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import sys
import time
import json
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.amp import autocast, GradScaler
from sklearn.metrics import (accuracy_score, precision_score, recall_score,
f1_score, roc_auc_score, matthews_corrcoef,
confusion_matrix)
from collections import Counter
from model import PeptEdge, count_parameters
from data_utils import load_genpept_data, get_dataloaders
def evaluate(model, loader, device, multilabel=False):
model.eval()
all_preds, all_labels, all_probs = [], [], []
with torch.no_grad():
for x, y in loader:
x, y = x.to(device), y.to(device)
logits = model(x)
if multilabel:
probs = torch.sigmoid(logits)
preds = (probs > 0.5).long()
else:
probs = torch.softmax(logits, dim=1)
preds = logits.argmax(dim=1)
all_preds.append(preds.cpu())
all_labels.append(y.cpu())
all_probs.append(probs.cpu())
preds = torch.cat(all_preds).numpy()
labels = torch.cat(all_labels).numpy()
probs = torch.cat(all_probs).numpy()
if multilabel:
from sklearn.metrics import average_precision_score
ap_per_class = []
for i in range(probs.shape[1]):
ap_per_class.append(average_precision_score(labels[:, i], probs[:, i]))
mAP = np.mean(ap_per_class)
f1 = f1_score(labels, preds, average='macro', zero_division=0)
acc = accuracy_score(labels.flatten(), preds.flatten())
return {
'accuracy': float(acc),
'f1_macro': float(f1),
'mAP': float(mAP),
}
else:
if probs.shape[1] == 2:
auc = roc_auc_score(labels, probs[:, 1])
else:
auc = roc_auc_score(labels, probs, multi_class='ovr')
mcc = matthews_corrcoef(labels, preds)
sens = recall_score(labels, preds, pos_label=1, zero_division=0)
spec = recall_score(labels, preds, pos_label=0, zero_division=0)
return {
'accuracy': float(accuracy_score(labels, preds)),
'precision': float(precision_score(labels, preds, zero_division=0)),
'recall': float(recall_score(labels, preds, zero_division=0)),
'specificity': float(spec),
'f1': float(f1_score(labels, preds, zero_division=0)),
'auc': float(auc),
'mcc': float(mcc),
}
def train_epoch(model, loader, criterion, optimizer, scaler, device, use_amp=True):
model.train()
total_loss = 0
for x, y in loader:
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
if use_amp:
with autocast(device_type='cuda'):
logits = model(x)
loss = criterion(logits, y)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
else:
logits = model(x)
loss = criterion(logits, y)
loss.backward()
optimizer.step()
total_loss += loss.item()
return total_loss / len(loader)
def train(config=None):
if config is None:
config = {
'batch_size': 64,
'lr': 5e-4,
'weight_decay': 1e-4,
'epochs': 150,
'd_model': 128,
'n_heads': 4,
'num_layers': 3,
'ff_dim': 256,
'dropout': 0.2,
'max_len': 200,
'use_amp': True,
'patience': 20,
'label_smoothing': 0.05,
}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Device: {device}')
print(f'Config: {json.dumps(config, indent=2)}')
sequences, labels = load_genpept_data()
print(f'Dataset: {len(sequences)} sequences, {Counter(labels)}')
train_loader, val_loader, test_loader = get_dataloaders(
sequences, labels, batch_size=config['batch_size'], max_len=config['max_len']
)
model = PeptEdge(
vocab_size=21,
max_len=config['max_len'],
d_model=config['d_model'],
n_heads=config['n_heads'],
num_layers=config['num_layers'],
ff_dim=config['ff_dim'],
num_classes=2,
dropout=config['dropout'],
).to(device)
total_params = count_parameters(model)
print(f'Model params: {total_params:,}')
criterion = nn.CrossEntropyLoss(label_smoothing=config['label_smoothing'])
optimizer = optim.AdamW(model.parameters(), lr=config['lr'],
weight_decay=config['weight_decay'])
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=config['epochs'])
scaler = GradScaler('cuda') if device.type == 'cuda' else None
best_val_f1 = 0
best_state = None
patience_counter = 0
history = []
print(f'\n{"Epoch":>5} | {"Train Loss":>10} | {"Val Acc":>8} | {"Val F1":>8} | {"Val AUC":>8} | {"Best":>5} | {"LR":>10}')
print('-' * 60)
for epoch in range(config['epochs']):
train_loss = train_epoch(model, train_loader, criterion, optimizer,
scaler, device, config['use_amp'])
val_metrics = evaluate(model, val_loader, device)
scheduler.step()
current_lr = scheduler.get_last_lr()[0]
is_best = val_metrics['f1'] > best_val_f1
if is_best:
best_val_f1 = val_metrics['f1']
best_state = model.state_dict().copy()
patience_counter = 0
else:
patience_counter += 1
history.append({
'epoch': epoch + 1,
'train_loss': train_loss,
**val_metrics
})
if (epoch + 1) % 2 == 0 or epoch == 0:
print(f'{epoch + 1:>5} | {train_loss:>10.4f} | {val_metrics["accuracy"]:>8.4f} | '
f'{val_metrics["f1"]:>8.4f} | {val_metrics["auc"]:>8.4f} | '
f'{"*" if is_best else "":>5} | {current_lr:>10.2e}')
if patience_counter >= config['patience']:
print(f'Early stopping at epoch {epoch + 1}')
break
model.load_state_dict(best_state)
test_metrics = evaluate(model, test_loader, device)
print('\n' + '=' * 50)
print('TEST SET RESULTS')
print('=' * 50)
for k, v in test_metrics.items():
print(f' {k}: {v:.4f}')
print(f' params: {total_params:,}')
results = {
'config': config,
'total_params': total_params,
'best_val_f1': best_val_f1,
'test_metrics': test_metrics,
'history': history,
}
os.makedirs('results', exist_ok=True)
torch.save({
'model_state_dict': best_state,
'config': config,
'test_metrics': test_metrics,
'total_params': total_params,
}, 'results/peptedge_genpept.pt')
with open('results/training_results.json', 'w') as f:
json.dump(results, f, indent=2, default=str)
print(f'\nModel saved to results/peptedge_genpept.pt')
print(f'Results saved to results/training_results.json')
return results
if __name__ == '__main__':
results = train()
print(f"\nSOTA Comparison:")
print(f" ESM-2 LoRA (650M params): 88.3% F1")
print(f" PeptEdge ({results['total_params']:,} params): {results['test_metrics']['f1']:.2%} F1")
improvement = results['test_metrics']['f1'] - 0.883
print(f" Δ: {improvement:+.2%}")
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