Kontur.ipynb

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            "_view_module": "@jupyter-widgets/base",
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            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
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      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSCkP1EbmoS4",
        "outputId": "baa8336c-2f9a-4b9d-8f24-70229b0c3504"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xq23MwVIho5s",
        "outputId": "f5dc30ac-e8b3-4f6c-81e7-9ecedbbd4daa"
      },
      "source": [
        "!wget https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ru.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-04-30 16:01:16--  https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ru.zip\n",
            "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 172.67.9.4, 104.22.75.142, 104.22.74.142, ...\n",
            "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|172.67.9.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8344906764 (7.8G) [application/zip]\n",
            "Saving to: ‘wiki.ru.zip’\n",
            "\n",
            "wiki.ru.zip         100%[===================>]   7.77G  40.8MB/s    in 3m 20s  \n",
            "\n",
            "2021-04-30 16:04:36 (39.7 MB/s) - ‘wiki.ru.zip’ saved [8344906764/8344906764]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGfzD5S7ycyz",
        "outputId": "fcdba7bb-36af-4a30-ee79-8c5b7ba5a9da"
      },
      "source": [
        "!unzip wiki.ru.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  wiki.ru.zip\n",
            "  inflating: wiki.ru.vec             \n",
            "  inflating: wiki.ru.bin             \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nk_2DiR77Zu2"
      },
      "source": [
        "import re\n",
        "import shutil\n",
        "\n",
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from torch import nn, optim\n",
        "from gensim.models import FastText\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tqdm.autonotebook import tqdm"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ_lGf_4ywlE"
      },
      "source": [
        "model = FastText.load_fasttext_format('wiki.ru.bin')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "gtN0Hu_VG9Vn",
        "outputId": "458f9515-97b3-42a7-9142-7ea6a235520b"
      },
      "source": [
        "shutil.copy('/content/drive/MyDrive/Contur/kontur_srs_internship_test_task.zip', './')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'./kontur_srs_internship_test_task.zip'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNczvb6lOwtz",
        "outputId": "c303e052-bd13-4652-f992-a0ab36af2384"
      },
      "source": [
        "!unzip kontur_srs_internship_test_task.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  kontur_srs_internship_test_task.zip\n",
            "  inflating: train.txt               \n",
            "  inflating: __MACOSX/._train.txt    \n",
            "  inflating: test.txt                \n",
            "  inflating: __MACOSX/._test.txt     \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s74aGlR4zxQp"
      },
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7T7XBSJG9Zh"
      },
      "source": [
        "def read_file(path_to_file):\n",
        "    data = []\n",
        "    with open(path_to_file) as f:\n",
        "        for line in f:\n",
        "            data.append(line.strip())\n",
        "    return data"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wpe3m3YAG9b3"
      },
      "source": [
        "train = pd.DataFrame(read_file('.//train.txt'), columns=['text'])\n",
        "test = pd.DataFrame(read_file('.//test.txt'), columns=['text'])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4IZubAj4SdK"
      },
      "source": [
        "train, val = train_test_split(train, test_size=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70IqGeORPBYL",
        "outputId": "a464a5c7-e7a7-4d13-9c73-097333281a54"
      },
      "source": [
        "print('Train shape:     ', train.shape[0],\n",
        "      '\\nValidation shape:', val.shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape:      4709229 \n",
            "Validation shape: 523248\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVScQYGZiZS6"
      },
      "source": [
        "def simple_tokenizer(text):\n",
        "    tokens = re.findall(r'[\\w]+', text)\n",
        "    return tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JvVa42nn_Gs"
      },
      "source": [
        "def get_labels(tokens):\n",
        "    result = []\n",
        "    for token in tokens:\n",
        "        if token.islower():\n",
        "            result.append(0)\n",
        "        elif token.isupper():\n",
        "            result.append(2)\n",
        "        else:\n",
        "            result.append(1)\n",
