Bump version: 0.1.1-dev0 → 0.1.1-rc0

"Use of assert detected. The enclosed code will be removed when compiling to
optimised byte code."

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.0-dev0
+current_version = 0.1.1-rc0
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?

.github/workflows/pythonapp.yml

@@ -1,7 +1,7 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: Tests
+name: tests
 
 on:
   push:
@@ -24,6 +24,9 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install .
+    - name: Install extras
+      run: |
+        pip install -r requirements.txt
     - name: Lint with flake8
       run: |
         pip install flake8

.travis.yml

@@ -0,0 +1,18 @@
+dist: bionic
+sudo: false
+language: python
+python: 3.8
+cache:
+  directories:
+    - ./tests/artifacts
+
+install:
+  - pip install . --progress-bar off
+  - pip install -r requirements.txt
+
+script:
+  - coverage run -m pytest
+
+# Push the results to codecov
+after_success:
+  - bash <(curl -s https://codecov.io/bash)

MANIFEST.in

@@ -1,9 +1,11 @@
 include .bumpversion.cfg
 include LICENSE
 include tox.ini
+include *.yml
 recursive-include docs *.bat
 recursive-include docs *.png
 recursive-include docs *.py
 recursive-include docs *.rst
 recursive-include docs Makefile
 recursive-include examples *.py
+recursive-include tests *.py

README.md

@@ -3,8 +3,14 @@
 ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
 prototype-based machine learning algorithms.
 
-![Tests](https://github.com/si-cim/prototorch/workflows/Tests/badge.svg)
+[![Build Status](https://travis-ci.org/si-cim/prototorch.svg?branch=master)](https://travis-ci.org/si-cim/prototorch)
+![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg)
+[![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/si-cim/prototorch?color=yellow&label=version)](https://github.com/si-cim/prototorch/releases)
+[![PyPI](https://img.shields.io/pypi/v/prototorch)](https://pypi.org/project/prototorch/)
 [![codecov](https://codecov.io/gh/si-cim/prototorch/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/prototorch)
+[![Codacy Badge](https://api.codacy.com/project/badge/Grade/76273904bf9343f0a8b29cd8aca242e7)](https://www.codacy.com/gh/si-cim/prototorch?utm_source=github.com&utm_medium=referral&utm_content=si-cim/prototorch&utm_campaign=Badge_Grade)
+![PyPI - Downloads](https://img.shields.io/pypi/dm/prototorch?color=blue)
+[![GitHub license](https://img.shields.io/github/license/si-cim/prototorch)](https://github.com/si-cim/prototorch/blob/master/LICENSE)
 
