aml-pipelines-with-automated-machine-learning-step.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/NotebookVM/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.png)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Azure Machine Learning Pipeline with AutoMLStep (Udacity Course 2)\n",
        "This notebook demonstrates the use of AutoMLStep in Azure Machine Learning Pipeline."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction\n",
        "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Create or Attach existing AmlCompute to a workspace.\n",
        "3. Define data loading in a `TabularDataset`.\n",
        "4. Configure AutoML using `AutoMLConfig`.\n",
        "5. Use AutoMLStep\n",
        "6. Train the model using AmlCompute\n",
        "7. Explore the results.\n",
        "8. Test the best fitted model."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Azure Machine Learning and Pipeline SDK-specific imports"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "import os\n",
        "import csv\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "import pkg_resources\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core.dataset import Dataset\n",
        "\n",
        "from azureml.pipeline.steps import AutoMLStep\n",
        "\n",
        "# Check core SDK version number\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SDK version: 1.24.0\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "gather": {
          "logged": 1616512321709
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initialize Workspace\n",
        "Initialize a workspace object from persisted configuration. Make sure the config file is present at .\\config.json"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "quick-starts-ws-141212\n",
            "aml-quickstarts-141212\n",
            "southcentralus\n",
            "d4ad7261-832d-46b2-b093-22156001df5b\n"
          ]
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1616512324684
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create an Azure ML experiment\n",
        "Let's create an experiment named \"automlstep-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
        "\n",
        "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step.\n",
        "\n",
        "*Udacity Note:* There is no need to create an Azure ML experiment, this needs to re-use the experiment that was already created\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Choose a name for the run history container in the workspace.\n",
        "# NOTE: update these to match your existing experiment name\n",
        "experiment_name = 'project-experiment'\n",
        "project_folder = './pipeline-project'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "experiment"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "Experiment(Name: project-experiment,\nWorkspace: quick-starts-ws-141212)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>project-experiment</td><td>quick-starts-ws-141212</td><td><a href=\"https://ml.azure.com/experiments/project-experiment?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-141212/workspaces/quick-starts-ws-141212\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.Experiment?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1616512327092
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create or Attach an AmlCompute cluster\n",
        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource.\n",
        "\n",
        "**Udacity Note** There is no need to create a new compute target, it can re-use the previous cluster"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# NOTE: update the cluster name to match the existing cluster\n",
        "# Choose a name for your CPU cluster\n",
        "amlcompute_cluster_name = \"comp-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',# for GPU, use \"STANDARD_NC6\"\n",
        "                                                           #vm_priority = 'lowpriority', # optional\n",
        "                                                           max_nodes=4)\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
        "\n",
        "compute_target.wait_for_completion(show_output=True, min_node_count = 1, timeout_in_minutes = 10)\n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing cluster, use it.\n",
            "Succeeded\n",
            "AmlCompute wait for completion finished\n",
            "\n",
            "Minimum number of nodes requested have been provisioned\n"
          ]
        }
      ],
      "execution_count": 19,
      "metadata": {
        "gather": {
          "logged": 1616512331800
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data\n",
        "\n",
        "**Udacity note:** Make sure the `key` is the same name as the dataset that is uploaded, and that the description matches. If it is hard to find or unknown, loop over the `ws.datasets.keys()` and `print()` them.\n",
        "If it *isn't* found because it was deleted, it can be recreated with the link that has the CSV "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Try to load the dataset from the Workspace. Otherwise, create it from the file\n",
        "# NOTE: update the key to match the dataset name\n",
        "found = False\n",
        "key = \"bank-dataset\"\n",
        "description_text = \"Bank Marketing DataSet for Udacity Course 2\"\n",
        "\n",
        "if key in ws.datasets.keys(): \n",
        "        found = True\n",
        "        dataset = ws.datasets[key] \n",
        "\n",
        "if not found:\n",
        "        # Create AML Dataset and register it into Workspace\n",
        "        example_data = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv'\n",
        "        dataset = Dataset.Tabular.from_delimited_files(example_data)        \n",
        "        #Register Dataset in Workspace\n",
        "        dataset = dataset.register(workspace=ws,\n",
        "                                   name=key,\n",
        "                                   description=description_text)\n",
        "\n",
        "\n",
        "df = dataset.to_pandas_dataframe()\n",
        "df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "                age      duration      campaign         pdays      previous  \\\ncount  32950.000000  32950.000000  32950.000000  32950.000000  32950.000000   \nmean      40.040212    257.335205      2.561730    962.174780      0.174780   \nstd       10.432313    257.331700      2.763646    187.646785      0.496503   \nmin       17.000000      0.000000      1.000000      0.000000      0.000000   \n25%       32.000000    102.000000      1.000000    999.000000      0.000000   \n50%       38.000000    179.000000      2.000000    999.000000      0.000000   \n75%       47.000000    318.000000      3.000000    999.000000      0.000000   \nmax       98.000000   4918.000000     56.000000    999.000000      7.000000   \n\n       emp.var.rate  cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \ncount  32950.000000    32950.000000   32950.000000  32950.000000  32950.000000  \nmean       0.076228       93.574243     -40.518680      3.615654   5166.859608  \nstd        1.572242        0.578636       4.623004      1.735748     72.208448  \nmin       -3.400000       92.201000     -50.800000      0.634000   4963.600000  \n25%       -1.800000       93.075000     -42.700000      1.344000   5099.100000  \n50%        1.100000       93.749000     -41.800000      4.857000   5191.000000  \n75%        1.400000       93.994000     -36.400000      4.961000   5228.100000  \nmax        1.400000       94.767000     -26.900000      5.045000   5228.100000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>duration</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n      <td>32950.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>40.040212</td>\n      <td>257.335205</td>\n      <td>2.561730</td>\n      <td>962.174780</td>\n      <td>0.174780</td>\n      <td>0.076228</td>\n      <td>93.574243</td>\n      <td>-40.518680</td>\n      <td>3.615654</td>\n      <td>5166.859608</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>10.432313</td>\n      <td>257.331700</td>\n      <td>2.763646</td>\n      <td>187.646785</td>\n      <td>0.496503</td>\n      <td>1.572242</td>\n      <td>0.578636</td>\n      <td>4.623004</td>\n      <td>1.735748</td>\n      <td>72.208448</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>17.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-3.400000</td>\n      <td>92.201000</td>\n      <td>-50.800000</td>\n      <td>0.634000</td>\n      <td>4963.600000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>32.000000</td>\n      <td>102.000000</td>\n      <td>1.000000</td>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>-1.800000</td>\n      <td>93.075000</td>\n      <td>-42.700000</td>\n      <td>1.344000</td>\n      <td>5099.100000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>38.000000</td>\n      <td>179.000000</td>\n      <td>2.000000</td>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>1.100000</td>\n      <td>93.749000</td>\n      <td>-41.800000</td>\n      <td>4.857000</td>\n      <td>5191.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>47.000000</td>\n      <td>318.000000</td>\n      <td>3.000000</td>\n      <td>999.000000</td>\n      <td>0.000000</td>\n      <td>1.400000</td>\n      <td>93.994000</td>\n      <td>-36.400000</td>\n      <td>4.961000</td>\n      <td>5228.100000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>98.000000</td>\n      <td>4918.000000</td>\n      <td>56.000000</td>\n      <td>999.000000</td>\n      <td>7.000000</td>\n      <td>1.400000</td>\n      <td>94.767000</td>\n      <td>-26.900000</td>\n      <td>5.045000</td>\n      <td>5228.100000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "gather": {
          "logged": 1616512335664
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Review the Dataset Result\n",
        "\n",
        "You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records for all the steps in the TabularDataset, which makes it fast even against large datasets.\n",
        "\n",
