.bumpversion.cfg

@@ -1,13 +1,21 @@
 [bumpversion]
-current_version = 0.7.6
+current_version = 0.1.0-dev0
 commit = True
 tag = True
-parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
+parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
-serialize = {major}.{minor}.{patch}
+serialize = 
-message = build: bump version {current_version} → {new_version}
+	{major}.{minor}.{patch}-{release}{build}
+	{major}.{minor}.{patch}
+
+[bumpversion:part:release]
+optional_value = prod
+first_value = dev
+values = 
+	dev
+	rc
+	prod
 
 [bumpversion:file:setup.py]
 
 [bumpversion:file:./prototorch/__init__.py]
 
-[bumpversion:file:./docs/source/conf.py]

.codecov.yml

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

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,38 +0,0 @@
----
-name: Bug report
-about: Create a report to help us improve
-title: ''
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug**
-A clear and concise description of what the bug is.
-
-**Steps to reproduce the behavior**
-1. ...
-2. Run script '...' or this snippet:
-```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.
-
-**System and version information**
-- OS: [e.g. Ubuntu 20.10]
-- ProtoTorch Version: [e.g. 0.4.0]
-- Python Version: [e.g. 3.9.5]
-
-**Additional context**
-Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

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

.github/workflows/pythonapp.yml

@@ -1,75 +1,37 @@
 # This workflow will install Python dependencies, run tests and lint with a single version of Python
 # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
 
-name: tests
+name: Tests
 
 on:
   push:
+    branches: [ master, dev ]
   pull_request:
-    branches: [master]
+    branches: [ master ]
 
 jobs:
-  style:
+  build:
-    runs-on: ubuntu-latest
+
-    steps:
+    runs-on: ubuntu-latest
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.11
-        uses: actions/setup-python@v4
-        with:
-          python-version: "3.11"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install .[all]
-      - uses: pre-commit/action@v3.0.0
-  compatibility:
-    needs: style
-    strategy:
-      fail-fast: false
-      matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
-        os: [ubuntu-latest, windows-latest]
-        exclude:
-          - os: windows-latest
-            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
+    - uses: actions/checkout@v2
-      - name: Set up Python ${{ matrix.python-version }}
+    - name: Set up Python 3.8
-        uses: actions/setup-python@v4
+      uses: actions/setup-python@v1
       with:
-          python-version: ${{ matrix.python-version }}
+        python-version: 3.8
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-          pip install .[all]
+        pip install .
+    - name: Lint with flake8
+      run: |
+        pip install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
+        pip install pytest
         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

@@ -129,6 +129,14 @@ 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
 
@@ -146,13 +154,4 @@ dmypy.json
 # 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

@@ -1,53 +0,0 @@
-# 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

@@ -1,27 +0,0 @@
-# .readthedocs.yml
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Build documentation in the docs/ directory with Sphinx
-sphinx:
-  configuration: docs/source/conf.py
-  fail_on_warning: true
-
-# Build documentation with MkDocs
-# mkdocs:
-#   configuration: mkdocs.yml
-
-# Optionally build your docs in additional formats such as PDF and ePub
-formats: all
-
-# Optionally set the version of Python and requirements required to build your docs
-python:
-  version: 3.9
-  install:
-    - method: pip
-      path: .
-      extra_requirements:
-        - all

.remarkrc

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

LICENSE

@@ -1,7 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Saxon Institute for Computational Intelligence and Machine
+Copyright (c) 2020 si-cim
-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

MANIFEST.in

@@ -1,13 +1,9 @@
 include .bumpversion.cfg
 include LICENSE
 include tox.ini
-include *.md
-include *.txt
-include *.yml
 recursive-include docs *.bat
 recursive-include docs *.png
 recursive-include docs *.py
 recursive-include docs *.rst
 recursive-include docs Makefile
 recursive-include examples *.py
-recursive-include tests *.py

README.md

@@ -1,75 +1,49 @@
-# ProtoTorch: Prototype Learning in PyTorch
+# ProtoTorch
 
-![ProtoTorch Logo](https://prototorch.readthedocs.io/en/latest/_static/horizontal-lockup.png)
+ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge research in
+prototype-based machine learning algorithms.
 
-![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg)
+![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)
+[![codecov](https://codecov.io/gh/si-cim/prototorch/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/prototorch)
-[![PyPI](https://img.shields.io/pypi/v/prototorch)](https://pypi.org/project/prototorch/)
-[![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)
 
 ## Description
 
 This is a Python toolbox brewed at the Mittweida University of Applied Sciences
-in Germany for bleeding-edge research in Prototype-based Machine Learning
+in Germany for bleeding-edge research in Learning Vector Quantization (LVQ)
-methods and other interpretable models. The focus of ProtoTorch is ease-of-use,
+and potentially other prototype-based methods. Although, there are
-extensibility and speed.
+other (perhaps more extensive) LVQ toolboxes available out there, the focus of
+ProtoTorch is ease-of-use, extensibility and speed.
+
+Many popular prototype-based Machine Learning (ML) algorithms like K-Nearest
+Neighbors (KNN), Generalized Learning Vector Quantization (GLVQ) and Generalized
+Matrix Learning Vector Quantization (GMLVQ) are implemented using the "nn" API
+provided by PyTorch.
 
 ## Installation
 
 ProtoTorch can be installed using `pip`.
-```bash
-pip install -U prototorch
 ```
-To also install the extras, use
+pip install prototorch
-```bash
-pip install -U prototorch[all]
 ```
 
