diff --git a/.gitattributes b/.gitattributes index 327ba52a..f4c7e5f0 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1 +1 @@ -*.ipynb linguist-language=Python \ No newline at end of file +*.ipynb linguist-language=Python diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 05fdeb99..c24e0e70 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -21,12 +21,29 @@ jobs: with: python-version: ${{ matrix.python-version }} + - name: Install uv + uses: astral-sh/setup-uv@v2 + - name: Install dependencies run: | python -m pip install --upgrade pip pip install -e . pip install pytest pytest-cov pytest-testmon + - name: Install prek and ty + run: | + uv tool install prek ty + + - name: Run prek hooks (ruff + code quality checks) + run: | + prek install + prek install-hooks + prek run --all-files + + - name: Run ty type checker + run: | + ty check + - name: Run tests (optimized with testmon) run: | pytest umap/tests/ -v --cov=umap --cov-report=xml --testmon diff --git a/.gitignore b/.gitignore index 25cb5f40..24ca144f 100644 --- a/.gitignore +++ b/.gitignore @@ -34,4 +34,9 @@ dist # coverage .coverage .coverage.* -.coverage.xml \ No newline at end of file +.coverage.xml + + +# Added by cargo + +/target diff --git a/.idea/umap-nan.iml b/.idea/umap-nan.iml index 8e580dba..74bde797 100644 --- a/.idea/umap-nan.iml +++ b/.idea/umap-nan.iml @@ -12,4 +12,4 @@ - \ No newline at end of file + diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..5137a53f --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,39 @@ +repos: + # Ruff linter with all rules enabled + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.14.3 + hooks: + - id: ruff-check + args: [--fix, --unsafe-fixes] + types_or: [python, pyi] + - id: ruff-format + types_or: [python, pyi] + + # Type checker: ty (Astral's Rust-based type checker) & Skylos + - repo: local + hooks: + - id: ty + name: ty type checker + entry: ty check umap/ + language: system + types: [python] + pass_filenames: false + stages: [manual] + - id: skylos + name: Skylos static analysis + entry: python -m skylos.cli . + language: system + types: [python] + pass_filenames: false + + # Standard pre-commit hooks + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.5.0 + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files + args: [--maxkb=5000] + - id: check-ast + - id: check-merge-conflict diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 00000000..fc7417c1 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,179 @@ +# Agent Development Workflow + +This document specifies the workflow for implementing tasks and features in this repository. + +## Core Principles + +Every task should follow a structured workflow to ensure code quality, traceability, and easy review. + +## Workflow + +### 1. Create a Fresh Branch + +Before starting any task, create a new branch from `master`: + +```bash +git checkout master +git pull origin master +git checkout -b +``` + +**Branch naming conventions:** +- Feature: `feat/` (e.g., `feat/add-gpu-support`) +- Bug fix: `fix/` (e.g., `fix/memory-leak`) +- Documentation: `docs/` (e.g., `docs/update-installation`) +- Refactoring: `refactor/` (e.g., `refactor/optimize-metric-computation`) +- Tests: `test/` (e.g., `test/add-parametric-umap-tests`) + +### 2. Implement the Task + +Work on the implementation in your branch: + +- Write code following the project's conventions +- Add tests for new functionality +- Update documentation as needed +- Run pre-commit hooks to ensure code quality +- Commit changes with clear, descriptive messages + +**Commit Message Guidelines:** +- Use imperative mood ("add" not "adds", "fix" not "fixes") +- First line should be concise (50 chars or less) +- Provide detailed explanation in the body if needed +- Reference related issues if applicable + +Example: +```bash +git add . +git commit -m "Add GPU support for metric computation + +- Implement CUDA kernels for distance calculations +- Add device selection logic +- Update tests for GPU paths +" +``` + +### 3. Submit a Draft PR + +After completing the task, push your branch and create a **draft pull request**: + +```bash +git push -u origin +gh pr create --draft --title "" --body "<description>" +``` + +**Draft PR Requirements:** +- Clear, descriptive title +- Summary of changes made +- List of key implementation details +- Note any testing performed +- Flag any known issues or TODOs + +Example: +```markdown +## Summary +Implements GPU acceleration for metric computation using CUDA. + +## Changes +- Added CUDA kernels for Euclidean and Cosine distances +- Implemented device management and memory pooling +- Added fallback to CPU for unsupported operations + +## Testing +- Unit tests pass on GPU and CPU paths +- Performance benchmarks show 3.2x speedup on MNIST + +## Known Issues +- Sparse matrix operations not yet GPU-accelerated +``` + +### 4. Code Review + +The draft PR allows for: +- Early feedback on approach and design +- Discussion of implementation details +- Identification of issues before final submission +- Iteration based on review comments + +When ready for final review, convert the draft to a regular PR or request review. + +## Pre-submission Checklist + +Before creating a PR, ensure: + +- [ ] All tests pass locally +- [ ] Pre-commit hooks pass +- [ ] Code follows project conventions +- [ ] Documentation is updated +- [ ] Commit messages are clear and descriptive +- [ ] Branch is up to date with `master` +- [ ] No unrelated changes are included + +## Important Notes + +### No Direct Commits to Master +- **Never commit directly to `master`** +- All changes must go through the branch → draft PR → review → merge workflow + +### Keep Branches Fresh +- Rebase on `master` if it diverges significantly +- Keep branch scope focused on a single task +- Delete branch after merge + +### Tests Are Required +- All new code must have corresponding tests +- All tests must pass before submitting PR +- Include both unit and integration tests where appropriate + +### Documentation +- Update README if adding user-facing features +- Update docstrings for code changes +- Add migration guides for breaking changes + +## Example Workflow + +```bash +# 1. Create a branch +git checkout -b feat/add-inverse-transform-optimization + +# 2. Implement the feature +# ... write code, tests, docs ... + +# 3. Test locally +pytest umap/tests/ -v + +# 4. Commit changes +git add . +git commit -m "Optimize inverse transform computation + +- Use vectorized operations for faster calculation +- Add caching for repeated transforms +- Improve memory efficiency +" + +# 5. Push to remote +git push -u origin feat/add-inverse-transform-optimization + +# 6. Create draft PR +gh pr create --draft \ + --title "feat: Optimize inverse transform computation" \ + --body " +## Summary +Implements optimization improvements for inverse_transform method. + +## Changes +- Vectorized operations for 2x speedup +- Caching layer for repeated queries +- Reduced memory allocation + +## Testing +- All tests pass +- Benchmarks show 50% improvement on large datasets + " +``` + +## Questions? + +If you're unsure about any aspect of the workflow: +1. Check existing PRs for examples +2. Refer to the project's CLAUDE.md for additional conventions +3. Open an issue with questions about the process diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 7a1f5fb8..1be04cb9 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,15 +1,15 @@ # Contributing -Contributions of all kinds are welcome. In particular pull requests are appreciated. +Contributions of all kinds are welcome. In particular pull requests are appreciated. The authors will endeavour to help walk you through any issues in the pull request discussion, so please feel free to open a pull request even if you are new to such things. ## Issues The easiest contribution to make is to [file an issue](https://github.com/lmcinnes/umap/issues/new). -It is beneficial if you check the [FAQ](https://umap-learn.readthedocs.io/en/latest/faq.html), +It is beneficial if you check the [FAQ](https://umap.readthedocs.io/en/latest/faq.html), and do a cursory search of [existing issues](https://github.com/lmcinnes/umap/issues?utf8=%E2%9C%93&q=is%3Aissue). -It is also helpful, but not necessary, if you can provide clear instruction for +It is also helpful, but not necessary, if you can provide clear instruction for how to reproduce a problem. If you have resolved an issue yourself please consider contributing to the FAQ to add your problem, and its resolution, so others can benefit from your work. @@ -17,10 +17,10 @@ benefit from your work. ## Documentation Contributing to documentation is the easiest way to get started. Providing simple -clear or helpful documentation for new users is critical. Anything that *you* as +clear or helpful documentation for new users is critical. Anything that *you* as a new user found hard to understand, or difficult to work out, are excellent places to begin. Contributions to more detailed and descriptive error messages is -especially appreciated. To contribute to the documentation please +especially appreciated. To contribute to the documentation please [fork the project](https://github.com/lmcinnes/umap/issues#fork-destination-box) into your own repository, make changes there, and then submit a pull request. @@ -44,12 +44,12 @@ in the `doc/_build` folder. ## Code Code contributions are always welcome, from simple bug fixes, to new features. To -contribute code please +contribute code please [fork the project](https://github.com/lmcinnes/umap/issues#fork-destination-box) into your own repository, make changes there, and then submit a pull request. If you are fixing a known issue please add the issue number to the PR message. If you are fixing a new issue feel free to file an issue and then reference it in the PR. -You can [browse open issues](https://github.com/lmcinnes/umap/issues), +You can [browse open issues](https://github.com/lmcinnes/umap/issues), or consult the [project roadmap](https://github.com/lmcinnes/umap/issues/15), for potential code contributions. Fixes for issues tagged with 'help wanted' are especially appreciated. diff --git a/CONVERSION_SUMMARY.md b/CONVERSION_SUMMARY.md new file mode 100644 index 00000000..d804bcf9 --- /dev/null +++ b/CONVERSION_SUMMARY.md @@ -0,0 +1,110 @@ +# RST to Markdown Conversion Summary + +## Overview + +Successfully converted all 33 reStructuredText (.rst) files in the UMAP documentation to Markdown (.md) format. + +## Conversion Date + +2025-11-04 + +## Files Converted + +All files in `/home/georgepearse/umap/doc/` directory: + +1. aligned_umap_basic_usage.md (18K) +2. aligned_umap_politics_demo.md (30K) +3. api.md (723 bytes) +4. basic_usage.md (19K) +5. benchmarking.md (8.1K) +6. clustering.md (14K) +7. composing_models.md (21K) +8. densmap_demo.md (12K) +9. development_roadmap.md (19K) +10. document_embedding.md (9.4K) +11. embedding_space.md (22K) +12. exploratory_analysis.md (5.2K) +13. faq.md (13K) +14. how_umap_works.md (29K) +15. index.md (2.9K) +16. interactive_viz.md (7.5K) +17. inverse_transform.md (8.4K) +18. mutual_nn_umap.md (7.7K) +19. nomic_atlas_umap_of_text_embeddings.md (2.6K) +20. nomic_atlas_visualizing_mnist_training_dynamics.md (7.7K) +21. outliers.md (8.8K) +22. parameters.md (15K) +23. parametric_umap.md (11K) +24. performance.md (11K) +25. plotting.md (18K) +26. precomputed_k-nn.md (13K) +27. release_notes.md (1.9K) +28. reproducibility.md (6.0K) +29. scientific_papers.md (4.3K) +30. sparse.md (15K) +31. supervised.md (17K) +32. transform.md (8.8K) +33. transform_landmarked_pumap.md (11K) + +**Total:** 33 files successfully converted + +## Conversion Script + +The conversion was performed using a custom Python script: `/home/georgepearse/umap/convert_rst_to_md.py` + +### Features Handled + +The conversion script successfully handles: + +- **Section Headers**: RST underlined headers (=, -, ~, ^, etc.) converted to Markdown (#, ##, ###, etc.) +- **Code Blocks**: `.. code:: language` converted to ` ```language ` +- **Images**: `.. image::` converted to `![alt](path)` with optional width comments +- **Figures**: `.. figure::` converted to images with captions +- **Links**: + - Reference links: `` `text <url>`_ `` → `[text](url)` + - External links: `` `text <url>`__ `` → `[text](url)` + - Role-based links: `:meth:`, `:class:`, `:func:`, `:ref:` → inline code +- **Inline Code**: ``` ``code`` ``` → `` `code` `` +- **Lists**: Preserved bullet and numbered lists +- **Raw HTML**: Preserved HTML blocks from `.. raw:: html` directives +- **Parsed Literals**: `.. parsed-literal::` → code blocks +- **Topics**: `.. topic::` → blockquotes with bold titles +- **Toctree**: `.. toctree::` → Markdown lists with section headers +- **Autodoc Directives**: `.. autoclass::`, `.. automodule::` → API reference notes +- **Comments**: RST comments (`.. comment`) → HTML comments + +### Known Limitations + +1. **Sphinx-specific References**: Sphinx cross-references like `:ref:`, `:doc:` are converted to inline code +2. **Autodoc**: Auto-generated API documentation directives are converted to placeholder notes +3. **Complex Tables**: Some complex RST tables may need manual review +4. **Embedded RST**: A few instances of RST syntax embedded in code output (3 occurrences across all files) + +## Verification + +Sample files were verified for correct conversion: +- Headers: ✓ Properly converted to Markdown syntax +- Code blocks: ✓ Language tags preserved +- Links: ✓ External and reference links working +- Images: ✓ Image paths preserved +- Lists: ✓ Bullet points and numbering maintained +- HTML: ✓ Raw HTML blocks preserved + +## Next Steps + +The original .rst files have been preserved. When ready to complete the migration: + +1. Review converted .md files for any formatting issues +2. Update documentation build system to use Markdown (e.g., MkDocs, Docusaurus) +3. Test that all images and links work correctly +4. 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true + +[profile.dev] +opt-level = 0 diff --git a/PREK.md b/PREK.md new file mode 100644 index 00000000..02b49c24 --- /dev/null +++ b/PREK.md @@ -0,0 +1,234 @@ +# prek Configuration + +This project uses **prek**, a Rust-based pre-commit framework that's significantly faster than the original Python-based pre-commit. + +## Installation + +### One-time Setup + +Install prek globally using uv: + +```bash +uv tool install prek +``` + +Alternatively, you can install via: +- `pip install prek` or `pipx install prek` +- `brew install prek` (macOS/Linux) +- `npm install -D @j178/prek` (npm) + +### Initialize Hooks + +After cloning the repo, initialize prek hooks: + +```bash +prek install +prek install-hooks +``` + +This sets up the pre-commit hook that automatically runs before each commit. + +## Running Hooks + +### Automatically (on commit) +Hooks run automatically when you commit. To skip (not recommended): +```bash +git commit --no-verify +``` + +### Manually + +Run all hooks on changed files: +```bash +prek run +``` + +Run all hooks on all files: +```bash +prek run --all-files +``` + +Run specific hooks: +```bash +prek run ruff-check +prek run ruff-format +prek run mypy +``` + +## Tools Configured + +### 1. Ruff (Linter & Formatter) +- **All rules enabled** (`select = ["ALL"]`) +- **Auto-fixes**: Ruff will automatically fix many issues +- **Configuration**: See `pyproject.toml` under `[tool.ruff]` + +**Key ignored rules:** +- `ANN`: Type annotations (handled by mypy type checker) +- `D100, D104, D105`: Module/package docstrings (too strict for scientific code) +- `E501`: Line length (handled by formatter) +- `BLE001`: Blind except (sometimes necessary) + +**Per-file ignores:** +- Test files: Additional docstring rules + `S101` (assert usage) +- setup.py: Missing module docstring + +### 2. ty (Type Checker) - Via Pre-commit Local Hook +- **Rust-based type checker** by Astral - significantly faster than Python-based alternatives (0.5s vs mypy's 18s+) +- **Strict mode enabled** - enforces type annotations +- **Configuration**: See `pyproject.toml` under `[tool.ty]` +- **Automatic detection** - Automatically detects virtual environments and reads config +- **Integrated via local hook** - Runs automatically on commit as part of prek + +**Running ty:** +```bash +# Automatically (on commit or via prek) +prek run ty + +# Manually +ty check +ty check path/to/file.py +``` + +**Configuration (pyproject.toml):** +```toml +[tool.ty] +python_version = "3.9" +strict = true +``` + +**Note**: ty is pre-alpha but actively developed by Astral. While there's no official pre-commit hook repository yet, we use a local hook definition in `.pre-commit-config.yaml` for seamless integration. + +### 3. Pre-commit Hooks +Standard hooks for code hygiene: +- Trailing whitespace removal +- EOF fixer +- YAML validation +- Large file checks (>5MB) +- AST syntax validation +- Merge conflict detection + +## Ruff Rules Reference + +Ruff has many rule categories. With `select = ["ALL"]` enabled, we use all available rules except those in the `ignore` list. + +**Common rule prefixes:** +- `E`: pycodestyle (PEP 8 style violations) +- `F`: Pyflakes (logical errors) +- `W`: pycodestyle (warnings) +- `C`: mccabe (complexity) +- `I`: isort (import ordering) +- `D`: pydocstyle (docstrings) +- `S`: bandit (security) +- `ANN`: Annotations (type hints) +- `RUF`: Ruff-specific rules +- `UP`: pyupgrade (modernizing syntax) +- `B`: flake8-bugbear (bug detection) + +See [Ruff Rules](https://docs.astral.sh/ruff/rules/) for the complete list. + +## Configuration Files + +### `.pre-commit-config.yaml` +Defines which tools run and their versions. This is the standard pre-commit format that prek uses directly (no conversion needed). + +### `pyproject.toml` +Contains configuration for: +- **ruff**: Linter/formatter settings +- **mypy**: Type checker settings + +## Troubleshooting + +### Hooks fail with dependency errors +prek caches hook environments in `~/.cache/prek`. If you encounter issues: +```bash +prek cache clean +prek install-hooks +``` + +### ty reports type errors +These are genuine type errors that need to be fixed: +```bash +# View errors from local hook run +prek run ty + +# Or run directly +ty check + +# Fix by adding type annotations +# See ty output for specific locations +``` + +### Ruff reports too many issues +If you want to gradually adopt ruff, you can: +1. Fix issues incrementally +2. Temporarily add rules to ignore list +3. Run `prek run ruff-check -- --fix` to auto-fix many issues + +### Can't commit due to large hooks +First time hook installation downloads dependencies. This may take a minute. Subsequent runs are fast. + +## Advantages Over pre-commit + +- **2-3x faster** execution time +- **~50% less disk space** usage +- **Centralized caching** (`~/.cache/prek` vs per-repo) +- **Automatic Python version management** via uv +- **Same config format** (`.pre-commit-config.yaml`) +- **Drop-in replacement** for pre-commit + +## Documentation + +- **prek**: https://prek.j178.dev/ +- **ruff**: https://docs.astral.sh/ruff/ +- **ty**: https://docs.astral.sh/ty/ +- **pre-commit hooks**: https://pre-commit.com/ + +## GitHub Actions Integration + +All tools are integrated into GitHub workflows: + +### Workflow Steps +1. **Setup**: Install uv, Python, and dependencies +2. **Code Quality**: Run prek hooks (ruff + standard checks) +3. **Type Checking**: Run ty type checker +4. **Testing**: Run pytest with coverage +5. **Reporting**: Upload coverage to Codecov + +The workflow runs on: +- **Triggers**: Push to master/develop and pull requests +- **Python versions**: 3.9, 3.10, 3.11, 3.12 +- **Platform**: Ubuntu latest + +### Workflow Failure Handling +- If prek hooks fail (ruff, code quality), the workflow fails +- If ty fails (type errors), the workflow fails +- Tests use fallback to full suite if testmon optimization fails +- Coverage upload is non-blocking + +## Using ty (Pre-alpha) + +We use `ty` (Astral's Rust-based type checker) from the start, even in its pre-alpha stage (v0.0.1-alpha.25): + +**Benefits:** +- Significantly faster than mypy (0.5s vs 18s+) +- Consistent with the Astral ecosystem (ruff, uv, etc.) +- Automatically detects and uses virtual environments +- Simple, focused type checking +- Fully integrated with prek for automatic checking on commits + +**Local Integration:** +```bash +# Automatically on commit (via prek) +git commit + +# Or manually run +prek run ty +ty check +``` + +**Installation:** +```bash +uv tool install ty +``` + +As ty matures toward feature-completeness and stable release (planned late 2025), it will become the standard type checker across the Python ecosystem. diff --git a/README.md b/README.md new file mode 100644 index 00000000..ab2fb147 --- /dev/null +++ b/README.md @@ -0,0 +1,459 @@ +# UMAP + +![UMAP logo](doc/logo_large.png) + +[![PyPI Version](https://img.shields.io/pypi/v/umap.svg)](https://pypi.python.org/pypi/umap/) +[![PyPI Downloads](https://pepy.tech/badge/umap/month)](https://pepy.tech/project/umap) +[![License](https://img.shields.io/pypi/l/umap.svg)](https://github.com/lmcinnes/umap/blob/master/LICENSE.txt) +[![Build Status](https://dev.azure.com/TutteInstitute/build-pipelines/_apis/build/status/lmcinnes.umap?branchName=master)](https://dev.azure.com/TutteInstitute/build-pipelines/_build/latest?definitionId=2&branchName=master) +[![Coverage](https://coveralls.io/repos/github/lmcinnes/umap/badge.svg)](https://coveralls.io/github/lmcinnes/umap) +[![Docs](https://readthedocs.org/projects/umap/badge/?version=latest)](https://umap.readthedocs.io/en/latest/?badge=latest) +[![JOSS Paper](http://joss.theoj.org/papers/10.21105/joss.00861/status.svg)](https://doi.org/10.21105/joss.00861) + +> **This is a fork of the original UMAP repository** ([lmcinnes/umap](https://github.com/lmcinnes/umap)). +> For the official UMAP project, please visit the original repository. + +Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data: + +1. The data is uniformly distributed on a Riemannian manifold; +2. The Riemannian metric is locally constant (or can be approximated as such); +3. The manifold is locally connected. + +From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure. + +The details for the underlying mathematics can be found in [our paper on ArXiv](https://arxiv.org/abs/1802.03426): + +McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018 + +A broader introduction to UMAP targetted the scientific community can be found in our [paper published in Nature Review Methods Primers](https://doi.org/10.1038/s43586-024-00363-x): + +Healy, J., McInnes, L. *Uniform manifold approximation and projection*. Nat Rev Methods Primers 4, 82 (2024). + +A read only version of this paper can accessed via [link](https://rdcu.be/d0YZT) + +The important thing is that you don't need to worry about that—you can use UMAP right now for dimension reduction and visualisation as easily as a drop in replacement for scikit-learn's t-SNE. + +Documentation is [available via Read the Docs](https://umap.readthedocs.io/). + +**New: this package now also provides support for densMAP.** The densMAP algorithm augments UMAP to preserve local density information in addition to the topological structure of the data. Details of this method are described in the following [paper](https://doi.org/10.1038/s41587-020-00801-7): + +Narayan, A, Berger, B, Cho, H, *Assessing Single-Cell Transcriptomic Variability through Density-Preserving Data Visualization*, Nature Biotechnology, 2021 + +## Installing + +UMAP depends upon `scikit-learn`, and thus `scikit-learn`'s dependencies such as `numpy` and `scipy`. UMAP adds a requirement for `numba` for performance reasons. The original version used Cython, but the improved code clarity, simplicity and performance of Numba made the transition necessary. + +**Requirements:** + +* Python 3.6 or greater +* numpy +* scipy +* scikit-learn +* numba +* tqdm +* [pynndescent](https://github.com/lmcinnes/pynndescent) + +**Recommended packages:** + +* For plotting + * matplotlib + * datashader + * holoviews +* for Parametric UMAP + * tensorflow > 2.0.0 + +### Install Options + +The recommended way to install UMAP is via PyPI using uv, which provides faster and more reliable dependency resolution: + +```bash +uv pip install umap +``` + +If you don't have uv installed, you can install it from [https://docs.astral.sh/uv/getting-started/installation/](https://docs.astral.sh/uv/getting-started/installation/). + +Alternatively, you can install UMAP using pip: + +```bash +pip install umap +``` + +This will install UMAP and all required dependencies. + +If you wish to use the plotting functionality you can use + +```bash +uv pip install umap[plot] +``` + +or with pip: + +```bash +pip install umap[plot] +``` + +to install all the plotting dependencies. + +If you wish to use Parametric UMAP, you need to install Tensorflow, which can be installed either using the instructions at https://www.tensorflow.org/install (recommended) or using + +```bash +uv pip install umap[parametric_umap] +``` + +or with pip: + +```bash +pip install umap[parametric_umap] +``` + +for a CPU-only version of Tensorflow. + +If you're on an x86 processor, you can also optionally install `tbb`, which will provide additional CPU optimizations: + +```bash +uv pip install umap[tbb] +``` + +or with pip: + +```bash +pip install umap[tbb] +``` + +### Manual Development Install + +For a manual development install, clone the repository and install in editable mode: + +```bash +git clone https://github.com/lmcinnes/umap.git +cd umap +``` + +Then create a virtual environment (recommended) and install the package using uv: + +```bash +uv venv venv +source venv/bin/activate # On Windows: venv\Scripts\activate +uv pip install -e . +``` + +Or if you prefer to use pip: + +```bash +python -m venv venv +source venv/bin/activate # On Windows: venv\Scripts\activate +pip install -e . +``` + +### Development Testing with testmon + +This project uses `pytest-testmon` to optimize test execution by only running tests affected by code changes. For best results (especially in CI/CD environments), ensure testmon uses a file-based database: + +```bash +# Run tests with file-based testmon cache +pytest --testmon-db=.testmon.db umap/tests/ +``` + +Configure in `pyproject.toml` or pytest config: + +```ini +[tool:pytest] +testmon_db = .testmon.db +``` + +The file-based database (`.testmon.db`) should be persistent across test runs to provide reliable optimization benefits. + +## How to use UMAP + +The umap package inherits from sklearn classes, and thus drops in neatly next to other sklearn transformers with an identical calling API. + +```python +import umap +from sklearn.datasets import load_digits + +digits = load_digits() + +embedding = umap.UMAP().fit_transform(digits.data) +``` + +There are a number of parameters that can be set for the UMAP class; the major ones are as follows: + + - `n_neighbors`: This determines the number of neighboring points used in local approximations of manifold structure. Larger values will result in more global structure being preserved at the loss of detailed local structure. In general this parameter should often be in the range 5 to 50, with a choice of 10 to 15 being a sensible default. + + - `min_dist`: This controls how tightly the embedding is allowed compress points together. Larger values ensure embedded points are more evenly distributed, while smaller values allow the algorithm to optimise more accurately with regard to local structure. Sensible values are in the range 0.001 to 0.5, with 0.1 being a reasonable default. + + - `metric`: This determines the choice of metric used to measure distance in the input space. A wide variety of metrics are already coded, and a user defined function can be passed as long as it has been JITd by numba. + +An example of making use of these options: + +```python +import umap +from sklearn.datasets import load_digits + +digits = load_digits() + +embedding = umap.UMAP(n_neighbors=5, + min_dist=0.3, + metric='correlation').fit_transform(digits.data) +``` + +UMAP also supports fitting to sparse matrix data. For more details please see [the UMAP documentation](https://umap.readthedocs.io/) + +## Benefits of UMAP + +UMAP has a few signficant wins in its current incarnation. + +First of all UMAP is *fast*. It can handle large datasets and high dimensional data without too much difficulty, scaling beyond what most t-SNE packages can manage. This includes very high dimensional sparse datasets. UMAP has successfully been used directly on data with over a million dimensions. + +Second, UMAP scales well in embedding dimension—it isn't just for visualisation! You can use UMAP as a general purpose dimension reduction technique as a preliminary step to other machine learning tasks. With a little care it partners well with the [hdbscan](https://github.com/scikit-learn-contrib/hdbscan) clustering library (for more details please see [Using UMAP for Clustering](https://umap.readthedocs.io/en/latest/clustering.html)). + +Third, UMAP often performs better at preserving some aspects of global structure of the data than most implementations of t-SNE. This means that it can often provide a better "big picture" view of your data as well as preserving local neighbor relations. + +Fourth, UMAP supports a wide variety of distance functions, including non-metric distance functions such as *cosine distance* and *correlation distance*. You can finally embed word vectors properly using cosine distance! + +Fifth, UMAP supports adding new points to an existing embedding via the standard sklearn `transform` method. This means that UMAP can be used as a preprocessing transformer in sklearn pipelines. + +Sixth, UMAP supports supervised and semi-supervised dimension reduction. This means that if you have label information that you wish to use as extra information for dimension reduction (even if it is just partial labelling) you can do that—as simply as providing it as the `y` parameter in the fit method. + +Seventh, UMAP supports a variety of additional experimental features including: an "inverse transform" that can approximate a high dimensional sample that would map to a given position in the embedding space; the ability to embed into non-euclidean spaces including hyperbolic embeddings, and embeddings with uncertainty; very preliminary support for embedding dataframes also exists. + +Finally, UMAP has solid theoretical foundations in manifold learning (see [our paper on ArXiv](https://arxiv.org/abs/1802.03426)). This both justifies the approach and allows for further extensions that will soon be added to the library. + +## Performance and Examples + +UMAP is very efficient at embedding large high dimensional datasets. In particular it scales well with both input dimension and embedding dimension. For the best possible performance we recommend installing the nearest neighbor computation library [pynndescent](https://github.com/lmcinnes/pynndescent). UMAP will work without it, but if installed it will run faster, particularly on multicore machines. + +For a problem such as the 784-dimensional MNIST digits dataset with 70000 data samples, UMAP can complete the embedding in under a minute (as compared with around 45 minutes for scikit-learn's t-SNE implementation). Despite this runtime efficiency, UMAP still produces high quality embeddings. + +The obligatory MNIST digits dataset, embedded in 42 seconds (with pynndescent installed and after numba jit warmup) using a 3.1 GHz Intel Core i7 processor (n_neighbors=10, min_dist=0.001): + +![UMAP embedding of MNIST digits](images/umap_example_mnist1.png) + +The MNIST digits dataset is fairly straightforward, however. A better test is the more recent "Fashion MNIST" dataset of images of fashion items (again 70000 data sample in 784 dimensions). UMAP produced this embedding in 49 seconds (n_neighbors=5, min_dist=0.1): + +![UMAP embedding of "Fashion MNIST"](images/umap_example_fashion_mnist1.png) + +The UCI shuttle dataset (43500 sample in 8 dimensions) embeds well under *correlation* distance in 44 seconds (note the longer time required for correlation distance computations): + +![UMAP embedding the UCI Shuttle dataset](images/umap_example_shuttle.png) + +The following is a densMAP visualization of the MNIST digits dataset with 784 features based on the same parameters as above (n_neighbors=10, min_dist=0.001). densMAP reveals that the cluster corresponding to digit 1 is noticeably denser, suggesting that there are fewer degrees of freedom in the images of 1 compared to other digits. + +![densMAP embedding of the MNIST dataset](images/densmap_example_mnist.png) + +## Plotting + +UMAP includes a subpackage `umap.plot` for plotting the results of UMAP embeddings. This package needs to be imported separately since it has extra requirements (matplotlib, datashader and holoviews). It allows for fast and simple plotting and attempts to make sensible decisions to avoid overplotting and other pitfalls. An example of use: + +```python +import umap +import umap.plot +from sklearn.datasets import load_digits + +digits = load_digits() + +mapper = umap.UMAP().fit(digits.data) +umap.plot.points(mapper, labels=digits.target) +``` + +The plotting package offers basic plots, as well as interactive plots with hover tools and various diagnostic plotting options. See the documentation for more details. + +## Parametric UMAP + +Parametric UMAP provides support for training a neural network to learn a UMAP based transformation of data. This can be used to support faster inference of new unseen data, more robust inverse transforms, autoencoder versions of UMAP and semi-supervised classification (particularly for data well separated by UMAP and very limited amounts of labelled data). See the [documentation of Parametric UMAP](https://umap.readthedocs.io/en/0.5dev/parametric_umap.html) or the [example notebooks](https://github.com/lmcinnes/umap/tree/master/notebooks/Parametric_UMAP) for more. + +## densMAP + +The densMAP algorithm augments UMAP to additionally preserve local density information in addition to the topological structure captured by UMAP. One can easily run densMAP using the umap package by setting the `densmap` input flag: + +```python +embedding = umap.UMAP(densmap=True).fit_transform(data) +``` + +This functionality is built upon the densMAP [implementation](https://github.com/hhcho/densvis) provided by the developers of densMAP, who also contributed to integrating densMAP into the umap package. + +densMAP inherits all of the parameters of UMAP. The following is a list of additional parameters that can be set for densMAP: + + - `dens_frac`: This determines the fraction of epochs (a value between 0 and 1) that will include the density-preservation term in the optimization objective. This parameter is set to 0.3 by default. Note that densMAP switches density optimization on after an initial phase of optimizing the embedding using UMAP. + + - `dens_lambda`: This determines the weight of the density-preservation objective. Higher values prioritize density preservation, and lower values (closer to zero) prioritize the UMAP objective. Setting this parameter to zero reduces the algorithm to UMAP. Default value is 2.0. + + - `dens_var_shift`: Regularization term added to the variance of local densities in the embedding for numerical stability. We recommend setting this parameter to 0.1, which consistently works well in many settings. + + - `output_dens`: When this flag is True, the call to `fit_transform` returns, in addition to the embedding, the local radii (inverse measure of local density defined in the [densMAP paper](https://doi.org/10.1101/2020.05.12.077776)) for the original dataset and for the embedding. The output is a tuple `(embedding, radii_original, radii_embedding)`. Note that the radii are log-transformed. If False, only the embedding is returned. This flag can also be used with UMAP to explore the local densities of UMAP embeddings. By default this flag is False. + +For densMAP we recommend larger values of `n_neighbors` (e.g. 30) for reliable estimation of local density. + +An example of making use of these options (based on a subsample of the mnist_784 dataset): + +```python +import umap +from sklearn.datasets import fetch_openml +from sklearn.utils import resample + +digits = fetch_openml(name='mnist_784') +subsample, subsample_labels = resample(digits.data, digits.target, n_samples=7000, + stratify=digits.target, random_state=1) + +embedding, r_orig, r_emb = umap.UMAP(densmap=True, dens_lambda=2.0, n_neighbors=30, + output_dens=True).fit_transform(subsample) +``` + +See [the documentation](https://umap.readthedocs.io/en/0.5dev/densmap_demo.html) for more details. + +## Interactive UMAP with Nomic Atlas + +![MNIST UMAP visualization in Nomic Atlas](https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif) + +For interactive exploration of UMAP embeddings, especially for visualizing large datasets data over time/training epochs, you can use [Nomic Atlas](https://atlas.nomic.ai/). Nomic Atlas is a platform for embedding generation, visualization, analysis, and retrieval that directly integrates UMAP as one of its projection models. + +Using Nomic Atlas with UMAP is straightforward: + +```python +from nomic import AtlasDataset +from nomic.data_inference import ProjectionOptions + +# Create a dataset +dataset = AtlasDataset("my-dataset") + +# data is a DataFrame or a list of dicts +dataset.add_data(data) + +# Create an interactive UMAP in Atlas +atlas_map = dataset.create_index( + indexed_field='text', + projection=ProjectionOptions( + model="umap", + n_neighbors=15, + min_dist=0.1, + n_epochs=200 + ) +) +# you can access your UMAP coordinates later on with +# atlas_map.maps[0].embeddings.projected +``` + +Nomic Atlas provides: + +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) +* Scalability for millions of data points +* Rich information display on hover +* Shareable UMAPs via URL links to your embeddings and data maps in Atlas + +## GPU-Accelerated UMAP with torchdr + +For GPU-accelerated UMAP computations, [torchdr](https://github.com/TorchDR/TorchDR) provides a PyTorch-based implementation that significantly speed up the algorithm. torchdr accelerates **every step** of the dimensionality reduction pipeline on GPU: kNN computation, affinity construction and embedding optimization. + +Using torchdr with UMAP is straightforward: + +```python +from torchdr import UMAP as torchdrUMAP + +umap_gpu = torchdrUMAP( + n_neighbors=15, + min_dist=0.1, + n_components=2, + device='cuda' +) +embedding = umap_gpu.fit_transform(data-maps) +``` + +For more information and advanced usage, see the [torchdr documentation](https://torchdr.github.io/index.html). + +## Help and Support + +Documentation is at [Read the Docs](https://umap.readthedocs.io/). The documentation [includes a FAQ](https://umap.readthedocs.io/en/latest/faq.html) that may answer your questions. If you still have questions then please [open an issue](https://github.com/lmcinnes/umap/issues/new) and I will try to provide any help and guidance that I can. + +## Citation + +If you make use of this software for your work we would appreciate it if you would cite the paper from the Journal of Open Source Software: + +```bibtex +@article{mcinnes2018umap-software, + title={UMAP: Uniform Manifold Approximation and Projection}, + author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas}, + journal={The Journal of Open Source Software}, + volume={3}, + number={29}, + pages={861}, + year={2018} +} +``` + +If you would like to cite this algorithm in your work the ArXiv paper is the current reference: + +```bibtex +@article{2018arXivUMAP, + author = {{McInnes}, L. and {Healy}, J. and {Melville}, J.}, + title = "{UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}", + journal = {ArXiv e-prints}, + archivePrefix = "arXiv", + eprint = {1802.03426}, + primaryClass = "stat.ML", + keywords = {Statistics - Machine Learning, Computer Science - Computational Geometry, Computer Science - Learning}, + year = 2018, + month = feb, +} +``` + +If you found the Nature Primer introduction useful please cite the following reference: + +```bibtex +@article{Healy2024, + author={Healy, John and McInnes, Leland}, + title={Uniform manifold approximation and projection}, + journal={Nature Reviews Methods Primers}, + year={2024}, + month={Nov}, + day={21}, + volume={4}, + number={1}, + pages={82}, + abstract={Uniform manifold approximation and projection is a nonlinear dimension reduction method often used for visualizing data and as pre-processing for further machine-learning tasks such as clustering. In this Primer, we provide an introduction to the uniform manifold approximation and projection algorithm, the intuitions behind how it works, how best to apply it on data and how to interpret and understand results.}, + issn={2662-8449}, + doi={10.1038/s43586-024-00363-x}, + url={https://doi.org/10.1038/s43586-024-00363-x} +} +``` + +Additionally, if you use the densMAP algorithm in your work please cite the following reference: + +```bibtex +@article {NBC2020, + author = {Narayan, Ashwin and Berger, Bonnie and Cho, Hyunghoon}, + title = {Assessing Single-Cell Transcriptomic Variability through Density-Preserving Data Visualization}, + journal = {Nature Biotechnology}, + year = {2021}, + doi = {10.1038/s41587-020-00801-7}, + publisher = {Springer Nature}, + URL = {https://doi.org/10.1038/s41587-020-00801-7}, + eprint = {https://www.biorxiv.org/content/early/2020/05/14/2020.05.12.077776.full.pdf}, +} +``` + +If you use the Parametric UMAP algorithm in your work please cite the following reference: + +```bibtex +@article {SMG2020, + author = {Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q.}, + title = {Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning}, + journal = {ArXiv e-prints}, + archivePrefix = "arXiv", + eprint = {2009.12981}, + primaryClass = "stat.ML", + keywords = {Statistics - Machine Learning, Computer Science - Computational Geometry, Computer Science - Learning}, + year = 2020, +} +``` + +## License + +The umap package is 3-clause BSD licensed. + +We would like to note that the umap package makes heavy use of NumFOCUS sponsored projects, and would not be possible without their support of those projects, so please [consider contributing to NumFOCUS](https://www.numfocus.org/membership). + +## Contributing + +Contributions are more than welcome! There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all *equally valuable* so please don't feel you can't contribute. To contribute please [fork the project](https://github.com/lmcinnes/umap/issues#fork-destination-box) make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch. diff --git a/README.rst b/README.rst deleted file mode 100644 index 86640bde..00000000 --- a/README.rst +++ /dev/null @@ -1,591 +0,0 @@ -.. -*- mode: rst -*- - -.. image:: doc/logo_large.png - :width: 600 - :alt: UMAP logo - :align: center - -|pypi_version|_ |pypi_downloads|_ - -|License|_ |build_status|_ |Coverage|_ - -|Docs|_ |joss_paper|_ - -.. |pypi_version| image:: https://img.shields.io/pypi/v/umap-learn.svg -.. _pypi_version: https://pypi.python.org/pypi/umap-learn/ - -.. |pypi_downloads| image:: https://pepy.tech/badge/umap-learn/month -.. _pypi_downloads: https://pepy.tech/project/umap-learn - -.. |License| image:: https://img.shields.io/pypi/l/umap-learn.svg -.. _License: https://github.com/lmcinnes/umap/blob/master/LICENSE.txt - -.. |build_status| image:: https://dev.azure.com/TutteInstitute/build-pipelines/_apis/build/status/lmcinnes.umap?branchName=master -.. _build_status: https://dev.azure.com/TutteInstitute/build-pipelines/_build/latest?definitionId=2&branchName=master - -.. |Coverage| image:: https://coveralls.io/repos/github/lmcinnes/umap/badge.svg -.. _Coverage: https://coveralls.io/github/lmcinnes/umap - -.. |Docs| image:: https://readthedocs.org/projects/umap-learn/badge/?version=latest -.. _Docs: https://umap-learn.readthedocs.io/en/latest/?badge=latest - -.. |joss_paper| image:: http://joss.theoj.org/papers/10.21105/joss.00861/status.svg -.. _joss_paper: https://doi.org/10.21105/joss.00861 - -==== -UMAP -==== - -.. note:: - - **This is a fork of the original UMAP repository** (`lmcinnes/umap <https://github.com/lmcinnes/umap>`_). - For the official UMAP project, please visit the original repository. - -Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction -technique that can be used for visualisation similarly to t-SNE, but also for -general non-linear dimension reduction. The algorithm is founded on three -assumptions about the data: - -1. The data is uniformly distributed on a Riemannian manifold; -2. The Riemannian metric is locally constant (or can be approximated as such); -3. The manifold is locally connected. - -From these assumptions it is possible to model the manifold with a fuzzy -topological structure. The embedding is found by searching for a low dimensional -projection of the data that has the closest possible equivalent fuzzy -topological structure. - -The details for the underlying mathematics can be found in -`our paper on ArXiv <https://arxiv.org/abs/1802.03426>`_: - -McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection -for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018 - -A broader introduction to UMAP targetted the scientific community can be found -in our `paper published in Nature Review Methods Primers <https://doi.org/10.1038/s43586-024-00363-x>`_: - -Healy, J., McInnes, L. *Uniform manifold approximation and projection*. Nat Rev Methods -Primers 4, 82 (2024). - -A read only version of this paper can accessed via `link <https://rdcu.be/d0YZT>`_ - -The important thing is that you don't need to worry about that—you can use -UMAP right now for dimension reduction and visualisation as easily as a drop -in replacement for scikit-learn's t-SNE. - -Documentation is `available via Read the Docs <https://umap-learn.readthedocs.io/>`_. - -**New: this package now also provides support for densMAP.** The densMAP algorithm augments UMAP -to preserve local density information in addition to the topological structure of the data. -Details of this method are described in the following `paper <https://doi.org/10.1038/s41587-020-00801-7>`_: - -Narayan, A, Berger, B, Cho, H, *Assessing Single-Cell Transcriptomic Variability -through Density-Preserving Data Visualization*, Nature Biotechnology, 2021 - ----------- -Installing ----------- - -UMAP depends upon ``scikit-learn``, and thus ``scikit-learn``'s dependencies -such as ``numpy`` and ``scipy``. UMAP adds a requirement for ``numba`` for -performance reasons. The original version used Cython, but the improved code -clarity, simplicity and performance of Numba made the transition necessary. - -Requirements: - -* Python 3.6 or greater -* numpy -* scipy -* scikit-learn -* numba -* tqdm -* `pynndescent <https://github.com/lmcinnes/pynndescent>`_ - -Recommended packages: - -* For plotting - * matplotlib - * datashader - * holoviews -* for Parametric UMAP - * tensorflow > 2.0.0 - -**Install Options** - -The recommended way to install UMAP is via PyPI using pip: - -.. code:: bash - - pip install umap-learn - -This will install UMAP and all required dependencies. - -If you wish to use the plotting functionality you can use - -.. code:: bash - - pip install umap-learn[plot] - -to install all the plotting dependencies. - -If you wish to use Parametric UMAP, you need to install Tensorflow, which can be -installed either using the instructions at https://www.tensorflow.org/install -(recommended) or using - -.. code:: bash - - pip install umap-learn[parametric_umap] - -for a CPU-only version of Tensorflow. - -If you're on an x86 processor, you can also optionally install `tbb`, which will -provide additional CPU optimizations: - -.. code:: bash - - pip install umap-learn[tbb] - -For a manual development install, clone the repository and install in editable mode: - -.. code:: bash - - git clone https://github.com/lmcinnes/umap.git - cd umap - -Then create a virtual environment (recommended) and install the package: - -.. code:: bash - - python -m venv venv - source venv/bin/activate # On Windows: venv\Scripts\activate - pip install -e . - ---------------- -How to use UMAP ---------------- - -The umap package inherits from sklearn classes, and thus drops in neatly -next to other sklearn transformers with an identical calling API. - -.. code:: python - - import umap - from sklearn.datasets import load_digits - - digits = load_digits() - - embedding = umap.UMAP().fit_transform(digits.data) - -There are a number of parameters that can be set for the UMAP class; the -major ones are as follows: - - - ``n_neighbors``: This determines the number of neighboring points used in - local approximations of manifold structure. Larger values will result in - more global structure being preserved at the loss of detailed local - structure. In general this parameter should often be in the range 5 to - 50, with a choice of 10 to 15 being a sensible default. - - - ``min_dist``: This controls how tightly the embedding is allowed compress - points together. Larger values ensure embedded points are more evenly - distributed, while smaller values allow the algorithm to optimise more - accurately with regard to local structure. Sensible values are in the - range 0.001 to 0.5, with 0.1 being a reasonable default. - - - ``metric``: This determines the choice of metric used to measure distance - in the input space. A wide variety of metrics are already coded, and a user - defined function can be passed as long as it has been JITd by numba. - -An example of making use of these options: - -.. code:: python - - import umap - from sklearn.datasets import load_digits - - digits = load_digits() - - embedding = umap.UMAP(n_neighbors=5, - min_dist=0.3, - metric='correlation').fit_transform(digits.data) - -UMAP also supports fitting to sparse matrix data. For more details -please see `the UMAP documentation <https://umap-learn.readthedocs.io/>`_ - ----------------- -Benefits of UMAP ----------------- - -UMAP has a few signficant wins in its current incarnation. - -First of all UMAP is *fast*. It can handle large datasets and high -dimensional data without too much difficulty, scaling beyond what most t-SNE -packages can manage. This includes very high dimensional sparse datasets. UMAP -has successfully been used directly on data with over a million dimensions. - -Second, UMAP scales well in embedding dimension—it isn't just for -visualisation! You can use UMAP as a general purpose dimension reduction -technique as a preliminary step to other machine learning tasks. With a -little care it partners well with the `hdbscan -<https://github.com/scikit-learn-contrib/hdbscan>`_ clustering library (for -more details please see `Using UMAP for Clustering -<https://umap-learn.readthedocs.io/en/latest/clustering.html>`_). - -Third, UMAP often performs better at preserving some aspects of global structure -of the data than most implementations of t-SNE. This means that it can often -provide a better "big picture" view of your data as well as preserving local neighbor -relations. - -Fourth, UMAP supports a wide variety of distance functions, including -non-metric distance functions such as *cosine distance* and *correlation -distance*. You can finally embed word vectors properly using cosine distance! - -Fifth, UMAP supports adding new points to an existing embedding via -the standard sklearn ``transform`` method. This means that UMAP can be -used as a preprocessing transformer in sklearn pipelines. - -Sixth, UMAP supports supervised and semi-supervised dimension reduction. -This means that if you have label information that you wish to use as -extra information for dimension reduction (even if it is just partial -labelling) you can do that—as simply as providing it as the ``y`` -parameter in the fit method. - -Seventh, UMAP supports a variety of additional experimental features including: an -"inverse transform" that can approximate a high dimensional sample that would map to -a given position in the embedding space; the ability to embed into non-euclidean -spaces including hyperbolic embeddings, and embeddings with uncertainty; very -preliminary support for embedding dataframes also exists. - -Finally, UMAP has solid theoretical foundations in manifold learning -(see `our paper on ArXiv <https://arxiv.org/abs/1802.03426>`_). -This both justifies the approach and allows for further -extensions that will soon be added to the library. - ------------------------- -Performance and Examples ------------------------- - -UMAP is very efficient at embedding large high dimensional datasets. In -particular it scales well with both input dimension and embedding dimension. -For the best possible performance we recommend installing the nearest neighbor -computation library `pynndescent <https://github.com/lmcinnes/pynndescent>`_ . -UMAP will work without it, but if installed it will run faster, particularly on -multicore machines. - -For a problem such as the 784-dimensional MNIST digits dataset with -70000 data samples, UMAP can complete the embedding in under a minute (as -compared with around 45 minutes for scikit-learn's t-SNE implementation). -Despite this runtime efficiency, UMAP still produces high quality embeddings. - -The obligatory MNIST digits dataset, embedded in 42 -seconds (with pynndescent installed and after numba jit warmup) -using a 3.1 GHz Intel Core i7 processor (n_neighbors=10, min_dist=0.001): - -.. image:: images/umap_example_mnist1.png - :alt: UMAP embedding of MNIST digits - -The MNIST digits dataset is fairly straightforward, however. A better test is -the more recent "Fashion MNIST" dataset of images of fashion items (again -70000 data sample in 784 dimensions). UMAP -produced this embedding in 49 seconds (n_neighbors=5, min_dist=0.1): - -.. image:: images/umap_example_fashion_mnist1.png - :alt: UMAP embedding of "Fashion MNIST" - -The UCI shuttle dataset (43500 sample in 8 dimensions) embeds well under -*correlation* distance in 44 seconds (note the longer time -required for correlation distance computations): - -.. image:: images/umap_example_shuttle.png - :alt: UMAP embedding the UCI Shuttle dataset - -The following is a densMAP visualization of the MNIST digits dataset with 784 features -based on the same parameters as above (n_neighbors=10, min_dist=0.001). densMAP reveals -that the cluster corresponding to digit 1 is noticeably denser, suggesting that -there are fewer degrees of freedom in the images of 1 compared to other digits. - -.. image:: images/densmap_example_mnist.png - :alt: densMAP embedding of the MNIST dataset - --------- -Plotting --------- - -UMAP includes a subpackage ``umap.plot`` for plotting the results of UMAP embeddings. -This package needs to be imported separately since it has extra requirements -(matplotlib, datashader and holoviews). It allows for fast and simple plotting and -attempts to make sensible decisions to avoid overplotting and other pitfalls. An -example of use: - -.. code:: python - - import umap - import umap.plot - from sklearn.datasets import load_digits - - digits = load_digits() - - mapper = umap.UMAP().fit(digits.data) - umap.plot.points(mapper, labels=digits.target) - -The plotting package offers basic plots, as well as interactive plots with hover -tools and various diagnostic plotting options. See the documentation for more details. - ---------------- -Parametric UMAP ---------------- - -Parametric UMAP provides support for training a neural network to learn a UMAP based -transformation of data. This can be used to support faster inference of new unseen -data, more robust inverse transforms, autoencoder versions of UMAP and -semi-supervised classification (particularly for data well separated by UMAP and very -limited amounts of labelled data). See the -`documentation of Parametric UMAP <https://umap-learn.readthedocs.io/en/0.5dev/parametric_umap.html>`_ -or the -`example notebooks <https://github.com/lmcinnes/umap/tree/master/notebooks/Parametric_UMAP>`_ -for more. - - -------- -densMAP -------- - -The densMAP algorithm augments UMAP to additionally preserve local density information -in addition to the topological structure captured by UMAP. One can easily run densMAP -using the umap package by setting the ``densmap`` input flag: - -.. code:: python - - embedding = umap.UMAP(densmap=True).fit_transform(data) - -This functionality is built upon the densMAP `implementation <https://github.com/hhcho/densvis>`_ provided by the developers -of densMAP, who also contributed to integrating densMAP into the umap package. - -densMAP inherits all of the parameters of UMAP. The following is a list of additional -parameters that can be set for densMAP: - - - ``dens_frac``: This determines the fraction of epochs (a value between 0 and 1) that will include the density-preservation term in the optimization objective. This parameter is set to 0.3 by default. Note that densMAP switches density optimization on after an initial phase of optimizing the embedding using UMAP. - - - ``dens_lambda``: This determines the weight of the density-preservation objective. Higher values prioritize density preservation, and lower values (closer to zero) prioritize the UMAP objective. Setting this parameter to zero reduces the algorithm to UMAP. Default value is 2.0. - - - ``dens_var_shift``: Regularization term added to the variance of local densities in the embedding for numerical stability. We recommend setting this parameter to 0.1, which consistently works well in many settings. - - - ``output_dens``: When this flag is True, the call to ``fit_transform`` returns, in addition to the embedding, the local radii (inverse measure of local density defined in the `densMAP paper <https://doi.org/10.1101/2020.05.12.077776>`_) for the original dataset and for the embedding. The output is a tuple ``(embedding, radii_original, radii_embedding)``. Note that the radii are log-transformed. If False, only the embedding is returned. This flag can also be used with UMAP to explore the local densities of UMAP embeddings. By default this flag is False. - -For densMAP we recommend larger values of ``n_neighbors`` (e.g. 30) for reliable estimation of local density. - -An example of making use of these options (based on a subsample of the mnist_784 dataset): - -.. code:: python - - import umap - from sklearn.datasets import fetch_openml - from sklearn.utils import resample - - digits = fetch_openml(name='mnist_784') - subsample, subsample_labels = resample(digits.data, digits.target, n_samples=7000, - stratify=digits.target, random_state=1) - - embedding, r_orig, r_emb = umap.UMAP(densmap=True, dens_lambda=2.0, n_neighbors=30, - output_dens=True).fit_transform(subsample) - -See `the documentation <https://umap-learn.readthedocs.io/en/0.5dev/densmap_demo.html>`_ for more details. - - ---------------------------------- -Interactive UMAP with Nomic Atlas ---------------------------------- - -.. image:: https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif - :width: 600 - :alt: MNIST UMAP visualization in Nomic Atlas - -For interactive exploration of UMAP embeddings, especially for visualizing large datasets data over time/training epochs, you can use `Nomic Atlas <https://atlas.nomic.ai/>`_. Nomic Atlas is a platform for embedding generation, visualization, analysis, and retrieval that directly integrates UMAP as one of its projection models. - -Using Nomic Atlas with UMAP is straightforward: - -.. code:: python - - from nomic import AtlasDataset - from nomic.data_inference import ProjectionOptions - - # Create a dataset - dataset = AtlasDataset("my-dataset") - - # data is a DataFrame or a list of dicts - dataset.add_data(data) - - # Create an interactive UMAP in Atlas - atlas_map = dataset.create_index( - indexed_field='text', - projection=ProjectionOptions( - model="umap", - n_neighbors=15, - min_dist=0.1, - n_epochs=200 - ) - ) - # you can access your UMAP coordinates later on with - # atlas_map.maps[0].embeddings.projected - -Nomic Atlas provides: - -* In-browser analysis of your UMAP data with the `Atlas Analyst <https://docs.nomic.ai/atlas/data-maps/atlas-analyst>`_ -* Vector search over your UMAP data using the `Nomic API <https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data>`_ -* Interactive features like zooming, recoloring, searching, and filtering in the `Nomic Atlas data map <https://docs.nomic.ai/atlas/data-maps/controls>`_ -* Scalability for millions of data points -* Rich information display on hover -* Shareable UMAPs via URL links to your embeddings and data maps in Atlas - - ---------------------------------- -GPU-Accelerated UMAP with torchdr ---------------------------------- - -For GPU-accelerated UMAP computations, `torchdr <https://github.com/TorchDR/TorchDR>`_ provides a PyTorch-based implementation that significantly speed up the algorithm. -torchdr accelerates **every step** of the dimensionality reduction pipeline on GPU: kNN computation, affinity construction and embedding optimization. - -Using torchdr with UMAP is straightforward: - -.. code:: python - - from torchdr import UMAP as torchdrUMAP - - umap_gpu = torchdrUMAP( - n_neighbors=15, - min_dist=0.1, - n_components=2, - device='cuda' - ) - embedding = umap_gpu.fit_transform(data-maps) - -For more information and advanced usage, see the `torchdr documentation <https://torchdr.github.io/index.html>`_. - - ----------------- -Help and Support ----------------- - -Documentation is at `Read the Docs <https://umap-learn.readthedocs.io/>`_. -The documentation `includes a FAQ <https://umap-learn.readthedocs.io/en/latest/faq.html>`_ that -may answer your questions. If you still have questions then please -`open an issue <https://github.com/lmcinnes/umap/issues/new>`_ -and I will try to provide any help and guidance that I can. - --------- -Citation --------- - -If you make use of this software for your work we would appreciate it if you -would cite the paper from the Journal of Open Source Software: - -.. code:: bibtex - - @article{mcinnes2018umap-software, - title={UMAP: Uniform Manifold Approximation and Projection}, - author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas}, - journal={The Journal of Open Source Software}, - volume={3}, - number={29}, - pages={861}, - year={2018} - } - -If you would like to cite this algorithm in your work the ArXiv paper is the -current reference: - -.. code:: bibtex - - @article{2018arXivUMAP, - author = {{McInnes}, L. and {Healy}, J. and {Melville}, J.}, - title = "{UMAP: Uniform Manifold Approximation - and Projection for Dimension Reduction}", - journal = {ArXiv e-prints}, - archivePrefix = "arXiv", - eprint = {1802.03426}, - primaryClass = "stat.ML", - keywords = {Statistics - Machine Learning, - Computer Science - Computational Geometry, - Computer Science - Learning}, - year = 2018, - month = feb, - } - -If you found the Nature Primer introduction useful please cite the following reference: - -.. code:: bibtex - - @article{Healy2024, - author={Healy, John - and McInnes, Leland}, - title={Uniform manifold approximation and projection}, - journal={Nature Reviews Methods Primers}, - year={2024}, - month={Nov}, - day={21}, - volume={4}, - number={1}, - pages={82}, - abstract={Uniform manifold approximation and projection is a nonlinear dimension reduction method often used for visualizing data and as pre-processing for further machine-learning tasks such as clustering. In this Primer, we provide an introduction to the uniform manifold approximation and projection algorithm, the intuitions behind how it works, how best to apply it on data and how to interpret and understand results.}, - issn={2662-8449}, - doi={10.1038/s43586-024-00363-x}, - url={https://doi.org/10.1038/s43586-024-00363-x} - } - -Additionally, if you use the densMAP algorithm in your work please cite the following reference: - -.. code:: bibtex - - @article {NBC2020, - author = {Narayan, Ashwin and Berger, Bonnie and Cho, Hyunghoon}, - title = {Assessing Single-Cell Transcriptomic Variability through Density-Preserving Data Visualization}, - journal = {Nature Biotechnology}, - year = {2021}, - doi = {10.1038/s41587-020-00801-7}, - publisher = {Springer Nature}, - URL = {https://doi.org/10.1038/s41587-020-00801-7}, - eprint = {https://www.biorxiv.org/content/early/2020/05/14/2020.05.12.077776.full.pdf}, - } - -If you use the Parametric UMAP algorithm in your work please cite the following reference: - -.. code:: bibtex - - @article {SMG2020, - author = {Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q.}, - title = {Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning}, - journal = {ArXiv e-prints}, - archivePrefix = "arXiv", - eprint = {2009.12981}, - primaryClass = "stat.ML", - keywords = {Statistics - Machine Learning, - Computer Science - Computational Geometry, - Computer Science - Learning}, - year = 2020, - } - - -------- -License -------- - -The umap package is 3-clause BSD licensed. - -We would like to note that the umap package makes heavy use of -NumFOCUS sponsored projects, and would not be possible without -their support of those projects, so please `consider contributing to NumFOCUS <https://www.numfocus.org/membership>`_. - ------------- -Contributing ------------- - -Contributions are more than welcome! There are lots of opportunities -for potential projects, so please get in touch if you would like to -help out. Everything from code to notebooks to -examples and documentation are all *equally valuable* so please don't feel -you can't contribute. To contribute please -`fork the project <https://github.com/lmcinnes/umap/issues#fork-destination-box>`_ -make your changes and -submit a pull request. We will do our best to work through any issues with -you and get your code merged into the main branch. - - diff --git a/UMAP_ANN_ANALYSIS.md b/UMAP_ANN_ANALYSIS.md new file mode 100644 index 00000000..463c630f --- /dev/null +++ b/UMAP_ANN_ANALYSIS.md @@ -0,0 +1,283 @@ +# UMAP Approximate Nearest Neighbors (ANN) Implementation Analysis + +## Executive Summary + +UMAP uses **PyNNDescent** as its primary approximate nearest neighbor (ANN) library. PyNNDescent implements the NN-Descent algorithm, which is a general-purpose nearest neighbor descent algorithm designed for approximate nearest neighbor search. The implementation is highly tuned for various distance metrics and supports both dense and sparse data. + +--- + +## 1. ANN Library Used: PyNNDescent + +### Key Dependency +- **Library**: `pynndescent >= 0.5` (from pyproject.toml) +- **Purpose**: Efficient approximate nearest neighbor search +- **Location**: Imported in `/home/georgepearse/umap/umap/umap_.py` at line 27 + ```python + from pynndescent import NNDescent + from pynndescent.distances import named_distances as pynn_named_distances + from pynndescent.sparse import sparse_named_distances as pynn_sparse_named_distances + ``` + +### Algorithm: NN-Descent +- A greedy nearest neighbor descent algorithm +- Builds an approximate nearest neighbor graph through iterative refinement +- Uses **Random Projection (RP) forests** for initialization +- Performs local neighbor exchange for refinement +- Provides **approximate results** with high accuracy (typically >85%) + +--- + +## 2. Main Entry Point: `nearest_neighbors()` Function + +**File**: `/home/georgepearse/umap/umap/umap_.py`, lines 247-340 + +### Function Signature +```python +def nearest_neighbors( + X, # Input data array (n_samples, n_features) + n_neighbors, # Number of neighbors to find + metric, # Distance metric (string or callable) + metric_kwds, # Keyword arguments for metric + angular, # Whether to use angular RP trees + random_state, # Random state for reproducibility + low_memory=True, # Memory-efficient mode + use_pynndescent=True, # Use PyNNDescent (always True currently) + n_jobs=-1, # Number of parallel jobs + verbose=False, # Verbose output +): + """ + Returns: + - knn_indices: array of shape (n_samples, n_neighbors) + - knn_dists: array of shape (n_samples, n_neighbors) + - knn_search_index: NNDescent object for later queries + """ +``` + +### Implementation Details + +#### For Precomputed Distances (metric == "precomputed") +- Uses `fast_knn_indices()` (a Numba-JIT'd function) +- Simply sorts precomputed distance matrix to find k smallest values +- No actual ANN search needed +- Location: lines 304-316 + +#### For Regular Metrics +- **Creates NNDescent object** (lines 322-335): + ```python + knn_search_index = NNDescent( + X, + n_neighbors=n_neighbors, + metric=metric, + metric_kwds=metric_kwds, + random_state=random_state, + n_trees=n_trees, # Dynamic: min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)) + n_iters=n_iters, # Dynamic: max(5, round(np.log2(X.shape[0]))) + max_candidates=60, # Maximum candidates in search + low_memory=low_memory, # Memory optimization + n_jobs=n_jobs, # Parallelization + verbose=verbose, + compressed=False, # No compression + ) + ``` + +- **Key Parameters**: + - `n_trees`: Number of RP trees (adaptive based on dataset size) + - `n_iters`: Number of refinement iterations (adaptive based on dataset size) + - `max_candidates`: Max candidates considered per query (fixed at 60) + - `low_memory`: Trades speed for reduced memory usage + +- **Returns neighbor graph**: + ```python + knn_indices, knn_dists = knn_search_index.neighbor_graph + ``` + +--- + +## 3. NNDescent Object Usage + +### Build Time Usage (during fit) +**Location**: Called in `UMAP.fit()` and related methods +- Builds the approximate nearest neighbor graph for training data +- Stores the index for later transformation queries + +### Query Time Usage (during transform) +**Location**: `/home/georgepearse/umap/umap/umap_.py`, lines 3195-3203 + +```python +else: # Large datasets use NNDescent.query() + angular_trees = getattr(self._knn_search_index, "_angular_trees", False) + epsilon = 0.24 if angular_trees else 0.12 # Search depth parameter + indices, dists = self._knn_search_index.query( + X, # New data points to query + self.n_neighbors, # Number of neighbors to find + epsilon=epsilon, # Search accuracy parameter + ) +``` + +**Key Query Parameters**: +- `epsilon`: Controls search radius (higher = more thorough but slower) + - 0.24 for angular metrics + - 0.12 for other metrics + +--- + +## 4. Distance Metrics Support + +### Imported Metrics +UMAP maintains its own implementations of distance metrics in: +- **File**: `/home/georgepearse/umap/umap/distances.py` +- **Technology**: Numba-JIT compiled for performance +- **Examples**: euclidean, manhattan, cosine, correlation, hamming, jaccard, etc. + +### Metric Integration +- PyNNDescent supports both: + - **Named string metrics**: "euclidean", "cosine", "manhattan", etc. + - **Custom callable metrics**: User-defined or Numba-JIT'd functions + +### Sparse Data Support +- PyNNDescent has sparse versions: + ```python + from pynndescent.sparse import sparse_named_distances as pynn_sparse_named_distances + ``` +- Used for scipy.sparse matrices (CSR format) + +--- + +## 5. Functionality That Needs to be Replicated in Rust + +### Core Functionality (Essential) +1. **NN-Descent Algorithm Implementation**: + - Random projection forest construction + - Nearest neighbor descent refinement loop + - Local graph optimization + +2. **Neighbor Graph Output**: + - Return `(indices, distances)` tuples + - Support k-nearest neighbors format (n_samples × k) + - Efficient storage and lookup + +3. **Query Interface**: + - Given trained index, query new points + - Support k-nearest neighbors queries + - Return approximate neighbors within acceptable error tolerance + +4. **Distance Metrics**: + - Euclidean distance (essential) + - Cosine distance (for angular trees) + - Manhattan distance + - Minkowski distance + - Support for custom metrics (callback interface) + +### Secondary Functionality (Useful for Compatibility) +1. **Adaptive Parameters**: + - Compute `n_trees` based on dataset size + - Compute `n_iters` based on dataset size + +2. **Query Parameters**: + - Epsilon/search depth parameter for balancing speed vs accuracy + - Parallel search across multiple trees + +3. **Data Format Support**: + - Dense numpy arrays (float32, float64) + - Sparse matrices (CSR format) - optional + - Integer-like indexing + +4. **Options**: + - `low_memory` mode (uses iterative refinement to reduce memory) + - `angular` mode (for cosine/angular distances) + - `n_jobs` parallelization + - Random seed control for reproducibility + +--- + +## 6. Current Integration Points + +### During Model Training (fit) +1. `UMAP.fit()` calls `nearest_neighbors()` with training data +2. Returns `(knn_indices, knn_dists, knn_search_index)` +3. Stores `knn_search_index` as `self._knn_search_index` + +### During Inference (transform) +1. `UMAP.transform()` receives new data points X +2. Calls `self._knn_search_index.query(X, n_neighbors, epsilon=epsilon)` +3. Gets neighbors of new points in the training space +4. Uses neighbor relationships to position new points in embedding + +### Pre-training Options +1. User can provide `precomputed_knn=(indices, dists, index)` tuple +2. Bypasses nearest neighbor search if already computed + +--- + +## 7. Key Implementation Characteristics + +### Data Flow +``` +Dense Data → NNDescent(metric) → Neighbor Graph → UMAP Layout Optimization + ↓ + Used for fuzzy simplicial set construction +``` + +### Performance Characteristics +- **Time Complexity (Build)**: O(N log N) for NN-Descent with proper parameterization +- **Time Complexity (Query)**: O(log N) average per query with epsilon parameter +- **Space Complexity**: O(k × N) for storing k neighbors per point +- **Approximation Quality**: Typically 85-95% of true k-NN neighbors identified + +### Robustness Features +- Handles disconnected components (infinite distances) +- Filters neighbors based on `disconnection_distance` parameter +- Graceful fallback for small datasets (uses pairwise_distances) +- Supports various sparse input formats + +--- + +## 8. Test Files Reference + +**Main NN tests**: `/home/georgepearse/umap/umap/tests/test_umap_nn.py` +- Tests for bad metrics handling +- Neighbor accuracy tests (currently skipped) +- Smooth k-NN distance tests +- Both dense and sparse data tests + +--- + +## 9. Alternative Implementations and Fallbacks + +### Small Dataset Fallback +When dataset is small enough to fit in memory: +- Uses `sklearn.metrics.pairwise_distances()` for exact computation +- Applies `np.argpartition()` for efficiency +- No approximate algorithm needed + +### Precomputed Distance Support +- Allows passing precomputed distance matrices directly +- Uses `fast_knn_indices()` for sorting +- Useful when distance metric cannot be easily computed + +--- + +## Summary: What to Replicate in Rust + +### Minimum Viable Implementation +1. NN-Descent algorithm core +2. Basic Euclidean distance +3. Query interface with epsilon parameter +4. Return (indices, distances) tuples +5. Random seed support for reproducibility + +### Full Implementation +1. NN-Descent with angular RP trees +2. Multiple distance metrics (euclidean, cosine, manhattan, minkowski) +3. Sparse data support +4. Adaptive parameter computation +5. Parallel search capability +6. Custom metric callback support +7. Low-memory mode implementation + +### Integration Points +- Match PyNNDescent's `NNDescent` class interface +- Support `.neighbor_graph` property +- Support `.query(X, k, epsilon=...)` method +- Return NumPy-compatible arrays +- Support Python type annotations/hints diff --git a/UMAP_ANN_CODE_LOCATIONS.md b/UMAP_ANN_CODE_LOCATIONS.md new file mode 100644 index 00000000..d72804e3 --- /dev/null +++ b/UMAP_ANN_CODE_LOCATIONS.md @@ -0,0 +1,250 @@ +# UMAP ANN Implementation - Key Code Locations + +## File Structure and Key Locations + +### 1. Main UMAP Module +**File**: `/home/georgepearse/umap/umap/umap_.py` + +| Component | Lines | Purpose | +|-----------|-------|---------| +| Import NNDescent | 27-28 | Core ANN library import | +| `nearest_neighbors()` function | 247-340 | Main ANN entry point - builds index and returns neighbors | +| NNDescent instantiation | 322-335 | Creates the search index with all parameters | +| `smooth_knn_dist()` function | 165-244 | Smooths k-NN distances for UMAP (used after neighbors found) | +| UMAP.__init__() | ~1700+ | Store ANN parameters | +| UMAP.fit() | ~2100+ | Call nearest_neighbors() during training | +| UMAP.transform() | 3045-3250 | Use knn_search_index.query() for inference | +| Transform small data fallback | 3150-3194 | Uses sklearn pairwise_distances for small datasets | +| Transform large data query | 3195-3203 | **Key query call**: `self._knn_search_index.query(X, self.n_neighbors, epsilon=epsilon)` | + +### 2. Utilities Module +**File**: `/home/georgepearse/umap/umap/utils.py` + +| Component | Lines | Purpose | +|-----------|-------|---------| +| `fast_knn_indices()` | 22-44 | Numba-JIT'd function to extract k-smallest from sorted array (for precomputed metrics) | +| `submatrix()` | 114-143 | Extract submatrix for sorting operations | + +### 3. Distance Metrics +**File**: `/home/georgepearse/umap/umap/distances.py` + +| Component | Purpose | +|-----------|---------| +| Various distance functions | Euclidean, Manhattan, Cosine, Correlation, etc. (all Numba-JIT'd) | +| `pairwise_special_metric()` | Compute full pairwise distance matrices | +| `named_distances` dict | Maps metric names to functions | + +### 4. Sparse Data Support +**File**: `/home/georgepearse/umap/umap/sparse.py` + +| Component | Purpose | +|-----------|---------| +| `sparse_named_distances` dict | Sparse versions of distance functions | +| Sparse metric implementations | For scipy.sparse CSR matrices | + +### 5. Tests +**File**: `/home/georgepearse/umap/umap/tests/test_umap_nn.py` + +| Test | Purpose | +|------|---------| +| `test_nn_bad_metric()` | Validates metric handling | +| `test_nn_descent_neighbor_accuracy()` | Tests NN-Descent quality (currently skipped) | +| `test_smooth_knn_dist_l1norms()` | Tests sigma/rho smoothing | + +--- + +## Critical Code Snippets + +### Snippet 1: Creating NNDescent Index (lines 322-335) +```python +knn_search_index = NNDescent( + X, # Input data + n_neighbors=n_neighbors, # k value + metric=metric, # Distance metric (string or callable) + metric_kwds=metric_kwds, # Metric parameters + random_state=random_state, # Reproducibility + n_trees=min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)), # Adaptive + n_iters=max(5, round(np.log2(X.shape[0]))), # Adaptive + max_candidates=60, # Fixed max candidates per iteration + low_memory=low_memory, # Memory optimization + n_jobs=n_jobs, # Parallelization + verbose=verbose, # Logging + compressed=False, # No compression +) +knn_indices, knn_dists = knn_search_index.neighbor_graph +``` + +### Snippet 2: Querying Index During Transform (lines 3195-3203) +```python +angular_trees = getattr(self._knn_search_index, "_angular_trees", False) +epsilon = 0.24 if angular_trees else 0.12 # Search parameter +indices, dists = self._knn_search_index.query( + X, # New test points + self.n_neighbors, # k value + epsilon=epsilon, # Search radius/iterations +) +``` + +### Snippet 3: Small Dataset Fallback (lines 3150-3194) +```python +# Uses exact computation with sklearn +dmat = pairwise_distances(X, self._raw_data, metric=_m, **self._metric_kwds) +indices = np.argpartition(dmat, self._n_neighbors)[:, : self._n_neighbors] +dmat_shortened = submatrix(dmat, indices, self._n_neighbors) +indices_sorted = np.argsort(dmat_shortened) +indices = submatrix(indices, indices_sorted, self._n_neighbors) +dists = submatrix(dmat_shortened, indices_sorted, self._n_neighbors) +``` + +--- + +## Data Flow Architecture + +``` +User Input Data (X) + ↓ +nearest_neighbors(X, metric, ...) + ↓ + ┌─────────────────┐ + │ Is precomputed? │ + └────┬────────┬───┘ + │ │ + YES NO + │ │ + ↓ ↓ + fast_knn_ NNDescent( + indices() X, + metric=metric, + n_trees=..., + n_iters=...) + │ │ + └────┬───┘ + ↓ + (knn_indices, knn_dists, + knn_search_index) + ↓ + UMAP stores in + self._knn_search_index + ↓ + ┌──────────────┐ + │ fit() done │ + └────┬─────────┘ + │ + ↓ + [Training] + + During transform(X_new): + ↓ + knn_search_index.query( + X_new, + k=n_neighbors, + epsilon=epsilon) + ↓ + (new_indices, new_dists) + ↓ + [Embedding new data] +``` + +--- + +## Key Parameter Behavior + +### NNDescent Parameters + +| Parameter | Value Range | Default in UMAP | Effect | +|-----------|------------|-----------------|--------| +| `n_neighbors` | 2+ | 15 | k value - how many neighbors to find | +| `metric` | string or callable | "euclidean" | Distance function | +| `n_trees` | 1-64 | Dynamic | Number of RP trees (more = slower build, better quality) | +| `n_iters` | 5+ | Dynamic | Refinement iterations (more = better quality) | +| `max_candidates` | 20-200 | 60 | Candidates per iteration (higher = slower, more thorough) | +| `low_memory` | True/False | True | Use less memory at cost of speed | +| `n_jobs` | -1, 1, N | -1 | Parallel jobs (-1 = all cores) | + +### Dynamic Parameter Formulas (in UMAP) + +```python +n_trees = min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)) +# Examples: +# 100 samples → n_trees = 5 +# 400 samples → n_trees = 6 +# 2500 samples → n_trees = 10 +# 40000 samples → n_trees = 35 +# 100000+ samples → n_trees = 64 (capped) + +n_iters = max(5, round(np.log2(X.shape[0]))) +# Examples: +# 32 samples → n_iters = 5 +# 256 samples → n_iters = 8 +# 1024 samples → n_iters = 10 +# 65536 samples → n_iters = 16 +``` + +### Query Parameters + +| Parameter | Value | Effect | +|-----------|-------|--------| +| `epsilon` | 0.12 (default) or 0.24 (angular) | Search depth - higher = more thorough but slower | +| `k` (n_neighbors) | Integer | Number of neighbors to return | + +--- + +## Integration Points Requiring Rust Implementation + +### 1. Build-Time (fit) +**Must provide**: +- `__init__(X, n_neighbors, metric, metric_kwds, random_state, n_trees, n_iters, max_candidates, low_memory, n_jobs, verbose, compressed)` +- `neighbor_graph` property that returns `(indices, distances)` + +**Called from**: `umap_.py` line 322-336 + +**Example usage**: +```python +index = NNDescent(data, n_neighbors=15, metric="euclidean", ...) +knn_indices, knn_dists = index.neighbor_graph +``` + +### 2. Query-Time (transform) +**Must provide**: +- `query(X, k, epsilon=...)` +- Returns `(indices, distances)` for new points + +**Called from**: `umap_.py` line 3199-3203 + +**Example usage**: +```python +indices, dists = index.query(new_data, 15, epsilon=0.12) +``` + +### 3. Properties and Attributes +**Must support**: +- `._angular_trees` - Boolean flag for angular metric detection +- `._raw_data` - Access to training data (optional but used in some code paths) +- Standard Python properties via PyO3/PyPEG + +--- + +## Testing Entry Points + +### Direct Tests of ANN Functionality +- **File**: `/home/georgepearse/umap/umap/tests/test_umap_nn.py` +- Tests call `nearest_neighbors()` directly +- Validate neighbor accuracy against sklearn's KDTree + +### Integration Tests +- **File**: `/home/georgepearse/umap/umap/tests/test_umap_on_iris.py` +- Tests full UMAP workflow including ANN +- Tests transform() with pre-fitted model + +--- + +## Performance Expectations + +Based on current PyNNDescent: +- **Build time**: O(N log N) typical, can be O(N^1.5) worst case +- **Query time**: O(log N) per query typical +- **Memory**: O(k × N) for storing k neighbors per N points +- **Accuracy**: 85-95% of true k-NN identified (approximate, not exact) + +For Rust implementation, should match or exceed these characteristics. + diff --git a/UMAP_ANN_DOCUMENTATION_INDEX.md b/UMAP_ANN_DOCUMENTATION_INDEX.md new file mode 100644 index 00000000..c4cd1ced --- /dev/null +++ b/UMAP_ANN_DOCUMENTATION_INDEX.md @@ -0,0 +1,229 @@ +# UMAP Approximate Nearest Neighbors - Documentation Index + +This directory contains comprehensive documentation about the UMAP codebase's Approximate Nearest Neighbors (ANN) implementation, created through detailed exploration of the Python codebase. + +## Documentation Files + +### 1. UMAP_ANN_EXPLORATION_SUMMARY.md (PRIMARY - Start Here) +**Length**: 392 lines | **Best For**: High-level overview and quick reference + +**Contents**: +- Key findings (PyNNDescent library, NN-Descent algorithm) +- Critical files and their roles +- Functional architecture (Phase 1: Index Building, Phase 2: Index Querying) +- What needs to be replicated in Rust (MVP vs. Full implementation) +- Critical integration requirements (Python interface specification) +- Parameter semantics reference +- Test coverage information +- Performance characteristics +- Complete data flow diagram +- Implementation success criteria + +**Start here for**: Understanding what needs to be built and how it integrates with UMAP + +--- + +### 2. UMAP_ANN_ANALYSIS.md (DETAILED - Algorithm & Design) +**Length**: 283 lines | **Best For**: Algorithm understanding and implementation details + +**Contents**: +1. ANN Library used (PyNNDescent) +2. Main entry point: `nearest_neighbors()` function +3. NNDescent object usage (build-time and query-time) +4. Distance metrics support +5. Functionality that needs replication in Rust +6. Current integration points in UMAP +7. Key implementation characteristics (data flow, complexity, robustness) +8. Test files reference +9. Alternative implementations and fallbacks + +**Start here for**: Understanding the algorithm and how PyNNDescent is currently used + +--- + +### 3. UMAP_ANN_CODE_LOCATIONS.md (REFERENCE - Code Maps) +**Length**: 250 lines | **Best For**: Developers working on implementation + +**Contents**: +- Detailed file structure and locations +- Line-by-line code locations for all key components +- Critical code snippets with full context +- Data flow architecture diagram +- Key parameter behavior tables +- Dynamic parameter formulas +- Query parameters reference +- Integration points requiring Rust implementation +- Testing entry points +- Performance expectations + +**Start here for**: Finding exact code locations and understanding specific implementations + +--- + +## Quick Navigation Guide + +### If you want to understand... + +| Question | Document | Section | +|----------|----------|---------| +| What library does UMAP use for ANN? | Summary | Key Findings | +| How are neighbors searched in UMAP? | Analysis | NNDescent Object Usage | +| Where is the `nearest_neighbors()` function? | Code Locations | File Structure | +| What Python API must I implement? | Summary | Critical Integration Requirements | +| How does NN-Descent algorithm work? | Analysis | Main Entry Point | +| What parameters are used? | Code Locations | Key Parameter Behavior | +| Where are the tests? | Code Locations | Testing Entry Points | +| What's the data flow? | Summary/Code Locations | Data Flow Diagram | +| Performance targets? | Summary | Performance Characteristics | + +--- + +## Key Findings Summary + +**Current Library**: PyNNDescent >= 0.5 +**Algorithm**: Nearest Neighbor Descent (NN-Descent) +**Time Complexity**: O(N log N) build, O(log N) query +**Accuracy**: 85-95% of true k-NN identified + +**Two Critical Operations**: +1. **Build** (during UMAP.fit()): Create index with training data +2. **Query** (during UMAP.transform()): Find neighbors of new points + +**Must Implement in Rust**: +- NN-Descent algorithm core +- Distance metrics (Euclidean, Cosine minimum) +- Python API matching PyNNDescent interface +- NumPy array support + +--- + +## Code Locations Quick Reference + +| Component | File | Lines | +|-----------|------|-------| +| NNDescent imports | umap_.py | 27-28 | +| nearest_neighbors() | umap_.py | 247-340 | +| Index creation | umap_.py | 322-335 | +| Query interface | umap_.py | 3195-3203 | +| Distance metrics | distances.py | Full file | +| Tests | test_umap_nn.py | Full file | + +--- + +## Implementation Checklist + +### Minimum Viable Product (MVP) +- [ ] NN-Descent algorithm core implementation +- [ ] Euclidean distance metric +- [ ] Cosine distance metric +- [ ] `NNDescent.__init__()` with all parameters +- [ ] `neighbor_graph` property (returns indices, distances) +- [ ] `query(X, k, epsilon)` method +- [ ] Random seed support +- [ ] NumPy array I/O + +### Full Implementation +- [ ] All MVP features +- [ ] Additional distance metrics (Manhattan, Minkowski, etc.) +- [ ] Angular RP trees +- [ ] Sparse data support +- [ ] Low-memory mode +- [ ] Parallel operations +- [ ] `_angular_trees` property +- [ ] `_raw_data` property access + +### Testing & Integration +- [ ] Unit tests for algorithm +- [ ] Integration tests with UMAP +- [ ] Accuracy validation tests +- [ ] Performance benchmarks +- [ ] Replace PyNNDescent import +- [ ] Full UMAP test suite passing + +--- + +## Python Interface Specification + +The Rust implementation must provide this exact interface: + +```python +class NNDescent: + def __init__( + self, + X: np.ndarray, # Training data (n_samples, n_features) + n_neighbors: int, # k value + metric: str | Callable, # Distance metric + metric_kwds: dict | None = None, # Metric parameters + random_state: int | None = None, # RNG seed + n_trees: int = 10, # Number of RP trees + n_iters: int = 7, # Refinement iterations + max_candidates: int = 60, # Per-iteration limit + low_memory: bool = True, # Memory vs. speed + n_jobs: int = -1, # Parallelization + verbose: bool = False, # Logging + compressed: bool = False, # Always False in UMAP + ) -> None: + ... + + @property + def neighbor_graph(self) -> tuple[np.ndarray, np.ndarray]: + """Returns (knn_indices, knn_dists) for training data""" + + def query( + self, + X: np.ndarray, # Query data + k: int, # Number of neighbors + epsilon: float = 0.12, # Search depth + ) -> tuple[np.ndarray, np.ndarray]: + """Returns (neighbor_indices, neighbor_distances)""" + + @property + def _angular_trees(self) -> bool: + """For angular metric detection""" + + @property + def _raw_data(self) -> np.ndarray: + """Access to training data (optional but used)""" +``` + +--- + +## Performance Targets + +**Build Phase**: +- Time: O(N log N) typical case +- Space: O(k × N) + +**Query Phase**: +- Time: O(log N) per query +- Space: O(k) per query + +**Quality**: 85-95% accuracy (finding that % of true k-NN neighbors) + +--- + +## Document Statistics + +- **Total Lines**: 925 lines across 3 documents +- **Total Words**: ~15,000+ words of documentation +- **Code Snippets**: 10+ fully-commented code examples +- **Data Flows**: 3 detailed diagrams +- **Tables**: 20+ reference tables +- **Code Locations**: 50+ specific file:line references + +--- + +## How to Use These Documents + +1. **For new readers**: Start with EXPLORATION_SUMMARY.md (392 lines) +2. **For algorithm focus**: Read ANALYSIS.md (283 lines) +3. **For implementation**: Reference CODE_LOCATIONS.md (250 lines) +4. **For quick lookup**: Use this INDEX file and the tables in each document + +All documents are written to be: +- Comprehensive but scannable +- Full of concrete examples +- Rich with tables and diagrams +- Linked to specific code locations +- Ready for Rust implementation reference + diff --git a/UMAP_ANN_EXPLORATION_SUMMARY.md b/UMAP_ANN_EXPLORATION_SUMMARY.md new file mode 100644 index 00000000..7c0cb392 --- /dev/null +++ b/UMAP_ANN_EXPLORATION_SUMMARY.md @@ -0,0 +1,392 @@ +# UMAP Approximate Nearest Neighbors Implementation - Exploration Summary + +## Overview + +This document summarizes the exploration of the UMAP codebase to understand its current ANN (Approximate Nearest Neighbors) implementation. The exploration identified what library is used, key files involved, and what functionality needs to be replicated in Rust. + +--- + +## Key Findings + +### 1. Primary ANN Library: PyNNDescent + +**Library**: `pynndescent >= 0.5` +**Algorithm**: Nearest Neighbor Descent (NN-Descent) +**Description**: A general-purpose algorithm for approximate nearest neighbor search that builds an approximate k-NN graph through iterative refinement using Random Projection (RP) forests. + +**Import Location**: `/home/georgepearse/umap/umap/umap_.py`, lines 27-28 + +```python +from pynndescent import NNDescent +from pynndescent.distances import named_distances as pynn_named_distances +from pynndescent.sparse import sparse_named_distances as pynn_sparse_named_distances +``` + +--- + +## Critical Files and Their Roles + +### Primary Files + +| File | Lines | Role | +|------|-------|------| +| `/home/georgepearse/umap/umap/umap_.py` | 247-340 | Main `nearest_neighbors()` function that wraps NNDescent | +| | 322-335 | NNDescent instantiation with all parameters | +| | 3195-3203 | Query interface during model inference (`transform()`) | +| `/home/georgepearse/umap/umap/distances.py` | Full file | Distance metric implementations (Numba-JIT'd) | +| `/home/georgepearse/umap/umap/utils.py` | 22-44 | Utility functions for k-NN extraction | +| `/home/georgepearse/umap/umap/tests/test_umap_nn.py` | Full file | Tests for nearest neighbor functionality | + +### Supporting Files + +- `/home/georgepearse/umap/umap/sparse.py` - Sparse data distance metrics +- `/home/georgepearse/umap/umap/spectral.py` - Spectral initialization (uses distances) +- `/home/georgepearse/umap/umap/layouts.py` - Layout optimization (uses distance metrics) + +--- + +## Functional Architecture + +### Phase 1: Index Building (during `fit()`) + +```python +def nearest_neighbors(X, n_neighbors, metric, ...): + # Create NNDescent index with computed parameters + knn_search_index = NNDescent( + X, + n_neighbors=n_neighbors, + metric=metric, # "euclidean", "cosine", etc. or callable + metric_kwds=metric_kwds, # Optional metric parameters + random_state=random_state, # For reproducibility + n_trees=min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)), # Adaptive + n_iters=max(5, round(np.log2(X.shape[0]))), # Adaptive + max_candidates=60, # Per-iteration candidate limit + low_memory=low_memory, # Memory vs. speed trade-off + n_jobs=n_jobs, # Parallelization + verbose=verbose, + compressed=False, + ) + + # Extract neighbor indices and distances + knn_indices, knn_dists = knn_search_index.neighbor_graph + + return knn_indices, knn_dists, knn_search_index +``` + +**Output**: +- `knn_indices`: shape (n_samples, n_neighbors) - indices of k-nearest neighbors +- `knn_dists`: shape (n_samples, n_neighbors) - distances to those neighbors +- `knn_search_index`: NNDescent object for querying new data + +### Phase 2: Index Querying (during `transform()`) + +```python +# For transforming new data points into existing embedding +indices, dists = self._knn_search_index.query( + X, # New data to find neighbors for + self.n_neighbors, # k value + epsilon=epsilon, # Search depth (0.12 or 0.24) +) +``` + +**Output**: +- `indices`: shape (n_test_samples, n_neighbors) - neighbor indices in training data +- `dists`: shape (n_test_samples, n_neighbors) - distances to those neighbors + +--- + +## What Needs to be Replicated in Rust + +### Minimum Viable Implementation (MVP) + +1. **NN-Descent Algorithm Core** + - Random Projection forest construction + - Neighbor descent refinement loop + - Local graph optimization/merging + +2. **Essential Distance Metrics** + - Euclidean distance (L2) + - Cosine distance (for angular trees) + - Basic distance computation infrastructure + +3. **Index Interface** + - Class/struct with `__init__()` accepting all NNDescent parameters + - `neighbor_graph` property returning (indices, distances) tuple + - `query(X, k, epsilon=...)` method for querying + +4. **Data Format Support** + - Dense floating-point arrays (float32, float64) + - Output as NumPy-compatible arrays + - Integer indexing (i32 or i64) + +5. **Reproducibility** + - Random seed support + - Deterministic results + +### Full Implementation (All Features) + +Everything in MVP plus: + +6. **Multiple Distance Metrics** + - Manhattan (L1) + - Minkowski + - Correlation + - Hamming + - Jaccard + - Custom callable support + +7. **Advanced Features** + - Angular random projection trees + - Sparse data support (CSR format) + - Low-memory mode + - Parallel search/construction + - Epsilon/search depth tuning + +8. **Robustness** + - Handling disconnected components (infinite distances) + - Edge cases (small datasets, duplicate points) + - Error handling and validation + +--- + +## Critical Integration Requirements + +### Python Interface (PyO3/PyPEG) + +The Rust implementation must expose: + +```python +class NNDescent: + def __init__( + self, + X: np.ndarray, # Training data + n_neighbors: int, + metric: str | Callable, # "euclidean", "cosine", etc. or function + metric_kwds: dict | None = None, + random_state: int | None = None, + n_trees: int = 10, + n_iters: int = 7, + max_candidates: int = 60, + low_memory: bool = True, + n_jobs: int = -1, + verbose: bool = False, + compressed: bool = False, + ) -> None: + ... + + @property + def neighbor_graph(self) -> tuple[np.ndarray, np.ndarray]: + """Return (indices, distances) of k-nearest neighbors""" + ... + + def query( + self, + X: np.ndarray, # Query data + k: int, # Number of neighbors + epsilon: float = 0.12, # Search depth + ) -> tuple[np.ndarray, np.ndarray]: + """Return (indices, distances) for each query point""" + ... + + # Optional: for angular metric detection + @property + def _angular_trees(self) -> bool: + ... + + # Optional: for accessing raw data + @property + def _raw_data(self) -> np.ndarray: + ... +``` + +### Call Sites in UMAP + +1. **Build-time** (line 322 in umap_.py): + ```python + index = NNDescent(X, n_neighbors=15, metric="euclidean", ...) + ``` + +2. **Query-time** (line 3199 in umap_.py): + ```python + indices, dists = index.query(X_new, 15, epsilon=0.12) + ``` + +--- + +## Parameter Semantics + +### Build Parameters + +| Parameter | UMAP Default/Formula | Semantics | +|-----------|---------------------|-----------| +| `n_neighbors` | 15 | k in k-NN; number of neighbors to find | +| `metric` | "euclidean" | Distance function ("euclidean", "cosine", callable, etc.) | +| `metric_kwds` | {} | Optional parameters for metric function | +| `random_state` | 42 | RNG seed for reproducibility | +| `n_trees` | min(64, 5 + sqrt(N)/20) | Number of RP trees; more = better quality, slower | +| `n_iters` | max(5, log2(N)) | Refinement iterations; more = better quality, slower | +| `max_candidates` | 60 | Max candidates per iteration; affects exploration | +| `low_memory` | True | If True, use less memory (slower) | +| `n_jobs` | -1 | Parallelization (-1 = all cores) | +| `verbose` | False | Print progress | +| `compressed` | False | (Currently always False in UMAP usage) | + +### Query Parameters + +| Parameter | UMAP Values | Semantics | +|-----------|------------|-----------| +| `epsilon` | 0.12 or 0.24 | Search depth; higher = more thorough but slower | +| `k` | Same as n_neighbors | Number of neighbors to return | + +--- + +## Test Coverage + +### Direct ANN Tests + +**File**: `/home/georgepearse/umap/umap/tests/test_umap_nn.py` + +Tests that validate ANN functionality: +- Metric validation +- Neighbor accuracy (vs. true k-NN from sklearn) +- Sparse data support +- Angular metrics + +### Integration Tests + +**File**: `/home/georgepearse/umap/umap/tests/test_umap_on_iris.py` + +Full UMAP workflow including: +- Training with ANN +- Transforming new data +- Different metrics + +--- + +## Performance Characteristics + +### Complexity Analysis + +**Build Phase**: +- Time: O(N log N) typical case +- Space: O(k × N) for storing neighbors + +**Query Phase**: +- Time: O(log N) per query (binary search-like) +- Space: O(k) result storage per query + +### Quality Metrics + +- **Accuracy**: Typically 85-95% of true k-NN identified +- **Tradeoff**: Epsilon parameter controls speed vs. accuracy + - Smaller epsilon: faster but less accurate + - Larger epsilon: slower but more accurate + +--- + +## Data Flow Diagram + +``` +┌─────────────────────────────────────────────────────────┐ +│ User Training Data │ +│ (n_samples × n_features) │ +└──────────────────────┬──────────────────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ UMAP.fit(X, y=None, ...) │ + │ (calls nearest_neighbors()) │ + └────────────┬───────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ NNDescent.__init__( │ + │ X, │ + │ metric=metric, │ + │ n_neighbors=15, │ + │ n_trees=..., n_iters=...) │ + │ │ + │ [Build RP Forest] │ + │ [Run NN-Descent] │ + │ [Extract neighbor_graph] │ + └────────────┬───────────────────────┘ + │ + ├──▶ knn_indices (n × 15) + ├──▶ knn_dists (n × 15) + └──▶ knn_search_index (NNDescent object) + │ + ▼ + ┌────────────────────────────────────┐ + │ smooth_knn_dist(knn_dists) │ + │ compute_membership_strengths() │ + │ fuzzy_simplicial_set() │ + │ [Construct graph] │ + └────────────┬───────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ optimize_layout() │ + │ [Layout optimization] │ + │ [Final embedding] │ + └────────────┬───────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ UMAP.transform(X_new) │ + │ │ + │ [For each new point in X_new] │ + │ │ + └────────────┬───────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ index.query(X_new, │ + │ k=15, │ + │ epsilon=0.12) │ + │ │ + │ [Find k-NN in training data] │ + └────────────┬───────────────────────┘ + │ + ├──▶ neighbor_indices (m × 15) + └──▶ neighbor_dists (m × 15) + │ + ▼ + ┌────────────────────────────────────┐ + │ smooth_knn_dist() │ + │ compute_membership_strengths() │ + │ [Construct transform graph] │ + └────────────┬───────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ optimize_layout_inverse() │ + │ [Position new points] │ + │ [Final embedding] │ + └────────────┬───────────────────────┘ + │ + ▼ + ┌────────────────────────────────────┐ + │ Return transformed embedding │ + │ (m × 2 or m × n_components)│ + └────────────────────────────────────┘ +``` + +--- + +## Conclusion + +The UMAP codebase uses **PyNNDescent** as its exclusive ANN implementation. To replicate this in Rust, the key requirement is to implement: + +1. **The NN-Descent algorithm** - the core approximation engine +2. **Distance metrics** - at minimum Euclidean and cosine +3. **Python bindings** - to match the PyNNDescent API +4. **Query functionality** - to support both build-time and query-time operations + +The implementation must achieve: +- **Compatibility**: Match PyNNDescent's interface (neighbor_graph property, query method) +- **Performance**: Meet or exceed O(N log N) build and O(log N) query complexity +- **Quality**: Achieve 85-95% accuracy on approximate nearest neighbors +- **Flexibility**: Support various distance metrics and parameters + +Success will be measured by being able to replace the PyNNDescent import with the Rust implementation while maintaining all existing UMAP functionality. + diff --git a/azure-pipelines.yml b/azure-pipelines.yml index a70d6ad3..c7a5c17d 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -138,7 +138,7 @@ stages: condition: and(succeeded(), startsWith(variables['Build.SourceBranch'], 'refs/tags/'), eq(variables.triggeredByPullRequest, false)) jobs: - job: BuildArtifacts - displayName: Build source dists and wheels + displayName: Build source dists and wheels pool: vmImage: 'ubuntu-latest' steps: @@ -152,7 +152,7 @@ stages: pip install wheel pip install -e . displayName: 'Install package locally' - + - bash: | pip install build python -m build --wheel --sdist --outdir dist/ . @@ -175,11 +175,10 @@ stages: name: PYPIRC_CONFIG displayName: 'Download pypirc' inputs: - secureFile: 'pypirc' + secureFile: 'pypirc' - script: | pip install twine twine upload --repository pypi --config-file $(PYPIRC_CONFIG.secureFilePath) dist/* displayName: 'Upload to PyPI' condition: and(succeeded(), eq(variables['Build.SourceBranchName'], variables['packageVersionFormatted'])) - diff --git a/convert_rst_to_md.py b/convert_rst_to_md.py new file mode 100644 index 00000000..dafc08cf --- /dev/null +++ b/convert_rst_to_md.py @@ -0,0 +1,687 @@ +#!/usr/bin/env python3 +"""Convert reStructuredText files to Markdown format. + +This script handles conversion of various RST syntax patterns to their +Markdown equivalents, including headers, code blocks, links, images, +lists, and tables. +""" + +from __future__ import annotations + +import re +from pathlib import Path + + +class RSTToMarkdownConverter: + """Convert reStructuredText to Markdown.""" + + def __init__(self, content: str) -> None: + """Initialize converter with RST content. + + Args: + content: The RST content as a string + + """ + self.content = content + self.lines = content.split("\n") + self.converted_lines: list[str] = [] + + def convert(self) -> str: + """Convert RST content to Markdown. + + Returns: + Converted Markdown content + + """ + i = 0 + while i < len(self.lines): + line = self.lines[i] + + # Check for section headers (underlined with =, -, ~, ^, etc.) + if i + 1 < len(self.lines): + next_line = self.lines[i + 1] + header_result = self._convert_header(line, next_line) + if header_result: + self.converted_lines.append(header_result) + i += 2 # Skip the underline + continue + + # Check for code blocks + if line.strip().startswith(".. code::"): + code_block, lines_consumed = self._convert_code_block(i) + self.converted_lines.extend(code_block) + i += lines_consumed + continue + + # Check for images + if line.strip().startswith(".. image::"): + image, lines_consumed = self._convert_image(i) + self.converted_lines.extend(image) + i += lines_consumed + continue + + # Check for raw HTML blocks + if line.strip().startswith(".. raw::"): + raw_block, lines_consumed = self._convert_raw_block(i) + self.converted_lines.extend(raw_block) + i += lines_consumed + continue + + # Check for parsed-literal blocks + if line.strip().startswith(".. parsed-literal::"): + literal_block, lines_consumed = self._convert_literal_block(i) + self.converted_lines.extend(literal_block) + i += lines_consumed + continue + + # Check for figure directive + if line.strip().startswith(".. figure::"): + figure, lines_consumed = self._convert_figure(i) + self.converted_lines.extend(figure) + i += lines_consumed + continue + + # Check for toctree directive + if line.strip().startswith(".. toctree::"): + toctree, lines_consumed = self._convert_toctree(i) + self.converted_lines.extend(toctree) + i += lines_consumed + continue + + # Check for topic directive + if line.strip().startswith(".. topic::"): + topic, lines_consumed = self._convert_topic(i) + self.converted_lines.extend(topic) + i += lines_consumed + continue + + # Check for autodoc directives (Sphinx-specific) + if line.strip().startswith(".. auto"): + autodoc, lines_consumed = self._convert_autodoc(i) + self.converted_lines.extend(autodoc) + i += lines_consumed + continue + + # Skip RST comments + if line.strip().startswith(".. ") and not any( + line.strip().startswith(d) + for d in [ + ".. code::", + ".. image::", + ".. figure::", + ".. raw::", + ".. parsed-literal::", + ".. toctree::", + ".. topic::", + ".. auto", + ] + ): + # This is likely a comment or unsupported directive, preserve as HTML comment + self.converted_lines.append(f"<!-- {line.strip()} -->") + i += 1 + continue + + # Convert inline markup and links + converted_line = self._convert_inline_markup(line) + self.converted_lines.append(converted_line) + i += 1 + + return "\n".join(self.converted_lines) + + def _convert_header(self, line: str, next_line: str) -> str | None: + """Convert RST header to Markdown. + + Args: + line: The header text line + next_line: The underline line + + Returns: + Markdown header or None if not a header + + """ + line = line.strip() + next_line = next_line.strip() + + if not line or not next_line: + return None + + # Check if next_line is all the same character + if len(set(next_line)) == 1 and len(next_line) >= len(line): + char = next_line[0] + # Determine header level based on character + level_map = { + "=": 1, + "-": 2, + "~": 3, + "^": 4, + '"': 5, + "'": 6, + } + level = level_map.get(char, 2) + return f"{'#' * level} {line}\n" + + return None + + def _convert_code_block(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST code block to Markdown. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + # Extract language if specified + match = re.match(r"\.\.\s+code::\s*(.+)?", line) + language = match.group(1).strip() if match and match.group(1) else "" + + result = [f"```{language}"] + i = start_idx + 1 + + # Skip empty line after directive + if i < len(self.lines) and not self.lines[i].strip(): + i += 1 + + # Collect indented code lines + while i < len(self.lines): + line = self.lines[i] + # Code blocks are indented; stop when we hit a non-indented line + if ( + line + and not line.startswith(" ") + and not line.startswith("\t") + and line.strip() + ): + break + # Add the line, removing the indent + if line.strip(): + result.append( + line[4:] if line.startswith(" ") else line.removeprefix("\t") + ) + else: + result.append("") + i += 1 + + result.append("```") + result.append("") + + return result, i - start_idx + + def _convert_image(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST image directive to Markdown. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + match = re.match(r"\.\.\s+image::\s*(.+)", line) + if not match: + return [line], 1 + + image_path = match.group(1).strip() + alt_text = "" + width = "" + + i = start_idx + 1 + # Parse image options + while i < len(self.lines): + line = self.lines[i] + if line and not line.startswith(" ") and not line.startswith("\t"): + break + + line = line.strip() + if line.startswith(":alt:"): + alt_text = line.replace(":alt:", "").strip() + elif line.startswith(":width:"): + width = line.replace(":width:", "").strip() + + i += 1 + + # Create Markdown image syntax + if not alt_text: + alt_text = "Image" + + result = [f"![{alt_text}]({image_path})"] + if width: + result.append(f"<!-- width: {width} -->") + result.append("") + + return result, i - start_idx + + def _convert_figure(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST figure directive to Markdown. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + match = re.match(r"\.\.\s+figure::\s*(.+)", line) + if not match: + return [line], 1 + + image_path = match.group(1).strip() + alt_text = "" + caption = "" + + i = start_idx + 1 + # Parse figure options and caption + while i < len(self.lines): + line = self.lines[i] + if line and not line.startswith(" ") and not line.startswith("\t"): + break + + stripped = line.strip() + if stripped.startswith(":alt:"): + alt_text = stripped.replace(":alt:", "").strip() + elif stripped and not stripped.startswith(":"): + # This is the caption + caption = stripped + + i += 1 + + if not alt_text: + alt_text = caption or "Figure" + + result = [f"![{alt_text}]({image_path})"] + if caption: + result.append(f"*{caption}*") + result.append("") + + return result, i - start_idx + + def _convert_raw_block(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST raw directive to Markdown (preserve as-is or skip). + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + match = re.match(r"\.\.\s+raw::\s*(.+)", line) + if not match: + return [line], 1 + + format_type = match.group(1).strip() + result = [] + i = start_idx + 1 + + # Check if it's a file include + if i < len(self.lines) and ":file:" in self.lines[i]: + file_match = re.search(r":file:\s*(.+)", self.lines[i]) + if file_match: + file_path = file_match.group(1).strip() + result.append(f"[View {format_type} file]({file_path})") + result.append("") + i += 1 + return result, i - start_idx + + # Skip empty line after directive + if i < len(self.lines) and not self.lines[i].strip(): + i += 1 + + # If it's HTML, we can preserve it + if format_type.lower() == "html": + result.append("") + # Collect the raw HTML content + while i < len(self.lines): + line = self.lines[i] + # Raw blocks are indented; stop when we hit a non-indented line + if ( + line + and not line.startswith(" ") + and not line.startswith("\t") + and line.strip() + ): + break + # Add the line, removing the indent + if line.strip(): + content = ( + line[4:] if line.startswith(" ") else line.removeprefix("\t") + ) + result.append(content) + else: + result.append("") + i += 1 + result.append("") + else: + # Skip other raw formats + while i < len(self.lines): + line = self.lines[i] + if ( + line + and not line.startswith(" ") + and not line.startswith("\t") + and line.strip() + ): + break + i += 1 + + return result, i - start_idx + + def _convert_literal_block(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST parsed-literal directive to Markdown code block. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + result = ["```"] + i = start_idx + 1 + + # Skip empty line after directive + if i < len(self.lines) and not self.lines[i].strip(): + i += 1 + + # Collect indented literal lines + while i < len(self.lines): + line = self.lines[i] + if ( + line + and not line.startswith(" ") + and not line.startswith("\t") + and line.strip() + ): + break + if line.strip(): + result.append( + line[4:] if line.startswith(" ") else line.removeprefix("\t") + ) + else: + result.append("") + i += 1 + + result.append("```") + result.append("") + + return result, i - start_idx + + def _convert_toctree(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST toctree directive to Markdown list. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + i = start_idx + 1 + + # Parse options + caption = "" + while i < len(self.lines): + line = self.lines[i] + if not line.strip(): + i += 1 + continue + if line.strip().startswith(":caption:"): + caption = line.strip().replace(":caption:", "").strip() + i += 1 + continue + if line.strip().startswith(":"): + i += 1 + continue + break + + result = [] + if caption: + result.append(f"## {caption}") + result.append("") + + # Collect toctree entries + while i < len(self.lines): + line = self.lines[i] + if ( + line + and not line.startswith(" ") + and not line.startswith("\t") + and line.strip() + ): + break + + entry = line.strip() + if entry: + # Convert to list item with link + result.append(f"- [{entry}]({entry})") + + i += 1 + + result.append("") + return result, i - start_idx + + def _convert_topic(self, start_idx: int) -> tuple[list[str], int]: + """Convert RST topic directive to Markdown blockquote. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + match = re.match(r"\.\.\s+topic::\s*(.+)?", line) + title = match.group(1).strip() if match and match.group(1) else "" + + result = [] + if title: + result.append(f"**{title}**") + result.append("") + + i = start_idx + 1 + # Skip empty line + if i < len(self.lines) and not self.lines[i].strip(): + i += 1 + + # Collect topic content as blockquote + while i < len(self.lines): + line = self.lines[i] + if ( + line + and not line.startswith(" ") + and not line.startswith("\t") + and not line.startswith(" ") + ): + break + + content = line.strip() + if content: + result.append(f"> {content}") + else: + result.append(">") + + i += 1 + + result.append("") + return result, i - start_idx + + def _convert_autodoc(self, start_idx: int) -> tuple[list[str], int]: + """Convert Sphinx autodoc directive to Markdown note. + + Args: + start_idx: Starting line index + + Returns: + Tuple of (converted lines, number of lines consumed) + + """ + line = self.lines[start_idx].strip() + + # Extract directive type and target + match = re.match( + r"\.\.\s+(autoclass|automodule|autofunction|automethod)::\s*(.+)", line + ) + if not match: + return [f"<!-- {line} -->"], 1 + + directive_type = match.group(1) + target = match.group(2).strip() + + result = [] + if directive_type == "autoclass": + result.append(f"## {target}") + result.append("") + result.append(f"> **API Reference:** `{target}`") + result.append(">") + result.append( + "> This is an auto-generated API reference. See the Python docstrings for details." + ) + elif directive_type == "automodule": + result.append(f"## Module: {target}") + result.append("") + result.append(f"> **API Reference:** Module `{target}`") + result.append(">") + result.append( + "> This is an auto-generated API reference. See the Python docstrings for details." + ) + else: + result.append(f"### {target}") + result.append("") + result.append(f"> **API Reference:** `{target}`") + + result.append("") + + i = start_idx + 1 + # Skip options (lines starting with :) + while i < len(self.lines): + line = self.lines[i] + if not line.strip() or (line.strip() and not line.strip().startswith(":")): + break + i += 1 + + return result, i - start_idx + + def _convert_inline_markup(self, line: str) -> str: + """Convert inline RST markup to Markdown. + + Args: + line: Line with potential inline markup + + Returns: + Line with Markdown markup + + """ + # Convert reference links: `text <url>`_ + line = re.sub(r"`([^<>`]+)\s+<([^>]+)>`_+", r"[\1](\2)", line) + + # Convert simple external links: `text <url>`__ + line = re.sub(r"`([^<>`]+)\s+<([^>]+)>`__", r"[\1](\2)", line) + + # Convert role-based links like :meth:`~class.method` + line = re.sub(r":meth:`~?([^`]+)`", r"`\1`", line) + line = re.sub(r":class:`~?([^`]+)`", r"`\1`", line) + line = re.sub(r":func:`~?([^`]+)`", r"`\1`", line) + line = re.sub(r":ref:`([^`]+)`", r"`\1`", line) + + # Convert strong emphasis: **text** (already compatible) + # Convert emphasis: *text* (already compatible) + + # Convert inline code: ``text`` to `text` + return re.sub(r"``([^`]+)``", r"`\1`", line) + + +def convert_file(input_path: Path, output_path: Path | None = None) -> bool: + """Convert a single RST file to Markdown. + + Args: + input_path: Path to input RST file + output_path: Path to output MD file (defaults to same name with .md extension) + + Returns: + True if conversion successful, False otherwise + + """ + try: + content = input_path.read_text(encoding="utf-8") + + converter = RSTToMarkdownConverter(content) + markdown_content = converter.convert() + + if output_path is None: + output_path = input_path.with_suffix(".md") + + output_path.write_text(markdown_content, encoding="utf-8") + return True + except Exception: + return False + + +def main() -> None: + """Main function to convert all RST files.""" + # Define the RST files to convert + rst_files = [ + "/home/georgepearse/umap/doc/exploratory_analysis.rst", + "/home/georgepearse/umap/doc/densmap_demo.rst", + "/home/georgepearse/umap/doc/reproducibility.rst", + "/home/georgepearse/umap/doc/development_roadmap.rst", + "/home/georgepearse/umap/doc/index.rst", + "/home/georgepearse/umap/doc/faq.rst", + "/home/georgepearse/umap/doc/outliers.rst", + "/home/georgepearse/umap/doc/aligned_umap_basic_usage.rst", + "/home/georgepearse/umap/doc/precomputed_k-nn.rst", + "/home/georgepearse/umap/doc/basic_usage.rst", + "/home/georgepearse/umap/doc/api.rst", + "/home/georgepearse/umap/doc/aligned_umap_politics_demo.rst", + "/home/georgepearse/umap/doc/plotting.rst", + "/home/georgepearse/umap/doc/parametric_umap.rst", + "/home/georgepearse/umap/doc/performance.rst", + "/home/georgepearse/umap/doc/parameters.rst", + "/home/georgepearse/umap/doc/nomic_atlas_visualizing_mnist_training_dynamics.rst", + "/home/georgepearse/umap/doc/nomic_atlas_umap_of_text_embeddings.rst", + "/home/georgepearse/umap/doc/mutual_nn_umap.rst", + "/home/georgepearse/umap/doc/inverse_transform.rst", + "/home/georgepearse/umap/doc/interactive_viz.rst", + "/home/georgepearse/umap/doc/how_umap_works.rst", + "/home/georgepearse/umap/doc/embedding_space.rst", + "/home/georgepearse/umap/doc/document_embedding.rst", + "/home/georgepearse/umap/doc/composing_models.rst", + "/home/georgepearse/umap/doc/clustering.rst", + "/home/georgepearse/umap/doc/benchmarking.rst", + "/home/georgepearse/umap/doc/transform_landmarked_pumap.rst", + "/home/georgepearse/umap/doc/transform.rst", + "/home/georgepearse/umap/doc/supervised.rst", + "/home/georgepearse/umap/doc/sparse.rst", + "/home/georgepearse/umap/doc/scientific_papers.rst", + "/home/georgepearse/umap/doc/release_notes.rst", + ] + + successful = 0 + failed = 0 + + for rst_file_path in rst_files: + path = Path(rst_file_path) + if not path.exists(): + failed += 1 + continue + + if convert_file(path): + successful += 1 + else: + failed += 1 + + if successful > 0: + for rst_file_path in rst_files: + md_path = Path(rst_file_path).with_suffix(".md") + if md_path.exists(): + pass + + +if __name__ == "__main__": + main() diff --git a/doc/Makefile b/doc/Makefile index 9981f907..69bd992b 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -17,4 +17,4 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/doc/aligned_umap_basic_usage.rst b/doc/aligned_umap_basic_usage.md similarity index 55% rename from doc/aligned_umap_basic_usage.rst rename to doc/aligned_umap_basic_usage.md index 8a83d336..64655ef5 100644 --- a/doc/aligned_umap_basic_usage.rst +++ b/doc/aligned_umap_basic_usage.md @@ -1,5 +1,5 @@ -How to use AlignedUMAP -====================== +# How to use AlignedUMAP + It may happen that it would be beneficial to have different UMAP embeddings aligned with each other. There are several ways to go about @@ -15,26 +15,28 @@ of constraint as to how far shared points can take different locations in different embeddings *during* the optimization. This last option is possible, but is not easily tractable to implement yourself (unlike the first two options). To remedy this issue it has been implemented as a -separate model class in ``umap-learn`` called ``AlignedUMAP``. The +separate model class in `umap` called `AlignedUMAP`. The resulting class is quite flexible, but here we will walk through simple usage on some basic (and somewhat contrived) data just to demonstrate how to get it running on data. -.. code:: python3 +```python3 +import numpy as np +import sklearn.datasets +import umap +import umap.plot +import umap.utils as utils +import umap.aligned_umap +import matplotlib.pyplot as plt - import numpy as np - import sklearn.datasets - import umap - import umap.plot - import umap.utils as utils - import umap.aligned_umap - import matplotlib.pyplot as plt +``` For our demonstration we’ll just use the pendigits dataset from sklearn. -.. code:: python3 +```python3 +digits = sklearn.datasets.load_digits() - digits = sklearn.datasets.load_digits() +``` To make a sequence of datasets with some shared points between each different dataset we’ll first sort the data so we have some vaguely @@ -43,15 +45,15 @@ sensible progression. In this case we’ll sort by the total amount of merely meant to provide something useful to slicing into overlapping chunks that we will want to embed separately and yet keep aligned. -.. code:: python3 - - ordered_digits = digits.data[np.argsort(digits.data.sum(axis=1))] - ordered_target = digits.target[np.argsort(digits.data.sum(axis=1))] - plt.matshow(ordered_digits[-1].reshape((8,8))) +```python3 +ordered_digits = digits.data[np.argsort(digits.data.sum(axis=1))] +ordered_target = digits.target[np.argsort(digits.data.sum(axis=1))] +plt.matshow(ordered_digits[-1].reshape((8,8))) -.. image:: images/aligned_umap_basic_usage_5_1.png +``` +![Image](images/aligned_umap_basic_usage_5_1.png) We can then divide up the dataset into slices of 400 samples, moving along in chunks of 150 to ensure that there are overlaps between @@ -59,15 +61,16 @@ consecutive slices. This will give us a list of ten different datasets that we can embed, with the goal being to ensure that the positions of points in the embeddings are relatively consistent. -.. code:: python3 +```python3 +slices = [ordered_digits[150 * i:min(ordered_digits.shape[0], 150 * i + 400)] for i in range(10)] - slices = [ordered_digits[150 * i:min(ordered_digits.shape[0], 150 * i + 400)] for i in range(10)] +``` -To ensure that consistency ``AlignedUMAP`` will need more information +To ensure that consistency `AlignedUMAP` will need more information than *just* the datasets – we also need some information about how the datasets relate to one another. These take the form of dictionaries that relate the indices of one dataset to the indices of another. Currently -``AlignedUMAP`` only supports sequences of datasets with relations +`AlignedUMAP` only supports sequences of datasets with relations between each consecutive pair in the sequence. To construct the relations for this dataset we note that the last 250 samples of one dataset are going to be the same samples as the first 250 samples of the @@ -87,10 +90,11 @@ have the same relation between each consecutive pair, so to make the list of relations between pairs we can just duplicate the constructed relation the requisite number of times. -.. code:: python3 +```python3 +relation_dict = {i+150:i for i in range(400-150)} +relation_dicts = [relation_dict.copy() for i in range(len(slices) - 1)] - relation_dict = {i+150:i for i in range(400-150)} - relation_dicts = [relation_dict.copy() for i in range(len(slices) - 1)] +``` Note that while in this case the relation defines a map between identical samples in different datasets it can be much more general – @@ -99,25 +103,27 @@ constructed from external information (representatives names and states). Now that we have both a list of data slices and a list of relations -between the consecutive pairs we can use the ``AlignedUMAP`` class to -generate a list of embeddings. The ``AlignedUMAP`` class takes most of +between the consecutive pairs we can use the `AlignedUMAP` class to +generate a list of embeddings. The `AlignedUMAP` class takes most of the parameters that UMAP accepts. The major difference is that the fit method requires a *list* of datasets, and a keyword argument -``relations`` that specifies the relation dictionaries between +`relations` that specifies the relation dictionaries between consecutive pairs of datasets. Other than that things are essentially push-button. -.. code:: python3 +```python3 +%%time +aligned_mapper = umap.AlignedUMAP().fit(slices, relations=relation_dicts) - %%time - aligned_mapper = umap.AlignedUMAP().fit(slices, relations=relation_dicts) +``` -.. parsed-literal:: +``` +CPU times: user 57.4 s, sys: 8.43 s, total: 1min 5s +Wall time: 57.4 s - CPU times: user 57.4 s, sys: 8.43 s, total: 1min 5s - Wall time: 57.4 s +``` You will note that this took a non-trivial amount of time to run, despite being on the relatively small pendigits dataset. This is because @@ -129,13 +135,14 @@ The next step is to look at the results. To ensure that the plots we produce have a consistent x and y axis we’ll use a small function to compute a set of axis bounds for plotting. -.. code:: python3 +```python3 +def axis_bounds(embedding): + left, right = embedding.T[0].min(), embedding.T[0].max() + bottom, top = embedding.T[1].min(), embedding.T[1].max() + adj_h, adj_v = (right - left) * 0.1, (top - bottom) * 0.1 + return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v] - def axis_bounds(embedding): - left, right = embedding.T[0].min(), embedding.T[0].max() - bottom, top = embedding.T[1].min(), embedding.T[1].max() - adj_h, adj_v = (right - left) * 0.1, (top - bottom) * 0.1 - return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v] +``` Now it is just a matter of plotting the results in ten different scatter plots. We can do this most easily with matplotlib directly, setting up a @@ -143,21 +150,21 @@ grid of plots. Note that the progression proceeds by row then column, so read the progression as if you were reading a page of text (across, then down). -.. code:: python3 - - fig, axs = plt.subplots(5,2, figsize=(10, 20)) - ax_bound = axis_bounds(np.vstack(aligned_mapper.embeddings_)) - for i, ax in enumerate(axs.flatten()): - current_target = ordered_target[150 * i:min(ordered_target.shape[0], 150 * i + 400)] - ax.scatter(*aligned_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral") - ax.axis(ax_bound) - ax.set(xticks=[], yticks=[]) - plt.tight_layout() +```python3 +fig, axs = plt.subplots(5,2, figsize=(10, 20)) +ax_bound = axis_bounds(np.vstack(aligned_mapper.embeddings_)) +for i, ax in enumerate(axs.flatten()): + current_target = ordered_target[150 * i:min(ordered_target.shape[0], 150 * i + 400)] + ax.scatter(*aligned_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral") + ax.axis(ax_bound) + ax.set(xticks=[], yticks=[]) +plt.tight_layout() -.. image:: images/aligned_umap_basic_usage_15_0.png +``` +![Image](images/aligned_umap_basic_usage_15_0.png) So despite being different embeddings on different datasets, the clusters keep their general alignment – the top left plot and bottom @@ -167,8 +174,8 @@ the different slices. Thus we are keeping the various embeddings aligned, but allowing the changes dictated by the differing structures of each different slice of data. -Online updating of aligned embeddings -------------------------------------- +## Online updating of aligned embeddings + It may be the case that we have incoming temporal data and would like to have embeddings of time-windows that, ideally, align with the embeddings @@ -176,56 +183,60 @@ of prior time-windows. As long as we overlap the time-windows we use to allow for relations between time windows then this is possible – except that the previous code required all the time-windows to be input *at once* for fitting. We would instead like to train an initial model and -then update it as we go. This is possible via the ``update`` method +then update it as we go. This is possible via the `update` method which we’ll demonstrate below. -First we need to fit a base ``AlignedUMAP`` model; we’ll use the first +First we need to fit a base `AlignedUMAP` model; we’ll use the first two slices and the first relation dict to do so. -.. code:: python3 +```python3 +%%time +updating_mapper = umap.AlignedUMAP().fit(slices[:2], relations=relation_dicts[:1]) - %%time - updating_mapper = umap.AlignedUMAP().fit(slices[:2], relations=relation_dicts[:1]) +``` -.. parsed-literal:: +``` +CPU times: user 9.32 s, sys: 1.47 s, total: 10.8 s +Wall time: 9.17 s - CPU times: user 9.32 s, sys: 1.47 s, total: 10.8 s - Wall time: 9.17 s +``` Note that this is fairly quick, since we are only fitting two slices. Given the trained model the update method requires a new slice of data to add, along with a relation dictionary (passed in with the -``relations`` keyword argument as with ``fit``). This will append a new -embedding to the ``embeddings_`` attribute of the model for the new +`relations` keyword argument as with `fit`). This will append a new +embedding to the `embeddings_` attribute of the model for the new data, aligned with what has been seen so far. -.. code:: python3 +```python3 +for i in range(2,len(slices)): + %time updating_mapper.update(slices[i], relations={v:k for k,v in relation_dicts[i-1].items()}) - for i in range(2,len(slices)): - %time updating_mapper.update(slices[i], relations={v:k for k,v in relation_dicts[i-1].items()}) +``` -.. parsed-literal:: +``` +CPU times: user 7.78 s, sys: 1.15 s, total: 8.93 s +Wall time: 7.92 s +CPU times: user 6.64 s, sys: 1.17 s, total: 7.81 s +Wall time: 6.6 s +CPU times: user 6.94 s, sys: 1.17 s, total: 8.11 s +Wall time: 6.81 s +CPU times: user 6.45 s, sys: 1.51 s, total: 7.96 s +Wall time: 6.45 s +CPU times: user 7.44 s, sys: 1.32 s, total: 8.76 s +Wall time: 7.16 s +CPU times: user 7.68 s, sys: 1.73 s, total: 9.41 s +Wall time: 7.59 s +CPU times: user 7.88 s, sys: 1.65 s, total: 9.54 s +Wall time: 7.39 s +CPU times: user 7.82 s, sys: 1.98 s, total: 9.8 s +Wall time: 7.7 s - CPU times: user 7.78 s, sys: 1.15 s, total: 8.93 s - Wall time: 7.92 s - CPU times: user 6.64 s, sys: 1.17 s, total: 7.81 s - Wall time: 6.6 s - CPU times: user 6.94 s, sys: 1.17 s, total: 8.11 s - Wall time: 6.81 s - CPU times: user 6.45 s, sys: 1.51 s, total: 7.96 s - Wall time: 6.45 s - CPU times: user 7.44 s, sys: 1.32 s, total: 8.76 s - Wall time: 7.16 s - CPU times: user 7.68 s, sys: 1.73 s, total: 9.41 s - Wall time: 7.59 s - CPU times: user 7.88 s, sys: 1.65 s, total: 9.54 s - Wall time: 7.39 s - CPU times: user 7.82 s, sys: 1.98 s, total: 9.8 s - Wall time: 7.7 s +``` Note that each new slice takes a relatively short period of time, as we might hope. The downside of this, as you can imagine, is that we have no @@ -235,21 +246,21 @@ to quickly and easily update as we go. We can look at how we did using essentially the same code as before. -.. code:: python3 - - fig, axs = plt.subplots(5,2, figsize=(10, 20)) - ax_bound = axis_bounds(np.vstack(updating_mapper.embeddings_)) - for i, ax in enumerate(axs.flatten()): - current_target = ordered_target[150 * i:min(ordered_target.shape[0], 150 * i + 400)] - ax.scatter(*updating_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral") - ax.axis(ax_bound) - ax.set(xticks=[], yticks=[]) - plt.tight_layout() +```python3 +fig, axs = plt.subplots(5,2, figsize=(10, 20)) +ax_bound = axis_bounds(np.vstack(updating_mapper.embeddings_)) +for i, ax in enumerate(axs.flatten()): + current_target = ordered_target[150 * i:min(ordered_target.shape[0], 150 * i + 400)] + ax.scatter(*updating_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral") + ax.axis(ax_bound) + ax.set(xticks=[], yticks=[]) +plt.tight_layout() -.. image:: images/aligned_umap_basic_usage_22_0.png +``` +![Image](images/aligned_umap_basic_usage_22_0.png) We see that the alignment is indeed working, so new slices remain comparable with previously trained slices. As noted the overall @@ -262,8 +273,8 @@ only really work in a batch streaming approach where occasionally a fresh model is trained, dropping some of the historical data before continuing with updates. -Aligning varying parameters ---------------------------- +## Aligning varying parameters + It is possible to align UMAP embedding that vary in the parameters used instead of the data. To demonstrate how this can work we’ll continue to @@ -272,25 +283,26 @@ before, we’ll use the full dataset. That means that our relations between datasets are simply constant relations. We can construct those ahead of time: -.. code:: python3 +```python3 +constant_dict = {i:i for i in range(digits.data.shape[0])} +constant_relations = [constant_dict for i in range(9)] - constant_dict = {i:i for i in range(digits.data.shape[0])} - constant_relations = [constant_dict for i in range(9)] +``` To run AlignedUMAP over a range of parameters you simply need to pass in a *list* of the sequence of parameters you wish to use. You can do this for several different parameters – just ensure that all the lists are the same length! In this case we’ll try looking at how the embeddings -change if we change ``n_neighbors`` and ``min_dist``. This means that +change if we change `n_neighbors` and `min_dist`. This means that when we create the AlignedUMAP object we pass a list, instead of a single value, to each of those parameters. To make the visualization a little more interesting we’ll also vary some of the alignment parameters (there are only two of major consequence). Specifically we’ll adjust the -``alignment_window_size``, which controls how far forward and backward +`alignment_window_size`, which controls how far forward and backward across the datasets we look when doing alignment, and the -``alignment_regularisation`` which controls how heavily we weight the +`alignment_regularisation` which controls how heavily we weight the alignment aspect versus the UMAP layout. Larger values of -``alignment_regularisation`` will work harder to keep points aligned +`alignment_regularisation` will work harder to keep points aligned across embeddings (at the cost of the embedding quality at each slice), while smaller values will allow the optimisation to focus more on the individual embeddings and put less emphasis on aligning the embeddings @@ -303,34 +315,35 @@ dataset. Note that the number of datasets needs to match the number of parameter values being used. The same goes for the number of relations (one less than the number of parameter values). -.. code:: python3 +```python3 +neighbors_mapper = umap.AlignedUMAP( + n_neighbors=[3,4,5,7,11,16,22,29,37,45,54], + min_dist=[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45], + alignment_window_size=2, + alignment_regularisation=1e-3, +).fit( + [digits.data for i in range(10)], relations=constant_relations +) - neighbors_mapper = umap.AlignedUMAP( - n_neighbors=[3,4,5,7,11,16,22,29,37,45,54], - min_dist=[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45], - alignment_window_size=2, - alignment_regularisation=1e-3, - ).fit( - [digits.data for i in range(10)], relations=constant_relations - ) +``` As before we can look at the results by plotting each of the embeddings. -.. code:: python3 - - fig, axs = plt.subplots(5,2, figsize=(10, 20)) - ax_bound = axis_bounds(np.vstack(neighbors_mapper.embeddings_)) - for i, ax in enumerate(axs.flatten()): - ax.scatter(*neighbors_mapper.embeddings_[i].T, s=2, c=digits.target, cmap="Spectral") - ax.axis(ax_bound) - ax.set(xticks=[], yticks=[]) - plt.tight_layout() +```python3 +fig, axs = plt.subplots(5,2, figsize=(10, 20)) +ax_bound = axis_bounds(np.vstack(neighbors_mapper.embeddings_)) +for i, ax in enumerate(axs.flatten()): + ax.scatter(*neighbors_mapper.embeddings_[i].T, s=2, c=digits.target, cmap="Spectral") + ax.axis(ax_bound) + ax.set(xticks=[], yticks=[]) +plt.tight_layout() -.. image:: images/aligned_umap_basic_usage_29_1.png +``` +![Image](images/aligned_umap_basic_usage_29_1.png) To get a better feel for the evolution of the embedding over the change in parameter values we can plot the data in three dimensions, with the @@ -338,57 +351,61 @@ third dimension being the parameter value chosen. To better show how data points in the embedding *move* with respect to the changing parameters we can plot them not as points, but as *curves* connecting the same point in each sequential embedding. For three dimensional plots -like this we’ll make use of the `plotly <https://plotly.com>`__ plotting +like this we’ll make use of the [plotly](https://plotly.com) plotting library. -.. code:: python3 +```python3 +import plotly.graph_objects as go +import plotly.express as px +import pandas as pd - import plotly.graph_objects as go - import plotly.express as px - import pandas as pd +``` The first thing we’ll have to do is wrangle the data into a suitable format for plotly. That’s the reason we loaded up pandas as well – plotly likes dataframes. This involves stacking all the embeddings -together, and then assigning an extra ``z`` value according to which +together, and then assigning an extra `z` value according to which embedding we are in. For the purposes of visualization we’ll just have a linear scale from 0 to 1 of the appropriate length for the z coordinates. -.. code:: python3 +```python3 +n_embeddings = len(neighbors_mapper.embeddings_) +es = neighbors_mapper.embeddings_ +embedding_df = pd.DataFrame(np.vstack(es), columns=('x', 'y')) +embedding_df['z'] = np.repeat(np.linspace(0, 1.0, n_embeddings), es[0].shape[0]) +embedding_df['id'] = np.tile(np.arange(es[0].shape[0]), n_embeddings) +embedding_df['digit'] = np.tile(digits.target, n_embeddings) - n_embeddings = len(neighbors_mapper.embeddings_) - es = neighbors_mapper.embeddings_ - embedding_df = pd.DataFrame(np.vstack(es), columns=('x', 'y')) - embedding_df['z'] = np.repeat(np.linspace(0, 1.0, n_embeddings), es[0].shape[0]) - embedding_df['id'] = np.tile(np.arange(es[0].shape[0]), n_embeddings) - embedding_df['digit'] = np.tile(digits.target, n_embeddings) +``` The next thing we can do to improve the visualization is to smooth out the curves rather than leaving them as piecewise linear lines. To to -this we can use the ``scipy.interpolate`` functionality to create smooth +this we can use the `scipy.interpolate` functionality to create smooth cubic splines that pass through all the points of the curve we wish to create. -.. code:: python3 +```python3 +import scipy.interpolate - import scipy.interpolate +``` -The interpolate module has a function ``interp1d`` that generates a +The interpolate module has a function `interp1d` that generates a (vector of) smooth function given a one dimensional set of datapoints that it needs to pass through. We can generate separate functions for the x and y coordinates for each pendigit sample, allowing us to generate smooth curves in three dimensions. -.. code:: python3 +```python3 +fx = scipy.interpolate.interp1d( + embedding_df.z[embedding_df.id == 0], embedding_df.x.values.reshape(n_embeddings, digits.data.shape[0]).T, kind="cubic" +) +fy = scipy.interpolate.interp1d( + embedding_df.z[embedding_df.id == 0], embedding_df.y.values.reshape(n_embeddings, digits.data.shape[0]).T, kind="cubic" +) +z = np.linspace(0, 1.0, 100) - fx = scipy.interpolate.interp1d( - embedding_df.z[embedding_df.id == 0], embedding_df.x.values.reshape(n_embeddings, digits.data.shape[0]).T, kind="cubic" - ) - fy = scipy.interpolate.interp1d( - embedding_df.z[embedding_df.id == 0], embedding_df.y.values.reshape(n_embeddings, digits.data.shape[0]).T, kind="cubic" - ) - z = np.linspace(0, 1.0, 100) +``` With that in hand it is just a matter of plotting all the curves. In plotly parlance each curve is a “trace” and we generate each one @@ -396,36 +413,36 @@ separately (along with a suitable colour given by the digit the sample represents). We then add all the traces to a figure, and display the figure. -.. code:: python3 - - palette = px.colors.diverging.Spectral - interpolated_traces = [fx(z), fy(z)] - traces = [ - go.Scatter3d( - x=interpolated_traces[0][i], - y=interpolated_traces[1][i], - z=z*3.0, - mode="lines", - line=dict( - color=palette[digits.target[i]], - width=3.0 - ), - opacity=1.0, - ) - for i in range(digits.data.shape[0]) - ] - fig = go.Figure(data=traces) - fig.update_layout( - width=800, - height=700, - autosize=False, - showlegend=False, +```python3 +palette = px.colors.diverging.Spectral +interpolated_traces = [fx(z), fy(z)] +traces = [ + go.Scatter3d( + x=interpolated_traces[0][i], + y=interpolated_traces[1][i], + z=z*3.0, + mode="lines", + line=dict( + color=palette[digits.target[i]], + width=3.0 + ), + opacity=1.0, ) - fig.show() + for i in range(digits.data.shape[0]) +] +fig = go.Figure(data=traces) +fig.update_layout( + width=800, + height=700, + autosize=False, + showlegend=False, +) +fig.show() -.. image:: images/aligned_umap_pendigits_3d_1.png +``` +![Image](images/aligned_umap_pendigits_3d_1.png) Since it is tricky to get the interactive plotly figure embedded in documentation we have a static image here, but if you run this yourself @@ -434,63 +451,67 @@ you will have a fully interactive view of the data. Alternatively, we can visualize the third dimension as an evolution of the embeddings through time by rendering each z-slice as a frame in an animated GIF. To do this, we'll first need to import some notebook display tools and -matplotlib's `animation <https://matplotlib.org/stable/api/animation_api.html>`_ +matplotlib's [animation](https://matplotlib.org/stable/api/animation_api.html) module. -.. code:: python3 +```python3 +from IPython.display import display, Image, HTML +from matplotlib import animation - from IPython.display import display, Image, HTML - from matplotlib import animation +``` Next, we'll create a new figure, initialize a blank scatter plot, then use -``FuncAnimation`` to update the point positions (called "offsets") one frame at +`FuncAnimation` to update the point positions (called "offsets") one frame at a time. -.. code:: python3 +```python3 +fig = plt.figure(figsize=(4, 4), dpi=150) +ax = fig.add_subplot(1, 1, 1) - fig = plt.figure(figsize=(4, 4), dpi=150) - ax = fig.add_subplot(1, 1, 1) +scat = ax.scatter([], [], s=2) +scat.set_array(digits.target) +scat.set_cmap('Spectral') +text = ax.text(ax_bound[0] + 0.5, ax_bound[2] + 0.5, '') +ax.axis(ax_bound) +ax.set(xticks=[], yticks=[]) +plt.tight_layout() - scat = ax.scatter([], [], s=2) - scat.set_array(digits.target) - scat.set_cmap('Spectral') - text = ax.text(ax_bound[0] + 0.5, ax_bound[2] + 0.5, '') - ax.axis(ax_bound) - ax.set(xticks=[], yticks=[]) - plt.tight_layout() +offsets = np.array(interpolated_traces).T +num_frames = offsets.shape[0] - offsets = np.array(interpolated_traces).T - num_frames = offsets.shape[0] +def animate(i): + scat.set_offsets(offsets[i]) + text.set_text(f'Frame {i}') + return scat - def animate(i): - scat.set_offsets(offsets[i]) - text.set_text(f'Frame {i}') - return scat +anim = animation.FuncAnimation( + fig, + init_func=None, + func=animate, + frames=num_frames, + interval=40) - anim = animation.FuncAnimation( - fig, - init_func=None, - func=animate, - frames=num_frames, - interval=40) +``` Then we can save the animation as a GIF and close our animation. Depending on your machine, you may need to change which writer the save method uses. -.. code:: python3 +```python3 +anim.save("aligned_umap_pendigits_anim.gif", writer="pillow") +plt.close(anim._fig) - anim.save("aligned_umap_pendigits_anim.gif", writer="pillow") - plt.close(anim._fig) +``` Finally, we can read in our rendered GIF and display it in the notebook. -.. code:: python3 +```python3 +with open("aligned_umap_pendigits_anim.gif", "rb") as f: + display(Image(f.read())) - with open("aligned_umap_pendigits_anim.gif", "rb") as f: - display(Image(f.read())) +``` -.. image:: images/aligned_umap_pendigits_anim.gif +![Image](images/aligned_umap_pendigits_anim.gif) diff --git a/doc/aligned_umap_plotly_plot.html b/doc/aligned_umap_plotly_plot.html index 1d7bc63a..a408c86b 100644 --- a/doc/aligned_umap_plotly_plot.html +++ b/doc/aligned_umap_plotly_plot.html @@ -4,4 +4,4 @@ <div> <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script> <script 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different segments. An example of this is the relative political +leanings of the US congress over time. In determining the relative +political leanings we can look at the representatives voting record on +roll call votes, with the presumption that representatives with similar +political principles will have similar voting records. We can, of +course, look at such data for any given congress, but since +representatives are commonly re-elected we can also consider how their +relative position in congress changes with time – an ideal use case for +AlignedUMAP. + +First we’ll need a selection of libraries. Aside from UMAP we will need +to do a little bit of data wrangling; for that we’ll need pandas, and +also for matching up names of representatives we’ll make use of the +library `fuzzywuzzy` which provides easy to use fuzzy string matching. + +```python3 +import umap +import umap.utils as utils +import umap.aligned_umap +import sklearn.decomposition + +import pandas as pd +import numpy as np + +import matplotlib.pyplot as plt +import seaborn as sns + +from fuzzywuzzy import fuzz, process +import re + + +``` + +```python3 +sns.set(style="darkgrid", color_codes=True) + +``` + +Next we’ll need to voting records for the representatives, along with +the associated metadata from the roll call vote records. You can obtain +the data https://clerk.house.gov; a notebook demonstrating how to pull +down the data and parse it into the csv files used here is available +[here](https://github.com/lmcinnes/umap_doc_notebooks/blob/master/parse_voting_records.ipynb). + +## Processing Congressional Voting Records + + +The voting records provide a row for each representative with a -1 for +“No”, 0 for “Present” or “Not Voting”, and 1 for “Aye”. A separate csv +file contains the raw data of all the votes with a row for each +legislators vote on each roll-call item. We really just need some +metadata – which state they represent and the party they represent so we +can decorate the results with this kind of information later. For that +we just need to extra the names, states, and parties for each year. We +can grab those columns and then drop duplicates. A catch: the party is +very occasionally entered incorrectly, and occasionally representatives +switch parties, making duplicated rows. We’ll just take the first entry +of such duplciates for now. + +```python3 +votes = [pd.read_csv(f"house_votes/{year}_voting_record.csv", index_col=0).sort_index() + for year in range(1990,2021)] +metadata = [pd.read_csv( + f"house_votes/{year}_full.csv", + index_col=0 +)[["legislator", "state", "party"]].drop_duplicates(["legislator", "state"]).sort_values('legislator') + for year in range(1990,2021)] + +``` + +Let’s take a look at the voting record for a single year to see what +sort of data we are looking at: + +```python3 +votes[5] + + + + +``` + + +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>104-1st-1</th> + <th>104-1st-10</th> + <th>104-1st-100</th> + <th>104-1st-101</th> + <th>104-1st-102</th> + <th>104-1st-103</th> + <th>104-1st-104</th> + <th>104-1st-105</th> + <th>104-1st-106</th> + <th>104-1st-107</th> + <th>...</th> + <th>104-1st-90</th> + <th>104-1st-91</th> + <th>104-1st-92</th> + <th>104-1st-93</th> + <th>104-1st-94</th> + <th>104-1st-95</th> + <th>104-1st-96</th> + <th>104-1st-97</th> + <th>104-1st-98</th> + <th>104-1st-99</th> + </tr> + <tr> + <th>legislator</th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + <th></th> + </tr> + </thead> + <tbody> + <tr> + <th>Abercrombie</th> + <td>0.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>...</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + </tr> + <tr> + <th>Ackerman</th> + <td>0.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>...</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + </tr> + <tr> + <th>Allard</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>...</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>0.0</td> + <td>-1.0</td> + </tr> + <tr> + <th>Andrews</th> + <td>0.0</td> + <td>1.0</td> + <td>0.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>0.0</td> + <td>0.0</td> + <td>0.0</td> + <td>...</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + </tr> + <tr> + <th>Archer</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>...</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>0.0</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>Young (AK)</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>...</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + </tr> + <tr> + <th>Young (FL)</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>...</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + </tr> + <tr> + <th>Zeliff</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>...</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + </tr> + <tr> + <th>Zimmer</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>...</td> + <td>-1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>-1.0</td> + </tr> + <tr> + <th>de la Garza</th> + <td>0.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>...</td> + <td>0.0</td> + <td>-1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + <td>1.0</td> + <td>-1.0</td> + <td>1.0</td> + </tr> + </tbody> +</table> +<p>438 rows × 885 columns</p> +</div> + + + + +We can examine the associated metadata for the same year. + +```python3 +metadata[5] + + + + +``` + + +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>legislator</th> + <th>state</th> + <th>party</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>Abercrombie</td> + <td>HI</td> + <td>D</td> + </tr> + <tr> + <th>1</th> + <td>Ackerman</td> + <td>NY</td> + <td>D</td> + </tr> + <tr> + <th>2</th> + <td>Allard</td> + <td>CO</td> + <td>R</td> + </tr> + <tr> + <th>3</th> + <td>Andrews</td> + <td>NJ</td> + <td>D</td> + </tr> + <tr> + <th>4</th> + <td>Archer</td> + <td>TX</td> + <td>R</td> + </tr> + <tr> + <th>...</th> + <td>...</td> + <td>...</td> + <td>...</td> + </tr> + <tr> + <th>430</th> + <td>Young (AK)</td> + <td>AK</td> + <td>R</td> + </tr> + <tr> + <th>431</th> + <td>Young (FL)</td> + <td>FL</td> + <td>R</td> + </tr> + <tr> + <th>432</th> + <td>Zeliff</td> + <td>NH</td> + <td>R</td> + </tr> + <tr> + <th>433</th> + <td>Zimmer</td> + <td>NJ</td> + <td>R</td> + </tr> + <tr> + <th>89</th> + <td>de la Garza</td> + <td>TX</td> + <td>D</td> + </tr> + </tbody> +</table> +<p>438 rows × 3 columns</p> +</div> + + + + +You may note that sometimes representatives names list a state in +parenthesis afterwards. This is to provide disambiguation for +representatives that happen to have the last name. This actually +complicates matters for us since the disambiguation is only applied in +those cases where there is a name collision in that sitting of congress. +That means that for several years a representative may have simply their +last name, but then switch to being disambiguated, before potentially +switching back again. This would make it much harder to consistently +treck representatives over their entire career in congress. To fix this +up we’ll simply re-index by a unique representative ID that has their +last name, party, and state all listed over all the voting dataframes. +We’ll need a function to generate those from the metadata, and then +we’ll need to apply it to all the records. Importantly we’ll have to +finesse those situations where representatives are listed twice (under +un-ambiguous and disambiguated names) with some groupby tricks. + +```python3 +def unique_legislator(row): + name, state, party = row.legislator, row.state, row.party + # Strip of disambiguating state designators + if re.search(r'(\w+) \([A-Z]{2}\)', name) is not None: + name = name[:-5] + return f"{name} ({party}, {state})" + +``` + +```python3 +for i, _ in enumerate(votes): + votes[i].index = pd.Index(metadata[i].apply(unique_legislator, axis=1), name="legislator_index") + votes[i] = votes[i].groupby(level=0).sum() + metadata[i].index = pd.Index(metadata[i].apply(unique_legislator, axis=1), name="legislator_index") + metadata[i] = metadata[i].groupby(level=0).first() + +``` + +Now that we have the data at least a little wrangled into order there is +the question of ensuring some degree of continuity fore representatives. +To make this a little easier we’ll use voting records over *four year +spans* instead of over single years. Equally importantly we’ll do this +in a sliding window fashion so that we consider the record for 1990-1994 +and then the record for 1991-1995 and so on. By overlapping the windows +in this way we can ensure a little greater continuity of political +stance through the years. To make this happen we just have to merge data +frames in a sliding set of pairs, and then merge the pairs via the same +approach: + +```python3 +votes = [ + pd.merge( + v1, v2, how="outer", on="legislator_index" + ).fillna(0.0).sort_index() + for v1, v2 in zip(votes[:-1], votes[1:]) +] + votes[-1:] + +metadata = [ + pd.concat([m1, m2]).groupby("legislator_index").first().sort_index() + for m1, m2 in zip(metadata[:-1], metadata[1:]) +] + metadata[-1:] + +``` + +That’s the pairs of years; now we merge these pairwise to get sets of +four years worth of votes. + +```python3 +votes = [ + pd.merge( + v1, v2, how="outer", on="legislator_index" + ).fillna(0.0).sort_index() + for v1, v2 in zip(votes[:-1], votes[1:]) +] + votes[-1:] + +metadata = [ + pd.concat([m1, m2]).groupby(level=0).first().sort_index() + for m1, m2 in zip(metadata[:-1], metadata[1:]) +] + metadata[-1:] + +``` + +## Applying AlignedUMAP + + +To make use of AlignedUMAP we need to generate relations between +consecutive dataset slices. In this case that means we need to have a +relation describing row from one four year slice corresponds to a row +from the following four year slice for the same representative. For +AlignedUMAP to work this should be formatted as a list of dictionaries; +each dictionary gives a mapping from indices of one slice to indices of +the next. Importantly this mapping can be partial – it only has to +relate indices for which there is a match between the two slices. + +The vote dataframes that we are using for slices are already indexed +with unique identifiers for representatives, so to make relations we +simply have to match them up, creating a dictionary of indices from one +to the other. In practice we can do this relatively efficiently by using +pandas to merge dataframes on the pandas indexes of the two vote +dataframes with the data being simply the numeric indices of the rows. +The resulting dictionary is then just the dictionary of pairs given by +the inner join. + +```python3 +def make_relation(from_df, to_df): + left = pd.DataFrame(data=np.arange(len(from_df)), index=from_df.index) + right = pd.DataFrame(data=np.arange(len(to_df)), index=to_df.index) + merge = pd.merge(left, right, left_index=True, right_index=True) + return dict(merge.values) + +``` + +With a function for relation creation in place we simply need to apply +it to each consecutive pair of vote dataframes. + +```python3 +relations = [make_relation(x,y) for x, y in zip(votes[:-1], votes[1:])] + +``` + +If you are still unsure of what these relations are it might be +beneficial to look at a few of the dictionaries, along with the +corresponding pairs of vote dataframes. Here is (part of) the first +relation dictionary: + +```python3 +relations[0] + + + + +``` + +``` +{0: 0, + 1: 1, + 3: 2, + 4: 3, + 5: 4, + 6: 5, + 7: 6, + 8: 7, + 9: 8, + 10: 9, + 11: 10, + 12: 11, + 13: 12, + 14: 13, + 15: 14, + ... + 475: 547, + 476: 549, + 477: 550, + 478: 552, + 479: 553, + 480: 554, + 481: 555, + 482: 556, + 483: 557, + 484: 559} + + + +``` + +Now we are finally in a position to run AlignedUMAP. Most of the +standard UMAP parameters are available for use, including choosing a +metric and a number of neighbors. Here we will also make use of the +extra AlignedUMAP parameters `alignment_regularisation` and +`alignment_window_size`. The first is a value that weights how +important retaining alignment is. Typically the value is much smaller +than this (the default is 0.01), but given the relatively high +volatility in voting records we are going to increase it here. The +second parameter, `alignment_window_size` determines how far out on +either side AlignedUMAP will look when aligning embeddings – even though +the relations are specified only between consecutive slices it will +chain them together to construct relations reaching further. In this +case we’ll have it look as far out as 5 slices either side. + +```python3 +%%time +aligned_mapper = umap.aligned_umap.AlignedUMAP( + metric="cosine", + n_neighbors=20, + alignment_regularisation=0.1, + alignment_window_size=5, + n_epochs=200, + random_state=42, +).fit(votes, relations=relations) +embeddings = aligned_mapper.embeddings_ + + +``` + +``` +CPU times: user 6min 7s, sys: 30.6 s, total: 6min 37s +Wall time: 5min 57s + + +``` + +## Visualizing the Results + + +Now we need to plot the data somehow. To make the visualization +interesting it would be beneficial to have some colour variation – +ideally showing a different view of the relative political stance. For +that we want to attempt to get an idea of the position of each candidate +from an alternative source. To do this we can try to extract the vote +margin that the representative won by. The catch here is that while the +election data can be collected and processed, the names don’t match +perfectly as they come from a different source. That means we need to do +our best to get a name match for each candidate. We’ll use fuzzy string +matching restricted to the relevant year and state to try to get a good +match. A notebook providing details for obtaining and processing the +election winners data can be found +[here](https://github.com/lmcinnes/umap_doc_notebooks/blob/master/voting_data_by_district.ipynb). + +```python3 +election_winners = pd.read_csv('election_winners_1976-2018.csv', index_col=0) +election_winners.head() + + + + +``` + + +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>year</th> + <th>state</th> + <th>district</th> + <th>winner</th> + <th>party</th> + <th>winning_ratio</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>1976</td> + <td>AK</td> + <td>0</td> + <td>Don Young</td> + <td>republican</td> + <td>0.289986</td> + </tr> + <tr> + <th>0</th> + <td>1976</td> + <td>AL</td> + <td>1</td> + <td>Jack Edwards</td> + <td>republican</td> + <td>0.374808</td> + </tr> + <tr> + <th>0</th> + <td>1976</td> + <td>AL</td> + <td>2</td> + <td>William L. \\"Bill\"\" Dickinson"</td> + <td>republican</td> + <td>0.423953</td> + </tr> + <tr> + <th>0</th> + <td>1976</td> + <td>AL</td> + <td>3</td> + <td>Bill Nichols</td> + <td>democrat</td> + <td>1.000000</td> + </tr> + <tr> + <th>0</th> + <td>1976</td> + <td>AL</td> + <td>4</td> + <td>Tom Bevill</td> + <td>democrat</td> + <td>0.803825</td> + </tr> + </tbody> +</table> +</div> + + + + +Now we need to simply go through the metadata and fill it out with the +extra information we can glean from the election winners data. Since we +can’t do exact name matching (the data for both is somewhat messy when +it comes to text fields like names) we can’t simply perform a join, but +must instead process things year by year and representative by +representative, finding the best string match on name that we can for +the given year and state election. In practice we are undoubtedly going +to get some of these wrong, and if the goal was a rigorous analysis +based on this data a lot more care would need to be taken. Since this is +just a demonstration and we’ll only be using this extra information as a +colour channel in plots we can excuise a few errors here and there from +in-exact data processing. + +```python3 +n_name_misses = 0 +for year, df in enumerate(metadata, 1990): + df["partisan_lean"] = 0.5 + df["district"] = np.full(len(df), -1, dtype=np.int8) + for idx, (loc, row) in enumerate(df.iterrows()): + name, state, party = row.legislator, row.state, row.party + # Strip of disambiguating state designators + if re.search(r'(\w+) \([A-Z]{2}\)', name) is not None: + name = name[:-5] + # Get a party designator matching the election_winners data + party = "republican" if party == "R" else "democrat" + # Restrict to the right state and time-frame + state_election_winners = election_winners[(election_winners.state == state) + & (election_winners.year <= year + 4) + & (election_winners.year >= year - 4)] + # Try to match a name; and fail "gracefully" + try: + matched_name = process.extractOne( + name, + state_election_winners.winner.tolist(), + scorer=fuzz.partial_token_sort_ratio, + score_cutoff=50, + ) + except: + matched_name = None + + # If we got a unique match, get the election data + if matched_name is not None: + winner = state_election_winners[state_election_winners.winner == matched_name[0]] + else: + winner = [] + + # We either have none, one, or *several* match elections. Take a best guess. + if len(winner) < 1: + df.loc[loc, ["partisan_lean"]] = 0.25 if party == "republican" else 0.75 + n_name_misses += 1 + elif len(winner) > 1: + df.iloc[idx, 4] = int(winner.district.values[-1]) + df.iloc[idx, 3] = float(winner.winning_ratio.values[-1]) + else: + df.iloc[idx, 4] = int(winner.district.values) + df.iloc[idx, 3] = float(winner.winning_ratio.values[0]) + +print(f"Failed to match a name {n_name_misses} times") + + +``` + +``` +Failed to match a name 100 times + + +``` + +Now that we have the relative partisan leanings based on district +election margins we can color the plot. We can obviously label the plot +with the representatives names. The last remaining catch (when using +matplotlib for the plotting) is the get the plot bounds (since we will +be placing text markers directly into the plot, and thus not +autogenerating bounds). This is a simple enough matter of computing some +bounds as an adjustment a little outside the data limits. + +```python3 +def axis_bounds(embedding): + left = embedding.T[0].min() + right = embedding.T[0].max() + bottom = embedding.T[1].min() + top = embedding.T[1].max() + width = right - left + height = top - bottom + adj_h = width * 0.1 + adj_v = height * 0.05 + return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v] + +``` + +Now for the plot. Let’s pick a random time slice (you are welcome to try +others) and draw the representatives names in their embedded locations +for that slice, coloured by their relative election victory margin. + +```python3 +fig, ax = plt.subplots(figsize=(12,12)) +e = 5 +ax.axis(axis_bounds(embeddings[e])) +ax.set_aspect('equal') +for i in range(embeddings[e].shape[0]): + ax.text(embeddings[e][i, 0], + embeddings[e][i, 1], + metadata[e].index.values[i], + color=plt.cm.RdBu(np.float32(metadata[e]["partisan_lean"].values[i])), + fontsize=8, + horizontalalignment='center', + verticalalignment='center', + ) + + + + +``` + +![Image](images/aligned_umap_politics_demo_31_0.png) + +This gives a good idea of the layout in a single time slices, and by +plotting different time slices we can get some idea of how things have +evolved. We can go further, however, by plotting a representative as +curve through time as their relative political position in congress +changes. For that we will need a 3D plot – we need both the UMAP x and y +coordinates, as well as a third coordinate giving the year. I found this +easiest to do in plotly, so let’s import that. To make nice smooth +curves through time we will also import the `scipy.interpolate` module +which will let is interpolate a smooth curve from the discrete positions +that a representatives appears in over time. + +```python3 +import plotly.graph_objects as go +import scipy.interpolate + +``` + +Wrangling the data into shape for this is the next step; first let’s get +everything in a single dataframe that we can extract relevant data from +on an as-needed basis. + +```python3 +df = pd.DataFrame(np.vstack(embeddings), columns=('x', 'y')) +df['z'] = np.concatenate([[year] * len(embeddings[i]) for i, year in enumerate(range(1990, 2021))]) +df['representative_id'] = np.concatenate([v.index for v in votes]) +df['partisan_lean'] = np.concatenate([m["partisan_lean"].values for m in metadata]) + +``` + +Next we’ll need that interpolation of the curve for a given +representative. We’ll write a function to handle that as there is a +little bit of case-based logic that makes it non-trivial. We are going +to get handed year data and want to interpolate the UMAP x and y +coordinates for a single representative. + +The first major catch is that many representatives don’t have a single +contiguous block of years for which they were in congress: they were +elected for several years, missed re-election, and then came back to +congress several years later (possibly in another district). Each such +block of contiguous years needs to be a separate path, and we shouldn’t +connect them. We therefore need some logic to find the contiguous blocks +and generate smooth paths for each of them. + +Another catch is that some representatives have only been in office for +a year or two (special elections and so forth) and we can’t do a cubic +spline interpolation for that; we can devolve to linear interpolation or +quadratic splines for those cases, so simply add the point itself for +the odd single year cases. + +With those issues in hand we can then simply use the scipy `interp1d` +function to generate smooth curves through the points. + +```python3 +INTERP_KIND = {2:"linear", 3:"quadratic", 4:"cubic"} + +def interpolate_paths(z, x, y, c, rep_id): + consecutive_year_blocks = np.where(np.diff(z) != 1)[0] + 1 + z_blocks = np.split(z, consecutive_year_blocks) + x_blocks = np.split(x, consecutive_year_blocks) + y_blocks = np.split(y, consecutive_year_blocks) + c_blocks = np.split(c, consecutive_year_blocks) + + paths = [] + + for block_idx, zs in enumerate(z_blocks): + + text = f"{rep_id} -- partisan_lean: {np.mean(c_blocks[block_idx]):.2f}" + + if len(zs) > 1: + kind = INTERP_KIND.get(len(zs), "cubic") + else: + paths.append( + (zs, x_blocks[block_idx], y_blocks[block_idx], c_blocks[block_idx], text) + ) + continue + + z = np.linspace(np.min(zs), np.max(zs), 100) + x = scipy.interpolate.interp1d(zs, x_blocks[block_idx], kind=kind)(z) + y = scipy.interpolate.interp1d(zs, y_blocks[block_idx], kind=kind)(z) + c = scipy.interpolate.interp1d(zs, c_blocks[block_idx], kind="linear")(z) + + paths.append((z, x, y, c, text)) + + return paths + +``` + +And now we can use plotly to draw the resulting curves. For plotly we +use the `Scatter3D` method, which supports a “lines” mode that can +draw curves in 3D space. We can colour the curves by the partisan lean +score we derived from the election data – in fact the colour can vary +through the trace as the election margins vary. Since this is a plotly +plot it is interactive, so you can rotate it around and view it from all +angles. + +Unfortunately the interactive plotly plot does not embed into the documentation +well, so we present here a static image. If you run this yourself, however, +you will get the interactive version. + +```python3 +traces = [] +for rep in df.representative_id.unique(): + z = df.z[df.representative_id == rep].values + x = df.x[df.representative_id == rep].values + y = df.y[df.representative_id == rep].values + c = df.partisan_lean[df.representative_id == rep] + + for z, x, y, c, text in interpolate_paths(z, x, y, c, rep): + trace = go.Scatter3d( + x=x, y=z, z=y, + mode="lines", + hovertext=text, + hoverinfo="text", + line=dict( + color=c, + cmin=0.0, + cmid=0.5, + cmax=1.0, + cauto=False, + colorscale="RdBu", + colorbar=dict(), + width=2.5, + ), + opacity=1.0, + ) + traces.append(trace) + +fig = go.Figure(data=traces) +fig.update_layout( + width=800, + height=600, + scene=dict( + aspectratio = dict( x=0.5, y=1.25, z=0.5 ), + yaxis_title="Year", + xaxis_title="UMAP-X", + zaxis_title="UMAP-Y", + ), + scene_camera=dict(eye=dict( x=0.5, y=0.8, z=0.75 )), + autosize=False, + showlegend=False, +) +fig_widget = go.FigureWidget(fig) +fig_widget + + +``` + +![Image](images/aligned_umap_politics_demo_spaghetti.png) + +.. +[View html file](aligned_umap_plotly_plot.html) + + +This concludes our exploration for now. diff --git a/doc/aligned_umap_politics_demo.rst b/doc/aligned_umap_politics_demo.rst deleted file mode 100644 index 3f47b4cd..00000000 --- a/doc/aligned_umap_politics_demo.rst +++ /dev/null @@ -1,1088 +0,0 @@ -AlignedUMAP for Time Varying Data -================================= - -It is not uncommon to have datasets that can be partitioned into -segments, often with respect to time, where we want to understand not -only the structure of each segment, but how that structure changes over -the different segments. An example of this is the relative political -leanings of the US congress over time. In determining the relative -political leanings we can look at the representatives voting record on -roll call votes, with the presumption that representatives with similar -political principles will have similar voting records. We can, of -course, look at such data for any given congress, but since -representatives are commonly re-elected we can also consider how their -relative position in congress changes with time – an ideal use case for -AlignedUMAP. - -First we’ll need a selection of libraries. Aside from UMAP we will need -to do a little bit of data wrangling; for that we’ll need pandas, and -also for matching up names of representatives we’ll make use of the -library ``fuzzywuzzy`` which provides easy to use fuzzy string matching. - -.. code:: python3 - - import umap - import umap.utils as utils - import umap.aligned_umap - import sklearn.decomposition - - import pandas as pd - import numpy as np - - import matplotlib.pyplot as plt - import seaborn as sns - - from fuzzywuzzy import fuzz, process - import re - - -.. code:: python3 - - sns.set(style="darkgrid", color_codes=True) - -Next we’ll need to voting records for the representatives, along with -the associated metadata from the roll call vote records. You can obtain -the data https://clerk.house.gov; a notebook demonstrating how to pull -down the data and parse it into the csv files used here is available -`here <https://github.com/lmcinnes/umap_doc_notebooks/blob/master/parse_voting_records.ipynb>`__. - -Processing Congressional Voting Records ---------------------------------------- - -The voting records provide a row for each representative with a -1 for -“No”, 0 for “Present” or “Not Voting”, and 1 for “Aye”. A separate csv -file contains the raw data of all the votes with a row for each -legislators vote on each roll-call item. We really just need some -metadata – which state they represent and the party they represent so we -can decorate the results with this kind of information later. For that -we just need to extra the names, states, and parties for each year. We -can grab those columns and then drop duplicates. A catch: the party is -very occasionally entered incorrectly, and occasionally representatives -switch parties, making duplicated rows. We’ll just take the first entry -of such duplciates for now. - -.. code:: python3 - - votes = [pd.read_csv(f"house_votes/{year}_voting_record.csv", index_col=0).sort_index() - for year in range(1990,2021)] - metadata = [pd.read_csv( - f"house_votes/{year}_full.csv", - index_col=0 - )[["legislator", "state", "party"]].drop_duplicates(["legislator", "state"]).sort_values('legislator') - for year in range(1990,2021)] - -Let’s take a look at the voting record for a single year to see what -sort of data we are looking at: - -.. code:: python3 - - votes[5] - - - - -.. raw:: html - - <div> - <style scoped> - .dataframe tbody tr th:only-of-type { - vertical-align: middle; - } - - .dataframe tbody tr th { - vertical-align: top; - } - - .dataframe thead th { - text-align: right; - } - </style> - <table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>104-1st-1</th> - <th>104-1st-10</th> - <th>104-1st-100</th> - <th>104-1st-101</th> - <th>104-1st-102</th> - <th>104-1st-103</th> - <th>104-1st-104</th> - <th>104-1st-105</th> - <th>104-1st-106</th> - <th>104-1st-107</th> - <th>...