 ## Description
 
@@ -22,12 +28,12 @@ provided by PyTorch.
 ## Installation
 
 ProtoTorch can be installed using `pip`.
-```
+```bash
 pip install prototorch
 ```
 
 To install the bleeding-edge features and improvements:
-```
+```bash
 git clone https://github.com/si-cim/prototorch.git
 git checkout dev
 cd prototorch
@@ -47,3 +53,16 @@ API, with more algorithms and techniques coming soon. If you would simply like
 to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
 lets you do this without requiring a black-belt in high-performance Tensor
 computation.
+
+## Bibtex
+
+If you would like to cite the package, please use this:
+```bibtex
+@misc{Ravichandran2020,
+  author = {Ravichandran, J},
+  title = {ProtoTorch},
+  year = {2020},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/si-cim/prototorch}}
+}

RELEASE.md

@@ -0,0 +1,11 @@
+# ProtoTorch Releases
+
+## Release 0.1.1-dev0
+
+### Includes
+ - Minor bugfixes.
+ - 100% line coverage.
+
+## Release 0.1.0-dev0
+
+Initial public release of ProtoTorch.

examples/glvq_iris.py

@@ -1,4 +1,4 @@
-"""ProtoTorch GLVQ example using 2D Iris data"""
+"""ProtoTorch GLVQ example using 2D Iris data."""
 
 import numpy as np
 import torch
@@ -8,7 +8,7 @@ from sklearn.preprocessing import StandardScaler
 
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules.losses import GLVQLoss
-from prototorch.modules.prototypes import AddPrototypes1D
+from prototorch.modules.prototypes import Prototypes1D
 
 # Prepare and preprocess the data
 scaler = StandardScaler()
@@ -21,11 +21,12 @@ x_train = scaler.transform(x_train)
 # Define the GLVQ model
 class Model(torch.nn.Module):
     def __init__(self, **kwargs):
+        """GLVQ model."""
         super().__init__()
-        self.p1 = AddPrototypes1D(input_dim=2,
+        self.p1 = Prototypes1D(input_dim=2,
-                                  prototypes_per_class=1,
+                               prototypes_per_class=1,
-                                  nclasses=3,
+                               nclasses=3,
-                                  prototype_initializer='zeros')
+                               prototype_initializer='zeros')
 
     def forward(self, x):
         protos = self.p1.prototypes
@@ -41,13 +42,17 @@ model = Model()
 optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
 criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
 
+x_in = torch.Tensor(x_train)
+y_in = torch.Tensor(y_train)
+
 # Training loop
-fig = plt.figure('Prototype Visualization')
+title = 'Prototype Visualization'
+fig = plt.figure(title)
 for epoch in range(70):
-    # Compute loss.
+    # Compute loss
-    distances, plabels = model(torch.tensor(x_train))
+    dis, plabels = model(x_in)
-    loss = criterion([distances, plabels], torch.tensor(y_train))
+    loss = criterion([dis, plabels], y_in)
-    print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():02.02f}')
+    print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f}')
 
     # Take a gradient descent step
     optimizer.zero_grad()
@@ -60,6 +65,9 @@ for epoch in range(70):
     # Visualize the data and the prototypes
     ax = fig.gca()
     ax.cla()
+    ax.set_title(title)
+    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],
@@ -71,28 +79,17 @@ for epoch in range(70):
                s=50)
 
     # Paint decision regions
-    border = 1
-    resolution = 50
     x = np.vstack((x_train, protos))
-    x_min, x_max = x[:, 0].min(), x[:, 0].max()
+    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
-    y_min, y_max = x[:, 1].min(), x[:, 1].max()
+    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
-    x_min, x_max = x_min - border, x_max + border
+    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
-    y_min, y_max = y_min - border, y_max + border
+                         np.arange(y_min, y_max, 1 / 50))
-    try:
-        xx, yy = np.meshgrid(np.arange(x_min, x_max, 1.0 / resolution),
-                             np.arange(y_min, y_max, 1.0 / resolution))
-    except ValueError as ve:
-        print(ve)
-        raise ValueError(f'x_min: {x_min}, x_max: {x_max}. '
-                         f'x_min - x_max is {x_max - x_min}.')
-    except MemoryError as me:
-        print(me)
-        raise ValueError('Too many points. ' 'Try reducing the resolution.')
     mesh_input = np.c_[xx.ravel(), yy.ravel()]
 
-    torch_input = torch.from_numpy(mesh_input)
+    torch_input = torch.Tensor(mesh_input)
     d = model(torch_input)[0]
-    y_pred = np.argmin(d.detach().numpy(), axis=1)
+    y_pred = np.argmin(d.detach().numpy(),
+                       axis=1)  # assume one prototype per class
     y_pred = y_pred.reshape(xx.shape)
 
     # Plot voronoi regions
@@ -100,4 +97,5 @@ for epoch in range(70):
 
     ax.set_xlim(left=x_min + 0, right=x_max - 0)
     ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
+
     plt.pause(0.1)

prototorch/__init__.py

@@ -1 +1,11 @@
-__version__ = '0.1.0-dev0'
+"""ProtoTorch package."""
+
+__version__ = '0.1.1-rc0'
+
+from prototorch import datasets, functions, modules
+
+__all__ = [
+    'datasets',
+    'functions',
+    'modules',
+]

prototorch/datasets/__init__.py

@@ -0,0 +1,7 @@
+"""ProtoTorch datasets."""
+
+from .tecator import Tecator
+
+__all__ = [
+    'Tecator',
+]

prototorch/datasets/abstract.py

@@ -0,0 +1,87 @@
+"""ProtoTorch abstract dataset classes.
+
+Based on `torchvision.VisionDataset` and `torchvision.MNIST`
+
+For the original code, see:
+https://github.com/pytorch/vision/blob/master/torchvision/datasets/vision.py
+https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py
+"""
+
+import os
+
+import torch
+
+
+class Dataset(torch.utils.data.Dataset):
+    """Abstract dataset class to be inherited."""
+    _repr_indent = 2
+
+    def __init__(self, root):
+        if isinstance(root, torch._six.string_classes):
+            root = os.path.expanduser(root)
+        self.root = root
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def __len__(self):
+        raise NotImplementedError
+
+
+class ProtoDataset(Dataset):
+    """Abstract dataset class to be inherited."""
+    training_file = 'training.pt'
+    test_file = 'test.pt'
+
+    def __init__(self, root, train=True, download=True, verbose=True):
+        super().__init__(root)
+        self.train = train  # training set or test set
+        self.verbose = verbose
+
+        if download:
+            self.download()
+
+        if not self._check_exists():
+            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))
+
+    @property
+    def raw_folder(self):
+        return os.path.join(self.root, self.__class__.__name__, 'raw')
+
+    @property
+    def processed_folder(self):
+        return os.path.join(self.root, self.__class__.__name__, 'processed')
+
+    @property
+    def class_to_idx(self):
+        return {_class: i for i, _class in enumerate(self.classes)}
+
+    def _check_exists(self):
+        return (os.path.exists(
+            os.path.join(self.processed_folder, self.training_file))
+                and os.path.exists(
+                    os.path.join(self.processed_folder, self.test_file)))
+
+    def __repr__(self):
+        head = 'Dataset ' + self.__class__.__name__
+        body = ['Number of datapoints: {}'.format(self.__len__())]
+        if self.root is not None:
+            body.append('Root location: {}'.format(self.root))
+        body += self.extra_repr().splitlines()
+        lines = [head] + [' ' * self._repr_indent + line for line in body]
+        return '\n'.join(lines)
+
+    def extra_repr(self):
+        return f"Split: {'Train' if self.train is True else 'Test'}"
+
+    def __len__(self):
+        return len(self.data)
+
+    def download(self):
+        raise NotImplementedError

prototorch/datasets/tecator.py

@@ -0,0 +1,102 @@
+"""Tecator dataset for classification.
+
+URL:
+    http://lib.stat.cmu.edu/datasets/tecator
+
+LICENCE / TERMS / COPYRIGHT:
+    This is the Tecator data set: The task is to predict the fat content
+    of a meat sample on the basis of its near infrared absorbance spectrum.
