fix logging

meta_automl/data_preparation/datasets_loaders/openml_datasets_loader.py

@@ -2,8 +2,6 @@
 
 from typing import List, Union, Optional, Sequence
 
-from golem.core.log import default_log
-
 from meta_automl.data_preparation.dataset import OpenMLDataset, OpenMLDatasetIDType
 from meta_automl.data_preparation.datasets_loaders import DatasetsLoader
 
@@ -37,7 +35,3 @@ def load_single(self, dataset_id: Union[OpenMLDatasetIDType, str],
         self.dataset_ids.add(dataset.id_)
 
         return dataset
-
-    @property
-    def _log(self):
-        return default_log(self)

meta_automl/data_preparation/meta_features_extractors/pymfe_extractor.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 
+import logging
 from typing import List, Union, Dict, Any
 
 import pandas as pd
-from golem.core.log import default_log
 from pymfe.mfe import MFE
 
 from meta_automl.data_preparation.dataset import DatasetBase, DatasetIDType
@@ -18,7 +18,6 @@ def __init__(self, extractor_params: Dict[str, Any] = None, datasets_loader: Dat
         self.extractor_params = extractor_params if extractor_params is not None else self.default_params
         self._datasets_loader = datasets_loader or OpenMLDatasetsLoader()
         self._extractor = MFE(**self.extractor_params)
-        self._logger = default_log(self)
 
     @property
     def datasets_loader(self) -> DatasetsLoader:
@@ -35,7 +34,7 @@ def extract(self, datasets_or_ids: List[Union[DatasetBase, DatasetIDType]],
             if not isinstance(dataset, DatasetBase):
                 dataset = self._datasets_loader.load_single(dataset)
 
-            self._logger.info(f'Extracting meta features of the dataset {dataset}...')
+            logging.critical(f'Extracting meta features of the dataset {dataset}...')
             if (use_cached and
                     (mfs := self._get_meta_features_cache(dataset.id_, meta_feature_names))):
                 meta_features[dataset.id_] = mfs