Automated autopep8 fixes

aimclub · Jan 16, 2025 · 5fba668 · 5fba668
1 parent 10e87b9
commit 5fba668
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Showing 4 changed files with 14 additions and 15 deletions.
diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py
@@ -127,8 +127,8 @@ def __init_solver(self, input_data):
             metric=self.manager.learning_config.config['optimisation_loss'],
             problem=self.manager.automl_config.config['task'],
             task_params=self.manager.industrial_config.task_params
-            if self.manager.industrial_config.is_forecasting_context else self.manager.automl_config.config['task_params'],
-            optimizer=self.manager.automl_config.optimisation_strategy,
+            if self.manager.industrial_config.is_forecasting_context else self.manager.automl_config.config
+            ['task_params'], optimizer=self.manager.automl_config.optimisation_strategy,
             available_operations=self.manager.automl_config.config['available_operations'],
             initial_assumption=self.manager.automl_config.config['initial_assumption'])
         return input_data
@@ -245,8 +245,8 @@ def fit(self,
         def fit_function(train_data): return \
             Either(value=train_data, monoid=[train_data,
                                              not isinstance(self.manager.industrial_config.strategy, Callable)]). \
-                either(left_function=lambda data: self.manager.industrial_config.strategy.fit(data),
-                       right_function=lambda data: self.manager.solver.fit(data))
+            either(left_function=lambda data: self.manager.industrial_config.strategy.fit(data),
+                   right_function=lambda data: self.manager.solver.fit(data))
 
         Either.insert(self._process_input_data(input_data)). \
             then(lambda data: self.__init_industrial_backend(data)). \
@@ -367,7 +367,7 @@ def get_metrics(self,
                     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(

diff --git a/fedot_ind/api/utils/api_init.py b/fedot_ind/api/utils/api_init.py
@@ -208,7 +208,7 @@ def __init__(self):
                      'learning_config': self.with_learning_config,
                      'compute_config': self.with_compute_config}
         self.optimisation_agent = {"Industrial": IndustrialEvoOptimizer,
-                                 'Fedot': FedotEvoOptimizer}
+                                   'Fedot': FedotEvoOptimizer}
 
     def null_state_object(self):
         self.solver = None

diff --git a/fedot_ind/api/utils/checkers_collections.py b/fedot_ind/api/utils/checkers_collections.py
@@ -116,16 +116,16 @@ def encode_idx(dict_with_target): return Either(value=dict_with_target,
         def define_horizon(dict_with_idx): return Either(value=dict_with_idx,
                                                          monoid=[dict_with_idx, self.strategy_params is None]).either(
             left_function=lambda dict: dict | {'have_predict_horizon':
-                                                   all([self.strategy_params['data_type'] == 'time_series',
-                                                        'detection_window' in self.strategy_params.keys()])},
+                                               all([self.strategy_params['data_type'] == 'time_series',
+                                                    'detection_window' in self.strategy_params.keys()])},
             right_function=lambda dict: dict | {'have_predict_horizon': False})
 
         def define_task(dict_with_horizon): return Either(value=dict_with_horizon,
                                                           monoid=[dict_with_horizon,
                                                                   dict_with_horizon['have_predict_horizon']]).either(
             right_function=lambda dict: dict |
-                                        {'task': fedot_task('ts_forecasting',
-                                                            self.strategy_params['detection_window'])},
+            {'task': fedot_task('ts_forecasting',
+                                self.strategy_params['detection_window'])},
             left_function=lambda dict: dict | {'task': fedot_task(self.task)})
 
         encoded_dict = Either.insert(data_dict). \
@@ -177,7 +177,7 @@ def _check_input_data_features(self):
             then(lambda data: np.where(np.isnan(data), 0, data)). \
             then(lambda data_without_nan: np.where(np.isinf(data_without_nan), 0, data_without_nan)). \
             then(lambda data_without_inf: NumpyConverter(data=data_without_inf).convert_to_torch_format()
-        if self.task != 'ts_forecasting' else data_without_inf).value
+                 if self.task != 'ts_forecasting' else data_without_inf).value
 
     def _check_input_data_target(self):
         """Checks and preprocesses the features in the input data.
@@ -215,9 +215,9 @@ def _convert_ts2tabular(self, input_data, sampling_strategy):
             channel_start, channel_end = list(sampling_strategy['channels'].values())
             element_start, element_end = list(sampling_strategy['elements'].values())
             input_data.features = self.input_data.features[
-                                  sample_start:sample_end,
-                                  channel_start:channel_end,
-                                  element_start:element_end]
+                sample_start:sample_end,
+                channel_start:channel_end,
+                element_start:element_end]
         fg_list = self.strategy_params['feature_generator']
         ts2tabular_model = TabularExtractor({'feature_domain': fg_list,
                                              'reduce_dimension': False})

diff --git a/fedot_ind/core/repository/constanst_repository.py b/fedot_ind/core/repository/constanst_repository.py
@@ -380,7 +380,6 @@ class FedotOperationConstant(Enum):
                                  'lora_strategy',
                                  'sampling_strategy']
 
-
     FEDOT_ENSEMBLE_ASSUMPTIONS = {
         'classification': PipelineBuilder().add_node('logit'),
         'regression': PipelineBuilder().add_node('treg')