14.6382\n", + "Epoch 1/10, cost time: 2.00s\n", + "train loss: 15.2049\n", + "Validation, Loss: 15.2768, Metric: -15.2768\n", + "val loss: 15.2768, val metric: -15.2768\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.78s\n", - "train loss: 14.5299\n", - "Validation, Loss: 14.5703, Metric: -14.5703\n", - "val loss: 14.5703\n", + "Epoch 2/10, cost time: 2.05s\n", + "train loss: 15.1382\n", + "Validation, Loss: 15.1853, Metric: -15.1853\n", + "val loss: 15.1853, val metric: -15.1853\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.62s\n", - "train loss: 14.4353\n", - "Validation, Loss: 14.4728, Metric: -14.4728\n", - "val loss: 14.4728\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 15.0584\n", + "Validation, Loss: 15.1178, Metric: -15.1178\n", + "val loss: 15.1178, val metric: -15.1178\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.70s\n", - "train loss: 14.3435\n", - "Validation, Loss: 14.4078, Metric: -14.4078\n", - "val loss: 14.4078\n", + "Epoch 4/10, cost time: 2.08s\n", + "train loss: 14.9508\n", + "Validation, Loss: 14.9971, Metric: -14.9971\n", + "val loss: 14.9971, val metric: -14.9971\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.78s\n", - "train loss: 14.2574\n", - "Validation, Loss: 14.3771, Metric: -14.3771\n", - "val loss: 14.3771\n", + "Epoch 5/10, cost time: 2.07s\n", + "train loss: 14.8535\n", + "Validation, Loss: 14.9023, Metric: -14.9023\n", + "val loss: 14.9023, val metric: -14.9023\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.66s\n", - "train loss: 14.2107\n", - "Validation, Loss: 14.2036, Metric: -14.2036\n", - "val loss: 14.2036\n", + "Epoch 6/10, cost time: 2.11s\n", + "train loss: 14.7680\n", + "Validation, Loss: 14.8101, Metric: -14.8101\n", + "val loss: 14.8101, val metric: -14.8101\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.63s\n", - "train loss: 14.1034\n", - "Validation, Loss: 14.1513, Metric: -14.1513\n", - "val loss: 14.1513\n", + "Epoch 7/10, cost time: 2.06s\n", + "train loss: 14.6690\n", + "Validation, Loss: 14.7321, Metric: -14.7321\n", + "val loss: 14.7321, val metric: -14.7321\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.77s\n", - "train loss: 13.9854\n", - "Validation, Loss: 14.0232, Metric: -14.0232\n", - "val loss: 14.0232\n", + "Epoch 8/10, cost time: 2.06s\n", + "train loss: 14.5852\n", + "Validation, Loss: 14.6153, Metric: -14.6153\n", + "val loss: 14.6153, val metric: -14.6153\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.71s\n", - "train loss: 13.8830\n", - "Validation, Loss: 13.9311, Metric: -13.9311\n", - "val loss: 13.9311\n", + "Epoch 9/10, cost time: 2.07s\n", + "train loss: 14.4852\n", + "Validation, Loss: 14.5029, Metric: -14.5029\n", + "val loss: 14.5029, val metric: -14.5029\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.65s\n", - "train loss: 13.7840\n", - "Validation, Loss: 13.8271, Metric: -13.8271\n", - "val loss: 13.8271\n", + "Epoch 10/10, cost time: 2.06s\n", + "train loss: 14.3642\n", + "Validation, Loss: 14.3978, Metric: -14.3978\n", + "val loss: 14.3978, val metric: -14.3978\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -13.82713508605957\n", + "Fold 0. Score: -14.397769927978516\n", "length of train dataset: 494\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.61s\n", - "train loss: 14.1206\n", - "Validation, Loss: 13.9856, Metric: -13.9856\n", - "val loss: 13.9856\n", + "Epoch 1/10, cost time: 2.06s\n", + "train loss: 15.3321\n", + "Validation, Loss: 15.1772, Metric: -15.1772\n", + "val loss: 15.1772, val metric: -15.1772\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.62s\n", - "train loss: 14.0371\n", - "Validation, Loss: 13.8985, Metric: -13.8985\n", - "val loss: 13.8985\n", + "Epoch 2/10, cost time: 2.08s\n", + "train loss: 15.2465\n", + "Validation, Loss: 15.0918, Metric: -15.0918\n", + "val loss: 15.0918, val metric: -15.0918\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.64s\n", - "train loss: 13.9648\n", - "Validation, Loss: 13.8339, Metric: -13.8339\n", - "val loss: 13.8339\n", + "Epoch 3/10, cost time: 2.09s\n", + "train loss: 15.1606\n", + "Validation, Loss: 15.0096, Metric: -15.0096\n", + "val loss: 15.0096, val metric: -15.0096\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.60s\n", - "train loss: 13.8822\n", - "Validation, Loss: 13.7395, Metric: -13.7395\n", - "val loss: 13.7395\n", + "Epoch 4/10, cost time: 2.06s\n", + "train loss: 15.0829\n", + "Validation, Loss: 14.9350, Metric: -14.9350\n", + "val loss: 14.9350, val metric: -14.9350\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.60s\n", - "train loss: 13.7903\n", - "Validation, Loss: 13.6687, Metric: -13.6687\n", - "val loss: 13.6687\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 14.9794\n", + "Validation, Loss: 14.8179, Metric: -14.8179\n", + "val loss: 14.8179, val metric: -14.8179\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.61s\n", - "train loss: 13.7069\n", - "Validation, Loss: 13.5543, Metric: -13.5543\n", - "val loss: 13.5543\n", + "Epoch 6/10, cost time: 2.15s\n", + "train loss: 14.8815\n", + "Validation, Loss: 14.7188, Metric: -14.7188\n", + "val loss: 14.7188, val metric: -14.7188\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.60s\n", - "train loss: 13.6136\n", - "Validation, Loss: 13.4649, Metric: -13.4649\n", - "val loss: 13.4649\n", + "Epoch 7/10, cost time: 2.10s\n", + "train loss: 14.8099\n", + "Validation, Loss: 14.6202, Metric: -14.6202\n", + "val loss: 14.6202, val metric: -14.6202\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.60s\n", - "train loss: 13.5222\n", - "Validation, Loss: 13.3781, Metric: -13.3781\n", - "val loss: 13.3781\n", + "Epoch 8/10, cost time: 2.08s\n", + "train loss: 14.6788\n", + "Validation, Loss: 14.5351, Metric: -14.5351\n", + "val loss: 14.5351, val metric: -14.5351\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.60s\n", - "train loss: 13.4350\n", - "Validation, Loss: 13.2843, Metric: -13.2843\n", - "val loss: 13.2843\n", + "Epoch 9/10, cost time: 2.09s\n", + "train loss: 14.5753\n", + "Validation, Loss: 14.4359, Metric: -14.4359\n", + "val loss: 14.4359, val metric: -14.4359\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.59s\n", - "train loss: 13.3280\n", - "Validation, Loss: 13.1827, Metric: -13.1827\n", - "val loss: 13.1827\n", + "Epoch 10/10, cost time: 2.08s\n", + "train loss: 14.4740\n", + "Validation, Loss: 14.3063, Metric: -14.3063\n", + "val loss: 14.3063, val metric: -14.3063\n", "Last epoch model saved to checkpoints_finetuned/trainer_5/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_5/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_5/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_5/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -13.182726860046387\n", - "Mean score: -13.5049\n", - "Std: 0.3222\n", + "Fold 1. Score: -14.306349754333496\n", + "Mean score: -14.3521\n", + "Std: 0.0457\n", "length of train dataset: 493\n", "length of val dataset: 494\n", - "Epoch 1/10, cost time: 0.60s\n", - "train loss: 16.3563\n", - "Validation, Loss: 16.3956, Metric: -16.3956\n", - "val loss: 16.3956\n", + "Epoch 1/10, cost time: 2.09s\n", + "train loss: 16.7145\n", + "Validation, Loss: 16.7511, Metric: -16.7511\n", + "val loss: 16.7511, val metric: -16.7511\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.64s\n", - "train loss: 16.2662\n", - "Validation, Loss: 16.3085, Metric: -16.3085\n", - "val loss: 16.3085\n", + "Epoch 2/10, cost time: 2.07s\n", + "train loss: 16.6236\n", + "Validation, Loss: 16.6682, Metric: -16.6682\n", + "val loss: 16.6682, val metric: -16.6682\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.71s\n", - "train loss: 16.1773\n", - "Validation, Loss: 16.2451, Metric: -16.2451\n", - "val loss: 16.2451\n", + "Epoch 3/10, cost time: 2.06s\n", + "train loss: 16.5325\n", + "Validation, Loss: 16.5596, Metric: -16.5596\n", + "val loss: 16.5596, val metric: -16.5596\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.62s\n", - "train loss: 16.0996\n", - "Validation, Loss: 16.1621, Metric: -16.1621\n", - "val loss: 16.1621\n", + "Epoch 4/10, cost time: 2.05s\n", + "train loss: 16.4272\n", + "Validation, Loss: 16.4611, Metric: -16.4611\n", + "val loss: 16.4611, val metric: -16.4611\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.61s\n", - "train loss: 16.0046\n", - "Validation, Loss: 16.0087, Metric: -16.0087\n", - "val loss: 16.0087\n", + "Epoch 5/10, cost time: 2.08s\n", + "train loss: 16.3243\n", + "Validation, Loss: 16.3963, Metric: -16.3963\n", + "val loss: 16.3963, val metric: -16.3963\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.62s\n", - "train loss: 15.8729\n", - "Validation, Loss: 15.9024, Metric: -15.9024\n", - "val loss: 15.9024\n", + "Epoch 6/10, cost time: 2.08s\n", + "train loss: 16.2325\n", + "Validation, Loss: 16.2513, Metric: -16.2513\n", + "val loss: 16.2513, val metric: -16.2513\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.61s\n", - "train loss: 15.7679\n", - "Validation, Loss: 15.8665, Metric: -15.8665\n", - "val loss: 15.8665\n", + "Epoch 7/10, cost time: 2.04s\n", + "train loss: 16.1230\n", + "Validation, Loss: 16.1378, Metric: -16.1378\n", + "val loss: 16.1378, val metric: -16.1378\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.61s\n", - "train loss: 15.6997\n", - "Validation, Loss: 15.6833, Metric: -15.6833\n", - "val loss: 15.6833\n", + "Epoch 8/10, cost time: 2.15s\n", + "train loss: 16.0079\n", + "Validation, Loss: 16.0250, Metric: -16.0250\n", + "val loss: 16.0250, val metric: -16.0250\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.65s\n", - "train loss: 15.5581\n", - "Validation, Loss: 15.5959, Metric: -15.5959\n", - "val loss: 15.5959\n", + "Epoch 9/10, cost time: 2.05s\n", + "train loss: 15.8990\n", + "Validation, Loss: 15.9274, Metric: -15.9274\n", + "val loss: 15.9274, val metric: -15.9274\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.62s\n", - "train loss: 15.4514\n", - "Validation, Loss: 15.4734, Metric: -15.4734\n", - "val loss: 15.4734\n", + "Epoch 10/10, cost time: 2.06s\n", + "train loss: 15.7968\n", + "Validation, Loss: 15.8163, Metric: -15.8163\n", + "val loss: 15.8163, val metric: -15.8163\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_0/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_0/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_0/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: -15.473418235778809\n", + "Fold 0. Score: -15.816303253173828\n", "length of train dataset: 494\n", "length of val dataset: 493\n", - "Epoch 1/10, cost time: 0.66s\n", - "train loss: 17.0522\n", - "Validation, Loss: 16.9092, Metric: -16.9092\n", - "val loss: 16.9092\n", + "Epoch 1/10, cost time: 2.06s\n", + "train loss: 16.6161\n", + "Validation, Loss: 16.4749, Metric: -16.4749\n", + "val loss: 16.4749, val metric: -16.4749\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_0.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_0.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_0.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 2/10, cost time: 0.68s\n", - "train loss: 16.9659\n", - "Validation, Loss: 16.8336, Metric: -16.8336\n", - "val loss: 16.8336\n", + "Epoch 2/10, cost time: 2.05s\n", + "train loss: 16.5317\n", + "Validation, Loss: 16.3905, Metric: -16.3905\n", + "val loss: 16.3905, val metric: -16.3905\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_1.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_1.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_1.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 3/10, cost time: 0.63s\n", - "train loss: 16.8774\n", - "Validation, Loss: 16.7134, Metric: -16.7134\n", - "val loss: 16.7134\n", + "Epoch 3/10, cost time: 2.07s\n", + "train loss: 16.4369\n", + "Validation, Loss: 16.2813, Metric: -16.2813\n", + "val loss: 16.2813, val metric: -16.2813\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_2.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_2.pth\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_2.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 4/10, cost time: 0.68s\n", - "train loss: 16.7588\n", - "Validation, Loss: 16.6066, Metric: -16.6066\n", - "val loss: 16.6066\n", + "Epoch 4/10, cost time: 2.05s\n", + "train loss: 16.3491\n", + "Validation, Loss: 16.2151, Metric: -16.2151\n", + "val loss: 16.2151, val metric: -16.2151\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_3.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_3.pth\n", "Removing worst model snapshot: from epoch 0\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_3.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 5/10, cost time: 0.69s\n", - "train loss: 16.6589\n", - "Validation, Loss: 16.5006, Metric: -16.5006\n", - "val loss: 16.5006\n", + "Epoch 5/10, cost time: 2.05s\n", + "train loss: 16.2300\n", + "Validation, Loss: 16.0982, Metric: -16.0982\n", + "val loss: 16.0982, val metric: -16.0982\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_4.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_4.pth\n", "Removing worst model snapshot: from epoch 1\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_4.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 6/10, cost time: 0.65s\n", - "train loss: 16.5478\n", - "Validation, Loss: 16.4862, Metric: -16.4862\n", - "val loss: 16.4862\n", + "Epoch 6/10, cost time: 2.07s\n", + "train loss: 16.1425\n", + "Validation, Loss: 15.9748, Metric: -15.9748\n", + "val loss: 15.9748, val metric: -15.9748\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_5.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_5.pth\n", "Removing worst model snapshot: from epoch 2\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_5.