File size: 18,470 Bytes
ce9ca82 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 |
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Finetuneing ESM-2 Models for CAFA-5"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finetune an ESM-2 Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.utils.data import DataLoader, Dataset\n",
"from transformers import AutoTokenizer, EsmForSequenceClassification\n",
"from accelerate import Accelerator\n",
"from sklearn.model_selection import train_test_split\n",
"from torchmetrics.classification import MultilabelF1Score\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, average_precision_score\n",
"import datetime\n",
"import pandas as pd\n",
"\n",
"# Load the data\n",
"data = pd.read_csv(\"C:/Users/OWO/Desktop/amelie_vscode/cafa5/data/merged_protein_data.tsv\", sep=\"\\t\")\n",
"# Use only the first 100 entries\n",
"# data = data.head(100)\n",
"\n",
"# Initialize the accelerator\n",
"accelerator = Accelerator()\n",
"device = accelerator.device\n",
"\n",
"# Data Preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(\"facebook/esm2_t6_8M_UR50D\")\n",
"MAX_LENGTH = tokenizer.model_max_length\n",
"NUM_EPOCHS = 3\n",
"LR = 5e-4\n",
"BATCH_SIZE = 2\n",
"\n",
"class ProteinDataset(Dataset):\n",
" def __init__(self, sequences, labels):\n",
" self.sequences = sequences\n",
" self.labels = labels\n",
"\n",
" def __len__(self):\n",
" return len(self.sequences)\n",
"\n",
" def __getitem__(self, idx):\n",
" sequence = self.sequences[idx]\n",
" label = self.labels[idx]\n",
" encoding = tokenizer(sequence, return_tensors=\"pt\", padding='max_length', truncation=True, max_length=MAX_LENGTH)\n",
" return {\n",
" 'input_ids': encoding['input_ids'].flatten(),\n",
" 'attention_mask': encoding['attention_mask'].flatten(),\n",
" 'labels': torch.tensor(label, dtype=torch.float)\n",
" }\n",
"\n",
"def encode_labels(go_terms, unique_terms):\n",
" encoded = []\n",
" for terms in go_terms:\n",
" encoding = [1 if term in terms else 0 for term in unique_terms]\n",
" encoded.append(encoding)\n",
" return encoded\n",
"\n",
"train_sequences, val_sequences, train_labels, val_labels = train_test_split(data['sequence'], data['term'], test_size=0.1)\n",
"\n",
"# Reset the indices\n",
"train_sequences = train_sequences.reset_index(drop=True)\n",
"val_sequences = val_sequences.reset_index(drop=True)\n",
"train_labels = train_labels.reset_index(drop=True)\n",
"val_labels = val_labels.reset_index(drop=True)\n",
"\n",
"unique_terms = list(set(term for sublist in data['term'] for term in sublist))\n",
"train_labels_encoded = encode_labels(train_labels, unique_terms)\n",
"val_labels_encoded = encode_labels(val_labels, unique_terms)\n",
"\n",
"train_dataset = ProteinDataset(train_sequences, train_labels_encoded)\n",
"val_dataset = ProteinDataset(val_sequences, val_labels_encoded)\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
"val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE)\n",
"\n",
"# Model Training\n",
"model = EsmForSequenceClassification.from_pretrained(\"facebook/esm2_t6_8M_UR50D\", num_labels=len(unique_terms), problem_type=\"multi_label_classification\")\n",
"model = model.to(device)\n",
"model.train()\n",
"\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=LR)\n",
"optimizer, model = accelerator.prepare(optimizer, model)\n",
"\n",
"# Initialize metrics\n",
"f1_metric = MultilabelF1Score(num_labels=len(unique_terms), threshold=0.5)\n",
"f1_metric = f1_metric.to(device)\n",
"\n",
"num_epochs = NUM_EPOCHS\n",
"\n",
"for epoch in range(num_epochs):\n",
" total_loss = 0\n",
" for batch in train_loader:\n",
" optimizer.zero_grad()\n",
" input_ids = batch['input_ids'].to(device)\n",
" attention_mask = batch['attention_mask'].to(device)\n",
