File size: 17,450 Bytes
cd123bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 |
{
"cells": [
{
"cell_type": "markdown",
"id": "9bfb61e1",
"metadata": {},
"source": [
"# Сравниваем модели и сохраняем в `src/models/pretrained`"
]
},
{
"cell_type": "markdown",
"id": "f0574ac3",
"metadata": {},
"source": [
"- Импорты\n",
"- Константы\n",
"- Считывание датасетов"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5a237c5c",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import time\n",
"import torch\n",
"import warnings\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
"for warn in [UserWarning, FutureWarning]: warnings.filterwarnings(\"ignore\", category = warn)\n",
"\n",
"from src.data_utils.config import DatasetConfig\n",
"from src.data_utils.dataset_params import DatasetName\n",
"from src.data_utils.dataset_generator import DatasetGenerator\n",
"from src.models.models import TransformerClassifier, CustomMambaClassifier, LSTMClassifier\n",
"\n",
"MAX_SEQ_LEN = 300\n",
"EMBEDDING_DIM = 128\n",
"BATCH_SIZE = 32\n",
"LEARNING_RATE = 1e-4\n",
"NUM_EPOCHS = 5 # для быстрого сравнения моделей\n",
"NUM_CLASSES = 2\n",
"\n",
"SAVE_DIR = \"../pretrained_comparison\"\n",
"os.makedirs(SAVE_DIR, exist_ok=True)\n",
"DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"config = DatasetConfig(\n",
" load_from_disk=True,\n",
" path_to_data=\"../datasets\"\n",
")\n",
"\n",
"generator = DatasetGenerator(DatasetName.IMDB, config=config)\n",
"(X_train, y_train), (X_val, y_val), (X_test, y_test) = generator.generate_dataset()\n",
"VOCAB_SIZE = len(generator.vocab)"
]
},
{
"cell_type": "markdown",
"id": "5b95192d",
"metadata": {},
"source": [
"Вспомогательные функции для трейна/валидации/теста"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b2a4534c",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def train_and_evaluate(model, train_loader, val_loader, optimizer, criterion, num_epochs, device, model_name, save_path):\n",
" best_val_f1 = 0.0\n",
" history = {'train_loss': [], 'val_loss': [], 'val_accuracy': [], 'val_f1': []}\n",
" \n",
" print(f\"--- Начало обучения модели: {model_name} на устройстве {device} ---\")\n",
"\n",
" for epoch in range(num_epochs):\n",
" model.train()\n",
" start_time = time.time()\n",
" total_train_loss = 0\n",
"\n",
" for batch_X, batch_y in train_loader:\n",
" batch_X, batch_y = batch_X.to(device), batch_y.to(device)\n",
" optimizer.zero_grad()\n",
" outputs = model(batch_X)\n",
" loss = criterion(outputs, batch_y)\n",
" loss.backward()\n",
" optimizer.step()\n",
" total_train_loss += loss.item()\n",
" \n",
" avg_train_loss = total_train_loss / len(train_loader)\n",
" history['train_loss'].append(avg_train_loss)\n",
"\n",
" model.eval()\n",
" total_val_loss = 0\n",
" all_preds = []\n",
" all_labels = []\n",
"\n",
" with torch.no_grad():\n",
" for batch_X, batch_y in val_loader:\n",
" batch_X, batch_y = batch_X.to(device), batch_y.to(device)\n",
" outputs = model(batch_X)\n",
" loss = criterion(outputs, batch_y)\n",
" total_val_loss += loss.item()\n",
" \n",
" _, predicted = torch.max(outputs.data, 1)\n",
" all_preds.extend(predicted.cpu().numpy())\n",
" all_labels.extend(batch_y.cpu().numpy())\n",
" \n",
" avg_val_loss = total_val_loss / len(val_loader)\n",
