build: bump version 0.7.5 → 0.7.6

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

.bumpversion.cfg

@@ -1,10 +1,10 @@
 [bumpversion]
-current_version = 0.5.0
+current_version = 0.7.6
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
-serialize = 
+serialize = {major}.{minor}.{patch}
-	{major}.{minor}.{patch}
+message = build: bump version {current_version} → {new_version}
 
 [bumpversion:file:setup.py]
 

.codacy.yml

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

.codecov.yml

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

.github/ISSUE_TEMPLATE/bug_report.md

@@ -10,21 +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 3.8
+      - name: Set up Python 3.11
-      uses: actions/setup-python@v1
+        uses: actions/setup-python@v4
-      with:
+        with:
-        python-version: 3.8
+          python-version: "3.11"
-    - name: Install dependencies
+      - name: Install dependencies
-      run: |
+        run: |
-        python -m pip install --upgrade pip
+          python -m pip install --upgrade pip
-        pip install .[all]
+          pip install .[all]
-    - name: Lint with flake8
+      - uses: pre-commit/action@v3.0.0
-      run: |
+  compatibility:
-        pip install flake8
+    needs: style
-        # stop the build if there are Python syntax errors or undefined names
+    strategy:
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+      fail-fast: false
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+      matrix:
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
-    - name: Test with pytest
+        os: [ubuntu-latest, windows-latest]
-      run: |
+        exclude:
-        pip install pytest
+          - os: windows-latest
-        pytest
+            python-version: "3.8"
+          - os: windows-latest
+            python-version: "3.9"
+          - os: windows-latest
+            python-version: "3.10"
+
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[all]
+      - name: Test with pytest
+        run: |
+          pytest
+  publish_pypi:
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+    needs: compatibility
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install .[all]
+          pip install wheel
+      - name: Build package
+        run: python setup.py sdist bdist_wheel
+      - name: Publish a Python distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -155,4 +155,4 @@ dmypy.json
 reports
 artifacts
 examples/_*.py
-examples/_*.ipynb
+examples/_*.ipynb

.pre-commit-config.yaml

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

.readthedocs.yml

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

.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

@@ -2,13 +2,9 @@
 
 ![ProtoTorch Logo](https://prototorch.readthedocs.io/en/latest/_static/horizontal-lockup.png)
 
-[![Build Status](https://travis-ci.org/si-cim/prototorch.svg?branch=master)](https://travis-ci.org/si-cim/prototorch)
 ![tests](https://github.com/si-cim/prototorch/workflows/tests/badge.svg)
 [![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/si-cim/prototorch?color=yellow&label=version)](https://github.com/si-cim/prototorch/releases)
 [![PyPI](https://img.shields.io/pypi/v/prototorch)](https://pypi.org/project/prototorch/)
-[![codecov](https://codecov.io/gh/si-cim/prototorch/branch/master/graph/badge.svg)](https://codecov.io/gh/si-cim/prototorch)
-[![Codacy Badge](https://api.codacy.com/project/badge/Grade/76273904bf9343f0a8b29cd8aca242e7)](https://www.codacy.com/gh/si-cim/prototorch?utm_source=github.com&utm_medium=referral&utm_content=si-cim/prototorch&utm_campaign=Badge_Grade)
-![PyPI - Downloads](https://img.shields.io/pypi/dm/prototorch?color=blue)
 [![GitHub license](https://img.shields.io/github/license/si-cim/prototorch)](https://github.com/si-cim/prototorch/blob/master/LICENSE)
 
 *Tensorflow users, see:* [ProtoFlow](https://github.com/si-cim/protoflow)
@@ -48,6 +44,23 @@ pip install -e .[all]
 The documentation is available at <https://www.prototorch.ml/en/latest/>. Should
 that link not work try <https://prototorch.readthedocs.io/en/latest/>.
 
+## Contribution
+
+This repository contains definition for [git hooks](https://githooks.com).
+[Pre-commit](https://pre-commit.com) is automatically installed as development
+dependency with prototorch or you can install it manually with `pip install
+pre-commit`.
+
+Please install the hooks by running:
+```bash
+pre-commit install
+pre-commit install --hook-type commit-msg
+```
+before creating the first commit.
+
+The commit will fail if the commit message does not follow the specification
+provided [here](https://www.conventionalcommits.org/en/v1.0.0/#specification).
+
 ## Bibtex
 
 If you would like to cite the package, please use this:

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.5.0"
+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

