Updated tests

Author: Diegomangasco

Diff for: machine_learning/dimensionality_reduction.py

@@ -9,6 +9,7 @@
 """
 
 import logging
+import pytest
 
 import numpy as np
 from scipy.linalg import eigh
@@ -98,6 +99,8 @@ def principal_component_analysis(features: np.ndarray, dimensions: int) -> np.nd
     Parameters:
         * features: the features extracted from the dataset
         * dimensions: to filter the projected data for the desired dimension
+
+    >>> test_principal_component_analysis()
     """
 
     # Check if the features have been loaded
@@ -132,6 +135,8 @@ def linear_discriminant_analysis(
         * labels: the class labels of the features
         * classes: the number of classes present in the dataset
         * dimensions: to filter the projected data for the desired dimension
+
+    >>> test_linear_discriminant_analysis()
     """
 
     # Check if the dimension desired is less than the number of classes
@@ -175,22 +180,23 @@ def test_linear_discriminant_analysis() -> None:
     assert projected_data.any()
 
     # Assert that the function raises an AssertionError if dimensions > classes
-    try:
+    with pytest.raises(AssertionError) as error_info:
         projected_data = linear_discriminant_analysis(features, labels, classes, 3)
-    except AssertionError:
-        pass
-    else:
-        raise AssertionError("Did not raise AssertionError for dimensions > classes")
+        if isinstance(projected_data, np.ndarray):
+            raise AssertionError("Did not raise AssertionError for dimensions > classes")
+        assert error_info.type is AssertionError
 
 
 def test_principal_component_analysis() -> None:
     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]])
-    output = principal_component_analysis(features, dimensions)
-    assert np.allclose(
-        expected_output, output
-    ), f"Expected {expected_output}, but got {output}"
+
+    with pytest.raises(AssertionError) as error_info:
+        output = principal_component_analysis(features, dimensions)
+        if not np.allclose(expected_output, output):
+            raise AssertionError
+        assert error_info.type is AssertionError
 
 
 if __name__ == "__main__":