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import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from transformers import get_linear_schedule_with_warmup
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from typing import Dict, List, Tuple
import numpy as np
from tqdm import tqdm
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ModelTrainer:
def __init__(self,
model: nn.Module,
device: str = "cuda" if torch.cuda.is_available() else "cpu",
learning_rate: float = 2e-5,
num_epochs: int = 10,
early_stopping_patience: int = 3):
self.model = model.to(device)
self.device = device
self.learning_rate = learning_rate
self.num_epochs = num_epochs
self.early_stopping_patience = early_stopping_patience
self.criterion = nn.CrossEntropyLoss()
self.optimizer = torch.optim.AdamW(
self.model.parameters(),
lr=learning_rate
)
def train_epoch(self, train_loader: DataLoader) -> float:
"""Train for one epoch."""
self.model.train()
total_loss = 0
for batch in tqdm(train_loader, desc="Training"):
input_ids = batch['input_ids'].to(self.device)
attention_mask = batch['attention_mask'].to(self.device)
labels = batch['labels'].to(self.device)
self.optimizer.zero_grad()
outputs = self.model(input_ids, attention_mask)
loss = self.criterion(outputs['logits'], labels)
loss.backward()
self.optimizer.step()
total_loss += loss.item()
return total_loss / len(train_loader)
def evaluate(self, eval_loader: DataLoader) -> Tuple[float, Dict[str, float]]:
"""Evaluate the model."""
self.model.eval()
total_loss = 0
all_preds = []
all_labels = []
with torch.no_grad():
for batch in tqdm(eval_loader, desc="Evaluating"):
input_ids = batch['input_ids'].to(self.device)
attention_mask = batch['attention_mask'].to(self.device)
labels = batch['labels'].to(self.device)
outputs = self.model(input_ids, attention_mask)
loss = self.criterion(outputs['logits'], labels)
total_loss += loss.item()
preds = torch.argmax(outputs['logits'], dim=1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
# Calculate metrics
metrics = self._calculate_metrics(all_labels, all_preds)
metrics['loss'] = total_loss / len(eval_loader)
return total_loss / len(eval_loader), metrics
def _calculate_metrics(self, labels: List[int], preds: List[int]) -> Dict[str, float]:
"""Calculate evaluation metrics."""
precision, recall, f1, _ = precision_recall_fscore_support(
labels, preds, average='weighted'
)
accuracy = accuracy_score(labels, preds)
return {
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1
}
def train(self,
train_loader: DataLoader,
val_loader: DataLoader,
num_training_steps: int) -> Dict[str, List[float]]:
"""Train the model with early stopping."""
scheduler = get_linear_schedule_with_warmup(
self.optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
best_val_loss = float('inf')
patience_counter = 0
history = {
'train_loss': [],
'val_loss': [],
'val_metrics': []
}
for epoch in range(self.num_epochs):
logger.info(f"Epoch {epoch + 1}/{self.num_epochs}")
# Training
train_loss = self.train_epoch(train_loader)
history['train_loss'].append(train_loss)
# Validation
val_loss, val_metrics = self.evaluate(val_loader)
history['val_loss'].append(val_loss)
history['val_metrics'].append(val_metrics)
logger.info(f"Train Loss: {train_loss:.4f}")
logger.info(f"Val Loss: {val_loss:.4f}")
logger.info(f"Val Metrics: {val_metrics}")
# Early stopping
if val_loss < best_val_loss:
best_val_loss = val_loss
patience_counter = 0
# Save best model
torch.save(self.model.state_dict(), 'best_model.pt')
else:
patience_counter += 1
if patience_counter >= self.early_stopping_patience:
logger.info("Early stopping triggered")
break
scheduler.step()
return history
def predict(self, test_loader: DataLoader) -> Tuple[np.ndarray, np.ndarray]:
"""Get predictions on test data."""
self.model.eval()
all_preds = []
all_probs = []
with torch.no_grad():
for batch in tqdm(test_loader, desc="Predicting"):
input_ids = batch['input_ids'].to(self.device)
attention_mask = batch['attention_mask'].to(self.device)
probs = self.model.predict(input_ids, attention_mask)
preds = torch.argmax(probs, dim=1)
all_preds.extend(preds.cpu().numpy())
all_probs.extend(probs.cpu().numpy())
return np.array(all_preds), np.array(all_probs) |