+    -------------------------------------------------------------------------
+    1. Statement of permission from Tecator (the original data source)
+
+    These data are recorded on a Tecator Infratec Food and Feed Analyzer
+    working in the wavelength range 850 - 1050 nm by the Near Infrared
+    Transmission (NIT) principle. Each sample contains finely chopped pure
+    meat with different moisture, fat and protein contents.
+
+    If results from these data are used in a publication we want you to
+    mention the instrument and company name (Tecator) in the publication.
+    In addition, please send a preprint of your article to
+
+        Karin Thente, Tecator AB,
+        Box 70, S-263 21 Hoganas, Sweden
+
+    The data are available in the public domain with no responsability from
+    the original data source. The data can be redistributed as long as this
+    permission note is attached.
+
+    For more information about the instrument - call Perstorp Analytical's
+    representative in your area.
+
+Description:
+    For each meat sample the data consists of a 100 channel spectrum of
+    absorbances and the contents of moisture (water), fat and protein.
+    The absorbance is -log10 of the transmittance
+    measured by the spectrometer. The three contents, measured in percent,
+    are determined by analytic chemistry.
+"""
+
+import os
+
+import numpy as np
+import torch
+from torchvision.datasets.utils import download_file_from_google_drive
+
+from prototorch.datasets.abstract import ProtoDataset
+
+
+class Tecator(ProtoDataset):
+    """Tecator dataset for classification."""
+    resources = [
+        ('1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0',
+         'ba5607c580d0f91bb27dc29d13c2f8df'),
+    ]  # (google_storage_id, md5hash)
+    classes = ['0 - low_fat', '1 - high_fat']
+
+    def __getitem__(self, index):
+        img, target = self.data[index], int(self.targets[index])
+        return img, target
+
+    def download(self):
+        """Download the data if it doesn't exist in already."""
+        if self._check_exists():
+            return
+
+        if self.verbose:
+            print('Making directories...')
+        os.makedirs(self.raw_folder, exist_ok=True)
+        os.makedirs(self.processed_folder, exist_ok=True)
+
+        if self.verbose:
+            print('Downloading...')
+        for fileid, md5 in self.resources:
+            filename = 'tecator.npz'
+            download_file_from_google_drive(fileid,
+                                            root=self.raw_folder,
+                                            filename=filename,
+                                            md5=md5)
+
+        if self.verbose:
+            print('Processing...')
+        with np.load(os.path.join(self.raw_folder, 'tecator.npz'),
+                     allow_pickle=False) as f:
+            x_train, y_train = f['x_train'], f['y_train']
+            x_test, y_test = f['x_test'], f['y_test']
+        training_set = [
+            torch.tensor(x_train, dtype=torch.float32),
+            torch.tensor(y_train),
+        ]
+        test_set = [
+            torch.tensor(x_test, dtype=torch.float32),
+            torch.tensor(y_test),
+        ]
+
+        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:
+            torch.save(test_set, f)
+
+        if self.verbose:
+            print('Done!')

prototorch/functions/__init__.py

@@ -0,0 +1,12 @@
+"""ProtoTorch functions."""
+
+from .activations import identity, sigmoid_beta, swish_beta
+from .competitions import knnc, wtac
+
+__all__ = [
+    'identity',
+    'sigmoid_beta',
+    'swish_beta',
+    'knnc',
+    'wtac',
+]

prototorch/functions/activations.py

@@ -5,44 +5,60 @@ import torch
 ACTIVATIONS = dict()
 
 
-def register_activation(func):
+# def register_activation(scriptf):
-    ACTIVATIONS[func.__name__] = func
+#     ACTIVATIONS[scriptf.name] = scriptf
-    return func
+#     return scriptf
+def register_activation(function):
+    """Add the activation function to the registry."""
+    ACTIVATIONS[function.__name__] = function
+    return function
 
 
 @register_activation
-def identity(input, **kwargs):
+# @torch.jit.script
-    """:math:`f(x) = x`"""
+def identity(x, beta=torch.tensor(0)):
-    return input
+    """Identity activation function.
+
+    Definition:
+    :math:`f(x) = x`
+    """
+    return x
 
 
 @register_activation
-def sigmoid_beta(input, beta=10):
+# @torch.jit.script
-    """:math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`
+def sigmoid_beta(x, beta=torch.tensor(10)):
+    r"""Sigmoid activation function with scaling.
+
+    Definition:
+    :math:`f(x) = \frac{1}{1 + e^{-\beta x}}`
 
     Keyword Arguments:
-        beta (float): Parameter :math:`\\beta`
+        beta (`torch.tensor`): Scaling parameter :math:`\beta`
     """
-    out = torch.reciprocal(1.0 + torch.exp(-beta * input))
+    out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x))
     return out
 
 
 @register_activation
-def swish_beta(input, beta=10):
+# @torch.jit.script
-    """:math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`
+def swish_beta(x, beta=torch.tensor(10)):
+    r"""Swish activation function with scaling.
+
+    Definition:
+    :math:`f(x) = \frac{x}{1 + e^{-\beta x}}`
 
     Keyword Arguments:
-        beta (float): Parameter :math:`\\beta`
+        beta (`torch.tensor`): Scaling parameter :math:`\beta`
     """
-    out = input * sigmoid_beta(input, beta=beta)
+    out = x * sigmoid_beta(x, beta=beta)
     return out
 
 
 def get_activation(funcname):
+    """Deserialize the activation function."""
     if callable(funcname):
         return funcname
-    else:
+    if funcname in ACTIVATIONS:
-        if funcname in ACTIVATIONS:
+        return ACTIVATIONS.get(funcname)
-            return ACTIVATIONS.get(funcname)
+    raise NameError(f'Activation {funcname} was not found.')
-        else:
-            raise NameError(f'Activation {funcname} was not found.')

prototorch/functions/competitions.py

@@ -3,13 +3,43 @@
 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:
+        # 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'))
+    # 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)
+        cdists = torch.where(matcher, distances.T, inf).T
+        winning_distances[i] = torch.min(cdists, dim=1,
+                                         keepdim=True).values.squeeze()
+    if labels.ndim == 2:
+        # Transpose to return with `batch_size` first and
+        # reverse the columns to fix the ordering of the classes
+        return torch.flip(winning_distances.T, dims=(1, ))
+
+    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, dim=1).indices
+    winning_indices = torch.topk(-distances, k=k.item(), dim=1).indices
     winning_labels = labels[winning_indices].squeeze()
     return winning_labels

prototorch/functions/distances.py

@@ -28,26 +28,19 @@ def euclidean_distance(x, y):
 
 
 def lpnorm_distance(x, y, p):
-    """Compute :math:`{\\langle x, y \\rangle}_p`.
+    r"""Compute :math:`{\langle x, y \rangle}_p`.
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
     """
-    # # DEPRECATED in favor of torch.cdist
-    # expanded_x = x.unsqueeze(dim=1)
-    # batchwise_difference = y - expanded_x
-    # differences_raised = torch.pow(batchwise_difference, p)
-    # distances_raised = torch.sum(differences_raised, axis=2)
-    # distances = torch.pow(distances_raised, 1.0 / p)
-    # return distances
     distances = torch.cdist(x, y, p=p)
     return distances
 
 
 def omega_distance(x, y, omega):
-    """Omega distance.
+    r"""Omega distance.
 
-    Compute :math:`{\\langle \\Omega x, \\Omega y \\rangle}_p`
+    Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -60,9 +53,9 @@ def omega_distance(x, y, omega):
 
 
 def lomega_distance(x, y, omegas):
-    """Localized Omega distance.
+    r"""Localized Omega distance.
 
-    Compute :math:`{\\langle \\Omega_k x, \\Omega_k y_k \\rangle}_p`
+    Compute :math:`{\langle \Omega_k x, \Omega_k y_k \rangle}_p`
 
