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"""Implements evaluation on JetBrains CI builds repair baselines
Please see https://github.com/JetBrains-Research/lca-baselines/tree/main/ci-builds-repair
and https://huggingface.co/datasets/JetBrains-Research/lca-ci-builds-repair
TODOs:
- Add more flags
"""
import json
import os
from pathlib import Path
import ruamel.yaml
from evaluation.utils.shared import (
EvalMetadata,
get_default_sandbox_config_for_eval,
make_metadata,
)
from openhands.core.config import (
LLMConfig,
OpenHandsConfig,
get_parser,
load_openhands_config,
)
from openhands.core.logger import openhands_logger as logger
from openhands.core.main import create_runtime
from openhands.events.action import CmdRunAction
from openhands.events.observation import CmdOutputObservation
from openhands.runtime.base import Runtime
from openhands.utils.async_utils import call_async_from_sync
def get_config(
metadata: EvalMetadata,
) -> OpenHandsConfig:
sandbox_config = get_default_sandbox_config_for_eval()
sandbox_config.base_container_image = 'python:3.12-bookworm'
config = OpenHandsConfig(
default_agent=metadata.agent_class,
run_as_openhands=False,
runtime='docker',
max_iterations=metadata.max_iterations,
sandbox=sandbox_config,
# do not mount workspace
workspace_base=None,
workspace_mount_path=None,
)
config.set_llm_config(metadata.llm_config)
agent_config = config.get_agent_config(metadata.agent_class)
agent_config.enable_prompt_extensions = False
return config
config = load_openhands_config()
def load_bench_config():
script_dir = os.path.dirname(
os.path.abspath(__file__)
) # Get the absolute path of the script
config_path = os.path.join(script_dir, 'config.yaml')
yaml = ruamel.yaml.YAML(typ='rt')
with open(config_path, 'r') as file:
return yaml.load(file)
bench_config = load_bench_config()
def run_eval(
runtime: Runtime,
):
"""Run the evaluation and create report"""
logger.info(f'{"-" * 50} BEGIN Runtime Initialization Fn {"-" * 50}')
obs: CmdOutputObservation
lca_path = bench_config['LCA_PATH']
lca_ci_path = os.path.join(
lca_path, 'lca-baselines', 'ci-builds-repair', 'ci-builds-repair-benchmark'
)
model_name = bench_config['model_name']
action = CmdRunAction(command=f'mkdir {lca_path}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
action = CmdRunAction(command=f'cd {lca_path}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
lca_repo_url = 'https://github.com/juanmichelini/lca-baselines'
action = CmdRunAction(command=f'git clone {lca_repo_url}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
action = CmdRunAction(command=f'cd {lca_ci_path}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
action = CmdRunAction(command='git switch open-hands-integration')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
script_dir = os.path.dirname(
os.path.abspath(__file__)
) # Get the absolute path of the script
config_path = os.path.join(script_dir, 'config.yaml')
runtime.copy_to(config_path, lca_ci_path)
token_gh = bench_config['token_gh']
commandf = f'export TOKEN_GH={token_gh}'
action = CmdRunAction(command=commandf)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
action = CmdRunAction(command='poetry install')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
# Set up the task environment
commandf = f'poetry run python run_eval_jobs.py --model-name "{model_name}" --config-path "{lca_ci_path}/config.yaml" --job-ids-file "/tmp/output_lca.jsonl" --result-filename "testfile.jsonl" > /tmp/single_output.txt'
action = CmdRunAction(command=commandf)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(f'run_eval_jobs.py gave {obs.content} !')
# assert obs.exit_code == 0
commandf = 'cat /tmp/single_output.txt'
action = CmdRunAction(command=commandf)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(f' {commandf} gave {obs.content}!')
testfile_path = os.path.join(bench_config['out_folder'], 'testfile.jsonl')
commandf = f'cat {testfile_path}'
action = CmdRunAction(command=commandf)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
report_str = obs.content
logger.info(f'{"-" * 50} END Runtime Initialization Fn {"-" * 50}')
return report_str
def process_predictions(predictions_path: str):
output_path = Path(predictions_path)
if output_path.suffix != '.jsonl':
raise ValueError('output_path must end in .jsonl')
output_lca_path = output_path.with_name(output_path.stem + '_lca.jsonl')
with output_path.open() as infile, output_lca_path.open('w') as outfile:
for line in infile:
data = json.loads(line)
json.dump(data.get('test_result'), outfile)
outfile.write('\n')
return str(output_lca_path)
if __name__ == '__main__':
parser = get_parser()
parser.add_argument(
'-s',
'--eval-split',
type=str,
default='test',
choices=['test'],
help='data split to evaluate on, must be test',
)
parser.add_argument(
'--predictions-path',
type=str,
help='Path to the directory containing the output.jsonl with the predictions.',
)
args, _ = parser.parse_known_args()
data_split = args.eval_split
llm_config = LLMConfig(model='dummy_model')
metadata = make_metadata(
llm_config,
f'jetbrains-lca-ci--{data_split}',
args.agent_cls,
args.max_iterations,
args.eval_note,
args.predictions_path,
)
# prepare image
config = get_config(metadata)
runtime = create_runtime(config)
call_async_from_sync(runtime.connect)
logger.info('Converting output.jsonl into output_lca.jsonl')
predictions_lca_path = process_predictions(
os.path.join(args.predictions_path, 'output.jsonl')
)
runtime.copy_to(predictions_lca_path, '/tmp')
# get results
results_str = run_eval(runtime)
results_path = os.path.join(args.predictions_path, 'results.jsonl')
with open(results_path, 'w') as file:
file.write(results_str)
logger.info(f'Saved results to {results_path}')
# make a summary
resolved_instances = []
unresolved_instances = []
for line in results_str.strip().splitlines():
data = json.loads(line)
conclusion = data.get('conclusion')
if conclusion == 'success':
resolved_instances.append(data)
elif conclusion == 'failure':
unresolved_instances.append(data)
completed_instances = resolved_instances + unresolved_instances
report = {
'success': len(resolved_instances),
'failure': len(unresolved_instances),
'resolved_instances': resolved_instances,
'unresolved_instances': unresolved_instances,
'completed_instances': completed_instances,
}
print(f'Results: {report}')
report_path = os.path.join(args.predictions_path, 'report.jsonl')
with open(report_path, 'w') as out_f:
out_f.write(json.dumps(report) + '\n')
logger.info(f'Saved report of results in swebench format to {report_path}')
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