build: bump version 0.7.5 → 0.7.6

it was necessary to update the pre-commit definition for a successfull
commit.

.bumpversion.cfg

@@ -1,10 +1,10 @@
 [bumpversion]
-current_version = 0.4.2
+current_version = 0.7.6
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
-serialize = 
+serialize = {major}.{minor}.{patch}
-	{major}.{minor}.{patch}
+message = build: bump version {current_version} → {new_version}
 
 [bumpversion:file:setup.py]
 

.codacy.yml

@@ -1,15 +0,0 @@
-# To validate the contents of your configuration file
-# run the following command in the folder where the configuration file is located:
-# codacy-analysis-cli validate-configuration --directory `pwd`
-# To analyse, run:
-# codacy-analysis-cli analyse --tool remark-lint --directory `pwd`
----
-engines:
-  pylintpython3:
-    exclude_paths:
-      - config/engines.yml
-  remark-lint:
-    exclude_paths:
-      - config/engines.yml
-exclude_paths:
-  - 'tests/**'

.codecov.yml

@@ -1,2 +0,0 @@
-comment:
-  require_changes: yes

.github/ISSUE_TEMPLATE/bug_report.md

@@ -10,22 +10,29 @@ assignees: ''
 **Describe the bug**
 A clear and concise description of what the bug is.
 
-**To Reproduce**
+**Steps to reproduce the behavior**
-Steps to reproduce the behavior:
+1. ...
-1. Install Prototorch by running '...'
+2. Run script '...' or this snippet:
-2. Run script '...'
+```python
+import prototorch as pt
+
+...
+```
 3. See errors
 
 **Expected behavior**
 A clear and concise description of what you expected to happen.
 
+**Observed behavior**
+A clear and concise description of what actually happened.
+
 **Screenshots**
 If applicable, add screenshots to help explain your problem.
 
-**Desktop (please complete the following information):**
+**System and version information**
- - OS: [e.g. Ubuntu 20.10]
+- OS: [e.g. Ubuntu 20.10]
- - Prototorch Version: [e.g. v0.4.0]
+- ProtoTorch Version: [e.g. 0.4.0]
- - Python Version: [e.g. 3.9.5]
+- Python Version: [e.g. 3.9.5]
 
 **Additional context**
 Add any other context about the problem here.

.github/workflows/pythonapp.yml

@@ -5,33 +5,71 @@ name: tests
 
 on:
   push:
-    branches: [ master, dev ]
   pull_request:
-    branches: [ master ]
+    branches: [master]
 
 jobs:
-  build:
+  style:
-
     runs-on: ubuntu-latest
-
     steps:
-    - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
-    - name: Set up Python 3.8
+      - name: Set up Python 3.11
-      uses: actions/setup-python@v1
+        uses: actions/setup-python@v4
-      with:
+        with:
-        python-version: 3.8
+          python-version: "3.11"
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install .[all]
+          pip install .[all]
-    - name: Lint with flake8
+      - uses: pre-commit/action@v3.0.0
-      run: |
+  compatibility:
-        pip install flake8
+    needs: style
-        # stop the build if there are Python syntax errors or undefined names
+    strategy:
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+      fail-fast: false
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+      matrix:
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
-    - name: Test with pytest
+        os: [ubuntu-latest, windows-latest]
-      run: |
+        exclude:
-        pip install pytest
+          - os: windows-latest
-        pytest
+            python-version: "3.8"
+          - os: windows-latest
+            python-version: "3.9"
+          - os: windows-latest
+            python-version: "3.10"
+
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[all]
+      - name: Test with pytest
+        run: |
+          pytest
+  publish_pypi:
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+    needs: compatibility
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[all]
+          pip install wheel
+      - name: Build package
+        run: python setup.py sdist bdist_wheel
+      - name: Publish a Python distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -129,14 +129,6 @@ dmypy.json
 
 # End of https://www.gitignore.io/api/python
 
-# ProtoFlow
-core
-checkpoint
-logs/
-saved_weights/
-scratch*
-
-
 # Created by https://www.gitignore.io/api/visualstudiocode
 # Edit at https://www.gitignore.io/?templates=visualstudiocode
 
@@ -154,4 +146,13 @@ scratch*
 # End of https://www.gitignore.io/api/visualstudiocode
 .vscode/
 
+# Vim
+*~
+*.swp
+*.swo
+
+# Artifacts created by ProtoTorch
 reports
+artifacts
+examples/_*.py
+examples/_*.ipynb

.pre-commit-config.yaml

@@ -0,0 +1,53 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+
+repos:
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v4.4.0
+  hooks:
+  - id: trailing-whitespace
+  - id: end-of-file-fixer
+  - id: check-yaml
+  - id: check-added-large-files
+  - id: check-ast
+  - id: check-case-conflict
+
+- repo: https://github.com/myint/autoflake
+  rev: v2.1.1
+  hooks:
+  - id: autoflake
+
+- repo: http://github.com/PyCQA/isort
+  rev: 5.12.0
+  hooks:
+  - id: isort
+
+- repo: https://github.com/pre-commit/mirrors-mypy
+  rev: v1.3.0
+  hooks:
+  - id: mypy
+    files: prototorch
+    additional_dependencies: [types-pkg_resources]
+
+- repo: https://github.com/pre-commit/mirrors-yapf
+  rev: v0.32.0
+  hooks:
+  - id: yapf
+
+- repo: https://github.com/pre-commit/pygrep-hooks
+  rev: v1.10.0
+  hooks:
+  - id: python-use-type-annotations
+  - id: python-no-log-warn
+  - id: python-check-blanket-noqa
+
+- repo: https://github.com/asottile/pyupgrade
+  rev: v3.7.0
+  hooks:
+  - id: pyupgrade
+
+- repo: https://github.com/si-cim/gitlint
+  rev: v0.15.2-unofficial
+  hooks:
+  - id: gitlint
+    args: [--contrib=CT1, --ignore=B6, --msg-filename]

.readthedocs.yml

@@ -19,7 +19,7 @@ formats: all
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.8
+  version: 3.9
   install:
     - method: pip
       path: .

.remarkrc

@@ -0,0 +1,7 @@
+{
+  "plugins": [
+    "remark-preset-lint-recommended",
+    ["remark-lint-list-item-indent", false],
+    ["no-emphasis-as-header", false]
+  ]
+}

.travis.yml

@@ -1,34 +0,0 @@
-dist: bionic
-sudo: false
-language: python
-python: 3.8
-cache:
-  directories:
-  - "./tests/artifacts"
-install:
-- pip install .[all] --progress-bar off
-
-# Generate code coverage report
-script:
-- coverage run -m pytest
-
-# Push the results to codecov
-after_success:
-- bash <(curl -s https://codecov.io/bash)
-
-# Publish on PyPI
-deploy:
-  provider: pypi
-  username: __token__
-  password:
-    secure: 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
-  on:
-    tags: true
-    skip_existing: true
-
-# The password is encrypted with:
-# `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`
-# See https://docs.travis-ci.com/user/deployment/pypi and
-# https://github.com/travis-ci/travis.rb#installation
-# for more details
-# Note: The encrypt command does not work well in ZSH.

LICENSE

@@ -1,6 +1,7 @@
 MIT License
 
-Copyright (c) 2020 si-cim
+Copyright (c) 2020 Saxon Institute for Computational Intelligence and Machine
+Learning (SICIM)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,13 +2,9 @@
 
 ![ProtoTorch Logo](https://prototorch.readthedocs.io/en/latest/_static/horizontal-lockup.png)
 
-[![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)
 
 *Tensorflow users, see:* [ProtoFlow](https://github.com/si-cim/protoflow)
@@ -48,6 +44,23 @@ pip install -e .[all]
 The documentation is available at <https://www.prototorch.ml/en/latest/>. Should
 that link not work try <https://prototorch.readthedocs.io/en/latest/>.
 
+## Contribution
+
+This repository contains definition for [git hooks](https://githooks.com).
+[Pre-commit](https://pre-commit.com) is automatically installed as development
+dependency with prototorch or you can install it manually with `pip install
+pre-commit`.
+
+Please install the hooks by running:
+```bash
+pre-commit install
+pre-commit install --hook-type commit-msg
+```
+before creating the first commit.
+
+The commit will fail if the commit message does not follow the specification
+provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
+
 ## Bibtex
 
 If you would like to cite the package, please use this:

RELEASE.md

@@ -1,13 +1,16 @@
 # ProtoTorch Releases
 
+## Release 0.5.0
+
+- Breaking: Removed deprecated `prototorch.modules.Prototypes1D`.
+  - Use `prototorch.components.LabeledComponents` instead.
+
 ## Release 0.2.0
 
-### Includes
 - Fixes in example scripts.
 
 ## Release 0.1.1-dev0
 
-### Includes
 - Minor bugfixes.
 - 100% line coverage.
 

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

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.4.2"
+release = "0.7.6"
 
 # -- General configuration ---------------------------------------------------
 
@@ -46,6 +46,7 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinx_rtd_theme",
     "sphinxcontrib.katex",
+    'sphinx_autodoc_typehints',
 ]
 
 # katex_prerender = True
@@ -119,7 +120,7 @@ html_css_files = [
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = "protoflowdoc"
+htmlhelp_basename = "prototorchdoc"
 
 # -- Options for LaTeX output ---------------------------------------------
 
@@ -179,6 +180,9 @@ texinfo_documents = [
 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/cbc_iris.py

@@ -0,0 +1,100 @@
+"""ProtoTorch CBC example using 2D Iris data."""
+
+import logging
+
+import torch
+from matplotlib import pyplot as plt
+
+import prototorch as pt
+
+
+class CBC(torch.nn.Module):
+
+    def __init__(self, data, **kwargs):
+        super().__init__(**kwargs)
+        self.components_layer = pt.components.ReasoningComponents(
+            distribution=[2, 1, 2],
+            components_initializer=pt.initializers.SSCI(data, noise=0.1),
+            reasonings_initializer=pt.initializers.PPRI(components_first=True),
+        )
+
+    def forward(self, x):
+        components, reasonings = self.components_layer()
+        sims = pt.similarities.euclidean_similarity(x, components)
+        probs = pt.competitions.cbcc(sims, reasonings)
+        return probs
+
+
+class VisCBC2D():
+
+    def __init__(self, model, data):
+        self.model = model
+        self.x_train, self.y_train = pt.utils.parse_data_arg(data)
+        self.title = "Components Visualization"
+        self.fig = plt.figure(self.title)
+        self.border = 0.1
+        self.resolution = 100
+        self.cmap = "viridis"
+
+    def on_train_epoch_end(self):
+        x_train, y_train = self.x_train, self.y_train
+        _components = self.model.components_layer._components.detach()
+        ax = self.fig.gca()
+        ax.cla()
+        ax.set_title(self.title)
+        ax.axis("off")
+        ax.scatter(
+            x_train[:, 0],
+            x_train[:, 1],
+            c=y_train,
+            cmap=self.cmap,
+            edgecolor="k",
+            marker="o",
+            s=30,
+        )
+        ax.scatter(
+            _components[:, 0],
+            _components[:, 1],
+            c="w",
+            cmap=self.cmap,
+            edgecolor="k",
+            marker="D",
+            s=50,
+        )
+        x = torch.vstack((x_train, _components))
+        mesh_input, xx, yy = pt.utils.mesh2d(x, self.border, self.resolution)
+        with torch.no_grad():
+            y_pred = self.model(
+                torch.Tensor(mesh_input).type_as(_components)).argmax(1)
+        y_pred = y_pred.cpu().reshape(xx.shape)
+        ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
+        plt.pause(0.2)
+
+
+if __name__ == "__main__":
+    train_ds = pt.datasets.Iris(dims=[0, 2])
+
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
+
+    model = CBC(train_ds)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
+    criterion = pt.losses.MarginLoss(margin=0.1)
+    vis = VisCBC2D(model, train_ds)
+
+    for epoch in range(200):
+        correct = 0.0
+        for x, y in train_loader:
+            y_oh = torch.eye(3)[y]
+            y_pred = model(x)
+            loss = criterion(y_pred, y_oh).mean(0)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            correct += (y_pred.argmax(1) == y).float().sum(0)
+
+        acc = 100 * correct / len(train_ds)
+        logging.info(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
+        vis.on_train_epoch_end()

examples/glvq_iris.py

@@ -1,120 +0,0 @@
-"""ProtoTorch GLVQ example using 2D Iris data."""
-
-import numpy as np
-import torch
-from matplotlib import pyplot as plt
-from sklearn.datasets import load_iris
-from sklearn.preprocessing import StandardScaler
-from torchinfo import summary
-
-from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import euclidean_distance
-from prototorch.modules.losses import GLVQLoss
-from prototorch.modules.prototypes import Prototypes1D
-
-# Prepare and preprocess the data
-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)
-
-
-# Define the GLVQ model
-class Model(torch.nn.Module):
-    def __init__(self):
-        """GLVQ model for training on 2D Iris data."""
-        super().__init__()
-        self.proto_layer = Prototypes1D(
-            input_dim=2,
-            prototypes_per_class=3,
-            nclasses=3,
-            prototype_initializer="stratified_random",
-            data=[x_train, y_train],
-        )
-
-    def forward(self, x):
-        protos = self.proto_layer.prototypes
-        plabels = self.proto_layer.prototype_labels
-        dis = euclidean_distance(x, protos)
-        return dis, plabels
-
-
-# Build the GLVQ model
-model = Model()
-
-# Print summary using torchinfo (might be buggy/incorrect)
-print(summary(model))
-
-# Optimize using SGD optimizer from `torch.optim`
-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
-title = "Prototype Visualization"
-fig = plt.figure(title)
-for epoch in range(70):
-    # Compute loss
-    dis, plabels = model(x_in)
-    loss = criterion([dis, plabels], y_in)
-    with torch.no_grad():
-        pred = wtac(dis, plabels)
-        correct = pred.eq(y_in.view_as(pred)).sum().item()
-    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()
-    loss.backward()
-    optimizer.step()
-
-    # Get the prototypes form the model
-    protos = model.proto_layer.prototypes.data.numpy()
-    if np.isnan(np.sum(protos)):
-        print("Stopping training because of `nan` in prototypes.")
-        break
-
-    # 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],
-        protos[:, 1],
-        c=plabels,
-        cmap=cmap,
-        edgecolor="k",
-        marker="D",
-        s=50,
-    )
-
-    # Paint decision regions
-    x = np.vstack((x_train, protos))
-    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
-    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
-    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
-                         np.arange(y_min, y_max, 1 / 50))
-    mesh_input = np.c_[xx.ravel(), yy.ravel()]
-
-    torch_input = torch.Tensor(mesh_input)
-    d = model(torch_input)[0]
-    w_indices = torch.argmin(d, dim=1)
-    y_pred = torch.index_select(plabels, 0, w_indices)
-    y_pred = y_pred.reshape(xx.shape)
-
-    # Plot voronoi regions
-    ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
-
-    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)

examples/gmlvq.py

@@ -0,0 +1,76 @@
+"""ProtoTorch GMLVQ example using Iris data."""
+
+import torch
+
+import prototorch as pt
+
+
+class GMLVQ(torch.nn.Module):
+    """
+    Implementation of Generalized Matrix Learning Vector Quantization.
+    """
+
+    def __init__(self, data, **kwargs):
+        super().__init__(**kwargs)
+
+        self.components_layer = pt.components.LabeledComponents(
+            distribution=[1, 1, 1],
+            components_initializer=pt.initializers.SMCI(data, noise=0.1),
+        )
+
+        self.backbone = pt.transforms.Omega(
+            len(data[0][0]),
+            len(data[0][0]),
+            pt.initializers.RandomLinearTransformInitializer(),
+        )
+
+    def forward(self, data):
+        """
+        Forward function that returns a tuple of dissimilarities and label information.
+        Feed into GLVQLoss to get a complete GMLVQ model.
+        """
+        components, label = self.components_layer()
+
+        latent_x = self.backbone(data)
+        latent_components = self.backbone(components)
+
+        distance = pt.distances.squared_euclidean_distance(
+            latent_x, latent_components)
+
+        return distance, label
+
+    def predict(self, data):
+        """
+        The GMLVQ has a modified prediction step, where a competition layer is applied.
+        """
+        components, label = self.components_layer()
+        distance = pt.distances.squared_euclidean_distance(data, components)
+        winning_label = pt.competitions.wtac(distance, label)
+        return winning_label
+
+
+if __name__ == "__main__":
+    train_ds = pt.datasets.Iris()
+
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
+
+    model = GMLVQ(train_ds)
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.05)
+    criterion = pt.losses.GLVQLoss()
+
+    for epoch in range(200):
+        correct = 0.0
+        for x, y in train_loader:
+            d, labels = model(x)
+            loss = criterion(d, y, labels).mean(0)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            with torch.no_grad():
+                y_pred = model.predict(x)
+                correct += (y_pred == y).float().sum(0)
+
+        acc = 100 * correct / len(train_ds)
+        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")

examples/gmlvq_tecator.py

@@ -1,104 +0,0 @@
-"""ProtoTorch "siamese" GMLVQ example using Tecator."""
-
-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
-
-# Prepare the dataset and dataloader
-train_data = Tecator(root="./artifacts", train=True)
-train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
-
-
-class Model(torch.nn.Module):
-    def __init__(self, **kwargs):
-        """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.omega = torch.nn.Linear(in_features=100,
-                                     out_features=100,
-                                     bias=False)
-        torch.nn.init.eye_(self.omega.weight)
-
-    def forward(self, x):
-        protos = self.p1.prototypes
-        plabels = self.p1.prototype_labels
-
-        # Process `x` and `protos` through `omega`
-        x_map = self.omega(x)
-        protos_map = self.omega(protos)
-
-        # Compute distances and output
-        dis = sed(x_map, protos_map)
-        return dis, plabels
-
-
-# Build the GLVQ model
-model = Model()
-
-# Print a summary of the model
-print(model)
-
-# Optimize using Adam optimizer from `torch.optim`
-optimizer = torch.optim.Adam(model.parameters(), lr=0.001_0)
-scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=75, gamma=0.1)
-criterion = GLVQLoss(squashing="identity", beta=10)
-
-# Training loop
-for epoch in range(150):
-    epoch_loss = 0.0  # zero-out epoch loss
-    optimizer.zero_grad()  # zero-out gradients
-    for xb, yb in train_loader:
-        # Compute loss
-        distances, plabels = model(xb)
-        loss = criterion([distances, plabels], yb)
-        epoch_loss += loss.item()
-        # Backprop
-        loss.backward()
-    # Take a gradient descent step
-    optimizer.step()
-    scheduler.step()
-
-    lr = optimizer.param_groups[0]["lr"]
-    print(f"Epoch: {epoch + 1:03d} Loss: {epoch_loss:06.02f} lr: {lr:07.06f}")
-
-# Get the omega matrix form the model
-omega = model.omega.weight.data.numpy().T
-
-# Visualize the lambda matrix
-title = "Lambda Matrix Visualization"
-fig = plt.figure(title)
-ax = fig.gca()
-ax.set_title(title)
-im = ax.imshow(omega.dot(omega.T), cmap="viridis")
-plt.show()
-
-# Get the prototypes form the model
-protos = model.p1.prototypes.data.numpy()
-plabels = model.p1.prototype_labels
-
-# Visualize the prototypes
-title = "Tecator Prototypes"
-fig = plt.figure(title)
-ax = fig.gca()
-ax.set_title(title)
-ax.set_xlabel("Spectral frequencies")
-ax.set_ylabel("Absorption")
-clabels = ["Class 0 - Low fat", "Class 1 - High fat"]
-handles, colors = get_legend_handles(clabels, marker="line", zero_indexed=True)
-for x, y in zip(protos, plabels):
-    ax.plot(x, c=colors[int(y)])
-ax.legend(handles, clabels)
-plt.show()