" \n",
" accuracy = accuracy_score(all_labels, all_preds)\n",
" precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='binary')\n",
" \n",
" history['val_loss'].append(avg_val_loss)\n",
" history['val_accuracy'].append(accuracy)\n",
" history['val_f1'].append(f1)\n",
"\n",
" epoch_time = time.time() - start_time\n",
" print(f\"Эпоха {epoch+1}/{num_epochs} | Время: {epoch_time:.2f}с | Train Loss: {avg_train_loss:.4f} | \"\n",
" f\"Val Loss: {avg_val_loss:.4f} | Val Acc: {accuracy:.4f} | Val F1: {f1:.4f}\")\n",
"\n",
" if f1 > best_val_f1:\n",
" best_val_f1 = f1\n",
" torch.save(model.state_dict(), save_path)\n",
" print(f\" -> Модель сохранена, новый лучший Val F1: {best_val_f1:.4f}\")\n",
" \n",
" print(f\"--- Обучение модели {model_name} завершено ---\")\n",
" return history\n",
"\n",
"def evaluate_on_test(model, test_loader, device, criterion):\n",
" model.eval()\n",
" total_test_loss = 0\n",
" all_preds = []\n",
" all_labels = []\n",
"\n",
" with torch.no_grad():\n",
" for batch_X, batch_y in test_loader:\n",
" batch_X, batch_y = batch_X.to(device), batch_y.to(device)\n",
" outputs = model(batch_X)\n",
" loss = criterion(outputs, batch_y)\n",
" total_test_loss += loss.item()\n",
" \n",
" _, predicted = torch.max(outputs.data, 1)\n",
" all_preds.extend(predicted.cpu().numpy())\n",
" all_labels.extend(batch_y.cpu().numpy())\n",
" \n",
" avg_test_loss = total_test_loss / len(test_loader)\n",
" \n",
" accuracy = accuracy_score(all_labels, all_preds)\n",
" precision, recall, f1, _ = precision_recall_fscore_support(all_labels, all_preds, average='binary')\n",
" \n",
" return {'loss': avg_test_loss, 'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1_score': f1}"
]
},
{
"cell_type": "markdown",
"id": "1be50523",
"metadata": {},
"source": [
"Создание даталоадера"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "cccc5bea",
"metadata": {},
"outputs": [],
"source": [
"def create_dataloader(X, y, batch_size, shuffle=True):\n",
" X_tensor = torch.as_tensor(X, dtype=torch.long)\n",
" y_tensor = torch.as_tensor(y, dtype=torch.long)\n",
" dataset = TensorDataset(X_tensor, y_tensor)\n",
" return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)\n",
"\n",
"train_loader = create_dataloader(X_train, y_train, BATCH_SIZE)\n",
"val_loader = create_dataloader(X_val, y_val, BATCH_SIZE, shuffle=False)\n",
"test_loader = create_dataloader(X_test, y_test, BATCH_SIZE, shuffle=False)"
]
},
{
"cell_type": "markdown",
"id": "4938b9f3",
"metadata": {},
"source": [
"Сравнения моделей\n",
"\n",
"Смотрим первые 5 эпох чтобы выбрать лучшую модель, с которой будем играться дальше"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0244aafa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Начало обучения модели: CustomMamba на устройстве cuda ---\n",
"Эпоха 1/5 | Время: 337.85с | Train Loss: 0.6768 | Val Loss: 0.6168 | Val Acc: 0.6592 | Val F1: 0.5937\n",
" -> Модель сохранена, новый лучший Val F1: 0.5937\n",
"Эпоха 2/5 | Время: 345.54с | Train Loss: 0.5266 | Val Loss: 0.4964 | Val Acc: 0.7580 | Val F1: 0.7552\n",
" -> Модель сохранена, новый лучший Val F1: 0.7552\n",
"Эпоха 3/5 | Время: 343.23с | Train Loss: 0.4329 | Val Loss: 0.4586 | Val Acc: 0.7812 | Val F1: 0.7830\n",
