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evaluation.py
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evaluation.py
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import argparse
import json
import os
import traceback
from datasets import load_dataset, load_from_disk
from collections import Counter
from rich import print
from swebench import (
KEY_INSTANCE_ID,
KEY_MODEL,
KEY_PREDICTION,
get_eval_report,
get_logs_eval,
get_model_report,
get_resolution_status,
run_evaluation,
get_eval_refs,
)
from swebench.harness.constants import (
INSTALL_FAIL,
)
from unidiff import PatchSet
def main(predictions_path, log_dir, swe_bench_tasks, testbed, skip_existing, timeout, verbose, conda_link, log_suffix, num_processes):
# Check if paths exist
if not os.path.exists(predictions_path):
raise FileNotFoundError(f"Predictions path {predictions_path} does not exist")
eval_refs = get_eval_refs(swe_bench_tasks)
for k, v in eval_refs.items():
eval_refs[k] = {key: v[key] for key in [KEY_INSTANCE_ID, "FAIL_TO_PASS", "PASS_TO_PASS"]}
# Change model_name_or_patch field to directory name for all predictions
directory = os.path.dirname(predictions_path)
directory_name = directory.rsplit("/", 1)[-1]
pred_path_orig = predictions_path
pred_path_temp = predictions_path.replace(".jsonl", "_filtered.jsonl")
pred_total, pred_will_eval = 0, 0
with open(pred_path_temp, "w") as f:
for l in open(pred_path_orig, "r").readlines():
pred_total += 1
p = json.loads(l)
# Exclude predictions w/ empty strings
if p[KEY_PREDICTION] is not None and p[KEY_PREDICTION].strip() != "":
p[KEY_MODEL] = directory_name
json.dump(p, f)
f.write("\n")
pred_will_eval += 1
print(
f"Found {pred_total} total predictions, will evaluate {pred_will_eval} ({pred_total-pred_will_eval} are empty)"
)
# Run evaluation
predictions_path = pred_path_temp
try:
print("🏃 Beginning evaluation...")
run_evaluation(
predictions_path=predictions_path,
log_dir=log_dir,
swe_bench_tasks=swe_bench_tasks,
testbed=testbed,
skip_existing=skip_existing,
timeout=timeout,
verbose=verbose,
conda_link=conda_link,
log_suffix=log_suffix,
num_processes=num_processes
)
print("✅ Finished evaluation")
except Exception as e:
print(f"❌ Evaluation failed: {e}\n{traceback.format_exc()}")
pass
print("==================================")
os.remove(pred_path_temp)
# Get predictions, define log_dir
predictions = [json.loads(l) for l in open(pred_path_orig, "r").readlines()]
log_dir = os.path.join(log_dir, directory_name)
print(f"Log directory for evaluation run: {log_dir}")
# Iterate through predictions
scorecards = []
for p in predictions:
scorecard = {KEY_INSTANCE_ID: p[KEY_INSTANCE_ID], "statuses": [], "stats": {}}
# Add trajectory statistics if traj_path exists
traj_path = os.path.join(directory, f"{p[KEY_INSTANCE_ID]}.traj")
if os.path.exists(traj_path):
traj_data = json.load(open(traj_path, "r"))
scorecard["stats"]["traj_num_steps"] = len(traj_data["trajectory"])
scorecard["stats"]["traj_action_dist"] = dict(
Counter(
[
entry["action"].strip().split()[0]
if entry["role"] == "assistant" and "action" in entry and len(entry["action"]) > 0
else None
for entry in traj_data["history"]
]
)
)
scorecard["exit_status"] = (
traj_data["info"]["exit_status"]
if "exit_status" in traj_data["info"]
else "n/a"
)
# Check that a prediction was generated
if p[KEY_PREDICTION] is None or p[KEY_PREDICTION].strip() == "":
scorecard["statuses"].append("not_generated")
scorecards.append(scorecard)
continue
scorecard["statuses"].append("generated")
# Get log file
log_path = os.path.join(
log_dir, f"{p[KEY_INSTANCE_ID]}.{directory_name}.eval.log"
)
if not os.path.exists(log_path):
scorecard["statuses"].append("build_failure")
scorecards.append(scorecard)
continue
# Get evaluation logs
eval_sm, found = get_logs_eval(log_path)
# Check that the prediction generated
if not found:
scorecards.append(scorecard)
continue
