diff --git a/.github/workflows/code-quality.yml b/.github/workflows/code-quality.yml index ea7e499..eb873e8 100644 --- a/.github/workflows/code-quality.yml +++ b/.github/workflows/code-quality.yml @@ -2,171 +2,69 @@ name: Code Quality on: push: - branches: [ main, dev/jmlr, feature/gpu_fix ] + branches: [main] pull_request: - branches: [ main, dev/jmlr, feature/gpu_fix ] + branches: [main] jobs: - code-quality: + lint: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: "3.9" - - - name: Cache pip dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-quality-${{ hashFiles('pyproject.toml') }} - restore-keys: | - ${{ runner.os }}-pip-quality- - ${{ runner.os }}-pip- - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -e ".[dev, security, linting]" - # pip install ruff mypy bandit safety - - - name: Check code formatting with Black - run: | - black --check --diff torchsom/ tests/ - - - name: Check import sorting with isort - run: | - isort --check-only --diff torchsom/ tests/ - - - name: Lint with Ruff - run: | - ruff check torchsom/ tests/ --output-format=github - - - name: Type checking with MyPy - run: | - mypy torchsom/ --ignore-missing-imports --strict - # mypy torchsom/ - continue-on-error: true - - - name: Check for security issues with Bandit (library code) - run: | - bandit -r torchsom/ -f json -o bandit-report-library.json --skip B101,B311,B601 - continue-on-error: true - - - name: Check for security issues with Bandit (tests) - run: | - bandit -r tests/ -f json -o bandit-report-tests.json --skip B101,B311,B601 - continue-on-error: true - - # - name: Check for known security vulnerabilities - # run: | - # safety check --json --output safety-report.json - # continue-on-error: true - - - name: Upload security reports - uses: actions/upload-artifact@v4 - if: always() - with: - name: security-reports - path: | - bandit-report-library.json - bandit-report-tests.json - # safety-report.json + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python + run: uv python install 3.12 + + - name: Sync dependencies + run: uv sync --extra linting + + - name: Ruff format check + run: uv run ruff format --check torchsom/ tests/ + + - name: Ruff lint + run: uv run ruff check torchsom/ tests/ --output-format=github + + - name: Type checking with mypy + run: uv run mypy torchsom/ --ignore-missing-imports + continue-on-error: true docstring-quality: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: "3.9" - - - name: Cache pip dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-quality-${{ hashFiles('pyproject.toml') }} - restore-keys: | - ${{ runner.os }}-pip-quality- - ${{ runner.os }}-pip- - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -e ".[dev, docs]" - # pip install pydocstyle interrogate - - - name: Check docstring style - run: | - pydocstyle torchsom/ --convention=google - continue-on-error: true - - - name: Check docstring coverage - run: | - interrogate torchsom/ --verbose --ignore-init-method --ignore-magic --ignore-module --fail-under=80 - continue-on-error: true - - complexity-analysis: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: "3.9" - - - name: Cache pip dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-quality-${{ hashFiles('pyproject.toml') }} - restore-keys: | - ${{ runner.os }}-pip-quality- - ${{ runner.os }}-pip- - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -e ".[dev, linting]" - # pip install radon - - - name: Analyze code complexity - run: | - radon cc torchsom/ --show-complexity --min B - radon mi torchsom/ --show --min B - continue-on-error: true - - check-dependencies: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python + run: uv python install 3.12 + + - name: Sync dependencies + run: uv sync --extra docs + + - name: Docstring coverage + run: uv run interrogate torchsom/ --verbose --ignore-init-method --ignore-magic --ignore-module --fail-under=80 + continue-on-error: true + + complexity: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v4 - - - name: Set up Python 3.9 - uses: actions/setup-python@v4 - with: - python-version: "3.9" - - - name: Cache pip dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-quality-${{ hashFiles('pyproject.toml') }} - restore-keys: | - ${{ runner.os }}-pip-quality- - ${{ runner.os }}-pip- - - - name: Install pip-tools - run: | - python -m pip install --upgrade pip - pip install -e ".[security]" - # pip install pip-tools - - - name: Check for dependency conflicts - run: | - pip-compile pyproject.toml --dry-run --verbose - continue-on-error: true + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python + run: uv python install 3.12 + + - name: Sync dependencies + run: uv sync --extra linting + + - name: Complexity analysis + run: | + uv run radon cc torchsom/ --show-complexity --min B + uv run radon mi torchsom/ --show --min B + continue-on-error: true diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index b3c9a36..f6b72ba 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -2,7 +2,7 @@ name: Build and Deploy Docs on: push: - branches: [main, dev/jmlr, feature/gpu_fix] + branches: [main] jobs: build-and-deploy: @@ -10,22 +10,17 @@ jobs: steps: - uses: actions/checkout@v4 + - name: Install uv + uses: astral-sh/setup-uv@v5 + - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.9' + run: uv python install 3.12 - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -e '.[dev, docs]' - # pip install -e '.[dev]' - # pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints + - name: Sync dependencies + run: uv sync --extra dev --extra docs - name: Build docs - run: | - cd docs - make html + run: uv run sphinx-build -b html docs/source/ docs/build/html - name: Deploy to GitHub Pages uses: peaceiris/actions-gh-pages@v3 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5685294..666fc07 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -3,7 +3,7 @@ name: Release on: push: tags: - - 'v*' + - "v*" jobs: test-before-release: @@ -19,72 +19,60 @@ jobs: id-token: write steps: - - uses: actions/checkout@v4 - with: - fetch-depth: 0 + - uses: actions/checkout@v4 + with: + fetch-depth: 0 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: "3.9" + - name: Install uv + uses: astral-sh/setup-uv@v5 - - name: Cache pip dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-release-${{ hashFiles('pyproject.toml') }} - restore-keys: | - ${{ runner.os }}-pip-release- - ${{ runner.os }}-pip- + - name: Set up Python + run: uv python install 3.12 - - name: Install build dependencies - run: | - python -m pip install --upgrade pip - pip install build twine commitizen pygithub + - name: Sync release dependencies + run: uv sync --extra release - - name: Generate CHANGELOG - run: | - if git log $(git describe --tags --abbrev=0)..HEAD --oneline | grep -q .; then - cz changelog - else - echo "No new commits" - fi + - name: Generate CHANGELOG + run: | + if git log $(git describe --tags --abbrev=0)..HEAD --oneline | grep -q .; then + uv run cz changelog + else + echo "No new commits" + fi - - name: Commit updated CHANGELOG.md - run: | - git config user.name "github-actions[bot]" - git config user.email "github-actions[bot]@users.noreply.github.com" - git add CHANGELOG.md - git commit -m "chore: update changelog for $GITHUB_REF_NAME" || echo "No changes to commit" - git push origin HEAD:main + - name: Commit updated CHANGELOG.md + run: | + git config user.name "github-actions[bot]" + git config user.email "github-actions[bot]@users.noreply.github.com" + git add CHANGELOG.md + git commit -m "chore: update changelog for $GITHUB_REF_NAME" || echo "No changes to commit" + git push origin HEAD:main - - name: Build package - run: | - python -m build + - name: Build package + run: uv build - - name: Check package - run: | - twine check dist/* + - name: Check package + run: uv run twine check dist/* - - name: Extract changelog - id: changelog - run: | - echo "RELEASE_NOTES<> $GITHUB_ENV - sed -n '/## \['"$GITHUB_REF_NAME"'\]/,/^## \[/{/^## \[/b;p}' CHANGELOG.md >> $GITHUB_ENV - echo "EOF" >> $GITHUB_ENV + - name: Extract changelog + id: changelog + run: | + echo "RELEASE_NOTES<> $GITHUB_ENV + sed -n '/## \['"$GITHUB_REF_NAME"'\]/,/^## \[/{/^## \[/b;p}' CHANGELOG.md >> $GITHUB_ENV + echo "EOF" >> $GITHUB_ENV - - name: Create Release with changelog notes - uses: actions/create-release@v1.1.4 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - with: - tag_name: ${{ github.ref_name }} - release_name: Release ${{ github.ref }} - body: ${{ env.RELEASE_NOTES }} - draft: false - prerelease: false + - name: Create Release + uses: actions/create-release@v1.1.4 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ github.ref_name }} + release_name: Release ${{ github.ref }} + body: ${{ env.RELEASE_NOTES }} + draft: false + prerelease: false - - name: Publish to PyPI - uses: pypa/gh-action-pypi-publish@release/v1 - with: - password: ${{ secrets.PYPI_API_TOKEN }} + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 1b86d0e..d22fe60 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -2,9 +2,9 @@ name: Tests on: push: - branches: [ main, dev/jmlr, feature/gpu_fix ] + branches: [main] pull_request: - branches: [ main, dev/jmlr, feature/gpu_fix ] + branches: [main] workflow_call: jobs: @@ -13,161 +13,49 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest, windows-latest, macos-latest] # macos doesn't work with CUDA - python-version: ["3.9", "3.10", "3.11"] - pytorch-version: ["2.7.0+cu126"] + os: [ubuntu-latest, windows-latest, macos-latest] + python-version: ["3.10", "3.11", "3.12"] exclude: - # Exclude some combinations to reduce CI load: macos doesn't work with CUDA, and ubuntu 3.9 triggers Codecov not available - # - os: ubuntu-latest - # python-version: "3.9" - - os: macos-latest - python-version: "3.9" - os: macos-latest python-version: "3.10" - os: macos-latest python-version: "3.11" - steps: - - uses: actions/checkout@v4 - # with: - # fetch-depth: 0 - - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Cache pip dependencies - uses: actions/cache@v3 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml') }} - restore-keys: | - ${{ runner.os }}-pip-${{ matrix.python-version }}- - ${{ runner.os }}-pip- - - - name: Install PyTorch ${{ matrix.pytorch-version }} - run: | - python -m pip install --upgrade pip - pip install torch==${{ matrix.pytorch-version }} --index-url https://download.pytorch.org/whl/cu126 - # pip install torch==${{ matrix.pytorch-version }} --index-url https://download.pytorch.org/whl/cpu - - - name: Install package, dev, and test dependencies - run: pip install -e ".[dev, tests]" - - - name: Run unit and gpu tests with coverage - # Automatically uses pytest configuration from pyproject.toml - run: pytest - env: - PYTORCH_VERSION: ${{ matrix.pytorch-version }} - # run: | - # pytest tests/unit/ -v \ - # --cov=torchsom \ - # --cov-report=xml \ - # --cov-report=html \ - # --cov-report=term-missing \ - # --cov-config=pyproject.toml \ - # --junit-xml=junit.xml \ - # --timeout=300 \ - # -n auto \ - # -m "unit or gpu" - - - name: Analyze code with SonarQube - if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.9' && matrix.pytorch-version == '2.7.0+cu126' - uses: SonarSource/sonarqube-scan-action@v5 - env: - SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} - # with: - # token: ${{ env.SONAR_TOKEN }} - # organization: michelin - # projectKey: michelin_TorchSOM - - - name: Upload test results - uses: actions/upload-artifact@v4 - if: always() - with: - name: test-results-${{ matrix.os }}-py${{ matrix.python-version }}-torch${{ matrix.pytorch-version }} - path: | - coverage.xml - htmlcov/ - junit.xml - - # test-gpu: - # runs-on: self-hosted - # if: github.event_name == 'push' && github.ref == 'refs/heads/main' - # strategy: - # fail-fast: false - # matrix: - # python-version: ["3.10"] - - # steps: - # - uses: actions/checkout@v4 - - # - name: Set up Python ${{ matrix.python-version }} - # uses: actions/setup-python@v4 - # with: - # python-version: ${{ matrix.python-version }} - - # - name: Install CUDA PyTorch - # run: | - # python -m pip install --upgrade pip - # pip install torch --index-url https://download.pytorch.org/whl/cu118 - - # - name: Install package, dev, and test dependencies - # run: pip install -e ".[dev, tests]" - - # - name: Run GPU tests - # run: | - # pytest tests/unit/ -v \ - # --cov=torchsom \ - # --cov-report=xml \ - # --cov-report=term-missing \ - # --junit-xml=junit-gpu.xml \ - # --timeout=600 \ - # -m "gpu" - # env: - # CUDA_VISIBLE_DEVICES: 0 - - # - name: Upload GPU test results - # uses: actions/upload-artifact@v4 - # if: always() - # with: - # name: gpu-test-results - # path: | - # coverage.xml - # junit-gpu.xml - - # integration-tests: - # runs-on: ubuntu-latest - # needs: test - # if: false - # # github.event_name == 'push' - # steps: - # - uses: actions/checkout@v4 - - # - name: Set up Python 3.10 - # uses: actions/setup-python@v4 - # with: - # python-version: "3.10" - - # - name: Install package, dev, and test dependencies - # run: pip install -e ".[dev, tests]" - - # - name: Run integration tests - # run: | - # pytest tests/ -v \ - # --cov=torchsom \ - # --cov-report=xml \ - # --cov-report=term-missing \ - # --junit-xml=junit-integration.xml \ - # --timeout=600 \ - # -m "integration" - # continue-on-error: true + - os: macos-latest + python-version: "3.12" - # - name: Upload integration test results - # uses: actions/upload-artifact@v4 - # if: always() - # with: - # name: integration-test-results - # path: | - # coverage.xml - # junit-integration.xml + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python ${{ matrix.python-version }} + run: uv python install ${{ matrix.python-version }} + + - name: Sync dependencies + run: uv sync --extra dev --extra tests + + - name: Install PyTorch (CPU) + run: uv pip install torch --index-url https://download.pytorch.org/whl/cpu + + - name: Run tests + run: uv run --no-sync pytest + + - name: Analyze code with SonarQube + if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.12' + # Advisory only: a SonarCloud auth/infra failure (e.g. token 403) must not + # fail the test job or block the release pipeline (release.yml reuses this workflow). + continue-on-error: true + uses: SonarSource/sonarqube-scan-action@v5 + env: + SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} + + - name: Upload test results + uses: actions/upload-artifact@v4 + if: always() + with: + name: test-results-${{ matrix.os }}-py${{ matrix.python-version }} + path: | + coverage.xml + htmlcov/ + junit.xml diff --git a/.gitignore b/.gitignore index dd65744..679e450 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,8 @@ benchmark/environments/ benchmark/jobs/ benchmark/.amlignore +arXiv-2510.11147v1/ + # # Too large for GitHub # data/benchmark/blobs_20000_300.csv @@ -104,3 +106,9 @@ Desktop.ini # Ruff .ruff_cache/ + +# LaTeX +*.aux +*.log +*.synctex.gz +docs/figures/*.pdf diff --git a/.markdownlint.json b/.markdownlint.json index 3433b97..ad1c171 100644 --- a/.markdownlint.json +++ b/.markdownlint.json @@ -3,7 +3,7 @@ "line_length": 500 }, "MD033": { - "allowed_elements": ["div", "p", "br", "table", "tr", "td", "details", "summary", "b", "img"] + "allowed_elements": ["div", "p", "br", "table", "tr", "td", "details", "summary", "b", "img", "sub"] }, "MD036": false, "MD007": false, diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 9450333..e335bdc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.6.0 + rev: v5.0.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer @@ -9,81 +9,29 @@ repos: - id: check-toml - id: check-json - id: debug-statements - # - id: check-added-large-files - # args: ['--maxkb=10000'] - # - id: name-tests-test - # args: ['--pytest-test-first'] - - - repo: https://github.com/psf/black - rev: 24.3.0 - hooks: - - id: black - name: black - args: ['--config', 'pyproject.toml'] - language_version: python3 - # args: ['--line-length=88'] - - - repo: https://github.com/pycqa/isort - rev: 5.13.2 - hooks: - - id: isort - name: isort - args: ['--settings-path', 'pyproject.toml'] - # args: ['--profile=black'] + - id: check-added-large-files + args: ["--maxkb=5000"] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.3.4 + rev: v0.9.7 hooks: + - id: ruff-format + name: ruff format - id: ruff - name: ruff - args: ['--config', 'pyproject.toml'] - # args: [--fix, --exit-non-zero-on-fix] + name: ruff lint + args: [--fix, --exit-non-zero-on-fix] - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.9.0 + rev: v1.15.0 hooks: - id: mypy name: mypy - args: ['--config-file', 'pyproject.toml', '--ignore-missing-imports', '--strict'] - # additional_dependencies: [torch, numpy, pydantic, types-requests] - - - repo: https://github.com/PyCQA/bandit - rev: 1.7.5 - hooks: - - id: bandit - name: bandit - args: ['--configfile', 'pyproject.toml'] - # args: ['-r', 'torchsom/', '--exclude', 'tests', '--skip', 'B101,B311,B601'] - # - id: bandit - # name: bandit-tests - # args: ['-r', 'tests/', '--skip', 'B101,B311,B601'] - - - repo: https://github.com/pycqa/pydocstyle - rev: 6.3.0 - hooks: - - id: pydocstyle - name: pydocstyle - args: ['--config', 'pyproject.toml'] - # additional_dependencies: [toml] + args: ["--config-file", "pyproject.toml", "--ignore-missing-imports"] + additional_dependencies: [types-requests, pydantic] - repo: https://github.com/econchick/interrogate - rev: 1.5.0 + rev: 1.7.0 hooks: - id: interrogate name: interrogate - args: ['--config', 'pyproject.toml'] - # args: ['torchsom/', '--verbose', '--ignore-init-method', '--ignore-magic', '--ignore-module', '--fail-under=80'] - - # - repo: local - # hooks: - # - id: pytest-check - # name: pytest-check - # description: Run pytest with configuration from pyproject.toml - # # stages: [commit] - # types: [python] - # entry: pytest - # language: system - # # args: [] # LONG Leave empty to use pytest configuration from pyproject.toml - # args: ['-x', '-v', 'tests/unit/', '-m', 'gpu', '--tb=short'] # SHORT - # pass_filenames: false - # always_run: true + args: ["--config", "pyproject.toml"] diff --git a/Makefile b/Makefile index 24f0249..f67470f 100644 --- a/Makefile +++ b/Makefile @@ -1,312 +1,125 @@ # TorchSOM Development Makefile -# Run `make help` to see available commands +# Run `make` or `make help` to see available commands. +# Uses `uv` (https://docs.astral.sh/uv/) for environment and dependency management. -# Note on pyproject.toml integration: -# - black, isort, ruff: read [tool.black], [tool.isort], [tool.ruff] automatically -# - mypy: must pass --config-file pyproject.toml -# - pytest: reads --cov-config=pyproject.toml for coverage config -# - bandit: does NOT read TOML, uses CLI options only -# - interrogate: can use --config if needed -# - sphinx-build: uses conf.py, TOML not involved -# - pip-compile: reads dependencies from pyproject.toml - -.PHONY: help install test test-quick test-gpu test-integration lint format security docs clean clean-docs precommit complexity dependencies ci all publish fix check coverage install-dev install-tests install-security install-linting install-docs install-precommit install-all test-coverage check-black check-isort check-ruff check-mypy lint-all format-black format-isort format-ruff format-all check-docstrings measure-docstrings-coverage build-docs clean-build clean-test clean-lint clean-security clean-python clean-all check-cc check-mi complexity-all build-dist upload-dist - -# -------------------------- -# Aliases for commands -# -------------------------- - -install: install-all -cov: test-coverage -check: lint-all -fix: format-all -complexity: complexity-all -clean: clean-all - -# -------------------------- -# Default help target -# -------------------------- - -help: ## Show this help message - @echo "TorchSOM Development Commands:" - @echo "" - @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf " \033[36m%-20s\033[0m %s\n", $$1, $$2}' - @echo "" - @echo "Examples:" - @echo "" - @echo " make install # Install development dependencies" - @echo " make test # Run all tests with coverage" - @echo " make lint # Run all code quality checks" - @echo " make format # Auto-fix formatting issues" - @echo " make all # Run full CI simulation" - @echo "" - -# -------------------------- -# Environment Setup -# -------------------------- - -install-dev: ## Install development dependencies - pip install -e ".[dev]" - -install-tests: ## Install test dependencies - pip install -e ".[tests]" - -install-security: ## Install security dependencies - pip install -e ".[security]" - -install-linting: ## Install linting dependencies - pip install -e ".[linting]" - -install-docs: ## Install documentation dependencies - pip install -e ".[docs]" - -install-precommit: ## Install pre-commit dependencies - pip install pre-commit - pre-commit install - -install-cz: ## Install Commitizen - pip install commitizen - -install-all: ## Install all dependencies - @echo "πŸ“¦ Installing all dependencies..." - $(MAKE) install-dev - $(MAKE) install-tests - $(MAKE) install-security - $(MAKE) install-linting - $(MAKE) install-docs - $(MAKE) install-precommit +SHELL := /bin/bash +.DEFAULT_GOAL := help +MAKEFLAGS += --no-print-directory # -------------------------- -# Testing +# Sub-makefiles # -------------------------- -TESTS ?= tests/unit/ # Default test path - -test-gpu: ## Run GPU tests (requires CUDA) - @echo "πŸ–₯️ Running GPU tests..." - pytest $(TESTS) -v -x -m "gpu" -# -v verbose, -x exit on first failure, -m marker: run only tests with the corresponding markers - -test-integration: ## Run integration tests - @echo "πŸ–₯️ Running integration tests..." - pytest $(TESTS) -v -m "integration" - -test-coverage: ## Run all tests with coverage - @echo "πŸ§ͺ Running tests with coverage..." - pytest -# pytest $(TESTS) -v \ -# --cov=torchsom \ -# --cov-report=term-missing \ -# --cov-report=html \ -# --cov-config=pyproject.toml \ -# --junit-xml=junit.xml \ -# -m "unit or gpu" +include makefiles/ci.mk +include makefiles/clean.mk +include makefiles/complexity.mk +include makefiles/docs.mk +include makefiles/format.mk +include makefiles/install.mk +include makefiles/lint.mk +include makefiles/release.mk +include makefiles/security.mk +include makefiles/test.mk # -------------------------- -# Code Quality +# Convenience Aliases # -------------------------- -check-black: ## Run black check - black --check --diff torchsom/ tests/ - -check-isort: ## Run isort check - isort --check-only --diff torchsom/ tests/ - -check-ruff: ## Run ruff check - ruff check torchsom/ tests/ - -check-mypy: ## Run mypy check - mypy torchsom/ --ignore-missing-imports --strict - -lint-all: ## Run all code quality checks - @echo "πŸ” Running code quality checks (formatting, sorting, linting, type checking)..." - $(MAKE) check-black - $(MAKE) check-isort - $(MAKE) check-ruff - $(MAKE) check-mypy - @echo "βœ… All quality checks passed!" - -format-black: ## Auto-fix black formatting - black torchsom/ tests/ +.PHONY: install cov check fix complexity clean dev qa workflow -format-isort: ## Auto-fix isort formatting - isort torchsom/ tests/ +install: install-all ## Alias: sync all dependencies +cov: test-coverage ## Alias: run tests with coverage +check: lint-all ## Alias: run all linting checks +fix: format-all ## Alias: auto-fix formatting +complexity: complexity-all ## Alias: run complexity analysis +clean: clean-all ## Alias: clean all generated files -format-ruff: ## Auto-fix ruff formatting - ruff check --fix torchsom/ tests/ - -format-all: ## Auto-fix formatting and imports - @echo "🎨 Auto-fixing code formatting, imports, and linting..." - $(MAKE) format-black - $(MAKE) format-isort - $(MAKE) format-ruff - @echo "βœ… Formatting applied!" - -precommit: ## Run pre-commit hooks on all files - @echo "πŸ”§ Running pre-commit hooks..." - pre-commit run --all-files - -# -------------------------- -# Security -# -------------------------- - -security: ## Run security scans - @echo "πŸ”’ Running security scans..." - bandit -r torchsom/ --exclude tests --skip B101,B311,B601 - bandit -r tests --skip B101,B311,B601 - @echo "βœ… Security scans completed!" - -# -------------------------- -# Documentation -# -------------------------- +dev: install-all ## Setup dev environment (install + pre-commit) -check-docstrings: ## Check docstrings - pydocstyle torchsom/ --convention=google - -measure-docstrings-coverage: ## Assess docstring coverage - interrogate torchsom/ --verbose --ignore-init-method --ignore-magic --ignore-module --fail-under=80 - -build-docs: ## Build documentation - sphinx-build -b html --keep-going docs/source/ docs/build/html -# sphinx-build -b html docs/source/ docs/build/html - -docs: ## Check documentation quality - @echo "πŸ“š Checking documentation..." - $(MAKE) check-docstrings - $(MAKE) measure-docstrings-coverage - $(MAKE) build-docs - @echo "βœ… Documentation checks and build complete!" - -# -------------------------- -# Cleanup -# -------------------------- - -clean-docs: ## Remove Sphinx build artifacts - @echo "🧹 Cleaning documentation build..." - rm -rf docs/build/html/ - @echo "βœ… Documentation cleaned!" - -clean-build: ## Remove build and distribution artifacts - @echo "🧹 Cleaning build artifacts..." - rm -rf build/ dist/ *.egg-info/ - @echo "βœ… Build artifacts cleaned!" - -clean-test: ## Remove test and coverage artifacts - @echo "🧹 Cleaning test artifacts..." - rm -rf .pytest_cache/ .coverage htmlcov/ - rm -f junit.xml coverage.xml - @echo "βœ… Test artifacts cleaned!" - -clean-lint: ## Remove linting and type checking cache - @echo "🧹 Cleaning linting cache..." - rm -rf .mypy_cache/ .ruff_cache/ - @echo "βœ… Linting cache cleaned!" - -clean-security: ## Remove security scan reports - @echo "🧹 Cleaning security reports..." - rm -f bandit-report.json safety-report.json pip-audit-report.json - @echo "βœ… Security reports cleaned!" - -clean-python: ## Remove Python cache files - @echo "🧹 Cleaning Python cache..." - find . -type f -name "*.pyc" -delete - find . -type d -name __pycache__ -delete - @echo "βœ… Python cache cleaned!" - -clean-all: ## Clean up all generated files (runs all clean- commands) - @echo "🧹 Cleaning up all generated files..." - @echo "" - $(MAKE) clean-build - $(MAKE) clean-test - $(MAKE) clean-lint - $(MAKE) clean-security - $(MAKE) clean-python - $(MAKE) clean-docs - @echo "" - @echo "βœ… All cleanup completed!" - -# -------------------------- -# Complexity Analysis -# -------------------------- - -check-cc: ## Check cyclomatic complexity - radon cc torchsom/ --show-complexity --min B - -check-mi: ## Check maintainability index - radon mi torchsom/ --show --min B - -complexity-all: ## Run cyclomatic complexity and maintainability analysis - @echo "πŸ” Running complexity analysis..." - $(MAKE) check-cc - $(MAKE) check-mi - @echo "βœ… Complexity analysis completed!" - -# -------------------------- -# Dependencies -# -------------------------- - -dependencies: ## Check for dependency conflicts: : super long with pip-compile - @echo "πŸ” Checking for dependency conflicts..." - pip check - @echo "βœ… Dependency checks completed!" -# pip-compile pyproject.toml --dry-run --verbose - -# -------------------------- -# Changelog / Release Notes -# -------------------------- - -changelog: ## Generate or update CHANGELOG.md based on commits - cz changelog - -bump: ## Bump version and update changelog: should be used on the release branch (main) - cz bump --changelog -# To preview the changes: -# cz bump --dry-run - -# release-changelog: changelog ## Generate changelog and commit it automatically -# git config user.name "github-actions[bot]" -# git config user.email "github-actions[bot]@users.noreply.github.com" -# git add CHANGELOG.md -# git commit -m "chore: update changelog for $(shell git describe --tags --abbrev=0)" || echo "No changes to commit" - - -# -------------------------- -# CI / Full Pipeline -# -------------------------- - -ci: ## Run CI pipeline (without tests) - @echo "" +qa: ## Run full QA suite (check + fix + cov) $(MAKE) check $(MAKE) fix - $(MAKE) security - $(MAKE) complexity - $(MAKE) dependencies - $(MAKE) docs - @echo "πŸŽ‰ All checks passed (without tests)! Ready to push!" - -all: ## Run full CI simulation (includes tests) - @echo "" - $(MAKE) ci $(MAKE) cov - @echo "" - @echo "πŸŽ‰ All checks passed! Ready to push!" # -------------------------- -# Publishing +# Help # -------------------------- -build-dist: ## Build distribution - python -m build +# Color codes +CYAN := \033[36m +GREEN := \033[32m +YELLOW := \033[33m +BOLD := \033[1m +RESET := \033[0m -upload-dist: ## Upload distribution - twine upload dist/* - -publish: ## Build and upload to PyPI (manually but is triggered by .github/workflows/release.yml with tag modifications) - @echo "πŸ“¦ Publishing to PyPI..." - @bash -c '\ - source .env; \ - export TWINE_USERNAME TWINE_PASSWORD; \ - $(MAKE) build-dist; \ - $(MAKE) upload-dist \ - ' - @echo "βœ… Published to PyPI!" +help: ## Show this help message + @printf "\n$(BOLD)TorchSOM Development Commands$(RESET) (powered by uv)\n\n" + @printf "$(GREEN)Setup$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/install.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Code Quality$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/lint.mk makefiles/format.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Testing$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/test.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Documentation$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/docs.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Security & Complexity$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/security.mk makefiles/complexity.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Release$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/release.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)CI / Pipelines$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/ci.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Cleanup$(RESET)\n" + @grep -hE '^[a-zA-Z_-]+:.*?## .*$$' makefiles/clean.mk | awk 'BEGIN {FS = ":.*?## "}; {printf " $(CYAN)%-28s$(RESET) %s\n", $$1, $$2}' + @printf "\n$(GREEN)Aliases$(RESET)\n" + @printf " $(CYAN)%-28s$(RESET) %s\n" "install" "Sync all dependencies" + @printf " $(CYAN)%-28s$(RESET) %s\n" "cov" "Run tests with coverage" + @printf " $(CYAN)%-28s$(RESET) %s\n" "check" "Run all linting checks" + @printf " $(CYAN)%-28s$(RESET) %s\n" "fix" "Auto-fix formatting" + @printf " $(CYAN)%-28s$(RESET) %s\n" "dev" "Setup dev environment" + @printf " $(CYAN)%-28s$(RESET) %s\n" "qa" "Full QA (check + fix + cov)" + @printf " $(CYAN)%-28s$(RESET) %s\n" "clean" "Clean all generated files" + @printf "\n$(GREEN)Reference$(RESET)\n" + @printf " $(CYAN)%-28s$(RESET) %s\n" "workflow" "Print the recommended development workflow" + @printf "\n" + + +workflow: ## Print the recommended development workflow + @printf "\n$(BOLD)TorchSOM β€” Development Workflow$(RESET)\n" + @printf "Run $(CYAN)make workflow$(RESET) any time to see this reference.\n\n" + @printf "$(BOLD)━━━ 1. First-time setup (once per machine) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━$(RESET)\n" + @printf " $(CYAN)make dev$(RESET)\n" + @printf " Installs all dependency groups and wires up pre-commit hooks.\n\n" + @printf "$(BOLD)━━━ 2. Inner loop β€” while coding ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━$(RESET)\n" + @printf " $(CYAN)make fix$(RESET) Auto-format code (ruff format + ruff --fix)\n" + @printf " $(CYAN)make test-unit$(RESET) Run unit tests fast, fail on first error (-x, no coverage)\n" + @printf " $(CYAN)make test-smoke$(RESET) Quick sanity check on core functionality only\n" + @printf " Repeat freely. Format before testing so failures are logic issues, not style.\n\n" + @printf "$(BOLD)━━━ 3. Before every commit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━$(RESET)\n" + @printf " Pre-commit runs automatically if installed via $(CYAN)make dev$(RESET).\n" + @printf " To run it manually: $(CYAN)make precommit$(RESET)\n" + @printf " If it modifies files, stage them and commit again.\n\n" + @printf " Use Conventional Commits for messages β€” required for auto-changelog:\n" + @printf " $(YELLOW)feat$(RESET): new feature $(YELLOW)fix$(RESET): bug fix $(YELLOW)refactor$(RESET): no behaviour change\n" + @printf " $(YELLOW)docs$(RESET): docs only $(YELLOW)test$(RESET): tests $(YELLOW)chore$(RESET): tooling / deps\n\n" + @printf "$(BOLD)━━━ 4. Before opening a Pull Request ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━$(RESET)\n" + @printf " Run in order (each step gates the next):\n\n" + @printf " $(CYAN)make fix$(RESET) 1. Auto-fix style β€” clean diff before anything else\n" + @printf " $(CYAN)make check$(RESET) 2. Lint (ruff) + type-check (mypy) β€” catch errors cheaply\n" + @printf " $(CYAN)make cov$(RESET) 3. Full test suite with coverage report\n" + @printf " $(CYAN)make security$(RESET) 4. Audit dependencies for known CVEs (pip-audit)\n" + @printf " $(CYAN)make complexity$(RESET) 5. Flag functions with high cyclomatic complexity (radon)\n" + @printf " $(CYAN)make docs$(RESET) 6. Docstring coverage (β‰₯80%%) + build Sphinx HTML\n\n" + @printf " Or in one shot: $(CYAN)make all$(RESET) (ci + fix + cov)\n\n" + @printf "$(BOLD)━━━ 5. Release (main branch only) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━$(RESET)\n" + @printf " $(CYAN)make bump$(RESET) Bump version + auto-generate CHANGELOG.md from commits\n" + @printf " $(CYAN)make publish$(RESET) Build dist and upload to PyPI (reads .env for credentials)\n\n" + @printf "$(BOLD)━━━ Quick reference table ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━$(RESET)\n" + @printf " $(BOLD)%-20s %-22s %s$(RESET)\n" "When" "Command" "Why" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "First clone" "make dev" "Install everything + hooks" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "While coding" "make fix" "Auto-format before anything" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "" "make test-unit" "Fast feedback loop" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "Before commit" "make precommit" "Style gate (auto if hooks set)" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "Before PR" "make all" "Full quality gate" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "Release branch" "make bump" "Version bump + changelog" + @printf " %-20s $(CYAN)%-22s$(RESET) %s\n" "" "make publish" "Ship to PyPI" + @printf "\n" diff --git a/README.md b/README.md index 5b26745..36d2bb5 100644 --- a/README.md +++ b/README.md @@ -2,44 +2,40 @@
- [![PyPI version](https://img.shields.io/pypi/v/torchsom.svg?color=blue)](https://pypi.org/project/torchsom/) [![Python versions](https://img.shields.io/pypi/pyversions/torchsom.svg?color=blue)](https://pypi.org/project/torchsom/) [![PyTorch versions](https://img.shields.io/badge/PyTorch-2.7-EE4C2C.svg)](https://pytorch.org/) [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/license/apache-2-0) +[![arXiv](https://img.shields.io/badge/arXiv-2510.11147-b31b1b.svg)](https://arxiv.org/abs/2510.11147) + + - -[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=michelin_TorchSOM&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=michelin_TorchSOM) -[![Reliability Rating](https://sonarcloud.io/api/project_badges/measure?project=michelin_TorchSOM&metric=reliability_rating)](https://sonarcloud.io/summary/new_code?id=michelin_TorchSOM) -[![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=michelin_TorchSOM&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=michelin_TorchSOM) -[![Maintainability Rating](https://sonarcloud.io/api/project_badges/measure?project=michelin_TorchSOM&metric=sqale_rating)](https://sonarcloud.io/summary/new_code?id=michelin_TorchSOM) -[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=michelin_TorchSOM&metric=coverage)](https://sonarcloud.io/summary/new_code?id=michelin_TorchSOM) - - - - [![Tests](https://github.com/michelin/TorchSOM/workflows/Tests/badge.svg)](https://github.com/michelin/TorchSOM/actions/workflows/test.yml) [![Code Quality](https://github.com/michelin/TorchSOM/workflows/Code%20Quality/badge.svg)](https://github.com/michelin/TorchSOM/actions/workflows/code-quality.yml) +[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=michelin_TorchSOM&metric=coverage)](https://sonarcloud.io/summary/new_code?id=michelin_TorchSOM) [![Downloads](https://static.pepy.tech/badge/torchsom)](https://pepy.tech/project/torchsom) [![GitHub stars](https://img.shields.io/github/stars/michelin/TorchSOM.svg?style=social&label=Star)](https://github.com/michelin/TorchSOM) - - - -[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) -[![Imports: isort](https://img.shields.io/badge/imports-isort-6c757d.svg?color=blue)](https://pycqa.github.io/isort/) -[![Ruff](https://img.shields.io/badge/linter-ruff-6c757d.svg?color=blue)](https://github.com/astral-sh/ruff)

- TorchSOM_logo + TorchSOM_logo

-**A modern, comprehensive and GPU-accelerated PyTorch implementation of Self-Organizing Maps for scalable ML workflows** +*GPU-accelerated Self-Organizing Maps in PyTorch with a scikit-learn API, rich visualization, and clustering --- from dimensionality reduction to Just-In-Time Learning.* -[πŸ“„ Paper](https://arxiv.org/abs/2510.11147) -| [πŸ“š Documentation](https://opensource.michelin.io/TorchSOM/) -| [πŸš€ Quick Start](#-quick-start) -| [πŸ“Š Examples](notebooks/) -| [🀝 Contributing](CONTRIBUTING.md) +[Paper](https://arxiv.org/abs/2510.11147) +| [Documentation](https://opensource.michelin.io/TorchSOM/) +| [Quick Start](#quick-start) +| [Examples](notebooks/) +| [Contributing](CONTRIBUTING.md) **⭐ If you find [`torchsom`](https://github.com/michelin/TorchSOM) valuable, please consider starring this repository ⭐** @@ -47,204 +43,190 @@ --- -## 🎯 Overview +## Overview -Self-Organizing Maps (SOMs) remain **highly relevant in modern machine learning** (ML) due to their **interpretability**, **topology preservation**, and **computational efficiency**. They excel and are widely used in domains such as energy systems, biology, internet of things (IoT), environmental science, and industrial applications. +[Self-Organizing Maps (SOMs)](https://en.wikipedia.org/wiki/Self-organizing_map) remain highly relevant in modern machine learning due to their **interpretability**, **topology preservation**, and **computational efficiency**. They are widely used in energy systems, biology, IoT, environmental science, and industrial applications. -Despite their utility, the SOM ecosystem is fragmented. Existing implementations are often **outdated**, **unmaintained**, and **lack GPU acceleration or modern deep learning** (DL) **framework integration**, limiting adoption and scalability. +Despite their utility, the Python SOM ecosystem is fragmented - existing implementations are often **outdated**, **unmaintained**, and **lack GPU acceleration** or integration with modern deep learning frameworks. -**`torchsom`** addresses these gaps as a **reference PyTorch library** for SOMs. It provides: +**`torchsom`** addresses these gaps as a **reference PyTorch library** for SOMs, providing: -- **GPU-accelerated training** -- **Advanced clustering capabilities** -- A **scikit-learn-style API** for ease of use -- **Rich visualization tools** -- **Robust software engineering practices** +- **GPU-accelerated training** via PyTorch CUDA backend +- **Advanced clustering** (K-Means, GMM, HDBSCAN) on the SOM latent space +- A **scikit-learn-style API** for ease of use and extensibility +- **Rich visualization tools** for both rectangular and hexagonal topologies +- **Just-In-Time Learning (JITL)** for supervised regression and classification -`torchsom` enables researchers and practitioners to integrate SOMs seamlessly into workflows, from exploratory data analysis to advanced model architectures. +This library accompanies the paper: [*torchsom: The Reference PyTorch Library for Self-Organizing Maps*](https://arxiv.org/abs/2510.11147) (Berthier et al., 2025). If you use `torchsom` in academic or industrial work, please cite both the paper and the software (see [Citation](#citation)). -This library accompanies the paper: [`torchsom`: The Reference PyTorch Library for Self-Organizing Maps](https://arxiv.org/abs/2510.11147). If you use `torchsom` in academic or industrial work, please cite both the paper and the software (see [`CITATION`](CITATION.cff)). +### Key Results -> **Note**: See the comparison table below to understand how `torchsom` differs from other SOM libraries, and explore our [Visualization Gallery](#-visualization-gallery) for example outputs. +Benchmarked against [MiniSom](https://github.com/JustGlowing/minisom) on synthetic datasets (240–16,000 samples, 4–300 features) with identical hyperparameters: -## ⚑ Why `torchsom`? +| Metric | Improvement | +| --- | --- | +| **Training speed** | Up to **99% faster** (GPU) and **77–98% faster** (CPU) | +| **Topographic Error** | **34–81% lower** β€” better topology preservation | +| **Quantization Error** | Comparable fidelity across all configurations | -Unlike legacy implementations, `torchsom` is engineered from the ground up for modern ML workflows: +> Hardware: Intel Xeon Platinum 8370C (CPU), NVIDIA Tesla T4 (GPU). See the [paper](https://arxiv.org/abs/2510.11147) for full benchmark tables. +> +> **Reproducing the JMLR benchmarks.** All scripts, configurations, and the exact MiniSom pin (`v2.3.5` / `65b6ba6`) used to produce these numbers are released under [`benchmark/`](benchmark/) β€” see [`benchmark/README.md`](benchmark/README.md) for a step-by-step walkthrough. Two annotated tags pin the version of record: `jmlr-submission-v1` (original October 2025 submission) and `jmlr-revision-v1` (accepted revised version). `git checkout ` reproduces the corresponding Table 2. -| | [torchsom](https://github.com/michelin/TorchSOM) | [MiniSom](https://github.com/JustGlowing/minisom) | [SimpSOM](https://github.com/fcomitani/simpsom) | [SOMPY](https://github.com/sevamoo/SOMPY) | [somoclu](https://github.com/peterwittek/somoclu) | [som-pbc](https://github.com/alexarnimueller/som) | -|---|---|---|---|---|---|---| -| **Architecture Section** | | | | | | | -| Framework | PyTorch | NumPy | NumPy | NumPy | C++/CUDA | NumPy | -| GPU Acceleration | βœ… CUDA | ❌ | βœ… CuPy/CUML | ❌ | βœ… CUDA | ❌ | -| API Design | scikit-learn | Custom | Custom | MATLAB | Custom | custom | -| **Development Quality Section** | | | | | | | -| Maintenance | βœ… Active | βœ… Active | ⚠️ Minimal | ⚠️ Minimal | ⚠️ Minimal | ❌ | -| Documentation | βœ… Rich | ❌ | ⚠️ Basic | ❌ | ⚠️ Basic | ⚠️ Basic | -| Test Coverage | βœ… 90% | ❌ | 🟠 ~53% | ❌ | ⚠️ Minimal | ❌ | -| PyPI Distribution | βœ… | βœ… | βœ… | ❌ | βœ… | ❌ | -| **Functionality Section** | | | | | | | -| Visualization | βœ… Advanced | ❌ | 🟠 Moderate | 🟠 Moderate | ⚠️ Basic | ⚠️ Basic | -| Clustering | βœ… Advanced | ❌ | ❌ | ❌ | ❌ | ❌ | -| JITL support | βœ… Built-in | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOM Variants | 🚧 In development | ❌ | 🟠 PBC | ❌ | 🟠 PBC | 🟠 PBC | -| Extensibility | βœ… High | 🟠 Moderate | ⚠️ Low | ⚠️ Low | ⚠️ Low | ⚠️ Low | +--- -> **Note**: `torchsom` supports **Just-In-Time Learning (JITL)**. -> Given an online query, JITL collects relevant datapoints to form a local buffer (selected first by topology, then by distance). A lightweight local model can then be trained on this buffer, enabling efficient supervised learning (regression or classification). +## How It Works ---- +A SOM is an unsupervised neural network that maps high-dimensional data onto a low-dimensional grid (typically 2D) while preserving topological relationships. At each training step, the **Best Matching Unit (BMU)** β€” the neuron closest to the input β€” is identified, and its weights along with its neighbors are updated: + +$$\mathbf{w}_{ij}(t+1) = \mathbf{w}_{ij}(t) + \alpha(t) \cdot h_{ij}(t) \cdot \bigl(\mathbf{x} - \mathbf{w}_{ij}(t)\bigr)$$ + +where $\alpha(t)$ is the learning rate, $h_{ij}(t)$ is a neighborhood function (e.g., Gaussian) centered on the BMU, and $\mathbf{x} \in \mathbb{R}^k$ is the input vector. The BMU is found by: -## πŸ“‘ Table of Contents +$$\text{BMU} = \underset{i,j}{\operatorname{argmin}}\, \lVert \mathbf{x} - \mathbf{w}_{ij} \rVert_2$$ -- [Quick Start](#-quick-start) -- [Tutorials](#-tutorials) -- [Installation](#-installation) -- [Documentation](#-documentation) -- [Citation](#-citation) -- [Contributing](#-contributing) -- [Acknowledgments](#-acknowledgments) -- [License](#-license) -- [Related Work and References](#-related-work-and-references) +Training quality is assessed via **Quantization Error** (representation fidelity) and **Topographic Error** (topology preservation). See the [documentation](https://opensource.michelin.io/TorchSOM/) for the full mathematical background. --- -## πŸš€ Quick Start +## Why `torchsom`? + +| | [torchsom](https://github.com/michelin/TorchSOM) | [MiniSom](https://github.com/JustGlowing/minisom) | [SimpSOM](https://github.com/fcomitani/simpsom) | [SOMPY](https://github.com/sevamoo/SOMPY) | [somoclu](https://github.com/peterwittek/somoclu) | [som-pbc](https://github.com/alexarnimueller/som) | +| --- | --- | --- | --- | --- | --- | --- | +| Framework | **PyTorch** | NumPy | NumPy | NumPy | C++/CUDA | NumPy | +| GPU Acceleration | βœ… CUDA | ❌ | βœ… CuPy/CUML | ❌ | βœ… CUDA | ❌ | +| API Design | **scikit-learn** | Custom | Custom | MATLAB | Custom | Custom | +| Maintenance | βœ… Active | βœ… Active | ⚠️ Minimal | ⚠️ Minimal | ⚠️ Minimal | ❌ | +| Documentation | βœ… Rich | ❌ | ⚠️ Basic | ❌ | ⚠️ Basic | ⚠️ Basic | +| Test Coverage | βœ… 90% | ❌ | ~53% | ❌ | Minimal | ❌ | +| Visualization | βœ… Advanced | ❌ | Moderate | Moderate | Basic | Basic | +| Clustering | βœ… Advanced | ❌ | ❌ | ❌ | ❌ | ❌ | +| JITL Support | βœ… Built-in | ❌ | ❌ | ❌ | ❌ | ❌ | +| SOM Variants | PBC, Growing*, Hierarchical* | ❌ | PBC | ❌ | PBC | PBC | + +*\* Work in progress* + +> **Just-In-Time Learning (JITL)**: Given an online query, JITL collects relevant samples by topology and distance to form a local buffer. A lightweight local model is then trained on this buffer, enabling efficient supervised learning (regression or classification). -Get started with `torchsom` in just a few lines of code: +--- + +## Quick Start ```python import torch from torchsom.core import SOM from torchsom.visualization import SOMVisualizer -# Create a 10x10 map for 3D input som = SOM(x=10, y=10, num_features=3, epochs=50) -# Train SOM for 50 epochs on 1000 samples X = torch.randn(1000, 3) som.initialize_weights(data=X, mode="pca") -QE, TE = som.fit(data=X) +q_errors, t_errors = som.fit(data=X) -# Visualize results -visualizer = SOMVisualizer(som=som, config=None) -visualizer.plot_training_errors(quantization_errors=QE, topographic_errors=TE, save_path=None) -visualizer.plot_hit_map(data=X, batch_size=256, save_path=None) +visualizer = SOMVisualizer(som=som) +visualizer.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors +) +visualizer.plot_hit_map(data=X, batch_size=256) visualizer.plot_distance_map( - save_path=None, - distance_metric=som.distance_fn_name, - neighborhood_order=som.neighborhood_order, - scaling="sum" + distance_metric=som.distance_fn_name, + neighborhood_order=som.neighborhood_order, + scaling="sum", ) ``` -## πŸ““ Tutorials +--- -Explore our comprehensive collection of Jupyter notebooks: +## Tutorials + +Explore our collection of Jupyter notebooks: + +| Notebook | Task | Dataset | +| --- | --- | --- | +| [`iris.ipynb`](notebooks/iris.ipynb) | Multiclass classification | Iris | +| [`wine.ipynb`](notebooks/wine.ipynb) | Multiclass classification | Wine | +| [`boston_housing.ipynb`](notebooks/boston_housing.ipynb) | Regression | Boston Housing | +| [`energy_efficiency.ipynb`](notebooks/energy_efficiency.ipynb) | Multi-output regression | Energy Efficiency | +| [`clustering.ipynb`](notebooks/clustering.ipynb) | Clustering analysis | Synthetic blobs | + +### Visualization Gallery + + + + + + + + + + + + + + + + + +
D-Matrix (U-Matrix)
Inter-neuron distances
D-Matrix		Hit Map
BMU activation frequency
Hit Map		Mean Map
Target value distribution
Mean Map
Component Planes
Feature-wise weight distribution
Component Plane 1		Classification Map
Dominant class per neuron
Classification Map		HDBSCAN Cluster Map
Cluster assignment
HDBSCAN Cluster Map
Component Planes
Another feature dimension
Component Plane 2		K-Means Elbow
Optimal cluster selection
K-Means Elbow		Cluster Quality Metrics
Algorithm comparison
Cluster Metrics
-- πŸ“Š [`iris.ipynb`](notebooks/iris.ipynb): Multiclass classification -- 🍷 [`wine.ipynb`](notebooks/wine.ipynb): Multiclass classification -- 🏠 [`boston_housing.ipynb`](notebooks/boston_housing.ipynb): Regression -- ⚑ [`energy_efficiency.ipynb`](notebooks/energy_efficiency.ipynb): Multi-output regression -- 🎯 [`clustering.ipynb`](notebooks/clustering.ipynb): SOM-based clustering analysis +--- -### 🎨 Visualization Gallery +## Installation -