examples/gtlvq_mnist.py

@@ -1,184 +0,0 @@
-"""
-ProtoTorch GTLVQ example using MNIST data.
-The GTLVQ is placed as an classification model on
-top of a CNN, considered as featurer extractor.
-Initialization of subpsace and prototypes in
-Siamnese fashion
-For more info about GTLVQ see:
-DOI:10.1109/IJCNN.2016.7727534
-"""
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torchvision
-from torchvision import transforms
-
-from prototorch.functions.helper import calculate_prototype_accuracy
-from prototorch.modules.losses import GLVQLoss
-from prototorch.modules.models import GTLVQ
-
-# Parameters and options
-n_epochs = 50
-batch_size_train = 64
-batch_size_test = 1000
-learning_rate = 0.1
-momentum = 0.5
-log_interval = 10
-cuda = "cuda:1"
-random_seed = 1
-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,
-)
-
-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
-class CNNGTLVQ(torch.nn.Module):
-    def __init__(
-        self,
-        num_classes,
-        subspace_data,
-        prototype_data,
-        tangent_projection_type="local",
-        prototypes_per_class=2,
-        bottleneck_dim=128,
-    ):
-        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),
-        )
-
-        # 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,
-        )
-
-    def forward(self, x):
-        # Feature Extraction
-        x = self.fe(x)
-
-        # GTLVQ Forward pass
-        dis = self.gtlvq(x)
-        return dis
-
-
-# Get init data
-subspace_data = torch.cat(
-    [next(iter(train_loader))[0],
-     next(iter(test_loader))[0]])
-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)
-
-# 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)
-
-# Training loop
-for epoch in range(n_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)
-        optimizer.zero_grad()
-
-        distances = model(x_train)
-        plabels = model.gtlvq.cls.prototype_labels.to(device)
-
-        # Compute loss.
-        loss = criterion([distances, plabels], y_train)
-        loss.backward()
-        optimizer.step()
-
-        # GTLVQ uses projected SGD, which means to orthogonalize the subspaces after every gradient update.
-        model.gtlvq.orthogonalize_subspace()
-
-        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}")
-
-    # Test
-    with torch.no_grad():
-        model.eval()
-        correct = 0
-        total = 0
-        for x_test, y_test in test_loader:
-            x_test, y_test = x_test.to(device), y_test.to(device)
-            test_distances = model(torch.tensor(x_test))
-            test_plabels = model.gtlvq.cls.prototype_labels.to(device)
-            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 %%" %
-              (torch.true_divide(correct, total) * 100))
-
-# Save the model
-PATH = "./glvq_mnist_model.pth"
-torch.save(model.state_dict(), PATH)

examples/lgmlvq_iris.py

@@ -1,110 +0,0 @@
-"""ProtoTorch LGMLVQ example using 2D Iris data."""
-
-import numpy as np
-import torch
-from matplotlib import pyplot as plt
-from sklearn.datasets import load_iris
-from sklearn.metrics import accuracy_score
-
-from prototorch.functions.competitions import stratified_min
-from prototorch.functions.distances import lomega_distance
-from prototorch.functions.init import eye_
-from prototorch.modules.losses import GLVQLoss
-from prototorch.modules.prototypes import Prototypes1D
-
-# Prepare training data
-x_train, y_train = load_iris(True)
-x_train = x_train[:, [0, 2]]
-
-
-# Define the model
-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],
-        )
-        omegas = torch.zeros(5, 2, 2)
-        self.omegas = torch.nn.Parameter(omegas)
-        eye_(self.omegas)
-
-    def forward(self, x):
-        protos = self.p1.prototypes
-        plabels = self.p1.prototype_labels
-        omegas = self.omegas
-        dis = lomega_distance(x, protos, omegas)
-        return dis, plabels
-
-
-# Build the model
-model = Model()
-
-# Optimize using Adam optimizer from `torch.optim`
-optimizer = torch.optim.Adam(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
-title = "Prototype Visualization"
-fig = plt.figure(title)
-for epoch in range(100):
-    # Compute loss
-    dis, plabels = model(x_in)
-    loss = criterion([dis, plabels], y_in)
-    y_pred = np.argmin(stratified_min(dis, plabels).detach().numpy(), axis=1)
-    acc = accuracy_score(y_train, y_pred)
-    log_string = f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} "
-    log_string += f"Acc: {acc * 100:05.02f}%"
-    print(log_string)
-
-    # Take a gradient descent step
-    optimizer.zero_grad()
-    loss.backward()
-    optimizer.step()
-
-    # Get the prototypes form the model
-    protos = model.p1.prototypes.data.numpy()
-
-    # 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],
-        protos[:, 1],
-        c=plabels,
-        cmap=cmap,
-        edgecolor="k",
-        marker="D",
-        s=50,
-    )
-
-    # Paint decision regions
-    x = np.vstack((x_train, protos))
-    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
-    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
-    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
-                         np.arange(y_min, y_max, 1 / 50))
-    mesh_input = np.c_[xx.ravel(), yy.ravel()]
-
-    d, plabels = model(torch.Tensor(mesh_input))
-    y_pred = np.argmin(stratified_min(d, plabels).detach().numpy(), axis=1)
-    y_pred = y_pred.reshape(xx.shape)
-
-    # Plot voronoi regions
-    ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
-
-    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)

examples/new_components.py

@@ -1,39 +1,35 @@
 """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()
+import prototorch as pt
-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)
+ds = pt.datasets.Iris()
-y_train = torch.Tensor(y_train)
-num_classes = len(torch.unique(y_train))
 
-# CREATE NEW COMPONENTS
+unsupervised = pt.components.Components(
-from prototorch.components import *
+    6,
-from prototorch.components.initializers import *
+    initializer=pt.initializers.ZCI(2),
-
+)
-unsupervised = Components(6, SelectionInitializer(x_train))
 print(unsupervised())
 
-prototypes = LabeledComponents(
+prototypes = pt.components.LabeledComponents(
-    (3, 2), StratifiedSelectionInitializer(x_train, y_train))
+    (3, 2),
+    components_initializer=pt.initializers.SSCI(ds),
+)
 print(prototypes())
 
-components = ReasoningComponents(
+components = pt.components.ReasoningComponents(
-    (3, 6), StratifiedSelectionInitializer(x_train, y_train))
+    (3, 2),
-print(components())
+    components_initializer=pt.initializers.SSCI(ds),
+    reasonings_initializer=pt.initializers.PPRI(),
+)
+print(prototypes())
 
-# TEST SERIALIZATION
+# Test Serialization
 import io
 
 save = io.BytesIO()
@@ -41,25 +37,20 @@ torch.save(unsupervised, save)
 save.seek(0)
 serialized_unsupervised = torch.load(save)
 
-assert torch.all(unsupervised.components == serialized_unsupervised.components
+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
+assert torch.all(prototypes.components == serialized_prototypes.components)
-                 ), "Serialization of Components failed."
+assert torch.all(prototypes.labels == serialized_prototypes.labels)
-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
+assert torch.all(components.components == serialized_components.components)
-                 ), "Serialization of Components failed."
+assert torch.all(components.reasonings == serialized_components.reasonings)
-assert torch.all(components.reasonings == serialized_components.reasonings
-                 ), "Serialization of Components failed."

prototorch/__init__.py

@@ -1,21 +1,40 @@
-"""ProtoTorch package."""
+"""ProtoTorch package"""
 
-# Core Setup
-__version__ = "0.4.2"
-
-__all_core__ = [
-    "datasets",
-    "functions",
-    "modules",
-]
-
-from .datasets import *
-
-# Plugin Loader
 import pkgutil
 
 import pkg_resources
 
+from . import datasets  # noqa: F401
+from . import nn  # noqa: F401
+from . import utils  # noqa: F401
+from .core import competitions  # noqa: F401
+from .core import components  # noqa: F401
+from .core import distances  # noqa: F401
+from .core import initializers  # noqa: F401
+from .core import losses  # noqa: F401
+from .core import pooling  # noqa: F401
+from .core import similarities  # noqa: F401
+from .core import transforms  # noqa: F401
+
+# Core Setup
+__version__ = "0.7.6"
+
+__all_core__ = [
+    "competitions",
+    "components",
+    "core",
+    "datasets",
+    "distances",
+    "initializers",
+    "losses",
+    "nn",
+    "pooling",
+    "similarities",
+    "transforms",
+    "utils",
+]
+
+# Plugin Loader
 __path__ = pkgutil.extend_path(__path__, __name__)
 
 

prototorch/components/__init__.py

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

prototorch/components/components.py

@@ -1,151 +0,0 @@
-"""ProtoTorch components modules."""
-
-import warnings
-from typing import Tuple
-
-import torch
-from prototorch.components.initializers import (ClassAwareInitializer,
-                                                ComponentsInitializer,
-                                                EqualLabelsInitializer,
-                                                UnequalLabelsInitializer,
-                                                ZeroReasoningsInitializer)
-from prototorch.functions.initializers import get_initializer
-from torch.nn.parameter import Parameter
-
-
-class Components(torch.nn.Module):
-    """Components is a set of learnable Tensors."""
-    def __init__(self,
-                 number_of_components=None,
-                 initializer=None,
-                 *,
-                 initialized_components=None,
-                 dtype=torch.float32):
-        super().__init__()
-
-        # Ignore all initialization settings if initialized_components is given.
-        if initialized_components is not None:
-            self._components = Parameter(initialized_components)
-            if number_of_components is not None or initializer is not None:
-                wmsg = "Arguments ignored while initializing Components"
-                warnings.warn(wmsg)
-        else:
-            self._initialize_components(number_of_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, number_of_components, initializer):
-        self._precheck_initializer(initializer)
-        self._components = Parameter(
-            initializer.generate(number_of_components))
-
-    @property
-    def components(self):
-        """Tensor containing the component tensors."""
-        return self._components.detach().cpu()
-
-    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:
-            super().__init__(initialized_components=initialized_components[0])
-            self._labels = initialized_components[1]
-        else:
-            self._initialize_labels(distribution)
-            super().__init__(number_of_components=len(self._labels),
-                             initializer=initializer)
-
-    def _initialize_components(self, number_of_components, initializer):
-        if isinstance(initializer, ClassAwareInitializer):
-            self._precheck_initializer(initializer)
-            self._components = Parameter(
-                initializer.generate(number_of_components, self.distribution))
-        else:
-            super()._initialize_components(self, number_of_components,
-                                           initializer)
-
-    def _initialize_labels(self, distribution):
-        if 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
-        self._labels = labels.generate()
-
-    @property
-    def component_labels(self):
-        """Tensor containing the component tensors."""
-        return self._labels.detach().cpu()
-
-    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:
-            super().__init__(initialized_components=initialized_components[0])
-            self._reasonings = initialized_components[1]
-        else:
-            self._initialize_reasonings(reasonings)
-            super().__init__(number_of_components=len(self._reasonings),
-                             initializer=initializer)
-
-    def _initialize_reasonings(self, reasonings):
-        if type(reasonings) == tuple:
-            num_classes, number_of_components = reasonings
-            reasonings = ZeroReasoningsInitializer(num_classes,
-                                                   number_of_components)
-
-        self._reasonings = reasonings.generate()
-
-    @property
-    def reasonings(self):
-        """Returns Reasoning Matrix.
-
-        Dimension NxCx2
-
-        """
-        return self._reasonings.detach().cpu()
-
-    def forward(self):
-        return super().forward(), self._reasonings

prototorch/components/initializers.py

@@ -1,197 +0,0 @@
-"""ProtoTroch Initializers."""
-import warnings
-from collections.abc import Iterable
-from itertools import chain
-
-import torch
-from torch.utils.data import DataLoader, Dataset
-
-
-def parse_init_arg(arg):
-    if isinstance(arg, Dataset):
-        data, labels = next(iter(DataLoader(arg, batch_size=len(arg))))
-        # data = data.view(len(arg), -1)  # flatten
-    else:
-        data, labels = 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 PositionAwareInitializer(ComponentsInitializer):
-    def __init__(self, positions):
-        super().__init__()
-        self.data = positions
-
-
-class SelectionInitializer(PositionAwareInitializer):
-    def generate(self, length):
-        indices = torch.LongTensor(length).random_(0, len(self.data))
-        return self.data[indices]
-
-
-class MeanInitializer(PositionAwareInitializer):
-    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, arg):
-        super().__init__()
-        data, labels = parse_init_arg(arg)
-        self.data = data
-        self.labels = labels
-
-        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]
-        samples_list = [
-            init.generate(n) for init, n in zip(self.initializers, dist)
-        ]
-        return torch.vstack(samples_list)
-
-
-class StratifiedMeanInitializer(ClassAwareInitializer):
-    def __init__(self, arg):
-        super().__init__(arg)
-
-        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, arg, *, noise=None):
-        super().__init__(arg)
-        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(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(samples)
-            samples = samples + self.noise
-        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 = range(len(self.dist))
-        labels = list(chain(*[[i] * n for i, n in zip(clabels, self.dist)]))
-        return torch.tensor(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()
-
-
-# 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

prototorch/core/__init__.py

@@ -0,0 +1,10 @@
+"""ProtoTorch core"""
+
+from .competitions import *
+from .components import *
+from .distances import *
+from .initializers import *
+from .losses import *
+from .pooling import *
+from .similarities import *
+from .transforms import *

prototorch/core/competitions.py

@@ -0,0 +1,93 @@
+"""ProtoTorch competitions"""
+
+import torch
+
+
+def wtac(distances: torch.Tensor, labels: torch.LongTensor):
+    """Winner-Takes-All-Competition.
+
+    Returns the labels corresponding to the winners.
+
+    """
+    winning_indices = torch.min(distances, dim=1).indices
+    winning_labels = labels[winning_indices].squeeze()
+    return winning_labels
+
+
+def knnc(distances: torch.Tensor, labels: torch.LongTensor, k: int = 1):
+    """K-Nearest-Neighbors-Competition.
+
+    Returns the labels corresponding to the winners.
+
+    """
+    winning_indices = torch.topk(-distances, k=k, dim=1).indices
+    winning_labels = torch.mode(labels[winning_indices], dim=1).values
+    return winning_labels
+
+
+def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
+    """Classification-By-Components Competition.
+
+    Returns probability distributions over the classes.
+
+    `detections` must be of shape [batch_size, num_components].
+    `reasonings` must be of shape [num_components, num_classes, 2].
+
+    """
+    A, B = reasonings.permute(2, 1, 0).clamp(0, 1)
+    pk = A
+    nk = (1 - A) * B
+    numerator = (detections @ (pk - nk).T) + nk.sum(1)
+    probs = numerator / ((pk + nk).sum(1) + 1e-8)
+    return probs
+
+
+class WTAC(torch.nn.Module):
+    """Winner-Takes-All-Competition Layer.
+
+    Thin wrapper over the `wtac` function.
+
+    """
+
+    def forward(self, distances, labels):  # pylint: disable=no-self-use
+        return wtac(distances, labels)
+
+
+class LTAC(torch.nn.Module):
+    """Loser-Takes-All-Competition Layer.
+
+    Thin wrapper over the `wtac` function.
+
+    """
+
+    def forward(self, probs, labels):  # pylint: disable=no-self-use
+        return wtac(-1.0 * probs, labels)
+
+
+class KNNC(torch.nn.Module):
+    """K-Nearest-Neighbors-Competition.
+
+    Thin wrapper over the `knnc` function.
+
+    """
+
+    def __init__(self, k=1, **kwargs):
+        super().__init__(**kwargs)
+        self.k = k
+
+    def forward(self, distances, labels):
+        return knnc(distances, labels, k=self.k)
+
+    def extra_repr(self):
+        return f"k: {self.k}"
+
+
+class CBCC(torch.nn.Module):
+    """Classification-By-Components Competition.
+
+    Thin wrapper over the `cbcc` function.
+
+    """
+
+    def forward(self, detections, reasonings):  # pylint: disable=no-self-use
+        return cbcc(detections, reasonings)