        "    return result"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB_NCbI_ntL7"
      },
      "source": [
        "dict_transfrom = {\n",
        "    0:'lower',\n",
        "    1:'capitalize',\n",
        "    2:'upper',\n",
        "    3:'pad'\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSyql1jFnqOX"
      },
      "source": [
        "def get_vec_and_labels(text, max_size):\n",
        "        tokens = simple_tokenizer(text)\n",
        "        labels = get_labels(tokens)\n",
        "        vecs = []\n",
        "        for token in tokens:\n",
        "            try:\n",
        "                if token in model.wv.vocab:\n",
        "                    vecs.append(model.wv[token.lower()])\n",
        "                else:\n",
        "                    vecs.append(model.wv.word_vec(token.lower()))\n",
        "            except:\n",
        "                vecs.append(model.wv['<unk>'])\n",
        "        lenght = len(labels)\n",
        "        if max_size is None:\n",
        "            return vecs, tokens, labels\n",
        "        elif len(labels) < max_size:\n",
        "            labels.extend([3 for _ in range(max_size - lenght)])\n",
        "            vecs.extend([model.wv['<pad>'] for _ in range(max_size - lenght)])\n",
        "        else:\n",
        "            labels = labels[:max_size]\n",
        "            vecs = vecs[:max_size]\n",
        "        return vecs, labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BmgcROgG9Xw"
      },
      "source": [
        "class RegDataset(Dataset):\n",
        "    def __init__(self, text, text2vec_label, max_size=60):\n",
        "        self.text = text\n",
        "        self.transform = text2vec_label\n",
        "        self.maxsize = max_size\n",
        "    def __len__(self):\n",
        "        return len(self.text)\n",
        "\n",
        "    def __getitem__(self, ind):\n",
        "        vecs, labels = self.transform(self.text.iloc[ind, 0], self.maxsize)\n",
        "        return torch.tensor(vecs), torch.tensor(labels, dtype=torch.long)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pogWesK05oUH"
      },
      "source": [
        "train_dataset = RegDataset(train, get_vec_and_labels, 20)\n",
        "val_dataset = RegDataset(val, get_vec_and_labels, 20)\n",
        "test_dataset = RegDataset(val, get_vec_and_labels, 60)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdLHaHVs5wKR"
      },
      "source": [
        "train_loader = DataLoader(train_dataset, batch_size=1000, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=1000)\n",
        "test_loader = DataLoader(test_dataset, batch_size=1000)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4cWPSwX-xPP"
      },
      "source": [
        "class RNN_Reg(nn.Module):\n",
        "    def __init__(self, in_size, hid_dim, n_layers, dropout, bidirectional):\n",
        "        super().__init__()\n",
        "        self.rnn = nn.LSTM(\n",
        "            input_size=in_size,\n",
        "            hidden_size=hid_dim,\n",
        "            num_layers=n_layers,\n",
        "            dropout=dropout,\n",
        "            bidirectional=bidirectional\n",
        "        )\n",
        "        self.lin = nn.Sequential(\n",
        "            nn.Linear(hid_dim * (2 if bidirectional else 1), 100),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout),\n",
        "            nn.Linear(100, 4)\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        # x shape: batch, seq, hid_dim\n",
        "        seq_len = x.size(1)\n",
        "        x = x.permute(1, 0, 2)\n",
        "        output, (h_n, c_n) = self.rnn(x)\n",
        "        output = output.permute(1, 2, 0)\n",
        "        output_model = torch.zeros((output.size(0), 4, output.size(2))).to(x.device)\n",
        "        for j in range(seq_len):\n",
        "            output_model[:, :, j] = self.lin(output[:, :, j])\n",
        "             \n",
        "        return output_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lI6EwXGSvY8"
      },
      "source": [
        "NN = RNN_Reg(300, 200, 2, 0.5, True).to(device)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ut9YeJOpdbMg"
      },
      "source": [
        "loss_func = nn.CrossEntropyLoss(weight=torch.tensor([1, 1, 1, 1e-10]).to(device))\n",
        "opt = optim.Adam(NN.parameters(), lr=3e-4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tq-QJQEMkvVL"
      },
      "source": [
        "torch.cuda.empty_cache()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXuAHSkdSNPC"
      },
      "source": [
        "max_epochs = 1\n",
        "patience = 3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377,
          "referenced_widgets": [
            "7ad20a95864c494fac8f64aab5ec157c",