- - - - - - - - - - - - - - - -
- πŸ—ΊοΈ D-Matrix Visualization
-

Michelin production line (regression)

- U-Matrix - 		- πŸ“ Hit Map Visualization
-

Michelin production line (regression)

- Hit Map - 		- πŸ“Š Mean Map Visualization
-

Michelin production line (regression)

- Mean Map -
- πŸ—ΊοΈ Component Planes Visualization
-

Another Michelin line (regression)

- - - - - -
- Component Plane 1 - - Component Plane 2 -
- 		- 🏷️ Classification Map
-

Wine dataset (multi-classification)

- Classification Map -
- πŸ“Š Cluster Metrics
-

Clustering analysis

- Cluster Metrics - 		- πŸ“ˆ K-Means Elbow
-

Optimal cluster selection

- K-Means Elbow - 		- 🎯 HDBSCAN Cluster Map
-

Cluster visualization

- HDBSCAN Cluster Map -
-

+This project uses [`uv`](https://docs.astral.sh/uv/) for fast, reproducible dependency management. ---- +### From PyPI -## πŸ’Ύ Installation +```bash +uv add torchsom +``` -### πŸ“¦ PyPI +With optional [FAISS](https://github.com/facebookresearch/faiss) acceleration for BMU search: ```bash -pip install torchsom +uv add torchsom[faiss] ``` -### πŸ”§ Development Version +### Development Setup ```bash git clone https://github.com/michelin/TorchSOM.git cd TorchSOM -python3.9 -m venv .torchsom_env -source .torchsom_env/bin/activate -pip install -e ".[all]" +uv sync --all-extras # creates .venv and installs everything +``` + +All Make targets use `uv run` so the correct environment is always activated: + +```bash +make help # see all available commands +make cov # run tests with coverage +make check # lint / type-check +make fix # auto-format +make docs # build documentation ``` --- -## πŸ“š Documentation +## Documentation -Comprehensive documentation is available at [opensource.michelin.io/TorchSOM](https://opensource.michelin.io/TorchSOM/) +Comprehensive documentation is available at **[opensource.michelin.io/TorchSOM](https://opensource.michelin.io/TorchSOM/)**, including: + +- **Getting Started**: installation, quick start, SOM concepts +- **User Guide**: visualization, architecture, benchmarks +- **API Reference**: core, utils, visualization, configs +- **Additional Resources**: FAQ, troubleshooting, changelog --- -## πŸ“ Citation +## Citation -If you use `torchsom` in your academic, research or industrial work, please cite both the paper and software: +If you use `torchsom` in your academic, research, or industrial work, please cite both the paper and the software: ```bibtex @misc{berthier2025torchsom, title={torchsom: The Reference PyTorch Library for Self-Organizing Maps}, - author = {Berthier, Louis and Shokry, Ahmed and Moreaud, Maxime and Ramelet, Guillaume and Moulines, Eric}, + author={Berthier, Louis and Shokry, Ahmed and Moreaud, Maxime + and Ramelet, Guillaume and Moulines, Eric}, year={2025}, eprint={2510.11147}, archivePrefix={arXiv}, @@ -259,65 +241,52 @@ If you use `torchsom` in your academic, research or industrial work, please cite year={2025}, version={1.1.1}, url={https://github.com/michelin/TorchSOM}, - note = {Documentation available at \url{https://opensource.michelin.io/TorchSOM/}} + note={Documentation available at \url{https://opensource.michelin.io/TorchSOM/}} } ``` - - -For more details, please refer to the [CITATION](CITATION.cff) file. +For more details, see the [CITATION](CITATION.cff) file. --- -## 🀝 Contributing +## Contributing We welcome contributions from the community! See our [Contributing Guide](CONTRIBUTING.md) and [Code of Conduct](CODE_OF_CONDUCT.md) for details. - **GitHub Issues**: [Report bugs or request features](https://github.com/michelin/TorchSOM/issues) - --- -## πŸ™ Acknowledgments +## Acknowledgments - [Centre de MathΓ©matiques AppliquΓ©es (CMAP)](https://cmap.ip-paris.fr/) at Γ‰cole Polytechnique - [Manufacture FranΓ§aise des Pneumatiques Michelin](https://www.michelin.com/) for collaboration - [Giuseppe Vettigli](https://github.com/JustGlowing) for [MiniSom](https://github.com/JustGlowing/minisom) inspiration - The [PyTorch](https://pytorch.org/) team for the amazing framework -- Logo created using [DALL-E](https://openai.com/index/dall-e-3/) --- -## πŸ“„ License +## License `torchsom` is licensed under the [Apache License 2.0](LICENSE). See the [LICENSE](LICENSE) file for details. --- -## πŸ“š Related Work and References +## Related Work and References -### πŸ“– Foundational Literature Papers +### Foundational Literature +- Kohonen, T. (1982). [Self-organized formation of topologically correct feature maps](https://doi.org/10.1007/BF00337288). *Biological Cybernetics*, 43(1), 59–69. +- Kohonen, T. (1990). [The self-organizing map](https://doi.org/10.1109/5.58325). *Proceedings of the IEEE*, 78(9), 1464–1480. - Kohonen, T. (2001). [Self-Organizing Maps](https://link.springer.com/book/10.1007/978-3-642-56927-2). Springer. -### πŸ”— Related Softwares +### Related Software - [MiniSom](https://github.com/JustGlowing/minisom): Minimalistic Python SOM -- [SimpSOM](https://github.com/fcomitani/simpsom):Simple Self-Organizing Maps +- [SimpSOM](https://github.com/fcomitani/simpsom): Simple Self-Organizing Maps - [SOMPY](https://github.com/sevamoo/SOMPY): Python SOM library - [somoclu](https://github.com/peterwittek/somoclu): Massively Parallel Self-Organizing Maps -- [som-pbc](https://github.com/alexarnimueller/som): A simple self-organizing map implementation in Python with periodic boundary conditions +- [som-pbc](https://github.com/alexarnimueller/som): SOM with periodic boundary conditions - [SOM Toolbox](http://www.cis.hut.fi/projects/somtoolbox/): MATLAB implementation --- diff --git a/TODO.MD b/TODO.MD new file mode 100644 index 0000000..9ae5a33 --- /dev/null +++ b/TODO.MD @@ -0,0 +1,12 @@ + +- (DONE) Change pydoc to furo for online documentation +- Refine online documentation to better match current package. +- Implement FAISS for optimized search (BMU identification, sample collection, ...) +- Further develop SOM mechanisms: + - PBC + - Growing + - HIerarchical +- Enhance visualisation: + - Record at each training epoch the design of the maps (distance/U-matrix) + - Identify the BMU (black) + surroundings (purple) + neurons identified by KNN search (blue) +- Handle issues for instance when training the SOM if the batch contains only one sample diff --git a/benchmark/README.md b/benchmark/README.md index 18ab2b3..7016023 100644 --- a/benchmark/README.md +++ b/benchmark/README.md @@ -1,41 +1,48 @@ -# Benchmark for JMLR-MLOSS submission +# Benchmark β€” JMLR-MLOSS reproducibility -This repository is dedicated to compare the performances between [`torchsom`](https://github.com/michelin/TorchSOM) and [`Minisom`](https://github.com/JustGlowing/minisom), using dataset examples available at [data/benchmark](../data/benchmark/) +This folder contains the **complete, runnable artefacts** used to generate every benchmark number reported in the *torchsom* JMLR-MLOSS paper: -## Local setup +- [`benchmark.py`](benchmark.py) β€” non-interactive comparison script (CLI) +- [`benchmark.ipynb`](benchmark.ipynb) β€” interactive notebook with plots +- [`configs/`](configs/) β€” YAML configurations describing each dataset / map-size combination + +It compares `torchsom` against [`MiniSom`](https://github.com/JustGlowing/minisom) on `scikit-learn`'s synthetic `make_blobs` data, varying sample size (240 / 4 000 / 16 000) and feature count (4 / 50 / 300), with identical SOM hyperparameters: 25Γ—15 grid, PCA initialization, rectangular topology, 100 epochs, Gaussian neighborhood, Euclidean distance. Each configuration is repeated 10Γ— and reported as mean Β± std. -I assume you've already followed the instruction in the global [README](../README.md), having at disposal an environment <.torchsom_env>. -Now, using this environment: +## Reproducibility tags + +| Tag | Use | +| -------------------- | ---------------------------------------------------------------------------------------------- | +| `jmlr-submission-v1` | Exact code as submitted to JMLR-MLOSS in October 2025 β€” reproduces the original Table 2. | +| `jmlr-revision-v1` | Code accompanying the accepted (revised) version β€” same benchmark scripts, same MiniSom pin. | ```bash -# Activate it -source .torchsom_env/bin/activate -# Install latest MiniSom version used for benchmarking: v2.3.5 -pip install git+https://github.com/JustGlowing/minisom.git@65b6ba6776f63db4536a85afa34bd7b2c6555960 +# Reproduce the original submission's Table 2 +git checkout jmlr-submission-v1 +# Reproduce the revised version's Table 2 (same numbers; benchmark code unchanged) +git checkout jmlr-revision-v1 ``` -Now you're free to experiment the notebook comparing both methods: notebook [benchmark.ipynb](benchmark.ipynb) or script [benchmark.py](benchmark.py) +The MiniSom pin (`65b6ba6` = v2.3.5, 7 April 2025) is identical on both tags, so the comparison baseline is stable. - +| Backend | Smallest config (240 Γ— 4) | Largest config (16 000 Γ— 300) | +| ---------------------- | ------------------------- | ----------------------------- | +| MiniSom (CPU) | ~2 s / repeat | ~32 min / repeat | +| torchsom (CPU) | <1 s / repeat | ~30 s / repeat | +| torchsom (GPU, T4) | <1 s / repeat | ~12 s / repeat | diff --git a/benchmark/benchmark.ipynb b/benchmark/benchmark.ipynb index 3f551cc..9abdb6b 100644 --- a/benchmark/benchmark.ipynb +++ b/benchmark/benchmark.ipynb @@ -15,17 +15,18 @@ "metadata": {}, "outputs": [], "source": [ - "import time\n", "import random\n", + "import time\n", + "from pathlib import Path\n", + "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import yaml\n", - "from pathlib import Path\n", + "from minisom import MiniSom\n", "\n", "from torchsom.core import SOM\n", - "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", - "from minisom import MiniSom" + "from torchsom.visualization import SOMVisualizer, VisualizationConfig" ] }, { @@ -77,7 +78,7 @@ "metadata": {}, "outputs": [], "source": [ - "device_log = \"cuda\" # \"cpu\" or \"cuda\"" + "device_log = \"cuda\" # \"cpu\" or \"cuda\"" ] }, { @@ -107,8 +108,8 @@ "metadata": {}, "outputs": [], "source": [ - "n_samples = 5000 # 300 | 5000 | 20000\n", - "n_features = 4 # 4 | 50 | 300\n", + "n_samples = 5000 # 300 | 5000 | 20000\n", + "n_features = 4 # 4 | 50 | 300\n", "data_path = f\"../data/benchmark/blobs_{n_samples}_{n_features}.csv\"" ] }, @@ -129,7 +130,7 @@ "metadata": {}, "outputs": [], "source": [ - "feature_columns = blobs_df.columns[:-1] \n", + "feature_columns = blobs_df.columns[:-1]\n", "feature_names = feature_columns.to_list()\n", "# feature_names" ] @@ -161,7 +162,10 @@ "\n", "\n", "shuffled_indices = torch.randperm(len(all_features), device=device)\n", - "all_features, all_targets = all_features[shuffled_indices], all_targets[shuffled_indices]\n", + "all_features, all_targets = (\n", + " all_features[shuffled_indices],\n", + " all_targets[shuffled_indices],\n", + ")\n", "\n", "train_ratio = 0.8\n", "train_count = int(train_ratio * len(all_features))\n", @@ -209,7 +213,7 @@ "topology = \"rectangular\"\n", "\n", "# ! To ensure a fair comparison with MiniSom training mechanism, we need to use the full data for each epoch\n", - "batch_size = train_features.shape[0] \n", + "batch_size = train_features.shape[0]\n", "# batch_size = 16" ] }, @@ -220,7 +224,7 @@ "metadata": {}, "outputs": [], "source": [ - "save_path = f\"results/blob_{n_samples}_{n_features}/{topology}/{device}\" \n", + "save_path = f\"results/blob_{n_samples}_{n_features}/{topology}/{device}\"\n", "record_file = Path(f\"{save_path}/results.yml\")\n", "record_file.parent.mkdir(parents=True, exist_ok=True)" ] @@ -288,10 +292,10 @@ " epochs=epochs,\n", " initialization_mode=\"pca\",\n", " # * Additional parameters for TorchSOM\n", - " batch_size=batch_size, # Important to ensure one pass over the data per eopch. One epoch = train_features.shape[0] samples\n", - " neighborhood_order=3, # Not used for the benchmark (time and learning curves)\n", - " device=device, # Important to specify GPU usage\n", - ") " + " batch_size=batch_size, # Important to ensure one pass over the data per eopch. One epoch = train_features.shape[0] samples\n", + " neighborhood_order=3, # Not used for the benchmark (time and learning curves)\n", + " device=device, # Important to specify GPU usage\n", + ")" ] }, { @@ -345,9 +349,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_train_QE = torchsom.quantization_error(\n", - " data=train_features\n", - ")\n", + "full_train_QE = torchsom.quantization_error(data=train_features)\n", "# full_train_QE" ] }, @@ -358,9 +360,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_train_TE = torchsom.topographic_error(\n", - " data=train_features\n", - ")\n", + "full_train_TE = torchsom.topographic_error(data=train_features)\n", "# full_train_TE" ] }, @@ -371,9 +371,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_test_QE = torchsom.quantization_error(\n", - " data=test_features\n", - ")\n", + "full_test_QE = torchsom.quantization_error(data=test_features)\n", "# full_test_QE" ] }, @@ -384,9 +382,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_test_TE = torchsom.topographic_error(\n", - " data=test_features\n", - ")\n", + "full_test_TE = torchsom.topographic_error(data=test_features)\n", "# full_test_TE" ] }, @@ -409,7 +405,6 @@ " \"final_full_train_TE\": f\"{full_train_TE:.2f}\",\n", " \"final_full_test_QE\": f\"{full_test_QE:.2f}\",\n", " \"final_full_test_TE\": f\"{full_test_TE:.2f}\",\n", - " \n", " },\n", "}" ] @@ -468,9 +463,7 @@ "outputs": [], "source": [ "visualizer.plot_training_errors(\n", - " quantization_errors=QE, \n", - " topographic_errors=TE, \n", - " save_path=save_path\n", + " quantization_errors=QE, topographic_errors=TE, save_path=save_path\n", ")" ] }, @@ -529,10 +522,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_component_planes(\n", - " component_names=feature_names,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_component_planes(component_names=feature_names, save_path=save_path)" ] }, { @@ -587,7 +577,7 @@ " num_iteration=epochs,\n", " random_order=True,\n", " verbose=True,\n", - " use_epochs=True, # ! Important: If true: num_iterations x train_features.shape[0] in total samples , if False: num_iterations samples\n", + " use_epochs=True, # ! Important: If true: num_iterations x train_features.shape[0] in total samples , if False: num_iterations samples\n", " )\n", " end = time.perf_counter()\n", " times_fit.append(end - start)" @@ -623,9 +613,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_train_QE = som.quantization_error(\n", - " data=train_features_np\n", - ")\n", + "full_train_QE = som.quantization_error(data=train_features_np)\n", "# full_train_QE" ] }, @@ -636,9 +624,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_train_TE = som.topographic_error(\n", - " data=train_features_np\n", - ")\n", + "full_train_TE = som.topographic_error(data=train_features_np)\n", "# full_train_TE" ] }, @@ -649,9 +635,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_test_QE = som.quantization_error(\n", - " data=test_features_np\n", - ")\n", + "full_test_QE = som.quantization_error(data=test_features_np)\n", "# full_test_QE" ] }, @@ -662,9 +646,7 @@ "metadata": {}, "outputs": [], "source": [ - "full_test_TE = som.topographic_error(\n", - " data=test_features_np\n", - ")\n", + "full_test_TE = som.topographic_error(data=test_features_np)\n", "# full_test_TE" ] }, diff --git a/benchmark/benchmark.py b/benchmark/benchmark.py index 8997548..63aeaa1 100644 --- a/benchmark/benchmark.py +++ b/benchmark/benchmark.py @@ -12,7 +12,7 @@ import random import time from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import numpy as np import pandas as pd @@ -63,7 +63,7 @@ def ensure_dir( def compute_errors( - som: Union[SOM, MiniSom], + som: SOM | MiniSom, train_features: torch.Tensor, test_features: torch.Tensor, ) -> tuple[float, float, float, float]: @@ -92,10 +92,10 @@ def run_benchmark( readable=True, help="Path to YAML configuration file.", ), - data_path: Optional[Path] = typer.Option( + data_path: Path | None = typer.Option( None, help="Override dataset CSV path (takes precedence over config)." ), - output_dir: Optional[Path] = typer.Option( + output_dir: Path | None = typer.Option( None, help="Override output directory root (ignored on Azure if outputs env is set).", ), diff --git a/docs/figures/architecture.tex b/docs/figures/architecture.tex new file mode 100644 index 0000000..579bf12 --- /dev/null +++ b/docs/figures/architecture.tex @@ -0,0 +1,81 @@ +% SOM architecture figure for the TorchSOM documentation. +% Input features x_1..x_k fully connected to an m x n grid of neurons w_ij. +% Recovered from the JMLR paper history (commit b970394) and kept here so the +% docs figure stays reproducible. +% +% Regenerate the PNG with: +% pdflatex architecture.tex +% pdftocairo -png -singlefile -r 300 architecture.pdf ../source/_static/som/architecture +\documentclass[tikz,border=4pt]{standalone} +\usepackage{amssymb} + +% Layout parameters (mirror the paper's imports.tex). +\def\xspace{2} % horizontal spacing +\def\yspace{1.5} % vertical spacing +\def\xoffset{3} % horizontal offset to centre the SOM grid +\def\yoffset{0} % vertical offset +\def\m{3} % rows +\def\n{3} % columns + +\begin{document} +\begin{tikzpicture}[scale=1, every node/.style={scale=1}] + % Input features: only the 1st and kth (last) are drawn. + \coordinate (x1) at (0, {-(1-0.5)*\yspace-1}); + \coordinate (xk) at (0, {-(3-0.5)*\yspace-1}); + % SOM grid weights, sharing the y-coordinates of x1 and xk. + \foreach \j in {1,...,\n} { + \foreach \i in {1,...,\m} { + \coordinate (w\i\j) at ({\j*\xspace + \xoffset}, {-(\i-0.5)*\yspace-1}); + } + } + + % "Input Features" box. + \draw[thick, rounded corners, fill=gray!15] (-0.8, {1+-1*\yspace-0.5}) rectangle (0.8, {-0.5-3*\yspace-0.5}); + \node[above, font=\bfseries] at (0, {1+-1*\yspace-0.5}) {Input Features ($k \times 1$)}; + % "SOM Grid" box. + \draw[thick, rounded corners, fill=gray!15] + ({1*\xspace + \xoffset - 0.8}, {1+-1*\yspace-0.5}) rectangle + ({3*\xspace + \xoffset + 0.8}, {{-0.5-3*\yspace-0.5}}); + \node[above, font=\bfseries] at ({2*\xspace + \xoffset}, {1+-1*\yspace-0.5}) {SOM Grid ($m \times n$)}; + + % Connections from the first and last feature to corner and centre neurons. + \draw[black!70, ->] (x1) -- (w11); + \draw[black!70, ->] (x1) -- (w\m\n); + \draw[black!70, ->] (x1) -- (w1\n); + \draw[black!70, ->] (x1) -- (w\m1); + \draw[black!70, ->] (x1) -- (w22); + \draw[black!70, ->] (xk) -- (w11); + \draw[black!70, ->] (xk) -- (w\m\n); + \draw[black!70, ->] (xk) -- (w1\n); + \draw[black!70, ->] (xk) -- (w\m1); + \draw[black!70, ->] (xk) -- (w22); + + % Input feature nodes. + \node[circle, draw, thick, minimum size=10mm, fill=white] (node1) at (x1) {$x_{1}$}; + \node at (0, -3) {$\vdots$}; + \node[circle, draw, thick, minimum size=10mm, fill=white] (nodek) at (xk) {$x_{k}$}; + % SOM grid neurons: corners and centre. + \node[circle, draw, thick, minimum size=10mm, fill=white] at (w11) {$w_{11}$}; + \node[circle, draw, thick, minimum size=10mm, fill=white] at (w1\n) {$w_{1n}$}; + \node[circle, draw, thick, minimum size=10mm, fill=white] at (w\m1) {$w_{m1}$}; + \node[circle, draw, thick, minimum size=10mm, fill=white] at (w\m\n) {$w_{mn}$}; + \node[circle, draw, thick, minimum size=10mm, fill=white] at (w22) {$w_{rr}$}; + + % Vertical dots for the left and right columns. + \node at ({1*\xspace + \xoffset}, {-2*\yspace+\yoffset}) {$\vdots$}; + \node at ({3*\xspace + \xoffset}, {-2*\yspace+\yoffset}) {$\vdots$}; + % Horizontal dots for the top and bottom rows. + \node at ({2*\xspace + \xoffset}, {-1*\yspace+\yoffset}) {$\cdots$}; + \node at ({2*\xspace + \xoffset}, {-3*\yspace+\yoffset}) {$\cdots$}; + + % Column count arrow. + \draw[<->, thick] ({1*\xspace + \xoffset - 0.5}, {-3*\yspace - 1.2+\yoffset}) -- ({3*\xspace + \xoffset + 0.5}, {-3*\yspace - 1.2+\yoffset}); + \node[below] at ({2*\xspace + \xoffset}, {-3*\yspace - 1.2+\yoffset}) {$n$ columns}; + % Row count arrow. + \draw[<->, thick] ({3*\xspace + \xoffset + 1.0}, {-1.2*\yspace + 0.5+\yoffset}) -- ({3*\xspace + \xoffset + 1.0}, {-3*\yspace - 0.8+\yoffset}); + \node[right] at ({3*\xspace + \xoffset + 1.0}, {-2*\yspace+\yoffset}) {$m$ rows}; + % Feature count arrow. + \draw[<->, thick] (-1, {0.8-1*\yspace-0.5}) -- (-1, {-0.3-3*\yspace-0.5}); + \node[left] at (-1., {-2.1*\yspace}) {$k$ features}; +\end{tikzpicture} +\end{document} diff --git a/docs/figures/topologies.tex b/docs/figures/topologies.tex new file mode 100644 index 0000000..47914b0 --- /dev/null +++ b/docs/figures/topologies.tex @@ -0,0 +1,61 @@ +% Neighborhood-order figure for the TorchSOM documentation. +% Mirrors the JMLR paper (Appendix B): rectangular Chebyshev blocks (left) and +% hexagonal hop-distance rings (right) around the BMU, for orders o = 1, 2, 3. +% +% Regenerate the PNG with: +% pdflatex topologies.tex +% pdftocairo -png -singlefile -r 300 topologies.pdf ../source/_static/som/topologies +\documentclass[tikz,border=6pt]{standalone} +\usepackage{amssymb} +\usetikzlibrary{shapes.geometric} + +% Colour-coding for the BMU and neighborhood orders (matches the paper). +\colorlet{bmucolor}{black!80} +\colorlet{order1color}{blue!60} +\colorlet{order2color}{teal!60} +\colorlet{order3color}{purple!40} + +% Fill a unit grid cell with a 1% margin. +\newcommand{\fillrect}[3]{% + \fill[#3] (#1+0.01,#2+0.01) rectangle ++ (0.98,0.98); +} + +\begin{document} +\begin{tikzpicture} + % --- Rectangular grid (left): Chebyshev (max-norm) blocks --- + \begin{scope}[scale=0.5] + \foreach \x in {1,...,7} { + \foreach \y in {1,...,7} { + \pgfmathtruncatemacro{\ord}{max(abs(\x-4),abs(\y-4))} + \ifcase\ord + \fillrect{\x}{\y}{bmucolor}\or + \fillrect{\x}{\y}{order1color}\or + \fillrect{\x}{\y}{order2color}\or + \fillrect{\x}{\y}{order3color} + \fi + } + } + \draw[step=1, gray!50, very thin] (1,1) grid (8,8); + \node[font=\bfseries, anchor=north] at (4.5,0.7) {Rectangular}; + \end{scope} + % --- Hexagonal grid (right): hop-distance rings --- + \begin{scope}[xshift=6cm, yshift=2.25cm] + \foreach \q in {-3,...,3} { + \foreach \r in {-3,...,3} { + \pgfmathtruncatemacro{\hd}{(abs(\q)+abs(\r)+abs(\q+\r))/2} 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a/docs/source/additional_resources/changelog.rst b/docs/source/additional_resources/changelog.rst index 0e55817..1cad228 100644 --- a/docs/source/additional_resources/changelog.rst +++ b/docs/source/additional_resources/changelog.rst @@ -1,132 +1,55 @@ Changelog ========= -All notable changes to TorchSOM will be documented in this file. +All notable changes to TorchSOM are documented in this file. -The format is based on `Keep a Changelog `_, and this project adheres to `Semantic Versioning `_. +The format is based on `Keep a Changelog `_, +and this project adheres to `Semantic Versioning `_. -.. ! Below are not used but keeping them could be relevant for the future +For the full commit history, see the +`GitHub releases `_ page. -.. .. [Unreleased] -.. ------------ +The auto-generated ``CHANGELOG.md`` in the repository root is maintained by +`Commitizen `_ and provides +a detailed, commit-level changelog. -.. Added -.. ~~~~~ -.. - Comprehensive documentation with tutorials and examples -.. - Advanced visualization capabilities with customizable configurations -.. - GPU acceleration support for training -.. - Multiple SOM variants (Growing SOM, Hierarchical SOM) -.. - Pydantic-based configuration management -.. - Type hints throughout the codebase -.. - Performance benchmarking tools -.. Changed -.. ~~~~~~~ -.. - Improved API consistency with scikit-learn patterns -.. - Enhanced error handling and validation -.. - Optimized memory usage for large datasets +v1.1.1 +------ -.. Fixed -.. ~~~~~ -.. - Edge cases in distance calculations -.. - Memory leaks during long training sessions -.. - Visualization issues with hexagonal topologies +- Periodic Boundary Conditions (PBC) support for toroidal SOM topologies +- FAISS backend for accelerated BMU search (``uv add torchsom[faiss]``) +- Configurable search backend (``auto``, ``torch``, ``faiss``) +- Additional quality-of-life improvements and bug fixes -.. .. [0.1.0] - 2024-01-15 -.. -------------------- +v1.0.0 +------ -.. Added -.. ~~~~~ -.. - Initial release of TorchSOM -.. - Basic SOM implementation with PyTorch backend -.. - Core utilities for distance functions, neighborhoods, and decay -.. - Basic visualization with matplotlib -.. - Standard SOM training algorithms -.. - Support for rectangular and hexagonal topologies +Initial public release of TorchSOM, accompanying the +`arXiv paper `_. -.. Features -.. ~~~~~~~~ -.. - **Core SOM Implementation**: Complete self-organizing map with customizable parameters -.. - **Multiple Distance Functions**: Euclidean, Cosine, Manhattan, and Chebyshev distances -.. - **Neighborhood Functions**: Gaussian, Mexican Hat, Bubble, and Triangle neighborhoods -.. - **Flexible Topologies**: Support for both rectangular and hexagonal grid layouts -.. - **Visualization Tools**: Basic plotting capabilities for distance maps and hit maps -.. - **GPU Support**: Automatic device detection and CUDA acceleration -.. - **Configuration Management**: Structured parameter validation with Pydantic +**Features** -.. Technical Details -.. ~~~~~~~~~~~~~~~~~ -.. - Minimum Python version: 3.8 -.. - PyTorch dependency: >=1.10.0 -.. - Full type annotation support -.. - Comprehensive test coverage -.. - CI/CD pipeline with GitHub Actions +- Classical SOM implementation with PyTorch backend +- GPU-accelerated training with batch learning +- scikit-learn-style API (``fit``, ``build_map``, ``cluster``) +- Rectangular and hexagonal grid topologies +- Four distance functions: Euclidean, Cosine, Manhattan, Chebyshev +- Four neighborhood functions: Gaussian, Mexican Hat, Bubble, Triangle +- Multiple decay schedulers for learning rate and neighborhood width +- PCA and random weight initialization +- Comprehensive visualization suite (seven visualization types) +- Clustering integration (K-Means, GMM, HDBSCAN) +- Just-In-Time Learning (JITL) via ``collect_samples()`` +- Pydantic-based configuration with validation +- 90% test coverage +- Full documentation with tutorials and API reference -.. Migration Guide -.. --------------- - -.. From v0.0.x to v0.1.0 -.. ~~~~~~~~~~~~~~~~~~~~~ - -.. Breaking Changes -.. ................ - -.. - **Import paths changed**: Update import statements - -.. .. code-block:: python - -.. # Old -.. from torchsom.som import SOM - -.. # New -.. from torchsom import SOM - -.. - **Parameter names**: Some parameter names were standardized - -.. .. code-block:: python - -.. # Old -.. som = SOM(map_size=(10, 10), learning_rate=0.5) - -.. # New -.. som = SOM(x=10, y=10, learning_rate=0.5) - -.. - **Visualization API**: Updated method signatures - -.. .. code-block:: python - -.. # Old -.. som.plot_distance_map() - -.. # New -.. from torchsom.visualization import SOMVisualizer -.. viz = SOMVisualizer(som) -.. viz.plot_distance_map() - -.. From v0.1.0 to v0.2.0 -.. ~~~~~~~~~~~~~~~~~~~~~ - -.. Breaking Changes -.. ................ - -.. Deprecated Features -.. ~~~~~~~~~~~~~~~~~~~ - -.. The following features are deprecated and will be removed in v0.2.0: - -.. - XXX -.. - XXX - -.. Upgrade Steps -.. ~~~~~~~~~~~~~ - -.. 1. XXX -.. 2. XXX How to Contribute ----------------- -We welcome contributions! See our `contributing guide `_ for: +We welcome contributions! See our `contributing guide `_ for details. Report Issues ~~~~~~~~~~~~~ diff --git a/docs/source/additional_resources/faq.rst b/docs/source/additional_resources/faq.rst index 9df51e9..3ed1c8b 100644 --- a/docs/source/additional_resources/faq.rst +++ b/docs/source/additional_resources/faq.rst @@ -17,7 +17,7 @@ TorchSOM offers several advantages: - **GPU acceleration** through PyTorch - **Modern Python practices** with type hints and Pydantic validation - **Comprehensive visualization suite** with matplotlib integration -- **Flexible architecture** supporting multiple SOM variants +- **Flexible topologies**: rectangular and hexagonal grids, optionally toroidal via periodic boundary conditions Installation and Setup ---------------------- @@ -25,7 +25,7 @@ Installation and Setup Which Python versions are supported? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -We recommend using Python 3.9+. +TorchSOM requires Python 3.10 or higher. Do I need a GPU to use TorchSOM? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -102,10 +102,10 @@ White neurons typically indicate: This is normal for sparse data or oversized maps. -How do I interpret the distance map (D-Matrix)? +How do I interpret the distance map (U-matrix)? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -In the D-Matrix: +In the U-matrix: - **Light areas**: High distances between neighboring neurons (cluster boundaries) - **Dark areas**: Low distances (within clusters) @@ -177,25 +177,28 @@ Integration Questions How do I cite TorchSOM in my research? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Please cite TorchSOM as: +Please cite both the paper and the software: .. code-block:: bibtex - # Conference Paper - @inproceedings{Berthier2025TorchSOM, + @misc{berthier2025torchsom, title={torchsom: The Reference PyTorch Library for Self-Organizing Maps}, - author={Berthier, Louis}, - booktitle={Conference Name}, - year={2025} + author={Berthier, Louis and Shokry, Ahmed and Moreaud, Maxime + and Ramelet, Guillaume and Moulines, Eric}, + year={2025}, + eprint={2510.11147}, + archivePrefix={arXiv}, + primaryClass={stat.ML}, + url={https://arxiv.org/abs/2510.11147} } - # GitHub Repository - @software{Berthier_TorchSOM_The_Reference_2025, + @software{berthier2025torchsom_software, author={Berthier, Louis}, title={torchsom: The Reference PyTorch Library for Self-Organizing Maps}, + year={2025}, + version={1.1.1}, url={https://github.com/michelin/TorchSOM}, - version={1.0.0}, - year={2025} + note={Documentation available at \url{https://opensource.michelin.io/TorchSOM/}} } Getting Help diff --git a/docs/source/additional_resources/troubleshooting.rst b/docs/source/additional_resources/troubleshooting.rst index 5c15a0e..2ec376f 100644 --- a/docs/source/additional_resources/troubleshooting.rst +++ b/docs/source/additional_resources/troubleshooting.rst @@ -21,14 +21,13 @@ Package Issues .. code-block:: bash - pip install torchsom + uv pip install torchsom -2. If using conda environment, make sure it's activated: +2. If you use a virtual environment, make sure it is active. With uv, prefix commands with ``uv run`` so the project environment is always used: .. code-block:: bash - conda activate your_environment - pip install torchsom + uv run python -c "import torchsom" 3. Check installation: @@ -94,7 +93,7 @@ CUDA not available .. code-block:: bash # For CUDA 11.8 - pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 + uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 2. **Check CUDA installation**: diff --git a/docs/source/api/configs.rst b/docs/source/api/configs.rst index 9649ea7..2f1d88d 100644 --- a/docs/source/api/configs.rst +++ b/docs/source/api/configs.rst @@ -11,33 +11,25 @@ SOM Configuration :undoc-members: :show-inheritance: -Saving Configuration --------------------- +Loading and Saving +------------------ -Loading Configuration -~~~~~~~~~~~~~~~~~~~~~ +``SOMConfig`` is a Pydantic model, so it round-trips through dictionaries, JSON, and +YAML for reproducible experiments. .. code-block:: python import yaml from torchsom.configs import SOMConfig - # Load from YAML file - with open("som_config.yaml", "r") as f: - config_dict = yaml.safe_load(f) - config = SOMConfig(**config_dict) + # Load from a YAML file + with open("som_config.yaml") as f: + config = SOMConfig(**yaml.safe_load(f)) -Exporting Configuration -~~~~~~~~~~~~~~~~~~~~~~~ + # Export to a dict or a JSON string + config_dict = config.model_dump() + config_json = config.model_dump_json(indent=2) -.. code-block:: python - - import yaml - - # Export to dictionary and JSON - config_dict = config.dict() - config_json = config.json(indent=2) - - # Save to YAML file + # Save back to YAML with open("exported_config.yaml", "w") as f: - yaml.dump(config.dict(), f, default_flow_style=False) + yaml.dump(config_dict, f, default_flow_style=False) diff --git a/docs/source/api/core.rst b/docs/source/api/core.rst index ed283ab..f3e2d15 100644 --- a/docs/source/api/core.rst +++ b/docs/source/api/core.rst @@ -45,44 +45,18 @@ Example usage data=X, ) -SOM Variants (WORK IN PROGRESS) -------------------------------- +Periodic boundary conditions +---------------------------- -Periodic Boundary Conditioned SOM -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Periodic boundary conditions are **not a separate class**. They are enabled with the +``pbc=True`` argument of :class:`~torchsom.core.SOM`, which wraps the grid into a +torus for both rectangular and hexagonal topologies, removing edge effects. See +:doc:`../user_guide/topologies` for when and how to use them. -Growing SOM -~~~~~~~~~~~ +Roadmap +------- -.. .. automodule:: torchsom.core.growing -.. :members: -.. :undoc-members: -.. :show-inheritance: - -.. .. automodule:: torchsom.core.growing.components -.. :members: -.. :undoc-members: -.. :show-inheritance: - -.. .. automodule:: torchsom.core.growing.growing_som -.. :members: -.. :undoc-members: -.. :show-inheritance: - -Hierarchical SOM -~~~~~~~~~~~~~~~~ - -.. .. automodule:: torchsom.core.hierarchical -.. :members: -.. :undoc-members: -.. :show-inheritance: - -.. .. automodule:: torchsom.core.hierarchical.components -.. :members: -.. :undoc-members: -.. :show-inheritance: - -.. .. automodule:: torchsom.core.hierarchical.hierarchical_som -.. :members: -.. :undoc-members: -.. :show-inheritance: +Growing and Hierarchical SOM variants are planned (see the paper's Conclusion). They +live under ``torchsom.core.growing`` and ``torchsom.core.hierarchical`` as +work-in-progress modules, are not yet part of the public API, and are therefore not +documented here. Track progress in the :doc:`../additional_resources/changelog`. diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst new file mode 100644 index 0000000..0c8a919 --- /dev/null +++ b/docs/source/api/index.rst @@ -0,0 +1,88 @@ +API Reference +============= + +The public API is small by design. Most workflows use the :class:`~torchsom.core.SOM` +class, the :class:`~torchsom.visualization.SOMVisualizer`, and (optionally) the +:class:`~torchsom.configs.SOMConfig`. The pages below document every module in full. + +.. grid:: 2 + :gutter: 3 + + .. grid-item-card:: Core + :link: core + :link-type: doc + + ``torchsom.core`` β€” the ``SOM`` class and its ``BaseSOM`` interface. + + .. grid-item-card:: Utils + :link: utils + :link-type: doc + + ``torchsom.utils`` β€” distances, neighborhoods, decay, clustering, metrics, search. + + .. grid-item-card:: Visualization + :link: visualization + :link-type: doc + + ``torchsom.visualization`` β€” ``SOMVisualizer`` and ``VisualizationConfig``. + + .. grid-item-card:: Configs + :link: configs + :link-type: doc + + ``torchsom.configs`` β€” the Pydantic ``SOMConfig`` model. + + +Public objects at a glance +-------------------------- + +.. list-table:: + :header-rows: 1 + :widths: 34 66 + + * - Object + - Purpose + * - :class:`torchsom.core.SOM` + - The Self-Organizing Map: ``fit``, ``build_map``, ``cluster``, ``collect_samples``. + * - :class:`torchsom.core.BaseSOM` + - Abstract base defining the SOM interface and shared attributes. + * - :class:`torchsom.visualization.SOMVisualizer` + - Factory that renders every plot for the SOM's topology. + * - :class:`torchsom.visualization.VisualizationConfig` + - Styling options for figures (size, fonts, colormap, DPI, hex settings). + * - :class:`torchsom.configs.SOMConfig` + - Validated, serializable configuration for a ``SOM``. + * - ``torchsom.DISTANCE_FUNCTIONS`` + - Registry of distance metrics (``euclidean``, ``cosine``, ``manhattan``, ``chebyshev``). + * - ``torchsom.NEIGHBORHOOD_FUNCTIONS`` + - Registry of neighborhood kernels (``gaussian``, ``mexican_hat``, ``bubble``, ``triangle``). + * - ``torchsom.DECAY_FUNCTIONS`` + - Registry of learning-rate and neighborhood-width decay schedules. + + +Import conventions +------------------ + +The most common objects are re-exported at the top level: + +.. code-block:: python + + from torchsom import SOM, SOMVisualizer + from torchsom.visualization import VisualizationConfig + from torchsom.configs import SOMConfig + +The function registries let you list or extend the available options: + +.. code-block:: python + + from torchsom import DISTANCE_FUNCTIONS, NEIGHBORHOOD_FUNCTIONS, DECAY_FUNCTIONS + + print(list(DISTANCE_FUNCTIONS)) # available distance metrics + print(list(NEIGHBORHOOD_FUNCTIONS)) # available neighborhood kernels + + +.. seealso:: + + The :doc:`../user_guide/architecture` page explains how these modules fit together, + and the :doc:`../user_guide/training` guide maps the constructor arguments to + training behavior. diff --git a/docs/source/api/visualization.rst b/docs/source/api/visualization.rst index 640a7e4..bc33aef 100644 --- a/docs/source/api/visualization.rst +++ b/docs/source/api/visualization.rst @@ -16,23 +16,20 @@ Example .. code-block:: python - from torchsom import SOM - from torchsom.visualization import SOMVisualizer import torch + from torchsom import SOM, SOMVisualizer data = torch.randn(500, 3) som = SOM(x=12, y=10, num_features=3, epochs=20) - som.initialize_weights(data, mode="pca") - q_errors, t_errors = som.fit(data) - - viz = SOMVisualizer(som) - viz.plot_all( - quantization_errors=q_errors, - topographic_errors=t_errors, - data=data, - target=None, - save_path=None, - ) + som.initialize_weights(data=data, mode="pca") + q_errors, t_errors = som.fit(data=data) + + viz = SOMVisualizer(som=som) + viz.plot_distance_map() + viz.plot_hit_map(data=data) + +See the :doc:`../user_guide/visualization_help` gallery for every plot, including +``plot_all`` and the supervised maps. Visualization Configuration --------------------------- @@ -54,4 +51,6 @@ Notes ----- - The visualizer is a factory that forwards to topology-specific implementations (hexagonal or rectangular). -- For supervised maps (metric/score/rank/classification), you can either pass data and target directly or precompute ``bmus_data_map = som.build_map("bmus_data", data, return_indices=True)`` to speed up repeated plots. +- The supervised maps (metric/score/rank/classification) and ``plot_all`` require a + pre-computed ``bmus_data_map = som.build_map("bmus_data", data=data)``; build it once + and reuse it across plots. diff --git a/docs/source/conf.py b/docs/source/conf.py index 32a6b5a..afb4444 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -7,15 +7,23 @@ from sphinx.application import Sphinx +def _read_version() -> str: + """Read the package version from pyproject.toml.""" + pyproject = Path(__file__).resolve().parents[2] / "pyproject.toml" + if pyproject.exists(): + for line in pyproject.read_text().splitlines(): + if line.strip().startswith('version = "'): + return line.split('"')[1] + return "0.0.0" + + def _copy_repo_assets_to_static() -> None: """Copy repo-level assets/ into docs/_static/assets for reliable access.""" - print("Copying repo-level assets/ into docs/_static/assets for reliable access.") repo_root = Path(__file__).resolve().parents[2] src = repo_root / "assets" dst = Path(__file__).parent / "_static" / "assets" if src.exists(): dst.parent.mkdir(parents=True, exist_ok=True) - shutil.copytree(src, dst, dirs_exist_ok=True) with suppress(Exception): shutil.copytree(src, dst, dirs_exist_ok=True) @@ -25,32 +33,28 @@ def setup(app: Sphinx) -> None: # noqa: ARG001 _copy_repo_assets_to_static() -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - # -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = "TorchSOM" copyright = "2025, Manufacture FranΓ§aise des Pneumatiques Michelin" author = "Louis Berthier" -release = "0.0.1" +release = _read_version() # -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ - "sphinx.ext.duration", # Reports build duration per step. - "sphinx.ext.doctest", # Allows running doctests in code snippets embedded in the documentation. - "sphinx.ext.autodoc", # Automatically includes documentation from Python docstrings. - "sphinx.ext.autosummary", # Generates summary tables for modules/functions/classes with short descriptions. - "sphinx.ext.intersphinx", # Links to objects in external documentation projects (e.g., Python, NumPy). - "sphinx.ext.viewcode", # Adds links to highlighted source code. - "sphinx.ext.githubpages", # Adds .nojekyll file needed for GitHub Pages deployment. - "sphinx_copybutton", # Adds a "copy to clipboard" button on code blocks. - "sphinx.ext.napoleon", # Enables parsing of NumPy/Google-style docstrings. + "sphinx.ext.duration", + "sphinx.ext.doctest", + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.intersphinx", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", + "sphinx.ext.githubpages", + "sphinx.ext.napoleon", + "sphinx.ext.todo", + "sphinx_copybutton", + "sphinx_design", ] autodoc_typehints = "description" @@ -58,36 +62,56 @@ def setup(app: Sphinx) -> None: # noqa: ARG001 templates_path = ["_templates"] exclude_patterns = [] +# -- MathJax configuration --------------------------------------------------- +# Define LaTeX macros used in the math so the docs match the JMLR paper's +# notation. MathJax v3 core does not provide \coloneqq (it comes from the +# mathtools package in LaTeX), so it is declared here. +mathjax3_config = { + "tex": { + "macros": { + "coloneqq": "\\mathrel{:=}", + }, + }, +} + +# -- Intersphinx mapping ----------------------------------------------------- + +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), + "torch": ("https://pytorch.org/docs/stable", None), + "sklearn": ("https://scikit-learn.org/stable", None), + "numpy": ("https://numpy.org/doc/stable", None), + "matplotlib": ("https://matplotlib.org/stable", None), + "pydantic": ("https://docs.pydantic.dev/latest", None), +} # -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = "sphinx_rtd_theme" # "alabaster" "sphinx_rtd_theme" +html_theme = "furo" html_theme_options = { - # "analytics_id": "G-XXXXXXXXXX", # Google Analytics ID to enable pageview tracking, provided in the Google Analytics account. - "analytics_anonymize_ip": False, # If True, anonymizes user IP addresses in analytics data. - "logo_only": False, # If True, displays only the logo without project title text. - "prev_next_buttons_location": "both", # Places "Previous" and "Next" navigation buttons at the bottom of the page. Options: 'top', 'bottom', 'both'. - "style_external_links": False, # If True, adds an external link icon to all external hyperlinks. - "vcs_pageview_mode": "", # Version control system mode (e.g., 'blob' for GitHub). Empty disables this. - "style_nav_header_background": "#B22222", # Changes the hex color of the top left header containing the logo. - "flyout_display": "hidden", # Controls behavior of sidebar flyouts. 'hidden' disables them. - "version_selector": True, # Enables a UI component for selecting documentation versions (requires configuration). - "language_selector": True, # Enables a UI component for selecting languages (requires localization setup). - "collapse_navigation": False, # Collapses sub-sections in the sidebar for cleaner appearance. If False then expend. - "sticky_navigation": True, # Keeps sidebar navigation fixed while scrolling. - "navigation_depth": 4, # Enables deeper table-of-contents nesting in the sidebar. - "includehidden": True, # Includes hidden TOC entries in the sidebar. - "titles_only": False, # If True, shows only the page titles (no section titles) in the sidebar. + "source_repository": "https://github.com/michelin/TorchSOM", + "source_branch": "main", + "source_directory": "docs/source/", + "light_css_variables": { + "color-brand-primary": "#B22222", + "color-brand-content": "#B22222", + }, + "dark_css_variables": { + "color-brand-primary": "#E05555", + "color-brand-content": "#E05555", + }, + "sidebar_hide_name": False, + "navigation_with_keys": True, } -html_context = { - "display_github": True, # Enable GitHub link in the header - "github_user": "LouisTier", - "github_repo": "TorchSOM", - "github_version": "dev_org", - "conf_py_path": "/docs/source/", -} html_static_path = ["_static"] html_logo = "_static/assets/logo.png" -# html_favicon = "../../favicon.ico" # Icon shown in the browser tab + +# -- Todo extension ---------------------------------------------------------- +# Author-facing only: keep ``.. todo::`` notes out of the published HTML. +todo_include_todos = False + +# -- Linkcheck --------------------------------------------------------------- +# PyTorch's documentation renders anchors client-side, so linkcheck cannot verify +# them from static HTML even though intersphinx resolves the targets at build time. +linkcheck_anchors_ignore_for_url = [r"https://docs\.pytorch\.org/.*"] diff --git a/docs/source/getting_started/basic_concepts.rst b/docs/source/getting_started/basic_concepts.rst index 6506dbe..21d4201 100644 --- a/docs/source/getting_started/basic_concepts.rst +++ b/docs/source/getting_started/basic_concepts.rst @@ -41,29 +41,47 @@ The SOM Algorithm Mathematical Foundation ~~~~~~~~~~~~~~~~~~~~~~~ -**Distance Calculation** -The similarity between an input vector :math:`\mathbf{x}` and a neuron's weight vector :math:`\mathbf{w}` is most commonly measured using the Euclidean distance. -Alternative distance functions are also supported; see :ref:`distance_functions_section` for a comprehensive list. +**Setup and notation** +A SOM approximates a distribution over an input space :math:`\mathcal{X} \subseteq \mathbb{R}^d` by a two-dimensional lattice of :math:`I \times J` neurons. +Each neuron at grid position :math:`(i, j)`, with :math:`i \in \{1, \dots, I\}` and :math:`j \in \{1, \dots, J\}`, carries a *codebook* (weight) vector :math:`\mathbf{w}_{ij} \in \mathbb{R}^l` with :math:`l = d`, and the full parameter set is the tensor .. math:: - \text{BMU} = w_{\mathrm{bmu}} = \underset{i,j}{\operatorname{argmin}}\, \| \mathbf{x} - \mathbf{w}_{ij} \|_2 = \underset{i,j}{\operatorname{argmin}}\, \sqrt{\sum_{l=1}^{k} (x_l - w_{ij,l})^2} + \mathbf{W} \coloneqq [\mathbf{w}_{ij}]_{i \le I,\, j \le J} \in \mathbb{R}^{I \times J \times l} -**Weight Update Rule** -The weights of the neurons are updated according to the following rule: +Training uses a data set :math:`\{\mathbf{x}_k\}_{k=1}^{N} \subset \mathbb{R}^d` over epochs :math:`t \in \{0, 1, \dots, T\}`. + +**Best matching unit and projection** +Similarity in feature space is measured by a distance :math:`\delta` (see :ref:`distance_functions_section`). +For an input :math:`\mathbf{x}`, the Best Matching Unit (BMU) is the neuron whose codebook minimizes :math:`\delta`: + +.. math:: + \mathrm{BMU}(\mathbf{x}) \coloneqq \operatorname*{arg\,min}_{(i,j)}\, \delta(\mathbf{x}, \mathbf{w}_{ij}) + +which induces a projection onto grid coordinates and a latent codebook retrieval: + +.. math:: + \psi : \mathbb{R}^d \rightarrow \{1, \dots, I\} \times \{1, \dots, J\}, \qquad \psi(\mathbf{x}) \coloneqq \mathrm{BMU}(\mathbf{x}), \qquad \mathbf{z} \coloneqq \mathbf{w}_{\psi(\mathbf{x})} \in \mathbb{R}^l + +The latent vector :math:`\mathbf{z}` is the representation used for clustering, visualization, and just-in-time learning (JITL) retrieval. + +**Competitive update** +A SOM learns by a neighborhood-weighted competitive rule rather than gradient descent: at each step the BMU for the presented sample :math:`\mathbf{x}` is found, and each neuron is moved toward :math:`\mathbf{x}` by a step scaled by its grid proximity to the BMU: .. math:: - \mathbf{w}_{ij}(t+1) = \mathbf{w}_{ij}(t) + \alpha(t) \cdot h_{ij}(t) \cdot (\mathbf{x} - \mathbf{w}_{ij}(t)) + \mathbf{w}_{ij}(t+1) \coloneqq \mathbf{w}_{ij}(t) + \alpha(t)\, h_{ij}(t)\, (\mathbf{x} - \mathbf{w}_{ij}(t)) where: -- :math:`\mathbf{w}_{ij}(t) \in \mathbb{R}^k`: weight vector of the neuron at row :math:`i`, column :math:`j` at iteration :math:`t` -- :math:`\alpha(t) \in \mathbb{R}`: learning rate at iteration :math:`t` -- :math:`h_{ij}(t) \in \mathbb{R}`: neighborhood function value for neuron :math:`(i, j)` at iteration :math:`t` -- :math:`\mathbf{x} \in \mathbb{R}^k`: input feature vector +- :math:`\mathbf{w}_{ij}(t) \in \mathbb{R}^l`: codebook vector of neuron :math:`(i, j)` at epoch :math:`t` +- :math:`\alpha(t) \in \mathbb{R}^+`: learning rate at epoch :math:`t` (see the decay schedules below) +- :math:`h_{ij}(t) \in \mathbb{R}`: neighborhood weight for neuron :math:`(i, j)` at epoch :math:`t` +- :math:`\mathbf{x} \in \mathbb{R}^d`: input feature vector Core Components --------------- +.. _grid_topology_section: + 1. Grid Topology ~~~~~~~~~~~~~~~~ @@ -77,6 +95,19 @@ SOMs arrange neurons in a regular grid structure, which determines the map's top - Uniform neighborhood distances - Reduces topology errors, often preferred for advanced analysis +**Periodic Boundary Conditions (PBC)** + - Available for both rectangular and hexagonal grids + - Opposite edges of the map are identified, eliminating boundary artifacts at the borders + - Useful when the input space has no natural boundary (e.g., cyclic features, angular data) + or when uniform neuron utilization is required across the entire map + +Under periodic boundary conditions, grid distances follow the minimum-image convention: + +.. math:: + d_{\mathrm{grid}}\big((i,j),(i',j')\big) \coloneqq \min_{\mathbf{s} \in \mathcal{S}} \big\lVert \gamma(i,j) - \gamma(i',j') + \mathbf{s} \big\rVert_2 + +where :math:`\gamma(\cdot)` maps a grid index to its coordinates and :math:`\mathcal{S}` enumerates translations by the grid periods (:math:`\mathcal{S} = \{\mathbf{0}\}` without PBC), so neighborhoods wrap across boundaries and corner neurons are not penalized. + .. image:: ../_static/som/topologies.png :alt: Topologies with neighborhood orders :width: 600px @@ -85,36 +116,38 @@ SOMs arrange neurons in a regular grid structure, which determines the map's top 2. Neighborhood Function ~~~~~~~~~~~~~~~~~~~~~~~~ -The neighborhood function determines how much each neuron is influenced by the BMU during weight updates: +The neighborhood function determines how much each neuron is influenced by the BMU during weight updates. +Let :math:`\rho \coloneqq d_{\mathrm{grid}}\big((i, j), \mathrm{BMU}\big)` denote the *grid-space* distance from neuron :math:`(i, j)` to the BMU, induced by the lattice geometry. TorchSOM provides four neighborhood kernels of width :math:`\sigma(t)`: **Gaussian** (most common): .. math:: - h_{ij}^{\mathrm{Gaussian}}(t) = \exp\left(-\frac{d_{ij}^2}{2\,\sigma(t)^2}\right) + h_{ij}^{\mathrm{gaussian}}(t) \coloneqq \exp\left(-\frac{\rho^2}{2\,\sigma(t)^2}\right) -**Mexican Hat**: +**Mexican hat** (the Ricker wavelet rescaled to a unit peak; it dips below zero in an outer ring, so :math:`h_{ij}(t) \in \mathbb{R}`): .. math:: - h_{ij}^{\mathrm{Mexican}}(t) = \frac{1}{\pi\,\sigma(t)^4} \left(1 - \frac{d_{ij}^2}{2\,\sigma(t)^2}\right) \exp\left(-\frac{d_{ij}^2}{2\,\sigma(t)^2}\right) + h_{ij}^{\mathrm{mexican}}(t) \coloneqq \left(1 - \frac{\rho^2}{4\,\sigma(t)^2}\right) \exp\left(-\frac{\rho^2}{2\,\sigma(t)^2}\right) **Bubble**: .. math:: - h_{ij}^{\mathrm{Bubble}}(t) = \begin{cases} - 1, & \text{if } d_{ij} \leq \sigma(t) \\ - 0, & \text{otherwise} - \end{cases} + h_{ij}^{\mathrm{bubble}}(t) \coloneqq \mathbb{I}\big(\rho \le \sigma(t)\big) **Triangle**: .. math:: - h_{ij}^{\mathrm{Triangle}}(t) = \max\left(0,\, 1 - \frac{d_{ij}}{\sigma(t)}\right) + h_{ij}^{\mathrm{triangle}}(t) \coloneqq \max\left(0,\, 1 - \frac{\rho}{\sigma(t)}\right) -When updating neuron weights, the distance :math:`d_{ij}` is computed in the grid (map) space, not in the input feature space: +The grid distance is computed in the map space, not the input feature space. On a rectangular grid it is Euclidean in the neuron coordinates, .. math:: - d_{ij} = \sqrt{(i - c_x)^2 + (j - c_y)^2} + \rho = \sqrt{(i - c_i)^2 + (j - c_j)^2} -where: +where :math:`(c_i, c_j)` are the BMU coordinates and :math:`(i, j)` those of neuron :math:`\mathbf{w}_{ij}` (periodic boundaries wrap this distance; see :ref:`grid_topology_section`). + +Discrete neighborhoods are controlled by an integer **order** :math:`o \in \mathbb{N}^+`. On a rectangular grid, the order-:math:`o` neighborhood of the BMU at :math:`(i, j)` is the Chebyshev ball + +.. math:: + N_o(\mathrm{BMU}) \coloneqq \big\{ (i', j') : \max(|i' - i|,\, |j' - j|) \le o \big\} -- :math:`(c_x, c_y)`: coordinates of the BMU in the grid -- :math:`(i, j)`: coordinates of neuron :math:`w_{ij}` +a :math:`(2o + 1) \times (2o + 1)` block of neurons; the hexagonal grid uses the analogous hop-distance rings. The order :math:`o` sets the support of the discrete weight update and the sample-retrieval neighborhoods used for JITL (the ``neighborhood_order`` parameter and the ``collect_samples`` retrieval modes). 3. Schedule Learning Rate and Neighborhood Radius Decay ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -126,11 +159,11 @@ The learning rate :math:`\alpha(t)` controls the magnitude of weight vector upda **Inverse Decay**: .. math:: - \alpha(t+1) = \alpha(t) \cdot \frac{\gamma}{\gamma + t} % , \quad \text{where } \gamma = \frac{T}{100} + \alpha(t+1) \coloneqq \alpha(t) \cdot \frac{\gamma}{\gamma + t} **Linear Decay**: .. math:: - \alpha(t+1) = \alpha(t) \cdot \left( 1 - \frac{t}{T} \right) + \alpha(t+1) \coloneqq \alpha(t) \cdot \left( 1 - \frac{t}{T} \right) These schedulers guarantee convergence to :math:`\alpha(T) = 0`, corresponding to zero weight updates in the final training phase, which is essential for achieving precise local weight adjustments. @@ -141,11 +174,11 @@ The neighborhood radius controls the size of the neighborhood of the BMU during **Inverse Decay**: .. math:: - \sigma(t+1) = \frac{\sigma(t)}{1 + t \cdot \frac{\sigma(t) - 1}{T}} + \sigma(t+1) \coloneqq \frac{\sigma(t)}{1 + t \cdot \frac{\sigma(t) - 1}{T}} **Linear Decay**: .. math:: - \sigma(t+1) = \sigma(t) + t \cdot \frac{1 - \sigma(t)}{T} + \sigma(t+1) \coloneqq \sigma(t) + t \cdot \frac{1 - \sigma(t)}{T} These schedulers guarantee convergence to :math:`\sigma(T) = 1`, corresponding to single-neuron updates in the final training phase, which is essential for achieving precise local weight adjustments. @@ -155,7 +188,7 @@ Asymptotic Decay For arbitrary dynamic parameters requiring exponential-like decay characteristics, TorchSOM implements a general asymptotic decay scheduler: .. math:: - \theta(t+1) = \frac{\theta(t)}{1 + \frac{t}{T/2}} + \theta(t+1) \coloneqq \frac{\theta(t)}{1 + \frac{t}{T/2}} where: @@ -171,63 +204,50 @@ where: 4. Distance Functions ~~~~~~~~~~~~~~~~~~~~~ -Different ways to measure similarity: +The feature-space distance :math:`\delta` used by the BMU search is configurable. Writing :math:`x_a` and :math:`w_a` for the :math:`a`-th components: **Euclidean**: .. math:: - d_{\text{Euclidean}}(x, w_{ij}) = \sqrt{\sum_{l=1}^k {(x_l - w_{ij,l})}^2} + \delta_{\mathrm{euclidean}}(\mathbf{x}, \mathbf{w}) \coloneqq \sqrt{\sum_{a=1}^{d} (x_a - w_a)^2} -**Cosine**: +**Manhattan**: .. math:: - d_{\text{cosine}}(x, w_{ij}) = 1 - \frac{x \cdot w_{ij}}{\|x\| \|w_{ij}\|} + \delta_{\mathrm{manhattan}}(\mathbf{x}, \mathbf{w}) \coloneqq \sum_{a=1}^{d} |x_a - w_a| -**Manhattan**: +**Cosine**: .. math:: - d_{\text{Manhattan}}(x, w_{ij}) = \sum_{l=1}^k |x_l - w_{ij,l}| + \delta_{\mathrm{cosine}}(\mathbf{x}, \mathbf{w}) \coloneqq 1 - \frac{\mathbf{x} \cdot \mathbf{w}}{\lVert \mathbf{x} \rVert\, \lVert \mathbf{w} \rVert} **Chebyshev**: .. math:: - d_{\text{Chebyshev}}(x, w_{ij}) = \max_{l} |x_l - w_{ij,l}| + \delta_{\mathrm{chebyshev}}(\mathbf{x}, \mathbf{w}) \coloneqq \max_{a \le d} |x_a - w_a| -where: - - :math:`x \in \mathbb{R}^k`: input feature vector - - :math:`w_{ij} \in \mathbb{R}^k`: weight vector of the neuron at row :math:`i`, column :math:`j` - - :math:`k \in \mathbb{N}`: number of features - - :math:`l \in \{1, \ldots, k\}`: feature index +where :math:`\mathbf{x}, \mathbf{w} \in \mathbb{R}^d` and :math:`d \in \mathbb{N}` is the number of features. 5. Quality Metrics ~~~~~~~~~~~~~~~~~~ -**Quantization Error** -Average distance between data points and their BMUs. Lower is better, measures how well the map represents the data. - -**Quantization Error** +**Quantization Error (QE)** -Average distance between data points and their BMUs. Lower is better; measures how well the map represents the data. +Average distance between data points and their BMUs. Lower is better; it measures how well the map represents the data. .. math:: + \mathrm{QE} \coloneqq \frac{1}{N} \sum_{k=1}^{N} \big\lVert \mathbf{x}_k - \mathbf{w}_{\mathrm{BMU}(\mathbf{x}_k)} \big\rVert_2 - \mathrm{QE} = \frac{1}{N} \sum_{i=1}^{N} \left\| x_i - w_{\mathrm{BMU}}(x_i) \right\|_2 +**Topographic Error (TE)** -**Topographic Error** - -Percentage of data points whose BMU and second-BMU are not neighbors. Lower is better; measures topology preservation. +Fraction of data points whose BMU and second-BMU are not grid-adjacent. Lower is better; it measures topology preservation. .. math:: - - \mathrm{TE} = \frac{1}{N} \sum_{i=1}^{N} \mathbb{I} \left( d_{\mathrm{grid}} \left( w_{\mathrm{BMU}}(x_i),\ w_{\mathrm{2nd\text{-}BMU}}(x_i) \right) > d_{\mathrm{th}} \right ) - + \mathrm{TE} \coloneqq \frac{1}{N} \sum_{k=1}^{N} \mathbb{I}\big( d_{\mathrm{grid}}(\mathrm{BMU}(\mathbf{x}_k),\, \mathrm{BMU}_2(\mathbf{x}_k)) > d_{\mathrm{th}} \big) where: - - :math:`N \in \mathbb{N}`: Number of training samples - - :math:`x_i \in \mathbb{R}^k`: The :math:`i`-th input training sample - - :math:`w_{\text{BMU}}(x_i) \in \mathbb{R}^k`: Weight vector of the Best Matching Unit (BMU) for input :math:`x_i` - - :math:`w_{\text{2nd-BMU}}(x_i) \in \mathbb{R}^k`: Weight vector of the second BMU for input :math:`x_i` - - :math:`d_{\text{th}} \in \mathbb{R}^+`: Threshold distance for topological adjacency (typically :math:`d_{\text{th}} = 1`) - - :math:`\mathbb{I}(\cdot) \in \{0, 1\}`: Indicator function - - :math:`\| \cdot \|_2`: Euclidean norm in feature space - - :math:`d_{\text{grid}}(\cdot, \cdot)`: Grid space distance between BMUs of input :math:`x_i` + - :math:`N \in \mathbb{N}`: number of training samples + - :math:`\mathbf{x}_k \in \mathbb{R}^d`: the :math:`k`-th input sample + - :math:`\mathrm{BMU}_2(\mathbf{x}_k)`: the second-closest neuron in feature space + - :math:`d_{\mathrm{th}} \in \mathbb{R}^+`: grid-adjacency threshold (typically :math:`d_{\mathrm{th}} = 1`) + - :math:`\mathbb{I}(\cdot) \in \{0, 1\}`: indicator function Strengths and Weaknesses @@ -273,9 +293,13 @@ Interpretation 3. **Validate findings** with other analysis methods 4. **Document parameter choices** for reproducibility -Next Steps +Next steps ---------- Now that you understand the basics, explore: -- :doc:`../user_guide/visualization_help` - Visualization techniques +- :doc:`../user_guide/architecture` β€” How these concepts map to the package structure and APIs +- :doc:`../user_guide/topologies` β€” Choosing a topology and periodic boundary conditions +- :doc:`../user_guide/training` β€” Decay schedules and training configuration +- :doc:`../user_guide/visualization_help` β€” Visualization gallery +- :doc:`quickstart` β€” A minimal end-to-end example diff --git a/docs/source/getting_started/installation.rst b/docs/source/getting_started/installation.rst index 3e32ccf..30e4e9e 100644 --- a/docs/source/getting_started/installation.rst +++ b/docs/source/getting_started/installation.rst @@ -9,7 +9,7 @@ Systems TorchSOM requires: -- **Python**: 3.9 or higher +- **Python**: 3.10 or higher - **PyTorch**: 2.7 or higher - **Operating System**: Linux, macOS, or Windows @@ -27,11 +27,11 @@ Installation Methods Install from PyPI (Recommended) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -The easiest way to install TorchSOM is using pip: +The easiest way to install TorchSOM is using uv: .. code-block:: bash - pip install torchsom + uv add torchsom This will install TorchSOM with all required dependencies. @@ -47,10 +47,10 @@ For the latest development version: cd TorchSOM # For standard users: install the main package - pip install -e . + uv sync - # For contributors and developers: install with development dependencies - pip install -e ".[all]" + # For contributors and developers: install with all optional dependencies + uv sync --all-extras Ensure GPU Support ~~~~~~~~~~~~~~~~~~ @@ -61,8 +61,8 @@ To ensure GPU acceleration, install a CUDA-enabled PyTorch per the official sele # Select the right command for your system at https://pytorch.org/get-started/locally/ # Example (CUDA 11.8): - pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 - pip install torchsom + uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 + uv add torchsom Verification ------------ @@ -98,14 +98,14 @@ Core Dependencies Optional Dependencies ~~~~~~~~~~~~~~~~~~~~~ -The following optional dependency groups can be installed via pip, e.g.: +The following optional dependency groups can be installed via uv, e.g.: -- ``pip install .[dev]`` -- ``pip install .[tests]`` -- ``pip install .[docs]`` -- ``pip install .[security]`` -- ``pip install .[linting]`` -- ``pip install .[all]`` +- ``uv sync --extra dev`` +- ``uv sync --extra tests`` +- ``uv sync --extra docs`` +- ``uv sync --extra security`` +- ``uv sync --extra linting`` +- ``uv sync --all-extras`` These are useful for development, testing, documentation, security, and linting. @@ -137,7 +137,7 @@ Documentation Dependencies ^^^^^^^^^^^^^^^^^^^^^^^^^^ - **sphinx**: Documentation building and formatting -- **sphinx-rtd-theme**: ReadTheDocs theme for documentation +- **furo**: Documentation theme - **sphinx-autodoc-typehints**: Type hints for documentation - **sphinx-copybutton**: Copy button for documentation - **pydocstyle**: Documentation style checking @@ -163,14 +163,14 @@ Getting Help If you encounter installation issues: -1. Check the `troubleshooting guide <../troubleshooting.html>`_ +1. Check the :doc:`../additional_resources/troubleshooting` 2. Search existing `GitHub Issues `_ 3. Create a new issue with your system details and error message -Next Steps +Next steps ---------- Once installed, continue with: -- :doc:`quickstart` - Your first SOM in 5 minutes -- :doc:`basic_concepts` - Understanding SOM fundamentals +- :doc:`quickstart` β€” Your first SOM in a few minutes +- :doc:`basic_concepts` β€” Understanding SOM fundamentals diff --git a/docs/source/getting_started/quickstart.rst b/docs/source/getting_started/quickstart.rst index ea82f08..6cad05d 100644 --- a/docs/source/getting_started/quickstart.rst +++ b/docs/source/getting_started/quickstart.rst @@ -1,7 +1,7 @@ -Quick Start Guide -================= +Quick Start +=========== -This guide will get you up and running with TorchSOM in just a few minutes! +This guide gets you up and running with TorchSOM in a few minutes. Your First SOM -------------- @@ -31,8 +31,8 @@ Let's create and train your first Self-Organizing Map: q_errors, t_errors = som.fit(data=data) print("Training completed!") - print(f"Final quantization error: {QE[-1]:.4f}") - print(f"Final topographic error: {TE[-1]:.4f}") + print(f"Final quantization error: {q_errors[-1]:.4f}") + print(f"Final topographic error: {t_errors[-1]:.4f}") Basic Visualization ------------------- @@ -176,19 +176,25 @@ Here's a complete example with data preprocessing and multiple visualizations: # Create visualizer viz = SOMVisualizer(som) + # Pre-compute the BMU -> sample-indices map (required by plot_all) + bmus_map = som.build_map("bmus_data", data=data) + # Generate all visualizations viz.plot_all( quantization_errors=q_errors, topographic_errors=t_errors, + bmus_data_map=bmus_map, data=data, target=labels, - save_path="som_results" + save_path="som_results", ) -What's Next? ------------- +Next steps +---------- Now that you've created your first SOM, explore: -- :doc:`basic_concepts` - Understand how SOMs work -- :doc:`../user_guide/visualization_help` - Comprehensive visualization guide +- :doc:`basic_concepts` β€” Understand how SOMs work +- :doc:`../user_guide/architecture` β€” How the package is organized +- :doc:`../tutorials/index` β€” End-to-end worked examples +- :doc:`../user_guide/visualization_help` β€” Visualization gallery diff --git a/docs/source/index.rst b/docs/source/index.rst index c6bca60..5395173 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,47 +1,95 @@ -.. TorchSOM documentation master file, created by - sphinx-quickstart on Tue May 6 18:37:48 2025. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - TorchSOM Documentation ====================== -.. .. image:: _static/assets/logo.png -.. :alt: TorchSOM Logo -.. :width: 200px -.. :align: center +.. image:: _static/assets/logo.png + :alt: TorchSOM Logo + :width: 280px + :align: center + +.. raw:: html + +

