王子睿

restructure + add files

8c10cf2 11 months ago

7.3 kB

#!/usr/bin/env python3
"""
Compute the success rate of each model on the SocialMediaManager-MCP task.
Success criterion: the `status` field in `metadata.json` equals "success".
(status="success" means a Shakespeare-style X post was generated and passed validation.)
"""

import json
import re
from pathlib import Path
from collections import defaultdict

# 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 = "SocialMediaManager-MCP"


def check_status_success(metadata_file: Path) -> tuple[bool, dict]:
    """
    Check whether the `status` field in metadata.json equals "success".
    Args:
        metadata_file: Path to metadata.json file
    Returns:
        (success: bool, metadata: dict) - success flag and metadata
    """
    if not metadata_file.exists():
        return False, {}

    try:
        with open(metadata_file, "r", encoding="utf-8") as f:
            metadata = json.load(f)

        status = metadata.get("status", "unknown")
        return status == "success", metadata

    except Exception as e:
        print(f"  ❌ Error reading {metadata_file}: {e}")
        return False, {}


def analyze_model_results():
    """Analyze execution results for all models."""
    results = {}

    for model in MODELS:
        test_results_dir = BASE_DIR / model / PROJECT_NAME / "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 over all session subfolders
        for session_dir in sorted(test_results_dir.iterdir()):
            if not session_dir.is_dir():
                continue

            metadata_file = session_dir / "metadata.json"

            # metadata.json is required for classification
            if not metadata_file.exists():
                continue

            try:
                # Check whether status is success
                is_success, metadata = check_status_success(metadata_file)

                # Extract metadata fields
                topic = metadata.get("topic", "unknown")
                retry_count = metadata.get("retry_count", 0)
                valid = metadata.get("valid", False)
                duration = metadata.get("duration_seconds", 0)
                crew_retry_count = metadata.get("crew_retry_count", 0)
                status = metadata.get("status", "unknown")

                model_stats["total_count"] += 1
                if is_success:
                    model_stats["success_count"] += 1
                else:
                    model_stats["failure_count"] += 1

                # Build status description
                if is_success:
                    reason = f"✅ Success (RETRY {retry_count}, crew RETRY {crew_retry_count})"
                else:
                    reason = f"❌ Failure: status={status}"

                model_stats["sessions"].append(
                    {
                        "session": session_dir.name,
                        "success": is_success,
                        "status": status,
                        "topic": (
                            topic[:80] if isinstance(topic, str) else str(topic)[:80]
                        ),
                        "retry_count": retry_count,
                        "crew_retry_count": crew_retry_count,
                        "valid": valid,
                        "duration_seconds": round(duration, 2),
                        "reason": reason,
                    }
                )

            except Exception as e:
                print(f"❌ Error processing {session_dir}: {e}")

        results[model] = model_stats

    return results


def print_summary(results):
    """Print a summary table."""
    print("\n" + "=" * 100)
    print(f"Model execution summary - {PROJECT_NAME}")
    print("Success criterion: status == 'success' in metadata.json")
    print("=" * 100 + "\n")

    # Header
    print(
        f"{'Model':<35} {'Total':<10} {'Success':<10} {'Failure':<10} {'Success Rate':<15}"
    )
    print("-" * 100)

    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:<35} {total:<10} {success:<10} {failure:<10} {success_rate:>6.1f}%"
            )
        else:
            print(f"{model:<35} {'No data':<10}")

    print("=" * 100)


def print_failure_details(results):
    """Print details of failed sessions."""
    print("\n" + "=" * 100)
    print("Failure details")
    print("=" * 100 + "\n")

    for model, stats in results.items():
        failures = [s for s in stats["sessions"] if not s["success"]]
        if not failures:
            continue

        print(f"\n{model} - Failed sessions: {len(failures)}")
        print("-" * 100)

        for i, session in enumerate(failures, 1):
            print(f"{i}. {session['session']}")
            print(f"   Topic: {session['topic']}")
            print(f"   Status: {session['status']}")
            print(f"   Reason: {session['reason']}")
            print()


def save_detailed_results(results, output_file="success_detailed_results.json"):
    """Save detailed results to a 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_rate.csv"):
    """Save summary to a 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(%)", "Criteria"]
        )

        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}",
                    "status='success' in metadata.json",
                ]
            )

    print(f"✅ CSV summary saved to: {output_path}")


if __name__ == "__main__":
    print(f"Starting success-rate analysis for {PROJECT_NAME}...")
    print("Success criterion: status == 'success' in metadata.json\n")

    results = analyze_model_results()
    print_summary(results)
    print_failure_details(results)
    save_detailed_results(results)
    save_csv_summary(results)

    print("\n✅ Done")

	#!/usr/bin/env python3
	"""
	Compute the success rate of each model on the SocialMediaManager-MCP task.