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            "f644258ebaed4ba3ad0c19cbd5762e5d",
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            "5cbb4fe120404481a26484a8d12dd6f0",
            "bd53efac246a444398a85e3c25188e6c",
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            "945148fec88b4a1eba5bb5e4247d0031",
            "25d341ab45c24de2888efff167f9b3c7",
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            "7ecf3b17720d4466a3c08c0fe95de94c",
            "567f53c41eb64aa293398240fe3f3f3a",
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            "f6285d493ed74242975c864f7739966d"
          ]
        },
        "id": "SGtyPtNdb073",
        "outputId": "a14a27db-c846-463d-f800-261db4ba9123"
      },
      "source": [
        "min_loss = np.inf\n",
        "\n",
        "cur_patience = 0\n",
        "\n",
        "for epoch in range(1, max_epochs + 1):\n",
        "    train_loss = 0.0\n",
        "    NN.train()\n",
        "    pbar = tqdm(enumerate(train_loader), total=len(train_loader), leave=False)\n",
        "    pbar.set_description(f\"Epoch {epoch}\")\n",
        "    for it, batch in pbar:\n",
        "        with torch.set_grad_enabled(True):\n",
        "\n",
        "            opt.zero_grad()\n",
        "            data = batch[0].to(device)\n",
        "            label = batch[1].to(device)\n",
        "\n",
        "            output = NN(data)\n",
        "\n",
        "            loss = loss_func(output, label)\n",
        "            loss.backward()\n",
        "            train_loss += loss.item()\n",
        "\n",
        "            opt.step()\n",
        "\n",
        "    train_loss /= len(train_loader)\n",
        "\n",
        "    val_loss = 0.0\n",
        "    NN.eval()\n",
        "\n",
        "    pbar = tqdm(enumerate(val_loader), total=len(val_loader), leave=False)\n",
        "    pbar.set_description(f\"Epoch {epoch}\")\n",
        "    for it, batch in pbar:\n",
        "        with torch.no_grad():\n",
        "            data = batch[0].to(device)\n",
        "            label = batch[1].to(device)\n",
        "\n",
        "            output = NN(data)\n",
        "\n",
        "            loss = loss_func(output, label)\n",
        "            val_loss += loss.item()        \n",
        "\n",
        "    val_loss /= len(val_loader)\n",
        "    if val_loss < min_loss:\n",
        "        min_loss = val_loss\n",
        "        best_model = NN.state_dict()\n",
        "    else:\n",
        "        cur_patience += 1\n",
        "        if cur_patience == patience:\n",
        "            cur_patience = 0\n",
        "            break\n",
        "    \n",
        "    print(f'Epoch: {epoch}, Training Loss: {train_loss:.6f}, Validation Loss: {val_loss:.6f}')\n",
        "NN.load_state_dict(best_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7ad20a95864c494fac8f64aab5ec157c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4710.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=524.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 1, Training Loss: 0.249313, Validation Loss: 0.208511\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          },
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        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-56-234570327157>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mpbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_description\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Epoch {epoch}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m   1103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1105\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m             \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampler_iter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 517\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    518\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_collation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-53-047a7c39f40c>\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, ind)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mvecs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvecs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lae1kFPRb0-c"
      },
      "source": [
        "def change_register_corpus(text):\n",
        "    with torch.no_grad():\n",
        "        vecs, tokens, _ = get_vec_and_labels(text, None)\n",
        "        input = torch.tensor(vecs).unsqueeze(0).to(device)\n",
        "        output = torch.argmax(NN(input), dim=1).squeeze(0).cpu().tolist()\n",
        "        sub_string = re.sub(r'[\\w]+', '/', text)\n",
        "        true_tokens = change_register(output, tokens)\n",
        "        return change_tokens(true_tokens, sub_string)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4gpv1jmK5J9"