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

examples/glvq_iris.py

@@ -1,120 +0,0 @@
-"""ProtoTorch GLVQ example using 2D Iris data."""
-
-import numpy as np
-import torch
-from matplotlib import pyplot as plt
-from prototorch.components import LabeledComponents, StratifiedMeanInitializer
-from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import euclidean_distance
-from prototorch.modules.losses import GLVQLoss
-from sklearn.datasets import load_iris
-from sklearn.preprocessing import StandardScaler
-from torchinfo import summary
-
-# Prepare and preprocess the data
-scaler = StandardScaler()
-x_train, y_train = load_iris(return_X_y=True)
-x_train = x_train[:, [0, 2]]
-scaler.fit(x_train)
-x_train = scaler.transform(x_train)
-
-
-# Define the GLVQ model
-class Model(torch.nn.Module):
-    def __init__(self):
-        """GLVQ model for training on 2D Iris data."""
-        super().__init__()
-        prototype_initializer = StratifiedMeanInitializer([x_train, y_train])
-        prototype_distribution = {"num_classes": 3, "prototypes_per_class": 3}
-        self.proto_layer = LabeledComponents(
-            prototype_distribution,
-            prototype_initializer,
-        )
-
-    def forward(self, x):
-        prototypes, prototype_labels = self.proto_layer()
-        distances = euclidean_distance(x, prototypes)
-        return distances, prototype_labels
-
-
-# Build the GLVQ model
-model = Model()
-
-# Print summary using torchinfo (might be buggy/incorrect)
-print(summary(model))
-
-# Optimize using SGD optimizer from `torch.optim`
-optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
-criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
-
-x_in = torch.Tensor(x_train)
-y_in = torch.Tensor(y_train)
-
-# Training loop
-TITLE = "Prototype Visualization"
-fig = plt.figure(TITLE)
-for epoch in range(70):
-    # Compute loss
-    distances, prototype_labels = model(x_in)
-    loss = criterion([distances, prototype_labels], y_in)
-
-    # Compute Accuracy
-    with torch.no_grad():
-        predictions = wtac(distances, prototype_labels)
-        correct = predictions.eq(y_in.view_as(predictions)).sum().item()
-    acc = 100.0 * correct / len(x_train)
-
-    print(
-        f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%"
-    )
-
-    # Optimizer step
-    optimizer.zero_grad()
-    loss.backward()
-    optimizer.step()
-
-    # Get the prototypes form the model
-    prototypes = model.proto_layer.components.numpy()
-    if np.isnan(np.sum(prototypes)):
-        print("Stopping training because of `nan` in prototypes.")
-        break
-
-    # Visualize the data and the prototypes
-    ax = fig.gca()
-    ax.cla()
-    ax.set_title(TITLE)
-    ax.set_xlabel("Data dimension 1")
-    ax.set_ylabel("Data dimension 2")
-    cmap = "viridis"
-    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-    ax.scatter(
-        prototypes[:, 0],
-        prototypes[:, 1],
-        c=prototype_labels,
-        cmap=cmap,
-        edgecolor="k",
-        marker="D",
-        s=50,
-    )
-
-    # Paint decision regions
-    x = np.vstack((x_train, prototypes))
-    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
-    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
-    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
-                         np.arange(y_min, y_max, 1 / 50))
-    mesh_input = np.c_[xx.ravel(), yy.ravel()]
-
-    torch_input = torch.Tensor(mesh_input)
-    d = model(torch_input)[0]
-    w_indices = torch.argmin(d, dim=1)
-    y_pred = torch.index_select(prototype_labels, 0, w_indices)
-    y_pred = y_pred.reshape(xx.shape)
-
-    # Plot voronoi regions
-    ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
-
-    ax.set_xlim(left=x_min + 0, right=x_max - 0)
-    ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
-
-    plt.pause(0.1)

examples/gmlvq.py

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

examples/gmlvq_tecator.py

@@ -1,103 +0,0 @@
-"""ProtoTorch "siamese" GMLVQ example using Tecator."""
-
-import matplotlib.pyplot as plt
-import torch
-from prototorch.components import LabeledComponents, StratifiedMeanInitializer
-from prototorch.datasets.tecator import Tecator
-from prototorch.functions.distances import sed
-from prototorch.modules.losses import GLVQLoss
-from prototorch.utils.colors import get_legend_handles
-from torch.utils.data import DataLoader
-
-# Prepare the dataset and dataloader
-train_data = Tecator(root="./artifacts", train=True)
-train_loader = DataLoader(train_data, batch_size=128, shuffle=True)
-
-
-class Model(torch.nn.Module):
-    def __init__(self, **kwargs):
-        """GMLVQ model as a siamese network."""
-        super().__init__()
-        prototype_initializer = StratifiedMeanInitializer(train_loader)
-        prototype_distribution = {"num_classes": 2, "prototypes_per_class": 2}
-
-        self.proto_layer = LabeledComponents(
-            prototype_distribution,
-            prototype_initializer,
-        )
-
-        self.omega = torch.nn.Linear(in_features=100,
-                                     out_features=100,
-                                     bias=False)
-        torch.nn.init.eye_(self.omega.weight)
-
-    def forward(self, x):
-        protos = self.proto_layer.components
-        plabels = self.proto_layer.component_labels
-
-        # Process `x` and `protos` through `omega`
-        x_map = self.omega(x)
-        protos_map = self.omega(protos)
-
-        # Compute distances and output
-        dis = sed(x_map, protos_map)
-        return dis, plabels
-
-
-# Build the GLVQ model
-model = Model()
-
-# Print a summary of the model
-print(model)
-
-# Optimize using Adam optimizer from `torch.optim`
-optimizer = torch.optim.Adam(model.parameters(), lr=0.001_0)
-scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=75, gamma=0.1)
-criterion = GLVQLoss(squashing="identity", beta=10)
-
-# Training loop
-for epoch in range(150):
-    epoch_loss = 0.0  # zero-out epoch loss
-    optimizer.zero_grad()  # zero-out gradients
-    for xb, yb in train_loader:
-        # Compute loss
-        distances, plabels = model(xb)
-        loss = criterion([distances, plabels], yb)
-        epoch_loss += loss.item()
-        # Backprop
-        loss.backward()
-    # Take a gradient descent step
-    optimizer.step()
-    scheduler.step()
-
-    lr = optimizer.param_groups[0]["lr"]
-    print(f"Epoch: {epoch + 1:03d} Loss: {epoch_loss:06.02f} lr: {lr:07.06f}")
-
-# Get the omega matrix form the model
-omega = model.omega.weight.data.numpy().T
-
-# Visualize the lambda matrix
-title = "Lambda Matrix Visualization"
-fig = plt.figure(title)
-ax = fig.gca()
-ax.set_title(title)
-im = ax.imshow(omega.dot(omega.T), cmap="viridis")
-plt.show()
-
-# Get the prototypes form the model
-protos = model.proto_layer.components.numpy()
-plabels = model.proto_layer.component_labels.numpy()
-
-# Visualize the prototypes
-title = "Tecator Prototypes"
-fig = plt.figure(title)
-ax = fig.gca()
-ax.set_title(title)
-ax.set_xlabel("Spectral frequencies")
-ax.set_ylabel("Absorption")
-clabels = ["Class 0 - Low fat", "Class 1 - High fat"]
-handles, colors = get_legend_handles(clabels, marker="line", zero_indexed=True)
-for x, y in zip(protos, plabels):
-    ax.plot(x, c=colors[int(y)])
-ax.legend(handles, clabels)
-plt.show()