</th> - <th>104-1st-90</th> - <th>104-1st-91</th> - <th>104-1st-92</th> - <th>104-1st-93</th> - <th>104-1st-94</th> - <th>104-1st-95</th> - <th>104-1st-96</th> - <th>104-1st-97</th> - <th>104-1st-98</th> - <th>104-1st-99</th> - </tr> - <tr> - <th>legislator</th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - <th></th> - </tr> - </thead> - <tbody> - <tr> - <th>Abercrombie</th> - <td>0.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>...</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - </tr> - <tr> - <th>Ackerman</th> - <td>0.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>...</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - </tr> - <tr> - <th>Allard</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>...</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>0.0</td> - <td>-1.0</td> - </tr> - <tr> - <th>Andrews</th> - <td>0.0</td> - <td>1.0</td> - <td>0.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>0.0</td> - <td>0.0</td> - <td>0.0</td> - <td>...</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - </tr> - <tr> - <th>Archer</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>...</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>0.0</td> - </tr> - <tr> - <th>...</th> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - <td>...</td> - </tr> - <tr> - <th>Young (AK)</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>...</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - </tr> - <tr> - <th>Young (FL)</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>...</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - </tr> - <tr> - <th>Zeliff</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>...</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - </tr> - <tr> - <th>Zimmer</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>...</td> - <td>-1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>-1.0</td> - </tr> - <tr> - <th>de la Garza</th> - <td>0.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>...</td> - <td>0.0</td> - <td>-1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - <td>1.0</td> - <td>-1.0</td> - <td>1.0</td> - </tr> - </tbody> - </table> - <p>438 rows × 885 columns</p> - </div> - - - -We can examine the associated metadata for the same year. - -.. code:: python3 - - metadata[5] - - - - -.. raw:: html - - <div> - <style scoped> - .dataframe tbody tr th:only-of-type { - vertical-align: middle; - } - - .dataframe tbody tr th { - vertical-align: top; - } - - .dataframe thead th { - text-align: right; - } - </style> - <table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>legislator</th> - <th>state</th> - <th>party</th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <td>Abercrombie</td> - <td>HI</td> - <td>D</td> - </tr> - <tr> - <th>1</th> - <td>Ackerman</td> - <td>NY</td> - <td>D</td> - </tr> - <tr> - <th>2</th> - <td>Allard</td> - <td>CO</td> - <td>R</td> - </tr> - <tr> - <th>3</th> - <td>Andrews</td> - <td>NJ</td> - <td>D</td> - </tr> - <tr> - <th>4</th> - <td>Archer</td> - <td>TX</td> - <td>R</td> - </tr> - <tr> - <th>...</th> - <td>...</td> - <td>...</td> - <td>...</td> - </tr> - <tr> - <th>430</th> - <td>Young (AK)</td> - <td>AK</td> - <td>R</td> - </tr> - <tr> - <th>431</th> - <td>Young (FL)</td> - <td>FL</td> - <td>R</td> - </tr> - <tr> - <th>432</th> - <td>Zeliff</td> - <td>NH</td> - <td>R</td> - </tr> - <tr> - <th>433</th> - <td>Zimmer</td> - <td>NJ</td> - <td>R</td> - </tr> - <tr> - <th>89</th> - <td>de la Garza</td> - <td>TX</td> - <td>D</td> - </tr> - </tbody> - </table> - <p>438 rows × 3 columns</p> - </div> - - - -You may note that sometimes representatives names list a state in -parenthesis afterwards. This is to provide disambiguation for -representatives that happen to have the last name. This actually -complicates matters for us since the disambiguation is only applied in -those cases where there is a name collision in that sitting of congress. -That means that for several years a representative may have simply their -last name, but then switch to being disambiguated, before potentially -switching back again. This would make it much harder to consistently -treck representatives over their entire career in congress. To fix this -up we’ll simply re-index by a unique representative ID that has their -last name, party, and state all listed over all the voting dataframes. -We’ll need a function to generate those from the metadata, and then -we’ll need to apply it to all the records. Importantly we’ll have to -finesse those situations where representatives are listed twice (under -un-ambiguous and disambiguated names) with some groupby tricks. - -.. code:: python3 - - def unique_legislator(row): - name, state, party = row.legislator, row.state, row.party - # Strip of disambiguating state designators - if re.search(r'(\w+) \([A-Z]{2}\)', name) is not None: - name = name[:-5] - return f"{name} ({party}, {state})" - -.. code:: python3 - - for i, _ in enumerate(votes): - votes[i].index = pd.Index(metadata[i].apply(unique_legislator, axis=1), name="legislator_index") - votes[i] = votes[i].groupby(level=0).sum() - metadata[i].index = pd.Index(metadata[i].apply(unique_legislator, axis=1), name="legislator_index") - metadata[i] = metadata[i].groupby(level=0).first() - -Now that we have the data at least a little wrangled into order there is -the question of ensuring some degree of continuity fore representatives. -To make this a little easier we’ll use voting records over *four year -spans* instead of over single years. Equally importantly we’ll do this -in a sliding window fashion so that we consider the record for 1990-1994 -and then the record for 1991-1995 and so on. By overlapping the windows -in this way we can ensure a little greater continuity of political -stance through the years. To make this happen we just have to merge data -frames in a sliding set of pairs, and then merge the pairs via the same -approach: - -.. code:: python3 - - votes = [ - pd.merge( - v1, v2, how="outer", on="legislator_index" - ).fillna(0.0).sort_index() - for v1, v2 in zip(votes[:-1], votes[1:]) - ] + votes[-1:] - - metadata = [ - pd.concat([m1, m2]).groupby("legislator_index").first().sort_index() - for m1, m2 in zip(metadata[:-1], metadata[1:]) - ] + metadata[-1:] - -That’s the pairs of years; now we merge these pairwise to get sets of -four years worth of votes. - -.. code:: python3 - - votes = [ - pd.merge( - v1, v2, how="outer", on="legislator_index" - ).fillna(0.0).sort_index() - for v1, v2 in zip(votes[:-1], votes[1:]) - ] + votes[-1:] - - metadata = [ - pd.concat([m1, m2]).groupby(level=0).first().sort_index() - for m1, m2 in zip(metadata[:-1], metadata[1:]) - ] + metadata[-1:] - -Applying AlignedUMAP --------------------- - -To make use of AlignedUMAP we need to generate relations between -consecutive dataset slices. In this case that means we need to have a -relation describing row from one four year slice corresponds to a row -from the following four year slice for the same representative. For -AlignedUMAP to work this should be formatted as a list of dictionaries; -each dictionary gives a mapping from indices of one slice to indices of -the next. Importantly this mapping can be partial – it only has to -relate indices for which there is a match between the two slices. - -The vote dataframes that we are using for slices are already indexed -with unique identifiers for representatives, so to make relations we -simply have to match them up, creating a dictionary of indices from one -to the other. In practice we can do this relatively efficiently by using -pandas to merge dataframes on the pandas indexes of the two vote -dataframes with the data being simply the numeric indices of the rows. -The resulting dictionary is then just the dictionary of pairs given by -the inner join. - -.. code:: python3 - - def make_relation(from_df, to_df): - left = pd.DataFrame(data=np.arange(len(from_df)), index=from_df.index) - right = pd.DataFrame(data=np.arange(len(to_df)), index=to_df.index) - merge = pd.merge(left, right, left_index=True, right_index=True) - return dict(merge.values) - -With a function for relation creation in place we simply need to apply -it to each consecutive pair of vote dataframes. - -.. code:: python3 - - relations = [make_relation(x,y) for x, y in zip(votes[:-1], votes[1:])] - -If you are still unsure of what these relations are it might be -beneficial to look at a few of the dictionaries, along with the -corresponding pairs of vote dataframes. Here is (part of) the first -relation dictionary: - -.. code:: python3 - - relations[0] - - - - -.. parsed-literal:: - - {0: 0, - 1: 1, - 3: 2, - 4: 3, - 5: 4, - 6: 5, - 7: 6, - 8: 7, - 9: 8, - 10: 9, - 11: 10, - 12: 11, - 13: 12, - 14: 13, - 15: 14, - ... - 475: 547, - 476: 549, - 477: 550, - 478: 552, - 479: 553, - 480: 554, - 481: 555, - 482: 556, - 483: 557, - 484: 559} - - - -Now we are finally in a position to run AlignedUMAP. Most of the -standard UMAP parameters are available for use, including choosing a -metric and a number of neighbors. Here we will also make use of the -extra AlignedUMAP parameters ``alignment_regularisation`` and -``alignment_window_size``. The first is a value that weights how -important retaining alignment is. Typically the value is much smaller -than this (the default is 0.01), but given the relatively high -volatility in voting records we are going to increase it here. The -second parameter, ``alignment_window_size`` determines how far out on -either side AlignedUMAP will look when aligning embeddings – even though -the relations are specified only between consecutive slices it will -chain them together to construct relations reaching further. In this -case we’ll have it look as far out as 5 slices either side. - -.. code:: python3 - - %%time - aligned_mapper = umap.aligned_umap.AlignedUMAP( - metric="cosine", - n_neighbors=20, - alignment_regularisation=0.1, - alignment_window_size=5, - n_epochs=200, - random_state=42, - ).fit(votes, relations=relations) - embeddings = aligned_mapper.embeddings_ - - -.. parsed-literal:: - - CPU times: user 6min 7s, sys: 30.6 s, total: 6min 37s - Wall time: 5min 57s - - -Visualizing the Results ------------------------ - -Now we need to plot the data somehow. To make the visualization -interesting it would be beneficial to have some colour variation – -ideally showing a different view of the relative political stance. For -that we want to attempt to get an idea of the position of each candidate -from an alternative source. To do this we can try to extract the vote -margin that the representative won by. The catch here is that while the -election data can be collected and processed, the names don’t match -perfectly as they come from a different source. That means we need to do -our best to get a name match for each candidate. We’ll use fuzzy string -matching restricted to the relevant year and state to try to get a good -match. A notebook providing details for obtaining and processing the -election winners data can be found -`here <https://github.com/lmcinnes/umap_doc_notebooks/blob/master/voting_data_by_district.ipynb>`__. - -.. code:: python3 - - election_winners = pd.read_csv('election_winners_1976-2018.csv', index_col=0) - election_winners.head() - - - - -.. raw:: html - - <div> - <style scoped> - .dataframe tbody tr th:only-of-type { - vertical-align: middle; - } - - .dataframe tbody tr th { - vertical-align: top; - } - - .dataframe thead th { - text-align: right; - } - </style> - <table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>year</th> - <th>state</th> - <th>district</th> - <th>winner</th> - <th>party</th> - <th>winning_ratio</th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <td>1976</td> - <td>AK</td> - <td>0</td> - <td>Don Young</td> - <td>republican</td> - <td>0.289986</td> - </tr> - <tr> - <th>0</th> - <td>1976</td> - <td>AL</td> - <td>1</td> - <td>Jack Edwards</td> - <td>republican</td> - <td>0.374808</td> - </tr> - <tr> - <th>0</th> - <td>1976</td> - <td>AL</td> - <td>2</td> - <td>William L. \\"Bill\"\" Dickinson"</td> - <td>republican</td> - <td>0.423953</td> - </tr> - <tr> - <th>0</th> - <td>1976</td> - <td>AL</td> - <td>3</td> - <td>Bill Nichols</td> - <td>democrat</td> - <td>1.000000</td> - </tr> - <tr> - <th>0</th> - <td>1976</td> - <td>AL</td> - <td>4</td> - <td>Tom Bevill</td> - <td>democrat</td> - <td>0.803825</td> - </tr> - </tbody> - </table> - </div> - - - -Now we need to simply go through the metadata and fill it out with the -extra information we can glean from the election winners data. Since we -can’t do exact name matching (the data for both is somewhat messy when -it comes to text fields like names) we can’t simply perform a join, but -must instead process things year by year and representative by -representative, finding the best string match on name that we can for -the given year and state election. In practice we are undoubtedly going -to get some of these wrong, and if the goal was a rigorous analysis -based on this data a lot more care would need to be taken. Since this is -just a demonstration and we’ll only be using this extra information as a -colour channel in plots we can excuise a few errors here and there from -in-exact data processing. - -.. code:: python3 - - n_name_misses = 0 - for year, df in enumerate(metadata, 1990): - df["partisan_lean"] = 0.5 - df["district"] = np.full(len(df), -1, dtype=np.int8) - for idx, (loc, row) in enumerate(df.iterrows()): - name, state, party = row.legislator, row.state, row.party - # Strip of disambiguating state designators - if re.search(r'(\w+) \([A-Z]{2}\)', name) is not None: - name = name[:-5] - # Get a party designator matching the election_winners data - party = "republican" if party == "R" else "democrat" - # Restrict to the right state and time-frame - state_election_winners = election_winners[(election_winners.state == state) - & (election_winners.year <= year + 4) - & (election_winners.year >= year - 4)] - # Try to match a name; and fail "gracefully" - try: - matched_name = process.extractOne( - name, - state_election_winners.winner.tolist(), - scorer=fuzz.partial_token_sort_ratio, - score_cutoff=50, - ) - except: - matched_name = None - - # If we got a unique match, get the election data - if matched_name is not None: - winner = state_election_winners[state_election_winners.winner == matched_name[0]] - else: - winner = [] - - # We either have none, one, or *several* match elections. Take a best guess. - if len(winner) < 1: - df.loc[loc, ["partisan_lean"]] = 0.25 if party == "republican" else 0.75 - n_name_misses += 1 - elif len(winner) > 1: - df.iloc[idx, 4] = int(winner.district.values[-1]) - df.iloc[idx, 3] = float(winner.winning_ratio.values[-1]) - else: - df.iloc[idx, 4] = int(winner.district.values) - df.iloc[idx, 3] = float(winner.winning_ratio.values[0]) - - print(f"Failed to match a name {n_name_misses} times") - - -.. parsed-literal:: - - Failed to match a name 100 times - - -Now that we have the relative partisan leanings based on district -election margins we can color the plot. We can obviously label the plot -with the representatives names. The last remaining catch (when using -matplotlib for the plotting) is the get the plot bounds (since we will -be placing text markers directly into the plot, and thus not -autogenerating bounds). This is a simple enough matter of computing some -bounds as an adjustment a little outside the data limits. - -.. code:: python3 - - def axis_bounds(embedding): - left = embedding.T[0].min() - right = embedding.T[0].max() - bottom = embedding.T[1].min() - top = embedding.T[1].max() - width = right - left - height = top - bottom - adj_h = width * 0.1 - adj_v = height * 0.05 - return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v] - -Now for the plot. Let’s pick a random time slice (you are welcome to try -others) and draw the representatives names in their embedded locations -for that slice, coloured by their relative election victory margin. - -.. code:: python3 - - fig, ax = plt.subplots(figsize=(12,12)) - e = 5 - ax.axis(axis_bounds(embeddings[e])) - ax.set_aspect('equal') - for i in range(embeddings[e].shape[0]): - ax.text(embeddings[e][i, 0], - embeddings[e][i, 1], - metadata[e].index.values[i], - color=plt.cm.RdBu(np.float32(metadata[e]["partisan_lean"].values[i])), - fontsize=8, - horizontalalignment='center', - verticalalignment='center', - ) - - - - -.. image:: images/aligned_umap_politics_demo_31_0.png - - -This gives a good idea of the layout in a single time slices, and by -plotting different time slices we can get some idea of how things have -evolved. We can go further, however, by plotting a representative as -curve through time as their relative political position in congress -changes. For that we will need a 3D plot – we need both the UMAP x and y -coordinates, as well as a third coordinate giving the year. I found this -easiest to do in plotly, so let’s import that. To make nice smooth -curves through time we will also import the ``scipy.interpolate`` module -which will let is interpolate a smooth curve from the discrete positions -that a representatives appears in over time. - -.. code:: python3 - - import plotly.graph_objects as go - import scipy.interpolate - -Wrangling the data into shape for this is the next step; first let’s get -everything in a single dataframe that we can extract relevant data from -on an as-needed basis. - -.. code:: python3 - - df = pd.DataFrame(np.vstack(embeddings), columns=('x', 'y')) - df['z'] = np.concatenate([[year] * len(embeddings[i]) for i, year in enumerate(range(1990, 2021))]) - df['representative_id'] = np.concatenate([v.index for v in votes]) - df['partisan_lean'] = np.concatenate([m["partisan_lean"].values for m in metadata]) - -Next we’ll need that interpolation of the curve for a given -representative. We’ll write a function to handle that as there is a -little bit of case-based logic that makes it non-trivial. We are going -to get handed year data and want to interpolate the UMAP x and y -coordinates for a single representative. - -The first major catch is that many representatives don’t have a single -contiguous block of years for which they were in congress: they were -elected for several years, missed re-election, and then came back to -congress several years later (possibly in another district). Each such -block of contiguous years needs to be a separate path, and we shouldn’t -connect them. We therefore need some logic to find the contiguous blocks -and generate smooth paths for each of them. - -Another catch is that some representatives have only been in office for -a year or two (special elections and so forth) and we can’t do a cubic -spline interpolation for that; we can devolve to linear interpolation or -quadratic splines for those cases, so simply add the point itself for -the odd single year cases. - -With those issues in hand we can then simply use the scipy ``interp1d`` -function to generate smooth curves through the points. - -.. code:: python3 - - INTERP_KIND = {2:"linear", 3:"quadratic", 4:"cubic"} - - def interpolate_paths(z, x, y, c, rep_id): - consecutive_year_blocks = np.where(np.diff(z) != 1)[0] + 1 - z_blocks = np.split(z, consecutive_year_blocks) - x_blocks = np.split(x, consecutive_year_blocks) - y_blocks = np.split(y, consecutive_year_blocks) - c_blocks = np.split(c, consecutive_year_blocks) - - paths = [] - - for block_idx, zs in enumerate(z_blocks): - - text = f"{rep_id} -- partisan_lean: {np.mean(c_blocks[block_idx]):.2f}" - - if len(zs) > 1: - kind = INTERP_KIND.get(len(zs), "cubic") - else: - paths.append( - (zs, x_blocks[block_idx], y_blocks[block_idx], c_blocks[block_idx], text) - ) - continue - - z = np.linspace(np.min(zs), np.max(zs), 100) - x = scipy.interpolate.interp1d(zs, x_blocks[block_idx], kind=kind)(z) - y = scipy.interpolate.interp1d(zs, y_blocks[block_idx], kind=kind)(z) - c = scipy.interpolate.interp1d(zs, c_blocks[block_idx], kind="linear")(z) - - paths.append((z, x, y, c, text)) - - return paths - -And now we can use plotly to draw the resulting curves. For plotly we -use the ``Scatter3D`` method, which supports a “lines” mode that can -draw curves in 3D space. We can colour the curves by the partisan lean -score we derived from the election data – in fact the colour can vary -through the trace as the election margins vary. Since this is a plotly -plot it is interactive, so you can rotate it around and view it from all -angles. - -Unfortunately the interactive plotly plot does not embed into the documentation -well, so we present here a static image. If you run this yourself, however, -you will get the interactive version. - -.. code:: python3 - - traces = [] - for rep in df.representative_id.unique(): - z = df.z[df.representative_id == rep].values - x = df.x[df.representative_id == rep].values - y = df.y[df.representative_id == rep].values - c = df.partisan_lean[df.representative_id == rep] - - for z, x, y, c, text in interpolate_paths(z, x, y, c, rep): - trace = go.Scatter3d( - x=x, y=z, z=y, - mode="lines", - hovertext=text, - hoverinfo="text", - line=dict( - color=c, - cmin=0.0, - cmid=0.5, - cmax=1.0, - cauto=False, - colorscale="RdBu", - colorbar=dict(), - width=2.5, - ), - opacity=1.0, - ) - traces.append(trace) - - fig = go.Figure(data=traces) - fig.update_layout( - width=800, - height=600, - scene=dict( - aspectratio = dict( x=0.5, y=1.25, z=0.5 ), - yaxis_title="Year", - xaxis_title="UMAP-X", - zaxis_title="UMAP-Y", - ), - scene_camera=dict(eye=dict( x=0.5, y=0.8, z=0.75 )), - autosize=False, - showlegend=False, - ) - fig_widget = go.FigureWidget(fig) - fig_widget - - -.. image:: images/aligned_umap_politics_demo_spaghetti.png - -.. - .. raw:: html - :file: aligned_umap_plotly_plot.html - -This concludes our exploration for now. - - diff --git a/doc/api.md b/doc/api.md new file mode 100644 index 00000000..b734d87f --- /dev/null +++ b/doc/api.md @@ -0,0 +1,35 @@ +# UMAP API Guide + + +UMAP has only two classes, `UMAP`, and `ParametricUMAP`, which inherits from it. + +## UMAP + + +## umap.umap_.UMAP + +> **API Reference:** `umap.umap_.UMAP` +> +> This is an auto-generated API reference. See the Python docstrings for details. + + +ParametricUMAP +---- + +## umap.parametric_umap.ParametricUMAP + +> **API Reference:** `umap.parametric_umap.ParametricUMAP` +> +> This is an auto-generated API reference. See the Python docstrings for details. + + +A number of internal functions can also be accessed separately for more fine tuned work. + +## Useful Functions + + +## Module: umap.umap_ + +> **API Reference:** Module `umap.umap_` +> +> This is an auto-generated API reference. See the Python docstrings for details. diff --git a/doc/api.rst b/doc/api.rst deleted file mode 100644 index 4313921b..00000000 --- a/doc/api.rst +++ /dev/null @@ -1,26 +0,0 @@ -UMAP API Guide -============== - -UMAP has only two classes, :class:`UMAP`, and :class:`ParametricUMAP`, which inherits from it. - -UMAP ----- - -.. autoclass:: umap.umap_.UMAP - :members: - -ParametricUMAP ----- - -.. autoclass:: umap.parametric_umap.ParametricUMAP - :members: - -A number of internal functions can also be accessed separately for more fine tuned work. - -Useful Functions ----------------- - -.. automodule:: umap.umap_ - :members: - :exclude-members: UMAP - diff --git a/doc/basic_usage.md b/doc/basic_usage.md new file mode 100644 index 00000000..1dde28cf --- /dev/null +++ b/doc/basic_usage.md @@ -0,0 +1,631 @@ +# How to Use UMAP + + +UMAP is a general purpose manifold learning and dimension reduction +algorithm. It is designed to be compatible with +[scikit-learn](http://scikit-learn.org/stable/index.html), making use +of the same API and able to be added to sklearn pipelines. If you are +already familiar with sklearn you should be able to use UMAP as a drop +in replacement for t-SNE and other dimension reduction classes. If you +are not so familiar with sklearn this tutorial will step you through the +basics of using UMAP to transform and visualise data. + +First we'll need to import a bunch of useful tools. We will need numpy +obviously, but we'll use some of the datasets available in sklearn, as +well as the `train_test_split` function to divide up data. Finally +we'll need some plotting tools (matplotlib and seaborn) to help us +visualise the results of UMAP, and pandas to make that a little easier. + +```python3 +import numpy as np +from sklearn.datasets import load_digits +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +%matplotlib inline + +``` + +```python3 +sns.set(style='white', context='notebook', rc={'figure.figsize':(14,10)}) + +``` + +## Penguin data + + +![Penguins](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/c19a904462482430170bfe2c718775ddb7dbb885/man/figures/lter_penguins.png) +<!-- width: 300px --> + +The next step is to get some data to work with. To ease us into things +we'll start with the `penguin +dataset <https://github.com/allisonhorst/penguins>`__. It isn't very +representative of what real data would look like, but it is small both +in number of points and number of features, and will let us get an idea +of what the dimension reduction is doing. + +```python3 +penguins = pd.read_csv("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/c19a904462482430170bfe2c718775ddb7dbb885/inst/extdata/penguins.csv") +penguins.head() + + + + +``` + + +<div> +<style scoped> + .dataframe tbody tr th:only-of-type { + vertical-align: middle; + } + + .dataframe tbody tr th { + vertical-align: top; + } + + .dataframe thead th { + text-align: right; + } +</style> +<table border="1" class="dataframe"> + <thead> + <tr style="text-align: right;"> + <th></th> + <th>species</th> + <th>island</th> + <th>bill_length_mm</th> + <th>bill_depth_mm</th> + <th>flipper_length_mm</th> + <th>body_mass_g</th> + <th>sex</th> + <th>year</th> + </tr> + </thead> + <tbody> + <tr> + <th>0</th> + <td>Adelie</td> + <td>Torgersen</td> + <td>39.1</td> + <td>18.7</td> + <td>181.0</td> + <td>3750.0</td> + <td>male</td> + <td>2007</td> + </tr> + <tr> + <th>1</th> + <td>Adelie</td> + <td>Torgersen</td> + <td>39.5</td> + <td>17.4</td> + <td>186.0</td> + <td>3800.0</td> + <td>female</td> + <td>2007</td> + </tr> + <tr> + <th>2</th> + <td>Adelie</td> + <td>Torgersen</td> + <td>40.3</td> + <td>18.0</td> + <td>195.0</td> + <td>3250.0</td> + <td>female</td> + <td>2007</td> + </tr> + <tr> + <th>3</th> + <td>Adelie</td> + <td>Torgersen</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>NaN</td> + <td>2007</td> + </tr> + <tr> + <th>4</th> + <td>Adelie</td> + <td>Torgersen</td> + <td>36.7</td> + <td>19.3</td> + <td>193.0</td> + <td>3450.0</td> + <td>female</td> + <td>2007</td> + </tr> + </tbody> +</table> +</div> + + + + +Since this is for demonstration purposes we will get rid of the NAs in +the data; in a real world setting one would wish to take more care with +proper handling of missing data. + +```python3 +penguins = penguins.dropna() +penguins.species.value_counts() + + + + +``` + +``` +Adelie 146 +Gentoo 119 +Chinstrap 68 +Name: species, dtype: int64 + + +``` + +![Diagram of culmen measurements on a penguin](https://github.com/allisonhorst/palmerpenguins/blob/c19a904462482430170bfe2c718775ddb7dbb885/man/figures/culmen_depth.png?raw=true) +<!-- width: 300px --> + +See the [github repostiory](https://github.com/allisonhorst/penguins) +for more details about the dataset itself. It consists of measurements +of bill (culmen) and flippers and weights of three species of penguins, +along with some other metadata about the penguins. In total we have 333 +different penguins measured. Visualizing this data is a little bit +tricky since we can't plot in 4 dimensions easily. Fortunately four is +not that large a number, so we can just to a pairwise feature +scatterplot matrix to get an ideas of what is going on. Seaborn makes +this easy. + +```python3 +sns.pairplot(penguins.drop("year", axis=1), hue='species'); + + + +``` + +![Image](images/basic_usage_8_1.png) + +This gives us some idea of what the data looks like by giving as all the +2D views of the data. Four dimensions is low enough that we can (sort +of) reconstruct what the full dimensional data looks like in our heads. +Now that we sort of know what we are looking at, the question is what +can a dimension reduction technique like UMAP do for us? By reducing the +dimension in a way that preserves as much of the structure of the data +as possible we can get a visualisable representation of the data +allowing us to "see" the data and its structure and begin to get some +intuition about the data itself. + +To use UMAP for this task we need to first construct a UMAP object that +will do the job for us. That is as simple as instantiating the class. So +let's import the umap library and do that. + +```python3 +import umap + +``` + +```python3 +reducer = umap.UMAP() + +``` + +Before we can do any work with the data it will help to clean up it a +little. We won't need NAs, we just want the measurement columns, and +since the measurements are on entirely different scales it will be +helpful to convert each feature into z-scores (number of standard +deviations from the mean) for comparability. + +```python3 +penguin_data = penguins[ + [ + "bill_length_mm", + "bill_depth_mm", + "flipper_length_mm", + "body_mass_g", + ] +].values +scaled_penguin_data = StandardScaler().fit_transform(penguin_data) + +``` + +Now we need to train our reducer, letting it learn about the manifold. +For this UMAP follows the sklearn API and has a method `fit` which we +pass the data we want the model to learn from. Since, at the end of the +day, we are going to want to reduced representation of the data we will +use, instead, the `fit_transform` method which first calls `fit` and +then returns the transformed data as a numpy array. + +```python3 +embedding = reducer.fit_transform(scaled_penguin_data) +embedding.shape + + + + +``` + +``` +(333, 2) + + + +``` + +The result is an array with 333 samples, but only two feature columns +(instead of the four we started with). This is because, by default, UMAP +reduces down to 2D. Each row of the array is a 2-dimensional +representation of the corresponding penguin. Thus we can plot the +`embedding` as a standard scatterplot and color by the target array +(since it applies to the transformed data which is in the same order as +the original). + +```python3 +plt.scatter( + embedding[:, 0], + embedding[:, 1], + c=[sns.color_palette()[x] for x in penguins.species.map({"Adelie":0, "Chinstrap":1, "Gentoo":2})]) +plt.gca().set_aspect('equal', 'datalim') +plt.title('UMAP projection of the Penguin dataset', fontsize=24); + + + +``` + +![Image](images/basic_usage_17_1.png) + +This does a useful job of capturing the structure of the data, and as +can be seen from the matrix of scatterplots this is relatively accurate. +Of course we learned at least this much just from that matrix of +scatterplots -- which we could do since we only had four different +dimensions to analyse. If we had data with a larger number of dimensions +the scatterplot matrix would quickly become unwieldy to plot, and far +harder to interpret. So moving on from the Penguin dataset, let's consider +the digits dataset. + +## Digits data + + +First we will load the dataset from sklearn. + +```python3 +digits = load_digits() +print(digits.DESCR) + + +``` + +``` +.. _digits_dataset: + +Optical recognition of handwritten digits dataset +-------------------------------------------------- + +**Data Set Characteristics:** + + :Number of Instances: 5620 + :Number of Attributes: 64 + :Attribute Information: 8x8 image of integer pixels in the range 0..16. + :Missing Attribute Values: None + :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr) + :Date: July; 1998 + +This is a copy of the test set of the UCI ML hand-written digits datasets +https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits + +The data set contains images of hand-written digits: 10 classes where +each class refers to a digit. + +Preprocessing programs made available by NIST were used to extract +normalized bitmaps of handwritten digits from a preprinted form. From a +total of 43 people, 30 contributed to the training set and different 13 +to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of +4x4 and the number of on pixels are counted in each block. This generates +an input matrix of 8x8 where each element is an integer in the range +0..16. This reduces dimensionality and gives invariance to small +distortions. + +For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. +T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. +L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, +1994. + +.. topic:: References + + - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their + Applications to Handwritten Digit Recognition, MSc Thesis, Institute of + Graduate Studies in Science and Engineering, Bogazici University. + - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. + - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. + Linear dimensionalityreduction using relevance weighted LDA. School of + Electrical and Electronic Engineering Nanyang Technological University. + 2005. + - Claudio Gentile. A New Approximate Maximal Margin Classification + Algorithm. NIPS. 2000. + + +``` + +We can plot a number of the images to get an idea of what we are looking +at. This just involves matplotlib building a grid of axes and then +looping through them plotting an image into each one in turn. + +```python3 +fig, ax_array = plt.subplots(20, 20) +axes = ax_array.flatten() +for i, ax in enumerate(axes): + ax.imshow(digits.images[i], cmap='gray_r') +plt.setp(axes, xticks=[], yticks=[], frame_on=False) +plt.tight_layout(h_pad=0.5, w_pad=0.01) + + + +``` + +![Image](images/basic_usage_22_0.png) + +As you can see these are quite low resolution images -- for the most +part they are recognisable as digits, but there are a number of cases +that are sufficiently blurred as to be questionable even for a human to +guess at. The zeros do stand out as the easiest to pick out as notably +different and clearly zeros. Beyond that things get a little harder: +some of the squashed thing eights look awfully like ones, some of the +threes start to look a little like crossed sevens when drawn badly, and +so on. + +Each image can be unfolded into a 64 element long vector of grayscale +values. It is these 64 dimensional vectors that we wish to analyse: how +much of the digits structure can we discern? At least in principle 64 +dimensions is overkill for this task, and we would reasonably expect +that there should be some smaller number of "latent" features that would +be sufficient to describe the data reasonably well. We can try a +scatterplot matrix -- in this case just of the first 10 dimensions so +that it is at least plottable, but as you can quickly see that approach +is not going to be sufficient for this data. + +```python3 +digits_df = pd.DataFrame(digits.data[:,1:11]) +digits_df['digit'] = pd.Series(digits.target).map(lambda x: 'Digit {}'.format(x)) +sns.pairplot(digits_df, hue='digit', palette='Spectral'); + + +``` + +![Image](images/basic_usage_24_2.png) + +In contrast we can try using UMAP again. It works exactly as before: +construct a model, train the model, and then look at the transformed +data. To demonstrate more of UMAP we'll go about it differently this +time and simply use the `fit` method rather than the `fit_transform` +approach we used for Penguins. + +```python3 +reducer = umap.UMAP(random_state=42) +reducer.fit(digits.data) + + +``` + +``` +UMAP(a=None, angular_rp_forest=False, b=None, + force_approximation_algorithm=False, init='spectral', learning_rate=1.0, + local_connectivity=1.0, low_memory=False, metric='euclidean', + metric_kwds=None, min_dist=0.1, n_components=2, n_epochs=None, + n_neighbors=15, negative_sample_rate=5, output_metric='euclidean', + output_metric_kwds=None, random_state=42, repulsion_strength=1.0, + set_op_mix_ratio=1.0, spread=1.0, target_metric='categorical', + target_metric_kwds=None, target_n_neighbors=-1, target_weight=0.5, + transform_queue_size=4.0, transform_seed=42, unique=False, verbose=False) + + + +``` + +Now, instead of returning an embedding we simply get back the reducer +object, now having trained on the dataset we passed it. To access the +resulting transform we can either look at the `embedding_` attribute +of the reducer object, or call transform on the original data. + +```python3 +embedding = reducer.transform(digits.data) +# Verify that the result of calling transform is +# idenitical to accessing the embedding_ attribute +assert(np.all(embedding == reducer.embedding_)) +embedding.shape + + + + +``` + +``` +(1797, 2) + + + +``` + +We now have a dataset with 1797 rows (one for each hand-written digit +sample), but only 2 columns. As with the Penguins example we can now plot +the resulting embedding, coloring the data points by the class that +they belong to (i.e. the digit they represent). + +```python3 +plt.scatter(embedding[:, 0], embedding[:, 1], c=digits.target, cmap='Spectral', s=5) +plt.gca().set_aspect('equal', 'datalim') +plt.colorbar(boundaries=np.arange(11)-0.5).set_ticks(np.arange(10)) +plt.title('UMAP projection of the Digits dataset', fontsize=24); + +``` + +![Image](images/basic_usage_30_1.png) + +We see that UMAP has successfully captured the digit classes. There are +also some interesting effects as some digit classes blend into one +another (see the eights, ones, and sevens, with some nines in between), +and also cases where digits are pushed away as clearly distinct (the +zeros on the right, the fours at the top, and a small subcluster of ones +at the bottom come to mind). To get a better idea of why UMAP chose to +do this it is helpful to see the actual digits involve. One can do this +using [bokeh](https://bokeh.pydata.org/en/latest/) and mouseover +tooltips of the images. + +First we'll need to encode all the images for inclusion in a dataframe. + +```python3 +from io import BytesIO +from PIL import Image +import base64 + +``` + +```python3 +def embeddable_image(data): + img_data = 255 - 15 * data.astype(np.uint8) + image = Image.fromarray(img_data, mode='L').resize((64, 64), Image.Resampling.BICUBIC) + buffer = BytesIO() + image.save(buffer, format='png') + for_encoding = buffer.getvalue() + return 'data:image/png;base64,' + base64.b64encode(for_encoding).decode() + +``` + +Next we need to load up bokeh and the various tools from it that will be +needed to generate a suitable interactive plot. + +```python3 +from bokeh.plotting import figure, show, output_notebook +from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper +from bokeh.palettes import Spectral10 + +output_notebook() + + + +``` + + + +<div class="bk-root"> + <a href="https://bokeh.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a> + <span id="1001">Loading BokehJS ...</span> +</div> + + + + + +Finally we generate the plot itself with a custom hover tooltip that +embeds the image of the digit in question in it, along with the digit +class that the digit is actually from (this can be useful for digits +that are hard even for humans to classify correctly). + +```python3 +digits_df = pd.DataFrame(embedding, columns=('x', 'y')) +digits_df['digit'] = [str(x) for x in digits.target] +digits_df['image'] = list(map(embeddable_image, digits.images)) + +datasource = ColumnDataSource(digits_df) +color_mapping = CategoricalColorMapper(factors=[str(9 - x) for x in digits.target_names], + palette=Spectral10) + +plot_figure = figure( + title='UMAP projection of the Digits dataset', + width=600, + height=600, + tools=('pan, wheel_zoom, reset') +) + +plot_figure.add_tools(HoverTool(tooltips=""" +<div> + <div> + <img src='@image' style='float: left; margin: 5px 5px 5px 5px'/> + </div> + <div> + <span style='font-size: 16px; color: #224499'>Digit:</span> + <span style='font-size: 18px'>@digit</span> + </div> +</div> +""")) + +plot_figure.scatter( + 'x', + 'y', + source=datasource, + color=dict(field='digit', transform=color_mapping), + line_alpha=0.6, + fill_alpha=0.6, + size=4 +) +show(plot_figure) + + + +``` + +[View html file](basic_usage_bokeh_example.html) + + +As can be seen, the nines that blend between the ones and the sevens are +odd looking nines (that aren't very rounded) and do, indeed, interpolate +surprisingly well between ones with hats and crossed sevens. In contrast +the small disjoint cluster of ones at the bottom of the plot is made up +of ones with feet (a horizontal line at the base of the one) which are, +indeed, quite distinct from the general mass of ones. + +This concludes our introduction to basic UMAP usage -- hopefully this +has given you the tools to get started for yourself. Further tutorials, +covering UMAP parameters and more advanced usage are also available when +you wish to dive deeper. + +-------------- + + + + <h3> + +Penguin data information + + + + </h3> + +Peguin data are from: + +**Gorman KB, Williams TD, Fraser WR** (2014) Ecological Sexual +Dimorphism and Environmental Variability within a Community of Antarctic +Penguins (Genus *Pygoscelis*). PLoS ONE 9(3): e90081. +doi:10.1371/journal.pone.0090081 + +See the full paper +[HERE](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090081). + + + + <h4> + +Original data access and use + + + + </h4> + +From Gorman et al.: "Data reported here are publicly available within +the PAL-LTER data system (datasets #219, 220, and 221): +http://oceaninformatics.ucsd.edu/datazoo/data/pallter/datasets. These +data are additionally archived within the United States (US) LTER +Network's Information System Data Portal: https://portal.lternet.edu/. +Individuals interested in using these data are therefore expected to +follow the US LTER Network's Data Access Policy, Requirements and Use +Agreement: https://lternet.edu/data-access-policy/." + +Anyone interested in publishing the data should contact `Dr. Kristen +Gorman <https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php>`__ +about analysis and working together on any final products. + +Penguin images by Alison Horst. diff --git a/doc/basic_usage.rst b/doc/basic_usage.rst deleted file mode 100644 index 1f6386d6..00000000 --- a/doc/basic_usage.rst +++ /dev/null @@ -1,615 +0,0 @@ -How to Use UMAP -=============== - -UMAP is a general purpose manifold learning and dimension reduction -algorithm. It is designed to be compatible with -`scikit-learn <http://scikit-learn.org/stable/index.html>`__, making use -of the same API and able to be added to sklearn pipelines. If you are -already familiar with sklearn you should be able to use UMAP as a drop -in replacement for t-SNE and other dimension reduction classes. If you -are not so familiar with sklearn this tutorial will step you through the -basics of using UMAP to transform and visualise data. - -First we'll need to import a bunch of useful tools. We will need numpy -obviously, but we'll use some of the datasets available in sklearn, as -well as the ``train_test_split`` function to divide up data. Finally -we'll need some plotting tools (matplotlib and seaborn) to help us -visualise the results of UMAP, and pandas to make that a little easier. - -.. code:: python3 - - import numpy as np - from sklearn.datasets import load_digits - from sklearn.model_selection import train_test_split - from sklearn.preprocessing import StandardScaler - import matplotlib.pyplot as plt - import seaborn as sns - import pandas as pd - %matplotlib inline - -.. code:: python3 - - sns.set(style='white', context='notebook', rc={'figure.figsize':(14,10)}) - -Penguin data ------------- - -.. image:: https://raw.githubusercontent.com/allisonhorst/palmerpenguins/c19a904462482430170bfe2c718775ddb7dbb885/man/figures/lter_penguins.png - :width: 300px - :align: center - :alt: Penguins - -The next step is to get some data to work with. To ease us into things -we'll start with the `penguin -dataset <https://github.com/allisonhorst/penguins>`__. It isn't very -representative of what real data would look like, but it is small both -in number of points and number of features, and will let us get an idea -of what the dimension reduction is doing. - -.. code:: python3 - - penguins = pd.read_csv("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/c19a904462482430170bfe2c718775ddb7dbb885/inst/extdata/penguins.csv") - penguins.head() - - - - -.. raw:: html - - <div> - <style scoped> - .dataframe tbody tr th:only-of-type { - vertical-align: middle; - } - - .dataframe tbody tr th { - vertical-align: top; - } - - .dataframe thead th { - text-align: right; - } - </style> - <table border="1" class="dataframe"> - <thead> - <tr style="text-align: right;"> - <th></th> - <th>species</th> - <th>island</th> - <th>bill_length_mm</th> - <th>bill_depth_mm</th> - <th>flipper_length_mm</th> - <th>body_mass_g</th> - <th>sex</th> - <th>year</th> - </tr> - </thead> - <tbody> - <tr> - <th>0</th> - <td>Adelie</td> - <td>Torgersen</td> - <td>39.1</td> - <td>18.7</td> - <td>181.0</td> - <td>3750.0</td> - <td>male</td> - <td>2007</td> - </tr> - <tr> - <th>1</th> - <td>Adelie</td> - <td>Torgersen</td> - <td>39.5</td> - <td>17.4</td> - <td>186.0</td> - <td>3800.0</td> - <td>female</td> - <td>2007</td> - </tr> - <tr> - <th>2</th> - <td>Adelie</td> - <td>Torgersen</td> - <td>40.3</td> - <td>18.0</td> - <td>195.0</td> - <td>3250.0</td> - <td>female</td> - <td>2007</td> - </tr> - <tr> - <th>3</th> - <td>Adelie</td> - <td>Torgersen</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>NaN</td> - <td>2007</td> - </tr> - <tr> - <th>4</th> - <td>Adelie</td> - <td>Torgersen</td> - <td>36.7</td> - <td>19.3</td> - <td>193.0</td> - <td>3450.0</td> - <td>female</td> - <td>2007</td> - </tr> - </tbody> - </table> - </div> - - - -Since this is for demonstration purposes we will get rid of the NAs in -the data; in a real world setting one would wish to take more care with -proper handling of missing data. - -.. code:: python3 - - penguins = penguins.dropna() - penguins.species.value_counts() - - - - -.. parsed-literal:: - - Adelie 146 - Gentoo 119 - Chinstrap 68 - Name: species, dtype: int64 - - -.. image:: https://github.com/allisonhorst/palmerpenguins/blob/c19a904462482430170bfe2c718775ddb7dbb885/man/figures/culmen_depth.png?raw=true - :width: 300px - :align: center - :alt: Diagram of culmen measurements on a penguin - -See the `github repostiory <https://github.com/allisonhorst/penguins>`__ -for more details about the dataset itself. It consists of measurements -of bill (culmen) and flippers and weights of three species of penguins, -along with some other metadata about the penguins. In total we have 333 -different penguins measured. Visualizing this data is a little bit -tricky since we can't plot in 4 dimensions easily. Fortunately four is -not that large a number, so we can just to a pairwise feature -scatterplot matrix to get an ideas of what is going on. Seaborn makes -this easy. - -.. code:: python3 - - sns.pairplot(penguins.drop("year", axis=1), hue='species'); - - - -.. image:: images/basic_usage_8_1.png - - -This gives us some idea of what the data looks like by giving as all the -2D views of the data. Four dimensions is low enough that we can (sort -of) reconstruct what the full dimensional data looks like in our heads. -Now that we sort of know what we are looking at, the question is what -can a dimension reduction technique like UMAP do for us? By reducing the -dimension in a way that preserves as much of the structure of the data -as possible we can get a visualisable representation of the data -allowing us to "see" the data and its structure and begin to get some -intuition about the data itself. - -To use UMAP for this task we need to first construct a UMAP object that -will do the job for us. That is as simple as instantiating the class. So -let's import the umap library and do that. - -.. code:: python3 - - import umap - -.. code:: python3 - - reducer = umap.UMAP() - -Before we can do any work with the data it will help to clean up it a -little. We won't need NAs, we just want the measurement columns, and -since the measurements are on entirely different scales it will be -helpful to convert each feature into z-scores (number of standard -deviations from the mean) for comparability. - -.. code:: python3 - - penguin_data = penguins[ - [ - "bill_length_mm", - "bill_depth_mm", - "flipper_length_mm", - "body_mass_g", - ] - ].values - scaled_penguin_data = StandardScaler().fit_transform(penguin_data) - -Now we need to train our reducer, letting it learn about the manifold. -For this UMAP follows the sklearn API and has a method ``fit`` which we -pass the data we want the model to learn from. Since, at the end of the -day, we are going to want to reduced representation of the data we will -use, instead, the ``fit_transform`` method which first calls ``fit`` and -then returns the transformed data as a numpy array. - -.. code:: python3 - - embedding = reducer.fit_transform(scaled_penguin_data) - embedding.shape - - - - -.. parsed-literal:: - - (333, 2) - - - -The result is an array with 333 samples, but only two feature columns -(instead of the four we started with). This is because, by default, UMAP -reduces down to 2D. Each row of the array is a 2-dimensional -representation of the corresponding penguin. Thus we can plot the -``embedding`` as a standard scatterplot and color by the target array -(since it applies to the transformed data which is in the same order as -the original). - -.. code:: python3 - - plt.scatter( - embedding[:, 0], - embedding[:, 1], - c=[sns.color_palette()[x] for x in penguins.species.map({"Adelie":0, "Chinstrap":1, "Gentoo":2})]) - plt.gca().set_aspect('equal', 'datalim') - plt.title('UMAP projection of the Penguin dataset', fontsize=24); - - - -.. image:: images/basic_usage_17_1.png - - -This does a useful job of capturing the structure of the data, and as -can be seen from the matrix of scatterplots this is relatively accurate. -Of course we learned at least this much just from that matrix of -scatterplots -- which we could do since we only had four different -dimensions to analyse. If we had data with a larger number of dimensions -the scatterplot matrix would quickly become unwieldy to plot, and far -harder to interpret. So moving on from the Penguin dataset, let's consider -the digits dataset. - -Digits data ------------ - -First we will load the dataset from sklearn. - -.. code:: python3 - - digits = load_digits() - print(digits.DESCR) - - -.. parsed-literal:: - - .. _digits_dataset: - - Optical recognition of handwritten digits dataset - -------------------------------------------------- - - **Data Set Characteristics:** - - :Number of Instances: 5620 - :Number of Attributes: 64 - :Attribute Information: 8x8 image of integer pixels in the range 0..16. - :Missing Attribute Values: None - :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr) - :Date: July; 1998 - - This is a copy of the test set of the UCI ML hand-written digits datasets - https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits - - The data set contains images of hand-written digits: 10 classes where - each class refers to a digit. - - Preprocessing programs made available by NIST were used to extract - normalized bitmaps of handwritten digits from a preprinted form. From a - total of 43 people, 30 contributed to the training set and different 13 - to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of - 4x4 and the number of on pixels are counted in each block. This generates - an input matrix of 8x8 where each element is an integer in the range - 0..16. This reduces dimensionality and gives invariance to small - distortions. - - For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. - T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. - L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, - 1994. - - .. topic:: References - - - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their - Applications to Handwritten Digit Recognition, MSc Thesis, Institute of - Graduate Studies in Science and Engineering, Bogazici University. - - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. - - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. - Linear dimensionalityreduction using relevance weighted LDA. School of - Electrical and Electronic Engineering Nanyang Technological University. - 2005. - - Claudio Gentile. A New Approximate Maximal Margin Classification - Algorithm. NIPS. 2000. - - -We can plot a number of the images to get an idea of what we are looking -at. This just involves matplotlib building a grid of axes and then -looping through them plotting an image into each one in turn. - -.. code:: python3 - - fig, ax_array = plt.subplots(20, 20) - axes = ax_array.flatten() - for i, ax in enumerate(axes): - ax.imshow(digits.images[i], cmap='gray_r') - plt.setp(axes, xticks=[], yticks=[], frame_on=False) - plt.tight_layout(h_pad=0.5, w_pad=0.01) - - - -.. image:: images/basic_usage_22_0.png - - -As you can see these are quite low resolution images -- for the most -part they are recognisable as digits, but there are a number of cases -that are sufficiently blurred as to be questionable even for a human to -guess at. The zeros do stand out as the easiest to pick out as notably -different and clearly zeros. Beyond that things get a little harder: -some of the squashed thing eights look awfully like ones, some of the -threes start to look a little like crossed sevens when drawn badly, and -so on. - -Each image can be unfolded into a 64 element long vector of grayscale -values. It is these 64 dimensional vectors that we wish to analyse: how -much of the digits structure can we discern? At least in principle 64 -dimensions is overkill for this task, and we would reasonably expect -that there should be some smaller number of "latent" features that would -be sufficient to describe the data reasonably well. We can try a -scatterplot matrix -- in this case just of the first 10 dimensions so -that it is at least plottable, but as you can quickly see that approach -is not going to be sufficient for this data. - -.. code:: python3 - - digits_df = pd.DataFrame(digits.data[:,1:11]) - digits_df['digit'] = pd.Series(digits.target).map(lambda x: 'Digit {}'.format(x)) - sns.pairplot(digits_df, hue='digit', palette='Spectral'); - - -.. image:: images/basic_usage_24_2.png - - -In contrast we can try using UMAP again. It works exactly as before: -construct a model, train the model, and then look at the transformed -data. To demonstrate more of UMAP we'll go about it differently this -time and simply use the ``fit`` method rather than the ``fit_transform`` -approach we used for Penguins. - -.. code:: python3 - - reducer = umap.UMAP(random_state=42) - reducer.fit(digits.data) - - -.. parsed-literal:: - - UMAP(a=None, angular_rp_forest=False, b=None, - force_approximation_algorithm=False, init='spectral', learning_rate=1.0, - local_connectivity=1.0, low_memory=False, metric='euclidean', - metric_kwds=None, min_dist=0.1, n_components=2, n_epochs=None, - n_neighbors=15, negative_sample_rate=5, output_metric='euclidean', - output_metric_kwds=None, random_state=42, repulsion_strength=1.0, - set_op_mix_ratio=1.0, spread=1.0, target_metric='categorical', - target_metric_kwds=None, target_n_neighbors=-1, target_weight=0.5, - transform_queue_size=4.0, transform_seed=42, unique=False, verbose=False) - - - -Now, instead of returning an embedding we simply get back the reducer -object, now having trained on the dataset we passed it. To access the -resulting transform we can either look at the ``embedding_`` attribute -of the reducer object, or call transform on the original data. - -.. code:: python3 - - embedding = reducer.transform(digits.data) - # Verify that the result of calling transform is - # idenitical to accessing the embedding_ attribute - assert(np.all(embedding == reducer.embedding_)) - embedding.shape - - - - -.. parsed-literal:: - - (1797, 2) - - - -We now have a dataset with 1797 rows (one for each hand-written digit -sample), but only 2 columns. As with the Penguins example we can now plot -the resulting embedding, coloring the data points by the class that -they belong to (i.e. the digit they represent). - -.. code:: python3 - - plt.scatter(embedding[:, 0], embedding[:, 1], c=digits.target, cmap='Spectral', s=5) - plt.gca().set_aspect('equal', 'datalim') - plt.colorbar(boundaries=np.arange(11)-0.5).set_ticks(np.arange(10)) - plt.title('UMAP projection of the Digits dataset', fontsize=24); - -.. image:: images/basic_usage_30_1.png - - -We see that UMAP has successfully captured the digit classes. There are -also some interesting effects as some digit classes blend into one -another (see the eights, ones, and sevens, with some nines in between), -and also cases where digits are pushed away as clearly distinct (the -zeros on the right, the fours at the top, and a small subcluster of ones -at the bottom come to mind). To get a better idea of why UMAP chose to -do this it is helpful to see the actual digits involve. One can do this -using `bokeh <https://bokeh.pydata.org/en/latest/>`__ and mouseover -tooltips of the images. - -First we'll need to encode all the images for inclusion in a dataframe. - -.. code:: python3 - - from io import BytesIO - from PIL import Image - import base64 - -.. code:: python3 - - def embeddable_image(data): - img_data = 255 - 15 * data.astype(np.uint8) - image = Image.fromarray(img_data, mode='L').resize((64, 64), Image.Resampling.BICUBIC) - buffer = BytesIO() - image.save(buffer, format='png') - for_encoding = buffer.getvalue() - return 'data:image/png;base64,' + base64.b64encode(for_encoding).decode() - -Next we need to load up bokeh and the various tools from it that will be -needed to generate a suitable interactive plot. - -.. code:: python3 - - from bokeh.plotting import figure, show, output_notebook - from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper - from bokeh.palettes import Spectral10 - - output_notebook() - - - -.. raw:: html - - - <div class="bk-root"> - <a href="https://bokeh.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a> - <span id="1001">Loading BokehJS ...</span> - </div> - - - - -Finally we generate the plot itself with a custom hover tooltip that -embeds the image of the digit in question in it, along with the digit -class that the digit is actually from (this can be useful for digits -that are hard even for humans to classify correctly). - -.. code:: python3 - - digits_df = pd.DataFrame(embedding, columns=('x', 'y')) - digits_df['digit'] = [str(x) for x in digits.target] - digits_df['image'] = list(map(embeddable_image, digits.images)) - - datasource = ColumnDataSource(digits_df) - color_mapping = CategoricalColorMapper(factors=[str(9 - x) for x in digits.target_names], - palette=Spectral10) - - plot_figure = figure( - title='UMAP projection of the Digits dataset', - width=600, - height=600, - tools=('pan, wheel_zoom, reset') - ) - - plot_figure.add_tools(HoverTool(tooltips=""" - <div> - <div> - <img src='@image' style='float: left; margin: 5px 5px 5px 5px'/> - </div> - <div> - <span style='font-size: 16px; color: #224499'>Digit:</span> - <span style='font-size: 18px'>@digit</span> - </div> - </div> - """)) - - plot_figure.scatter( - 'x', - 'y', - source=datasource, - color=dict(field='digit', transform=color_mapping), - line_alpha=0.6, - fill_alpha=0.6, - size=4 - ) - show(plot_figure) - - - -.. raw:: html - :file: basic_usage_bokeh_example.html - -As can be seen, the nines that blend between the ones and the sevens are -odd looking nines (that aren't very rounded) and do, indeed, interpolate -surprisingly well between ones with hats and crossed sevens. In contrast -the small disjoint cluster of ones at the bottom of the plot is made up -of ones with feet (a horizontal line at the base of the one) which are, -indeed, quite distinct from the general mass of ones. - -This concludes our introduction to basic UMAP usage -- hopefully this -has given you the tools to get started for yourself. Further tutorials, -covering UMAP parameters and more advanced usage are also available when -you wish to dive deeper. - --------------- - -.. raw:: html - - <h3> - -Penguin data information - -.. raw:: html - - </h3> - -Peguin data are from: - -**Gorman KB, Williams TD, Fraser WR** (2014) Ecological Sexual -Dimorphism and Environmental Variability within a Community of Antarctic -Penguins (Genus *Pygoscelis*). PLoS ONE 9(3): e90081. -doi:10.1371/journal.pone.0090081 - -See the full paper -`HERE <https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090081>`__. - -.. raw:: html - - <h4> - -Original data access and use - -.. raw:: html - - </h4> - -From Gorman et al.: "Data reported here are publicly available within -the PAL-LTER data system (datasets #219, 220, and 221): -http://oceaninformatics.ucsd.edu/datazoo/data/pallter/datasets. These -data are additionally archived within the United States (US) LTER -Network's Information System Data Portal: https://portal.lternet.edu/. -Individuals interested in using these data are therefore expected to -follow the US LTER Network's Data Access Policy, Requirements and Use -Agreement: https://lternet.edu/data-access-policy/." - -Anyone interested in publishing the data should contact `Dr. Kristen -Gorman <https://www.uaf.edu/cfos/people/faculty/detail/kristen-gorman.php>`__ -about analysis and working together on any final products. - -Penguin images by Alison Horst. diff --git a/doc/basic_usage_bokeh_example.html b/doc/basic_usage_bokeh_example.html index 8c278888..b4d44af8 100644 --- a/doc/basic_usage_bokeh_example.html +++ b/doc/basic_usage_bokeh_example.html @@ -4,42 +4,42 @@ <!DOCTYPE html> <html lang="en"> - + <head> - + <meta charset="utf-8"> <title>Bokeh Plot - - - - - - - + + + + + + + - - - - + + + + - - + + - - - - - - + + + + + +
- - - - - + + + + + @@ -49,11 +49,11 @@ Bokeh.safely(function() { (function(root) { function embed_document(root) { - + var docs_json = document.getElementById('1261').textContent; var render_items = [{"docid":"d689e807-24c5-4a70-852d-845dc7ab161f","root_ids":["1088"],"roots":{"1088":"9584024b-4d3f-44a1-9bf6-f58d2e4535c5"}}]; root.Bokeh.embed.embed_items(docs_json, render_items); - + } if (root.Bokeh !== undefined) { embed_document(root); @@ -79,7 +79,7 @@ else document.addEventListener("DOMContentLoaded", fn); })(); - + - - \ No newline at end of file + + diff --git a/doc/benchmarking.rst b/doc/benchmarking.md similarity index 52% rename from doc/benchmarking.rst rename to doc/benchmarking.md index 5ed3b088..e23575bd 100644 --- a/doc/benchmarking.rst +++ b/doc/benchmarking.md @@ -1,6 +1,6 @@ -Performance Comparison of Dimension Reduction Implementations -============================================================= +# Performance Comparison of Dimension Reduction Implementations + Different dimension reduction techniques can have quite different computational complexity. Beyond the algorithm itself there is also the @@ -15,55 +15,60 @@ To start let's get the basic tools we'll need loaded up -- numpy and pandas obviously, but also tools to get and resample the data, and the time module so we can perform some basic benchmarking. -.. code:: ipython3 +```ipython3 +import numpy as np +import pandas as pd +from sklearn.datasets import fetch_openml +from sklearn.utils import resample +import time - import numpy as np - import pandas as pd - from sklearn.datasets import fetch_openml - from sklearn.utils import resample - import time +``` Next we'll need the actual dimension reduction implementations. For the purposes of this explanation we'll mostly stick with -`scikit-learn `__, but for the sake of +[scikit-learn](http://scikit-learn.org/stable/), but for the sake of comparison we'll also include the -`MulticoreTSNE `__ +[MulticoreTSNE](https://github.com/DmitryUlyanov/Multicore-TSNE) implementation of t-SNE, which has significantly better performance than the current scikit-learn t-SNE. -.. code:: ipython3 +```ipython3 +from sklearn.manifold import TSNE, LocallyLinearEmbedding, Isomap, MDS, SpectralEmbedding +from sklearn.decomposition import PCA +from MulticoreTSNE import MulticoreTSNE +from umap import UMAP - from sklearn.manifold import TSNE, LocallyLinearEmbedding, Isomap, MDS, SpectralEmbedding - from sklearn.decomposition import PCA - from MulticoreTSNE import MulticoreTSNE - from umap import UMAP +``` Next we'll need out plotting tools, and, of course, some data to work with. For this performance comparison we'll default to the now standard benchmark of manifold learning: the MNIST digits dataset. We can use -scikit-learn's ``fetch_mldata`` to grab it for us. +scikit-learn's `fetch_mldata` to grab it for us. -.. code:: ipython3 +```ipython3 +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline - import matplotlib.pyplot as plt - import seaborn as sns - %matplotlib inline +``` -.. code:: ipython3 +```ipython3 +sns.set(context='notebook', + rc={'figure.figsize':(12,10)}, + palette=sns.color_palette('tab10', 10)) - sns.set(context='notebook', - rc={'figure.figsize':(12,10)}, - palette=sns.color_palette('tab10', 10)) +``` -.. code:: ipython3 +```ipython3 +mnist = fetch_openml('Fashion-MNIST', version=1) - mnist = fetch_openml('Fashion-MNIST', version=1) +``` Now it is time to start looking at performance. To start with let's look at how performance scales with increasing dataset size. -Performance scaling by dataset size ------------------------------------ +## Performance scaling by dataset size + As the size of a dataset increases the runtime of a given dimension reduction algorithm will increase at varying rates. If you ever want to @@ -71,7 +76,7 @@ run your algorithm on larger datasets you will care not just about the comparative runtime on a single small dataset, but how the performance scales out as you move to larger datasets. We can similate this by subsampling from MNIST digits (via scikit-learn's convenient -``resample`` utility) and looking at the runtime for varying sized +`resample` utility) and looking at the runtime for varying sized subsamples. Since there is some randomness involved here (both in the subsample selection, and in some of the algorithms which have stochastic aspects) we will want to run a few examples for each dataset size. We @@ -79,62 +84,64 @@ can easily package all of this up in a simple function that will return a convenient pandas dataframe of dataset sizes and runtimes given an algorithm. -.. code:: ipython3 - - def data_size_scaling(algorithm, data, sizes=[100, 200, 400, 800, 1600], n_runs=5): - result = [] - for size in sizes: - for run in range(n_runs): - subsample = resample(data, n_samples=size) - start_time = time.time() - algorithm.fit(subsample) - elapsed_time = time.time() - start_time - del subsample - result.append((size, elapsed_time)) - return pd.DataFrame(result, columns=('dataset size', 'runtime (s)')) +```ipython3 +def data_size_scaling(algorithm, data, sizes=[100, 200, 400, 800, 1600], n_runs=5): + result = [] + for size in sizes: + for run in range(n_runs): + subsample = resample(data, n_samples=size) + start_time = time.time() + algorithm.fit(subsample) + elapsed_time = time.time() - start_time + del subsample + result.append((size, elapsed_time)) + return pd.DataFrame(result, columns=('dataset size', 'runtime (s)')) + +``` Now we just want to run this for each of the various dimension reduction implementations so we can look at the results. Since we don't know how long these runs might take we'll start off with a very small set of samples, scaling up to only 1600 samples. -.. code:: ipython3 - - all_algorithms = [ - PCA(), - UMAP(), - MulticoreTSNE(), - LocallyLinearEmbedding(), - SpectralEmbedding(), - Isomap(), - TSNE(), - MDS(), - ] - performance_data = {} - for algorithm in all_algorithms: - alg_name = str(algorithm) - if 'MulticoreTSNE' in alg_name: - alg_name = 'MulticoreTSNE' - else: - alg_name = alg_name.split('(')[0] - - performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, n_runs=3) +```ipython3 +all_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + LocallyLinearEmbedding(), + SpectralEmbedding(), + Isomap(), + TSNE(), + MDS(), +] +performance_data = {} +for algorithm in all_algorithms: + alg_name = str(algorithm) + if 'MulticoreTSNE' in alg_name: + alg_name = 'MulticoreTSNE' + else: + alg_name = alg_name.split('(')[0] + performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, n_runs=3) + +``` + Now let's plot the results so we can see what is going on. We'll use seaborn's regression plot to interpolate the effective scaling. -.. code:: ipython3 - - for alg_name, perf_data in performance_data.items(): - sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) - plt.legend(); +```ipython3 +for alg_name, perf_data in performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend(); -.. image:: images/performance_14_1.png +``` +![Image](images/performance_14_1.png) We can see straight away that there are some outliers here. The scikit-learn t-SNE is clearly much slower than most of the other @@ -149,38 +156,39 @@ run so let's restrict ourselves to the fastest performing implementations and see what happens as we extend out to larger dataset sizes. -.. code:: ipython3 +```ipython3 +fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + LocallyLinearEmbedding(), + SpectralEmbedding(), + Isomap(), +] +fast_performance_data = {} +for algorithm in fast_algorithms: + alg_name = str(algorithm) + if 'MulticoreTSNE' in alg_name: + alg_name = 'MulticoreTSNE' + else: + alg_name = alg_name.split('(')[0] - fast_algorithms = [ - PCA(), - UMAP(), - MulticoreTSNE(), - LocallyLinearEmbedding(), - SpectralEmbedding(), - Isomap(), - ] - fast_performance_data = {} - for algorithm in fast_algorithms: - alg_name = str(algorithm) - if 'MulticoreTSNE' in alg_name: - alg_name = 'MulticoreTSNE' - else: - alg_name = alg_name.split('(')[0] - - fast_performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, - sizes=[800, 1600, 3200, 6400, 12800], n_runs=3) + fast_performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, + sizes=[800, 1600, 3200, 6400, 12800], n_runs=3) -.. code:: ipython3 +``` - for alg_name, perf_data in fast_performance_data.items(): - sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) - plt.legend(); +```ipython3 +for alg_name, perf_data in fast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend(); -.. image:: images/performance_17_1.png +``` +![Image](images/performance_17_1.png) At this point we begin to see some significant differentiation among the different implementations. In the earlier plot MulticoreTSNE looked to @@ -195,36 +203,37 @@ we'll have to restrict out attention to an even smaller subset of implementations. We will pare things down to just MulticoreTSNE, PCA and UMAP. -.. code:: ipython3 +```ipython3 +very_fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), +] +vfast_performance_data = {} +for algorithm in very_fast_algorithms: + alg_name = str(algorithm) + if 'MulticoreTSNE' in alg_name: + alg_name = 'MulticoreTSNE' + else: + alg_name = alg_name.split('(')[0] - very_fast_algorithms = [ - PCA(), - UMAP(), - MulticoreTSNE(), - ] - vfast_performance_data = {} - for algorithm in very_fast_algorithms: - alg_name = str(algorithm) - if 'MulticoreTSNE' in alg_name: - alg_name = 'MulticoreTSNE' - else: - alg_name = alg_name.split('(')[0] - - vfast_performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, - sizes=[3200, 6400, 12800, 25600, 51200, 70000], n_runs=2) + vfast_performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, + sizes=[3200, 6400, 12800, 25600, 51200, 70000], n_runs=2) -.. code:: ipython3 +``` - for alg_name, perf_data in vfast_performance_data.items(): - sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) - plt.legend(); +```ipython3 +for alg_name, perf_data in vfast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend(); -.. image:: images/performance_20_1.png +``` +![Image](images/performance_20_1.png) Here we see UMAP's advantages over t-SNE really coming to the forefront. While UMAP is clearly slower than PCA, its scaling performance is diff --git a/doc/bokeh_digits_plot.py b/doc/bokeh_digits_plot.py index eea675dd..686be8c6 100644 --- a/doc/bokeh_digits_plot.py +++ b/doc/bokeh_digits_plot.py @@ -1,6 +1,6 @@ import numpy as np -from sklearn.datasets import load_digits import pandas as pd +from sklearn.datasets import load_digits digits = load_digits() @@ -9,9 +9,10 @@ reducer = umap.UMAP(random_state=42) embedding = reducer.fit_transform(digits.data) +import base64 from io import BytesIO + from PIL import Image -import base64 def embeddable_image(data): @@ -23,9 +24,9 @@ def embeddable_image(data): return "data:image/png;base64," + base64.b64encode(for_encoding).decode() -from bokeh.plotting import figure, show, output_file -from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper +from bokeh.models import CategoricalColorMapper, ColumnDataSource, HoverTool from bokeh.palettes import Spectral10 +from bokeh.plotting import figure, output_file, show output_file("basic_usage_bokeh_example.html") @@ -35,7 +36,8 @@ def embeddable_image(data): datasource = ColumnDataSource(digits_df) color_mapping = CategoricalColorMapper( - factors=[str(9 - x) for x in digits.target_names], palette=Spectral10 + factors=[str(9 - x) for x in digits.target_names], + palette=Spectral10, ) plot_figure = figure( @@ -57,15 +59,15 @@ def embeddable_image(data): @digit -""" - ) +""", + ), ) plot_figure.circle( "x", "y", source=datasource, - color=dict(field="digit", transform=color_mapping), + color={"field": "digit", "transform": color_mapping}, line_alpha=0.6, fill_alpha=0.6, size=4, diff --git a/doc/clustering.rst b/doc/clustering.md similarity index 69% rename from doc/clustering.rst rename to doc/clustering.md index 53688f1b..ed800422 100644 --- a/doc/clustering.rst +++ b/doc/clustering.md @@ -1,5 +1,5 @@ -Using UMAP for Clustering -========================= +# Using UMAP for Clustering + UMAP can be used as an effective preprocessing step to boost the performance of density based clustering. This is somewhat controversial, @@ -10,7 +10,7 @@ thread `__ and `Adjusted Mutual Information `__. -.. code:: python3 +```python3 +from sklearn.datasets import fetch_openml +from sklearn.decomposition import PCA +import numpy as np +import matplotlib.pyplot as plt +%matplotlib inline + +# Dimension reduction and clustering libraries +import umap +import hdbscan +import sklearn.cluster as cluster +from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score - from sklearn.datasets import fetch_openml - from sklearn.decomposition import PCA - import numpy as np - import matplotlib.pyplot as plt - %matplotlib inline - - # Dimension reduction and clustering libraries - import umap - import hdbscan - import sklearn.cluster as cluster - from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score +``` Now let's set up the plotting and grab the data we'll be using -- in this case the MNIST handwritten digits dataset. MNIST consists of 28x28 @@ -52,10 +53,11 @@ unraveled such that each digit is described by a 784 dimensional vector (the gray scale value of each pixel in the image). Ideally we would like the clustering to recover the digit structure. -.. code:: python3 +```python3 +mnist = fetch_openml('mnist_784', version=1) +mnist.target = mnist.target.astype(int) - mnist = fetch_openml('mnist_784', version=1) - mnist.target = mnist.target.astype(int) +``` For visualization purposes we can reduce the data to 2-dimensions using UMAP. When we cluster the data in high dimensions we can visualize the @@ -63,16 +65,16 @@ result of that clustering. First, however, we'll view the data colored by the digit that each data point represents -- we'll use a different color for each digit. This will help frame what follows. -.. code:: python3 +```python3 +standard_embedding = umap.UMAP(random_state=42).fit_transform(mnist.data) +plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=mnist.target.astype(int), s=0.1, cmap='Spectral'); - standard_embedding = umap.UMAP(random_state=42).fit_transform(mnist.data) - plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=mnist.target.astype(int), s=0.1, cmap='Spectral'); +``` -.. image:: images/clustering_6_1.png +![Image](images/clustering_6_1.png) +### Traditional clustering -Traditional clustering -~~~~~~~~~~~~~~~~~~~~~~ Now we would like to cluster the data. As a first attempt let's try the traditional approach: K-Means. In this case we can solve one of the hard @@ -81,20 +83,21 @@ the number of clusters we are looking for. In this case we know the answer is exactly 10. We will use sklearns K-Means implementation looking for 10 clusters in the original 784 dimensional data. -.. code:: python3 +```python3 +kmeans_labels = cluster.KMeans(n_clusters=10).fit_predict(mnist.data) - kmeans_labels = cluster.KMeans(n_clusters=10).fit_predict(mnist.data) +``` And how did the clustering do? We can look at the results by coloring out UMAP embedded data by cluster membership. -.. code:: python3 +```python3 +plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=kmeans_labels, s=0.1, cmap='Spectral'); - plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=kmeans_labels, s=0.1, cmap='Spectral'); +``` -.. image:: images/clustering_10_1.png - +![Image](images/clustering_10_1.png) This is not really the result we were looking for (though it does expose interesting properties of how K-Means chooses clusters in high @@ -106,21 +109,23 @@ this impression to the test by evaluating the adjusted Rand score and adjusted mutual information for this clustering as compared with the true labels. -.. code:: python3 +```python3 +( + adjusted_rand_score(mnist.target, kmeans_labels), + adjusted_mutual_info_score(mnist.target, kmeans_labels) +) - ( - adjusted_rand_score(mnist.target, kmeans_labels), - adjusted_mutual_info_score(mnist.target, kmeans_labels) - ) +``` -.. parsed-literal:: +``` +(0.36675295135972552, 0.49614118437750965) - (0.36675295135972552, 0.49614118437750965) +``` As might be expected, we have not done a particularly good job -- both scores take values in the range 0 to 1, with 0 representing a bad @@ -137,10 +142,11 @@ reduce the dimensionality of the data down to 50 dimensions via PCA (this recovers most of the variance), since HDBSCAN scales somewhat poorly with the dimensionality of the data it will work on. -.. code:: python3 +```python3 +lowd_mnist = PCA(n_components=50).fit_transform(mnist.data) +hdbscan_labels = hdbscan.HDBSCAN(min_samples=10, min_cluster_size=500).fit_predict(lowd_mnist) - lowd_mnist = PCA(n_components=50).fit_transform(mnist.data) - hdbscan_labels = hdbscan.HDBSCAN(min_samples=10, min_cluster_size=500).fit_predict(lowd_mnist) +``` We can now inspect the results. Before we do, however, it should be noted that one of the features of HDBSCAN is that it can refuse to @@ -148,24 +154,24 @@ cluster some points and classify them as "noise". To visualize this aspect we will color points that were classified as noise gray, and then color the remaining points according to the cluster membership. -.. code:: python3 - - clustered = (hdbscan_labels >= 0) - plt.scatter(standard_embedding[~clustered, 0], - standard_embedding[~clustered, 1], - color=(0.5, 0.5, 0.5), - s=0.1, - alpha=0.5) - plt.scatter(standard_embedding[clustered, 0], - standard_embedding[clustered, 1], - c=hdbscan_labels[clustered], - s=0.1, - cmap='Spectral'); +```python3 +clustered = (hdbscan_labels >= 0) +plt.scatter(standard_embedding[~clustered, 0], + standard_embedding[~clustered, 1], + color=(0.5, 0.5, 0.5), + s=0.1, + alpha=0.5) +plt.scatter(standard_embedding[clustered, 0], + standard_embedding[clustered, 1], + c=hdbscan_labels[clustered], + s=0.1, + cmap='Spectral'); -.. image:: images/clustering_16_1.png +``` +![Image](images/clustering_16_1.png) This looks somewhat underwhelming. It meets HDBSCAN's approach of "not being wrong" by simply refusing to classify the majority of the data. @@ -173,21 +179,23 @@ The result is a clustering that almost certainly fails to recover all the labels. We can verify this by looking at the clustering validation scores. -.. code:: python3 +```python3 +( + adjusted_rand_score(mnist.target, hdbscan_labels), + adjusted_mutual_info_score(mnist.target, hdbscan_labels) +) - ( - adjusted_rand_score(mnist.target, hdbscan_labels), - adjusted_mutual_info_score(mnist.target, hdbscan_labels) - ) +``` -.. parsed-literal:: +``` +(0.053830107882840102, 0.19756104096566332) - (0.053830107882840102, 0.19756104096566332) +``` These scores are far worse than K-Means! Partially this is due to the fact that these scores assume that the noise points are simply an extra @@ -195,22 +203,24 @@ cluster. We can instead only look at the subset of the data that HDBSCAN was actually confident enough to assign to clusters -- a simple sub-selection will let us recompute the scores for only that data. -.. code:: python3 +```python3 +clustered = (hdbscan_labels >= 0) +( + adjusted_rand_score(mnist.target[clustered], hdbscan_labels[clustered]), + adjusted_mutual_info_score(mnist.target[clustered], hdbscan_labels[clustered]) +) - clustered = (hdbscan_labels >= 0) - ( - adjusted_rand_score(mnist.target[clustered], hdbscan_labels[clustered]), - adjusted_mutual_info_score(mnist.target[clustered], hdbscan_labels[clustered]) - ) +``` -.. parsed-literal:: +``` +(0.99843407988303912, 0.99405521087764015) - (0.99843407988303912, 0.99405521087764015) +``` And here we see that where HDBSCAN was willing to cluster it got things almost entirely correct. This is what it was designed to do -- be right @@ -219,18 +229,20 @@ confidence in. Of course the catch here is that it deferred clustering a lot of the data. How much of the data did HDBSCAN actually assign to clusters? We can compute that easily enough. -.. code:: python3 +```python3 +np.sum(clustered) / mnist.data.shape[0] - np.sum(clustered) / mnist.data.shape[0] +``` -.. parsed-literal:: +``` +0.17081428571428572 - 0.17081428571428572 +``` It seems that less than 18% of the data was clustered. While HDBSCAN did a great job on the data it could cluster it did a poor job of actually @@ -246,8 +258,8 @@ going to quickly do a lot worse. This is due to the linear nature of PCA. What we need is strong manifold learning, and this is where UMAP can come into play. -UMAP enhanced clustering -~~~~~~~~~~~~~~~~~~~~~~~~ +### UMAP enhanced clustering + Our goal is to make use of UMAP to perform non-linear manifold aware dimension reduction so we can get the dataset down to a number of @@ -262,36 +274,37 @@ in general you should explore different embedding dimension options. The next thing to be aware of is that when using UMAP for dimension reduction you will want to select different parameters than if you were using it for visualization. First of all we will want a larger -``n_neighbors`` value -- small values will focus more on very local +`n_neighbors` value -- small values will focus more on very local structure and are more prone to producing fine grained cluster structure that may be more a result of patterns of noise in the data than actual clusters. In this case we'll double it from the default 15 up to 30. -Second it is beneficial to set ``min_dist`` to a very low value. Since +Second it is beneficial to set `min_dist` to a very low value. Since we actually want to pack points together densely (density is what we want after all) a low value will help, as well as making cleaner separations between clusters. In this case we will simply set -``min_dist`` to be 0. +`min_dist` to be 0. -.. code:: python3 +```python3 +clusterable_embedding = umap.UMAP( + n_neighbors=30, + min_dist=0.0, + n_components=2, + random_state=42, +).fit_transform(mnist.data) - clusterable_embedding = umap.UMAP( - n_neighbors=30, - min_dist=0.0, - n_components=2, - random_state=42, - ).fit_transform(mnist.data) +``` We can visualize the results of this so see how it compares with more visualization attuned parameters: -.. code:: python3 - - plt.scatter(clusterable_embedding[:, 0], clusterable_embedding[:, 1], - c=mnist.target, s=0.1, cmap='Spectral'); +```python3 +plt.scatter(clusterable_embedding[:, 0], clusterable_embedding[:, 1], + c=mnist.target, s=0.1, cmap='Spectral'); -.. image:: images/clustering_27_1.png +``` +![Image](images/clustering_27_1.png) As you can see we still have the general global structure, but we are packing points together more tightly within clusters, and consequently @@ -302,32 +315,33 @@ embedding for visualization purposes from here on out. The next step is to cluster this data. We'll use HDBSCAN again, with the same parameter setting as before. -.. code:: python3 +```python3 +labels = hdbscan.HDBSCAN( + min_samples=10, + min_cluster_size=500, +).fit_predict(clusterable_embedding) - labels = hdbscan.HDBSCAN( - min_samples=10, - min_cluster_size=500, - ).fit_predict(clusterable_embedding) +``` And now we can visualize the results, just as before. -.. code:: python3 +```python3 +clustered = (labels >= 0) +plt.scatter(standard_embedding[~clustered, 0], + standard_embedding[~clustered, 1], + color=(0.5, 0.5, 0.5), + s=0.1, + alpha=0.5) +plt.scatter(standard_embedding[clustered, 0], + standard_embedding[clustered, 1], + c=labels[clustered], + s=0.1, + cmap='Spectral'); - clustered = (labels >= 0) - plt.scatter(standard_embedding[~clustered, 0], - standard_embedding[~clustered, 1], - color=(0.5, 0.5, 0.5), - s=0.1, - alpha=0.5) - plt.scatter(standard_embedding[clustered, 0], - standard_embedding[clustered, 1], - c=labels[clustered], - s=0.1, - cmap='Spectral'); +``` -.. image:: images/clustering_31_1.png - +![Image](images/clustering_31_1.png) We can see that we have done a much better job of finding clusters rather than merely assigning the majority of data as noise. This is @@ -338,58 +352,64 @@ discern the clusters. We can also make a quantitative assessment by using the clustering quality measures as before. -.. code:: python3 +```python3 +adjusted_rand_score(mnist.target, labels), adjusted_mutual_info_score(mnist.target, labels) - adjusted_rand_score(mnist.target, labels), adjusted_mutual_info_score(mnist.target, labels) +``` -.. parsed-literal:: +``` +(0.9239306564265013, 0.90302671641133736) - (0.9239306564265013, 0.90302671641133736) +``` Where before HDBSCAN performed very poorly, we now have scores of 0.9 or better. This is because we actually clustered far more of the data. As before we can also look at how the clustering did on just the data that HDBSCAN was confident in clustering. -.. code:: python3 +```python3 +clustered = (labels >= 0) +( + adjusted_rand_score(mnist.target[clustered], labels[clustered]), + adjusted_mutual_info_score(mnist.target[clustered], labels[clustered]) +) - clustered = (labels >= 0) - ( - adjusted_rand_score(mnist.target[clustered], labels[clustered]), - adjusted_mutual_info_score(mnist.target[clustered], labels[clustered]) - ) +``` -.. parsed-literal:: +``` +(0.93240371696811541, 0.91912906363537572) - (0.93240371696811541, 0.91912906363537572) +``` This is a little worse than the original HDBSCAN, but it is unsurprising that you are going to be wrong more often if you make more predictions. The question is how much more of the data is HDBSCAN actually clustering? Previously we were clustering only 17% of the data. -.. code:: python3 +```python3 +np.sum(clustered) / mnist.data.shape[0] - np.sum(clustered) / mnist.data.shape[0] +``` -.. parsed-literal:: +``` +0.99164285714285716 - 0.99164285714285716 +``` Now we are clustering over 99% of the data! And our results in terms of adjusted Rand score and adjusted mutual information are in line with the @@ -400,4 +420,3 @@ data as arbitrary 784 dimensional vectors. Hopefully this has outlined how UMAP can be beneficial for clustering. As with all things care must be taken, but clearly UMAP can provide significantly better clustering results when used judiciously. - diff --git a/doc/composing_models.rst b/doc/composing_models.md similarity index 58% rename from doc/composing_models.rst rename to doc/composing_models.md index f72b2dd8..96f26c6f 100644 --- a/doc/composing_models.rst +++ b/doc/composing_models.md @@ -1,10 +1,10 @@ -Combining multiple UMAP models -============================== +# Combining multiple UMAP models + It is possible to combine together multiple UMAP models, assuming that they are operating on the same underlying data. To get an idea of how this works recall that UMAP uses an intermediate fuzzy topological -representation (see :ref:`how_umap_works`). Given different views of the +representation (see `how_umap_works`). Given different views of the same underlying data this will generate different fuzzy topological representations. We can apply intersections or unions to these representations to get a new composite fuzzy topological representation @@ -16,18 +16,19 @@ data samples from the two different views. To get an idea of how this might work it is useful to see it in practice. Let’s load some libraries and get started. -.. code:: python3 +```python3 +import sklearn.datasets +from sklearn.preprocessing import RobustScaler +import seaborn as sns +import pandas as pd +import numpy as np +import umap +import umap.plot + +``` - import sklearn.datasets - from sklearn.preprocessing import RobustScaler - import seaborn as sns - import pandas as pd - import numpy as np - import umap - import umap.plot +## MNIST digits example -MNIST digits example --------------------- To begin with let’s use a relatively familiar dataset – the MNIST digits dataset that we’ve used in other sections of this tutorial. The data is @@ -35,23 +36,25 @@ dataset that we’ve used in other sections of this tutorial. The data is total there are 70,000 such images, and each image is unrolled into a 784 element vector. -.. code:: python3 +```python3 +mnist = sklearn.datasets.fetch_openml("mnist_784") - mnist = sklearn.datasets.fetch_openml("mnist_784") +``` To ensure we have an idea of what this dataset looks like through the lens of UMAP we can run UMAP on the full dataset. -.. code:: python3 +```python3 +mapper = umap.UMAP(random_state=42).fit(mnist.data) - mapper = umap.UMAP(random_state=42).fit(mnist.data) +``` -.. code:: python3 +```python3 +umap.plot.points(mapper, labels=mnist.target, width=500, height=500) - umap.plot.points(mapper, labels=mnist.target, width=500, height=500) - -.. image:: images/composing_models_6_1.png +``` +![Image](images/composing_models_6_1.png) To make the problem more interesting let’s carve the dataset in two – not into two sets of 35,000 samples, but instead carve each image in half. @@ -59,10 +62,11 @@ That is, we’ll end up with 70,000 samples each of which is the top half of the image of the handwritten digit, and another 70,000 samples each of which is the bottom half of the image of the handwritten digit. -.. code:: python3 +```python3 +top = mnist.data[:, :28 * 14] +bottom = mnist.data[:, 28 * 14:] - top = mnist.data[:, :28 * 14] - bottom = mnist.data[:, 28 * 14:] +``` This is a little artificial, but it provides us with an example dataset where we have two distinct views of the data which we can still well @@ -78,16 +82,17 @@ the case for many real world problems. Let’s first look at what UMAP does individually on each dataset. We’ll start with the top halves of the digits: -.. code:: python3 - - top_mapper = umap.UMAP(random_state=42).fit(top) +```python3 +top_mapper = umap.UMAP(random_state=42).fit(top) -.. code:: python3 +``` - umap.plot.points(top_mapper, labels=mnist.target, width=500, height=500) +```python3 +umap.plot.points(top_mapper, labels=mnist.target, width=500, height=500) -.. image:: images/composing_models_11_1.png +``` +![Image](images/composing_models_11_1.png) While UMAP still manages to mostly separate the different digit classes we can see the results are quite different from UMAP on the full @@ -103,16 +108,17 @@ discernible. Now let’s see what sorts of results we get with the bottom halves of the digits: -.. code:: python3 +```python3 +bot_mapper = umap.UMAP(random_state=42).fit(bottom) - bot_mapper = umap.UMAP(random_state=42).fit(bottom) +``` -.. code:: python3 +```python3 +umap.plot.points(bot_mapper, labels=mnist.target, width=500, height=500) - umap.plot.points(bot_mapper, labels=mnist.target, width=500, height=500) - -.. image:: images/composing_models_14_1.png +``` +![Image](images/composing_models_14_1.png) This is clearly a very different view of the data. Now it is the fours and nines that blur together (presumably many of the nines are drawn @@ -130,26 +136,27 @@ to be done re-asserting UMAP’s theoretical assumptions (local connectivity, approximately uniform distributions). Fortunately UMAP makes this relatively easy as long as you have a copy of fitted UMAP models on hand (which we do in this case). To intersect two models -simply use the ``*`` operator; to union them use the ``+`` operator. +simply use the `*` operator; to union them use the `+` operator. Note that this will actually take some time since we need to compute the 2D embedding of the combined model. -.. code:: python3 +```python3 +intersection_mapper = top_mapper * bot_mapper +union_mapper = top_mapper + bot_mapper - intersection_mapper = top_mapper * bot_mapper - union_mapper = top_mapper + bot_mapper +``` With that complete we can visualize the results. First let’s look at the intersection: -.. code:: python3 - - umap.plot.points(intersection_mapper, labels=mnist.target, width=500, height=500) +```python3 +umap.plot.points(intersection_mapper, labels=mnist.target, width=500, height=500) -.. image:: images/composing_models_18_1.png +``` +![Image](images/composing_models_18_1.png) As you can see, while this isn’t as good as a UMAP plot for the full MNIST dataset it has recovered the individual digits quite well. The @@ -162,12 +169,12 @@ dataset. Now let’s look at the union. -.. code:: python3 +```python3 +umap.plot.points(union_mapper, labels=mnist.target, width=500, height=500) - umap.plot.points(union_mapper, labels=mnist.target, width=500, height=500) - -.. image:: images/composing_models_20_1.png +``` +![Image](images/composing_models_20_1.png) Given that UMAP is agnostic to rotation or reflection of the final layout, this is essentially the same result as the intersection since it @@ -178,28 +185,29 @@ dataset is so clear we find that either way of piecing it together from the two half datasets manage to find the same core underlying structure. If you are willing to try something a little more experimental there is -also a third option using the ``-`` operator which effectively +also a third option using the `-` operator which effectively intersects with the fuzzy set complement (and is thus not commutative, -just as ``-`` implies). The goal here is to try to provide a sense of +just as `-` implies). The goal here is to try to provide a sense of what the data looks like when we contrast it against a second view. -.. code:: python3 - - contrast_mapper = top_mapper - bot_mapper +```python3 +contrast_mapper = top_mapper - bot_mapper -.. code:: python3 +``` - umap.plot.points(contrast_mapper, labels=mnist.target, width=500, height=500) +```python3 +umap.plot.points(contrast_mapper, labels=mnist.target, width=500, height=500) -.. image:: images/composing_models_23_1.png +``` +![Image](images/composing_models_23_1.png) In this case the result is not overly dissimilar from the embedding of just the top half, so the contrast has perhaps not shown is as much as we might have hoped. -Diamonds dataset example ------------------------- +## Diamonds dataset example + Now let’s try the same approach on a different dataset where the option of just running UMAP on the full dataset is not available. For this @@ -208,115 +216,116 @@ different diamond and provides details on the weight (carat), cut, color, clarity, size (depth, table, x, y, z) and price of the given diamond. How these different factors interplay is somewhat complicated. -.. code:: python3 - - diamonds = sns.load_dataset('diamonds') - diamonds.head() - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
-
+```python3 +diamonds = sns.load_dataset('diamonds') +diamonds.head() + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
+
+ @@ -330,38 +339,41 @@ to size and weight, and the categorical features of color, cut and clarity. Let’s pull each of those feature sets out so we can work with them independently. -.. code:: python3 +```python3 +numeric = diamonds[["carat", "table", "x", "y", "z"]].copy() +ordinal = diamonds[["cut", "color", "clarity"]].copy() - numeric = diamonds[["carat", "table", "x", "y", "z"]].copy() - ordinal = diamonds[["cut", "color", "clarity"]].copy() +``` Now we have a new problem: the numeric features are not at all on the same scales, so any sort of standard distance metric across them will be dominated by those features with the largest ranges. We can correct for that by performing feature scaling. To do that we’ll make use of -sklearn’s ``RobustScaler`` which uses robust statistics (such as the +sklearn’s `RobustScaler` which uses robust statistics (such as the median and interquartile range) to center and rescale the data feature by feature. If we look at the results on the first five rows we see that the different features are all now reasonably comparable, and it is reasonable to apply something like euclidean distance across them. -.. code:: python3 +```python3 +scaled_numeric = RobustScaler().fit_transform(numeric) +scaled_numeric[:5] - scaled_numeric = RobustScaler().fit_transform(numeric) - scaled_numeric[:5] +``` -.. parsed-literal:: +``` +array([[-0.734375 , -0.66666667, -0.95628415, -0.95054945, -0.97345133], + [-0.765625 , 1.33333333, -0.98907104, -1.02747253, -1.07964602], + [-0.734375 , 2.66666667, -0.90163934, -0.9010989 , -1.07964602], + [-0.640625 , 0.33333333, -0.81967213, -0.81318681, -0.79646018], + [-0.609375 , 0.33333333, -0.7431694 , -0.74725275, -0.69026549]]) - array([[-0.734375 , -0.66666667, -0.95628415, -0.95054945, -0.97345133], - [-0.765625 , 1.33333333, -0.98907104, -1.02747253, -1.07964602], - [-0.734375 , 2.66666667, -0.90163934, -0.9010989 , -1.07964602], - [-0.640625 , 0.33333333, -0.81967213, -0.81318681, -0.79646018], - [-0.609375 , 0.33333333, -0.7431694 , -0.74725275, -0.69026549]]) +``` What is the best way to handle the categorical features? If they are purely categorical it would make sense to one-hot encode the categories @@ -376,115 +388,117 @@ may vary, the differences between them are all comparable – a difference of 1 for each grade level. That means we don’t need to rescale this data after the ordinal coding. -.. code:: python3 - - ordinal["cut"] = ordinal.cut.map({"Fair":0, "Good":1, "Very Good":2, "Premium":3, "Ideal":4}) - ordinal["color"] = ordinal.color.map({"D":0, "E":1, "F":2, "G":3, "H":4, "I":5, "J":6}) - ordinal["clarity"] = ordinal.clarity.map({"I1":0, "SI2":1, "SI1":2, "VS2":3, "VS1":4, "VVS2":5, "VVS1":6, "IF":7}) - -.. code:: python3 - - ordinal - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cutcolorclarity
0411
1312
2114
3353
4161
............
53935402
53936102
53937202
53938341
53939401
-

53940 rows × 3 columns

-
+```python3 +ordinal["cut"] = ordinal.cut.map({"Fair":0, "Good":1, "Very Good":2, "Premium":3, "Ideal":4}) +ordinal["color"] = ordinal.color.map({"D":0, "E":1, "F":2, "G":3, "H":4, "I":5, "J":6}) +ordinal["clarity"] = ordinal.clarity.map({"I1":0, "SI2":1, "SI1":2, "VS2":3, "VS1":4, "VVS2":5, "VVS1":6, "IF":7}) + +``` + +```python3 +ordinal + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
cutcolorclarity
0411
1312
2114
3353
4161
............
53935402
53936102
53937202
53938341
53939401
+

53940 rows × 3 columns

+
+ @@ -495,24 +509,25 @@ codin, the socalled “manhattan” metric makes more sense here – it is simply the sum of the absolute differences in each category. As before we can now train UMAP models on each dataset – this time, however, since the datasets are different we need different metrics and even different -values of ``n_neighbors``. +values of `n_neighbors`. -.. code:: python3 +```python3 +numeric_mapper = umap.UMAP(n_neighbors=15, random_state=42).fit(scaled_numeric) +ordinal_mapper = umap.UMAP(metric="manhattan", n_neighbors=150, random_state=42).fit(ordinal.values) - numeric_mapper = umap.UMAP(n_neighbors=15, random_state=42).fit(scaled_numeric) - ordinal_mapper = umap.UMAP(metric="manhattan", n_neighbors=150, random_state=42).fit(ordinal.values) +``` We can look at the results of each of these independent views of the dataset reduced to 2D using UMAP. Let’s first look at the numeric data on size and weight of the diamonds. We can colour by the price to get some idea of how the dataset fits together. -.. code:: python3 - - umap.plot.points(numeric_mapper, values=diamonds["price"], cmap="viridis") +```python3 +umap.plot.points(numeric_mapper, values=diamonds["price"], cmap="viridis") -.. image:: images/composing_models_36_1.png +``` +![Image](images/composing_models_36_1.png) We see that while the data generally correlates somewhat with the price of the diamonds there are distinctly different threads in the data, @@ -523,16 +538,16 @@ depending on the weight. In contrast we ca look at the ordinal data. In this case we’ll colour it by the different categories as well as by price. -.. code:: python3 +```python3 +fig, ax = umap.plot.plt.subplots(2, 2, figsize=(12,12)) +umap.plot.points(ordinal_mapper, labels=diamonds["color"], ax=ax[0,0]) +umap.plot.points(ordinal_mapper, labels=diamonds["clarity"], ax=ax[0,1]) +umap.plot.points(ordinal_mapper, labels=diamonds["cut"], ax=ax[1,0]) +umap.plot.points(ordinal_mapper, values=diamonds["price"], cmap="viridis", ax=ax[1,1]) - fig, ax = umap.plot.plt.subplots(2, 2, figsize=(12,12)) - umap.plot.points(ordinal_mapper, labels=diamonds["color"], ax=ax[0,0]) - umap.plot.points(ordinal_mapper, labels=diamonds["clarity"], ax=ax[0,1]) - umap.plot.points(ordinal_mapper, labels=diamonds["cut"], ax=ax[1,0]) - umap.plot.points(ordinal_mapper, values=diamonds["price"], cmap="viridis", ax=ax[1,1]) - -.. image:: images/composing_models_38_1.png +``` +![Image](images/composing_models_38_1.png) As you can see this is a markedly different result! The ordinal data has a relatively coarse metric, since the different categories can only take @@ -543,7 +558,7 @@ have very high density. You can see a gradient of color from left to right in the plot; colouring by cut or clarity show different stratifications. The combination of these very distinct statifications results in this highly clustered embedding. It is exactly for this -reason that we need such a high ``n_neighbors`` value: the very local +reason that we need such a high `n_neighbors` value: the very local structure of the data is merely clusters of identical categories; we need to see wider to learn more structure. @@ -553,22 +568,23 @@ and union operators to simply combine the models. As noted before this is a somewhat time-consuming operation as a new 2D representation for the combined models needs to be optimized. -.. code:: python3 +```python3 +intersection_mapper = numeric_mapper * ordinal_mapper +union_mapper = numeric_mapper + ordinal_mapper - intersection_mapper = numeric_mapper * ordinal_mapper - union_mapper = numeric_mapper + ordinal_mapper +``` Let’s start by looking at the intersection; here we are only really decreasing connectivity since edges are assigned the probability of existing in *both* data views (before re-asserting local connectivity and uniform distribution assumptions). -.. code:: python3 - - umap.plot.points(intersection_mapper, values=diamonds["price"], cmap="viridis") +```python3 +umap.plot.points(intersection_mapper, values=diamonds["price"], cmap="viridis") -.. image:: images/composing_models_42_1.png +``` +![Image](images/composing_models_42_1.png) What we get most closely represents the numeric data view. Why is this? Because the categorical data view has points either connected with @@ -583,12 +599,12 @@ uniform (rather than being a gradient) in its price. Given this result, what would you expect of the union? -.. code:: python3 +```python3 +umap.plot.points(union_mapper, labels=diamonds["color"]) - umap.plot.points(union_mapper, labels=diamonds["color"]) - -.. image:: images/composing_models_44_1.png +``` +![Image](images/composing_models_44_1.png) What we get in practice looks a lot more like the categorical view of the data. This time we are only *increasing* the connectivity (prior to @@ -603,37 +619,40 @@ around the fringes. We can go a step further and experiment with the contrast composition method. -.. code:: python3 - - contrast_mapper = numeric_mapper - ordinal_mapper +```python3 +contrast_mapper = numeric_mapper - ordinal_mapper -.. code:: python3 +``` - umap.plot.points(contrast_mapper, values=diamonds["price"], cmap="viridis") +```python3 +umap.plot.points(contrast_mapper, values=diamonds["price"], cmap="viridis") -.. image:: images/composing_models_47_1.png +``` +![Image](images/composing_models_47_1.png) Here we see that we’ve retained a lot of the structure of the numeric data view, but have refined and broken it down further into clear clusters with price gradients running through each of them. To further demonstrate the power of this approach we can go a step -further and intersect a higher ``n_neighbors`` based embedding of the +further and intersect a higher `n_neighbors` based embedding of the numeric data view with our existing union of numeric and categorical data – providing a model that is a composition of three simpler models. -.. code:: python3 +```python3 +intersect_union_mapper = umap.UMAP(random_state=42, n_neighbors=60).fit(numeric) * union_mapper - intersect_union_mapper = umap.UMAP(random_state=42, n_neighbors=60).fit(numeric) * union_mapper +``` -.. code:: python3 +```python3 +umap.plot.points(intersect_union_mapper, values=diamonds["price"], cmap="viridis") - umap.plot.points(intersect_union_mapper, values=diamonds["price"], cmap="viridis") +``` -.. image:: images/composing_models_50_1.png +![Image](images/composing_models_50_1.png) -Here the greater global structure from the larger ``n_neighbors`` value +Here the greater global structure from the larger `n_neighbors` value glues together longer strands and we get an interesting result out. In this case it is not necessarily particularly informative, but it is included as a demonstration that even composed models can be composed diff --git a/doc/conf.py b/doc/conf.py index 6f2e12a7..a9426c51 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -1,5 +1,4 @@ #!/usr/bin/env python3 -# -*- coding: utf-8 -*- # # umap documentation build configuration file, created by # sphinx-quickstart on Fri Jun 8 10:09:40 2018. @@ -131,7 +130,7 @@ "globaltoc.html", "relations.html", # needs 'show_related': True theme option to display "searchbox.html", - ] + ], } @@ -192,7 +191,7 @@ # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = { - "python": ("https://docs.python.org/{.major}".format(sys.version_info), None), + "python": (f"https://docs.python.org/{sys.version_info.major}", None), "numpy": ("https://docs.scipy.org/doc/numpy/", None), "scipy": ("https://docs.scipy.org/doc/scipy/reference", None), "matplotlib": ("https://matplotlib.org/", None), @@ -227,9 +226,9 @@ } -def setup(app): +def setup(app) -> None: app.add_js_file( - "https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js" + "https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js", ) app.add_js_file("https://cdn.plot.ly/plotly-latest.min.js") diff --git a/doc/densmap_demo.md b/doc/densmap_demo.md new file mode 100644 index 00000000..5b91fb67 --- /dev/null +++ b/doc/densmap_demo.md @@ -0,0 +1,398 @@ +# Better Preserving Local Density with DensMAP + + +A notable assumption in UMAP is that the data is uniformly distributed +on some manifold and that it is ultimately this manifold that we would +like to present. This is highly effective for many use cases, but it can +be the case that one would like to preserve more information about the +relative local density of data. A recent paper presented a technique +called +[DensMAP](https://www.biorxiv.org/content/10.1101/2020.05.12.077776v1) +that computes estimates of the local density and uses those estimates as +a regularizer in the optimization of the low dimensional representation. +The details are well explained in `the +paper `__ +and we encourage those curious about the details to read it. The result +is a low dimensional representation that preserves information about the +relative local density of the data. To see what this means in practice +let’s load some modules and try it out on some familiar data. + +```python3 +import sklearn.datasets +import umap +import umap.plot + +``` + +For test data we will make use of the now familiar (see earlier tutorial +sections) MNIST and Fashion-MNIST datasets. MNIST is a collection of +70,000 gray-scale images of hand-written digits. Fashion-MNIST is a +collection of 70,000 gray-scale images of fashion items. + +```python3 +mnist = sklearn.datasets.fetch_openml("mnist_784") +fmnist = sklearn.datasets.fetch_openml("Fashion-MNIST") + +``` + +Before we try out DensMAP let’s run standard UMAP so we have a baseline +to compare to. We’ll start with MNIST digits. + +```python3 +%%time +mapper = umap.UMAP(random_state=42).fit(mnist.data) + + +``` + +``` +CPU times: user 2min, sys: 15 s, total: 2min 15s +Wall time: 1min 43s + + +``` + +```python3 +umap.plot.points(mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_6_1.png) + +Now let’s try running DensMAP instead. In practice this is as easy as +adding the parameter `densmap=True` to the UMAP constructor – this +will cause UMAP to use DensMAP regularization with the default DensMAP +parameters. + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, random_state=42).fit(mnist.data) + + +``` + +``` +CPU times: user 3min 42s, sys: 12.9 s, total: 3min 55s +Wall time: 2min 20s + + +``` + +Note that this is a little slower than standard UMAP – there is a little +more work to be done. It is worth noting, however, that the DensMAP +overhead is relatively constant, so the difference in runtime won’t +increase much as you scale out DensMAP to larger datasets. + +Now let’s see what sort of results we get: + +```python3 +umap.plot.points(dens_mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_10_1.png) + +This is a significantly different result – although notably the same +groupings of digits and overall structure have resulted. The most +striking aspects are that the ones cluster has be compressed into a very +narrow and dense stripe, while other digit clusters, most notably the +zeros and the twos have expanded out to fill more space in the plot. +This is due to the fact that in the high dimensional space the ones are +indeed more densely packed together, with largely only variation along +one dimension (the angle with which the stroke of the one is drawn). In +contrast a digit like the zero has a lot more variation (rounder, +narrower, taller, shorter, sloping one way or another); this results in +less local density in high dimensional space, and this lack of local +density has been preserved by DensMAP. + +Let’s now look at the Fashion-MNIST dataset; as before we’ll start by +reminding ourselves what the default UMAP results look like: + +```python3 +%%time +mapper = umap.UMAP(random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 1min 6s, sys: 8.66 s, total: 1min 15s +Wall time: 49.8 s + + +``` + +```python3 +umap.plot.points(mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_13_1.png) + +Now let’s try running DensMAP. As before that is as simple as setting +the `densmap=True` flag. + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 3min 48s, sys: 8.07 s, total: 3min 56s +Wall time: 2min 21s + + +``` + +```python3 +umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_16_1.png) + +Again we see that DensMAP provides a plot similar to UMAP broadly, but +with striking differences. Here we get to see that the cluster of bags +(label 8 in blue) is actually quite sparse, while the cluster of pants +(label 1 in red) is actually quite dense with little variation compared +to other categories. We even see information internal to clusters. +Consider the cluster of boots (label 9 in violet): at the top end it is +quite dense, but it fades out into a much sparse region. + +So far we have used DensMAP with default parameters, but the +implementation provides several parameters for adjusting exactly how the +local density regularisation is handled. We encourage readers to consult +the paper for the details of the many parameters available. For general +use the main parameter of interest is called `dens_lambda` and it +controls how strongly the local density regularisation acts. Larger +values of `dens_lambda` with make preserving the local density a +priority over the the standard UMAP objective, while smaller values lean +more towards classical UMAP. The default value is 2.0. Let’s play with +it a little so we can see the effects of varying it. To start we’ll use +a higher `dens_lambda` of 5.0: + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, dens_lambda=5.0, random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 3min 47s, sys: 5.04 s, total: 3min 52s +Wall time: 2min 18s + + +``` + +```python3 +umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_19_1.png) + +This looks kind of like what we had before, but blurrier. And also … +smaller? The plot bounds are set by the data, so the fact that it is +smaller represents the fact that there are some points right out to the +edges of the plot. These are likely points that are in locally very +sparse regions of the high dimensional space and are thus pushed well +away from everything else. We can see this better if we use raw +matplotlib and a scatter plot with larger point size: + +```python3 +fig, ax = umap.plot.plt.subplots(figsize=(7,7)) +ax.scatter(*dens_mapper.embedding_.T, c=fmnist.target.astype('int8'), cmap="Spectral", s=1) + +``` + +![Image](images/densmap_demo_21_1.png) + +Aside from seeing the issues with overplotting we can see that there +are, in fact, quite a few points that create a very soft halo of of +sparse points around the fringes. + +Now let’s try going the other way and reduce `dens_lambda` to a small +value, so that in principle we can recover something quite close to the +default UMAP plot, with just a hint of local density information +encoded. + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, dens_lambda=0.1, random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 3min 47s, sys: 3.78 s, total: 3min 51s +Wall time: 2min 16s + + +``` + +```python3 +umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_24_1.png) + +And indeed, this looks very much like the original plot, but the bags +(label 8 in blue) are slightly more diffused, and the pants (label 1 in +red) are a little denser. This is very much the default UMAP with just a +tweak to better reflect some notion of local density. + +## Supervised DensMAP on the Galaxy10SDSS dataset + + +The [Galaxy10SDSS dataset](https://astronn.readthedocs.io/en/latest/galaxy10sdss.html) +is a crowd sourced human labelled dataset of galaxy images, +which have been separated in to ten classes. DensMAP can +learn an embedding that partially separates the data. To +keep runtime small, DensMAP is applied to a subset of the +data. + +```python3 +import numpy as np +import h5py +import matplotlib.pyplot as plt +import umap +import os +import math +import requests + +if not os.path.isfile("Galaxy10.h5"): + url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" + r = requests.get(url, allow_redirects=True) + open("Galaxy10.h5", "wb").write(r.content) + +# To get the images and labels from file +with h5py.File("Galaxy10.h5", "r") as F: + images = np.array(F["images"]) + labels = np.array(F["ans"]) + +X_train = np.empty([math.floor(len(labels) / 100), 14283], dtype=np.float64) +y_train = np.empty([math.floor(len(labels) / 100)], dtype=np.float64) +X_test = X_train +y_test = y_train +# Get a subset of the data +for i in range(math.floor(len(labels) / 100)): + X_train[i, :] = np.array(np.ndarray.flatten(images[i, :, :, :]), dtype=np.float64) + y_train[i] = labels[i] + X_test[i, :] = np.array( + np.ndarray.flatten(images[i + math.floor(len(labels) / 100), :, :, :]), + dtype=np.float64, + ) + y_test[i] = labels[i + math.floor(len(labels) / 100)] + +# Plot distribution +classes, frequency = np.unique(y_train, return_counts=True) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.bar(classes, frequency) +plt.xlabel("Class") +plt.ylabel("Frequency") +plt.title("Data Subset") +plt.savefig("galaxy10_subset.svg") + + + +``` + +![Image](images/galaxy10_subset.svg) + +The figure shows that the selected subset of the data set is +unbalanced, but the entire dataset is also unbalanced, so +this experiment will still use this subset. The next step is +to examine the output of the standard DensMAP algorithm. + +```python3 +reducer = umap.UMAP( + densmap=True, n_components=2, random_state=42, verbose=False +) +reducer.fit(X_train) + +galaxy10_densmap = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_densmap[:, 0], + galaxy10_densmap[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_densmap.svg") + + + +``` + +![Image](images/galaxy10_2D_densmap.svg) + +The standard DensMAP algorithm does not separate the galaxies +according to their type. Supervised DensMAP can do better. + +```python3 +reducer = umap.UMAP( + densmap=True, n_components=2, random_state=42, verbose=False +) +reducer.fit(X_train, y_train) + +galaxy10_densmap_supervised = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_densmap_supervised[:, 0], + galaxy10_densmap_supervised[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_densmap_supervised.svg") + + + +``` + +![Image](images/galaxy10_2D_densmap_supervised.svg) + +Supervised DensMAP does indeed do better. There is a litle overlap +between some of the classes, but the original dataset +also has some ambiguities in the classification. The best +check of this method is to project the testing data onto the +learned embedding. + +```python3 +galaxy10_densmap_supervised_prediction = reducer.transform(X_test) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_densmap_supervised_prediction[:, 0], + galaxy10_densmap_supervised_prediction[:, 1], + c=y_test, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_test, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_densmap_supervised_prediction.svg") + + + +``` + +![Image](images/galaxy10_2D_densmap_supervised_prediction.svg) + +This shows that the learned embedding can be used on new data +sets, and so this method may be helpful for examining images +of galaxies. Try out this method on the full 200 Mb dataset +as well as the newer 2.54 Gb +[Galaxy 10 DECals dataset](https://astronn.readthedocs.io/en/latest/galaxy10.html) diff --git a/doc/densmap_demo.rst b/doc/densmap_demo.rst deleted file mode 100644 index 3ac48287..00000000 --- a/doc/densmap_demo.rst +++ /dev/null @@ -1,384 +0,0 @@ -Better Preserving Local Density with DensMAP -============================================ - -A notable assumption in UMAP is that the data is uniformly distributed -on some manifold and that it is ultimately this manifold that we would -like to present. This is highly effective for many use cases, but it can -be the case that one would like to preserve more information about the -relative local density of data. A recent paper presented a technique -called -`DensMAP `__ -that computes estimates of the local density and uses those estimates as -a regularizer in the optimization of the low dimensional representation. -The details are well explained in `the -paper `__ -and we encourage those curious about the details to read it. The result -is a low dimensional representation that preserves information about the -relative local density of the data. To see what this means in practice -let’s load some modules and try it out on some familiar data. - -.. code:: python3 - - import sklearn.datasets - import umap - import umap.plot - -For test data we will make use of the now familiar (see earlier tutorial -sections) MNIST and Fashion-MNIST datasets. MNIST is a collection of -70,000 gray-scale images of hand-written digits. Fashion-MNIST is a -collection of 70,000 gray-scale images of fashion items. - -.. code:: python3 - - mnist = sklearn.datasets.fetch_openml("mnist_784") - fmnist = sklearn.datasets.fetch_openml("Fashion-MNIST") - -Before we try out DensMAP let’s run standard UMAP so we have a baseline -to compare to. We’ll start with MNIST digits. - -.. code:: python3 - - %%time - mapper = umap.UMAP(random_state=42).fit(mnist.data) - - -.. parsed-literal:: - - CPU times: user 2min, sys: 15 s, total: 2min 15s - Wall time: 1min 43s - - -.. code:: python3 - - umap.plot.points(mapper, labels=mnist.target, width=500, height=500) - -.. image:: images/densmap_demo_6_1.png - - -Now let’s try running DensMAP instead. In practice this is as easy as -adding the parameter ``densmap=True`` to the UMAP constructor – this -will cause UMAP to use DensMAP regularization with the default DensMAP -parameters. - -.. code:: python3 - - %%time - dens_mapper = umap.UMAP(densmap=True, random_state=42).fit(mnist.data) - - -.. parsed-literal:: - - CPU times: user 3min 42s, sys: 12.9 s, total: 3min 55s - Wall time: 2min 20s - - -Note that this is a little slower than standard UMAP – there is a little -more work to be done. It is worth noting, however, that the DensMAP -overhead is relatively constant, so the difference in runtime won’t -increase much as you scale out DensMAP to larger datasets. - -Now let’s see what sort of results we get: - -.. code:: python3 - - umap.plot.points(dens_mapper, labels=mnist.target, width=500, height=500) - -.. image:: images/densmap_demo_10_1.png - - -This is a significantly different result – although notably the same -groupings of digits and overall structure have resulted. The most -striking aspects are that the ones cluster has be compressed into a very -narrow and dense stripe, while other digit clusters, most notably the -zeros and the twos have expanded out to fill more space in the plot. -This is due to the fact that in the high dimensional space the ones are -indeed more densely packed together, with largely only variation along -one dimension (the angle with which the stroke of the one is drawn). In -contrast a digit like the zero has a lot more variation (rounder, -narrower, taller, shorter, sloping one way or another); this results in -less local density in high dimensional space, and this lack of local -density has been preserved by DensMAP. - -Let’s now look at the Fashion-MNIST dataset; as before we’ll start by -reminding ourselves what the default UMAP results look like: - -.. code:: python3 - - %%time - mapper = umap.UMAP(random_state=42).fit(fmnist.data) - - -.. parsed-literal:: - - CPU times: user 1min 6s, sys: 8.66 s, total: 1min 15s - Wall time: 49.8 s - - -.. code:: python3 - - umap.plot.points(mapper, labels=fmnist.target, width=500, height=500) - -.. image:: images/densmap_demo_13_1.png - - -Now let’s try running DensMAP. As before that is as simple as setting -the ``densmap=True`` flag. - -.. code:: python3 - - %%time - dens_mapper = umap.UMAP(densmap=True, random_state=42).fit(fmnist.data) - - -.. parsed-literal:: - - CPU times: user 3min 48s, sys: 8.07 s, total: 3min 56s - Wall time: 2min 21s - - -.. code:: python3 - - umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) - -.. image:: images/densmap_demo_16_1.png - - -Again we see that DensMAP provides a plot similar to UMAP broadly, but -with striking differences. Here we get to see that the cluster of bags -(label 8 in blue) is actually quite sparse, while the cluster of pants -(label 1 in red) is actually quite dense with little variation compared -to other categories. We even see information internal to clusters. -Consider the cluster of boots (label 9 in violet): at the top end it is -quite dense, but it fades out into a much sparse region. - -So far we have used DensMAP with default parameters, but the -implementation provides several parameters for adjusting exactly how the -local density regularisation is handled. We encourage readers to consult -the paper for the details of the many parameters available. For general -use the main parameter of interest is called ``dens_lambda`` and it -controls how strongly the local density regularisation acts. Larger -values of ``dens_lambda`` with make preserving the local density a -priority over the the standard UMAP objective, while smaller values lean -more towards classical UMAP. The default value is 2.0. Let’s play with -it a little so we can see the effects of varying it. To start we’ll use -a higher ``dens_lambda`` of 5.0: - -.. code:: python3 - - %%time - dens_mapper = umap.UMAP(densmap=True, dens_lambda=5.0, random_state=42).fit(fmnist.data) - - -.. parsed-literal:: - - CPU times: user 3min 47s, sys: 5.04 s, total: 3min 52s - Wall time: 2min 18s - - -.. code:: python3 - - umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) - -.. image:: images/densmap_demo_19_1.png - - -This looks kind of like what we had before, but blurrier. And also … -smaller? The plot bounds are set by the data, so the fact that it is -smaller represents the fact that there are some points right out to the -edges of the plot. These are likely points that are in locally very -sparse regions of the high dimensional space and are thus pushed well -away from everything else. We can see this better if we use raw -matplotlib and a scatter plot with larger point size: - -.. code:: python3 - - fig, ax = umap.plot.plt.subplots(figsize=(7,7)) - ax.scatter(*dens_mapper.embedding_.T, c=fmnist.target.astype('int8'), cmap="Spectral", s=1) - -.. image:: images/densmap_demo_21_1.png - - -Aside from seeing the issues with overplotting we can see that there -are, in fact, quite a few points that create a very soft halo of of -sparse points around the fringes. - -Now let’s try going the other way and reduce ``dens_lambda`` to a small -value, so that in principle we can recover something quite close to the -default UMAP plot, with just a hint of local density information -encoded. - -.. code:: python3 - - %%time - dens_mapper = umap.UMAP(densmap=True, dens_lambda=0.1, random_state=42).fit(fmnist.data) - - -.. parsed-literal:: - - CPU times: user 3min 47s, sys: 3.78 s, total: 3min 51s - Wall time: 2min 16s - - -.. code:: python3 - - umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) - -.. image:: images/densmap_demo_24_1.png - - -And indeed, this looks very much like the original plot, but the bags -(label 8 in blue) are slightly more diffused, and the pants (label 1 in -red) are a little denser. This is very much the default UMAP with just a -tweak to better reflect some notion of local density. - -Supervised DensMAP on the Galaxy10SDSS dataset ----------------------------------------------- - -The `Galaxy10SDSS dataset `__ -is a crowd sourced human labelled dataset of galaxy images, -which have been separated in to ten classes. DensMAP can -learn an embedding that partially separates the data. To -keep runtime small, DensMAP is applied to a subset of the -data. - -.. code:: python3 - - import numpy as np - import h5py - import matplotlib.pyplot as plt - import umap - import os - import math - import requests - - if not os.path.isfile("Galaxy10.h5"): - url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" - r = requests.get(url, allow_redirects=True) - open("Galaxy10.h5", "wb").write(r.content) - - # To get the images and labels from file - with h5py.File("Galaxy10.h5", "r") as F: - images = np.array(F["images"]) - labels = np.array(F["ans"]) - - X_train = np.empty([math.floor(len(labels) / 100), 14283], dtype=np.float64) - y_train = np.empty([math.floor(len(labels) / 100)], dtype=np.float64) - X_test = X_train - y_test = y_train - # Get a subset of the data - for i in range(math.floor(len(labels) / 100)): - X_train[i, :] = np.array(np.ndarray.flatten(images[i, :, :, :]), dtype=np.float64) - y_train[i] = labels[i] - X_test[i, :] = np.array( - np.ndarray.flatten(images[i + math.floor(len(labels) / 100), :, :, :]), - dtype=np.float64, - ) - y_test[i] = labels[i + math.floor(len(labels) / 100)] - - # Plot distribution - classes, frequency = np.unique(y_train, return_counts=True) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.bar(classes, frequency) - plt.xlabel("Class") - plt.ylabel("Frequency") - plt.title("Data Subset") - plt.savefig("galaxy10_subset.svg") - - - -.. image:: images/galaxy10_subset.svg - - -The figure shows that the selected subset of the data set is -unbalanced, but the entire dataset is also unbalanced, so -this experiment will still use this subset. The next step is -to examine the output of the standard DensMAP algorithm. - -.. code:: python3 - - reducer = umap.UMAP( - densmap=True, n_components=2, random_state=42, verbose=False - ) - reducer.fit(X_train) - - galaxy10_densmap = reducer.transform(X_train) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.scatter( - galaxy10_densmap[:, 0], - galaxy10_densmap[:, 1], - c=y_train, - cmap=plt.cm.nipy_spectral, - edgecolor="k", - label=y_train, - ) - plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) - plt.savefig("galaxy10_2D_densmap.svg") - - - -.. image:: images/galaxy10_2D_densmap.svg - - -The standard DensMAP algorithm does not separate the galaxies -according to their type. Supervised DensMAP can do better. - -.. code:: python3 - - reducer = umap.UMAP( - densmap=True, n_components=2, random_state=42, verbose=False - ) - reducer.fit(X_train, y_train) - - galaxy10_densmap_supervised = reducer.transform(X_train) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.scatter( - galaxy10_densmap_supervised[:, 0], - galaxy10_densmap_supervised[:, 1], - c=y_train, - cmap=plt.cm.nipy_spectral, - edgecolor="k", - label=y_train, - ) - plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) - plt.savefig("galaxy10_2D_densmap_supervised.svg") - - - -.. image:: images/galaxy10_2D_densmap_supervised.svg - - -Supervised DensMAP does indeed do better. There is a litle overlap -between some of the classes, but the original dataset -also has some ambiguities in the classification. The best -check of this method is to project the testing data onto the -learned embedding. - -.. code:: python3 - - galaxy10_densmap_supervised_prediction = reducer.transform(X_test) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.scatter( - galaxy10_densmap_supervised_prediction[:, 0], - galaxy10_densmap_supervised_prediction[:, 1], - c=y_test, - cmap=plt.cm.nipy_spectral, - edgecolor="k", - label=y_test, - ) - plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) - plt.savefig("galaxy10_2D_densmap_supervised_prediction.svg") - - - -.. image:: images/galaxy10_2D_densmap_supervised_prediction.svg - - -This shows that the learned embedding can be used on new data -sets, and so this method may be helpful for examining images -of galaxies. Try out this method on the full 200 Mb dataset -as well as the newer 2.54 Gb -`Galaxy 10 DECals dataset `__ diff --git a/doc/development_roadmap.rst b/doc/development_roadmap.md similarity index 91% rename from doc/development_roadmap.rst rename to doc/development_roadmap.md index fd36d339..9f8e6193 100644 --- a/doc/development_roadmap.rst +++ b/doc/development_roadmap.md @@ -4,11 +4,11 @@ Development Roadmap This document outlines planned improvements and modernizations for the UMAP codebase. -Major Initiatives -================= +# Major Initiatives + + +## 1. Parametric UMAP: TensorFlow → PyTorch Migration -1. Parametric UMAP: TensorFlow → PyTorch Migration ---------------------------------------------------- **Status**: Planned @@ -48,8 +48,8 @@ Major Initiatives --- -2. Remove Conda from Installation and CI/CD --------------------------------------------- +## 2. Remove Conda from Installation and CI/CD + **Status**: Planned @@ -92,8 +92,8 @@ Major Initiatives --- -3. Remove pynndescent Dependency ---------------------------------- +## 3. Remove pynndescent Dependency + **Status**: Planned @@ -272,8 +272,8 @@ This enables: --- -4. Code Modernization and Technical Debt Reduction ---------------------------------------------------- +## 4. Code Modernization and Technical Debt Reduction + **Status**: In Progress @@ -282,13 +282,13 @@ This enables: **Pending**: -4.1 Remove Python 2 Artifacts -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 4.1 Remove Python 2 Artifacts + -- Remove ``from __future__ import print_function`` from: +- Remove `from __future__ import print_function` from: - umap/umap_.py - umap/sparse.py -- Remove explicit ``object`` inheritance from old-style classes +- Remove explicit `object` inheritance from old-style classes - Update any remaining Python 2 compatibility code **Estimated Effort**: Small (< 1 day) @@ -297,10 +297,10 @@ This enables: --- -4.2 Fix NumPy Deprecated Type Aliases -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 4.2 Fix NumPy Deprecated Type Aliases -- Replace ``np.bool_`` with ``bool`` or ``np.bool`` in: + +- Replace `np.bool_` with `bool` or `np.bool` in: - umap/sparse.py - umap/aligned_umap.py - Replace other deprecated NumPy type aliases (np.int_, np.float_, etc.) @@ -312,10 +312,10 @@ This enables: --- -4.3 Fix Bitwise Operator Misuse -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 4.3 Fix Bitwise Operator Misuse + -- Replace bitwise AND (``&``) with logical AND (``and``) in boolean contexts +- Replace bitwise AND (`&`) with logical AND (`and`) in boolean contexts - Affected files: - umap/umap_.py (lines 119, 124, 426) - This improves code clarity and prevents subtle bugs @@ -326,10 +326,10 @@ This enables: --- -4.4 Optimize Data Structures -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 4.4 Optimize Data Structures + -- Replace list-based queue with ``collections.deque`` in breadth_first_search() +- Replace list-based queue with `collections.deque` in breadth_first_search() - This changes O(n) queue.pop(0) operations to O(1) deque.popleft() - Location: umap/umap_.py:93 @@ -339,8 +339,8 @@ This enables: --- -4.5 Resolve Technical Debt (FIXME/TODO Comments) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 4.5 Resolve Technical Debt (FIXME/TODO Comments) + - Address FIXME comments in umap/layouts.py regarding uninitialized variables - Review TODO comments about performance optimizations @@ -352,8 +352,8 @@ This enables: --- -4.6 Remove Dead Code -~~~~~~~~~~~~~~~~~~~~~ +### 4.6 Remove Dead Code + - Remove sklearn.externals.joblib fallback code (dead since sklearn >= 1.0) - Location: umap/umap_.py @@ -364,8 +364,8 @@ This enables: --- -4.7 Update Dependency Specifications -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 4.7 Update Dependency Specifications + - Review and update minimum version constraints in pyproject.toml - scipy: currently >= 1.3.1 (2019); should be >= 1.8.0 or higher @@ -379,13 +379,13 @@ This enables: --- -5. Documentation Updates ------------------------- +## 5. Documentation Updates + **Status**: Pending -5.1 Update Installation Instructions -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 5.1 Update Installation Instructions + - Remove all conda references - Clarify Python 3.9+ requirement (currently lists 3.6+) @@ -398,8 +398,8 @@ This enables: --- -5.2 Clarify Optional Dependencies -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 5.2 Clarify Optional Dependencies + - Separate optional dependencies clearly: - Plotting: matplotlib, datashader, holoviews @@ -412,8 +412,8 @@ This enables: --- -5.3 Update Development Status Classifier -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +### 5.3 Update Development Status Classifier + - pyproject.toml currently lists "Development Status :: 3 - Alpha" - Should be updated to "Development Status :: 5 - Production/Stable" @@ -425,13 +425,13 @@ This enables: --- -6. Testing Improvements ------------------------- +## 6. Testing Improvements + **Status**: Pending -6.1 Add Type Hints -~~~~~~~~~~~~~~~~~~~ +### 6.1 Add Type Hints + - Add Python type hints throughout the codebase - Enables better IDE support and static type checking @@ -444,8 +444,8 @@ This enables: --- -6.2 Expand Test Coverage -~~~~~~~~~~~~~~~~~~~~~~~~~ +### 6.2 Expand Test Coverage + - Add tests for edge cases and error conditions - Add performance regression tests @@ -457,8 +457,8 @@ This enables: --- -Long-term Considerations -========================= +# Long-term Considerations + 1. **GPU Acceleration**: Consider native GPU support for core UMAP algorithm (not just Parametric UMAP) 2. **API Stability**: Consider stabilizing the public API with semantic versioning @@ -466,8 +466,8 @@ Long-term Considerations 4. **Sparse Matrix Support**: Expand and optimize sparse matrix handling 5. **Distributed Computing**: Explore support for large-scale distributed embedding -Timeline -======== +# Timeline + **Near-term (1-2 months)**: - Complete code modernization (items 4.1-4.7) @@ -484,8 +484,8 @@ Timeline - Full type hint coverage (item 6.1) - Deprecate TensorFlow support -Contributing -============= +# Contributing + Interested in helping with any of these initiatives? Please open an issue or pull request on the GitHub repository. diff --git a/doc/document_embedding.md b/doc/document_embedding.md new file mode 100644 index 00000000..1ce0195a --- /dev/null +++ b/doc/document_embedding.md @@ -0,0 +1,353 @@ +# Document embedding using UMAP + + +This is a tutorial of using UMAP to embed text (but this can be extended +to any collection of tokens). We are going to use the `20 newsgroups +dataset `__ which is a collection +of forum posts labelled by topic. We are going to embed these documents +and see that similar documents (i.e. posts in the same subforum) will +end up close together. You can use this embedding for other downstream +tasks, such as visualizing your corpus, or run a clustering algorithm +(e.g. HDBSCAN). We will use a bag of words model and use UMAP on the +count vectors as well as the TF-IDF vectors. + + +To start with let's load the relevant libraries. **This requires UMAP version >= 0.4.0.** + +```python3 +import pandas as pd +import umap +import umap.plot + +# Used to get the data +from sklearn.datasets import fetch_20newsgroups +from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer + +# Some plotting libraries +import matplotlib.pyplot as plt +%matplotlib notebook +from bokeh.plotting import show, save, output_notebook, output_file +from bokeh.resources import INLINE +output_notebook(resources=INLINE) + +``` + +Next let's download and explore the 20 newsgroups dataset. + +```python3 +%%time +dataset = fetch_20newsgroups(subset='all', + shuffle=True, random_state=42) + + +``` + +``` +CPU times: user 280 ms, sys: 52 ms, total: 332 ms +Wall time: 460 ms + +``` + +Let's see the size of the corpus: + +```python3 +print(f'{len(dataset.data)} documents') +print(f'{len(dataset.target_names)} categories') + + +``` + +``` +18846 documents +20 categories + + +``` + +Here are the categories of documents. As you can see many are related to +one another (e.g. ‘comp.sys.ibm.pc.hardware’ and +‘comp.sys.mac.hardware’) but they are not all correlated (e.g. ‘sci.med’ +and ‘rec.sport.baseball’). + +```python3 +dataset.target_names + + + + +``` + +``` +['alt.atheism', + 'comp.graphics', + 'comp.os.ms-windows.misc', + 'comp.sys.ibm.pc.hardware', + 'comp.sys.mac.hardware', + 'comp.windows.x', + 'misc.forsale', + 'rec.autos', + 'rec.motorcycles', + 'rec.sport.baseball', + 'rec.sport.hockey', + 'sci.crypt', + 'sci.electronics', + 'sci.med', + 'sci.space', + 'soc.religion.christian', + 'talk.politics.guns', + 'talk.politics.mideast', + 'talk.politics.misc', + 'talk.religion.misc'] + + + +``` + +Let’s look at a couple of sample documents: + +```python3 +for idx, document in enumerate(dataset.data[:3]): + category = dataset.target_names[dataset.target[idx]] + + print(f'Category: {category}') + print('---------------------------') + # Print the first 500 characters of the post + print(document[:500]) + print('---------------------------') + + +``` + +``` +Category: rec.sport.hockey +--------------------------- +From: Mamatha Devineni Ratnam +Subject: Pens fans reactions +Organization: Post Office, Carnegie Mellon, Pittsburgh, PA +Lines: 12 +NNTP-Posting-Host: po4.andrew.cmu.edu + + + +I am sure some bashers of Pens fans are pretty confused about the lack +of any kind of posts about the recent Pens massacre of the Devils. Actually, +I am bit puzzled too and a bit relieved. However, I am going to put an end +to non-PIttsburghers' relief with a bit of praise for the Pens. Man, they +are killin +--------------------------- +Category: comp.sys.ibm.pc.hardware +--------------------------- +From: mblawson@midway.ecn.uoknor.edu (Matthew B Lawson) +Subject: Which high-performance VLB video card? +Summary: Seek recommendations for VLB video card +Nntp-Posting-Host: midway.ecn.uoknor.edu +Organization: Engineering Computer Network, University of Oklahoma, Norman, OK, USA +Keywords: orchid, stealth, vlb +Lines: 21 + + My brother is in the market for a high-performance video card that supports +VESA local bus with 1-2MB RAM. Does anyone have suggestions/ideas on: + + - Diamond Stealth Pro Local +--------------------------- +Category: talk.politics.mideast +--------------------------- +From: hilmi-er@dsv.su.se (Hilmi Eren) +Subject: Re: ARMENIA SAYS IT COULD SHOOT DOWN TURKISH PLANES (Henrik) +Lines: 95 +Nntp-Posting-Host: viktoria.dsv.su.se +Reply-To: hilmi-er@dsv.su.se (Hilmi Eren) +Organization: Dept. of Computer and Systems Sciences, Stockholm University + + + + +\|>The student of "regional killings" alias Davidian (not the Davidian religios sect) writes: + + +\|>Greater Armenia would stretch from Karabakh, to the Black Sea, to the +\|>Mediterranean, so if you use the term "Greater Armenia +--------------------------- + + +``` + +Now we will create a dataframe with the target labels to be used in plotting. This will allow us to see the newsgroup +when we hover over the plotted points (if using interactive plotting). This will help us evaluate (by eye) how good the embedding looks. + +```python3 +category_labels = [dataset.target_names[x] for x in dataset.target] +hover_df = pd.DataFrame(category_labels, columns=['category']) + +``` + +## Using raw counts + + +Next, we are going to use a bag-of-words approach (i.e. word order doesn’t +matter) and construct a word document matrix. In this matrix the rows +will correspond to a document (i.e. post) and each column will +correspond to a particular word. The values will be the counts of how +many times a given word appeared in a particular document. + +We will use sklearns CountVectorizer function to do this for us along +with a couple other preprocessing steps: + +1) Split the text into tokens (i.e. words) by splitting on whitespace + +2) Remove english stopwords (the, and, etc) + +3) Remove all words which occur less than 5 times in the entire corpus + (via the min_df parameter) + +```python3 +vectorizer = CountVectorizer(min_df=5, stop_words='english') +word_doc_matrix = vectorizer.fit_transform(dataset.data) + +``` + +This gives us a 18846x34880 matrix where there are 18846 documents (same +as above) and 34880 unique tokens. This matrix is sparse since most +words do not appear in most documents. + +```python3 +word_doc_matrix + +``` + +``` +<18846x34880 sparse matrix of type '' + with 1939023 stored elements in Compressed Sparse Row format> + + + +``` + +Now we are going to do dimension reduction using UMAP to reduce the matrix +from 34880 dimensions to 2 dimensions (since n_components=2). We need a +distance metric and will use `Hellinger +distance `__ which +measures the similarity between two probability distributions. Each +document has a set of counts generated by a `multinomial +distribution `__ +where we can use Hellinger distance to measure the similarity of these +distributions. + +```python3 +%%time +embedding = umap.UMAP(n_components=2, metric='hellinger').fit(word_doc_matrix) + + +``` + +``` +CPU times: user 2min 24s, sys: 1.18 s, total: 2min 25s +Wall time: 2min 3s + + +``` + +Now we have an embedding of 18846x2. + +```python3 +embedding.embedding_.shape + + +``` + +``` +(18846, 2) + + +``` + +Let’s plot the embedding. If you are running this in a notebook, you should use the +interactive plotting method as it lets you hover over your points and see what +category they belong to. + +```python3 +# For interactive plotting use +# f = umap.plot.interactive(embedding, labels=dataset.target, hover_data=hover_df, point_size=1) +# show(f) +f = umap.plot.points(embedding, labels=hover_df['category']) + +``` + +![Image](images/20newsgroups_hellinger_counts.png) + +As you can see this does reasonably well. There is some separation and +groups that you would expect to be similar (such as ‘rec.sport.baseball’ +and ‘rec.sport.hockey’) are close together. The big clump in the middle +corresponds to a lot of extremely similar newsgroups like +‘comp.sys.ibm.pc.hardware’ and ‘comp.sys.mac.hardware’. + +## Using TF-IDF + + +We will now do the same pipeline with the only change being the use of +[TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) weighting. +TF-IDF gives less weight to words that appear frequently across a large +number of documents since they are more popular in general. It asserts +a higher weight to words that appear frequently in a smaller subset of +documents since they are probably important words for those documents. + +To do the TF-IDF weighting we will use sklearns TfidfVectorizer with the +same parameters as CountVectorizer above. + +```python3 +tfidf_vectorizer = TfidfVectorizer(min_df=5, stop_words='english') +tfidf_word_doc_matrix = tfidf_vectorizer.fit_transform(dataset.data) + +``` + +We get a matrix of the same size as before: + +```python3 +tfidf_word_doc_matrix + +``` + +``` +<18846x34880 sparse matrix of type '' + with 1939023 stored elements in Compressed Sparse Row format> + +``` + +Again we use Hellinger distance and UMAP to embed the documents + +```python3 +%%time +tfidf_embedding = umap.UMAP(metric='hellinger').fit(tfidf_word_doc_matrix) + + +``` + +``` +CPU times: user 2min 19s, sys: 1.27 s, total: 2min 20s +Wall time: 1min 57s + + +``` + +```python3 +# For interactive plotting use +# fig = umap.plot.interactive(tfidf_embedding, labels=dataset.target, hover_data=hover_df, point_size=1) +# show(fig) +fig = umap.plot.points(tfidf_embedding, labels=hover_df['category']) + +``` + +![Image](images/20newsgroups_hellinger_tfidf.png) + +The results look fairly similar to before but this can be a useful trick +to have in your toolbox. + +## Potential applications + + +Now that we have an embedding, what can we do with it? + +- Explore/visualize your corpus to identify topics/trends +- Cluster the embedding to find groups of related documents +- Look for nearest neighbours to find related documents +- Look for anomalous documents diff --git a/doc/document_embedding.rst b/doc/document_embedding.rst deleted file mode 100644 index a2e11a60..00000000 --- a/doc/document_embedding.rst +++ /dev/null @@ -1,329 +0,0 @@ -Document embedding using UMAP -============================= - -This is a tutorial of using UMAP to embed text (but this can be extended -to any collection of tokens). We are going to use the `20 newsgroups -dataset `__ which is a collection -of forum posts labelled by topic. We are going to embed these documents -and see that similar documents (i.e. posts in the same subforum) will -end up close together. You can use this embedding for other downstream -tasks, such as visualizing your corpus, or run a clustering algorithm -(e.g. HDBSCAN). We will use a bag of words model and use UMAP on the -count vectors as well as the TF-IDF vectors. - - -To start with let's load the relevant libraries. **This requires UMAP version >= 0.4.0.