prototorch/core/components.py

@@ -0,0 +1,380 @@
+"""ProtoTorch components"""
+
+import inspect
+from typing import Union
+
+import torch
+from torch.nn.parameter import Parameter
+
+from prototorch.utils import parse_distribution
+
+from .initializers import (
+    AbstractClassAwareCompInitializer,
+    AbstractComponentsInitializer,
+    AbstractLabelsInitializer,
+    AbstractReasoningsInitializer,
+    LabelsInitializer,
+    PurePositiveReasoningsInitializer,
+    RandomReasoningsInitializer,
+)
+
+
+def validate_initializer(initializer, instanceof):
+    """Check if the initializer is valid."""
+    if not isinstance(initializer, instanceof):
+        emsg = f"`initializer` has to be an instance " \
+            f"of some subtype of {instanceof}. " \
+            f"You have provided: {initializer} instead. "
+        helpmsg = ""
+        if inspect.isclass(initializer):
+            helpmsg = f"Perhaps you meant to say, {initializer.__name__}() " \
+                f"with the brackets instead of just {initializer.__name__}?"
+        raise TypeError(emsg + helpmsg)
+    return True
+
+
+def gencat(ins, attr, init, *iargs, **ikwargs):
+    """Generate new items and concatenate with existing items."""
+    new_items = init.generate(*iargs, **ikwargs)
+    if hasattr(ins, attr):
+        items = torch.cat([getattr(ins, attr), new_items])
+    else:
+        items = new_items
+    return items, new_items
+
+
+def removeind(ins, attr, indices):
+    """Remove items at specified indices."""
+    mask = torch.ones(len(ins), dtype=torch.bool)
+    mask[indices] = False
+    items = getattr(ins, attr)[mask]
+    return items, mask
+
+
+def get_cikwargs(init, distribution):
+    """Return appropriate key-word arguments for a component initializer."""
+    if isinstance(init, AbstractClassAwareCompInitializer):
+        cikwargs = dict(distribution=distribution)
+    else:
+        distribution = parse_distribution(distribution)
+        num_components = sum(distribution.values())
+        cikwargs = dict(num_components=num_components)
+    return cikwargs
+
+
+class AbstractComponents(torch.nn.Module):
+    """Abstract class for all components modules."""
+
+    @property
+    def num_components(self):
+        """Current number of components."""
+        return len(self._components)
+
+    @property
+    def components(self):
+        """Detached Tensor containing the components."""
+        return self._components.detach().cpu()
+
+    def _register_components(self, components):
+        self.register_parameter("_components", Parameter(components))
+
+    def extra_repr(self):
+        return f"components: (shape: {tuple(self._components.shape)})"
+
+    def __len__(self):
+        return self.num_components
+
+
+class Components(AbstractComponents):
+    """A set of adaptable Tensors."""
+
+    def __init__(self, num_components: int,
+                 initializer: AbstractComponentsInitializer):
+        super().__init__()
+        self.add_components(num_components, initializer)
+
+    def add_components(self, num_components: int,
+                       initializer: AbstractComponentsInitializer):
+        """Generate and add new components."""
+        assert validate_initializer(initializer, AbstractComponentsInitializer)
+        _components, new_components = gencat(self, "_components", initializer,
+                                             num_components)
+        self._register_components(_components)
+        return new_components
+
+    def remove_components(self, indices):
+        """Remove components at specified indices."""
+        _components, mask = removeind(self, "_components", indices)
+        self._register_components(_components)
+        return mask
+
+    def forward(self):
+        """Simply return the components parameter Tensor."""
+        return self._components
+
+
+class AbstractLabels(torch.nn.Module):
+    """Abstract class for all labels modules."""
+
+    @property
+    def labels(self):
+        return self._labels.cpu()
+
+    @property
+    def num_labels(self):
+        return len(self._labels)
+
+    @property
+    def unique_labels(self):
+        return torch.unique(self._labels)
+
+    @property
+    def num_unique(self):
+        return len(self.unique_labels)
+
+    @property
+    def distribution(self):
+        unique, counts = torch.unique(self._labels,
+                                      sorted=True,
+                                      return_counts=True)
+        return dict(zip(unique.tolist(), counts.tolist()))
+
+    def _register_labels(self, labels):
+        self.register_buffer("_labels", labels)
+
+    def extra_repr(self):
+        r = f"num_labels: {self.num_labels}, num_unique: {self.num_unique}"
+        if len(self.distribution) < 11:  # avoid lengthy representations
+            d = self.distribution
+            unique, counts = list(d.keys()), list(d.values())
+            r += f", unique: {unique}, counts: {counts}"
+        return r
+
+    def __len__(self):
+        return self.num_labels
+
+
+class Labels(AbstractLabels):
+    """A set of standalone labels."""
+
+    def __init__(self,
+                 distribution: Union[dict, list, tuple],
+                 initializer: AbstractLabelsInitializer = LabelsInitializer()):
+        super().__init__()
+        self.add_labels(distribution, initializer)
+
+    def add_labels(
+        self,
+        distribution: Union[dict, tuple, list],
+        initializer: AbstractLabelsInitializer = LabelsInitializer()):
+        """Generate and add new labels."""
+        assert validate_initializer(initializer, AbstractLabelsInitializer)
+        _labels, new_labels = gencat(self, "_labels", initializer,
+                                     distribution)
+        self._register_labels(_labels)
+        return new_labels
+
+    def remove_labels(self, indices):
+        """Remove labels at specified indices."""
+        _labels, mask = removeind(self, "_labels", indices)
+        self._register_labels(_labels)
+        return mask
+
+    def forward(self):
+        """Simply return the labels."""
+        return self._labels
+
+
+class LabeledComponents(AbstractComponents):
+    """A set of adaptable components and corresponding unadaptable labels."""
+
+    def __init__(
+        self,
+        distribution: Union[dict, list, tuple],
+        components_initializer: AbstractComponentsInitializer,
+        labels_initializer: AbstractLabelsInitializer = LabelsInitializer()):
+        super().__init__()
+        self.add_components(distribution, components_initializer,
+                            labels_initializer)
+
+    @property
+    def distribution(self):
+        unique, counts = torch.unique(self._labels,
+                                      sorted=True,
+                                      return_counts=True)
+        return dict(zip(unique.tolist(), counts.tolist()))
+
+    @property
+    def num_classes(self):
+        return len(self.distribution.keys())
+
+    @property
+    def labels(self):
+        """Tensor containing the component labels."""
+        return self._labels.cpu()
+
+    def _register_labels(self, labels):
+        self.register_buffer("_labels", labels)
+
+    def add_components(
+        self,
+        distribution,
+        components_initializer,
+        labels_initializer: AbstractLabelsInitializer = LabelsInitializer()):
+        """Generate and add new components and labels."""
+        assert validate_initializer(components_initializer,
+                                    AbstractComponentsInitializer)
+        assert validate_initializer(labels_initializer,
+                                    AbstractLabelsInitializer)
+        cikwargs = get_cikwargs(components_initializer, distribution)
+        _components, new_components = gencat(self, "_components",
+                                             components_initializer,
+                                             **cikwargs)
+        _labels, new_labels = gencat(self, "_labels", labels_initializer,
+                                     distribution)
+        self._register_components(_components)
+        self._register_labels(_labels)
+        return new_components, new_labels
+
+    def remove_components(self, indices):
+        """Remove components and labels at specified indices."""
+        _components, mask = removeind(self, "_components", indices)
+        _labels, mask = removeind(self, "_labels", indices)
+        self._register_components(_components)
+        self._register_labels(_labels)
+        return mask
+
+    def forward(self):
+        """Simply return the components parameter Tensor and labels."""
+        return self._components, self._labels
+
+
+class Reasonings(torch.nn.Module):
+    """A set of standalone reasoning matrices.
+
+    The `reasonings` tensor is of shape [num_components, num_classes, 2].
+
+    """
+
+    def __init__(
+        self,
+        distribution: Union[dict, list, tuple],
+        initializer:
+        AbstractReasoningsInitializer = RandomReasoningsInitializer(),
+    ):
+        super().__init__()
+        self.add_reasonings(distribution, initializer)
+
+    @property
+    def num_classes(self):
+        return self._reasonings.shape[1]
+
+    @property
+    def reasonings(self):
+        """Tensor containing the reasoning matrices."""
+        return self._reasonings.detach().cpu()
+
+    def _register_reasonings(self, reasonings):
+        self.register_buffer("_reasonings", reasonings)
+
+    def add_reasonings(
+        self,
+        distribution: Union[dict, list, tuple],
+        initializer:
+        AbstractReasoningsInitializer = RandomReasoningsInitializer()):
+        """Generate and add new reasonings."""
+        assert validate_initializer(initializer, AbstractReasoningsInitializer)
+        _reasonings, new_reasonings = gencat(self, "_reasonings", initializer,
+                                             distribution)
+        self._register_reasonings(_reasonings)
+        return new_reasonings
+
+    def remove_reasonings(self, indices):
+        """Remove reasonings at specified indices."""
+        _reasonings, mask = removeind(self, "_reasonings", indices)
+        self._register_reasonings(_reasonings)
+        return mask
+
+    def forward(self):
+        """Simply return the reasonings."""
+        return self._reasonings
+
+
+class ReasoningComponents(AbstractComponents):
+    r"""A set of components and a corresponding adapatable reasoning matrices.
+
+    Every component has its own reasoning matrix.
+
+    A reasoning matrix is an 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,
+        distribution: Union[dict, list, tuple],
+        components_initializer: AbstractComponentsInitializer,
+        reasonings_initializer:
+        AbstractReasoningsInitializer = PurePositiveReasoningsInitializer()):
+        super().__init__()
+        self.add_components(distribution, components_initializer,
+                            reasonings_initializer)
+
+    @property
+    def num_classes(self):
+        return self._reasonings.shape[1]
+
+    @property
+    def reasonings(self):
+        """Tensor containing the reasoning matrices."""
+        return self._reasonings.detach().cpu()
+
+    @property
+    def reasoning_matrices(self):
+        """Reasoning matrices for each class."""
+        with torch.no_grad():
+            A, B = self._reasonings.permute(2, 1, 0).clamp(0, 1)
+            pk = A
+            nk = (1 - pk) * B
+            ik = 1 - pk - nk
+            matrices = torch.stack([pk, nk, ik], dim=-1).permute(1, 2, 0)
+        return matrices.cpu()
+
+    def _register_reasonings(self, reasonings):
+        self.register_parameter("_reasonings", Parameter(reasonings))
+
+    def add_components(self, distribution, components_initializer,
+                       reasonings_initializer: AbstractReasoningsInitializer):
+        """Generate and add new components and reasonings."""
+        assert validate_initializer(components_initializer,
+                                    AbstractComponentsInitializer)
+        assert validate_initializer(reasonings_initializer,
+                                    AbstractReasoningsInitializer)
+        cikwargs = get_cikwargs(components_initializer, distribution)
+        _components, new_components = gencat(self, "_components",
+                                             components_initializer,
+                                             **cikwargs)
+        _reasonings, new_reasonings = gencat(self, "_reasonings",
+                                             reasonings_initializer,
+                                             distribution)
+        self._register_components(_components)
+        self._register_reasonings(_reasonings)
+        return new_components, new_reasonings
+
+    def remove_components(self, indices):
+        """Remove components and reasonings at specified indices."""
+        _components, mask = removeind(self, "_components", indices)
+        _reasonings, mask = removeind(self, "_reasonings", indices)
+        self._register_components(_components)
+        self._register_reasonings(_reasonings)
+        return mask
+
+    def forward(self):
+        """Simply return the components and reasonings."""
+        return self._components, self._reasonings

prototorch/core/distances.py

@@ -0,0 +1,95 @@
+"""ProtoTorch distances"""
+
+import torch
+
+
+def squared_euclidean_distance(x, y):
+    r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm y`.
+
+    Compute :math:`{\langle \bm x - \bm y \rangle}_2`
+
+    **Alias:**
+    ``prototorch.functions.distances.sed``
+    """
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    expanded_x = x.unsqueeze(dim=1)
+    batchwise_difference = y - expanded_x
+    differences_raised = torch.pow(batchwise_difference, 2)
+    distances = torch.sum(differences_raised, axis=2)
+    return distances
+
+
+def euclidean_distance(x, y):
+    r"""Compute the Euclidean distance between :math:`x` and :math:`y`.
+
+    Compute :math:`\sqrt{{\langle \bm x - \bm y \rangle}_2}`
+
+    :returns: Distance Tensor of shape :math:`X \times Y`
+    :rtype: `torch.tensor`
+    """
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    distances_raised = squared_euclidean_distance(x, y)
+    distances = torch.sqrt(distances_raised)
+    return distances
+
+
+def euclidean_distance_v2(x, y):
+    x, y = (arr.view(arr.size(0), -1) for arr in (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)
+    return distances
+
+
+def lpnorm_distance(x, y, p):
+    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 p: p parameter of the lp norm
+    """
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    distances = torch.cdist(x, y, p=p)
+    return distances
+
+
+def omega_distance(x, y, omega):
+    r"""Omega distance.
+
+    Compute :math:`{\| \Omega \bm x - \Omega \bm y \|}_p`
+
+    :param `torch.tensor` omega: Two dimensional matrix
+    """
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    projected_x = x @ omega
+    projected_y = y @ omega
+    distances = squared_euclidean_distance(projected_x, projected_y)
+    return distances
+
+
+def lomega_distance(x, y, omegas):
+    r"""Localized Omega distance.
+
+    Compute :math:`{\| \Omega_k \bm x - \Omega_k \bm y_k \|}_p`
+
+    :param `torch.tensor` omegas: Three dimensional matrix
+    """
+    x, y = (arr.view(arr.size(0), -1) for arr in (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
+    distances = torch.sum(differences_squared, dim=2)
+    distances = distances.permute(1, 0)
+    return distances
+
+
+# Aliases
+sed = squared_euclidean_distance

