Bump version: 0.4.4 → 0.4.5

The component initializers behave differently based on the type of the
`distribution` argument. If it is a Python
[list](https://docs.python.org/3/tutorial/datastructures.html), it is assumed
that there are as many entries in this list as there are classes, and the number
at each location of this list describes the number of prototypes to be used for
that particular class. So, `[1, 1, 1]` implies that we have three classes with
one prototype per class. If it is a Python
[tuple](https://docs.python.org/3/tutorial/datastructures.html), it a shorthand
of `(num_classes, prototypes_per_class)` is assumed. If it is a Python
[dictionary](https://docs.python.org/3/tutorial/datastructures.html), the
key-value pairs describe the class label and the number of prototypes for that
class respectively. So, `{0: 2, 1: 2, 2: 2}` implies that we have three classes
with labels `{1, 2, 3}`, each equipped with two prototypes.

.bumpversion.cfg

@@ -1,20 +1,11 @@
 [bumpversion]
-current_version = 0.3.0-dev0
+current_version = 0.4.5
 commit = True
 tag = True
-parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
+parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
 serialize = 
-	{major}.{minor}.{patch}-{release}{build}
 	{major}.{minor}.{patch}
 
-[bumpversion:part:release]
-optional_value = prod
-first_value = dev
-values = 
-	dev
-	rc
-	prod
-
 [bumpversion:file:setup.py]
 
 [bumpversion:file:./prototorch/__init__.py]

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,31 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Install Prototorch by running '...'
+2. Run script '...'
+3. See errors
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. Ubuntu 20.10]
+ - Prototorch Version: [e.g. v0.4.0]
+ - Python Version: [e.g. 3.9.5]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.github/workflows/pythonapp.yml

@@ -23,10 +23,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install .
-    - name: Install extras
-      run: |
-        pip install -r requirements.txt
+        pip install .[all]
     - name: Lint with flake8
       run: |
         pip install flake8

.gitignore

@@ -154,4 +154,5 @@ scratch*
 # End of https://www.gitignore.io/api/visualstudiocode
 .vscode/
 
-reports
+reports
+artifacts

.travis.yml

@@ -4,11 +4,11 @@ language: python
 python: 3.8
 cache:
   directories:
+  - "$HOME/.cache/pip"
   - "./tests/artifacts"
-  # - "$HOME/.prototorch/datasets"
+  - "$HOME/datasets"
 install:
-- pip install . --progress-bar off
-- pip install -r requirements.txt
+- pip install .[all] --progress-bar off
 
 # Generate code coverage report
 script:
@@ -25,8 +25,8 @@ deploy:
   password:
     secure: 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
   on:
-  tags: true
-  skip_existing: true
+    tags: true
+    skip_existing: true
 