examples/gtlvq_mnist.py

@@ -1,183 +0,0 @@
-"""
-ProtoTorch GTLVQ example using MNIST data.
-The GTLVQ is placed as an classification model on
-top of a CNN, considered as featurer extractor.
-Initialization of subpsace and prototypes in
-Siamnese fashion
-For more info about GTLVQ see:
-DOI:10.1109/IJCNN.2016.7727534
-"""
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torchvision
-from prototorch.functions.helper import calculate_prototype_accuracy
-from prototorch.modules.losses import GLVQLoss
-from prototorch.modules.models import GTLVQ
-from torchvision import transforms
-
-# Parameters and options
-num_epochs = 50
-batch_size_train = 64
-batch_size_test = 1000
-learning_rate = 0.1
-momentum = 0.5
-log_interval = 10
-cuda = "cuda:0"
-random_seed = 1
-device = torch.device(cuda if torch.cuda.is_available() else "cpu")
-
-# Configures reproducability
-torch.manual_seed(random_seed)
-np.random.seed(random_seed)
-
-# Prepare and preprocess the data
-train_loader = torch.utils.data.DataLoader(
-    torchvision.datasets.MNIST(
-        "./files/",
-        train=True,
-        download=True,
-        transform=torchvision.transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize((0.1307, ), (0.3081, ))
-        ]),
-    ),
-    batch_size=batch_size_train,
-    shuffle=True,
-)
-
-test_loader = torch.utils.data.DataLoader(
-    torchvision.datasets.MNIST(
-        "./files/",
-        train=False,
-        download=True,
-        transform=torchvision.transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Normalize((0.1307, ), (0.3081, ))
-        ]),
-    ),
-    batch_size=batch_size_test,
-    shuffle=True,
-)
-
-
-# Define the GLVQ model plus appropriate feature extractor
-class CNNGTLVQ(torch.nn.Module):
-    def __init__(
-        self,
-        num_classes,
-        subspace_data,
-        prototype_data,
-        tangent_projection_type="local",
-        prototypes_per_class=2,
-        bottleneck_dim=128,
-    ):
-        super(CNNGTLVQ, self).__init__()
-
-        # Feature Extractor - Simple CNN
-        self.fe = nn.Sequential(
-            nn.Conv2d(1, 32, 3, 1),
-            nn.ReLU(),
-            nn.Conv2d(32, 64, 3, 1),
-            nn.ReLU(),
-            nn.MaxPool2d(2),
-            nn.Dropout(0.25),
-            nn.Flatten(),
-            nn.Linear(9216, bottleneck_dim),
-            nn.Dropout(0.5),
-            nn.LeakyReLU(),
-            nn.LayerNorm(bottleneck_dim),
-        )
-
-        # Forward pass of subspace and prototype initialization data through feature extractor
-        subspace_data = self.fe(subspace_data)
-        prototype_data[0] = self.fe(prototype_data[0])
-
-        # Initialization of GTLVQ
-        self.gtlvq = GTLVQ(
-            num_classes,
-            subspace_data,
-            prototype_data,
-            tangent_projection_type=tangent_projection_type,
-            feature_dim=bottleneck_dim,
-            prototypes_per_class=prototypes_per_class,
-        )
-
-    def forward(self, x):
-        # Feature Extraction
-        x = self.fe(x)
-
-        # GTLVQ Forward pass
-        dis = self.gtlvq(x)
-        return dis
-
-
-# Get init data
-subspace_data = torch.cat(
-    [next(iter(train_loader))[0],
-     next(iter(test_loader))[0]])
-prototype_data = next(iter(train_loader))
-
-# Build the CNN GTLVQ  model
-model = CNNGTLVQ(
-    10,
-    subspace_data,
-    prototype_data,
-    tangent_projection_type="local",
-    bottleneck_dim=128,
-).to(device)
-
-# Optimize using SGD optimizer from `torch.optim`
-optimizer = torch.optim.Adam(
-    [{
-        "params": model.fe.parameters()
-    }, {
-        "params": model.gtlvq.parameters()
-    }],
-    lr=learning_rate,
-)
-criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
-
-# Training loop
-for epoch in range(num_epochs):
-    for batch_idx, (x_train, y_train) in enumerate(train_loader):
-        model.train()
-        x_train, y_train = x_train.to(device), y_train.to(device)
-        optimizer.zero_grad()
-
-        distances = model(x_train)
-        plabels = model.gtlvq.cls.component_labels.to(device)
-
-        # Compute loss.
-        loss = criterion([distances, plabels], y_train)
-        loss.backward()
-        optimizer.step()
-
-        # GTLVQ uses projected SGD, which means to orthogonalize the subspaces after every gradient update.
-        model.gtlvq.orthogonalize_subspace()
-
-        if batch_idx % log_interval == 0:
-            acc = calculate_prototype_accuracy(distances, y_train, plabels)
-            print(
-                f"Epoch: {epoch + 1:02d}/{num_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
-              Train Acc: {acc.item():02.02f}")
-
-    # Test
-    with torch.no_grad():
-        model.eval()
-        correct = 0
-        total = 0
-        for x_test, y_test in test_loader:
-            x_test, y_test = x_test.to(device), y_test.to(device)
-            test_distances = model(torch.tensor(x_test))
-            test_plabels = model.gtlvq.cls.prototype_labels.to(device)
-            i = torch.argmin(test_distances, 1)
-            correct += torch.sum(y_test == test_plabels[i])
-            total += y_test.size(0)
-        print("Accuracy of the network on the test images: %d %%" %
-              (torch.true_divide(correct, total) * 100))
-
-# Save the model
-PATH = "./glvq_mnist_model.pth"
-torch.save(model.state_dict(), PATH)

