move seed setting out of the methods

cases/credit_scoring/credit_scoring_problem.py

@@ -9,6 +9,7 @@
 from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
 
 
 def calculate_validation_metric(pipeline: Pipeline, dataset_to_validate: InputData) -> float:
@@ -27,7 +28,6 @@ def run_credit_scoring_problem(train_file_path, test_file_path,
                                **composer_args):
     automl = Fedot(problem='classification',
                    timeout=timeout,
-                   seed=42,
                    preset=BEST_QUALITY_PRESET_NAME,
                    logging_level=logging.DEBUG,
                    **composer_args)
@@ -63,6 +63,8 @@ def get_scoring_data():
 
 
 if __name__ == '__main__':
+    set_random_seed(42)
+
     full_path_train, full_path_test = get_scoring_data()
     run_credit_scoring_problem(full_path_train,
                                full_path_test,

examples/advanced/additional_learning.py

@@ -9,10 +9,10 @@
 from fedot.core.pipelines.pipeline import Pipeline
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
 
 
 def run_additional_learning_example():
-    seed = 42
     train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
     test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
 
@@ -23,7 +23,7 @@ def run_additional_learning_example():
 
     problem = 'classification'
 
-    auto_model = Fedot(problem=problem, seed=seed, timeout=5, preset='best_quality',
+    auto_model = Fedot(problem=problem, timeout=5, preset='best_quality',
                        initial_assumption=PipelineBuilder().add_node('scaling').add_node('logit').build())
 
     auto_model.fit(features=deepcopy(train_data.head(1000)), target='target')
@@ -41,15 +41,15 @@ def run_additional_learning_example():
     train_data = train_data.head(5000)
     timeout = 1
 
-    auto_model_from_atomized = Fedot(problem=problem, seed=seed, preset='best_quality', timeout=timeout,
+    auto_model_from_atomized = Fedot(problem=problem, preset='best_quality', timeout=timeout,
                                      logging_level=logging.INFO,
                                      initial_assumption=atomized_model)
     auto_model_from_atomized.fit(features=deepcopy(train_data), target='target')
     auto_model_from_atomized.predict_proba(features=deepcopy(test_data))
     auto_model_from_atomized.current_pipeline.show()
     print('auto_model_from_atomized', auto_model_from_atomized.get_metrics(deepcopy(test_data_target)))
 
-    auto_model_from_pipeline = Fedot(problem=problem, seed=seed, preset='best_quality', timeout=timeout,
+    auto_model_from_pipeline = Fedot(problem=problem, preset='best_quality', timeout=timeout,
                                      logging_level=logging.INFO,
                                      initial_assumption=non_atomized_model)
     auto_model_from_pipeline.fit(features=deepcopy(train_data), target='target')
@@ -59,4 +59,6 @@ def run_additional_learning_example():
 
 
 if __name__ == '__main__':
+    set_random_seed(42)
+
     run_additional_learning_example()

examples/advanced/multimodal_text_num_example.py

@@ -2,6 +2,7 @@
 from fedot.core.data.data_split import train_test_data_setup
 from fedot.core.data.multi_modal import MultiModalData
 from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
 
 
 def run_multi_modal_example(file_path: str, visualization: bool = False, with_tuning: bool = True,
@@ -23,12 +24,11 @@ def run_multi_modal_example(file_path: str, visualization: bool = False, with_tu
         F1 metrics of the model.
     """
     task = 'classification'
-    seed = 42
     path = fedot_project_root().joinpath(file_path)
     data = MultiModalData.from_csv(file_path=path, task=task, target_columns='variety', index_col=None)
     fit_data, predict_data = train_test_data_setup(data, shuffle_flag=True, split_ratio=0.7)
 
-    automl_model = Fedot(problem=task, timeout=timeout, seed=seed, with_tuning=with_tuning, n_jobs=1)
+    automl_model = Fedot(problem=task, timeout=timeout, with_tuning=with_tuning, n_jobs=1)
     automl_model.fit(features=fit_data,
                      target=fit_data.target)
 