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 7/10, cost time: 0.94s\n", - "train loss: 16.4392\n", - "Validation, Loss: 16.2885, Metric: -16.2885\n", - "val loss: 16.2885\n", + "Epoch 7/10, cost time: 2.08s\n", + "train loss: 16.0127\n", + "Validation, Loss: 15.9073, Metric: -15.9073\n", + "val loss: 15.9073, val metric: -15.9073\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_6.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_6.pth\n", "Removing worst model snapshot: from epoch 3\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_6.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 8/10, cost time: 0.77s\n", - "train loss: 16.3296\n", - "Validation, Loss: 16.1659, Metric: -16.1659\n", - "val loss: 16.1659\n", + "Epoch 8/10, cost time: 2.07s\n", + "train loss: 15.9301\n", + "Validation, Loss: 15.7657, Metric: -15.7657\n", + "val loss: 15.7657, val metric: -15.7657\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_7.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_7.pth\n", "Removing worst model snapshot: from epoch 4\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_7.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 9/10, cost time: 0.66s\n", - "train loss: 16.2050\n", - "Validation, Loss: 16.1123, Metric: -16.1123\n", - "val loss: 16.1123\n", + "Epoch 9/10, cost time: 2.08s\n", + "train loss: 15.8140\n", + "Validation, Loss: 15.6519, Metric: -15.6519\n", + "val loss: 15.6519, val metric: -15.6519\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_8.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_8.pth\n", "Removing worst model snapshot: from epoch 5\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_8.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", - "Epoch 10/10, cost time: 0.66s\n", - "train loss: 16.0964\n", - "Validation, Loss: 15.9350, Metric: -15.9350\n", - "val loss: 15.9350\n", + "Epoch 10/10, cost time: 2.07s\n", + "train loss: 15.6804\n", + "Validation, Loss: 15.5294, Metric: -15.5294\n", + "val loss: 15.5294, val metric: -15.5294\n", "Last epoch model saved to checkpoints_finetuned/trainer_6/fold_1/model_9.pth\n", "Last epoch optimizer saved to checkpoints_finetuned/trainer_6/fold_1/opt_9.pth\n", "Removing worst model snapshot: from epoch 6\n", "Best model snapshot saved to checkpoints_finetuned/trainer_6/fold_1/model_9.pth\n", "Checkpoint manager saved to checkpoints_finetuned/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: -15.935033798217773\n", - "Mean score: -15.7042\n", - "Std: 0.2308\n" + "Fold 1. Score: -15.52938461303711\n", + "Mean score: -15.6728\n", + "Std: 0.1435\n" ] }, { "data": { "text/plain": [ - "(185.71449208259583, )" + "(542.532794713974,\n", + " )" ] }, "execution_count": 15, @@ -3290,14 +3304,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "trainer_params = {\n", " \"device\": \"cpu\",\n", " \"num_workers\": 0,\n", - " \"n_epochs\": 0,\n", + " \"n_epochs\": 1,\n", " \"pretrained_path\": \"checkpoints_finetuned/\",\n", " # Average checkpoints for the final model\n", " \"average_snapshots\": True,\n", @@ -3321,7 +3335,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3329,85 +3343,203 @@ "output_type": "stream", "text": [ "length of train dataset: 496\n", - "length of val dataset: 497\n", + "length of val dataset: 497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1, cost time: 2.15s\n", + "train loss: 2.3693\n", + "Validation, Loss: 2.3725, Metric: -2.3725\n", + "val loss: 2.3725, val metric: -2.3725\n", + "Last epoch model saved to checkpoints/trainer_0/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -2.3724923133850098\n", "length of train dataset: 497\n", "length of val dataset: 496\n", + "Epoch 1/1, cost time: 2.09s\n", + "train loss: 3.4084\n", + "Validation, Loss: 3.3751, Metric: -3.3751\n", + "val loss: 3.3751, val metric: -3.3751\n", + "Last epoch model saved to checkpoints/trainer_0/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_0/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_0/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_0/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n", + "Fold 1. Score: -3.37510347366333\n", + "Mean score: -2.8738\n", + "Std: 0.5013\n", "length of train dataset: 496\n", "length of val dataset: 496\n", + "Epoch 1/1, cost time: 2.07s\n", + "train loss: 4.4695\n", + "Validation, Loss: 4.4801, Metric: -4.4801\n", + "val loss: 4.4801, val metric: -4.4801\n", + "Last epoch model saved to checkpoints/trainer_1/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -4.480130195617676\n", "length of train dataset: 496\n", "length of val dataset: 496\n", + "Epoch 1/1, cost time: 2.09s\n", + "train loss: 3.8120\n", + "Validation, Loss: 3.7744, Metric: -3.7744\n", + "val loss: 3.7744, val metric: -3.7744\n", + "Last epoch model saved to checkpoints/trainer_1/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_1/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_1/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_1/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n", + "Fold 1. Score: -3.774390935897827\n", + "Mean score: -4.1273\n", + "Std: 0.3529\n", "length of train dataset: 495\n", "length of val dataset: 496\n", + "Epoch 1/1, cost time: 2.08s\n", + "train loss: 5.9567\n", + "Validation, Loss: 5.9638, Metric: -5.9638\n", + "val loss: 5.9638, val metric: -5.9638\n", + "Last epoch model saved to checkpoints/trainer_2/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -5.963770866394043\n", "length of train dataset: 496\n", "length of val dataset: 495\n", + "Epoch 1/1, cost time: 2.09s\n", + "train loss: 6.2336\n", + "Validation, Loss: 6.1705, Metric: -6.1705\n", + "val loss: 6.1705, val metric: -6.1705\n", + "Last epoch model saved to checkpoints/trainer_2/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_2/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_2/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_2/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n", + "Fold 1. Score: -6.170502185821533\n", + "Mean score: -6.0671\n", + "Std: 0.1034\n", "length of train dataset: 495\n", "length of val dataset: 495\n", + "Epoch 1/1, cost time: 2.08s\n", + "train loss: 7.4179\n", + "Validation, Loss: 7.4461, Metric: -7.4461\n", + "val loss: 7.4461, val metric: -7.4461\n", + "Last epoch model saved to checkpoints/trainer_3/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -7.446073055267334\n", "length of train dataset: 495\n", "length of val dataset: 495\n", + "Epoch 1/1, cost time: 2.08s\n", + "train loss: 9.3169\n", + "Validation, Loss: 9.2158, Metric: -9.2158\n", + "val loss: 9.2158, val metric: -9.2158\n", + "Last epoch model saved to checkpoints/trainer_3/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_3/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_3/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_3/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n", + "Fold 1. Score: -9.215822219848633\n", + "Mean score: -8.3309\n", + "Std: 0.8849\n", "length of train dataset: 494\n", "length of val dataset: 495\n", + "Epoch 1/1, cost time: 2.20s\n", + "train loss: 11.1697\n", + "Validation, Loss: 11.2258, Metric: -11.2258\n", + "val loss: 11.2258, val metric: -11.2258\n", + "Last epoch model saved to checkpoints/trainer_4/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -11.225826263427734\n", "length of train dataset: 495\n", "length of val dataset: 494\n", + "Epoch 1/1, cost time: 2.10s\n", + "train loss: 10.7208\n", + "Validation, Loss: 10.6222, Metric: -10.6222\n", + "val loss: 10.6222, val metric: -10.6222\n", + "Last epoch model saved to checkpoints/trainer_4/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_4/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_4/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_4/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n", + "Fold 1. Score: -10.62221908569336\n", + "Mean score: -10.924\n", + "Std: 0.3018\n", "length of train dataset: 494\n", "length of val dataset: 494\n", + "Epoch 1/1, cost time: 2.09s\n", + "train loss: 14.2562\n", + "Validation, Loss: 14.2929, Metric: -14.2929\n", + "val loss: 14.2929, val metric: -14.2929\n", + "Last epoch model saved to checkpoints/trainer_5/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -14.292855262756348\n", "length of train dataset: 494\n", "length of val dataset: 494\n", + "Epoch 1/1, cost time: 2.08s\n", + "train loss: 14.3666\n", + "Validation, Loss: 14.1999, Metric: -14.1999\n", + "val loss: 14.1999, val metric: -14.1999\n", + "Last epoch model saved to checkpoints/trainer_5/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_5/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_5/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_5/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n", + "Fold 1. Score: -14.199889183044434\n", + "Mean score: -14.2464\n", + "Std: 0.0465\n", "length of train dataset: 493\n", "length of val dataset: 494\n", + "Epoch 1/1, cost time: 2.05s\n", + "train loss: 15.6696\n", + "Validation, Loss: 15.6848, Metric: -15.6848\n", + "val loss: 15.6848, val metric: -15.6848\n", + "Last epoch model saved to checkpoints/trainer_6/fold_0/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_0/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_0/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_0/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 0. Score: nan\n", + "Fold 0. Score: -15.684831619262695\n", "length of train dataset: 494\n", "length of val dataset: 493\n", + "Epoch 1/1, cost time: 2.08s\n", + "train loss: 15.5737\n", + "Validation, Loss: 15.4131, Metric: -15.4131\n", + "val loss: 15.4131, val metric: -15.4131\n", + "Last epoch model saved to checkpoints/trainer_6/fold_1/model_0.pth\n", + "Last epoch optimizer saved to checkpoints/trainer_6/fold_1/opt_0.pth\n", + "Best model snapshot saved to checkpoints/trainer_6/fold_1/model_0.pth\n", + "Checkpoint manager saved to checkpoints/trainer_6/fold_1/es_checkpoint_manager.pth\n", "Training finished.\n", - "Fold 1. Score: nan\n", - "Mean score: nan\n", - "Std: nan\n" + "Fold 1. Score: -15.413114547729492\n", + "Mean score: -15.549\n", + "Std: 0.1359\n" ] }, { "data": { "text/plain": [ - "(0.10583090782165527,\n", - " )" + "(54.834699869155884,\n", + " )" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3421,31 +3553,24 @@ "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n", + "length of test dataset: 1\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", - "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", - "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n", "length of test dataset: 1\n", - "Validation, Loss: nan, Metric: nan\n", - "Validation, Loss: nan, Metric: nan\n" + "length of test dataset: 1\n" ] } ], @@ -3489,31 +3614,31 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1997.192505\n", + " 1997.147827\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1997.647217\n", + " 1997.578491\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1997.732788\n", + " 1997.824463\n", " \n", " \n", " 3\n", " 0\n", " 2022-09-30\n", - " 1998.238525\n", + " 1998.122559\n", " \n", " \n", " 4\n", " 0\n", " 2022-10-01\n", - " 1998.347656\n", + " 1998.426758\n", " \n", " \n", " ...\n", @@ -3525,31 +3650,31 @@ " 65\n", " 9\n", " 2022-09-29\n", - " 11004.697266\n", + " 11004.701172\n", " \n", " \n", " 66\n", " 9\n", " 2022-09-30\n", - " 11005.898438\n", + " 11006.19043\n", " \n", " \n", " 67\n", " 9\n", " 2022-10-01\n", - " 11007.662109\n", + " 11007.738281\n", " \n", " \n", " 68\n", " 9\n", " 2022-10-02\n", - " 11009.277344\n", + " 11009.234375\n", " \n", " \n", " 69\n", " 9\n", " 2022-10-03\n", - " 11010.523438\n", + " 11010.298828\n", " \n", " \n", "\n", @@ -3558,17 +3683,17 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1997.192505\n", - "1 0 2022-09-28 1997.647217\n", - "2 0 2022-09-29 1997.732788\n", - "3 0 2022-09-30 1998.238525\n", - "4 0 2022-10-01 1998.347656\n", + "0 0 2022-09-27 1997.147827\n", + "1 0 2022-09-28 1997.578491\n", + "2 0 2022-09-29 1997.824463\n", + "3 0 2022-09-30 1998.122559\n", + "4 0 2022-10-01 1998.426758\n", ".. .. ... ...\n", - "65 9 2022-09-29 11004.697266\n", - "66 9 2022-09-30 11005.898438\n", - "67 9 2022-10-01 11007.662109\n", - "68 9 2022-10-02 11009.277344\n", - "69 9 2022-10-03 11010.523438\n", + "65 9 2022-09-29 11004.701172\n", + "66 9 2022-09-30 11006.19043\n", + "67 9 2022-10-01 11007.738281\n", + "68 9 2022-10-02 11009.234375\n", + "69 9 2022-10-03 11010.298828\n", "\n", "[70 rows x 3 columns]" ] @@ -3585,7 +3710,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "tsururu_test", "language": "python", "name": "python3" }, @@ -3599,7 +3724,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.13" }, "orig_nbformat": 4 }, From dbfa6effa905bf27b9db0bfe40eac49cd87fb79e Mon Sep 17 00:00:00 2001 From: Aleksandr Yugay Date: Mon, 20 Jan 2025 15:44:44 +0300 Subject: [PATCH 37/50] fix conftest --- tests/test_pipeline/conftest.