+ GPU-accelerated Self-Organizing Maps in PyTorch with a scikit-learn API, + advanced visualization, and clustering. +

+ +TorchSOM is the PyTorch-native reference implementation of the Self-Organizing Map +(SOM). It pairs a familiar scikit-learn-style API with GPU-accelerated batch +training, a built-in clustering interface, just-in-time-learning support, and a +visualization suite for both rectangular and hexagonal topologies. It accompanies the +paper *torchsom: The Reference PyTorch Library for Self-Organizing Maps* +(`Berthier et al., 2025 `_). + +.. grid:: 2 + :gutter: 3 + + .. grid-item-card:: Getting Started + :link: getting_started/quickstart + :link-type: doc + + Install TorchSOM and train your first Self-Organizing Map in a few minutes. + + .. grid-item-card:: Tutorials + :link: tutorials/index + :link-type: doc + + End-to-end worked examples on real datasets: classification, regression, and clustering. -**TorchSOM** is a modern PyTorch-based library for training and visualizing **Self-Organizing Maps (SOMs)**, -a powerful unsupervised learning algorithm used for clustering, dimensionality reduction, and data exploration. + .. grid-item-card:: User Guide + :link: user_guide/architecture + :link-type: doc -Built with PyTorch at its core, TorchSOM seamlessly integrates with modern deep learning workflows while -providing GPU acceleration for high-performance computing. + Architecture, topologies, training, clustering, JITL, visualization, and benchmarks. + + .. grid-item-card:: API Reference + :link: api/index + :link-type: doc + + Full API documentation for ``torchsom.core``, ``torchsom.utils``, and ``torchsom.visualization``. -.. note:: - 🌟 If you find this project interesting, we would be grateful for your support by starring - this `GitHub repository `_. Key Features ------------ -πŸš€ **Performance** - - GPU-accelerated training with PyTorch - - Efficient batch processing and memory management - - Optimized for large-scale data +.. grid:: 2 + :gutter: 2 + + .. grid-item:: + + **Performance** + + - GPU-accelerated batch training with PyTorch + - 77–99% faster training than MiniSom (see :doc:`user_guide/benchmarks`) + - Quantization-error parity with lower topographic error + - Optional FAISS backend for BMU search on large maps + + .. grid-item:: + + **Flexibility** + + - Rectangular and hexagonal topologies, optionally toroidal via periodic boundary conditions + - Four distance metrics and four neighborhood kernels + - Configurable learning-rate and neighborhood-width decay schedules + - Configurable BMU search backend (PyTorch or FAISS) -🎯 **Flexibility** - - Multiple SOM variants (classical, growing, hierarchical, ...) - - Configurable topologies (rectangular, hexagonal) - - Extensive customization options + .. grid-item:: -πŸ“Š **Visualization** - - Rich visualization suite with matplotlib - - Publication-ready figures + **Visualization** + + - Seven visualization types for both rectangular and hexagonal topologies + - Distance, hit, component-plane, classification, and metric maps + - Score and rank maps for per-neuron reliability in regression + - Clustering diagnostics: elbow, silhouette, and algorithm comparison + + .. grid-item:: + + **Developer-Friendly** + + - scikit-learn-style API (``fit``, ``build_map``, ``cluster``) + - Pydantic-based configuration with validation + - Full type hints throughout + - 90% test coverage, Apache 2.0 licensed -πŸ”§ **Developer-Friendly** - - Clean, modular architecture - - Comprehensive API documentation - - Type hints and validation with Pydantic Quick Start ----------- @@ -50,36 +98,52 @@ Install TorchSOM: .. code-block:: bash - pip install torchsom + uv add torchsom -Basic usage: +Train and visualize a SOM: .. code-block:: python import torch - from torchsom import SOM - from torchsom.visualization import SOMVisualizer + from torchsom import SOM, SOMVisualizer - # Create a 10x10 map for 3D input som = SOM(x=10, y=10, num_features=3, epochs=50) - # Train SOM for 50 epochs on 1000 samples X = torch.randn(1000, 3) som.initialize_weights(data=X, mode="pca") q_errors, t_errors = som.fit(data=X) - # Visualize results - visualizer = SOMVisualizer(som=som, config=None) - visualizer.plot_training_errors(quantization_errors=q_errors, topographic_errors=t_errors, save_path=None) - visualizer.plot_distance_map(save_path=None) - visualizer.plot_hit_map(data=X, save_path=None) + viz = SOMVisualizer(som=som) + viz.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors + ) + viz.plot_distance_map() + viz.plot_hit_map(data=X) + + +Citation +-------- + +If you use TorchSOM in your work, please cite: + +.. code-block:: bibtex + + @misc{berthier2025torchsom, + title={torchsom: The Reference PyTorch Library for Self-Organizing Maps}, + author={Berthier, Louis and Shokry, Ahmed and Moreaud, Maxime + and Ramelet, Guillaume and Moulines, Eric}, + year={2025}, + eprint={2510.11147}, + archivePrefix={arXiv}, + primaryClass={stat.ML}, + url={https://arxiv.org/abs/2510.11147} + } -Documentation Structure ------------------------ .. toctree:: :maxdepth: 2 :caption: Getting Started + :hidden: getting_started/installation getting_started/quickstart @@ -88,13 +152,35 @@ Documentation Structure .. toctree:: :maxdepth: 2 :caption: User Guide + :hidden: + user_guide/architecture + user_guide/topologies + user_guide/training + user_guide/clustering + user_guide/jitl user_guide/visualization_help + user_guide/benchmarks + user_guide/comparison + +.. toctree:: + :maxdepth: 2 + :caption: Tutorials + :hidden: + + tutorials/index + tutorials/iris + tutorials/wine + tutorials/boston_housing + tutorials/energy_efficiency + tutorials/clustering_walkthrough .. toctree:: :maxdepth: 2 :caption: API Reference + :hidden: + api/index api/core api/utils api/visualization @@ -103,24 +189,29 @@ Documentation Structure .. toctree:: :maxdepth: 2 :caption: Additional Resources + :hidden: additional_resources/changelog additional_resources/faq additional_resources/troubleshooting -Support & Community -------------------- -- πŸ“– **Documentation**: You're reading it! -- πŸ› **Bug Reports**: `GitHub Issues `_ +Support +------- + +- **Documentation**: You're reading it! +- **Bug Reports**: `GitHub Issues `_ +- **Source Code**: `GitHub `_ + License ------- -TorchSOM is released under the Apache License 2.0 License. See the `LICENSE `_ file for details. +TorchSOM is released under the `Apache License 2.0 `_. + Indices and Tables -================== +=================== * :ref:`genindex` * :ref:`modindex` diff --git a/docs/source/tutorials/boston_housing.rst b/docs/source/tutorials/boston_housing.rst new file mode 100644 index 0000000..e492b58 --- /dev/null +++ b/docs/source/tutorials/boston_housing.rst @@ -0,0 +1,179 @@ +Boston Housing β€” Regression +=========================== + +The Boston Housing dataset (506 samples, 13 numeric features, one continuous target) +is a standard regression benchmark. The target ``MEDV`` is the median home value. This +tutorial trains a map, checks convergence, and reads the structure through the +U-matrix and hit map, then turns to the regression-specific views: the mean and std +target maps, the score map, and the rank map. + +.. note:: + + Full runnable notebook: + `notebooks/boston_housing.ipynb `_. + The figures below are its outputs. + + +1. Load and standardize the data +-------------------------------- + +The dataset ships in the repo as a CSV. The features are every column except the last; +``MEDV`` is the continuous target. The BMU search compares raw feature distances, so +standardizing the features is essential. + +.. code-block:: python + + import torch + import pandas as pd + from sklearn.preprocessing import StandardScaler + + # boston_housing.csv ships in the repo under data/notebooks/ + df = pd.read_csv("data/notebooks/boston_housing.csv") + + feature_df = df.iloc[:, :-1] # all columns except the last + target_series = df.iloc[:, -1] # last column: MEDV + + features = torch.tensor( + StandardScaler().fit_transform(feature_df), dtype=torch.float32 + ) + targets = torch.tensor(target_series.values, dtype=torch.float32) # continuous + feature_names = list(feature_df.columns) + + +2. Train the SOM +---------------- + +.. code-block:: python + + from torchsom import SOM + + som = SOM( + x=25, + y=15, + num_features=features.shape[1], + epochs=100, + batch_size=16, + sigma=1.45, + learning_rate=0.95, + neighborhood_order=3, + topology="rectangular", + initialization_mode="pca", + random_seed=42, + ) + som.initialize_weights(data=features, mode=som.initialization_mode) + q_errors, t_errors = som.fit(data=features) + + +3. Check convergence +-------------------- + +.. code-block:: python + + from torchsom import SOMVisualizer + + viz = SOMVisualizer(som=som) + viz.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors + ) + +.. image:: /_static/results/boston/rectangular/training_errors.png + :width: 600px + :align: center + :alt: Boston Housing training curve + +Both errors fall and flatten β€” training is long enough. + + +4. Map structure +---------------- + +The U-matrix exposes cluster boundaries; the hit map shows where the data lands. + +.. code-block:: python + + viz.plot_distance_map( + distance_metric=som.distance_fn_name, + neighborhood_order=som.neighborhood_order, + ) + viz.plot_hit_map(data=features) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/boston/rectangular/distance_map.png + :width: 100% + :alt: Boston Housing U-matrix + - .. image:: /_static/results/boston/rectangular/hit_map.png + :width: 100% + :alt: Boston Housing hit map + + +5. Target landscape +------------------- + +Build the BMUβ†’sample map once, then summarize the target over each neuron. The mean +map is a smooth regression surface over the topology: neighboring neurons hold similar +predicted values. The std map flags neurons whose mapped samples disagree on the +target, marking regions where a single prediction is less trustworthy. + +.. code-block:: python + + bmus_map = som.build_map("bmus_data", data=features) + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=targets, + reduction_parameter="mean", + ) + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=targets, + reduction_parameter="std", + ) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/boston/rectangular/mean_target_map.png + :width: 100% + :alt: Boston Housing mean target map + - .. image:: /_static/results/boston/rectangular/std_target_map.png + :width: 100% + :alt: Boston Housing std target map + + +6. Per-neuron reliability +------------------------- + +The score map combines target variance, sample count, and statistical significance into +a single value where lower is better. The rank map orders neurons by std, so rank 1 is +the lowest-std, most reliable neuron. Together they pinpoint which neurons give +trustworthy regression estimates. + +.. code-block:: python + + viz.plot_score_map( + bmus_data_map=bmus_map, + target=targets, + total_samples=features.shape[0], + ) + viz.plot_rank_map(bmus_data_map=bmus_map, target=targets) + +.. image:: /_static/results/boston/rectangular/score_map.png + :width: 600px + :align: center + :alt: Boston Housing score map + +.. image:: /_static/results/boston/rectangular/rank_map.png + :width: 600px + :align: center + :alt: Boston Housing rank map + + +Next steps +---------- + +- :doc:`energy_efficiency` β€” Another regression example +- :doc:`../user_guide/visualization_help` β€” Every plot explained +- :doc:`../user_guide/clustering` β€” Group neurons into clusters diff --git a/docs/source/tutorials/clustering_walkthrough.rst b/docs/source/tutorials/clustering_walkthrough.rst new file mode 100644 index 0000000..c4901ec --- /dev/null +++ b/docs/source/tutorials/clustering_walkthrough.rst @@ -0,0 +1,205 @@ +Clustering β€” Synthetic Blobs +============================ + +Synthetic blobs (300 samples, 4 features, 3 well-separated Gaussian clusters) make the +full clustering workflow easy to follow. This tutorial trains a map, picks the number +of clusters with the elbow and silhouette diagnostics, clusters the neurons, draws the +cluster map, and compares the three algorithms objectively. The integer blob ids serve +as a known ground-truth class for the classification map. + +.. note:: + + Full runnable notebook: + `notebooks/clustering.ipynb `_. + The figures below are its outputs. + + +1. Generate and standardize the data +------------------------------------- + +The BMU search compares raw feature distances, so standardizing is essential. + +.. code-block:: python + + import torch + from sklearn.datasets import make_blobs + from sklearn.preprocessing import StandardScaler + + X, y = make_blobs(n_samples=300, centers=3, n_features=4, random_state=42) + features = torch.tensor( + StandardScaler().fit_transform(X), dtype=torch.float32 + ) + targets = torch.tensor(y, dtype=torch.long) # 0, 1, 2 + + +2. Train the SOM +---------------- + +.. code-block:: python + + from torchsom import SOM + + som = SOM( + x=25, + y=15, + num_features=features.shape[1], + epochs=100, + batch_size=16, + sigma=1.45, + learning_rate=0.95, + neighborhood_order=3, + topology="rectangular", + initialization_mode="pca", + random_seed=42, + ) + som.initialize_weights(data=features, mode=som.initialization_mode) + q_errors, t_errors = som.fit(data=features) + + +3. Check convergence +-------------------- + +.. code-block:: python + + from torchsom import SOMVisualizer + + viz = SOMVisualizer(som=som) + viz.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors + ) + +.. image:: /_static/results/clustering/rectangular/training_errors.png + :width: 600px + :align: center + :alt: Blobs training curve + +Both errors fall and flatten β€” training is long enough. + + +4. Map structure +---------------- + +The U-matrix exposes cluster boundaries; the hit map shows where the data lands. + +.. code-block:: python + + viz.plot_distance_map( + distance_metric=som.distance_fn_name, + neighborhood_order=som.neighborhood_order, + ) + viz.plot_hit_map(data=features) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/clustering/rectangular/distance_map.png + :width: 100% + :alt: Blobs U-matrix + - .. image:: /_static/results/clustering/rectangular/hit_map.png + :width: 100% + :alt: Blobs hit map + +The U-matrix shows three basins of low inter-neuron distance separated by clear ridges, +matching the three Gaussian clusters in the data. + + +5. Ground-truth classes +----------------------- + +Build the BMUβ†’sample map once, then color each neuron by its dominant blob id. + +.. code-block:: python + + bmus_map = som.build_map("bmus_data", data=features) + viz.plot_classification_map( + bmus_data_map=bmus_map, + data=features, + target=targets, + neighborhood_order=som.neighborhood_order, + ) + +.. image:: /_static/results/clustering/rectangular/classification_map.png + :width: 600px + :align: center + :alt: Blobs classification map + +The three blobs occupy distinct, contiguous regions of the grid, confirming the map has +preserved the cluster structure. + + +6. Choose the number of clusters +-------------------------------- + +The elbow plot tracks within-cluster dispersion against ``k``; the bend marks a good +choice. + +.. code-block:: python + + viz.plot_elbow_analysis(max_k=10, feature_space="weights") + +.. image:: /_static/results/clustering/rectangular/elbow_analysis.png + :width: 600px + :align: center + :alt: Blobs elbow analysis + +The curve bends sharply at ``k=3``, agreeing with the three basins seen in the U-matrix. + + +7. Cluster the neurons +---------------------- + +With ``k=3`` chosen, cluster the codebook vectors and draw the result. +:meth:`~torchsom.core.SOM.cluster` returns a dictionary; pass it straight to the +visualizer. The silhouette plot reports how cleanly each neuron sits in its cluster. + +.. code-block:: python + + result = som.cluster(method="kmeans", n_clusters=3, feature_space="weights") + viz.plot_cluster_map(cluster_result=result) + viz.plot_silhouette_analysis(cluster_result=result) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/clustering/rectangular/cluster_map.png + :width: 100% + :alt: Blobs cluster map + - .. image:: /_static/results/clustering/rectangular/silhouette_analysis.png + :width: 100% + :alt: Blobs silhouette analysis + +The cluster map's boundaries follow the U-matrix ridges, so the three neuron groups +line up with the data's natural separation. ``feature_space`` can be ``"weights"``, +``"positions"``, or ``"combined"`` β€” see :doc:`../user_guide/clustering` for the +trade-offs. + + +8. Compare algorithms +--------------------- + +Rather than picking by eye, score K-Means, GMM, and HDBSCAN side by side. ``n_clusters`` +is ignored by HDBSCAN, which finds ``k`` itself. + +.. code-block:: python + + results = [ + som.cluster(method=m, feature_space="weights") + for m in ("kmeans", "gmm", "hdbscan") + ] + viz.plot_cluster_quality_comparison(results_list=results) + +.. image:: /_static/results/clustering/rectangular/clustering_metrics_comparison.png + :width: 600px + :align: center + :alt: Blobs clustering metrics comparison + +The panel scores each method with silhouette, Davies–Bouldin, and Calinski–Harabasz, so +the final choice is driven by metrics rather than appearance. + + +Next steps +---------- + +- :doc:`../user_guide/clustering` β€” The clustering API and feature spaces in full +- :doc:`../user_guide/visualization_help` β€” Every plot explained +- :doc:`iris` β€” A classification tutorial on the Iris dataset diff --git a/docs/source/tutorials/energy_efficiency.rst b/docs/source/tutorials/energy_efficiency.rst new file mode 100644 index 0000000..786ee0e --- /dev/null +++ b/docs/source/tutorials/energy_efficiency.rst @@ -0,0 +1,187 @@ +Energy Efficiency β€” Multi-target Regression +=========================================== + +The Energy Efficiency dataset (768 samples, 8 building-design features, 2 continuous +targets) is a multi-target regression problem: each building has a Heating Load and a +Cooling Load. This tutorial trains one SOM on the 8 features, then projects each target +separately onto that same map to show how a single SOM supports multi-target analysis. + +.. note:: + + Full runnable notebook: + `notebooks/energy_efficiency.ipynb `_. + The figures below are its outputs. + + +1. Load and standardize the data +-------------------------------- + +The first 8 columns are building-design features; the last two columns are the targets +(``Heating Load``, then ``Cooling Load``). The BMU search compares raw feature +distances, so standardizing the features is essential. + +.. code-block:: python + + import torch + import pandas as pd + from sklearn.preprocessing import StandardScaler + + # energy_efficiency.csv ships in the repo + df = pd.read_csv("data/notebooks/energy_efficiency.csv") + + feature_cols = df.columns[:8] + features = torch.tensor( + StandardScaler().fit_transform(df[feature_cols].values), dtype=torch.float32 + ) + heating = torch.tensor(df["Heating Load"].values, dtype=torch.float32) + cooling = torch.tensor(df["Cooling Load"].values, dtype=torch.float32) + + +2. Train the SOM +---------------- + +.. code-block:: python + + from torchsom import SOM + + som = SOM( + x=25, + y=15, + num_features=features.shape[1], + epochs=100, + batch_size=16, + sigma=1.45, + learning_rate=0.95, + neighborhood_order=3, + topology="rectangular", + initialization_mode="pca", + random_seed=42, + ) + som.initialize_weights(data=features, mode=som.initialization_mode) + q_errors, t_errors = som.fit(data=features) + + +3. Check convergence +-------------------- + +.. code-block:: python + + from torchsom import SOMVisualizer + + viz = SOMVisualizer(som=som) + viz.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors + ) + +.. image:: /_static/results/energy/rectangular/training_errors.png + :width: 600px + :align: center + :alt: Energy efficiency training curve + +Both errors fall and flatten β€” training is long enough. + + +4. Inspect the map structure +---------------------------- + +The U-matrix exposes cluster boundaries; the hit map shows where the data lands. + +.. code-block:: python + + viz.plot_distance_map( + distance_metric=som.distance_fn_name, + neighborhood_order=som.neighborhood_order, + ) + viz.plot_hit_map(data=features) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/energy/rectangular/distance_map.png + :width: 100% + :alt: Energy efficiency U-matrix + - .. image:: /_static/results/energy/rectangular/hit_map.png + :width: 100% + :alt: Energy efficiency hit map + + +5. Heating load landscape +------------------------- + +Build the BMUβ†’sample map once, then reuse it for every target. Here it colors each +neuron by the mean heating load of its samples, and by the standard deviation. + +.. code-block:: python + + bmus_map = som.build_map("bmus_data", data=features) + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=heating, + reduction_parameter="mean", + ) + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=heating, + reduction_parameter="std", + ) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/energy/rectangular/heating/mean_target_map.png + :width: 100% + :alt: Heating load mean map + - .. image:: /_static/results/energy/rectangular/heating/std_target_map.png + :width: 100% + :alt: Heating load standard deviation map + +The mean map shows heating load varying smoothly across the grid, so nearby neurons +hold buildings with similar loads. The std map reveals where the target is consistent: +low values mark neurons whose samples share nearly the same heating load. + + +6. Cooling load landscape +------------------------- + +The same ``bmus_map`` is reused β€” no retraining and no rebuild β€” now projecting the +cooling load onto the identical map. + +.. code-block:: python + + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=cooling, + reduction_parameter="mean", + ) + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=cooling, + reduction_parameter="std", + ) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/energy/rectangular/cooling/mean_target_map.png + :width: 100% + :alt: Cooling load mean map + - .. image:: /_static/results/energy/rectangular/cooling/std_target_map.png + :width: 100% + :alt: Cooling load standard deviation map + +Heating and cooling loads produce similar but not identical landscapes on the same map: +the two targets are correlated yet diverge in regions where building geometry affects +them differently. The std map again marks where the cooling load is consistent within +each neuron. + + +Next steps +---------- + +- :doc:`clustering_walkthrough` β€” Group neurons into clusters +- :doc:`boston_housing` β€” Single-target regression on a SOM +- :doc:`../user_guide/visualization_help` β€” Every plot explained diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst new file mode 100644 index 0000000..f615585 --- /dev/null +++ b/docs/source/tutorials/index.rst @@ -0,0 +1,72 @@ +Tutorials +========= + +End-to-end walkthroughs on public datasets. Each tutorial loads and standardizes the +data, trains a SOM, and reads the result through the visualization suite. Every figure +on these pages is an actual output of the matching notebook in the +`notebooks/ `_ directory, so +you can reproduce them end to end. + +.. grid:: 2 + :gutter: 3 + + .. grid-item-card:: Iris β€” Classification + :link: iris + :link-type: doc + + The classic 4-feature, 3-class dataset. Train a SOM and read the classification + map and component planes. + + .. grid-item-card:: Wine β€” Classification + :link: wine + :link-type: doc + + 13 chemical features, 3 cultivars. A higher-dimensional classification example. + + .. grid-item-card:: Boston Housing β€” Regression + :link: boston_housing + :link-type: doc + + Map a continuous target with mean, std, score, and rank maps. + + .. grid-item-card:: Energy Efficiency β€” Multi-target + :link: energy_efficiency + :link-type: doc + + Two regression targets (heating and cooling load) on one map. + + .. grid-item-card:: Clustering β€” Synthetic blobs + :link: clustering_walkthrough + :link-type: doc + + Cluster the neurons and use the elbow, silhouette, and comparison diagnostics. + + +.. list-table:: Datasets at a glance + :header-rows: 1 + :widths: 26 14 14 46 + + * - Tutorial + - Task + - Features + - Visualizations highlighted + * - :doc:`iris` + - Classification + - 4 + - Classification map, component planes + * - :doc:`wine` + - Classification + - 13 + - Classification map, U-matrix + * - :doc:`boston_housing` + - Regression + - 13 + - Mean / std / score / rank maps + * - :doc:`energy_efficiency` + - Regression (Γ—2) + - 8 + - Per-target metric maps + * - :doc:`clustering_walkthrough` + - Clustering + - 4 + - Cluster map, elbow, silhouette, comparison diff --git a/docs/source/tutorials/iris.rst b/docs/source/tutorials/iris.rst new file mode 100644 index 0000000..c2d981b --- /dev/null +++ b/docs/source/tutorials/iris.rst @@ -0,0 +1,172 @@ +Iris β€” Classification +====================== + +The Iris dataset (150 samples, 4 features, 3 species) is the classic first SOM. This +tutorial trains a map, checks convergence, and reads the structure through the +U-matrix, hit map, classification map, and component planes. + +.. note:: + + Full runnable notebook: + `notebooks/iris.ipynb `_. + The figures below are its outputs. + + +1. Load and standardize the data +-------------------------------- + +The BMU search compares raw feature distances, so standardizing is essential. + +.. code-block:: python + + import torch + from sklearn.datasets import load_iris + from sklearn.preprocessing import StandardScaler + + bunch = load_iris() + features = torch.tensor( + StandardScaler().fit_transform(bunch.data), dtype=torch.float32 + ) + targets = torch.tensor(bunch.target, dtype=torch.long) # 0, 1, 2 + feature_names = list(bunch.feature_names) + + +2. Train the SOM +---------------- + +.. code-block:: python + + from torchsom import SOM + + som = SOM( + x=25, + y=15, + num_features=features.shape[1], + epochs=100, + batch_size=16, + sigma=1.45, + learning_rate=0.95, + neighborhood_order=3, + topology="rectangular", + initialization_mode="pca", + random_seed=42, + ) + som.initialize_weights(data=features, mode=som.initialization_mode) + q_errors, t_errors = som.fit(data=features) + + +3. Check convergence +-------------------- + +.. code-block:: python + + from torchsom import SOMVisualizer + + viz = SOMVisualizer(som=som) + viz.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors + ) + +.. image:: /_static/results/iris/rectangular/training_errors.png + :width: 600px + :align: center + :alt: Iris training curve + +Both errors fall and flatten β€” training is long enough. + + +4. Inspect the map structure +---------------------------- + +The U-matrix exposes cluster boundaries; the hit map shows where the data lands. + +.. code-block:: python + + viz.plot_distance_map( + distance_metric=som.distance_fn_name, + neighborhood_order=som.neighborhood_order, + ) + viz.plot_hit_map(data=features) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/iris/rectangular/distance_map.png + :width: 100% + :alt: Iris U-matrix + - .. image:: /_static/results/iris/rectangular/hit_map.png + :width: 100% + :alt: Iris hit map + + +5. Classification map +--------------------- + +Build the BMUβ†’sample map once, then color each neuron by its dominant class. + +.. code-block:: python + + bmus_map = som.build_map("bmus_data", data=features) + viz.plot_classification_map( + bmus_data_map=bmus_map, + data=features, + target=targets, + neighborhood_order=som.neighborhood_order, + ) + +.. image:: /_static/results/iris/rectangular/classification_map.png + :width: 600px + :align: center + :alt: Iris classification map + +*Iris setosa* separates cleanly, while *versicolor* and *virginica* share a boundary β€” +exactly the overlap known in this dataset, recovered here without supervision. + + +6. Component planes +------------------- + +One heat map per feature reveals which features drive the separation. + +.. code-block:: python + + viz.plot_component_planes(component_names=feature_names) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/iris/rectangular/component_planes/Petal_Length.png + :width: 100% + :alt: Petal length component plane + - .. image:: /_static/results/iris/rectangular/component_planes/Petal_Width.png + :width: 100% + :alt: Petal width component plane + * - .. image:: /_static/results/iris/rectangular/component_planes/Sepal_Length.png + :width: 100% + :alt: Sepal length component plane + - .. image:: /_static/results/iris/rectangular/component_planes/Sepal_Width.png + :width: 100% + :alt: Sepal width component plane + +Petal length and width vary together across the grid and align with the class +regions, confirming they are the most discriminative features. + + +Hexagonal variant +----------------- + +Set ``topology="hexagonal"`` for the same analysis on a hexagonal grid; the visualizer +renders hexagon cells automatically: + +.. image:: /_static/results/iris/hexagonal/classification_map.png + :width: 600px + :align: center + :alt: Iris classification map on a hexagonal grid + + +Next steps +---------- + +- :doc:`wine` β€” A higher-dimensional classification example +- :doc:`boston_housing` β€” From classification to regression +- :doc:`../user_guide/visualization_help` β€” Every plot explained diff --git a/docs/source/tutorials/wine.rst b/docs/source/tutorials/wine.rst new file mode 100644 index 0000000..064ddbb --- /dev/null +++ b/docs/source/tutorials/wine.rst @@ -0,0 +1,159 @@ +Wine β€” Classification +====================== + +The Wine dataset (178 samples, 13 chemical features, 3 cultivars) is a +higher-dimensional classification example than iris. This tutorial trains a map, +checks convergence, and reads the structure through the U-matrix, hit map, +classification map, and component planes. + +.. note:: + + Full runnable notebook: + `notebooks/wine.ipynb `_. + The figures below are its outputs. + + +1. Load and standardize the data +-------------------------------- + +The 13 chemical features span very different scales, so standardizing is essential +before the BMU search compares raw feature distances. + +.. code-block:: python + + import torch + from sklearn.datasets import load_wine + from sklearn.preprocessing import StandardScaler + + bunch = load_wine() + features = torch.tensor( + StandardScaler().fit_transform(bunch.data), dtype=torch.float32 + ) + targets = torch.tensor(bunch.target, dtype=torch.long) # 0, 1, 2 + feature_names = list(bunch.feature_names) + + +2. Train the SOM +---------------- + +.. code-block:: python + + from torchsom import SOM + + som = SOM( + x=25, + y=15, + num_features=features.shape[1], + epochs=100, + batch_size=16, + sigma=1.45, + learning_rate=0.95, + neighborhood_order=3, + topology="rectangular", + initialization_mode="pca", + random_seed=42, + ) + som.initialize_weights(data=features, mode=som.initialization_mode) + q_errors, t_errors = som.fit(data=features) + + +3. Check convergence +-------------------- + +.. code-block:: python + + from torchsom import SOMVisualizer + + viz = SOMVisualizer(som=som) + viz.plot_training_errors( + quantization_errors=q_errors, topographic_errors=t_errors + ) + +.. image:: /_static/results/wine/rectangular/training_errors.png + :width: 600px + :align: center + :alt: Wine training curve + +Both errors fall and flatten β€” training is long enough. + + +4. Inspect the map structure +---------------------------- + +The U-matrix exposes cluster boundaries; the hit map shows where the data lands. + +.. code-block:: python + + viz.plot_distance_map( + distance_metric=som.distance_fn_name, + neighborhood_order=som.neighborhood_order, + ) + viz.plot_hit_map(data=features) + +.. list-table:: + :widths: 50 50 + + * - .. image:: /_static/results/wine/rectangular/distance_map.png + :width: 100% + :alt: Wine U-matrix + - .. image:: /_static/results/wine/rectangular/hit_map.png + :width: 100% + :alt: Wine hit map + + +5. Classification map +--------------------- + +Build the BMUβ†’sample map once, then color each neuron by its dominant class. + +.. code-block:: python + + bmus_map = som.build_map("bmus_data", data=features) + viz.plot_classification_map( + bmus_data_map=bmus_map, + data=features, + target=targets, + neighborhood_order=som.neighborhood_order, + ) + +.. image:: /_static/results/wine/rectangular/classification_map.png + :width: 600px + :align: center + :alt: Wine classification map + +The three cultivars occupy distinct regions of the grid. The 13 features give a +clearer 3-class separation than iris, with little overlap between the classes. + + +6. Component planes +------------------- + +One heat map per feature reveals which features drive the separation. With 13 +features there is one plane per feature; see the notebook for the full set. + +.. code-block:: python + + viz.plot_component_planes(component_names=feature_names) + +The planes that vary together across the grid and align with the class regions are +the most discriminative chemical measurements. + + +Hexagonal variant +----------------- + +Set ``topology="hexagonal"`` for the same analysis on a hexagonal grid; the visualizer +renders hexagon cells automatically: + +.. image:: /_static/results/wine/hexagonal/classification_map.png + :width: 600px + :align: center + :alt: Wine classification map on a hexagonal grid + + +Next steps +---------- + +- :doc:`iris` β€” The classic first SOM classification example +- :doc:`boston_housing` β€” From classification to regression +- :doc:`../user_guide/visualization_help` β€” Every plot explained diff --git a/docs/source/user_guide/architecture.rst b/docs/source/user_guide/architecture.rst new file mode 100644 index 0000000..9cafe1f --- /dev/null +++ b/docs/source/user_guide/architecture.rst @@ -0,0 +1,272 @@ +Package Architecture +==================== + +TorchSOM follows a modular design built around three core components that provide +a complete SOM implementation with native PyTorch integration. + + +Module Overview +--------------- + +.. code-block:: text + + torchsom/ + β”œβ”€β”€ core/ # SOM implementations + β”‚ β”œβ”€β”€ base_som.py # Abstract base class (BaseSOM) + β”‚ β”œβ”€β”€ som.py # Classical SOM (batch learning) + β”‚ β”œβ”€β”€ growing/ # Growing SOM variant (WIP) + β”‚ └── hierarchical/ # Hierarchical SOM variant (WIP) + β”œβ”€β”€ utils/ # Training utilities + β”‚ β”œβ”€β”€ distances.py # Distance functions (Euclidean, Cosine, Manhattan, Chebyshev) + β”‚ β”œβ”€β”€ neighborhood.py # Neighborhood kernels (Gaussian, Mexican Hat, Bubble, Triangle) + β”‚ β”œβ”€β”€ decay.py # Schedulers for learning rate and neighborhood width + β”‚ β”œβ”€β”€ initialization.py # Weight initialization (random, PCA) + β”‚ β”œβ”€β”€ grid.py # Grid coordinate generation + β”‚ β”œβ”€β”€ topology.py # Topology utilities + β”‚ β”œβ”€β”€ maps.py # Map computation (hit, distance, metric, score, rank, classification) + β”‚ β”œβ”€β”€ metrics.py # Quality metrics (QE, TE) + β”‚ β”œβ”€β”€ clustering.py # Clustering algorithms (K-Means, GMM, HDBSCAN) + β”‚ └── search.py # BMU search backends (PyTorch, FAISS) + β”œβ”€β”€ visualization/ # Visualization suite + β”‚ β”œβ”€β”€ base.py # SOMVisualizer factory + β”‚ β”œβ”€β”€ base_visualizer.py # Abstract base with shared methods + β”‚ β”œβ”€β”€ rectangular.py # Rectangular topology visualizer + β”‚ β”œβ”€β”€ hexagonal.py # Hexagonal topology visualizer + β”‚ β”œβ”€β”€ clustering.py # Clustering visualization + β”‚ └── config.py # VisualizationConfig + └── configs/ # Configuration management + └── som_config.py # SOMConfig (Pydantic model) + + +Core Module (``torchsom.core``) +------------------------------- + +The core module implements classical SOM algorithms within the PyTorch ecosystem. +The main ``SOM`` class inherits from ``BaseSOM`` and provides: + +- **``fit(data)``** β€” Train the SOM with automatic GPU acceleration and batch processing. + Returns per-epoch quantization and topographic errors for convergence monitoring. + +- **``build_map(map_type, ...)``** β€” Generate various map types for visualization: + ``"hit"``, ``"distance"``, ``"metric"``, ``"score"``, ``"rank"``, ``"classification"``, ``"bmus_data"``. + +- **``cluster(method, ...)``** β€” Cluster SOM neurons using K-Means, GMM, or HDBSCAN + on the weight space, latent space, or both. + +- **``collect_samples(...)``** β€” Identify relevant historical samples for a given query + using topology and latent-space distances, enabling Just-In-Time Learning (JITL) + applications. Three retrieval modes are available through ``retrieval_mode`` + (``"bmu_only"``, ``"bmu_neighborhood"``, ``"bmu_neighborhood_knn"``), with the + neighborhood extent set by ``neighborhood_order``. + +- **``identify_bmus(data)``** β€” Find Best Matching Units for input data using the + configured search backend (PyTorch or FAISS). + +Class Hierarchy +~~~~~~~~~~~~~~~ + +.. code-block:: text + + BaseSOM (abstract) + └── SOM + β”œβ”€β”€ fit() + β”œβ”€β”€ build_map() + β”œβ”€β”€ build_multiple_maps() + β”œβ”€β”€ cluster() + β”œβ”€β”€ collect_samples() + β”œβ”€β”€ identify_bmus() + β”œβ”€β”€ quantization_error() + └── topographic_error() + +``BaseSOM`` defines the interface and common attributes (grid dimensions, topology, +device placement). ``SOM`` implements the full training loop with batch learning, +where each epoch shuffles the data, processes it in batches, and updates weights +using the neighborhood-weighted update rule. + +.. note:: + + The current release ships the classical ``SOM`` with rectangular and hexagonal + topologies, each optionally wrapped into a torus via :ref:`periodic boundary + conditions `. Growing and Hierarchical SOM variants are on the + roadmap (see the paper's Conclusion) and are not part of the public API yet. + + +Utilities Module (``torchsom.utils``) +------------------------------------- + +This module provides essential components for SOM parameterization and training. + +Distance Functions +~~~~~~~~~~~~~~~~~~ + +Four distance metrics are available for BMU selection in feature space: + +- **Euclidean** (default): :math:`\delta(\mathbf{x}, \mathbf{w}) \coloneqq \sqrt{\sum_{a=1}^{d} (x_a - w_a)^2}` +- **Manhattan**: :math:`\delta(\mathbf{x}, \mathbf{w}) \coloneqq \sum_{a=1}^{d} |x_a - w_a|` +- **Cosine**: :math:`\delta(\mathbf{x}, \mathbf{w}) \coloneqq 1 - \frac{\mathbf{x} \cdot \mathbf{w}}{\lVert \mathbf{x} \rVert \lVert \mathbf{w} \rVert}` +- **Chebyshev**: :math:`\delta(\mathbf{x}, \mathbf{w}) \coloneqq \max_{a \le d} |x_a - w_a|` + +Neighborhood Functions +~~~~~~~~~~~~~~~~~~~~~~ + +Four neighborhood kernels control the spatial extent of weight updates around the BMU: + +- **Gaussian** (default): Smooth, continuous influence decay +- **Mexican Hat** (Ricker wavelet): Excitatory center with inhibitory surround +- **Bubble** (Step): Binary on/off within a fixed radius +- **Triangle** (Linear): Linear decay from BMU to radius boundary + +Decay Schedulers +~~~~~~~~~~~~~~~~ + +Learning rate (:math:`\alpha`) and neighborhood width (:math:`\sigma`) decay over training: + +- **Asymptotic decay** (default): :math:`\theta(t+1) \coloneqq \frac{\theta(t)}{1 + t / (T/2)}` +- **Inverse decay**: Gradual asymptotic reduction to 0 (for :math:`\alpha`) or 1 (for :math:`\sigma`) +- **Linear decay**: Uniform reduction to 0 (for :math:`\alpha`) or 1 (for :math:`\sigma`) + + +Grid Topologies +~~~~~~~~~~~~~~~ + +Maps use either a ``"rectangular"`` or ``"hexagonal"`` grid. Setting ``pbc=True`` wraps the +grid into a torus (periodic boundary conditions): grid-distance calculations then use the +minimum-image convention, so neighborhoods wrap across opposite edges and corner neurons are +no longer disadvantaged by the map boundary. + +BMU Search Backends +~~~~~~~~~~~~~~~~~~~ + +The backend is chosen with the ``search_backend`` argument (``"auto"``, ``"torch"``, or +``"faiss"``); ``"auto"`` uses FAISS when it is installed and falls back to PyTorch otherwise. + +- **PyTorch** (default): Full pairwise distance computation on GPU/CPU +- **FAISS** (optional): Approximate nearest-neighbor search for large maps, + enabled with ``uv add torchsom[faiss]`` + + +Visualization Module (``torchsom.visualization``) +-------------------------------------------------- + +The visualization module provides seven visualization types for both rectangular +and hexagonal topologies: + +.. list-table:: + :header-rows: 1 + :widths: 32 15 53 + + * - Visualization + - Setting + - Purpose + * - U-matrix (distance map) + - Unsupervised + - Inter-neuron distances and cluster boundaries + * - Hit map + - Unsupervised + - BMU activation frequency and data density + * - Component planes + - Unsupervised + - Per-feature weight distribution across the grid + * - Classification & metric maps + - Supervised + - Dominant class, or mean/std of a target, per neuron + * - Score & rank maps + - Supervised + - Per-neuron reliability for regression + * - Training curve + - Unsupervised + - QE and TE convergence during training + * - Clustering maps + - Unsupervised + - Cluster assignment plus elbow, silhouette, and comparison diagnostics + +The ``SOMVisualizer`` class acts as a factory that delegates to topology-specific +implementations (``RectangularVisualizer`` or ``HexagonalVisualizer``), and routes +clustering plots to a ``ClusteringVisualizer``. See the :doc:`visualization_help` +gallery for every plot with example figures. + +.. code-block:: text + + SOMVisualizer (factory) + β”œβ”€β”€ delegates to ──► RectangularVisualizer + β”‚ └── inherits BaseVisualizer + └── delegates to ──► HexagonalVisualizer + └── inherits BaseVisualizer + + +Training Data Flow +------------------ + +The end-to-end workflow for training and analyzing a SOM: + +.. code-block:: text + + Input Data (N x k) + β”‚ + β–Ό + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Initialization β”‚ PCA or random sampling from data + β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Training Loop β”‚ For each epoch: + β”‚ β”‚ 1. Shuffle data + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ 2. For each batch: + β”‚ β”‚ BMU Search β”‚ β”‚ - Compute distances (feature space) + β”‚ β”‚ (PyTorch / β”‚ β”‚ - Find BMU per sample + β”‚ β”‚ FAISS) β”‚ β”‚ + β”‚ β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜ β”‚ + β”‚ β–Ό β”‚ 3. Compute neighborhood influence (grid space) + β”‚ β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” β”‚ 4. Update weights: + β”‚ β”‚ Weight β”‚ β”‚ w(t+1) = w(t) + Ξ±(t) Β· h(t) Β· (x - w(t)) + β”‚ β”‚ Update β”‚ β”‚ 5. Decay Ξ±(t) and Οƒ(t) + β”‚ β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ β”‚ 6. Compute QE and TE + β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Analysis β”‚ build_map(), cluster(), collect_samples() + β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β–Ό + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Visualization β”‚ SOMVisualizer.plot_*() + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + + +Configuration +------------- + +SOM parameters are managed through ``SOMConfig``, a Pydantic model that validates +all inputs at construction time: + +.. code-block:: python + + from torchsom.configs import SOMConfig + + config = SOMConfig( + x=25, y=15, + topology="hexagonal", + epochs=100, + learning_rate=0.95, + sigma=1.75, + neighborhood_function="gaussian", + distance_function="euclidean", + initialization_mode="pca", + ) + +The config can be serialized to/from YAML or JSON for reproducible experiments. +See the :doc:`../api/configs` for full parameter documentation. + + +Next steps +---------- + +- :doc:`topologies` β€” Choosing a grid topology and using periodic boundary conditions +- :doc:`training` β€” Initialization, decay schedules, and BMU search backends +- :doc:`clustering` β€” Clustering SOM neurons and reading the diagnostics +- :doc:`jitl` β€” Retrieving relevant samples for just-in-time learning +- :doc:`visualization_help` β€” Visualization gallery +- :doc:`../getting_started/basic_concepts` β€” Mathematical foundations +- :doc:`../api/core` β€” Full API reference diff --git a/docs/source/user_guide/benchmarks.rst b/docs/source/user_guide/benchmarks.rst new file mode 100644 index 0000000..667e300 --- /dev/null +++ b/docs/source/user_guide/benchmarks.rst @@ -0,0 +1,265 @@ +Benchmarks +========== + +TorchSOM's computational performance and fidelity are evaluated against +`MiniSom `_, the most widely adopted +and actively maintained SOM library. These benchmarks are from +`the paper `_ (Section 4, Appendix D). + + +Methodology +----------- + +Synthetic datasets are generated using scikit-learn's ``make_blobs()``, varying +both sample size and feature dimensionality. Both implementations use identical +hyperparameters: + +- **Grid size**: 25 x 15 (rectangular) +- **Initialization**: PCA +- **Training iterations**: 100 epochs +- **Neighborhood function**: Gaussian +- **Distance function**: Euclidean +- **Seed**: Fixed for reproducibility + +Hardware: + +- **CPU**: Intel Xeon Platinum 8370C (Ice Lake) @ 3.4 GHz, 8 cores, 16 GB RAM +- **GPU**: NVIDIA Tesla T4, 2560 CUDA cores, 16 GB RAM + +Each configuration is averaged over **10 independent runs**. + + +Small Rectangular Map (25 x 15) +------------------------------- + +.. list-table:: + :header-rows: 1 + :widths: 8 8 10 10 10 10 10 10 10 10 10 + + * - Samples + - Features + - QE (MiniSom) + - TE% (MiniSom) + - Time (MiniSom) + - QE (CPU) + - TE% (CPU) + - Time (CPU) + - QE (GPU) + - TE% (GPU) + - Time (GPU) + * - 240 + - 4 + - 0.17 + - 26 + - 1.82 s + - 0.24 + - 12 + - 0.41 s + - 0.24 + - 10 + - 0.24 s + * - 240 + - 50 + - 1.79 + - 49 + - 3.84 s + - 1.79 + - 12 + - 0.50 s + - 1.83 + - 15 + - 0.25 s + * - 240 + - 300 + - 5.43 + - 71 + - 15.4 s + - 5.21 + - 47 + - 0.72 s + - 5.21 + - 30 + - 0.28 s + * - 4,000 + - 4 + - 0.16 + - 31 + - 54.6 s + - 0.23 + - 6 + - 4.87 s + - 0.23 + - 7 + - 2.61 s + * - 4,000 + - 50 + - 1.66 + - 56 + - 89.2 s + - 1.77 + - 14 + - 5.33 s + - 1.74 + - 16 + - 2.62 s + * - 4,000 + - 300 + - 5.14 + - 74 + - 261 s + - 5.13 + - 19 + - 8.32 s + - 5.13 + - 20 + - 2.77 s + * - 16,000 + - 4 + - 0.15 + - 32 + - 1,054 s + - 0.23 + - 6 + - 20.2 s + - 0.23 + - 6 + - 10.9 s + * - 16,000 + - 50 + - 1.64 + - 57 + - 1,220 s + - 1.75 + - 13 + - 21.9 s + - 1.75 + - 14 + - 10.8 s + * - 16,000 + - 300 + - 5.15 + - 75 + - 1,939 s + - 5.14 + - 18 + - 30.4 s + - 5.14 + - 17 + - 11.6 s + +*QE = Quantization Error (lower is better). TE% = Topographic Error as percentage (lower is better).