DL_RNN/RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation of packages\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install torch\n",
    "%pip install torchtext\n",
    "%pip install datasets\n",
    "%pip install pandas\n",
    "%pip install scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import torch\n",
    "import pandas as pd\n",
    "from torchtext.vocab import vocab\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    train_dataset = pd.read_csv(\n",
    "        \"train/train.tsv.xz\", compression=\"xz\", sep=\"\\t\", names=[\"Label\", \"Text\"]\n",
    "    )\n",
    "    dev_0_dataset = pd.read_csv(\"dev-0/in.tsv\", sep=\"\\t\", names=[\"Text\"])\n",
    "    dev_0_labels = pd.read_csv(\"dev-0/expected.tsv\", sep=\"\\t\", names=[\"Label\"])\n",
    "    test_A_dataset = pd.read_csv(\"test-A/in.tsv\", sep=\"\\t\", names=[\"Text\"])\n",
    "\n",
    "    return train_dataset, dev_0_dataset, dev_0_labels, test_A_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset, dev_0_dataset, dev_0_labels, test_A_dataset = read_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the training data into training and validation sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_texts, val_texts, train_labels, val_labels = train_test_split(\n",
    "    train_dataset[\"Text\"], train_dataset[\"Label\"], test_size=0.1, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = pd.DataFrame({\"Text\": train_texts, \"Label\": train_labels})\n",
    "val_dataset = pd.DataFrame({\"Text\": val_texts, \"Label\": val_labels})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenize the text and labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset[\"tokenized_text\"] = train_dataset[\"Text\"].apply(lambda x: x.split())\n",
    "train_dataset[\"tokenized_labels\"] = train_dataset[\"Label\"].apply(lambda x: x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset[\"tokenized_text\"] = val_dataset[\"Text\"].apply(lambda x: x.split())\n",
    "val_dataset[\"tokenized_labels\"] = val_dataset[\"Label\"].apply(lambda x: x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_0_dataset[\"tokenized_text\"] = dev_0_dataset[\"Text\"].apply(lambda x: x.split())\n",
    "dev_0_dataset[\"tokenized_labels\"] = dev_0_labels[\"Label\"].apply(lambda x: x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_A_dataset[\"tokenized_text\"] = test_A_dataset[\"Text\"].apply(lambda x: x.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a vocab object which maps tokens to indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_vocab(dataset):\n",
    "    counter = Counter()\n",
    "    for document in dataset:\n",
    "        counter.update(document)\n",
    "    return vocab(counter, specials=[\"<unk>\", \"<pad>\", \"<bos>\", \"<eos>\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = build_vocab(train_dataset[\"tokenized_text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map indices to tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "itos = v.get_itos()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of tokens in the vocabulary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22154"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(itos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index of the 'rejects' token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9086"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[\"rejects\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index of the '\\<unk\\>' token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[\"<unk>\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the default index to the unknown token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "v.set_default_index(v[\"<unk>\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use cuda if available\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorize the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_process(dt):\n",
    "    return [\n",
    "        torch.tensor(\n",
    "            [v[\"<bos>\"]] + [v[token] for token in document] + [v[\"<eos>\"]],\n",
    "            dtype=torch.long,\n",
    "            device=device,\n",
    "        )\n",
    "        for document in dt\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_tokens_ids = data_process(train_dataset[\"tokenized_text\"])\n",
    "val_tokens_ids = data_process(val_dataset[\"tokenized_text\"])\n",
    "dev_0_tokens_ids = data_process(dev_0_dataset[\"tokenized_text\"])\n",
    "test_A_tokens_ids = data_process(test_A_dataset[\"tokenized_text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a mapping from label to index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"O\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\", \"B-MISC\", \"I-MISC\"]\n",
    "\n",
    "label_to_index = {label: idx for idx, label in enumerate(labels)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorize the labels (NER)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def labels_process(dt, label_to_index):\n",
    "    return [\n",
    "        torch.tensor(\n",
    "            [0] + [label_to_index[label] for label in document] + [0],\n",
