Use github actions for CI (#10)

* chore: Absolute imports

* feat: Add new mesh util

* chore: replace bumpversion

original fork no longer maintained, move config

* ci: remove old configuration files

* ci: update github action

* ci: add python 3.10 test

* chore: update pre-commit hooks

* ci: update supported python versions

supported are 3.7, 3.8 and 3.9.

3.6 had EOL in december 2021.
3.10 has no pytorch distribution yet.

* ci: add windows test

* ci: update action

less windows tests, pre commit

* ci: fix typo

* chore: run precommit for all files

* ci: two step tests

* ci: compatibility waits for style

* fix: init file had missing imports

* ci: add deployment script

* ci: skip complete publish step

* ci: cleanup readme

.bumpversion.cfg

@@ -1,13 +0,0 @@
-[bumpversion]
-current_version = 0.7.1
-commit = True
-tag = True
-parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
-serialize = {major}.{minor}.{patch}
-message = build: bump version {current_version} → {new_version}
-
-[bumpversion:file:setup.py]
-
-[bumpversion:file:./prototorch/__init__.py]
-
-[bumpversion:file:./docs/source/conf.py]

.codacy.yml

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

.codecov.yml

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

.github/workflows/pythonapp.yml

@@ -5,33 +5,69 @@ name: tests
 
 on:
   push:
-    branches: [ master, dev ]
   pull_request:
     branches: [ master ]
 
 jobs:
-  build:
+  style:
-
     runs-on: ubuntu-latest
-
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python 3.9
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v2
       with:
         python-version: 3.9
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install .[all]
-    - name: Lint with flake8
+    - uses: pre-commit/action@v2.0.3
+  compatibility:
+    needs: style
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.7", "3.8", "3.9"]
+        os: [ubuntu-latest, windows-latest]
+        exclude:
+        - os: windows-latest
+          python-version: "3.7"
+        - os: windows-latest
+          python-version: "3.8"
+
+    runs-on: ${{ matrix.os }}
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
       run: |
-        pip install flake8
+        python -m pip install --upgrade pip
-        # stop the build if there are Python syntax errors or undefined names
+        pip install .[all]
-        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@v2
+    - name: Set up Python 3.9
+      uses: actions/setup-python@v2
+      with:
+        python-version: "3.9"
+    - 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 }}

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.0.1
+  rev: v4.1.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
@@ -18,19 +18,19 @@ repos:
   - id: autoflake
 
 - repo: http://github.com/PyCQA/isort
-  rev: 5.8.0
+  rev: 5.10.1
   hooks:
   - id: isort
 
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.902
+  rev: v0.931
   hooks:
   - id: mypy
     files: prototorch
     additional_dependencies: [types-pkg_resources]
 
 - repo: https://github.com/pre-commit/mirrors-yapf
-  rev: v0.31.0
+  rev: v0.32.0
   hooks:
   - id: yapf
 
@@ -42,7 +42,7 @@ repos:
   - id: python-check-blanket-noqa
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v2.19.4
+  rev: v2.31.0
   hooks:
   - id: pyupgrade
 

.travis.yml

@@ -1,46 +0,0 @@
-dist: bionic
-sudo: false
-language: python
-python:
-  - 3.9
-  - 3.8
-  - 3.7
-  - 3.6
-cache:
-  directories:
-  - "$HOME/.cache/pip"
-  - "./tests/artifacts"
-  - "$HOME/datasets"
-install:
-- pip install .[all] --progress-bar off
-
-# Generate code coverage report
-script:
-- coverage run -m pytest
-
-# Push the results to codecov
-after_success:
-- bash <(curl -s https://codecov.io/bash)
-
-# Publish on PyPI
-jobs:
-  include:
-    - stage: build
-      python: 3.9
-      script: echo "Starting Pypi build"
-      deploy:
-        provider: pypi
-        username: __token__
-        distributions: "sdist bdist_wheel"
-        password:
-          secure: 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
-        on:
-          tags: true
-          skip_existing: true
-
-# The password is encrypted with:
-# `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`
-# See https://docs.travis-ci.com/user/deployment/pypi and
-# https://github.com/travis-ci/travis.rb#installation
-# for more details
-# Note: The encrypt command does not work well in ZSH.

