CaliBench / SMART /toy_example /softece_Experiment.py
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import sys
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from tqdm import tqdm
import json
import time

# Add the parent directory to the path to import from PureLogits
current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.dirname(current_dir)
grandparent_dir = os.path.dirname(parent_dir)
sys.path.append(grandparent_dir)
sys.path.append(parent_dir)

# Import necessary components from the calibrator
from calibrator.Component.metrics import (
    BrierLoss, CrossEntropyLoss, MSELoss, SoftECE, ECE
)

# Set random seed for reproducibility
def set_seed(seed):
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

# Load logits and labels from cache directory
def load_data(cache_dir):
    val_logits = np.load(os.path.join(cache_dir, "val_logits.npy"))
    val_labels = np.load(os.path.join(cache_dir, "val_labels.npy"))
    test_logits = np.load(os.path.join(cache_dir, "test_logits.npy"))
    test_labels = np.load(os.path.join(cache_dir, "test_labels.npy"))
    
    print(f"Loaded data: val_logits shape: {val_logits.shape}, val_labels shape: {val_labels.shape}")
    print(f"Loaded data: test_logits shape: {test_logits.shape}, test_labels shape: {test_labels.shape}")
    
    return val_logits, val_labels, test_logits, test_labels

# Compute hardness as the gap between top logit and runner-up
def compute_hardness(logits):
    logits_tensor = torch.tensor(logits, dtype=torch.float32)
    sorted_logits, _ = torch.sort(logits_tensor, descending=True)
    logit_gap = (sorted_logits[0] - sorted_logits[1]).item()
    return logit_gap

# Custom ECE wrapper to ensure it returns a tensor that can be backpropagated
class ECEWrapper(nn.Module):
    def __init__(self, n_bins=15):
        super(ECEWrapper, self).__init__()
        self.ece = ECE(n_bins=n_bins)
        
    def forward(self, logits, labels):
        # ECE returns a scalar value, we need to wrap it in a tensor with requires_grad=True
        ece_value = self.ece(logits, labels)
        # Check if the result is already a tensor with grad
        if isinstance(ece_value, torch.Tensor) and ece_value.requires_grad:
            return ece_value
        # Otherwise, create a tensor with requires_grad=True
        if isinstance(ece_value, torch.Tensor):
            return ece_value.clone().detach().requires_grad_(True)
        else:
            return torch.tensor(ece_value, requires_grad=True, device=logits.device)

# Custom Brier Loss implementation that ensures it's different from MSE
class CustomBrierLoss(nn.Module):
    def __init__(self):
        super(CustomBrierLoss, self).__init__()
        
    def forward(self, logits, labels):
        # Get predicted probabilities
        outputs = F.softmax(logits, dim=1)
        
        # Convert labels to one-hot
        one_hot = torch.zeros(labels.size(0), outputs.size(1), device=labels.device)
        one_hot.scatter_(1, labels.unsqueeze(1), 1)
        
        # Compute Brier loss
        brier_score = torch.mean(torch.sum((outputs - one_hot) ** 2, dim=1))
        
        # Explicitly scale by 0.5 to differentiate from MSE
        return brier_score * 0.5

# Experiment 1: Compare gradient stability of different calibration objectives
def experiment_1_gradient_stability(test_logits, test_labels, cache_dir, output_dir="results"):
    print("\nExperiment 1: Comparing Gradient Stability of Different Calibration Objectives")
    
    # Create output directory
    os.makedirs(output_dir, exist_ok=True)
    
    # Convert data to PyTorch tensors
    logits_tensor = torch.tensor(test_logits, dtype=torch.float32)
    labels_tensor = torch.tensor(test_labels, dtype=torch.long)
    
    # Initialize different loss functions
    soft_ece = SoftECE(n_bins=15)
    traditional_ece = ECEWrapper(n_bins=15)  # Use wrapper for ECE
    brier_score = BrierLoss()
    cross_entropy = CrossEntropyLoss()
    mse_loss = MSELoss()
    
    # Temperature parameter with gradient tracking
    temperature = nn.Parameter(torch.ones(1))
    
    # Sample a subset of data for visualization (to avoid clutter)
    num_samples = 1000
    indices = np.random.choice(len(test_logits), num_samples, replace=False)
    
    # Confidence bins for analysis
    confidence_bins = np.linspace(0.5, 1.0, 10)
    bin_width = confidence_bins[1] - confidence_bins[0]
    
    # Store gradients for each loss function across confidence levels
    results = {
        "SoftECE": {"grads": [], "conf_bins": []},
        "ECE": {"grads": [], "conf_bins": []},
        "Brier": {"grads": [], "conf_bins": []},
        "CrossEntropy": {"grads": [], "conf_bins": []},
        "MSE": {"grads": [], "conf_bins": []}
    }
    
    # Calculate gradients for different temperature values
    temp_values = [0.5, 0.8, 1.0, 1.2, 1.5, 2.0]
    
    for temp_val in temp_values:
        print(f"\nAnalyzing gradients at temperature = {temp_val}")
        
