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import json
import os
import traceback
from pathlib import Path
import ale_bench
from ale_bench.result import CaseResult, JudgeResult, Result
def result_feedback(result: Result) -> CaseResult:
if result.overall_judge_result == JudgeResult.ACCEPTED:
return result.case_results[0]
else:
selected_case_idx = 0
for idx, case_result in enumerate(result.case_results):
if case_result.judge_result == result.overall_judge_result:
selected_case_idx = idx
break
return result.case_results[selected_case_idx]
def main(program_path: str, results_dir: str, problem_id: str) -> None:
"""Runs the evaluation using the shinka.eval utility."""
print(f"Problem ID: {problem_id}")
print(f"Evaluating program: {program_path}")
print(f"Saving results to: {results_dir}")
root_dir = Path(__file__).resolve().parent
session_file = root_dir / results_dir / "session.json"
# create results_dir if it doesn't exist
if not os.path.exists(results_dir):
os.makedirs(results_dir)
try:
session = None
if not session_file.exists():
session = ale_bench.start(
problem_id=problem_id,
lite_version=True,
num_workers=13,
)
else:
session = ale_bench.restart(session_saved_file=session_file, num_workers=13)
if not session:
raise RuntimeError("Failed to start or restart the session.")
code = Path(program_path).read_text()
print("Problem metadata: ", session.problem.metadata)
maximize = session.problem.metadata.score_type == "maximize"
print("MAXIMIZE SCORE: ", maximize)
# DEFAULT LITE EVAL USES ONLY 5 TEST CASES
# public_result = session.public_eval(code=code, code_language="cpp20")
# ALE-AGENT: USE SPECIFIED NUMBER OF GENERATED TEST CASES
num_public_cases = 50
cases = session.case_gen(list(range(num_public_cases)))
public_result = session.case_eval(
cases, code, code_language="cpp20", skip_local_visualization=True
)
# Store the public_result as JSON in the results directory
public_result_json_path = Path(results_dir) / "public_result.json"
public_json_str = public_result.model_dump_json(indent=4)
public_result_json_path.write_text(public_json_str)
public_json = json.loads(public_json_str)
public_passed_cases, public_failed_cases = 0, 0
for case in public_json["case_results"]:
if case["judge_result"] == "ACCEPTED":
public_passed_cases += 1
else:
public_failed_cases += 1
print(
f"Passed {public_passed_cases} cases, failed {public_failed_cases} cases out of {num_public_cases}"
)
print(
f"Initial Public Score: {public_result.overall_absolute_score} - Mean Score: {public_result.overall_absolute_score / num_public_cases}"
)
extracted_case = result_feedback(public_result)
session.save(session_file)
public_metrics = {
"judge_result": public_result.overall_judge_result.value,
"max_execution_time_sec": max(
[
case_result.execution_time
for case_result in public_result.case_results
]
),
"max_memory_usage_mib": max(
[case_result.memory_usage for case_result in public_result.case_results]
)
// 1024
// 1024,
"num_passed_cases": public_passed_cases,
"num_failed_cases": public_failed_cases,
"standard_error": extracted_case.error_str,
"message": extracted_case.message,
}
if maximize:
score_to_opt = public_result.overall_absolute_score / num_public_cases
else:
score_to_opt = public_result.overall_absolute_score / num_public_cases * -1
metrics = {
"combined_score": score_to_opt,
"public": public_metrics,
}
correct = public_metrics["judge_result"] == "ACCEPTED"
error = ""
private_result, final_rank, final_performance = session.private_eval(
code, code_language="cpp20"
)
# Store the private_result as JSON in the results directory
private_result_json_path = Path(results_dir) / "private_result.json"
private_json_str = private_result.model_dump_json(indent=4)
private_result_json_path.write_text(private_json_str)
private_json = json.loads(private_json_str)
private_passed_cases, private_failed_cases = 0, 0
num_private_cases = len(private_json["case_results"])
for case in private_json["case_results"]:
if case["judge_result"] == "ACCEPTED":
private_passed_cases += 1
else:
private_failed_cases += 1
print(
f"Passed {private_passed_cases} cases, failed {private_failed_cases} cases out of {num_private_cases}"
)
print(
f"Final Private Score: {private_result.overall_absolute_score} - Mean Score: {private_result.overall_absolute_score / num_private_cases}"
)
print(f"Rank: {final_rank}, Performance: {final_performance}")
private_metrics = {
"private_rank": final_rank,
"private_performance": final_performance,
"num_private_passed_cases": private_passed_cases,
"num_private_failed_cases": private_failed_cases,
}
metrics["private"] = private_metrics
# Monitor resource consumption
print(f"Current Resource Usage: {session.current_resource_usage}")
print(f"Remaining Resources: {session.remaining_resource_usage}")
except Exception as e:
print(f"Evaluation failed completely: {str(e)}")
print(traceback.format_exc())
metrics = {
"combined_score": 0.0,
"public": {"judge_result": "REJECTED"},
"private": {
"private_rank": 0,
"private_performance": 0,
"num_private_passed_cases": 0,
"num_private_failed_cases": 0,
},
}
correct = False
error = str(e)
# Save correct to JSON file
correct_file = os.path.join(results_dir, "correct.json")
with open(correct_file, "w") as f:
json.dump({"correct": correct, "error": error}, f, indent=4)
print(f"Correct saved to {correct_file}")
# Save metrics to JSON file
metrics_file = os.path.join(
results_dir,
"metrics.json",
)
with open(metrics_file, "w") as f:
json.dump(metrics, f, indent=4)
print(f"Metrics saved to {metrics_file}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Agent evaluation script using shinka.eval"
)
parser.add_argument(
"--program_path",
type=str,
default="initial.cpp",
help="Path to the program to evaluate",
)
parser.add_argument(
"--results_dir",
type=str,
default="results",
help="Directory to save results and logs (metrics.json, correct.json)",
)
parser.add_argument(
"--problem_id",
type=str,
default="ahc046",
help="Problem ID",
)
parsed_args = parser.parse_args()
main(
parsed_args.program_path,
parsed_args.results_dir,
parsed_args.problem_id,
)
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