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8c10cf2 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | #!/usr/bin/env python3
"""
Collect score statistics for the SQLAssistant-A2A project across models
Data source: "score" field in execution_log.json file in each session directory
"""
import json
from pathlib import Path
import csv
# Define base path and model list
BASE_DIR = Path("/Users/wzr/TOSEM-2025/RESULTS")
MODELS = [
"DeepSeek-R1",
"DeepSeek-V3-1",
"GPT-4o-mini",
"GPT-5",
"Gemini-2.5-flash",
"Gemini-2.5-flash-nothinking",
"Qwen3-235b",
]
PROJECT_NAME = "SQLAssistant-A2A"
def collect_scores():
"""Collect scores from all models (from score field in execution_log.json)"""
results = {}
for model in MODELS:
test_results_dir = BASE_DIR / model / PROJECT_NAME / "test_results"
if not test_results_dir.exists():
print(f"⚠️ Model {model}'s test_results directory does not exist")
continue
scores = []
session_details = []
# Iterate through all session subdirectories
for session_dir in sorted(test_results_dir.iterdir()):
if not session_dir.is_dir():
continue
log_file = session_dir / "execution_log.json"
if not log_file.exists():
continue
try:
with open(log_file, "r", encoding="utf-8") as f:
log_data = json.load(f)
# Extract numeric score from score field in execution_log.json
# Example structure: "score": {"total_score": 70, "max_score": 100, "percentage": 70, ...}
score_field = log_data.get("score")
score = None
if isinstance(score_field, (int, float)):
score = score_field
elif isinstance(score_field, dict):
# For SQLAssistant-A2A, use percentage as numeric score (0-100)
score = score_field.get("percentage")
if isinstance(score, (int, float)):
scores.append(score)
session_details.append(
{
"session": session_dir.name,
"score": score,
}
)
except Exception as e:
print(f"❌ Error reading {log_file}: {e}")
# Calculate statistics
if scores:
stats = {
"model": model,
"total_samples": len(scores),
"mean_score": sum(scores) / len(scores),
"min_score": min(scores),
"max_score": max(scores),
# In SQLAssistant-A2A, percentage max score is 100
"perfect_count": sum(1 for s in scores if s == 100.0),
"perfect_rate": sum(1 for s in scores if s == 100.0)
/ len(scores)
* 100,
"scores": scores,
"session_details": session_details,
}
else:
stats = {
"model": model,
"total_samples": 0,
"mean_score": 0.0,
"min_score": 0.0,
"max_score": 0.0,
"perfect_count": 0,
"perfect_rate": 0.0,
"scores": [],
"session_details": [],
}
results[model] = stats
return results
def print_summary(results: dict):
"""Print statistics summary"""
print("\n" + "=" * 100)
print(f"Score Statistics - {PROJECT_NAME}")
print("=" * 100)
print(
f"\n{'Model':<35} {'Samples':<10} {'Mean Score':<12} {'Min Score':<10} {'Max Score':<10} {'Perfect Count':<10} {'Perfect Rate':<10}"
)
print("-" * 100)
for model, stats in results.items():
if stats["total_samples"] > 0:
print(
f"{model:<35} {stats['total_samples']:<10} "
f"{stats['mean_score']:<12.4f} {stats['min_score']:<10.4f} "
f"{stats['max_score']:<10.4f} {stats['perfect_count']:<10} "
f"{stats['perfect_rate']:<10.1f}%"
)
else:
print(f"{model:<35} {'No data':<10}")
print("=" * 100)
def save_results(results: dict):
"""Save results to files"""
output_dir = Path(__file__).parent
# Save detailed JSON
json_file = output_dir / "score_analysis.json"
with open(json_file, "w", encoding="utf-8") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"\n✅ Detailed JSON results saved: {json_file}")
# Save CSV summary
csv_file = output_dir / "score_summary.csv"
with open(csv_file, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(
[
"Model",
"Total_Samples",
"Mean_Score",
"Min_Score",
"Max_Score",
"Perfect_Count",
"Perfect_Rate(%)",
]
)
for model, stats in results.items():
if stats["total_samples"] > 0:
writer.writerow(
[
model,
stats["total_samples"],
f"{stats['mean_score']:.4f}",
f"{stats['min_score']:.4f}",
f"{stats['max_score']:.4f}",
stats["perfect_count"],
f"{stats['perfect_rate']:.2f}",
]
)
print(f"✅ CSV summary saved: {csv_file}")
if __name__ == "__main__":
print(f"Starting score statistics for {PROJECT_NAME} project...")
results = collect_scores()
print_summary(results)
save_results(results)
print("\n✅ Analysis complete!")
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