Update components

examples/new_components.py

@@ -1,8 +1,10 @@
-#
-# DATASET
-#
-import torch
+"""This example script shows the usage of the new components architecture.
 
+Serialization/deserialization also works as expected.
+"""
+
+# DATASET
+import torch
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
 
@@ -16,9 +18,7 @@ x_train = torch.Tensor(x_train)
 y_train = torch.Tensor(y_train)
 num_classes = len(torch.unique(y_train))
 
-#
 # CREATE NEW COMPONENTS
-#
 from prototorch.components import *
 from prototorch.components.initializers import *
 
@@ -33,9 +33,7 @@ components = ReasoningComponents(
     (3, 6), StratifiedSelectionInitializer(x_train, y_train))
 print(components())
 
-#
 # TEST SERIALIZATION
-#
 import io
 
 save = io.BytesIO()
@@ -53,8 +51,8 @@ serialized_prototypes = torch.load(save)
 
 assert torch.all(prototypes.components == serialized_prototypes.components
                  ), "Serialization of Components failed."
-assert torch.all(prototypes.labels == serialized_prototypes.labels
-                 ), "Serialization of Components failed."
+assert torch.all(prototypes.component_labels == serialized_prototypes.
+                 component_labels), "Serialization of Components failed."
 
 save = io.BytesIO()
 torch.save(components, save)

prototorch/components/__init__.py

@@ -1,7 +1,2 @@
-from prototorch.components.components import Components, LabeledComponents, ReasoningComponents
-
-__all__ = [
-    "Components",
-    "LabeledComponents",
-    "ReasoningComponents",
-]
+from prototorch.components.components import *
+from prototorch.components.initializers import *

prototorch/components/components.py

@@ -1,18 +1,18 @@
 """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 typing import Tuple
 
+import torch
+from prototorch.components.initializers import (ComponentsInitializer,
+                                                EqualLabelInitializer,
+                                                ZeroReasoningsInitializer)
 from prototorch.functions.initializers import get_initializer
+from torch.nn.parameter import Parameter
 
 
 class Components(torch.nn.Module):
-    """
-    Components is a set of learnable Tensors.
-    """
+    """Components is a set of learnable Tensors."""
     def __init__(self,
                  number_of_components=None,
                  initializer=None,
@@ -31,14 +31,16 @@ class Components(torch.nn.Module):
             self._initialize_components(number_of_components, initializer)
 
     def _initialize_components(self, number_of_components, initializer):
+        if not isinstance(initializer, ComponentsInitializer):
+            emsg = f"`initializer` has to be some kind of `ComponentsInitializer`. " \
+                f"You provided: {initializer=} instead."
+            raise TypeError(emsg)
         self._components = Parameter(
             initializer.generate(number_of_components))
 
     @property
     def components(self):
-        """
-        Tensor containing the component tensors.
-        """
+        """Tensor containing the component tensors."""
         return self._components.detach().cpu()
 
     def forward(self):
@@ -49,8 +51,8 @@ class Components(torch.nn.Module):
 
 
 class LabeledComponents(Components):
-    """
-    LabeledComponents generate a set of components and a set of labels.
+    """LabeledComponents generate a set of components and a set of labels.
+
     Every Component has a label assigned.
     """
     def __init__(self,
@@ -62,11 +64,11 @@ class LabeledComponents(Components):
             super().__init__(initialized_components=initialized_components[0])
             self._labels = initialized_components[1]
         else:
-            self._initialize_labels(labels, initializer)
+            self._initialize_labels(labels)
             super().__init__(number_of_components=len(self._labels),
                              initializer=initializer)
 
-    def _initialize_labels(self, labels, initializer):
+    def _initialize_labels(self, labels):
         if type(labels) == tuple:
             num_classes, prototypes_per_class = labels
             labels = EqualLabelInitializer(num_classes, prototypes_per_class)
@@ -74,10 +76,8 @@ class LabeledComponents(Components):
         self._labels = labels.generate()
 
     @property
-    def labels(self):
-        """
-        Tensor containing the component tensors.
-        """
+    def component_labels(self):
+        """Tensor containing the component tensors."""
         return self._labels.detach().cpu()
 
     def forward(self):
@@ -85,16 +85,19 @@ class LabeledComponents(Components):
 
 
 class ReasoningComponents(Components):
-    """
-    ReasoningComponents generate a set of components and a set of reasoning matrices.
+    """ReasoningComponents generate a set of components and a set of reasoning matrices.
+
     Every Component has a reasoning matrix assigned.
 
-    A reasoning matrix is a Nx2 matrix, where N is the number of Classes.
-    The first element is called positive reasoning :math:`p`, the second negative reasoning :math:`n`.
-    A components can reason in favour (positive) of a class, against (negative) a class or not at all (neutral).
+    A reasoning matrix is a Nx2 matrix, where N is the number of Classes. The
+    first element is called positive reasoning :math:`p`, the second negative
+    reasoning :math:`n`. A components can reason in favour (positive) of a
+    class, against (negative) a class or not at all (neutral).
+
+    It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0
+    \leq n+p \leq 1`. Therefore :math:`n` and :math:`p` are two elements of a
+    three element probability distribution.
 
-    It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0 \leq n+p \leq 1`.
-    Therefore :math:`n` and  :math:`p` are two elements of a three element probability distribution.
     """
     def __init__(self,
                  reasonings=None,
@@ -119,10 +122,10 @@ class ReasoningComponents(Components):
 
     @property
     def reasonings(self):
-        """
-        Returns Reasoning Matrix.
+        """Returns Reasoning Matrix.
 
         Dimension NxCx2
+
         """
         return self._reasonings.detach().cpu()