1079 lines
28 KiB
Plaintext
1079 lines
28 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import Counter\n",
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"import torch\n",
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"import pandas as pd\n",
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"from torchtext.vocab import vocab\n",
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"from sklearn.model_selection import train_test_split\n",
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"from tqdm.notebook import tqdm\n",
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"from sklearn.metrics import accuracy_score"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CUDA nie jest dostępna. Model będzie uruchomiony na CPU.\n"
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]
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}
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],
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"source": [
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"if torch.cuda.is_available():\n",
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" print(\"CUDA jest dostępna!\")\n",
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" print(f\"Nazwa urządzenia: {torch.cuda.get_device_name(0)}\")\n",
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" device = torch.device(\"cuda\")\n",
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"else:\n",
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" print(\"CUDA nie jest dostępna. Model będzie uruchomiony na CPU.\")\n",
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" device = torch.device(\"cpu\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data = pd.read_csv(\"train.tsv\", sep='\\t', header=None, names=['labels', 'documents'])\n",
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"train_data[\"tokenized_documents\"] = train_data[\"documents\"].apply(lambda x: x.split())\n",
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"train_data[\"tokenized_labels\"] = train_data[\"labels\"].apply(lambda x: x.split())\n",
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"\n",
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"X_train, X_val, y_train, y_val = train_test_split(\n",
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" train_data[\"tokenized_documents\"], train_data[\"tokenized_labels\"], test_size=0.2, random_state=42\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def build_vocab(dataset):\n",
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" counter = Counter()\n",
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" for document in dataset:\n",
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" counter.update(document)\n",
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" return vocab(counter, specials=[\"<unk>\", \"<pad>\", \"<bos>\", \"<eos>\"])\n",
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"\n",
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"train_vocab = build_vocab(X_train)\n",
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"itos = train_vocab.get_itos()\n",
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"train_vocab.set_default_index(train_vocab[\"<unk>\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def data_process(dt):\n",
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" return [\n",
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" torch.tensor(\n",
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" [train_vocab[\"<bos>\"]] + [train_vocab[token] for token in document] + [train_vocab[\"<eos>\"]],\n",
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" dtype=torch.long,\n",
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" )\n",
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" for document in dt\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_tokens_ids = data_process(X_train)\n",
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"val_tokens_ids = data_process(X_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"labels = [\"O\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\", \"B-MISC\", \"I-MISC\"]\n",
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"\n",
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"label_to_index = {label: idx for idx, label in enumerate(labels)}\n",
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"\n",
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"def labels_process(dt, label_to_index):\n",
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" return [\n",
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" torch.tensor(\n",
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" [0] + [label_to_index[label] for label in document] + [0],\n",
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" dtype=torch.long,\n",
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" device=device,\n",
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" )\n",
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" for document in dt\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_labels = labels_process(y_train, label_to_index)\n",
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"val_labels = labels_process(y_val, label_to_index)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"all_label_indices = [\n",
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" label_to_index[label]\n",
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" for document in y_train\n",
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" for label in document\n",
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"]\n",
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"\n",
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"num_tags = max(all_label_indices) + 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"class LSTM(torch.nn.Module):\n",
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"\n",
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" def __init__(self):\n",
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" super(LSTM, self).__init__()\n",
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" self.emb = torch.nn.Embedding(len(train_vocab.get_itos()), 100)\n",
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" self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)\n",
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" self.fc1 = torch.nn.Linear(256, num_tags)\n",
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"\n",
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" def forward(self, x):\n",
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" emb = torch.relu(self.emb(x))\n",
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" lstm_output, (h_n, c_n) = self.rec(emb)\n",
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" out_weights = self.fc1(lstm_output)\n",
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" return out_weights"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"lstm = LSTM()\n",
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"criterion = torch.nn.CrossEntropyLoss()\n",
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"optimizer = torch.optim.Adam(lstm.parameters())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_scores(y_true, y_pred):\n",
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" acc_score = 0\n",