prototorch/core/initializers.py

@@ -0,0 +1,555 @@
+"""ProtoTorch code initializers"""
+
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Iterable
+from typing import (
+    Callable,
+    Type,
+    Union,
+)
+
+import torch
+
+from prototorch.utils import parse_data_arg, parse_distribution
+
+
+# Components
+class AbstractComponentsInitializer(ABC):
+    """Abstract class for all components initializers."""
+    ...
+
+
+class LiteralCompInitializer(AbstractComponentsInitializer):
+    """'Generate' the provided components.
+
+    Use this to 'generate' pre-initialized components elsewhere.
+
+    """
+
+    def __init__(self, components):
+        self.components = components
+
+    def generate(self, num_components: int = 0):
+        """Ignore `num_components` and simply return `self.components`."""
+        provided_num_components = len(self.components)
+        if provided_num_components != num_components:
+            wmsg = f"The number of components ({provided_num_components}) " \
+                f"provided to {self.__class__.__name__} " \
+                f"does not match the expected number ({num_components})."
+            warnings.warn(wmsg)
+        if not isinstance(self.components, torch.Tensor):
+            wmsg = f"Converting components to {torch.Tensor}..."
+            warnings.warn(wmsg)
+            self.components = torch.Tensor(self.components)
+        return self.components
+
+
+class ShapeAwareCompInitializer(AbstractComponentsInitializer):
+    """Abstract class for all dimension-aware components initializers."""
+
+    def __init__(self, shape: Union[Iterable, int]):
+        if isinstance(shape, Iterable):
+            self.component_shape = tuple(shape)
+        else:
+            self.component_shape = (shape, )
+
+    @abstractmethod
+    def generate(self, num_components: int):
+        ...
+
+
+class ZerosCompInitializer(ShapeAwareCompInitializer):
+    """Generate zeros corresponding to the components shape."""
+
+    def generate(self, num_components: int):
+        components = torch.zeros((num_components, ) + self.component_shape)
+        return components
+
+
+class OnesCompInitializer(ShapeAwareCompInitializer):
+    """Generate ones corresponding to the components shape."""
+
+    def generate(self, num_components: int):
+        components = torch.ones((num_components, ) + self.component_shape)
+        return components
+
+
+class FillValueCompInitializer(OnesCompInitializer):
+    """Generate components with the provided `fill_value`."""
+
+    def __init__(self, shape, fill_value: float = 1.0):
+        super().__init__(shape)
+        self.fill_value = fill_value
+
+    def generate(self, num_components: int):
+        ones = super().generate(num_components)
+        components = ones.fill_(self.fill_value)
+        return components
+
+
+class UniformCompInitializer(OnesCompInitializer):
+    """Generate components by sampling from a continuous uniform distribution."""
+
+    def __init__(self, shape, minimum=0.0, maximum=1.0, scale=1.0):
+        super().__init__(shape)
+        self.minimum = minimum
+        self.maximum = maximum
+        self.scale = scale
+
+    def generate(self, num_components: int):
+        ones = super().generate(num_components)
+        components = self.scale * ones.uniform_(self.minimum, self.maximum)
+        return components
+
+
+class RandomNormalCompInitializer(OnesCompInitializer):
+    """Generate components by sampling from a standard normal distribution."""
+
+    def __init__(self, shape, shift=0.0, scale=1.0):
+        super().__init__(shape)
+        self.shift = shift
+        self.scale = scale
+
+    def generate(self, num_components: int):
+        ones = super().generate(num_components)
+        components = self.scale * (torch.randn_like(ones) + self.shift)
+        return components
+
+
+class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
+    """Abstract class for all data-aware components initializers.
+
+    Components generated by data-aware components initializers inherit the shape
+    of the provided data.
+
+    `data` has to be a torch tensor.
+
+    """
+
+    def __init__(self,
+                 data: torch.Tensor,
+                 noise: float = 0.0,
+                 transform: Callable = torch.nn.Identity()):
+        self.data = data
+        self.noise = noise
+        self.transform = transform
+
+    def generate_end_hook(self, samples):
+        drift = torch.rand_like(samples) * self.noise
+        components = self.transform(samples + drift)
+        return components
+
+    @abstractmethod
+    def generate(self, num_components: int):
+        ...
+        return self.generate_end_hook(...)
+
+    def __del__(self):
+        del self.data
+
+
+class DataAwareCompInitializer(AbstractDataAwareCompInitializer):
+    """'Generate' the components from the provided data."""
+
+    def generate(self, num_components: int = 0):
+        """Ignore `num_components` and simply return transformed `self.data`."""
+        components = self.generate_end_hook(self.data)
+        return components
+
+
+class SelectionCompInitializer(AbstractDataAwareCompInitializer):
+    """Generate components by uniformly sampling from the provided data."""
+
+    def generate(self, num_components: int):
+        indices = torch.LongTensor(num_components).random_(0, len(self.data))
+        samples = self.data[indices]
+        components = self.generate_end_hook(samples)
+        return components
+
+
+class MeanCompInitializer(AbstractDataAwareCompInitializer):
+    """Generate components by computing the mean of the provided data."""
+
+    def generate(self, num_components: int):
+        mean = self.data.mean(dim=0)
+        repeat_dim = [num_components] + [1] * len(mean.shape)
+        samples = mean.repeat(repeat_dim)
+        components = self.generate_end_hook(samples)
+        return components
+
+
+class AbstractClassAwareCompInitializer(AbstractComponentsInitializer):
+    """Abstract class for all class-aware components initializers.
+
+    Components generated by class-aware components initializers inherit the shape
+    of the provided data.
+
+    `data` could be a torch Dataset or DataLoader or a list/tuple of data and
+    target tensors.
+
+    """
+
+    def __init__(self,
+                 data,
+                 noise: float = 0.0,
+                 transform: Callable = torch.nn.Identity()):
+        self.data, self.targets = parse_data_arg(data)
+        self.noise = noise
+        self.transform = transform
+        self.clabels = torch.unique(self.targets).int().tolist()
+        self.num_classes = len(self.clabels)
+
+    def generate_end_hook(self, samples):
+        drift = torch.rand_like(samples) * self.noise
+        components = self.transform(samples + drift)
+        return components
+
+    @abstractmethod
+    def generate(self, distribution: Union[dict, list, tuple]):
+        ...
+        return self.generate_end_hook(...)
+
+    def __del__(self):
+        del self.data
+        del self.targets
+
+
+class ClassAwareCompInitializer(AbstractClassAwareCompInitializer):
+    """'Generate' components from provided data and requested distribution."""
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        """Ignore `distribution` and simply return transformed `self.data`."""
+        components = self.generate_end_hook(self.data)
+        return components
+
+
+class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
+    """Abstract class for all stratified components initializers."""
+
+    @property
+    @abstractmethod
+    def subinit_type(self) -> Type[AbstractDataAwareCompInitializer]:
+        ...
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        distribution = parse_distribution(distribution)
+        components = torch.tensor([])
+        for k, v in distribution.items():
+            stratified_data = self.data[self.targets == k]
+            if len(stratified_data) == 0:
+                raise ValueError(f"No data available for class {k}.")
+            initializer = self.subinit_type(
+                stratified_data,
+                noise=self.noise,
+                transform=self.transform,
+            )
+            samples = initializer.generate(num_components=v)
+            components = torch.cat([components, samples])
+        return components
+
+
+class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
+    """Generate components using stratified sampling from the provided data."""
+
+    @property
+    def subinit_type(self):
+        return SelectionCompInitializer
+
+
+class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
+    """Generate components at stratified means of the provided data."""
+
+    @property
+    def subinit_type(self):
+        return MeanCompInitializer
+
+
+# Labels
+class AbstractLabelsInitializer(ABC):
+    """Abstract class for all labels initializers."""
+
+    @abstractmethod
+    def generate(self, distribution: Union[dict, list, tuple]):
+        ...
+
+
+class LiteralLabelsInitializer(AbstractLabelsInitializer):
+    """'Generate' the provided labels.
+
+    Use this to 'generate' pre-initialized labels elsewhere.
+
+    """
+
+    def __init__(self, labels):
+        self.labels = labels
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        """Ignore `distribution` and simply return `self.labels`.
+
+        Convert to long tensor, if necessary.
+        """
+        labels = self.labels
+        if not isinstance(labels, torch.LongTensor):
+            wmsg = f"Converting labels to {torch.LongTensor}..."
+            warnings.warn(wmsg)
+            labels = torch.LongTensor(labels)
+        return labels
+
+
+class DataAwareLabelsInitializer(AbstractLabelsInitializer):
+    """'Generate' the labels from a torch Dataset."""
+
+    def __init__(self, data):
+        self.data, self.targets = parse_data_arg(data)
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        """Ignore `num_components` and simply return `self.targets`."""
+        return self.targets
+
+
+class LabelsInitializer(AbstractLabelsInitializer):
+    """Generate labels from `distribution`."""
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        distribution = parse_distribution(distribution)
+        labels_list = []
+        for k, v in distribution.items():
+            labels_list.extend([k] * v)
+        labels = torch.LongTensor(labels_list)
+        return labels
+
+
+class OneHotLabelsInitializer(LabelsInitializer):
+    """Generate one-hot-encoded labels from `distribution`."""
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        distribution = parse_distribution(distribution)
+        num_classes = len(distribution.keys())
+        # this breaks if class labels are not [0,...,nclasses]
+        labels = torch.eye(num_classes)[super().generate(distribution)]
+        return labels
+
+
+# Reasonings
+def compute_distribution_shape(distribution):
+    distribution = parse_distribution(distribution)
+    num_components = sum(distribution.values())
+    num_classes = len(distribution.keys())
+    return (num_components, num_classes, 2)
+
+
+class AbstractReasoningsInitializer(ABC):
+    """Abstract class for all reasonings initializers."""
+
+    def __init__(self, components_first: bool = True):
+        self.components_first = components_first
+
+    def generate_end_hook(self, reasonings):
+        if not self.components_first:
+            reasonings = reasonings.permute(2, 1, 0)
+        return reasonings
+
+    @abstractmethod
+    def generate(self, distribution: Union[dict, list, tuple]):
+        ...
+        return self.generate_end_hook(...)
+
+
+class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
+    """'Generate' the provided reasonings.
+
+    Use this to 'generate' pre-initialized reasonings elsewhere.
+
+    """
+
+    def __init__(self, reasonings, **kwargs):
+        super().__init__(**kwargs)
+        self.reasonings = reasonings
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        """Ignore `distributuion` and simply return self.reasonings."""
+        reasonings = self.reasonings
+        if not isinstance(reasonings, torch.Tensor):
+            wmsg = f"Converting reasonings to {torch.Tensor}..."
+            warnings.warn(wmsg)
+            reasonings = torch.Tensor(reasonings)
+        reasonings = self.generate_end_hook(reasonings)
+        return reasonings
+
+
+class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
+    """Reasonings are all initialized with zeros."""
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        shape = compute_distribution_shape(distribution)
+        reasonings = torch.zeros(*shape)
+        reasonings = self.generate_end_hook(reasonings)
+        return reasonings
+
+
+class OnesReasoningsInitializer(AbstractReasoningsInitializer):
+    """Reasonings are all initialized with ones."""
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        shape = compute_distribution_shape(distribution)
+        reasonings = torch.ones(*shape)
+        reasonings = self.generate_end_hook(reasonings)
+        return reasonings
+
+
+class RandomReasoningsInitializer(AbstractReasoningsInitializer):
+    """Reasonings are randomly initialized."""
+
+    def __init__(self, minimum=0.4, maximum=0.6, **kwargs):
+        super().__init__(**kwargs)
+        self.minimum = minimum
+        self.maximum = maximum
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        shape = compute_distribution_shape(distribution)
+        reasonings = torch.ones(*shape).uniform_(self.minimum, self.maximum)
+        reasonings = self.generate_end_hook(reasonings)
+        return reasonings
+
+
+class PurePositiveReasoningsInitializer(AbstractReasoningsInitializer):
+    """Each component reasons positively for exactly one class."""
+
+    def generate(self, distribution: Union[dict, list, tuple]):
+        num_components, num_classes, _ = compute_distribution_shape(
+            distribution)
+        A = OneHotLabelsInitializer().generate(distribution)
+        B = torch.zeros(num_components, num_classes)
+        reasonings = torch.stack([A, B], dim=-1)
+        reasonings = self.generate_end_hook(reasonings)
+        return reasonings
+
+
+# Transforms
+class AbstractTransformInitializer(ABC):
+    """Abstract class for all transform initializers."""
+    ...
+
+
+class AbstractLinearTransformInitializer(AbstractTransformInitializer):
+    """Abstract class for all linear transform initializers."""
+
+    def __init__(self, out_dim_first: bool = False):
+        self.out_dim_first = out_dim_first
+
+    def generate_end_hook(self, weights):
+        if self.out_dim_first:
+            weights = weights.permute(1, 0)
+        return weights
+
+    @abstractmethod
+    def generate(self, in_dim: int, out_dim: int):
+        ...
+        return self.generate_end_hook(...)
+
+
+class ZerosLinearTransformInitializer(AbstractLinearTransformInitializer):
+    """Initialize a matrix with zeros."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        weights = torch.zeros(in_dim, out_dim)
+        return self.generate_end_hook(weights)
+
+
+class OnesLinearTransformInitializer(AbstractLinearTransformInitializer):
+    """Initialize a matrix with ones."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        weights = torch.ones(in_dim, out_dim)
+        return self.generate_end_hook(weights)
+
+
+class RandomLinearTransformInitializer(AbstractLinearTransformInitializer):
+    """Initialize a matrix with random values."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        weights = torch.rand(in_dim, out_dim)
+        return self.generate_end_hook(weights)
+
+
+class EyeLinearTransformInitializer(AbstractLinearTransformInitializer):
+    """Initialize a matrix with the largest possible identity matrix."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        weights = torch.zeros(in_dim, out_dim)
+        I = torch.eye(min(in_dim, out_dim))
+        weights[:I.shape[0], :I.shape[1]] = I
+        return self.generate_end_hook(weights)
+
+
+class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
+    """Abstract class for all data-aware linear transform initializers."""
+
+    def __init__(self,
+                 data: torch.Tensor,
+                 noise: float = 0.0,
+                 transform: Callable = torch.nn.Identity(),
+                 out_dim_first: bool = False):
+        super().__init__(out_dim_first)
+        self.data = data
+        self.noise = noise
+        self.transform = transform
+
+    def generate_end_hook(self, weights: torch.Tensor):
+        drift = torch.rand_like(weights) * self.noise
+        weights = self.transform(weights + drift)
+        if self.out_dim_first:
+            weights = weights.permute(1, 0)
+        return weights
+
+
+class PCALinearTransformInitializer(AbstractDataAwareLTInitializer):
+    """Initialize a matrix with Eigenvectors from the data."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        _, _, weights = torch.pca_lowrank(self.data, q=out_dim)
+        return self.generate_end_hook(weights)
+
+
+class LiteralLinearTransformInitializer(AbstractDataAwareLTInitializer):
+    """'Generate' the provided weights."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        return self.generate_end_hook(self.data)
+
+
+# Aliases - Components
+CACI = ClassAwareCompInitializer
+DACI = DataAwareCompInitializer
+FVCI = FillValueCompInitializer
+LCI = LiteralCompInitializer
+MCI = MeanCompInitializer
+OCI = OnesCompInitializer
+RNCI = RandomNormalCompInitializer
+SCI = SelectionCompInitializer
+SMCI = StratifiedMeanCompInitializer
+SSCI = StratifiedSelectionCompInitializer
+UCI = UniformCompInitializer
+ZCI = ZerosCompInitializer
+
+# Aliases - Labels
+DLI = DataAwareLabelsInitializer
+LI = LabelsInitializer
+LLI = LiteralLabelsInitializer
+OHLI = OneHotLabelsInitializer
+
+# Aliases - Reasonings
+LRI = LiteralReasoningsInitializer
+ORI = OnesReasoningsInitializer
+PPRI = PurePositiveReasoningsInitializer
+RRI = RandomReasoningsInitializer
+ZRI = ZerosReasoningsInitializer
+
+# Aliases - Transforms
+ELTI = Eye = EyeLinearTransformInitializer
+OLTI = OnesLinearTransformInitializer
+RLTI = RandomLinearTransformInitializer
+ZLTI = ZerosLinearTransformInitializer
+PCALTI = PCALinearTransformInitializer
+LLTI = LiteralLinearTransformInitializer

prototorch/core/losses.py

@@ -0,0 +1,184 @@
+"""ProtoTorch losses"""
+
+import torch
+
+from prototorch.nn.activations import get_activation
+
+
+# Helpers
+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
+        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"))
+    d_matching = torch.where(matcher, distances, inf)
+    d_unmatching = torch.where(not_matcher, distances, inf)
+    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
+
+
+# GLVQ
+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
+
+
+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
+
+
+# Probabilistic
+def _get_class_probabilities(probabilities, targets, prototype_labels):
+    # Create Label Mapping
+    uniques = prototype_labels.unique(sorted=True).tolist()
+    key_val = {key: val for key, val in zip(uniques, range(len(uniques)))}
+
+    target_indices = torch.LongTensor(list(map(key_val.get, targets.tolist())))
+
+    whole = probabilities.sum(dim=1)
+    correct = probabilities[torch.arange(len(probabilities)), target_indices]
+    wrong = whole - correct
+
+    return whole, correct, wrong
+
+
+def nllr_loss(probabilities, targets, prototype_labels):
+    """Compute the Negative Log-Likelihood Ratio loss."""
+    _, correct, wrong = _get_class_probabilities(probabilities, targets,
+                                                 prototype_labels)
+
+    likelihood = correct / wrong
+    log_likelihood = torch.log(likelihood)
+    return -1.0 * log_likelihood
+
+
+def rslvq_loss(probabilities, targets, prototype_labels):
+    """Compute the Robust Soft Learning Vector Quantization (RSLVQ) loss."""
+    whole, correct, _ = _get_class_probabilities(probabilities, targets,
+                                                 prototype_labels)
+
+    likelihood = correct / whole
+    log_likelihood = torch.log(likelihood)
+    return -1.0 * log_likelihood
+
+
+def margin_loss(y_pred, y_true, margin=0.3):
+    """Compute the margin loss."""
+    dp = torch.sum(y_true * y_pred, dim=-1)
+    dm = torch.max(y_pred - y_true, dim=-1).values
+    return torch.nn.functional.relu(dm - dp + margin)
+
+
+class GLVQLoss(torch.nn.Module):
+
+    def __init__(self,
+                 margin=0.0,
+                 transfer_fn="identity",
+                 beta=10,
+                 add_dp=False,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.margin = margin
+        self.transfer_fn = get_activation(transfer_fn)
+        self.beta = torch.tensor(beta)
+        self.add_dp = add_dp
+
+    def forward(self, outputs, targets, plabels):
+        # mu = glvq_loss(outputs, targets, plabels)
+        dp, dm = _get_dp_dm(outputs, targets, plabels)
+        mu = (dp - dm) / (dp + dm)
+        if self.add_dp:
+            mu = mu + dp
+        batch_loss = self.transfer_fn(mu + self.margin, beta=self.beta)
+        return batch_loss.sum()
+
+
+class MarginLoss(torch.nn.modules.loss._Loss):
+
+    def __init__(self,
+                 margin=0.3,
+                 size_average=None,
+                 reduce=None,
+                 reduction="mean"):
+        super().__init__(size_average, reduce, reduction)
+        self.margin = margin
+
+    def forward(self, y_pred, y_true):
+        return margin_loss(y_pred, y_true, self.margin)
+
+
+class NeuralGasEnergy(torch.nn.Module):
+
+    def __init__(self, lm, **kwargs):
+        super().__init__(**kwargs)
+        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)
+
+
+class GrowingNeuralGasEnergy(NeuralGasEnergy):
+
+    def __init__(self, topology_layer, **kwargs):
+        super().__init__(**kwargs)
+        self.topology_layer = topology_layer
+
+    @staticmethod
+    def _nghood_fn(rankings, topology):
+        winner = rankings[:, 0]
+
+        weights = torch.zeros_like(rankings, dtype=torch.float)
+        weights[torch.arange(rankings.shape[0]), winner] = 1.0
+
+        neighbours = topology.get_neighbours(winner)
+
+        weights[neighbours] = 0.1
+
+        return weights

prototorch/core/pooling.py

@@ -0,0 +1,108 @@
+"""ProtoTorch pooling"""
+
+from typing import Callable
+
+import torch
+
+
+def stratify_with(values: torch.Tensor,
+                  labels: torch.LongTensor,
+                  fn: Callable,
+                  fill_value: float = 0.0) -> (torch.Tensor):
+    """Apply an arbitrary stratification strategy on the columns on `values`.
+
+    The outputs correspond to sorted labels.
+    """
+    clabels = torch.unique(labels, dim=0, sorted=True)
+    num_classes = clabels.size()[0]
+    if values.size()[1] == num_classes:
+        # skip if stratification is trivial
+        return values
+    batch_size = values.size()[0]
+    winning_values = torch.zeros(num_classes, batch_size, device=labels.device)
+    filler = torch.full_like(values.T, fill_value=fill_value)
+    for i, cl in enumerate(clabels):
+        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), num_classes)
+        cdists = torch.where(matcher, values.T, filler).T
+        winning_values[i] = fn(cdists)
+    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_values.T, dims=(1, ))
+
+    return winning_values.T  # return with `batch_size` first
+
+
+def stratified_sum_pooling(values: torch.Tensor,
+                           labels: torch.LongTensor) -> (torch.Tensor):
+    """Group-wise sum."""
+    winning_values = stratify_with(
+        values,
+        labels,
+        fn=lambda x: torch.sum(x, dim=1, keepdim=True).squeeze(),
+        fill_value=0.0)
+    return winning_values
+
+
+def stratified_min_pooling(values: torch.Tensor,
+                           labels: torch.LongTensor) -> (torch.Tensor):
+    """Group-wise minimum."""
+    winning_values = stratify_with(
+        values,
+        labels,
+        fn=lambda x: torch.min(x, dim=1, keepdim=True).values.squeeze(),
+        fill_value=float("inf"))
+    return winning_values
+
+
+def stratified_max_pooling(values: torch.Tensor,
+                           labels: torch.LongTensor) -> (torch.Tensor):
+    """Group-wise maximum."""
+    winning_values = stratify_with(
+        values,
+        labels,
+        fn=lambda x: torch.max(x, dim=1, keepdim=True).values.squeeze(),
+        fill_value=-1.0 * float("inf"))
+    return winning_values
+
+
+def stratified_prod_pooling(values: torch.Tensor,
+                            labels: torch.LongTensor) -> (torch.Tensor):
+    """Group-wise maximum."""
+    winning_values = stratify_with(
+        values,
+        labels,
+        fn=lambda x: torch.prod(x, dim=1, keepdim=True).squeeze(),
+        fill_value=1.0)
+    return winning_values
+
+
+class StratifiedSumPooling(torch.nn.Module):
+    """Thin wrapper over the `stratified_sum_pooling` function."""
+
+    def forward(self, values, labels):  # pylint: disable=no-self-use
+        return stratified_sum_pooling(values, labels)
+
+
+class StratifiedProdPooling(torch.nn.Module):
+    """Thin wrapper over the `stratified_prod_pooling` function."""
+
+    def forward(self, values, labels):  # pylint: disable=no-self-use
+        return stratified_prod_pooling(values, labels)
+
+
+class StratifiedMinPooling(torch.nn.Module):
+    """Thin wrapper over the `stratified_min_pooling` function."""
+
+    def forward(self, values, labels):  # pylint: disable=no-self-use
+        return stratified_min_pooling(values, labels)
+
+
+class StratifiedMaxPooling(torch.nn.Module):
+    """Thin wrapper over the `stratified_max_pooling` function."""
+
+    def forward(self, values, labels):  # pylint: disable=no-self-use
+        return stratified_max_pooling(values, labels)

prototorch/core/similarities.py

@@ -1,7 +1,19 @@
-"""ProtoTorch similarity functions."""
+"""ProtoTorch similarities."""
 
 import torch
 
+from .distances import euclidean_distance
+
+
+def gaussian(x, variance=1.0):
+    return torch.exp(-(x * x) / (2 * variance))
+
+
+def euclidean_similarity(x, y, variance=1.0):
+    distances = euclidean_distance(x, y)
+    similarities = gaussian(distances, variance)
+    return similarities
+
 
 def cosine_similarity(x, y):
     """Compute the cosine similarity between :math:`x` and :math:`y`.
@@ -9,6 +21,7 @@ def cosine_similarity(x, y):
     Expected dimension of x is 2.
     Expected dimension of y is 2.
     """
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     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

prototorch/core/transforms.py

@@ -0,0 +1,47 @@
+"""ProtoTorch transforms"""
+
+import torch
+from torch.nn.parameter import Parameter
+
+from .initializers import (
+    AbstractLinearTransformInitializer,
+    EyeLinearTransformInitializer,
+)
+
+
+class LinearTransform(torch.nn.Module):
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        initializer:
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
+        super().__init__()
+        self.set_weights(in_dim, out_dim, initializer)
+
+    @property
+    def weights(self):
+        return self._weights.detach().cpu()
+
+    def _register_weights(self, weights):
+        self.register_parameter("_weights", Parameter(weights))
+
+    def set_weights(
+        self,
+        in_dim: int,
+        out_dim: int,
+        initializer:
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
+        weights = initializer.generate(in_dim, out_dim)
+        self._register_weights(weights)
+
+    def forward(self, x):
+        return x @ self._weights
+
+    def extra_repr(self):
+        return f"weights: (shape: {tuple(self._weights.shape)})"
+
+
+# Aliases
+Omega = LinearTransform

prototorch/datasets/__init__.py

@@ -1,11 +1,13 @@
-"""ProtoTorch datasets."""
+"""ProtoTorch datasets"""
 
-from .abstract import NumpyDataset
+from .abstract import CSVDataset, NumpyDataset
+from .sklearn import (
+    Blobs,
+    Circles,
+    Iris,
+    Moons,
+    Random,
+)
 from .spiral import Spiral
 from .tecator import Tecator
-
+from .xor import XOR
-__all__ = [
-    "NumpyDataset",
-    "Spiral",
-    "Tecator",
-]

prototorch/datasets/abstract.py

@@ -1,31 +1,26 @@
-"""ProtoTorch abstract dataset classes.
+"""ProtoTorch abstract dataset classes
 
-Based on `torchvision.VisionDataset` and `torchvision.MNIST`
+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 numpy as np
 import torch
 