    "            dtype=torch.long,\n",
    "            device=device,\n",
    "        )\n",
    "        for document in dt\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = labels_process(train_dataset[\"tokenized_labels\"], label_to_index)\n",
    "val_labels = labels_process(val_dataset[\"tokenized_labels\"], label_to_index)\n",
    "dev_0_labels = labels_process(dev_0_dataset[\"tokenized_labels\"], label_to_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for evaluation (returns precision, recall, and F1 score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores(y_true, y_pred):\n",
    "    acc_score = 0\n",
    "    tp = 0\n",
    "    fp = 0\n",
    "    selected_items = 0\n",
    "    relevant_items = 0\n",
    "\n",
    "    for p, t in zip(y_pred, y_true):\n",
    "        if p == t:\n",
    "            acc_score += 1\n",
    "\n",
    "        if p > 0 and p == t:\n",
    "            tp += 1\n",
    "\n",
    "        if p > 0:\n",
    "            selected_items += 1\n",
    "\n",
    "        if t > 0:\n",
    "            relevant_items += 1\n",
    "\n",
    "    if selected_items == 0:\n",
    "        precision = 1.0\n",
    "    else:\n",
    "        precision = tp / selected_items\n",
    "\n",
    "    if relevant_items == 0:\n",
    "        recall = 1.0\n",
    "    else:\n",
    "        recall = tp / relevant_items\n",
    "\n",
    "    if precision + recall == 0.0:\n",
    "        f1 = 0.0\n",
    "    else:\n",
    "        f1 = 2 * precision * recall / (precision + recall)\n",
    "\n",
    "    return precision, recall, f1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the number of unique tags\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "all_label_indices = [\n",
    "    label_to_index[label]\n",
    "    for document in train_dataset[\"tokenized_labels\"]\n",
    "    for label in document\n",
    "]\n",
    "\n",
    "num_tags = max(all_label_indices) + 1\n",
    "\n",
    "print(num_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation of a recurrent neural network LSTM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers, num_tags):\n",
    "        super(LSTM, self).__init__()\n",
    "        self.embedding = torch.nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.rec = torch.nn.LSTM(\n",
    "            embedding_dim, hidden_dim, num_layers, batch_first=True, bidirectional=True\n",
    "        )\n",
    "        self.fc1 = torch.nn.Linear(hidden_dim * 2, num_tags)\n",
    "\n",
    "    def forward(self, x):\n",
    "        embedding = torch.relu(self.embedding(x))\n",
    "        lstm_output, _ = self.rec(embedding)\n",
    "        out_weights = self.fc1(lstm_output)\n",
    "        return out_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the LSTM model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm = LSTM(len(v.get_itos()), 100, 100, 1, num_tags).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the loss function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the optimizer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(lstm.parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for model evaluation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_model(dataset_tokens, dataset_labels, model):\n",
    "    Y_true = []\n",
    "    Y_pred = []\n",
    "    for i in tqdm(range(len(dataset_labels))):\n",
    "        batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
    "        tags = list(dataset_labels[i].cpu().numpy())\n",
    "        Y_true += tags\n",
    "\n",
    "        Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
    "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
    "        Y_pred += list(Y_batch_pred.cpu().numpy())\n",
    "\n",
    "    return get_scores(Y_true, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function for returning the predictions labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_labels(dataset_tokens, model, label_to_index):\n",
    "    Y_pred = []\n",
    "    inv_label_to_index = {\n",
    "        v: k for k, v in label_to_index.items()\n",
    "    }  # Create the inverse mapping\n",
    "\n",
    "    for i in tqdm(range(len(dataset_tokens))):\n",
    "        batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
    "        Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
    "        Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
    "        predicted_labels = [inv_label_to_index[label.item()] for label in Y_batch_pred]\n",
    "        predicted_labels = predicted_labels[1:-1]\n",
    "        Y_pred.append(\" \".join(predicted_labels))\n",
    "\n",
    "    return Y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_EPOCHS = 20"
   ]
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     "text": [
      "(0.8324958123953099, 0.7146965775093471, 0.7691117301145157)\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.839452603471295, 0.7233247052056371, 0.7770739996910242)\n"
     ]
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    {
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    {
