fix tests, calculate features and X_prediction

ml_garden/core/steps/calculate_features.py

@@ -65,14 +65,16 @@ def __init__(
                 f" features: {list(self.feature_extractors.keys())}"
             )
 
-    def _convert_column_to_datetime(self, df: pd.DataFrame, column: str) -> pd.DataFrame:
+    def _convert_column_to_datetime(self, df: pd.DataFrame, column: str, log: bool) -> pd.DataFrame:
         """Convert a column to datetime.
         Parameters
         ----------
         df : pd.DataFrame
             The DataFrame containing the column to convert
         column : str
             The name of the column to convert
+        log: bool
+            If True, logs information.
         Returns
         -------
         pd.DataFrame
@@ -85,14 +87,15 @@ def _convert_column_to_datetime(self, df: pd.DataFrame, column: str) -> pd.DataF
                     df[column],
                     errors="raise",
                 )
-                self.logger.info(f"Column '{column}' automatically converted to datetime.")
+                if log:
+                    self.logger.info(f"Column '{column}' automatically converted to datetime.")
             except ValueError as e:
                 self.logger.error(f"Error converting column '{column}' to datetime: {e}")
             except Exception as e:
                 self.logger.error(f"Unexpected error converting column '{column}' to datetime: {e}")
         else:
-            self.logger.debug(f"Column '{column}' is already a datetime type.")
-
+            if log:
+                self.logger.debug(f"Column '{column}' is already a datetime type.")
         return df
 
     def _extract_feature(self, df: pd.DataFrame, column: str, feature: str) -> None:
@@ -122,6 +125,14 @@ def _extract_feature(self, df: pd.DataFrame, column: str, feature: str) -> None:
             )
             raise ValueError(error_message)
 
+    def _drop_datetime_columns(self, df: pd.DataFrame, log: bool) -> pd.DataFrame:
+        """Drop the datetime columns from the `df`."""
+        if self.datetime_columns:
+            if log:
+                self.logger.info(f"Dropping original datetime columns: {self.datetime_columns}")
+            return df.drop(columns=self.datetime_columns)
+        return df
+
     def execute(self, data: DataContainer) -> DataContainer:
         """Execute the step.
         Parameters
@@ -135,21 +146,18 @@ def execute(self, data: DataContainer) -> DataContainer:
         """
         self.logger.info("Calculating features")
 
-        if not data.is_train:
-            data.flow = self._create_datetime_features(data.flow, log=True)
+        datasets = [
+            ("X_prediction", data.X_prediction, True),
+            ("X_train", data.X_train, True),
+            ("X_validation", data.X_validation, False),
+            ("X_test", data.X_test, False),
+        ]
 
-        if data.train is not None:
-            data.train = self._create_datetime_features(data.train, log=True)
-
-        if data.validation is not None:
-            data.validation = self._create_datetime_features(data.validation)
-
-        if data.test is not None:
-            data.test = self._create_datetime_features(data.test)
-
-        ## add datetime columns to ignore columns for training
-        if self.datetime_columns:
-            data.columns_to_ignore_for_training.extend(self.datetime_columns)
+        for attr_name, dataset, should_log in datasets:
+            if dataset is not None:
+                dataset = self._create_datetime_features(dataset, log=should_log)
+                dataset = self._drop_datetime_columns(dataset, log=should_log)
+                setattr(data, attr_name, dataset)
 
         return data
 
@@ -173,7 +181,7 @@ def _create_datetime_features(
         if self.datetime_columns:
             for column in self.datetime_columns:
                 if column in df.columns:
-                    df = self._convert_column_to_datetime(df, column)
+                    df = self._convert_column_to_datetime(df, column, log)
 
                     if self.features:
                         for feature in self.features:
@@ -191,4 +199,7 @@ def _create_datetime_features(
             if log:
                 self.logger.warning("No datetime columns specified. Skipping feature extraction.")
 
