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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 SQLAssistant-MCP project across models.
Data source: "score" field in execution_log.json for each session.
"""
import json
from pathlib import Path
import csv
# Base directory 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-MCP"
def collect_scores():
"""Collect scores from all models (from execution_log.json score field)."""
results = {}
for model in MODELS:
test_results_dir = BASE_DIR / model / PROJECT_NAME / "test_results"
if not test_results_dir.exists():
print(f"[WARN] Model {model} test_results directory not found")
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 execution_log.json score field
# Structure example: "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-MCP, 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),
# For SQLAssistant-MCP, percentage max 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':<12} {'Min':<10} {'Max':<10} {'Perfect':<10} {'Perfect%':<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[OK] 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"[OK] CSV summary saved: {csv_file}")
if __name__ == "__main__":
print(f"Starting score statistics for {PROJECT_NAME}...")
results = collect_scores()
print_summary(results)
save_results(results)
print("\n[OK] Analysis complete!")
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