 # The password is encrypted with:
 # `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`

README.md

@@ -31,15 +31,15 @@ To also install the extras, use
 pip install -U prototorch[all]
 ```
 
-*Note: If you're using [ZSH](https://www.zsh.org/), the square brackets `[ ]`
-have to be escaped like so: `\[\]`, making the install command `pip install -U
-prototorch\[all\]`.*
+*Note: If you're using [ZSH](https://www.zsh.org/) (which is also the default
+shell on MacOS now), the square brackets `[ ]` have to be escaped like so:
+`\[\]`, making the install command `pip install -U prototorch\[all\]`.*
 
 To install the bleeding-edge features and improvements:
 ```bash
 git clone https://github.com/si-cim/prototorch.git
-git checkout dev
 cd prototorch
+git checkout dev
 pip install -e .[all]
 ```
 

docs/source/api.rst

@@ -1,13 +1,24 @@
-.. ProtoFlow API Reference
+.. ProtoTorch API Reference
 
-ProtoFlow API Reference
+ProtoTorch API Reference
 ======================================
 
 Datasets
 --------------------------------------
+
+Common Datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 .. automodule:: prototorch.datasets
    :members:
-   :undoc-members:
+
+
+Abstract Datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Abstract Datasets are used to build your own datasets.
+
+.. autoclass:: prototorch.datasets.abstract.NumpyDataset
+   :members:
 
 Functions
 --------------------------------------

docs/source/conf.py

@@ -12,9 +12,8 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath("../../"))
 
-import sphinx_rtd_theme
+sys.path.insert(0, os.path.abspath("../../"))
 
 # -- Project information -----------------------------------------------------
 
@@ -24,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.3.0-dev0"
+release = "0.4.5"
 
 # -- General configuration ---------------------------------------------------
 
@@ -47,6 +46,7 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinx_rtd_theme",
     "sphinxcontrib.katex",
+    'sphinx_autodoc_typehints',
 ]
 
 # katex_prerender = True
@@ -128,15 +128,12 @@ latex_elements = {
     # The paper size ("letterpaper" or "a4paper").
     #
     # "papersize": "letterpaper",
-
     # The font size ("10pt", "11pt" or "12pt").
     #
     # "pointsize": "10pt",
-
     # Additional stuff for the LaTeX preamble.
     #
     # "preamble": "",
-
     # Latex figure (float) alignment
     #
     # "figure_align": "htbp",
@@ -146,15 +143,21 @@ latex_elements = {
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, "prototorch.tex", "ProtoTorch Documentation",
-     "Jensun Ravichandran", "manual"),
+    (
+        master_doc,
+        "prototorch.tex",
+        "ProtoTorch Documentation",
+        "Jensun Ravichandran",
+        "manual",
+    ),
 ]
 
 # -- Options for manual page output ---------------------------------------
 
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author], 1)]
+man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author],
+              1)]
 
 # -- Options for Texinfo output -------------------------------------------
 
@@ -162,15 +165,24 @@ man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author], 1)
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, "prototorch", "ProtoTorch Documentation", author, "prototorch",
-     "Prototype-based machine learning in PyTorch.",
-     "Miscellaneous"),
+    (
+        master_doc,
+        "prototorch",
+        "ProtoTorch Documentation",
+        author,
+        "prototorch",
+        "Prototype-based machine learning in PyTorch.",
+        "Miscellaneous",
+    ),
 ]
 
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {
     "python": ("https://docs.python.org/", None),
     "numpy": ("https://docs.scipy.org/doc/numpy/", None),
+    "torch": ('https://pytorch.org/docs/stable/', None),
+    "pytorch_lightning":
+    ("https://pytorch-lightning.readthedocs.io/en/stable/", None),
 }
 
 # -- Options for Epub output ----------------------------------------------

examples/glvq_iris.py

@@ -27,9 +27,10 @@ class Model(torch.nn.Module):
         self.proto_layer = Prototypes1D(
             input_dim=2,
             prototypes_per_class=3,
-            nclasses=3,
+            num_classes=3,
             prototype_initializer="stratified_random",
-            data=[x_train, y_train])
+            data=[x_train, y_train],
+        )
 
     def forward(self, x):
         protos = self.proto_layer.prototypes
@@ -61,8 +62,10 @@ for epoch in range(70):
     with torch.no_grad():
         pred = wtac(dis, plabels)
         correct = pred.eq(y_in.view_as(pred)).sum().item()
-    acc = 100. * correct / len(x_train)
-    print(f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%")
+    acc = 100.0 * correct / len(x_train)
+    print(
+        f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%"
+    )
 
     # Take a gradient descent step
     optimizer.zero_grad()
@@ -83,13 +86,15 @@ for epoch in range(70):
     ax.set_ylabel("Data dimension 2")
     cmap = "viridis"
     ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-    ax.scatter(protos[:, 0],
-               protos[:, 1],
-               c=plabels,
-               cmap=cmap,
-               edgecolor="k",
-               marker="D",
-               s=50)
+    ax.scatter(
+        protos[:, 0],
+        protos[:, 1],
+        c=plabels,
+        cmap=cmap,
+        edgecolor="k",
+        marker="D",
+        s=50,
+    )
 
     # Paint decision regions
     x = np.vstack((x_train, protos))

examples/gmlvq_tecator.py

@@ -2,13 +2,12 @@
 
 import matplotlib.pyplot as plt
 import torch
-from torch.utils.data import DataLoader
-
 from prototorch.datasets.tecator import Tecator
 from prototorch.functions.distances import sed
 from prototorch.modules import Prototypes1D
 from prototorch.modules.losses import GLVQLoss
 from prototorch.utils.colors import get_legend_handles
+from torch.utils.data import DataLoader
 
 # Prepare the dataset and dataloader
 train_data = Tecator(root="./artifacts", train=True)
@@ -20,11 +19,13 @@ class Model(torch.nn.Module):
         """GMLVQ model as a siamese network."""
         super().__init__()
         x, y = train_data.data, train_data.targets
-        self.p1 = Prototypes1D(input_dim=100,
-                               prototypes_per_class=2,
-                               nclasses=2,
-                               prototype_initializer="stratified_random",
-                               data=[x, y])
+        self.p1 = Prototypes1D(
+            input_dim=100,
+            prototypes_per_class=2,
+            num_classes=2,
+            prototype_initializer="stratified_random",
+            data=[x, y],
+        )
         self.omega = torch.nn.Linear(in_features=100,
                                      out_features=100,
                                      bias=False)

examples/gtlvq_mnist.py

@@ -12,13 +12,13 @@ import numpy as np
 import torch
 import torch.nn as nn
 import torchvision
-from torchvision import transforms
-from prototorch.modules.losses import GLVQLoss
 from prototorch.functions.helper import calculate_prototype_accuracy
+from prototorch.modules.losses import GLVQLoss
 from prototorch.modules.models import GTLVQ
+from torchvision import transforms
 
 # Parameters and options
-n_epochs = 50
+num_epochs = 50
 batch_size_train = 64
 batch_size_test = 1000
 learning_rate = 0.1
@@ -26,32 +26,40 @@ momentum = 0.5
 log_interval = 10
 cuda = "cuda:1"
 random_seed = 1
-device = torch.device(cuda if torch.cuda.is_available() else 'cpu')
+device = torch.device(cuda if torch.cuda.is_available() else "cpu")
 
 # Configures reproducability
 torch.manual_seed(random_seed)
 np.random.seed(random_seed)
 
 # Prepare and preprocess the data
-train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(
-    './files/',
-    train=True,
-    download=True,
-    transform=torchvision.transforms.Compose(
-        [transforms.ToTensor(),
-         transforms.Normalize((0.1307, ), (0.3081, ))])),
-                                           batch_size=batch_size_train,
-                                           shuffle=True)
+train_loader = torch.utils.data.DataLoader(
+    torchvision.datasets.MNIST(
+        "./files/",
+        train=True,
+        download=True,
+        transform=torchvision.transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307, ), (0.3081, ))
+        ]),
+    ),
+    batch_size=batch_size_train,
+    shuffle=True,
+)
 
-test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(
-    './files/',
-    train=False,
-    download=True,
-    transform=torchvision.transforms.Compose(
-        [transforms.ToTensor(),
-         transforms.Normalize((0.1307, ), (0.3081, ))])),
-                                          batch_size=batch_size_test,
-                                          shuffle=True)
+test_loader = torch.utils.data.DataLoader(
+    torchvision.datasets.MNIST(
+        "./files/",
+        train=False,
+        download=True,
+        transform=torchvision.transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307, ), (0.3081, ))
+        ]),
+    ),
+    batch_size=batch_size_test,
+    shuffle=True,
+)
 
 
 # Define the GLVQ model plus appropriate feature extractor
@@ -67,25 +75,34 @@ class CNNGTLVQ(torch.nn.Module):
     ):
         super(CNNGTLVQ, self).__init__()
 
-        #Feature Extractor - Simple CNN
-        self.fe = nn.Sequential(nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
-                                nn.Conv2d(32, 64, 3, 1), nn.ReLU(),
-                                nn.MaxPool2d(2), nn.Dropout(0.25),
-                                nn.Flatten(), nn.Linear(9216, bottleneck_dim),
-                                nn.Dropout(0.5), nn.LeakyReLU(),
-                                nn.LayerNorm(bottleneck_dim))
+        # Feature Extractor - Simple CNN
+        self.fe = nn.Sequential(
+            nn.Conv2d(1, 32, 3, 1),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, 3, 1),
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Dropout(0.25),
+            nn.Flatten(),
+            nn.Linear(9216, bottleneck_dim),
+            nn.Dropout(0.5),
+            nn.LeakyReLU(),
+            nn.LayerNorm(bottleneck_dim),
+        )
 
         # Forward pass of subspace and prototype initialization data through feature extractor
         subspace_data = self.fe(subspace_data)
         prototype_data[0] = self.fe(prototype_data[0])
 
         # Initialization of GTLVQ
-        self.gtlvq = GTLVQ(num_classes,
-                           subspace_data,
-                           prototype_data,
-                           tangent_projection_type=tangent_projection_type,
-                           feature_dim=bottleneck_dim,
-                           prototypes_per_class=prototypes_per_class)
+        self.gtlvq = GTLVQ(
+            num_classes,
+            subspace_data,
+            prototype_data,
+            tangent_projection_type=tangent_projection_type,
+            feature_dim=bottleneck_dim,
+            prototypes_per_class=prototypes_per_class,
+        )
 
     def forward(self, x):
         # Feature Extraction
@@ -103,23 +120,27 @@ subspace_data = torch.cat(
 prototype_data = next(iter(train_loader))
 
 # Build the CNN GTLVQ  model
-model = CNNGTLVQ(10,
-                 subspace_data,
-                 prototype_data,
-                 tangent_projection_type="local",
-                 bottleneck_dim=128).to(device)
+model = CNNGTLVQ(
+    10,
+    subspace_data,
+    prototype_data,
+    tangent_projection_type="local",
+    bottleneck_dim=128,
+).to(device)
 
 # Optimize using SGD optimizer from `torch.optim`
-optimizer = torch.optim.Adam([{
-    'params': model.fe.parameters()
-}, {
-    'params': model.gtlvq.parameters()
-}],
-                             lr=learning_rate)
-criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
+optimizer = torch.optim.Adam(
+    [{
+        "params": model.fe.parameters()
+    }, {
+        "params": model.gtlvq.parameters()
+    }],
+    lr=learning_rate,
+)
+criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
 
 # Training loop
-for epoch in range(n_epochs):
+for epoch in range(num_epochs):
     for batch_idx, (x_train, y_train) in enumerate(train_loader):
         model.train()
         x_train, y_train = x_train.to(device), y_train.to(device)
@@ -139,8 +160,8 @@ for epoch in range(n_epochs):
         if batch_idx % log_interval == 0:
             acc = calculate_prototype_accuracy(distances, y_train, plabels)
             print(
-                f'Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
-              Train Acc: {acc.item():02.02f}')
+                f"Epoch: {epoch + 1:02d}/{num_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
+              Train Acc: {acc.item():02.02f}")
 
     # Test
     with torch.no_grad():
@@ -154,9 +175,9 @@ for epoch in range(n_epochs):
             i = torch.argmin(test_distances, 1)
             correct += torch.sum(y_test == test_plabels[i])
             total += y_test.size(0)
-        print('Accuracy of the network on the test images: %d %%' %
+        print("Accuracy of the network on the test images: %d %%" %
               (torch.true_divide(correct, total) * 100))
 
 # Save the model
-PATH = './glvq_mnist_model.pth'
+PATH = "./glvq_mnist_model.pth"
 torch.save(model.state_dict(), PATH)

examples/lgmlvq_iris.py

@@ -22,10 +22,12 @@ class Model(torch.nn.Module):
     def __init__(self):
         """Local-GMLVQ model."""
         super().__init__()
-        self.p1 = Prototypes1D(input_dim=2,
-                               prototype_distribution=[1, 2, 2],
-                               prototype_initializer="stratified_random",
-                               data=[x_train, y_train])
+        self.p1 = Prototypes1D(
+            input_dim=2,
+            prototype_distribution=[1, 2, 2],
+            prototype_initializer="stratified_random",
+            data=[x_train, y_train],
+        )
         omegas = torch.zeros(5, 2, 2)
         self.omegas = torch.nn.Parameter(omegas)
         eye_(self.omegas)
@@ -76,14 +78,16 @@ for epoch in range(100):
     ax.set_xlabel("Data dimension 1")
     ax.set_ylabel("Data dimension 2")
     cmap = "viridis"
-    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
-    ax.scatter(protos[:, 0],
-               protos[:, 1],
-               c=plabels,
-               cmap=cmap,
-               edgecolor='k',
-               marker='D',
-               s=50)
+    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
+    ax.scatter(
+        protos[:, 0],
+        protos[:, 1],
+        c=plabels,
+        cmap=cmap,
+        edgecolor="k",
+        marker="D",
+        s=50,
+    )
 
     # Paint decision regions
     x = np.vstack((x_train, protos))

examples/new_components.py

@@ -0,0 +1,65 @@
+"""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
+
+scaler = StandardScaler()
+x_train, y_train = load_iris(return_X_y=True)
+x_train = x_train[:, [0, 2]]
+scaler.fit(x_train)
+x_train = scaler.transform(x_train)
+
+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 *
