File size: 6,041 Bytes
f1f2c2c | 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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | 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:
grid_size = entry['rows']
num_circles = entry['path_size']-2
total_counts += 1
# Calculate per-category accuracy
#if grid_size not in category_accuracies:
# category_accuracies[grid_size] = {'correct': 0 , 'total': 0}
if num_circles not in category_accuracies:
category_accuracies[num_circles] = {'correct': 0, 'total': 0}
category_accuracies[num_circles]['total'] += 1
#category_accuracies['total'] += 1
# category_accuracies[(grid_size,num_circles)]['total'] += 1
if entry["ERROR"]:
continue
# Check if the output is correct
output = entry["Output"]
clean_output = [s.lower() for s in output]
clean_gold_output = [s.lower() for s in entry["gold_output"]]
if clean_output == clean_gold_output:
correct_counts += 1
category_accuracies[num_circles]['correct'] += 1
# Calculate overall accuracy
overall_accuracy = correct_counts / total_counts * 100
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.")
"""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:
grid_size = entry['rows']
path_size = entry['path_size']
total_counts += 1
# Calculate per-category accuracy
if grid_size not in category_accuracies:
category_accuracies[grid_size] = {'correct': 0 , 'total': 0}
if path_size not in category_accuracies[grid_size]:
category_accuracies[grid_size][path_size] = {'correct': 0, 'total': 0}
category_accuracies[grid_size][path_size]['total'] += 1
category_accuracies[grid_size]['total'] += 1
# category_accuracies[(grid_size,path_size)]['total'] += 1
if entry["ERROR"]:
continue
# Check if the output is correct
output = entry["Output"]
clean_output = [s.lower() for s in output]
clean_gold_output = [s.lower() for s in entry["gold_output"]]
if clean_output == clean_gold_output:
correct_counts += 1
category_accuracies[grid_size]['correct'] += 1
category_accuracies[grid_size][path_size]['correct'] += 1
# Calculate overall accuracy
overall_accuracy = correct_counts / total_counts * 100
for grid_size in category_accuracies:
category_accuracies[grid_size]["Accuracy"] = category_accuracies[grid_size]['correct'] / category_accuracies[grid_size]['total'] * 100
for num_queens in category_accuracies[grid_size]:
if num_queens == 'correct' or num_queens == 'total' or num_queens == 'Accuracy':
continue
# print(category_accuracies[grid_size][num_queens])
category_accuracies[grid_size][num_queens]["Accuracy"] = category_accuracies[grid_size][num_queens]['correct'] / category_accuracies[grid_size][num_queens]['total'] * 100
# category_accuracies["Overall Accuracy"] = overall_accuracy
final_results = {}
for grid_size in category_accuracies:
# print(grid_size)
final_results[grid_size] = {}
final_results[grid_size]["Overall Accuracy"] = category_accuracies[grid_size]["Accuracy"]
final_results[grid_size]["Category-wise Accuracy"] = {}
for num_crosses in category_accuracies[grid_size]:
if num_crosses == 'correct' or num_crosses == 'total' or num_crosses == 'Accuracy':
continue
final_results[grid_size]["Category-wise Accuracy"][num_crosses] = category_accuracies[grid_size][num_crosses]["Accuracy"]
final_results["Overall Accuracy"] = overall_accuracy
# Save results to eval.json
with open(args.output, 'w') as eval_file:
json.dump(final_results, eval_file, indent=4)
print("Evaluation results saved to eval.json.")"""
|