Bump version: 0.2.0 → 0.3.0-dev0

Changes:
1. Change single-quotes to double-quotes.

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.2.0
+current_version = 0.3.0-dev0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

docs/source/api.rst

@@ -11,8 +11,26 @@ Datasets
 
 Functions
 --------------------------------------
-.. automodule:: prototorch.functions
+
+**Dimensions:**
+
+- :math:`B` ... Batch size
+- :math:`P` ... Number of prototypes
+- :math:`n_x` ... Data dimension for vectorial data
+- :math:`n_w` ... Data dimension for vectorial prototypes
+
+Activations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: prototorch.functions.activations
    :members:
+   :exclude-members: register_activation, get_activation
+   :undoc-members:
+
+Distances
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: prototorch.functions.distances
+   :members:
+   :exclude-members: sed
    :undoc-members:
 
 Modules

docs/source/conf.py

@@ -24,7 +24,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.2.0"
+release = "0.3.0-dev0"
 
 # -- General configuration ---------------------------------------------------
 

prototorch/__init__.py

@@ -1,11 +1,46 @@
 """ProtoTorch package."""
 
-__version__ = '0.2.0'
+# #############################################
+# Core Setup
+# #############################################
+__version__ = "0.3.0-dev0"
 
 from prototorch import datasets, functions, modules
 
-__all__ = [
-    'datasets',
-    'functions',
-    'modules',
+__all_core__ = [
+    "datasets",
+    "functions",
+    "modules",
 ]
+
+# #############################################
+# Plugin Loader
+# #############################################
+import pkgutil
+import pkg_resources
+
+__path__ = pkgutil.extend_path(__path__, __name__)
+
+
+def discover_plugins():
+    return {
+        entry_point.name: entry_point.load()
+        for entry_point in pkg_resources.iter_entry_points("prototorch.plugins")
+    }
+
+
+discovered_plugins = discover_plugins()
+locals().update(discovered_plugins)
+
+# Generate combines __version__ and __all__
+version_plugins = "\n".join(
+    [
+        "- " + name + ": v" + plugin.__version__
+        for name, plugin in discovered_plugins.items()
+    ]
+)
+if version_plugins != "":
+    version_plugins = "\nPlugins: \n" + version_plugins
+
+version = "core: v" + __version__ + version_plugins
+__all__ = __all_core__ + list(discovered_plugins.keys())

prototorch/datasets/abstract.py

@@ -14,6 +14,7 @@ import torch
 
 class Dataset(torch.utils.data.Dataset):
     """Abstract dataset class to be inherited."""
+
     _repr_indent = 2
 
     def __init__(self, root):
@@ -30,8 +31,9 @@ class Dataset(torch.utils.data.Dataset):
 
 class ProtoDataset(Dataset):
     """Abstract dataset class to be inherited."""
-    training_file = 'training.pt'
-    test_file = 'test.pt'
+
+    training_file = "training.pt"
+    test_file = "test.pt"
 
     def __init__(self, root, train=True, download=True, verbose=True):
         super().__init__(root)
@@ -39,43 +41,44 @@ class ProtoDataset(Dataset):
         self.verbose = verbose
 
         if download:
-            self.download()
+            self._download()
 
         if not self._check_exists():
-            raise RuntimeError('Dataset not found. '
-                               'You can use download=True to download it')
+            raise RuntimeError(
+                "Dataset not found. " "You can use download=True to download it"
+            )
 
         data_file = self.training_file if self.train else self.test_file
 
         self.data, self.targets = torch.load(
-            os.path.join(self.processed_folder, data_file))
+            os.path.join(self.processed_folder, data_file)
+        )
 
     @property
     def raw_folder(self):
-        return os.path.join(self.root, self.__class__.__name__, 'raw')
+        return os.path.join(self.root, self.__class__.__name__, "raw")
 
     @property
     def processed_folder(self):
-        return os.path.join(self.root, self.__class__.__name__, 'processed')
+        return os.path.join(self.root, self.__class__.__name__, "processed")
 
     @property
     def class_to_idx(self):
         return {_class: i for i, _class in enumerate(self.classes)}
 
     def _check_exists(self):
-        return (os.path.exists(
-            os.path.join(self.processed_folder, self.training_file))
-                and os.path.exists(
-                    os.path.join(self.processed_folder, self.test_file)))
+        return os.path.exists(
+            os.path.join(self.processed_folder, self.training_file)
+        ) and os.path.exists(os.path.join(self.processed_folder, self.test_file))
 
     def __repr__(self):
-        head = 'Dataset ' + self.__class__.__name__
-        body = ['Number of datapoints: {}'.format(self.__len__())]
+        head = "Dataset " + self.__class__.__name__
+        body = ["Number of datapoints: {}".format(self.__len__())]
         if self.root is not None:
-            body.append('Root location: {}'.format(self.root))
+            body.append("Root location: {}".format(self.root))
         body += self.extra_repr().splitlines()
-        lines = [head] + [' ' * self._repr_indent + line for line in body]
-        return '\n'.join(lines)
+        lines = [head] + [" " * self._repr_indent + line for line in body]
+        return "\n".join(lines)
 
     def extra_repr(self):
         return f"Split: {'Train' if self.train is True else 'Test'}"
@@ -83,5 +86,5 @@ class ProtoDataset(Dataset):
     def __len__(self):
         return len(self.data)
 
-    def download(self):
+    def _download(self):
         raise NotImplementedError

prototorch/datasets/tecator.py

@@ -46,42 +46,45 @@ from prototorch.datasets.abstract import ProtoDataset
 
 
 class Tecator(ProtoDataset):
-    """Tecator dataset for classification."""
-    resources = [
-        ('1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0',
-         'ba5607c580d0f91bb27dc29d13c2f8df'),
+    """
+    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__
+    for classification.
+    """
+
+    _resources = [
+        ("1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0", "ba5607c580d0f91bb27dc29d13c2f8df"),
     ]  # (google_storage_id, md5hash)
-    classes = ['0 - low_fat', '1 - high_fat']
+    classes = ["0 - low_fat", "1 - high_fat"]
 
     def __getitem__(self, index):
         img, target = self.data[index], int(self.targets[index])
         return img, target
 
-    def download(self):
+    def _download(self):
         """Download the data if it doesn't exist in already."""
         if self._check_exists():
             return
 
         if self.verbose:
-            print('Making directories...')
+            print("Making directories...")
         os.makedirs(self.raw_folder, exist_ok=True)
         os.makedirs(self.processed_folder, exist_ok=True)
 
         if self.verbose:
-            print('Downloading...')
-        for fileid, md5 in self.resources:
-            filename = 'tecator.npz'
-            download_file_from_google_drive(fileid,
-                                            root=self.raw_folder,
-                                            filename=filename,
-                                            md5=md5)
+            print("Downloading...")
+        for fileid, md5 in self._resources:
+            filename = "tecator.npz"
+            download_file_from_google_drive(
+                fileid, root=self.raw_folder, filename=filename, md5=md5
+            )
 
         if self.verbose:
-            print('Processing...')
-        with np.load(os.path.join(self.raw_folder, 'tecator.npz'),
-                     allow_pickle=False) as f:
-            x_train, y_train = f['x_train'], f['y_train']
-            x_test, y_test = f['x_test'], f['y_test']
+            print("Processing...")
+        with np.load(
+            os.path.join(self.raw_folder, "tecator.npz"), allow_pickle=False
+        ) as f:
+            x_train, y_train = f["x_train"], f["y_train"]
+            x_test, y_test = f["x_test"], f["y_test"]
         training_set = [
             torch.tensor(x_train, dtype=torch.float32),
             torch.tensor(y_train),
@@ -91,12 +94,10 @@ class Tecator(ProtoDataset):
             torch.tensor(y_test),
         ]
 
-        with open(os.path.join(self.processed_folder, self.training_file),
-                  'wb') as f:
+        with open(os.path.join(self.processed_folder, self.training_file), "wb") as f:
             torch.save(training_set, f)
-        with open(os.path.join(self.processed_folder, self.test_file),
-                  'wb') as f:
+        with open(os.path.join(self.processed_folder, self.test_file), "wb") as f:
             torch.save(test_set, f)
 
         if self.verbose:
-            print('Done!')
+            print("Done!")

prototorch/functions/distances.py

@@ -1,15 +1,24 @@
 """ProtoTorch distance functions."""
 
 import torch
-from prototorch.functions.helper import equal_int_shape, _int_and_mixed_shape, _check_shapes
+from prototorch.functions.helper import (
+    equal_int_shape,
+    _int_and_mixed_shape,
+    _check_shapes,
+)
 import numpy as np
 
 
 def squared_euclidean_distance(x, y):
-    """Compute the squared Euclidean distance between :math:`x` and :math:`y`.
+    r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm y`.
 