+
+unsupervised = Components(6, SelectionInitializer(x_train))
+print(unsupervised())
+
+prototypes = LabeledComponents(
+    (3, 2), StratifiedSelectionInitializer(x_train, y_train))
+print(prototypes())
+
+components = ReasoningComponents(
+    (3, 6), StratifiedSelectionInitializer(x_train, y_train))
+print(components())
+
+# TEST SERIALIZATION
+import io
+
+save = io.BytesIO()
+torch.save(unsupervised, save)
+save.seek(0)
+serialized_unsupervised = torch.load(save)
+
+assert torch.all(unsupervised.components == serialized_unsupervised.components
+                 ), "Serialization of Components failed."
+
+save = io.BytesIO()
+torch.save(prototypes, save)
+save.seek(0)
+serialized_prototypes = torch.load(save)
+
+assert torch.all(prototypes.components == serialized_prototypes.components
+                 ), "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)
+save.seek(0)
+serialized_components = torch.load(save)
+
+assert torch.all(components.components == serialized_components.components
+                 ), "Serialization of Components failed."
+assert torch.all(components.reasonings == serialized_components.reasonings
+                 ), "Serialization of Components failed."

prototorch/__init__.py

@@ -1,31 +1,32 @@
 """ProtoTorch package."""
 
-# #############################################
-# Core Setup
-# #############################################
-__version__ = "0.3.0-dev0"
+import pkgutil
 
-from prototorch import datasets, functions, modules
+import pkg_resources
+
+from . import components, datasets, functions, modules, utils
+from .datasets import *
+
+# Core Setup
+__version__ = "0.4.5"
 
 __all_core__ = [
     "datasets",
     "functions",
     "modules",
+    "components",
+    "utils",
 ]
 
-# #############################################
 # 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")
+        for entry_point in pkg_resources.iter_entry_points(
+            "prototorch.plugins")
     }
 
 
@@ -33,14 +34,12 @@ 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()
-    ]
-)
+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())
+__all__ = __all_core__ + list(discovered_plugins.keys())

prototorch/components/__init__.py

@@ -0,0 +1,2 @@
+from prototorch.components.components import *
+from prototorch.components.initializers import *

prototorch/components/components.py

@@ -0,0 +1,161 @@
+"""ProtoTorch components modules."""
+
+import warnings
+
+import torch
+from prototorch.components.initializers import (ClassAwareInitializer,
+                                                ComponentsInitializer,
+                                                CustomLabelsInitializer,
+                                                EqualLabelsInitializer,
+                                                UnequalLabelsInitializer,
+                                                ZeroReasoningsInitializer)
+from torch.nn.parameter import Parameter
+
+
+class Components(torch.nn.Module):
+    """Components is a set of learnable Tensors."""
+    def __init__(self,
+                 num_components=None,
+                 initializer=None,
+                 *,
+                 initialized_components=None):
+        super().__init__()
+
+        self.num_components = num_components
+
+        # Ignore all initialization settings if initialized_components is given.
+        if initialized_components is not None:
+            self.register_parameter("_components",
+                                    Parameter(initialized_components))
+            if num_components is not None or initializer is not None:
+                wmsg = "Arguments ignored while initializing Components"
+                warnings.warn(wmsg)
+        else:
+            self._initialize_components(initializer)
+
+    def _precheck_initializer(self, initializer):
+        if not isinstance(initializer, ComponentsInitializer):
+            emsg = f"`initializer` has to be some subtype of " \
+                f"{ComponentsInitializer}. " \
+                f"You have provided: {initializer=} instead."
+            raise TypeError(emsg)
+
+    def _initialize_components(self, initializer):
+        self._precheck_initializer(initializer)
+        _components = initializer.generate(self.num_components)
+        self.register_parameter("_components", Parameter(_components))
+
+    @property
+    def components(self):
+        """Tensor containing the component tensors."""
+        return self._components.detach()
+
+    def forward(self):
+        return self._components
+
+    def extra_repr(self):
+        return f"components.shape: {tuple(self._components.shape)}"
+
+
+class LabeledComponents(Components):
+    """LabeledComponents generate a set of components and a set of labels.
+
+    Every Component has a label assigned.
+    """
+    def __init__(self,
+                 distribution=None,
+                 initializer=None,
+                 *,
+                 initialized_components=None):
+        if initialized_components is not None:
+            components, component_labels = initialized_components
+            super().__init__(initialized_components=components)
+            self._labels = component_labels
+        else:
+            _labels = self._initialize_labels(distribution)
+            super().__init__(len(_labels), initializer=initializer)
+            self.register_buffer("_labels", _labels)
+
+    def _initialize_components(self, initializer):
+        if isinstance(initializer, ClassAwareInitializer):
+            self._precheck_initializer(initializer)
+            _components = initializer.generate(self.num_components,
+                                               self.distribution)
+            self.register_parameter("_components", Parameter(_components))
+        else:
+            super()._initialize_components(initializer)
+
+    def _initialize_labels(self, distribution):
+        if type(distribution) == dict:
+            if "num_classes" in distribution.keys():
+                labels = EqualLabelsInitializer(
+                    distribution["num_classes"],
+                    distribution["prototypes_per_class"])
+            else:
+                labels = CustomLabelsInitializer(distribution)
+        elif type(distribution) == tuple:
+            num_classes, prototypes_per_class = distribution
+            labels = EqualLabelsInitializer(num_classes, prototypes_per_class)
+        elif type(distribution) == list:
+            labels = UnequalLabelsInitializer(distribution)
+
+        self.distribution = labels.distribution
+        return labels.generate()
+
+    @property
+    def component_labels(self):
+        """Tensor containing the component tensors."""
+        return self._labels.detach()
+
+    def forward(self):
+        return super().forward(), self._labels
+
+
+class ReasoningComponents(Components):
+    """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).
+
+    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,
+                 initializer=None,
+                 *,
+                 initialized_components=None):
+        if initialized_components is not None:
+            components, reasonings = initialized_components
+
+            super().__init__(initialized_components=components)
+            self.register_parameter("_reasonings", reasonings)
+        else:
+            self._initialize_reasonings(reasonings)
+            super().__init__(len(self._reasonings), initializer=initializer)
+
+    def _initialize_reasonings(self, reasonings):
+        if type(reasonings) == tuple:
+            num_classes, num_components = reasonings
+            reasonings = ZeroReasoningsInitializer(num_classes, num_components)
+
+        _reasonings = reasonings.generate()
+        self.register_parameter("_reasonings", _reasonings)
+
+    @property
+    def reasonings(self):
+        """Returns Reasoning Matrix.
+
+        Dimension NxCx2
+
+        """
+        return self._reasonings.detach()
+
+    def forward(self):
+        return super().forward(), self._reasonings

prototorch/components/initializers.py

@@ -0,0 +1,228 @@
+"""ProtoTroch Initializers."""
+import warnings
+from collections.abc import Iterable
+from itertools import chain
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+
+def parse_data_arg(data_arg):
+    if isinstance(data_arg, Dataset):
+        data_arg = DataLoader(data_arg, batch_size=len(data_arg))
+
+    if isinstance(data_arg, DataLoader):
+        data = torch.tensor([])
+        labels = torch.tensor([])
+        for x, y in data_arg:
+            data = torch.cat([data, x])
+            labels = torch.cat([labels, y])
+    else:
+        data, labels = data_arg
+        if not isinstance(data, torch.Tensor):
+            wmsg = f"Converting data to {torch.Tensor}."
+            warnings.warn(wmsg)
+            data = torch.Tensor(data)
+        if not isinstance(labels, torch.Tensor):
+            wmsg = f"Converting labels to {torch.Tensor}."
+            warnings.warn(wmsg)
+            labels = torch.Tensor(labels)
+    return data, labels
+
+
+# Components
+class ComponentsInitializer(object):
+    def generate(self, number_of_components):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class DimensionAwareInitializer(ComponentsInitializer):
+    def __init__(self, c_dims):
+        super().__init__()
+        if isinstance(c_dims, Iterable):
+            self.components_dims = tuple(c_dims)
+        else:
+            self.components_dims = (c_dims, )
+
+
+class OnesInitializer(DimensionAwareInitializer):
+    def generate(self, length):
+        gen_dims = (length, ) + self.components_dims
+        return torch.ones(gen_dims)
+
+
+class ZerosInitializer(DimensionAwareInitializer):
+    def generate(self, length):
+        gen_dims = (length, ) + self.components_dims
+        return torch.zeros(gen_dims)
+
+
+class UniformInitializer(DimensionAwareInitializer):
+    def __init__(self, c_dims, min=0.0, max=1.0):
+        super().__init__(c_dims)
+
+        self.min = min
+        self.max = max
+
+    def generate(self, length):
+        gen_dims = (length, ) + self.components_dims
+        return torch.ones(gen_dims).uniform_(self.min, self.max)
+
+
+class DataAwareInitializer(ComponentsInitializer):
+    def __init__(self, data):
+        super().__init__()
+        self.data = data
+
+
+class SelectionInitializer(DataAwareInitializer):
+    def generate(self, length):
+        indices = torch.LongTensor(length).random_(0, len(self.data))
+        return self.data[indices]
+
+
+class MeanInitializer(DataAwareInitializer):
+    def generate(self, length):
+        mean = torch.mean(self.data, dim=0)
+        repeat_dim = [length] + [1] * len(mean.shape)
+        return mean.repeat(repeat_dim)
+
+
+class ClassAwareInitializer(ComponentsInitializer):
+    def __init__(self, data, transform=torch.nn.Identity()):
+        super().__init__()
+        data, labels = parse_data_arg(data)
+        self.data = data
+        self.labels = labels
+
+        self.transform = transform
+
+        self.clabels = torch.unique(self.labels)
+        self.num_classes = len(self.clabels)
+
+    def _get_samples_from_initializer(self, length, dist):
+        if not dist:
+            per_class = length // self.num_classes
+            dist = self.num_classes * [per_class]
+        if type(dist) == dict:
+            dist = dist.values()
+        samples_list = [
+            init.generate(n) for init, n in zip(self.initializers, dist)
+        ]
+        out = torch.vstack(samples_list)
+        with torch.no_grad():
+            out = self.transform(out)
+        return out
+
+    def __del__(self):
+        del self.data
+        del self.labels
+
+
+class StratifiedMeanInitializer(ClassAwareInitializer):
+    def __init__(self, data, **kwargs):
+        super().__init__(data, **kwargs)
+
+        self.initializers = []
+        for clabel in self.clabels:
+            class_data = self.data[self.labels == clabel]
+            class_initializer = MeanInitializer(class_data)
+            self.initializers.append(class_initializer)
+
+    def generate(self, length, dist=[]):
+        samples = self._get_samples_from_initializer(length, dist)
+        return samples
+
+
+class StratifiedSelectionInitializer(ClassAwareInitializer):
+    def __init__(self, data, noise=None, **kwargs):
+        super().__init__(data, **kwargs)
+        self.noise = noise
+
+        self.initializers = []
+        for clabel in self.clabels:
+            class_data = self.data[self.labels == clabel]
+            class_initializer = SelectionInitializer(class_data)
+            self.initializers.append(class_initializer)
+
+    def add_noise_v1(self, x):
+        return x + self.noise
+
+    def add_noise_v2(self, x):
+        """Shifts some dimensions of the data randomly."""
+        n1 = torch.rand_like(x)
+        n2 = torch.rand_like(x)
+        mask = torch.bernoulli(n1) - torch.bernoulli(n2)
+        return x + (self.noise * mask)
+
+    def generate(self, length, dist=[]):
+        samples = self._get_samples_from_initializer(length, dist)
+        if self.noise is not None:
+            samples = self.add_noise_v1(samples)
+        return samples
+
+
+# Labels
+class LabelsInitializer:
+    def generate(self):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class UnequalLabelsInitializer(LabelsInitializer):
+    def __init__(self, dist):
+        self.dist = dist
+
+    @property
+    def distribution(self):
+        return self.dist
+
+    def generate(self, clabels=None, dist=None):
+        if not clabels:
+            clabels = range(len(self.dist))
+        if not dist:
+            dist = self.dist
+        labels = list(chain(*[[i] * n for i, n in zip(clabels, dist)]))
+        return torch.LongTensor(labels)
+
+
+class EqualLabelsInitializer(LabelsInitializer):
+    def __init__(self, classes, per_class):
+        self.classes = classes
+        self.per_class = per_class
+
+    @property
+    def distribution(self):
+        return self.classes * [self.per_class]
+
+    def generate(self):
+        return torch.arange(self.classes).repeat(self.per_class, 1).T.flatten()
+
+
+class CustomLabelsInitializer(UnequalLabelsInitializer):
+    def generate(self):
+        clabels = list(self.dist.keys())
+        dist = list(self.dist.values())
+        return super().generate(clabels, dist)
+
+
+# Reasonings
+class ReasoningsInitializer:
+    def generate(self, length):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class ZeroReasoningsInitializer(ReasoningsInitializer):
+    def __init__(self, classes, length):
+        self.classes = classes
+        self.length = length
+
+    def generate(self):
+        return torch.zeros((self.length, self.classes, 2))
+
+
+# Aliases
+SSI = StratifiedSampleInitializer = StratifiedSelectionInitializer
+SMI = StratifiedMeanInitializer
+Random = RandomInitializer = UniformInitializer
+Zeros = ZerosInitializer
+Ones = OnesInitializer

prototorch/datasets/__init__.py

@@ -1,7 +1,8 @@
 """ProtoTorch datasets."""
 
+from .abstract import NumpyDataset
+from .iris import Iris
+from .spiral import Spiral
 from .tecator import Tecator
 
-__all__ = [
-    'Tecator',
-]
+__all__ = ['Iris', 'Spiral', 'Tecator']

prototorch/datasets/abstract.py

@@ -12,6 +12,15 @@ import os
 import torch
 
 
+class NumpyDataset(torch.utils.data.TensorDataset):
+    """Create a PyTorch TensorDataset from NumPy arrays."""
+    def __init__(self, data, targets):
+        self.data = data
+        self.targets = targets
+        tensors = [torch.Tensor(data), torch.Tensor(targets)]
+        super().__init__(*tensors)
+
+
 class Dataset(torch.utils.data.Dataset):
     """Abstract dataset class to be inherited."""
 
@@ -44,15 +53,13 @@ class ProtoDataset(Dataset):
             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):
@@ -68,8 +75,9 @@ class ProtoDataset(Dataset):
 
     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))
+            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__

prototorch/datasets/iris.py

@@ -0,0 +1,40 @@
+"""Thin wrapper for the Iris classification dataset from sklearn.
+
+URL:
+    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
+
+"""
+
+from typing import Sequence
+
+from prototorch.datasets.abstract import NumpyDataset
+from sklearn.datasets import load_iris
+
+
+class Iris(NumpyDataset):
+    """
+    Iris Dataset by Ronald Fisher introduced in 1936.
+
+    The dataset contains four measurements from flowers of three species of iris.
+
+    .. list-table:: Iris
+        :header-rows: 1
+
+        * - dimensions
+          - classes
+          - training size
+          - validation size
+          - test size
+        * - 4
+          - 3
+          - 150
+          - 0
+          - 0
+
+    :param dims: select a subset of dimensions
+    """
+    def __init__(self, dims: Sequence[int] = None):
+        x, y = load_iris(return_X_y=True)
+        if dims:
+            x = x[:, dims]
+        super().__init__(x, y)

prototorch/datasets/spiral.py

@@ -0,0 +1,57 @@
+"""Spiral dataset for binary classification."""
+
+import numpy as np
+import torch
+
+
+def make_spiral(num_samples=500, noise=0.3):
+    """Generates the Spiral Dataset.
+    
+    For use in Prototorch use `prototorch.datasets.Spiral` instead.
+    """
+    def get_samples(n, delta_t):
+        points = []
+        for i in range(n):
+            r = i / num_samples * 5
+            t = 1.75 * i / n * 2 * np.pi + delta_t
+            x = r * np.sin(t) + np.random.rand(1) * noise
+            y = r * np.cos(t) + np.random.rand(1) * noise
+            points.append([x, y])
+        return points
+
+    n = num_samples // 2
+    positive = get_samples(n=n, delta_t=0)
+    negative = get_samples(n=n, delta_t=np.pi)
+    x = np.concatenate(
+        [np.array(positive).reshape(n, -1),
+         np.array(negative).reshape(n, -1)],
+        axis=0)
+    y = np.concatenate([np.zeros(n), np.ones(n)])
+    return x, y
+
+
+class Spiral(torch.utils.data.TensorDataset):
+    """Spiral dataset for binary classification.
+
+    This datasets consists of two spirals of two different classes.
+
+    .. list-table:: Spiral
+        :header-rows: 1
+
+        * - dimensions
+          - classes
+          - training size
+          - validation size
+          - test size
+        * - 2
+          - 2
+          - num_samples
+          - 0
+          - 0
+
+    :param num_samples: number of random samples
+    :param noise: noise added to the spirals
+    """
+    def __init__(self, num_samples: int = 500, noise: float = 0.3):
+        x, y = make_spiral(num_samples, noise)
+        super().__init__(torch.Tensor(x), torch.LongTensor(y))

prototorch/datasets/tecator.py

@@ -40,19 +40,34 @@ import os
 
 import numpy as np
 import torch
-from torchvision.datasets.utils import download_file_from_google_drive
-
 from prototorch.datasets.abstract import ProtoDataset
+from torchvision.datasets.utils import download_file_from_google_drive
 
 
 class Tecator(ProtoDataset):
     """
-    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__
-    for classification.
+    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__ for classification.
+
+    The dataset contains wavelength measurements of meat.
+
+    .. list-table:: Tecator
+        :header-rows: 1
+
+        * - dimensions
+          - classes
+          - training size
+          - validation size
+          - test size
+        * - 100
+          - 2
+          - 129
+          - 43
+          - 43
     """
 
     _resources = [
-        ("1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0", "ba5607c580d0f91bb27dc29d13c2f8df"),
+        ("1P9WIYnyxFPh6f1vqAbnKfK8oYmUgyV83",
+         "ba5607c580d0f91bb27dc29d13c2f8df"),
     ]  # (google_storage_id, md5hash)
     classes = ["0 - low_fat", "1 - high_fat"]
 
@@ -74,15 +89,15 @@ class Tecator(ProtoDataset):
             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
-            )
+            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:
+        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 = [
@@ -94,9 +109,11 @@ 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:

prototorch/functions/__init__.py

@@ -4,9 +4,9 @@ from .activations import identity, sigmoid_beta, swish_beta
 from .competitions import knnc, wtac
 
 __all__ = [
-    'identity',
-    'sigmoid_beta',
-    'swish_beta',
-    'knnc',
-    'wtac',
+    "identity",
+    "sigmoid_beta",
+    "swish_beta",
+    "knnc",
+    "wtac",
 ]

prototorch/functions/activations.py

@@ -16,40 +16,43 @@ def register_activation(function):
 
 @register_activation
 # @torch.jit.script
-def identity(x, beta=torch.tensor(0)):
+def identity(x, beta=0.0):
     """Identity activation function.
 
     Definition:
     :math:`f(x) = x`
+
+    Keyword Arguments:
+        beta (`float`): Ignored.
     """
     return x
 
 
 @register_activation
 # @torch.jit.script
-def sigmoid_beta(x, beta=torch.tensor(10)):
+def sigmoid_beta(x, beta=10.0):
     r"""Sigmoid activation function with scaling.
 
     Definition:
     :math:`f(x) = \frac{1}{1 + e^{-\beta x}}`
 
     Keyword Arguments:
-        beta (`torch.tensor`): Scaling parameter :math:`\beta`
+        beta (`float`): Scaling parameter :math:`\beta`
     """
-    out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x))
+    out = 1.0 / (1.0 + torch.exp(-1.0 * beta * x))
     return out
 
 
 @register_activation
 # @torch.jit.script
-def swish_beta(x, beta=torch.tensor(10)):
+def swish_beta(x, beta=10.0):
     r"""Swish activation function with scaling.
 
     Definition:
     :math:`f(x) = \frac{x}{1 + e^{-\beta x}}`
 
     Keyword Arguments:
-        beta (`torch.tensor`): Scaling parameter :math:`\beta`
+        beta (`float`): Scaling parameter :math:`\beta`
     """
     out = x * sigmoid_beta(x, beta=beta)
     return out
@@ -61,4 +64,4 @@ def get_activation(funcname):
         return funcname
     if funcname in ACTIVATIONS:
         return ACTIVATIONS.get(funcname)
-    raise NameError(f'Activation {funcname} was not found.')
+    raise NameError(f"Activation {funcname} was not found.")

prototorch/functions/competitions.py

@@ -3,23 +3,22 @@
 import torch
 
 
-# @torch.jit.script
 def stratified_min(distances, labels):
     clabels = torch.unique(labels, dim=0)
-    nclasses = clabels.size()[0]
-    if distances.size()[1] == nclasses:
+    num_classes = clabels.size()[0]
+    if distances.size()[1] == num_classes:
         # skip if only one prototype per class
         return distances
     batch_size = distances.size()[0]
-    winning_distances = torch.zeros(nclasses, batch_size)
-    inf = torch.full_like(distances.T, fill_value=float('inf'))
+    winning_distances = torch.zeros(num_classes, batch_size)
+    inf = torch.full_like(distances.T, fill_value=float("inf"))
     # distances_to_wpluses = torch.where(matcher, distances, inf)
     for i, cl in enumerate(clabels):
         # cdists = distances.T[labels == cl]
         matcher = torch.eq(labels.unsqueeze(dim=1), cl)
         if labels.ndim == 2:
             # if the labels are one-hot vectors
-            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+            matcher = torch.eq(torch.sum(matcher, dim=-1), num_classes)
         cdists = torch.where(matcher, distances.T, inf).T
         winning_distances[i] = torch.min(cdists, dim=1,
                                          keepdim=True).values.squeeze()
@@ -31,15 +30,15 @@ def stratified_min(distances, labels):
     return winning_distances.T  # return with `batch_size` first
 
 
-# @torch.jit.script
 def wtac(distances, labels):
     winning_indices = torch.min(distances, dim=1).indices
     winning_labels = labels[winning_indices].squeeze()
     return winning_labels
 
 
-# @torch.jit.script
-def knnc(distances, labels, k):
-    winning_indices = torch.topk(-distances, k=k.item(), dim=1).indices
-    winning_labels = labels[winning_indices].squeeze()
+def knnc(distances, labels, k=1):
+    winning_indices = torch.topk(-distances, k=k, dim=1).indices
+    # winning_labels = torch.mode(labels[winning_indices].squeeze(),
+    #                             dim=1).values
+    winning_labels = torch.mode(labels[winning_indices], dim=1).values
     return winning_labels

prototorch/functions/distances.py

@@ -1,12 +1,9 @@
 """ProtoTorch distance functions."""
 
-import torch
-from prototorch.functions.helper import (
-    equal_int_shape,
-    _int_and_mixed_shape,
-    _check_shapes,
-)
 import numpy as np
+import torch
+from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
+                                         equal_int_shape, get_flat)
 
 
 def squared_euclidean_distance(x, y):
@@ -14,12 +11,10 @@ def squared_euclidean_distance(x, y):
 
     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``
     """
+    x, y = get_flat(x, y)
     expanded_x = x.unsqueeze(dim=1)
     batchwise_difference = y - expanded_x
     differences_raised = torch.pow(batchwise_difference, 2)
@@ -32,30 +27,40 @@ def euclidean_distance(x, y):
 
     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`
     """
+    x, y = get_flat(x, y)
     distances_raised = squared_euclidean_distance(x, y)
     distances = torch.sqrt(distances_raised)
     return distances
 
 
+def euclidean_distance_v2(x, y):
+    x, y = get_flat(x, y)
+    diff = y - x.unsqueeze(1)
+    pairwise_distances = (diff @ diff.permute((0, 2, 1))).sqrt()
+    # Passing `dim1=-2` and `dim2=-1` to `diagonal()` takes the
+    # batch diagonal. See:
+    # https://pytorch.org/docs/stable/generated/torch.diagonal.html
+    distances = torch.diagonal(pairwise_distances, dim1=-2, dim2=-1)
+    # print(f"{diff.shape=}")  # (nx, ny, ndim)
+    # print(f"{pairwise_distances.shape=}")  # (nx, ny, ny)
+    # print(f"{distances.shape=}")  # (nx, ny)
+    return distances
+
+
 def lpnorm_distance(x, y, p):
-    r"""
-    Calculates the lp-norm between :math:`\bm x` and :math:`\bm y`.
+    r"""Calculate the lp-norm between :math:`\bm x` and :math:`\bm y`.
     Also known as Minkowski distance.
 
     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
     """
+    x, y = get_flat(x, y)
     distances = torch.cdist(x, y, p=p)
     return distances
 
@@ -65,10 +70,9 @@ def omega_distance(x, y, omega):
 
     Compute :math:`{\| \Omega \bm x - \Omega \bm y \|}_p`
 
-    :param `torch.tensor` x: Two dimensional vector
-    :param `torch.tensor` y: Two dimensional vector
     :param `torch.tensor` omega: Two dimensional matrix
     """
+    x, y = get_flat(x, y)
     projected_x = x @ omega
     projected_y = y @ omega
     distances = squared_euclidean_distance(projected_x, projected_y)
@@ -80,15 +84,14 @@ def lomega_distance(x, y, omegas):
 
     Compute :math:`{\| \Omega_k \bm x - \Omega_k \bm y_k \|}_p`
 
-    :param `torch.tensor` x: Two dimensional vector
-    :param `torch.tensor` y: Two dimensional vector
     :param `torch.tensor` omegas: Three dimensional matrix
     """
+    x, y = get_flat(x, y)
     projected_x = x @ omegas
     projected_y = torch.diagonal(y @ omegas).T
     expanded_y = torch.unsqueeze(projected_y, dim=1)
     batchwise_difference = expanded_y - projected_x
-    differences_squared = batchwise_difference ** 2
+    differences_squared = batchwise_difference**2
     distances = torch.sum(differences_squared, dim=2)
     distances = distances.permute(1, 0)
     return distances
@@ -107,26 +110,18 @@ def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
     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])
-            + "."
-        )
+            + 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:
@@ -173,19 +168,18 @@ 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])
 
@@ -193,21 +187,18 @@ 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
-            ).T  # K.batch_dot(projectors, protos, [1, 1]))
+            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)
+                return torch.sum(torch.dot(signals, projector)**2, axis=1)
 
-            diss = (
-                torch.transpose(map(projected_norm, projectors))
-                - 2 * torch.dot(signals, projected_protos)
-                + torch.sum(projected_protos ** 2, axis=0, keepdims=True)
-            )
+            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:
@@ -216,8 +207,7 @@ 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])
 
@@ -233,12 +223,12 @@ 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)
@@ -251,13 +241,13 @@ 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))
             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)

prototorch/functions/helper.py

@@ -1,6 +1,11 @@
 import torch
 
 
+def get_flat(*args):
+    rv = [x.view(x.size(0), -1) for x in args]
+    return rv
+
+
 def calculate_prototype_accuracy(y_pred, y_true, plabels):
     """Computes the accuracy of a prototype based model.
     via Winner-Takes-All rule.
@@ -23,7 +28,7 @@ def predict_label(y_pred, plabels):
 
 def mixed_shape(inputs):
     if not torch.is_tensor(inputs):
-        raise ValueError('Input must be a tensor.')
+        raise ValueError("Input must be a tensor.")
     else:
         int_shape = list(inputs.shape)
         # sometimes int_shape returns mixed integer types
@@ -39,11 +44,11 @@ def mixed_shape(inputs):
 def equal_int_shape(shape_1, shape_2):
     if not isinstance(shape_1,
                       (tuple, list)) or not isinstance(shape_2, (tuple, list)):
-        raise ValueError('Input shapes must list or tuple.')
+        raise ValueError("Input shapes must list or tuple.")
     for shape in [shape_1, shape_2]:
         if not all([isinstance(x, int) or x is None for x in shape]):
             raise ValueError(
-                'Input shapes must be list or tuple of int and None values.')
+                "Input shapes must be list or tuple of int and None values.")
 
     if len(shape_1) != len(shape_2):
         return False

prototorch/functions/initializers.py

@@ -15,59 +15,59 @@ def register_initializer(function):
 
 def labels_from(distribution, one_hot=True):
     """Takes a distribution tensor and returns a labels tensor."""