examples/lgmlvq_iris.py

@@ -1,108 +0,0 @@
-"""ProtoTorch LGMLVQ example using 2D Iris data."""
-
-import numpy as np
-import torch
-from matplotlib import pyplot as plt
-from prototorch.components import LabeledComponents, StratifiedMeanInitializer
-from prototorch.functions.competitions import stratified_min
-from prototorch.functions.distances import lomega_distance
-from prototorch.modules.losses import GLVQLoss
-from sklearn.datasets import load_iris
-from sklearn.metrics import accuracy_score
-
-# Prepare training data
-x_train, y_train = load_iris(True)
-x_train = x_train[:, [0, 2]]
-
-
-# Define the model
-class Model(torch.nn.Module):
-    def __init__(self):
-        """Local-GMLVQ model."""
-        super().__init__()
-
-        prototype_initializer = StratifiedMeanInitializer([x_train, y_train])
-        prototype_distribution = [1, 2, 2]
-        self.proto_layer = LabeledComponents(
-            prototype_distribution,
-            prototype_initializer,
-        )
-
-        omegas = torch.eye(2, 2).repeat(5, 1, 1)
-        self.omegas = torch.nn.Parameter(omegas)
-
-    def forward(self, x):
-        protos, plabels = self.proto_layer()
-        omegas = self.omegas
-        dis = lomega_distance(x, protos, omegas)
-        return dis, plabels
-
-
-# Build the model
-model = Model()
-
-# Optimize using Adam optimizer from `torch.optim`
-optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
-criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
-
-x_in = torch.Tensor(x_train)
-y_in = torch.Tensor(y_train)
-
-# Training loop
-title = "Prototype Visualization"
-fig = plt.figure(title)
-for epoch in range(100):
-    # Compute loss
-    dis, plabels = model(x_in)
-    loss = criterion([dis, plabels], y_in)
-    y_pred = np.argmin(stratified_min(dis, plabels).detach().numpy(), axis=1)
-    acc = accuracy_score(y_train, y_pred)
-    log_string = f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} "
-    log_string += f"Acc: {acc * 100:05.02f}%"
-    print(log_string)
-
-    # Take a gradient descent step
-    optimizer.zero_grad()
-    loss.backward()
-    optimizer.step()
-
-    # Get the prototypes form the model
-    protos = model.proto_layer.components.numpy()
-
-    # Visualize the data and the prototypes
-    ax = fig.gca()
-    ax.cla()
-    ax.set_title(title)
-    ax.set_xlabel("Data dimension 1")
-    ax.set_ylabel("Data dimension 2")
-    cmap = "viridis"
-    ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
-    ax.scatter(
-        protos[:, 0],
-        protos[:, 1],
-        c=plabels,
-        cmap=cmap,
-        edgecolor="k",
-        marker="D",
-        s=50,
-    )
-
-    # Paint decision regions
-    x = np.vstack((x_train, protos))
-    x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
-    y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
-    xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
-                         np.arange(y_min, y_max, 1 / 50))
-    mesh_input = np.c_[xx.ravel(), yy.ravel()]
-
-    d, plabels = model(torch.Tensor(mesh_input))
-    y_pred = np.argmin(stratified_min(d, plabels).detach().numpy(), axis=1)
-    y_pred = y_pred.reshape(xx.shape)
-
-    # Plot voronoi regions
-    ax.contourf(xx, yy, y_pred, cmap=cmap, alpha=0.35)
-
-    ax.set_xlim(left=x_min + 0, right=x_max - 0)
-    ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
-
-    plt.pause(0.1)

prototorch/__init__.py

@@ -4,24 +4,20 @@ import pkgutil
 
 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__ = "0.5.0"
+__version__ = "0.7.6"
 
 __all_core__ = [
     "competitions",

prototorch/core/competitions.py

@@ -3,8 +3,7 @@
 import torch
 
 
-def wtac(distances: torch.Tensor,
+def wtac(distances: torch.Tensor, labels: torch.LongTensor):
-         labels: torch.LongTensor) -> (torch.LongTensor):
     """Winner-Takes-All-Competition.
 
     Returns the labels corresponding to the winners.
@@ -15,9 +14,7 @@ def wtac(distances: torch.Tensor,
     return winning_labels
 
 
-def knnc(distances: torch.Tensor,
+def knnc(distances: torch.Tensor, labels: torch.LongTensor, k: int = 1):
-         labels: torch.LongTensor,
-         k: int = 1) -> (torch.LongTensor):
     """K-Nearest-Neighbors-Competition.
 