     Expected dimension of x is 2.
     Expected dimension of y is 2.
@@ -76,3 +69,7 @@ def lomega_distance(x, y, omegas):
     distances = torch.sum(differences_squared, dim=2)
     distances = distances.permute(1, 0)
     return distances
+
+
+# Aliases
+sed = squared_euclidean_distance

prototorch/functions/initializers.py

@@ -7,87 +7,97 @@ import torch
 INITIALIZERS = dict()
 
 
-def register_initializer(func):
+def register_initializer(function):
-    INITIALIZERS[func.__name__] = func
+    """Add the initializer to the registry."""
-    return func
+    INITIALIZERS[function.__name__] = function
+    return function
 
 
-def labels_from(distribution):
+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)]
     # labels = [l for cl in llist for l in cl]  # flatten the list of lists
-    labels = list(chain(*llist))  # flatten using itertools.chain
+    flat_llist = list(chain(*llist))  # flatten label list with itertools.chain
-    return torch.tensor(labels, requires_grad=False)
+    plabels = torch.tensor(flat_llist, requires_grad=False)
+    if one_hot:
+        return torch.eye(nclasses)[plabels]
+    return plabels
 
 
 @register_initializer
-def ones(x_train, y_train, prototype_distribution):
+def ones(x_train, y_train, prototype_distribution, one_hot=True):
     nprotos = torch.sum(prototype_distribution)
     protos = torch.ones(nprotos, *x_train.shape[1:])
-    plabels = labels_from(prototype_distribution)
+    plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
-def zeros(x_train, y_train, prototype_distribution):
+def zeros(x_train, y_train, prototype_distribution, one_hot=True):
     nprotos = torch.sum(prototype_distribution)
     protos = torch.zeros(nprotos, *x_train.shape[1:])
-    plabels = labels_from(prototype_distribution)
+    plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
-def rand(x_train, y_train, prototype_distribution):
+def rand(x_train, y_train, prototype_distribution, one_hot=True):
     nprotos = torch.sum(prototype_distribution)
     protos = torch.rand(nprotos, *x_train.shape[1:])
-    plabels = labels_from(prototype_distribution)
+    plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
-def randn(x_train, y_train, prototype_distribution):
+def randn(x_train, y_train, prototype_distribution, one_hot=True):
     nprotos = torch.sum(prototype_distribution)
     protos = torch.randn(nprotos, *x_train.shape[1:])
-    plabels = labels_from(prototype_distribution)
+    plabels = labels_from(prototype_distribution, one_hot)
     return protos, plabels
 
 
 @register_initializer
-def stratified_mean(x_train, y_train, prototype_distribution):
+def stratified_mean(x_train, y_train, prototype_distribution, one_hot=True):
     nprotos = torch.sum(prototype_distribution)
     pdim = x_train.shape[1]
     protos = torch.empty(nprotos, pdim)
-    plabels = labels_from(prototype_distribution)
+    plabels = labels_from(prototype_distribution, one_hot)
-    for i, l in enumerate(plabels):
+    for i, label in enumerate(plabels):
-        xl = x_train[y_train == l]
+        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)
+        xl = x_train[matcher]
         mean_xl = torch.mean(xl, dim=0)
         protos[i] = mean_xl
+    plabels = labels_from(prototype_distribution, one_hot=one_hot)
     return protos, plabels
 
 
 @register_initializer
-def stratified_random(x_train, y_train, prototype_distribution):
+def stratified_random(x_train, y_train, prototype_distribution, one_hot=True):
-    gen = torch.manual_seed(torch.initial_seed())
     nprotos = torch.sum(prototype_distribution)
     pdim = x_train.shape[1]
     protos = torch.empty(nprotos, pdim)
-    plabels = labels_from(prototype_distribution)
+    plabels = labels_from(prototype_distribution, one_hot)
-    for i, l in enumerate(plabels):
+    for i, label in enumerate(plabels):
-        xl = x_train[y_train == l]
+        matcher = torch.eq(label.unsqueeze(dim=0), y_train)
-        rand_index = torch.zeros(1).long().random_(0,
+        if one_hot:
-                                                   xl.shape[1] - 1,
+            nclasses = y_train.size()[1]
-                                                   generator=gen)
+            matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+        xl = x_train[matcher]
+        rand_index = torch.zeros(1).long().random_(0, xl.shape[0] - 1)
         random_xl = xl[rand_index]
         protos[i] = random_xl
+    plabels = labels_from(prototype_distribution, one_hot=one_hot)
     return protos, plabels
 
 
 def get_initializer(funcname):
+    """Deserialize the initializer."""
     if callable(funcname):
         return funcname
-    else:
+    if funcname in INITIALIZERS:
-        if funcname in INITIALIZERS:
+        return INITIALIZERS.get(funcname)
-            return INITIALIZERS.get(funcname)
+    raise NameError(f'Initializer {funcname} was not found.')
-        else:
-            raise NameError(f'Initializer {funcname} was not found.')

prototorch/functions/losses.py

@@ -3,23 +3,24 @@
 import torch
 
 
-def glvq_loss(distances, target_labels, prototype_labels):
+def _get_dp_dm(distances, targets, plabels):
-    """GLVQ loss function with support for one-hot labels."""
+    matcher = torch.eq(targets.unsqueeze(dim=1), plabels)
-    matcher = torch.eq(target_labels.unsqueeze(dim=1), prototype_labels)
+    if plabels.ndim == 2:
-    if prototype_labels.ndim == 2:
         # if the labels are one-hot vectors
-        nclasses = target_labels.size()[1]
+        nclasses = targets.size()[1]
         matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
     not_matcher = torch.bitwise_not(matcher)
 
-    dplus_criterion = distances * matcher > 0.0
-    dminus_criterion = distances * not_matcher > 0.0
-
     inf = torch.full_like(distances, fill_value=float('inf'))
-    distances_to_wpluses = torch.where(dplus_criterion, distances, inf)
+    d_matching = torch.where(matcher, distances, inf)
-    distances_to_wminuses = torch.where(dminus_criterion, distances, inf)
+    d_unmatching = torch.where(not_matcher, distances, inf)
-    dpluses = torch.min(distances_to_wpluses, dim=1, keepdim=True).values
+    dp = torch.min(d_matching, dim=1, keepdim=True).values
-    dminuses = torch.min(distances_to_wminuses, dim=1, keepdim=True).values
+    dm = torch.min(d_unmatching, dim=1, keepdim=True).values
+    return dp, dm
 
-    mu = (dpluses - dminuses) / (dpluses + dminuses)
+
+def glvq_loss(distances, target_labels, prototype_labels):
+    """GLVQ loss function with support for one-hot labels."""
+    dp, dm = _get_dp_dm(distances, target_labels, prototype_labels)
+    mu = (dp - dm) / (dp + dm)
     return mu

prototorch/modules/__init__.py

@@ -0,0 +1,7 @@
+"""ProtoTorch modules."""
+
+from .prototypes import Prototypes1D
+
+__all__ = [
+    'Prototypes1D',
+]

prototorch/modules/losses.py

@@ -7,12 +7,11 @@ from prototorch.functions.losses import glvq_loss
 
 
 class GLVQLoss(torch.nn.Module):
-    """GLVQ Loss."""
     def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
         super().__init__(**kwargs)
         self.margin = margin
         self.squashing = get_activation(squashing)
-        self.beta = beta
+        self.beta = torch.tensor(beta)
 
     def forward(self, outputs, targets):
         distances, plabels = outputs

prototorch/modules/prototypes.py

@@ -1,57 +1,165 @@
 """ProtoTorch prototype modules."""
 
+import warnings
+
+import numpy as np
 import torch
 
+from prototorch.functions.competitions import wtac
+from prototorch.functions.distances import sed
 from prototorch.functions.initializers import get_initializer
 
 
-class AddPrototypes1D(torch.nn.Module):
+class _Prototypes(torch.nn.Module):
+    """Abstract prototypes class."""
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _validate_prototype_distribution(self):
+        if 0 in self.prototype_distribution:
+            warnings.warn('Are you sure about the `0` in '
+                          '`prototype_distribution`?')
+
+    def extra_repr(self):