scorecard["statuses"].append("applied")
with open(log_path, "r") as f:
log_contents = f.read()
if INSTALL_FAIL in log_contents:
scorecard["statuses"].append("install_fail")
# Get resolution status
report = get_eval_report(eval_sm, eval_refs[p[KEY_INSTANCE_ID]])
scorecard["test_results"] = {
"failure": {
"FAIL_TO_PASS": report["FAIL_TO_PASS"]["failure"],
"PASS_TO_PASS": report["PASS_TO_PASS"]["failure"],
},
"success": {
"FAIL_TO_PASS": report["FAIL_TO_PASS"]["success"],
"PASS_TO_PASS": report["PASS_TO_PASS"]["success"],
}
}
resolution_status = get_resolution_status(report)
scorecard["statuses"].append(resolution_status)
diff_obj = PatchSet(p[KEY_PREDICTION])
scorecard["patch_files"] = [
x.path
for x in diff_obj.modified_files
+ diff_obj.added_files
+ diff_obj.removed_files
]
scorecard["patch_lines_add"] = sum([f.added for f in diff_obj])
scorecard["patch_lines_del"] = sum([f.removed for f in diff_obj])
scorecards.append(scorecard)
# Calculate cumulative results
get_ids_with_status = lambda x: [
s[KEY_INSTANCE_ID] for s in scorecards if x in s["statuses"]
]
report = {
"# Not Generated": len(get_ids_with_status("not_generated")),
"# Generated": len(get_ids_with_status("generated")),
"# Applied": len(get_ids_with_status("applied")),
"# Resolved": len(get_ids_with_status("RESOLVED_FULL")),
"# Install Fail": len(get_ids_with_status("install_fail")),
}
print(f"== Evaluation Report ==\n{report}")
report_exits = dict(
Counter([s["exit_status"] if "exit_status" in s else "n/a" for s in scorecards])
)
# Save to summary, scorecard json
path_scorecards = os.path.join(directory, "scorecards.json")
with open(path_scorecards, "w") as f:
json.dump(scorecards, fp=f, indent=2)
print(f"- Wrote per-instance scorecards to {path_scorecards}")
path_results = os.path.join(directory, "results.json")
with open(path_results, "w") as f:
json.dump(
{
"report": report,
"report_exits": report_exits,
"not_generated": get_ids_with_status("not_generated"),
"generated": get_ids_with_status("generated"),
"applied": get_ids_with_status("applied"),
"resolved": get_ids_with_status("RESOLVED_FULL"),
"install_fail": get_ids_with_status("install_fail"),
},
fp=f,
indent=2,
)
print(f"- Wrote summary of run to {path_results}")
# Sanity check against get_model_report
report = get_model_report(
directory_name, pred_path_orig, swe_bench_tasks, log_dir
)
by_outcome = {}
by_outcome_func = lambda status: len(
[
instance_id
for _, v in report.items()
if isinstance(v, dict)
for instance_id in v[status]
]
)
by_outcome["# Not Generated"] = by_outcome_func("none")
by_outcome["# Generated"] = by_outcome_func("generated")
by_outcome["# Applied"] = by_outcome_func("applied")
by_outcome["# Resolved"] = by_outcome_func("resolved")
by_outcome["# Install Fail"] = by_outcome_func("install_fail")
print(f"Reference Report:\n{by_outcome}")
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"--predictions_path",
type=str,
help="Path to predictions file (.jsonl)",
required=True,
)
parser.add_argument(
"--log_dir", type=str, help="Path to log directory", required=True
)
parser.add_argument(
"--swe_bench_tasks",
type=str,
help="Path to SWE-bench task instances file",
required=True,
)
parser.add_argument(
"--testbed", type=str, help="Path to testbed directory", required=True
)
parser.add_argument(
"--skip_existing", action="store_true", help="(Optional) Skip existing logs"
)
parser.add_argument(
"--timeout",
type=int,
help="(Optional) Timeout in seconds (default: 900)",
default=900,
)
parser.add_argument(
"--verbose", action="store_true", help="(Optional) Verbose mode"
)
parser.add_argument(
"--conda_link", default=None, type=str, help="(Optional) URL to conda installation to use"
)
parser.add_argument(
"--log_suffix", default=None, type=str, help="(Optional) Log suffix"
)
parser.add_argument(
"--num_processes", default=-1, type=int, help="Num processes"
)
args = parser.parse_args()
main(**vars(args))