        # Set temperature to current value
        with torch.no_grad():
            temperature.fill_(temp_val)
        
        # Process samples in batches to avoid memory issues
        batch_size = 100
        num_batches = (num_samples + batch_size - 1) // batch_size
        
        for batch_idx in tqdm(range(num_batches)):
            start_idx = batch_idx * batch_size
            end_idx = min((batch_idx + 1) * batch_size, num_samples)
            batch_indices = indices[start_idx:end_idx]
            
            batch_logits = logits_tensor[batch_indices]
            batch_labels = labels_tensor[batch_indices]
            
            # Apply temperature scaling
            scaled_logits = batch_logits / temperature
            probs = F.softmax(scaled_logits, dim=1)
            
            # Calculate confidence (max probability)
            confidences = torch.max(probs, dim=1)[0].detach().numpy()
            
            # Calculate gradients for each loss function
            for loss_name, loss_fn in [
                ("SoftECE", soft_ece),
                ("ECE", traditional_ece),
                ("Brier", brier_score),
                ("CrossEntropy", cross_entropy),
                ("MSE", mse_loss)
            ]:
                temperature.grad = None
                
                if loss_name == "SoftECE":
                    loss = loss_fn(scaled_logits, batch_labels)
                elif loss_name == "ECE":
                    loss = loss_fn(scaled_logits, batch_labels)
                elif loss_name == "Brier":
                    loss = loss_fn(scaled_logits, batch_labels)
                elif loss_name == "CrossEntropy":
                    loss = loss_fn(scaled_logits, batch_labels)
                elif loss_name == "MSE":
                    loss = loss_fn(scaled_logits, batch_labels)
                
                loss.backward(retain_graph=True)
                
                # Store gradient magnitude for each sample
                if temperature.grad is not None:
                    grad_magnitude = temperature.grad.item()
                    
                    # Store gradients by confidence bin
                    for i, conf in enumerate(confidences):
                        bin_idx = np.digitize(conf, confidence_bins) - 1
                        if 0 <= bin_idx < len(confidence_bins):
                            results[loss_name]["grads"].append(grad_magnitude)
                            results[loss_name]["conf_bins"].append(confidence_bins[bin_idx])
    
    # Plot gradient magnitudes across confidence levels
    plt.figure(figsize=(10, 6))
    
    for loss_name in ["SoftECE", "ECE", "Brier", "CrossEntropy", "MSE"]:
        # Group gradients by confidence bin
        binned_grads = {}
        for grad, bin_val in zip(results[loss_name]["grads"], results[loss_name]["conf_bins"]):
            if bin_val not in binned_grads:
                binned_grads[bin_val] = []
            binned_grads[bin_val].append(grad)
        
        # Calculate mean and std of gradients for each bin
        bin_centers = []
        mean_grads = []
        std_grads = []
        
        for bin_val in sorted(binned_grads.keys()):
            if binned_grads[bin_val]:  # Ensure there are gradients for this bin
                bin_centers.append(bin_val)
                mean_grads.append(np.mean(np.abs(binned_grads[bin_val])))
                std_grads.append(np.std(np.abs(binned_grads[bin_val])))
        
        # Plot mean gradient magnitude with error bars
        plt.errorbar(bin_centers, mean_grads, yerr=std_grads, label=loss_name, marker='o', capsize=4)
    
    plt.xlabel('Confidence Level')
    plt.ylabel('Gradient Magnitude')
    plt.title('Gradient Stability Across Confidence Levels')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'gradient_stability.png'), dpi=300)
    plt.close()
    
    # Save results
    with open(os.path.join(output_dir, 'gradient_stability_results.json'), 'w') as f:
        # Convert numpy arrays to lists for JSON serialization
        serializable_results = {}
        for loss_name, data in results.items():
            serializable_results[loss_name] = {
                "grads": [float(g) for g in data["grads"]],
                "conf_bins": [float(c) for c in data["conf_bins"]]
            }
        json.dump(serializable_results, f, indent=2)
    
    print(f"Gradient stability analysis complete. Results saved to {output_dir}")

# Experiment 2: Gradient information content analysis
def experiment_2_gradient_information(test_logits, test_labels, cache_dir, output_dir="results"):
    print("\nExperiment 2: Analyzing Gradient Information Content of Different Calibration Objectives")
    
    # Create output directory
    os.makedirs(output_dir, exist_ok=True)
    
    # Convert data to PyTorch tensors
    logits_tensor = torch.tensor(test_logits, dtype=torch.float32)
    labels_tensor = torch.tensor(test_labels, dtype=torch.long)
    
    # Sample a subset of data
    num_samples = 2000
    indices = np.random.choice(len(test_logits), num_samples, replace=False)
    
    # Initialize different loss functions
    soft_ece = SoftECE(n_bins=15, sigma=0.05)
    traditional_ece = ECEWrapper(n_bins=15)
    brier_score = BrierLoss()
    cross_entropy = CrossEntropyLoss()
    mse_loss = MSELoss()
    