" -> Модель сохранена, новый лучший Val F1: 0.7830\n",
"Эпоха 4/5 | Время: 342.62с | Train Loss: 0.3730 | Val Loss: 0.4596 | Val Acc: 0.7928 | Val F1: 0.8056\n",
" -> Модель сохранена, новый лучший Val F1: 0.8056\n",
"Эпоха 5/5 | Время: 340.21с | Train Loss: 0.3127 | Val Loss: 0.4469 | Val Acc: 0.7996 | Val F1: 0.8124\n",
" -> Модель сохранена, новый лучший Val F1: 0.8124\n",
"--- Обучение модели CustomMamba завершено ---\n",
"--- Оценка лучшей модели CustomMamba на тестовых данных ---\n",
"Результаты для CustomMamba: {'loss': 0.44949763529239944, 'accuracy': 0.8062, 'precision': 0.778874269005848, 'recall': 0.8541082164328657, 'f1_score': 0.8147581724335691}\n",
"------------------------------------------------------------\n",
"--- Начало обучения модели: Lib_LSTM на устройстве cuda ---\n",
"Эпоха 1/5 | Время: 5.09с | Train Loss: 0.6930 | Val Loss: 0.6922 | Val Acc: 0.5170 | Val F1: 0.4221\n",
" -> Модель сохранена, новый лучший Val F1: 0.4221\n",
"Эпоха 2/5 | Время: 5.03с | Train Loss: 0.6911 | Val Loss: 0.6899 | Val Acc: 0.5324 | Val F1: 0.4880\n",
" -> Модель сохранена, новый лучший Val F1: 0.4880\n",
"Эпоха 3/5 | Время: 5.03с | Train Loss: 0.6864 | Val Loss: 0.6837 | Val Acc: 0.5530 | Val F1: 0.5605\n",
" -> Модель сохранена, новый лучший Val F1: 0.5605\n",
"Эпоха 4/5 | Время: 5.03с | Train Loss: 0.6740 | Val Loss: 0.6589 | Val Acc: 0.6096 | Val F1: 0.6208\n",
" -> Модель сохранена, новый лучший Val F1: 0.6208\n",
"Эпоха 5/5 | Время: 5.04с | Train Loss: 0.6489 | Val Loss: 0.6501 | Val Acc: 0.6498 | Val F1: 0.6460\n",
" -> Модель сохранена, новый лучший Val F1: 0.6460\n",
"--- Обучение модели Lib_LSTM завершено ---\n",
"--- Оценка лучшей модели Lib_LSTM на тестовых данных ---\n",
"Результаты для Lib_LSTM: {'loss': 0.6330309821541902, 'accuracy': 0.6644, 'precision': 0.6724356268467708, 'recall': 0.6384769539078157, 'f1_score': 0.655016447368421}\n",
"------------------------------------------------------------\n",
"--- Начало обучения модели: Lib_Transformer на устройстве cuda ---\n",
"Эпоха 1/5 | Время: 4.28с | Train Loss: 0.6712 | Val Loss: 0.6773 | Val Acc: 0.5292 | Val F1: 0.1729\n",
" -> Модель сохранена, новый лучший Val F1: 0.1729\n",
"Эпоха 2/5 | Время: 4.14с | Train Loss: 0.5753 | Val Loss: 0.5631 | Val Acc: 0.7308 | Val F1: 0.7701\n",
" -> Модель сохранена, новый лучший Val F1: 0.7701\n",
"Эпоха 3/5 | Время: 4.17с | Train Loss: 0.4836 | Val Loss: 0.5106 | Val Acc: 0.7622 | Val F1: 0.7830\n",
" -> Модель сохранена, новый лучший Val F1: 0.7830\n",
"Эпоха 4/5 | Время: 4.16с | Train Loss: 0.4399 | Val Loss: 0.4880 | Val Acc: 0.7814 | Val F1: 0.7763\n",
"Эпоха 5/5 | Время: 4.13с | Train Loss: 0.4014 | Val Loss: 0.4611 | Val Acc: 0.7946 | Val F1: 0.8078\n",
" -> Модель сохранена, новый лучший Val F1: 0.8078\n",
"--- Обучение модели Lib_Transformer завершено ---\n",
"--- Оценка лучшей модели Lib_Transformer на тестовых данных ---\n",
"Результаты для Lib_Transformer: {'loss': 0.4671077333438169, 'accuracy': 0.7938, 'precision': 0.7661818181818182, 'recall': 0.8444889779559118, 'f1_score': 0.8034318398474738}\n",
"------------------------------------------------------------\n",
"\n",
"\n",
"--- Итоговая таблица сравнения моделей на тестовых данных ---\n",
