File size: 5,996 Bytes
961dad9 | 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 | #!/usr/bin/env python3
import os
import json
import logging
from typing import Dict, List, Any
from datetime import datetime
try:
from openai import OpenAI
except ImportError:
print("Error: openai package not found. Install with: pip install openai")
exit(1)
from env.environment import CodeGuardianEnv
from env.models import AgentState
from inference import setup_openai_client, run_episode
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def run_benchmark(num_episodes: int = 10) -> Dict[str, Any]:
"""Run benchmarking across multiple episodes and return results."""
try:
# Setup
client, model_name = setup_openai_client()
agent_state = AgentState()
episode_results = []
logger.info(f"Starting benchmark with {num_episodes} episodes using model: {model_name}")
print(f"\n{'='*60}")
print(f"CODEGUARDIAN BENCHMARK - {model_name}")
print(f"{'='*60}")
print(f"{'Episode':<10} {'Score':<10} {'Steps':<10} {'Performance'}")
print("-" * 50)
for episode in range(num_episodes):
env = CodeGuardianEnv()
episode_reward, episode_history = run_episode(env, client, model_name, agent_state)
# Calculate performance metrics
steps = len(episode_history)
performance = "Excellent" if episode_reward >= 0.8 else "Good" if episode_reward >= 0.5 else "Poor"
episode_results.append({
"episode": episode + 1,
"reward": episode_reward,
"steps": steps,
"history": episode_history
})
# Print table row
print(f"{episode + 1:<10} {episode_reward:<10.2f} {steps:<10} {performance}")
# Update agent state for learning
agent_state.episode += 1
agent_state.improvement_history.append(episode_reward)
if episode_reward > agent_state.best_score:
agent_state.best_score = episode_reward
# Calculate aggregate metrics
rewards = [ep["reward"] for ep in episode_results]
avg_score = sum(rewards) / len(rewards)
best_score = max(rewards)
worst_score = min(rewards)
print("-" * 50)
print(f"\n{'='*60}")
print("BENCHMARK SUMMARY")
print(f"{'='*60}")
print(f"Model: {model_name}")
print(f"Episodes: {num_episodes}")
print(f"Average Score: {avg_score:.3f}")
print(f"Best Score: {best_score:.3f}")
print(f"Worst Score: {worst_score:.3f}")
print(f"Score Std Dev: {(sum((r - avg_score)**2 for r in rewards) / len(rewards))**0.5:.3f}")
print(f"Improvement Trend: {'Upward' if rewards[-1] > rewards[0] else 'Downward' if rewards[-1] < rewards[0] else 'Stable'}")
# Create benchmark result object
benchmark_result = {
"model": model_name,
"timestamp": datetime.now().isoformat(),
"episodes": num_episodes,
"avg_score": avg_score,
"best_score": best_score,
"worst_score": worst_score,
"score_std": (sum((r - avg_score)**2 for r in rewards) / len(rewards))**0.5,
"improvement_trend": "upward" if rewards[-1] > rewards[0] else "downward" if rewards[-1] < rewards[0] else "stable",
"episode_results": episode_results
}
# Save benchmark results
results_file = f"benchmark_results_{model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
with open(results_file, 'w') as f:
json.dump(benchmark_result, f, indent=2)
logger.info(f"Benchmark results saved to: {results_file}")
print(f"\nDetailed results saved to: {results_file}")
return benchmark_result
except Exception as e:
logger.error(f"Benchmark failed: {e}")
raise
def compare_benchmarks(results_files: List[str]) -> None:
"""Compare multiple benchmark result files."""
print(f"\n{'='*60}")
print("BENCHMARK COMPARISON")
print(f"{'='*60}")
benchmarks = []
for file in results_files:
try:
with open(file, 'r') as f:
benchmarks.append(json.load(f))
except Exception as e:
logger.warning(f"Failed to load {file}: {e}")
if not benchmarks:
print("No valid benchmark files found")
return
print(f"{'Model':<20} {'Avg Score':<12} {'Best Score':<12} {'Episodes':<10}")
print("-" * 60)
for bench in benchmarks:
print(f"{bench['model']:<20} {bench['avg_score']:<12.3f} {bench['best_score']:<12.3f} {bench['episodes']:<10}")
# Find best performing model
best_model = max(benchmarks, key=lambda x: x['avg_score'])
print(f"\n🏆 Best Performing Model: {best_model['model']} (Avg: {best_model['avg_score']:.3f})")
def main():
"""Main benchmark execution."""
import sys
# Parse command line arguments
num_episodes = 10
if len(sys.argv) > 1:
try:
num_episodes = int(sys.argv[1])
except ValueError:
print("Usage: python benchmark.py [num_episodes]")
return 1
try:
# Run benchmark
result = run_benchmark(num_episodes)
# Check for comparison mode
if len(sys.argv) > 2 and sys.argv[2] == "--compare":
# Find existing benchmark files
import glob
benchmark_files = glob.glob("benchmark_results_*.json")
if benchmark_files:
compare_benchmarks(benchmark_files)
return 0
except Exception as e:
logger.error(f"Benchmark execution failed: {e}")
return 1
if __name__ == "__main__":
exit(main())
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