diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..07760e8 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,14 @@ +# Check http://editorconfig.org for more information +# This is the main config file for this project: +root = true + +[*] +charset = utf-8 +end_of_line = lf +insert_final_newline = true +indent_style = space +trim_trailing_whitespace = true +indent_size = 4 + +[*.md] +trim_trailing_whitespace = false diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..d2b0e2e --- /dev/null +++ b/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,132 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +[INSERT CONTACT METHOD]. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 0000000..52cc24b --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,53 @@ +# Contributing to `nearness` + +Thanks for contributing! Before implementing features and changes, please submit an issue to discuss the proposed +changes. + +## How to submit a pull request + +1. [Fork this repository](https://github.com/davnn/nearness/fork). +2. Clone the forked repository and add a new branch with the feature name. + +Before submitting your code as a pull request please do the following steps: + +1. Add any changes you want +2. Add tests for the new changes +3. Edit documentation if you have changed something significant +4. Run `task format` to format your changes. +5. Run `task lint` to ensure code style checks. +6. Run `task typing` to ensure type checks. +7. Run `task safety` to ensure safety checks. +8. Run `task test` to verify that all tests are passing. + +Conveniently, you can run `task check`, to combine the mentioned commands before contributing a commit. +We use [gitmoji](https://gitmoji.dev/) to categorize different kinds of commits. + +## Contributing without `task` + +We use [task](https://taskfile.dev/) to provide pre-configured CLI commands for the project, but `task` is +not required, you can also run the commands directly from the CLI. Have a look at `Taskfile.yml` for a reference of +commands. + +## Set up a poetry environment + +We use [micromamba](https://github.com/mamba-org/mamba) to set up a Python environment +for [poetry](https://python-poetry.org/). Make sure that ``micromamba`` is installed +and available. Run `task env-create` to create an empty Python 3.10 environment named `nearness`. After you have +successfully created the environment activate it with `micromamba activate nearness`. If `poetry` is not already +installed, run `task poetry-install`. Using the activated `nearness` environment check if poetry is using the right +environment with `poetry env info`. Once the poetry setup is complete, you are ready to install the dependencies. + +Note that Conda, Mamba or Micromamba is not necessary, you can use a tool of your choice to manage your Python +environment. One benefit of using Conda is that we can override packages that are not easy to install with `pip`, +for example, dependencies with GPU-support. + +## Install dependencies + +We use [`poetry`](https://github.com/python-poetry/poetry) to manage the dependencies. With an active poetry env, +run `task install` to install all dependencies into environment. After the dependencies are installed, run +`task pre-commit-install` to add the [pre-commit](https://pre-commit.com/) hooks. + +## Other help + +You can contribute by spreading a word about this library. It would also be a huge contribution to write a short article +on how you are using this project. You can also share your best practices with us. diff --git a/.github/ISSUE_TEMPLATE/bug.yml b/.github/ISSUE_TEMPLATE/bug.yml new file mode 100644 index 0000000..9ff6930 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug.yml @@ -0,0 +1,45 @@ +name: πŸ› Bug report +description: Report an issue +title: "πŸ› " +labels: [ bug ] + +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + - type: textarea + id: bug-description + attributes: + label: Describe the bug + description: A clear description the bug. If you want to contribute to fix this issue, tell us in the description. + placeholder: Description + validations: + required: true + - type: textarea + id: reproduction + attributes: + label: Reproduction + description: A [minimal reproduction](https://stackoverflow.com/help/minimal-reproducible-example) is **required**, otherwise the issue might be closed without further notice. + placeholder: Reproduction + validations: + required: true + - type: textarea + id: system-info + attributes: + label: System Info + description: Output of `task sysinfo` + render: Shell + placeholder: Paste the output of the following command + validations: + required: true + - type: checkboxes + id: validations + attributes: + label: Validations + description: Before submitting the issue, please make sure you do the following + options: + - label: Read the [Contributing Guide](https://github.com/davnn/nearness/blob/main/.github/CONTRIBUTING.md). + required: true + - label: Check that there isn't already an issue that reports the same bug to avoid creating duplicates. + required: true diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..5b22dcb --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,9 @@ +# Configuration: https://help.github.com/en/github/building-a-strong-community/configuring-issue-templates-for-your-repository +blank_issues_enabled: false +contact_links: + - name: ⁉ Questions and Discussions + url: https://github.com/davnn/nearness/discussions + about: Please do not use issues for question and discussion. + - name: 😍 Contribution Guide + url: https://github.com/davnn/nearness/blob/main/.github/CONTRIBUTING.md + about: Please read through before making a contribution. diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml new file mode 100644 index 0000000..30b2fe0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/documentation.yml @@ -0,0 +1,27 @@ +name: πŸ“ Documentation improvement +description: Something is erroneous, unclear or missing in the docs. +title: "πŸ“ " +labels: [ documentation ] + +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to help us improve the documentation! + - type: textarea + id: documentation-description + attributes: + label: Description + description: 'Explain how you would like to improve the docs here!' + validations: + required: true + - type: checkboxes + id: checkboxes + attributes: + label: Validations + description: Before submitting the issue, please make sure you do the following + options: + - label: Read the [Contributing Guide](https://github.com/davnn/nearness/blob/main/.github/CONTRIBUTING.md). + required: true + - label: Check that there isn't already an issue requesting the same. + required: true diff --git a/.github/ISSUE_TEMPLATE/feature.yml b/.github/ISSUE_TEMPLATE/feature.yml new file mode 100644 index 0000000..865aa58 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature.yml @@ -0,0 +1,34 @@ +name: πŸš€ Feature proposal or enhancement +description: Propose a new feature or enhancement +title: "πŸš€ " +labels: [ feature ] + +body: + - type: markdown + attributes: + value: | + Thanks for your interest in the project and taking the time to fill out this feature report! + - type: textarea + id: feature-description + attributes: + label: Description of the problem + description: 'As a user I want [goal] so that [benefit]. If you want to contribute with a PR, tell us in the description. Thanks!' + validations: + required: true + - type: textarea + id: suggested-solution + attributes: + label: Solution + description: 'We could provide following implementation...' + validations: + required: true + - type: checkboxes + id: checkboxes + attributes: + label: Validations + description: Before submitting the issue, please make sure you do the following + options: + - label: Read the [Contributing Guide](https://github.com/davnn/nearness/blob/main/.github/CONTRIBUTING.md). + required: true + - label: Check that there isn't already an issue requesting the same feature. + required: true diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..f6dd472 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,22 @@ +## Description + + + +## Related Issue + + + +## Type of Change + + + +- [ ] πŸ”§ Bug fix +- [ ] πŸ” Security fix +- [ ] πŸš€ Improvement / New feature +- [ ] πŸ“š Examples / Docs / Tutorials + +## Checklist + + + +- [ ] I've checked the code using `task check`. diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml new file mode 100644 index 0000000..384a5a2 --- /dev/null +++ b/.github/release-drafter.yml @@ -0,0 +1,28 @@ +# Release drafter configuration https://github.com/release-drafter/release-drafter#configuration +# Emojis were chosen to match the https://gitmoji.carloscuesta.me/ + +name-template: "v$NEXT_PATCH_VERSION" +tag-template: "v$NEXT_PATCH_VERSION" + +categories: + - title: ":rocket: Features" + labels: [ feature, enhancement ] + - title: ":wrench: Fixes & Refactoring" + labels: [ bug, fix, refactoring ] + - title: ":package: Build System & CI/CD" + labels: [ build ] + - title: ":boom: Breaking Changes" + labels: [ breaking ] + - title: ":pencil: Documentation" + labels: [ documentation ] + - title: ":arrow_up: Dependencies updates" + labels: [ dependencies ] + +template: | + ## What’s Changed + + $CHANGES + + ## :busts_in_silhouette: List of contributors + + $CONTRIBUTORS diff --git a/.github/system_info.py b/.github/system_info.py new file mode 100644 index 0000000..998c47b --- /dev/null +++ b/.github/system_info.py @@ -0,0 +1,19 @@ +import json +import platform +import sys + +import nearness + + +def get_system_info(): + info = { + "library": nearness.get_version(), + "python": sys.version, + "platform": platform.platform(), + "architecture": platform.machine(), + } + return info + + +if __name__ == "__main__": + print(json.dumps(get_system_info(), indent=4)) diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml new file mode 100644 index 0000000..a2a5456 --- /dev/null +++ b/.github/workflows/check.yml @@ -0,0 +1,20 @@ +name: Check + +on: + push: + branches: [ main ] + pull_request: + types: [ opened, reopened, synchronize ] + +jobs: + check: + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, windows-latest, macos-latest ] + python: [ "3.10" ] + uses: ./.github/workflows/setup.yml + with: + os: ${{ matrix.os }} + python: ${{ matrix.python }} + command: task check diff --git a/.github/workflows/release-drafter.yml b/.github/workflows/release-drafter.yml new file mode 100644 index 0000000..3252425 --- /dev/null +++ b/.github/workflows/release-drafter.yml @@ -0,0 +1,27 @@ +name: Release Drafter + +on: + push: + # branches to consider in the event; optional, defaults to all + branches: [ main ] + # pull_request event is required only for autolabeler + pull_request: + # Only following types are handled by the action, but one can default to all as well + types: [ opened, reopened, synchronize ] + # pull_request_target event is required for autolabeler to support PRs from forks + pull_request_target: + types: [ opened, reopened, synchronize ] + +permissions: + contents: read + +jobs: + update_release_draft: + permissions: + contents: write # for release-drafter/release-drafter to create a GitHub release + pull-requests: write # for release-drafter/release-drafter to add label to PR + runs-on: ubuntu-latest + steps: + - uses: release-drafter/release-drafter@v5 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..f9cae36 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,20 @@ +name: Release + +on: + release: + types: [ published ] + workflow_dispatch: + +jobs: + build: + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest ] + python: [ "3.12" ] + uses: ./.github/workflows/setup.yml + secrets: inherit + with: + os: ${{ matrix.os }} + python: ${{ matrix.python }} + command: task build && task publish diff --git a/.github/workflows/setup.yml b/.github/workflows/setup.yml new file mode 100644 index 0000000..7736539 --- /dev/null +++ b/.github/workflows/setup.yml @@ -0,0 +1,57 @@ +name: Setup + +on: + workflow_call: + inputs: + python: + required: true + type: string + os: + required: true + type: string + command: + required: true + type: string + +env: + POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_TOKEN }} + +jobs: + setup: + runs-on: ${{ inputs.os }} + steps: + - uses: actions/checkout@v4 + + - name: Install Task + uses: arduino/setup-task@v1 + with: + version: 3.x + repo-token: ${{ secrets.GITHUB_TOKEN }} + + - name: Install poetry + run: curl -sSL https://install.python-poetry.org | python3 - + + - name: Set up poetry path (windows) + if: inputs.os == 'windows-latest' + run: echo "C:\Users\runneradmin\AppData\Roaming\Python\Scripts" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append + + - name: Set up poetry path (macos) + if: inputs.os == 'macos-latest' + run: echo "/Users/runner/.local/bin" >> $GITHUB_PATH + + - name: Set up poetry path (linux) + if: inputs.os == 'ubuntu-latest' + run: echo "/home/runner/.local/bin" >> $GITHUB_PATH + + - name: Set up Python ${{ inputs.python }} + uses: actions/setup-python@v5 + with: + python-version: ${{ inputs.python }} + cache: "poetry" + + - name: Run install + run: task poetry-install + + - name: Command + run: ${{ inputs.command }} + shell: bash diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml new file mode 100644 index 0000000..55194e7 --- /dev/null +++ b/.github/workflows/stale.yml @@ -0,0 +1,19 @@ +name: "Close stale issues and PRs" +on: + schedule: + - cron: "00 6 * * *" + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - uses: actions/stale@v9 + with: + stale-issue-message: "This issue is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 10 days." + stale-pr-message: "This PR is stale because it has been open 60 days with no activity. Remove stale label or comment or this will be closed in 10 days." + close-issue-message: "This issue was closed because it has been stalled for 10 days with no activity." + close-pr-message: "This PR was closed because it has been stalled for 10 days with no activity." + days-before-issue-stale: 30 + days-before-pr-stale: 60 + days-before-issue-close: 10 + days-before-pr-close: 10 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..cdc748d --- /dev/null +++ b/.gitignore @@ -0,0 +1,631 @@ +# Additional ignores + +.benchmarks/ +.ruff_cache/ +.ipynb_checkpoints/ +*.ipynb + +# Created by https://www.gitignore.io/api/osx,python,pycharm,windows,visualstudio,visualstudiocode +# Edit at https://www.gitignore.io/?templates=osx,python,pycharm,windows,visualstudio,visualstudiocode + +### OSX ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +.idea/ +*.iml +*.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +### PyCharm Patch ### +# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 + +# *.iml +# modules.xml +# .idea/misc.xml +# *.ipr + +# Sonarlint plugin +.idea/**/sonarlint/ + +# SonarQube Plugin +.idea/**/sonarIssues.xml + +# Markdown Navigator plugin +.idea/**/markdown-navigator.xml +.idea/**/markdown-navigator/ + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# pyenv +.python-version + +# poetry +.venv + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# Mr Developer +.mr.developer.cfg +.project +.pydevproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Plugins +.secrets.baseline + +### VisualStudioCode ### +.vscode/* +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json + +### VisualStudioCode Patch ### +# Ignore all local history of files +.history + +### Windows ### +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +### VisualStudio ### +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore + +# User-specific files +*.rsuser +*.suo +*.user +*.userosscache +*.sln.docstates + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Mono auto generated files +mono_crash.* + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +[Aa][Rr][Mm]/ +[Aa][Rr][Mm]64/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ + +# Visual Studio 2015/2017 cache/options directory +.vs/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# Visual Studio 2017 auto generated files +Generated\ Files/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUnit +*.VisualState.xml +TestResult.xml +nunit-*.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# Benchmark Results +BenchmarkDotNet.Artifacts/ + +# .NET Core +project.lock.json +project.fragment.lock.json +artifacts/ + +# StyleCop +StyleCopReport.xml + +# Files built by Visual Studio +*_i.c +*_p.c +*_h.h +*.ilk +*.obj +*.iobj +*.pch +*.pdb +*.ipdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*_wpftmp.csproj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile +*.VC.db +*.VC.VC.opendb + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# Visual Studio Trace Files +*.e2e + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# JustCode is a .NET coding add-in +.JustCode + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# AxoCover is a Code Coverage Tool +.axoCover/* +!.axoCover/settings.json + +# Visual Studio code coverage results +*.coverage +*.coveragexml + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# Note: Comment the next line if you want to checkin your web deploy settings, +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# Microsoft Azure Web App publish settings. Comment the next line if you want to +# checkin your Azure Web App publish settings, but sensitive information contained +# in these scripts will be unencrypted +PublishScripts/ + +# NuGet Packages +*.nupkg +# NuGet Symbol Packages +*.snupkg +# The packages folder can be ignored because of Package Restore +**/[Pp]ackages/* +# except build/, which is used as an MSBuild target. +!**/[Pp]ackages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/[Pp]ackages/repositories.config +# NuGet v3's project.json files produces more ignorable files +*.nuget.props +*.nuget.targets + +# Microsoft Azure Build Output +csx/ +*.build.csdef + +# Microsoft Azure Emulator +ecf/ +rcf/ + +# Windows Store app package directories and files +AppPackages/ +BundleArtifacts/ +Package.StoreAssociation.xml +_pkginfo.txt +*.appx +*.appxbundle +*.appxupload + +# Visual Studio cache files +# files ending in .cache can be ignored +*.[Cc]ache +# but keep track of directories ending in .cache +!?*.[Cc]ache/ + +# Others +ClientBin/ +~$* +*~ +*.dbmdl +*.dbproj.schemaview +*.jfm +*.pfx +*.publishsettings +orleans.codegen.cs + +# Including strong name files can present a security risk +# (https://github.com/github/gitignore/pull/2483#issue-259490424) +#*.snk + +# Since there are multiple workflows, uncomment next line to ignore bower_components +# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) +#bower_components/ + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm +ServiceFabricBackup/ +*.rptproj.bak + +# SQL Server files +*.mdf +*.ldf +*.ndf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings +*.rptproj.rsuser +*- [Bb]ackup.rdl +*- [Bb]ackup ([0-9]).rdl +*- [Bb]ackup ([0-9][0-9]).rdl + +# Microsoft Fakes +FakesAssemblies/ + +# GhostDoc plugin setting file +*.GhostDoc.xml + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat +node_modules/ + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# Visual Studio 6 auto-generated workspace file (contains which files were open etc.) +*.vbw + +# Visual Studio LightSwitch build output +**/*.HTMLClient/GeneratedArtifacts +**/*.DesktopClient/GeneratedArtifacts +**/*.DesktopClient/ModelManifest.xml +**/*.Server/GeneratedArtifacts +**/*.Server/ModelManifest.xml +_Pvt_Extensions + +# Paket dependency manager +.paket/paket.exe +paket-files/ + +# FAKE - F# Make +.fake/ + +# CodeRush personal settings +.cr/personal + +# Python Tools for Visual Studio (PTVS) +*.pyc + +# Cake - Uncomment if you are using it +# tools/** +# !tools/packages.config + +# Tabs Studio +*.tss + +# Telerik's JustMock configuration file +*.jmconfig + +# BizTalk build output +*.btp.cs +*.btm.cs +*.odx.cs +*.xsd.cs + +# OpenCover UI analysis results +OpenCover/ + +# Azure Stream Analytics local run output +ASALocalRun/ + +# MSBuild Binary and Structured Log +*.binlog + +# NVidia Nsight GPU debugger configuration file +*.nvuser + +# MFractors (Xamarin productivity tool) working folder +.mfractor/ + +# Local History for Visual Studio +.localhistory/ + +# BeatPulse healthcheck temp database +healthchecksdb + +# Backup folder for Package Reference Convert tool in Visual Studio 2017 +MigrationBackup/ + +### Terraform ### + +# Local .terraform directories +**/.terraform/* + +# .tfstate files +*.tfstate +*.tfstate.* + +# Crash log files +crash.log +crash.*.log + +# Exclude all .tfvars files, which are likely to contain sensitive data, such as +# password, private keys, and other secrets. These should not be part of version +# control as they are data points which are potentially sensitive and subject +# to change depending on the environment. +*.tfvars +*.tfvars.json + +# Ignore override files as they are usually used to override resources locally and so +# are not checked in +override.tf +override.tf.json +*_override.tf +*_override.tf.json + +# Include override files you do wish to add to version control using negated pattern +# !example_override.tf + +# Include tfplan files to ignore the plan output of command: terraform plan -out=tfplan +# example: *tfplan* + +# Ignore CLI configuration files +.terraformrc +terraform.rc + +# End of https://www.gitignore.io/api/osx,python,pycharm,windows,visualstudio,visualstudiocode diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..860c9ae --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,39 @@ +default_language_version: + python: python3.10 + +default_stages: [ commit, push ] + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: check-toml + - id: check-yaml + - id: check-json + - id: debug-statements + - id: trailing-whitespace + - id: check-added-large-files + - id: check-merge-conflict + - id: fix-byte-order-marker + - id: name-tests-test + args: [ --pytest-test-first ] + exclude: ^tests/utilities + - id: end-of-file-fixer + - id: no-commit-to-branch + + - repo: local + hooks: + - id: ruff + name: ruff + entry: ruff check + types: [ python ] + language: system + exclude: (^tests|^.github) + + - repo: local + hooks: + - id: black + name: black + entry: black --config pyproject.toml + types: [ python ] + language: system diff --git a/.safety-policy.yml b/.safety-policy.yml new file mode 100644 index 0000000..c76e10f --- /dev/null +++ b/.safety-policy.yml @@ -0,0 +1,2 @@ +security: + continue-on-vulnerability-error: True # Suppress non-zero exit codes when vulnerabilities are found. diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..eda9379 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024-present David Muhr + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..23403cf --- /dev/null +++ b/README.md @@ -0,0 +1,17 @@ +

+ +

+ +------------------------------------------------------------------------------------------- + +[![Check Status](https://github.com/davnn/nearness/actions/workflows/check.yml/badge.svg)](https://github.com/davnn/nearness/actions?query=workflow%3Acheck) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![Security: bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://github.com/PyCQA/bandit) +[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/davnn/nearness/blob/main/.pre-commit-config.yaml) +[![Semantic Versions](https://img.shields.io/badge/%20%20%F0%9F%93%A6%F0%9F%9A%80-semantic--versions-e10079.svg)](https://github.com/davnn/nearness/releases) +![Coverage Report](https://raw.githubusercontent.com/davnn/nearness/main/assets/coverage.svg) + +*nearness* is a unified interface for (approximate) nearest neighbors search. + +This library currently in an *experimental* stage trying +to [standardize and improve its API](https://github.com/davnn/nearness/issues/1). diff --git a/Taskfile.yml b/Taskfile.yml new file mode 100644 index 0000000..561b114 --- /dev/null +++ b/Taskfile.yml @@ -0,0 +1,80 @@ +version: "3" + +env: + CONDA: micromamba + +vars: + PROJECT: nearness + TESTS: tests + +tasks: + env-create: + cmd: $CONDA env create -n {{.PROJECT}} --file env.yml --yes + + env-remove: + cmd: $CONDA env remove -n {{.PROJECT}} --yes + + poetry-setup: + cmds: + - curl -sSL https://install.python-poetry.org | python - + - poetry config virtualenvs.create false + + poetry-remove: + cmd: curl -sSL https://install.python-poetry.org | python - --uninstall + + poetry-install: + cmd: poetry install --all-extras --with dev --no-interaction + + poetry-update-dev: + cmd: poetry add pytest@latest pytest-html@latest hypothesis@latest coverage@latest pytest-cov@latest pytest-benchmark@latest coverage-badge@latest ruff@latest pre-commit@latest black@latest pyright@latest bandit@latest safety@latest -G dev + + poetry-use: + cmds: + - | + $CONDA activate {{.PROJECT}} + poetry env use system + + pre-commit-install: + cmd: poetry run pre-commit install + + pre-commit: + cmd: poetry run pre-commit run --all-files + + lint: + cmds: + - poetry run ruff check src/{{.PROJECT}} + - poetry run black --diff --check --config pyproject.toml src/{{.PROJECT}} {{.TESTS}} + + format: + cmds: + - poetry run ruff check src/{{.PROJECT}} --fix + - poetry run black --config pyproject.toml src/{{.PROJECT}} {{.TESTS}} + + test: + cmds: + - poetry run pytest -rsx -c pyproject.toml --cov-report=html --cov=src/{{.PROJECT}} {{.TESTS}}/ + - poetry run coverage-badge -o assets/coverage.svg -f + + typing: + cmd: poetry run pyright + + safety: + cmds: + - poetry run safety check --full-report + - poetry run bandit -ll --recursive src/{{.PROJECT}} {{.TESTS}} + + check: + cmds: + - task: lint + - task: typing + - task: test + - task: safety + + build: + cmd: poetry build + + publish: + cmd: poetry publish --skip-existing + + sysinfo: + cmd: poetry run python .github/system_info.py diff --git a/assets/coverage.svg b/assets/coverage.svg new file mode 100644 index 0000000..a8c7e72 --- /dev/null +++ b/assets/coverage.svg @@ -0,0 +1,21 @@ + + + + + + + + + + + + + + + + coverage + coverage + 92% + 92% + + diff --git a/assets/nearness.png b/assets/nearness.png new file mode 100644 index 0000000..6361cdb Binary files /dev/null and b/assets/nearness.png differ diff --git a/env.yml b/env.yml new file mode 100644 index 0000000..df5d941 --- /dev/null +++ b/env.yml @@ -0,0 +1,5 @@ +name: nearness +channels: + - conda-forge +dependencies: + - conda-forge::python=3.10 diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..7ba53bd --- /dev/null +++ b/poetry.lock @@ -0,0 +1,4187 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "absl-py" +version = 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(approximate) nearest neighbors algorithms." +readme = "README.md" +authors = ["David Muhr "] +license = "MIT" +repository = "https://github.com/davnn/nearness" +packages = [{ include = "nearness", from = "src" }] + +# Pypi classifiers: https://pypi.org/classifiers/ +classifiers = [ + "Development Status :: 3 - Alpha", + "License :: OSI Approved :: MIT License", + "Topic :: Software Development :: Libraries", + "Topic :: Scientific/Engineering", + "Topic :: Utilities", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Intended Audience :: Education", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3 :: Only", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX", + "Operating System :: Unix", + "Operating System :: MacOS", + "Typing :: Typed" +] + +[tool.poetry.dependencies] +python = ">=3.10,<3.13" +safecheck = "^0.3" +joblib = "^1" +typing-extensions = 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at module level (F406) + "B008", # do not perform function calls in argument defaults + "S101", # do not use assert, + "PD901", # do not use 'df' as a variable name + "PLR0911", # too many return statements + "ANN101", # missing type annotations for 'self' + "D107", # missing docstring for '__init__' + "TCH", # require type checking block + "PLR0913", # too many function arguments + "F722", # syntax error forward annotations (error on jaxtyping annotation)) + "UP037", # remove quotes from type annotation (format breaks jaxtyping annotations) + "F821", # undefined name (error on jaxtyping annotations) + "UP035", # deprecated import (we actually want to import from typing_extensions) + "ARG002", # unused method argument (we use the arguments in a meta-class, but ruff doesn't know) + "PLR2004", # magic values are sometimes useful + "PYI041", # use float instead of int | float, this clashes with typechecking + "E731", # no lambdas + "B905", # zip without strict arguments + "ANN401", # no typed Any +] + +[tool.ruff.lint.isort] +# Combine multiple `from foo import bar as baz` statements with the same source +# (`foo`) into a single statement. +combine-as-imports = true +# Imports of the form `from foo import bar as baz` show one `import bar as baz` +# per line. Useful for __init__.py files that just re-export symbols. +force-wrap-aliases = true + +[tool.ruff.lint.flake8-quotes] +docstring-quotes = "double" + +[tool.ruff.lint.flake8-tidy-imports.banned-api] +"beartype".msg = "Do not import from beartype, import from safecheck instead." +"jaxtyping".msg = "Do not import from jaxtype, import from safecheck instead." +"typing".msg = "Do not import from typing, import from typing_extensions instead." + +[tool.pyright] +include = ["src/nearness"] +reportPrivateImportUsage = false +reportConstantRedefinition = true +reportDuplicateImport = true +reportFunctionMemberAccess = true +reportOptionalMemberAccess = false +reportImportCycles = true +reportIncompatibleMethodOverride = true +reportIncompatibleVariableOverride = true +reportIncompleteStub = "warning" +reportInconsistentConstructor = true +reportInvalidStubStatement = "warning" +reportMatchNotExhaustive = true +reportMissingParameterType = true +reportMissingTypeArgument = false +reportOverlappingOverload = false +reportPrivateUsage = false +reportTypeCommentUsage = "warning" +reportUnknownArgumentType = false +reportUnknownLambdaType = false +reportUnknownMemberType = false +reportUnknownParameterType = false +reportUnknownVariableType = false +reportUnnecessaryCast = true +reportUnnecessaryComparison = true +reportUnnecessaryContains = true +reportUnnecessaryIsInstance = true +reportUnnecessaryTypeIgnoreComment = true +reportUnusedClass = true +reportUnusedImport = true +reportUnusedFunction = true +reportUnusedVariable = true +reportUnusedExpression = true +reportUnusedCallResult = false +reportUntypedBaseClass = false +reportUntypedClassDecorator = false +reportUntypedFunctionDecorator = false +reportUntypedNamedTuple = false +reportCallInDefaultInitializer = false +reportImplicitStringConcatenation = true +reportMissingSuperCall = true +reportPropertyTypeMismatch = true +reportUninitializedInstanceVariable = true +reportMissingImports = false + +[tool.pytest.ini_options] +# https://docs.pytest.org/en/6.2.x/customize.html#pyproject-toml +# Directories that are not visited by pytest collector: +norecursedirs = ["hooks", "*.egg", ".eggs", "dist", "build", "docs", ".tox", ".git", "__pycache__"] +addopts = ["--strict-markers", "--tb=short"] +filterwarnings = ["ignore::UserWarning", "ignore::DeprecationWarning"] + +[tool.coverage.run] +source = ["tests"] +branch = true + +[tool.coverage.paths] +source = ["src/nearness"] + +[tool.coverage.report] +show_missing = true +exclude_lines = ["pragma: no cover", "@overload", "if __name__ == .__main__.:", "if TYPE_CHECKING:"] diff --git a/src/nearness/__init__.py b/src/nearness/__init__.py new file mode 100644 index 0000000..a670fbe --- /dev/null +++ b/src/nearness/__init__.py @@ -0,0 +1,94 @@ +"""Entrypoint to the library exposing all available algorithms (if they are importable). + +An algorithm can only be imported if the required libraries are installed. For example, +importing ``AnnoyNeighbors`` requires the ``annoy`` package to be available. + +The additional libraries can be installed using nearness' package extras. For example, +to install nearness with ``annoy``, run ``pip install nearness[annoy]`` or install ``annoy`` +by other means, for example, using ``conda install conda-forge::python-annoy``. + +Installing nearness without any extras exposes only the abstract base class ``NearestNeighbors`` +to define your own nearest neighbors algorithm. However, if you already have installed a +package that would otherwise be included as an extra dependency, you can use the corresponding +algorithm. For example, if you have installed ``nearness`` and ``numpy``, the algorithm +``NumpyNeighbors`` is exposed. +""" +from ._base import NearestNeighbors + +__all__ = [ + "NearestNeighbors", +] + + +def get_version() -> str: + """Return the package version or "unknown" if no version can be found.""" + from importlib import metadata + + try: + return metadata.version(__name__) + except metadata.PackageNotFoundError: # pragma: no cover + return "no-version-found-in-package-metadata" + + +try: + from ._annoy import AnnoyNeighbors + + __all__ += ["AnnoyNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._faiss import FaissNeighbors + + __all__ += ["FaissNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._hnsw import HNSWNeighbors + + __all__ += ["HNSWNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._jax import JaxNeighbors + + __all__ += ["JaxNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._numpy import NumpyNeighbors + + __all__ += ["NumpyNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._scann import ScannNeighbors + + __all__ += ["ScannNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._scipy import ScipyNeighbors + + __all__ += ["ScipyNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._sklearn import SklearnNeighbors + + __all__ += ["SklearnNeighbors"] +except ImportError: # pragma: no cover + ... + +try: + from ._torch import TorchNeighbors + + __all__ += ["TorchNeighbors"] +except ImportError: # pragma: no cover + ... diff --git a/src/nearness/_annoy.py b/src/nearness/_annoy.py new file mode 100644 index 0000000..7d58fe4 --- /dev/null +++ b/src/nearness/_annoy.py @@ -0,0 +1,86 @@ +from pathlib import Path + +import numpy as np +from annoy import AnnoyIndex +from safecheck import Float, Int64, Is, NumpyArray, Real, typecheck +from typing_extensions import Annotated, Iterable, Literal, overload + +from ._base import NearestNeighbors + + +class AnnoyNeighbors(NearestNeighbors): + available_metrics = Literal["angular", "euclidean", "manhattan", "hamming", "dot"] + positive_int = Annotated[int, Is[lambda i: i > 0]] + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "euclidean", + n_trees: positive_int = 32, + n_search_neighbors: positive_int = 128, + random_seed: int | None = None, + on_disk_build: str | None = None, + load_dim: positive_int | None = None, + prefault: bool = False, + ) -> None: + super().__init__() + self._model = None + + @overload + def fit(self, data: Iterable[Real[NumpyArray, "d"]]) -> "AnnoyNeighbors": + ... + + @overload + def fit(self, data: Real[NumpyArray, "n d"]) -> "AnnoyNeighbors": + ... + + def fit(self, data): # type: ignore[reportGeneralTypeIssues] + n_samples, n_dim = data.shape + self.parameters.load_dim = n_dim + model = AnnoyIndex(n_dim, metric=self.parameters.metric) + + if (seed := self.parameters.random_seed) is not None: + model.set_seed(seed) + + if (path := self.parameters.on_disk_build) is not None: + model.on_disk_build(path) + + for i in range(n_samples): + model.add_item(i, data[i].tolist()) + + model.build(self.parameters.n_trees) + self._model = model + return self + + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "{n_neighbors}"], Float[NumpyArray, "{n_neighbors}"]]: + """No runtime type checks, because query is used in the ``query_batch`` loop.""" + idx, dist = self._model.get_nns_by_vector( + point.tolist(), + n_neighbors, + self.parameters.n_search_neighbors, + include_distances=True, + ) + return np.array(idx, dtype=np.int64), np.array(dist, dtype=point.dtype) + + def query_idx(self, point: Float[NumpyArray, "d"], n_neighbors: int) -> Int64[NumpyArray, "{n_neighbors}"]: + idx = self._model.get_nns_by_vector( + point.tolist(), + n_neighbors, + self.parameters.n_search_neighbors, + ) + return np.array(idx, dtype=np.int64) + + def save(self, file: str | Path) -> None: + self._model.save(str(file), prefault=self.parameters.prefault) + + def load(self, file: str | Path) -> "NearestNeighbors": + model = AnnoyIndex(self.parameters.load_dim, metric=self.parameters.metric) + model.load(str(file), prefault=self.parameters.prefault) + self._model = model + self.__fitted__ = True + return self diff --git a/src/nearness/_base.py b/src/nearness/_base.py new file mode 100644 index 0000000..dd6cea9 --- /dev/null +++ b/src/nearness/_base.py @@ -0,0 +1,321 @@ +import inspect +import threading +import types +from abc import ABCMeta, abstractmethod +from copy import copy, deepcopy +from dataclasses import fields, make_dataclass +from functools import partial, wraps +from logging import getLogger +from multiprocessing.pool import ThreadPool +from pathlib import Path +from warnings import warn + +import joblib +import numpy as np +from typing_extensions import Any, Callable, Self + +from ._config import Config, config + +logger = getLogger(__name__) + +__all__ = [ + "NearestNeighbors", +] + + +class InvalidSignatureError(ValueError): + ... + + +class ParameterTypeError(TypeError): + ... + + +class NearestNeighborsMeta(ABCMeta): + """Metaclass for NearestNeighbors. + + This metaclass is used to: + 1. Check if the implemented ``__init__`` only uses keyword-only parameters. + 2. Dynamically set an attribute ``_parameters_`` containing all ``__init__`` parameters. + 3. Dynamically set an attribute ``_config_`` containing a copy of the global ``config`` object. + + The parameters are modeled as a ``dataclasses.dataclass``, therefore, allowing: + 1. Parameter access using ``instance.parameters.name`` or ``getattr(instance.parameters, name)`` + 2. Parameter updates using ``instance.parameters.name = value`` or ``setattr(instance.parameters, name, value)`` + """ + + def __new__(cls, name: str, bases: tuple[type, ...], attrs: dict[str, Any]) -> type: + """Check the signature of ``__init__`` to ensure keyword-only arguments.""" + if "__init__" in attrs: + parameters = inspect.signature(attrs["__init__"]).parameters + for name, parameter in parameters.items(): + if name != "self" and (kind := parameter.kind) is not inspect.Parameter.KEYWORD_ONLY: + msg = ( + "Only keyword-only arguments are allowed for classes inheriting from 'nearness." + f"NearestNeighbors', but found parameter '{parameter}' of kind '{kind}'." + f" Hint: You can enforce keyword-only arguments using the 'single star' syntax before the" + f" parameters, e.g. '___init___(self, *, {parameter}, ...), see " + f"https://peps.python.org/pep-3102/." + ) + raise InvalidSignatureError(msg) + + return super().__new__(cls, name, bases, attrs) + + def __call__(cls, *_: Any, **kwargs: Any) -> "NearestNeighbors": + """Dynamically set the ``_parameters_`` attribute and wrap the fit method and query methods. + + :param _: We ensure that there are only keyword-only arguments in ``__new__``. + :param kwargs: The mapping of parameters of the algorithm. + :return: The modified object. + """ + # filter the self parameter + parameters = {k: v for k, v in inspect.signature(cls.__init__).parameters.items() if k != "self"} + + # We don't want threads to override class-bound ``_parameters_`` or ``_config_``. + # Because we use thread local storage, the ``_config_`` and ``_parameters`` attribute on the class should + # never be visible to another parallel class instantiation. + thread_local = threading.local() + + # Set the default config, such that it can be manipulated in ``__init__``, and deepcopy the config such that + # a manipulation of ``cls._config_`` does not manipulate the global ``config`` object. + # We must set the attribute here before ``__call__``, because the config should be usable in ``__init__``. + thread_local.config = deepcopy(config) + cls._config_ = thread_local.config # type: ignore[reportUninitializedInstanceVariable] + logger.info("Determined config object as '%s'", cls._config_) + + # same for the parameters, we set the parameters before the call such that they are usable in ``__init__`` + thread_local.parameters = _create_parameter_class(parameters, kwargs) + cls._parameters_ = thread_local.parameters # type: ignore[reportUninitializedInstanceVariable] + logger.info("Determined parameters as '%s'", cls._parameters_) + + # now we set all the relevant attributes on the ``instance``, as they should not be class-bound. + # order is important here as ``__wrap_fit_method`` and ``__wrap_check_method`` depend on the set attributes + obj = type.__call__(cls, **kwargs) + obj._parameters_, obj._config_ = cls._parameters_, cls._config_ # noqa: SLF001 + # make sure that the wrapped methods are in sync when the config is changed after class instantiation + obj._config_.register_callback( # noqa: SLF001 + "methods_require_check", + partial(cls._check_callback, obj=obj), + ) + cls._wrap_fit_method(obj) + cls._wrap_check_method(obj) + del cls._parameters_ + del cls._config_ + return obj + + def _check_callback(cls, _: Any, *, obj: "NearestNeighbors") -> None: + """A callback to refresh the ``__fitted__`` attributes when the 'methods_require_check' attribute is set .""" + cls._wrap_check_method(obj) + + def _wrap_fit_method(cls, obj: "NearestNeighbors") -> None: + """Wrap the ``fit`` method to ensure it sets the ``__fitted__`` attribute and unwraps the query methods. + + This feels a bit like magic, but the alternatives would be to: + 1. Add a decorator to every ``fit`` method that sets ``__fitted__``. + 2. Set the ``__fitted__`` attribute on every ``fit``-like method and check for ``__fitted__`` everywhere. + """ + fit = obj.fit + + @wraps(fit) + def wrapper(*args: Any, **kwargs: Any) -> Any: + result = fit(*args, **kwargs) + logger.debug("Called wrapped fit method with successful fit result, setting '__fitted__'.") + obj.__fitted__ = True + if obj._config_.remove_check_after_fit: # noqa: SLF001 + cls._unwrap_check_method(obj) + return result + + # we set a ``__fit__`` attribute to denote that the method has been decorated + wrapper.__fit__ = True # type: ignore[reportGeneralTypeIssues] + obj.fit = wrapper # override the default fit with the wrapped fit + + def _wrap_check_method(cls, obj: "NearestNeighbors") -> None: + """Wrap or unwrap methods according to ``methods_require_check``. + + For each wrapped method, we ensure that ``__fitted__`` is True, before the method can be called. + Methods not in ``methods_require_check`` are unwrapped. + """ + logger.debug("Starting to wrap methods to enable fit checking.") + available_attributes = dir(obj) + methods_to_wrap = obj._config_.methods_require_check # noqa: SLF001 + for attribute_name in available_attributes: + attribute = getattr(obj, attribute_name) + has_check = hasattr(attribute, "__check__") + if attribute_name in methods_to_wrap: + if has_check: + continue + # check if the ``method`` attribute is a bound method + if isinstance(attribute, types.MethodType): + logger.debug("Wrapping attribute '%s'.", attribute_name) + # update the method with the wrapped ``__fitted__`` check + setattr(obj, attribute_name, wraps(attribute)(_create_check_wrapper(obj, attribute))) + else: + msg = ( + f"Attempting to enable '__fitted__' check for invalid attribute '{attribute_name}', because " + f"'{attribute_name}' is not a bound method, instead it's an attribute of '{type(attribute)}'." + ) + warn(msg, stacklevel=1) + elif has_check: # if an attribute has a ``__check__`` attribute, but is not in the set to check, we unwrap. + logger.debug( + "Attribute '%s' is not in '%s', unwrapping the attribute.", + attribute_name, + methods_to_wrap, + ) + setattr(obj, attribute_name, attribute.__wrapped__) + + for attribute_name in methods_to_wrap: + if attribute_name not in available_attributes: + msg = ( + f"Attempting to enable '__fitted__' check for missing attribute '{attribute_name}', because " + f"'{attribute_name}' does not exist on class {obj}." + ) + warn(msg, stacklevel=1) + + def _unwrap_check_method(cls, obj: "NearestNeighbors") -> None: + """Unwrap all existing methods in ``methods_require_check`` to disable the ``__fitted__`` check. + + This is just a performance optimization, it retrieves the original method and removes the implicitly + generated function wrapper (decorator). It should be safe to unwrap the methods if ``__fitted__`` + is only set in ``fit``, but unsafe when ``__fitted__`` is manually set to ``False`` after ``fit``. + If manually setting the ``__fitted__`` attribute is required, then the configuration should be + set to ``remove_check_after_fit = False``. + """ + logger.debug("Starting to unwrap all fit checking methods.") + for method_name in obj._config_.methods_require_check: # noqa: SLF001 + # we set an __requires_fit__ attribute on the wrapper, because using ``__wrapped__`` alone is not + # safe (methods also use ``__wrapped__`` starting with Python 3.10) + if hasattr(obj, method_name) and hasattr(method := getattr(obj, method_name), "__check__"): + logger.debug("Unwrapping method '%s'.", method_name) + setattr(obj, method_name, method.__wrapped__) + + +class NearestNeighbors(metaclass=NearestNeighborsMeta): + """Abstract base class for nearest neighbors search algorithms. + + The minimal implementation consists of a ``fit`` and ``query`` method. The default ``query_batch`` method uses + a thread pool to parallelize calls to ``query``, but all methods may be overridden for efficiency purposes. + It is expected that ``query`` returns a tuple of indices and distances to the nearest neighbors. + + The default interface to all methods is based on NumPy N-dimensional arrays, but implementations might ``overload`` + the methods such that other data types can be implemented. An important consideration for all possible methods + and overloads is that they should be type stable, preferably allowing only floating-point arrays as input + and returning floating-point distances of equal type as output. + """ + + __fitted__ = False + + @abstractmethod + def fit(self, data: np.ndarray) -> Self: + ... + + @abstractmethod + def query(self, point: np.ndarray, n_neighbors: int) -> tuple[np.ndarray, np.ndarray]: + ... + + def query_idx(self, point: np.ndarray, n_neighbors: int) -> np.ndarray: + idx, _ = self.query(point, n_neighbors) + return idx + + def query_dist(self, point: np.ndarray, n_neighbors: int) -> np.ndarray: + _, dist = self.query(point, n_neighbors) + return dist + + def query_batch(self, points: np.ndarray, n_neighbors: int) -> tuple[np.ndarray, np.ndarray]: + pool = ThreadPool() + result = pool.map(lambda q: self.query(q, n_neighbors), points) + idx, dist = zip(*result) + return np.stack(idx), np.stack(dist) + + def query_batch_idx(self, points: np.ndarray, n_neighbors: int) -> np.ndarray: + idx, _ = self.query_batch(points, n_neighbors) + return idx + + def query_batch_dist(self, points: np.ndarray, n_neighbors: int) -> np.ndarray: + _, dist = self.query_batch(points, n_neighbors) + return dist + + def add(self, data: np.ndarray) -> Self: + """Partially add elements to the index. + + Not all methods implement ``add``, but this can be useful if the data does not fit into memory. + + :param data: Data to add to the index. + :raises: NotImplementedError if ``add`` is not available for the class. + """ + msg = ( + f"Method 'add' is not implemented for '{self}', use 'fit' to learn the entire database at once, " + f"or use a different indexing structure that enables partial addition of data. " + f"If you would like to implement 'add' for '{self}', please open a pull request at " + f"https://github.com/davnn/nearness" + ) + raise NotImplementedError(msg) + + def save(self, file: str | Path) -> None: + # unwrap the fit method such that it equals the original one (if it has the special ``__check__`` attribute) + self.fit = ( + self.fit.__wrapped__ # type: ignore[reportFunctionMemberAccess] + if hasattr(self.fit, "__fit__") + else self.fit + ) + # the dynamic dataclass is not pickleable, therefore we "serialize" it and override ``_parameters_`` on load + original_params = copy(self._parameters_) + try: + parameter_fields = fields(self._parameters_) + parameter_types = [(f.name, f.type) for f in parameter_fields] + parameter_values = {f.name: getattr(self._parameters_, f.name) for f in parameter_fields} + del self._parameters_ + joblib.dump( + [self, parameter_types, parameter_values], + filename=file, + protocol=self.config.save_protocol, + compress=self.config.save_compression, + ) + finally: + self._parameters_ = original_params # type: ignore[reportUninitializedInstanceVariable] + + def load(self, file: str | Path) -> "NearestNeighbors": + # we might wrap the fit_method again here, but it's not strictly necessary if we assume that + # the model is ``__fitted__`` already, i.e. a check is unnecessary. + model, parameter_types, parameter_values = joblib.load(file) + model.__fitted__ = True + model._parameters_ = make_dataclass("Parameters", parameter_types)(**parameter_values) # noqa: SLF001 + return self + + @property + def config(self) -> "Config": + return self._config_ + + @property + def parameters(self) -> Any: + """The parameters are dynamically set on class creation due to the ``NearestNeighborsMeta`` metaclass. + + All ``__init__`` arguments are considered parameters, and it is suggested to type-hint every parameter, + because those type hints are used in ``set_parameters`` to validate the input arguments. + + :return: + """ + return self._parameters_ + + +def _create_parameter_class( + parameters: dict[str, inspect.Parameter], + kwargs: dict[str, Any], +) -> "types.Parameters": # type: ignore[reportGeneralTypeIssues] + empty = inspect.Parameter.empty + parameter_types = [(k, Any if (a := v.annotation) is empty else a) for k, v in parameters.items()] + # determine the parameter values + parameter_values = {k: kwargs[k] if k in kwargs else v.default for k, v in parameters.items()} + return make_dataclass("Parameters", parameter_types)(**parameter_values) + + +def _create_check_wrapper(obj: NearestNeighbors, method: Callable[..., Any]) -> Callable[..., Any]: + def wrapper(*args: Any, **kwargs: Any) -> Any: + logger.debug("Called method with fit check enabled.") + if not obj.__fitted__: + msg = f"Attempted to call '{method.__qualname__}', but 'fit' has not yet been called." + raise AssertionError(msg) + return method(*args, **kwargs) + + # we set a ``__check__`` attribute, to be able to (easily) distinguish the wrapper from the bound method + wrapper.__check__ = True # type: ignore[reportFunctionMemberAccess] + return wrapper diff --git a/src/nearness/_config.py b/src/nearness/_config.py new file mode 100644 index 0000000..47747eb --- /dev/null +++ b/src/nearness/_config.py @@ -0,0 +1,61 @@ +from dataclasses import asdict, dataclass + +from typing_extensions import Any, Callable + +__all__ = ["Config", "config"] + + +@dataclass +class Config: + """The global NearestNeighbors configuration. + + Each instance of ``NearestNeighbors`` creates a current (deep) copy of the global ``config`` object. It is, + therefore, possible to adapt class behaviour before instantiation by modification of ``config``, and, because + each class owns a copy of ``config``, it can be configured individually. + + We further provide a simple event interface using callbacks. You can observe ``__setattr__`` events using + ``register_callback`` and provide a function receiving the newly set value. To enable the callback-based + observability it's necessary that all fields are immutable, the only way to update a field is to set it. + """ + + def __init__(self) -> None: + super().__init__() + self._callbacks = {} + + methods_require_check: frozenset[str] = frozenset( + { + "save", + "query", + "query_idx", + "query_dist", + "query_batch", + "query_batch_idx", + "query_batch_dist", + }, + ) + remove_check_after_fit: bool = True + save_protocol: int | None = None + save_compression: int = 0 + + def register_callback(self, field_name: str, callback: Callable[[Any], Any]) -> None: + if field_name in asdict(self): + # initialize the list of callbacks if there is no callback yet + if field_name not in self._callbacks: + self._callbacks[field_name] = [] + self._callbacks[field_name].append(callback) + else: + msg = ( + f"Cannot register a callback for attribute '{field_name}', attribute " + f"does not exist on class {self}." + ) + raise AssertionError(msg) + + def __setattr__(self, name: str, value: Any) -> None: + super().__setattr__(name, value) + if name in self.__annotations__: + callbacks = self._callbacks.get(name, []) + for callback in callbacks: + callback(value) + + +config = Config() diff --git a/src/nearness/_faiss.py b/src/nearness/_faiss.py new file mode 100644 index 0000000..bcc6c2a --- /dev/null +++ b/src/nearness/_faiss.py @@ -0,0 +1,118 @@ +from pathlib import Path + +import faiss +import numpy as np +from safecheck import Float, Float32, Int64, Is, NumpyArray, typecheck +from typing_extensions import Annotated, Any, Protocol, runtime_checkable + +from ._base import NearestNeighbors + + +@runtime_checkable +class FaissIndexFactory(Protocol): + def __call__(self, dim: int) -> faiss.Index: + ... + + +class FaissNeighbors(NearestNeighbors): + """A fairly simple wrapper around ``faiss.Index``. + + A Faiss index is built from any combination of the following. + + 1. Vector transform β€” a pre-processing step applied to vectors before indexing (PCA, OPQ). + 2. Coarse quantizer β€” rough organization of vectors to subdomains (for restricting search + scope, includes IVF, IMI, and HNSW). + 3. Fine quantizer β€” a finer compression of vectors into smaller domains (for compressing + index size, such as PQ). + 4. Refinement β€” a final step at search-time which re-orders results using distance calculations + on the original flat vectors. Alternatively, another index (non-flat) index can be used. + + Note that coarse quantization refers to the 'clustering' of vectors (such as inverted indexing with IVF). + By using coarse quantization, we enable non-exhaustive search by limiting the search scope. + Fine quantization describes the compression of vectors into codes (as with PQ). The purpose fine quantization + is to reduce the memory usage of the index. + + References + ---------- + - https://github.com/facebookresearch/faiss + - https://www.pinecone.io/learn/series/faiss/ + """ + + positive_integer = Annotated[int, Is[lambda i: i > 0]] + float_fraction = Annotated[float, Is[lambda f: 0 < f < 1]] + + @typecheck + def __init__( + self, + *, + index_or_factory: str | FaissIndexFactory | faiss.Index = "Flat", + add_data_on_fit: bool = True, + sample_train_points: positive_integer | float_fraction | None = None, + sample_with_replacement: bool = False, + rng: np.random.Generator | Any = None, # (Any-types are validated by default_rng) + ) -> None: + super().__init__() + self.parameters.rng = rng if isinstance(rng, np.random.Generator) else np.random.default_rng(rng) + + # to be defined in ``fit`` + self._index = None + + @typecheck + def fit(self, data: Float[NumpyArray, "n d"]) -> "FaissNeighbors": + n_samples, dim = data.shape + index = self.parameters.index_or_factory + + if isinstance(index, str): + # if the index is a string, we treat it as an index factory + self._index = faiss.index_factory(dim, index) + elif isinstance(index, faiss.Index): + # if the index is an existing index, simply use that + self._index = index + else: + # otherwise the index should be a callable returning an index + self._index = index(dim) + + if (n_train := self.parameters.sample_train_points) is not None: + if isinstance(n_train, float): + # we make sure float is 0 < float < 1 in ``__init__`` + n_train = int(n_train * n_samples) + + sample = self.parameters.rng.choice(data, size=n_train, replace=self.parameters.sample_with_replacement) + self._index.train(sample) # type: ignore[reportGeneralTypeIssues] + else: + self._index.train(data) # type: ignore[reportGeneralTypeIssues] + + if self.parameters.add_data_on_fit: + # data might be added directly on fit, or using the ``add`` method + self._index.add(data) # type: ignore[reportGeneralTypeIssues] + + return self + + @typecheck + def add(self, data: Float[NumpyArray, "n d"]) -> "FaissNeighbors": + self._index.add(data) # type: ignore[reportGeneralTypeIssues] + return self + + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "{n_neighbors}"], Float32[NumpyArray, "{n_neighbors}"]]: + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors) + return idx.ravel(), dist.ravel() + + def query_batch( + self, + points: Float[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "m {n_neighbors}"], Float32[NumpyArray, "m {n_neighbors}"]]: + dist, idx = self._index.search(points, n_neighbors) # type: ignore[reportGeneralTypeIssues] + return idx, dist + + def save(self, file: str | Path) -> None: + faiss.write_index(self._index, str(file)) + + def load(self, file: str | Path) -> "FaissNeighbors": + self._index = faiss.read_index(str(file)) + self.__fitted__ = True + return self diff --git a/src/nearness/_hnsw.py b/src/nearness/_hnsw.py new file mode 100644 index 0000000..3974cd6 --- /dev/null +++ b/src/nearness/_hnsw.py @@ -0,0 +1,67 @@ +from hnswlib import BFIndex, Index +from safecheck import Float, Float32, NumpyArray, UInt64, typecheck +from typing_extensions import Literal + +from ._base import NearestNeighbors + + +class HNSWNeighbors(NearestNeighbors): + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + + available_metrics = Literal["l2", "ip", "cosine"] + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "l2", + n_index_neighbors: int = 256, + n_search_neighbors: int | None = None, + num_links: int = 16, + num_threads: int = -1, + random_seed: int = 0, + use_bruteforce: bool = False, + load_dim: int | None = None, + ) -> None: + super().__init__() + self._index_constructor = BFIndex if use_bruteforce else Index + self._model = None + + @typecheck + def fit(self, data: Float[NumpyArray, "n d"]) -> "HNSWNeighbors": + n_samples, n_dim = data.shape + index = self._index_constructor(space=self.parameters.metric, dim=n_dim) + + if self.parameters.use_bruteforce: + index.init_index(max_elements=n_samples) + index.add_items(data) + else: + index.init_index( + max_elements=n_samples, + ef_construction=self.parameters.n_index_neighbors, + M=self.parameters.num_links, + random_seed=self.parameters.random_seed, + ) + index.add_items(data) + + if (n_search := self.parameters.n_search_neighbors) is not None: + index.set_ef(n_search) + + self._model = index + return self + + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[UInt64[NumpyArray, "{n_neighbors}"], Float32[NumpyArray, "{n_neighbors}"]]: + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors) + return idx.ravel(), dist.ravel() + + def query_batch( + self, + points: Float[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[UInt64[NumpyArray, "m {n_neighbors}"], Float32[NumpyArray, "m {n_neighbors}"]]: + idx, dist = self._model.knn_query(points, k=n_neighbors, num_threads=self.parameters.num_threads) + return idx, dist diff --git a/src/nearness/_jax.py b/src/nearness/_jax.py new file mode 100644 index 0000000..705629b --- /dev/null +++ b/src/nearness/_jax.py @@ -0,0 +1,125 @@ +import jax +import jax.numpy as jnp +import numpy as np +from safecheck import Float, Float32, Int32, JaxArray, NumpyArray, typecheck +from typing_extensions import Literal, overload + +from ._base import NearestNeighbors + + +def cdist_minkowski( + data: Float[NumpyArray | JaxArray, "n d"], + query: Float[NumpyArray | JaxArray, "m d"], + p: int | float = 2, +) -> Float[NumpyArray | JaxArray, "m n"]: + """Reference implementation of Euclidean distance with Jax. + + :param data: Matrix of input data points. + :param query: Matrix of query data points. + :param p: The power parameter for the Minkowski distance metric. + :return: Matrix of distances between query and data. + """ + diff = jnp.expand_dims(query, 1) - jnp.expand_dims(data, 0) + return jnp.linalg.norm(diff, ord=p, axis=-1, keepdims=False) + + +def cdist_euclidean_mm( + data: Float[NumpyArray | JaxArray, "n d"], + query: Float[NumpyArray | JaxArray, "m d"], +) -> Float[NumpyArray | JaxArray, "m n"]: + """Probably the fastest possible implementation of Euclidean distance with NumPy. + + :param data: Matrix of input data points. + :param query: Matrix of query data points. + :return: Matrix of distances between query and data. + """ + # Calculate the squared differences raised to the power of p + squared_diff_p = ( + jnp.expand_dims(jnp.einsum("ij,ij->i", data, data), 0) + - 2.0 * jnp.dot(query, data.T) + + jnp.expand_dims(jnp.einsum("ij,ij->i", query, query), 1) + ) + + # Ensure that negative values close to zero are treated as zeros due to potential numerical errors + return jnp.sqrt(jnp.maximum(squared_diff_p, 0.0)) + + +class JaxNeighbors(NearestNeighbors): + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + + available_metrics = Literal["minkowski"] + compute_modes = Literal["use_mm_for_euclid_dist", "donot_use_mm_for_euclid_dist"] + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "minkowski", + p: int = 2, + compute_mode: compute_modes = "use_mm_for_euclid_dist", + approximate_recall_target: float = 0.95, + ) -> None: + super().__init__() + # to be defined in ``fit`` + self._data: Float[JaxArray, "n d"] | None = None + + @typecheck + def fit(self, data: Float[NumpyArray | JaxArray, "n d"]) -> "JaxNeighbors": + self._data = jnp.asarray(data) + return self + + @overload + def query( + self, + point: Float[JaxArray, "d"], + n_neighbors: int, + ) -> tuple[Int32[JaxArray, "{n_neighbors}"], Float32[JaxArray, "{n_neighbors}"]]: + ... + + @overload + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int32[NumpyArray, "{n_neighbors}"], Float32[NumpyArray, "{n_neighbors}"]]: + ... + + def query(self, point, n_neighbors): # type: ignore[reportGeneralTypeIssues] + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors) + return idx.ravel(), dist.ravel() + + @overload + def query_batch( + self, + points: Float[JaxArray, "m d"], + n_neighbors: int, + ) -> tuple[Int32[JaxArray, "m {n_neighbors}"], Float32[JaxArray, "m {n_neighbors}"]]: + ... + + @overload + def query_batch( + self, + points: Float[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[Int32[NumpyArray, "m {n_neighbors}"], Float32[NumpyArray, "m {n_neighbors}"]]: + ... + + def query_batch(self, points, n_neighbors): # type: ignore[reportGeneralTypeIssues] + # the data is implicitly casted to a jax array + is_numpy = isinstance(points, NumpyArray) # type: ignore[reportUnnecessaryIsInstance,reportGeneralTypeIssues] + if self.parameters.p == 2 and self.parameters.compute_mode == "use_mm_for_euclid_dist": + distance = cdist_euclidean_mm(self._data, points) # type: ignore[reportGeneralTypeIssues] + else: + distance = cdist_minkowski(self._data, points, p=self.parameters.p) # type: ignore[reportGeneralTypeIssues] + + dist, idx = jax.lax.approx_min_k( + distance, # type: ignore[reportGeneralTypeIssues] + n_neighbors, + recall_target=self.parameters.approximate_recall_target, + ) + # ``np.asarray`` should be the best option to ensure no copy is made, https://github.com/google/jax/issues/1961 + if is_numpy: + return np.asarray(idx), np.asarray(dist) + + return idx, dist diff --git a/src/nearness/_numpy.py b/src/nearness/_numpy.py new file mode 100644 index 0000000..7fc8c83 --- /dev/null +++ b/src/nearness/_numpy.py @@ -0,0 +1,99 @@ +import numpy as np +from safecheck import Float, Int64, NumpyArray, typecheck +from typing_extensions import Literal + +from ._base import NearestNeighbors + + +def cdist_minkowski( + data: Float[NumpyArray, "n d"], + query: Float[NumpyArray, "m d"], + p: int | float = 2, +) -> Float[NumpyArray, "m n"]: + """Reference implementation of Euclidean distance with NumPy. + + :param data: Matrix of input data points. + :param query: Matrix of query data points. + :param p: The power parameter for the Minkowski distance metric. + :return: Matrix of distances between query and data. + """ + diff = query[:, np.newaxis, :] - data[np.newaxis, :, :] + return np.linalg.norm(diff, ord=p, axis=-1, keepdims=False) + + +def cdist_euclidean_mm( + data: Float[NumpyArray, "n d"], + query: Float[NumpyArray, "m d"], +) -> Float[NumpyArray, "m n"]: + """Probably the fastest possible implementation of Euclidean distance with NumPy. + + :param data: Matrix of input data points. + :param query: Matrix of query data points. + :return: Matrix of distances between query and data. + """ + # Calculate the squared differences raised to the power of p + squared_diff_p = ( + np.einsum("ij,ij->i", data, data)[np.newaxis, :] + - 2.0 * np.dot(query, data.T) + + np.einsum("ij,ij->i", query, query)[:, np.newaxis] + ) + + # Ensure that negative values close to zero are treated as zeros due to potential numerical errors + np.maximum(squared_diff_p, 0.0, out=squared_diff_p) + return np.sqrt(squared_diff_p) + + +def min_k(arr: Float[NumpyArray, "m n"], k: int) -> tuple[Int64[NumpyArray, "m {k}"], Float[NumpyArray, "m {k}"]]: + """Similar to torch.topk, but only implemented for matrices and returning the smallest (min) items. + + :param arr: Matrix of data points (distances). + :param k: Number of smallest elements to determine. + """ + indices = np.argpartition(arr, kth=range(k), axis=1)[:, :k] + values = np.take_along_axis(arr, indices, axis=1) + return indices, values + + +class NumpyNeighbors(NearestNeighbors): + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + + available_metrics = Literal["minkowski"] + compute_modes = Literal["use_mm_for_euclid_dist", "donot_use_mm_for_euclid_dist"] + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "minkowski", + p: int = 2, + compute_mode: compute_modes = "use_mm_for_euclid_dist", + ) -> None: + super().__init__() + # to be defined in ``fit`` + self._data: Float[NumpyArray, "n d"] | None = None + + @typecheck + def fit(self, data: Float[NumpyArray, "n d"]) -> "NumpyNeighbors": + self._data = data + return self + + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "{n_neighbors}"], Float[NumpyArray, "{n_neighbors}"]]: + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors) + return idx.ravel(), dist.ravel() + + def query_batch( + self, + points: Float[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "m {n_neighbors}"], Float[NumpyArray, "m {n_neighbors}"]]: + if self.parameters.p == 2 and self.parameters.compute_mode == "use_mm_for_euclid_dist": + distance = cdist_euclidean_mm(self._data, points) # type: ignore[reportGeneralTypeIssues] + else: + distance = cdist_minkowski(self._data, points, p=self.parameters.p) # type: ignore[reportGeneralTypeIssues] + + return min_k(distance, k=n_neighbors) diff --git a/src/nearness/_scann.py b/src/nearness/_scann.py new file mode 100644 index 0000000..a0580ef --- /dev/null +++ b/src/nearness/_scann.py @@ -0,0 +1,180 @@ +from dataclasses import asdict, dataclass +from pathlib import Path + +import scann +from safecheck import Float, Float32, NumpyArray, UInt32, typecheck +from typing_extensions import Literal, NotRequired, TypedDict, get_type_hints + +from ._base import NearestNeighbors + + +@dataclass +class ScannTreeConfig: + num_leaves: int = 1000 + num_leaves_to_search: int = 10 + training_sample_size: int = 100000 + min_partition_size: int = 50 + training_iterations: int = 10 + spherical: bool = False + quantize_centroids: bool = False + random_init: bool = True + + +@dataclass +class ScannHashingConfig: + dimensions_per_block: int = 2 + anisotropic_quantization_threshold: float = 0.2 + training_sample_size: int = 100000 + min_cluster_size: int = 100 + hash_type: str = "lut16" + training_iterations: int = 10 + + +@dataclass +class ScannBruteForceConfig: + quantize: bool = False + + +@dataclass +class ScannReorderConfig: + reordering_num_neighbors: int = 100 + quantize: bool = False + + +@dataclass +class ScannSearchConfig: + pre_reorder_num_neighbors: int | None = None + leaves_to_search: int | None = None + + +def typedict(data_class: type) -> type[TypedDict]: # type: ignore[reportGeneralTypeIssues] + """Convert the dataclass to a typed dictionary. + + All dictionary items are set to ``NotRequired`` because we have default initializers + for all config items where ``typedict`` is used. + + :param data_class: Configuration data class with default values. + :return: TypedDict representation of the dataclass. + """ + field_types: dict[str, type] = { + k: NotRequired[t] for k, t in get_type_hints(data_class).items() # type: ignore[reportGeneralTypeIssues] + } + return TypedDict(f"{data_class.__name__}TypedDict", field_types) # type: ignore[reportGeneralTypeIssues] + + +class ScannNeighbors(NearestNeighbors): + """ScaNN performs search in three phases as described in the following. + + 1. Partitioning (optional): ScaNN partitions the dataset during training time, and at query time selects the top + partitions to pass onto the scoring stage. + 2. Scoring: ScaNN computes the distances from the query to all datapoints in the dataset + (if partitioning isn't enabled) or all datapoints in a partition to search (if partitioning is enabled). + These distances aren't necessarily exact, but brute-force scoring can be enabled. + 3. Rescoring (optional): ScaNN takes the best k' distances from scoring and re-computes these distances more + accurately. From these k' re-computed distances the top k are selected. + + Source: https://github.com/google-research/google-research/blob/master/scann/docs/algorithms.md + """ + + available_metrics = Literal["dot_product", "squared_l2"] + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "squared_l2", + n_neighbors: int = 1, + n_training_threads: int = 0, + use_tree: bool = True, + use_reorder: bool = False, + use_bruteforce: bool = False, + search_parallel: bool = False, + tree_config: ScannTreeConfig | typedict(ScannTreeConfig) | None = None, # type: ignore[reportGeneralTypeIssues] + bruteforce_config: ScannBruteForceConfig | typedict(ScannBruteForceConfig) | None = None, # type: ignore[reportGeneralTypeIssues] + hashing_config: ScannHashingConfig | typedict(ScannHashingConfig) | None = None, # type: ignore[reportGeneralTypeIssues] + reorder_config: ScannReorderConfig | typedict(ScannReorderConfig) | None = None, # type: ignore[reportGeneralTypeIssues] + search_config: ScannSearchConfig | typedict(ScannSearchConfig) | None = None, # type: ignore[reportGeneralTypeIssues] + ) -> None: + super().__init__() + # safely initialize the mutable config parameters + to_config = lambda c, v: c() if v is None else c(**v) if isinstance(v, dict) else v + self.parameters.tree_config = to_config(ScannTreeConfig, tree_config) + self.parameters.bruteforce_config = to_config(ScannBruteForceConfig, bruteforce_config) + self.parameters.hashing_config = to_config(ScannHashingConfig, hashing_config) + self.parameters.reorder_config = to_config(ScannReorderConfig, reorder_config) + self.parameters.search_config = to_config(ScannSearchConfig, search_config) + + # to be defined in ``fit`` + self._index = None + + @typecheck + def fit(self, data: Float[NumpyArray, "n d"]) -> "ScannNeighbors": + parameters = self.parameters + score_config = parameters.bruteforce_config if parameters.use_bruteforce else parameters.hashing_config + score_method = "score_brute_force" if parameters.use_bruteforce else "score_ah" + + # set up the builder + searcher = scann.scann_ops_pybind.builder( + db=data, + num_neighbors=parameters.n_neighbors, + distance_measure=parameters.metric, + ) + + # set number of threads + searcher.set_n_training_threads(self.parameters.n_training_threads) + + # provide the tree config + if self.parameters.use_tree: + searcher.tree(**asdict(parameters.tree_config)) + + # provide the score config + getattr(searcher, score_method)(**asdict(score_config)) + + # provide the reorder config + if self.parameters.use_reorder: + searcher.reorder(**asdict(parameters.reorder_config)) + + self._index = searcher.build() + return self + + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[UInt32[NumpyArray, "{n_neighbors}"], Float32[NumpyArray, "{n_neighbors}"]]: + idx, dist = self._index.search( + point, + final_num_neighbors=n_neighbors, + **asdict(self.parameters.search_config), + ) + return idx, dist + + def query_batch( + self, + points: Float[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[UInt32[NumpyArray, "m {n_neighbors}"], Float32[NumpyArray, "m {n_neighbors}"]]: + search_method = "search_batched_parallel" if self.parameters.search_parallel else "search_batched" + idx, dist = getattr(self._index, search_method)( + points, + final_num_neighbors=n_neighbors, + **asdict(self.parameters.search_config), + ) + return idx, dist + + def save(self, file: str | Path) -> None: + self._assert_is_directory(file) + self._index.serialize(str(file)) + + def load(self, file: str | Path) -> "ScannNeighbors": + self._assert_is_directory(file) + searcher = scann.scann_ops_pybind.load_searcher(str(file)) + self._index = searcher + self.__fitted__ = True + return self + + @staticmethod + def _assert_is_directory(file: str | Path) -> None: + if not Path(file).is_dir(): + msg = f"ScaNN requires an artifacts directory for storage, but found no directory at '{file}'." + raise AssertionError(msg) diff --git a/src/nearness/_scipy.py b/src/nearness/_scipy.py new file mode 100644 index 0000000..ba0dbd3 --- /dev/null +++ b/src/nearness/_scipy.py @@ -0,0 +1,84 @@ +from safecheck import Float, Int64, NumpyArray, typecheck +from scipy.spatial.distance import cdist +from typing_extensions import Literal, TypedDict + +from ._base import NearestNeighbors +from ._numpy import min_k + + +class ScipyNeighbors(NearestNeighbors): + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + + available_metrics = Literal[ + "braycurtis", + "canberra", + "chebyshev", + "cityblock", + "correlation", + "cosine", + "dice", + "euclidean", + "hamming", + "jaccard", + "jensenshannon", + "kulczynski1", + "mahalanobis", + "matching", + "minkowski", + "rogerstanimoto", + "russellrao", + "seuclidean", + "sokalmichener", + "sokalsneath", + "sqeuclidean", + "yule", + ] + + class MetricArgs(TypedDict): + p: float + w: NumpyArray + V: NumpyArray + VI: NumpyArray + out: NumpyArray + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "euclidean", + metric_args: MetricArgs | None = None, + ) -> None: + super().__init__() + if metric_args is None: + self.parameters.metric_args = {} + + # to be defined in ``fit`` + self._data: Float[NumpyArray, "n d"] | None = None + + @typecheck + def fit(self, data: Float[NumpyArray, "n d"]) -> "ScipyNeighbors": + self._data = data + return self + + def query( + self, + point: Float[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "{n_neighbors}"], Float[NumpyArray, "{n_neighbors}"]]: + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors) + return idx.ravel(), dist.ravel() + + def query_batch( + self, + points: Float[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "m {n_neighbors}"], Float[NumpyArray, "m {n_neighbors}"]]: + distance = cdist( + points, + self._data, # type: ignore[reportGeneralTypeIssues] + metric=self.parameters.metric, + **self.parameters.metric_args, + ) + idx, dist = min_k(distance, k=n_neighbors) + return idx, dist diff --git a/src/nearness/_sklearn.py b/src/nearness/_sklearn.py new file mode 100644 index 0000000..12cd770 --- /dev/null +++ b/src/nearness/_sklearn.py @@ -0,0 +1,58 @@ +from safecheck import Float64, Int64, NumpyArray, Real, typecheck +from sklearn.neighbors import NearestNeighbors as SklearnNearestNeighbors +from typing_extensions import Any, Literal + +from ._base import NearestNeighbors + + +class SklearnNeighbors(NearestNeighbors): + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + + available_algorithms = Literal["auto", "brute", "ball_tree", "kd_tree"] + + @typecheck + def __init__( + self, + *, + n_neighbors: int = 5, + algorithm: available_algorithms = "auto", + leaf_size: int = 30, + metric: str = "minkowski", # dynamically checked + p: int = 2, + metric_params: dict[str, Any] | None = None, + n_jobs: int | None = None, + ) -> None: + super().__init__() + # to be set in ``fit`` + self._model: None | SklearnNearestNeighbors = None + + @typecheck + def fit(self, data: Real[NumpyArray, "n d"]) -> "SklearnNeighbors": + self._model = SklearnNearestNeighbors( + n_neighbors=self.parameters.n_neighbors, + algorithm=self.parameters.algorithm, + leaf_size=self.parameters.leaf_size, + metric=self.parameters.metric, + p=self.parameters.p, + metric_params=self.parameters.metric_params, + n_jobs=self.parameters.n_jobs, + ) + self._model.fit(data) + return self + + def query( + self, + point: Real[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "{n_neighbors}"], Float64[NumpyArray, "{n_neighbors}"]]: + """CPU-based nearest neighbors algorithm based on scikit-learn. Note: The distances and indices are sorted!.""" + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors=n_neighbors) + return idx.ravel(), dist.ravel() + + def query_batch( + self, + points: Real[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "m {n_neighbors}"], Float64[NumpyArray, "m {n_neighbors}"]]: + dist, idx = self._model.kneighbors(points, n_neighbors=n_neighbors) + return idx, dist diff --git a/src/nearness/_torch.py b/src/nearness/_torch.py new file mode 100644 index 0000000..7b28f7e --- /dev/null +++ b/src/nearness/_torch.py @@ -0,0 +1,88 @@ +# type: ignore[reportGeneralTypeIssues,reportIncompatibleMethodOverride] +# It appears to be impossible to correctly type the overrides without bloat. + + +import torch +from safecheck import AbstractDtype, Int64, NumpyArray, TorchArray, typecheck +from typing_extensions import Literal, overload + +from ._base import NearestNeighbors + + +class SupportedFloat(AbstractDtype): + dtypes = ["float32", "float64"] # noqa: RUF012 + + +class TorchNeighbors(NearestNeighbors): + available_metrics = Literal["minkowski"] + compute_modes = Literal[ + "use_mm_for_euclid_dist", + "donot_use_mm_for_euclid_dist", + "use_mm_for_euclid_dist_if_necessary", + ] + + @typecheck + def __init__( + self, + *, + metric: available_metrics = "minkowski", + p: int = 2, + compute_mode: compute_modes = "use_mm_for_euclid_dist", + force_dtype: torch.dtype | None = None, + force_device: torch.device | str | None = None, + ) -> None: + super().__init__() + # to be defined in ``fit`` + self._data: SupportedFloat[TorchArray, "n d"] | None = None + + @typecheck + def fit(self, data: SupportedFloat[NumpyArray | TorchArray, "n d"]) -> "TorchNeighbors": + self._data = torch.as_tensor(data, device=self.parameters.force_device, dtype=self.parameters.force_dtype) + return self + + @overload + def query( + self, + point: SupportedFloat[NumpyArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "{n_neighbors}"], SupportedFloat[NumpyArray, "{n_neighbors}"]]: + ... + + @overload + def query( + self, + point: SupportedFloat[TorchArray, "d"], + n_neighbors: int, + ) -> tuple[Int64[TorchArray, "{n_neighbors}"], SupportedFloat[TorchArray, "{n_neighbors}"]]: + ... + + def query(self, point, n_neighbors): + idx, dist = self.query_batch(point.reshape(1, -1), n_neighbors) + return idx.ravel(), dist.ravel() + + @overload + def query_batch( + self, + points: SupportedFloat[NumpyArray, "m d"], + n_neighbors: int, + ) -> tuple[Int64[NumpyArray, "m {n_neighbors}"], SupportedFloat[NumpyArray, "m {n_neighbors}"]]: + ... + + @overload + def query_batch( + self, + points: SupportedFloat[TorchArray, "m d"], + n_neighbors: int, + ) -> tuple[Int64[TorchArray, "m {n_neighbors}"], SupportedFloat[TorchArray, "m {n_neighbors}"]]: + ... + + def query_batch(self, points, n_neighbors): + is_numpy = isinstance(points, NumpyArray) + points = torch.as_tensor(points, dtype=self._data.dtype, device=self._data.device) + dist = torch.cdist(points, self._data, p=self.parameters.p, compute_mode=self.parameters.compute_mode) + dist, idx = torch.topk(dist, k=n_neighbors, dim=1, largest=False) + + if is_numpy: + return idx.cpu().numpy(), dist.cpu().numpy() + + return idx, dist diff --git a/src/nearness/py.typed b/src/nearness/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_benchmark.py b/tests/test_benchmark.py new file mode 100644 index 0000000..81663e6 --- /dev/null +++ b/tests/test_benchmark.py @@ -0,0 +1,48 @@ +import pytest +from faiss.contrib.datasets import SyntheticDataset + +from nearness import * +from .utilities import pytest_param_if_value_available + +data = SyntheticDataset( + d=128, # data dimensionality + nt=100_000, # number of training points + nb=100, # batch query size + nq=1, # number of single queries +) + +candidates = { + "annoy": lambda: AnnoyNeighbors(metric="euclidean", n_search_neighbors=256), + "faiss-ivf-pq": lambda: FaissNeighbors(index_or_factory="OPQ8,IVF128,PQ8", sample_train_points=10_000), + "faiss-hnsw-pq": lambda: FaissNeighbors(index_or_factory="OPQ8,HNSW_PQ8"), + "faiss-nsg-pq": lambda: FaissNeighbors(index_or_factory="OPQ8,NSG_PQ8"), + "faiss-brute": lambda: FaissNeighbors(index_or_factory="Flat"), + "hnsw": lambda: HNSWNeighbors(metric="l2", num_threads=-1, n_search_neighbors=128, n_index_neighbors=256), + "hnsw-brute": lambda: HNSWNeighbors(metric="l2", num_threads=-1, use_bruteforce=True), + "jax": lambda: JaxNeighbors(compute_mode="use_mm_for_euclid_dist", approximate_recall_target=0.9), + "numpy": lambda: NumpyNeighbors(metric="minkowski", p=2, compute_mode="use_mm_for_euclid_dist"), + "scann": lambda: ScannNeighbors(search_parallel=True, use_tree=True, use_bruteforce=False, use_reorder=False), + "scann-brute": lambda: ScannNeighbors(search_parallel=True, use_bruteforce=True, use_tree=False, use_reorder=False), + "scipy": lambda: ScipyNeighbors(metric="euclidean"), + "sklearn": lambda: SklearnNeighbors(metric="euclidean", n_jobs=-1), + "torch": lambda: TorchNeighbors(metric="minkowski", p=2, compute_mode="use_mm_for_euclid_dist"), +} +candidates = [pytest_param_if_value_available(k, v) for k, v in candidates.items()] + + +@pytest.mark.parametrize("n_neighbors", [1]) +@pytest.mark.parametrize("model", candidates) +def test_benchmark_single_query(n_neighbors, model, benchmark): + fit_data = data.get_train() + query_data = data.get_queries() + model.fit(fit_data) + benchmark(lambda query: model.query(query, n_neighbors=n_neighbors), *query_data) + + +@pytest.mark.parametrize("n_neighbors", [1]) +@pytest.mark.parametrize("model", candidates) +def test_benchmark_batch_query(n_neighbors, model, benchmark): + fit_data = data.get_train() + query_data = data.get_database() + model.fit(fit_data) + benchmark(lambda query: model.query_batch(query, n_neighbors=n_neighbors), query_data) diff --git a/tests/test_nearness.py b/tests/test_nearness.py new file mode 100644 index 0000000..5d975f4 --- /dev/null +++ b/tests/test_nearness.py @@ -0,0 +1,480 @@ +import threading +from dataclasses import dataclass + +import faiss +from faiss.contrib.datasets import SyntheticDataset +from typing_extensions import Self, Literal + +from numpy.typing import DTypeLike +import hypothesis +import hypothesis.strategies as st +from sklearn.decomposition import PCA +from sklearn.model_selection import train_test_split +from sklearn.manifold import MDS +from sklearn.datasets import load_digits + +import nearness +from nearness import * +from nearness._base import InvalidSignatureError +from nearness._config import config +from .utilities import * + +hypothesis.settings.register_profile("no-deadline", deadline=None) +hypothesis.settings.load_profile("no-deadline") + + +@dataclass +class Candidate: + """Create a test candidate to be checked against a corresponding scikit-learn reference implementation.""" + + implementation: NearestNeighbors + reference: SklearnNeighbors + + +@dataclass +class Data: + fit: np.ndarray + query: np.ndarray + batch: np.ndarray + + +candidates = { + # comparing two equal models might seem unnecessary, but some tests are only performed on the ``implementation`` + "sklearn": lambda: Candidate( + implementation=SklearnNeighbors(metric="minkowski", p=2), reference=SklearnNeighbors(metric="minkowski", p=2) + ), + "numpy": lambda: Candidate( + implementation=NumpyNeighbors(metric="minkowski", p=2, compute_mode="donot_use_mm_for_euclid_dist"), + reference=SklearnNeighbors(metric="minkowski", p=2), + ), + "torch": lambda: Candidate( + implementation=TorchNeighbors(metric="minkowski", p=2, compute_mode="donot_use_mm_for_euclid_dist"), + reference=SklearnNeighbors(metric="minkowski", p=2), + ), + # it's important to only use a single tree for testing (with little data), otherwise it's possible that + # some points are never visited + "annoy": lambda: Candidate( + implementation=AnnoyNeighbors(metric="euclidean", n_trees=1, n_search_neighbors=32, random_seed=0), + reference=SklearnNeighbors(metric="minkowski", p=2), + ), + "hnsw-brute": lambda: Candidate( + implementation=HNSWNeighbors(metric="l2", use_bruteforce=True), reference=SklearnNeighbors(metric="sqeuclidean") + ), + "hnsw": lambda: Candidate( + implementation=HNSWNeighbors(metric="l2", n_index_neighbors=32, n_search_neighbors=32, num_links=32), + reference=SklearnNeighbors(metric="sqeuclidean"), + ), + "scipy": lambda: Candidate( + implementation=ScipyNeighbors(metric="euclidean"), reference=SklearnNeighbors(metric="euclidean") + ), + "faiss": lambda: Candidate( + implementation=FaissNeighbors(index_or_factory="Flat"), reference=SklearnNeighbors(metric="sqeuclidean") + ), + "scann": lambda: Candidate( + implementation=ScannNeighbors(use_bruteforce=True, use_tree=False, use_reorder=False), + reference=SklearnNeighbors(metric="sqeuclidean"), + ), + "jax": lambda: Candidate( + implementation=JaxNeighbors(compute_mode="donot_use_mm_for_euclid_dist"), + reference=SklearnNeighbors(metric="euclidean"), + ), +} + +candidates = [pytest_param_if_value_available(k, v) for k, v in candidates.items()] +DataGenerationMethod = Literal["random", "mds", "digits", "synthetic"] + + +def make_data( + dim: int, + fit_size: int = 1, + batch_size: int = 1, + dtype: DTypeLike = np.float64, + method: DataGenerationMethod = "random", +) -> Data: + # TODO: Evaluate if it makes sense to add more realistic datasets for testing and benchmarking, such as: + # https://github.com/facebookresearch/faiss/blob/main/contrib/datasets.py + rs = np.random.RandomState(0) + + if method == "digits": + x, _ = load_digits(return_X_y=True) + x = PCA(n_components=dim, random_state=rs).fit_transform(x).astype(dtype) + fit, batch = train_test_split(x, train_size=fit_size, test_size=batch_size, random_state=rs) + return Data(fit=fit, batch=batch, query=batch[0]) + + if method == "mds": + mds = MDS( + n_components=dim, + dissimilarity="precomputed", + normalized_stress="auto", + n_init=1, + max_iter=10, + random_state=rs, + ) + n_data = fit_size + batch_size + 1 + x = np.arange(start=1, stop=n_data * n_data + 1) + rs.shuffle(x) + x = x.reshape(n_data, n_data) + x = np.tril(x, k=-1) + x = x + x.T + x = mds.fit_transform(x).astype(dtype) + batch = x[:batch_size] + fit = x[batch_size:] + return Data(fit=fit, batch=batch, query=batch[0]) + + if method == "random": + fit = rs.rand(fit_size, dim).astype(dtype) + batch = rs.rand(batch_size, dim).astype(dtype) + query = rs.rand(dim).astype(dtype) + return Data(fit=fit, batch=batch, query=query) + + if method == "synthetic": + data = SyntheticDataset(d=dim, nt=fit_size, nb=batch_size, nq=1) + return Data(fit=data.get_train(), batch=data.get_database(), query=data.get_queries()[0]) + + msg = f"Invalid data generation method, found {method}." + raise ValueError(msg) + + +data_strategy = st.builds( + make_data, + dim=st.integers(min_value=1, max_value=32), + fit_size=st.integers(min_value=1, max_value=32), + batch_size=st.integers(min_value=1, max_value=32), + dtype=st.sampled_from([np.float32, np.float64]), +) + + +@st.composite +def neighbors_strategy(draw): + data = draw(data_strategy) + max_neighbors = len(data.fit) + n_neighbors = st.integers(min_value=1, max_value=max_neighbors) + return data, draw(n_neighbors) + + +@pytest.mark.parametrize("method", ["query", "query_idx", "query_dist"]) +@pytest.mark.parametrize("candidate", candidates) +def test_query_fail_not_fit(method, candidate): + # This test should run before any .fit has been called on ``implementation``. + vec = np.zeros(1, dtype=np.float64) + implementation = candidate.implementation + + with pytest.raises(Exception): + getattr(implementation, method)(vec, n_neighbors=1) + + +@pytest.mark.parametrize("method", ["query_batch", "query_batch_idx", "query_batch_dist"]) +@pytest.mark.parametrize("candidate", candidates) +def test_query_batch_fail_not_fit(method, candidate): + # This test should run before any .fit has been called on ``implementation``. + mat = np.zeros((1, 1), dtype=np.float64) + implementation = candidate.implementation + + with pytest.raises(Exception): + getattr(implementation, method)(mat, n_neighbors=1) + + +@hypothesis.given(data=data_strategy) +@pytest.mark.parametrize("candidate", candidates) +def test_fit_return_self(data, candidate): + # Test if the fit method works correctly + model = candidate.implementation + assert model == model.fit(data.fit) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +@pytest.mark.parametrize("candidate", candidates) +def test_query(data_and_neighbors, candidate): + data, n_neighbors = data_and_neighbors + + candidate.reference.fit(data.fit) + candidate.implementation.fit(data.fit) + + idx_ref, dist_ref = candidate.reference.query(data.query, n_neighbors=n_neighbors) + idx_imp, dist_imp = candidate.implementation.query(data.query, n_neighbors=n_neighbors) + + assert array_equal(idx_ref, idx_imp) + assert approx_equal(dist_ref, dist_imp) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +@pytest.mark.parametrize("candidate", candidates) +def test_query_idx_dist(data_and_neighbors, candidate): + data, n_neighbors = data_and_neighbors + + candidate.implementation.fit(data.fit) + idx_ref, dist_ref = candidate.implementation.query(data.query, n_neighbors=n_neighbors) + idx_check = candidate.implementation.query_idx(data.query, n_neighbors=n_neighbors) + dist_check = candidate.implementation.query_dist(data.query, n_neighbors=n_neighbors) + + assert array_equal(idx_ref, idx_check) + assert approx_equal(dist_ref, dist_check) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +@pytest.mark.parametrize("candidate", candidates) +def test_query_batch(data_and_neighbors, candidate): + data, n_neighbors = data_and_neighbors + + candidate.reference.fit(data.fit) + candidate.implementation.fit(data.fit) + + idx_ref, dist_ref = candidate.reference.query_batch(data.batch, n_neighbors=n_neighbors) + idx_imp, dist_imp = candidate.implementation.query_batch(data.batch, n_neighbors=n_neighbors) + + assert array_equal(idx_ref, idx_imp) + assert approx_equal(dist_ref, dist_imp) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +@pytest.mark.parametrize("candidate", candidates) +def test_query_batch_idx_dist(data_and_neighbors, candidate): + data, n_neighbors = data_and_neighbors + + candidate.implementation.fit(data.fit) + idx_ref, dist_ref = candidate.implementation.query_batch(data.batch, n_neighbors=n_neighbors) + idx_check = candidate.implementation.query_batch_idx(data.batch, n_neighbors=n_neighbors) + dist_check = candidate.implementation.query_batch_dist(data.batch, n_neighbors=n_neighbors) + + assert array_equal(idx_ref, idx_check) + assert approx_equal(dist_ref, dist_check) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +@hypothesis.settings(suppress_health_check=[hypothesis.HealthCheck.function_scoped_fixture]) +@pytest.mark.parametrize("candidate", candidates) +def test_save_load_identity(data_and_neighbors, candidate, tmp_path, request): + key = request.node.callspec.id + save_path = tmp_path / "file" + + if key == "hnsw-brute": + pytest.skip("Saving of HSNW index with use_bruteforce=True is not possible currently.") + + if key == "scann": + # faiss requires a dictionary to save to instead of a file + save_path.mkdir(exist_ok=True) + + data, n_neighbors = data_and_neighbors + saved_model = candidate.implementation.fit(data.fit) + saved_model.save(save_path) + loaded_model = candidate.implementation.load(save_path) + + idx_save, dist_save = saved_model.query_batch(data.batch, n_neighbors=n_neighbors) + idx_load, dist_load = loaded_model.query_batch(data.batch, n_neighbors=n_neighbors) + + assert array_equal(idx_save, idx_load) + assert approx_equal(dist_save, dist_load) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +@hypothesis.settings(suppress_health_check=[hypothesis.HealthCheck.function_scoped_fixture]) +def test_annoy_save_load_identity(data_and_neighbors, tmp_path): + """Must use unique model names for both models, because only one ``tmp_path`` is created (function-scoped).""" + data, n_neighbors = data_and_neighbors + _, n_dim = data.fit.shape + + # build models and save + save_model = AnnoyNeighbors(n_trees=1, n_search_neighbors=32) + save_model.fit(data.fit) + save_model.save(tmp_path / "save_model") + disk_model = AnnoyNeighbors(n_trees=1, n_search_neighbors=32, on_disk_build=str(tmp_path / "other_model")) + disk_model.fit(data.fit) + + # load models + load_model_save = AnnoyNeighbors(n_trees=1, n_search_neighbors=32, load_dim=n_dim).load(tmp_path / "save_model") + load_model_disk = AnnoyNeighbors(n_trees=1, n_search_neighbors=32, load_dim=n_dim).load(tmp_path / "other_model") + + idx_save, dist_save = save_model.query_batch(data.batch, n_neighbors=n_neighbors) + idx_disk, dist_disk = disk_model.query_batch(data.batch, n_neighbors=n_neighbors) + idx_load_save, dist_load_save = load_model_save.query_batch(data.batch, n_neighbors=n_neighbors) + idx_load_disk, dist_load_disk = load_model_disk.query_batch(data.batch, n_neighbors=n_neighbors) + + # save models equal load models + assert array_equal(idx_save, idx_disk, idx_load_save, idx_load_disk) + assert approx_equal(dist_save, dist_disk, dist_load_save, dist_load_disk) + + +@hypothesis.given(data_and_neighbors=neighbors_strategy()) +def test_faiss_index_creation(data_and_neighbors): + data, n_neighbors = data_and_neighbors + model_str = FaissNeighbors(index_or_factory="Flat") + model_str.fit(data.fit) + model_fun = FaissNeighbors(index_or_factory=lambda d: faiss.IndexFlat(d)) + model_fun.fit(data.fit) + model_raw = FaissNeighbors(index_or_factory=faiss.IndexFlat) + model_raw.fit(data.fit) + + idx_str, dist_str = model_str.query(data.query, n_neighbors) + idx_fun, dist_fun = model_fun.query(data.query, n_neighbors) + idx_raw, dist_raw = model_raw.query(data.query, n_neighbors) + + assert array_equal(idx_str, idx_fun, idx_raw) + assert approx_equal(dist_str, dist_fun, dist_raw) + + +def test_thread_safety(): + # Number of threads to create + num_threads = 100 + original_thread_ids = set() + captured_thread_ids = set() + + class Model(NearestNeighbors): + def __init__(self, *, thread_id: int): + super().__init__() + + def fit(self, data: np.ndarray) -> Self: + ... + + def query(self, point: np.ndarray, n_neighbors: int) -> tuple[np.ndarray, np.ndarray]: + ... + + # Use a threading.Event to synchronize threads + start_event = threading.Event() + + def create_instance(): + # Wait for the start signal + start_event.wait() + + # Get the current thread id + thread_id = threading.current_thread().ident + original_thread_ids.add(thread_id) + + # Set the thread id on the model + model = Model(thread_id=thread_id) + captured_id = model.parameters.thread_id + captured_thread_ids.add(captured_id) + + # Perform any assertions on the instance + assert thread_id == model.parameters.thread_id + + # Create and start threads + threads = [threading.Thread(target=create_instance) for _ in range(num_threads)] + for thread in threads: + thread.start() + + # Signal threads to start creating instances + start_event.set() + + # Join threads + for thread in threads: + thread.join() + + # Perform any additional assertions or checks + assert len(captured_thread_ids) == len(original_thread_ids) + assert captured_thread_ids == original_thread_ids + + +def test_missing_abstract_method(): + class N(NearestNeighbors): + ... + + with pytest.raises(TypeError, match="Can't instantiate abstract class"): + N() + + +def test_keyword_only(): + with pytest.raises(InvalidSignatureError): + + class N(NearestNeighbors): + def __init__(self, a): + ... + + +def test_warn_check(): + class ModelNoConfig(NearestNeighbors): + no_method = True + + def __init__(self): + ... + + def fit(self, data): + ... + + def query(self, point, n_neighbors): + ... + + class ModelWrongAttribute(NearestNeighbors): + no_method = True + + def __init__(self): + self.config.methods_require_check = self.config.methods_require_check | {"no_method"} + + def fit(self, data): + ... + + def query(self, point, n_neighbors): + ... + + class ModelMissingAttribute(NearestNeighbors): + def __init__(self): + self.config.methods_require_check = self.config.methods_require_check | {"missing_method"} + + def fit(self, data): + ... + + def query(self, point, n_neighbors): + ... + + with pytest.warns(UserWarning, match="Attempting to enable '__fitted__' check for invalid attribute"): + ModelWrongAttribute() + + with pytest.warns(UserWarning, match="Attempting to enable '__fitted__' check for missing attribute"): + ModelMissingAttribute() + + with pytest.warns(UserWarning, match="Attempting to enable '__fitted__' check for invalid attribute"): + model = ModelNoConfig() + model.config.methods_require_check = model.config.methods_require_check | {"no_method"} + + with pytest.warns(UserWarning, match="Attempting to enable '__fitted__' check for missing attribute"): + model = ModelNoConfig() + model.config.methods_require_check = model.config.methods_require_check | {"missing_method"} + + +def test_check_attribute(): + class Model(NearestNeighbors): + def __init__(self): + self.config.remove_check_after_fit = False + + def fit(self, data): + return self + + def query(self, point, n_neighbors): + ... + + model = Model() + + # make sure that all methods have a ``__check__`` attribute + for method in model.config.methods_require_check: + assert hasattr(getattr(model, method), "__check__") + + # make sure that the checks are not removed when configured as such + model.fit(None) + + # the methods should still have the attribute (as configured in ``__init__``) + for method in model.config.methods_require_check: + assert hasattr(getattr(model, method), "__check__") + + model.config.remove_check_after_fit = True + model.fit(None) + + # # now after ``fit``, the check should be removed from all methods (as configured) + for method in model.config.methods_require_check: + assert not hasattr(getattr(model, method), "__check__") + + # resetting the methods should enable the fit checks again + model.config.methods_require_check = config.methods_require_check + + for method in model.config.methods_require_check: + assert hasattr(getattr(model, method), "__check__") + + # removing methods should also remove their check + model.config.methods_require_check = model.config.methods_require_check - {"query"} + assert not hasattr(model.query, "__check__") + + # adding methods should also add their check + model.config.methods_require_check = model.config.methods_require_check | {"query"} + assert hasattr(model.query, "__check__") + + +def test_retrieve_version(): + nearness.get_version() diff --git a/tests/utilities.py b/tests/utilities.py new file mode 100644 index 0000000..e0d5921 --- /dev/null +++ b/tests/utilities.py @@ -0,0 +1,28 @@ +from copy import copy + +import pytest +import numpy as np +from typing_extensions import Any + + +def approx_equal(x: np.ndarray, y: np.ndarray, *arrays: np.ndarray, rtol: float = 1e-2, atol: float = 1e-4) -> bool: + """Ensure that all input arrays and their elements are approximately equal.""" + result = np.allclose(x, y, rtol=rtol, atol=atol) + for array in arrays: + result = result and np.allclose(x, array, rtol=rtol, atol=atol) + return result + + +def array_equal(x: np.ndarray, y: np.ndarray, *arrays: np.ndarray, equal_nan: bool = False) -> bool: + """Ensure that all input arrays and their elements are equal.""" + result = np.array_equal(x, y, equal_nan=equal_nan) + for array in arrays: + result = result and np.array_equal(x, array, equal_nan=equal_nan) + return result + + +def pytest_param_if_value_available(key: str, lazy_value: Any) -> Any: # type: ignore[ANN401] + try: + return pytest.param(copy(lazy_value()), id=key) + except NameError: + return pytest.param(None, id=key, marks=pytest.mark.skip)