+        if log:
+            self.logger.info(f"Created new features: {self.features}")
+
         return df

ml_garden/core/steps/encode.py

@@ -52,11 +52,9 @@ def execute(self, data: DataContainer) -> DataContainer:
         self.logger.info("Encoding data")
 
         if not data.is_train:
-            categorical_features, numeric_features = self._get_feature_types(
-                data.flow.drop(columns=data.columns_to_ignore_for_training)
-            )
+            categorical_features, numeric_features = self._get_feature_types(data.X_prediction)
             data.X_prediction, _, _ = self._apply_encoding(
-                X=data.flow,
+                X=data.X_prediction,
                 y=None,
                 categorical_features=categorical_features,
                 numeric_features=numeric_features,
@@ -66,8 +64,6 @@ def execute(self, data: DataContainer) -> DataContainer:
             return data
 
         categorical_features, numeric_features = self._get_feature_types(data.X_train)
-        self.logger.info(f"New categorical features: {categorical_features}")
-        self.logger.info(f"New numeric features: {numeric_features}")
 
         data.X_train, data.y_train, data._encoder = self._apply_encoding(
             X=data.X_train,

ml_garden/core/steps/fit_model.py

@@ -298,7 +298,6 @@ def predict(self, data: DataContainer) -> DataContainer:
             The updated data container
         """
         self.logger.info(f"Predicting with {self.model_class.__name__} model")
-        data.X_prediction = data.flow.drop(columns=data.columns_to_ignore_for_training)
         data.flow[data.prediction_column] = data.model.predict(data.X_prediction)
         data.predictions = data.flow[data.prediction_column]
         return data

ml_garden/core/steps/tabular_split.py

@@ -13,7 +13,7 @@
 class TabularSplitStep(PipelineStep):
     """Split the data."""
 
-    used_for_prediction = False
+    used_for_prediction = True
     used_for_training = True
 
     def __init__(
@@ -129,6 +129,13 @@ def execute(self, data: DataContainer) -> DataContainer:
 
         Where df is the DataFrame used as input to the SplitStep
         """
+        if not data.is_train:
+            data.X_prediction = data.flow
+            if data.columns_to_ignore_for_training:
+                data.X_prediction = data.X_prediction.drop(
+                    columns=data.columns_to_ignore_for_training
+                )
+            return data
 
         self.logger.info("Splitting tabular data...")
         df = data.flow

tests/core/steps/test_calculate_features.py

@@ -62,7 +62,7 @@ def input_data() -> pd.DataFrame:
 def data(input_data: pd.DataFrame) -> DataContainer:
     data = DataContainer({"is_train": True})
     data.columns_to_ignore_for_training = []
-    data.train = input_data
+    data.X_train = input_data
     return data
 
 
@@ -72,7 +72,7 @@ def test_skipping_with_no_parameters(data: DataContainer):
     result = calculate_features_step.execute(data)
 
     assert isinstance(result, DataContainer)
-    assert result.train.equals(data.train)
+    assert result.X_train.equals(data.X_train)
 
 
 def test_feature_names(data: DataContainer):
@@ -87,22 +87,22 @@ def test_feature_names(data: DataContainer):
     result = calculate_features_step.execute(data)
 
     assert isinstance(result, DataContainer)
-    assert "creation_date_year" in result.train.columns
-    assert "creation_date_month" in result.train.columns
-    assert "creation_date_day" in result.train.columns
-    assert "creation_date_hour" in result.train.columns
-    assert "creation_date_minute" in result.train.columns
-    assert "creation_date_second" in result.train.columns
-    assert "creation_date_weekday" in result.train.columns
-    assert "creation_date_dayofyear" in result.train.columns
-    assert "deletion_date_year" in result.train.columns
-    assert "deletion_date_month" in result.train.columns
-    assert "deletion_date_day" in result.train.columns
-    assert "deletion_date_hour" in result.train.columns
-    assert "deletion_date_minute" in result.train.columns
-    assert "deletion_date_second" in result.train.columns
-    assert "deletion_date_weekday" in result.train.columns
-    assert "deletion_date_dayofyear" in result.train.columns
+    assert "creation_date_year" in result.X_train.columns
+    assert "creation_date_month" in result.X_train.columns
+    assert "creation_date_day" in result.X_train.columns
+    assert "creation_date_hour" in result.X_train.columns
+    assert "creation_date_minute" in result.X_train.columns
+    assert "creation_date_second" in result.X_train.columns
+    assert "creation_date_weekday" in result.X_train.columns
+    assert "creation_date_dayofyear" in result.X_train.columns
+    assert "deletion_date_year" in result.X_train.columns
+    assert "deletion_date_month" in result.X_train.columns
+    assert "deletion_date_day" in result.X_train.columns
+    assert "deletion_date_hour" in result.X_train.columns
+    assert "deletion_date_minute" in result.X_train.columns
+    assert "deletion_date_second" in result.X_train.columns
+    assert "deletion_date_weekday" in result.X_train.columns
+    assert "deletion_date_dayofyear" in result.X_train.columns
 
 
 def test_date_columns_are_ignored_for_training(data: DataContainer):
@@ -117,8 +117,8 @@ def test_date_columns_are_ignored_for_training(data: DataContainer):
     result = calculate_features_step.execute(data)
 
