Automated autopep8 fixes

fedot_ind/api/main.py

@@ -163,7 +163,7 @@ class FedotIndustrial(Fedot):
         # [self.config_dict.pop(x, None) for x in industrial_params]
 
         industrial_params = set(self.config_dict.keys()) - \
-                            set(FEDOT_API_PARAMS.keys())
+            set(FEDOT_API_PARAMS.keys())
         for param in industrial_params:
             self.config_dict.pop(param, None)
 
@@ -399,7 +399,7 @@ class FedotIndustrial(Fedot):
                     predicted_probs=probs,
                     rounding_order=rounding_order,
                     metric_names=metric_names) for strategy,
-                                                   probs in self.predicted_probs.items()}
+                probs in self.predicted_probs.items()}
 
         else:
             metric_dict = self._metric_evaluation_loop(

fedot_ind/api/utils/checkers_collections.py

@@ -92,7 +92,7 @@ class DataCheck:
             forecast_length=self.task_params['forecast_length']))
         if self.industrial_task_params is None:
             features_array = features_array[:-
-            self.task_params['forecast_length']]
+                                            self.task_params['forecast_length']]
             target = features_array
         return InputData.from_numpy_time_series(
             features_array=features_array, target_array=target, task=task)

fedot_ind/core/repository/constanst_repository.py

@@ -31,7 +31,7 @@ from fedot_ind.core.operation.transformation.data.hankel import HankelMatrix
 
 def beta_thr(beta):
     return 0.56 * np.power(beta, 3) - 0.95 * \
-           np.power(beta, 2) + 1.82 * beta + 1.43
+        np.power(beta, 2) + 1.82 * beta + 1.43
 
 
 class ComputationalConstant(Enum):

fedot_ind/tools/example_utils.py

@@ -94,9 +94,9 @@ def industrial_common_modelling_loop(
         finetune: bool = False,
         api_config: dict = None,
         metric_names: tuple = (
-                'r2',
-                'rmse',
-                'mae')):
+            'r2',
+            'rmse',
+            'mae')):
     industrial = FedotIndustrial(**api_config)
     dataset_is_dict = isinstance(dataset_name, dict) and 'train_data' in dataset_name.keys()
     industrial_strategy = 'industrial_strategy' in api_config.keys()
@@ -144,11 +144,11 @@ def create_comprasion_df(df, metric: str = 'rmse'):
     df_full = df_full[df_full['Unnamed: 0'] == metric]
     df_full = df_full.drop('Unnamed: 0', axis=1)
     df_full['Difference_industrial_All'] = (
-            df_full.iloc[:, 1:3].min(axis=1) - df_full['industrial'])
+        df_full.iloc[:, 1:3].min(axis=1) - df_full['industrial'])
     df_full['Difference_industrial_AG'] = (
-            df_full.iloc[:, 1:2].min(axis=1) - df_full['industrial'])
+        df_full.iloc[:, 1:2].min(axis=1) - df_full['industrial'])
     df_full['Difference_industrial_NBEATS'] = (
-            df_full.iloc[:, 2:3].min(axis=1) - df_full['industrial'])
+        df_full.iloc[:, 2:3].min(axis=1) - df_full['industrial'])
     df_full['industrial_Wins_All'] = df_full.apply(
         lambda row: 'Win' if row.loc['Difference_industrial_All'] > 0 else 'Loose', axis=1)
     df_full['industrial_Wins_AG'] = df_full.apply(

fedot_ind/tools/loader.py

@@ -70,7 +70,7 @@ class DataLoader:
 
     def local_skab_load(self, directory='other', group=None):
         path_to_result = PROJECT_PATH + \
-                         f'/examples/data/detection/data/{directory}'
+            f'/examples/data/detection/data/{directory}'
         folder_dict = {'other': [i for i in range(15)],
                        'valve1': [i for i in range(16)],
                        'valve2': [i for i in range(4)]}
@@ -433,8 +433,8 @@ class DataLoader:
                     elif data_started:
                         # Check that a full set of metadata has been provided
                         incomplete_regression_meta_data = not has_problem_name_tag or not has_timestamps_tag or \
-                                                          not has_univariate_tag or not has_target_labels_tag or \
-                                                          not has_data_tag
+                            not has_univariate_tag or not has_target_labels_tag or \
+                            not has_data_tag
                         incomplete_classification_meta_data = \
                             not has_problem_name_tag or not has_timestamps_tag \
                             or not has_univariate_tag or not has_class_labels_tag \
@@ -830,7 +830,7 @@ class DataLoader:
         if line_num:
             # Check that the file contained both metadata and data
             complete_regression_meta_data = has_problem_name_tag and has_timestamps_tag and has_univariate_tag \
-                                            and has_target_labels_tag and has_data_tag
+                and has_target_labels_tag and has_data_tag
             complete_classification_meta_data = \
                 has_problem_name_tag and has_timestamps_tag \
                 and has_univariate_tag and has_class_labels_tag and has_data_tag