" loss accuracy precision recall f1_score\n",
"CustomMamba 0.449498 0.8062 0.778874 0.854108 0.814758\n",
"Lib_LSTM 0.633031 0.6644 0.672436 0.638477 0.655016\n",
"Lib_Transformer 0.467108 0.7938 0.766182 0.844489 0.803432\n"
]
}
],
"source": [
"model_configs = {\n",
" \"CustomMamba\": {\n",
" \"class\": CustomMambaClassifier,\n",
" \"params\": {'vocab_size': VOCAB_SIZE, 'd_model': EMBEDDING_DIM, 'd_state': 8, \n",
" 'd_conv': 4, 'num_layers': 2, 'num_classes': NUM_CLASSES},\n",
" },\n",
"\n",
" \"Lib_LSTM\": {\n",
" \"class\": LSTMClassifier,\n",
" \"params\": {'vocab_size': VOCAB_SIZE, 'embed_dim': EMBEDDING_DIM, 'hidden_dim': 128, \n",
" 'num_layers': 2, 'num_classes': NUM_CLASSES, 'dropout': 0.5},\n",
" },\n",
" \"Lib_Transformer\": {\n",
" \"class\": TransformerClassifier,\n",
" \"params\": {'vocab_size': VOCAB_SIZE, 'embed_dim': EMBEDDING_DIM, 'num_heads': 4, \n",
" 'num_layers': 2, 'num_classes': NUM_CLASSES, 'max_seq_len': MAX_SEQ_LEN},\n",
" },\n",
"}\n",
"\n",
"results = {}\n",
"for model_name, config in model_configs.items():\n",
"\n",
" model_path = os.path.join(SAVE_DIR, f\"best_model_{model_name.lower()}.pth\")\n",
" \n",
" model = config['class'](**config['params']).to(DEVICE)\n",
" optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n",
" criterion = nn.CrossEntropyLoss()\n",
" \n",
" train_and_evaluate(\n",
" model=model, train_loader=train_loader, val_loader=val_loader,\n",
" optimizer=optimizer, criterion=criterion, num_epochs=NUM_EPOCHS,\n",
" device=DEVICE, model_name=model_name, save_path=model_path\n",
" )\n",
" \n",
" print(f\"--- Оценка лучшей модели {model_name} на тестовых данных ---\")\n",
" if os.path.exists(model_path):\n",
" best_model = config['class'](**config['params']).to(DEVICE)\n",
" best_model.load_state_dict(torch.load(model_path))\n",
" test_metrics = evaluate_on_test(best_model, test_loader, DEVICE, criterion)\n",
" results[model_name] = test_metrics\n",
" print(f\"Результаты для {model_name}: {test_metrics}\")\n",
" else:\n",
" print(f\"Файл лучшей модели для {model_name} не найден. Пропускаем оценку.\")\n",
"\n",
" print(\"-\" * 60)\n",
" \n",
"if results:\n",
" results_df = pd.DataFrame(results).T\n",
" print(\"\\n\\n--- Итоговая таблица сравнения моделей на тестовых данных ---\")\n",
" print(results_df.to_string())\n",
"else:\n",
" print(\"Не удалось получить результаты ни для одной модели.\")\n"
]
},
{
"cell_type": "markdown",
"id": "404db766",
"metadata": {},
"source": [
"По результатам видно, что LSTM и Transformer обучаются быстро, но Mamba обучается хорошо. Дальнейшие шаги следующие \n",
" - Пробуем сравнить Transformer и Mamba более детально, играем с гиперпараметрами\n",
" - LSTM проигрывает Transformer и по времени, и по качеству, поэтому в следующий этап сравнения не пойдет\n",
" \n",
"Цель следующего иследования: найти идеальный баланс между временем и качеством. Поставим больше эпох, меньший lr для обоих моделей, увеличим датасет (в текущем сетапе было 10'000 сэмплов на трейн и по 5'000 на валидацию/тест)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "monkey-coding-dl-project-rj23F0vJ-py3.12",
"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": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|