 
-class NumpyDataset(torch.utils.data.TensorDataset):
-    """Create a PyTorch TensorDataset from NumPy arrays."""
-    def __init__(self, *arrays):
-        tensors = [torch.Tensor(arr) for arr in arrays]
-        super().__init__(*tensors)
-
-
 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):
+        if isinstance(root, str):
             root = os.path.expanduser(root)
         self.root = root
 
@@ -42,7 +37,7 @@ class ProtoDataset(Dataset):
     training_file = "training.pt"
     test_file = "test.pt"
 
-    def __init__(self, root, train=True, download=True, verbose=True):
+    def __init__(self, root="", train=True, download=True, verbose=True):
         super().__init__(root)
         self.train = train  # training set or test set
         self.verbose = verbose
@@ -94,3 +89,27 @@ class ProtoDataset(Dataset):
 
     def _download(self):
         raise NotImplementedError
+
+
+class NumpyDataset(torch.utils.data.TensorDataset):
+    """Create a PyTorch TensorDataset from NumPy arrays."""
+
+    def __init__(self, data, targets):
+        self.data = torch.Tensor(data)
+        self.targets = torch.LongTensor(targets)
+        tensors = [self.data, self.targets]
+        super().__init__(*tensors)
+
+
+class CSVDataset(NumpyDataset):
+    """Create a Dataset from a CSV file."""
+
+    def __init__(self, filepath, target_col=-1, delimiter=',', skip_header=0):
+        raw = np.genfromtxt(
+            filepath,
+            delimiter=delimiter,
+            skip_header=skip_header,
+        )
+        data = np.delete(raw, 1, target_col)
+        targets = raw[:, target_col]
+        super().__init__(data, targets)

prototorch/datasets/sklearn.py

@@ -0,0 +1,165 @@
+"""Thin wrappers for a few scikit-learn datasets.
+
+URL:
+    https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets
+
+"""
+
+from __future__ import annotations
+
+import warnings
+from typing import Sequence
+
+from sklearn.datasets import (
+    load_iris,
+    make_blobs,
+    make_circles,
+    make_classification,
+    make_moons,
+)
+
+from prototorch.datasets.abstract import NumpyDataset
+
+
+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 = None):
+        x, y = load_iris(return_X_y=True)
+        if dims is not None:
+            x = x[:, dims]
+        super().__init__(x, y)
+
+
+class Blobs(NumpyDataset):
+    """Generate isotropic Gaussian blobs for clustering.
+
+    Read more at
+    https://scikit-learn.org/stable/datasets/sample_generators.html#sample-generators.
+
+    """
+
+    def __init__(
+        self,
+        num_samples: int = 300,
+        num_features: int = 2,
+        seed: None | int = 0,
+    ):
+        x, y = make_blobs(
+            num_samples,
+            num_features,
+            centers=None,
+            random_state=seed,
+            shuffle=False,
+        )
+        super().__init__(x, y)
+
+
+class Random(NumpyDataset):
+    """Generate a random n-class classification problem.
+
+    Read more at
+    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html.
+
+    Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
+    """
+
+    def __init__(
+        self,
+        num_samples: int = 300,
+        num_features: int = 2,
+        num_classes: int = 2,
+        num_clusters: int = 2,
+        num_informative: None | int = None,
+        separation: float = 1.0,
+        seed: None | int = 0,
+    ):
+        if not num_informative:
+            import math
+            num_informative = math.ceil(math.log2(num_classes * num_clusters))
+            if num_features < num_informative:
+                warnings.warn("Generating more features than requested.")
+                num_features = num_informative
+        x, y = make_classification(
+            num_samples,
+            num_features,
+            n_informative=num_informative,
+            n_redundant=0,
+            n_classes=num_classes,
+            n_clusters_per_class=num_clusters,
+            class_sep=separation,
+            random_state=seed,
+            shuffle=False,
+        )
+        super().__init__(x, y)
+
+
+class Circles(NumpyDataset):
+    """Make a large circle containing a smaller circle in 2D.
+
+    A simple toy dataset to visualize clustering and classification algorithms.
+
+    Read more at
+    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
+
+    """
+
+    def __init__(
+        self,
+        num_samples: int = 300,
+        noise: float = 0.3,
+        factor: float = 0.8,
+        seed: None | int = 0,
+    ):
+        x, y = make_circles(
+            num_samples,
+            noise=noise,
+            factor=factor,
+            random_state=seed,
+            shuffle=False,
+        )
+        super().__init__(x, y)
+
+
+class Moons(NumpyDataset):
+    """Make two interleaving half circles.
+
+    A simple toy dataset to visualize clustering and classification algorithms.
+
+    Read more at
+    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
+
+    """
+
+    def __init__(
+        self,
+        num_samples: int = 300,
+        noise: float = 0.3,
+        seed: None | int = 0,
+    ):
+        x, y = make_moons(
+            num_samples,
+            noise=noise,
+            random_state=seed,
+            shuffle=False,
+        )
+        super().__init__(x, y)

prototorch/datasets/spiral.py

@@ -4,18 +4,23 @@ import numpy as np
 import torch
 
 
-def make_spiral(n_samples=500, noise=0.3):
+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 / n_samples * 5
+            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 = n_samples // 2
+    n = num_samples // 2
     positive = get_samples(n=n, delta_t=0)
     negative = get_samples(n=n, delta_t=np.pi)
     x = np.concatenate(
@@ -27,7 +32,28 @@ def make_spiral(n_samples=500, noise=0.3):
 
 
 class Spiral(torch.utils.data.TensorDataset):
-    """Spiral dataset for binary classification."""
+    """Spiral dataset for binary classification.
-    def __init__(self, n_samples=500, noise=0.3):
+
-        x, y = make_spiral(n_samples, noise)
+    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

@@ -36,6 +36,7 @@ Description:
     are determined by analytic chemistry.
 """
 
+import logging
 import os
 
 import numpy as np
@@ -47,8 +48,23 @@ from prototorch.datasets.abstract import ProtoDataset
 
 class Tecator(ProtoDataset):
     """
-    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__
+    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__ for classification.
-    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 = [
@@ -66,13 +82,11 @@ class Tecator(ProtoDataset):
         if self._check_exists():
             return
 
-        if self.verbose:
+        logging.debug("Making directories...")
-            print("Making directories...")
         os.makedirs(self.raw_folder, exist_ok=True)
         os.makedirs(self.processed_folder, exist_ok=True)
 
-        if self.verbose:
+        logging.debug("Downloading...")
-            print("Downloading...")
         for fileid, md5 in self._resources:
             filename = "tecator.npz"
             download_file_from_google_drive(fileid,
@@ -80,19 +94,18 @@ class Tecator(ProtoDataset):
                                             filename=filename,
                                             md5=md5)
 
-        if self.verbose:
+        logging.debug("Processing...")
-            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(x_train),
-            torch.tensor(y_train),
+            torch.LongTensor(y_train),
         ]
         test_set = [
-            torch.tensor(x_test, dtype=torch.float32),
+            torch.Tensor(x_test),
-            torch.tensor(y_test),
+            torch.LongTensor(y_test),
         ]
 
         with open(os.path.join(self.processed_folder, self.training_file),
@@ -102,5 +115,4 @@ class Tecator(ProtoDataset):
                   "wb") as f:
             torch.save(test_set, f)
 
-        if self.verbose:
+        logging.debug("Done!")
-            print("Done!")

prototorch/datasets/xor.py

@@ -0,0 +1,19 @@
+"""Exclusive-or (XOR) dataset for binary classification."""
+
+import torch
+
+
+def make_xor(num_samples=500):
+    x = torch.rand(num_samples, 2)
+    y = torch.zeros(num_samples)
+    y[torch.logical_and(x[:, 0] > 0.5, x[:, 1] < 0.5)] = 1
+    y[torch.logical_and(x[:, 1] > 0.5, x[:, 0] < 0.5)] = 1
+    return x, y
+
+
+class XOR(torch.utils.data.TensorDataset):
+    """Exclusive-or (XOR) dataset for binary classification."""
+
+    def __init__(self, num_samples: int = 500):
+        x, y = make_xor(num_samples)
+        super().__init__(x, y)

prototorch/functions/__init__.py

@@ -1,12 +0,0 @@
-"""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/competitions.py

@@ -1,45 +0,0 @@
-"""ProtoTorch competition functions."""
-
-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.item(), dim=1).indices
-    winning_labels = labels[winning_indices].squeeze()
-    return winning_labels

prototorch/functions/distances.py

@@ -1,265 +0,0 @@
-"""ProtoTorch distance functions."""
-
-import numpy as np
-import torch
-
-from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
-                                         equal_int_shape)
-
-
-def squared_euclidean_distance(x, y):
-    r"""Compute the squared Euclidean distance between :math:`\bm x` and :math:`\bm 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``
-    """
-    expanded_x = x.unsqueeze(dim=1)
-    batchwise_difference = y - expanded_x
-    differences_raised = torch.pow(batchwise_difference, 2)
-    distances = torch.sum(differences_raised, axis=2)
-    return distances
-
-
-def euclidean_distance(x, y):
-    r"""Compute the Euclidean distance between :math:`x` and :math:`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`
-    """
-    distances_raised = squared_euclidean_distance(x, y)
-    distances = torch.sqrt(distances_raised)
-    return distances
-
-
-def euclidean_distance_v2(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"""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
-    """
-    distances = torch.cdist(x, y, p=p)
-    return distances
-
-
-def omega_distance(x, y, omega):
-    r"""Omega distance.
-
-    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
-    """
-    projected_x = x @ omega
-    projected_y = y @ omega
-    distances = squared_euclidean_distance(projected_x, projected_y)
-    return distances
-
-
-def lomega_distance(x, y, omegas):
-    r"""Localized Omega distance.
-
-    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
-    """
-    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
-    distances = torch.sum(differences_squared, dim=2)
-    distances = distances.permute(1, 0)
-    return distances
-
-
-def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
-    r"""Computes an euclidean distances matrix given two distinct vectors.
-    last dimension must be the vector dimension!
-    compute the distance via the identity of the dot product. This avoids the memory overhead due to the subtraction!
-
-    - ``x.shape = (number_of_x_vectors, vector_dim)``
-    - ``y.shape = (number_of_y_vectors, vector_dim)``
-
-    output: matrix of distances (number_of_x_vectors, number_of_y_vectors)
-    """
-    for tensor in [x, y]:
-        if tensor.ndim != 2:
-            raise ValueError(
-                "The tensor dimension must be two. You provide: tensor.ndim=" +
-                str(tensor.ndim) + ".")
-    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]) + ".")
-
-    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))
-
-    if not squared:
-        if epsilon == 0:
-            diss = torch.sqrt(diss)
-        else:
-            diss = torch.sqrt(torch.max(diss, epsilon))
-
-    return diss
-
-
-def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
-    r"""Tangent distances based on the tensorflow implementation of Sascha Saralajews
-
-    For more info about Tangen distances see
-
-    DOI:10.1109/IJCNN.2016.7727534.
-
-    The subspaces is always assumed as transposed and must be orthogonal!
-    For local non sparse signals subspaces must be provided!
-
-    - shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
-    - shape(protos): proto_number x dim1 x dim2 x ... x dimN
-    - shape(subspaces): (optional [proto_number]) x prod(dim1 * dim2 * ... * dimN)  x prod(projected_atom_shape)
-
-    subspace should be orthogonalized
-    Pytorch implementation of Sascha Saralajew's tensorflow code.
-    Translation by Christoph Raab
-    """
-    signal_shape, signal_int_shape = _int_and_mixed_shape(signals)
-    proto_shape, proto_int_shape = _int_and_mixed_shape(protos)
-    subspace_int_shape = tuple(subspaces.shape)
-
-    # check if the shapes are correct
-    _check_shapes(signal_int_shape, proto_int_shape)
-
-    atom_axes = list(range(3, len(signal_int_shape)))
-    # for sparse signals, we use the memory efficient implementation
-    if signal_int_shape[1] == 1:
-        signals = torch.reshape(signals, [-1, np.prod(signal_shape[3:])])
-
-        if len(atom_axes) > 1:
-            protos = torch.reshape(protos, [proto_shape[0], -1])
-
-        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))
-
-            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 = torch.reshape(
-                diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
-
-            return torch.permute(diss, [0, 2, 1])
-
-        else:
-
-            # 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])
-
-            projected_protos = (protos @ subspaces
-                                ).T  # K.batch_dot(projectors, protos, [1, 1]))
-
-            def projected_norm(projector):
-                return torch.sum(torch.dot(signals, projector)**2, axis=1)
-
-            diss = (torch.transpose(map(projected_norm, projectors)) -
-                    2 * torch.dot(signals, projected_protos) +
-                    torch.sum(projected_protos**2, axis=0, keepdims=True))
-
-            if not squared:
-                if epsilon == 0:
-                    diss = torch.sqrt(diss)
-                else:
-                    diss = torch.sqrt(torch.max(diss, epsilon))
-
-            diss = torch.reshape(
-                diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
-
-            return torch.permute(diss, [0, 2, 1])
-
-    else:
-        signals = signals.permute([0, 2, 1] + atom_axes)
-
-        diff = signals - protos
-
-        # global tangent space
-        if subspaces.ndim == 2:
-            # Scope Projectors
-            projectors = subspaces  #
-
-            # Scope: Tangentspace Projections
-            diff = torch.reshape(
-                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:],
-            )
-
-            diss = torch.norm(projected_diff, 2, dim=-1)
-            return diss.permute([0, 2, 1])
-
-        # local tangent spaces
-        else:
-            # Scope: Calculate Projectors
-            projectors = subspaces
-
-            # Scope: Tangentspace Projections
-            diff = torch.reshape(
-                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:],
-            )
-
-            diss = torch.norm(projected_diff, 2, dim=-1)
-            return diss.permute([1, 0, 2]).squeeze(-1)
-
-
-# Aliases
-sed = squared_euclidean_distance

prototorch/functions/helper.py

@@ -1,89 +0,0 @@
-import torch
-
-
-def calculate_prototype_accuracy(y_pred, y_true, plabels):
-    """Computes the accuracy of a prototype based model.
-    via Winner-Takes-All rule.
-    Requirement:
-    y_pred.shape == y_true.shape
-    unique(y_pred) in plabels
-    """
-    with torch.no_grad():
-        idx = torch.argmin(y_pred, axis=1)
-        return torch.true_divide(torch.sum(y_true == plabels[idx]),
-                                 len(y_pred)) * 100
-
-
-def predict_label(y_pred, plabels):
-    r""" Predicts labels given a prediction of a prototype based model.
-    """
-    with torch.no_grad():
-        return plabels[torch.argmin(y_pred, 1)]
-
-
-def mixed_shape(inputs):
-    if not torch.is_tensor(inputs):
-        raise ValueError("Input must be a tensor.")
-    else:
-        int_shape = list(inputs.shape)
-        # sometimes int_shape returns mixed integer types
-        int_shape = [int(i) if i is not None else i for i in int_shape]
-        tensor_shape = inputs.shape
-
-        for i, s in enumerate(int_shape):
-            if s is None:
-                int_shape[i] = tensor_shape[i]
-        return tuple(int_shape)
-
-
-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.")
-    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.")
-
-    if len(shape_1) != len(shape_2):
-        return False
-    else:
-        for axis, value in enumerate(shape_1):
-            if value is not None and shape_2[axis] not in {value, None}:
-                return False
-        return True
-
-
-def _check_shapes(signal_int_shape, proto_int_shape):
-    if len(signal_int_shape) < 4:
-        raise ValueError(
-            "The number of signal dimensions must be >=4. You provide: " +
-            str(len(signal_int_shape)))
-
-    if len(proto_int_shape) < 2:
-        raise ValueError(
-            "The number of proto dimensions must be >=2. You provide: " +
-            str(len(proto_int_shape)))
-
-    if not equal_int_shape(signal_int_shape[3:], proto_int_shape[1:]):
-        raise ValueError(
-            "The atom shape of signals must be equal protos. You provide: signals.shape[3:]="
-            + str(signal_int_shape[3:]) + " != protos.shape[1:]=" +
-            str(proto_int_shape[1:]))
-
-    # not a sparse signal
-    if signal_int_shape[1] != 1:
-        if not equal_int_shape(signal_int_shape[1:2], proto_int_shape[0:1]):
-            raise ValueError(
-                "If the signal is not sparse, the number of prototypes must be equal in signals and "
-                "protos. You provide: " + str(signal_int_shape[1]) + " != " +
-                str(proto_int_shape[0]))
-
-    return True
-
-
-def _int_and_mixed_shape(tensor):
-    shape = mixed_shape(tensor)
-    int_shape = tuple([i if isinstance(i, int) else None for i in shape])
-
-    return shape, int_shape

prototorch/functions/initializers.py

@@ -1,107 +0,0 @@
-"""ProtoTorch initialization functions."""
-
-from itertools import chain
-
-import torch
-
-INITIALIZERS = dict()
-
-
-def register_initializer(function):
-    """Add the initializer to the registry."""
-    INITIALIZERS[function.__name__] = function
-    return 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)]
-    # 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 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:])
-    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:])
-    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:])
-    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:])
-    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)
-    pdim = x_train.shape[1]
-    protos = torch.empty(nprotos, 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)
-        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,
-                      one_hot=True,
-                      epsilon=1e-7):
-    nprotos = torch.sum(prototype_distribution)
-    pdim = x_train.shape[1]
-    protos = torch.empty(nprotos, 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)
-        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 + epsilon
-    plabels = labels_from(prototype_distribution, one_hot=one_hot)
-    return protos, plabels
-
-
-def get_initializer(funcname):
-    """Deserialize the initializer."""
-    if callable(funcname):
-        return funcname
-    if funcname in INITIALIZERS:
-        return INITIALIZERS.get(funcname)
-    raise NameError(f"Initializer {funcname} was not found.")

prototorch/functions/losses.py

@@ -1,56 +0,0 @@
-"""ProtoTorch loss functions."""
-
-import torch
-
-
-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)
-    return matcher
-
-
-def _get_dp_dm(distances, targets, plabels):
-    """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"))
-    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
-
-
-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
-
-
-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

@@ -1,35 +0,0 @@
-# -*- coding: utf-8 -*-
-from __future__ import absolute_import, division, print_function
-
-import torch
-
-
-def orthogonalization(tensors):
-    r""" Orthogonalization of a given tensor via polar decomposition.
-    """
-    u, _, v = torch.svd(tensors, compute_uv=True)
-    u_shape = tuple(list(u.shape))
-    v_shape = tuple(list(v.shape))
-
-    # reshape to (num x N x M)
-    u = torch.reshape(u, (-1, u_shape[-2], u_shape[-1]))
-    v = torch.reshape(v, (-1, v_shape[-2], v_shape[-1]))
-
-    out = u @ v.permute([0, 2, 1])
-
-    out = torch.reshape(out, u_shape[:-1] + (v_shape[-2], ))
-
-    return out
-
-
-def trace_normalization(tensors):
-    r""" Trace normalization
-    """
-    epsilon = torch.tensor([1e-10], dtype=torch.float64)
-    # Scope trace_normalization
-    constant = torch.trace(tensors)
-
-    if epsilon != 0:
-        constant = torch.max(constant, epsilon)
-
-    return tensors / constant

prototorch/modules/__init__.py

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

prototorch/modules/losses.py

@@ -1,40 +0,0 @@
-"""ProtoTorch losses."""
-
-import torch
-
-from prototorch.functions.activations import get_activation
-from prototorch.functions.losses import glvq_loss
-
-
-class GLVQLoss(torch.nn.Module):
-    def __init__(self, margin=0.0, squashing="identity", beta=10, **kwargs):
-        super().__init__(**kwargs)
-        self.margin = margin
-        self.squashing = get_activation(squashing)
-        self.beta = torch.tensor(beta)
-
-    def forward(self, outputs, targets):
-        distances, plabels = outputs
-        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