@@ -44,4 +44,6 @@ def run_multi_modal_example(file_path: str, visualization: bool = False, with_tu
 
 
 if __name__ == '__main__':
+    set_random_seed(42)
+
     run_multi_modal_example(file_path='examples/data/multimodal_wine.csv', visualization=True)

examples/advanced/multiobj_optimisation.py

@@ -2,6 +2,7 @@
 
 from fedot.api.main import Fedot
 from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
 
 
 def run_classification_multiobj_example(visualization=False, timeout=1, with_tuning=True):
@@ -12,7 +13,7 @@ def run_classification_multiobj_example(visualization=False, timeout=1, with_tun
     problem = 'classification'
 
     metric_names = ['f1', 'node_number']
-    auto_model = Fedot(problem=problem, timeout=timeout, preset='best_quality', seed=42,
+    auto_model = Fedot(problem=problem, timeout=timeout, preset='best_quality',
                        metric=metric_names,
                        with_tuning=with_tuning)
     auto_model.fit(features=train_data, target='class')
@@ -27,4 +28,6 @@ def run_classification_multiobj_example(visualization=False, timeout=1, with_tun
 
 
 if __name__ == '__main__':
+    set_random_seed(42)
+
     run_classification_multiobj_example(visualization=True)

examples/advanced/remote_execution/ts_composer_with_integration.py

@@ -1,6 +1,7 @@
 from fedot.api.main import Fedot
 from fedot.core.data.multi_modal import MultiModalData
 from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
 from fedot.remote.infrastructure.clients.test_client import TestClient
 from fedot.remote.remote_evaluator import RemoteEvaluator, RemoteTaskParams
 
@@ -60,14 +61,16 @@ def run_automl(data: MultiModalData, features_to_use,
                        'cv_folds': None,
                        'validation_blocks': None}
 
-    automl = Fedot(problem='ts_forecasting', seed=1, timeout=timeout, **composer_params)
+    automl = Fedot(problem='ts_forecasting', timeout=timeout, **composer_params)
 
     obtained_pipeline = automl.fit(data)
     forecast = automl.forecast(data)
     return forecast, obtained_pipeline
 
 
 if __name__ == '__main__':
+    set_random_seed(1)
+
     features_to_use = ['wind_speed', 'sea_height']
 
     data = MultiModalData.from_csv_time_series(

examples/simple/classification/api_classification.py

@@ -1,20 +1,20 @@
 from fedot.api.main import Fedot
 from fedot.core.utils import fedot_project_root
+from fedot.core.utils import set_random_seed
 
 
 def run_classification_example(timeout: float = None, visualization=False, with_tuning=True):
-    seed = 42
     problem = 'classification'
     train_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_train.csv'
     test_data_path = f'{fedot_project_root()}/cases/data/scoring/scoring_test.csv'
 
-    baseline_model = Fedot(problem=problem, timeout=timeout, seed=seed)
+    baseline_model = Fedot(problem=problem, timeout=timeout)
     baseline_model.fit(features=train_data_path, target='target', predefined_model='rf')
 
     baseline_model.predict(features=test_data_path)
     print(baseline_model.get_metrics())
 
-    auto_model = Fedot(problem=problem, seed=seed, timeout=timeout, n_jobs=-1, preset='best_quality',
+    auto_model = Fedot(problem=problem, timeout=timeout, n_jobs=-1, preset='best_quality',
                        max_pipeline_fit_time=5, metric='roc_auc', with_tuning=with_tuning)
     auto_model.fit(features=train_data_path, target='target')
     prediction = auto_model.predict_proba(features=test_data_path)
@@ -26,4 +26,6 @@ def run_classification_example(timeout: float = None, visualization=False, with_
 
 
 if __name__ == '__main__':
+    set_random_seed(42)
+
     run_classification_example(timeout=10.0, visualization=True)

test/unit/common_tests.py

@@ -3,9 +3,12 @@
 import numpy as np
 
 from fedot.core.data.data import InputData, OutputData
+from fedot.core.utils import set_random_seed
 
 
 def is_predict_ignores_target(predict_func, input_data, data_arg_name, predict_args=None):
+    set_random_seed(0)
+
     if predict_args is None:
         predict_args = {}