README.md

@@ -2,12 +2,9 @@
 
 ![ProtoTorch Logo](https://prototorch.readthedocs.io/en/latest/_static/horizontal-lockup.png)
 
-[![Build Status](https://api.travis-ci.com/si-cim/prototorch.svg?branch=master)](https://travis-ci.com/github/si-cim/prototorch)
 ![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg)
 [![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/si-cim/prototorch?color=yellow&label=version)](https://github.com/si-cim/prototorch/releases)
 [![PyPI](https://img.shields.io/pypi/v/prototorch)](https://pypi.org/project/prototorch/)
-[![codecov](https://codecov.io/gh/si-cim/prototorch/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/prototorch)
-[![Codacy Badge](https://api.codacy.com/project/badge/Grade/76273904bf9343f0a8b29cd8aca242e7)](https://www.codacy.com/gh/si-cim/prototorch?utm_source=github.com&utm_medium=referral&utm_content=si-cim/prototorch&utm_campaign=Badge_Grade)
 [![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)

examples/cbc_iris.py

@@ -7,6 +7,7 @@ import prototorch as pt
 
 
 class CBC(torch.nn.Module):
+
     def __init__(self, data, **kwargs):
         super().__init__(**kwargs)
         self.components_layer = pt.components.ReasoningComponents(
@@ -23,6 +24,7 @@ class CBC(torch.nn.Module):
 
 
 class VisCBC2D():
+
     def __init__(self, model, data):
         self.model = model
         self.x_train, self.y_train = pt.utils.parse_data_arg(data)

prototorch/__init__.py

@@ -1,25 +1,20 @@
 """ProtoTorch package"""
 
 import pkgutil
-from typing import List
 
 import pkg_resources
 
-from . import (
+from . import datasets  # noqa: F401
-    datasets,
+from . import nn  # noqa: F401
-    nn,
+from . import utils  # noqa: F401
-    utils,
+from .core import competitions  # noqa: F401
-)
+from .core import components  # noqa: F401
-from .core import (
+from .core import distances  # noqa: F401
-    competitions,
+from .core import initializers  # noqa: F401
-    components,
+from .core import losses  # noqa: F401
-    distances,
+from .core import pooling  # noqa: F401
-    initializers,
+from .core import similarities  # noqa: F401
-    losses,
+from .core import transforms  # noqa: F401
-    pooling,
-    similarities,
-    transforms,
-)
 
 # Core Setup
 __version__ = "0.7.1"
@@ -40,7 +35,7 @@ __all_core__ = [
 ]
 
 # Plugin Loader
-__path__: List[str] = pkgutil.extend_path(__path__, __name__)
+__path__ = pkgutil.extend_path(__path__, __name__)
 
 
 def discover_plugins():

prototorch/core/competitions.py

@@ -48,6 +48,7 @@ class WTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
+
     def forward(self, distances, labels):  # pylint: disable=no-self-use
         return wtac(distances, labels)
 
@@ -58,6 +59,7 @@ class LTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
+
     def forward(self, probs, labels):  # pylint: disable=no-self-use
         return wtac(-1.0 * probs, labels)
 
@@ -68,6 +70,7 @@ class KNNC(torch.nn.Module):
     Thin wrapper over the `knnc` function.
 
     """
+
     def __init__(self, k=1, **kwargs):
         super().__init__(**kwargs)
         self.k = k
@@ -85,5 +88,6 @@ class CBCC(torch.nn.Module):
     Thin wrapper over the `cbcc` function.
 
     """
+
     def forward(self, detections, reasonings):  # pylint: disable=no-self-use
         return cbcc(detections, reasonings)

prototorch/core/components.py

@@ -6,7 +6,8 @@ from typing import Union
 import torch
 from torch.nn.parameter import Parameter
 
-from ..utils import parse_distribution
+from prototorch.utils import parse_distribution
+
 from .initializers import (
     AbstractClassAwareCompInitializer,
     AbstractComponentsInitializer,
@@ -63,6 +64,7 @@ def get_cikwargs(init, distribution):
 
 class AbstractComponents(torch.nn.Module):
     """Abstract class for all components modules."""
+
     @property
     def num_components(self):
         """Current number of components."""
@@ -85,6 +87,7 @@ class AbstractComponents(torch.nn.Module):
 
 class Components(AbstractComponents):
     """A set of adaptable Tensors."""
+
     def __init__(self, num_components: int,
                  initializer: AbstractComponentsInitializer):
         super().__init__()
@@ -112,6 +115,7 @@ class Components(AbstractComponents):
 
 class AbstractLabels(torch.nn.Module):
     """Abstract class for all labels modules."""
+
     @property
     def labels(self):
         return self._labels.cpu()
@@ -152,6 +156,7 @@ class AbstractLabels(torch.nn.Module):
 
 class Labels(AbstractLabels):
     """A set of standalone labels."""
+
     def __init__(self,
                  distribution: Union[dict, list, tuple],
                  initializer: AbstractLabelsInitializer = LabelsInitializer()):
@@ -182,6 +187,7 @@ class Labels(AbstractLabels):
 
 class LabeledComponents(AbstractComponents):
     """A set of adaptable components and corresponding unadaptable labels."""
+
     def __init__(
         self,
         distribution: Union[dict, list, tuple],
@@ -249,6 +255,7 @@ class Reasonings(torch.nn.Module):
     The `reasonings` tensor is of shape [num_components, num_classes, 2].
 
     """
+
     def __init__(
         self,
         distribution: Union[dict, list, tuple],
@@ -308,6 +315,7 @@ class ReasoningComponents(AbstractComponents):
     three element probability distribution.
 
     """
+
     def __init__(
         self,
         distribution: Union[dict, list, tuple],

prototorch/core/initializers.py

@@ -11,7 +11,7 @@ from typing import (
 
 import torch
 
-from ..utils import parse_data_arg, parse_distribution
+from prototorch.utils import parse_data_arg, parse_distribution
 
 
 # Components
@@ -26,6 +26,7 @@ class LiteralCompInitializer(AbstractComponentsInitializer):
     Use this to 'generate' pre-initialized components elsewhere.
 
     """
+
     def __init__(self, components):
         self.components = components
 
@@ -40,6 +41,7 @@ class LiteralCompInitializer(AbstractComponentsInitializer):
 
 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)
@@ -53,6 +55,7 @@ class ShapeAwareCompInitializer(AbstractComponentsInitializer):
 
 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
@@ -60,6 +63,7 @@ class ZerosCompInitializer(ShapeAwareCompInitializer):
 
 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
@@ -67,6 +71,7 @@ class OnesCompInitializer(ShapeAwareCompInitializer):
 
 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
@@ -79,6 +84,7 @@ class FillValueCompInitializer(OnesCompInitializer):
 
 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
@@ -93,6 +99,7 @@ class UniformCompInitializer(OnesCompInitializer):
 
 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
@@ -113,6 +120,7 @@ class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
     `data` has to be a torch tensor.
 
     """
+
     def __init__(self,
                  data: torch.Tensor,
                  noise: float = 0.0,
@@ -137,6 +145,7 @@ class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
 
 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)
@@ -145,6 +154,7 @@ class DataAwareCompInitializer(AbstractDataAwareCompInitializer):
 
 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]
@@ -154,6 +164,7 @@ class SelectionCompInitializer(AbstractDataAwareCompInitializer):
 
 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)
@@ -172,6 +183,7 @@ class AbstractClassAwareCompInitializer(AbstractComponentsInitializer):
     target tensors.
 
     """
+
     def __init__(self,
                  data,
                  noise: float = 0.0,
@@ -199,6 +211,7 @@ class AbstractClassAwareCompInitializer(AbstractComponentsInitializer):
 
 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)
@@ -207,6 +220,7 @@ class ClassAwareCompInitializer(AbstractClassAwareCompInitializer):
 
 class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
     """Abstract class for all stratified components initializers."""
+
     @property
     @abstractmethod
     def subinit_type(self) -> Type[AbstractDataAwareCompInitializer]:
@@ -229,6 +243,7 @@ class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
 
 class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
     """Generate components using stratified sampling from the provided data."""
+
     @property
     def subinit_type(self):
         return SelectionCompInitializer
@@ -236,6 +251,7 @@ class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
 
 class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
     """Generate components at stratified means of the provided data."""
+
     @property
     def subinit_type(self):
         return MeanCompInitializer
@@ -244,6 +260,7 @@ class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
 # Labels
 class AbstractLabelsInitializer(ABC):
     """Abstract class for all labels initializers."""
+
     @abstractmethod
     def generate(self, distribution: Union[dict, list, tuple]):
         ...
@@ -255,6 +272,7 @@ class LiteralLabelsInitializer(AbstractLabelsInitializer):
     Use this to 'generate' pre-initialized labels elsewhere.
 
     """
+
     def __init__(self, labels):
         self.labels = labels
 
@@ -273,6 +291,7 @@ class LiteralLabelsInitializer(AbstractLabelsInitializer):
 
 class DataAwareLabelsInitializer(AbstractLabelsInitializer):
     """'Generate' the labels from a torch Dataset."""
+
     def __init__(self, data):
         self.data, self.targets = parse_data_arg(data)
 
@@ -283,6 +302,7 @@ class DataAwareLabelsInitializer(AbstractLabelsInitializer):
 
 class LabelsInitializer(AbstractLabelsInitializer):
     """Generate labels from `distribution`."""
+
     def generate(self, distribution: Union[dict, list, tuple]):
         distribution = parse_distribution(distribution)
         labels_list = []
@@ -294,6 +314,7 @@ class LabelsInitializer(AbstractLabelsInitializer):
 
 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())
@@ -312,6 +333,7 @@ def compute_distribution_shape(distribution):
 
 class AbstractReasoningsInitializer(ABC):
     """Abstract class for all reasonings initializers."""
+
     def __init__(self, components_first: bool = True):
         self.components_first = components_first
 
@@ -332,6 +354,7 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
     Use this to 'generate' pre-initialized reasonings elsewhere.
 
     """
+
     def __init__(self, reasonings, **kwargs):
         super().__init__(**kwargs)
         self.reasonings = reasonings
@@ -349,6 +372,7 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
 
 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)
@@ -358,6 +382,7 @@ class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
 
 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)
@@ -367,6 +392,7 @@ class OnesReasoningsInitializer(AbstractReasoningsInitializer):
 
 class RandomReasoningsInitializer(AbstractReasoningsInitializer):
     """Reasonings are randomly initialized."""
+
     def __init__(self, minimum=0.4, maximum=0.6, **kwargs):
         super().__init__(**kwargs)
         self.minimum = minimum
@@ -381,6 +407,7 @@ class RandomReasoningsInitializer(AbstractReasoningsInitializer):
 
 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)
@@ -399,6 +426,7 @@ class AbstractTransformInitializer(ABC):
 
 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
 
@@ -415,6 +443,7 @@ class AbstractLinearTransformInitializer(AbstractTransformInitializer):
 
 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)
@@ -422,6 +451,7 @@ class ZerosLinearTransformInitializer(AbstractLinearTransformInitializer):
 
 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)
@@ -429,6 +459,7 @@ class OnesLinearTransformInitializer(AbstractLinearTransformInitializer):
 
 class EyeTransformInitializer(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))
@@ -438,6 +469,7 @@ class EyeTransformInitializer(AbstractLinearTransformInitializer):
 
 class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
     """Abstract class for all data-aware linear transform initializers."""
+
     def __init__(self,
                  data: torch.Tensor,
                  noise: float = 0.0,
@@ -458,6 +490,7 @@ class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
 
 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)

prototorch/core/losses.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from ..nn.activations import get_activation
+from prototorch.nn.activations import get_activation
 