@@ -1,195 +0,0 @@
-import torch
-from torch import nn
-
-from prototorch.functions.distances import (euclidean_distance_matrix,
-                                            tangent_distance)
-from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
-from prototorch.functions.normalization import orthogonalization
-from prototorch.modules.prototypes import Prototypes1D
-
-
-class GTLVQ(nn.Module):
-    r""" Generalized Tangent Learning Vector Quantization
-
-    Parameters
-    ----------
-    num_classes: int
-        Number of classes of the given classification problem.
-
-    subspace_data: torch.tensor of shape (n_batch,feature_dim,feature_dim)
-        Subspace data for the point approximation, required
-
-    prototype_data: torch.tensor of shape (n_init_data,feature_dim) (optional)
-        prototype data for initalization of the prototypes used in GTLVQ.
-
-    subspace_size: int (default=256,optional)
-        Subspace dimension of the Projectors. Currently only supported
-        with tagnent_projection_type=global.
-
-    tangent_projection_type: string
-        Specifies the tangent projection type
-        options:    local
-                    local_proj
-                    global
-        local: computes the tangent distances without emphasizing projected
-        data. Only distances are available
-        local_proj: computs tangent distances and returns the projected data
-        for further use. Be careful: data is repeated by number of prototypes
-        global: Number of subspaces is set to one and every prototypes
-        uses the same.
-
-    prototypes_per_class: int (default=2,optional)
-    Number of prototypes per class
-
-    feature_dim: int (default=256)
-    Dimensionality of the feature space specified as integer.
-    Prototype dimension.
-
-    Notes
-    -----
-    The GTLVQ [1] is a prototype-based classification learning model. The
-    GTLVQ uses the Tangent-Distances for a local point approximation
-    of an assumed data manifold via prototypial representations.
-
-    The GTLVQ requires subspace projectors for transforming the data
-    and prototypes into the affine subspace. Every prototype is
-    equipped with a specific subpspace and represents a point
-    approximation of the assumed manifold.
-
-    In practice prototypes and data are projected on this manifold
-    and pairwise euclidean distance computes.
-
-    References
-    ----------
-    .. [1] Saralajew, Sascha; Villmann, Thomas: Transfer learning
-    in classification based on manifolc. models and its relation
-    to tangent metric learning. In: 2017 International Joint
-    Conference on Neural Networks (IJCNN).
-    Bd. 2017-May : IEEE, 2017, S. 1756–1765
-    """
-    def __init__(
-        self,
-        num_classes,
-        subspace_data=None,
-        prototype_data=None,
-        subspace_size=256,
-        tangent_projection_type="local",
-        prototypes_per_class=2,
-        feature_dim=256,
-    ):
-        super(GTLVQ, self).__init__()
-
-        self.num_protos = num_classes * prototypes_per_class
-        self.subspace_size = feature_dim if subspace_size is None else subspace_size
-        self.feature_dim = feature_dim
-
-        if subspace_data is None:
-            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":
-                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)
-        elif self.tpt == "gloabl":
-            dis = self.global_tangent_distances(x)
-        else:
-            dis = (x @ self.cls.prototypes.T) / (
-                torch.norm(x, dim=1, keepdim=True) @ torch.norm(
-                    self.cls.prototypes, dim=1, keepdim=True).T)
-        return dis
-
-    def init_gobal_subspace(self, data, num_subspaces):
-        _, _, 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))
-
-    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 global_tangent_distances(self, x):
-        # Tangent Projection
-        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)
-
-        # 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)
-
-    def get_parameters(self):
-        return {
-            "params": self.cls.prototypes,
-        }, {
-            "params": self.subspaces
-        }
-
-    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))
-                self.subspaces.copy_(ortho_subpsaces)

prototorch/modules/prototypes.py

@@ -1,137 +0,0 @@
-"""ProtoTorch prototype modules."""
-
-import warnings
-
-import torch
-
-from prototorch.functions.initializers import get_initializer
-
-
-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):
-    """Create a learnable set of one-dimensional 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,
-    ):
-        warnings.warn(
-            PendingDeprecationWarning(
-                "Prototypes1D will be replaced in future versions."))
-
-        # 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:
-                kwargs_nclasses = sum(prototype_distribution)
-            else:
-                if "nclasses" not in kwargs:
-                    raise NameError("`prototype_distribution` required if "
-                                    "both `data` and `nclasses` are not "
-                                    "provided.")
-                kwargs_nclasses = kwargs.pop("nclasses")
-            input_dim = kwargs.pop("input_dim")
-            if prototype_initializer in [
-                    "stratified_mean", "stratified_random"
-            ]:
-                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]
-
-        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]
-
-        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)
-
-        if not prototype_distribution:
-            prototype_distribution = [prototypes_per_class] * nclasses
-        with torch.no_grad():
-            self.prototype_distribution = torch.tensor(prototype_distribution)
-
-        self._validate_prototype_distribution()
-
-        self.prototype_initializer = get_initializer(prototype_initializer)
-        prototypes, prototype_labels = self.prototype_initializer(
-            x_train,
-            y_train,
-            prototype_distribution=self.prototype_distribution,
-            one_hot=one_hot_labels,
-        )
-
-        # Register module parameters
-        self.prototypes = torch.nn.Parameter(prototypes)
-        self.prototype_labels = torch.nn.Parameter(
-            prototype_labels.type(dtype)).requires_grad_(False)

prototorch/nn/__init__.py

@@ -0,0 +1,4 @@
+"""ProtoTorch Neural Network Module"""
+
+from .activations import *
+from .wrappers import *

prototorch/nn/activations.py

@@ -1,21 +1,18 @@
-"""ProtoTorch activation functions."""
+"""ProtoTorch activations"""
 
 import torch
 
 ACTIVATIONS = dict()
 
 
-# def register_activation(scriptf):
+def register_activation(fn):
-#     ACTIVATIONS[scriptf.name] = scriptf
-#     return scriptf
-def register_activation(function):
     """Add the activation function to the registry."""
-    ACTIVATIONS[function.__name__] = function
+    name = fn.__name__
-    return function
+    ACTIVATIONS[name] = fn
+    return fn
 
 
 @register_activation
-# @torch.jit.script
 def identity(x, beta=0.0):
     """Identity activation function.
 
@@ -29,7 +26,6 @@ def identity(x, beta=0.0):
 
 
 @register_activation
-# @torch.jit.script
 def sigmoid_beta(x, beta=10.0):
     r"""Sigmoid activation function with scaling.
 
@@ -44,7 +40,6 @@ def sigmoid_beta(x, beta=10.0):
 
 
 @register_activation
-# @torch.jit.script
 def swish_beta(x, beta=10.0):
     r"""Swish activation function with scaling.
 
@@ -62,6 +57,10 @@ def get_activation(funcname):
     """Deserialize the activation function."""
     if callable(funcname):
         return funcname
-    if funcname in ACTIVATIONS:
+    elif funcname in ACTIVATIONS:
         return ACTIVATIONS.get(funcname)
-    raise NameError(f"Activation {funcname} was not found.")
+    else:
+        emsg = f"Unable to find matching function for `{funcname}` " \
+            f"in `prototorch.nn.activations`. "
+        helpmsg = f"Possible values are {list(ACTIVATIONS.keys())}."
+        raise NameError(emsg + helpmsg)

prototorch/nn/wrappers.py

@@ -0,0 +1,38 @@
+"""ProtoTorch wrappers."""
+
+import torch
+
+
+class LambdaLayer(torch.nn.Module):
+
+    def __init__(self, fn, name=None):
+        super().__init__()
+        self.fn = fn
+        self.name = name or fn.__name__  # lambda fns get <lambda>
+
+    def forward(self, *args, **kwargs):
+        return self.fn(*args, **kwargs)
+
+    def extra_repr(self):
+        return self.name
+
+
+class LossLayer(torch.nn.modules.loss._Loss):
+
+    def __init__(self,
+                 fn,
+                 name=None,
+                 size_average=None,
+                 reduce=None,
+                 reduction: str = "mean") -> None:
+        super().__init__(size_average=size_average,
+                         reduce=reduce,
+                         reduction=reduction)
+        self.fn = fn
+        self.name = name or fn.__name__  # lambda fns get <lambda>
+
+    def forward(self, *args, **kwargs):
+        return self.fn(*args, **kwargs)
+
+    def extra_repr(self):
+        return self.name

prototorch/utils/__init__.py

@@ -0,0 +1,13 @@
+"""ProtoTorch utils module"""
+
+from .colors import (
+    get_colors,
+    get_legend_handles,
+    hex_to_rgb,
+    rgb_to_hex,
+)
+from .utils import (
+    mesh2d,
+    parse_data_arg,
+    parse_distribution,
+)

prototorch/utils/celluloid.py

@@ -1,46 +0,0 @@
-"""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

@@ -1,78 +1,60 @@
-"""ProtoFlow color utilities."""
+"""ProtoTorch color utilities"""
 
 import matplotlib.lines as mlines
+import torch
 from matplotlib import cm
-from matplotlib.colors import Normalize, to_hex, to_rgb
+from matplotlib.colors import (
+    Normalize,
+    to_hex,
+    to_rgb,
+)
 
 
-def color_scheme(n,
+def hex_to_rgb(hex_values):
-                 cmap="viridis",
+    for v in hex_values:
-                 form="hex",
+        v = v.lstrip('#')
-                 tikz=False,
+        lv = len(v)
-                 zero_indexed=False):
+        c = [int(v[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]
-    """Return *n* colors from the color scheme.
+        yield c
 
-    Arguments:
-        n (int): number of colors to return
 
-    Keyword Arguments:
+def rgb_to_hex(rgb_values):
-        cmap (str): Name of a matplotlib `colormap\
+    for v in rgb_values:
-            <https://matplotlib.org/3.1.1/gallery/color/colormap_reference.html>`_.
+        c = "%02x%02x%02x" % tuple(v)
-        form (str): Colorformat (supports "hex" and "rgb").
+        yield c
-        tikz (bool): Output as `TikZ <https://github.com/pgf-tikz/pgf>`_
-            command.
-        zero_indexed (bool): Use zero indexing for output array.
 
-    Returns:
+
-        (list): List of colors
+def get_colors(vmax, vmin=0, cmap="viridis"):
-    """
     cmap = cm.get_cmap(cmap)
-    colornorm = Normalize(vmin=1, vmax=n)
+    colornorm = Normalize(vmin=vmin, vmax=vmax)
-    hex_map = dict()
+    colors = dict()
-    rgb_map = dict()
+    for c in range(vmin, vmax + 1):
-    for cl in range(1, n + 1):
+        colors[c] = to_hex(cmap(colornorm(c)))
-        if zero_indexed:
+    return colors
-            hex_map[cl - 1] = to_hex(cmap(colornorm(cl)))
-            rgb_map[cl - 1] = to_rgb(cmap(colornorm(cl)))
-        else:
-            hex_map[cl] = to_hex(cmap(colornorm(cl)))
-            rgb_map[cl] = to_rgb(cmap(colornorm(cl)))
-    if tikz:
-        for k, v in rgb_map.items():
-            print(f"\\definecolor{{color-{k}}}{{rgb}}{{{v[0]},{v[1]},{v[2]}}}")
-    if form == "hex":
-        return hex_map
-    elif form == "rgb":
-        return rgb_map
-    else:
-        return hex_map
 
 
-def get_legend_handles(labels, marker="dots", zero_indexed=False):
+def get_legend_handles(colors, labels, marker="dots", zero_indexed=False):
-    """Return matplotlib legend handles and colors."""
     handles = list()
-    n = len(labels)
+    for color, label in zip(colors.values(), labels):
-    colors = color_scheme(n,
-                          cmap="viridis",
-                          form="hex",
-                          zero_indexed=zero_indexed)
-    for label, color in zip(labels, colors.values()):
         if marker == "dots":
             handle = mlines.Line2D(
-                [],
+                xdata=[],
-                [],
+                ydata=[],
+                label=label,
                 color="white",
                 markerfacecolor=color,
                 marker="o",
                 markersize=10,
                 markeredgecolor="k",
-                label=label,
             )
         else:
-            handle = mlines.Line2D([], [],
+            handle = mlines.Line2D(
-                                   color=color,
+                xdata=[],
-                                   marker="",
+                ydata=[],
-                                   markersize=15,
+                label=label,
-                                   label=label)
+                color=color,
-            handles.append(handle)
+                marker="",
-    return handles, colors
+                markersize=15,
+            )
+        handles.append(handle)
+    return handles

prototorch/utils/utils.py

@@ -1,243 +1,136 @@
-"""Utilities that provide various small functionalities."""
+"""ProtoTorch utilities"""
 
-import os
+import warnings
-import pickle
+from typing import (
-import sys
+    Dict,
-from time import time
+    Iterable,
+    List,
+    Optional,
+    Union,
+)
 
-import matplotlib.pyplot as plt
 import numpy as np
+import torch
+from torch.utils.data import DataLoader, Dataset
 
 
-def progressbar(title, value, end, bar_width=20):
+def generate_mesh(
-    percent = float(value) / end
+    minima: torch.TensorType,
-    arrow = "=" * int(round(percent * bar_width) - 1) + ">"
+    maxima: torch.TensorType,
-    spaces = "." * (bar_width - len(arrow))
+    border: float = 1.0,
-    sys.stdout.write("\r{}: [{}] {}%".format(title, arrow + spaces,
+    resolution: int = 100,
-                                             int(round(percent * 100))))
+    device: Optional[torch.device] = None,
-    sys.stdout.flush()
+):
-    if percent == 1.0:
+    # Apply Border
-        print()
+    ptp = maxima - minima
+    shift = border * ptp
+    minima -= shift
+    maxima += shift
+
+    # Generate Mesh
+    minima = minima.to(device).unsqueeze(1)
+    maxima = maxima.to(device).unsqueeze(1)
+
+    factors = torch.linspace(0, 1, resolution, device=device)
+    marginals = factors * maxima + ((1 - factors) * minima)
+
+    single_dimensions = torch.meshgrid(*marginals)
+    mesh_input = torch.stack([dim.ravel() for dim in single_dimensions], dim=1)
+
+    return mesh_input, single_dimensions
 
 
-def prettify_string(inputs, start="", sep=" ", end="\n"):
+def mesh2d(x=None, border: float = 1.0, resolution: int = 100):
-    outputs = start + " ".join(inputs.split()) + end
+    if x is not None:
-    return outputs
+        x_shift = border * np.ptp(x[:, 0])
-
+        y_shift = border * np.ptp(x[:, 1])
-
+        x_min, x_max = x[:, 0].min() - x_shift, x[:, 0].max() + x_shift
-def pretty_print(inputs):
+        y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
-    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
+        x_min, x_max = -border, border
-
+        y_min, y_max = -border, border
-        new_arr = copy.deepcopy(arr)
+    xx, yy = np.meshgrid(np.linspace(x_min, x_max, resolution),
-    for k, v in replacement_dict.items():
+                         np.linspace(y_min, y_max, resolution))
-        new_arr[arr == k] = v
+    mesh = np.c_[xx.ravel(), yy.ravel()]
-    return new_arr
+    return mesh, xx, yy
 
 
-def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
+def distribution_from_list(list_dist: List[int],
-    """Split a classification dataset in such a way so as to
+                           clabels: Optional[Iterable[int]] = None):
-    preserve the class distribution in subsamples of the dataset.
+    clabels = clabels or list(range(len(list_dist)))
+    distribution = dict(zip(clabels, list_dist))
+    return distribution
+
+
+def parse_distribution(
+        user_distribution,
+        clabels: Optional[Iterable[int]] = None) -> Dict[int, int]:
+    """Parse user-provided distribution.
+
+    Return a dictionary with integer keys that represent the class labels and
+    values that denote the number of components/prototypes with that class
+    label.
+
+    The argument `user_distribution` could be any one of a number of allowed
+    formats. If it is a Python list, it is assumed that there are as many
+    entries in this list as there are classes, and the value at each index of
+    this list describes the number of prototypes 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, a shorthand of (num_classes, prototypes_per_class)
+    is assumed. If it is a Python dictionary, 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. If however, the dictionary contains
+    the keys "num_classes" and "per_class", they are parsed to use their values
+    as one might expect.
+
     """
-    if train + val > 1.0:
+    if isinstance(user_distribution, dict):
-        raise ValueError("Invalid split values for train and val.")
+        if "num_classes" in user_distribution.keys():
-    Y = data[:, -1]
+            num_classes = int(user_distribution["num_classes"])
-    labels = set(Y)
+            per_class = int(user_distribution["per_class"])
-    hist = dict()
+            return distribution_from_list([per_class] * num_classes, clabels)
-    for l in labels:
+        else:
-        data_l = data[Y == l]
+            return user_distribution
-        nl = len(data_l)
+    elif isinstance(user_distribution, tuple):
-        nl_train = int(nl * train)
+        assert len(user_distribution) == 2
-        nl_val = int(nl * val)
+        num_classes, per_class = user_distribution
-        nl_test = nl - (nl_train + nl_val)
+        num_classes, per_class = int(num_classes), int(per_class)
-        hist[l] = (nl_train, nl_val, nl_test)
+        return distribution_from_list([per_class] * num_classes, clabels)
-
+    elif isinstance(user_distribution, list):
-    train_data = list()
+        return distribution_from_list(user_distribution, clabels)
-    val_data = list()
+    else:
-    test_data = list()
+        msg = f"`distribution` was not understood." \
-    for l, (nl_train, nl_val, nl_test) in hist.items():
+            f"You have provided: {user_distribution}."
-        data_l = data[Y == l]
+        raise ValueError(msg)
-        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"):
+def parse_data_arg(data_arg: Union[Dataset, DataLoader, list, tuple]):
-    plt.figure(title)
+    """Return data and target as torch tensors."""
-    plt.clf()
+    if isinstance(data_arg, Dataset):
-    plt.title(title)
+        if hasattr(data_arg, "__len__"):
-    dist, counts = np.unique(data[:, -1], return_counts=True)
+            ds_size = len(data_arg)  # type: ignore
-    plt.bar(dist, counts)
+            loader = DataLoader(data_arg, batch_size=ds_size)
-    plt.xticks(dist)
+            data, targets = next(iter(loader))
-    print("Call matplotlib.pyplot.show() to see the plot.")
+        else:
+            emsg = f"Dataset {data_arg} is not sized (`__len__` unimplemented)."
+            raise TypeError(emsg)
 
