legacy/benchmark.py

# ============================================
# PentachoraViT CIFAR-100 Evaluation
# ============================================

import torch
import torch.nn.functional as F
from collections import defaultdict
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt

def evaluate_pentachora_vit(model, test_loader, device='cuda'):
    """Properly evaluate PentachoraViT model."""
    model.eval()

    # Get class names
    class_names = get_cifar100_class_names()

    # Check model configuration
    print(f"Model Configuration:")
    print(f"  Internal dim: {model.dim}")
    print(f"  Vocab dim: {model.vocab_dim}")
    print(f"  Num classes: {model.num_classes}")

    # Get the class crystals
    if hasattr(model, 'cls_tokens') and hasattr(model.cls_tokens, 'class_pentachora'):
        crystals = model.cls_tokens.class_pentachora  # [100, 5, vocab_dim]
        print(f"  Crystal shape: {crystals.shape}")
    else:
        print("  No crystals found!")
        return None

    # Track metrics
    all_predictions = []
    all_targets = []
    all_confidences = []
    geometric_alignments_by_class = defaultdict(list)
    aux_predictions = []

    with torch.no_grad():
        for images, targets in tqdm(test_loader, desc="Evaluating"):
            images = images.to(device)
            targets = targets.to(device)

            # Get model outputs dictionary
            outputs = model(images)

            # Main predictions from primary head
            logits = outputs['logits']  # [batch, 100]
            probs = F.softmax(logits, dim=1)
            confidence, predicted = torch.max(probs, 1)

            # Store predictions
            all_predictions.extend(predicted.cpu().numpy())
            all_targets.extend(targets.cpu().numpy())
            all_confidences.extend(confidence.cpu().numpy())

            # Auxiliary predictions
            if 'aux_logits' in outputs:
                aux_probs = F.softmax(outputs['aux_logits'], dim=1)
                _, aux_pred = torch.max(aux_probs, 1)
                aux_predictions.extend(aux_pred.cpu().numpy())

            # Geometric alignments - these show how patches align with class crystals
            if 'geometric_alignments' in outputs:
                # Shape: [batch, num_patches, num_classes]
                geo_align = outputs['geometric_alignments']
                # Average over patches to get per-sample class alignments
                geo_align_mean = geo_align.mean(dim=1)  # [batch, num_classes]

                for i, target_class in enumerate(targets):
                    class_idx = target_class.item()
                    # Store alignment score for the true class
                    geometric_alignments_by_class[class_idx].append(
                        geo_align_mean[i, class_idx].item()
                    )

    # Convert to numpy arrays
    all_predictions = np.array(all_predictions)
    all_targets = np.array(all_targets)
    all_confidences = np.array(all_confidences)

    # Calculate per-class metrics
    class_results = []
    for class_idx in range(len(class_names)):
        mask = all_targets == class_idx
        if mask.sum() == 0:
            continue

        class_preds = all_predictions[mask]
        correct = (class_preds == class_idx).sum()
        total = mask.sum()
        accuracy = 100.0 * correct / total

        # Average confidence for this class
        class_conf = all_confidences[mask].mean()

        # Geometric alignment for this class
        geo_align = np.mean(geometric_alignments_by_class[class_idx]) if geometric_alignments_by_class[class_idx] else 0

        # Crystal statistics
        class_crystal = crystals[class_idx].detach().cpu()  # [5, vocab_dim]
        vertex_variance = class_crystal.var(dim=0).mean().item()

        # Crystal norm (average magnitude)
        crystal_norm = class_crystal.norm(dim=-1).mean().item()

        class_results.append({
            'class_idx': class_idx,
            'class_name': class_names[class_idx],
            'accuracy': accuracy,
            'correct': int(correct),
            'total': int(total),
            'avg_confidence': class_conf,
            'geometric_alignment': geo_align,
            'vertex_variance': vertex_variance,
            'crystal_norm': crystal_norm
        })

    # Sort by accuracy
    class_results.sort(key=lambda x: x['accuracy'], reverse=True)

    # Overall metrics
    overall_acc = 100.0 * (all_predictions == all_targets).mean()

    # Auxiliary head accuracy if available
    aux_acc = None
    if aux_predictions:
        aux_predictions = np.array(aux_predictions)
        aux_acc = 100.0 * (aux_predictions == all_targets).mean()

    # Print results
    print(f"\n" + "="*80)
    print(f"EVALUATION RESULTS")
    print(f"="*80)
    print(f"\nOverall Accuracy: {overall_acc:.2f}%")
    if aux_acc:
        print(f"Auxiliary Head Accuracy: {aux_acc:.2f}%")

    # Top 10 classes
    print(f"\nTop 10 Classes:")
    print(f"{'Class':<20} {'Acc%':<8} {'Conf':<8} {'GeoAlign':<10} {'CrystalNorm':<12}")
    print("-"*70)
    for r in class_results[:10]:
        print(f"{r['class_name']:<20} {r['accuracy']:>6.1f} {r['avg_confidence']:>7.3f} "
              f"{r['geometric_alignment']:>9.3f} {r['crystal_norm']:>11.3f}")

    # Bottom 10 classes
    print(f"\nBottom 10 Classes:")
    print(f"{'Class':<20} {'Acc%':<8} {'Conf':<8} {'GeoAlign':<10} {'CrystalNorm':<12}")
    print("-"*70)
    for r in class_results[-10:]:
        print(f"{r['class_name']:<20} {r['accuracy']:>6.1f} {r['avg_confidence']:>7.3f} "
              f"{r['geometric_alignment']:>9.3f} {r['crystal_norm']:>11.3f}")

