13_cnn_rnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "73ca9bd9-f475-43d8-9549-6ebb319e27af",
   "metadata": {},
   "source": [
    "#### Модели CNN, SepCNN, LSTM на базе предобученных статических эмбеддингов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75595727-61da-4a02-b8fa-df589158c70f",
   "metadata": {
    "id": "75595727-61da-4a02-b8fa-df589158c70f"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af46418a-840e-41f7-bf70-6543c5e0f477",
   "metadata": {
    "id": "af46418a-840e-41f7-bf70-6543c5e0f477"
   },
   "outputs": [],
   "source": [
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff82ec20-992e-4012-b592-6c52cc8a556c",
   "metadata": {
    "id": "ff82ec20-992e-4012-b592-6c52cc8a556c"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "23f68e03-87b6-4054-87c5-501d80d1c2e1",
   "metadata": {
    "id": "23f68e03-87b6-4054-87c5-501d80d1c2e1"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3e0264f-e676-4c68-9b1d-b2be886fb758",
   "metadata": {
    "id": "a3e0264f-e676-4c68-9b1d-b2be886fb758"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import random_split\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "425b2e09-06ac-4365-8d46-2dcf315c57ce",
   "metadata": {
    "id": "425b2e09-06ac-4365-8d46-2dcf315c57ce"
   },
   "outputs": [],
   "source": [
    "def set_seed(seed):\n",
    "    np.random.seed(seed)\n",
    "    torch.random.manual_seed(seed)\n",
    "    torch.cuda.random.manual_seed(seed)\n",
    "    torch.cuda.random.manual_seed_all(seed)\n",
    "\n",
    "def get_available_device():\n",
    "    cuda = torch.cuda.is_available()\n",
    "    return torch.device('cuda' if cuda else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec676150-0eb6-45f3-9f5a-05a577c82401",
   "metadata": {
    "id": "ec676150-0eb6-45f3-9f5a-05a577c82401"
   },
   "outputs": [],
   "source": [
    "def emb(data):\n",
    "    emb = torch.nn.Embedding(*data.shape)\n",
    "    emb.weight.data.copy_(torch.tensor(data))\n",
    "    return emb\n",
    "\n",
    "# def emb(num_embeddings, embedding_dim):\n",
    "#     return torch.nn.Embedding(num_embeddings, embedding_dim)\n",
    "\n",
    "def conv(in_channels, out_channels, kernel_size):\n",
    "    return torch.nn.Sequential(\n",
    "        torch.nn.Conv1d(in_channels, out_channels, kernel_size), \n",
    "        torch.nn.ReLU(), \n",
    "        torch.nn.AdaptiveMaxPool1d(1), \n",
    "        torch.nn.Flatten()\n",
    "    )\n",
    "\n",
    "def sep_conv(in_channels, out_channels, kernel_size):\n",
    "    return torch.nn.Sequential(\n",
    "        torch.nn.Conv1d(in_channels, in_channels, kernel_size, groups=in_channels), \n",
    "        torch.nn.Conv1d(in_channels, out_channels, 1),\n",
    "        torch.nn.ReLU(), \n",
    "        torch.nn.AdaptiveMaxPool1d(1), \n",
    "        torch.nn.Flatten()\n",
    "    )\n",
    "\n",
    "def lstm(input_size, hidden_size):\n",
    "    return torch.nn.LSTM(input_size, hidden_size, batch_first=True)\n",
    "\n",
    "def mlp(in_features, out_features):\n",
    "    return torch.nn.Sequential(\n",
    "        torch.nn.Linear(in_features, out_features // 2), \n",
    "        torch.nn.Dropout(0.2), \n",
    "        torch.nn.Linear(out_features // 2, out_features)\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0524a7ee-4a4e-4579-a4ed-53ee8ba7f866",
   "metadata": {
    "id": "0524a7ee-4a4e-4579-a4ed-53ee8ba7f866"
   },
   "outputs": [],
   "source": [
    "class CNN(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, emb_list):\n",
    "        super().__init__()  \n",
    "        self.emb = emb(emb_list)\n",
    "        self.conv2 = conv(300, 100, 2)\n",
    "        self.conv3 = conv(300, 100, 3)\n",
    "        self.conv4 = conv(300, 100, 4)\n",
    "        self.mlp = mlp(300, 6)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.emb(x)\n",
    "        x = x.permute(0,2,1)\n",
    "        x2 = self.conv2(x)\n",
    "        x3 = self.conv3(x)\n",
    "        x4 = self.conv4(x)\n",
    "        x = torch.concat((x2,x3,x4), -1)\n",
    "        return self.mlp(x)\n",
    "\n",
    "# model = CNN()\n",
    "# token_indices = [[1,5,7,8],[8,4,2,9]]\n",