-
+    elif isinstance(data_arg, DataLoader):
-def ntimer(n=10):
+        data = torch.tensor([])
-    """Wraps a function which wraps another function to time it."""
+        targets = torch.tensor([])
-    if n < 1:
+        for x, y in data_arg:
-        raise (Exception(f"Invalid n = {n} given."))
+            data = torch.cat([data, x])
-
+            targets = torch.cat([targets, y])
-    def timer(func):
+    else:
-        """Wraps `func` with a timer and returns the wrapped `func`."""
+        assert len(data_arg) == 2
-        def wrapper(*args, **kwargs):
+        data, targets = data_arg
-            rv = None
+        if not isinstance(data, torch.Tensor):
-            before = time()
+            wmsg = f"Converting data to {torch.Tensor}..."
-            for _ in range(n):
+            warnings.warn(wmsg)
-                rv = func(*args, **kwargs)
+            data = torch.Tensor(data)
-            after = time()
+        if not isinstance(targets, torch.LongTensor):
-            elapsed = after - before
+            wmsg = f"Converting targets to {torch.LongTensor}..."
-            print(f"Elapsed: {elapsed*1e3:02.02f} ms")
+            warnings.warn(wmsg)
-            return rv
+            targets = torch.LongTensor(targets)
-
+    return data, targets
-        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

setup.cfg

@@ -0,0 +1,16 @@
+[pylint]
+disable =
+	too-many-arguments,
+	too-few-public-methods,
+	fixme,
+
+
+[pycodestyle]
+max-line-length = 79
+
+[isort]
+multi_line_output = 3
+include_trailing_comma = True
+force_grid_wrap = 3
+use_parentheses = True
+line_length = 79

setup.py

@@ -1,10 +1,12 @@
 """
-  _____           _     _______             _
+
- |  __ \         | |   |__   __|           | |
+ ######
- | |__) | __ ___ | |_ ___ | | ___  _ __ ___| |__
+ #     # #####   ####  #####  ####  #####  ####  #####   ####  #    #
- |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \
+ #     # #    # #    #   #   #    #   #   #    # #    # #    # #    #
- | |   | | | (_) | || (_) | | (_) | | | (__| | | |
+ ######  #    # #    #   #   #    #   #   #    # #    # #      ######
- |_|   |_|  \___/ \__\___/|_|\___/|_|  \___|_| |_|
+ #       #####  #    #   #   #    #   #   #    # #####  #      #    #
+ #       #   #  #    #   #   #    #   #   #    # #   #  #    # #    #
+ #       #    #  ####    #    ####    #    ####  #    #  ####  #    #
 
 ProtoTorch Core Package
 """
@@ -13,36 +15,43 @@ from setuptools import find_packages, setup
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
 
-with open("README.md", "r") as fh:
+with open("README.md", encoding="utf-8") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "torch>=1.3.1",
+    "torch>=2.0.0",
-    "torchvision>=0.5.0",
+    "torchvision",
-    "numpy>=1.9.1",
+    "numpy",
+    "scikit-learn",
+    "matplotlib",
 ]
 DATASETS = [
     "requests",
     "tqdm",
 ]
-DEV = ["bumpversion"]
+DEV = [
+    "bump2version",
+    "pre-commit",
+]
 DOCS = [
     "recommonmark",
     "sphinx",
     "sphinx_rtd_theme",
     "sphinxcontrib-katex",
+    "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
-    "sklearn",
-    "matplotlib",
     "torchinfo",
 ]
-TESTS = ["codecov", "pytest"]
+TESTS = [
+    "flake8",
+    "pytest",
+]
 ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.4.2",
+    version="0.7.6",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -53,30 +62,33 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
+    python_requires=">=3.8",
     install_requires=INSTALL_REQUIRES,
     extras_require={
-        "docs": DOCS,
         "datasets": DATASETS,
+        "dev": DEV,
+        "docs": DOCS,
         "examples": EXAMPLES,
         "tests": TESTS,
         "all": ALL,
     },
     classifiers=[
-        "Development Status :: 2 - Pre-Alpha",
         "Environment :: Console",
+        "Natural Language :: English",
+        "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: MIT License",
-        "Natural Language :: English",
-        "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",
         "Topic :: Software Development :: Libraries :: Python Modules",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
     ],
     packages=find_packages(),
     zip_safe=False,

tests/test_core.py

@@ -0,0 +1,777 @@
+"""ProtoTorch core test suite"""
+
+import unittest
+
+import numpy as np
+import pytest
+import torch
+
+import prototorch as pt
+from prototorch.utils import parse_distribution
+
+
+# Utils
+def test_parse_distribution_dict_0():
+    distribution = {"num_classes": 1, "per_class": 0}
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 0}
+
+
+def test_parse_distribution_dict_1():
+    distribution = dict(num_classes=3, per_class=2)
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 2, 1: 2, 2: 2}
+
+
+def test_parse_distribution_dict_2():
+    distribution = {0: 1, 2: 2, -1: 3}
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 1, 2: 2, -1: 3}
+
+
+def test_parse_distribution_tuple():
+    distribution = (2, 3)
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 3, 1: 3}
+
+
+def test_parse_distribution_list():
+    distribution = [1, 1, 0, 2]
+    distribution = parse_distribution(distribution)
+    assert distribution == {0: 1, 1: 1, 2: 0, 3: 2}
+
+
+def test_parse_distribution_custom_labels():
+    distribution = [1, 1, 0, 2]
+    clabels = [1, 2, 5, 3]
+    distribution = parse_distribution(distribution, clabels)
+    assert distribution == {1: 1, 2: 1, 5: 0, 3: 2}
+
+
+# Components initializers
+def test_literal_comp_generate():
+    protos = torch.rand(4, 3, 5, 5)
+    c = pt.initializers.LiteralCompInitializer(protos)
+    components = c.generate([])
+    assert torch.allclose(components, protos)
+
+
+def test_literal_comp_generate_from_list():
+    protos = [[0, 1], [2, 3], [4, 5]]
+    c = pt.initializers.LiteralCompInitializer(protos)
+    with pytest.warns(UserWarning):
+        components = c.generate([])
+    assert torch.allclose(components, torch.Tensor(protos))
+
+
+def test_shape_aware_raises_error():
+    with pytest.raises(TypeError):
+        _ = pt.initializers.ShapeAwareCompInitializer(shape=(2, ))
+
+
+def test_data_aware_comp_generate():
+    protos = torch.rand(4, 3, 5, 5)
+    c = pt.initializers.DataAwareCompInitializer(protos)
+    components = c.generate(num_components="IgnoreMe!")
+    assert torch.allclose(components, protos)
+
+
+def test_class_aware_comp_generate():
+    protos = torch.rand(4, 2, 3, 5, 5)
+    plabels = torch.tensor([0, 0, 1, 1]).long()
+    c = pt.initializers.ClassAwareCompInitializer([protos, plabels])
+    components = c.generate(distribution=[])
+    assert torch.allclose(components, protos)
+
+
+def test_zeros_comp_generate():
+    shape = (3, 5, 5)
+    c = pt.initializers.ZerosCompInitializer(shape)
+    components = c.generate(num_components=4)
+    assert torch.allclose(components, torch.zeros(4, 3, 5, 5))
+
+
+def test_ones_comp_generate():
+    c = pt.initializers.OnesCompInitializer(2)
+    components = c.generate(num_components=3)
+    assert torch.allclose(components, torch.ones(3, 2))
+
+
+def test_fill_value_comp_generate():
+    c = pt.initializers.FillValueCompInitializer(2, 0.0)
+    components = c.generate(num_components=3)
+    assert torch.allclose(components, torch.zeros(3, 2))
+
+
+def test_uniform_comp_generate_min_max_bound():
+    c = pt.initializers.UniformCompInitializer(2, -1.0, 1.0)
+    components = c.generate(num_components=1024)
+    assert components.min() >= -1.0
+    assert components.max() <= 1.0
+
+
+def test_random_comp_generate_mean():
+    c = pt.initializers.RandomNormalCompInitializer(2, -1.0)
+    components = c.generate(num_components=1024)
+    assert torch.allclose(components.mean(),
+                          torch.tensor(-1.0),
+                          rtol=1e-05,
+                          atol=1e-01)
+
+
+def test_comp_generate_0_components():
+    c = pt.initializers.ZerosCompInitializer(2)
+    _ = c.generate(num_components=0)
+
+
+def test_stratified_mean_comp_generate():
+    # yapf: disable
+    x = torch.Tensor(
+        [[0,  -1, -2],
+         [10, 11, 12],
+         [0,   0,  0],
+         [2,   2,  2]])
+    y = torch.LongTensor([0, 0, 1, 1])
+    desired = torch.Tensor(
+        [[5.0, 5.0, 5.0],
+         [1.0, 1.0, 1.0]])
+    # yapf: enable
+    c = pt.initializers.StratifiedMeanCompInitializer(data=[x, y])
+    actual = c.generate([1, 1])
+    assert torch.allclose(actual, desired)
+
+
+def test_stratified_selection_comp_generate():
+    # yapf: disable
+    x = torch.Tensor(
+        [[0, 0, 0],
+         [1, 1, 1],
+         [0, 0, 0],
+         [1, 1, 1]])
+    y = torch.LongTensor([0, 1, 0, 1])
+    desired = torch.Tensor(
+        [[0, 0, 0],
+         [1, 1, 1]])
+    # yapf: enable
+    c = pt.initializers.StratifiedSelectionCompInitializer(data=[x, y])
+    actual = c.generate([1, 1])
+    assert torch.allclose(actual, desired)
+
+
+# Labels initializers
+def test_literal_labels_init():
+    l = pt.initializers.LiteralLabelsInitializer([0, 0, 1, 2])
+    with pytest.warns(UserWarning):
+        labels = l.generate([])
+    assert torch.allclose(labels, torch.LongTensor([0, 0, 1, 2]))
+
+
+def test_labels_init_from_list():
+    l = pt.initializers.LabelsInitializer()
+    components = l.generate(distribution=[1, 1, 1])
+    assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
+
+
+def test_labels_init_from_tuple_legal():
+    l = pt.initializers.LabelsInitializer()
+    components = l.generate(distribution=(3, 1))
+    assert torch.allclose(components, torch.LongTensor([0, 1, 2]))
+
+
+def test_labels_init_from_tuple_illegal():
+    l = pt.initializers.LabelsInitializer()
+    with pytest.raises(AssertionError):
+        _ = l.generate(distribution=(1, 1, 1))
+
+
+def test_data_aware_labels_init():
+    data, targets = [0, 1, 2, 3], [0, 0, 1, 1]
+    ds = pt.datasets.NumpyDataset(data, targets)
+    l = pt.initializers.DataAwareLabelsInitializer(ds)
+    labels = l.generate([])
+    assert torch.allclose(labels, torch.LongTensor(targets))
+
+
+# Reasonings initializers
+def test_literal_reasonings_init():
+    r = pt.initializers.LiteralReasoningsInitializer([0, 0, 1, 2])
+    with pytest.warns(UserWarning):
+        reasonings = r.generate([])
+    assert torch.allclose(reasonings, torch.Tensor([0, 0, 1, 2]))
+
+
+def test_random_reasonings_init():
+    r = pt.initializers.RandomReasoningsInitializer(0.2, 0.8)
+    reasonings = r.generate(distribution=[0, 1])
+    assert torch.numel(reasonings) == 1 * 2 * 2
+    assert reasonings.min() >= 0.2
+    assert reasonings.max() <= 0.8
+
+
+def test_zeros_reasonings_init():
+    r = pt.initializers.ZerosReasoningsInitializer()
+    reasonings = r.generate(distribution=[0, 1])
+    assert torch.allclose(reasonings, torch.zeros(1, 2, 2))
+
+
+def test_ones_reasonings_init():
+    r = pt.initializers.ZerosReasoningsInitializer()
+    reasonings = r.generate(distribution=[1, 2, 3])
+    assert torch.allclose(reasonings, torch.zeros(6, 3, 2))
+
+
+def test_pure_positive_reasonings_init_one_per_class():
+    r = pt.initializers.PurePositiveReasoningsInitializer(
+        components_first=False)
+    reasonings = r.generate(distribution=(4, 1))
+    assert torch.allclose(reasonings[0], torch.eye(4))
+
+
+def test_pure_positive_reasonings_init_unrepresented_classes():
+    r = pt.initializers.PurePositiveReasoningsInitializer()
+    reasonings = r.generate(distribution=[9, 0, 0, 0])
+    assert reasonings.shape[0] == 9
+    assert reasonings.shape[1] == 4
+    assert reasonings.shape[2] == 2
+
+
+def test_random_reasonings_init_channels_not_first():
+    r = pt.initializers.RandomReasoningsInitializer(components_first=False)
+    reasonings = r.generate(distribution=[0, 0, 0, 1])
+    assert reasonings.shape[0] == 2
+    assert reasonings.shape[1] == 4
+    assert reasonings.shape[2] == 1
+
+
+# Transform initializers
+def test_eye_transform_init_square():
+    t = pt.initializers.EyeLinearTransformInitializer()
+    I = t.generate(3, 3)
+    assert torch.allclose(I, torch.eye(3))
+
+
+def test_eye_transform_init_narrow():
+    t = pt.initializers.EyeLinearTransformInitializer()
+    actual = t.generate(3, 2)
+    desired = torch.Tensor([[1, 0], [0, 1], [0, 0]])
+    assert torch.allclose(actual, desired)
+
+
+def test_eye_transform_init_wide():
+    t = pt.initializers.EyeLinearTransformInitializer()
+    actual = t.generate(2, 3)
+    desired = torch.Tensor([[1, 0, 0], [0, 1, 0]])
+    assert torch.allclose(actual, desired)
+
+
+# Transforms
+def test_linear_transform_default_eye_init():
+    l = pt.transforms.LinearTransform(2, 4)
+    actual = l.weights
+    desired = torch.Tensor([[1, 0, 0, 0], [0, 1, 0, 0]])
+    assert torch.allclose(actual, desired)
+
+
+def test_linear_transform_forward():
+    l = pt.transforms.LinearTransform(4, 2)
+    actual_weights = l.weights
+    desired_weights = torch.Tensor([[1, 0], [0, 1], [0, 0], [0, 0]])
+    assert torch.allclose(actual_weights, desired_weights)
+    actual_outputs = l(torch.Tensor([[1.1, 2.2, 3.3, 4.4], \
+                                     [1.1, 2.2, 3.3, 4.4], \
+                                     [5.5, 6.6, 7.7, 8.8]]))
+    desired_outputs = torch.Tensor([[1.1, 2.2], [1.1, 2.2], [5.5, 6.6]])
+    assert torch.allclose(actual_outputs, desired_outputs)
+
+
+def test_linear_transform_zeros_init():
+    l = pt.transforms.LinearTransform(
+        in_dim=2,
+        out_dim=4,
+        initializer=pt.initializers.ZerosLinearTransformInitializer(),
+    )
+    actual = l.weights
+    desired = torch.zeros(2, 4)
+    assert torch.allclose(actual, desired)
+
+
+def test_linear_transform_out_dim_first():
+    l = pt.transforms.LinearTransform(
+        in_dim=2,
+        out_dim=4,
+        initializer=pt.initializers.OLTI(out_dim_first=True),
+    )
+    assert l.weights.shape[0] == 4
+    assert l.weights.shape[1] == 2
+
+
+# Components
+def test_components_no_initializer():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components(3, None)
+
+
+def test_components_no_num_components():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components(initializer=pt.initializers.OCI(2))
+
+
+def test_components_none_num_components():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components(None, initializer=pt.initializers.OCI(2))
+
+
+def test_components_no_args():
+    with pytest.raises(TypeError):
+        _ = pt.components.Components()
+
+
+def test_components_zeros_init():
+    c = pt.components.Components(3, pt.initializers.ZCI(2))
+    assert torch.allclose(c.components, torch.zeros(3, 2))
+
+
+def test_labeled_components_dict_init():
+    c = pt.components.LabeledComponents({0: 3}, pt.initializers.OCI(2))
+    assert torch.allclose(c.components, torch.ones(3, 2))
+    assert torch.allclose(c.labels, torch.zeros(3, dtype=torch.long))
+
+
+def test_labeled_components_list_init():
+    c = pt.components.LabeledComponents([3], pt.initializers.OCI(2))
+    assert torch.allclose(c.components, torch.ones(3, 2))
+    assert torch.allclose(c.labels, torch.zeros(3, dtype=torch.long))
+
+
+def test_labeled_components_tuple_init():
+    c = pt.components.LabeledComponents({0: 1, 1: 2}, pt.initializers.OCI(2))
+    assert torch.allclose(c.components, torch.ones(3, 2))
+    assert torch.allclose(c.labels, torch.LongTensor([0, 1, 1]))
+
+
+# Labels
+def test_standalone_labels_dict_init():
+    l = pt.components.Labels({0: 3})
+    assert torch.allclose(l.labels, torch.zeros(3, dtype=torch.long))
+
+
+def test_standalone_labels_list_init():
+    l = pt.components.Labels([3])
+    assert torch.allclose(l.labels, torch.zeros(3, dtype=torch.long))
+
+
+def test_standalone_labels_tuple_init():
+    l = pt.components.Labels({0: 1, 1: 2})
+    assert torch.allclose(l.labels, torch.LongTensor([0, 1, 1]))
+
+
+# Losses
+def test_glvq_loss_int_labels():
+    d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
+    labels = torch.tensor([0, 1])
+    targets = torch.ones(100)
+    batch_loss = pt.losses.glvq_loss(distances=d,
+                                     target_labels=targets,
+                                     prototype_labels=labels)
+    loss_value = torch.sum(batch_loss, dim=0)
+    assert loss_value == -100
+
+
+def test_glvq_loss_one_hot_labels():
+    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 = pt.losses.glvq_loss(distances=d,
+                                     target_labels=targets,
+                                     prototype_labels=labels)
+    loss_value = torch.sum(batch_loss, dim=0)
+    assert loss_value == -100
+
+
+def test_glvq_loss_one_hot_unequal():
+    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 = pt.losses.glvq_loss(distances=d,
+                                     target_labels=targets,
+                                     prototype_labels=labels)
+    loss_value = torch.sum(batch_loss, dim=0)
+    assert loss_value == -100
+
+
+# Activations
+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(pt.nn.ACTIVATIONS)
+
+    def test_funcname_deserialization(self):
+        for funcname in self.flist:
+            f = pt.nn.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 = pt.nn.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):
+                _ = pt.nn.get_activation(funcname)
+
+    def test_identity(self):
+        actual = pt.nn.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 = pt.nn.sigmoid_beta(self.x, beta=1.0)
+        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 = pt.nn.swish_beta(self.x, beta=1.0)
+        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
+
+
+# Competitions
+class TestCompetitions(unittest.TestCase):
+
+    def setUp(self):
+        pass
+
+    def test_wtac(self):
+        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])
+        competition_layer = pt.competitions.WTAC()
+        actual = competition_layer(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([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
+        labels = torch.tensor([0, 1, 1])
+        competition_layer = pt.competitions.WTAC()
+        actual = competition_layer(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], [3.0, 2.01]])
+        labels = torch.tensor([[0, 1], [1, 0]])
+        competition_layer = pt.competitions.WTAC()
+        actual = competition_layer(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([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
+        labels = torch.tensor([0, 1, 2, 3])
+        competition_layer = pt.competitions.KNNC(k=1)
+        actual = competition_layer(d, labels)
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        pass
+
+
+# Pooling
+class TestPooling(unittest.TestCase):
+
+    def setUp(self):
+        pass
+
+    def test_stratified_min(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
+        labels = torch.tensor([0, 0, 1, 2])
+        pooling_layer = pt.pooling.StratifiedMinPooling()
+        actual = pooling_layer(d, labels)
+        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, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
+        labels = torch.tensor([0, 0, 1, 2])
+        labels = torch.eye(3)[labels]
+        pooling_layer = pt.pooling.StratifiedMinPooling()
+        actual = pooling_layer(d, labels)
+        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_trivial(self):
+        d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
+        labels = torch.tensor([0, 1, 2])
+        pooling_layer = pt.pooling.StratifiedMinPooling()
+        actual = pooling_layer(d, labels)
+        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_max(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
+        labels = torch.tensor([0, 0, 3, 2, 0])
+        pooling_layer = pt.pooling.StratifiedMaxPooling()
+        actual = pooling_layer(d, labels)
+        desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_max_one_hot(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
+        labels = torch.tensor([0, 0, 2, 1, 0])
+        labels = torch.nn.functional.one_hot(labels, num_classes=3)
+        pooling_layer = pt.pooling.StratifiedMaxPooling()
+        actual = pooling_layer(d, labels)
+        desired = torch.tensor([[9.0, 3.0, 2.0], [9.0, 1.0, 0.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_sum(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
+        labels = torch.LongTensor([0, 0, 1, 2])
+        pooling_layer = pt.pooling.StratifiedSumPooling()
+        actual = pooling_layer(d, labels)
+        desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_sum_one_hot(self):
+        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]
+        pooling_layer = pt.pooling.StratifiedSumPooling()
+        actual = pooling_layer(d, labels)
+        desired = torch.tensor([[1.0, 2.0, 3.0], [17.0, 0.0, 1.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_stratified_prod(self):
+        d = torch.tensor([[1.0, 0.0, 2.0, 3.0, 9.0], [9.0, 8.0, 0, 1, 7.0]])
+        labels = torch.tensor([0, 0, 3, 2, 0])
+        pooling_layer = pt.pooling.StratifiedProdPooling()
+        actual = pooling_layer(d, labels)
+        desired = torch.tensor([[0.0, 3.0, 2.0], [504.0, 1.0, 0.0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        pass
+
+
+# Distances
+class TestDistances(unittest.TestCase):
+
+    def setUp(self):
+        self.nx, self.mx = 32, 2048
+        self.ny, self.my = 8, 2048
+        self.x = torch.randn(self.nx, self.mx)
+        self.y = torch.randn(self.ny, self.my)
+
+    def test_manhattan(self):
+        actual = pt.distances.lpnorm_distance(self.x, self.y, p=1)
+        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=1,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def test_euclidean(self):
+        actual = pt.distances.euclidean_distance(self.x, self.y)
+        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=2,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=3)
+        self.assertIsNone(mismatch)
+
+    def test_squared_euclidean(self):
+        actual = pt.distances.squared_euclidean_distance(self.x, self.y)
+        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=2,
+                    keepdim=False,
+                )**2)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_p0(self):
+        actual = pt.distances.lpnorm_distance(self.x, self.y, p=0)
+        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=0,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_p2(self):
+        actual = pt.distances.lpnorm_distance(self.x, self.y, p=2)
+        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=2,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_p3(self):
+        actual = pt.distances.lpnorm_distance(self.x, self.y, p=3)
+        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=3,
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_lpnorm_pinf(self):
+        actual = pt.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"),
+                    keepdim=False,
+                )
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=4)
+        self.assertIsNone(mismatch)
+
+    def test_omega_identity(self):
+        omega = torch.eye(self.mx, self.my)
+        actual = pt.distances.omega_distance(self.x, self.y, omega=omega)
+        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=2,
+                    keepdim=False,
+                )**2)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def test_lomega_identity(self):
+        omega = torch.eye(self.mx, self.my)
+        omegas = torch.stack([omega for _ in range(self.ny)], dim=0)
+        actual = pt.distances.lomega_distance(self.x, self.y, omegas=omegas)
+        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=2,
+                    keepdim=False,
+                )**2)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=2)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x, self.y

