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#!/usr/bin/env python3
"""Run multiple trials of OpenEvolve to get statistics."""
import json
import os
import shutil
import subprocess
import sys
from pathlib import Path
# Run from the example directory
os.chdir(Path(__file__).parent)
def run_trial(trial_num: int, max_iterations: int = 100, seed: int = None):
"""Run a single OpenEvolve trial."""
output_dir = f"openevolve_output_trial_{trial_num}"
# Clean output directory
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
# Update config with new seed if provided
if seed is not None:
# Read config
with open("config.yaml", "r") as f:
config_content = f.read()
# Replace seed
import re
config_content = re.sub(r'random_seed:\s*\d+', f'random_seed: {seed}', config_content)
# Write temp config
temp_config = f"config_trial_{trial_num}.yaml"
with open(temp_config, "w") as f:
f.write(config_content)
else:
temp_config = "config.yaml"
# Run OpenEvolve
cmd = [
"openevolve-run",
"initial_program.py",
"evaluator.py",
"--config", temp_config,
"--iterations", str(max_iterations),
"--output", output_dir,
]
print(f"\n{'='*60}")
print(f"TRIAL {trial_num + 1}: Running OpenEvolve with seed {seed}")
print('='*60)
result = subprocess.run(cmd, capture_output=True, text=True)
# Clean up temp config
if seed is not None and os.path.exists(temp_config):
os.remove(temp_config)
# Parse results from log
solution_found_at = None
best_score = 0.0
log_dir = Path(output_dir) / "logs"
if log_dir.exists():
log_files = list(log_dir.glob("*.log"))
if log_files:
with open(log_files[0], "r") as f:
log_content = f.read()
import re
# Find best score
score_matches = re.findall(r'combined_score[=:]\s*([\d.]+)', log_content)
if score_matches:
best_score = max(float(s) for s in score_matches)
# Look for first 100% solution - find the "New best" line with 1.0000
new_best_matches = re.findall(r'New best solution found at iteration (\d+):', log_content)
perfect_matches = re.findall(r'Iteration (\d+):.*?combined_score=1\.0000', log_content)
if perfect_matches:
solution_found_at = int(perfect_matches[0])
elif best_score >= 1.0 and new_best_matches:
# Fallback: find last new best if we have 100%
solution_found_at = int(new_best_matches[-1])
return {
"trial": trial_num,
"seed": seed,
"solution_found_at": solution_found_at,
"best_score": best_score,
"max_iterations": max_iterations,
}
def run_trials(num_trials: int = 3, max_iterations: int = 100, base_seed: int = 100):
"""Run multiple trials and collect statistics."""
results = []
solutions_found = []
for trial in range(num_trials):
seed = base_seed + trial * 111 # Different seeds for each trial
result = run_trial(trial, max_iterations, seed)
results.append(result)
if result["solution_found_at"] is not None:
solutions_found.append(result["solution_found_at"])
print(f"Trial {trial + 1}: SUCCESS at iteration {result['solution_found_at']}")
else:
print(f"Trial {trial + 1}: FAILED (best score: {result['best_score']:.2%})")
# Calculate statistics
success_rate = len(solutions_found) / num_trials
avg_iterations = sum(solutions_found) / len(solutions_found) if solutions_found else float('inf')
min_iterations = min(solutions_found) if solutions_found else None
max_iterations_found = max(solutions_found) if solutions_found else None
print(f"\n{'='*60}")
print("OPENEVOLVE TRIAL RESULTS")
print('='*60)
print(f"Trials: {num_trials}")
print(f"Max iterations per trial: {max_iterations}")
print(f"Success rate: {success_rate:.0%} ({len(solutions_found)}/{num_trials})")
if solutions_found:
print(f"Avg iterations to solution: {avg_iterations:.1f}")
print(f"Min iterations: {min_iterations}")
print(f"Max iterations: {max_iterations_found}")
print('='*60)
# Save summary
summary = {
"config": {
"num_trials": num_trials,
"max_iterations": max_iterations,
},
"summary": {
"success_rate": success_rate,
"avg_iterations_to_solution": avg_iterations if solutions_found else None,
"min_iterations": min_iterations,
"max_iterations": max_iterations_found,
"solutions_found": len(solutions_found),
},
"trials": results,
}
with open("openevolve_trials_results.json", "w") as f:
json.dump(summary, f, indent=2)
print(f"\nResults saved to: openevolve_trials_results.json")
# Clean up trial output directories
for trial in range(num_trials):
output_dir = f"openevolve_output_trial_{trial}"
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
return summary
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--trials", type=int, default=3, help="Number of trials")
parser.add_argument("--iterations", type=int, default=100, help="Max iterations per trial")
parser.add_argument("--seed", type=int, default=100, help="Base random seed")
args = parser.parse_args()
run_trials(num_trials=args.trials, max_iterations=args.iterations, base_seed=args.seed)
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