refactor(KDP): adding missing layers

kdp/layers_factory.py

@@ -169,7 +169,7 @@ def crossing_layer(name: str, **kwargs) -> tf.keras.layers.Layer:
         )
 
     @staticmethod
-    def create_crossing_layer(name: str, **kwargs) -> tf.keras.layers.Layer:
+    def flatten_layer(name: str, **kwargs) -> tf.keras.layers.Layer:
         """Create a crossing layer.
 
         Args:
@@ -180,7 +180,7 @@ def create_crossing_layer(name: str, **kwargs) -> tf.keras.layers.Layer:
             An instance of the Flatten layer.
         """
         return PreprocessorLayerFactory.create_layer(
-            layer_class=tf.keras.layers.HashedCrossing,
+            layer_class=tf.keras.layers.Flatten,
             name=name,
             **kwargs,
         )

kdp/processor.py

@@ -443,7 +443,7 @@ def _add_pipeline_cross(self, stats: dict) -> None:
 
             _feature_name_crossed = f"{feature_a}_x_{feature_b}"
             preprocessor.add_processing_step(
-                layer_creator=PreprocessorLayerFactory.create_crossing_layer,
+                layer_creator=PreprocessorLayerFactory.crossing_layer,
                 nr_bins=nr_bins,
                 name=f"cross_{_feature_name_crossed}",
             )
@@ -466,28 +466,27 @@ def _add_pipeline_text(self, feature_name: str, input_layer) -> None:
             feature_name (str): The name of the feature to be preprocessed.
             input_layer: The input layer for the feature.
         """
+        # getting feature object
+        _feature = self.features_specs[feature_name]
+
+        # initializing preprocessor
         preprocessor = FeaturePreprocessor(name=feature_name)
 
-        # checking if we have custom setting per feature
-        _feature_config = self.text_features_config.get(feature_name) or self.text_features_config
         # getting stop words for text preprocessing
-        _stop_words = _feature_config.get("stop_words")
+        _stop_words = _feature.kwargs.get("stop_words", [])
 
         if _stop_words:
             preprocessor.add_processing_step(
-                layer_creator=PreprocessorLayerFactory.create_text_preprocessing_layer,
+                layer_creator=PreprocessorLayerFactory.text_preprocessing_layer,
                 stop_words=_stop_words,
                 name=f"text_preprocessor_{feature_name}",
             )
         preprocessor.add_processing_step(
-            layer_creator=PreprocessorLayerFactory.create_text_vectorization_layer,
-            conf=_feature_config,
+            layer_creator=PreprocessorLayerFactory.text_vectorization_layer,
             name=f"text_vactorizer_{feature_name}",
         )
 
         self.outputs[feature_name] = preprocessor.chain(input_layer=input_layer)
-        # updating output vector dim
-        self.output_dims += _feature_config["output_sequence_length"]
 
     def _prepare_outputs(self) -> None:
         """Preparing the outputs of the model.