      },
      "source": [
        "def change_register(transform, tokens):\n",
        "    for i in range(len(tokens)):\n",
        "        if transform[i] == 0:\n",
        "            tokens[i] = tokens[i].lower()\n",
        "        elif transform[i] == 1:\n",
        "            tokens[i] = tokens[i].capitalize()\n",
        "        elif transform[i] == 2:\n",
        "            tokens[i] = tokens[i].upper()\n",
        "        elif transform[i] == 3:\n",
        "            tokens[i] = tokens[i].lower()\n",
        "    return tokens"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_yw4igTMtte"
      },
      "source": [
        "def change_tokens(tokens, text):\n",
        "    for token in tokens:\n",
        "        text = re.sub(r'/', token, text, count=1)\n",
        "    return text"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srrfpiZ0b1MD"
      },
      "source": [
        "test['predicted'] = test.text.apply(change_register_corpus)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRqEJtM7Vfcf"
      },
      "source": [
        "test.to_csv('result.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zg5WKOpIVo4I"
      },
      "source": [
        "with open('test_right.txt', 'w') as f:\n",
        "    for i in range(test.shape[0]):\n",
        "        f.write(test.iloc[i, 1]+'\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5xVous_O0CS"
      },
      "source": [
        "result = pd.DataFrame(read_file('.//result.txt'), columns=['text'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yysh50ivPGh6"
      },
      "source": [
        "test['result'] = result['text']"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "tJR9stttQXOV",
        "outputId": "b420046a-8201-4561-c92b-32a84e20200f"
      },
      "source": [
        "test.head(10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ РАДИО...</td>\n",
              "      <td>Общество с ограниченной ответственностью Радио...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ГБУ \"АЗНАКАЙЛЕС\"</td>\n",
              "      <td>ГБУ \"Азнакайлес\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ОАО \"АЗНАКАЕВСКОЕ ПАТП\"</td>\n",
              "      <td>ОАО \"Азнакаевское патп\"</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"АЗНА...</td>\n",
              "      <td>Общество с ограниченной ответственностью \"Азна...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"АЛНАЗ\"</td>\n",
              "      <td>Общество с ограниченной ответственностью \"Алназ\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"МАГИ...</td>\n",
              "      <td>Общество с ограниченной ответственностью \"Маги...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"ЭНТЕР\"</td>\n",
              "      <td>Общество с ограниченной ответственностью \"Энтер\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ЗАКРЫТОЕ АКЦИОНЕРНОЕ ОБЩЕСТВО \"АЗГЕОФИЗИКА\"</td>\n",
              "      <td>Закрытое акционерное общество \"Азгеофизика\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ГИАГИНСКАЯ РАЙОННАЯ ОБЩЕСТВЕННАЯ ОРГАНИЗАЦИЯ А...</td>\n",
              "      <td>Гиагинская районная общественная организация А...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>РО ПП \"КОПРФ\" В РА</td>\n",
              "      <td>РО ПП \"Копрф\" в ра</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                text                                             result\n",
              "0  ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ РАДИО...  Общество с ограниченной ответственностью Радио...\n",
              "1                                   ГБУ \"АЗНАКАЙЛЕС\"                                   ГБУ \"Азнакайлес\"\n",
              "2                            ОАО \"АЗНАКАЕВСКОЕ ПАТП\"                            ОАО \"Азнакаевское патп\"\n",
              "3  ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"АЗНА...  Общество с ограниченной ответственностью \"Азна...\n",
              "4   ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"АЛНАЗ\"   Общество с ограниченной ответственностью \"Алназ\"\n",
              "5  ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"МАГИ...  Общество с ограниченной ответственностью \"Маги...\n",
              "6   ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ \"ЭНТЕР\"   Общество с ограниченной ответственностью \"Энтер\"\n",
              "7        ЗАКРЫТОЕ АКЦИОНЕРНОЕ ОБЩЕСТВО \"АЗГЕОФИЗИКА\"        Закрытое акционерное общество \"Азгеофизика\"\n",
              "8  ГИАГИНСКАЯ РАЙОННАЯ ОБЩЕСТВЕННАЯ ОРГАНИЗАЦИЯ А...  Гиагинская районная общественная организация А...\n",
              "9                                 РО ПП \"КОПРФ\" В РА                                 РО ПП \"Копрф\" в ра"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    }
  ]
}