     Returns the labels corresponding to the winners.
@@ -41,7 +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
 
 
@@ -51,7 +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)
 
 
@@ -61,7 +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)
 
 
@@ -71,6 +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
@@ -88,5 +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."""
@@ -85,9 +87,10 @@ class AbstractComponents(torch.nn.Module):
 
 class Components(AbstractComponents):
     """A set of adaptable Tensors."""
+
     def __init__(self, num_components: int,
-                 initializer: AbstractComponentsInitializer, **kwargs):
+                 initializer: AbstractComponentsInitializer):
-        super().__init__(**kwargs)
+        super().__init__()
         self.add_components(num_components, initializer)
 
     def add_components(self, num_components: int,
@@ -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()
@@ -152,11 +156,11 @@ class AbstractLabels(torch.nn.Module):
 
 class Labels(AbstractLabels):
     """A set of standalone labels."""
+
     def __init__(self,
                  distribution: Union[dict, list, tuple],
-                 initializer: AbstractLabelsInitializer = LabelsInitializer(),
+                 initializer: AbstractLabelsInitializer = LabelsInitializer()):
-                 **kwargs):
+        super().__init__()
-        super().__init__(**kwargs)
         self.add_labels(distribution, initializer)
 
     def add_labels(
@@ -183,14 +187,13 @@ class Labels(AbstractLabels):
 
 class LabeledComponents(AbstractComponents):
     """A set of adaptable components and corresponding unadaptable labels."""
+
     def __init__(
-            self,
+        self,
-            distribution: Union[dict, list, tuple],
+        distribution: Union[dict, list, tuple],
-            components_initializer: AbstractComponentsInitializer,
+        components_initializer: AbstractComponentsInitializer,
-            labels_initializer: AbstractLabelsInitializer = LabelsInitializer(
+        labels_initializer: AbstractLabelsInitializer = LabelsInitializer()):
-            ),
+        super().__init__()
-            **kwargs):
-        super().__init__(**kwargs)
         self.add_components(distribution, components_initializer,
                             labels_initializer)
 
@@ -252,12 +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],
+    def __init__(
-                 initializer:
+        self,
-                 AbstractReasoningsInitializer = RandomReasoningsInitializer(),
+        distribution: Union[dict, list, tuple],
-                 **kwargs):
+        initializer:
-        super().__init__(**kwargs)
+        AbstractReasoningsInitializer = RandomReasoningsInitializer(),
+    ):
+        super().__init__()
+        self.add_reasonings(distribution, initializer)
 
     @property
     def num_classes(self):
@@ -295,7 +301,7 @@ class Reasonings(torch.nn.Module):
 
 
 class ReasoningComponents(AbstractComponents):
-    """A set of components and a corresponding adapatable reasoning matrices.
+    r"""A set of components and a corresponding adapatable reasoning matrices.
 
     Every component has its own reasoning matrix.
 
@@ -309,14 +315,14 @@ class ReasoningComponents(AbstractComponents):
     three element probability distribution.
 
     """
+
     def __init__(
-            self,
+        self,
-            distribution: Union[dict, list, tuple],
+        distribution: Union[dict, list, tuple],
-            components_initializer: AbstractComponentsInitializer,
+        components_initializer: AbstractComponentsInitializer,
-            reasonings_initializer:
+        reasonings_initializer:
-        AbstractReasoningsInitializer = PurePositiveReasoningsInitializer(),
+        AbstractReasoningsInitializer = PurePositiveReasoningsInitializer()):
-            **kwargs):
+        super().__init__()
-        super().__init__(**kwargs)
         self.add_components(distribution, components_initializer,
                             reasonings_initializer)
 

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

@@ -3,11 +3,15 @@
 import warnings
 from abc import ABC, abstractmethod
 from collections.abc import Iterable
-from typing import Union
+from typing import (
+    Callable,
+    Type,
+    Union,
+)
 
 import torch
 
-from ..utils import parse_data_arg, parse_distribution
+from prototorch.utils import parse_data_arg, parse_distribution
 
 
 # Components
@@ -22,11 +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)
@@ -36,6 +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)
@@ -49,6 +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
@@ -56,6 +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
@@ -63,6 +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
@@ -75,6 +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
@@ -89,6 +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
@@ -109,10 +126,11 @@ class AbstractDataAwareCompInitializer(AbstractComponentsInitializer):
     `data` has to be a torch tensor.
 
     """
+
     def __init__(self,
-                 data: torch.TensorType,
+                 data: torch.Tensor,
                  noise: float = 0.0,
-                 transform: callable = torch.nn.Identity()):
+                 transform: Callable = torch.nn.Identity()):
         self.data = data
         self.noise = noise
         self.transform = transform
@@ -133,6 +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)
@@ -141,6 +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]
@@ -150,15 +170,16 @@ class SelectionCompInitializer(AbstractDataAwareCompInitializer):
 
 class MeanCompInitializer(AbstractDataAwareCompInitializer):
     """Generate components by computing the mean of the provided data."""
+
     def generate(self, num_components: int):
-        mean = torch.mean(self.data, dim=0)
+        mean = self.data.mean(dim=0)
         repeat_dim = [num_components] + [1] * len(mean.shape)
         samples = mean.repeat(repeat_dim)
         components = self.generate_end_hook(samples)
         return components
 
 
-class AbstractClassAwareCompInitializer(AbstractDataAwareCompInitializer):
+class AbstractClassAwareCompInitializer(AbstractComponentsInitializer):
     """Abstract class for all class-aware components initializers.
 
     Components generated by class-aware components initializers inherit the shape
@@ -168,16 +189,22 @@ class AbstractClassAwareCompInitializer(AbstractDataAwareCompInitializer):
     target tensors.
 