-*Note: If you're using [ZSH](https://www.zsh.org/) (which is also the default
-shell on MacOS now), the square brackets `[ ]` have to be escaped like so:
-`\[\]`, making the install command `pip install -U prototorch\[all\]`.*
-
 To install the bleeding-edge features and improvements:
-```bash
+```
 git clone https://github.com/si-cim/prototorch.git
-cd prototorch
 git checkout dev
-pip install -e .[all]
+cd prototorch
+pip install -e .
 ```
 
-## Documentation
+## Usage
 
-The documentation is available at <https://www.prototorch.ml/en/latest/>. Should
+ProtoTorch is modular. It is very easy to use the modular pieces provided by
-that link not work try <https://prototorch.readthedocs.io/en/latest/>.
+ProtoTorch, like the layers, losses, callbacks and metrics to build your own
+prototype-based(instance-based) models. These pieces blend-in seamlessly with
+numpy and PyTorch to allow you mix and match the modules from ProtoTorch with
+other PyTorch modules.
 
-## Contribution
+ProtoTorch comes prepackaged with many popular LVQ algorithms in a convenient
-
+API, with more algorithms and techniques coming soon. If you would simply like
-This repository contains definition for [git hooks](https://githooks.com).
+to be able to use those algorithms to train large ML models on a GPU, ProtoTorch
-[Pre-commit](https://pre-commit.com) is automatically installed as development
+lets you do this without requiring a black-belt in high-performance Tensor
-dependency with prototorch or you can install it manually with `pip install
+computation.
-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:
-```bibtex
-@misc{Ravichandran2020b,
-  author = {Ravichandran, J},
-  title = {ProtoTorch},
-  year = {2020},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/si-cim/prototorch}}
-}

RELEASE.md

@@ -1,19 +0,0 @@
-# ProtoTorch Releases
-
-## Release 0.5.0
-
-- Breaking: Removed deprecated `prototorch.modules.Prototypes1D`.
-  - Use `prototorch.components.LabeledComponents` instead.
-
-## Release 0.2.0
-
-- Fixes in example scripts.
-
-## Release 0.1.1-dev0
-
-- Minor bugfixes.
-- 100% line coverage.
-
-## Release 0.1.0-dev0
-
-Initial public release of ProtoTorch.

docs/Makefile

@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= python3 -m sphinx
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/make.bat

@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-  set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-  echo.
-  echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-  echo.installed, then set the SPHINXBUILD environment variable to point
-  echo.to the full path of the 'sphinx-build' executable. Alternatively you
-  echo.may add the Sphinx directory to PATH.
-  echo.
-  echo.If you don't have Sphinx installed, grab it from
-  echo.http://sphinx-doc.org/
-  exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd

docs/requirements.txt

@@ -1,4 +0,0 @@
-torch==1.6.0
-matplotlib==3.1.2
-sphinx_rtd_theme==0.5.0
-sphinxcontrib-katex==0.6.1

docs/source/api.rst

@@ -1,57 +0,0 @@
-.. ProtoTorch API Reference
-
-ProtoTorch API Reference
-======================================
-
-Datasets
---------------------------------------
-
-Common Datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. automodule:: prototorch.datasets
-   :members:
-
-
-Abstract Datasets
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Abstract Datasets are used to build your own datasets.
-
-.. autoclass:: prototorch.datasets.abstract.NumpyDataset
-   :members:
-
-Functions
---------------------------------------
-
-**Dimensions:**
-
-- :math:`B` ... Batch size
-- :math:`P` ... Number of prototypes
-- :math:`n_x` ... Data dimension for vectorial data
-- :math:`n_w` ... Data dimension for vectorial prototypes
-
-Activations
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. automodule:: prototorch.functions.activations
-   :members:
-   :exclude-members: register_activation, get_activation
-   :undoc-members:
-
-Distances
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. automodule:: prototorch.functions.distances
-   :members:
-   :exclude-members: sed
-   :undoc-members:
-
-Modules
---------------------------------------
-.. automodule:: prototorch.modules
-   :members:
-   :undoc-members:
-
-Utilities
---------------------------------------
-.. automodule:: prototorch.utils
-   :members:
-   :undoc-members:

docs/source/conf.py

@@ -1,192 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# This file only contains a selection of the most common options. For a full
-# list see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Path setup --------------------------------------------------------------
-
-# If extensions (or modules to document with autodoc) are in another directory,
-# add these directories to sys.path here. If the directory is relative to the
-# documentation root, use os.path.abspath to make it absolute, like shown here.
-#
-import os
-import sys
-
-sys.path.insert(0, os.path.abspath("../../"))
-
-# -- Project information -----------------------------------------------------
-
-project = "ProtoTorch"
-copyright = "2021, Jensun Ravichandran"
-author = "Jensun Ravichandran"
-
-# The full version, including alpha/beta/rc tags
-#
-release = "0.7.6"
-
-# -- General configuration ---------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-needs_sphinx = "1.6"
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named "sphinx.ext.*") or your custom
-# ones.
-extensions = [
-    "recommonmark",
-    "sphinx.ext.autodoc",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.doctest",
-    "sphinx.ext.intersphinx",
-    "sphinx.ext.todo",
-    "sphinx.ext.coverage",
-    "sphinx.ext.napoleon",
-    "sphinx.ext.viewcode",
-    "sphinx_rtd_theme",
-    "sphinxcontrib.katex",
-    'sphinx_autodoc_typehints',
-]
-
-# katex_prerender = True
-katex_prerender = False
-
-napoleon_use_ivar = True
-
-# Add any paths that contain templates here, relative to this directory.
-templates_path = ["_templates"]
-
-# The suffix(es) of source filenames.
-# You can specify multiple suffix as a list of string:
-#
-source_suffix = [".rst", ".md"]
-
-# The master toctree document.
-master_doc = "index"
-
-# List of patterns, relative to source directory, that match files and
-# directories to ignore when looking for source files.
-# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
-
-# The name of the Pygments (syntax highlighting) style to use. Choose from:
-# ["default", "emacs", "friendly", "colorful", "autumn", "murphy", "manni",
-#  "monokai", "perldoc", "pastie", "borland", "trac", "native", "fruity", "bw",
-#  "vim", "vs", "tango", "rrt", "xcode", "igor", "paraiso-light", "paraiso-dark",
-#  "lovelace", "algol", "algol_nu", "arduino", "rainbo w_dash", "abap",
-#  "solarized-dark", "solarized-light", "sas", "stata", "stata-light",
-#  "stata-dark", "inkpot"]
-pygments_style = "monokai"
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = True
-
-# Disable docstring inheritance
-autodoc_inherit_docstrings = False
-
-# -- Options for HTML output -------------------------------------------------
-
-# The theme to use for HTML and HTML Help pages.  See the documentation for
-# a list of builtin themes.
-# https://sphinx-themes.org/
-html_theme = "sphinx_rtd_theme"
-
-html_logo = "_static/img/horizontal-lockup.png"
-
-html_theme_options = {
-    "logo_only": True,
-    "display_version": True,
-    "prev_next_buttons_location": "bottom",
-    "style_external_links": False,
-    "style_nav_header_background": "#ffffff",
-    # Toc options
-    "collapse_navigation": True,
-    "sticky_navigation": True,
