Update dimensionality_reduction.py

Author: Sid101098

machine_learning/dimensionality_reduction.py

@@ -414,36 +414,34 @@ def test_multidimensional_scaling() -> None:
         logging.error(f"MDS test failed: {e}")
         raise
 
-
 def test_linear_discriminant_analysis() -> None:
+    """Test function for Linear Discriminant Analysis."""
     # Create dummy dataset with 2 classes and 3 features
     features = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]])
     labels = np.array([0, 0, 0, 1, 1])
     classes = 2
-    dimensions = 2
-
-    # Assert that the function raises an AssertionError if dimensions > classes
-    with pytest.raises(AssertionError) as error_info:  # noqa: PT012
-        projected_data = linear_discriminant_analysis(
-            features, labels, classes, dimensions
-        )
-        if isinstance(projected_data, np.ndarray):
-            raise AssertionError(
-                "Did not raise AssertionError for dimensions > classes"
-            )
-        assert error_info.type is AssertionError
+    dimensions = 1  # Changed to 1 since classes=2 and dimensions must be < classes
 
+    try:
+        # This should work since dimensions < classes
+        lda_result = linear_discriminant_analysis(features, labels, classes, dimensions)
+        assert lda_result.shape == (dimensions, features.shape[1])
+        logging.info("LDA test passed")
+    except Exception as e:
+        logging.error(f"LDA test failed: {e}")
+        raise
 
 def test_principal_component_analysis() -> None:
+    """Test function for Principal Component Analysis."""
     features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
     dimensions = 2
-    expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
+    expected_output = np.array(
+        [[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]
+    )
 
-    with pytest.raises(AssertionError) as error_info:  # noqa: PT012
-        output = principal_component_analysis(features, dimensions)
-        if not np.allclose(expected_output, output):
-            raise AssertionError
-        assert error_info.type is AssertionError
+    output = principal_component_analysis(features, dimensions)
+    if not np.allclose(expected_output, output):
+        raise AssertionError("PCA output does not match expected result")
 
 
 def test_dimensionality_reduction() -> None: