flake8 fix

Author: dengdifan

autoPyTorch/pipeline/components/preprocessing/time_series_preprocessing/scaling/utils.py

@@ -135,7 +135,7 @@ def transform(self, X: Union[pd.DataFrame, np.ndarray]) -> Union[pd.DataFrame, n
                 X_abs = np.abs(X)
                 mean_abs_ = X_abs.mean(0, keepdims=True)
                 scale = np.where(mean_abs_ == 0.0, np.max(X_abs), mean_abs_)
-                scale[scale  < VERY_SMALL_VALUE] = 1
+                scale[scale < VERY_SMALL_VALUE] = 1
                 return X / scale
 
             elif self.mode == "none":

autoPyTorch/pipeline/components/setup/forecasting_target_scaling/utils.py

@@ -6,6 +6,7 @@
 
 from autoPyTorch.constants import VERY_SMALL_VALUE
 
+
 # Similar to / inspired by
 # https://github.com/tslearn-team/tslearn/blob/a3cf3bf/tslearn/preprocessing/preprocessing.py
 class TargetScaler(BaseEstimator):

test/test_pipeline/components/preprocessing/forecasting/test_scaling.py

@@ -38,13 +38,10 @@ def setUp(self) -> None:
                                    'numerical_columns': numerical_columns,
                                    'static_features': self.static_features,
                                    'is_small_preprocess': True}
-        very_small_values = np.array([[1e-10, 0., 1e-15],
-                                               [1e-10, 0., 1e-15]])
 
         self.small_data = pd.DataFrame(np.array([[1e-10, 0., 1e-15],
                                                 [-1e-10, 0., +1e-15]]), columns=columns, index=[0] * 2)
 
-
     def test_base_and_standard_scaler(self):
         scaler_component = BaseScaler(scaling_mode='standard')
         X = {

test/test_pipeline/components/setup/forecasting/test_forecasting_target_scaling.py

@@ -96,7 +96,7 @@ def test_target_mean_abs_scalar(self):
         self.assertIsNone(loc_full)
 
         _, _, _, scale = scalar(
-            torch.Tensor([[1e-10, 1e-10, 1e-10],[1e-15,1e-15, 1e-15]]).reshape([2, 3, 1])
+            torch.Tensor([[1e-10, 1e-10, 1e-10], [1e-15, 1e-15, 1e-15]]).reshape([2, 3, 1])
         )
         self.assertTrue(torch.equal(scale.flatten(), torch.Tensor([1e-10, 1.])))
 
@@ -184,7 +184,7 @@ def test_target_standard_scalar(self):
         self.assertTrue(torch.equal(scale, scale_full))
 
         _, _, _, scale = scalar(
-            torch.Tensor([[1e-10, -1e-10, 1e-10],[1e-15, -1e-15, 1e-15]]).reshape([2, 3, 1])
+            torch.Tensor([[1e-10, -1e-10, 1e-10], [1e-15, -1e-15, 1e-15]]).reshape([2, 3, 1])
         )
         self.assertTrue(torch.all(torch.isclose(scale.flatten(), torch.Tensor([1.1547e-10, 1.]))))
 
@@ -256,7 +256,7 @@ def test_target_min_max_scalar(self):
         self.assertTrue(torch.equal(scale, scale_full))
 
         _, _, _, scale = scalar(
-            torch.Tensor([[1e-10, 1e-10, 1e-10],[1e-15,1e-15, 1e-15]]).reshape([2, 3, 1])
+            torch.Tensor([[1e-10, 1e-10, 1e-10], [1e-15, 1e-15, 1e-15]]).reshape([2, 3, 1])
         )
         self.assertTrue(torch.equal(scale.flatten(), torch.Tensor([1e-10, 1.])))
 
@@ -326,6 +326,6 @@ def test_target_max_abs_scalar(self):
         self.assertTrue(torch.equal(scale, scale_full))
 
         _, _, _, scale = scalar(
-            torch.Tensor([[1e-10, 1e-10, 1e-10],[1e-15,1e-15, 1e-15]]).reshape([2, 3, 1])
+            torch.Tensor([[1e-10, 1e-10, 1e-10], [1e-15, 1e-15, 1e-15]]).reshape([2, 3, 1])
         )
         self.assertTrue(torch.equal(scale.flatten(), torch.Tensor([1e-10, 1.])))