	Success criterion: the `status` field in `metadata.json` equals "success".
	(status="success" means a Shakespeare-style X post was generated and passed validation.)
	"""

	import json
	import re
	from pathlib import Path
	from collections import defaultdict

	# 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 = "SocialMediaManager-MCP"


	def check_status_success(metadata_file: Path) -> tuple[bool, dict]:
	"""
	Check whether the `status` field in metadata.json equals "success".

	Args:
	metadata_file: Path to metadata.json file

	Returns:
	(success: bool, metadata: dict) - success flag and metadata
	"""
	if not metadata_file.exists():
	return False, {}

	try:
	with open(metadata_file, "r", encoding="utf-8") as f:
	metadata = json.load(f)

	status = metadata.get("status", "unknown")
	return status == "success", metadata

	except Exception as e:
	print(f" ❌ Error reading {metadata_file}: {e}")
	return False, {}


	def analyze_model_results():
	"""Analyze execution results for all models."""
	results = {}

	for model in MODELS:
	test_results_dir = BASE_DIR / model / PROJECT_NAME / "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 over all session subfolders
	for session_dir in sorted(test_results_dir.iterdir()):
	if not session_dir.is_dir():
	continue

	metadata_file = session_dir / "metadata.json"

	# metadata.json is required for classification
	if not metadata_file.exists():
	continue

	try:
	# Check whether status is success
	is_success, metadata = check_status_success(metadata_file)

	# Extract metadata fields
	topic = metadata.get("topic", "unknown")
	retry_count = metadata.get("retry_count", 0)
	valid = metadata.get("valid", False)
	duration = metadata.get("duration_seconds", 0)
	crew_retry_count = metadata.get("crew_retry_count", 0)
	status = metadata.get("status", "unknown")

	model_stats["total_count"] += 1
	if is_success:
	model_stats["success_count"] += 1
	else:
	model_stats["failure_count"] += 1

	# Build status description
	if is_success:
	reason = f"✅ Success (RETRY {retry_count}, crew RETRY {crew_retry_count})"
	else:
	reason = f"❌ Failure: status={status}"

	model_stats["sessions"].append(
	{
	"session": session_dir.name,
	"success": is_success,
	"status": status,
	"topic": (
	topic[:80] if isinstance(topic, str) else str(topic)[:80]
	),
	"retry_count": retry_count,
	"crew_retry_count": crew_retry_count,
	"valid": valid,
	"duration_seconds": round(duration, 2),
	"reason": reason,
	}
	)

	except Exception as e:
	print(f"❌ Error processing {session_dir}: {e}")

	results[model] = model_stats

	return results


	def print_summary(results):
	"""Print a summary table."""
	print("\n" + "=" * 100)
	print(f"Model execution summary - {PROJECT_NAME}")
	print("Success criterion: status == 'success' in metadata.json")
	print("=" * 100 + "\n")

	# Header
	print(
	f"{'Model':<35} {'Total':<10} {'Success':<10} {'Failure':<10} {'Success Rate':<15}"
	)
	print("-" * 100)

	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:<35} {total:<10} {success:<10} {failure:<10} {success_rate:>6.1f}%"
	)
	else:
	print(f"{model:<35} {'No data':<10}")

	print("=" * 100)


	def print_failure_details(results):
	"""Print details of failed sessions."""
	print("\n" + "=" * 100)
	print("Failure details")
	print("=" * 100 + "\n")

	for model, stats in results.items():
	failures = [s for s in stats["sessions"] if not s["success"]]
	if not failures:
	continue

	print(f"\n{model} - Failed sessions: {len(failures)}")
	print("-" * 100)

	for i, session in enumerate(failures, 1):
	print(f"{i}. {session['session']}")
	print(f" Topic: {session['topic']}")
	print(f" Status: {session['status']}")
	print(f" Reason: {session['reason']}")
	print()


	def save_detailed_results(results, output_file="success_detailed_results.json"):
	"""Save detailed results to a 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_rate.csv"):
	"""Save summary to a 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(%)", "Criteria"]
	)

	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}",
	"status='success' in metadata.json",
	]
	)

	print(f"✅ CSV summary saved to: {output_path}")


	if __name__ == "__main__":
	print(f"Starting success-rate analysis for {PROJECT_NAME}...")
	print("Success criterion: status == 'success' in metadata.json\n")

	results = analyze_model_results()
	print_summary(results)
	print_failure_details(results)
	save_detailed_results(results)
	save_csv_summary(results)

	print("\n✅ Done")