+        return f'prototypes.shape: {tuple(self.prototypes.shape)}'
+
+    def forward(self):
+        return self.prototypes, self.prototype_labels
+
+
+class Prototypes1D(_Prototypes):
+    r"""Create a learnable set of one-dimensional prototypes.
+
+    TODO Complete this doc-string
+
+    Kwargs:
+        prototypes_per_class: number of prototypes to use per class.
+            Default: ``1``
+        prototype_initializer: prototype initializer.
+            Default: ``'ones'``
+        prototype_distribution: prototype distribution vector.
+            Default: ``None``
+        input_dim: dimension of the incoming data.
+        nclasses: number of classes.
+        data: If set to ``None``, data-dependent initializers will be ignored.
+            Default: ``None``
+
+    Shape:
+        - Input: :math:`(N, H_{in})`
+            where :math:`H_{in} = \text{input_dim}`.
+        - Output: :math:`(N, H_{out})`
+            where :math:`H_{out} = \text{total_prototypes}`.
+
+    Attributes:
+        prototypes: the learnable weights of the module of shape
+            :math:`(\text{total_prototypes}, \text{prototype_dimension})`.
+        prototype_labels: the non-learnable labels of the prototypes.
+
+    Examples::
+
+        >>> p = Prototypes1D(input_dim=20, nclasses=10)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([20, 10])
+    """
     def __init__(self,
                  prototypes_per_class=1,
-                 prototype_distribution=None,
                  prototype_initializer='ones',
+                 prototype_distribution=None,
                  data=None,
+                 dtype=torch.float32,
+                 one_hot_labels=False,
                  **kwargs):
 
+        # Convert tensors to python lists before processing
+        if prototype_distribution is not None:
+            if not isinstance(prototype_distribution, list):
+                prototype_distribution = prototype_distribution.tolist()
+
         if data is None:
             if 'input_dim' not in kwargs:
                 raise NameError('`input_dim` required if '
                                 'no `data` is provided.')
-            if prototype_distribution is not None:
+            if prototype_distribution:
-                nclasses = sum(prototype_distribution)
+                kwargs_nclasses = sum(prototype_distribution)
             else:
                 if 'nclasses' not in kwargs:
                     raise NameError('`prototype_distribution` required if '
                                     'both `data` and `nclasses` are not '
                                     'provided.')
-                nclasses = kwargs.pop('nclasses')
+                kwargs_nclasses = kwargs.pop('nclasses')
             input_dim = kwargs.pop('input_dim')
-            # input_shape = (input_dim, )
+            if prototype_initializer in [
-            x_train = torch.rand(nclasses, input_dim)
+                    'stratified_mean', 'stratified_random'
-            y_train = torch.arange(nclasses)
+            ]:
+                warnings.warn(
+                    f'`prototype_initializer`: `{prototype_initializer}` '
+                    'requires `data`, but `data` is not provided. '
+                    'Using randomly generated data instead.')
+            x_train = torch.rand(kwargs_nclasses, input_dim)
+            y_train = torch.arange(kwargs_nclasses)
+            if one_hot_labels:
+                y_train = torch.eye(kwargs_nclasses)[y_train]
+            data = [x_train, y_train]
 
-        else:
+        x_train, y_train = data
-            x_train, y_train = data
+        x_train = torch.as_tensor(x_train).type(dtype)
-            x_train = torch.as_tensor(x_train)
+        y_train = torch.as_tensor(y_train).type(torch.int)
-            y_train = torch.as_tensor(y_train)
+        nclasses = torch.unique(y_train, dim=-1).shape[-1]
+
+        if nclasses == 1:
+            warnings.warn('Are you sure about having one class only?')
+
+        if x_train.ndim != 2:
+            raise ValueError('`data[0].ndim != 2`.')
+
+        if y_train.ndim == 2:
+            if y_train.shape[1] == 1 and one_hot_labels:
+                raise ValueError('`one_hot_labels` is set to `True` '
+                                 'but target labels are not one-hot-encoded.')
+            if y_train.shape[1] != 1 and not one_hot_labels:
+                raise ValueError('`one_hot_labels` is set to `False` '
+                                 'but target labels in `data` '
+                                 'are one-hot-encoded.')
+        if y_train.ndim == 1 and one_hot_labels:
+            raise ValueError('`one_hot_labels` is set to `True` '
+                             'but target labels are not one-hot-encoded.')
+
+        # Verify input dimension if `input_dim` is provided
+        if 'input_dim' in kwargs:
+            input_dim = kwargs.pop('input_dim')
+            if input_dim != x_train.shape[1]:
+                raise ValueError(f'Provided `input_dim`={input_dim} does '
+                                 'not match data dimension '
+                                 f'`data[0].shape[1]`={x_train.shape[1]}')
+
+        # 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}')
 
         super().__init__(**kwargs)
-        self.prototypes_per_class = prototypes_per_class
+
+        if not prototype_distribution:
+            prototype_distribution = [prototypes_per_class] * nclasses
         with torch.no_grad():
-            if not prototype_distribution:
+            self.prototype_distribution = torch.tensor(prototype_distribution)
-                num_classes = torch.unique(y_train).shape[0]
+
-                self.prototype_distribution = torch.tensor(
+        self._validate_prototype_distribution()
-                    [self.prototypes_per_class] * num_classes)
+
-            else:
-                self.prototype_distribution = torch.tensor(
-                    prototype_distribution)
         self.prototype_initializer = get_initializer(prototype_initializer)
         prototypes, prototype_labels = self.prototype_initializer(
             x_train,
             y_train,
-            prototype_distribution=self.prototype_distribution)
+            prototype_distribution=self.prototype_distribution,
+            one_hot=one_hot_labels,
+        )
+
+        # Register module parameters
         self.prototypes = torch.nn.Parameter(prototypes)
         self.prototype_labels = prototype_labels
-
-    def forward(self):
-        return self.prototypes, self.prototype_labels

requirements.txt

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

setup.py

@@ -10,7 +10,7 @@ with open('README.md', 'r') as fh:
     long_description = fh.read()
 
 setup(name='prototorch',
-      version='0.1.0-dev0',
+      version='0.1.1-rc0',
       description='Highly extensible, GPU-supported '
       'Learning Vector Quantization (LVQ) toolbox '
       'built using PyTorch and its nn API.',
@@ -27,6 +27,7 @@ setup(name='prototorch',
           'numpy>=1.9.1',
       ],
       extras_require={
+          'datasets': ['requests'],
           'examples': [
               'sklearn',
               'matplotlib',

tests/test_datasets.py

@@ -0,0 +1,95 @@
+"""ProtoTorch datasets test suite."""
+
+import os
+import shutil
+import unittest
+
+import torch
+
+from prototorch.datasets import abstract, tecator
+
+
+class TestAbstract(unittest.TestCase):
+    def test_getitem(self):
+        with self.assertRaises(NotImplementedError):
+            abstract.Dataset('./artifacts')[0]
+
+    def test_len(self):
+        with self.assertRaises(NotImplementedError):
+            len(abstract.Dataset('./artifacts'))
+
+
+class TestProtoDataset(unittest.TestCase):
+    def test_getitem(self):
+        with self.assertRaises(NotImplementedError):
+            abstract.ProtoDataset('./artifacts')[0]
+
+    def test_download(self):
+        with self.assertRaises(NotImplementedError):
+            abstract.ProtoDataset('./artifacts').download()
+
+
+class TestTecator(unittest.TestCase):
+    def setUp(self):
+        self.artifacts_dir = './artifacts/Tecator'
+        self._remove_artifacts()
+
+    def _remove_artifacts(self):
+        if os.path.exists(self.artifacts_dir):
+            shutil.rmtree(self.artifacts_dir)
+
+    def test_download_false(self):
+        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]
+        _ = tecator.Tecator(rootdir, download=True, verbose=False)
+        _ = tecator.Tecator(rootdir, download=False, verbose=False)
+
+    def test_repr(self):
+        rootdir = self.artifacts_dir.rpartition('/')[0]
+        train = tecator.Tecator(rootdir, download=True, verbose=True)
+        self.assertTrue('Split: Train' in train.__repr__())
+
+    def test_download_train(self):
+        rootdir = self.artifacts_dir.rpartition('/')[0]
+        train = tecator.Tecator(root=rootdir,
+                                train=True,
+                                download=True,
+                                verbose=False)
+        train = tecator.Tecator(root=rootdir, download=True, verbose=False)
+        x_train, y_train = train.data, train.targets
+        self.assertEqual(x_train.shape[0], 144)
+        self.assertEqual(y_train.shape[0], 144)
+        self.assertEqual(x_train.shape[1], 100)
+
+    def test_download_test(self):
+        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)
+        self.assertEqual(y_test.shape[0], 71)
+        self.assertEqual(x_test.shape[1], 100)
+
+    def test_class_to_idx(self):
+        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]
+        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]
+        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
+
+    def tearDown(self):
+        pass

tests/test_functions.py

@@ -6,7 +6,148 @@ import numpy as np
 import torch
 
 from prototorch.functions import (activations, competitions, distances,
-                                  initializers)
+                                  initializers, losses)
+
+
+class TestActivations(unittest.TestCase):
+    def setUp(self):
+        self.flist = ['identity', 'sigmoid_beta', 'swish_beta']
+        self.x = torch.randn(1024, 1)
+
+    def test_registry(self):
+        self.assertIsNotNone(activations.ACTIVATIONS)
+
+    def test_funcname_deserialization(self):
+        for funcname in self.flist:
+            f = activations.get_activation(funcname)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+
+    # def test_torch_script(self):
+    #     for funcname in self.flist:
+    #         f = activations.get_activation(funcname)
+    #         self.assertIsInstance(f, torch.jit.ScriptFunction)
+
+    def test_callable_deserialization(self):
+        def dummy(x, **kwargs):
+            return x
+
+        for f in [dummy, lambda x: x]:
+            f = activations.get_activation(f)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+            self.assertEqual(1, f(1))
+
+    def test_unknown_deserialization(self):
+        for funcname in ['blubb', 'foobar']:
+            with self.assertRaises(NameError):
+                _ = activations.get_activation(funcname)
+
+    def test_identity(self):
+        actual = activations.identity(self.x)
+        desired = self.x
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_sigmoid_beta1(self):
+        actual = activations.sigmoid_beta(self.x, beta=torch.tensor(1))
+        desired = torch.sigmoid(self.x)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_swish_beta1(self):
+        actual = activations.swish_beta(self.x, beta=torch.tensor(1))
+        desired = self.x * torch.sigmoid(self.x)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x
+
+
+class TestCompetitions(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_wtac(self):
+        d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
+        labels = torch.tensor([0, 1, 2, 3])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_wtac_unequal_dist(self):
+        d = torch.tensor([[2., 3., 4.], [2., 3., 1.]])
+        labels = torch.tensor([0, 1, 1])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([0, 1])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_wtac_one_hot(self):
+        d = torch.tensor([[1.99, 3.01], [3., 2.01]])
+        labels = torch.tensor([[0, 1], [1, 0]])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([[0, 1], [1, 0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_min(self):
+        d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
+        labels = torch.tensor([0, 0, 1, 2])
+        actual = competitions.stratified_min(d, labels)
+        desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
+        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]])
+        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.]])
+        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]])
+        labels = torch.tensor([0, 1, 2])
+        actual = competitions.stratified_min(d, labels)
+        desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
+        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.]])
+        labels = torch.tensor([0, 1, 2, 3])
+        actual = competitions.knnc(d, labels, k=torch.tensor([1]))
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        pass
 
 
 class TestDistances(unittest.TestCase):
@@ -167,103 +308,12 @@ class TestDistances(unittest.TestCase):
         del self.x, self.y
 
 
-class TestActivations(unittest.TestCase):
-    def setUp(self):
-        self.x = torch.randn(1024, 1)
-
-    def test_registry(self):
-        self.assertIsNotNone(activations.ACTIVATIONS)
-
-    def test_funcname_deserialization(self):
-        flist = ['identity', 'sigmoid_beta', 'swish_beta']
-        for funcname in flist:
-            f = activations.get_activation(funcname)
-            iscallable = callable(f)
-            self.assertTrue(iscallable)
-
-    def test_callable_deserialization(self):
-        def dummy(x, **kwargs):
-            return x
-
-        for f in [dummy, lambda x: x]:
-            f = activations.get_activation(f)
-            iscallable = callable(f)
-            self.assertTrue(iscallable)
-            self.assertEqual(1, f(1))
-
-    def test_unknown_deserialization(self):
-        for funcname in ['blubb', 'foobar']:
-            with self.assertRaises(NameError):
-                _ = activations.get_activation(funcname)
-
-    def test_identity(self):
-        actual = activations.identity(self.x)
-        desired = self.x
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_sigmoid_beta1(self):
-        actual = activations.sigmoid_beta(self.x, beta=1)
-        desired = torch.sigmoid(self.x)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_swish_beta1(self):
-        actual = activations.swish_beta(self.x, beta=1)
-        desired = self.x * torch.sigmoid(self.x)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        del self.x
-
-
-class TestCompetitions(unittest.TestCase):
-    def setUp(self):
-        pass
-
-    def test_wtac(self):
-        d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
-        labels = torch.tensor([0, 1, 2, 3])
-        actual = competitions.wtac(d, labels)
-        desired = torch.tensor([2, 0])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_wtac_one_hot(self):
-        d = torch.tensor([[1.99, 3.01], [3., 2.01]])
-        labels = torch.tensor([[0, 1], [1, 0]])
-        actual = competitions.wtac(d, labels)
-        desired = torch.tensor([[0, 1], [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.]])
-        labels = torch.tensor([0, 1, 2, 3])
-        actual = competitions.knnc(d, labels, k=1)
-        desired = torch.tensor([2, 0])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        pass
-
-
 class TestInitializers(unittest.TestCase):
     def setUp(self):
+        self.flist = [
+            'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
+            'stratified_random'
+        ]
         self.x = torch.tensor(
             [[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
             dtype=torch.float32)