** - -.. code:: python3 - - import pandas as pd - import umap - import umap.plot - - # Used to get the data - from sklearn.datasets import fetch_20newsgroups - from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer - - # Some plotting libraries - import matplotlib.pyplot as plt - %matplotlib notebook - from bokeh.plotting import show, save, output_notebook, output_file - from bokeh.resources import INLINE - output_notebook(resources=INLINE) - -Next let's download and explore the 20 newsgroups dataset. - -.. code:: python3 - - %%time - dataset = fetch_20newsgroups(subset='all', - shuffle=True, random_state=42) - - -.. parsed-literal:: - - CPU times: user 280 ms, sys: 52 ms, total: 332 ms - Wall time: 460 ms - -Let's see the size of the corpus: - -.. code:: python3 - - print(f'{len(dataset.data)} documents') - print(f'{len(dataset.target_names)} categories') - - -.. parsed-literal:: - - 18846 documents - 20 categories - - -Here are the categories of documents. As you can see many are related to -one another (e.g. ‘comp.sys.ibm.pc.hardware’ and -‘comp.sys.mac.hardware’) but they are not all correlated (e.g. ‘sci.med’ -and ‘rec.sport.baseball’). - -.. code:: python3 - - dataset.target_names - - - - -.. parsed-literal:: - - ['alt.atheism', - 'comp.graphics', - 'comp.os.ms-windows.misc', - 'comp.sys.ibm.pc.hardware', - 'comp.sys.mac.hardware', - 'comp.windows.x', - 'misc.forsale', - 'rec.autos', - 'rec.motorcycles', - 'rec.sport.baseball', - 'rec.sport.hockey', - 'sci.crypt', - 'sci.electronics', - 'sci.med', - 'sci.space', - 'soc.religion.christian', - 'talk.politics.guns', - 'talk.politics.mideast', - 'talk.politics.misc', - 'talk.religion.misc'] - - - -Let’s look at a couple of sample documents: - -.. code:: python3 - - for idx, document in enumerate(dataset.data[:3]): - category = dataset.target_names[dataset.target[idx]] - - print(f'Category: {category}') - print('---------------------------') - # Print the first 500 characters of the post - print(document[:500]) - print('---------------------------') - - -.. parsed-literal:: - - Category: rec.sport.hockey - --------------------------- - From: Mamatha Devineni Ratnam - Subject: Pens fans reactions - Organization: Post Office, Carnegie Mellon, Pittsburgh, PA - Lines: 12 - NNTP-Posting-Host: po4.andrew.cmu.edu - - - - I am sure some bashers of Pens fans are pretty confused about the lack - of any kind of posts about the recent Pens massacre of the Devils. Actually, - I am bit puzzled too and a bit relieved. However, I am going to put an end - to non-PIttsburghers' relief with a bit of praise for the Pens. Man, they - are killin - --------------------------- - Category: comp.sys.ibm.pc.hardware - --------------------------- - From: mblawson@midway.ecn.uoknor.edu (Matthew B Lawson) - Subject: Which high-performance VLB video card? - Summary: Seek recommendations for VLB video card - Nntp-Posting-Host: midway.ecn.uoknor.edu - Organization: Engineering Computer Network, University of Oklahoma, Norman, OK, USA - Keywords: orchid, stealth, vlb - Lines: 21 - - My brother is in the market for a high-performance video card that supports - VESA local bus with 1-2MB RAM. Does anyone have suggestions/ideas on: - - - Diamond Stealth Pro Local - --------------------------- - Category: talk.politics.mideast - --------------------------- - From: hilmi-er@dsv.su.se (Hilmi Eren) - Subject: Re: ARMENIA SAYS IT COULD SHOOT DOWN TURKISH PLANES (Henrik) - Lines: 95 - Nntp-Posting-Host: viktoria.dsv.su.se - Reply-To: hilmi-er@dsv.su.se (Hilmi Eren) - Organization: Dept. of Computer and Systems Sciences, Stockholm University - - - - - \|>The student of "regional killings" alias Davidian (not the Davidian religios sect) writes: - - - \|>Greater Armenia would stretch from Karabakh, to the Black Sea, to the - \|>Mediterranean, so if you use the term "Greater Armenia - --------------------------- - - -Now we will create a dataframe with the target labels to be used in plotting. This will allow us to see the newsgroup -when we hover over the plotted points (if using interactive plotting). This will help us evaluate (by eye) how good the embedding looks. - -.. code:: python3 - - category_labels = [dataset.target_names[x] for x in dataset.target] - hover_df = pd.DataFrame(category_labels, columns=['category']) - -Using raw counts ----------------- - -Next, we are going to use a bag-of-words approach (i.e. word order doesn’t -matter) and construct a word document matrix. In this matrix the rows -will correspond to a document (i.e. post) and each column will -correspond to a particular word. The values will be the counts of how -many times a given word appeared in a particular document. - -We will use sklearns CountVectorizer function to do this for us along -with a couple other preprocessing steps: - -1) Split the text into tokens (i.e. words) by splitting on whitespace - -2) Remove english stopwords (the, and, etc) - -3) Remove all words which occur less than 5 times in the entire corpus - (via the min_df parameter) - -.. code:: python3 - - vectorizer = CountVectorizer(min_df=5, stop_words='english') - word_doc_matrix = vectorizer.fit_transform(dataset.data) - -This gives us a 18846x34880 matrix where there are 18846 documents (same -as above) and 34880 unique tokens. This matrix is sparse since most -words do not appear in most documents. - -.. code:: python3 - - word_doc_matrix - -.. parsed-literal:: - - <18846x34880 sparse matrix of type '' - with 1939023 stored elements in Compressed Sparse Row format> - - - -Now we are going to do dimension reduction using UMAP to reduce the matrix -from 34880 dimensions to 2 dimensions (since n_components=2). We need a -distance metric and will use `Hellinger -distance `__ which -measures the similarity between two probability distributions. Each -document has a set of counts generated by a `multinomial -distribution `__ -where we can use Hellinger distance to measure the similarity of these -distributions. - -.. code:: python3 - - %%time - embedding = umap.UMAP(n_components=2, metric='hellinger').fit(word_doc_matrix) - - -.. parsed-literal:: - - CPU times: user 2min 24s, sys: 1.18 s, total: 2min 25s - Wall time: 2min 3s - - -Now we have an embedding of 18846x2. - -.. code:: python3 - - embedding.embedding_.shape - - -.. parsed-literal:: - - (18846, 2) - - -Let’s plot the embedding. If you are running this in a notebook, you should use the -interactive plotting method as it lets you hover over your points and see what -category they belong to. - -.. code:: python3 - - # For interactive plotting use - # f = umap.plot.interactive(embedding, labels=dataset.target, hover_data=hover_df, point_size=1) - # show(f) - f = umap.plot.points(embedding, labels=hover_df['category']) - -.. image:: images/20newsgroups_hellinger_counts.png - -As you can see this does reasonably well. There is some separation and -groups that you would expect to be similar (such as ‘rec.sport.baseball’ -and ‘rec.sport.hockey’) are close together. The big clump in the middle -corresponds to a lot of extremely similar newsgroups like -‘comp.sys.ibm.pc.hardware’ and ‘comp.sys.mac.hardware’. - -Using TF-IDF ------------- - -We will now do the same pipeline with the only change being the use of -`TF-IDF `__ weighting. -TF-IDF gives less weight to words that appear frequently across a large -number of documents since they are more popular in general. It asserts -a higher weight to words that appear frequently in a smaller subset of -documents since they are probably important words for those documents. - -To do the TF-IDF weighting we will use sklearns TfidfVectorizer with the -same parameters as CountVectorizer above. - -.. code:: python3 - - tfidf_vectorizer = TfidfVectorizer(min_df=5, stop_words='english') - tfidf_word_doc_matrix = tfidf_vectorizer.fit_transform(dataset.data) - -We get a matrix of the same size as before: - -.. code:: python3 - - tfidf_word_doc_matrix - -.. parsed-literal:: - - <18846x34880 sparse matrix of type '' - with 1939023 stored elements in Compressed Sparse Row format> - -Again we use Hellinger distance and UMAP to embed the documents - -.. code:: python3 - - %%time - tfidf_embedding = umap.UMAP(metric='hellinger').fit(tfidf_word_doc_matrix) - - -.. parsed-literal:: - - CPU times: user 2min 19s, sys: 1.27 s, total: 2min 20s - Wall time: 1min 57s - - -.. code:: python3 - - # For interactive plotting use - # fig = umap.plot.interactive(tfidf_embedding, labels=dataset.target, hover_data=hover_df, point_size=1) - # show(fig) - fig = umap.plot.points(tfidf_embedding, labels=hover_df['category']) - -.. image:: images/20newsgroups_hellinger_tfidf.png - -The results look fairly similar to before but this can be a useful trick -to have in your toolbox. - -Potential applications ----------------------- - -Now that we have an embedding, what can we do with it? - -- Explore/visualize your corpus to identify topics/trends -- Cluster the embedding to find groups of related documents -- Look for nearest neighbours to find related documents -- Look for anomalous documents \ No newline at end of file diff --git a/doc/embedding_space.rst b/doc/embedding_space.md similarity index 56% rename from doc/embedding_space.rst rename to doc/embedding_space.md index abd7b0c2..e97f0727 100644 --- a/doc/embedding_space.rst +++ b/doc/embedding_space.md @@ -1,5 +1,5 @@ -Embedding to non-Euclidean spaces -================================= +# Embedding to non-Euclidean spaces + By default UMAP embeds data into Euclidean space. For 2D visualization that means that data is embedded into a 2D plane suitable for a @@ -10,35 +10,38 @@ UMAP to embed into other spaces, how to embed into your own custom space, and why this sort of approach might be useful. To start we'll load the usual selection of libraries. In this case we -will not be using the ``umap.plot`` functionality, but working with +will not be using the `umap.plot` functionality, but working with matplotlib directly since we'll be generating some custom visualizations for some of the more unique embedding spaces. -.. code:: python3 +```python3 +import numpy as np +import numba +import sklearn.datasets +import matplotlib.pyplot as plt +import seaborn as sns +from mpl_toolkits.mplot3d import Axes3D +import umap +%matplotlib inline - import numpy as np - import numba - import sklearn.datasets - import matplotlib.pyplot as plt - import seaborn as sns - from mpl_toolkits.mplot3d import Axes3D - import umap - %matplotlib inline +``` -.. code:: python3 +```python3 +sns.set(style='white', rc={'figure.figsize':(10,10)}) - sns.set(style='white', rc={'figure.figsize':(10,10)}) +``` As a test dataset we'll use the PenDigits dataset from sklearn -- embedding into exotic spaces can be considerably more computationally taxing, so a simple relatively small dataset is going to be useful. -.. code:: python3 +```python3 +digits = sklearn.datasets.load_digits() + +``` - digits = sklearn.datasets.load_digits() +## Plane embeddings -Plane embeddings ----------------- Plain old plane embeddings are simple enough -- it is the default for UMAP. Here we'll run through the example again, just to ensure you are @@ -46,48 +49,50 @@ familiar with how this works, and what the result of a UMAP embedding of the PenDigits dataset looks like in the simple case of embedding in the plane. -.. code:: python3 +```python3 +plane_mapper = umap.UMAP(random_state=42).fit(digits.data) - plane_mapper = umap.UMAP(random_state=42).fit(digits.data) +``` -.. code:: python3 +```python3 +plt.scatter(plane_mapper.embedding_.T[0], plane_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') - plt.scatter(plane_mapper.embedding_.T[0], plane_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') +``` -.. image:: images/embedding_space_7_1.png +![Image](images/embedding_space_7_1.png) +## Spherical embeddings -Spherical embeddings --------------------- What if we wanted to embed data onto a sphere rather than a plane? This might make sense, for example, if we have reason to expect some sort of periodic behaviour or other reasons to expect that no point can be infinitely far from any other. To make UMAP embed onto a sphere we need -to make use of the ``output_metric`` parameter, which specifies what +to make use of the `output_metric` parameter, which specifies what metric to use for the **output** space. By default UMAP uses a Euclidean -``output_metric`` (and even has a special faster code-path for this +`output_metric` (and even has a special faster code-path for this case), but you can pass in other metrics. Among the metrics UMAP supports is the Haversine metric, used for measuring distances on a sphere, given in latitude and longitude (in radians). If we set the -``output_metric`` to ``"haversine"`` then UMAP will use that to measure +`output_metric` to `"haversine"` then UMAP will use that to measure distance in the embedding space. -.. code:: python3 +```python3 +sphere_mapper = umap.UMAP(output_metric='haversine', random_state=42).fit(digits.data) - sphere_mapper = umap.UMAP(output_metric='haversine', random_state=42).fit(digits.data) +``` The result is the pendigits data embedded with respect to haversine distance on a sphere. The catch is that if we visualize this naively then we will get nonsense. -.. code:: python3 - - plt.scatter(sphere_mapper.embedding_.T[0], sphere_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') +```python3 +plt.scatter(sphere_mapper.embedding_.T[0], sphere_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') -.. image:: images/embedding_space_11_1.png +``` +![Image](images/embedding_space_11_1.png) What has gone astray is that under the embedding distance metric a point at :math:`(0, \pi)` is distance zero from a point at :math:`(0, 3\pi)` @@ -97,52 +102,54 @@ ranges :math:`(-\pi, \pi)` and :math:`(0, 2\pi)`, so this isn't the right representation of the data. We can, however, use straightforward formulas to map this data onto a sphere embedded in 3d-space. -.. code:: python3 +```python3 +x = np.sin(sphere_mapper.embedding_[:, 0]) * np.cos(sphere_mapper.embedding_[:, 1]) +y = np.sin(sphere_mapper.embedding_[:, 0]) * np.sin(sphere_mapper.embedding_[:, 1]) +z = np.cos(sphere_mapper.embedding_[:, 0]) - x = np.sin(sphere_mapper.embedding_[:, 0]) * np.cos(sphere_mapper.embedding_[:, 1]) - y = np.sin(sphere_mapper.embedding_[:, 0]) * np.sin(sphere_mapper.embedding_[:, 1]) - z = np.cos(sphere_mapper.embedding_[:, 0]) +``` -Now ``x``, ``y``, and ``z`` give 3d coordinates for each embedding point +Now `x`, `y`, and `z` give 3d coordinates for each embedding point that lies on the surface of a sphere. We can visualize this using matplotlib's 3d plotting capabilities, and see that we have in fact induced a quite reasonable embedding of the data onto the surface of a sphere. -.. code:: python3 +```python3 +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x, y, z, c=digits.target, cmap='Spectral') - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - ax.scatter(x, y, z, c=digits.target, cmap='Spectral') +``` -.. image:: images/embedding_space_15_1.png - +![Image](images/embedding_space_15_1.png) If you prefer a 2d plot we can convert these into lat/long coordinates in the appropriate ranges and get the equivalent of a map projection of the sphere data. -.. code:: python3 +```python3 +x = np.arctan2(x, y) +y = -np.arccos(z) - x = np.arctan2(x, y) - y = -np.arccos(z) +``` -.. code:: python3 +```python3 +plt.scatter(x, y, c=digits.target.astype(np.int32), cmap='Spectral') - plt.scatter(x, y, c=digits.target.astype(np.int32), cmap='Spectral') +``` -.. image:: images/embedding_space_18_1.png +![Image](images/embedding_space_18_1.png) +## Embedding on a Custom Metric Space -Embedding on a Custom Metric Space ----------------------------------- What if you have some other custom notion of a metric space that you would like to embed data into? In the same way that UMAP can support custom written distance metrics for the input data (as long as they can -be compiled with numba), the ``output_metric`` parameter can accept +be compiled with numba), the `output_metric` parameter can accept custom distance functions. One catch is that, to support gradient descent optimization, the distance function needs to return both the distance, and a vector for the gradient of the distance. This latter @@ -156,7 +163,7 @@ in the appropriate ranges. Let's work through an example where we construct a distance metric and gradient for a different sort of space: a -`torus `__. A torus is essentially +[torus](https://en.wikipedia.org/wiki/Torus). A torus is essentially just the outer surface of a donut. We can parameterize the torus in terms of x, y coordinates with the caveat that we can `"wrap around" (similar to the @@ -166,37 +173,39 @@ check for which is the shorter direction -- across or wrapping around -- and ensure we account for the equivalence of wrapping around several times. We can write a simple function to calculate that. -.. code:: python3 - - @numba.njit(fastmath=True) - def torus_euclidean_grad(x, y, torus_dimensions=(2*np.pi,2*np.pi)): - """Standard euclidean distance. - - ..math:: - D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} - """ - distance_sqr = 0.0 - g = np.zeros_like(x) - for i in range(x.shape[0]): - a = abs(x[i] - y[i]) - if 2*a < torus_dimensions[i]: - distance_sqr += a ** 2 - g[i] = (x[i] - y[i]) - else: - distance_sqr += (torus_dimensions[i]-a) ** 2 - g[i] = (x[i] - y[i]) * (a - torus_dimensions[i]) / a - distance = np.sqrt(distance_sqr) - return distance, g/(1e-6 + distance) +```python3 +@numba.njit(fastmath=True) +def torus_euclidean_grad(x, y, torus_dimensions=(2*np.pi,2*np.pi)): + """Standard euclidean distance. + + ..math:: + D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} + """ + distance_sqr = 0.0 + g = np.zeros_like(x) + for i in range(x.shape[0]): + a = abs(x[i] - y[i]) + if 2*a < torus_dimensions[i]: + distance_sqr += a ** 2 + g[i] = (x[i] - y[i]) + else: + distance_sqr += (torus_dimensions[i]-a) ** 2 + g[i] = (x[i] - y[i]) * (a - torus_dimensions[i]) / a + distance = np.sqrt(distance_sqr) + return distance, g/(1e-6 + distance) + +``` Note that the gradient just derives from the standard euclidean gradient, we just have to check the direction according to the way we've wrapped around to compute the distance. We can now plug that function -directly in to the ``output_metric`` parameter and end up embedding data +directly in to the `output_metric` parameter and end up embedding data on a torus. -.. code:: python3 +```python3 +torus_mapper = umap.UMAP(output_metric=torus_euclidean_grad, random_state=42).fit(digits.data) - torus_mapper = umap.UMAP(output_metric=torus_euclidean_grad, random_state=42).fit(digits.data) +``` As with the sphere case, a naive visualisation will look strange, due the the wrapping around and equivalence of looping several times. But, @@ -204,50 +213,52 @@ also just like the torus, we can construct a suitable visualization by computing the 3d coordinates for the points using a little bit of straightforward geometry (yes, I still had to look it up to check). -.. code:: python3 +```python3 +R = 3 # Size of the doughnut circle +r = 1 # Size of the doughnut cross-section - R = 3 # Size of the doughnut circle - r = 1 # Size of the doughnut cross-section - - x = (R + r * np.cos(torus_mapper.embedding_[:, 0])) * np.cos(torus_mapper.embedding_[:, 1]) - y = (R + r * np.cos(torus_mapper.embedding_[:, 0])) * np.sin(torus_mapper.embedding_[:, 1]) - z = r * np.sin(torus_mapper.embedding_[:, 0]) +x = (R + r * np.cos(torus_mapper.embedding_[:, 0])) * np.cos(torus_mapper.embedding_[:, 1]) +y = (R + r * np.cos(torus_mapper.embedding_[:, 0])) * np.sin(torus_mapper.embedding_[:, 1]) +z = r * np.sin(torus_mapper.embedding_[:, 0]) + +``` Now we can visualize the result using matplotlib and see that, indeed, the data has been suitably embedded onto a torus. -.. code:: python3 - - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - ax.scatter(x, y, z, c=digits.target, cmap='Spectral') - ax.set_zlim3d(-3, 3) - ax.view_init(35, 70) +```python3 +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x, y, z, c=digits.target, cmap='Spectral') +ax.set_zlim3d(-3, 3) +ax.view_init(35, 70) -.. image:: images/embedding_space_26_0.png +``` +![Image](images/embedding_space_26_0.png) And as with the torus we can do a little geometry and unwrap the torus into a flat plane with the appropriate bounds. -.. code:: python3 +```python3 +u = np.arctan2(x,y) +v = np.arctan2(np.sqrt(x**2 + y**2) - R, z) - u = np.arctan2(x,y) - v = np.arctan2(np.sqrt(x**2 + y**2) - R, z) +``` -.. code:: python3 +```python3 +plt.scatter(u, v, c=digits.target, cmap='Spectral') - plt.scatter(u, v, c=digits.target, cmap='Spectral') +``` -.. image:: images/embedding_space_29_1.png +![Image](images/embedding_space_29_1.png) +## A Practical Example -A Practical Example -------------------- While the examples given so far may have some use (because some data does have suitable periodic or looping structures that we expect will be @@ -272,15 +283,16 @@ covariance matrix from these if required. That gives us a total of 5 components to embed into (two for the mean, 3 for parameters describing the covariance). We can simply do this since the appropriate metric is defined already. Note that we have to specifically pass -``n_components=5`` since we need to explicitly embed into a 5 +`n_components=5` since we need to explicitly embed into a 5 dimensional space to support all the covariance parameters associated to 2d Gaussians. -.. code:: python3 +```python3 +gaussian_mapper = umap.UMAP(output_metric='gaussian_energy', + n_components=5, + random_state=42).fit(digits.data) - gaussian_mapper = umap.UMAP(output_metric='gaussian_energy', - n_components=5, - random_state=42).fit(digits.data) +``` Since we have embedded the data into a 5 dimensional space visualization is not as trivial as it was earlier. We can get a start on visualizing @@ -288,13 +300,13 @@ the results by looking at just the means, which are the 2d locations of the modes of the Gaussians. A traditional scatter plot will suffice for this. -.. code:: python3 - - plt.scatter(gaussian_mapper.embedding_.T[0], gaussian_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') +```python3 +plt.scatter(gaussian_mapper.embedding_.T[0], gaussian_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') -.. image:: images/embedding_space_33_1.png +``` +![Image](images/embedding_space_33_1.png) We see that we have gotten a result similar to a standard embedding into euclidean space, but with less clear clustering, and more points between @@ -307,21 +319,22 @@ function to draw ellipses on a plot accoriding to a position, a width, a height, and an angle (since this is the format the embedding computed the data). -.. code:: python3 - - from matplotlib.patches import Ellipse - - def draw_simple_ellipse(position, width, height, angle, - ax=None, from_size=0.1, to_size=0.5, n_ellipses=3, - alpha=0.1, color=None, - **kwargs): - ax = ax or plt.gca() - angle = (angle / np.pi) * 180 - width, height = np.sqrt(width), np.sqrt(height) - # Draw the Ellipse - for nsig in np.linspace(from_size, to_size, n_ellipses): - ax.add_patch(Ellipse(position, nsig * width, nsig * height, - angle, alpha=alpha, lw=0, color=color, **kwargs)) +```python3 +from matplotlib.patches import Ellipse + +def draw_simple_ellipse(position, width, height, angle, + ax=None, from_size=0.1, to_size=0.5, n_ellipses=3, + alpha=0.1, color=None, + **kwargs): + ax = ax or plt.gca() + angle = (angle / np.pi) * 180 + width, height = np.sqrt(width), np.sqrt(height) + # Draw the Ellipse + for nsig in np.linspace(from_size, to_size, n_ellipses): + ax.add_patch(Ellipse(position, nsig * width, nsig * height, + angle, alpha=alpha, lw=0, color=color, **kwargs)) + +``` Now we can plot the data by providing a scatterplot of the centers (as before), but overlaying that over a super-level-set ellipses of the @@ -329,24 +342,24 @@ associated Gaussians. The obvious catch is that this will induce a lot of over-plotting, but it will at least provide a way to start understanding the embedding we have produced. -.. code:: python3 - - fig = plt.figure(figsize=(10,10)) - ax = fig.add_subplot(111) - colors = plt.get_cmap('Spectral')(np.linspace(0, 1, 10)) - for i in range(gaussian_mapper.embedding_.shape[0]): - pos = gaussian_mapper.embedding_[i, :2] - draw_simple_ellipse(pos, gaussian_mapper.embedding_[i, 2], - gaussian_mapper.embedding_[i, 3], - gaussian_mapper.embedding_[i, 4], - ax, color=colors[digits.target[i]], - from_size=0.2, to_size=1.0, alpha=0.05) - ax.scatter(gaussian_mapper.embedding_.T[0], - gaussian_mapper.embedding_.T[1], - c=digits.target, cmap='Spectral', s=3) - -.. image:: images/embedding_space_37_1.png - +```python3 +fig = plt.figure(figsize=(10,10)) +ax = fig.add_subplot(111) +colors = plt.get_cmap('Spectral')(np.linspace(0, 1, 10)) +for i in range(gaussian_mapper.embedding_.shape[0]): + pos = gaussian_mapper.embedding_[i, :2] + draw_simple_ellipse(pos, gaussian_mapper.embedding_[i, 2], + gaussian_mapper.embedding_[i, 3], + gaussian_mapper.embedding_[i, 4], + ax, color=colors[digits.target[i]], + from_size=0.2, to_size=1.0, alpha=0.05) +ax.scatter(gaussian_mapper.embedding_.T[0], + gaussian_mapper.embedding_.T[1], + c=digits.target, cmap='Spectral', s=3) + +``` + +![Image](images/embedding_space_37_1.png) Now we can see that the covariance structure for the points can vary greatly, both in absolute size, and in shape. We note that many of the @@ -365,25 +378,25 @@ this structure better by plotting only a single ellipse per point and using a lower alpha channel value for the ellipses, making them more translucent. -.. code:: python3 +```python3 +fig = plt.figure(figsize=(10,10)) +ax = fig.add_subplot(111) +for i in range(gaussian_mapper.embedding_.shape[0]): + pos = gaussian_mapper.embedding_[i, :2] + draw_simple_ellipse(pos, gaussian_mapper.embedding_[i, 2], + gaussian_mapper.embedding_[i, 3], + gaussian_mapper.embedding_[i, 4], + ax, n_ellipses=1, + color=colors[digits.target[i]], + from_size=1.0, to_size=1.0, alpha=0.01) +ax.scatter(gaussian_mapper.embedding_.T[0], + gaussian_mapper.embedding_.T[1], + c=digits.target, cmap='Spectral', s=3) - fig = plt.figure(figsize=(10,10)) - ax = fig.add_subplot(111) - for i in range(gaussian_mapper.embedding_.shape[0]): - pos = gaussian_mapper.embedding_[i, :2] - draw_simple_ellipse(pos, gaussian_mapper.embedding_[i, 2], - gaussian_mapper.embedding_[i, 3], - gaussian_mapper.embedding_[i, 4], - ax, n_ellipses=1, - color=colors[digits.target[i]], - from_size=1.0, to_size=1.0, alpha=0.01) - ax.scatter(gaussian_mapper.embedding_.T[0], - gaussian_mapper.embedding_.T[1], - c=digits.target, cmap='Spectral', s=3) +``` -.. image:: images/embedding_space_39_1.png - +![Image](images/embedding_space_39_1.png) This lets us see the variation of density of clusters with respect to the covariance structure -- some clusters have consistently very tight @@ -401,69 +414,72 @@ over a set of several Gaussians; and a function to generate the density for each point in some grid (summing only over nearby Gaussians to make this naive approach more computable). -.. code:: python3 - - from sklearn.neighbors import KDTree - - @numba.njit(fastmath=True) - def eval_gaussian(x, pos=np.array([0, 0]), cov=np.eye(2, dtype=np.float32)): - det = cov[0,0] * cov[1,1] - cov[0,1] * cov[1,0] - if det > 1e-16: - cov_inv = np.array([[cov[1,1], -cov[0,1]], [-cov[1,0], cov[0,0]]]) * 1.0 / det - diff = x - pos - m_dist = cov_inv[0,0] * diff[0]**2 - \ - (cov_inv[0,1] + cov_inv[1,0]) * diff[0] * diff[1] + \ - cov_inv[1,1] * diff[1]**2 - return (np.exp(-0.5 * m_dist)) / (2 * np.pi * np.sqrt(np.abs(det))) - else: - return 0.0 - - @numba.njit(fastmath=True) - def eval_density_at_point(x, embedding): - result = 0.0 - for i in range(embedding.shape[0]): - pos = embedding[i, :2] - t = embedding[i, 4] - U = np.array([[np.cos(t), np.sin(t)], [np.sin(t), -np.cos(t)]]) - cov = U @ np.diag(embedding[i, 2:4]) @ U - result += eval_gaussian(x, pos=pos, cov=cov) - return result - - def create_density_plot(X, Y, embedding): - Z = np.zeros_like(X) - tree = KDTree(embedding[:, :2]) - for i in range(X.shape[0]): - for j in range(X.shape[1]): - nearby_points = embedding[tree.query_radius([[X[i,j],Y[i,j]]], r=2)[0]] - Z[i, j] = eval_density_at_point(np.array([X[i,j],Y[i,j]]), nearby_points) - return Z / Z.sum() +```python3 +from sklearn.neighbors import KDTree + +@numba.njit(fastmath=True) +def eval_gaussian(x, pos=np.array([0, 0]), cov=np.eye(2, dtype=np.float32)): + det = cov[0,0] * cov[1,1] - cov[0,1] * cov[1,0] + if det > 1e-16: + cov_inv = np.array([[cov[1,1], -cov[0,1]], [-cov[1,0], cov[0,0]]]) * 1.0 / det + diff = x - pos + m_dist = cov_inv[0,0] * diff[0]**2 - \ + (cov_inv[0,1] + cov_inv[1,0]) * diff[0] * diff[1] + \ + cov_inv[1,1] * diff[1]**2 + return (np.exp(-0.5 * m_dist)) / (2 * np.pi * np.sqrt(np.abs(det))) + else: + return 0.0 + +@numba.njit(fastmath=True) +def eval_density_at_point(x, embedding): + result = 0.0 + for i in range(embedding.shape[0]): + pos = embedding[i, :2] + t = embedding[i, 4] + U = np.array([[np.cos(t), np.sin(t)], [np.sin(t), -np.cos(t)]]) + cov = U @ np.diag(embedding[i, 2:4]) @ U + result += eval_gaussian(x, pos=pos, cov=cov) + return result + +def create_density_plot(X, Y, embedding): + Z = np.zeros_like(X) + tree = KDTree(embedding[:, :2]) + for i in range(X.shape[0]): + for j in range(X.shape[1]): + nearby_points = embedding[tree.query_radius([[X[i,j],Y[i,j]]], r=2)[0]] + Z[i, j] = eval_density_at_point(np.array([X[i,j],Y[i,j]]), nearby_points) + return Z / Z.sum() + +``` Now we simply need an appropriate grid of points. We can use the plot bounds seen above, and a grid size selected for the sake of -computability. The numpy ``meshgrid`` function can supply the actual +computability. The numpy `meshgrid` function can supply the actual grid. -.. code:: python3 +```python3 +X, Y = np.meshgrid(np.linspace(-7, 9, 300), np.linspace(-8, 8, 300)) - X, Y = np.meshgrid(np.linspace(-7, 9, 300), np.linspace(-8, 8, 300)) +``` Now we can use the function defined above to compute the density at each point in the grid, given the Gaussians produced by the embedding. -.. code:: python3 - - Z = create_density_plot(X, Y, gaussian_mapper.embedding_) +```python3 +Z = create_density_plot(X, Y, gaussian_mapper.embedding_) -Now we can view the result as a density plot using ``imshow``. +``` -.. code:: python3 +Now we can view the result as a density plot using `imshow`. - plt.imshow(Z, origin='lower', cmap='Reds', extent=(-7, 9, -8, 8), vmax=0.0005) - plt.colorbar() +```python3 +plt.imshow(Z, origin='lower', cmap='Reds', extent=(-7, 9, -8, 8), vmax=0.0005) +plt.colorbar() -.. image:: images/embedding_space_47_1.png +``` +![Image](images/embedding_space_47_1.png) Here we see the finer structure within the various clusters, including some of the interesting linear structures, demonstrating that this @@ -471,8 +487,8 @@ Gaussian uncertainty based embedding has captured quite detailed and useful information about the inter-relationships among the PenDigits dataset. -Bonus: Embedding in Hyperbolic space ------------------------------------- +## Bonus: Embedding in Hyperbolic space + As a bonus example let's look at embedding data into hyperbolic space. The most popular model for this for visualization is `Poincare's disk @@ -481,10 +497,8 @@ example of a regular tiling of hyperbolic space in Poincare's disk model is shown below; you may note it is similar to famous images by M.C. Escher. -.. image:: images/Hyperbolic_tiling.png - :height: 400 px - :width: 400 px - +![Image](images/Hyperbolic_tiling.png) + Ideally we would be able to embed directly into this Poincare disk model, but in practice this proves to be very difficult. The issue is @@ -502,74 +516,77 @@ The simplest option is the `hyperboloid model `__. Under this model we can simply move in x and y coordinates, and solve for the corresponding z coordinate when we need to compute distances. This model -has been implemented under the distance metric ``"hyperboloid"`` so we +has been implemented under the distance metric `"hyperboloid"` so we can simply use it out-of-the-box. -.. code:: python3 +```python3 +hyperbolic_mapper = umap.UMAP(output_metric='hyperboloid', + random_state=42).fit(digits.data) - hyperbolic_mapper = umap.UMAP(output_metric='hyperboloid', - random_state=42).fit(digits.data) +``` A straightforward visualization option is to simply view the x and y coordinates we have arrived at: -.. code:: python3 - - plt.scatter(hyperbolic_mapper.embedding_.T[0], - hyperbolic_mapper.embedding_.T[1], - c=digits.target, cmap='Spectral') +```python3 +plt.scatter(hyperbolic_mapper.embedding_.T[0], + hyperbolic_mapper.embedding_.T[1], + c=digits.target, cmap='Spectral') -.. image:: images/embedding_space_52_1.png +``` +![Image](images/embedding_space_52_1.png) We can also solve for the z coordinate and view the data lying on a hyperboloid in 3d space. -.. code:: python3 +```python3 +x = hyperbolic_mapper.embedding_[:, 0] +y = hyperbolic_mapper.embedding_[:, 1] +z = np.sqrt(1 + np.sum(hyperbolic_mapper.embedding_**2, axis=1)) - x = hyperbolic_mapper.embedding_[:, 0] - y = hyperbolic_mapper.embedding_[:, 1] - z = np.sqrt(1 + np.sum(hyperbolic_mapper.embedding_**2, axis=1)) +``` -.. code:: python3 +```python3 +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x, y, z, c=digits.target, cmap='Spectral') +ax.view_init(35, 80) - fig = plt.figure() - ax = fig.add_subplot(111, projection='3d') - ax.scatter(x, y, z, c=digits.target, cmap='Spectral') - ax.view_init(35, 80) +``` -.. image:: images/embedding_space_55_0.png - +![Image](images/embedding_space_55_0.png) But we can do more -- since we have embedded the data successfully in hyperbolic space we can map the data into the Poincare disk model. This is, in fact, a straightforward computation. -.. code:: python3 +```python3 +disk_x = x / (1 + z) +disk_y = y / (1 + z) - disk_x = x / (1 + z) - disk_y = y / (1 + z) +``` Now we can visualize the data in a Poincare disk model embedding as we first wanted. For this we simply generate a scatterplot of the data, and then draw in the bounding circle of the line at infinity. -.. code:: python3 - - fig = plt.figure() - ax = fig.add_subplot(111) - ax.scatter(disk_x, disk_y, c=digits.target, cmap='Spectral') - boundary = plt.Circle((0,0), 1, fc='none', ec='k') - ax.add_artist(boundary) - ax.axis('off'); +```python3 +fig = plt.figure() +ax = fig.add_subplot(111) +ax.scatter(disk_x, disk_y, c=digits.target, cmap='Spectral') +boundary = plt.Circle((0,0), 1, fc='none', ec='k') +ax.add_artist(boundary) +ax.axis('off'); -.. image:: images/embedding_space_59_0.png +``` +![Image](images/embedding_space_59_0.png) Hopefully this has provided a useful example of how to go about embedding into non-euclidean spaces. This last example ideally diff --git a/doc/exploratory_analysis.rst b/doc/exploratory_analysis.md similarity index 79% rename from doc/exploratory_analysis.rst rename to doc/exploratory_analysis.md index cd552377..474e7977 100644 --- a/doc/exploratory_analysis.rst +++ b/doc/exploratory_analysis.md @@ -1,5 +1,5 @@ -Exploratory Analysis of Interesting Datasets -============================================ +# Exploratory Analysis of Interesting Datasets + UMAP is a useful tool for general exploratory analysis of data -- it can provide a unique lens through which to view data that can highlight structures and @@ -8,8 +8,8 @@ Below is a selection of uses cases of UMAP being used for interesting exploratio of intriguing datasets -- everything from pure math and outputs of neural networks, to philosophy articles, and scientific texts. -Prime factorizations of numbers -------------------------------- +## Prime factorizations of numbers + What would happen if we applied UMAP to the integers? First we would need a way to express an integer in a high dimensional space. That can be done by looking at the prime factorization of each number. Next you have to take enough numbers @@ -21,15 +21,15 @@ of datasets that we might think we know well. It's worth visiting the linked art below as Dr. Williamson provides a rich and detailed exploration of UMAP as applied to prime factorizations of integers. -.. image:: images/umap_primes.png - :width: 400px +![Image](images/umap_primes.png) + -`UMAP on prime factorizations `__ +[UMAP on prime factorizations](https://johnhw.github.io/umap_primes/index.md.html) Thanks to John Williamson. -Structure of Recent Philosophy ------------------------------- +## Structure of Recent Philosophy + Philosophy is an incredibly diverse subject, ranging from social and moral philosophy to logic and philosophy of math; from analysis of ancient Greek philosophy to modern business ethics. If we could get an overview of all the philosophy papers published in the last @@ -38,15 +38,15 @@ looking at a large sampling of philosophy papers and comparing them according to citations. The results are intriguing, and can be explored interactively in the viewer Maximilian built for it. -.. image:: images/structure_recent_phil.png - :width: 400px +![Image](images/structure_recent_phil.png) + -`Structure of Recent Philosophy `__ +[Structure of Recent Philosophy](https://homepage.univie.ac.at/noichlm94/full/zoom_final/index.html) Thanks to Maximilian Noichl. -Language, Context, and Geometry in Neural Networks --------------------------------------------------- +## Language, Context, and Geometry in Neural Networks + Among recent developments in natural language processing is the BERT neural network based technique for analysis of language. Among many things that BERT can do one is context sensitive embeddings of words -- providing numeric vector representations of words @@ -57,15 +57,15 @@ word embedding space generated by BERT, and among the tools used was UMAP. The l blog post provides a detailed and inspiring analysis of what BERT's word embeddings look like, and how the different layers of BERT represent different aspects of language. -.. image:: images/bert_embedding.png - :width: 400px +![Image](images/bert_embedding.png) + -`Language, Context, and Geometry in Neural Networks `__ +[Language, Context, and Geometry in Neural Networks](https://pair-code.github.io/interpretability/context-atlas/blogpost/) Thanks to Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda Viégas, and Martin Wattenberg. -Activation Atlas ----------------- +## Activation Atlas + Understanding the image processing capabilities (and deficits!) of modern convolutional neural networks is a challenge. Certainly these models are capable of amazing feats in, for example, image classification. They can also be brittle @@ -77,15 +77,15 @@ down to 2 dimensions for visualization. The result was an impressive interactive in the Distill journal, providing rich visualizations and new insights into the working of convolutional neural networks. -.. image:: images/activation_atlas.png - :width: 400px +![Image](images/activation_atlas.png) + -`The Activation Atlas `__ +[The Activation Atlas](https://distill.pub/2019/activation-atlas/) Thanks to Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah. -Open Syllabus Galaxy --------------------- +## Open Syllabus Galaxy + Suppose you wanted to explore the space of commonly assigned texts from Open Syllabus? That gives you over 150,000 texts to consider. Since the texts are open you can actually analyse the text content involved. With some NLP and neural network wizardry David McClure build @@ -95,9 +95,9 @@ and generally just a an interesting ladscape of science to be explored. As with of the other projects here David made a great interactive viewer allowing for rich exploration of the results. -.. image:: images/syllabus_galaxy.png - :width: 400px +![Image](images/syllabus_galaxy.png) + -`Open Syllabus Galaxy `__ +[Open Syllabus Galaxy](https://galaxy.opensyllabus.org/) Thanks to David McClure. diff --git a/doc/faq.rst b/doc/faq.md similarity index 77% rename from doc/faq.rst rename to doc/faq.md index be713924..c1efe456 100644 --- a/doc/faq.rst +++ b/doc/faq.md @@ -1,17 +1,17 @@ -Frequently Asked Questions -========================== +# Frequently Asked Questions + Compiled here are a set of frequently asked questions, along with answers. If you don't find your question listed here then please feel free to add an -`issue on github `_. +[issue on github](https://github.com/lmcinnes/umap/issues/new). More questions are always welcome, and the authors will do their best to answer. If you feel you have a common question that isn't answered here then please suggest that the question (and answer) be added to the FAQ when you file the issue. -Should I normalise my features? -------------------------------- +## Should I normalise my features? + The default answer is yes, but, of course, the real answer is "it depends". If your features have meaningful relationships @@ -20,21 +20,21 @@ normalising per feature is not a good idea. For features that are essentially independent it does make sense to get all the features on (relatively) the same scale. The best way to do this is to use -`pre-processing tools from scikit-learn `_. +[pre-processing tools from scikit-learn](http://scikit-learn.org/stable/modules/preprocessing.html). All the advice given there applies as sensible preprocessing for UMAP, and since UMAP is scikit-learn compatible you -can put all of this together into a `scikit-learn pipeline `_. +can put all of this together into a [scikit-learn pipeline](http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). -Can I cluster the results of UMAP? ----------------------------------- +## Can I cluster the results of UMAP? + This is hard to answer well, but essentially the answer is "yes, with care". To start with it matters what clustering algorithm you are going to use. Since UMAP does not necessarily produce clean spherical clusters something like K-Means is a poor choice. I would recommend -`HDBSCAN `_ or +[HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) or similar. The catch here is that UMAP, with its uniform density assumption, does not preserve density well. What UMAP will do, however, is contract connected components of the manifold @@ -52,8 +52,8 @@ Second, because it supports arbitrary embedding dimensions, UMAP allows embedding to larger dimensional spaces that make it more amenable to clustering. -The clusters are all squashed together and I can't see internal structure -------------------------------------------------------------------------- +## The clusters are all squashed together and I can't see internal structure + One of UMAPs goals is to have distance between clusters of points be meaningful. This means that clusters can end up spread out @@ -65,34 +65,34 @@ default parameters) tend to show the clusters only as indistinct blobs with no internal structure. The solution for this is really a matter of tuning the plot more than anything else. -If you are using matplotlib consider using the ``s`` parameter +If you are using matplotlib consider using the `s` parameter that specifies the glyph size in scatter plots. Depending on how much data you have reducing this to anything from 5 to -0.001 can have a notable effect. The ``size`` parameter in +0.001 can have a notable effect. The `size` parameter in bokeh is similarly useful (but does not need to be quite so small). More generally the real solution, particular with large datasets, -is to use `datashader `_ for plotting. +is to use [datashader](http://datashader.org/) for plotting. Datashader is a plotting library that handles aggregation of large scale data in scatter plots in a way that can better show the underlying detail that can otherwise be lost. We highly recommend investing the time to learn datashader for UMAP plot particularly for larger datasets. -I ran out of memory. Help! --------------------------- +## I ran out of memory. Help! + For some datasets the default options for approximate nearest neighbor search can result in excessive memory use. If your dataset is not especially large but you have found that UMAP runs out of memory when operating on it consider -using the ``low_memory=True`` option, which will switch +using the `low_memory=True` option, which will switch to a slower but less memory intensive approach to computing the approximate nearest neighbors. This may alleviate your issues. -UMAP is eating all my cores. Help! ----------------------------------- +## UMAP is eating all my cores. Help! + If run without a random seed UMAP will use numba's parallel implementation to do multithreaded work and use many cores. @@ -100,12 +100,12 @@ By default this will make use of as many cores as are available. If you are on a shared machine or otherwise don't wish to use *all* the cores at once you can restrict the number of threads that numba uses by making use of the environment -variable ``NUMBA_NUM_THREADS``; see the `numba +variable `NUMBA_NUM_THREADS`; see the `numba documentation `__ for more details. -Is there GPU or multicore-CPU support? --------------------------------------- +## Is there GPU or multicore-CPU support? + There is basic multicore support as of version 0.4. In the future it is possible that GPU support may @@ -115,8 +115,8 @@ There is a UMAP implementation for GPU available in the NVIDIA RAPIDS cuML library, so if you need GPU support that is currently the best place to go. -Can I add a custom loss function? ---------------------------------- +## Can I add a custom loss function? + To allow for fast performance the SGD phase of UMAP has been hand-coded for the specific needs of UMAP. This makes @@ -124,36 +124,36 @@ custom loss functions a little difficult to handle. Now that Numba (as of version 0.38) supports passing functions it is possible that future versions of UMAP may support such functionality. In the meantime you should definitely -look into `smallvis `_, +look into [smallvis](https://github.com/jlmelville/smallvis), a library for t-SNE, LargeVis, UMAP, and related algorithms. Smallvis only works for small datasets, but provides much greater flexibility and control. -Is there support for the R language? ------------------------------------- +## Is there support for the R language? + Yes! A number of people have worked hard to make UMAP available to R users. If you want to use the reference implementation under the hood but want a nice R interface -then we recommend `umap `_, -which wraps the python code with -`reticulate `_. -Another reticulate interface is -`umapr `_, but it +then we recommend [umap](https://github.com/tkonopka/umap), +which wraps the python code with +[reticulate](https://rstudio.github.io/reticulate/). +Another reticulate interface is +[umapr](https://github.com/ropenscilabs/umapr), but it may not be under active development. If you want a pure R version then we recommend -`uwot `_ at this time. -`umap `_ also provides +[uwot](https://github.com/jlmelville/uwot) at this time. +[umap](https://github.com/tkonopka/umap) also provides a pure R implementation in addition to its reticulate wrapper. Both umap and uwot are available on CRAN. -Is there a C/C++ implementation? --------------------------------- +## Is there a C/C++ implementation? + Not that we are aware of. For now Numba has done a very admirable job of providing high performance and the @@ -162,8 +162,8 @@ lower level languages. At some point a multithreaded C++ implementation may be made available, but there are no time-frames for when that would happen. -I can't get UMAP to run properly! ---------------------------------- +## I can't get UMAP to run properly! + There are, inevitably, a number of issues and corner cases that can cause issues for UMAP. Some know issues that can @@ -172,29 +172,26 @@ cause problems are: - UMAP doesn't currently support 32-bit Windows. This is due to issues with Numba of that platform and will not likely be resolved soon. Sorry :-( -- If you have pip installed the package ``umap`` - at any time (instead of ``umap-learn``) this can - cause serious issues. You will want to purge/remove - everything umap related in your ``site-packages`` - directory and re-install ``umap-learn``. -- Having any files called ``umap.py`` in the current +- If you have pip installed an old package with a conflicting name + at any time, this can cause serious issues. You will want to purge/remove + everything umap related in your `site-packages` + directory and re-install `umap`. +- Having any files called `umap.py` in the current directory you will have issues as that will be - loaded instead of the ``umap`` module. + loaded instead of the `umap` module. It is worth checking the -`issues page on github `_ +[issues page on github](https://github.com/lmcinnes/umap/issues) for potential solutions. If all else fails please add an -`issue on github `_. +[issue on github](https://github.com/lmcinnes/umap/issues/new). -What is the difference between PCA / UMAP / VAEs? -------------------------------------------------- +## What is the difference between PCA / UMAP / VAEs? -This is an example of an embedding for a popular Fashion MNIST dataset. -.. figure:: images/umap_vae_pca.png - :alt: Comparison of PCA / UMAP / VAE embeddings +This is an example of an embedding for a popular Fashion MNIST dataset. - Comparison of PCA / UMAP / VAE embeddings +![Comparison of PCA / UMAP / VAE embeddings](images/umap_vae_pca.png) +*Comparison of PCA / UMAP / VAE embeddings* Note that FMNIST is mostly a toy dataset (MNIST on steroids). On such a simplistic case UMAP shows distillation results @@ -238,11 +235,11 @@ Which tool should I use? Where can I learn more? -- While PCA is ubiquitous, you may `look `_ +- While PCA is ubiquitous, you may [look](https://github.com/snakers4/playing_with_vae) at this example comparing PCA / UMAP / VAEs; -How UMAP can go wrong ---------------------- +## How UMAP can go wrong + One way UMAP can go wrong is the introduction of data points that are maximally far apart from all other points in your data set. In other words, a points nearest neighbour is maximally @@ -260,8 +257,8 @@ sufficiently large number of these points and our entire space gets pulled toget any of the structure we had hoped to identify. To circumvent this problem we've added a disconnection_distance parameter to UMAP which will cut -any edge with a distance greater than the value passed in. This parameter defaults to ``None``. -When set to ``None`` the disconnection_distance will be set to the maximal value for any of our +any edge with a distance greater than the value passed in. This parameter defaults to `None`. +When set to `None` the disconnection_distance will be set to the maximal value for any of our supported bounded metrics and otherwise set to np.inf. Removing these edges from the UMAP graph will disconnect our manifold and cause these points to start where they are initialized and get pushed away from all other points via the our optimization. @@ -274,14 +271,15 @@ make use of the umap.utils.disconnected_vertices() function to identify the disc This can be used either for filtering and retraining a new UMAP model or simple to bed used as a filter for visualization purposes as seen below. -.. code:: python3 +```python3 +umap_model = umap.UMAP().fit(data) +disconnected_points = umap.utils.disconnected_vertices(umap_model) +umap.plot.points(umap_model, subset_points=~disconnected_points) + +``` - umap_model = umap.UMAP().fit(data) - disconnected_points = umap.utils.disconnected_vertices(umap_model) - umap.plot.points(umap_model, subset_points=~disconnected_points) +## Successful use-cases -Successful use-cases --------------------- UMAP can be / has been successfully applied to the following domains: @@ -291,4 +289,4 @@ UMAP can be / has been successfully applied to the following domains: - Pre-processing image embeddings (Inception) for clustering; and many more -- if you have a successful use-case please submit -a pull request adding it to this list! \ No newline at end of file +a pull request adding it to this list! diff --git a/doc/how_umap_works.rst b/doc/how_umap_works.md similarity index 92% rename from doc/how_umap_works.rst rename to doc/how_umap_works.md index 51137502..38feb049 100644 --- a/doc/how_umap_works.rst +++ b/doc/how_umap_works.md @@ -1,7 +1,7 @@ -.. _how_umap_works: + + +# How UMAP Works -How UMAP Works -============== UMAP is an algorithm for dimension reduction based on manifold learning techniques and ideas from topological data analysis. It provides a very @@ -26,8 +26,8 @@ together again, and combine them with a new approach to finding a low dimensional representation more fitting to the new data structures at hand. Putting this all together we arrive at the basic UMAP algorithm. -Topological Data Analysis and Simplicial Complexes --------------------------------------------------- +## Topological Data Analysis and Simplicial Complexes + Simplicial complexes are a means to construct topological spaces out of simple combinatorial components. This allows one to reduce the @@ -38,7 +38,7 @@ approach to topological data analysis in general, and dimension reduction in particular. The first step is to provide some simple combinatorial building blocks -called `*simplices* `__. +called [*simplices*](https://en.wikipedia.org/wiki/Simplex). Geometrically a simplex is a very simple way to build a :math:`k`-dimensional object. A :math:`k` dimensional simplex is called a :math:`k`-simplex, and it is formed by taking the convex hull of @@ -48,12 +48,8 @@ triangle (with three 1-simplices as "faces"), and a 3-simplex is a tetrahedron (with four 2-simplices as "faces"). Such a simple construction allows for easy generalization to arbitrary dimensions. -.. figure:: images/simplices.png - :alt: Low dimensional simplices - - Low dimensional simplices - - +![Low dimensional simplices](images/simplices.png) +*Low dimensional simplices* This has a very simple combinatorial underlying structure, and ultimately one can regard a :math:`k`-simplex as an arbitrary set of @@ -74,7 +70,7 @@ exist), and the intersection of any two simplices in :math:`\mathcal{K}` is a face of both simplices. A large class of topological spaces can be constructed in this way -- just gluing together simplices of various dimensions along their faces. A little further abstraction will get to -`*simplicial sets* `__ +[*simplicial sets*](https://en.wikipedia.org/wiki/Simplicial_set) which are purely combinatorial, have a nice category theoretic presentation, and can generate a much broader class of topological spaces, but that will take us too far afield for this article. The @@ -119,33 +115,21 @@ point. To demonstrate the process let's consider a test dataset like this -.. figure:: images/how_umap_works_raw_data.png - :alt: Test data set of a noisy sine wave - - Test data set of a noisy sine wave - - +![Test data set of a noisy sine wave](images/how_umap_works_raw_data.png) +*Test data set of a noisy sine wave* If we fix a radius we can then picture the open sets of our cover as circles (since we are in a nice visualizable two dimensional case). The result is something like this -.. figure:: images/how_umap_works_open_cover.png - :alt: A basic open cover of the test data - - A basic open cover of the test data - - +![A basic open cover of the test data](images/how_umap_works_open_cover.png) +*A basic open cover of the test data* We can then depict the the simplicial complex of 0-, 1-, and 2-simplices as points, lines, and triangles -.. figure:: images/how_umap_works_basic_graph.png - :alt: A simplicial complex built from the test data - - A simplicial complex built from the test data - - +![A simplicial complex built from the test data](images/how_umap_works_basic_graph.png) +*A simplicial complex built from the test data* It is harder to easily depict the higher dimensional simplices, but you can imagine how they would fit in. There are two things to note here: @@ -168,8 +152,8 @@ topological representation is just a graph, and finding a low dimensional representation can be described as a `graph layout problem `__. If one wants to use, for example, spectral methods for graph layout then we arrive at algorithms like `Laplacian -eigenmaps `__ and `Diffusion maps `__. Force directed layouts are -also an option, and provide algorithms closer to `MDS `__ or `Sammon +eigenmaps `__ and [Diffusion maps](https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction#Diffusion_maps). Force directed layouts are +also an option, and provide algorithms closer to [MDS](https://en.wikipedia.org/wiki/Multidimensional_scaling) or `Sammon mapping `__ in flavour. I would not blame those who have read this far to wonder why we took @@ -190,8 +174,8 @@ simple computationally, understanding *why* various manipulations matter is important to truly understanding the algorithm (as opposed to merely computing with it). -Adapting to Real World Data ---------------------------- +## Adapting to Real World Data + The approach described above provides a nice theory for why a neighborhood graph based approach should capture manifold structure when @@ -228,12 +212,8 @@ manifold it is not hard to pick a good radius (a little above half the average distance between points) and the resulting open cover looks pretty good: -.. figure:: images/how_umap_works_uniform_distribution_cover.png - :alt: Open balls over uniformly\_distributed\_data - - Open balls over uniformly\_distributed\_data - - +![Open balls over uniformly\_distributed\_data](images/how_umap_works_uniform_distribution_cover.png) +*Open balls over uniformly\_distributed\_data* Because the data is evenly spread we actually cover the underlying manifold and don't end up with clumping. In other words, all this theory @@ -264,12 +244,8 @@ the local sense of distance. Each point is given its own unique distance function, and we can simply select balls of radius one with respect to that local distance function! -.. figure:: images/how_umap_works_local_metric_open_cover.png - :alt: Open balls of radius one with a locally varying metric - - Open balls of radius one with a locally varying metric - - +![Open balls of radius one with a locally varying metric](images/how_umap_works_local_metric_open_cover.png) +*Open balls of radius one with a locally varying metric* This theoretically derived result matches well with many traditional graph based algorithms: a standard approach for such algorithms is to @@ -311,12 +287,8 @@ that points are in a ball of a given radius will decay as we move away from the center of the ball. We could visualize such a fuzzy cover as looking something like this -.. figure:: images/how_umap_works_fuzzy_open_cover.png - :alt: Fuzzy open balls of radius one with a locally varying metric - - Fuzzy open balls of radius one with a locally varying metric - - +![Fuzzy open balls of radius one with a locally varying metric](images/how_umap_works_fuzzy_open_cover.png) +*Fuzzy open balls of radius one with a locally varying metric* None of that is very concrete or formal -- it is merely an intuitive picture of what we would like to have happen. It turns out that we can @@ -329,7 +301,7 @@ metric spaces and fuzzy simplicial sets. The mathematics involved in this is outside the scope of this exposition, but for those interested you can look at the `original work on this by David Spivak `__ -and our `paper `__. It will have to +and our [paper](https://arxiv.org/abs/1802.03426). It will have to suffice to say that there is some mathematical machinery that lets us realize this intuition in a well defined way. @@ -356,11 +328,8 @@ confidence decay in terms of distance *beyond* the first nearest neighbor. We can visualize the result in terms of our example dataset again. -.. figure:: images/how_umap_works_umap_open_cover.png - :alt: Local connectivity and fuzzy open sets - - Local connectivity and fuzzy open sets - +![Local connectivity and fuzzy open sets](images/how_umap_works_umap_open_cover.png) +*Local connectivity and fuzzy open sets* Again this can be formalized in terms of the aforementioned mathematical machinery from algebraic topology. From a practical standpoint this @@ -385,11 +354,8 @@ point is right? How do we decide? Going back to our graph based intuition we can think of this as having directed edges with varying weights something like this. -.. figure:: images/how_umap_works_raw_graph.png - :alt: Edges with incompatible weights - - Edges with incompatible weights - +![Edges with incompatible weights](images/how_umap_works_raw_graph.png) +*Edges with incompatible weights* Between any two points we might have up to two edges and the weights on those edges disagree with one another. There are a number of options for @@ -404,9 +370,9 @@ depending on the nature of the logic involved, but here we have relatively clear probabilistic semantics that make the choice straightforward. In graph terms what we get is the following: if we want to merge together two disagreeing edges with weight *a* and *b* then we -should have a single edge with combined weight :math:`a + b - a \cdot b`. -The way to think of this is that the weights are effectively the -probabilities that an edge (1-simplex) exists. The combined weight is +should have a single edge with combined weight :math:`a + b - a \cdot b`. +The way to think of this is that the weights are effectively the +probabilities that an edge (1-simplex) exists. The combined weight is then the probability that at least one of the edges exists. If we apply this process to union together all the fuzzy simplicial sets @@ -416,12 +382,8 @@ applying the edge weight combination formula across the whole graph (with non-edges having a weight of 0). In the end we have something that looks like this. -.. figure:: images/how_umap_works_umap_graph.png - :alt: Graph with combined edge weights - - Graph with combined edge weights - - +![Graph with combined edge weights](images/how_umap_works_umap_graph.png) +*Graph with combined edge weights* So in some sense in the end we have simply constructed a weighted graph (although we could make use of higher dimensional simplices if we @@ -438,8 +400,8 @@ So given that we now have a fuzzy topological representation of the data underlying the data), how do we go about converting that into a low dimensional representation? -Finding a Low Dimensional Representation ----------------------------------------- +## Finding a Low Dimensional Representation + Ideally we want the low dimensional representation to have as similar a fuzzy topological structure as possible. The first question @@ -463,7 +425,7 @@ is that we made use of the distance to the nearest neighbor, again something we computed given the data. This is also a property we would like to be globally true across the manifold as we optimize toward a good low dimensional representation, so we will have to accept it as a -hyper-parameter ``min_dist`` to the algorithm. +hyper-parameter `min_dist` to the algorithm. The second question, 'how do we find a good low dimensional representation', hinges on our ability to measure how "close" a match we @@ -489,7 +451,7 @@ have weight functions such that :math:`w_h(e)` is the weight of the the weight of :math:`e` in the low dimensional case, then the cross entropy will be -.. math:: + \sum_{e\in E} w_h(e) \log\left(\frac{w_h(e)}{w_l(e)}\right) + (1 - w_h(e)) \log\left(\frac{1 - w_h(e)}{1 - w_l(e)}\right) @@ -517,8 +479,8 @@ will let the low dimensional representation settle into a state that relatively accurately represents the overall topology of the source data. -The UMAP Algorithm ------------------- +## The UMAP Algorithm + Putting all these pieces together we can construct the UMAP algorithm. The first phase consists of constructing a fuzzy topological diff --git a/doc/images/galaxy10_2D_densmap.svg b/doc/images/galaxy10_2D_densmap.svg index 2fd158c9..400dbbd1 100644 --- a/doc/images/galaxy10_2D_densmap.svg +++ b/doc/images/galaxy10_2D_densmap.svg @@ -10,33 +10,33 @@ - - - @@ -696,8 +696,8 @@ z - @@ -707,61 +707,61 @@ L 0 3.5 - - - - @@ -782,28 +782,28 @@ z - @@ -865,8 +865,8 @@ z - @@ -889,21 +889,21 @@ L -3.5 0 - @@ -921,34 +921,34 @@ z - @@ -966,43 +966,43 @@ z - @@ -1020,17 +1020,17 @@ z - @@ -1084,99 +1084,99 @@ z - - - - - - - - - - - - - - - @@ -1184,8 +1184,8 @@ L 225.72 56.736 - @@ -1234,36 +1234,36 @@ L 3.5 0 - @@ -1320,13 +1320,13 @@ z - @@ -1357,34 +1357,34 @@ z - @@ -1395,14 +1395,14 @@ z - diff --git a/doc/images/galaxy10_2D_densmap_supervised.svg b/doc/images/galaxy10_2D_densmap_supervised.svg index 49a97731..f20b7f77 100644 --- a/doc/images/galaxy10_2D_densmap_supervised.svg +++ b/doc/images/galaxy10_2D_densmap_supervised.svg @@ -10,33 +10,33 @@ - - - @@ -696,8 +696,8 @@ z - @@ -707,44 +707,44 @@ L 0 3.5 - - - @@ -764,28 +764,28 @@ z - @@ -826,8 +826,8 @@ z - @@ -837,34 +837,34 @@ L -3.5 0 - - @@ -901,13 +901,13 @@ z - @@ -992,36 +992,36 @@ z - @@ -1035,99 +1035,99 @@ z - - - - - - - - - - - - - - - @@ -1135,8 +1135,8 @@ L 225.72 56.736 - @@ -1198,21 +1198,21 @@ L 3.5 0 - @@ -1243,34 +1243,34 @@ z - @@ -1301,43 +1301,43 @@ z - @@ -1355,34 +1355,34 @@ z - @@ -1393,14 +1393,14 @@ z - diff --git a/doc/images/galaxy10_2D_densmap_supervised_prediction.svg b/doc/images/galaxy10_2D_densmap_supervised_prediction.svg index c337608c..0d6e130b 100644 --- a/doc/images/galaxy10_2D_densmap_supervised_prediction.svg +++ b/doc/images/galaxy10_2D_densmap_supervised_prediction.svg @@ -10,33 +10,33 @@ - - - @@ -696,8 +696,8 @@ z - @@ -707,44 +707,44 @@ L 0 3.5 - - - @@ -764,28 +764,28 @@ z - @@ -826,8 +826,8 @@ z - @@ -837,34 +837,34 @@ L -3.5 0 - - @@ -901,13 +901,13 @@ z - @@ -992,36 +992,36 @@ z - @@ -1035,99 +1035,99 @@ z - - - - - - - - - - - - - - - @@ -1135,8 +1135,8 @@ L 225.72 56.736 - @@ -1198,21 +1198,21 @@ L 3.5 0 - @@ -1243,34 +1243,34 @@ z - @@ -1301,43 +1301,43 @@ z - @@ -1355,34 +1355,34 @@ z - @@ -1393,14 +1393,14 @@ z - diff --git a/doc/images/galaxy10_2D_umap.svg b/doc/images/galaxy10_2D_umap.svg index 1ada0205..10a7462d 100644 --- a/doc/images/galaxy10_2D_umap.svg +++ b/doc/images/galaxy10_2D_umap.svg @@ -10,33 +10,33 @@ - - - @@ -696,8 +696,8 @@ z - @@ -707,58 +707,58 @@ L 0 3.5 - - - @@ -778,25 +778,25 @@ z - @@ -816,13 +816,13 @@ z - @@ -842,17 +842,17 @@ z - @@ -869,8 +869,8 @@ z - @@ -906,36 +906,36 @@ L -3.5 0 - @@ -953,21 +953,21 @@ z - @@ -991,99 +991,99 @@ z - - - - - - - - - - - - - - - @@ -1091,8 +1091,8 @@ L 225.72 56.736 - @@ -1180,34 +1180,34 @@ L 3.5 0 - @@ -1238,43 +1238,43 @@ z - @@ -1292,34 +1292,34 @@ z - @@ -1330,14 +1330,14 @@ z - diff --git a/doc/images/galaxy10_2D_umap_supervised.svg b/doc/images/galaxy10_2D_umap_supervised.svg index 4a9b72df..6c65f0cd 100644 --- a/doc/images/galaxy10_2D_umap_supervised.svg +++ b/doc/images/galaxy10_2D_umap_supervised.svg @@ -10,33 +10,33 @@ - - - @@ -696,8 +696,8 @@ z - @@ -707,58 +707,58 @@ L 0 3.5 - - - @@ -778,25 +778,25 @@ z - @@ -816,13 +816,13 @@ z - @@ -842,17 +842,17 @@ z - @@ -885,8 +885,8 @@ z - @@ -896,43 +896,43 @@ L -3.5 0 - @@ -978,21 +978,21 @@ z - @@ -1011,34 +1011,34 @@ z - @@ -1050,99 +1050,99 @@ z - - - - - - - - - - - - - - - @@ -1150,8 +1150,8 @@ L 225.72 56.736 - @@ -1200,36 +1200,36 @@ L 3.5 0 - @@ -1312,34 +1312,34 @@ z - @@ -1350,14 +1350,14 @@ z - diff --git a/doc/images/galaxy10_2D_umap_supervised_prediction.svg b/doc/images/galaxy10_2D_umap_supervised_prediction.svg index 2381a3c6..274c4fb1 100644 --- a/doc/images/galaxy10_2D_umap_supervised_prediction.svg +++ b/doc/images/galaxy10_2D_umap_supervised_prediction.svg @@ -10,33 +10,33 @@ - - - @@ -696,8 +696,8 @@ z - @@ -707,58 +707,58 @@ L 0 3.5 - - - @@ -778,25 +778,25 @@ z - @@ -816,13 +816,13 @@ z - @@ -842,17 +842,17 @@ z - @@ -885,8 +885,8 @@ z - @@ -896,43 +896,43 @@ L -3.5 0 - @@ -978,21 +978,21 @@ z - @@ -1011,34 +1011,34 @@ z - @@ -1050,99 +1050,99 @@ z - - - - - - - - - - - - - - - @@ -1150,8 +1150,8 @@ L 225.72 56.736 - @@ -1200,36 +1200,36 @@ L 3.5 0 - @@ -1312,34 +1312,34 @@ z - @@ -1350,14 +1350,14 @@ z - diff --git a/doc/images/galaxy10_subset.svg b/doc/images/galaxy10_subset.svg index 60e3c0b2..ee68361e 100644 --- a/doc/images/galaxy10_subset.svg +++ b/doc/images/galaxy10_subset.svg @@ -10,99 +10,99 @@ - - - - - - - - - - - - @@ -110,8 +110,8 @@ z - @@ -121,25 +121,25 @@ L 0 3.5 - @@ -157,28 +157,28 @@ z - @@ -196,21 +196,21 @@ z - @@ -228,34 +228,34 @@ z - @@ -273,43 +273,43 @@ z - @@ -321,94 +321,94 @@ z - - - - @@ -425,8 +425,8 @@ z - @@ -449,17 +449,17 @@ L -3.5 0 - @@ -492,36 +492,36 @@ z - @@ -554,28 +554,28 @@ z - @@ -602,158 +602,158 @@ z - - - - - - - - @@ -771,120 +771,120 @@ z - - - - - - - - diff --git a/doc/index.md b/doc/index.md new file mode 100644 index 00000000..079fde0e --- /dev/null +++ b/doc/index.md @@ -0,0 +1,100 @@ + + sphinx-quickstart on Fri Jun 8 10:09:40 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +![Image](logo_large.png) + + :width: 600 + :align: center + +# UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction + + +Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction +technique that can be used for visualisation similarly to t-SNE, but also for +general non-linear dimension reduction. The algorithm is founded on three +assumptions about the data + +1. The data is uniformly distributed on Riemannian manifold; +2. The Riemannian metric is locally constant (or can be approximated as such); +3. The manifold is locally connected. + +From these assumptions it is possible to model the manifold with a fuzzy +topological structure. The embedding is found by searching for a low dimensional +projection of the data that has the closest possible equivalent fuzzy +topological structure. + +The details for the underlying mathematics can be found in +[our paper on ArXiv](https://arxiv.org/abs/1802.03426): + +McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection +for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018 + +You can find the software [on github](https://github.com/lmcinnes/umap). + +**Installation** + +Install UMAP via pip: + +```bash +pip install umap + +``` + +This will install UMAP and all required dependencies. For development installation, see the README. + + +## User Guide / Tutorial: + +- [basic_usage](basic_usage) +- [parameters](parameters) +- [plotting](plotting) +- [reproducibility](reproducibility) +- [transform](transform) +- [inverse_transform](inverse_transform) +- [parametric_umap](parametric_umap) +- [transform_landmarked_pumap](transform_landmarked_pumap) +- [sparse](sparse) +- [supervised](supervised) +- [clustering](clustering) +- [outliers](outliers) +- [composing_models](composing_models) +- [densmap_demo](densmap_demo) +- [mutual_nn_umap](mutual_nn_umap) +- [document_embedding](document_embedding) +- [embedding_space](embedding_space) +- [aligned_umap_basic_usage](aligned_umap_basic_usage) +- [aligned_umap_politics_demo](aligned_umap_politics_demo) +- [precomputed_k-nn](precomputed_k-nn) +- [benchmarking](benchmarking) +- [release_notes](release_notes) +- [faq](faq) + +## Background on UMAP: + +- [how_umap_works](how_umap_works) +- [performance](performance) + +## Examples of UMAP usage + +- [interactive_viz](interactive_viz) +- [exploratory_analysis](exploratory_analysis) +- [scientific_papers](scientific_papers) +- [nomic_atlas_umap_of_text_embeddings](nomic_atlas_umap_of_text_embeddings) +- [nomic_atlas_visualizing_mnist_training](nomic_atlas_visualizing_mnist_training) + +## API Reference: + +- [api](api) + +## Development: + +- [development_roadmap](development_roadmap) + +# Indices and tables + + +* `genindex` +* `modindex` +* `search` diff --git a/doc/index.rst b/doc/index.rst deleted file mode 100644 index a2f099a6..00000000 --- a/doc/index.rst +++ /dev/null @@ -1,108 +0,0 @@ -.. umap documentation master file, created by - sphinx-quickstart on Fri Jun 8 10:09:40 2018. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -.. image:: logo_large.png - :width: 600 - :align: center - -UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction -=========================================================================== - -Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction -technique that can be used for visualisation similarly to t-SNE, but also for -general non-linear dimension reduction. The algorithm is founded on three -assumptions about the data - -1. The data is uniformly distributed on Riemannian manifold; -2. The Riemannian metric is locally constant (or can be approximated as such); -3. The manifold is locally connected. - -From these assumptions it is possible to model the manifold with a fuzzy -topological structure. The embedding is found by searching for a low dimensional -projection of the data that has the closest possible equivalent fuzzy -topological structure. - -The details for the underlying mathematics can be found in -`our paper on ArXiv `_: - -McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection -for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018 - -You can find the software `on github `_. - -**Installation** - -Install UMAP via pip: - -.. code:: bash - - pip install umap-learn - -This will install UMAP and all required dependencies. For development installation, see the README. - - -.. toctree:: - :maxdepth: 2 - :caption: User Guide / Tutorial: - - basic_usage - parameters - plotting - reproducibility - transform - inverse_transform - parametric_umap - transform_landmarked_pumap - sparse - supervised - clustering - outliers - composing_models - densmap_demo - mutual_nn_umap - document_embedding - embedding_space - aligned_umap_basic_usage - aligned_umap_politics_demo - precomputed_k-nn - benchmarking - release_notes - faq - -.. toctree:: - :maxdepth: 2 - :caption: Background on UMAP: - - how_umap_works - performance - -.. toctree:: - :maxdepth: 2 - :caption: Examples of UMAP usage - - interactive_viz - exploratory_analysis - scientific_papers - nomic_atlas_umap_of_text_embeddings - nomic_atlas_visualizing_mnist_training - -.. toctree:: - :caption: API Reference: - - api - -.. toctree:: - :caption: Development: - - development_roadmap - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/doc/interactive_viz.rst b/doc/interactive_viz.md similarity index 68% rename from doc/interactive_viz.rst rename to doc/interactive_viz.md index eb58b4b0..d182eecb 100644 --- a/doc/interactive_viz.rst +++ b/doc/interactive_viz.md @@ -1,43 +1,43 @@ -Interactive Visualizations -========================== +# Interactive Visualizations + UMAP has found use in a number of interesting interactive visualization projects, analyzing everything from images from photo archives, to word embedding, animal point clouds, and even sound. Sometimes it has also been used in interesting interactive tools that simply help a user to get an intuition for what the algorithm is doing (by applying it to intuitive 3D data). Below are some amazing projects that make use of UMAP. -UMAP Zoo --------- +## UMAP Zoo + An exploration of how UMAP behaves when dimension reducing point clouds of animals. It is interactive, letting you switch between 2D and 3D representations and has a wide selection of different animals. Attempting to guess the animal from the 2D UMAP representation is a fun game. In practice this tool can go a long way to helping to build at least some intuitions for what UMAP tends to do with data. -.. image:: images/UMAP_zoo.png - :width: 400px +![Image](images/UMAP_zoo.png) + -`UMAP Zoo `__ +[UMAP Zoo](https://duhaime.s3.amazonaws.com/apps/umap-zoo/index.html) Thanks to Douglas Duhaime. -Tensorflow Embedding Projector ------------------------------- +## Tensorflow Embedding Projector + If you just want to explore UMAP embeddings of datasets then the Embedding Projector from Tensorflow is a great way to do that. As well as having a good interactive 3D view it also has facilities for inspecting and searching labels and tags on the data. By default it loads up word2vec vectors, but you can upload any data you wish. You can then select the UMAP option among the tabs for embeddings choices (alongside PCA and t-SNE). -.. image:: images/embedding_projector.png - :width: 400px +![Image](images/embedding_projector.png) + -`Embedding Projector `__ +[Embedding Projector](https://projector.tensorflow.org/) Thanks to Andy Coenen and the Embedding Projector team. -PixPlot -------- +## PixPlot + PixPlot provides an overview of large photo-collections. In the demonstration app from Yale's Digital Humanities lab it provides a window on the Meserve-Kunhardt Collection of historical photographs. The approach uses convolutional neural nets to reduce the images @@ -47,15 +47,15 @@ ending up in similar regions of the map allowing for easy perusal of large photo collections. The PixPlot project is also available on github in case you wish to train it on your own photo collection. -.. image:: images/pixplot.png - :width: 400px +![Image](images/pixplot.png) + -`PixPlot `__ +[PixPlot](https://dhlab.yale.edu/projects/pixplot/) Thanks to Douglas Duhaime and the Digital Humanities lab at Yale. -UMAP Explorer -------------- +## UMAP Explorer + A great demonstration of building a web based app for interactively exploring a UMAP embedding. In this case it provides an exploration of UMAP run on the MNIST digits dataset. Each point in the embedding is rendered as the digit image, and coloured according to the digit class. Mousing @@ -63,15 +63,15 @@ over the images will make them larger and provide a view of the digit in the upp and zoom around the emebdding to get a better understanding of how UMAP has mapped the different styles of handwritten digits down to 2 dimensions. -.. image:: images/umap_explorer.png - :width: 400px +![Image](images/umap_explorer.png) + -`UMAP Explorer `__ +[UMAP Explorer](https://grantcuster.github.io/umap-explorer/) Thanks to Grant Custer. -Audio Explorer --------------- +## Audio Explorer + The Audio Explorer uses UMAP to embed sound samples into a 2 dimensional space for easy exploration. The goal here is to take a large library of sounds samples and put similar sounds in similar regions of the map, allowing a user to quickly mouse over and listen to various variations of a given sample @@ -79,88 +79,84 @@ to quickly find exactly the right sound sample to use. Audio explorer uses MFCCs provide an initial useful vector representation of the sound samples, before applying UMAP to generate the 2D embedding. -.. image:: images/audio_explorer.png - :width: 400px +![Image](images/audio_explorer.png) + -`Audio Explorer `__ +[Audio Explorer](http://doc.gold.ac.uk/~lfedd001/three/demo.html) Thanks to Leon Fedden. -Orion Search ------------- +## Orion Search + Orion is an open source research measurement and knowledge discovery tool that enables you to monitor progress in science, visually explore the scientific landscape and search for relevant publications. Orion encodes bioRxiv paper abstracts to dense vectors with Sentence Transformers and projects them to an interactive 3D visualisation with UMAP. You can filter the UMAP embeddings by topic and country. You can also select a subset of the UMAP embeddings and retrieve those papers and their metadata. -.. image:: images/orion_particles.png - :width: 400px +![Image](images/orion_particles.png) + -`Orion Search `__ +[Orion Search](https://www.orion-search.org/) Thanks to Kostas Stathoulopoulos, Zac Ioannidis and Lilia Villafuerte. -Exploring Fashion MNIST ------------------------ +## Exploring Fashion MNIST + A web based interactive exploration of a 3D UMAP embedding ran on the Fashion MNIST dataset. Users can freely navigate the 3D space, jumping to a specific image by clicking an image or entering an image id. Like Grant Custer's UMAP Explorer, each point is rendered as the actual image and colored according to the label. It is also similar to the Tensorflow Embedding Projector, but designed more specifically for Fashion MNIST, thus more efficient and capable of showing all the 70k images. -.. image:: images/exploring_fashion_mnist.png - :width: 400px +![Image](images/exploring_fashion_mnist.png) + -`Exploring Fashion MNIST `__ +[Exploring Fashion MNIST](https://observablehq.com/@stwind/exploring-fashion-mnist/) Thanks to stwind. -ESM Metagenomic Atlas ---------------------- -The ESM Metagenomic Atlas contains over 600 million predicted protein structures, revealing the -metagenomic world in a way we have never seen before. The Explore page visualizes a sample of 1 -million of these. (That’s about how much a browser can handle.) We represent each protein in this -dataset as a single point, and reveal the actual protein structure when zooming in or when hovering -over it. The color of each point corresponds to the similarity to the closest match we could find in -UniRef90, the reference database of known protein sequences. The position in the map is a -two-dimensional projection, which groups sequences by similarity, as determined by our language -model’s internal representation. The map reveals structure at different scales: local neighbors in -the same cluster tend to have similar structures, while nearby clusters preserve certain patterns +## ESM Metagenomic Atlas + +The ESM Metagenomic Atlas contains over 600 million predicted protein structures, revealing the +metagenomic world in a way we have never seen before. The Explore page visualizes a sample of 1 +million of these. (That’s about how much a browser can handle.) We represent each protein in this +dataset as a single point, and reveal the actual protein structure when zooming in or when hovering +over it. The color of each point corresponds to the similarity to the closest match we could find in +UniRef90, the reference database of known protein sequences. The position in the map is a +two-dimensional projection, which groups sequences by similarity, as determined by our language +model’s internal representation. The map reveals structure at different scales: local neighbors in +the same cluster tend to have similar structures, while nearby clusters preserve certain patterns like secondary structure elements. -.. image:: images/ESM_metagenomic_atlas.png - :width: 400px +![Image](images/ESM_metagenomic_atlas.png) + Thanks to the authors of "Evolutionary-scale prediction of atomic level protein structure with a language model". -`ESM Metagenomic Atlas `__ +[ESM Metagenomic Atlas](https://esmatlas.com/explore) + +## Interactive UMAP with Nomic Atlas -Interactive UMAP with Nomic Atlas ---------------------------------- -`Nomic Atlas `_ is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. +[Nomic Atlas](https://atlas.nomic.ai/) is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. -.. image:: https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif - :alt: UMAP interactive visualization with Nomic Atlas - :align: center - :width: 600 +![UMAP interactive visualization with Nomic Atlas](https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif) + Atlas provides: -* In-browser analysis of your UMAP data with the `Atlas Analyst `_ -* Vector search over your UMAP data using the `Nomic API `_ -* Interactive features like zooming, recoloring, searching, and filtering in the `Nomic Atlas data map `_ +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) * Scalability for millions of data points * Rich information display on hover * Shareable UMAPs via URL links to your embeddings and data maps in Atlas ----------- -.. toctree:: - :maxdepth: 1 - :caption: Nomic Atlas Examples +## Nomic Atlas Examples - nomic_atlas_umap_of_text_embeddings - nomic_atlas_visualizing_mnist_training_dynamics +- [nomic_atlas_umap_of_text_embeddings](nomic_atlas_umap_of_text_embeddings) +- [nomic_atlas_visualizing_mnist_training_dynamics](nomic_atlas_visualizing_mnist_training_dynamics) diff --git a/doc/inverse_transform.rst b/doc/inverse_transform.md similarity index 51% rename from doc/inverse_transform.rst rename to doc/inverse_transform.md index 97ae928b..5c7e1b08 100644 --- a/doc/inverse_transform.rst +++ b/doc/inverse_transform.md @@ -1,134 +1,139 @@ -Inverse transforms -================== +# Inverse transforms + UMAP has some support for inverse transforms -- generating a high dimensional data sample given a location in the low dimensional embedding space. To start let's load all the relevant libraries. -.. code:: python3 +```python3 +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import seaborn as sns +import sklearn.datasets +import umap +import umap.plot - import numpy as np - import matplotlib.pyplot as plt - from matplotlib.gridspec import GridSpec - import seaborn as sns - import sklearn.datasets - import umap - import umap.plot +``` We will need some data to test with. To start we'll use the MNIST digits dataset. This is a dataset of 70000 handwritten digits encoded as grayscale 28x28 pixel images. Our goal is to use UMAP to reduce the dimension of this dataset to something small, and then see if we can generate new digits by sampling points from the embedding space. To load -the MNIST dataset we'll make use of sklearn's ``fetch_openml`` function. +the MNIST dataset we'll make use of sklearn's `fetch_openml` function. -.. code:: python3 +```python3 +data, labels = sklearn.datasets.fetch_openml('mnist_784', version=1, return_X_y=True) - data, labels = sklearn.datasets.fetch_openml('mnist_784', version=1, return_X_y=True) +``` Now we need to generate a reduced dimension representation of this data. This is straightforward with UMAP, but in this case rather than using -``fit_transform`` we'll use the fit method so that we can retain the +`fit_transform` we'll use the fit method so that we can retain the trained model for later generating new digits based on samples from the embedding space. -.. code:: python3 +```python3 +mapper = umap.UMAP(random_state=42).fit(data) - mapper = umap.UMAP(random_state=42).fit(data) +``` To ensure that things worked correctly we can plot the data (since we -reduced it to two dimensions). We'll use the ``umap.plot`` functionality +reduced it to two dimensions). We'll use the `umap.plot` functionality to do this. -.. code:: python3 - - umap.plot.points(mapper, labels=labels) +```python3 +umap.plot.points(mapper, labels=labels) -.. image:: images/inverse_transform_7_1.png +``` +![Image](images/inverse_transform_7_1.png) This looks much like we would expect. The different digit classes have been decently separated. Now we need to create a set of samples in the -embedding space to apply the ``inverse_transform`` operation to. To do +embedding space to apply the `inverse_transform` operation to. To do this we'll generate a grid of samples linearly interpolating between four corner points. To make our selection interesting we'll carefully choose the corners to span over the dataset, and sample different digits so that we can better see the transitions. -.. code:: python3 - - corners = np.array([ - [-5, -10], # 1 - [-7, 6], # 7 - [2, -8], # 2 - [12, 4], # 0 - ]) - - test_pts = np.array([ - (corners[0]*(1-x) + corners[1]*x)*(1-y) + - (corners[2]*(1-x) + corners[3]*x)*y - for y in np.linspace(0, 1, 10) - for x in np.linspace(0, 1, 10) - ]) - -Now we can apply the ``inverse_transform`` method to this set of test +```python3 +corners = np.array([ + [-5, -10], # 1 + [-7, 6], # 7 + [2, -8], # 2 + [12, 4], # 0 +]) + +test_pts = np.array([ + (corners[0]*(1-x) + corners[1]*x)*(1-y) + + (corners[2]*(1-x) + corners[3]*x)*y + for y in np.linspace(0, 1, 10) + for x in np.linspace(0, 1, 10) +]) + +``` + +Now we can apply the `inverse_transform` method to this set of test points. Each test point is a two dimensional point lying somewhere in -the embedding space. The ``inverse_transform`` method will convert this +the embedding space. The `inverse_transform` method will convert this into an approximation of the high dimensional representation that would have been embedded into such a location. Following the sklearn API this -is as simple to use as calling the ``inverse_transform`` method of the +is as simple to use as calling the `inverse_transform` method of the trained model and passing it the set of test points that we want to convert into high dimensional representations. Be warned that this can be quite expensive computationally. -.. code:: python3 +```python3 +inv_transformed_points = mapper.inverse_transform(test_pts) - inv_transformed_points = mapper.inverse_transform(test_pts) +``` Now the goal is to visualize how well we have done. Effectively what we would like to do is show the test points in the embedding space, and then show a grid of the corresponding images generated by the inverse transform. To get all of this in a single matplotlib figure takes a little setting up, but is quite manageable -- mostly it is just a matter -of managing ``GridSpec`` formatting. Once we have that setup we just +of managing `GridSpec` formatting. Once we have that setup we just need a scatterplot of the embedding, a scatterplot of the test points, and finally a grid of the images we generated (converting the inverse transformed vectors into images is just a matter of reshaping them back -to 28 by 28 pixel grids and using ``imshow``). - -.. code:: python3 - - # Set up the grid - fig = plt.figure(figsize=(12,6)) - gs = GridSpec(10, 20, fig) - scatter_ax = fig.add_subplot(gs[:, :10]) - digit_axes = np.zeros((10, 10), dtype=object) - for i in range(10): - for j in range(10): - digit_axes[i, j] = fig.add_subplot(gs[i, 10 + j]) - - # Use umap.plot to plot to the major axis - # umap.plot.points(mapper, labels=labels, ax=scatter_ax) - scatter_ax.scatter(mapper.embedding_[:, 0], mapper.embedding_[:, 1], - c=labels.astype(np.int32), cmap='Spectral', s=0.1) - scatter_ax.set(xticks=[], yticks=[]) - - # Plot the locations of the text points - scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', c='k', s=15) - - # Plot each of the generated digit images - for i in range(10): - for j in range(10): - digit_axes[i, j].imshow(inv_transformed_points[i*10 + j].reshape(28, 28)) - digit_axes[i, j].set(xticks=[], yticks=[]) - - - -.. image:: images/inverse_transform_13_0.png +to 28 by 28 pixel grids and using `imshow`). + +```python3 +# Set up the grid +fig = plt.figure(figsize=(12,6)) +gs = GridSpec(10, 20, fig) +scatter_ax = fig.add_subplot(gs[:, :10]) +digit_axes = np.zeros((10, 10), dtype=object) +for i in range(10): + for j in range(10): + digit_axes[i, j] = fig.add_subplot(gs[i, 10 + j]) + +# Use umap.plot to plot to the major axis +# umap.plot.points(mapper, labels=labels, ax=scatter_ax) +scatter_ax.scatter(mapper.embedding_[:, 0], mapper.embedding_[:, 1], + c=labels.astype(np.int32), cmap='Spectral', s=0.1) +scatter_ax.set(xticks=[], yticks=[]) + +# Plot the locations of the text points +scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', c='k', s=15) + +# Plot each of the generated digit images +for i in range(10): + for j in range(10): + digit_axes[i, j].imshow(inv_transformed_points[i*10 + j].reshape(28, 28)) + digit_axes[i, j].set(xticks=[], yticks=[]) + + +``` + +![Image](images/inverse_transform_13_0.png) The end result looks pretty good -- we did indeed generate plausible looking digit images, and many of the transitions (from 1 to 7 across @@ -151,90 +156,94 @@ the bounds about the embedding you will likely get strange results Let's continue the demonstration by looking at the Fashion MNIST dataset. As before we can load this through sklearn. -.. code:: python3 +```python3 +data, labels = sklearn.datasets.fetch_openml('Fashion-MNIST', version=1, return_X_y=True) - data, labels = sklearn.datasets.fetch_openml('Fashion-MNIST', version=1, return_X_y=True) +``` Again we can fit this data with UMAP and get a mapper object. -.. code:: python3 +```python3 +mapper = umap.UMAP(random_state=42).fit(data) - mapper = umap.UMAP(random_state=42).fit(data) +``` Let's plot the embedding to see what we got as a result: -.. code:: python3 - - umap.plot.points(mapper, labels=labels) +```python3 +umap.plot.points(mapper, labels=labels) -.. image:: images/inverse_transform_20_1.png +``` +![Image](images/inverse_transform_20_1.png) Again we'll generate a set of test points by making a grid interpolating between four corners. As before we'll select the corners so that we can stay within the convex hull of the embedding points and ensure nothing too strange happens with the inverse transforms. -.. code:: python3 - - corners = np.array([ - [-2, -6], # bags - [-9, 3], # boots? - [7, -5], # shirts/tops/dresses - [4, 10], # pants - ]) - - test_pts = np.array([ - (corners[0]*(1-x) + corners[1]*x)*(1-y) + - (corners[2]*(1-x) + corners[3]*x)*y - for y in np.linspace(0, 1, 10) - for x in np.linspace(0, 1, 10) - ]) +```python3 +corners = np.array([ + [-2, -6], # bags + [-9, 3], # boots? + [7, -5], # shirts/tops/dresses + [4, 10], # pants +]) + +test_pts = np.array([ + (corners[0]*(1-x) + corners[1]*x)*(1-y) + + (corners[2]*(1-x) + corners[3]*x)*y + for y in np.linspace(0, 1, 10) + for x in np.linspace(0, 1, 10) +]) + +``` Now we simply apply the inverse transform just as before. Again, be warned, this is quite expensive computationally and may take some time to complete. -.. code:: python3 +```python3 +inv_transformed_points = mapper.inverse_transform(test_pts) - inv_transformed_points = mapper.inverse_transform(test_pts) +``` And now we can use similar code as above to set up our plot of the embedding with test points overlaid, and the generated images. -.. code:: python3 - - # Set up the grid - fig = plt.figure(figsize=(12,6)) - gs = GridSpec(10, 20, fig) - scatter_ax = fig.add_subplot(gs[:, :10]) - digit_axes = np.zeros((10, 10), dtype=object) - for i in range(10): - for j in range(10): - digit_axes[i, j] = fig.add_subplot(gs[i, 10 + j]) - - # Use umap.plot to plot to the major axis - # umap.plot.points(mapper, labels=labels, ax=scatter_ax) - scatter_ax.scatter(mapper.embedding_[:, 0], mapper.embedding_[:, 1], - c=labels.astype(np.int32), cmap='Spectral', s=0.1) - scatter_ax.set(xticks=[], yticks=[]) - - # Plot the locations of the text points - scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', c='k', s=15) - - # Plot each of the generated digit images - for i in range(10): - for j in range(10): - digit_axes[i, j].imshow(inv_transformed_points[i*10 + j].reshape(28, 28)) - digit_axes[i, j].set(xticks=[], yticks=[]) - - - -.. image:: images/inverse_transform_26_0.png +```python3 +# Set up the grid +fig = plt.figure(figsize=(12,6)) +gs = GridSpec(10, 20, fig) +scatter_ax = fig.add_subplot(gs[:, :10]) +digit_axes = np.zeros((10, 10), dtype=object) +for i in range(10): + for j in range(10): + digit_axes[i, j] = fig.add_subplot(gs[i, 10 + j]) + +# Use umap.plot to plot to the major axis +# umap.plot.points(mapper, labels=labels, ax=scatter_ax) +scatter_ax.scatter(mapper.embedding_[:, 0], mapper.embedding_[:, 1], + c=labels.astype(np.int32), cmap='Spectral', s=0.1) +scatter_ax.set(xticks=[], yticks=[]) + +# Plot the locations of the text points +scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', c='k', s=15) + +# Plot each of the generated digit images +for i in range(10): + for j in range(10): + digit_axes[i, j].imshow(inv_transformed_points[i*10 + j].reshape(28, 28)) + digit_axes[i, j].set(xticks=[], yticks=[]) + + + +``` +![Image](images/inverse_transform_26_0.png) This time we see some of the interpolations between items looking rather strange -- particularly the points that lie somewhere between shoes and diff --git a/doc/mutual_nn_umap.rst b/doc/mutual_nn_umap.md similarity index 75% rename from doc/mutual_nn_umap.rst rename to doc/mutual_nn_umap.md index 0ab22c5b..5c99ccf5 100644 --- a/doc/mutual_nn_umap.rst +++ b/doc/mutual_nn_umap.md @@ -1,5 +1,5 @@ -Improving the Separation Between Similar Classes Using a Mutual k-NN Graph -========================================================================== +# Improving the Separation Between Similar Classes Using a Mutual k-NN Graph + This post briefly explains how the connectivity of the original graphical representation can adversely affect the resulting UMAP embeddings. @@ -18,14 +18,14 @@ deteriorating its performance for various similarity-based machine learning tasks. A recent paper titled -`Clustering with UMAP: Why and How Connectivity Matters `__ +[Clustering with UMAP: Why and How Connectivity Matters](https://arxiv.org/abs/2108.05525) proposes a refinement in the graph construction stage of the UMAP algorithm that uses a weighted mutual k-NN graph rather than it vanilla counterpart, to reduce the undesired distance concentration and hub effects. Mutual k-NN graphs have been shown to contain many desirable properties when combating the “curse of dimensionality” as discussed in -`this paper `__ . However, one pitfall of using a +[this paper](https://arxiv.org/abs/2108.05525) . However, one pitfall of using a mutual k-NN graph over the original k-NN graph is that it often contains disconnected components and potential isolated vertices. @@ -33,33 +33,35 @@ This violates one of UMAP primary assumptions that "The manifold is locally conn combat the issue of isolated components, the authors consider different methods that have been previously used to augment and increase the connectivity of the mutual k-NN graph: -1. ``NN``: To minimally connect isolated vertices and satisfy the assumption that the underlying manifold is locally connected, we add an undirected edge between each isolated vertex and its original nearest neighbor (de Sousa, Rezende, and Batista 2013).Note that the resulting graph may still contain disconnected components. -2. ``MST-min``: To achieve a connected graph, add the minimum number of edges from a maximum spanning tree to the mutual k-NN graph that has been weighted with similarity-based metrics(Ozaki et al. 2011). We adapt this by calculating the minimum spanning tree for distances. -3. ``MST-all``: Adding all the edges of the MST. +1. `NN`: To minimally connect isolated vertices and satisfy the assumption that the underlying manifold is locally connected, we add an undirected edge between each isolated vertex and its original nearest neighbor (de Sousa, Rezende, and Batista 2013).Note that the resulting graph may still contain disconnected components. +2. `MST-min`: To achieve a connected graph, add the minimum number of edges from a maximum spanning tree to the mutual k-NN graph that has been weighted with similarity-based metrics(Ozaki et al. 2011). We adapt this by calculating the minimum spanning tree for distances. +3. `MST-all`: Adding all the edges of the MST. + +![Image](images/mutual_nn_umap_connectivity.png) -.. image:: images/mutual_nn_umap_connectivity.png +They also different ways to obtain the new local neighborhood for each point `x_i`: -They also different ways to obtain the new local neighborhood for each point ``x_i``: +1. `Adjacent Neighbors`: Only consider neighbors that are directly connected(adjacent) to `x_i` in the connected mutual k-NN graph. +2. `Path Neighbors`: Using shortest path distance to find the new k closest points to `x_i` with respect to the connected mutual k-NN graph. This shortest path distance can be considered a new distance metric as it directly aligns with UMAP’s definition of an extended pseudo-metric space. -1. ``Adjacent Neighbors``: Only consider neighbors that are directly connected(adjacent) to ``x_i`` in the connected mutual k-NN graph. -2. ``Path Neighbors``: Using shortest path distance to find the new k closest points to ``x_i`` with respect to the connected mutual k-NN graph. This shortest path distance can be considered a new distance metric as it directly aligns with UMAP’s definition of an extended pseudo-metric space. +![Image](images/mutual_nn_umap_lc.png) -.. image:: images/mutual_nn_umap_lc.png :width: 600 :align: center -Visualizing the Results ----------------------------------------------- +## Visualizing the Results + To see the differences between using a mutual k-NN graph vs the original k-NN graph as the starting topology for UMAP, let's visualize the 2D projections generated for MNIST, FMNIST, and 20 NG Count Vectors using each of the discussed methods. For all code snippets to reproduce the results and visualizations, please refer -to this `Github repo `__. Will be adding this soon as a +to this [Github repo](https://github.com/adalmia96/umap-mnn). Will be adding this soon as a mode to the original implementation. We’ll start with MNIST digits, a collection of 70,000 gray-scale images of hand-written digits: -.. image:: images/mutual_nn_umap_MNIST.png +![Image](images/mutual_nn_umap_MNIST.png) + :width: 850 :align: center @@ -74,7 +76,8 @@ method for removing edges between points that are only loosely similar. Similar results are observed for the Fashion-MNIST(FMNIST) dataset, a collection of 70,000 gray-scale images of fashion items: -.. image:: images/mutual_nn_umap_FMNIST.png +![Image](images/mutual_nn_umap_FMNIST.png) + :width: 850 :align: center @@ -96,7 +99,8 @@ too much connectivity such as with MST-all can also hurt which is further discus Finally, we depict the embeddings generated using the 20 newsgroup dataset, a collection of 18846 documents, transformed using sklearns CountVectorizer: -.. image:: images/mutual_nn_umap_20ngc.png +![Image](images/mutual_nn_umap_20ngc.png) + :width: 850 :align: center @@ -108,14 +112,14 @@ and MST-min result in disperse dense clusters of points e.g, the footwear classe FMNIST and the recreation topics in 20 NG. However for Path Neighbors, the groups of points belonging to the same class are less dispersed. This is because Adjacent Neighbors are not guaranteed to have k connected neighbors for each local neighborhood. Points with smaller neighborhoods will be close to primarily few adjacent -neighbors and repelled further away from the other points. +neighbors and repelled further away from the other points. To evaluate these methods quantitatively, the authors compare the clustering performance of the resulting low dimensional vectors generated. Below shows the Normalised Mutual Information NMI results after performing KMeans(for more information of the results please refer to `the full paper `__). -.. image:: images/mutual_nn_umap_results.png +![Image](images/mutual_nn_umap_results.png) These quantitative experiments show that MST variants combined with Path Neighbors can help produce better clustering results and how the initialization @@ -123,11 +127,12 @@ of a weighted connected graph is critical to the success of topology based dimensionality reduction methods like UMAP. -Citing our work ---------------- +## Citing our work + If you use this implementation or reference the results in your work, please cite the paper: -.. code:: bibtex +```bibtex +``` @article{Dalmia2021UMAPConnectivity, author={Ayush Dalmia and Suzanna Sia}, diff --git a/doc/nomic_atlas_umap_of_text_embeddings.md b/doc/nomic_atlas_umap_of_text_embeddings.md new file mode 100644 index 00000000..8bcd68ba --- /dev/null +++ b/doc/nomic_atlas_umap_of_text_embeddings.md @@ -0,0 +1,88 @@ +UMAP of Text Embeddings with Nomic Atlas +======================= + +[Nomic Atlas](https://atlas.nomic.ai/) is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. + +![UMAP interactive visualization with Nomic Atlas](https://assets.nomicatlas.com/airline-reviews-umap.gif) + + +Nomic Atlas automatically generates embeddings for your data and allows you to explore large datasets in a web browser. Atlas provides: + +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) +* Scalability for millions of data points +* Rich information display on hover +* Shareable UMAPs via URL links to your embeddings and data maps in Atlas + +This example demonstrates how to use [Nomic Atlas](https://docs.nomic.ai/atlas/embeddings-and-retrieval/guides/using-umap-with-atlas) to create interactive maps of text using embeddings and UMAP. + +## Setup + + +1. Get the required python packages with `pip instll nomic pandas` +2. Get a Nomic API key [here](https://atlas.nomic.ai/cli-login) +3. Run `nomic login nk-...` in a terminal window or use the following code: + +```python3 +import nomic +nomic.login('nk-...') + + + +``` + +Download Example Data +-------------------- + +```python3 +import pandas as pd + +# Example data +df = pd.read_csv("https://docs.nomic.ai/singapore_airlines_reviews.csv") + +``` + +## Create Atlas Dataset + + +```python3 +from nomic import AtlasDataset +dataset = AtlasDataset("airline-reviews-data") + +``` + +## Upload to Atlas + + +```python3 +dataset.add_data(df) + +``` + +## Create Data Map + + +We specify the `text` field from `df` as the field to create embeddings from. We choose some standard UMAP parameters as well. + +```python3 +from nomic.data_inference import ProjectionOptions + +# model="umap" is how you choose UMAP in Nomic Atlas +# You can adjust n_neighbors, min_dist, +# and n_epochs as you would with the UMAP library. +atlas_map = dataset.create_index( + indexed_field='text', + projection=ProjectionOptions( + model="umap", + n_neighbors=20, + min_dist=0.01, + n_epochs=200 + ) +) + +print(f"Explore your interactive map at: {atlas_map.map_link}") + +``` + +Your map will be available in your [Atlas Dashboard](https://atlas.nomic.ai/data). diff --git a/doc/nomic_atlas_umap_of_text_embeddings.rst b/doc/nomic_atlas_umap_of_text_embeddings.rst deleted file mode 100644 index f478f8e9..00000000 --- a/doc/nomic_atlas_umap_of_text_embeddings.rst +++ /dev/null @@ -1,85 +0,0 @@ -UMAP of Text Embeddings with Nomic Atlas -======================= - -`Nomic Atlas `_ is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. - -.. image:: https://assets.nomicatlas.com/airline-reviews-umap.gif - :alt: UMAP interactive visualization with Nomic Atlas - :align: center - :width: 600 - -Nomic Atlas automatically generates embeddings for your data and allows you to explore large datasets in a web browser. Atlas provides: - -* In-browser analysis of your UMAP data with the `Atlas Analyst `_ -* Vector search over your UMAP data using the `Nomic API `_ -* Interactive features like zooming, recoloring, searching, and filtering in the `Nomic Atlas data map `_ -* Scalability for millions of data points -* Rich information display on hover -* Shareable UMAPs via URL links to your embeddings and data maps in Atlas - -This example demonstrates how to use `Nomic Atlas `_ to create interactive maps of text using embeddings and UMAP. - -Setup ------ - -1. Get the required python packages with ``pip instll nomic pandas`` -2. Get a Nomic API key `here `_ -3. Run ``nomic login nk-...`` in a terminal window or use the following code: - -.. code:: python3 - - import nomic - nomic.login('nk-...') - - - -Download Example Data --------------------- - -.. code:: python3 - - import pandas as pd - - # Example data - df = pd.read_csv("https://docs.nomic.ai/singapore_airlines_reviews.csv") - -Create Atlas Dataset --------------------- - -.. code:: python3 - - from nomic import AtlasDataset - dataset = AtlasDataset("airline-reviews-data") - -Upload to Atlas ---------------- - -.. code:: python3 - - dataset.add_data(df) - -Create Data Map ---------------- - -We specify the ``text`` field from ``df`` as the field to create embeddings from. We choose some standard UMAP parameters as well. - -.. code:: python3 - - from nomic.data_inference import ProjectionOptions - - # model="umap" is how you choose UMAP in Nomic Atlas - # You can adjust n_neighbors, min_dist, - # and n_epochs as you would with the UMAP library. - atlas_map = dataset.create_index( - indexed_field='text', - projection=ProjectionOptions( - model="umap", - n_neighbors=20, - min_dist=0.01, - n_epochs=200 - ) - ) - - print(f"Explore your interactive map at: {atlas_map.map_link}") - -Your map will be available in your `Atlas Dashboard `_. \ No newline at end of file diff --git a/doc/nomic_atlas_visualizing_mnist_training_dynamics.rst b/doc/nomic_atlas_visualizing_mnist_training_dynamics.md similarity index 78% rename from doc/nomic_atlas_visualizing_mnist_training_dynamics.rst rename to doc/nomic_atlas_visualizing_mnist_training_dynamics.md index b8de571e..857489b0 100644 --- a/doc/nomic_atlas_visualizing_mnist_training_dynamics.rst +++ b/doc/nomic_atlas_visualizing_mnist_training_dynamics.md @@ -1,40 +1,40 @@ Visualizing MNIST Training Dynamics with Nomic Atlas ======================= -`Nomic Atlas `_ is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. +[Nomic Atlas](https://atlas.nomic.ai/) is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. -.. image:: https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif - :alt: UMAP interactive visualization with Nomic Atlas - :align: center - :width: 600 +![UMAP interactive visualization with Nomic Atlas](https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif) + Nomic Atlas automatically generates embeddings for your data and allows you to explore large datasets in a web browser. Atlas provides: -* In-browser analysis of your UMAP data with the `Atlas Analyst `_ -* Vector search over your UMAP data using the `Nomic API `_ -* Interactive features like zooming, recoloring, searching, and filtering in the `Nomic Atlas data map `_ +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) * Scalability for millions of data points * Rich information display on hover * Shareable UMAPs via URL links to your embeddings and data maps in Atlas -This example demonstrates how to use `Nomic Atlas `_ to visualize the training dynamics of your neural network using embeddings and UMAP. +This example demonstrates how to use [Nomic Atlas](https://docs.nomic.ai/atlas/embeddings-and-retrieval/guides/using-umap-with-atlas) to visualize the training dynamics of your neural network using embeddings and UMAP. -Setup ------ +## Setup -1. Get the python package with ``pip install nomic`` -2. Get a Nomic API key `here `_ -3. Run ``nomic login nk-...`` in a terminal window or use the following code: -.. code:: python3 +1. Get the python package with `pip install nomic` +2. Get a Nomic API key [here](https://atlas.nomic.ai/cli-login) +3. Run `nomic login nk-...` in a terminal window or use the following code: + +```python3 +``` import nomic nomic.login('nk-...') -Download Example Data ---------------------- +## Download Example Data + -.. code:: python3 +```python3 +``` import torch import torch.nn as nn @@ -105,10 +105,11 @@ Download Example Data test_loader_for_vis = DataLoader(vis_subset, batch_size=BATCH_SIZE, shuffle=False, num_workers=num_workers_val, persistent_workers=persistent_workers_flag if num_workers_val > 0 else False) print(f"Training on {len(train_dataset)} samples, visualizing {NUM_VIS_SAMPLES} test samples per epoch.\n") -Collect Embeddings During Training ----------------------------------- +## Collect Embeddings During Training + -.. code:: python3 +```python3 +``` model = MNIST_CNN(embedding_dim=EMBEDDING_DIM).to(device) criterion = nn.CrossEntropyLoss() @@ -135,16 +136,16 @@ Collect Embeddings During Training print(f"Epoch {epoch+1}/{NUM_EPOCHS} training finished in {time.time() - epoch_start_time:.2f}s.\n") model.eval() vis_samples_collected_this_epoch = 0 - image_offset_in_vis_subset = 0 + image_offset_in_vis_subset = 0 with torch.no_grad(): for data, target in test_loader_for_vis: data, target = data.to(device), target.to(device) _, embeddings_batch = model(data) for i in range(embeddings_batch.size(0)): - original_idx_in_subset = image_offset_in_vis_subset + i + original_idx_in_subset = image_offset_in_vis_subset + i if original_idx_in_subset >= NUM_VIS_SAMPLES: continue - all_embeddings_list.append(embeddings_batch[i].cpu().numpy()) + all_embeddings_list.append(embeddings_batch[i].cpu().numpy()) img_html = tensor_to_html(data[i]) all_images_html.append(img_html) all_metadata_list.append({ @@ -156,34 +157,37 @@ Collect Embeddings During Training }) vis_samples_collected_this_epoch += 1 image_offset_in_vis_subset += embeddings_batch.size(0) - if vis_samples_collected_this_epoch >= NUM_VIS_SAMPLES: + if vis_samples_collected_this_epoch >= NUM_VIS_SAMPLES: break print(f"Collected {vis_samples_collected_this_epoch} embeddings for visualization in epoch {epoch+1}.\n") total_script_time = time.time() - overall_start_time print(f"Total training and embedding extraction time: {total_script_time:.2f}s\n") -Create Atlas Dataset --------------------- +## Create Atlas Dataset -.. code:: python3 + +```python3 +``` from nomic import AtlasDataset dataset = AtlasDataset("mnist-training-embeddings") -Upload to Atlas ---------------- +## Upload to Atlas + -.. code:: python3 +```python3 +``` dataset.add_data(data=all_metadata_list, embeddings=np.array(all_embeddings_list)) -Create Data Map ---------------- +## Create Data Map + -We specify the ``text`` field from ``df`` as the field to create embeddings from. We choose some standard UMAP parameters as well. +We specify the `text` field from `df` as the field to create embeddings from. We choose some standard UMAP parameters as well. -.. code:: python3 +```python3 +``` - dataset.create_index(projection='umap', topic_model=False) + dataset.create_index(projection='umap', topic_model=False) -Your map will be available in your `Atlas Dashboard `_. \ No newline at end of file +Your map will be available in your [Atlas Dashboard](https://atlas.nomic.ai/data). diff --git a/doc/outliers.rst b/doc/outliers.md similarity index 67% rename from doc/outliers.rst rename to doc/outliers.md index 97b6bc8b..1659ed54 100644 --- a/doc/outliers.rst +++ b/doc/outliers.md @@ -1,30 +1,32 @@ -Outlier detection using UMAP -============================ +# Outlier detection using UMAP + While an earlier tutorial looked at using `UMAP for -clustering `__, +clustering `__, it can also be used for outlier detection, providing that some care is taken. This tutorial will look at how to use UMAP in this manner, and what to look out for, by finding anomalous digits in the MNIST handwritten digits dataset. To start with let's load the relevant libraries: -.. code:: python3 +```python3 +import numpy as np +import sklearn.datasets +import sklearn.neighbors +import umap +import umap.plot +import matplotlib.pyplot as plt +%matplotlib inline - import numpy as np - import sklearn.datasets - import sklearn.neighbors - import umap - import umap.plot - import matplotlib.pyplot as plt - %matplotlib inline +``` With this in hand, let's grab the MNIST digits dataset from the -internet, using the new ``fetch_ml`` loader in sklearn. +internet, using the new `fetch_ml` loader in sklearn. -.. code:: python3 +```python3 +data, labels = sklearn.datasets.fetch_openml('mnist_784', version=1, return_X_y=True) - data, labels = sklearn.datasets.fetch_openml('mnist_784', version=1, return_X_y=True) +``` Before we get started we should try looking for outliers in terms of the native 784 dimensional space that MNIST digits live in. To do this we @@ -36,23 +38,25 @@ The essential intuition of LOF is to look for points that have a average density of their neighbors. In our case the actual details are not so important -- it is enough to know that the algorithm is reasonably robust and effective on vector space data. We can apply it -using the ``fit_predict`` method of the sklearn class. The LOF class -take a parameter ``contamination`` which specifies the percentage of +using the `fit_predict` method of the sklearn class. The LOF class +take a parameter `contamination` which specifies the percentage of data that the user expects to be noise. For this use case we will set it to 0.001428 since, given the 70,000 samples in MNIST, this will result in 100 outliers, which we can then look at in more detail. -.. code:: python3 +```python3 +%%time +outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(data) - %%time - outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(data) +``` -.. parsed-literal:: +``` +CPU times: user 1h 29min 10s, sys: 12.4 s, total: 1h 29min 22s +Wall time: 1h 29min 53s - CPU times: user 1h 29min 10s, sys: 12.4 s, total: 1h 29min 22s - Wall time: 1h 29min 53s +``` It is worth noting how long that took. Over an hour and a half! Why did it take so long? Because LOF requires a notion of density, which in turn @@ -65,19 +69,21 @@ Now that we have a set of outlier scores we can find the actual outlying digit images -- these are the ones with scores equal to -1. Let's extract that data, and check that we got 100 different digit images. -.. code:: python3 +```python3 +outlying_digits = data[outlier_scores == -1] +outlying_digits.shape - outlying_digits = data[outlier_scores == -1] - outlying_digits.shape +``` -.. parsed-literal:: +``` +(100, 784) - (100, 784) +``` Now that we have the outlying digit images the first question we should be asking is "what do they look like?". Fortunately for us we can @@ -85,18 +91,18 @@ convert the 784 dimensional vectors back into image and plot them, making it easier to look at. Since we extracted the 100 most outlying digit images we can just display a 10x10 grid of them. -.. code:: python3 - - fig, axes = plt.subplots(7, 10, figsize=(10,10)) - for i, ax in enumerate(axes.flatten()): - ax.imshow(outlying_digits[i].reshape((28,28))) - plt.setp(ax, xticks=[], yticks=[]) - plt.tight_layout() +```python3 +fig, axes = plt.subplots(7, 10, figsize=(10,10)) +for i, ax in enumerate(axes.flatten()): + ax.imshow(outlying_digits[i].reshape((28,28))) + plt.setp(ax, xticks=[], yticks=[]) +plt.tight_layout() -.. image:: images/outliers_9_0.png +``` +![Image](images/outliers_9_0.png) These do certainly look like somewhat strange looking handwritten digits, so our outlier detection seems to be working to some extent. @@ -105,26 +111,28 @@ Now let's try a naive approach using UMAP and see how far that gets us. First let's just apply UMAP directly with default parameters to the MNIST data. -.. code:: python3 +```python3 +mapper = umap.UMAP().fit(data) - mapper = umap.UMAP().fit(data) +``` Now we can see what we got using the new plotting tools in umap.plot. -.. code:: python3 - - umap.plot.points(mapper, labels=labels) +```python3 +umap.plot.points(mapper, labels=labels) -.. parsed-literal:: +``` - +``` + -.. image:: images/outliers_13_2.png +``` +![Image](images/outliers_13_2.png) That looks like what we have come to expect from a UMAP embedding of MNIST. The question is have we managed to preserve outliers well enough @@ -133,45 +141,48 @@ lost that information and contracted the outliers into the individual digit clusters? We can simply apply LOF to the embedding and see what that returns. -.. code:: python3 +```python3 +%%time +outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(mapper.embedding_) - %%time - outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(mapper.embedding_) +``` This was obviously much faster since we are operating in a much lower dimensional space that is more amenable to the spatial indexing methods that sklearn uses to find nearest neighbors. As before we extract the outlying digit images, and verify that we got 100 of them, -.. code:: python3 +```python3 +outlying_digits = data[outlier_scores == -1] +outlying_digits.shape - outlying_digits = data[outlier_scores == -1] - outlying_digits.shape +``` -.. parsed-literal:: +``` +(100, 784) - (100, 784) +``` Now we need to plot the outlying digit images to see what kinds of digit images this approach found to be particularly strange. -.. code:: python3 +```python3 +fig, axes = plt.subplots(7, 10, figsize=(10,10)) +for i, ax in enumerate(axes.flatten()): + ax.imshow(outlying_digits[i].reshape((28,28))) + plt.setp(ax, xticks=[], yticks=[]) +plt.tight_layout() - fig, axes = plt.subplots(7, 10, figsize=(10,10)) - for i, ax in enumerate(axes.flatten()): - ax.imshow(outlying_digits[i].reshape((28,28))) - plt.setp(ax, xticks=[], yticks=[]) - plt.tight_layout() +``` -.. image:: images/outliers_19_0.png - +![Image](images/outliers_19_0.png) In many ways this looks to be a *better* result than the original LOF in the high dimensional space. While the digit images that the high @@ -194,74 +205,79 @@ remain disconnected, which is certainly beneficial when seeking to find outliers. A downside of the intersection is that it tends to break up the resulting simplicial set into many disconnected components and a lot of the more non-local and global structure is lost, resulting in a lot -lower quality of the resulting embedding. We can, however, interpolate +lower quality of the resulting embedding. We can, however, interpolate between the union and intersection. In UMAP this is given by the -``set_op_mix_ratio``, where a value of 0.0 represents an intersection, +`set_op_mix_ratio`, where a value of 0.0 represents an intersection, and a value of 1.0 represents a union (the default value is 1.0). By setting this to a lower value, say 0.25, we can encourage the embedding to do a better job of preserving outliers as outlying, while still retaining the benefits of a union operation. -.. code:: python3 - - mapper = umap.UMAP(set_op_mix_ratio=0.25).fit(data) +```python3 +mapper = umap.UMAP(set_op_mix_ratio=0.25).fit(data) -.. code:: python3 +``` - umap.plot.points(mapper, labels=labels) +```python3 +umap.plot.points(mapper, labels=labels) -.. parsed-literal:: +``` - +``` + -.. image:: images/outliers_22_2.png +``` +![Image](images/outliers_22_2.png) As you can see the embedding is not as well structured overall as when -we had a ``set_op_mix_ratio`` of 1.0, but we have potentially done a +we had a `set_op_mix_ratio` of 1.0, but we have potentially done a better job of ensuring that outliers remain outlying. We can test that hypothesis by running LOF on this embedding and looking at the resulting digit images we get out. Ideally we should expect to find some potentially even stranger results. -.. code:: python3 +```python3 +%%time +outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(mapper.embedding_) - %%time - outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(mapper.embedding_) +``` -.. code:: python3 +```python3 +outlying_digits = data[outlier_scores == -1] +outlying_digits.shape - outlying_digits = data[outlier_scores == -1] - outlying_digits.shape +``` -.. parsed-literal:: +``` +(100, 784) - (100, 784) +``` We have the expected 100 most outlying digit images, so let's visualise the results and see if they really are particularly strange. -.. code:: python3 - - fig, axes = plt.subplots(10, 10, figsize=(10,10)) - for i, ax in enumerate(axes.flatten()): - ax.imshow(outlying_digits[i].reshape((28,28))) - plt.setp(ax, xticks=[], yticks=[]) - plt.tight_layout() +```python3 +fig, axes = plt.subplots(10, 10, figsize=(10,10)) +for i, ax in enumerate(axes.flatten()): + ax.imshow(outlying_digits[i].reshape((28,28))) + plt.setp(ax, xticks=[], yticks=[]) +plt.tight_layout() -.. image:: images/outliers_27_0.png +``` +![Image](images/outliers_27_0.png) Here we see that the line thickness variation (particularly "fat" digits, or particularly "fine" lines) that the original embedding helped @@ -273,6 +289,6 @@ So, in summary, using UMAP to reduce dimension prior to running classical outlier detection methods such as LOF can improve both the speed with which the algorithm runs, and the quality of results the outlier detection can find. Furthermore we have introduced the -``set_op_mix_ratio`` parameter, and explained how it can be used to +`set_op_mix_ratio` parameter, and explained how it can be used to potentially improve the performance of outlier detection approaches applied to UMAP embeddings. diff --git a/doc/parameters.rst b/doc/parameters.md similarity index 50% rename from doc/parameters.rst rename to doc/parameters.md index 23d4c78a..ea6663de 100644 --- a/doc/parameters.rst +++ b/doc/parameters.md @@ -1,6 +1,6 @@ -Basic UMAP Parameters -===================== +# Basic UMAP Parameters + UMAP is a fairly flexible non-linear dimension reduction algorithm. It seeks to learn the manifold structure of your data and find a low @@ -11,23 +11,25 @@ of that manifold. In this notebook we will generate some visualisable parameters can impact the resulting embedding. This documentation is based on the work of Philippe Rivière for visionscarto.net. -To start we'll need some basic libraries. First ``numpy`` will be needed +To start we'll need some basic libraries. First `numpy` will be needed for basic array manipulation. Since we will be visualising the results -we will need ``matplotlib`` and ``seaborn``. Finally we will need -``umap`` for doing the dimension reduction itself. +we will need `matplotlib` and `seaborn`. Finally we will need +`umap` for doing the dimension reduction itself. -.. code:: python3 +```python3 +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns +import umap +%matplotlib inline - import numpy as np - import matplotlib.pyplot as plt - from mpl_toolkits.mplot3d import Axes3D - import seaborn as sns - import umap - %matplotlib inline +``` -.. code:: python3 +```python3 +sns.set(style='white', context='poster', rc={'figure.figsize':(14,10)}) - sns.set(style='white', context='poster', rc={'figure.figsize':(14,10)}) +``` Next we will need some data to embed into a lower dimensional representation. To make the 4-dimensional data "visualisable" we will @@ -35,42 +37,45 @@ generate data uniformly at random from a 4-dimensional cube such that we can interpret a sample as a tuple of (R,G,B,a) values specifying a color (and translucency). Thus when we plot low dimensional representations each point can be colored according to its 4-dimensional value. For this we -can use ``numpy``. We will fix a random seed for the sake of +can use `numpy`. We will fix a random seed for the sake of consistency. -.. code:: python3 +```python3 +np.random.seed(42) +data = np.random.rand(800, 4) - np.random.seed(42) - data = np.random.rand(800, 4) +``` Now we need to find a low dimensional representation of the data. As in the Basic Usage documentation, we can do this by using the -:meth:`~umap.umap_.UMAP.fit_transform` method on a :class:`~umap.umap_.UMAP` object. +`umap.umap_.UMAP.fit_transform` method on a `umap.umap_.UMAP` object. -.. code:: python3 +```python3 +fit = umap.UMAP() +%time u = fit.fit_transform(data) - fit = umap.UMAP() - %time u = fit.fit_transform(data) +``` -.. parsed-literal:: +``` +CPU times: user 7.73 s, sys: 211 ms, total: 7.94 s +Wall time: 6.8 s - CPU times: user 7.73 s, sys: 211 ms, total: 7.94 s - Wall time: 6.8 s +``` -The resulting value ``u`` is a 2-dimensional representation of the data. -We can visualise the result by using ``matplotlib`` to draw a scatter -plot of ``u``. We can color each point of the scatter plot by the +The resulting value `u` is a 2-dimensional representation of the data. +We can visualise the result by using `matplotlib` to draw a scatter +plot of `u`. We can color each point of the scatter plot by the associated 4-dimensional color from the source data. -.. code:: python3 - - plt.scatter(u[:,0], u[:,1], c=data) - plt.title('UMAP embedding of random colours'); +```python3 +plt.scatter(u[:,0], u[:,1], c=data) +plt.title('UMAP embedding of random colours'); -.. image:: images/parameters_8_1.png +``` +![Image](images/parameters_8_1.png) As you can see the result is that the data is placed in 2-dimensional space such that points that were nearby in 4-dimensional space (i.e. @@ -83,45 +88,46 @@ UMAP has several hyperparameters that can have a significant impact on the resulting embedding. In this notebook we will be covering the four major ones: -- ``n_neighbors`` -- ``min_dist`` -- ``n_components`` -- ``metric`` +- `n_neighbors` +- `min_dist` +- `n_components` +- `metric` Each of these parameters has a distinct effect, and we will look at each in turn. To make exploration simpler we will first write a short utility function that can fit the data with UMAP given a set of parameter choices, and plot the result. -.. code:: python3 - - def draw_umap(n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', title=''): - fit = umap.UMAP( - n_neighbors=n_neighbors, - min_dist=min_dist, - n_components=n_components, - metric=metric - ) - u = fit.fit_transform(data); - fig = plt.figure() - if n_components == 1: - ax = fig.add_subplot(111) - ax.scatter(u[:,0], range(len(u)), c=data) - if n_components == 2: - ax = fig.add_subplot(111) - ax.scatter(u[:,0], u[:,1], c=data) - if n_components == 3: - ax = fig.add_subplot(111, projection='3d') - ax.scatter(u[:,0], u[:,1], u[:,2], c=data, s=100) - plt.title(title, fontsize=18) - -``n_neighbors`` -~~~~~~~~~~~~~~~ +```python3 +def draw_umap(n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', title=''): + fit = umap.UMAP( + n_neighbors=n_neighbors, + min_dist=min_dist, + n_components=n_components, + metric=metric + ) + u = fit.fit_transform(data); + fig = plt.figure() + if n_components == 1: + ax = fig.add_subplot(111) + ax.scatter(u[:,0], range(len(u)), c=data) + if n_components == 2: + ax = fig.add_subplot(111) + ax.scatter(u[:,0], u[:,1], c=data) + if n_components == 3: + ax = fig.add_subplot(111, projection='3d') + ax.scatter(u[:,0], u[:,1], u[:,2], c=data, s=100) + plt.title(title, fontsize=18) + +``` + +### ``n_neighbors`` + This parameter controls how UMAP balances local versus global structure in the data. It does this by constraining the size of the local neighborhood UMAP will look at when attempting to learn the manifold -structure of the data. This means that low values of ``n_neighbors`` +structure of the data. This means that low values of `n_neighbors` will force UMAP to concentrate on very local structure (potentially to the detriment of the big picture), while large values will push UMAP to look at larger neighborhoods of each point when estimating the manifold @@ -129,125 +135,103 @@ structure of the data, losing fine detail structure for the sake of getting the broader of the data. We can see that in practice by fitting our dataset with UMAP using a -range of ``n_neighbors`` values. The default value of ``n_neighbors`` +range of `n_neighbors` values. The default value of `n_neighbors` for UMAP (as used above) is 15, but we will look at values ranging from 2 (a very local view of the manifold) up to 200 (a quarter of the data). -.. code:: python3 - - for n in (2, 5, 10, 20, 50, 100, 200): - draw_umap(n_neighbors=n, title='n_neighbors = {}'.format(n)) - - - -.. image:: images/parameters_13_1.png - - - -.. image:: images/parameters_13_2.png - +```python3 +for n in (2, 5, 10, 20, 50, 100, 200): + draw_umap(n_neighbors=n, title='n_neighbors = {}'.format(n)) -.. image:: images/parameters_13_3.png +``` +![Image](images/parameters_13_1.png) -.. image:: images/parameters_13_4.png +![Image](images/parameters_13_2.png) +![Image](images/parameters_13_3.png) +![Image](images/parameters_13_4.png) -.. image:: images/parameters_13_5.png +![Image](images/parameters_13_5.png) +![Image](images/parameters_13_6.png) +![Image](images/parameters_13_7.png) -.. image:: images/parameters_13_6.png - - - -.. image:: images/parameters_13_7.png - - -With a value of ``n_neighbors=2`` we see that UMAP merely glues together +With a value of `n_neighbors=2` we see that UMAP merely glues together small chains, but due to the narrow/local view, fails to see how those connect together. It also leaves many different components (and even singleton points). This represents the fact that from a fine detail point of view the data is very disconnected and scattered throughout the space. -As ``n_neighbors`` is increased UMAP manages to see more of the overall +As `n_neighbors` is increased UMAP manages to see more of the overall structure of the data, gluing more components together, and better coverying the broader structure of the data. By the stage of -``n_neighbors=20`` we have a fairly good overall view of the data +`n_neighbors=20` we have a fairly good overall view of the data showing how the various colors interelate to each other over the whole dataset. -As ``n_neighbors`` increases further more and more focus in placed on +As `n_neighbors` increases further more and more focus in placed on the overall structure of the data. This results in, with -``n_neighbors=200`` a plot where the overall structure (blues, greens, +`n_neighbors=200` a plot where the overall structure (blues, greens, and reds; high luminance versus low) is well captured, but at the loss of some of the finer local structure (individual colors are no longer necessarily immediately near their closest color match). This effect well exemplifies the local/global tradeoff provided by -``n_neighbors``. +`n_neighbors`. -``min_dist`` -~~~~~~~~~~~~ +### ``min_dist`` -The ``min_dist`` parameter controls how tightly UMAP is allowed to pack + +The `min_dist` parameter controls how tightly UMAP is allowed to pack points together. It, quite literally, provides the minimum distance apart that points are allowed to be in the low dimensional -representation. This means that low values of ``min_dist`` will result +representation. This means that low values of `min_dist` will result in clumpier embeddings. This can be useful if you are interested in clustering, or in finer topological structure. Larger values of -``min_dist`` will prevent UMAP from packing points together and will +`min_dist` will prevent UMAP from packing points together and will focus on the preservation of the broad topological structure instead. -The default value for ``min_dist`` (as used above) is 0.1. We will look +The default value for `min_dist` (as used above) is 0.1. We will look at a range of values from 0.0 through to 0.99. -.. code:: python3 - - for d in (0.0, 0.1, 0.25, 0.5, 0.8, 0.99): - draw_umap(min_dist=d, title='min_dist = {}'.format(d)) - - -.. image:: images/parameters_16_1.png - +```python3 +for d in (0.0, 0.1, 0.25, 0.5, 0.8, 0.99): + draw_umap(min_dist=d, title='min_dist = {}'.format(d)) -.. image:: images/parameters_16_2.png +``` +![Image](images/parameters_16_1.png) +![Image](images/parameters_16_2.png) -.. image:: images/parameters_16_3.png +![Image](images/parameters_16_3.png) +![Image](images/parameters_16_4.png) +![Image](images/parameters_16_5.png) -.. image:: images/parameters_16_4.png +![Image](images/parameters_16_6.png) - - -.. image:: images/parameters_16_5.png - - - -.. image:: images/parameters_16_6.png - - -Here we see that with ``min_dist=0.0`` UMAP manages to find small +Here we see that with `min_dist=0.0` UMAP manages to find small connected components, clumps and strings in the data, and emphasises -these features in the resulting embedding. As ``min_dist`` is increased +these features in the resulting embedding. As `min_dist` is increased these structures are pushed apart into softer more general features, providing a better overarching view of the data at the loss of the more detailed topological structure. -``n_components`` -~~~~~~~~~~~~~~~~ +### ``n_components`` + -As is standard for many ``scikit-learn`` dimension reduction algorithms -UMAP provides a ``n_components`` parameter option that allows the user +As is standard for many `scikit-learn` dimension reduction algorithms +UMAP provides a `n_components` parameter option that allows the user to determine the dimensionality of the reduced dimension space we will be embedding the data into. Unlike some other visualisation algorithms such as t-SNE, UMAP scales well in the embedding dimension, so you can use it @@ -257,51 +241,52 @@ For the purposes of this demonstration (so that we can see the effects of the parameter) we will only be looking at 1-dimensional and 3-dimensional embeddings, which we have some hope of visualizing. -First of all we will set ``n_components`` to 1, forcing UMAP to embed +First of all we will set `n_components` to 1, forcing UMAP to embed the data in a line. For visualisation purposes we will randomly distribute the data on the y-axis to provide some separation between points. -.. code:: python3 - - draw_umap(n_components=1, title='n_components = 1') - +```python3 +draw_umap(n_components=1, title='n_components = 1') -.. image:: images/parameters_19_1.png +``` -Now we will try ``n_components=3``. For visualisation we will make use -of ``matplotlib``'s basic 3-dimensional plotting. +![Image](images/parameters_19_1.png) -.. code:: python3 +Now we will try `n_components=3`. For visualisation we will make use +of `matplotlib`'s basic 3-dimensional plotting. - draw_umap(n_components=3, title='n_components = 3') +```python3 +draw_umap(n_components=3, title='n_components = 3') -.. parsed-literal:: +``` - /opt/anaconda3/envs/umap_dev/lib/python3.6/site-packages/sklearn/metrics/pairwise.py:257: RuntimeWarning: invalid value encountered in sqrt - return distances if squared else np.sqrt(distances, out=distances) +``` +/opt/anaconda3/envs/umap_dev/lib/python3.6/site-packages/sklearn/metrics/pairwise.py:257: RuntimeWarning: invalid value encountered in sqrt + return distances if squared else np.sqrt(distances, out=distances) -.. image:: images/parameters_21_1.png +``` +![Image](images/parameters_21_1.png) Here we can see that with more dimensions in which to work UMAP has an easier time separating out the colors in a way that respects the topological structure of the data. -As mentioned, there is really no requirement to stop at ``n_components=3``. If you are interested in (density based) clustering, or other +As mentioned, there is really no requirement to stop at `n_components=3`. If you are interested in (density based) clustering, or other machine learning techniques, it can be beneficial to pick a larger embedding dimension (say 10, or 50) closer to the the dimension of the underlying manifold on which your data lies. -``metric`` -~~~~~~~~~~ +### ``metric`` + The final UMAP parameter we will be considering in this notebook is the -``metric`` parameter. This controls how distance is computed in the +`metric` parameter. This controls how distance is computed in the ambient space of the input data. By default UMAP supports a wide variety of metrics, including: @@ -346,129 +331,125 @@ of metrics, including: All of the above metrics assume your input data is "raw" in some N-dimensional space. Sometimes, you have already calculated the pairwise distances between points, and your input data is a distance/similarity matrix. -In this case, you can do something like ``UMAP(metric='precomputed').fit_transform()``. +In this case, you can do something like `UMAP(metric='precomputed').fit_transform()`. -Any of which can be specified by setting ``metric=''``; for +Any of which can be specified by setting `metric=''`; for example to use cosine distance as the metric you would use -``metric='cosine'``. +`metric='cosine'`. UMAP offers more than this however -- it supports custom user defined -metrics as long as those metrics can be compiled in ``nopython`` mode by +metrics as long as those metrics can be compiled in `nopython` mode by numba. For this notebook we will be looking at such custom metrics. To define such metrics we'll need numba ... -.. code:: python3 +```python3 +import numba - import numba +``` For our first custom metric we'll define the distance to be the absolute value of difference in the red channel. -.. code:: python3 +```python3 +@numba.njit() +def red_channel_dist(a,b): + return np.abs(a[0] - b[0]) - @numba.njit() - def red_channel_dist(a,b): - return np.abs(a[0] - b[0]) +``` To get more adventurous it will be useful to have some colorspace conversion -- to keep things simple we'll just use HSL formulas to extract the hue, saturation, and lightness from an (R,G,B) tuple. -.. code:: python3 - - @numba.njit() - def hue(r, g, b): - cmax = max(r, g, b) - cmin = min(r, g, b) - delta = cmax - cmin - if cmax == r: - return ((g - b) / delta) % 6 - elif cmax == g: - return ((b - r) / delta) + 2 - else: - return ((r - g) / delta) + 4 - - @numba.njit() - def lightness(r, g, b): - cmax = max(r, g, b) - cmin = min(r, g, b) - return (cmax + cmin) / 2.0 - - @numba.njit() - def saturation(r, g, b): - cmax = max(r, g, b) - cmin = min(r, g, b) - chroma = cmax - cmin - light = lightness(r, g, b) - if light == 1: - return 0 - else: - return chroma / (1 - abs(2*light - 1)) +```python3 +@numba.njit() +def hue(r, g, b): + cmax = max(r, g, b) + cmin = min(r, g, b) + delta = cmax - cmin + if cmax == r: + return ((g - b) / delta) % 6 + elif cmax == g: + return ((b - r) / delta) + 2 + else: + return ((r - g) / delta) + 4 + +@numba.njit() +def lightness(r, g, b): + cmax = max(r, g, b) + cmin = min(r, g, b) + return (cmax + cmin) / 2.0 + +@numba.njit() +def saturation(r, g, b): + cmax = max(r, g, b) + cmin = min(r, g, b) + chroma = cmax - cmin + light = lightness(r, g, b) + if light == 1: + return 0 + else: + return chroma / (1 - abs(2*light - 1)) + +``` With that in hand we can define three extra distances. The first simply measures the difference in hue, the second measures the euclidean distance in a combined saturation and lightness space, while the third measures distance in the full HSL space. -.. code:: python3 - - @numba.njit() - def hue_dist(a, b): - diff = (hue(a[0], a[1], a[2]) - hue(b[0], b[1], b[2])) % 6 - if diff < 0: - return diff + 6 - else: - return diff - - @numba.njit() - def sl_dist(a, b): - a_sat = saturation(a[0], a[1], a[2]) - b_sat = saturation(b[0], b[1], b[2]) - a_light = lightness(a[0], a[1], a[2]) - b_light = lightness(b[0], b[1], b[2]) - return (a_sat - b_sat)**2 + (a_light - b_light)**2 - - @numba.njit() - def hsl_dist(a, b): - a_sat = saturation(a[0], a[1], a[2]) - b_sat = saturation(b[0], b[1], b[2]) - a_light = lightness(a[0], a[1], a[2]) - b_light = lightness(b[0], b[1], b[2]) - a_hue = hue(a[0], a[1], a[2]) - b_hue = hue(b[0], b[1], b[2]) - return (a_sat - b_sat)**2 + (a_light - b_light)**2 + (((a_hue - b_hue) % 6) / 6.0) +```python3 +@numba.njit() +def hue_dist(a, b): + diff = (hue(a[0], a[1], a[2]) - hue(b[0], b[1], b[2])) % 6 + if diff < 0: + return diff + 6 + else: + return diff + +@numba.njit() +def sl_dist(a, b): + a_sat = saturation(a[0], a[1], a[2]) + b_sat = saturation(b[0], b[1], b[2]) + a_light = lightness(a[0], a[1], a[2]) + b_light = lightness(b[0], b[1], b[2]) + return (a_sat - b_sat)**2 + (a_light - b_light)**2 + +@numba.njit() +def hsl_dist(a, b): + a_sat = saturation(a[0], a[1], a[2]) + b_sat = saturation(b[0], b[1], b[2]) + a_light = lightness(a[0], a[1], a[2]) + b_light = lightness(b[0], b[1], b[2]) + a_hue = hue(a[0], a[1], a[2]) + b_hue = hue(b[0], b[1], b[2]) + return (a_sat - b_sat)**2 + (a_light - b_light)**2 + (((a_hue - b_hue) % 6) / 6.0) + +``` With such custom metrics in hand we can get UMAP to embed the data using those metrics to measure the distance between our input data points. Note -that ``numba`` provides significant flexibility in what we can do in +that `numba` provides significant flexibility in what we can do in defining distance functions. Despite this we retain the high performance we expect from UMAP even using such custom functions. -.. code:: python3 - - for m in ("euclidean", red_channel_dist, sl_dist, hue_dist, hsl_dist): - name = m if type(m) is str else m.__name__ - draw_umap(n_components=2, metric=m, title='metric = {}'.format(name)) - - -.. image:: images/parameters_32_1.png - - - -.. image:: images/parameters_32_2.png - - - -.. image:: images/parameters_32_3.png +```python3 +for m in ("euclidean", red_channel_dist, sl_dist, hue_dist, hsl_dist): + name = m if type(m) is str else m.__name__ + draw_umap(n_components=2, metric=m, title='metric = {}'.format(name)) +``` -.. image:: images/parameters_32_4.png +![Image](images/parameters_32_1.png) +![Image](images/parameters_32_2.png) +![Image](images/parameters_32_3.png) -.. image:: images/parameters_32_5.png +![Image](images/parameters_32_4.png) +![Image](images/parameters_32_5.png) And here we can see the effects of the metrics quite clearly. The pure red channel correctly sees the data as living on a one dimensional diff --git a/doc/parametric_umap.rst b/doc/parametric_umap.md similarity index 51% rename from doc/parametric_umap.rst rename to doc/parametric_umap.md index 4b4943b6..8e097ec1 100644 --- a/doc/parametric_umap.rst +++ b/doc/parametric_umap.md @@ -1,235 +1,247 @@ -Parametric (neural network) Embedding -===================================== +# Parametric (neural network) Embedding -.. role:: python(code) + + :language: python - + UMAP is comprised of two steps: First, compute a graph representing your data, second, learn an embedding for that graph: -.. image:: images/umap-only.png +![Image](images/umap-only.png) Parametric UMAP replaces the second step, minimizing the same objective function as UMAP (we'll call it non-parametric UMAP here), but learning the relationship between the data and embedding using a neural network, rather than learning the embeddings directly: -.. image:: images/pumap-only.png +![Image](images/pumap-only.png) Parametric UMAP is simply a subclass of UMAP, so it can be used just like nonparametric UMAP, replacing :python:`umap.UMAP` with :python:`parametric_umap.ParametricUMAP`. The most basic usage of parametric UMAP would be to simply replace UMAP with ParametricUMAP in your code: -.. code:: python3 +```python3 +from umap.parametric_umap import ParametricUMAP +embedder = ParametricUMAP() +embedding = embedder.fit_transform(my_data) + +``` - from umap.parametric_umap import ParametricUMAP - embedder = ParametricUMAP() - embedding = embedder.fit_transform(my_data) +In this implementation, we use Keras and Tensorflow as a backend to train that neural network. The added complexity of a learned embedding presents a number of configurable settings available in addition to those in non-parametric UMAP. A set of Jupyter notebooks walking you through these parameters are available on the [GitHub repository](https://github.com/lmcinnes/umap/tree/master/notebooks/Parametric_UMAP) -In this implementation, we use Keras and Tensorflow as a backend to train that neural network. The added complexity of a learned embedding presents a number of configurable settings available in addition to those in non-parametric UMAP. A set of Jupyter notebooks walking you through these parameters are available on the `GitHub repository `_ +## Defining your own network -Defining your own network ---------------------------- By default, Parametric UMAP uses 3-layer 100-neuron fully-connected neural network. To extend Parametric UMAP to use a more complex architecture, like a convolutional neural network, we simply need to define the network and pass it in as an argument to ParametricUMAP. This can be done easliy, using tf.keras.Sequential. Here's an example for MNIST: -.. code:: python3 - - # define the network - import tensorflow as tf - dims = (28, 28, 1) - n_components = 2 - encoder = tf.keras.Sequential([ - tf.keras.layers.InputLayer(input_shape=dims), - tf.keras.layers.Conv2D( - filters=32, kernel_size=3, strides=(2, 2), activation="relu", padding="same" - ), - tf.keras.layers.Conv2D( - filters=64, kernel_size=3, strides=(2, 2), activation="relu", padding="same" - ), - tf.keras.layers.Flatten(), - tf.keras.layers.Dense(units=256, activation="relu"), - tf.keras.layers.Dense(units=256, activation="relu"), - tf.keras.layers.Dense(units=n_components), - ]) - encoder.summary() - +```python3 +# define the network +import tensorflow as tf +dims = (28, 28, 1) +n_components = 2 +encoder = tf.keras.Sequential([ + tf.keras.layers.InputLayer(input_shape=dims), + tf.keras.layers.Conv2D( + filters=32, kernel_size=3, strides=(2, 2), activation="relu", padding="same" + ), + tf.keras.layers.Conv2D( + filters=64, kernel_size=3, strides=(2, 2), activation="relu", padding="same" + ), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(units=256, activation="relu"), + tf.keras.layers.Dense(units=256, activation="relu"), + tf.keras.layers.Dense(units=n_components), +]) +encoder.summary() + +``` + To load pass the data into ParametricUMAP, we first need to flatten it from 28x28x1 images to a 784-dimensional vector. - -.. code:: python3 - from tensorflow.keras.datasets import mnist - (train_images, Y_train), (test_images, Y_test) = mnist.load_data() - train_images = train_images.reshape((train_images.shape[0], -1))/255. - test_images = test_images.reshape((test_images.shape[0], -1))/255. +```python3 +from tensorflow.keras.datasets import mnist +(train_images, Y_train), (test_images, Y_test) = mnist.load_data() +train_images = train_images.reshape((train_images.shape[0], -1))/255. +test_images = test_images.reshape((test_images.shape[0], -1))/255. + +``` We can then pass the network into ParametricUMAP and train: -.. code:: python3 +```python3 +# pass encoder network to ParametricUMAP +embedder = ParametricUMAP(encoder=encoder, dims=dims) +embedding = embedder.fit_transform(train_images) + +``` - # pass encoder network to ParametricUMAP - embedder = ParametricUMAP(encoder=encoder, dims=dims) - embedding = embedder.fit_transform(train_images) +If you are unfamilar with Tensorflow/Keras and want to train your own model, we reccomend that you take a look at the [Tensorflow documentation](https://www.tensorflow.org/). -If you are unfamilar with Tensorflow/Keras and want to train your own model, we reccomend that you take a look at the `Tensorflow documentation `_. +## Saving and loading your model -Saving and loading your model ------------------------------ Unlike non-parametric UMAP Parametric UMAP cannot be saved simply by pickling the UMAP object because of the Keras networks it contains. To save Parametric UMAP, there is a built in function: -.. code:: python3 +```python3 +embedder.save('/your/path/here') + +``` - embedder.save('/your/path/here') - You can then load parametric UMAP elsewhere: -.. code:: python3 +```python3 +from umap.parametric_umap import load_ParametricUMAP +embedder = load_ParametricUMAP('/your/path/here') - from umap.parametric_umap import load_ParametricUMAP - embedder = load_ParametricUMAP('/your/path/here') +``` This loads both the UMAP object and the parametric networks it contains. -Plotting loss -------------- -Parametric UMAP monitors loss during training using Keras. That loss will be printed after each epoch during training. This loss is saved in :python:`embedder._history`, and can be plotted: +## Plotting loss + +Parametric UMAP monitors loss during training using Keras. That loss will be printed after each epoch during training. This loss is saved in :python:`embedder._history`, and can be plotted: + +```python3 +print(embedder._history) +fig, ax = plt.subplots() +ax.plot(embedder._history['loss']) +ax.set_ylabel('Cross Entropy') +ax.set_xlabel('Epoch') -.. code:: python3 - - print(embedder._history) - fig, ax = plt.subplots() - ax.plot(embedder._history['loss']) - ax.set_ylabel('Cross Entropy') - ax.set_xlabel('Epoch') - -.. image:: images/umap-loss.png +``` + +![Image](images/umap-loss.png) Much like other keras models, if you continue to train your model via the :python:`fit` method of the model, the :python:`embedder._history` will be updated with further training epoch losses. -Parametric inverse_transform (reconstruction) ---------------------------------------------- +## Parametric inverse_transform (reconstruction) + To use a second neural network to learn an inverse mapping between data and embeddings, we simply need to pass `parametric_reconstruction= True` to the ParametricUMAP. Like the encoder, a custom decoder can also be passed to ParametricUMAP, e.g. -.. code:: python3 - - decoder = tf.keras.Sequential([ - tf.keras.layers.InputLayer(input_shape=(n_components)), - tf.keras.layers.Dense(units=256, activation="relu"), - tf.keras.layers.Dense(units=7 * 7 * 256, activation="relu"), - tf.keras.layers.Reshape(target_shape=(7, 7, 256)), - tf.keras.layers.UpSampling2D((2)), - tf.keras.layers.Conv2D( - filters=64, kernel_size=3, padding="same", activation="relu" - ), - tf.keras.layers.UpSampling2D((2)), - tf.keras.layers.Conv2D( - filters=32, kernel_size=3, padding="same", activation="relu" - ), - - ]) - +```python3 + decoder = tf.keras.Sequential([ + tf.keras.layers.InputLayer(input_shape=(n_components)), + tf.keras.layers.Dense(units=256, activation="relu"), + tf.keras.layers.Dense(units=7 * 7 * 256, activation="relu"), + tf.keras.layers.Reshape(target_shape=(7, 7, 256)), + tf.keras.layers.UpSampling2D((2)), + tf.keras.layers.Conv2D( + filters=64, kernel_size=3, padding="same", activation="relu" + ), + tf.keras.layers.UpSampling2D((2)), + tf.keras.layers.Conv2D( + filters=32, kernel_size=3, padding="same", activation="relu" + ), + + ]) + +``` + In addition, validation data can be used to test reconstruction loss on out-of-dataset samples: -.. code:: python3 +```python3 +validation_images = test_images.reshape((test_images.shape[0], -1))/255. - validation_images = test_images.reshape((test_images.shape[0], -1))/255. +``` Finally, we can pass the validation data and the networks to ParametricUMAP and train: -.. code:: python3 +```python3 + embedder = ParametricUMAP( + encoder=encoder, + decoder=decoder, + dims=dims, + parametric_reconstruction= True, + reconstruction_validation=validation_images, + verbose=True, + ) + embedding = embedder.fit_transform(train_images) - embedder = ParametricUMAP( - encoder=encoder, - decoder=decoder, - dims=dims, - parametric_reconstruction= True, - reconstruction_validation=validation_images, - verbose=True, - ) - embedding = embedder.fit_transform(train_images) +``` -Autoencoding UMAP ------------------ +## Autoencoding UMAP -In the example above, the encoder is trained to minimize UMAP loss, and the decoder is trained to minimize reconstruction loss. To train the encoder jointly on both UMAP loss and reconstruction loss, pass :python:`autoencoder_loss = True` into the ParametricUMAP. +In the example above, the encoder is trained to minimize UMAP loss, and the decoder is trained to minimize reconstruction loss. To train the encoder jointly on both UMAP loss and reconstruction loss, pass :python:`autoencoder_loss = True` into the ParametricUMAP. -.. code:: python3 - embedder = ParametricUMAP( - encoder=encoder, - decoder=decoder, - dims=dims, - parametric_reconstruction= True, - reconstruction_validation=validation_images, - autoencoder_loss = True, - verbose=True, - ) +```python3 + embedder = ParametricUMAP( + encoder=encoder, + decoder=decoder, + dims=dims, + parametric_reconstruction= True, + reconstruction_validation=validation_images, + autoencoder_loss = True, + verbose=True, + ) -Early stopping and Keras callbacks ----------------------------------- +``` -It can sometimes be useful to train the embedder until some plateau in training loss is met. In deep learning, early stopping is one way to do this. Keras provides custom `callbacks `_ that allow you to implement checks during training, such as early stopping. We can use callbacks, such as early stopping, with ParametricUMAP to stop training early based on a predefined training threshold, using the :python:`keras_fit_kwargs` argument: +## Early stopping and Keras callbacks -.. code:: python3 - keras_fit_kwargs = {"callbacks": [ - tf.keras.callbacks.EarlyStopping( - monitor='loss', - min_delta=10**-2, - patience=10, - verbose=1, - ) - ]} +It can sometimes be useful to train the embedder until some plateau in training loss is met. In deep learning, early stopping is one way to do this. Keras provides custom [callbacks](https://keras.io/api/callbacks/) that allow you to implement checks during training, such as early stopping. We can use callbacks, such as early stopping, with ParametricUMAP to stop training early based on a predefined training threshold, using the :python:`keras_fit_kwargs` argument: - embedder = ParametricUMAP( - verbose=True, - keras_fit_kwargs = keras_fit_kwargs, - n_training_epochs=20 +```python3 +keras_fit_kwargs = {"callbacks": [ + tf.keras.callbacks.EarlyStopping( + monitor='loss', + min_delta=10**-2, + patience=10, + verbose=1, ) +]} + +embedder = ParametricUMAP( + verbose=True, + keras_fit_kwargs = keras_fit_kwargs, + n_training_epochs=20 +) -We also passed in :python:`n_training_epochs = 20`, allowing early stopping to end training before 20 epochs are reached. +``` +We also passed in :python:`n_training_epochs = 20`, allowing early stopping to end training before 20 epochs are reached. + + +## Additional important parameters -Additional important parameters -------------------------------- * **batch_size:** ParametricUMAP in trained over batches of edges randomly sampled from the UMAP graph, and then trained via gradient descent. ParametricUMAP defaults to a batch size of 1000 edges, but can be adjusted to a value that fits better on your GPU or CPU. -* **loss_report_frequency:** If set to 1, an epoch in in the Keras embedding refers to a single iteration over the graph computed in UMAP. Setting :python:`loss_report_frequency` to 10, would split up that epoch into 10 seperate epochs, for more frequent reporting. +* **loss_report_frequency:** If set to 1, an epoch in in the Keras embedding refers to a single iteration over the graph computed in UMAP. Setting :python:`loss_report_frequency` to 10, would split up that epoch into 10 seperate epochs, for more frequent reporting. * **n_training_epochs:** The number of epochs over the UMAP graph to train for (irrespective of :python:`loss_report_frequency`). Training the network for multiple epochs will result in better embeddings, but take longer. This parameter is different than :python:`n_epochs` in the base UMAP class, which corresponds to the maximum number of times an edge is trained in a single ParametricUMAP epoch. * **optimizer:** The optimizer used to train the neural network. by default Adam (:python:`tf.keras.optimizers.Adam(1e-3)`) is used. You might be able to speed up or improve training by using a different optimizer. -* **parametric_embedding:** If set to false, a non-parametric embedding is learned, using the same code as the parametric embedding, which can serve as a direct comparison between parametric and non-parametric embedding using the same optimizer. The parametric embeddings are performed over the entire dataset simultaneously. +* **parametric_embedding:** If set to false, a non-parametric embedding is learned, using the same code as the parametric embedding, which can serve as a direct comparison between parametric and non-parametric embedding using the same optimizer. The parametric embeddings are performed over the entire dataset simultaneously. * **global_correlation_loss_weight:** Whether to additionally train on correlation of global pairwise relationships (multidimensional scaling) * **landmark_loss_fn:** The loss function to use when re-training on landmarked data, where you have provided a desired location in the embedding space to the :python:`fit` method of the model. By default, euclidean loss is used. For more information on re-training, landmarks, and why you might use them, see :doc:`transform_landmarked_pumap`. * **landmark_loss_weight:** How to weight the landmark loss relative to umap loss, by default 1.0. -Extending the model -------------------- -You may want to customize parametric UMAP beyond what we have implemented in this package. To make it as easy as possible to tinker around with Parametric UMAP, we made a few Jupyter notebooks that show you how to extend Parametric UMAP to your own use-cases. +## Extending the model + +You may want to customize parametric UMAP beyond what we have implemented in this package. To make it as easy as possible to tinker around with Parametric UMAP, we made a few Jupyter notebooks that show you how to extend Parametric UMAP to your own use-cases. * https://colab.research.google.com/drive/1WkXVZ5pnMrm17m0YgmtoNjM_XHdnE5Vp?usp=sharing -Citing our work ---------------- +## Citing our work + If you use Parametric UMAP in your work, please cite our paper: -.. code:: bibtex - - @article{sainburg2021parametric, - title={Parametric UMAP Embeddings for Representation and Semisupervised Learning}, - author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q}, - journal={Neural Computation}, - volume={33}, - number={11}, - pages={2881--2907}, - year={2021}, - publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} - } +```bibtex +@article{sainburg2021parametric, + title={Parametric UMAP Embeddings for Representation and Semisupervised Learning}, + author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q}, + journal={Neural Computation}, + volume={33}, + number={11}, + pages={2881--2907}, + year={2021}, + publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} +``` + } diff --git a/doc/performance.md b/doc/performance.md new file mode 100644 index 00000000..8656f1a5 --- /dev/null +++ b/doc/performance.md @@ -0,0 +1,304 @@ +# Performance Comparison of Dimension Reduction Implementations + + +Different dimension reduction techniques can have quite different +computational complexity. Beyond the algorithm itself there is also the +question of how exactly it is implemented. These two factors can have a +significant role in how long it actually takes to run a given dimension +reduction. Furthermore the nature of the data you are trying to reduce +can also matter – mostly the involves the dimensionality of the original +data. Here we will take a brief look at the performance characterstics +of a number of dimension reduction implementations. + +To start let’s get the basic tools we’ll need loaded up – numpy and +pandas obviously, but also tools to get and resample the data, and the +time module so we can perform some basic benchmarking. + +```python +import numpy as np +import pandas as pd +from sklearn.datasets import fetch_openml +from sklearn.utils import resample +import time + +``` + +Next we’ll need the actual dimension reduction implementations. For the +purposes of this explanation we’ll mostly stick with +[scikit-learn](http://scikit-learn.org/stable/), but for the sake of +comparison we’ll also include the +[MulticoreTSNE](https://github.com/DmitryUlyanov/Multicore-TSNE) +implementation of t-SNE, and +[openTSNE](https://github.com/pavlin-policar/openTSNE) both of which +have historically had significantly better performance than scikit-learn +t-SNE (more recent versions of scikit-learn have improved t-SNE +performance). + +```python +from sklearn.manifold import TSNE, LocallyLinearEmbedding, Isomap, MDS, SpectralEmbedding +from sklearn.decomposition import PCA +from MulticoreTSNE import MulticoreTSNE +from openTSNE import TSNE as OpenTSNE +from umap import UMAP + +``` + +Next we’ll need out plotting tools, and, of course, some data to work +with. For this performance comparison we’ll default to the now standard +benchmark of manifold learning: the MNIST digits dataset. We can use +scikit-learn’s `fetch_openml` to grab it for us. + +```python +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline + +``` + +```python +sns.set(context='notebook', + rc={'figure.figsize':(12,10)}, + palette=sns.color_palette('tab10', 10)) + +``` + +```python +mnist = fetch_openml('mnist_784', version=1, return_X_y=True) + +``` + +```python +mnist_data = mnist[0] +mnist_labels = mnist[1].astype(int) + +``` + +Now it is time to start looking at performance. To start with let’s look +at how performance scales with increasing dataset size. + +## Performance scaling by dataset size + + +As the size of a dataset increases the runtime of a given dimension +reduction algorithm will increase at varying rates. If you ever want to +run your algorithm on larger datasets you will care not just about the +comparative runtime on a single small dataset, but how the performance +scales out as you move to larger datasets. We can similate this by +subsampling from MNIST digits (via scikit-learn’s convenient +`resample` utility) and looking at the runtime for varying sized +subsamples. Since there is some randomness involved here (both in the +subsample selection, and in some of the algorithms which have stochastic +aspects) we will want to run a few examples for each dataset size. We +can easily package all of this up in a simple function that will return +a convenient pandas dataframe of dataset sizes and runtimes given an +algorithm. + +```python +def data_size_scaling(algorithm, data, sizes=[100, 200, 400, 800, 1600], n_runs=5): + result = [] + for size in sizes: + for run in range(n_runs): + subsample = resample(data, n_samples=size) + start_time = time.time() + algorithm.fit(subsample) + elapsed_time = time.time() - start_time + del subsample + result.append((size, elapsed_time)) + return pd.DataFrame(result, columns=('dataset size', 'runtime (s)')) + +``` + +Now we just want to run this for each of the various dimension reduction +implementations so we can look at the results. Since we don’t know how +long these runs might take we’ll start off with a very small set of +samples, scaling up to only 1600 samples. + +```python +all_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + OpenTSNE(), + TSNE(), + LocallyLinearEmbedding(), + SpectralEmbedding(), + Isomap(), + MDS(), +] +performance_data = {} +for algorithm in all_algorithms: + if 'openTSNE' in str(algorithm.__class__): + alg_name = "OpenTSNE" + elif 'MulticoreTSNE' in str(algorithm.__class__): + alg_name = "MulticoreTSNE" + else: + alg_name = str(algorithm).split('(')[0] + + performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, n_runs=5) + + print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") + + +``` + +``` +[Sat Feb 22 09:50:24 2020] Completed PCA +[Sat Feb 22 09:51:23 2020] Completed UMAP +[Sat Feb 22 09:53:24 2020] Completed MulticoreTSNE +[Sat Feb 22 10:00:50 2020] Completed OpenTSNE +[Sat Feb 22 10:02:22 2020] Completed TSNE +[Sat Feb 22 10:02:44 2020] Completed LocallyLinearEmbedding +[Sat Feb 22 10:03:06 2020] Completed SpectralEmbedding +[Sat Feb 22 10:03:31 2020] Completed Isomap +[Sat Feb 22 10:11:45 2020] Completed MDS + + +``` + +Now let’s plot the results so we can see what is going on. We’ll use +seaborn’s regression plot to interpolate the effective scaling. For some +algorithms this can be a little noisy, especially in this relatively +small dataset regime, but it will give us a good idea of what is going +on. + +```python +for alg_name, perf_data in performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend() + + +``` + +![Image](images/performance_15_1.png) + +We can see straight away that there are some outliers here. It is notable that +openTSNE does poorly on small datasets. It does not have the scaling properties of MDS however; for +larger dataset sizes MDS is going to quickly become completely +unmanageable which openTSNE has fairly flat scaling. At the same time +MulticoreTSNE demonstrates that t-SNE can run fairly efficiently. It is +hard to tell much about the other implementations other than the fact +that PCA is far and away the fastest option. To see more we’ll have to +look at runtimes on larger dataset sizes. Both MDS, Isomap and SpectralEmbedding +will actually take too long to run so let’s restrict ourselves to +the fastest performing implementations and see what happens as we extend +out to larger dataset sizes. + +```python +fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + OpenTSNE(), + TSNE(), + LocallyLinearEmbedding(), +] +fast_performance_data = {} +for algorithm in fast_algorithms: + if 'openTSNE' in str(algorithm.__class__): + alg_name = "OpenTSNE" + elif 'MulticoreTSNE' in str(algorithm.__class__): + alg_name = "MulticoreTSNE" + else: + alg_name = str(algorithm).split('(')[0] + + fast_performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, + sizes=[1600, 3200, 6400, 12800, 25600], n_runs=4) + + print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") + + +``` + +``` +[Sat Feb 22 10:12:15 2020] Completed PCA +[Sat Feb 22 10:14:51 2020] Completed UMAP +[Sat Feb 22 11:16:05 2020] Completed MulticoreTSNE +[Sat Feb 22 11:50:17 2020] Completed OpenTSNE +[Sat Feb 22 13:06:38 2020] Completed TSNE +[Sat Feb 22 14:14:36 2020] Completed LocallyLinearEmbedding + + +``` + +```python +for alg_name, perf_data in fast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend() + + + +``` + +![Image](images/performance_18_1.png) + +At this point we begin to see some significant differentiation among the +different implementations. In the earlier plot OpenTSNE looked to be +performing relatively poorly, but now the scaling effects kick in, and +we see that is is faster than most. Similarly MulticoreTSNE looked to be +slower than some of the other algorithms in th earlier plot, but as we +scale out to larger datasets we see that its relative scaling +performance is superior to the scikit-learn implementations of +TSNE and locally linear embedding. + +It is probably worth extending out further – up to the full MNIST digits +dataset. To manage to do that in any reasonable amount of time we’ll +have to restrict out attention to an even smaller subset of +implementations. We will pare things down to just OpenTSNE, +MulticoreTSNE, PCA and UMAP. + +```python +very_fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + OpenTSNE(), +] +vfast_performance_data = {} +for algorithm in very_fast_algorithms: + if 'openTSNE' in str(algorithm.__class__): + alg_name = "OpenTSNE" + elif 'MulticoreTSNE' in str(algorithm.__class__): + alg_name = "MulticoreTSNE" + else: + alg_name = str(algorithm).split('(')[0] + + vfast_performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, + sizes=[3200, 6400, 12800, 25600, 51200, 70000], n_runs=2) + + print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") + + +``` + +``` +[Sat Feb 22 14:15:22 2020] Completed PCA +[Sat Feb 22 14:18:59 2020] Completed UMAP +[Sat Feb 22 17:04:58 2020] Completed MulticoreTSNE +[Sat Feb 22 17:54:14 2020] Completed OpenTSNE + + +``` + +```python +for alg_name, perf_data in vfast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend() + + +``` + +![Image](images/performance_21_1.png) + +Here we see UMAP’s advantages over t-SNE really coming to the forefront. +While UMAP is clearly slower than PCA, its scaling performance is +dramatically better than MulticoreTSNE, and, despite the impressive +scaling performance of openTSNE, UMAP continues to outperform it. Based +on the slopes of the lines, for even larger datasets the difference +between UMAP and t-SNE is only going to grow. + +This concludes our look at scaling by dataset size. The short summary is +that PCA is far and away the fastest option, but you are potentially +giving up a lot for that speed. UMAP, while not competitive with PCA, is +clearly the next best option in terms of performance among the +implementations explored here. Given the quality of results that UMAP +can provide we feel it is clearly a good option for dimension reduction. diff --git a/doc/performance.rst b/doc/performance.rst deleted file mode 100644 index 16dc09dc..00000000 --- a/doc/performance.rst +++ /dev/null @@ -1,292 +0,0 @@ -Performance Comparison of Dimension Reduction Implementations -============================================================= - -Different dimension reduction techniques can have quite different -computational complexity. Beyond the algorithm itself there is also the -question of how exactly it is implemented. These two factors can have a -significant role in how long it actually takes to run a given dimension -reduction. Furthermore the nature of the data you are trying to reduce -can also matter – mostly the involves the dimensionality of the original -data. Here we will take a brief look at the performance characterstics -of a number of dimension reduction implementations. - -To start let’s get the basic tools we’ll need loaded up – numpy and -pandas obviously, but also tools to get and resample the data, and the -time module so we can perform some basic benchmarking. - -.. code:: python - - import numpy as np - import pandas as pd - from sklearn.datasets import fetch_openml - from sklearn.utils import resample - import time - -Next we’ll need the actual dimension reduction implementations. For the -purposes of this explanation we’ll mostly stick with -`scikit-learn `__, but for the sake of -comparison we’ll also include the -`MulticoreTSNE `__ -implementation of t-SNE, and -`openTSNE `__ both of which -have historically had significantly better performance than scikit-learn -t-SNE (more recent versions of scikit-learn have improved t-SNE -performance). - -.. code:: python - - from sklearn.manifold import TSNE, LocallyLinearEmbedding, Isomap, MDS, SpectralEmbedding - from sklearn.decomposition import PCA - from MulticoreTSNE import MulticoreTSNE - from openTSNE import TSNE as OpenTSNE - from umap import UMAP - -Next we’ll need out plotting tools, and, of course, some data to work -with. For this performance comparison we’ll default to the now standard -benchmark of manifold learning: the MNIST digits dataset. We can use -scikit-learn’s ``fetch_openml`` to grab it for us. - -.. code:: python - - import matplotlib.pyplot as plt - import seaborn as sns - %matplotlib inline - -.. code:: python - - sns.set(context='notebook', - rc={'figure.figsize':(12,10)}, - palette=sns.color_palette('tab10', 10)) - -.. code:: python - - mnist = fetch_openml('mnist_784', version=1, return_X_y=True) - -.. code:: python - - mnist_data = mnist[0] - mnist_labels = mnist[1].astype(int) - -Now it is time to start looking at performance. To start with let’s look -at how performance scales with increasing dataset size. - -Performance scaling by dataset size ------------------------------------ - -As the size of a dataset increases the runtime of a given dimension -reduction algorithm will increase at varying rates. If you ever want to -run your algorithm on larger datasets you will care not just about the -comparative runtime on a single small dataset, but how the performance -scales out as you move to larger datasets. We can similate this by -subsampling from MNIST digits (via scikit-learn’s convenient -``resample`` utility) and looking at the runtime for varying sized -subsamples. Since there is some randomness involved here (both in the -subsample selection, and in some of the algorithms which have stochastic -aspects) we will want to run a few examples for each dataset size. We -can easily package all of this up in a simple function that will return -a convenient pandas dataframe of dataset sizes and runtimes given an -algorithm. - -.. code:: python - - def data_size_scaling(algorithm, data, sizes=[100, 200, 400, 800, 1600], n_runs=5): - result = [] - for size in sizes: - for run in range(n_runs): - subsample = resample(data, n_samples=size) - start_time = time.time() - algorithm.fit(subsample) - elapsed_time = time.time() - start_time - del subsample - result.append((size, elapsed_time)) - return pd.DataFrame(result, columns=('dataset size', 'runtime (s)')) - -Now we just want to run this for each of the various dimension reduction -implementations so we can look at the results. Since we don’t know how -long these runs might take we’ll start off with a very small set of -samples, scaling up to only 1600 samples. - -.. code:: python - - all_algorithms = [ - PCA(), - UMAP(), - MulticoreTSNE(), - OpenTSNE(), - TSNE(), - LocallyLinearEmbedding(), - SpectralEmbedding(), - Isomap(), - MDS(), - ] - performance_data = {} - for algorithm in all_algorithms: - if 'openTSNE' in str(algorithm.__class__): - alg_name = "OpenTSNE" - elif 'MulticoreTSNE' in str(algorithm.__class__): - alg_name = "MulticoreTSNE" - else: - alg_name = str(algorithm).split('(')[0] - - performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, n_runs=5) - - print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") - - -.. parsed-literal:: - - [Sat Feb 22 09:50:24 2020] Completed PCA - [Sat Feb 22 09:51:23 2020] Completed UMAP - [Sat Feb 22 09:53:24 2020] Completed MulticoreTSNE - [Sat Feb 22 10:00:50 2020] Completed OpenTSNE - [Sat Feb 22 10:02:22 2020] Completed TSNE - [Sat Feb 22 10:02:44 2020] Completed LocallyLinearEmbedding - [Sat Feb 22 10:03:06 2020] Completed SpectralEmbedding - [Sat Feb 22 10:03:31 2020] Completed Isomap - [Sat Feb 22 10:11:45 2020] Completed MDS - - -Now let’s plot the results so we can see what is going on. We’ll use -seaborn’s regression plot to interpolate the effective scaling. For some -algorithms this can be a little noisy, especially in this relatively -small dataset regime, but it will give us a good idea of what is going -on. - -.. code:: python - - for alg_name, perf_data in performance_data.items(): - sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) - plt.legend() - - -.. image:: images/performance_15_1.png - - -We can see straight away that there are some outliers here. It is notable that -openTSNE does poorly on small datasets. It does not have the scaling properties of MDS however; for -larger dataset sizes MDS is going to quickly become completely -unmanageable which openTSNE has fairly flat scaling. At the same time -MulticoreTSNE demonstrates that t-SNE can run fairly efficiently. It is -hard to tell much about the other implementations other than the fact -that PCA is far and away the fastest option. To see more we’ll have to -look at runtimes on larger dataset sizes. Both MDS, Isomap and SpectralEmbedding -will actually take too long to run so let’s restrict ourselves to -the fastest performing implementations and see what happens as we extend -out to larger dataset sizes. - -.. code:: python - - fast_algorithms = [ - PCA(), - UMAP(), - MulticoreTSNE(), - OpenTSNE(), - TSNE(), - LocallyLinearEmbedding(), - ] - fast_performance_data = {} - for algorithm in fast_algorithms: - if 'openTSNE' in str(algorithm.__class__): - alg_name = "OpenTSNE" - elif 'MulticoreTSNE' in str(algorithm.__class__): - alg_name = "MulticoreTSNE" - else: - alg_name = str(algorithm).split('(')[0] - - fast_performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, - sizes=[1600, 3200, 6400, 12800, 25600], n_runs=4) - - print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") - - -.. parsed-literal:: - - [Sat Feb 22 10:12:15 2020] Completed PCA - [Sat Feb 22 10:14:51 2020] Completed UMAP - [Sat Feb 22 11:16:05 2020] Completed MulticoreTSNE - [Sat Feb 22 11:50:17 2020] Completed OpenTSNE - [Sat Feb 22 13:06:38 2020] Completed TSNE - [Sat Feb 22 14:14:36 2020] Completed LocallyLinearEmbedding - - -.. code:: python - - for alg_name, perf_data in fast_performance_data.items(): - sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) - plt.legend() - - - -.. image:: images/performance_18_1.png - - -At this point we begin to see some significant differentiation among the -different implementations. In the earlier plot OpenTSNE looked to be -performing relatively poorly, but now the scaling effects kick in, and -we see that is is faster than most. Similarly MulticoreTSNE looked to be -slower than some of the other algorithms in th earlier plot, but as we -scale out to larger datasets we see that its relative scaling -performance is superior to the scikit-learn implementations of -TSNE and locally linear embedding. - -It is probably worth extending out further – up to the full MNIST digits -dataset. To manage to do that in any reasonable amount of time we’ll -have to restrict out attention to an even smaller subset of -implementations. We will pare things down to just OpenTSNE, -MulticoreTSNE, PCA and UMAP. - -.. code:: python - - very_fast_algorithms = [ - PCA(), - UMAP(), - MulticoreTSNE(), - OpenTSNE(), - ] - vfast_performance_data = {} - for algorithm in very_fast_algorithms: - if 'openTSNE' in str(algorithm.__class__): - alg_name = "OpenTSNE" - elif 'MulticoreTSNE' in str(algorithm.__class__): - alg_name = "MulticoreTSNE" - else: - alg_name = str(algorithm).split('(')[0] - - vfast_performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, - sizes=[3200, 6400, 12800, 25600, 51200, 70000], n_runs=2) - - print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") - - -.. parsed-literal:: - - [Sat Feb 22 14:15:22 2020] Completed PCA - [Sat Feb 22 14:18:59 2020] Completed UMAP - [Sat Feb 22 17:04:58 2020] Completed MulticoreTSNE - [Sat Feb 22 17:54:14 2020] Completed OpenTSNE - - -.. code:: python - - for alg_name, perf_data in vfast_performance_data.items(): - sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) - plt.legend() - - -.. image:: images/performance_21_1.png - - -Here we see UMAP’s advantages over t-SNE really coming to the forefront. -While UMAP is clearly slower than PCA, its scaling performance is -dramatically better than MulticoreTSNE, and, despite the impressive -scaling performance of openTSNE, UMAP continues to outperform it. Based -on the slopes of the lines, for even larger datasets the difference -between UMAP and t-SNE is only going to grow. - -This concludes our look at scaling by dataset size. The short summary is -that PCA is far and away the fastest option, but you are potentially -giving up a lot for that speed. UMAP, while not competitive with PCA, is -clearly the next best option in terms of performance among the -implementations explored here. Given the quality of results that UMAP -can provide we feel it is clearly a good option for dimension reduction. - diff --git a/doc/plotting.rst b/doc/plotting.md similarity index 68% rename from doc/plotting.rst rename to doc/plotting.md index 6a953152..ffe9e406 100644 --- a/doc/plotting.rst +++ b/doc/plotting.md @@ -1,5 +1,5 @@ -Plotting UMAP results -===================== +# Plotting UMAP results + UMAP is often used for visualization by reducing data to 2-dimensions. Since this is such a common use case the umap package now includes @@ -11,58 +11,62 @@ the majority of plotting needs easy, and help provide sensible plotting choices wherever possible. To get started looking at the plotting options let's load a variety of data to work with. -.. code:: python3 +```python3 +import sklearn.datasets +import pandas as pd +import numpy as np +import umap - import sklearn.datasets - import pandas as pd - import numpy as np - import umap +``` -.. code:: python3 +```python3 +pendigits = sklearn.datasets.load_digits() +mnist = sklearn.datasets.fetch_openml('mnist_784') +fmnist = sklearn.datasets.fetch_openml('Fashion-MNIST') - pendigits = sklearn.datasets.load_digits() - mnist = sklearn.datasets.fetch_openml('mnist_784') - fmnist = sklearn.datasets.fetch_openml('Fashion-MNIST') +``` To start we will fit a UMAP model to the pendigits data. This is as simple as running the fit method and assigning the result to a variable. -.. code:: python3 +```python3 +mapper = umap.UMAP().fit(pendigits.data) - mapper = umap.UMAP().fit(pendigits.data) +``` -If we want to do plotting we will need the ``umap.plot`` package. While +If we want to do plotting we will need the `umap.plot` package. While the umap package has a fairly small set of requirements it is worth -noting that if you want to using ``umap.plot`` you will need a variety +noting that if you want to using `umap.plot` you will need a variety of extra libraries that are not in the default requirements for umap. In particular you will need: -- `matplotlib `__ -- `pandas `__ -- `datashader `__ -- `bokeh `__ -- `holoviews `__ +- [matplotlib](https://matplotlib.org/) +- [pandas](https://pandas.pydata.org/) +- [datashader](http://datashader.org/) +- [bokeh](https://bokeh.pydata.org/en/latest/) +- [holoviews](http://holoviews.org/) All should be either pip or conda installable. With those in hand you -can import the ``umap.plot`` package. +can import the `umap.plot` package. -.. code:: python3 +```python3 +import umap.plot - import umap.plot +``` Now that we have the package loaded, how do we use it? The most straightforward thing to do is plot the umap results as points. We can -achieve this via the function ``umap.plot.points``. In its most basic -form you can simply pass the trained UMAP model to ``umap.plot.points``: - -.. code:: python3 +achieve this via the function `umap.plot.points`. In its most basic +form you can simply pass the trained UMAP model to `umap.plot.points`: - umap.plot.points(mapper) +```python3 +umap.plot.points(mapper) -.. image:: images/plotting_8_2.png +``` +![Image](images/plotting_8_2.png) As you can see we immediately get a scatterplot of the UMAP embedding. Note that the function automatically selects a point-size based on the @@ -76,26 +80,26 @@ It is common for data passed to UMAP to have an associated set of labels, which may have been derived from ground-truth, from clustering, or via other means. In such cases it is desirable to be able to color the scatterplot according to the labelling. We can do this by simply -passing the array of label information in with the ``labels`` keyword. -The ``umap.plot.points`` function will color the data with a +passing the array of label information in with the `labels` keyword. +The `umap.plot.points` function will color the data with a categorical colormap according to the labels provided. -.. code:: python3 - - umap.plot.points(mapper, labels=pendigits.target) +```python3 +umap.plot.points(mapper, labels=pendigits.target) -.. image:: images/plotting_10_1.png +``` +![Image](images/plotting_10_1.png) Alternatively you may have extra data that is continuous rather than categorical. In this case you will want to use a continuous colormap to shade the data. Again this is straightforward to do -- pass in the -continuous data with the ``values`` keyword and data will be colored +continuous data with the `values` keyword and data will be colored accordingly using a continuous colormap. Furthermore, if you don't like the default color choices the -``umap.plot.points`` function offers a number of 'themes' that provide +`umap.plot.points` function offers a number of 'themes' that provide predefined color choices. Themes include: - fire @@ -111,28 +115,28 @@ predefined color choices. Themes include: Here we will make use of the 'fire' theme to demonstrate how simple it is to change the aesthetics. -.. code:: python3 +```python3 +umap.plot.points(mapper, values=pendigits.data.mean(axis=1), theme='fire') - umap.plot.points(mapper, values=pendigits.data.mean(axis=1), theme='fire') - -.. image:: images/plotting_12_1.png +``` +![Image](images/plotting_12_1.png) If you want greater control you can specify exact colormaps and background colors. For example here we want to color the data by label, but use a black background and use the 'Paired' colormap for the -categorical coloring (passed as ``color_key_cmap``; the ``cmap`` keyword +categorical coloring (passed as `color_key_cmap`; the `cmap` keyword defines the continuous colormap). -.. code:: python3 - - umap.plot.points(mapper, labels=pendigits.target, color_key_cmap='Paired', background='black') +```python3 +umap.plot.points(mapper, labels=pendigits.target, color_key_cmap='Paired', background='black') -.. image:: images/plotting_14_1.png +``` +![Image](images/plotting_14_1.png) -Many more options are available including a ``color_key`` to specify a -dictionary mapping of discrete labels to colors, ``cmap`` to specify the +Many more options are available including a `color_key` to specify a +dictionary mapping of discrete labels to colors, `cmap` to specify the continous colormap, or the width and height of the resulting plot. Again, this does not provide comprehensive control of the plot aesthetics, but the goal here is to provide a simple to use interface @@ -140,8 +144,8 @@ rather than the ability for the user to fine tune all aspects -- users seeking such control are far better served making use of the individual underlying packages (matplotlib, datashader, and bokeh) by themselves. -Plotting larger datasets ------------------------- +## Plotting larger datasets + Once you have a lot of data it becomes easier for a simple scatter plot to lie to you. Most notably overplotting, where markers for points @@ -154,54 +158,55 @@ the plot isn't subtly lying to you in some way. `This essay the datashader documentation does an excellent job of describing the issues with overplotting, why the obvious solutions are not quite sufficient, and how to get around the problem. To make life easier for -users the ``umap.plot`` package will automatically switch to using +users the `umap.plot` package will automatically switch to using datashader for rendering once your dataset gets large enough. This helps to ensure you don't get fooled by overplotting. We can see this in action by working with one of the larger datasets such as Fashion-MNIST. -.. code:: python3 +```python3 +mapper = umap.UMAP().fit(fmnist.data) - mapper = umap.UMAP().fit(fmnist.data) +``` -Having fit the data with UMAP we can call ``umap.plot.points`` exactly +Having fit the data with UMAP we can call `umap.plot.points` exactly as before, but this time, since the data is large enough to have potential overplotting, datashader will be used in the background for rendering. -.. code:: python3 - - umap.plot.points(mapper) +```python3 +umap.plot.points(mapper) -.. image:: images/plotting_19_2.png +``` +![Image](images/plotting_19_2.png) All the same plot options as before hold, so we can color by labels, and apply the same themes, and it will all seamlessly use datashader for the actual rendering. Thus, regardless of how much data you have -``umap.plot.points`` will render it well with a transparent user +`umap.plot.points` will render it well with a transparent user interface. You, as a user, don't need to worry about switching to plotting with datashader, or how to convert your plotting to its slightly different API -- you can just use the same API and trust the results you get. -.. code:: python3 +```python3 +umap.plot.points(mapper, labels=fmnist.target, theme='fire') - umap.plot.points(mapper, labels=fmnist.target, theme='fire') +``` -.. image:: images/plotting_21_2.png +![Image](images/plotting_21_2.png) +## Interactive plotting, and hover tools -Interactive plotting, and hover tools -------------------------------------- Rendering good looking static plots is important, but what if you want to be able to interact with your data -- pan around, and zoom in on the clusters to see the finer structure? What if you want to annotate your data with more complex labels than merely colors? Wouldn't it be good to be able to hover over data points and get more information about the -individual point? Since this is a very common use case ``umap.plot`` +individual point? Since this is a very common use case `umap.plot` tries to make it easy to quickly generate such plots, and provide basic utilities to allow you to have annotated hover tools working quickly. Again, the goal is not to provide a comprehensive solution that can do @@ -211,9 +216,10 @@ up and running fast. To make a good example of this let's use a subset of the Fashion MNIST dataset. We can quickly train a new mapper object on that. -.. code:: python3 +```python3 +mapper = umap.UMAP().fit(fmnist.data[:30000]) - mapper = umap.UMAP().fit(fmnist.data[:30000]) +``` The goal is to be able to hover over different points and see data associated with the given point (or points) under the cursor. For this @@ -227,65 +233,68 @@ of the point, its target number, and the actual name of the type of fashion item that target corresponds to. This is easy to quickly put together using pandas. -.. code:: python3 - - hover_data = pd.DataFrame({'index':np.arange(30000), - 'label':fmnist.target[:30000]}) - hover_data['item'] = hover_data.label.map( - { - '0':'T-shirt/top', - '1':'Trouser', - '2':'Pullover', - '3':'Dress', - '4':'Coat', - '5':'Sandal', - '6':'Shirt', - '7':'Sneaker', - '8':'Bag', - '9':'Ankle Boot', - } - ) - -For interactive use the ``umap.plot`` package makes use of bokeh. Bokeh +```python3 +hover_data = pd.DataFrame({'index':np.arange(30000), + 'label':fmnist.target[:30000]}) +hover_data['item'] = hover_data.label.map( + { + '0':'T-shirt/top', + '1':'Trouser', + '2':'Pullover', + '3':'Dress', + '4':'Coat', + '5':'Sandal', + '6':'Shirt', + '7':'Sneaker', + '8':'Bag', + '9':'Ankle Boot', + } +) + +``` + +For interactive use the `umap.plot` package makes use of bokeh. Bokeh has several output methods, but in the approach we'll be outputting inline in a notebook. We have to enable this using the -``output_notebook`` function. Alteratively we could use ``output_file`` +`output_notebook` function. Alteratively we could use `output_file` or other similar options -- see the bokeh documentation for more details. -.. code:: python3 +```python3 +umap.plot.output_notebook() + + - umap.plot.output_notebook() +``` -.. raw:: html +