-    nclasses = distribution.shape[0]
-    llist = [[i] * n for i, n in zip(range(nclasses), distribution)]
+    num_classes = distribution.shape[0]
+    llist = [[i] * n for i, n in zip(range(num_classes), distribution)]
     # labels = [l for cl in llist for l in cl]  # flatten the list of lists
     flat_llist = list(chain(*llist))  # flatten label list with itertools.chain
     plabels = torch.tensor(flat_llist, requires_grad=False)
     if one_hot:
-        return torch.eye(nclasses)[plabels]
+        return torch.eye(num_classes)[plabels]
     return plabels
 
 
 @register_initializer
 def ones(x_train, y_train, prototype_distribution, one_hot=True):
-    nprotos = torch.sum(prototype_distribution)
-    protos = torch.ones(nprotos, *x_train.shape[1:])
+    num_protos = torch.sum(prototype_distribution)
+    protos = torch.ones(num_protos, *x_train.shape[1:])
     plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
 def zeros(x_train, y_train, prototype_distribution, one_hot=True):
-    nprotos = torch.sum(prototype_distribution)
-    protos = torch.zeros(nprotos, *x_train.shape[1:])
+    num_protos = torch.sum(prototype_distribution)
+    protos = torch.zeros(num_protos, *x_train.shape[1:])
     plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
 def rand(x_train, y_train, prototype_distribution, one_hot=True):
-    nprotos = torch.sum(prototype_distribution)
-    protos = torch.rand(nprotos, *x_train.shape[1:])
+    num_protos = torch.sum(prototype_distribution)
+    protos = torch.rand(num_protos, *x_train.shape[1:])
     plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
 def randn(x_train, y_train, prototype_distribution, one_hot=True):
-    nprotos = torch.sum(prototype_distribution)
-    protos = torch.randn(nprotos, *x_train.shape[1:])
+    num_protos = torch.sum(prototype_distribution)
+    protos = torch.randn(num_protos, *x_train.shape[1:])
     plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
 def stratified_mean(x_train, y_train, prototype_distribution, one_hot=True):
-    nprotos = torch.sum(prototype_distribution)
+    num_protos = torch.sum(prototype_distribution)
     pdim = x_train.shape[1]
-    protos = torch.empty(nprotos, pdim)
+    protos = torch.empty(num_protos, pdim)
     plabels = labels_from(prototype_distribution, one_hot)
     for i, label in enumerate(plabels):
         matcher = torch.eq(label.unsqueeze(dim=0), y_train)
         if one_hot:
-            nclasses = y_train.size()[1]
-            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+            num_classes = y_train.size()[1]
+            matcher = torch.eq(torch.sum(matcher, dim=-1), num_classes)
         xl = x_train[matcher]
         mean_xl = torch.mean(xl, dim=0)
         protos[i] = mean_xl
@@ -81,15 +81,15 @@ def stratified_random(x_train,
                       prototype_distribution,
                       one_hot=True,
                       epsilon=1e-7):
-    nprotos = torch.sum(prototype_distribution)
+    num_protos = torch.sum(prototype_distribution)
     pdim = x_train.shape[1]
-    protos = torch.empty(nprotos, pdim)
+    protos = torch.empty(num_protos, pdim)
     plabels = labels_from(prototype_distribution, one_hot)
     for i, label in enumerate(plabels):
         matcher = torch.eq(label.unsqueeze(dim=0), y_train)
         if one_hot:
-            nclasses = y_train.size()[1]
-            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+            num_classes = y_train.size()[1]
+            matcher = torch.eq(torch.sum(matcher, dim=-1), num_classes)
         xl = x_train[matcher]
         rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1)
         random_xl = xl[rand_index]
@@ -104,4 +104,4 @@ def get_initializer(funcname):
         return funcname
     if funcname in INITIALIZERS:
         return INITIALIZERS.get(funcname)
-    raise NameError(f'Initializer {funcname} was not found.')
+    raise NameError(f"Initializer {funcname} was not found.")

prototorch/functions/losses.py

@@ -3,20 +3,29 @@
 import torch
 
 
-def _get_dp_dm(distances, targets, plabels):
-    matcher = torch.eq(targets.unsqueeze(dim=1), plabels)
-    if plabels.ndim == 2:
+def _get_matcher(targets, labels):
+    """Returns a boolean tensor."""
+    matcher = torch.eq(targets.unsqueeze(dim=1), labels)
+    if labels.ndim == 2:
         # if the labels are one-hot vectors
-        nclasses = targets.size()[1]
-        matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+        num_classes = targets.size()[1]
+        matcher = torch.eq(torch.sum(matcher, dim=-1), num_classes)
+    return matcher
+
+
+def _get_dp_dm(distances, targets, plabels, with_indices=False):
+    """Returns the d+ and d- values for a batch of distances."""
+    matcher = _get_matcher(targets, plabels)
     not_matcher = torch.bitwise_not(matcher)
 
-    inf = torch.full_like(distances, fill_value=float('inf'))
+    inf = torch.full_like(distances, fill_value=float("inf"))
     d_matching = torch.where(matcher, distances, inf)
     d_unmatching = torch.where(not_matcher, distances, inf)
-    dp = torch.min(d_matching, dim=1, keepdim=True).values
-    dm = torch.min(d_unmatching, dim=1, keepdim=True).values
-    return dp, dm
+    dp = torch.min(d_matching, dim=-1, keepdim=True)
+    dm = torch.min(d_unmatching, dim=-1, keepdim=True)
+    if with_indices:
+        return dp, dm
+    return dp.values, dm.values
 
 
 def glvq_loss(distances, target_labels, prototype_labels):
@@ -24,3 +33,27 @@ def glvq_loss(distances, target_labels, prototype_labels):
     dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
     mu = (dp - dm) / (dp + dm)
     return mu
+
+
+def lvq1_loss(distances, target_labels, prototype_labels):
+    """LVQ1 loss function with support for one-hot labels.
+
+    See Section 4 [Sado&Yamada]
+    https://papers.nips.cc/paper/1995/file/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf
+    """
+    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
+    mu = dp
+    mu[dp > dm] = -dm[dp > dm]
+    return mu
+
+
+def lvq21_loss(distances, target_labels, prototype_labels):
+    """LVQ2.1 loss function with support for one-hot labels.
+
+    See Section 4 [Sado&Yamada]
+    https://papers.nips.cc/paper/1995/file/9c3b1830513cc3b8fc4b76635d32e692-Paper.pdf
+    """
+    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
+    mu = dp - dm
+
+    return mu

prototorch/functions/normalization.py

@@ -1,7 +1,5 @@
 # -*- coding: utf-8 -*-
-from __future__ import print_function
-from __future__ import absolute_import
-from __future__ import division
+from __future__ import absolute_import, division, print_function
 
 import torch
 

prototorch/functions/similarities.py

@@ -0,0 +1,18 @@
+"""ProtoTorch similarity functions."""
+
+import torch
+
+
+def cosine_similarity(x, y):
+    """Compute the cosine similarity between :math:`x` and :math:`y`.
+
+    Expected dimension of x is 2.
+    Expected dimension of y is 2.
+    """
+    norm_x = x.pow(2).sum(1).sqrt()
+    norm_y = y.pow(2).sum(1).sqrt()
+    norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T
+    epsilon = torch.finfo(norm_mat.dtype).eps
+    norm_mat.clamp_(min=epsilon)
+    similarities = (x @ y.T) / norm_mat
+    return similarities

prototorch/modules/__init__.py

@@ -3,5 +3,5 @@
 from .prototypes import Prototypes1D
 
 __all__ = [
-    'Prototypes1D',
+    "Prototypes1D",
 ]

prototorch/modules/losses.py

@@ -7,7 +7,7 @@ from prototorch.functions.losses import glvq_loss
 
 
 class GLVQLoss(torch.nn.Module):
-    def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
+    def __init__(self, margin=0.0, squashing="identity", beta=10, **kwargs):
         super().__init__(**kwargs)
         self.margin = margin
         self.squashing = get_activation(squashing)
@@ -18,3 +18,23 @@ class GLVQLoss(torch.nn.Module):
         mu = glvq_loss(distances, targets, prototype_labels=plabels)
         batch_loss = self.squashing(mu + self.margin, beta=self.beta)
         return torch.sum(batch_loss, dim=0)
+
+
+class NeuralGasEnergy(torch.nn.Module):
+    def __init__(self, lm):
+        super().__init__()
+        self.lm = lm
+
+    def forward(self, d):
+        order = torch.argsort(d, dim=1)
+        ranks = torch.argsort(order, dim=1)
+        cost = torch.sum(self._nghood_fn(ranks, self.lm) * d)
+
+        return cost, order
+
+    def extra_repr(self):
+        return f"lambda: {self.lm}"
+
+    @staticmethod
+    def _nghood_fn(rankings, lm):
+        return torch.exp(-rankings / lm)

prototorch/modules/models.py

@@ -1,10 +1,8 @@
-from torch import nn
 import torch
-from prototorch.modules.prototypes import Prototypes1D
-from prototorch.functions.distances import tangent_distance, euclidean_distance_matrix
+from prototorch.functions.distances import euclidean_distance_matrix
 from prototorch.functions.normalization import orthogonalization
-from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
-
+from prototorch.modules.prototypes import Prototypes1D
+from torch import nn
 
 class GTLVQ(nn.Module):
     r""" Generalized Tangent Learning Vector Quantization
@@ -71,50 +69,42 @@ class GTLVQ(nn.Module):
         subspace_data=None,
         prototype_data=None,
         subspace_size=256,
-        tangent_projection_type='local',
+        tangent_projection_type="local",
         prototypes_per_class=2,
         feature_dim=256,
     ):
         super(GTLVQ, self).__init__()
 
         self.num_protos = num_classes * prototypes_per_class
+        self.num_protos_class = prototypes_per_class
         self.subspace_size = feature_dim if subspace_size is None else subspace_size
         self.feature_dim = feature_dim
+        self.num_classes = num_classes
+
+        self.cls = Prototypes1D(
+            input_dim=feature_dim,
+            prototypes_per_class=prototypes_per_class,
+            nclasses=num_classes,
+            prototype_initializer="stratified_mean",
+            data=prototype_data,
+        )
 
         if subspace_data is None:
-            raise ValueError('Init Data must be specified!')
+            raise ValueError("Init Data must be specified!")
 
         self.tpt = tangent_projection_type
         with torch.no_grad():
-            if self.tpt == 'local' or self.tpt == 'local_proj':
-                self.init_local_subspace(subspace_data)
-            elif self.tpt == 'global':
+            if self.tpt == "local":
+                self.init_local_subspace(subspace_data, subspace_size,
+                                         self.num_protos)
+            elif self.tpt == "global":
                 self.init_gobal_subspace(subspace_data, subspace_size)
             else:
                 self.subspaces = None
 
-        # Hypothesis-Margin-Classifier
-        self.cls = Prototypes1D(input_dim=feature_dim,
-                                prototypes_per_class=prototypes_per_class,
-                                nclasses=num_classes,
-                                prototype_initializer='stratified_mean',
-                                data=prototype_data)
-
     def forward(self, x):
-        # Tangent Projection
-        if self.tpt == 'local_proj':
-            x_conform = x.unsqueeze(1).repeat_interleave(self.num_protos,
-                                                         1).unsqueeze(2)
-            dis, proj_x = self.local_tangent_projection(x_conform)
-
-            proj_x = proj_x.reshape(x.shape[0] * self.num_protos,
-                                    self.feature_dim)
-            return proj_x, dis
-        elif self.tpt == "local":
-            x_conform = x.unsqueeze(1).repeat_interleave(self.num_protos,
-                                                         1).unsqueeze(2)
-            dis = tangent_distance(x_conform, self.cls.prototypes,
-                                   self.subspaces)
+        if self.tpt == "local":
+            dis = self.local_tangent_distances(x)
         elif self.tpt == "gloabl":
             dis = self.global_tangent_distances(x)
         else:
@@ -127,51 +117,39 @@ class GTLVQ(nn.Module):
         _, _, v = torch.svd(data)
         subspace = (torch.eye(v.shape[0]) - (v @ v.T)).T
         subspaces = subspace[:, :num_subspaces]
-        self.subspaces = torch.nn.Parameter(
-            subspaces).clone().detach().requires_grad_(True)
+        self.subspaces = nn.Parameter(subspaces, requires_grad=True)
 
-    def init_local_subspace(self, data):
-        _, _, v = torch.svd(data)
-        inital_projector = (torch.eye(v.shape[0]) - (v @ v.T)).T
-        subspaces = inital_projector.unsqueeze(0).repeat_interleave(
-            self.num_protos, 0)
-        self.subspaces = torch.nn.Parameter(
-            subspaces).clone().detach().requires_grad_(True)
+    def init_local_subspace(self, data,num_subspaces,num_protos):
+        data = data -  torch.mean(data,dim=0)
+        _,_,v = torch.svd(data,some=False)
+        v = v[:,:num_subspaces]
+        subspaces = v.unsqueeze(0).repeat_interleave(num_protos,0)
+        self.subspaces = nn.Parameter(subspaces,requires_grad=True)
 
     def global_tangent_distances(self, x):
         # Tangent Projection
-        x, projected_prototypes = x @ self.subspaces, self.cls.prototypes @ self.subspaces
+        x, projected_prototypes = (
+            x @ self.subspaces,
+            self.cls.prototypes @ self.subspaces,
+        )
         # Euclidean Distance
         return euclidean_distance_matrix(x, projected_prototypes)
 
-    def local_tangent_projection(self,
-                                 signals):
-        # Note: subspaces is always assumed as transposed and must be orthogonal!
-        # 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
-        # Origin Source Code
-        # Origin Author:
-        protos = self.cls.prototypes
-        subspaces = self.subspaces
-        signal_shape, signal_int_shape = _int_and_mixed_shape(signals)
-        _, proto_int_shape = _int_and_mixed_shape(protos)
+    def local_tangent_distances(self, x):
 
-        # check if the shapes are correct
-        _check_shapes(signal_int_shape, proto_int_shape)
-
-        # Tangent Data Projections
-        projected_protos = torch.bmm(protos.unsqueeze(1), subspaces).squeeze(1)
-        data = signals.squeeze(2).permute([1, 0, 2])
-        projected_data = torch.bmm(data, subspaces)
-        projected_data = projected_data.permute([1, 0, 2]).unsqueeze(1)
-        diff = projected_data - projected_protos
-        projected_diff = torch.reshape(
-            diff, (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), projected_data.squeeze(1)
+        # Tangent Distance
+        x = x.unsqueeze(1).expand(x.size(0), self.cls.prototypes.size(0),
+                                  x.size(-1))
+        protos = self.cls.prototypes.unsqueeze(0).expand(
+            x.size(0), self.cls.prototypes.size(0), x.size(-1))
+        projectors = torch.eye(
+            self.subspaces.shape[-2], device=x.device) - torch.bmm(
+                self.subspaces, self.subspaces.permute([0, 2, 1]))
+        diff = (x - protos)
+        diff = diff.permute([1, 0, 2])
+        diff = torch.bmm(diff, projectors)
+        diff = torch.norm(diff,2,dim=-1).T
+        return diff
 
     def get_parameters(self):
         return {
@@ -183,8 +161,7 @@ class GTLVQ(nn.Module):
     def orthogonalize_subspace(self):
         if self.subspaces is not None:
             with torch.no_grad():
-                ortho_subpsaces = orthogonalization(
-                    self.subspaces
-                ) if self.tpt == 'global' else torch.nn.init.orthogonal_(
-                    self.subspaces)
+                ortho_subpsaces = (orthogonalization(self.subspaces)
+                                   if self.tpt == "global" else
+                                   torch.nn.init.orthogonal_(self.subspaces))
                 self.subspaces.copy_(ortho_subpsaces)

prototorch/modules/prototypes.py

@@ -3,7 +3,6 @@
 import warnings
 
 import torch
-
 from prototorch.functions.initializers import get_initializer
 
 
@@ -29,14 +28,19 @@ class Prototypes1D(_Prototypes):
 
     TODO Complete this doc-string.
     """
-    def __init__(self,
-                 prototypes_per_class=1,
-                 prototype_initializer="ones",
-                 prototype_distribution=None,
-                 data=None,
-                 dtype=torch.float32,
-                 one_hot_labels=False,
-                 **kwargs):
+    def __init__(
+        self,
+        prototypes_per_class=1,
+        prototype_initializer="ones",
+        prototype_distribution=None,
+        data=None,
+        dtype=torch.float32,
+        one_hot_labels=False,
+        **kwargs,
+    ):
+        warnings.warn(
+            PendingDeprecationWarning(
+                "Prototypes1D will be replaced in future versions."))
 
         # Convert tensors to python lists before processing
         if prototype_distribution is not None:
@@ -48,13 +52,13 @@ class Prototypes1D(_Prototypes):
                 raise NameError("`input_dim` required if "
                                 "no `data` is provided.")
             if prototype_distribution:
-                kwargs_nclasses = sum(prototype_distribution)
+                kwargs_num_classes = sum(prototype_distribution)
             else:
-                if "nclasses" not in kwargs:
+                if "num_classes" not in kwargs:
                     raise NameError("`prototype_distribution` required if "
-                                    "both `data` and `nclasses` are not "
+                                    "both `data` and `num_classes` are not "
                                     "provided.")
-                kwargs_nclasses = kwargs.pop("nclasses")
+                kwargs_num_classes = kwargs.pop("num_classes")
             input_dim = kwargs.pop("input_dim")
             if prototype_initializer in [
                     "stratified_mean", "stratified_random"
@@ -63,18 +67,18 @@ class Prototypes1D(_Prototypes):
                     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)
+            x_train = torch.rand(kwargs_num_classes, input_dim)
+            y_train = torch.arange(kwargs_num_classes)
             if one_hot_labels:
-                y_train = torch.eye(kwargs_nclasses)[y_train]
+                y_train = torch.eye(kwargs_num_classes)[y_train]
             data = [x_train, y_train]
 
         x_train, y_train = data
         x_train = torch.as_tensor(x_train).type(dtype)
         y_train = torch.as_tensor(y_train).type(torch.int)
-        nclasses = torch.unique(y_train, dim=-1).shape[-1]
+        num_classes = torch.unique(y_train, dim=-1).shape[-1]
 
-        if nclasses == 1:
+        if num_classes == 1:
             warnings.warn("Are you sure about having one class only?")
 
         if x_train.ndim != 2:
@@ -100,19 +104,20 @@ class Prototypes1D(_Prototypes):
                                  "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 kwargs_nclasses != nclasses:
-                raise ValueError(f"Provided `nclasses={kwargs_nclasses}` does "
-                                 "not match data labels "
-                                 "`torch.unique(data[1]).shape[0]`"
-                                 f"={nclasses}")
+        # Verify the number of classes if `num_classes` is provided
+        if "num_classes" in kwargs:
+            kwargs_num_classes = kwargs.pop("num_classes")
+            if kwargs_num_classes != num_classes:
+                raise ValueError(
+                    f"Provided `num_classes={kwargs_num_classes}` does "
+                    "not match data labels "
+                    "`torch.unique(data[1]).shape[0]`"
+                    f"={num_classes}")
 
         super().__init__(**kwargs)
 
         if not prototype_distribution:
-            prototype_distribution = [prototypes_per_class] * nclasses
+            prototype_distribution = [prototypes_per_class] * num_classes
         with torch.no_grad():
             self.prototype_distribution = torch.tensor(prototype_distribution)
 

prototorch/utils/__init__.py

@@ -1 +0,0 @@
-from .colors import color_scheme, get_legend_handles

prototorch/utils/celluloid.py

@@ -0,0 +1,46 @@
+"""Easy matplotlib animation. From https://github.com/jwkvam/celluloid."""
+
+from collections import defaultdict
+from typing import Dict, List
+
+from matplotlib.animation import ArtistAnimation
+from matplotlib.artist import Artist
+from matplotlib.figure import Figure
+
+__version__ = "0.2.0"
+
+
+class Camera:
+    """Make animations easier."""
+    def __init__(self, figure: Figure) -> None:
+        """Create camera from matplotlib figure."""
+        self._figure = figure
+        # need to keep track off artists for each axis
+        self._offsets: Dict[str, Dict[int, int]] = {
+            k: defaultdict(int)
+            for k in
+            ["collections", "patches", "lines", "texts", "artists", "images"]
+        }
+        self._photos: List[List[Artist]] = []
+
+    def snap(self) -> List[Artist]:
+        """Capture current state of the figure."""
+        frame_artists: List[Artist] = []
+        for i, axis in enumerate(self._figure.axes):
+            if axis.legend_ is not None:
+                axis.add_artist(axis.legend_)
+            for name in self._offsets:
+                new_artists = getattr(axis, name)[self._offsets[name][i]:]
+                frame_artists += new_artists
+                self._offsets[name][i] += len(new_artists)
+        self._photos.append(frame_artists)
+        return frame_artists
+
+    def animate(self, *args, **kwargs) -> ArtistAnimation:
+        """Animate the snapshots taken.
+        Uses matplotlib.animation.ArtistAnimation
+        Returns
+        -------
+        ArtistAnimation
+        """
+        return ArtistAnimation(self._figure, self._photos, *args, **kwargs)

prototorch/utils/colors.py

@@ -1,13 +1,14 @@
 """ProtoFlow color utilities."""
 
-from matplotlib import cm
-from matplotlib.colors import Normalize
-from matplotlib.colors import to_hex
-from matplotlib.colors import to_rgb
 import matplotlib.lines as mlines
+from matplotlib import cm
+from matplotlib.colors import Normalize, to_hex, to_rgb
 
 
-def color_scheme(n, cmap="viridis", form="hex", tikz=False,
+def color_scheme(n,
+                 cmap="viridis",
+                 form="hex",
+                 tikz=False,
                  zero_indexed=False):
     """Return *n* colors from the color scheme.
 
@@ -57,13 +58,16 @@ def get_legend_handles(labels, marker="dots", zero_indexed=False):
                           zero_indexed=zero_indexed)
     for label, color in zip(labels, colors.values()):
         if marker == "dots":
-            handle = mlines.Line2D([], [],
-                                   color="white",
-                                   markerfacecolor=color,
-                                   marker="o",
-                                   markersize=10,
-                                   markeredgecolor="k",
-                                   label=label)
+            handle = mlines.Line2D(
+                [],
+                [],
+                color="white",
+                markerfacecolor=color,
+                marker="o",
+                markersize=10,
+                markeredgecolor="k",
+                label=label,
+            )
         else:
             handle = mlines.Line2D([], [],
                                    color=color,

prototorch/utils/utils.py

@@ -0,0 +1,243 @@
+"""Utilities that provide various small functionalities."""
+
+import os
+import pickle
+import sys
+from time import time
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def progressbar(title, value, end, bar_width=20):
+    percent = float(value) / end
+    arrow = "=" * int(round(percent * bar_width) - 1) + ">"
+    spaces = "." * (bar_width - len(arrow))
+    sys.stdout.write("\r{}: [{}] {}%".format(title, arrow + spaces,
+                                             int(round(percent * 100))))
+    sys.stdout.flush()
+    if percent == 1.0:
+        print()
+
+
+def prettify_string(inputs, start="", sep=" ", end="\n"):
+    outputs = start + " ".join(inputs.split()) + end
+    return outputs
+
+
+def pretty_print(inputs):
+    print(prettify_string(inputs))
+
+
+def writelog(self, *logs, logdir="./logs", logfile="run.txt"):
+    f = os.path.join(logdir, logfile)
+    with open(f, "a+") as fh:
+        for log in logs:
+            fh.write(log)
+            fh.write("\n")
+
+
+def start_tensorboard(self, logdir="./logs"):
+    cmd = f"tensorboard --logdir={logdir} --port=6006"
+    os.system(cmd)
+
+
+def make_directory(save_dir):
+    if not os.path.exists(save_dir):
+        print(f"Making directory {save_dir}.")
+        os.mkdir(save_dir)
+
+
+def make_gif(filenames, duration, output_file=None):
+    try:
+        import imageio
+    except ModuleNotFoundError as e:
+        print("Please install Protoflow with [other] extra requirements.")
+        raise (e)
+
+    images = list()
+    for filename in filenames:
+        images.append(imageio.imread(filename))
+    if not output_file:
+        output_file = f"makegif.gif"
+    if images:
+        imageio.mimwrite(output_file, images, duration=duration)
+
+
+def gif_from_dir(directory,
+                 duration,
+                 prefix="",
+                 output_file=None,
+                 verbose=True):
+    images = os.listdir(directory)
+    if verbose:
+        print(f"Making gif from {len(images)} images under {directory}.")
+    filenames = list()
+    # Sort images
+    images = sorted(
+        images,
+        key=lambda img: int(os.path.splitext(img)[0].replace(prefix, "")))
+    for image in images:
+        fname = os.path.join(directory, image)
+        filenames.append(fname)
+    if not output_file:
+        output_file = os.path.join(directory, "makegif.gif")
+    make_gif(filenames=filenames, duration=duration, output_file=output_file)
+
+
+def accuracy_score(y_true, y_pred):
+    accuracy = np.sum(y_true == y_pred)
+    normalized_acc = accuracy / float(len(y_true))
+    return normalized_acc
+
+
+def predict_and_score(clf,
+                      x_test,
+                      y_test,
+                      verbose=False,
+                      title="Test accuracy"):
+    y_pred = clf.predict(x_test)
+    accuracy = np.sum(y_test == y_pred)
+    normalized_acc = accuracy / float(len(y_test))
+    if verbose:
+        print(f"{title}: {normalized_acc * 100:06.04f}%")
+    return normalized_acc
+
+
+def remove_nan_rows(arr):
+    """Remove all rows with `nan` values in `arr`."""
+    mask = np.isnan(arr).any(axis=1)
+    return arr[~mask]
+
+
+def remove_nan_cols(arr):
+    """Remove all columns with `nan` values in `arr`."""
+    mask = np.isnan(arr).any(axis=0)
+    return arr[~mask]
+
+
+def replace_in(arr, replacement_dict, inplace=False):
+    """Replace the keys found in `arr` with the values from
+    the `replacement_dict`.
+    """
+    if inplace:
+        new_arr = arr
+    else:
+        import copy
+
+        new_arr = copy.deepcopy(arr)
+    for k, v in replacement_dict.items():
+        new_arr[arr == k] = v
+    return new_arr
+
+
+def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
+    """Split a classification dataset in such a way so as to
+    preserve the class distribution in subsamples of the dataset.
+    """
+    if train + val > 1.0:
+        raise ValueError("Invalid split values for train and val.")
+    Y = data[:, -1]
+    labels = set(Y)
+    hist = dict()
+    for l in labels:
+        data_l = data[Y == l]
+        nl = len(data_l)
+        nl_train = int(nl * train)
+        nl_val = int(nl * val)
+        nl_test = nl - (nl_train + nl_val)
+        hist[l] = (nl_train, nl_val, nl_test)
+
+    train_data = list()
+    val_data = list()
+    test_data = list()
+    for l, (nl_train, nl_val, nl_test) in hist.items():
+        data_l = data[Y == l]
+        if shuffle:
+            np.random.shuffle(data_l)
+        train_l = data_l[:nl_train]
+        val_l = data_l[nl_train:nl_train + nl_val]
+        test_l = data_l[nl_train + nl_val:nl_train + nl_val + nl_test]
+        train_data.append(train_l)
+        val_data.append(val_l)
+        test_data.append(test_l)
+
+    def _squash(data_list):
+        data = np.array(data_list[0])
+        for item in data_list[1:]:
+            data = np.vstack((data, np.array(item)))
+        return data
+
+    train_data = _squash(train_data)
+    if val_data:
+        val_data = _squash(val_data)
+    if test_data:
+        test_data = _squash(test_data)
+    if return_xy:
+        x_train = train_data[:, :-1]
+        y_train = train_data[:, -1]
+        x_val = val_data[:, :-1]
+        y_val = val_data[:, -1]
+        x_test = test_data[:, :-1]
+        y_test = test_data[:, -1]
+        return (x_train, y_train), (x_val, y_val), (x_test, y_test)
+    return train_data, val_data, test_data
+
+
+def class_histogram(data, title="Untitled"):
+    plt.figure(title)
+    plt.clf()
+    plt.title(title)
+    dist, counts = np.unique(data[:, -1], return_counts=True)
+    plt.bar(dist, counts)
+    plt.xticks(dist)
+    print("Call matplotlib.pyplot.show() to see the plot.")
+
+
+def ntimer(n=10):
+    """Wraps a function which wraps another function to time it."""
+    if n < 1:
+        raise (Exception(f"Invalid n = {n} given."))
+
+    def timer(func):
+        """Wraps `func` with a timer and returns the wrapped `func`."""
+        def wrapper(*args, **kwargs):
+            rv = None
+            before = time()
+            for _ in range(n):
+                rv = func(*args, **kwargs)
+            after = time()
+            elapsed = after - before
+            print(f"Elapsed: {elapsed*1e3:02.02f} ms")
+            return rv
+
+        return wrapper
+
+    return timer
+
+
+def memoize(verbose=True):
+    """Wraps a function which wraps another function that memoizes."""
+    def memoizer(func):
+        """Memoize (cache) return values of `func`.
+        Wraps `func` and returns the wrapped `func` so that `func`
