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| #!/usr/bin/env python3 | |
| """ | |
| Test the enhanced domain-aware pattern detection | |
| """ | |
| import sys | |
| import os | |
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) | |
| from uslap_forest_v3_domain_aware import SelfReader, PatternDetector | |
| def test_enhanced_detection(): | |
| """Test the enhanced pattern detection""" | |
| print("="*60) | |
| print("🧪 TESTING ENHANCED DOMAIN-AWARE DETECTION") | |
| print("="*60) | |
| filepath = "USLaP_Final_Data_Consolidated_Master.xlsx" | |
| if not os.path.exists(filepath): | |
| print(f"File not found: {filepath}") | |
| return | |
| # COMPONENT 1: Read | |
| print("\n📖 Reading file...") | |
| reader = SelfReader(filepath) | |
| reader.read_all() | |
| print(f"\n📊 Total entries: {reader.total_entries}") | |
| # COMPONENT 2: Enhanced detection | |
| print("\n🔍 Running enhanced pattern detection...") | |
| detector = PatternDetector(reader) | |
| gaps, candidates = detector.detect_all() | |
| print(f"\n📊 RESULTS:") | |
| print(f" Total gaps found: {len(gaps)}") | |
| print(f" Total candidates: {len(candidates)}") | |
| # Analyze gaps by type | |
| gap_types = {} | |
| for gap in gaps: | |
| gap_type = gap['type'] | |
| gap_types[gap_type] = gap_types.get(gap_type, 0) + 1 | |
| if gap_types: | |
| print(f"\n Gap types:") | |
| for gap_type, count in sorted(gap_types.items()): | |
| print(f" {gap_type}: {count}") | |
| # Show top gaps by priority | |
| if gaps: | |
| print(f"\n Top gaps (by priority):") | |
| sorted_gaps = sorted(gaps, key=lambda x: x.get('priority', 0), reverse=True) | |
| for i, gap in enumerate(sorted_gaps[:10]): | |
| gap_type = gap['type'] | |
| if gap_type == 'missing_domain_variant': | |
| print(f" {i+1}. {gap_type}: ratio {gap['ratio']} missing {gap['missing_domain']} domain") | |
| print(f" Existing domains: {gap['existing_domains']}") | |
| print(f" Network: {gap['network']}, Priority: {gap['priority']}") | |
| elif gap_type == 'network_needs_ratio': | |
| print(f" {i+1}. {gap_type}: network {gap['network']} needs ratio") | |
| print(f" Entry count: {gap['count']}, Priority: {gap['priority']}") | |
| elif gap_type == 'entry_needs_network': | |
| print(f" {i+1}. {gap_type}: entry {gap['entry']} needs network") | |
| print(f" Term: {gap['term']}, Score: {gap['score']}, Priority: {gap['priority']}") | |
| else: | |
| print(f" {i+1}. {gap_type}: {gap}") | |
| # Show what would be generated | |
| print(f"\n🎯 WHAT WOULD BE GENERATED (highest priority):") | |
| if gaps: | |
| top_gap = sorted_gaps[0] | |
| if top_gap['type'] == 'missing_domain_variant': | |
| ratio = top_gap['ratio'] | |
| missing_domain = top_gap['missing_domain'] | |
| network = top_gap['network'] | |
| print(f" For ratio {ratio} missing {missing_domain} domain:") | |
| if missing_domain == 'maqam': | |
| print(f" → maqam proportion ({ratio})") | |
| elif missing_domain == 'rhythmic': | |
| print(f" → tempo ratio {ratio}") | |
| elif missing_domain == 'spiritual/geometric': | |
| print(f" → sacred proportion {ratio}") | |
| elif missing_domain == 'musical': | |
| print(f" → musical interval {ratio}") | |
| print(f" Network: {network}") | |
| # Analyze ratio families | |
| print(f"\n📈 RATIO FAMILY ANALYSIS:") | |
| # Group entries by ratio | |
| ratio_groups = {} | |
| for node_id, node in reader.graph.items(): | |
| term = node.get('en_term', '') | |
| import re | |
| match = re.search(r'(\d+)/(\d+)', term) | |
| if match: | |
| ratio = match.group(0) | |
| if ratio not in ratio_groups: | |
| ratio_groups[ratio] = [] | |
| ratio_groups[ratio].append(node_id) | |
| if ratio_groups: | |
| print(f" Found {len(ratio_groups)} ratio families:") | |
| for ratio, entries in sorted(ratio_groups.items()): | |
| print(f" {ratio}: {len(entries)} entries") | |
| print(f"\n✅ Test complete.") | |
| if __name__ == "__main__": | |
| test_enhanced_detection() |