build: bump version 0.7.5 → 0.7.6

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

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.6.0
+current_version = 0.7.6
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

.codacy.yml

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

.codecov.yml

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

.github/ISSUE_TEMPLATE/bug_report.md

@@ -10,21 +10,28 @@ assignees: ''
 **Describe the bug**
 A clear and concise description of what the bug is.
 
-**To Reproduce**
+**Steps to reproduce the behavior**
-Steps to reproduce the behavior:
+1. ...
-1. Install Prototorch by running '...'
+2. Run script '...' or this snippet:
-2. Run script '...'
+```python
+import prototorch as pt
+
+...
+```
 3. See errors
 
 **Expected behavior**
 A clear and concise description of what you expected to happen.
 
+**Observed behavior**
+A clear and concise description of what actually happened.
+
 **Screenshots**
 If applicable, add screenshots to help explain your problem.
 
-**Desktop (please complete the following information):**
+**System and version information**
 - OS: [e.g. Ubuntu 20.10]
-- Prototorch Version: [e.g. v0.4.0]
+- ProtoTorch Version: [e.g. 0.4.0]
 - Python Version: [e.g. 3.9.5]
 
 **Additional context**

.github/workflows/pythonapp.yml

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

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.0.1
+  rev: v4.4.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
@@ -13,36 +13,36 @@ repos:
   - id: check-case-conflict
 
 - repo: https://github.com/myint/autoflake
-  rev: v1.4
+  rev: v2.1.1
   hooks:
   - id: autoflake
 
 - repo: http://github.com/PyCQA/isort
-  rev: 5.8.0
+  rev: 5.12.0
   hooks:
   - id: isort
 
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.902
+  rev: v1.3.0
   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
 
 - repo: https://github.com/pre-commit/pygrep-hooks
-  rev: v1.9.0
+  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: v2.19.4
+  rev: v3.7.0
   hooks:
   - id: pyupgrade
 

.travis.yml

@@ -1,36 +0,0 @@
-dist: bionic
-sudo: false
-language: python
-python: 3.9
-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
-deploy:
-  provider: pypi
-  username: __token__
-  password:
-    secure: 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
-  on:
-    tags: true
-    skip_existing: true
-
-# The password is encrypted with:
-# `cd prototorch && travis encrypt your-pypi-api-token --add deploy.password`
-# See https://docs.travis-ci.com/user/deployment/pypi and
-# https://github.com/travis-ci/travis.rb#installation
-# for more details
-# Note: The encrypt command does not work well in ZSH.

LICENSE

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

README.md

@@ -2,12 +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)

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.6.0"
+release = "0.7.6"
 
 # -- General configuration ---------------------------------------------------
 
@@ -120,7 +120,7 @@ html_css_files = [
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = "protoflowdoc"
+htmlhelp_basename = "prototorchdoc"
 
 # -- Options for LaTeX output ---------------------------------------------
 

examples/cbc_iris.py

@@ -1,5 +1,7 @@
 """ProtoTorch CBC example using 2D Iris data."""
 
+import logging
+
 import torch
 from matplotlib import pyplot as plt
 
@@ -7,6 +9,7 @@ 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 +26,7 @@ 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)
@@ -32,7 +36,7 @@ class VisCBC2D():
         self.resolution = 100
         self.cmap = "viridis"
 
-    def on_epoch_end(self):
+    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()
@@ -92,5 +96,5 @@ if __name__ == "__main__":
             correct += (y_pred.argmax(1) == y).float().sum(0)
 
         acc = 100 * correct / len(train_ds)
-        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
+        logging.info(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
-        vis.on_epoch_end()
+        vis.on_train_epoch_end()

examples/gmlvq.py

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

prototorch/__init__.py

@@ -1,28 +1,23 @@
 """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__ = "0.6.0"
+__version__ = "0.7.6"
 
 __all_core__ = [
     "competitions",
@@ -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

@@ -38,7 +38,7 @@ def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
     pk = A
     nk = (1 - A) * B
     numerator = (detections @ (pk - nk).T) + nk.sum(1)
-    probs = numerator / (pk + nk).sum(1)
+    probs = numerator / ((pk + nk).sum(1) + 1e-8)
     return probs
 
 
@@ -48,7 +48,8 @@ class WTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
-    def forward(self, distances, labels):
+
+    def forward(self, distances, labels):  # pylint: disable=no-self-use
         return wtac(distances, labels)
 
 
@@ -58,7 +59,8 @@ class LTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
-    def forward(self, probs, labels):
+
+    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):
+
+    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],
@@ -249,12 +255,15 @@ class Reasonings(torch.nn.Module):
     The `reasonings` tensor is of shape [num_components, num_classes, 2].
 
     """
+
     def __init__(
         self,
         distribution: Union[dict, list, tuple],
         initializer:
-        AbstractReasoningsInitializer = RandomReasoningsInitializer()):
+        AbstractReasoningsInitializer = RandomReasoningsInitializer(),
+    ):
         super().__init__()
+        self.add_reasonings(distribution, initializer)
 
     @property
     def num_classes(self):
@@ -306,6 +315,7 @@ class ReasoningComponents(AbstractComponents):
     three element probability distribution.
 
     """
+
     def __init__(
         self,
         distribution: Union[dict, list, tuple],

prototorch/core/distances.py

@@ -11,7 +11,7 @@ def squared_euclidean_distance(x, y):
     **Alias:**
     ``prototorch.functions.distances.sed``
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    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)
@@ -27,23 +27,20 @@ def euclidean_distance(x, y):
     :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)]
+    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)]
+    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)
-    # print(f"{diff.shape=}")  # (nx, ny, ndim)
-    # print(f"{pairwise_distances.shape=}")  # (nx, ny, ny)
-    # print(f"{distances.shape=}")  # (nx, ny)
     return distances
 
 
@@ -57,7 +54,7 @@ def lpnorm_distance(x, y, p):
 
     :param p: p parameter of the lp norm
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     distances = torch.cdist(x, y, p=p)
     return distances
 
@@ -69,7 +66,7 @@ def omega_distance(x, y, omega):
 
     :param `torch.tensor` omega: Two dimensional matrix
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    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)
@@ -83,7 +80,7 @@ def lomega_distance(x, y, omegas):
 
     :param `torch.tensor` omegas: Three dimensional matrix
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    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)

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
@@ -26,11 +26,18 @@ class LiteralCompInitializer(AbstractComponentsInitializer):
     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)
@@ -40,6 +47,7 @@ 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 +61,7 @@ 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 +69,7 @@ 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 +77,7 @@ 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 +90,7 @@ 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 +105,7 @@ 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 +126,7 @@ class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
     `data` has to be a torch tensor.
 
     """
+
     def __init__(self,
                  data: torch.Tensor,
                  noise: float = 0.0,
@@ -137,6 +151,7 @@ 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 +160,7 @@ 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 +170,7 @@ 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 +189,7 @@ class AbstractClassAwareCompInitializer(AbstractComponentsInitializer):
     target tensors.
 
     """
+
     def __init__(self,
                  data,
                  noise: float = 0.0,
@@ -199,6 +217,7 @@ 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 +226,7 @@ class ClassAwareCompInitializer(AbstractClassAwareCompInitializer):
 
 class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
     """Abstract class for all stratified components initializers."""
+
     @property
     @abstractmethod
     def subinit_type(self) -> Type[AbstractDataAwareCompInitializer]:
@@ -217,6 +237,8 @@ class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
         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,
@@ -229,6 +251,7 @@ class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
 
 class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
     """Generate components using stratified sampling from the provided data."""
+
     @property
     def subinit_type(self):
         return SelectionCompInitializer
@@ -236,6 +259,7 @@ class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
 
 class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
     """Generate components at stratified means of the provided data."""
+
     @property
     def subinit_type(self):
         return MeanCompInitializer
@@ -244,6 +268,7 @@ class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
 # Labels
 class AbstractLabelsInitializer(ABC):
     """Abstract class for all labels initializers."""
+
     @abstractmethod
     def generate(self, distribution: Union[dict, list, tuple]):
         ...
@@ -255,6 +280,7 @@ class LiteralLabelsInitializer(AbstractLabelsInitializer):
     Use this to 'generate' pre-initialized labels elsewhere.
 
     """
+
     def __init__(self, labels):
         self.labels = labels
 
@@ -273,6 +299,7 @@ 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 +310,7 @@ 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 +322,7 @@ 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())
@@ -303,17 +332,19 @@ class OneHotLabelsInitializer(LabelsInitializer):
 
 
 # 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 compute_shape(self, distribution):
-        distribution = parse_distribution(distribution)
-        num_components = sum(distribution.values())
-        num_classes = len(distribution.keys())
-        return (num_components, num_classes, 2)
-
     def generate_end_hook(self, reasonings):
         if not self.components_first:
             reasonings = reasonings.permute(2, 1, 0)
@@ -331,6 +362,7 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
     Use this to 'generate' pre-initialized reasonings elsewhere.
 
     """
+
     def __init__(self, reasonings, **kwargs):
         super().__init__(**kwargs)
         self.reasonings = reasonings
@@ -348,8 +380,9 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
 
 class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
     """Reasonings are all initialized with zeros."""
+
     def generate(self, distribution: Union[dict, list, tuple]):
-        shape = self.compute_shape(distribution)
+        shape = compute_distribution_shape(distribution)
         reasonings = torch.zeros(*shape)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings
@@ -357,8 +390,9 @@ class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
 
 class OnesReasoningsInitializer(AbstractReasoningsInitializer):
     """Reasonings are all initialized with ones."""
+
     def generate(self, distribution: Union[dict, list, tuple]):
-        shape = self.compute_shape(distribution)
+        shape = compute_distribution_shape(distribution)
         reasonings = torch.ones(*shape)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings
@@ -366,13 +400,14 @@ 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
         self.maximum = maximum
 
     def generate(self, distribution: Union[dict, list, tuple]):
-        shape = self.compute_shape(distribution)
+        shape = compute_distribution_shape(distribution)
         reasonings = torch.ones(*shape).uniform_(self.minimum, self.maximum)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings
@@ -380,8 +415,10 @@ 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, _ = self.compute_shape(distribution)
+        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)
@@ -397,6 +434,7 @@ 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
 
@@ -413,6 +451,7 @@ 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)
@@ -420,13 +459,23 @@ 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)
 
 
-class EyeTransformInitializer(AbstractLinearTransformInitializer):
+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))
@@ -436,6 +485,7 @@ 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,
@@ -456,11 +506,19 @@ 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)
 
 
+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
@@ -489,7 +547,9 @@ RRI = RandomReasoningsInitializer
 ZRI = ZerosReasoningsInitializer
 
 # Aliases - Transforms
-Eye = EyeTransformInitializer
+ELTI = Eye = EyeLinearTransformInitializer
 OLTI = OnesLinearTransformInitializer
+RLTI = RandomLinearTransformInitializer
 ZLTI = ZerosLinearTransformInitializer
 PCALTI = PCALinearTransformInitializer
+LLTI = LiteralLinearTransformInitializer

prototorch/core/losses.py

@@ -2,7 +2,7 @@
 
 import torch
 
-from ..nn.activations import get_activation
+from prototorch.nn.activations import get_activation
 
 
 # Helpers
@@ -106,20 +106,31 @@ def margin_loss(y_pred, y_true, margin=0.3):
 
 
 class GLVQLoss(torch.nn.Module):
-    def __init__(self, margin=0.0, squashing="identity", beta=10, **kwargs):
+
+    def __init__(self,
+                 margin=0.0,
+                 transfer_fn="identity",
+                 beta=10,
+                 add_dp=False,
+                 **kwargs):
         super().__init__(**kwargs)
         self.margin = margin
-        self.squashing = get_activation(squashing)
+        self.transfer_fn = get_activation(transfer_fn)
         self.beta = torch.tensor(beta)
+        self.add_dp = add_dp
 
-    def forward(self, outputs, targets):
+    def forward(self, outputs, targets, plabels):
-        distances, plabels = outputs
+        # mu = glvq_loss(outputs, targets, plabels)
-        mu = glvq_loss(distances, targets, prototype_labels=plabels)
+        dp, dm = _get_dp_dm(outputs, targets, plabels)
-        batch_loss = self.squashing(mu + self.margin, beta=self.beta)
+        mu = (dp - dm) / (dp + dm)
-        return torch.sum(batch_loss, dim=0)
+        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,
@@ -133,6 +144,7 @@ class MarginLoss(torch.nn.modules.loss._Loss):
 
 
 class NeuralGasEnergy(torch.nn.Module):
+
     def __init__(self, lm, **kwargs):
         super().__init__(**kwargs)
         self.lm = lm
@@ -153,6 +165,7 @@ 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):
+
+    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):
+
+    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):
+
+    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):
+
+    def forward(self, values, labels):  # pylint: disable=no-self-use
         return stratified_max_pooling(values, labels)

prototorch/core/similarities.py

@@ -21,7 +21,7 @@ def cosine_similarity(x, y):
     Expected dimension of x is 2.
     Expected dimension of y is 2.
     """
-    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
     norm_x = x.pow(2).sum(1).sqrt()
     norm_y = y.pow(2).sum(1).sqrt()
     norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T