    "# model(torch.tensor(token_indices)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77dc678c-75c9-4809-bb8f-6cbd589861f6",
   "metadata": {
    "id": "77dc678c-75c9-4809-bb8f-6cbd589861f6"
   },
   "outputs": [],
   "source": [
    "class SepCNN(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, emb_list):\n",
    "        super().__init__()  \n",
    "        self.emb = emb(emb_list)\n",
    "        self.conv2 = sep_conv(300, 100, 2)\n",
    "        self.conv3 = sep_conv(300, 100, 3)\n",
    "        self.conv4 = sep_conv(300, 100, 4)\n",
    "        self.mlp = mlp(300, 6)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.emb(x)\n",
    "        x = x.permute(0,2,1)\n",
    "        x2 = self.conv2(x)\n",
    "        x3 = self.conv3(x)\n",
    "        x4 = self.conv4(x)\n",
    "        x = torch.concat((x2,x3,x4), -1)\n",
    "        return self.mlp(x)\n",
    "\n",
    "# model = SepCNN()\n",
    "# token_indices = [[1,5,7,8],[8,4,2,9]]\n",
    "# model(torch.tensor(token_indices)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a399736-837d-45c1-8061-58d8c0e4b5af",
   "metadata": {
    "id": "6a399736-837d-45c1-8061-58d8c0e4b5af"
   },
   "outputs": [],
   "source": [
    "class LSTM(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, emb_list):\n",
    "        super().__init__()  \n",
    "        self.emb = emb(emb_list)\n",
    "        self.lstm = lstm(300, 300)\n",
    "        self.mlp = mlp(300, 6)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = self.emb(x)\n",
    "        x, _ = self.lstm(x)\n",
    "        return self.mlp(x[:,-1,:])\n",
    "\n",
    "# model = LSTM()\n",
    "# token_indices = [[1,5,7,3],[8,4,2,4]]\n",
    "# model(torch.tensor(token_indices)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a8c226f-807f-4bcf-b9b7-1407fa85aa0e",
   "metadata": {
    "id": "0a8c226f-807f-4bcf-b9b7-1407fa85aa0e"
   },
   "outputs": [],
   "source": [
    "class MyDataset(Dataset):\n",
    "    \n",
    "    def __init__(self, df, w2i):\n",
    "        super(Dataset).__init__()\n",
    "        self.df, self.w2i = df, w2i\n",
    "    \n",
    "    def tokenize(self, text):\n",
    "        return [self.w2i[w] for w in text.split() if w in self.w2i]\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        s = df.iloc[idx]['title'] + '. ' + df.iloc[idx]['text']\n",
    "        ids = torch.tensor(self.tokenize(s), dtype=torch.long)\n",
    "        topic = torch.tensor(df.iloc[idx]['topic'], dtype=torch.long)\n",
    "        return ids, topic\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "# dataset = MyDataset(df, w2i)\n",
    "# dataset[333]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1a766c00-566f-4bd9-a28f-c157f69b171b",
   "metadata": {
    "id": "1a766c00-566f-4bd9-a28f-c157f69b171b"
   },
   "outputs": [],
   "source": [
    "def pad(X):\n",
    "    max_len = len(max(X, key=len))\n",
    "    max_len = max(4, max_len) # convs\n",
    "    l = lambda x: torch.tensor([len(x)])\n",
    "    f = lambda to_pad: torch.full((to_pad,), w2i['PAD'])\n",
    "    p = lambda x: torch.cat((x, f(max_len-len(x)), l(x)))\n",
    "    return torch.stack([p(x) for x in X])\n",
    "\n",
    "def collate_fn(batch):\n",
    "    X, y = zip(*batch)\n",
    "    return pad(X), torch.stack(y)\n",
    "\n",
    "# loader = DataLoader(dataset, batch_size=32, shuffle=True, collate_fn=collate_fn)\n",
    "# batch = next(iter(loader))\n",
    "# batch[0].shape, batch[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eDpaBMW59asR",
   "metadata": {
    "id": "eDpaBMW59asR"
   },
   "outputs": [],
   "source": [
    "def metrics_stat(y_true, y_pred):\n",
    "    return {'acc': accuracy_score(y_true, y_pred),\n",
    "            'f1': f1_score(y_true, y_pred, average='macro')}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "993efde0-936e-4f35-9a32-5de39e04c493",
   "metadata": {
    "id": "993efde0-936e-4f35-9a32-5de39e04c493"
   },
   "outputs": [],
   "source": [
    "def nn_epoch(model, optimizer, loss_fn, \n",
    "             loader, device, train=False):\n",
    "    \n",
    "    model = model.to(device)\n",