-    "navigation_depth": 4,
-    "includehidden": True,
-    "titles_only": False,
-}
-
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
-
-html_css_files = [
-    "https://cdn.jsdelivr.net/npm/katex@0.11.1/dist/katex.min.css",
-]
-
-# -- Options for HTMLHelp output ------------------------------------------
-
-# Output file base name for HTML help builder.
-htmlhelp_basename = "prototorchdoc"
-
-# -- Options for LaTeX output ---------------------------------------------
-
-latex_elements = {
-    # The paper size ("letterpaper" or "a4paper").
-    #
-    # "papersize": "letterpaper",
-    # The font size ("10pt", "11pt" or "12pt").
-    #
-    # "pointsize": "10pt",
-    # Additional stuff for the LaTeX preamble.
-    #
-    # "preamble": "",
-    # Latex figure (float) alignment
-    #
-    # "figure_align": "htbp",
-}
-
-# Grouping the document tree into LaTeX files. List of tuples
-# (source start file, target name, title,
-#  author, documentclass [howto, manual, or own class]).
-latex_documents = [
-    (
-        master_doc,
-        "prototorch.tex",
-        "ProtoTorch Documentation",
-        "Jensun Ravichandran",
-        "manual",
-    ),
-]
-
-# -- Options for manual page output ---------------------------------------
-
-# One entry per manual page. List of tuples
-# (source start file, name, description, authors, manual section).
-man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author],
-              1)]
-
-# -- Options for Texinfo output -------------------------------------------
-
-# Grouping the document tree into Texinfo files. List of tuples
-# (source start file, target name, title, author,
-#  dir menu entry, description, category)
-texinfo_documents = [
-    (
-        master_doc,
-        "prototorch",
-        "ProtoTorch Documentation",
-        author,
-        "prototorch",
-        "Prototype-based machine learning in PyTorch.",
-        "Miscellaneous",
-    ),
-]
-
-# Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {
-    "python": ("https://docs.python.org/", None),
-    "numpy": ("https://docs.scipy.org/doc/numpy/", None),
-    "torch": ('https://pytorch.org/docs/stable/', None),
-    "pytorch_lightning":
-    ("https://pytorch-lightning.readthedocs.io/en/stable/", None),
-}
-
-# -- Options for Epub output ----------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-epub-output
-
-epub_cover = ()
-version = release

docs/source/index.rst

@@ -1,22 +0,0 @@
-.. ProtoTorch documentation master file
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
-About ProtoTorch
-================
-
-.. toctree::
-   :hidden:
-   :maxdepth: 3
-   :caption: Contents:
-
-   self
-   api
-
-ProtoTorch is a PyTorch-based Python toolbox for bleeding-edge
-research in prototype-based machine learning algorithms.
-
-Indices
-=======
-* :ref:`genindex`
-* :ref:`modindex`

examples/cbc_iris.py

@@ -1,100 +0,0 @@
-"""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

@@ -0,0 +1,103 @@
+"""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 prototorch.functions.distances import euclidean_distance
+from prototorch.modules.losses import GLVQLoss
+from prototorch.modules.prototypes import AddPrototypes1D
+
+# Prepare and preprocess the data
+scaler = StandardScaler()
+x_train, y_train = load_iris(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, **kwargs):
+        super().__init__()
+        self.p1 = AddPrototypes1D(input_dim=2,
+                                  prototypes_per_class=1,
+                                  nclasses=3,
+                                  prototype_initializer='zeros')
+
+    def forward(self, x):
+        protos = self.p1.prototypes
+        plabels = self.p1.prototype_labels
+        dis = euclidean_distance(x, protos)
+        return dis, plabels
+
+
+# Build the GLVQ model
+model = Model()
+
+# Optimize using SGD optimizer from `torch.optim`
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
+
+# Training loop
+fig = plt.figure('Prototype Visualization')
+for epoch in range(70):
+    # Compute loss.
+    distances, plabels = model(torch.tensor(x_train))
+    loss = criterion([distances, plabels], torch.tensor(y_train))
+    print(f'Epoch: {epoch + 1:03d} Loss: {loss.item():02.02f}')
+
+    # 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()
+    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
+    border = 1
+    resolution = 50
+    x = np.vstack((x_train, protos))
+    x_min, x_max = x[:, 0].min(), x[:, 0].max()
+    y_min, y_max = x[:, 1].min(), x[:, 1].max()
+    x_min, x_max = x_min - border, x_max + border
+    y_min, y_max = y_min - border, y_max + border
+    try:
+        xx, yy = np.meshgrid(np.arange(x_min, x_max, 1.0 / resolution),
+                             np.arange(y_min, y_max, 1.0 / resolution))
+    except ValueError as ve:
+        print(ve)
+        raise ValueError(f'x_min: {x_min}, x_max: {x_max}. '
+                         f'x_min - x_max is {x_max - x_min}.')
+    except MemoryError as me:
+        print(me)
+        raise ValueError('Too many points. ' 'Try reducing the resolution.')
+    mesh_input = np.c_[xx.ravel(), yy.ravel()]
+
+    torch_input = torch.from_numpy(mesh_input)
+    d = model(torch_input)[0]
+    y_pred = np.argmin(d.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/gmlvq.py

@@ -1,76 +0,0 @@
-"""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/new_components.py

@@ -1,56 +0,0 @@
-"""This example script shows the usage of the new components architecture.
-
-Serialization/deserialization also works as expected.
-
-"""
-
-import torch
-
-import prototorch as pt
-
-ds = pt.datasets.Iris()
-
-unsupervised = pt.components.Components(
-    6,
-    initializer=pt.initializers.ZCI(2),
-)
-print(unsupervised())
-
-prototypes = pt.components.LabeledComponents(
-    (3, 2),
-    components_initializer=pt.initializers.SSCI(ds),
-)
-print(prototypes())
-
-components = pt.components.ReasoningComponents(
-    (3, 2),
-    components_initializer=pt.initializers.SSCI(ds),
-    reasonings_initializer=pt.initializers.PPRI(),
-)
-print(prototypes())
-
-# Test Serialization
-import io
-
-save = io.BytesIO()
-torch.save(unsupervised, save)
-save.seek(0)
-serialized_unsupervised = torch.load(save)
-
-assert torch.all(unsupervised.components == serialized_unsupervised.components)
-
-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.labels == serialized_prototypes.labels)
-
-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.reasonings == serialized_components.reasonings)

prototorch/__init__.py

@@ -1,61 +1 @@
-"""ProtoTorch package"""
+__version__ = '0.1.0-dev0'
-
-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__)
-
-
-def discover_plugins():
-    return {
-        entry_point.name: entry_point.load()
-        for entry_point in pkg_resources.iter_entry_points(
-            "prototorch.plugins")
-    }
-
-
-discovered_plugins = discover_plugins()
-locals().update(discovered_plugins)
-
-# Generate combines __version__ and __all__
-version_plugins = "\n".join([
-    "- " + name + ": v" + plugin.__version__
-    for name, plugin in discovered_plugins.items()
-])
-if version_plugins != "":
-    version_plugins = "\nPlugins: \n" + version_plugins
-
-version = "core: v" + __version__ + version_plugins
-__all__ = __all_core__ + list(discovered_plugins.keys())

prototorch/core/__init__.py

@@ -1,10 +0,0 @@
-"""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

@@ -1,93 +0,0 @@
-"""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

@@ -1,380 +0,0 @@
-"""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

@@ -1,95 +0,0 @@
-"""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