+        is executed when the results are not available in the cache.
+        """
+        cache = {}
+
+        def wrapper(*args, **kwargs):
+            t = (pickle.dumps(args), pickle.dumps(kwargs))
+            if t not in cache:
+                if verbose:
+                    print(f"Adding NEW rv {func.__name__}{args}{kwargs} "
+                          "to cache.")
+                cache[t] = func(*args, **kwargs)
+            else:
+                if verbose:
+                    print(f"Using OLD rv {func.__name__}{args}{kwargs} "
+                          "from cache.")
+            return cache[t]
+
+        return wrapper
+
+    return memoizer

requirements.txt

@@ -1,5 +0,0 @@
-matplotlib==3.1.2
-pytest==5.3.4
-requests==2.22.0
-codecov==2.0.22
-tqdm==4.44.1

setup.py

@@ -1,15 +1,14 @@
 """
-  _____           _     _______             _ 
- |  __ \         | |   |__   __|           | |  
+  _____           _     _______             _
+ |  __ \         | |   |__   __|           | |
  | |__) | __ ___ | |_ ___ | | ___  _ __ ___| |__
- |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \ 
+ |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \
  | |   | | | (_) | || (_) | | (_) | | | (__| | | |
  |_|   |_|  \___/ \__\___/|_|\___/|_|  \___|_| |_|
 
 ProtoTorch Core Package
 """
-from setuptools import setup
-from setuptools import find_packages
+from setuptools import find_packages, setup
 
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
@@ -21,28 +20,30 @@ INSTALL_REQUIRES = [
     "torch>=1.3.1",
     "torchvision>=0.5.0",
     "numpy>=1.9.1",
-]
-DOCS = [
-    "recommonmark",
-    "sphinx",
-    "sphinx_rtd_theme",
-    "sphinxcontrib-katex",
+    "sklearn",
 ]
 DATASETS = [
     "requests",
     "tqdm",
 ]
+DEV = ["bumpversion"]
+DOCS = [
+    "recommonmark",
+    "sphinx",
+    "sphinx_rtd_theme",
+    "sphinxcontrib-katex",
+    "sphinx-autodoc-typehints",
+]
 EXAMPLES = [
-    "sklearn",
     "matplotlib",
     "torchinfo",
 ]
-TESTS = ["pytest"]
-ALL = DOCS + DATASETS + EXAMPLES + TESTS
+TESTS = ["codecov", "pytest"]
+ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.3.0-dev0",
+    version="0.4.5",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -72,6 +73,7 @@ setup(
         "Programming Language :: Python :: 3.6",
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
         "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Software Development :: Libraries",

tests/test_components.py

@@ -0,0 +1,25 @@
+"""ProtoTorch components test suite."""
+
+import prototorch as pt
+import torch
+
+
+def test_labcomps_zeros_init():
+    protos = torch.zeros(3, 2)
+    c = pt.components.LabeledComponents(
+        distribution=[1, 1, 1],
+        initializer=pt.components.Zeros(2),
+    )
+    assert (c.components == protos).any() == True
+
+
+def test_labcomps_warmstart():
+    protos = torch.randn(3, 2)
+    plabels = torch.tensor([1, 2, 3])
+    c = pt.components.LabeledComponents(
+        distribution=[1, 1, 1],
+        initializer=None,
+        initialized_components=[protos, plabels],
+    )
+    assert (c.components == protos).any() == True
+    assert (c.component_labels == plabels).any() == True

tests/test_datasets.py

@@ -12,26 +12,26 @@ from prototorch.datasets import abstract, tecator
 class TestAbstract(unittest.TestCase):
     def test_getitem(self):
         with self.assertRaises(NotImplementedError):
-            abstract.Dataset('./artifacts')[0]
+            abstract.Dataset("./artifacts")[0]
 
     def test_len(self):
         with self.assertRaises(NotImplementedError):
-            len(abstract.Dataset('./artifacts'))
+            len(abstract.Dataset("./artifacts"))
 
 
 class TestProtoDataset(unittest.TestCase):
     def test_getitem(self):
         with self.assertRaises(NotImplementedError):
-            abstract.ProtoDataset('./artifacts')[0]
+            abstract.ProtoDataset("./artifacts")[0]
 
     def test_download(self):
         with self.assertRaises(NotImplementedError):
-            abstract.ProtoDataset('./artifacts').download()
+            abstract.ProtoDataset("./artifacts").download()
 
 
 class TestTecator(unittest.TestCase):
     def setUp(self):
-        self.artifacts_dir = './artifacts/Tecator'
+        self.artifacts_dir = "./artifacts/Tecator"
         self._remove_artifacts()
 
     def _remove_artifacts(self):
@@ -39,23 +39,23 @@ class TestTecator(unittest.TestCase):
             shutil.rmtree(self.artifacts_dir)
 
     def test_download_false(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         self._remove_artifacts()
         with self.assertRaises(RuntimeError):
             _ = tecator.Tecator(rootdir, download=False)
 
     def test_download_caching(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         _ = tecator.Tecator(rootdir, download=True, verbose=False)
         _ = tecator.Tecator(rootdir, download=False, verbose=False)
 
     def test_repr(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         train = tecator.Tecator(rootdir, download=True, verbose=True)
-        self.assertTrue('Split: Train' in train.__repr__())
+        self.assertTrue("Split: Train" in train.__repr__())
 
     def test_download_train(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         train = tecator.Tecator(root=rootdir,
                                 train=True,
                                 download=True,
@@ -67,7 +67,7 @@ class TestTecator(unittest.TestCase):
         self.assertEqual(x_train.shape[1], 100)
 
     def test_download_test(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         test = tecator.Tecator(root=rootdir, train=False, verbose=False)
         x_test, y_test = test.data, test.targets
         self.assertEqual(x_test.shape[0], 71)
@@ -75,19 +75,19 @@ class TestTecator(unittest.TestCase):
         self.assertEqual(x_test.shape[1], 100)
 
     def test_class_to_idx(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         test = tecator.Tecator(root=rootdir, train=False, verbose=False)
         _ = test.class_to_idx
 
     def test_getitem(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         test = tecator.Tecator(root=rootdir, train=False, verbose=False)
         x, y = test[0]
         self.assertEqual(x.shape[0], 100)
         self.assertIsInstance(y, int)
 
     def test_loadable_with_dataloader(self):
-        rootdir = self.artifacts_dir.rpartition('/')[0]
+        rootdir = self.artifacts_dir.rpartition("/")[0]
         test = tecator.Tecator(root=rootdir, train=False, verbose=False)
         _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
 

tests/test_functions.py

@@ -4,14 +4,13 @@ import unittest
 
 import numpy as np
 import torch
-
 from prototorch.functions import (activations, competitions, distances,
                                   initializers, losses)
 
 
 class TestActivations(unittest.TestCase):
     def setUp(self):
-        self.flist = ['identity', 'sigmoid_beta', 'swish_beta']
+        self.flist = ["identity", "sigmoid_beta", "swish_beta"]
         self.x = torch.randn(1024, 1)
 
     def test_registry(self):
@@ -39,7 +38,7 @@ class TestActivations(unittest.TestCase):
             self.assertEqual(1, f(1))
 
     def test_unknown_deserialization(self):
-        for funcname in ['blubb', 'foobar']:
+        for funcname in ["blubb", "foobar"]:
             with self.assertRaises(NameError):
                 _ = activations.get_activation(funcname)
 
@@ -52,7 +51,7 @@ class TestActivations(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_sigmoid_beta1(self):
-        actual = activations.sigmoid_beta(self.x, beta=torch.tensor(1))
+        actual = activations.sigmoid_beta(self.x, beta=1.0)
         desired = torch.sigmoid(self.x)
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
@@ -60,7 +59,7 @@ class TestActivations(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_swish_beta1(self):
-        actual = activations.swish_beta(self.x, beta=torch.tensor(1))
+        actual = activations.swish_beta(self.x, beta=1.0)
         desired = self.x * torch.sigmoid(self.x)
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
@@ -76,7 +75,7 @@ class TestCompetitions(unittest.TestCase):
         pass
 
     def test_wtac(self):
-        d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
+        d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
         labels = torch.tensor([0, 1, 2, 3])
         actual = competitions.wtac(d, labels)
         desired = torch.tensor([2, 0])
@@ -86,7 +85,7 @@ class TestCompetitions(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_wtac_unequal_dist(self):
-        d = torch.tensor([[2., 3., 4.], [2., 3., 1.]])
+        d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
         labels = torch.tensor([0, 1, 1])
         actual = competitions.wtac(d, labels)
         desired = torch.tensor([0, 1])
@@ -96,7 +95,7 @@ class TestCompetitions(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_wtac_one_hot(self):
-        d = torch.tensor([[1.99, 3.01], [3., 2.01]])
+        d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
         labels = torch.tensor([[0, 1], [1, 0]])
         actual = competitions.wtac(d, labels)
         desired = torch.tensor([[0, 1], [1, 0]])
@@ -106,40 +105,40 @@ class TestCompetitions(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_stratified_min(self):
-        d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
         labels = torch.tensor([0, 0, 1, 2])
         actual = competitions.stratified_min(d, labels)
-        desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
+        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
     def test_stratified_min_one_hot(self):
-        d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
         labels = torch.tensor([0, 0, 1, 2])
         labels = torch.eye(3)[labels]
         actual = competitions.stratified_min(d, labels)
-        desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
+        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
     def test_stratified_min_simple(self):
-        d = torch.tensor([[0., 2., 3.], [8., 0, 1]])
+        d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
         labels = torch.tensor([0, 1, 2])
         actual = competitions.stratified_min(d, labels)
-        desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
+        desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
     def test_knnc_k1(self):
-        d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
+        d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
         labels = torch.tensor([0, 1, 2, 3])
-        actual = competitions.knnc(d, labels, k=torch.tensor([1]))
+        actual = competitions.knnc(d, labels, k=1)
         desired = torch.tensor([2, 0])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
@@ -194,12 +193,12 @@ class TestDistances(unittest.TestCase):
         desired = torch.empty(self.nx, self.ny)
         for i in range(self.nx):
             for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
+                desired[i][j] = (torch.nn.functional.pairwise_distance(
                     self.x[i].reshape(1, -1),
                     self.y[j].reshape(1, -1),
                     p=2,
                     keepdim=False,
-                )**2
+                )**2)
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=2)
@@ -254,14 +253,14 @@ class TestDistances(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_lpnorm_pinf(self):
-        actual = distances.lpnorm_distance(self.x, self.y, p=float('inf'))
+        actual = distances.lpnorm_distance(self.x, self.y, p=float("inf"))
         desired = torch.empty(self.nx, self.ny)
         for i in range(self.nx):
             for j in range(self.ny):
                 desired[i][j] = torch.nn.functional.pairwise_distance(
                     self.x[i].reshape(1, -1),
                     self.y[j].reshape(1, -1),
-                    p=float('inf'),
+                    p=float("inf"),
                     keepdim=False,
                 )
         mismatch = np.testing.assert_array_almost_equal(actual,
@@ -275,12 +274,12 @@ class TestDistances(unittest.TestCase):
         desired = torch.empty(self.nx, self.ny)
         for i in range(self.nx):
             for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
+                desired[i][j] = (torch.nn.functional.pairwise_distance(
                     self.x[i].reshape(1, -1),
                     self.y[j].reshape(1, -1),
                     p=2,
                     keepdim=False,
-                )**2
+                )**2)
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=2)
@@ -293,12 +292,12 @@ class TestDistances(unittest.TestCase):
         desired = torch.empty(self.nx, self.ny)
         for i in range(self.nx):
             for j in range(self.ny):
-                desired[i][j] = torch.nn.functional.pairwise_distance(
+                desired[i][j] = (torch.nn.functional.pairwise_distance(
                     self.x[i].reshape(1, -1),
                     self.y[j].reshape(1, -1),
                     p=2,
                     keepdim=False,
-                )**2
+                )**2)
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=2)
@@ -311,8 +310,12 @@ class TestDistances(unittest.TestCase):
 class TestInitializers(unittest.TestCase):
     def setUp(self):
         self.flist = [
-            'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
-            'stratified_random'
+            "zeros",
+            "ones",
+            "rand",
+            "randn",
+            "stratified_mean",
+            "stratified_random",
         ]
         self.x = torch.tensor(
             [[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
@@ -340,7 +343,7 @@ class TestInitializers(unittest.TestCase):
             self.assertEqual(1, f(1))
 
     def test_unknown_deserialization(self):
-        for funcname in ['blubb', 'foobar']:
+        for funcname in ["blubb", "foobar"]:
             with self.assertRaises(NameError):
                 _ = initializers.get_initializer(funcname)
 
@@ -383,7 +386,7 @@ class TestInitializers(unittest.TestCase):
     def test_stratified_mean_equal1(self):
         pdist = torch.tensor([1, 1])
         actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
-        desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
+        desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -393,7 +396,7 @@ class TestInitializers(unittest.TestCase):
         pdist = torch.tensor([1, 1])
         actual, _ = initializers.stratified_random(self.x, self.y, pdist,
                                                    False)
-        desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
+        desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -402,8 +405,8 @@ class TestInitializers(unittest.TestCase):
     def test_stratified_mean_equal2(self):
         pdist = torch.tensor([2, 2])
         actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
-        desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