    "    torch.set_grad_enabled(train)\n",
    "    (model.eval, model.train)[int(train)]()\n",
    "    \n",
    "    pbar, total_loss = tqdm(loader), 0\n",
    "    preds = torch.empty(0, dtype=torch.int8)\n",
    "    targets = torch.empty(0, dtype=torch.int8)\n",
    "  \n",
    "    for input_ids, labels in pbar:\n",
    "        \n",
    "        input_ids = input_ids.to(device)\n",
    "        labels = labels.to(device).long()\n",
    "        \n",
    "        if train:\n",
    "            optimizer.zero_grad()\n",
    "        \n",
    "        logits = model(input_ids)\n",
    "        loss = loss_fn(logits, labels)\n",
    "        \n",
    "        if train: \n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "        \n",
    "        targets = torch.cat((targets, labels.cpu()))\n",
    "        preds = torch.cat((preds, torch.argmax(logits.cpu(), 1)))\n",
    "        pbar.set_description(f'loss: {loss.item():.4}')\n",
    "        total_loss += loss.item()\n",
    "    \n",
    "    avg_loss = total_loss / len(loader)\n",
    "    pbar.set_description(f'loss: {avg_loss:.4}')\n",
    "    return {'preds': preds, 'targets': targets, 'loss': avg_loss}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89d8ec83-831e-4236-b35c-943322de2c5e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "89d8ec83-831e-4236-b35c-943322de2c5e",
    "outputId": "259d106e-5ca6-47a4-faa3-cecad53b0d1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "251cbbe0-fc09-4c1a-8885-a2f21b08936a",
   "metadata": {
    "id": "251cbbe0-fc09-4c1a-8885-a2f21b08936a"
   },
   "outputs": [],
   "source": [
    "!unzip /content/drive/MyDrive/shared/topic/data.zip -d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8b54dea-9f3e-47f4-9c64-b17fd38f058a",
   "metadata": {
    "id": "e8b54dea-9f3e-47f4-9c64-b17fd38f058a"
   },
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "device = get_available_device()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c2894f76-0ab2-4d1b-b591-cd63e9824986",
   "metadata": {
    "id": "c2894f76-0ab2-4d1b-b591-cd63e9824986"
   },
   "outputs": [],
   "source": [
    "df = pd.read_feather('data/prep.ftr')\n",
    "w2v_embs = load_pickle('data/w2v_embs.bin')\n",
    "w2v_embs['PAD'] = np.zeros((300,), dtype=np.float32)\n",
    "w2i = {k: i for i, k in enumerate(w2v_embs.keys())}\n",
    "embs = np.stack(list(w2v_embs.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8baf9baf-24b7-4bde-88e8-c394c7792a84",
   "metadata": {
    "id": "8baf9baf-24b7-4bde-88e8-c394c7792a84"
   },
   "outputs": [],
   "source": [
    "dataset = MyDataset(df, w2i)\n",
    "train_size = int(0.800 * len(dataset))\n",
    "valid_size = len(dataset) - train_size\n",
    "generator = torch.Generator().manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bbf86b7b-a871-4736-8da0-8978f8856863",
   "metadata": {
    "id": "bbf86b7b-a871-4736-8da0-8978f8856863"
   },
   "outputs": [],
   "source": [
    "train_dataset, valid_dataset = random_split(dataset, [train_size, valid_size], generator)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=collate_fn)\n",
    "valid_loader = DataLoader(valid_dataset, batch_size=32, shuffle=False, collate_fn=collate_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b94e1836-abe4-49c0-ad1d-36ffbed90742",
   "metadata": {
    "id": "b94e1836-abe4-49c0-ad1d-36ffbed90742"
   },
   "outputs": [],
   "source": [
    "model = LSTM(embs)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n",
    "loss_fn = torch.nn.CrossEntropyLoss(ignore_index=w2i['PAD'])"
   ]
  },
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      "train {'acc': 0.7237523356646594, 'f1': 0.6813519418742978}\n"
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      "valid {'acc': 0.9338439466864125, 'f1': 0.9276628103892647}\n"
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     "text": [
      "train {'acc': 0.9009446787340264, 'f1': 0.8896533347622763}\n"
     ]
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     "text": [
      "valid {'acc': 0.9355215660866346, 'f1': 0.9295253870754764}\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 6\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    \n",