@@ -1,555 +0,0 @@
-"""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

@@ -1,184 +0,0 @@
-"""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

@@ -1,108 +0,0 @@
-"""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

@@ -1,31 +0,0 @@
-"""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`.
-
-    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
-    epsilon = torch.finfo(norm_mat.dtype).eps
-    norm_mat.clamp_(min=epsilon)
-    similarities = (x @ y.T) / norm_mat
-    return similarities

prototorch/core/transforms.py

@@ -1,47 +0,0 @@
-"""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

@@ -1,13 +0,0 @@
-"""ProtoTorch datasets"""
-
-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

prototorch/datasets/abstract.py

@@ -1,115 +0,0 @@
-"""ProtoTorch abstract dataset classes
-
-Based on `torchvision.VisionDataset` and `torchvision.MNIST`.
-
-For the original code, see:
-https://github.com/pytorch/vision/blob/master/torchvision/datasets/vision.py
-https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py
-
-"""
-
-import os
-
-import numpy as np
-import torch
-
-
-class Dataset(torch.utils.data.Dataset):
-    """Abstract dataset class to be inherited."""
-
-    _repr_indent = 2
-
-    def __init__(self, root):
-        if isinstance(root, str):
-            root = os.path.expanduser(root)
-        self.root = root
-
-    def __getitem__(self, index):
-        raise NotImplementedError
-
-    def __len__(self):
-        raise NotImplementedError
-
-
-class ProtoDataset(Dataset):
-    """Abstract dataset class to be inherited."""
-
-    training_file = "training.pt"
-    test_file = "test.pt"
-
-    def __init__(self, root="", train=True, download=True, verbose=True):
-        super().__init__(root)
-        self.train = train  # training set or test set
-        self.verbose = verbose
-
-        if download:
-            self._download()
-
-        if not self._check_exists():
-            raise RuntimeError("Dataset not found. "
-                               "You can use download=True to download it")
-
-        data_file = self.training_file if self.train else self.test_file
-
-        self.data, self.targets = torch.load(
-            os.path.join(self.processed_folder, data_file))
-
-    @property
-    def raw_folder(self):
-        return os.path.join(self.root, self.__class__.__name__, "raw")
-
-    @property
-    def processed_folder(self):
-        return os.path.join(self.root, self.__class__.__name__, "processed")
-
-    @property
-    def class_to_idx(self):
-        return {_class: i for i, _class in enumerate(self.classes)}
-
-    def _check_exists(self):
-        return os.path.exists(
-            os.path.join(
-                self.processed_folder, self.training_file)) and os.path.exists(
-                    os.path.join(self.processed_folder, self.test_file))
-
-    def __repr__(self):
-        head = "Dataset " + self.__class__.__name__
-        body = ["Number of datapoints: {}".format(self.__len__())]
-        if self.root is not None:
-            body.append("Root location: {}".format(self.root))
-        body += self.extra_repr().splitlines()
-        lines = [head] + [" " * self._repr_indent + line for line in body]
-        return "\n".join(lines)
-
-    def extra_repr(self):
-        return f"Split: {'Train' if self.train is True else 'Test'}"
-
-    def __len__(self):
-        return len(self.data)
-
-    def _download(self):
-        raise NotImplementedError
-
-
-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

@@ -1,165 +0,0 @@
-"""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

@@ -1,59 +0,0 @@
-"""Spiral dataset for binary classification."""
-
-import numpy as np
-import torch
-
-
-def make_spiral(num_samples=500, noise=0.3):
-    """Generates the Spiral Dataset.
-
-    For use in Prototorch use `prototorch.datasets.Spiral` instead.
-    """
-
-    def get_samples(n, delta_t):
-        points = []
-        for i in range(n):
-            r = i / num_samples * 5
-            t = 1.75 * i / n * 2 * np.pi + delta_t
-            x = r * np.sin(t) + np.random.rand(1) * noise
-            y = r * np.cos(t) + np.random.rand(1) * noise
-            points.append([x, y])
-        return points
-
-    n = num_samples // 2
-    positive = get_samples(n=n, delta_t=0)
-    negative = get_samples(n=n, delta_t=np.pi)
-    x = np.concatenate(
-        [np.array(positive).reshape(n, -1),
-         np.array(negative).reshape(n, -1)],
-        axis=0)
-    y = np.concatenate([np.zeros(n), np.ones(n)])
-    return x, y
-
-
-class Spiral(torch.utils.data.TensorDataset):
-    """Spiral dataset for binary classification.
-
-    This datasets consists of two spirals of two different classes.
-
-    .. list-table:: Spiral
-        :header-rows: 1
-
-        * - dimensions
-          - classes
-          - training size
-          - validation size
-          - test size
-        * - 2
-          - 2
-          - num_samples
-          - 0
-          - 0
-
-    :param num_samples: number of random samples
-    :param noise: noise added to the spirals
-    """
-
-    def __init__(self, num_samples: int = 500, noise: float = 0.3):
-        x, y = make_spiral(num_samples, noise)
-        super().__init__(torch.Tensor(x), torch.LongTensor(y))

prototorch/datasets/tecator.py

@@ -1,118 +0,0 @@
-"""Tecator dataset for classification.
-
-URL:
-    http://lib.stat.cmu.edu/datasets/tecator
-
-LICENCE / TERMS / COPYRIGHT:
-    This is the Tecator data set: The task is to predict the fat content
-    of a meat sample on the basis of its near infrared absorbance spectrum.
-    -------------------------------------------------------------------------
-    1. Statement of permission from Tecator (the original data source)
-
-    These data are recorded on a Tecator Infratec Food and Feed Analyzer
-    working in the wavelength range 850 - 1050 nm by the Near Infrared
-    Transmission (NIT) principle. Each sample contains finely chopped pure
-    meat with different moisture, fat and protein contents.
-
-    If results from these data are used in a publication we want you to
-    mention the instrument and company name (Tecator) in the publication.
-    In addition, please send a preprint of your article to
-
-        Karin Thente, Tecator AB,
-        Box 70, S-263 21 Hoganas, Sweden
-
-    The data are available in the public domain with no responsability from
-    the original data source. The data can be redistributed as long as this
-    permission note is attached.
-
-    For more information about the instrument - call Perstorp Analytical's
-    representative in your area.
-
-Description:
-    For each meat sample the data consists of a 100 channel spectrum of
-    absorbances and the contents of moisture (water), fat and protein.
-    The absorbance is -log10 of the transmittance
-    measured by the spectrometer. The three contents, measured in percent,
-    are determined by analytic chemistry.
-"""
-
-import logging
-import os
-
-import numpy as np
-import torch
-from torchvision.datasets.utils import download_file_from_google_drive
-
-from prototorch.datasets.abstract import ProtoDataset
-
-
-class Tecator(ProtoDataset):
-    """
-    `Tecator Dataset <http://lib.stat.cmu.edu/datasets/tecator>`__ for classification.
-
-    The dataset contains wavelength measurements of meat.
-
-    .. list-table:: Tecator
-        :header-rows: 1
-
-        * - dimensions
-          - classes
-          - training size
-          - validation size
-          - test size
-        * - 100
-          - 2
-          - 129
-          - 43