    # Create synthetic miscalibration by distorting temperatures
    # We'll create 5 different scenarios ranging from underconfident to overconfident
    temp_scenarios = [0.5, 0.8, 1.0, 1.2, 2.0]
    
    # Store gradient direction consistency and correlation with calibration error
    results = {
        "temp_scenario": [],
        "ece_values": [],
        "gradient_consistency": {},
        "gradient_correlation": {},
        "gradient_entropy": {}
    }
    
    for loss_name in ["SoftECE", "ECE", "Brier", "CrossEntropy", "MSE"]:
        results["gradient_consistency"][loss_name] = []
        results["gradient_correlation"][loss_name] = []
        results["gradient_entropy"][loss_name] = []
    
    # Create confidence bins for analysis
    confidence_bins = np.linspace(0.1, 1.0, 10)
    
    for temperature_value in temp_scenarios:
        print(f"\nAnalyzing scenario with temperature = {temperature_value}")
        
        # Set up temperature parameter for this scenario
        temperature = nn.Parameter(torch.ones(1) * temperature_value)
        
        # Generate scaled logits for the whole dataset to calculate overall ECE
        with torch.no_grad():
            overall_scaled_logits = logits_tensor / temperature
            overall_probs = F.softmax(overall_scaled_logits, dim=1)
            ece_metric = ECE(n_bins=15)
            ece_value = ece_metric(overall_scaled_logits, labels_tensor).item()
        
        results["temp_scenario"].append(temperature_value)
        results["ece_values"].append(ece_value)
        
        print(f"Overall ECE for temperature {temperature_value}: {ece_value:.4f}")
        
        # For each loss function, analyze gradient properties
        for loss_name, loss_fn in [
            ("SoftECE", soft_ece),
            ("ECE", traditional_ece),
            ("Brier", brier_score),
            ("CrossEntropy", cross_entropy),
            ("MSE", mse_loss)
        ]:
            print(f"Analyzing gradients for {loss_name}")
            
            # Calculate gradients for each sample in batches
            batch_size = 100
            num_batches = (num_samples + batch_size - 1) // batch_size
            
            # Store all gradients and corresponding confidences
            all_grads = []
            all_confidences = []
            all_is_correct = []
            
            for batch_idx in tqdm(range(num_batches)):
                start_idx = batch_idx * batch_size
                end_idx = min((batch_idx + 1) * batch_size, num_samples)
                batch_indices = indices[start_idx:end_idx]
                
                batch_logits = logits_tensor[batch_indices]
                batch_labels = labels_tensor[batch_indices]
                
                # Calculate gradients per sample
                for i in range(len(batch_logits)):
                    sample_logits = batch_logits[i:i+1]
                    sample_label = batch_labels[i:i+1]
                    
                    # Reset parameter to temperature_value for each sample
                    temperature = nn.Parameter(torch.ones(1) * temperature_value)
                    
                    # Apply temperature scaling
                    scaled_logits = sample_logits / temperature
                    probs = F.softmax(scaled_logits, dim=1)
                    
                    # Check if prediction is correct
                    pred = torch.argmax(probs, dim=1)
                    is_correct = (pred == sample_label).float().item()
                    
                    # Calculate confidence
                    confidence = torch.max(probs).item()
                    
                    # Calculate loss and gradient
                    temperature.grad = None
                    
                    try:
                        loss = loss_fn(scaled_logits, sample_label)
                        loss.backward()
                        
                        # Only store if gradient was successfully calculated
                        if temperature.grad is not None:
                            all_grads.append(temperature.grad.item())
                            all_confidences.append(confidence)
                            all_is_correct.append(is_correct)
                    except Exception as e:
                        # Skip samples that cause errors in gradient calculation
                        print(f"  Skipping sample due to error: {e}")
                        continue
            
            # Skip further analysis if we don't have enough gradient data
            if len(all_grads) < 10:
                print(f"  Not enough gradient data for {loss_name}, skipping analysis")
                continue
                
            # Calculate gradient consistency per confidence bin
            # (correlation between gradient direction and correctness)
            bin_grad_consistency = []
            bin_grad_entropy = []
            
            for i in range(len(confidence_bins) - 1):
                bin_start = confidence_bins[i]
                bin_end = confidence_bins[i+1]
                
                # Find samples in this bin
                bin_indices = []
                for j in range(len(all_confidences)):
                    if bin_start <= all_confidences[j] < bin_end:
                        bin_indices.append(j)
                
                if len(bin_indices) > 5:  # Only consider bins with enough samples
                    bin_grads = [all_grads[j] for j in bin_indices]
                    bin_correct = [all_is_correct[j] for j in bin_indices]
                    
                    # Calculate consistency: avg sign of gradient * (conf - accuracy)
                    bin_conf = np.mean([all_confidences[j] for j in bin_indices])
                    bin_acc = np.mean(bin_correct)
                    calibration_error = bin_conf - bin_acc
                    
