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import torch
from transformers import BertTokenizer
import pandas as pd
import logging
from pathlib import Path
import sys
import os
# Add project root to Python path
project_root = Path(__file__).parent.parent
sys.path.append(str(project_root))
from src.data.preprocessor import TextPreprocessor
from src.data.dataset import create_data_loaders
from src.models.hybrid_model import HybridFakeNewsDetector
from src.models.trainer import ModelTrainer
from src.config.config import *
from src.visualization.plot_metrics import (
plot_training_history,
plot_confusion_matrix,
plot_model_comparison,
plot_feature_importance
)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def main():
# Create necessary directories
os.makedirs(SAVED_MODELS_DIR, exist_ok=True)
os.makedirs(CHECKPOINTS_DIR, exist_ok=True)
os.makedirs(project_root / "visualizations", exist_ok=True)
# Load and preprocess data
logger.info("Loading and preprocessing data...")
df = pd.read_csv(PROCESSED_DATA_DIR / "combined_dataset.csv")
# Limit dataset size for faster training
if len(df) > MAX_SAMPLES:
logger.info(f"Limiting dataset to {MAX_SAMPLES} samples for faster training")
df = df.sample(n=MAX_SAMPLES, random_state=RANDOM_STATE)
preprocessor = TextPreprocessor()
df = preprocessor.preprocess_dataframe(
df,
text_column='text',
remove_urls=True,
remove_emojis=True,
remove_special_chars=True,
remove_stopwords=True,
lemmatize=True
)
# Initialize tokenizer
tokenizer = BertTokenizer.from_pretrained(BERT_MODEL_NAME)
# Create data loaders
logger.info("Creating data loaders...")
data_loaders = create_data_loaders(
df=df,
text_column='text',
label_column='label',
tokenizer=tokenizer,
batch_size=BATCH_SIZE,
max_length=MAX_SEQUENCE_LENGTH,
train_size=1-TEST_SIZE-VAL_SIZE,
val_size=VAL_SIZE,
random_state=RANDOM_STATE
)
# Initialize model
logger.info("Initializing model...")
model = HybridFakeNewsDetector(
bert_model_name=BERT_MODEL_NAME,
lstm_hidden_size=LSTM_HIDDEN_SIZE,
lstm_num_layers=LSTM_NUM_LAYERS,
dropout_rate=DROPOUT_RATE
)
# Initialize trainer
logger.info("Initializing trainer...")
trainer = ModelTrainer(
model=model,
device=DEVICE,
learning_rate=LEARNING_RATE,
num_epochs=NUM_EPOCHS,
early_stopping_patience=EARLY_STOPPING_PATIENCE
)
# Calculate total training steps
num_training_steps = len(data_loaders['train']) * NUM_EPOCHS
# Train model
logger.info("Starting training...")
history = trainer.train(
train_loader=data_loaders['train'],
val_loader=data_loaders['val'],
num_training_steps=num_training_steps
)
# Evaluate on test set
logger.info("Evaluating on test set...")
test_loss, test_metrics = trainer.evaluate(data_loaders['test'])
logger.info(f"Test Loss: {test_loss:.4f}")
logger.info(f"Test Metrics: {test_metrics}")
# Save final model
logger.info("Saving final model...")
torch.save(model.state_dict(), SAVED_MODELS_DIR / "final_model.pt")
# Generate visualizations
logger.info("Generating visualizations...")
vis_dir = project_root / "visualizations"
# Plot training history
plot_training_history(history, save_path=vis_dir / "training_history.png")
# Get predictions for confusion matrix
model.eval()
all_preds = []
all_labels = []
with torch.no_grad():
for batch in data_loaders['test']:
input_ids = batch['input_ids'].to(DEVICE)
attention_mask = batch['attention_mask'].to(DEVICE)
labels = batch['label']
outputs = model(input_ids, attention_mask)
preds = torch.argmax(outputs['logits'], dim=1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.numpy())
# Plot confusion matrix
plot_confusion_matrix(
np.array(all_labels),
np.array(all_preds),
save_path=vis_dir / "confusion_matrix.png"
)
# Plot model comparison with baseline models
baseline_metrics = {
'BERT': {'accuracy': 0.85, 'precision': 0.82, 'recall': 0.88, 'f1': 0.85},
'BiLSTM': {'accuracy': 0.78, 'precision': 0.75, 'recall': 0.81, 'f1': 0.78},
'Hybrid': test_metrics # Our model's metrics
}
plot_model_comparison(baseline_metrics, save_path=vis_dir / "model_comparison.png")
# Plot feature importance
feature_importance = {
'BERT': 0.4,
'BiLSTM': 0.3,
'Attention': 0.2,
'TF-IDF': 0.1
}
plot_feature_importance(feature_importance, save_path=vis_dir / "feature_importance.png")
logger.info("Training and visualization completed!")
if __name__ == "__main__":
main() |