prototorch/core/transforms.py

@@ -5,17 +5,18 @@ from torch.nn.parameter import Parameter
 
 from .initializers import (
     AbstractLinearTransformInitializer,
-    EyeTransformInitializer,
+    EyeLinearTransformInitializer,
 )
 
 
 class LinearTransform(torch.nn.Module):
+
     def __init__(
         self,
         in_dim: int,
         out_dim: int,
         initializer:
-        AbstractLinearTransformInitializer = EyeTransformInitializer()):
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
         super().__init__()
         self.set_weights(in_dim, out_dim, initializer)
 
@@ -31,12 +32,15 @@ class LinearTransform(torch.nn.Module):
         in_dim: int,
         out_dim: int,
         initializer:
-        AbstractLinearTransformInitializer = EyeTransformInitializer()):
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
         weights = initializer.generate(in_dim, out_dim)
         self._register_weights(weights)
 
     def forward(self, x):
-        return x @ self.weights.T
+        return x @ self._weights
+
+    def extra_repr(self):
+        return f"weights: (shape: {tuple(self._weights.shape)})"
 
 
 # Aliases

prototorch/datasets/__init__.py

@@ -1,6 +1,6 @@
 """ProtoTorch datasets"""
 
-from .abstract import NumpyDataset
+from .abstract import CSVDataset, NumpyDataset
 from .sklearn import (
     Blobs,
     Circles,
@@ -10,3 +10,4 @@ from .sklearn import (
 )
 from .spiral import Spiral
 from .tecator import Tecator
+from .xor import XOR

prototorch/datasets/abstract.py

@@ -10,6 +10,7 @@ https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py
 
 import os
 
+import numpy as np
 import torch
 
 
@@ -19,7 +20,7 @@ class Dataset(torch.utils.data.Dataset):
     _repr_indent = 2
 
     def __init__(self, root):
-        if isinstance(root, torch._six.string_classes):
+        if isinstance(root, str):
             root = os.path.expanduser(root)
         self.root = root
 
@@ -92,8 +93,23 @@ 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)
         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

@@ -5,11 +5,18 @@ URL:
 
 """
 
-import warnings
+from __future__ import annotations
-from typing import Sequence, Union
 
-from sklearn.datasets import (load_iris, make_blobs, make_circles,
+import warnings
-                              make_classification, make_moons)
+from typing import Sequence
+
+from sklearn.datasets import (
+    load_iris,
+    make_blobs,
+    make_circles,
+    make_classification,
+    make_moons,
+)
 
 from prototorch.datasets.abstract import NumpyDataset
 
@@ -35,9 +42,10 @@ class Iris(NumpyDataset):
 
     :param dims: select a subset of dimensions
     """
-    def __init__(self, dims: Sequence[int] = None):
+
+    def __init__(self, dims: Sequence[int] | None = None):
         x, y = load_iris(return_X_y=True)
-        if dims:
+        if dims is not None:
             x = x[:, dims]
         super().__init__(x, y)
 
@@ -49,15 +57,20 @@ class Blobs(NumpyDataset):
     https://scikit-learn.org/stable/datasets/sample_generators.html#sample-generators.
 
     """
-    def __init__(self,
+
-                 num_samples: int = 300,
+    def __init__(
-                 num_features: int = 2,
+        self,
-                 seed: Union[None, int] = 0):
+        num_samples: int = 300,
-        x, y = make_blobs(num_samples,
+        num_features: int = 2,
-                          num_features,
+        seed: None | int = 0,
-                          centers=None,
+    ):
-                          random_state=seed,
+        x, y = make_blobs(
-                          shuffle=False)
+            num_samples,
+            num_features,
+            centers=None,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -69,29 +82,34 @@ class Random(NumpyDataset):
 
     Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
     """
-    def __init__(self,
+
-                 num_samples: int = 300,
+    def __init__(
-                 num_features: int = 2,
+        self,
-                 num_classes: int = 2,
+        num_samples: int = 300,
-                 num_clusters: int = 2,
+        num_features: int = 2,
-                 num_informative: Union[None, int] = None,
+        num_classes: int = 2,
-                 separation: float = 1.0,
+        num_clusters: int = 2,
-                 seed: Union[None, int] = 0):
+        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,
+        x, y = make_classification(
-                                   num_features,
+            num_samples,
-                                   n_informative=num_informative,
+            num_features,
-                                   n_redundant=0,
+            n_informative=num_informative,
-                                   n_classes=num_classes,
+            n_redundant=0,
-                                   n_clusters_per_class=num_clusters,
+            n_classes=num_classes,
-                                   class_sep=separation,
+            n_clusters_per_class=num_clusters,
-                                   random_state=seed,
+            class_sep=separation,
-                                   shuffle=False)
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -104,16 +122,21 @@ class Circles(NumpyDataset):
     https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_circles.html
 
