import json import argparse if __name__ == "__main__": # Parse command line arguments parser = argparse.ArgumentParser(description="Evaluation script for comparing_size task") parser.add_argument( '--answer', '-a', type=str, default='answer.json', help='Path to the answer JSON file (default: answer.json)' ) parser.add_argument( '--output', '-o', type=str, default='eval.json', help='Path to the output JSON file (default: eval.json)' ) args = parser.parse_args() # Assuming your JSON data is stored in a file called 'results.json' with open(args.answer, 'r') as f: data = json.load(f) # Initialize variables to calculate accuracies correct_counts = 0 total_counts = 0 category_accuracies = {} # Iterate through the JSON data for entry in data: #num_objects = entry['circles'] + entry['triangles'] + entry['squares'] num_objects = entry['vanished'] total_counts += 1 # Calculate per-category accuracy if num_objects not in category_accuracies: category_accuracies[num_objects] = {'correct': 0, 'total': 0} category_accuracies[num_objects]['total'] += 1 try: prediction = entry["Output"] if prediction == entry['vanished']: correct_counts += 1 category_accuracies[num_objects]['correct'] += 1 except: continue # Calculate overall accuracy overall_accuracy = correct_counts / total_counts * 100 # Calculate accuracy for each category category_accuracy_percentages = { k: (v['correct'] / v['total'] * 100) for k, v in category_accuracies.items() } # Prepare results for saving eval_results = { "Overall Accuracy": overall_accuracy, "Category-wise Accuracy": category_accuracy_percentages } # Save results to eval.json with open(args.output, 'w') as eval_file: json.dump(eval_results, eval_file, indent=4) print("Evaluation results saved to eval.json.")