Fix unit tests

Author: thomasmeissnercrm

bluecast/tests/test_custom_model_recipes.py

@@ -132,27 +132,27 @@ def test_linear_predict(regression_data, linear_model):
     X_train, X_test, y_train, y_test = regression_data
     linear_model.fit(X_train, X_test, y_train, y_test)
 
-    predictions = linear_model.predict(X_test)
+    result = linear_model.predict(X_test)
+
+    # predict returns (preds, preds) tuple to match BaseClassMlModel interface
+    assert isinstance(result, tuple), "Predictions should be a tuple."
+    predictions = result[0]
 
-    # Check the types of the returned values
     assert isinstance(predictions, np.ndarray), "Predictions should be a numpy array."
 
-    # Check the shape of the returned values
     assert predictions.shape == (
         X_test.shape[0],
     ), "Predictions should have the correct shape."
 
-    # Optionally, check the values of predictions are finite (not NaN or inf)
     assert np.all(np.isfinite(predictions)), "Predictions should be finite values."
 
 
 def test_linear_predict_range(regression_data, linear_model):
     X_train, X_test, y_train, y_test = regression_data
     linear_model.fit(X_train, X_test, y_train, y_test)
 
-    predictions = linear_model.predict(X_test)
+    predictions, _ = linear_model.predict(X_test)
 
-    # Check if predictions are within a reasonable range
     assert np.all(
         predictions >= y_train.min() - 10
     ), "Predictions should not be too low."

bluecast/tests/test_preprocessing_recipes.py

@@ -102,6 +102,12 @@ def test_no_numerical_columns():
     preprocessing = PreprocessingForLinearModels(num_columns=[])
     transformed_df, transformed_target = preprocessing.fit_transform(df, target)
 
-    # Since there are no numerical columns, the DataFrame should remain unchanged
-    pd.testing.assert_frame_equal(transformed_df, df)
+    # Since there are no numerical columns, all original columns should be present
+    assert set(transformed_df.columns) == set(df.columns)
+    for col in df.columns:
+        pd.testing.assert_series_equal(
+            transformed_df[col].reset_index(drop=True),
+            df[col].reset_index(drop=True),
+            check_names=False,
+        )
     pd.testing.assert_series_equal(transformed_target, target)