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/test_pipeline/conftest.py b/tests/test_pipeline/conftest.py index b342855..0529a9a 100644 --- a/tests/test_pipeline/conftest.py +++ b/tests/test_pipeline/conftest.py @@ -1,11 +1,13 @@ import pandas as pd import pytest -from tsururu.dataset import TSDataset @pytest.fixture(scope="package") def get_dataset(): + + from tsururu.dataset import TSDataset + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") # Add some features with different names From 00e4b77ba329af17682ef10c0bb7a8a78b6b3b14 Mon Sep 17 00:00:00 2001 From: Aleksandr Yugay Date: Mon, 20 Jan 2025 15:49:36 +0300 Subject: [PATCH 38/50] fix torch import --- .../torch_based/data_provider.py | 5 +- tsururu/model_training/torch_based/metrics.py | 12 +++-- tsururu/model_training/trainer.py | 48 ++++++++++++------- tsururu/models/torch_based/dlinear.py | 11 +++-- .../torch_based/layers/decomposition.py | 11 +++-- .../models/torch_based/layers/patch_tst.py | 17 ++++--- .../torch_based/layers/positional_encoding.py | 8 ++-- tsururu/models/torch_based/layers/rev_in.py | 11 +++-- tsururu/models/torch_based/layers/utils.py | 9 ++-- tsururu/models/torch_based/patch_tst.py | 9 ++-- 10 files changed, 87 insertions(+), 54 deletions(-) diff --git a/tsururu/model_training/torch_based/data_provider.py b/tsururu/model_training/torch_based/data_provider.py index 3e5e163..f1b9013 100644 --- a/tsururu/model_training/torch_based/data_provider.py +++ b/tsururu/model_training/torch_based/data_provider.py @@ -9,8 +9,9 @@ import torch from torch.utils.data import Dataset except ImportError: + from abc import ABC torch = None - Dataset = None + Dataset = ABC class Dataset_NN(Dataset): @@ -141,7 +142,7 @@ def _get_adjusted_data(self, idx_X: np.ndarray, idx_y: np.ndarray) -> dict: } def _reshape_tensors( - self, X_tensor: torch.Tensor, y_tensor: torch.Tensor, index_of_sample: int + self, X_tensor: "torch.Tensor", y_tensor: "torch.Tensor", index_of_sample: int ) -> tuple: """Reshapes and transposes tensors to fit the neural network input. diff --git a/tsururu/model_training/torch_based/metrics.py b/tsururu/model_training/torch_based/metrics.py index 04e70bf..bd8e70c 100644 --- a/tsururu/model_training/torch_based/metrics.py +++ b/tsururu/model_training/torch_based/metrics.py @@ -2,20 +2,22 @@ try: import torch - import torch.nn as nn + from torch.nn import Module, MSELoss except ImportError: + from abc import ABC torch = None - nn = None + Module = ABC + MSELoss = ABC -class NegativeMSEMetric(nn.Module): +class NegativeMSEMetric(Module): """Custom metric that returns the negative of the Mean Squared Error (MSE).""" def __init__(self): super(NegativeMSEMetric, self).__init__() - self.mse_loss = nn.MSELoss() + self.mse_loss = MSELoss() - def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + def forward(self, input: "torch.Tensor", target: "torch.Tensor") -> "torch.Tensor": """Computes the negative mean squared error between input and target. Args: diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index f4b0a89..7571ee5 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -179,14 +179,14 @@ def __init__( n_epochs: int = 10, batch_size: int = 32, drop_last: bool = False, - device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"), + device: Optional["torch.device"] = None, device_ids: List[int] = [0], num_workers: int = 4, metric: Callable = NegativeMSEMetric(), - criterion: torch.nn.Module = torch.nn.MSELoss(), - optimizer: Optional[torch.optim.Optimizer] = torch.optim.Adam, + criterion: Optional["torch.nn.Module"] = None, + optimizer: Optional["torch.optim.Optimizer"] = None, optimizer_params: Dict = {}, - scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None, + scheduler: Optional["torch.optim.lr_scheduler.LRScheduler"] = None, scheduler_params: Dict = {}, scheduler_after_epoch: bool = True, pretrained_path: Optional[Union[Path, str]] = None, @@ -200,6 +200,15 @@ def __init__( train_shuffle: bool = True, verbose: int = 1, ): + if device is None: + device = torch.device("cuda") + + if criterion is None: + criterion = torch.nn.MSELoss() + + if optimizer is None: + optimizer = torch.optim.Adam + self.model_base = model self.model_params = model_params self.validator_base = validator @@ -284,9 +293,9 @@ def init_trainer_one_fold(self, num_features: int): def load_trainer_one_fold( self, fold_i: int, - model: nn.Module, - optimizer: torch.optim.Optimizer, - scheduler: Optional[torch.optim.lr_scheduler._LRScheduler], + model: "nn.Module", + optimizer: "torch.optim.Optimizer", + scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"], ): """Loads pretrained model, optimizer, and scheduler states for one fold. @@ -312,13 +321,13 @@ def load_trainer_one_fold( def train_model( self, - train_loader: torch.utils.data.DataLoader, - val_loader: torch.utils.data.DataLoader, - model: nn.Module, - optimizer: torch.optim.Optimizer, - scheduler: Optional[torch.optim.lr_scheduler._LRScheduler], + train_loader: "torch.utils.data.DataLoader", + val_loader: "torch.utils.data.DataLoader", + model: "nn.Module", + optimizer: "torch.optim.Optimizer", + scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"], ) -> Tuple[ - nn.Module, torch.optim.Optimizer, Optional[torch.optim.lr_scheduler._LRScheduler], float + "nn.Module", "torch.optim.Optimizer", Optional["torch.optim.lr_scheduler._LRScheduler"], float ]: """Trains the model for all epochs. @@ -427,16 +436,18 @@ def train_model( def validate_model( self, - val_loader: torch.utils.data.DataLoader, - model: nn.Module, + val_loader: "torch.utils.data.DataLoader", + model: "nn.Module", return_outputs: bool = False, - ) -> Union[float, Tuple[float, float], Tuple[float, float, torch.Tensor, torch.Tensor]]: + inference: bool = False, + ) -> Union[float, Tuple[float, float], Tuple[float, float, "torch.Tensor", "torch.Tensor"]]: """Validates the model on the validation data. Args: val_loader: data loader for the validation data. model: model to be validated. return_outputs: whether to return the outputs and targets. + inference: if True, skips logging and assumes test data. Returns: validation loss, metric, and optionally the outputs and targets. @@ -464,7 +475,8 @@ def validate_model( loss = self.criterion(all_outputs, all_targets).item() metric = self.metric(all_outputs, all_targets).item() if self.metric else 0.0 - logger.info(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") + if not inference: + logger.info(f"Validation, Loss: {loss:.4f}, Metric: {metric:.4f}") if return_outputs: return loss, metric, all_outputs, all_targets @@ -579,7 +591,7 @@ def predict(self, data: dict, pipeline: Pipeline) -> np.ndarray: logger.info(f"length of test dataset: {len(test_dataset)}") models_preds = [ - self.validate_model(test_loader, model, return_outputs=True)[2].cpu() + self.validate_model(test_loader, model, return_outputs=True, inference=True)[2].cpu() for model in self.models ] diff --git a/tsururu/models/torch_based/dlinear.py b/tsururu/models/torch_based/dlinear.py index 3292d83..b23e1e4 100644 --- a/tsururu/models/torch_based/dlinear.py +++ b/tsururu/models/torch_based/dlinear.py @@ -5,12 +5,15 @@ try: import torch import torch.nn as nn + from torch.nn import Module except ImportError: + from abc import ABC torch = None nn = None + Module = ABC -class DLinear_NN(nn.Module): +class DLinear_NN(Module): """DLInear model from the paper https://arxiv.org/pdf/2205.13504.pdf. Args: @@ -69,7 +72,7 @@ def __init__( self.trainer_type = "DLTrainer" - def encoder(self, x: torch.Tensor) -> torch.Tensor: + def encoder(self, x: "torch.Tensor") -> "torch.Tensor": """Encode the input sequence by decomposing it into seasonal and trend components. Args: @@ -99,7 +102,7 @@ def encoder(self, x: torch.Tensor) -> torch.Tensor: return x.permute(0, 2, 1) - def forecast(self, x_enc: torch.Tensor) -> torch.Tensor: + def forecast(self, x_enc: "torch.Tensor") -> "torch.Tensor": """Forecast the output sequence. Args: @@ -111,7 +114,7 @@ def forecast(self, x_enc: torch.Tensor) -> torch.Tensor: """ return self.encoder(x_enc) - def forward(self, x_enc: torch.Tensor) -> torch.Tensor: + def forward(self, x_enc: "torch.Tensor") -> "torch.Tensor": """Forward pass of the model. Args: diff --git a/tsururu/models/torch_based/layers/decomposition.py b/tsururu/models/torch_based/layers/decomposition.py index f6db4c2..7e13b1d 100644 --- a/tsururu/models/torch_based/layers/decomposition.py +++ b/tsururu/models/torch_based/layers/decomposition.py @@ -3,12 +3,15 @@ try: import torch import torch.nn as nn + from torch.nn import Module except ImportError: + from abc import ABC torch = None nn = None + Module = ABC -class moving_avg(nn.Module): +class moving_avg(Module): """Moving average block to highlight the trend of time series. Args: @@ -22,7 +25,7 @@ def __init__(self, kernel_size: int, stride: int): self.kernel_size = kernel_size self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0) - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x: "torch.Tensor") -> "torch.Tensor": """Forward pass for computing the moving average. Args: @@ -42,7 +45,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return x -class series_decomp(nn.Module): +class series_decomp(Module): """Series decomposition block. Args: @@ -54,7 +57,7 @@ def __init__(self, kernel_size: int): super(series_decomp, self).__init__() self.moving_avg = moving_avg(kernel_size, stride=1) - def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + def forward(self, x: "torch.Tensor") -> tuple["torch.Tensor", "torch.Tensor"]: """Forward pass for decomposing the series into trend and remainder. Args: diff --git a/tsururu/models/torch_based/layers/patch_tst.py b/tsururu/models/torch_based/layers/patch_tst.py index 7d88f64..404a0d1 100644 --- a/tsururu/models/torch_based/layers/patch_tst.py +++ b/tsururu/models/torch_based/layers/patch_tst.py @@ -13,15 +13,18 @@ import torch.nn as nn import torch.nn.functional as F from torch import Tensor + from torch.nn import Module except ImportError: + from abc import ABC torch = None nn = None Tensor = None F = None + Module = ABC # Cell -class PatchTST_backbone(nn.Module): +class PatchTST_backbone(Module): """Backbone for Patch Time Series Transformer. Args: @@ -215,7 +218,7 @@ def create_pretrain_head(self, head_nf, vars, dropout): return nn.Sequential(nn.Dropout(dropout), nn.Conv1d(head_nf, vars, 1)) -class Flatten_Head(nn.Module): +class Flatten_Head(Module): """Flatten head for PatchTST. Args: @@ -271,7 +274,7 @@ def forward(self, x): return x -class TSTiEncoder(nn.Module): +class TSTiEncoder(Module): """Time Series Transformer independent encoder. Args: @@ -398,7 +401,7 @@ def forward(self, x: Tensor) -> Tensor: # Cell -class TSTEncoder(nn.Module): +class TSTEncoder(Module): """Time Series Transformer encoder. Args: @@ -493,7 +496,7 @@ def forward( return output -class TSTEncoderLayer(nn.Module): +class TSTEncoderLayer(Module): """Encoder layer for the Time Series Transformer. Args: @@ -634,7 +637,7 @@ def forward( return src -class _MultiheadAttention(nn.Module): +class _MultiheadAttention(Module): """Multi-Head Attention Layer. Args: @@ -752,7 +755,7 @@ def forward( return output, attn_weights -class _ScaledDotProductAttention(nn.Module): +class _ScaledDotProductAttention(Module): """Scaled Dot-Product Attention module. Args: diff --git a/tsururu/models/torch_based/layers/positional_encoding.py b/tsururu/models/torch_based/layers/positional_encoding.py index a8777ae..f6585c9 100644 --- a/tsururu/models/torch_based/layers/positional_encoding.py +++ b/tsururu/models/torch_based/layers/positional_encoding.py @@ -11,7 +11,7 @@ nn = None -def PositionalEncoding(q_len: int, d_model: int, normalize: bool = True) -> torch.Tensor: +def PositionalEncoding(q_len: int, d_model: int, normalize: bool = True) -> "torch.Tensor": """Generate positional encoding. Args: @@ -42,7 +42,7 @@ def Coord2dPosEncoding( normalize: bool = True, eps: float = 1e-3, verbose: bool = False, -) -> torch.Tensor: +) -> "torch.Tensor": """Generate 2D coordinate positional encoding. Args: @@ -82,7 +82,7 @@ def Coord2dPosEncoding( def Coord1dPosEncoding( q_len: int, exponential: bool = False, normalize: bool = True -) -> torch.Tensor: +) -> "torch.Tensor": """Generate 1D coordinate positional encoding. Args: @@ -104,7 +104,7 @@ def Coord1dPosEncoding( def positional_encoding( pe: Optional[str], learn_pe: bool, q_len: int, d_model: int -) -> nn.Parameter: +) -> "nn.Parameter": """Initialize positional encoding. Args: diff --git a/tsururu/models/torch_based/layers/rev_in.py b/tsururu/models/torch_based/layers/rev_in.py index 2f58df5..e02cf0d 100644 --- a/tsururu/models/torch_based/layers/rev_in.py +++ b/tsururu/models/torch_based/layers/rev_in.py @@ -3,12 +3,15 @@ try: import torch import torch.nn as nn + from torch.nn import Module except ImportError: + from abc import ABC torch = None nn = None + Module = ABC -class RevIN(nn.Module): +class RevIN(Module): def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False): """Reversible Instance Normalization (RevIN) module. @@ -56,7 +59,7 @@ def _init_params(self): self.affine_weight = nn.Parameter(torch.ones(self.num_features)) self.affine_bias = nn.Parameter(torch.zeros(self.num_features)) - def _get_statistics(self, x: torch.Tensor): + def _get_statistics(self, x: "torch.Tensor"): """Compute mean and standard deviation for normalization. Args: @@ -72,7 +75,7 @@ def _get_statistics(self, x: torch.Tensor): torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps ).detach() - def _normalize(self, x: torch.Tensor) -> torch.Tensor: + def _normalize(self, x: "torch.Tensor") -> "torch.Tensor": """Normalize the input tensor. Args: @@ -93,7 +96,7 @@ def _normalize(self, x: torch.Tensor) -> torch.Tensor: return x - def _denormalize(self, x: torch.Tensor) -> torch.Tensor: + def _denormalize(self, x: "torch.Tensor") -> "torch.Tensor": """Denormalize the input tensor. Args: diff --git a/tsururu/models/torch_based/layers/utils.py b/tsururu/models/torch_based/layers/utils.py index 56627ba..65e90b9 100644 --- a/tsururu/models/torch_based/layers/utils.py +++ b/tsururu/models/torch_based/layers/utils.py @@ -5,12 +5,15 @@ try: import torch import torch.nn as nn + from torch.nn import Module except ImportError: + from abc import ABC torch = None nn = None + Module = ABC -def get_activation_fn(activation: Union[str, Callable[[], nn.Module]]) -> nn.Module: +def get_activation_fn(activation: Union[str, Callable[[], Module]]) -> Module: """Get the activation function based on the provided name or callable. Args: @@ -32,7 +35,7 @@ def get_activation_fn(activation: Union[str, Callable[[], nn.Module]]) -> nn.Mod raise ValueError(f'{activation} is not available. You can use "relu", "gelu", or a callable') -class Transpose(nn.Module): +class Transpose(Module): """Module for transposing tensors with optional contiguous memory layout. Args: @@ -46,7 +49,7 @@ def __init__(self, *dims: int, contiguous: bool = False): self.dims = dims self.contiguous = contiguous - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x: "torch.Tensor") -> "torch.Tensor": """Forward pass for transposing the tensor. Args: diff --git a/tsururu/models/torch_based/patch_tst.py b/tsururu/models/torch_based/patch_tst.py index 0ee987e..f4720f6 100644 --- a/tsururu/models/torch_based/patch_tst.py +++ b/tsururu/models/torch_based/patch_tst.py @@ -8,12 +8,15 @@ try: import torch import torch.nn as nn + from torch.nn import Module except ImportError: + from abc import ABC torch = None nn = None + Module = ABC -class PatchTST_NN(nn.Module): +class PatchTST_NN(Module): """PatchTST_NN model from the paper https://arxiv.org/abs/2211.14730. Args: @@ -85,7 +88,7 @@ def __init__( act: str = "gelu", key_padding_mask: Union[str, bool] = "auto", padding_var: Optional[int] = None, - attn_mask: Optional[torch.Tensor] = None, + attn_mask: Optional["torch.Tensor"] = None, res_attention: bool = True, pre_norm: bool = False, store_attn: bool = False, @@ -242,7 +245,7 @@ def __init__( **kwargs ) - def forward(self, x: torch.Tensor) -> torch.Tensor: + def forward(self, x: "torch.Tensor") -> "torch.Tensor": """Forward pass for the PatchTST_NN model. Args: From 6e30ad699134549624bd71d5e2e118380fc17de5 Mon Sep 17 00:00:00 2001 From: Aleksandr Yugay Date: Mon, 20 Jan 2025 15:54:50 +0300 Subject: [PATCH 39/50] fix warning in SeriesToSeriesTransformer --- tsururu/transformers/base.py | 4 ++-- tsururu/transformers/numeric.py | 9 ++++----- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/tsururu/transformers/base.py b/tsururu/transformers/base.py index 909696d..ff1e5c0 100644 --- a/tsururu/transformers/base.py +++ b/tsururu/transformers/base.py @@ -459,8 +459,8 @@ def _transform(self, data: dict, data_key: str) -> dict: data[data_key] = ( data[data_key] .groupby(data["id_column_name"], sort=False) - .apply(self._transform_segment, data["id_column_name"]) - .reset_index(level=data["id_column_name"], drop=True) + .apply(lambda group: self._transform_segment(group, group.name), include_groups=False) + .reset_index(level=data["id_column_name"], drop=False) ) return data diff --git a/tsururu/transformers/numeric.py b/tsururu/transformers/numeric.py index 6512d2d..095412e 100644 --- a/tsururu/transformers/numeric.py +++ b/tsururu/transformers/numeric.py @@ -101,19 +101,18 @@ def _get_mask_mean_std(self, segment, column_name, current_id): return column_mask, mean, std - def _transform_segment(self, segment: pd.Series, id_column_name: str) -> pd.Series: + def _transform_segment(self, segment: pd.Series, current_id: str) -> pd.Series: """Transform segment (points with similar id) of "elongated series" for features' and targets' further generation. Args: segment: segment of "elongated series" to transform. - id_column_name: name of id column. + current_id: id of current segment. Returns: transformed segment of "elongated series". """ - current_id = segment[id_column_name].values[0] for i, column_name in enumerate(self.input_features): column_mask, mean, std = self._get_mask_mean_std( @@ -349,7 +348,7 @@ def transform(self, data: dict) -> dict: # Update the params if self.transform_target is True if self.transform_target: try: - feature = re.compile("^(.*)__(lag_\d+)$").findall(self.input_features[0])[0][0] + feature = re.compile(r"^(.*)__(lag_\d+)$").findall(self.input_features[0])[0][0] except IndexError: raise ValueError( "There is no lags in data['raw_ts_X']! Make sure that you initialize LastKnownNormalizer AFTER LagTransformer!" @@ -379,7 +378,7 @@ def generate(self, data: dict) -> dict: last_lag_idx_by_feature = {} feature_by_idx = {} for i, column in enumerate(self.input_features): - feature, lag_suffix = re.compile("^(.*)__(lag_\d+)$").findall(column)[0] + feature, lag_suffix = re.compile(r"^(.*)__(lag_\d+)$").findall(column)[0] feature_by_idx[i] = feature if lag_suffix == self.last_lag_substring: last_lag_idx_by_feature[feature] = i From aef23bab02b350b5ee211ec14e61330a2aece296 Mon Sep 17 00:00:00 2001 From: Aleksandr Yugay Date: Mon, 20 Jan 2025 15:55:20 +0300 Subject: [PATCH 40/50] delete TimesNet --- tsururu/models/__init__.py | 4 +- tsururu/models/torch_based/times_net.py | 205 ------------------------ 2 files changed, 1 insertion(+), 208 deletions(-) delete mode 100644 tsururu/models/torch_based/times_net.py diff --git a/tsururu/models/__init__.py b/tsururu/models/__init__.py index bf03385..99865e7 100644 --- a/tsururu/models/__init__.py +++ b/tsururu/models/__init__.py @@ -4,7 +4,6 @@ from .boost import CatBoost from .torch_based.dlinear import DLinear_NN from .torch_based.patch_tst import PatchTST_NN -from .torch_based.times_net import TimesNet_NN # Factory Object @@ -14,7 +13,6 @@ def __init__(self): "CatBoost": CatBoost, "DLinear_NN": DLinear_NN, "PatchTST_NN": PatchTST_NN, - "TimesNet_NN": TimesNet_NN, } def get_allowed(self): @@ -30,4 +28,4 @@ def create_model(self, model_name, model_params): return self.models[model_name](**model_params) -__all__ = ["CatBoost", "Estimator", "ModelsFactory", "DLinear_NN", "PatchTST_NN", "TimesNet_NN"] +__all__ = ["CatBoost", "Estimator", "ModelsFactory", "DLinear_NN", "PatchTST_NN"] diff --git a/tsururu/models/torch_based/times_net.py b/tsururu/models/torch_based/times_net.py deleted file mode 100644 index 851e365..0000000 --- a/tsururu/models/torch_based/times_net.py +++ /dev/null @@ -1,205 +0,0 @@ -"""TimesNet neural network for time series forecasting.""" - -from typing import Tuple - -import numpy as np - - -from .layers.convolution import Inception_Block_V1 -from .layers.embedding import DataEmbedding - -try: - import torch - import torch.fft - import torch.nn as nn - import torch.nn.functional as F -except ImportError: - torch = None - nn = None - torch.fft = None - F = None - - -def FFT_for_Period(x: torch.Tensor, k: int = 2) -> Tuple[np.ndarray, torch.Tensor]: - """Compute the FFT for the input tensor and find the top-k periods. - - Args: - x: input tensor of shape [B, T, C]. - k: number of top periods to return. Default is 2. - - Returns: - Tuple containing: - - array of top-k periods - - tensor of top-k period weights - - """ - # [B, T, C] - xf = torch.fft.rfft(x, dim=1) - # find period by amplitudes - frequency_list = abs(xf).mean(0).mean(-1) - frequency_list[0] = 0 - _, top_list = torch.topk(frequency_list, k) - top_list = top_list.detach().cpu().numpy() - period = x.shape[1] // top_list - - return period, abs(xf).mean(-1)[:, top_list] - - -class TimesBlock(nn.Module): - """TimesBlock module for time series forecasting. - - Args: - seq_len: length of the input sequence. - pred_len: length of the prediction sequence. - top_k: number of top periods to consider. - d_model: dimension of the model. - d_ff: dimension of the feed-forward network. - num_kernels: number of kernels for convolutional layers. - - """ - - def __init__( - self, seq_len: int, pred_len: int, top_k: int, d_model: int, d_ff: int, num_kernels: int - ): - super(TimesBlock, self).__init__() - - self.seq_len = seq_len - self.pred_len = pred_len - self.k = top_k - - self.conv = nn.Sequential( - Inception_Block_V1(d_model, d_ff, num_kernels=num_kernels), - nn.GELU(), - Inception_Block_V1(d_ff, d_model, num_kernels=num_kernels), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """Forward pass for the TimesBlock module. - - Args: - x: input tensor of shape [B, T, N]. - - Returns: - Output tensor of shape [B, T, N]. - - """ - B, T, N = x.size() - period_list, period_weight = FFT_for_Period(x, self.k) - - res = [] - for i in range(self.k): - period = period_list[i] - if (self.seq_len + self.pred_len) % period != 0: - length = (((self.seq_len + self.pred_len) // period) + 1) * period - padding = torch.zeros([B, (length - (self.seq_len + self.pred_len)), N]).to( - x.device - ) - out = torch.cat([x, padding], dim=1) - else: - length = self.seq_len + self.pred_len - out = x - out = out.reshape(B, length // period, period, N).permute(0, 3, 1, 2).contiguous() - out = self.conv(out) - out = out.permute(0, 2, 3, 1).reshape(B, -1, N) - res.append(out[:, : (self.seq_len + self.pred_len), :]) - res = torch.stack(res, dim=-1) - period_weight = F.softmax(period_weight, dim=1) - period_weight = period_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1) - res = torch.sum(res * period_weight, -1) - res = res + x - - return res - - -class TimesNet_NN(nn.Module): - """TimesNet neural network for time series forecasting. - - Args: - seq_len: length of the input sequence. - pred_len: length of the prediction sequence. - e_layers: number of encoder layers. Default is 2. - enc_in: number of input channels. Default is 7. - d_model: dimension of the model. Default is 512. - d_ff: dimension of the feed-forward network. Default is 2048. - num_kernels: number of kernels for convolutional layers. Default is 6. - top_k: number of top periods to consider. Default is 5. - c_out: number of output channels. Default is 7. - dropout: dropout rate. Default is 0.1. - embed: type of embedding. Default is "timeF". - freq: frequency of the data. Default is "h". - - """ - - def __init__( - self, - seq_len: int, - pred_len: int, - e_layers: int = 2, - enc_in: int = 7, - d_model: int = 512, - d_ff: int = 2048, - num_kernels: int = 6, - top_k: int = 5, - c_out: int = 7, - dropout: float = 0.1, - embed: str = "timeF", - freq: str = "h", - ): - super(TimesNet_NN, self).__init__() - - self.seq_len = seq_len - self.pred_len = pred_len - self.e_layers = e_layers - self.d_model = d_model - self.d_ff = d_ff - self.num_kernels = num_kernels - self.top_k = top_k - self.c_out = c_out - self.dropout = dropout - self.embed = embed - self.freq = freq - - self.model = nn.ModuleList( - [ - TimesBlock(seq_len, pred_len, top_k, d_model, d_ff, num_kernels) - for _ in range(self.e_layers) - ] - ) - - self.enc_embedding = DataEmbedding(enc_in, d_model, embed, freq, dropout) - self.layer_norm = nn.LayerNorm(d_model) - self.predict_linear = nn.Linear(seq_len, pred_len + seq_len) - self.projection = nn.Linear(d_model, c_out, bias=True) - - def forward(self, x_enc: torch.Tensor, x_mark_enc: torch.Tensor) -> torch.Tensor: - """Forward pass for the TimesNet neural network. - - Args: - x_enc: input tensor of shape [B, T, C]. - x_mark_enc: time features tensor of shape [B, T, F]. - - Returns: - Output tensor of shape [B, pred_len, C]. - - """ - means = x_enc.mean(1, keepdim=True).detach() - x_enc = x_enc - means - stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) - x_enc /= stdev - - enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C] - enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(0, 2, 1) - - for i in range(self.e_layers): - enc_out = self.layer_norm(self.model) - - dec_out = self.projection(enc_out) - - dec_out = dec_out * ( - stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1) - ) - dec_out = dec_out + ( - means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len + self.seq_len, 1) - ) - - return dec_out[:, -self.pred_len :, :] # [B, L, D] From 821eed6f9af0c897b48508c86bfed20e058a0231 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 23 Jan 2025 22:35:37 +0300 Subject: [PATCH 41/50] fix pandas version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 6e69ba4..b8b409d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ classifiers = [ python = ">=3.9, <3.12" scikit-learn = "^1.3.2" numpy = "^1.26.3" -pandas = "^2.1.4" +pandas = "^2.2" holidays = "^0.40" scipy = "^1.11.4" catboost = { version = ">=1.2.0", optional = true } From 5f08714d2e2adacfb4f6848d7f4a6c16619d6145 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 23 Jan 2025 22:36:09 +0300 Subject: [PATCH 42/50] add autotyping for target, date, id columns --- tsururu/dataset/dataset.