-                                [1., 1., 1.]])
+        desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
+                                [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -413,8 +416,8 @@ class TestInitializers(unittest.TestCase):
         pdist = torch.tensor([2, 2])
         actual, _ = initializers.stratified_random(self.x, self.y, pdist,
                                                    False)
-        desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
-                                [0., 0., 0.]])
+        desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
+                                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -423,8 +426,8 @@ class TestInitializers(unittest.TestCase):
     def test_stratified_mean_unequal(self):
         pdist = torch.tensor([1, 3])
         actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
-        desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
-                                [1., 1., 1.]])
+        desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
+                                [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -434,8 +437,8 @@ class TestInitializers(unittest.TestCase):
         pdist = torch.tensor([1, 3])
         actual, _ = initializers.stratified_random(self.x, self.y, pdist,
                                                    False)
-        desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
-                                [0., 0., 0.]])
+        desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
+                                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -444,8 +447,8 @@ class TestInitializers(unittest.TestCase):
     def test_stratified_mean_unequal_one_hot(self):
         pdist = torch.tensor([1, 3])
         y = torch.eye(2)[self.y]
-        desired1 = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
-                                 [1., 1., 1.]])
+        desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
+                                 [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
         actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
         desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
         mismatch = np.testing.assert_array_almost_equal(actual1,
@@ -460,8 +463,8 @@ class TestInitializers(unittest.TestCase):
         pdist = torch.tensor([1, 3])
         y = torch.eye(2)[self.y]
         actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
-        desired1 = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
-                                 [0., 0., 0.]])
+        desired1 = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
+                                 [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
         desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
         mismatch = np.testing.assert_array_almost_equal(actual1,
                                                         desired1,

tests/test_modules.py

@@ -4,7 +4,6 @@ import unittest
 
 import numpy as np
 import torch
-
 from prototorch.modules import losses, prototypes
 
 
@@ -18,21 +17,23 @@ class TestPrototypes(unittest.TestCase):
 
     def test_prototypes1d_init_without_input_dim(self):
         with self.assertRaises(NameError):
-            _ = prototypes.Prototypes1D(nclasses=2)
+            _ = prototypes.Prototypes1D(num_classes=2)
 
-    def test_prototypes1d_init_without_nclasses(self):
+    def test_prototypes1d_init_without_num_classes(self):
         with self.assertRaises(NameError):
             _ = prototypes.Prototypes1D(input_dim=1)
 
-    def test_prototypes1d_init_with_nclasses_1(self):
+    def test_prototypes1d_init_with_num_classes_1(self):
         with self.assertWarns(UserWarning):
-            _ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
+            _ = prototypes.Prototypes1D(num_classes=1, input_dim=1)
 
     def test_prototypes1d_init_without_pdist(self):
-        p1 = prototypes.Prototypes1D(input_dim=6,
-                                     nclasses=2,
-                                     prototypes_per_class=4,
-                                     prototype_initializer='ones')
+        p1 = prototypes.Prototypes1D(
+            input_dim=6,
+            num_classes=2,
+            prototypes_per_class=4,
+            prototype_initializer="ones",
+        )
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.ones(8, 6)
@@ -45,7 +46,7 @@ class TestPrototypes(unittest.TestCase):
         pdist = [2, 2]
         p1 = prototypes.Prototypes1D(input_dim=3,
                                      prototype_distribution=pdist,
-                                     prototype_initializer='zeros')
+                                     prototype_initializer="zeros")
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(4, 3)
@@ -58,16 +59,17 @@ class TestPrototypes(unittest.TestCase):
         with self.assertWarns(UserWarning):
             _ = prototypes.Prototypes1D(
                 input_dim=3,
-                nclasses=2,
+                num_classes=2,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
-                data=None)
+                prototype_initializer="stratified_mean",
+                data=None,
+            )
 
     def test_prototypes1d_init_torch_pdist(self):
         pdist = torch.tensor([2, 2])
         p1 = prototypes.Prototypes1D(input_dim=3,
                                      prototype_distribution=pdist,
-                                     prototype_initializer='zeros')
+                                     prototype_initializer="zeros")
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(4, 3)
@@ -77,24 +79,30 @@ class TestPrototypes(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_prototypes1d_init_without_inputdim_with_data(self):
-        _ = prototypes.Prototypes1D(nclasses=2,
-                                    prototypes_per_class=1,
-                                    prototype_initializer='stratified_mean',
-                                    data=[[[1.], [0.]], [1, 0]])
+        _ = prototypes.Prototypes1D(
+            num_classes=2,
+            prototypes_per_class=1,
+            prototype_initializer="stratified_mean",
+            data=[[[1.0], [0.0]], [1, 0]],
+        )
 
     def test_prototypes1d_init_with_int_data(self):
-        _ = prototypes.Prototypes1D(nclasses=2,
-                                    prototypes_per_class=1,
-                                    prototype_initializer='stratified_mean',
-                                    data=[[[1], [0]], [1, 0]])
+        _ = prototypes.Prototypes1D(
+            num_classes=2,
+            prototypes_per_class=1,
+            prototype_initializer="stratified_mean",
+            data=[[[1], [0]], [1, 0]],
+        )
 
     def test_prototypes1d_init_one_hot_without_data(self):
-        _ = prototypes.Prototypes1D(input_dim=1,
-                                    nclasses=2,
-                                    prototypes_per_class=1,
-                                    prototype_initializer='stratified_mean',
-                                    data=None,
-                                    one_hot_labels=True)
+        _ = prototypes.Prototypes1D(
+            input_dim=1,
+            num_classes=2,
+            prototypes_per_class=1,
+            prototype_initializer="stratified_mean",
+            data=None,
+            one_hot_labels=True,
+        )
 
     def test_prototypes1d_init_one_hot_labels_false(self):
         """Test if ValueError is raised when `one_hot_labels` is set to `False`
@@ -103,11 +111,12 @@ class TestPrototypes(unittest.TestCase):
         with self.assertRaises(ValueError):
             _ = prototypes.Prototypes1D(
                 input_dim=1,
-                nclasses=2,
+                num_classes=2,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
-                data=([[0.], [1.]], [[0, 1], [1, 0]]),
-                one_hot_labels=False)
+                prototype_initializer="stratified_mean",
+                data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
+                one_hot_labels=False,
+            )
 
     def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
         """Test if ValueError is raised when `one_hot_labels` is set to `True`
@@ -116,11 +125,12 @@ class TestPrototypes(unittest.TestCase):
         with self.assertRaises(ValueError):
             _ = prototypes.Prototypes1D(
                 input_dim=1,
-                nclasses=2,
+                num_classes=2,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
-                data=([[0.], [1.]], [0, 1]),
-                one_hot_labels=True)
+                prototype_initializer="stratified_mean",
+                data=([[0.0], [1.0]], [0, 1]),
+                one_hot_labels=True,
+            )
 
     def test_prototypes1d_init_one_hot_labels_true(self):
         """Test if ValueError is raised when `one_hot_labels` is set to `True`
@@ -130,53 +140,57 @@ class TestPrototypes(unittest.TestCase):
         with self.assertRaises(ValueError):
             _ = prototypes.Prototypes1D(
                 input_dim=1,
-                nclasses=2,
+                num_classes=2,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
-                data=([[0.], [1.]], [[0], [1]]),
-                one_hot_labels=True)
+                prototype_initializer="stratified_mean",
+                data=([[0.0], [1.0]], [[0], [1]]),
+                one_hot_labels=True,
+            )
 
     def test_prototypes1d_init_with_int_dtype(self):
         with self.assertRaises(RuntimeError):
             _ = prototypes.Prototypes1D(
-                nclasses=2,
+                num_classes=2,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
+                prototype_initializer="stratified_mean",
                 data=[[[1], [0]], [1, 0]],
-                dtype=torch.int32)
+                dtype=torch.int32,
+            )
 
     def test_prototypes1d_inputndim_with_data(self):
         with self.assertRaises(ValueError):
             _ = prototypes.Prototypes1D(input_dim=1,
-                                        nclasses=1,
+                                        num_classes=1,
                                         prototypes_per_class=1,
-                                        data=[[1.], [1]])
+                                        data=[[1.0], [1]])
 
     def test_prototypes1d_inputdim_with_data(self):
         with self.assertRaises(ValueError):
             _ = prototypes.Prototypes1D(
                 input_dim=2,
-                nclasses=2,
+                num_classes=2,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
-                data=[[[1.], [0.]], [1, 0]])
+                prototype_initializer="stratified_mean",
+                data=[[[1.0], [0.0]], [1, 0]],
+            )
 
-    def test_prototypes1d_nclasses_with_data(self):
-        """Test ValueError raise if provided `nclasses` is not the same
+    def test_prototypes1d_num_classes_with_data(self):
+        """Test ValueError raise if provided `num_classes` is not the same
         as the one computed from the provided `data`.
         """
         with self.assertRaises(ValueError):
             _ = prototypes.Prototypes1D(
                 input_dim=1,
-                nclasses=1,
+                num_classes=1,
                 prototypes_per_class=1,
-                prototype_initializer='stratified_mean',
-                data=[[[1.], [2.]], [1, 2]])
+                prototype_initializer="stratified_mean",
+                data=[[[1.0], [2.0]], [1, 2]],
+            )
 
     def test_prototypes1d_init_with_ppc(self):
         p1 = prototypes.Prototypes1D(data=[self.x, self.y],
                                      prototypes_per_class=2,
-                                     prototype_initializer='zeros')
+                                     prototype_initializer="zeros")
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(4, 3)
@@ -186,9 +200,11 @@ class TestPrototypes(unittest.TestCase):
         self.assertIsNone(mismatch)
 
     def test_prototypes1d_init_with_pdist(self):
-        p1 = prototypes.Prototypes1D(data=[self.x, self.y],
-                                     prototype_distribution=[6, 9],
-                                     prototype_initializer='zeros')
+        p1 = prototypes.Prototypes1D(
+            data=[self.x, self.y],
+            prototype_distribution=[6, 9],
+            prototype_initializer="zeros",
+        )
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(15, 3)
@@ -201,10 +217,12 @@ class TestPrototypes(unittest.TestCase):
         def my_initializer(*args, **kwargs):
             return torch.full((2, 99), 99.0), torch.tensor([0, 1])
 
-        p1 = prototypes.Prototypes1D(input_dim=99,
-                                     nclasses=2,
-                                     prototypes_per_class=1,
-                                     prototype_initializer=my_initializer)
+        p1 = prototypes.Prototypes1D(
+            input_dim=99,
+            num_classes=2,
+            prototypes_per_class=1,
+            prototype_initializer=my_initializer,
+        )
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = 99 * torch.ones(2, 99)
@@ -231,7 +249,7 @@ class TestPrototypes(unittest.TestCase):
     def test_prototypes1d_validate_extra_repr_not_empty(self):
         p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
         rep = p1.extra_repr()
-        self.assertNotEqual(rep, '')
+        self.assertNotEqual(rep, "")
 
     def tearDown(self):
         del self.x, self.y, self.gen
@@ -243,11 +261,11 @@ class TestLosses(unittest.TestCase):
         pass
 
     def test_glvqloss_init(self):
-        _ = losses.GLVQLoss(0, 'swish_beta', beta=20)
+        _ = losses.GLVQLoss(0, "swish_beta", beta=20)
 
     def test_glvqloss_forward_1ppc(self):
         criterion = losses.GLVQLoss(margin=0,
-                                    squashing='sigmoid_beta',
+                                    squashing="sigmoid_beta",
                                     beta=100)
         d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
         labels = torch.tensor([0, 1])
@@ -259,7 +277,7 @@ class TestLosses(unittest.TestCase):
 
     def test_glvqloss_forward_2ppc(self):
         criterion = losses.GLVQLoss(margin=0,
-                                    squashing='sigmoid_beta',
+                                    squashing="sigmoid_beta",
                                     beta=100)
         d = torch.stack([
             torch.ones(100),

tox.ini

@@ -1,15 +0,0 @@
-# tox (https://tox.readthedocs.io/) is a tool for running tests
-# in multiple virtualenvs. This configuration file will run the
-# test suite on all supported python versions. To use it, "pip install tox"
-# and then run "tox" from this directory.
-
-[tox]
-envlist = py36,py37,py38
-
-[testenv]
-deps =
-    pytest
-    coverage
-commands =
-    pip install -e .
-    coverage run -m pytest