     assert isinstance(result, DataContainer)
-    assert "creation_date" in result.columns_to_ignore_for_training
-    assert "deletion_date" in result.columns_to_ignore_for_training
+    assert "creation_date" not in result.X_train.columns
+    assert "deletion_date" not in result.X_train.columns
 
 
 def test_output_dtypes(data: DataContainer):
@@ -133,14 +133,14 @@ def test_output_dtypes(data: DataContainer):
     result = calculate_features_step.execute(data)
 
     assert isinstance(result, DataContainer)
-    assert result.train["creation_date_year"].dtype == np.dtype("uint16")
-    assert result.train["creation_date_month"].dtype == np.dtype("uint8")
-    assert result.train["creation_date_day"].dtype == np.dtype("uint8")
-    assert result.train["creation_date_hour"].dtype == np.dtype("uint8")
-    assert result.train["creation_date_minute"].dtype == np.dtype("uint8")
-    assert result.train["creation_date_second"].dtype == np.dtype("uint8")
-    assert result.train["creation_date_weekday"].dtype == np.dtype("uint8")
-    assert result.train["creation_date_dayofyear"].dtype == np.dtype("uint16")
+    assert result.X_train["creation_date_year"].dtype == np.dtype("uint16")
+    assert result.X_train["creation_date_month"].dtype == np.dtype("uint8")
+    assert result.X_train["creation_date_day"].dtype == np.dtype("uint8")
+    assert result.X_train["creation_date_hour"].dtype == np.dtype("uint8")
+    assert result.X_train["creation_date_minute"].dtype == np.dtype("uint8")
+    assert result.X_train["creation_date_second"].dtype == np.dtype("uint8")
+    assert result.X_train["creation_date_weekday"].dtype == np.dtype("uint8")
+    assert result.X_train["creation_date_dayofyear"].dtype == np.dtype("uint16")
 
 
 def test_output_values(data: DataContainer):
@@ -155,28 +155,28 @@ def test_output_values(data: DataContainer):
     result = calculate_features_step.execute(data)
 
     assert isinstance(result, DataContainer)
-    assert result.train["creation_date_year"].equals(
+    assert result.X_train["creation_date_year"].equals(
         pd.Series([2023, 2023, 2023, 2023, 2023, 2023, 2024, 2024], dtype="uint16")
     )
-    assert result.train["creation_date_month"].equals(
+    assert result.X_train["creation_date_month"].equals(
         pd.Series([1, 1, 1, 1, 1, 11, 2, 3], dtype="uint8")
     )
-    assert result.train["creation_date_day"].equals(
+    assert result.X_train["creation_date_day"].equals(
         pd.Series([1, 2, 3, 4, 5, 1, 28, 28], dtype="uint8")
     )
-    assert result.train["creation_date_hour"].equals(
+    assert result.X_train["creation_date_hour"].equals(
         pd.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="uint8")
     )
-    assert result.train["creation_date_minute"].equals(
+    assert result.X_train["creation_date_minute"].equals(
         pd.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="uint8")
     )
-    assert result.train["creation_date_second"].equals(
+    assert result.X_train["creation_date_second"].equals(
         pd.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="uint8")
     )
-    assert result.train["creation_date_weekday"].equals(
+    assert result.X_train["creation_date_weekday"].equals(
         pd.Series([6, 0, 1, 2, 3, 2, 2, 3], dtype="uint8")
     )
-    assert result.train["creation_date_dayofyear"].equals(
+    assert result.X_train["creation_date_dayofyear"].equals(
         pd.Series([1, 2, 3, 4, 5, 305, 59, 88], dtype="uint16")
     )
 
@@ -214,7 +214,7 @@ def test_init_with_unsupported_features():
 
 def test_execute_with_prediction(data: DataContainer):
     data.is_train = False
-    data.flow = data.train.copy()
+    data.X_prediction = data.X_train.copy()
 
     datetime_columns = ["creation_date"]
     features = ["year", "month", "day"]
@@ -226,6 +226,6 @@ def test_execute_with_prediction(data: DataContainer):
     result = calculate_features_step.execute(data)
 
     assert isinstance(result, DataContainer)
-    assert "creation_date_year" in result.flow.columns
-    assert "creation_date_month" in result.flow.columns
-    assert "creation_date_day" in result.flow.columns
+    assert "creation_date_year" in result.X_prediction.columns
+    assert "creation_date_month" in result.X_prediction.columns
+    assert "creation_date_day" in result.X_prediction.columns