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index b7523b6..1742642 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -43,6 +43,11 @@ class TSDataset: """ + def _auto_type_columns(self, columns_params, column_name, default_type): + if columns_params.get(column_name, None) is not None: + if columns_params[column_name].get("type", None) is None: + columns_params[column_name]["type"] = default_type + def _check_single_column(self): """Check that `target`, `id`, `date` columns contains only one column. @@ -73,11 +78,11 @@ def _check_regular(self, print_freq_period_info): if print_freq_period_info: logger.info(info) - + # Try to reconstruct regular data min_data = self.data.min() max_data = self.data.max() - + reconstructed_data = pd.date_range( start=min_data[self.date_column], end=max_data[self.date_column], @@ -85,9 +90,8 @@ def _check_regular(self, print_freq_period_info): ) reconstructed_data = np.tile(reconstructed_data, ts_count) - if ( - reconstructed_data.shape[0] != self.data.shape[0] - or not np.all(reconstructed_data == self.data[self.date_column].values) + if reconstructed_data.shape[0] != self.data.shape[0] or not np.all( + reconstructed_data == self.data[self.date_column].values ): logger.warning( f""" @@ -105,6 +109,10 @@ def __init__( print_freq_period_info: bool = True, ): # Columns typing + self._auto_type_columns(columns_params, "date", "datetime") + self._auto_type_columns(columns_params, "id", "categorical") + self._auto_type_columns(columns_params, "target", "continious") + for _, role_dict in columns_params.items(): column_name = role_dict["columns"][0] column_type = role_dict["type"] From feeabe595ce1b8f53621c805fe561bbe5b58270b Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 23 Jan 2025 23:17:51 +0300 Subject: [PATCH 43/50] add Quick_tour, Preprocessing, License parts and some small fixes to README --- README.md | 88 +++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 69 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index fe8ed21..a5fb373 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,70 @@ ![tsururu_logo](imgs/tsururu_logo.png) -# Tsururu – a time series forecasting strategies framework +# Tsururu (TSForesight) – a time series forecasting strategies framework -Much attention is now paid to what models to use for time series forecasting, but not to how exactly to perform training and inference. +Tsururu is a Python-based library that provides a wide range of multi-series and multi-point-ahead prediction strategies, compatible with any underlying model, including neural networks. -Tsururu is a Python-based library which aims at overcoming the aforementioned problems and provides a large number of multi-series and multi-point-ahead prediction strategies that can be used with any underlying model, including neural networks. +While much attention is currently focused on selecting models for time series forecasting, the crucial aspect of how to perform training and inference often goes overlooked. Tsururu aims to address this gap. Also tsururu provides various preprocessing techniques. + +## Quick tour + +```python +from tsururu.dataset import Pipeline, TSDataset +from tsururu.model_training.trainer import MLTrainer +from tsururu.model_training.validator import KFoldCrossValidator +from tsururu.models.boost import CatBoost +from tsururu.strategies import RecursiveStrategy + +dataset_params = { + "target": {"columns": ["value"]}, + "date": {"columns": ["date"]}, + "id": {"columns": ["id"]}, +} + +dataset = TSDataset( + data=pd.read_csv(df_path), + columns_params=dataset_params, +) + +pipeline = Pipeline.easy_setup( + dataset_params, {"target_lags": 3, "date_lags": 1}, multivariate=False +) +trainer = MLTrainer(model=CatBoost, validator=KFoldCrossValidator) +strategy = RecursiveStrategy(horizon=3, history=7, trainer=trainer, pipeline=pipeline) + +fit_time, _ = strategy.fit(dataset) +forecast_time, current_pred = strategy.predict(dataset) +``` + + +## Installation +To install Tsururu on your machine from PyPI: +```bash +# Base functionality: +pip install -U tsururu + +# For partial installation use corresponding option +# Extra dependencies: [catboost, torch] or use 'all' to install all dependencies +pip install -U tsururu[catboost] +``` + + +## Other tutorials and examples + +* [Tutorial_1_Quick_Start](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_1_Quick_start.ipynb) for simple usage examples +* [Tutorial_2_Strategies](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_2_Strategies.ipynb) covers forecasting strategies. +* [Tutorial_3_Transformers_and_Pipeline](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_3_Transformers_and_Pipeline.ipynb) provides a description of available data preprocessing techniques. +* [Tutorial_4_Neural_Networks](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_4_Neural_Networks.ipynb) demonstrates working with neural networks. +* [Example_1_All_configurations](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Example_1_All_configurations.py) script for benchmarking multiple configurations from available strategies, models and preprocessing methods on a dataset. + + ## Multi-series prediction strategies: - _Local-modelling_: - An individual model for each time series. - - Each time series as independent from others. + - Each time series is independent from others. - _Global-modelling_: - A single model for all time series. - Features created from each series do not overlap with other series. Series are related but modeled separately. @@ -37,21 +90,18 @@ Also tsururu provides various preprocessing techniques. - A hybrid of Direct and MIMO. One model is trained, but Direct’s features are deployed across the forecast horizon. - Note 1: To use FlatWideMIMO with date-related features, h lags of them must be included (with help of LagTransformer). -## Installation - -To install tsururu via pip you can use: - -`pip install -U tsururu` -## Quick tour +## Preprocessing +- _StandardScalerTransformer_: scales features to have zero mean and unit variance. +- _DifferenceNormalizer_: transforms features by subtracting or dividing by their previous value. +- _TimeToNumGenerator_ and _DateSeasonsGenerator_: generates seasonal features (e.g., month, quarter, day of the week) from date information. +- _LabelEncodingTransformer_ and _OneHotEncodingTransformer_: encodes categorical features. +- _MissingValuesImputer_: handles missing values by imputing them with a chosen strategy. +- _LagTransformer_: generates for lags. +- _LastKnownNormalizer_: normalizes lagged features by the last known value in history, either by subtracting it or dividing by it. -For usage example please see: + +# License +This project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/sb-ai-lab/tsururu/blob/master/LICENSE) file for more details. -* [Tutorial_1_Quick_Start](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_1_Quick_start.ipynb) for simple usage examples - -## Other tutorials and examples - -* [Tutorial_2_Strategies](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_2_Strategies.ipynb) covers forecasting strategies. -* [Tutorial_3_Transformers_and_Pipeline](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_3_Transformers_and_Pipeline.ipynb) provides a description of available data preprocessing techniques. -* [Tutorial_4_Neural_Networks](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Tutorial_4_Neural_Networks.ipynb) demonstrates working with neural networks. -* [Example_1_All_configurations](https://github.com/sb-ai-lab/tsururu/blob/main/examples/Example_1_All_configurations.py) script for benchmarking multiple configurations from available strategies, models and preprocessing methods on a dataset. +[Back to top](#toc) \ No newline at end of file From f2cdd58b4a78fd008bb940a69bc43e9871191f96 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 23 Jan 2025 23:18:43 +0300 Subject: [PATCH 44/50] add empty dict as deafult model_params value in mltrainer --- tsururu/model_training/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tsururu/model_training/trainer.py b/tsururu/model_training/trainer.py index 7571ee5..7de3e6c 100644 --- a/tsururu/model_training/trainer.py +++ b/tsururu/model_training/trainer.py @@ -41,7 +41,7 @@ class MLTrainer: def __init__( self, model: Estimator, - model_params: Dict, + model_params: Dict = {}, validator: Optional[Validator] = None, validation_params: Dict = {}, ): From f6251afede09296199baed36c5346a00a0e0fac8 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Thu, 23 Jan 2025 23:19:50 +0300 Subject: [PATCH 45/50] add another way to initialize pipeline and sliding window validation and fix backtest example (Tutorial_1_Quick_start.ipynb) --- examples/Tutorial_1_Quick_start.ipynb | 920 +++++++++++++++++++++----- 1 file changed, 744 insertions(+), 176 deletions(-) diff --git a/examples/Tutorial_1_Quick_start.ipynb b/examples/Tutorial_1_Quick_start.ipynb index 775e651..2c8228a 100644 --- a/examples/Tutorial_1_Quick_start.ipynb +++ b/examples/Tutorial_1_Quick_start.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -261,13 +261,75 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "pipeline = Pipeline.easy_setup(dataset_params, pipeline_easy_params, multivariate=False)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_params = {\n", + " \"target\": {\n", + " \"columns\": [\"value\"],\n", + " \"features\": {\n", + " \"StandardScalerTransformer\":\n", + " {\n", + " \"transform_target\": True, \n", + " \"transform_features\": True\n", + " },\n", + " \"LagTransformer\": {\"lags\": 7},\n", + " },\n", + " },\n", + " \"date\": {\n", + " \"columns\": [\"date\"],\n", + " \"features\": {\n", + " \"DateSeasonsGenerator\": {\n", + " # Use seasonality features from the date column as \n", + " # features with datetime lags\n", + " # Possible values: [\n", + " # \"y\": year, \"m\": month, \"d\": day, \n", + " # \"wd\": weekday, \"doy\": dayofyear,\n", + " # \"hour\": hour, \"min\": minute, \"sec\": second, \n", + " # \"ms\": microsecond, \"ns\": nanosecond\n", + " # ]\n", + " \"seasonalities\": ['doy', 'm', 'wd'], \n", + " # Use date from target point to make datetime features\n", + " \"from_target_date\": True,\n", + " },\n", + " \"LagTransformer\": {\"lags\": 3}\n", + " },\n", + " },\n", + " \"id\": {\n", + " \"columns\": [\"id\"],\n", + " \"features\": {\n", + " \"LagTransformer\": {\"lags\": 1},\n", + " },\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline = Pipeline.from_dict(pipeline_params, multivariate=False)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -286,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -392,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -402,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -411,27 +473,27 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0:\tlearn: 0.9603852\ttest: 0.9667066\tbest: 0.9667066 (0)\ttotal: 53.5ms\tremaining: 53.5s\n", - "500:\tlearn: 0.0043385\ttest: 0.0045465\tbest: 0.0045465 (500)\ttotal: 482ms\tremaining: 481ms\n", - "999:\tlearn: 0.0027344\ttest: 0.0030471\tbest: 0.0030471 (999)\ttotal: 910ms\tremaining: 0us\n", - "bestTest = 0.003047062205\n", + "0:\tlearn: 0.9606080\ttest: 0.9667407\tbest: 0.9667407 (0)\ttotal: 4.58ms\tremaining: 4.58s\n", + "500:\tlearn: 0.0051947\ttest: 0.0053699\tbest: 0.0053699 (500)\ttotal: 611ms\tremaining: 608ms\n", + "999:\tlearn: 0.0031608\ttest: 0.0033676\tbest: 0.0033676 (999)\ttotal: 1.41s\tremaining: 0us\n", + "bestTest = 0.003367620955\n", "bestIteration = 999\n", - "Fold 0. Score: 0.003047062205400275\n", - "0:\tlearn: 0.9660362\ttest: 0.9613700\tbest: 0.9613700 (0)\ttotal: 1.22ms\tremaining: 1.22s\n", - "500:\tlearn: 0.0043259\ttest: 0.0046427\tbest: 0.0046427 (500)\ttotal: 430ms\tremaining: 428ms\n", - "999:\tlearn: 0.0026124\ttest: 0.0029169\tbest: 0.0029169 (999)\ttotal: 973ms\tremaining: 0us\n", - "bestTest = 0.002916854444\n", + "Fold 0. Score: 0.0033676209549416128\n", + "0:\tlearn: 0.9659554\ttest: 0.9614093\tbest: 0.9614093 (0)\ttotal: 1.3ms\tremaining: 1.3s\n", + "500:\tlearn: 0.0052698\ttest: 0.0054766\tbest: 0.0054766 (500)\ttotal: 664ms\tremaining: 662ms\n", + "999:\tlearn: 0.0031515\ttest: 0.0033391\tbest: 0.0033391 (999)\ttotal: 1.21s\tremaining: 0us\n", + "bestTest = 0.003339095317\n", "bestIteration = 999\n", - "Fold 1. Score: 0.002916854443892958\n", - "Mean score: 0.003\n", - "Std: 0.0001\n" + "Fold 1. Score: 0.0033390953168007786\n", + "Mean score: 0.0034\n", + "Std: 0.0\n" ] } ], @@ -441,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -458,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -492,181 +554,181 @@ " 0\n", " 0\n", " 2022-09-27\n", - " 1997.088714\n", + " 1992.837874\n", " \n", " \n", " 1\n", " 0\n", " 2022-09-28\n", - " 1997.191979\n", + " 1993.026917\n", " \n", " \n", " 2\n", " 0\n", " 2022-09-29\n", - " 1995.496356\n", + " 1981.524299\n", " \n", " \n", " 3\n", " 1\n", " 2022-09-27\n", - " 2997.475196\n", + " 2993.408144\n", " \n", " \n", " 4\n", " 1\n", " 2022-09-28\n", - " 2997.553282\n", + " 2993.582053\n", " \n", " \n", " 5\n", " 1\n", " 2022-09-29\n", - " 2995.826568\n", + " 2982.066608\n", " \n", " \n", " 6\n", " 2\n", " 2022-09-27\n", - " 3997.392796\n", + " 3993.473395\n", " \n", " \n", " 7\n", " 2\n", " 2022-09-28\n", - " 3997.46765\n", + " 3993.639452\n", " \n", " \n", " 8\n", " 2\n", " 2022-09-29\n", - " 3995.743526\n", + " 3982.13657\n", " \n", " \n", " 9\n", " 3\n", " 2022-09-27\n", - " 4997.548721\n", + " 4993.459173\n", " \n", " \n", " 10\n", " 3\n", " 2022-09-28\n", - " 4997.572455\n", + " 4993.613307\n", " \n", " \n", " 11\n", " 3\n", " 2022-09-29\n", - " 4995.842271\n", + " 4982.113216\n", " \n", " \n", " 12\n", " 4\n", " 2022-09-27\n", - " 5997.700033\n", + " 5993.457714\n", " \n", " \n", " 13\n", " 4\n", " 2022-09-28\n", - " 5997.725905\n", + " 5993.606981\n", " \n", " \n", " 14\n", " 4\n", " 2022-09-29\n", - " 5995.998695\n", + " 5982.113868\n", " \n", " \n", " 15\n", " 5\n", " 2022-09-27\n", - " 6997.694582\n", + " 6993.463906\n", " \n", " \n", " 16\n", " 5\n", " 2022-09-28\n", - " 6997.722867\n", + " 6993.614611\n", " \n", " \n", " 17\n", " 5\n", " 2022-09-29\n", - " 6995.997412\n", + " 6982.121499\n", " \n", " \n", " 18\n", " 6\n", " 2022-09-27\n", - " 7997.620737\n", + " 7993.419185\n", " \n", " \n", " 19\n", " 6\n", " 2022-09-28\n", - " 7997.648131\n", + " 7993.569889\n", " \n", " \n", " 20\n", " 6\n", " 2022-09-29\n", - " 7995.923171\n", + " 7982.076777\n", " \n", " \n", " 21\n", " 7\n", " 2022-09-27\n", - " 8997.53187\n", + " 8993.367361\n", " \n", " \n", " 22\n", " 7\n", " 2022-09-28\n", - " 8997.559264\n", + " 8993.518065\n", " \n", " \n", " 23\n", " 7\n", " 2022-09-29\n", - " 8995.845535\n", + " 8982.024953\n", " \n", " \n", " 24\n", " 8\n", " 2022-09-27\n", - " 9997.5219\n", + " 9993.261674\n", " \n", " \n", " 25\n", " 8\n", " 2022-09-28\n", - " 9997.546915\n", + " 9993.413249\n", " \n", " \n", " 26\n", " 8\n", " 2022-09-29\n", - " 9995.828639\n", + " 9981.921657\n", " \n", " \n", " 27\n", " 9\n", " 2022-09-27\n", - " 10997.380235\n", + " 10993.141321\n", " \n", " \n", " 28\n", " 9\n", " 2022-09-28\n", - " 10997.403622\n", + " 10993.292934\n", " \n", " \n", " 29\n", " 9\n", " 2022-09-29\n", - " 10995.683994\n", + " 10981.801303\n", " \n", " \n", "\n", @@ -674,39 +736,39 @@ ], "text/plain": [ " id date value\n", - "0 0 2022-09-27 1997.088714\n", - "1 0 2022-09-28 1997.191979\n", - "2 0 2022-09-29 1995.496356\n", - "3 1 2022-09-27 2997.475196\n", - "4 1 2022-09-28 2997.553282\n", - "5 1 2022-09-29 2995.826568\n", - "6 2 2022-09-27 3997.392796\n", - "7 2 2022-09-28 3997.46765\n", - "8 2 2022-09-29 3995.743526\n", - "9 3 2022-09-27 4997.548721\n", - "10 3 2022-09-28 4997.572455\n", - "11 3 2022-09-29 4995.842271\n", - "12 4 2022-09-27 5997.700033\n", - "13 4 2022-09-28 5997.725905\n", - "14 4 2022-09-29 5995.998695\n", - "15 5 2022-09-27 6997.694582\n", - "16 5 2022-09-28 6997.722867\n", - "17 