adding n_jobs param and fixes

Author: aPovidlo

fedot/core/data/data.py

@@ -532,7 +532,7 @@ def get_not_encoded_data(self):
             else:
                 cat_features_names = np.array([f'cat_feature_{i}' for i in range(1, cat_features.shape[1] + 1)])
 
-        if num_features and cat_features:
+        if num_features is not None and cat_features is not None:
             new_features = np.hstack((num_features, cat_features))
             new_features_names = np.hstack((num_features_names, cat_features_names))
             new_features_idx = np.array(range(new_features.shape[1]))

fedot/core/operations/evaluation/operation_implementations/models/boostings_implementations.py

@@ -13,15 +13,13 @@
 
 
 class FedotCatBoostImplementation(ModelImplementation):
-    __operation_params = ['use_eval_set']
+    __operation_params = ['use_eval_set', 'n_jobs']
 
     def __init__(self, params: Optional[OperationParameters] = None):
         super().__init__(params)
 
-        self.params.update(**self.params.to_dict())
-
         # TODO: Adding checking params compatibility with each other
-        # self.check_params(self.params.to_dict())
+        self.params.update(**self.check_and_update_params(self.params.to_dict()))
 
         self.model_params = {k: v for k, v in self.params.to_dict().items() if k not in self.__operation_params}
         self.model = None
@@ -30,15 +28,13 @@ def fit(self, input_data: InputData):
         input_data = input_data.get_not_encoded_data()
 
         if self.params.get('use_eval_set'):
+            # TODO: Using this method for tuning
             train_input, eval_input = train_test_data_setup(input_data)
 
             train_input = self.convert_to_pool(train_input)
             eval_input = self.convert_to_pool(eval_input)
 
-            self.model.fit(
-                X=train_input,
-                eval_set=eval_input,
-            )
+            self.model.fit(X=train_input, eval_set=eval_input)
 
         else:
             train_input = self.convert_to_pool(input_data)
@@ -53,7 +49,9 @@ def predict(self, input_data: InputData):
         return prediction
 
     @staticmethod
-    def check_params(params):
+    def check_and_update_params(params):
+        params['thread_count'] = params['n_jobs']
+
         if params['use_best_model'] or params['early_stopping_rounds'] and not params['use_eval_set']:
             params['use_best_model'] = False
             params['early_stopping_rounds'] = False

fedot/core/repository/data/default_operation_params.json

@@ -15,15 +15,17 @@
     "iterations": 1000,
     "use_eval_set": false,
     "use_best_model": false,
-    "early_stopping_rounds": 100
+    "early_stopping_rounds": null,
+    "n_jobs": 1
   },
   "catboostreg": {
     "allow_writing_files": false,
     "verbose": false,
     "iterations": 1000,
     "use_eval_set": false,
     "use_best_model": false,
-    "early_stopping_rounds": 100
+    "early_stopping_rounds": null,
+    "n_jobs": 1
   },
   "lgbm": {
     "num_leaves": 32,

test/integration/models/test_strategy.py

@@ -47,8 +47,8 @@ def test_boosting_classification_operation():
     )
 
     for model_name in model_names:
-        pipeline = PipelineBuilder().add_node(model_name).build()
-        pipeline.fit(train_data, n_jobs=-1)
+        pipeline = PipelineBuilder().add_node(model_name, params={'n_jobs': -1}).build()
+        pipeline.fit(train_data)
         predicted_output = pipeline.predict(test_data, output_mode='labels')
         metric = roc_auc(test_data.target, predicted_output.predict)