-          - 43
-    """
-
-    _resources = [
-        ("1P9WIYnyxFPh6f1vqAbnKfK8oYmUgyV83",
-         "ba5607c580d0f91bb27dc29d13c2f8df"),
-    ]  # (google_storage_id, md5hash)
-    classes = ["0 - low_fat", "1 - high_fat"]
-
-    def __getitem__(self, index):
-        img, target = self.data[index], int(self.targets[index])
-        return img, target
-
-    def _download(self):
-        """Download the data if it doesn't exist in already."""
-        if self._check_exists():
-            return
-
-        logging.debug("Making directories...")
-        os.makedirs(self.raw_folder, exist_ok=True)
-        os.makedirs(self.processed_folder, exist_ok=True)
-
-        logging.debug("Downloading...")
-        for fileid, md5 in self._resources:
-            filename = "tecator.npz"
-            download_file_from_google_drive(fileid,
-                                            root=self.raw_folder,
-                                            filename=filename,
-                                            md5=md5)
-
-        logging.debug("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),
-            torch.LongTensor(y_train),
-        ]
-        test_set = [
-            torch.Tensor(x_test),
-            torch.LongTensor(y_test),
-        ]
-
-        with open(os.path.join(self.processed_folder, self.training_file),
-                  "wb") as f:
-            torch.save(training_set, f)
-        with open(os.path.join(self.processed_folder, self.test_file),
-                  "wb") as f:
-            torch.save(test_set, f)
-
-        logging.debug("Done!")

prototorch/datasets/xor.py

@@ -1,19 +0,0 @@
-"""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/activations.py

@@ -0,0 +1,48 @@
+"""ProtoTorch activation functions."""
+
+import torch
+
+ACTIVATIONS = dict()
+
+
+def register_activation(func):
+    ACTIVATIONS[func.__name__] = func
+    return func
+
+
+@register_activation
+def identity(input, **kwargs):
+    """:math:`f(x) = x`"""
+    return input
+
+
+@register_activation
+def sigmoid_beta(input, beta=10):
+    """:math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`
+
+    Keyword Arguments:
+        beta (float): Parameter :math:`\\beta`
+    """
+    out = torch.reciprocal(1.0 + torch.exp(-beta * input))
+    return out
+
+
+@register_activation
+def swish_beta(input, beta=10):
+    """:math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`
+
+    Keyword Arguments:
+        beta (float): Parameter :math:`\\beta`
+    """
+    out = input * sigmoid_beta(input, beta=beta)
+    return out
+
+
+def get_activation(funcname):
+    if callable(funcname):
+        return funcname
+    else:
+        if funcname in ACTIVATIONS:
+            return ACTIVATIONS.get(funcname)
+        else:
+            raise NameError(f'Activation {funcname} was not found.')

prototorch/functions/competitions.py

@@ -0,0 +1,15 @@
+"""ProtoTorch competition functions."""
+
+import torch
+
+
+def wtac(distances, labels):
+    winning_indices = torch.min(distances, dim=1).indices
+    winning_labels = labels[winning_indices].squeeze()
+    return winning_labels
+
+
+def knnc(distances, labels, k):
+    winning_indices = torch.topk(-distances, k=k, dim=1).indices
+    winning_labels = labels[winning_indices].squeeze()
+    return winning_labels

prototorch/functions/distances.py

@@ -0,0 +1,78 @@
+"""ProtoTorch distance functions."""
+
+import torch
+
+
+def squared_euclidean_distance(x, y):
+    """Compute the squared Euclidean distance between :math:`x` and :math:`y`.
+
+    Expected dimension of x is 2.
+    Expected dimension of y is 2.
+    """
+    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):
+    """Compute the Euclidean distance between :math:`x` and :math:`y`.
+
+    Expected dimension of x is 2.
+    Expected dimension of y is 2.
+    """
+    distances_raised = squared_euclidean_distance(x, y)
+    distances = torch.sqrt(distances_raised)
+    return distances
+
+
+def lpnorm_distance(x, y, p):
+    """Compute :math:`{\\langle x, y \\rangle}_p`.
+
+    Expected dimension of x is 2.
+    Expected dimension of y is 2.
+    """
+    # # DEPRECATED in favor of torch.cdist
+    # expanded_x = x.unsqueeze(dim=1)
+    # batchwise_difference = y - expanded_x
+    # differences_raised = torch.pow(batchwise_difference, p)
+    # distances_raised = torch.sum(differences_raised, axis=2)
+    # distances = torch.pow(distances_raised, 1.0 / p)
+    # return distances
+    distances = torch.cdist(x, y, p=p)
+    return distances
+
+
+def omega_distance(x, y, omega):
+    """Omega distance.
+
+    Compute :math:`{\\langle \\Omega x, \\Omega y \\rangle}_p`
+
+    Expected dimension of x is 2.
+    Expected dimension of y is 2.
+    Expected dimension of omega is 2.
+    """
+    projected_x = x @ omega
+    projected_y = y @ omega
+    distances = squared_euclidean_distance(projected_x, projected_y)
+    return distances
+
+
+def lomega_distance(x, y, omegas):
+    """Localized Omega distance.
+
+    Compute :math:`{\\langle \\Omega_k x, \\Omega_k y_k \\rangle}_p`
+
+    Expected dimension of x is 2.
+    Expected dimension of y is 2.
+    Expected dimension of omegas is 3.
+    """
+    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

prototorch/functions/initializers.py

@@ -0,0 +1,93 @@
+"""ProtoTorch initialization functions."""
+
+from itertools import chain
+
+import torch
+
+INITIALIZERS = dict()
+
+
+def register_initializer(func):
+    INITIALIZERS[func.__name__] = func
+    return func
+
+
+def labels_from(distribution):
+    """Takes a distribution tensor and returns a labels tensor."""
+    nclasses = distribution.shape[0]
+    llist = [[i] * n for i, n in zip(range(nclasses), distribution)]
+    # labels = [l for cl in llist for l in cl]  # flatten the list of lists
+    labels = list(chain(*llist))  # flatten using itertools.chain
+    return torch.tensor(labels, requires_grad=False)
+
+
+@register_initializer
+def ones(x_train, y_train, prototype_distribution):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.ones(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution)
+    return protos, plabels
+
+
+@register_initializer
+def zeros(x_train, y_train, prototype_distribution):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.zeros(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution)
+    return protos, plabels
+
+
+@register_initializer
+def rand(x_train, y_train, prototype_distribution):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.rand(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution)
+    return protos, plabels
+
+
+@register_initializer
+def randn(x_train, y_train, prototype_distribution):
+    nprotos = torch.sum(prototype_distribution)
+    protos = torch.randn(nprotos, *x_train.shape[1:])
+    plabels = labels_from(prototype_distribution)
+    return protos, plabels
+
+
+@register_initializer
+def stratified_mean(x_train, y_train, prototype_distribution):
+    nprotos = torch.sum(prototype_distribution)
+    pdim = x_train.shape[1]
+    protos = torch.empty(nprotos, pdim)
+    plabels = labels_from(prototype_distribution)
+    for i, l in enumerate(plabels):
+        xl = x_train[y_train == l]
+        mean_xl = torch.mean(xl, dim=0)
+        protos[i] = mean_xl
+    return protos, plabels
+
+
+@register_initializer
+def stratified_random(x_train, y_train, prototype_distribution):
+    gen = torch.manual_seed(torch.initial_seed())
+    nprotos = torch.sum(prototype_distribution)
+    pdim = x_train.shape[1]
+    protos = torch.empty(nprotos, pdim)
+    plabels = labels_from(prototype_distribution)