-    Expected dimension of x is 2.
-    Expected dimension of y is 2.
+    Compute :math:`{\langle \bm x - \bm y \rangle}_2`
+
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+
+    **Alias:**
+    ``prototorch.functions.distances.sed``
     """
     expanded_x = x.unsqueeze(dim=1)
     batchwise_difference = y - expanded_x
@@ -19,10 +28,15 @@ def squared_euclidean_distance(x, y):
 
 
 def euclidean_distance(x, y):
-    """Compute the Euclidean distance between :math:`x` and :math:`y`.
+    r"""Compute the Euclidean distance between :math:`x` and :math:`y`.
 
-    Expected dimension of x is 2.
-    Expected dimension of y is 2.
+    Compute :math:`\sqrt{{\langle \bm x - \bm y \rangle}_2}`
+
+    :param `torch.tensor` x: Input Tensor of shape :math:`X \times N`
+    :param `torch.tensor` y: Input Tensor of shape :math:`Y \times N`
+
+    :returns: Distance Tensor of shape :math:`X \times Y`
+    :rtype: `torch.tensor`
     """
     distances_raised = squared_euclidean_distance(x, y)
     distances = torch.sqrt(distances_raised)
@@ -30,10 +44,17 @@ def euclidean_distance(x, y):
 
 
 def lpnorm_distance(x, y, p):
-    r"""Compute :math:`{\langle x, y \rangle}_p`.
+    r"""
+    Calculates the lp-norm between :math:`\bm x` and :math:`\bm y`.
+    Also known as Minkowski distance.
 
-    Expected dimension of x is 2.
-    Expected dimension of y is 2.
+    Compute :math:`{\| \bm x - \bm y \|}_p`.
+
+    Calls ``torch.cdist``
+
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+    :param p: p parameter of the lp norm
     """
     distances = torch.cdist(x, y, p=p)
     return distances
@@ -42,11 +63,11 @@ def lpnorm_distance(x, y, p):
 def omega_distance(x, y, omega):
     r"""Omega distance.
 
-    Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
+    Compute :math:`{\| \Omega \bm x - \Omega \bm y \|}_p`
 
-    Expected dimension of x is 2.
-    Expected dimension of y is 2.
-    Expected dimension of omega is 2.
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+    :param `torch.tensor` omega: Two dimensional matrix
     """
     projected_x = x @ omega
     projected_y = y @ omega
@@ -57,11 +78,11 @@ def omega_distance(x, y, omega):
 def lomega_distance(x, y, omegas):
     r"""Localized Omega distance.
 
-    Compute :math:`{\langle \Omega_k x, \Omega_k y_k \rangle}_p`
+    Compute :math:`{\| \Omega_k \bm x - \Omega_k \bm y_k \|}_p`
 
-    Expected dimension of x is 2.
-    Expected dimension of y is 2.
-    Expected dimension of omegas is 3.
+    :param `torch.tensor` x: Two dimensional vector
+    :param `torch.tensor` y: Two dimensional vector
+    :param `torch.tensor` omegas: Three dimensional matrix
     """
     projected_x = x @ omegas
     projected_y = torch.diagonal(y @ omegas).T
@@ -74,31 +95,38 @@ def lomega_distance(x, y, omegas):
 
 
 def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
-    r""" Computes an euclidean distanes matrix given two distinct vectors.
+    r"""Computes an euclidean distances matrix given two distinct vectors.
     last dimension must be the vector dimension!
     compute the distance via the identity of the dot product. This avoids the memory overhead due to the subtraction!
 