5 2022-09-29 6995.997412\n", - "18 6 2022-09-27 7997.620737\n", - "19 6 2022-09-28 7997.648131\n", - "20 6 2022-09-29 7995.923171\n", - "21 7 2022-09-27 8997.53187\n", - "22 7 2022-09-28 8997.559264\n", - "23 7 2022-09-29 8995.845535\n", - "24 8 2022-09-27 9997.5219\n", - "25 8 2022-09-28 9997.546915\n", - "26 8 2022-09-29 9995.828639\n", - "27 9 2022-09-27 10997.380235\n", - "28 9 2022-09-28 10997.403622\n", - "29 9 2022-09-29 10995.683994" + "0 0 2022-09-27 1992.837874\n", + "1 0 2022-09-28 1993.026917\n", + "2 0 2022-09-29 1981.524299\n", + "3 1 2022-09-27 2993.408144\n", + "4 1 2022-09-28 2993.582053\n", + "5 1 2022-09-29 2982.066608\n", + "6 2 2022-09-27 3993.473395\n", + "7 2 2022-09-28 3993.639452\n", + "8 2 2022-09-29 3982.13657\n", + "9 3 2022-09-27 4993.459173\n", + "10 3 2022-09-28 4993.613307\n", + "11 3 2022-09-29 4982.113216\n", + "12 4 2022-09-27 5993.457714\n", + "13 4 2022-09-28 5993.606981\n", + "14 4 2022-09-29 5982.113868\n", + "15 5 2022-09-27 6993.463906\n", + "16 5 2022-09-28 6993.614611\n", + "17 5 2022-09-29 6982.121499\n", + "18 6 2022-09-27 7993.419185\n", + "19 6 2022-09-28 7993.569889\n", + "20 6 2022-09-29 7982.076777\n", + "21 7 2022-09-27 8993.367361\n", + "22 7 2022-09-28 8993.518065\n", + "23 7 2022-09-29 8982.024953\n", + "24 8 2022-09-27 9993.261674\n", + "25 8 2022-09-28 9993.413249\n", + "26 8 2022-09-29 9981.921657\n", + "27 9 2022-09-27 10993.141321\n", + "28 9 2022-09-28 10993.292934\n", + "29 9 2022-09-29 10981.801303" ] }, - "execution_count": 13, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -722,47 +784,80 @@ "## Backtest validation of pipeline" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Backtest focuses on evaluating models on the most recent horizons with iterative retraining as newer data becomes available. " + ] + }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "freq: Day; period: 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0:\tlearn: 0.9616026\ttest: 0.9653712\tbest: 0.9653712 (0)\ttotal: 2.58ms\tremaining: 2.58s\n", - "500:\tlearn: 0.0042762\ttest: 0.0045139\tbest: 0.0045139 (500)\ttotal: 702ms\tremaining: 699ms\n", - "999:\tlearn: 0.0026729\ttest: 0.0029726\tbest: 0.0029726 (999)\ttotal: 1.13s\tremaining: 0us\n", - "bestTest = 0.002972644218\n", + "freq: Day; period: 1\n", + "0:\tlearn: 0.9618043\ttest: 0.9656878\tbest: 0.9656878 (0)\ttotal: 1.34ms\tremaining: 1.34s\n", + "500:\tlearn: 0.0051787\ttest: 0.0052308\tbest: 0.0052308 (500)\ttotal: 557ms\tremaining: 555ms\n", + "999:\tlearn: 0.0030887\ttest: 0.0032962\tbest: 0.0032962 (999)\ttotal: 1.13s\tremaining: 0us\n", + "bestTest = 0.00329621851\n", + "bestIteration = 999\n", + "Fold 0. Score: 0.0032962185095815415\n", + "0:\tlearn: 0.9647141\ttest: 0.9623340\tbest: 0.9623340 (0)\ttotal: 1.21ms\tremaining: 1.21s\n", + "500:\tlearn: 0.0055104\ttest: 0.0057187\tbest: 0.0057187 (500)\ttotal: 565ms\tremaining: 563ms\n", + "999:\tlearn: 0.0033140\ttest: 0.0035696\tbest: 0.0035696 (999)\ttotal: 1.16s\tremaining: 0us\n", + "bestTest = 0.003569589613\n", + "bestIteration = 999\n", + "Fold 1. Score: 0.003569589613068342\n", + "Mean score: 0.0034\n", + "Std: 0.0001\n", + "freq: Day; period: 1\n", + "freq: Day; period: 1\n", + "0:\tlearn: 0.9635630\ttest: 0.9636501\tbest: 0.9636501 (0)\ttotal: 2.2ms\tremaining: 2.2s\n", + "500:\tlearn: 0.0051732\ttest: 0.0053000\tbest: 0.0053000 (500)\ttotal: 578ms\tremaining: 576ms\n", + "999:\tlearn: 0.0031204\ttest: 0.0032928\tbest: 0.0032928 (999)\ttotal: 1.19s\tremaining: 0us\n", + "bestTest = 0.003292811776\n", "bestIteration = 999\n", - "Fold 0. Score: 0.0029726442175072565\n", - "0:\tlearn: 0.9646266\ttest: 0.9624752\tbest: 0.9624752 (0)\ttotal: 1.52ms\tremaining: 1.52s\n", - "500:\tlearn: 0.0043855\ttest: 0.0045979\tbest: 0.0045979 (500)\ttotal: 429ms\tremaining: 427ms\n", - "999:\tlearn: 0.0026660\ttest: 0.0029718\tbest: 0.0029718 (999)\ttotal: 854ms\tremaining: 0us\n", - "bestTest = 0.002971764957\n", + "Fold 0. Score: 0.0032928117763904234\n", + "0:\tlearn: 0.9632624\ttest: 0.9635496\tbest: 0.9635496 (0)\ttotal: 2.09ms\tremaining: 2.09s\n", + "500:\tlearn: 0.0053458\ttest: 0.0056056\tbest: 0.0056056 (500)\ttotal: 567ms\tremaining: 565ms\n", + "999:\tlearn: 0.0032541\ttest: 0.0035628\tbest: 0.0035628 (999)\ttotal: 1.13s\tremaining: 0us\n", + "bestTest = 0.003562794598\n", "bestIteration = 999\n", - "Fold 1. Score: 0.002971764957214351\n", - "Mean score: 0.003\n", - "Std: 0.0\n", + "Fold 1. Score: 0.003562794597514985\n", + "Mean score: 0.0034\n", + "Std: 0.0001\n", + "freq: Day; period: 1\n", + "freq: Day; period: 1\n", + "0:\tlearn: 0.9672679\ttest: 0.9599529\tbest: 0.9599529 (0)\ttotal: 1.92ms\tremaining: 1.92s\n", + "500:\tlearn: 0.0052718\ttest: 0.0054426\tbest: 0.0054426 (500)\ttotal: 620ms\tremaining: 617ms\n", + "999:\tlearn: 0.0030843\ttest: 0.0033111\tbest: 0.0033111 (999)\ttotal: 1.19s\tremaining: 0us\n", + "bestTest = 0.003311056772\n", + "bestIteration = 999\n", + "Fold 0. Score: 0.003311056771697718\n", + "0:\tlearn: 0.9591189\ttest: 0.9679140\tbest: 0.9679140 (0)\ttotal: 1.78ms\tremaining: 1.78s\n", + "500:\tlearn: 0.0053303\ttest: 0.0056656\tbest: 0.0056656 (500)\ttotal: 572ms\tremaining: 570ms\n", + "999:\tlearn: 0.0031389\ttest: 0.0034467\tbest: 0.0034467 (999)\ttotal: 1.15s\tremaining: 0us\n", + "bestTest = 0.003446668742\n", + "bestIteration = 999\n", + "Fold 1. Score: 0.003446668741714999\n", + "Mean score: 0.0034\n", + "Std: 0.0001\n", "freq: Day; period: 1\n" ] } ], "source": [ - "ids, test, pred, fit_time, forecast_time = strategy.back_test(dataset, cv=1)" + "ids, test, pred, fit_time, forecast_time = strategy.back_test(dataset, cv=3)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -789,234 +884,707 @@ " y_true_1\n", " y_pred_1\n", " id_1\n", + " y_true_2\n", + " y_pred_2\n", + " id_2\n", + " y_true_3\n", + " y_pred_3\n", + " id_3\n", " \n", " \n", " \n", " \n", " 0\n", " 1997.0\n", - " 1993.68704\n", + " 1993.466612\n", + " 0\n", + " 1994.0\n", + " 1989.660865\n", + " 0\n", + " 1991.0\n", + " 1985.570009\n", " 0\n", " \n", " \n", " 1\n", " 1998.0\n", - " 1994.202883\n", + " 1993.89183\n", + " 0\n", + " 1995.0\n", + " 1989.906115\n", + " 0\n", + " 1992.0\n", + " 1986.309039\n", " 0\n", " \n", " \n", " 2\n", " 1999.0\n", - " 1994.669445\n", + " 1994.653188\n", + " 0\n", + " 1996.0\n", + " 1990.068356\n", + " 0\n", + " 1993.0\n", + " 1984.264202\n", " 0\n", " \n", " \n", " 3\n", " 2997.0\n", - " 2993.991901\n", + " 2994.040063\n", + " 1\n", + " 2994.0\n", + " 2990.189639\n", + " 1\n", + " 2991.0\n", + " 2986.108931\n", " 1\n", " \n", " \n", " 4\n", " 2998.0\n", - " 2994.586288\n", + " 2994.45766\n", + " 1\n", + " 2995.0\n", + " 2990.460632\n", + " 1\n", + " 2992.0\n", + " 2986.8504\n", " 1\n", " \n", " \n", " 5\n", " 2999.0\n", - " 2995.082926\n", + " 2995.221471\n", + " 1\n", + " 2996.0\n", + " 2990.630855\n", + " 1\n", + " 2993.0\n", + " 2984.845248\n", " 1\n", " \n", " \n", " 6\n", " 3997.0\n", - " 3993.914717\n", + " 3994.116753\n", + " 2\n", + " 3994.0\n", + " 3990.252623\n", + " 2\n", + " 3991.0\n", + " 3986.186164\n", " 2\n", " \n", " \n", " 7\n", " 3998.0\n", - " 3994.509697\n", + " 3994.53536\n", + " 2\n", + " 3995.0\n", + " 3990.536104\n", + " 2\n", + " 3992.0\n", + " 3986.91842\n", " 2\n", " \n", " \n", " 8\n", " 3999.0\n", - " 3995.015013\n", + 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9990.335604\n", + " 8\n", + " 9992.0\n", + " 9986.709841\n", " 8\n", " \n", " \n", " 26\n", " 9999.0\n", - " 9995.1378\n", + " 9995.078784\n", + " 8\n", + " 9996.0\n", + " 9990.505597\n", + " 8\n", + " 9993.0\n", + " 9984.701365\n", " 8\n", " \n", " \n", " 27\n", " 10997.0\n", - " 10993.687397\n", + " 10993.797982\n", + " 9\n", + " 10994.0\n", + " 10989.921308\n", + " 9\n", + " 10991.0\n", + " 10985.872866\n", " 9\n", " \n", " \n", " 28\n", " 10998.0\n", - " 10994.270971\n", + " 10994.218889\n", + " 9\n", + " 10995.0\n", + " 10990.215972\n", + " 9\n", + " 10992.0\n", + " 10986.59057\n", " 9\n", " \n", " \n", " 29\n", " 10999.0\n", - " 10994.990313\n", + " 10994.958791\n", + " 9\n", + " 10996.0\n", + " 10990.385965\n", + " 9\n", + " 10993.0\n", + " 10984.582094\n", + " 9\n", + " \n", + " \n", + "\n", + "" + ], + "text/plain": [ + " y_true_1 y_pred_1 id_1 y_true_2 y_pred_2 id_2 y_true_3 \\\n", + "0 1997.0 1993.466612 0 1994.0 1989.660865 0 1991.0 \n", + "1 1998.0 1993.89183 0 1995.0 1989.906115 0 1992.0 \n", + "2 1999.0 1994.653188 0 1996.0 1990.068356 0 1993.0 \n", + "3 2997.0 2994.040063 1 2994.0 2990.189639 1 2991.0 \n", + "4 2998.0 2994.45766 1 2995.0 2990.460632 1 2992.0 \n", + "5 2999.0 2995.221471 1 2996.0 2990.630855 1 2993.0 \n", + "6 3997.0 3994.116753 2 3994.0 3990.252623 2 3991.0 \n", + "7 3998.0 3994.53536 2 3995.0 3990.536104 2 3992.0 \n", + "8 3999.0 3995.289903 2 3996.0 3990.707172 2 3993.0 \n", + "9 4997.0 4994.105357 3 4994.0 4990.238486 3 4991.0 \n", + "10 4998.0 4994.523963 3 4995.0 4990.524742 3 4992.0 \n", + "11 4999.0 4995.27642 3 4996.0 4990.69581 3 4993.0 \n", + "12 5997.0 5994.106691 4 5994.0 5990.233471 4 5991.0 \n", + "13 5998.0 5994.526708 4 5995.0 5990.526663 4 5992.0 \n", + "14 5999.0 5995.273601 4 5996.0 5990.69714 4 5993.0 \n", + "15 6997.0 6994.116021 5 6994.0 6990.241056 5 6991.0 \n", + "16 6998.0 6994.536038 5 6995.0 6990.534248 5 6992.0 \n", + "17 6999.0 6995.280723 5 6996.0 6990.70424 5 6993.0 \n", + "18 7997.0 7994.072629 6 7994.0 7990.196603 6 7991.0 \n", + "19 7998.0 7994.492646 6 7995.0 7990.489794 6 7992.0 \n", + "20 7999.0 7995.236135 6 7996.0 7990.659787 6 7993.0 \n", + "21 8997.0 8994.020961 7 8994.0 8990.145089 7 8991.0 \n", + "22 8998.0 8994.440977 7 8995.0 8990.438281 7 8992.0 \n", + "23 8999.0 8995.184466 7 8996.0 8990.608273 7 8993.0 \n", + "24 9997.0 9993.917974 8 9994.0 9990.040901 8 9991.0 \n", + "25 9998.0 9994.338881 8 9995.0 9990.335604 8 9992.0 \n", + "26 9999.0 9995.078784 8 9996.0 9990.505597 8 9993.0 \n", + "27 10997.0 10993.797982 9 10994.0 10989.921308 9 10991.0 \n", + "28 10998.0 10994.218889 9 10995.0 10990.215972 9 10992.0 \n", + "29 10999.0 10994.958791 9 10996.0 10990.385965 9 10993.0 \n", + "\n", + " y_pred_3 id_3 \n", + "0 1985.570009 0 \n", + "1 1986.309039 0 \n", + "2 1984.264202 0 \n", + "3 2986.108931 1 \n", + "4 2986.8504 1 \n", + "5 2984.845248 1 \n", + "6 3986.186164 2 \n", + "7 3986.91842 2 \n", + "8 3984.909912 2 \n", + "9 4986.174836 3 \n", + "10 4986.905019 3 \n", + "11 4984.895818 3 \n", + "12 5986.178833 4 \n", + "13 5986.903486 4 \n", + "14 5984.895642 4 \n", + "15 6986.188108 5 \n", + "16 6986.910565 5 \n", + "17 6984.901779 5 \n", + "18 7986.144976 6 \n", + "19 7986.866245 6 \n", + "20 7984.857459 6 \n", + "21 8986.093618 7 \n", + "22 8986.814888 7 \n", + "23 8984.806101 7 \n", + "24 9985.992136 8 \n", + "25 9986.709841 8 \n", + "26 9984.701365 8 \n", + "27 10985.872866 9 \n", + "28 10986.59057 9 \n", + "29 10984.582094 9 " + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_results(cv=3, regime=\"global\", y_true=test, y_pred=pred, ids=ids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sliding Window Validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sliding window validation is a technique often used in research papers dedicated to time series forecasting. \n", + "\n", + "The testing part is further subdivided using rolling windows, where a “history” window and a “horizon” window are repeatedly created with a fixed step size." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train shape: (7310, 3)\n", + "Test shape: (2690, 3)\n", + "freq: Day; period: 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n" + ] + } + ], + "source": [ + "full_df = pd.read_csv(df_path)\n", + "\n", + "train_df = full_df[full_df[\"date\"] < \"2022-01-01\"]\n", + "test_df = full_df[full_df[\"date\"] >= \"2022-01-01\"]\n", + "\n", + "print(f\"Train shape: {train_df.shape}\")\n", + "print(f\"Test shape: {test_df.shape}\")\n", + "\n", + "train_dataset = TSDataset(\n", + " data=train_df,\n", + " columns_params=dataset_params,\n", + " print_freq_period_info=True,\n", + ")\n", + "\n", + "test_dataset = TSDataset(\n", + " data=test_df,\n", + " columns_params=dataset_params,\n", + " print_freq_period_info=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0:\tlearn: 0.9606080\ttest: 0.9667407\tbest: 0.9667407 (0)\ttotal: 1.15ms\tremaining: 1.15s\n", + "500:\tlearn: 0.0051947\ttest: 0.0053699\tbest: 0.0053699 (500)\ttotal: 558ms\tremaining: 556ms\n", + "999:\tlearn: 0.0031608\ttest: 0.0033676\tbest: 0.0033676 (999)\ttotal: 1.21s\tremaining: 0us\n", + "bestTest = 0.003367620955\n", + "bestIteration = 999\n", + "Fold 0. Score: 0.0033676209549416128\n", + "0:\tlearn: 0.9659554\ttest: 0.9614093\tbest: 0.9614093 (0)\ttotal: 2ms\tremaining: 2s\n", + "500:\tlearn: 0.0052698\ttest: 0.0054766\tbest: 0.0054766 (500)\ttotal: 570ms\tremaining: 568ms\n", + "999:\tlearn: 0.0031515\ttest: 0.0033391\tbest: 0.0033391 (999)\ttotal: 1.14s\tremaining: 0us\n", + "bestTest = 0.003339095317\n", + "bestIteration = 999\n", + "Fold 1. Score: 0.0033390953168007786\n", + "Mean score: 0.0034\n", + "Std: 0.0\n" + ] + } + ], + "source": [ + "fit_time, _ = strategy.fit(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "freq: Day; period: 1\n", + "\n", + " It seems that the data is not regular. Please, check the data and the frequency info. \n", + " For multivariate regime it is critical to have regular data.\n", + " For global regime each regular part of time series will be processed as separate time series. \n", + " \n" + ] + } + ], + "source": [ + "forecast_time, current_pred = strategy.predict(dataset, test_all=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is normal to see this warning when using the sliding window validation." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatevalue
002020-01-081008.4361
102020-01-091008.769569
202020-01-101009.748942
302020-01-091008.769569
402020-01-101009.71512
............