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" tp = 0\n",
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" fp = 0\n",
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" selected_items = 0\n",
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" relevant_items = 0\n",
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"\n",
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" for p, t in zip(y_pred, y_true):\n",
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" if p == t:\n",
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" acc_score += 1\n",
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"\n",
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" if p > 0 and p == t:\n",
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" tp += 1\n",
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"\n",
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" if p > 0:\n",
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" selected_items += 1\n",
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"\n",
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" if t > 0:\n",
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" relevant_items += 1\n",
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"\n",
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" if selected_items == 0:\n",
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" precision = 1.0\n",
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" else:\n",
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" precision = tp / selected_items\n",
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"\n",
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" if relevant_items == 0:\n",
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" recall = 1.0\n",
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" else:\n",
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" recall = tp / relevant_items\n",
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"\n",
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" if precision + recall == 0.0:\n",
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" f1 = 0.0\n",
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" else:\n",
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" f1 = 2 * precision * recall / (precision + recall)\n",
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"\n",
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" return precision, recall, f1\n",
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"\n",
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"def eval_model(dataset_tokens, dataset_labels, model):\n",
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" Y_true = []\n",
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" Y_pred = []\n",
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" for i in tqdm(range(len(dataset_labels))):\n",
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" batch_tokens = dataset_tokens[i].unsqueeze(0)\n",
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" tags = list(dataset_labels[i].numpy())\n",
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" Y_true += tags\n",
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"\n",
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" Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n",
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" Y_batch_pred = torch.argmax(Y_batch_pred_weights, 1)\n",
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" Y_pred += list(Y_batch_pred.numpy())\n",
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"\n",
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" return get_scores(Y_true, Y_pred)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"text": [
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"(0.5042095416276894, 0.07628078120577413, 0.1325138291333743)\n"
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"(0.6831020812685827, 0.3901783187093122, 0.4966672671590705)\n"
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],
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"source": [
|
|
"NUM_EPOCHS = 20\n",
|
|
"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",
|
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" tags = train_labels[i].unsqueeze(1)\n",
|
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"\n",
|
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" predicted_tags = lstm(batch_tokens)\n",
|
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"\n",
|
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" optimizer.zero_grad()\n",
|
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" loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))\n",
|
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"\n",
|
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" loss.backward()\n",
|
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" optimizer.step()\n",
|
|
"\n",
|
|
" lstm.eval()\n",
|
|
" print(eval_model(val_tokens_ids, val_labels, lstm))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
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|
"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"model_id": "7966898b2ca64115a34fad154c0518dc",
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},
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"metadata": {},
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2574e1adda9946e8a2fb0201726beba7",
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"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",
|
|
" }\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\n",
|
|
"\n",
|
|
"dev_data = pd.read_csv(\"dev-0/in.tsv\", sep=\"\\t\", names=[\"Text\"])\n",
|
|
"dev_labels = pd.read_csv(\"dev-0/expected.tsv\", sep=\"\\t\", names=[\"Label\"])\n",
|
|
"test_A_data = pd.read_csv(\"test-A/in.tsv\", sep=\"\\t\", names=[\"Text\"])\n",
|
|
"dev_data[\"tokenized_text\"] = dev_data[\"Text\"].apply(lambda x: x.split())\n",
|
|
"dev_labels[\"tokenized_labels\"] = dev_labels[\"Label\"].apply(lambda x: x.split())\n",
|
|
"test_A_data[\"tokenized_text\"] = test_A_data[\"Text\"].apply(lambda x: x.split())\n",
|
|
"dev_0_tokens_ids = data_process(dev_data[\"tokenized_text\"])\n",
|
|
"test_A_tokens_ids = data_process(test_A_data[\"tokenized_text\"])\n",
|
|
"dev_0_labels = labels_process(dev_labels[\"tokenized_labels\"], label_to_index)\n",
|
|
"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)\n",
|
|
"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": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Accuracy: 0.9441390252001624\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"with open('dev-0/out.tsv', 'r') as file:\n",
|
|
" predicted_labels = [line.strip().split()[1:] for line in file]\n",
|
|
"\n",
|
|
"with open('dev-0/expected.tsv', 'r') as file:\n",
|
|
" true_labels = [line.strip().split()[1:] for line in file]\n",
|
|
"\n",
|
|
"predicted_labels = [label for sublist in predicted_labels for label in sublist]\n",
|
|
"true_labels = [label for sublist in true_labels for label in sublist]\n",
|
|
"\n",
|
|
"accuracy = accuracy_score(true_labels, predicted_labels)\n",
|
|
"print(\"Accuracy:\", accuracy)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.11"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|