        "`TabularDataset` objects are composed of a list of transformation steps (optional)."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.take(5).to_pandas_dataframe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "   age          job  marital    education  default housing loan    contact  \\\n0   57   technician  married  high.school       no      no  yes   cellular   \n1   55      unknown  married      unknown  unknown     yes   no  telephone   \n2   33  blue-collar  married     basic.9y       no      no   no   cellular   \n3   36       admin.  married  high.school       no      no   no  telephone   \n4   27    housemaid  married  high.school       no     yes   no   cellular   \n\n  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n0   may         mon  ...         1    999         1      failure         -1.8   \n1   may         thu  ...         2    999         0  nonexistent          1.1   \n2   may         fri  ...         1    999         1      failure         -1.8   \n3   jun         fri  ...         4    999         0  nonexistent          1.4   \n4   jul         fri  ...         2    999         0  nonexistent          1.4   \n\n   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n0          92.893          -46.2      1.299       5099.1  no  \n1          93.994          -36.4      4.860       5191.0  no  \n2          92.893          -46.2      1.313       5099.1  no  \n3          94.465          -41.8      4.967       5228.1  no  \n4          93.918          -42.7      4.963       5228.1  no  \n\n[5 rows x 21 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>month</th>\n      <th>day_of_week</th>\n      <th>...</th>\n      <th>campaign</th>\n      <th>pdays</th>\n      <th>previous</th>\n      <th>poutcome</th>\n      <th>emp.var.rate</th>\n      <th>cons.price.idx</th>\n      <th>cons.conf.idx</th>\n      <th>euribor3m</th>\n      <th>nr.employed</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57</td>\n      <td>technician</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>cellular</td>\n      <td>may</td>\n      <td>mon</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>1</td>\n      <td>failure</td>\n      <td>-1.8</td>\n      <td>92.893</td>\n      <td>-46.2</td>\n      <td>1.299</td>\n      <td>5099.1</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55</td>\n      <td>unknown</td>\n      <td>married</td>\n      <td>unknown</td>\n      <td>unknown</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>may</td>\n      <td>thu</td>\n      <td>...</td>\n      <td>2</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.1</td>\n      <td>93.994</td>\n      <td>-36.4</td>\n      <td>4.860</td>\n      <td>5191.0</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>33</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>basic.9y</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>may</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>1</td>\n      <td>999</td>\n      <td>1</td>\n      <td>failure</td>\n      <td>-1.8</td>\n      <td>92.893</td>\n      <td>-46.2</td>\n      <td>1.313</td>\n      <td>5099.1</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>36</td>\n      <td>admin.</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>telephone</td>\n      <td>jun</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>4</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.4</td>\n      <td>94.465</td>\n      <td>-41.8</td>\n      <td>4.967</td>\n      <td>5228.1</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>27</td>\n      <td>housemaid</td>\n      <td>married</td>\n      <td>high.school</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>jul</td>\n      <td>fri</td>\n      <td>...</td>\n      <td>2</td>\n      <td>999</td>\n      <td>0</td>\n      <td>nonexistent</td>\n      <td>1.4</td>\n      <td>93.918</td>\n      <td>-42.7</td>\n      <td>4.963</td>\n      <td>5228.1</td>\n      <td>no</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1616512338601
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train\n",
        "This creates a general AutoML settings object.\n",
        "**Udacity notes:** These inputs must match what was used when training in the portal. `label_column_name` has to be `y` for example."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "automl_settings = {\n",
        "    \"experiment_timeout_minutes\": 20,\n",
        "    \"max_concurrent_iterations\": 5,\n",
        "    \"primary_metric\" : 'AUC_weighted'\n",
        "}\n",
        "automl_config = AutoMLConfig(compute_target=compute_target,\n",
        "                             task = \"classification\",\n",
        "                             training_data=dataset,\n",
        "                             label_column_name=\"y\",   \n",
        "                             path = project_folder,\n",
        "                             enable_early_stopping= True,\n",
        "                             featurization= 'auto',\n",
        "                             debug_log = \"automl_errors.log\",\n",
        "                             **automl_settings\n",
        "                            )"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "gather": {
          "logged": 1616512343126
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Create Pipeline and AutoMLStep\n",
        "\n",
        "You can define outputs for the AutoMLStep using TrainingOutput."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core import PipelineData, TrainingOutput\n",
        "\n",
        "ds = ws.get_default_datastore()\n",
        "metrics_output_name = 'metrics_output'\n",
        "best_model_output_name = 'best_model_output'\n",
        "\n",
        "metrics_data = PipelineData(name='metrics_data',\n",
        "                           datastore=ds,\n",
        "                           pipeline_output_name=metrics_output_name,\n",
        "                           training_output=TrainingOutput(type='Metrics'))\n",
        "model_data = PipelineData(name='model_data',\n",
        "                           datastore=ds,\n",
        "                           pipeline_output_name=best_model_output_name,\n",
        "                           training_output=TrainingOutput(type='Model'))"
      ],
      "outputs": [],
      "execution_count": 23,
      "metadata": {
        "gather": {
          "logged": 1616512347009
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create an AutoMLStep."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "automl_step = AutoMLStep(\n",
        "    name='automl_module',\n",
        "    automl_config=automl_config,\n",
        "    outputs=[metrics_data, model_data],\n",
        "    allow_reuse=True)"
      ],
      "outputs": [],
      "execution_count": 24,
      "metadata": {
        "tags": [
          "automlstep-remarks-sample1"
        ],
        "gather": {
          "logged": 1616512349717
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core import Pipeline\n",
        "pipeline = Pipeline(\n",
        "    description=\"pipeline_with_automlstep\",\n",
        "    workspace=ws,    \n",
        "    steps=[automl_step])"
      ],
      "outputs": [],
      "execution_count": 25,
      "metadata": {
        "tags": [
          "automlstep-remarks-sample2"
        ],
        "gather": {
          "logged": 1616512352562
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run = experiment.submit(pipeline)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created step automl_module [87b9c81b][791cc67d-4bfd-4abf-9a04-0434aa036e5e], (This step is eligible to reuse a previous run's output)\n",
            "Submitted PipelineRun b12b6172-5d06-4f99-9cea-84e85eed2b40\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/project-experiment/runs/b12b6172-5d06-4f99-9cea-84e85eed2b40?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-141212/workspaces/quick-starts-ws-141212\n"
          ]
        }
      ],
      "execution_count": 26,
      "metadata": {
        "gather": {
          "logged": 1616512361052
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_PipelineWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', …",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6b4ea8877fa44fde8a42684c84056720"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Running\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/project-experiment/runs/b12b6172-5d06-4f99-9cea-84e85eed2b40?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-141212/workspaces/quick-starts-ws-141212\", \"run_id\": \"b12b6172-5d06-4f99-9cea-84e85eed2b40\", \"run_properties\": {\"run_id\": \"b12b6172-5d06-4f99-9cea-84e85eed2b40\", \"created_utc\": \"2021-03-23T15:12:39.51331Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\"}, \"tags\": {\"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": null, \"status\": \"Running\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg141212.blob.core.windows.net/azureml/ExperimentRun/dcid.b12b6172-5d06-4f99-9cea-84e85eed2b40/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=YWD%2BTmjv%2F9G%2FAagfdYExto1lZWMK%2FMBusRbkUmtTFs0%3D&st=2021-03-23T15%3A02%3A44Z&se=2021-03-23T23%3A12%3A44Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg141212.blob.core.windows.net/azureml/ExperimentRun/dcid.b12b6172-5d06-4f99-9cea-84e85eed2b40/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=NraAHBXDdRnXmsLps97QbuYowaNYW3LLHF6fv5VCWmg%3D&st=2021-03-23T15%3A02%3A44Z&se=2021-03-23T23%3A12%3A44Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg141212.blob.core.windows.net/azureml/ExperimentRun/dcid.b12b6172-5d06-4f99-9cea-84e85eed2b40/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=4uIAypP0BYTtVokrOWZk5fulFpZ0ZLILyy%2Fhk0tabq8%3D&st=2021-03-23T15%3A02%3A44Z&se=2021-03-23T23%3A12%3A44Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:13:40\", \"run_number\": \"28\", \"run_queued_details\": {\"status\": \"Running\", \"details\": null}}, \"child_runs\": [{\"run_id\": \"058f59b1-d96b-485c-af4f-fbe8f855d202\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-03-23T15:13:01.821542Z\", \"created_time\": \"2021-03-23T15:12:43.379101Z\", \"end_time\": \"\", \"duration\": \"0:13:37\", \"run_number\": 29, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-03-23T15:12:43.379101Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-03-23 15:12:43Z] Submitting 1 runs, first five are: 87b9c81b:058f59b1-d96b-485c-af4f-fbe8f855d202\\n\", \"graph\": {\"datasource_nodes\": {\"889ad44e\": {\"node_id\": \"889ad44e\", \"name\": \"bank-dataset\"}}, \"module_nodes\": {\"87b9c81b\": {\"node_id\": \"87b9c81b\", \"name\": \"automl_module\", \"status\": \"Running\", \"_is_reused\": false, \"run_id\": \"058f59b1-d96b-485c-af4f-fbe8f855d202\"}}, \"edges\": [{\"source_node_id\": \"889ad44e\", \"source_node_name\": \"bank-dataset\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"87b9c81b\", \"dst_node_name\": \"automl_module\"}], \"child_runs\": [{\"run_id\": \"058f59b1-d96b-485c-af4f-fbe8f855d202\", \"name\": \"automl_module\", \"status\": \"Running\", \"start_time\": \"2021-03-23T15:13:01.821542Z\", \"created_time\": \"2021-03-23T15:12:43.379101Z\", \"end_time\": \"\", \"duration\": \"0:13:37\", \"run_number\": 29, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-03-23T15:12:43.379101Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.24.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 27,
      "metadata": {
        "gather": {
          "logged": 1616512364997
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run.wait_for_completion()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PipelineRunId: b12b6172-5d06-4f99-9cea-84e85eed2b40\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/project-experiment/runs/b12b6172-5d06-4f99-9cea-84e85eed2b40?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-141212/workspaces/quick-starts-ws-141212\n",
