| """
|
| Evaluation functions for model testing and visualization.
|
| """
|
|
|
| import torch
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| import numpy as np
|
| import matplotlib.pyplot as plt
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| import seaborn as sns
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| from tqdm import tqdm
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| from sklearn.metrics import classification_report, confusion_matrix
|
|
|
|
|
| def evaluate_model(model, test_loader, device, class_names=['Not Smoking', 'Smoking']):
|
| """
|
| Evaluate model on test set and return predictions and labels.
|
|
|
| Args:
|
| model: PyTorch model
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| test_loader: DataLoader for test data
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| device: Device to evaluate on (cuda/cpu)
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| class_names: List of class names for reporting
|
|
|
| Returns:
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| tuple: (all_predictions, all_labels, test_accuracy)
|
| """
|
| model.eval()
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| all_preds = []
|
| all_labels = []
|
|
|
| print("🧪 Evaluating on Test Set...")
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| print(f" Test batches: {len(test_loader)}\n")
|
|
|
| with torch.no_grad():
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| for images, labels in tqdm(test_loader, desc="Testing"):
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| images = images.to(device)
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| outputs = model(images)
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| _, predicted = outputs.max(1)
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|
|
| all_preds.extend(predicted.cpu().numpy())
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| all_labels.extend(labels.numpy())
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|
|
|
|
| test_acc = 100. * sum(p == l for p, l in zip(all_preds, all_labels)) / len(all_labels)
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|
|
| return all_preds, all_labels, test_acc
|
|
|
|
|
| def print_classification_report(predictions, labels, class_names=['Not Smoking', 'Smoking']):
|
| """
|
| Print detailed classification metrics.
|
|
|
| Args:
|
| predictions: List of predicted labels
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| labels: List of true labels
|
| class_names: List of class names
|
| """
|
| print(f"\n{'='*60}")
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| print(f"📊 TEST SET RESULTS")
|
| print(f"{'='*60}")
|
|
|
|
|
| test_acc = 100. * sum(p == l for p, l in zip(predictions, labels)) / len(labels)
|
| print(f"\n Overall Accuracy: {test_acc:.2f}%\n")
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|
|
|
|
| print("\nDetailed Classification Report:")
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| print(classification_report(labels, predictions, target_names=class_names, digits=4))
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| print(f"{'='*60}")
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|
|
|
|
| def plot_confusion_matrix(predictions, labels, class_names=['Not Smoking', 'Smoking'],
|
| save_path=None):
|
| """
|
| Plot confusion matrix.
|
|
|
| Args:
|
| predictions: List of predicted labels
|
| labels: List of true labels
|
| class_names: List of class names
|
| save_path: Optional path to save the figure
|
|
|
| Returns:
|
| matplotlib figure
|
| """
|
| cm = confusion_matrix(labels, predictions)
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|
|
| fig, ax = plt.subplots(figsize=(8, 6))
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| sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
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| xticklabels=class_names, yticklabels=class_names,
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| cbar_kws={'label': 'Count'}, ax=ax)
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| ax.set_title('Confusion Matrix - Test Set', fontsize=14, fontweight='bold')
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| ax.set_ylabel('True Label')
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| ax.set_xlabel('Predicted Label')
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| plt.tight_layout()
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|
|
| if save_path:
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| plt.savefig(save_path, dpi=300, bbox_inches='tight')
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| print(f"Confusion matrix saved to {save_path}")
|
|
|
| return fig
|
|
|
|
|
| def plot_training_history(history, save_path=None):
|
| """
|
| Plot training and validation loss/accuracy curves.
|
|
|
| Args:
|
| history: Dictionary with keys 'train_loss', 'val_loss', 'train_acc', 'val_acc'
|
| save_path: Optional path to save the figure
|
|
|
| Returns:
|
| matplotlib figure
|
| """
|
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
|
|
|
|
|
| ax1.plot(history['train_loss'], label='Train Loss', marker='o', linewidth=2)
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| ax1.plot(history['val_loss'], label='Val Loss', marker='s', linewidth=2)
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| ax1.set_xlabel('Epoch', fontsize=12)
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| ax1.set_ylabel('Loss', fontsize=12)
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| ax1.set_title('Training and Validation Loss', fontsize=14, fontweight='bold')
|
| ax1.legend(fontsize=11)
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| ax1.grid(True, alpha=0.3)
|
|
|
|
|
| ax2.plot(history['train_acc'], label='Train Accuracy', marker='o', linewidth=2)
|
| ax2.plot(history['val_acc'], label='Val Accuracy', marker='s', linewidth=2)
|
| ax2.set_xlabel('Epoch', fontsize=12)
|
| ax2.set_ylabel('Accuracy (%)', fontsize=12)
|
| ax2.set_title('Training and Validation Accuracy', fontsize=14, fontweight='bold')
|
| ax2.legend(fontsize=11)
|
| ax2.grid(True, alpha=0.3)
|
|
|
| plt.tight_layout()
|
|
|
| if save_path:
|
| plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
| print(f"Training history saved to {save_path}")
|
|
|
| return fig
|
|
|
|
|
| def get_predictions_with_confidence(model, dataloader, device):
|
| """
|
| Get predictions along with confidence scores.
|
|
|
| Args:
|
| model: PyTorch model
|
| dataloader: DataLoader for data
|
| device: Device to run inference on
|
|
|
| Returns:
|
| tuple: (predictions, confidences, labels)
|
| """
|
| model.eval()
|
| all_preds = []
|
| all_confidences = []
|
| all_labels = []
|
|
|
| with torch.no_grad():
|
| for images, labels in dataloader:
|
| images = images.to(device)
|
| outputs = model(images)
|
|
|
|
|
| probs = torch.softmax(outputs, dim=1)
|
| confidences, predicted = probs.max(1)
|
|
|
| all_preds.extend(predicted.cpu().numpy())
|
| all_confidences.extend(confidences.cpu().numpy())
|
| all_labels.extend(labels.numpy())
|
|
|
| return np.array(all_preds), np.array(all_confidences), np.array(all_labels)
|
|
|
|
|
| def analyze_errors(model, dataloader, device, dataset, num_samples=10):
|
| """
|
| Analyze misclassified samples.
|
|
|
| Args:
|
| model: PyTorch model
|
| dataloader: DataLoader for data
|
| device: Device to run inference on
|
| dataset: Original dataset to access images
|
| num_samples: Number of error samples to display
|
|
|
| Returns:
|
| List of dictionaries with error information
|
| """
|
| predictions, confidences, labels = get_predictions_with_confidence(model, dataloader, device)
|
|
|
|
|
| errors = []
|
| for idx, (pred, conf, label) in enumerate(zip(predictions, confidences, labels)):
|
| if pred != label:
|
| errors.append({
|
| 'index': idx,
|
| 'true_label': label,
|
| 'predicted_label': pred,
|
| 'confidence': conf,
|
| 'image_path': dataset.image_paths[idx]
|
| })
|
|
|
| print(f"\n🔍 Error Analysis:")
|
| print(f" Total errors: {len(errors)}")
|
| print(f" Error rate: {100 * len(errors) / len(labels):.2f}%")
|
|
|
|
|
| errors.sort(key=lambda x: x['confidence'], reverse=True)
|
|
|
| return errors[:num_samples] |