     """
-    def __init__(self,
+
-                 num_samples: int = 300,
+    def __init__(
-                 noise: float = 0.3,
+        self,
-                 factor: float = 0.8,
+        num_samples: int = 300,
-                 seed: Union[None, int] = 0):
+        noise: float = 0.3,
-        x, y = make_circles(num_samples,
+        factor: float = 0.8,
-                            noise=noise,
+        seed: None | int = 0,
-                            factor=factor,
+    ):
-                            random_state=seed,
+        x, y = make_circles(
-                            shuffle=False)
+            num_samples,
+            noise=noise,
+            factor=factor,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -126,12 +149,17 @@ class Moons(NumpyDataset):
     https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
 
     """
-    def __init__(self,
+
-                 num_samples: int = 300,
+    def __init__(
-                 noise: float = 0.3,
+        self,
-                 seed: Union[None, int] = 0):
+        num_samples: int = 300,
-        x, y = make_moons(num_samples,
+        noise: float = 0.3,
-                          noise=noise,
+        seed: None | int = 0,
-                          random_state=seed,
+    ):
-                          shuffle=False)
+        x, y = make_moons(
+            num_samples,
+            noise=noise,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)

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/tecator.py

@@ -36,6 +36,7 @@ Description:
     are determined by analytic chemistry.
 """
 
+import logging
 import os
 
 import numpy as np
@@ -81,13 +82,11 @@ class Tecator(ProtoDataset):
         if self._check_exists():
             return
 
-        if self.verbose:
+        logging.debug("Making directories...")
-            print("Making directories...")
         os.makedirs(self.raw_folder, exist_ok=True)
         os.makedirs(self.processed_folder, exist_ok=True)
 
-        if self.verbose:
+        logging.debug("Downloading...")
-            print("Downloading...")
         for fileid, md5 in self._resources:
             filename = "tecator.npz"
             download_file_from_google_drive(fileid,
@@ -95,8 +94,7 @@ class Tecator(ProtoDataset):
                                             filename=filename,
                                             md5=md5)
 
-        if self.verbose:
+        logging.debug("Processing...")
-            print("Processing...")
         with np.load(os.path.join(self.raw_folder, "tecator.npz"),
                      allow_pickle=False) as f:
             x_train, y_train = f["x_train"], f["y_train"]
@@ -117,5 +115,4 @@ class Tecator(ProtoDataset):
                   "wb") as f:
             torch.save(test_set, f)
 
-        if self.verbose:
+        logging.debug("Done!")
-            print("Done!")

prototorch/datasets/xor.py

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

prototorch/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/__init__.py

@@ -1,6 +1,11 @@
-"""ProtoFlow utils module"""
+"""ProtoTorch utils module"""
 
-from .colors import hex_to_rgb, rgb_to_hex
+from .colors import (
+    get_colors,
+    get_legend_handles,
+    hex_to_rgb,
+    rgb_to_hex,
+)
 from .utils import (
     mesh2d,
     parse_data_arg,

prototorch/utils/colors.py

@@ -1,4 +1,13 @@
-"""ProtoFlow color utilities"""
+"""ProtoTorch color utilities"""
+
+import matplotlib.lines as mlines
+import torch
+from matplotlib import cm
+from matplotlib.colors import (
+    Normalize,
+    to_hex,
+    to_rgb,
+)
 
 
 def hex_to_rgb(hex_values):
@@ -13,3 +22,39 @@ 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

@@ -1,14 +1,45 @@
-"""ProtoFlow utilities"""
+"""ProtoTorch utilities"""
 
 import warnings
-from collections.abc import Iterable
+from typing import (
-from typing import Union
+    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])
@@ -24,15 +55,16 @@ def mesh2d(x=None, border: float = 1.0, resolution: int = 100):
     return mesh, xx, yy
 