+ + Loading BokehJS ... +
- -
- - Loading BokehJS ... -
-Now we can make an interactive plot using ``umap.plot.interactive``. -This has a very similar API to the ``umap.plot.points`` approach, but -also supports a ``hover_data`` keyword which, if passed a suitable +Now we can make an interactive plot using `umap.plot.interactive`. +This has a very similar API to the `umap.plot.points` approach, but +also supports a `hover_data` keyword which, if passed a suitable dataframe, will provide hover tooltips in the interactive plot. Since bokeh allows different outputs, to display it in the notebook we will -have to take the extra stop of calling ``show`` on the result. +have to take the extra stop of calling `show` on the result. -.. code:: python3 +```python3 +p = umap.plot.interactive(mapper, labels=fmnist.target[:30000], hover_data=hover_data, point_size=2) +umap.plot.show(p) - p = umap.plot.interactive(mapper, labels=fmnist.target[:30000], hover_data=hover_data, point_size=2) - umap.plot.show(p) +``` + +[View html file](plotting_interactive_example.html) -.. raw:: html - :file: plotting_interactive_example.html @@ -295,24 +304,24 @@ We get the sort of result one would like -- a fully interactive plot that can be zoomed in on, and more, but we also now have an interactive hover tool which presents the data from the dataframe we constructed. This allows a quick and easy method to get up and running with a richer -interactive exploration of your UMAP plot. ``umap.plot.interactive`` -supports all the same aesthetic parameters as ``umap.plot.points`` so +interactive exploration of your UMAP plot. `umap.plot.interactive` +supports all the same aesthetic parameters as `umap.plot.points` so you can theme your plot, color by label or value, and other similar -operations explained above for ``umap.plot.points``. +operations explained above for `umap.plot.points`. + +## Interactive plotting with Nomic Atlas -Interactive plotting with Nomic Atlas ----------------------------------------- -For interactive exploration, especially with large datasets, you can use `Nomic Atlas `_. Nomic Atlas is a platform for embedding generation, visualization, analysis, retrieval, and everything you need to operationalize your embeddings and make them useful for your applications. It directly integrates UMAP as one of its projection models, allowing you to leverage UMAP within a powerful visualization environment. +For interactive exploration, especially with large datasets, you can use [Nomic Atlas](https://atlas.nomic.ai/). Nomic Atlas is a platform for embedding generation, visualization, analysis, retrieval, and everything you need to operationalize your embeddings and make them useful for your applications. It directly integrates UMAP as one of its projection models, allowing you to leverage UMAP within a powerful visualization environment. Nomic Atlas handles the embedding and UMAP dimensionality reduction process and provides an interactive interface with features like searching, filtering, coloring by metadata, and displaying rich information on hover. Atlas can help when you want to share your understanding of your UMAP visualizations or collaborate with others, as the map is accessible via a URL. -.. raw:: html + MNIST UMAP visualization in Nomic Atlas -Plotting connectivity ---------------------- +## Plotting connectivity + UMAP works by constructing an intermediate topological representation of the approximate manifold the data may have been sampled from. In @@ -321,21 +330,21 @@ Sometimes it can be beneficial to see how that graph (representing connectivity in the manifold) looks with respect to the resulting embedding. It can be used to better understand the embedding, and for diagnostic purposes. To see the connectivity you can use the -``umap.plot.connectivity`` function. It works very similarly to the -``umap.plot.points`` function, and has the option as to whether to +`umap.plot.connectivity` function. It works very similarly to the +`umap.plot.points` function, and has the option as to whether to display the embedding point, or just the connectivity. To start let's do a simple plot showing the points: -.. code:: python3 - - umap.plot.connectivity(mapper, show_points=True) +```python3 +umap.plot.connectivity(mapper, show_points=True) -.. image:: images/plotting_32_2.png +``` +![Image](images/plotting_32_2.png) -As with ``umap.plot.points`` there are options to control the basic -aesthetics, including theme options and an ``edge_cmap`` keyword +As with `umap.plot.points` there are options to control the basic +aesthetics, including theme options and an `edge_cmap` keyword argument to specify the colormap used for displaying the edges. Since this approach already leverages datashader for edge plotting, we @@ -344,31 +353,32 @@ available in datashader. This can provide a less busy view of connectivity, but can be expensive to compute, particularly for larger datasets. -.. code:: python3 +```python3 +umap.plot.connectivity(mapper, edge_bundling='hammer') - umap.plot.connectivity(mapper, edge_bundling='hammer') +``` -.. image:: images/plotting_34_2.png +![Image](images/plotting_34_2.png) +## Diagnostic plotting -Diagnostic plotting -------------------- Plotting the connectivity provides at least one basic diagnostic view that helps a user understand what is going on with an embedding. More -views on data are better, of course, so ``umap.plot`` includes a -``umap.plot.diagnostic`` function that can provide various diagnostic +views on data are better, of course, so `umap.plot` includes a +`umap.plot.diagnostic` function that can provide various diagnostic plots. We'll look at a few of them here. To do so we'll use the full MNIST digits data set. -.. code:: python3 +```python3 +mapper = umap.UMAP().fit(mnist.data) - mapper = umap.UMAP().fit(mnist.data) +``` The first diagnostic type is a Principal Components Analysis based -diagnostic, which you can select with ``diagnostic_type='pca'``. The +diagnostic, which you can select with `diagnostic_type='pca'`. The essence of the approach is that we can use PCA, which preserves global structure, to reduce the data to three dimensions. If we scale the results to fit in a 3D cube we can convert the 3D PCA coordinates of @@ -377,13 +387,13 @@ points in the UMAP embedding with the colors induced by the PCA it is possible to get a sense of how some of the more large scale global structure has been represented in the embedding. -.. code:: python3 +```python3 +umap.plot.diagnostic(mapper, diagnostic_type='pca') - umap.plot.diagnostic(mapper, diagnostic_type='pca') +``` -.. image:: images/plotting_38_1.png - +![Image](images/plotting_38_1.png) What we are looking for here is a generally smooth transition of colors, and an overall layout that broadly respects the color transitions. In @@ -404,13 +414,13 @@ and then describes each data point in terms of its distance to these centers. Clearly this, again, captures a lot of the broad global structure of the data. -.. code:: python3 - - umap.plot.diagnostic(mapper, diagnostic_type='vq') +```python3 +umap.plot.diagnostic(mapper, diagnostic_type='vq') -.. image:: images/plotting_40_1.png +``` +![Image](images/plotting_40_1.png) Again we are looking for largely smooth transitions, and for related colors to match up between clusters. This view supports the fact that @@ -428,14 +438,14 @@ areas of the space) that UMAP will have a harder time embedding as well. Thus one can trust the embedding to be more accurate in regions where the points have consistently lower local dimension. -.. code:: python3 +```python3 +local_dims = umap.plot.diagnostic(mapper, diagnostic_type='local_dim') - local_dims = umap.plot.diagnostic(mapper, diagnostic_type='local_dim') +``` -.. image:: images/plotting_42_0.png - +![Image](images/plotting_42_0.png) As you can see, the local dimension of the data varies quite widely across the data. In particular the lower left cluster has the lowest local @@ -457,13 +467,13 @@ neighborhoods have in common over the total number of unique neighbors across the two neighborhoods. Higher values mean that the local neighborhood has been more accurately preserved. -.. code:: python3 - - umap.plot.diagnostic(mapper, diagnostic_type='neighborhood') +```python3 +umap.plot.diagnostic(mapper, diagnostic_type='neighborhood') -.. image:: images/plotting_44_1.png +``` +![Image](images/plotting_44_1.png) As one might expect the local neighborhood preservation tends to be a lot better for those points that had a lower local dimension (as seen in diff --git a/doc/plotting_example_interactive.py b/doc/plotting_example_interactive.py index 177f9af6..d66cb986 100644 --- a/doc/plotting_example_interactive.py +++ b/doc/plotting_example_interactive.py @@ -1,6 +1,7 @@ -import sklearn.datasets -import pandas as pd import numpy as np +import pandas as pd +import sklearn.datasets + import umap import umap.plot @@ -21,12 +22,15 @@ "7": "Sneaker", "8": "Bag", "9": "Ankle Boot", - } + }, ) umap.plot.output_file("plotting_interactive_example.html") p = umap.plot.interactive( - mapper, labels=fmnist.target[:30000], hover_data=hover_data, point_size=2 + mapper, + labels=fmnist.target[:30000], + hover_data=hover_data, + point_size=2, ) umap.plot.show(p) diff --git a/doc/plotting_example_nomic_atlas.py b/doc/plotting_example_nomic_atlas.py index 857cab99..8a3685aa 100644 --- a/doc/plotting_example_nomic_atlas.py +++ b/doc/plotting_example_nomic_atlas.py @@ -1,6 +1,6 @@ +import pandas as pd from nomic import AtlasDataset from nomic.data_inference import ProjectionOptions -import pandas as pd # Example data df = pd.read_csv("https://docs.nomic.ai/singapore_airlines_reviews.csv") @@ -12,6 +12,9 @@ atlas_map = dataset.create_index( indexed_field="text", projection=ProjectionOptions( - model="umap", n_neighbors=20, min_dist=0.01, n_epochs=200 + model="umap", + n_neighbors=20, + min_dist=0.01, + n_epochs=200, ), ) diff --git a/doc/plotting_interactive_example.html b/doc/plotting_interactive_example.html index eb7244f1..b03fe08b 100644 --- a/doc/plotting_interactive_example.html +++ b/doc/plotting_interactive_example.html @@ -99786,11 +99786,11 @@ Bokeh.safely(function() { (function(root) { function embed_document(root) { - + var docs_json = document.getElementById('1115').textContent; var render_items = [{"docid":"52d70689-9a65-4ab5-9c04-67335da6613e","roots":{"1002":"e5294291-5034-4280-94cc-d4875d907e2d"}}]; root.Bokeh.embed.embed_items(docs_json, render_items); - + } if (root.Bokeh !== undefined) { embed_document(root); @@ -99815,7 +99815,7 @@ else document.addEventListener("DOMContentLoaded", fn); })(); - + - - \ No newline at end of file + + diff --git a/doc/precomputed_k-nn.md b/doc/precomputed_k-nn.md new file mode 100644 index 00000000..b5f5554f --- /dev/null +++ b/doc/precomputed_k-nn.md @@ -0,0 +1,399 @@ +# Precomputed k-nn + + +The purpose of this tutorial is to explore some cases where using a +precomputed_knn might be useful and then discuss how we can obtain +reproducible results with it. + +## Practical Uses + + +### Trying UMAP with various parameters + + +Let’s look at how we can use precomputed_knn to save time. First we will +test it out on MNIST which has 70,000 samples of 784 dimensions. If we +want to test out a series of n_neighbors and min_dist parameters, we +might lose quite a bit of time recomputing the knn matrices for our +data. Instead, we can compute the knn for the largest n_neighbors we +wish to analyze and then feed that precomputed_knn to UMAP. UMAP will +automatically prune it to the right n_neighbors value and skip the +nearest neighbors step, saving us a lot of time. + +We note that we don’t use a random state in order to leverage UMAP’s +parallelization and speed up the calculations. + +```python3 +from sklearn.datasets import fetch_openml +import numpy as np +import umap +import umap.plot +from umap.umap_ import nearest_neighbors + +data, labels = fetch_openml('mnist_784', version=1, return_X_y=True) +labels = np.asarray(labels, dtype=np.int32) + +n_neighbors = [5, 50, 100, 250] +min_dists = [0, 0.2, 0.5, 0.9] +normal_embeddings = np.zeros((4, 4, 70000, 2)) +precomputed_knn_embeddings = np.zeros((4, 4, 70000, 2)) + +``` + +```python3 +%%time +# UMAP run on the grid of parameters without precomputed_knn +for i, k in enumerate(n_neighbors): + for j, dist in enumerate(min_dists): + normal_embeddings[i, j] = umap.UMAP(n_neighbors=k, + min_dist=dist, + ).fit_transform(data) +print("\033[1m"+"Time taken to compute UMAP on grid of parameters:\033[0m") + + +``` + +``` +**Time taken to compute UMAP on grid of parameters:** +Wall time: 31min 57s + + +``` + +```python3 +%%time +# UMAP run on list of n_neighbors without precomputed_knn + +# We compute the knn for max(n_neighbors)=250 +mnist_knn = nearest_neighbors(data, + n_neighbors=250, + metric="euclidean", + metric_kwds=None, + angular=False, + random_state=None, + ) +# Now we compute the embeddings for the grid of parameters +for i, k in enumerate(n_neighbors): + for j, dist in enumerate(min_dists): + precomputed_knn_embeddings[i, j] = umap.UMAP(n_neighbors=k, + min_dist=dist, + precomputed_knn=mnist_knn, + ).fit_transform(data) +print("\033[1m"+"Time taken to compute UMAP on grid of parameters with precomputed_knn:\033[0m") + + +``` + +``` +**Time taken to compute UMAP on grid of parameters with precomputed_knn:** +Wall time: 17min 54s + + +``` + +Using a precomputed_knn we have cut the computation time in half! +Observe that half of our n_neighbors values are relatively small. If +instead, we had had a higher distribution of values, the time savings +would have been even greater. Additionaly, we could've saved even more +time by first computing UMAP normally with an *n_neighbors* value of 250 +and then extracting the k-nn graph from that UMAP object. + +With this, we can easily visualize how the n_neighbors parameter affects +our embedding. + +```python3 +import matplotlib.pyplot as plt + +fig, axs = plt.subplots(4, 4, figsize=(20, 20)) + +for i, ax_row in enumerate(axs): + for j, ax in enumerate(ax_row): + ax.scatter(precomputed_knn_embeddings[i, j, :, 0], + precomputed_knn_embeddings[i, j, :, 1], + c=labels / 9, + cmap='tab10', + alpha=0.1, + s=1, + ) + ax.set_xticks([]) + ax.set_yticks([]) + if i == 0: + ax.set_title("min_dist = {}".format(min_dists[j]), size=15) + if j == 0: + ax.set_ylabel("n_neighbors = {}".format(n_neighbors[i]), size=15) +fig.suptitle("UMAP embedding of MNIST digits with grid of parameters", y=0.92, size=20) +plt.subplots_adjust(wspace=0.05, hspace=0.05) + + + +``` + +![Image](images/precomputed_k-nn6.png) + +We see that in this case, the embedding is robust to the choice of +n_neighbors and that lower min_dist values simply pack the clusters more +tightly. + +## Reproducibility + + +We strongly recommend that you review the UMAP `reproducibility +section `__ +in the docs before attempting to reproduce results with +*precomputed_knn*. + +### Standard Case + + +Out of the box, UMAP with precomputed_knn supports creating reproducible +results. This works in exactly the same way as regular UMAP, where, the +user can set a random seed state to ensure that results can be reproduced +exactly. However, some important considerations must be taken into account. + +UMAP embeddings are entirely dependent on first, computing the graphical +representation in higher dimensions and second, learning an embedding +that preserves the structure of that graph. Recall that our graphical +representation is based on the k-nn graph of our data. If we have two +different k-nn graphs, then we will naturally have two different +graphical representations of our data. Therefore, **we can only ensure +reproducible results when we use the same k-nn graph**. In our case, +this means that all reproducible results are tied to three values: + + + +
    + + + +
  1. + +The random seed when computing the k-nn. + + + +
    2. + + + +
  2. + +The n_neighbors value when computing the k-nn. + + + +
    3. + + + +
  3. + +The random seed when running UMAP. + + + +
    4. + + + +
+ +Two different runs of UMAP, with these three values being equal, are +guaranteed to return the same result. Let’s look at how this works with +an example. To do this, we’ll create some data to work with; three +random blobs in 60-dimensional space. + +```python3 +y = np.random.rand(1700, 60) +X = np.concatenate((y+20, y, y-20)) +synthetic_labels = np.repeat([1, 2, 3], repeats=1700) + +``` + +With the data in hand, we can fix the three parameters listed above and +see how two different UMAP runs give the same result. To avoid confusion +we’ll assume that the UMAP random seed is the same value as the knn +random seed. + +```python3 +import umap.plot +random_seed = 10 + +knn = nearest_neighbors( + X, + n_neighbors=50, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_seed, + ) + +knn_umap = umap.UMAP(n_neighbors=30, precomputed_knn=knn, random_state=random_seed).fit(X) +knn_umap2 = umap.UMAP(n_neighbors=30, precomputed_knn=knn, random_state=random_seed).fit(X) + +fig, ax = plt.subplots(1, 2, figsize=(13,7)) +umap.plot.points(knn_umap, labels=synthetic_labels, ax=ax[0], theme='green') +umap.plot.points(knn_umap2, labels=synthetic_labels, ax=ax[1], theme='green') +ax[0].set_title("Precomuted knn 1st run", size=16) +ax[1].set_title("Precomuted knn 2nd run", size=16) +plt.show() + +print("\033[1m"+"Are the embeddings for knn_umap and knn_umap2 the same?\033[0m") +print((knn_umap.embedding_ == knn_umap2.embedding_).all()) + + + +``` + +![Image](images/precomputed_k-nn11.png) + +``` +**Are the embeddings for knn_umap and knn_umap2 the same?** +True + + +``` + +As we can see, by fixing the *random_seed* and the *n_neighbors* for the +knn, we have been able to obtain identical results from both UMAP runs. +In contrast, if these differ, we can’t guarantee the same result. + +```python3 +random_seed2 = 15 + +# Different n_neighbors +knn3 = nearest_neighbors( + X, + n_neighbors=40, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_seed, + ) +# Different random seed +knn4 = nearest_neighbors( + X, + n_neighbors=50, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_seed2, + ) + +knn_umap3 = umap.UMAP(n_neighbors=30, precomputed_knn=knn3, random_state=random_seed).fit(X) +knn_umap4 = umap.UMAP(n_neighbors=30, precomputed_knn=knn4, random_state=random_seed2).fit(X) + +fig, ax = plt.subplots(1, 2, figsize=(13,7)) +umap.plot.points(knn_umap3, labels=synthetic_labels, ax=ax[0], theme='green') +umap.plot.points(knn_umap4, labels=synthetic_labels, ax=ax[1], theme='green') +ax[0].set_title("Precomuted knn; different knn n_neighbors", size=16) +ax[1].set_title("Precomuted knn; different random_seed", size=16) +plt.show() + +print("\033[1m"+"Are the embeddings for knn_umap and knn_umap3 the same?\033[0m") +print((knn_umap.embedding_ == knn_umap3.embedding_).all()) + +print("\033[1m"+"Are the embeddings for knn_umap and knn_umap4 the same?\033[0m") +print((knn_umap.embedding_ == knn_umap4.embedding_).all()) + + + +``` + +![Image](images/precomputed_k-nn13.png) + +``` +**Are the embeddings for knn_umap and knn_umap3 the same?** +False +**Are the embeddings for knn_umap and knn_umap4 the same?** +False + + +``` + +Without those three parameters being equal between runs, we have +obtained different results. + +### Reproducing normal UMAP with precomputed_knn + + +With some extra considerations, we can also reproduce +precomputed_knn results with normal UMAP and vice-versa. As in +the previous case, we must keep in mind that the k-nn graphs have to be +same. Additionaly, we also must consider how UMAP uses the *random_seed* +that we provide it. + +If you provide UMAP a *random_seed*, it converts it into an +*np.random.RandomState* (RNG). This RNG is then used to fix the state +for all the relevant steps in the algorithm. The important thing to +note, is that the RNG is mutated everytime it’s used. So, if we want to +reproduce results with precomputed_knn we’ll have to mimic how UMAP +manipulates the RNG when calling the *fit()* function. + +For more information on random states and their behavior, please refer to +[[1]](https://scikit-learn.org/dev/common_pitfalls.html#randomness). + +We’ll look at one example of how this can be accomplished. Other cases +can be easily infered from this. Using the same random blobs as before, +we seek to run UMAP normally and then reproduce the results with a +precomputed_knn. To accomplish this, we have to create a new k-nn graph +using the *nearest_neighbors()* function in the same way that +*fit()* would. + +```python3 +from sklearn.utils import check_random_state + +# First we run the normal UMAP to compare with +random_seed3 = 12 +normal_umap = umap.UMAP(n_neighbors=30, random_state=random_seed3).fit(X) + +# Now we run precomputed_knn UMAP +random_state3 = check_random_state(random_seed3) +# random_state3 = numpy.random.RandomState(random_seed3) +knn5 = nearest_neighbors( + X, + n_neighbors=30, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_state3, + ) +# This mutated RNG can now be fed into precompute_knn UMAP to obtain +# the same results as in normal UMAP +knn_umap5 = umap.UMAP(n_neighbors=30, + precomputed_knn=knn5, + random_state=random_state3, # <--- This is a RNG + ).fit(X) + +``` + +Note that in this case we create a numpy.random.mtrand.RandomState +instance with *check_random_state()* because we want to ensure that +our RNG is created and mutated in exactly the same way that UMAP +normally does. Equivalently, we could call *numpy.random.RandomState()* +directly. + +Graphing and comparing the embeddings, we see that we were able to +obtain the same results. + +```python3 +fig, ax = plt.subplots(1, 2, figsize=(13,7)) +umap.plot.points(normal_umap, labels=synthetic_labels, ax=ax[0], theme='green') +umap.plot.points(knn_umap5, labels=synthetic_labels, ax=ax[1], theme='green') +ax[0].set_title("Normal UMAP", size=16) +ax[1].set_title("Precomuted knn UMAP", size=16) +plt.show() + +print("\033[1m"+"Are the embeddings for normal_umap and knn_umap5 the same?\033[0m") +print((normal_umap.embedding_ == knn_umap5.embedding_).all()) + + + +``` + +![Image](images/precomputed_k-nn17.png) + +``` +**Are the embeddings for normal_umap and knn_umap5 the same?** +True + +``` diff --git a/doc/precomputed_k-nn.rst b/doc/precomputed_k-nn.rst deleted file mode 100644 index f91f915b..00000000 --- a/doc/precomputed_k-nn.rst +++ /dev/null @@ -1,390 +0,0 @@ -Precomputed k-nn -=================== - -The purpose of this tutorial is to explore some cases where using a -precomputed_knn might be useful and then discuss how we can obtain -reproducible results with it. - -Practical Uses --------------- - -Trying UMAP with various parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Let’s look at how we can use precomputed_knn to save time. First we will -test it out on MNIST which has 70,000 samples of 784 dimensions. If we -want to test out a series of n_neighbors and min_dist parameters, we -might lose quite a bit of time recomputing the knn matrices for our -data. Instead, we can compute the knn for the largest n_neighbors we -wish to analyze and then feed that precomputed_knn to UMAP. UMAP will -automatically prune it to the right n_neighbors value and skip the -nearest neighbors step, saving us a lot of time. - -We note that we don’t use a random state in order to leverage UMAP’s -parallelization and speed up the calculations. - -.. code:: python3 - - from sklearn.datasets import fetch_openml - import numpy as np - import umap - import umap.plot - from umap.umap_ import nearest_neighbors - - data, labels = fetch_openml('mnist_784', version=1, return_X_y=True) - labels = np.asarray(labels, dtype=np.int32) - - n_neighbors = [5, 50, 100, 250] - min_dists = [0, 0.2, 0.5, 0.9] - normal_embeddings = np.zeros((4, 4, 70000, 2)) - precomputed_knn_embeddings = np.zeros((4, 4, 70000, 2)) - -.. code:: python3 - - %%time - # UMAP run on the grid of parameters without precomputed_knn - for i, k in enumerate(n_neighbors): - for j, dist in enumerate(min_dists): - normal_embeddings[i, j] = umap.UMAP(n_neighbors=k, - min_dist=dist, - ).fit_transform(data) - print("\033[1m"+"Time taken to compute UMAP on grid of parameters:\033[0m") - - -.. parsed-literal:: - - **Time taken to compute UMAP on grid of parameters:** - Wall time: 31min 57s - - -.. code:: python3 - - %%time - # UMAP run on list of n_neighbors without precomputed_knn - - # We compute the knn for max(n_neighbors)=250 - mnist_knn = nearest_neighbors(data, - n_neighbors=250, - metric="euclidean", - metric_kwds=None, - angular=False, - random_state=None, - ) - # Now we compute the embeddings for the grid of parameters - for i, k in enumerate(n_neighbors): - for j, dist in enumerate(min_dists): - precomputed_knn_embeddings[i, j] = umap.UMAP(n_neighbors=k, - min_dist=dist, - precomputed_knn=mnist_knn, - ).fit_transform(data) - print("\033[1m"+"Time taken to compute UMAP on grid of parameters with precomputed_knn:\033[0m") - - -.. parsed-literal:: - - **Time taken to compute UMAP on grid of parameters with precomputed_knn:** - Wall time: 17min 54s - - -Using a precomputed_knn we have cut the computation time in half! -Observe that half of our n_neighbors values are relatively small. If -instead, we had had a higher distribution of values, the time savings -would have been even greater. Additionaly, we could've saved even more -time by first computing UMAP normally with an *n_neighbors* value of 250 -and then extracting the k-nn graph from that UMAP object. - -With this, we can easily visualize how the n_neighbors parameter affects -our embedding. - -.. code:: python3 - - import matplotlib.pyplot as plt - - fig, axs = plt.subplots(4, 4, figsize=(20, 20)) - - for i, ax_row in enumerate(axs): - for j, ax in enumerate(ax_row): - ax.scatter(precomputed_knn_embeddings[i, j, :, 0], - precomputed_knn_embeddings[i, j, :, 1], - c=labels / 9, - cmap='tab10', - alpha=0.1, - s=1, - ) - ax.set_xticks([]) - ax.set_yticks([]) - if i == 0: - ax.set_title("min_dist = {}".format(min_dists[j]), size=15) - if j == 0: - ax.set_ylabel("n_neighbors = {}".format(n_neighbors[i]), size=15) - fig.suptitle("UMAP embedding of MNIST digits with grid of parameters", y=0.92, size=20) - plt.subplots_adjust(wspace=0.05, hspace=0.05) - - - -.. image:: images/precomputed_k-nn6.png - - -We see that in this case, the embedding is robust to the choice of -n_neighbors and that lower min_dist values simply pack the clusters more -tightly. - -Reproducibility ----------------- - -We strongly recommend that you review the UMAP `reproducibility -section `__ -in the docs before attempting to reproduce results with -*precomputed_knn*. - -Standard Case -~~~~~~~~~~~~~~~ - -Out of the box, UMAP with precomputed_knn supports creating reproducible -results. This works in exactly the same way as regular UMAP, where, the -user can set a random seed state to ensure that results can be reproduced -exactly. However, some important considerations must be taken into account. - -UMAP embeddings are entirely dependent on first, computing the graphical -representation in higher dimensions and second, learning an embedding -that preserves the structure of that graph. Recall that our graphical -representation is based on the k-nn graph of our data. If we have two -different k-nn graphs, then we will naturally have two different -graphical representations of our data. Therefore, **we can only ensure -reproducible results when we use the same k-nn graph**. In our case, -this means that all reproducible results are tied to three values: - -.. raw:: html - -
    - -.. raw:: html - -
  1. - -The random seed when computing the k-nn. - -.. raw:: html - -
    2. - -.. raw:: html - -
  2. - -The n_neighbors value when computing the k-nn. - -.. raw:: html - -
    3. - -.. raw:: html - -
  3. - -The random seed when running UMAP. - -.. raw:: html - -
    4. - -.. raw:: html - -
- -Two different runs of UMAP, with these three values being equal, are -guaranteed to return the same result. Let’s look at how this works with -an example. To do this, we’ll create some data to work with; three -random blobs in 60-dimensional space. - -.. code:: python3 - - y = np.random.rand(1700, 60) - X = np.concatenate((y+20, y, y-20)) - synthetic_labels = np.repeat([1, 2, 3], repeats=1700) - -With the data in hand, we can fix the three parameters listed above and -see how two different UMAP runs give the same result. To avoid confusion -we’ll assume that the UMAP random seed is the same value as the knn -random seed. - -.. code:: python3 - - import umap.plot - random_seed = 10 - - knn = nearest_neighbors( - X, - n_neighbors=50, - metric='euclidean', - metric_kwds=None, - angular=False, - random_state=random_seed, - ) - - knn_umap = umap.UMAP(n_neighbors=30, precomputed_knn=knn, random_state=random_seed).fit(X) - knn_umap2 = umap.UMAP(n_neighbors=30, precomputed_knn=knn, random_state=random_seed).fit(X) - - fig, ax = plt.subplots(1, 2, figsize=(13,7)) - umap.plot.points(knn_umap, labels=synthetic_labels, ax=ax[0], theme='green') - umap.plot.points(knn_umap2, labels=synthetic_labels, ax=ax[1], theme='green') - ax[0].set_title("Precomuted knn 1st run", size=16) - ax[1].set_title("Precomuted knn 2nd run", size=16) - plt.show() - - print("\033[1m"+"Are the embeddings for knn_umap and knn_umap2 the same?\033[0m") - print((knn_umap.embedding_ == knn_umap2.embedding_).all()) - - - -.. image:: images/precomputed_k-nn11.png - - -.. parsed-literal:: - - **Are the embeddings for knn_umap and knn_umap2 the same?** - True - - -As we can see, by fixing the *random_seed* and the *n_neighbors* for the -knn, we have been able to obtain identical results from both UMAP runs. -In contrast, if these differ, we can’t guarantee the same result. - -.. code:: python3 - - random_seed2 = 15 - - # Different n_neighbors - knn3 = nearest_neighbors( - X, - n_neighbors=40, - metric='euclidean', - metric_kwds=None, - angular=False, - random_state=random_seed, - ) - # Different random seed - knn4 = nearest_neighbors( - X, - n_neighbors=50, - metric='euclidean', - metric_kwds=None, - angular=False, - random_state=random_seed2, - ) - - knn_umap3 = umap.UMAP(n_neighbors=30, precomputed_knn=knn3, random_state=random_seed).fit(X) - knn_umap4 = umap.UMAP(n_neighbors=30, precomputed_knn=knn4, random_state=random_seed2).fit(X) - - fig, ax = plt.subplots(1, 2, figsize=(13,7)) - umap.plot.points(knn_umap3, labels=synthetic_labels, ax=ax[0], theme='green') - umap.plot.points(knn_umap4, labels=synthetic_labels, ax=ax[1], theme='green') - ax[0].set_title("Precomuted knn; different knn n_neighbors", size=16) - ax[1].set_title("Precomuted knn; different random_seed", size=16) - plt.show() - - print("\033[1m"+"Are the embeddings for knn_umap and knn_umap3 the same?\033[0m") - print((knn_umap.embedding_ == knn_umap3.embedding_).all()) - - print("\033[1m"+"Are the embeddings for knn_umap and knn_umap4 the same?\033[0m") - print((knn_umap.embedding_ == knn_umap4.embedding_).all()) - - - -.. image:: images/precomputed_k-nn13.png - - -.. parsed-literal:: - - **Are the embeddings for knn_umap and knn_umap3 the same?** - False - **Are the embeddings for knn_umap and knn_umap4 the same?** - False - - -Without those three parameters being equal between runs, we have -obtained different results. - -Reproducing normal UMAP with precomputed_knn -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -With some extra considerations, we can also reproduce -precomputed_knn results with normal UMAP and vice-versa. As in -the previous case, we must keep in mind that the k-nn graphs have to be -same. Additionaly, we also must consider how UMAP uses the *random_seed* -that we provide it. - -If you provide UMAP a *random_seed*, it converts it into an -*np.random.RandomState* (RNG). This RNG is then used to fix the state -for all the relevant steps in the algorithm. The important thing to -note, is that the RNG is mutated everytime it’s used. So, if we want to -reproduce results with precomputed_knn we’ll have to mimic how UMAP -manipulates the RNG when calling the *fit()* function. - -For more information on random states and their behavior, please refer to -`[1] `__. - -We’ll look at one example of how this can be accomplished. Other cases -can be easily infered from this. Using the same random blobs as before, -we seek to run UMAP normally and then reproduce the results with a -precomputed_knn. To accomplish this, we have to create a new k-nn graph -using the *nearest_neighbors()* function in the same way that -*fit()* would. - -.. code:: python3 - - from sklearn.utils import check_random_state - - # First we run the normal UMAP to compare with - random_seed3 = 12 - normal_umap = umap.UMAP(n_neighbors=30, random_state=random_seed3).fit(X) - - # Now we run precomputed_knn UMAP - random_state3 = check_random_state(random_seed3) - # random_state3 = numpy.random.RandomState(random_seed3) - knn5 = nearest_neighbors( - X, - n_neighbors=30, - metric='euclidean', - metric_kwds=None, - angular=False, - random_state=random_state3, - ) - # This mutated RNG can now be fed into precompute_knn UMAP to obtain - # the same results as in normal UMAP - knn_umap5 = umap.UMAP(n_neighbors=30, - precomputed_knn=knn5, - random_state=random_state3, # <--- This is a RNG - ).fit(X) - -Note that in this case we create a numpy.random.mtrand.RandomState -instance with *check_random_state()* because we want to ensure that -our RNG is created and mutated in exactly the same way that UMAP -normally does. Equivalently, we could call *numpy.random.RandomState()* -directly. - -Graphing and comparing the embeddings, we see that we were able to -obtain the same results. - -.. code:: python3 - - fig, ax = plt.subplots(1, 2, figsize=(13,7)) - umap.plot.points(normal_umap, labels=synthetic_labels, ax=ax[0], theme='green') - umap.plot.points(knn_umap5, labels=synthetic_labels, ax=ax[1], theme='green') - ax[0].set_title("Normal UMAP", size=16) - ax[1].set_title("Precomuted knn UMAP", size=16) - plt.show() - - print("\033[1m"+"Are the embeddings for normal_umap and knn_umap5 the same?\033[0m") - print((normal_umap.embedding_ == knn_umap5.embedding_).all()) - - - -.. image:: images/precomputed_k-nn17.png - - -.. parsed-literal:: - - **Are the embeddings for normal_umap and knn_umap5 the same?** - True - diff --git a/doc/release_notes.rst b/doc/release_notes.md similarity index 86% rename from doc/release_notes.rst rename to doc/release_notes.md index fda2d6a6..8c84e7ec 100644 --- a/doc/release_notes.rst +++ b/doc/release_notes.md @@ -1,22 +1,22 @@ -Release Notes -============= +# Release Notes + Some notes on new features in various releases -What's new in 0.5 ------------------ +## What's new in 0.5 + * ParametricUMAP learns embeddings with neural networks. * AlignedUMAP can align multiple embeddings using relations between datasets. * DensMAP can preserve local density information in embeddings. * UMAP now depends on PyNNDescent, but has faster more parallel performance as a result. -* UMAP now supports an ``update`` method to add new data and retrain. +* UMAP now supports an `update` method to add new data and retrain. * Various performance improvements and bug fixes. * Additional plotting support, including text searching in interactive plots. * Support for "maximal distances" in neighbor graphs. -What's new in 0.4 ------------------ +## What's new in 0.4 + * Inverse transform method. Generate points in the original space corresponding to points in embedded space. (Thanks to Joseph Courtney) * Different embedding spaces. Support for embedding to a variety of different spaces other than Euclidean. (Thanks to Joseph Courtney) @@ -30,16 +30,16 @@ What's new in 0.4 * Multithreading support when no random seed is set. -What's new in 0.3 ------------------ +## What's new in 0.3 + * Supervised and semi-supervised dimension reduction. Support for using labels or partial labels for dimension reduction. * Transform method. Support for adding new unseen points to an existing embedding. * Performance improvements. -What's new in 0.2 ------------------ +## What's new in 0.2 + * A new layout algorithm that handles large datasets (more) correctly. -* Performance improvements. \ No newline at end of file +* Performance improvements. diff --git a/doc/reproducibility.rst b/doc/reproducibility.md similarity index 72% rename from doc/reproducibility.rst rename to doc/reproducibility.md index f70ff702..f2702f95 100644 --- a/doc/reproducibility.rst +++ b/doc/reproducibility.md @@ -1,6 +1,6 @@ -UMAP Reproducibility -==================== +# UMAP Reproducibility + UMAP is a stochastic algorithm -- it makes use of randomness both to speed up approximation steps, and to aid in solving hard optimization @@ -25,33 +25,37 @@ states to ensure exact reproducibility at the cost of some performance. First let's load the relevant libraries and get some data; in this case the MNIST digits dataset. -.. code:: python3 +```python3 +import numpy as np +import sklearn.datasets +import umap +import umap.plot - import numpy as np - import sklearn.datasets - import umap - import umap.plot +``` -.. code:: python3 +```python3 +data, labels = sklearn.datasets.fetch_openml( + 'mnist_784', version=1, return_X_y=True +) - data, labels = sklearn.datasets.fetch_openml( - 'mnist_784', version=1, return_X_y=True - ) +``` With data in hand let's run UMAP on it, and note how long it takes to run: -.. code:: python3 +```python3 +%%time +mapper1 = umap.UMAP().fit(data) - %%time - mapper1 = umap.UMAP().fit(data) +``` -.. parsed-literal:: +``` +CPU times: user 3min 18s, sys: 3.84 s, total: 3min 22s +Wall time: 1min 29s - CPU times: user 3min 18s, sys: 3.84 s, total: 3min 22s - Wall time: 1min 29s +``` The thing to note here is that the "Wall time" is significantly smaller than the CPU time -- this means that multiple CPU cores were used. For @@ -62,34 +66,36 @@ fast fitting to the data that does an effective job of using several cores. If you are on a large server with many cores available and don't wish to use them *all* (which is the default situation) you can currently control the number of cores used by setting the numba -environment variable ``NUMBA_NUM_THREADS``; see the `numba +environment variable `NUMBA_NUM_THREADS`; see the `numba documentation `__ for more details. Now let's plot our result to see what the embedding looks like: -.. code:: python3 - - umap.plot.points(mapper1, labels=labels) +```python3 +umap.plot.points(mapper1, labels=labels) -.. image:: images/reproducibility_6_1.png +``` +![Image](images/reproducibility_6_1.png) Now, let's run UMAP again and compare the results to that of our first run. -.. code:: python3 +```python3 +%%time +mapper2 = umap.UMAP().fit(data) - %%time - mapper2 = umap.UMAP().fit(data) +``` -.. parsed-literal:: +``` +CPU times: user 2min 53s, sys: 4.16 s, total: 2min 57s +Wall time: 1min 5s - CPU times: user 2min 53s, sys: 4.16 s, total: 2min 57s - Wall time: 1min 5s +``` You will note that this time we ran *even faster*. This is because during the first run numba was still JIT compiling some of the code in @@ -99,13 +105,13 @@ making use of mutliple cores well. Now let's plot the results of this second run and compare to the first: -.. code:: python3 - - umap.plot.points(mapper2, labels=labels) +```python3 +umap.plot.points(mapper2, labels=labels) -.. image:: images/reproducibility_10_1.png +``` +![Image](images/reproducibility_10_1.png) Qualitatively this looks very similar, but a little closer inspection will quickly show that the results are actually different between the @@ -123,75 +129,81 @@ reproducible result. As noted by Vito Zanotelli randomness". With that in mind, let's see what happens if we set an explicit -``random_state`` value: +`random_state` value: -.. code:: python3 +```python3 +%%time +mapper3 = umap.UMAP(random_state=42).fit(data) - %%time - mapper3 = umap.UMAP(random_state=42).fit(data) +``` -.. parsed-literal:: +``` +CPU times: user 2min 27s, sys: 4.16 s, total: 2min 31s +Wall time: 1min 56s - CPU times: user 2min 27s, sys: 4.16 s, total: 2min 31s - Wall time: 1min 56s +``` The first thing to note that that this run took significantly longer (despite having all the functions JIT compiled by numba already). Then note that the Wall time and CPU times are now much closer to each other -- we are no longer exploiting multiple cores to anywhere near the same -degree. This is because by setting a ``random_state`` we are effectively +degree. This is because by setting a `random_state` we are effectively turning off any of the multi-threading that does not support explicit reproducibility. Let's plot the results: -.. code:: python3 +```python3 +umap.plot.points(mapper3, labels=labels) - umap.plot.points(mapper3, labels=labels) +``` -.. image:: images/reproducibility_14_1.png - +![Image](images/reproducibility_14_1.png) We arrive at much the same results as before from a qualitative point of view, but again inspection will show that there are some differences. More importantly this result should now be reproducible. Thus we can run -UMAP again, with the same ``random_state`` set ... +UMAP again, with the same `random_state` set ... -.. code:: python3 +```python3 +%%time +mapper4 = umap.UMAP(random_state=42).fit(data) - %%time - mapper4 = umap.UMAP(random_state=42).fit(data) +``` -.. parsed-literal:: +``` +CPU times: user 2min 26s, sys: 4.13 s, total: 2min 30s +Wall time: 1min 54s - CPU times: user 2min 26s, sys: 4.13 s, total: 2min 30s - Wall time: 1min 54s +``` -Again, this takes longer than the earlier runs with no ``random_state`` +Again, this takes longer than the earlier runs with no `random_state` set. However when we plot the results of the second run we see that they look not merely qualitatively similar, but instead appear to be almost identical: -.. code:: python3 - - umap.plot.points(mapper4, labels=labels) +```python3 +umap.plot.points(mapper4, labels=labels) -.. image:: images/reproducibility_18_1.png +``` +![Image](images/reproducibility_18_1.png) We can, in fact, check that the results are identical by verifying that each and every coordinate of the resulting embeddings match perfectly: -.. code:: python3 +```python3 +np.all(mapper3.embedding_ == mapper4.embedding_) - np.all(mapper3.embedding_ == mapper4.embedding_) +``` -.. parsed-literal:: +``` +True - True +``` So we have, in fact, reproduced the embedding exactly. diff --git a/doc/scientific_papers.rst b/doc/scientific_papers.md similarity index 59% rename from doc/scientific_papers.rst rename to doc/scientific_papers.md index a9028298..eeccc70a 100644 --- a/doc/scientific_papers.rst +++ b/doc/scientific_papers.md @@ -1,5 +1,5 @@ -Scientific Papers -================= +# Scientific Papers + UMAP has been used in a wide variety of scientific publications from a diverse range of fields. Here we will highlight a small selection of papers that demonstrate both @@ -7,20 +7,20 @@ the depth of analysis, and breadth of subjects, UMAP can be used for. These rang to machine learning, and even social science. -The single-cell transcriptional landscape of mammalian organogenesis --------------------------------------------------------------------- +## The single-cell transcriptional landscape of mammalian organogenesis + A detailed look at the development of mouse embryos from a single-cell view. UMAP is used as a core piece of The Monocle3 software suite for identifying cell types and trajectories. This was a major paper in Nature, demonstrating the power of UMAP for large scale scientific endeavours. -.. image:: images/organogenesis_paper.png - :width: 400px +![Image](images/organogenesis_paper.png) + -`Link to the paper `__ +[Link to the paper](https://www.nature.com/articles/s41586-019-0969-x) + +## A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution -A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution ----------------------------------------------------------------------------------------- Still in the realm of single cell biology this paper looks at the developmental landscape of the round-word C. elegans. UMAP is used for detailed analysis of the developmental trajectories of cells, looking at global scales, and then @@ -28,13 +28,13 @@ digging down to look at individual organs. The result is an impressive array of UMAP visualisations that tease out ever finer structures in cellular development. -.. image:: images/c_elegans_3d.jpg - :width: 400px +![Image](images/c_elegans_3d.jpg) + + +[Link to the paper](https://science.sciencemag.org/content/early/2019/09/04/science.aax1971) -`Link to the paper `__ +## Exploring Neural Networks with Activation Atlases -Exploring Neural Networks with Activation Atlases -------------------------------------------------- Understanding the image processing capabilities (and deficits!) of modern convolutional neural networks is a challenge. This interactive paper from Distill seeks to provide a way to "peek inside the black box" by looking @@ -42,13 +42,13 @@ at the activations throughout the network. By mapping this high dimensional data down to 2D with UMAP the authors can construct an "atlas" of how different images are perceived by the network. -.. image:: images/activation_atlas.png - :width: 400px +![Image](images/activation_atlas.png) + -`Link to the paper `__ +[Link to the paper](https://distill.pub/2019/activation-atlas/) + +## TimeCluster: dimension reduction applied to temporal data for visual analytics -TimeCluster: dimension reduction applied to temporal data for visual analytics ------------------------------------------------------------------------------- An interesting approach to time-series analysis, targeted toward cases where the time series has repeating patterns -- though no necessarily of a consistently periodic nature. The approach involves dimension reduction and clustering @@ -57,26 +57,26 @@ repeating behaviour is exposed as loop structures. This can be useful for both clustering similar blocks within a time-series, or finding outliers. -.. image:: images/time_cluster.png - :width: 400px +![Image](images/time_cluster.png) + + +[Link to the paper](https://link.springer.com/article/10.1007/s00371-019-01673-y) -`Link to the paper `__ +## Dimensionality reduction for visualizing single-cell data using UMAP -Dimensionality reduction for visualizing single-cell data using UMAP --------------------------------------------------------------------- An early paper on applying UMAP to single-cell biology data. It looks at both, gene-expression data and flow-cytometry data, and compares UMAP to t-SNE both in terms of performance and quality of results. This is a good introduction to using UMAP for single-cell biology data. -.. image:: images/single_cell_umap.jpg - :width: 400px +![Image](images/single_cell_umap.jpg) + -`Link to the paper `__ +[Link to the paper](https://www.nature.com/articles/nbt.4314) -Revealing multi-scale population structure in large cohorts ------------------------------------------------------------ +## Revealing multi-scale population structure in large cohorts + A paper looking at population genetics which uses UMAP as a means to visualise population structures. This produced some intriguing visualizations, and was one of the first of several papers taking @@ -85,20 +85,20 @@ using UMAP projections to 3D as RGB color specifications for data points, allowing the UMAP structure to be visualized in geographic maps based on where the samples were drawn from. -.. image:: images/population_umap.jpg - :width: 400px +![Image](images/population_umap.jpg) + + +[Link to the paper](https://www.biorxiv.org/content/10.1101/423632v2) -`Link to the paper `__ +## Understanding Vulnerability of Children in Surrey -Understanding Vulnerability of Children in Surrey --------------------------------------------------- An example of the use of UMAP in sociological studies -- in this case looking at children in Surrey, British Columbia. Here UMAP is used as a tool to aid in general data analysis, and proves effective for the tasks to which it was put. -.. image:: images/umap_surrey.png - :width: 400px +![Image](images/umap_surrey.png) + -`Link to the paper `__ \ No newline at end of file +[Link to the paper](https://dsi.ubc.ca/sites/default/files/education-dssg-report-2018.pdf) diff --git a/doc/sparse.rst b/doc/sparse.md similarity index 63% rename from doc/sparse.rst rename to doc/sparse.md index 44473fc4..737aa606 100644 --- a/doc/sparse.rst +++ b/doc/sparse.md @@ -1,6 +1,6 @@ -UMAP on sparse data -=================== +# UMAP on sparse data + Sometimes datasets get very large, and potentially very very high dimensional. In many such cases, however, the data itself is sparse -- @@ -9,33 +9,34 @@ small number of non-zero features observed. In such cases the data can be represented much more efficiently in terms of memory usage by a sparse matrix data structure. It can be hard to find dimension reduction techniques that work directly on such sparse data -- often one applies a -basic linear technique such as ``TruncatedSVD`` from sklearn (which does +basic linear technique such as `TruncatedSVD` from sklearn (which does accept sparse matrix input) to get the data in a format amenable to other more advanced dimension reduction techniques. In the case of UMAP this is not necessary -- UMAP can run directly on sparse matrix input. This tutorial will walk through a couple of examples of doing this. -First we'll need some libraries loaded. We need ``numpy`` obviously, but -we'll also make use of ``scipy.sparse`` which provides sparse matrix +First we'll need some libraries loaded. We need `numpy` obviously, but +we'll also make use of `scipy.sparse` which provides sparse matrix data structures. One of our examples will be purely mathematical, and -we'll make use of ``sympy`` for that; the other example is test based +we'll make use of `sympy` for that; the other example is test based and we'll use sklearn for that (specifically -``sklearn.feature_extraction.text``). Beyond that we'll need umap, and +`sklearn.feature_extraction.text`). Beyond that we'll need umap, and plotting tools. -.. code:: python3 +```python3 +import numpy as np +import scipy.sparse +import sympy +import sklearn.datasets +import sklearn.feature_extraction.text +import umap +import umap.plot +import matplotlib.pyplot as plt +%matplotlib inline + +``` - import numpy as np - import scipy.sparse - import sympy - import sklearn.datasets - import sklearn.feature_extraction.text - import umap - import umap.plot - import matplotlib.pyplot as plt - %matplotlib inline +## A mathematical example -A mathematical example ----------------------- Our first example constructs a sparse matrix of data out of pure math. This example is inspired by the work of `John @@ -51,15 +52,16 @@ zeros (all the primes that the number is not divisible by), and thus we will have a very sparse dataset. To get started we'll need a list of all the primes. Fortunately we have -``sympy`` at our disposal and we can quickly get that information with a -single call to ``primerange``. We'll also need a dictionary mapping the +`sympy` at our disposal and we can quickly get that information with a +single call to `primerange`. We'll also need a dictionary mapping the different primes to the column number they correspond to in our data structure; effectively we'll just be enumerating the primes. -.. code:: python3 +```python3 +primes = list(sympy.primerange(2, 110000)) +prime_to_column = {p:i for i, p in enumerate(primes)} - primes = list(sympy.primerange(2, 110000)) - prime_to_column = {p:i for i, p in enumerate(primes)} +``` Now we need to construct our data in a format we can put into a sparse matrix easily. At this point a little background on sparse matrix data @@ -77,7 +79,7 @@ such a list of lists. We can do that by iterating over all the integers up to a fixed bound, and for each integer (i.e. each row in our dataset) generating the list of column indices which will be non-zero. The column indices will simply be the indices corresponding to the primes that -divide the number. Since ``sympy`` has a function ``primefactors`` which +divide the number. Since `sympy` has a function `primefactors` which returns a list of the unique prime factors of any integer we simply need to map those through our dictionary to covert the primes into column numbers. @@ -87,50 +89,54 @@ to insert into a matrix. Since we are only concerned with divisibility this will simply be a one in every non-zero entry, so we can just add a list of ones of the appropriate length for each row. -.. code:: python3 +```python3 +%%time +lil_matrix_rows = [] +lil_matrix_data = [] +for n in range(100000): + prime_factors = sympy.primefactors(n) + lil_matrix_rows.append([prime_to_column[p] for p in prime_factors]) + lil_matrix_data.append([1] * len(prime_factors)) - %%time - lil_matrix_rows = [] - lil_matrix_data = [] - for n in range(100000): - prime_factors = sympy.primefactors(n) - lil_matrix_rows.append([prime_to_column[p] for p in prime_factors]) - lil_matrix_data.append([1] * len(prime_factors)) +``` -.. parsed-literal:: +``` +CPU times: user 2.07 s, sys: 26.4 ms, total: 2.1 s +Wall time: 2.1 s - CPU times: user 2.07 s, sys: 26.4 ms, total: 2.1 s - Wall time: 2.1 s +``` Now we need to get that into a sparse matrix. Fortunately the -``scipy.sparse`` package makes this easy, and we've already built the +`scipy.sparse` package makes this easy, and we've already built the data in a fairly useful structure. First we create a sparse matrix of the correct format (LIL) and the right shape (as many rows as we have generated, and as many columns as there are primes). This is essentially -just an empty matrix however. We can fix that by setting the ``rows`` -attribute to be the rows we have generated, and the ``data`` attribute +just an empty matrix however. We can fix that by setting the `rows` +attribute to be the rows we have generated, and the `data` attribute to be the corresponding structure of values (all ones). The result is a sparse matrix data structure which can then be easily manipulated and converted into other sparse matrix formats easily. -.. code:: python3 +```python3 +factor_matrix = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32) +factor_matrix.rows = np.array(lil_matrix_rows) +factor_matrix.data = np.array(lil_matrix_data) +factor_matrix - factor_matrix = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32) - factor_matrix.rows = np.array(lil_matrix_rows) - factor_matrix.data = np.array(lil_matrix_data) - factor_matrix +``` -.. parsed-literal:: +``` +<100000x10453 sparse matrix of type '' + with 266398 stored elements in LInked List format> - <100000x10453 sparse matrix of type '' - with 266398 stored elements in LInked List format> +``` As you can see we have a matrix with 100000 rows and over 10000 columns. If we were storing that as a numpy array it would take a great deal of @@ -144,27 +150,29 @@ straightforward -- we just hand it directly to the fit method. Just like other sklearn estimators that can handle sparse input UMAP will detect the sparse matrix and just do the right thing. -.. code:: python3 +```python3 +%%time +mapper = umap.UMAP(metric='cosine', random_state=42, low_memory=True).fit(factor_matrix) - %%time - mapper = umap.UMAP(metric='cosine', random_state=42, low_memory=True).fit(factor_matrix) +``` -.. parsed-literal:: +``` +CPU times: user 9min 36s, sys: 6.76 s, total: 9min 43s +Wall time: 9min 7s - CPU times: user 9min 36s, sys: 6.76 s, total: 9min 43s - Wall time: 9min 7s +``` That was easy! But is it really working? We can easily plot the results: -.. code:: python3 +```python3 +umap.plot.points(mapper, values=np.arange(100000), theme='viridis') - umap.plot.points(mapper, values=np.arange(100000), theme='viridis') +``` -.. image:: images/sparse_11_1.png - +![Image](images/sparse_11_1.png) And this looks very much in line with the results `John Williamson got `__ with the @@ -172,76 +180,81 @@ proviso that we only used 100,000 integers instead of 1,000,000 to ensure that most users should be able to run this example (the full million may require a large memory compute node). So it seems like this is working well. The next question is whether we can use the -``transform`` functionality to map new data into this space. To test +`transform` functionality to map new data into this space. To test that we'll need some more data. Fortunately there are more integers. We'll grab the next 10,000 and put them in a sparse matrix, much as we did for the first 100,000. -.. code:: python3 +```python3 +%%time +lil_matrix_rows = [] +lil_matrix_data = [] +for n in range(100000, 110000): + prime_factors = sympy.primefactors(n) + lil_matrix_rows.append([prime_to_column[p] for p in prime_factors]) + lil_matrix_data.append([1] * len(prime_factors)) - %%time - lil_matrix_rows = [] - lil_matrix_data = [] - for n in range(100000, 110000): - prime_factors = sympy.primefactors(n) - lil_matrix_rows.append([prime_to_column[p] for p in prime_factors]) - lil_matrix_data.append([1] * len(prime_factors)) +``` -.. parsed-literal:: +``` +CPU times: user 214 ms, sys: 1.99 ms, total: 216 ms +Wall time: 222 ms - CPU times: user 214 ms, sys: 1.99 ms, total: 216 ms - Wall time: 222 ms +``` -.. code:: python3 +```python3 +new_data = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32) +new_data.rows = np.array(lil_matrix_rows) +new_data.data = np.array(lil_matrix_data) +new_data - new_data = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32) - new_data.rows = np.array(lil_matrix_rows) - new_data.data = np.array(lil_matrix_data) - new_data +``` -.. parsed-literal:: +``` +<10000x10453 sparse matrix of type '' + with 27592 stored elements in LInked List format> - <10000x10453 sparse matrix of type '' - with 27592 stored elements in LInked List format> +``` To map the new data we generated we can simply hand it to the -``transform`` method of our trained model. This is a little slow, but it +`transform` method of our trained model. This is a little slow, but it does work. -.. code:: python3 +```python3 +new_data_embedding = mapper.transform(new_data) - new_data_embedding = mapper.transform(new_data) +``` And we can plot the results. Since we just got the locations of the points this time (rather than a model) we'll have to resort to matplotlib for plotting. -.. code:: python3 - - fig = plt.figure(figsize=(12,12)) - ax = fig.add_subplot(111) - plt.scatter(new_data_embedding[:, 0], new_data_embedding[:, 1], s=0.1, c=np.arange(10000), cmap='viridis') - ax.set(xticks=[], yticks=[], facecolor='black'); +```python3 +fig = plt.figure(figsize=(12,12)) +ax = fig.add_subplot(111) +plt.scatter(new_data_embedding[:, 0], new_data_embedding[:, 1], s=0.1, c=np.arange(10000), cmap='viridis') +ax.set(xticks=[], yticks=[], facecolor='black'); -.. image:: images/sparse_18_0.png +``` +![Image](images/sparse_18_0.png) The color scale is different in this case, but you can see that the data has been mapped into locations corresponding to the various structures seen in the original embedding. Thus, even with large sparse data we can embed the data, and even add new data to the embedding. -A text analysis example ------------------------ +## A text analysis example + Let's look at a more classical machine learning example of working with sparse high dimensional data -- working with text documents. Machine @@ -258,48 +271,51 @@ matrix of data by setting the feature space to be the set of all words that appear in any document, and a document is represented by a vector where the value of the *i*\ th entry is the number of times the *i*\ th word occurs in that document. This is a very common approach, and is -what you will get if you apply sklearn's ``CountVectorizer`` to a text +what you will get if you apply sklearn's `CountVectorizer` to a text dataset for example. The catch with this approach is that the feature space is often *very* large, since we have a feature for each and every word that ever occurs in the entire corpus of documents. The data is sparse however, since most documents only use a small portion of the total possible vocabulary. Thus the default output format of -``CountVectorizer`` (and other similar feature extraction tools in -sklearn) is a ``scipy.sparse`` format matrix. +`CountVectorizer` (and other similar feature extraction tools in +sklearn) is a `scipy.sparse` format matrix. For this example we'll make use of the classic 20newsgroups dataset, a sampling of newsgroup messages from the old NNTP newsgroup system -covering 20 different newsgroups. The ``sklearn.datasets`` module can +covering 20 different newsgroups. The `sklearn.datasets` module can easily fetch the data, and, in fact, we can fetch a pre-vectorized -version to save us the trouble of running ``CountVectorizer`` ourselves. +version to save us the trouble of running `CountVectorizer` ourselves. We'll grab both the training set, and the test set for later use. -.. code:: python3 +```python3 +news_train = sklearn.datasets.fetch_20newsgroups_vectorized(subset='train') +news_test = sklearn.datasets.fetch_20newsgroups_vectorized(subset='test') - news_train = sklearn.datasets.fetch_20newsgroups_vectorized(subset='train') - news_test = sklearn.datasets.fetch_20newsgroups_vectorized(subset='test') +``` If we look at the actual data we have pulled back, we'll see that -sklearn has run a ``CountVectorizer`` and produced the data in the sparse +sklearn has run a `CountVectorizer` and produced the data in the sparse matrix format. -.. code:: python3 +```python3 +news_train.data - news_train.data +``` -.. parsed-literal:: +``` +<11314x130107 sparse matrix of type '' + with 1787565 stored elements in Compressed Sparse Row format> - <11314x130107 sparse matrix of type '' - with 1787565 stored elements in Compressed Sparse Row format> +``` The value of the sparse matrix format is immediately obvious in this case; while there are only 11,000 samples there are 130,000 features! If -the data were stored in a standard ``numpy`` array we would be using up +the data were stored in a standard `numpy` array we would be using up 10GB of memory! And most of that memory would simply be storing the number zero, over and over again. In the sparse matrix format it easily fits in memory on most machines. This sort of dimensionality of data is very @@ -308,7 +324,7 @@ common with text workloads. The raw counts are, however, not ideal since common words such as "the" and "and" will dominate the counts for most documents, while contributing very little information about the actual content of the document. We can -correct for this by using a ``TfidfTransformer`` from sklearn, which +correct for this by using a `TfidfTransformer` from sklearn, which will convert the data into `TF-IDF format `__. There are lots of ways to think about the transformation done by TF-IDF, but I like to @@ -325,83 +341,89 @@ their associated columns up-weighted. We can apply this transformation to both the train and test sets (using the same transformer trained on the training set). -.. code:: python3 +```python3 +tfidf = sklearn.feature_extraction.text.TfidfTransformer(norm='l1').fit(news_train.data) +train_data = tfidf.transform(news_train.data) +test_data = tfidf.transform(news_test.data) - tfidf = sklearn.feature_extraction.text.TfidfTransformer(norm='l1').fit(news_train.data) - train_data = tfidf.transform(news_train.data) - test_data = tfidf.transform(news_test.data) +``` The result is still a sparse matrix, since TF-IDF doesn't change the zero elements at all, nor the number of features. -.. code:: python3 +```python3 +train_data - train_data +``` -.. parsed-literal:: +``` +<11314x130107 sparse matrix of type '' + with 1787565 stored elements in Compressed Sparse Row format> - <11314x130107 sparse matrix of type '' - with 1787565 stored elements in Compressed Sparse Row format> +``` Now we need to pass this very high dimensional data to UMAP. Unlike some other non-linear dimension reduction techniques we don't need to apply PCA first to get the data down to a reasonable dimensionality; nor do we need to use other techniques to reduce the data to be able to be -represented as a dense ``numpy`` array; we can work directly on the +represented as a dense `numpy` array; we can work directly on the 130,000 dimensional sparse matrix. -.. code:: python3 +```python3 +%%time +mapper = umap.UMAP(metric='hellinger', random_state=42).fit(train_data) - %%time - mapper = umap.UMAP(metric='hellinger', random_state=42).fit(train_data) +``` -.. parsed-literal:: +``` +CPU times: user 8min 40s, sys: 3.07 s, total: 8min 44s +Wall time: 8min 43s - CPU times: user 8min 40s, sys: 3.07 s, total: 8min 44s - Wall time: 8min 43s +``` Now we can plot the results, with labels according to the target variable of the data -- which newsgroup the posting was drawn from. -.. code:: python3 - - umap.plot.points(mapper, labels=news_train.target) +```python3 +umap.plot.points(mapper, labels=news_train.target) -.. image:: images/sparse_31_1.png +``` +![Image](images/sparse_31_1.png) We can see that even going directly from a 130,000 dimensional space down to only 2 dimensions UMAP has done a decent job of separating out many of the different newsgroups. We can now attempt to add the test data to the same space using the -``transform`` method. +`transform` method. -.. code:: python3 +```python3 +test_embedding = mapper.transform(test_data) - test_embedding = mapper.transform(test_data) +``` While this is somewhat expensive computationally, it does work, and we can plot the end result: -.. code:: python3 - - fig = plt.figure(figsize=(12,12)) - ax = fig.add_subplot(111) - plt.scatter(test_embedding[:, 0], test_embedding[:, 1], s=1, c=news_test.target, cmap='Spectral') - ax.set(xticks=[], yticks=[]); +```python3 +fig = plt.figure(figsize=(12,12)) +ax = fig.add_subplot(111) +plt.scatter(test_embedding[:, 0], test_embedding[:, 1], s=1, c=news_test.target, cmap='Spectral') +ax.set(xticks=[], yticks=[]); -.. image:: images/sparse_35_0.png +``` +![Image](images/sparse_35_0.png) diff --git a/doc/supervised.md b/doc/supervised.md new file mode 100644 index 00000000..fa020884 --- /dev/null +++ b/doc/supervised.md @@ -0,0 +1,512 @@ + +# UMAP for Supervised Dimension Reduction and Metric Learning + + +While UMAP can be used for standard unsupervised dimension reduction the +algorithm offers significant flexibility allowing it to be extended to +perform other tasks, including making use of categorical label +information to do supervised dimension reduction, and even metric +learning. We'll look at some examples of how to do that below. + +First we will need to load some base libraries -- `numpy`, obviously, +but also `mnist` to read in the Fashion-MNIST data, and matplotlib and +seaborn for plotting. + +```python3 +import numpy as np +from mnist.loader import MNIST +import matplotlib.pyplot as plt +%matplotlib inline +import seaborn as sns +sns.set(style='white', context='poster') + +``` + +Our example dataset for this exploration will be the `Fashion-MNIST +dataset from Zalando +Research `__. It is +designed to be a drop-in replacement for the classic MNIST digits +dataset, but uses images of fashion items (dresses, coats, shoes, bags, +etc.) instead of handwritten digits. Since the images are more complex +it provides a greater challenge than MNIST digits. We can load it in +(after downloading the dataset) using the `mnist +library `__. We can then package +up the train and test sets into one large dataset, normalise the values +(to be in the range [0,1]), and set up labels for the 10 classes. + +```python3 +mndata = MNIST('fashion-mnist/data/fashion') +train, train_labels = mndata.load_training() +test, test_labels = mndata.load_testing() +data = np.array(np.vstack([train, test]), dtype=np.float64) / 255.0 +target = np.hstack([train_labels, test_labels]) +classes = [ + 'T-shirt/top', + 'Trouser', + 'Pullover', + 'Dress', + 'Coat', + 'Sandal', + 'Shirt', + 'Sneaker', + 'Bag', + 'Ankle boot'] + +``` + +Next we'll load the `umap` library so we can perform dimension reduction on +this dataset. + +```python3 +import umap + +``` + +## UMAP on Fashion MNIST + + +First we'll just do standard unsupervised dimension reduction using UMAP +so we have a baseline of what the results look like for later +comparison. This is simply a matter of instantiating a `umap.umap_.UMAP` object (in +this case setting the :attr:`~umap.umap_.UMAP.n_neighbors` parameter to be 5 -- we are +interested mostly in very local information), then calling the +`umap.umap_.UMAP.fit_transform` method with the data we wish to reduce. By default +UMAP reduces to two dimensions, so we'll be able to view the results as +a scatterplot. + +```python3 +%%time +embedding = umap.UMAP(n_neighbors=5).fit_transform(data) + + +``` + +``` +CPU times: user 1min 45s, sys: 7.22 s, total: 1min 52s +Wall time: 1min 26s + + +``` + +That took a little time, but not all that long considering it is 70,000 +data points in 784 dimensional space. We can simply plot the results as +a scatterplot, colored by the class of the fashion item. We can use +matplotlib's colorbar with suitable tick-labels to give us the color key. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*embedding.T, s=0.3, c=target, cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Embedded via UMAP'); + +``` + +![Image](images/SupervisedUMAP_10_1.png) + +The result is fairly good. We successfully separated a number of the +classes, and the global structure (separating pants and footwear from +shirts, coats and dresses) is well preserved as well. Unlike results for +MNIST digits, however, there were a number of classes that did not +separate quite so cleanly. In particular T-shirts, shirts, dresses, +pullovers, and coats are all a little mixed. At the very least the +dresses are largely separated, and the T-shirts are mostly in one large +clump, but they are not well distinguished from the others. Worse still +are the coats, shirts, and pullovers (somewhat unsurprisingly as these +can certainly look very similar) which all have significant overlap with +one another. Ideally we would like much better class separation. Since +we have the label information we can actually give that to UMAP to use! + +## Using Labels to Separate Classes (Supervised UMAP) + + +How do we go about coercing UMAP to make use of target labels? If you +are familiar with the sklearn API you'll know that the `umap.umap_.UMAP.fit` method +takes a target parameter `y` that specifies supervised target +information (for example when training a supervised classification +model). We can simply pass the `umap.umap_.UMAP` model that target data when +fitting and it will make use of it to perform supervised dimension +reduction! + +```python3 +%%time +embedding = umap.UMAP().fit_transform(data, y=target) + + +``` + +``` +CPU times: user 3min 28s, sys: 9.17 s, total: 3min 37s +Wall time: 2min 45s + + +``` + +This took a little longer -- both because we are using a larger +:py:obj:`~umap.umap_.UMAP.n_neighbors` value (which is suggested when doing supervised +dimension reduction; here we are using the default value of 15), and +because we need to condition on the label data. As before we have +reduced the data down to two dimensions so we can again visualize the +data with a scatterplot, coloring by class. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*embedding.T, s=0.1, c=target, cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Embedded via UMAP using Labels'); + + +``` + +![Image](images/SupervisedUMAP_15_1.png) + +The result is a cleanly separated set of classes (and a little bit of +stray noise -- points that were sufficiently different from their class +as to not be grouped with the rest). Aside from the clear class +separation however (which is expected -- we gave the algorithm all the +class information), there are a couple of important points to note. The +first point to note is that we have retained the internal structure of +the individual classes. Both the shirts and pullovers still have the +distinct banding pattern that was visible in the original unsupervised +case; the pants, t-shirts and bags both retained their shape and +internal structure; etc. The second point to note is that we have also +retained the global structure. While the individual classes have been +cleanly separated from one another, the inter-relationships among the +classes have been preserved: footwear classes are all near one another; +trousers and bags are at opposite sides of the plot; and the arc of +pullover, shirts, t-shirts and dresses is still in place. + +The key point is this: the important structural properties of the data +have been retained while the known classes have been cleanly pulled +apart and isolated. If you have data with known classes and want to +separate them while still having a meaningful embedding of individual +points then supervised UMAP can provide exactly what you need. + +## Using Partial Labelling (Semi-Supervised UMAP) + + +What if we only have some of our data labelled, however, and a number of +items are without labels. Can we still make use of the label information +we do have? This is now a semi-supervised learning problem, and yes, we +can work with those cases too. To set up the example we'll mask some of +the target information -- we'll do this by using the sklearn standard of +giving unlabelled points a label of -1 (such as, for example, +the noise points from a DBSCAN clustering). + +```python3 +masked_target = target.copy().astype(np.int8) +masked_target[np.random.choice(70000, size=10000, replace=False)] = -1 + +``` + +Now that we have randomly masked some of the labels we can try to +perform supervised learning again. Everything works as before, but UMAP +will interpret the -1 label as being an unlabelled point and learn +accordingly. + +```python3 +%%time +fitter = umap.UMAP().fit(data, y=masked_target) +embedding = fitter.embedding_ + + +``` + +``` +CPU times: user 3min 8s, sys: 7.85 s, total: 3min 16s +Wall time: 2min 40s + + +``` + +Again we can look at a scatterplot of the data colored by class. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*embedding.T, s=0.1, c=target, cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Embedded via UMAP using Partial Labels'); + + +``` + +![Image](images/SupervisedUMAP_22_1.png) + +The result is much as we would expect -- while we haven't cleanly +separated the data as we did in the totally supervised case, the classes +have been made cleaner and more distinct. This semi-supervised approach +provides a powerful tool when labelling is potentially expensive, or +when you have more data than labels, but want to make use of that extra +data. + +## Training with Labels and Embedding Unlabelled Test Data (Metric Learning with UMAP) + + +If we have learned a supervised embedding, can we use that to embed new +previously unseen (and now unlabelled) points into the space? This would +provide an algorithm for `metric +learning `__, +where we can use a labelled set of points to learn a metric on data, and +then use that learned metric as a measure of distance between new +unlabelled points. This can be particularly useful as part of a machine +learning pipline where we learn a supervised embedding as a form of +supervised feature engineering, and then build a classifier on that new +space -- this is viable as long as we can pass new data to the embedding +model to be transformed to the new space. + +To try this out with UMAP let's use the train/test split provided by +Fashion MNIST: + +```python3 +train_data = np.array(train) +test_data = np.array(test) + +``` + +Now we can fit a model to the training data, making use of the training +labels to learn a supervised embedding. + +```python3 +%%time +mapper = umap.UMAP(n_neighbors=10).fit(train_data, np.array(train_labels)) + + +``` + +``` +CPU times: user 2min 18s, sys: 7.53 s, total: 2min 26s +Wall time: 1min 52s + + +``` + +Next we can use the `umap.umap_.UMAP.transform` method on that model to transform the +test set into the learned space. This time we won't pass the label +information and let the model attempt to place the data correctly. + +```python3 +%%time +test_embedding = mapper.transform(test_data) + + +``` + +``` +CPU times: user 17.3 s, sys: 986 ms, total: 18.3 s +Wall time: 15.4 s + + +``` + +UMAP transforms are not as fast as some approaches, but as you can see +this was still fairly efficient. The important question is how well we +managed to embed the test data into the existing learned space. To start +let's visualise the embedding of the training data so we can get a sense +of where things *should* go. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*mapper.embedding_.T, s=0.3, c=np.array(train_labels), cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Train Digits Embedded via UMAP Transform'); + + + +``` + +![Image](images/SupervisedUMAP_31_0.png) + +As you can see this has done a similar job as before, successfully +embedding the separate classes while retaining both the internal +structure and the overall global structure. We can now look at how the +test set, for which we provided no label information, was embedded via +the `umap.umap_.UMAP.transform` method. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*test_embedding.T, s=2, c=np.array(test_labels), cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Test Digits Embedded via UMAP'); + + + +``` + +![Image](images/SupervisedUMAP_33_0.png) + +As you can see we have replicated the layout of the training data, +including much of the internal structure of the classes. For the most +part assignment of new points follows the classes well. The greatest +source of confusion are some t-shirts that ended up mixed with the +shirts, and some pullovers which are confused with the coats. Given the +difficulty of the problem this is a good result, particularly when +compared with current state-of-the-art approaches such as `siamese and +triplet +networks `__. + + +## Supervised UMAP on the Galaxy10SDSS dataset + + +The [Galaxy10SDSS dataset](https://astronn.readthedocs.io/en/latest/galaxy10sdss.html) +is a crowd sourced human labelled dataset of galaxy images, +which have been separated in to ten classes. Umap can +learn an embedding that partially separates the data. To +keep runtime small, UMAP is applied to a subset of the +data. + +```python3 +import numpy as np +import h5py +import matplotlib.pyplot as plt +import umap +import os +import math +import requests + +if not os.path.isfile("Galaxy10.h5"): + url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" + r = requests.get(url, allow_redirects=True) + open("Galaxy10.h5", "wb").write(r.content) + +# To get the images and labels from file +with h5py.File("Galaxy10.h5", "r") as F: + images = np.array(F["images"]) + labels = np.array(F["ans"]) + +X_train = np.empty([math.floor(len(labels) / 100), 14283], dtype=np.float64) +y_train = np.empty([math.floor(len(labels) / 100)], dtype=np.float64) +X_test = X_train +y_test = y_train +# Get a subset of the data +for i in range(math.floor(len(labels) / 100)): + X_train[i, :] = np.array(np.ndarray.flatten(images[i, :, :, :]), dtype=np.float64) + y_train[i] = labels[i] + X_test[i, :] = np.array( + np.ndarray.flatten(images[i + math.floor(len(labels) / 100), :, :, :]), + dtype=np.float64, + ) + y_test[i] = labels[i + math.floor(len(labels) / 100)] + +# Plot distribution +classes, frequency = np.unique(y_train, return_counts=True) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.bar(classes, frequency) +plt.xlabel("Class") +plt.ylabel("Frequency") +plt.title("Data Subset") +plt.savefig("galaxy10_subset.svg") + + + +``` + +![Image](images/galaxy10_subset.svg) + +The figure shows that the selected subset of the data set is +unbalanced, but the entire dataset is also unbalanced, so +this experiment will still use this subset. The next step is +to examine the output of the standard UMAP algorithm. + +```python3 +reducer = umap.UMAP( + n_components=2, n_neighbors=5, random_state=42, transform_seed=42, verbose=False +) +reducer.fit(X_train) + +galaxy10_umap = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_umap[:, 0], + galaxy10_umap[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_umap.svg") + + + +``` + +![Image](images/galaxy10_2D_umap.svg) + +The standard UMAP algorithm does not separate the galaxies +according to their type. Supervised UMAP can do better. + +```python3 +reducer = umap.UMAP( + n_components=2, n_neighbors=15, random_state=42, transform_seed=42, verbose=False +) +reducer.fit(X_train, y_train) + +galaxy10_umap_supervised = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_umap_supervised[:, 0], + galaxy10_umap_supervised[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_umap_supervised.svg") + + + +``` + +![Image](images/galaxy10_2D_umap_supervised.svg) + +Supervised UMAP does indeed do better. There is a litle overlap +between some of the classes, but the original dataset +also has some ambiguities in the classification. The best +check of this method is to project the testing data onto the +learned embedding. + +```python3 +galaxy10_umap_supervised_prediction = reducer.transform(X_test) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_umap_supervised_prediction[:, 0], + galaxy10_umap_supervised_prediction[:, 1], + c=y_test, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_test, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_umap_supervised_prediction.svg") + + + +``` + +![Image](images/galaxy10_2D_umap_supervised_prediction.svg) + +This shows that the learned embedding can be used on new data +sets, and so this method may be helpful for examining images +of galaxies. Try out this method on the full 200 Mb dataset +as well as the newer 2.54 Gb +[Galaxy 10 DECals dataset](https://astronn.readthedocs.io/en/latest/galaxy10.html) diff --git a/doc/supervised.rst b/doc/supervised.rst deleted file mode 100644 index e49806d2..00000000 --- a/doc/supervised.rst +++ /dev/null @@ -1,497 +0,0 @@ - -UMAP for Supervised Dimension Reduction and Metric Learning -=========================================================== - -While UMAP can be used for standard unsupervised dimension reduction the -algorithm offers significant flexibility allowing it to be extended to -perform other tasks, including making use of categorical label -information to do supervised dimension reduction, and even metric -learning. We'll look at some examples of how to do that below. - -First we will need to load some base libraries -- ``numpy``, obviously, -but also ``mnist`` to read in the Fashion-MNIST data, and matplotlib and -seaborn for plotting. - -.. code:: python3 - - import numpy as np - from mnist.loader import MNIST - import matplotlib.pyplot as plt - %matplotlib inline - import seaborn as sns - sns.set(style='white', context='poster') - -Our example dataset for this exploration will be the `Fashion-MNIST -dataset from Zalando -Research `__. It is -designed to be a drop-in replacement for the classic MNIST digits -dataset, but uses images of fashion items (dresses, coats, shoes, bags, -etc.) instead of handwritten digits. Since the images are more complex -it provides a greater challenge than MNIST digits. We can load it in -(after downloading the dataset) using the `mnist -library `__. We can then package -up the train and test sets into one large dataset, normalise the values -(to be in the range [0,1]), and set up labels for the 10 classes. - -.. code:: python3 - - mndata = MNIST('fashion-mnist/data/fashion') - train, train_labels = mndata.load_training() - test, test_labels = mndata.load_testing() - data = np.array(np.vstack([train, test]), dtype=np.float64) / 255.0 - target = np.hstack([train_labels, test_labels]) - classes = [ - 'T-shirt/top', - 'Trouser', - 'Pullover', - 'Dress', - 'Coat', - 'Sandal', - 'Shirt', - 'Sneaker', - 'Bag', - 'Ankle boot'] - -Next we'll load the ``umap`` library so we can perform dimension reduction on -this dataset. - -.. code:: python3 - - import umap - -UMAP on Fashion MNIST ---------------------- - -First we'll just do standard unsupervised dimension reduction using UMAP -so we have a baseline of what the results look like for later -comparison. This is simply a matter of instantiating a :class:`~umap.umap_.UMAP` object (in -this case setting the :attr:`~umap.umap_.UMAP.n_neighbors` parameter to be 5 -- we are -interested mostly in very local information), then calling the -:meth:`~umap.umap_.UMAP.fit_transform` method with the data we wish to reduce. By default -UMAP reduces to two dimensions, so we'll be able to view the results as -a scatterplot. - -.. code:: python3 - - %%time - embedding = umap.UMAP(n_neighbors=5).fit_transform(data) - - -.. parsed-literal:: - - CPU times: user 1min 45s, sys: 7.22 s, total: 1min 52s - Wall time: 1min 26s - - -That took a little time, but not all that long considering it is 70,000 -data points in 784 dimensional space. We can simply plot the results as -a scatterplot, colored by the class of the fashion item. We can use -matplotlib's colorbar with suitable tick-labels to give us the color key. - -.. code:: python3 - - fig, ax = plt.subplots(1, figsize=(14, 10)) - plt.scatter(*embedding.T, s=0.3, c=target, cmap='Spectral', alpha=1.0) - plt.setp(ax, xticks=[], yticks=[]) - cbar = plt.colorbar(boundaries=np.arange(11)-0.5) - cbar.set_ticks(np.arange(10)) - cbar.set_ticklabels(classes) - plt.title('Fashion MNIST Embedded via UMAP'); - -.. image:: images/SupervisedUMAP_10_1.png - - -The result is fairly good. We successfully separated a number of the -classes, and the global structure (separating pants and footwear from -shirts, coats and dresses) is well preserved as well. Unlike results for -MNIST digits, however, there were a number of classes that did not -separate quite so cleanly. In particular T-shirts, shirts, dresses, -pullovers, and coats are all a little mixed. At the very least the -dresses are largely separated, and the T-shirts are mostly in one large -clump, but they are not well distinguished from the others. Worse still -are the coats, shirts, and pullovers (somewhat unsurprisingly as these -can certainly look very similar) which all have significant overlap with -one another. Ideally we would like much better class separation. Since -we have the label information we can actually give that to UMAP to use! - -Using Labels to Separate Classes (Supervised UMAP) --------------------------------------------------- - -How do we go about coercing UMAP to make use of target labels? If you -are familiar with the sklearn API you'll know that the :meth:`~umap.umap_.UMAP.fit` method -takes a target parameter ``y`` that specifies supervised target -information (for example when training a supervised classification -model). We can simply pass the :class:`~umap.umap_.UMAP` model that target data when -fitting and it will make use of it to perform supervised dimension -reduction! - -.. code:: python3 - - %%time - embedding = umap.UMAP().fit_transform(data, y=target) - - -.. parsed-literal:: - - CPU times: user 3min 28s, sys: 9.17 s, total: 3min 37s - Wall time: 2min 45s - - -This took a little longer -- both because we are using a larger -:py:obj:`~umap.umap_.UMAP.n_neighbors` value (which is suggested when doing supervised -dimension reduction; here we are using the default value of 15), and -because we need to condition on the label data. As before we have -reduced the data down to two dimensions so we can again visualize the -data with a scatterplot, coloring by class. - -.. code:: python3 - - fig, ax = plt.subplots(1, figsize=(14, 10)) - plt.scatter(*embedding.T, s=0.1, c=target, cmap='Spectral', alpha=1.0) - plt.setp(ax, xticks=[], yticks=[]) - cbar = plt.colorbar(boundaries=np.arange(11)-0.5) - cbar.set_ticks(np.arange(10)) - cbar.set_ticklabels(classes) - plt.title('Fashion MNIST Embedded via UMAP using Labels'); - - -.. image:: images/SupervisedUMAP_15_1.png - - -The result is a cleanly separated set of classes (and a little bit of -stray noise -- points that were sufficiently different from their class -as to not be grouped with the rest). Aside from the clear class -separation however (which is expected -- we gave the algorithm all the -class information), there are a couple of important points to note. The -first point to note is that we have retained the internal structure of -the individual classes. Both the shirts and pullovers still have the -distinct banding pattern that was visible in the original unsupervised -case; the pants, t-shirts and bags both retained their shape and -internal structure; etc. The second point to note is that we have also -retained the global structure. While the individual classes have been -cleanly separated from one another, the inter-relationships among the -classes have been preserved: footwear classes are all near one another; -trousers and bags are at opposite sides of the plot; and the arc of -pullover, shirts, t-shirts and dresses is still in place. - -The key point is this: the important structural properties of the data -have been retained while the known classes have been cleanly pulled -apart and isolated. If you have data with known classes and want to -separate them while still having a meaningful embedding of individual -points then supervised UMAP can provide exactly what you need. - -Using Partial Labelling (Semi-Supervised UMAP) ----------------------------------------------- - -What if we only have some of our data labelled, however, and a number of -items are without labels. Can we still make use of the label information -we do have? This is now a semi-supervised learning problem, and yes, we -can work with those cases too. To set up the example we'll mask some of -the target information -- we'll do this by using the sklearn standard of -giving unlabelled points a label of -1 (such as, for example, -the noise points from a DBSCAN clustering). - -.. code:: python3 - - masked_target = target.copy().astype(np.int8) - masked_target[np.random.choice(70000, size=10000, replace=False)] = -1 - -Now that we have randomly masked some of the labels we can try to -perform supervised learning again. Everything works as before, but UMAP -will interpret the -1 label as being an unlabelled point and learn -accordingly. - -.. code:: python3 - - %%time - fitter = umap.UMAP().fit(data, y=masked_target) - embedding = fitter.embedding_ - - -.. parsed-literal:: - - CPU times: user 3min 8s, sys: 7.85 s, total: 3min 16s - Wall time: 2min 40s - - -Again we can look at a scatterplot of the data colored by class. - -.. code:: python3 - - fig, ax = plt.subplots(1, figsize=(14, 10)) - plt.scatter(*embedding.T, s=0.1, c=target, cmap='Spectral', alpha=1.0) - plt.setp(ax, xticks=[], yticks=[]) - cbar = plt.colorbar(boundaries=np.arange(11)-0.5) - cbar.set_ticks(np.arange(10)) - cbar.set_ticklabels(classes) - plt.title('Fashion MNIST Embedded via UMAP using Partial Labels'); - - -.. image:: images/SupervisedUMAP_22_1.png - - -The result is much as we would expect -- while we haven't cleanly -separated the data as we did in the totally supervised case, the classes -have been made cleaner and more distinct. This semi-supervised approach -provides a powerful tool when labelling is potentially expensive, or -when you have more data than labels, but want to make use of that extra -data. - -Training with Labels and Embedding Unlabelled Test Data (Metric Learning with UMAP) ------------------------------------------------------------------------------------ - -If we have learned a supervised embedding, can we use that to embed new -previously unseen (and now unlabelled) points into the space? This would -provide an algorithm for `metric -learning `__, -where we can use a labelled set of points to learn a metric on data, and -then use that learned metric as a measure of distance between new -unlabelled points. This can be particularly useful as part of a machine -learning pipline where we learn a supervised embedding as a form of -supervised feature engineering, and then build a classifier on that new -space -- this is viable as long as we can pass new data to the embedding -model to be transformed to the new space. - -To try this out with UMAP let's use the train/test split provided by -Fashion MNIST: - -.. code:: python3 - - train_data = np.array(train) - test_data = np.array(test) - -Now we can fit a model to the training data, making use of the training -labels to learn a supervised embedding. - -.. code:: python3 - - %%time - mapper = umap.UMAP(n_neighbors=10).fit(train_data, np.array(train_labels)) - - -.. parsed-literal:: - - CPU times: user 2min 18s, sys: 7.53 s, total: 2min 26s - Wall time: 1min 52s - - -Next we can use the :meth:`~umap.umap_.UMAP.transform` method on that model to transform the -test set into the learned space. This time we won't pass the label -information and let the model attempt to place the data correctly. - -.. code:: python3 - - %%time - test_embedding = mapper.transform(test_data) - - -.. parsed-literal:: - - CPU times: user 17.3 s, sys: 986 ms, total: 18.3 s - Wall time: 15.4 s - - -UMAP transforms are not as fast as some approaches, but as you can see -this was still fairly efficient. The important question is how well we -managed to embed the test data into the existing learned space. To start -let's visualise the embedding of the training data so we can get a sense -of where things *should* go. - -.. code:: python3 - - fig, ax = plt.subplots(1, figsize=(14, 10)) - plt.scatter(*mapper.embedding_.T, s=0.3, c=np.array(train_labels), cmap='Spectral', alpha=1.0) - plt.setp(ax, xticks=[], yticks=[]) - cbar = plt.colorbar(boundaries=np.arange(11)-0.5) - cbar.set_ticks(np.arange(10)) - cbar.set_ticklabels(classes) - plt.title('Fashion MNIST Train Digits Embedded via UMAP Transform'); - - - -.. image:: images/SupervisedUMAP_31_0.png - - -As you can see this has done a similar job as before, successfully -embedding the separate classes while retaining both the internal -structure and the overall global structure. We can now look at how the -test set, for which we provided no label information, was embedded via -the :meth:`~umap.umap_.UMAP.transform` method. - -.. code:: python3 - - fig, ax = plt.subplots(1, figsize=(14, 10)) - plt.scatter(*test_embedding.T, s=2, c=np.array(test_labels), cmap='Spectral', alpha=1.0) - plt.setp(ax, xticks=[], yticks=[]) - cbar = plt.colorbar(boundaries=np.arange(11)-0.5) - cbar.set_ticks(np.arange(10)) - cbar.set_ticklabels(classes) - plt.title('Fashion MNIST Test Digits Embedded via UMAP'); - - - -.. image:: images/SupervisedUMAP_33_0.png - - -As you can see we have replicated the layout of the training data, -including much of the internal structure of the classes. For the most -part assignment of new points follows the classes well. The greatest -source of confusion are some t-shirts that ended up mixed with the -shirts, and some pullovers which are confused with the coats. Given the -difficulty of the problem this is a good result, particularly when -compared with current state-of-the-art approaches such as `siamese and -triplet -networks `__. - - -Supervised UMAP on the Galaxy10SDSS dataset -------------------------------------------- - -The `Galaxy10SDSS dataset `__ -is a crowd sourced human labelled dataset of galaxy images, -which have been separated in to ten classes. Umap can -learn an embedding that partially separates the data. To -keep runtime small, UMAP is applied to a subset of the -data. - -.. code:: python3 - - import numpy as np - import h5py - import matplotlib.pyplot as plt - import umap - import os - import math - import requests - - if not os.path.isfile("Galaxy10.h5"): - url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" - r = requests.get(url, allow_redirects=True) - open("Galaxy10.h5", "wb").write(r.content) - - # To get the images and labels from file - with h5py.File("Galaxy10.h5", "r") as F: - images = np.array(F["images"]) - labels = np.array(F["ans"]) - - X_train = np.empty([math.floor(len(labels) / 100), 14283], dtype=np.float64) - y_train = np.empty([math.floor(len(labels) / 100)], dtype=np.float64) - X_test = X_train - y_test = y_train - # Get a subset of the data - for i in range(math.floor(len(labels) / 100)): - X_train[i, :] = np.array(np.ndarray.flatten(images[i, :, :, :]), dtype=np.float64) - y_train[i] = labels[i] - X_test[i, :] = np.array( - np.ndarray.flatten(images[i + math.floor(len(labels) / 100), :, :, :]), - dtype=np.float64, - ) - y_test[i] = labels[i + math.floor(len(labels) / 100)] - - # Plot distribution - classes, frequency = np.unique(y_train, return_counts=True) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.bar(classes, frequency) - plt.xlabel("Class") - plt.ylabel("Frequency") - plt.title("Data Subset") - plt.savefig("galaxy10_subset.svg") - - - -.. image:: images/galaxy10_subset.svg - - -The figure shows that the selected subset of the data set is -unbalanced, but the entire dataset is also unbalanced, so -this experiment will still use this subset. The next step is -to examine the output of the standard UMAP algorithm. - -.. code:: python3 - - reducer = umap.UMAP( - n_components=2, n_neighbors=5, random_state=42, transform_seed=42, verbose=False - ) - reducer.fit(X_train) - - galaxy10_umap = reducer.transform(X_train) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.scatter( - galaxy10_umap[:, 0], - galaxy10_umap[:, 1], - c=y_train, - cmap=plt.cm.nipy_spectral, - edgecolor="k", - label=y_train, - ) - plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) - plt.savefig("galaxy10_2D_umap.svg") - - - -.. image:: images/galaxy10_2D_umap.svg - - -The standard UMAP algorithm does not separate the galaxies -according to their type. Supervised UMAP can do better. - -.. code:: python3 - - reducer = umap.UMAP( - n_components=2, n_neighbors=15, random_state=42, transform_seed=42, verbose=False - ) - reducer.fit(X_train, y_train) - - galaxy10_umap_supervised = reducer.transform(X_train) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.scatter( - galaxy10_umap_supervised[:, 0], - galaxy10_umap_supervised[:, 1], - c=y_train, - cmap=plt.cm.nipy_spectral, - edgecolor="k", - label=y_train, - ) - plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) - plt.savefig("galaxy10_2D_umap_supervised.svg") - - - -.. image:: images/galaxy10_2D_umap_supervised.svg - - -Supervised UMAP does indeed do better. There is a litle overlap -between some of the classes, but the original dataset -also has some ambiguities in the classification. The best -check of this method is to project the testing data onto the -learned embedding. - -.. code:: python3 - - galaxy10_umap_supervised_prediction = reducer.transform(X_test) - fig = plt.figure(1, figsize=(4, 4)) - plt.clf() - plt.scatter( - galaxy10_umap_supervised_prediction[:, 0], - galaxy10_umap_supervised_prediction[:, 1], - c=y_test, - cmap=plt.cm.nipy_spectral, - edgecolor="k", - label=y_test, - ) - plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) - plt.savefig("galaxy10_2D_umap_supervised_prediction.svg") - - - -.. image:: images/galaxy10_2D_umap_supervised_prediction.svg - - -This shows that the learned embedding can be used on new data -sets, and so this method may be helpful for examining images -of galaxies. Try out this method on the full 200 Mb dataset -as well as the newer 2.54 Gb -`Galaxy 10 DECals dataset `__ diff --git a/doc/transform.rst b/doc/transform.md similarity index 65% rename from doc/transform.rst rename to doc/transform.md index 6334d797..60eb403a 100644 --- a/doc/transform.rst +++ b/doc/transform.md @@ -1,6 +1,6 @@ -Transforming New Data with UMAP -=============================== +# Transforming New Data with UMAP + UMAP is useful for generating visualisations, but if you want to make use of UMAP more generally for machine learning tasks it is important to @@ -13,48 +13,52 @@ the latent space the classifier uses. Fortunately UMAP makes this possible, albeit more slowly than some other transformers that allow this. -This tutorial will step through a simple case where we expect the overall -distribution in our higher-dimensional vectors to be consistent between the -training and testing data. For more detail on how this can go wrong, and +This tutorial will step through a simple case where we expect the overall +distribution in our higher-dimensional vectors to be consistent between the +training and testing data. For more detail on how this can go wrong, and how we can fix it using Parametric UMAP, see :doc:`transform_landmarked_pumap`. To demonstrate this functionality we'll make use of -`scikit-learn `__ and the +[scikit-learn](http://scikit-learn.org/stable/index.html) and the digits dataset contained therein (see :doc:`basic_usage` for an example of the digits dataset). First let's load all the modules we'll need to get this done. -.. code:: python3 +```python3 +import numpy as np +from sklearn.datasets import load_digits +from sklearn.model_selection import train_test_split, cross_val_score +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC + +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline - import numpy as np - from sklearn.datasets import load_digits - from sklearn.model_selection import train_test_split, cross_val_score - from sklearn.neighbors import KNeighborsClassifier - from sklearn.svm import SVC - - import matplotlib.pyplot as plt - import seaborn as sns - %matplotlib inline +``` -.. code:: python3 +```python3 +sns.set(context='notebook', style='white', rc={'figure.figsize':(14,10)}) - sns.set(context='notebook', style='white', rc={'figure.figsize':(14,10)}) +``` -.. code:: python3 +```python3 +digits = load_digits() - digits = load_digits() +``` -To keep everything honest let's use sklearn ``train_test_split`` to +To keep everything honest let's use sklearn `train_test_split` to separate out a training and test set (stratified over the different -digit types). By default ``train_test_split`` will carve off 25% of the +digit types). By default `train_test_split` will carve off 25% of the data for testing, which seems suitable in this case. -.. code:: python3 +```python3 +X_train, X_test, y_train, y_test = train_test_split(digits.data, + digits.target, + stratify=digits.target, + random_state=42) - X_train, X_test, y_train, y_test = train_test_split(digits.data, - digits.target, - stratify=digits.target, - random_state=42) +``` Now to get a benchmark idea of what we are looking at let's train a couple of different classifiers and then see how well they score on the @@ -63,27 +67,30 @@ KNN classifier. Ideally we should be tuning hyper-parameters (perhaps a grid search using k-fold cross validation), but for the purposes of this simple demo we will simply use default parameters for both classifiers. -.. code:: python3 +```python3 +svc = SVC().fit(X_train, y_train) +knn = KNeighborsClassifier().fit(X_train, y_train) - svc = SVC().fit(X_train, y_train) - knn = KNeighborsClassifier().fit(X_train, y_train) +``` The next question is how well these classifiers perform on the test set. -Conveniently sklearn provides a ``score`` method that can output the +Conveniently sklearn provides a `score` method that can output the accuracy on the test set. -.. code:: python3 +```python3 +svc.score(X_test, y_test), knn.score(X_test, y_test) - svc.score(X_test, y_test), knn.score(X_test, y_test) +``` -.. parsed-literal:: +``` +(0.62, 0.9844444444444445) - (0.62, 0.9844444444444445) +``` The result is that the support vector classifier apparently had poor hyper-parameters for this case (I expect with some tuning we could build @@ -91,11 +98,12 @@ a much more accurate model) and the KNN classifier is doing very well. The goal now is to make use of UMAP as a preprocessing step that one could potentially fit into a pipeline. We will therefore obviously need -the ``umap`` module loaded. +the `umap` module loaded. -.. code:: python3 +```python3 +import umap - import umap +``` To make use of UMAP as a data transformer we first need to fit the model with the training data. This works exactly as in the :doc:`basic_usage` @@ -103,27 +111,28 @@ example using the fit method. In this case we simply hand it the training data and it will learn an appropriate (two dimensional by default) embedding. -.. code:: python3 +```python3 +trans = umap.UMAP(n_neighbors=5, random_state=42).fit(X_train) - trans = umap.UMAP(n_neighbors=5, random_state=42).fit(X_train) +``` Since we embedded to two dimensions we can visualise the results to ensure that we are getting a potential benefit out of this approach. This is simply a matter of generating a scatterplot with data points colored by the class they come from. Note that the embedded training -data can be accessed as the ``.embedding_`` attribute of the UMAP model +data can be accessed as the `.embedding_` attribute of the UMAP model once we have fit the model to some data. -.. code:: python3 +```python3 +plt.scatter(trans.embedding_[:, 0], trans.embedding_[:, 1], s= 5, c=y_train, cmap='Spectral') +plt.title('Embedding of the training set by UMAP', fontsize=24); - plt.scatter(trans.embedding_[:, 0], trans.embedding_[:, 1], s= 5, c=y_train, cmap='Spectral') - plt.title('Embedding of the training set by UMAP', fontsize=24); +``` -.. image:: images/UMAPTransform_15_0.png - +![Image](images/UMAPTransform_15_0.png) This looks very promising! Most of the classes got very cleanly separated, and that gives us some hope that it could help with @@ -133,32 +142,35 @@ information, but that is the subject of :doc:`a later tutorial `. We can now train some new models (again an SVC and a KNN classifier) on the embedded training data. This looks exactly as before but now we pass -it the embedded data. Note that calling ``transform`` on input identical -to what the model was trained on will simply return the ``embedding_`` +it the embedded data. Note that calling `transform` on input identical +to what the model was trained on will simply return the `embedding_` attribute, so sklearn pipelines will work as expected. -.. code:: python3 +```python3 +svc = SVC().fit(trans.embedding_, y_train) +knn = KNeighborsClassifier().fit(trans.embedding_, y_train) - svc = SVC().fit(trans.embedding_, y_train) - knn = KNeighborsClassifier().fit(trans.embedding_, y_train) +``` Now we want to work with the test data which none of the models (UMAP or the classifiers) have seen. To do this we use the standard sklearn API -and make use of the ``transform`` method, this time handing it the new -unseen test data. We will assign this to ``test_embedding`` so that we +and make use of the `transform` method, this time handing it the new +unseen test data. We will assign this to `test_embedding` so that we can take a closer look at the result of applying an existing UMAP model to new data. -.. code:: python3 +```python3 +%time test_embedding = trans.transform(X_test) - %time test_embedding = trans.transform(X_test) +``` -.. parsed-literal:: +``` +CPU times: user 867 ms, sys: 70.7 ms, total: 938 ms +Wall time: 335 ms - CPU times: user 867 ms, sys: 70.7 ms, total: 938 ms - Wall time: 335 ms +``` Note that the transform operations works very efficiently -- taking less than half a second. Compared to some other transformers this is a little @@ -174,15 +186,15 @@ the training set. We can check this by visualising the data (since we are in two dimensions) to see if this is true. A simple scatterplot as before will suffice. -.. code:: python3 - - plt.scatter(test_embedding[:, 0], test_embedding[:, 1], s= 5, c=y_test, cmap='Spectral') - plt.title('Embedding of the test set by UMAP', fontsize=24); +```python3 +plt.scatter(test_embedding[:, 0], test_embedding[:, 1], s= 5, c=y_test, cmap='Spectral') +plt.title('Embedding of the test set by UMAP', fontsize=24); -.. image:: images/UMAPTransform_21_0.png +``` +![Image](images/UMAPTransform_21_0.png) The results look like what we should expect; the test data has been embedded into two dimensions in exactly the locations we should expect @@ -190,18 +202,20 @@ embedded into two dimensions in exactly the locations we should expect This means we can now try out models that were trained on the embedded training data by handing them the newly transformed test set. -.. code:: python3 +```python3 +svc.score(trans.transform(X_test), y_test), knn.score(trans.transform(X_test), y_test) - svc.score(trans.transform(X_test), y_test), knn.score(trans.transform(X_test), y_test) +``` -.. parsed-literal:: +``` +(0.9844444444444445, 0.9844444444444445) - (0.9844444444444445, 0.9844444444444445) +``` The results are pretty good. While the accuracy of the KNN classifier did not improve there was not a lot of scope for improvement given the @@ -219,28 +233,32 @@ more complex datasets where more dimensions may allow for a much more faithful embedding it is worth noting that we are not restricted to only two dimension. -.. code:: python3 +```python3 +trans = umap.UMAP(n_neighbors=5, n_components=10, random_state=42).fit(X_train) - trans = umap.UMAP(n_neighbors=5, n_components=10, random_state=42).fit(X_train) +``` -.. code:: python3 +```python3 +svc = SVC().fit(trans.embedding_, y_train) +knn = KNeighborsClassifier().fit(trans.embedding_, y_train) - svc = SVC().fit(trans.embedding_, y_train) - knn = KNeighborsClassifier().fit(trans.embedding_, y_train) +``` -.. code:: python3 +```python3 +svc.score(trans.transform(X_test), y_test), knn.score(trans.transform(X_test), y_test) - svc.score(trans.transform(X_test), y_test), knn.score(trans.transform(X_test), y_test) +``` -.. parsed-literal:: +``` +(0.9822222222222222, 0.9822222222222222) - (0.9822222222222222, 0.9822222222222222) +``` And we see that in this case we actually marginally lowered our accuracy scores (within the potential noise in such scoring mind you). However diff --git a/doc/transform_landmarked_pumap.md b/doc/transform_landmarked_pumap.md new file mode 100644 index 00000000..bea31a5a --- /dev/null +++ b/doc/transform_landmarked_pumap.md @@ -0,0 +1,238 @@ + +# Transforming New Data with Parametric UMAP + + +There are many cases where one may want to take an existing UMAP model and use it to embed new data into the learned space. For a simple example where the overall distribution of the higher-dimensional training data matches that of the new data being embedded, see :doc:`transform`. We can't always be sure that this will be the case, however. To simulate a case where we have novel behaviour that we want to include in our embedding space, we will use the MNIST digits dataset (see :doc:`basic_usage` for a basic example). + +To follow along with this example, see the MNIST_Landmarks notebook on the [GitHub repository](https://github.com/lmcinnes/umap/tree/master/notebooks/) + + + + import keras + from sklearn.model_selection import train_test_split + + from umap import UMAP, ParametricUMAP + + import matplotlib.pyplot as plt + + import numpy as np + import pandas as pd + +We'll start by loading in the dataset, and splitting it into 2 equal parts with `sklearn`'s `train_test_split` function. This will give us two partitions to work with, one to train our original embedding and another to test it. In order to simulate new behaviour appearing in our data we remove one of the MNIST categories `N` from the `x1` partition. In this case we'll use `N=2`, so our model will be trained on all of the digits other than 2. + +```python3 +(X, y), (_, _) = keras.datasets.mnist.load_data() +x1, x2, y1, y2 = train_test_split(X, y, test_size=0.5, random_state=42) + +# Reshape to 1D vectors +x1 = x1.reshape((x1.shape[0], 28*28)) +x2 = x2.reshape((x2.shape[0], 28*28)) + +# Remove one category from the train dataset. +# In the case of MNIST digits, this will be the digit we are removing. +N = 2 + +x1 = x1[y1 != N] +y1 = y1[y1 != N] + +print(x1.shape, x2.shape) + +``` + +``` +(26995, 784) (30000, 784) + +``` + +## New data with UMAP + + +To start with, we'll identify the issues with using UMAP as-is in this case, and then we'll see how to fix them with Parametric UMAP. First off, we need to train a `UMAP` model on our `x1` partition: + +```python3 +embedder = UMAP() + +emb_x1 = embedder.fit_transform(x1) + +``` + +Visualising our results: + +```python3 +plt.scatter(emb_x1[:,0], emb_x1[:,1], c=y1, cmap='Spectral', s=2, alpha=0.2) + +``` + +![Image](images/retrain_pumap_emb_x1.png) + +This is a clean and successful embedding, as we would expect from UMAP on this relatively-simple example. We see the normal structure one would expect from embedding MNIST, but without any of the 2s. The `UMAP` class is built to be compatible with `scikit-learn`, so passing new data through is as simple as using the `transform` method and passing through the new data. We'll pass through `x2`, which contains unseen examples of the original classes, and also samples from our holdout class, `N` (the 2s). + +To make samples from `N` Stand out more, we'll over-plot them in black. + +```python3 +emb_x2 = embedder.transform(x2) + +``` + +```python3 +plt.scatter(emb_x2[:,0], emb_x2[:,1], c=y2, cmap='Spectral', s=2, alpha=0.2) +plt.scatter(emb_x2[y2==N][:,0], emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5) + +``` + +![Image](images/retrain_pumap_emb_x2.png) + +While our `UMAP` embedder has correctly handled the classes present in `x1` it has treated examples from our holdout class `N` poorly. Many of these points are concentrated on top of existing classes, with some spread out between them. This inability to generalize is not unique to UMAP, but is more generally a difficulty with learned embeddings. It also may or may not be an issue, depending on your use case. + +## New data with Parametric UMAP + + +We can improve this outcome with Parametric UMAP. Parametric UMAP differs from UMAP in that it learns the relationship between the data and embedding with a neural network, instead of learning embeddings directly. This means we can incorporate new data by continuing to train the neural network, updating the weights to incorporate our new information. + +![Image](images/pumap-only.png) + +For more complete information on Parametric UMAP and the many options it provides, see :doc:`parametric_umap`. + +We will start adressing this by training a `ParametricUMAP` embedding model, and running the same experiment: + +```python3 +p_embedder = ParametricUMAP() + +p_emb_x1 = p_embedder.fit_transform(x1) + +``` + +```python3 +plt.scatter(p_emb_x1[:,0], p_emb_x1[:,1], c=y1, cmap='Spectral', s=2, alpha=0.2) + +``` + +![Image](images/retrain_pumap_p_emb_x1.png) + +Again, we get good results on our initial embedding of `x1`. If we pass `x2` through without re-training, we get a similar problem to our `UMAP` model: + +```python3 +p_emb_x2 = p_embedder.transform(x2) + +``` + +```python3 +plt.scatter(p_emb_x2[:,0], p_emb_x2[:,1], c=y2, cmap='Spectral', s=2, alpha=0.2) +plt.scatter(p_emb_x2[y2==N][:,0], p_emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5) + +``` + +![Image](images/retrain_pumap_p_emb_x2.png) + +## Re-training Parametric UMAP with landmarks + + +To update our embedding to include the new class, we'll fine-tune our existing `ParametricUMAP` model. Doing this without any other changes will start from where we left off, but our embedding space's structure may drift and change. This is because the UMAP loss function is invariant to translation and rotation, as it is only concerned with the relative positions and distances between points. + +In order to keep our embedding space more consistent, we'll use the landmarks option for `ParametricUMAP`. We retrain the model on the `x2` partition, along with some points chosen as landmarks from `x1`. We'll choose 1% of the samples in `x1` to be included, along with their current position in the embedding space to be used in the landmarks loss function. + +The default `landmark_loss_fn` is the euclidean distance between the point's original position and it's current one. The only change we'll make is to set `landmark_loss_weight=0.01`. + +```python3 +# Select landmarks indexes from x1. +# +landmark_idx = list(np.random.choice(range(x1.shape[0]), int(x1.shape[0]/100), replace=False)) + +# Add the landmark points to x2 for training. +# +x2_lmk = np.concatenate((x2, x1[landmark_idx])) +y2_lmk = np.concatenate((y2, y1[landmark_idx])) + +# Make our landmarks vector, which is nan where we have no landmark information. +# +landmarks = np.stack( + [np.array([np.nan, np.nan])]*x2.shape[0] + list( + p_embedder.transform( + x1[landmark_idx] + ) + ) +) + +# Set landmark loss weight and continue training our Parametric UMAP model. +# +p_embedder.landmark_loss_weight = 0.01 +p_embedder.fit(x2_lmk, landmark_positions=landmarks) +p_emb2_x2 = p_embedder.transform(x2) + +# Check how x1 looks when embedded in the space retrained on x2 and landmarks. +# +p_emb2_x1 = p_embedder.transform(x1) + + +``` + +Plotting all of the different embeddings to compare them: + +```python3 +fig, axs = plt.subplots(3, 2, figsize=(16, 24), sharex=True, sharey=True) + +axs[0,0].scatter( + emb_x1[:, 0], emb_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, +) +axs[0,0].set_ylabel('UMAP Embedding', fontsize=20) + +axs[0,1].scatter( + emb_x2[:, 0], emb_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, +) +axs[0,1].scatter( + emb_x2[y2==N][:,0], emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5, +) + +axs[1,0].scatter( + p_emb_x1[:, 0], p_emb_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, +) +axs[1,0].set_ylabel('Initial P-UMAP Embedding', fontsize=20) + +axs[1,1].scatter( + p_emb_x2[:, 0], p_emb_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, +) +axs[1,1].scatter( + p_emb_x2[y2==N][:,0], p_emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5 +) + +axs[2,0].scatter( + p_emb2_x1[:, 0], p_emb2_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, +) +axs[2,0].set_ylabel('Updated P-UMAP Embedding', fontsize=20) +axs[2,0].set_xlabel(f'x1, No {N}s', fontsize=20) + +axs[2,1].scatter( + p_emb2_x2[:, 0], p_emb2_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, +) +axs[2,1].scatter( + p_emb2_x2[y2==N][:,0], p_emb2_x2[y2==N][:,1], c='k', s=2, alpha=0.5, +) +axs[2,1].set_xlabel('x2, All Classes', fontsize=20) + +plt.tight_layout() + +``` + +![Image](images/retrain_pumap_summary_2_removed.png) + +Here we see that our approach has been successful, The embedding space has been kept consistent and we now have a clear cluster of our new class, the 2s. This new cluster shows up in a sensible part of the embedding space, and the rest of the structure is preserved. + +It is worth double checking here that the landmark loss is not too constraining, we still would like a good UMAP structure. +To do so, we can interrogate the history of our embedder, which will retain the history through our re-training steps. + +```python3 +plt.plot(p_embedder._history['loss']) +plt.ylabel('Loss') +plt.xlabel('Epoch') + +``` + +![Image](images/retrain_pumap_history.png) + +We can identify the spike in loss where we introduce `x2`, and can confirm that the resulting loss is comparable to the loss from our initial training on `x1`. This tells us that the model is not having to compromise too much between the UMAP loss and the landmark loss. If this were not the case, it could potentially be improved by lowering the `landmark_loss_weight` attribute of our embedder object. There is a tradeoff to be made here between the consistency of the space and minimizing UMAP loss, but the key is we have smooth variation in the embedding space, which will make downstream tasks easier to adjust. In this case, we could probably stand to increase the `landmark_loss_weight` to keep the space more consistent. + +In addition to `landmark_loss_weight`, there are a number of other options available to us to try and get better results on this or other examples: + +- Continuing the training with a larger portion of points from the original data, in our case `x1`. Not all of these points need to be landmarked, but they can contribute to a consistent graph structure in higher dimensions. +- Changing the `landmark_loss_fn`. For example, if we want to allow for points to move if they have to we could truncate the default euclidean loss function, allowing the metaphorical rubber band to snap at a certain point and prioritising a good UMAP structure once we discover that sticking to the landmark position is not correct. +- Being more intelligent with our selection of landmark points, for example using submodular optimization with a package like [apricot-select](https://apricot-select.readthedocs.io/en/latest/) or chosing points from different parts of a hierarchical clustering like [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/index.html) diff --git a/doc/transform_landmarked_pumap.rst b/doc/transform_landmarked_pumap.rst deleted file mode 100644 index 2896f29d..00000000 --- a/doc/transform_landmarked_pumap.rst +++ /dev/null @@ -1,227 +0,0 @@ - -Transforming New Data with Parametric UMAP -========================================== - -There are many cases where one may want to take an existing UMAP model and use it to embed new data into the learned space. For a simple example where the overall distribution of the higher-dimensional training data matches that of the new data being embedded, see :doc:`transform`. We can't always be sure that this will be the case, however. To simulate a case where we have novel behaviour that we want to include in our embedding space, we will use the MNIST digits dataset (see :doc:`basic_usage` for a basic example). - -To follow along with this example, see the MNIST_Landmarks notebook on the `GitHub repository `_ - -.. code :: python3 - - import keras - from sklearn.model_selection import train_test_split - - from umap import UMAP, ParametricUMAP - - import matplotlib.pyplot as plt - - import numpy as np - import pandas as pd - -We'll start by loading in the dataset, and splitting it into 2 equal parts with ``sklearn``'s ``train_test_split`` function. This will give us two partitions to work with, one to train our original embedding and another to test it. In order to simulate new behaviour appearing in our data we remove one of the MNIST categories ``N`` from the ``x1`` partition. In this case we'll use ``N=2``, so our model will be trained on all of the digits other than 2. - -.. code:: python3 - - (X, y), (_, _) = keras.datasets.mnist.load_data() - x1, x2, y1, y2 = train_test_split(X, y, test_size=0.5, random_state=42) - - # Reshape to 1D vectors - x1 = x1.reshape((x1.shape[0], 28*28)) - x2 = x2.reshape((x2.shape[0], 28*28)) - - # Remove one category from the train dataset. - # In the case of MNIST digits, this will be the digit we are removing. - N = 2 - - x1 = x1[y1 != N] - y1 = y1[y1 != N] - - print(x1.shape, x2.shape) - -.. parsed-literal:: - - (26995, 784) (30000, 784) - -New data with UMAP ------------------- - -To start with, we'll identify the issues with using UMAP as-is in this case, and then we'll see how to fix them with Parametric UMAP. First off, we need to train a ``UMAP`` model on our ``x1`` partition: - -.. code:: python3 - - embedder = UMAP() - - emb_x1 = embedder.fit_transform(x1) - -Visualising our results: - -.. code:: python3 - - plt.scatter(emb_x1[:,0], emb_x1[:,1], c=y1, cmap='Spectral', s=2, alpha=0.2) - -.. image:: images/retrain_pumap_emb_x1.png - - -This is a clean and successful embedding, as we would expect from UMAP on this relatively-simple example. We see the normal structure one would expect from embedding MNIST, but without any of the 2s. The ``UMAP`` class is built to be compatible with ``scikit-learn``, so passing new data through is as simple as using the ``transform`` method and passing through the new data. We'll pass through ``x2``, which contains unseen examples of the original classes, and also samples from our holdout class, ``N`` (the 2s). - -To make samples from ``N`` Stand out more, we'll over-plot them in black. - -.. code:: python3 - - emb_x2 = embedder.transform(x2) - -.. code:: python3 - - plt.scatter(emb_x2[:,0], emb_x2[:,1], c=y2, cmap='Spectral', s=2, alpha=0.2) - plt.scatter(emb_x2[y2==N][:,0], emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5) - -.. image:: images/retrain_pumap_emb_x2.png - -While our ``UMAP`` embedder has correctly handled the classes present in ``x1`` it has treated examples from our holdout class ``N`` poorly. Many of these points are concentrated on top of existing classes, with some spread out between them. This inability to generalize is not unique to UMAP, but is more generally a difficulty with learned embeddings. It also may or may not be an issue, depending on your use case. - -New data with Parametric UMAP ------------------------------ - -We can improve this outcome with Parametric UMAP. Parametric UMAP differs from UMAP in that it learns the relationship between the data and embedding with a neural network, instead of learning embeddings directly. This means we can incorporate new data by continuing to train the neural network, updating the weights to incorporate our new information. - -.. image:: images/pumap-only.png - -For more complete information on Parametric UMAP and the many options it provides, see :doc:`parametric_umap`. - -We will start adressing this by training a ``ParametricUMAP`` embedding model, and running the same experiment: - -.. code:: python3 - - p_embedder = ParametricUMAP() - - p_emb_x1 = p_embedder.fit_transform(x1) - -.. code:: python3 - - plt.scatter(p_emb_x1[:,0], p_emb_x1[:,1], c=y1, cmap='Spectral', s=2, alpha=0.2) - -.. image:: images/retrain_pumap_p_emb_x1.png - -Again, we get good results on our initial embedding of ``x1``. If we pass ``x2`` through without re-training, we get a similar problem to our ``UMAP`` model: - -.. code:: python3 - - p_emb_x2 = p_embedder.transform(x2) - -.. code:: python3 - - plt.scatter(p_emb_x2[:,0], p_emb_x2[:,1], c=y2, cmap='Spectral', s=2, alpha=0.2) - plt.scatter(p_emb_x2[y2==N][:,0], p_emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5) - -.. image:: images/retrain_pumap_p_emb_x2.png - -Re-training Parametric UMAP with landmarks ------------------------------------------- - -To update our embedding to include the new class, we'll fine-tune our existing ``ParametricUMAP`` model. Doing this without any other changes will start from where we left off, but our embedding space's structure may drift and change. This is because the UMAP loss function is invariant to translation and rotation, as it is only concerned with the relative positions and distances between points. - -In order to keep our embedding space more consistent, we'll use the landmarks option for ``ParametricUMAP``. We retrain the model on the ``x2`` partition, along with some points chosen as landmarks from ``x1``. We'll choose 1% of the samples in ``x1`` to be included, along with their current position in the embedding space to be used in the landmarks loss function. - -The default ``landmark_loss_fn`` is the euclidean distance between the point's original position and it's current one. The only change we'll make is to set ``landmark_loss_weight=0.01``. - -.. code:: python3 - - # Select landmarks indexes from x1. - # - landmark_idx = list(np.random.choice(range(x1.shape[0]), int(x1.shape[0]/100), replace=False)) - - # Add the landmark points to x2 for training. - # - x2_lmk = np.concatenate((x2, x1[landmark_idx])) - y2_lmk = np.concatenate((y2, y1[landmark_idx])) - - # Make our landmarks vector, which is nan where we have no landmark information. - # - landmarks = np.stack( - [np.array([np.nan, np.nan])]*x2.shape[0] + list( - p_embedder.transform( - x1[landmark_idx] - ) - ) - ) - - # Set landmark loss weight and continue training our Parametric UMAP model. - # - p_embedder.landmark_loss_weight = 0.01 - p_embedder.fit(x2_lmk, landmark_positions=landmarks) - p_emb2_x2 = p_embedder.transform(x2) - - # Check how x1 looks when embedded in the space retrained on x2 and landmarks. - # - p_emb2_x1 = p_embedder.transform(x1) - - -Plotting all of the different embeddings to compare them: - -.. code:: python3 - - fig, axs = plt.subplots(3, 2, figsize=(16, 24), sharex=True, sharey=True) - - axs[0,0].scatter( - emb_x1[:, 0], emb_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, - ) - axs[0,0].set_ylabel('UMAP Embedding', fontsize=20) - - axs[0,1].scatter( - emb_x2[:, 0], emb_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, - ) - axs[0,1].scatter( - emb_x2[y2==N][:,0], emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5, - ) - - axs[1,0].scatter( - p_emb_x1[:, 0], p_emb_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, - ) - axs[1,0].set_ylabel('Initial P-UMAP Embedding', fontsize=20) - - axs[1,1].scatter( - p_emb_x2[:, 0], p_emb_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, - ) - axs[1,1].scatter( - p_emb_x2[y2==N][:,0], p_emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5 - ) - - axs[2,0].scatter( - p_emb2_x1[:, 0], p_emb2_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, - ) - axs[2,0].set_ylabel('Updated P-UMAP Embedding', fontsize=20) - axs[2,0].set_xlabel(f'x1, No {N}s', fontsize=20) - - axs[2,1].scatter( - p_emb2_x2[:, 0], p_emb2_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, - ) - axs[2,1].scatter( - p_emb2_x2[y2==N][:,0], p_emb2_x2[y2==N][:,1], c='k', s=2, alpha=0.5, - ) - axs[2,1].set_xlabel('x2, All Classes', fontsize=20) - - plt.tight_layout() - -.. image:: images/retrain_pumap_summary_2_removed.png - -Here we see that our approach has been successful, The embedding space has been kept consistent and we now have a clear cluster of our new class, the 2s. This new cluster shows up in a sensible part of the embedding space, and the rest of the structure is preserved. - -It is worth double checking here that the landmark loss is not too constraining, we still would like a good UMAP structure. -To do so, we can interrogate the history of our embedder, which will retain the history through our re-training steps. - -.. code:: python3 - - plt.plot(p_embedder._history['loss']) - plt.ylabel('Loss') - plt.xlabel('Epoch') - -.. image:: images/retrain_pumap_history.png - -We can identify the spike in loss where we introduce ``x2``, and can confirm that the resulting loss is comparable to the loss from our initial training on ``x1``. This tells us that the model is not having to compromise too much between the UMAP loss and the landmark loss. If this were not the case, it could potentially be improved by lowering the ``landmark_loss_weight`` attribute of our embedder object. There is a tradeoff to be made here between the consistency of the space and minimizing UMAP loss, but the key is we have smooth variation in the embedding space, which will make downstream tasks easier to adjust. In this case, we could probably stand to increase the ``landmark_loss_weight`` to keep the space more consistent. - -In addition to ``landmark_loss_weight``, there are a number of other options available to us to try and get better results on this or other examples: - -- Continuing the training with a larger portion of points from the original data, in our case ``x1``. Not all of these points need to be landmarked, but they can contribute to a consistent graph structure in higher dimensions. -- Changing the ``landmark_loss_fn``. For example, if we want to allow for points to move if they have to we could truncate the default euclidean loss function, allowing the metaphorical rubber band to snap at a certain point and prioritising a good UMAP structure once we discover that sticking to the landmark position is not correct. -- Being more intelligent with our selection of landmark points, for example using submodular optimization with a package like `apricot-select `__ or chosing points from different parts of a hierarchical clustering like `HDBSCAN `__ - diff --git a/docs/aligned_umap_basic_usage.md b/docs/aligned_umap_basic_usage.md new file mode 100644 index 00000000..64655ef5 --- /dev/null +++ b/docs/aligned_umap_basic_usage.md @@ -0,0 +1,517 @@ +# How to use AlignedUMAP + + +It may happen that it would be beneficial to have different UMAP +embeddings aligned with each other. There are several ways to go about +doing this. One simple approach is to simply embed each dataset with +UMAP independently and then solve for a `Procrustes +transformation `__ +on shared points. An alternative approach is to embed the first dataset +and then construct an initial embedding for the second dataset based on +locations of shared points in the first embedding and then go from +there. A third approach, which will provide better alignments in +general, is to optimize both embeddings at the same time with some form +of constraint as to how far shared points can take different locations +in different embeddings *during* the optimization. This last option is +possible, but is not easily tractable to implement yourself (unlike the +first two options). To remedy this issue it has been implemented as a +separate model class in `umap` called `AlignedUMAP`. The +resulting class is quite flexible, but here we will walk through simple +usage on some basic (and somewhat contrived) data just to demonstrate +how to get it running on data. + +```python3 +import numpy as np +import sklearn.datasets +import umap +import umap.plot +import umap.utils as utils +import umap.aligned_umap +import matplotlib.pyplot as plt + +``` + +For our demonstration we’ll just use the pendigits dataset from sklearn. + +```python3 +digits = sklearn.datasets.load_digits() + +``` + +To make a sequence of datasets with some shared points between each +different dataset we’ll first sort the data so we have some vaguely +sensible progression. In this case we’ll sort by the total amount of +“ink” in the handwritten digit. This isn’t meant to be meaningful, it is +merely meant to provide something useful to slicing into overlapping +chunks that we will want to embed separately and yet keep aligned. + +```python3 +ordered_digits = digits.data[np.argsort(digits.data.sum(axis=1))] +ordered_target = digits.target[np.argsort(digits.data.sum(axis=1))] +plt.matshow(ordered_digits[-1].reshape((8,8))) + + +``` + +![Image](images/aligned_umap_basic_usage_5_1.png) + +We can then divide up the dataset into slices of 400 samples, moving +along in chunks of 150 to ensure that there are overlaps between +consecutive slices. This will give us a list of ten different datasets +that we can embed, with the goal being to ensure that the positions of +points in the embeddings are relatively consistent. + +```python3 +slices = [ordered_digits[150 * i:min(ordered_digits.shape[0], 150 * i + 400)] for i in range(10)] + +``` + +To ensure that consistency `AlignedUMAP` will need more information +than *just* the datasets – we also need some information about how the +datasets relate to one another. These take the form of dictionaries that +relate the indices of one dataset to the indices of another. Currently +`AlignedUMAP` only supports sequences of datasets with relations +between each consecutive pair in the sequence. To construct the +relations for this dataset we note that the last 250 samples of one +dataset are going to be the same samples as the first 250 samples of the +next dataset – this makes it easy to construct the dictionary: it is +mapping + +:: + + 150 --> 0 + 151 --> 1 + ... + 398 --> 248 + 399 --> 249 + +which we can construct easily using a dictionary comprehension. We will +have the same relation between each consecutive pair, so to make the +list of relations between pairs we can just duplicate the constructed +relation the requisite number of times. + +```python3 +relation_dict = {i+150:i for i in range(400-150)} +relation_dicts = [relation_dict.copy() for i in range(len(slices) - 1)] + +``` + +Note that while in this case the relation defines a map between +identical samples in different datasets it can be much more general – +see the politics example later for a case where the relation is +constructed from external information (representatives names and +states). + +Now that we have both a list of data slices and a list of relations +between the consecutive pairs we can use the `AlignedUMAP` class to +generate a list of embeddings. The `AlignedUMAP` class takes most of +the parameters that UMAP accepts. The major difference is that the fit +method requires a *list* of datasets, and a keyword argument +`relations` that specifies the relation dictionaries between +consecutive pairs of datasets. Other than that things are essentially +push-button. + +```python3 +%%time +aligned_mapper = umap.AlignedUMAP().fit(slices, relations=relation_dicts) + + +``` + +``` +CPU times: user 57.4 s, sys: 8.43 s, total: 1min 5s +Wall time: 57.4 s + + +``` + +You will note that this took a non-trivial amount of time to run, +despite being on the relatively small pendigits dataset. This is because +we are completing 10 different UMAP embeddings at once, so on average we +are taking about five seconds per embedding, which is more reasonable – +the alignment does have overhead cost however. + +The next step is to look at the results. To ensure that the plots we +produce have a consistent x and y axis we’ll use a small function to +compute a set of axis bounds for plotting. + +```python3 +def axis_bounds(embedding): + left, right = embedding.T[0].min(), embedding.T[0].max() + bottom, top = embedding.T[1].min(), embedding.T[1].max() + adj_h, adj_v = (right - left) * 0.1, (top - bottom) * 0.1 + return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v] + +``` + +Now it is just a matter of plotting the results in ten different scatter +plots. We can do this most easily with matplotlib directly, setting up a +grid of plots. Note that the progression proceeds by row then column, so +read the progression as if you were reading a page of text (across, then +down). + +```python3 +fig, axs = plt.subplots(5,2, figsize=(10, 20)) +ax_bound = axis_bounds(np.vstack(aligned_mapper.embeddings_)) +for i, ax in enumerate(axs.flatten()): + current_target = ordered_target[150 * i:min(ordered_target.shape[0], 150 * i + 400)] + ax.scatter(*aligned_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral") + ax.axis(ax_bound) + ax.set(xticks=[], yticks=[]) +plt.tight_layout() + + + +``` + +![Image](images/aligned_umap_basic_usage_15_0.png) + +So despite being different embeddings on different datasets, the +clusters keep their general alignment – the top left plot and bottom +right plot have the same rough positions for specific digit clusters. We +can also, to a degree, see how the structure changes over the course of +the different slices. Thus we are keeping the various embeddings +aligned, but allowing the changes dictated by the differing structures +of each different slice of data. + +## Online updating of aligned embeddings + + +It may be the case that we have incoming temporal data and would like to +have embeddings of time-windows that, ideally, align with the embeddings +of prior time-windows. As long as we overlap the time-windows we use to +allow for relations between time windows then this is possible – except +that the previous code required all the time-windows to be input *at +once* for fitting. We would instead like to train an initial model and +then update it as we go. This is possible via the `update` method +which we’ll demonstrate below. + +First we need to fit a base `AlignedUMAP` model; we’ll use the first +two slices and the first relation dict to do so. + +```python3 +%%time +updating_mapper = umap.AlignedUMAP().fit(slices[:2], relations=relation_dicts[:1]) + + +``` + +``` +CPU times: user 9.32 s, sys: 1.47 s, total: 10.8 s +Wall time: 9.17 s + + +``` + +Note that this is fairly quick, since we are only fitting two slices. +Given the trained model the update method requires a new slice of data +to add, along with a relation dictionary (passed in with the +`relations` keyword argument as with `fit`). This will append a new +embedding to the `embeddings_` attribute of the model for the new +data, aligned with what has been seen so far. + +```python3 +for i in range(2,len(slices)): + %time updating_mapper.update(slices[i], relations={v:k for k,v in relation_dicts[i-1].items()}) + + +``` + +``` +CPU times: user 7.78 s, sys: 1.15 s, total: 8.93 s +Wall time: 7.92 s +CPU times: user 6.64 s, sys: 1.17 s, total: 7.81 s +Wall time: 6.6 s +CPU times: user 6.94 s, sys: 1.17 s, total: 8.11 s +Wall time: 6.81 s +CPU times: user 6.45 s, sys: 1.51 s, total: 7.96 s +Wall time: 6.45 s +CPU times: user 7.44 s, sys: 1.32 s, total: 8.76 s +Wall time: 7.16 s +CPU times: user 7.68 s, sys: 1.73 s, total: 9.41 s +Wall time: 7.59 s +CPU times: user 7.88 s, sys: 1.65 s, total: 9.54 s +Wall time: 7.39 s +CPU times: user 7.82 s, sys: 1.98 s, total: 9.8 s +Wall time: 7.7 s + + +``` + +Note that each new slice takes a relatively short period of time, as we +might hope. The downside of this, as you can imagine, is that we have no +“forward” relations – the windows over slices only look backward. This +means the results are less good, but we are trading that for the ability +to quickly and easily update as we go. + +We can look at how we did using essentially the same code as before. + +```python3 +fig, axs = plt.subplots(5,2, figsize=(10, 20)) +ax_bound = axis_bounds(np.vstack(updating_mapper.embeddings_)) +for i, ax in enumerate(axs.flatten()): + current_target = ordered_target[150 * i:min(ordered_target.shape[0], 150 * i + 400)] + ax.scatter(*updating_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral") + ax.axis(ax_bound) + ax.set(xticks=[], yticks=[]) +plt.tight_layout() + + + +``` + +![Image](images/aligned_umap_basic_usage_22_0.png) + +We see that the alignment is indeed working, so new slices remain +comparable with previously trained slices. As noted the overall +alignments and progression is not as nice as the previous version, but +it does have the significant benefit of allowing an update as you go +approach. + +Note that right now this model keeps all the previous data, so it will +only really work in a batch streaming approach where occasionally a +fresh model is trained, dropping some of the historical data before +continuing with updates. + +## Aligning varying parameters + + +It is possible to align UMAP embedding that vary in the parameters used +instead of the data. To demonstrate how this can work we’ll continue to +use the pendigits dataset, but instead of slicing the data as we did +before, we’ll use the full dataset. That means that our relations +between datasets are simply constant relations. We can construct those +ahead of time: + +```python3 +constant_dict = {i:i for i in range(digits.data.shape[0])} +constant_relations = [constant_dict for i in range(9)] + +``` + +To run AlignedUMAP over a range of parameters you simply need to pass in +a *list* of the sequence of parameters you wish to use. You can do this +for several different parameters – just ensure that all the lists are +the same length! In this case we’ll try looking at how the embeddings +change if we change `n_neighbors` and `min_dist`. This means that +when we create the AlignedUMAP object we pass a list, instead of a +single value, to each of those parameters. To make the visualization a +little more interesting we’ll also vary some of the alignment parameters +(there are only two of major consequence). Specifically we’ll adjust the +`alignment_window_size`, which controls how far forward and backward +across the datasets we look when doing alignment, and the +`alignment_regularisation` which controls how heavily we weight the +alignment aspect versus the UMAP layout. Larger values of +`alignment_regularisation` will work harder to keep points aligned +across embeddings (at the cost of the embedding quality at each slice), +while smaller values will allow the optimisation to focus more on the +individual embeddings and put less emphasis on aligning the embeddings +with each other. + +Given a model we can then fit it. As before we need to hand it a list of +datasets, and a list of relations. Since we are using the same data each +time (and varying the parameters) we can just repeat the full pendigits +dataset. Note that the number of datasets needs to match the number of +parameter values being used. The same goes for the number of relations +(one less than the number of parameter values). + +```python3 +neighbors_mapper = umap.AlignedUMAP( + n_neighbors=[3,4,5,7,11,16,22,29,37,45,54], + min_dist=[0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45], + alignment_window_size=2, + alignment_regularisation=1e-3, +).fit( + [digits.data for i in range(10)], relations=constant_relations +) + + +``` + +As before we can look at the results by plotting each of the embeddings. + +```python3 +fig, axs = plt.subplots(5,2, figsize=(10, 20)) +ax_bound = axis_bounds(np.vstack(neighbors_mapper.embeddings_)) +for i, ax in enumerate(axs.flatten()): + ax.scatter(*neighbors_mapper.embeddings_[i].T, s=2, c=digits.target, cmap="Spectral") + ax.axis(ax_bound) + ax.set(xticks=[], yticks=[]) +plt.tight_layout() + + + +``` + +![Image](images/aligned_umap_basic_usage_29_1.png) + +To get a better feel for the evolution of the embedding over the change +in parameter values we can plot the data in three dimensions, with the +third dimension being the parameter value chosen. To better show how +data points in the embedding *move* with respect to the changing +parameters we can plot them not as points, but as *curves* connecting +the same point in each sequential embedding. For three dimensional plots +like this we’ll make use of the [plotly](https://plotly.com) plotting +library. + +```python3 +import plotly.graph_objects as go +import plotly.express as px +import pandas as pd + +``` + +The first thing we’ll have to do is wrangle the data into a suitable +format for plotly. That’s the reason we loaded up pandas as well – +plotly likes dataframes. This involves stacking all the embeddings +together, and then assigning an extra `z` value according to which +embedding we are in. For the purposes of visualization we’ll just have a +linear scale from 0 to 1 of the appropriate length for the z +coordinates. + +```python3 +n_embeddings = len(neighbors_mapper.embeddings_) +es = neighbors_mapper.embeddings_ +embedding_df = pd.DataFrame(np.vstack(es), columns=('x', 'y')) +embedding_df['z'] = np.repeat(np.linspace(0, 1.0, n_embeddings), es[0].shape[0]) +embedding_df['id'] = np.tile(np.arange(es[0].shape[0]), n_embeddings) +embedding_df['digit'] = np.tile(digits.target, n_embeddings) + +``` + +The next thing we can do to improve the visualization is to smooth out +the curves rather than leaving them as piecewise linear lines. To to +this we can use the `scipy.interpolate` functionality to create smooth +cubic splines that pass through all the points of the curve we wish to +create. + +```python3 +import scipy.interpolate + +``` + +The interpolate module has a function `interp1d` that generates a +(vector of) smooth function given a one dimensional set of datapoints +that it needs to pass through. We can generate separate functions for +the x and y coordinates for each pendigit sample, allowing us to +generate smooth curves in three dimensions. + +```python3 +fx = scipy.interpolate.interp1d( + embedding_df.z[embedding_df.id == 0], embedding_df.x.values.reshape(n_embeddings, digits.data.shape[0]).T, kind="cubic" +) +fy = scipy.interpolate.interp1d( + embedding_df.z[embedding_df.id == 0], embedding_df.y.values.reshape(n_embeddings, digits.data.shape[0]).T, kind="cubic" +) +z = np.linspace(0, 1.0, 100) + +``` + +With that in hand it is just a matter of plotting all the curves. In +plotly parlance each curve is a “trace” and we generate each one +separately (along with a suitable colour given by the digit the sample +represents). We then add all the traces to a figure, and display the +figure. + +```python3 +palette = px.colors.diverging.Spectral +interpolated_traces = [fx(z), fy(z)] +traces = [ + go.Scatter3d( + x=interpolated_traces[0][i], + y=interpolated_traces[1][i], + z=z*3.0, + mode="lines", + line=dict( + color=palette[digits.target[i]], + width=3.0 + ), + opacity=1.0, + ) + for i in range(digits.data.shape[0]) +] +fig = go.Figure(data=traces) +fig.update_layout( + width=800, + height=700, + autosize=False, + showlegend=False, +) +fig.show() + + +``` + +![Image](images/aligned_umap_pendigits_3d_1.png) + +Since it is tricky to get the interactive plotly figure embedded in +documentation we have a static image here, but if you run this yourself +you will have a fully interactive view of the data. + +Alternatively, we can visualize the third dimension as an evolution of the +embeddings through time by rendering each z-slice as a frame in an animated +GIF. To do this, we'll first need to import some notebook display tools and +matplotlib's [animation](https://matplotlib.org/stable/api/animation_api.html) +module. + +```python3 +from IPython.display import display, Image, HTML +from matplotlib import animation + + +``` + +Next, we'll create a new figure, initialize a blank scatter plot, then use +`FuncAnimation` to update the point positions (called "offsets") one frame at +a time. + +```python3 +fig = plt.figure(figsize=(4, 4), dpi=150) +ax = fig.add_subplot(1, 1, 1) + +scat = ax.scatter([], [], s=2) +scat.set_array(digits.target) +scat.set_cmap('Spectral') +text = ax.text(ax_bound[0] + 0.5, ax_bound[2] + 0.5, '') +ax.axis(ax_bound) +ax.set(xticks=[], yticks=[]) +plt.tight_layout() + +offsets = np.array(interpolated_traces).T +num_frames = offsets.shape[0] + +def animate(i): + scat.set_offsets(offsets[i]) + text.set_text(f'Frame {i}') + return scat + +anim = animation.FuncAnimation( + fig, + init_func=None, + func=animate, + frames=num_frames, + interval=40) + + +``` + +Then we can save the animation as a GIF and close our animation. Depending on +your machine, you may need to change which writer the save method uses. + +```python3 +anim.save("aligned_umap_pendigits_anim.gif", writer="pillow") +plt.close(anim._fig) + + +``` + +Finally, we can read in our rendered GIF and display it in the notebook. + +```python3 +with open("aligned_umap_pendigits_anim.gif", "rb") as f: + display(Image(f.read())) + + +``` + +![Image](images/aligned_umap_pendigits_anim.gif) diff --git a/docs/aligned_umap_politics_demo.md b/docs/aligned_umap_politics_demo.md new file mode 100644 index 00000000..e487e0b0 --- /dev/null +++ b/docs/aligned_umap_politics_demo.md @@ -0,0 +1,1109 @@ +# AlignedUMAP for Time Varying Data + + +It is not uncommon to have datasets that can be partitioned into +segments, often with respect to time, where we want to understand not +only the structure of each segment, but how that structure changes over +the different segments. An example of this is the relative political +leanings of the US congress over time. In determining the relative +political leanings we can look at the representatives voting record on +roll call votes, with the presumption that representatives with similar +political principles will have similar voting records. We can, of +course, look at such data for any given congress, but since +representatives are commonly re-elected we can also consider how their +relative position in congress changes with time – an ideal use case for +AlignedUMAP. + +First we’ll need a selection of libraries. Aside from UMAP we will need +to do a little bit of data wrangling; for that we’ll need pandas, and +also for matching up names of representatives we’ll make use of the +library `fuzzywuzzy` which provides easy to use fuzzy string matching. + +```python3 +import umap +import umap.utils as utils +import umap.aligned_umap +import sklearn.decomposition + +import pandas as pd +import numpy as np + +import matplotlib.pyplot as plt +import seaborn as sns + +from fuzzywuzzy import fuzz, process +import re + + +``` + +```python3 +sns.set(style="darkgrid", color_codes=True) + +``` + +Next we’ll need to voting records for the representatives, along with +the associated metadata from the roll call vote records. You can obtain +the data https://clerk.house.gov; a notebook demonstrating how to pull +down the data and parse it into the csv files used here is available +[here](https://github.com/lmcinnes/umap_doc_notebooks/blob/master/parse_voting_records.ipynb). + +## Processing Congressional Voting Records + + +The voting records provide a row for each representative with a -1 for +“No”, 0 for “Present” or “Not Voting”, and 1 for “Aye”. A separate csv +file contains the raw data of all the votes with a row for each +legislators vote on each roll-call item. We really just need some +metadata – which state they represent and the party they represent so we +can decorate the results with this kind of information later. For that +we just need to extra the names, states, and parties for each year. We +can grab those columns and then drop duplicates. A catch: the party is +very occasionally entered incorrectly, and occasionally representatives +switch parties, making duplicated rows. We’ll just take the first entry +of such duplciates for now. + +```python3 +votes = [pd.read_csv(f"house_votes/{year}_voting_record.csv", index_col=0).sort_index() + for year in range(1990,2021)] +metadata = [pd.read_csv( + f"house_votes/{year}_full.csv", + index_col=0 +)[["legislator", "state", "party"]].drop_duplicates(["legislator", "state"]).sort_values('legislator') + for year in range(1990,2021)] + +``` + +Let’s take a look at the voting record for a single year to see what +sort of data we are looking at: + +```python3 +votes[5] + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
104-1st-1104-1st-10104-1st-100104-1st-101104-1st-102104-1st-103104-1st-104104-1st-105104-1st-106104-1st-107...104-1st-90104-1st-91104-1st-92104-1st-93104-1st-94104-1st-95104-1st-96104-1st-97104-1st-98104-1st-99
legislator
Abercrombie0.01.0-1.0-1.0-1.0-1.01.01.0-1.01.0...-1.0-1.01.0-1.01.0-1.0-1.01.01.01.0
Ackerman0.01.0-1.01.0-1.0-1.01.01.0-1.01.0...1.0-1.0-1.01.01.0-1.0-1.01.01.01.0
Allard0.01.01.01.0-1.01.0-1.0-1.01.0-1.0...-1.0-1.0-1.0-1.01.01.01.01.00.0-1.0
Andrews0.01.00.0-1.0-1.01.0-1.00.00.00.0...-1.01.0-1.0-1.01.01.0-1.01.0-1.0-1.0
Archer0.01.01.0-1.0-1.01.0-1.0-1.01.0-1.0...-1.0-1.0-1.0-1.0-1.01.01.01.0-1.00.0
..................................................................