-    x.shape = (number_of_x_vectors, vector_dim)
-    y.shape = (number_of_y_vectors, vector_dim)
+    - ``x.shape = (number_of_x_vectors, vector_dim)``
+    - ``y.shape = (number_of_y_vectors, vector_dim)``
 
     output: matrix of distances (number_of_x_vectors, number_of_y_vectors)
     """
     for tensor in [x, y]:
         if tensor.ndim != 2:
             raise ValueError(
-                'The tensor dimension must be two. You provide: tensor.ndim=' +
-                str(tensor.ndim) + '.')
+                "The tensor dimension must be two. You provide: tensor.ndim="
+                + str(tensor.ndim)
+                + "."
+            )
     if not equal_int_shape([tuple(x.shape)[1]], [tuple(y.shape)[1]]):
         raise ValueError(
-            'The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]='
-            + str(tuple(x.shape)[1]) + ' and  tuple(y.shape)(y)[1]=' +
-            str(tuple(y.shape)[1]) + '.')
+            "The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]="
+            + str(tuple(x.shape)[1])
+            + " and  tuple(y.shape)(y)[1]="
+            + str(tuple(y.shape)[1])
+            + "."
+        )
 
     y = torch.transpose(y)
 
-    diss = torch.sum(x**2, axis=1,
-                     keepdims=True) - 2 * torch.dot(x, y) + torch.sum(
-                         y**2, axis=0, keepdims=True)
+    diss = (
+        torch.sum(x ** 2, axis=1, keepdims=True)
+        - 2 * torch.dot(x, y)
+        + torch.sum(y ** 2, axis=0, keepdims=True)
+    )
 
     if not squared:
         if epsilon == 0:
@@ -111,12 +139,18 @@ def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
 
 def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
     r"""Tangent distances based on the tensorflow implementation of Sascha Saralajews
-    For more info about Tangen distances see DOI:10.1109/IJCNN.2016.7727534.
+
+    For more info about Tangen distances see
+
+    DOI:10.1109/IJCNN.2016.7727534.
+
     The subspaces is always assumed as transposed and must be orthogonal!
     For local non sparse signals subspaces must be provided!
-    shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
-    shape(protos): proto_number x dim1 x dim2 x ... x dimN
-    shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN)  x prod(projected_atom_shape)
+
+    - shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
+    - shape(protos): proto_number x dim1 x dim2 x ... x dimN
+    - shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN)  x prod(projected_atom_shape)
+
     subspace should be orthogonalized
     Pytorch implementation of Sascha Saralajew's tensorflow code.
     Translation by Christoph Raab
@@ -139,18 +173,19 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
         if subspaces.ndim == 2:
             # clean solution without map if the matrix_scope is global
             projectors = torch.eye(subspace_int_shape[-2]) - torch.dot(
-                subspaces, torch.transpose(subspaces))
+                subspaces, torch.transpose(subspaces)
+            )
 
             projected_signals = torch.dot(signals, projectors)
             projected_protos = torch.dot(protos, projectors)
 
-            diss = euclidean_distance_matrix(projected_signals,
-                                             projected_protos,
-                                             squared=squared,
-                                             epsilon=epsilon)
+            diss = euclidean_distance_matrix(
+                projected_signals, projected_protos, squared=squared, epsilon=epsilon
+            )
 
             diss = torch.reshape(
-                diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
+                diss, [signal_shape[0], signal_shape[2], proto_shape[0]]
+            )
 
             return torch.permute(diss, [0, 2, 1])
 
@@ -158,18 +193,21 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
 
             # no solution without map possible --> memory efficient but slow!
             projectors = torch.eye(subspace_int_shape[-2]) - torch.bmm(
-                subspaces,
-                subspaces)  #K.batch_dot(subspaces, subspaces, [2, 2])
+                subspaces, subspaces
+            )  # K.batch_dot(subspaces, subspaces, [2, 2])
 
-            projected_protos = (protos @ subspaces
+            projected_protos = (
+                protos @ subspaces
             ).T  # K.batch_dot(projectors, protos, [1, 1]))
 
             def projected_norm(projector):
                 return torch.sum(torch.dot(signals, projector) ** 2, axis=1)
 
