File size: 5,283 Bytes
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 | #!/usr/bin/env python3
"""
Calculate success rates for models on RecruitmentAssistant-MCP task
Success criteria: reports folder exists and contains multiple (>=2) md files
"""
import json
import os
from pathlib import Path
from collections import defaultdict
# 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 = "RecruitmentAssistant-MCP"
def check_reports_success(reports_dir: Path) -> dict:
"""
Check if reports folder meets success criteria
Success criteria:
1. reports folder exists
2. Contains multiple (>=2) md files
Returns:
{
'success': bool,
'reports_exists': bool,
'md_count': int,
'md_files': list
}
"""
result = {"success": False, "reports_exists": False, "md_count": 0, "md_files": []}
# Check if reports folder exists
if not reports_dir.exists() or not reports_dir.is_dir():
return result
result["reports_exists"] = True
# Count md files
md_files = sorted([f.name for f in reports_dir.glob("*.md")])
result["md_count"] = len(md_files)
result["md_files"] = md_files
# Determine success: at least 2 md files
result["success"] = result["md_count"] >= 2
return result
def analyze_model_results():
"""Analyze execution results for each model"""
results = {}
for model in MODELS:
test_results_dir = BASE_DIR / model / PROJECT_NAME / "test_results"
if not test_results_dir.exists():
print(
f"Warning: test_results directory not found for model {model}: {test_results_dir}"
)
continue
model_stats = {
"success_count": 0,
"failure_count": 0,
"total_count": 0,
"sessions": [],
}
# Iterate through all session subdirectories
for session_dir in sorted(test_results_dir.iterdir()):
if not session_dir.is_dir():
continue
reports_dir = session_dir / "reports"
# Check reports folder and md files
check_result = check_reports_success(reports_dir)
model_stats["total_count"] += 1
if check_result["success"]:
model_stats["success_count"] += 1
else:
model_stats["failure_count"] += 1
model_stats["sessions"].append(
{
"session": session_dir.name,
"success": check_result["success"],
"reports_exists": check_result["reports_exists"],
"md_count": check_result["md_count"],
"md_files": check_result["md_files"],
}
)
results[model] = model_stats
return results
def print_summary(results):
"""Print statistics summary"""
print("\n" + "=" * 80)
print(f"Model Execution Results - {PROJECT_NAME}")
print("=" * 80 + "\n")
for model, stats in results.items():
total = stats["total_count"]
success = stats["success_count"]
failure = stats["failure_count"]
if total > 0:
success_rate = (success / total) * 100
print(f"{model}")
print(f" Total: {total} runs")
print(
f" Success: {success} runs (reports exists with >=2 md files, {success_rate:.1f}%)"
)
print(f" Failure: {failure} runs ({100-success_rate:.1f}%)")
print()
else:
print(f"{model}")
print(f" No data")
print()
print("=" * 80)
def save_detailed_results(results, output_file="success_detailed_results.json"):
"""Save detailed results to JSON file"""
output_path = Path(__file__).parent / output_file
with open(output_path, "w", encoding="utf-8") as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"\nDetailed results saved to: {output_path}")
def save_csv_summary(results, output_file="success_rate.csv"):
"""Save summary to CSV file"""
import csv
output_path = Path(__file__).parent / output_file
with open(output_path, "w", newline="", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerow(["Model", "Total", "Success", "Failure", "Success_Rate(%)"])
for model, stats in results.items():
total = stats["total_count"]
success = stats["success_count"]
failure = stats["failure_count"]
success_rate = (success / total * 100) if total > 0 else 0
writer.writerow([model, total, success, failure, f"{success_rate:.2f}"])
print(f"CSV summary saved to: {output_path}")
if __name__ == "__main__":
print(f"Starting success rate calculation - {PROJECT_NAME}")
print(
f"Success criteria: reports folder exists and contains multiple (>=2) md files\n"
)
results = analyze_model_results()
print_summary(results)
save_detailed_results(results)
save_csv_summary(results)
print("\nCalculation completed!")
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