 
 # Helpers
@@ -106,6 +106,7 @@ def margin_loss(y_pred, y_true, margin=0.3):
 
 
 class GLVQLoss(torch.nn.Module):
+
     def __init__(self, margin=0.0, transfer_fn="identity", beta=10, **kwargs):
         super().__init__(**kwargs)
         self.margin = margin
@@ -119,6 +120,7 @@ class GLVQLoss(torch.nn.Module):
 
 
 class MarginLoss(torch.nn.modules.loss._Loss):
+
     def __init__(self,
                  margin=0.3,
                  size_average=None,
@@ -132,6 +134,7 @@ class MarginLoss(torch.nn.modules.loss._Loss):
 
 
 class NeuralGasEnergy(torch.nn.Module):
+
     def __init__(self, lm, **kwargs):
         super().__init__(**kwargs)
         self.lm = lm
@@ -152,6 +155,7 @@ class NeuralGasEnergy(torch.nn.Module):
 
 
 class GrowingNeuralGasEnergy(NeuralGasEnergy):
+
     def __init__(self, topology_layer, **kwargs):
         super().__init__(**kwargs)
         self.topology_layer = topology_layer

prototorch/core/pooling.py

@@ -82,23 +82,27 @@ def stratified_prod_pooling(values: torch.Tensor,
 
 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/transforms.py

@@ -10,6 +10,7 @@ from .initializers import (
 
 
 class LinearTransform(torch.nn.Module):
+
     def __init__(
         self,
         in_dim: int,

prototorch/datasets/abstract.py

@@ -93,6 +93,7 @@ class ProtoDataset(Dataset):
 
 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)
@@ -102,6 +103,7 @@ class NumpyDataset(torch.utils.data.TensorDataset):
 
 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,

prototorch/datasets/sklearn.py

@@ -8,8 +8,13 @@ URL:
 import warnings
 from typing import Sequence, Union
 
-from sklearn.datasets import (load_iris, make_blobs, make_circles,
+from sklearn.datasets import (
-                              make_classification, make_moons)
+    load_iris,
+    make_blobs,
+    make_circles,
+    make_classification,
+    make_moons,
+)
 
 from prototorch.datasets.abstract import NumpyDataset
 
@@ -35,6 +40,7 @@ class Iris(NumpyDataset):
 
     :param dims: select a subset of dimensions
     """
+
     def __init__(self, dims: Sequence[int] = None):
         x, y = load_iris(return_X_y=True)
         if dims:
@@ -49,6 +55,7 @@ class Blobs(NumpyDataset):
     https://scikit-learn.org/stable/datasets/sample_generators.html#sample-generators.
 
     """
+
     def __init__(self,
                  num_samples: int = 300,
                  num_features: int = 2,
@@ -69,6 +76,7 @@ class Random(NumpyDataset):
 
     Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
     """
+
     def __init__(self,
                  num_samples: int = 300,
                  num_features: int = 2,
@@ -104,6 +112,7 @@ class Circles(NumpyDataset):
     https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
 
     """
+
     def __init__(self,
                  num_samples: int = 300,
                  noise: float = 0.3,
@@ -126,6 +135,7 @@ class Moons(NumpyDataset):
     https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
 
     """
+
     def __init__(self,
                  num_samples: int = 300,
                  noise: float = 0.3,

prototorch/datasets/spiral.py

@@ -9,6 +9,7 @@ def make_spiral(num_samples=500, noise=0.3):
 
     For use in Prototorch use `prototorch.datasets.Spiral` instead.
     """
+
     def get_samples(n, delta_t):
         points = []
         for i in range(n):
@@ -52,6 +53,7 @@ class Spiral(torch.utils.data.TensorDataset):
     :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/xor.py

@@ -13,6 +13,7 @@ def make_xor(num_samples=500):
 