+    for i, l in enumerate(plabels):
+        xl = x_train[y_train == l]
+        rand_index = torch.zeros(1).long().random_(0,
+                                                   xl.shape[1] - 1,
+                                                   generator=gen)
+        random_xl = xl[rand_index]
+        protos[i] = random_xl
+    return protos, plabels
+
+
+def get_initializer(funcname):
+    if callable(funcname):
+        return funcname
+    else:
+        if funcname in INITIALIZERS:
+            return INITIALIZERS.get(funcname)
+        else:
+            raise NameError(f'Initializer {funcname} was not found.')

prototorch/functions/losses.py

@@ -0,0 +1,25 @@
+"""ProtoTorch loss functions."""
+
+import torch
+
+
+def glvq_loss(distances, target_labels, prototype_labels):
+    """GLVQ loss function with support for one-hot labels."""
+    matcher = torch.eq(target_labels.unsqueeze(dim=1), prototype_labels)
+    if prototype_labels.ndim == 2:
+        # if the labels are one-hot vectors
+        nclasses = target_labels.size()[1]
+        matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
+    not_matcher = torch.bitwise_not(matcher)
+
+    dplus_criterion = distances * matcher > 0.0
+    dminus_criterion = distances * not_matcher > 0.0
+
+    inf = torch.full_like(distances, fill_value=float('inf'))
+    distances_to_wpluses = torch.where(dplus_criterion, distances, inf)
+    distances_to_wminuses = torch.where(dminus_criterion, distances, inf)
+    dpluses = torch.min(distances_to_wpluses, dim=1, keepdim=True).values
+    dminuses = torch.min(distances_to_wminuses, dim=1, keepdim=True).values
+
+    mu = (dpluses - dminuses) / (dpluses + dminuses)
+    return mu

prototorch/modules/losses.py

@@ -0,0 +1,21 @@
+"""ProtoTorch losses."""
+
+import torch
+
+from prototorch.functions.activations import get_activation
+from prototorch.functions.losses import glvq_loss
+
+
+class GLVQLoss(torch.nn.Module):
+    """GLVQ Loss."""
+    def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
+        super().__init__(**kwargs)
+        self.margin = margin
+        self.squashing = get_activation(squashing)
+        self.beta = beta
+
+    def forward(self, outputs, targets):
+        distances, plabels = outputs
+        mu = glvq_loss(distances, targets, plabels)
+        batch_loss = self.squashing(mu + self.margin, beta=self.beta)
+        return torch.sum(batch_loss, dim=0)

prototorch/modules/prototypes.py

@@ -0,0 +1,57 @@
+"""ProtoTorch prototype modules."""
+
+import torch
+
+from prototorch.functions.initializers import get_initializer
+
+
+class AddPrototypes1D(torch.nn.Module):
+    def __init__(self,
+                 prototypes_per_class=1,
+                 prototype_distribution=None,
+                 prototype_initializer='ones',
+                 data=None,
+                 **kwargs):
+
+        if data is None:
+            if 'input_dim' not in kwargs:
+                raise NameError('`input_dim` required if '
+                                'no `data` is provided.')
+            if prototype_distribution is not None:
+                nclasses = sum(prototype_distribution)
+            else:
+                if 'nclasses' not in kwargs:
+                    raise NameError('`prototype_distribution` required if '
+                                    'both `data` and `nclasses` are not '
+                                    'provided.')
+                nclasses = kwargs.pop('nclasses')
+            input_dim = kwargs.pop('input_dim')
+            # input_shape = (input_dim, )
+            x_train = torch.rand(nclasses, input_dim)
+            y_train = torch.arange(nclasses)
+
+        else:
+            x_train, y_train = data
+            x_train = torch.as_tensor(x_train)
+            y_train = torch.as_tensor(y_train)
+
+        super().__init__(**kwargs)
+        self.prototypes_per_class = prototypes_per_class
+        with torch.no_grad():
+            if not prototype_distribution:
+                num_classes = torch.unique(y_train).shape[0]
+                self.prototype_distribution = torch.tensor(
+                    [self.prototypes_per_class] * num_classes)
+            else:
+                self.prototype_distribution = torch.tensor(
+                    prototype_distribution)
+        self.prototype_initializer = get_initializer(prototype_initializer)
+        prototypes, prototype_labels = self.prototype_initializer(
+            x_train,
+            y_train,
+            prototype_distribution=self.prototype_distribution)
+        self.prototypes = torch.nn.Parameter(prototypes)
+        self.prototype_labels = prototype_labels
+
+    def forward(self):
+        return self.prototypes, self.prototype_labels

prototorch/nn/__init__.py

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

prototorch/nn/activations.py

@@ -1,66 +0,0 @@
-"""ProtoTorch activations"""
-
-import torch
-
-ACTIVATIONS = dict()
-
-
-def register_activation(fn):
-    """Add the activation function to the registry."""
-    name = fn.__name__
-    ACTIVATIONS[name] = fn
-    return fn
-
-
-@register_activation
-def identity(x, beta=0.0):
-    """Identity activation function.
-
-    Definition:
-    :math:`f(x) = x`
-
-    Keyword Arguments:
-        beta (`float`): Ignored.
-    """
-    return x
-
-
-@register_activation
-def sigmoid_beta(x, beta=10.0):
-    r"""Sigmoid activation function with scaling.
-
-    Definition:
-    :math:`f(x) = \frac{1}{1 + e^{-\beta x}}`
-
-    Keyword Arguments:
-        beta (`float`): Scaling parameter :math:`\beta`
-    """
-    out = 1.0 / (1.0 + torch.exp(-1.0 * beta * x))
-    return out
-
-
-@register_activation
-def swish_beta(x, beta=10.0):
-    r"""Swish activation function with scaling.
-
-    Definition:
-    :math:`f(x) = \frac{x}{1 + e^{-\beta x}}`
-
-    Keyword Arguments:
-        beta (`float`): Scaling parameter :math:`\beta`
-    """
-    out = x * sigmoid_beta(x, beta=beta)
-    return out
-
-
-def get_activation(funcname):
-    """Deserialize the activation function."""
-    if callable(funcname):
-        return funcname
-    elif funcname in ACTIVATIONS:
-        return ACTIVATIONS.get(funcname)
-    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

@@ -1,38 +0,0 @@
-"""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

@@ -1,13 +0,0 @@
-"""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/colors.py

@@ -1,60 +0,0 @@
-"""ProtoTorch color utilities"""
-
-import matplotlib.lines as mlines
-import torch
-from matplotlib import cm
-from matplotlib.colors import (
-    Normalize,
-    to_hex,
-    to_rgb,
-)
-
-
-def hex_to_rgb(hex_values):
-    for v in hex_values:
-        v = v.lstrip('#')
-        lv = len(v)
-        c = [int(v[i:i + lv // 3], 16) for i in range(0, lv, lv // 3)]
-        yield c
-
-
-def rgb_to_hex(rgb_values):
-    for v in rgb_values:
-        c = "%02x%02x%02x" % tuple(v)
-        yield c
-
-
-def get_colors(vmax, vmin=0, cmap="viridis"):
-    cmap = cm.get_cmap(cmap)
-    colornorm = Normalize(vmin=vmin, vmax=vmax)
-    colors = dict()
-    for c in range(vmin, vmax + 1):
-        colors[c] = to_hex(cmap(colornorm(c)))
-    return colors
-
-
-def get_legend_handles(colors, labels, marker="dots", zero_indexed=False):
-    handles = list()
-    for color, label in zip(colors.values(), labels):
-        if marker == "dots":
-            handle = mlines.Line2D(
-                xdata=[],
-                ydata=[],
-                label=label,
-                color="white",
-                markerfacecolor=color,
-                marker="o",
-                markersize=10,
-                markeredgecolor="k",