                    # Compute correlation between gradient and calibration error
                    # A well-behaved gradient should be negative when confidence > accuracy
                    # and positive when confidence < accuracy
                    grad_signs = np.sign(bin_grads)
                    ce_sign = np.sign(calibration_error)
                    correct_direction = -1 * ce_sign  # Gradient should point in opposite direction of error
                    
                    consistency = np.mean(grad_signs == correct_direction)
                    bin_grad_consistency.append(consistency)
                    
                    # Calculate entropy of gradient distribution in this bin
                    # Normalize gradients for comparison
                    if len(bin_grads) > 0 and np.std(bin_grads) > 0:
                        norm_grads = (bin_grads - np.mean(bin_grads)) / np.std(bin_grads)
                        # Use histogram to estimate entropy
                        hist, _ = np.histogram(norm_grads, bins=10, density=True)
                        hist = hist[hist > 0]  # Avoid log(0)
                        entropy = -np.sum(hist * np.log(hist))
                        bin_grad_entropy.append(entropy)
            
            # Store results
            if bin_grad_consistency:
                avg_consistency = np.mean(bin_grad_consistency)
                results["gradient_consistency"][loss_name].append(avg_consistency)
                print(f"  {loss_name} gradient consistency: {avg_consistency:.4f}")
            
            if bin_grad_entropy:
                avg_entropy = np.mean(bin_grad_entropy)
                results["gradient_entropy"][loss_name].append(avg_entropy)
                print(f"  {loss_name} gradient entropy: {avg_entropy:.4f}")
            
            # Calculate correlation between gradient and calibration error
            # For overall dataset
            if len(all_grads) > 0:
                accuracies = np.array(all_is_correct)
                confidences = np.array(all_confidences)
                grads = np.array(all_grads)
                
                # Ensure we have valid data
                if len(grads) > 0 and not np.all(np.isnan(grads)):
                    cal_errors = confidences - accuracies
                    # Correlation should be negative (gradient points opposite to error)
                    correlation = np.corrcoef(cal_errors, -grads)[0, 1]
                    if not np.isnan(correlation):
                        results["gradient_correlation"][loss_name].append(correlation)
                        print(f"  {loss_name} gradient-error correlation: {correlation:.4f}")
    
    # Plot results
    # 1. Plot gradient consistency across different calibration scenarios
    plt.figure(figsize=(10, 6))
    for loss_name in ["SoftECE", "ECE", "Brier", "CrossEntropy", "MSE"]:
        if loss_name in results["gradient_consistency"] and len(results["gradient_consistency"][loss_name]) > 0:
            plt.plot(results["temp_scenario"], results["gradient_consistency"][loss_name], 
                    marker='o', label=loss_name)
    
    plt.xlabel('Temperature (Lower = More Overconfident)')
    plt.ylabel('Gradient Direction Consistency')
    plt.title('Gradient Consistency Across Calibration Scenarios')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'gradient_consistency.png'), dpi=300)
    plt.close()
    
    # 2. Plot gradient-error correlation
    plt.figure(figsize=(10, 6))
    for loss_name in ["SoftECE", "ECE", "Brier", "CrossEntropy", "MSE"]:
        if loss_name in results["gradient_correlation"] and len(results["gradient_correlation"][loss_name]) > 0:
            plt.plot(results["temp_scenario"], results["gradient_correlation"][loss_name], 
                    marker='o', label=loss_name)
    
    plt.xlabel('Temperature (Lower = More Overconfident)')
    plt.ylabel('Correlation between Gradient and Calibration Error')
    plt.title('Gradient-Error Correlation Across Calibration Scenarios')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'gradient_correlation.png'), dpi=300)
    plt.close()
    
    # 3. Plot gradient entropy (information content)
    plt.figure(figsize=(10, 6))
    for loss_name in ["SoftECE", "ECE", "Brier", "CrossEntropy", "MSE"]:
        if loss_name in results["gradient_entropy"] and len(results["gradient_entropy"][loss_name]) > 0:
            plt.plot(results["temp_scenario"], results["gradient_entropy"][loss_name], 
                    marker='o', label=loss_name)
    
    plt.xlabel('Temperature (Lower = More Overconfident)')
    plt.ylabel('Gradient Distribution Entropy')
    plt.title('Gradient Information Content Across Calibration Scenarios')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'gradient_entropy.png'), dpi=300)
    plt.close()
    
    # Save results
    with open(os.path.join(output_dir, 'gradient_information_results.json'), 'w') as f:
        # Ensure all data is serializable
        serializable_results = {
            "temp_scenario": list(results["temp_scenario"]),
            "ece_values": list(results["ece_values"]),
            "gradient_consistency": {k: list(v) for k, v in results["gradient_consistency"].items()},
            "gradient_correlation": {k: list(v) for k, v in results["gradient_correlation"].items()},
            "gradient_entropy": {k: list(v) for k, v in results["gradient_entropy"].items()}
        }
        json.dump(serializable_results, f, indent=2)
    
    print(f"Gradient information analysis complete. Results saved to {output_dir}")