" labels = batch['labels'].to(device)\n",
"\n",
" outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
" loss = outputs.loss\n",
" accelerator.backward(loss)\n",
" optimizer.step()\n",
"\n",
" total_loss += loss.item()\n",
"\n",
" print(f'Epoch {epoch + 1}/{num_epochs}, Training loss: {total_loss/len(train_loader)}')\n",
"\n",
" model.eval()\n",
" predictions = []\n",
" true_labels_list = []\n",
" with torch.no_grad():\n",
" for batch in val_loader:\n",
" input_ids = batch['input_ids'].to(device)\n",
" attention_mask = batch['attention_mask'].to(device)\n",
" labels = batch['labels'].to(device)\n",
"\n",
" outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n",
" logits = outputs.logits\n",
" predictions.append(torch.sigmoid(logits))\n",
" true_labels_list.append(labels)\n",
"\n",
" predictions_tensor = torch.cat(predictions, dim=0).cpu().numpy()\n",
" true_labels_tensor = torch.cat(true_labels_list, dim=0).cpu().numpy()\n",
"\n",
" threshold = 0.5\n",
" predictions_bin = (predictions_tensor > threshold).astype(int)\n",
"\n",
" # Compute metrics\n",
" val_f1 = f1_metric(torch.tensor(predictions_tensor).to(device), torch.tensor(true_labels_tensor).to(device))\n",
" val_accuracy = accuracy_score(true_labels_tensor.flatten(), predictions_bin.flatten())\n",
" val_precision = precision_score(true_labels_tensor.flatten(), predictions_bin.flatten(), average='micro')\n",
" val_recall = recall_score(true_labels_tensor.flatten(), predictions_bin.flatten(), average='micro')\n",
" val_auc = average_precision_score(true_labels_tensor, predictions_tensor, average='micro')\n",
"\n",
" # Print metrics\n",
" print(f'Validation F1 Score: {val_f1}')\n",
" print(f'Validation Accuracy: {val_accuracy}')\n",
" print(f'Validation Precision: {val_precision}')\n",
" print(f'Validation Recall: {val_recall}')\n",
" print(f'Validation AUC: {val_auc}')\n",
"\n",
" timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n",
" model_path = f'./esm2_t6_8M_finetuned_cafa5_{timestamp}'\n",
" model.save_pretrained(model_path)\n",
" tokenizer.save_pretrained(model_path)\n",
"\n",
" print(f'Model checkpoint saved to {model_path}')\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save the Train/Validation Split Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"# After you've created the train and validation splits:\n",
"data_splits = {\n",
" \"train_sequences\": train_sequences,\n",
" \"val_sequences\": val_sequences,\n",
" \"train_labels\": train_labels,\n",
" \"val_labels\": val_labels\n",
"}\n",
"\n",
"with open('data_splits.pkl', 'wb') as file:\n",
" pickle.dump(data_splits, file)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reload the Data Later"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"\n",
"# Load the data splits\n",
"with open('data_splits.pkl', 'rb') as file:\n",
" data_splits = pickle.load(file)\n",
"\n",
"train_sequences = data_splits[\"train_sequences\"]\n",
"val_sequences = data_splits[\"val_sequences\"]\n",
"train_labels = data_splits[\"train_labels\"]\n",
"val_labels = data_splits[\"val_labels\"]\n",
"\n",
"# Now, the rest of your code can proceed as it is, \n",
"# with the train and validation sets loaded from the pickle file."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from torch.utils.data import DataLoader, Dataset\n",
"from transformers import AutoTokenizer, EsmForSequenceClassification\n",
"from accelerate import Accelerator\n",
"from sklearn.model_selection import train_test_split\n",
"from torchmetrics.classification import MultilabelF1Score\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, average_precision_score\n",
"import datetime\n",
"import pandas as pd\n",
"\n",
"# Load the data\n",
