diff --git a/.github/dependabot.yml b/.github/dependabot.yml index b3340cd..cd07852 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -1,18 +1,14 @@ version: 2 updates: - # Maintain dependencies for GitHub Actions as recommended in SPEC8: - # https://github.com/scientific-python/specs/pull/325 - # At the time of writing, release critical workflows such as - # pypa/gh-action-pypi-publish should use hash-based versioning for security - # reasons. This strategy may be generalized to all other github actions - # in the future. + - package-ecosystem: "uv" + directory: "/" + schedule: + interval: "daily" + labels: + - "dependencies" - package-ecosystem: "github-actions" directory: "/" schedule: interval: "weekly" - groups: - actions: - patterns: - - "*" - reviewers: - - "glemaitre" + labels: + - "dependencies" \ No newline at end of file diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..07a5809 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,23 @@ +name: CI + +on: + push: + branches: + - main + pull_request: + branches: + - main + +permissions: + contents: read + +jobs: + lint: + uses: ./.github/workflows/lint.yml + + tests: + uses: ./.github/workflows/pytest.yml + secrets: inherit + + docs: + uses: ./.github/workflows/docs.yml diff --git a/.github/workflows/deploy-gh-pages.yml b/.github/workflows/deploy-gh-pages.yml index c95814d..05464b3 100644 --- a/.github/workflows/deploy-gh-pages.yml +++ b/.github/workflows/deploy-gh-pages.yml @@ -4,31 +4,53 @@ on: push: branches: - main + tags: + - "v*" pull_request: branches: - main jobs: + docs-check: + uses: ./.github/workflows/docs.yml + deploy-gh-pages: + needs: docs-check + if: ${{ github.event_name == 'push' }} runs-on: ubuntu-latest + permissions: + contents: write steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - - uses: prefix-dev/setup-pixi@v0.8.3 - with: - pixi-version: v0.41.4 - environments: doc - frozen: true - - name: Build documentation - run: pixi run -e doc build-doc + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.13" - - name: Update the main gh-page website - if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }} - uses: peaceiris/actions-gh-pages@v4.0.0 + - name: Set up uv + uses: astral-sh/setup-uv@v6 with: - github_token: ${{ secrets.GITHUB_TOKEN }} - publish_dir: ./site - commit_message: "[ci skip] ${{ github.event.head_commit.message }}" + enable-cache: true + + - name: Install docs dependencies + run: uv sync --frozen --no-default-groups --group docs + + - name: Deploy docs as dev + if: ${{ github.ref == 'refs/heads/main' }} + run: | + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + uv run mike deploy --push --update-aliases dev + + - name: Deploy versioned docs and update latest + if: ${{ startsWith(github.ref, 'refs/tags/v') }} + run: | + VERSION="${GITHUB_REF_NAME#v}" + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + uv run mike deploy --push --update-aliases "$VERSION" latest + uv run mike set-default --push latest diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..2359c79 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,32 @@ +name: Docs Check + +on: + workflow_call: + workflow_dispatch: + +permissions: + contents: read + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.13" + + - name: Set up uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + + - name: Install docs dependencies + run: uv sync --frozen --no-default-groups --group docs + + - name: Build documentation (strict, execute notebooks) + run: uv run mkdocs build --strict \ No newline at end of file diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index d0d3440..43850a2 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -1,23 +1,30 @@ name: Linter on: - push: - branches: - - main - pull_request: - branches: - - main + workflow_call: + workflow_dispatch: jobs: - build: + lint: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: prefix-dev/setup-pixi@v0.8.3 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.13" + + - name: Set up uv + uses: astral-sh/setup-uv@v6 with: - pixi-version: v0.41.4 - environments: lint - frozen: true + enable-cache: true + + - name: Install lint dependencies + run: uv sync --frozen --no-default-groups --group lint + + - name: Check format with Black + run: uv run black --check --diff . - name: Run linter - run: pixi run -e lint lint + run: uv run ruff check . diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 2c5ad14..6e766d3 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -1,110 +1,105 @@ -name: Publish Python 🐍 distribution 📦 to PyPI and TestPyPI +name: Publish Distribution on: - push: - branches: [main] - tags: ['v*'] # Only run on version tags - pull_request: - branches: [main] + push: + tags: + - "v*" + workflow_dispatch: + +permissions: + contents: read jobs: - build: - name: Build distribution 📦 - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: "3.x" - cache: 'pip' # Enable pip caching - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install build wheel - - name: Build a binary wheel and a source tarball - run: python -m build - - name: Store the distribution packages - uses: actions/upload-artifact@v4 - with: - name: python-package-distributions - path: dist/ - retention-days: 7 # Set a reasonable retention period + build: + name: Build distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.13" + + - name: Set up uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: true + + - name: Build sdist and wheel + run: uv build + + - name: Upload built artifacts + uses: actions/upload-artifact@v4 + with: + name: python-package-distributions + path: dist/ + if-no-files-found: error + + publish-testpypi: + name: Publish to TestPyPI + needs: build + if: github.event_name == 'workflow_dispatch' + runs-on: ubuntu-latest + environment: + name: testpypi + url: https://test.pypi.org/p/ikpykit + permissions: + id-token: write + steps: + - name: Download built artifacts + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ + + - name: Publish to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + + publish-pypi: + name: Publish to PyPI + needs: build + if: startsWith(github.ref, 'refs/tags/v') + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/ikpykit + permissions: + id-token: write + steps: + - name: Download built artifacts + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ - publish-to-testpypi: - name: Publish to TestPyPI - needs: [build] - runs-on: ubuntu-latest - if: github.event_name == 'push' && (github.ref == 'refs/heads/main' || startsWith(github.ref, 'refs/tags/')) - environment: - name: testpypi - url: https://test.pypi.org/p/ikpykit # Update to your actual package name - permissions: - id-token: write - steps: - - name: Download artifact - uses: actions/download-artifact@v4 - with: - name: python-package-distributions - path: dist/ - - name: Publish to TestPyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - repository-url: https://test.pypi.org/legacy/ + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 - publish-to-pypi: - name: Publish to PyPI - needs: [build] - runs-on: ubuntu-latest - if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') - environment: - name: pypi - url: https://pypi.org/p/ikpykit # Update to your actual package name - permissions: - id-token: write - steps: - - name: Download artifact - uses: actions/download-artifact@v4 - with: - name: python-package-distributions - path: dist/ - - name: Publish 📦 to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 + github-release: + name: Create GitHub Release + needs: publish-pypi + if: startsWith(github.ref, 'refs/tags/v') + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - name: Download built artifacts + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ - github-release: - name: Create GitHub Release - needs: [publish-to-pypi] - runs-on: ubuntu-latest - if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') - permissions: - contents: write - id-token: write - steps: - - name: Download artifact - uses: actions/download-artifact@v4 - with: - name: python-package-distributions - path: dist/ - - name: Sign the dists with Sigstore - uses: sigstore/gh-action-sigstore-python@v3.0.0 - with: - inputs: >- - ./dist/*.tar.gz - ./dist/*.whl - - name: Extract release notes - id: extract-release-notes - run: | - version=${GITHUB_REF_NAME#v} - echo "version=$version" >> $GITHUB_OUTPUT - - name: Create GitHub Release - env: - GITHUB_TOKEN: ${{ github.token }} - run: | - gh release create "$GITHUB_REF_NAME" \ - --repo "$GITHUB_REPOSITORY" \ - --title "Release $GITHUB_REF_NAME" \ - --notes "Release $GITHUB_REF_NAME" - - name: Upload artifacts to GitHub Release - env: - GITHUB_TOKEN: ${{ github.token }} - run: gh release upload "$GITHUB_REF_NAME" dist/** --repo "$GITHUB_REPOSITORY" + - name: Create GitHub release and upload artifacts + env: + GH_TOKEN: ${{ github.token }} + run: | + gh release create "$GITHUB_REF_NAME" \ + --repo "$GITHUB_REPOSITORY" \ + --title "Release $GITHUB_REF_NAME" \ + --generate-notes \ + dist/* diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 149e684..bbe43e3 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -1,42 +1,40 @@ name: Unit Tests on: - push: - branches: - - main - pull_request: - branches: - - main + workflow_call: + workflow_dispatch: jobs: - build: + test: strategy: matrix: os: [windows-latest, ubuntu-latest, macos-latest] - environment: [test] + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 with: - fetch-depth: 0 # 确保拉取完整的 Git 历史记录 + fetch-depth: 0 - name: Set up Python uses: actions/setup-python@v5 with: - python-version: "3.x" + python-version: ${{ matrix.python-version }} + + - name: Set up uv + uses: astral-sh/setup-uv@v6 + with: + enable-cache: true - name: Install dependencies run: | - python -m pip install --upgrade pip - pip install build setuptools-scm - - - name: Build the package - run: python -m build # 确保生成 _version.py 文件 + uv sync --frozen --no-default-groups --group dev - name: Run tests - run: pixi run -e ${{ matrix.environment }} test + run: uv run pytest --cov=ikpykit --cov-report=xml - name: Upload coverage reports to Codecov + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.13' uses: codecov/codecov-action@v4.5.0 with: token: ${{ secrets.CODECOV_TOKEN }} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 346262d..6340e0a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,16 +1,37 @@ +fail_fast: false + repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 + - repo: https://github.com/abravalheri/validate-pyproject + rev: v0.24.1 hooks: - - id: check-yaml - - id: end-of-file-fixer - - id: trailing-whitespace - - repo: https://github.com/psf/black - rev: 24.8.0 + - id: validate-pyproject + + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v3.1.0 hooks: - - id: black + - id: prettier + types_or: [yaml, json5] + - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.9 + # Ruff version. + rev: v0.12.1 + hooks: + # Run the linter. + - id: ruff-check + args: [--fix, --exit-non-zero-on-fix] + # Run the formatter. + - id: ruff-format + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: no-commit-to-branch + name: prevent commits to main + args: [--branch, main] + + - repo: https://github.com/codespell-project/codespell + rev: v2.4.1 hooks: - - id: ruff - args: ["--fix", "--show-source"] + - id: codespell # See pyproject.toml for args + additional_dependencies: + - tomli diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/README.md b/README.md index 1ef46eb..ba41626 100644 --- a/README.md +++ b/README.md @@ -9,8 +9,8 @@ ![Python](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue) [![PyPI](https://img.shields.io/pypi/v/ikpykit)](https://pypi.org/project/ikpykit/) -[![codecov](https://codecov.io/gh/IsolationKernel/ikpykit/branch/master/graph/badge.svg)](https://codecov.io/gh/IsolationKernel/ikpykit) -[![Build status](https://github.com/IsolationKernel/ikpykit/actions/workflows/python-app.yml/badge.svg)](https://github.com/IsolationKernel/ikpykit/actions/workflows/python-app.yml/badge.svg) +[![codecov](https://codecov.io/gh/IsolationKernel/ikpykit/branch/main/graph/badge.svg)](https://codecov.io/gh/IsolationKernel/ikpykit) +[![Build status](https://github.com/IsolationKernel/ikpykit/actions/workflows/ci.yml/badge.svg)](https://github.com/IsolationKernel/ikpykit/actions/workflows/ci.yml) [![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/IsolationKernel/ikpykit/graphs/commit-activity) [![Downloads](https://static.pepy.tech/badge/ikpykit)](https://pepy.tech/project/ikpykit) @@ -49,7 +49,15 @@ IKPyKit is specifically built to harness the power of Isolation Kernel, providin ## Installation & Dependencies -To install the basic version of `ikpykit` with core dependencies, run the following: +Recommended installation (with `uv`): + +Install `uv` first: + +```bash +uv pip install ikpykit +``` + +If you prefer classic pip: ```bash pip install ikpykit diff --git a/docs/README.md b/docs/README.md index b3497cd..5aa5d26 100644 --- a/docs/README.md +++ b/docs/README.md @@ -9,8 +9,8 @@ ![Python](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-blue) [![PyPI](https://img.shields.io/pypi/v/ikpykit)](https://pypi.org/project/ikpykit/) -[![codecov](https://codecov.io/gh/IsolationKernel/ikpykit/branch/master/graph/badge.svg)](https://codecov.io/gh/IsolationKernel/ikpykit) -[![Build status](https://github.com/IsolationKernel/ikpykit/actions/workflows/python-app.yml/badge.svg)](https://github.com/IsolationKernel/ikpykit/actions/workflows/python-app.yml/badge.svg) +[![codecov](https://codecov.io/gh/IsolationKernel/ikpykit/branch/main/graph/badge.svg)](https://codecov.io/gh/IsolationKernel/ikpykit) +[![Build status](https://github.com/IsolationKernel/ikpykit/actions/workflows/ci.yml/badge.svg)](https://github.com/IsolationKernel/ikpykit/actions/workflows/ci.yml) [![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) [![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/IsolationKernel/ikpykit/graphs/commit-activity) [![Downloads](https://static.pepy.tech/badge/ikpykit)](https://pepy.tech/project/ikpykit) @@ -49,13 +49,21 @@ IKPyKit is specifically built to harness the power of Isolation Kernel, providin ## Installation & Dependencies -To install the basic version of `IKPyKit` with core dependencies, run the following: +Recommended installation (with `uv`): + +Install `uv` first: + +```bash +uv pip install ikpykit +``` + +If you prefer classic pip: ```bash pip install ikpykit ``` -For more installation options, including dependencies and additional features, check out our [Installation Guide](./quick-start/how-to-install.html). +For more installation options, including dependencies and additional features, check out our [Installation Guide](./quick-start/how-to-install.md). --- @@ -147,13 +155,13 @@ clf.predict([[0.1, 0.3], [0, 0.7], [90, 85]]) ## Features -IKPyKit provides a set of key features designed to make machine learning tasks easy and efficient. For a detailed overview, see the [User Guides](./user_guides/table-of-contents.html). +IKPyKit provides a set of key features designed to make machine learning tasks easy and efficient. For a detailed overview, see the [User Guides](./user_guides/table-of-contents.md). --- ## Examples and tutorials -Explore our extensive list of examples and tutorials to get you started with IKPyKit. You can find them [here](./examples/examples_english.html). +Explore our extensive list of examples and tutorials to get you started with IKPyKit. You can find them [here](./examples/examples_english.md). --- @@ -162,15 +170,15 @@ Explore our extensive list of examples and tutorials to get you started with IKP Primarily, IKPyKit development consists of adding and creating new algorithms, new validation strategies, or improving the performance of the current code. However, there are many other ways to contribute: - Submit a bug report or feature request on [GitHub Issues](https://github.com/IsolationKernel/ikpykit/issues). -- Contribute a Jupyter notebook to our [examples](./examples/examples_english.html). +- Contribute a Jupyter notebook to our [examples](./examples/examples_english.md). - Write [unit or integration tests](https://docs.pytest.org/en/latest/) for our project. - Answer questions on our issues, Stack Overflow, and elsewhere. - Translate our documentation into another language. - Write a blog post, tweet, or share our project with others. -For more information on how to contribute to IKPyKit, see our [Contribution Guide](./contributing/contribution.html). +For more information on how to contribute to IKPyKit, see our [Contribution Guide](./contributing/contribution.md). -Visit our [authors section](./authors/authors.html) to meet all the contributors to IKPyKit. +Visit our [authors section](./authors/authors.md) to meet all the contributors to IKPyKit. --- diff --git a/docs/examples/examples_english.md b/docs/examples/examples_english.md index b525342..4ba4c95 100644 --- a/docs/examples/examples_english.md +++ b/docs/examples/examples_english.md @@ -6,4 +6,4 @@ Practical examples and tutorials to help you understand and apply ikpykit. Anomaly Detection -- [INNE](../user_guides/inne.html) +- [INNE](../user_guides/inne.ipynb) diff --git a/docs/faq/table-of-contents.md b/docs/faq/table-of-contents.md index c7e78b4..7d2d937 100644 --- a/docs/faq/table-of-contents.md +++ b/docs/faq/table-of-contents.md @@ -1,20 +1,49 @@ -# Frequently Asked Questions and forecasting tips +# Frequently Asked Questions -Thank you for choosing skforecast and visiting our Frequently Asked Questions (FAQ) page. Here, we aim to provide solutions to commonly encountered issues on our Github repository. If your question is not answered on this page, we encourage you to create a new issue on our [Github](https://github.com/skforecast/skforecast/issues) so that it can be addressed, and other users can also benefit from it. Additionally, we have included some forecasting tips to help you get the most out of skforecast. +This page collects common questions about IKPyKit usage, installation, and model behavior. -General Forecasting Tips +## 1. Which Python versions are supported? -+ [Avoid negative predictions when forecasting](../faq/non-negative-predictions.html) -+ [Forecasting time series with missing values](../faq/forecasting-time-series-with-missing-values.html) -+ [Forecasting with delayed historical data](../faq/forecasting-with-delayed-historical-data.html) -+ [Backtesting vs One-step-ahead](../faq/parameters-search-backtesting-vs-one-step-ahead.html) +IKPyKit supports Python 3.9 and above. -Feature Engineering +## 2. How do I install IKPyKit? -+ [Cyclical features in time series](../faq/cyclical-features-time-series.html) -+ [Time series aggregation](../faq/time-series-aggregation.html) +Recommended installation (with `uv`): -Performance Optimization +Install `uv` first: -+ [Parallelization in skforecast](../faq/parallelization-skforecast.html) -+ [Profiling skforecast](../faq/profiling-skforecast.html) +```bash +uv pip install ikpykit +``` + +If you prefer classic pip: + +```bash +pip install ikpykit +``` + +For more options, see [How to install](../quick-start/how-to-install.md). + + +## 3. Why do I sometimes see `-1` labels in clustering outputs? + +Some clustering methods can leave points unassigned and mark them as `-1` (outliers). +For example, IDKC may stop before every point is assigned because of threshold and +early-stop criteria. + +## 4. How can I report a bug or request a feature? + +Open an issue on GitHub: + +- https://github.com/IsolationKernel/ikpykit/issues + +When reporting bugs, include: +- IKPyKit version +- Python version +- Minimal reproducible code snippet +- Full traceback or error output + +## 5. Where can I find runnable examples? + +See [Examples and tutorials](../examples/examples_english.md) and +[User Guides](../user_guides/table-of-contents.md). diff --git a/docs/quick-start/how-to-install.md b/docs/quick-start/how-to-install.md index db7e21d..4c6f14b 100644 --- a/docs/quick-start/how-to-install.md +++ b/docs/quick-start/how-to-install.md @@ -6,7 +6,15 @@ This guide will help you install `ikpykit`. The default installation of `ikpykit ## **Basic installation** -To install the basic version of `ikpykit` with its core dependencies, run: +We recommend using `uv` for package installation: + +Install `uv` first: + +```bash +uv pip install ikpykit +``` + +If you prefer classic pip, run: ```bash pip install ikpykit @@ -16,7 +24,7 @@ If you're feeling brave, feel free to install the bleeding edge: NOTE: Do so at Latest (unstable): ```bash -pip install git+https://github.com/IsolationKernel/ikpykit.git@main --upgrade +uv pip install git+https://github.com/IsolationKernel/ikpykit.git@main --upgrade ``` Once the installation is completed, you can check whether the installation was successful through: diff --git a/docs/releases/releases.md b/docs/releases/releases.md index 1630c98..7891e5e 100644 --- a/docs/releases/releases.md +++ b/docs/releases/releases.md @@ -8,3 +8,28 @@ All significant changes to this project are documented in this release file. | Enhancement | Improvement in existing functionality | | API Change | Changes in the API | | Fix | Bug fix | + +## Released (2026-03-22) +- Feature IDKC now supports optional `force_assign_unassigned` to force final assignment of remaining `-1` samples. +- Enhancement CI/CD workflows were unified around `uv` with reusable pipelines for lint, tests, and docs checks. +- Enhancement Installation guidance was standardized to recommend `uv pip install`, with classic `pip` retained as fallback. +- Enhancement A top-level CI entry workflow was introduced to run lint, tests, and docs validation in a consistent matrix-driven flow. +- Enhancement Docs deployment was refactored to support branch/tag version publishing using `mike` with `dev` and `latest` aliases. +- Enhancement PyPI release workflow was modernized with `uv build`, OIDC publishing, and GitHub Release artifact upload. +- Enhancement Shared cluster bookkeeping was consolidated in `KCluster` (`build_labels`, `total_points`) and reused by IDKC/PSKC. +- Enhancement IDKC and PSKC internals were refactored into clearer helper methods without changing their core algorithmic intent. +- Enhancement Project formatting and linting gates were strengthened by integrating `black` with existing `ruff` checks. +- Fix Notebook web rendering reliability was improved by enabling `mkdocs-jupyter` execution and validating notebook build in strict docs checks. +- Fix Documentation build failures caused by notebook runtime dependencies were resolved by adding docs-time plotting dependencies. +- Fix User guide notebook example import paths were corrected to match actual public API symbols. + +- Fix Multiple Ruff and warning issues were resolved across anomaly, kernel, stream, graph, and utility modules. +- Fix Pytest warning noise was reduced via dependency/config updates (`pytest-asyncio`, `pytest-timeout`) and timeout tuning. + +- Fix IDKC post-processing logic for unassigned points was corrected to prevent invalid cluster transitions. + +- Fix Graph test sparse matrix construction was optimized to avoid efficiency warnings (`lil` construction then `csr` conversion). +- Fix Time-series test regex matching for exception messages was fixed using escaped expected patterns. + +- Fix FAQ content was rewritten for IKPyKit and reconnected to site navigation. +- Fix Multiple documentation links and navigation targets were corrected to pass `mkdocs --strict`. diff --git a/docs/user_guides/inne.ipynb b/docs/user_guides/inne.ipynb index ec8a9fd..4817cfa 100644 --- a/docs/user_guides/inne.ipynb +++ b/docs/user_guides/inne.ipynb @@ -1,43 +1,89 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# IsolationNNE example\n\nAn example using :class:`ikpykit.anomaly.IsolationNNE` for anomaly\ndetection.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nimport matplotlib.pyplot as plt\nfrom ikpykit.anomaly import IsolationNNE\n\nrng = np.random.RandomState(42)\n\n# Generate train data\nX = 0.3 * rng.randn(100, 2)\nX_train = np.r_[X + 2, X - 2]\n# Generate some regular novel observations\nX = 0.3 * rng.randn(20, 2)\nX_test = np.r_[X + 2, X - 2]\n# Generate some abnormal novel observations\nX_outliers = rng.uniform(low=-4, high=4, size=(20, 2))\n\n# fit the model\nclf = IsolationNNE()\nclf.fit(X_train)\ny_pred_train = clf.predict(X_train)\ny_pred_test = clf.predict(X_test)\ny_pred_outliers = clf.predict(X_outliers)\n\n# plot the line, the samples, and the nearest vectors to the plane\nxx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))\nZ = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\nZ = Z.reshape(xx.shape)\n\nplt.title(\"IsolationNNE\")\nplt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)\n\nb1 = plt.scatter(X_train[:, 0], X_train[:, 1], c=\"white\", s=20, edgecolor=\"k\")\nb2 = plt.scatter(X_test[:, 0], X_test[:, 1], c=\"green\", s=20, edgecolor=\"k\")\nc = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c=\"red\", s=20, edgecolor=\"k\")\nplt.axis(\"tight\")\nplt.xlim((-5, 5))\nplt.ylim((-5, 5))\nplt.legend(\n [b1, b2, c],\n [\"training observations\", \"new regular observations\", \"new abnormal observations\"],\n loc=\"upper left\",\n)\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# INNE example\n", + "\n", + "An example using :class:`ikpykit.anomaly.INNE` for anomaly\n", + "detection.\n" + ] }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from ikpykit.anomaly import INNE\n", + "\n", + "rng = np.random.RandomState(42)\n", + "\n", + "# Generate train data\n", + "X = 0.3 * rng.randn(100, 2)\n", + "X_train = np.r_[X + 2, X - 2]\n", + "# Generate some regular novel observations\n", + "X = 0.3 * rng.randn(20, 2)\n", + "X_test = np.r_[X + 2, X - 2]\n", + "# Generate some abnormal novel observations\n", + "X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))\n", + "\n", + "# fit the model\n", + "clf = INNE()\n", + "clf.fit(X_train)\n", + "y_pred_train = clf.predict(X_train)\n", + "y_pred_test = clf.predict(X_test)\n", + "y_pred_outliers = clf.predict(X_outliers)\n", + "\n", + "# plot the line, the samples, and the nearest vectors to the plane\n", + "xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))\n", + "Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n", + "Z = Z.reshape(xx.shape)\n", + "\n", + "plt.title(\"INNE\")\n", + "plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)\n", + "\n", + "b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c=\"white\", s=20, edgecolor=\"k\")\n", + "b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c=\"green\", s=20, edgecolor=\"k\")\n", + "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c=\"red\", s=20, edgecolor=\"k\")\n", + "plt.axis(\"tight\")\n", + "plt.xlim((-5, 5))\n", + "plt.ylim((-5, 5))\n", + "plt.legend(\n", + " [b1, b2, c],\n", + " [\"training observations\", \"new regular observations\", \"new abnormal observations\"],\n", + " loc=\"upper left\",\n", + ")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/user_guides/table-of-contents.md b/docs/user_guides/table-of-contents.md index 78c61fa..156b0a2 100644 --- a/docs/user_guides/table-of-contents.md +++ b/docs/user_guides/table-of-contents.md @@ -4,6 +4,6 @@ Welcome to the ikpykit user guides! This comprehensive collection of guides is d Anomaly Detection -- [INNE](../user_guides/inne.html) +- [INNE](../user_guides/inne.ipynb) We hope you find these guides helpful. If you have any questions or need further assistance, please don't hesitate to reach out to the ikpykit community. diff --git a/examples/plot_inne.py b/examples/plot_inne.py index e189d60..73566f9 100644 --- a/examples/plot_inne.py +++ b/examples/plot_inne.py @@ -12,8 +12,9 @@ """ -import numpy as np import matplotlib.pyplot as plt +import numpy as np + from ikpykit.anomaly import INNE rng = np.random.RandomState(42) diff --git a/ikpykit/__init__.py b/ikpykit/__init__.py index 9c1f1a4..66f939c 100644 --- a/ikpykit/__init__.py +++ b/ikpykit/__init__.py @@ -19,20 +19,20 @@ from .trajectory import IKAT, TIDKC __all__ = [ - "IsoDisKernel", - "IsoKernel", - "IDKD", - "INNE", + "ICID", "IDKC", - "PSKC", + "IDKD", "IKAHC", - "IsoGraphKernel", - "IKGOD", - "IKGAD", - "ICID", "IKAT", - "TIDKC", - "STREAMKHC", + "IKGAD", + "IKGOD", "IKTOD", + "INNE", + "PSKC", + "STREAMKHC", + "TIDKC", + "IsoDisKernel", + "IsoGraphKernel", + "IsoKernel", "__version__", ] diff --git a/ikpykit/anomaly/_idkd.py b/ikpykit/anomaly/_idkd.py index 856d8e2..43f6069 100644 --- a/ikpykit/anomaly/_idkd.py +++ b/ikpykit/anomaly/_idkd.py @@ -124,18 +124,18 @@ def fit(self, X, y=None): max_samples = min(16, n_samples) else: raise ValueError( - "max_samples (%s) is not supported." + f"max_samples ({self.max_samples}) is not supported." 'Valid choices are: "auto", int or' - "float" % self.max_samples + "float" ) elif isinstance(self.max_samples, numbers.Integral): if self.max_samples > n_samples: warn( - "max_samples (%s) is greater than the " - "total number of samples (%s). max_samples " - "will be set to n_samples for estimation." - % (self.max_samples, n_samples) + f"max_samples ({self.max_samples}) is greater than the " + f"total number of samples ({n_samples}). max_samples " + "will be set to n_samples for estimation.", + stacklevel=2, ) max_samples = n_samples else: @@ -143,7 +143,7 @@ def fit(self, X, y=None): else: # float if not 0.0 < self.max_samples <= 1.0: raise ValueError( - "max_samples must be in (0, 1], got %r" % self.max_samples + f"max_samples must be in (0, 1], got {self.max_samples!r}" ) max_samples = int(self.max_samples * X.shape[0]) @@ -154,7 +154,7 @@ def fit(self, X, y=None): if self.contamination != "auto": if not (0.0 < self.contamination <= 0.5): raise ValueError( - "contamination must be in (0, 0.5], got: %f" % self.contamination + f"contamination must be in (0, 0.5], got: {self.contamination:f}" ) if self.contamination == "auto": diff --git a/ikpykit/anomaly/_inne.py b/ikpykit/anomaly/_inne.py index 90b386d..5d95d6e 100644 --- a/ikpykit/anomaly/_inne.py +++ b/ikpykit/anomaly/_inne.py @@ -116,18 +116,18 @@ def fit(self, X, y=None): max_samples = min(16, n_samples) else: raise ValueError( - "max_samples (%s) is not supported." + f"max_samples ({self.max_samples}) is not supported." 'Valid choices are: "auto", int or' - "float" % self.max_samples + "float" ) elif isinstance(self.max_samples, numbers.Integral): if self.max_samples > n_samples: warn( - "max_samples (%s) is greater than the " - "total number of samples (%s). max_samples " - "will be set to n_samples for estimation." - % (self.max_samples, n_samples) + f"max_samples ({self.max_samples}) is greater than the " + f"total number of samples ({n_samples}). max_samples " + "will be set to n_samples for estimation.", + stacklevel=2, ) max_samples = n_samples else: @@ -135,7 +135,7 @@ def fit(self, X, y=None): else: # float if not 0.0 < self.max_samples <= 1.0: raise ValueError( - "max_samples must be in (0, 1], got %r" % self.max_samples + f"max_samples must be in (0, 1], got {self.max_samples!r}" ) max_samples = int(self.max_samples * X.shape[0]) @@ -146,7 +146,7 @@ def fit(self, X, y=None): if self.contamination != "auto": if not (0.0 < self.contamination <= 0.5): raise ValueError( - "contamination must be in (0, 0.5], got: %f" % self.contamination + f"contamination must be in (0, 0.5], got: {self.contamination:f}" ) if self.contamination == "auto": diff --git a/ikpykit/anomaly/tests/test_inne.py b/ikpykit/anomaly/tests/test_inne.py index f6b09e4..4e71ac0 100644 --- a/ikpykit/anomaly/tests/test_inne.py +++ b/ikpykit/anomaly/tests/test_inne.py @@ -123,7 +123,7 @@ def test_fit_time(): t1 = time.time() clf.fit(data) t2 = time.time() - anomaly_labels = clf.predict(data) + clf.predict(data) t3 = time.time() print(t2 - t1) print(t3 - t2) @@ -132,7 +132,7 @@ def test_fit_time(): t1 = time.time() clf2.fit(data) t2 = time.time() - anomaly_labels = clf2.predict(data) + clf2.predict(data) t3 = time.time() print(t2 - t1) print(t3 - t2) diff --git a/ikpykit/cluster/__init__.py b/ikpykit/cluster/__init__.py index 183df1b..decd602 100644 --- a/ikpykit/cluster/__init__.py +++ b/ikpykit/cluster/__init__.py @@ -6,4 +6,4 @@ from ._ikahc import IKAHC from ._pskc import PSKC -__all__ = ["PSKC", "IKAHC", "IDKC"] +__all__ = ["IDKC", "IKAHC", "PSKC"] diff --git a/ikpykit/cluster/_idkc.py b/ikpykit/cluster/_idkc.py index fc9ad85..dfffb1a 100644 --- a/ikpykit/cluster/_idkc.py +++ b/ikpykit/cluster/_idkc.py @@ -8,14 +8,15 @@ work. If not, see . """ +import numbers +from warnings import warn + import numpy as np import scipy.sparse as sp from sklearn.base import BaseEstimator, ClusterMixin from sklearn.metrics._pairwise_distances_reduction import ArgKmin from sklearn.utils.extmath import safe_sparse_dot from sklearn.utils.validation import check_array, check_random_state -import numbers -from warnings import warn from ikpykit.kernel import IsoKernel @@ -72,6 +73,10 @@ class IDKC(BaseEstimator, ClusterMixin): Whether to perform post-processing refinement of clusters through iterative reassignment. Improves cluster quality but adds computational overhead. + force_assign_unassigned : bool, default=False + Whether to force-assign all remaining unassigned points (label -1) + to their nearest existing cluster center at the end of training. + random_state : int, RandomState instance or None, default=None Controls the randomness of the algorithm. Pass an int for reproducible results. @@ -118,6 +123,7 @@ def __init__( n_init_samples, init_center=None, is_post_process=True, + force_assign_unassigned=False, random_state=None, ): self.n_estimators = n_estimators @@ -128,6 +134,7 @@ def __init__( self.v = v self.n_init_samples = n_init_samples self.is_post_process = is_post_process + self.force_assign_unassigned = force_assign_unassigned self.init_center = init_center self.random_state = random_state self.clusters_ = [] @@ -164,7 +171,8 @@ def fit(self, X, y=None): if self.n_init_samples > X.shape[0]: self.n_init_samples = X.shape[0] raise warn( - f"Number of initial samples n_init_samples={self.n_init_samples} is greater than the number of samples in the dataset. Setting n_init_samples to {X.shape[0]}" + f"Number of initial samples n_init_samples={self.n_init_samples} is greater than the number of samples in the dataset. Setting n_init_samples to {X.shape[0]}", + stacklevel=2, ) else: self.n_init_samples = int(self.n_init_samples) @@ -188,8 +196,11 @@ def fit(self, X, y=None): if self.is_post_process: self._post_process(data_ik) + if self.force_assign_unassigned: + self._force_assign_unassigned_points(data_ik) + self.is_fitted_ = True - self.labels_ = self._get_labels(X) + self.labels_ = self._get_labels(data_ik) return self def _fit(self, X): @@ -213,12 +224,12 @@ def _fit(self, X): break assigned_mask = np.zeros_like(tmp_labels) for i in range(self.k): - I = np.logical_and(tmp_labels == i, tmp_similarity > r) - if np.sum(I) > 0: + mask = np.logical_and(tmp_labels == i, tmp_similarity > r) + if np.sum(mask) > 0: self.clusters_[i].add_points( - self.data_index[I], X[self.data_index][I] + self.data_index[mask], X[self.data_index][mask] ) - assigned_mask += I + assigned_mask += mask self._update_centers(X) self.data_index = np.delete(self.data_index, np.where(assigned_mask > 0)[0]) self.it_ += 1 @@ -253,32 +264,63 @@ def _post_process(self, X): threshold = max(int(np.ceil(X.shape[0] * 0.01)), 1) for _ in range(100): # Maximum iterations old_labels = self._get_labels(X) - data_index = np.arange(X.shape[0]) - # Compute new assignments - similarity = np.asarray(safe_sparse_dot(X, self.center_matrix.T)) - new_labels = np.argmax(similarity, axis=1) - # Find points that changed clusters - change_id = new_labels != old_labels - if np.sum(change_id) < threshold or len(np.unique(new_labels)) < self.k: + new_labels = self._predict_labels_from_similarity(X) + change_mask = new_labels != old_labels + + if self._should_stop_post_process(change_mask, new_labels, threshold): break - # Update cluster assignments - old_label, new_label = old_labels[change_id], new_labels[change_id] - changed_points = data_index[change_id] - - for old_cluster, new_cluster, point_idx in zip( - old_label, new_label, changed_points - ): - self._change_points( - self.clusters_[old_cluster], - self.clusters_[new_cluster], - point_idx, - X, - ) + + self._apply_post_process_changes(X, old_labels, new_labels, change_mask) # Update centers after reassignment self._update_centers(X) return self + def _predict_labels_from_similarity(self, X): + """Predict labels using current cluster centers and kernel means.""" + similarity = np.asarray(safe_sparse_dot(X, self.center_matrix.T)) + return np.argmax(similarity, axis=1) + + def _should_stop_post_process(self, change_mask, new_labels, threshold): + """Return True if post-processing should stop for this iteration.""" + return np.sum(change_mask) < threshold or len(np.unique(new_labels)) < self.k + + def _apply_post_process_changes(self, X, old_labels, new_labels, change_mask): + """Apply both unassigned-to-cluster assignments and cluster reassignments.""" + data_index = np.arange(X.shape[0]) + assign_from_unassigned = np.logical_and(old_labels < 0, change_mask) + reassign_points = np.logical_and(old_labels >= 0, change_mask) + + self._assign_unassigned_points( + X, data_index, new_labels, assign_from_unassigned + ) + self._reassign_points(X, data_index, old_labels, new_labels, reassign_points) + + def _assign_unassigned_points(self, X, data_index, new_labels, assign_mask): + """Assign points with label -1 to their new target clusters.""" + for new_cluster, point_idx in zip( + new_labels[assign_mask], + data_index[assign_mask], + ): + if point_idx not in self.clusters_[new_cluster].points: + self.clusters_[new_cluster].add_points(point_idx, X[point_idx]) + + def _reassign_points(self, X, data_index, old_labels, new_labels, reassign_mask): + """Move already assigned points between clusters.""" + old_label = old_labels[reassign_mask] + new_label = new_labels[reassign_mask] + changed_points = data_index[reassign_mask] + + for source_id, target_id, point_idx in zip( + old_label, new_label, changed_points + ): + self._change_points( + self.clusters_[source_id], + self.clusters_[target_id], + point_idx, + X, + ) + @property def center_matrix(self): c_mean = np.vstack( @@ -326,18 +368,56 @@ def _get_klc(self, X): def _get_labels(self, X): """Get cluster labels for all points in the dataset.""" - n_samples = X.shape[0] - labels = np.full( - n_samples, -1, dtype=int - ) # Default to -1 for unassigned points - for i, cluster in enumerate(self.clusters_): - labels[cluster.points_] = i - return labels + return KCluster.build_labels(self.clusters_, X.shape[0]) + + def _force_assign_unassigned_points(self, X): + """Force-assign any remaining unassigned points to nearest non-empty cluster.""" + labels = self._get_labels(X) + unassigned_idx = np.where(labels == -1)[0] + if unassigned_idx.size == 0: + return self + + non_empty_clusters = [ + cluster + for cluster in self.clusters_ + if cluster.n_points > 0 and cluster.kernel_mean is not None + ] + if len(non_empty_clusters) == 0: + raise ValueError("Cannot assign unassigned points: no non-empty clusters") + + center_matrix = np.vstack( + [ + c.kernel_mean.toarray() if sp.issparse(c.kernel_mean) else c.kernel_mean + for c in non_empty_clusters + ] + ) + similarity = np.asarray(safe_sparse_dot(X[unassigned_idx], center_matrix.T)) + target_local_idx = np.argmax(similarity, axis=1) + + for local_idx, point_idx in zip(target_local_idx, unassigned_idx): + non_empty_clusters[local_idx].add_points(point_idx, X[point_idx]) + + self._update_centers(X) + return self def _change_points(self, source_cluster, target_cluster, point_idx, X): """Move a point from one cluster to another, updating both clusters.""" - target_cluster.add_points(point_idx, X[point_idx]) - source_cluster.delete_points(point_idx, X[point_idx]) + actual_source = source_cluster + if point_idx not in actual_source.points: + actual_source = next( + (cluster for cluster in self.clusters_ if point_idx in cluster.points), + None, + ) + + if actual_source is None: + raise ValueError(f"Point {point_idx} not found in any cluster") + + if actual_source.id == target_cluster.id: + return self + + actual_source.delete_points(point_idx, X[point_idx]) + if point_idx not in target_cluster.points: + target_cluster.add_points(point_idx, X[point_idx]) return self def _update_centers(self, X): diff --git a/ikpykit/cluster/_kcluster.py b/ikpykit/cluster/_kcluster.py index 49c24da..03b8ef0 100644 --- a/ikpykit/cluster/_kcluster.py +++ b/ikpykit/cluster/_kcluster.py @@ -15,7 +15,7 @@ from scipy import sparse as sp -class KCluster(object): +class KCluster: def __init__(self, id: int) -> None: self.id = id self.center = None @@ -34,31 +34,34 @@ def add_points(self, ids, X): self.points_.extend(ids) def delete_points(self, points, X): - self.reduce_kernel_mean_(X) if isinstance(points, Integral): - try: - self.points_.remove(points) - except: - print(f"Point {points} not in cluster {self.id}") + if points not in self.points_: + raise ValueError(f"Point {points} not in cluster {self.id}") + self.points_.remove(points) + self.reduce_kernel_mean_(X) elif isinstance(points, Iterable): + missing_points = [p for p in points if p not in self.points_] + if missing_points: + raise ValueError(f"Points {missing_points} not in cluster {self.id}") for p in points: self.points_.remove(p) + self.reduce_kernel_mean_(X) def reduce_kernel_mean_(self, X): if self.kernel_mean_ is None: raise ValueError("Kernel mean is not initialized.") else: - self.kernel_mean_ = (self.kernel_mean_ * self.n_points - X.sum(axis=0)).sum(axis=0) / ( - self.n_points - X.shape[0] - ) + self.kernel_mean_ = (self.kernel_mean_ * self.n_points - X.sum(axis=0)).sum( + axis=0 + ) / (self.n_points - X.shape[0]) def increment_kernel_mean_(self, X): if self.kernel_mean_ is None: self.kernel_mean_ = X else: - self.kernel_mean_ = sp.vstack((self.kernel_mean_ * self.n_points, X)).sum(axis=0) / ( - self.n_points + X.shape[0] - ) + self.kernel_mean_ = sp.vstack((self.kernel_mean_ * self.n_points, X)).sum( + axis=0 + ) / (self.n_points + X.shape[0]) @property def n_points(self): @@ -71,3 +74,19 @@ def points(self): @property def kernel_mean(self): return self.kernel_mean_ + + @staticmethod + def build_labels(clusters, n_samples): + """Build label array from a sequence of clusters. + + Unassigned samples are marked as -1. + """ + labels = np.full(n_samples, -1, dtype=int) + for cluster_id, cluster in enumerate(clusters): + labels[cluster.points_] = cluster_id + return labels + + @staticmethod + def total_points(clusters): + """Return total number of points currently assigned across clusters.""" + return sum(cluster.n_points for cluster in clusters) diff --git a/ikpykit/cluster/_pskc.py b/ikpykit/cluster/_pskc.py index c43c988..3b82a1f 100644 --- a/ikpykit/cluster/_pskc.py +++ b/ikpykit/cluster/_pskc.py @@ -138,50 +138,75 @@ def fit(self, X, y=None): return self def _fit(self, X): - k = 1 + cluster_id = 1 point_indices = np.arange(X.shape[0]) while len(point_indices) > 0: - center_id = np.argmax( - safe_sparse_dot(X[point_indices], X[point_indices].mean(axis=0).T) - ) - c_k = KCluster(k) - c_k, point_indices = self._update_cluster( - c_k, + cluster, point_indices = self._create_seed_cluster( X, point_indices, - center_id, + cluster_id, ) - self.clusters_.append(c_k) + self.clusters_.append(cluster) if len(point_indices) == 0: break - nn_dists = ( - safe_sparse_dot(X[point_indices], X[point_indices].mean(axis=0).T) - / self.n_estimators + cluster, point_indices, threshold = self._attach_nearest_global_point( + cluster, + X, + point_indices, ) - nn_index = np.argmax(nn_dists) - nn_dist = nn_dists[nn_index] - c_k, point_indices = self._update_cluster(c_k, X, point_indices, nn_index) - r = (1 - self.v) * nn_dist - if r <= self.tau: - print("break") + if threshold <= self.tau: break - while r > self.tau: - S = ( - safe_sparse_dot(X[point_indices], c_k.kernel_mean.T) - / self.n_estimators - ) - x = np.where(S > r)[0] # Use [0] to get the indices as a 1D array - if len(x) == 0: - break - c_k, point_indices = self._update_cluster(c_k, X, point_indices, x) - r = (1 - self.v) * r + cluster, point_indices = self._expand_cluster( + cluster, + X, + point_indices, + threshold, + ) assert self._get_n_points() == X.shape[0] - len(point_indices) - k += 1 + cluster_id += 1 return self + def _create_seed_cluster(self, X, point_indices, cluster_id): + """Create a new cluster by selecting the point most similar to global mean.""" + center_idx = np.argmax(self._similarity_to_global_mean(X, point_indices)) + cluster = KCluster(cluster_id) + return self._update_cluster(cluster, X, point_indices, center_idx) + + def _attach_nearest_global_point(self, cluster, X, point_indices): + """Attach the current best remaining point and compute initial threshold.""" + nn_dists = self._similarity_to_global_mean(X, point_indices) / self.n_estimators + nn_index = np.argmax(nn_dists) + nn_dist = nn_dists[nn_index] + cluster, point_indices = self._update_cluster( + cluster, X, point_indices, nn_index + ) + threshold = (1 - self.v) * nn_dist + return cluster, point_indices, threshold + + def _expand_cluster(self, cluster, X, point_indices, threshold): + """Expand a cluster by iteratively adding points above decayed threshold.""" + r = threshold + while r > self.tau: + similarities = ( + safe_sparse_dot(X[point_indices], cluster.kernel_mean.T) + / self.n_estimators + ) + selected = np.where(r < similarities)[0] + if len(selected) == 0: + break + cluster, point_indices = self._update_cluster( + cluster, X, point_indices, selected + ) + r = (1 - self.v) * r + return cluster, point_indices + + def _similarity_to_global_mean(self, X, point_indices): + """Compute similarity of points to mean of the current active set.""" + return safe_sparse_dot(X[point_indices], X[point_indices].mean(axis=0).T) + def _update_cluster( self, c_k, @@ -195,15 +220,8 @@ def _update_cluster( def _get_labels(self, X): """Get cluster labels for all points in the dataset.""" - n_samples = X.shape[0] - labels = np.full( - n_samples, -1, dtype=int - ) # Default to -1 for unassigned points - for i, cluster in enumerate(self.clusters_): - labels[cluster.points_] = i - return labels + return KCluster.build_labels(self.clusters_, X.shape[0]) def _get_n_points(self): check_is_fitted(self) - n_points = sum([c.n_points for c in self.clusters_]) - return n_points + return KCluster.total_points(self.clusters_) diff --git a/ikpykit/cluster/tests/test_idkc.py b/ikpykit/cluster/tests/test_idkc.py index 3321e58..002f282 100644 --- a/ikpykit/cluster/tests/test_idkc.py +++ b/ikpykit/cluster/tests/test_idkc.py @@ -8,10 +8,11 @@ work. If not, see . """ -import pytest import numpy as np +import pytest from sklearn import metrics from sklearn.datasets import make_blobs, make_circles + from ikpykit.cluster import IDKC @@ -96,7 +97,7 @@ def test_idkc_parameter_validation(): def test_idkc_methods(): # Test with different isolation methods - X, true_labels = make_blobs(n_samples=100, centers=3, random_state=42) + X, _true_labels = make_blobs(n_samples=100, centers=3, random_state=42) methods = ["anne", "inne"] for method in methods: @@ -117,7 +118,7 @@ def test_idkc_methods(): def test_idkc_init_center(): # Test with explicit init_center - X, true_labels = make_blobs(n_samples=100, centers=3, random_state=42) + X, _true_labels = make_blobs(n_samples=100, centers=3, random_state=42) # Choose specific points as initial centers init_centers = [0, 33, 66] @@ -140,7 +141,7 @@ def test_idkc_init_center(): def test_idkc_post_processing(): # Test with and without post-processing - X, true_labels = make_blobs(n_samples=100, centers=3, random_state=42) + X, _true_labels = make_blobs(n_samples=100, centers=3, random_state=42) # With post-processing idkc_with_pp = IDKC( @@ -179,7 +180,7 @@ def test_idkc_post_processing(): def test_idkc_non_linear_clusters(): # Test on a dataset with non-linearly separable clusters - X, true_labels = make_circles( + X, _true_labels = make_circles( n_samples=200, factor=0.5, noise=0.05, random_state=42 ) @@ -224,7 +225,51 @@ def test_idkc_empty_clusters(): random_state=42, ) - labels = idkc.fit_predict(X) + idkc.fit_predict(X) # Should still have k=3 clusters in the model assert len(idkc.clusters_) == 3 + + +def test_idkc_force_assign_unassigned_optional_flag(): + class _IDKCInjectUnassigned(IDKC): + def _post_process(self, X): + super()._post_process(X) + # Simulate an unassigned point after post-process. + for cluster in self.clusters_: + if cluster.n_points > 1: + point_idx = cluster.points[0] + cluster.delete_points(point_idx, X[point_idx]) + self.injected_unassigned_idx_ = point_idx + break + return self + + X, _ = make_blobs(n_samples=120, centers=3, random_state=42) + + base = _IDKCInjectUnassigned( + n_estimators=100, + max_samples=10, + method="anne", + k=3, + kn=5, + v=0.1, + n_init_samples=30, + force_assign_unassigned=False, + random_state=42, + ) + base_labels = base.fit_predict(X) + assert np.any(base_labels == -1) + + forced = _IDKCInjectUnassigned( + n_estimators=100, + max_samples=10, + method="anne", + k=3, + kn=5, + v=0.1, + n_init_samples=30, + force_assign_unassigned=True, + random_state=42, + ) + forced_labels = forced.fit_predict(X) + assert not np.any(forced_labels == -1) diff --git a/ikpykit/cluster/tests/test_pskc.py b/ikpykit/cluster/tests/test_pskc.py index c9c6047..ee6c239 100644 --- a/ikpykit/cluster/tests/test_pskc.py +++ b/ikpykit/cluster/tests/test_pskc.py @@ -89,7 +89,7 @@ def test_pskc_properties(simple_data): def test_pskc_blob_data(blob_data): """Test PSKC on blob data.""" - X, y = blob_data + X, _y = blob_data pskc = PSKC(random_state=42) pskc.fit(X) assert pskc.is_fitted_ diff --git a/ikpykit/graph/__init__.py b/ikpykit/graph/__init__.py index 6763ea6..fe07568 100644 --- a/ikpykit/graph/__init__.py +++ b/ikpykit/graph/__init__.py @@ -12,6 +12,6 @@ from ._isographkernel import IsoGraphKernel __all__ = [ - "IsoGraphKernel", "IKGOD", + "IsoGraphKernel", ] diff --git a/ikpykit/graph/_ikgod.py b/ikpykit/graph/_ikgod.py index 9a1b5d1..b07c72c 100644 --- a/ikpykit/graph/_ikgod.py +++ b/ikpykit/graph/_ikgod.py @@ -143,7 +143,8 @@ def fit(self, adjacency, features, y=None): warn( f"max_samples ({self.max_samples}) is greater than the " f"total number of samples ({n_samples}). max_samples " - f"will be set to n_samples for estimation." + f"will be set to n_samples for estimation.", + stacklevel=2, ) max_samples = n_samples else: diff --git a/ikpykit/graph/_isographkernel.py b/ikpykit/graph/_isographkernel.py index 4aef9a6..ba530b5 100644 --- a/ikpykit/graph/_isographkernel.py +++ b/ikpykit/graph/_isographkernel.py @@ -142,7 +142,7 @@ def transform( features : sparse.csr_matrix, np.ndarray Features, array of shape (n_nodes, n_features). h : int - The number of iterations for Weisfeiler–Lehman embedding. + The number of iterations for Weisfeiler-Lehman embedding. dense_output : bool, default=False Whether to return a dense array. @@ -161,7 +161,7 @@ def transform( if sp.issparse(embedding) and hasattr(embedding, "toarray"): return embedding.toarray() else: - warn("The IsoKernel transform output is already dense.") + warn("The IsoKernel transform output is already dense.", stacklevel=2) return embedding def _wlembedding( @@ -190,7 +190,7 @@ def _wlembedding( degrees = get_degrees(adjacency) tmp_embedding = features embedding = copy.deepcopy(features) - for it in range(h + 1)[1:]: + for _it in range(h + 1)[1:]: updated_embedding = np.empty(features.shape) for i in range(n_nodes): # TODO: Add weights neighbors = get_neighbors(adjacency, i) @@ -224,7 +224,7 @@ def fit_transform( features : sparse.csr_matrix, np.ndarray Features, array of shape (n_nodes, n_features). h : int - The number of iterations for Weisfeiler–Lehman embedding. + The number of iterations for Weisfeiler-Lehman embedding. dense_output : bool, default=False Whether to return a dense array. diff --git a/ikpykit/graph/tests/test_ikgod.py b/ikpykit/graph/tests/test_ikgod.py index 952573e..31f8cba 100644 --- a/ikpykit/graph/tests/test_ikgod.py +++ b/ikpykit/graph/tests/test_ikgod.py @@ -34,10 +34,11 @@ def test_ikgod_with_synthetic_data(): # Create a simple adjacency matrix (a ring graph) n_samples = X.shape[0] - adjacency = sp.csr_matrix((n_samples, n_samples)) + adjacency = sp.lil_matrix((n_samples, n_samples)) for i in range(n_samples): adjacency[i, (i + 1) % n_samples] = 1 adjacency[(i + 1) % n_samples, i] = 1 + adjacency = adjacency.tocsr() # Initialize and fit the model model = IKGOD(n_estimators=50, random_state=42, h=2) @@ -124,7 +125,7 @@ def test_ikgod_h_hop_neighbors(): ] ) - features = np.random.rand(5, 2) + np.random.rand(5, 2) # h=1 should see only direct neighbors model_h1 = IKGOD(h=1, n_estimators=10) diff --git a/ikpykit/graph/tests/test_isographkernel.py b/ikpykit/graph/tests/test_isographkernel.py index ce114ae..738a2fb 100644 --- a/ikpykit/graph/tests/test_isographkernel.py +++ b/ikpykit/graph/tests/test_isographkernel.py @@ -42,11 +42,11 @@ def test_isographkernel_transform(): """Test IsoGraphKernel transform method.""" # Create sample features and adjacency matrix features = np.random.rand(10, 5) - adjacency = np.zeros((10, 10)) + adjacency = sp.lil_matrix((10, 10)) for i in range(10): adjacency[i, (i + 1) % 10] = 1 adjacency[(i + 1) % 10, i] = 1 - adjacency = sp.csr_matrix(adjacency) + adjacency = adjacency.tocsr() # Initialize, fit and transform igk = IsoGraphKernel(n_estimators=50, random_state=42) @@ -65,11 +65,11 @@ def test_isographkernel_fit_transform(): """Test IsoGraphKernel fit_transform method.""" # Create sample features and adjacency matrix features = np.random.rand(10, 5) - adjacency = np.zeros((10, 10)) + adjacency = sp.lil_matrix((10, 10)) for i in range(10): adjacency[i, (i + 1) % 10] = 1 adjacency[(i + 1) % 10, i] = 1 - adjacency = sp.csr_matrix(adjacency) + adjacency = adjacency.tocsr() # Test fit_transform with sparse output igk = IsoGraphKernel(n_estimators=50, random_state=42) @@ -88,11 +88,11 @@ def test_isographkernel_with_different_h_values(): """Test IsoGraphKernel with different h values for WL embedding.""" # Create sample features and adjacency matrix features = np.random.rand(10, 5) - adjacency = np.zeros((10, 10)) + adjacency = sp.lil_matrix((10, 10)) for i in range(10): adjacency[i, (i + 1) % 10] = 1 adjacency[(i + 1) % 10, i] = 1 - adjacency = sp.csr_matrix(adjacency) + adjacency = adjacency.tocsr() igk = IsoGraphKernel(n_estimators=50, random_state=42) igk.fit(features) diff --git a/ikpykit/graph/utils.py b/ikpykit/graph/utils.py index cb4c3d8..8c2768d 100644 --- a/ikpykit/graph/utils.py +++ b/ikpykit/graph/utils.py @@ -33,10 +33,7 @@ def get_degrees(input_matrix: sp.csr_matrix, transpose: bool = False) -> np.ndar degrees : np.ndarray Array of degrees. """ - if transpose: - matrix = sp.csr_matrix(input_matrix.T) - else: - matrix = input_matrix + matrix = sp.csr_matrix(input_matrix.T) if transpose else input_matrix degrees = matrix.indptr[1:] - matrix.indptr[:-1] return degrees @@ -53,10 +50,7 @@ def get_neighbors( to get the neighbors of a column node. """ - if transpose: - matrix = sp.csr_matrix(input_matrix.T) - else: - matrix = input_matrix + matrix = sp.csr_matrix(input_matrix.T) if transpose else input_matrix neighbors = matrix.indices[matrix.indptr[node] : matrix.indptr[node + 1]] return neighbors diff --git a/ikpykit/group/utils.py b/ikpykit/group/utils.py index bd7bf7c..bedccca 100644 --- a/ikpykit/group/utils.py +++ b/ikpykit/group/utils.py @@ -8,14 +8,14 @@ work. If not, see . """ -from typing import Any, List, Optional, Union +from typing import Any, Optional, Union import numpy as np from sklearn.utils.validation import check_array def check_format( - X: Union[List[Any], np.ndarray], + X: Union[list[Any], np.ndarray], allow_empty: bool = False, n_features: Optional[int] = None, ) -> np.ndarray: diff --git a/ikpykit/kernel/_ik_inne.py b/ikpykit/kernel/_ik_inne.py index 135dd36..2965d47 100644 --- a/ikpykit/kernel/_ik_inne.py +++ b/ikpykit/kernel/_ik_inne.py @@ -116,7 +116,7 @@ def transform(self, X): """ check_is_fitted(self) X = check_array(X) - n, m = X.shape + n, _m = X.shape embedding = None for i in range(self.n_estimators): nearest_index, nearest_values = pairwise_distances_argmin_min( diff --git a/ikpykit/kernel/_isokernel.py b/ikpykit/kernel/_isokernel.py index 74e4d2c..14ff9e0 100644 --- a/ikpykit/kernel/_isokernel.py +++ b/ikpykit/kernel/_isokernel.py @@ -98,17 +98,17 @@ def fit(self, X, y=None): max_samples = min(16, n_samples) else: raise ValueError( - "max_samples (%s) is not supported." + f"max_samples ({self.max_samples}) is not supported." 'Valid choices are: "auto", int or' - "float" % self.max_samples + "float" ) elif isinstance(self.max_samples, numbers.Integral): if self.max_samples > n_samples: warn( - "max_samples (%s) is greater than the " - "total number of samples (%s). max_samples " - "will be set to n_samples for estimation." - % (self.max_samples, n_samples) + f"max_samples ({self.max_samples}) is greater than the " + f"total number of samples ({n_samples}). max_samples " + "will be set to n_samples for estimation.", + stacklevel=2, ) max_samples = n_samples else: @@ -116,7 +116,7 @@ def fit(self, X, y=None): else: # float if not 0.0 < self.max_samples <= 1.0: raise ValueError( - "max_samples must be in (0, 1], got %r" % self.max_samples + f"max_samples must be in (0, 1], got {self.max_samples!r}" ) max_samples = int(self.max_samples * X.shape[0]) self.max_samples_ = max_samples @@ -135,8 +135,8 @@ def fit(self, X, y=None): ) else: raise ValueError( - "method (%s) is not supported." - 'Valid choices are: "anne", "inne" or "iforest"' % self.method + f"method ({self.method}) is not supported." + 'Valid choices are: "anne", "inne" or "iforest"' ) self.iso_kernel_.fit(X) @@ -184,5 +184,5 @@ def transform(self, X, dense_output=False): if sp.issparse(X_trans) and hasattr(X_trans, "toarray"): return X_trans.toarray() else: - warn("The IsoKernel transform output is already dense.") + warn("The IsoKernel transform output is already dense.", stacklevel=2) return X_trans diff --git a/ikpykit/stream/changedetect/_icid.py b/ikpykit/stream/changedetect/_icid.py index 4074a89..e08b8c5 100644 --- a/ikpykit/stream/changedetect/_icid.py +++ b/ikpykit/stream/changedetect/_icid.py @@ -83,7 +83,7 @@ class ICID(BaseEstimator): ---------- .. [1] Y. Cao, Y. Zhu, K. M. Ting, F. D. Salim, H. X. Li, L. Yang, G. Li (2024). Detecting change intervals with isolation distributional kernel. - Journal of Artificial Intelligence Research, 79:273–306. + Journal of Artificial Intelligence Research, 79:273-306. Examples -------- @@ -110,7 +110,7 @@ class ICID(BaseEstimator): def __init__( self, n_estimators=200, - max_samples_list=[2, 4, 8, 16, 32, 64], + max_samples_list=None, method="inne", stability_method="entropy", adjust_rate=0.1, @@ -118,6 +118,8 @@ def __init__( window_size=10, random_state=None, ): + if max_samples_list is None: + max_samples_list = [2, 4, 8, 16, 32, 64] self.n_estimators = n_estimators self.max_samples_list = max_samples_list self.method = method @@ -167,8 +169,6 @@ def fit_predict_batch(self, X): ---------- X : np.array of shape (n_samples, n_features) The input instances. - window_size : int, default=10 - The size of the sliding window. Returns ------- @@ -178,9 +178,9 @@ def fit_predict_batch(self, X): self.fit(X) is_inlier = np.ones(len(self.interval_score_), dtype=int) threshold = self._determine_anomaly_bounds() - is_inlier[ - self.interval_score_ > threshold - ] = -1 # Higher scores indicate change + is_inlier[self.interval_score_ > threshold] = ( + -1 + ) # Higher scores indicate change return is_inlier def predict_online(self, X): diff --git a/ikpykit/stream/cluster/_inode.py b/ikpykit/stream/cluster/_inode.py index c75c816..42fc111 100644 --- a/ikpykit/stream/cluster/_inode.py +++ b/ikpykit/stream/cluster/_inode.py @@ -12,7 +12,7 @@ import random import string from collections import defaultdict, deque -from typing import Dict, List, Optional, Tuple +from typing import Optional import numpy as np from numba import jit @@ -55,9 +55,9 @@ def __init__(self): self.id = "id" + "".join( random.choice(string.ascii_uppercase + string.digits) for _ in range(15) ) - self.children: List["INODE"] = [] - self.parent: Optional["INODE"] = None - self.pts: List[Tuple] = [] # each pt is a tuple of (label, id) + self.children: list[INODE] = [] + self.parent: Optional[INODE] = None + self.pts: list[tuple] = [] # each pt is a tuple of (label, id) self.ikv = None # Isolation kernel value self.point_counter: int = 0 @@ -245,7 +245,7 @@ def purity(self, cluster=None): A float representing the purity of this node """ if cluster: - pts = [p for l in self.leaves() for p in l.pts] + pts = [p for leaf in self.leaves() for p in leaf.pts] return ( float(len([pt for pt in pts if pt[0] == cluster])) / len(pts) if pts @@ -265,31 +265,31 @@ def clusters(self): """ return self.true_leaves() - def class_counts(self) -> Dict: + def class_counts(self) -> dict: """Produce a map from label to the # of descendant points with label.""" label_to_count = defaultdict(float) - pts = [p for l in self.leaves() for p in l.pts] + pts = [p for leaf in self.leaves() for p in leaf.pts] for x in pts: - l, _ = x - label_to_count[l] += 1.0 + label, _ = x + label_to_count[label] += 1.0 return label_to_count def pure_class(self): """If this node has purity 1.0, return its label; else return None.""" cc = self.class_counts() if len(cc) == 1: - return list(cc.keys())[0] + return next(iter(cc.keys())) else: return None - def siblings(self) -> List["INODE"]: + def siblings(self) -> list["INODE"]: """Return a list of my siblings.""" if self.parent: return [child for child in self.parent.children if child != self] else: return [] - def aunts(self) -> List["INODE"]: + def aunts(self) -> list["INODE"]: """Return a list of all of my aunts.""" if self.parent and self.parent.parent: return [ @@ -298,7 +298,7 @@ def aunts(self) -> List["INODE"]: else: return [] - def _ancestors(self) -> List["INODE"]: + def _ancestors(self) -> list["INODE"]: """Return all of this nodes ancestors in order to the root.""" anc = [] curr = self @@ -314,9 +314,9 @@ def depth(self) -> int: def height(self) -> int: """Return the height of this node (maximum depth of any leaf).""" leaves = self.leaves() - return max([l.depth() for l in leaves]) if leaves else 0 + return max([leaf.depth() for leaf in leaves]) if leaves else 0 - def descendants(self) -> List["INODE"]: + def descendants(self) -> list["INODE"]: """Return all descendants of the current node.""" d = [] queue = deque([self]) @@ -327,7 +327,7 @@ def descendants(self) -> List["INODE"]: queue.extend(n.children) return d - def leaves(self) -> List["INODE"]: + def leaves(self) -> list["INODE"]: """Return the list of leaves under this node.""" lvs = [] queue = deque([self]) @@ -348,7 +348,7 @@ def lca(self, other: "INODE") -> "INODE": Returns: A node that is the lowest common ancestor """ - ancestors = set(self._ancestors() + [self]) + ancestors = {*self._ancestors(), self} curr_node = other while curr_node not in ancestors: if not curr_node.parent: diff --git a/ikpykit/stream/cluster/_streakhc.py b/ikpykit/stream/cluster/_streakhc.py index 6b2aa19..902747f 100644 --- a/ikpykit/stream/cluster/_streakhc.py +++ b/ikpykit/stream/cluster/_streakhc.py @@ -10,7 +10,7 @@ from __future__ import annotations -from typing import Literal, Optional, Union +from typing import Literal import numpy as np from sklearn.base import BaseEstimator, ClusterMixin @@ -94,9 +94,9 @@ def __init__( self, method: Literal["inne", "anne"] = "anne", n_estimators: int = 200, - max_samples: Union[Literal["auto"], int, float] = "auto", + max_samples: Literal["auto"] | int | float = "auto", max_leaf: int = 5000, - random_state: Optional[Union[int, np.random.RandomState]] = None, + random_state: int | np.random.RandomState | None = None, ): self.method = method self.n_estimators = n_estimators @@ -108,7 +108,7 @@ def __init__( self.iso_kernel_ = None self.n_features_in_ = None - def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> STREAMKHC: + def fit(self, X: np.ndarray, y: np.ndarray | None = None) -> STREAMKHC: """Fit the model with a batch of data points. Parameters @@ -189,7 +189,7 @@ def _process_batch(self, X_ikv: np.ndarray, y: np.ndarray) -> None: ) self.point_counter_ += 1 - def fit_online(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> STREAMKHC: + def fit_online(self, X: np.ndarray, y: np.ndarray | None = None) -> STREAMKHC: """Fit the model with a stream of data points. Parameters diff --git a/ikpykit/stream/cluster/utils/Graphviz.py b/ikpykit/stream/cluster/utils/Graphviz.py index 01dedf3..2280006 100644 --- a/ikpykit/stream/cluster/utils/Graphviz.py +++ b/ikpykit/stream/cluster/utils/Graphviz.py @@ -1,4 +1,7 @@ -class Graphviz(object): +import contextlib + + +class Graphviz: def __init__(self): self.internal_color = "lavenderblush4" self.colors = [ @@ -125,17 +128,14 @@ def __init__(self): def format_id(self, ID): if not ID.startswith("id"): return ( - ("id%s" % ID) + (f"id{ID}") .replace("-", "") .replace("#", "_HASH_") .replace(".", "_DOT_") ) else: return ( - ("%s" % ID) - .replace("-", "") - .replace("#", "_HASH_") - .replace(".", "_DOT_") + (f"{ID}").replace("-", "").replace("#", "_HASH_").replace(".", "_DOT_") ) def clean_label(self, s): @@ -145,15 +145,13 @@ def get_node_label(self, node): lbl = [] lbl.append(self.format_id(node.id)) lbl.append("
") - lbl.append("num pts: %d" % len(node.leaves())) + lbl.append(f"num pts: {len(node.leaves())}") lbl.append("
") - try: - lbl.append("purity: %f" % node.purity()) - except Exception: - pass + with contextlib.suppress(Exception): + lbl.append(f"purity: {node.purity():f}") try: lbl.append("
") - lbl.append("across: %s" % node.best_across_debug) + lbl.append(f"across: {node.best_across_debug}") except Exception: pass return "".join(lbl) @@ -174,7 +172,7 @@ def format_graphiz_node(self, node): if node.purity() == 1.0: if hasattr(node, "pts"): curr_node = node - while curr_node.pts == None: + while curr_node.pts is None: curr_node = curr_node.children[0] if len(curr_node.pts) > 0: w_gt = [x for x in curr_node.pts if x[1] and x[1] != "None"] @@ -188,47 +186,28 @@ def format_graphiz_node(self, node): if node.parent is None: s.append( - "\n%s[shape=%s;style=filled;width=1;color=%s;label=<%s
%s
>]" - % ( - self.format_id(node.id), - shape, - color, - self.get_node_label(node), - color, - ) + f"\n{self.format_id(node.id)}[shape={shape};style=filled;width=1;color={color};label=<{self.get_node_label(node)}
{color}
>]" ) s.append("\nROOTNODE[shape=star;style=filled;color=gold;label=]") - s.append("\nROOTNODE->%s" % self.format_id(node.id)) + s.append(f"\nROOTNODE->{self.format_id(node.id)}") else: leaf_m = "" if hasattr(node, "pts") and node.pts and len(node.pts) > 0: if hasattr(node.pts[0][0], "mid"): leaf_m = ( - "%s|%s" % (node.pts[0][0].mid, node.pts[0][0].gt) + f"{node.pts[0][0].mid}|{node.pts[0][0].gt}" if node.is_leaf() else "" ) else: leaf_m = ( - "%s|%s" % (node.pts[0][1], node.pts[0][0]) - if node.is_leaf() - else "" + f"{node.pts[0][1]}|{node.pts[0][0]}" if node.is_leaf() else "" ) s.append( - "\n%s[shape=%s;style=filled;width=1;color=%s;label=<%s
" - "%s
%s
>]" - % ( - self.format_id(node.id), - shape, - color, - self.get_node_label(node), - color, - leaf_m, - ) - ) - s.append( - "\n%s->%s" % (self.format_id(node.parent.id), self.format_id(node.id)) + f"\n{self.format_id(node.id)}[shape={shape};style=filled;width=1;color={color};label=<{self.get_node_label(node)}
" + f"{color}
{leaf_m}
>]" ) + s.append(f"\n{self.format_id(node.parent.id)}->{self.format_id(node.id)}") return "".join(s) def graphviz_tree( diff --git a/ikpykit/stream/cluster/utils/deltasep_utils.py b/ikpykit/stream/cluster/utils/deltasep_utils.py index 7beb7b7..328b8f7 100644 --- a/ikpykit/stream/cluster/utils/deltasep_utils.py +++ b/ikpykit/stream/cluster/utils/deltasep_utils.py @@ -13,9 +13,7 @@ limitations under the License. """ -""" -Utilities for creating delta separated data sets. -""" +# Utilities for creating delta separated data sets. import numpy as np @@ -84,7 +82,7 @@ def _create_constrained_dataset(centers, delta, size): dataset = [] count = 0 for i, c in enumerate(centers): - for j in range(size): + for _j in range(size): x = np.random.multivariate_normal(c, np.identity(np.size(c))) - c direction = x / np.linalg.norm(x) magnitude = np.random.uniform(0.0, 0.5 * delta) @@ -322,10 +320,10 @@ def _random_standard_centers(n=100): def _from_file(filename): - with open(filename, "r") as f: + with open(filename) as f: clustering = [] for line in f: splits = line.split("\t") - l, vec = int(splits[0]), np.array([float(x) for x in splits[1:]]) - clustering.append((vec, l)) + label, vec = int(splits[0]), np.array([float(x) for x in splits[1:]]) + clustering.append((vec, label)) return clustering diff --git a/ikpykit/stream/cluster/utils/dendrogram_purity.py b/ikpykit/stream/cluster/utils/dendrogram_purity.py index 0d6cd25..8f21c38 100644 --- a/ikpykit/stream/cluster/utils/dendrogram_purity.py +++ b/ikpykit/stream/cluster/utils/dendrogram_purity.py @@ -16,7 +16,7 @@ from itertools import combinations, groupby import numpy as np -from tqdm._tqdm import trange +from tqdm import trange def expected_dendrogram_purity(root): @@ -44,11 +44,11 @@ def get_cluster(x): cluster_to_leaves = { c: list(ls) for c, ls in groupby(sorted(leaves, key=get_cluster), get_cluster) } - leaf_to_cluster = {l: l.pts[0][0] for l in leaves} + leaf_to_cluster = {leaf: leaf.pts[0][0] for leaf in leaves} non_singleton_leaves = [ - l - for l in leaf_to_cluster.keys() - if len(cluster_to_leaves[leaf_to_cluster[l]]) > 1 + leaf + for leaf in leaf_to_cluster + if len(cluster_to_leaves[leaf_to_cluster[leaf]]) > 1 ] if len(non_singleton_leaves) == 0.0: return 1.0 @@ -58,7 +58,7 @@ def get_cluster(x): # from the same class unformly at random. samps = len(non_singleton_leaves) * 5 # TODO (AK): pick 5 in a better way. unnormalized_purity = 0.0 - for i in trange(samps): + for _i in trange(samps): rand_leaf = np.random.choice(non_singleton_leaves) cluster = leaf_to_cluster[rand_leaf] rand_cluster_member = np.random.choice(cluster_to_leaves[cluster]) diff --git a/ikpykit/stream/cluster/utils/dendrogram_purity_pool.py b/ikpykit/stream/cluster/utils/dendrogram_purity_pool.py index 8db22b9..5a91e7b 100644 --- a/ikpykit/stream/cluster/utils/dendrogram_purity_pool.py +++ b/ikpykit/stream/cluster/utils/dendrogram_purity_pool.py @@ -33,7 +33,7 @@ def __init__( *args, **kwargs, ): - super(Producer, self).__init__(*args, **kwargs) + super().__init__(*args, **kwargs) self.samp_queue = samp_queue self.result_queue = result_queue self.non_singleton_leaves = non_singleton_leaves @@ -47,8 +47,8 @@ def run(self): print("bye") queueLock.release() break - print("剩余采样数：", self.samp_queue.qsize()) - samp = self.samp_queue.get() + print("Remaining samples:", self.samp_queue.qsize()) + self.samp_queue.get() queueLock.release() self.get_purity() @@ -94,11 +94,11 @@ def get_cluster(x): cluster_to_leaves = { c: list(ls) for c, ls in groupby(sorted(leaves, key=get_cluster), get_cluster) } - leaf_to_cluster = {l: l.pts[0][0] for l in leaves} + leaf_to_cluster = {leaf: leaf.pts[0][0] for leaf in leaves} non_singleton_leaves = [ - l - for l in leaf_to_cluster.keys() - if len(cluster_to_leaves[leaf_to_cluster[l]]) > 1 + leaf + for leaf in leaf_to_cluster + if len(cluster_to_leaves[leaf_to_cluster[leaf]]) > 1 ] if len(non_singleton_leaves) == 0.0: return 1.0 diff --git a/ikpykit/stream/cluster/utils/file_utils.py b/ikpykit/stream/cluster/utils/file_utils.py index e850c8e..07ded30 100644 --- a/ikpykit/stream/cluster/utils/file_utils.py +++ b/ikpykit/stream/cluster/utils/file_utils.py @@ -39,8 +39,12 @@ def load_data(filename): split_sep = "," elif filename.endswith(".tsv"): split_sep = "\t" - with open(filename, "r") as f: + with open(filename) as f: for line in f: splits = line.strip().split(sep=split_sep) - pid, l, vec = splits[0], splits[1], np.array([float(x) for x in splits[2:]]) - yield ((l, pid, vec)) + pid, label, vec = ( + splits[0], + splits[1], + np.array([float(x) for x in splits[2:]]), + ) + yield ((label, pid, vec)) diff --git a/ikpykit/stream/cluster/utils/logger.py b/ikpykit/stream/cluster/utils/logger.py index 6b509c7..c5cca86 100644 --- a/ikpykit/stream/cluster/utils/logger.py +++ b/ikpykit/stream/cluster/utils/logger.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ Copyright (c) 2021 Xin Han @@ -6,14 +5,16 @@ License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/. """ + # coding: utf-8 import logging from logging import handlers +from typing import ClassVar -class Logger(object): - level_relations = { +class Logger: + level_relations: ClassVar[dict[str, int]] = { "debug": logging.DEBUG, "info": logging.INFO, "warning": logging.WARNING, diff --git a/ikpykit/stream/cluster/utils/serialize_trees.py b/ikpykit/stream/cluster/utils/serialize_trees.py index 9d3d2da..1197495 100644 --- a/ikpykit/stream/cluster/utils/serialize_trees.py +++ b/ikpykit/stream/cluster/utils/serialize_trees.py @@ -14,10 +14,10 @@ """ import math +from queue import Queue import numpy as np from numba import jit -from queue import Queue @jit(nopython=True) @@ -81,8 +81,7 @@ def serliaze_tree_to_file_with_point_ids(root, fn): ) dis = getDistance(curr_node) if curr_node.parent else "None" fout.write( - "%s\t%s\t%s\t%s\t%s\n" - % ( + "{}\t{}\t{}\t{}\t{}\n".format( curr_node_id, sibling_node_id, curr_node.parent.id if curr_node.parent else "None", @@ -117,8 +116,7 @@ def serliaze_tree_to_file(root, fn): while not queue.empty(): curr_node = queue.get() fout.write( - "%s\t%s\t%s\t%s\n" - % ( + "{}\t{}\t{}\t{}\n".format( curr_node.id, curr_node.parent.id if curr_node.parent else "None", curr_node.pts[0][1] if curr_node.is_leaf() else "None", @@ -141,8 +139,7 @@ def serliaze_collapsed_tree_to_file_with_point_ids(root, fn): else curr_node.id ) fout.write( - "%s\t%s\t%s\n" - % ( + "{}\t{}\t{}\n".format( curr_node_id, curr_node.parent.id if curr_node.parent else "None", ( diff --git a/ikpykit/stream/tests/test_icid.py b/ikpykit/stream/tests/test_icid.py index 6b573e4..e8626c6 100644 --- a/ikpykit/stream/tests/test_icid.py +++ b/ikpykit/stream/tests/test_icid.py @@ -9,6 +9,7 @@ from ikpykit.stream import ICID rng = check_random_state(42) +pytestmark = pytest.mark.timeout(120) def test_icid_init(): diff --git a/ikpykit/timeseries/anomaly/_iktod.py b/ikpykit/timeseries/anomaly/_iktod.py index f6c8170..4c6c326 100644 --- a/ikpykit/timeseries/anomaly/_iktod.py +++ b/ikpykit/timeseries/anomaly/_iktod.py @@ -151,7 +151,8 @@ def fit(self, X) -> "IKTOD": if rest_samples != 0: warnings.warn( f"The last sequence of series has {rest_samples} samples, " - f"which are less than other sequence." + f"which are less than other sequence.", + stacklevel=2, ) # Fit the model diff --git a/ikpykit/timeseries/tests/test_iktod.py b/ikpykit/timeseries/tests/test_iktod.py index 3c1dae8..77aa4b2 100644 --- a/ikpykit/timeseries/tests/test_iktod.py +++ b/ikpykit/timeseries/tests/test_iktod.py @@ -1,3 +1,5 @@ +import re + import numpy as np import pytest @@ -51,9 +53,11 @@ def test_iktod_invalid_input(): detector = IKTOD(period_length=period_length) - with pytest.raises( - ValueError, match="Time series length.