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bf8df4f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | import json
import os
from datetime import datetime
import torch
import torch.optim as optim
import os
import json
from datetime import datetime
import torch
from sklearn.metrics import (
accuracy_score, precision_score, recall_score,
f1_score, roc_auc_score
)
def evaluate_metrics(model, dataloader, device, threshold=0.5):
"""Run model on a dataloader and compute classification metrics."""
model.eval()
all_logits = []
all_targets = []
with torch.no_grad():
for batch in dataloader:
x_batch, y_batch = batch[0].to(device), batch[1].to(device)
logits = model(x_batch)
all_logits.append(logits.cpu())
all_targets.append(y_batch.cpu())
logits = torch.cat(all_logits)
targets = torch.cat(all_targets)
probs = torch.sigmoid(logits).numpy()
preds = (probs > threshold).astype(int)
targets_np = targets.numpy().astype(int)
# average='macro' works for both binary and multi-label;
# switch to 'binary' if you have a single output column and want binary-specific behavior
avg = "macro" if targets_np.ndim > 1 and targets_np.shape[1] > 1 else "binary"
metrics = {
"accuracy": float(accuracy_score(targets_np, preds)),
"precision": float(precision_score(targets_np, preds, average=avg, zero_division=0)),
"recall": float(recall_score(targets_np, preds, average=avg, zero_division=0)),
"f1": float(f1_score(targets_np, preds, average=avg, zero_division=0)),
}
return metrics
def train_mlp(model, train_loader, optimizer, criterion, num_epochs=300,
save_dir="checkpoints", device="cpu", patience=20,
val_loader=None):
os.makedirs(save_dir, exist_ok=True)
hyperparams = {
"num_epochs": num_epochs,
"learning_rate": optimizer.param_groups[0]["lr"],
"optimizer": optimizer.__class__.__name__,
"criterion": criterion.__class__.__name__,
"batch_size": train_loader.batch_size,
"model_class": model.__class__.__name__,
"device": str(device),
"patience": patience,
"timestamp": datetime.now().isoformat(),
}
train_loss_history = []
val_loss_history = []
best_loss = float("inf")
epochs_without_improvement = 0
model.to(device)
for epoch in range(num_epochs):
# ---------- Training phase ----------
model.train()
total_loss = 0.0
for batch in train_loader:
x_batch, y_batch = batch[0].to(device), batch[1].to(device)
optimizer.zero_grad()
logits = model(x_batch)
loss = criterion(logits, y_batch)
loss.backward()
optimizer.step()
total_loss += loss.item()
avg_train_loss = total_loss / len(train_loader)
train_loss_history.append(avg_train_loss)
# ---------- Validation phase ----------
avg_val_loss = None
if val_loader is not None:
model.eval()
val_loss = 0.0
with torch.no_grad():
for batch in val_loader:
x_batch, y_batch = batch[0].to(device), batch[1].to(device)
logits = model(x_batch)
val_loss += criterion(logits, y_batch).item()
avg_val_loss = val_loss / len(val_loader)
val_loss_history.append(avg_val_loss)
# ---------- Checkpointing ----------
monitor_loss = avg_val_loss if avg_val_loss is not None else avg_train_loss
if monitor_loss < best_loss:
best_loss = monitor_loss
epochs_without_improvement = 0
torch.save({
"epoch": epoch + 1,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"loss": best_loss,
"architecture": {
"input_size": model.input_size,
"output_size": model.output_size,
"n_neurons": model.n_neurons,
"dropout_rates": model.dropout_rates,
},
"hyperparams": hyperparams,
}, os.path.join(save_dir, "best_model.pt"))
else:
epochs_without_improvement += 1
# ---------- Logging ----------
log_msg = f"Epoch [{epoch+1}/{num_epochs}] | Train Loss: {avg_train_loss:.6f}"
if avg_val_loss is not None:
log_msg += f" | Val Loss: {avg_val_loss:.6f}"
log_msg += f" | Best: {best_loss:.6f}"
print(log_msg)
# ---------- Early stopping ----------
if epochs_without_improvement >= patience:
print(f"Early stopping at epoch {epoch+1} "
f"(no improvement for {patience} epochs)")
break
# ---------- Final save ----------
torch.save({
"epoch": epoch + 1,
"model_state_dict": model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"loss": avg_train_loss if avg_val_loss is None else avg_val_loss,
"architecture": {
"input_size": model.input_size,
"output_size": model.output_size,
"n_neurons": model.n_neurons,
"dropout_rates": model.dropout_rates,
},
"hyperparams": hyperparams,
}, os.path.join(save_dir, "final_model.pt"))
# ---------- Final evaluation: load best model, compute all metrics ----------
print("\nLoading best model for final evaluation...")
checkpoint = torch.load(os.path.join(save_dir, "best_model.pt"), map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
print("\nComputing metrics on training set...")
train_metrics = evaluate_metrics(model, train_loader, device)
print(f" Train: {train_metrics}")
val_metrics = None
if val_loader is not None:
print("\nComputing metrics on validation set...")
val_metrics = evaluate_metrics(model, val_loader, device)
print(f" Val: {val_metrics}")
# ---------- JSON log ----------
log_data = {
"hyperparams": hyperparams,
"train_loss_history": train_loss_history,
"val_loss_history": val_loss_history,
"final_metrics": {
"train": train_metrics,
"val": val_metrics,
},
}
with open(os.path.join(save_dir, "training_log.json"), "w") as f:
json.dump(log_data, f, indent=2)
print(f"\nTraining complete. Best loss: {best_loss:.6f}")
print(f"Checkpoints saved to {save_dir}/")
return {
"train_loss_history": train_loss_history,
"val_loss_history": val_loss_history,
"final_metrics": {"train": train_metrics, "val": val_metrics},
} |