* + + +Key Findings +------------ + +Training Speed +~~~~~~~~~~~~~~ + +TorchSOM trains substantially faster than MiniSom: + +- **CPU**: 77--98% faster across all configurations +- **GPU**: Up to 99% faster for large, high-dimensional datasets +- The 16,000-sample / 300-feature case: MiniSom takes **32 minutes**, + torchsom (GPU) takes **12 seconds** β€” a **167x speedup** + +These improvements hold even though torchsom computes both QE and TE at every +epoch (an :math:`\mathcal{O}(2 \times \text{batch} \times \text{epochs})` +overhead that MiniSom does not incur). + +Topographic Error +~~~~~~~~~~~~~~~~~ + +TorchSOM consistently produces maps with **34--81% lower Topographic Error**, +indicating significantly better topology preservation. This means the spatial +relationships in the input data are more faithfully represented on the SOM grid. + +Quantization Error +~~~~~~~~~~~~~~~~~~ + +Both libraries achieve comparable Quantization Error across all configurations, +confirming that torchsom's batch learning approach maintains representation +fidelity equivalent to MiniSom's online learning. + + +Large Map Results (90 x 70) +---------------------------- + +For large maps, torchsom is the only viable option. MiniSom fails to complete +within reasonable time for the largest configurations: + +.. list-table:: + :header-rows: 1 + :widths: 10 10 15 15 15 15 15 15 + + * - Samples + - Features + - QE (MiniSom) + - Time (MiniSom) + - QE (CPU) + - Time (CPU) + - QE (GPU) + - Time (GPU) + * - 240 + - 300 + - 5.45 + - 364 s + - 5.17 + - 7.07 s + - 5.18 + - 0.40 s + * - 4,000 + - 300 + - 5.0 + - 6,149 s + - 5.11 + - 77.8 s + - 5.10 + - 4.57 s + * - 16,000 + - 300 + - N/A + - N/A + - 5.11 + - 321 s + - 5.12 + - 19.5 s + +*MiniSom is unable to process the 16,000 x 300 configuration on a 90 x 70 grid +within a reasonable time.* + + +Interpretation +-------------- + +TorchSOM's advantages stem from two design decisions: + +1. **Batch learning**: Instead of updating weights sample-by-sample (online), + torchsom processes entire batches, enabling vectorized operations and + efficient GPU utilization. + +2. **PyTorch backend**: Leverages optimized BLAS routines and CUDA kernels for + distance computation, BMU search, and weight updates, all operating on + contiguous tensor memory. + +Together, these enable torchsom to scale to datasets and map sizes that are +impractical with existing libraries, while maintaining or improving map quality. + + +Reproducibility +--------------- + +All benchmark scripts and configuration files live in the +`benchmark/ `_ directory. +The exact paper states are pinned by two Git tags β€” ``jmlr-submission-v1`` (original +submission) and ``jmlr-revision-v1`` (this revision) β€” and the comparison uses +MiniSom v2.3.5 (commit ``65b6ba6``). + + +Further reading +--------------- + +- :doc:`comparison` β€” Feature-by-feature comparison with other SOM libraries +- Full benchmark tables (including hexagonal maps): `arXiv paper, Appendix D `_ +- :doc:`architecture` β€” Package design and module overview +- :doc:`../getting_started/basic_concepts` β€” SOM mathematical foundations diff --git a/docs/source/user_guide/clustering.rst b/docs/source/user_guide/clustering.rst new file mode 100644 index 0000000..d9d77ef --- /dev/null +++ b/docs/source/user_guide/clustering.rst @@ -0,0 +1,152 @@ +Clustering +========== + +A trained SOM already organizes data into a topology-preserving grid. Clustering goes +one step further and groups the *neurons* themselves into a small number of regions, +turning the map into an explicit segmentation. TorchSOM exposes this through a single +method, :meth:`~torchsom.core.SOM.cluster`, with three algorithms and built-in +diagnostics for choosing among them. + + +The ``cluster`` method +---------------------- + +.. code-block:: python + + result = som.cluster( + method="kmeans", # "kmeans", "gmm", or "hdbscan" + n_clusters=4, # ignored by HDBSCAN, which finds k itself + feature_space="weights", # "weights", "positions", or "combined" + ) + +It returns a dictionary describing the clustering, including ``labels`` (one cluster +id per neuron), ``method``, ``n_clusters``, ``feature_space``, and a ``metrics`` block +(silhouette, Davies–Bouldin, Calinski–Harabasz). Pass the whole dictionary to the +visualizer to draw it. + +Choosing the feature space +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. list-table:: + :header-rows: 1 + :widths: 22 78 + + * - ``feature_space`` + - Clusters neurons by… + * - ``"weights"`` + - their codebook vectors β€” groups neurons that encode similar inputs. The usual choice. + * - ``"positions"`` + - their grid coordinates β€” groups neurons that are spatially close. + * - ``"combined"`` + - both, balancing feature similarity with spatial contiguity. + + +Choosing an algorithm +--------------------- + +.. list-table:: + :header-rows: 1 + :widths: 16 18 66 + + * - Method + - Needs ``n_clusters``? + - Best for + * - ``"kmeans"`` + - Yes + - Compact, roughly spherical clusters; fast baseline. + * - ``"gmm"`` + - Yes + - Elliptical clusters and soft assignments. + * - ``"hdbscan"`` + - No + - Arbitrary shapes and noise; density-based, finds ``k`` automatically. + +HDBSCAN labels low-density neurons as noise (cluster id ``-1``), which the cluster map +renders as an "Uncertain" category. + + +Choosing the number of clusters +------------------------------- + +For K-Means and GMM, use the elbow and silhouette diagnostics rather than guessing. + +.. code-block:: python + + from torchsom import SOMVisualizer + + viz = SOMVisualizer(som=som) + + # Elbow: within-cluster dispersion vs k; look for the bend + viz.plot_elbow_analysis(max_k=10, feature_space="weights") + + # Silhouette: how cleanly points sit in their cluster (higher is better) + result = som.cluster(method="kmeans", n_clusters=4, feature_space="weights") + viz.plot_silhouette_analysis(cluster_result=result) + + +Comparing algorithms objectively +-------------------------------- + +Instead of picking by eye, score several configurations side by side: + +.. code-block:: python + + results = [ + som.cluster(method=m, feature_space="weights") + for m in ("kmeans", "gmm", "hdbscan") + ] + viz.plot_cluster_quality_comparison(results_list=results) + +The comparison reports silhouette, Davies–Bouldin, and Calinski–Harabasz scores for +each method, so the final choice is driven by metrics. + + +Visualizing the result +---------------------- + +.. code-block:: python + + result = som.cluster(method="hdbscan", feature_space="weights") + viz.plot_cluster_map(cluster_result=result) + +.. image:: /_static/results/clustering/rectangular/cluster_map.png + :width: 600px + :align: center + :alt: Cluster assignment overlaid on the SOM grid + +Read the cluster map together with the :doc:`U-matrix `: cluster +boundaries should fall along the U-matrix ridges (regions of large inter-neuron +distance). + + +End-to-end example +------------------ + +.. code-block:: python + + import torch + from sklearn.datasets import make_blobs + from sklearn.preprocessing import StandardScaler + + from torchsom import SOM, SOMVisualizer + + X, _ = make_blobs(n_samples=1000, centers=4, n_features=5, random_state=42) + data = torch.tensor(StandardScaler().fit_transform(X), dtype=torch.float32) + + som = SOM(x=25, y=15, num_features=5, epochs=100, batch_size=16, + topology="hexagonal", initialization_mode="pca", random_seed=42) + som.initialize_weights(data=data, mode=som.initialization_mode) + som.fit(data=data) + + viz = SOMVisualizer(som=som) + viz.plot_elbow_analysis(max_k=10, feature_space="weights") # pick k + result = som.cluster(method="kmeans", n_clusters=4, feature_space="weights") + viz.plot_cluster_map(cluster_result=result) + + +Next steps +---------- + +- :doc:`../tutorials/clustering_walkthrough` β€” A full clustering tutorial on synthetic blobs +- :doc:`visualization_help` β€” All clustering diagnostics with example figures +- :doc:`../api/utils` β€” Clustering and metric utilities diff --git a/docs/source/user_guide/comparison.rst b/docs/source/user_guide/comparison.rst new file mode 100644 index 0000000..ba846c7 --- /dev/null +++ b/docs/source/user_guide/comparison.rst @@ -0,0 +1,154 @@ +Comparison with Other Libraries +=============================== + +Several Python libraries implement Self-Organizing Maps. They differ in technical +architecture, maintenance, and built-in capabilities. The table below reproduces the +comparison from the paper (Table 1); it is the basis for TorchSOM's positioning. + +Libraries compared: +`TorchSOM `_, +`MiniSom `_, +`SimpSOM `_, +`SOMPY `_, +`somoclu `_, and +`som-pbc `_. + + +Feature matrix +-------------- + +.. list-table:: + :header-rows: 1 + :stub-columns: 1 + :widths: 22 14 12 12 12 12 12 + + * - + - TorchSOM + - MiniSom + - SimpSOM + - SOMPY + - somoclu + - som-pbc + * - **Framework** + - PyTorch + - NumPy + - NumPy + - NumPy + - C++ + - NumPy + * - **GPU acceleration** + - CUDA (PyTorch) + - βœ— + - CuPy / CUML + - βœ— + - CUDA C++ + - βœ— + * - **API design** + - scikit-learn + - Custom + - Custom + - MATLAB + - Custom + - Custom + * - **Maintenance** + - Active + - Active + - Minimal + - Minimal + - Minimal + - βœ— + * - **Documentation** + - Rich + - Basic [#minisom-docs]_ + - Basic + - βœ— + - Basic + - Basic + * - **Test coverage** + - 90% + - 98% + - 53% + - βœ— + - Minimal + - βœ— + * - **PyPI distribution** + - βœ“ + - βœ“ + - βœ“ + - βœ— + - βœ“ + - βœ— + * - **Visualization** + - Advanced + - βœ— + - Moderate + - Moderate + - Basic + - Basic + * - **Clustering (built-in)** + - βœ“ + - Examples only [#minisom-clust]_ + - βœ“ + - βœ— + - βœ— + - βœ— + * - **JITL support** + - βœ“ + - βœ— + - βœ— + - βœ— + - βœ— + - βœ— + * - **SOM variants** + - Multiple [#variants]_ + - βœ— + - PBC + - βœ— + - PBC + - PBC + * - **Extensibility** + - High + - Moderate + - Low + - Low + - Low + - Low + +.. [#minisom-docs] MiniSom ships example notebooks and partial in-code docstrings, + but no narrative documentation site comparable to this one. +.. [#minisom-clust] Clustering is not a built-in MiniSom feature; it requires + user-supplied code on top of MiniSom primitives. TorchSOM provides + :meth:`~torchsom.core.SOM.cluster` directly. +.. [#variants] In the current release, "Multiple" means rectangular and hexagonal + topologies, each optionally toroidal via periodic boundary conditions. Growing and + Hierarchical variants are on the roadmap (see the paper's Conclusion). + + +Where TorchSOM fits +------------------- + +Existing libraries each address a specific niche: MiniSom is a minimalist, +NumPy-based implementation well suited to teaching and prototyping, while somoclu +targets HPC environments through CUDA C++. TorchSOM is the only library in this +comparison that combines, in a single modular codebase: + +- a native PyTorch backend with GPU acceleration, +- a scikit-learn-compatible API, +- an advanced built-in visualization suite, +- a built-in clustering interface, +- just-in-time-learning support, and +- multiple grid topologies with configurable neighborhood retrieval modes. + +It is further supported by this narrative documentation site and a community-oriented +development process, making it a complete and scalable reference for both research and +production. The performance side of this comparison β€” quantization-error parity with +substantially lower topographic error and training time β€” is documented in +:doc:`benchmarks`. + + +Next steps +---------- + +- :doc:`benchmarks` β€” Quantitative speed and quality comparison with MiniSom +- :doc:`architecture` β€” How TorchSOM is organized +- :doc:`../getting_started/quickstart` β€” Try it yourself diff --git a/docs/source/user_guide/jitl.rst b/docs/source/user_guide/jitl.rst new file mode 100644 index 0000000..635a46f --- /dev/null +++ b/docs/source/user_guide/jitl.rst @@ -0,0 +1,127 @@ +Just-in-Time Learning +===================== + +Just-in-time learning (JITL) builds a small, local model on demand: for each new +query, it retrieves the most relevant historical samples and trains (or predicts) on +just those. A SOM is a natural retrieval index for this β€” its topology already places +similar samples on neighboring neurons. TorchSOM exposes this through +:meth:`~torchsom.core.SOM.collect_samples`, which gathers the historical samples that +land on the query's BMU and its neighborhood. + +This pattern is common in industrial soft sensing and adaptive monitoring, where the +process drifts and a single global model goes stale. + + +The retrieval index +------------------- + +``collect_samples`` needs a BMUβ†’sample-index map of the historical data β€” the same +``"bmus_data"`` map used by the visualizations. Build it once after training: + +.. code-block:: python + + som.initialize_weights(data=historical_samples, mode="pca") + som.fit(data=historical_samples) + + bmus_idx_map = som.build_map("bmus_data", data=historical_samples) + +``bmus_idx_map`` maps each grid cell ``(i, j)`` to the list of historical-sample +indices whose BMU is that cell. + + +Retrieving samples for a query +------------------------------ + +.. code-block:: python + + data_buffer, output_buffer = som.collect_samples( + query_sample=query, # tensor [num_features] + historical_samples=historical_samples, # [N, num_features] + historical_outputs=historical_outputs, # [N] + bmus_idx_map=bmus_idx_map, + retrieval_mode="bmu_neighborhood_knn", + min_buffer_threshold=50, + ) + +It returns the matched inputs and their outputs (``data_buffer``, ``output_buffer``), +ready to fit a local regressor. Pass ``return_indices=True`` to also get the indices +of the retrieved samples. + + +Retrieval modes +--------------- + +The ``retrieval_mode`` argument trades recall against locality. All modes start from +the query's BMU cell; they differ in how far they expand: + +.. list-table:: + :header-rows: 1 + :widths: 26 16 58 + + * - ``retrieval_mode`` + - Expands? + - Strategy + * - ``"bmu_only"`` + - No + - Only samples mapped to the query's BMU cell. Tightest, smallest buffer. + * - ``"bmu_neighborhood"`` + - Topological + - BMU plus its grid neighbors up to ``neighborhood_order`` hops. No fallback. + * - ``"bmu_neighborhood_knn"`` *(default)* + - Topological + KNN + - Same as above, then a nearest-neighbor fallback in weight space when the + buffer is still below ``min_buffer_threshold``. + +The KNN fallback in the default mode guarantees a usable buffer size even in sparse +regions of the map: if the BMU and its neighbors hold too few samples, the nearest +remaining neurons (by codebook distance) are pulled in until +``min_buffer_threshold`` is exceeded. The neighborhood extent is the SOM's +``neighborhood_order``, and under :ref:`periodic boundary conditions ` +the neighborhood wraps across edges. + + +Typical workflow +---------------- + +.. code-block:: python + + from sklearn.linear_model import LinearRegression + + # 1. Train the SOM once on the historical buffer and index it + som.initialize_weights(data=historical_samples, mode="pca") + som.fit(data=historical_samples) + bmus_idx_map = som.build_map("bmus_data", data=historical_samples) + + # 2. For each incoming query, retrieve a local set and fit a local model + for query in stream: + X_local, y_local = som.collect_samples( + query_sample=query, + historical_samples=historical_samples, + historical_outputs=historical_outputs, + bmus_idx_map=bmus_idx_map, + retrieval_mode="bmu_neighborhood_knn", + ) + local_model = LinearRegression().fit( + X_local.cpu().numpy(), y_local.cpu().numpy() + ) + prediction = local_model.predict(query.reshape(1, -1).cpu().numpy()) + + +Choosing a mode +--------------- + +- Start with ``"bmu_neighborhood_knn"`` (the default) β€” it adapts the buffer size to + local data density. +- Use ``"bmu_neighborhood"`` when you want strictly local samples and accept a + variable, possibly small, buffer. +- Use ``"bmu_only"`` for the most local model, or to inspect exactly which samples a + single neuron represents. +- Tune ``min_buffer_threshold`` to the minimum sample count your local model needs. + + +Next steps +---------- + +- :doc:`topologies` β€” How ``neighborhood_order`` and PBC shape retrieval +- :doc:`../getting_started/basic_concepts` β€” The latent representation behind retrieval +- :doc:`../api/core` β€” ``collect_samples`` and ``build_map`` reference diff --git a/docs/source/user_guide/topologies.rst b/docs/source/user_guide/topologies.rst new file mode 100644 index 0000000..cbab720 --- /dev/null +++ b/docs/source/user_guide/topologies.rst @@ -0,0 +1,111 @@ +Topologies & Boundary Conditions +================================ + +The grid topology fixes how neurons are arranged and, therefore, which neurons count +as neighbors during training. TorchSOM supports two topologies β€” ``"rectangular"`` +and ``"hexagonal"`` β€” and an optional periodic (toroidal) wrap for either. This guide +covers when to pick each and how to enable them. For the underlying geometry and the +grid-distance definitions, see :ref:`grid_topology_section` in +:doc:`../getting_started/basic_concepts`. + + +Rectangular vs hexagonal +------------------------ + +.. image:: /_static/som/topologies.png + :width: 600px + :align: center + :alt: Rectangular and hexagonal neighborhoods at increasing order + +.. list-table:: + :header-rows: 1 + :widths: 22 39 39 + + * - + - Rectangular + - Hexagonal + * - Neighbors + - 8 (Chebyshev block) + - 6 (hop-distance rings) + * - Order-:math:`o` cell count + - :math:`(2o+1)^2` + - Hexagonal ring of radius :math:`o` + * - Reading the map + - Most intuitive; axis-aligned + - Uniform neighbor distances + * - Typical use + - General-purpose default + - Lower topographic error; preferred for finer analysis + +Both topologies expose the same API; only the ``topology`` argument changes: + +.. code-block:: python + + from torchsom import SOM + + rect = SOM(x=25, y=15, num_features=4, topology="rectangular") + hexg = SOM(x=25, y=15, num_features=4, topology="hexagonal") + +The :doc:`visualization_help` gallery renders square cells for rectangular maps and +hexagon cells for hexagonal maps automatically β€” your plotting code does not change. + +.. tip:: + + If you are unsure, start rectangular for a quick, readable first pass, then switch + to hexagonal when you want the lowest topographic error for a final map. + + +.. _topologies-pbc: + +Periodic boundary conditions (toroidal maps) +-------------------------------------------- + +By default the grid has edges, so corner and border neurons have fewer neighbors and +tend to be under-used. Setting ``pbc=True`` wraps opposite edges together, turning the +grid into a torus. Grid distances then use the *minimum-image convention*, so +neighborhoods wrap across boundaries and no neuron is disadvantaged by its position. + +.. code-block:: python + + from torchsom import SOM + + som = SOM( + x=25, + y=15, + num_features=4, + topology="hexagonal", + pbc=True, # wrap the lattice into a torus + ) + +When to enable PBC: + +- The input space has **no natural boundary** β€” cyclic or angular features + (hour-of-day, wind direction, phase). +- You want **uniform neuron utilization** and no edge artifacts on the U-matrix. + +When to leave it off (the default): + +- The data has genuine extremes you *want* pushed to the map borders. +- You need the most directly interpretable 2-D layout. + +PBC works with both topologies and changes only the grid-distance computation; every +other part of the API (training, visualization, clustering, JITL) is unaffected. + + +Effect on neighborhoods and JITL +-------------------------------- + +The neighborhood order :math:`o` (the ``neighborhood_order`` argument) sets how far the +discrete neighborhood extends β€” a :math:`(2o+1)\times(2o+1)` block on a rectangular +grid, or hop-distance rings on a hexagonal grid. The same order governs the +neighborhood used by :doc:`jitl` sample retrieval. Under PBC these neighborhoods wrap +across edges, which matters when you rely on ``collect_samples`` near a boundary. + + +Next steps +---------- + +- :doc:`training` β€” Decay schedules, initialization, and BMU search backends +- :doc:`visualization_help` β€” See both topologies rendered +- :ref:`grid_topology_section` β€” The grid-distance math behind PBC +- :doc:`../api/core` β€” ``SOM`` constructor reference diff --git a/docs/source/user_guide/training.rst b/docs/source/user_guide/training.rst new file mode 100644 index 0000000..3a69cc4 --- /dev/null +++ b/docs/source/user_guide/training.rst @@ -0,0 +1,195 @@ +Training +======== + +This guide covers how to configure and monitor SOM training: initialization, the +decay schedules for the learning rate and neighborhood width, the key +hyperparameters, and the BMU search backend. The update rule itself is derived in +:doc:`../getting_started/basic_concepts`. + + +The training loop in one call +------------------------------ + +Training is two steps β€” initialize the weights, then fit: + +.. code-block:: python + + import torch + from torchsom import SOM + + data = torch.randn(2000, 8) + + som = SOM(x=25, y=15, num_features=8, epochs=100, batch_size=16) + som.initialize_weights(data=data, mode="pca") + q_errors, t_errors = som.fit(data=data) + +``fit`` shuffles the data each epoch, processes it in batches, applies the +neighborhood-weighted update, decays the learning rate and neighborhood width, and +records the quantization error (QE) and topographic error (TE) per epoch. The two +returned lists are your convergence trace. + + +Initialization +-------------- + +``initialize_weights`` seeds the codebook before training: + +.. list-table:: + :header-rows: 1 + :widths: 20 80 + + * - Mode + - Behavior + * - ``"pca"`` + - Spread weights along the first two principal components of the data. Faster, + more reproducible convergence β€” the recommended default. + * - ``"random"`` + - Sample initial weights randomly from the data range. + +.. code-block:: python + + som.initialize_weights(data=data, mode="pca") # or "random" + +Initialization quality strongly affects the final map; PCA initialization usually +reaches a lower QE/TE in fewer epochs. + + +Decay schedules +--------------- + +The learning rate :math:`\alpha(t)` and neighborhood width :math:`\sigma(t)` shrink +over training so that updates start broad (global ordering) and end local (fine +tuning). Pick a schedule per parameter: + +.. list-table:: + :header-rows: 1 + :widths: 22 38 40 + + * - Schedule + - Learning rate (``lr_decay_function``) + - Neighborhood width (``sigma_decay_function``) + * - Asymptotic *(default)* + - ``"asymptotic_decay"`` + - ``"asymptotic_decay"`` + * - Inverse + - ``"lr_inverse_decay_to_zero"`` + - ``"sig_inverse_decay_to_one"`` + * - Linear + - ``"lr_linear_decay_to_zero"`` + - ``"sig_linear_decay_to_one"`` + +The inverse and linear schedules guarantee :math:`\alpha(T) \to 0` and +:math:`\sigma(T) \to 1` by the final epoch β€” zero global drift and single-neuron +updates at the end, which is what gives the map its fine local structure. The exact +formulas are in :doc:`../getting_started/basic_concepts`. + +.. code-block:: python + + som = SOM( + x=25, y=15, num_features=8, + learning_rate=0.95, # initial alpha + sigma=1.75, # initial neighborhood width + lr_decay_function="lr_linear_decay_to_zero", + sigma_decay_function="sig_inverse_decay_to_one", + ) + + +Key hyperparameters +------------------- + +.. list-table:: + :header-rows: 1 + :widths: 24 12 64 + + * - Parameter + - Default + - Guidance + * - ``epochs`` + - 10 + - Full passes over the data. Increase until QE/TE flatten. + * - ``batch_size`` + - 5 + - Larger batches use the GPU more efficiently; raise it for big data. + * - ``learning_rate`` + - 0.5 + - Initial step size, typically 0.1–1.0. + * - ``sigma`` + - 1.0 + - Initial neighborhood radius. Scale with the grid size. + * - ``neighborhood_order`` + - 1 + - Discrete neighborhood extent; also used by :doc:`jitl` retrieval. + * - ``neighborhood_function`` + - ``"gaussian"`` + - Also ``"mexican_hat"``, ``"bubble"``, ``"triangle"``. + * - ``distance_function`` + - ``"euclidean"`` + - Also ``"cosine"``, ``"manhattan"``, ``"chebyshev"``. + * - ``random_seed`` + - 42 + - Fix for reproducible runs. + +.. tip:: + + Always standardize features before training (e.g. scikit-learn's + ``StandardScaler``). The BMU search compares raw feature distances, so + unscaled features let large-magnitude columns dominate. + + +BMU search backend +------------------ + +Finding the Best-Matching Unit is the per-step bottleneck. The ``search_backend`` +argument selects the implementation: + +.. list-table:: + :header-rows: 1 + :widths: 18 82 + + * - Value + - Behavior + * - ``"auto"`` *(default)* + - Use FAISS if it is installed, otherwise the PyTorch backend. + * - ``"torch"`` + - Full pairwise distance computation on GPU/CPU. No extra dependency. + * - ``"faiss"`` + - Approximate nearest-neighbor search, faster for large maps and + high-dimensional inputs. Install with ``uv add torchsom[faiss]``. + +.. code-block:: python + + som = SOM(x=90, y=70, num_features=300, search_backend="auto") + +For the default 25Γ—15 grids, the PyTorch backend is already fast; FAISS pays off on +large maps (e.g. 90Γ—70) or high-dimensional data. + + +Monitoring convergence +---------------------- + +Use the returned error traces to decide whether training was long enough: + +.. code-block:: python + + q_errors, t_errors = som.fit(data=data) + print(f"final QE = {q_errors[-1]:.4f}, final TE = {t_errors[-1]:.4f}") + +Plot them with :meth:`~torchsom.visualization.SOMVisualizer.plot_training_errors` +(see :doc:`visualization_help`). Both curves should fall and then flatten; if either +is still dropping at the last epoch, raise ``epochs``. + +You can also compute the metrics on held-out data: + +.. code-block:: python + + qe = som.quantization_error(data=test_data) + te = som.topographic_error(data=test_data) + + +Next steps +---------- + +- :doc:`topologies` β€” Grid choice and periodic boundary conditions +- :doc:`visualization_help` β€” Plotting the training curve and maps +- :doc:`../tutorials/index` β€” Full runs on real datasets +- :doc:`../api/core` β€” ``SOM`` and ``fit`` reference diff --git a/docs/source/user_guide/visualization_help.rst b/docs/source/user_guide/visualization_help.rst index 2c8ff0b..e3c3fd4 100644 --- a/docs/source/user_guide/visualization_help.rst +++ b/docs/source/user_guide/visualization_help.rst @@ -1,367 +1,479 @@ -SOM Visualization Guide -======================= +Visualization Gallery +===================== + +TorchSOM ships a visualization suite that turns a trained map into figures you can +read. Every plot is produced by the :class:`~torchsom.visualization.SOMVisualizer` +class and works for both rectangular and hexagonal topologies. + +This page is the practical companion to the paper: it walks through each +visualization type with runnable code, an example figure, and notes on how to read it. + + +The seven visualization types +------------------------------ + +TorchSOM groups its plots into seven types, spanning unsupervised structure, +supervised target landscapes, and clustering model selection: + +.. list-table:: + :header-rows: 1 + :widths: 5 28 15 52 + + * - # + - Type + - Setting + - What it shows + * - 1 + - U-matrix (distance map) + - Unsupervised + - Inter-neuron distances; reveals cluster boundaries + * - 2 + - Hit map + - Unsupervised + - BMU activation frequency and data density + * - 3 + - Component planes + - Unsupervised + - Per-feature weight distribution across the grid + * - 4 + - Classification & metric maps + - Supervised + - Dominant class, or mean/std of a target, per neuron + * - 5 + - Score & rank maps + - Supervised + - Per-neuron reliability for regression + * - 6 + - Training curve + - Unsupervised + - QE and TE convergence during training + * - 7 + - Clustering maps + - Unsupervised + - Cluster assignment plus elbow, silhouette, and comparison diagnostics + +The numbered subsections below follow a natural workflow rather than this table's +order. + + +Setup +----- + +All examples reuse the SOM and the BMU map built here. Train once, then build the +``bmus_data`` map a single time and pass it to every supervised plot: -This comprehensive guide covers all visualization capabilities available in TorchSOM for analyzing and interpreting Self-Organizing Maps effectively. +.. code-block:: python -Overview --------- + import torch + from sklearn.datasets import load_iris + from sklearn.preprocessing import StandardScaler -TorchSOM provides a rich set of visualization tools through the :class:`SOMVisualizer` class, supporting both rectangular and hexagonal topologies. All visualizations are designed to help you understand: + from torchsom import SOM, SOMVisualizer -- **Training Progress**: How well your SOM is learning over time -- **Data Distribution**: How input data maps onto the SOM grid -- **Topology Preservation**: Whether neighborhood relationships are maintained -- **Feature Representation**: How individual features are distributed across neurons -- **Cluster Structure**: Identification of natural groupings in your data + # 1. Load and standardise the data + bunch = load_iris() + features = torch.tensor( + StandardScaler().fit_transform(bunch.data), dtype=torch.float32 + ) + targets = torch.tensor(bunch.target, dtype=torch.long) + feature_names = list(bunch.feature_names) + + # 2. Train a SOM + som = SOM( + x=25, + y=15, + num_features=features.shape[1], + epochs=100, + batch_size=16, + topology="rectangular", + initialization_mode="pca", + random_seed=42, + ) + som.initialize_weights(data=features, mode=som.initialization_mode) + q_errors, t_errors = som.fit(data=features) -Quick Start ------------ + # 3. Pre-compute the BMU -> sample-indices map ONCE and reuse it + bmus_map = som.build_map("bmus_data", data=features) -Basic Visualization Setup -~~~~~~~~~~~~~~~~~~~~~~~~~ + # 4. Create a visualizer (the topology is inferred from the SOM) + viz = SOMVisualizer(som=som) -.. code-block:: python +.. tip:: - from torchsom import SOM - from torchsom.visualization import SOMVisualizer, VisualizationConfig - import torch + Pass ``save_path="some/folder"`` to any ``plot_*`` method to write a file instead + of opening an interactive window. The file name is taken from each method's + ``fig_name`` argument and the format from :class:`VisualizationConfig.save_format`. - # Train a SOM - data = torch.randn(1000, 4) - som = SOM(x=20, y=15, num_features=4, epochs=50) - som.initialize_weights(data=data, mode="pca") - q_errors, t_errors = som.fit(data) - # Create visualizer with default configuration - visualizer = SOMVisualizer(som=som) +Customizing the output +----------------------- - # Generate all visualizations at once - visualizer.plot_all( - quantization_errors=q_errors, - topographic_errors=t_errors, - data=data, - save_path="som_results" - ) - -Custom Configuration -~~~~~~~~~~~~~~~~~~~~ - -The :class:`VisualizationConfig` class provides comprehensive customization options: +:class:`~torchsom.visualization.VisualizationConfig` controls styling. Pass an +instance to the visualizer: .. code-block:: python + from torchsom import SOMVisualizer + from torchsom.visualization import VisualizationConfig + config = VisualizationConfig( - figsize=(12, 8), # Figure size in inches - fontsize={ # Font sizes for different elements - "title": 16, - "axis": 13, - "legend": 11 - }, - fontweight={ # Font weights - "title": "bold", - "axis": "normal" - }, - cmap="viridis", # Default colormap - dpi=150, # Resolution for saved figures - grid_alpha=0.3, # Grid transparency - colorbar_pad=0.01, # Colorbar padding - save_format="png", # Save format (png, pdf, eps, svg) - hexgrid_size=None # Hexagonal grid size (auto if None) + figsize=(12, 8), # figure size in inches + fontsize={"title": 16, "axis": 13, "legend": 11}, + fontweight={"title": "bold", "axis": "normal", "legend": "normal"}, + cmap="viridis", # default colormap + dpi=300, # resolution for saved figures + grid_alpha=0.3, # grid transparency + colorbar_pad=0.01, # colorbar padding + save_format="png", # png, pdf, eps, or svg + hex_radius=0.5, # hexagon radius (hexagonal topology) + hex_border_color="black", + hex_border_width=0.3, ) + viz = SOMVisualizer(som=som, config=config) -Visualization Types -------------------- -Training Errors -~~~~~~~~~~~~~~~ +1. Training curve +----------------- -Monitors SOM learning progress by plotting quantization and topographic errors over epochs. +Plots quantization error (QE) and topographic error (TE) per epoch. This is the +first thing to check after training. .. code-block:: python - visualizer.plot_training_errors( + viz.plot_training_errors( quantization_errors=q_errors, topographic_errors=t_errors, - save_path="results" ) -**Interpretation:** - -- **Quantization Error**: Measures how well the SOM represents the input data (lower is better) -- **Topographic Error**: Measures topology preservation (lower percentage is better) -- **Convergence**: Both errors should generally decrease and stabilize during training - -.. image:: ../_static/assets/michelin_training_errors.png +.. image:: /_static/results/iris/rectangular/training_errors.png :width: 600px :align: center - :alt: Training Errors Example + :alt: Training curve showing QE and TE per epoch + +How to read it: + +- **QE** measures how well neurons represent the data (lower is better). +- **TE** measures topology preservation (lower is better). +- Both should fall and then flatten. A curve that is still dropping at the last + epoch means training was too short. -Distance Map (U-Matrix) -~~~~~~~~~~~~~~~~~~~~~~~ -The unified distance matrix shows the distance between each neuron and its neighbors, revealing cluster boundaries. +2. U-matrix (distance map) +-------------------------- + +The unified distance matrix shows, for each neuron, the average distance to its +grid neighbors. Ridges of large distance separate clusters. .. code-block:: python - visualizer.plot_distance_map( - save_path=save_path + viz.plot_distance_map( distance_metric=som.distance_fn_name, neighborhood_order=som.neighborhood_order, scaling="sum", ) -**Interpretation:** - -- **Dark Regions**: Small distances between neighboring neurons (cluster boundaries) -- **Light Regions**: Large distances between neighboring neurons (within clusters) -- **Topology**: Works with both rectangular and hexagonal grids - -.. image:: ../_static/assets/michelin_dmatrix.png +.. image:: /_static/results/iris/rectangular/distance_map.png :width: 600px :align: center - :alt: Distance Matrix Example + :alt: U-matrix (distance map) -Hit Map -~~~~~~~ +How to read it: -Shows the frequency of neuron activation, indicating how often each neuron was selected as the Best Matching Unit (BMU). +- **Light ridges** = large inter-neuron distance = cluster boundaries. +- **Dark basins** = similar neighbors = the interior of a cluster. +- Works identically on rectangular and hexagonal grids. -.. code-block:: python - visualizer.plot_hit_map( - data=train_features, - save_path=save_path, - batch_size=train_features.shape[0], - ) +3. Hit map +---------- -**Interpretation:** +Counts how often each neuron is selected as the BMU, exposing where the data +concentrates and which neurons are unused ("dead"). -- **Bright Areas**: Frequently activated neurons (high data density) -- **Dark Areas**: Rarely activated neurons (low data density or dead neurons) -- **Usage**: Identifies data distribution patterns and potential dead neurons +.. code-block:: python + + viz.plot_hit_map(data=features) -.. image:: ../_static/assets/michelin_hitmap.png +.. image:: /_static/results/iris/rectangular/hit_map.png :width: 600px :align: center - :alt: Hit Map Example + :alt: Hit map of BMU activation frequency -Component Planes -~~~~~~~~~~~~~~~~ +How to read it: -Individual visualizations for each input feature dimension, showing how feature weights are distributed across the map. +- **Bright cells** = frequently activated neurons = dense regions of the input space. +- **Empty cells** = rarely or never activated. A large empty border often means the + map is bigger than the data needs. -.. code-block:: python - visualizer.plot_component_planes( - component_names=feature_names, - save_path=save_path - ) +4. Component planes +------------------- -**Interpretation:** +One heat map per input feature, showing how that feature's weight varies across the +grid. Comparing planes reveals correlations and gradients. -- **One Plane per Feature**: Shows weight values for each input dimension -- **Pattern Analysis**: Reveals feature level in different map regions +.. code-block:: python + + viz.plot_component_planes(component_names=feature_names) -.. image:: ../_static/assets/michelin_cp12.png +.. image:: /_static/results/iris/rectangular/component_planes/Petal_Length.png :width: 600px :align: center - :alt: Component Plane of feature 12 + :alt: Component plane for the petal-length feature -Supervised Maps -~~~~~~~~~~~~~~~ +How to read it: + +- Each plane uses the same grid layout, so regions that are bright in two planes + indicate features that co-vary. +- Smooth gradients indicate good topology preservation for that feature. + + +Supervised maps +--------------- -Visualizations for supervised learning tasks, including both classification and regression, help interpret how target information is distributed across the SOM map. +The next two groups use a ``target`` vector aligned with the input data. They rely +on the pre-computed ``bmus_map`` from `Setup`_. -Classification Case -^^^^^^^^^^^^^^^^^^^ +5a. Classification map +~~~~~~~~~~~~~~~~~~~~~~~ -Displays the most frequent class label assigned to each neuron, providing insight into class separation and cluster structure. +For classification targets, shows the dominant class assigned to each neuron. .. code-block:: python - visualizer.plot_classification_map( - data=train_features, - target=train_targets, - save_path=save_path, + viz.plot_classification_map( bmus_data_map=bmus_map, + data=features, + target=targets, neighborhood_order=som.neighborhood_order, ) -**Interpretation:** - -- **Color Coding**: Each color represents a different class label. -- **Cluster Identification**: Reveals spatial organization of classes on the map. -- **Decision Boundaries**: Boundaries between colors indicate class separation. - -.. image:: ../_static/assets/wine_classificationmap.png +.. image:: /_static/results/iris/rectangular/classification_map.png :width: 600px :align: center - :alt: Classification Map Example + :alt: Classification map of dominant class per neuron -Regression Case -^^^^^^^^^^^^^^^ +How to read it: -Analyzes the distribution of continuous target values (e.g., for regression tasks) using statistical summaries per neuron. +- Each color is a class; contiguous regions of one color show the map has + organized that class into a coherent territory. +- Boundaries between colors are decision boundaries learned without supervision. -Mean Map -"""""""" +5b. Metric maps (mean / std) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Shows the average target value for samples mapped to each neuron. +For regression targets, summarize the target values that land on each neuron. +``reduction_parameter`` selects the statistic. .. code-block:: python - visualizer.plot_metric_map( - data=train_features, - target=train_targets, - reduction_parameter="mean", - save_path=save_path, + # Mean target value per neuron + viz.plot_metric_map( bmus_data_map=bmus_map, + data=features, + target=targets, + reduction_parameter="mean", ) -**Interpretation:** - -- **Color Scale**: Indicates the mean target value per neuron. -- **Smooth Transitions**: Suggest good topology preservation. -- **Hot Spots**: Highlight neurons with extreme target values. + # Spread of target values per neuron + viz.plot_metric_map( + bmus_data_map=bmus_map, + data=features, + target=targets, + reduction_parameter="std", + ) -.. image:: ../_static/assets/michelin_meanmap.png +.. image:: /_static/results/boston/rectangular/mean_target_map.png :width: 600px :align: center - :alt: Mean Map Example + :alt: Mean target value per neuron (Boston Housing) + +How to read it: + +- The **mean** map is a smooth regression surface over the topology; gradients show + how the target changes across the input space. +- The **std** map flags neurons with inconsistent targets β€” high values mark + unreliable regions for prediction. -Standard Deviation Map -"""""""""""""""""""""" -Shows the variability of target values for each neuron, useful for assessing prediction reliability. +6. Score & rank maps +-------------------- + +These rank neurons by how trustworthy their target estimates are, complementing the +global QE/TE numbers with a *per-neuron* reliability view. + +Score map +~~~~~~~~~~ .. code-block:: python - visualizer.plot_metric_map( - data=train_features, - target=train_targets, - reduction_parameter="std", - save_path=save_path, + viz.plot_score_map( bmus_data_map=bmus_map, + target=targets, + total_samples=features.shape[0], ) -**Interpretation:** +.. image:: /_static/results/boston/rectangular/score_map.png + :width: 600px + :align: center + :alt: Per-neuron reliability score map -- **Low Values**: Neurons with consistent (low-variance) target valuesβ€”good for prediction. -- **High Values**: Neurons with variable (high-variance) target valuesβ€”less reliable. -- **Quality Assessment**: Helps identify the most reliable neurons for regression tasks. +The score balances local spread, sample count, and statistical significance: -.. .. image:: ../_static/assets/michelin_stdmap.png -.. :width: 600px -.. :align: center -.. :alt: Standard Deviation Map Example +.. math:: + S_{ij} = \frac{\sigma_{ij}}{\sqrt{n_{ij}}} \cdot \log\!\left(\frac{N}{n_{ij}}\right) -Advanced Visualizations -~~~~~~~~~~~~~~~~~~~~~~~ +where :math:`\sigma_{ij}` is the standard deviation of the targets assigned to +neuron :math:`(i, j)`, :math:`n_{ij}` the number of samples it received, and +:math:`N` the total sample count. **Lower scores are better** (stable, +well-supported neurons). -Score Map -^^^^^^^^^ +Rank map +~~~~~~~~ + +.. code-block:: python + + viz.plot_rank_map(bmus_data_map=bmus_map, target=targets) + +.. image:: /_static/results/boston/rectangular/rank_map.png + :width: 600px + :align: center + :alt: Neurons ranked by target standard deviation -Evaluates neuron representativeness using a composite score combining standard error and sample distribution. +Ranks neurons by target standard deviation (rank 1 = lowest spread = most reliable), +giving a quick shortlist of neurons to trust for prediction. + + +7. Clustering maps & diagnostics +-------------------------------- + +Beyond a single cluster assignment, TorchSOM ships diagnostics for model selection +and objective algorithm comparison. Clustering is computed by +:meth:`~torchsom.core.SOM.cluster` and visualized by the methods below. See the +:doc:`clustering` guide for the full workflow. + +Cluster map +~~~~~~~~~~~~ .. code-block:: python - visualizer.plot_score_map( - bmus_data_map=bmus_map, - target=train_targets, - total_samples=train_features.shape[0], - save_path=save_path, + cluster_result = som.cluster( + method="hdbscan", # "kmeans", "gmm", or "hdbscan" + feature_space="weights", # "weights", "positions", or "combined" ) + viz.plot_cluster_map(cluster_result=cluster_result) -.. math:: - S_{ij} = \frac{\sigma_{ij}}{\sqrt{n_{ij}}} \cdot \log\left(\frac{N}{n_{ij}}\right) +.. image:: /_static/results/clustering/rectangular/cluster_map.png + :width: 600px + :align: center + :alt: Cluster assignment overlaid on the SOM grid -where: +Elbow analysis +~~~~~~~~~~~~~~~ - - :math:`S_{ij} \in \mathbb{R}^+`: Reliability score of neuron at position :math:`(i, j)` - - :math:`\sigma_{ij} \in \mathbb{R}^+`: Standard deviation of target values assigned to neuron at position :math:`(i, j)` - - :math:`n_{ij} \in \mathbb{N}`: Number of samples assigned to neuron at position :math:`(i, j)` - - :math:`N \in \mathbb{N}`: Total number of samples in the latent space +Tracks within-cluster dispersion as :math:`k` grows; the "elbow" marks the point of +diminishing returns β€” a common heuristic for choosing :math:`k` for K-Means or GMM. -**Interpretation:** +.. code-block:: python -- **Lower Scores**: Better neuron representativeness -- **Usage**: Identifies most reliable neurons for analysis + viz.plot_elbow_analysis(max_k=10, feature_space="weights") -.. .. image:: ../_static/assets/michelin_scoremap.png -.. :width: 600px -.. :align: center -.. :alt: Score Map Example +.. image:: /_static/results/clustering/rectangular/elbow_analysis.png + :width: 600px + :align: center + :alt: Elbow analysis for K selection -Rank Map -^^^^^^^^ +Silhouette analysis +~~~~~~~~~~~~~~~~~~~~~ -Ranks neurons based on their target value standard deviations. +Measures how well each point fits its cluster versus the nearest alternative +(coefficient in :math:`[-1, 1]`, higher is better). .. code-block:: python - visualizer.plot_rank_map( - bmus_data_map=bmus_map, - target=train_targets, - save_path=save_path, - ) + viz.plot_silhouette_analysis(cluster_result=cluster_result) + +.. image:: /_static/results/clustering/rectangular/silhouette_analysis.png + :width: 600px + :align: center + :alt: Silhouette analysis of a clustering result + +Algorithm comparison +~~~~~~~~~~~~~~~~~~~~~~ -**Interpretation:** +Evaluates several algorithms side by side with standardized metrics, so the choice +of method is data-driven rather than visual. -- **Rank 1**: Lowest standard deviation (best predictive neurons) -- **Higher Ranks**: Increasing standard deviation -- **Selection**: Use top-ranked neurons for reliable predictions +.. code-block:: python + + results = [ + som.cluster(method=m, feature_space="weights") + for m in ("kmeans", "gmm", "hdbscan") + ] + viz.plot_cluster_quality_comparison(results_list=results) + +.. image:: /_static/results/clustering/rectangular/clustering_metrics_comparison.png + :width: 600px + :align: center + :alt: Clustering algorithm quality comparison -.. .. image:: ../_static/assets/michelin_rankmap.png -.. :width: 600px -.. :align: center -.. :alt: Rank Map Example -Cluster Map -^^^^^^^^^^^ +Generating everything at once +------------------------------ -Clusters neurons based on algorithms like HDBSCAN, KMeans, or GMMs. +:meth:`~torchsom.visualization.SOMVisualizer.plot_all` produces the full set in one +call. It needs the pre-computed ``bmus_map``: .. code-block:: python - cluster = som.cluster( - method="hdbscan", # hdbscan, kmeans, gmm - n_clusters=n_clusters, - feature_space="weights", + viz.plot_all( + quantization_errors=q_errors, + topographic_errors=t_errors, + bmus_data_map=bmus_map, + data=features, + target=targets, + component_names=feature_names, + save_path="som_results", ) - visualizer.plot_cluster_map( - cluster_result=cluster, - save_path=save_path, - ) +Toggle individual plots with the boolean flags (``training_errors``, ``distance_map``, +``hit_map``, ``score_map``, ``rank_map``, ``metric_map``, ``component_planes``). -.. image:: ../_static/assets/hdbscan_cluster_map.png - :width: 600px - :align: center - :alt: Cluster Map Example -Troubleshooting ---------------- +Hexagonal topology +------------------ -**White Cells in Visualizations**: - - Indicates neurons with zero values or NaN - - Check for dead neurons in hit map - - Verify data preprocessing and normalization +Every plot above works unchanged on a hexagonal map β€” build the SOM with +``topology="hexagonal"`` and the same visualizer renders hexagon cells instead of +squares. For example, the iris U-matrix and a clustering result on a hexagonal grid: -**Memory Issues**: - - Reduce batch size in visualization functions - - Use CPU-only mode for very large SOMs - - Clear GPU cache with ``torch.cuda.empty_cache()`` +.. list-table:: + :widths: 50 50 -**Topology Preservation**: - - High topographic error indicates poor topology preservation - - Consider adjusting learning rate, sigma, or training epochs - - Use PCA initialization for better convergence + * - .. image:: /_static/results/iris/hexagonal/distance_map.png + :width: 100% + :alt: Hexagonal U-matrix + - .. image:: /_static/results/clustering/hexagonal/cluster_map.png + :width: 100% + :alt: Hexagonal cluster map -References ----------- -For more examples and detailed usage, see: +Per-neuron reliability, in context +----------------------------------- + +The score and rank maps quantify *which* neurons are trustworthy, not just whether +the map as a whole is well organized. Lower scores correspond to stable, +well-supported neurons; higher scores flag regions with poor generalization or +sensitivity to outliers. Together, the seven visualization types make TorchSOM a +systematic framework for inspecting self-organizing models across supervised and +unsupervised regimes β€” training diagnostics, data distribution, feature +interpretation, supervised target landscapes, and clustering model selection. + + +Next steps +---------- -- `TorchSOM Examples `_ -- `API Documentation <../api/visualization.html>`_ -- `Getting Started Guide <../getting_started/quickstart.html>`_ +- :doc:`clustering` β€” the full clustering workflow and diagnostics +- :doc:`../tutorials/index` β€” end-to-end worked examples that produce these figures +- :doc:`../api/visualization` β€” complete visualization API reference +- :doc:`../additional_resources/troubleshooting` β€” fixing blank cells, memory, and topology issues diff --git a/makefiles/ci.mk b/makefiles/ci.mk new file mode 100644 index 0000000..5f9da9c --- /dev/null +++ b/makefiles/ci.mk @@ -0,0 +1,23 @@ +# -------------------------- +# CI / Full Pipeline +# -------------------------- + +.PHONY: ci all + +ci: ## Run CI pipeline (lint + security + complexity + docs, no tests) + @echo "" + $(MAKE) check + $(MAKE) security + $(MAKE) complexity + $(MAKE) docs + @echo "" + @echo "All CI checks passed (without tests)!" + +all: ## Run full CI simulation (CI + format + tests) + @start=$$(date +%s); \ + $(MAKE) ci; \ + $(MAKE) fix; \ + $(MAKE) cov; \ + elapsed=$$(( $$(date +%s) - $$start )); \ + echo ""; \ + echo "All checks passed in $${elapsed}s! Ready to push." diff --git a/makefiles/clean.mk b/makefiles/clean.mk new file mode 100644 index 0000000..d4912c6 --- /dev/null +++ b/makefiles/clean.mk @@ -0,0 +1,38 @@ +# -------------------------- +# Cleanup +# -------------------------- + +.PHONY: clean-docs clean-build clean-test clean-lint clean-security clean-python clean-notebooks clean-all + +clean-docs: ## Remove Sphinx build artifacts + rm -rf docs/build/html/ + +clean-build: ## Remove build and distribution artifacts + rm -rf build/ dist/ *.egg-info/ + +clean-test: ## Remove test and coverage artifacts + rm -rf .pytest_cache/ .coverage htmlcov/ + rm -f junit.xml coverage.xml + +clean-lint: ## Remove linting and type checking cache + rm -rf .mypy_cache/ .ruff_cache/ + +clean-security: ## Remove security scan reports + rm -f bandit-report.json safety-report.json pip-audit-report.json + +clean-python: ## Remove Python cache files + find . -type f -name "*.pyc" -delete + find . -type d -name __pycache__ -delete + +clean-notebooks: ## Clear Jupyter notebook outputs + find notebooks/ -name "*.ipynb" -exec uv run jupyter nbconvert --clear-output --inplace {} \; 2>/dev/null || true + +clean-all: ## Clean all generated files + @echo "Cleaning all generated files..." + $(MAKE) clean-build + $(MAKE) clean-test + $(MAKE) clean-lint + $(MAKE) clean-security + $(MAKE) clean-python + $(MAKE) clean-docs + @echo "Cleanup complete." diff --git a/makefiles/complexity.mk b/makefiles/complexity.mk new file mode 100644 index 0000000..79c3226 --- /dev/null +++ b/makefiles/complexity.mk @@ -0,0 +1,17 @@ +# -------------------------- +# Complexity Analysis +# -------------------------- + +.PHONY: check-cc check-mi complexity-all + +check-cc: ## Check cyclomatic complexity (flag B+ ratings) + uv run radon cc torchsom/ --show-complexity --min B + +check-mi: ## Check maintainability index (flag B+ ratings) + uv run radon mi torchsom/ --show --min B + +complexity-all: ## Run cyclomatic complexity and maintainability analysis + @echo "Running complexity analysis..." + $(MAKE) check-cc + $(MAKE) check-mi + @echo "Complexity analysis completed." diff --git a/makefiles/docs.mk b/makefiles/docs.mk new file mode 100644 index 0000000..6f3e8e9 --- /dev/null +++ b/makefiles/docs.mk @@ -0,0 +1,29 @@ +# -------------------------- +# Documentation +# -------------------------- +# Docstring style is enforced by ruff's D rules (pydocstyle convention: google). +# Docstring coverage is measured by interrogate. + +.PHONY: measure-docstrings-coverage build-docs open-docs serve-docs docs + +measure-docstrings-coverage: ## Assess docstring coverage + uv run interrogate torchsom/ --verbose --ignore-init-method --ignore-magic --ignore-module --fail-under=80 + +build-docs: ## Build Sphinx documentation + uv run sphinx-build -b html --keep-going docs/source/ docs/build/html + +open-docs: ## Open built documentation in browser + @if [ -f docs/build/html/index.html ]; then \ + python -m webbrowser docs/build/html/index.html; \ + else \ + echo "Documentation not built yet. Run 'make docs' first."; \ + fi + +serve-docs: ## Serve documentation with live-reload (requires sphinx-autobuild) + uv run sphinx-autobuild docs/source/ docs/build/html --open-browser --watch torchsom/ + +docs: ## Build documentation and measure docstring coverage + @echo "Building documentation..." + $(MAKE) measure-docstrings-coverage + $(MAKE) build-docs + @echo "Documentation complete!" diff --git a/makefiles/format.mk b/makefiles/format.mk new file mode 100644 index 0000000..c9701c5 --- /dev/null +++ b/makefiles/format.mk @@ -0,0 +1,17 @@ +# -------------------------- +# Formatting +# -------------------------- + +.PHONY: format-ruff format-all precommit + +format-ruff: ## Auto-fix formatting, imports, and lint issues + uv run ruff format torchsom/ tests/ + uv run ruff check --fix torchsom/ tests/ + +format-all: ## Auto-fix all formatting issues + @echo "Auto-fixing code formatting..." + $(MAKE) format-ruff + @echo "Formatting applied." + +precommit: ## Run pre-commit hooks on all files + uv run pre-commit run --all-files diff --git a/makefiles/install.mk b/makefiles/install.mk new file mode 100644 index 0000000..668cdac --- /dev/null +++ b/makefiles/install.mk @@ -0,0 +1,30 @@ +# -------------------------- +# Environment Setup (uv) +# -------------------------- + +.PHONY: install-dev install-tests install-security install-linting install-docs install-precommit install-cz install-all + +install-dev: ## Sync development dependencies + uv sync --extra dev + +install-tests: ## Sync test dependencies + uv sync --extra tests + +install-security: ## Sync security dependencies + uv sync --extra security + +install-linting: ## Sync linting dependencies + uv sync --extra linting + +install-docs: ## Sync documentation dependencies + uv sync --extra docs + +install-precommit: ## Install pre-commit hooks + uv run pre-commit install + +install-cz: ## Install Commitizen + uv add --dev commitizen + +install-all: ## Sync all dependencies and install hooks + uv sync --all-extras + $(MAKE) install-precommit diff --git a/makefiles/lint.mk b/makefiles/lint.mk new file mode 100644 index 0000000..8cb6805 --- /dev/null +++ b/makefiles/lint.mk @@ -0,0 +1,17 @@ +# -------------------------- +# Code Quality +# -------------------------- + +.PHONY: check-ruff check-mypy lint-all + +check-ruff: ## Run ruff linter (style, imports, security, docstrings) + uv run ruff check torchsom/ tests/ + +check-mypy: ## Run mypy type checker + uv run mypy torchsom/ --ignore-missing-imports + +lint-all: ## Run all code quality checks (ruff + mypy) + @echo "Running code quality checks..." + $(MAKE) check-ruff + $(MAKE) check-mypy + @echo "All quality checks passed!" diff --git a/makefiles/release.mk b/makefiles/release.mk new file mode 100644 index 0000000..0f0aea1 --- /dev/null +++ b/makefiles/release.mk @@ -0,0 +1,33 @@ +# -------------------------- +# Changelog / Release Notes +# -------------------------- + +.PHONY: changelog bump + +changelog: ## Generate or update CHANGELOG.md based on commits + uv run cz changelog + +bump: ## Bump version and update changelog (use on release branch: main) + uv run cz bump --changelog + +# -------------------------- +# Publishing +# -------------------------- + +.PHONY: build-dist upload-dist publish + +build-dist: ## Build distribution packages + uv build + +upload-dist: ## Upload distribution to PyPI + uv run twine upload dist/* + +publish: ## Build and upload to PyPI + @echo "Publishing to PyPI..." + @bash -c '\ + source .env; \ + export TWINE_USERNAME TWINE_PASSWORD; \ + $(MAKE) build-dist; \ + $(MAKE) upload-dist \ + ' + @echo "Published to PyPI." diff --git a/makefiles/security.mk b/makefiles/security.mk new file mode 100644 index 0000000..4033bfd --- /dev/null +++ b/makefiles/security.mk @@ -0,0 +1,12 @@ +# -------------------------- +# Security +# -------------------------- +# Security linting (bandit-equivalent) is handled by ruff's S rules in lint.mk. +# This target runs an explicit audit of installed dependencies. + +.PHONY: security + +security: ## Audit installed packages for known vulnerabilities + @echo "Auditing dependencies..." + uv run pip-audit + @echo "Security audit completed." diff --git a/makefiles/test.mk b/makefiles/test.mk new file mode 100644 index 0000000..97eb660 --- /dev/null +++ b/makefiles/test.mk @@ -0,0 +1,23 @@ +# -------------------------- +# Testing +# -------------------------- + +TESTS ?= tests/unit/ + +.PHONY: test-unit test-smoke test-gpu test-integration test-coverage + +test-unit: ## Run unit tests (fast, no coverage) + uv run pytest $(TESTS) -v -x -m "unit" --no-cov + +test-smoke: ## Run smoke tests for basic functionality + uv run pytest $(TESTS) -v -x -m "smoke" --no-cov + +test-gpu: ## Run GPU tests (requires CUDA) + uv run pytest $(TESTS) -v -x -m "gpu" + +test-integration: ## Run integration tests + uv run pytest $(TESTS) -v -m "integration" + +test-coverage: ## Run all tests with coverage + @echo "Running tests with coverage..." + uv run pytest diff --git a/notebooks/boston_housing.ipynb b/notebooks/boston_housing.ipynb index d47c4aa..a23db08 100644 --- a/notebooks/boston_housing.ipynb +++ b/notebooks/boston_housing.ipynb @@ -14,23 +14,23 @@ "outputs": [], "source": [ "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from torchsom.core import SOM\n", - "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", "from sklearn.metrics import (\n", " mean_absolute_error,\n", " mean_squared_error,\n", - " root_mean_squared_error,\n", " r2_score,\n", + " root_mean_squared_error,\n", ")\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from torchsom.core import SOM\n", + "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", "\n", "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" @@ -73,7 +73,9 @@ "source": [ "# boston_df_scaled = boston_df\n", "scaler = StandardScaler()\n", - "boston_df_scaled = pd.DataFrame(scaler.fit_transform(boston_df), columns=boston_df.columns)" + "boston_df_scaled = pd.DataFrame(\n", + " scaler.fit_transform(boston_df), columns=boston_df.columns\n", + ")" ] }, { @@ -129,7 +131,10 @@ "\n", "\n", "shuffled_indices = torch.randperm(len(all_features), device=device)\n", - "all_features, all_targets = all_features[shuffled_indices], all_targets[shuffled_indices]\n", + "all_features, all_targets = (\n", + " all_features[shuffled_indices],\n", + " all_targets[shuffled_indices],\n", + ")\n", "\n", "train_ratio = 0.8\n", "train_count = int(train_ratio * len(all_features))\n", @@ -170,7 +175,7 @@ " initialization_mode=\"pca\",\n", " device=device,\n", " random_seed=random_seed,\n", - ") " + ")" ] }, { @@ -179,10 +184,7 @@ "metadata": {}, "outputs": [], "source": [ - "som.initialize_weights(\n", - " data=train_features,\n", - " mode=som.initialization_mode\n", - ")" + "som.initialize_weights(data=train_features, mode=som.initialization_mode)" ] }, { @@ -191,9 +193,7 @@ "metadata": {}, "outputs": [], "source": [ - "QE, TE = som.fit(\n", - " data=train_features\n", - ")" + "QE, TE = som.fit(data=train_features)" ] }, { @@ -218,7 +218,7 @@ "outputs": [], "source": [ "visualizer = SOMVisualizer(som=som, config=VisualizationConfig(save_format=\"pdf\"))\n", - "save_path = f\"results/boston/{som.topology}\" # Set to None if you want a direct plot" + "save_path = f\"results/boston/{som.topology}\" # Set to None if you want a direct plot" ] }, { @@ -228,9 +228,7 @@ "outputs": [], "source": [ "visualizer.plot_training_errors(\n", - " quantization_errors=QE, \n", - " topographic_errors=TE, \n", - " save_path=save_path\n", + " quantization_errors=QE, topographic_errors=TE, save_path=save_path\n", ")" ] }, @@ -305,7 +303,7 @@ " # fig_name=\"rank_map\",\n", " bmus_data_map=bmus_map,\n", " target=train_targets,\n", - " save_path=save_path\n", + " save_path=save_path,\n", ")" ] }, @@ -330,10 +328,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_component_planes(\n", - " component_names=feature_names,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_component_planes(component_names=feature_names, save_path=save_path)" ] }, { @@ -352,41 +347,40 @@ "outputs": [], "source": [ "predictions = []\n", - "for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets)):\n", - " \n", + "for _idx, (test_feature, _test_target) in enumerate(zip(test_features, test_targets)):\n", " collected_features, collected_targets = som.collect_samples(\n", " query_sample=test_feature,\n", " historical_samples=train_features,\n", " historical_outputs=train_targets,\n", - " min_buffer_threshold=125, # Collect 30 historical samples to train a model\n", + " min_buffer_threshold=125, # Collect 30 historical samples to train a model\n", " bmus_idx_map=bmus_map,\n", " )\n", - " \n", + "\n", " X = collected_features.cpu().numpy()\n", " y = collected_targets.cpu().numpy().ravel()\n", - " test_feature_np = test_feature.cpu().numpy().reshape(1, -1) \n", - " \n", + " test_feature_np = test_feature.cpu().numpy().reshape(1, -1)\n", + "\n", " reg = MLPRegressor(\n", " hidden_layer_sizes=(8, 16, 16, 8),\n", " max_iter=250,\n", " learning_rate_init=0.005,\n", " activation=\"relu\",\n", " solver=\"adam\",\n", - " batch_size='auto', \n", + " batch_size=\"auto\",\n", " random_state=random_seed,\n", " shuffle=True,\n", " verbose=False,\n", " ).fit(X, y)\n", - " \n", + "\n", " # plt.plot(reg.loss_curve_)\n", " # plt.xlabel(\"Iteration\")\n", " # plt.ylabel(\"Loss\")\n", " # plt.title(\"MLPRegressor Training Loss Curve\")\n", " # plt.grid(True)\n", " # plt.show()\n", - " \n", + "\n", " reg_prediction = reg.predict(test_feature_np)\n", - " predictions.append(reg_prediction[0]) " + " predictions.append(reg_prediction[0])" ] }, { @@ -396,9 +390,9 @@ "outputs": [], "source": [ "y_pred = np.array(predictions)\n", - "y_true = test_targets.cpu().numpy() \n", + "y_true = test_targets.cpu().numpy()\n", "\n", - "mae = mean_absolute_error(y_true, y_pred) \n", + "mae = mean_absolute_error(y_true, y_pred)\n", "mse = mean_squared_error(y_true, y_pred)\n", "rmse = root_mean_squared_error(y_true, y_pred)\n", "r2 = r2_score(y_true, y_pred)\n", @@ -420,8 +414,8 @@ "\n", "plt.figure(figsize=(10, 5))\n", "plt.scatter(\n", - " y_true, \n", - " y_pred, \n", + " y_true,\n", + " y_pred,\n", " alpha=0.8,\n", " color=\"blue\",\n", " marker=\"o\",\n", @@ -435,7 +429,7 @@ " label=\"Perfect Prediction\",\n", " color=\"red\",\n", " alpha=0.8,\n", - " linewidth=2\n", + " linewidth=2,\n", ")\n", "plt.xlabel(\"Ground Truth Values\")\n", "plt.ylabel(\"Predictions\")\n", diff --git a/notebooks/clustering.ipynb b/notebooks/clustering.ipynb index be59c0e..3eece7e 100644 --- a/notebooks/clustering.ipynb +++ b/notebooks/clustering.ipynb @@ -14,6 +14,7 @@ "outputs": [], "source": [ "import warnings\n", + "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", @@ -84,7 +85,7 @@ "metadata": {}, "outputs": [], "source": [ - "feature_columns = blobs_df.columns[:-1] \n", + "feature_columns = blobs_df.columns[:-1]\n", "feature_names = feature_columns.to_list()\n", "feature_names" ] @@ -114,7 +115,10 @@ "\n", "\n", "shuffled_indices = torch.randperm(len(all_features), device=device)\n", - "all_features, all_targets = all_features[shuffled_indices], all_targets[shuffled_indices]\n", + "all_features, all_targets = (\n", + " all_features[shuffled_indices],\n", + " all_targets[shuffled_indices],\n", + ")\n", "\n", "train_ratio = 0.8\n", "train_count = int(train_ratio * len(all_features))\n", @@ -145,7 +149,7 @@ " learning_rate=0.95,\n", " neighborhood_order=3,\n", " epochs=100,\n", - " batch_size=16, # 16 or train_features.shape[0]\n", + " batch_size=16, # 16 or train_features.shape[0]\n", " topology=\"hexagonal\",\n", " distance_function=\"euclidean\",\n", " neighborhood_function=\"gaussian\",\n", @@ -155,7 +159,7 @@ " initialization_mode=\"pca\",\n", " device=device,\n", " random_seed=random_seed,\n", - ") " + ")" ] }, { @@ -164,10 +168,7 @@ "metadata": {}, "outputs": [], "source": [ - "som.initialize_weights(\n", - " data=train_features,\n", - " mode=som.initialization_mode\n", - ")" + "som.initialize_weights(data=train_features, mode=som.initialization_mode)" ] }, { @@ -176,9 +177,7 @@ "metadata": {}, "outputs": [], "source": [ - "QE, TE = som.fit(\n", - " data=train_features\n", - ")" + "QE, TE = som.fit(data=train_features)" ] }, { @@ -203,7 +202,7 @@ "outputs": [], "source": [ "visualizer = SOMVisualizer(som=som, config=VisualizationConfig(save_format=\"pdf\"))\n", - "save_path = f\"results/clustering/blob_{blobs_df.shape[0]}_{blobs_df.shape[1]}_{len(blobs_df['Species'].unique())}/{som.topology}\" # Set to None if you want a direct plot" + "save_path = f\"results/clustering/blob_{blobs_df.shape[0]}_{blobs_df.shape[1]}_{len(blobs_df['Species'].unique())}/{som.topology}\" # Set to None if you want a direct plot" ] }, { @@ -213,9 +212,7 @@ "outputs": [], "source": [ "visualizer.plot_training_errors(\n", - " quantization_errors=QE, \n", - " topographic_errors=TE, \n", - " save_path=save_path\n", + " quantization_errors=QE, topographic_errors=TE, save_path=save_path\n", ")" ] }, @@ -270,10 +267,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_component_planes(\n", - " component_names=feature_names,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_component_planes(component_names=feature_names, save_path=save_path)" ] }, { @@ -290,8 +284,8 @@ "outputs": [], "source": [ "cluster = som.cluster(\n", - " method=\"hdbscan\", # hdbscan, kmeans, gmm\n", - " n_clusters=len(blobs_df['Species'].unique()),\n", + " method=\"hdbscan\", # hdbscan, kmeans, gmm\n", + " n_clusters=len(blobs_df[\"Species\"].unique()),\n", " feature_space=\"weights\",\n", ")" ] @@ -314,11 +308,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_elbow_analysis(\n", - " max_k=10,\n", - " feature_space=\"weights\",\n", - " save_path=save_path\n", - ")" + "visualizer.plot_elbow_analysis(max_k=10, feature_space=\"weights\", save_path=save_path)" ] }, { @@ -342,10 +332,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_cluster_quality_comparison(\n", - " results_list=results,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_cluster_quality_comparison(results_list=results, save_path=save_path)" ] }, { diff --git a/notebooks/energy_efficiency.ipynb b/notebooks/energy_efficiency.ipynb index 4f436f3..85a997d 100644 --- a/notebooks/energy_efficiency.ipynb +++ b/notebooks/energy_efficiency.ipynb @@ -14,23 +14,23 @@ "outputs": [], "source": [ "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from torchsom.core import SOM\n", - "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", "from sklearn.metrics import (\n", " mean_absolute_error,\n", " mean_squared_error,\n", - " root_mean_squared_error,\n", " r2_score,\n", + " root_mean_squared_error,\n", ")\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "from sklearn.neural_network import MLPRegressor\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from torchsom.core import SOM\n", + "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", "\n", "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" @@ -73,7 +73,9 @@ "source": [ "# energy_df_scaled = energy_df\n", "scaler = StandardScaler()\n", - "energy_df_scaled = pd.DataFrame(scaler.fit_transform(energy_df), columns=energy_df.columns)" + "energy_df_scaled = pd.DataFrame(\n", + " scaler.fit_transform(energy_df), columns=energy_df.columns\n", + ")" ] }, { @@ -130,19 +132,33 @@ "\n", "shuffled_indices = torch.randperm(len(all_features), device=device)\n", "all_features = all_features[shuffled_indices]\n", - "all_targets_heating, all_targets_cooling = all_targets_heating[shuffled_indices], all_targets_cooling[shuffled_indices]\n", + "all_targets_heating, all_targets_cooling = (\n", + " all_targets_heating[shuffled_indices],\n", + " all_targets_cooling[shuffled_indices],\n", + ")\n", "\n", "train_ratio = 0.8\n", "train_count = int(train_ratio * len(all_features))\n", "\n", "train_features = all_features[:train_count]\n", - "train_targets_heating, train_targets_cooling = all_targets_heating[:train_count], all_targets_cooling[:train_count]\n", + "train_targets_heating, train_targets_cooling = (\n", + " all_targets_heating[:train_count],\n", + " all_targets_cooling[:train_count],\n", + ")\n", "\n", "test_features = all_features[train_count:]\n", - "test_targets_heating, test_targets_cooling = all_targets_heating[train_count:], all_targets_cooling[train_count:]\n", + "test_targets_heating, test_targets_cooling = (\n", + " all_targets_heating[train_count:],\n", + " all_targets_cooling[train_count:],\n", + ")\n", "\n", "print(train_features.shape, test_features.shape)\n", - "print(train_targets_heating.shape, train_targets_cooling.shape, test_targets_heating.shape, test_targets_cooling.shape)" + "print(\n", + " train_targets_heating.shape,\n", + " train_targets_cooling.shape,\n", + " test_targets_heating.shape,\n", + " test_targets_cooling.shape,\n", + ")" ] }, { @@ -184,10 +200,7 @@ "metadata": {}, "outputs": [], "source": [ - "som.initialize_weights(\n", - " data=train_features,\n", - " mode=som.initialization_mode\n", - ")" + "som.initialize_weights(data=train_features, mode=som.initialization_mode)" ] }, { @@ -196,9 +209,7 @@ "metadata": {}, "outputs": [], "source": [ - "QE, TE = som.fit(\n", - " data=train_features\n", - ")" + "QE, TE = som.fit(data=train_features)" ] }, { @@ -223,7 +234,7 @@ "outputs": [], "source": [ "visualizer = SOMVisualizer(som=som, config=VisualizationConfig(save_format=\"pdf\"))\n", - "save_path = f\"results/energy/{som.topology}\" # Set to None if you want a direct plot" + "save_path = f\"results/energy/{som.topology}\" # Set to None if you want a direct plot" ] }, { @@ -233,9 +244,7 @@ "outputs": [], "source": [ "visualizer.plot_training_errors(\n", - " quantization_errors=QE, \n", - " topographic_errors=TE, \n", - " save_path=save_path\n", + " quantization_errors=QE, topographic_errors=TE, save_path=save_path\n", ")" ] }, @@ -274,10 +283,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_component_planes(\n", - " component_names=feature_names,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_component_planes(component_names=feature_names, save_path=save_path)" ] }, { @@ -338,7 +344,7 @@ " # fig_name=\"rank_map\",\n", " bmus_data_map=bmus_map,\n", " target=train_targets_heating,\n", - " save_path=heating_path\n", + " save_path=heating_path,\n", ")" ] }, @@ -415,7 +421,7 @@ " # fig_name=\"rank_map\",\n", " bmus_data_map=bmus_map,\n", " target=train_targets_cooling,\n", - " save_path=cooling_path\n", + " save_path=cooling_path,\n", ")" ] }, @@ -450,41 +456,42 @@ "outputs": [], "source": [ "predictions = []\n", - "for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets_heating)):\n", - " \n", + "for _idx, (test_feature, _test_target) in enumerate(\n", + " zip(test_features, test_targets_heating)\n", + "):\n", " collected_features, collected_targets = som.collect_samples(\n", " query_sample=test_feature,\n", " historical_samples=train_features,\n", " historical_outputs=train_targets_heating,\n", - " min_buffer_threshold=150, # Collect 425 historical samples to train a model\n", + " min_buffer_threshold=150, # Collect 425 historical samples to train a model\n", " bmus_idx_map=bmus_map,\n", " )\n", - " \n", + "\n", " X = collected_features.cpu().numpy()\n", " y = collected_targets.cpu().numpy().ravel()\n", - " test_feature_np = test_feature.cpu().numpy().reshape(1, -1) \n", - " \n", + " test_feature_np = test_feature.cpu().numpy().reshape(1, -1)\n", + "\n", " reg = MLPRegressor(\n", " hidden_layer_sizes=(8, 16, 16, 8),\n", " max_iter=200,\n", " learning_rate_init=0.008,\n", " activation=\"relu\",\n", " solver=\"adam\",\n", - " batch_size='auto', \n", + " batch_size=\"auto\",\n", " random_state=random_seed,\n", " shuffle=True,\n", " verbose=False,\n", " ).fit(X, y)\n", - " \n", + "\n", " # plt.plot(reg.loss_curve_)\n", " # plt.xlabel(\"Iteration\")\n", " # plt.ylabel(\"Loss\")\n", " # plt.title(\"MLPRegressor Training Loss Curve\")\n", " # plt.grid(True)\n", " # plt.show()\n", - " \n", + "\n", " reg_prediction = reg.predict(test_feature_np)\n", - " predictions.append(reg_prediction[0]) " + " predictions.append(reg_prediction[0])" ] }, { @@ -494,9 +501,9 @@ "outputs": [], "source": [ "y_pred = np.array(predictions)\n", - "y_true = test_targets_heating.cpu().numpy() \n", + "y_true = test_targets_heating.cpu().numpy()\n", "\n", - "mae = mean_absolute_error(y_true, y_pred) \n", + "mae = mean_absolute_error(y_true, y_pred)\n", "mse = mean_squared_error(y_true, y_pred)\n", "rmse = root_mean_squared_error(y_true, y_pred)\n", "r2 = r2_score(y_true, y_pred)\n", @@ -518,8 +525,8 @@ "\n", "plt.figure(figsize=(10, 5))\n", "plt.scatter(\n", - " y_true, \n", - " y_pred, \n", + " y_true,\n", + " y_pred,\n", " alpha=0.8,\n", " color=\"blue\",\n", " marker=\"o\",\n", @@ -533,7 +540,7 @@ " label=\"Perfect Prediction\",\n", " color=\"red\",\n", " alpha=0.8,\n", - " linewidth=2\n", + " linewidth=2,\n", ")\n", "plt.xlabel(\"Ground Truth Values - Heating\")\n", "plt.ylabel(\"Predictions\")\n", diff --git a/notebooks/get_data.ipynb b/notebooks/get_data.ipynb index 9d44d84..31a5e0f 100644 --- a/notebooks/get_data.ipynb +++ b/notebooks/get_data.ipynb @@ -7,7 +7,7 @@ "outputs": [], "source": [ "import pandas as pd\n", - "from sklearn.datasets import fetch_openml, make_blobs # make_moons, make_circles\n", + "from sklearn.datasets import fetch_openml, make_blobs # make_moons, make_circles\n", "from sklearn.preprocessing import StandardScaler" ] }, @@ -36,11 +36,11 @@ "source": [ "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n", "columns = [\n", - " \"Sepal Length\", \n", - " \"Sepal Width\", \n", - " \"Petal Length\", \n", - " \"Petal Width\", \n", - " \"Species\" # Target\n", + " \"Sepal Length\",\n", + " \"Sepal Width\",\n", + " \"Petal Length\",\n", + " \"Petal Width\",\n", + " \"Species\", # Target\n", "]\n", "df = pd.read_csv(url, header=None, names=columns)" ] @@ -81,20 +81,20 @@ "source": [ "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\"\n", "columns = [\n", - " \"Class\", # Target\n", - " \"Alcohol\", \n", - " \"Malic Acid\", \n", - " \"Ash\", \n", + " \"Class\", # Target\n", + " \"Alcohol\",\n", + " \"Malic Acid\",\n", + " \"Ash\",\n", " \"Alcalinity of Ash\",\n", - " \"Magnesium\", \n", + " \"Magnesium\",\n", " \"Total Phenols\",\n", " \"Flavanoids\",\n", " \"Nonflavanoid Phenols\",\n", - " \"Proanthocyanins\", \n", - " \"Color Intensity\", \n", - " \"Hue\", \n", - " \"OD280/OD315\", \n", - " \"Proline\"\n", + " \"Proanthocyanins\",\n", + " \"Color Intensity\",\n", + " \"Hue\",\n", + " \"OD280/OD315\",\n", + " \"Proline\",\n", "]\n", "\n", "df = pd.read_csv(url, header=None, names=columns)" @@ -178,19 +178,21 @@ "metadata": {}, "outputs": [], "source": [ - "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx\"\n", + "url = (\n", + " \"https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx\"\n", + ")\n", "df = pd.read_excel(url)\n", "df.columns = [\n", - " \"Relative Compactness\", \n", - " \"Surface Area\", \n", - " \"Wall Area\", \n", - " \"Roof Area\", \n", - " \"Overall Height\", \n", - " \"Orientation\", \n", - " \"Glazing Area\", \n", - " \"Glazing Area Distribution\", \n", - " \"Heating Load\", \n", - " \"Cooling Load\"\n", + " \"Relative Compactness\",\n", + " \"Surface Area\",\n", + " \"Wall Area\",\n", + " \"Roof Area\",\n", + " \"Overall Height\",\n", + " \"Orientation\",\n", + " \"Glazing Area\",\n", + " \"Glazing Area Distribution\",\n", + " \"Heating Load\",\n", + " \"Cooling Load\",\n", "]" ] }, @@ -225,8 +227,8 @@ "metadata": {}, "outputs": [], "source": [ - "n_samples = 300 # Small: 300 | Medium: 5000 | Large: 20000\n", - "n_features = 4 # Small: 4 | Medium: 50 | Large: 300" + "n_samples = 300 # Small: 300 | Medium: 5000 | Large: 20000\n", + "n_features = 4 # Small: 4 | Medium: 50 | Large: 300" ] }, { @@ -236,14 +238,14 @@ "outputs": [], "source": [ "X, y, centers = make_blobs(\n", - " n_samples=n_samples, \n", - " n_features=n_features, \n", - " centers=3, \n", + " n_samples=n_samples,\n", + " n_features=n_features,\n", + " centers=3,\n", " cluster_std=1.0,\n", - " center_box=(-10.0, 10.0), \n", - " shuffle=True, \n", - " random_state=42, \n", - " return_centers=True \n", + " center_box=(-10.0, 10.0),\n", + " shuffle=True,\n", + " random_state=42,\n", + " return_centers=True,\n", ")" ] }, @@ -253,8 +255,8 @@ "metadata": {}, "outputs": [], "source": [ - "df = pd.DataFrame(X, columns=[f\"Feature {i+1}\" for i in range(X.shape[1])])\n", - "df[\"Species\"] = [int(label)+1 for label in y]" + "df = pd.DataFrame(X, columns=[f\"Feature {i + 1}\" for i in range(X.shape[1])])\n", + "df[\"Species\"] = [int(label) + 1 for label in y]" ] }, { diff --git a/notebooks/iris.ipynb b/notebooks/iris.ipynb index 89fc760..8b4e86d 100644 --- a/notebooks/iris.ipynb +++ b/notebooks/iris.ipynb @@ -14,26 +14,22 @@ "outputs": [], "source": [ "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "import torch\n", - "\n", - "from torchsom.core import SOM\n", - "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", "from sklearn.metrics import (\n", - " f1_score, \n", - " accuracy_score, \n", - " recall_score, \n", - " precision_score, \n", - " confusion_matrix, \n", + " ConfusionMatrixDisplay,\n", " classification_report,\n", - " ConfusionMatrixDisplay\n", + " confusion_matrix,\n", ")\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from torchsom.core import SOM\n", + "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", "\n", "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" @@ -74,7 +70,7 @@ "metadata": {}, "outputs": [], "source": [ - "feature_columns = iris_df.columns[:-1] \n", + "feature_columns = iris_df.columns[:-1]\n", "scaler = StandardScaler()\n", "iris_df[feature_columns] = scaler.fit_transform(iris_df[feature_columns])" ] @@ -85,10 +81,8 @@ "metadata": {}, "outputs": [], "source": [ - "iris_df['Species'] = iris_df['Species'].map({\n", - " 'Iris-setosa': 1, \n", - " 'Iris-versicolor': 2, \n", - " 'Iris-virginica': 3}\n", + "iris_df[\"Species\"] = iris_df[\"Species\"].map(\n", + " {\"Iris-setosa\": 1, \"Iris-versicolor\": 2, \"Iris-virginica\": 3}\n", ")" ] }, @@ -145,7 +139,10 @@ "\n", "\n", "shuffled_indices = torch.randperm(len(all_features), device=device)\n", - "all_features, all_targets = all_features[shuffled_indices], all_targets[shuffled_indices]\n", + "all_features, all_targets = (\n", + " all_features[shuffled_indices],\n", + " all_targets[shuffled_indices],\n", + ")\n", "\n", "train_ratio = 0.8\n", "train_count = int(train_ratio * len(all_features))\n", @@ -186,7 +183,7 @@ " initialization_mode=\"pca\",\n", " device=device,\n", " random_seed=random_seed,\n", - ") " + ")" ] }, { @@ -195,10 +192,7 @@ "metadata": {}, "outputs": [], "source": [ - "som.initialize_weights(\n", - " data=train_features,\n", - " mode=som.initialization_mode\n", - ")" + "som.initialize_weights(data=train_features, mode=som.initialization_mode)" ] }, { @@ -207,9 +201,7 @@ "metadata": {}, "outputs": [], "source": [ - "QE, TE = som.fit(\n", - " data=train_features\n", - ")" + "QE, TE = som.fit(data=train_features)" ] }, { @@ -234,7 +226,7 @@ "outputs": [], "source": [ "visualizer = SOMVisualizer(som=som, config=VisualizationConfig(save_format=\"pdf\"))\n", - "save_path = f\"results/iris/{som.topology}\" # Set to None if you want a direct plot" + "save_path = f\"results/iris/{som.topology}\" # Set to None if you want a direct plot" ] }, { @@ -244,9 +236,7 @@ "outputs": [], "source": [ "visualizer.plot_training_errors(\n", - " quantization_errors=QE, \n", - " topographic_errors=TE, \n", - " save_path=save_path\n", + " quantization_errors=QE, topographic_errors=TE, save_path=save_path\n", ")" ] }, @@ -301,10 +291,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_component_planes(\n", - " component_names=feature_names,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_component_planes(component_names=feature_names, save_path=save_path)" ] }, { @@ -323,41 +310,40 @@ "outputs": [], "source": [ "predictions = []\n", - "for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets)):\n", - " \n", + "for _idx, (test_feature, _test_target) in enumerate(zip(test_features, test_targets)):\n", " collected_features, collected_targets = som.collect_samples(\n", " query_sample=test_feature,\n", " historical_samples=train_features,\n", " historical_outputs=train_targets,\n", - " min_buffer_threshold=30, # Collect 20 historical samples to train a model\n", + " min_buffer_threshold=30, # Collect 20 historical samples to train a model\n", " bmus_idx_map=bmus_map,\n", " )\n", - " \n", + "\n", " X = collected_features.cpu().numpy()\n", " y = collected_targets.cpu().numpy().ravel()\n", - " test_feature_np = test_feature.cpu().numpy().reshape(1, -1) \n", - " \n", + " test_feature_np = test_feature.cpu().numpy().reshape(1, -1)\n", + "\n", " clf = MLPClassifier(\n", " hidden_layer_sizes=(8),\n", " max_iter=200,\n", " learning_rate_init=0.008,\n", " activation=\"relu\",\n", " solver=\"adam\",\n", - " batch_size='auto', \n", + " batch_size=\"auto\",\n", " random_state=random_seed,\n", " shuffle=True,\n", " verbose=False,\n", " ).fit(X, y)\n", - " \n", + "\n", " # plt.plot(clf.loss_curve_)\n", " # plt.xlabel(\"Iteration\")\n", " # plt.ylabel(\"Loss\")\n", " # plt.title(\"MLPClassifier Training Loss Curve\")\n", " # plt.grid(True)\n", " # plt.show()\n", - " \n", + "\n", " clf_prediction = clf.predict(test_feature_np)\n", - " predictions.append(clf_prediction[0]) " + " predictions.append(clf_prediction[0])" ] }, { @@ -387,7 +373,7 @@ "outputs": [], "source": [ "class_report = classification_report(y_true, y_pred)\n", - "print('\\t\\t\\tClassification report:\\n\\n', class_report, '\\n')" + "print(\"\\t\\t\\tClassification report:\\n\\n\", class_report, \"\\n\")" ] }, { @@ -399,7 +385,7 @@ "cm = confusion_matrix(y_true, y_pred)\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[1, 2, 3])\n", "fig, ax = plt.subplots(figsize=(6, 6))\n", - "disp.plot(ax=ax, cmap='Blues', values_format='d')\n", + "disp.plot(ax=ax, cmap=\"Blues\", values_format=\"d\")\n", "plt.title(\"Confusion Matrix\")\n", "plt.grid(False)\n", "plt.tight_layout()\n", diff --git a/notebooks/wine.ipynb b/notebooks/wine.ipynb index 4327df9..f227474 100644 --- a/notebooks/wine.ipynb +++ b/notebooks/wine.ipynb @@ -14,26 +14,22 @@ "outputs": [], "source": [ "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", "import torch\n", - "\n", - "from torchsom.core import SOM\n", - "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", "from sklearn.metrics import (\n", - " f1_score, \n", - " accuracy_score, \n", - " recall_score, \n", - " precision_score,\n", - " confusion_matrix, \n", + " ConfusionMatrixDisplay,\n", " classification_report,\n", - " ConfusionMatrixDisplay\n", + " confusion_matrix,\n", ")\n", - "from sklearn.exceptions import ConvergenceWarning, DataConversionWarning\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "from torchsom.core import SOM\n", + "from torchsom.visualization import SOMVisualizer, VisualizationConfig\n", "\n", "warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)\n", "warnings.filterwarnings(\"ignore\", category=DataConversionWarning)" @@ -66,7 +62,7 @@ "wine_df = pd.read_csv(\n", " filepath_or_buffer=\"../data/notebooks/wine.csv\",\n", ")\n", - "wine_df.rename(columns={'OD280/OD315': 'OD280_OD315'}, inplace=True)" + "wine_df.rename(columns={\"OD280/OD315\": \"OD280_OD315\"}, inplace=True)" ] }, { @@ -75,7 +71,7 @@ "metadata": {}, "outputs": [], "source": [ - "feature_columns = wine_df.columns[1:] \n", + "feature_columns = wine_df.columns[1:]\n", "scaler = StandardScaler()\n", "wine_df[feature_columns] = scaler.fit_transform(wine_df[feature_columns])" ] @@ -133,7 +129,10 @@ "\n", "\n", "shuffled_indices = torch.randperm(len(all_features))\n", - "all_features, all_targets = all_features[shuffled_indices], all_targets[shuffled_indices]\n", + "all_features, all_targets = (\n", + " all_features[shuffled_indices],\n", + " all_targets[shuffled_indices],\n", + ")\n", "\n", "train_ratio = 0.8\n", "train_count = int(train_ratio * len(all_features))\n", @@ -172,9 +171,9 @@ " lr_decay_function=\"asymptotic_decay\",\n", " sigma_decay_function=\"asymptotic_decay\",\n", " initialization_mode=\"pca\",\n", - " device=\"cpu\", # \"cpu\" or \"cuda\"\n", + " device=\"cpu\", # \"cpu\" or \"cuda\"\n", " random_seed=random_seed,\n", - ") " + ")" ] }, { @@ -183,10 +182,7 @@ "metadata": {}, "outputs": [], "source": [ - "som.initialize_weights(\n", - " data=train_features,\n", - " mode=som.initialization_mode\n", - ")" + "som.initialize_weights(data=train_features, mode=som.initialization_mode)" ] }, { @@ -195,9 +191,7 @@ "metadata": {}, "outputs": [], "source": [ - "QE, TE = som.fit(\n", - " data=train_features\n", - ")" + "QE, TE = som.fit(data=train_features)" ] }, { @@ -222,7 +216,7 @@ "outputs": [], "source": [ "visualizer = SOMVisualizer(som=som, config=VisualizationConfig(save_format=\"pdf\"))\n", - "save_path = f\"results/wine/{som.topology}\" # Set to None if you want a direct plot" + "save_path = f\"results/wine/{som.topology}\" # Set to None if you want a direct plot" ] }, { @@ -232,9 +226,7 @@ "outputs": [], "source": [ "visualizer.plot_training_errors(\n", - " quantization_errors=QE, \n", - " topographic_errors=TE, \n", - " save_path=save_path\n", + " quantization_errors=QE, topographic_errors=TE, save_path=save_path\n", ")" ] }, @@ -289,10 +281,7 @@ "metadata": {}, "outputs": [], "source": [ - "visualizer.plot_component_planes(\n", - " component_names=feature_names,\n", - " save_path=save_path\n", - ")" + "visualizer.plot_component_planes(component_names=feature_names, save_path=save_path)" ] }, { @@ -311,41 +300,40 @@ "outputs": [], "source": [ "predictions = []\n", - "for idx, (test_feature, test_target) in enumerate(zip(test_features, test_targets)):\n", - " \n", + "for _idx, (test_feature, _test_target) in enumerate(zip(test_features, test_targets)):\n", " collected_features, collected_targets = som.collect_samples(\n", " query_sample=test_feature,\n", " historical_samples=train_features,\n", " historical_outputs=train_targets,\n", - " min_buffer_threshold=30, # Collect 30 historical samples to train a model\n", + " min_buffer_threshold=30, # Collect 30 historical samples to train a model\n", " bmus_idx_map=bmus_map,\n", " )\n", - " \n", + "\n", " X = collected_features.cpu().numpy()\n", " y = collected_targets.cpu().numpy().ravel()\n", - " test_feature_np = test_feature.cpu().numpy().reshape(1, -1) \n", - " \n", + " test_feature_np = test_feature.cpu().numpy().reshape(1, -1)\n", + "\n", " clf = MLPClassifier(\n", " hidden_layer_sizes=(8, 8, 8),\n", " max_iter=200,\n", " learning_rate_init=0.001,\n", " activation=\"relu\",\n", " solver=\"adam\",\n", - " batch_size='auto', \n", + " batch_size=\"auto\",\n", " random_state=random_seed,\n", " shuffle=True,\n", " verbose=False,\n", " ).fit(X, y)\n", - " \n", + "\n", " # plt.plot(clf.loss_curve_)\n", " # plt.xlabel(\"Iteration\")\n", " # plt.ylabel(\"Loss\")\n", " # plt.title(\"MLPClassifier Training Loss Curve\")\n", " # plt.grid(True)\n", " # plt.show()\n", - " \n", + "\n", " clf_prediction = clf.predict(test_feature_np)\n", - " predictions.append(clf_prediction[0]) " + " predictions.append(clf_prediction[0])" ] }, { @@ -355,7 +343,7 @@ "outputs": [], "source": [ "y_pred = np.array(predictions)\n", - "y_true = test_targets.cpu().numpy() \n", + "y_true = test_targets.cpu().numpy()\n", "\n", "# accuracy = accuracy_score(y_true, y_pred)\n", "# precision = precision_score(y_true, y_pred, average='macro') # or 'weighted' or 'micro'\n", @@ -375,7 +363,7 @@ "outputs": [], "source": [ "class_report = classification_report(y_true, y_pred)\n", - "print('\\t\\t\\tClassification report:\\n\\n', class_report, '\\n')" + "print(\"\\t\\t\\tClassification report:\\n\\n\", class_report, \"\\n\")" ] }, { @@ -387,7 +375,7 @@ "cm = confusion_matrix(y_true, y_pred)\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[1, 2, 3])\n", "fig, ax = plt.subplots(figsize=(6, 6))\n", - "disp.plot(ax=ax, cmap='Blues', values_format='d')\n", + "disp.plot(ax=ax, cmap=\"Blues\", values_format=\"d\")\n", "plt.title(\"Confusion Matrix\")\n", "plt.grid(False)\n", "plt.tight_layout()\n", diff --git a/pyproject.toml b/pyproject.toml index 3af604a..75d45f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,9 @@ -# This section defines how the package should be built when running `python -m build` [build-system] -build-backend = "setuptools.build_meta" # Use setuptools as the backend -requires = [ - "setuptools", # The core packaging tool - "setuptools-git-versioning", # Automatically derive the version from Git tags -] +build-backend = "setuptools.build_meta" +requires = ["setuptools", "setuptools-git-versioning"] + +# ==================== Project Metadata ==================== -# Main project/package metadata [project] name = "torchsom" authors = [ @@ -15,15 +12,11 @@ authors = [ maintainers = [ {name = "Louis Berthier", email = "louis-desire-romeo.berthier@michelin.com"}, ] -dynamic = [ - "version", # Version will be determined dynamically (via git) - "readme", # README will be dynamically loaded -] +dynamic = ["version", "readme"] description = "torchsom: The Reference PyTorch Library for Self-Organizing Maps" -requires-python = ">=3.9" # Minimum Python version required -# https://pypi.org/classifiers/ +requires-python = ">=3.10" classifiers = [ - "Development Status :: 4 - Beta", # 3 - Alpha, 4 - Beta, 5 - Production/Stable + "Development Status :: 4 - Beta", "Natural Language :: English", "Operating System :: OS Independent", "Intended Audience :: Developers", @@ -34,12 +27,10 @@ classifiers = [ "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Visualization", "Topic :: Software Development :: Libraries", - "Topic :: Software Development :: Testing :: Unit", - "Topic :: Software Development :: Version Control :: Git", - "Programming Language :: Python", - "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "License :: OSI Approved :: Apache Software License", ] dependencies = [ @@ -64,48 +55,40 @@ keywords = [ ] [project.urls] -"Source" = "https://github.com/michelin/torchsom" -"Documentation" = "https://opensource.michelin.io/TorchSOM/" -"Tracker" = "https://github.com/michelin/TorchSOM/issues" -"License" = "https://github.com/michelin/TorchSOM/blob/main/LICENSE" -"Contributing" = "https://github.com/michelin/TorchSOM/blob/main/CONTRIBUTING.md" +Source = "https://github.com/michelin/torchsom" +Documentation = "https://opensource.michelin.io/TorchSOM/" +Tracker = "https://github.com/michelin/TorchSOM/issues" +License = "https://github.com/michelin/TorchSOM/blob/main/LICENSE" +Contributing = "https://github.com/michelin/TorchSOM/blob/main/CONTRIBUTING.md" "Code of Conduct" = "https://github.com/michelin/TorchSOM/blob/main/CODE_OF_CONDUCT.md" -"Citation" = "https://github.com/michelin/TorchSOM/blob/main/CITATION.cff" -# "Changelog" = "https://github.com/michelin/torchsom/releases" -# "Author" = "https://github.com/michelin/TorchSOM/blob/main/AUTHORS.md" +Citation = "https://github.com/michelin/TorchSOM/blob/main/CITATION.cff" -# Configuration for setuptools-git-versioning plugin [tool.setuptools-git-versioning] -enabled = true # Enables git-based versioning -# dev_template and dirty_template define version strings for development and dirty states -dev_template = "{tag}.dev{ccount}" # Format when there are commits after a tag -dirty_template = "{tag}.post{ccount}+dirty" # Format when the working tree is dirty +enabled = true +dev_template = "{tag}.dev{ccount}" +dirty_template = "{tag}.post{ccount}+dirty" -# Dynamically load the README from file [tool.setuptools.dynamic] -readme = {file = "README.md", content-type = "text/markdown"} # Long description for PyPI and docs +readme = {file = "README.md", content-type = "text/markdown"} -# Package discovery configuration [tool.setuptools.packages.find] -where = ["."] # Search in current directory -include = ["torchsom*"] # Include packages matching this pattern (e.g., torchsom, torchsom.utils) +where = ["."] +include = ["torchsom*"] + +# ==================== Optional Dependencies ==================== -# Optional dependencies that can be installed with [dev] extra: `pip install .