@@ -274,11 +324,7 @@ class TestInitializers(unittest.TestCase):
         self.assertIsNotNone(initializers.INITIALIZERS)
 
     def test_funcname_deserialization(self):
-        flist = [
+        for funcname in self.flist:
-            'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
-            'stratified_random'
-        ]
-        for funcname in flist:
             f = initializers.get_initializer(funcname)
             iscallable = callable(f)
             self.assertTrue(iscallable)
@@ -336,7 +382,7 @@ class TestInitializers(unittest.TestCase):
 
     def test_stratified_mean_equal1(self):
         pdist = torch.tensor([1, 1])
-        actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
+        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
         desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
@@ -345,8 +391,9 @@ class TestInitializers(unittest.TestCase):
 
     def test_stratified_random_equal1(self):
         pdist = torch.tensor([1, 1])
-        actual, _ = initializers.stratified_random(self.x, self.y, pdist)
+        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
-        desired = torch.tensor([[0., -1., -2.], [2., 2., 2.]])
+                                                   False)
+        desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
@@ -354,7 +401,7 @@ class TestInitializers(unittest.TestCase):
 
     def test_stratified_mean_equal2(self):
         pdist = torch.tensor([2, 2])
-        actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
+        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.]])
         mismatch = np.testing.assert_array_almost_equal(actual,
@@ -362,9 +409,20 @@ class TestInitializers(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
+    def test_stratified_random_equal2(self):
+        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.]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
     def test_stratified_mean_unequal(self):
         pdist = torch.tensor([1, 3])
-        actual, _ = initializers.stratified_mean(self.x, self.y, pdist)
+        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.]])
         mismatch = np.testing.assert_array_almost_equal(actual,
@@ -374,14 +432,86 @@ class TestInitializers(unittest.TestCase):
 
     def test_stratified_random_unequal(self):
         pdist = torch.tensor([1, 3])
-        actual, _ = initializers.stratified_random(self.x, self.y, pdist)
+        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
-        desired = torch.tensor([[0., -1., -2.], [2., 2., 2.], [0., 0., 0.],
+                                                   False)
+        desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
                                 [0., 0., 0.]])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
+    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.]])
+        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,
+                                                        desired1,
+                                                        decimal=5)
+        mismatch = np.testing.assert_array_almost_equal(actual2,
+                                                        desired2,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_random_unequal_one_hot(self):
+        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.]])
+        desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
+        mismatch = np.testing.assert_array_almost_equal(actual1,
+                                                        desired1,
+                                                        decimal=5)
+        mismatch = np.testing.assert_array_almost_equal(actual2,
+                                                        desired2,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
     def tearDown(self):
         del self.x, self.y, self.gen
         _ = torch.seed()
+
+
+class TestLosses(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_glvq_loss_int_labels(self):
+        d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+        labels = torch.tensor([0, 1])
+        targets = torch.ones(100)
+        batch_loss = losses.glvq_loss(distances=d,
+                                      target_labels=targets,
+                                      prototype_labels=labels)
+        loss_value = torch.sum(batch_loss, dim=0)
+        self.assertEqual(loss_value, -100)
+
+    def test_glvq_loss_one_hot_labels(self):
+        d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+        labels = torch.tensor([[0, 1], [1, 0]])
+        wl = torch.tensor([1, 0])
+        targets = torch.stack([wl for _ in range(100)], dim=0)
+        batch_loss = losses.glvq_loss(distances=d,
+                                      target_labels=targets,
+                                      prototype_labels=labels)
+        loss_value = torch.sum(batch_loss, dim=0)
+        self.assertEqual(loss_value, -100)
+
+    def test_glvq_loss_one_hot_unequal(self):
+        dlist = [torch.ones(100), torch.zeros(100), torch.zeros(100)]
+        d = torch.stack(dlist, dim=1)
+        labels = torch.tensor([[0, 1], [1, 0], [1, 0]])
+        wl = torch.tensor([1, 0])
+        targets = torch.stack([wl for _ in range(100)], dim=0)
+        batch_loss = losses.glvq_loss(distances=d,
+                                      target_labels=targets,
+                                      prototype_labels=labels)
+        loss_value = torch.sum(batch_loss, dim=0)
+        self.assertEqual(loss_value, -100)
+
+    def tearDown(self):
+        pass

tests/test_modules.py

@@ -5,7 +5,7 @@ import unittest
 import numpy as np
 import torch
 
-from prototorch.modules import prototypes, losses
+from prototorch.modules import losses, prototypes
 
 
 class TestPrototypes(unittest.TestCase):
@@ -16,19 +16,23 @@ class TestPrototypes(unittest.TestCase):
         self.y = torch.tensor([0, 0, 1, 1])
         self.gen = torch.manual_seed(42)
 
-    def test_addprototypes1d_init_without_input_dim(self):
+    def test_prototypes1d_init_without_input_dim(self):
         with self.assertRaises(NameError):
-            _ = prototypes.AddPrototypes1D(nclasses=1)
+            _ = prototypes.Prototypes1D(nclasses=2)
 
-    def test_addprototypes1d_init_without_nclasses(self):
+    def test_prototypes1d_init_without_nclasses(self):
         with self.assertRaises(NameError):
-            _ = prototypes.AddPrototypes1D(input_dim=1)
+            _ = prototypes.Prototypes1D(input_dim=1)
 
-    def test_addprototypes1d_init_without_pdist(self):
+    def test_prototypes1d_init_with_nclasses_1(self):
-        p1 = prototypes.AddPrototypes1D(input_dim=6,
+        with self.assertWarns(UserWarning):
-                                        nclasses=2,
+            _ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
-                                        prototypes_per_class=4,
+
-                                        prototype_initializer='ones')
+    def test_prototypes1d_init_without_pdist(self):
+        p1 = prototypes.Prototypes1D(input_dim=6,
+                                     nclasses=2,
+                                     prototypes_per_class=4,
+                                     prototype_initializer='ones')
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.ones(8, 6)
@@ -37,11 +41,11 @@ class TestPrototypes(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
-    def test_addprototypes1d_init_without_data(self):
+    def test_prototypes1d_init_without_data(self):
         pdist = [2, 2]
-        p1 = prototypes.AddPrototypes1D(input_dim=3,
+        p1 = prototypes.Prototypes1D(input_dim=3,
-                                        prototype_distribution=pdist,
+                                     prototype_distribution=pdist,
-                                        prototype_initializer='zeros')
+                                     prototype_initializer='zeros')
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(4, 3)
@@ -50,23 +54,20 @@ class TestPrototypes(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
