Claude Context

#!/usr/bin/env python3 """ Generate swe_verified_15min1h_2files_instances.json from the subset analysis """ import json import re from datasets import load_dataset def parse_patch_files(patch_content): """Parse patch content to extract the number of modified files""" if not patch_content: return [] file_pattern = r'^diff --git a/(.*?) b/(.*?)$' files = [] for line in patch_content.split('\n'): match = re.match(file_pattern, line) if match: file_path = match.group(1) files.append(file_path) return files def main(): print("Loading SWE-bench_Verified dataset...") dataset = load_dataset("princeton-nlp/SWE-bench_Verified") instances = list(dataset['test']) print("Filtering instances for: 15min-1hour difficulty + 2 patch files...") # Filter for the specific subset subset_instances = [] for instance in instances: difficulty = instance.get('difficulty', 'Unknown') # Parse main patch to count files patch_content = instance.get('patch', '') patch_files = parse_patch_files(patch_content) oracle_count = len(patch_files) # Check if it matches our criteria if difficulty == '15 min - 1 hour' and oracle_count == 2: subset_instances.append(instance) print(f"Found {len(subset_instances)} instances matching criteria") # Create the JSON structure that _prepare_instances expects output_data = { "metadata": { "description": "SWE-bench_Verified subset: 15min-1hour difficulty with 2 patch files", "source_dataset": "princeton-nlp/SWE-bench_Verified", "extraction_date": "2024", "filter_criteria": { "difficulty": "15 min - 1 hour", "patch_files_count": 2 }, "total_instances": len(subset_instances), "statistics": { "total_instances_in_original": 500, "subset_count": len(subset_instances), "percentage_of_original": round((len(subset_instances) / 500) * 100, 1) } }, "instances": subset_instances } # Save to JSON file output_file = "swe_verified_15min1h_2files_instances.json" with open(output_file, 'w') as f: json.dump(output_data, f, indent=2) print(f"Generated {output_file} with {len(subset_instances)} instances") # Verify the structure print("\nVerifying JSON structure...") with open(output_file, 'r') as f: loaded_data = json.load(f) print(f"✓ Contains 'instances' key: {'instances' in loaded_data}") print(f"✓ Contains 'metadata' key: {'metadata' in loaded_data}") print(f"✓ Number of instances: {len(loaded_data['instances'])}") print(f"✓ First instance has required fields:") if loaded_data['instances']: first_instance = loaded_data['instances'][0] required_fields = ['instance_id', 'repo', 'base_commit', 'problem_statement'] for field in required_fields: has_field = field in first_instance print(f" - {field}: {'✓' if has_field else '✗'}") print(f"\nFile successfully generated: {output_file}") print("This file can be used with BaseRetrieval._prepare_instances()") if __name__ == "__main__": main()