Pre-commits

tests/preprocessing/test_preprocessor_remapper.py

@@ -40,9 +40,7 @@ def input_remapper():
     statistics = {}
     name_to_index = {"x": 0, "y": 1, "z": 2, "q": 3, "d": 4, "other": 5}
     data_indices = IndexCollection(config=config, name_to_index=name_to_index)
-    return Remapper(
-        config=config.data.remapper, data_indices=data_indices, statistics=statistics
-    )
+    return Remapper(config=config.data.remapper, data_indices=data_indices, statistics=statistics)
 
 
 @pytest.fixture()
@@ -62,9 +60,7 @@ def input_remapper_1d():
     statistics = {}
     name_to_index = {"x": 0, "y": 1, "d": 2}
     data_indices = IndexCollection(config=config, name_to_index=name_to_index)
-    return Remapper(
-        config=config.data.remapper, data_indices=data_indices, statistics=statistics
-    )
+    return Remapper(config=config.data.remapper, data_indices=data_indices, statistics=statistics)
 
 
 @pytest.fixture()
@@ -92,28 +88,20 @@ def input_imputer():
     }
     name_to_index = {"x": 0, "y": 1, "z": 2, "q": 3, "d": 4, "other": 5}
     data_indices = IndexCollection(config=config, name_to_index=name_to_index)
-    return InputImputer(
-        config=config.data.imputer, data_indices=data_indices, statistics=statistics
-    )
+    return InputImputer(config=config.data.imputer, data_indices=data_indices, statistics=statistics)
 
 
 def test_remap_not_inplace(input_remapper) -> None:
-    x = torch.Tensor(
-        [[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]]
-    )
+    x = torch.Tensor([[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]])
     input_remapper(x, in_place=False)
     assert torch.allclose(
         x,
-        torch.Tensor(
-            [[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]]
-        ),
+        torch.Tensor([[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]]),
     )
 
 
 def test_remap(input_remapper) -> None:
-    x = torch.Tensor(
-        [[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]]
-    )
+    x = torch.Tensor([[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]])
     expected_output = torch.Tensor(
         [
             [1.0, 2.0, 3.0, 4.0, 5.0, -0.8660254, 0.5],
@@ -130,31 +118,21 @@ def test_inverse_transform(input_remapper) -> None:
             [6.0, 7.0, 8.0, 9.0, 10.0, -0.93358043, -0.35836795],
         ]
     )
-    expected_output = torch.Tensor(
-        [[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]]
-    )
+    expected_output = torch.Tensor([[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]])
     assert torch.allclose(input_remapper.inverse_transform(x), expected_output)
 
 
 def test_remap_inverse_transform(input_remapper) -> None:
-    x = torch.Tensor(
-        [[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]]
-    )
+    x = torch.Tensor([[1.0, 2.0, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, 201.0, 10.0]])
     assert torch.allclose(
-        input_remapper.inverse_transform(
-            input_remapper.transform(x, in_place=False), in_place=False
-        ),
+        input_remapper.inverse_transform(input_remapper.transform(x, in_place=False), in_place=False),
         x,
     )
 
 
 def test_transform_loss_mask(input_imputer, input_remapper) -> None:
-    x = torch.Tensor(
-        [[1.0, np.nan, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, np.nan, 10.0]]
-    )
-    expected_output = torch.Tensor(
-        [[1.0, 0.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 0.0, 0.0]]
-    )
+    x = torch.Tensor([[1.0, np.nan, 3.0, 4.0, 150.0, 5.0], [6.0, 7.0, 8.0, 9.0, np.nan, 10.0]])
+    expected_output = torch.Tensor([[1.0, 0.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 0.0, 0.0]])
     input_imputer.transform(x)
     input_remapper.transform(x)
     loss_mask_training = input_imputer.loss_mask_training
@@ -164,9 +142,7 @@ def test_transform_loss_mask(input_imputer, input_remapper) -> None:
 
 def test_remap_log1p(input_remapper_1d) -> None:
     x = torch.Tensor([[1.0, 2.0, 150.0], [6.0, 7.0, 201.0]])
-    expected_output = torch.Tensor(
-        [[1.0, 2.0, np.log1p(150.0)], [6.0, 7.0, np.log1p(201.0)]]
-    )
+    expected_output = torch.Tensor([[1.0, 2.0, np.log1p(150.0)], [6.0, 7.0, np.log1p(201.0)]])
     assert torch.allclose(input_remapper_1d.transform(x), expected_output)