# Experiment 3: Sample efficiency and overfitting resistance analysis
def experiment_3_sample_efficiency(val_logits, val_labels, test_logits, test_labels, cache_dir, output_dir="results"):
    print("\nExperiment 3: Analyzing Sample Efficiency and Overfitting Resistance")
    
    # Create output directory
    os.makedirs(output_dir, exist_ok=True)
    
    # Convert data to PyTorch tensors
    val_logits_tensor = torch.tensor(val_logits, dtype=torch.float32)
    val_labels_tensor = torch.tensor(val_labels, dtype=torch.long)
    test_logits_tensor = torch.tensor(test_logits, dtype=torch.float32)
    test_labels_tensor = torch.tensor(test_labels, dtype=torch.long)
    
    # Define different validation set sizes to test
    val_set_sizes = [10, 20, 30, 40,50, 100, 150]
    val_set_sizes = [min(size, len(val_logits)) for size in val_set_sizes]
    
    # Define loss functions to compare
    loss_functions = [
        ("SoftECE", lambda: SoftECE(n_bins=15, sigma=0.05)),
        ("Brier", lambda: CustomBrierLoss()),
        ("CrossEntropy", lambda: CrossEntropyLoss()),
        ("MSE", lambda: MSELoss())
    ]
    
    # Number of training steps to monitor
    max_steps = 100
    step_interval = 5  # Record metrics every n steps
    
    # Store results
    results = {
        "val_set_sizes": val_set_sizes,
        "training_curves": {size: {} for size in val_set_sizes},
        "final_metrics": {size: {} for size in val_set_sizes},
    }
    
    # Train on different validation set sizes
    for val_size in val_set_sizes:
        print(f"\nTraining with validation set size: {val_size}")
        
        # Sample a subset of validation data
        indices = np.random.choice(len(val_logits), val_size, replace=False)
        subset_logits = val_logits_tensor[indices]
        subset_labels = val_labels_tensor[indices]
        
        # For each loss function
        for loss_name, loss_fn_creator in loss_functions:
            print(f"  Training with {loss_name}")
            
            # Initialize results storage for this scenario
            if loss_name not in results["training_curves"][val_size]:
                results["training_curves"][val_size][loss_name] = {
                    "steps": [],
                    "train_loss": [],
                    "train_ece": [],
                    "test_ece": [],
                    "test_acc": []
                }
            
            if loss_name not in results["final_metrics"][val_size]:
                results["final_metrics"][val_size][loss_name] = {}
            
            # Initialize temperature and optimizer
            temperature = nn.Parameter(torch.ones(1))
            optimizer = torch.optim.Adam([temperature], lr=0.01)
            
            # Create loss function
            loss_fn = loss_fn_creator()
            
            # Training loop with tracking
            for step in range(max_steps):
                optimizer.zero_grad()
                
                # Apply temperature scaling
                scaled_logits = subset_logits / temperature
                
                # Calculate loss
                loss = loss_fn(scaled_logits, subset_labels)
                
                # Backpropagation
                loss.backward()
                optimizer.step()
                
                # Record metrics at specified intervals
                if step % step_interval == 0 or step == max_steps - 1:
                    with torch.no_grad():
                        # Calculate training metrics
                        train_ece = ECE(n_bins=15)(scaled_logits, subset_labels).item()
                        
                        # Calculate test metrics
                        test_scaled_logits = test_logits_tensor / temperature
                        test_probs = F.softmax(test_scaled_logits, dim=1)
                        test_ece = ECE(n_bins=15)(test_scaled_logits, test_labels_tensor).item()
                        test_acc = (torch.argmax(test_probs, dim=1) == test_labels_tensor).float().mean().item()
                        
                        # Store metrics
                        results["training_curves"][val_size][loss_name]["steps"].append(step)
                        results["training_curves"][val_size][loss_name]["train_loss"].append(loss.item())
                        results["training_curves"][val_size][loss_name]["train_ece"].append(train_ece)
                        results["training_curves"][val_size][loss_name]["test_ece"].append(test_ece)
                        results["training_curves"][val_size][loss_name]["test_acc"].append(test_acc)
            
            # Store final metrics
            results["final_metrics"][val_size][loss_name] = {
                "temperature": temperature.item(),
                "train_ece": train_ece,
                "test_ece": test_ece,
                "test_acc": test_acc,
                "train_test_ece_gap": abs(train_ece - test_ece)  # Measure of overfitting
            }
            
            print(f"    Final temperature: {temperature.item():.4f}, Test ECE: {test_ece:.4f}")
    
