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import os, sys, json
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from datetime import datetime
import argparse, numpy as np, torch
from torch.utils.data import TensorDataset, DataLoader
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import confusion_matrix
# Add project-specific imports
from scripts.preprocess_dataset import preprocess_dataset
from models.figure2_cnn import Figure2CNN
from models.resnet_cnn import ResNet1D
# Argument parser for CLI usage
parser = argparse.ArgumentParser(
description="Run 10-fold CV on Raman data with optional preprocessing.")
parser.add_argument("--target-len", type=int, default=500)
parser.add_argument("--baseline", action="store_true")
parser.add_argument("--smooth", action="store_true")
parser.add_argument("--normalize", action="store_true")
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--epochs", type=int, default=10)
parser.add_argument("--learning-rate", type=float, default=1e-3)
parser.add_argument("--model", type=str, default="figure2",
choices=["figure2", "resnet"])
args = parser.parse_args()
# Constants
# Raman-only dataset (RDWP)
DATASET_PATH = 'datasets/rdwp'
DEVICE = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
NUM_FOLDS = 10
# Ensure output dirs exist
os.makedirs("outputs", exist_ok=True)
os.makedirs("outputs/logs", exist_ok=True)
print("Preprocessing Configuration:")
print(f" Reseample to : {args.target_len}")
print(f" Baseline Correct: {'✅' if args.baseline else '❌'}")
print(f" Smoothing : {'✅' if args.smooth else '❌'}")
print(f" Normalization : {'✅' if args.normalize else '❌'}")
# Load + Preprocess data
print("🔄 Loading and preprocessing data ...")
X, y = preprocess_dataset(
DATASET_PATH,
target_len=args.target_len,
baseline_correction=args.baseline,
apply_smoothing=args.smooth,
normalize=args.normalize
)
X, y = np.array(X, np.float32), np.array(y, np.int64)
print(f"✅ Data Loaded: {X.shape[0]} samples, {X.shape[1]} features each.")
print(f"🔍 Using model: {args.model}")
# CV
skf = StratifiedKFold(n_splits=NUM_FOLDS, shuffle=True, random_state=42)
fold_accuracies = []
all_conf_matrices = []
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y), 1):
print(f"\n🔁 Fold {fold}/{NUM_FOLDS}")
X_train, X_val = X[train_idx], X[val_idx]
y_train, y_val = y[train_idx], y[val_idx]
train_loader = DataLoader(
TensorDataset(torch.tensor(X_train), torch.tensor(y_train)),
batch_size=args.batch_size, shuffle=True)
val_loader = DataLoader(
TensorDataset(torch.tensor(X_val), torch.tensor(y_val)),batch_size=args.batch_size)
# Model selection
model = (Figure2CNN if args.model == "figure2" else ResNet1D)(
input_length=args.target_len).to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
criterion = torch.nn.CrossEntropyLoss()
for epoch in range(args.epochs):
model.train()
RUNNING_LOSS = 0.0
for inputs, labels in train_loader:
inputs = inputs.unsqueeze(1).to(DEVICE)
labels = labels.to(DEVICE)
optimizer.zero_grad()
loss = criterion(model(inputs), labels)
loss.backward()
optimizer.step()
RUNNING_LOSS += loss.item()
# After fold loop (outside the epoch loop), print 1 line:
print(f"✅ Fold {fold} done. Final loss: {RUNNING_LOSS:.4f}")
# Evaluation
model.eval()
all_true, all_pred = [], []
with torch.no_grad():
for inputs, labels in val_loader:
inputs = inputs.unsqueeze(1).to(DEVICE)
labels = labels.to(DEVICE)
outputs = model(inputs)
_, predicted = torch.max(outputs, 1)
all_true.extend(labels.cpu().numpy())
all_pred.extend(predicted.cpu().numpy())
acc = 100 * np.mean(np.array(all_true) == np.array(all_pred))
fold_accuracies.append(acc)
all_conf_matrices.append(confusion_matrix(all_true, all_pred))
print(f"✅ Fold {fold} Accuracy: {acc:.2f}%")
# Save model checkpoint **after** final fold
model_path = f"outputs/{args.model}_model.pth"
torch.save(model.state_dict(), model_path)
# Summary
mean_acc, std_acc = np.mean(fold_accuracies), np.std(fold_accuracies)
print("\n📊 Cross-Validation Results:")
for i, a in enumerate(fold_accuracies, 1):
print(f"Fold {i}: {a:.2f}%")
print(f"\n✅ Mean Accuracy: {mean_acc:.2f}% ± {std_acc:.2f}%")
print(f"✅ Model saved to {model_path}")
# Save diagnostics
def save_diagnostics_log(fold_acc, confs, args_param, output_path):
fold_metrics = [{"fold": i+1, "accuracy": acc,
"confusion_matrix": c.tolist()}
for i, (a, c) in enumerate(zip(fold_acc, confs))]
log = {
"timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"preprocessing": {
"target_len": args_param.target_len,
"baseline": args_param.baseline,
"smooth": args_param.smooth,
"normalize": args_param.normalize,
},
"fold_metrics": fold_metrics,
"overall": {
"mean_accuracy": float(np.mean(fold_acc)),
"std_accuracy": float(np.std(fold_acc)),
"num_folds": len(fold_acc),
"batch_size": args_param.batch_size,
"epochs": args_param.epochs,
"learning_rate": args_param.learning_rate,
"device": str(DEVICE)
}
}
with open(output_path, "w", encoding="utf-8") as f:
json.dump(log, f, indent=2)
print(f"🧠 Diagnostics written to {output_path}")
log_path = f"outputs/logs/raman_{args.model}_diagnostics.json"
save_diagnostics_log(fold_accuracies, all_conf_matrices, args, log_path) |