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import pandas as pd
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from transformers import RobertaTokenizer, RobertaModel
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from tqdm import tqdm
import argparse
# 1. Dataset Class
class DepressionDataset(Dataset):
def __init__(self, df, tokenizer, max_length=256):
self.texts = df['clean_text'].values
self.labels = df['is_depression'].values
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
text = str(self.texts[idx])
label = self.labels[idx]
encoding = self.tokenizer.encode_plus(
text,
add_special_tokens=True,
max_length=self.max_length,
padding='max_length',
truncation=True,
return_attention_mask=True,
return_tensors='pt'
)
return {
'input_ids': encoding['input_ids'].flatten(),
'attention_mask': encoding['attention_mask'].flatten(),
'label': torch.tensor(label, dtype=torch.long)
}
# 2. Model Class
class DepressionClassifier(nn.Module):
def __init__(self, dropout_rate=0.1):
super(DepressionClassifier, self).__init__()
self.roberta = RobertaModel.from_pretrained('roberta-base')
self.dropout = nn.Dropout(dropout_rate)
self.classifier = nn.Linear(768, 2)
def forward(self, input_ids, attention_mask):
outputs = self.roberta(
input_ids=input_ids,
attention_mask=attention_mask
)
sequence_output = outputs.last_hidden_state[:, 0, :]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
return logits
# 3. Prepare data loaders
def prepare_dataloaders(df, batch_size=16):
# Split data
train_df, temp_df = train_test_split(df, test_size=0.3, stratify=df['is_depression'], random_state=42)
val_df, test_df = train_test_split(temp_df, test_size=0.5, stratify=temp_df['is_depression'], random_state=42)
# Initialize tokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
# Create datasets
train_dataset = DepressionDataset(train_df, tokenizer)
val_dataset = DepressionDataset(val_df, tokenizer)
test_dataset = DepressionDataset(test_df, tokenizer)
# Create dataloaders
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
return train_loader, val_loader, test_loader
# 4. Training function
def train_model(model, train_loader, val_loader, device, epochs=3, learning_rate=2e-5):
# Move model to device
model = model.to(device)
# Initialize optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
# Initialize loss function
loss_fn = nn.CrossEntropyLoss()
# Training loop
best_accuracy = 0
for epoch in range(epochs):
print(f'Epoch {epoch + 1}/{epochs}')
# TRAINING
model.train()
train_loss = 0
train_preds = []
train_labels = []
# Progress bar for training
progress_bar = tqdm(train_loader, desc="Training")
for batch in progress_bar:
# Get batch data
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['label'].to(device)
# Forward pass
optimizer.zero_grad()
outputs = model(input_ids, attention_mask)
loss = loss_fn(outputs, labels)
# Backward pass
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
# Track metrics
train_loss += loss.item()
_, preds = torch.max(outputs, dim=1)
train_preds.extend(preds.cpu().tolist())
train_labels.extend(labels.cpu().tolist())
# Update progress bar
progress_bar.set_postfix({'loss': loss.item()})
# Calculate training metrics
avg_train_loss = train_loss / len(train_loader)
train_accuracy = accuracy_score(train_labels, train_preds)
# VALIDATION
model.eval()
val_loss = 0
val_preds = []
val_labels = []
with torch.no_grad():
for batch in tqdm(val_loader, desc="Validation"):
# Get batch data
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['label'].to(device)
# Forward pass
outputs = model(input_ids, attention_mask)
loss = loss_fn(outputs, labels)
# Track metrics
val_loss += loss.item()
_, preds = torch.max(outputs, dim=1)
val_preds.extend(preds.cpu().tolist())
val_labels.extend(labels.cpu().tolist())
# Calculate validation metrics
avg_val_loss = val_loss / len(val_loader)
val_accuracy = accuracy_score(val_labels, val_preds)
# Print metrics
print(f'Train Loss: {avg_train_loss:.4f} | Train Accuracy: {train_accuracy:.4f}')
print(f'Val Loss: {avg_val_loss:.4f} | Val Accuracy: {val_accuracy:.4f}')
# Save best model
if val_accuracy > best_accuracy:
torch.save(model.state_dict(), 'best_model.pt')
best_accuracy = val_accuracy
print(f'New best model saved with accuracy: {val_accuracy:.4f}')
print('-' * 50)
# Load best model
model.load_state_dict(torch.load('best_model.pt'))
return model
# 5. Evaluation function
def evaluate_model(model, test_loader, device):
model.eval()
test_preds = []
test_labels = []
with torch.no_grad():
for batch in tqdm(test_loader, desc="Testing"):
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['label'].to(device)
outputs = model(input_ids, attention_mask)
_, preds = torch.max(outputs, dim=1)
test_preds.extend(preds.cpu().tolist())
test_labels.extend(labels.cpu().tolist())
# Calculate metrics
accuracy = accuracy_score(test_labels, test_preds)
precision, recall, f1, _ = precision_recall_fscore_support(
test_labels, test_preds, average='binary'
)
return {
'accuracy': accuracy,
'precision': precision,
'recall': recall,
'f1': f1
}
# 6. Main function
def main():
parser = argparse.ArgumentParser(description='Train depression classifier')
parser.add_argument('--data_path', type=str, default='depression_dataset_reddit_cleaned_final.csv',
help='Path to the cleaned dataset')
parser.add_argument('--batch_size', type=int, default=16, help='Batch size for training')
parser.add_argument('--epochs', type=int, default=3, help='Number of training epochs')
parser.add_argument('--learning_rate', type=float, default=2e-5, help='Learning rate')
args = parser.parse_args()
# Check for GPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device: {device}')
# Load data
df = pd.read_csv(args.data_path)
print(f'Loaded dataset with {len(df)} examples')
# Prepare data
train_loader, val_loader, test_loader = prepare_dataloaders(
df, batch_size=args.batch_size
)
print(f'Training samples: {len(train_loader.dataset)}')
print(f'Validation samples: {len(val_loader.dataset)}')
print(f'Testing samples: {len(test_loader.dataset)}')
# Create model
model = DepressionClassifier()
print('Model created')
# Train model
print('Starting training...')
trained_model = train_model(
model,
train_loader,
val_loader,
device,
epochs=args.epochs,
learning_rate=args.learning_rate
)
# Evaluate model
print('Evaluating model...')
metrics = evaluate_model(trained_model, test_loader, device)
# Print results
print('\nTest Results:')
for metric, value in metrics.items():
print(f'{metric}: {value:.4f}')
if __name__ == '__main__':
main() |