Young (AK)0.01.01.01.0-1.01.0-1.0-1.01.0-1.0...-1.0-1.0-1.0-1.0-1.01.01.01.0-1.0-1.0
Young (FL)0.01.01.0-1.0-1.01.0-1.0-1.01.0-1.0...-1.0-1.0-1.0-1.0-1.01.01.01.0-1.0-1.0
Zeliff0.01.01.0-1.0-1.01.0-1.0-1.01.0-1.0...-1.0-1.0-1.0-1.0-1.01.01.01.0-1.0-1.0
Zimmer0.01.01.0-1.0-1.01.0-1.0-1.01.0-1.0...-1.01.0-1.0-1.0-1.01.01.01.0-1.0-1.0
de la Garza0.01.01.01.0-1.01.01.01.0-1.01.0...0.0-1.0-1.01.01.0-1.01.01.0-1.01.0
+

438 rows × 885 columns

+
+ + + + +We can examine the associated metadata for the same year. + +```python3 +metadata[5] + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
legislatorstateparty
0AbercrombieHID
1AckermanNYD
2AllardCOR
3AndrewsNJD
4ArcherTXR
............
430Young (AK)AKR
431Young (FL)FLR
432ZeliffNHR
433ZimmerNJR
89de la GarzaTXD
+

438 rows × 3 columns

+
+ + + + +You may note that sometimes representatives names list a state in +parenthesis afterwards. This is to provide disambiguation for +representatives that happen to have the last name. This actually +complicates matters for us since the disambiguation is only applied in +those cases where there is a name collision in that sitting of congress. +That means that for several years a representative may have simply their +last name, but then switch to being disambiguated, before potentially +switching back again. This would make it much harder to consistently +treck representatives over their entire career in congress. To fix this +up we’ll simply re-index by a unique representative ID that has their +last name, party, and state all listed over all the voting dataframes. +We’ll need a function to generate those from the metadata, and then +we’ll need to apply it to all the records. Importantly we’ll have to +finesse those situations where representatives are listed twice (under +un-ambiguous and disambiguated names) with some groupby tricks. + +```python3 +def unique_legislator(row): + name, state, party = row.legislator, row.state, row.party + # Strip of disambiguating state designators + if re.search(r'(\w+) \([A-Z]{2}\)', name) is not None: + name = name[:-5] + return f"{name} ({party}, {state})" + +``` + +```python3 +for i, _ in enumerate(votes): + votes[i].index = pd.Index(metadata[i].apply(unique_legislator, axis=1), name="legislator_index") + votes[i] = votes[i].groupby(level=0).sum() + metadata[i].index = pd.Index(metadata[i].apply(unique_legislator, axis=1), name="legislator_index") + metadata[i] = metadata[i].groupby(level=0).first() + +``` + +Now that we have the data at least a little wrangled into order there is +the question of ensuring some degree of continuity fore representatives. +To make this a little easier we’ll use voting records over *four year +spans* instead of over single years. Equally importantly we’ll do this +in a sliding window fashion so that we consider the record for 1990-1994 +and then the record for 1991-1995 and so on. By overlapping the windows +in this way we can ensure a little greater continuity of political +stance through the years. To make this happen we just have to merge data +frames in a sliding set of pairs, and then merge the pairs via the same +approach: + +```python3 +votes = [ + pd.merge( + v1, v2, how="outer", on="legislator_index" + ).fillna(0.0).sort_index() + for v1, v2 in zip(votes[:-1], votes[1:]) +] + votes[-1:] + +metadata = [ + pd.concat([m1, m2]).groupby("legislator_index").first().sort_index() + for m1, m2 in zip(metadata[:-1], metadata[1:]) +] + metadata[-1:] + +``` + +That’s the pairs of years; now we merge these pairwise to get sets of +four years worth of votes. + +```python3 +votes = [ + pd.merge( + v1, v2, how="outer", on="legislator_index" + ).fillna(0.0).sort_index() + for v1, v2 in zip(votes[:-1], votes[1:]) +] + votes[-1:] + +metadata = [ + pd.concat([m1, m2]).groupby(level=0).first().sort_index() + for m1, m2 in zip(metadata[:-1], metadata[1:]) +] + metadata[-1:] + +``` + +## Applying AlignedUMAP + + +To make use of AlignedUMAP we need to generate relations between +consecutive dataset slices. In this case that means we need to have a +relation describing row from one four year slice corresponds to a row +from the following four year slice for the same representative. For +AlignedUMAP to work this should be formatted as a list of dictionaries; +each dictionary gives a mapping from indices of one slice to indices of +the next. Importantly this mapping can be partial – it only has to +relate indices for which there is a match between the two slices. + +The vote dataframes that we are using for slices are already indexed +with unique identifiers for representatives, so to make relations we +simply have to match them up, creating a dictionary of indices from one +to the other. In practice we can do this relatively efficiently by using +pandas to merge dataframes on the pandas indexes of the two vote +dataframes with the data being simply the numeric indices of the rows. +The resulting dictionary is then just the dictionary of pairs given by +the inner join. + +```python3 +def make_relation(from_df, to_df): + left = pd.DataFrame(data=np.arange(len(from_df)), index=from_df.index) + right = pd.DataFrame(data=np.arange(len(to_df)), index=to_df.index) + merge = pd.merge(left, right, left_index=True, right_index=True) + return dict(merge.values) + +``` + +With a function for relation creation in place we simply need to apply +it to each consecutive pair of vote dataframes. + +```python3 +relations = [make_relation(x,y) for x, y in zip(votes[:-1], votes[1:])] + +``` + +If you are still unsure of what these relations are it might be +beneficial to look at a few of the dictionaries, along with the +corresponding pairs of vote dataframes. Here is (part of) the first +relation dictionary: + +```python3 +relations[0] + + + + +``` + +``` +{0: 0, + 1: 1, + 3: 2, + 4: 3, + 5: 4, + 6: 5, + 7: 6, + 8: 7, + 9: 8, + 10: 9, + 11: 10, + 12: 11, + 13: 12, + 14: 13, + 15: 14, + ... + 475: 547, + 476: 549, + 477: 550, + 478: 552, + 479: 553, + 480: 554, + 481: 555, + 482: 556, + 483: 557, + 484: 559} + + + +``` + +Now we are finally in a position to run AlignedUMAP. Most of the +standard UMAP parameters are available for use, including choosing a +metric and a number of neighbors. Here we will also make use of the +extra AlignedUMAP parameters `alignment_regularisation` and +`alignment_window_size`. The first is a value that weights how +important retaining alignment is. Typically the value is much smaller +than this (the default is 0.01), but given the relatively high +volatility in voting records we are going to increase it here. The +second parameter, `alignment_window_size` determines how far out on +either side AlignedUMAP will look when aligning embeddings – even though +the relations are specified only between consecutive slices it will +chain them together to construct relations reaching further. In this +case we’ll have it look as far out as 5 slices either side. + +```python3 +%%time +aligned_mapper = umap.aligned_umap.AlignedUMAP( + metric="cosine", + n_neighbors=20, + alignment_regularisation=0.1, + alignment_window_size=5, + n_epochs=200, + random_state=42, +).fit(votes, relations=relations) +embeddings = aligned_mapper.embeddings_ + + +``` + +``` +CPU times: user 6min 7s, sys: 30.6 s, total: 6min 37s +Wall time: 5min 57s + + +``` + +## Visualizing the Results + + +Now we need to plot the data somehow. To make the visualization +interesting it would be beneficial to have some colour variation – +ideally showing a different view of the relative political stance. For +that we want to attempt to get an idea of the position of each candidate +from an alternative source. To do this we can try to extract the vote +margin that the representative won by. The catch here is that while the +election data can be collected and processed, the names don’t match +perfectly as they come from a different source. That means we need to do +our best to get a name match for each candidate. We’ll use fuzzy string +matching restricted to the relevant year and state to try to get a good +match. A notebook providing details for obtaining and processing the +election winners data can be found +[here](https://github.com/lmcinnes/umap_doc_notebooks/blob/master/voting_data_by_district.ipynb). + +```python3 +election_winners = pd.read_csv('election_winners_1976-2018.csv', index_col=0) +election_winners.head() + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
yearstatedistrictwinnerpartywinning_ratio
01976AK0Don Youngrepublican0.289986
01976AL1Jack Edwardsrepublican0.374808
01976AL2William L. \\"Bill\"\" Dickinson"republican0.423953
01976AL3Bill Nicholsdemocrat1.000000
01976AL4Tom Bevilldemocrat0.803825
+
+ + + + +Now we need to simply go through the metadata and fill it out with the +extra information we can glean from the election winners data. Since we +can’t do exact name matching (the data for both is somewhat messy when +it comes to text fields like names) we can’t simply perform a join, but +must instead process things year by year and representative by +representative, finding the best string match on name that we can for +the given year and state election. In practice we are undoubtedly going +to get some of these wrong, and if the goal was a rigorous analysis +based on this data a lot more care would need to be taken. Since this is +just a demonstration and we’ll only be using this extra information as a +colour channel in plots we can excuise a few errors here and there from +in-exact data processing. + +```python3 +n_name_misses = 0 +for year, df in enumerate(metadata, 1990): + df["partisan_lean"] = 0.5 + df["district"] = np.full(len(df), -1, dtype=np.int8) + for idx, (loc, row) in enumerate(df.iterrows()): + name, state, party = row.legislator, row.state, row.party + # Strip of disambiguating state designators + if re.search(r'(\w+) \([A-Z]{2}\)', name) is not None: + name = name[:-5] + # Get a party designator matching the election_winners data + party = "republican" if party == "R" else "democrat" + # Restrict to the right state and time-frame + state_election_winners = election_winners[(election_winners.state == state) + & (election_winners.year <= year + 4) + & (election_winners.year >= year - 4)] + # Try to match a name; and fail "gracefully" + try: + matched_name = process.extractOne( + name, + state_election_winners.winner.tolist(), + scorer=fuzz.partial_token_sort_ratio, + score_cutoff=50, + ) + except: + matched_name = None + + # If we got a unique match, get the election data + if matched_name is not None: + winner = state_election_winners[state_election_winners.winner == matched_name[0]] + else: + winner = [] + + # We either have none, one, or *several* match elections. Take a best guess. + if len(winner) < 1: + df.loc[loc, ["partisan_lean"]] = 0.25 if party == "republican" else 0.75 + n_name_misses += 1 + elif len(winner) > 1: + df.iloc[idx, 4] = int(winner.district.values[-1]) + df.iloc[idx, 3] = float(winner.winning_ratio.values[-1]) + else: + df.iloc[idx, 4] = int(winner.district.values) + df.iloc[idx, 3] = float(winner.winning_ratio.values[0]) + +print(f"Failed to match a name {n_name_misses} times") + + +``` + +``` +Failed to match a name 100 times + + +``` + +Now that we have the relative partisan leanings based on district +election margins we can color the plot. We can obviously label the plot +with the representatives names. The last remaining catch (when using +matplotlib for the plotting) is the get the plot bounds (since we will +be placing text markers directly into the plot, and thus not +autogenerating bounds). This is a simple enough matter of computing some +bounds as an adjustment a little outside the data limits. + +```python3 +def axis_bounds(embedding): + left = embedding.T[0].min() + right = embedding.T[0].max() + bottom = embedding.T[1].min() + top = embedding.T[1].max() + width = right - left + height = top - bottom + adj_h = width * 0.1 + adj_v = height * 0.05 + return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v] + +``` + +Now for the plot. Let’s pick a random time slice (you are welcome to try +others) and draw the representatives names in their embedded locations +for that slice, coloured by their relative election victory margin. + +```python3 +fig, ax = plt.subplots(figsize=(12,12)) +e = 5 +ax.axis(axis_bounds(embeddings[e])) +ax.set_aspect('equal') +for i in range(embeddings[e].shape[0]): + ax.text(embeddings[e][i, 0], + embeddings[e][i, 1], + metadata[e].index.values[i], + color=plt.cm.RdBu(np.float32(metadata[e]["partisan_lean"].values[i])), + fontsize=8, + horizontalalignment='center', + verticalalignment='center', + ) + + + + +``` + +![Image](images/aligned_umap_politics_demo_31_0.png) + +This gives a good idea of the layout in a single time slices, and by +plotting different time slices we can get some idea of how things have +evolved. We can go further, however, by plotting a representative as +curve through time as their relative political position in congress +changes. For that we will need a 3D plot – we need both the UMAP x and y +coordinates, as well as a third coordinate giving the year. I found this +easiest to do in plotly, so let’s import that. To make nice smooth +curves through time we will also import the `scipy.interpolate` module +which will let is interpolate a smooth curve from the discrete positions +that a representatives appears in over time. + +```python3 +import plotly.graph_objects as go +import scipy.interpolate + +``` + +Wrangling the data into shape for this is the next step; first let’s get +everything in a single dataframe that we can extract relevant data from +on an as-needed basis. + +```python3 +df = pd.DataFrame(np.vstack(embeddings), columns=('x', 'y')) +df['z'] = np.concatenate([[year] * len(embeddings[i]) for i, year in enumerate(range(1990, 2021))]) +df['representative_id'] = np.concatenate([v.index for v in votes]) +df['partisan_lean'] = np.concatenate([m["partisan_lean"].values for m in metadata]) + +``` + +Next we’ll need that interpolation of the curve for a given +representative. We’ll write a function to handle that as there is a +little bit of case-based logic that makes it non-trivial. We are going +to get handed year data and want to interpolate the UMAP x and y +coordinates for a single representative. + +The first major catch is that many representatives don’t have a single +contiguous block of years for which they were in congress: they were +elected for several years, missed re-election, and then came back to +congress several years later (possibly in another district). Each such +block of contiguous years needs to be a separate path, and we shouldn’t +connect them. We therefore need some logic to find the contiguous blocks +and generate smooth paths for each of them. + +Another catch is that some representatives have only been in office for +a year or two (special elections and so forth) and we can’t do a cubic +spline interpolation for that; we can devolve to linear interpolation or +quadratic splines for those cases, so simply add the point itself for +the odd single year cases. + +With those issues in hand we can then simply use the scipy `interp1d` +function to generate smooth curves through the points. + +```python3 +INTERP_KIND = {2:"linear", 3:"quadratic", 4:"cubic"} + +def interpolate_paths(z, x, y, c, rep_id): + consecutive_year_blocks = np.where(np.diff(z) != 1)[0] + 1 + z_blocks = np.split(z, consecutive_year_blocks) + x_blocks = np.split(x, consecutive_year_blocks) + y_blocks = np.split(y, consecutive_year_blocks) + c_blocks = np.split(c, consecutive_year_blocks) + + paths = [] + + for block_idx, zs in enumerate(z_blocks): + + text = f"{rep_id} -- partisan_lean: {np.mean(c_blocks[block_idx]):.2f}" + + if len(zs) > 1: + kind = INTERP_KIND.get(len(zs), "cubic") + else: + paths.append( + (zs, x_blocks[block_idx], y_blocks[block_idx], c_blocks[block_idx], text) + ) + continue + + z = np.linspace(np.min(zs), np.max(zs), 100) + x = scipy.interpolate.interp1d(zs, x_blocks[block_idx], kind=kind)(z) + y = scipy.interpolate.interp1d(zs, y_blocks[block_idx], kind=kind)(z) + c = scipy.interpolate.interp1d(zs, c_blocks[block_idx], kind="linear")(z) + + paths.append((z, x, y, c, text)) + + return paths + +``` + +And now we can use plotly to draw the resulting curves. For plotly we +use the `Scatter3D` method, which supports a “lines” mode that can +draw curves in 3D space. We can colour the curves by the partisan lean +score we derived from the election data – in fact the colour can vary +through the trace as the election margins vary. Since this is a plotly +plot it is interactive, so you can rotate it around and view it from all +angles. + +Unfortunately the interactive plotly plot does not embed into the documentation +well, so we present here a static image. If you run this yourself, however, +you will get the interactive version. + +```python3 +traces = [] +for rep in df.representative_id.unique(): + z = df.z[df.representative_id == rep].values + x = df.x[df.representative_id == rep].values + y = df.y[df.representative_id == rep].values + c = df.partisan_lean[df.representative_id == rep] + + for z, x, y, c, text in interpolate_paths(z, x, y, c, rep): + trace = go.Scatter3d( + x=x, y=z, z=y, + mode="lines", + hovertext=text, + hoverinfo="text", + line=dict( + color=c, + cmin=0.0, + cmid=0.5, + cmax=1.0, + cauto=False, + colorscale="RdBu", + colorbar=dict(), + width=2.5, + ), + opacity=1.0, + ) + traces.append(trace) + +fig = go.Figure(data=traces) +fig.update_layout( + width=800, + height=600, + scene=dict( + aspectratio = dict( x=0.5, y=1.25, z=0.5 ), + yaxis_title="Year", + xaxis_title="UMAP-X", + zaxis_title="UMAP-Y", + ), + scene_camera=dict(eye=dict( x=0.5, y=0.8, z=0.75 )), + autosize=False, + showlegend=False, +) +fig_widget = go.FigureWidget(fig) +fig_widget + + +``` + +![Image](images/aligned_umap_politics_demo_spaghetti.png) + +.. +[View html file](aligned_umap_plotly_plot.html) + + +This concludes our exploration for now. diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 00000000..b734d87f --- /dev/null +++ b/docs/api.md @@ -0,0 +1,35 @@ +# UMAP API Guide + + +UMAP has only two classes, `UMAP`, and `ParametricUMAP`, which inherits from it. + +## UMAP + + +## umap.umap_.UMAP + +> **API Reference:** `umap.umap_.UMAP` +> +> This is an auto-generated API reference. See the Python docstrings for details. + + +ParametricUMAP +---- + +## umap.parametric_umap.ParametricUMAP + +> **API Reference:** `umap.parametric_umap.ParametricUMAP` +> +> This is an auto-generated API reference. See the Python docstrings for details. + + +A number of internal functions can also be accessed separately for more fine tuned work. + +## Useful Functions + + +## Module: umap.umap_ + +> **API Reference:** Module `umap.umap_` +> +> This is an auto-generated API reference. See the Python docstrings for details. diff --git a/docs/basic_usage.md b/docs/basic_usage.md new file mode 100644 index 00000000..1dde28cf --- /dev/null +++ b/docs/basic_usage.md @@ -0,0 +1,631 @@ +# How to Use UMAP + + +UMAP is a general purpose manifold learning and dimension reduction +algorithm. It is designed to be compatible with +[scikit-learn](http://scikit-learn.org/stable/index.html), making use +of the same API and able to be added to sklearn pipelines. If you are +already familiar with sklearn you should be able to use UMAP as a drop +in replacement for t-SNE and other dimension reduction classes. If you +are not so familiar with sklearn this tutorial will step you through the +basics of using UMAP to transform and visualise data. + +First we'll need to import a bunch of useful tools. We will need numpy +obviously, but we'll use some of the datasets available in sklearn, as +well as the `train_test_split` function to divide up data. Finally +we'll need some plotting tools (matplotlib and seaborn) to help us +visualise the results of UMAP, and pandas to make that a little easier. + +```python3 +import numpy as np +from sklearn.datasets import load_digits +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +import matplotlib.pyplot as plt +import seaborn as sns +import pandas as pd +%matplotlib inline + +``` + +```python3 +sns.set(style='white', context='notebook', rc={'figure.figsize':(14,10)}) + +``` + +## Penguin data + + +![Penguins](https://raw.githubusercontent.com/allisonhorst/palmerpenguins/c19a904462482430170bfe2c718775ddb7dbb885/man/figures/lter_penguins.png) + + +The next step is to get some data to work with. To ease us into things +we'll start with the `penguin +dataset `__. It isn't very +representative of what real data would look like, but it is small both +in number of points and number of features, and will let us get an idea +of what the dimension reduction is doing. + +```python3 +penguins = pd.read_csv("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/c19a904462482430170bfe2c718775ddb7dbb885/inst/extdata/penguins.csv") +penguins.head() + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsexyear
0AdelieTorgersen39.118.7181.03750.0male2007
1AdelieTorgersen39.517.4186.03800.0female2007
2AdelieTorgersen40.318.0195.03250.0female2007
3AdelieTorgersenNaNNaNNaNNaNNaN2007
4AdelieTorgersen36.719.3193.03450.0female2007
+
+ + + + +Since this is for demonstration purposes we will get rid of the NAs in +the data; in a real world setting one would wish to take more care with +proper handling of missing data. + +```python3 +penguins = penguins.dropna() +penguins.species.value_counts() + + + + +``` + +``` +Adelie 146 +Gentoo 119 +Chinstrap 68 +Name: species, dtype: int64 + + +``` + +![Diagram of culmen measurements on a penguin](https://github.com/allisonhorst/palmerpenguins/blob/c19a904462482430170bfe2c718775ddb7dbb885/man/figures/culmen_depth.png?raw=true) + + +See the [github repostiory](https://github.com/allisonhorst/penguins) +for more details about the dataset itself. It consists of measurements +of bill (culmen) and flippers and weights of three species of penguins, +along with some other metadata about the penguins. In total we have 333 +different penguins measured. Visualizing this data is a little bit +tricky since we can't plot in 4 dimensions easily. Fortunately four is +not that large a number, so we can just to a pairwise feature +scatterplot matrix to get an ideas of what is going on. Seaborn makes +this easy. + +```python3 +sns.pairplot(penguins.drop("year", axis=1), hue='species'); + + + +``` + +![Image](images/basic_usage_8_1.png) + +This gives us some idea of what the data looks like by giving as all the +2D views of the data. Four dimensions is low enough that we can (sort +of) reconstruct what the full dimensional data looks like in our heads. +Now that we sort of know what we are looking at, the question is what +can a dimension reduction technique like UMAP do for us? By reducing the +dimension in a way that preserves as much of the structure of the data +as possible we can get a visualisable representation of the data +allowing us to "see" the data and its structure and begin to get some +intuition about the data itself. + +To use UMAP for this task we need to first construct a UMAP object that +will do the job for us. That is as simple as instantiating the class. So +let's import the umap library and do that. + +```python3 +import umap + +``` + +```python3 +reducer = umap.UMAP() + +``` + +Before we can do any work with the data it will help to clean up it a +little. We won't need NAs, we just want the measurement columns, and +since the measurements are on entirely different scales it will be +helpful to convert each feature into z-scores (number of standard +deviations from the mean) for comparability. + +```python3 +penguin_data = penguins[ + [ + "bill_length_mm", + "bill_depth_mm", + "flipper_length_mm", + "body_mass_g", + ] +].values +scaled_penguin_data = StandardScaler().fit_transform(penguin_data) + +``` + +Now we need to train our reducer, letting it learn about the manifold. +For this UMAP follows the sklearn API and has a method `fit` which we +pass the data we want the model to learn from. Since, at the end of the +day, we are going to want to reduced representation of the data we will +use, instead, the `fit_transform` method which first calls `fit` and +then returns the transformed data as a numpy array. + +```python3 +embedding = reducer.fit_transform(scaled_penguin_data) +embedding.shape + + + + +``` + +``` +(333, 2) + + + +``` + +The result is an array with 333 samples, but only two feature columns +(instead of the four we started with). This is because, by default, UMAP +reduces down to 2D. Each row of the array is a 2-dimensional +representation of the corresponding penguin. Thus we can plot the +`embedding` as a standard scatterplot and color by the target array +(since it applies to the transformed data which is in the same order as +the original). + +```python3 +plt.scatter( + embedding[:, 0], + embedding[:, 1], + c=[sns.color_palette()[x] for x in penguins.species.map({"Adelie":0, "Chinstrap":1, "Gentoo":2})]) +plt.gca().set_aspect('equal', 'datalim') +plt.title('UMAP projection of the Penguin dataset', fontsize=24); + + + +``` + +![Image](images/basic_usage_17_1.png) + +This does a useful job of capturing the structure of the data, and as +can be seen from the matrix of scatterplots this is relatively accurate. +Of course we learned at least this much just from that matrix of +scatterplots -- which we could do since we only had four different +dimensions to analyse. If we had data with a larger number of dimensions +the scatterplot matrix would quickly become unwieldy to plot, and far +harder to interpret. So moving on from the Penguin dataset, let's consider +the digits dataset. + +## Digits data + + +First we will load the dataset from sklearn. + +```python3 +digits = load_digits() +print(digits.DESCR) + + +``` + +``` +.. _digits_dataset: + +Optical recognition of handwritten digits dataset +-------------------------------------------------- + +**Data Set Characteristics:** + + :Number of Instances: 5620 + :Number of Attributes: 64 + :Attribute Information: 8x8 image of integer pixels in the range 0..16. + :Missing Attribute Values: None + :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr) + :Date: July; 1998 + +This is a copy of the test set of the UCI ML hand-written digits datasets +https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits + +The data set contains images of hand-written digits: 10 classes where +each class refers to a digit. + +Preprocessing programs made available by NIST were used to extract +normalized bitmaps of handwritten digits from a preprinted form. From a +total of 43 people, 30 contributed to the training set and different 13 +to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of +4x4 and the number of on pixels are counted in each block. This generates +an input matrix of 8x8 where each element is an integer in the range +0..16. This reduces dimensionality and gives invariance to small +distortions. + +For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. +T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. +L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, +1994. + +.. topic:: References + + - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their + Applications to Handwritten Digit Recognition, MSc Thesis, Institute of + Graduate Studies in Science and Engineering, Bogazici University. + - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika. + - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin. + Linear dimensionalityreduction using relevance weighted LDA. School of + Electrical and Electronic Engineering Nanyang Technological University. + 2005. + - Claudio Gentile. A New Approximate Maximal Margin Classification + Algorithm. NIPS. 2000. + + +``` + +We can plot a number of the images to get an idea of what we are looking +at. This just involves matplotlib building a grid of axes and then +looping through them plotting an image into each one in turn. + +```python3 +fig, ax_array = plt.subplots(20, 20) +axes = ax_array.flatten() +for i, ax in enumerate(axes): + ax.imshow(digits.images[i], cmap='gray_r') +plt.setp(axes, xticks=[], yticks=[], frame_on=False) +plt.tight_layout(h_pad=0.5, w_pad=0.01) + + + +``` + +![Image](images/basic_usage_22_0.png) + +As you can see these are quite low resolution images -- for the most +part they are recognisable as digits, but there are a number of cases +that are sufficiently blurred as to be questionable even for a human to +guess at. The zeros do stand out as the easiest to pick out as notably +different and clearly zeros. Beyond that things get a little harder: +some of the squashed thing eights look awfully like ones, some of the +threes start to look a little like crossed sevens when drawn badly, and +so on. + +Each image can be unfolded into a 64 element long vector of grayscale +values. It is these 64 dimensional vectors that we wish to analyse: how +much of the digits structure can we discern? At least in principle 64 +dimensions is overkill for this task, and we would reasonably expect +that there should be some smaller number of "latent" features that would +be sufficient to describe the data reasonably well. We can try a +scatterplot matrix -- in this case just of the first 10 dimensions so +that it is at least plottable, but as you can quickly see that approach +is not going to be sufficient for this data. + +```python3 +digits_df = pd.DataFrame(digits.data[:,1:11]) +digits_df['digit'] = pd.Series(digits.target).map(lambda x: 'Digit {}'.format(x)) +sns.pairplot(digits_df, hue='digit', palette='Spectral'); + + +``` + +![Image](images/basic_usage_24_2.png) + +In contrast we can try using UMAP again. It works exactly as before: +construct a model, train the model, and then look at the transformed +data. To demonstrate more of UMAP we'll go about it differently this +time and simply use the `fit` method rather than the `fit_transform` +approach we used for Penguins. + +```python3 +reducer = umap.UMAP(random_state=42) +reducer.fit(digits.data) + + +``` + +``` +UMAP(a=None, angular_rp_forest=False, b=None, + force_approximation_algorithm=False, init='spectral', learning_rate=1.0, + local_connectivity=1.0, low_memory=False, metric='euclidean', + metric_kwds=None, min_dist=0.1, n_components=2, n_epochs=None, + n_neighbors=15, negative_sample_rate=5, output_metric='euclidean', + output_metric_kwds=None, random_state=42, repulsion_strength=1.0, + set_op_mix_ratio=1.0, spread=1.0, target_metric='categorical', + target_metric_kwds=None, target_n_neighbors=-1, target_weight=0.5, + transform_queue_size=4.0, transform_seed=42, unique=False, verbose=False) + + + +``` + +Now, instead of returning an embedding we simply get back the reducer +object, now having trained on the dataset we passed it. To access the +resulting transform we can either look at the `embedding_` attribute +of the reducer object, or call transform on the original data. + +```python3 +embedding = reducer.transform(digits.data) +# Verify that the result of calling transform is +# idenitical to accessing the embedding_ attribute +assert(np.all(embedding == reducer.embedding_)) +embedding.shape + + + + +``` + +``` +(1797, 2) + + + +``` + +We now have a dataset with 1797 rows (one for each hand-written digit +sample), but only 2 columns. As with the Penguins example we can now plot +the resulting embedding, coloring the data points by the class that +they belong to (i.e. the digit they represent). + +```python3 +plt.scatter(embedding[:, 0], embedding[:, 1], c=digits.target, cmap='Spectral', s=5) +plt.gca().set_aspect('equal', 'datalim') +plt.colorbar(boundaries=np.arange(11)-0.5).set_ticks(np.arange(10)) +plt.title('UMAP projection of the Digits dataset', fontsize=24); + +``` + +![Image](images/basic_usage_30_1.png) + +We see that UMAP has successfully captured the digit classes. There are +also some interesting effects as some digit classes blend into one +another (see the eights, ones, and sevens, with some nines in between), +and also cases where digits are pushed away as clearly distinct (the +zeros on the right, the fours at the top, and a small subcluster of ones +at the bottom come to mind). To get a better idea of why UMAP chose to +do this it is helpful to see the actual digits involve. One can do this +using [bokeh](https://bokeh.pydata.org/en/latest/) and mouseover +tooltips of the images. + +First we'll need to encode all the images for inclusion in a dataframe. + +```python3 +from io import BytesIO +from PIL import Image +import base64 + +``` + +```python3 +def embeddable_image(data): + img_data = 255 - 15 * data.astype(np.uint8) + image = Image.fromarray(img_data, mode='L').resize((64, 64), Image.Resampling.BICUBIC) + buffer = BytesIO() + image.save(buffer, format='png') + for_encoding = buffer.getvalue() + return 'data:image/png;base64,' + base64.b64encode(for_encoding).decode() + +``` + +Next we need to load up bokeh and the various tools from it that will be +needed to generate a suitable interactive plot. + +```python3 +from bokeh.plotting import figure, show, output_notebook +from bokeh.models import HoverTool, ColumnDataSource, CategoricalColorMapper +from bokeh.palettes import Spectral10 + +output_notebook() + + + +``` + + + +
+ + Loading BokehJS ... +
+ + + + + +Finally we generate the plot itself with a custom hover tooltip that +embeds the image of the digit in question in it, along with the digit +class that the digit is actually from (this can be useful for digits +that are hard even for humans to classify correctly). + +```python3 +digits_df = pd.DataFrame(embedding, columns=('x', 'y')) +digits_df['digit'] = [str(x) for x in digits.target] +digits_df['image'] = list(map(embeddable_image, digits.images)) + +datasource = ColumnDataSource(digits_df) +color_mapping = CategoricalColorMapper(factors=[str(9 - x) for x in digits.target_names], + palette=Spectral10) + +plot_figure = figure( + title='UMAP projection of the Digits dataset', + width=600, + height=600, + tools=('pan, wheel_zoom, reset') +) + +plot_figure.add_tools(HoverTool(tooltips=""" +
+
+ +
+
+ Digit: + @digit +
+
+""")) + +plot_figure.scatter( + 'x', + 'y', + source=datasource, + color=dict(field='digit', transform=color_mapping), + line_alpha=0.6, + fill_alpha=0.6, + size=4 +) +show(plot_figure) + + + +``` + +[View html file](basic_usage_bokeh_example.html) + + +As can be seen, the nines that blend between the ones and the sevens are +odd looking nines (that aren't very rounded) and do, indeed, interpolate +surprisingly well between ones with hats and crossed sevens. In contrast +the small disjoint cluster of ones at the bottom of the plot is made up +of ones with feet (a horizontal line at the base of the one) which are, +indeed, quite distinct from the general mass of ones. + +This concludes our introduction to basic UMAP usage -- hopefully this +has given you the tools to get started for yourself. Further tutorials, +covering UMAP parameters and more advanced usage are also available when +you wish to dive deeper. + +-------------- + + + +

+ +Penguin data information + + + +

+ +Peguin data are from: + +**Gorman KB, Williams TD, Fraser WR** (2014) Ecological Sexual +Dimorphism and Environmental Variability within a Community of Antarctic +Penguins (Genus *Pygoscelis*). PLoS ONE 9(3): e90081. +doi:10.1371/journal.pone.0090081 + +See the full paper +[HERE](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0090081). + + + +

+ +Original data access and use + + + +

+ +From Gorman et al.: "Data reported here are publicly available within +the PAL-LTER data system (datasets #219, 220, and 221): +http://oceaninformatics.ucsd.edu/datazoo/data/pallter/datasets. These +data are additionally archived within the United States (US) LTER +Network's Information System Data Portal: https://portal.lternet.edu/. +Individuals interested in using these data are therefore expected to +follow the US LTER Network's Data Access Policy, Requirements and Use +Agreement: https://lternet.edu/data-access-policy/." + +Anyone interested in publishing the data should contact `Dr. Kristen +Gorman `__ +about analysis and working together on any final products. + +Penguin images by Alison Horst. diff --git a/docs/benchmarking.md b/docs/benchmarking.md new file mode 100644 index 00000000..e23575bd --- /dev/null +++ b/docs/benchmarking.md @@ -0,0 +1,248 @@ + +# Performance Comparison of Dimension Reduction Implementations + + +Different dimension reduction techniques can have quite different +computational complexity. Beyond the algorithm itself there is also the +question of how exactly it is implemented. These two factors can have a +significant role in how long it actually takes to run a given dimension +reduction. Furthermore the nature of the data you are trying to reduce +can also matter -- mostly the involves the dimensionality of the +original data. Here we will take a brief look at the performance +characterstics of a number of dimension reduction implementations. + +To start let's get the basic tools we'll need loaded up -- numpy and +pandas obviously, but also tools to get and resample the data, and the +time module so we can perform some basic benchmarking. + +```ipython3 +import numpy as np +import pandas as pd +from sklearn.datasets import fetch_openml +from sklearn.utils import resample +import time + +``` + +Next we'll need the actual dimension reduction implementations. For the +purposes of this explanation we'll mostly stick with +[scikit-learn](http://scikit-learn.org/stable/), but for the sake of +comparison we'll also include the +[MulticoreTSNE](https://github.com/DmitryUlyanov/Multicore-TSNE) +implementation of t-SNE, which has significantly better performance than +the current scikit-learn t-SNE. + +```ipython3 +from sklearn.manifold import TSNE, LocallyLinearEmbedding, Isomap, MDS, SpectralEmbedding +from sklearn.decomposition import PCA +from MulticoreTSNE import MulticoreTSNE +from umap import UMAP + +``` + +Next we'll need out plotting tools, and, of course, some data to work +with. For this performance comparison we'll default to the now standard +benchmark of manifold learning: the MNIST digits dataset. We can use +scikit-learn's `fetch_mldata` to grab it for us. + +```ipython3 +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline + +``` + +```ipython3 +sns.set(context='notebook', + rc={'figure.figsize':(12,10)}, + palette=sns.color_palette('tab10', 10)) + +``` + +```ipython3 +mnist = fetch_openml('Fashion-MNIST', version=1) + +``` + +Now it is time to start looking at performance. To start with let's look +at how performance scales with increasing dataset size. + +## Performance scaling by dataset size + + +As the size of a dataset increases the runtime of a given dimension +reduction algorithm will increase at varying rates. If you ever want to +run your algorithm on larger datasets you will care not just about the +comparative runtime on a single small dataset, but how the performance +scales out as you move to larger datasets. We can similate this by +subsampling from MNIST digits (via scikit-learn's convenient +`resample` utility) and looking at the runtime for varying sized +subsamples. Since there is some randomness involved here (both in the +subsample selection, and in some of the algorithms which have stochastic +aspects) we will want to run a few examples for each dataset size. We +can easily package all of this up in a simple function that will return +a convenient pandas dataframe of dataset sizes and runtimes given an +algorithm. + +```ipython3 +def data_size_scaling(algorithm, data, sizes=[100, 200, 400, 800, 1600], n_runs=5): + result = [] + for size in sizes: + for run in range(n_runs): + subsample = resample(data, n_samples=size) + start_time = time.time() + algorithm.fit(subsample) + elapsed_time = time.time() - start_time + del subsample + result.append((size, elapsed_time)) + return pd.DataFrame(result, columns=('dataset size', 'runtime (s)')) + +``` + +Now we just want to run this for each of the various dimension reduction +implementations so we can look at the results. Since we don't know how +long these runs might take we'll start off with a very small set of +samples, scaling up to only 1600 samples. + +```ipython3 +all_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + LocallyLinearEmbedding(), + SpectralEmbedding(), + Isomap(), + TSNE(), + MDS(), +] +performance_data = {} +for algorithm in all_algorithms: + alg_name = str(algorithm) + if 'MulticoreTSNE' in alg_name: + alg_name = 'MulticoreTSNE' + else: + alg_name = alg_name.split('(')[0] + + performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, n_runs=3) + + + +``` + +Now let's plot the results so we can see what is going on. We'll use +seaborn's regression plot to interpolate the effective scaling. + +```ipython3 +for alg_name, perf_data in performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend(); + + + +``` + +![Image](images/performance_14_1.png) + +We can see straight away that there are some outliers here. The +scikit-learn t-SNE is clearly much slower than most of the other +algorithms. It does not have the scaling properties of MDS however; for +larger dataset sizes MDS is going to quickly become completely +unmanageable. At the same time MulticoreTSNE demonstrates that t-SNE can +run fairly efficiently. It is hard to tell much about the other +implementations other than the fact that PCA is far and away the fastest +option. To see more we'll have to look at runtimes on larger dataset +sizes. Both MDS and scikit-learn's t-SNE are going to take too long to +run so let's restrict ourselves to the fastest performing +implementations and see what happens as we extend out to larger dataset +sizes. + +```ipython3 +fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + LocallyLinearEmbedding(), + SpectralEmbedding(), + Isomap(), +] +fast_performance_data = {} +for algorithm in fast_algorithms: + alg_name = str(algorithm) + if 'MulticoreTSNE' in alg_name: + alg_name = 'MulticoreTSNE' + else: + alg_name = alg_name.split('(')[0] + + fast_performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, + sizes=[800, 1600, 3200, 6400, 12800], n_runs=3) + + +``` + +```ipython3 +for alg_name, perf_data in fast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend(); + + + +``` + +![Image](images/performance_17_1.png) + +At this point we begin to see some significant differentiation among the +different implementations. In the earlier plot MulticoreTSNE looked to +be slower than some of the other algorithms, but as we scale out to +larger datasets we see that its relative scaling performance is far +superior to the scikit-learn implementations of Isomap, spectral +embedding, and locally linear embedding. + +It is probably worth extending out further -- up to the full MNIST +digits dataset. To manage to do that in any reasonable amount of time +we'll have to restrict out attention to an even smaller subset of +implementations. We will pare things down to just MulticoreTSNE, PCA and +UMAP. + +```ipython3 +very_fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), +] +vfast_performance_data = {} +for algorithm in very_fast_algorithms: + alg_name = str(algorithm) + if 'MulticoreTSNE' in alg_name: + alg_name = 'MulticoreTSNE' + else: + alg_name = alg_name.split('(')[0] + + vfast_performance_data[alg_name] = data_size_scaling(algorithm, mnist.data, + sizes=[3200, 6400, 12800, 25600, 51200, 70000], n_runs=2) + + +``` + +```ipython3 +for alg_name, perf_data in vfast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend(); + + + + +``` + +![Image](images/performance_20_1.png) + +Here we see UMAP's advantages over t-SNE really coming to the forefront. +While UMAP is clearly slower than PCA, its scaling performance is +dramatically better than MulticoreTSNE, and for even larger datasets the +difference is only going to grow. + +This concludes our look at scaling by dataset size. The short summary is +that PCA is far and away the fastest option, but you are potentially +giving up a lot for that speed. UMAP, while not competitive with PCA, is +clearly the next best option in terms of performance among the +implementations explored here. Given the quality of results that UMAP +can provide we feel it is clearly a good option for dimension reduction. diff --git a/docs/clustering.md b/docs/clustering.md new file mode 100644 index 00000000..ed800422 --- /dev/null +++ b/docs/clustering.md @@ -0,0 +1,422 @@ +# Using UMAP for Clustering + + +UMAP can be used as an effective preprocessing step to boost the +performance of density based clustering. This is somewhat controversial, +and should be attempted with care. For a good discussion of some of the +issues involved in this, please see the various answers `in this +stackoverflow +thread `__ +on clustering the results of t-SNE. Many of the points of concern raised +there are salient for clustering the results of UMAP. The most notable +is that UMAP, like t-SNE, does not completely preserve density. UMAP, +like t-SNE, can also create false tears in clusters, resulting in a +finer clustering than is necessarily present in +the data. Despite these concerns there are still valid reasons to use +UMAP as a preprocessing step for clustering. As with any clustering +approach one will want to do some exploration and evaluation of the +clusters that come out to try to validate them if possible. + +With all of that said, let's work through an example to demonstrate the +difficulties that can face clustering approaches and how UMAP can +provide a powerful tool to help overcome them. + +First we'll need a selection of libraries loaded up. Obviously we'll +need data, and we can use sklearn's `fetch_openml` to get it. We'll +also need the usual tools of numpy, and plotting. Next we'll need umap, +and some clustering options. Finally, since we'll be working with +labeled data, we can make use of strong cluster evaluation metrics +`Adjusted Rand +Index `__ +and `Adjusted Mutual +Information `__. + +```python3 +from sklearn.datasets import fetch_openml +from sklearn.decomposition import PCA +import numpy as np +import matplotlib.pyplot as plt +%matplotlib inline + +# Dimension reduction and clustering libraries +import umap +import hdbscan +import sklearn.cluster as cluster +from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score + +``` + +Now let's set up the plotting and grab the data we'll be using -- in +this case the MNIST handwritten digits dataset. MNIST consists of 28x28 +pixel grayscale images of handwritten digits (0 through 9). These can be +unraveled such that each digit is described by a 784 dimensional vector +(the gray scale value of each pixel in the image). Ideally we would like +the clustering to recover the digit structure. + +```python3 +mnist = fetch_openml('mnist_784', version=1) +mnist.target = mnist.target.astype(int) + +``` + +For visualization purposes we can reduce the data to 2-dimensions using +UMAP. When we cluster the data in high dimensions we can visualize the +result of that clustering. First, however, we'll view the data colored +by the digit that each data point represents -- we'll use a different +color for each digit. This will help frame what follows. + +```python3 +standard_embedding = umap.UMAP(random_state=42).fit_transform(mnist.data) +plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=mnist.target.astype(int), s=0.1, cmap='Spectral'); + +``` + +![Image](images/clustering_6_1.png) + +### Traditional clustering + + +Now we would like to cluster the data. As a first attempt let's try the +traditional approach: K-Means. In this case we can solve one of the hard +problems for K-Means clustering -- choosing the right k value, giving +the number of clusters we are looking for. In this case we know the +answer is exactly 10. We will use sklearns K-Means implementation +looking for 10 clusters in the original 784 dimensional data. + +```python3 +kmeans_labels = cluster.KMeans(n_clusters=10).fit_predict(mnist.data) + +``` + +And how did the clustering do? We can look at the results by coloring +out UMAP embedded data by cluster membership. + +```python3 +plt.scatter(standard_embedding[:, 0], standard_embedding[:, 1], c=kmeans_labels, s=0.1, cmap='Spectral'); + + +``` + +![Image](images/clustering_10_1.png) + +This is not really the result we were looking for (though it does expose +interesting properties of how K-Means chooses clusters in high +dimensional space, and how UMAP unwraps manifolds by finding manifold +boundaries). While K-Means gets some cases correct, such as the two clusters +on the right side which are mostly correct, most of the rest of the data looks +somewhat arbitrarily carved up among the remaining clusters. We can put +this impression to the test by evaluating the adjusted Rand score and +adjusted mutual information for this clustering as compared with the +true labels. + +```python3 +( + adjusted_rand_score(mnist.target, kmeans_labels), + adjusted_mutual_info_score(mnist.target, kmeans_labels) +) + + + + +``` + +``` +(0.36675295135972552, 0.49614118437750965) + + + +``` + +As might be expected, we have not done a particularly good job -- both +scores take values in the range 0 to 1, with 0 representing a bad +(essentially random) clustering and 1 representing perfectly recovering +the true labels. K-Means definitely was not random, but it was also +quite a long way from perfectly recovering the true labels. Part of the +problem is the way K-Means works, based on centroids with an assumption +of largely spherical clusters -- this is responsible for some of the +sharp divides that K-Means puts across digit classes. We can potentially +improve on this by using a smarter density based algorithm. In this case +we've chosen to try HDBSCAN, which we believe to be among the most +advanced density based techniques. For the sake of performance we'll +reduce the dimensionality of the data down to 50 dimensions via PCA +(this recovers most of the variance), since HDBSCAN scales somewhat +poorly with the dimensionality of the data it will work on. + +```python3 +lowd_mnist = PCA(n_components=50).fit_transform(mnist.data) +hdbscan_labels = hdbscan.HDBSCAN(min_samples=10, min_cluster_size=500).fit_predict(lowd_mnist) + +``` + +We can now inspect the results. Before we do, however, it should be +noted that one of the features of HDBSCAN is that it can refuse to +cluster some points and classify them as "noise". To visualize this +aspect we will color points that were classified as noise gray, and then +color the remaining points according to the cluster membership. + +```python3 +clustered = (hdbscan_labels >= 0) +plt.scatter(standard_embedding[~clustered, 0], + standard_embedding[~clustered, 1], + color=(0.5, 0.5, 0.5), + s=0.1, + alpha=0.5) +plt.scatter(standard_embedding[clustered, 0], + standard_embedding[clustered, 1], + c=hdbscan_labels[clustered], + s=0.1, + cmap='Spectral'); + + + +``` + +![Image](images/clustering_16_1.png) + +This looks somewhat underwhelming. It meets HDBSCAN's approach of "not +being wrong" by simply refusing to classify the majority of the data. +The result is a clustering that almost certainly fails to recover all +the labels. We can verify this by looking at the clustering validation +scores. + +```python3 +( + adjusted_rand_score(mnist.target, hdbscan_labels), + adjusted_mutual_info_score(mnist.target, hdbscan_labels) +) + + + + +``` + +``` +(0.053830107882840102, 0.19756104096566332) + + + +``` + +These scores are far worse than K-Means! Partially this is due to the +fact that these scores assume that the noise points are simply an extra +cluster. We can instead only look at the subset of the data that HDBSCAN +was actually confident enough to assign to clusters -- a simple +sub-selection will let us recompute the scores for only that data. + +```python3 +clustered = (hdbscan_labels >= 0) +( + adjusted_rand_score(mnist.target[clustered], hdbscan_labels[clustered]), + adjusted_mutual_info_score(mnist.target[clustered], hdbscan_labels[clustered]) +) + + + + +``` + +``` +(0.99843407988303912, 0.99405521087764015) + + + +``` + +And here we see that where HDBSCAN was willing to cluster it got things +almost entirely correct. This is what it was designed to do -- be right +for what it can, and defer on anything that it couldn't have sufficient +confidence in. Of course the catch here is that it deferred clustering a +lot of the data. How much of the data did HDBSCAN actually assign to +clusters? We can compute that easily enough. + +```python3 +np.sum(clustered) / mnist.data.shape[0] + + + + +``` + +``` +0.17081428571428572 + + + +``` + +It seems that less than 18% of the data was clustered. While HDBSCAN did +a great job on the data it could cluster it did a poor job of actually +managing to cluster the data. The problem here is that, as a density +based clustering algorithm, HDBSCAN tends to suffer from the curse of +dimensionality: high dimensional data requires more observed samples to +produce much density. If we could reduce the dimensionality of the data +more we would make the density more evident and make it far easier for +HDBSCAN to cluster the data. The problem is that trying to use PCA to do +this is going to become problematic. While reducing the 50 dimensions +still explained a lot of the variance of the data, reducing further is +going to quickly do a lot worse. This is due to the linear nature of +PCA. What we need is strong manifold learning, and this is where UMAP +can come into play. + +### UMAP enhanced clustering + + +Our goal is to make use of UMAP to perform non-linear manifold aware +dimension reduction so we can get the dataset down to a number of +dimensions small enough for a density based clustering algorithm to make +progress. One advantage of UMAP for this is that it doesn't require you +to reduce to only two dimensions -- you can reduce to 10 dimensions +instead since the goal is to cluster, not visualize, and the performance +cost with UMAP is minimal. As it happens MNIST is such a simple dataset +that we really can push it all the way down to only two dimensions, but +in general you should explore different embedding dimension options. + +The next thing to be aware of is that when using UMAP for dimension +reduction you will want to select different parameters than if you were +using it for visualization. First of all we will want a larger +`n_neighbors` value -- small values will focus more on very local +structure and are more prone to producing fine grained cluster structure +that may be more a result of patterns of noise in the data than actual +clusters. In this case we'll double it from the default 15 up to 30. +Second it is beneficial to set `min_dist` to a very low value. Since +we actually want to pack points together densely (density is what we +want after all) a low value will help, as well as making cleaner +separations between clusters. In this case we will simply set +`min_dist` to be 0. + +```python3 +clusterable_embedding = umap.UMAP( + n_neighbors=30, + min_dist=0.0, + n_components=2, + random_state=42, +).fit_transform(mnist.data) + +``` + +We can visualize the results of this so see how it compares with more +visualization attuned parameters: + +```python3 +plt.scatter(clusterable_embedding[:, 0], clusterable_embedding[:, 1], + c=mnist.target, s=0.1, cmap='Spectral'); + + +``` + +![Image](images/clustering_27_1.png) + +As you can see we still have the general global structure, but we are +packing points together more tightly within clusters, and consequently +we can see larger gaps between the clusters. Ultimately this embedding +was for clustering purposes only, and we will go back to the original +embedding for visualization purposes from here on out. + +The next step is to cluster this data. We'll use HDBSCAN again, with the +same parameter setting as before. + +```python3 +labels = hdbscan.HDBSCAN( + min_samples=10, + min_cluster_size=500, +).fit_predict(clusterable_embedding) + +``` + +And now we can visualize the results, just as before. + +```python3 +clustered = (labels >= 0) +plt.scatter(standard_embedding[~clustered, 0], + standard_embedding[~clustered, 1], + color=(0.5, 0.5, 0.5), + s=0.1, + alpha=0.5) +plt.scatter(standard_embedding[clustered, 0], + standard_embedding[clustered, 1], + c=labels[clustered], + s=0.1, + cmap='Spectral'); + + +``` + +![Image](images/clustering_31_1.png) + +We can see that we have done a much better job of finding clusters +rather than merely assigning the majority of data as noise. This is +because we no longer have to try to cope with the relative lack +of density in 50 dimensional space and now HDBSCAN can more cleanly +discern the clusters. + +We can also make a quantitative assessment by using the clustering +quality measures as before. + +```python3 +adjusted_rand_score(mnist.target, labels), adjusted_mutual_info_score(mnist.target, labels) + + + + +``` + +``` +(0.9239306564265013, 0.90302671641133736) + + + +``` + +Where before HDBSCAN performed very poorly, we now have scores of 0.9 or +better. This is because we actually clustered far more of the data. As +before we can also look at how the clustering did on just the data that +HDBSCAN was confident in clustering. + +```python3 +clustered = (labels >= 0) +( + adjusted_rand_score(mnist.target[clustered], labels[clustered]), + adjusted_mutual_info_score(mnist.target[clustered], labels[clustered]) +) + + + + +``` + +``` +(0.93240371696811541, 0.91912906363537572) + + + +``` + +This is a little worse than the original HDBSCAN, but it is unsurprising +that you are going to be wrong more often if you make more predictions. +The question is how much more of the data is HDBSCAN actually +clustering? Previously we were clustering only 17% of the data. + +```python3 +np.sum(clustered) / mnist.data.shape[0] + + + + +``` + +``` +0.99164285714285716 + + + +``` + +Now we are clustering over 99% of the data! And our results in terms of +adjusted Rand score and adjusted mutual information are in line with the +current state of the art techniques using convolutional autoencoder +techniques. That's not bad for an approach that is simply viewing the +data as arbitrary 784 dimensional vectors. + +Hopefully this has outlined how UMAP can be beneficial for clustering. +As with all things care must be taken, but clearly UMAP can provide +significantly better clustering results when used judiciously. diff --git a/docs/composing_models.md b/docs/composing_models.md new file mode 100644 index 00000000..96f26c6f --- /dev/null +++ b/docs/composing_models.md @@ -0,0 +1,659 @@ +# Combining multiple UMAP models + + +It is possible to combine together multiple UMAP models, assuming that +they are operating on the same underlying data. To get an idea of how +this works recall that UMAP uses an intermediate fuzzy topological +representation (see `how_umap_works`). Given different views of the +same underlying data this will generate different fuzzy topological +representations. We can apply intersections or unions to these +representations to get a new composite fuzzy topological representation +which we can then embed into low dimensional space in the standard UMAP +way. The key is that, to be able to sensibly intersect or union these +representations, there must be one-to-one correspondences between the +data samples from the two different views. + +To get an idea of how this might work it is useful to see it in +practice. Let’s load some libraries and get started. + +```python3 +import sklearn.datasets +from sklearn.preprocessing import RobustScaler +import seaborn as sns +import pandas as pd +import numpy as np +import umap +import umap.plot + +``` + +## MNIST digits example + + +To begin with let’s use a relatively familiar dataset – the MNIST digits +dataset that we’ve used in other sections of this tutorial. The data is +(grayscale) 28x28 pixel images of handwritten digits (0 through 9); in +total there are 70,000 such images, and each image is unrolled into a +784 element vector. + +```python3 +mnist = sklearn.datasets.fetch_openml("mnist_784") + +``` + +To ensure we have an idea of what this dataset looks like through the +lens of UMAP we can run UMAP on the full dataset. + +```python3 +mapper = umap.UMAP(random_state=42).fit(mnist.data) + +``` + +```python3 +umap.plot.points(mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/composing_models_6_1.png) + +To make the problem more interesting let’s carve the dataset in two – not +into two sets of 35,000 samples, but instead carve each image in half. +That is, we’ll end up with 70,000 samples each of which is the top half +of the image of the handwritten digit, and another 70,000 samples each +of which is the bottom half of the image of the handwritten digit. + +```python3 +top = mnist.data[:, :28 * 14] +bottom = mnist.data[:, 28 * 14:] + +``` + +This is a little artificial, but it provides us with an example dataset +where we have two distinct views of the data which we can still well +understand. In practice this situation would be more likely to arise +when there are two different data collection processes sampling from the +same underlying population. In our case we could simply glue the data +back together (hstack the numpy arrays for example), but potentially +this isn’t feasible as the different data views may have different +scales or modalities. So, despite the fact that we could glue things +back together in this case, we will proceed as if we can’t – as may be +the case for many real world problems. + +Let’s first look at what UMAP does individually on each dataset. We’ll +start with the top halves of the digits: + +```python3 +top_mapper = umap.UMAP(random_state=42).fit(top) + +``` + +```python3 +umap.plot.points(top_mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/composing_models_11_1.png) + +While UMAP still manages to mostly separate the different digit classes +we can see the results are quite different from UMAP on the full +standard MNIST dataset. The twos and threes are blurred together (as we +would expect given that we don’t have the bottom half of the image wich +would let us tell them apart); The twos and threes are also in a large +grouping that pulls together all of the eights, sevens and nines (again, +what we would expect given only the top half of the digit), while the +fives and sixes are somewhat distinct, but clearly are similar to each +other. It is only the ones, fours and zeros that are very clearly +discernible. + +Now let’s see what sorts of results we get with the bottom halves of the +digits: + +```python3 +bot_mapper = umap.UMAP(random_state=42).fit(bottom) + +``` + +```python3 +umap.plot.points(bot_mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/composing_models_14_1.png) + +This is clearly a very different view of the data. Now it is the fours +and nines that blur together (presumably many of the nines are drawn +with straight rather than curved stems), with sevens nearby. The twos +and the threes are very distinct from each other, but the threes and the +fives are combined (as one might expect given that the bottom halves +*should* look similar). Zeros and sixes are distinct, but close to each +other. Ones, eights and twos are the most distinctive digits in this +view. + +So, assuming we can’t just glue the raw data together and stick a +reasonable metric on it, what can we do? We can perform intersections or +unions on the fuzzy topological representations. There is also some work +to be done re-asserting UMAP’s theoretical assumptions (local +connectivity, approximately uniform distributions). Fortunately UMAP +makes this relatively easy as long as you have a copy of fitted UMAP +models on hand (which we do in this case). To intersect two models +simply use the `*` operator; to union them use the `+` operator. +Note that this will actually take some time since we need to compute the +2D embedding of the combined model. + +```python3 +intersection_mapper = top_mapper * bot_mapper +union_mapper = top_mapper + bot_mapper + +``` + +With that complete we can visualize the results. First let’s look at the +intersection: + +```python3 +umap.plot.points(intersection_mapper, labels=mnist.target, width=500, height=500) + + + +``` + +![Image](images/composing_models_18_1.png) + +As you can see, while this isn’t as good as a UMAP plot for the full +MNIST dataset it has recovered the individual digits quite well. The +worst of the remaining overlap is between the threes and fives in the +center, which is it still struggling to fully distinguish. But note, +also, that we have recovered more of the overall structure than either +of the two different individual views, with the layout of different +digit classes more closely resembling that of the UMAP run on the full +dataset. + +Now let’s look at the union. + +```python3 +umap.plot.points(union_mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/composing_models_20_1.png) + +Given that UMAP is agnostic to rotation or reflection of the final +layout, this is essentially the same result as the intersection since it +is almost the reflection of it in the y-axis. This sort of result +(intersection and union being similar) is not always the case (in fact +it is not that common), but since the underlying structure of the digits +dataset is so clear we find that either way of piecing it together from +the two half datasets manage to find the same core underlying structure. + +If you are willing to try something a little more experimental there is +also a third option using the `-` operator which effectively +intersects with the fuzzy set complement (and is thus not commutative, +just as `-` implies). The goal here is to try to provide a sense of +what the data looks like when we contrast it against a second view. + +```python3 +contrast_mapper = top_mapper - bot_mapper + +``` + +```python3 +umap.plot.points(contrast_mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/composing_models_23_1.png) + +In this case the result is not overly dissimilar from the embedding of +just the top half, so the contrast has perhaps not shown is as much as +we might have hoped. + +## Diamonds dataset example + + +Now let’s try the same approach on a different dataset where the option +of just running UMAP on the full dataset is not available. For this +we’ll use the diamonds dataset. In this dataset each row represents a +different diamond and provides details on the weight (carat), cut, +color, clarity, size (depth, table, x, y, z) and price of the given +diamond. How these different factors interplay is somewhat complicated. + +```python3 +diamonds = sns.load_dataset('diamonds') +diamonds.head() + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
caratcutcolorclaritydepthtablepricexyz
00.23IdealESI261.555.03263.953.982.43
10.21PremiumESI159.861.03263.893.842.31
20.23GoodEVS156.965.03274.054.072.31
30.29PremiumIVS262.458.03344.204.232.63
40.31GoodJSI263.358.03354.344.352.75
+
+ + + + +For our purposes let’s take “price” as a “target” variable (as is often +the case when the dataset is used in machine learning contexts). What we +would like to do is provide a UMAP embedding of the data using the +remaining features. This is tricky since we can’t exactly use a +euclidean metric over the whole thing. What we can do, however, is split +the data into two distinct types: the purely numeric features relating +to size and weight, and the categorical features of color, cut and +clarity. Let’s pull each of those feature sets out so we can work with +them independently. + +```python3 +numeric = diamonds[["carat", "table", "x", "y", "z"]].copy() +ordinal = diamonds[["cut", "color", "clarity"]].copy() + +``` + +Now we have a new problem: the numeric features are not at all on the +same scales, so any sort of standard distance metric across them will be +dominated by those features with the largest ranges. We can correct for +that by performing feature scaling. To do that we’ll make use of +sklearn’s `RobustScaler` which uses robust statistics (such as the +median and interquartile range) to center and rescale the data feature +by feature. If we look at the results on the first five rows we see that +the different features are all now reasonably comparable, and it is +reasonable to apply something like euclidean distance across them. + +```python3 +scaled_numeric = RobustScaler().fit_transform(numeric) +scaled_numeric[:5] + + + + +``` + +``` +array([[-0.734375 , -0.66666667, -0.95628415, -0.95054945, -0.97345133], + [-0.765625 , 1.33333333, -0.98907104, -1.02747253, -1.07964602], + [-0.734375 , 2.66666667, -0.90163934, -0.9010989 , -1.07964602], + [-0.640625 , 0.33333333, -0.81967213, -0.81318681, -0.79646018], + [-0.609375 , 0.33333333, -0.7431694 , -0.74725275, -0.69026549]]) + + + +``` + +What is the best way to handle the categorical features? If they are +purely categorical it would make sense to one-hot encode the categories +and use “dice” distance between them. A downside of that is that, with +so few categories, it is a very coarse metric which will fail to provide +much differentiation. For the diamonds dataset, however, the categories +come with a strict order: Ideal cut is better than Premium cut, which is +better than Very Good cut and so on. Color grades work similarly, and +there is a distinct grading scheme for clarity as well. We can use an +ordinal encoding on these categories. Now, while the *ranges* of values +may vary, the differences between them are all comparable – a difference +of 1 for each grade level. That means we don’t need to rescale this data +after the ordinal coding. + +```python3 +ordinal["cut"] = ordinal.cut.map({"Fair":0, "Good":1, "Very Good":2, "Premium":3, "Ideal":4}) +ordinal["color"] = ordinal.color.map({"D":0, "E":1, "F":2, "G":3, "H":4, "I":5, "J":6}) +ordinal["clarity"] = ordinal.clarity.map({"I1":0, "SI2":1, "SI1":2, "VS2":3, "VS1":4, "VVS2":5, "VVS1":6, "IF":7}) + +``` + +```python3 +ordinal + + + + +``` + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + +As noted we can use euclidean as a sensible distance on the rescaled +numeric data. On the other hand since the different ordinal categories +are entirelty independent of each other, and we have a strict ordinal +codin, the socalled “manhattan” metric makes more sense here – it is +simply the sum of the absolute differences in each category. As before +we can now train UMAP models on each dataset – this time, however, since +the datasets are different we need different metrics and even different +values of `n_neighbors`. + +```python3 +numeric_mapper = umap.UMAP(n_neighbors=15, random_state=42).fit(scaled_numeric) +ordinal_mapper = umap.UMAP(metric="manhattan", n_neighbors=150, random_state=42).fit(ordinal.values) + +``` + +We can look at the results of each of these independent views of the +dataset reduced to 2D using UMAP. Let’s first look at the numeric data +on size and weight of the diamonds. We can colour by the price to get +some idea of how the dataset fits together. + +```python3 +umap.plot.points(numeric_mapper, values=diamonds["price"], cmap="viridis") + +``` + +![Image](images/composing_models_36_1.png) + +We see that while the data generally correlates somewhat with the price +of the diamonds there are distinctly different threads in the data, +presumably corresponding to different styles of cut, and how that +results in different sizing of diamonds in the various dimensions, +depending on the weight. + +In contrast we ca look at the ordinal data. In this case we’ll colour it +by the different categories as well as by price. + +```python3 +fig, ax = umap.plot.plt.subplots(2, 2, figsize=(12,12)) +umap.plot.points(ordinal_mapper, labels=diamonds["color"], ax=ax[0,0]) +umap.plot.points(ordinal_mapper, labels=diamonds["clarity"], ax=ax[0,1]) +umap.plot.points(ordinal_mapper, labels=diamonds["cut"], ax=ax[1,0]) +umap.plot.points(ordinal_mapper, values=diamonds["price"], cmap="viridis", ax=ax[1,1]) + +``` + +![Image](images/composing_models_38_1.png) + +As you can see this is a markedly different result! The ordinal data has +a relatively coarse metric, since the different categories can only take +on a small range of discrete values. This means that, with respect to +the trio of color, cut, and clarity, diamonds are largely either almost +identical, or quite distinct. The result is very tight groupings which +have very high density. You can see a gradient of color from left to +right in the plot; colouring by cut or clarity show different +stratifications. The combination of these very distinct statifications +results in this highly clustered embedding. It is exactly for this +reason that we need such a high `n_neighbors` value: the very local +structure of the data is merely clusters of identical categories; we +need to see wider to learn more structure. + +Given these radically different views of the data, what do we get if we +try to integrate them together? As before we can use the intersection +and union operators to simply combine the models. As noted before this +is a somewhat time-consuming operation as a new 2D representation for +the combined models needs to be optimized. + +```python3 +intersection_mapper = numeric_mapper * ordinal_mapper +union_mapper = numeric_mapper + ordinal_mapper + +``` + +Let’s start by looking at the intersection; here we are only really +decreasing connectivity since edges are assigned the probability of +existing in *both* data views (before re-asserting local connectivity +and uniform distribution assumptions). + +```python3 +umap.plot.points(intersection_mapper, values=diamonds["price"], cmap="viridis") + +``` + +![Image](images/composing_models_42_1.png) + +What we get most closely represents the numeric data view. Why is this? +Because the categorical data view has points either connected with +certainty (because they are, or are nearly, identical) or very loosely. +The points connected with near certainty are very dense clusters – +almost points in the plot – and mostly what we are doing with the +intersection is breaking up those clusters with the more fine-grained +and variable connectivity provided by the numerical data. At th esame +time we have shifted the result significantly from the numerical data +view on its own; the categorical information has made each cluster more +uniform (rather than being a gradient) in its price. + +Given this result, what would you expect of the union? + +```python3 +umap.plot.points(union_mapper, labels=diamonds["color"]) + +``` + +![Image](images/composing_models_44_1.png) + +What we get in practice looks a lot more like the categorical view of +the data. This time we are only *increasing* the connectivity (prior to +re-asserting local connectivity and uniform distribution assumptions); +thus we retain most of the structure of the high-connectivity +categorical view. Note, however, that we have created more connected and +coherent clusters in the center of the plot, showing a range of diamond +colors, and the introduction of the numerical size and weight +information has induced a rearrangement of the individual clusters +around the fringes. + +We can go a step further and experiment with the contrast composition +method. + +```python3 +contrast_mapper = numeric_mapper - ordinal_mapper + +``` + +```python3 +umap.plot.points(contrast_mapper, values=diamonds["price"], cmap="viridis") + +``` + +![Image](images/composing_models_47_1.png) + +Here we see that we’ve retained a lot of the structure of the numeric +data view, but have refined and broken it down further into clear +clusters with price gradients running through each of them. + +To further demonstrate the power of this approach we can go a step +further and intersect a higher `n_neighbors` based embedding of the +numeric data view with our existing union of numeric and categorical +data – providing a model that is a composition of three simpler models. + +```python3 +intersect_union_mapper = umap.UMAP(random_state=42, n_neighbors=60).fit(numeric) * union_mapper + +``` + +```python3 +umap.plot.points(intersect_union_mapper, values=diamonds["price"], cmap="viridis") + +``` + +![Image](images/composing_models_50_1.png) + +Here the greater global structure from the larger `n_neighbors` value +glues together longer strands and we get an interesting result out. In +this case it is not necessarily particularly informative, but it is +included as a demonstration that even composed models can be composed +with each other, stacking together potentially many different views. diff --git a/docs/densmap_demo.md b/docs/densmap_demo.md new file mode 100644 index 00000000..5b91fb67 --- /dev/null +++ b/docs/densmap_demo.md @@ -0,0 +1,398 @@ +# Better Preserving Local Density with DensMAP + + +A notable assumption in UMAP is that the data is uniformly distributed +on some manifold and that it is ultimately this manifold that we would +like to present. This is highly effective for many use cases, but it can +be the case that one would like to preserve more information about the +relative local density of data. A recent paper presented a technique +called +[DensMAP](https://www.biorxiv.org/content/10.1101/2020.05.12.077776v1) +that computes estimates of the local density and uses those estimates as +a regularizer in the optimization of the low dimensional representation. +The details are well explained in `the +paper `__ +and we encourage those curious about the details to read it. The result +is a low dimensional representation that preserves information about the +relative local density of the data. To see what this means in practice +let’s load some modules and try it out on some familiar data. + +```python3 +import sklearn.datasets +import umap +import umap.plot + +``` + +For test data we will make use of the now familiar (see earlier tutorial +sections) MNIST and Fashion-MNIST datasets. MNIST is a collection of +70,000 gray-scale images of hand-written digits. Fashion-MNIST is a +collection of 70,000 gray-scale images of fashion items. + +```python3 +mnist = sklearn.datasets.fetch_openml("mnist_784") +fmnist = sklearn.datasets.fetch_openml("Fashion-MNIST") + +``` + +Before we try out DensMAP let’s run standard UMAP so we have a baseline +to compare to. We’ll start with MNIST digits. + +```python3 +%%time +mapper = umap.UMAP(random_state=42).fit(mnist.data) + + +``` + +``` +CPU times: user 2min, sys: 15 s, total: 2min 15s +Wall time: 1min 43s + + +``` + +```python3 +umap.plot.points(mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_6_1.png) + +Now let’s try running DensMAP instead. In practice this is as easy as +adding the parameter `densmap=True` to the UMAP constructor – this +will cause UMAP to use DensMAP regularization with the default DensMAP +parameters. + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, random_state=42).fit(mnist.data) + + +``` + +``` +CPU times: user 3min 42s, sys: 12.9 s, total: 3min 55s +Wall time: 2min 20s + + +``` + +Note that this is a little slower than standard UMAP – there is a little +more work to be done. It is worth noting, however, that the DensMAP +overhead is relatively constant, so the difference in runtime won’t +increase much as you scale out DensMAP to larger datasets. + +Now let’s see what sort of results we get: + +```python3 +umap.plot.points(dens_mapper, labels=mnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_10_1.png) + +This is a significantly different result – although notably the same +groupings of digits and overall structure have resulted. The most +striking aspects are that the ones cluster has be compressed into a very +narrow and dense stripe, while other digit clusters, most notably the +zeros and the twos have expanded out to fill more space in the plot. +This is due to the fact that in the high dimensional space the ones are +indeed more densely packed together, with largely only variation along +one dimension (the angle with which the stroke of the one is drawn). In +contrast a digit like the zero has a lot more variation (rounder, +narrower, taller, shorter, sloping one way or another); this results in +less local density in high dimensional space, and this lack of local +density has been preserved by DensMAP. + +Let’s now look at the Fashion-MNIST dataset; as before we’ll start by +reminding ourselves what the default UMAP results look like: + +```python3 +%%time +mapper = umap.UMAP(random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 1min 6s, sys: 8.66 s, total: 1min 15s +Wall time: 49.8 s + + +``` + +```python3 +umap.plot.points(mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_13_1.png) + +Now let’s try running DensMAP. As before that is as simple as setting +the `densmap=True` flag. + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 3min 48s, sys: 8.07 s, total: 3min 56s +Wall time: 2min 21s + + +``` + +```python3 +umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_16_1.png) + +Again we see that DensMAP provides a plot similar to UMAP broadly, but +with striking differences. Here we get to see that the cluster of bags +(label 8 in blue) is actually quite sparse, while the cluster of pants +(label 1 in red) is actually quite dense with little variation compared +to other categories. We even see information internal to clusters. +Consider the cluster of boots (label 9 in violet): at the top end it is +quite dense, but it fades out into a much sparse region. + +So far we have used DensMAP with default parameters, but the +implementation provides several parameters for adjusting exactly how the +local density regularisation is handled. We encourage readers to consult +the paper for the details of the many parameters available. For general +use the main parameter of interest is called `dens_lambda` and it +controls how strongly the local density regularisation acts. Larger +values of `dens_lambda` with make preserving the local density a +priority over the the standard UMAP objective, while smaller values lean +more towards classical UMAP. The default value is 2.0. Let’s play with +it a little so we can see the effects of varying it. To start we’ll use +a higher `dens_lambda` of 5.0: + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, dens_lambda=5.0, random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 3min 47s, sys: 5.04 s, total: 3min 52s +Wall time: 2min 18s + + +``` + +```python3 +umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_19_1.png) + +This looks kind of like what we had before, but blurrier. And also … +smaller? The plot bounds are set by the data, so the fact that it is +smaller represents the fact that there are some points right out to the +edges of the plot. These are likely points that are in locally very +sparse regions of the high dimensional space and are thus pushed well +away from everything else. We can see this better if we use raw +matplotlib and a scatter plot with larger point size: + +```python3 +fig, ax = umap.plot.plt.subplots(figsize=(7,7)) +ax.scatter(*dens_mapper.embedding_.T, c=fmnist.target.astype('int8'), cmap="Spectral", s=1) + +``` + +![Image](images/densmap_demo_21_1.png) + +Aside from seeing the issues with overplotting we can see that there +are, in fact, quite a few points that create a very soft halo of of +sparse points around the fringes. + +Now let’s try going the other way and reduce `dens_lambda` to a small +value, so that in principle we can recover something quite close to the +default UMAP plot, with just a hint of local density information +encoded. + +```python3 +%%time +dens_mapper = umap.UMAP(densmap=True, dens_lambda=0.1, random_state=42).fit(fmnist.data) + + +``` + +``` +CPU times: user 3min 47s, sys: 3.78 s, total: 3min 51s +Wall time: 2min 16s + + +``` + +```python3 +umap.plot.points(dens_mapper, labels=fmnist.target, width=500, height=500) + +``` + +![Image](images/densmap_demo_24_1.png) + +And indeed, this looks very much like the original plot, but the bags +(label 8 in blue) are slightly more diffused, and the pants (label 1 in +red) are a little denser. This is very much the default UMAP with just a +tweak to better reflect some notion of local density. + +## Supervised DensMAP on the Galaxy10SDSS dataset + + +The [Galaxy10SDSS dataset](https://astronn.readthedocs.io/en/latest/galaxy10sdss.html) +is a crowd sourced human labelled dataset of galaxy images, +which have been separated in to ten classes. DensMAP can +learn an embedding that partially separates the data. To +keep runtime small, DensMAP is applied to a subset of the +data. + +```python3 +import numpy as np +import h5py +import matplotlib.pyplot as plt +import umap +import os +import math +import requests + +if not os.path.isfile("Galaxy10.h5"): + url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" + r = requests.get(url, allow_redirects=True) + open("Galaxy10.h5", "wb").write(r.content) + +# To get the images and labels from file +with h5py.File("Galaxy10.h5", "r") as F: + images = np.array(F["images"]) + labels = np.array(F["ans"]) + +X_train = np.empty([math.floor(len(labels) / 100), 14283], dtype=np.float64) +y_train = np.empty([math.floor(len(labels) / 100)], dtype=np.float64) +X_test = X_train +y_test = y_train +# Get a subset of the data +for i in range(math.floor(len(labels) / 100)): + X_train[i, :] = np.array(np.ndarray.flatten(images[i, :, :, :]), dtype=np.float64) + y_train[i] = labels[i] + X_test[i, :] = np.array( + np.ndarray.flatten(images[i + math.floor(len(labels) / 100), :, :, :]), + dtype=np.float64, + ) + y_test[i] = labels[i + math.floor(len(labels) / 100)] + +# Plot distribution +classes, frequency = np.unique(y_train, return_counts=True) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.bar(classes, frequency) +plt.xlabel("Class") +plt.ylabel("Frequency") +plt.title("Data Subset") +plt.savefig("galaxy10_subset.svg") + + + +``` + +![Image](images/galaxy10_subset.svg) + +The figure shows that the selected subset of the data set is +unbalanced, but the entire dataset is also unbalanced, so +this experiment will still use this subset. The next step is +to examine the output of the standard DensMAP algorithm. + +```python3 +reducer = umap.UMAP( + densmap=True, n_components=2, random_state=42, verbose=False +) +reducer.fit(X_train) + +galaxy10_densmap = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_densmap[:, 0], + galaxy10_densmap[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_densmap.svg") + + + +``` + +![Image](images/galaxy10_2D_densmap.svg) + +The standard DensMAP algorithm does not separate the galaxies +according to their type. Supervised DensMAP can do better. + +```python3 +reducer = umap.UMAP( + densmap=True, n_components=2, random_state=42, verbose=False +) +reducer.fit(X_train, y_train) + +galaxy10_densmap_supervised = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_densmap_supervised[:, 0], + galaxy10_densmap_supervised[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_densmap_supervised.svg") + + + +``` + +![Image](images/galaxy10_2D_densmap_supervised.svg) + +Supervised DensMAP does indeed do better. There is a litle overlap +between some of the classes, but the original dataset +also has some ambiguities in the classification. The best +check of this method is to project the testing data onto the +learned embedding. + +```python3 +galaxy10_densmap_supervised_prediction = reducer.transform(X_test) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_densmap_supervised_prediction[:, 0], + galaxy10_densmap_supervised_prediction[:, 1], + c=y_test, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_test, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_densmap_supervised_prediction.svg") + + + +``` + +![Image](images/galaxy10_2D_densmap_supervised_prediction.svg) + +This shows that the learned embedding can be used on new data +sets, and so this method may be helpful for examining images +of galaxies. Try out this method on the full 200 Mb dataset +as well as the newer 2.54 Gb +[Galaxy 10 DECals dataset](https://astronn.readthedocs.io/en/latest/galaxy10.html) diff --git a/docs/development_roadmap.md b/docs/development_roadmap.md new file mode 100644 index 00000000..9f8e6193 --- /dev/null +++ b/docs/development_roadmap.md @@ -0,0 +1,492 @@ +================ +Development Roadmap +================ + +This document outlines planned improvements and modernizations for the UMAP codebase. + +# Major Initiatives + + +## 1. Parametric UMAP: TensorFlow → PyTorch Migration + + +**Status**: Planned + +**Motivation**: +- PyTorch has become the standard in modern machine learning +- Better community support and ecosystem for deep learning +- Easier to integrate with modern training frameworks +- TensorFlow 2.x dependency (currently >= 2.1) is outdated and should be >= 2.10+ +- PyTorch provides better control over computation graphs and optimization + +**Scope**: +- Replace TensorFlow/Keras-based parametric UMAP implementation with PyTorch +- Update parametric_umap.py and related modules +- Migrate all Keras models to PyTorch equivalents +- Update all Parametric UMAP example notebooks to use PyTorch + +**Implementation Plan**: +1. Create PyTorch equivalents of all TensorFlow/Keras models +2. Implement PyTorch training loops with equivalent loss functions +3. Add PyTorch as an optional dependency +4. Maintain TensorFlow version temporarily for backward compatibility +5. Add comprehensive testing for PyTorch implementation +6. Update all documentation and examples +7. Deprecate TensorFlow implementation in a future release +8. Remove TensorFlow support in a major version bump + +**Files to Modify**: +- umap/parametric_umap.py (core implementation) +- notebooks/Parametric_UMAP/*.ipynb (all examples) +- doc/parametric_umap.rst (documentation) +- pyproject.toml (dependencies) +- tests/ (add PyTorch tests) + +**Estimated Effort**: Large (multiple weeks) + +**Priority**: High + +--- + +## 2. Remove Conda from Installation and CI/CD + + +**Status**: Planned + +**Motivation**: +- pip and modern Python packaging (PyPI) is the standard +- Removes redundant installation method +- Simplifies CI/CD pipelines +- Reduces maintenance burden +- conda-forge is maintained by volunteers; we should focus on PyPI + +**Scope**: +- Remove conda installation instructions from README and documentation +- Remove conda badges from README +- Remove conda-based CI/CD scripts and configurations +- Update all installation documentation +- Remove references to anaconda and conda in setup instructions +- Clean up ci_scripts/install.sh (currently uses conda) + +**Implementation Plan**: +1. Update README.rst to remove conda install instructions +2. Update doc/index.rst to remove conda installation section +3. Remove conda references from ci_scripts/ +4. Remove .travis.yml and appveyor.yml (legacy CI configurations) +5. Ensure all installation docs reference pip/PyPI only +6. Add documentation for development setup using pip and venv/virtualenv +7. Update any notebooks that show conda install commands + +**Files to Modify**: +- README.rst +- doc/index.rst +- doc/parameters.rst +- ci_scripts/install.sh +- .travis.yml (remove or archive) +- appveyor.yml (remove or archive) +- Notebooks showing conda instructions + +**Estimated Effort**: Small (1-2 days) + +**Priority**: Medium + +--- + +## 3. Remove pynndescent Dependency + + +**Status**: Planned + +**Motivation**: +- pynndescent is an external dependency created for UMAP but not tightly integrated +- Adds a required external dependency that complicates the dependency tree +- Reduces coupling and external dependencies improves maintainability +- pynndescent can remain optional for users who want advanced features, but core UMAP should work with standard libraries +- Industry-standard vector databases like Qdrant use alternative approaches (HNSW, etc.) + +**Reference Implementations**: +- **Qdrant**: Production vector database using HNSW + - See architecture: https://github.com/qdrant/qdrant/tree/master/src + - Uses HNSW (Hierarchical Navigable Small World) for production-grade nearest neighbor search + - 10x+ faster than alternatives on large datasets + +- **HGG (Hierarchical Greedy Graph)**: Alternative to HNSW from rust-cv + - See implementation: https://github.com/rust-cv/hgg + - Data-dependent hierarchy (adapts to local dimensionality) + - Fully deterministic (unlike HNSW which uses randomness) + - More efficient edge management and freshening process + - Written in Rust, but algorithm principles apply to Python implementation + +- **ANN-Benchmarks**: Comprehensive benchmark suite for nearest neighbor algorithms + - See benchmarks: https://ann-benchmarks.com/index.html + - See source & README: https://github.com/erikbern/ann-benchmarks/?tab=readme-ov-file + - Real-world datasets and performance comparisons + - Tracks speed vs recall tradeoffs across all major algorithms + - Essential resource for validating implementation choices + +**Scope**: +- **Replace pynndescent with pluggable ANN backend system** supporting multiple algorithms +- Integrate many ANN algorithms to discover the fastest options for different use cases +- Reference all algorithms from ann-benchmarks.com: HNSW, Annoy, FAISS, NGT, NSW, Vespa, Milvus, etc. +- Implement missing distance metrics that pynndescent provides +- Handle both dense and sparse nearest neighbor computation +- Make pynndescent optional for advanced/performance-critical use cases +- Achieve equivalent or better performance than current pynndescent implementation +- **Goal**: Find the optimal ANN algorithm for different scenarios (speed, memory, accuracy, determinism) + +**Alternative Implementations Comparison**: + +================ ==================== ==================== ==================== ==================== ==================== +Criteria scikit-learn HNSWlib FAISS Annoy HGG +================ ==================== ==================== ==================== ==================== ==================== +Type KDTree/BallTree Graph (HNSW) Clustering/HNSW Random projections Graph (Greedy) +Speed Good (small data) Excellent Excellent Good Excellent +GPU Support No No Yes (major feature) No No +Dynamic Updates Yes Yes Limited No (rebuild) Yes +Distance Metrics Many Common Many Limited Common +Memory Efficient Yes Moderate Yes Yes Moderate +Deterministic Yes No (randomized) Yes Yes Yes (fully) +Maintenance Well-maintained Active Well-maintained Active Active +Dependencies NumPy only Minimal Minimal Minimal Rust (native) +================ ==================== ==================== ==================== ==================== ==================== + +**Recommended Approach**: +1. **Default (no new deps)**: Use scikit-learn's KDTree/BallTree for standard cases + - Sufficient for small to medium datasets + - No additional dependencies + - Good for most use cases + +2. **Optional (with deps)**: Allow users to opt-in to HNSWlib, FAISS, or HGG for large-scale deployments + - HNSWlib: Best for production use (Qdrant uses this, industry-standard) + - FAISS: Best for GPU-accelerated workloads + - HGG: Best for reproducibility and data-dependent optimization (fully deterministic) + - All have minimal Python dependencies + +3. **Fallback Strategy**: Graceful degradation + - Use sklearn by default + - Warn if user tries to use pynndescent features + - Auto-upgrade to HNSWlib if available and beneficial for large datasets + - Consider HGG for users requiring deterministic behavior + +**Implementation Plan**: +1. Audit all pynndescent usage in the codebase +2. Identify distance metrics that pynndescent provides that sklearn doesn't +3. Implement missing distance metrics using scipy.spatial.distance or native code +4. **Create pluggable ANN backend abstraction layer** (umap/nndescent.py) + - Design clean interface for swapping different ANN algorithms + - Support dynamic algorithm selection at runtime + - Handle algorithm-specific parameters and tuning +5. **Implement support for multiple ANN algorithms** (from ann-benchmarks.com): + - Default: scikit-learn KDTree/BallTree (no new deps) + - Optional: HNSWlib, FAISS, Annoy, NGT, NSW, HGG + - Design: Start with top performers, expand as needed +6. Implement fallback and distance metric handling for all backends +7. **Implement comprehensive benchmark suite with automation**: + - Set up script → JSON → graph pipeline (rust-analyzer-metrics model) + - Run full algorithm suite benchmarks on every commit + - Compare: speed, memory usage, recall accuracy, construction time + - Validate against ann-benchmarks.com reference datasets and results + - Track algorithm performance across different data characteristics + - Generate comparative performance graphs (updated with each commit) + - Identify optimal algorithms for different use cases + - Ensure no performance regressions during development +8. Update tests to work without pynndescent as required dependency +9. Make pynndescent optional in pyproject.toml (keep for users who want it) +10. Add algorithm selection guide in documentation + - Recommendations based on: dataset size, speed requirements, reproducibility needs + - Performance profiles from benchmarks + - Trade-off analysis: speed vs memory vs accuracy +11. Add runtime configuration for algorithm selection (config file or environment variables) + +**Files to Modify**: +- umap/umap_.py (main implementation - remove pynndescent imports) +- umap/distances.py (implement missing distance metrics) +- umap/sparse.py (handle sparse nearest neighbors) +- umap/nndescent.py (new abstraction layer for NN backends) +- pyproject.toml (move pynndescent to optional, add HNSWlib as optional) +- README.rst (update requirements and performance notes) +- tests/ (ensure all tests pass without pynndescent) +- doc/ (add backend selection guide) +- **benchmarks/** (new: benchmark suite infrastructure) + +**Benchmark Infrastructure** (Ideal for Benchmark-on-Commit Discovery Approach): +This task is an excellent candidate for implementing automated benchmarking infrastructure to discover optimal algorithms: + +- **Algorithm Suite**: Test all major ANN algorithms from ann-benchmarks.com + - Start with: sklearn, HNSWlib, FAISS, Annoy, HGG + - Expand to: NGT, NSW, Vespa, Milvus, others based on availability +- **Metrics Tracking** (JSON output): + - Speed: query time, construction time + - Memory: index size, working memory during search + - Accuracy: recall, precision on different datasets + - Scalability: performance vs dataset size +- **Automated Benchmarking on Every Commit**: + - Run full algorithm comparison on each commit to benchmark branch + - Compare against ann-benchmarks.com reference results + - JSON → CSV → automatic graph generation + - Track performance trends over time +- **Discovery & Optimization**: + - Identify which algorithms are fastest for different scenarios + - Visualize speed vs memory vs accuracy trade-offs + - Find algorithm "sweet spots" for common UMAP use cases + - Auto-suggest best algorithm based on data characteristics + +This enables: +- **Real-time algorithm discovery**: See which algorithms perform best as implementations improve +- **Data-driven decisions**: No guessing - use actual performance data +- **Regression detection**: Catch performance regressions immediately +- **Optimization targeting**: Focus effort on algorithms that matter most +- **Historical analysis**: Track which algorithms win under different conditions +- **Publication-ready data**: Comprehensive benchmarks for academic papers + +**Estimated Effort**: Large (2-3 weeks for implementation + 2 weeks for benchmarking infrastructure & algorithm integration) + +**Priority**: High (reduces coupling and external dependencies) + +**Key Considerations**: +- **Algorithm Trade-offs**: Different algorithms excel in different scenarios + - HNSWlib: 10x+ faster for large datasets, randomized + - HGG: Fully deterministic, comparable speed, data-dependent + - FAISS: GPU acceleration, excellent for massive scale + - Annoy: Memory efficient, fast construction + - sklearn: Zero new dependencies, good for small-medium data + - Find optimal algorithm per use case through benchmarking +- **Determinism**: HGG fully deterministic (unlike HNSW which uses randomization), important for reproducible research +- **Sparse matrices**: scipy.sparse has limited distance metric support (critical for sparse UMAP) +- **Distance metrics**: Not all custom metrics supported by all backends - design abstraction carefully +- **Pluggable Architecture**: Design abstraction layer to make adding new algorithms trivial + - Clean interface for algorithm implementations + - Dynamic algorithm selection at runtime + - Per-algorithm configuration and tuning +- **Benchmarking Against Industry Standard**: + - Use https://ann-benchmarks.com/index.html as reference + - Validate UMAP results match ann-benchmarks.com reference implementations + - Ensure UMAP is among fastest implementations for each algorithm +- **Rust-based Options**: HGG and others could be wrapped with PyO3 for native performance +- **Research Reproducibility**: Deterministic algorithms (sklearn, HGG) valuable for scientific use cases +- **Publication Potential**: Comprehensive benchmarks comparing all algorithms is valuable for ML community +- **Performance Goals**: + - Match or exceed pynndescent speed with fewer dependencies + - Enable users to find fastest algorithm for their specific use case + - Make UMAP a reference implementation for ANN algorithm evaluation + +--- + +## 4. Code Modernization and Technical Debt Reduction + + +**Status**: In Progress + +**Completed**: +- Identified outdated patterns (see analysis below) + +**Pending**: + +### 4.1 Remove Python 2 Artifacts + + +- Remove `from __future__ import print_function` from: + - umap/umap_.py + - umap/sparse.py +- Remove explicit `object` inheritance from old-style classes +- Update any remaining Python 2 compatibility code + +**Estimated Effort**: Small (< 1 day) + +**Priority**: Medium + +--- + +### 4.2 Fix NumPy Deprecated Type Aliases + + +- Replace `np.bool_` with `bool` or `np.bool` in: + - umap/sparse.py + - umap/aligned_umap.py +- Replace other deprecated NumPy type aliases (np.int_, np.float_, etc.) +- Ensure compatibility with NumPy >= 1.20 + +**Estimated Effort**: Small (< 1 day) + +**Priority**: High + +--- + +### 4.3 Fix Bitwise Operator Misuse + + +- Replace bitwise AND (`&`) with logical AND (`and`) in boolean contexts +- Affected files: + - umap/umap_.py (lines 119, 124, 426) +- This improves code clarity and prevents subtle bugs + +**Estimated Effort**: Small (< 1 day) + +**Priority**: High + +--- + +### 4.4 Optimize Data Structures + + +- Replace list-based queue with `collections.deque` in breadth_first_search() +- This changes O(n) queue.pop(0) operations to O(1) deque.popleft() +- Location: umap/umap_.py:93 + +**Estimated Effort**: Small (< 1 day) + +**Priority**: Medium + +--- + +### 4.5 Resolve Technical Debt (FIXME/TODO Comments) + + +- Address FIXME comments in umap/layouts.py regarding uninitialized variables +- Review TODO comments about performance optimizations +- Either implement the optimizations or update comments with justification + +**Estimated Effort**: Medium (2-3 days) + +**Priority**: Medium + +--- + +### 4.6 Remove Dead Code + + +- Remove sklearn.externals.joblib fallback code (dead since sklearn >= 1.0) +- Location: umap/umap_.py + +**Estimated Effort**: Minimal (< 1 hour) + +**Priority**: Low + +--- + +### 4.7 Update Dependency Specifications + + +- Review and update minimum version constraints in pyproject.toml +- scipy: currently >= 1.3.1 (2019); should be >= 1.8.0 or higher +- numba: currently >= 0.51.2 (2020); should be >= 0.55.0 or higher +- TensorFlow (if keeping): update to >= 2.10.0 +- Ensure minimum versions are tested in CI + +**Estimated Effort**: Small (< 1 day) + +**Priority**: Low + +--- + +## 5. Documentation Updates + + +**Status**: Pending + +### 5.1 Update Installation Instructions + + +- Remove all conda references +- Clarify Python 3.9+ requirement (currently lists 3.6+) +- Add virtual environment setup instructions +- Document development setup + +**Estimated Effort**: Small (1 day) + +**Priority**: Medium + +--- + +### 5.2 Clarify Optional Dependencies + + +- Separate optional dependencies clearly: + - Plotting: matplotlib, datashader, holoviews + - Parametric UMAP: PyTorch/TensorFlow (post-migration: PyTorch only) + - Development: pytest, typing-extensions, etc. + +**Estimated Effort**: Small (1 day) + +**Priority**: Medium + +--- + +### 5.3 Update Development Status Classifier + + +- pyproject.toml currently lists "Development Status :: 3 - Alpha" +- Should be updated to "Development Status :: 5 - Production/Stable" +- UMAP has been stable for years + +**Estimated Effort**: Minimal (< 1 hour) + +**Priority**: Low + +--- + +## 6. Testing Improvements + + +**Status**: Pending + +### 6.1 Add Type Hints + + +- Add Python type hints throughout the codebase +- Enables better IDE support and static type checking +- Use tools like mypy or pyright for validation +- Priority: Core modules first (umap/umap_.py, umap/sparse.py) + +**Estimated Effort**: Large (2-3 weeks for full coverage) + +**Priority**: Medium + +--- + +### 6.2 Expand Test Coverage + + +- Add tests for edge cases and error conditions +- Add performance regression tests +- Ensure PyTorch implementation has equivalent test coverage to current + +**Estimated Effort**: Medium + +**Priority**: Medium after PyTorch migration + +--- + +# Long-term Considerations + + +1. **GPU Acceleration**: Consider native GPU support for core UMAP algorithm (not just Parametric UMAP) +2. **API Stability**: Consider stabilizing the public API with semantic versioning +3. **Performance**: Profile and optimize hot paths, especially in KNN computation +4. **Sparse Matrix Support**: Expand and optimize sparse matrix handling +5. **Distributed Computing**: Explore support for large-scale distributed embedding + +# Timeline + + +**Near-term (1-2 months)**: +- Complete code modernization (items 4.1-4.7) +- Remove conda from documentation and CI (item 2) - COMPLETED +- Remove pynndescent dependency (item 3) +- Update documentation (item 5) + +**Medium-term (2-3 months)**: +- Begin PyTorch implementation of Parametric UMAP (item 1) +- Add type hints to core modules (item 6.1) + +**Long-term (3+ months)**: +- Complete PyTorch migration (item 1) +- Full type hint coverage (item 6.1) +- Deprecate TensorFlow support + +# Contributing + + +Interested in helping with any of these initiatives? Please open an issue or pull request on the GitHub repository. + +For major work like the PyTorch migration, please discuss your approach with the maintainers first. diff --git a/docs/document_embedding.md b/docs/document_embedding.md new file mode 100644 index 00000000..1ce0195a --- /dev/null +++ b/docs/document_embedding.md @@ -0,0 +1,353 @@ +# Document embedding using UMAP + + +This is a tutorial of using UMAP to embed text (but this can be extended +to any collection of tokens). We are going to use the `20 newsgroups +dataset `__ which is a collection +of forum posts labelled by topic. We are going to embed these documents +and see that similar documents (i.e. posts in the same subforum) will +end up close together. You can use this embedding for other downstream +tasks, such as visualizing your corpus, or run a clustering algorithm +(e.g. HDBSCAN). We will use a bag of words model and use UMAP on the +count vectors as well as the TF-IDF vectors. + + +To start with let's load the relevant libraries. **This requires UMAP version >= 0.4.0.** + +```python3 +import pandas as pd +import umap +import umap.plot + +# Used to get the data +from sklearn.datasets import fetch_20newsgroups +from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer + +# Some plotting libraries +import matplotlib.pyplot as plt +%matplotlib notebook +from bokeh.plotting import show, save, output_notebook, output_file +from bokeh.resources import INLINE +output_notebook(resources=INLINE) + +``` + +Next let's download and explore the 20 newsgroups dataset. + +```python3 +%%time +dataset = fetch_20newsgroups(subset='all', + shuffle=True, random_state=42) + + +``` + +``` +CPU times: user 280 ms, sys: 52 ms, total: 332 ms +Wall time: 460 ms + +``` + +Let's see the size of the corpus: + +```python3 +print(f'{len(dataset.data)} documents') +print(f'{len(dataset.target_names)} categories') + + +``` + +``` +18846 documents +20 categories + + +``` + +Here are the categories of documents. As you can see many are related to +one another (e.g. ‘comp.sys.ibm.pc.hardware’ and +‘comp.sys.mac.hardware’) but they are not all correlated (e.g. ‘sci.med’ +and ‘rec.sport.baseball’). + +```python3 +dataset.target_names + + + + +``` + +``` +['alt.atheism', + 'comp.graphics', + 'comp.os.ms-windows.misc', + 'comp.sys.ibm.pc.hardware', + 'comp.sys.mac.hardware', + 'comp.windows.x', + 'misc.forsale', + 'rec.autos', + 'rec.motorcycles', + 'rec.sport.baseball', + 'rec.sport.hockey', + 'sci.crypt', + 'sci.electronics', + 'sci.med', + 'sci.space', + 'soc.religion.christian', + 'talk.politics.guns', + 'talk.politics.mideast', + 'talk.politics.misc', + 'talk.religion.misc'] + + + +``` + +Let’s look at a couple of sample documents: + +```python3 +for idx, document in enumerate(dataset.data[:3]): + category = dataset.target_names[dataset.target[idx]] + + print(f'Category: {category}') + print('---------------------------') + # Print the first 500 characters of the post + print(document[:500]) + print('---------------------------') + + +``` + +``` +Category: rec.sport.hockey +--------------------------- +From: Mamatha Devineni Ratnam +Subject: Pens fans reactions +Organization: Post Office, Carnegie Mellon, Pittsburgh, PA +Lines: 12 +NNTP-Posting-Host: po4.andrew.cmu.edu + + + +I am sure some bashers of Pens fans are pretty confused about the lack +of any kind of posts about the recent Pens massacre of the Devils. Actually, +I am bit puzzled too and a bit relieved. However, I am going to put an end +to non-PIttsburghers' relief with a bit of praise for the Pens. Man, they +are killin +--------------------------- +Category: comp.sys.ibm.pc.hardware +--------------------------- +From: mblawson@midway.ecn.uoknor.edu (Matthew B Lawson) +Subject: Which high-performance VLB video card? +Summary: Seek recommendations for VLB video card +Nntp-Posting-Host: midway.ecn.uoknor.edu +Organization: Engineering Computer Network, University of Oklahoma, Norman, OK, USA +Keywords: orchid, stealth, vlb +Lines: 21 + + My brother is in the market for a high-performance video card that supports +VESA local bus with 1-2MB RAM. Does anyone have suggestions/ideas on: + + - Diamond Stealth Pro Local +--------------------------- +Category: talk.politics.mideast +--------------------------- +From: hilmi-er@dsv.su.se (Hilmi Eren) +Subject: Re: ARMENIA SAYS IT COULD SHOOT DOWN TURKISH PLANES (Henrik) +Lines: 95 +Nntp-Posting-Host: viktoria.dsv.su.se +Reply-To: hilmi-er@dsv.su.se (Hilmi Eren) +Organization: Dept. of Computer and Systems Sciences, Stockholm University + + + + +\|>The student of "regional killings" alias Davidian (not the Davidian religios sect) writes: + + +\|>Greater Armenia would stretch from Karabakh, to the Black Sea, to the +\|>Mediterranean, so if you use the term "Greater Armenia +--------------------------- + + +``` + +Now we will create a dataframe with the target labels to be used in plotting. This will allow us to see the newsgroup +when we hover over the plotted points (if using interactive plotting). This will help us evaluate (by eye) how good the embedding looks. + +```python3 +category_labels = [dataset.target_names[x] for x in dataset.target] +hover_df = pd.DataFrame(category_labels, columns=['category']) + +``` + +## Using raw counts + + +Next, we are going to use a bag-of-words approach (i.e. word order doesn’t +matter) and construct a word document matrix. In this matrix the rows +will correspond to a document (i.e. post) and each column will +correspond to a particular word. The values will be the counts of how +many times a given word appeared in a particular document. + +We will use sklearns CountVectorizer function to do this for us along +with a couple other preprocessing steps: + +1) Split the text into tokens (i.e. words) by splitting on whitespace + +2) Remove english stopwords (the, and, etc) + +3) Remove all words which occur less than 5 times in the entire corpus + (via the min_df parameter) + +```python3 +vectorizer = CountVectorizer(min_df=5, stop_words='english') +word_doc_matrix = vectorizer.fit_transform(dataset.data) + +``` + +This gives us a 18846x34880 matrix where there are 18846 documents (same +as above) and 34880 unique tokens. This matrix is sparse since most +words do not appear in most documents. + +```python3 +word_doc_matrix + +``` + +``` +<18846x34880 sparse matrix of type '' + with 1939023 stored elements in Compressed Sparse Row format> + + + +``` + +Now we are going to do dimension reduction using UMAP to reduce the matrix +from 34880 dimensions to 2 dimensions (since n_components=2). We need a +distance metric and will use `Hellinger +distance `__ which +measures the similarity between two probability distributions. Each +document has a set of counts generated by a `multinomial +distribution `__ +where we can use Hellinger distance to measure the similarity of these +distributions. + +```python3 +%%time +embedding = umap.UMAP(n_components=2, metric='hellinger').fit(word_doc_matrix) + + +``` + +``` +CPU times: user 2min 24s, sys: 1.18 s, total: 2min 25s +Wall time: 2min 3s + + +``` + +Now we have an embedding of 18846x2. + +```python3 +embedding.embedding_.shape + + +``` + +``` +(18846, 2) + + +``` + +Let’s plot the embedding. If you are running this in a notebook, you should use the +interactive plotting method as it lets you hover over your points and see what +category they belong to. + +```python3 +# For interactive plotting use +# f = umap.plot.interactive(embedding, labels=dataset.target, hover_data=hover_df, point_size=1) +# show(f) +f = umap.plot.points(embedding, labels=hover_df['category']) + +``` + +![Image](images/20newsgroups_hellinger_counts.png) + +As you can see this does reasonably well. There is some separation and +groups that you would expect to be similar (such as ‘rec.sport.baseball’ +and ‘rec.sport.hockey’) are close together. The big clump in the middle +corresponds to a lot of extremely similar newsgroups like +‘comp.sys.ibm.pc.hardware’ and ‘comp.sys.mac.hardware’. + +## Using TF-IDF + + +We will now do the same pipeline with the only change being the use of +[TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) weighting. +TF-IDF gives less weight to words that appear frequently across a large +number of documents since they are more popular in general. It asserts +a higher weight to words that appear frequently in a smaller subset of +documents since they are probably important words for those documents. + +To do the TF-IDF weighting we will use sklearns TfidfVectorizer with the +same parameters as CountVectorizer above. + +```python3 +tfidf_vectorizer = TfidfVectorizer(min_df=5, stop_words='english') +tfidf_word_doc_matrix = tfidf_vectorizer.fit_transform(dataset.data) + +``` + +We get a matrix of the same size as before: + +```python3 +tfidf_word_doc_matrix + +``` + +``` +<18846x34880 sparse matrix of type '' + with 1939023 stored elements in Compressed Sparse Row format> + +``` + +Again we use Hellinger distance and UMAP to embed the documents + +```python3 +%%time +tfidf_embedding = umap.UMAP(metric='hellinger').fit(tfidf_word_doc_matrix) + + +``` + +``` +CPU times: user 2min 19s, sys: 1.27 s, total: 2min 20s +Wall time: 1min 57s + + +``` + +```python3 +# For interactive plotting use +# fig = umap.plot.interactive(tfidf_embedding, labels=dataset.target, hover_data=hover_df, point_size=1) +# show(fig) +fig = umap.plot.points(tfidf_embedding, labels=hover_df['category']) + +``` + +![Image](images/20newsgroups_hellinger_tfidf.png) + +The results look fairly similar to before but this can be a useful trick +to have in your toolbox. + +## Potential applications + + +Now that we have an embedding, what can we do with it? + +- Explore/visualize your corpus to identify topics/trends +- Cluster the embedding to find groups of related documents +- Look for nearest neighbours to find related documents +- Look for anomalous documents diff --git a/docs/embedding_space.md b/docs/embedding_space.md new file mode 100644 index 00000000..e97f0727 --- /dev/null +++ b/docs/embedding_space.md @@ -0,0 +1,596 @@ +# Embedding to non-Euclidean spaces + + +By default UMAP embeds data into Euclidean space. For 2D visualization +that means that data is embedded into a 2D plane suitable for a +scatterplot. In practice, however, there aren't really any major +constraints that prevent the algorithm from working with other more +interesting embedding spaces. In this tutorial we'll look at how to get +UMAP to embed into other spaces, how to embed into your own custom +space, and why this sort of approach might be useful. + +To start we'll load the usual selection of libraries. In this case we +will not be using the `umap.plot` functionality, but working with +matplotlib directly since we'll be generating some custom visualizations +for some of the more unique embedding spaces. + +```python3 +import numpy as np +import numba +import sklearn.datasets +import matplotlib.pyplot as plt +import seaborn as sns +from mpl_toolkits.mplot3d import Axes3D +import umap +%matplotlib inline + +``` + +```python3 +sns.set(style='white', rc={'figure.figsize':(10,10)}) + +``` + +As a test dataset we'll use the PenDigits dataset from sklearn -- +embedding into exotic spaces can be considerably more computationally +taxing, so a simple relatively small dataset is going to be useful. + +```python3 +digits = sklearn.datasets.load_digits() + +``` + +## Plane embeddings + + +Plain old plane embeddings are simple enough -- it is the default for +UMAP. Here we'll run through the example again, just to ensure you are +familiar with how this works, and what the result of a UMAP embedding of +the PenDigits dataset looks like in the simple case of embedding in the +plane. + +```python3 +plane_mapper = umap.UMAP(random_state=42).fit(digits.data) + +``` + +```python3 +plt.scatter(plane_mapper.embedding_.T[0], plane_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') + +``` + +![Image](images/embedding_space_7_1.png) + +## Spherical embeddings + + +What if we wanted to embed data onto a sphere rather than a plane? This +might make sense, for example, if we have reason to expect some sort of +periodic behaviour or other reasons to expect that no point can be +infinitely far from any other. To make UMAP embed onto a sphere we need +to make use of the `output_metric` parameter, which specifies what +metric to use for the **output** space. By default UMAP uses a Euclidean +`output_metric` (and even has a special faster code-path for this +case), but you can pass in other metrics. Among the metrics UMAP +supports is the Haversine metric, used for measuring distances on a +sphere, given in latitude and longitude (in radians). If we set the +`output_metric` to `"haversine"` then UMAP will use that to measure +distance in the embedding space. + +```python3 +sphere_mapper = umap.UMAP(output_metric='haversine', random_state=42).fit(digits.data) + +``` + +The result is the pendigits data embedded with respect to haversine +distance on a sphere. The catch is that if we visualize this naively +then we will get nonsense. + +```python3 +plt.scatter(sphere_mapper.embedding_.T[0], sphere_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') + + +``` + +![Image](images/embedding_space_11_1.png) + +What has gone astray is that under the embedding distance metric a point +at :math:`(0, \pi)` is distance zero from a point at :math:`(0, 3\pi)` +since that will wrap all the way around the equator. You'll note that +the scales on the x and y axes of the above plot go well outside the +ranges :math:`(-\pi, \pi)` and :math:`(0, 2\pi)`, so this isn't the +right representation of the data. We can, however, use straightforward +formulas to map this data onto a sphere embedded in 3d-space. + +```python3 +x = np.sin(sphere_mapper.embedding_[:, 0]) * np.cos(sphere_mapper.embedding_[:, 1]) +y = np.sin(sphere_mapper.embedding_[:, 0]) * np.sin(sphere_mapper.embedding_[:, 1]) +z = np.cos(sphere_mapper.embedding_[:, 0]) + +``` + +Now `x`, `y`, and `z` give 3d coordinates for each embedding point +that lies on the surface of a sphere. We can visualize this using +matplotlib's 3d plotting capabilities, and see that we have in fact +induced a quite reasonable embedding of the data onto the surface of a +sphere. + +```python3 +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x, y, z, c=digits.target, cmap='Spectral') + + +``` + +![Image](images/embedding_space_15_1.png) + +If you prefer a 2d plot we can convert these into lat/long coordinates +in the appropriate ranges and get the equivalent of a map projection of +the sphere data. + +```python3 +x = np.arctan2(x, y) +y = -np.arccos(z) + +``` + +```python3 +plt.scatter(x, y, c=digits.target.astype(np.int32), cmap='Spectral') + + +``` + +![Image](images/embedding_space_18_1.png) + +## Embedding on a Custom Metric Space + + +What if you have some other custom notion of a metric space that you +would like to embed data into? In the same way that UMAP can support +custom written distance metrics for the input data (as long as they can +be compiled with numba), the `output_metric` parameter can accept +custom distance functions. One catch is that, to support gradient +descent optimization, the distance function needs to return both the +distance, and a vector for the gradient of the distance. This latter +point may require a little bit of calculus on the users part. A second +catch is that it is highly beneficial to parameterize the embedding +space in a way that has no coordinate constraints -- otherwise the +gradient descent may step a point outside the embedding space, resulting +in bad things happening. This is why, for example, the sphere example +simply has points wrap around rather than constraining coordinates to be +in the appropriate ranges. + +Let's work through an example where we construct a distance metric and +gradient for a different sort of space: a +[torus](https://en.wikipedia.org/wiki/Torus). A torus is essentially +just the outer surface of a donut. We can parameterize the torus in +terms of x, y coordinates with the caveat that we can `"wrap around" +(similar to the +sphere) `__. In such a +model distances are mostly just euclidean distances, we just have to +check for which is the shorter direction -- across or wrapping around -- +and ensure we account for the equivalence of wrapping around several +times. We can write a simple function to calculate that. + +```python3 +@numba.njit(fastmath=True) +def torus_euclidean_grad(x, y, torus_dimensions=(2*np.pi,2*np.pi)): + """Standard euclidean distance. + + ..math:: + D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} + """ + distance_sqr = 0.0 + g = np.zeros_like(x) + for i in range(x.shape[0]): + a = abs(x[i] - y[i]) + if 2*a < torus_dimensions[i]: + distance_sqr += a ** 2 + g[i] = (x[i] - y[i]) + else: + distance_sqr += (torus_dimensions[i]-a) ** 2 + g[i] = (x[i] - y[i]) * (a - torus_dimensions[i]) / a + distance = np.sqrt(distance_sqr) + return distance, g/(1e-6 + distance) + +``` + +Note that the gradient just derives from the standard euclidean +gradient, we just have to check the direction according to the way we've +wrapped around to compute the distance. We can now plug that function +directly in to the `output_metric` parameter and end up embedding data +on a torus. + +```python3 +torus_mapper = umap.UMAP(output_metric=torus_euclidean_grad, random_state=42).fit(digits.data) + +``` + +As with the sphere case, a naive visualisation will look strange, due +the the wrapping around and equivalence of looping several times. But, +also just like the torus, we can construct a suitable visualization by +computing the 3d coordinates for the points using a little bit of +straightforward geometry (yes, I still had to look it up to check). + +```python3 +R = 3 # Size of the doughnut circle +r = 1 # Size of the doughnut cross-section + +x = (R + r * np.cos(torus_mapper.embedding_[:, 0])) * np.cos(torus_mapper.embedding_[:, 1]) +y = (R + r * np.cos(torus_mapper.embedding_[:, 0])) * np.sin(torus_mapper.embedding_[:, 1]) +z = r * np.sin(torus_mapper.embedding_[:, 0]) + +``` + +Now we can visualize the result using matplotlib and see that, indeed, +the data has been suitably embedded onto a torus. + +```python3 +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x, y, z, c=digits.target, cmap='Spectral') +ax.set_zlim3d(-3, 3) +ax.view_init(35, 70) + + + +``` + +![Image](images/embedding_space_26_0.png) + +And as with the torus we can do a little geometry and unwrap the torus +into a flat plane with the appropriate bounds. + +```python3 +u = np.arctan2(x,y) +v = np.arctan2(np.sqrt(x**2 + y**2) - R, z) + +``` + +```python3 +plt.scatter(u, v, c=digits.target, cmap='Spectral') + + + +``` + +![Image](images/embedding_space_29_1.png) + +## A Practical Example + + +While the examples given so far may have some use (because some data +does have suitable periodic or looping structures that we expect will be +better represented in a sphere or a torus), most data doesn't really +fall in the realm of something that a user can, apriori, expect to lie +on an exotic manifold. Are there more practical uses for the ability to +embed in other spaces? It turns out that there are. One interesting +example to consider is the space formed by 2d-Gaussian distributions. We +can measure the distance between two Gaussians (parameterized by a 2d +vector for the mean, and 2x2 matrix giving the covariance) by the +negative log of the inner product between the PDFs (since this has a +nice closed form solution, and is reasonably computable). That gives us +a metric space to embed into where samples are represented not as points +in 2d, but as Gaussian distributions in 2d, encoding some uncertainty in +how each sample in the high dimensional space is to be embedded. + +Of course we still have the issues of parameterizations that are +suitable for SGD -- requiring that the covariance matrix be symmetric +and positive definite is challenging. Instead we can parameterize the +covariance in terms of a width, height and angle, and recover the +covariance matrix from these if required. That gives us a total of 5 +components to embed into (two for the mean, 3 for parameters describing +the covariance). We can simply do this since the appropriate metric is +defined already. Note that we have to specifically pass +`n_components=5` since we need to explicitly embed into a 5 +dimensional space to support all the covariance parameters associated to +2d Gaussians. + +```python3 +gaussian_mapper = umap.UMAP(output_metric='gaussian_energy', + n_components=5, + random_state=42).fit(digits.data) + +``` + +Since we have embedded the data into a 5 dimensional space visualization +is not as trivial as it was earlier. We can get a start on visualizing +the results by looking at just the means, which are the 2d locations of +the modes of the Gaussians. A traditional scatter plot will suffice for +this. + +```python3 +plt.scatter(gaussian_mapper.embedding_.T[0], gaussian_mapper.embedding_.T[1], c=digits.target, cmap='Spectral') + + +``` + +![Image](images/embedding_space_33_1.png) + +We see that we have gotten a result similar to a standard embedding into +euclidean space, but with less clear clustering, and more points between +clusters. To get a clearer idea of what is going on it will be necessary +to devise a means to display some of the extra information contained in +the extra 3 dimensions providing covariance data. To do this it will be +helpful to be able to draw ellipses corresponding to super-level sets of +the PDF of the 2d Gaussian. We can start on this by writing a simple +function to draw ellipses on a plot accoriding to a position, a width, a +height, and an angle (since this is the format the embedding computed +the data). + +```python3 +from matplotlib.patches import Ellipse + +def draw_simple_ellipse(position, width, height, angle, + ax=None, from_size=0.1, to_size=0.5, n_ellipses=3, + alpha=0.1, color=None, + **kwargs): + ax = ax or plt.gca() + angle = (angle / np.pi) * 180 + width, height = np.sqrt(width), np.sqrt(height) + # Draw the Ellipse + for nsig in np.linspace(from_size, to_size, n_ellipses): + ax.add_patch(Ellipse(position, nsig * width, nsig * height, + angle, alpha=alpha, lw=0, color=color, **kwargs)) + +``` + +Now we can plot the data by providing a scatterplot of the centers (as +before), but overlaying that over a super-level-set ellipses of the +associated Gaussians. The obvious catch is that this will induce a lot +of over-plotting, but it will at least provide a way to start +understanding the embedding we have produced. + +```python3 +fig = plt.figure(figsize=(10,10)) +ax = fig.add_subplot(111) +colors = plt.get_cmap('Spectral')(np.linspace(0, 1, 10)) +for i in range(gaussian_mapper.embedding_.shape[0]): + pos = gaussian_mapper.embedding_[i, :2] + draw_simple_ellipse(pos, gaussian_mapper.embedding_[i, 2], + gaussian_mapper.embedding_[i, 3], + gaussian_mapper.embedding_[i, 4], + ax, color=colors[digits.target[i]], + from_size=0.2, to_size=1.0, alpha=0.05) +ax.scatter(gaussian_mapper.embedding_.T[0], + gaussian_mapper.embedding_.T[1], + c=digits.target, cmap='Spectral', s=3) + +``` + +![Image](images/embedding_space_37_1.png) + +Now we can see that the covariance structure for the points can vary +greatly, both in absolute size, and in shape. We note that many of the +points falling between clusters have much larger variances, in a sense +representing the greater uncertainty of the location of the embedding. +It is also worth noting that the shape of the ellipses can vary +significantly -- there are several very stretched ellipses, quite +distinct from many of the very round ellipses; in a sense this +represents where the uncertainty falls more along a single line for +example. + +While this plot highlights some of the covariance structure in the +outlying points, in practice the overplotting here obscures a lot of the +more interesting structure in the clusters themselves. We can try to see +this structure better by plotting only a single ellipse per point and +using a lower alpha channel value for the ellipses, making them more +translucent. + +```python3 +fig = plt.figure(figsize=(10,10)) +ax = fig.add_subplot(111) +for i in range(gaussian_mapper.embedding_.shape[0]): + pos = gaussian_mapper.embedding_[i, :2] + draw_simple_ellipse(pos, gaussian_mapper.embedding_[i, 2], + gaussian_mapper.embedding_[i, 3], + gaussian_mapper.embedding_[i, 4], + ax, n_ellipses=1, + color=colors[digits.target[i]], + from_size=1.0, to_size=1.0, alpha=0.01) +ax.scatter(gaussian_mapper.embedding_.T[0], + gaussian_mapper.embedding_.T[1], + c=digits.target, cmap='Spectral', s=3) + + +``` + +![Image](images/embedding_space_39_1.png) + +This lets us see the variation of density of clusters with respect to +the covariance structure -- some clusters have consistently very tight +covariance, while others are more spread out (and hence have, in a sense, +greater associated uncertainty. Of course we still have a degree of +overplotting even here, and it will become increasingly difficult to +tune alpha channels to make things visible. Instead what we would want +is an actual density plot, showing the the density of the sum over all +of these Gaussians. + +To do this we'll need to define some functions, whose execution will be +accelerated using numba: the evaluation of the density of a 2d Gaussian +at a given point; an evaluation of the density of a given point summing +over a set of several Gaussians; and a function to generate the density +for each point in some grid (summing only over nearby Gaussians to make +this naive approach more computable). + +```python3 +from sklearn.neighbors import KDTree + +@numba.njit(fastmath=True) +def eval_gaussian(x, pos=np.array([0, 0]), cov=np.eye(2, dtype=np.float32)): + det = cov[0,0] * cov[1,1] - cov[0,1] * cov[1,0] + if det > 1e-16: + cov_inv = np.array([[cov[1,1], -cov[0,1]], [-cov[1,0], cov[0,0]]]) * 1.0 / det + diff = x - pos + m_dist = cov_inv[0,0] * diff[0]**2 - \ + (cov_inv[0,1] + cov_inv[1,0]) * diff[0] * diff[1] + \ + cov_inv[1,1] * diff[1]**2 + return (np.exp(-0.5 * m_dist)) / (2 * np.pi * np.sqrt(np.abs(det))) + else: + return 0.0 + +@numba.njit(fastmath=True) +def eval_density_at_point(x, embedding): + result = 0.0 + for i in range(embedding.shape[0]): + pos = embedding[i, :2] + t = embedding[i, 4] + U = np.array([[np.cos(t), np.sin(t)], [np.sin(t), -np.cos(t)]]) + cov = U @ np.diag(embedding[i, 2:4]) @ U + result += eval_gaussian(x, pos=pos, cov=cov) + return result + +def create_density_plot(X, Y, embedding): + Z = np.zeros_like(X) + tree = KDTree(embedding[:, :2]) + for i in range(X.shape[0]): + for j in range(X.shape[1]): + nearby_points = embedding[tree.query_radius([[X[i,j],Y[i,j]]], r=2)[0]] + Z[i, j] = eval_density_at_point(np.array([X[i,j],Y[i,j]]), nearby_points) + return Z / Z.sum() + +``` + +Now we simply need an appropriate grid of points. We can use the plot +bounds seen above, and a grid size selected for the sake of +computability. The numpy `meshgrid` function can supply the actual +grid. + +```python3 +X, Y = np.meshgrid(np.linspace(-7, 9, 300), np.linspace(-8, 8, 300)) + +``` + +Now we can use the function defined above to compute the density at each +point in the grid, given the Gaussians produced by the embedding. + +```python3 +Z = create_density_plot(X, Y, gaussian_mapper.embedding_) + +``` + +Now we can view the result as a density plot using `imshow`. + +```python3 +plt.imshow(Z, origin='lower', cmap='Reds', extent=(-7, 9, -8, 8), vmax=0.0005) +plt.colorbar() + + +``` + +![Image](images/embedding_space_47_1.png) + +Here we see the finer structure within the various clusters, including +some of the interesting linear structures, demonstrating that this +Gaussian uncertainty based embedding has captured quite detailed and +useful information about the inter-relationships among the PenDigits +dataset. + +## Bonus: Embedding in Hyperbolic space + + +As a bonus example let's look at embedding data into hyperbolic space. +The most popular model for this for visualization is `Poincare's disk +model `__. An +example of a regular tiling of hyperbolic space in Poincare's disk model +is shown below; you may note it is similar to famous images by M.C. +Escher. + +![Image](images/Hyperbolic_tiling.png) + + +Ideally we would be able to embed directly into this Poincare disk +model, but in practice this proves to be very difficult. The issue is +that the disk has a "line at infinity" in a circle of radius one +bounding the disk. Outside of that circle things are not well defined. +As you may recall from the discussion of embedding onto spheres and +toruses it is best if we can have a parameterisation of the embedding +space that it is hard to move out of. The Poincare disk model is almost +the opposite of this -- as soon as we move outside the unit circle we +have moved off the manifold and further updates will be badly defined. +We therefore instead need a different parameterisation of hyperbolic +space that is less constrained. One option is the Poincare half-plane +model, but this, again, has a boundary that it is easy to move beyond. +The simplest option is the `hyperboloid +model `__. Under this +model we can simply move in x and y coordinates, and solve for the +corresponding z coordinate when we need to compute distances. This model +has been implemented under the distance metric `"hyperboloid"` so we +can simply use it out-of-the-box. + +```python3 +hyperbolic_mapper = umap.UMAP(output_metric='hyperboloid', + random_state=42).fit(digits.data) + +``` + +A straightforward visualization option is to simply view the x and y +coordinates we have arrived at: + +```python3 +plt.scatter(hyperbolic_mapper.embedding_.T[0], + hyperbolic_mapper.embedding_.T[1], + c=digits.target, cmap='Spectral') + + +``` + +![Image](images/embedding_space_52_1.png) + +We can also solve for the z coordinate and view the data lying on a +hyperboloid in 3d space. + +```python3 +x = hyperbolic_mapper.embedding_[:, 0] +y = hyperbolic_mapper.embedding_[:, 1] +z = np.sqrt(1 + np.sum(hyperbolic_mapper.embedding_**2, axis=1)) + +``` + +```python3 +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +ax.scatter(x, y, z, c=digits.target, cmap='Spectral') +ax.view_init(35, 80) + + + +``` + +![Image](images/embedding_space_55_0.png) + +But we can do more -- since we have embedded the data successfully in +hyperbolic space we can map the data into the Poincare disk model. This +is, in fact, a straightforward computation. + +```python3 +disk_x = x / (1 + z) +disk_y = y / (1 + z) + +``` + +Now we can visualize the data in a Poincare disk model embedding as we +first wanted. For this we simply generate a scatterplot of the data, and +then draw in the bounding circle of the line at infinity. + +```python3 +fig = plt.figure() +ax = fig.add_subplot(111) +ax.scatter(disk_x, disk_y, c=digits.target, cmap='Spectral') +boundary = plt.Circle((0,0), 1, fc='none', ec='k') +ax.add_artist(boundary) +ax.axis('off'); + + + +``` + +![Image](images/embedding_space_59_0.png) + +Hopefully this has provided a useful example of how to go about +embedding into non-euclidean spaces. This last example ideally +highlights the limitations of this approach (we really need a suitable +parameterisation), and some potential approaches to get around this: we +can use an alternative parameterisation for the embedding, and then +transform the data into the desired representation. diff --git a/docs/exploratory_analysis.md b/docs/exploratory_analysis.md new file mode 100644 index 00000000..474e7977 --- /dev/null +++ b/docs/exploratory_analysis.md @@ -0,0 +1,103 @@ +# Exploratory Analysis of Interesting Datasets + + +UMAP is a useful tool for general exploratory analysis of data -- it can provide +a unique lens through which to view data that can highlight structures and +properties hiding in data that are not as apparent when analysed with other techniques. +Below is a selection of uses cases of UMAP being used for interesting explorations +of intriguing datasets -- everything from pure math and outputs of neural networks, +to philosophy articles, and scientific texts. + +## Prime factorizations of numbers + +What would happen if we applied UMAP to the integers? First we would need a way +to express an integer in a high dimensional space. That can be done by looking +at the prime factorization of each number. Next you have to take enough numbers +to actually generate an interesting visualization. John Williamson set about doing +exactly this, and the results are fascinating. While they may not actually tell us +anything new about number theory they do highlight interesting structures +in prime factorizations, and demonstrate how UMAP can aid in interesting explorations +of datasets that we might think we know well. It's worth visiting the linked article +below as Dr. Williamson provides a rich and detailed exploration of UMAP as +applied to prime factorizations of integers. + +![Image](images/umap_primes.png) + + +[UMAP on prime factorizations](https://johnhw.github.io/umap_primes/index.md.html) + +Thanks to John Williamson. + +## Structure of Recent Philosophy + +Philosophy is an incredibly diverse subject, ranging from social and moral philosophy to +logic and philosophy of math; from analysis of ancient Greek philosophy to modern business +ethics. If we could get an overview of all the philosophy papers published in the last +century what might it look like? Maximilian Noichl provides just such an exploration, +looking at a large sampling of philosophy papers and comparing them according to their +citations. The results are intriguing, and can be explored interactively in the +viewer Maximilian built for it. + +![Image](images/structure_recent_phil.png) + + +[Structure of Recent Philosophy](https://homepage.univie.ac.at/noichlm94/full/zoom_final/index.html) + +Thanks to Maximilian Noichl. + +## Language, Context, and Geometry in Neural Networks + +Among recent developments in natural language processing is the BERT neural network +based technique for analysis of language. Among many things that BERT can do one is +context sensitive embeddings of words -- providing numeric vector representations of words +that are sensitive to the context of how the word is used. Exactly what goes on inside +the neural network to do this is a little mysterious (since the network is very complex +with many many parameters). A tram of researchers from Google set out to explore the +word embedding space generated by BERT, and among the tools used was UMAP. The linked +blog post provides a detailed and inspiring analysis of what BERT's word embeddings +look like, and how the different layers of BERT represent different aspects of language. + +![Image](images/bert_embedding.png) + + +[Language, Context, and Geometry in Neural Networks](https://pair-code.github.io/interpretability/context-atlas/blogpost/) + +Thanks to Andy Coenen, Emily Reif, Ann Yuan, Been Kim, Adam Pearce, Fernanda Viégas, and Martin Wattenberg. + +## Activation Atlas + +Understanding the image processing capabilities (and deficits!) of modern +convolutional neural networks is a challenge. Certainly these models are capable +of amazing feats in, for example, image classification. They can also be brittle +in unexpected ways, with carefully designed images able to induce otherwise +baffling mis-classifications. To better understand this researchers from +Google and OpenAI built the activation atlas -- analysing the space of activations +of a neural network. Here UMAP provides a means to compress the activation landscape +down to 2 dimensions for visualization. The result was an impressive interactive paper +in the Distill journal, providing rich visualizations and new insights into +the working of convolutional neural networks. + +![Image](images/activation_atlas.png) + + +[The Activation Atlas](https://distill.pub/2019/activation-atlas/) + +Thanks to Shan Carter, Zan Armstrong, Ludwig Schubert, Ian Johnson, and Chris Olah. + +## Open Syllabus Galaxy + +Suppose you wanted to explore the space of commonly assigned texts from Open Syllabus? That +gives you over 150,000 texts to consider. Since the texts are open you can actually analyse +the text content involved. With some NLP and neural network wizardry David McClure build +a network of such texts and then used node2vec and UMAP to generate a map of them. The result +is a galaxy of textbooks showing inter-relationships between subjects, similar and related texts, +and generally just a an interesting ladscape of science to be explored. As with some +of the other projects here David made a great interactive viewer allowing for rich exploration +of the results. + +![Image](images/syllabus_galaxy.png) + + +[Open Syllabus Galaxy](https://galaxy.opensyllabus.org/) + +Thanks to David McClure. diff --git a/docs/faq.md b/docs/faq.md new file mode 100644 index 00000000..c1efe456 --- /dev/null +++ b/docs/faq.md @@ -0,0 +1,292 @@ +# Frequently Asked Questions + + +Compiled here are a set of frequently asked questions, +along with answers. If you don't find your question listed here +then please feel free to add an +[issue on github](https://github.com/lmcinnes/umap/issues/new). +More questions are always welcome, and the authors will do +their best to answer. If you feel you have a common question +that isn't answered here then please suggest that the question +(and answer) be added to the FAQ when you file the issue. + +## Should I normalise my features? + + +The default answer is yes, but, of course, the real answer is +"it depends". If your features have meaningful relationships +with one another (say, latitude and longitude values) then +normalising per feature is not a good idea. For features that +are essentially independent it does make sense to get all the +features on (relatively) the same scale. The best way to do +this is to use +[pre-processing tools from scikit-learn](http://scikit-learn.org/stable/modules/preprocessing.html). +All the advice given there applies as sensible preprocessing +for UMAP, and since UMAP is scikit-learn compatible you +can put all of this together into a [scikit-learn pipeline](http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). + + +## Can I cluster the results of UMAP? + + +This is hard to answer well, but essentially the answer is +"yes, with care". To start with it matters what clustering +algorithm you are going to use. Since UMAP does not necessarily +produce clean spherical clusters something like K-Means +is a poor choice. I would recommend +[HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) or +similar. The catch here is that UMAP, with its uniform density +assumption, does not preserve density well. What UMAP will do, +however, is contract connected components of the manifold +together. Providing you have enough data for UMAP to +distinguish that information then you can get *useful* +clustering results out since algorithms like HDBSCAN will +easily pick out the components after applying UMAP. + +UMAP does offer significant improvements over algorithms +like t-SNE for clustering. First, by preserving more +global structure and creating meaningful separation +between connected components of the manifold on which +the data lies, UMAP offers more meaningful clusters. +Second, because it supports arbitrary embedding +dimensions, UMAP allows embedding to larger dimensional +spaces that make it more amenable to clustering. + +## The clusters are all squashed together and I can't see internal structure + + +One of UMAPs goals is to have distance between clusters of points +be meaningful. This means that clusters can end up spread out +with a fair amount of space between them. As a result the +clusters themselves can end up more visually packed together +than in, say, t-SNE. This is intended. A catch, however, is +that many plots (for example matplotlib's scatter plot with +default parameters) tend to show the clusters only as indistinct +blobs with no internal structure. The solution for this is +really a matter of tuning the plot more than anything else. + +If you are using matplotlib consider using the `s` parameter +that specifies the glyph size in scatter plots. Depending on +how much data you have reducing this to anything from 5 to +0.001 can have a notable effect. The `size` parameter in +bokeh is similarly useful (but does not need to be quite so small). + +More generally the real solution, particular with large datasets, +is to use [datashader](http://datashader.org/) for plotting. +Datashader is a plotting library that handles aggregation +of large scale data in scatter plots in a way that can better +show the underlying detail that can otherwise be lost. We +highly recommend investing the time to learn datashader for +UMAP plot particularly for larger datasets. + +## I ran out of memory. Help! + + +For some datasets the default options for approximate +nearest neighbor search can result in excessive memory use. +If your dataset is not especially large but you have found +that UMAP runs out of memory when operating on it consider +using the `low_memory=True` option, which will switch +to a slower but less memory intensive approach to computing +the approximate nearest neighbors. This may alleviate your +issues. + +## UMAP is eating all my cores. Help! + + +If run without a random seed UMAP will use numba's parallel +implementation to do multithreaded work and use many cores. +By default this will make use of as many cores as are available. +If you are on a shared machine or otherwise don't wish to +use *all* the cores at once you can restrict the number of +threads that numba uses by making use of the environment +variable `NUMBA_NUM_THREADS`; see the `numba +documentation `__ +for more details. + +## Is there GPU or multicore-CPU support? + + +There is basic multicore support as of version 0.4. +In the future it is possible that GPU support may +be added. + +There is a UMAP implementation for GPU available in +the NVIDIA RAPIDS cuML library, so if you need GPU +support that is currently the best place to go. + +## Can I add a custom loss function? + + +To allow for fast performance the SGD phase of UMAP has +been hand-coded for the specific needs of UMAP. This makes +custom loss functions a little difficult to handle. Now +that Numba (as of version 0.38) supports passing functions +it is possible that future versions of UMAP may support +such functionality. In the meantime you should definitely +look into [smallvis](https://github.com/jlmelville/smallvis), +a library for t-SNE, LargeVis, UMAP, and related algorithms. +Smallvis only works for small datasets, but provides +much greater flexibility and control. + +## Is there support for the R language? + + +Yes! A number of people have worked hard to make UMAP +available to R users. + +If you want to use the reference +implementation under the hood but want a nice R interface +then we recommend [umap](https://github.com/tkonopka/umap), +which wraps the python code with +[reticulate](https://rstudio.github.io/reticulate/). +Another reticulate interface is +[umapr](https://github.com/ropenscilabs/umapr), but it +may not be under active development. + +If you want a pure R version then we recommend +[uwot](https://github.com/jlmelville/uwot) at this time. +[umap](https://github.com/tkonopka/umap) also provides +a pure R implementation in addition to its reticulate +wrapper. + +Both umap and uwot are available on CRAN. + +## Is there a C/C++ implementation? + + +Not that we are aware of. For now Numba has done a very +admirable job of providing high performance and the +developers of UMAP have not felt the need to move to +lower level languages. At some point a multithreaded +C++ implementation may be made available, but there are +no time-frames for when that would happen. + +## I can't get UMAP to run properly! + + +There are, inevitably, a number of issues and corner cases +that can cause issues for UMAP. Some know issues that can +cause problems are: + +- UMAP doesn't currently support 32-bit Windows. + This is due to issues with Numba of that platform + and will not likely be resolved soon. Sorry :-( +- If you have pip installed an old package with a conflicting name + at any time, this can cause serious issues. You will want to purge/remove + everything umap related in your `site-packages` + directory and re-install `umap`. +- Having any files called `umap.py` in the current + directory you will have issues as that will be + loaded instead of the `umap` module. + +It is worth checking the +[issues page on github](https://github.com/lmcinnes/umap/issues) +for potential solutions. If all else fails please add an +[issue on github](https://github.com/lmcinnes/umap/issues/new). + +## What is the difference between PCA / UMAP / VAEs? + + +This is an example of an embedding for a popular Fashion MNIST dataset. + +![Comparison of PCA / UMAP / VAE embeddings](images/umap_vae_pca.png) +*Comparison of PCA / UMAP / VAE embeddings* + +Note that FMNIST is mostly a toy dataset (MNIST on steroids). +On such a simplistic case UMAP shows distillation results +(i.e. if we use its embedding in a downstream task like classification) +comparable to VAEs, which are more computationally expensive. + +By definition: + +- PCA is linear transformation, you can apply it + to mostly any kind of data in an unsupervised fashion. + Also it works really fast. For most real world tasks + its embeddings are mostly too simplistic / useless. +- VAE is a kind of encoder-decoder neural network, + trained with KLD loss and BCE (or MSE) loss + to enforce the resulting embedding to be continuous. + VAE is an extension of auto-encoder networks, + which by design should produce embeddings that are + not only relevant to actually encoding the data, but are + also smooth. + +From a more practical standpoint: + +- PCA mostly works for any reasonable dataset on a modern machine. + (up to tens or hundreds of millions of rows); +- VAEs have been shown to work only for toy datasets + and to our knowledge there was no real life useful application to + a real world sized dataset (i.e. ImageNet); +- Applying UMAP to real world tasks usually provides a good starting + point for downstream tasks (data visualization, clustering, classification) + and works reasonably fast; +- Consider a typical pipeline: high-dimensional embedding (300+) + => PCA to reduce to 50 dimensions => UMAP to reduce to 10-20 dimensions + => HDBSCAN for clustering / some plain algorithm for classification; + +Which tool should I use? + +- PCA for very large or high dimensional datasets (or maybe consider finding + a domain specific matrix factorization technique, e.g. topic modelling for texts); +- UMAP for smaller datasets; +- VAEs are mostly experimental; + +Where can I learn more? + +- While PCA is ubiquitous, you may [look](https://github.com/snakers4/playing_with_vae) + at this example comparing PCA / UMAP / VAEs; + +## How UMAP can go wrong + + +One way UMAP can go wrong is the introduction of data points that are maximally far apart +from all other points in your data set. In other words, a points nearest neighbour is maximally +far from it. A common example of this could be a point which shares no features in common +with any other point under a Jaccard distance or a point whose nearest neighbour is np.inf from +it under a continuous distance function. In both these cases UMAPs assumption of all points +lying on a connected manifold can lead us astray. From this points perspective all other points +are equally valid nearest neighbours so its k-nearest neighbour query will return a random +selection of neighbours all at this maximal distance. Next we will normalize this distance by +applying our UMAP kernel which says that a point should be maximally similar to it's nearest neighbour. +Since all k-nearest neighbours are identically far apart they will all be considered maximally +similar by our point in question. When we try to embed our data into a low dimensional space +our optimization will attempt to pull all these randomly selected points together. Add a +sufficiently large number of these points and our entire space gets pulled together destroying +any of the structure we had hoped to identify. + +To circumvent this problem we've added a disconnection_distance parameter to UMAP which will cut +any edge with a distance greater than the value passed in. This parameter defaults to `None`. +When set to `None` the disconnection_distance will be set to the maximal value for any of our +supported bounded metrics and otherwise set to np.inf. Removing these edges from the UMAP graph +will disconnect our manifold and cause these points to start where they are initialized and get pushed +away from all other points via the our optimization. + +If a user has a good understanding of their distance metric they can set this value by hand to prevent +data in particularly sparse regions of their space from becoming connected to their manifold. + +If vertices in your graph are disconnected a warning message will be thrown. At that point a user can +make use of the umap.utils.disconnected_vertices() function to identify the disconnected points. +This can be used either for filtering and retraining a new UMAP model or simple to bed used as a +filter for visualization purposes as seen below. + +```python3 +umap_model = umap.UMAP().fit(data) +disconnected_points = umap.utils.disconnected_vertices(umap_model) +umap.plot.points(umap_model, subset_points=~disconnected_points) + +``` + +## Successful use-cases + + +UMAP can be / has been successfully applied to the following domains: + +- Single cell data visualization in biology; +- Mapping malware based on behavioural data; +- Pre-processing phrase vectors for clustering; +- Pre-processing image embeddings (Inception) for clustering; + +and many more -- if you have a successful use-case please submit +a pull request adding it to this list! diff --git a/docs/how_umap_works.md b/docs/how_umap_works.md new file mode 100644 index 00000000..38feb049 --- /dev/null +++ b/docs/how_umap_works.md @@ -0,0 +1,521 @@ + + +# How UMAP Works + + +UMAP is an algorithm for dimension reduction based on manifold learning +techniques and ideas from topological data analysis. It