    "    res = nn_epoch(\n",
    "        model, optimizer, loss_fn, \n",
    "        train_loader, device, train=True\n",
    "    )\n",
    "\n",
    "    print('train', metrics_stat(res['targets'], res['preds']))\n",
    "    \n",
    "    res = nn_epoch(\n",
    "        model, optimizer, loss_fn, \n",
    "        valid_loader, device, train=False\n",
    "    )\n",
    "    \n",
    "    print('valid', metrics_stat(res['targets'], res['preds']))\n",
    "    torch.save(model.state_dict(), f'data/model_{epoch+1:02}.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ioL1510gtNMS",
   "metadata": {
    "id": "ioL1510gtNMS"
   },
   "outputs": [],
   "source": [
    "# CNN acc 0.929 after 5 epochs\n",
    "# SepCNN acc 0.924 after 5 epochs\n",
    "# LSTM acc 0.936 after 6 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8b224f18-9e84-4f00-bae9-101604801862",
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   "source": [
    "res = nn_epoch(model, None, loss_fn, valid_loader, device, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e311567-33ec-4312-b4e7-fe0abe1738d3",
   "metadata": {
    "colab": {
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    "outputId": "65030aa6-e29c-47ee-f1dc-d733de8c6974"
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    {
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      "text/plain": [
       "0.9355215660866346"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(res['targets'], res['preds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8f5ccbac-83bb-414b-91dc-08216b800ebc",
   "metadata": {
    "colab": {
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    },
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    "outputId": "54bff3aa-90e4-4b58-b5ca-238e118d7363"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9295253870754764"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(res['targets'], res['preds'], average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f7fe354-6740-44f6-96d5-da9a156d7c38",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2f7fe354-6740-44f6-96d5-da9a156d7c38",
    "outputId": "ace361d6-504a-4ba4-d02b-2fddbd7e48bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7956   284   367   284    33   138]\n",
      " [  271 10077   253    80    22     7]\n",
      " [  433   222 25905   338    54   301]\n",
      " [  312    94   502  9670     5   106]\n",
      " [   24   164   127    10 12527     1]\n",
      " [  366    57   513   190    15 14724]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(res['targets'], res['preds']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "83c884a4-970d-4f7b-b2da-119b485a0614",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "83c884a4-970d-4f7b-b2da-119b485a0614",
    "outputId": "a7c156d5-4fce-40a2-ed71-dd55df61d11b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.88      0.86      9062\n",
      "           1       0.92      0.94      0.93     10710\n",
      "           2       0.94      0.95      0.94     27253\n",
      "           3       0.91      0.90      0.91     10689\n",
      "           4       0.99      0.97      0.98     12853\n",
      "           5       0.96      0.93      0.95     15865\n",
      "\n",
      "    accuracy                           0.94     86432\n",
      "   macro avg       0.93      0.93      0.93     86432\n",
      "weighted avg       0.94      0.94      0.94     86432\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(res['targets'], res['preds']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e9844e0d-4bde-4037-a68a-5098628d2917",
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    },
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    "outputId": "1adbd209-207a-4ebb-c60c-922364152819"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Интернет и СМИ', 'Культура', 'Мир', 'Наука и техника', 'Спорт', 'Экономика']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
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    }
   ],
   "source": [
    "list(load_json('data/labels.json').keys())"
   ]
  }
 ],
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