 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/nn/wrappers.py

@@ -4,6 +4,7 @@ import torch
 
 
 class LambdaLayer(torch.nn.Module):
+
     def __init__(self, fn, name=None):
         super().__init__()
         self.fn = fn
@@ -17,6 +18,7 @@ class LambdaLayer(torch.nn.Module):
 
 
 class LossLayer(torch.nn.modules.loss._Loss):
+
     def __init__(self,
                  fn,
                  name=None,

prototorch/utils/utils.py

@@ -13,6 +13,32 @@ 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: 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])

setup.cfg

@@ -12,4 +12,18 @@ multi_line_output = 3
 include_trailing_comma = True
 force_grid_wrap = 3
 use_parentheses = True
-line_length = 79
+line_length = 79
+
+[bumpversion]
+current_version = 0.7.1
+commit = True
+tag = True
+parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
+serialize = {major}.{minor}.{patch}
+message = build: bump version {current_version} → {new_version}
+
+[bumpversion:file:setup.py]
+
+[bumpversion:file:./prototorch/__init__.py]
+
+[bumpversion:file:./docs/source/conf.py]

setup.py

@@ -29,7 +29,7 @@ DATASETS = [
     "tqdm",
 ]
 DEV = [
-    "bumpversion",
+    "bump2version",
     "pre-commit",
 ]
 DOCS = [
@@ -43,7 +43,10 @@ EXAMPLES = [
     "matplotlib",
     "torchinfo",
 ]
-TESTS = ["codecov", "pytest"]
+TESTS = [
+    "flake8",
+    "pytest",
+]
 ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
@@ -59,7 +62,7 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
-    python_requires=">=3.6",
+    python_requires=">=3.7,<3.10",
     install_requires=INSTALL_REQUIRES,
     extras_require={
         "datasets": DATASETS,
@@ -82,7 +85,6 @@ setup(
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.6",
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",

tests/test_core.py

@@ -404,6 +404,7 @@ def test_glvq_loss_one_hot_unequal():
 
 # Activations
 class TestActivations(unittest.TestCase):
+
     def setUp(self):
         self.flist = ["identity", "sigmoid_beta", "swish_beta"]
         self.x = torch.randn(1024, 1)
@@ -418,6 +419,7 @@ class TestActivations(unittest.TestCase):
             self.assertTrue(iscallable)
 
     def test_callable_deserialization(self):
+
         def dummy(x, **kwargs):
             return x
 
@@ -462,6 +464,7 @@ class TestActivations(unittest.TestCase):
 
 # Competitions
 class TestCompetitions(unittest.TestCase):
+
     def setUp(self):
         pass
 
@@ -515,6 +518,7 @@ class TestCompetitions(unittest.TestCase):
 
 # Pooling
 class TestPooling(unittest.TestCase):
+
     def setUp(self):
         pass
 
@@ -615,6 +619,7 @@ class TestPooling(unittest.TestCase):
 
 # Distances
 class TestDistances(unittest.TestCase):
+
     def setUp(self):
         self.nx, self.mx = 32, 2048
         self.ny, self.my = 8, 2048

tests/test_datasets.py

@@ -12,6 +12,7 @@ from prototorch.datasets.abstract import Dataset, ProtoDataset
 
 
 class TestAbstract(unittest.TestCase):
+
     def setUp(self):
         self.ds = Dataset("./artifacts")
 
@@ -28,6 +29,7 @@ class TestAbstract(unittest.TestCase):
 
 
 class TestProtoDataset(unittest.TestCase):
+
     def test_download(self):
         with self.assertRaises(NotImplementedError):
             _ = ProtoDataset("./artifacts", download=True)
@@ -38,6 +40,7 @@ class TestProtoDataset(unittest.TestCase):
 
 
 class TestNumpyDataset(unittest.TestCase):
+
     def test_list_init(self):
         ds = pt.datasets.NumpyDataset([1], [1])
         self.assertEqual(len(ds), 1)
@@ -50,6 +53,7 @@ class TestNumpyDataset(unittest.TestCase):
 
 
 class TestCSVDataset(unittest.TestCase):
+
     def setUp(self):
         data = np.random.rand(100, 4)
         targets = np.random.randint(2, size=(100, 1))
@@ -67,12 +71,14 @@ class TestCSVDataset(unittest.TestCase):
 
 
 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()
 
@@ -88,24 +94,28 @@ class TestIris(unittest.TestCase):
 
 
 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)