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507afd7f06
Co-authored-by: Kush Dave Jain <kdjain@pit.isri.cmu.edu> Co-authored-by: openhands <openhands@all-hands.dev> Co-authored-by: Graham Neubig <neubig@gmail.com>
68 lines
2.0 KiB
Python
Executable File
68 lines
2.0 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import pandas as pd
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parser = argparse.ArgumentParser(
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description='Compare two TestGenEval 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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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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# Get the intersection of the ids
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df = pd.merge(df1, df2, on='id', how='inner')
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def _get_coverage(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('test_pass', False)
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df['test_pass_x'] = df['test_pass_x'].apply(_get_coverage)
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df['test_pass_y'] = df['test_pass_y'].apply(_get_coverage)
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df['diff'] = df.apply(lambda x: x['test_pass_x'] != x['test_pass_y'], axis=1)
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df_diff = df[df['diff']].sort_values(
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by=['test_pass_x', 'test_pass_y'], ascending=[False, False]
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)
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# skip if any of the pass is nan, which means one of the eval is not finished yet
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df_diff = df_diff[df_diff['test_pass_x'].notna() & df_diff['test_pass_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[['id', 'test_pass_x', 'test_pass_y', 'report_x', 'report_y']]
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# x pass but y not
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print('-' * 100)
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df_diff_x_only = df_diff[df_diff['test_pass_x'] & ~df_diff['test_pass_y']].sort_values(
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by='id'
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)
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print(f'# x pass but y not={df_diff_x_only.shape[0]}')
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print(df_diff_x_only[['id', 'report_x', 'report_y']])
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# y pass but x not
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print('-' * 100)
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df_diff_y_only = df_diff[~df_diff['test_pass_x'] & df_diff['test_pass_y']].sort_values(
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by='id'
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)
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print(f'# y pass but x not={df_diff_y_only.shape[0]}')
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print(df_diff_y_only[['id', 'report_x', 'report_y']])
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# get instance_id from df_diff_y_only
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print('-' * 100)
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print('Instances that x pass but y not:')
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print(df_diff_x_only['id'].tolist())
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print('-' * 100)
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print('Instances that y pass but x not:')
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print(df_diff_y_only['id'].tolist())
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