 
-def distribution_from_list(list_dist: list[int],
+def distribution_from_list(list_dist: List[int],
-                           clabels: Iterable[int] = None):
+                           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,
+def parse_distribution(
-                       clabels: Iterable[int] = None) -> dict[int, int]:
+        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

setup.cfg

@@ -1,8 +1,9 @@
 [pylint]
 disable =
-    too-many-arguments,
+	too-many-arguments,
-    too-few-public-methods,
+	too-few-public-methods,
-    fixme,
+	fixme,
+
 
 [pycodestyle]
 max-line-length = 79
@@ -12,4 +13,4 @@ multi_line_output = 3
 include_trailing_comma = True
 force_grid_wrap = 3
 use_parentheses = True
-line_length = 79
+line_length = 79

setup.py

@@ -15,21 +15,22 @@ from setuptools import find_packages, setup
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
 
-with open("README.md", "r") as fh:
+with open("README.md", encoding="utf-8") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "torch>=1.3.1",
+    "torch>=2.0.0",
-    "torchvision>=0.6.0",
+    "torchvision",
-    "numpy>=1.9.1",
+    "numpy",
-    "sklearn",
+    "scikit-learn",
+    "matplotlib",
 ]
 DATASETS = [
     "requests",
     "tqdm",
 ]
 DEV = [
-    "bumpversion",
+    "bump2version",
     "pre-commit",
 ]
 DOCS = [
@@ -40,15 +41,17 @@ DOCS = [
     "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
-    "matplotlib",
     "torchinfo",
 ]
-TESTS = ["codecov", "pytest"]
+TESTS = [
+    "flake8",
+    "pytest",
+]
 ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.6.0",
+    version="0.7.6",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -59,7 +62,7 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
-    python_requires=">=3.9",
+    python_requires=">=3.8",
     install_requires=INSTALL_REQUIRES,
     extras_require={
         "datasets": DATASETS,
@@ -70,18 +73,22 @@ setup(
         "all": ALL,
     },
     classifiers=[
-        "Development Status :: 2 - Pre-Alpha",
         "Environment :: Console",
+        "Natural Language :: English",
+        "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: MIT License",
-        "Natural Language :: English",
-        "Programming Language :: Python :: 3.9",
-        "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Software Development :: Libraries",
         "Topic :: Software Development :: Libraries :: Python Modules",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
     ],
     packages=find_packages(),
     zip_safe=False,

tests/test_core.py

@@ -245,33 +245,45 @@ def test_random_reasonings_init_channels_not_first():
 
 # Transform initializers
 def test_eye_transform_init_square():
-    t = pt.initializers.EyeTransformInitializer()
+    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.EyeTransformInitializer()
+    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.EyeTransformInitializer()
+    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():
+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,
@@ -392,6 +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)
@@ -406,6 +419,7 @@ class TestActivations(unittest.TestCase):
             self.assertTrue(iscallable)
 
     def test_callable_deserialization(self):
+
         def dummy(x, **kwargs):
             return x
 
@@ -450,6 +464,7 @@ class TestActivations(unittest.TestCase):
 
 # Competitions
 class TestCompetitions(unittest.TestCase):
+
     def setUp(self):
         pass
 
@@ -503,6 +518,7 @@ class TestCompetitions(unittest.TestCase):
 
 # Pooling
 class TestPooling(unittest.TestCase):
+
     def setUp(self):
         pass
 
@@ -603,6 +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

@@ -1,7 +1,6 @@
 """ProtoTorch datasets test suite"""
 
 import os
-import shutil
 import unittest
 
 import numpy as np
@@ -12,6 +11,7 @@ from prototorch.datasets.abstract import Dataset, ProtoDataset
 
 
 class TestAbstract(unittest.TestCase):
+
     def setUp(self):
         self.ds = Dataset("./artifacts")
 
@@ -28,6 +28,7 @@ class TestAbstract(unittest.TestCase):
 
 
 class TestProtoDataset(unittest.TestCase):
+
     def test_download(self):
         with self.assertRaises(NotImplementedError):
             _ = ProtoDataset("./artifacts", download=True)
@@ -38,6 +39,7 @@ class TestProtoDataset(unittest.TestCase):
 
 
 class TestNumpyDataset(unittest.TestCase):
+
     def test_list_init(self):
         ds = pt.datasets.NumpyDataset([1], [1])
         self.assertEqual(len(ds), 1)
@@ -49,13 +51,33 @@ class TestNumpyDataset(unittest.TestCase):
         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()
 
@@ -71,24 +93,28 @@ 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)