⬆️ set version 0.6.0

setup.py

@@ -10,8 +10,8 @@
 
 setup(
     name='mljar-supervised',
-    version='0.5.5',
-    description='Automated Machine Learning for Supervised tasks',
+    version='0.6.0',
+    description='Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning',
     long_description=long_description,
     long_description_content_type="text/markdown",
     url='https://github.com/mljar/mljar-supervised',

supervised/__init__.py

@@ -1,3 +1,3 @@
-__version__ = "0.5.5"
+__version__ = "0.6.0"
 
 from supervised.automl import AutoML

supervised/preprocessing/preprocessing.py

@@ -131,6 +131,11 @@ def fit_and_transform(self, X_train, y_train):
             X_train.drop(cols_to_remove, axis=1, inplace=True)
         self._remove_columns = cols_to_remove
 
+        numeric_cols = [] # get numeric cols before text transformations
+        # needed for golden features
+        if X_train is not None and "golden_features" in self._params:
+            numeric_cols = X_train.select_dtypes(include="number").columns.tolist()
+
         # there can be missing values in the text data,
         # but we don't want to handle it by fill missing methods
         # zeros will be imputed by text_transform method
@@ -164,9 +169,7 @@ def fit_and_transform(self, X_train, y_train):
 
         # golden features
         golden_columns = []
-
         if "golden_features" in self._params:
-            numeric_cols = X_train.select_dtypes(include="number").columns.tolist()
             results_path = self._params["golden_features"]["results_path"]
             ml_task = self._params["golden_features"]["ml_task"]
             # if ml_task in [BINARY_CLASSIFICATION]: