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8c10cf2 c4e7970 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 | #!/usr/bin/env python3
"""
Calculate success rates for each model in EmailResponder task
"""
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",
]
def analyze_model_results():
"""Analyze execution results for each model"""
results = {}
for model in MODELS:
test_results_dir = BASE_DIR / model / "EmailResponder" / "test_results"
if not test_results_dir.exists():
print(
f"⚠️ 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
execution_log = session_dir / "execution_log.json"
if not execution_log.exists():
continue
try:
with open(execution_log, "r", encoding="utf-8") as f:
log_data = json.load(f)
success = log_data.get("success", False)
email_index = log_data.get("email_index", "unknown")
model_stats["total_count"] += 1
if success:
model_stats["success_count"] += 1
else:
model_stats["failure_count"] += 1
model_stats["sessions"].append(
{
"session": session_dir.name,
"success": success,
"email_index": email_index,
"error": log_data.get("error"),
}
)
except Exception as e:
print(f"❌ Error reading {execution_log}: {e}")
results[model] = model_stats
return results
def print_summary(results):
"""Print statistical summary"""
print("\n" + "=" * 80)
print("Model Execution Results - EmailResponder")
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} times")
print(f" Success: {success} times ({success_rate:.1f}%)")
print(f" Failure: {failure} times ({100-success_rate:.1f}%)")
print()
else:
print(f"📊 {model}")
print(f" No data")
print()
print("=" * 80)
def save_detailed_results(results, output_file="success-finish_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"\n✅ Detailed results saved to: {output_path}")
def save_csv_summary(results, output_file="success-finish_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__":
results = analyze_model_results()
print_summary(results)
save_detailed_results(results)
save_csv_summary(results)
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