File size: 2,167 Bytes
e70dfd2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | 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_gpt4o.json',
help='Path to the answer JSON file (default: answer.json)'
)
parser.add_argument(
'--output', '-o',
type=str,
default='eval_gpt4o.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_rows = entry['Rows']
num_columns = entry['Columns']
total_counts += 1
# Calculate per-category accuracy
# category_key = f"{num_rows}"
category_key = len(entry['path'])-2
if category_key not in category_accuracies:
category_accuracies[category_key] = {'correct': 0, 'total': 0}
category_accuracies[category_key]['total'] += 1
try:
predicted_output = entry['Output']
except:
continue
if predicted_output == entry['path']:
correct_counts += 1
category_accuracies[category_key]['correct'] += 1
# 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(f"Evaluation results saved to {args.output}.")
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