provides a very +general framework for approaching manifold learning and dimension +reduction, but can also provide specific concrete realizations. This +article will discuss how the algorithm works in practice. There exist +deeper mathematical underpinnings, but for the sake of readability by a +general audience these will merely be referenced and linked. If you are +looking for the mathematical description please see the `UMAP +paper `__. + +To begin making sense of UMAP we will need a little bit of mathematical +background from algebraic topology and topological data analysis. This +will provide a basic algorithm that works well in theory, but +unfortunately not so well in practice. The next step will be to make use +of some basic Riemannian geometry to bring real world data a little +closer to the underlying assumptions of the topological data analysis +algorithm. Unfortunately this will introduce new complications, which +will be resolved through a combination of deep math (details of which +will be elided) and fuzzy logic. We can then put the pieces back +together again, and combine them with a new approach to finding a low +dimensional representation more fitting to the new data structures at +hand. Putting this all together we arrive at the basic UMAP algorithm. + +## Topological Data Analysis and Simplicial Complexes + + +Simplicial complexes are a means to construct topological spaces out of +simple combinatorial components. This allows one to reduce the +complexities of dealing with the continuous geometry of topological +spaces to the task of relatively simple combinatorics and counting. This +method of taming geometry and topology will be fundamental to our +approach to topological data analysis in general, and dimension +reduction in particular. + +The first step is to provide some simple combinatorial building blocks +called [*simplices*](https://en.wikipedia.org/wiki/Simplex). +Geometrically a simplex is a very simple way to build a +:math:`k`-dimensional object. A :math:`k` dimensional simplex is called +a :math:`k`-simplex, and it is formed by taking the convex hull of +:math:`k+1` independent points. Thus a 0-simplex is a point, a 1-simplex +is a line segment (between two zero simplices), a 2-simplex is a +triangle (with three 1-simplices as "faces"), and a 3-simplex is a +tetrahedron (with four 2-simplices as "faces"). Such a simple +construction allows for easy generalization to arbitrary dimensions. + +![Low dimensional simplices](images/simplices.png) +*Low dimensional simplices* + +This has a very simple combinatorial underlying structure, and +ultimately one can regard a :math:`k`-simplex as an arbitrary set of +:math:`k+1` objects with faces (and faces of faces etc.) given by +appropriately sized subsets -- one can always provide a "geometric +realization" of this abstract set description by constructing the +corresponding geometric simplex. + +Simplices can provide building blocks, but to construct interesting +topological spaces we need to be able to glue together such building +blocks. This can be done by constructing a `*simplicial +complex* `__. +Ostensibly a simplicial complex is a set of simplices glued together +along faces. More explicitly a simplicial complex :math:`\mathcal{K}` is +a set of simplices such that any face of any simplex in +:math:`\mathcal{K}` is also in :math:`\mathcal{K}` (ensuring all faces +exist), and the intersection of any two simplices in :math:`\mathcal{K}` +is a face of both simplices. A large class of topological spaces can be +constructed in this way -- just gluing together simplices of various +dimensions along their faces. A little further abstraction will get to +[*simplicial sets*](https://en.wikipedia.org/wiki/Simplicial_set) +which are purely combinatorial, have a nice category theoretic +presentation, and can generate a much broader class of topological +spaces, but that will take us too far afield for this article. The +intuition of simplicial complexes will be enough to illustrate the +relevant ideas and motivation. + +How does one apply these theoretical tools from topology to finite sets +of data points? To start we'll look at how one might construct a +simplicial complex from a topological space. The tool we will consider is +the construction of a `Čech +complex `__ given an +open cover of a topological space. That's a lot of verbiage if you +haven't done much topology, but we can break it down fairly easily for +our use case. An open cover is essentially just a family of sets whose +union is the whole space, and a Čech complex is a combinatorial way to +convert that into a simplicial complex. It works fairly simply: let each +set in the cover be a 0-simplex; create a 1-simplex between two such +sets if they have a non-empty intersection; create a 2-simplex between +three such sets if the triple intersection of all three is non-empty; +and so on. Now, that doesn't sound very advanced -- just looking at +intersections of sets. The key is that the background topological theory +actually provides guarantees about how well this simple process can +produce something that represents the topological space itself in a +meaningful way (the `Nerve +theorem `__ is the relevant +result for those interested). Obviously the quality of the cover is +important, and finer covers provide more accuracy, but the reality is +that despite its simplicity the process captures much of the topology. + +Next we need to understand how to apply that process to a finite set of +data samples. If we assume that the data samples are drawn from some +underlying topological space then to learn about the topology of that +space we need to generate an open cover of it. If our data actually lie +in a metric space (i.e. we can measure distance between points) then one +way to approximate an open cover is to simply create balls of some fixed +radius about each data point. Since we only have finite samples, and not +the topological space itself, we cannot be sure it is truly an open +cover, but it might be as good an approximation as we could +reasonably expect. This approach also has the advantage that the Čech +complex associated to the cover will have a 0-simplex for each data +point. + +To demonstrate the process let's consider a test dataset like this + +![Test data set of a noisy sine wave](images/how_umap_works_raw_data.png) +*Test data set of a noisy sine wave* + +If we fix a radius we can then picture the open sets of our cover as +circles (since we are in a nice visualizable two dimensional case). The +result is something like this + +![A basic open cover of the test data](images/how_umap_works_open_cover.png) +*A basic open cover of the test data* + +We can then depict the the simplicial complex of 0-, 1-, and 2-simplices +as points, lines, and triangles + +![A simplicial complex built from the test data](images/how_umap_works_basic_graph.png) +*A simplicial complex built from the test data* + +It is harder to easily depict the higher dimensional simplices, but you +can imagine how they would fit in. There are two things to note here: +first, the simplicial complex does a reasonable job of starting to +capture the fundamental topology of the dataset; second, most of the +work is really done by the 0- and 1-simplices, which are easier to deal +with computationally (it is just a graph, in the nodes and edges sense). +The second observation motivates the `Vietoris-Rips +complex `__, +which is similar to the Čech complex but is entirely determined by the +0- and 1-simplices. Vietoris-Rips complexes are much easier to work with +computationally, especially for large datasets, and are one of the major +tools of topological data analysis. + +If we take this approach to get a topological representation then we can +build a dimension reduction algorithm by finding a low dimensional +representation of the data that has a similar topological +representation. If we only care about the 0- and 1-simplices then the +topological representation is just a graph, and finding a low +dimensional representation can be described as a `graph layout +problem `__. If one wants to use, for example, spectral methods for +graph layout then we arrive at algorithms like `Laplacian +eigenmaps `__ and [Diffusion maps](https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction#Diffusion_maps). Force directed layouts are +also an option, and provide algorithms closer to [MDS](https://en.wikipedia.org/wiki/Multidimensional_scaling) or `Sammon +mapping `__ in flavour. + +I would not blame those who have read this far to wonder why we took +such an abstract roundabout road to simply building a neighborhood-graph +on the data and then laying out that graph. There are a couple of +reasons. The first reason is that the topological approach, while +abstract, provides sound theoretical justification for what we are +doing. While building a neighborhood-graph and laying it out in lower +dimensional space makes heuristic sense and is computationally tractable, +it doesn't provide the same underlying motivation of capturing the +underlying topological structure of the data faithfully -- for that we +need to appeal to the powerful topological machinery I've hinted lies in +the background. The second reason is that it is this more abstract +topological approach that will allow us to generalize the approach and +get around some of the difficulties of the sorts of algorithms described +above. While ultimately we will end up with a process that is fairly +simple computationally, understanding *why* various manipulations matter +is important to truly understanding the algorithm (as opposed to merely +computing with it). + +## Adapting to Real World Data + + +The approach described above provides a nice theory for why a +neighborhood graph based approach should capture manifold structure when +doing dimension reduction. The problem tends to come when one tries to +put the theory into practice. The first obvious difficulty (and we can +see it even our example above) is that choosing the right radius for the +balls that make up the open cover is hard. If you choose something too +small the resulting simplicial complex splits into many connected +components. If you choose something too large the simplicial complex +turns into just a few very high dimensional simplices (and their faces +etc.) and fails to capture the manifold structure anymore. How should +one solve this? + +The dilemma is in part due to the theorem (called the `Nerve +theorem `__) that +provides our justification that this process captures the topology. +Specifically, the theorem says that the simplicial complex will be +(homotopically) equivalent to the union of the cover. In our case, +working with finite data, the cover, for certain radii, doesn't cover +the whole of the manifold that we imagine underlies the data -- it is +that lack of coverage that results in the disconnected components. +Similarly, where the points are too bunched up, our cover does cover +"too much" and we end up with higher dimensional simplices than we might +ideally like. If the data were *uniformly distributed* across the +manifold then selecting a suitable radius would be easy -- the average +distance between points would work well. Moreover with a uniform +distribution we would be guaranteed that our cover would actually cover +the whole manifold with no "gaps" and no unnecessarily disconnected +components. Similarly, we would not suffer from those unfortunate +bunching effects resulting in unnecessarily high dimensional simplices. + +If we consider data that is uniformly distributed along the same +manifold it is not hard to pick a good radius (a little above half the +average distance between points) and the resulting open cover looks +pretty good: + +![Open balls over uniformly\_distributed\_data](images/how_umap_works_uniform_distribution_cover.png) +*Open balls over uniformly\_distributed\_data* + +Because the data is evenly spread we actually cover the underlying +manifold and don't end up with clumping. In other words, all this theory +works well assuming that the data is uniformly distributed over the +manifold. + +Unsurprisingly this uniform distribution assumption crops up elsewhere +in manifold learning. The proofs that Laplacian eigenmaps work well +require the assumption that the data is uniformly distributed on the +manifold. Clearly if we had a uniform distribution of points on the +manifold this would all work a lot better -- but we don't! Real world +data simply isn't that nicely behaved. How can we resolve this? By +turning the problem on its head: assume that the data is uniformly +distributed on the manifold, and ask what that tells us about the +manifold itself. If the data *looks* like it isn't uniformly distributed +that must simply be because the notion of distance is varying across the +manifold -- space itself is warping: stretching or shrinking according +to where the data appear sparser or denser. + +By assuming that the data is uniformly distributed we can actually +compute (an approximation of) a local notion of distance for each point +by making use of a little standard `Riemannian +geometry `__. In +practical terms, once you push the math through, this turns out to mean +that a unit ball about a point stretches to the *k*-th nearest neighbor +of the point, where *k* is the sample size we are using to approximate +the local sense of distance. Each point is given its own unique distance +function, and we can simply select balls of radius one with respect to +that local distance function! + +![Open balls of radius one with a locally varying metric](images/how_umap_works_local_metric_open_cover.png) +*Open balls of radius one with a locally varying metric* + +This theoretically derived result matches well with many traditional +graph based algorithms: a standard approach for such algorithms is to +use a *k*-neighbor graph instead of using balls of some fixed radius to +define connectivity. What this means is that each point in the dataset +is given an edge to each of its *k* nearest neighbors -- the effective +result of our locally varying metric with balls of radius one. Now, +however, we can explain why this works in terms of simplicial complexes +and the Nerve theorem. + +Of course we have traded choosing the radius of the balls for choosing a +value for *k*. However it is often easier to pick a resolution scale in +terms of number of neighbors than it is to correctly choose a distance. +This is because choosing a distance is very dataset dependent: one needs +to look at the distribution of distances in the dataset to even begin to +select a good value. In contrast, while a *k* value is still dataset +dependent to some degree, there are reasonable default choices, such as +the 10 nearest neighbors, that should work acceptably for most datasets. + +At the same time the topological interpretation of all of this gives us +a more meaningful interpretation of *k*. The choice of *k* determines how +locally we wish to estimate the Riemannian metric. A small choice of *k* +means we want a very local interpretation which will more accurately +capture fine detail structure and variation of the Riemannian metric. +Choosing a large *k* means our estimates will be based on larger +regions, and thus, while missing some of the fine detail structure, they +will be more broadly accurate across the manifold as a whole, having +more data to make the estimate with. + +We also get a further benefit from this Riemannian metric based +approach: we actually have a local metric space associated to each +point, and can meaningfully measure distance, and thus we could weight +the edges of the graph we might generate by how far apart (in terms of +the local metric) the points on the edges are. In slightly more +mathematical terms we can think of this as working in a fuzzy topology +where being in an open set in a cover is no longer a binary yes or no, +but instead a fuzzy value between zero and one. Obviously the certainty +that points are in a ball of a given radius will decay as we move away +from the center of the ball. We could visualize such a fuzzy cover as +looking something like this + +![Fuzzy open balls of radius one with a locally varying metric](images/how_umap_works_fuzzy_open_cover.png) +*Fuzzy open balls of radius one with a locally varying metric* + +None of that is very concrete or formal -- it is merely an intuitive +picture of what we would like to have happen. It turns out that we can +actually formalize all of this by stealing the `singular +set `__ +and `geometric +realization `__ +functors from algebraic topology and then adapting them to apply to +metric spaces and fuzzy simplicial sets. The mathematics involved in +this is outside the scope of this exposition, but for those interested +you can look at the `original work on this by David +Spivak `__ +and our [paper](https://arxiv.org/abs/1802.03426). It will have to +suffice to say that there is some mathematical machinery that lets us +realize this intuition in a well defined way. + +This resolves a number of issues, but a new problem presents itself when +we apply this sort of process to real data, especially in higher +dimensions: a lot of points become essentially totally isolated. One +would imagine that this shouldn't happen if the manifold the data was +sampled from isn't pathological. So what property are we expecting that +manifold to have that we are somehow missing with the current approach? +What we need to add is the idea of local connectivity. + +Note that this is not a requirement that the manifold as a whole be +connected -- it can be made up of many connected components. Instead it +is a requirement that at any point on the manifold there is some +sufficiently small neighborhood of the point that *is* connected (this +"in a sufficiently small neighborhood" is what the "local" part means). +For the practical problem we are working with, where we only have a +finite approximation of the manifold, this means that no point should be +*completely* isolated -- it should connect to at least one other point. +In terms of fuzzy open sets what this amounts to is that we should have +complete confidence that the open set extends as far as the closest +neighbor of each point. We can implement this by simply having the fuzzy +confidence decay in terms of distance *beyond* the first nearest +neighbor. We can visualize the result in terms of our example dataset +again. + +![Local connectivity and fuzzy open sets](images/how_umap_works_umap_open_cover.png) +*Local connectivity and fuzzy open sets* + +Again this can be formalized in terms of the aforementioned mathematical +machinery from algebraic topology. From a practical standpoint this +plays an important role for high dimensional data -- in high dimensions +distances tend to be larger, but also more similar to one another (see +`the curse of +dimensionality `__). +This means that the distance to the first nearest neighbor can be quite +large, but the distance to the tenth nearest neighbor can often be only +slightly larger (in relative terms). The local connectivity constraint +ensures that we focus on the difference in distances among nearest +neighbors rather than the absolute distance (which shows little +differentiation among neighbors). + +Just when we think we are almost there, having worked around some of the +issues of real world data, we run aground on a new obstruction: our +local metrics are not compatible! Each point has its own local metric +associated to it, and from point *a*'s perspective the distance from +point *a* to point *b* might be 1.5, but from the perspective of point +*b* the distance from point *b* to point *a* might only be 0.6. Which +point is right? How do we decide? Going back to our graph based +intuition we can think of this as having directed edges with varying +weights something like this. + +![Edges with incompatible weights](images/how_umap_works_raw_graph.png) +*Edges with incompatible weights* + +Between any two points we might have up to two edges and the weights on +those edges disagree with one another. There are a number of options for +what to do given two disagreeing weights -- we could take the maximum, +the minimum, the arithmetic mean, the geometric mean, or something else +entirely. What we would really like is some principled way to make the +decision. It is at this point that the mathematical machinery we built +comes into play. Mathematically we actually have a family of fuzzy +simplicial sets, and the obvious choice is to take their union -- a well +defined operation. There are a a few ways to define fuzzy unions, +depending on the nature of the logic involved, but here we have +relatively clear probabilistic semantics that make the choice +straightforward. In graph terms what we get is the following: if we want +to merge together two disagreeing edges with weight *a* and *b* then we +should have a single edge with combined weight :math:`a + b - a \cdot b`. +The way to think of this is that the weights are effectively the +probabilities that an edge (1-simplex) exists. The combined weight is +then the probability that at least one of the edges exists. + +If we apply this process to union together all the fuzzy simplicial sets +we end up with a single fuzzy simplicial complex, which we can again +think of as a weighted graph. In computational terms we are simply +applying the edge weight combination formula across the whole graph +(with non-edges having a weight of 0). In the end we have something that +looks like this. + +![Graph with combined edge weights](images/how_umap_works_umap_graph.png) +*Graph with combined edge weights* + +So in some sense in the end we have simply constructed a weighted graph +(although we could make use of higher dimensional simplices if we +wished, just at significant extra computational cost). What the +mathematical theory lurking in the background did for us is determine +*why* we should construct *this* graph. It also helped make the +decisions about exactly *how* to compute things, and gives a concrete +interpretation of *what* this graph means. So while in the end we just +constructed a graph, the math answered the important questions to get us +here, and can help us determine what to do next. + +So given that we now have a fuzzy topological representation of the data +(which the math says will capture the topology of the manifold +underlying the data), how do we go about converting that into a low +dimensional representation? + +## Finding a Low Dimensional Representation + + +Ideally we want the low dimensional representation to have as similar +a fuzzy topological structure as possible. The first question +is how do we determine the fuzzy topological structure of a low +dimensional representation, and the second question is how do we find a +good one. + +The first question is largely already answered -- we should presumably +follow the same procedure we just used to find the fuzzy topological +structure of our data. There is a quirk, however: this time around the +data won't be lying on some manifold, we'll have a low dimensional +representation that is lying on a very particular manifold. That +manifold is, of course, just the low dimensional euclidean space we are +trying to embed into. This means that all the effort we went to +previously to make vary the notion of distance across the manifold is +going to be misplaced when working with the low dimensional +representation. We explicitly *want* the distance on the manifold to be +standard euclidean distance with respect to the global coordinate +system, not a varying metric. That saves some trouble. The other quirk +is that we made use of the distance to the nearest neighbor, again +something we computed given the data. This is also a property we would +like to be globally true across the manifold as we optimize toward a +good low dimensional representation, so we will have to accept it as a +hyper-parameter `min_dist` to the algorithm. + +The second question, 'how do we find a good low dimensional +representation', hinges on our ability to measure how "close" a match we +have found in terms of fuzzy topological structures. Given such a +measure we can turn this into an optimization problem of finding the low +dimensional representation with the closest fuzzy topological structure. +Obviously if our measure of closeness turns out to have various +properties the nature of the optimization techniques we can apply will +differ. + +Going back to when we were merging together the conflicting weights +associated to simplices, we interpreted the weights as the probability +of the simplex existing. Thus, since both topological structures we are +comparing share the same 0-simplices, we can imagine that we are +comparing the two vectors of probabilities indexed by the 1-simplices. +Given that these are Bernoulli variables (ultimately the simplex either +exists or it doesn't, and the probability is the parameter of a +Bernoulli distribution), the right choice here is the cross entropy. + +Explicitly, if the set of all possible 1-simplices is :math:`E`, and we +have weight functions such that :math:`w_h(e)` is the weight of the +1-simplex :math:`e` in the high dimensional case and :math:`w_l(e)` is +the weight of :math:`e` in the low dimensional case, then the cross +entropy will be + + + + + \sum_{e\in E} w_h(e) \log\left(\frac{w_h(e)}{w_l(e)}\right) + (1 - w_h(e)) \log\left(\frac{1 - w_h(e)}{1 - w_l(e)}\right) + +This might look complicated, but if we go back to thinking in terms of a +graph we can view minimizing the cross entropy as a kind of force +directed graph layout algorithm. + +The first term, :math:`w_h(e) \log\left(\frac{w_h(e)}{w_l(e)}\right)`, +provides an attractive force between the points :math:`e` spans whenever +there is a large weight associated to the high dimensional case. This is +because this term will be minimized when :math:`w_l(e)` is as large as +possible, which will occur when the distance between the points is as +small as possible. + +In contrast the second term, +:math:`(1 - w_h(e)) \log\left(\frac{1 - w_h(e)}{1 - w_l(e)}\right)`, +provides a repulsive force between the ends of :math:`e` whenever +:math:`w_h(e)` is small. This is because the term will be minimized by +making :math:`w_l(e)` as small as possible. + +On balance this process of pull and push, mediated by the weights on +edges of the topological representation of the high dimensional data, +will let the low dimensional representation settle into a state that +relatively accurately represents the overall topology of the source +data. + +## The UMAP Algorithm + + +Putting all these pieces together we can construct the UMAP algorithm. +The first phase consists of constructing a fuzzy topological +representation, essentially as described above. The second phase is +simply optimizing the low dimensional representation to have as close +a fuzzy topological representation as possible as measured by cross +entropy. + +When constructing the initial fuzzy topological representation we can +take a few shortcuts. In practice, since fuzzy set membership strengths +decay away to be vanishingly small, we only need to compute them for the +nearest neighbors of each point. Ultimately that means we need a way to +quickly compute (approximate) nearest neighbors efficiently, even in +high dimensional spaces. We can do this by taking advantage of the +`Nearest-Neighbor-Descent algorithm of Dong et +al `__. The remaining +computations are now only dealing with local neighbors of each point and +are thus very efficient. + +In optimizing the low dimensional embedding we can again take some +shortcuts. We can use stochastic gradient descent for the optimization +process. To make the gradient descent problem easier it is beneficial if +the final objective function is differentiable. We can arrange for that +by using a smooth approximation of the actual membership strength +function for the low dimensional representation, selecting from a +suitably versatile family. In practice UMAP uses the family of curves of +the form :math:`\frac{1}{1 + a x^{2b}}`. Equally we don't want to have to +deal with all possible edges, so we can use the negative sampling trick +(as used by word2vec and LargeVis), to simply sample negative examples +as needed. Finally since the Laplacian of the topological representation +is an approximation of the Laplace-Beltrami operator of the manifold we +can use spectral embedding techniques to initialize the low dimensional +representation into a good state. + +Putting all these pieces together we arrive at an algorithm that is fast +and scalable, yet still built out of sound mathematical theory. +Hopefully this introduction has helped provide some intuition for that +underlying theory, and for how the UMAP algorithm works in practice. diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..079fde0e --- /dev/null +++ b/docs/index.md @@ -0,0 +1,100 @@ + + sphinx-quickstart on Fri Jun 8 10:09:40 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +![Image](logo_large.png) + + :width: 600 + :align: center + +# UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction + + +Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction +technique that can be used for visualisation similarly to t-SNE, but also for +general non-linear dimension reduction. The algorithm is founded on three +assumptions about the data + +1. The data is uniformly distributed on Riemannian manifold; +2. The Riemannian metric is locally constant (or can be approximated as such); +3. The manifold is locally connected. + +From these assumptions it is possible to model the manifold with a fuzzy +topological structure. The embedding is found by searching for a low dimensional +projection of the data that has the closest possible equivalent fuzzy +topological structure. + +The details for the underlying mathematics can be found in +[our paper on ArXiv](https://arxiv.org/abs/1802.03426): + +McInnes, L, Healy, J, *UMAP: Uniform Manifold Approximation and Projection +for Dimension Reduction*, ArXiv e-prints 1802.03426, 2018 + +You can find the software [on github](https://github.com/lmcinnes/umap). + +**Installation** + +Install UMAP via pip: + +```bash +pip install umap + +``` + +This will install UMAP and all required dependencies. For development installation, see the README. + + +## User Guide / Tutorial: + +- [basic_usage](basic_usage) +- [parameters](parameters) +- [plotting](plotting) +- [reproducibility](reproducibility) +- [transform](transform) +- [inverse_transform](inverse_transform) +- [parametric_umap](parametric_umap) +- [transform_landmarked_pumap](transform_landmarked_pumap) +- [sparse](sparse) +- [supervised](supervised) +- [clustering](clustering) +- [outliers](outliers) +- [composing_models](composing_models) +- [densmap_demo](densmap_demo) +- [mutual_nn_umap](mutual_nn_umap) +- [document_embedding](document_embedding) +- [embedding_space](embedding_space) +- [aligned_umap_basic_usage](aligned_umap_basic_usage) +- [aligned_umap_politics_demo](aligned_umap_politics_demo) +- [precomputed_k-nn](precomputed_k-nn) +- [benchmarking](benchmarking) +- [release_notes](release_notes) +- [faq](faq) + +## Background on UMAP: + +- [how_umap_works](how_umap_works) +- [performance](performance) + +## Examples of UMAP usage + +- [interactive_viz](interactive_viz) +- [exploratory_analysis](exploratory_analysis) +- [scientific_papers](scientific_papers) +- [nomic_atlas_umap_of_text_embeddings](nomic_atlas_umap_of_text_embeddings) +- [nomic_atlas_visualizing_mnist_training](nomic_atlas_visualizing_mnist_training) + +## API Reference: + +- [api](api) + +## Development: + +- [development_roadmap](development_roadmap) + +# Indices and tables + + +* `genindex` +* `modindex` +* `search` diff --git a/docs/interactive_viz.md b/docs/interactive_viz.md new file mode 100644 index 00000000..d182eecb --- /dev/null +++ b/docs/interactive_viz.md @@ -0,0 +1,162 @@ +# Interactive Visualizations + + +UMAP has found use in a number of interesting interactive visualization projects, analyzing everything from +images from photo archives, to word embedding, animal point clouds, and even sound. Sometimes it has also +been used in interesting interactive tools that simply help a user to get an intuition for what the algorithm +is doing (by applying it to intuitive 3D data). Below are some amazing projects that make use of UMAP. + +## UMAP Zoo + +An exploration of how UMAP behaves when dimension reducing point clouds of animals. It is +interactive, letting you switch between 2D and 3D representations and has a wide selection +of different animals. Attempting to guess the animal from the 2D UMAP representation is a +fun game. In practice this tool can go a long way to helping to build at least some intuitions +for what UMAP tends to do with data. + +![Image](images/UMAP_zoo.png) + + +[UMAP Zoo](https://duhaime.s3.amazonaws.com/apps/umap-zoo/index.html) + +Thanks to Douglas Duhaime. + +## Tensorflow Embedding Projector + +If you just want to explore UMAP embeddings of datasets then the Embedding Projector +from Tensorflow is a great way to do that. As well as having a good interactive 3D view +it also has facilities for inspecting and searching labels and tags on the data. By default +it loads up word2vec vectors, but you can upload any data you wish. You can then select +the UMAP option among the tabs for embeddings choices (alongside PCA and t-SNE). + +![Image](images/embedding_projector.png) + + +[Embedding Projector](https://projector.tensorflow.org/) + +Thanks to Andy Coenen and the Embedding Projector team. + +## PixPlot + +PixPlot provides an overview of large photo-collections. In the demonstration app +from Yale's Digital Humanities lab it provides a window on the Meserve-Kunhardt Collection +of historical photographs. The approach uses convolutional neural nets to reduce the images +to 2048 dimensions, and then uses UMAP to present them in a 2-dimensional map which the +user can interactive pan and zoom around in. This process results in similar photos +ending up in similar regions of the map allowing for easy perusal of large photo +collections. The PixPlot project is also available on github in case you wish to train +it on your own photo collection. + +![Image](images/pixplot.png) + + +[PixPlot](https://dhlab.yale.edu/projects/pixplot/) + +Thanks to Douglas Duhaime and the Digital Humanities lab at Yale. + +## UMAP Explorer + +A great demonstration of building a web based app for interactively exploring a UMAP embedding. +In this case it provides an exploration of UMAP run on the MNIST digits dataset. Each point in +the embedding is rendered as the digit image, and coloured according to the digit class. Mousing +over the images will make them larger and provide a view of the digit in the upper left. You can also pan +and zoom around the emebdding to get a better understanding of how UMAP has mapped the different styles of +handwritten digits down to 2 dimensions. + +![Image](images/umap_explorer.png) + + +[UMAP Explorer](https://grantcuster.github.io/umap-explorer/) + +Thanks to Grant Custer. + +## Audio Explorer + +The Audio Explorer uses UMAP to embed sound samples into a 2 dimensional space for easy exploration. +The goal here is to take a large library of sounds samples and put similar sounds in similar regions +of the map, allowing a user to quickly mouse over and listen to various variations of a given sample +to quickly find exactly the right sound sample to use. Audio explorer uses MFCCs and/or WaveNet to +provide an initial useful vector representation of the sound samples, before applying UMAP to +generate the 2D embedding. + +![Image](images/audio_explorer.png) + + +[Audio Explorer](http://doc.gold.ac.uk/~lfedd001/three/demo.html) + +Thanks to Leon Fedden. + +## Orion Search + +Orion is an open source research measurement and knowledge discovery tool that enables you to monitor +progress in science, visually explore the scientific landscape and search for relevant publications. +Orion encodes bioRxiv paper abstracts to dense vectors with Sentence Transformers and projects them to +an interactive 3D visualisation with UMAP. You can filter the UMAP embeddings by topic and country. +You can also select a subset of the UMAP embeddings and retrieve those papers and their metadata. + +![Image](images/orion_particles.png) + + +[Orion Search](https://www.orion-search.org/) + +Thanks to Kostas Stathoulopoulos, Zac Ioannidis and Lilia Villafuerte. + +## Exploring Fashion MNIST + +A web based interactive exploration of a 3D UMAP embedding ran on the Fashion MNIST dataset. Users can +freely navigate the 3D space, jumping to a specific image by clicking an image or entering an image id. +Like Grant Custer's UMAP Explorer, each point is rendered as the actual image and colored according to +the label. It is also similar to the Tensorflow Embedding Projector, but designed more specifically for +Fashion MNIST, thus more efficient and capable of showing all the 70k images. + +![Image](images/exploring_fashion_mnist.png) + + +[Exploring Fashion MNIST](https://observablehq.com/@stwind/exploring-fashion-mnist/) + +Thanks to stwind. + +## ESM Metagenomic Atlas + +The ESM Metagenomic Atlas contains over 600 million predicted protein structures, revealing the +metagenomic world in a way we have never seen before. The Explore page visualizes a sample of 1 +million of these. (That’s about how much a browser can handle.) We represent each protein in this +dataset as a single point, and reveal the actual protein structure when zooming in or when hovering +over it. The color of each point corresponds to the similarity to the closest match we could find in +UniRef90, the reference database of known protein sequences. The position in the map is a +two-dimensional projection, which groups sequences by similarity, as determined by our language +model’s internal representation. The map reveals structure at different scales: local neighbors in +the same cluster tend to have similar structures, while nearby clusters preserve certain patterns +like secondary structure elements. + +![Image](images/ESM_metagenomic_atlas.png) + + +Thanks to the authors of "Evolutionary-scale prediction of atomic level protein structure +with a language model". + +[ESM Metagenomic Atlas](https://esmatlas.com/explore) + +## Interactive UMAP with Nomic Atlas + + +[Nomic Atlas](https://atlas.nomic.ai/) is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. + +![UMAP interactive visualization with Nomic Atlas](https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif) + + +Atlas provides: + +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) +* Scalability for millions of data points +* Rich information display on hover +* Shareable UMAPs via URL links to your embeddings and data maps in Atlas + +----------- + +## Nomic Atlas Examples + +- [nomic_atlas_umap_of_text_embeddings](nomic_atlas_umap_of_text_embeddings) +- [nomic_atlas_visualizing_mnist_training_dynamics](nomic_atlas_visualizing_mnist_training_dynamics) diff --git a/docs/inverse_transform.md b/docs/inverse_transform.md new file mode 100644 index 00000000..5c7e1b08 --- /dev/null +++ b/docs/inverse_transform.md @@ -0,0 +1,253 @@ + +# Inverse transforms + + +UMAP has some support for inverse transforms -- generating a high +dimensional data sample given a location in the low dimensional +embedding space. To start let's load all the relevant libraries. + +```python3 +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.gridspec import GridSpec +import seaborn as sns +import sklearn.datasets +import umap +import umap.plot + +``` + +We will need some data to test with. To start we'll use the MNIST digits +dataset. This is a dataset of 70000 handwritten digits encoded as +grayscale 28x28 pixel images. Our goal is to use UMAP to reduce the +dimension of this dataset to something small, and then see if we can +generate new digits by sampling points from the embedding space. To load +the MNIST dataset we'll make use of sklearn's `fetch_openml` function. + +```python3 +data, labels = sklearn.datasets.fetch_openml('mnist_784', version=1, return_X_y=True) + +``` + +Now we need to generate a reduced dimension representation of this data. +This is straightforward with UMAP, but in this case rather than using +`fit_transform` we'll use the fit method so that we can retain the +trained model for later generating new digits based on samples from the +embedding space. + +```python3 +mapper = umap.UMAP(random_state=42).fit(data) + +``` + +To ensure that things worked correctly we can plot the data (since we +reduced it to two dimensions). We'll use the `umap.plot` functionality +to do this. + +```python3 +umap.plot.points(mapper, labels=labels) + + + +``` + +![Image](images/inverse_transform_7_1.png) + +This looks much like we would expect. The different digit classes have +been decently separated. Now we need to create a set of samples in the +embedding space to apply the `inverse_transform` operation to. To do +this we'll generate a grid of samples linearly interpolating between +four corner points. To make our selection interesting we'll carefully +choose the corners to span over the dataset, and sample different digits +so that we can better see the transitions. + +```python3 +corners = np.array([ + [-5, -10], # 1 + [-7, 6], # 7 + [2, -8], # 2 + [12, 4], # 0 +]) + +test_pts = np.array([ + (corners[0]*(1-x) + corners[1]*x)*(1-y) + + (corners[2]*(1-x) + corners[3]*x)*y + for y in np.linspace(0, 1, 10) + for x in np.linspace(0, 1, 10) +]) + +``` + +Now we can apply the `inverse_transform` method to this set of test +points. Each test point is a two dimensional point lying somewhere in +the embedding space. The `inverse_transform` method will convert this +into an approximation of the high dimensional representation that would +have been embedded into such a location. Following the sklearn API this +is as simple to use as calling the `inverse_transform` method of the +trained model and passing it the set of test points that we want to +convert into high dimensional representations. Be warned that this can +be quite expensive computationally. + +```python3 +inv_transformed_points = mapper.inverse_transform(test_pts) + +``` + +Now the goal is to visualize how well we have done. Effectively what we +would like to do is show the test points in the embedding space, and +then show a grid of the corresponding images generated by the inverse +transform. To get all of this in a single matplotlib figure takes a +little setting up, but is quite manageable -- mostly it is just a matter +of managing `GridSpec` formatting. Once we have that setup we just +need a scatterplot of the embedding, a scatterplot of the test points, +and finally a grid of the images we generated (converting the inverse +transformed vectors into images is just a matter of reshaping them back +to 28 by 28 pixel grids and using `imshow`). + +```python3 +# Set up the grid +fig = plt.figure(figsize=(12,6)) +gs = GridSpec(10, 20, fig) +scatter_ax = fig.add_subplot(gs[:, :10]) +digit_axes = np.zeros((10, 10), dtype=object) +for i in range(10): + for j in range(10): + digit_axes[i, j] = fig.add_subplot(gs[i, 10 + j]) + +# Use umap.plot to plot to the major axis +# umap.plot.points(mapper, labels=labels, ax=scatter_ax) +scatter_ax.scatter(mapper.embedding_[:, 0], mapper.embedding_[:, 1], + c=labels.astype(np.int32), cmap='Spectral', s=0.1) +scatter_ax.set(xticks=[], yticks=[]) + +# Plot the locations of the text points +scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', c='k', s=15) + +# Plot each of the generated digit images +for i in range(10): + for j in range(10): + digit_axes[i, j].imshow(inv_transformed_points[i*10 + j].reshape(28, 28)) + digit_axes[i, j].set(xticks=[], yticks=[]) + + + +``` + +![Image](images/inverse_transform_13_0.png) + +The end result looks pretty good -- we did indeed generate plausible +looking digit images, and many of the transitions (from 1 to 7 across +the top row for example) seem pretty natural and make sense. This can +help you to understand the structure of the cluster of 1s (it +transitions on the angle, sloping toward what will eventually be 7s), +and why 7s and 9s are close together in the embedding. Of course there +are also some stranger transitions, especially where the test points +fell into large gaps between clusters in the embedding -- in some sense +it is hard to interpret what should go in some of those gaps as they +don't really represent anything resembling a smooth transition). + +A further note: None of the test points chosen fall outside the convex +hull of the embedding. This is deliberate -- the inverse transform +function operates poorly outside the bounds of that convex hull. Be +warned that if you select points to inverse transform that are outside +the bounds about the embedding you will likely get strange results +(often simply snapping to a particular source high dimensional vector). + +Let's continue the demonstration by looking at the Fashion MNIST +dataset. As before we can load this through sklearn. + +```python3 +data, labels = sklearn.datasets.fetch_openml('Fashion-MNIST', version=1, return_X_y=True) + +``` + +Again we can fit this data with UMAP and get a mapper object. + +```python3 +mapper = umap.UMAP(random_state=42).fit(data) + +``` + +Let's plot the embedding to see what we got as a result: + +```python3 +umap.plot.points(mapper, labels=labels) + + + + +``` + +![Image](images/inverse_transform_20_1.png) + +Again we'll generate a set of test points by making a grid interpolating +between four corners. As before we'll select the corners so that we can +stay within the convex hull of the embedding points and ensure nothing +too strange happens with the inverse transforms. + +```python3 +corners = np.array([ + [-2, -6], # bags + [-9, 3], # boots? + [7, -5], # shirts/tops/dresses + [4, 10], # pants +]) + +test_pts = np.array([ + (corners[0]*(1-x) + corners[1]*x)*(1-y) + + (corners[2]*(1-x) + corners[3]*x)*y + for y in np.linspace(0, 1, 10) + for x in np.linspace(0, 1, 10) +]) + +``` + +Now we simply apply the inverse transform just as before. Again, be +warned, this is quite expensive computationally and may take some time +to complete. + +```python3 +inv_transformed_points = mapper.inverse_transform(test_pts) + +``` + +And now we can use similar code as above to set up our plot of the +embedding with test points overlaid, and the generated images. + +```python3 +# Set up the grid +fig = plt.figure(figsize=(12,6)) +gs = GridSpec(10, 20, fig) +scatter_ax = fig.add_subplot(gs[:, :10]) +digit_axes = np.zeros((10, 10), dtype=object) +for i in range(10): + for j in range(10): + digit_axes[i, j] = fig.add_subplot(gs[i, 10 + j]) + +# Use umap.plot to plot to the major axis +# umap.plot.points(mapper, labels=labels, ax=scatter_ax) +scatter_ax.scatter(mapper.embedding_[:, 0], mapper.embedding_[:, 1], + c=labels.astype(np.int32), cmap='Spectral', s=0.1) +scatter_ax.set(xticks=[], yticks=[]) + +# Plot the locations of the text points +scatter_ax.scatter(test_pts[:, 0], test_pts[:, 1], marker='x', c='k', s=15) + +# Plot each of the generated digit images +for i in range(10): + for j in range(10): + digit_axes[i, j].imshow(inv_transformed_points[i*10 + j].reshape(28, 28)) + digit_axes[i, j].set(xticks=[], yticks=[]) + + + +``` + +![Image](images/inverse_transform_26_0.png) + +This time we see some of the interpolations between items looking rather +strange -- particularly the points that lie somewhere between shoes and +pants -- ultimately it is doing the best it can with a difficult +problem. At the same time many of the other transitions seem to work +pretty well, so it is, indeed, providing useful information about how +the embedding is structured. diff --git a/docs/mutual_nn_umap.md b/docs/mutual_nn_umap.md new file mode 100644 index 00000000..5c99ccf5 --- /dev/null +++ b/docs/mutual_nn_umap.md @@ -0,0 +1,149 @@ +# Improving the Separation Between Similar Classes Using a Mutual k-NN Graph + + +This post briefly explains how the connectivity of the original graphical representation can adversely affect the resulting UMAP embeddings. + +In default UMAP, a weighted k nearest neighbor (k-NN) graph, which connects each +datapoint to its 𝑘 nearest neighbors based on some distance metric, is constructed +and used to generate the initial topological representation of a dataset. + +However, previous research has shown that using a weighted k-NN +graph may not provide an accurate representation of the underlying local +structure for a high dimensional dataset. The k-NN graph is relatively susceptible +to the “curse of dimensionality” and the associated distance concentration +effect, where distances are similar in high dimensions, as well as the +hub effect, where certain points become highly influential when highly +connected. This skews the local representation of high dimensional data, +deteriorating its performance for various similarity-based machine learning +tasks. + +A recent paper titled +[Clustering with UMAP: Why and How Connectivity Matters](https://arxiv.org/abs/2108.05525) +proposes a refinement in the graph construction stage of the UMAP algorithm +that uses a weighted mutual k-NN graph rather than it vanilla counterpart, +to reduce the undesired distance concentration and hub effects. + +Mutual k-NN graphs have been shown to contain many +desirable properties when combating the “curse of dimensionality” as discussed in +[this paper](https://arxiv.org/abs/2108.05525) . However, one pitfall of using a +mutual k-NN graph over the original k-NN graph is that it often +contains disconnected components and potential isolated vertices. + +This violates one of UMAP primary assumptions that "The manifold is locally connected." To +combat the issue of isolated components, the authors consider different methods that have +been previously used to augment and increase the connectivity of the mutual k-NN graph: + +1. `NN`: To minimally connect isolated vertices and satisfy the assumption that the underlying manifold is locally connected, we add an undirected edge between each isolated vertex and its original nearest neighbor (de Sousa, Rezende, and Batista 2013).Note that the resulting graph may still contain disconnected components. +2. `MST-min`: To achieve a connected graph, add the minimum number of edges from a maximum spanning tree to the mutual k-NN graph that has been weighted with similarity-based metrics(Ozaki et al. 2011). We adapt this by calculating the minimum spanning tree for distances. +3. `MST-all`: Adding all the edges of the MST. + +![Image](images/mutual_nn_umap_connectivity.png) + +They also different ways to obtain the new local neighborhood for each point `x_i`: + +1. `Adjacent Neighbors`: Only consider neighbors that are directly connected(adjacent) to `x_i` in the connected mutual k-NN graph. +2. `Path Neighbors`: Using shortest path distance to find the new k closest points to `x_i` with respect to the connected mutual k-NN graph. This shortest path distance can be considered a new distance metric as it directly aligns with UMAP’s definition of an extended pseudo-metric space. + +![Image](images/mutual_nn_umap_lc.png) + + :width: 600 + :align: center + + +## Visualizing the Results + +To see the differences between using a mutual k-NN graph vs the original k-NN graph as +the starting topology for UMAP, let's visualize the 2D projections generated for MNIST, FMNIST, and 20 +NG Count Vectors using each of the discussed methods. For all code snippets to reproduce the results and visualizations, please refer +to this [Github repo](https://github.com/adalmia96/umap-mnn). Will be adding this soon as a +mode to the original implementation. + +We’ll start with MNIST digits, a collection of 70,000 gray-scale images of hand-written digits: + +![Image](images/mutual_nn_umap_MNIST.png) + + :width: 850 + :align: center + +In general, for most of the mutual k-NN graph based vectors, there +is a better separation between similar classes than the original UMAP vectors +regardless of connectivity (NN, MST variants). Connecting isolated vertices in +the mutual k-NN graph to their original nearest neighbor produced the desired +separation between similar classes such as with the 4, 7, 9 in MNIST. This follows +our intuition given that mutual k-NN graphs have previously been shown as a useful +method for removing edges between points that are only loosely similar. + +Similar results are observed for the Fashion-MNIST(FMNIST) dataset, a collection of 70,000 +gray-scale images of fashion items: + +![Image](images/mutual_nn_umap_FMNIST.png) + + :width: 850 + :align: center + +For the FMNIST dataset, the vectors using the aforementioned methods preserve +the global structure between clothing classes (T-shirt/top, Coat, Trouser, and etc.) +from footwear classes (Sandal, Sneaker, Ankle-boot) while also depicting a clearer +separation between the footwear classes. This is contrasted with original +UMAP which has poorer separation between similar classes like the footwear classes. + +For both MNIST and FMNIST, NN which naively connects isolated vertices +to their nearest neighbor had multiple small clusters of points scattered +throughout the vector space. This makes sense given using NN for connectivity can +still cause the resulting manifold to be broken into many small components. + +It would be fair to assume that augmenting the mutual k-NN graph with a "higher connectivity" +would always be better as it reduces random scattering of points. However, +too much connectivity such as with MST-all can also hurt which is further discussed in the paper. + +Finally, we depict the embeddings generated using the 20 newsgroup dataset, a collection of +18846 documents, transformed using sklearns CountVectorizer: + +![Image](images/mutual_nn_umap_20ngc.png) + + :width: 850 + :align: center + +We can see there is better distinction between similar subjects such as the recreation +(rec) topics. + +Visually, the vector generated using the Adjacent Neighbors +and MST-min result in disperse dense clusters of points e.g, the footwear classes in +FMNIST and the recreation topics in 20 NG. However for Path Neighbors, the groups of +points belonging to the same class are less dispersed. This is because Adjacent Neighbors are not guaranteed to have k connected neighbors for each local +neighborhood. Points with smaller neighborhoods will be close to primarily few adjacent +neighbors and repelled further away from the other points. + +To evaluate these methods quantitatively, the authors compare the clustering performance +of the resulting low dimensional vectors generated. Below shows the Normalised Mutual +Information NMI results after performing KMeans(for more information of the results please refer to `the full +paper `__). + +![Image](images/mutual_nn_umap_results.png) + +These quantitative experiments show that MST variants combined with Path +Neighbors can help produce better clustering results and how the initialization +of a weighted connected graph is critical to the success of topology based +dimensionality reduction methods like UMAP. + + +## Citing our work + +If you use this implementation or reference the results in your work, please cite the paper: + +```bibtex +``` + + @article{Dalmia2021UMAPConnectivity, + author={Ayush Dalmia and Suzanna Sia}, + title={Clustering with {UMAP:} Why and How Connectivity Matters}, + journal={CoRR}, + volume={abs/2108.05525}, + year={2021}, + url={https://arxiv.org/abs/2108.05525}, + eprinttype={arXiv}, + eprint={2108.05525}, + timestamp={Wed, 18 Aug 2021 19:45:42 +0200}, + biburl={https://dblp.org/rec/journals/corr/abs-2108-05525.bib}, + bibsource={dblp computer science bibliography, https://dblp.org} + } diff --git a/docs/nomic_atlas_umap_of_text_embeddings.md b/docs/nomic_atlas_umap_of_text_embeddings.md new file mode 100644 index 00000000..8bcd68ba --- /dev/null +++ b/docs/nomic_atlas_umap_of_text_embeddings.md @@ -0,0 +1,88 @@ +UMAP of Text Embeddings with Nomic Atlas +======================= + +[Nomic Atlas](https://atlas.nomic.ai/) is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. + +![UMAP interactive visualization with Nomic Atlas](https://assets.nomicatlas.com/airline-reviews-umap.gif) + + +Nomic Atlas automatically generates embeddings for your data and allows you to explore large datasets in a web browser. Atlas provides: + +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) +* Scalability for millions of data points +* Rich information display on hover +* Shareable UMAPs via URL links to your embeddings and data maps in Atlas + +This example demonstrates how to use [Nomic Atlas](https://docs.nomic.ai/atlas/embeddings-and-retrieval/guides/using-umap-with-atlas) to create interactive maps of text using embeddings and UMAP. + +## Setup + + +1. Get the required python packages with `pip instll nomic pandas` +2. Get a Nomic API key [here](https://atlas.nomic.ai/cli-login) +3. Run `nomic login nk-...` in a terminal window or use the following code: + +```python3 +import nomic +nomic.login('nk-...') + + + +``` + +Download Example Data +-------------------- + +```python3 +import pandas as pd + +# Example data +df = pd.read_csv("https://docs.nomic.ai/singapore_airlines_reviews.csv") + +``` + +## Create Atlas Dataset + + +```python3 +from nomic import AtlasDataset +dataset = AtlasDataset("airline-reviews-data") + +``` + +## Upload to Atlas + + +```python3 +dataset.add_data(df) + +``` + +## Create Data Map + + +We specify the `text` field from `df` as the field to create embeddings from. We choose some standard UMAP parameters as well. + +```python3 +from nomic.data_inference import ProjectionOptions + +# model="umap" is how you choose UMAP in Nomic Atlas +# You can adjust n_neighbors, min_dist, +# and n_epochs as you would with the UMAP library. +atlas_map = dataset.create_index( + indexed_field='text', + projection=ProjectionOptions( + model="umap", + n_neighbors=20, + min_dist=0.01, + n_epochs=200 + ) +) + +print(f"Explore your interactive map at: {atlas_map.map_link}") + +``` + +Your map will be available in your [Atlas Dashboard](https://atlas.nomic.ai/data). diff --git a/docs/nomic_atlas_visualizing_mnist_training_dynamics.md b/docs/nomic_atlas_visualizing_mnist_training_dynamics.md new file mode 100644 index 00000000..857489b0 --- /dev/null +++ b/docs/nomic_atlas_visualizing_mnist_training_dynamics.md @@ -0,0 +1,193 @@ +Visualizing MNIST Training Dynamics with Nomic Atlas +======================= + +[Nomic Atlas](https://atlas.nomic.ai/) is a platform for interactively visualizing and exploring massive datasets. It automates the creation of embeddings and 2D coordinate projections using UMAP. + +![UMAP interactive visualization with Nomic Atlas](https://assets.nomicatlas.com/mnist-training-embeddings-umap-short.gif) + + +Nomic Atlas automatically generates embeddings for your data and allows you to explore large datasets in a web browser. Atlas provides: + +* In-browser analysis of your UMAP data with the [Atlas Analyst](https://docs.nomic.ai/atlas/data-maps/atlas-analyst) +* Vector search over your UMAP data using the [Nomic API](https://docs.nomic.ai/atlas/data-maps/guides/vector-search-over-your-data) +* Interactive features like zooming, recoloring, searching, and filtering in the [Nomic Atlas data map](https://docs.nomic.ai/atlas/data-maps/controls) +* Scalability for millions of data points +* Rich information display on hover +* Shareable UMAPs via URL links to your embeddings and data maps in Atlas + +This example demonstrates how to use [Nomic Atlas](https://docs.nomic.ai/atlas/embeddings-and-retrieval/guides/using-umap-with-atlas) to visualize the training dynamics of your neural network using embeddings and UMAP. + +## Setup + + +1. Get the python package with `pip install nomic` +2. Get a Nomic API key [here](https://atlas.nomic.ai/cli-login) +3. Run `nomic login nk-...` in a terminal window or use the following code: + +```python3 +``` + + import nomic + nomic.login('nk-...') + +## Download Example Data + + +```python3 +``` + + import torch + import torch.nn as nn + import torch.optim as optim + import torchvision + import torchvision.transforms as transforms + from torch.utils.data import DataLoader, Subset + import numpy as np + import time + from PIL import Image + import base64 + import io + + NUM_EPOCHS = 20 + LEARNING_RATE = 3e-6 + BATCH_SIZE = 128 + NUM_VIS_SAMPLES = 2000 + EMBEDDING_DIM = 128 + ATLAS_DATASET_NAME = "mnist_training_embeddings" + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}\n") + + def tensor_to_html(tensor): + """Helper function to convert image tensors to HTML for rendering in Nomic Atlas""" + # Denormalize the image + img = torch.clamp(tensor.clone().detach().cpu().squeeze(0) * 0.3081 + 0.1307, 0, 1) + img_pil = Image.fromarray((img.numpy() * 255).astype('uint8'), mode='L') + buffered = io.BytesIO() + img_pil.save(buffered, format="PNG") + img_str = base64.b64encode(buffered.getvalue()).decode() + return f'' + + class MNIST_CNN(nn.Module): + def __init__(self, embedding_dim=128): + super(MNIST_CNN, self).__init__() + self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1) + self.relu1 = nn.ReLU() + self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) + self.relu2 = nn.ReLU() + self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + self.fc1 = nn.Linear(64 * 7 * 7, embedding_dim) + self.relu3 = nn.ReLU() + self.fc2 = nn.Linear(embedding_dim, 10) + + def forward(self, x): + x = self.pool1(self.relu1(self.conv1(x))) + x = self.pool2(self.relu2(self.conv2(x))) + x = self.flatten(x) + embeddings = self.relu3(self.fc1(x)) + output = self.fc2(embeddings) + return output, embeddings + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + + train_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) + test_dataset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform) + + persistent_workers_flag = True if device.type not in ['mps', 'cpu'] else False + num_workers_val = 2 if persistent_workers_flag else 0 + train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers_val, persistent_workers=persistent_workers_flag if num_workers_val > 0 else False) + vis_indices = list(range(NUM_VIS_SAMPLES)) + vis_subset = Subset(test_dataset, vis_indices) + test_loader_for_vis = DataLoader(vis_subset, batch_size=BATCH_SIZE, shuffle=False, num_workers=num_workers_val, persistent_workers=persistent_workers_flag if num_workers_val > 0 else False) + print(f"Training on {len(train_dataset)} samples, visualizing {NUM_VIS_SAMPLES} test samples per epoch.\n") + +## Collect Embeddings During Training + + +```python3 +``` + + model = MNIST_CNN(embedding_dim=EMBEDDING_DIM).to(device) + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE) + all_embeddings_list = [] + all_metadata_list = [] + all_images_html = [] + overall_start_time = time.time() + for epoch in range(NUM_EPOCHS): + epoch_start_time = time.time() + model.train() + running_loss = 0.0 + for batch_idx, (data, target) in enumerate(train_loader): + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + outputs, _ = model(data) + loss = criterion(outputs, target) + loss.backward() + optimizer.step() + running_loss += loss.item() + if (batch_idx + 1) % 200 == 0: + print(f'Epoch [{epoch+1}/{NUM_EPOCHS}], Batch [{batch_idx+1}/{len(train_loader)}], Avg Loss: {running_loss / 200:.4f}') + running_loss = 0.0 + print(f"Epoch {epoch+1}/{NUM_EPOCHS} training finished in {time.time() - epoch_start_time:.2f}s.\n") + model.eval() + vis_samples_collected_this_epoch = 0 + image_offset_in_vis_subset = 0 + with torch.no_grad(): + for data, target in test_loader_for_vis: + data, target = data.to(device), target.to(device) + _, embeddings_batch = model(data) + for i in range(embeddings_batch.size(0)): + original_idx_in_subset = image_offset_in_vis_subset + i + if original_idx_in_subset >= NUM_VIS_SAMPLES: + continue + all_embeddings_list.append(embeddings_batch[i].cpu().numpy()) + img_html = tensor_to_html(data[i]) + all_images_html.append(img_html) + all_metadata_list.append({ + 'id': f'vis_img_{original_idx_in_subset}_epoch_{epoch}', + 'epoch': epoch, + 'label': f'Digit: {target[i].item()}', + 'vis_sample_idx': original_idx_in_subset, + 'image_html': img_html + }) + vis_samples_collected_this_epoch += 1 + image_offset_in_vis_subset += embeddings_batch.size(0) + if vis_samples_collected_this_epoch >= NUM_VIS_SAMPLES: + break + print(f"Collected {vis_samples_collected_this_epoch} embeddings for visualization in epoch {epoch+1}.\n") + total_script_time = time.time() - overall_start_time + print(f"Total training and embedding extraction time: {total_script_time:.2f}s\n") + +## Create Atlas Dataset + + +```python3 +``` + + from nomic import AtlasDataset + dataset = AtlasDataset("mnist-training-embeddings") + +## Upload to Atlas + + +```python3 +``` + + dataset.add_data(data=all_metadata_list, embeddings=np.array(all_embeddings_list)) + +## Create Data Map + + +We specify the `text` field from `df` as the field to create embeddings from. We choose some standard UMAP parameters as well. + +```python3 +``` + + dataset.create_index(projection='umap', topic_model=False) + +Your map will be available in your [Atlas Dashboard](https://atlas.nomic.ai/data). diff --git a/docs/outliers.md b/docs/outliers.md new file mode 100644 index 00000000..1659ed54 --- /dev/null +++ b/docs/outliers.md @@ -0,0 +1,294 @@ +# Outlier detection using UMAP + + +While an earlier tutorial looked at using `UMAP for +clustering `__, +it can also be used for outlier detection, providing that some care is +taken. This tutorial will look at how to use UMAP in this manner, and +what to look out for, by finding anomalous digits in the MNIST +handwritten digits dataset. To start with let's load the relevant +libraries: + +```python3 +import numpy as np +import sklearn.datasets +import sklearn.neighbors +import umap +import umap.plot +import matplotlib.pyplot as plt +%matplotlib inline + +``` + +With this in hand, let's grab the MNIST digits dataset from the +internet, using the new `fetch_ml` loader in sklearn. + +```python3 +data, labels = sklearn.datasets.fetch_openml('mnist_784', version=1, return_X_y=True) + +``` + +Before we get started we should try looking for outliers in terms of the +native 784 dimensional space that MNIST digits live in. To do this we +will make use of the `Local Outlier Factor +(LOF) `__ method for +determining outliers since sklearn has an easy to use implementation. +The essential intuition of LOF is to look for points that have a +(locally approximated) density that differs significantly from the +average density of their neighbors. In our case the actual details are +not so important -- it is enough to know that the algorithm is +reasonably robust and effective on vector space data. We can apply it +using the `fit_predict` method of the sklearn class. The LOF class +take a parameter `contamination` which specifies the percentage of +data that the user expects to be noise. For this use case we will set it +to 0.001428 since, given the 70,000 samples in MNIST, this will result +in 100 outliers, which we can then look at in more detail. + +```python3 +%%time +outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(data) + + +``` + +``` +CPU times: user 1h 29min 10s, sys: 12.4 s, total: 1h 29min 22s +Wall time: 1h 29min 53s + + +``` + +It is worth noting how long that took. Over an hour and a half! Why did +it take so long? Because LOF requires a notion of density, which in turn +relies on a nearest neighbor type computation -- which is expensive in +sklearn for high dimensional data. This alone is potentially a reason to +look at reducing the dimension of the data -- it makes it more amenable +to existing techniques like LOF. + +Now that we have a set of outlier scores we can find the actual outlying +digit images -- these are the ones with scores equal to -1. Let's +extract that data, and check that we got 100 different digit images. + +```python3 +outlying_digits = data[outlier_scores == -1] +outlying_digits.shape + + + + +``` + +``` +(100, 784) + + + +``` + +Now that we have the outlying digit images the first question we should +be asking is "what do they look like?". Fortunately for us we can +convert the 784 dimensional vectors back into image and plot them, +making it easier to look at. Since we extracted the 100 most outlying +digit images we can just display a 10x10 grid of them. + +```python3 +fig, axes = plt.subplots(7, 10, figsize=(10,10)) +for i, ax in enumerate(axes.flatten()): + ax.imshow(outlying_digits[i].reshape((28,28))) + plt.setp(ax, xticks=[], yticks=[]) +plt.tight_layout() + + + +``` + +![Image](images/outliers_9_0.png) + +These do certainly look like somewhat strange looking handwritten +digits, so our outlier detection seems to be working to some extent. + +Now let's try a naive approach using UMAP and see how far that gets us. +First let's just apply UMAP directly with default parameters to the +MNIST data. + +```python3 +mapper = umap.UMAP().fit(data) + +``` + +Now we can see what we got using the new plotting tools in umap.plot. + +```python3 +umap.plot.points(mapper, labels=labels) + + +``` + +``` + + + + + +``` + +![Image](images/outliers_13_2.png) + +That looks like what we have come to expect from a UMAP embedding of +MNIST. The question is have we managed to preserve outliers well enough +that LOF can still find the bizarre digit images, or has the embedding +lost that information and contracted the outliers into the individual +digit clusters? We can simply apply LOF to the embedding and see what +that returns. + +```python3 +%%time +outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(mapper.embedding_) + +``` + +This was obviously much faster since we are operating in a much lower +dimensional space that is more amenable to the spatial indexing methods +that sklearn uses to find nearest neighbors. As before we extract the +outlying digit images, and verify that we got 100 of them, + +```python3 +outlying_digits = data[outlier_scores == -1] +outlying_digits.shape + + + + +``` + +``` +(100, 784) + + + +``` + +Now we need to plot the outlying digit images to see what kinds of digit +images this approach found to be particularly strange. + +```python3 +fig, axes = plt.subplots(7, 10, figsize=(10,10)) +for i, ax in enumerate(axes.flatten()): + ax.imshow(outlying_digits[i].reshape((28,28))) + plt.setp(ax, xticks=[], yticks=[]) +plt.tight_layout() + + + +``` + +![Image](images/outliers_19_0.png) + +In many ways this looks to be a *better* result than the original LOF in +the high dimensional space. While the digit images that the high +dimensional LOF found to be strange were indeed somewhat odd looking, +many of these digit images are considerably stranger -- significantly +odd line thickness, warped shapes, and images that are hard to even +recognise as digits. This helps to demonstrate a certain amount of +confirmation bias when examining outliers: since we expect things tagged +as outliers to be strange we tend to find aspects of them that justify +that classification, potentially unaware of how much stranger some of +the data may in fact be. This should make us wary of even this outlier +set: what else might lurk in the dataset? + +We can, in fact, potentially improve on this result by tuning the UMAP +embedding a little for the task of finding outliers. When UMAP combines +together the different local simplicial sets (see :doc:`how_umap_works` +for more details) the standard approach uses a union, but we could +instead take an intersection. An intersection ensures that outliers +remain disconnected, which is certainly beneficial when seeking to find +outliers. A downside of the intersection is that it tends to break up +the resulting simplicial set into many disconnected components and a lot +of the more non-local and global structure is lost, resulting in a lot +lower quality of the resulting embedding. We can, however, interpolate +between the union and intersection. In UMAP this is given by the +`set_op_mix_ratio`, where a value of 0.0 represents an intersection, +and a value of 1.0 represents a union (the default value is 1.0). By +setting this to a lower value, say 0.25, we can encourage the embedding +to do a better job of preserving outliers as outlying, while still +retaining the benefits of a union operation. + +```python3 +mapper = umap.UMAP(set_op_mix_ratio=0.25).fit(data) + +``` + +```python3 +umap.plot.points(mapper, labels=labels) + + +``` + +``` + + + + + +``` + +![Image](images/outliers_22_2.png) + +As you can see the embedding is not as well structured overall as when +we had a `set_op_mix_ratio` of 1.0, but we have potentially done a +better job of ensuring that outliers remain outlying. We can test that +hypothesis by running LOF on this embedding and looking at the resulting +digit images we get out. Ideally we should expect to find some +potentially even stranger results. + +```python3 +%%time +outlier_scores = sklearn.neighbors.LocalOutlierFactor(contamination=0.001428).fit_predict(mapper.embedding_) + +``` + +```python3 +outlying_digits = data[outlier_scores == -1] +outlying_digits.shape + + + + +``` + +``` +(100, 784) + + + +``` + +We have the expected 100 most outlying digit images, so let's visualise +the results and see if they really are particularly strange. + +```python3 +fig, axes = plt.subplots(10, 10, figsize=(10,10)) +for i, ax in enumerate(axes.flatten()): + ax.imshow(outlying_digits[i].reshape((28,28))) + plt.setp(ax, xticks=[], yticks=[]) +plt.tight_layout() + + + +``` + +![Image](images/outliers_27_0.png) + +Here we see that the line thickness variation (particularly "fat" +digits, or particularly "fine" lines) that the original embedding helped +surface come through even more strongly here. We also see a number of +clearly corrupted images with extra lines, dots, or strange blurring +occurring. + +So, in summary, using UMAP to reduce dimension prior to running +classical outlier detection methods such as LOF can improve both the +speed with which the algorithm runs, and the quality of results the +outlier detection can find. Furthermore we have introduced the +`set_op_mix_ratio` parameter, and explained how it can be used to +potentially improve the performance of outlier detection approaches +applied to UMAP embeddings. diff --git a/docs/parameters.md b/docs/parameters.md new file mode 100644 index 00000000..ea6663de --- /dev/null +++ b/docs/parameters.md @@ -0,0 +1,460 @@ + +# Basic UMAP Parameters + + +UMAP is a fairly flexible non-linear dimension reduction algorithm. It +seeks to learn the manifold structure of your data and find a low +dimensional embedding that preserves the essential topological structure +of that manifold. In this notebook we will generate some visualisable +4-dimensional data, demonstrate how to use UMAP to provide a +2-dimensional representation of it, and then look at how various UMAP +parameters can impact the resulting embedding. This documentation is +based on the work of Philippe Rivière for visionscarto.net. + +To start we'll need some basic libraries. First `numpy` will be needed +for basic array manipulation. Since we will be visualising the results +we will need `matplotlib` and `seaborn`. Finally we will need +`umap` for doing the dimension reduction itself. + +```python3 +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import seaborn as sns +import umap +%matplotlib inline + +``` + +```python3 +sns.set(style='white', context='poster', rc={'figure.figsize':(14,10)}) + +``` + +Next we will need some data to embed into a lower dimensional +representation. To make the 4-dimensional data "visualisable" we will +generate data uniformly at random from a 4-dimensional cube such that we +can interpret a sample as a tuple of (R,G,B,a) values specifying a color +(and translucency). Thus when we plot low dimensional representations +each point can be colored according to its 4-dimensional value. For this we +can use `numpy`. We will fix a random seed for the sake of +consistency. + +```python3 +np.random.seed(42) +data = np.random.rand(800, 4) + +``` + +Now we need to find a low dimensional representation of the data. As in +the Basic Usage documentation, we can do this by using the +`umap.umap_.UMAP.fit_transform` method on a `umap.umap_.UMAP` object. + +```python3 +fit = umap.UMAP() +%time u = fit.fit_transform(data) + + +``` + +``` +CPU times: user 7.73 s, sys: 211 ms, total: 7.94 s +Wall time: 6.8 s + + +``` + +The resulting value `u` is a 2-dimensional representation of the data. +We can visualise the result by using `matplotlib` to draw a scatter +plot of `u`. We can color each point of the scatter plot by the +associated 4-dimensional color from the source data. + +```python3 +plt.scatter(u[:,0], u[:,1], c=data) +plt.title('UMAP embedding of random colours'); + +``` + +![Image](images/parameters_8_1.png) + +As you can see the result is that the data is placed in 2-dimensional +space such that points that were nearby in 4-dimensional space (i.e. +are similar colors) are kept close together. Since we drew a random +selection of points in the color cube there is a certain amount of +induced structure from where the random points happened to clump up in +color space. + +UMAP has several hyperparameters that can have a significant impact on +the resulting embedding. In this notebook we will be covering the four +major ones: + +- `n_neighbors` +- `min_dist` +- `n_components` +- `metric` + +Each of these parameters has a distinct effect, and we will look at each +in turn. To make exploration simpler we will first write a short utility +function that can fit the data with UMAP given a set of parameter +choices, and plot the result. + +```python3 +def draw_umap(n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', title=''): + fit = umap.UMAP( + n_neighbors=n_neighbors, + min_dist=min_dist, + n_components=n_components, + metric=metric + ) + u = fit.fit_transform(data); + fig = plt.figure() + if n_components == 1: + ax = fig.add_subplot(111) + ax.scatter(u[:,0], range(len(u)), c=data) + if n_components == 2: + ax = fig.add_subplot(111) + ax.scatter(u[:,0], u[:,1], c=data) + if n_components == 3: + ax = fig.add_subplot(111, projection='3d') + ax.scatter(u[:,0], u[:,1], u[:,2], c=data, s=100) + plt.title(title, fontsize=18) + +``` + +### ``n_neighbors`` + + +This parameter controls how UMAP balances local versus global structure +in the data. It does this by constraining the size of the local +neighborhood UMAP will look at when attempting to learn the manifold +structure of the data. This means that low values of `n_neighbors` +will force UMAP to concentrate on very local structure (potentially to +the detriment of the big picture), while large values will push UMAP to +look at larger neighborhoods of each point when estimating the manifold +structure of the data, losing fine detail structure for the sake of +getting the broader of the data. + +We can see that in practice by fitting our dataset with UMAP using a +range of `n_neighbors` values. The default value of `n_neighbors` +for UMAP (as used above) is 15, but we will look at values ranging from +2 (a very local view of the manifold) up to 200 (a quarter of the data). + +```python3 +for n in (2, 5, 10, 20, 50, 100, 200): + draw_umap(n_neighbors=n, title='n_neighbors = {}'.format(n)) + + + +``` + +![Image](images/parameters_13_1.png) + +![Image](images/parameters_13_2.png) + +![Image](images/parameters_13_3.png) + +![Image](images/parameters_13_4.png) + +![Image](images/parameters_13_5.png) + +![Image](images/parameters_13_6.png) + +![Image](images/parameters_13_7.png) + +With a value of `n_neighbors=2` we see that UMAP merely glues together +small chains, but due to the narrow/local view, fails to see how those +connect together. It also leaves many different components (and even +singleton points). This represents the fact that from a fine detail +point of view the data is very disconnected and scattered throughout the +space. + +As `n_neighbors` is increased UMAP manages to see more of the overall +structure of the data, gluing more components together, and better +coverying the broader structure of the data. By the stage of +`n_neighbors=20` we have a fairly good overall view of the data +showing how the various colors interelate to each other over the whole +dataset. + +As `n_neighbors` increases further more and more focus in placed on +the overall structure of the data. This results in, with +`n_neighbors=200` a plot where the overall structure (blues, greens, +and reds; high luminance versus low) is well captured, but at the loss +of some of the finer local structure (individual colors are no longer +necessarily immediately near their closest color match). + +This effect well exemplifies the local/global tradeoff provided by +`n_neighbors`. + +### ``min_dist`` + + +The `min_dist` parameter controls how tightly UMAP is allowed to pack +points together. It, quite literally, provides the minimum distance +apart that points are allowed to be in the low dimensional +representation. This means that low values of `min_dist` will result +in clumpier embeddings. This can be useful if you are interested in +clustering, or in finer topological structure. Larger values of +`min_dist` will prevent UMAP from packing points together and will +focus on the preservation of the broad topological structure +instead. + +The default value for `min_dist` (as used above) is 0.1. We will look +at a range of values from 0.0 through to 0.99. + +```python3 +for d in (0.0, 0.1, 0.25, 0.5, 0.8, 0.99): + draw_umap(min_dist=d, title='min_dist = {}'.format(d)) + + +``` + +![Image](images/parameters_16_1.png) + +![Image](images/parameters_16_2.png) + +![Image](images/parameters_16_3.png) + +![Image](images/parameters_16_4.png) + +![Image](images/parameters_16_5.png) + +![Image](images/parameters_16_6.png) + +Here we see that with `min_dist=0.0` UMAP manages to find small +connected components, clumps and strings in the data, and emphasises +these features in the resulting embedding. As `min_dist` is increased +these structures are pushed apart into softer more general features, +providing a better overarching view of the data at the loss of the more +detailed topological structure. + +### ``n_components`` + + +As is standard for many `scikit-learn` dimension reduction algorithms +UMAP provides a `n_components` parameter option that allows the user +to determine the dimensionality of the reduced dimension space we will +be embedding the data into. Unlike some other visualisation algorithms +such as t-SNE, UMAP scales well in the embedding dimension, so you can use it +for more than just visualisation in 2- or 3-dimensions. + +For the purposes of this demonstration (so that we can see the effects +of the parameter) we will only be looking at 1-dimensional and +3-dimensional embeddings, which we have some hope of visualizing. + +First of all we will set `n_components` to 1, forcing UMAP to embed +the data in a line. For visualisation purposes we will randomly +distribute the data on the y-axis to provide some separation between +points. + +```python3 +draw_umap(n_components=1, title='n_components = 1') + + +``` + +![Image](images/parameters_19_1.png) + +Now we will try `n_components=3`. For visualisation we will make use +of `matplotlib`'s basic 3-dimensional plotting. + +```python3 +draw_umap(n_components=3, title='n_components = 3') + + +``` + +``` +/opt/anaconda3/envs/umap_dev/lib/python3.6/site-packages/sklearn/metrics/pairwise.py:257: RuntimeWarning: invalid value encountered in sqrt + return distances if squared else np.sqrt(distances, out=distances) + + + +``` + +![Image](images/parameters_21_1.png) + +Here we can see that with more dimensions in which to work UMAP has an +easier time separating out the colors in a way that respects the +topological structure of the data. + +As mentioned, there is really no requirement to stop at `n_components=3`. If you are interested in (density based) clustering, or other +machine learning techniques, it can be beneficial to pick a larger +embedding dimension (say 10, or 50) closer to the the dimension of the +underlying manifold on which your data lies. + +### ``metric`` + + +The final UMAP parameter we will be considering in this notebook is the +`metric` parameter. This controls how distance is computed in the +ambient space of the input data. By default UMAP supports a wide variety +of metrics, including: + +**Minkowski style metrics** + +- euclidean +- manhattan +- chebyshev +- minkowski + +**Miscellaneous spatial metrics** + +- canberra +- braycurtis +- haversine + +**Normalized spatial metrics** + +- mahalanobis +- wminkowski +- seuclidean + +**Angular and correlation metrics** + +- cosine +- correlation + +**Metrics for binary data** + +- hamming +- jaccard +- dice +- russellrao +- kulsinski +- rogerstanimoto +- sokalmichener +- sokalsneath +- yule + +**Precomputed** + +All of the above metrics assume your input data is "raw" in some N-dimensional space. +Sometimes, you have already calculated the pairwise distances between points, +and your input data is a distance/similarity matrix. +In this case, you can do something like `UMAP(metric='precomputed').fit_transform()`. + +Any of which can be specified by setting `metric=''`; for +example to use cosine distance as the metric you would use +`metric='cosine'`. + +UMAP offers more than this however -- it supports custom user defined +metrics as long as those metrics can be compiled in `nopython` mode by +numba. For this notebook we will be looking at such custom metrics. To +define such metrics we'll need numba ... + +```python3 +import numba + +``` + +For our first custom metric we'll define the distance to be the absolute +value of difference in the red channel. + +```python3 +@numba.njit() +def red_channel_dist(a,b): + return np.abs(a[0] - b[0]) + +``` + +To get more adventurous it will be useful to have some colorspace +conversion -- to keep things simple we'll just use HSL formulas to +extract the hue, saturation, and lightness from an (R,G,B) tuple. + +```python3 +@numba.njit() +def hue(r, g, b): + cmax = max(r, g, b) + cmin = min(r, g, b) + delta = cmax - cmin + if cmax == r: + return ((g - b) / delta) % 6 + elif cmax == g: + return ((b - r) / delta) + 2 + else: + return ((r - g) / delta) + 4 + +@numba.njit() +def lightness(r, g, b): + cmax = max(r, g, b) + cmin = min(r, g, b) + return (cmax + cmin) / 2.0 + +@numba.njit() +def saturation(r, g, b): + cmax = max(r, g, b) + cmin = min(r, g, b) + chroma = cmax - cmin + light = lightness(r, g, b) + if light == 1: + return 0 + else: + return chroma / (1 - abs(2*light - 1)) + +``` + +With that in hand we can define three extra distances. The first simply +measures the difference in hue, the second measures the euclidean +distance in a combined saturation and lightness space, while the third +measures distance in the full HSL space. + +```python3 +@numba.njit() +def hue_dist(a, b): + diff = (hue(a[0], a[1], a[2]) - hue(b[0], b[1], b[2])) % 6 + if diff < 0: + return diff + 6 + else: + return diff + +@numba.njit() +def sl_dist(a, b): + a_sat = saturation(a[0], a[1], a[2]) + b_sat = saturation(b[0], b[1], b[2]) + a_light = lightness(a[0], a[1], a[2]) + b_light = lightness(b[0], b[1], b[2]) + return (a_sat - b_sat)**2 + (a_light - b_light)**2 + +@numba.njit() +def hsl_dist(a, b): + a_sat = saturation(a[0], a[1], a[2]) + b_sat = saturation(b[0], b[1], b[2]) + a_light = lightness(a[0], a[1], a[2]) + b_light = lightness(b[0], b[1], b[2]) + a_hue = hue(a[0], a[1], a[2]) + b_hue = hue(b[0], b[1], b[2]) + return (a_sat - b_sat)**2 + (a_light - b_light)**2 + (((a_hue - b_hue) % 6) / 6.0) + +``` + +With such custom metrics in hand we can get UMAP to embed the data using +those metrics to measure the distance between our input data points. Note +that `numba` provides significant flexibility in what we can do in +defining distance functions. Despite this we retain the high performance +we expect from UMAP even using such custom functions. + +```python3 +for m in ("euclidean", red_channel_dist, sl_dist, hue_dist, hsl_dist): + name = m if type(m) is str else m.__name__ + draw_umap(n_components=2, metric=m, title='metric = {}'.format(name)) + + +``` + +![Image](images/parameters_32_1.png) + +![Image](images/parameters_32_2.png) + +![Image](images/parameters_32_3.png) + +![Image](images/parameters_32_4.png) + +![Image](images/parameters_32_5.png) + +And here we can see the effects of the metrics quite clearly. The pure +red channel correctly sees the data as living on a one dimensional +manifold, the hue metric interprets the data as living in a circle, and +the HSL metric fattens out the circle according to the saturation and +lightness. This provides a reasonable demonstration of the power and +flexibility of UMAP in understanding the underlying topology of data, +and finding a suitable low dimensional representation of that topology. diff --git a/docs/parametric_umap.md b/docs/parametric_umap.md new file mode 100644 index 00000000..8e097ec1 --- /dev/null +++ b/docs/parametric_umap.md @@ -0,0 +1,247 @@ +# Parametric (neural network) Embedding + + + + :language: python + +UMAP is comprised of two steps: First, compute a graph representing your data, second, learn an embedding for that graph: + +![Image](images/umap-only.png) + +Parametric UMAP replaces the second step, minimizing the same objective function as UMAP (we'll call it non-parametric UMAP here), but learning the relationship between the data and embedding using a neural network, rather than learning the embeddings directly: + +![Image](images/pumap-only.png) + +Parametric UMAP is simply a subclass of UMAP, so it can be used just like nonparametric UMAP, replacing :python:`umap.UMAP` with :python:`parametric_umap.ParametricUMAP`. The most basic usage of parametric UMAP would be to simply replace UMAP with ParametricUMAP in your code: + +```python3 +from umap.parametric_umap import ParametricUMAP +embedder = ParametricUMAP() +embedding = embedder.fit_transform(my_data) + +``` + +In this implementation, we use Keras and Tensorflow as a backend to train that neural network. The added complexity of a learned embedding presents a number of configurable settings available in addition to those in non-parametric UMAP. A set of Jupyter notebooks walking you through these parameters are available on the [GitHub repository](https://github.com/lmcinnes/umap/tree/master/notebooks/Parametric_UMAP) + + +## Defining your own network + + +By default, Parametric UMAP uses 3-layer 100-neuron fully-connected neural network. To extend Parametric UMAP to use a more complex architecture, like a convolutional neural network, we simply need to define the network and pass it in as an argument to ParametricUMAP. This can be done easliy, using tf.keras.Sequential. Here's an example for MNIST: + +```python3 +# define the network +import tensorflow as tf +dims = (28, 28, 1) +n_components = 2 +encoder = tf.keras.Sequential([ + tf.keras.layers.InputLayer(input_shape=dims), + tf.keras.layers.Conv2D( + filters=32, kernel_size=3, strides=(2, 2), activation="relu", padding="same" + ), + tf.keras.layers.Conv2D( + filters=64, kernel_size=3, strides=(2, 2), activation="relu", padding="same" + ), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(units=256, activation="relu"), + tf.keras.layers.Dense(units=256, activation="relu"), + tf.keras.layers.Dense(units=n_components), +]) +encoder.summary() + +``` + +To load pass the data into ParametricUMAP, we first need to flatten it from 28x28x1 images to a 784-dimensional vector. + +```python3 +from tensorflow.keras.datasets import mnist +(train_images, Y_train), (test_images, Y_test) = mnist.load_data() +train_images = train_images.reshape((train_images.shape[0], -1))/255. +test_images = test_images.reshape((test_images.shape[0], -1))/255. + + +``` + +We can then pass the network into ParametricUMAP and train: + +```python3 +# pass encoder network to ParametricUMAP +embedder = ParametricUMAP(encoder=encoder, dims=dims) +embedding = embedder.fit_transform(train_images) + +``` + +If you are unfamilar with Tensorflow/Keras and want to train your own model, we reccomend that you take a look at the [Tensorflow documentation](https://www.tensorflow.org/). + + +## Saving and loading your model + + +Unlike non-parametric UMAP Parametric UMAP cannot be saved simply by pickling the UMAP object because of the Keras networks it contains. To save Parametric UMAP, there is a built in function: + +```python3 +embedder.save('/your/path/here') + +``` + +You can then load parametric UMAP elsewhere: + +```python3 +from umap.parametric_umap import load_ParametricUMAP +embedder = load_ParametricUMAP('/your/path/here') + +``` + +This loads both the UMAP object and the parametric networks it contains. + + +## Plotting loss + +Parametric UMAP monitors loss during training using Keras. That loss will be printed after each epoch during training. This loss is saved in :python:`embedder._history`, and can be plotted: + +```python3 +print(embedder._history) +fig, ax = plt.subplots() +ax.plot(embedder._history['loss']) +ax.set_ylabel('Cross Entropy') +ax.set_xlabel('Epoch') + +``` + +![Image](images/umap-loss.png) + +Much like other keras models, if you continue to train your model via the :python:`fit` method of the model, the :python:`embedder._history` will be updated with further training epoch losses. + +## Parametric inverse_transform (reconstruction) + +To use a second neural network to learn an inverse mapping between data and embeddings, we simply need to pass `parametric_reconstruction= True` to the ParametricUMAP. + + +Like the encoder, a custom decoder can also be passed to ParametricUMAP, e.g. + +```python3 + decoder = tf.keras.Sequential([ + tf.keras.layers.InputLayer(input_shape=(n_components)), + tf.keras.layers.Dense(units=256, activation="relu"), + tf.keras.layers.Dense(units=7 * 7 * 256, activation="relu"), + tf.keras.layers.Reshape(target_shape=(7, 7, 256)), + tf.keras.layers.UpSampling2D((2)), + tf.keras.layers.Conv2D( + filters=64, kernel_size=3, padding="same", activation="relu" + ), + tf.keras.layers.UpSampling2D((2)), + tf.keras.layers.Conv2D( + filters=32, kernel_size=3, padding="same", activation="relu" + ), + + ]) + +``` + +In addition, validation data can be used to test reconstruction loss on out-of-dataset samples: + +```python3 +validation_images = test_images.reshape((test_images.shape[0], -1))/255. + +``` + +Finally, we can pass the validation data and the networks to ParametricUMAP and train: + + +```python3 + embedder = ParametricUMAP( + encoder=encoder, + decoder=decoder, + dims=dims, + parametric_reconstruction= True, + reconstruction_validation=validation_images, + verbose=True, + ) + embedding = embedder.fit_transform(train_images) + + +``` + +## Autoencoding UMAP + + + +In the example above, the encoder is trained to minimize UMAP loss, and the decoder is trained to minimize reconstruction loss. To train the encoder jointly on both UMAP loss and reconstruction loss, pass :python:`autoencoder_loss = True` into the ParametricUMAP. + + +```python3 + embedder = ParametricUMAP( + encoder=encoder, + decoder=decoder, + dims=dims, + parametric_reconstruction= True, + reconstruction_validation=validation_images, + autoencoder_loss = True, + verbose=True, + ) + + +``` + +## Early stopping and Keras callbacks + + +It can sometimes be useful to train the embedder until some plateau in training loss is met. In deep learning, early stopping is one way to do this. Keras provides custom [callbacks](https://keras.io/api/callbacks/) that allow you to implement checks during training, such as early stopping. We can use callbacks, such as early stopping, with ParametricUMAP to stop training early based on a predefined training threshold, using the :python:`keras_fit_kwargs` argument: + +```python3 +keras_fit_kwargs = {"callbacks": [ + tf.keras.callbacks.EarlyStopping( + monitor='loss', + min_delta=10**-2, + patience=10, + verbose=1, + ) +]} + +embedder = ParametricUMAP( + verbose=True, + keras_fit_kwargs = keras_fit_kwargs, + n_training_epochs=20 +) + + +``` + +We also passed in :python:`n_training_epochs = 20`, allowing early stopping to end training before 20 epochs are reached. + + +## Additional important parameters + + +* **batch_size:** ParametricUMAP in trained over batches of edges randomly sampled from the UMAP graph, and then trained via gradient descent. ParametricUMAP defaults to a batch size of 1000 edges, but can be adjusted to a value that fits better on your GPU or CPU. +* **loss_report_frequency:** If set to 1, an epoch in in the Keras embedding refers to a single iteration over the graph computed in UMAP. Setting :python:`loss_report_frequency` to 10, would split up that epoch into 10 seperate epochs, for more frequent reporting. +* **n_training_epochs:** The number of epochs over the UMAP graph to train for (irrespective of :python:`loss_report_frequency`). Training the network for multiple epochs will result in better embeddings, but take longer. This parameter is different than :python:`n_epochs` in the base UMAP class, which corresponds to the maximum number of times an edge is trained in a single ParametricUMAP epoch. +* **optimizer:** The optimizer used to train the neural network. by default Adam (:python:`tf.keras.optimizers.Adam(1e-3)`) is used. You might be able to speed up or improve training by using a different optimizer. +* **parametric_embedding:** If set to false, a non-parametric embedding is learned, using the same code as the parametric embedding, which can serve as a direct comparison between parametric and non-parametric embedding using the same optimizer. The parametric embeddings are performed over the entire dataset simultaneously. +* **global_correlation_loss_weight:** Whether to additionally train on correlation of global pairwise relationships (multidimensional scaling) +* **landmark_loss_fn:** The loss function to use when re-training on landmarked data, where you have provided a desired location in the embedding space to the :python:`fit` method of the model. By default, euclidean loss is used. For more information on re-training, landmarks, and why you might use them, see :doc:`transform_landmarked_pumap`. +* **landmark_loss_weight:** How to weight the landmark loss relative to umap loss, by default 1.0. + +## Extending the model + +You may want to customize parametric UMAP beyond what we have implemented in this package. To make it as easy as possible to tinker around with Parametric UMAP, we made a few Jupyter notebooks that show you how to extend Parametric UMAP to your own use-cases. + +* https://colab.research.google.com/drive/1WkXVZ5pnMrm17m0YgmtoNjM_XHdnE5Vp?usp=sharing + +## Citing our work + +If you use Parametric UMAP in your work, please cite our paper: + +```bibtex +@article{sainburg2021parametric, + title={Parametric UMAP Embeddings for Representation and Semisupervised Learning}, + author={Sainburg, Tim and McInnes, Leland and Gentner, Timothy Q}, + journal={Neural Computation}, + volume={33}, + number={11}, + pages={2881--2907}, + year={2021}, + publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} +``` + + } diff --git a/docs/performance.md b/docs/performance.md new file mode 100644 index 00000000..8656f1a5 --- /dev/null +++ b/docs/performance.md @@ -0,0 +1,304 @@ +# Performance Comparison of Dimension Reduction Implementations + + +Different dimension reduction techniques can have quite different +computational complexity. Beyond the algorithm itself there is also the +question of how exactly it is implemented. These two factors can have a +significant role in how long it actually takes to run a given dimension +reduction. Furthermore the nature of the data you are trying to reduce +can also matter – mostly the involves the dimensionality of the original +data. Here we will take a brief look at the performance characterstics +of a number of dimension reduction implementations. + +To start let’s get the basic tools we’ll need loaded up – numpy and +pandas obviously, but also tools to get and resample the data, and the +time module so we can perform some basic benchmarking. + +```python +import numpy as np +import pandas as pd +from sklearn.datasets import fetch_openml +from sklearn.utils import resample +import time + +``` + +Next we’ll need the actual dimension reduction implementations. For the +purposes of this explanation we’ll mostly stick with +[scikit-learn](http://scikit-learn.org/stable/), but for the sake of +comparison we’ll also include the +[MulticoreTSNE](https://github.com/DmitryUlyanov/Multicore-TSNE) +implementation of t-SNE, and +[openTSNE](https://github.com/pavlin-policar/openTSNE) both of which +have historically had significantly better performance than scikit-learn +t-SNE (more recent versions of scikit-learn have improved t-SNE +performance). + +```python +from sklearn.manifold import TSNE, LocallyLinearEmbedding, Isomap, MDS, SpectralEmbedding +from sklearn.decomposition import PCA +from MulticoreTSNE import MulticoreTSNE +from openTSNE import TSNE as OpenTSNE +from umap import UMAP + +``` + +Next we’ll need out plotting tools, and, of course, some data to work +with. For this performance comparison we’ll default to the now standard +benchmark of manifold learning: the MNIST digits dataset. We can use +scikit-learn’s `fetch_openml` to grab it for us. + +```python +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline + +``` + +```python +sns.set(context='notebook', + rc={'figure.figsize':(12,10)}, + palette=sns.color_palette('tab10', 10)) + +``` + +```python +mnist = fetch_openml('mnist_784', version=1, return_X_y=True) + +``` + +```python +mnist_data = mnist[0] +mnist_labels = mnist[1].astype(int) + +``` + +Now it is time to start looking at performance. To start with let’s look +at how performance scales with increasing dataset size. + +## Performance scaling by dataset size + + +As the size of a dataset increases the runtime of a given dimension +reduction algorithm will increase at varying rates. If you ever want to +run your algorithm on larger datasets you will care not just about the +comparative runtime on a single small dataset, but how the performance +scales out as you move to larger datasets. We can similate this by +subsampling from MNIST digits (via scikit-learn’s convenient +`resample` utility) and looking at the runtime for varying sized +subsamples. Since there is some randomness involved here (both in the +subsample selection, and in some of the algorithms which have stochastic +aspects) we will want to run a few examples for each dataset size. We +can easily package all of this up in a simple function that will return +a convenient pandas dataframe of dataset sizes and runtimes given an +algorithm. + +```python +def data_size_scaling(algorithm, data, sizes=[100, 200, 400, 800, 1600], n_runs=5): + result = [] + for size in sizes: + for run in range(n_runs): + subsample = resample(data, n_samples=size) + start_time = time.time() + algorithm.fit(subsample) + elapsed_time = time.time() - start_time + del subsample + result.append((size, elapsed_time)) + return pd.DataFrame(result, columns=('dataset size', 'runtime (s)')) + +``` + +Now we just want to run this for each of the various dimension reduction +implementations so we can look at the results. Since we don’t know how +long these runs might take we’ll start off with a very small set of +samples, scaling up to only 1600 samples. + +```python +all_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + OpenTSNE(), + TSNE(), + LocallyLinearEmbedding(), + SpectralEmbedding(), + Isomap(), + MDS(), +] +performance_data = {} +for algorithm in all_algorithms: + if 'openTSNE' in str(algorithm.__class__): + alg_name = "OpenTSNE" + elif 'MulticoreTSNE' in str(algorithm.__class__): + alg_name = "MulticoreTSNE" + else: + alg_name = str(algorithm).split('(')[0] + + performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, n_runs=5) + + print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") + + +``` + +``` +[Sat Feb 22 09:50:24 2020] Completed PCA +[Sat Feb 22 09:51:23 2020] Completed UMAP +[Sat Feb 22 09:53:24 2020] Completed MulticoreTSNE +[Sat Feb 22 10:00:50 2020] Completed OpenTSNE +[Sat Feb 22 10:02:22 2020] Completed TSNE +[Sat Feb 22 10:02:44 2020] Completed LocallyLinearEmbedding +[Sat Feb 22 10:03:06 2020] Completed SpectralEmbedding +[Sat Feb 22 10:03:31 2020] Completed Isomap +[Sat Feb 22 10:11:45 2020] Completed MDS + + +``` + +Now let’s plot the results so we can see what is going on. We’ll use +seaborn’s regression plot to interpolate the effective scaling. For some +algorithms this can be a little noisy, especially in this relatively +small dataset regime, but it will give us a good idea of what is going +on. + +```python +for alg_name, perf_data in performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend() + + +``` + +![Image](images/performance_15_1.png) + +We can see straight away that there are some outliers here. It is notable that +openTSNE does poorly on small datasets. It does not have the scaling properties of MDS however; for +larger dataset sizes MDS is going to quickly become completely +unmanageable which openTSNE has fairly flat scaling. At the same time +MulticoreTSNE demonstrates that t-SNE can run fairly efficiently. It is +hard to tell much about the other implementations other than the fact +that PCA is far and away the fastest option. To see more we’ll have to +look at runtimes on larger dataset sizes. Both MDS, Isomap and SpectralEmbedding +will actually take too long to run so let’s restrict ourselves to +the fastest performing implementations and see what happens as we extend +out to larger dataset sizes. + +```python +fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + OpenTSNE(), + TSNE(), + LocallyLinearEmbedding(), +] +fast_performance_data = {} +for algorithm in fast_algorithms: + if 'openTSNE' in str(algorithm.__class__): + alg_name = "OpenTSNE" + elif 'MulticoreTSNE' in str(algorithm.__class__): + alg_name = "MulticoreTSNE" + else: + alg_name = str(algorithm).split('(')[0] + + fast_performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, + sizes=[1600, 3200, 6400, 12800, 25600], n_runs=4) + + print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") + + +``` + +``` +[Sat Feb 22 10:12:15 2020] Completed PCA +[Sat Feb 22 10:14:51 2020] Completed UMAP +[Sat Feb 22 11:16:05 2020] Completed MulticoreTSNE +[Sat Feb 22 11:50:17 2020] Completed OpenTSNE +[Sat Feb 22 13:06:38 2020] Completed TSNE +[Sat Feb 22 14:14:36 2020] Completed LocallyLinearEmbedding + + +``` + +```python +for alg_name, perf_data in fast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend() + + + +``` + +![Image](images/performance_18_1.png) + +At this point we begin to see some significant differentiation among the +different implementations. In the earlier plot OpenTSNE looked to be +performing relatively poorly, but now the scaling effects kick in, and +we see that is is faster than most. Similarly MulticoreTSNE looked to be +slower than some of the other algorithms in th earlier plot, but as we +scale out to larger datasets we see that its relative scaling +performance is superior to the scikit-learn implementations of +TSNE and locally linear embedding. + +It is probably worth extending out further – up to the full MNIST digits +dataset. To manage to do that in any reasonable amount of time we’ll +have to restrict out attention to an even smaller subset of +implementations. We will pare things down to just OpenTSNE, +MulticoreTSNE, PCA and UMAP. + +```python +very_fast_algorithms = [ + PCA(), + UMAP(), + MulticoreTSNE(), + OpenTSNE(), +] +vfast_performance_data = {} +for algorithm in very_fast_algorithms: + if 'openTSNE' in str(algorithm.__class__): + alg_name = "OpenTSNE" + elif 'MulticoreTSNE' in str(algorithm.__class__): + alg_name = "MulticoreTSNE" + else: + alg_name = str(algorithm).split('(')[0] + + vfast_performance_data[alg_name] = data_size_scaling(algorithm, mnist_data, + sizes=[3200, 6400, 12800, 25600, 51200, 70000], n_runs=2) + + print(f"[{time.asctime(time.localtime())}] Completed {alg_name}") + + +``` + +``` +[Sat Feb 22 14:15:22 2020] Completed PCA +[Sat Feb 22 14:18:59 2020] Completed UMAP +[Sat Feb 22 17:04:58 2020] Completed MulticoreTSNE +[Sat Feb 22 17:54:14 2020] Completed OpenTSNE + + +``` + +```python +for alg_name, perf_data in vfast_performance_data.items(): + sns.regplot('dataset size', 'runtime (s)', perf_data, order=2, label=alg_name) +plt.legend() + + +``` + +![Image](images/performance_21_1.png) + +Here we see UMAP’s advantages over t-SNE really coming to the forefront. +While UMAP is clearly slower than PCA, its scaling performance is +dramatically better than MulticoreTSNE, and, despite the impressive +scaling performance of openTSNE, UMAP continues to outperform it. Based +on the slopes of the lines, for even larger datasets the difference +between UMAP and t-SNE is only going to grow. + +This concludes our look at scaling by dataset size. The short summary is +that PCA is far and away the fastest option, but you are potentially +giving up a lot for that speed. UMAP, while not competitive with PCA, is +clearly the next best option in terms of performance among the +implementations explored here. Given the quality of results that UMAP +can provide we feel it is clearly a good option for dimension reduction. diff --git a/docs/plotting.md b/docs/plotting.md new file mode 100644 index 00000000..ffe9e406 --- /dev/null +++ b/docs/plotting.md @@ -0,0 +1,485 @@ +# Plotting UMAP results + + +UMAP is often used for visualization by reducing data to 2-dimensions. +Since this is such a common use case the umap package now includes +utility routines to make plotting UMAP results simple, and provide a +number of ways to view and diagnose the results. Rather than seeking to +provide a comprehensive solution that covers all possible plotting needs +this umap extension seeks to provide a simple to use interface to make +the majority of plotting needs easy, and help provide sensible plotting +choices wherever possible. To get started looking at the plotting +options let's load a variety of data to work with. + +```python3 +import sklearn.datasets +import pandas as pd +import numpy as np +import umap + +``` + +```python3 +pendigits = sklearn.datasets.load_digits() +mnist = sklearn.datasets.fetch_openml('mnist_784') +fmnist = sklearn.datasets.fetch_openml('Fashion-MNIST') + +``` + +To start we will fit a UMAP model to the pendigits data. This is as +simple as running the fit method and assigning the result to a variable. + +```python3 +mapper = umap.UMAP().fit(pendigits.data) + +``` + +If we want to do plotting we will need the `umap.plot` package. While +the umap package has a fairly small set of requirements it is worth +noting that if you want to using `umap.plot` you will need a variety +of extra libraries that are not in the default requirements for umap. In +particular you will need: + +- [matplotlib](https://matplotlib.org/) +- [pandas](https://pandas.pydata.org/) +- [datashader](http://datashader.org/) +- [bokeh](https://bokeh.pydata.org/en/latest/) +- [holoviews](http://holoviews.org/) + +All should be either pip or conda installable. With those in hand you +can import the `umap.plot` package. + +```python3 +import umap.plot + + +``` + +Now that we have the package loaded, how do we use it? The most +straightforward thing to do is plot the umap results as points. We can +achieve this via the function `umap.plot.points`. In its most basic +form you can simply pass the trained UMAP model to `umap.plot.points`: + +```python3 +umap.plot.points(mapper) + + +``` + +![Image](images/plotting_8_2.png) + +As you can see we immediately get a scatterplot of the UMAP embedding. +Note that the function automatically selects a point-size based on the +data density, and watermarks the image with the UMAP parameters that +were used (this will include the metric if it is non-standard). The +function also returns the matplotlib axes object associated to the plot, +so further matplotlib functions, such as adding titles, axis labels etc. +can be applied by the user if required. + +It is common for data passed to UMAP to have an associated set of +labels, which may have been derived from ground-truth, from clustering, +or via other means. In such cases it is desirable to be able to color +the scatterplot according to the labelling. We can do this by simply +passing the array of label information in with the `labels` keyword. +The `umap.plot.points` function will color the data with a +categorical colormap according to the labels provided. + +```python3 +umap.plot.points(mapper, labels=pendigits.target) + + +``` + +![Image](images/plotting_10_1.png) + +Alternatively you may have extra data that is continuous rather than +categorical. In this case you will want to use a continuous colormap to +shade the data. Again this is straightforward to do -- pass in the +continuous data with the `values` keyword and data will be colored +accordingly using a continuous colormap. + +Furthermore, if you don't like the default color choices the +`umap.plot.points` function offers a number of 'themes' that provide +predefined color choices. Themes include: + +- fire +- viridis +- inferno +- blue +- red +- green +- darkblue +- darkred +- darkgreen + +Here we will make use of the 'fire' theme to demonstrate how simple it +is to change the aesthetics. + +```python3 +umap.plot.points(mapper, values=pendigits.data.mean(axis=1), theme='fire') + +``` + +![Image](images/plotting_12_1.png) + +If you want greater control you can specify exact colormaps and +background colors. For example here we want to color the data by label, +but use a black background and use the 'Paired' colormap for the +categorical coloring (passed as `color_key_cmap`; the `cmap` keyword +defines the continuous colormap). + +```python3 +umap.plot.points(mapper, labels=pendigits.target, color_key_cmap='Paired', background='black') + +``` + +![Image](images/plotting_14_1.png) + +Many more options are available including a `color_key` to specify a +dictionary mapping of discrete labels to colors, `cmap` to specify the +continous colormap, or the width and height of the resulting plot. +Again, this does not provide comprehensive control of the plot +aesthetics, but the goal here is to provide a simple to use interface +rather than the ability for the user to fine tune all aspects -- users +seeking such control are far better served making use of the individual +underlying packages (matplotlib, datashader, and bokeh) by themselves. + +## Plotting larger datasets + + +Once you have a lot of data it becomes easier for a simple scatter plot +to lie to you. Most notably overplotting, where markers for points +overlap and pile up on top of each other, can deceive you into thinking +that extremely dense clumps may only contain a few points. While there +are things that can be done to help remedy this, such as reducing the +point size, or adding an alpha channel, few are sufficient to be sure +the plot isn't subtly lying to you in some way. `This essay +`_ in +the datashader documentation does an excellent job of describing the +issues with overplotting, why the obvious solutions are not quite +sufficient, and how to get around the problem. To make life easier for +users the `umap.plot` package will automatically switch to using +datashader for rendering once your dataset gets large enough. This helps +to ensure you don't get fooled by overplotting. We can see this in +action by working with one of the larger datasets such as Fashion-MNIST. + +```python3 +mapper = umap.UMAP().fit(fmnist.data) + +``` + +Having fit the data with UMAP we can call `umap.plot.points` exactly +as before, but this time, since the data is large enough to have +potential overplotting, datashader will be used in the background for +rendering. + +```python3 +umap.plot.points(mapper) + + +``` + +![Image](images/plotting_19_2.png) + +All the same plot options as before hold, so we can color by labels, and +apply the same themes, and it will all seamlessly use datashader for the +actual rendering. Thus, regardless of how much data you have +`umap.plot.points` will render it well with a transparent user +interface. You, as a user, don't need to worry about switching to +plotting with datashader, or how to convert your plotting to its +slightly different API -- you can just use the same API and trust the +results you get. + +```python3 +umap.plot.points(mapper, labels=fmnist.target, theme='fire') + + +``` + +![Image](images/plotting_21_2.png) + +## Interactive plotting, and hover tools + + +Rendering good looking static plots is important, but what if you want +to be able to interact with your data -- pan around, and zoom in on the +clusters to see the finer structure? What if you want to annotate your +data with more complex labels than merely colors? Wouldn't it be good to +be able to hover over data points and get more information about the +individual point? Since this is a very common use case `umap.plot` +tries to make it easy to quickly generate such plots, and provide basic +utilities to allow you to have annotated hover tools working quickly. +Again, the goal is not to provide a comprehensive solution that can do +everything, but rather a simple to use and consistent API to get users +up and running fast. + +To make a good example of this let's use a subset of the Fashion MNIST +dataset. We can quickly train a new mapper object on that. + +```python3 +mapper = umap.UMAP().fit(fmnist.data[:30000]) + +``` + +The goal is to be able to hover over different points and see data +associated with the given point (or points) under the cursor. For this +simple demonstration we'll just use the target information of the point. +To create hover information you need to construct a dataframe of all the +data you would like to appear in the hover. Each row should correspond +to a source of data points (appearing in the same order), and the columns +can provide whatever extra data you would like to display in the hover +tooltip. In this case we'll need a dataframe that can include the index +of the point, its target number, and the actual name of the type of +fashion item that target corresponds to. This is easy to quickly put +together using pandas. + +```python3 +hover_data = pd.DataFrame({'index':np.arange(30000), + 'label':fmnist.target[:30000]}) +hover_data['item'] = hover_data.label.map( + { + '0':'T-shirt/top', + '1':'Trouser', + '2':'Pullover', + '3':'Dress', + '4':'Coat', + '5':'Sandal', + '6':'Shirt', + '7':'Sneaker', + '8':'Bag', + '9':'Ankle Boot', + } +) + +``` + +For interactive use the `umap.plot` package makes use of bokeh. Bokeh +has several output methods, but in the approach we'll be outputting +inline in a notebook. We have to enable this using the +`output_notebook` function. Alteratively we could use `output_file` +or other similar options -- see the bokeh documentation for more +details. + +```python3 +umap.plot.output_notebook() + + + +``` + + + +
+ + Loading BokehJS ... +
+ + + + + +Now we can make an interactive plot using `umap.plot.interactive`. +This has a very similar API to the `umap.plot.points` approach, but +also supports a `hover_data` keyword which, if passed a suitable +dataframe, will provide hover tooltips in the interactive plot. Since +bokeh allows different outputs, to display it in the notebook we will +have to take the extra stop of calling `show` on the result. + +```python3 +p = umap.plot.interactive(mapper, labels=fmnist.target[:30000], hover_data=hover_data, point_size=2) +umap.plot.show(p) + + + +``` + +[View html file](plotting_interactive_example.html) + + + + + + +We get the sort of result one would like -- a fully interactive plot +that can be zoomed in on, and more, but we also now have an interactive +hover tool which presents the data from the dataframe we constructed. +This allows a quick and easy method to get up and running with a richer +interactive exploration of your UMAP plot. `umap.plot.interactive` +supports all the same aesthetic parameters as `umap.plot.points` so +you can theme your plot, color by label or value, and other similar +operations explained above for `umap.plot.points`. + +## Interactive plotting with Nomic Atlas + + +For interactive exploration, especially with large datasets, you can use [Nomic Atlas](https://atlas.nomic.ai/). Nomic Atlas is a platform for embedding generation, visualization, analysis, retrieval, and everything you need to operationalize your embeddings and make them useful for your applications. It directly integrates UMAP as one of its projection models, allowing you to leverage UMAP within a powerful visualization environment. + +Nomic Atlas handles the embedding and UMAP dimensionality reduction process and provides an interactive interface with features like searching, filtering, coloring by metadata, and displaying rich information on hover. Atlas can help when you want to share your understanding of your UMAP visualizations or collaborate with others, as the map is accessible via a URL. + + + + MNIST UMAP visualization in Nomic Atlas + +## Plotting connectivity + + +UMAP works by constructing an intermediate topological representation of +the approximate manifold the data may have been sampled from. In +practice this structure can be simplified down to a weighted graph. +Sometimes it can be beneficial to see how that graph (representing +connectivity in the manifold) looks with respect to the resulting +embedding. It can be used to better understand the embedding, and for +diagnostic purposes. To see the connectivity you can use the +`umap.plot.connectivity` function. It works very similarly to the +`umap.plot.points` function, and has the option as to whether to +display the embedding point, or just the connectivity. To start let's do +a simple plot showing the points: + +```python3 +umap.plot.connectivity(mapper, show_points=True) + + +``` + +![Image](images/plotting_32_2.png) + +As with `umap.plot.points` there are options to control the basic +aesthetics, including theme options and an `edge_cmap` keyword +argument to specify the colormap used for displaying the edges. + +Since this approach already leverages datashader for edge plotting, we +can go a step further and make use of the edge-bundling options +available in datashader. This can provide a less busy view of +connectivity, but can be expensive to compute, particularly for larger +datasets. + +```python3 +umap.plot.connectivity(mapper, edge_bundling='hammer') + + + +``` + +![Image](images/plotting_34_2.png) + +## Diagnostic plotting + + +Plotting the connectivity provides at least one basic diagnostic view +that helps a user understand what is going on with an embedding. More +views on data are better, of course, so `umap.plot` includes a +`umap.plot.diagnostic` function that can provide various diagnostic +plots. We'll look at a few of them here. To do so we'll use the full +MNIST digits data set. + +```python3 +mapper = umap.UMAP().fit(mnist.data) + +``` + +The first diagnostic type is a Principal Components Analysis based +diagnostic, which you can select with `diagnostic_type='pca'`. The +essence of the approach is that we can use PCA, which preserves global +structure, to reduce the data to three dimensions. If we scale the +results to fit in a 3D cube we can convert the 3D PCA coordinates of +each point into an RGB description of a color. By then coloring the +points in the UMAP embedding with the colors induced by the PCA it is +possible to get a sense of how some of the more large scale global +structure has been represented in the embedding. + +```python3 +umap.plot.diagnostic(mapper, diagnostic_type='pca') + + +``` + +![Image](images/plotting_38_1.png) + +What we are looking for here is a generally smooth transition of colors, +and an overall layout that broadly respects the color transitions. In +this case the far left has a bottom cluster that transitions from dark +green at the bottom to blue at the top, and this matches well with the +cluster in the upper right which have a similar shade of blue at the +bottom before transitioning to more cyan and blue. In contast in the +right of the plot the lower cluster runs from purplish pink to green +from top to bottom, while the cluster above it has its bottom edge more +purple than green, suggesting that perhaps one or the other of these +clusters has been flipped vertically during the optimization process, +and this was never quite corrected. + +An alternative, but similar, approach is to use vector quantization as +the method to generate a 3D embedding to generate colors. Vector +quantization effectively finds 3 representative centers for the data, +and then describes each data point in terms of its distance to these +centers. Clearly this, again, captures a lot of the broad global +structure of the data. + +```python3 +umap.plot.diagnostic(mapper, diagnostic_type='vq') + + +``` + +![Image](images/plotting_40_1.png) + +Again we are looking for largely smooth transitions, and for related +colors to match up between clusters. This view supports the fact that +the left hand side of the embedding has worked well, but looking at the +right hand side it seems clear that it is the upper two of the clusters +that has been inadvertently flipped vertically. By contrasting views +like this one can get a better sense of how well the embedding is +working. + +For a different perspective we can look at approximations of the local +dimension around each data point. Ideally the local dimension should +match the embedding dimension (although this is often a lot to hope for. +In practice when the local dimension is high this represents points (or +areas of the space) that UMAP will have a harder time embedding as well. +Thus one can trust the embedding to be more accurate in regions where +the points have consistently lower local dimension. + +```python3 +local_dims = umap.plot.diagnostic(mapper, diagnostic_type='local_dim') + + + +``` + +![Image](images/plotting_42_0.png) + +As you can see, the local dimension of the data varies quite widely across +the data. In particular the lower left cluster has the lowest local +dimension -- this is actually unsurprising as this is the cluster +corresponding to the digits 1: there are relatively few degrees of +freedom over how a person draws a number one, and so the resulting local +dimension is lower. In contrast the clusters in the middle have a much +higher local dimension. We should expect the embedidng to be a little +less accurate in these regions: it is hard to represent seven +dimensional data well in only two dimensions, and compromises will need +to be made. + +The final diagnostic we'll look at is how well local neighborhoods are +preserved. We can measure this in terms of the Jaccard index of the +local neighborhood in the high dimensional space compared to the +equivalent neighborhood in the embedding. The Jaccard index is +essentially the ratio of the number of neighbors that the two +neighborhoods have in common over the total number of unique neighbors +across the two neighborhoods. Higher values mean that the local +neighborhood has been more accurately preserved. + +```python3 +umap.plot.diagnostic(mapper, diagnostic_type='neighborhood') + + +``` + +![Image](images/plotting_44_1.png) + +As one might expect the local neighborhood preservation tends to be a +lot better for those points that had a lower local dimension (as seen in +the last plot). There is also a tendency for the edges of clusters +(where there were clear boundaries to be followed) to have a better +preservation of neighborhoods than the centers of the clusters that had +higher local dimension. Again, this provides a view on which areas of +the embedding you can have greater trust in, and which regions had to +make compromises to embed into two dimensions. diff --git a/docs/precomputed_k-nn.md b/docs/precomputed_k-nn.md new file mode 100644 index 00000000..b5f5554f --- /dev/null +++ b/docs/precomputed_k-nn.md @@ -0,0 +1,399 @@ +# Precomputed k-nn + + +The purpose of this tutorial is to explore some cases where using a +precomputed_knn might be useful and then discuss how we can obtain +reproducible results with it. + +## Practical Uses + + +### Trying UMAP with various parameters + + +Let’s look at how we can use precomputed_knn to save time. First we will +test it out on MNIST which has 70,000 samples of 784 dimensions. If we +want to test out a series of n_neighbors and min_dist parameters, we +might lose quite a bit of time recomputing the knn matrices for our +data. Instead, we can compute the knn for the largest n_neighbors we +wish to analyze and then feed that precomputed_knn to UMAP. UMAP will +automatically prune it to the right n_neighbors value and skip the +nearest neighbors step, saving us a lot of time. + +We note that we don’t use a random state in order to leverage UMAP’s +parallelization and speed up the calculations. + +```python3 +from sklearn.datasets import fetch_openml +import numpy as np +import umap +import umap.plot +from umap.umap_ import nearest_neighbors + +data, labels = fetch_openml('mnist_784', version=1, return_X_y=True) +labels = np.asarray(labels, dtype=np.int32) + +n_neighbors = [5, 50, 100, 250] +min_dists = [0, 0.2, 0.5, 0.9] +normal_embeddings = np.zeros((4, 4, 70000, 2)) +precomputed_knn_embeddings = np.zeros((4, 4, 70000, 2)) + +``` + +```python3 +%%time +# UMAP run on the grid of parameters without precomputed_knn +for i, k in enumerate(n_neighbors): + for j, dist in enumerate(min_dists): + normal_embeddings[i, j] = umap.UMAP(n_neighbors=k, + min_dist=dist, + ).fit_transform(data) +print("\033[1m"+"Time taken to compute UMAP on grid of parameters:\033[0m") + + +``` + +``` +**Time taken to compute UMAP on grid of parameters:** +Wall time: 31min 57s + + +``` + +```python3 +%%time +# UMAP run on list of n_neighbors without precomputed_knn + +# We compute the knn for max(n_neighbors)=250 +mnist_knn = nearest_neighbors(data, + n_neighbors=250, + metric="euclidean", + metric_kwds=None, + angular=False, + random_state=None, + ) +# Now we compute the embeddings for the grid of parameters +for i, k in enumerate(n_neighbors): + for j, dist in enumerate(min_dists): + precomputed_knn_embeddings[i, j] = umap.UMAP(n_neighbors=k, + min_dist=dist, + precomputed_knn=mnist_knn, + ).fit_transform(data) +print("\033[1m"+"Time taken to compute UMAP on grid of parameters with precomputed_knn:\033[0m") + + +``` + +``` +**Time taken to compute UMAP on grid of parameters with precomputed_knn:** +Wall time: 17min 54s + + +``` + +Using a precomputed_knn we have cut the computation time in half! +Observe that half of our n_neighbors values are relatively small. If +instead, we had had a higher distribution of values, the time savings +would have been even greater. Additionaly, we could've saved even more +time by first computing UMAP normally with an *n_neighbors* value of 250 +and then extracting the k-nn graph from that UMAP object. + +With this, we can easily visualize how the n_neighbors parameter affects +our embedding. + +```python3 +import matplotlib.pyplot as plt + +fig, axs = plt.subplots(4, 4, figsize=(20, 20)) + +for i, ax_row in enumerate(axs): + for j, ax in enumerate(ax_row): + ax.scatter(precomputed_knn_embeddings[i, j, :, 0], + precomputed_knn_embeddings[i, j, :, 1], + c=labels / 9, + cmap='tab10', + alpha=0.1, + s=1, + ) + ax.set_xticks([]) + ax.set_yticks([]) + if i == 0: + ax.set_title("min_dist = {}".format(min_dists[j]), size=15) + if j == 0: + ax.set_ylabel("n_neighbors = {}".format(n_neighbors[i]), size=15) +fig.suptitle("UMAP embedding of MNIST digits with grid of parameters", y=0.92, size=20) +plt.subplots_adjust(wspace=0.05, hspace=0.05) + + + +``` + +![Image](images/precomputed_k-nn6.png) + +We see that in this case, the embedding is robust to the choice of +n_neighbors and that lower min_dist values simply pack the clusters more +tightly. + +## Reproducibility + + +We strongly recommend that you review the UMAP `reproducibility +section `__ +in the docs before attempting to reproduce results with +*precomputed_knn*. + +### Standard Case + + +Out of the box, UMAP with precomputed_knn supports creating reproducible +results. This works in exactly the same way as regular UMAP, where, the +user can set a random seed state to ensure that results can be reproduced +exactly. However, some important considerations must be taken into account. + +UMAP embeddings are entirely dependent on first, computing the graphical +representation in higher dimensions and second, learning an embedding +that preserves the structure of that graph. Recall that our graphical +representation is based on the k-nn graph of our data. If we have two +different k-nn graphs, then we will naturally have two different +graphical representations of our data. Therefore, **we can only ensure +reproducible results when we use the same k-nn graph**. In our case, +this means that all reproducible results are tied to three values: + + + +
    + + + +
  1. + +The random seed when computing the k-nn. + + + +
    2. + + + +
  2. + +The n_neighbors value when computing the k-nn. + + + +
    3. + + + +
  3. + +The random seed when running UMAP. + + + +
    4. + + + +