*must be greater than period_length" - ): + expected_message = ( + f"Time series length ({X.shape[0]}) must be greater than " + f"period_length ({period_length})." + ) + with pytest.raises(ValueError, match=re.escape(expected_message)): detector.fit(X) diff --git a/ikpykit/trajectory/dataloader/base.py b/ikpykit/trajectory/dataloader/base.py index 095db53..b1e721d 100644 --- a/ikpykit/trajectory/dataloader/base.py +++ b/ikpykit/trajectory/dataloader/base.py @@ -1,6 +1,5 @@ from __future__ import annotations -import abc import inspect import os import pathlib @@ -25,7 +24,7 @@ def get_data_home(): return data_home -class Dataset(abc.ABC): +class Dataset: """Base class for all datasets. All datasets inherit from this class, be they stored in a file or generated on the fly. diff --git a/ikpykit/trajectory/tests/test_ikat.py b/ikpykit/trajectory/tests/test_ikat.py index e446e49..71c6eb1 100644 --- a/ikpykit/trajectory/tests/test_ikat.py +++ b/ikpykit/trajectory/tests/test_ikat.py @@ -129,5 +129,5 @@ def test_IKAT_decision_function(trajectory_data, method): def test_IKAT_check_is_fitted(): ikat = IKAT() - with pytest.raises(Exception): # Should raise some kind of not fitted error + with pytest.raises(ValueError): # Should raise some kind of not fitted error ikat.predict([np.random.rand(5, 2)]) diff --git a/ikpykit/trajectory/utils.py b/ikpykit/trajectory/utils.py index 7986e12..5d45626 100644 --- a/ikpykit/trajectory/utils.py +++ b/ikpykit/trajectory/utils.py @@ -8,13 +8,13 @@ work. If not, see . """ -from typing import Any, List, Optional, Union +from typing import Any, Optional, Union import numpy as np def check_format( - X: Union[List[Any], np.ndarray], + X: Union[list[Any], np.ndarray], allow_empty: bool = False, n_features: Optional[int] = 2, ) -> np.ndarray: diff --git a/mkdocs.yml b/mkdocs.yml index 3c0585f..e202b44 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -24,6 +24,7 @@ nav: - Examples and tutorials: - Examples: examples/examples_english.md + - API Reference: - Isolation Kernel: - Isolation Kernel: api/kernel/isolation_kernel.md @@ -54,6 +55,9 @@ nav: - Releases: releases/releases.md + - FAQ: + - Table of contents: faq/table-of-contents.md + - Contributing: - How to contribute: contributing/contribution.md @@ -86,7 +90,7 @@ plugins: - mkdocs-jupyter: ignore_h1_titles: True include_source: True - execute: False + execute: True allow_errors: False include_requirejs: True - search: diff --git a/pyproject.toml b/pyproject.toml index 24563b0..d56c4c9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,6 @@ [project] name = "ikpykit" dynamic = ["version"] - description = "Isolation Kernel Toolkit in Python." readme = "README.md" authors = [ @@ -10,7 +9,6 @@ authors = [ { name = "Ye Zhu", email = "ye.zhu@ieee.org" }, { name = "Kaiming Ting", email = "tingkm@nju.edu.cn" }, ] - classifiers = [ "Operating System :: MacOS", "Operating System :: Microsoft :: Windows", @@ -22,9 +20,9 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "License :: OSI Approved :: BSD License", ] - keywords = [ "data-science", "machine-learning", @@ -35,142 +33,112 @@ keywords = [ "graph embedding", "isolation kernel", ] - dependencies = [ - "scikit-learn >= 1.2", - "pandas >= 1.5", - "numpy <= 1.22", - "numba >= 0.54", - "tqdm >= 4.62.3", + "numba>=0.60.0", + "numpy>=2.0.2", + "pandas>=2.3.3", + "scikit-learn>=1.6.1", + "tqdm>=4.67.1", ] - requires-python = ">=3.9" +[project.license] +file = "LICENSE" + [project.urls] Homepage = "https://isolationkernel.github.io/ikpykit/" Repository = "https://github.com/IsolationKernel/ikpykit" Documentation = "https://isolationkernel.github.io/ikpykit/" "Release Notes" = "https://isolationkernel.github.io/ikpykit/latest/releases/releases" +[build-system] +requires = ["hatchling", "uv-dynamic-versioning>=0.7.0"] +build-backend = "hatchling.build" -[project.license] -file = "LICENSE" +[tool.hatch.version] +source = "uv-dynamic-versioning" -[build-system] -requires = ["setuptools>=61", "toml", "build", "setuptools-scm>=8"] -build-backend = "setuptools.build_meta" - - -[tool.setuptools_scm] -version_file = "ikpykit/_version.py" -local_scheme = "no-local-version" - -[tool.setuptools.packages.find] -where = ["."] # list of folders that contain the packages (["."] by default) -include = ["ikpykit", "ikpykit*"] -exclude = [ - "ikpykit/**/tests/*", -] # exclude packages matching these glob patterns (empty by default) -namespaces = false # to disable scanning PEP 420 namespaces (true by default) - -[tool.pixi.project] -channels = ["conda-forge"] -platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] - -[tool.pixi.dependencies] -python = ">=3.9" -scikit-learn = ">=1.4.2" -numba = ">=0.54" -numpy = "<=1.22" - -[tool.pixi.pypi-dependencies] -ikpykit = { path = ".", editable = true } - -[tool.pixi.feature.lint.dependencies] -# The version below should be aligned with the one of `.pre-commit-config.yaml` -black = "24.8.0" -pre-commit = "3.7.1" -ruff = "0.9.9" - -[tool.pixi.feature.lint.tasks] -black = { cmd = "black --check --diff ikpykit && black --check --diff examples" } -ruff = { cmd = "ruff check --output-format=full ikpykit && ruff check --output-format=full examples" } -lint = { depends-on = ["black", "ruff"] } - -[tool.pixi.feature.test.dependencies] -pytest = "*" -pytest-cov = "*" - -[tool.pixi.feature.test.tasks] -test = { cmd = "pytest -vsl --cov=ikpykit --cov-report=xml ikpykit" } - -[tool.pixi.feature.doc.dependencies] -mike = "2.1.3" -mkdocs = "1.6.1" -mkdocs-jupyter = "0.25.0" -mkdocs-material = "9.5.39" -mkdocstrings = "0.26.1" -mkdocstrings-python = "1.11.1" -notebook = "6.4.12" -jupyter_contrib_nbextensions = "0.7.0" -black = "24.8.0" - -[tool.pixi.feature.doc.tasks] -clean-doc = { cmd = "rm -rf site" } -build-doc = { cmd = "mkdocs build" } - -[tool.pixi.environments] -doc = ["doc"] -lint = ["lint"] -test = ["test"] -dev = ["doc", "lint", "test"] +[tool.hatch.metadata] +allow-direct-references = true + +[tool.hatch.build.hooks.version] +path = "ikpykit/_version.py" + +[tool.uv-dynamic-versioning] +vcs = "git" +style = "pep440" +bump = true +fallback-version = "0.0.0" + +[tool.pytest.ini_options] +asyncio_mode = "auto" +# filterwarnings = ["error::DeprecationWarning"] +filterwarnings = [ + # Suppress OAuth in-memory token storage warnings in tests + # Tests intentionally use ephemeral storage; this warning is for end users + "ignore:Using in-memory token storage:UserWarning", +] +timeout = 30 + +[tool.uv] +default-groups = ["dev", "lint", "docs"] + +[dependency-groups] +dev = [ + "pytest>=8.4.2", + "pytest-asyncio>=1.2.0", + "pytest-cov>=7.0.0", + "pytest-timeout>=2.4.0", +] +docs = [ + "black==24.8.0", + "jupyter-contrib-nbextensions==0.7.0", + "matplotlib>=3.9.0", + "mike==2.1.3", + "mkdocs==1.6.1", + "mkdocs-jupyter==0.25.0", + "mkdocstrings==0.26.1", + "mkdocstrings-python==1.11.1", + "notebook==6.4.12", +] +lint = ["black==24.8.0", "pre-commit>=4.3.0", "ruff>=0.14.5"] [tool.black] line-length = 88 -target_version = ['py38', 'py39', 'py310'] -preview = true -exclude = ''' -/( - \.eggs # exclude a few common directories in the - | \.git # root of the project - | \.vscode -)/ -''' -force-exclude = "ikpykit/_version.py" - -[tool.ruff] -# max line length for black -line-length = 88 -target-version = "py38" -exclude = [ - ".git", - "__pycache__", - "dist", - "doc/_build", - "doc/auto_examples", - "build", - "ikpykit/_version.py", -] +target-version = ["py39"] [tool.ruff.lint] -# all rules can be found here: https://beta.ruff.rs/docs/rules/ -select = ["E", "F", "W", "I"] +fixable = ["ALL"] ignore = [ - # space before : (needed for how black formats slicing) - "E203", - # do not assign a lambda expression, use a def - "E731", - # do not use variables named 'l', 'O', or 'I' - "E741", + "COM812", + "PLR0913", # Too many arguments, sometimes necessary + "SIM102", # Dont require combining if statements + +] +extend-select = [ + "B", # flake8-bugbear: Catches actual bugs like mutable default arguments + "C4", # flake8-comprehensions: More efficient/readable comprehensions + "I", # flake8-builtins: Catches builtins that are not explicitly imported + "PIE", # flake8-pie: More idiomatic Python code + "RUF", # Ruff-specific: Modern best practices unique to Ruff + "SIM", # flake8-simplify: Simplifies verbose code patterns + "UP", # flake8-unused-imports: Catches unused imports + ] [tool.ruff.lint.per-file-ignores] -# It's fine not to put the import at the top of the file in the examples -# folder. -"examples/*" = ["E402"] -"doc/conf.py" = ["E402"] -"doc/_templates/numpydoc_docstring.py" = ["F821", "W292"] +"__init__.py" = ["F401", "I001", "RUF013"] +# allow imports not at the top of the file +"ikpykit/__init__.py" = ["E402"] +"!ikpykit/**.py" = [ + # Only enforce extended ruff rules for code in ikpykit/ + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "PIE", # flake8-pie + "RUF", # Ruff-specific + "SIM", # flake8-simplify -[tool.pytest.ini_options] -addopts = "--doctest-modules --color=yes" -doctest_optionflags = "NORMALIZE_WHITESPACE" +] + +[tool.codespell] +ignore-words-list = "asend,shttp,te" diff --git a/pyproject_old.toml b/pyproject_old.toml new file mode 100644 index 0000000..24563b0 --- /dev/null +++ b/pyproject_old.toml @@ -0,0 +1,176 @@ +[project] +name = "ikpykit" +dynamic = ["version"] + +description = "Isolation Kernel Toolkit in Python." +readme = "README.md" +authors = [ + { name = "Xin Han", email = "xhan197@outlook.com" }, + { name = "Yixiao Ma", email = "mayx@lamda.nju.edu.cn" }, + { name = "Ye Zhu", email = "ye.zhu@ieee.org" }, + { name = "Kaiming Ting", email = "tingkm@nju.edu.cn" }, +] + +classifiers = [ + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX", + "Operating System :: Unix", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: BSD License", +] + +keywords = [ + "data-science", + "machine-learning", + "data-mining", + "anomaly detection", + "scikit-learn", + "cluster", + "graph embedding", + "isolation kernel", +] + +dependencies = [ + "scikit-learn >= 1.2", + "pandas >= 1.5", + "numpy <= 1.22", + "numba >= 0.54", + "tqdm >= 4.62.3", +] + +requires-python = ">=3.9" + +[project.urls] +Homepage = "https://isolationkernel.github.io/ikpykit/" +Repository = "https://github.com/IsolationKernel/ikpykit" +Documentation = "https://isolationkernel.github.io/ikpykit/" +"Release Notes" = "https://isolationkernel.github.io/ikpykit/latest/releases/releases" + + +[project.license] +file = "LICENSE" + +[build-system] +requires = ["setuptools>=61", "toml", "build", "setuptools-scm>=8"] +build-backend = "setuptools.build_meta" + + +[tool.setuptools_scm] +version_file = "ikpykit/_version.py" +local_scheme = "no-local-version" + +[tool.setuptools.packages.find] +where = ["."] # list of folders that contain the packages (["."] by default) +include = ["ikpykit", "ikpykit*"] +exclude = [ + "ikpykit/**/tests/*", +] # exclude packages matching these glob patterns (empty by default) +namespaces = false # to disable scanning PEP 420 namespaces (true by default) + +[tool.pixi.project] +channels = ["conda-forge"] +platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] + +[tool.pixi.dependencies] +python = ">=3.9" +scikit-learn = ">=1.4.2" +numba = ">=0.54" +numpy = "<=1.22" + +[tool.pixi.pypi-dependencies] +ikpykit = { path = ".", editable = true } + +[tool.pixi.feature.lint.dependencies] +# The version below should be aligned with the one of `.pre-commit-config.yaml` +black = "24.8.0" +pre-commit = "3.7.1" +ruff = "0.9.9" + +[tool.pixi.feature.lint.tasks] +black = { cmd = "black --check --diff ikpykit && black --check --diff examples" } +ruff = { cmd = "ruff check --output-format=full ikpykit && ruff check --output-format=full examples" } +lint = { depends-on = ["black", "ruff"] } + +[tool.pixi.feature.test.dependencies] +pytest = "*" +pytest-cov = "*" + +[tool.pixi.feature.test.tasks] +test = { cmd = "pytest -vsl --cov=ikpykit --cov-report=xml ikpykit" } + +[tool.pixi.feature.doc.dependencies] +mike = "2.1.3" +mkdocs = "1.6.1" +mkdocs-jupyter = "0.25.0" +mkdocs-material = "9.5.39" +mkdocstrings = 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