    # Plot results
    # 1. Plot final test ECE vs validation set size
    plt.figure(figsize=(10, 6))
    
    print("\nFinal Test ECE values for plotting:")
    for loss_name, _ in loss_functions:
        test_eces = [results["final_metrics"][size][loss_name]["test_ece"] for size in val_set_sizes]
        print(f"  {loss_name}: {test_eces}")
        line, = plt.plot(val_set_sizes, test_eces, marker='o', linewidth=2, markersize=8)
        plt.annotate(loss_name, (val_set_sizes[-1], test_eces[-1]), 
                     xytext=(5, 0), textcoords='offset points', va='center')
    
    plt.xlabel('Validation Set Size')
    plt.ylabel('Test ECE')
    plt.title('Sample Efficiency: Test ECE vs. Validation Set Size')
    plt.legend([loss_name for loss_name, _ in loss_functions])
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'sample_efficiency_ece.png'), dpi=300)
    plt.close()
    
    # 2. Plot train-test ECE gap vs validation set size (measure of overfitting)
    plt.figure(figsize=(10, 6))
    
    print("\nTrain-Test ECE Gap values for plotting:")
    for loss_name, _ in loss_functions:
        gaps = [results["final_metrics"][size][loss_name]["train_test_ece_gap"] for size in val_set_sizes]
        print(f"  {loss_name}: {gaps}")
        line, = plt.plot(val_set_sizes, gaps, marker='o', linewidth=2, markersize=8)
        plt.annotate(loss_name, (val_set_sizes[-1], gaps[-1]), 
                     xytext=(5, 0), textcoords='offset points', va='center')
    
    plt.xlabel('Validation Set Size')
    plt.ylabel('|Train ECE - Test ECE|')
    plt.title('Overfitting Resistance: Train-Test Gap vs. Validation Set Size')
    plt.legend([loss_name for loss_name, _ in loss_functions])
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'overfitting_resistance.png'), dpi=300)
    plt.close()
    
    # 3. Plot training curves for different loss functions on smallest dataset
    smallest_size = val_set_sizes[0]
    
    plt.figure(figsize=(12, 8))
    for loss_name, _ in loss_functions:
        steps = results["training_curves"][smallest_size][loss_name]["steps"]
        test_eces = results["training_curves"][smallest_size][loss_name]["test_ece"]
        plt.plot(steps, test_eces, marker='.', label=f"{loss_name}")
    
    plt.xlabel('Training Step')
    plt.ylabel('Test ECE')
    plt.title(f'Training Stability with Small Dataset (n={smallest_size})')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, f'training_stability_{smallest_size}.png'), dpi=300)
    plt.close()
    
    # 4. Plot training curves for SoftECE across different dataset sizes
    plt.figure(figsize=(12, 8))
    for val_size in val_set_sizes:
        steps = results["training_curves"][val_size]["SoftECE"]["steps"]
        test_eces = results["training_curves"][val_size]["SoftECE"]["test_ece"]
        plt.plot(steps, test_eces, marker='.', label=f"n={val_size}")
    
    plt.xlabel('Training Step')
    plt.ylabel('Test ECE')
    plt.title('SoftECE Training Curves Across Dataset Sizes')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'softece_across_sizes.png'), dpi=300)
    plt.close()
    
    # Save results
    with open(os.path.join(output_dir, 'sample_efficiency_results.json'), 'w') as f:
        # Ensure all data is serializable
        serializable_results = {
            "val_set_sizes": results["val_set_sizes"],
            "training_curves": {},
            "final_metrics": {}
        }
        
        # Convert training curves
        for size in results["training_curves"]:
            serializable_results["training_curves"][str(size)] = {}
            for loss_name in results["training_curves"][size]:
                serializable_results["training_curves"][str(size)][loss_name] = {
                    k: [float(v_i) for v_i in v] 
                    for k, v in results["training_curves"][size][loss_name].items()
                }
        
        # Convert final metrics
        for size in results["final_metrics"]:
            serializable_results["final_metrics"][str(size)] = {}
            for loss_name in results["final_metrics"][size]:
                serializable_results["final_metrics"][str(size)][loss_name] = {
                    k: float(v) for k, v in results["final_metrics"][size][loss_name].items()
                }
        
        json.dump(serializable_results, f, indent=2)
    
    print(f"Sample efficiency analysis complete. Results saved to {output_dir}")

# Experiment 4: Bias-variance tradeoff visualization
def experiment_4_bias_variance_tradeoff(val_logits, val_labels, test_logits, test_labels, cache_dir, output_dir="results"):
    print("\nExperiment 4: Visualizing Bias-Variance Tradeoff")
    
    # Create output directory
    os.makedirs(output_dir, exist_ok=True)
    
    # Convert data to PyTorch tensors
    val_logits_tensor = torch.tensor(val_logits, dtype=torch.float32)
    val_labels_tensor = torch.tensor(val_labels, dtype=torch.long)
    test_logits_tensor = torch.tensor(test_logits, dtype=torch.float32)
    test_labels_tensor = torch.tensor(test_labels, dtype=torch.long)
    
    # Define loss functions to compare
    loss_functions = [
        ("SoftECE", lambda: SoftECE(n_bins=15, sigma=0.05)),
        ("ECE", lambda: ECEWrapper(n_bins=15)),
        ("Brier", lambda: CustomBrierLoss()),
        ("CrossEntropy", lambda: CrossEntropyLoss()),
        ("MSE", lambda: MSELoss())
    ]
    