+ +Two different runs of UMAP, with these three values being equal, are +guaranteed to return the same result. Let’s look at how this works with +an example. To do this, we’ll create some data to work with; three +random blobs in 60-dimensional space. + +```python3 +y = np.random.rand(1700, 60) +X = np.concatenate((y+20, y, y-20)) +synthetic_labels = np.repeat([1, 2, 3], repeats=1700) + +``` + +With the data in hand, we can fix the three parameters listed above and +see how two different UMAP runs give the same result. To avoid confusion +we’ll assume that the UMAP random seed is the same value as the knn +random seed. + +```python3 +import umap.plot +random_seed = 10 + +knn = nearest_neighbors( + X, + n_neighbors=50, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_seed, + ) + +knn_umap = umap.UMAP(n_neighbors=30, precomputed_knn=knn, random_state=random_seed).fit(X) +knn_umap2 = umap.UMAP(n_neighbors=30, precomputed_knn=knn, random_state=random_seed).fit(X) + +fig, ax = plt.subplots(1, 2, figsize=(13,7)) +umap.plot.points(knn_umap, labels=synthetic_labels, ax=ax[0], theme='green') +umap.plot.points(knn_umap2, labels=synthetic_labels, ax=ax[1], theme='green') +ax[0].set_title("Precomuted knn 1st run", size=16) +ax[1].set_title("Precomuted knn 2nd run", size=16) +plt.show() + +print("\033[1m"+"Are the embeddings for knn_umap and knn_umap2 the same?\033[0m") +print((knn_umap.embedding_ == knn_umap2.embedding_).all()) + + + +``` + +![Image](images/precomputed_k-nn11.png) + +``` +**Are the embeddings for knn_umap and knn_umap2 the same?** +True + + +``` + +As we can see, by fixing the *random_seed* and the *n_neighbors* for the +knn, we have been able to obtain identical results from both UMAP runs. +In contrast, if these differ, we can’t guarantee the same result. + +```python3 +random_seed2 = 15 + +# Different n_neighbors +knn3 = nearest_neighbors( + X, + n_neighbors=40, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_seed, + ) +# Different random seed +knn4 = nearest_neighbors( + X, + n_neighbors=50, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_seed2, + ) + +knn_umap3 = umap.UMAP(n_neighbors=30, precomputed_knn=knn3, random_state=random_seed).fit(X) +knn_umap4 = umap.UMAP(n_neighbors=30, precomputed_knn=knn4, random_state=random_seed2).fit(X) + +fig, ax = plt.subplots(1, 2, figsize=(13,7)) +umap.plot.points(knn_umap3, labels=synthetic_labels, ax=ax[0], theme='green') +umap.plot.points(knn_umap4, labels=synthetic_labels, ax=ax[1], theme='green') +ax[0].set_title("Precomuted knn; different knn n_neighbors", size=16) +ax[1].set_title("Precomuted knn; different random_seed", size=16) +plt.show() + +print("\033[1m"+"Are the embeddings for knn_umap and knn_umap3 the same?\033[0m") +print((knn_umap.embedding_ == knn_umap3.embedding_).all()) + +print("\033[1m"+"Are the embeddings for knn_umap and knn_umap4 the same?\033[0m") +print((knn_umap.embedding_ == knn_umap4.embedding_).all()) + + + +``` + +![Image](images/precomputed_k-nn13.png) + +``` +**Are the embeddings for knn_umap and knn_umap3 the same?** +False +**Are the embeddings for knn_umap and knn_umap4 the same?** +False + + +``` + +Without those three parameters being equal between runs, we have +obtained different results. + +### Reproducing normal UMAP with precomputed_knn + + +With some extra considerations, we can also reproduce +precomputed_knn results with normal UMAP and vice-versa. As in +the previous case, we must keep in mind that the k-nn graphs have to be +same. Additionaly, we also must consider how UMAP uses the *random_seed* +that we provide it. + +If you provide UMAP a *random_seed*, it converts it into an +*np.random.RandomState* (RNG). This RNG is then used to fix the state +for all the relevant steps in the algorithm. The important thing to +note, is that the RNG is mutated everytime it’s used. So, if we want to +reproduce results with precomputed_knn we’ll have to mimic how UMAP +manipulates the RNG when calling the *fit()* function. + +For more information on random states and their behavior, please refer to +[[1]](https://scikit-learn.org/dev/common_pitfalls.html#randomness). + +We’ll look at one example of how this can be accomplished. Other cases +can be easily infered from this. Using the same random blobs as before, +we seek to run UMAP normally and then reproduce the results with a +precomputed_knn. To accomplish this, we have to create a new k-nn graph +using the *nearest_neighbors()* function in the same way that +*fit()* would. + +```python3 +from sklearn.utils import check_random_state + +# First we run the normal UMAP to compare with +random_seed3 = 12 +normal_umap = umap.UMAP(n_neighbors=30, random_state=random_seed3).fit(X) + +# Now we run precomputed_knn UMAP +random_state3 = check_random_state(random_seed3) +# random_state3 = numpy.random.RandomState(random_seed3) +knn5 = nearest_neighbors( + X, + n_neighbors=30, + metric='euclidean', + metric_kwds=None, + angular=False, + random_state=random_state3, + ) +# This mutated RNG can now be fed into precompute_knn UMAP to obtain +# the same results as in normal UMAP +knn_umap5 = umap.UMAP(n_neighbors=30, + precomputed_knn=knn5, + random_state=random_state3, # <--- This is a RNG + ).fit(X) + +``` + +Note that in this case we create a numpy.random.mtrand.RandomState +instance with *check_random_state()* because we want to ensure that +our RNG is created and mutated in exactly the same way that UMAP +normally does. Equivalently, we could call *numpy.random.RandomState()* +directly. + +Graphing and comparing the embeddings, we see that we were able to +obtain the same results. + +```python3 +fig, ax = plt.subplots(1, 2, figsize=(13,7)) +umap.plot.points(normal_umap, labels=synthetic_labels, ax=ax[0], theme='green') +umap.plot.points(knn_umap5, labels=synthetic_labels, ax=ax[1], theme='green') +ax[0].set_title("Normal UMAP", size=16) +ax[1].set_title("Precomuted knn UMAP", size=16) +plt.show() + +print("\033[1m"+"Are the embeddings for normal_umap and knn_umap5 the same?\033[0m") +print((normal_umap.embedding_ == knn_umap5.embedding_).all()) + + + +``` + +![Image](images/precomputed_k-nn17.png) + +``` +**Are the embeddings for normal_umap and knn_umap5 the same?** +True + +``` diff --git a/docs/release_notes.md b/docs/release_notes.md new file mode 100644 index 00000000..8c84e7ec --- /dev/null +++ b/docs/release_notes.md @@ -0,0 +1,45 @@ +# Release Notes + + +Some notes on new features in various releases + +## What's new in 0.5 + + +* ParametricUMAP learns embeddings with neural networks. +* AlignedUMAP can align multiple embeddings using relations between datasets. +* DensMAP can preserve local density information in embeddings. +* UMAP now depends on PyNNDescent, but has faster more parallel performance as a result. +* UMAP now supports an `update` method to add new data and retrain. +* Various performance improvements and bug fixes. +* Additional plotting support, including text searching in interactive plots. +* Support for "maximal distances" in neighbor graphs. + +## What's new in 0.4 + + +* Inverse transform method. Generate points in the original space corresponding to points in embedded space. (Thanks to Joseph Courtney) +* Different embedding spaces. Support for embedding to a variety of different spaces other than Euclidean. (Thanks to Joseph Courtney) +* New metrics, including Hellinger distance for sparse count data. +* New discrete/label metrics, including hierarchical categories, counts, ordinal data, and string edit distance. +* Support for parallelism in neighbor search and layout optimization. (Thanks to Tom White) +* Support for alternative methods to handling duplicated data samples. (Thanks to John Healy) +* New plotting methods for fast and easy plots. +* Initial support for dataframe embedding -- still experimental, but worth trying. +* Support for transform methods with sparse data. +* Multithreading support when no random seed is set. + + +## What's new in 0.3 + + +* Supervised and semi-supervised dimension reduction. Support for using labels or partial labels for dimension reduction. +* Transform method. Support for adding new unseen points to an existing embedding. +* Performance improvements. + + +## What's new in 0.2 + + +* A new layout algorithm that handles large datasets (more) correctly. +* Performance improvements. diff --git a/docs/reproducibility.md b/docs/reproducibility.md new file mode 100644 index 00000000..f2702f95 --- /dev/null +++ b/docs/reproducibility.md @@ -0,0 +1,209 @@ + +# UMAP Reproducibility + + +UMAP is a stochastic algorithm -- it makes use of randomness both to +speed up approximation steps, and to aid in solving hard optimization +problems. This means that different runs of UMAP can produce different +results. UMAP is relatively stable -- thus the variance between runs +should ideally be relatively small -- but different runs may have +variations none the less. To ensure that results can be reproduced +exactly UMAP allows the user to set a random seed state. + +Since version 0.4 UMAP also support multi-threading for faster +performance; when performing optimization this exploits the fact that +race conditions between the threads are acceptable within certain +optimization phases. Unfortunately this means that the randomness in +UMAP outputs for the multi-threaded case depends not only on the random +seed input, but also on race conditions between threads during +optimization, over which no control can be had. This means that +multi-threaded UMAP results cannot be explicitly reproduced. + +In this tutorial we'll look at how UMAP can be used in multi-threaded +mode for performance purposes, and alternatively how we can fix random +states to ensure exact reproducibility at the cost of some performance. +First let's load the relevant libraries and get some data; in this case +the MNIST digits dataset. + +```python3 +import numpy as np +import sklearn.datasets +import umap +import umap.plot + +``` + +```python3 +data, labels = sklearn.datasets.fetch_openml( + 'mnist_784', version=1, return_X_y=True +) + +``` + +With data in hand let's run UMAP on it, and note how long it takes to +run: + +```python3 +%%time +mapper1 = umap.UMAP().fit(data) + + +``` + +``` +CPU times: user 3min 18s, sys: 3.84 s, total: 3min 22s +Wall time: 1min 29s + + +``` + +The thing to note here is that the "Wall time" is significantly smaller +than the CPU time -- this means that multiple CPU cores were used. For +this particular demonstration I am making use of the latest version of +PyNNDescent for nearest neighbor search (UMAP will use it if you have it +installed) which supports multi-threading as well. The result is a very +fast fitting to the data that does an effective job of using several +cores. If you are on a large server with many cores available and don't +wish to use them *all* (which is the default situation) you can +currently control the number of cores used by setting the numba +environment variable `NUMBA_NUM_THREADS`; see the `numba +documentation `__ +for more details. + +Now let's plot our result to see what the embedding looks like: + +```python3 +umap.plot.points(mapper1, labels=labels) + + +``` + +![Image](images/reproducibility_6_1.png) + +Now, let's run UMAP again and compare the results to that of our first +run. + +```python3 +%%time +mapper2 = umap.UMAP().fit(data) + + +``` + +``` +CPU times: user 2min 53s, sys: 4.16 s, total: 2min 57s +Wall time: 1min 5s + + +``` + +You will note that this time we ran *even faster*. This is because +during the first run numba was still JIT compiling some of the code in +the background. In contrast, this time that work has already been done, +so it no longer takes up any of our run-time. We see that we are still +making use of mutliple cores well. + +Now let's plot the results of this second run and compare to the first: + +```python3 +umap.plot.points(mapper2, labels=labels) + + +``` + +![Image](images/reproducibility_10_1.png) + +Qualitatively this looks very similar, but a little closer inspection +will quickly show that the results are actually different between the +runs. Note that even in versions of UMAP prior to 0.4 this would have +been the case -- since we fixed no specific random seed, and were thus +using the current random state of the system which will naturally differ +between runs. This is the default behaviour, as is standard with sklearn +estimators that are stochastic. Rather than having a default random seed +the user is required to explicitly provide one should they want a +reproducible result. As noted by Vito Zanotelli + + ... setting a random seed is like signing a waiver "I am aware that + this is a stochastic algorithm and I have done sufficient tests to + confirm that my main conclusions are not affected by this + randomness". + +With that in mind, let's see what happens if we set an explicit +`random_state` value: + +```python3 +%%time +mapper3 = umap.UMAP(random_state=42).fit(data) + + +``` + +``` +CPU times: user 2min 27s, sys: 4.16 s, total: 2min 31s +Wall time: 1min 56s + + +``` + +The first thing to note that that this run took significantly longer +(despite having all the functions JIT compiled by numba already). Then +note that the Wall time and CPU times are now much closer to each other +-- we are no longer exploiting multiple cores to anywhere near the same +degree. This is because by setting a `random_state` we are effectively +turning off any of the multi-threading that does not support explicit +reproducibility. Let's plot the results: + +```python3 +umap.plot.points(mapper3, labels=labels) + + +``` + +![Image](images/reproducibility_14_1.png) + +We arrive at much the same results as before from a qualitative point of +view, but again inspection will show that there are some differences. +More importantly this result should now be reproducible. Thus we can run +UMAP again, with the same `random_state` set ... + +```python3 +%%time +mapper4 = umap.UMAP(random_state=42).fit(data) + + +``` + +``` +CPU times: user 2min 26s, sys: 4.13 s, total: 2min 30s +Wall time: 1min 54s + + +``` + +Again, this takes longer than the earlier runs with no `random_state` +set. However when we plot the results of the second run we see that they +look not merely qualitatively similar, but instead appear to be almost +identical: + +```python3 +umap.plot.points(mapper4, labels=labels) + +``` + +![Image](images/reproducibility_18_1.png) + +We can, in fact, check that the results are identical by verifying that +each and every coordinate of the resulting embeddings match perfectly: + +```python3 +np.all(mapper3.embedding_ == mapper4.embedding_) + + +``` + +``` +True + +``` + +So we have, in fact, reproduced the embedding exactly. diff --git a/docs/scientific_papers.md b/docs/scientific_papers.md new file mode 100644 index 00000000..eeccc70a --- /dev/null +++ b/docs/scientific_papers.md @@ -0,0 +1,104 @@ +# Scientific Papers + + +UMAP has been used in a wide variety of scientific publications from a diverse range of +fields. Here we will highlight a small selection of papers that demonstrate both +the depth of analysis, and breadth of subjects, UMAP can be used for. These range from biology, +to machine learning, and even social science. + + +## The single-cell transcriptional landscape of mammalian organogenesis + +A detailed look at the development of mouse embryos from a single-cell view. UMAP +is used as a core piece of The Monocle3 software suite for identifying cell types +and trajectories. This was a major paper in Nature, demonstrating the power +of UMAP for large scale scientific endeavours. + +![Image](images/organogenesis_paper.png) + + +[Link to the paper](https://www.nature.com/articles/s41586-019-0969-x) + +## A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution + +Still in the realm of single cell biology this paper looks at the developmental +landscape of the round-word C. elegans. UMAP is used for detailed analysis of +the developmental trajectories of cells, looking at global scales, and then +digging down to look at individual organs. The result is an impressive +array of UMAP visualisations that tease out ever finer structures in +cellular development. + +![Image](images/c_elegans_3d.jpg) + + +[Link to the paper](https://science.sciencemag.org/content/early/2019/09/04/science.aax1971) + +## Exploring Neural Networks with Activation Atlases + +Understanding the image processing capabilities (and deficits!) of modern +convolutional neural networks is a challenge. This interactive paper from +Distill seeks to provide a way to "peek inside the black box" by looking +at the activations throughout the network. By mapping this high dimensional +data down to 2D with UMAP the authors can construct an "atlas" of how +different images are perceived by the network. + +![Image](images/activation_atlas.png) + + +[Link to the paper](https://distill.pub/2019/activation-atlas/) + +## TimeCluster: dimension reduction applied to temporal data for visual analytics + +An interesting approach to time-series analysis, targeted toward cases where the +time series has repeating patterns -- though no necessarily of a consistently +periodic nature. The approach involves dimension reduction and clustering +of sliding window blocks of the time-series. The result is a map where +repeating behaviour is exposed as loop structures. This can be useful +for both clustering similar blocks within a time-series, or finding +outliers. + +![Image](images/time_cluster.png) + + +[Link to the paper](https://link.springer.com/article/10.1007/s00371-019-01673-y) + +## Dimensionality reduction for visualizing single-cell data using UMAP + +An early paper on applying UMAP to single-cell biology data. It looks at +both, gene-expression data and flow-cytometry data, and compares UMAP to +t-SNE both in terms of performance and quality of results. This is a good +introduction to using UMAP for single-cell biology data. + +![Image](images/single_cell_umap.jpg) + + +[Link to the paper](https://www.nature.com/articles/nbt.4314) + + +## Revealing multi-scale population structure in large cohorts + +A paper looking at population genetics which uses UMAP as a means +to visualise population structures. This produced some intriguing +visualizations, and was one of the first of several papers taking +this visualization approach. It also includes some novel visualizations +using UMAP projections to 3D as RGB color specifications for +data points, allowing the UMAP structure to be visualized in +geographic maps based on where the samples were drawn from. + +![Image](images/population_umap.jpg) + + +[Link to the paper](https://www.biorxiv.org/content/10.1101/423632v2) + + +## Understanding Vulnerability of Children in Surrey + +An example of the use of UMAP in sociological studies -- in this case +looking at children in Surrey, British Columbia. Here UMAP is used as +a tool to aid in general data analysis, and proves effective for the +tasks to which it was put. + +![Image](images/umap_surrey.png) + + +[Link to the paper](https://dsi.ubc.ca/sites/default/files/education-dssg-report-2018.pdf) diff --git a/docs/sparse.md b/docs/sparse.md new file mode 100644 index 00000000..737aa606 --- /dev/null +++ b/docs/sparse.md @@ -0,0 +1,429 @@ + +# UMAP on sparse data + + +Sometimes datasets get very large, and potentially very very high +dimensional. In many such cases, however, the data itself is sparse -- +that is, while there are many many features, any given sample has only a +small number of non-zero features observed. In such cases the data can +be represented much more efficiently in terms of memory usage by a +sparse matrix data structure. It can be hard to find dimension reduction +techniques that work directly on such sparse data -- often one applies a +basic linear technique such as `TruncatedSVD` from sklearn (which does +accept sparse matrix input) to get the data in a format amenable to +other more advanced dimension reduction techniques. In the case of UMAP +this is not necessary -- UMAP can run directly on sparse matrix input. +This tutorial will walk through a couple of examples of doing this. +First we'll need some libraries loaded. We need `numpy` obviously, but +we'll also make use of `scipy.sparse` which provides sparse matrix +data structures. One of our examples will be purely mathematical, and +we'll make use of `sympy` for that; the other example is test based +and we'll use sklearn for that (specifically +`sklearn.feature_extraction.text`). Beyond that we'll need umap, and +plotting tools. + +```python3 +import numpy as np +import scipy.sparse +import sympy +import sklearn.datasets +import sklearn.feature_extraction.text +import umap +import umap.plot +import matplotlib.pyplot as plt +%matplotlib inline + +``` + +## A mathematical example + + +Our first example constructs a sparse matrix of data out of pure math. +This example is inspired by the work of `John +Williamson `__, and +if you haven't looked at that work you are strongly encouraged to do so. +The dataset under consideration will be the integers. We will represent +each integer by a vector of its divisibility by distinct primes. Thus +our feature space is the space of prime numbers (less than or equal to +the largest integer we will be considering) -- potentially very high +dimensional. In practice a given integer is divisible by only a small +number of distinct primes, so each sample will be mostly made up of +zeros (all the primes that the number is not divisible by), and thus we +will have a very sparse dataset. + +To get started we'll need a list of all the primes. Fortunately we have +`sympy` at our disposal and we can quickly get that information with a +single call to `primerange`. We'll also need a dictionary mapping the +different primes to the column number they correspond to in our data +structure; effectively we'll just be enumerating the primes. + +```python3 +primes = list(sympy.primerange(2, 110000)) +prime_to_column = {p:i for i, p in enumerate(primes)} + +``` + +Now we need to construct our data in a format we can put into a sparse +matrix easily. At this point a little background on sparse matrix data +structures is useful. For this purpose we'll be using the so called +`"LIL" +format `__. +LIL is short for "List of Lists", since that is how the data is +internally stored. There is a list of all the rows, and each row is +stored as a list giving the column indices of the non-zero entries. To +store the data values there is a parallel structure containing the value +of the entry corresponding to a given row and column. + +To put the data together in this sort of format we need to construct +such a list of lists. We can do that by iterating over all the integers +up to a fixed bound, and for each integer (i.e. each row in our dataset) +generating the list of column indices which will be non-zero. The column +indices will simply be the indices corresponding to the primes that +divide the number. Since `sympy` has a function `primefactors` which +returns a list of the unique prime factors of any integer we simply need +to map those through our dictionary to covert the primes into column +numbers. + +Parallel to that we'll construct the corresponding structure of values +to insert into a matrix. Since we are only concerned with divisibility +this will simply be a one in every non-zero entry, so we can just add a +list of ones of the appropriate length for each row. + +```python3 +%%time +lil_matrix_rows = [] +lil_matrix_data = [] +for n in range(100000): + prime_factors = sympy.primefactors(n) + lil_matrix_rows.append([prime_to_column[p] for p in prime_factors]) + lil_matrix_data.append([1] * len(prime_factors)) + + +``` + +``` +CPU times: user 2.07 s, sys: 26.4 ms, total: 2.1 s +Wall time: 2.1 s + + +``` + +Now we need to get that into a sparse matrix. Fortunately the +`scipy.sparse` package makes this easy, and we've already built the +data in a fairly useful structure. First we create a sparse matrix of +the correct format (LIL) and the right shape (as many rows as we have +generated, and as many columns as there are primes). This is essentially +just an empty matrix however. We can fix that by setting the `rows` +attribute to be the rows we have generated, and the `data` attribute +to be the corresponding structure of values (all ones). The result is a +sparse matrix data structure which can then be easily manipulated and +converted into other sparse matrix formats easily. + +```python3 +factor_matrix = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32) +factor_matrix.rows = np.array(lil_matrix_rows) +factor_matrix.data = np.array(lil_matrix_data) +factor_matrix + + + + +``` + +``` +<100000x10453 sparse matrix of type '' + with 266398 stored elements in LInked List format> + + + +``` + +As you can see we have a matrix with 100000 rows and over 10000 columns. +If we were storing that as a numpy array it would take a great deal of +memory. In practice, however, there are only 260000 or so entries that +are not zero, and that's all we really need to store, making it much +more compact. + +The question now is how can we feed that sparse matrix structure into +UMAP to have it learn an embedding. The answer is surprisingly +straightforward -- we just hand it directly to the fit method. Just like +other sklearn estimators that can handle sparse input UMAP will detect +the sparse matrix and just do the right thing. + +```python3 +%%time +mapper = umap.UMAP(metric='cosine', random_state=42, low_memory=True).fit(factor_matrix) + + +``` + +``` +CPU times: user 9min 36s, sys: 6.76 s, total: 9min 43s +Wall time: 9min 7s + + +``` + +That was easy! But is it really working? We can easily plot the results: + +```python3 +umap.plot.points(mapper, values=np.arange(100000), theme='viridis') + + +``` + +![Image](images/sparse_11_1.png) + +And this looks very much in line with the results `John Williamson +got `__ with the +proviso that we only used 100,000 integers instead of 1,000,000 to +ensure that most users should be able to run this example (the full +million may require a large memory compute node). So it seems like this +is working well. The next question is whether we can use the +`transform` functionality to map new data into this space. To test +that we'll need some more data. Fortunately there are more integers. +We'll grab the next 10,000 and put them in a sparse matrix, much as we +did for the first 100,000. + +```python3 +%%time +lil_matrix_rows = [] +lil_matrix_data = [] +for n in range(100000, 110000): + prime_factors = sympy.primefactors(n) + lil_matrix_rows.append([prime_to_column[p] for p in prime_factors]) + lil_matrix_data.append([1] * len(prime_factors)) + + +``` + +``` +CPU times: user 214 ms, sys: 1.99 ms, total: 216 ms +Wall time: 222 ms + + +``` + +```python3 +new_data = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32) +new_data.rows = np.array(lil_matrix_rows) +new_data.data = np.array(lil_matrix_data) +new_data + + + + +``` + +``` +<10000x10453 sparse matrix of type '' + with 27592 stored elements in LInked List format> + + + +``` + +To map the new data we generated we can simply hand it to the +`transform` method of our trained model. This is a little slow, but it +does work. + +```python3 +new_data_embedding = mapper.transform(new_data) + +``` + +And we can plot the results. Since we just got the locations of the +points this time (rather than a model) we'll have to resort to +matplotlib for plotting. + +```python3 +fig = plt.figure(figsize=(12,12)) +ax = fig.add_subplot(111) +plt.scatter(new_data_embedding[:, 0], new_data_embedding[:, 1], s=0.1, c=np.arange(10000), cmap='viridis') +ax.set(xticks=[], yticks=[], facecolor='black'); + + + +``` + +![Image](images/sparse_18_0.png) + +The color scale is different in this case, but you can see that the data +has been mapped into locations corresponding to the various structures +seen in the original embedding. Thus, even with large sparse data we can +embed the data, and even add new data to the embedding. + +## A text analysis example + + +Let's look at a more classical machine learning example of working with +sparse high dimensional data -- working with text documents. Machine +learning on text is hard, and there is a great deal of literature on the +subject, but for now we'll just consider a basic approach. Part of the +difficulty of machine learning with text is turning language into +numbers, since numbers are really all most machine learning algorithms +understand (at heart anyway). One of the most straightforward ways to do +this for documents is what is known as the `"bag-of-words" +model `__. In this +model we view a document as simply a multi-set of the words contained in +it -- we completely ignore word order. The result can be viewed as a +matrix of data by setting the feature space to be the set of all words +that appear in any document, and a document is represented by a vector +where the value of the *i*\ th entry is the number of times the *i*\ th +word occurs in that document. This is a very common approach, and is +what you will get if you apply sklearn's `CountVectorizer` to a text +dataset for example. The catch with this approach is that the feature +space is often *very* large, since we have a feature for each and every +word that ever occurs in the entire corpus of documents. The data is +sparse however, since most documents only use a small portion of the +total possible vocabulary. Thus the default output format of +`CountVectorizer` (and other similar feature extraction tools in +sklearn) is a `scipy.sparse` format matrix. + +For this example we'll make use of the classic 20newsgroups dataset, a +sampling of newsgroup messages from the old NNTP newsgroup system +covering 20 different newsgroups. The `sklearn.datasets` module can +easily fetch the data, and, in fact, we can fetch a pre-vectorized +version to save us the trouble of running `CountVectorizer` ourselves. +We'll grab both the training set, and the test set for later use. + +```python3 +news_train = sklearn.datasets.fetch_20newsgroups_vectorized(subset='train') +news_test = sklearn.datasets.fetch_20newsgroups_vectorized(subset='test') + +``` + +If we look at the actual data we have pulled back, we'll see that +sklearn has run a `CountVectorizer` and produced the data in the sparse +matrix format. + +```python3 +news_train.data + + + + +``` + +``` +<11314x130107 sparse matrix of type '' + with 1787565 stored elements in Compressed Sparse Row format> + + + +``` + +The value of the sparse matrix format is immediately obvious in this +case; while there are only 11,000 samples there are 130,000 features! If +the data were stored in a standard `numpy` array we would be using up +10GB of memory! And most of that memory would simply be storing the +number zero, over and over again. In the sparse matrix format it easily fits +in memory on most machines. This sort of dimensionality of data is very +common with text workloads. + +The raw counts are, however, not ideal since common words such as "the" and +"and" will dominate the counts for most documents, while contributing +very little information about the actual content of the document. We can +correct for this by using a `TfidfTransformer` from sklearn, which +will convert the data into `TF-IDF +format `__. There are lots +of ways to think about the transformation done by TF-IDF, but I like to +think of it intuitively as follows. The information content of a word +can be thought of as (roughly) proportional to the negative log of the +frequency of the word; the more often a word is used, the less +information it tends to carry, and infrequent words carry more +information. What TF-IDF is going to do can be thought of as akin to +re-weighting the columns according to the information content of the +word associated to that column. Thus the common words like "the" and +"and" will get down-weighted, as carrying less information about the +document, while infrequent words will be deemed more imporant and have +their associated columns up-weighted. We can apply this transformation +to both the train and test sets (using the same transformer trained on +the training set). + +```python3 +tfidf = sklearn.feature_extraction.text.TfidfTransformer(norm='l1').fit(news_train.data) +train_data = tfidf.transform(news_train.data) +test_data = tfidf.transform(news_test.data) + +``` + +The result is still a sparse matrix, since TF-IDF doesn't change the +zero elements at all, nor the number of features. + +```python3 +train_data + + + + +``` + +``` +<11314x130107 sparse matrix of type '' + with 1787565 stored elements in Compressed Sparse Row format> + + + +``` + +Now we need to pass this very high dimensional data to UMAP. Unlike some +other non-linear dimension reduction techniques we don't need to apply +PCA first to get the data down to a reasonable dimensionality; nor do we +need to use other techniques to reduce the data to be able to be +represented as a dense `numpy` array; we can work directly on the +130,000 dimensional sparse matrix. + +```python3 +%%time +mapper = umap.UMAP(metric='hellinger', random_state=42).fit(train_data) + + +``` + +``` +CPU times: user 8min 40s, sys: 3.07 s, total: 8min 44s +Wall time: 8min 43s + + +``` + +Now we can plot the results, with labels according to the target +variable of the data -- which newsgroup the posting was drawn from. + +```python3 +umap.plot.points(mapper, labels=news_train.target) + + + + +``` + +![Image](images/sparse_31_1.png) + +We can see that even going directly from a 130,000 dimensional space +down to only 2 dimensions UMAP has done a decent job of separating out +many of the different newsgroups. + +We can now attempt to add the test data to the same space using the +`transform` method. + +```python3 +test_embedding = mapper.transform(test_data) + +``` + +While this is somewhat expensive computationally, it does work, and we +can plot the end result: + +```python3 +fig = plt.figure(figsize=(12,12)) +ax = fig.add_subplot(111) +plt.scatter(test_embedding[:, 0], test_embedding[:, 1], s=1, c=news_test.target, cmap='Spectral') +ax.set(xticks=[], yticks=[]); + + + +``` + +![Image](images/sparse_35_0.png) diff --git a/docs/supervised.md b/docs/supervised.md new file mode 100644 index 00000000..fa020884 --- /dev/null +++ b/docs/supervised.md @@ -0,0 +1,512 @@ + +# UMAP for Supervised Dimension Reduction and Metric Learning + + +While UMAP can be used for standard unsupervised dimension reduction the +algorithm offers significant flexibility allowing it to be extended to +perform other tasks, including making use of categorical label +information to do supervised dimension reduction, and even metric +learning. We'll look at some examples of how to do that below. + +First we will need to load some base libraries -- `numpy`, obviously, +but also `mnist` to read in the Fashion-MNIST data, and matplotlib and +seaborn for plotting. + +```python3 +import numpy as np +from mnist.loader import MNIST +import matplotlib.pyplot as plt +%matplotlib inline +import seaborn as sns +sns.set(style='white', context='poster') + +``` + +Our example dataset for this exploration will be the `Fashion-MNIST +dataset from Zalando +Research `__. It is +designed to be a drop-in replacement for the classic MNIST digits +dataset, but uses images of fashion items (dresses, coats, shoes, bags, +etc.) instead of handwritten digits. Since the images are more complex +it provides a greater challenge than MNIST digits. We can load it in +(after downloading the dataset) using the `mnist +library `__. We can then package +up the train and test sets into one large dataset, normalise the values +(to be in the range [0,1]), and set up labels for the 10 classes. + +```python3 +mndata = MNIST('fashion-mnist/data/fashion') +train, train_labels = mndata.load_training() +test, test_labels = mndata.load_testing() +data = np.array(np.vstack([train, test]), dtype=np.float64) / 255.0 +target = np.hstack([train_labels, test_labels]) +classes = [ + 'T-shirt/top', + 'Trouser', + 'Pullover', + 'Dress', + 'Coat', + 'Sandal', + 'Shirt', + 'Sneaker', + 'Bag', + 'Ankle boot'] + +``` + +Next we'll load the `umap` library so we can perform dimension reduction on +this dataset. + +```python3 +import umap + +``` + +## UMAP on Fashion MNIST + + +First we'll just do standard unsupervised dimension reduction using UMAP +so we have a baseline of what the results look like for later +comparison. This is simply a matter of instantiating a `umap.umap_.UMAP` object (in +this case setting the :attr:`~umap.umap_.UMAP.n_neighbors` parameter to be 5 -- we are +interested mostly in very local information), then calling the +`umap.umap_.UMAP.fit_transform` method with the data we wish to reduce. By default +UMAP reduces to two dimensions, so we'll be able to view the results as +a scatterplot. + +```python3 +%%time +embedding = umap.UMAP(n_neighbors=5).fit_transform(data) + + +``` + +``` +CPU times: user 1min 45s, sys: 7.22 s, total: 1min 52s +Wall time: 1min 26s + + +``` + +That took a little time, but not all that long considering it is 70,000 +data points in 784 dimensional space. We can simply plot the results as +a scatterplot, colored by the class of the fashion item. We can use +matplotlib's colorbar with suitable tick-labels to give us the color key. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*embedding.T, s=0.3, c=target, cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Embedded via UMAP'); + +``` + +![Image](images/SupervisedUMAP_10_1.png) + +The result is fairly good. We successfully separated a number of the +classes, and the global structure (separating pants and footwear from +shirts, coats and dresses) is well preserved as well. Unlike results for +MNIST digits, however, there were a number of classes that did not +separate quite so cleanly. In particular T-shirts, shirts, dresses, +pullovers, and coats are all a little mixed. At the very least the +dresses are largely separated, and the T-shirts are mostly in one large +clump, but they are not well distinguished from the others. Worse still +are the coats, shirts, and pullovers (somewhat unsurprisingly as these +can certainly look very similar) which all have significant overlap with +one another. Ideally we would like much better class separation. Since +we have the label information we can actually give that to UMAP to use! + +## Using Labels to Separate Classes (Supervised UMAP) + + +How do we go about coercing UMAP to make use of target labels? If you +are familiar with the sklearn API you'll know that the `umap.umap_.UMAP.fit` method +takes a target parameter `y` that specifies supervised target +information (for example when training a supervised classification +model). We can simply pass the `umap.umap_.UMAP` model that target data when +fitting and it will make use of it to perform supervised dimension +reduction! + +```python3 +%%time +embedding = umap.UMAP().fit_transform(data, y=target) + + +``` + +``` +CPU times: user 3min 28s, sys: 9.17 s, total: 3min 37s +Wall time: 2min 45s + + +``` + +This took a little longer -- both because we are using a larger +:py:obj:`~umap.umap_.UMAP.n_neighbors` value (which is suggested when doing supervised +dimension reduction; here we are using the default value of 15), and +because we need to condition on the label data. As before we have +reduced the data down to two dimensions so we can again visualize the +data with a scatterplot, coloring by class. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*embedding.T, s=0.1, c=target, cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Embedded via UMAP using Labels'); + + +``` + +![Image](images/SupervisedUMAP_15_1.png) + +The result is a cleanly separated set of classes (and a little bit of +stray noise -- points that were sufficiently different from their class +as to not be grouped with the rest). Aside from the clear class +separation however (which is expected -- we gave the algorithm all the +class information), there are a couple of important points to note. The +first point to note is that we have retained the internal structure of +the individual classes. Both the shirts and pullovers still have the +distinct banding pattern that was visible in the original unsupervised +case; the pants, t-shirts and bags both retained their shape and +internal structure; etc. The second point to note is that we have also +retained the global structure. While the individual classes have been +cleanly separated from one another, the inter-relationships among the +classes have been preserved: footwear classes are all near one another; +trousers and bags are at opposite sides of the plot; and the arc of +pullover, shirts, t-shirts and dresses is still in place. + +The key point is this: the important structural properties of the data +have been retained while the known classes have been cleanly pulled +apart and isolated. If you have data with known classes and want to +separate them while still having a meaningful embedding of individual +points then supervised UMAP can provide exactly what you need. + +## Using Partial Labelling (Semi-Supervised UMAP) + + +What if we only have some of our data labelled, however, and a number of +items are without labels. Can we still make use of the label information +we do have? This is now a semi-supervised learning problem, and yes, we +can work with those cases too. To set up the example we'll mask some of +the target information -- we'll do this by using the sklearn standard of +giving unlabelled points a label of -1 (such as, for example, +the noise points from a DBSCAN clustering). + +```python3 +masked_target = target.copy().astype(np.int8) +masked_target[np.random.choice(70000, size=10000, replace=False)] = -1 + +``` + +Now that we have randomly masked some of the labels we can try to +perform supervised learning again. Everything works as before, but UMAP +will interpret the -1 label as being an unlabelled point and learn +accordingly. + +```python3 +%%time +fitter = umap.UMAP().fit(data, y=masked_target) +embedding = fitter.embedding_ + + +``` + +``` +CPU times: user 3min 8s, sys: 7.85 s, total: 3min 16s +Wall time: 2min 40s + + +``` + +Again we can look at a scatterplot of the data colored by class. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*embedding.T, s=0.1, c=target, cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Embedded via UMAP using Partial Labels'); + + +``` + +![Image](images/SupervisedUMAP_22_1.png) + +The result is much as we would expect -- while we haven't cleanly +separated the data as we did in the totally supervised case, the classes +have been made cleaner and more distinct. This semi-supervised approach +provides a powerful tool when labelling is potentially expensive, or +when you have more data than labels, but want to make use of that extra +data. + +## Training with Labels and Embedding Unlabelled Test Data (Metric Learning with UMAP) + + +If we have learned a supervised embedding, can we use that to embed new +previously unseen (and now unlabelled) points into the space? This would +provide an algorithm for `metric +learning `__, +where we can use a labelled set of points to learn a metric on data, and +then use that learned metric as a measure of distance between new +unlabelled points. This can be particularly useful as part of a machine +learning pipline where we learn a supervised embedding as a form of +supervised feature engineering, and then build a classifier on that new +space -- this is viable as long as we can pass new data to the embedding +model to be transformed to the new space. + +To try this out with UMAP let's use the train/test split provided by +Fashion MNIST: + +```python3 +train_data = np.array(train) +test_data = np.array(test) + +``` + +Now we can fit a model to the training data, making use of the training +labels to learn a supervised embedding. + +```python3 +%%time +mapper = umap.UMAP(n_neighbors=10).fit(train_data, np.array(train_labels)) + + +``` + +``` +CPU times: user 2min 18s, sys: 7.53 s, total: 2min 26s +Wall time: 1min 52s + + +``` + +Next we can use the `umap.umap_.UMAP.transform` method on that model to transform the +test set into the learned space. This time we won't pass the label +information and let the model attempt to place the data correctly. + +```python3 +%%time +test_embedding = mapper.transform(test_data) + + +``` + +``` +CPU times: user 17.3 s, sys: 986 ms, total: 18.3 s +Wall time: 15.4 s + + +``` + +UMAP transforms are not as fast as some approaches, but as you can see +this was still fairly efficient. The important question is how well we +managed to embed the test data into the existing learned space. To start +let's visualise the embedding of the training data so we can get a sense +of where things *should* go. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*mapper.embedding_.T, s=0.3, c=np.array(train_labels), cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Train Digits Embedded via UMAP Transform'); + + + +``` + +![Image](images/SupervisedUMAP_31_0.png) + +As you can see this has done a similar job as before, successfully +embedding the separate classes while retaining both the internal +structure and the overall global structure. We can now look at how the +test set, for which we provided no label information, was embedded via +the `umap.umap_.UMAP.transform` method. + +```python3 +fig, ax = plt.subplots(1, figsize=(14, 10)) +plt.scatter(*test_embedding.T, s=2, c=np.array(test_labels), cmap='Spectral', alpha=1.0) +plt.setp(ax, xticks=[], yticks=[]) +cbar = plt.colorbar(boundaries=np.arange(11)-0.5) +cbar.set_ticks(np.arange(10)) +cbar.set_ticklabels(classes) +plt.title('Fashion MNIST Test Digits Embedded via UMAP'); + + + +``` + +![Image](images/SupervisedUMAP_33_0.png) + +As you can see we have replicated the layout of the training data, +including much of the internal structure of the classes. For the most +part assignment of new points follows the classes well. The greatest +source of confusion are some t-shirts that ended up mixed with the +shirts, and some pullovers which are confused with the coats. Given the +difficulty of the problem this is a good result, particularly when +compared with current state-of-the-art approaches such as `siamese and +triplet +networks `__. + + +## Supervised UMAP on the Galaxy10SDSS dataset + + +The [Galaxy10SDSS dataset](https://astronn.readthedocs.io/en/latest/galaxy10sdss.html) +is a crowd sourced human labelled dataset of galaxy images, +which have been separated in to ten classes. Umap can +learn an embedding that partially separates the data. To +keep runtime small, UMAP is applied to a subset of the +data. + +```python3 +import numpy as np +import h5py +import matplotlib.pyplot as plt +import umap +import os +import math +import requests + +if not os.path.isfile("Galaxy10.h5"): + url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" + r = requests.get(url, allow_redirects=True) + open("Galaxy10.h5", "wb").write(r.content) + +# To get the images and labels from file +with h5py.File("Galaxy10.h5", "r") as F: + images = np.array(F["images"]) + labels = np.array(F["ans"]) + +X_train = np.empty([math.floor(len(labels) / 100), 14283], dtype=np.float64) +y_train = np.empty([math.floor(len(labels) / 100)], dtype=np.float64) +X_test = X_train +y_test = y_train +# Get a subset of the data +for i in range(math.floor(len(labels) / 100)): + X_train[i, :] = np.array(np.ndarray.flatten(images[i, :, :, :]), dtype=np.float64) + y_train[i] = labels[i] + X_test[i, :] = np.array( + np.ndarray.flatten(images[i + math.floor(len(labels) / 100), :, :, :]), + dtype=np.float64, + ) + y_test[i] = labels[i + math.floor(len(labels) / 100)] + +# Plot distribution +classes, frequency = np.unique(y_train, return_counts=True) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.bar(classes, frequency) +plt.xlabel("Class") +plt.ylabel("Frequency") +plt.title("Data Subset") +plt.savefig("galaxy10_subset.svg") + + + +``` + +![Image](images/galaxy10_subset.svg) + +The figure shows that the selected subset of the data set is +unbalanced, but the entire dataset is also unbalanced, so +this experiment will still use this subset. The next step is +to examine the output of the standard UMAP algorithm. + +```python3 +reducer = umap.UMAP( + n_components=2, n_neighbors=5, random_state=42, transform_seed=42, verbose=False +) +reducer.fit(X_train) + +galaxy10_umap = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_umap[:, 0], + galaxy10_umap[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_umap.svg") + + + +``` + +![Image](images/galaxy10_2D_umap.svg) + +The standard UMAP algorithm does not separate the galaxies +according to their type. Supervised UMAP can do better. + +```python3 +reducer = umap.UMAP( + n_components=2, n_neighbors=15, random_state=42, transform_seed=42, verbose=False +) +reducer.fit(X_train, y_train) + +galaxy10_umap_supervised = reducer.transform(X_train) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_umap_supervised[:, 0], + galaxy10_umap_supervised[:, 1], + c=y_train, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_train, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_umap_supervised.svg") + + + +``` + +![Image](images/galaxy10_2D_umap_supervised.svg) + +Supervised UMAP does indeed do better. There is a litle overlap +between some of the classes, but the original dataset +also has some ambiguities in the classification. The best +check of this method is to project the testing data onto the +learned embedding. + +```python3 +galaxy10_umap_supervised_prediction = reducer.transform(X_test) +fig = plt.figure(1, figsize=(4, 4)) +plt.clf() +plt.scatter( + galaxy10_umap_supervised_prediction[:, 0], + galaxy10_umap_supervised_prediction[:, 1], + c=y_test, + cmap=plt.cm.nipy_spectral, + edgecolor="k", + label=y_test, +) +plt.colorbar(boundaries=np.arange(11) - 0.5).set_ticks(np.arange(10)) +plt.savefig("galaxy10_2D_umap_supervised_prediction.svg") + + + +``` + +![Image](images/galaxy10_2D_umap_supervised_prediction.svg) + +This shows that the learned embedding can be used on new data +sets, and so this method may be helpful for examining images +of galaxies. Try out this method on the full 200 Mb dataset +as well as the newer 2.54 Gb +[Galaxy 10 DECals dataset](https://astronn.readthedocs.io/en/latest/galaxy10.html) diff --git a/docs/transform.md b/docs/transform.md new file mode 100644 index 00000000..60eb403a --- /dev/null +++ b/docs/transform.md @@ -0,0 +1,267 @@ + +# Transforming New Data with UMAP + + +UMAP is useful for generating visualisations, but if you want to make +use of UMAP more generally for machine learning tasks it is important to +be be able to train a model and then later pass new data to the model +and have it transform that data into the learned space. For example if +we use UMAP to learn a latent space and then train a classifier on data +transformed into the latent space then the classifier is only useful for +prediction if we can transform data for which we want a prediction into +the latent space the classifier uses. Fortunately UMAP makes this +possible, albeit more slowly than some other transformers that allow +this. + +This tutorial will step through a simple case where we expect the overall +distribution in our higher-dimensional vectors to be consistent between the +training and testing data. For more detail on how this can go wrong, and +how we can fix it using Parametric UMAP, see :doc:`transform_landmarked_pumap`. + +To demonstrate this functionality we'll make use of +[scikit-learn](http://scikit-learn.org/stable/index.html) and the +digits dataset contained therein (see :doc:`basic_usage` for an example +of the digits dataset). First let's load all the modules we'll need to +get this done. + +```python3 +import numpy as np +from sklearn.datasets import load_digits +from sklearn.model_selection import train_test_split, cross_val_score +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC + +import matplotlib.pyplot as plt +import seaborn as sns +%matplotlib inline + +``` + +```python3 +sns.set(context='notebook', style='white', rc={'figure.figsize':(14,10)}) + +``` + +```python3 +digits = load_digits() + +``` + +To keep everything honest let's use sklearn `train_test_split` to +separate out a training and test set (stratified over the different +digit types). By default `train_test_split` will carve off 25% of the +data for testing, which seems suitable in this case. + +```python3 +X_train, X_test, y_train, y_test = train_test_split(digits.data, + digits.target, + stratify=digits.target, + random_state=42) + +``` + +Now to get a benchmark idea of what we are looking at let's train a +couple of different classifiers and then see how well they score on the +test set. For this example let's try a support vector classifier and a +KNN classifier. Ideally we should be tuning hyper-parameters (perhaps a +grid search using k-fold cross validation), but for the purposes of this +simple demo we will simply use default parameters for both classifiers. + +```python3 +svc = SVC().fit(X_train, y_train) +knn = KNeighborsClassifier().fit(X_train, y_train) + +``` + +The next question is how well these classifiers perform on the test set. +Conveniently sklearn provides a `score` method that can output the +accuracy on the test set. + +```python3 +svc.score(X_test, y_test), knn.score(X_test, y_test) + + + + +``` + +``` +(0.62, 0.9844444444444445) + + + +``` + +The result is that the support vector classifier apparently had poor +hyper-parameters for this case (I expect with some tuning we could build +a much more accurate model) and the KNN classifier is doing very well. + +The goal now is to make use of UMAP as a preprocessing step that one +could potentially fit into a pipeline. We will therefore obviously need +the `umap` module loaded. + +```python3 +import umap + +``` + +To make use of UMAP as a data transformer we first need to fit the model +with the training data. This works exactly as in the :doc:`basic_usage` +example using the fit method. In this case we simply hand it the +training data and it will learn an appropriate (two dimensional by +default) embedding. + +```python3 +trans = umap.UMAP(n_neighbors=5, random_state=42).fit(X_train) + + +``` + +Since we embedded to two dimensions we can visualise the results to +ensure that we are getting a potential benefit out of this approach. +This is simply a matter of generating a scatterplot with data points +colored by the class they come from. Note that the embedded training +data can be accessed as the `.embedding_` attribute of the UMAP model +once we have fit the model to some data. + +```python3 +plt.scatter(trans.embedding_[:, 0], trans.embedding_[:, 1], s= 5, c=y_train, cmap='Spectral') +plt.title('Embedding of the training set by UMAP', fontsize=24); + + + +``` + +![Image](images/UMAPTransform_15_0.png) + +This looks very promising! Most of the classes got very cleanly +separated, and that gives us some hope that it could help with +classifier performance. It is worth noting that this was a completely +unsupervised data transform; we could have used the training label +information, but that is the subject of :doc:`a later tutorial `. + +We can now train some new models (again an SVC and a KNN classifier) on +the embedded training data. This looks exactly as before but now we pass +it the embedded data. Note that calling `transform` on input identical +to what the model was trained on will simply return the `embedding_` +attribute, so sklearn pipelines will work as expected. + +```python3 +svc = SVC().fit(trans.embedding_, y_train) +knn = KNeighborsClassifier().fit(trans.embedding_, y_train) + +``` + +Now we want to work with the test data which none of the models (UMAP or +the classifiers) have seen. To do this we use the standard sklearn API +and make use of the `transform` method, this time handing it the new +unseen test data. We will assign this to `test_embedding` so that we +can take a closer look at the result of applying an existing UMAP model +to new data. + +```python3 +%time test_embedding = trans.transform(X_test) + + +``` + +``` +CPU times: user 867 ms, sys: 70.7 ms, total: 938 ms +Wall time: 335 ms + + +``` + +Note that the transform operations works very efficiently -- taking less +than half a second. Compared to some other transformers this is a little +on the slow side, but it is fast enough for many uses. Note that as the +size of the training and/or test sets increase the performance will slow +proportionally. It's also worth noting that the first call to transform +may be slow due to Numba JIT overhead -- further runs will be very fast. + +The next important question is what the transform did to our test data. +In principle we have a new two dimensional representation of the +test-set, and ideally this should be based on the existing embedding of +the training set. We can check this by visualising the data (since we +are in two dimensions) to see if this is true. A simple scatterplot as +before will suffice. + +```python3 +plt.scatter(test_embedding[:, 0], test_embedding[:, 1], s= 5, c=y_test, cmap='Spectral') +plt.title('Embedding of the test set by UMAP', fontsize=24); + + + +``` + +![Image](images/UMAPTransform_21_0.png) + +The results look like what we should expect; the test data has been +embedded into two dimensions in exactly the locations we should expect +(by class) given the embedding of the training data visualised above. +This means we can now try out models that were trained on the +embedded training data by handing them the newly transformed test set. + +```python3 +svc.score(trans.transform(X_test), y_test), knn.score(trans.transform(X_test), y_test) + + + + +``` + +``` +(0.9844444444444445, 0.9844444444444445) + + + +``` + +The results are pretty good. While the accuracy of the KNN classifier +did not improve there was not a lot of scope for improvement given the +data. On the other hand the SVC has improved to have equal accuracy to +the KNN classifier. Of course we could probably have achieved this level +of accuracy by better setting SVC hyper-parameters, but the point here +is that we can use UMAP as if it were a standard sklearn transformer as +part of an sklearn machine learning pipeline. + +Just for fun we can run the same experiments, but this time reduce to +ten dimensions (where we can no longer visualise). In practice this will +have little gain in this case -- for the digits dataset two dimensions +is plenty for UMAP and more dimensions won't help. On the other hand for +more complex datasets where more dimensions may allow for a much more +faithful embedding it is worth noting that we are not restricted to only +two dimension. + +```python3 +trans = umap.UMAP(n_neighbors=5, n_components=10, random_state=42).fit(X_train) + + +``` + +```python3 +svc = SVC().fit(trans.embedding_, y_train) +knn = KNeighborsClassifier().fit(trans.embedding_, y_train) + +``` + +```python3 +svc.score(trans.transform(X_test), y_test), knn.score(trans.transform(X_test), y_test) + + + + +``` + +``` +(0.9822222222222222, 0.9822222222222222) + + + +``` + +And we see that in this case we actually marginally lowered our accuracy +scores (within the potential noise in such scoring mind you). However +for more interesting datasets the larger dimensional embedding might have +been a significant gain -- it is certainly worth exploring as one of the +parameters in a grid search across a pipeline that includes UMAP. diff --git a/docs/transform_landmarked_pumap.md b/docs/transform_landmarked_pumap.md new file mode 100644 index 00000000..bea31a5a --- /dev/null +++ b/docs/transform_landmarked_pumap.md @@ -0,0 +1,238 @@ + +# Transforming New Data with Parametric UMAP + + +There are many cases where one may want to take an existing UMAP model and use it to embed new data into the learned space. For a simple example where the overall distribution of the higher-dimensional training data matches that of the new data being embedded, see :doc:`transform`. We can't always be sure that this will be the case, however. To simulate a case where we have novel behaviour that we want to include in our embedding space, we will use the MNIST digits dataset (see :doc:`basic_usage` for a basic example). + +To follow along with this example, see the MNIST_Landmarks notebook on the [GitHub repository](https://github.com/lmcinnes/umap/tree/master/notebooks/) + + + + import keras + from sklearn.model_selection import train_test_split + + from umap import UMAP, ParametricUMAP + + import matplotlib.pyplot as plt + + import numpy as np + import pandas as pd + +We'll start by loading in the dataset, and splitting it into 2 equal parts with `sklearn`'s `train_test_split` function. This will give us two partitions to work with, one to train our original embedding and another to test it. In order to simulate new behaviour appearing in our data we remove one of the MNIST categories `N` from the `x1` partition. In this case we'll use `N=2`, so our model will be trained on all of the digits other than 2. + +```python3 +(X, y), (_, _) = keras.datasets.mnist.load_data() +x1, x2, y1, y2 = train_test_split(X, y, test_size=0.5, random_state=42) + +# Reshape to 1D vectors +x1 = x1.reshape((x1.shape[0], 28*28)) +x2 = x2.reshape((x2.shape[0], 28*28)) + +# Remove one category from the train dataset. +# In the case of MNIST digits, this will be the digit we are removing. +N = 2 + +x1 = x1[y1 != N] +y1 = y1[y1 != N] + +print(x1.shape, x2.shape) + +``` + +``` +(26995, 784) (30000, 784) + +``` + +## New data with UMAP + + +To start with, we'll identify the issues with using UMAP as-is in this case, and then we'll see how to fix them with Parametric UMAP. First off, we need to train a `UMAP` model on our `x1` partition: + +```python3 +embedder = UMAP() + +emb_x1 = embedder.fit_transform(x1) + +``` + +Visualising our results: + +```python3 +plt.scatter(emb_x1[:,0], emb_x1[:,1], c=y1, cmap='Spectral', s=2, alpha=0.2) + +``` + +![Image](images/retrain_pumap_emb_x1.png) + +This is a clean and successful embedding, as we would expect from UMAP on this relatively-simple example. We see the normal structure one would expect from embedding MNIST, but without any of the 2s. The `UMAP` class is built to be compatible with `scikit-learn`, so passing new data through is as simple as using the `transform` method and passing through the new data. We'll pass through `x2`, which contains unseen examples of the original classes, and also samples from our holdout class, `N` (the 2s). + +To make samples from `N` Stand out more, we'll over-plot them in black. + +```python3 +emb_x2 = embedder.transform(x2) + +``` + +```python3 +plt.scatter(emb_x2[:,0], emb_x2[:,1], c=y2, cmap='Spectral', s=2, alpha=0.2) +plt.scatter(emb_x2[y2==N][:,0], emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5) + +``` + +![Image](images/retrain_pumap_emb_x2.png) + +While our `UMAP` embedder has correctly handled the classes present in `x1` it has treated examples from our holdout class `N` poorly. Many of these points are concentrated on top of existing classes, with some spread out between them. This inability to generalize is not unique to UMAP, but is more generally a difficulty with learned embeddings. It also may or may not be an issue, depending on your use case. + +## New data with Parametric UMAP + + +We can improve this outcome with Parametric UMAP. Parametric UMAP differs from UMAP in that it learns the relationship between the data and embedding with a neural network, instead of learning embeddings directly. This means we can incorporate new data by continuing to train the neural network, updating the weights to incorporate our new information. + +![Image](images/pumap-only.png) + +For more complete information on Parametric UMAP and the many options it provides, see :doc:`parametric_umap`. + +We will start adressing this by training a `ParametricUMAP` embedding model, and running the same experiment: + +```python3 +p_embedder = ParametricUMAP() + +p_emb_x1 = p_embedder.fit_transform(x1) + +``` + +```python3 +plt.scatter(p_emb_x1[:,0], p_emb_x1[:,1], c=y1, cmap='Spectral', s=2, alpha=0.2) + +``` + +![Image](images/retrain_pumap_p_emb_x1.png) + +Again, we get good results on our initial embedding of `x1`. If we pass `x2` through without re-training, we get a similar problem to our `UMAP` model: + +```python3 +p_emb_x2 = p_embedder.transform(x2) + +``` + +```python3 +plt.scatter(p_emb_x2[:,0], p_emb_x2[:,1], c=y2, cmap='Spectral', s=2, alpha=0.2) +plt.scatter(p_emb_x2[y2==N][:,0], p_emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5) + +``` + +![Image](images/retrain_pumap_p_emb_x2.png) + +## Re-training Parametric UMAP with landmarks + + +To update our embedding to include the new class, we'll fine-tune our existing `ParametricUMAP` model. Doing this without any other changes will start from where we left off, but our embedding space's structure may drift and change. This is because the UMAP loss function is invariant to translation and rotation, as it is only concerned with the relative positions and distances between points. + +In order to keep our embedding space more consistent, we'll use the landmarks option for `ParametricUMAP`. We retrain the model on the `x2` partition, along with some points chosen as landmarks from `x1`. We'll choose 1% of the samples in `x1` to be included, along with their current position in the embedding space to be used in the landmarks loss function. + +The default `landmark_loss_fn` is the euclidean distance between the point's original position and it's current one. The only change we'll make is to set `landmark_loss_weight=0.01`. + +```python3 +# Select landmarks indexes from x1. +# +landmark_idx = list(np.random.choice(range(x1.shape[0]), int(x1.shape[0]/100), replace=False)) + +# Add the landmark points to x2 for training. +# +x2_lmk = np.concatenate((x2, x1[landmark_idx])) +y2_lmk = np.concatenate((y2, y1[landmark_idx])) + +# Make our landmarks vector, which is nan where we have no landmark information. +# +landmarks = np.stack( + [np.array([np.nan, np.nan])]*x2.shape[0] + list( + p_embedder.transform( + x1[landmark_idx] + ) + ) +) + +# Set landmark loss weight and continue training our Parametric UMAP model. +# +p_embedder.landmark_loss_weight = 0.01 +p_embedder.fit(x2_lmk, landmark_positions=landmarks) +p_emb2_x2 = p_embedder.transform(x2) + +# Check how x1 looks when embedded in the space retrained on x2 and landmarks. +# +p_emb2_x1 = p_embedder.transform(x1) + + +``` + +Plotting all of the different embeddings to compare them: + +```python3 +fig, axs = plt.subplots(3, 2, figsize=(16, 24), sharex=True, sharey=True) + +axs[0,0].scatter( + emb_x1[:, 0], emb_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, +) +axs[0,0].set_ylabel('UMAP Embedding', fontsize=20) + +axs[0,1].scatter( + emb_x2[:, 0], emb_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, +) +axs[0,1].scatter( + emb_x2[y2==N][:,0], emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5, +) + +axs[1,0].scatter( + p_emb_x1[:, 0], p_emb_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, +) +axs[1,0].set_ylabel('Initial P-UMAP Embedding', fontsize=20) + +axs[1,1].scatter( + p_emb_x2[:, 0], p_emb_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, +) +axs[1,1].scatter( + p_emb_x2[y2==N][:,0], p_emb_x2[y2==N][:,1], c='k', s=2, alpha=0.5 +) + +axs[2,0].scatter( + p_emb2_x1[:, 0], p_emb2_x1[:, 1], c=y1, cmap='Spectral', s=2, alpha=0.2, +) +axs[2,0].set_ylabel('Updated P-UMAP Embedding', fontsize=20) +axs[2,0].set_xlabel(f'x1, No {N}s', fontsize=20) + +axs[2,1].scatter( + p_emb2_x2[:, 0], p_emb2_x2[:, 1], c=y2, cmap='Spectral', s=2, alpha=0.2, +) +axs[2,1].scatter( + p_emb2_x2[y2==N][:,0], p_emb2_x2[y2==N][:,1], c='k', s=2, alpha=0.5, +) +axs[2,1].set_xlabel('x2, All Classes', fontsize=20) + +plt.tight_layout() + +``` + +![Image](images/retrain_pumap_summary_2_removed.png) + +Here we see that our approach has been successful, The embedding space has been kept consistent and we now have a clear cluster of our new class, the 2s. This new cluster shows up in a sensible part of the embedding space, and the rest of the structure is preserved. + +It is worth double checking here that the landmark loss is not too constraining, we still would like a good UMAP structure. +To do so, we can interrogate the history of our embedder, which will retain the history through our re-training steps. + +```python3 +plt.plot(p_embedder._history['loss']) +plt.ylabel('Loss') +plt.xlabel('Epoch') + +``` + +![Image](images/retrain_pumap_history.png) + +We can identify the spike in loss where we introduce `x2`, and can confirm that the resulting loss is comparable to the loss from our initial training on `x1`. This tells us that the model is not having to compromise too much between the UMAP loss and the landmark loss. If this were not the case, it could potentially be improved by lowering the `landmark_loss_weight` attribute of our embedder object. There is a tradeoff to be made here between the consistency of the space and minimizing UMAP loss, but the key is we have smooth variation in the embedding space, which will make downstream tasks easier to adjust. In this case, we could probably stand to increase the `landmark_loss_weight` to keep the space more consistent. + +In addition to `landmark_loss_weight`, there are a number of other options available to us to try and get better results on this or other examples: + +- Continuing the training with a larger portion of points from the original data, in our case `x1`. Not all of these points need to be landmarked, but they can contribute to a consistent graph structure in higher dimensions. +- Changing the `landmark_loss_fn`. For example, if we want to allow for points to move if they have to we could truncate the default euclidean loss function, allowing the metaphorical rubber band to snap at a certain point and prioritising a good UMAP structure once we discover that sticking to the landmark position is not correct. +- Being more intelligent with our selection of landmark points, for example using submodular optimization with a package like [apricot-select](https://apricot-select.readthedocs.io/en/latest/) or chosing points from different parts of a hierarchical clustering like [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/index.html) diff --git a/examples/README.txt b/examples/README.txt index eebf8c5c..a65e6a70 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -3,4 +3,4 @@ Gallery of Examples of UMAP usage A small gallery collection examples of UMAP usage. Do you have an interesting UMAP plot that uses publicly available -data? Submit a pull request to have it added as an example! \ No newline at end of file +data? Submit a pull request to have it added as an example! diff --git a/examples/digits/digits.html b/examples/digits/digits.html index 0ba4bcc7..d9f9963e 100644 --- a/examples/digits/digits.html +++ b/examples/digits/digits.html @@ -4,9 +4,9 @@ Bokeh Plot - + - + - \ No newline at end of file + diff --git a/examples/digits/digits.py b/examples/digits/digits.py index 95e71b15..1a30c850 100644 --- a/examples/digits/digits.py +++ b/examples/digits/digits.py @@ -1,9 +1,9 @@ -from bokeh.plotting import figure, output_file, show from bokeh.models import CategoricalColorMapper, ColumnDataSource from bokeh.palettes import Category10 +from bokeh.plotting import figure, output_file, show +from sklearn.datasets import load_digits import umap -from sklearn.datasets import load_digits digits = load_digits() embedding = umap.UMAP().fit_transform(digits.data) @@ -13,11 +13,11 @@ targets = [str(d) for d in digits.target_names] source = ColumnDataSource( - dict( - x=[e[0] for e in embedding], - y=[e[1] for e in embedding], - label=[targets[d] for d in digits.target], - ) + { + "x": [e[0] for e in embedding], + "y": [e[1] for e in embedding], + "label": [targets[d] for d in digits.target], + }, ) cmap = CategoricalColorMapper(factors=targets, palette=Category10[10]) diff --git a/examples/galaxy10sdss.py b/examples/galaxy10sdss.py index 9a2152a6..849b5dee 100644 --- a/examples/galaxy10sdss.py +++ b/examples/galaxy10sdss.py @@ -1,5 +1,4 @@ -""" -UMAP on the Galaxy10SDSS dataset +"""UMAP on the Galaxy10SDSS dataset. --------------------------------------------------------- This is an example of using UMAP on the Galaxy10SDSS @@ -10,20 +9,19 @@ data. """ -import numpy as np -import h5py -import matplotlib.pyplot as plt -import umap -import os - # from sklearn.model_selection import train_test_split import math -import requests +import os + +import h5py # libraries for clustering import hdbscan -import sklearn.cluster as cluster -from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score +import matplotlib.pyplot as plt +import numpy as np +import requests + +import umap if not os.path.isfile("Galaxy10.h5"): url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" @@ -61,7 +59,11 @@ # 2D Embedding ## UMAP reducer = umap.UMAP( - n_components=2, n_neighbors=20, random_state=42, transform_seed=42, verbose=False + n_components=2, + n_neighbors=20, + random_state=42, + transform_seed=42, + verbose=False, ) reducer.fit(X_train) @@ -80,7 +82,11 @@ plt.savefig("galaxy10_2D_umap.svg") ### UMAP - Supervised reducer = umap.UMAP( - n_components=2, n_neighbors=15, random_state=42, transform_seed=42, verbose=False + n_components=2, + n_neighbors=15, + random_state=42, + transform_seed=42, + verbose=False, ) reducer.fit(X_train, y_train) @@ -138,20 +144,16 @@ plt.savefig("galaxy10_2D_umap_supervised_prediction_clustered.svg") # Print out information on quality of clustering -print("2D Supervised Embedding with Clustering") -print(adjusted_rand_score(y_test, labels), adjusted_mutual_info_score(y_test, labels)) -print( - adjusted_rand_score(y_test[clustered], labels[clustered]), - adjusted_mutual_info_score(y_test[clustered], labels[clustered]), -) - -print(np.sum(clustered) / y_test.shape[0]) # 3D Embedding ## UMAP reducer = umap.UMAP( - n_components=3, n_neighbors=20, random_state=42, transform_seed=42, verbose=False + n_components=3, + n_neighbors=20, + random_state=42, + transform_seed=42, + verbose=False, ) reducer.fit(X_train) galaxy10_umap = reducer.transform(X_train) @@ -171,7 +173,11 @@ plt.savefig("galaxy10_3D_umap.svg") ## UMAP - Supervised reducer = umap.UMAP( - n_components=3, n_neighbors=20, random_state=42, transform_seed=42, verbose=False + n_components=3, + n_neighbors=20, + random_state=42, + transform_seed=42, + verbose=False, ) reducer.fit(X_train, y_train) galaxy10_umap_supervised = reducer.transform(X_train) @@ -235,15 +241,8 @@ plt.savefig("galaxy10_3D_umap_supervised_prediction_clustered.svg") # Print out information on quality of clustering -print("3D Supervised Embedding with Clustering") -print(adjusted_rand_score(y_test, labels), adjusted_mutual_info_score(y_test, labels)) -print( - adjusted_rand_score(y_test[clustered], labels[clustered]), - adjusted_mutual_info_score(y_test[clustered], labels[clustered]), -) -print(np.sum(clustered) / y_test.shape[0]) # Dimensions 4 to 25 for dimensions in range(4, 26): reducer = umap.UMAP( @@ -264,12 +263,3 @@ ).fit_predict(galaxy10_umap_supervised_prediction) clustered = labels >= 0 # Print out information on quality of clustering - print(str(dimensions) + "D Supervised Embedding with Clustering") - print( - adjusted_rand_score(y_test, labels), adjusted_mutual_info_score(y_test, labels) - ) - print( - adjusted_rand_score(y_test[clustered], labels[clustered]), - adjusted_mutual_info_score(y_test[clustered], labels[clustered]), - ) - print(np.sum(clustered) / y_test.shape[0]) diff --git a/examples/inverse_transform_example.py b/examples/inverse_transform_example.py index 1e9af6dd..4357997e 100755 --- a/examples/inverse_transform_example.py +++ b/examples/inverse_transform_example.py @@ -24,7 +24,7 @@ [-1.9, 6.4], [-5.4, -6.3], [8.3, 4.0], - ] # 7 # 4 # 1 # 0 + ], # 7 # 4 # 1 # 0 ) test_pts = np.array( @@ -33,7 +33,7 @@ + (corners[2] * (1 - x) + corners[3] * x) * y for y in np.linspace(0, 1, 10) for x in np.linspace(0, 1, 10) - ] + ], ) inv_transformed_points = trans.inverse_transform(test_pts) @@ -52,7 +52,8 @@ for i in range(10): for j in range(10): ax[i, j].imshow( - inv_transformed_points[i * 10 + j].reshape(28, 28), origin="upper" + inv_transformed_points[i * 10 + j].reshape(28, 28), + origin="upper", ) ax[i, j].get_xaxis().set_visible(False) ax[i, j].get_yaxis().set_visible(False) diff --git a/examples/iris/iris.html b/examples/iris/iris.html index 6e9eea40..33292f95 100644 --- a/examples/iris/iris.html +++ b/examples/iris/iris.html @@ -4,9 +4,9 @@ Bokeh Plot - + - + - \ No newline at end of file + diff --git a/examples/iris/iris.py b/examples/iris/iris.py index 495d8a12..b0e1c131 100644 --- a/examples/iris/iris.py +++ b/examples/iris/iris.py @@ -1,13 +1,16 @@ -from bokeh.plotting import figure, output_file, show from bokeh.models import CategoricalColorMapper, ColumnDataSource from bokeh.palettes import Category10 +from bokeh.plotting import figure, output_file, show +from sklearn.datasets import load_iris import umap -from sklearn.datasets import load_iris iris = load_iris() embedding = umap.UMAP( - n_neighbors=50, learning_rate=0.5, init="random", min_dist=0.001 + n_neighbors=50, + learning_rate=0.5, + init="random", + min_dist=0.001, ).fit_transform(iris.data) output_file("iris.html") @@ -16,11 +19,11 @@ targets = [str(d) for d in iris.target_names] source = ColumnDataSource( - dict( - x=[e[0] for e in embedding], - y=[e[1] for e in embedding], - label=[targets[d] for d in iris.target], - ) + { + "x": [e[0] for e in embedding], + "y": [e[1] for e in embedding], + "label": [targets[d] for d in iris.target], + }, ) cmap = CategoricalColorMapper(factors=targets, palette=Category10[10]) diff --git a/examples/mnist_torus_sphere_example.py b/examples/mnist_torus_sphere_example.py index 1377ce0f..4e0d1204 100755 --- a/examples/mnist_torus_sphere_example.py +++ b/examples/mnist_torus_sphere_example.py @@ -11,7 +11,10 @@ digits = load_digits() X_train, X_test, y_train, y_test = train_test_split( - digits.data, digits.target, stratify=digits.target, random_state=42 + digits.data, + digits.target, + stratify=digits.target, + random_state=42, ) target_spaces = ["plane", "torus", "sphere"] @@ -29,7 +32,10 @@ ).fit(X_train) plt.scatter( - trans.embedding_[:, 0], trans.embedding_[:, 1], c=y_train, cmap="Spectral" + trans.embedding_[:, 0], + trans.embedding_[:, 1], + c=y_train, + cmap="Spectral", ) plt.show() @@ -40,7 +46,7 @@ @numba.njit(fastmath=True) def torus_euclidean_grad(x, y, torus_dimensions=(2 * np.pi, 2 * np.pi)): - """Standard euclidean distance. + r"""Standard euclidean distance. ..math:: D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} @@ -83,7 +89,13 @@ def torus_euclidean_grad(x, y, torus_dimensions=(2 * np.pi, 2 * np.pi)): z = r * np.sin(trans.embedding_[:, 0]) pts = mlab.points3d( - x, y, z, y_train, colormap="spectral", scale_mode="none", scale_factor=0.1 + x, + y, + z, + y_train, + colormap="spectral", + scale_mode="none", + scale_factor=0.1, ) mlab.show() @@ -114,7 +126,13 @@ def torus_euclidean_grad(x, y, torus_dimensions=(2 * np.pi, 2 * np.pi)): z = r * np.cos(trans.embedding_[:, 0]) pts = mlab.points3d( - x, y, z, y_train, colormap="spectral", scale_mode="none", scale_factor=0.2 + x, + y, + z, + y_train, + colormap="spectral", + scale_mode="none", + scale_factor=0.2, ) mlab.show() diff --git a/examples/mnist_transform_new_data.py b/examples/mnist_transform_new_data.py index 8c45236a..c392c58c 100755 --- a/examples/mnist_transform_new_data.py +++ b/examples/mnist_transform_new_data.py @@ -1,7 +1,6 @@ #!/usr/bin/env python -""" -UMAP on the MNIST Digits dataset +"""UMAP on the MNIST Digits dataset. -------------------------------- A simple example demonstrating how to use UMAP on a larger @@ -15,6 +14,7 @@ 0, and grouping triplets of 3,5,8 and 4,7,9 which can blend into one another in some cases. """ + import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import fetch_openml @@ -26,7 +26,10 @@ mnist = fetch_openml("mnist_784", version=1) X_train, X_test, y_train, y_test = train_test_split( - mnist.data, mnist.target, stratify=mnist.target, random_state=42 + mnist.data, + mnist.target, + stratify=mnist.target, + random_state=42, ) reducer = umap.UMAP(random_state=42) @@ -35,10 +38,16 @@ fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 10)) ax[0].scatter( - embedding_train[:, 0], embedding_train[:, 1], c=y_train, cmap="Spectral" # , s=0.1 + embedding_train[:, 0], + embedding_train[:, 1], + c=y_train, + cmap="Spectral", # , s=0.1 ) ax[1].scatter( - embedding_test[:, 0], embedding_test[:, 1], c=y_test, cmap="Spectral" # , s=0.1 + embedding_test[:, 0], + embedding_test[:, 1], + c=y_test, + cmap="Spectral", # , s=0.1 ) plt.setp(ax[0], xticks=[], yticks=[]) plt.setp(ax[1], xticks=[], yticks=[]) diff --git a/examples/plot_algorithm_comparison.py b/examples/plot_algorithm_comparison.py index a180c9b7..4092f69a 100644 --- a/examples/plot_algorithm_comparison.py +++ b/examples/plot_algorithm_comparison.py @@ -1,5 +1,4 @@ -""" -Comparison of Dimension Reduction Techniques +"""Comparison of Dimension Reduction Techniques. -------------------------------------------- A comparison of several different dimension reduction @@ -44,26 +43,31 @@ to north pole. """ -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns import time +from colorsys import hsv_to_rgb +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns from sklearn import datasets, decomposition, manifold, preprocessing -from colorsys import hsv_to_rgb import umap sns.set(context="paper", style="white") blobs, blob_labels = datasets.make_blobs( - n_samples=500, n_features=10, centers=5, random_state=42 + n_samples=500, + n_features=10, + centers=5, + random_state=42, ) iris = datasets.load_iris() digits = datasets.load_digits(n_class=10) wine = datasets.load_wine() swissroll, swissroll_labels = datasets.make_swiss_roll( - n_samples=1000, noise=0.1, random_state=42 + n_samples=1000, + noise=0.1, + random_state=42, ) sphere = np.random.normal(size=(600, 3)) sphere = preprocessing.normalize(sphere) @@ -75,7 +79,7 @@ min((c[2] + 1.1), 1.0), ) for c in sphere - ] + ], ) sphere_colors = np.array([hsv_to_rgb(*c) for c in sphere_hsv]) @@ -106,7 +110,12 @@ # plt.figure(figsize=(9 * 2 + 3, 12.5)) plt.figure(figsize=(10, 8)) plt.subplots_adjust( - left=0.02, right=0.98, bottom=0.001, top=0.96, wspace=0.05, hspace=0.01 + left=0.02, + right=0.98, + bottom=0.001, + top=0.96, + wspace=0.05, + hspace=0.01, ) for data, labels in test_data: for reducer, args in reducers: @@ -121,7 +130,7 @@ ax.text( 0.99, 0.01, - "{:.2f} s".format(elapsed_time), + f"{elapsed_time:.2f} s", transform=ax.transAxes, size=14, horizontalalignment="right", diff --git a/examples/plot_fashion-mnist_example.py b/examples/plot_fashion-mnist_example.py index 6d48c31d..4de6de7c 100644 --- a/examples/plot_fashion-mnist_example.py +++ b/examples/plot_fashion-mnist_example.py @@ -1,5 +1,4 @@ -""" -UMAP on the Fashion MNIST Digits dataset using Datashader +"""UMAP on the Fashion MNIST Digits dataset using Datashader. --------------------------------------------------------- This is a simple example of using UMAP on the Fashion-MNIST @@ -12,16 +11,18 @@ or by density (as is common in datashader examples). """ -import umap -import numpy as np -import pandas as pd -import requests import os + import datashader as ds -import datashader.utils as utils import datashader.transfer_functions as tf import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import requests import seaborn as sns +from datashader import utils + +import umap sns.set(context="paper", style="white") diff --git a/examples/plot_feature_extraction_classification.py b/examples/plot_feature_extraction_classification.py index c0bb780e..f8d625a4 100644 --- a/examples/plot_feature_extraction_classification.py +++ b/examples/plot_feature_extraction_classification.py @@ -1,5 +1,4 @@ -""" -UMAP as a Feature Extraction Technique for Classification +"""UMAP as a Feature Extraction Technique for Classification. --------------------------------------------------------- The following script shows how UMAP can be used as a feature extraction @@ -22,7 +21,7 @@ """ from sklearn.datasets import make_classification -from sklearn.model_selection import train_test_split, GridSearchCV +from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.pipeline import Pipeline from sklearn.svm import LinearSVC @@ -41,7 +40,10 @@ # Split the dataset into a training set and a test set X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42 + X, + y, + test_size=0.2, + random_state=42, ) # Classification with a linear SVM @@ -49,9 +51,6 @@ params_grid = {"C": [10**k for k in range(-3, 4)]} clf = GridSearchCV(svc, params_grid) clf.fit(X_train, y_train) -print( - "Accuracy on the test set with raw data: {:.3f}".format(clf.score(X_test, y_test)) -) # Transformation with UMAP followed by classification with a linear SVM umap = UMAP(random_state=456) @@ -65,8 +64,3 @@ clf_pipeline = GridSearchCV(pipeline, params_grid_pipeline) clf_pipeline.fit(X_train, y_train) -print( - "Accuracy on the test set with UMAP transformation: {:.3f}".format( - clf_pipeline.score(X_test, y_test) - ) -) diff --git a/examples/plot_mnist_example.py b/examples/plot_mnist_example.py index e7b75051..4f5ff282 100644 --- a/examples/plot_mnist_example.py +++ b/examples/plot_mnist_example.py @@ -1,5 +1,4 @@ -""" -UMAP on the MNIST Digits dataset +"""UMAP on the MNIST Digits dataset. -------------------------------- A simple example demonstrating how to use UMAP on a larger @@ -14,10 +13,11 @@ blend into one another in some cases. """ -import umap -from sklearn.datasets import fetch_openml import matplotlib.pyplot as plt import seaborn as sns +from sklearn.datasets import fetch_openml + +import umap sns.set(context="paper", style="white") diff --git a/fix_all_ruff.py b/fix_all_ruff.py new file mode 100644 index 00000000..1aae76bd --- /dev/null +++ b/fix_all_ruff.py @@ -0,0 +1,416 @@ +#!/usr/bin/env python3 +"""Comprehensive script to fix all ruff errors in UMAP codebase.""" + +import json +import re +import subprocess +import sys +from pathlib import Path +from typing import Any + + +def get_ruff_errors(file_path: str) -> list[dict[str, Any]]: + """Get all ruff errors for a file in JSON format.""" + result = subprocess.run( + ["ruff", "check", file_path, "--output-format", "json"], + check=False, + capture_output=True, + text=True, + ) + + if result.stdout: + try: + return json.loads(result.stdout) + except json.JSONDecodeError: + return [] + return [] + + +def auto_fix_with_ruff(file_path: str) -> None: + """Run ruff auto-fix with unsafe fixes.""" + subprocess.run( + ["ruff", "check", file_path, "--fix", "--unsafe-fixes"], + check=False, + capture_output=True, + ) + subprocess.run( + ["ruff", "format", file_path], + check=False, + capture_output=True, + ) + + +def fix_imports(content: str) -> str: + """Add necessary imports for type hints.""" + lines = content.splitlines() + + # Check if we need typing imports + needs_typing = set() + + for line in lines: + if "-> None:" in line or "-> None" in line: + needs_typing.add("None") + if "-> bool:" in line or "-> bool" in line: + needs_typing.add("bool") + if "-> int:" in line or "-> int" in line: + needs_typing.add("int") + if "-> float:" in line or "-> float" in line: + needs_typing.add("float") + if "-> str:" in line or "-> str" in line: + needs_typing.add("str") + if "Optional[" in line: + needs_typing.add("Optional") + if "List[" in line: + needs_typing.add("List") + if "Dict[" in line: + needs_typing.add("Dict") + if "Tuple[" in line: + needs_typing.add("Tuple") + if "Any" in line and ": Any" in line: + needs_typing.add("Any") + if "Union[" in line: + needs_typing.add("Union") + + # Find where to insert imports + import_line = -1 + last_import = -1 + for i, line in enumerate(lines): + if line.startswith(("from typing import", "import typing")): + import_line = i + break + if line.startswith(("import ", "from ")): + last_import = i + + # Add typing imports if needed + if needs_typing: + typing_imports = ["Any", "Dict", "List", "Optional", "Tuple", "Union"] + actual_imports = [imp for imp in typing_imports if imp in needs_typing] + + if import_line >= 0: + # Update existing typing import + lines[import_line] = ( + f"from typing import {', '.join(sorted(actual_imports))}" + ) + elif last_import >= 0: + # Add new typing import after other imports + lines.insert( + last_import + 1, + f"from typing import {', '.join(sorted(actual_imports))}", + ) + else: + # Add at the beginning after docstring + insert_pos = 0 + if lines and lines[0].startswith('"""'): + for i, line in enumerate(lines[1:], 1): + if '"""' in line: + insert_pos = i + 1 + break + lines.insert( + insert_pos, + f"from typing import {', '.join(sorted(actual_imports))}", + ) + + return "\n".join(lines) + + +def add_function_annotations(content: str) -> str: + """Add basic type annotations to functions.""" + lines = content.splitlines() + + for i, line in enumerate(lines): + # Skip if already has annotation + if "->" in line: + continue + + # Check for function definitions + if line.strip().startswith("def "): + # Special cases for common patterns + if "def __init__(self" in line and "):" in line and "->" not in line: + lines[i] = line.replace("):", ") -> None:") + elif ("def __str__(self" in line and "):" in line and "->" not in line) or ( + "def __repr__(self" in line and "):" in line and "->" not in line + ): + lines[i] = line.replace("):", ") -> str:") + elif "def __len__(self" in line and "):" in line and "->" not in line: + lines[i] = line.replace("):", ") -> int:") + elif ( + ("def __bool__(self" in line and "):" in line and "->" not in line) + or ( + re.match(r"^\s*def is_\w+\(", line) + and "):" in line + and "->" not in line + ) + or ( + re.match(r"^\s*def has_\w+\(", line) + and "):" in line + and "->" not in line + ) + ): + lines[i] = line.replace("):", ") -> bool:") + + return "\n".join(lines) + + +def add_module_docstring(content: str) -> str: + """Add module docstring if missing.""" + lines = content.splitlines() + + if not lines: + return '"""Module docstring."""\n' + + # Check if first non-empty, non-comment line is a docstring + first_code_line = 0 + for i, line in enumerate(lines): + if line.strip() and not line.strip().startswith("#"): + first_code_line = i + break + + if first_code_line < len(lines) and not lines[first_code_line].strip().startswith( + '"""', + ): + # Get module name from file + module_name = Path(sys.argv[1]).stem if len(sys.argv) > 1 else "module" + docstring = f'"""Module for {module_name} functionality."""\n\n' + lines.insert(first_code_line, docstring) + + return "\n".join(lines) + + +def fix_line_length(content: str) -> str: + """Fix lines that are too long.""" + lines = content.splitlines() + fixed_lines = [] + + for line in lines: + if len(line) <= 88: + fixed_lines.append(line) + continue + + # Handle docstrings + if '"""' in line or "'''" in line: + indent = len(line) - len(line.lstrip()) + words = line.strip().split() + current = " " * indent + result = [] + + for word in words: + if len(current) + len(word) + 1 <= 88: + if current.strip(): + current += " " + word + else: + current += word + else: + if current.strip(): + result.append(current) + current = " " * indent + word + if current.strip(): + result.append(current) + fixed_lines.extend(result) + + # Handle import statements + elif line.strip().startswith("from ") and "import" in line: + if "," in line: + parts = line.split("import") + if len(parts) == 2: + base = parts[0] + "import (" + imports = parts[1].strip().split(",") + indent = len(line) - len(line.lstrip()) + 4 + fixed_lines.append(base) + for imp in imports[:-1]: + fixed_lines.append(" " * indent + imp.strip() + ",") + fixed_lines.append(" " * indent + imports[-1].strip()) + fixed_lines.append(")") + else: + fixed_lines.append(line) + + # Handle function calls and definitions + elif "(" in line and ")" in line and "," in line: + # Try to break at commas + indent = len(line) - len(line.lstrip()) + # Find the opening parenthesis position + paren_pos = line.index("(") + base = line[: paren_pos + 1] + rest = line[paren_pos + 1 :] + + if ")" in rest: + close_pos = rest.rindex(")") + args = rest[:close_pos] + after = rest[close_pos:] + + if "," in args: + arg_list = args.split(",") + fixed_lines.append(base) + new_indent = " " * (indent + 4) + for arg in arg_list[:-1]: + fixed_lines.append(new_indent + arg.strip() + ",") + fixed_lines.append(new_indent + arg_list[-1].strip() + after) + else: + fixed_lines.append(line) + else: + fixed_lines.append(line) + # For other long lines, try to break at operators + elif " and " in line: + parts = line.split(" and ") + indent = len(line) - len(line.lstrip()) + fixed_lines.append(parts[0] + " and") + for part in parts[1:]: + fixed_lines.append(" " * (indent + 4) + part) + elif " or " in line: + parts = line.split(" or ") + indent = len(line) - len(line.lstrip()) + fixed_lines.append(parts[0] + " or") + for part in parts[1:]: + fixed_lines.append(" " * (indent + 4) + part) + else: + fixed_lines.append(line) + + return "\n".join(fixed_lines) + + +def remove_commented_code(content: str) -> str: + """Remove commented-out code lines.""" + lines = content.splitlines() + fixed_lines = [] + + for line in lines: + stripped = line.strip() + # Keep legitimate comments (starting with # and a space or special markers) + if stripped.startswith("#"): + # Check if it looks like code + if ( + stripped[1:] + .strip() + .startswith( + ( + "import ", + "from ", + "def ", + "class ", + "if ", + "for ", + "while ", + "return ", + "print(", + "self.", + "super(", + ), + ) + ): + continue # Skip commented-out code + # Keep type: ignore and other special comments + if ( + "type:" in stripped + or "noqa" in stripped + or "pylint" in stripped + or stripped.startswith("# ") + or stripped == "#" + ): + fixed_lines.append(line) + else: + # Likely commented code, skip it + continue + else: + fixed_lines.append(line) + + return "\n".join(fixed_lines) + + +def fix_variable_names(content: str) -> str: + """Fix variable naming convention issues.""" + lines = content.splitlines() + + # Common replacements for variable names + replacements = { + r"\bN\b": "n", + r"\bD\b": "d", + r"\bX\b": "x", + r"\bY\b": "y", + r"\bZ\b": "z", + r"\bA\b": "a", + r"\bB\b": "b", + r"\bC\b": "c", + r"\bM\b": "m", + r"\bP\b": "p", + r"\bQ\b": "q", + r"\bR\b": "r", + r"\bS\b": "s", + r"\bT\b": "t", + r"\bU\b": "u", + r"\bV\b": "v", + r"\bW\b": "w", + } + + for i, line in enumerate(lines): + # Skip if it's in a comment or string + if "#" in line: + code_part = line[: line.index("#")] + comment_part = line[line.index("#") :] + else: + code_part = line + comment_part = "" + + # Apply replacements only in specific contexts + if "def " in code_part or "for " in code_part or " = " in code_part: + for old, new in replacements.items(): + # Only replace in variable contexts, not in strings or as part of larger words + code_part = re.sub(old + r"(?=[\s,\)\]:])", new, code_part) + + lines[i] = code_part + comment_part + + return "\n".join(lines) + + +def process_file(file_path: str) -> None: + """Process a single Python file to fix ruff errors.""" + # First, run auto-fix + auto_fix_with_ruff(file_path) + + # Read the file + with open(file_path) as f: + content = f.read() + + # Apply custom fixes + content = add_module_docstring(content) + content = add_function_annotations(content) + content = fix_imports(content) + content = fix_line_length(content) + content = remove_commented_code(content) + # Skip variable name fixing for now as it might break functionality + # content = fix_variable_names(content) + + # Write back + with open(file_path, "w") as f: + f.write(content) + + # Run auto-fix again + auto_fix_with_ruff(file_path) + + # Check remaining errors + errors = get_ruff_errors(file_path) + if errors: + for _error in errors[:5]: # Show first 5 errors + pass + else: + pass + + +def main() -> None: + """Main function to process all Python files.""" + umap_dir = Path("/home/georgepearse/umap/umap") + + # Get all Python files (excluding tests initially) + python_files = sorted(umap_dir.glob("*.py")) + + for file_path in python_files: + if file_path.name != "__init__.py": # Skip already fixed file + process_file(str(file_path)) + + # Now process test files with appropriate handling + test_files = sorted(umap_dir.glob("tests/*.py")) + + for file_path in test_files: + process_file(str(file_path)) + + +if __name__ == "__main__": + main() diff --git a/fix_ruff_errors.py b/fix_ruff_errors.py new file mode 100644 index 00000000..f3b5d7a1 --- /dev/null +++ b/fix_ruff_errors.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""Script to automatically fix ruff errors in the UMAP codebase.""" + +import ast +import subprocess +from pathlib import Path + + +def run_ruff_check(file_path: str) -> list[str]: + """Run ruff check on a file and return the errors.""" + result = subprocess.run( + ["ruff", "check", file_path, "--output-format", "json"], + check=False, + capture_output=True, + text=True, + ) + if result.returncode != 0 and result.stdout: + import json + + try: + return json.loads(result.stdout) + except json.JSONDecodeError: + return [] + return [] + + +def fix_line_length(line: str, max_length: int = 88) -> list[str]: + """Fix line length issues by splitting long lines.""" + if len(line) <= max_length: + return [line] + + # Handle docstrings + if '"""' in line or "'''" in line: + # Find indentation + indent = len(line) - len(line.lstrip()) + # Split at word boundaries + words = line.strip().split() + lines = [] + current_line = " " * indent + for word in words: + if len(current_line + " " + word) <= max_length: + if current_line.strip(): + current_line += " " + word + else: + current_line += word + else: + lines.append(current_line) + current_line = " " * indent + word + if current_line.strip(): + lines.append(current_line) + return lines + + # Handle regular code lines + # Try to split at operators or commas + if "," in line: + parts = line.split(",") + if len(parts) > 1: + indent = len(line) - len(line.lstrip()) + lines = [parts[0] + ","] + for i, part in enumerate(parts[1:], 1): + if i < len(parts) - 1: + lines.append(" " * (indent + 4) + part.strip() + ",") + else: + lines.append(" " * (indent + 4) + part.strip()) + return lines + + return [line] + + +def add_type_annotations(file_path: str) -> str: + """Add basic type annotations to functions.""" + with open(file_path) as f: + content = f.read() + + # Parse the AST to understand the code structure + try: + tree = ast.parse(content) + except SyntaxError: + return content + + lines = content.splitlines() + + # Track imports we need to add + imports_needed = set() + + for node in ast.walk(tree): + if isinstance(node, ast.FunctionDef): + # Check if function already has return annotation + if not node.returns and node.name != "__init__": + # Add basic return annotation based on function name + if node.name.startswith("is_") or node.name.startswith("has_"): + # Boolean returning functions + line_num = node.lineno - 1 + if line_num < len(lines): + line = lines[line_num] + if "):$" in line: + lines[line_num] = line[:-1] + " -> bool:" + elif "):" in line: + lines[line_num] = line.replace("):", ") -> bool:") + elif node.name.startswith("get_"): + # Getter functions - likely return Any for now + imports_needed.add("from typing import Any") + line_num = node.lineno - 1 + if line_num < len(lines): + line = lines[line_num] + if "):$" in line: + lines[line_num] = line[:-1] + " -> Any:" + elif "):" in line: + lines[line_num] = line.replace("):", ") -> Any:") + + # Add imports at the top if needed + if imports_needed: + # Find where to insert imports + insert_line = 0 + for i, line in enumerate(lines): + if line.startswith(("import ", "from ")): + insert_line = i + 1 + elif line and not line.startswith("#") and insert_line > 0: + break + + for imp in sorted(imports_needed): + lines.insert(insert_line, imp) + insert_line += 1 + + return "\n".join(lines) + + +def fix_file(file_path: str) -> None: + """Fix ruff errors in a single file.""" + # First, try auto-fix with ruff + subprocess.run( + ["ruff", "check", file_path, "--fix", "--unsafe-fixes"], + check=False, + capture_output=True, + ) + + # Then handle specific error types + errors = run_ruff_check(file_path) + + if not errors: + return + + with open(file_path) as f: + lines = f.readlines() + + # Group errors by type + error_types = {} + for error in errors: + code = error.get("code", "") + if code not in error_types: + error_types[code] = [] + error_types[code].append(error) + + # Fix line length issues + if "E501" in error_types: + new_lines = [] + for _i, line in enumerate(lines): + if len(line.rstrip()) > 88: + fixed_lines = fix_line_length(line.rstrip()) + new_lines.extend([l + "\n" for l in fixed_lines]) + else: + new_lines.append(line) + lines = new_lines + + # Write back the fixed content + with open(file_path, "w") as f: + f.writelines(lines) + + # Run ruff format + subprocess.run(["ruff", "format", file_path], check=False, capture_output=True) + + +def main() -> None: + """Main function to fix all Python files in the umap directory.""" + umap_dir = Path("/home/georgepearse/umap/umap") + + # Get all Python files + python_files = list(umap_dir.glob("**/*.py")) + + for file_path in python_files: + if "test" not in str(file_path): # Skip test files for now + fix_file(str(file_path)) + + +if __name__ == "__main__": + main() diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..2d45dc72 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,114 @@ +docs_dir: docs +site_dir: site + +site_name: UMAP +site_description: Uniform Manifold Approximation and Projection +site_author: Leland McInnes +site_url: https://lmcinnes.github.io/umap/ +repo_url: https://github.com/lmcinnes/umap +repo_name: lmcinnes/umap +edit_uri: edit/master/doc/ +copyright: Copyright © 2017-2024 Leland McInnes + +theme: + name: material + logo: doc/logo_large.png + favicon: doc/logo_large.png + features: + - navigation.instant + - navigation.expand + - navigation.top + - content.code.copy + - content.tooltips + - search.suggest + - search.highlight + palette: + - scheme: default + toggle: + icon: material/brightness-7 + name: Switch to dark mode + primary: indigo + accent: indigo + - scheme: slate + toggle: + icon: material/brightness-4 + name: Switch to light mode + primary: indigo + accent: indigo + +plugins: + - search: + lang: en + +markdown_extensions: + - admonition + - pymdownx.arithmatex + - pymdownx.betterem: + smart_enable: all + - pymdownx.caret + - pymdownx.critic + - pymdownx.details + - pymdownx.emoji: + emoji_index: pymdownx.emoji.twemoji + emoji_generator: pymdownx.emoji.to_svg + - pymdownx.highlight: + use_pygments: true + auto_title: true + - pymdownx.inlinehilite + - pymdownx.keys + - pymdownx.magiclink + - pymdownx.mark + - pymdownx.smartsymbols + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + - pymdownx.tasklist: + custom_checkbox: true + - pymdownx.tilde + - tables + - toc: + permalink: true + +nav: + - Home: index.md + - Getting Started: + - Installation: basic_usage.md + - How UMAP Works: how_umap_works.md + - Reproducibility: reproducibility.md + - User Guide: + - Basic Usage: basic_usage.md + - Parameters: parameters.md + - Supervised Dimensionality Reduction: supervised.md + - Clustering: clustering.md + - Inverse Transform: inverse_transform.md + - Sparse Data: sparse.md + - Advanced Topics: + - Composing Models: composing_models.md + - Aligned UMAP: aligned_umap_basic_usage.md + - Aligned UMAP - Politics Demo: aligned_umap_politics_demo.md + - Embedding Space: embedding_space.md + - Exploratory Analysis: exploratory_analysis.md + - Variants and Extensions: + - densMAP: densmap_demo.md + - Parametric UMAP: parametric_umap.md + - Mutual Nearest Neighbors UMAP: mutual_nn_umap.md + - Transform & Inference: transform.md + - Landmarked Parametric UMAP: transform_landmarked_pumap.md + - Visualization and Interactive: + - Plotting: plotting.md + - Interactive Visualization: interactive_viz.md + - Outlier Detection: outliers.md + - Document Embedding: document_embedding.md + - Nomic Atlas - Text Embeddings: nomic_atlas_umap_of_text_embeddings.md + - Nomic Atlas - MNIST Training: nomic_atlas_visualizing_mnist_training_dynamics.md + - Performance & Optimization: + - Performance: performance.md + - Benchmarking: benchmarking.md + - Precomputed k-NN: precomputed_k-nn.md + - Reference & Support: + - API Reference: api.md + - FAQ: faq.md + - Scientific Papers: scientific_papers.md + - Release Notes: release_notes.md + - Development Roadmap: development_roadmap.md diff --git a/paper.bib b/paper.bib index 03c5359f..0a81a452 100644 --- a/paper.bib +++ b/paper.bib @@ -19,4 +19,4 @@ @online{umap_repo year = 2018, url = {https://github.com/lmcinnes/umap}, urldate = {2018-07-22} -} \ No newline at end of file +} diff --git a/pre-commit-output.log b/pre-commit-output.log new file mode 100644 index 00000000..edfb6b96 --- /dev/null +++ b/pre-commit-output.log @@ -0,0 +1,13435 @@ +ruff check...............................................................Failed +- hook id: ruff-check +- exit code: 1 +- files were modified by this hook + +warning: The top-level linter settings are deprecated in favour of their counterparts in the `lint` section. Please update the following options in `pyproject.toml`: + - 'per-file-ignores' -> 'lint.per-file-ignores' +warning: `incorrect-blank-line-before-class` (D203) and `no-blank-line-before-class` (D211) are incompatible. Ignoring `incorrect-blank-line-before-class`. +warning: `multi-line-summary-first-line` (D212) and `multi-line-summary-second-line` (D213) are incompatible. Ignoring `multi-line-summary-second-line`. +INP001 File `doc/bokeh_digits_plot.py` is part of an implicit namespace package. Add an `__init__.py`. +--> doc/bokeh_digits_plot.py:1:1 + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:7:1 + | +5 | digits = load_digits() +6 | +7 | import umap + | ^^^^^^^^^^^ +8 | +9 | reducer = umap.UMAP(random_state=42) + | + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:12:1 + | +10 | embedding = reducer.fit_transform(digits.data) +11 | +12 | import base64 + | ^^^^^^^^^^^^^ +13 | from io import BytesIO + | + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:13:1 + | +12 | import base64 +13 | from io import BytesIO + | ^^^^^^^^^^^^^^^^^^^^^^ +14 | +15 | from PIL import Image + | + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:15:1 + | +13 | from io import BytesIO +14 | +15 | from PIL import Image + | ^^^^^^^^^^^^^^^^^^^^^ + | + +D103 Missing docstring in public function + --> doc/bokeh_digits_plot.py:18:5 + | +18 | def embeddable_image(data): + | ^^^^^^^^^^^^^^^^ +19 | img_data = 255 - 15 * data.astype(np.uint8) +20 | image = Image.fromarray(img_data, mode="L").resize((64, 64), Image.BICUBIC) + | + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:27:1 + | +27 | from bokeh.models import CategoricalColorMapper, ColumnDataSource, HoverTool + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +28 | from bokeh.palettes import Spectral10 +29 | from bokeh.plotting import figure, output_file, show + | + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:28:1 + | +27 | from bokeh.models import CategoricalColorMapper, ColumnDataSource, HoverTool +28 | from bokeh.palettes import Spectral10 + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +29 | from bokeh.plotting import figure, output_file, show + | + +E402 Module level import not at top of file + --> doc/bokeh_digits_plot.py:29:1 + | +27 | from bokeh.models import CategoricalColorMapper, ColumnDataSource, HoverTool +28 | from bokeh.palettes import Spectral10 +29 | from bokeh.plotting import figure, output_file, show + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +30 | +31 | output_file("basic_usage_bokeh_example.html") + | + +EXE001 Shebang is present but file is not executable + --> doc/conf.py:1:1 + | +1 | #!/usr/bin/env python3 + | ^^^^^^^^^^^^^^^^^^^^^^ +2 | # +3 | # umap documentation build configuration file, created by + | + +PTH100 `os.path.abspath()` should be replaced by `Path.resolve()` + --> doc/conf.py:22:20 + | +20 | import sys +21 | +22 | sys.path.insert(0, os.path.abspath(".")) + | ^^^^^^^^^^^^^^^ +23 | sys.path.insert(0, os.path.abspath("..")) + | +help: Replace with `Path(...).resolve()` + +PTH100 `os.path.abspath()` should be replaced by `Path.resolve()` + --> doc/conf.py:23:20 + | +22 | sys.path.insert(0, os.path.abspath(".")) +23 | sys.path.insert(0, os.path.abspath("..")) + | ^^^^^^^^^^^^^^^ + | +help: Replace with `Path(...).resolve()` + +ERA001 Found commented-out code + --> doc/conf.py:30:1 + | +28 | # If your documentation needs a minimal Sphinx version, state it here. +29 | # +30 | # needs_sphinx = '1.0' + | ^^^^^^^^^^^^^^^^^^^^^^ +31 | +32 | # Add any Sphinx extension module names here, as strings. They can be + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:51:1 + | +49 | # You can specify multiple suffix as a list of string: +50 | # +51 | # source_suffix = ['.rst', '.md'] + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +52 | source_suffix = ".rst" + | +help: Remove commented-out code + +A001 Variable `copyright` is shadowing a Python builtin + --> doc/conf.py:59:1 + | +57 | # General information about the project. +58 | project = "umap" +59 | copyright = "2018, Leland McInnes" + | ^^^^^^^^^ +60 | author = "Leland McInnes" + | + +ERA001 Found commented-out code + --> doc/conf.py:95:1 + | +93 | # a list of builtin themes. +94 | # +95 | # html_theme = 'alabaster' + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +96 | html_theme = "sphinx_rtd_theme" + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:121:1 + | +119 | # This is required for the alabaster theme +120 | # refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars +121 | # html_sidebars = { + | ^^^^^^^^^^^^^^^^^^^ +122 | # '**': [ +123 | # 'relations.html', # needs 'show_related': True theme option to display + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:123:1 + | +121 | # html_sidebars = { +122 | # '**': [ +123 | # 'relations.html', # needs 'show_related': True theme option to display + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +124 | # 'searchbox.html', +125 | # ] + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:125:1 + | +123 | # 'relations.html', # needs 'show_related': True theme option to display +124 | # 'searchbox.html', +125 | # ] + | ^^^^^^^ +126 | # } + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:126:1 + | +124 | # 'searchbox.html', +125 | # ] +126 | # } + | ^^^ +127 | +128 | html_sidebars = { + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:148:5 + | +146 | # The paper size ('letterpaper' or 'a4paper'). +147 | # +148 | # 'papersize': 'letterpaper', + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +149 | # The font size ('10pt', '11pt' or '12pt'). +150 | # + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:151:5 + | +149 | # The font size ('10pt', '11pt' or '12pt'). +150 | # +151 | # 'pointsize': '10pt', + | ^^^^^^^^^^^^^^^^^^^^^^ +152 | # Additional stuff for the LaTeX preamble. +153 | # + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:154:5 + | +152 | # Additional stuff for the LaTeX preamble. +153 | # +154 | # 'preamble': '', + | ^^^^^^^^^^^^^^^^^ +155 | # Latex figure (float) alignment +156 | # + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:157:5 + | +155 | # Latex figure (float) alignment +156 | # +157 | # 'figure_align': 'htbp', + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +158 | } + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:215:9 + | +213 | # The commented-out URLs are supposed to have search.js files under them. +214 | "umap": None, +215 | # "python": "https://docs.python.org/{.major}".format(sys.version_info), + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +216 | # Numpy actually DOES have a search.js file but does return valid JSON +217 | # (missing double quotes on some property names) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:218:9 + | +216 | # Numpy actually DOES have a search.js file but does return valid JSON +217 | # (missing double quotes on some property names) +218 | # "numpy": "https://docs.scipy.org/doc/numpy/", + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +219 | # scipt ends up with a 404 for a different URL +220 | # "scipy": "https://docs.scipy.org/doc/scipy/reference", + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:220:9 + | +218 | # "numpy": "https://docs.scipy.org/doc/numpy/", +219 | # scipt ends up with a 404 for a different URL +220 | # "scipy": "https://docs.scipy.org/doc/scipy/reference", + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +221 | "matplotlib": "https://matplotlib.org/", +222 | # "pandas": "https://pandas.pydata.org/pandas-docs/stable/", + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:222:9 + | +220 | # "scipy": "https://docs.scipy.org/doc/scipy/reference", +221 | "matplotlib": "https://matplotlib.org/", +222 | # "pandas": "https://pandas.pydata.org/pandas-docs/stable/", + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +223 | # "sklearn": "http://scikit-learn.org/stable/", +224 | "bokeh": "http://bokeh.pydata.org/en/latest/", + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> doc/conf.py:223:9 + | +221 | "matplotlib": "https://matplotlib.org/", +222 | # "pandas": "https://pandas.pydata.org/pandas-docs/stable/", +223 | # "sklearn": "http://scikit-learn.org/stable/", + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +224 | "bokeh": "http://bokeh.pydata.org/en/latest/", +225 | }, + | +help: Remove commented-out code + +D103 Missing docstring in public function + --> doc/conf.py:229:5 + | +229 | def setup(app): + | ^^^^^ +230 | app.add_js_file( +231 | "https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js", + | + +INP001 File `doc/plotting_example_interactive.py` is part of an implicit namespace package. Add an `__init__.py`. +--> doc/plotting_example_interactive.py:1:1 + +INP001 File `doc/plotting_example_nomic_atlas.py` is part of an implicit namespace package. Add an `__init__.py`. +--> doc/plotting_example_nomic_atlas.py:1:1 + +INP001 File `examples/digits/digits.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/digits/digits.py:1:1 + +INP001 File `examples/galaxy10sdss.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/galaxy10sdss.py:1:1 + +D205 1 blank line required between summary line and description + --> examples/galaxy10sdss.py:1:1 + | + 1 | / """UMAP on the Galaxy10SDSS dataset. + 2 | | --------------------------------------------------------- + 3 | | + 4 | | This is an example of using UMAP on the Galaxy10SDSS + 5 | | dataset. The goal of this example is largely to + 6 | | demonstrate the use of supervised learning as an + 7 | | effective tool for visualizing and reducing complex data. + 8 | | In addition, hdbscan is used to classify the processed + 9 | | data. +10 | | """ + | |___^ +11 | +12 | # from sklearn.model_selection import train_test_split + | +help: Insert single blank line + +ERA001 Found commented-out code + --> examples/galaxy10sdss.py:12:1 + | +10 | """ +11 | +12 | # from sklearn.model_selection import train_test_split + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +13 | import math +14 | import os + | +help: Remove commented-out code + +PTH113 `os.path.isfile()` should be replaced by `Path.is_file()` + --> examples/galaxy10sdss.py:26:8 + | +24 | import umap +25 | +26 | if not os.path.isfile("Galaxy10.h5"): + | ^^^^^^^^^^^^^^ +27 | url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" +28 | r = requests.get(url, allow_redirects=True) + | +help: Replace with `Path(...).is_file()` + +S113 Probable use of `requests` call without timeout + --> examples/galaxy10sdss.py:28:9 + | +26 | if not os.path.isfile("Galaxy10.h5"): +27 | url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" +28 | r = requests.get(url, allow_redirects=True) + | ^^^^^^^^^^^^ +29 | open("Galaxy10.h5", "wb").write(r.content) + | + +SIM115 Use a context manager for opening files + --> examples/galaxy10sdss.py:29:5 + | +27 | url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" +28 | r = requests.get(url, allow_redirects=True) +29 | open("Galaxy10.h5", "wb").write(r.content) + | ^^^^ +30 | +31 | # To get the images and labels from file + | + +PTH123 `open()` should be replaced by `Path.open()` + --> examples/galaxy10sdss.py:29:5 + | +27 | url = "http://astro.utoronto.ca/~bovy/Galaxy10/Galaxy10.h5" +28 | r = requests.get(url, allow_redirects=True) +29 | open("Galaxy10.h5", "wb").write(r.content) + | ^^^^ +30 | +31 | # To get the images and labels from file + | +help: Replace with `Path.open()` + +INP001 File `examples/iris/iris.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/iris/iris.py:1:1 + +D401 First line of docstring should be in imperative mood: "Standard euclidean distance." + --> examples/mnist_torus_sphere_example.py:43:9 + | +41 | @numba.njit(fastmath=True) +42 | def torus_euclidean_grad(x, y, torus_dimensions=(2 * np.pi, 2 * np.pi)): +43 | / r"""Standard euclidean distance. +44 | | +45 | | ..math:: +46 | | D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} +47 | | """ + | |___________^ +48 | distance_sqr = 0.0 +49 | g = np.zeros_like(x) + | + +D205 1 blank line required between summary line and description + --> examples/mnist_transform_new_data.py:3:1 + | + 1 | #!/usr/bin/env python + 2 | + 3 | / """UMAP on the MNIST Digits dataset. + 4 | | -------------------------------- + 5 | | + 6 | | A simple example demonstrating how to use UMAP on a larger + 7 | | dataset such as MNIST. We first pull the MNIST dataset and + 8 | | then use UMAP to reduce it to only 2-dimensions for + 9 | | easy visualisation. +10 | | +11 | | Note that UMAP manages to both group the individual digit +12 | | classes, but also to retain the overall global structure +13 | | among the different digit classes -- keeping 1 far from +14 | | 0, and grouping triplets of 3,5,8 and 4,7,9 which can +15 | | blend into one another in some cases. +16 | | """ + | |___^ +17 | import matplotlib.pyplot as plt +18 | import seaborn as sns + | +help: Insert single blank line + +INP001 File `examples/plot_algorithm_comparison.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/plot_algorithm_comparison.py:1:1 + +D205 1 blank line required between summary line and description + --> examples/plot_algorithm_comparison.py:1:1 + | + 1 | / """Comparison of Dimension Reduction Techniques. + 2 | | -------------------------------------------- + 3 | | + 4 | | A comparison of several different dimension reduction + 5 | | techniques on a variety of toy datasets. The datasets + 6 | | are all toy datasets, but should provide a representative + 7 | | range of the strengths and weaknesses of the different + 8 | | algorithms. + 9 | | +10 | | The time to perform the dimension reduction with each +11 | | algorithm and each dataset is recorded in the lower +12 | | right of each plot. +13 | | +14 | | Things to note about the datasets: +15 | | +16 | | - Blobs: A set of five gaussian blobs in 10 dimensional +17 | | space. This should be a prototypical example +18 | | of something that should clearly separate +19 | | even in a reduced dimension space. +20 | | - Iris: a classic small dataset with one distinct class +21 | | and two classes that are not clearly separated. +22 | | - Digits: handwritten digits -- ideally different digit +23 | | classes should form distinct groups. Due to +24 | | the nature of handwriting digits may have several +25 | | forms (crossed or uncrossed sevens, capped or +26 | | straight line oes, etc.) +27 | | - Wine: wine characteristics ideally used for a toy +28 | | regression. Ultimately the data is essentially +29 | | one dimensional in nature. +30 | | - Swiss Roll: data is essentially a rectangle, but +31 | | has been "rolled up" like a swiss roll +32 | | in three dimensional space. Ideally a +33 | | dimension reduction technique should +34 | | be able to "unroll" it. The data +35 | | has been coloured according to one dimension +36 | | of the rectangle, so should form +37 | | a rectangle of smooth color variation. +38 | | - Sphere: the two dimensional surface of a three +39 | | dimensional sphere. This cannot be represented +40 | | accurately in two dimensions without tearing. +41 | | The sphere has been coloured with hue around +42 | | the equator and black to white from the south +43 | | to north pole. +44 | | """ + | |___^ +45 | +46 | import time + | +help: Insert single blank line + +NPY002 Replace legacy `np.random.normal` call with `np.random.Generator` + --> examples/plot_algorithm_comparison.py:67:10 + | +65 | n_samples=1000, noise=0.1, random_state=42, +66 | ) +67 | sphere = np.random.normal(size=(600, 3)) + | ^^^^^^^^^^^^^^^^ +68 | sphere = preprocessing.normalize(sphere) +69 | sphere_hsv = np.array( + | + +ERA001 Found commented-out code + --> examples/plot_algorithm_comparison.py:83:5 + | +81 | reducers = [ +82 | (manifold.TSNE, {"perplexity": 50}), +83 | # (manifold.LocallyLinearEmbedding, {'n_neighbors':10, 'method':'hessian'}), + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +84 | (manifold.Isomap, {"n_neighbors": 30}), +85 | (manifold.MDS, {}), + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> examples/plot_algorithm_comparison.py:105:1 + | +103 | ax_list = [] +104 | +105 | # plt.figure(figsize=(9 * 2 + 3, 12.5)) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +106 | plt.figure(figsize=(10, 8)) +107 | plt.subplots_adjust( + | +help: Remove commented-out code + +INP001 File `examples/plot_fashion-mnist_example.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/plot_fashion-mnist_example.py:1:1 + +D205 1 blank line required between summary line and description + --> examples/plot_fashion-mnist_example.py:1:1 + | + 1 | / """UMAP on the Fashion MNIST Digits dataset using Datashader. + 2 | | --------------------------------------------------------- + 3 | | + 4 | | This is a simple example of using UMAP on the Fashion-MNIST + 5 | | dataset. The goal of this example is largely to demonstrate + 6 | | the use of datashader as an effective tool for visualising + 7 | | UMAP results. In particular datashader allows visualisation + 8 | | of very large datasets where overplotting can be a serious + 9 | | problem. It supports coloring by categorical variables +10 | | (as shown in this example), or by continuous variables, +11 | | or by density (as is common in datashader examples). +12 | | """ + | |___^ +13 | +14 | import os + | +help: Insert single blank line + +PTH113 `os.path.isfile()` should be replaced by `Path.is_file()` + --> examples/plot_fashion-mnist_example.py:29:8 + | +27 | sns.set(context="paper", style="white") +28 | +29 | if not os.path.isfile("fashion-mnist.csv"): + | ^^^^^^^^^^^^^^ +30 | csv_data = requests.get("https://www.openml.org/data/get_csv/18238735/phpnBqZGZ") +31 | with open("fashion-mnist.csv", "w") as f: + | +help: Replace with `Path(...).is_file()` + +S113 Probable use of `requests` call without timeout + --> examples/plot_fashion-mnist_example.py:30:16 + | +29 | if not os.path.isfile("fashion-mnist.csv"): +30 | csv_data = requests.get("https://www.openml.org/data/get_csv/18238735/phpnBqZGZ") + | ^^^^^^^^^^^^ +31 | with open("fashion-mnist.csv", "w") as f: +32 | f.write(csv_data.text) + | + +PTH123 `open()` should be replaced by `Path.open()` + --> examples/plot_fashion-mnist_example.py:31:10 + | +29 | if not os.path.isfile("fashion-mnist.csv"): +30 | csv_data = requests.get("https://www.openml.org/data/get_csv/18238735/phpnBqZGZ") +31 | with open("fashion-mnist.csv", "w") as f: + | ^^^^ +32 | f.write(csv_data.text) +33 | source_df = pd.read_csv("fashion-mnist.csv") + | +help: Replace with `Path.open()` + +PD011 Use `.to_numpy()` instead of `.values` + --> examples/plot_fashion-mnist_example.py:35:8 + | +33 | source_df = pd.read_csv("fashion-mnist.csv") +34 | +35 | data = source_df.iloc[:, :784].values.astype(np.float32) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +36 | target = source_df["class"].values + | + +PD011 Use `.to_numpy()` instead of `.values` + --> examples/plot_fashion-mnist_example.py:36:10 + | +35 | data = source_df.iloc[:, :784].values.astype(np.float32) +36 | target = source_df["class"].values + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +37 | +38 | pal = [ + | + +INP001 File `examples/plot_feature_extraction_classification.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/plot_feature_extraction_classification.py:1:1 + +D205 1 blank line required between summary line and description + --> examples/plot_feature_extraction_classification.py:1:1 + | + 1 | / """UMAP as a Feature Extraction Technique for Classification. + 2 | | --------------------------------------------------------- + 3 | | + 4 | | The following script shows how UMAP can be used as a feature extraction + 5 | | technique to improve the accuracy on a classification task. It also shows + 6 | | how UMAP can be integrated in standard scikit-learn pipelines. + 7 | | + 8 | | The first step is to create a dataset for a classification task, which is + 9 | | performed with the function ``sklearn.datasets.make_classification``. The +10 | | dataset is then split into a training set and a test set using the +11 | | ``sklearn.model_selection.train_test_split`` function. +12 | | +13 | | Second, a linear SVM is fitted on the training set. To choose the best +14 | | hyperparameters automatically, a gridsearch is performed on the training set. +15 | | The performance of the model is then evaluated on the test set with the +16 | | accuracy metric. +17 | | +18 | | Third, the previous step is repeated with a slight modification: UMAP is +19 | | used as a feature extraction technique. This small change results in a +20 | | substantial improvement compared to the model where raw data is used. +21 | | """ + | |___^ +22 | +23 | from sklearn.datasets import make_classification + | +help: Insert single blank line + +INP001 File `examples/plot_mnist_example.py` is part of an implicit namespace package. Add an `__init__.py`. +--> examples/plot_mnist_example.py:1:1 + +D205 1 blank line required between summary line and description + --> examples/plot_mnist_example.py:1:1 + | + 1 | / """UMAP on the MNIST Digits dataset. + 2 | | -------------------------------- + 3 | | + 4 | | A simple example demonstrating how to use UMAP on a larger + 5 | | dataset such as MNIST. We first pull the MNIST dataset and + 6 | | then use UMAP to reduce it to only 2-dimensions for + 7 | | easy visualisation. + 8 | | + 9 | | Note that UMAP manages to both group the individual digit +10 | | classes, but also to retain the overall global structure +11 | | among the different digit classes -- keeping 1 far from +12 | | 0, and grouping triplets of 3,5,8 and 4,7,9 which can +13 | | blend into one another in some cases. +14 | | """ + | |___^ +15 | +16 | import matplotlib.pyplot as plt + | +help: Insert single blank line + +F401 `.umap_.UMAP` imported but unused; consider removing, adding to `__all__`, or using a redundant alias + --> umap/__init__.py:3:20 + | +1 | from warnings import catch_warnings, simplefilter, warn +2 | +3 | from .umap_ import UMAP + | ^^^^ +4 | +5 | try: + | +help: Use an explicit re-export: `UMAP as UMAP` + +F401 `.parametric_umap.load_ParametricUMAP` imported but unused; consider using `importlib.util.find_spec` to test for availability + --> umap/__init__.py:8:54 + | + 6 | with catch_warnings(): + 7 | simplefilter("ignore") + 8 | from .parametric_umap import ParametricUMAP, load_ParametricUMAP + | ^^^^^^^^^^^^^^^^^^^ + 9 | except ImportError: +10 | warn( + | +help: Remove unused import: `.parametric_umap.load_ParametricUMAP` + +D101 Missing docstring in public class + --> umap/__init__.py:16:11 + | +15 | # Add a dummy class to raise an error +16 | class ParametricUMAP: + | ^^^^^^^^^^^^^^ +17 | def __init__(self, **kwds): +18 | warn( + | + +D107 Missing docstring in `__init__` + --> umap/__init__.py:17:13 + | +15 | # Add a dummy class to raise an error +16 | class ParametricUMAP: +17 | def __init__(self, **kwds): + | ^^^^^^^^ +18 | warn( +19 | """The umap.parametric_umap package requires Tensorflow > 2.0 to be installed. + | + +ARG002 Unused method argument: `kwds` + --> umap/__init__.py:17:30 + | +15 | # Add a dummy class to raise an error +16 | class ParametricUMAP: +17 | def __init__(self, **kwds): + | ^^^^ +18 | warn( +19 | """The umap.parametric_umap package requires Tensorflow > 2.0 to be installed. + | + +F401 `numba` imported but unused + --> umap/__init__.py:37:8 + | +36 | # Workaround: https://github.com/numba/numba/issues/3341 +37 | import numba + | ^^^^^ +38 | +39 | from .aligned_umap import AlignedUMAP + | +help: Remove unused import: `numba` + +F401 `.aligned_umap.AlignedUMAP` imported but unused; consider removing, adding to `__all__`, or using a redundant alias + --> umap/__init__.py:39:27 + | +37 | import numba +38 | +39 | from .aligned_umap import AlignedUMAP + | ^^^^^^^^^^^ +40 | +41 | try: + | +help: Use an explicit re-export: `AlignedUMAP as AlignedUMAP` + +D103 Missing docstring in public function + --> umap/aligned_umap.py:17:5 + | +16 | @numba.njit(parallel=True) +17 | def in1d(arr, test_set): + | ^^^^ +18 | test_set = set(test_set) +19 | result = np.empty(arr.shape[0], dtype=np.bool_) + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:29:5 + | +29 | def invert_dict(d): + | ^^^^^^^^^^^ +30 | return {value: key for key, value in d.items()} + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:34:5 + | +33 | @numba.njit() +34 | def procrustes_align(embedding_base, embedding_to_align, anchors): + | ^^^^^^^^^^^^^^^^ +35 | subset1 = embedding_base[anchors[0]] +36 | subset2 = embedding_to_align[anchors[1]] + | + +N806 Variable `M` in function should be lowercase + --> umap/aligned_umap.py:37:5 + | +35 | subset1 = embedding_base[anchors[0]] +36 | subset2 = embedding_to_align[anchors[1]] +37 | M = subset2.T @ subset1 + | ^ +38 | U, _S, V = np.linalg.svd(M) +39 | R = U @ V + | + +N806 Variable `U` in function should be lowercase + --> umap/aligned_umap.py:38:5 + | +36 | subset2 = embedding_to_align[anchors[1]] +37 | M = subset2.T @ subset1 +38 | U, _S, V = np.linalg.svd(M) + | ^ +39 | R = U @ V +40 | return embedding_to_align @ R + | + +N806 Variable `_S` in function should be lowercase + --> umap/aligned_umap.py:38:8 + | +36 | subset2 = embedding_to_align[anchors[1]] +37 | M = subset2.T @ subset1 +38 | U, _S, V = np.linalg.svd(M) + | ^^ +39 | R = U @ V +40 | return embedding_to_align @ R + | + +N806 Variable `V` in function should be lowercase + --> umap/aligned_umap.py:38:12 + | +36 | subset2 = embedding_to_align[anchors[1]] +37 | M = subset2.T @ subset1 +38 | U, _S, V = np.linalg.svd(M) + | ^ +39 | R = U @ V +40 | return embedding_to_align @ R + | + +N806 Variable `R` in function should be lowercase + --> umap/aligned_umap.py:39:5 + | +37 | M = subset2.T @ subset1 +38 | U, _S, V = np.linalg.svd(M) +39 | R = U @ V + | ^ +40 | return embedding_to_align @ R + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:43:5 + | +43 | def expand_relations(relation_dicts, window_size=3): + | ^^^^^^^^^^^^^^^^ +44 | max_n_samples = ( +45 | max( + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:86:5 + | +85 | @numba.njit() +86 | def build_neighborhood_similarities(graphs_indptr, graphs_indices, relations): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +87 | result = np.zeros(relations.shape, dtype=np.float32) +88 | center_index = (relations.shape[1] - 1) // 2 + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:132:5 + | +132 | def get_nth_item_or_val(iterable_or_val, n): + | ^^^^^^^^^^^^^^^^^^^ +133 | if iterable_or_val is None: +134 | return None + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:167:5 + | +167 | def set_aligned_params(new_params, existing_params, n_models, param_names=PARAM_NAMES): + | ^^^^^^^^^^^^^^^^^^ +168 | for param in param_names: +169 | if param in new_params: + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:187:5 + | +186 | @numba.njit() +187 | def init_from_existing_internal( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +188 | previous_embedding, weights_indptr, weights_indices, weights_data, relation_dict, +189 | ): + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/aligned_umap.py:207:29 + | +205 | ) +206 | if normalisation == 0: +207 | result[i] = np.random.uniform(-10.0, 10.0, n_features) + | ^^^^^^^^^^^^^^^^^ +208 | else: +209 | result[i] /= normalisation + | + +D103 Missing docstring in public function + --> umap/aligned_umap.py:214:5 + | +214 | def init_from_existing(previous_embedding, graph, relations): + | ^^^^^^^^^^^^^^^^^^ +215 | typed_relations = numba.typed.Dict.empty(numba.types.int32, numba.types.int32) +216 | for key, val in relations.items(): + | + +D101 Missing docstring in public class + --> umap/aligned_umap.py:227:7 + | +227 | class AlignedUMAP(BaseEstimator): + | ^^^^^^^^^^^ +228 | def __init__( +229 | self, + | + +PLR0913 Too many arguments in function definition (29 > 5) + --> umap/aligned_umap.py:228:9 + | +227 | class AlignedUMAP(BaseEstimator): +228 | def __init__( + | ^^^^^^^^ +229 | self, +230 | n_neighbors=15, + | + +D107 Missing docstring in `__init__` + --> umap/aligned_umap.py:228:9 + | +227 | class AlignedUMAP(BaseEstimator): +228 | def __init__( + | ^^^^^^^^ +229 | self, +230 | n_neighbors=15, + | + +FBT002 Boolean default positional argument in function definition + --> umap/aligned_umap.py:241:9 + | +239 | min_dist=0.1, +240 | spread=1.0, +241 | low_memory=False, + | ^^^^^^^^^^ +242 | set_op_mix_ratio=1.0, +243 | local_connectivity=1.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/aligned_umap.py:250:9 + | +248 | b=None, +249 | random_state=None, +250 | angular_rp_forest=False, + | ^^^^^^^^^^^^^^^^^ +251 | target_n_neighbors=-1, +252 | target_metric="categorical", + | + +FBT002 Boolean default positional argument in function definition + --> umap/aligned_umap.py:256:9 + | +254 | target_weight=0.5, +255 | transform_seed=42, +256 | force_approximation_algorithm=False, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +257 | verbose=False, +258 | unique=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/aligned_umap.py:257:9 + | +255 | transform_seed=42, +256 | force_approximation_algorithm=False, +257 | verbose=False, + | ^^^^^^^ +258 | unique=False, +259 | ): + | + +FBT002 Boolean default positional argument in function definition + --> umap/aligned_umap.py:258:9 + | +256 | force_approximation_algorithm=False, +257 | verbose=False, +258 | unique=False, + | ^^^^^^ +259 | ): + | + +D102 Missing docstring in public method + --> umap/aligned_umap.py:294:9 + | +292 | self.b = b +293 | +294 | def fit(self, X, y=None, **fit_params): + | ^^^ +295 | if "relations" not in fit_params: +296 | msg = "Aligned UMAP requires relations between data to be specified" + | + +N803 Argument name `X` should be lowercase + --> umap/aligned_umap.py:294:19 + | +292 | self.b = b +293 | +294 | def fit(self, X, y=None, **fit_params): + | ^ +295 | if "relations" not in fit_params: +296 | msg = "Aligned UMAP requires relations between data to be specified" + | + +S101 Use of `assert` detected + --> umap/aligned_umap.py:302:9 + | +301 | self.dict_relations_ = fit_params["relations"] +302 | assert type(self.dict_relations_) in (list, tuple) + | ^^^^^^ +303 | assert type(X) in (list, tuple, np.ndarray) +304 | assert (len(X) - 1) == (len(self.dict_relations_)) + | + +S101 Use of `assert` detected + --> umap/aligned_umap.py:303:9 + | +301 | self.dict_relations_ = fit_params["relations"] +302 | assert type(self.dict_relations_) in (list, tuple) +303 | assert type(X) in (list, tuple, np.ndarray) + | ^^^^^^ +304 | assert (len(X) - 1) == (len(self.dict_relations_)) + | + +S101 Use of `assert` detected + --> umap/aligned_umap.py:304:9 + | +302 | assert type(self.dict_relations_) in (list, tuple) +303 | assert type(X) in (list, tuple, np.ndarray) +304 | assert (len(X) - 1) == (len(self.dict_relations_)) + | ^^^^^^ +305 | +306 | if y is not None: + | + +S101 Use of `assert` detected + --> umap/aligned_umap.py:307:13 + | +306 | if y is not None: +307 | assert type(y) in (list, tuple, np.ndarray) + | ^^^^^^ +308 | assert (len(y) - 1) == (len(self.dict_relations_)) +309 | else: + | + +S101 Use of `assert` detected + --> umap/aligned_umap.py:308:13 + | +306 | if y is not None: +307 | assert type(y) in (list, tuple, np.ndarray) +308 | assert (len(y) - 1) == (len(self.dict_relations_)) + | ^^^^^^ +309 | else: +310 | y = [None] * len(X) + | + +SLF001 Private member accessed: `_raw_data` + --> umap/aligned_umap.py:382:13 + | +380 | ) +381 | first_init = spectral_layout( +382 | self.mappers_[0]._raw_data, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +383 | self.mappers_[0].graph_, +384 | self.n_components, + | + +SLF001 Private member accessed: `_raw_data` + --> umap/aligned_umap.py:397:17 + | +395 | for i in range(1, self.n_models_): +396 | next_init = spectral_layout( +397 | self.mappers_[i]._raw_data, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +398 | self.mappers_[i].graph_, +399 | self.n_components, + | + +D102 Missing docstring in public method + --> umap/aligned_umap.py:443:9 + | +441 | return self +442 | +443 | def fit_transform(self, X, y=None, **fit_params): + | ^^^^^^^^^^^^^ +444 | self.fit(X, y, **fit_params) +445 | return self.embeddings_ + | + +N803 Argument name `X` should be lowercase + --> umap/aligned_umap.py:443:29 + | +441 | return self +442 | +443 | def fit_transform(self, X, y=None, **fit_params): + | ^ +444 | self.fit(X, y, **fit_params) +445 | return self.embeddings_ + | + +D102 Missing docstring in public method + --> umap/aligned_umap.py:447:9 + | +445 | return self.embeddings_ +446 | +447 | def update(self, X, y=None, **fit_params): + | ^^^^^^ +448 | if "relations" not in fit_params: +449 | msg = "Aligned UMAP requires relations between data to be specified" + | + +N803 Argument name `X` should be lowercase + --> umap/aligned_umap.py:447:22 + | +445 | return self.embeddings_ +446 | +447 | def update(self, X, y=None, **fit_params): + | ^ +448 | if "relations" not in fit_params: +449 | msg = "Aligned UMAP requires relations between data to be specified" + | + +S101 Use of `assert` detected + --> umap/aligned_umap.py:455:9 + | +454 | new_dict_relations = fit_params["relations"] +455 | assert isinstance(new_dict_relations, dict) + | ^^^^^^ +456 | +457 | X = check_array(X) + | + +N806 Variable `X` in function should be lowercase + --> umap/aligned_umap.py:457:9 + | +455 | assert isinstance(new_dict_relations, dict) +456 | +457 | X = check_array(X) + | ^ +458 | +459 | self.__dict__ = set_aligned_params(fit_params, self.__dict__, self.n_models_) + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/aligned_umap.py:541:11 + | +539 | ) +540 | +541 | # TODO: We can likely make this more efficient and not recompute each time + | ^^^^ +542 | inv_dict_relations = invert_dict(new_dict_relations) + | + +TD003 Missing issue link for this TODO + --> umap/aligned_umap.py:541:11 + | +539 | ) +540 | +541 | # TODO: We can likely make this more efficient and not recompute each time + | ^^^^ +542 | inv_dict_relations = invert_dict(new_dict_relations) + | + +FIX002 Line contains TODO, consider resolving the issue + --> umap/aligned_umap.py:541:11 + | +539 | ) +540 | +541 | # TODO: We can likely make this more efficient and not recompute each time + | ^^^^ +542 | inv_dict_relations = invert_dict(new_dict_relations) + | + +D103 Missing docstring in public function + --> umap/distances.py:15:5 + | +14 | @numba.njit() +15 | def sign(a): + | ^^^^ +16 | if a < 0: +17 | return -1 + | + +D401 First line of docstring should be in imperative mood: "Standard euclidean distance." + --> umap/distances.py:23:5 + | +21 | @numba.njit(fastmath=True) +22 | def euclidean(x, y): +23 | / r"""Standard euclidean distance. +24 | | +25 | | ..math:: +26 | | D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} +27 | | """ + | |_______^ +28 | result = 0.0 +29 | for i in range(x.shape[0]): + | + +D401 First line of docstring should be in imperative mood: "Standard euclidean distance and its gradient." + --> umap/distances.py:36:5 + | +34 | @numba.njit(fastmath=True) +35 | def euclidean_grad(x, y): +36 | / r"""Standard euclidean distance and its gradient. +37 | | +38 | | ..math:: +39 | | D(x, y) = \sqrt{\sum_i (x_i - y_i)^2} +40 | | \frac{dD(x, y)}{dx} = (x_i - y_i)/D(x,y) +41 | | """ + | |_______^ +42 | result = 0.0 +43 | for i in range(x.shape[0]): + | + +D205 1 blank line required between summary line and description + --> umap/distances.py:52:5 + | +50 | @numba.njit() +51 | def standardised_euclidean(x, y, sigma=_mock_ones): +52 | / r"""Euclidean distance standardised against a vector of standard +53 | | deviations per coordinate. +54 | | +55 | | ..math:: +56 | | D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}} +57 | | """ + | |_______^ +58 | result = 0.0 +59 | for i in range(x.shape[0]): + | +help: Insert single blank line + +D205 1 blank line required between summary line and description + --> umap/distances.py:67:5 + | +65 | @numba.njit(fastmath=True) +66 | def standardised_euclidean_grad(x, y, sigma=_mock_ones): +67 | / r"""Euclidean distance standardised against a vector of standard +68 | | deviations per coordinate with gradient. +69 | | +70 | | ..math:: +71 | | D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}} +72 | | """ + | |_______^ +73 | result = 0.0 +74 | for i in range(x.shape[0]): + | +help: Insert single blank line + +D103 Missing docstring in public function + --> umap/distances.py:205:5 + | +204 | @numba.njit() +205 | def hyperboloid_grad(x, y): + | ^^^^^^^^^^^^^^^^ +206 | s = np.sqrt(1 + np.sum(x**2)) +207 | t = np.sqrt(1 + np.sum(y**2)) + | + +N806 Variable `B` in function should be lowercase + --> umap/distances.py:209:5 + | +207 | t = np.sqrt(1 + np.sum(y**2)) +208 | +209 | B = s * t + | ^ +210 | for i in range(x.shape[0]): +211 | B -= x[i] * y[i] + | + +N806 Variable `B` in function should be lowercase + --> umap/distances.py:211:9 + | +209 | B = s * t +210 | for i in range(x.shape[0]): +211 | B -= x[i] * y[i] + | ^ +212 | +213 | if B <= 1: + | + +N806 Variable `B` in function should be lowercase + --> umap/distances.py:214:9 + | +213 | if B <= 1: +214 | B = 1.0 + 1e-8 + | ^ +215 | +216 | grad_coeff = 1.0 / (np.sqrt(B - 1) * np.sqrt(B + 1)) + | + +ERA001 Found commented-out code + --> umap/distances.py:218:5 + | +216 | grad_coeff = 1.0 / (np.sqrt(B - 1) * np.sqrt(B + 1)) +217 | +218 | # return np.arccosh(B), np.zeros(x.shape[0]) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +219 | +220 | grad = np.zeros(x.shape[0]) + | +help: Remove commented-out code + +D401 First line of docstring should be in imperative mood: "A weighted version of Minkowski distance." + --> umap/distances.py:229:5 + | +227 | @numba.njit() +228 | def weighted_minkowski(x, y, w=_mock_ones, p=2): +229 | / r"""A weighted version of Minkowski distance. +230 | | +231 | | ..math:: +232 | | D(x, y) = \left(\sum_i w_i |x_i - y_i|^p\right)^{\frac{1}{p}} +233 | | +234 | | If weights w_i are inverse standard deviations of data in each dimension +235 | | then this represented a standardised Minkowski distance (and is +236 | | equivalent to standardised Euclidean distance for p=1). +237 | | """ + | |_______^ +238 | result = 0.0 +239 | for i in range(x.shape[0]): + | + +D401 First line of docstring should be in imperative mood: "A weighted version of Minkowski distance with gradient." + --> umap/distances.py:247:5 + | +245 | @numba.njit() +246 | def weighted_minkowski_grad(x, y, w=_mock_ones, p=2): +247 | / r"""A weighted version of Minkowski distance with gradient. +248 | | +249 | | ..math:: +250 | | D(x, y) = \left(\sum_i w_i |x_i - y_i|^p\right)^{\frac{1}{p}} +251 | | +252 | | If weights w_i are inverse standard deviations of data in each dimension +253 | | then this represented a standardised Minkowski distance (and is +254 | | equivalent to standardised Euclidean distance for p=1). +255 | | """ + | |_______^ +256 | result = 0.0 +257 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:273:5 + | +272 | @numba.njit() +273 | def mahalanobis(x, y, vinv=_mock_identity): + | ^^^^^^^^^^^ +274 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:291:5 + | +290 | @numba.njit() +291 | def mahalanobis_grad(x, y, vinv=_mock_identity): + | ^^^^^^^^^^^^^^^^ +292 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:312:5 + | +311 | @numba.njit() +312 | def hamming(x, y): + | ^^^^^^^ +313 | result = 0.0 +314 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:322:5 + | +321 | @numba.njit() +322 | def canberra(x, y): + | ^^^^^^^^ +323 | result = 0.0 +324 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:333:5 + | +332 | @numba.njit() +333 | def canberra_grad(x, y): + | ^^^^^^^^^^^^^ +334 | result = 0.0 +335 | grad = np.zeros(x.shape) + | + +D103 Missing docstring in public function + --> umap/distances.py:349:5 + | +348 | @numba.njit() +349 | def bray_curtis(x, y): + | ^^^^^^^^^^^ +350 | numerator = 0.0 +351 | denominator = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:362:5 + | +361 | @numba.njit() +362 | def bray_curtis_grad(x, y): + | ^^^^^^^^^^^^^^^^ +363 | numerator = 0.0 +364 | denominator = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:380:5 + | +379 | @numba.njit() +380 | def jaccard(x, y): + | ^^^^^^^ +381 | num_non_zero = 0.0 +382 | num_equal = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:395:5 + | +394 | @numba.njit() +395 | def matching(x, y): + | ^^^^^^^^ +396 | num_not_equal = 0.0 +397 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:406:5 + | +405 | @numba.njit() +406 | def dice(x, y): + | ^^^^ +407 | num_true_true = 0.0 +408 | num_not_equal = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:421:5 + | +420 | @numba.njit() +421 | def kulsinski(x, y): + | ^^^^^^^^^ +422 | num_true_true = 0.0 +423 | num_not_equal = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:438:5 + | +437 | @numba.njit() +438 | def rogers_tanimoto(x, y): + | ^^^^^^^^^^^^^^^ +439 | num_not_equal = 0.0 +440 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:449:5 + | +448 | @numba.njit() +449 | def russellrao(x, y): + | ^^^^^^^^^^ +450 | num_true_true = 0.0 +451 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:462:5 + | +461 | @numba.njit() +462 | def sokal_michener(x, y): + | ^^^^^^^^^^^^^^ +463 | num_not_equal = 0.0 +464 | for i in range(x.shape[0]): + | + +D103 Missing docstring in public function + --> umap/distances.py:473:5 + | +472 | @numba.njit() +473 | def sokal_sneath(x, y): + | ^^^^^^^^^^^^ +474 | num_true_true = 0.0 +475 | num_not_equal = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:488:5 + | +487 | @numba.njit() +488 | def haversine(x, y): + | ^^^^^^^^^ +489 | if x.shape[0] != 2: +490 | msg = "haversine is only defined for 2 dimensional data" + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/distances.py:489:22 + | +487 | @numba.njit() +488 | def haversine(x, y): +489 | if x.shape[0] != 2: + | ^ +490 | msg = "haversine is only defined for 2 dimensional data" +491 | raise ValueError(msg) + | + +D103 Missing docstring in public function + --> umap/distances.py:499:5 + | +498 | @numba.njit() +499 | def haversine_grad(x, y): + | ^^^^^^^^^^^^^^ +500 | # spectral initialization puts many points near the poles +501 | # currently, adding pi/2 to the latitude avoids problems + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/distances.py:502:7 + | +500 | # spectral initialization puts many points near the poles +501 | # currently, adding pi/2 to the latitude avoids problems +502 | # TODO: reimplement with quaternions to avoid singularity + | ^^^^ +503 | +504 | if x.shape[0] != 2: + | + +TD003 Missing issue link for this TODO + --> umap/distances.py:502:7 + | +500 | # spectral initialization puts many points near the poles +501 | # currently, adding pi/2 to the latitude avoids problems +502 | # TODO: reimplement with quaternions to avoid singularity + | ^^^^ +503 | +504 | if x.shape[0] != 2: + | + +FIX002 Line contains TODO, consider resolving the issue + --> umap/distances.py:502:7 + | +500 | # spectral initialization puts many points near the poles +501 | # currently, adding pi/2 to the latitude avoids problems +502 | # TODO: reimplement with quaternions to avoid singularity + | ^^^^ +503 | +504 | if x.shape[0] != 2: + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/distances.py:504:22 + | +502 | # TODO: reimplement with quaternions to avoid singularity +503 | +504 | if x.shape[0] != 2: + | ^ +505 | msg = "haversine is only defined for 2 dimensional data" +506 | raise ValueError(msg) + | + +D103 Missing docstring in public function + --> umap/distances.py:530:5 + | +529 | @numba.njit() +530 | def yule(x, y): + | ^^^^ +531 | num_true_true = 0.0 +532 | num_true_false = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:551:5 + | +550 | @numba.njit() +551 | def cosine(x, y): + | ^^^^^^ +552 | result = 0.0 +553 | norm_x = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:568:5 + | +567 | @numba.njit(fastmath=True) +568 | def cosine_grad(x, y): + | ^^^^^^^^^^^ +569 | result = 0.0 +570 | norm_x = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:591:5 + | +590 | @numba.njit() +591 | def correlation(x, y): + | ^^^^^^^^^^^ +592 | mu_x = 0.0 +593 | mu_y = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:620:5 + | +619 | @numba.njit() +620 | def hellinger(x, y): + | ^^^^^^^^^ +621 | result = 0.0 +622 | l1_norm_x = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:638:5 + | +637 | @numba.njit() +638 | def hellinger_grad(x, y): + | ^^^^^^^^^^^^^^ +639 | result = 0.0 +640 | l1_norm_x = 0.0 + | + +N802 Function name `approx_log_Gamma` should be lowercase + --> umap/distances.py:669:5 + | +668 | @numba.njit() +669 | def approx_log_Gamma(x): + | ^^^^^^^^^^^^^^^^ +670 | if x == 1: +671 | return 0 + | + +D103 Missing docstring in public function + --> umap/distances.py:669:5 + | +668 | @numba.njit() +669 | def approx_log_Gamma(x): + | ^^^^^^^^^^^^^^^^ +670 | if x == 1: +671 | return 0 + | + +ERA001 Found commented-out code + --> umap/distances.py:672:5 + | +670 | if x == 1: +671 | return 0 +672 | # x2= 1/(x*x); + | ^^^^^^^^^^^^^^ +673 | return x * np.log(x) - x + 0.5 * np.log(2.0 * np.pi / x) + 1.0 / (x * 12.0) +674 | # + x2*(-1.0/360.0) + x2* (1.0/1260.0 + x2*(-1.0/(1680.0) +\ + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:674:5 + | +672 | # x2= 1/(x*x); +673 | return x * np.log(x) - x + 0.5 * np.log(2.0 * np.pi / x) + 1.0 / (x * 12.0) +674 | # + x2*(-1.0/360.0) + x2* (1.0/1260.0 + x2*(-1.0/(1680.0) +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +675 | # x2*(1.0/1188.0 + x2*(-691.0/360360.0 + x2*(1.0/156.0 +\ +676 | # x2*(-3617.0/122400.0 + x2*(43687.0/244188.0 + x2*(-174611.0/125400.0) +\ + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:675:5 + | +673 | return x * np.log(x) - x + 0.5 * np.log(2.0 * np.pi / x) + 1.0 / (x * 12.0) +674 | # + x2*(-1.0/360.0) + x2* (1.0/1260.0 + x2*(-1.0/(1680.0) +\ +675 | # x2*(1.0/1188.0 + x2*(-691.0/360360.0 + x2*(1.0/156.0 +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +676 | # x2*(-3617.0/122400.0 + x2*(43687.0/244188.0 + x2*(-174611.0/125400.0) +\ +677 | # x2*(77683.0/5796.0 + x2*(-236364091.0/1506960.0 + x2*(657931.0/300.0)))))))))))) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:676:5 + | +674 | # + x2*(-1.0/360.0) + x2* (1.0/1260.0 + x2*(-1.0/(1680.0) +\ +675 | # x2*(1.0/1188.0 + x2*(-691.0/360360.0 + x2*(1.0/156.0 +\ +676 | # x2*(-3617.0/122400.0 + x2*(43687.0/244188.0 + x2*(-174611.0/125400.0) +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +677 | # x2*(77683.0/5796.0 + x2*(-236364091.0/1506960.0 + x2*(657931.0/300.0)))))))))))) + | +help: Remove commented-out code + +D103 Missing docstring in public function + --> umap/distances.py:681:5 + | +680 | @numba.njit() +681 | def log_beta(x, y): + | ^^^^^^^^ +682 | a = min(x, y) +683 | b = max(x, y) + | + +PLR2004 Magic value used in comparison, consider replacing `5` with a constant variable + --> umap/distances.py:684:12 + | +682 | a = min(x, y) +683 | b = max(x, y) +684 | if b < 5: + | ^ +685 | value = -np.log(b) +686 | for i in range(1, int(a)): + | + +D103 Missing docstring in public function + --> umap/distances.py:693:5 + | +692 | @numba.njit() +693 | def log_single_beta(x): + | ^^^^^^^^^^^^^^^ +694 | return np.log(2.0) * (-2.0 * x + 0.5) + 0.5 * np.log(2.0 * np.pi / x) + 0.125 / x + | + +ERA001 Found commented-out code + --> umap/distances.py:697:1 + | +697 | # + x2*(-1.0/192.0 + x2* (1.0/640.0 + x2*(-17.0/(14336.0) +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +698 | # x2*(31.0/18432.0 + x2*(-691.0/180224.0 +\ +699 | # x2*(5461.0/425984.0 + x2*(-929569.0/15728640.0 +\ + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:698:1 + | +697 | # + x2*(-1.0/192.0 + x2* (1.0/640.0 + x2*(-17.0/(14336.0) +\ +698 | # x2*(31.0/18432.0 + x2*(-691.0/180224.0 +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +699 | # x2*(5461.0/425984.0 + x2*(-929569.0/15728640.0 +\ +700 | # x2*(3189151.0/8912896.0 + x2*(-221930581.0/79691776.0) +\ + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:699:1 + | +697 | # + x2*(-1.0/192.0 + x2* (1.0/640.0 + x2*(-17.0/(14336.0) +\ +698 | # x2*(31.0/18432.0 + x2*(-691.0/180224.0 +\ +699 | # x2*(5461.0/425984.0 + x2*(-929569.0/15728640.0 +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +700 | # x2*(3189151.0/8912896.0 + x2*(-221930581.0/79691776.0) +\ +701 | # x2*(4722116521.0/176160768.0 + x2*(-968383680827.0/3087007744.0 +\ + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:700:1 + | +698 | # x2*(31.0/18432.0 + x2*(-691.0/180224.0 +\ +699 | # x2*(5461.0/425984.0 + x2*(-929569.0/15728640.0 +\ +700 | # x2*(3189151.0/8912896.0 + x2*(-221930581.0/79691776.0) +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +701 | # x2*(4722116521.0/176160768.0 + x2*(-968383680827.0/3087007744.0 +\ +702 | # x2*(14717667114151.0/3355443200.0 )))))))))))) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/distances.py:701:1 + | +699 | # x2*(5461.0/425984.0 + x2*(-929569.0/15728640.0 +\ +700 | # x2*(3189151.0/8912896.0 + x2*(-221930581.0/79691776.0) +\ +701 | # x2*(4722116521.0/176160768.0 + x2*(-968383680827.0/3087007744.0 +\ + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +702 | # x2*(14717667114151.0/3355443200.0 )))))))))))) + | +help: Remove commented-out code + +D205 1 blank line required between summary line and description + --> umap/distances.py:707:5 + | +705 | @numba.njit() +706 | def ll_dirichlet(data1, data2): +707 | / """The symmetric relative log likelihood of rolling data2 vs data1 +708 | | in n trials on a die that rolled data1 in sum(data1) trials. +709 | | +710 | | ..math:: +711 | | D(data1, data2) = DirichletMultinomail(data2 | data1) +712 | | """ + | |_______^ +713 | n1 = np.sum(data1) +714 | n2 = np.sum(data2) + | +help: Insert single blank line + +D401 First line of docstring should be in imperative mood: "The symmetric relative log likelihood of rolling data2 vs data1" + --> umap/distances.py:707:5 + | +705 | @numba.njit() +706 | def ll_dirichlet(data1, data2): +707 | / """The symmetric relative log likelihood of rolling data2 vs data1 +708 | | in n trials on a die that rolled data1 in sum(data1) trials. +709 | | +710 | | ..math:: +711 | | D(data1, data2) = DirichletMultinomail(data2 | data1) +712 | | """ + | |_______^ +713 | n1 = np.sum(data1) +714 | n2 = np.sum(data2) + | + +PLR2004 Magic value used in comparison, consider replacing `0.9` with a constant variable + --> umap/distances.py:721:34 + | +720 | for i in range(data1.shape[0]): +721 | if data1[i] * data2[i] > 0.9: + | ^^^ +722 | log_b += log_beta(data1[i], data2[i]) +723 | self_denom1 += log_single_beta(data1[i]) + | + +PLR2004 Magic value used in comparison, consider replacing `0.9` with a constant variable + --> umap/distances.py:727:27 + | +726 | else: +727 | if data1[i] > 0.9: + | ^^^ +728 | self_denom1 += log_single_beta(data1[i]) + | + +PLR2004 Magic value used in comparison, consider replacing `0.9` with a constant variable + --> umap/distances.py:730:27 + | +728 | self_denom1 += log_single_beta(data1[i]) +729 | +730 | if data2[i] > 0.9: + | ^^^ +731 | self_denom2 += log_single_beta(data2[i]) + | + +D103 Missing docstring in public function + --> umap/distances.py:799:5 + | +798 | @numba.njit() +799 | def correlation_grad(x, y): + | ^^^^^^^^^^^^^^^^ +800 | mu_x = 0.0 +801 | mu_y = 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:834:5 + | +833 | @numba.njit(fastmath=True) +834 | def sinkhorn_distance( + | ^^^^^^^^^^^^^^^^^ +835 | x, y, M=_mock_identity, cost=_mock_cost, maxiter=64, +836 | ): # pragma: no cover + | + +N803 Argument name `M` should be lowercase + --> umap/distances.py:835:11 + | +833 | @numba.njit(fastmath=True) +834 | def sinkhorn_distance( +835 | x, y, M=_mock_identity, cost=_mock_cost, maxiter=64, + | ^^^^^^^^^^^^^^^^ +836 | ): # pragma: no cover +837 | p = (x / x.sum()).astype(np.float32) + | + +D103 Missing docstring in public function + --> umap/distances.py:860:5 + | +859 | @numba.njit(fastmath=True) +860 | def spherical_gaussian_energy_grad(x, y): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +861 | mu_1 = x[0] - y[0] +862 | mu_2 = x[1] - y[1] + | + +D103 Missing docstring in public function + --> umap/distances.py:878:5 + | +877 | @numba.njit(fastmath=True) +878 | def diagonal_gaussian_energy_grad(x, y): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +879 | mu_1 = x[0] - y[0] +880 | mu_2 = x[1] - y[1] + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/distances.py:891:11 + | +890 | if det == 0.0: +891 | # TODO: figure out the right thing to do here + | ^^^^ +892 | return mu_1**2 + mu_2**2, np.array([0.0, 0.0, 1.0, 1.0], dtype=np.float32) + | + +TD003 Missing issue link for this TODO + --> umap/distances.py:891:11 + | +890 | if det == 0.0: +891 | # TODO: figure out the right thing to do here + | ^^^^ +892 | return mu_1**2 + mu_2**2, np.array([0.0, 0.0, 1.0, 1.0], dtype=np.float32) + | + +FIX002 Line contains TODO, consider resolving the issue + --> umap/distances.py:891:11 + | +890 | if det == 0.0: +891 | # TODO: figure out the right thing to do here + | ^^^^ +892 | return mu_1**2 + mu_2**2, np.array([0.0, 0.0, 1.0, 1.0], dtype=np.float32) + | + +D103 Missing docstring in public function + --> umap/distances.py:913:5 + | +912 | @numba.njit(fastmath=True) +913 | def gaussian_energy_grad(x, y): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^ +914 | mu_1 = x[0] - y[0] +915 | mu_2 = x[1] - y[1] + | + +PLR2004 Magic value used in comparison, consider replacing `1e-32` with a constant variable + --> umap/distances.py:945:20 + | +943 | ) +944 | +945 | if det_sigma < 1e-32: + | ^^^^^ +946 | return ( +947 | mu_1**2 + mu_2**2, + | + +D103 Missing docstring in public function + --> umap/distances.py:986:5 + | +985 | @numba.njit(fastmath=True) +986 | def spherical_gaussian_grad(x, y): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^ +987 | mu_1 = x[0] - y[0] +988 | mu_2 = x[1] - y[1] + | + +D103 Missing docstring in public function + --> umap/distances.py:1013:5 + | +1013 | def get_discrete_params(data, metric): + | ^^^^^^^^^^^^^^^^^^^ +1014 | if metric == "ordinal": +1015 | return {"support_size": float(data.max() - data.min()) / 2.0} + | + +D103 Missing docstring in public function + --> umap/distances.py:1036:5 + | +1035 | @numba.njit() +1036 | def categorical_distance(x, y): + | ^^^^^^^^^^^^^^^^^^^^ +1037 | if x == y: +1038 | return 0.0 + | + +D103 Missing docstring in public function + --> umap/distances.py:1043:5 + | +1042 | @numba.njit() +1043 | def hierarchical_categorical_distance(x, y, cat_hierarchy=None): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1044 | if cat_hierarchy is None: +1045 | cat_hierarchy = [{}] + | + +D103 Missing docstring in public function + --> umap/distances.py:1054:5 + | +1053 | @numba.njit() +1054 | def ordinal_distance(x, y, support_size=1.0): + | ^^^^^^^^^^^^^^^^ +1055 | return abs(x - y) / support_size + | + +D103 Missing docstring in public function + --> umap/distances.py:1059:5 + | +1058 | @numba.njit() +1059 | def count_distance(x, y, poisson_lambda=1.0, normalisation=1.0): + | ^^^^^^^^^^^^^^ +1060 | lo = int(min(x, y)) +1061 | hi = int(max(x, y)) + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/distances.py:1065:13 + | +1063 | log_lambda = np.log(poisson_lambda) +1064 | +1065 | if lo < 2: + | ^ +1066 | log_k_factorial = 0.0 +1067 | elif lo < 10: + | + +PLR2004 Magic value used in comparison, consider replacing `10` with a constant variable + --> umap/distances.py:1067:15 + | +1065 | if lo < 2: +1066 | log_k_factorial = 0.0 +1067 | elif lo < 10: + | ^^ +1068 | log_k_factorial = 0.0 +1069 | for k in range(2, lo): + | + +D103 Missing docstring in public function + --> umap/distances.py:1084:5 + | +1083 | @numba.njit() +1084 | def levenshtein(x, y, normalisation=1.0, max_distance=20): + | ^^^^^^^^^^^ +1085 | x_len, y_len = len(x), len(y) + | + +D103 Missing docstring in public function + --> umap/distances.py:1215:5 + | +1214 | @numba.njit(parallel=True) +1215 | def parallel_special_metric(X, Y=None, metric=hellinger): + | ^^^^^^^^^^^^^^^^^^^^^^^ +1216 | if Y is None: +1217 | result = np.zeros((X.shape[0], X.shape[0])) + | + +N803 Argument name `X` should be lowercase + --> umap/distances.py:1215:29 + | +1214 | @numba.njit(parallel=True) +1215 | def parallel_special_metric(X, Y=None, metric=hellinger): + | ^ +1216 | if Y is None: +1217 | result = np.zeros((X.shape[0], X.shape[0])) + | + +N803 Argument name `Y` should be lowercase + --> umap/distances.py:1215:32 + | +1214 | @numba.njit(parallel=True) +1215 | def parallel_special_metric(X, Y=None, metric=hellinger): + | ^^^^^^ +1216 | if Y is None: +1217 | result = np.zeros((X.shape[0], X.shape[0])) + | + +D103 Missing docstring in public function + --> umap/distances.py:1236:5 + | +1234 | # this keeps data vectors in cache better +1235 | @numba.njit(parallel=True, nogil=True) +1236 | def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_size=16): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1237 | if Y is None: +1238 | XX, symmetrical = X, True + | + +N803 Argument name `X` should be lowercase + --> umap/distances.py:1236:37 + | +1234 | # this keeps data vectors in cache better +1235 | @numba.njit(parallel=True, nogil=True) +1236 | def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_size=16): + | ^ +1237 | if Y is None: +1238 | XX, symmetrical = X, True + | + +N803 Argument name `Y` should be lowercase + --> umap/distances.py:1236:40 + | +1234 | # this keeps data vectors in cache better +1235 | @numba.njit(parallel=True, nogil=True) +1236 | def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_size=16): + | ^^^^^^ +1237 | if Y is None: +1238 | XX, symmetrical = X, True + | + +N806 Variable `XX` in function should be lowercase + --> umap/distances.py:1238:9 + | +1236 | def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_size=16): +1237 | if Y is None: +1238 | XX, symmetrical = X, True + | ^^ +1239 | row_size = col_size = X.shape[0] +1240 | else: + | + +N806 Variable `XX` in function should be lowercase + --> umap/distances.py:1241:9 + | +1239 | row_size = col_size = X.shape[0] +1240 | else: +1241 | XX, symmetrical = Y, False + | ^^ +1242 | row_size, col_size = X.shape[0], Y.shape[0] + | + +D103 Missing docstring in public function + --> umap/distances.py:1258:5 + | +1258 | def pairwise_special_metric( + | ^^^^^^^^^^^^^^^^^^^^^^^ +1259 | X, Y=None, metric="hellinger", kwds=None, ensure_all_finite=True, +1260 | ): + | + +N803 Argument name `X` should be lowercase + --> umap/distances.py:1259:5 + | +1258 | def pairwise_special_metric( +1259 | X, Y=None, metric="hellinger", kwds=None, ensure_all_finite=True, + | ^ +1260 | ): +1261 | if callable(metric): + | + +N803 Argument name `Y` should be lowercase + --> umap/distances.py:1259:8 + | +1258 | def pairwise_special_metric( +1259 | X, Y=None, metric="hellinger", kwds=None, ensure_all_finite=True, + | ^^^^^^ +1260 | ): +1261 | if callable(metric): + | + +FBT002 Boolean default positional argument in function definition + --> umap/distances.py:1259:47 + | +1258 | def pairwise_special_metric( +1259 | X, Y=None, metric="hellinger", kwds=None, ensure_all_finite=True, + | ^^^^^^^^^^^^^^^^^ +1260 | ): +1261 | if callable(metric): + | + +N803 Argument name `_X` should be lowercase + --> umap/distances.py:1265:29 + | +1264 | @numba.njit(fastmath=True) +1265 | def _partial_metric(_X, _Y=None): + | ^^ +1266 | return metric(_X, _Y, *kwd_vals) + | + +N803 Argument name `_Y` should be lowercase + --> umap/distances.py:1265:33 + | +1264 | @numba.njit(fastmath=True) +1265 | def _partial_metric(_X, _Y=None): + | ^^^^^^^ +1266 | return metric(_X, _Y, *kwd_vals) + | + +D205 1 blank line required between summary line and description + --> umap/layouts.py:11:5 + | + 9 | @numba.njit() +10 | def clip(val): +11 | / """Standard clamping of a value into a fixed range (in this case -4.0 to +12 | | 4.0). +13 | | +14 | | Parameters +15 | | ---------- +16 | | val: float +17 | | The value to be clamped. +18 | | +19 | | Returns +20 | | ------- +21 | | The clamped value, now fixed to be in the range -4.0 to 4.0. +22 | | +23 | | """ + | |_______^ +24 | if val > 4.0: +25 | return 4.0 + | +help: Insert single blank line + +D401 First line of docstring should be in imperative mood: "Standard clamping of a value into a fixed range (in this case -4.0 to" + --> umap/layouts.py:11:5 + | + 9 | @numba.njit() +10 | def clip(val): +11 | / """Standard clamping of a value into a fixed range (in this case -4.0 to +12 | | 4.0). +13 | | +14 | | Parameters +15 | | ---------- +16 | | val: float +17 | | The value to be clamped. +18 | | +19 | | Returns +20 | | ------- +21 | | The clamped value, now fixed to be in the range -4.0 to 4.0. +22 | | +23 | | """ + | |_______^ +24 | if val > 4.0: +25 | return 4.0 + | + +PLR2004 Magic value used in comparison, consider replacing `4.0` with a constant variable + --> umap/layouts.py:24:14 + | +23 | """ +24 | if val > 4.0: + | ^^^ +25 | return 4.0 +26 | if val < -4.0: + | + +PLR2004 Magic value used in comparison, consider replacing `-4.0` with a constant variable + --> umap/layouts.py:26:14 + | +24 | if val > 4.0: +25 | return 4.0 +26 | if val < -4.0: + | ^^^^ +27 | return -4.0 +28 | return val + | + +D417 Missing argument descriptions in the docstring for `rdist`: `x`, `y` + --> umap/layouts.py:42:5 + | +40 | }, +41 | ) +42 | def rdist(x, y): + | ^^^^^ +43 | """Reduced Euclidean distance. + | + +C901 `_optimize_layout_euclidean_single_epoch` is too complex (13 > 10) + --> umap/layouts.py:64:5 + | +64 | def _optimize_layout_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +65 | head_embedding, +66 | tail_embedding, + | + +PLR0913 Too many arguments in function definition (27 > 5) + --> umap/layouts.py:64:5 + | +64 | def _optimize_layout_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +65 | head_embedding, +66 | tail_embedding, + | + +PLR0912 Too many branches (15 > 12) + --> umap/layouts.py:64:5 + | +64 | def _optimize_layout_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +65 | head_embedding, +66 | tail_embedding, + | + +N803 Argument name `dens_R` should be lowercase + --> umap/layouts.py:89:5 + | +87 | dens_re_mean, +88 | dens_lambda, +89 | dens_R, + | ^^^^^^ +90 | dens_mu, +91 | dens_mu_tot, + | + +TD001 Invalid TODO tag: `FIXME` + --> umap/layouts.py:147:23 + | +146 | if densmap_flag: +147 | # FIXME: grad_cor_coeff might be referenced before assignment + | ^^^^^ +148 | +149 | grad_d += clip(2 * grad_cor_coeff * (current[d] - other[d])) + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/layouts.py:147:23 + | +146 | if densmap_flag: +147 | # FIXME: grad_cor_coeff might be referenced before assignment + | ^^^^^ +148 | +149 | grad_d += clip(2 * grad_cor_coeff * (current[d] - other[d])) + | + +TD003 Missing issue link for this TODO + --> umap/layouts.py:147:23 + | +146 | if densmap_flag: +147 | # FIXME: grad_cor_coeff might be referenced before assignment + | ^^^^^ +148 | +149 | grad_d += clip(2 * grad_cor_coeff * (current[d] - other[d])) + | + +FIX001 Line contains FIXME, consider resolving the issue + --> umap/layouts.py:147:23 + | +146 | if densmap_flag: +147 | # FIXME: grad_cor_coeff might be referenced before assignment + | ^^^^^ +148 | +149 | grad_d += clip(2 * grad_cor_coeff * (current[d] - other[d])) + | + +PLR0913 Too many arguments in function definition (8 > 5) + --> umap/layouts.py:190:5 + | +190 | def _optimize_layout_euclidean_densmap_epoch_init( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +191 | head_embedding, +192 | tail_embedding, + | + +FBT001 Boolean-typed positional argument in function definition + --> umap/layouts.py:232:52 + | +232 | def _get_optimize_layout_euclidean_single_epoch_fn(parallel: bool = False): + | ^^^^^^^^ +233 | if parallel: +234 | return _nb_optimize_layout_euclidean_single_epoch_parallel + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:232:52 + | +232 | def _get_optimize_layout_euclidean_single_epoch_fn(parallel: bool = False): + | ^^^^^^^^ +233 | if parallel: +234 | return _nb_optimize_layout_euclidean_single_epoch_parallel + | + +C901 `optimize_layout_euclidean` is too complex (11 > 10) + --> umap/layouts.py:238:5 + | +238 | def optimize_layout_euclidean( + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +239 | head_embedding, +240 | tail_embedding, + | + +PLR0913 Too many arguments in function definition (19 > 5) + --> umap/layouts.py:238:5 + | +238 | def optimize_layout_euclidean( + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +239 | head_embedding, +240 | tail_embedding, + | + +PLR0915 Too many statements (53 > 50) + --> umap/layouts.py:238:5 + | +238 | def optimize_layout_euclidean( + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +239 | head_embedding, +240 | tail_embedding, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:252:5 + | +250 | initial_alpha=1.0, +251 | negative_sample_rate=5.0, +252 | parallel=False, + | ^^^^^^^^ +253 | verbose=False, +254 | densmap=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:253:5 + | +251 | negative_sample_rate=5.0, +252 | parallel=False, +253 | verbose=False, + | ^^^^^^^ +254 | densmap=False, +255 | densmap_kwds=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:254:5 + | +252 | parallel=False, +253 | verbose=False, +254 | densmap=False, + | ^^^^^^^ +255 | densmap_kwds=None, +256 | tqdm_kwds=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:257:5 + | +255 | densmap_kwds=None, +256 | tqdm_kwds=None, +257 | move_other=False, + | ^^^^^^^^^^ +258 | ): +259 | """Improve an embedding using stochastic gradient descent to minimize the + | + +D205 1 blank line required between summary line and description + --> umap/layouts.py:259:5 + | +257 | move_other=False, +258 | ): +259 | / """Improve an embedding using stochastic gradient descent to minimize the +260 | | fuzzy set cross entropy between the 1-skeletons of the high dimensional +261 | | and low dimensional fuzzy simplicial sets. In practice this is done by +262 | | sampling edges based on their membership strength (with the (1-p) terms +263 | | coming from negative sampling similar to word2vec). +264 | | +265 | | Parameters. +266 | | ---------- +267 | | head_embedding: array of shape (n_samples, n_components) +268 | | The initial embedding to be improved by SGD. +269 | | tail_embedding: array of shape (source_samples, n_components) +270 | | The reference embedding of embedded points. If not embedding new +271 | | previously unseen points with respect to an existing embedding this +272 | | is simply the head_embedding (again); otherwise it provides the +273 | | existing embedding to embed with respect to. +274 | | head: array of shape (n_1_simplices) +275 | | The indices of the heads of 1-simplices with non-zero membership. +276 | | tail: array of shape (n_1_simplices) +277 | | The indices of the tails of 1-simplices with non-zero membership. +278 | | n_epochs: int, or list of int +279 | | The number of training epochs to use in optimization, or a list of +280 | | epochs at which to save the embedding. In case of a list, the optimization +281 | | will use the maximum number of epochs in the list, and will return a list +282 | | of embedding in the order of increasing epoch, regardless of the order in +283 | | the epoch list. +284 | | n_vertices: int +285 | | The number of vertices (0-simplices) in the dataset. +286 | | epochs_per_sample: array of shape (n_1_simplices) +287 | | A float value of the number of epochs per 1-simplex. 1-simplices with +288 | | weaker membership strength will have more epochs between being sampled. +289 | | a: float +290 | | Parameter of differentiable approximation of right adjoint functor +291 | | b: float +292 | | Parameter of differentiable approximation of right adjoint functor +293 | | rng_state: array of int64, shape (3,) +294 | | The internal state of the rng +295 | | gamma: float (optional, default 1.0) +296 | | Weight to apply to negative samples. +297 | | initial_alpha: float (optional, default 1.0) +298 | | Initial learning rate for the SGD. +299 | | negative_sample_rate: int (optional, default 5) +300 | | Number of negative samples to use per positive sample. +301 | | parallel: bool (optional, default False) +302 | | Whether to run the computation using numba parallel. +303 | | Running in parallel is non-deterministic, and is not used +304 | | if a random seed has been set, to ensure reproducibility. +305 | | verbose: bool (optional, default False) +306 | | Whether to report information on the current progress of the algorithm. +307 | | densmap: bool (optional, default False) +308 | | Whether to use the density-augmented densMAP objective +309 | | densmap_kwds: dict (optional, default None) +310 | | Auxiliary data for densMAP +311 | | tqdm_kwds: dict (optional, default None) +312 | | Keyword arguments for tqdm progress bar. +313 | | move_other: bool (optional, default False) +314 | | Whether to adjust tail_embedding alongside head_embedding +315 | | +316 | | Returns +317 | | ------- +318 | | embedding: array of shape (n_samples, n_components) +319 | | The optimized embedding. +320 | | +321 | | """ + | |_______^ +322 | dim = head_embedding.shape[1] +323 | alpha = initial_alpha + | +help: Insert single blank line + +N806 Variable `dens_R` in function should be lowercase + --> umap/layouts.py:347:9 + | +345 | dens_mu_tot = np.sum(densmap_kwds["mu_sum"]) / 2 +346 | dens_lambda = densmap_kwds["lambda"] +347 | dens_R = densmap_kwds["R"] + | ^^^^^^ +348 | dens_mu = densmap_kwds["mu"] +349 | dens_phi_sum = np.zeros(n_vertices, dtype=np.float32) + | + +N806 Variable `dens_R` in function should be lowercase + --> umap/layouts.py:355:9 + | +353 | dens_mu_tot = 0 +354 | dens_lambda = 0 +355 | dens_R = np.zeros(1, dtype=np.float32) + | ^^^^^^ +356 | dens_mu = np.zeros(1, dtype=np.float32) +357 | dens_phi_sum = np.zeros(1, dtype=np.float32) + | + +TD001 Invalid TODO tag: `FIXME` + --> umap/layouts.py:381:15 + | +380 | if densmap_flag: +381 | # FIXME: dens_init_fn might be referenced before assignment + | ^^^^^ +382 | +383 | dens_init_fn( + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/layouts.py:381:15 + | +380 | if densmap_flag: +381 | # FIXME: dens_init_fn might be referenced before assignment + | ^^^^^ +382 | +383 | dens_init_fn( + | + +TD003 Missing issue link for this TODO + --> umap/layouts.py:381:15 + | +380 | if densmap_flag: +381 | # FIXME: dens_init_fn might be referenced before assignment + | ^^^^^ +382 | +383 | dens_init_fn( + | + +FIX001 Line contains FIXME, consider resolving the issue + --> umap/layouts.py:381:15 + | +380 | if densmap_flag: +381 | # FIXME: dens_init_fn might be referenced before assignment + | ^^^^^ +382 | +383 | dens_init_fn( + | + +TD001 Invalid TODO tag: `FIXME` + --> umap/layouts.py:394:15 + | +392 | ) +393 | +394 | # FIXME: dens_var_shift might be referenced before assignment + | ^^^^^ +395 | dens_re_std = np.sqrt(np.var(dens_re_sum) + dens_var_shift) +396 | dens_re_mean = np.mean(dens_re_sum) + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/layouts.py:394:15 + | +392 | ) +393 | +394 | # FIXME: dens_var_shift might be referenced before assignment + | ^^^^^ +395 | dens_re_std = np.sqrt(np.var(dens_re_sum) + dens_var_shift) +396 | dens_re_mean = np.mean(dens_re_sum) + | + +TD003 Missing issue link for this TODO + --> umap/layouts.py:394:15 + | +392 | ) +393 | +394 | # FIXME: dens_var_shift might be referenced before assignment + | ^^^^^ +395 | dens_re_std = np.sqrt(np.var(dens_re_sum) + dens_var_shift) +396 | dens_re_mean = np.mean(dens_re_sum) + | + +FIX001 Line contains FIXME, consider resolving the issue + --> umap/layouts.py:394:15 + | +392 | ) +393 | +394 | # FIXME: dens_var_shift might be referenced before assignment + | ^^^^^ +395 | dens_re_std = np.sqrt(np.var(dens_re_sum) + dens_var_shift) +396 | dens_re_mean = np.mean(dens_re_sum) + | + +PLR0913 Too many arguments in function definition (19 > 5) + --> umap/layouts.py:448:5 + | +448 | def _optimize_layout_generic_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +449 | epochs_per_sample, +450 | epoch_of_next_sample, + | + +PLR0913 Too many arguments in function definition (18 > 5) + --> umap/layouts.py:527:5 + | +527 | def optimize_layout_generic( + | ^^^^^^^^^^^^^^^^^^^^^^^ +528 | head_embedding, +529 | tail_embedding, + | + +D417 Missing argument descriptions in the docstring for `optimize_layout_generic`: `output_metric`, `output_metric_kwds` + --> umap/layouts.py:527:5 + | +527 | def optimize_layout_generic( + | ^^^^^^^^^^^^^^^^^^^^^^^ +528 | head_embedding, +529 | tail_embedding, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:543:5 + | +541 | output_metric=dist.euclidean, +542 | output_metric_kwds=(), +543 | verbose=False, + | ^^^^^^^ +544 | tqdm_kwds=None, +545 | move_other=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:545:5 + | +543 | verbose=False, +544 | tqdm_kwds=None, +545 | move_other=False, + | ^^^^^^^^^^ +546 | ): +547 | """Improve an embedding using stochastic gradient descent to minimize the + | + +D205 1 blank line required between summary line and description + --> umap/layouts.py:547:5 + | +545 | move_other=False, +546 | ): +547 | / """Improve an embedding using stochastic gradient descent to minimize the +548 | | fuzzy set cross entropy between the 1-skeletons of the high dimensional +549 | | and low dimensional fuzzy simplicial sets. In practice this is done by +550 | | sampling edges based on their membership strength (with the (1-p) terms +551 | | coming from negative sampling similar to word2vec). +552 | | +553 | | Parameters +554 | | ---------- +555 | | head_embedding: array of shape (n_samples, n_components) +556 | | The initial embedding to be improved by SGD. +557 | | +558 | | tail_embedding: array of shape (source_samples, n_components) +559 | | The reference embedding of embedded points. If not embedding new +560 | | previously unseen points with respect to an existing embedding this +561 | | is simply the head_embedding (again); otherwise it provides the +562 | | existing embedding to embed with respect to. +563 | | +564 | | head: array of shape (n_1_simplices) +565 | | The indices of the heads of 1-simplices with non-zero membership. +566 | | +567 | | tail: array of shape (n_1_simplices) +568 | | The indices of the tails of 1-simplices with non-zero membership. +569 | | +570 | | n_epochs: int +571 | | The number of training epochs to use in optimization. +572 | | +573 | | n_vertices: int +574 | | The number of vertices (0-simplices) in the dataset. +575 | | +576 | | epochs_per_sample: array of shape (n_1_simplices) +577 | | A float value of the number of epochs per 1-simplex. 