Automatic formatting.

examples/new_components.py

@@ -2,7 +2,6 @@
 # DATASET
 #
 import torch
-
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 

prototorch/components/__init__.py

@@ -1,4 +1,8 @@
-from prototorch.components.components import Components, LabeledComponents, ReasoningComponents
+from prototorch.components.components import (
+    Components,
+    LabeledComponents,
+    ReasoningComponents,
+)
 
 __all__ = [
     "Components",

prototorch/components/components.py

@@ -1,24 +1,28 @@
 """ProtoTorch components modules."""
 
-from typing import Tuple
 import warnings
-from prototorch.components.initializers import EqualLabelInitializer, ZeroReasoningsInitializer
+
 import torch
 from torch.nn.parameter import Parameter
 
-from prototorch.functions.initializers import get_initializer
+from prototorch.components.initializers import (
+    EqualLabelInitializer,
+    ZeroReasoningsInitializer,
+)
 
 
 class Components(torch.nn.Module):
     """
     Components is a set of learnable Tensors.
     """
-    def __init__(self,
+    def __init__(
-                 number_of_components=None,
+        self,
-                 initializer=None,
+        number_of_components=None,
-                 *,
+        initializer=None,
-                 initialized_components=None,
+        *,
-                 dtype=torch.float32):
+        initialized_components=None,
+        dtype=torch.float32,
+    ):
         super().__init__()
 
         # Ignore all initialization settings if initialized_components is given.
@@ -127,4 +131,4 @@ class ReasoningComponents(Components):
         return self._reasonings.detach().cpu()
 
     def forward(self):
-        return super().forward(), self._reasonings
+        return super().forward(), self._reasonings

prototorch/components/initializers.py

@@ -1,6 +1,7 @@
-import torch
 from collections.abc import Iterable
 
+import torch
+
 
 # Components
 class ComponentsInitializer:

prototorch/functions/distances.py

@@ -3,8 +3,11 @@
 import numpy as np
 import torch
 
-from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
+from prototorch.functions.helper import (
-                                         equal_int_shape)
+    _check_shapes,
+    _int_and_mixed_shape,
+    equal_int_shape,
+)
 
 
 def squared_euclidean_distance(x, y):

prototorch/modules/models.py

@@ -1,8 +1,7 @@
 import torch
 from torch import nn
 
-from prototorch.functions.distances import (euclidean_distance_matrix,
+from prototorch.functions.distances import euclidean_distance_matrix, tangent_distance
-                                            tangent_distance)
 from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
 from prototorch.functions.normalization import orthogonalization
 from prototorch.modules.prototypes import Prototypes1D

tests/test_functions.py

@@ -5,8 +5,13 @@ import unittest
 import numpy as np
 import torch
 
-from prototorch.functions import (activations, competitions, distances,
+from prototorch.functions import (
-                                  initializers, losses)
+    activations,
+    competitions,
+    distances,
+    initializers,
+    losses,
+)
 
 
 class TestActivations(unittest.TestCase):