"data = pd.read_csv(\"C:/Users/OWO/Desktop/amelie_vscode/cafa5/data/merged_protein_data.tsv\", sep=\"\\t\")\n",
"# Use only the first 100 entries\n",
"data = data.head(100)\n",
"\n",
"# Initialize the accelerator\n",
"accelerator = Accelerator()\n",
"device = accelerator.device\n",
"\n",
"# Data Preprocessing\n",
"tokenizer = AutoTokenizer.from_pretrained(\"facebook/esm2_t6_8M_UR50D\")\n",
"MAX_LENGTH = tokenizer.model_max_length\n",
"\n",
"class ProteinDataset(Dataset):\n",
" def __init__(self, sequences, labels):\n",
" self.sequences = sequences\n",
" self.labels = labels\n",
"\n",
" def __len__(self):\n",
" return len(self.sequences)\n",
"\n",
" def __getitem__(self, idx):\n",
" sequence = self.sequences[idx]\n",
" label = self.labels[idx]\n",
" encoding = tokenizer(sequence, return_tensors=\"pt\", padding='max_length', truncation=True, max_length=MAX_LENGTH)\n",
" return {\n",
" 'input_ids': encoding['input_ids'].flatten(),\n",
" 'attention_mask': encoding['attention_mask'].flatten(),\n",
" 'labels': torch.tensor(label, dtype=torch.float)\n",
" }\n",
"\n",
"def encode_labels(go_terms, unique_terms):\n",
" encoded = []\n",
" for terms in go_terms:\n",
" encoding = [1 if term in terms else 0 for term in unique_terms]\n",
" encoded.append(encoding)\n",
" return encoded\n",
"\n",
"# train_sequences, val_sequences, train_labels, val_labels = train_test_split(data['sequence'], data['term'], test_size=0.1)\n",
"\n",
"# Reset the indices\n",
"# train_sequences = train_sequences.reset_index(drop=True)\n",
"# val_sequences = val_sequences.reset_index(drop=True)\n",
"# train_labels = train_labels.reset_index(drop=True)\n",
"# val_labels = val_labels.reset_index(drop=True)\n",
"\n",
"unique_terms = list(set(term for sublist in data['term'] for term in sublist))\n",
"train_labels_encoded = encode_labels(train_labels, unique_terms)\n",
"val_labels_encoded = encode_labels(val_labels, unique_terms)\n",
"\n",
"train_dataset = ProteinDataset(train_sequences, train_labels_encoded)\n",
"val_dataset = ProteinDataset(val_sequences, val_labels_encoded)\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)\n",
"val_loader = DataLoader(val_dataset, batch_size=2)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fine-tune with LoRA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from collections import Counter\n",
"from peft import get_peft_config, get_peft_model, LoraConfig\n",
"import datetime\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, hamming_loss, average_precision_score\n",
"from torchmetrics.classification import MultilabelF1Score\n",
"\n",
"# Constants\n",
"MODEL_NAME = \"facebook/esm2_t6_8M_UR50D\" # Replace with your trained model above\n",
"BATCH_SIZE = 4\n",
"NUM_EPOCHS = 7\n",
"LR = 3e-5\n",
"\n",
"# Initialize model with LoRA\n",
"peft_config = LoraConfig(\n",
" task_type=\"SEQ_CLS\", \n",
" inference_mode=False, \n",
" r=16, \n",
" bias=\"none\",\n",
" lora_alpha=16, \n",
" lora_dropout=0.1, \n",
" target_modules=[\"query\", \"key\", \"value\"]\n",
")\n",
"\n",
"base_model = EsmForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=len(unique_terms), problem_type=\"multi_label_classification\")\n",
"model = get_peft_model(base_model, peft_config)\n",
"model = model.to(accelerator.device)\n",
"\n",
"optimizer = torch.optim.AdamW(model.parameters(), lr=LR)\n",
"optimizer, model = accelerator.prepare(optimizer, model)\n",
"\n",
"f1_metric = MultilabelF1Score(num_labels=len(unique_terms), threshold=0.5)\n",
"f1_metric = f1_metric.to(device)\n",
"\n",
"# Compute Class Weights\n",
"def compute_class_weights(terms, term_to_id):\n",
" all_terms = [term for terms_list in terms for term in terms_list]\n",
" term_counts = Counter(all_terms)\n",