            "PipelineRun Status: Running\n",
            "\n",
            "\n",
            "StepRunId: 058f59b1-d96b-485c-af4f-fbe8f855d202\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/experiments/project-experiment/runs/058f59b1-d96b-485c-af4f-fbe8f855d202?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-141212/workspaces/quick-starts-ws-141212\n",
            "StepRun( automl_module ) Status: NotStarted\n",
            "StepRun( automl_module ) Status: Running\n"
          ]
        }
      ],
      "execution_count": 28,
      "metadata": {
        "gather": {
          "logged": 1616512239842
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Examine Results\n",
        "\n",
        "### Retrieve the metrics of all child runs\n",
        "Outputs of above run can be used as inputs of other steps in pipeline. In this tutorial, we will examine the outputs by retrieve output data and running some tests."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "metrics_output = pipeline_run.get_pipeline_output(metrics_output_name)\n",
        "num_file_downloaded = metrics_output.download('.', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading azureml/058f59b1-d96b-485c-af4f-fbe8f855d202/metrics_data\n",
            "Downloaded azureml/058f59b1-d96b-485c-af4f-fbe8f855d202/metrics_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 29,
      "metadata": {
        "gather": {
          "logged": 1616515158573
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(metrics_output._path_on_datastore) as f:\n",
        "    metrics_output_result = f.read()\n",
        "    \n",
        "deserialized_metrics_output = json.loads(metrics_output_result)\n",
        "df = pd.DataFrame(deserialized_metrics_output)\n",
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "                                 058f59b1-d96b-485c-af4f-fbe8f855d202_3  \\\nweighted_accuracy                                  [0.8134669221076944]   \nprecision_score_micro                              [0.8075872534142641]   \nAUC_weighted                                       [0.8843607540655037]   \naverage_precision_score_micro                      [0.8353104548252442]   \nmatthews_correlation                               [0.4139824392025363]   \nnorm_macro_recall                                  [0.5678099535609165]   \nf1_score_weighted                                  [0.8360766763626712]   \nAUC_micro                                           [0.867283717224562]   \nf1_score_micro                                      [0.807587253414264]   \naccuracy                                           [0.8075872534142641]   \naverage_precision_score_weighted                   [0.9304181641544951]   \nprecision_score_macro                              [0.6509144555262674]   \nAUC_macro                                          [0.8843607540655037]   \naverage_precision_score_macro                      [0.7448150864800028]   \nrecall_score_micro                                 [0.8075872534142641]   \nf1_score_macro                                     [0.6749097416744476]   \nlog_loss                                           [0.5151473339191622]   \nbalanced_accuracy                                  [0.7839049767804582]   \nrecall_score_macro                                 [0.7839049767804582]   \nrecall_score_weighted                              [0.8075872534142641]   \nprecision_score_weighted                           [0.8932691707105429]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_5  \\\nweighted_accuracy                                  [0.9730611889183236]   \nprecision_score_micro                              [0.8977238239757208]   \nAUC_weighted                                       [0.8977728875033113]   \naverage_precision_score_micro                      [0.9682207204806685]   \nmatthews_correlation                              [0.31999379338174755]   \nnorm_macro_recall                                 [0.18855620203502088]   \nf1_score_weighted                                  [0.8734704046383025]   \nAUC_micro                                          [0.9674742390295684]   \nf1_score_micro                                     [0.8977238239757208]   \naccuracy                                           [0.8977238239757208]   \naverage_precision_score_weighted                   [0.9296047783413253]   \nprecision_score_macro                               [0.771526544069397]   \nAUC_macro                                          [0.8977728875033111]   \naverage_precision_score_macro                      [0.7350353031090393]   \nrecall_score_micro                                 [0.8977238239757208]   \nf1_score_macro                                     [0.6263947343363969]   \nlog_loss                                          [0.24040114103947297]   \nbalanced_accuracy                                  [0.5942781010175104]   \nrecall_score_macro                                 [0.5942781010175104]   \nrecall_score_weighted                              [0.8977238239757208]   \nprecision_score_weighted                            [0.877014103638037]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_2  \\\nweighted_accuracy                                  [0.9827992361603504]   \nprecision_score_micro                              [0.8937784522003035]   \nAUC_weighted                                       [0.9067254240553343]   \naverage_precision_score_micro                      [0.9700178644457927]   \nmatthews_correlation                               [0.2166736264828479]   \nnorm_macro_recall                                  [0.0704366237100511]   \nf1_score_weighted                                  [0.8527376441371826]   \nAUC_micro                                          [0.9690237426919438]   \nf1_score_micro                                     [0.8937784522003035]   \naccuracy                                           [0.8937784522003035]   \naverage_precision_score_weighted                   [0.9346650357763759]   \nprecision_score_macro                              [0.8332602979842244]   \nAUC_macro                                          [0.9067254240553342]   \naverage_precision_score_macro                      [0.7536148238449915]   \nrecall_score_micro                                 [0.8937784522003035]   \nf1_score_macro                                     [0.5385419967796974]   \nlog_loss                                          [0.23651897069392966]   \nbalanced_accuracy                                  [0.5352183118550256]   \nrecall_score_macro                                 [0.5352183118550256]   \nrecall_score_weighted                              [0.8937784522003035]   \nprecision_score_weighted                            [0.881243219016942]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_0  \\\nweighted_accuracy                                  [0.9514937218005303]   \nprecision_score_micro                              [0.9116843702579667]   \nAUC_weighted                                       [0.9450464668693167]   \naverage_precision_score_micro                      [0.9806603102489483]   \nmatthews_correlation                               [0.5323740218566827]   \nnorm_macro_recall                                  [0.5026785366965085]   \nf1_score_weighted                                  [0.9091539479147899]   \nAUC_micro                                           [0.979695082216353]   \nf1_score_micro                                     [0.9116843702579667]   \naccuracy                                           [0.9116843702579667]   \naverage_precision_score_weighted                   [0.9531771295804466]   \nprecision_score_macro                              [0.7819118765348991]   \nAUC_macro                                          [0.9450464668693166]   \naverage_precision_score_macro                      [0.8151093723721079]   \nrecall_score_micro                                 [0.9116843702579667]   \nf1_score_macro                                     [0.7653697272147331]   \nlog_loss                                          [0.17775706110025447]   \nbalanced_accuracy                                  [0.7513392683482543]   \nrecall_score_macro                                 [0.7513392683482543]   \nrecall_score_weighted                              [0.9116843702579667]   \nprecision_score_weighted                           [0.9072720074188747]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_1  \\\nweighted_accuracy                                  [0.9537972210153172]   \nprecision_score_micro                              [0.9071320182094081]   \nAUC_weighted                                       [0.9392346349984347]   \naverage_precision_score_micro                      [0.9791945367231853]   \nmatthews_correlation                                [0.488678780261868]   \nnorm_macro_recall                                 [0.43834549418631563]   \nf1_score_weighted                                  [0.9021127651963996]   \nAUC_micro                                          [0.9781770788959222]   \nf1_score_micro                                     [0.9071320182094081]   \naccuracy                                           [0.9071320182094081]   \naverage_precision_score_weighted                   [0.9505970434373063]   \nprecision_score_macro                              [0.7723958081530135]   \nAUC_macro                                          [0.9392346349984347]   \naverage_precision_score_macro                      [0.8065229883244922]   \nrecall_score_micro                                 [0.9071320182094081]   \nf1_score_macro                                     [0.7416848907681176]   \nlog_loss                                           [0.1874363495858499]   \nbalanced_accuracy                                  [0.7191727470931578]   \nrecall_score_macro                                 [0.7191727470931578]   \nrecall_score_weighted                              [0.9071320182094081]   \nprecision_score_weighted                           [0.8991976076061607]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_8  \\\nweighted_accuracy                                  [0.9843450583187134]   \nprecision_score_micro                              [0.8880121396054628]   \nAUC_weighted                                       [0.8978043779070737]   \naverage_precision_score_micro                       [0.966145841302633]   \nmatthews_correlation                                              [0.0]   \nnorm_macro_recall                                                 [0.0]   \nf1_score_weighted                                  [0.8353395018439429]   \nAUC_micro                                          [0.9671327090063806]   \nf1_score_micro                                     [0.8880121396054628]   \naccuracy                                           [0.8880121396054628]   \naverage_precision_score_weighted                   [0.9296135047518299]   \nprecision_score_macro                              [0.4440060698027314]   \nAUC_macro                                          [0.8978043779070737]   \naverage_precision_score_macro                      [0.7437635899759644]   \nrecall_score_micro                                 [0.8880121396054628]   \nf1_score_macro                                     [0.4703423886834914]   \nlog_loss                                           [0.2640780378739563]   \nbalanced_accuracy                                                 [0.5]   \nrecall_score_macro                                                [0.5]   \nrecall_score_weighted                              [0.8880121396054628]   \nprecision_score_weighted                            [0.788565560086672]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_4  \\\nweighted_accuracy                                  [0.9843450583187134]   \nprecision_score_micro                              [0.8880121396054628]   \nAUC_weighted                                       [0.9070060591241592]   \naverage_precision_score_micro                      [0.9701771124281748]   \nmatthews_correlation                                              [0.0]   \nnorm_macro_recall                                                 [0.0]   \nf1_score_weighted                                  [0.8353395018439429]   \nAUC_micro                                          [0.9689628604521034]   \nf1_score_micro                                     [0.8880121396054628]   \naccuracy                                           [0.8880121396054628]   \naverage_precision_score_weighted                   [0.9346302904136361]   \nprecision_score_macro                              [0.4440060698027314]   \nAUC_macro                                          [0.9070060591241593]   \naverage_precision_score_macro                      [0.7522881472130623]   \nrecall_score_micro                                 [0.8880121396054628]   \nf1_score_macro                                     [0.4703423886834914]   \nlog_loss                                          [0.24565149763737298]   \nbalanced_accuracy                                                 [0.5]   \nrecall_score_macro                                                [0.5]   \nrecall_score_weighted                              [0.8880121396054628]   \nprecision_score_weighted                            [0.788565560086672]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_6  \\\nweighted_accuracy                                  [0.9769284461975132]   \nprecision_score_micro                              [0.9001517450682853]   \nAUC_weighted                                       [0.8787369384288511]   \naverage_precision_score_micro                      [0.9637351997217339]   \nmatthews_correlation                                [0.331044134221627]   \nnorm_macro_recall                                 [0.18181725562983586]   \nf1_score_weighted                                   [0.874029224943002]   \nAUC_micro                                          [0.9638113571627587]   \nf1_score_micro                                     [0.9001517450682853]   \naccuracy                                           [0.9001517450682853]   \naverage_precision_score_weighted                   [0.9261914684092131]   \nprecision_score_macro                              [0.8013746369202237]   \nAUC_macro                                          [0.8787369384288513]   \naverage_precision_score_macro                      [0.7353653714991217]   \nrecall_score_micro                                 [0.9001517450682853]   \nf1_score_macro                                     [0.6238596220952524]   \nlog_loss                                           [0.2555320591882684]   \nbalanced_accuracy                                  [0.5909086278149179]   \nrecall_score_macro                                 [0.5909086278149179]   \nrecall_score_weighted                              [0.9001517450682853]   \nprecision_score_weighted                           [0.8830870489340036]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_10  \\\nweighted_accuracy                                   [0.7853295096127834]   \nprecision_score_micro                               [0.7787556904400607]   \nAUC_weighted                                        [0.8449861720079948]   \naverage_precision_score_micro                       [0.8165375336880235]   \nmatthews_correlation                                [0.3583490930507784]   \nnorm_macro_recall                                    [0.504554994285418]   \nf1_score_weighted                                   [0.8137222733923168]   \nAUC_micro                                           [0.8403098454687172]   \nf1_score_micro                                      [0.7787556904400607]   \naccuracy                                            [0.7787556904400607]   \naverage_precision_score_weighted                    [0.9167575337775034]   \nprecision_score_macro                               [0.6272547828727604]   \nAUC_macro                                           [0.8449861720079948]   \naverage_precision_score_macro                       [0.7130296686202923]   \nrecall_score_micro                                  [0.7787556904400607]   \nf1_score_macro                                      [0.6421108480393055]   \nlog_loss                                            [0.5523627651771427]   \nbalanced_accuracy                                    [0.752277497142709]   \nrecall_score_macro                                   [0.752277497142709]   \nrecall_score_weighted                               [0.7787556904400607]   \nprecision_score_weighted                             [0.882956314654666]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_9  \\\nweighted_accuracy                                  [0.7060238315303341]   \nprecision_score_micro                              [0.7098634294385432]   \nAUC_weighted                                       [0.8166985275457677]   \naverage_precision_score_micro                      [0.7919018986822479]   \nmatthews_correlation                              [0.29894467174429784]   \nnorm_macro_recall                                 [0.45065731586912583]   \nf1_score_weighted                                  [0.7619281474085273]   \nAUC_micro                                          [0.8168685252175435]   \nf1_score_micro                                     [0.7098634294385432]   \naccuracy                                           [0.7098634294385432]   \naverage_precision_score_weighted                   [0.9075281090202592]   \nprecision_score_macro                              [0.5991528525304791]   \nAUC_macro                                          [0.8166985275457677]   \naverage_precision_score_macro                      [0.7051892654657641]   \nrecall_score_micro                                 [0.7098634294385432]   \nf1_score_macro                                     [0.5885824653131508]   \nlog_loss                                           [0.5661148327428956]   \nbalanced_accuracy                                  [0.7253286579345629]   \nrecall_score_macro                                 [0.7253286579345629]   \nrecall_score_weighted                              [0.7098634294385432]   \nprecision_score_weighted                           [0.8764172454334369]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_13  \\\nweighted_accuracy                                   [0.9766010009385309]   \nprecision_score_micro                               [0.9062215477996965]   \nAUC_weighted                                        [0.9308878256246675]   \naverage_precision_score_micro                       [0.9766643355999638]   \nmatthews_correlation                                [0.3976739324324451]   \nnorm_macro_recall                                  [0.24549085203770704]   \nf1_score_weighted                                    [0.885603431576398]   \nAUC_micro                                           [0.9758990146932517]   \nf1_score_micro                                      [0.9062215477996965]   \naccuracy                                            [0.9062215477996965]   \naverage_precision_score_weighted                     [0.947605275820125]   \nprecision_score_macro                                [0.822098675416211]   \nAUC_macro                                           [0.9308878256246677]   \naverage_precision_score_macro                       [0.7985126174047921]   \nrecall_score_micro                                  [0.9062215477996965]   \nf1_score_macro                                      [0.6650154684677185]   \nlog_loss                                           [0.33655623030329523]   \nbalanced_accuracy                                   [0.6227454260188535]   \nrecall_score_macro                                  [0.6227454260188535]   \nrecall_score_weighted                               [0.9062215477996965]   \nprecision_score_weighted                            [0.8929725418691179]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_7  \\\nweighted_accuracy                                  [0.9843874836349229]   \nprecision_score_micro                              [0.8883156297420334]   \nAUC_weighted                                       [0.8720044753420877]   \naverage_precision_score_micro                      [0.9628945055280018]   \nmatthews_correlation                             [0.049063912865329386]   \nnorm_macro_recall                               [0.0027100271002709064]   \nf1_score_weighted                                  [0.8360791405851395]   \nAUC_micro                                          [0.9620013769886318]   \nf1_score_micro                                     [0.8883156297420334]   \naccuracy                                           [0.8883156297420334]   \naverage_precision_score_weighted                   [0.9230563356136842]   \nprecision_score_macro                              [0.9441408621736491]   \nAUC_macro                                          [0.8720044753420877]   \naverage_precision_score_macro                      [0.7211471018351185]   \nrecall_score_micro                                 [0.8883156297420334]   \nf1_score_macro                                    [0.47312070913357085]   \nlog_loss                                          [0.27030910523410084]   \nbalanced_accuracy                                  [0.5013550135501355]   \nrecall_score_macro                                 [0.5013550135501355]   \nrecall_score_weighted                              [0.8883156297420334]   \nprecision_score_weighted                           [0.9007928150046113]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_14  \\\nweighted_accuracy                                   [0.9841783463715489]   \nprecision_score_micro                               [0.8889226100151745]   \nAUC_weighted                                        [0.8943163525961986]   \naverage_precision_score_micro                       [0.9670009743929061]   \nmatthews_correlation                               [0.08505272438010367]   \nnorm_macro_recall                                  [0.01049834490142576]   \nf1_score_weighted                                    [0.838121172790458]   \nAUC_micro                                           [0.9664436620529103]   \nf1_score_micro                                      [0.8889226100151746]   \naccuracy                                            [0.8889226100151745]   \naverage_precision_score_weighted                    [0.9302722797235586]   \nprecision_score_macro                               [0.8445288753799393]   \nAUC_macro                                           [0.8943163525961986]   \naverage_precision_score_macro                       [0.7426680720283891]   \nrecall_score_micro                                  [0.8889226100151745]   \nf1_score_macro                                     [0.48125503272562103]   \nlog_loss                                           [0.26219431706539736]   \nbalanced_accuracy                                   [0.5052491724507129]   \nrecall_score_macro                                  [0.5052491724507129]   \nrecall_score_weighted                               [0.8889226100151745]   \nprecision_score_weighted                            [0.8790843638007296]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_15  \\\nweighted_accuracy                                   [0.9843450583187134]   \nprecision_score_micro                               [0.8880121396054628]   \nAUC_weighted                                        [0.8855198787804694]   \naverage_precision_score_micro                       [0.9656870174934193]   \nmatthews_correlation                                               [0.0]   \nnorm_macro_recall                                                  [0.0]   \nf1_score_weighted                                   [0.8353395018439429]   \nAUC_micro                                           [0.9646894061678959]   \nf1_score_micro                                      [0.8880121396054628]   \naccuracy                                            [0.8880121396054628]   \naverage_precision_score_weighted                    [0.9249830178919962]   \nprecision_score_macro                               [0.4440060698027314]   \nAUC_macro                                           [0.8855198787804694]   \naverage_precision_score_macro                       [0.7202537380617574]   \nrecall_score_micro                                  [0.8880121396054628]   \nf1_score_macro                                      [0.4703423886834914]   \nlog_loss                                            [0.2569340400852136]   \nbalanced_accuracy                                                  [0.5]   \nrecall_score_macro                                                 [0.5]   \nrecall_score_weighted                               [0.8880121396054628]   \nprecision_score_weighted                             [0.788565560086672]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_16  \\\nweighted_accuracy                                   [0.8456203679229198]   \nprecision_score_micro                               [0.8251896813353566]   \nAUC_weighted                                        [0.8399199217556086]   \naverage_precision_score_micro                       [0.7818769054641193]   \nmatthews_correlation                                [0.3791629811136251]   \nnorm_macro_recall                                   [0.4857969017147452]   \nf1_score_weighted                                   [0.8460707311907201]   \nAUC_micro                                           [0.8152561129775422]   \nf1_score_micro                                      [0.8251896813353566]   \naccuracy                                            [0.8251896813353566]   \naverage_precision_score_weighted                    [0.9119455591708515]   \nprecision_score_macro                               [0.6479677677435953]   \nAUC_macro                                           [0.8399199217556086]   \naverage_precision_score_macro                       [0.6971184763497635]   \nrecall_score_micro                                  [0.8251896813353566]   \nf1_score_macro                                      [0.6727173400213553]   \nlog_loss                                            [0.5306028903049885]   \nbalanced_accuracy                                   [0.7428984508573726]   \nrecall_score_macro                                  [0.7428984508573726]   \nrecall_score_weighted                               [0.8251896813353566]   \nprecision_score_weighted                            [0.8814332149636538]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_17  \\\nweighted_accuracy                                   [0.7390741876213046]   \nprecision_score_micro                               [0.7380880121396055]   \nAUC_weighted                                        [0.8091153604632423]   \naverage_precision_score_micro                       [0.7954273910570835]   \nmatthews_correlation                               [0.31836774985777505]   \nnorm_macro_recall                                   [0.4682317397336653]   \nf1_score_weighted                                   [0.7833284785713038]   \nAUC_micro                                           [0.8218743163988292]   \nf1_score_micro                                      [0.7380880121396055]   \naccuracy                                            [0.7380880121396055]   \naverage_precision_score_weighted                    [0.9062323533622272]   \nprecision_score_macro                               [0.6082348926272665]   \nAUC_macro                                           [0.8091153604632424]   \naverage_precision_score_macro                       [0.6991223063191867]   \nrecall_score_micro                                  [0.7380880121396055]   \nf1_score_macro                                      [0.6088490326197913]   \nlog_loss                                            [0.5753453288658256]   \nbalanced_accuracy                                   [0.7341158698668326]   \nrecall_score_macro                                  [0.7341158698668326]   \nrecall_score_weighted                               [0.7380880121396055]   \nprecision_score_weighted                            [0.8779622903470479]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_22  \\\nweighted_accuracy                                   [0.9638017774252939]   \nprecision_score_micro                                 [0.91350531107739]   \nAUC_weighted                                        [0.9445991178982194]   \naverage_precision_score_micro                       [0.9805449428850945]   \nmatthews_correlation                                  [0.50423840834671]   \nnorm_macro_recall                                  [0.42183989167301106]   \nf1_score_weighted                                   [0.9059001624604492]   \nAUC_micro                                           [0.9796031601658833]   \nf1_score_micro                                        [0.91350531107739]   \naccuracy                                              [0.91350531107739]   \naverage_precision_score_weighted                    [0.9537303389034844]   \nprecision_score_macro                               [0.8013659654657674]   \nAUC_macro                                           [0.9445991178982194]   \naverage_precision_score_macro                       [0.8179885620700982]   \nrecall_score_micro                                    [0.91350531107739]   \nf1_score_macro                                      [0.7451864020181578]   \nlog_loss                                            [0.2043178465521144]   \nbalanced_accuracy                                   [0.7109199458365055]   \nrecall_score_macro                                  [0.7109199458365055]   \nrecall_score_weighted                                 [0.91350531107739]   \nprecision_score_weighted                            [0.9038098049544845]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_23  \\\nweighted_accuracy                                   [0.9575007556650157]   \nprecision_score_micro                               [0.9125948406676783]   \nAUC_weighted                                        [0.9412907731264598]   \naverage_precision_score_micro                       [0.9798773723176037]   \nmatthews_correlation                                [0.5182831989820988]   \nnorm_macro_recall                                   [0.4634433459850662]   \nf1_score_weighted                                   [0.9077359298259612]   \nAUC_micro                                           [0.9789109815994712]   \nf1_score_micro                                      [0.9125948406676783]   \naccuracy                                            [0.9125948406676783]   \naverage_precision_score_weighted                    [0.9523313424166967]   \nprecision_score_macro                               [0.7898061615019645]   \nAUC_macro                                           [0.9412907731264598]   \naverage_precision_score_macro                       [0.8133700670556807]   \nrecall_score_micro                                  [0.9125948406676783]   \nf1_score_macro                                      [0.7562208685787566]   \nlog_loss                                           [0.19315052319891854]   \nbalanced_accuracy                                   [0.7317216729925331]   \nrecall_score_macro                                  [0.7317216729925331]   \nrecall_score_weighted                               [0.9125948406676783]   \nprecision_score_weighted                            [0.9051941264511486]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_11  \\\nweighted_accuracy                                   [0.7339306066693747]   \nprecision_score_micro                               [0.7538694992412747]   \nAUC_weighted                                         [0.901501721784135]   \naverage_precision_score_micro                       [0.8635040453333698]   \nmatthews_correlation                               [0.44363593167969023]   \nnorm_macro_recall                                   [0.6683597389630767]   \nf1_score_weighted                                   [0.7980005387650213]   \nAUC_micro                                           [0.8500510959493968]   \nf1_score_micro                                      [0.7538694992412747]   \naccuracy                                            [0.7538694992412747]   \naverage_precision_score_weighted                    [0.9312969618844966]   \nprecision_score_macro                               [0.6472357088584144]   \nAUC_macro                                           [0.9015017217841351]   \naverage_precision_score_macro                        [0.739794512680513]   \nrecall_score_micro                                  [0.7538694992412747]   \nf1_score_macro                                       [0.650493260252315]   \nlog_loss                                            [0.4654335118668296]   \nbalanced_accuracy                                   [0.8341798694815383]   \nrecall_score_macro                                  [0.8341798694815383]   \nrecall_score_weighted                               [0.7538694992412747]   \nprecision_score_weighted                            [0.9127299659265484]   \n\n                                 058f59b1-d96b-485c-af4f-fbe8f855d202_12  \nweighted_accuracy                                   [0.7339037028103151]  \nprecision_score_micro                               [0.7347496206373293]  \nAUC_weighted                                        [0.8541160736282688]  \naverage_precision_score_micro                       [0.8253907065495234]  \nmatthews_correlation                                [0.3221153271049788]  \nnorm_macro_recall                                  [0.47631365924048863]  \nf1_score_weighted                                    [0.780990681745509]  \nAUC_micro                                           [0.8372882074048831]  \nf1_score_micro                                      [0.7347496206373293]  \naccuracy                                            [0.7347496206373293]  \naverage_precision_score_weighted                    [0.9228010519654273]  \nprecision_score_macro                               [0.6089180227598306]  \nAUC_macro                                           [0.8541160736282689]  \naverage_precision_score_macro                       [0.7304329783698786]  \nrecall_score_micro                                  [0.7347496206373293]  \nf1_score_macro                                      [0.6081275337139075]  \nlog_loss                                            [0.5840565551646402]  \nbalanced_accuracy                                   [0.7381568296202443]  \nrecall_score_macro                                  [0.7381568296202443]  \nrecall_score_weighted                               [0.7347496206373293]  \nprecision_score_weighted                            [0.8795247475476895]  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_3</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_5</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_2</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_0</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_1</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_8</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_4</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_6</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_10</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_9</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_13</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_7</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_14</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_15</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_16</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_17</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_22</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_23</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_11</th>\n      <th>058f59b1-d96b-485c-af4f-fbe8f855d202_12</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>weighted_accuracy</th>\n      <td>[0.8134669221076944]</td>\n      <td>[0.9730611889183236]</td>\n      <td>[0.9827992361603504]</td>\n      <td>[0.9514937218005303]</td>\n      <td>[0.9537972210153172]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.9769284461975132]</td>\n      <td>[0.7853295096127834]</td>\n      <td>[0.7060238315303341]</td>\n      <td>[0.9766010009385309]</td>\n      <td>[0.9843874836349229]</td>\n      <td>[0.9841783463715489]</td>\n      <td>[0.9843450583187134]</td>\n      <td>[0.8456203679229198]</td>\n      <td>[0.7390741876213046]</td>\n      <td>[0.9638017774252939]</td>\n      <td>[0.9575007556650157]</td>\n      <td>[0.7339306066693747]</td>\n      <td>[0.7339037028103151]</td>\n    </tr>\n    <tr>\n      <th>precision_score_micro</th>\n      <td>[0.8075872534142641]</td>\n      <td>[0.8977238239757208]</td>\n      <td>[0.8937784522003035]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.7787556904400607]</td>\n      <td>[0.7098634294385432]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.8883156297420334]</td>\n      <td>[0.8889226100151745]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8251896813353566]</td>\n      <td>[0.7380880121396055]</td>\n      <td>[0.91350531107739]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.7538694992412747]</td>\n      <td>[0.7347496206373293]</td>\n    </tr>\n    <tr>\n      <th>AUC_weighted</th>\n      <td>[0.8843607540655037]</td>\n      <td>[0.8977728875033113]</td>\n      <td>[0.9067254240553343]</td>\n      <td>[0.9450464668693167]</td>\n      <td>[0.9392346349984347]</td>\n      <td>[0.8978043779070737]</td>\n      <td>[0.9070060591241592]</td>\n      <td>[0.8787369384288511]</td>\n      <td>[0.8449861720079948]</td>\n      <td>[0.8166985275457677]</td>\n      <td>[0.9308878256246675]</td>\n      <td>[0.8720044753420877]</td>\n      <td>[0.8943163525961986]</td>\n      <td>[0.8855198787804694]</td>\n      <td>[0.8399199217556086]</td>\n      <td>[0.8091153604632423]</td>\n      <td>[0.9445991178982194]</td>\n      <td>[0.9412907731264598]</td>\n      <td>[0.901501721784135]</td>\n      <td>[0.8541160736282688]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_micro</th>\n      <td>[0.8353104548252442]</td>\n      <td>[0.9682207204806685]</td>\n      <td>[0.9700178644457927]</td>\n      <td>[0.9806603102489483]</td>\n      <td>[0.9791945367231853]</td>\n      <td>[0.966145841302633]</td>\n      <td>[0.9701771124281748]</td>\n      <td>[0.9637351997217339]</td>\n      <td>[0.8165375336880235]</td>\n      <td>[0.7919018986822479]</td>\n      <td>[0.9766643355999638]</td>\n      <td>[0.9628945055280018]</td>\n      <td>[0.9670009743929061]</td>\n      <td>[0.9656870174934193]</td>\n      <td>[0.7818769054641193]</td>\n      <td>[0.7954273910570835]</td>\n      <td>[0.9805449428850945]</td>\n      <td>[0.9798773723176037]</td>\n      <td>[0.8635040453333698]</td>\n      <td>[0.8253907065495234]</td>\n    </tr>\n    <tr>\n      <th>matthews_correlation</th>\n      <td>[0.4139824392025363]</td>\n      <td>[0.31999379338174755]</td>\n      <td>[0.2166736264828479]</td>\n      <td>[0.5323740218566827]</td>\n      <td>[0.488678780261868]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.331044134221627]</td>\n      <td>[0.3583490930507784]</td>\n      <td>[0.29894467174429784]</td>\n      <td>[0.3976739324324451]</td>\n      <td>[0.049063912865329386]</td>\n      <td>[0.08505272438010367]</td>\n      <td>[0.0]</td>\n      <td>[0.3791629811136251]</td>\n      <td>[0.31836774985777505]</td>\n      <td>[0.50423840834671]</td>\n      <td>[0.5182831989820988]</td>\n      <td>[0.44363593167969023]</td>\n      <td>[0.3221153271049788]</td>\n    </tr>\n    <tr>\n      <th>norm_macro_recall</th>\n      <td>[0.5678099535609165]</td>\n      <td>[0.18855620203502088]</td>\n      <td>[0.0704366237100511]</td>\n      <td>[0.5026785366965085]</td>\n      <td>[0.43834549418631563]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.18181725562983586]</td>\n      <td>[0.504554994285418]</td>\n      <td>[0.45065731586912583]</td>\n      <td>[0.24549085203770704]</td>\n      <td>[0.0027100271002709064]</td>\n      <td>[0.01049834490142576]</td>\n      <td>[0.0]</td>\n      <td>[0.4857969017147452]</td>\n      <td>[0.4682317397336653]</td>\n      <td>[0.42183989167301106]</td>\n      <td>[0.4634433459850662]</td>\n      <td>[0.6683597389630767]</td>\n      <td>[0.47631365924048863]</td>\n    </tr>\n    <tr>\n      <th>f1_score_weighted</th>\n      <td>[0.8360766763626712]</td>\n      <td>[0.8734704046383025]</td>\n      <td>[0.8527376441371826]</td>\n      <td>[0.9091539479147899]</td>\n      <td>[0.9021127651963996]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.874029224943002]</td>\n      <td>[0.8137222733923168]</td>\n      <td>[0.7619281474085273]</td>\n      <td>[0.885603431576398]</td>\n      <td>[0.8360791405851395]</td>\n      <td>[0.838121172790458]</td>\n      <td>[0.8353395018439429]</td>\n      <td>[0.8460707311907201]</td>\n      <td>[0.7833284785713038]</td>\n      <td>[0.9059001624604492]</td>\n      <td>[0.9077359298259612]</td>\n      <td>[0.7980005387650213]</td>\n      <td>[0.780990681745509]</td>\n    </tr>\n    <tr>\n      <th>AUC_micro</th>\n      <td>[0.867283717224562]</td>\n      <td>[0.9674742390295684]</td>\n      <td>[0.9690237426919438]</td>\n      <td>[0.979695082216353]</td>\n      <td>[0.9781770788959222]</td>\n      <td>[0.9671327090063806]</td>\n      <td>[0.9689628604521034]</td>\n      <td>[0.9638113571627587]</td>\n      <td>[0.8403098454687172]</td>\n      <td>[0.8168685252175435]</td>\n      <td>[0.9758990146932517]</td>\n      <td>[0.9620013769886318]</td>\n      <td>[0.9664436620529103]</td>\n      <td>[0.9646894061678959]</td>\n      <td>[0.8152561129775422]</td>\n      <td>[0.8218743163988292]</td>\n      <td>[0.9796031601658833]</td>\n      <td>[0.9789109815994712]</td>\n      <td>[0.8500510959493968]</td>\n      <td>[0.8372882074048831]</td>\n    </tr>\n    <tr>\n      <th>f1_score_micro</th>\n      <td>[0.807587253414264]</td>\n      <td>[0.8977238239757208]</td>\n      <td>[0.8937784522003035]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.7787556904400607]</td>\n      <td>[0.7098634294385432]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.8883156297420334]</td>\n      <td>[0.8889226100151746]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8251896813353566]</td>\n      <td>[0.7380880121396055]</td>\n      <td>[0.91350531107739]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.7538694992412747]</td>\n      <td>[0.7347496206373293]</td>\n    </tr>\n    <tr>\n      <th>accuracy</th>\n      <td>[0.8075872534142641]</td>\n      <td>[0.8977238239757208]</td>\n      <td>[0.8937784522003035]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.7787556904400607]</td>\n      <td>[0.7098634294385432]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.8883156297420334]</td>\n      <td>[0.8889226100151745]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8251896813353566]</td>\n      <td>[0.7380880121396055]</td>\n      <td>[0.91350531107739]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.7538694992412747]</td>\n      <td>[0.7347496206373293]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_weighted</th>\n      <td>[0.9304181641544951]</td>\n      <td>[0.9296047783413253]</td>\n      <td>[0.9346650357763759]</td>\n      <td>[0.9531771295804466]</td>\n      <td>[0.9505970434373063]</td>\n      <td>[0.9296135047518299]</td>\n      <td>[0.9346302904136361]</td>\n      <td>[0.9261914684092131]</td>\n      <td>[0.9167575337775034]</td>\n      <td>[0.9075281090202592]</td>\n      <td>[0.947605275820125]</td>\n      <td>[0.9230563356136842]</td>\n      <td>[0.9302722797235586]</td>\n      <td>[0.9249830178919962]</td>\n      <td>[0.9119455591708515]</td>\n      <td>[0.9062323533622272]</td>\n      <td>[0.9537303389034844]</td>\n      <td>[0.9523313424166967]</td>\n      <td>[0.9312969618844966]</td>\n      <td>[0.9228010519654273]</td>\n    </tr>\n    <tr>\n      <th>precision_score_macro</th>\n      <td>[0.6509144555262674]</td>\n      <td>[0.771526544069397]</td>\n      <td>[0.8332602979842244]</td>\n      <td>[0.7819118765348991]</td>\n      <td>[0.7723958081530135]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.8013746369202237]</td>\n      <td>[0.6272547828727604]</td>\n      <td>[0.5991528525304791]</td>\n      <td>[0.822098675416211]</td>\n      <td>[0.9441408621736491]</td>\n      <td>[0.8445288753799393]</td>\n      <td>[0.4440060698027314]</td>\n      <td>[0.6479677677435953]</td>\n      <td>[0.6082348926272665]</td>\n      <td>[0.8013659654657674]</td>\n      <td>[0.7898061615019645]</td>\n      <td>[0.6472357088584144]</td>\n      <td>[0.6089180227598306]</td>\n    </tr>\n    <tr>\n      <th>AUC_macro</th>\n      <td>[0.8843607540655037]</td>\n      <td>[0.8977728875033111]</td>\n      <td>[0.9067254240553342]</td>\n      <td>[0.9450464668693166]</td>\n      <td>[0.9392346349984347]</td>\n      <td>[0.8978043779070737]</td>\n      <td>[0.9070060591241593]</td>\n      <td>[0.8787369384288513]</td>\n      <td>[0.8449861720079948]</td>\n      <td>[0.8166985275457677]</td>\n      <td>[0.9308878256246677]</td>\n      <td>[0.8720044753420877]</td>\n      <td>[0.8943163525961986]</td>\n      <td>[0.8855198787804694]</td>\n      <td>[0.8399199217556086]</td>\n      <td>[0.8091153604632424]</td>\n      <td>[0.9445991178982194]</td>\n      <td>[0.9412907731264598]</td>\n      <td>[0.9015017217841351]</td>\n      <td>[0.8541160736282689]</td>\n    </tr>\n    <tr>\n      <th>average_precision_score_macro</th>\n      <td>[0.7448150864800028]</td>\n      <td>[0.7350353031090393]</td>\n      <td>[0.7536148238449915]</td>\n      <td>[0.8151093723721079]</td>\n      <td>[0.8065229883244922]</td>\n      <td>[0.7437635899759644]</td>\n      <td>[0.7522881472130623]</td>\n      <td>[0.7353653714991217]</td>\n      <td>[0.7130296686202923]</td>\n      <td>[0.7051892654657641]</td>\n      <td>[0.7985126174047921]</td>\n      <td>[0.7211471018351185]</td>\n      <td>[0.7426680720283891]</td>\n      <td>[0.7202537380617574]</td>\n      <td>[0.6971184763497635]</td>\n      <td>[0.6991223063191867]</td>\n      <td>[0.8179885620700982]</td>\n      <td>[0.8133700670556807]</td>\n      <td>[0.739794512680513]</td>\n      <td>[0.7304329783698786]</td>\n    </tr>\n    <tr>\n      <th>recall_score_micro</th>\n      <td>[0.8075872534142641]</td>\n      <td>[0.8977238239757208]</td>\n      <td>[0.8937784522003035]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.7787556904400607]</td>\n      <td>[0.7098634294385432]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.8883156297420334]</td>\n      <td>[0.8889226100151745]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8251896813353566]</td>\n      <td>[0.7380880121396055]</td>\n      <td>[0.91350531107739]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.7538694992412747]</td>\n      <td>[0.7347496206373293]</td>\n    </tr>\n    <tr>\n      <th>f1_score_macro</th>\n      <td>[0.6749097416744476]</td>\n      <td>[0.6263947343363969]</td>\n      <td>[0.5385419967796974]</td>\n      <td>[0.7653697272147331]</td>\n      <td>[0.7416848907681176]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6238596220952524]</td>\n      <td>[0.6421108480393055]</td>\n      <td>[0.5885824653131508]</td>\n      <td>[0.6650154684677185]</td>\n      <td>[0.47312070913357085]</td>\n      <td>[0.48125503272562103]</td>\n      <td>[0.4703423886834914]</td>\n      <td>[0.6727173400213553]</td>\n      <td>[0.6088490326197913]</td>\n      <td>[0.7451864020181578]</td>\n      <td>[0.7562208685787566]</td>\n      <td>[0.650493260252315]</td>\n      <td>[0.6081275337139075]</td>\n    </tr>\n    <tr>\n      <th>log_loss</th>\n      <td>[0.5151473339191622]</td>\n      <td>[0.24040114103947297]</td>\n      <td>[0.23651897069392966]</td>\n      <td>[0.17775706110025447]</td>\n      <td>[0.1874363495858499]</td>\n      <td>[0.2640780378739563]</td>\n      <td>[0.24565149763737298]</td>\n      <td>[0.2555320591882684]</td>\n      <td>[0.5523627651771427]</td>\n      <td>[0.5661148327428956]</td>\n      <td>[0.33655623030329523]</td>\n      <td>[0.27030910523410084]</td>\n      <td>[0.26219431706539736]</td>\n      <td>[0.2569340400852136]</td>\n      <td>[0.5306028903049885]</td>\n      <td>[0.5753453288658256]</td>\n      <td>[0.2043178465521144]</td>\n      <td>[0.19315052319891854]</td>\n      <td>[0.4654335118668296]</td>\n      <td>[0.5840565551646402]</td>\n    </tr>\n    <tr>\n      <th>balanced_accuracy</th>\n      <td>[0.7839049767804582]</td>\n      <td>[0.5942781010175104]</td>\n      <td>[0.5352183118550256]</td>\n      <td>[0.7513392683482543]</td>\n      <td>[0.7191727470931578]</td>\n      <td>[0.5]</td>\n      <td>[0.5]</td>\n      <td>[0.5909086278149179]</td>\n      <td>[0.752277497142709]</td>\n      <td>[0.7253286579345629]</td>\n      <td>[0.6227454260188535]</td>\n      <td>[0.5013550135501355]</td>\n      <td>[0.5052491724507129]</td>\n      <td>[0.5]</td>\n      <td>[0.7428984508573726]</td>\n      <td>[0.7341158698668326]</td>\n      <td>[0.7109199458365055]</td>\n      <td>[0.7317216729925331]</td>\n      <td>[0.8341798694815383]</td>\n      <td>[0.7381568296202443]</td>\n    </tr>\n    <tr>\n      <th>recall_score_macro</th>\n      <td>[0.7839049767804582]</td>\n      <td>[0.5942781010175104]</td>\n      <td>[0.5352183118550256]</td>\n      <td>[0.7513392683482543]</td>\n      <td>[0.7191727470931578]</td>\n      <td>[0.5]</td>\n      <td>[0.5]</td>\n      <td>[0.5909086278149179]</td>\n      <td>[0.752277497142709]</td>\n      <td>[0.7253286579345629]</td>\n      <td>[0.6227454260188535]</td>\n      <td>[0.5013550135501355]</td>\n      <td>[0.5052491724507129]</td>\n      <td>[0.5]</td>\n      <td>[0.7428984508573726]</td>\n      <td>[0.7341158698668326]</td>\n      <td>[0.7109199458365055]</td>\n      <td>[0.7317216729925331]</td>\n      <td>[0.8341798694815383]</td>\n      <td>[0.7381568296202443]</td>\n    </tr>\n    <tr>\n      <th>recall_score_weighted</th>\n      <td>[0.8075872534142641]</td>\n      <td>[0.8977238239757208]</td>\n      <td>[0.8937784522003035]</td>\n      <td>[0.9116843702579667]</td>\n      <td>[0.9071320182094081]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.9001517450682853]</td>\n      <td>[0.7787556904400607]</td>\n      <td>[0.7098634294385432]</td>\n      <td>[0.9062215477996965]</td>\n      <td>[0.8883156297420334]</td>\n      <td>[0.8889226100151745]</td>\n      <td>[0.8880121396054628]</td>\n      <td>[0.8251896813353566]</td>\n      <td>[0.7380880121396055]</td>\n      <td>[0.91350531107739]</td>\n      <td>[0.9125948406676783]</td>\n      <td>[0.7538694992412747]</td>\n      <td>[0.7347496206373293]</td>\n    </tr>\n    <tr>\n      <th>precision_score_weighted</th>\n      <td>[0.8932691707105429]</td>\n      <td>[0.877014103638037]</td>\n      <td>[0.881243219016942]</td>\n      <td>[0.9072720074188747]</td>\n      <td>[0.8991976076061607]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8830870489340036]</td>\n      <td>[0.882956314654666]</td>\n      <td>[0.8764172454334369]</td>\n      <td>[0.8929725418691179]</td>\n      <td>[0.9007928150046113]</td>\n      <td>[0.8790843638007296]</td>\n      <td>[0.788565560086672]</td>\n      <td>[0.8814332149636538]</td>\n      <td>[0.8779622903470479]</td>\n      <td>[0.9038098049544845]</td>\n      <td>[0.9051941264511486]</td>\n      <td>[0.9127299659265484]</td>\n      <td>[0.8795247475476895]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 30,
      "metadata": {
        "gather": {
          "logged": 1616515163545
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Retrieve the Best Model"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Retrieve best model from Pipeline Run\n",
        "best_model_output = pipeline_run.get_pipeline_output(best_model_output_name)\n",
        "num_file_downloaded = best_model_output.download('.', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading azureml/058f59b1-d96b-485c-af4f-fbe8f855d202/model_data\n",
            "Downloaded azureml/058f59b1-d96b-485c-af4f-fbe8f855d202/model_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 31,
      "metadata": {
        "gather": {
          "logged": 1616515168089
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open(best_model_output._path_on_datastore, \"rb\" ) as f:\n",
        "    best_model = pickle.load(f)\n",
        "best_model"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "PipelineWithYTransformations(Pipeline={'memory': None,\n                                       'steps': [('datatransformer',\n                                                  DataTransformer(enable_dnn=None,\n                                                                  enable_feature_sweeping=None,\n                                                                  feature_sweeping_config=None,\n                                                                  feature_sweeping_timeout=None,\n                                                                  featurization_config=None,\n                                                                  force_text_dnn=None,\n                                                                  is_cross_validation=None,\n                                                                  is_onnx_compatible=None,\n                                                                  logger=None,\n                                                                  observer=None,\n                                                                  task=None,\n                                                                  working_dir=None))...\n                                                                     importance_type='split',\n                                                                     learning_rate=0.1,\n                                                                     max_depth=-1,\n                                                                     min_child_samples=20,\n                                                                     min_child_weight=0.001,\n                                                                     min_split_gain=0.0,\n                                                                     n_estimators=100,\n                                                                     n_jobs=1,\n                                                                     num_leaves=31,\n                                                                     objective=None,\n                                                                     random_state=None,\n                                                                     reg_alpha=0.0,\n                                                                     reg_lambda=0.0,\n                                                                     silent=True,\n                                                                     subsample=1.0,\n                                                                     subsample_for_bin=200000,\n                                                                     subsample_freq=0,\n                                                                     verbose=-10))],\n                                       'verbose': False},\n                             y_transformer={},\n                             y_transformer_name='LabelEncoder')"