tests/test_datasets.py

@@ -1,95 +1,186 @@
-"""ProtoTorch datasets test suite."""
+"""ProtoTorch datasets test suite"""
 
 import os
-import shutil
 import unittest
 
+import numpy as np
 import torch
 
-from prototorch.datasets import abstract, tecator
+import prototorch as pt
+from prototorch.datasets.abstract import Dataset, ProtoDataset
 
 
 class TestAbstract(unittest.TestCase):
+
+    def setUp(self):
+        self.ds = Dataset("./artifacts")
+
     def test_getitem(self):
         with self.assertRaises(NotImplementedError):
-            abstract.Dataset("./artifacts")[0]
+            _ = self.ds[0]
 
     def test_len(self):
         with self.assertRaises(NotImplementedError):
-            len(abstract.Dataset("./artifacts"))
+            _ = len(self.ds)
+
+    def tearDown(self):
+        del self.ds
 
 
 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()
+            _ = ProtoDataset("./artifacts", download=True)
 
-
+    def test_exists(self):
-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)
+            _ = ProtoDataset("./artifacts", 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):
+class TestNumpyDataset(unittest.TestCase):
-        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):
+    def test_list_init(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+        ds = pt.datasets.NumpyDataset([1], [1])
-        train = tecator.Tecator(root=rootdir,
+        self.assertEqual(len(ds), 1)
-                                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):
+    def test_numpy_init(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+        data = np.random.randn(3, 2)
-        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        targets = np.array([0, 1, 2])
-        x_test, y_test = test.data, test.targets
+        ds = pt.datasets.NumpyDataset(data, targets)
-        self.assertEqual(x_test.shape[0], 71)
+        self.assertEqual(len(ds), 3)
-        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):
+class TestCSVDataset(unittest.TestCase):
-        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):
+    def setUp(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+        data = np.random.rand(100, 4)
-        test = tecator.Tecator(root=rootdir, train=False, verbose=False)
+        targets = np.random.randint(2, size=(100, 1))
-        _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
+        arr = np.hstack([data, targets])
+        if not os.path.exists("./artifacts"):
+            os.mkdir("./artifacts")
+        np.savetxt("./artifacts/test.csv", arr, delimiter=",")
+
+    def test_len(self):
+        ds = pt.datasets.CSVDataset("./artifacts/test.csv")
+        self.assertEqual(len(ds), 100)
 
     def tearDown(self):
-        pass
+        os.remove("./artifacts/test.csv")
+
+
+class TestSpiral(unittest.TestCase):
+
+    def test_init(self):
+        ds = pt.datasets.Spiral(num_samples=10)
+        self.assertEqual(len(ds), 10)
+
+
+class TestIris(unittest.TestCase):
+
+    def setUp(self):
+        self.ds = pt.datasets.Iris()
+
+    def test_size(self):
+        self.assertEqual(len(self.ds), 150)
+
+    def test_dims(self):
+        self.assertEqual(self.ds.data.shape[1], 4)
+
+    def test_dims_selection(self):
+        ds = pt.datasets.Iris(dims=[0, 1])
+        self.assertEqual(ds.data.shape[1], 2)
+
+
+class TestBlobs(unittest.TestCase):
+
+    def test_size(self):
+        ds = pt.datasets.Blobs(num_samples=10)
+        self.assertEqual(len(ds), 10)
+
+
+class TestRandom(unittest.TestCase):
+
+    def test_size(self):
+        ds = pt.datasets.Random(num_samples=10)
+        self.assertEqual(len(ds), 10)
+
+
+class TestCircles(unittest.TestCase):
+
+    def test_size(self):
+        ds = pt.datasets.Circles(num_samples=10)
+        self.assertEqual(len(ds), 10)
+
+
+class TestMoons(unittest.TestCase):
+
+    def test_size(self):
+        ds = pt.datasets.Moons(num_samples=10)
+        self.assertEqual(len(ds), 10)
+
+
+# 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):
+#             _ = pt.datasets.Tecator(rootdir, download=False)
+
+#     def test_download_caching(self):
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
+#         _ = pt.datasets.Tecator(rootdir, download=True, verbose=False)
+#         _ = pt.datasets.Tecator(rootdir, download=False, verbose=False)
+
+#     def test_repr(self):
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
+#         train = pt.datasets.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 = pt.datasets.Tecator(root=rootdir,
+#                                     train=True,
+#                                     download=True,
+#                                     verbose=False)
+#         train = pt.datasets.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 = pt.datasets.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 = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
+#         _ = test.class_to_idx
+
+#     def test_getitem(self):
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
+#         test = pt.datasets.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 = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
+#         _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
+
+#     def tearDown(self):
+#         self._remove_artifacts()

tests/test_functions.py

@@ -1,521 +0,0 @@
-"""ProtoTorch functions test suite."""
-
-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.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=1.0)
-        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=1.0)
-        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([[2.0, 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])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_wtac_unequal_dist(self):
-        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])
-        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], [3.0, 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, 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([[0.0, 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, 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([[0.0, 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([[0.0, 2.0, 3.0], [8.0, 0, 1]])
-        labels = torch.tensor([0, 1, 2])
-        actual = competitions.stratified_min(d, labels)
-        desired = torch.tensor([[0.0, 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([[2.0, 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]))
-        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):
-    def setUp(self):
-        self.nx, self.mx = 32, 2048
-        self.ny, self.my = 8, 2048
-        self.x = torch.randn(self.nx, self.mx)
-        self.y = torch.randn(self.ny, self.my)
-
-    def test_manhattan(self):
-        actual = distances.lpnorm_distance(self.x, self.y, p=1)
-        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=1,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def test_euclidean(self):
-        actual = distances.euclidean_distance(self.x, self.y)
-        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=2,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=3)
-        self.assertIsNone(mismatch)
-
-    def test_squared_euclidean(self):
-        actual = distances.squared_euclidean_distance(self.x, self.y)
-        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=2,
-                    keepdim=False,
-                )**2)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_p0(self):
-        actual = distances.lpnorm_distance(self.x, self.y, p=0)
-        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=0,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_p2(self):
-        actual = distances.lpnorm_distance(self.x, self.y, p=2)
-        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=2,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_p3(self):
-        actual = distances.lpnorm_distance(self.x, self.y, p=3)
-        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=3,
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_lpnorm_pinf(self):
-        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"),
-                    keepdim=False,
-                )
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=4)
-        self.assertIsNone(mismatch)
-
-    def test_omega_identity(self):
-        omega = torch.eye(self.mx, self.my)
-        actual = distances.omega_distance(self.x, self.y, omega=omega)
-        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=2,
-                    keepdim=False,
-                )**2)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def test_lomega_identity(self):
-        omega = torch.eye(self.mx, self.my)
-        omegas = torch.stack([omega for _ in range(self.ny)], dim=0)
-        actual = distances.lomega_distance(self.x, self.y, omegas=omegas)
-        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=2,
-                    keepdim=False,
-                )**2)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=2)
-        self.assertIsNone(mismatch)
-
-    def tearDown(self):
-        del self.x, self.y
-
-
-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)
-        self.y = torch.tensor([0, 0, 1, 1])
-        self.gen = torch.manual_seed(42)
-
-    def test_registry(self):
-        self.assertIsNotNone(initializers.INITIALIZERS)
-
-    def test_funcname_deserialization(self):
-        for funcname in self.flist:
-            f = initializers.get_initializer(funcname)
-            iscallable = callable(f)
-            self.assertTrue(iscallable)
-
-    def test_callable_deserialization(self):
-        def dummy(x):
-            return x
-
-        for f in [dummy, lambda x: x]:
-            f = initializers.get_initializer(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):
-                _ = initializers.get_initializer(funcname)
-
-    def test_zeros(self):
-        pdist = torch.tensor([1, 1])
-        actual, _ = initializers.zeros(self.x, self.y, pdist)
-        desired = torch.zeros(2, 3)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_ones(self):
-        pdist = torch.tensor([1, 1])
-        actual, _ = initializers.ones(self.x, self.y, pdist)
-        desired = torch.ones(2, 3)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_rand(self):
-        pdist = torch.tensor([1, 1])
-        actual, _ = initializers.rand(self.x, self.y, pdist)
-        desired = torch.rand(2, 3, generator=torch.manual_seed(42))
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_randn(self):
-        pdist = torch.tensor([1, 1])
-        actual, _ = initializers.randn(self.x, self.y, pdist)
-        desired = torch.randn(2, 3, generator=torch.manual_seed(42))
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_mean_equal1(self):
-        pdist = torch.tensor([1, 1])
-        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
-        desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_random_equal1(self):
-        pdist = torch.tensor([1, 1])
-        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
-                                                   False)
-        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)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_mean_equal2(self):
-        pdist = torch.tensor([2, 2])
-        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
-        desired = torch.tensor([[5.0, 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)
-        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([[0.0, -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)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_mean_unequal(self):
-        pdist = torch.tensor([1, 3])
-        actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
-        desired = torch.tensor([[5.0, 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)
-        self.assertIsNone(mismatch)
-
-    def test_stratified_random_unequal(self):
-        pdist = torch.tensor([1, 3])
-        actual, _ = initializers.stratified_random(self.x, self.y, pdist,
-                                                   False)
-        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)
-        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([[5.0, 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,
-                                                        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([[0.0, -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,
-                                                        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

@@ -1,298 +0,0 @@
-"""ProtoTorch modules test suite."""
-
-import unittest
-
-import numpy as np
-import torch
-
-from prototorch.modules import losses, prototypes
-
-
-class TestPrototypes(unittest.TestCase):
-    def setUp(self):
-        self.x = torch.tensor(
-            [[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
-            dtype=torch.float32)
-        self.y = torch.tensor([0, 0, 1, 1])
-        self.gen = torch.manual_seed(42)
-
-    def test_prototypes1d_init_without_input_dim(self):
-        with self.assertRaises(NameError):
-            _ = prototypes.Prototypes1D(nclasses=2)
-
-    def test_prototypes1d_init_without_nclasses(self):
-        with self.assertRaises(NameError):
-            _ = prototypes.Prototypes1D(input_dim=1)
-
-    def test_prototypes1d_init_with_nclasses_1(self):
-        with self.assertWarns(UserWarning):
-            _ = prototypes.Prototypes1D(nclasses=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",
-        )
-        protos = p1.prototypes
-        actual = protos.detach().numpy()
-        desired = torch.ones(8, 6)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_prototypes1d_init_without_data(self):
-        pdist = [2, 2]
-        p1 = prototypes.Prototypes1D(input_dim=3,
-                                     prototype_distribution=pdist,
-                                     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_proto_init_without_data(self):
-        with self.assertWarns(UserWarning):
-            _ = prototypes.Prototypes1D(
-                input_dim=3,
-                nclasses=2,
-                prototypes_per_class=1,
-                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")
-        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_without_inputdim_with_data(self):
-        _ = prototypes.Prototypes1D(
-            nclasses=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]],
-        )
-
-    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=([[0.0], [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`
-        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=([[0.0], [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`
-        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=([[0.0], [1.0]], [[0], [1]]),
-                one_hot_labels=True,
-            )
-
-    def test_prototypes1d_init_with_int_dtype(self):
-        with self.assertRaises(RuntimeError):
-            _ = prototypes.Prototypes1D(
-                nclasses=2,
-                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=[[1.0], [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], [0.0]], [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=[[[1.0], [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")
-        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)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_prototypes1d_func_initializer(self):
-        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,
-        )
-        protos = p1.prototypes
-        actual = protos.detach().numpy()
-        desired = 99 * torch.ones(2, 99)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        decimal=5)
-        self.assertIsNone(mismatch)
-
-    def test_prototypes1d_forward(self):
-        p1 = prototypes.Prototypes1D(data=[self.x, self.y])
-        protos, _ = p1()
-        actual = protos.detach().numpy()
-        desired = torch.ones(2, 3)
-        mismatch = np.testing.assert_array_almost_equal(actual,
-                                                        desired,
-                                                        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()
-
-
-class TestLosses(unittest.TestCase):
-    def setUp(self):
-        pass
-
-    def test_glvqloss_init(self):
-        _ = losses.GLVQLoss(0, "swish_beta", beta=20)
-
-    def test_glvqloss_forward_1ppc(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 test_glvqloss_forward_2ppc(self):
-        criterion = losses.GLVQLoss(margin=0,
-                                    squashing="sigmoid_beta",
-                                    beta=100)
-        d = torch.stack([
-            torch.ones(100),
-            torch.ones(100),
-            torch.zeros(100),
-            torch.ones(100)
-        ],
-                        dim=1)
-        labels = torch.tensor([0, 0, 1, 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

tests/test_utils.py

@@ -0,0 +1,47 @@
+"""ProtoTorch utils test suite"""
+
+import numpy as np
+import torch
+
+import prototorch as pt
+
+
+def test_mesh2d_without_input():
+    mesh, xx, yy = pt.utils.mesh2d(border=2.0, resolution=10)
+    assert mesh.shape[0] == 100
+    assert mesh.shape[1] == 2
+    assert xx.shape[0] == 10
+    assert xx.shape[1] == 10
+    assert yy.shape[0] == 10
+    assert yy.shape[1] == 10
+    assert np.min(xx) == -2.0
+    assert np.max(xx) == 2.0
+    assert np.min(yy) == -2.0
+    assert np.max(yy) == 2.0
+
+
+def test_mesh2d_with_torch_input():
+    x = 10 * torch.rand(5, 2)
+    mesh, xx, yy = pt.utils.mesh2d(x, border=0.0, resolution=100)
+    assert mesh.shape[0] == 100 * 100
+    assert mesh.shape[1] == 2
+    assert xx.shape[0] == 100
+    assert xx.shape[1] == 100
+    assert yy.shape[0] == 100
+    assert yy.shape[1] == 100
+    assert np.min(xx) == x[:, 0].min()
+    assert np.max(xx) == x[:, 0].max()
+    assert np.min(yy) == x[:, 1].min()
+    assert np.max(yy) == x[:, 1].max()
+
+
+def test_hex_to_rgb():
+    red_rgb = list(pt.utils.hex_to_rgb(["#ff0000"]))[0]
+    assert red_rgb[0] == 255
+    assert red_rgb[1] == 0
+    assert red_rgb[2] == 0
+
+
+def test_rgb_to_hex():
+    blue_hex = list(pt.utils.rgb_to_hex([(0, 0, 255)]))[0]
+    assert blue_hex.lower() == "0000ff"