-            )
-        else:
-            handle = mlines.Line2D(
-                xdata=[],
-                ydata=[],
-                label=label,
-                color=color,
-                marker="",
-                markersize=15,
-            )
-        handles.append(handle)
-    return handles

prototorch/utils/utils.py

@@ -1,136 +0,0 @@
-"""ProtoTorch utilities"""
-
-import warnings
-from typing import (
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Union,
-)
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader, Dataset
-
-
-def generate_mesh(
-    minima: torch.TensorType,
-    maxima: torch.TensorType,
-    border: float = 1.0,
-    resolution: int = 100,
-    device: Optional[torch.device] = None,
-):
-    # Apply Border
-    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 mesh2d(x=None, border: float = 1.0, resolution: int = 100):
-    if x is not None:
-        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
-        y_min, y_max = x[:, 1].min() - y_shift, x[:, 1].max() + y_shift
-    else:
-        x_min, x_max = -border, border
-        y_min, y_max = -border, border
-    xx, yy = np.meshgrid(np.linspace(x_min, x_max, resolution),
-                         np.linspace(y_min, y_max, resolution))
-    mesh = np.c_[xx.ravel(), yy.ravel()]
-    return mesh, xx, yy
-
-
-def distribution_from_list(list_dist: List[int],
-                           clabels: Optional[Iterable[int]] = None):
-    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 isinstance(user_distribution, dict):
-        if "num_classes" in user_distribution.keys():
-            num_classes = int(user_distribution["num_classes"])
-            per_class = int(user_distribution["per_class"])
-            return distribution_from_list([per_class] * num_classes, clabels)
-        else:
-            return user_distribution
-    elif isinstance(user_distribution, tuple):
-        assert len(user_distribution) == 2
-        num_classes, per_class = user_distribution
-        num_classes, per_class = int(num_classes), int(per_class)
-        return distribution_from_list([per_class] * num_classes, clabels)
-    elif isinstance(user_distribution, list):
-        return distribution_from_list(user_distribution, clabels)
-    else:
-        msg = f"`distribution` was not understood." \
-            f"You have provided: {user_distribution}."
-        raise ValueError(msg)
-
-
-def parse_data_arg(data_arg: Union[Dataset, DataLoader, list, tuple]):
-    """Return data and target as torch tensors."""
-    if isinstance(data_arg, Dataset):
-        if hasattr(data_arg, "__len__"):
-            ds_size = len(data_arg)  # type: ignore
-            loader = DataLoader(data_arg, batch_size=ds_size)
-            data, targets = next(iter(loader))
-        else:
-            emsg = f"Dataset {data_arg} is not sized (`__len__` unimplemented)."
-            raise TypeError(emsg)
-
-    elif isinstance(data_arg, DataLoader):
-        data = torch.tensor([])
-        targets = torch.tensor([])
-        for x, y in data_arg:
-            data = torch.cat([data, x])
-            targets = torch.cat([targets, y])
-    else:
-        assert len(data_arg) == 2
-        data, targets = data_arg
-        if not isinstance(data, torch.Tensor):
-            wmsg = f"Converting data to {torch.Tensor}..."
-            warnings.warn(wmsg)
-            data = torch.Tensor(data)
-        if not isinstance(targets, torch.LongTensor):
-            wmsg = f"Converting targets to {torch.LongTensor}..."
-            warnings.warn(wmsg)
-            targets = torch.LongTensor(targets)
-    return data, targets

setup.cfg

@@ -1,16 +0,0 @@
-[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

@@ -1,95 +1,49 @@
-"""
+"""Install ProtoTorch."""
 
- ######
+from setuptools import setup
- #     # #####   ####  #####  ####  #####  ####  #####   ####  #    #
+from setuptools import find_packages
- #     # #    # #    #   #   #    #   #   #    # #    # #    # #    #
- ######  #    # #    #   #   #    #   #   #    # #    # #      ######
- #       #####  #    #   #   #    #   #   #    # #####  #      #    #
- #       #   #  #    #   #   #    #   #   #    # #   #  #    # #    #
- #       #    #  ####    #    ####    #    ####  #    #  ####  #    #
 
-ProtoTorch Core Package
+PROJECT_URL = 'https://github.com/si-cim/prototorch'
-"""
+DOWNLOAD_URL = 'https://github.com/si-cim/prototorch.git'
-from setuptools import find_packages, setup
 
-PROJECT_URL = "https://github.com/si-cim/prototorch"
+with open('README.md', 'r') as fh:
-DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
-
-with open("README.md", encoding="utf-8") as fh:
     long_description = fh.read()
 
-INSTALL_REQUIRES = [
+setup(name='prototorch',
-    "torch>=2.0.0",
+      version='0.1.0-dev0',
-    "torchvision",
+      description='Highly extensible, GPU-supported '
-    "numpy",
+      'Learning Vector Quantization (LVQ) toolbox '
-    "scikit-learn",
+      'built using PyTorch and its nn API.',
-    "matplotlib",
-]
-DATASETS = [
-    "requests",
-    "tqdm",
-]
-DEV = [
-    "bump2version",
-    "pre-commit",
-]
-DOCS = [
-    "recommonmark",
-    "sphinx",
-    "sphinx_rtd_theme",
-    "sphinxcontrib-katex",
-    "sphinx-autodoc-typehints",
-]
-EXAMPLES = [
-    "torchinfo",
-]
-TESTS = [
-    "flake8",
-    "pytest",
-]
-ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
-
-setup(
-    name="prototorch",
-    version="0.7.6",
-    description="Highly extensible, GPU-supported "
-    "Learning Vector Quantization (LVQ) toolbox "
-    "built using PyTorch and its nn API.",
       long_description=long_description,
-    long_description_content_type="text/markdown",
+      long_description_content_type='text/markdown',
-    author="Jensun Ravichandran",
+      author='Jensun Ravichandran',
-    author_email="jjensun@gmail.com",
+      author_email='jjensun@gmail.com',
       url=PROJECT_URL,
       download_url=DOWNLOAD_URL,
-    license="MIT",
+      license='MIT',
-    python_requires=">=3.8",
+      install_requires=[
-    install_requires=INSTALL_REQUIRES,
+          'torch>=1.3.1',
+          'torchvision>=0.5.0',
+          'numpy>=1.9.1',
+      ],
       extras_require={
-        "datasets": DATASETS,
+          'examples': [
-        "dev": DEV,
+              'sklearn',
-        "docs": DOCS,
+              'matplotlib',
-        "examples": EXAMPLES,
+          ],
-        "tests": TESTS,
+          'tests': ['pytest'],
-        "all": ALL,
       },
       classifiers=[
-        "Environment :: Console",
+          'Development Status :: 2 - Pre-Alpha', 'Environment :: Console',
-        "Natural Language :: English",
+          'Intended Audience :: Developers', 'Intended Audience :: Education',
-        "Development Status :: 4 - Beta",
+          'Intended Audience :: Science/Research',
-        "Intended Audience :: Developers",
+          'License :: OSI Approved :: MIT License',
-        "Intended Audience :: Education",
+          'Programming Language :: Python :: 3.6',
-        "Intended Audience :: Science/Research",
+          'Programming Language :: Python :: 3.7',
-        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+          'Programming Language :: Python :: 3.8',
-        "Topic :: Software Development :: Libraries",
+          'Operating System :: OS Independent',
-        "Topic :: Software Development :: Libraries :: Python Modules",
+          'Topic :: Scientific/Engineering :: Artificial Intelligence',
-        "License :: OSI Approved :: MIT License",
+          'Topic :: Software Development :: Libraries',
