🐛 features names fixed. removed_columns, added_columns are droped

tests/test_base.py

@@ -11,14 +11,18 @@ def __init__(self, required_columns, keep_required):
         self.keep_required = keep_required
 
     def _fit_implementation(self, X, y=None):
+        self.fit_check_features(X)
         if not self.keep_required:
-            self.removed_columns = self.required_columns
-        self.added_columns = ["new_col__°C__meteo"]
+            self.feature_names_out_ = [
+                col for col in X.columns if col not in self.required_columns
+            ]
+        self.feature_names_out_.append("new_col__°C__meteo")
         return self
 
     def _transform_implementation(self, X):
-        X[self.added_columns[0]] = X[self.required_columns] * 2
-        X.drop(self.removed_columns, axis=1, inplace=True)
+        X["new_col__°C__meteo"] = X[self.required_columns] * 2
+        if not self.keep_required:
+            X.drop(self.required_columns, axis=1, inplace=True)
         return X
 
 

tests/test_processing.py

@@ -444,6 +444,7 @@ def test_pd_add_time_lag(self):
 
         lager = AddTimeLag(time_lag=dt.timedelta(hours=1), drop_resulting_nan=True)
         lager.fit(df)
+        lager.get_feature_names_out()
         assert list(lager.get_feature_names_out()) == [
             "col0",
             "col1",
@@ -501,11 +502,13 @@ def test_pd_combine_columns(self):
 
         pd.testing.assert_frame_equal(trans.fit_transform(x_in), ref)
 
+        ref["combined_2"] = [2, 4]
         trans = CombineColumns(
             function=np.sum,
             tide_format_columns="°C",
             function_kwargs={"axis": 1},
             drop_columns=False,
+            result_column_name="combined_2",
         )
 
         pd.testing.assert_frame_equal(trans.fit_transform(x_in), ref)
@@ -720,13 +723,13 @@ def test_combiner(self):
         )
 
         combiner = ExpressionCombine(
-            variables_dict={
+            columns_dict={
                 "T1": "Tin__°C__building",
                 "T2": "Text__°C__outdoor",
                 "m": "mass_flwr__m3/h__hvac",
             },
             expression="(T1 - T2) * m * 1004 * 1.204",
-            result_col_name="loss_ventilation__J__hvac",
+            result_column_name="loss_ventilation__J__hvac",
         )
 
         res = combiner.fit_transform(test_df.copy())
@@ -748,7 +751,7 @@ def test_combiner(self):
             decimal=1,
         )
 
-        combiner.set_params(drop_variables=True)
+        combiner.set_params(drop_columns=True)
 
         res = combiner.fit_transform(test_df.copy())
 

tide/base.py

@@ -18,7 +18,6 @@
     process_stl_odd_args,
     get_data_blocks,
     get_idx_freq_delta_or_min_time_interval,
-    ensure_list,
     get_tags_max_level,
     NAME_LEVEL_MAP,
 )
@@ -56,15 +55,8 @@ class TideBaseMixin:
         Returns the names of the features as initially fitted.
     """
 
-    def __init__(
-        self,
-        required_columns: str | list[str] = None,
-        removed_columns: str | list[str] = None,
-        added_columns: str | list[str] = None,
-    ):
+    def __init__(self, required_columns: str | list[str] = None):
         self.required_columns = required_columns
-        self.removed_columns = removed_columns
-        self.added_columns = added_columns
 
     def check_required_features(self, X):
         if self.required_columns is not None:
@@ -73,7 +65,7 @@ def check_required_features(self, X):
 
     def fit_check_features(self, X):
         self.check_required_features(X)
-        self.feature_names_in_ = list(X.columns)
+        self.feature_names_in_ = self.feature_names_out_ = list(X.columns)
 
     def get_set_tags_values_columns(self, X, level: int | str, value: str):
         nb_tags = get_tags_max_level(X.columns)
@@ -96,18 +88,8 @@ def set_tags_values(self, X, tag_level: int, value: str):
         X.columns = self.get_set_tags_values_columns(X, tag_level, value)
 
     def get_feature_names_out(self, input_features=None):
-        if input_features is None:
-            check_is_fitted(self, attributes=["feature_names_in_"])
-            input_features = self.feature_names_in_
-
-        added_columns = ensure_list(self.added_columns)
-        removed_columns = ensure_list(self.removed_columns)
-        if isinstance(input_features, list):
-            input_features = np.array(input_features)
-        features_out = np.concatenate([input_features.copy(), np.array(added_columns)])
-        return np.array(
-            [feature for feature in features_out if feature not in removed_columns]
-        )
+        check_is_fitted(self, attributes=["feature_names_in_", "feature_names_out_"])
+        return self.feature_names_out_
 
     def get_feature_names_in(self):
         check_is_fitted(self, attributes=["feature_names_in_"])
@@ -160,15 +142,8 @@ class BaseProcessing(ABC, TransformerMixin, BaseEstimator, TideBaseMixin):
     def __init__(
         self,
         required_columns: str | list[str] = None,
-        removed_columns: str | list[str] = None,
-        added_columns: str | list[str] = None,
     ):
-        TideBaseMixin.__init__(
-            self,
-            required_columns=required_columns,
-            removed_columns=removed_columns,
-            added_columns=added_columns,
-        )
+        TideBaseMixin.__init__(self, required_columns=required_columns)
         TransformerMixin.__init__(self)
         BaseEstimator.__init__(self)