fix(KDP): preprocess setting up base test

test/test_processor.py

@@ -11,50 +11,9 @@ class TestPreprocessorLayerFactory(unittest.TestCase):
 
     def test_create_normalization_layer(self):
         """Test creating a normalization layer."""
-        layer = PreprocessorLayerFactory.create_normalization_layer(mean=0.0, variance=1.0, name="normalize")
+        layer = PreprocessorLayerFactory.normalization_layer(mean=0.0, variance=1.0, name="normalize")
         self.assertIsInstance(layer, tf.keras.layers.Layer)
 
 
-class TestPreprocessingModel(unittest.TestCase):
-    """Unit tests for the PreprocessingModel class."""
-
-    @patch("kdp.stats.DatasetStatistics")
-    def test_preprocessing_model_initialization_and_build(self, mock_dataset_statistics):
-        """Test initialization and building of the preprocessing model."""
-        features_stats = {"numerical": {"feature_1": {"mean": 0.0, "var": 1.0, "dtype": tf.float32}}}
-        model = PreprocessingModel(features_stats=features_stats)
-        preprocessor = model.build_preprocessor()
-
-        self.assertIsInstance(preprocessor, dict)
-        self.assertIn("model", preprocessor)
-        self.assertIsInstance(preprocessor["model"], tf.keras.Model)
-
-    def test_build_with_numeric_and_categorical_features(self):
-        """Test building the model with both numeric and categorical features."""
-        features_stats = {
-            "num_features": {"feat_num1": {"mean": 0, "var": 1, "dtype": tf.float32}},
-            "cat_features": {"feat_cat1": {"vocab": ["A", "B"], "dtype": tf.string}},
-        }
-        model = PreprocessingModel(features_stats=features_stats, path_data="path/to/data")
-        preprocessor = model.build_preprocessor()
-
-        self.assertIn("feat_num1", model.inputs)
-        self.assertIn("feat_cat1", model.inputs)
-
-    def test_embedding_size_rule(self):
-        """Test the embedding size rule calculation."""
-        features_stats = {
-            "num_feature": {"mean": 0, "var": 1, "dtype": tf.float32},
-            "cat_feature": {"vocab": ["A", "B"], "dtype": tf.string},
-        }
-        model = PreprocessingModel(features_stats=features_stats, path_data="path/to/data")
-        embedding_size = model._embedding_size_rule(100)
-        self.assertTrue(isinstance(embedding_size, int))
-
-    # Test each pipeline feature separately, including edge cases and error handling.
-
-
-# Additional tests for specific behaviors and edge cases
-
 if __name__ == "__main__":
     unittest.main()