Create test_cross_validation_stability.py

Implement proper model validation tests with CV stability checks

- Add cross-validation stability test with coefficient of variation thresholds
- Add overfitting detection test using performance indicators
- Remove empty test function with meaningful implementation
- Include proper error handling and pytest.skip for missing data

tests/test_cross_validation_stability.py

@@ -0,0 +1,72 @@
+import pytest
+import numpy as np
+import json
+from pathlib import Path
+from model.train import ModelTrainer
+
+def test_model_performance_thresholds():
+    """Test that model meets minimum performance requirements"""
+    trainer = ModelTrainer()
+    
+    # Load test results
+    metadata = trainer.load_metadata()
+    
+    assert metadata['test_f1'] >= 0.75, "F1 score below threshold"
+    assert metadata['test_accuracy'] >= 0.75, "Accuracy below threshold"
+
+def test_cross_validation_stability():
+    """Test CV results show acceptable stability (low variance)"""
+    # Load CV results
+    try:
+        from path_config import path_manager
+        cv_results_path = path_manager.get_logs_path("cv_results.json")
+        
+        if not cv_results_path.exists():
+            pytest.skip("No CV results available")
+            
+        with open(cv_results_path, 'r') as f:
+            cv_data = json.load(f)
+        
+        # Test CV stability - standard deviation should be reasonable
+        test_scores = cv_data.get('test_scores', {})
+        
+        if 'f1' in test_scores:
+            f1_std = test_scores['f1'].get('std', 0)
+            f1_mean = test_scores['f1'].get('mean', 0)
+            
+            # CV coefficient of variation should be < 0.15 (15%)
+            cv_coefficient = f1_std / f1_mean if f1_mean > 0 else 1
+            assert cv_coefficient < 0.15, f"CV results too unstable: CV={cv_coefficient:.3f}"
+            
+        if 'accuracy' in test_scores:
+            acc_std = test_scores['accuracy'].get('std', 0)
+            acc_mean = test_scores['accuracy'].get('mean', 0)
+            
+            cv_coefficient = acc_std / acc_mean if acc_mean > 0 else 1
+            assert cv_coefficient < 0.15, f"Accuracy CV too unstable: CV={cv_coefficient:.3f}"
+            
+    except FileNotFoundError:
+        pytest.skip("CV results file not found")
+
+def test_model_overfitting_indicators():
+    """Test that model doesn't show signs of severe overfitting"""
+    try:
+        from path_config import path_manager
+        cv_results_path = path_manager.get_logs_path("cv_results.json")
+        
+        if not cv_results_path.exists():
+            pytest.skip("No CV results available")
+            
+        with open(cv_results_path, 'r') as f:
+            cv_data = json.load(f)
+        
+        # Check overfitting score if available
+        perf_indicators = cv_data.get('performance_indicators', {})
+        overfitting_score = perf_indicators.get('overfitting_score')
+        
+        if overfitting_score is not None and overfitting_score != 'Unknown':
+            # Overfitting score should be reasonable (< 0.1 difference)
+            assert overfitting_score < 0.1, f"Model shows overfitting: {overfitting_score}"
+            
+    except (FileNotFoundError, KeyError):
+        pytest.skip("Performance indicators not available")