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benchmark/IOAI/IOAI-2025/Individual-Contest/Restroom/Solution/Scoring/metrics.py

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+import numpy as np
+import json
+import os
+import sys
+from pathlib import Path
+
+def calculate_precision_at_1(predictions, ground_truth):
+    """
+    Calculate precision@1 for the given predictions and ground truth.
+    
+    Args:
+        predictions (np.array): Array of predicted gallery indices for each query
+        ground_truth (np.array): Array of correct gallery indices for each query
+    
+    Returns:
+        float: Precision@1 score (percentage of correct predictions)
+    """
+    if len(predictions) != len(ground_truth):
+        raise ValueError(f"Predictions length ({len(predictions)}) doesn't match ground truth length ({len(ground_truth)})")
+    
+    # Count correct predictions
+    correct_predictions = np.sum(predictions == ground_truth)
+    total_predictions = len(predictions)
+    
+    # Calculate precision@1 as percentage
+    precision_at_1 = (correct_predictions / total_predictions)
+    
+    return precision_at_1
+
+def load_submission_file(filepath):
+    """
+    Load submission file and handle potential errors.
+    
+    Args:
+        filepath (str): Path to the submission file
+    
+    Returns:
+        np.array or None: Loaded array or None if file doesn't exist or is invalid
+    """
+    try:
+        if not os.path.exists(filepath):
+            print(f"Warning: Submission file {filepath} not found")
+            return None
+        
+        submission = np.load(filepath)
+        print(f"Loaded {filepath}: shape {submission.shape}, dtype {submission.dtype}")
+        return submission
+    
+    except Exception as e:
+        print(f"Error loading {filepath}: {str(e)}")
+        return None
+
+def load_ground_truth_file(filepath):
+    """
+    Load ground truth file.
+    
+    Args:
+        filepath (str): Path to the ground truth file
+    
+    Returns:
+        np.array: Loaded ground truth array
+    """
+    try:
+        if not os.path.exists(filepath):
+            raise FileNotFoundError(f"Ground truth file {filepath} not found")
+        
+        ground_truth = np.load(filepath)
+        print(f"Loaded ground truth {filepath}: shape {ground_truth.shape}, dtype {ground_truth.dtype}")
+        return ground_truth
+    
+    except Exception as e:
+        print(f"Error loading ground truth {filepath}: {str(e)}")
+        raise
+
+def evaluate_test_set(submission_file, ground_truth_file, test_name):
+    """
+    Evaluate a single test set.
+    
+    Args:
+        submission_file (str): Path to submission file
+        ground_truth_file (str): Path to ground truth file
+        test_name (str): Name of the test set for logging
+    
+    Returns:
+        float or None: Precision@1 score or None if evaluation failed
+    """
+    print(f"\n=== Evaluating {test_name} ===")
+    
+    # Load ground truth
+    try:
+        ground_truth = load_ground_truth_file(ground_truth_file)
+    except Exception as e:
+        print(f"Failed to load ground truth for {test_name}: {str(e)}")
+        return None
+    
+    # Load submission
+    submission = load_submission_file(submission_file)
+    if submission is None:
+        print(f"Failed to load submission for {test_name}")
+        return None
+    
+    # Validate submission format
+    if submission.shape != ground_truth.shape:
+        print(f"Shape mismatch for {test_name}: submission {submission.shape} vs ground truth {ground_truth.shape}")
+        return None
+    
+    # Calculate precision@1
+    try:
+        score = calculate_precision_at_1(submission, ground_truth)
+        print(f"{test_name} - Precision@1: {score:.2f}")
+        
+        # Log some statistics
+        correct_count = np.sum(submission == ground_truth)
+        total_count = len(submission)
+        print(f"{test_name} - Correct predictions: {correct_count}/{total_count}")
+        
+        return score
+    
+    except Exception as e:
+        print(f"Error calculating precision@1 for {test_name}: {str(e)}")
+        return None
+
+def main():
+    """
+    Main evaluation function.
+    """
+    print("Starting evaluation...")
+    BASE_PATH = Path(__file__).parent
+    SCORING_PATH = BASE_PATH
+
+    submission_a_file = SCORING_PATH / "submission_a.npy"
+    submission_b_file = SCORING_PATH / "submission_b.npy"
+    ground_truth_a_file = SCORING_PATH / "answer_a.npy"
+    ground_truth_b_file = SCORING_PATH / "answer_b.npy"
+    output_file = SCORING_PATH / "score.json"
+    
+    # Evaluate test set A
+    score_a = evaluate_test_set(submission_a_file, ground_truth_a_file, "Test Set A")
+    
+
+    # Evaluate test set B
+    score_b = evaluate_test_set(submission_b_file, ground_truth_b_file, "Test Set B")
+    
+
+    # Determine overall status
+    status = True
+    msg = "Success!"
+    
+    # Handle missing or failed evaluations
+    if score_a is None:
+        score_a = 0.0
+        status = False
+        msg = "Failed to evaluate Test Set A"
+    
+    if score_b is None:
+        score_b = 0.0
+        if status:  # Only update if not already failed
+            status = False
+            msg = "Failed to evaluate Test Set B"
+        else:
+            msg = "Failed to evaluate both test sets"
+    if score_a > 1:
+        score_a = 0.0
+    if score_b > 1:
+        score_b = 0.0
+    def sanitize_score(value):
+        """处理单个分数值，将NaN和inf替换为0"""
+        if not np.isfinite(value):
+            return 0.0
+        return value
+    # Create result dictionary
+    result = {
+        "status": status,
+        "score": {
+            "public_a": sanitize_score(score_a),
+            "private_b": sanitize_score(score_b),
+        },
+        "msg": msg,
+    }
+    
+    # Save results to JSON
+    try:
+        with open(output_file, 'w') as f:
+            json.dump(result, f, indent=4)
+        print(f"\nResults saved to {output_file}")
+    except Exception as e:
+        print(f"Error saving results to {output_file}: {str(e)}")
+        sys.exit(1)
+    
+    # Print final summary
+    print("\n=== EVALUATION SUMMARY ===")
+    print(f"Status: {status}")
+    print(f"Test Set A (public) Score: {score_a:.2f}")
+    print(f"Test Set B (private) Score: {score_b:.2f}")
+    print(f"Message: {msg}")
+    
+    return result
+
+if __name__ == "__main__":
+    main()