2972592022-09-2410996.789104
2972692022-09-2510997.211277
2972792022-09-2410996.789104
2972892022-09-2510997.211277
2972992022-09-2610997.953405
\n", + "

29730 rows × 3 columns

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" ], "text/plain": [ - " y_true_1 y_pred_1 id_1\n", - "0 1997.0 1993.68704 0\n", - "1 1998.0 1994.202883 0\n", - "2 1999.0 1994.669445 0\n", - "3 2997.0 2993.991901 1\n", - "4 2998.0 2994.586288 1\n", - "5 2999.0 2995.082926 1\n", - "6 3997.0 3993.914717 2\n", - "7 3998.0 3994.509697 2\n", - "8 3999.0 3995.015013 2\n", - "9 4997.0 4994.022114 3\n", - "10 4998.0 4994.615917 3\n", - "11 4999.0 4995.170471 3\n", - "12 5997.0 5994.175996 4\n", - "13 5998.0 5994.779734 4\n", - "14 5999.0 5995.319329 4\n", - "15 6997.0 6994.179421 5\n", - "16 6998.0 6994.784167 5\n", - "17 6999.0 6995.312331 5\n", - "18 7997.0 7994.113358 6\n", - "19 7998.0 7994.705541 6\n", - "20 7999.0 7995.236341 6\n", - "21 8997.0 8993.839156 7\n", - "22 8998.0 8994.431338 7\n", - "23 8999.0 8995.147741 7\n", - "24 9997.0 9993.838821 8\n", - "25 9998.0 9994.4268 8\n", - "26 9999.0 9995.1378 8\n", - "27 10997.0 10993.687397 9\n", - "28 10998.0 10994.270971 9\n", - "29 10999.0 10994.990313 9" + " id date value\n", + "0 0 2020-01-08 1008.4361\n", + "1 0 2020-01-09 1008.769569\n", + "2 0 2020-01-10 1009.748942\n", + "3 0 2020-01-09 1008.769569\n", + "4 0 2020-01-10 1009.71512\n", + "... .. ... ...\n", + "29725 9 2022-09-24 10996.789104\n", + "29726 9 2022-09-25 10997.211277\n", + "29727 9 2022-09-24 10996.789104\n", + "29728 9 2022-09-25 10997.211277\n", + "29729 9 2022-09-26 10997.953405\n", + "\n", + "[29730 rows x 3 columns]" ] }, - "execution_count": 15, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "get_results(cv=1, regime=\"global\", y_true=test, y_pred=pred, ids=ids)" + "current_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that after 2020-01-10 it is 2020-01-09 and this means that sliding window validation is working correctly." ] }, { @@ -1058,7 +1626,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -1083,7 +1651,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -1116,7 +1684,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -1146,7 +1714,7 @@ ], "metadata": { "kernelspec": { - "display_name": "tsururu_test", + "display_name": "py_3_10", "language": "python", "name": "python3" }, @@ -1160,7 +1728,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" }, "orig_nbformat": 4 }, From ce6e0033fd6926edc3ddb72d9e335ec0deeacf17 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Mon, 27 Jan 2025 13:45:44 +0300 Subject: [PATCH 46/50] fix "continuous" --- examples/Example_1_All_configurations.py | 2 +- examples/Tutorial_1_Quick_start.ipynb | 4 ++-- examples/Tutorial_2_Strategies.ipynb | 2 +- examples/Tutorial_3_Transformers_and_Pipeline.ipynb | 2 +- examples/Tutorial_4_Neural_Networks.ipynb | 2 +- tests/test_dataset/test_different_freqs.py | 2 +- tests/test_model_training/test_ml.py | 2 +- tests/test_model_training/test_nn.py | 2 +- tests/test_pipeline/conftest.py | 6 +++--- tests/test_pipeline/test_categorical_encoding.py | 2 +- tests/test_strategies/test_fwm.py | 2 +- tsururu/dataset/dataset.py | 6 +++--- 12 files changed, 17 insertions(+), 17 deletions(-) diff --git a/examples/Example_1_All_configurations.py b/examples/Example_1_All_configurations.py index a9cc994..6a58b84 100644 --- a/examples/Example_1_All_configurations.py +++ b/examples/Example_1_All_configurations.py @@ -187,7 +187,7 @@ def main(): dataset_params = { "target": { "columns": [TARGET_COLUMN], - "type": "continious", + "type": "continuous", }, "date": { "columns": [DATE_COLUMN], diff --git a/examples/Tutorial_1_Quick_start.ipynb b/examples/Tutorial_1_Quick_start.ipynb index 2c8228a..453ea75 100644 --- a/examples/Tutorial_1_Quick_start.ipynb +++ b/examples/Tutorial_1_Quick_start.ipynb @@ -133,7 +133,7 @@ "dataset_params = {\n", " \"target\": {\n", " \"columns\": [\"value\"],\n", - " \"type\": \"continious\",\n", + " \"type\": \"continuous\",\n", " },\n", " \"date\": {\n", " \"columns\": [\"date\"],\n", @@ -1636,7 +1636,7 @@ "dataset_params_2 = {\n", " \"target\": {\n", " \"columns\": [\"value\"],\n", - " \"type\": \"continious\",\n", + " \"type\": \"continuous\",\n", " },\n", " \"date\": {\n", " \"columns\": [\"date\"],\n", diff --git a/examples/Tutorial_2_Strategies.ipynb b/examples/Tutorial_2_Strategies.ipynb index b7310aa..614f258 100644 --- a/examples/Tutorial_2_Strategies.ipynb +++ b/examples/Tutorial_2_Strategies.ipynb @@ -879,7 +879,7 @@ "dataset_params = {\n", " \"target\": {\n", " \"columns\": [\"value\"],\n", - " \"type\": \"continious\",\n", + " \"type\": \"continuous\",\n", " },\n", " \"date\": {\n", " \"columns\": [\"date\"],\n", diff --git a/examples/Tutorial_3_Transformers_and_Pipeline.ipynb b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb index 505cc7a..de7f463 100644 --- a/examples/Tutorial_3_Transformers_and_Pipeline.ipynb +++ b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb @@ -206,7 +206,7 @@ "dataset_params = {\n", " \"target\": {\n", " \"columns\": [\"value\"],\n", - " \"type\": \"continious\",\n", + " \"type\": \"continuous\",\n", " },\n", " \"date\": {\n", " \"columns\": [\"date\"],\n", diff --git a/examples/Tutorial_4_Neural_Networks.ipynb b/examples/Tutorial_4_Neural_Networks.ipynb index 8344634..78daa45 100644 --- a/examples/Tutorial_4_Neural_Networks.ipynb +++ b/examples/Tutorial_4_Neural_Networks.ipynb @@ -99,7 +99,7 @@ "dataset_params = {\n", " \"target\": {\n", " \"columns\": [\"value\"],\n", - " \"type\": \"continious\",\n", + " \"type\": \"continuous\",\n", " },\n", " \"date\": {\n", " \"columns\": [\"date\"],\n", diff --git a/tests/test_dataset/test_different_freqs.py b/tests/test_dataset/test_different_freqs.py index ba5b3a4..a969de1 100644 --- a/tests/test_dataset/test_different_freqs.py +++ b/tests/test_dataset/test_different_freqs.py @@ -12,7 +12,7 @@ DATASET_PARAMS = { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": { "columns": ["date"], diff --git a/tests/test_model_training/test_ml.py b/tests/test_model_training/test_ml.py index ed7ede4..8ddc126 100644 --- a/tests/test_model_training/test_ml.py +++ b/tests/test_model_training/test_ml.py @@ -17,7 +17,7 @@ DATASET_PARAMS = { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": { "columns": ["date"], diff --git a/tests/test_model_training/test_nn.py b/tests/test_model_training/test_nn.py index 41089d5..8aaacc7 100644 --- a/tests/test_model_training/test_nn.py +++ b/tests/test_model_training/test_nn.py @@ -18,7 +18,7 @@ DATASET_PARAMS = { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": { "columns": ["date"], diff --git a/tests/test_pipeline/conftest.py b/tests/test_pipeline/conftest.py index 0529a9a..31e3aff 100644 --- a/tests/test_pipeline/conftest.py +++ b/tests/test_pipeline/conftest.py @@ -28,7 +28,7 @@ def get_dataset(): dataset_params = { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": { "columns": ["date"], @@ -45,7 +45,7 @@ def get_dataset(): "feature_0", "feature_2", ], - "type": "continious", + "type": "continuous", }, "exog_2": { "columns": [ @@ -53,7 +53,7 @@ def get_dataset(): "объем Продаж! (Q3_2023)...анализ#данных", "feature_1", ], - "type": "continious", + "type": "continuous", }, } diff --git a/tests/test_pipeline/test_categorical_encoding.py b/tests/test_pipeline/test_categorical_encoding.py index 30f2108..6f752ed 100644 --- a/tests/test_pipeline/test_categorical_encoding.py +++ b/tests/test_pipeline/test_categorical_encoding.py @@ -12,7 +12,7 @@ DATASET_PARAMS = { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": { "columns": ["date"], diff --git a/tests/test_strategies/test_fwm.py b/tests/test_strategies/test_fwm.py index b4dcc11..1373fc2 100644 --- a/tests/test_strategies/test_fwm.py +++ b/tests/test_strategies/test_fwm.py @@ -16,7 +16,7 @@ DATASET_PARAMS = { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": { "columns": ["date"], diff --git a/tsururu/dataset/dataset.py b/tsururu/dataset/dataset.py index 1742642..88d57db 100644 --- a/tsururu/dataset/dataset.py +++ b/tsururu/dataset/dataset.py @@ -24,7 +24,7 @@ class TSDataset: { "target": { "columns": ["value"], - "type": "continious", + "type": "continuous", }, "date": {...}, "id": {...}, @@ -111,12 +111,12 @@ def __init__( # Columns typing self._auto_type_columns(columns_params, "date", "datetime") self._auto_type_columns(columns_params, "id", "categorical") - self._auto_type_columns(columns_params, "target", "continious") + self._auto_type_columns(columns_params, "target", "continuous") for _, role_dict in columns_params.items(): column_name = role_dict["columns"][0] column_type = role_dict["type"] - if column_type == "continious": + if column_type == "continuous": data[column_name] = data[column_name].astype("float") elif column_type == "datetime": data[column_name] = pd.to_datetime(data[column_name]) From d2a7cdf9555ac11a3707bf8bc168c3cd2bc6e90b Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Mon, 27 Jan 2025 13:46:05 +0300 Subject: [PATCH 47/50] fix readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a5fb373..5b64a04 100644 --- a/README.md +++ b/README.md @@ -64,7 +64,7 @@ pip install -U tsururu[catboost] ## Multi-series prediction strategies: - _Local-modelling_: - An individual model for each time series. - - Each time series is independent from others. + - Each time series is modeled independently of the others. - _Global-modelling_: - A single model for all time series. - Features created from each series do not overlap with other series. Series are related but modeled separately. @@ -97,7 +97,7 @@ pip install -U tsururu[catboost] - _TimeToNumGenerator_ and _DateSeasonsGenerator_: generates seasonal features (e.g., month, quarter, day of the week) from date information. - _LabelEncodingTransformer_ and _OneHotEncodingTransformer_: encodes categorical features. - _MissingValuesImputer_: handles missing values by imputing them with a chosen strategy. -- _LagTransformer_: generates for lags. +- _LagTransformer_: generates lagged features. - _LastKnownNormalizer_: normalizes lagged features by the last known value in history, either by subtracting it or dividing by it. From e4bd3643ede677922556c1cb6ec018092915e143 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Mon, 27 Jan 2025 13:48:47 +0300 Subject: [PATCH 48/50] fix imgs in Tutorial_2 --- examples/Tutorial_2_Strategies.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/Tutorial_2_Strategies.ipynb b/examples/Tutorial_2_Strategies.ipynb index 614f258..6465144 100644 --- a/examples/Tutorial_2_Strategies.ipynb +++ b/examples/Tutorial_2_Strategies.ipynb @@ -10161,7 +10161,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"recursive\"" + "\"recursive\"" ] }, { @@ -15044,7 +15044,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"direct\"" + "\"direct\"" ] }, { @@ -22759,7 +22759,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"flatwidemimo\"" + "\"flatwidemimo\"" ] }, { From 97b603aee44465de32b9697938460ca77fa14e23 Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Mon, 27 Jan 2025 14:09:55 +0300 Subject: [PATCH 49/50] Add tables of contents to tutorials --- examples/Tutorial_1_Quick_start.ipynb | 65 +++++++--- examples/Tutorial_2_Strategies.ipynb | 116 +++++++++++++++--- ...Tutorial_3_Transformers_and_Pipeline.ipynb | 109 +++++++++++++--- examples/Tutorial_4_Neural_Networks.ipynb | 57 ++++++--- 4 files changed, 283 insertions(+), 64 deletions(-) diff --git a/examples/Tutorial_1_Quick_start.ipynb b/examples/Tutorial_1_Quick_start.ipynb index 453ea75..540b70e 100644 --- a/examples/Tutorial_1_Quick_start.ipynb +++ b/examples/Tutorial_1_Quick_start.ipynb @@ -4,7 +4,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction" + "**Table of contents** \n", + "- [Introduction](#toc1_) \n", + "- [Working with Data](#toc2_) \n", + " - [TSDataset](#toc2_1_1_) \n", + " - [Pipeline and Transformers](#toc2_1_2_) \n", + " - [What kind of transformers are there?](#toc2_1_2_1_) \n", + " - [Transformers must be assembled in order!](#toc2_1_2_2_) \n", + " - [How to build a Pipeline?](#toc2_1_2_3_) \n", + " - [Can I use exogenous variables in the pipeline?](#toc2_1_2_4_) \n", + " - [Model, Validator and Trainer](#toc2_1_2_5_) \n", + " - [Strategy](#toc2_1_2_6_) \n", + " - [Backtest validation of pipeline](#toc2_2_) \n", + " - [Sliding Window Validation](#toc2_3_) \n", + " - [Working with raw time series' granularity](#toc2_4_) \n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [Introduction](#toc0_)" ] }, { @@ -98,14 +127,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Working with Data" + "# [Working with Data](#toc0_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### TSDataset" + "### [TSDataset](#toc0_)" ] }, { @@ -148,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -171,7 +200,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Pipeline and Transformers" + "### [Pipeline and Transformers](#toc0_)" ] }, { @@ -192,7 +221,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What kind of transformers are there?" + "#### [What kind of transformers are there?](#toc0_)" ] }, { @@ -223,7 +252,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Transformers must be assembled in order!" + "#### [Transformers must be assembled in order!](#toc0_)" ] }, { @@ -239,7 +268,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### How to build a Pipeline?" + "#### [How to build a Pipeline?](#toc0_)" ] }, { @@ -323,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -334,7 +363,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Can I use exogenous variables in the pipeline?" + "#### [Can I use exogenous variables in the pipeline?](#toc0_)" ] }, { @@ -376,7 +405,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Model, Validator and Trainer" + "#### [Model, Validator and Trainer](#toc0_)" ] }, { @@ -395,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -427,7 +456,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Strategy" + "#### [Strategy](#toc0_)" ] }, { @@ -520,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -781,7 +810,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Backtest validation of pipeline" + "## [Backtest validation of pipeline](#toc0_)" ] }, { @@ -857,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1336,7 +1365,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Sliding Window Validation" + "## [Sliding Window Validation](#toc0_)" ] }, { @@ -1591,7 +1620,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Working with raw time series' granularity" + "## [Working with raw time series' granularity](#toc0_)" ] }, { diff --git a/examples/Tutorial_2_Strategies.ipynb b/examples/Tutorial_2_Strategies.ipynb index 6465144..5340bd0 100644 --- a/examples/Tutorial_2_Strategies.ipynb +++ b/examples/Tutorial_2_Strategies.ipynb @@ -4,7 +4,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction" + "**Table of contents** \n", + "- [Introduction](#toc1_) \n", + "- [Loading Data](#toc2_) \n", + "- [Description of Strategies](#toc3_) \n", + " - [Multi-series prediction strategies: local, global, multivariate](#toc3_1_) \n", + " - [Multi-point-ahead prediction strategies:](#toc3_2_) \n", + " - [MIMO (Multi-input-multi-output)](#toc3_2_1_) \n", + " - [global-modelling](#toc3_2_1_1_) \n", + " - [multivariate-modelling](#toc3_2_1_2_) \n", + " - [local-modelling](#toc3_2_1_3_) \n", + " - [Recursive](#toc3_2_2_) \n", + " - [Reduced](#toc3_2_2_1_) \n", + " - [Recursive + MIMO](#toc3_2_2_2_) \n", + " - [Direct](#toc3_2_3_) \n", + " - [Equal train size](#toc3_2_3_1_) \n", + " - [Direct + MIMO](#toc3_2_3_2_) \n", + " - [FlatWideMIMO](#toc3_2_4_) \n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [Introduction](#toc0_)" ] }, { @@ -92,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -612,7 +644,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Loading Data" + "# [Loading Data](#toc0_)" ] }, { @@ -900,7 +932,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -936,14 +968,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Description of Strategies" + "# [Description of Strategies](#toc0_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Multi-series prediction strategies:" + "## [Multi-series prediction strategies: local, global, multivariate](#toc0_)" ] }, { @@ -1005,7 +1037,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Multi-point-ahead prediction strategies:" + "## [Multi-point-ahead prediction strategies:](#toc0_)" ] }, { @@ -1046,7 +1078,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**MIMO (Multi-input-multi-output)**:\n", + "### [MIMO (Multi-input-multi-output)](#toc0_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "- One model is trained and used for the entire forecast horizon at once. " ] }, @@ -1068,7 +1106,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "__global-modelling__" + "#### [global-modelling](#toc0_)" ] }, { @@ -2147,7 +2185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "__multivariate-modelling__" + "#### [multivariate-modelling](#toc0_)" ] }, { @@ -3108,7 +3146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "__local-modelling__" + "#### [local-modelling](#toc0_)" ] }, { @@ -10151,7 +10189,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Recursive**:\n", + "### [Recursive](#toc0_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "- One model is used for the entire forecast horizon. \n", "- training: The model is trained to predict one point ahead.