          },
          "metadata": {}
        }
      ],
      "execution_count": 32,
      "metadata": {
        "gather": {
          "logged": 1616515188382
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_model.steps"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "[('datatransformer',\n  DataTransformer(enable_dnn=None, enable_feature_sweeping=None,\n                  feature_sweeping_config=None, feature_sweeping_timeout=None,\n                  featurization_config=None, force_text_dnn=None,\n                  is_cross_validation=None, is_onnx_compatible=None, logger=None,\n                  observer=None, task=None, working_dir=None)),\n ('MaxAbsScaler', MaxAbsScaler(copy=True)),\n ('LightGBMClassifier',\n  LightGBMClassifier(boosting_type='gbdt', class_weight=None,\n                     colsample_bytree=1.0, importance_type='split',\n                     learning_rate=0.1, max_depth=-1, min_child_samples=20,\n                     min_child_weight=0.001, min_split_gain=0.0, n_estimators=100,\n                     n_jobs=1, num_leaves=31, objective=None, random_state=None,\n                     reg_alpha=0.0, reg_lambda=0.0, silent=True, subsample=1.0,\n                     subsample_for_bin=200000, subsample_freq=0, verbose=-10))]"
          },
          "metadata": {}
        }
      ],
      "execution_count": 33,
      "metadata": {
        "gather": {
          "logged": 1616515203248
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Test the Model\n",
        "#### Load Test Data\n",
        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv')\n",
        "df_test = dataset_test.to_pandas_dataframe()\n",
        "df_test = df_test[pd.notnull(df_test['y'])]\n",
        "\n",
        "y_test = df_test['y']\n",
        "X_test = df_test.drop(['y'], axis=1)"
      ],
      "outputs": [],
      "execution_count": 34,
      "metadata": {
        "gather": {
          "logged": 1616515212356
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "\n",
        "We will use confusion matrix to see how our model works."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "ypred = best_model.predict(X_test)\n",
        "cm = confusion_matrix(y_test, ypred)"
      ],
      "outputs": [],
      "execution_count": 35,
      "metadata": {
        "gather": {
          "logged": 1616515216762
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the confusion matrix\n",
        "pd.DataFrame(cm).style.background_gradient(cmap='Blues', low=0, high=0.9)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "<pandas.io.formats.style.Styler at 0x7f9c568ab198>",
            "text/html": "<style  type=\"text/css\" >\n    #T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row0_col0 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }    #T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row0_col1 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row1_col0 {\n            background-color:  #f7fbff;\n            color:  #000000;\n        }    #T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row1_col1 {\n            background-color:  #63a8d3;\n            color:  #000000;\n        }</style><table id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8level0_row0\" class=\"row_heading level0 row0\" >0</th>\n                        <td id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row0_col0\" class=\"data row0 col0\" >28431</td>\n                        <td id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row0_col1\" class=\"data row0 col1\" >827</td>\n            </tr>\n            <tr>\n                        <th id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8level0_row1\" class=\"row_heading level0 row1\" >1</th>\n                        <td id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row1_col0\" class=\"data row1 col0\" >1246</td>\n                        <td id=\"T_dde9052e_8bf0_11eb_a71b_bbdb56e8efd8row1_col1\" class=\"data row1 col1\" >2446</td>\n            </tr>\n    </tbody></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 36,
      "metadata": {
        "gather": {
          "logged": 1616515220622
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Publish and run from REST endpoint\n",
        "\n",
        "Run the following code to publish the pipeline to your workspace. In your workspace in the portal, you can see metadata for the pipeline including run history and durations. You can also run the pipeline manually from the portal.\n",
        "\n",
        "Additionally, publishing the pipeline enables a REST endpoint to rerun the pipeline from any HTTP library on any platform.\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "published_pipeline = pipeline_run.publish_pipeline(\n",
        "    name=\"Bankmarketing Train\", description=\"Training bankmarketing pipeline\", version=\"1.0\")\n",
        "\n",
        "published_pipeline\n"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "Pipeline(Name: Bankmarketing Train,\nId: a3c971b6-8bb9-4e1b-bdb4-30fbe3e73f7e,\nStatus: Active,\nEndpoint: https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourceGroups/aml-quickstarts-141212/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-141212/PipelineRuns/PipelineSubmit/a3c971b6-8bb9-4e1b-bdb4-30fbe3e73f7e)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Id</th><th>Status</th><th>Endpoint</th></tr><tr><td>Bankmarketing Train</td><td><a href=\"https://ml.azure.com/pipelines/a3c971b6-8bb9-4e1b-bdb4-30fbe3e73f7e?wsid=/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourcegroups/aml-quickstarts-141212/workspaces/quick-starts-ws-141212\" target=\"_blank\" rel=\"noopener\">a3c971b6-8bb9-4e1b-bdb4-30fbe3e73f7e</a></td><td>Active</td><td><a href=\"https://southcentralus.api.azureml.ms/pipelines/v1.0/subscriptions/d4ad7261-832d-46b2-b093-22156001df5b/resourceGroups/aml-quickstarts-141212/providers/Microsoft.MachineLearningServices/workspaces/quick-starts-ws-141212/PipelineRuns/PipelineSubmit/a3c971b6-8bb9-4e1b-bdb4-30fbe3e73f7e\" target=\"_blank\" rel=\"noopener\">REST Endpoint</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 37,
      "metadata": {
        "gather": {
          "logged": 1616515224717
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Authenticate once again, to retrieve the `auth_header` so that the endpoint can be used"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
        "\n",
        "interactive_auth = InteractiveLoginAuthentication()\n",
        "auth_header = interactive_auth.get_authentication_header()\n",
        "\n"
      ],
      "outputs": [],
      "execution_count": 38,
      "metadata": {
        "gather": {
          "logged": 1616515229502
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get the REST url from the endpoint property of the published pipeline object. You can also find the REST url in your workspace in the portal. Build an HTTP POST request to the endpoint, specifying your authentication header. Additionally, add a JSON payload object with the experiment name and the batch size parameter. As a reminder, the process_count_per_node is passed through to ParallelRunStep because you defined it is defined as a PipelineParameter object in the step configuration.\n",
        "\n",
        "Make the request to trigger the run. Access the Id key from the response dict to get the value of the run id.\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "rest_endpoint = published_pipeline.endpoint\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=auth_header, \n",
        "                         json={\"ExperimentName\": \"pipeline-rest-endpoint\"}\n",
        "                        )"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    response.raise_for_status()\n",
        "except Exception:    \n",
        "    raise Exception(\"Received bad response from the endpoint: {}\\n\"\n",
        "                    \"Response Code: {}\\n\"\n",
        "                    \"Headers: {}\\n\"\n",
        "                    \"Content: {}\".format(rest_endpoint, response.status_code, response.headers, response.content))\n",
        "\n",
        "run_id = response.json().get('Id')\n",
        "print('Submitted pipeline run: ', run_id)"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use the run id to monitor the status of the new run. This will take another 10-15 min to run and will look similar to the previous pipeline run, so if you don't need to see another pipeline run, you can skip watching the full output."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core.run import PipelineRun\n",
        "from azureml.widgets import RunDetails\n",
        "\n",
        "published_pipeline_run = PipelineRun(ws.experiments[\"pipeline-rest-endpoint\"], run_id)\n",
        "RunDetails(published_pipeline_run).show()"
      ],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {}
    }
  ],
  "metadata": {
    "order_index": 11,
    "exclude_from_index": false,
    "task": "Demonstrates the use of AutoMLStep",
    "deployment": [
      "None"
    ],
    "authors": [
      {
        "name": "sanpil"
      }
    ],
    "star_tag": [
      "featured"
    ],
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "compute": [
      "AML Compute"
    ],
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "tags": [
      "None"
    ],
    "datasets": [
      "Custom"
    ],
    "categories": [
      "how-to-use-azureml",
      "machine-learning-pipelines",
      "intro-to-pipelines"
    ],
    "category": "tutorial",
    "framework": [
      "Automated Machine Learning"
    ],
    "friendly_name": "How to use AutoMLStep with AML Pipelines",
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}