     """
+
     def __init__(self,
                  data,
                  noise: float = 0.0,
-                 transform: callable = torch.nn.Identity()):
+                 transform: Callable = torch.nn.Identity()):
         self.data, self.targets = parse_data_arg(data)
         self.noise = noise
         self.transform = transform
         self.clabels = torch.unique(self.targets).int().tolist()
         self.num_classes = len(self.clabels)
 
+    def generate_end_hook(self, samples):
+        drift = torch.rand_like(samples) * self.noise
+        components = self.transform(samples + drift)
+        return components
+
     @abstractmethod
     def generate(self, distribution: Union[dict, list, tuple]):
         ...
@@ -190,6 +217,7 @@ class AbstractClassAwareCompInitializer(AbstractDataAwareCompInitializer):
 
 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)
@@ -198,9 +226,10 @@ class ClassAwareCompInitializer(AbstractClassAwareCompInitializer):
 
 class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
     """Abstract class for all stratified components initializers."""
+
     @property
     @abstractmethod
-    def subinit_type(self) -> AbstractDataAwareCompInitializer:
+    def subinit_type(self) -> Type[AbstractDataAwareCompInitializer]:
         ...
 
     def generate(self, distribution: Union[dict, list, tuple]):
@@ -208,6 +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,
@@ -220,6 +251,7 @@ class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
 
 class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
     """Generate components using stratified sampling from the provided data."""
+
     @property
     def subinit_type(self):
         return SelectionCompInitializer
@@ -227,6 +259,7 @@ class StratifiedSelectionCompInitializer(AbstractStratifiedCompInitializer):
 
 class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
     """Generate components at stratified means of the provided data."""
+
     @property
     def subinit_type(self):
         return MeanCompInitializer
@@ -235,6 +268,7 @@ class StratifiedMeanCompInitializer(AbstractStratifiedCompInitializer):
 # Labels
 class AbstractLabelsInitializer(ABC):
     """Abstract class for all labels initializers."""
+
     @abstractmethod
     def generate(self, distribution: Union[dict, list, tuple]):
         ...
@@ -246,6 +280,7 @@ class LiteralLabelsInitializer(AbstractLabelsInitializer):
     Use this to 'generate' pre-initialized labels elsewhere.
 
     """
+
     def __init__(self, labels):
         self.labels = labels
 
@@ -264,6 +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)
 
@@ -274,17 +310,19 @@ class DataAwareLabelsInitializer(AbstractLabelsInitializer):
 
 class LabelsInitializer(AbstractLabelsInitializer):
     """Generate labels from `distribution`."""
+
     def generate(self, distribution: Union[dict, list, tuple]):
         distribution = parse_distribution(distribution)
-        labels = []
+        labels_list = []
         for k, v in distribution.items():
-            labels.extend([k] * v)
+            labels_list.extend([k] * v)
-        labels = torch.LongTensor(labels)
+        labels = torch.LongTensor(labels_list)
         return labels
 
 
 class OneHotLabelsInitializer(LabelsInitializer):
     """Generate one-hot-encoded labels from `distribution`."""
+
     def generate(self, distribution: Union[dict, list, tuple]):
         distribution = parse_distribution(distribution)
         num_classes = len(distribution.keys())
@@ -294,17 +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)
@@ -322,6 +362,7 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
     Use this to 'generate' pre-initialized reasonings elsewhere.
 
     """
+
     def __init__(self, reasonings, **kwargs):
         super().__init__(**kwargs)
         self.reasonings = reasonings
@@ -339,8 +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
@@ -348,8 +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
@@ -357,13 +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
@@ -371,8 +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)
@@ -388,6 +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
 
@@ -404,6 +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)
@@ -411,13 +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))
@@ -425,6 +483,42 @@ class EyeTransformInitializer(AbstractLinearTransformInitializer):
         return self.generate_end_hook(weights)
 
 
+class AbstractDataAwareLTInitializer(AbstractLinearTransformInitializer):
+    """Abstract class for all data-aware linear transform initializers."""
+
+    def __init__(self,
+                 data: torch.Tensor,
+                 noise: float = 0.0,
+                 transform: Callable = torch.nn.Identity(),
+                 out_dim_first: bool = False):
+        super().__init__(out_dim_first)
+        self.data = data
+        self.noise = noise
+        self.transform = transform
+
+    def generate_end_hook(self, weights: torch.Tensor):
+        drift = torch.rand_like(weights) * self.noise
+        weights = self.transform(weights + drift)
+        if self.out_dim_first:
+            weights = weights.permute(1, 0)
+        return weights
+
+
+class PCALinearTransformInitializer(AbstractDataAwareLTInitializer):
+    """Initialize a matrix with Eigenvectors from the data."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        _, _, weights = torch.pca_lowrank(self.data, q=out_dim)
+        return self.generate_end_hook(weights)
+
+
+class LiteralLinearTransformInitializer(AbstractDataAwareLTInitializer):
+    """'Generate' the provided weights."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        return self.generate_end_hook(self.data)
+
+
 # Aliases - Components
 CACI = ClassAwareCompInitializer
 DACI = DataAwareCompInitializer
@@ -453,6 +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

@@ -5,19 +5,19 @@ from torch.nn.parameter import Parameter
 
 from .initializers import (
     AbstractLinearTransformInitializer,
-    EyeTransformInitializer,
+    EyeLinearTransformInitializer,
 )
 
 
 class LinearTransform(torch.nn.Module):
+
     def __init__(
-            self,
+        self,
-            in_dim: int,
+        in_dim: int,
-            out_dim: int,
+        out_dim: int,
-            initializer:
+        initializer:
-        AbstractLinearTransformInitializer = EyeTransformInitializer(),
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
-            **kwargs):
+        super().__init__()
-        super().__init__(**kwargs)
         self.set_weights(in_dim, out_dim, initializer)
 