    # Analyze correlations
    accuracies = [r['accuracy'] for r in class_results]
    geo_aligns = [r['geometric_alignment'] for r in class_results]
    crystal_norms = [r['crystal_norm'] for r in class_results]
    vertex_vars = [r['vertex_variance'] for r in class_results]

    print(f"\nCorrelations with Accuracy:")
    print(f"  Geometric Alignment: {np.corrcoef(accuracies, geo_aligns)[0,1]:.3f}")
    print(f"  Crystal Norm: {np.corrcoef(accuracies, crystal_norms)[0,1]:.3f}")
    print(f"  Vertex Variance: {np.corrcoef(accuracies, vertex_vars)[0,1]:.3f}")

    # Visualizations
    fig, axes = plt.subplots(2, 2, figsize=(12, 10))

    # 1. Accuracy distribution
    ax = axes[0, 0]
    ax.hist(accuracies, bins=20, edgecolor='black', alpha=0.7)
    ax.axvline(overall_acc, color='red', linestyle='--', label=f'Overall: {overall_acc:.1f}%')
    ax.set_xlabel('Accuracy (%)')
    ax.set_ylabel('Count')
    ax.set_title('Per-Class Accuracy Distribution')
    ax.legend()
    ax.grid(True, alpha=0.3)

    # 2. Accuracy vs Geometric Alignment
    ax = axes[0, 1]
    scatter = ax.scatter(geo_aligns, accuracies, c=crystal_norms, cmap='viridis', alpha=0.6)
    ax.set_xlabel('Geometric Alignment Score')
    ax.set_ylabel('Accuracy (%)')
    ax.set_title('Accuracy vs Geometric Alignment\n(color = crystal norm)')
    plt.colorbar(scatter, ax=ax)
    ax.grid(True, alpha=0.3)

    # 3. Crystal Analysis
    ax = axes[1, 0]
    ax.scatter(crystal_norms, accuracies, alpha=0.6)
    ax.set_xlabel('Crystal Norm (avg magnitude)')
    ax.set_ylabel('Accuracy (%)')
    ax.set_title('Accuracy vs Crystal Norm')
    ax.grid(True, alpha=0.3)

    # 4. Top/Bottom comparison
    ax = axes[1, 1]
    top10_acc = [r['accuracy'] for r in class_results[:10]]
    bottom10_acc = [r['accuracy'] for r in class_results[-10:]]
    top10_geo = [r['geometric_alignment'] for r in class_results[:10]]
    bottom10_geo = [r['geometric_alignment'] for r in class_results[-10:]]

    x = np.arange(10)
    width = 0.35
    ax.bar(x - width/2, top10_acc, width, label='Top 10 Accuracy', color='green', alpha=0.7)
    ax.bar(x + width/2, bottom10_acc, width, label='Bottom 10 Accuracy', color='red', alpha=0.7)
    ax.set_xlabel('Rank within group')
    ax.set_ylabel('Accuracy (%)')
    ax.set_title('Top 10 vs Bottom 10 Classes')
    ax.legend()
    ax.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.show()
    # ===================================================================================
    # FULL 100-CLASS DIAGNOSTIC SPECTRUM (SORTED BY CLASS IDX FOR CONSISTENCY)
    # ===================================================================================
    print(f"\n{'='*90}")
    print("Sparky — Full Class Spectrum")
    print(f"{'='*90}")
    print(f"{'Idx':<5} {'Class':<20} {'Acc%':<8} {'Conf':<8} {'GeoAlign':<10} {'CrystalNorm':<12} {'Variance':<10}")
    print("-" * 90)

    for r in sorted(class_results, key=lambda x: x['class_idx']):
        print(f"{r['class_idx']:<5} {r['class_name']:<20} "
              f"{r['accuracy']:>6.1f} {r['avg_confidence']:>7.3f} "
              f"{r['geometric_alignment']:>9.3f} {r['crystal_norm']:>11.3f} "
              f"{r['vertex_variance']:>9.8f}")

    return class_results, overall_acc

# Run evaluation
if 'model' in globals():
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    _, test_loader = get_cifar100_dataloaders(batch_size=100)

    results, overall = evaluate_pentachora_vit(model, test_loader, device)

    # Additional crystal analysis
    print("\nCrystal Geometry Analysis:")
    print("-"*50)

    # Get crystals
    crystals = model.cls_tokens.class_pentachora.detach().cpu()

    # Compute pairwise similarities between class crystals
    crystals_flat = crystals.mean(dim=1)  # Average over 5 vertices
    crystals_norm = F.normalize(crystals_flat, dim=1)
    similarities = torch.matmul(crystals_norm, crystals_norm.T)

    # Find confused pairs (high similarity, both low accuracy)
    print("\nMost similar classes with poor performance:")
    for i in range(100):
        for j in range(i+1, 100):
            if results[i]['accuracy'] < 20 and results[j]['accuracy'] < 20:
                sim = similarities[results[i]['class_idx'], results[j]['class_idx']].item()
                if sim > 0.5:
                    print(f"  {results[i]['class_name']:<15} ({results[i]['accuracy']:.1f}%) ↔ "
                          f"{results[j]['class_name']:<15} ({results[j]['accuracy']:.1f}%) : {sim:.3f}")
    
else:
    print("No model found in memory!")