Modernisation

.github/workflows/ci.yml

@@ -0,0 +1,68 @@
+name: CI
+
+on:
+  push:
+    branches: [ master, main ]
+  pull_request:
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Install Ruff
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install ruff
+
+      - name: Run Ruff
+        run: |
+          ruff --version
+          ruff check .
+
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10", "3.11", "3.12"]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Cache pip
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: pip-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }}
+          restore-keys: |
+            pip-${{ runner.os }}-${{ matrix.python-version }}-
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install .
+          python -m pip install coverage
+
+      - name: Run unit tests
+        run: |
+          python -m coverage run -m unittest
+
+      - name: Coverage report
+        run: |
+          python -m coverage report -m
+
+      # Removed Coveralls upload step

README.md

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+# scikit-rvm
+
+[![CI](https://github.com/JamesRitchie/scikit-rvm/actions/workflows/ci.yml/badge.svg?branch=master)](https://github.com/JamesRitchie/scikit-rvm/actions/workflows/ci.yml)
+
+scikit-rvm is a Python module implementing the [Relevance Vector Machine](https://en.wikipedia.org/wiki/Relevance_vector_machine) (RVM)
+machine learning technique using the [scikit-learn](https://scikit-learn.org/) API.
+
+## Quickstart
+
+With NumPy, SciPy and scikit-learn available in your environment, install with:
+
+```bash
+pip install https://github.com/JamesRitchie/scikit-rvm/archive/master.zip
+```
+
+Regression is done with the `RVR` class:
+
+```python
+>>> from skrvm import RVR
+>>> X = [[0, 0], [2, 2]]
+>>> y = [0.5, 2.5]
+>>> clf = RVR(kernel='linear')
+>>> clf.fit(X, y)
+RVR(alpha=1e-06, beta=1e-06, beta_fixed=False, bias_used=True, coef0=0.0,
+coef1=None, degree=3, kernel='linear', n_iter=3000,
+threshold_alpha=1000000000.0, tol=0.001, verbose=False)
+>>> clf.predict([[1, 1]])
+array([1.49995187])
+```
+
+Classification is done with the `RVC` class:
+
+```python
+>>> from skrvm import RVC
+>>> from sklearn.datasets import load_iris
+>>> iris = load_iris()
+>>> clf = RVC()
+>>> clf.fit(iris.data, iris.target)
+RVC(alpha=1e-06, beta=1e-06, beta_fixed=False, bias_used=True, coef0=0.0,
+coef1=None, degree=3, kernel='rbf', n_iter=3000, n_iter_posterior=50,
+threshold_alpha=1000000000.0, tol=0.001, verbose=False)
+>>> clf.score(iris.data, iris.target)
+0.98
+```
+
+## Theory
+
+The RVM is a sparse Bayesian analogue to the Support Vector Machine, with a
+number of advantages:
+
+- Provides probabilistic estimates, as opposed to the SVM's point estimates.
+- Typically provides a sparser solution than the SVM, which tends to have the
+  number of support vectors grow linearly with the size of the training set.
+- Does not need a complexity parameter to be selected in order to avoid
+  overfitting.
+
+However, it is more expensive to train than the SVM, although prediction is
+faster and no cross-validation runs are required.
+
+The RVM's original creator, Mike Tipping, provides a selection of papers offering
+detailed insight into the formulation of the RVM (and sparse Bayesian learning
+in general) on a [dedicated page](http://www.miketipping.com/sparsebayes.htm), along with a Matlab implementation.
+
+Most of this implementation was written working from Section 7.2 of Christopher
+M. Bishop's [Pattern Recognition and Machine Learning](http://research.microsoft.com/en-us/um/people/cmbishop/prml/).
+
+## Contributors
+
+- [James Ritchie](https://github.com/JamesRitchie)
+- [Jonathan Feinberg](https://github.com/jonathf)
+
+## Future Improvements
+
+- Implement the fast Sequential Sparse Bayesian Learning Algorithm outlined in
+  Section 7.2.3 of [Pattern Recognition and Machine Learning](http://research.microsoft.com/en-us/um/people/cmbishop/prml/)
+- Handle ill-conditioning errors more gracefully.
+- Implement more kernel choices.
+- Create more detailed examples with IPython notebooks.
+

pyproject.toml

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+[project]
+name = "scikit-rvm"
+version = "0.1.0a1"
+description = "Relevance Vector Machine implementation using the scikit-learn API"
+readme = { file = "README.md", content-type = "text/markdown" }
+authors = [
+  { name = "James Ritchie", email = "skrvm@james.jmsrt.ch" },
+]
+license = { text = "BSD" }
+keywords = ["machine-learning", "rvm", "sparse", "bayesian", "scikit-learn"]
+classifiers = [
+  "Development Status :: 3 - Alpha",
+  "License :: OSI Approved :: BSD License",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python :: 3",
+]
+requires-python = ">=3.10"
+dependencies = [
+  "numpy>=2.0",
+  "scipy>=1.12",
+  "scikit-learn>=1.5",
+]
+
+[dependency-groups]
+dev = [
+    "coverage>=7.10.3",
+    "ruff>=0.12.8",
+]
+
+[build-system]
+requires = ["setuptools>=61"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+exclude = ["tests*"]
+
+# Ruff configuration
+[tool.ruff]
+line-length = 100
+src = ["skrvm", "tests"]
+exclude = [
+  ".venv",
+  "build",
+  "dist",
+  "__pycache__",
+]
+
+[tool.ruff.lint.isort]
+known-first-party = ["skrvm"]

skrvm/__init__.py

@@ -1,7 +1,7 @@
 """skrvm implements RVM models."""
 
-from .rvm import RVR, RVC
+from .rvm import RVC, RVR
 
-__all__ = ['RVR', 'RVC']
+__all__ = ["RVR", "RVC"]
 
-__version__ = '0.1.0a1'
+__version__ = "0.1.0a1"