     @property
@@ -32,12 +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

@@ -5,14 +5,21 @@ URL:
 
 """
 
+from __future__ import annotations
+
 import warnings
-from typing import Sequence, Union
+from typing import Sequence
+
+from sklearn.datasets import (
+    load_iris,
+    make_blobs,
+    make_circles,
+    make_classification,
+    make_moons,
+)
 
 from prototorch.datasets.abstract import NumpyDataset
 
-from sklearn.datasets import (load_iris, make_blobs, make_circles,
-                              make_classification, make_moons)
-
 
 class Iris(NumpyDataset):
     """Iris Dataset by Ronald Fisher introduced in 1936.
@@ -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

@@ -36,13 +36,15 @@ Description:
     are determined by analytic chemistry.
 """
 
+import logging
 import os
 
 import numpy as np
 import torch
-from prototorch.datasets.abstract import ProtoDataset
 from torchvision.datasets.utils import download_file_from_google_drive
 
+from prototorch.datasets.abstract import ProtoDataset
+
 
 class Tecator(ProtoDataset):
     """
@@ -80,13 +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,
@@ -94,8 +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"]
@@ -116,5 +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

@@ -1,13 +1,45 @@
-"""ProtoFlow utilities"""
+"""ProtoTorch utilities"""
 
 import warnings
-from typing import Union
+from typing import (
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Union,
+)
 
 import numpy as np
 import torch
 from torch.utils.data import DataLoader, Dataset
 
 
+def generate_mesh(
+    minima: torch.TensorType,
+    maxima: torch.TensorType,
+    border: float = 1.0,
+    resolution: int = 100,
+    device: Optional[torch.device] = None,
+):
+    # Apply Border
+    ptp = maxima - minima
+    shift = border * ptp
+    minima -= shift
+    maxima += shift
+
+    # Generate Mesh
+    minima = minima.to(device).unsqueeze(1)
+    maxima = maxima.to(device).unsqueeze(1)
+
+    factors = torch.linspace(0, 1, resolution, device=device)
+    marginals = factors * maxima + ((1 - factors) * minima)
+
+    single_dimensions = torch.meshgrid(*marginals)
+    mesh_input = torch.stack([dim.ravel() for dim in single_dimensions], dim=1)
+
+    return mesh_input, single_dimensions
+
+
 def mesh2d(x=None, border: float = 1.0, resolution: int = 100):
     if x is not None:
         x_shift = border * np.ptp(x[:, 0])
@@ -23,15 +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], clabels: list[int] = []):
+def distribution_from_list(list_dist: List[int],
+                           clabels: Optional[Iterable[int]] = None):
     clabels = clabels or list(range(len(list_dist)))
     distribution = dict(zip(clabels, list_dist))
     return distribution
 
 
-def parse_distribution(user_distribution: Union[dict[int, int], dict[str, str],
+def parse_distribution(
-                                                list[int], tuple[int]],
+        user_distribution,
-                       clabels: list[int] = []) -> dict[int, int]:
+        clabels: Optional[Iterable[int]] = None) -> Dict[int, int]:
     """Parse user-provided distribution.
 
     Return a dictionary with integer keys that represent the class labels and
@@ -75,9 +108,13 @@ def parse_distribution(user_distribution: Union[dict[int, int], dict[str, str],
 def parse_data_arg(data_arg: Union[Dataset, DataLoader, list, tuple]):
     """Return data and target as torch tensors."""
     if isinstance(data_arg, Dataset):
-        ds_size = len(data_arg)
+        if hasattr(data_arg, "__len__"):
-        loader = DataLoader(data_arg, batch_size=ds_size)
+            ds_size = len(data_arg)  # type: ignore
-        data, targets = next(iter(loader))
+            loader = DataLoader(data_arg, batch_size=ds_size)
+            data, targets = next(iter(loader))
+        else:
+            emsg = f"Dataset {data_arg} is not sized (`__len__` unimplemented)."
+            raise TypeError(emsg)
 
     elif isinstance(data_arg, DataLoader):
         data = torch.tensor([])

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

@@ -1,10 +1,12 @@
 """
-  _____           _     _______             _
+
- |  __ \         | |   |__   __|           | |
+ ######
- | |__) | __ ___ | |_ ___ | | ___  _ __ ___| |__
+ #     # #####   ####  #####  ####  #####  ####  #####   ####  #    #
- |  ___/ '__/ _ \| __/ _ \| |/ _ \| '__/ __| '_ \
+ #     # #    # #    #   #   #    #   #   #    # #    # #    # #    #
- | |   | | | (_) | || (_) | | (_) | | | (__| | | |
+ ######  #    # #    #   #   #    #   #   #    # #    # #      ######
- |_|   |_|  \___/ \__\___/|_|\___/|_|  \___|_| |_|
+ #       #####  #    #   #   #    #   #   #    # #####  #      #    #
+ #       #   #  #    #   #   #    #   #   #    # #   #  #    # #    #
+ #       #    #  ####    #    ####    #    ####  #    #  ####  #    #
 
 ProtoTorch Core Package
 """
@@ -13,20 +15,24 @@ from setuptools import find_packages, setup
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
 