[dev]` [project.optional-dependencies] dev = [ "pandas", "openpyxl", - "black", - "isort", "rich", "typing_extensions", - "build", - "twine", - "commitizen", "notebook", "pyyaml", + "pre-commit", + "typer", + "minisom>=2.3.6", ] - tests = [ "pytest", "pytest-cov", @@ -113,91 +96,56 @@ tests = [ "pytest-xdist", "pytest-timeout", ] - docs = [ "sphinx", - "sphinx-rtd-theme", + "furo", "sphinx-autodoc-typehints", "sphinx-copybutton", - "pydocstyle", + "sphinx-design", + "sphinx-autobuild", "interrogate", ] - -security = [ - "bandit[toml]", - "safety", - "pip-audit", - "pip-tools", +faiss = [ + "faiss-cpu", ] - linting = [ "ruff", "mypy", "radon", - "certifi", ] - -all = [ - "pandas", - "openpyxl", - "black", - "isort", - "rich", - "typing_extensions", +release = [ "build", "twine", "commitizen", - "notebook", - "pyyaml", - "pytest", - "pytest-cov", - "pytest-html", - "pytest-xdist", - "pytest-timeout", - "sphinx", - "sphinx-rtd-theme", - "sphinx-autodoc-typehints", - "sphinx-copybutton", - "pydocstyle", - "interrogate", - "bandit[toml]", - "safety", +] +security = [ "pip-audit", - "pip-tools", - "ruff", - "mypy", - "radon", - "certifi", +] +all = [ + "torchsom[dev,tests,docs,faiss,linting,release,security]", ] -# ==================== Testing Configuration ==================== +# ==================== Testing ==================== [tool.pytest.ini_options] -# Directory containing test files testpaths = ["tests"] -# File name patterns pytest will recognize as test files python_files = ["test_*.py", "*_test.py"] -# Class name patterns pytest will recognize as test classes python_classes = ["Test*"] -# Function name patterns pytest will recognize as test functions python_functions = ["test_*"] -# Additional pytest CLI options: addopts = [ - "-v", # Verbose output - # "--strict-markers", # Fail if unknown @pytest.mark is used - # "--strict-config", # Fail if unknown config option is found - "--tb=short", # Short traceback format - "--durations=10", # Report 10 slowest tests - "--cov=torchsom", # Coverage of torchsom - "--cov-report=term-missing", # Show missing lines in the report - "--cov-report=xml", # Generate XML coverage report - "--cov-report=html", # Generate HTML coverage report - "--cov-config=pyproject.toml", # Use configuration from pyproject.toml - "--junit-xml=junit.xml", # Create JUnit XML file for CI compatibility - "-m unit or gpu", # default marker selection - "--maxfail=0", # continue after failures: 0 means never stop on failure, 1 means stop after 1 failure and 10 means stop after 10 failures + "-v", + "--strict-markers", + "--tb=short", + "--durations=10", + "--cov=torchsom", + "--cov-report=term-missing", + "--cov-report=xml", + "--cov-report=html", + "--cov-config=pyproject.toml", + "--junit-xml=junit.xml", + "-m", "unit or gpu", + "--maxfail=0", ] -# Custom pytest markers with descriptions markers = [ "unit: Unit tests", "integration: Integration tests", @@ -205,200 +153,134 @@ markers = [ "slow: Slow running tests", "smoke: Smoke tests for basic functionality", ] -# Configure pytest to handle and ignore specific warnings filterwarnings = [ "error", "ignore::UserWarning", "ignore::DeprecationWarning", "ignore::PendingDeprecationWarning", + # hdbscan 0.8.41 ships invalid-escape docstrings: DeprecationWarning on Python <=3.11, SyntaxWarning on 3.12+ + "ignore::SyntaxWarning", ] -# Timeout for each test in seconds timeout = 300 [tool.coverage.run] -# Directories or modules to measure coverage for source = ["torchsom"] -# Files to exclude from coverage stats omit = [ - "tests/**", # Exclude all test files - "docs/**", # Exclude documentation - "notebooks/**", # Exclude Jupyter notebooks - "torchsom/core/base_som.py", # Exclude base SOM implementation - "torchsom/core/growing/**", # Exclude growing SOM implementation - "torchsom/core/hierarchical/**", # Exclude hierarchical SOM implementation - "torchsom/visualization/**", # Exclude visualization utilities - "torchsom/logger.py", # Exclude logging utilities - "torchsom/version.py", # Exclude version info file - "**/conftest.py", # Exclude pytest config - "**/__init__.py", # Exclude package init files - "**/setup.py", # Exclude setup scripts + "tests/**", + "docs/**", + "notebooks/**", + "torchsom/core/base_som.py", + "torchsom/core/growing/**", + "torchsom/core/hierarchical/**", + "torchsom/visualization/**", + "torchsom/logger.py", + "torchsom/version.py", + "**/conftest.py", + "**/__init__.py", ] -# Measure branch coverage in addition to line coverage branch = true -# Allow combining coverage results from parallel runs parallel = true [tool.coverage.report] -# Lines to exclude from coverage measurement exclude_lines = [ - "pragma: no cover", # Explicit coverage skip marker - "def __repr__", # Representation methods - "raise AssertionError", # Assertion errors not tested - "raise NotImplementedError", # Abstract/unimplemented methods - "if __name__ == .__main__.:", # Script entry points - "if TYPE_CHECKING:", # Type hinting blocks + "pragma: no cover", + "def __repr__", + "raise AssertionError", + "raise NotImplementedError", + "if __name__ == .__main__.:", + "if TYPE_CHECKING:", ] -# Show missing lines in the report show_missing = true -# Report numbers with 2 decimal places precision = 2 -# Fail if coverage percentage is below 60% -fail_under = 20 +fail_under = 80 [tool.coverage.html] -# Output directory for HTML coverage report directory = "htmlcov" -# ==================== Code Quality Configuration ==================== - -[tool.black] -# Maximum line length for Black -line-length = 88 -# Target Python versions for Black -target-version = ["py39", "py310", "py311"] -# Include files with .pyi extension -include = "\\.pyi?$" -# Exclude directories from Black formatting -extend-exclude = """ -/( - # directories - \\.eggs - | \\.git - | \\.hg - | \\.mypy_cache - | \\.tox - | \\.venv - | build - | dist -)/ -""" - -[tool.isort] -# Use Black's configuration for isort -profile = "black" -# Maximum number of lines in a single import block -multi_line_output = 3 -# Maximum line length for isort -line_length = 88 -# Known first-party packages -known_first_party = ["torchsom"] -# Known third-party packages -known_third_party = ["torch", "numpy", "matplotlib", "pytest", "pydantic"] -# Force grid wrap for imports -force_grid_wrap = 0 -# Use parentheses for grouped imports -use_parentheses = true -# Ensure newline before comments -ensure_newline_before_comments = true +# ==================== Ruff (lint + format) ==================== +# Ruff replaces black, isort, flake8, bandit, and pyupgrade in a single tool. [tool.ruff] -# Allow Ruff to fix code automatically fix = true -# Allow Ruff to fix code automatically even if it's unsafe -unsafe-fixes = true -# Maximum line length for Ruff line-length = 88 -# Target Python versions for Ruff -target-version = "py39" +target-version = "py310" + +[tool.ruff.format] +docstring-code-format = true + [tool.ruff.lint] -# Select rules to apply select = [ - "E", # pycodestyle errors - "W", # pycodestyle warnings - "F", # pyflakes - "I", # isort - "B", # flake8-bugbear - "C4", # flake8-comprehensions - "UP", # pyupgrade - "ARG", # flake8-unused-arguments - "SIM", # flake8-simplify - "ICN", # flake8-import-conventions - "S", # bandit + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort (import sorting) + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "UP", # pyupgrade + "ARG", # flake8-unused-arguments + "SIM", # flake8-simplify + "ICN", # flake8-import-conventions + "S", # flake8-bandit (security) + "D", # pydocstyle + "RUF", # ruff-specific rules ] -# Ignore specific rules ignore = [ - "E501", # line too long, handled by black - "B008", # do not perform function calls in argument defaults - "C901", # too complex - "S101", # use of assert - "B904", # exception re-raise - "S311", # use of insecure random number generator - "SIM102", # use of nested if statements -] -# Exclude directories from Ruff -exclude = [ - ".bzr", - ".direnv", - ".eggs", - ".git", - ".git-rewrite", - ".hg", - ".mypy_cache", - ".nox", - ".pants.d", - ".pytype", - ".ruff_cache", - ".svn", - ".tox", - ".venv", - "__pypackages__", - "_build", - "buck-out", - "build", - "dist", - "node_modules", - "venv", + "E501", # line too long -- formatter handles this + "B008", # function calls in defaults (common in pydantic) + "C901", # too complex + "S101", # assert usage -- needed in tests and fine in library + "B904", # exception re-raise + "S311", # pseudo-random generators -- acceptable for SOM init + "SIM102", # nested if statements + "D100", # missing module docstring (handled by interrogate) + "D104", # missing public package docstring + "B905", # zip without an explicit 'strict=' parameter ] + +[tool.ruff.lint.pydocstyle] +convention = "google" + +[tool.ruff.lint.isort] +known-first-party = ["torchsom"] + [tool.ruff.lint.per-file-ignores] -# Ignore specific rules for test files -"tests/*" = ["S101", "ARG", "SIM"] +"tests/*" = ["S101", "ARG", "SIM", "D"] + +# ==================== Mypy ==================== [tool.mypy] -# Target Python version for mypy -python_version = "3.9" -warn_return_any = true # If True, mypy warns when a function returns Any. False disables this warning. -warn_unused_configs = true # Warn if there are unused mypy configuration options in the config file. -disallow_untyped_defs = true # Disallow defining functions without type annotations for all arguments and the return type. -disallow_incomplete_defs = true # Disallow functions with some, but not all, arguments annotated. -check_untyped_defs = true # Type-check the body of functions without type annotations. -disallow_untyped_decorators = true # Disallow decorators without type annotations. -no_implicit_optional = true # Treat function arguments without explicit Optional[...] as required (not implicitly optional). -warn_redundant_casts = true # Warn about unnecessary type casts. -warn_unused_ignores = true # Warn about # type: ignore comments that are not needed. -warn_no_return = true # Warn if a function declared to return a value implicitly returns None. -warn_unreachable = true # Warn about code that cannot be reached. -strict_equality = true # Require strict type equality for comparisons. -show_error_codes = true # Display mypy error codes in the output for reference. +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +check_untyped_defs = true +disallow_untyped_decorators = true +no_implicit_optional = true +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +warn_unreachable = true +strict_equality = true +show_error_codes = true disable_error_code = [ - "var-annotated", # Error code for variable annotated with Any - "operator", # Error code for operator module - "no-any-return", # Error code for function returning Any when it should return a more specific type - "index", # Error code for indexing with a non-integer type - "unreachable", # Error code for code that cannot be reached - "call-arg", # Error code for calling a function with the wrong arguments - "arg-type", # Error code for argument type mismatch - "assignment", # Error code for assigning a value to a variable with a different type - "misc", # Error code for miscellaneous type issues - "call-overload", # Error code for calling a function with the wrong number of arguments - "override", # Error code for overriding a method with a different signature (e.g. in a subclass) - # "return-value", # Error code for function returning a value with a different type than declared - "no-untyped-call", # Error code for calling a function without type annotations - "attr-defined", # Error code for attribute access on None - "union-attr", # Error code for union attribute access + "var-annotated", + "operator", + "no-any-return", + "index", + "unreachable", + "call-arg", + "arg-type", + "assignment", + "misc", + "call-overload", + "override", + "no-untyped-call", + "attr-defined", + "union-attr", ] [[tool.mypy.overrides]] -# Module-level type overrides for mypy module = [ "sklearn.*", "matplotlib.*", @@ -408,24 +290,12 @@ module = [ "pydantic.*", "pytest.*", "yaml.*", + "faiss.*", + "hdbscan.*", ] -# Ignore missing imports ignore_missing_imports = true -[tool.bandit] -# Exclude directories from Bandit -exclude_dirs = ["docs", "build", "dist"] -# Skip assert_used and shell_injection tests -skips = ["B101", "B311", "B601"] - -[tool.bandit.assert_used] -# Skip assert_used for test files -skips = ["*_test.py", "test_*.py"] -[tool.pydocstyle] -convention = "google" -match-dir = "torchsom" -# match = ".*\.py" # optional; defaults to all .py -# add-ignore = ["D104"] # example: ignore missing package docstring +# ==================== Interrogate ==================== [tool.interrogate] verbose = 1 @@ -433,8 +303,12 @@ ignore-init-method = true ignore-magic = true ignore-module = true fail-under = 80 +color = true +omit-covered-files = true path = ["torchsom"] -# exclude = ["tests", "docs"] + +# ==================== Commitizen ==================== + [tool.commitizen] name = "cz_conventional_commits" version = "1.1.1" @@ -448,5 +322,7 @@ github_url = "https://github.com/michelin/TorchSOM" version_scheme = "semver" changelog_start_rev = "v1.0.0" +# ==================== Sphinx ==================== + [tool.sphinx] suppress-warnings = ["misc.docutils"] diff --git a/tests/unit/test_pbc.py b/tests/unit/test_pbc.py new file mode 100644 index 0000000..eba4a64 --- /dev/null +++ b/tests/unit/test_pbc.py @@ -0,0 +1,281 @@ +"""Tests for Periodic Boundary Conditions (PBC) in the SOM.""" + +import math + +import pytest +import torch + +from torchsom.core.som import SOM +from torchsom.utils.metrics import calculate_topographic_error + +pytestmark = [ + pytest.mark.unit, +] + + +class TestPBCCoordinateDistances: + """Verify that PBC wraps coordinate distances correctly.""" + + def test_corner_neurons_are_close_with_pbc(self) -> None: + """Opposite corners should be close on a toroidal grid.""" + som = SOM(x=10, y=10, num_features=4, pbc=True, device="cpu") + idx_top_left = 0 + idx_bottom_right = 10 * 10 - 1 + + dist_sq = som.coord_distances_sq[idx_top_left, idx_bottom_right].item() + diag = math.sqrt(1.0**2 + 1.0**2) + assert math.sqrt(dist_sq) <= diag + 0.5 + + def test_edge_neurons_wrap_horizontally(self) -> None: + """Left-edge and right-edge neurons on the same row should be neighbours.""" + som = SOM(x=6, y=6, num_features=4, pbc=True, device="cpu") + left = 0 * 6 + 0 + right = 0 * 6 + 5 + + dist_sq = som.coord_distances_sq[left, right].item() + assert math.sqrt(dist_sq) <= 1.5 + + def test_edge_neurons_wrap_vertically(self) -> None: + """Top-row and bottom-row neurons in the same column should be neighbours.""" + som = SOM(x=6, y=6, num_features=4, pbc=True, device="cpu") + top = 0 * 6 + 0 + bottom = 5 * 6 + 0 + + dist_sq = som.coord_distances_sq[top, bottom].item() + assert math.sqrt(dist_sq) <= 1.5 + + def test_pbc_disabled_edges_are_far(self) -> None: + """Without PBC, opposite edges should have large distance.""" + som = SOM(x=10, y=10, num_features=4, pbc=False, device="cpu") + idx_0 = 0 * 10 + 0 + idx_far = 9 * 10 + 9 + + dist_sq_no_pbc = som.coord_distances_sq[idx_0, idx_far].item() + assert math.sqrt(dist_sq_no_pbc) > 10.0 + + def test_pbc_symmetric_distances(self) -> None: + """PBC distance matrix should remain symmetric.""" + som = SOM(x=8, y=8, num_features=4, pbc=True, device="cpu") + diff = som.coord_distances_sq - som.coord_distances_sq.T + assert torch.allclose(diff, torch.zeros_like(diff), atol=1e-6) + + def test_self_distance_is_zero(self) -> None: + """Distance from any neuron to itself should be zero.""" + som = SOM(x=6, y=6, num_features=4, pbc=True, device="cpu") + diag = torch.diag(som.coord_distances_sq) + assert torch.allclose(diag, torch.zeros_like(diag), atol=1e-7) + + +class TestPBCHexagonal: + """PBC tests specific to hexagonal topology.""" + + def test_hexagonal_pbc_corner_wrap(self) -> None: + """Opposite corners should be closer on a toroidal hexagonal grid.""" + som = SOM( + x=8, y=8, num_features=4, topology="hexagonal", pbc=True, device="cpu" + ) + top_left = 0 + bottom_right = 8 * 8 - 1 + dist_sq = som.coord_distances_sq[top_left, bottom_right].item() + max_non_pbc = math.sqrt(8.0**2 + (8.0 * math.sqrt(3) / 2) ** 2) + assert math.sqrt(dist_sq) < max_non_pbc + + def test_hexagonal_pbc_symmetric(self) -> None: + """PBC distance matrix should remain symmetric for hexagonal grids.""" + som = SOM( + x=6, y=6, num_features=4, topology="hexagonal", pbc=True, device="cpu" + ) + diff = som.coord_distances_sq - som.coord_distances_sq.T + assert torch.allclose(diff, torch.zeros_like(diff), atol=1e-6) + + +class TestPBCNeighborhood: + """Verify that PBC neighborhood influence wraps correctly.""" + + def test_corner_neuron_has_full_neighborhood(self) -> None: + """With PBC a corner neuron should have the same neighbourhood + influence sum as a centre neuron.""" + som = SOM( + x=10, + y=10, + num_features=4, + pbc=True, + neighborhood_function="gaussian", + sigma=2.0, + device="cpu", + ) + corner_idx = torch.tensor([0]) + center_idx = torch.tensor([5 * 10 + 5]) + + corner_nb = som._vectorized_neighborhood(corner_idx, sigma=2.0) + center_nb = som._vectorized_neighborhood(center_idx, sigma=2.0) + + assert torch.allclose(corner_nb.sum(), center_nb.sum(), atol=0.3) + + def test_without_pbc_corner_has_less_influence(self) -> None: + """Without PBC, corner neighbourhood sums should be lower than centre.""" + som = SOM( + x=10, + y=10, + num_features=4, + pbc=False, + neighborhood_function="gaussian", + sigma=2.0, + device="cpu", + ) + corner_idx = torch.tensor([0]) + center_idx = torch.tensor([5 * 10 + 5]) + + corner_nb = som._vectorized_neighborhood(corner_idx, sigma=2.0) + center_nb = som._vectorized_neighborhood(center_idx, sigma=2.0) + + assert corner_nb.sum() < center_nb.sum() + + +class TestPBCTraining: + """Verify that PBC-enabled SOMs train successfully.""" + + def test_fit_runs_without_error(self) -> None: + """PBC-enabled SOM should complete training and return epoch-level errors.""" + torch.manual_seed(42) + data = torch.randn(100, 4) + data = (data - data.mean(0)) / data.std(0) + + som = SOM( + x=5, + y=5, + num_features=4, + epochs=3, + batch_size=16, + pbc=True, + device="cpu", + random_seed=42, + search_backend="torch", + ) + q_errors, t_errors = som.fit(data, verbose=False) + assert len(q_errors) == 3 + assert len(t_errors) == 3 + assert all(isinstance(e, float) for e in q_errors) + + def test_pbc_quantization_error_is_finite(self) -> None: + """Quantization error after PBC training should be a finite number.""" + torch.manual_seed(42) + data = torch.randn(80, 4) + data = (data - data.mean(0)) / data.std(0) + + som = SOM( + x=5, + y=5, + num_features=4, + epochs=3, + batch_size=16, + pbc=True, + device="cpu", + random_seed=42, + search_backend="torch", + ) + som.fit(data, verbose=False) + qe = som.quantization_error(data) + assert math.isfinite(qe) + + def test_pbc_hexagonal_training(self) -> None: + """PBC training should work correctly on hexagonal topology grids.""" + torch.manual_seed(42) + data = torch.randn(80, 4) + data = (data - data.mean(0)) / data.std(0) + + som = SOM( + x=5, + y=5, + num_features=4, + epochs=3, + batch_size=16, + topology="hexagonal", + pbc=True, + device="cpu", + random_seed=42, + search_backend="torch", + ) + q_errors, _ = som.fit(data, verbose=False) + assert len(q_errors) == 3 + + +class TestPBCTopographicError: + """Verify topographic error handles PBC wrapping.""" + + def test_topographic_error_with_pbc(self) -> None: + """Topographic error with PBC enabled should be in [0, 1].""" + torch.manual_seed(42) + data = torch.randn(50, 4) + weights = torch.randn(5, 5, 4) + + from torchsom.utils.distances import DISTANCE_FUNCTIONS + + te = calculate_topographic_error( + data, + weights, + DISTANCE_FUNCTIONS["euclidean"], + topology="rectangular", + pbc=True, + ) + assert 0.0 <= te <= 1.0 + + def test_topographic_error_pbc_vs_no_pbc(self) -> None: + """PBC should generally produce equal or lower topographic error + because adjacency wraps around edges.""" + torch.manual_seed(42) + data = torch.randn(100, 4) + weights = torch.randn(5, 5, 4) + + from torchsom.utils.distances import DISTANCE_FUNCTIONS + + te_no_pbc = calculate_topographic_error( + data, + weights, + DISTANCE_FUNCTIONS["euclidean"], + topology="rectangular", + pbc=False, + ) + te_pbc = calculate_topographic_error( + data, + weights, + DISTANCE_FUNCTIONS["euclidean"], + topology="rectangular", + pbc=True, + ) + assert te_pbc <= te_no_pbc + + +class TestPBCCollectSamples: + """Verify collect_samples wraps neighbours with PBC.""" + + def test_collect_samples_wraps_with_pbc(self) -> None: + """collect_samples should return a non-empty buffer when PBC is active.""" + torch.manual_seed(42) + data = torch.randn(100, 4) + data = (data - data.mean(0)) / data.std(0) + + som = SOM( + x=5, + y=5, + num_features=4, + epochs=2, + batch_size=16, + pbc=True, + device="cpu", + random_seed=42, + search_backend="torch", + ) + som.fit(data, verbose=False) + + bmus_map = som.build_map("bmus_data", data=data, return_indices=True) + query = data[0] + buf_data, buf_out = som.collect_samples( + query_sample=query, + historical_samples=data, + historical_outputs=torch.randn(100), + bmus_idx_map=bmus_map, + min_buffer_threshold=5, + ) + assert buf_data.shape[0] > 0 + assert buf_out.shape[0] == buf_data.shape[0] diff --git a/tests/unit/utils/test_clustering.py b/tests/unit/utils/test_clustering.py index 4580625..4ae9fa9 100644 --- a/tests/unit/utils/test_clustering.py +++ b/tests/unit/utils/test_clustering.py @@ -22,6 +22,8 @@ class TestSOMClustering: + """Tests for clustering via the SOM.cluster() interface.""" + def test_som_cluster_basic( self, som_trained: SOM, @@ -175,13 +177,15 @@ def test_clustering_small_som( class TestClusteringUtilities: + """Tests for the standalone clustering utility functions.""" + def test_cluster_kmeans_basic( self, well_separated_clusters: tuple[torch.Tensor, torch.Tensor], device: str, ) -> None: """Test basic K-means clustering functionality.""" - data, expected_labels = well_separated_clusters + data, _expected_labels = well_separated_clusters data = data.to(device) # Test with known number of clusters @@ -258,8 +262,8 @@ def test_cluster_gmm_auto_components( assert isinstance(result, dict) assert result["n_clusters"] >= 1 - assert isinstance(result["bic"], (float, np.floating)) - assert isinstance(result["aic"], (float, np.floating)) + assert isinstance(result["bic"], float | np.floating) + assert isinstance(result["aic"], float | np.floating) def test_cluster_data_dispatcher( self, diff --git a/tests/unit/utils/test_grid.py b/tests/unit/utils/test_grid.py index f469840..e80ceed 100644 --- a/tests/unit/utils/test_grid.py +++ b/tests/unit/utils/test_grid.py @@ -20,6 +20,8 @@ class TestHexagonalCoordinates: + """Tests for hexagonal coordinate conversion and distance utilities.""" + def test_offset_to_axial_coords_even_rows(self) -> None: """Test offset to axial coordinate conversion for even rows.""" # Test even rows (0, 2, 4...) @@ -86,7 +88,7 @@ def test_cube_axial_roundtrip(self) -> None: test_axial_coords = [(0, 0), (1, 0), (0, 1), (-1, 0), (0, -1), (1, -1)] for q_orig, r_orig in test_axial_coords: - x, y, z = axial_to_cube_coords(q_orig, r_orig) + x, _y, z = axial_to_cube_coords(q_orig, r_orig) q_back, r_back = cube_to_axial_coords(x, z) assert (q_back, r_back) == (q_orig, r_orig) diff --git a/tests/unit/utils/test_search.py b/tests/unit/utils/test_search.py new file mode 100644 index 0000000..24f8e02 --- /dev/null +++ b/tests/unit/utils/test_search.py @@ -0,0 +1,271 @@ +"""Tests for BMU search strategies in torchsom.utils.search.""" + +from unittest.mock import patch + +import pytest +import torch + +from torchsom.utils.distances import DISTANCE_FUNCTIONS +from torchsom.utils.search import ( + FAISS_AVAILABLE, + BMUSearchStrategy, + FAISSSearch, + TorchBruteForceSearch, + create_search_strategy, +) + +pytestmark = [ + pytest.mark.unit, +] + + +class TestTorchBruteForceSearch: + """Tests for the default PyTorch brute-force search backend.""" + + def test_search_returns_correct_shapes(self) -> None: + """Search output tensors should have shape (batch, k).""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = TorchBruteForceSearch(distance_fn) + data = torch.randn(10, 4) + weights = torch.randn(5, 5, 4) + + distances, indices = strategy.search(data, weights, k=1) + assert distances.shape == (10, 1) + assert indices.shape == (10, 1) + + def test_search_k_greater_than_one(self) -> None: + """Search with k>1 should return k neighbours per sample.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = TorchBruteForceSearch(distance_fn) + data = torch.randn(8, 4) + weights = torch.randn(5, 5, 4) + + distances, indices = strategy.search(data, weights, k=3) + assert distances.shape == (8, 3) + assert indices.shape == (8, 3) + + def test_search_finds_nearest_neuron(self) -> None: + """Search should identify the geometrically closest neuron.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = TorchBruteForceSearch(distance_fn) + + weights = torch.zeros(3, 3, 2) + weights[1, 1] = torch.tensor([1.0, 0.0]) + weights[0, 0] = torch.tensor([10.0, 10.0]) + + data = torch.tensor([[1.0, 0.0]]) + _, indices = strategy.search(data, weights, k=1) + assert indices[0, 0].item() == 4 # flat index of (1,1) in 3x3 + + def test_indices_are_valid_flat_indices(self) -> None: + """Returned indices must lie within the flattened grid range.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = TorchBruteForceSearch(distance_fn) + data = torch.randn(20, 4) + weights = torch.randn(5, 6, 4) + + _, indices = strategy.search(data, weights, k=1) + assert (indices >= 0).all() + assert (indices < 30).all() + + def test_rebuild_index_is_noop(self) -> None: + """rebuild_index on brute-force strategy should be a no-op.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = TorchBruteForceSearch(distance_fn) + strategy.rebuild_index(torch.randn(5, 5, 4)) + + def test_distances_are_non_negative(self) -> None: + """All returned distances must be non-negative.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = TorchBruteForceSearch(distance_fn) + data = torch.randn(10, 4) + weights = torch.randn(5, 5, 4) + + distances, _ = strategy.search(data, weights, k=1) + assert (distances >= 0).all() + + @pytest.mark.parametrize( + "metric", ["euclidean", "cosine", "manhattan", "chebyshev"] + ) + def test_works_with_all_distance_functions(self, metric: str) -> None: + """Search should succeed for all supported distance metrics.""" + distance_fn = DISTANCE_FUNCTIONS[metric] + strategy = TorchBruteForceSearch(distance_fn) + data = torch.randn(5, 4) + weights = torch.randn(3, 3, 4) + + distances, indices = strategy.search(data, weights, k=1) + assert distances.shape == (5, 1) + assert indices.shape == (5, 1) + + +class TestFAISSSearchFallback: + """Tests for FAISS search with unsupported metrics (falls back to torch).""" + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_manhattan_falls_back_to_torch(self) -> None: + """Manhattan metric should silently fall back to PyTorch brute-force.""" + distance_fn = DISTANCE_FUNCTIONS["manhattan"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="manhattan") + data = torch.randn(5, 4) + weights = torch.randn(3, 3, 4) + + distances, indices = strategy.search(data, weights, k=1) + assert distances.shape == (5, 1) + assert indices.shape == (5, 1) + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_chebyshev_falls_back_to_torch(self) -> None: + """Chebyshev metric should silently fall back to PyTorch brute-force.""" + distance_fn = DISTANCE_FUNCTIONS["chebyshev"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="chebyshev") + data = torch.randn(5, 4) + weights = torch.randn(3, 3, 4) + + distances, _indices = strategy.search(data, weights, k=1) + assert distances.shape == (5, 1) + + +class TestFAISSSearchNative: + """Tests for FAISS search with natively supported metrics.""" + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_euclidean_search_shapes(self) -> None: + """FAISS euclidean search output should match (batch, k) shape.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="euclidean") + data = torch.randn(10, 4) + weights = torch.randn(5, 5, 4) + + distances, indices = strategy.search(data, weights, k=1) + assert distances.shape == (10, 1) + assert indices.shape == (10, 1) + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_cosine_search_shapes(self) -> None: + """FAISS cosine search output should match (batch, k) shape.""" + distance_fn = DISTANCE_FUNCTIONS["cosine"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="cosine") + data = torch.randn(10, 4) + weights = torch.randn(5, 5, 4) + + distances, indices = strategy.search(data, weights, k=1) + assert distances.shape == (10, 1) + assert indices.shape == (10, 1) + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_euclidean_finds_correct_bmu(self) -> None: + """FAISS should identify the geometrically closest neuron.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="euclidean") + + weights = torch.zeros(3, 3, 2) + weights[1, 1] = torch.tensor([1.0, 0.0]) + data = torch.tensor([[1.0, 0.0]]) + + _, indices = strategy.search(data, weights, k=1) + assert indices[0, 0].item() == 4 + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_rebuild_index_updates_search(self) -> None: + """Rebuilding the index after weight change should reflect new weights.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="euclidean") + weights = torch.randn(3, 3, 4) + strategy.rebuild_index(weights) + assert strategy._index is not None + + new_weights = torch.randn(3, 3, 4) + strategy.rebuild_index(new_weights) + + data = torch.randn(2, 4) + distances, _indices = strategy.search(data, new_weights, k=1) + assert distances.shape == (2, 1) + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_k_greater_than_one(self) -> None: + """FAISS search with k>1 should return k neighbours per sample.""" + distance_fn = DISTANCE_FUNCTIONS["euclidean"] + strategy = FAISSSearch(distance_fn=distance_fn, distance_fn_name="euclidean") + data = torch.randn(5, 4) + weights = torch.randn(4, 4, 4) + + distances, indices = strategy.search(data, weights, k=3) + assert distances.shape == (5, 3) + assert indices.shape == (5, 3) + + +class TestFAISSImportError: + """Tests for graceful handling when FAISS is not installed.""" + + def test_import_error_when_faiss_unavailable(self) -> None: + """FAISSSearch should raise ImportError when faiss is not installed.""" + with patch("torchsom.utils.search.FAISS_AVAILABLE", False): + with patch("torchsom.utils.search.faiss", None): + with pytest.raises(ImportError, match="faiss is required"): + FAISSSearch( + distance_fn=DISTANCE_FUNCTIONS["euclidean"], + distance_fn_name="euclidean", + ) + + +class TestCreateSearchStrategy: + """Tests for the strategy factory function.""" + + def test_torch_backend_returns_brute_force(self) -> None: + """backend='torch' should always return TorchBruteForceSearch.""" + strategy = create_search_strategy( + backend="torch", + distance_fn=DISTANCE_FUNCTIONS["euclidean"], + distance_fn_name="euclidean", + n_neurons=25, + ) + assert isinstance(strategy, TorchBruteForceSearch) + + def test_auto_without_faiss_returns_brute_force(self) -> None: + """auto backend falls back to brute-force when FAISS is unavailable.""" + with patch("torchsom.utils.search.FAISS_AVAILABLE", False): + strategy = create_search_strategy( + backend="auto", + distance_fn=DISTANCE_FUNCTIONS["euclidean"], + distance_fn_name="euclidean", + n_neurons=1000, + ) + assert isinstance(strategy, TorchBruteForceSearch) + + def test_auto_with_incompatible_metric_returns_brute_force(self) -> None: + """auto backend falls back to brute-force for FAISS-incompatible metrics.""" + strategy = create_search_strategy( + backend="auto", + distance_fn=DISTANCE_FUNCTIONS["manhattan"], + distance_fn_name="manhattan", + n_neurons=1000, + ) + assert isinstance(strategy, TorchBruteForceSearch) + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_auto_with_faiss_and_euclidean_returns_faiss(self) -> None: + """auto backend selects FAISS for euclidean metric on a large enough grid.""" + strategy = create_search_strategy( + backend="auto", + distance_fn=DISTANCE_FUNCTIONS["euclidean"], + distance_fn_name="euclidean", + n_neurons=256, + ) + assert isinstance(strategy, FAISSSearch) + + @pytest.mark.skipif(not FAISS_AVAILABLE, reason="faiss not installed") + def test_faiss_backend_returns_faiss(self) -> None: + """backend='faiss' should always return FAISSSearch.""" + strategy = create_search_strategy( + backend="faiss", + distance_fn=DISTANCE_FUNCTIONS["euclidean"], + distance_fn_name="euclidean", + n_neurons=25, + ) + assert isinstance(strategy, FAISSSearch) + + def test_strategy_is_abstract(self) -> None: + """BMUSearchStrategy cannot be instantiated directly.""" + with pytest.raises(TypeError): + BMUSearchStrategy() # type: ignore[abstract] diff --git a/torchsom/__init__.py b/torchsom/__init__.py index 59dff03..a205c30 100644 --- a/torchsom/__init__.py +++ b/torchsom/__init__.py @@ -6,15 +6,12 @@ from torchsom.utils.neighborhood import NEIGHBORHOOD_FUNCTIONS from torchsom.visualization import SOMVisualizer, VisualizationConfig -# from .version import __version__ - -# Define what should be imported when using 'from torchsom import *' __all__ = [ - "SOM", - "BaseSOM", - "DISTANCE_FUNCTIONS", "DECAY_FUNCTIONS", + "DISTANCE_FUNCTIONS", "NEIGHBORHOOD_FUNCTIONS", + "SOM", + "BaseSOM", "SOMVisualizer", "VisualizationConfig", ] diff --git a/torchsom/configs/som_config.py b/torchsom/configs/som_config.py index 92f4433..b84e68e 100644 --- a/torchsom/configs/som_config.py +++ b/torchsom/configs/som_config.py @@ -42,6 +42,19 @@ class SOMConfig(BaseModel): "random", description="Weight initialization method" ) + # Boundary conditions + pbc: bool = Field( + False, + description="Enable periodic boundary conditions (toroidal topology)", + ) + + # Search backend + search_backend: Literal["auto", "torch", "faiss"] = Field( + "auto", + description="BMU search backend. 