chore: import upstream snapshot with attribution

2026-07-13 12:12:13 +08:00
commit 0446c45d8e
898 changed files with 328024 additions and 0 deletions
@@ -0,0 +1,98 @@
+"""
+Compare performance before and after optimizations
+"""
+
+def read_baseline():
+    """Read baseline performance metrics"""
+    with open('performance_baseline.txt', 'r') as f:
+        content = f.read()
+    
+    # Extract key metrics
+    metrics = {}
+    lines = content.split('\n')
+    for i, line in enumerate(lines):
+        if 'Total Time:' in line:
+            metrics['total_time'] = float(line.split(':')[1].strip().split()[0])
+        elif 'Memory Used:' in line:
+            metrics['memory_mb'] = float(line.split(':')[1].strip().split()[0])
+        elif 'validate_coverage:' in line and i+1 < len(lines) and 'Avg Time:' in lines[i+2]:
+            metrics['validate_coverage_ms'] = float(lines[i+2].split(':')[1].strip().split()[0])
+        elif 'select_links:' in line and i+1 < len(lines) and 'Avg Time:' in lines[i+2]:
+            metrics['select_links_ms'] = float(lines[i+2].split(':')[1].strip().split()[0])
+        elif 'calculate_confidence:' in line and i+1 < len(lines) and 'Avg Time:' in lines[i+2]:
+            metrics['calculate_confidence_ms'] = float(lines[i+2].split(':')[1].strip().split()[0])
+            
+    return metrics
+
+
+def print_comparison(before_metrics, after_metrics):
+    """Print performance comparison"""
+    print("\n" + "="*80)
+    print("PERFORMANCE COMPARISON: BEFORE vs AFTER OPTIMIZATIONS")
+    print("="*80)
+    
+    # Total time
+    time_improvement = (before_metrics['total_time'] - after_metrics['total_time']) / before_metrics['total_time'] * 100
+    print(f"\n📊 Total Time:")
+    print(f"   Before: {before_metrics['total_time']:.2f} seconds")
+    print(f"   After:  {after_metrics['total_time']:.2f} seconds")
+    print(f"   Improvement: {time_improvement:.1f}% faster ✅" if time_improvement > 0 else f"   Slower: {-time_improvement:.1f}% ❌")
+    
+    # Memory
+    mem_improvement = (before_metrics['memory_mb'] - after_metrics['memory_mb']) / before_metrics['memory_mb'] * 100
+    print(f"\n💾 Memory Usage:")
+    print(f"   Before: {before_metrics['memory_mb']:.2f} MB")
+    print(f"   After:  {after_metrics['memory_mb']:.2f} MB")
+    print(f"   Improvement: {mem_improvement:.1f}% less memory ✅" if mem_improvement > 0 else f"   More memory: {-mem_improvement:.1f}% ❌")
+    
+    # Key operations
+    print(f"\n⚡ Key Operations:")
+    
+    # Validate coverage
+    if 'validate_coverage_ms' in before_metrics and 'validate_coverage_ms' in after_metrics:
+        val_improvement = (before_metrics['validate_coverage_ms'] - after_metrics['validate_coverage_ms']) / before_metrics['validate_coverage_ms'] * 100
+        print(f"\n   validate_coverage:")
+        print(f"     Before: {before_metrics['validate_coverage_ms']:.1f} ms")
+        print(f"     After:  {after_metrics['validate_coverage_ms']:.1f} ms")
+        print(f"     Improvement: {val_improvement:.1f}% faster ✅" if val_improvement > 0 else f"     Slower: {-val_improvement:.1f}% ❌")
+    
+    # Select links
+    if 'select_links_ms' in before_metrics and 'select_links_ms' in after_metrics:
+        sel_improvement = (before_metrics['select_links_ms'] - after_metrics['select_links_ms']) / before_metrics['select_links_ms'] * 100
+        print(f"\n   select_links:")
+        print(f"     Before: {before_metrics['select_links_ms']:.1f} ms")
+        print(f"     After:  {after_metrics['select_links_ms']:.1f} ms")
+        print(f"     Improvement: {sel_improvement:.1f}% faster ✅" if sel_improvement > 0 else f"     Slower: {-sel_improvement:.1f}% ❌")
+    
+    # Calculate confidence
+    if 'calculate_confidence_ms' in before_metrics and 'calculate_confidence_ms' in after_metrics:
+        calc_improvement = (before_metrics['calculate_confidence_ms'] - after_metrics['calculate_confidence_ms']) / before_metrics['calculate_confidence_ms'] * 100
+        print(f"\n   calculate_confidence:")
+        print(f"     Before: {before_metrics['calculate_confidence_ms']:.1f} ms")
+        print(f"     After:  {after_metrics['calculate_confidence_ms']:.1f} ms")
+        print(f"     Improvement: {calc_improvement:.1f}% faster ✅" if calc_improvement > 0 else f"     Slower: {-calc_improvement:.1f}% ❌")
+    
+    print("\n" + "="*80)
+    
+    # Overall assessment
+    if time_improvement > 50:
+        print("🎉 EXCELLENT OPTIMIZATION! More than 50% performance improvement!")
+    elif time_improvement > 30:
+        print("✅ GOOD OPTIMIZATION! Significant performance improvement!")
+    elif time_improvement > 10:
+        print("👍 DECENT OPTIMIZATION! Noticeable performance improvement!")
+    else:
+        print("🤔 MINIMAL IMPROVEMENT. Further optimization may be needed.")
+    
+    print("="*80)
+
+
+if __name__ == "__main__":
+    # Example usage - you'll run this after implementing optimizations
+    baseline = read_baseline()
+    print("Baseline metrics loaded:")
+    for k, v in baseline.items():
+        print(f"  {k}: {v}")
+    
+    print("\n⚠️  Run the performance test again after optimizations to compare!")
+    print("Then update this script with the new metrics to see the comparison.")