    # Bootstrap parameters
    n_bootstraps = 20  # Number of bootstrap samples
    bootstrap_size = min(500, len(val_logits))  # Size of each bootstrap sample
    
    # Store results
    results = {
        "bootstrap_metrics": {loss_name: {
            "temperatures": [],
            "test_eces": [],
            "calibrated_confidences": []
        } for loss_name, _ in loss_functions},
        "aggregated_metrics": {loss_name: {} for loss_name, _ in loss_functions}
    }
    
    # Create bootstrap samples
    print(f"Creating {n_bootstraps} bootstrap samples of size {bootstrap_size}")
    bootstrap_indices = []
    for i in range(n_bootstraps):
        # Sample with replacement
        indices = np.random.choice(len(val_logits), bootstrap_size, replace=True)
        bootstrap_indices.append(indices)
    
    # Train on each bootstrap sample
    for bootstrap_idx, indices in enumerate(bootstrap_indices):
        print(f"\nTraining on bootstrap sample {bootstrap_idx+1}/{n_bootstraps}")
        
        # Get bootstrap sample
        bootstrap_logits = val_logits_tensor[indices]
        bootstrap_labels = val_labels_tensor[indices]
        
        # Train each loss function on this bootstrap sample
        for loss_name, loss_fn_creator in loss_functions:
            # Initialize temperature and optimizer
            temperature = nn.Parameter(torch.ones(1))
            optimizer = torch.optim.Adam([temperature], lr=0.01)
            
            # Create loss function
            loss_fn = loss_fn_creator()
            
            # Train for a fixed number of epochs
            epochs = 100
            for epoch in range(epochs):
                optimizer.zero_grad()
                
                # Apply temperature scaling
                scaled_logits = bootstrap_logits / temperature
                
                # Calculate loss
                loss = loss_fn(scaled_logits, bootstrap_labels)
                
                # Backpropagation
                loss.backward()
                optimizer.step()
            
            # Evaluate on test set
            with torch.no_grad():
                # Apply learned temperature
                scaled_test_logits = test_logits_tensor / temperature
                test_probs = F.softmax(scaled_test_logits, dim=1)
                
                # Calculate metrics
                test_ece = ECE(n_bins=15)(scaled_test_logits, test_labels_tensor).item()
                
                # Store max confidences from a random subset for visualization
                subset_size = min(1000, len(test_logits))
                subset_indices = np.random.choice(len(test_logits), subset_size, replace=False)
                subset_confs = torch.max(test_probs[subset_indices], dim=1)[0].cpu().numpy()
                
                # Store results for this bootstrap run
                results["bootstrap_metrics"][loss_name]["temperatures"].append(temperature.item())
                results["bootstrap_metrics"][loss_name]["test_eces"].append(test_ece)
                results["bootstrap_metrics"][loss_name]["calibrated_confidences"].append(subset_confs)
            
            print(f"  {loss_name}: Temperature = {temperature.item():.4f}, Test ECE = {test_ece:.4f}")
    
    # Calculate aggregated metrics
    for loss_name, _ in loss_functions:
        temps = results["bootstrap_metrics"][loss_name]["temperatures"]
        eces = results["bootstrap_metrics"][loss_name]["test_eces"]
        
        # Calculate statistics
        results["aggregated_metrics"][loss_name] = {
            "temperature_mean": np.mean(temps),
            "temperature_std": np.std(temps),
            "temperature_cv": np.std(temps) / np.mean(temps) if np.mean(temps) > 0 else 0,  # Coefficient of variation
            "ece_mean": np.mean(eces),
            "ece_std": np.std(eces),
            "ece_cv": np.std(eces) / np.mean(eces) if np.mean(eces) > 0 else 0
        }
    
    # Prepare plots
    # 1. Box plots of learned temperatures
    plt.figure(figsize=(10, 6))
    temp_data = [results["bootstrap_metrics"][loss_name]["temperatures"] for loss_name, _ in loss_functions]
    plt.boxplot(temp_data, labels=[loss_name for loss_name, _ in loss_functions])
    plt.ylabel('Temperature Value')
    plt.title('Distribution of Learned Temperatures Across Bootstrap Samples')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'temperature_distribution.png'), dpi=300)
    plt.close()
    
    # 2. Box plots of test ECEs
    plt.figure(figsize=(10, 6))
    ece_data = [results["bootstrap_metrics"][loss_name]["test_eces"] for loss_name, _ in loss_functions]
    plt.boxplot(ece_data, labels=[loss_name for loss_name, _ in loss_functions])
    plt.ylabel('Test ECE')
    plt.title('Distribution of Test ECE Across Bootstrap Samples')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'ece_distribution.png'), dpi=300)
    plt.close()
    