\n", "- prediction: The model iteratively predicts each point, using previous predictions to update the features in the test data." @@ -12455,6 +12499,13 @@ "You can observe how `raw_ts_X` is progressively filled at each step." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Reduced](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -13541,6 +13592,13 @@ "current_pred.head(10)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Recursive + MIMO](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -15036,7 +15094,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Direct**:\n", + "### [Direct](#toc0_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "- Individual models are trained for each point in the forecast horizon." ] }, @@ -18090,6 +18154,13 @@ "current_pred.head(10)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Equal train size](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -21134,6 +21205,13 @@ "current_pred.head(10)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### [Direct + MIMO](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -22750,7 +22828,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**FlatWideMIMO**:\n", + "### [FlatWideMIMO](#toc0_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "- A hybrid of Direct and MIMO. One model is trained, but Direct’s features are deployed across the forecast horizon.\n", "- Note 1: To use FlatWideMIMO with date-related features, h lags of them must be included (with help of LagTransformer)." ] @@ -23642,7 +23726,7 @@ ], "metadata": { "kernelspec": { - "display_name": "tsururu_test", + "display_name": "py_3_10", "language": "python", "name": "python3" }, @@ -23656,7 +23740,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/examples/Tutorial_3_Transformers_and_Pipeline.ipynb b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb index de7f463..a7ad522 100644 --- a/examples/Tutorial_3_Transformers_and_Pipeline.ipynb +++ b/examples/Tutorial_3_Transformers_and_Pipeline.ipynb @@ -4,7 +4,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction" + "**Table of contents** \n", + "- [Introduction](#toc1_) \n", + "- [Working with Data](#toc2_) \n", + "- [Transformers](#toc3_) \n", + " - [Series2Series](#toc3_1_) \n", + " - [StandardScalerTransformer](#toc3_1_1_) \n", + " - [DifferenceNormalizer](#toc3_1_2_) \n", + " - [TimeToNumGenerator & DateSeasonsGenerator](#toc3_1_3_) \n", + " - [LabelEncodingTransformer & OneHotEncodingTransformer](#toc3_1_4_) \n", + " - [MissingValuesImputer](#toc3_1_5_) \n", + " - [Series2Features](#toc3_2_) \n", + " - [LagTransformer & TargetGenerator](#toc3_2_1_) \n", + " - [Features2Features](#toc3_3_) \n", + " - [LastKnownNormalizer](#toc3_3_1_) \n", + " - [Sequential & Union](#toc3_4_) \n", + "- [Pipeline](#toc4_) \n", + " - [Can I use exogenous variables in the pipeline?](#toc4_1_1_1_) \n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# [Introduction](#toc0_)" ] }, { @@ -50,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -64,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Working with Data" + "# [Working with Data](#toc0_)" ] }, { @@ -490,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -522,7 +554,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Transformers" + "# [Transformers](#toc0_)" ] }, { @@ -536,7 +568,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Series2Series" + "## [Series2Series](#toc0_)" ] }, { @@ -547,6 +579,13 @@ "- During the training phase, they add new columns to this long series. These can be new features derived from the existing data (such as in the case of date-related and id-related feature generators) or normalized values of the existing data." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [StandardScalerTransformer](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -967,6 +1006,13 @@ "We can see that in **raw_ts_X**, the variable `value` has been normalized, while in **raw_ts_y** it remains unchanged." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [DifferenceNormalizer](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1469,6 +1515,13 @@ "Let's import everything we need and define functions to obtain the results." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [TimeToNumGenerator & DateSeasonsGenerator](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1702,6 +1755,13 @@ "display(data[\"raw_ts_X\"].head())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [LabelEncodingTransformer & OneHotEncodingTransformer](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1915,6 +1975,13 @@ "display(data[\"raw_ts_X\"].head())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [MissingValuesImputer](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2137,7 +2204,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2184,7 +2251,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Series2Features" + "## [Series2Features](#toc0_)" ] }, { @@ -2195,6 +2262,13 @@ "- During the feature matrix generation stage, they take the transformed data from the long series and convert it into specific features for the model." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [LagTransformer & TargetGenerator](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2281,7 +2355,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Features2Features" + "## [Features2Features](#toc0_)" ] }, { @@ -2291,6 +2365,13 @@ "- These transformers work directly with the feature matrix generated by the previous transformers. They can make changes to the already existing feature matrix." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [LastKnownNormalizer](#toc0_)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -2308,7 +2389,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -2390,7 +2471,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Sequential & Union" + "## [Sequential & Union](#toc0_)" ] }, { @@ -2410,7 +2491,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Pipeline" + "# [Pipeline](#toc0_)" ] }, { @@ -2594,7 +2675,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2605,7 +2686,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Can I use exogenous variables in the pipeline?" + "#### [Can I use exogenous variables in the pipeline?](#toc0_)" ] }, { diff --git a/examples/Tutorial_4_Neural_Networks.ipynb b/examples/Tutorial_4_Neural_Networks.ipynb index 78daa45..e95e85d 100644 --- a/examples/Tutorial_4_Neural_Networks.ipynb +++ b/examples/Tutorial_4_Neural_Networks.ipynb @@ -1,5 +1,30 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Table of contents** \n", + "- [Initialize TSDataset, Pipeline, Model, Validator, Strategy](#toc1_1_) \n", + " - [TSDataset](#toc1_1_1_) \n", + " - [Pipeline](#toc1_1_2_) \n", + " - [Trainer](#toc1_1_3_) \n", + " - [Strategy](#toc1_1_4_) \n", + "- [Save and load checkpoints](#toc2_) \n", + " - [Save checkpoint](#toc2_1_1_) \n", + " - [Load checkpoint for finetune](#toc2_2_) \n", + " - [Load checkpoint for inference](#toc2_3_) \n", + "\n", + "\n", + "" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -31,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +96,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Initialize TSDataset, Pipeline, Model, Validator, Strategy" + "## [Initialize TSDataset, Pipeline, Model, Validator, Strategy](#toc0_)" ] }, { @@ -85,7 +110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### TSDataset" + "### [TSDataset](#toc0_)" ] }, { @@ -114,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -137,12 +162,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Pipeline" + "### [Pipeline](#toc0_)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Trainer" + "### [Trainer](#toc0_)" ] }, { @@ -172,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -206,7 +231,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Strategy" + "### [Strategy](#toc0_)" ] }, { @@ -540,7 +565,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Save and load checkpoints" + "# [Save and load checkpoints](#toc0_)" ] }, { @@ -552,7 +577,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -589,12 +614,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Save checkpoint" + "### [Save checkpoint](#toc0_)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1928,7 +1953,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Load checkpoint for finetune" + "## [Load checkpoint for finetune](#toc0_)" ] }, { @@ -1976,7 +2001,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -3299,7 +3324,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Load checkpoint for inference" + "## [Load checkpoint for inference](#toc0_)" ] }, { From 7a8f66fa12f9a54ca1ba7f5023ca007b30c0718d Mon Sep 17 00:00:00 2001 From: Alina Kostromina Date: Mon, 27 Jan 2025 15:10:49 +0300 Subject: [PATCH 50/50] fix le / ohe fit & transform behavior + add test on it --- .../test_categorical_encoding.py | 110 +++++++++++++++--- tsururu/transformers/categorical.py | 18 ++- 2 files changed, 112 insertions(+), 16 deletions(-) diff --git a/tests/test_pipeline/test_categorical_encoding.py b/tests/test_pipeline/test_categorical_encoding.py index 6f752ed..6707c98 100644 --- a/tests/test_pipeline/test_categorical_encoding.py +++ b/tests/test_pipeline/test_categorical_encoding.py @@ -54,19 +54,7 @@ def int_to_str(x): return str_map_arr[x] -def test_categorical_encoding(): - df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") - df["id2"] = df["id"] + 2 - df["id"] = df["id"].apply(int_to_str) - df["id2"] = df["id2"].apply(int_to_str) - - dataset = TSDataset( - data=df, - columns_params=DATASET_PARAMS, - print_freq_period_info=False, - ) - pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) - +def get_features_target_idx(dataset): features_idx = index_slicer.create_idx_data( dataset.data, HORIZON, @@ -84,6 +72,25 @@ def test_categorical_encoding(): date_column=dataset.date_column, delta=dataset.delta, ) + + return features_idx, target_idx + + +def test_categorical_encoding(): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + df["id2"] = df["id"] + 2 + df["id"] = df["id"].apply(int_to_str) + df["id2"] = df["id2"].apply(int_to_str) + + dataset = TSDataset( + data=df, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + features_idx, target_idx = get_features_target_idx(dataset) + data = Pipeline.create_data_dict_for_pipeline(dataset, features_idx, target_idx) data = pipeline.fit_transform(data, strategy_name="MIMOStrategy") X, _ = pipeline.generate(data) @@ -124,3 +131,80 @@ def test_categorical_encoding(): ], ) ) + + +def test_transform_method_not_all_categories_in_test(): + df = pd.read_csv("./datasets/global/simulated_data_to_check.csv") + df["id2"] = df["id"] + 2 + df["id"] = df["id"].apply(int_to_str) + df["id2"] = df["id2"].apply(int_to_str) + + # Create train and test datasets + df_train = df[df["date"] < "2022-01-01"] + df_test = df[df["date"] >= "2022-01-01"] + + # Remove id == "a" and id == "c" from test dataset + df_test = df_test[(df_test["id"] != "a") & (df_test["id"] != "c")] + + train_dataset = TSDataset( + data=df_train, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + test_dataset = TSDataset( + data=df_test, + columns_params=DATASET_PARAMS, + print_freq_period_info=False, + ) + + pipeline = Pipeline.from_dict(PIPELINE_PARAMS, multivariate=False) + + features_idx_train, target_idx_train = get_features_target_idx(train_dataset) + features_idx_test, target_idx_test = get_features_target_idx(test_dataset) + + train_data = Pipeline.create_data_dict_for_pipeline( + train_dataset, features_idx_train, target_idx_train + ) + train_data = pipeline.fit_transform(train_data, strategy_name="MIMOStrategy") + X_train, _ = pipeline.generate(train_data) + + test_data = Pipeline.create_data_dict_for_pipeline( + test_dataset, features_idx_test, target_idx_test + ) + test_data = pipeline.transform(test_data) + X_test, _ = pipeline.generate(test_data) + + id__label__lag_0_idx = pipeline.output_features == "id__label__lag_0" + id2__c_ohe__lag_0_idx = pipeline.output_features == "id2__c_ohe__lag_0" + id2__d_ohe__lag_0_idx = pipeline.output_features == "id2__d_ohe__lag_0" + id2__e_ohe__lag_0_idx = pipeline.output_features == "id2__e_ohe__lag_0" + + assert np.all( + np.unique(X_train[:, id__label__lag_0_idx], return_counts=True)[0] + == np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]) + ) + assert np.all( + np.unique(X_test[:, id__label__lag_0_idx], return_counts=True)[0] + == np.array([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]) + ) + + assert np.all( + np.unique(X_train[:, id2__c_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0, 1.0]) + ) + assert np.all( + np.unique(X_test[:, id2__c_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0]) + ) + + assert np.all( + np.unique(X_train[:, id2__d_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0, 1.0]) + ) + assert np.all( + np.unique(X_test[:, id2__d_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0, 1.0]) + ) + + assert np.all( + np.unique(X_train[:, id2__e_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0, 1.0]) + ) + assert np.all( + np.unique(X_test[:, id2__e_ohe__lag_0_idx], return_counts=True)[0] == np.array([0.0]) + ) diff --git a/tsururu/transformers/categorical.py b/tsururu/transformers/categorical.py index bcddda2..8d185d9 100644 --- a/tsururu/transformers/categorical.py +++ b/tsururu/transformers/categorical.py @@ -11,6 +11,10 @@ class LabelEncodingTransformer(FeaturesGenerator): """A transformer that encodes categorical features into integer values.""" + def __init__(self): + super().__init__() + self.les = {} + def fit( self, data: dict, input_features: Optional[Sequence[str]] = None ) -> "LabelEncodingTransformer": @@ -27,6 +31,8 @@ def fit( """ super().fit(data, input_features) + for column_name in self.input_features: + self.les[column_name] = LabelEncoder().fit(data["raw_ts_X"][column_name]) self.output_features = [f"{column_name}__label" for column_name in self.input_features] @@ -46,7 +52,7 @@ def transform(self, data: dict) -> dict: """ new_arr = np.empty((len(data["raw_ts_X"]), len(self.output_features)), np.int32) for i, column_name in enumerate(self.input_features): - new_arr[:, i] = LabelEncoder().fit_transform(data["raw_ts_X"][column_name]) + new_arr[:, i] = self.les[column_name].transform(data["raw_ts_X"][column_name]) data["raw_ts_X"][self.output_features] = new_arr return data @@ -71,6 +77,7 @@ class OneHotEncodingTransformer(FeaturesGenerator): def __init__(self, drop: str = None): super().__init__() self.drop = drop + self.ohes = {} def fit( self, data: dict, input_features: Optional[Sequence[str]] = None @@ -90,6 +97,11 @@ def fit( super().fit(data, input_features) self.output_features = [] + for column_name in self.input_features: + self.ohes[column_name] = OneHotEncoder(drop=self.drop).fit( + data["raw_ts_X"][column_name].values.reshape(-1, 1) + ) + if self.drop == "first": for column_name in self.input_features: for id_name in data["raw_ts_X"][column_name].unique()[1:]: @@ -132,8 +144,8 @@ def transform(self, data: dict) -> dict: """ result_data = [ - OneHotEncoder(drop=self.drop) - .fit_transform(data["raw_ts_X"][column_name].values.reshape(-1, 1)) + self.ohes[column_name] + .transform(data["raw_ts_X"][column_name].values.reshape(-1, 1)) .todense() for column_name in self.input_features ]

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