-            diss = torch.transpose(map(projected_norm, projectors)) \
-                    - 2 * torch.dot(signals, projected_protos) \
+            diss = (
+                torch.transpose(map(projected_norm, projectors))
+                - 2 * torch.dot(signals, projected_protos)
                 + torch.sum(projected_protos ** 2, axis=0, keepdims=True)
+            )
 
             if not squared:
                 if epsilon == 0:
@@ -178,7 +216,8 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
                     diss = torch.sqrt(torch.max(diss, epsilon))
 
             diss = torch.reshape(
-                diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
+                diss, [signal_shape[0], signal_shape[2], proto_shape[0]]
+            )
 
             return torch.permute(diss, [0, 2, 1])
 
@@ -194,12 +233,13 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
 
             # Scope: Tangentspace Projections
             diff = torch.reshape(
-                diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
+                diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1)
+            )
             projected_diff = diff @ projectors
             projected_diff = torch.reshape(
                 projected_diff,
-                (signal_shape[0], signal_shape[2], signal_shape[1]) +
-                signal_shape[3:])
+                (signal_shape[0], signal_shape[2], signal_shape[1]) + signal_shape[3:],
+            )
 
             diss = torch.norm(projected_diff, 2, dim=-1)
             return diss.permute([0, 2, 1])
@@ -211,13 +251,14 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
 
             # Scope: Tangentspace Projections
             diff = torch.reshape(
-                diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
+                diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1)
+            )
             diff = diff.permute([1, 0, 2])
             projected_diff = torch.bmm(diff, projectors)
             projected_diff = torch.reshape(
                 projected_diff,
-                (signal_shape[1], signal_shape[0], signal_shape[2]) +
-                signal_shape[3:])
+                (signal_shape[1], signal_shape[0], signal_shape[2]) + signal_shape[3:],
+            )
 
             diss = torch.norm(projected_diff, 2, dim=-1)
             return diss.permute([1, 0, 2]).squeeze(-1)

prototorch/modules/prototypes.py

@@ -14,11 +14,11 @@ class _Prototypes(torch.nn.Module):
 
     def _validate_prototype_distribution(self):
         if 0 in self.prototype_distribution:
-            warnings.warn('Are you sure about the `0` in '
-                          '`prototype_distribution`?')
+            warnings.warn("Are you sure about the `0` in "
+                          "`prototype_distribution`?")
 
     def extra_repr(self):
-        return f'prototypes.shape: {tuple(self.prototypes.shape)}'
+        return f"prototypes.shape: {tuple(self.prototypes.shape)}"
 
     def forward(self):
         return self.prototypes, self.prototype_labels
@@ -31,7 +31,7 @@ class Prototypes1D(_Prototypes):
     """
     def __init__(self,
                  prototypes_per_class=1,
-                 prototype_initializer='ones',
+                 prototype_initializer="ones",
                  prototype_distribution=None,
                  data=None,
                  dtype=torch.float32,
@@ -44,25 +44,25 @@ class Prototypes1D(_Prototypes):
                 prototype_distribution = prototype_distribution.tolist()
 
         if data is None:
-            if 'input_dim' not in kwargs:
-                raise NameError('`input_dim` required if '
-                                'no `data` is provided.')
+            if "input_dim" not in kwargs:
+                raise NameError("`input_dim` required if "
+                                "no `data` is provided.")
             if prototype_distribution:
                 kwargs_nclasses = sum(prototype_distribution)
             else:
-                if 'nclasses' not in kwargs:
-                    raise NameError('`prototype_distribution` required if '
-                                    'both `data` and `nclasses` are not '
-                                    'provided.')
-                kwargs_nclasses = kwargs.pop('nclasses')
-            input_dim = kwargs.pop('input_dim')
+                if "nclasses" not in kwargs:
+                    raise NameError("`prototype_distribution` required if "
+                                    "both `data` and `nclasses` are not "
+                                    "provided.")
+                kwargs_nclasses = kwargs.pop("nclasses")
+            input_dim = kwargs.pop("input_dim")
             if prototype_initializer in [
-                    'stratified_mean', 'stratified_random'
+                    "stratified_mean", "stratified_random"
             ]:
                 warnings.warn(
-                    f'`prototype_initializer`: `{prototype_initializer}` '
-                    'requires `data`, but `data` is not provided. '
-                    'Using randomly generated data instead.')
+                    f"`prototype_initializer`: `{prototype_initializer}` "
+                    "requires `data`, but `data` is not provided. "
+                    "Using randomly generated data instead.")
             x_train = torch.rand(kwargs_nclasses, input_dim)
             y_train = torch.arange(kwargs_nclasses)
             if one_hot_labels:
@@ -75,39 +75,39 @@ class Prototypes1D(_Prototypes):
         nclasses = torch.unique(y_train, dim=-1).shape[-1]
 
         if nclasses == 1:
-            warnings.warn('Are you sure about having one class only?')
+            warnings.warn("Are you sure about having one class only?")
 