" total_terms = sum(term_counts.values())\n",
" class_weights = {term: total_terms / count for term, count in term_counts.items()}\n",
" weights = torch.tensor([class_weights[term] for term in term_to_id.keys()], dtype=torch.float)\n",
" normalized_weights = weights / weights.sum()\n",
" return normalized_weights\n",
"\n",
"term_to_id = {term: idx for idx, term in enumerate(unique_terms)}\n",
"all_terms_combined = train_labels.tolist() + val_labels.tolist()\n",
"weights = compute_class_weights(all_terms_combined, term_to_id)\n",
"weights = weights.to(accelerator.device)\n",
"loss_criterion = torch.nn.BCEWithLogitsLoss(pos_weight=weights)\n",
"\n",
"# Training loop\n",
"for epoch in range(NUM_EPOCHS):\n",
" # Training Phase\n",
" model.train()\n",
" total_train_loss = 0\n",
" for batch in train_loader:\n",
" optimizer.zero_grad()\n",
" input_ids = batch['input_ids'].to(accelerator.device)\n",
" attention_mask = batch['attention_mask'].to(accelerator.device)\n",
" labels = batch['labels'].to(accelerator.device)\n",
"\n",
" outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
" logits = outputs.logits\n",
" loss = loss_criterion(logits, labels)\n",
" accelerator.backward(loss)\n",
" optimizer.step()\n",
"\n",
" total_train_loss += loss.item()\n",
"\n",
" avg_train_loss = total_train_loss / len(train_loader)\n",
"\n",
" # Validation Phase\n",
" model.eval()\n",
" total_val_loss = 0\n",
" predictions = []\n",
" true_labels = []\n",
" with torch.no_grad():\n",
" for batch in val_loader:\n",
" input_ids = batch['input_ids'].to(accelerator.device)\n",
" attention_mask = batch['attention_mask'].to(accelerator.device)\n",
" labels = batch['labels'].to(accelerator.device)\n",
"\n",
" outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
" logits = outputs.logits\n",
" loss = loss_criterion(logits, labels)\n",
"\n",
" total_val_loss += loss.item()\n",
" predictions.append(torch.sigmoid(logits).detach())\n",
" true_labels.append(labels.detach())\n",
"\n",
"\n",
" avg_val_loss = total_val_loss / len(val_loader)\n",
" \n",
" predictions_tensor = torch.cat(predictions, dim=0).cpu().numpy()\n",
" true_labels_tensor = torch.cat(true_labels, dim=0).cpu().numpy()\n",
"\n",
" threshold = 0.5\n",
" predictions_bin = (predictions_tensor > threshold).astype(int)\n",
"\n",
" val_f1 = f1_metric(torch.tensor(predictions_tensor).to(device), torch.tensor(true_labels_tensor).to(device))\n",
" val_accuracy = accuracy_score(true_labels_tensor.flatten(), predictions_bin.flatten())\n",
" val_precision = precision_score(true_labels_tensor.flatten(), predictions_bin.flatten(), average='micro')\n",
" val_recall = recall_score(true_labels_tensor.flatten(), predictions_bin.flatten(), average='micro')\n",
" val_auc = average_precision_score(true_labels_tensor, predictions_tensor, average='micro')\n",
"\n",
" print(f\"Epoch {epoch + 1}/{NUM_EPOCHS} - Training Loss: {avg_train_loss:.4f} - Validation Loss: {avg_val_loss:.4f}\")\n",
" print(f\"Validation Metrics - Accuracy: {val_accuracy:.4f} - Precision (Micro): {val_precision:.4f} - Recall (Micro): {val_recall:.4f} - AUC: {val_auc:.4f} - F1 Score: {val_f1:.4f}\")\n",
"\n",
" timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n",
" # Save model and tokenizer. Note that Accelerator has a save method for models.\n",
" model_path = f'./esm2_t6_8M_cafa5_lora_{timestamp}'\n",
" model.save_pretrained(model_path)\n",
" tokenizer.save_pretrained(model_path)\n",
" model.base_model.save_pretrained(model_path)\n",
" print(f'Model checkpoint saved to {model_path}')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "cafa_5",
"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": "3.9.17"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|