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Training script for multimodal fraudulent paper detection.
"""
import os
import sys
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, random_split
from transformers import get_linear_schedule_with_warmup
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_auc_score
from tqdm import tqdm
import json
from model import MultimodalFraudDetector
from data_loader import FraudPaperDataset, collate_fn
def compute_metrics(predictions, labels, probs):
preds = np.argmax(predictions, axis=1)
accuracy = accuracy_score(labels, preds)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary', zero_division=0)
try:
auc = roc_auc_score(labels, probs[:, 1])
except:
auc = 0.5
return {'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1': f1, 'auc': auc}
def train_epoch(model, dataloader, optimizer, scheduler, device, epoch):
model.train()
total_loss = 0
all_preds, all_labels, all_probs = [], [], []
pbar = tqdm(dataloader, desc=f"Epoch {epoch}")
for batch in pbar:
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
tabular = batch['tabular_features'].to(device)
metadata = batch['metadata_features'].to(device)
labels = batch['labels'].to(device)
outputs = model(text_input_ids=input_ids, text_attention_mask=attention_mask,
tabular_features=tabular, metadata_features=metadata)
logits = outputs['logits']
modality_scores = outputs['modality_scores']
anomaly_score = outputs['anomaly_score']
ce_loss = nn.CrossEntropyLoss()(logits, labels)
consistency_loss = torch.mean((modality_scores - 0.5) ** 2) * 0.1
fraud_mask = labels == 1
if fraud_mask.any():
anomaly_loss = torch.mean((anomaly_score[fraud_mask] - 1.0) ** 2)
anomaly_loss += torch.mean((anomaly_score[~fraud_mask] - 0.0) ** 2)
else:
anomaly_loss = torch.tensor(0.0, device=device)
loss = ce_loss + consistency_loss + 0.1 * anomaly_loss
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
total_loss += loss.item()
probs = torch.softmax(logits, dim=1).detach().cpu().numpy()
all_preds.append(logits.detach().cpu().numpy())
all_labels.append(labels.cpu().numpy())
all_probs.append(probs)
pbar.set_postfix({'loss': loss.item()})
all_preds = np.concatenate(all_preds)
all_labels = np.concatenate(all_labels)
all_probs = np.concatenate(all_probs)
metrics = compute_metrics(all_preds, all_labels, all_probs)
metrics['loss'] = total_loss / len(dataloader)
return metrics
def evaluate(model, dataloader, device):
model.eval()
total_loss = 0
all_preds, all_labels, all_probs = [], [], []
all_embeddings, all_anomaly = [], []
with torch.no_grad():
for batch in tqdm(dataloader, desc="Evaluating"):
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
tabular = batch['tabular_features'].to(device)
metadata = batch['metadata_features'].to(device)
labels = batch['labels'].to(device)
outputs = model(text_input_ids=input_ids, text_attention_mask=attention_mask,
tabular_features=tabular, metadata_features=metadata)
logits = outputs['logits']
loss = nn.CrossEntropyLoss()(logits, labels)
total_loss += loss.item()
probs = torch.softmax(logits, dim=1).cpu().numpy()
all_preds.append(logits.cpu().numpy())
all_labels.append(labels.cpu().numpy())
all_probs.append(probs)
all_embeddings.append(outputs['fused_embedding'].cpu().numpy())
all_anomaly.append(outputs['anomaly_score'].cpu().numpy())
all_preds = np.concatenate(all_preds)
all_labels = np.concatenate(all_labels)
all_probs = np.concatenate(all_probs)
all_embeddings = np.concatenate(all_embeddings)
all_anomaly = np.concatenate(all_anomaly)
metrics = compute_metrics(all_preds, all_labels, all_probs)
metrics['loss'] = total_loss / len(dataloader)
return metrics, all_embeddings, all_anomaly
def main():
print("=" * 60)
print("MULTIMODAL FRAUD DETECTION - TRAINING")
print("=" * 60)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Device: {device}")
output_dir = './outputs'
os.makedirs(output_dir, exist_ok=True)
# Load data
print("\nLoading dataset...")
dataset = FraudPaperDataset("Lihuchen/pubmed_retraction", split="train", max_length=256)
# Split
train_size = int(0.8 * len(dataset))
val_size = len(dataset) - train_size
train_ds, val_ds = random_split(dataset, [train_size, val_size])
train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=2, collate_fn=collate_fn)
val_loader = DataLoader(val_ds, batch_size=16, shuffle=False, num_workers=2, collate_fn=collate_fn)
print(f"Train: {len(train_ds)}, Val: {len(val_ds)}")
# Get dims
sample = next(iter(train_loader))
tabular_dim = sample['tabular_features'].shape[1]
metadata_dim = sample['metadata_features'].shape[1]
print(f"Tabular: {tabular_dim}, Metadata: {metadata_dim}")
# Model
print("\nBuilding model...")
model = MultimodalFraudDetector(
text_model="allenai/scibert_scivocab_uncased",
tabular_features=tabular_dim,
metadata_features=metadata_dim,
fused_dim=256,
freeze_text_layers=8
).to(device)
total_params = sum(p.numel() for p in model.parameters())
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Total params: {total_params:,}, Trainable: {trainable:,}")
# Optimizer
optimizer = optim.AdamW(model.parameters(), lr=2e-5, weight_decay=0.01)
total_steps = len(train_loader) * 3
warmup = int(total_steps * 0.1)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup, num_training_steps=total_steps)
# Train
best_f1 = 0
for epoch in range(1, 4):
print(f"\n=== Epoch {epoch}/3 ===")
train_metrics = train_epoch(model, train_loader, optimizer, scheduler, device, epoch)
print(f"Train - Loss: {train_metrics['loss']:.4f}, Acc: {train_metrics['accuracy']:.4f}, F1: {train_metrics['f1']:.4f}")
val_metrics, val_emb, val_anom = evaluate(model, val_loader, device)
print(f"Val - Loss: {val_metrics['loss']:.4f}, Acc: {val_metrics['accuracy']:.4f}, F1: {val_metrics['f1']:.4f}, AUC: {val_metrics['auc']:.4f}")
if val_metrics['f1'] > best_f1:
best_f1 = val_metrics['f1']
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'f1': best_f1,
}, os.path.join(output_dir, 'best_model.pt'))
print(f"Saved best model (F1: {best_f1:.4f})")
# Save embeddings
np.save(os.path.join(output_dir, 'val_embeddings.npy'), val_emb)
np.save(os.path.join(output_dir, 'val_anomaly.npy'), val_anom)
print(f"\nTraining complete! Best F1: {best_f1:.4f}")
print(f"Outputs saved to {output_dir}")
if __name__ == '__main__':
main()
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