-        "Operating System :: OS Independent",
+          'Topic :: Software Development :: Libraries :: Python Modules'
-        "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(),
+      packages=find_packages())
-    zip_safe=False,
-)

tests/test_core.py

@@ -1,777 +0,0 @@
-"""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

@@ -1,186 +0,0 @@
-"""ProtoTorch datasets test suite"""
-
-import os
-import unittest
-
-import numpy as np
-import torch
-
-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):
-            _ = self.ds[0]
-
-    def test_len(self):
-        with self.assertRaises(NotImplementedError):
-            _ = len(self.ds)
-
-    def tearDown(self):
-        del self.ds
-
-
-class TestProtoDataset(unittest.TestCase):
-
-    def test_download(self):
-        with self.assertRaises(NotImplementedError):
-            _ = ProtoDataset("./artifacts", download=True)
-
-    def test_exists(self):
-        with self.assertRaises(RuntimeError):
-            _ = ProtoDataset("./artifacts", download=False)
-
-
-class TestNumpyDataset(unittest.TestCase):
-
-    def test_list_init(self):
-        ds = pt.datasets.NumpyDataset([1], [1])
-        self.assertEqual(len(ds), 1)
-
-    def test_numpy_init(self):
-        data = np.random.randn(3, 2)
-        targets = np.array([0, 1, 2])
-        ds = pt.datasets.NumpyDataset(data, targets)
-        self.assertEqual(len(ds), 3)
-
-
-class TestCSVDataset(unittest.TestCase):
-
-    def setUp(self):
-        data = np.random.rand(100, 4)
-        targets = np.random.randint(2, size=(100, 1))
-        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):
-        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

@@ -0,0 +1,387 @@
+"""ProtoTorch functions test suite."""
+
+import unittest
+
+import numpy as np
+import torch
+
+from prototorch.functions import (activations, competitions, distances,
+                                  initializers)
+
+
+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 TestActivations(unittest.TestCase):
+    def setUp(self):
+        self.x = torch.randn(1024, 1)
+
+    def test_registry(self):
+        self.assertIsNotNone(activations.ACTIVATIONS)
+
+    def test_funcname_deserialization(self):
+        flist = ['identity', 'sigmoid_beta', 'swish_beta']
+        for funcname in flist:
+            f = activations.get_activation(funcname)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+
+    def test_callable_deserialization(self):
+        def dummy(x, **kwargs):
+            return x
+
+        for f in [dummy, lambda x: x]:
+            f = activations.get_activation(f)
+            iscallable = callable(f)
+            self.assertTrue(iscallable)
+            self.assertEqual(1, f(1))
+
+    def test_unknown_deserialization(self):
+        for funcname in ['blubb', 'foobar']:
+            with self.assertRaises(NameError):
+                _ = activations.get_activation(funcname)
+
+    def test_identity(self):
+        actual = activations.identity(self.x)
+        desired = self.x
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_sigmoid_beta1(self):
+        actual = activations.sigmoid_beta(self.x, beta=1)
+        desired = torch.sigmoid(self.x)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_swish_beta1(self):
+        actual = activations.swish_beta(self.x, beta=1)
+        desired = self.x * torch.sigmoid(self.x)
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x
+
+
+class TestCompetitions(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_wtac(self):
+        d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
+        labels = torch.tensor([0, 1, 2, 3])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_wtac_one_hot(self):
+        d = torch.tensor([[1.99, 3.01], [3., 2.01]])
+        labels = torch.tensor([[0, 1], [1, 0]])
+        actual = competitions.wtac(d, labels)
+        desired = torch.tensor([[0, 1], [1, 0]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def test_knnc_k1(self):
+        d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
+        labels = torch.tensor([0, 1, 2, 3])
+        actual = competitions.knnc(d, labels, k=1)
+        desired = torch.tensor([2, 0])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        pass
+
+
+class TestInitializers(unittest.TestCase):
+    def setUp(self):
+        self.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):
+        flist = [
+            'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
+            'stratified_random'
+        ]
+        for funcname in 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)
+        desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
+        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)
+        desired = torch.tensor([[0., -1., -2.], [2., 2., 2.]])
+        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)
+        desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
+                                [1., 1., 1.]])
+        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)
+        desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
+                                [1., 1., 1.]])
+        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)
+        desired = torch.tensor([[0., -1., -2.], [2., 2., 2.], [0., 0., 0.],
+                                [0., 0., 0.]])
+        mismatch = np.testing.assert_array_almost_equal(actual,
+                                                        desired,
+                                                        decimal=5)
+        self.assertIsNone(mismatch)
+
+    def tearDown(self):
+        del self.x, self.y, self.gen
+        _ = torch.seed()

tests/test_modules.py

@@ -0,0 +1,129 @@
+"""ProtoTorch modules test suite."""
+
+import unittest
+
+import numpy as np
+import torch
+
+from prototorch.modules import prototypes, losses
+
+
+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_addprototypes1d_init_without_input_dim(self):
+        with self.assertRaises(NameError):
+            _ = prototypes.AddPrototypes1D(nclasses=1)
+
+    def test_addprototypes1d_init_without_nclasses(self):
+        with self.assertRaises(NameError):
+            _ = prototypes.AddPrototypes1D(input_dim=1)
+
+    def test_addprototypes1d_init_without_pdist(self):
+        p1 = prototypes.AddPrototypes1D(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_addprototypes1d_init_without_data(self):
+        pdist = [2, 2]
+        p1 = prototypes.AddPrototypes1D(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_addprototypes1d_init_torch_pdist(self):
+    #     pdist = torch.tensor([2, 2])
+    #     p1 = prototypes.AddPrototypes1D(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_addprototypes1d_init_with_ppc(self):
+        p1 = prototypes.AddPrototypes1D(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_addprototypes1d_init_with_pdist(self):
+        p1 = prototypes.AddPrototypes1D(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_addprototypes1d_func_initializer(self):
+        def my_initializer(*args, **kwargs):
+            return torch.full((2, 99), 99), torch.tensor([0, 1])
+
+        p1 = prototypes.AddPrototypes1D(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_addprototypes1d_forward(self):
+        p1 = prototypes.AddPrototypes1D(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 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()
+
+    def tearDown(self):
+        pass

tests/test_utils.py

@@ -1,47 +0,0 @@
-"""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"

tox.ini

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