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     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8292120013188262, 0.7233247052056371, 0.7726574500768049)\n"
     ]
    },
    {
     "data": {
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       "version_minor": 0
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     },
     "metadata": {},
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    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8110863332271424, 0.73224043715847, 0.7696493349455865)\n"
     ]
    },
    {
     "data": {
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    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8210254756530152, 0.73224043715847, 0.7740954697476436)\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
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    {
     "data": {
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     },
     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.8173884938590821, 0.727351164797239, 0.7697458529904124)\n"
     ]
    }
   ],
   "source": [
    "for i in range(NUM_EPOCHS):\n",
    "    lstm.train()\n",
    "    for i in tqdm(range(len(train_labels))):\n",
    "        batch_tokens = train_tokens_ids[i].unsqueeze(0)\n",
    "        tags = train_labels[i].unsqueeze(1)\n",
    "\n",
    "        predicted_tags = lstm(batch_tokens)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
    "\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    lstm.eval()\n",
    "    print(eval_model(val_tokens_ids, val_labels, lstm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "0cd8fbb1f4a34b569773014519a586de",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.8173884938590821, 0.727351164797239, 0.7697458529904124)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_model(val_tokens_ids, val_labels, lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ac118471d25448a910260c4ffd01bf5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.8368401624215578, 0.7924726171055698, 0.8140523071398648)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_model(dev_0_tokens_ids, dev_0_labels, lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79cda761e6e9434f99bbc1e2022bf9a7",
       "version_major": 2,
       "version_minor": 0
      },
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       "  0%|          | 0/215 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dev_0_predictons = pred_labels(dev_0_tokens_ids, lstm, label_to_index)\n",
    "dev_0_predictons = pd.DataFrame(dev_0_predictons, columns=[\"Label\"])\n",
    "dev_0_predictons.to_csv(\"dev-0/out.tsv\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35e9a5e3a08b4404a6dfdd5a94418028",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/230 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_A_predictions = pred_labels(test_A_tokens_ids, lstm, label_to_index)\n",
    "test_A_predictions = pd.DataFrame(test_A_predictions, columns=[\"Label\"])\n",
    "test_A_predictions.to_csv(\"test-A/out.tsv\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correct labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_labels(input_file, output_file):\n",
    "    df = pd.read_csv(input_file, sep=\"\\t\", names=[\"Text\"])\n",
    "\n",
    "    corrected_lines = []\n",
    "\n",
    "    for line in df[\"Text\"]:\n",
    "        tokens = line.split(\" \")\n",
    "        corrected_tokens = []\n",
    "        previous_token = \"O\"\n",
    "\n",
    "        for token in tokens:\n",
    "            if (\n",
    "                token == \"I-ORG\"\n",
    "                and previous_token != \"B-ORG\"\n",
    "                and previous_token != \"I-ORG\"\n",
    "            ):\n",
    "                corrected_tokens.append(\"B-ORG\")\n",
    "            elif (\n",
    "                token == \"I-PER\"\n",
    "                and previous_token != \"B-PER\"\n",
    "                and previous_token != \"I-PER\"\n",
    "            ):\n",
    "                corrected_tokens.append(\"B-PER\")\n",
    "            elif (\n",
    "                token == \"I-LOC\"\n",
    "                and previous_token != \"B-LOC\"\n",
    "                and previous_token != \"I-LOC\"\n",
    "            ):\n",
    "                corrected_tokens.append(\"B-LOC\")\n",
    "            elif (\n",
    "                token == \"I-MISC\"\n",
    "                and previous_token != \"B-MISC\"\n",
    "                and previous_token != \"I-MISC\"\n",
    "            ):\n",
    "                corrected_tokens.append(\"B-MISC\")\n",
    "            else:\n",
    "                corrected_tokens.append(token)\n",
    "\n",
    "            previous_token = token\n",
    "\n",
    "        corrected_line = \" \".join(corrected_tokens)\n",
    "        corrected_lines.append(corrected_line)\n",
    "\n",
    "    df[\"Text\"] = corrected_lines\n",
    "    df.to_csv(output_file, sep=\"\\t\", index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "correct_labels(\"dev-0/out.tsv\", \"dev-0/out.tsv\")\n",
    "correct_labels(\"test-A/out.tsv\", \"test-A/out.tsv\")"
   ]
  }
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