fix doc issue

Author: yzhao062

pyod/test/test_hbos_auto_bins_fix.py

@@ -15,6 +15,7 @@
 """
 
 import numpy as np
+
 from pyod.models.hbos import HBOS
 
 
@@ -26,55 +27,58 @@ def test_hbos_auto_bins_with_out_of_range_values():
     exceeded the maximum training value for any feature.
     """
     print("Testing HBOS with n_bins='auto' and out-of-range test values...")
-    
+
     # Create training data with limited range
     np.random.seed(42)
     n_train = 100
     n_features = 5
-    
+
     # Training data ranges roughly from 0 to 10
     X_train = np.random.randn(n_train, n_features) * 2 + 5
     X_train = np.clip(X_train, 0, 10)
-    
+
     print(f"Training data shape: {X_train.shape}")
     print(f"Training data range per feature:")
     for i in range(n_features):
-        print(f"  Feature {i}: [{X_train[:, i].min():.2f}, {X_train[:, i].max():.2f}]")
-    
+        print(
+            f"  Feature {i}: [{X_train[:, i].min():.2f}, {X_train[:, i].max():.2f}]")
+
     # Initialize and fit HBOS with auto bins
     model = HBOS(n_bins='auto', contamination=0.1)
     model.fit(X_train)
-    
+
     print(f"\nNumber of bins per feature after training:")
     for i in range(n_features):
         print(f"  Feature {i}: {model.hist_[i].shape[0]} bins")
-    
+
     # Create test data with some values OUTSIDE the training range
     # This is the critical part that triggers the bug
     X_test = np.random.randn(10, n_features) * 3 + 5
     # Force some values to be higher than training max
-    X_test[0, 2] = X_train[:, 2].max() + 5  # Way above training max for feature 2
+    X_test[0, 2] = X_train[:,
+                   2].max() + 5  # Way above training max for feature 2
     X_test[1, 3] = X_train[:, 3].max() + 3  # Above training max for feature 3
     X_test[2, 0] = X_train[:, 0].min() - 2  # Below training min for feature 0
-    
+
     print(f"\nTest data shape: {X_test.shape}")
     print(f"Test data range per feature:")
     for i in range(n_features):
-        print(f"  Feature {i}: [{X_test[:, i].min():.2f}, {X_test[:, i].max():.2f}]")
-    
+        print(
+            f"  Feature {i}: [{X_test[:, i].min():.2f}, {X_test[:, i].max():.2f}]")
+
     # This would fail with IndexError before the fix
     try:
         predictions = model.predict(X_test)
         scores = model.decision_function(X_test)
-        
+
         print(f"\n✓ SUCCESS: Predictions completed without error!")
         print(f"  Predictions shape: {predictions.shape}")
         print(f"  Scores shape: {scores.shape}")
         print(f"  Number of outliers detected: {predictions.sum()}")
         print(f"  Outlier indices: {np.where(predictions == 1)[0].tolist()}")
-        
+
         return True
-        
+
     except IndexError as e:
         print(f"\n✗ FAILED: IndexError occurred!")
         print(f"  Error: {e}")
@@ -86,26 +90,26 @@ def test_hbos_auto_bins_edge_cases():
     """
     Additional edge case tests for HBOS with auto bins.
     """
-    print("\n" + "="*70)
+    print("\n" + "=" * 70)
     print("Testing edge cases...")
-    print("="*70)
-    
+    print("=" * 70)
+
     np.random.seed(123)
-    
+
     # Test 1: All test values above training range
     print("\nTest 1: All test values above training range")
     X_train = np.random.randn(50, 3) * 1 + 5
     model = HBOS(n_bins='auto', contamination=0.1)
     model.fit(X_train)
-    
+
     X_test = X_train.max() + np.random.rand(5, 3) * 3
     try:
         predictions = model.predict(X_test)
         print(f"  ✓ Success: {predictions.sum()} outliers detected")
     except IndexError as e:
         print(f"  ✗ Failed with IndexError: {e}")
         return False
-    
+
     # Test 2: All test values below training range
     print("\nTest 2: All test values below training range")
     X_test = X_train.min() - np.random.rand(5, 3) * 3
@@ -115,7 +119,7 @@ def test_hbos_auto_bins_edge_cases():
     except IndexError as e:
         print(f"  ✗ Failed with IndexError: {e}")
         return False
-    
+
     # Test 3: Mixed in-range and out-of-range values
     print("\nTest 3: Mixed in-range and out-of-range values")
     X_test = np.vstack([
@@ -131,75 +135,77 @@ def test_hbos_auto_bins_edge_cases():
     except IndexError as e:
         print(f"  ✗ Failed with IndexError: {e}")
         return False
-    
+
     return True
 
 
 def test_hbos_static_bins_comparison():
     """
     Compare behavior between auto and static bins to ensure consistency.
     """
-    print("\n" + "="*70)
+    print("\n" + "=" * 70)
     print("Comparing auto bins vs static bins behavior...")
-    print("="*70)
-    
+    print("=" * 70)
+
     np.random.seed(456)
     X_train = np.random.randn(100, 4) * 2 + 5
     X_test = np.vstack([
         X_train[:5],
         X_train.max(axis=0) + 2,  # One sample above all training ranges
     ])
-    
+
     # Test with auto bins
     model_auto = HBOS(n_bins='auto', contamination=0.1)
     model_auto.fit(X_train)
     try:
         pred_auto = model_auto.predict(X_test)
         score_auto = model_auto.decision_function(X_test)
         print(f"  Auto bins: ✓ Success")
-        print(f"    Outliers: {pred_auto.sum()}, Score range: [{score_auto.min():.4f}, {score_auto.max():.4f}]")
+        print(
+            f"    Outliers: {pred_auto.sum()}, Score range: [{score_auto.min():.4f}, {score_auto.max():.4f}]")
     except Exception as e:
         print(f"  Auto bins: ✗ Failed - {e}")
         return False
-    
+
     # Test with static bins
     model_static = HBOS(n_bins=10, contamination=0.1)
     model_static.fit(X_train)
     try:
         pred_static = model_static.predict(X_test)
         score_static = model_static.decision_function(X_test)
         print(f"  Static bins: ✓ Success")
-        print(f"    Outliers: {pred_static.sum()}, Score range: [{score_static.min():.4f}, {score_static.max():.4f}]")
+        print(
+            f"    Outliers: {pred_static.sum()}, Score range: [{score_static.min():.4f}, {score_static.max():.4f}]")
     except Exception as e:
         print(f"  Static bins: ✗ Failed - {e}")
         return False
-    
+
     print(f"\n  Both methods handled out-of-range values correctly!")
     return True
 
 
 if __name__ == "__main__":
-    print("="*70)
+    print("=" * 70)
     print("HBOS IndexError Fix Test Suite")
     print("Issue: https://github.com/yzhao062/pyod/issues/476")
-    print("="*70)
-    
+    print("=" * 70)
+
     all_passed = True
-    
+
     # Run main test
     all_passed &= test_hbos_auto_bins_with_out_of_range_values()
-    
+
     # Run edge case tests
     all_passed &= test_hbos_auto_bins_edge_cases()
-    
+
     # Run comparison test
     all_passed &= test_hbos_static_bins_comparison()
-    
-    print("\n" + "="*70)
+
+    print("\n" + "=" * 70)
     if all_passed:
         print("✓ ALL TESTS PASSED!")
         print("The fix correctly handles out-of-range test values.")
     else:
         print("✗ SOME TESTS FAILED!")
         print("The bug may not be fully fixed.")
-    print("="*70)
+    print("=" * 70)