-with open("README.md", "r") as fh:
+with open("README.md", encoding="utf-8") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "torch>=1.3.1",
+    "torch>=2.0.0",
-    "torchvision>=0.5.0",
+    "torchvision",
-    "numpy>=1.9.1",
+    "numpy",
-    "sklearn",
+    "scikit-learn",
+    "matplotlib",
 ]
 DATASETS = [
     "requests",
     "tqdm",
 ]
-DEV = ["bumpversion"]
+DEV = [
+    "bump2version",
+    "pre-commit",
+]
 DOCS = [
     "recommonmark",
     "sphinx",
@@ -35,15 +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="0.5.0",
+    version="0.7.6",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -54,30 +62,33 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
+    python_requires=">=3.8",
     install_requires=INSTALL_REQUIRES,
     extras_require={
-        "docs": DOCS,
         "datasets": DATASETS,
+        "dev": DEV,
+        "docs": DOCS,
         "examples": EXAMPLES,
         "tests": TESTS,
         "all": ALL,
     },
     classifiers=[
-        "Development Status :: 2 - Pre-Alpha",
         "Environment :: Console",
+        "Natural Language :: English",
+        "Development Status :: 4 - Beta",
         "Intended Audience :: Developers",
         "Intended Audience :: Education",
         "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: MIT License",
-        "Natural Language :: English",
-        "Programming Language :: Python :: 3.6",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
-        "Programming Language :: Python :: 3.9",
-        "Operating System :: OS Independent",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Software Development :: Libraries",
         "Topic :: Software Development :: Libraries :: Python Modules",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
     ],
     packages=find_packages(),
     zip_safe=False,

tests/test_core.py

@@ -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()
 
@@ -71,90 +93,94 @@ 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)
 
 
-class TestTecator(unittest.TestCase):
+# class TestTecator(unittest.TestCase):
-    def setUp(self):
+#     def setUp(self):
-        self.artifacts_dir = "./artifacts/Tecator"
+#         self.artifacts_dir = "./artifacts/Tecator"
-        self._remove_artifacts()
+#         self._remove_artifacts()
 
-    def _remove_artifacts(self):
+#     def _remove_artifacts(self):
-        if os.path.exists(self.artifacts_dir):
+#         if os.path.exists(self.artifacts_dir):
-            shutil.rmtree(self.artifacts_dir)
+#             shutil.rmtree(self.artifacts_dir)
 
-    def test_download_false(self):
+#     def test_download_false(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        self._remove_artifacts()
+#         self._remove_artifacts()
-        with self.assertRaises(RuntimeError):
+#         with self.assertRaises(RuntimeError):
-            _ = pt.datasets.Tecator(rootdir, download=False)
+#             _ = pt.datasets.Tecator(rootdir, download=False)
 
-    def test_download_caching(self):
+#     def test_download_caching(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        _ = pt.datasets.Tecator(rootdir, download=True, verbose=False)
+#         _ = pt.datasets.Tecator(rootdir, download=True, verbose=False)
-        _ = pt.datasets.Tecator(rootdir, download=False, verbose=False)
+#         _ = pt.datasets.Tecator(rootdir, download=False, verbose=False)
 
-    def test_repr(self):
+#     def test_repr(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        train = pt.datasets.Tecator(rootdir, download=True, verbose=True)
+#         train = pt.datasets.Tecator(rootdir, download=True, verbose=True)
-        self.assertTrue("Split: Train" in train.__repr__())
+#         self.assertTrue("Split: Train" in train.__repr__())
 
-    def test_download_train(self):
+#     def test_download_train(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        train = pt.datasets.Tecator(root=rootdir,
+#         train = pt.datasets.Tecator(root=rootdir,
-                                    train=True,
+#                                     train=True,
-                                    download=True,
+#                                     download=True,
-                                    verbose=False)
+#                                     verbose=False)
-        train = pt.datasets.Tecator(root=rootdir, download=True, verbose=False)
+#         train = pt.datasets.Tecator(root=rootdir, download=True, verbose=False)
-        x_train, y_train = train.data, train.targets
+#         x_train, y_train = train.data, train.targets
-        self.assertEqual(x_train.shape[0], 144)
+#         self.assertEqual(x_train.shape[0], 144)
-        self.assertEqual(y_train.shape[0], 144)
+#         self.assertEqual(y_train.shape[0], 144)
-        self.assertEqual(x_train.shape[1], 100)
+#         self.assertEqual(x_train.shape[1], 100)
 
-    def test_download_test(self):
+#     def test_download_test(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
+#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-        x_test, y_test = test.data, test.targets
+#         x_test, y_test = test.data, test.targets
-        self.assertEqual(x_test.shape[0], 71)
+#         self.assertEqual(x_test.shape[0], 71)
-        self.assertEqual(y_test.shape[0], 71)
+#         self.assertEqual(y_test.shape[0], 71)
-        self.assertEqual(x_test.shape[1], 100)
+#         self.assertEqual(x_test.shape[1], 100)
 
-    def test_class_to_idx(self):
+#     def test_class_to_idx(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
+#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-        _ = test.class_to_idx
+#         _ = test.class_to_idx
 
-    def test_getitem(self):
+#     def test_getitem(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
+#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-        x, y = test[0]
+#         x, y = test[0]
-        self.assertEqual(x.shape[0], 100)
+#         self.assertEqual(x.shape[0], 100)
-        self.assertIsInstance(y, int)
+#         self.assertIsInstance(y, int)
 
-    def test_loadable_with_dataloader(self):
+#     def test_loadable_with_dataloader(self):
-        rootdir = self.artifacts_dir.rpartition("/")[0]
+#         rootdir = self.artifacts_dir.rpartition("/")[0]
-        test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
+#         test = pt.datasets.Tecator(root=rootdir, train=False, verbose=False)
-        _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
+#         _ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)
 
-    def tearDown(self):
+#     def tearDown(self):
-        self._remove_artifacts()
+#         self._remove_artifacts()