'auto' uses FAISS when available and the " + "distance metric is compatible, otherwise falls back to PyTorch.", + ) + # Other parameters neighborhood_order: int = Field( 1, description="Neighborhood order for distance calculations", ge=1 diff --git a/torchsom/core/base_som.py b/torchsom/core/base_som.py index 708c54a..de75fcb 100644 --- a/torchsom/core/base_som.py +++ b/torchsom/core/base_som.py @@ -1,7 +1,6 @@ """Abstract base class for all SOM variants.""" from abc import ABC, abstractmethod -from typing import Optional import torch import torch.nn as nn @@ -75,7 +74,7 @@ def topographic_error( def initialize_weights( self, data: torch.Tensor, - mode: Optional[str] = None, + mode: str | None = None, ) -> None: """Initialize the SOM weights. diff --git a/torchsom/core/som.py b/torchsom/core/som.py index 911ed97..dd4253a 100644 --- a/torchsom/core/som.py +++ b/torchsom/core/som.py @@ -1,7 +1,9 @@ """PyTorch implementation of classic Self Organizing Maps using batch learning.""" +import math import warnings -from typing import Any, Callable, Optional, Union +from collections.abc import Callable +from typing import Any import torch import torch.nn as nn @@ -21,6 +23,7 @@ calculate_topographic_error, ) from torchsom.utils.neighborhood import NEIGHBORHOOD_FUNCTIONS +from torchsom.utils.search import create_search_strategy from torchsom.utils.topology import get_all_neighbors_up_to_order @@ -47,6 +50,8 @@ def __init__( neighborhood_function: str = "gaussian", distance_function: str = "euclidean", initialization_mode: str = "random", + pbc: bool = False, + search_backend: str = "auto", device: str = "cuda" if torch.cuda.is_available() else "cpu", random_seed: int = 42, ): @@ -67,6 +72,8 @@ def __init__( neighborhood_function (str, optional): Function to update the weights at each epoch (training). Defaults to "gaussian". distance_function (str, optional): Function to compute the distance between grid weights and input data. Defaults to "euclidean". initialization_mode (str, optional): Method to initialize SOM weights. Defaults to "random". + pbc (bool, optional): Enable periodic boundary conditions (toroidal topology). Defaults to False. + search_backend (str, optional): BMU search backend. ``"auto"`` uses FAISS when available, ``"torch"`` forces PyTorch brute-force, ``"faiss"`` forces FAISS. Defaults to "auto". device (str, optional): Allocate tensors on CPU or GPU. Defaults to "cuda" if available, else "cpu". random_seed (int, optional): Ensure reproducibility. Defaults to 42. @@ -89,6 +96,8 @@ def __init__( raise ValueError("Invalid distance function") if neighborhood_function not in NEIGHBORHOOD_FUNCTIONS: raise ValueError("Invalid neighborhood function") + if search_backend not in ("auto", "torch", "faiss"): + raise ValueError("search_backend must be 'auto', 'torch', or 'faiss'") self.x = x self.y = y @@ -99,6 +108,7 @@ def __init__( self.batch_size = batch_size self.device = device self.topology = topology + self.pbc = pbc self.random_seed = random_seed self.neighborhood_order = neighborhood_order self.distance_fn_name = distance_function @@ -109,6 +119,13 @@ def __init__( ) self.lr_decay_fn = DECAY_FUNCTIONS[lr_decay_function] self.sigma_decay_fn = DECAY_FUNCTIONS[sigma_decay_function] + self._search_strategy = create_search_strategy( + backend=search_backend, + distance_fn=self.distance_fn, + distance_fn_name=distance_function, + n_neurons=x * y, + device=device, + ) x_meshgrid, y_meshgrid = create_mesh_grid(x, y, device) self.xx, self.yy = adjust_meshgrid_topology(x_meshgrid, y_meshgrid, topology) @@ -119,12 +136,6 @@ def __init__( normalized_weights = weights / torch.norm(weights, dim=-1, keepdim=True) self.weights = nn.Parameter(normalized_weights, requires_grad=False) - """ - Pre-compute: - 1. Coordinate distance matrices for efficient distance calculations - 2. Neighbor offsets for topology operations - 3. Decay schedules for all epochs at once - """ self._precompute_coordinate_distances() self._precompute_neighbor_offsets() self.lr_schedule, self.sigma_schedule = self._precompute_decay_schedules( @@ -132,12 +143,22 @@ def __init__( ) def _precompute_coordinate_distances(self) -> None: - """Pre-compute coordinate distance matrices for all neuron pairs, used during neighborhood calculations.""" - # Pre-compute coordinates for all neurons: [x*y, 2] + """Pre-compute coordinate distance matrices for all neuron pairs, used during neighborhood calculations. + + When ``pbc=True``, applies the minimum-image convention so that + coordinate distances wrap around the grid boundaries (toroidal topology). + """ coords = torch.stack([self.xx.flatten(), self.yy.flatten()], dim=1) - # Calculate pairwise coordinate distances between all neurons: [x*y, x*y, 2] coord_diff = coords.unsqueeze(1) - coords.unsqueeze(0) - # Squared distance between each pair of neurons: [x*y, x*y] + + if self.pbc: + grid_size_x = float(self.x) + grid_size_y = float(self.y) + if self.topology == "hexagonal": + grid_size_y *= math.sqrt(3) / 2 + grid_size = torch.tensor([grid_size_x, grid_size_y], device=coords.device) + coord_diff = coord_diff - grid_size * torch.round(coord_diff / grid_size) + self.coord_distances_sq = torch.sum(coord_diff**2, dim=2) def _precompute_neighbor_offsets(self) -> None: @@ -150,6 +171,28 @@ def _precompute_neighbor_offsets(self) -> None: self._even_row_offsets = self._neighbor_offsets["even"] self._odd_row_offsets = self._neighbor_offsets["odd"] + def set_neighborhood_order( + self, + neighborhood_order: int, + ) -> None: + """Update the neighborhood order and recompute neighbor offsets. + + This only affects retrieval (``collect_samples``); trained weights + are untouched. + + Args: + neighborhood_order (int): New neighborhood order (>= 1). + + Raises: + ValueError: If neighborhood_order < 1. + """ + if neighborhood_order < 1: + raise ValueError( + f"neighborhood_order must be >= 1, got {neighborhood_order}" + ) + self.neighborhood_order = neighborhood_order + self._precompute_neighbor_offsets() + def _precompute_decay_schedules( self, epochs: int, @@ -223,8 +266,8 @@ def _update_weights( updates = (weighted_data - neighborhood_sum.unsqueeze(-1) * self.weights) * ( learning_rate / batch_size ) - # Update weights: [x, y, features] self.weights.data += updates + self._search_strategy.rebuild_index(self.weights) def _calculate_distances_to_neurons( self, @@ -254,33 +297,29 @@ def identify_bmus( ) -> torch.Tensor: """Find BMUs for input data. + Uses the configured search strategy (PyTorch brute-force or FAISS). + Args: - data (torch.Tensor): Input tensor of shape [batch_size, features] + data (torch.Tensor): Input tensor of shape [batch_size, features] or [features] Returns: - torch.Tensor: BMU coordinates as tensor [batch_size, 2] + torch.Tensor: BMU coordinates as tensor [batch_size, 2] or [2] """ - # Compute distances between data and all neurons: [batch_size, x, y] - distances = self._calculate_distances_to_neurons(data) + data = data.to(self.device) + single = data.dim() == 1 + if single: + data = data.unsqueeze(0) - # Handle single sample case: [x, y] - if distances.dim() == 2: - # Find the index of the minimum distance: [1] - index = torch.argmin(distances.view(-1)) - # Convert flat index to 2D coordinates: [1, 2] - row, col = torch.unravel_index(index, (self.x, self.y)) - return torch.stack([row, col], dim=0).to(data.device) - # Batch samples: [batch_size, x, y] - else: - batch_size = distances.shape[0] - # Flatten distances: [batch_size, x*y] - distances_flat = distances.view(batch_size, -1) - # Find the index of the minimum distance for each sample: [batch_size] - indices = torch.argmin(distances_flat, dim=1) - # Convert flat indices to 2D coordinates: [batch_size, 2] - rows = torch.div(indices, self.y, rounding_mode="floor") - cols = indices % self.y - return torch.stack([rows, cols], dim=1) + _, indices = self._search_strategy.search(data, self.weights, k=1) + flat_indices = indices.squeeze(1) + + rows = torch.div(flat_indices, self.y, rounding_mode="floor") + cols = flat_indices % self.y + result = torch.stack([rows, cols], dim=1) + + if single: + return result.squeeze(0) + return result def quantization_error( self, @@ -315,13 +354,13 @@ def topographic_error( if data.dim() == 1: data = data.unsqueeze(0) return calculate_topographic_error( - data, self.weights, self.distance_fn, self.topology + data, self.weights, self.distance_fn, self.topology, pbc=self.pbc ) def initialize_weights( self, data: torch.Tensor, - mode: Optional[str] = None, + mode: str | None = None, ) -> None: """Data should be normalized before initialization. @@ -408,6 +447,8 @@ def fit( return epoch_q_errors, epoch_t_errors + _VALID_RETRIEVAL_MODES = ("bmu_only", "bmu_neighborhood", "bmu_neighborhood_knn") + def collect_samples( self, query_sample: torch.Tensor, @@ -416,24 +457,45 @@ def collect_samples( bmus_idx_map: dict[tuple[int, int], list[int]], min_buffer_threshold: int = 50, return_indices: bool = False, - ) -> Union[ - tuple[torch.Tensor, torch.Tensor], - tuple[torch.Tensor, torch.Tensor, torch.Tensor], - ]: + retrieval_mode: str = "bmu_neighborhood_knn", + ) -> ( + tuple[torch.Tensor, torch.Tensor] + | tuple[torch.Tensor, torch.Tensor, torch.Tensor] + ): """Collect historical samples similar to the query sample using SOM projection. + Three retrieval modes control the collection strategy: + + - ``"bmu_only"``: Collect samples mapped to the query's BMU cell only. + - ``"bmu_neighborhood"``: Collect from BMU + topological neighbors + (up to ``neighborhood_order`` hops). No KNN fallback. + - ``"bmu_neighborhood_knn"`` (default): Same as ``bmu_neighborhood``, + plus KNN fallback in weight space when the buffer is below + ``min_buffer_threshold``. + Args: - query_sample (torch.Tensor): Query sample tensor [num_features] - historical_samples (torch.Tensor): Historical samples tensor [num_samples, num_features] - historical_outputs (torch.Tensor): Historical outputs tensor [num_samples] - bmus_idx_map (dict[tuple[int, int], list[int]]): BMU to data indices mapping - min_buffer_threshold (int): Minimum buffer threshold - return_indices (bool): If True, also return the indices of collected samples + query_sample (torch.Tensor): Query sample tensor [num_features]. + historical_samples (torch.Tensor): Historical samples tensor [num_samples, num_features]. + historical_outputs (torch.Tensor): Historical outputs tensor [num_samples]. + bmus_idx_map (dict[tuple[int, int], list[int]]): BMU to data indices mapping. + min_buffer_threshold (int): Minimum buffer size before KNN fallback triggers. + Only used when ``retrieval_mode="bmu_neighborhood_knn"``. + return_indices (bool): If True, also return the indices of collected samples. + retrieval_mode (str): Retrieval strategy. One of ``"bmu_only"``, + ``"bmu_neighborhood"``, or ``"bmu_neighborhood_knn"`` (default). Returns: If return_indices is False: (historical_data_buffer, historical_output_buffer) If return_indices is True: (historical_data_buffer, historical_output_buffer, indices_tensor) + + Raises: + ValueError: If ``retrieval_mode`` is not one of the valid modes. """ + if retrieval_mode not in self._VALID_RETRIEVAL_MODES: + raise ValueError( + f"retrieval_mode must be one of {self._VALID_RETRIEVAL_MODES}, " + f"got '{retrieval_mode}'" + ) query_sample = query_sample.to(self.device) bmu_pos = self.identify_bmus(query_sample) bmu_row, bmu_col = int(bmu_pos[0].item()), int(bmu_pos[1].item()) @@ -444,19 +506,28 @@ def collect_samples( row_type = "even" if bmu_row % 2 == 0 else "odd" offsets = self._neighbor_offsets[row_type] - # Collect samples from BMU and its topological neighbors + # Tier 1: Always collect from BMU cell collected_sample_indices = list(bmus_idx_map.get(bmu_tuple, [])) visited_neurons = {bmu_tuple} - for dx, dy in offsets: - nr, nc = bmu_row + dx, bmu_col + dy - if 0 <= nr < self.x and 0 <= nc < self.y: + + # Tier 2: Neighborhood expansion (skip for bmu_only) + if retrieval_mode != "bmu_only": + for dx, dy in offsets: + nr, nc = bmu_row + dx, bmu_col + dy + if self.pbc: + nr, nc = nr % self.x, nc % self.y + elif not (0 <= nr < self.x and 0 <= nc < self.y): + continue pos = (nr, nc) if pos not in visited_neurons and pos in bmus_idx_map: collected_sample_indices.extend(bmus_idx_map[pos]) visited_neurons.add(pos) - # If we need more samples, use distance-based collection - if len(collected_sample_indices) <= min_buffer_threshold: + # Tier 3: KNN fallback in weight space (only for bmu_neighborhood_knn) + if ( + retrieval_mode == "bmu_neighborhood_knn" + and len(collected_sample_indices) <= min_buffer_threshold + ): bmu_weights = self.weights[bmu_row, bmu_col] distances = self._calculate_distances_to_neurons(bmu_weights) @@ -468,7 +539,7 @@ def collect_samples( candidate_neurons.append((dist, r, c)) candidate_neurons.sort(key=lambda x: x[0]) - # Collect samples from candidate unvisitedneurons + # Collect from nearest unvisited neurons until threshold for _, r, c in candidate_neurons: collected_sample_indices.extend(bmus_idx_map[(r, c)]) visited_neurons.add((r, c)) @@ -490,9 +561,9 @@ def collect_samples( def build_map( self, map_type: str, - data: Optional[torch.Tensor] = None, - target: Optional[torch.Tensor] = None, - bmus_data_map: Optional[dict[tuple[int, int], list[int]]] = None, + data: torch.Tensor | None = None, + target: torch.Tensor | None = None, + bmus_data_map: dict[tuple[int, int], list[int]] | None = None, **kwargs: Any, ) -> torch.Tensor: """Unified method to build various types of maps. @@ -580,7 +651,7 @@ def build_multiple_maps( self, map_configs: list[dict[str, Any]], data: torch.Tensor, - target: Optional[torch.Tensor] = None, + target: torch.Tensor | None = None, batch_size: int = 1024, ) -> dict[str, torch.Tensor]: """Efficiently build multiple maps by reusing BMUs computation. @@ -595,13 +666,15 @@ def build_multiple_maps( dict[str, torch.Tensor]: Dictionary mapping map names to their results Example: - configs = [ - {'type': 'hit'}, - {'type': 'metric', 'kwargs': {'reduction_parameter': 'std'}}, - {'type': 'rank'}, - {'type': 'classification', 'kwargs': {'neighborhood_order': 2}} - ] - results = som.build_multiple_maps(configs, data, target) + .. code-block:: python + + configs = [ + {"type": "hit"}, + {"type": "metric", "kwargs": {"reduction_parameter": "std"}}, + {"type": "rank"}, + {"type": "classification", "kwargs": {"neighborhood_order": 2}}, + ] + results = som.build_multiple_maps(configs, data, target) """ data_dependent_maps = {"hit", "bmus_data"} bmus_dependent_maps = {"metric", "score", "rank", "classification"} @@ -639,7 +712,7 @@ def build_multiple_maps( def cluster( self, method: str = "kmeans", - n_clusters: Optional[int] = None, + n_clusters: int | None = None, feature_space: str = "weights", **kwargs: Any, ) -> dict[str, Any]: diff --git a/torchsom/utils/__init__.py b/torchsom/utils/__init__.py index 58d9bed..d66d0d8 100644 --- a/torchsom/utils/__init__.py +++ b/torchsom/utils/__init__.py @@ -16,7 +16,11 @@ hexagonal_distance_offset, offset_to_axial_coords, ) -from torchsom.utils.initialization import initialize_weights, pca_init, random_init +from torchsom.utils.initialization import ( + initialize_weights, + pca_init, + random_init, +) from torchsom.utils.metrics import ( calculate_calinski_harabasz_score, calculate_clustering_metrics, @@ -27,6 +31,13 @@ calculate_topological_clustering_quality, ) from torchsom.utils.neighborhood import NEIGHBORHOOD_FUNCTIONS +from torchsom.utils.search import ( + FAISS_AVAILABLE, + BMUSearchStrategy, + FAISSSearch, + TorchBruteForceSearch, + create_search_strategy, +) from torchsom.utils.topology import ( get_all_neighbors_up_to_order, get_hexagonal_offsets, @@ -34,31 +45,36 @@ ) __all__ = [ - "DISTANCE_FUNCTIONS", "DECAY_FUNCTIONS", + "DISTANCE_FUNCTIONS", + "FAISS_AVAILABLE", "NEIGHBORHOOD_FUNCTIONS", - "create_mesh_grid", + "BMUSearchStrategy", + "FAISSSearch", + "TorchBruteForceSearch", "adjust_meshgrid_topology", - "offset_to_axial_coords", "axial_to_offset_coords", - "hexagonal_distance_axial", - "hexagonal_distance_offset", - "grid_to_display_coords", - "initialize_weights", - "random_init", - "pca_init", + "calculate_calinski_harabasz_score", + "calculate_clustering_metrics", + "calculate_davies_bouldin_score", "calculate_quantization_error", - "calculate_topographic_error", "calculate_silhouette_score", - "calculate_davies_bouldin_score", - "calculate_calinski_harabasz_score", + "calculate_topographic_error", "calculate_topological_clustering_quality", - "calculate_clustering_metrics", "cluster_data", - "cluster_kmeans", "cluster_gmm", "cluster_hdbscan", + "cluster_kmeans", + "create_mesh_grid", + "create_search_strategy", + "get_all_neighbors_up_to_order", "get_hexagonal_offsets", "get_rectangular_offsets", - "get_all_neighbors_up_to_order", + "grid_to_display_coords", + "hexagonal_distance_axial", + "hexagonal_distance_offset", + "initialize_weights", + "offset_to_axial_coords", + "pca_init", + "random_init", ] diff --git a/torchsom/utils/clustering.py b/torchsom/utils/clustering.py index ec7e7c0..d686fd8 100644 --- a/torchsom/utils/clustering.py +++ b/torchsom/utils/clustering.py @@ -1,7 +1,7 @@ """Clustering algorithms for SOM analysis using scikit-learn.""" import warnings -from typing import TYPE_CHECKING, Any, Optional +from typing import TYPE_CHECKING, Any import numpy as np import torch @@ -15,7 +15,7 @@ def cluster_kmeans( data: torch.Tensor, - n_clusters: Optional[int] = None, + n_clusters: int | None = None, random_state: int = 42, **kwargs: Any, ) -> dict[str, Any]: @@ -67,7 +67,7 @@ def cluster_kmeans( def cluster_gmm( data: torch.Tensor, - n_components: Optional[int] = None, + n_components: int | None = None, random_state: int = 42, **kwargs: Any, ) -> dict[str, Any]: @@ -124,7 +124,7 @@ def cluster_gmm( def cluster_hdbscan( data: torch.Tensor, - min_cluster_size: Optional[int] = None, + min_cluster_size: int | None = None, **kwargs: Any, ) -> dict[str, Any]: # pragma: no cover """HDBSCAN clustering using scikit-learn. @@ -153,7 +153,7 @@ def cluster_hdbscan( ) # labels = clusterer.fit_predict(data_np) clusterer.fit(data_np) - labels, strengths = prediction.approximate_predict(clusterer, data_np) + labels, _strengths = prediction.approximate_predict(clusterer, data_np) # Calculate cluster centers (excluding noise points) unique_labels = np.unique(labels) @@ -315,7 +315,7 @@ def extract_clustering_features( def cluster_data( data: torch.Tensor, method: str = "kmeans", - n_clusters: Optional[int] = None, + n_clusters: int | None = None, **kwargs: Any, ) -> dict[str, Any]: """Main clustering function that dispatches to specific algorithms. diff --git a/torchsom/utils/grid.py b/torchsom/utils/grid.py index 3d25f42..c9fa91a 100644 --- a/torchsom/utils/grid.py +++ b/torchsom/utils/grid.py @@ -26,8 +26,9 @@ def create_mesh_grid( Returns: Tuple[torch.Tensor, torch.Tensor]: Two tensors (xx, yy) of shape (x, y), representing the x and y coordinates of the mesh grid. """ - x_tensor, y_tensor = torch.arange(x, device=device), torch.arange( - y, device=device + x_tensor, y_tensor = ( + torch.arange(x, device=device), + torch.arange(y, device=device), ) # Shape: (x) and (y) x_meshgrid, y_meshgrid = torch.meshgrid( x_tensor, y_tensor, indexing="ij" diff --git a/torchsom/utils/hexagonal_coordinates.py b/torchsom/utils/hexagonal_coordinates.py index 51e7878..f99c1e5 100644 --- a/torchsom/utils/hexagonal_coordinates.py +++ b/torchsom/utils/hexagonal_coordinates.py @@ -101,8 +101,10 @@ def hexagonal_distance_axial( """Calculate distance between two hexagons using axial coordinates. Args: - q1, r1: First hexagon's axial coordinates - q2, r2: Second hexagon's axial coordinates + q1 (float): First hexagon's q axial coordinate. + r1 (float): First hexagon's r axial coordinate. + q2 (float): Second hexagon's q axial coordinate. + r2 (float): Second hexagon's r axial coordinate. Returns: int: Distance in hex steps @@ -124,8 +126,10 @@ def hexagonal_distance_offset( """Calculate distance between two hexagons using offset coordinates. Args: - row1, col1: First hexagon's offset coordinates - row2, col2: Second hexagon's offset coordinates + row1 (int): First hexagon's row offset coordinate. + col1 (int): First hexagon's column offset coordinate. + row2 (int): Second hexagon's row offset coordinate. + col2 (int): Second hexagon's column offset coordinate. Returns: int: Distance in hex steps diff --git a/torchsom/utils/initialization.py b/torchsom/utils/initialization.py index ae0c95f..e463eed 100644 --- a/torchsom/utils/initialization.py +++ b/torchsom/utils/initialization.py @@ -37,7 +37,7 @@ def random_init( return sampled_weights except RuntimeError as e: - raise RuntimeError(f"Random initialization failed: {str(e)}") + raise RuntimeError(f"Random initialization failed: {e!s}") def pca_init( @@ -82,7 +82,7 @@ def pca_init( # Try SVD first (more stable than eigendecomposition) try: - U, S, V = torch.linalg.svd( + _U, _S, V = torch.linalg.svd( cov, driver=None, # Default is None, but also: "gesvd" (small), "gesvdj" (medium), and "gesvda" (large) full_matrices=True, # Default is True @@ -124,7 +124,7 @@ def pca_init( except Exception as e: warnings.warn( - f"PCA initialization failed: {str(e)}. Falling back to random initialization", + f"PCA initialization failed: {e!s}. Falling back to random initialization", stacklevel=2, ) return random_init(weights, data, device) diff --git a/torchsom/utils/maps.py b/torchsom/utils/maps.py index 6872951..40763a8 100644 --- a/torchsom/utils/maps.py +++ b/torchsom/utils/maps.py @@ -2,7 +2,7 @@ import random from collections import Counter, defaultdict -from typing import TYPE_CHECKING, Any, Optional +from typing import TYPE_CHECKING, Any import torch @@ -88,8 +88,8 @@ def build_hit_map( def build_distance_map( som_instance: "BaseSOM", - distance_metric: Optional[str] = None, - neighborhood_order: Optional[int] = None, + distance_metric: str | None = None, + neighborhood_order: int | None = None, scaling: str = "sum", ) -> torch.Tensor: """Build distance map showing neuron-to-neighbor distances. @@ -169,9 +169,9 @@ def build_distance_map( ], dim=1, ) - distance_map[ - valid_positions[:, 0], valid_positions[:, 1] - ] += distances + distance_map[valid_positions[:, 0], valid_positions[:, 1]] += ( + distances + ) counts[valid_positions[:, 0], valid_positions[:, 1]] += 1 else: @@ -279,7 +279,6 @@ def build_score_map( for bmu_pos, sample_indices in bmus_data_map.items(): if len(sample_indices) > 0: - # Multiple samples in neuron if len(sample_indices) > 1: if isinstance(sample_indices, list): diff --git a/torchsom/utils/metrics.py b/torchsom/utils/metrics.py index cb5d896..a9674e5 100644 --- a/torchsom/utils/metrics.py +++ b/torchsom/utils/metrics.py @@ -1,7 +1,8 @@ """Utility functions for metrics.""" import warnings -from typing import TYPE_CHECKING, Callable +from collections.abc import Callable +from typing import TYPE_CHECKING import torch from sklearn.metrics import ( @@ -52,6 +53,7 @@ def calculate_topographic_error( weights: torch.Tensor, distance_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], topology: str = "rectangular", + pbc: bool = False, ) -> float: """Calculate topographic error for a SOM. @@ -60,6 +62,7 @@ def calculate_topographic_error( weights (torch.Tensor): SOM weights [x, y, num_features] distance_fn (Callable): Function to compute distances between data and weights topology (str, optional): Grid configuration. Defaults to "rectangular". + pbc (bool, optional): Whether periodic boundary conditions are enabled. Defaults to False. Returns: float: Topographic error ratio @@ -78,14 +81,10 @@ def calculate_topographic_error( return float("nan") batch_size = data.shape[0] - # Calculate distances between each data point and all neurons: [batch_size, x, y] distances = distance_fn(data, weights) - # Flatten distances: [batch_size, x*y] distances_flat = distances.view(batch_size, -1) - # Get top 2 BMU indices efficiently: [batch_size, 2] _, indices = torch.topk(distances_flat, k=2, largest=False, dim=1) - # Convert flat indices to 2D coordinates bmu1_row, bmu1_col = ( torch.div(indices[:, 0], y_dim, rounding_mode="floor"), indices[:, 0] % y_dim, @@ -97,18 +96,56 @@ def calculate_topographic_error( if topology == "hexagonal": error_count = 0 for i in range(batch_size): - q1, r1 = offset_to_axial_coords(bmu1_row[i].item(), bmu1_col[i].item()) - q2, r2 = offset_to_axial_coords(bmu2_row[i].item(), bmu2_col[i].item()) - hex_distance = hexagonal_distance_axial(q1, r1, q2, r2) - if hex_distance > 1: + r1_val, c1_val = int(bmu1_row[i].item()), int(bmu1_col[i].item()) + r2_val, c2_val = int(bmu2_row[i].item()), int(bmu2_col[i].item()) + + if pbc: + min_dist = _pbc_hex_min_distance( + r1_val, c1_val, r2_val, c2_val, x_dim, y_dim + ) + else: + q1, r1 = offset_to_axial_coords(r1_val, c1_val) + q2, r2 = offset_to_axial_coords(r2_val, c2_val) + min_dist = hexagonal_distance_axial(q1, r1, q2, r2) + + if min_dist > 1: error_count += 1 return error_count / batch_size else: dx = (bmu2_row - bmu1_row).float() dy = (bmu2_col - bmu1_col).float() - distances = torch.sqrt(dx**2 + dy**2) + + if pbc: + dx = dx - x_dim * torch.round(dx / x_dim) + dy = dy - y_dim * torch.round(dy / y_dim) + + grid_distances = torch.sqrt(dx**2 + dy**2) threshold = 1.0 - return (distances > threshold).float().mean().item() + return (grid_distances > threshold).float().mean().item() + + +def _pbc_hex_min_distance( + r1: int, + c1: int, + r2: int, + c2: int, + x_dim: int, + y_dim: int, +) -> int: + """Compute the minimum hexagonal distance considering periodic images. + + Checks the direct distance and all 8 periodic translations to find + the shortest path on the wrapped hex grid. + """ + best = float("inf") + for dr in (-x_dim, 0, x_dim): + for dc in (-y_dim, 0, y_dim): + q1, rr1 = offset_to_axial_coords(r1, c1) + q2, rr2 = offset_to_axial_coords(r2 + dr, c2 + dc) + d = hexagonal_distance_axial(q1, rr1, q2, rr2) + if d < best: + best = d + return int(best) def calculate_silhouette_score( @@ -315,7 +352,8 @@ def calculate_topological_clustering_quality( avg_distance = pairwise_distances[mask].mean().item() # Normalize by maximum possible distance on grid - max_distance = max(som.x, som.y) + # max_distance = max(som.x, som.y) + max_distance = max(int(som.x), int(som.y)) normalized_distance = avg_distance / max_distance # Convert to coherence (inverse of distance) diff --git a/torchsom/utils/search.py b/torchsom/utils/search.py new file mode 100644 index 0000000..5a1cd25 --- /dev/null +++ b/torchsom/utils/search.py @@ -0,0 +1,271 @@ +"""BMU search strategies with optional FAISS acceleration. + +Provides a strategy abstraction for Best Matching Unit (BMU) search, +allowing transparent switching between PyTorch brute-force and FAISS-backed +nearest-neighbor search. + +FAISS natively supports L2 (Euclidean) and inner-product metrics. +Cosine distance is handled by normalizing vectors before inner-product search. +Manhattan and Chebyshev distances fall back to PyTorch brute-force. +""" + +from abc import ABC, abstractmethod +from collections.abc import Callable +from typing import Literal + +import torch + +try: + import faiss + + FAISS_AVAILABLE = True +except ImportError: + faiss = None + FAISS_AVAILABLE = False + + +_FAISS_COMPATIBLE_METRICS = {"euclidean", "cosine"} + +# FAISS adds overhead for small grids and can be unstable on some platforms +# (e.g. macOS/aarch64) when the index contains very few vectors. +_FAISS_MIN_NEURONS = 256 + + +class BMUSearchStrategy(ABC): + """Abstract interface for BMU search backends.""" + + @abstractmethod + def search( + self, + data: torch.Tensor, + weights: torch.Tensor, + k: int = 1, + ) -> tuple[torch.Tensor, torch.Tensor]: + """Find the k nearest neurons for each data sample. + + Args: + data: Input tensor of shape ``[batch_size, num_features]``. + weights: SOM weight tensor of shape ``[x, y, num_features]``. + k: Number of nearest neighbors to return. + + Returns: + A ``(distances, indices)`` tuple where *distances* has shape + ``[batch_size, k]`` and *indices* has shape ``[batch_size, k]`` + (flat neuron indices into the ``x*y`` grid). + """ + + @abstractmethod + def rebuild_index(self, weights: torch.Tensor) -> None: + """Rebuild the internal index after weight updates. + + Args: + weights: Updated SOM weight tensor of shape ``[x, y, num_features]``. + """ + + +class TorchBruteForceSearch(BMUSearchStrategy): + """Brute-force BMU search using PyTorch distance functions.""" + + def __init__( + self, + distance_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + ) -> None: + """Initialize with the given distance function. + + Args: + distance_fn (Callable[[torch.Tensor, torch.Tensor], torch.Tensor]): The distance function to use. + """ + self._distance_fn = distance_fn + + def search( + self, + data: torch.Tensor, + weights: torch.Tensor, + k: int = 1, + ) -> tuple[torch.Tensor, torch.Tensor]: + """Search using brute-force PyTorch distance computations. + + Args: + data (torch.Tensor): The input data tensor. + weights (torch.Tensor): The SOM weight tensor. + k (int): The number of nearest neighbors to return. + + Returns: + A tuple of (distances, indices) where distances is a tensor of shape (batch_size, k) and indices is a tensor of shape (batch_size, k). + """ + distances = self._distance_fn(data, weights) + batch_size = distances.shape[0] + distances_flat = distances.view(batch_size, -1) + if k == 1: + indices = torch.argmin(distances_flat, dim=1, keepdim=True) + else: + _, indices = torch.topk(distances_flat, k, dim=1, largest=False) + gathered = torch.gather(distances_flat, 1, indices) + return gathered, indices + + def rebuild_index(self, weights: torch.Tensor) -> None: + """No-op; brute-force search does not maintain an index.""" + pass + + +class FAISSSearch(BMUSearchStrategy): + """FAISS-backed BMU search for accelerated nearest-neighbor lookup. + + Falls back to :class:`TorchBruteForceSearch` when the chosen distance + metric is not natively supported by FAISS (i.e. anything other than + Euclidean or cosine). + """ + + def __init__( + self, + distance_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + distance_fn_name: str, + device: str = "cpu", + index_type: Literal["flat", "ivf"] = "flat", + nprobe: int = 8, + ) -> None: + """Initialize FAISS search with the given distance function and configuration.""" + if not FAISS_AVAILABLE: + raise ImportError( + "faiss is required for the FAISS search backend. " + "Install it with: pip install faiss-cpu (or faiss-gpu)" + ) + + self._distance_fn_name = distance_fn_name + self._device = device + self._index_type = index_type + self._nprobe = nprobe + self._index: faiss.Index | None = None + self._use_cosine = distance_fn_name == "cosine" + + if distance_fn_name not in _FAISS_COMPATIBLE_METRICS: + self._fallback = TorchBruteForceSearch(distance_fn) + else: + self._fallback = None + + def _build_index( + self, + vectors: torch.Tensor, + ) -> "faiss.Index": + """Build a FAISS index from a flat ``[n, d]`` float32 tensor.""" + n, d = vectors.shape + vectors_np = vectors.detach().cpu().float().numpy() + + if self._use_cosine: + faiss.normalize_L2(vectors_np) + + if self._index_type == "ivf" and n >= 256: + nlist = min(int(n**0.5), n // 4) + quantizer = faiss.IndexFlatL2(d) + index = faiss.IndexIVFFlat(quantizer, d, nlist) + index.train(vectors_np) + index.nprobe = self._nprobe + else: + index = faiss.IndexFlatL2(d) + + index.add(vectors_np) + + if self._device.startswith("cuda") and hasattr(faiss, "index_cpu_to_gpu"): + gpu_id = 0 + if ":" in self._device: + gpu_id = int(self._device.split(":")[1]) + res = faiss.StandardGpuResources() + index = faiss.index_cpu_to_gpu(res, gpu_id, index) + + return index + + def rebuild_index( + self, + weights: torch.Tensor, + ) -> None: + """Rebuild the FAISS index from the flattened weight tensor. + + Args: + weights (torch.Tensor): The SOM weight tensor. + """ + _x, _y, d = weights.shape + flat_weights = weights.view(-1, d) + if self._fallback is not None: + return + self._index = self._build_index(flat_weights) + + def search( + self, + data: torch.Tensor, + weights: torch.Tensor, + k: int = 1, + ) -> tuple[torch.Tensor, torch.Tensor]: + """Search using the FAISS index, or fall back to brute-force for unsupported metrics. + + Args: + data (torch.Tensor): The input data tensor. + weights (torch.Tensor): The SOM weight tensor. + k (int): The number of nearest neighbors to return. + + Returns: + A tuple of (distances, indices) where distances is a tensor of shape (batch_size, k) and indices is a tensor of shape (batch_size, k). + """ + if self._fallback is not None: + return self._fallback.search(data, weights, k) + + if self._index is None: + self.rebuild_index(weights) + + query = data.detach().cpu().float().numpy() + if self._use_cosine: + faiss.normalize_L2(query) + + distances_np, indices_np = self._index.search(query, k) + + distances = torch.from_numpy(distances_np).to(data.device) + indices = torch.from_numpy(indices_np).long().to(data.device) + + if self._use_cosine: + distances = torch.clamp(distances, min=0.0) + + return distances, indices + + +def create_search_strategy( + backend: Literal["auto", "torch", "faiss"], + distance_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], + distance_fn_name: str, + n_neurons: int, + device: str = "cpu", + faiss_index_type: Literal["flat", "ivf"] = "flat", + faiss_nprobe: int = 8, +) -> BMUSearchStrategy: + """Factory that instantiates the appropriate search strategy. + + Args: + backend (Literal["auto", "torch", "faiss"]): ``"auto"`` selects FAISS when available and the metric is + compatible, otherwise falls back to PyTorch. ``"torch"`` and + ``"faiss"`` force a specific backend. + distance_fn: The PyTorch distance callable (used by the torch backend + and as a FAISS fallback for unsupported metrics). + distance_fn_name: Name of the distance function (e.g. ``"euclidean"``). + n_neurons: Total number of neurons in the grid (``x * y``). When fewer + than :data:`_FAISS_MIN_NEURONS`, ``"auto"`` falls back to PyTorch + because FAISS adds overhead and can be unstable for tiny indices. + device: Compute device (``"cpu"`` or ``"cuda"``). + faiss_index_type: FAISS index structure, ``"flat"`` for exact search + or ``"ivf"`` for approximate. + faiss_nprobe: Number of cells to probe when using IVF indices. + + Returns: + A concrete :class:`BMUSearchStrategy` instance. + """ + auto_use_faiss = ( + FAISS_AVAILABLE + and distance_fn_name in _FAISS_COMPATIBLE_METRICS + and n_neurons >= _FAISS_MIN_NEURONS + ) + if backend == "faiss" or (backend == "auto" and auto_use_faiss): + return FAISSSearch( + distance_fn=distance_fn, + distance_fn_name=distance_fn_name, + device=device, + index_type=faiss_index_type, + nprobe=faiss_nprobe, + ) + return TorchBruteForceSearch(distance_fn) diff --git a/torchsom/utils/topology.py b/torchsom/utils/topology.py index 5791dc1..a158353 100644 --- a/torchsom/utils/topology.py +++ b/torchsom/utils/topology.py @@ -1,7 +1,5 @@ """Utility functions for topology.""" -from typing import Union - def get_rectangular_offsets( neighborhood_order: int = 1, @@ -109,7 +107,7 @@ def generate_axial_ring(distance: int) -> list[tuple[int, int]]: def get_all_neighbors_up_to_order( topology: str, max_order: int, -) -> Union[list[tuple[int, int]], dict[str, list[tuple[int, int]]]]: +) -> list[tuple[int, int]] | dict[str, list[tuple[int, int]]]: """Get all neighbors from order 1 up to max_order. Args: diff --git a/torchsom/visualization/__init__.py b/torchsom/visualization/__init__.py index 4807739..a2fb99f 100644 --- a/torchsom/visualization/__init__.py +++ b/torchsom/visualization/__init__.py @@ -8,10 +8,10 @@ from torchsom.visualization.rectangular import RectangularVisualizer __all__ = [ - "VisualizationConfig", - "SOMVisualizer", "BaseVisualizer", + "ClusteringVisualizer", "HexagonalVisualizer", "RectangularVisualizer", - "ClusteringVisualizer", + "SOMVisualizer", + "VisualizationConfig", ] diff --git a/torchsom/visualization/base.py b/torchsom/visualization/base.py index 2845d99..a2d6bc7 100644 --- a/torchsom/visualization/base.py +++ b/torchsom/visualization/base.py @@ -1,7 +1,7 @@ """Base class for all visualization methods.""" from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import matplotlib.pyplot as plt import torch @@ -23,7 +23,7 @@ class SOMVisualizer: def __init__( self, som: BaseSOM, - config: Optional[VisualizationConfig] = None, + config: VisualizationConfig | None = None, ) -> None: """Initialize the SOM visualizer factory. @@ -151,8 +151,8 @@ def plot_all( bmus_data_map: dict[tuple[int, int], list[int]], data: torch.Tensor, target: torch.Tensor, - component_names: Optional[list[str]] = None, - save_path: Optional[Union[str, Path]] = None, + component_names: list[str] | None = None, + save_path: str | Path | None = None, training_errors: bool = True, distance_map: bool = True, hit_map: bool = True, diff --git a/torchsom/visualization/base_visualizer.py b/torchsom/visualization/base_visualizer.py index be48e52..fd44338 100644 --- a/torchsom/visualization/base_visualizer.py +++ b/torchsom/visualization/base_visualizer.py @@ -2,7 +2,7 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import matplotlib.pyplot as plt import torch @@ -17,8 +17,8 @@ class BaseVisualizer(ABC): def __init__( self, som: BaseSOM, - config: Optional[VisualizationConfig] = None, - expected_topology: str = None, + config: VisualizationConfig | None = None, + expected_topology: str | None = None, ) -> None: """Initialize the base visualizer. @@ -36,7 +36,7 @@ def __init__( def _prepare_save_path( self, - save_path: Union[str, Path], + save_path: str | Path, ) -> Path: """Prepare directory for saving visualizations. @@ -52,7 +52,7 @@ def _prepare_save_path( def _save_plot( self, - save_path: Union[str, Path], + save_path: str | Path, name: str, ) -> None: """Save plot with specified configuration. @@ -79,8 +79,8 @@ def plot_grid( title: str, colorbar_label: str, filename: str, - save_path: Optional[Union[str, Path]] = None, - cmap: Optional[str] = None, + save_path: str | Path | None = None, + cmap: str | None = None, show_values: bool = False, value_format: str = ".2f", **kwargs: Any, @@ -103,9 +103,9 @@ def plot_grid( def plot_distance_map( self, fig_name: str = "distance_map", - save_path: Optional[Union[str, Path]] = None, - distance_metric: Optional[str] = None, - neighborhood_order: Optional[int] = None, + save_path: str | Path | None = None, + distance_metric: str | None = None, + neighborhood_order: int | None = None, scaling: str = "sum", ) -> None: """Plot the distance map (U-Matrix). @@ -135,7 +135,7 @@ def plot_hit_map( self, data: torch.Tensor, fig_name: str = "hit_map", - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, batch_size: int = 1024, ) -> None: """Plot hit map. @@ -161,8 +161,8 @@ def plot_classification_map( data: torch.Tensor, target: torch.Tensor, fig_name: str = "classification_map", - save_path: Optional[Union[str, Path]] = None, - neighborhood_order: Optional[int] = None, + save_path: str | Path | None = None, + neighborhood_order: int | None = None, ) -> None: """Plot classification map. @@ -195,8 +195,8 @@ def plot_metric_map( data: torch.Tensor, target: torch.Tensor, reduction_parameter: str = "mean", - fig_name: Optional[str] = None, - save_path: Optional[Union[str, Path]] = None, + fig_name: str | None = None, + save_path: str | Path | None = None, ) -> None: """Plot target metric map. @@ -235,7 +235,7 @@ def plot_score_map( target: torch.Tensor, total_samples: int, fig_name: str = "score_map", - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """Plot neuron representativeness score map. @@ -265,7 +265,7 @@ def plot_rank_map( bmus_data_map: dict[tuple[int, int], list[int]], target: torch.Tensor, fig_name: str = "rank_map", - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """Plot ranked neurons map. @@ -292,8 +292,8 @@ def plot_rank_map( def plot_component_planes( self, - component_names: Optional[list[str]] = None, - save_path: Optional[Union[str, Path]] = None, + component_names: list[str] | None = None, + save_path: str | Path | None = None, ) -> None: """Plot component planes. @@ -303,7 +303,7 @@ def plot_component_planes( """ n_components = self.som.weights.shape[-1] component_names = component_names or [ - f"Component_{i+1}" for i in range(n_components) + f"Component_{i + 1}" for i in range(n_components) ] for i, name in enumerate(component_names): component_weights = self.som.weights[:, :, i].cpu() @@ -321,7 +321,7 @@ def plot_training_errors( quantization_errors: list[float], topographic_errors: list[float], fig_name: str = "training_errors", - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """Plot training errors over epochs. @@ -337,7 +337,7 @@ def plot_training_errors( if isinstance(topographic_errors, torch.Tensor): topographic_errors = topographic_errors.cpu().numpy() - fig, (ax1, ax2) = plt.subplots( + _fig, (ax1, ax2) = plt.subplots( 2, 1, figsize=self.config.figsize, gridspec_kw={"hspace": 0.3} ) diff --git a/torchsom/visualization/clustering.py b/torchsom/visualization/clustering.py index f2f49c9..bdc3258 100644 --- a/torchsom/visualization/clustering.py +++ b/torchsom/visualization/clustering.py @@ -1,7 +1,7 @@ """Clustering visualization methods for Self-Organizing Maps.""" from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import matplotlib.pyplot as plt import numpy as np @@ -22,7 +22,7 @@ class ClusteringVisualizer: def __init__( self, som: SOM, - config: Optional[VisualizationConfig] = None, + config: VisualizationConfig | None = None, ) -> None: """Initialize the clustering visualizer. @@ -35,7 +35,7 @@ def __init__( def _prepare_save_path( self, - save_path: Union[str, Path], + save_path: str | Path, ) -> Path: """Prepare directory for saving visualizations. @@ -51,7 +51,7 @@ def _prepare_save_path( def _save_plot( self, - save_path: Union[str, Path], + save_path: str | Path, name: str, ) -> None: """Save plot with specified configuration. @@ -103,8 +103,8 @@ def _create_cluster_colormap( def plot_cluster_map( self, cluster_result: dict[str, Any], - title: Optional[str] = None, - save_path: Optional[Union[str, Path]] = None, + title: str | None = None, + save_path: str | Path | None = None, show_values: bool = False, **kwargs: Any, ) -> None: @@ -180,7 +180,7 @@ def plot_cluster_map( def plot_silhouette_analysis( self, cluster_result: dict[str, Any], - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """Plot silhouette analysis for clustering results. @@ -215,7 +215,7 @@ def plot_silhouette_analysis( unique_labels = np.unique(labels_clean) sample_silhouette_values = silhouette_samples(data_clean, labels_clean) - fig, ax = plt.subplots(figsize=self.config.figsize) + _fig, ax = plt.subplots(figsize=self.config.figsize) y_lower = 10 for label in sorted(unique_labels): @@ -253,7 +253,7 @@ def plot_elbow_analysis( self, max_k: int = 10, feature_space: str = "weights", - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """Plot elbow analysis for optimal K selection in K-means. @@ -277,13 +277,14 @@ def plot_elbow_analysis( ) inertias.append(result.get("inertia", 0)) - fig, ax = plt.subplots(figsize=self.config.figsize) + _fig, ax = plt.subplots(figsize=self.config.figsize) ax.plot(k_range, inertias, "bo-", linewidth=2, markersize=8) ax.set_xlabel("Number of Clusters (k)") ax.set_ylabel("Within-Cluster Sum of Squares (WCSS)") ax.set_title(f"Elbow Analysis for K-means ({feature_space} space)") ax.grid(True, alpha=0.3) for k, inertia in zip(k_range, inertias): + # for k, inertia in zip(k_range, inertias, strict=False): ax.annotate( f"k={k}\n{inertia:.2f}", (k, inertia), @@ -300,7 +301,7 @@ def plot_elbow_analysis( def plot_cluster_quality_comparison( self, results_list: list[dict[str, Any]], - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """Compare clustering quality metrics across different methods. @@ -329,7 +330,7 @@ def plot_cluster_quality_comparison( calinski_harabasz_scores.append(metrics.get("calinski_harabasz_score", 0)) n_clusters_list.append(result.get("n_clusters", 0)) - fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10)) + _fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10)) x_pos = np.arange(len(methods)) # Silhouette Score (higher is better) @@ -340,6 +341,7 @@ def plot_cluster_quality_comparison( ax1.set_xticklabels(methods, rotation=45, ha="right") ax1.grid(True, alpha=0.3) for bar, score in zip(bars1, silhouette_scores): + # for bar, score in zip(bars1, silhouette_scores, strict=False): height = bar.get_height() ax1.text( bar.get_x() + bar.get_width() / 2.0, @@ -358,6 +360,7 @@ def plot_cluster_quality_comparison( ax2.set_xticklabels(methods, rotation=45, ha="right") ax2.grid(True, alpha=0.3) for bar, score in zip(bars2, davies_bouldin_scores): + # for bar, score in zip(bars2, davies_bouldin_scores, strict=False): height = bar.get_height() ax2.text( bar.get_x() + bar.get_width() / 2.0, @@ -376,6 +379,7 @@ def plot_cluster_quality_comparison( ax3.set_xticklabels(methods, rotation=45, ha="right") ax3.grid(True, alpha=0.3) for bar, score in zip(bars3, calinski_harabasz_scores): + # for bar, score in zip(bars3, calinski_harabasz_scores, strict=False): height = bar.get_height() ax3.text( bar.get_x() + bar.get_width() / 2.0, @@ -394,6 +398,7 @@ def plot_cluster_quality_comparison( ax4.set_xticklabels(methods, rotation=45, ha="right") ax4.grid(True, alpha=0.3) for bar, count in zip(bars4, n_clusters_list): + # for bar, count in zip(bars4, n_clusters_list, strict=False): height = bar.get_height() ax4.text( bar.get_x() + bar.get_width() / 2.0, diff --git a/torchsom/visualization/hexagonal.py b/torchsom/visualization/hexagonal.py index b0e6a56..f1e5eb2 100644 --- a/torchsom/visualization/hexagonal.py +++ b/torchsom/visualization/hexagonal.py @@ -1,11 +1,12 @@ """Hexagonal-specific visualization methods for Self-Organizing Maps.""" from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import matplotlib.colors as mcolors import matplotlib.pyplot as plt import numpy as np +import numpy.typing as npt import torch from matplotlib.axes import Axes from matplotlib.colors import Colormap, Normalize @@ -26,7 +27,7 @@ class HexagonalVisualizer(BaseVisualizer): def __init__( self, som: SOM, - config: Optional[VisualizationConfig] = None, + config: VisualizationConfig | None = None, ) -> None: """Initialize the hexagonal visualizer.""" super().__init__(som, config, expected_topology="hexagonal") @@ -36,12 +37,12 @@ def _create_hexagonal_plot( map_data: torch.Tensor, title: str, colorbar_label: str, - cmap: Optional[Union[str, Colormap]] = None, + cmap: str | Colormap | None = None, show_values: bool = False, value_format: str = ".2f", - norm: Optional[Normalize] = None, - ticks: Optional[np.ndarray[int, Any]] = None, - tick_labels: Optional[list[str]] = None, + norm: Normalize | None = None, + ticks: npt.NDArray[Any] | None = None, + tick_labels: list[str] | None = None, ) -> tuple[Figure, Axes]: """Create a hexagonal plot with proper hexagonal patches. @@ -172,11 +173,11 @@ def plot_grid( title: str, colorbar_label: str, filename: str, - save_path: Optional[Union[str, Path]] = None, - cmap: Optional[Union[str, Colormap]] = None, + save_path: str | Path | None = None, + cmap: str | Colormap | None = None, show_values: bool = False, value_format: str = ".2f", - **kwargs: Any, # For compatibility with base interface (ignores is_component_plane etc) # noqa: ARG002 + **kwargs: Any, # For compatibility with base interface (ignores is_component_plane etc) ) -> None: """Plot hexagonal grid visualization. @@ -199,7 +200,7 @@ def plot_grid( masked_map[zero_mask] = float("nan") # Create the hexagonal plot - fig, ax = self._create_hexagonal_plot( + _fig, _ax = self._create_hexagonal_plot( masked_map, title, colorbar_label, diff --git a/torchsom/visualization/hexagonal_utils.py b/torchsom/visualization/hexagonal_utils.py index 4c6d9c5..c8ed161 100644 --- a/torchsom/visualization/hexagonal_utils.py +++ b/torchsom/visualization/hexagonal_utils.py @@ -1,7 +1,6 @@ """Utility functions for hexagonal grid visualization.""" import math -from typing import Union import matplotlib.colors as mcolors import matplotlib.pyplot as plt @@ -108,8 +107,8 @@ def create_hexagon_patch( def create_hexagonal_grid_patches( map_data: torch.Tensor, hex_radius: float = 0.4, - cmap: Union[Colormap, None] = None, - norm: Union[Normalize, None] = None, + cmap: Colormap | None = None, + norm: Normalize | None = None, edgecolor: str = "white", linewidth: float = 0.5, ) -> tuple[list[RegularPolygon], float, float, float, float]: @@ -118,7 +117,8 @@ def create_hexagonal_grid_patches( Args: map_data (torch.Tensor): Data to visualize [rows, cols] hex_radius (float): Radius of hexagonal cells - cmap_name (str): Name of the colormap to use + cmap (Colormap): Colormap instance to use for coloring patches. + norm (Normalize): Normalization instance to map data values to colormap range. edgecolor (str): Color of hexagon borders linewidth (float): Width of hexagon borders diff --git a/torchsom/visualization/rectangular.py b/torchsom/visualization/rectangular.py index 8f70a74..76ca88e 100644 --- a/torchsom/visualization/rectangular.py +++ b/torchsom/visualization/rectangular.py @@ -1,10 +1,11 @@ """Rectangular-specific visualization methods for Self-Organizing Maps.""" from pathlib import Path -from typing import Any, Optional, Union +from typing import Any import matplotlib.pyplot as plt import numpy as np +import numpy.typing as npt import torch from matplotlib.axes import Axes from matplotlib.colors import Colormap @@ -21,7 +22,7 @@ class RectangularVisualizer(BaseVisualizer): def __init__( self, som: SOM, - config: Optional[VisualizationConfig] = None, + config: VisualizationConfig | None = None, ) -> None: """Initialize the rectangular visualizer.""" super().__init__(som, 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