         if x_train.ndim != 2:
-            raise ValueError('`data[0].ndim != 2`.')
+            raise ValueError("`data[0].ndim != 2`.")
 
         if y_train.ndim == 2:
             if y_train.shape[1] == 1 and one_hot_labels:
-                raise ValueError('`one_hot_labels` is set to `True` '
-                                 'but target labels are not one-hot-encoded.')
+                raise ValueError("`one_hot_labels` is set to `True` "
+                                 "but target labels are not one-hot-encoded.")
             if y_train.shape[1] != 1 and not one_hot_labels:
-                raise ValueError('`one_hot_labels` is set to `False` '
-                                 'but target labels in `data` '
-                                 'are one-hot-encoded.')
+                raise ValueError("`one_hot_labels` is set to `False` "
+                                 "but target labels in `data` "
+                                 "are one-hot-encoded.")
         if y_train.ndim == 1 and one_hot_labels:
-            raise ValueError('`one_hot_labels` is set to `True` '
-                             'but target labels are not one-hot-encoded.')
+            raise ValueError("`one_hot_labels` is set to `True` "
+                             "but target labels are not one-hot-encoded.")
 
         # Verify input dimension if `input_dim` is provided
-        if 'input_dim' in kwargs:
-            input_dim = kwargs.pop('input_dim')
+        if "input_dim" in kwargs:
+            input_dim = kwargs.pop("input_dim")
             if input_dim != x_train.shape[1]:
-                raise ValueError(f'Provided `input_dim`={input_dim} does '
-                                 'not match data dimension '
-                                 f'`data[0].shape[1]`={x_train.shape[1]}')
+                raise ValueError(f"Provided `input_dim`={input_dim} does "
+                                 "not match data dimension "
+                                 f"`data[0].shape[1]`={x_train.shape[1]}")
 
         # Verify the number of classes if `nclasses` is provided
-        if 'nclasses' in kwargs:
-            kwargs_nclasses = kwargs.pop('nclasses')
+        if "nclasses" in kwargs:
+            kwargs_nclasses = kwargs.pop("nclasses")
             if kwargs_nclasses != nclasses:
-                raise ValueError(f'Provided `nclasses={kwargs_nclasses}` does '
-                                 'not match data labels '
-                                 '`torch.unique(data[1]).shape[0]`'
-                                 f'={nclasses}')
+                raise ValueError(f"Provided `nclasses={kwargs_nclasses}` does "
+                                 "not match data labels "
+                                 "`torch.unique(data[1]).shape[0]`"
+                                 f"={nclasses}")
 
         super().__init__(**kwargs)
 

setup.py

@@ -1,5 +1,13 @@
-"""Install ProtoTorch."""
+"""
+  _____           _     _______             _ 
+ |  __ \         | |   |__   __|           | |  
+ | |__) | __ ___ | |_ ___ | | ___  _ __ ___| |__
+ |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \ 
+ | |   | | | (_) | || (_) | | (_) | | | (__| | | |
+ |_|   |_|  \___/ \__\___/|_|\___/|_|  \___|_| |_|
 
+ProtoTorch Core Package
+"""
 from setuptools import setup
 from setuptools import find_packages
 
@@ -32,8 +40,9 @@ EXAMPLES = [
 TESTS = ["pytest"]
 ALL = DOCS + DATASETS + EXAMPLES + TESTS
 
-setup(name="prototorch",
-      version="0.2.0",
+setup(
+    name="prototorch",
+    version="0.3.0-dev0",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -68,4 +77,6 @@ setup(name="prototorch",
         "Topic :: Software Development :: Libraries",
         "Topic :: Software Development :: Libraries :: Python Modules",
     ],
-      packages=find_packages())
+    packages=find_packages(),
+    zip_safe=False,
+)