Automated autopep8 fixes

aimclub · Feb 26, 2025 · 24cc2e3 · 24cc2e3
1 parent e48fac8
commit 24cc2e3
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Showing 3 changed files with 29 additions and 27 deletions.
diff --git a/fedot_ind/core/operation/transformation/basis/eigen_basis.py b/fedot_ind/core/operation/transformation/basis/eigen_basis.py
@@ -147,16 +147,15 @@ def mode_func(x):
             svd_numbers = list(map(lambda dimension:
                                    [dimension_rank.append(self._transform_one_sample(signal, svd_flag=True))
                                     for signal in data[:, dimension, :]], number_of_dim))
-        
+
             to_comp = []
             for dim in number_of_dim:
                 dim_ranks = []
                 for sign in data[:, dim, :]:
                     r = self._transform_one_sample(sign, svd_flag=True)
                     dim_ranks.append(r.compute())
                 to_comp.append(dim_ranks)
-
-
+
         with threadpool_limits(limits=1, user_api='blas'):
             if not one_dim_predict:
                 list_of_ranks = np.array(to_comp).flatten().tolist()

diff --git a/tests/unit/core/models/model_impl/test_nbeats.py b/tests/unit/core/models/model_impl/test_nbeats.py
@@ -23,6 +23,8 @@ def ts_input_data():
     return train_test_data_setup(train_input, validation_blocks=None)
 
 # def test_nbeats_model(ts_input_data):
+
+
 def nbeats_model(ts_input_data):
     IndustrialModels().setup_repository()
     train, test = ts_input_data

diff --git a/tests/unit/models/test_linear_pipelines.py b/tests/unit/models/test_linear_pipelines.py
@@ -4,7 +4,6 @@
 from fedot_ind.core.architecture.pipelines.abstract_pipeline import AbstractPipeline
 from fedot_ind.core.repository.constanst_repository import VALID_LINEAR_REG_PIPELINE, VALID_LINEAR_CLF_PIPELINE, \
     VALID_LINEAR_DETECTION_PIPELINE, VALID_LINEAR_TSF_PIPELINE
-from tests.unit.api.fixtures import set_pytest_timeout_in_seconds
 
 
 class LinearPipelineCase:
@@ -20,21 +19,21 @@ def __init__(self, pipeline_label, node_list, data_list, task, task_params=None)
     def __str__(self) -> str:
         return self.pipeline_label
 
+
 def get_data():
     # random train data
     import numpy as np
     train_features = np.random.rand(100, 8)
     train_target = np.zeros(100)
     # train_target[50:60] = 1.0
-    
+
     test_features = np.random.rand(50, 8)
     test_target = np.zeros(50)
     test_target[30:40] = 1.0
 
     return dict(train_data=(train_features, train_target),
-                        test_data=(test_features, test_target)
-                        )
-
+                test_data=(test_features, test_target)
+                )
 
 
 LINEAR_REG_PIPELINE_CASES = [
@@ -43,11 +42,11 @@ def get_data():
         node_list=node_list,
         # data_list=['AppliancesEnergy'],
         data_list=[
-            dict(train_data = (np.random.rand(25, 100), np.random.rand(25)), 
-                 test_data = (np.random.rand(25, 100), np.random.rand(25))),
-                #    dict(train_data = (np.random.rand(25, 3, 100), np.random.rand(25)), 
-                        # test_data = (np.random.rand(25, 3, 100), np.random.rand(25)))
-                        ],
+            dict(train_data=(np.random.rand(25, 100), np.random.rand(25)),
+                 test_data=(np.random.rand(25, 100), np.random.rand(25))),
+            #    dict(train_data = (np.random.rand(25, 3, 100), np.random.rand(25)),
+            # test_data = (np.random.rand(25, 3, 100), np.random.rand(25)))
+        ],
         task='regression'
     ) for pipeline_label, node_list in VALID_LINEAR_REG_PIPELINE.items()
 ]
@@ -57,15 +56,15 @@ def get_data():
         pipeline_label=pipeline_label,
         node_list=node_list,
         data_list=[
-                   dict(train_data = (np.random.rand(25, 50), np.random.randint(0, 2, 25)), 
-                        test_data = (np.random.rand(25, 50), np.random.randint(0, 2, 25))),
-                    # dict(train_data = (np.random.rand(25, 3, 50), np.random.randint(0, 2, 25)), 
-                        # test_data = (np.random.rand(25, 3, 50), np.random.randint(0, 2, 25)))
-                        ],
+            dict(train_data=(np.random.rand(25, 50), np.random.randint(0, 2, 25)),
+                 test_data=(np.random.rand(25, 50), np.random.randint(0, 2, 25))),
+            # dict(train_data = (np.random.rand(25, 3, 50), np.random.randint(0, 2, 25)),
+            # test_data = (np.random.rand(25, 3, 50), np.random.randint(0, 2, 25)))
+        ],
         # data_list=[
-            # 'Earthquakes', 
-            # 'ERing'
-            # ],
+        # 'Earthquakes',
+        # 'ERing'
+        # ],
         task='classification'
     ) for pipeline_label, node_list in VALID_LINEAR_CLF_PIPELINE.items()
 ]
@@ -75,9 +74,9 @@ def get_data():
         pipeline_label=pipeline_label,
         node_list=node_list,
         # data_list=[dict(benchmark='M4', dataset='D2600', task_params={'forecast_length': 14})],
-        data_list=[dict(train_data = (np.random.rand(100), np.random.rand(100)), 
-                        test_data = (np.random.rand(14), np.random.rand(14)),
-                        task_params = {'forecast_length': 14})],
+        data_list=[dict(train_data=(np.random.rand(100), np.random.rand(100)),
+                        test_data=(np.random.rand(14), np.random.rand(14)),
+                        task_params={'forecast_length': 14})],
         task='ts_forecasting',
         task_params=dict(forecast_length=14)
     ) for pipeline_label, node_list in VALID_LINEAR_TSF_PIPELINE.items()
@@ -95,9 +94,9 @@ def get_data():
 ]
 
 # TODO: temporarily workaround skip topological_*
-BANNED_LINEAR_PIPELINE_LABELS = ['topological_clf', 
-                                 'topological_reg', 
-                                 'composite_reg', 
+BANNED_LINEAR_PIPELINE_LABELS = ['topological_clf',
+                                 'topological_reg',
+                                 'composite_reg',
                                  'topological_lgbm',
                                  'composite_clf',
                                  'stat_detector',
@@ -112,6 +111,8 @@ def get_data():
 
 # @set_pytest_timeout_in_seconds(300)
 # @pytest.mark.xfail()
+
+
 @pytest.mark.parametrize('pipeline_case', LINEAR_PIPELINE_CASES, ids=str)
 def test_valid_linear_pipelines(pipeline_case: LinearPipelineCase):
     if isinstance(pipeline_case.node_list, list):