    # 3. Scatter plot of temperature variance vs ECE mean
    plt.figure(figsize=(10, 6))
    temp_vars = [results["aggregated_metrics"][loss_name]["temperature_cv"] for loss_name, _ in loss_functions]
    ece_means = [results["aggregated_metrics"][loss_name]["ece_mean"] for loss_name, _ in loss_functions]
    
    plt.scatter(temp_vars, ece_means, s=100)
    
    # Add labels to points
    for i, (loss_name, _) in enumerate(loss_functions):
        plt.annotate(loss_name, (temp_vars[i], ece_means[i]), 
                     textcoords="offset points", xytext=(0,10), ha='center')
    
    plt.xlabel('Temperature Coefficient of Variation')
    plt.ylabel('Mean Test ECE')
    plt.title('Bias-Variance Tradeoff: Parameter Stability vs. Calibration Performance')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'bias_variance_tradeoff.png'), dpi=300)
    plt.close()
    
    # 4. Histograms of calibrated confidences for each method
    # We'll use the last bootstrap sample for visualization
    plt.figure(figsize=(15, 10))
    
    for i, (loss_name, _) in enumerate(loss_functions):
        plt.subplot(2, 3, i+1)
        confidences = results["bootstrap_metrics"][loss_name]["calibrated_confidences"][-1]
        plt.hist(confidences, bins=20, alpha=0.7)
        plt.title(f'{loss_name} Calibrated Confidences')
        plt.xlabel('Confidence')
        plt.ylabel('Count')
        plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'calibrated_confidence_distribution.png'), dpi=300)
    plt.close()
    
    # 5. Violin plots of ECE distribution
    plt.figure(figsize=(12, 6))
    
    # Create violin plot
    plt.violinplot(ece_data, showmeans=True, showmedians=True)
    
    # Add labels
    plt.xticks(range(1, len(loss_functions) + 1), [loss_name for loss_name, _ in loss_functions])
    plt.ylabel('Test ECE')
    plt.title('Density Distribution of Test ECE Across Bootstrap Samples')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(os.path.join(output_dir, 'ece_density_distribution.png'), dpi=300)
    plt.close()
    
    # Save results
    with open(os.path.join(output_dir, 'bias_variance_results.json'), 'w') as f:
        # Ensure all data is serializable
        serializable_results = {
            "bootstrap_metrics": {},
            "aggregated_metrics": {}
        }
        
        # Convert bootstrap metrics
        for loss_name in results["bootstrap_metrics"]:
            serializable_results["bootstrap_metrics"][loss_name] = {
                "temperatures": [float(t) for t in results["bootstrap_metrics"][loss_name]["temperatures"]],
                "test_eces": [float(e) for e in results["bootstrap_metrics"][loss_name]["test_eces"]],
                # Don't save all confidences as it's too much data
                "temperature_mean": float(np.mean(results["bootstrap_metrics"][loss_name]["temperatures"])),
                "temperature_std": float(np.std(results["bootstrap_metrics"][loss_name]["temperatures"])),
                "ece_mean": float(np.mean(results["bootstrap_metrics"][loss_name]["test_eces"])),
                "ece_std": float(np.std(results["bootstrap_metrics"][loss_name]["test_eces"]))
            }
        
        # Convert aggregated metrics
        for loss_name in results["aggregated_metrics"]:
            serializable_results["aggregated_metrics"][loss_name] = {
                k: float(v) for k, v in results["aggregated_metrics"][loss_name].items()
            }
        
        json.dump(serializable_results, f, indent=2)
    
    print(f"Bias-variance tradeoff analysis complete. Results saved to {output_dir}")

def main():
    # Set random seed
    set_seed(42)
    
    # Define cache directory containing logits and labels
    cache_dir = "/hdd/haolan/shats/PureLogits/cache/imagenet_vit_b_16_seed1_vs0.2"
    # cache_dir = "/hdd/haolan/shats/PureLogits/cache/cifar100_resnet50_cross_entropy_seed1"
    # cache_dir = "/hdd/haolan/shats/PureLogits/cache/imagenet_resnet50_seed1_vs0.2"
    # cache_dir = "/hdd/haolan/shats/PureLogits/cache/cifar10_resnet50_cross_entropy_seed1"
    
    # Load data
    val_logits, val_labels, test_logits, test_labels = load_data(cache_dir)
    
    # Create output directory
    output_dir = "experiment_results/softece_validation"
    os.makedirs(output_dir, exist_ok=True)
    
    # Run experiments
    # experiment_1_gradient_stability(test_logits, test_labels, cache_dir, output_dir)
    # experiment_2_gradient_information(test_logits, test_labels, cache_dir, output_dir)
    experiment_3_sample_efficiency(val_logits, val_labels, test_logits, test_labels, cache_dir, output_dir)
    # experiment_4_bias_variance_tradeoff(val_logits, val_labels, test_logits, test_labels, cache_dir, output_dir)
    
if __name__ == "__main__":
    start_time = time.time()
    main()
    elapsed_time = time.time() - start_time
    print(f"Experiments completed in {elapsed_time/60:.2f} minutes") 