-    # def test_addprototypes1d_init_torch_pdist(self):
+    def test_prototypes1d_proto_init_without_data(self):
-    #     pdist = torch.tensor([2, 2])
+        with self.assertWarns(UserWarning):
-    #     p1 = prototypes.AddPrototypes1D(input_dim=3,
+            _ = prototypes.Prototypes1D(
-    #                                     prototype_distribution=pdist,
+                input_dim=3,
-    #                                     prototype_initializer='zeros')
+                nclasses=2,
-    #     protos = p1.prototypes
+                prototypes_per_class=1,
-    #     actual = protos.detach().numpy()
+                prototype_initializer='stratified_mean',
-    #     desired = torch.zeros(4, 3)
+                data=None)
-    #     mismatch = np.testing.assert_array_almost_equal(actual,
-    #                                                     desired,
-    #                                                     decimal=5)
-    #     self.assertIsNone(mismatch)
 
-    def test_addprototypes1d_init_with_ppc(self):
+    def test_prototypes1d_init_torch_pdist(self):
-        p1 = prototypes.AddPrototypes1D(data=[self.x, self.y],
+        pdist = torch.tensor([2, 2])
-                                        prototypes_per_class=2,
+        p1 = prototypes.Prototypes1D(input_dim=3,
-                                        prototype_initializer='zeros')
+                                     prototype_distribution=pdist,
+                                     prototype_initializer='zeros')
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(4, 3)
@@ -75,10 +76,119 @@ class TestPrototypes(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
-    def test_addprototypes1d_init_with_pdist(self):
+    def test_prototypes1d_init_without_inputdim_with_data(self):
-        p1 = prototypes.AddPrototypes1D(data=[self.x, self.y],
+        _ = prototypes.Prototypes1D(nclasses=2,
-                                        prototype_distribution=[6, 9],
+                                    prototypes_per_class=1,
-                                        prototype_initializer='zeros')
+                                    prototype_initializer='stratified_mean',
+                                    data=[[[1.], [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]])
+
+    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)
+
+    def test_prototypes1d_init_one_hot_labels_false(self):
+        """Test if ValueError is raised when `one_hot_labels` is set to `False`
+        but the provided `data` has one-hot encoded labels.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer='stratified_mean',
+                data=([[0.], [1.]], [[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`
+        but the provided `data` does not contain one-hot encoded labels.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer='stratified_mean',
+                data=([[0.], [1.]], [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`
+        but the provided `data` contains 2D targets but
+        does not contain one-hot encoded labels.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer='stratified_mean',
+                data=([[0.], [1.]], [[0], [1]]),
+                one_hot_labels=True)
+
+    def test_prototypes1d_init_with_int_dtype(self):
+        with self.assertRaises(RuntimeError):
+            _ = prototypes.Prototypes1D(
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer='stratified_mean',
+                data=[[[1], [0]], [1, 0]],
+                dtype=torch.int32)
+
+    def test_prototypes1d_inputndim_with_data(self):
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(input_dim=1,
+                                        nclasses=1,
+                                        prototypes_per_class=1,
+                                        data=[[1.], [1]])
+
+    def test_prototypes1d_inputdim_with_data(self):
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=2,
+                nclasses=2,
+                prototypes_per_class=1,
+                prototype_initializer='stratified_mean',
+                data=[[[1.], [0.]], [1, 0]])
+
+    def test_prototypes1d_nclasses_with_data(self):
+        """Test ValueError raise if provided `nclasses` is not the same
+        as the one computed from the provided `data`.
+        """
+        with self.assertRaises(ValueError):
+            _ = prototypes.Prototypes1D(
+                input_dim=1,
+                nclasses=1,
+                prototypes_per_class=1,
+                prototype_initializer='stratified_mean',
+                data=[[[1.], [2.]], [1, 2]])
+
+    def test_prototypes1d_init_with_ppc(self):
+        p1 = prototypes.Prototypes1D(data=[self.x, self.y],
+                                     prototypes_per_class=2,
+                                     prototype_initializer='zeros')
+        protos = p1.prototypes
+        actual = protos.detach().numpy()
+        desired = torch.zeros(4, 3)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        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')
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = torch.zeros(15, 3)
@@ -87,14 +197,14 @@ class TestPrototypes(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
-    def test_addprototypes1d_func_initializer(self):
+    def test_prototypes1d_func_initializer(self):
         def my_initializer(*args, **kwargs):
             return torch.full((2, 99), 99), torch.tensor([0, 1])
 
-        p1 = prototypes.AddPrototypes1D(input_dim=99,
+        p1 = prototypes.Prototypes1D(input_dim=99,
-                                        nclasses=2,
+                                     nclasses=2,
-                                        prototypes_per_class=1,
+                                     prototypes_per_class=1,
-                                        prototype_initializer=my_initializer)
+                                     prototype_initializer=my_initializer)
         protos = p1.prototypes
         actual = protos.detach().numpy()
         desired = 99 * torch.ones(2, 99)
@@ -103,8 +213,8 @@ class TestPrototypes(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
-    def test_addprototypes1d_forward(self):
+    def test_prototypes1d_forward(self):
-        p1 = prototypes.AddPrototypes1D(data=[self.x, self.y])
+        p1 = prototypes.Prototypes1D(data=[self.x, self.y])
         protos, _ = p1()
         actual = protos.detach().numpy()
         desired = torch.ones(2, 3)
@@ -113,6 +223,16 @@ class TestPrototypes(unittest.TestCase):
                                                         decimal=5)
         self.assertIsNone(mismatch)
 
+    def test_prototypes1d_dist_validate(self):
+        p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
+        with self.assertWarns(UserWarning):
+            _ = p1._validate_prototype_distribution()
+
+    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, '')
+
     def tearDown(self):
         del self.x, self.y, self.gen
         _ = torch.seed()
@@ -123,7 +243,19 @@ class TestLosses(unittest.TestCase):
         pass
 
     def test_glvqloss_init(self):
-        _ = losses.GLVQLoss()
+        _ = losses.GLVQLoss(0, 'swish_beta', beta=20)
+
+    def test_glvqloss_forward(self):
+        criterion = losses.GLVQLoss(margin=0,
+                                    squashing='sigmoid_beta',
+                                    beta=100)
+        d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+        labels = torch.tensor([0, 1])
+        targets = torch.ones(100)
+        outputs = [d, labels]
+        loss = criterion(outputs, targets)
+        loss_value = loss.item()
+        self.assertAlmostEqual(loss_value, 0.0)
 
     def tearDown(self):
         pass

tox.ini

@@ -4,12 +4,12 @@
 # and then run "tox" from this directory.
 
 [tox]
-envlist = py36
+envlist = py36,py37,py38
 
 [testenv]
 deps =
-    numpy
+    pytest
-    unittest-xml-reporting
+    coverage
 commands =
-    python -m xmlrunner -o reports
+    pip install -e .
-
+    coverage run -m pytest