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Co-authored-by: Robert Brennan <accounts@rbren.io> Co-authored-by: openhands <openhands@all-hands.dev> Co-authored-by: Engel Nyst <enyst@users.noreply.github.com> Co-authored-by: Graham Neubig <neubig@gmail.com>
163 lines
5.2 KiB
Python
Executable File
163 lines
5.2 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import os
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import subprocess
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import pandas as pd
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from termcolor import colored
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parser = argparse.ArgumentParser(
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description='Compare two swe_bench output JSONL files and print the resolved diff'
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)
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parser.add_argument('input_file_1', type=str)
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parser.add_argument('input_file_2', type=str)
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parser.add_argument(
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'--show-paths',
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action='store_true',
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help='Show visualization paths for failed instances',
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)
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parser.add_argument(
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'--only-x-instances',
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action='store_true',
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help='Only show instances that are ran by X',
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)
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args = parser.parse_args()
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df1 = pd.read_json(args.input_file_1, orient='records', lines=True)
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df2 = pd.read_json(args.input_file_2, orient='records', lines=True)
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if args.only_x_instances:
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instance_ids_1 = set(df1['instance_id'].tolist())
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print(
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f'Before removing instances not in X={args.input_file_1}: Y={df2.shape[0]} instances'
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)
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df2 = df2[df2['instance_id'].isin(instance_ids_1)]
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print(
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f'After removing instances not in X={args.input_file_1}: Y={df2.shape[0]} instances'
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)
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# Add summarization step for each input file
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def summarize_file(file_path):
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script_dir = os.path.dirname(os.path.abspath(__file__))
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summarize_script = os.path.join(script_dir, 'summarize_outputs.py')
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print(f'\nSummary for {file_path}:')
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print('=' * 80)
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subprocess.run(['python', summarize_script, file_path], check=True)
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print('=' * 80)
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# Generate summaries
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summarize_file(args.input_file_1)
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summarize_file(args.input_file_2)
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# Get the intersection of the instance_ids
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df = pd.merge(df1, df2, on='instance_id', how='inner')
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def _get_resolved(report):
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if report is None:
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return False
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if isinstance(report, float):
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return False
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else:
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return report.get('resolved', False)
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df['resolved_x'] = df['report_x'].apply(_get_resolved)
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df['resolved_y'] = df['report_y'].apply(_get_resolved)
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df['diff'] = df.apply(lambda x: x['resolved_x'] != x['resolved_y'], axis=1)
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df_diff = df[df['diff']].sort_values(
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by=['resolved_x', 'resolved_y'], ascending=[False, False]
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)
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# skip if any of the resolved is nan, which means one of the eval is not finished yet
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df_diff = df_diff[df_diff['resolved_x'].notna() & df_diff['resolved_y'].notna()]
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print(f'X={args.input_file_1}')
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print(f'Y={args.input_file_2}')
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print(f'# diff={df_diff.shape[0]}')
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df_diff = df_diff[['instance_id', 'resolved_x', 'resolved_y', 'report_x', 'report_y']]
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# x resolved but y not
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print('-' * 100)
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df_diff_x_only = df_diff[df_diff['resolved_x'] & ~df_diff['resolved_y']].sort_values(
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by='instance_id'
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)
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print(f'# x resolved but y not={df_diff_x_only.shape[0]}')
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print(df_diff_x_only[['instance_id', 'report_x', 'report_y']])
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# y resolved but x not
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print('-' * 100)
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df_diff_y_only = df_diff[~df_diff['resolved_x'] & df_diff['resolved_y']].sort_values(
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by='instance_id'
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)
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print(f'# y resolved but x not={df_diff_y_only.shape[0]}')
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print(df_diff_y_only[['instance_id', 'report_x', 'report_y']])
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# get instance_id from df_diff_y_only
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x_only_by_repo = {}
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for instance_id in df_diff_x_only['instance_id'].tolist():
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repo = instance_id.split('__')[0]
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x_only_by_repo.setdefault(repo, []).append(instance_id)
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y_only_by_repo = {}
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for instance_id in df_diff_y_only['instance_id'].tolist():
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repo = instance_id.split('__')[0]
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y_only_by_repo.setdefault(repo, []).append(instance_id)
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print('-' * 100)
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print(
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colored('Repository comparison (x resolved vs y resolved):', 'cyan', attrs=['bold'])
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)
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all_repos = sorted(set(list(x_only_by_repo.keys()) + list(y_only_by_repo.keys())))
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# Calculate diffs and sort repos by diff magnitude
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repo_diffs = []
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for repo in all_repos:
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x_count = len(x_only_by_repo.get(repo, []))
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y_count = len(y_only_by_repo.get(repo, []))
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diff = y_count - x_count
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repo_diffs.append((repo, diff))
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# Sort by diff (descending) and then by repo name
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repo_diffs.sort(key=lambda x: (-x[1], x[0]))
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threshold = max(
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3, sum(d[1] for d in repo_diffs) / len(repo_diffs) * 1.5 if repo_diffs else 0
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)
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x_input_file_folder = os.path.join(os.path.dirname(args.input_file_1), 'output.viz')
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for repo, diff in repo_diffs:
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x_instances = x_only_by_repo.get(repo, [])
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y_instances = y_only_by_repo.get(repo, [])
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# Determine if this repo has a significant diff
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is_significant = diff >= threshold
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repo_color = 'red' if is_significant else 'yellow'
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print(f"\n{colored(repo, repo_color, attrs=['bold'])}:")
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print(
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colored(
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f'Difference: {diff} instances! (Larger diff = Y better)',
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repo_color,
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attrs=['bold'],
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)
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)
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print(colored(f'X resolved but Y failed: ({len(x_instances)} instances)', 'green'))
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if x_instances:
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print(' ' + str(x_instances))
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print(colored(f'Y resolved but X failed: ({len(y_instances)} instances)', 'red'))
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if y_instances:
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print(' ' + str(y_instances))
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if args.show_paths:
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print(
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colored(' Visualization path for X failed:', 'cyan', attrs=['bold'])
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)
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for instance_id in y_instances:
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instance_file = os.path.join(
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x_input_file_folder, f'false.{instance_id}.md'
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)
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print(f' {instance_file}')
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