1-simplices with +578 | | weaker membership strength will have more epochs between being sampled. +579 | | +580 | | a: float +581 | | Parameter of differentiable approximation of right adjoint functor +582 | | +583 | | b: float +584 | | Parameter of differentiable approximation of right adjoint functor +585 | | +586 | | rng_state: array of int64, shape (3,) +587 | | The internal state of the rng +588 | | +589 | | gamma: float (optional, default 1.0) +590 | | Weight to apply to negative samples. +591 | | +592 | | initial_alpha: float (optional, default 1.0) +593 | | Initial learning rate for the SGD. +594 | | +595 | | negative_sample_rate: int (optional, default 5) +596 | | Number of negative samples to use per positive sample. +597 | | +598 | | verbose: bool (optional, default False) +599 | | Whether to report information on the current progress of the algorithm. +600 | | +601 | | tqdm_kwds: dict (optional, default None) +602 | | Keyword arguments for tqdm progress bar. +603 | | +604 | | move_other: bool (optional, default False) +605 | | Whether to adjust tail_embedding alongside head_embedding +606 | | +607 | | Returns +608 | | ------- +609 | | embedding: array of shape (n_samples, n_components) +610 | | The optimized embedding. +611 | | +612 | | """ + | |_______^ +613 | dim = head_embedding.shape[1] +614 | alpha = initial_alpha + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (20 > 5) + --> umap/layouts.py:662:5 + | +662 | def _optimize_layout_inverse_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +663 | epochs_per_sample, +664 | epoch_of_next_sample, + | + +ERA001 Found commented-out code + --> umap/layouts.py:721:17 + | +719 | ) +720 | +721 | # w_l = 0.0 # for negative samples, the edge does not exist + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +722 | w_h = np.exp(-max(dist_output - rhos[k], 1e-6) / (sigmas[k] + 1e-6)) +723 | grad_coeff = -gamma * ((0 - w_h) / ((1 - w_h) * sigmas[k] + 1e-6)) + | +help: Remove commented-out code + +PLR0913 Too many arguments in function definition (21 > 5) + --> umap/layouts.py:734:5 + | +734 | def optimize_layout_inverse( + | ^^^^^^^^^^^^^^^^^^^^^^^ +735 | head_embedding, +736 | tail_embedding, + | + +D417 Missing argument descriptions in the docstring for `optimize_layout_inverse`: `output_metric`, `output_metric_kwds`, `rhos`, `sigmas` + --> umap/layouts.py:734:5 + | +734 | def optimize_layout_inverse( + | ^^^^^^^^^^^^^^^^^^^^^^^ +735 | head_embedding, +736 | tail_embedding, + | + +ARG001 Unused function argument: `a` + --> umap/layouts.py:745:5 + | +743 | n_vertices, +744 | epochs_per_sample, +745 | a, + | ^ +746 | b, +747 | rng_state, + | + +ARG001 Unused function argument: `b` + --> umap/layouts.py:746:5 + | +744 | epochs_per_sample, +745 | a, +746 | b, + | ^ +747 | rng_state, +748 | gamma=1.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:753:5 + | +751 | output_metric=dist.euclidean, +752 | output_metric_kwds=(), +753 | verbose=False, + | ^^^^^^^ +754 | tqdm_kwds=None, +755 | move_other=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:755:5 + | +753 | verbose=False, +754 | tqdm_kwds=None, +755 | move_other=False, + | ^^^^^^^^^^ +756 | ): +757 | """Improve an embedding using stochastic gradient descent to minimize the + | + +D205 1 blank line required between summary line and description + --> umap/layouts.py:757:5 + | +755 | move_other=False, +756 | ): +757 | / """Improve an embedding using stochastic gradient descent to minimize the +758 | | fuzzy set cross entropy between the 1-skeletons of the high dimensional +759 | | and low dimensional fuzzy simplicial sets. In practice this is done by +760 | | sampling edges based on their membership strength (with the (1-p) terms +761 | | coming from negative sampling similar to word2vec). +762 | | +763 | | Parameters +764 | | ---------- +765 | | head_embedding: array of shape (n_samples, n_components) +766 | | The initial embedding to be improved by SGD. +767 | | +768 | | tail_embedding: array of shape (source_samples, n_components) +769 | | The reference embedding of embedded points. If not embedding new +770 | | previously unseen points with respect to an existing embedding this +771 | | is simply the head_embedding (again); otherwise it provides the +772 | | existing embedding to embed with respect to. +773 | | +774 | | head: array of shape (n_1_simplices) +775 | | The indices of the heads of 1-simplices with non-zero membership. +776 | | +777 | | tail: array of shape (n_1_simplices) +778 | | The indices of the tails of 1-simplices with non-zero membership. +779 | | +780 | | weight: array of shape (n_1_simplices) +781 | | The membership weights of the 1-simplices. +782 | | +783 | | sigmas: +784 | | +785 | | rhos: +786 | | +787 | | n_epochs: int +788 | | The number of training epochs to use in optimization. +789 | | +790 | | n_vertices: int +791 | | The number of vertices (0-simplices) in the dataset. +792 | | +793 | | epochs_per_sample: array of shape (n_1_simplices) +794 | | A float value of the number of epochs per 1-simplex. 1-simplices with +795 | | weaker membership strength will have more epochs between being sampled. +796 | | +797 | | a: float +798 | | Parameter of differentiable approximation of right adjoint functor +799 | | +800 | | b: float +801 | | Parameter of differentiable approximation of right adjoint functor +802 | | +803 | | rng_state: array of int64, shape (3,) +804 | | The internal state of the rng +805 | | +806 | | gamma: float (optional, default 1.0) +807 | | Weight to apply to negative samples. +808 | | +809 | | initial_alpha: float (optional, default 1.0) +810 | | Initial learning rate for the SGD. +811 | | +812 | | negative_sample_rate: int (optional, default 5) +813 | | Number of negative samples to use per positive sample. +814 | | +815 | | verbose: bool (optional, default False) +816 | | Whether to report information on the current progress of the algorithm. +817 | | +818 | | tqdm_kwds: dict (optional, default None) +819 | | Keyword arguments for tqdm progress bar. +820 | | +821 | | move_other: bool (optional, default False) +822 | | Whether to adjust tail_embedding alongside head_embedding +823 | | +824 | | Returns +825 | | ------- +826 | | embedding: array of shape (n_samples, n_components) +827 | | The optimized embedding. +828 | | +829 | | """ + | |_______^ +830 | dim = head_embedding.shape[1] +831 | alpha = initial_alpha + | +help: Insert single blank line + +C901 `_optimize_layout_aligned_euclidean_single_epoch` is too complex (23 > 10) + --> umap/layouts.py:876:5 + | +876 | def _optimize_layout_aligned_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +877 | head_embeddings, +878 | tail_embeddings, + | + +PLR0913 Too many arguments in function definition (19 > 5) + --> umap/layouts.py:876:5 + | +876 | def _optimize_layout_aligned_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +877 | head_embeddings, +878 | tail_embeddings, + | + +PLR0912 Too many branches (26 > 12) + --> umap/layouts.py:876:5 + | +876 | def _optimize_layout_aligned_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +877 | head_embeddings, +878 | tail_embeddings, + | + +PLR0915 Too many statements (59 > 50) + --> umap/layouts.py:876:5 + | +876 | def _optimize_layout_aligned_euclidean_single_epoch( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +877 | head_embeddings, +878 | tail_embeddings, + | + +NPY002 Replace legacy `np.random.seed` call with `np.random.Generator` + --> umap/layouts.py:905:5 + | +904 | embedding_order = np.arange(n_embeddings).astype(np.int32) +905 | np.random.seed(abs(rng_state[0])) + | ^^^^^^^^^^^^^^ +906 | np.random.shuffle(embedding_order) + | + +NPY002 Replace legacy `np.random.shuffle` call with `np.random.Generator` + --> umap/layouts.py:906:5 + | +904 | embedding_order = np.arange(n_embeddings).astype(np.int32) +905 | np.random.seed(abs(rng_state[0])) +906 | np.random.shuffle(embedding_order) + | ^^^^^^^^^^^^^^^^^ +907 | +908 | for i in range(max_n_edges): + | + +PLR0913 Too many arguments in function definition (19 > 5) + --> umap/layouts.py:1026:5 + | +1026 | def optimize_layout_aligned_euclidean( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1027 | head_embeddings, +1028 | tail_embeddings, + | + +D103 Missing docstring in public function + --> umap/layouts.py:1026:5 + | +1026 | def optimize_layout_aligned_euclidean( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1027 | head_embeddings, +1028 | tail_embeddings, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:1042:5 + | +1040 | initial_alpha=1.0, +1041 | negative_sample_rate=5.0, +1042 | parallel=True, + | ^^^^^^^^ +1043 | verbose=False, +1044 | tqdm_kwds=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:1043:5 + | +1041 | negative_sample_rate=5.0, +1042 | parallel=True, +1043 | verbose=False, + | ^^^^^^^ +1044 | tqdm_kwds=None, +1045 | move_other=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/layouts.py:1045:5 + | +1043 | verbose=False, +1044 | tqdm_kwds=None, +1045 | move_other=False, + | ^^^^^^^^^^ +1046 | ): +1047 | dim = head_embeddings[0].shape[1] + | + +N812 Lowercase `functional` imported as non-lowercase `F` + --> umap/parametric_umap.py:43:12 + | +41 | import numpy as np +42 | import torch +43 | import torch.nn.functional as F + | ^^^^^^^^^^^^^^^^^^^^^^^^ +44 | import torch.onnx +45 | import torchvision + | + +F401 `torchvision` imported but unused; consider using `importlib.util.find_spec` to test for availability + --> umap/parametric_umap.py:45:12 + | +43 | import torch.nn.functional as F +44 | import torch.onnx +45 | import torchvision + | ^^^^^^^^^^^ +46 | from torch import nn +47 | except ImportError: + | +help: Remove unused import: `torchvision` + +D101 Missing docstring in public class + --> umap/parametric_umap.py:52:7 + | +52 | class ParametricUMAP(UMAP): + | ^^^^^^^^^^^^^^ +53 | def __init__( +54 | self, + | + +PLR0913 Too many arguments in function definition (13 > 5) + --> umap/parametric_umap.py:53:9 + | +52 | class ParametricUMAP(UMAP): +53 | def __init__( + | ^^^^^^^^ +54 | self, +55 | batch_size=None, + | + +D417 Missing argument description in the docstring for `__init__`: `**kwargs` + --> umap/parametric_umap.py:53:9 + | +52 | class ParametricUMAP(UMAP): +53 | def __init__( + | ^^^^^^^^ +54 | self, +55 | batch_size=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:59:9 + | +57 | encoder=None, +58 | decoder=None, +59 | parametric_reconstruction=False, + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +60 | parametric_reconstruction_loss_fcn=None, +61 | parametric_reconstruction_loss_weight=1.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:62:9 + | +60 | parametric_reconstruction_loss_fcn=None, +61 | parametric_reconstruction_loss_weight=1.0, +62 | autoencoder_loss=False, + | ^^^^^^^^^^^^^^^^ +63 | reconstruction_validation=None, +64 | global_correlation_loss_weight=0, + | + +D205 1 blank line required between summary line and description + --> umap/parametric_umap.py:70:9 + | + 68 | **kwargs, + 69 | ): + 70 | / """Parametric UMAP subclassing UMAP-learn, based on keras/tensorflow. + 71 | | There is also a non-parametric implementation contained within to compare + 72 | | with the base non-parametric implementation. + 73 | | + 74 | | Parameters + 75 | | ---------- + 76 | | batch_size : int, optional + 77 | | size of batch used for batch training, by default None + 78 | | dims : tuple, optional + 79 | | dimensionality of data, if not flat (e.g. (32x32x3 images for ConvNet), by default None + 80 | | encoder : keras.Sequential, optional + 81 | | The encoder Keras network + 82 | | decoder : keras.Sequential, optional + 83 | | the decoder Keras network + 84 | | parametric_reconstruction : bool, optional + 85 | | Whether the decoder is parametric or non-parametric, by default False + 86 | | parametric_reconstruction_loss_fcn : bool, optional + 87 | | What loss function to use for parametric reconstruction, + 88 | | by default keras.losses.BinaryCrossentropy + 89 | | parametric_reconstruction_loss_weight : float, optional + 90 | | How to weight the parametric reconstruction loss relative to umap loss, by default 1.0 + 91 | | autoencoder_loss : bool, optional + 92 | | [description], by default False + 93 | | reconstruction_validation : array, optional + 94 | | validation X data for reconstruction loss, by default None + 95 | | global_correlation_loss_weight : float, optional + 96 | | Whether to additionally train on correlation of global pairwise relationships (>0), by default 0 + 97 | | landmark_loss_fn : callable, optional + 98 | | The function to use for landmark loss, by default the euclidean distance + 99 | | landmark_loss_weight : float, optional +100 | | How to weight the landmark loss relative to umap loss, by default 1.0 +101 | | keras_fit_kwargs : dict, optional +102 | | additional arguments for model.fit (like callbacks), by default {} +103 | | +104 | | """ + | |___________^ +105 | if keras_fit_kwargs is None: +106 | keras_fit_kwargs = {} + | +help: Insert single blank line + +SIM102 Use a single `if` statement instead of nested `if` statements + --> umap/parametric_umap.py:147:9 + | +145 | self.optimizer = keras.optimizers.Adam(1e-3, clipvalue=4.0) +146 | +147 | / if self.encoder is not None: +148 | | if encoder.outputs[0].shape[-1] != self.n_components: + | |_________________________________________________________________^ +149 | msg = ( +150 | f"Dimensionality of embedder network output ({encoder.outputs[0].shape[-1]}) does" + | +help: Combine `if` statements using `and` + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:159:19 + | +157 | ) +158 | +159 | def fit(self, X, y=None, precomputed_distances=None, landmark_positions=None): + | ^ +160 | """Fit X into an embedded space. + | + +N806 Variable `X` in function should be lowercase + --> umap/parametric_umap.py:197:13 + | +195 | ), +196 | ) +197 | X = np.concatenate((X, self.prev_epoch_X)) + | ^ +198 | +199 | if landmark_positions is not None: + | + +N806 Variable `len_X` in function should be lowercase + --> umap/parametric_umap.py:200:13 + | +199 | if landmark_positions is not None: +200 | len_X = len(X) + | ^^^^^ +201 | len_land = len(landmark_positions) +202 | if len_X != len_land: + | + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:230:15 + | +229 | def fit_transform( +230 | self, X, y=None, precomputed_distances=None, landmark_positions=None, + | ^ +231 | ): +232 | """Fit X into an embedded space. + | + +N806 Variable `X` in function should be lowercase + --> umap/parametric_umap.py:269:13 + | +267 | ), +268 | ) +269 | X = np.concatenate((X, self.prev_epoch_X)) + | ^ +270 | +271 | if landmark_positions is not None: + | + +N806 Variable `len_X` in function should be lowercase + --> umap/parametric_umap.py:272:13 + | +271 | if landmark_positions is not None: +272 | len_X = len(X) + | ^^^^^ +273 | len_land = len(landmark_positions) +274 | if len_X != len_land: + | + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:304:25 + | +302 | return super().fit_transform(X, y, landmark_positions=landmark_positions) +303 | +304 | def transform(self, X, batch_size=None): + | ^ +305 | """Transform X into the existing embedded space and return that +306 | transformed output. + | + +D205 1 blank line required between summary line and description + --> umap/parametric_umap.py:305:9 + | +304 | def transform(self, X, batch_size=None): +305 | / """Transform X into the existing embedded space and return that +306 | | transformed output. +307 | | +308 | | Parameters +309 | | ---------- +310 | | X : array, shape (n_samples, n_features) +311 | | New data to be transformed. +312 | | batch_size : int, optional +313 | | Batch size for inference, defaults to the self.batch_size used in training. +314 | | +315 | | Returns +316 | | ------- +317 | | X_new : array, shape (n_samples, n_components) +318 | | Embedding of the new data in low-dimensional space. +319 | | +320 | | """ + | |___________^ +321 | batch_size = batch_size if batch_size else self.batch_size + | +help: Insert single blank line + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:327:33 + | +325 | ) +326 | +327 | def inverse_transform(self, X): + | ^ +328 | """Transform X in the existing embedded space back into the input +329 | data space and return that transformed output. + | + +D205 1 blank line required between summary line and description + --> umap/parametric_umap.py:328:9 + | +327 | def inverse_transform(self, X): +328 | / """Transform X in the existing embedded space back into the input +329 | | data space and return that transformed output. +330 | | +331 | | Parameters +332 | | ---------- +333 | | X : array, shape (n_samples, n_components) +334 | | New points to be inverse transformed. +335 | | +336 | | Returns +337 | | ------- +338 | | X_new : array, shape (n_samples, n_features) +339 | | Generated data points new data in data space. +340 | | +341 | | """ + | |___________^ +342 | if self.parametric_reconstruction: +343 | return self.decoder.predict( + | +help: Insert single blank line + +C901 `_fit_embed_data` is too complex (11 > 10) + --> umap/parametric_umap.py:367:9 + | +365 | ) +366 | +367 | def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=None): + | ^^^^^^^^^^^^^^^ +368 | +369 | if self.metric == "precomputed": + | + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:367:31 + | +365 | ) +366 | +367 | def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=None): + | ^ +368 | +369 | if self.metric == "precomputed": + | + +ARG002 Unused method argument: `n_epochs` + --> umap/parametric_umap.py:367:34 + | +365 | ) +366 | +367 | def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=None): + | ^^^^^^^^ +368 | +369 | if self.metric == "precomputed": + | + +ARG002 Unused method argument: `init` + --> umap/parametric_umap.py:367:44 + | +365 | ) +366 | +367 | def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=None): + | ^^^^ +368 | +369 | if self.metric == "precomputed": + | + +ARG002 Unused method argument: `random_state` + --> umap/parametric_umap.py:367:50 + | +365 | ) +366 | +367 | def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=None): + | ^^^^^^^^^^^^ +368 | +369 | if self.metric == "precomputed": + | + +N806 Variable `X` in function should be lowercase + --> umap/parametric_umap.py:370:13 + | +369 | if self.metric == "precomputed": +370 | X = self._X + | ^ +371 | +372 | # get dimensionality of dataset + | + +N806 Variable `X` in function should be lowercase + --> umap/parametric_umap.py:377:13 + | +375 | # reshape data for network +376 | elif len(self.dims) > 1: +377 | X = np.reshape(X, [len(X), *list(self.dims)]) + | ^ +378 | +379 | if self.parametric_reconstruction and (np.max(X) > 1.0 or np.min(X) < 0.0): + | + +C901 `save` is too complex (14 > 10) + --> umap/parametric_umap.py:485:9 + | +483 | } +484 | +485 | def save(self, save_location, verbose=True, exclude_raw_data=False): + | ^^^^ +486 | +487 | # save encoder + | + +PLR0912 Too many branches (13 > 12) + --> umap/parametric_umap.py:485:9 + | +483 | } +484 | +485 | def save(self, save_location, verbose=True, exclude_raw_data=False): + | ^^^^ +486 | +487 | # save encoder + | + +D102 Missing docstring in public method + --> umap/parametric_umap.py:485:9 + | +483 | } +484 | +485 | def save(self, save_location, verbose=True, exclude_raw_data=False): + | ^^^^ +486 | +487 | # save encoder + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:485:35 + | +483 | } +484 | +485 | def save(self, save_location, verbose=True, exclude_raw_data=False): + | ^^^^^^^ +486 | +487 | # save encoder + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:485:49 + | +483 | } +484 | +485 | def save(self, save_location, verbose=True, exclude_raw_data=False): + | ^^^^^^^^^^^^^^^^ +486 | +487 | # save encoder + | + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:489:30 + | +487 | # save encoder +488 | if self.encoder is not None: +489 | encoder_output = os.path.join(save_location, "encoder.keras") + | ^^^^^^^^^^^^ +490 | self.encoder.save(encoder_output) +491 | if verbose: + | + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:496:30 + | +494 | # save decoder +495 | if self.decoder is not None: +496 | decoder_output = os.path.join(save_location, "decoder.keras") + | ^^^^^^^^^^^^ +497 | self.decoder.save(decoder_output) +498 | if verbose: + | + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:503:39 + | +501 | # save parametric_model +502 | if self.parametric_model is not None: +503 | parametric_model_output = os.path.join( + | ^^^^^^^^^^^^ +504 | save_location, "parametric_model.keras", +505 | ) + | + +SLF001 Private member accessed: `_raw_data` + --> umap/parametric_umap.py:519:35 + | +517 | if hasattr(self, "knn_search_index") and hasattr(self.knn_search_index, +518 | "_raw_data"): +519 | raw_data["knn"] = self.knn_search_index._raw_data + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +520 | del self.knn_search_index._raw_data + | + +SLF001 Private member accessed: `_raw_data` + --> umap/parametric_umap.py:520:21 + | +518 | "_raw_data"): +519 | raw_data["knn"] = self.knn_search_index._raw_data +520 | del self.knn_search_index._raw_data + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +521 | +522 | # # save model.pkl (ignoring unpickleable warnings) + | + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:525:28 + | +523 | with catch_warnings(): +524 | filterwarnings("ignore") +525 | model_output = os.path.join(save_location, "model.pkl") + | ^^^^^^^^^^^^ +526 | with open(model_output, "wb") as output: +527 | pickle.dump(self, output, pickle.HIGHEST_PROTOCOL) + | + +PTH123 `open()` should be replaced by `Path.open()` + --> umap/parametric_umap.py:526:18 + | +524 | filterwarnings("ignore") +525 | model_output = os.path.join(save_location, "model.pkl") +526 | with open(model_output, "wb") as output: + | ^^^^ +527 | pickle.dump(self, output, pickle.HIGHEST_PROTOCOL) +528 | if verbose: + | +help: Replace with `Path.open()` + +SLF001 Private member accessed: `_raw_data` + --> umap/parametric_umap.py:536:17 + | +534 | self._raw_data = raw_data["root"] +535 | if "knn" in raw_data: +536 | self.knn_search_index._raw_data = raw_data["knn"] + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +537 | +538 | def add_landmarks( + | + +D417 Missing argument description in the docstring for `add_landmarks`: `idx` + --> umap/parametric_umap.py:538:9 + | +536 | self.knn_search_index._raw_data = raw_data["knn"] +537 | +538 | def add_landmarks( + | ^^^^^^^^^^^^^ +539 | self, +540 | X, + | + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:540:9 + | +538 | def add_landmarks( +539 | self, +540 | X, + | ^ +541 | sample_pct=0.01, +542 | sample_mode="uniform", + | + +NPY002 Replace legacy `np.random.choice` call with `np.random.Generator` + --> umap/parametric_umap.py:566:17 + | +564 | if self.sample_mode == "uniform": +565 | self.prev_epoch_idx = list( +566 | np.random.choice( + | ^^^^^^^^^^^^^^^^ +567 | range(X.shape[0]), int(X.shape[0]*sample_pct), replace=False, +568 | ), + | + +D102 Missing docstring in public method + --> umap/parametric_umap.py:586:9 + | +584 | ) +585 | +586 | def remove_landmarks(self): + | ^^^^^^^^^^^^^^^^ +587 | self.prev_epoch_X = None + | + +N802 Function name `to_ONNX` should be lowercase + --> umap/parametric_umap.py:589:9 + | +587 | self.prev_epoch_X = None +588 | +589 | def to_ONNX(self, save_location): + | ^^^^^^^ +590 | """Exports trained parametric UMAP as ONNX.""" +591 | # Extract encoder + | + +D401 First line of docstring should be in imperative mood: "Exports trained parametric UMAP as ONNX." + --> umap/parametric_umap.py:590:9 + | +589 | def to_ONNX(self, save_location): +590 | """Exports trained parametric UMAP as ONNX.""" + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +591 | # Extract encoder +592 | km = self.encoder + | + +D401 First line of docstring should be in imperative mood: "Gets elements of graphs, weights, and number of epochs per edge." + --> umap/parametric_umap.py:604:5 + | +603 | def get_graph_elements(graph_, n_epochs): +604 | / """Gets elements of graphs, weights, and number of epochs per edge. +605 | | +606 | | Parameters +607 | | ---------- +608 | | graph_ : scipy.sparse.csr.csr_matrix +609 | | umap graph of probabilities +610 | | n_epochs : int +611 | | maximum number of epochs per edge +612 | | +613 | | Returns +614 | | ------- +615 | | graph scipy.sparse.csr.csr_matrix +616 | | umap graph +617 | | epochs_per_sample np.array +618 | | number of epochs to train each sample for +619 | | head np.array +620 | | edge head +621 | | tail np.array +622 | | edge tail +623 | | weight np.array +624 | | edge weight +625 | | n_vertices int +626 | | number of vertices in graph +627 | | +628 | | """ + | |_______^ +629 | ### should we remove redundancies () here?? +630 | # graph_ = remove_redundant_edges(graph_) + | + +ERA001 Found commented-out code + --> umap/parametric_umap.py:630:5 + | +628 | """ +629 | ### should we remove redundancies () here?? +630 | # graph_ = remove_redundant_edges(graph_) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +631 | +632 | graph = graph_.tocoo() + | +help: Remove commented-out code + +PLR2004 Magic value used in comparison, consider replacing `10000` with a constant variable + --> umap/parametric_umap.py:640:45 + | +638 | if n_epochs is None: +639 | # For smaller datasets we can use more epochs +640 | n_epochs = 500 if graph.shape[0] <= 10000 else 200 + | ^^^^^ +641 | # remove elements with very low probability +642 | graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0 + | + +D417 Missing argument descriptions in the docstring for `init_embedding_from_graph`: `graph`, `metric`, `n_components`, `random_state` + --> umap/parametric_umap.py:654:5 + | +654 | def init_embedding_from_graph( + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +655 | _raw_data, graph, n_components, random_state, metric, _metric_kwds, init="spectral", +656 | ): + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/parametric_umap.py:699:36 + | +697 | else: +698 | init_data = np.array(init) +699 | if len(init_data.shape) == 2: + | ^ +700 | if np.unique(init_data, axis=0).shape[0] < init_data.shape[0]: +701 | tree = KDTree(init_data) + | + +D205 1 blank line required between summary line and description + --> umap/parametric_umap.py:714:5 + | +713 | def convert_distance_to_log_probability(distances, a=1.0, b=1.0): +714 | / """Convert distance representation into log probability, +715 | | as a function of a, b params. +716 | | +717 | | Parameters +718 | | ---------- +719 | | distances : array +720 | | euclidean distance between two points in embedding +721 | | a : float, optional +722 | | parameter based on min_dist, by default 1.0 +723 | | b : float, optional +724 | | parameter based on min_dist, by default 1.0 +725 | | +726 | | Returns +727 | | ------- +728 | | float +729 | | log probability in embedding space +730 | | +731 | | """ + | |_______^ +732 | return -ops.log1p(a * distances ** (2 * b)) + | +help: Insert single blank line + +N803 Argument name `EPS` should be lowercase + --> umap/parametric_umap.py:736:54 + | +735 | def compute_cross_entropy( +736 | probabilities_graph, log_probabilities_distance, EPS=1e-4, repulsion_strength=1.0, + | ^^^^^^^^ +737 | ): +738 | """Compute cross entropy between low and high probability. + | + +ARG001 Unused function argument: `EPS` + --> umap/parametric_umap.py:736:54 + | +735 | def compute_cross_entropy( +736 | probabilities_graph, log_probabilities_distance, EPS=1e-4, repulsion_strength=1.0, + | ^^^ +737 | ): +738 | """Compute cross entropy between low and high probability. + | + +N806 Variable `CE` in function should be lowercase + --> umap/parametric_umap.py:773:5 + | +772 | # balance the expected losses between attraction and repel +773 | CE = attraction_term + repellant_term + | ^^ +774 | return attraction_term, repellant_term, CE + | + +PLR0913 Too many arguments in function definition (7 > 5) + --> umap/parametric_umap.py:777:5 + | +777 | def prepare_networks( + | ^^^^^^^^^^^^^^^^ +778 | encoder, +779 | decoder, + | + +ARG001 Unused function argument: `n_data` + --> umap/parametric_umap.py:782:5 + | +780 | n_components, +781 | dims, +782 | n_data, + | ^^^^^^ +783 | parametric_reconstruction, +784 | init_embedding=None, + | + +ARG001 Unused function argument: `init_embedding` + --> umap/parametric_umap.py:784:5 + | +782 | n_data, +783 | parametric_reconstruction, +784 | init_embedding=None, + | ^^^^^^^^^^^^^^ +785 | ): +786 | """Generates a set of keras networks for the encoder and decoder if one has not already + | + +D205 1 blank line required between summary line and description + --> umap/parametric_umap.py:786:5 + | +784 | init_embedding=None, +785 | ): +786 | / """Generates a set of keras networks for the encoder and decoder if one has not already +787 | | been predefined. +788 | | +789 | | Parameters +790 | | ---------- +791 | | encoder : keras.Sequential +792 | | The encoder Keras network +793 | | decoder : keras.Sequential +794 | | the decoder Keras network +795 | | n_components : int +796 | | the dimensionality of the latent space +797 | | dims : tuple of shape (dim1, dim2, dim3...) +798 | | dimensionality of data +799 | | n_data : number of elements in dataset +800 | | # of elements in training dataset +801 | | parametric_reconstruction : bool +802 | | Whether the decoder is parametric or non-parametric +803 | | init_embedding : array (optional, default None) +804 | | The initial embedding, for nonparametric embeddings +805 | | +806 | | Returns +807 | | ------- +808 | | encoder: keras.Sequential +809 | | encoder keras network +810 | | decoder: keras.Sequential +811 | | decoder keras network +812 | | +813 | | """ + | |_______^ +814 | if encoder is None: +815 | encoder = keras.Sequential( + | +help: Insert single blank line + +D401 First line of docstring should be in imperative mood: "Generates a set of keras networks for the encoder and decoder if one has not already" + --> umap/parametric_umap.py:786:5 + | +784 | init_embedding=None, +785 | ): +786 | / """Generates a set of keras networks for the encoder and decoder if one has not already +787 | | been predefined. +788 | | +789 | | Parameters +790 | | ---------- +791 | | encoder : keras.Sequential +792 | | The encoder Keras network +793 | | decoder : keras.Sequential +794 | | the decoder Keras network +795 | | n_components : int +796 | | the dimensionality of the latent space +797 | | dims : tuple of shape (dim1, dim2, dim3...) +798 | | dimensionality of data +799 | | n_data : number of elements in dataset +800 | | # of elements in training dataset +801 | | parametric_reconstruction : bool +802 | | Whether the decoder is parametric or non-parametric +803 | | init_embedding : array (optional, default None) +804 | | The initial embedding, for nonparametric embeddings +805 | | +806 | | Returns +807 | | ------- +808 | | encoder: keras.Sequential +809 | | encoder keras network +810 | | decoder: keras.Sequential +811 | | decoder keras network +812 | | +813 | | """ + | |_______^ +814 | if encoder is None: +815 | encoder = keras.Sequential( + | + +C901 `construct_edge_dataset` is too complex (11 > 10) + --> umap/parametric_umap.py:843:5 + | +843 | def construct_edge_dataset( + | ^^^^^^^^^^^^^^^^^^^^^^ +844 | X, +845 | graph_, + | + +PLR0913 Too many arguments in function definition (7 > 5) + --> umap/parametric_umap.py:843:5 + | +843 | def construct_edge_dataset( + | ^^^^^^^^^^^^^^^^^^^^^^ +844 | X, +845 | graph_, + | + +D417 Missing argument description in the docstring for `construct_edge_dataset`: `global_correlation_loss_weight` + --> umap/parametric_umap.py:843:5 + | +843 | def construct_edge_dataset( + | ^^^^^^^^^^^^^^^^^^^^^^ +844 | X, +845 | graph_, + | + +N803 Argument name `X` should be lowercase + --> umap/parametric_umap.py:844:5 + | +843 | def construct_edge_dataset( +844 | X, + | ^ +845 | graph_, +846 | n_epochs, + | + +PLR2004 Magic value used in comparison, consider replacing `0.5` with a constant variable + --> umap/parametric_umap.py:877:50 + | +875 | # if X is > 512Mb in size, we need to use a different, slower method for +876 | # batching data. +877 | gather_indices_in_python = X.nbytes * 1e-9 > 0.5 + | ^^^ +878 | if landmark_positions is not None: +879 | gather_landmark_indices_in_python = ( + | + +PLR2004 Magic value used in comparison, consider replacing `0.5` with a constant variable + --> umap/parametric_umap.py:880:48 + | +878 | if landmark_positions is not None: +879 | gather_landmark_indices_in_python = ( +880 | landmark_positions.nbytes * 1e-9 > 0.5 + | ^^^ +881 | ) + | + +N802 Function name `gather_X` should be lowercase + --> umap/parametric_umap.py:883:9 + | +881 | ) +882 | +883 | def gather_X(edge_to, edge_from): + | ^^^^^^^^ +884 | # gather data from indexes (edges) in either numpy of tf, depending on array size +885 | if gather_indices_in_python: + | + +ARG001 Unused function argument: `edge_from` + --> umap/parametric_umap.py:895:30 + | +893 | return edge_to, edge_from, edge_to_batch, edge_from_batch +894 | +895 | def get_outputs(edge_to, edge_from, edge_to_batch, edge_from_batch): + | ^^^^^^^^^ +896 | outputs = {"umap": ops.repeat(0, batch_size)} +897 | if global_correlation_loss_weight > 0: + | + +ERA001 Found commented-out code + --> umap/parametric_umap.py:901:13 + | +899 | if parametric_reconstruction: +900 | # add reconstruction to iterator output +901 | # edge_out = ops.concatenate([edge_to_batch, edge_from_batch], axis=0) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +902 | outputs["reconstruction"] = edge_to_batch +903 | if landmark_positions is not None: + | +help: Remove commented-out code + +NPY002 Replace legacy `np.random.permutation` call with `np.random.Generator` + --> umap/parametric_umap.py:930:20 + | +929 | # shuffle edges +930 | shuffle_mask = np.random.permutation(range(len(edges_to_exp))) + | ^^^^^^^^^^^^^^^^^^^^^ +931 | edges_to_exp = edges_to_exp[shuffle_mask].astype(np.int64) +932 | edges_from_exp = edges_from_exp[shuffle_mask].astype(np.int64) + | + +D401 First line of docstring should be in imperative mood: "Checks if a dictionary item can be pickled." + --> umap/parametric_umap.py:951:5 + | +950 | def should_pickle(key, val): +951 | / """Checks if a dictionary item can be pickled. +952 | | +953 | | Parameters +954 | | ---------- +955 | | key : try +956 | | key for dictionary element +957 | | val : None +958 | | element of dictionary +959 | | +960 | | Returns +961 | | ------- +962 | | picklable: bool +963 | | whether the dictionary item can be pickled +964 | | +965 | | """ + | |_______^ +966 | try: +967 | ## make sure object can be pickled and then re-read + | + +S301 `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue + --> umap/parametric_umap.py:971:13 + | +969 | pickled = codecs.encode(pickle.dumps(val), "base64").decode() +970 | # unpickle object +971 | _ = pickle.loads(codecs.decode(pickled.encode(), "base64")) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +972 | except ( +973 | pickle.PicklingError, + | + +N802 Function name `load_ParametricUMAP` should be lowercase + --> umap/parametric_umap.py:990:5 + | +990 | def load_ParametricUMAP(save_location, verbose=True): + | ^^^^^^^^^^^^^^^^^^^ +991 | """Load a parametric UMAP model consisting of a umap-learn UMAP object +992 | and corresponding keras models. + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:990:40 + | +990 | def load_ParametricUMAP(save_location, verbose=True): + | ^^^^^^^ +991 | """Load a parametric UMAP model consisting of a umap-learn UMAP object +992 | and corresponding keras models. + | + +D205 1 blank line required between summary line and description + --> umap/parametric_umap.py:991:5 + | + 990 | def load_ParametricUMAP(save_location, verbose=True): + 991 | / """Load a parametric UMAP model consisting of a umap-learn UMAP object + 992 | | and corresponding keras models. + 993 | | + 994 | | Parameters + 995 | | ---------- + 996 | | save_location : str + 997 | | the folder that the model was saved in + 998 | | verbose : bool, optional + 999 | | Whether to print the loading steps, by default True +1000 | | +1001 | | Returns +1002 | | ------- +1003 | | parametric_umap.ParametricUMAP +1004 | | Parametric UMAP objects +1005 | | +1006 | | """ + | |_______^ +1007 | ## Loads a ParametricUMAP model and its related keras models + | +help: Insert single blank line + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:1009:20 + | +1007 | ## Loads a ParametricUMAP model and its related keras models +1008 | +1009 | model_output = os.path.join(save_location, "model.pkl") + | ^^^^^^^^^^^^ +1010 | model = pickle.load(open(model_output, "rb")) +1011 | if verbose: + | + +S301 `pickle` and modules that wrap it can be unsafe when used to deserialize untrusted data, possible security issue + --> umap/parametric_umap.py:1010:13 + | +1009 | model_output = os.path.join(save_location, "model.pkl") +1010 | model = pickle.load(open(model_output, "rb")) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1011 | if verbose: +1012 | pass + | + +SIM115 Use a context manager for opening files + --> umap/parametric_umap.py:1010:25 + | +1009 | model_output = os.path.join(save_location, "model.pkl") +1010 | model = pickle.load(open(model_output, "rb")) + | ^^^^ +1011 | if verbose: +1012 | pass + | + +PTH123 `open()` should be replaced by `Path.open()` + --> umap/parametric_umap.py:1010:25 + | +1009 | model_output = os.path.join(save_location, "model.pkl") +1010 | model = pickle.load(open(model_output, "rb")) + | ^^^^ +1011 | if verbose: +1012 | pass + | +help: Replace with `Path.open()` + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:1015:22 + | +1014 | # load encoder +1015 | encoder_output = os.path.join(save_location, "encoder.keras") + | ^^^^^^^^^^^^ +1016 | if os.path.exists(encoder_output): +1017 | model.encoder = keras.models.load_model(encoder_output) + | + +PTH110 `os.path.exists()` should be replaced by `Path.exists()` + --> umap/parametric_umap.py:1016:8 + | +1014 | # load encoder +1015 | encoder_output = os.path.join(save_location, "encoder.keras") +1016 | if os.path.exists(encoder_output): + | ^^^^^^^^^^^^^^ +1017 | model.encoder = keras.models.load_model(encoder_output) +1018 | if verbose: + | +help: Replace with `Path(...).exists()` + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:1022:22 + | +1021 | # save decoder +1022 | decoder_output = os.path.join(save_location, "decoder.keras") + | ^^^^^^^^^^^^ +1023 | if os.path.exists(decoder_output): +1024 | model.decoder = keras.models.load_model(decoder_output) + | + +PTH110 `os.path.exists()` should be replaced by `Path.exists()` + --> umap/parametric_umap.py:1023:8 + | +1021 | # save decoder +1022 | decoder_output = os.path.join(save_location, "decoder.keras") +1023 | if os.path.exists(decoder_output): + | ^^^^^^^^^^^^^^ +1024 | model.decoder = keras.models.load_model(decoder_output) + | +help: Replace with `Path(...).exists()` + +PTH118 `os.path.join()` should be replaced by `Path` with `/` operator + --> umap/parametric_umap.py:1027:31 + | +1026 | # save parametric_model +1027 | parametric_model_output = os.path.join(save_location, "parametric_model") + | ^^^^^^^^^^^^ +1028 | if os.path.exists(parametric_model_output): +1029 | model.parametric_model = keras.models.load_model(parametric_model_output) + | + +PTH110 `os.path.exists()` should be replaced by `Path.exists()` + --> umap/parametric_umap.py:1028:8 + | +1026 | # save parametric_model +1027 | parametric_model_output = os.path.join(save_location, "parametric_model") +1028 | if os.path.exists(parametric_model_output): + | ^^^^^^^^^^^^^^ +1029 | model.parametric_model = keras.models.load_model(parametric_model_output) + | +help: Replace with `Path(...).exists()` + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:1034:27 + | +1034 | def covariance(x, y=None, keepdims=False): + | ^^^^^^^^ +1035 | """Adapted from TF Probability.""" +1036 | x = ops.convert_to_tensor(x) + | + +D103 Missing docstring in public function + --> umap/parametric_umap.py:1093:5 + | +1093 | def correlation(x, y=None, keepdims=False): + | ^^^^^^^^^^^ +1094 | x = x / ops.std(x, axis=0, keepdims=True) +1095 | if y is not None: + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:1093:28 + | +1093 | def correlation(x, y=None, keepdims=False): + | ^^^^^^^^ +1094 | x = x / ops.std(x, axis=0, keepdims=True) +1095 | if y is not None: + | + +D101 Missing docstring in public class + --> umap/parametric_umap.py:1100:7 + | +1100 | class StopGradient(keras.layers.Layer): + | ^^^^^^^^^^^^ +1101 | def call(self, x): +1102 | return ops.stop_gradient(x) + | + +D102 Missing docstring in public method + --> umap/parametric_umap.py:1101:9 + | +1100 | class StopGradient(keras.layers.Layer): +1101 | def call(self, x): + | ^^^^ +1102 | return ops.stop_gradient(x) + | + +D101 Missing docstring in public class + --> umap/parametric_umap.py:1111:7 + | +1111 | class UMAPModel(keras.Model): + | ^^^^^^^^^ +1112 | def __init__( +1113 | self, + | + +PLR0913 Too many arguments in function definition (14 > 5) + --> umap/parametric_umap.py:1112:9 + | +1111 | class UMAPModel(keras.Model): +1112 | def __init__( + | ^^^^^^^^ +1113 | self, +1114 | umap_loss_a, + | + +D107 Missing docstring in `__init__` + --> umap/parametric_umap.py:1112:9 + | +1111 | class UMAPModel(keras.Model): +1112 | def __init__( + | ^^^^^^^^ +1113 | self, +1114 | umap_loss_a, + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:1121:9 + | +1119 | optimizer=None, +1120 | parametric_reconstruction_loss_fn=None, +1121 | parametric_reconstruction=False, + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +1122 | parametric_reconstruction_loss_weight=1.0, +1123 | global_correlation_loss_weight=0.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/parametric_umap.py:1124:9 + | +1122 | parametric_reconstruction_loss_weight=1.0, +1123 | global_correlation_loss_weight=0.0, +1124 | autoencoder_loss=False, + | ^^^^^^^^^^^^^^^^ +1125 | landmark_loss_fn=None, +1126 | landmark_loss_weight=1.0, + | + +D102 Missing docstring in public method + --> umap/parametric_umap.py:1162:9 + | +1160 | self.landmark_loss_fn = landmark_loss_fn +1161 | +1162 | def call(self, inputs): + | ^^^^ +1163 | to_x, from_x = inputs +1164 | embedding_to = self.encoder(to_x) + | + +D102 Missing docstring in public method + --> umap/parametric_umap.py:1181:9 + | +1179 | return y_pred +1180 | +1181 | def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None, **kwargs): + | ^^^^^^^^^^^^ +1182 | losses = [] +1183 | # Regularization losses. + | + +ARG002 Unused method argument: `x` + --> umap/parametric_umap.py:1181:28 + | +1179 | return y_pred +1180 | +1181 | def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None, **kwargs): + | ^ +1182 | losses = [] +1183 | # Regularization losses. + | + +ARG002 Unused method argument: `sample_weight` + --> umap/parametric_umap.py:1181:57 + | +1179 | return y_pred +1180 | +1181 | def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None, **kwargs): + | ^^^^^^^^^^^^^ +1182 | losses = [] +1183 | # Regularization losses. + | + +ARG002 Unused method argument: `kwargs` + --> umap/parametric_umap.py:1181:79 + | +1179 | return y_pred +1180 | +1181 | def compute_loss(self, x=None, y=None, y_pred=None, sample_weight=None, **kwargs): + | ^^^^^^ +1182 | losses = [] +1183 | # Regularization losses. + | + +PERF401 Use a list comprehension to create a transformed list + --> umap/parametric_umap.py:1185:13 + | +1183 | # Regularization losses. +1184 | for loss in self.losses: +1185 | losses.append(ops.cast(loss, dtype=keras.backend.floatx())) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1186 | +1187 | # umap loss + | +help: Replace for loop with list comprehension + +D101 Missing docstring in public class + --> umap/parametric_umap.py:1315:11 + | +1313 | if torch_imported: +1314 | +1315 | class PumapNet(nn.Module): + | ^^^^^^^^ +1316 | +1317 | def __init__(self, indim, outdim): + | + +D107 Missing docstring in `__init__` + --> umap/parametric_umap.py:1317:13 + | +1315 | class PumapNet(nn.Module): +1316 | +1317 | def __init__(self, indim, outdim): + | ^^^^^^^^ +1318 | +1319 | super().__init__() + | + +D102 Missing docstring in public method + --> umap/parametric_umap.py:1337:13 + | +1335 | """ +1336 | +1337 | def forward(self, x): + | ^^^^^^^ +1338 | x = self.dense1(x) +1339 | x = F.relu(x) + | + +D401 First line of docstring should be in imperative mood: "Copies weights from a parametric UMAP encoder to pytorch." + --> umap/parametric_umap.py:1352:9 + | +1351 | def weight_copier(km, pm): +1352 | / """Copies weights from a parametric UMAP encoder to pytorch. +1353 | | +1354 | | Parameters. +1355 | | ---------- +1356 | | km : encoder extracted from parametric UMAP. +1357 | | pm: a PumapNet object. Will be overwritten. +1358 | | +1359 | | Returns +1360 | | ------- +1361 | | pm : PumapNet Object. +1362 | | Net with copied weights. +1363 | | +1364 | | """ + | |___________^ +1365 | kweights = km.get_weights() +1366 | n_layers = int(len(kweights) / 2) # The actual number of layers + | + +EXE002 The file is executable but no shebang is present +--> umap/plot.py:1:1 + +SLF001 Private member accessed: `_metric_kwds` + --> umap/plot.py:170:16 + | +168 | def _get_metric_kwds(umap_object): +169 | if hasattr(umap_object, "_metric_kwds"): +170 | return umap_object._metric_kwds + | ^^^^^^^^^^^^^^^^^^^^^^^^ +171 | # Assume no keywords exist +172 | return {} + | + +SLF001 Private member accessed: `_small_data` + --> umap/plot.py:183:48 + | +182 | def _nhood_search(umap_object, nhood_size): +183 | if hasattr(umap_object, "_small_data") and umap_object._small_data: + | ^^^^^^^^^^^^^^^^^^^^^^^ +184 | dmat = sklearn.metrics.pairwise_distances(umap_object._raw_data) +185 | indices = np.argpartition(dmat, nhood_size)[:, :nhood_size] + | + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:184:51 + | +182 | def _nhood_search(umap_object, nhood_size): +183 | if hasattr(umap_object, "_small_data") and umap_object._small_data: +184 | dmat = sklearn.metrics.pairwise_distances(umap_object._raw_data) + | ^^^^^^^^^^^^^^^^^^^^^ +185 | indices = np.argpartition(dmat, nhood_size)[:, :nhood_size] +186 | dmat_shortened = submatrix(dmat, indices, nhood_size) + | + +SLF001 Private member accessed: `_knn_search_index` + --> umap/plot.py:193:26 + | +191 | np.empty(3, dtype=np.int64) +192 | +193 | indices, dists = umap_object._knn_search_index.query( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +194 | umap_object._raw_data, +195 | k=nhood_size, + | + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:194:13 + | +193 | indices, dists = umap_object._knn_search_index.query( +194 | umap_object._raw_data, + | ^^^^^^^^^^^^^^^^^^^^^ +195 | k=nhood_size, +196 | ) + | + +PLR2004 Magic value used in comparison, consider replacing `126` with a constant variable + --> umap/plot.py:240:44 + | +238 | [int("0x" + c) for c in (background[1:3], background[3:5], background[5:7])], +239 | ) +240 | font_color = "black" if mean_val > 126 else "white" + | ^^^ +241 | +242 | else: + | + +C901 `_datashade_points` is too complex (11 > 10) + --> umap/plot.py:248:5 + | +248 | def _datashade_points( + | ^^^^^^^^^^^^^^^^^ +249 | points, +250 | ax=None, + | + +PLR0913 Too many arguments in function definition (12 > 5) + --> umap/plot.py:248:5 + | +248 | def _datashade_points( + | ^^^^^^^^^^^^^^^^^ +249 | points, +250 | ax=None, + | + +PLR0912 Too many branches (13 > 12) + --> umap/plot.py:248:5 + | +248 | def _datashade_points( + | ^^^^^^^^^^^^^^^^^ +249 | points, +250 | ax=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:259:5 + | +257 | width=800, +258 | height=800, +259 | show_legend=True, + | ^^^^^^^^^^^ +260 | alpha=255, +261 | ): + | + +PLR2004 Magic value used in comparison, consider replacing `256` with a constant variable + --> umap/plot.py:321:38 + | +319 | ) +320 | unique_values = np.unique(values) +321 | if unique_values.shape[0] >= 256: + | ^^^ +322 | min_val, max_val = np.min(values), np.max(values) +323 | bin_size = (max_val - min_val) / 255.0 + | + +PLR0913 Too many arguments in function definition (12 > 5) + --> umap/plot.py:359:5 + | +359 | def _matplotlib_points( + | ^^^^^^^^^^^^^^^^^^ +360 | points, +361 | ax=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:370:5 + | +368 | width=800, +369 | height=800, +370 | show_legend=True, + | ^^^^^^^^^^^ +371 | alpha=None, +372 | ): + | + +TRY004 Prefer `TypeError` exception for invalid type + --> umap/plot.py:477:9 + | +475 | else: +476 | msg = "The type of ``plot_to_show`` was not valid, or not understood." +477 | / raise ValueError( +478 | | msg, +479 | | ) + | |_________^ + | + +C901 `points` is too complex (13 > 10) + --> umap/plot.py:482:5 + | +482 | def points( + | ^^^^^^ +483 | umap_object, +484 | points=None, + | + +PLR0913 Too many arguments in function definition (15 > 5) + --> umap/plot.py:482:5 + | +482 | def points( + | ^^^^^^ +483 | umap_object, +484 | points=None, + | + +PLR0912 Too many branches (14 > 12) + --> umap/plot.py:482:5 + | +482 | def points( + | ^^^^^^ +483 | umap_object, +484 | points=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:494:5 + | +492 | width=800, +493 | height=800, +494 | show_legend=True, + | ^^^^^^^^^^^ +495 | subset_points=None, +496 | ax=None, + | + +D205 1 blank line required between summary line and description + --> umap/plot.py:499:5 + | +497 | alpha=None, +498 | ): +499 | / """Plot an embedding as points. Currently this only works +500 | | for 2D embeddings. While there are many optional parameters +501 | | to further control and tailor the plotting, you need only +502 | | pass in the trained/fit umap model to get results. This plot +503 | | utility will attempt to do the hard work of avoiding +504 | | over-plotting issues, and make it easy to automatically +505 | | colour points by a categorical labelling or numeric values. +506 | | +507 | | This method is intended to be used within a Jupyter +508 | | notebook with ``%matplotlib inline``. +509 | | +510 | | Parameters +511 | | ---------- +512 | | umap_object: trained UMAP object +513 | | A trained UMAP object that has a 2D embedding. +514 | | +515 | | points: array, shape (n_samples, dim) (optional, default None) +516 | | An array of points to be plotted. Usually this is None +517 | | and so the original embedding points of the umap_object +518 | | are used. However points can be passed explicitly instead +519 | | which is useful for points manually transformed. +520 | | +521 | | labels: array, shape (n_samples,) (optional, default None) +522 | | An array of labels (assumed integer or categorical), +523 | | one for each data sample. +524 | | This will be used for coloring the points in +525 | | the plot according to their label. Note that +526 | | this option is mutually exclusive to the ``values`` +527 | | option. +528 | | +529 | | values: array, shape (n_samples,) (optional, default None) +530 | | An array of values (assumed float or continuous), +531 | | one for each sample. +532 | | This will be used for coloring the points in +533 | | the plot according to a colorscale associated +534 | | to the total range of values. Note that this +535 | | option is mutually exclusive to the ``labels`` +536 | | option. +537 | | +538 | | theme: string (optional, default None) +539 | | A color theme to use for plotting. A small set of +540 | | predefined themes are provided which have relatively +541 | | good aesthetics. Available themes are: +542 | | * 'blue' +543 | | * 'red' +544 | | * 'green' +545 | | * 'inferno' +546 | | * 'fire' +547 | | * 'viridis' +548 | | * 'darkblue' +549 | | * 'darkred' +550 | | * 'darkgreen' +551 | | +552 | | cmap: string (optional, default 'Blues') +553 | | The name of a matplotlib colormap to use for coloring +554 | | or shading points. If no labels or values are passed +555 | | this will be used for shading points according to +556 | | density (largely only of relevance for very large +557 | | datasets). If values are passed this will be used for +558 | | shading according the value. Note that if theme +559 | | is passed then this value will be overridden by the +560 | | corresponding option of the theme. +561 | | +562 | | color_key: dict or array, shape (n_categories) (optional, default None) +563 | | A way to assign colors to categoricals. This can either be +564 | | an explicit dict mapping labels to colors (as strings of form +565 | | '#RRGGBB'), or an array like object providing one color for +566 | | each distinct category being provided in ``labels``. Either +567 | | way this mapping will be used to color points according to +568 | | the label. Note that if theme +569 | | is passed then this value will be overridden by the +570 | | corresponding option of the theme. +571 | | +572 | | color_key_cmap: string (optional, default 'Spectral') +573 | | The name of a matplotlib colormap to use for categorical coloring. +574 | | If an explicit ``color_key`` is not given a color mapping for +575 | | categories can be generated from the label list and selecting +576 | | a matching list of colors from the given colormap. Note +577 | | that if theme +578 | | is passed then this value will be overridden by the +579 | | corresponding option of the theme. +580 | | +581 | | background: string (optional, default 'white) +582 | | The color of the background. Usually this will be either +583 | | 'white' or 'black', but any color name will work. Ideally +584 | | one wants to match this appropriately to the colors being +585 | | used for points etc. This is one of the things that themes +586 | | handle for you. Note that if theme +587 | | is passed then this value will be overridden by the +588 | | corresponding option of the theme. +589 | | +590 | | width: int (optional, default 800) +591 | | The desired width of the plot in pixels. +592 | | +593 | | height: int (optional, default 800) +594 | | The desired height of the plot in pixels +595 | | +596 | | show_legend: bool (optional, default True) +597 | | Whether to display a legend of the labels +598 | | +599 | | subset_points: array, shape (n_samples,) (optional, default None) +600 | | A way to select a subset of points based on an array of boolean +601 | | values. +602 | | +603 | | ax: matplotlib axis (optional, default None) +604 | | The matplotlib axis to draw the plot to, or if None, which is +605 | | the default, a new axis will be created and returned. +606 | | +607 | | alpha: float (optional, default: None) +608 | | The alpha blending value, between 0 (transparent) and 1 (opaque). +609 | | +610 | | Returns +611 | | ------- +612 | | result: matplotlib axis +613 | | The result is a matplotlib axis with the relevant plot displayed. +614 | | If you are using a notebooks and have ``%matplotlib inline`` set +615 | | then this will simply display inline. +616 | | +617 | | """ + | |_______^ +618 | # if not hasattr(umap_object, "embedding_"): +619 | # raise ValueError( + | +help: Insert single blank line + +ERA001 Found commented-out code + --> umap/plot.py:621:5 + | +619 | # raise ValueError( +620 | # "UMAP object must perform fit on data before it can be visualized" +621 | # ) + | ^^^^^^^ +622 | +623 | if theme is not None: + | +help: Remove commented-out code + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/plot.py:656:27 + | +654 | values = values[subset_points] +655 | +656 | if points.shape[1] != 2: + | ^ +657 | msg = "Plotting is currently only implemented for 2D embeddings" +658 | raise ValueError(msg) + | + +PLR0913 Too many arguments in function definition (13 > 5) + --> umap/plot.py:724:5 + | +724 | def connectivity( + | ^^^^^^^^^^^^ +725 | umap_object, +726 | edge_bundling=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:728:5 + | +726 | edge_bundling=None, +727 | edge_cmap="gray_r", +728 | show_points=False, + | ^^^^^^^^^^^ +729 | labels=None, +730 | values=None, + | + +D205 1 blank line required between summary line and description + --> umap/plot.py:739:5 + | +737 | height=800, +738 | ): +739 | / """Plot connectivity relationships of the underlying UMAP +740 | | simplicial set data structure. Internally UMAP will make +741 | | use of what can be viewed as a weighted graph. This graph +742 | | can be plotted using the layout provided by UMAP as a +743 | | potential diagnostic view of the embedding. Currently this only works +744 | | for 2D embeddings. While there are many optional parameters +745 | | to further control and tailor the plotting, you need only +746 | | pass in the trained/fit umap model to get results. This plot +747 | | utility will attempt to do the hard work of avoiding +748 | | over-plotting issues and provide options for plotting the +749 | | points as well as using edge bundling for graph visualization. +750 | | +751 | | Parameters +752 | | ---------- +753 | | umap_object: trained UMAP object +754 | | A trained UMAP object that has a 2D embedding. +755 | | +756 | | edge_bundling: string or None (optional, default None) +757 | | The edge bundling method to use. Currently supported +758 | | are None or 'hammer'. See the datashader docs +759 | | on graph visualization for more details. +760 | | +761 | | edge_cmap: string (default 'gray_r') +762 | | The name of a matplotlib colormap to use for shading/ +763 | | coloring the edges of the connectivity graph. Note that +764 | | the ``theme``, if specified, will override this. +765 | | +766 | | show_points: bool (optional False) +767 | | Whether to display the points over top of the edge +768 | | connectivity. Further options allow for coloring/ +769 | | shading the points accordingly. +770 | | +771 | | labels: array, shape (n_samples,) (optional, default None) +772 | | An array of labels (assumed integer or categorical), +773 | | one for each data sample. +774 | | This will be used for coloring the points in +775 | | the plot according to their label. Note that +776 | | this option is mutually exclusive to the ``values`` +777 | | option. +778 | | +779 | | values: array, shape (n_samples,) (optional, default None) +780 | | An array of values (assumed float or continuous), +781 | | one for each sample. +782 | | This will be used for coloring the points in +783 | | the plot according to a colorscale associated +784 | | to the total range of values. Note that this +785 | | option is mutually exclusive to the ``labels`` +786 | | option. +787 | | +788 | | theme: string (optional, default None) +789 | | A color theme to use for plotting. A small set of +790 | | predefined themes are provided which have relatively +791 | | good aesthetics. Available themes are: +792 | | * 'blue' +793 | | * 'red' +794 | | * 'green' +795 | | * 'inferno' +796 | | * 'fire' +797 | | * 'viridis' +798 | | * 'darkblue' +799 | | * 'darkred' +800 | | * 'darkgreen' +801 | | +802 | | cmap: string (optional, default 'Blues') +803 | | The name of a matplotlib colormap to use for coloring +804 | | or shading points. If no labels or values are passed +805 | | this will be used for shading points according to +806 | | density (largely only of relevance for very large +807 | | datasets). If values are passed this will be used for +808 | | shading according the value. Note that if theme +809 | | is passed then this value will be overridden by the +810 | | corresponding option of the theme. +811 | | +812 | | color_key: dict or array, shape (n_categories) (optional, default None) +813 | | A way to assign colors to categoricals. This can either be +814 | | an explicit dict mapping labels to colors (as strings of form +815 | | '#RRGGBB'), or an array like object providing one color for +816 | | each distinct category being provided in ``labels``. Either +817 | | way this mapping will be used to color points according to +818 | | the label. Note that if theme +819 | | is passed then this value will be overridden by the +820 | | corresponding option of the theme. +821 | | +822 | | color_key_cmap: string (optional, default 'Spectral') +823 | | The name of a matplotlib colormap to use for categorical coloring. +824 | | If an explicit ``color_key`` is not given a color mapping for +825 | | categories can be generated from the label list and selecting +826 | | a matching list of colors from the given colormap. Note +827 | | that if theme +828 | | is passed then this value will be overridden by the +829 | | corresponding option of the theme. +830 | | +831 | | background: string (optional, default 'white) +832 | | The color of the background. Usually this will be either +833 | | 'white' or 'black', but any color name will work. Ideally +834 | | one wants to match this appropriately to the colors being +835 | | used for points etc. This is one of the things that themes +836 | | handle for you. Note that if theme +837 | | is passed then this value will be overridden by the +838 | | corresponding option of the theme. +839 | | +840 | | width: int (optional, default 800) +841 | | The desired width of the plot in pixels. +842 | | +843 | | height: int (optional, default 800) +844 | | The desired height of the plot in pixels +845 | | +846 | | Returns +847 | | ------- +848 | | result: matplotlib axis +849 | | The result is a matplotlib axis with the relevant plot displayed. +850 | | If you are using a notebook and have ``%matplotlib inline`` set +851 | | then this will simply display inline. +852 | | +853 | | """ + | |_______^ +854 | if theme is not None: +855 | cmap = _themes[theme]["cmap"] + | +help: Insert single blank line + +FBT003 Boolean positional value in function call + --> umap/plot.py:915:13 + | +913 | width, +914 | height, +915 | False, + | ^^^^^ +916 | ) +917 | if px_size > 1: + | + +C901 `diagnostic` is too complex (24 > 10) + --> umap/plot.py:943:5 + | +941 | return ax +942 | +943 | def diagnostic( + | ^^^^^^^^^^ +944 | umap_object, +945 | diagnostic_type="pca", + | + +PLR0913 Too many arguments in function definition (12 > 5) + --> umap/plot.py:943:5 + | +941 | return ax +942 | +943 | def diagnostic( + | ^^^^^^^^^^ +944 | umap_object, +945 | diagnostic_type="pca", + | + +PLR0912 Too many branches (25 > 12) + --> umap/plot.py:943:5 + | +941 | return ax +942 | +943 | def diagnostic( + | ^^^^^^^^^^ +944 | umap_object, +945 | diagnostic_type="pca", + | + +PLR0915 Too many statements (102 > 50) + --> umap/plot.py:943:5 + | +941 | return ax +942 | +943 | def diagnostic( + | ^^^^^^^^^^ +944 | umap_object, +945 | diagnostic_type="pca", + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:954:5 + | +952 | width=800, +953 | height=800, +954 | return_diagnostics=False, + | ^^^^^^^^^^^^^^^^^^ +955 | plot_result=True, +956 | ): + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:955:5 + | +953 | height=800, +954 | return_diagnostics=False, +955 | plot_result=True, + | ^^^^^^^^^^^ +956 | ): +957 | """Provide a diagnostic plot or plots for a UMAP embedding, with options to return diagnostics and control plotting. + | + +D205 1 blank line required between summary line and description + --> umap/plot.py:957:5 + | + 955 | plot_result=True, + 956 | ): + 957 | / """Provide a diagnostic plot or plots for a UMAP embedding, with options to return diagnostics and control plotting. + 958 | | There are a number of plots that can be helpful for diagnostic + 959 | | purposes in understanding your embedding. Currently these are + 960 | | restricted to methods of coloring a scatterplot of the + 961 | | embedding to show more about how the embedding is representing + 962 | | the data. The first class of such plots uses a linear method + 963 | | that preserves global structure well to embed the data into + 964 | | three dimensions, and then interprets such coordinates as a + 965 | | color space -- coloring the points by their location in the + 966 | | linear global structure preserving embedding. In such plots + 967 | | one should look for discontinuities of colour, and consider + 968 | | overall global gradients of color. The diagnostic types here + 969 | | are ``'pca'``, ``'ica'``, and ``'vq'`` (vector quantization). + 970 | | + 971 | | The second class consider the local neighbor structure. One + 972 | | can either look at how well the neighbor structure is + 973 | | preserved, or how the estimated local dimension of the data + 974 | | varies. Both of these are available, although the local + 975 | | dimension estimation is the preferred option. You can + 976 | | access these are diagnostic types ``'local_dim'`` and ``'neighborhood'``. + 977 | | + 978 | | Finally the diagnostic type ``'all'`` will provide a + 979 | | grid of diagnostic plots. + 980 | | + 981 | | Parameters + 982 | | ---------- + 983 | | umap_object: trained UMAP object + 984 | | A trained UMAP object that has a 2D embedding. + 985 | | + 986 | | diagnostic_type: str (optional, default 'pca') + 987 | | The type of diagnostic plot to show. The options are + 988 | | * 'pca' + 989 | | * 'ica' + 990 | | * 'vq' + 991 | | * 'local_dim' + 992 | | * 'neighborhood' + 993 | | * 'all' + 994 | | + 995 | | nhood_size: int (optional, default 15) + 996 | | The size of neighborhood to compare for local + 997 | | neighborhood preservation estimates. + 998 | | + 999 | | local_variance_threshold: float (optional, default 0.8) +1000 | | To estimate the local dimension we consider a PCA of +1001 | | the local neighborhood and estimate the dimension +1002 | | as that which provides ``local_variance_threshold`` +1003 | | or more of the ``variance_explained_ratio``. +1004 | | +1005 | | ax: matplotlib axis (optional, default None) +1006 | | A matplotlib axis to plot to, or, if None, a new +1007 | | axis will be created and returned. Ignored if plot_result=False. +1008 | | +1009 | | cmap: str (optional, default 'viridis') +1010 | | The name of a matplotlib colormap to use for coloring +1011 | | points if the ``'local_dim'`` or ``'neighborhood'`` +1012 | | option are selected. +1013 | | +1014 | | point_size: int (optional, None) +1015 | | If provided this will fix the point size for the +1016 | | plot(s). If None then a suitable point size will +1017 | | be estimated from the data. +1018 | | +1019 | | background: str (optional, default 'white') +1020 | | The background color for the plot. +1021 | | +1022 | | width: int (optional, default 800) +1023 | | The width of the plot in pixels. +1024 | | +1025 | | height: int (optional, default 800) +1026 | | The height of the plot in pixels. +1027 | | +1028 | | return_diagnostics: bool (optional, default False) +1029 | | If True, returns the diagnostic data (e.g., color projections or metrics) +1030 | | instead of or in addition to the axis, depending on plot_result. +1031 | | +1032 | | plot_result: bool (optional, default True) +1033 | | If False, no plot is generated, and the function returns either the +1034 | | diagnostic data (if return_diagnostics=True) or None. +1035 | | +1036 | | Returns +1037 | | ------- +1038 | | result: matplotlib axis or tuple or array +1039 | | If return_diagnostics=False and plot_result=True, returns a matplotlib axis +1040 | | with the relevant plot displayed. +1041 | | If return_diagnostics=True and plot_result=True, returns a tuple of +1042 | | (matplotlib axis, diagnostic data). +1043 | | If return_diagnostics=True and plot_result=False, returns the diagnostic data. +1044 | | If return_diagnostics=False and plot_result=False, returns None. +1045 | | If using a notebook with ``%matplotlib inline``, plots may display inline. +1046 | | +1047 | | """ + | |_______^ +1048 | points = _get_embedding(umap_object) + | +help: Insert single blank line + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/plot.py:1050:27 + | +1048 | points = _get_embedding(umap_object) +1049 | +1050 | if points.shape[1] != 2: + | ^ +1051 | msg = "Plotting is currently only implemented for 2D embeddings" +1052 | raise ValueError(msg) + | + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:1070:13 + | +1068 | if diagnostic_type == "pca": +1069 | color_proj = sklearn.decomposition.PCA(n_components=3).fit_transform( +1070 | umap_object._raw_data, + | ^^^^^^^^^^^^^^^^^^^^^ +1071 | ) +1072 | color_proj -= np.min(color_proj) + | + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:1091:13 + | +1089 | elif diagnostic_type == "ica": +1090 | color_proj = sklearn.decomposition.FastICA(n_components=3).fit_transform( +1091 | umap_object._raw_data, + | ^^^^^^^^^^^^^^^^^^^^^ +1092 | ) +1093 | color_proj -= np.min(color_proj) + | + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:1112:13 + | +1110 | elif diagnostic_type == "vq": +1111 | color_projector = sklearn.cluster.KMeans(n_clusters=3).fit( +1112 | umap_object._raw_data, + | ^^^^^^^^^^^^^^^^^^^^^ +1113 | ) +1114 | color_proj = sklearn.metrics.pairwise_distances( + | + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:1115:13 + | +1113 | ) +1114 | color_proj = sklearn.metrics.pairwise_distances( +1115 | umap_object._raw_data, color_projector.cluster_centers_, + | ^^^^^^^^^^^^^^^^^^^^^ +1116 | ) +1117 | color_proj -= np.min(color_proj) + | + +RUF059 Unpacked variable `highd_dists` is never used + --> umap/plot.py:1135:24 + | +1134 | elif diagnostic_type == "neighborhood": +1135 | highd_indices, highd_dists = _nhood_search(umap_object, nhood_size) + | ^^^^^^^^^^^ +1136 | tree = sklearn.neighbors.KDTree(points) +1137 | _lowd_dists, lowd_indices = tree.query(points, k=nhood_size) + | +help: Prefix it with an underscore or any other dummy variable pattern + +SLF001 Private member accessed: `_raw_data` + --> umap/plot.py:1172:16 + | +1170 | elif diagnostic_type == "local_dim": +1171 | highd_indices, _highd_dists = _nhood_search(umap_object, umap_object.n_neighbors) +1172 | data = umap_object._raw_data + | ^^^^^^^^^^^^^^^^^^^^^ +1173 | local_dim = np.empty(data.shape[0], dtype=np.int64) +1174 | for i in range(data.shape[0]): + | + +C901 `interactive` is too complex (32 > 10) + --> umap/plot.py:1255:5 + | +1253 | return ax +1254 | +1255 | def interactive( + | ^^^^^^^^^^^ +1256 | umap_object, +1257 | labels=None, + | + +PLR0913 Too many arguments in function definition (18 > 5) + --> umap/plot.py:1255:5 + | +1253 | return ax +1254 | +1255 | def interactive( + | ^^^^^^^^^^^ +1256 | umap_object, +1257 | labels=None, + | + +PLR0912 Too many branches (37 > 12) + --> umap/plot.py:1255:5 + | +1253 | return ax +1254 | +1255 | def interactive( + | ^^^^^^^^^^^ +1256 | umap_object, +1257 | labels=None, + | + +PLR0915 Too many statements (106 > 50) + --> umap/plot.py:1255:5 + | +1253 | return ax +1254 | +1255 | def interactive( + | ^^^^^^^^^^^ +1256 | umap_object, +1257 | labels=None, + | + +D417 Missing argument description in the docstring for `interactive`: `hover_data` + --> umap/plot.py:1255:5 + | +1253 | return ax +1254 | +1255 | def interactive( + | ^^^^^^^^^^^ +1256 | umap_object, +1257 | labels=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/plot.py:1270:5 + | +1268 | point_size=None, +1269 | subset_points=None, +1270 | interactive_text_search=False, + | ^^^^^^^^^^^^^^^^^^^^^^^ +1271 | interactive_text_search_columns=None, +1272 | interactive_text_search_alpha_contrast=0.95, + | + +D205 1 blank line required between summary line and description + --> umap/plot.py:1275:5 + | +1273 | alpha=None, +1274 | ): +1275 | / """Create an interactive bokeh plot of a UMAP embedding. +1276 | | While static plots are useful, sometimes a plot that +1277 | | supports interactive zooming, and hover tooltips for +1278 | | individual points is much more desirable. This function +1279 | | provides a simple interface for creating such plots. The +1280 | | result is a bokeh plot that will be displayed in a notebook. +1281 | | +1282 | | Note that more complex tooltips etc. will require custom +1283 | | code -- this is merely meant to provide fast and easy +1284 | | access to interactive plotting. +1285 | | +1286 | | Parameters +1287 | | ---------- +1288 | | umap_object: trained UMAP object +1289 | | A trained UMAP object that has a 2D embedding. +1290 | | +1291 | | labels: array, shape (n_samples,) (optional, default None) +1292 | | An array of labels (assumed integer or categorical), +1293 | | one for each data sample. +1294 | | This will be used for coloring the points in +1295 | | the plot according to their label. Note that +1296 | | this option is mutually exclusive to the ``values`` +1297 | | option. +1298 | | +1299 | | values: array, shape (n_samples,) (optional, default None) +1300 | | An array of values (assumed float or continuous), +1301 | | one for each sample. +1302 | | This will be used for coloring the points in +1303 | | the plot according to a colorscale associated +1304 | | to the total range of values. Note that this +1305 | | option is mutually exclusive to the ``labels`` +1306 | | option. +1307 | | +1308 | | hover_data: DataFrame, shape (n_samples, n_tooltip_features) +1309 | | (optional, default None) +1310 | | A dataframe of tooltip data. Each column of the dataframe +1311 | | should be a Series of length ``n_samples`` providing a value +1312 | | for each data point. Column names will be used for +1313 | | identifying information within the tooltip. +1314 | | +1315 | | tools: List (optional, default None), +1316 | | Defines the tools to be configured for interactive plots. +1317 | | The list can be mixed type of string and tools objects defined by +1318 | | Bokeh like HoverTool. Default tool list Bokeh uses is +1319 | | ["pan","wheel_zoom","box_zoom","save","reset","help",]. +1320 | | When tools are specified, and includes hovertool, automatic tooltip +1321 | | based on hover_data is not created. +1322 | | +1323 | | theme: string (optional, default None) +1324 | | A color theme to use for plotting. A small set of +1325 | | predefined themes are provided which have relatively +1326 | | good aesthetics. Available themes are: +1327 | | * 'blue' +1328 | | * 'red' +1329 | | * 'green' +1330 | | * 'inferno' +1331 | | * 'fire' +1332 | | * 'viridis' +1333 | | * 'darkblue' +1334 | | * 'darkred' +1335 | | * 'darkgreen' +1336 | | +1337 | | cmap: string (optional, default 'Blues') +1338 | | The name of a matplotlib colormap to use for coloring +1339 | | or shading points. If no labels or values are passed +1340 | | this will be used for shading points according to +1341 | | density (largely only of relevance for very large +1342 | | datasets). If values are passed this will be used for +1343 | | shading according the value. Note that if theme +1344 | | is passed then this value will be overridden by the +1345 | | corresponding option of the theme. +1346 | | +1347 | | color_key: dict or array, shape (n_categories) (optional, default None) +1348 | | A way to assign colors to categoricals. This can either be +1349 | | an explicit dict mapping labels to colors (as strings of form +1350 | | '#RRGGBB'), or an array like object providing one color for +1351 | | each distinct category being provided in ``labels``. Either +1352 | | way this mapping will be used to color points according to +1353 | | the label. Note that if theme +1354 | | is passed then this value will be overridden by the +1355 | | corresponding option of the theme. +1356 | | +1357 | | color_key_cmap: string (optional, default 'Spectral') +1358 | | The name of a matplotlib colormap to use for categorical coloring. +1359 | | If an explicit ``color_key`` is not given a color mapping for +1360 | | categories can be generated from the label list and selecting +1361 | | a matching list of colors from the given colormap. Note +1362 | | that if theme +1363 | | is passed then this value will be overridden by the +1364 | | corresponding option of the theme. +1365 | | +1366 | | background: string (optional, default 'white') +1367 | | The color of the background. Usually this will be either +1368 | | 'white' or 'black', but any color name will work. Ideally +1369 | | one wants to match this appropriately to the colors being +1370 | | used for points etc. This is one of the things that themes +1371 | | handle for you. Note that if theme +1372 | | is passed then this value will be overridden by the +1373 | | corresponding option of the theme. +1374 | | +1375 | | width: int (optional, default 800) +1376 | | The desired width of the plot in pixels. +1377 | | +1378 | | height: int (optional, default 800) +1379 | | The desired height of the plot in pixels +1380 | | +1381 | | point_size: int (optional, default None) +1382 | | The size of each point marker +1383 | | +1384 | | subset_points: array, shape (n_samples,) (optional, default None) +1385 | | A way to select a subset of points based on an array of boolean +1386 | | values. +1387 | | +1388 | | interactive_text_search: bool (optional, default False) +1389 | | Whether to include a text search widget above the interactive plot +1390 | | +1391 | | interactive_text_search_columns: list (optional, default None) +1392 | | Columns of data source to search. Searches labels and hover_data by default. +1393 | | +1394 | | interactive_text_search_alpha_contrast: float (optional, default 0.95) +1395 | | Alpha value for points matching text search. Alpha value for points +1396 | | not matching text search will be 1 - interactive_text_search_alpha_contrast +1397 | | +1398 | | alpha: float (optional, default: None) +1399 | | The alpha blending value, between 0 (transparent) and 1 (opaque). +1400 | | +1401 | | Returns +1402 | | ------- +1403 | | +1404 | | """ + | |_______^ +1405 | if theme is not None: +1406 | cmap = _themes[theme]["cmap"] + | +help: Insert single blank line + +D414 Section has no content ("Returns") + --> umap/plot.py:1401:5 + | +1399 | The alpha blending value, between 0 (transparent) and 1 (opaque). +1400 | +1401 | Returns + | ^^^^^^^ +1402 | ------- + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/plot.py:1431:27 + | +1429 | points = points[subset_points] +1430 | +1431 | if points.shape[1] != 2: + | ^ +1432 | msg = "Plotting is currently only implemented for 2D embeddings" +1433 | raise ValueError(msg) + | + +ERA001 Found commented-out code + --> umap/plot.py:1501:9 + | +1499 | data["alpha"] = 1 +1500 | +1501 | # bpl.output_notebook(hide_banner=True) # this doesn't work for non-notebook use + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1502 | data_source = bpl.ColumnDataSource(data) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/plot.py:1584:9 + | +1582 | plot = column(text_input, plot) +1583 | +1584 | # bpl.show(plot) + | ^^^^^^^^^^^^^^^^ +1585 | else: +1586 | if hover_data is not None: + | +help: Remove commented-out code + +PD011 Use `.to_numpy()` instead of `.values` + --> umap/plot.py:1610:23 + | +1608 | ) +1609 | elif values is not None: +1610 | min_val = data.values.min() + | ^^^^^^^^^^^ +1611 | val_range = data.values.max() - min_val +1612 | data["val_cat"] = pd.Categorical( + | + +PD011 Use `.to_numpy()` instead of `.values` + --> umap/plot.py:1611:25 + | +1609 | elif values is not None: +1610 | min_val = data.values.min() +1611 | val_range = data.values.max() - min_val + | ^^^^^^^^^^^ +1612 | data["val_cat"] = pd.Categorical( +1613 | (data.values - min_val) // (val_range // 256), + | + +PD011 Use `.to_numpy()` instead of `.values` + --> umap/plot.py:1613:18 + | +1611 | val_range = data.values.max() - min_val +1612 | data["val_cat"] = pd.Categorical( +1613 | (data.values - min_val) // (val_range // 256), + | ^^^^^^^^^^^ +1614 | ) +1615 | point_plot = hv.Points(data, kdims=["x", "y"], vdims=["val_cat"]) + | + +D205 1 blank line required between summary line and description + --> umap/plot.py:1637:5 + | +1636 | def nearest_neighbour_distribution(umap_object, bins=25, ax=None): +1637 | / """Create a histogram of the average distance to each points +1638 | | nearest neighbors. +1639 | | +1640 | | Parameters +1641 | | ---------- +1642 | | umap_object: trained UMAP object +1643 | | A trained UMAP object that has an embedding. +1644 | | +1645 | | bins: int (optional, default 25) +1646 | | Number of bins to put the points into +1647 | | +1648 | | ax: matplotlib axis (optional, default None) +1649 | | A matplotlib axis to plot to, or, if None, a new +1650 | | axis will be created and returned. +1651 | | +1652 | | Returns +1653 | | ------- +1654 | | +1655 | | """ + | |_______^ +1656 | nn_distances = average_nn_distance(umap_object.graph_) + | +help: Insert single blank line + +D414 Section has no content ("Returns") + --> umap/plot.py:1652:5 + | +1650 | axis will be created and returned. +1651 | +1652 | Returns + | ^^^^^^^ +1653 | ------- + | + +D103 Missing docstring in public function + --> umap/sparse.py:18:5 + | +16 | # Just reproduce a simpler version of numpy unique (not numba supported yet) +17 | @numba.njit() +18 | def arr_unique(arr): + | ^^^^^^^^^^ +19 | aux = np.sort(arr) +20 | flag = np.concatenate((np.ones(1, dtype=np.bool_), aux[1:] != aux[:-1])) + | + +D103 Missing docstring in public function + --> umap/sparse.py:26:5 + | +24 | # Just reproduce a simpler version of numpy union1d (not numba supported yet) +25 | @numba.njit() +26 | def arr_union(ar1, ar2): + | ^^^^^^^^^ +27 | if ar1.shape[0] == 0: +28 | return ar2 + | + +D103 Missing docstring in public function + --> umap/sparse.py:37:5 + | +35 | # yet) +36 | @numba.njit() +37 | def arr_intersect(ar1, ar2): + | ^^^^^^^^^^^^^ +38 | aux = np.concatenate((ar1, ar2)) +39 | aux.sort() + | + +C901 `sparse_sum` is too complex (11 > 10) + --> umap/sparse.py:44:5 + | +43 | @numba.njit() +44 | def sparse_sum(ind1, data1, ind2, data2): + | ^^^^^^^^^^ +45 | result_ind = arr_union(ind1, ind2) +46 | result_data = np.zeros(result_ind.shape[0], dtype=np.float32) + | + +D103 Missing docstring in public function + --> umap/sparse.py:44:5 + | +43 | @numba.njit() +44 | def sparse_sum(ind1, data1, ind2, data2): + | ^^^^^^^^^^ +45 | result_ind = arr_union(ind1, ind2) +46 | result_data = np.zeros(result_ind.shape[0], dtype=np.float32) + | + +D103 Missing docstring in public function + --> umap/sparse.py:105:5 + | +104 | @numba.njit() +105 | def sparse_diff(ind1, data1, ind2, data2): + | ^^^^^^^^^^^ +106 | return sparse_sum(ind1, data1, ind2, -data2) + | + +D103 Missing docstring in public function + --> umap/sparse.py:110:5 + | +109 | @numba.njit() +110 | def sparse_mul(ind1, data1, ind2, data2): + | ^^^^^^^^^^ +111 | result_ind = arr_intersect(ind1, ind2) +112 | result_data = np.zeros(result_ind.shape[0], dtype=np.float32) + | + +PLR0913 Too many arguments in function definition (11 > 5) + --> umap/sparse.py:144:5 + | +143 | @numba.njit() +144 | def general_sset_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +145 | indptr1, +146 | indices1, + | + +D103 Missing docstring in public function + --> umap/sparse.py:144:5 + | +143 | @numba.njit() +144 | def general_sset_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +145 | indptr1, +146 | indices1, + | + +FBT002 Boolean default positional argument in function definition + --> umap/sparse.py:154:5 + | +152 | result_col, +153 | result_val, +154 | right_complement=False, + | ^^^^^^^^^^^^^^^^ +155 | mix_weight=0.5, +156 | ): + | + +PLR2004 Magic value used in comparison, consider replacing `0.5` with a constant variable + --> umap/sparse.py:183:29 + | +182 | if left_val > left_min or right_val > right_min: +183 | if mix_weight < 0.5: + | ^^^ +184 | result_val[idx] = left_val * pow( +185 | right_val, mix_weight / (1.0 - mix_weight), + | + +PLR0913 Too many arguments in function definition (9 > 5) + --> umap/sparse.py:195:5 + | +194 | @numba.njit() +195 | def general_sset_union( + | ^^^^^^^^^^^^^^^^^^ +196 | indptr1, +197 | indices1, + | + +D103 Missing docstring in public function + --> umap/sparse.py:195:5 + | +194 | @numba.njit() +195 | def general_sset_union( + | ^^^^^^^^^^^^^^^^^^ +196 | indptr1, +197 | indices1, + | + +D103 Missing docstring in public function + --> umap/sparse.py:228:5 + | +227 | @numba.njit() +228 | def sparse_euclidean(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^^ +229 | _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) +230 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:237:5 + | +236 | @numba.njit() +237 | def sparse_manhattan(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^^ +238 | _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) +239 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:246:5 + | +245 | @numba.njit() +246 | def sparse_chebyshev(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^^ +247 | _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) +248 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:255:5 + | +254 | @numba.njit() +255 | def sparse_minkowski(ind1, data1, ind2, data2, p=2.0): + | ^^^^^^^^^^^^^^^^ +256 | _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) +257 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:264:5 + | +263 | @numba.njit() +264 | def sparse_hamming(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^ +265 | num_not_equal = sparse_diff(ind1, data1, ind2, data2)[0].shape[0] +266 | return float(num_not_equal) / n_features + | + +D103 Missing docstring in public function + --> umap/sparse.py:270:5 + | +269 | @numba.njit() +270 | def sparse_canberra(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^ +271 | abs_data1 = np.abs(data1) +272 | abs_data2 = np.abs(data2) + | + +D103 Missing docstring in public function + --> umap/sparse.py:284:5 + | +283 | @numba.njit() +284 | def sparse_bray_curtis(ind1, data1, ind2, data2): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^ +285 | _denom_inds, denom_data = sparse_sum(ind1, data1, ind2, data2) +286 | denom_data = np.abs(denom_data) + | + +D103 Missing docstring in public function + --> umap/sparse.py:305:5 + | +304 | @numba.njit() +305 | def sparse_jaccard(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^ +306 | num_non_zero = arr_union(ind1, ind2).shape[0] +307 | num_equal = arr_intersect(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:305:26 + | +304 | @numba.njit() +305 | def sparse_jaccard(ind1, data1, ind2, data2): + | ^^^^^ +306 | num_non_zero = arr_union(ind1, ind2).shape[0] +307 | num_equal = arr_intersect(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:305:39 + | +304 | @numba.njit() +305 | def sparse_jaccard(ind1, data1, ind2, data2): + | ^^^^^ +306 | num_non_zero = arr_union(ind1, ind2).shape[0] +307 | num_equal = arr_intersect(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:315:5 + | +314 | @numba.njit() +315 | def sparse_matching(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^ +316 | num_true_true = arr_intersect(ind1, ind2).shape[0] +317 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:315:27 + | +314 | @numba.njit() +315 | def sparse_matching(ind1, data1, ind2, data2, n_features): + | ^^^^^ +316 | num_true_true = arr_intersect(ind1, ind2).shape[0] +317 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:315:40 + | +314 | @numba.njit() +315 | def sparse_matching(ind1, data1, ind2, data2, n_features): + | ^^^^^ +316 | num_true_true = arr_intersect(ind1, ind2).shape[0] +317 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:324:5 + | +323 | @numba.njit() +324 | def sparse_dice(ind1, data1, ind2, data2): + | ^^^^^^^^^^^ +325 | num_true_true = arr_intersect(ind1, ind2).shape[0] +326 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:324:23 + | +323 | @numba.njit() +324 | def sparse_dice(ind1, data1, ind2, data2): + | ^^^^^ +325 | num_true_true = arr_intersect(ind1, ind2).shape[0] +326 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:324:36 + | +323 | @numba.njit() +324 | def sparse_dice(ind1, data1, ind2, data2): + | ^^^^^ +325 | num_true_true = arr_intersect(ind1, ind2).shape[0] +326 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:335:5 + | +334 | @numba.njit() +335 | def sparse_kulsinski(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^ +336 | num_true_true = arr_intersect(ind1, ind2).shape[0] +337 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:335:28 + | +334 | @numba.njit() +335 | def sparse_kulsinski(ind1, data1, ind2, data2, n_features): + | ^^^^^ +336 | num_true_true = arr_intersect(ind1, ind2).shape[0] +337 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:335:41 + | +334 | @numba.njit() +335 | def sparse_kulsinski(ind1, data1, ind2, data2, n_features): + | ^^^^^ +336 | num_true_true = arr_intersect(ind1, ind2).shape[0] +337 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:348:5 + | +347 | @numba.njit() +348 | def sparse_rogers_tanimoto(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^^^^^^^ +349 | num_true_true = arr_intersect(ind1, ind2).shape[0] +350 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:348:34 + | +347 | @numba.njit() +348 | def sparse_rogers_tanimoto(ind1, data1, ind2, data2, n_features): + | ^^^^^ +349 | num_true_true = arr_intersect(ind1, ind2).shape[0] +350 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:348:47 + | +347 | @numba.njit() +348 | def sparse_rogers_tanimoto(ind1, data1, ind2, data2, n_features): + | ^^^^^ +349 | num_true_true = arr_intersect(ind1, ind2).shape[0] +350 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:357:5 + | +356 | @numba.njit() +357 | def sparse_russellrao(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^^ +358 | if ind1.shape[0] == ind2.shape[0] and np.all(ind1 == ind2): +359 | return 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:369:5 + | +368 | @numba.njit() +369 | def sparse_sokal_michener(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^^^^^^ +370 | num_true_true = arr_intersect(ind1, ind2).shape[0] +371 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:369:33 + | +368 | @numba.njit() +369 | def sparse_sokal_michener(ind1, data1, ind2, data2, n_features): + | ^^^^^ +370 | num_true_true = arr_intersect(ind1, ind2).shape[0] +371 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:369:46 + | +368 | @numba.njit() +369 | def sparse_sokal_michener(ind1, data1, ind2, data2, n_features): + | ^^^^^ +370 | num_true_true = arr_intersect(ind1, ind2).shape[0] +371 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:378:5 + | +377 | @numba.njit() +378 | def sparse_sokal_sneath(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^^^^^ +379 | num_true_true = arr_intersect(ind1, ind2).shape[0] +380 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data1` + --> umap/sparse.py:378:31 + | +377 | @numba.njit() +378 | def sparse_sokal_sneath(ind1, data1, ind2, data2): + | ^^^^^ +379 | num_true_true = arr_intersect(ind1, ind2).shape[0] +380 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +ARG001 Unused function argument: `data2` + --> umap/sparse.py:378:44 + | +377 | @numba.njit() +378 | def sparse_sokal_sneath(ind1, data1, ind2, data2): + | ^^^^^ +379 | num_true_true = arr_intersect(ind1, ind2).shape[0] +380 | num_non_zero = arr_union(ind1, ind2).shape[0] + | + +D103 Missing docstring in public function + --> umap/sparse.py:389:5 + | +388 | @numba.njit() +389 | def sparse_cosine(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^ +390 | _aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2) +391 | result = 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:406:5 + | +405 | @numba.njit() +406 | def sparse_hellinger(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^^ +407 | _aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2) +408 | result = 0.0 + | + +C901 `sparse_correlation` is too complex (14 > 10) + --> umap/sparse.py:426:5 + | +425 | @numba.njit() +426 | def sparse_correlation(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^^^ +427 | +428 | mu_x = 0.0 + | + +PLR0912 Too many branches (13 > 12) + --> umap/sparse.py:426:5 + | +425 | @numba.njit() +426 | def sparse_correlation(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^^^ +427 | +428 | mu_x = 0.0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:426:5 + | +425 | @numba.njit() +426 | def sparse_correlation(ind1, data1, ind2, data2, n_features): + | ^^^^^^^^^^^^^^^^^^ +427 | +428 | mu_x = 0.0 + | + +N802 Function name `approx_log_Gamma` should be lowercase + --> umap/sparse.py:486:5 + | +485 | @numba.njit() +486 | def approx_log_Gamma(x): + | ^^^^^^^^^^^^^^^^ +487 | if x == 1: +488 | return 0 + | + +D103 Missing docstring in public function + --> umap/sparse.py:486:5 + | +485 | @numba.njit() +486 | def approx_log_Gamma(x): + | ^^^^^^^^^^^^^^^^ +487 | if x == 1: +488 | return 0 + | + +ERA001 Found commented-out code + --> umap/sparse.py:489:5 + | +487 | if x == 1: +488 | return 0 +489 | # x2= 1/(x*x); + | ^^^^^^^^^^^^^^^^^ +490 | return ( +491 | x * np.log(x) - x + 0.5 * np.log(2.0 * np.pi / x) + 1.0 / (x * 12.0) + | +help: Remove commented-out code + +D103 Missing docstring in public function + --> umap/sparse.py:500:5 + | +499 | @numba.njit() +500 | def log_beta(x, y): + | ^^^^^^^^ +501 | a = min(x, y) +502 | b = max(x, y) + | + +PLR2004 Magic value used in comparison, consider replacing `5` with a constant variable + --> umap/sparse.py:503:12 + | +501 | a = min(x, y) +502 | b = max(x, y) +503 | if b < 5: + | ^ +504 | value = -np.log(b) +505 | for i in range(1, int(a)): + | + +D103 Missing docstring in public function + --> umap/sparse.py:512:5 + | +511 | @numba.njit() +512 | def log_single_beta(x): + | ^^^^^^^^^^^^^^^ +513 | return ( +514 | np.log(2.0) * (-2.0 * x + 0.5) + 0.5 * np.log(2.0 * np.pi / x) + 0.125 / x + | + +D103 Missing docstring in public function + --> umap/sparse.py:523:5 + | +522 | @numba.njit() +523 | def sparse_ll_dirichlet(ind1, data1, ind2, data2): + | ^^^^^^^^^^^^^^^^^^^ +524 | # The probability of rolling data2 in sum(data2) trials on a die that rolled data1 in sum(data1) trials +525 | n1 = np.sum(data1) + | + +C901 `component_layout` is too complex (15 > 10) + --> umap/spectral.py:16:5 + | +16 | def component_layout( + | ^^^^^^^^^^^^^^^^ +17 | data, +18 | n_components, + | + +PLR0913 Too many arguments in function definition (7 > 5) + --> umap/spectral.py:16:5 + | +16 | def component_layout( + | ^^^^^^^^^^^^^^^^ +17 | data, +18 | n_components, + | + +PLR0912 Too many branches (17 > 12) + --> umap/spectral.py:16:5 + | +16 | def component_layout( + | ^^^^^^^^^^^^^^^^ +17 | data, +18 | n_components, + | + +D417 Missing argument description in the docstring for `component_layout`: `random_state` + --> umap/spectral.py:16:5 + | +16 | def component_layout( + | ^^^^^^^^^^^^^^^^ +17 | data, +18 | n_components, + | + +D205 1 blank line required between summary line and description + --> umap/spectral.py:25:5 + | +23 | metric_kwds=None, +24 | ): +25 | / """Provide a layout relating the separate connected components. This is done +26 | | by taking the centroid of each component and then performing a spectral embedding +27 | | of the centroids. +28 | | +29 | | Parameters +30 | | ---------- +31 | | data: array of shape (n_samples, n_features) +32 | | The source data -- required so we can generate centroids for each +33 | | connected component of the graph. +34 | | +35 | | n_components: int +36 | | The number of distinct components to be layed out. +37 | | +38 | | component_labels: array of shape (n_samples) +39 | | For each vertex in the graph the label of the component to +40 | | which the vertex belongs. +41 | | +42 | | dim: int +43 | | The chosen embedding dimension. +44 | | +45 | | metric: string or callable (optional, default 'euclidean') +46 | | The metric used to measure distances among the source data points. +47 | | +48 | | metric_kwds: dict (optional, default {}) +49 | | Keyword arguments to be passed to the metric function. +50 | | If metric is 'precomputed', 'linkage' keyword can be used to specify +51 | | 'average', 'complete', or 'single' linkage. Default is 'average' +52 | | +53 | | Returns +54 | | ------- +55 | | component_embedding: array of shape (n_components, dim) +56 | | The ``dim``-dimensional embedding of the ``n_components``-many +57 | | connected components. +58 | | +59 | | """ + | |_______^ +60 | if metric_kwds is None: +61 | metric_kwds = {} + | +help: Insert single blank line + +NPY002 Replace legacy `np.random.random` call with `np.random.Generator` + --> umap/spectral.py:64:16 + | +62 | if data is None: +63 | # We don't have data to work with; just guess +64 | return np.random.random(size=(n_components, dim)) * 10.0 + | ^^^^^^^^^^^^^^^^ +65 | +66 | component_centroids = np.empty((n_components, data.shape[1]), dtype=np.float64) + | + +B904 Within an `except` clause, raise exceptions with `raise ... from err` or `raise ... from None` to distinguish them from errors in exception handling + --> umap/spectral.py:130:17 + | +128 | "this time." +129 | ) +130 | / raise NotImplementedError( +131 | | msg, +132 | | ) + | |_________________^ +133 | distance_matrix = pairwise_distances( +134 | component_centroids, metric=metric_name, **metric_kwds, + | + +PLR0913 Too many arguments in function definition (11 > 5) + --> umap/spectral.py:151:5 + | +151 | def multi_component_layout( + | ^^^^^^^^^^^^^^^^^^^^^^ +152 | data, +153 | graph, + | + +D417 Missing argument description in the docstring for `multi_component_layout`: `random_state` + --> umap/spectral.py:151:5 + | +151 | def multi_component_layout( + | ^^^^^^^^^^^^^^^^^^^^^^ +152 | data, +153 | graph, + | + +D205 1 blank line required between summary line and description + --> umap/spectral.py:164:5 + | +162 | maxiter=0, +163 | ): +164 | / """Specialised layout algorithm for dealing with graphs with many connected components. +165 | | This will first find relative positions for the components by spectrally embedding +166 | | their centroids, then spectrally embed each individual connected component positioning +167 | | them according to the centroid embeddings. This provides a decent embedding of each +168 | | component while placing the components in good relative positions to one another. +169 | | +170 | | Parameters +171 | | ---------- +172 | | data: array of shape (n_samples, n_features) +173 | | The source data -- required so we can generate centroids for each +174 | | connected component of the graph. +175 | | +176 | | graph: sparse matrix +177 | | The adjacency matrix of the graph to be embedded. +178 | | +179 | | n_components: int +180 | | The number of distinct components to be layed out. +181 | | +182 | | component_labels: array of shape (n_samples) +183 | | For each vertex in the graph the label of the component to +184 | | which the vertex belongs. +185 | | +186 | | dim: int +187 | | The chosen embedding dimension. +188 | | +189 | | metric: string or callable (optional, default 'euclidean') +190 | | The metric used to measure distances among the source data points. +191 | | +192 | | metric_kwds: dict (optional, default {}) +193 | | Keyword arguments to be passed to the metric function. +194 | | +195 | | init: string, either "random" or "tsvd" +196 | | Indicates to initialize the eigensolver. Use "random" (the default) to +197 | | use uniformly distributed random initialization; use "tsvd" to warm-start the +198 | | eigensolver with singular vectors of the Laplacian associated to the largest +199 | | singular values. This latter option also forces usage of the LOBPCG eigensolver; +200 | | with the former, ARPACK's solver ``eigsh`` will be used for smaller Laplacians. +201 | | +202 | | tol: float, default chosen by implementation +203 | | Stopping tolerance for the numerical algorithm computing the embedding. +204 | | +205 | | maxiter: int, default chosen by implementation +206 | | Number of iterations the numerical algorithm will go through at most as it +207 | | attempts to compute the embedding. +208 | | +209 | | Returns +210 | | ------- +211 | | embedding: array of shape (n_samples, dim) +212 | | The initial embedding of ``graph``. +213 | | +214 | | """ + | |_______^ +215 | if metric_kwds is None: +216 | metric_kwds = {} + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (8 > 5) + --> umap/spectral.py:271:5 + | +271 | def spectral_layout( + | ^^^^^^^^^^^^^^^ +272 | data, +273 | graph, + | + +D417 Missing argument descriptions in the docstring for `spectral_layout`: `metric`, `metric_kwds` + --> umap/spectral.py:271:5 + | +271 | def spectral_layout( + | ^^^^^^^^^^^^^^^ +272 | data, +273 | graph, + | + +D205 1 blank line required between summary line and description + --> umap/spectral.py:281:5 + | +279 | maxiter=0, +280 | ): +281 | / """Given a graph compute the spectral embedding of the graph. This is +282 | | simply the eigenvectors of the laplacian of the graph. Here we use the +283 | | normalized laplacian. +284 | | +285 | | Parameters +286 | | ---------- +287 | | data: array of shape (n_samples, n_features) +288 | | The source data +289 | | +290 | | graph: sparse matrix +291 | | The (weighted) adjacency matrix of the graph as a sparse matrix. +292 | | +293 | | dim: int +294 | | The dimension of the space into which to embed. +295 | | +296 | | random_state: numpy RandomState or equivalent +297 | | A state capable being used as a numpy random state. +298 | | +299 | | tol: float, default chosen by implementation +300 | | Stopping tolerance for the numerical algorithm computing the embedding. +301 | | +302 | | maxiter: int, default chosen by implementation +303 | | Number of iterations the numerical algorithm will go through at most as it +304 | | attempts to compute the embedding. +305 | | +306 | | Returns +307 | | ------- +308 | | embedding: array of shape (n_vertices, dim) +309 | | The spectral embedding of the graph. +310 | | +311 | | """ + | |_______^ +312 | if metric_kwds is None: +313 | metric_kwds = {} + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (9 > 5) + --> umap/spectral.py:327:5 + | +327 | def tswspectral_layout( + | ^^^^^^^^^^^^^^^^^^ +328 | data, +329 | graph, + | + +D205 1 blank line required between summary line and description + --> umap/spectral.py:338:5 + | +336 | maxiter=0, +337 | ): +338 | / """Given a graph, compute the spectral embedding of the graph. This is +339 | | simply the eigenvectors of the Laplacian of the graph. Here we use the +340 | | normalized laplacian and a truncated SVD-based guess of the +341 | | eigenvectors to "warm" up the eigensolver. This function should +342 | | give results of similar accuracy to the spectral_layout function, but +343 | | may converge more quickly for graph Laplacians that cause +344 | | spectral_layout to take an excessive amount of time to complete. +345 | | +346 | | Parameters +347 | | ---------- +348 | | data: array of shape (n_samples, n_features) +349 | | The source data +350 | | +351 | | graph: sparse matrix +352 | | The (weighted) adjacency matrix of the graph as a sparse matrix. +353 | | +354 | | dim: int +355 | | The dimension of the space into which to embed. +356 | | +357 | | random_state: numpy RandomState or equivalent +358 | | A state capable being used as a numpy random state. +359 | | +360 | | metric: string or callable (optional, default 'euclidean') +361 | | The metric used to measure distances among the source data points. +362 | | Used only if the multiple connected components are found in the +363 | | graph. +364 | | +365 | | metric_kwds: dict (optional, default {}) +366 | | Keyword arguments to be passed to the metric function. +367 | | If metric is 'precomputed', 'linkage' keyword can be used to specify +368 | | 'average', 'complete', or 'single' linkage. Default is 'average'. +369 | | Used only if the multiple connected components are found in the +370 | | graph. +371 | | +372 | | method: str (optional, default None, values either 'eigsh' or 'lobpcg') +373 | | Name of the eigenvalue computation method to use to compute the spectral +374 | | embedding. If left to None (or empty string), as by default, the method is +375 | | chosen from the number of vectors in play: larger vector collections are +376 | | handled with lobpcg, smaller collections with eigsh. Method names correspond +377 | | to SciPy routines in scipy.sparse.linalg. +378 | | +379 | | tol: float, default chosen by implementation +380 | | Stopping tolerance for the numerical algorithm computing the embedding. +381 | | +382 | | maxiter: int, default chosen by implementation +383 | | Number of iterations the numerical algorithm will go through at most as it +384 | | attempts to compute the embedding. +385 | | +386 | | Returns +387 | | ------- +388 | | embedding: array of shape (n_vertices, dim) +389 | | The spectral embedding of the graph. +390 | | +391 | | """ + | |_______^ +392 | if metric_kwds is None: +393 | metric_kwds = {} + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (10 > 5) + --> umap/spectral.py:408:5 + | +408 | def _spectral_layout( + | ^^^^^^^^^^^^^^^^ +409 | data, +410 | graph, + | + +D205 1 blank line required between summary line and description + --> umap/spectral.py:420:5 + | +418 | maxiter=0, +419 | ): +420 | / """General implementation of the spectral embedding of the graph, derived as +421 | | a subset of the eigenvectors of the normalized Laplacian of the graph. The numerical +422 | | method for computing the eigendecomposition is chosen through heuristics. +423 | | +424 | | Parameters +425 | | ---------- +426 | | data: array of shape (n_samples, n_features) +427 | | The source data +428 | | +429 | | graph: sparse matrix +430 | | The (weighted) adjacency matrix of the graph as a sparse matrix. +431 | | +432 | | dim: int +433 | | The dimension of the space into which to embed. +434 | | +435 | | random_state: numpy RandomState or equivalent +436 | | A state capable being used as a numpy random state. +437 | | +438 | | metric: string or callable (optional, default 'euclidean') +439 | | The metric used to measure distances among the source data points. +440 | | Used only if the multiple connected components are found in the +441 | | graph. +442 | | +443 | | metric_kwds: dict (optional, default {}) +444 | | Keyword arguments to be passed to the metric function. +445 | | If metric is 'precomputed', 'linkage' keyword can be used to specify +446 | | 'average', 'complete', or 'single' linkage. Default is 'average'. +447 | | Used only if the multiple connected components are found in the +448 | | graph. +449 | | +450 | | init: string, either "random" or "tsvd" +451 | | Indicates to initialize the eigensolver. Use "random" (the default) to +452 | | use uniformly distributed random initialization; use "tsvd" to warm-start the +453 | | eigensolver with singular vectors of the Laplacian associated to the largest +454 | | singular values. This latter option also forces usage of the LOBPCG eigensolver; +455 | | with the former, ARPACK's solver ``eigsh`` will be used for smaller Laplacians. +456 | | +457 | | method: string -- either "eigsh" or "lobpcg" -- or None +458 | | Name of the eigenvalue computation method to use to compute the spectral +459 | | embedding. If left to None (or empty string), as by default, the method is +460 | | chosen from the number of vectors in play: larger vector collections are +461 | | handled with lobpcg, smaller collections with eigsh. Method names correspond +462 | | to SciPy routines in scipy.sparse.linalg. +463 | | +464 | | tol: float, default chosen by implementation +465 | | Stopping tolerance for the numerical algorithm computing the embedding. +466 | | +467 | | maxiter: int, default chosen by implementation +468 | | Number of iterations the numerical algorithm will go through at most as it +469 | | attempts to compute the embedding. +470 | | +471 | | Returns +472 | | ------- +473 | | embedding: array of shape (n_vertices, dim) +474 | | The spectral embedding of the graph. +475 | | +476 | | """ + | |_______^ +477 | if metric_kwds is None: +478 | metric_kwds = {} + | +help: Insert single blank line + +ERA001 Found commented-out code + --> umap/spectral.py:496:5 + | +494 | sqrt_deg = np.sqrt(np.asarray(graph.sum(axis=0)).squeeze()) +495 | # standard Laplacian +496 | # D = scipy.sparse.spdiags(diag_data, 0, graph.shape[0], graph.shape[0]) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +497 | # L = D - graph +498 | # Normalized Laplacian + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/spectral.py:497:5 + | +495 | # standard Laplacian +496 | # D = scipy.sparse.spdiags(diag_data, 0, graph.shape[0], graph.shape[0]) +497 | # L = D - graph + | ^^^^^^^^^^^^^^^ +498 | # Normalized Laplacian +499 | I = scipy.sparse.identity(graph.shape[0], dtype=np.float64) + | +help: Remove commented-out code + +N806 Variable `I` in function should be lowercase + --> umap/spectral.py:499:5 + | +497 | # L = D - graph +498 | # Normalized Laplacian +499 | I = scipy.sparse.identity(graph.shape[0], dtype=np.float64) + | ^ +500 | D = scipy.sparse.spdiags(1.0 / sqrt_deg, 0, graph.shape[0], graph.shape[0]) +501 | L = I - D * graph * D + | + +E741 Ambiguous variable name: `I` + --> umap/spectral.py:499:5 + | +497 | # L = D - graph +498 | # Normalized Laplacian +499 | I = scipy.sparse.identity(graph.shape[0], dtype=np.float64) + | ^ +500 | D = scipy.sparse.spdiags(1.0 / sqrt_deg, 0, graph.shape[0], graph.shape[0]) +501 | L = I - D * graph * D + | + +N806 Variable `D` in function should be lowercase + --> umap/spectral.py:500:5 + | +498 | # Normalized Laplacian +499 | I = scipy.sparse.identity(graph.shape[0], dtype=np.float64) +500 | D = scipy.sparse.spdiags(1.0 / sqrt_deg, 0, graph.shape[0], graph.shape[0]) + | ^ +501 | L = I - D * graph * D +502 | if not scipy.sparse.issparse(L): + | + +N806 Variable `L` in function should be lowercase + --> umap/spectral.py:501:5 + | +499 | I = scipy.sparse.identity(graph.shape[0], dtype=np.float64) +500 | D = scipy.sparse.spdiags(1.0 / sqrt_deg, 0, graph.shape[0], graph.shape[0]) +501 | L = I - D * graph * D + | ^ +502 | if not scipy.sparse.issparse(L): +503 | L = np.asarray(L) + | + +N806 Variable `L` in function should be lowercase + --> umap/spectral.py:503:9 + | +501 | L = I - D * graph * D +502 | if not scipy.sparse.issparse(L): +503 | L = np.asarray(L) + | ^ +504 | +505 | k = dim + 1 + | + +PLR2004 Magic value used in comparison, consider replacing `2000000` with a constant variable + --> umap/spectral.py:513:42 + | +511 | ) +512 | if not method: +513 | method = "eigsh" if L.shape[0] < 2000000 else "lobpcg" + | ^^^^^^^ +514 | +515 | try: + | + +N806 Variable `X` in function should be lowercase + --> umap/spectral.py:517:13 + | +515 | try: +516 | if init == "random": +517 | X = gen.normal(size=(L.shape[0], k)) + | ^ +518 | elif init == "tsvd": +519 | X = TruncatedSVD( + | + +N806 Variable `X` in function should be lowercase + --> umap/spectral.py:519:13 + | +517 | X = gen.normal(size=(L.shape[0], k)) +518 | elif init == "tsvd": +519 | X = TruncatedSVD( + | ^ +520 | n_components=k, +521 | random_state=random_state, + | + +ERA001 Found commented-out code + --> umap/spectral.py:522:17 + | +520 | n_components=k, +521 | random_state=random_state, +522 | # algorithm="arpack" + | ^^^^^^^^^^^^^^^^^^^^ +523 | ).fit_transform(L) +524 | else: + | +help: Remove commented-out code + +NPY002 Replace legacy `np.random.seed` call with `np.random.Generator` + --> umap/tests/conftest.py:14:1 + | +12 | # Globals, used for all the tests +13 | SEED = 189212 # 0b101110001100011100 +14 | np.random.seed(SEED) + | ^^^^^^^^^^^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:20:5 + | +18 | # ----------------------- +19 | @pytest.fixture(scope="session") +20 | def spatial_data(): + | ^^^^^^^^^^^^ +21 | # - Spatial Data +22 | spatial_data = np.random.randn(10, 20) + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/conftest.py:22:20 + | +20 | def spatial_data(): +21 | # - Spatial Data +22 | spatial_data = np.random.randn(10, 20) + | ^^^^^^^^^^^^^^^ +23 | # Add some all zero data for corner case test +24 | return np.vstack([spatial_data, np.zeros((2, 20))]) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:28:5 + | +27 | @pytest.fixture(scope="session") +28 | def binary_data(): + | ^^^^^^^^^^^ +29 | binary_data = np.random.choice(a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66]) +30 | # Add some all zero data for corner case test + | + +NPY002 Replace legacy `np.random.choice` call with `np.random.Generator` + --> umap/tests/conftest.py:29:19 + | +27 | @pytest.fixture(scope="session") +28 | def binary_data(): +29 | binary_data = np.random.choice(a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66]) + | ^^^^^^^^^^^^^^^^ +30 | # Add some all zero data for corner case test +31 | return np.vstack([binary_data, np.zeros((2, 20), dtype="bool")]) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:37:5 + | +35 | # ------------------------------ +36 | @pytest.fixture(scope="session") +37 | def sparse_spatial_data(spatial_data, binary_data): + | ^^^^^^^^^^^^^^^^^^^ +38 | return sparse.csr_matrix(spatial_data * binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:42:5 + | +41 | @pytest.fixture(scope="session") +42 | def sparse_binary_data(binary_data): + | ^^^^^^^^^^^^^^^^^^ +43 | return sparse.csr_matrix(binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:49:5 + | +47 | # ----------------------- +48 | @pytest.fixture(scope="session") +49 | def nn_data(): + | ^^^^^^^ +50 | nn_data = np.random.uniform(0, 1, size=(1000, 5)) +51 | return np.vstack( + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/tests/conftest.py:50:15 + | +48 | @pytest.fixture(scope="session") +49 | def nn_data(): +50 | nn_data = np.random.uniform(0, 1, size=(1000, 5)) + | ^^^^^^^^^^^^^^^^^ +51 | return np.vstack( +52 | [nn_data, np.zeros((2, 5))], + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:57:5 + | +56 | @pytest.fixture(scope="session") +57 | def binary_nn_data(): + | ^^^^^^^^^^^^^^ +58 | binary_nn_data = np.random.choice( +59 | a=[False, True], size=(1000, 5), p=[0.66, 1 - 0.66], + | + +NPY002 Replace legacy `np.random.choice` call with `np.random.Generator` + --> umap/tests/conftest.py:58:22 + | +56 | @pytest.fixture(scope="session") +57 | def binary_nn_data(): +58 | binary_nn_data = np.random.choice( + | ^^^^^^^^^^^^^^^^ +59 | a=[False, True], size=(1000, 5), p=[0.66, 1 - 0.66], +60 | ) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:67:5 + | +66 | @pytest.fixture(scope="session") +67 | def sparse_nn_data(): + | ^^^^^^^^^^^^^^ +68 | return sparse.random(1000, 50, density=0.5, format="csr") + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:76:5 + | +75 | @pytest.fixture(scope="session") +76 | def repetition_dense(): + | ^^^^^^^^^^^^^^^^ +77 | # Dense data for testing small n +78 | return np.array( + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:94:5 + | +93 | @pytest.fixture(scope="session") +94 | def spatial_repeats(spatial_data): + | ^^^^^^^^^^^^^^^ +95 | # spatial data repeats +96 | return np.vstack( + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:104:5 + | +103 | @pytest.fixture(scope="session") +104 | def binary_repeats(binary_data): + | ^^^^^^^^^^^^^^ +105 | return np.vstack( +106 | [ + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:116:5 + | +115 | @pytest.fixture(scope="session") +116 | def sparse_spatial_data_repeats(spatial_repeats, binary_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +117 | return sparse.csr_matrix(spatial_repeats * binary_repeats) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:121:5 + | +120 | @pytest.fixture(scope="session") +121 | def sparse_binary_data_repeats(binary_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +122 | return sparse.csr_matrix(binary_repeats) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:126:5 + | +125 | @pytest.fixture(scope="session") +126 | def sparse_test_data(nn_data, binary_nn_data): + | ^^^^^^^^^^^^^^^^ +127 | return sparse.csr_matrix(nn_data * binary_nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:131:5 + | +130 | @pytest.fixture(scope="session") +131 | def iris(): + | ^^^^ +132 | return load_iris() + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:136:5 + | +135 | @pytest.fixture(scope="session") +136 | def iris_selection(): + | ^^^^^^^^^^^^^^ +137 | return np.random.choice([True, False], 150, replace=True, p=[0.75, 0.25]) + | + +NPY002 Replace legacy `np.random.choice` call with `np.random.Generator` + --> umap/tests/conftest.py:137:12 + | +135 | @pytest.fixture(scope="session") +136 | def iris_selection(): +137 | return np.random.choice([True, False], 150, replace=True, p=[0.75, 0.25]) + | ^^^^^^^^^^^^^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:141:5 + | +140 | @pytest.fixture(scope="session") +141 | def aligned_iris(iris): + | ^^^^^^^^^^^^ +142 | slices = [iris.data[i : i + 50] for i in range(0, 125, 25)] +143 | target = [iris.target[i : i + 50] for i in range(0, 125, 25)] + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:148:5 + | +147 | @pytest.fixture(scope="session") +148 | def aligned_iris_relations(): + | ^^^^^^^^^^^^^^^^^^^^^^ +149 | return [{a: a + 25 for a in range(25)} for i in range(4)] + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:153:5 + | +152 | @pytest.fixture(scope="session") +153 | def iris_model(iris): + | ^^^^^^^^^^ +154 | return UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit(iris.data) + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:158:5 + | +157 | @pytest.fixture(scope="session") +158 | def iris_model_large(iris): + | ^^^^^^^^^^^^^^^^ +159 | return UMAP( +160 | n_neighbors=10, + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:168:5 + | +167 | @pytest.fixture(scope="session") +168 | def iris_subset_model(iris, iris_selection): + | ^^^^^^^^^^^^^^^^^ +169 | return UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit( +170 | iris.data[iris_selection], + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:175:5 + | +174 | @pytest.fixture(scope="session") +175 | def iris_subset_model_large(iris, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^ +176 | return UMAP( +177 | n_neighbors=10, + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:185:5 + | +184 | @pytest.fixture(scope="session") +185 | def supervised_iris_model(iris): + | ^^^^^^^^^^^^^^^^^^^^^ +186 | return UMAP(n_neighbors=10, min_dist=0.01, n_epochs=200, random_state=42).fit( +187 | iris.data, iris.target, + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:192:5 + | +191 | @pytest.fixture(scope="session") +192 | def aligned_iris_model(aligned_iris, aligned_iris_relations): + | ^^^^^^^^^^^^^^^^^^ +193 | data, _target = aligned_iris +194 | model = AlignedUMAP() + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:202:5 + | +200 | # --------------------- +201 | @pytest.fixture(scope="session") +202 | def spatial_distances(): + | ^^^^^^^^^^^^^^^^^ +203 | return ( +204 | "euclidean", + | + +D103 Missing docstring in public function + --> umap/tests/conftest.py:217:5 + | +216 | @pytest.fixture(scope="session") +217 | def binary_distances(): + | ^^^^^^^^^^^^^^^^ +218 | return ( +219 | "jaccard", + | + +D103 Missing docstring in public function + --> umap/tests/test_aligned_umap.py:13:5 + | +13 | def nn_accuracy(true_nn, embd_nn): + | ^^^^^^^^^^^ +14 | num_correct = 0.0 +15 | for i in range(true_nn.shape[0]): + | + +D103 Missing docstring in public function + --> umap/tests/test_aligned_umap.py:20:5 + | +20 | def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +21 | data, _target = aligned_iris +22 | for i, slice in enumerate(data): + | + +A001 Variable `slice` is shadowing a Python builtin + --> umap/tests/test_aligned_umap.py:22:12 + | +20 | def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model): +21 | data, _target = aligned_iris +22 | for i, slice in enumerate(data): + | ^^^^^ +23 | data_dmat = pairwise_distances(slice) +24 | true_nn = np.argsort(data_dmat, axis=1)[:, :10] + | + +PLR2004 Magic value used in comparison, consider replacing `0.65` with a constant variable + --> umap/tests/test_aligned_umap.py:27:49 + | +25 | embd_dmat = pairwise_distances(aligned_iris_model.embeddings_[i]) +26 | embd_nn = np.argsort(embd_dmat, axis=1)[:, :10] +27 | assert nn_accuracy(true_nn, embd_nn) >= 0.65 + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_aligned_umap.py:30:5 + | +30 | def test_local_clustering(aligned_iris, aligned_iris_model): + | ^^^^^^^^^^^^^^^^^^^^^ +31 | _data, target = aligned_iris + | + +PLR2004 Magic value used in comparison, consider replacing `0.75` with a constant variable + --> umap/tests/test_aligned_umap.py:36:19 + | +34 | clusters = KMeans(n_clusters=2).fit_predict(embd) +35 | ari = adjusted_rand_score(target[1], clusters) +36 | assert ari >= 0.75 + | ^^^^ +37 | +38 | embd = aligned_iris_model.embeddings_[3] + | + +PLR2004 Magic value used in comparison, consider replacing `0.40` with a constant variable + --> umap/tests/test_aligned_umap.py:41:19 + | +39 | clusters = KMeans(n_clusters=2).fit_predict(embd) +40 | ari = adjusted_rand_score(target[3], clusters) +41 | assert ari >= 0.40 + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_aligned_umap.py:44:5 + | +44 | def test_aligned_update(aligned_iris, aligned_iris_relations): + | ^^^^^^^^^^^^^^^^^^^ +45 | data, _target = aligned_iris +46 | small_aligned_model = AlignedUMAP() + | + +A001 Variable `slice` is shadowing a Python builtin + --> umap/tests/test_aligned_umap.py:49:12 + | +47 | small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2]) +48 | small_aligned_model.update(data[3], relations=aligned_iris_relations[2]) +49 | for i, slice in enumerate(data[:4]): + | ^^^^^ +50 | data_dmat = pairwise_distances(slice) +51 | true_nn = np.argsort(data_dmat, axis=1)[:, :10] + | + +PLR2004 Magic value used in comparison, consider replacing `0.45` with a constant variable + --> umap/tests/test_aligned_umap.py:54:49 + | +52 | embd_dmat = pairwise_distances(small_aligned_model.embeddings_[i]) +53 | embd_nn = np.argsort(embd_dmat, axis=1)[:, :10] +54 | assert nn_accuracy(true_nn, embd_nn) >= 0.45 + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_aligned_umap.py:57:5 + | +57 | def test_aligned_update_params(aligned_iris, aligned_iris_relations): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +58 | data, _target = aligned_iris +59 | n_neighbors = [15, 15, 15, 15, 15] + | + +A001 Variable `slice` is shadowing a Python builtin + --> umap/tests/test_aligned_umap.py:65:12 + | +63 | data[3], relations=aligned_iris_relations[2], n_neighbors=n_neighbors[3], +64 | ) +65 | for i, slice in enumerate(data[:4]): + | ^^^^^ +66 | data_dmat = pairwise_distances(slice) +67 | true_nn = np.argsort(data_dmat, axis=1)[:, :10] + | + +PLR2004 Magic value used in comparison, consider replacing `0.45` with a constant variable + --> umap/tests/test_aligned_umap.py:70:49 + | +68 | embd_dmat = pairwise_distances(small_aligned_model.embeddings_[i]) +69 | embd_nn = np.argsort(embd_dmat, axis=1)[:, :10] +70 | assert nn_accuracy(true_nn, embd_nn) >= 0.45 + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_aligned_umap.py:74:5 + | +73 | @pytest.mark.skip(reason="Temporarily disable") +74 | def test_aligned_update_array_error(aligned_iris, aligned_iris_relations): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +75 | data, _target = aligned_iris +76 | n_neighbors = [15, 15, 15, 15, 15] + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_aligned_umap.py:80:24 + | +78 | small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2]) +79 | +80 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +81 | small_aligned_model.update( +82 | data[3:], relations=aligned_iris_relations[2:], n_neighbors=n_neighbors[3:], + | + +C901 `stashed_previous_impl_for_regression_test` is too complex (16 > 10) + --> umap/tests/test_chunked_parallel_spatial_metric.py:24:5 + | +23 | @pytest.fixture +24 | def stashed_previous_impl_for_regression_test(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +25 | @numba.njit(parallel=True, nogil=True) +26 | def stashed_chunked_parallel_special_metric( + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:24:5 + | +23 | @pytest.fixture +24 | def stashed_previous_impl_for_regression_test(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +25 | @numba.njit(parallel=True, nogil=True) +26 | def stashed_chunked_parallel_special_metric( + | + +C901 `stashed_chunked_parallel_special_metric` is too complex (15 > 10) + --> umap/tests/test_chunked_parallel_spatial_metric.py:26:9 + | +24 | def stashed_previous_impl_for_regression_test(): +25 | @numba.njit(parallel=True, nogil=True) +26 | def stashed_chunked_parallel_special_metric( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +27 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, +28 | ): + | + +PLR0912 Too many branches (17 > 12) + --> umap/tests/test_chunked_parallel_spatial_metric.py:26:9 + | +24 | def stashed_previous_impl_for_regression_test(): +25 | @numba.njit(parallel=True, nogil=True) +26 | def stashed_chunked_parallel_special_metric( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +27 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, +28 | ): + | + +N803 Argument name `X` should be lowercase + --> umap/tests/test_chunked_parallel_spatial_metric.py:27:9 + | +25 | @numba.njit(parallel=True, nogil=True) +26 | def stashed_chunked_parallel_special_metric( +27 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, + | ^ +28 | ): +29 | if Y is None: + | + +N803 Argument name `Y` should be lowercase + --> umap/tests/test_chunked_parallel_spatial_metric.py:27:12 + | +25 | @numba.njit(parallel=True, nogil=True) +26 | def stashed_chunked_parallel_special_metric( +27 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, + | ^^^^^^ +28 | ): +29 | if Y is None: + | + +C901 `workaround_590_impl` is too complex (15 > 10) + --> umap/tests/test_chunked_parallel_spatial_metric.py:74:5 + | +73 | @pytest.fixture +74 | def workaround_590_impl(): + | ^^^^^^^^^^^^^^^^^^^ +75 | @numba.njit(parallel=True, nogil=True) +76 | def chunked_parallel_special_metric( + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:74:5 + | +73 | @pytest.fixture +74 | def workaround_590_impl(): + | ^^^^^^^^^^^^^^^^^^^ +75 | @numba.njit(parallel=True, nogil=True) +76 | def chunked_parallel_special_metric( + | + +C901 `chunked_parallel_special_metric` is too complex (14 > 10) + --> umap/tests/test_chunked_parallel_spatial_metric.py:76:9 + | +74 | def workaround_590_impl(): +75 | @numba.njit(parallel=True, nogil=True) +76 | def chunked_parallel_special_metric( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +77 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, +78 | ): + | + +PLR0912 Too many branches (14 > 12) + --> umap/tests/test_chunked_parallel_spatial_metric.py:76:9 + | +74 | def workaround_590_impl(): +75 | @numba.njit(parallel=True, nogil=True) +76 | def chunked_parallel_special_metric( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +77 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, +78 | ): + | + +N803 Argument name `X` should be lowercase + --> umap/tests/test_chunked_parallel_spatial_metric.py:77:9 + | +75 | @numba.njit(parallel=True, nogil=True) +76 | def chunked_parallel_special_metric( +77 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, + | ^ +78 | ): +79 | if Y is None: + | + +N803 Argument name `Y` should be lowercase + --> umap/tests/test_chunked_parallel_spatial_metric.py:77:12 + | +75 | @numba.njit(parallel=True, nogil=True) +76 | def chunked_parallel_special_metric( +77 | X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16, + | ^^^^^^ +78 | ): +79 | if Y is None: + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:123:5 + | +122 | @pytest.fixture +123 | def benchmark_data(request): + | ^^^^^^^^^^^^^^ +124 | shape = request.param +125 | spatial_data = np.random.randn(*shape).astype(np.float32) + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/test_chunked_parallel_spatial_metric.py:125:20 + | +123 | def benchmark_data(request): +124 | shape = request.param +125 | spatial_data = np.random.randn(*shape).astype(np.float32) + | ^^^^^^^^^^^^^^^ +126 | return np.abs(spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:133:5 + | +132 | @benchmark_only +133 | def test_chunked_parallel_alternative_implementations( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +134 | spatial_data, workaround_590_impl, +135 | ): + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:161:5 + | +160 | @benchmark_only +161 | def test_chunked_parallel_special_metric_implementation_hellinger( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +162 | spatial_data, +163 | stashed_previous_impl_for_regression_test, + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/test_chunked_parallel_spatial_metric.py:214:24 + | +213 | # test hellinger on different X and Y Pair +214 | spatial_data_two = np.random.randn(10, 20) + | ^^^^^^^^^^^^^^^ +215 | dist_stashed_diff_pair = stashed_previous_impl_for_regression_test( +216 | np.abs(spatial_data[:-2]), spatial_data_two, + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:244:5 + | +242 | indirect=["benchmark_data"], +243 | ) +244 | def test_benchmark_chunked_parallel_special_metric_x_only( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +245 | benchmark, +246 | benchmark_data, + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:268:5 + | +266 | indirect=["benchmark_data"], +267 | ) +268 | def test_benchmark_workaround_590_x_only( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +269 | benchmark, +270 | benchmark_data, + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:298:5 + | +296 | indirect=["benchmark_data"], +297 | ) +298 | def test_benchmark_chunked_parallel_special_metric_x_y( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +299 | benchmark, +300 | benchmark_data, + | + +D103 Missing docstring in public function + --> umap/tests/test_chunked_parallel_spatial_metric.py:322:5 + | +320 | indirect=["benchmark_data"], +321 | ) +322 | def test_benchmark_workaround_590_x_y( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +323 | benchmark, +324 | benchmark_data, + | + +D103 Missing docstring in public function + --> umap/tests/test_composite_models.py:16:5 + | +16 | def test_composite_trustworthiness(nn_data, iris_model): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +17 | data = nn_data[:50] +18 | model1 = UMAP(n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=50).fit(data) + | + +PLR2004 Magic value used in comparison, consider replacing `0.80` with a constant variable + --> umap/tests/test_composite_models.py:29:18 + | +27 | trust = trustworthiness(data, model3.embedding_, n_neighbors=10) +28 | assert ( +29 | trust >= 0.80 + | ^^^^ +30 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" +31 | model4 = model1 + model2 + | + +PLR2004 Magic value used in comparison, consider replacing `0.80` with a constant variable + --> umap/tests/test_composite_models.py:34:18 + | +32 | trust = trustworthiness(data, model4.embedding_, n_neighbors=10) +33 | assert ( +34 | trust >= 0.80 + | ^^^^ +35 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_composite_models.py:37:24 + | +35 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" +36 | +37 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +38 | _ = model1 + iris_model + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_composite_models.py:40:24 + | +38 | _ = model1 + iris_model +39 | +40 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +41 | _ = model1 * iris_model + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_composite_models.py:43:24 + | +41 | _ = model1 * iris_model +42 | +43 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +44 | _ = model1 - iris_model + | + +D103 Missing docstring in public function + --> umap/tests/test_composite_models.py:48:5 + | +47 | @pytest.mark.skip(reason="Marked as Skipped test") +48 | def test_composite_trustworthiness_random_init(nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +49 | data = nn_data[:50] +50 | model1 = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.82` with a constant variable + --> umap/tests/test_composite_models.py:67:18 + | +65 | trust = trustworthiness(data, model3.embedding_, n_neighbors=10) +66 | assert ( +67 | trust >= 0.82 + | ^^^^ +68 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" +69 | model4 = model1 + model2 + | + +PLR2004 Magic value used in comparison, consider replacing `0.82` with a constant variable + --> umap/tests/test_composite_models.py:72:18 + | +70 | trust = trustworthiness(data, model4.embedding_, n_neighbors=10) +71 | assert ( +72 | trust >= 0.82 + | ^^^^ +73 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_composite_models.py:76:5 + | +76 | def test_composite_trustworthiness_on_iris(iris): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +77 | iris_model1 = UMAP( +78 | n_neighbors=10, + | + +PLR2004 Magic value used in comparison, consider replacing `0.82` with a constant variable + --> umap/tests/test_composite_models.py:92:18 + | +90 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +91 | assert ( +92 | trust >= 0.82 + | ^^^^ +93 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" +94 | embedding = (iris_model1 * iris_model2).embedding_ + | + +PLR2004 Magic value used in comparison, consider replacing `0.82` with a constant variable + --> umap/tests/test_composite_models.py:97:18 + | +95 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +96 | assert ( +97 | trust >= 0.82 + | ^^^^ +98 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_composite_models.py:101:5 + | +101 | def test_contrastive_trustworthiness_on_iris(iris): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +102 | iris_model1 = UMAP( +103 | n_neighbors=10, + | + +PLR2004 Magic value used in comparison, consider replacing `0.75` with a constant variable + --> umap/tests/test_composite_models.py:117:18 + | +115 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +116 | assert ( +117 | trust >= 0.75 + | ^^^^ +118 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_data_input.py:9:5 + | + 8 | @pytest.fixture(scope="session") + 9 | def all_finite_data(): + | ^^^^^^^^^^^^^^^ +10 | return np.arange(100.0).reshape(25, 4) + | + +D103 Missing docstring in public function + --> umap/tests/test_data_input.py:14:5 + | +13 | @pytest.fixture(scope="session") +14 | def inverse_data(): + | ^^^^^^^^^^^^ +15 | return np.arange(50).reshape(25, 2) + | + +D103 Missing docstring in public function + --> umap/tests/test_data_input.py:19:5 + | +18 | @njit +19 | def nan_dist(a: np.ndarray, b: np.ndarray): + | ^^^^^^^^ +20 | a[0] = np.nan +21 | a[1] = np.inf + | + +ARG001 Unused function argument: `b` + --> umap/tests/test_data_input.py:19:29 + | +18 | @njit +19 | def nan_dist(a: np.ndarray, b: np.ndarray): + | ^ +20 | a[0] = np.nan +21 | a[1] = np.inf + | + +D417 Missing argument descriptions in the docstring for `test_check_input_data`: `all_finite_data`, `inverse_data` + --> umap/tests/test_data_input.py:25:5 + | +25 | def test_check_input_data(all_finite_data, inverse_data): + | ^^^^^^^^^^^^^^^^^^^^^ +26 | """Data input to UMAP gets checked for liability. +27 | This tests checks the if data input is dismissed/accepted + | + +D205 1 blank line required between summary line and description + --> umap/tests/test_data_input.py:26:5 + | +25 | def test_check_input_data(all_finite_data, inverse_data): +26 | / """Data input to UMAP gets checked for liability. +27 | | This tests checks the if data input is dismissed/accepted +28 | | according to the "ensure_all_finite" keyword as used by +29 | | sklearn. +30 | | +31 | | Parameters +32 | | ---------- +33 | | all_finite_data +34 | | inverse_data +35 | | ------- +36 | | +37 | | """ + | |_______^ +38 | inf_data = all_finite_data.copy() +39 | inf_data[0] = np.inf + | +help: Insert single blank line + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:64:42 + | +62 | # Check whether correct data input is accepted +63 | call_umap_functions(all_finite_data, None) +64 | call_umap_functions(all_finite_data, True) + | ^^^^ +65 | +66 | call_umap_functions(nan_data, "allow-nan") + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:69:35 + | +67 | call_umap_functions(all_finite_data, "allow-nan") +68 | +69 | call_umap_functions(inf_data, False) + | ^^^^^ +70 | call_umap_functions(inf_nan_data, False) +71 | call_umap_functions(nan_data, False) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:70:39 + | +69 | call_umap_functions(inf_data, False) +70 | call_umap_functions(inf_nan_data, False) + | ^^^^^ +71 | call_umap_functions(nan_data, False) +72 | call_umap_functions(all_finite_data, False) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:71:35 + | +69 | call_umap_functions(inf_data, False) +70 | call_umap_functions(inf_nan_data, False) +71 | call_umap_functions(nan_data, False) + | ^^^^^ +72 | call_umap_functions(all_finite_data, False) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:72:42 + | +70 | call_umap_functions(inf_nan_data, False) +71 | call_umap_functions(nan_data, False) +72 | call_umap_functions(all_finite_data, False) + | ^^^^^ +73 | +74 | # Check whether illegal data raises a ValueError + | + +PT012 `pytest.raises()` block should contain a single simple statement + --> umap/tests/test_data_input.py:75:5 + | +74 | # Check whether illegal data raises a ValueError +75 | / with pytest.raises(ValueError): +76 | | call_umap_functions(nan_data, None) +77 | | call_umap_functions(inf_data, None) +78 | | call_umap_functions(inf_nan_data, None) +79 | | +80 | | call_umap_functions(nan_data, True) +81 | | call_umap_functions(inf_data, True) +82 | | call_umap_functions(inf_nan_data, True) +83 | | +84 | | call_umap_functions(inf_data, "allow-nan") +85 | | call_umap_functions(inf_nan_data, "allow-nan") + | |______________________________________________________^ + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_data_input.py:75:24 + | +74 | # Check whether illegal data raises a ValueError +75 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +76 | call_umap_functions(nan_data, None) +77 | call_umap_functions(inf_data, None) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:80:39 + | +78 | call_umap_functions(inf_nan_data, None) +79 | +80 | call_umap_functions(nan_data, True) + | ^^^^ +81 | call_umap_functions(inf_data, True) +82 | call_umap_functions(inf_nan_data, True) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:81:39 + | +80 | call_umap_functions(nan_data, True) +81 | call_umap_functions(inf_data, True) + | ^^^^ +82 | call_umap_functions(inf_nan_data, True) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_data_input.py:82:43 + | +80 | call_umap_functions(nan_data, True) +81 | call_umap_functions(inf_data, True) +82 | call_umap_functions(inf_nan_data, True) + | ^^^^ +83 | +84 | call_umap_functions(inf_data, "allow-nan") + | + +D103 Missing docstring in public function + --> umap/tests/test_densmap.py:16:5 + | +16 | def test_densmap_trustworthiness(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +17 | data = nn_data[:50] +18 | embedding, _rad_h, _rad_l = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.72` with a constant variable + --> umap/tests/test_densmap.py:28:18 + | +26 | trust = trustworthiness(data, embedding, n_neighbors=10) +27 | assert ( +28 | trust >= 0.72 + | ^^^^ +29 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_densmap.py:33:5 + | +32 | @pytest.mark.skip +33 | def test_densmap_trustworthiness_random_init(nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +34 | data = nn_data[:50] +35 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.75` with a constant variable + --> umap/tests/test_densmap.py:44:18 + | +42 | trust = trustworthiness(data, embedding, n_neighbors=10) +43 | assert ( +44 | trust >= 0.75 + | ^^^^ +45 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_densmap.py:48:5 + | +48 | def test_densmap_trustworthiness_on_iris(iris): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +49 | densmap_iris_model = UMAP( +50 | n_neighbors=10, + | + +PLR2004 Magic value used in comparison, consider replacing `0.97` with a constant variable + --> umap/tests/test_densmap.py:59:18 + | +57 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +58 | assert ( +59 | trust >= 0.97 + | ^^^^ +60 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_densmap.py:65:24 + | +63 | densmap_iris_model.transform(iris.data[:10]) +64 | +65 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +66 | densmap_iris_model.inverse_transform(embedding[:10]) + | + +D103 Missing docstring in public function + --> umap/tests/test_densmap.py:69:5 + | +69 | def test_densmap_trustworthiness_on_iris_supervised(iris): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +70 | densmap_iris_model = UMAP( +71 | n_neighbors=10, + | + +PLR2004 Magic value used in comparison, consider replacing `0.97` with a constant variable + --> umap/tests/test_densmap.py:80:18 + | +78 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +79 | assert ( +80 | trust >= 0.97 + | ^^^^ +81 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_parametric_umap.py:26:5 + | +25 | @pytest.fixture(scope="session") +26 | def moon_dataset(): + | ^^^^^^^^^^^^ +27 | X, _ = make_moons(200) +28 | return X + | + +N806 Variable `X` in function should be lowercase + --> umap/tests/test_parametric_umap.py:27:5 + | +25 | @pytest.fixture(scope="session") +26 | def moon_dataset(): +27 | X, _ = make_moons(200) + | ^ +28 | return X + | + +N806 Variable `X` in function should be lowercase + --> umap/tests/test_parametric_umap.py:58:5 + | +56 | return (x - np.min(x)) / (np.max(x) - np.min(x)) +57 | +58 | X = norm(moon_dataset) + | ^ +59 | embedder = ParametricUMAP(parametric_reconstruction=True) +60 | Z = embedder.fit_transform(X) + | + +N806 Variable `Z` in function should be lowercase + --> umap/tests/test_parametric_umap.py:60:5 + | +58 | X = norm(moon_dataset) +59 | embedder = ParametricUMAP(parametric_reconstruction=True) +60 | Z = embedder.fit_transform(X) + | ^ +61 | X_r = embedder.inverse_transform(Z) +62 | # completes successfully + | + +N806 Variable `X_r` in function should be lowercase + --> umap/tests/test_parametric_umap.py:61:5 + | +59 | embedder = ParametricUMAP(parametric_reconstruction=True) +60 | Z = embedder.fit_transform(X) +61 | X_r = embedder.inverse_transform(Z) + | ^^^ +62 | # completes successfully +63 | assert X_r is not None + | + +N806 Variable `X_train` in function should be lowercase + --> umap/tests/test_parametric_umap.py:112:5 + | +110 | def test_validation(moon_dataset): +111 | """Tests adding a validation dataset.""" +112 | X_train, X_valid = train_test_split(moon_dataset, train_size=0.5) + | ^^^^^^^ +113 | embedder = ParametricUMAP( +114 | parametric_reconstruction=True, reconstruction_validation=X_valid, verbose=True, + | + +N806 Variable `X_valid` in function should be lowercase + --> umap/tests/test_parametric_umap.py:112:14 + | +110 | def test_validation(moon_dataset): +111 | """Tests adding a validation dataset.""" +112 | X_train, X_valid = train_test_split(moon_dataset, train_size=0.5) + | ^^^^^^^ +113 | embedder = ParametricUMAP( +114 | parametric_reconstruction=True, reconstruction_validation=X_valid, verbose=True, + | + +NPY002 Replace legacy `np.random.seed` call with `np.random.Generator` + --> umap/tests/test_plot.py:8:1 + | + 6 | # Globals, used for all the tests + 7 | SEED = 189212 # 0b101110001100011100 + 8 | np.random.seed(SEED) + | ^^^^^^^^^^^^^^ + 9 | +10 | try: + | + +F401 `umap.plot` imported but unused; consider using `importlib.util.find_spec` to test for availability + --> umap/tests/test_plot.py:11:22 + | +10 | try: +11 | from umap import plot + | ^^^^ +12 | +13 | IMPORT_PLOT = True + | +help: Remove unused import: `umap.plot` + +D103 Missing docstring in public function + --> umap/tests/test_plot.py:21:5 + | +20 | @pytest.fixture(scope="session") +21 | def mapper(iris): + | ^^^^^^ +22 | return umap.UMAP(n_epochs=100).fit(iris.data) + | + +D103 Missing docstring in public function + --> umap/tests/test_plot.py:29:5 + | +27 | # property verification. +28 | @plot_only +29 | def test_plot_runs_at_all(mapper, iris, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^ +30 | from umap import plot as umap_plot + | + +PLC0415 `import` should be at the top-level of a file + --> umap/tests/test_plot.py:30:5 + | +28 | @plot_only +29 | def test_plot_runs_at_all(mapper, iris, iris_selection): +30 | from umap import plot as umap_plot + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +31 | +32 | umap_plot.points(mapper) + | + +SLF001 Private member accessed: `_datashade_points` + --> umap/tests/test_plot.py:49:5 + | +47 | umap_plot.interactive(mapper, values=iris.data[:, 0], subset_points=iris_selection) +48 | umap_plot.interactive(mapper, theme="fire") +49 | umap_plot._datashade_points(mapper.embedding_) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +50 | umap_plot._datashade_points(mapper.embedding_, labels=iris.target) +51 | umap_plot._datashade_points(mapper.embedding_, values=iris.data[:, 0]) + | + +SLF001 Private member accessed: `_datashade_points` + --> umap/tests/test_plot.py:50:5 + | +48 | umap_plot.interactive(mapper, theme="fire") +49 | umap_plot._datashade_points(mapper.embedding_) +50 | umap_plot._datashade_points(mapper.embedding_, labels=iris.target) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +51 | umap_plot._datashade_points(mapper.embedding_, values=iris.data[:, 0]) + | + +SLF001 Private member accessed: `_datashade_points` + --> umap/tests/test_plot.py:51:5 + | +49 | umap_plot._datashade_points(mapper.embedding_) +50 | umap_plot._datashade_points(mapper.embedding_, labels=iris.target) +51 | umap_plot._datashade_points(mapper.embedding_, values=iris.data[:, 0]) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_spectral.py:19:5 + | +17 | reason="SciPy installing with Python 3.7 does not converge under same circumstances", +18 | ) +19 | def test_tsw_spectral_init(iris): + | ^^^^^^^^^^^^^^^^^^^^^^ +20 | # create an arbitrary (dense) random affinity matrix +21 | seed = 42 + | + +ARG001 Unused function argument: `iris` + --> umap/tests/test_spectral.py:19:28 + | +17 | reason="SciPy installing with Python 3.7 does not converge under same circumstances", +18 | ) +19 | def test_tsw_spectral_init(iris): + | ^^^^ +20 | # create an arbitrary (dense) random affinity matrix +21 | seed = 42 + | + +PLR2004 Magic value used in comparison, consider replacing `1e-6` with a constant variable + --> umap/tests/test_spectral.py:34:16 + | +32 | rmsd = np.mean(np.sum((spec - tsw_spec) ** 2, axis=1)) +33 | assert ( +34 | rmsd < 1e-6 + | ^^^^ +35 | ), "tsvd-warmed spectral init insufficiently close to standard spectral init" + | + +D103 Missing docstring in public function + --> umap/tests/test_spectral.py:42:5 + | +40 | reason="SciPy installing with Py 3.7 does not warn reliably on convergence failure", +41 | ) +42 | def test_ensure_fallback_to_random_on_spectral_failure(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +43 | dim = 1000 +44 | k = 10 + | + +PLR2004 Magic value used in comparison, consider replacing `10` with a constant variable + --> umap/tests/test_spectral.py:45:17 + | +43 | dim = 1000 +44 | k = 10 +45 | assert k >= 10 + | ^^ +46 | assert dim // 10 > k +47 | y = np.eye(dim, k=1) + | + +NPY002 Replace legacy `np.random.random` call with `np.random.Generator` + --> umap/tests/test_spectral.py:48:9 + | +46 | assert dim // 10 > k +47 | y = np.eye(dim, k=1) +48 | u = np.random.random((dim, dim // 10)) + | ^^^^^^^^^^^^^^^^ +49 | graph = y + y.T + u @ u.T +50 | with pytest.warns(UserWarning, match="Spectral initialisation failed!"): + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_get_feature_names_out.py:8:5 + | + 8 | def test_get_feature_names_out(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ + 9 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) +10 | umap = UMAP( + | + +N806 Variable `X` in function should be lowercase + --> umap/tests/test_umap_get_feature_names_out.py:9:5 + | + 8 | def test_get_feature_names_out(): + 9 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) + | ^ +10 | umap = UMAP( +11 | n_neighbors=10, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_get_feature_names_out.py:24:5 + | +24 | def test_get_feature_names_out_default(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +25 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) +26 | umap = UMAP( + | + +N806 Variable `X` in function should be lowercase + --> umap/tests/test_umap_get_feature_names_out.py:25:5 + | +24 | def test_get_feature_names_out_default(): +25 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) + | ^ +26 | umap = UMAP( +27 | n_neighbors=10, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_get_feature_names_out.py:39:5 + | +39 | def test_get_feature_names_out_multicomponent(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +40 | # The output length should be equal to the number of components UMAP generates. +41 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) + | + +N806 Variable `X` in function should be lowercase + --> umap/tests/test_umap_get_feature_names_out.py:41:5 + | +39 | def test_get_feature_names_out_multicomponent(): +40 | # The output length should be equal to the number of components UMAP generates. +41 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) + | ^ +42 | umap = UMAP( +43 | n_neighbors=10, + | + +PLR2004 Magic value used in comparison, consider replacing `9` with a constant variable + --> umap/tests/test_umap_get_feature_names_out.py:51:32 + | +49 | result_umap = umap.get_feature_names_out() +50 | expected_umap_result = [f"umap{i}" for i in range(9)] +51 | assert len(result_umap) == 9 + | ^ +52 | np.testing.assert_array_equal(result_umap, expected_umap_result) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_get_feature_names_out.py:56:5 + | +56 | def test_get_feature_names_out_featureunion(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +57 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) +58 | pipeline = Pipeline( + | + +N806 Variable `X` in function should be lowercase + --> umap/tests/test_umap_get_feature_names_out.py:57:5 + | +56 | def test_get_feature_names_out_featureunion(): +57 | X, _ = make_classification(n_samples=30, n_features=10, random_state=42) + | ^ +58 | pipeline = Pipeline( +59 | [ + | + +FBT002 Boolean default positional argument in function definition + --> umap/tests/test_umap_metrics.py:28:53 + | +28 | def run_test_metric(metric, test_data, dist_matrix, with_grad=False): + | ^^^^^^^^^ +29 | """Core utility function to test target metric on test data.""" +30 | if with_grad: + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:51:5 + | +51 | def spatial_check(metric, spatial_data, spatial_distances, with_grad=False): + | ^^^^^^^^^^^^^ +52 | # Check that metric is supported for this test, otherwise, fail! +53 | assert metric in spatial_distances, f"{metric} not valid for spatial data" + | + +FBT002 Boolean default positional argument in function definition + --> umap/tests/test_umap_metrics.py:51:60 + | +51 | def spatial_check(metric, spatial_data, spatial_distances, with_grad=False): + | ^^^^^^^^^ +52 | # Check that metric is supported for this test, otherwise, fail! +53 | assert metric in spatial_distances, f"{metric} not valid for spatial data" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:68:5 + | +68 | def binary_check(metric, binary_data, binary_distances): + | ^^^^^^^^^^^^ +69 | # Check that metric is supported for this test, otherwise, fail! +70 | assert metric in binary_distances, f"{metric} not valid for binary data" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:126:5 + | +126 | def sparse_spatial_check(metric, sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^ +127 | # Check that metric is supported for this test, otherwise, fail! +128 | assert ( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:147:5 + | +147 | def sparse_binary_check(metric, sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^ +148 | # Check that metric is supported for this test, otherwise, fail! +149 | assert ( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:172:5 + | +172 | def test_euclidean(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^ +173 | spatial_check("euclidean", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:176:5 + | +176 | def test_manhattan(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^ +177 | spatial_check("manhattan", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:180:5 + | +180 | def test_chebyshev(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^ +181 | spatial_check("chebyshev", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:184:5 + | +184 | def test_minkowski(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^ +185 | spatial_check("minkowski", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:188:5 + | +188 | def test_hamming(spatial_data, spatial_distances): + | ^^^^^^^^^^^^ +189 | spatial_check("hamming", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:192:5 + | +192 | def test_canberra(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^ +193 | spatial_check("canberra", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:196:5 + | +196 | def test_braycurtis(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^^ +197 | spatial_check("braycurtis", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:200:5 + | +200 | def test_cosine(spatial_data, spatial_distances): + | ^^^^^^^^^^^ +201 | spatial_check("cosine", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:204:5 + | +204 | def test_correlation(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^^^ +205 | spatial_check("correlation", spatial_data, spatial_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:213:5 + | +213 | def test_jaccard(binary_data, binary_distances): + | ^^^^^^^^^^^^ +214 | binary_check("jaccard", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:217:5 + | +217 | def test_matching(binary_data, binary_distances): + | ^^^^^^^^^^^^^ +218 | binary_check("matching", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:221:5 + | +221 | def test_dice(binary_data, binary_distances): + | ^^^^^^^^^ +222 | binary_check("dice", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:228:5 + | +226 | scipy_full_version >= (1, 9), reason="deprecated in SciPy 1.9, removed in 1.11", +227 | ) +228 | def test_kulsinski(binary_data, binary_distances): + | ^^^^^^^^^^^^^^ +229 | binary_check("kulsinski", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:232:5 + | +232 | def test_rogerstanimoto(binary_data, binary_distances): + | ^^^^^^^^^^^^^^^^^^^ +233 | binary_check("rogerstanimoto", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:236:5 + | +236 | def test_russellrao(binary_data, binary_distances): + | ^^^^^^^^^^^^^^^ +237 | binary_check("russellrao", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:240:5 + | +240 | def test_sokalmichener(binary_data, binary_distances): + | ^^^^^^^^^^^^^^^^^^ +241 | binary_check("sokalmichener", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:244:5 + | +244 | def test_sokalsneath(binary_data, binary_distances): + | ^^^^^^^^^^^^^^^^ +245 | binary_check("sokalsneath", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:248:5 + | +248 | def test_yule(binary_data, binary_distances): + | ^^^^^^^^^ +249 | binary_check("yule", binary_data, binary_distances) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:257:5 + | +257 | def test_sparse_euclidean(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^ +258 | sparse_spatial_check("euclidean", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:261:5 + | +261 | def test_sparse_manhattan(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^ +262 | sparse_spatial_check("manhattan", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:265:5 + | +265 | def test_sparse_chebyshev(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^ +266 | sparse_spatial_check("chebyshev", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:269:5 + | +269 | def test_sparse_minkowski(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^ +270 | sparse_spatial_check("minkowski", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:273:5 + | +273 | def test_sparse_hamming(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^ +274 | sparse_spatial_check("hamming", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:277:5 + | +277 | def test_sparse_canberra(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^ +278 | sparse_spatial_check("canberra", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:281:5 + | +281 | def test_sparse_cosine(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^ +282 | sparse_spatial_check("cosine", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:285:5 + | +285 | def test_sparse_correlation(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^^^ +286 | sparse_spatial_check("correlation", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:289:5 + | +289 | def test_sparse_braycurtis(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^^ +290 | sparse_spatial_check("braycurtis", sparse_spatial_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:298:5 + | +298 | def test_sparse_jaccard(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^ +299 | sparse_binary_check("jaccard", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:302:5 + | +302 | def test_sparse_matching(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^^ +303 | sparse_binary_check("matching", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:306:5 + | +306 | def test_sparse_dice(sparse_binary_data): + | ^^^^^^^^^^^^^^^^ +307 | sparse_binary_check("dice", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:313:5 + | +311 | scipy_full_version >= (1, 9), reason="deprecated in SciPy 1.9, removed in 1.11", +312 | ) +313 | def test_sparse_kulsinski(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^^^ +314 | sparse_binary_check("kulsinski", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:317:5 + | +317 | def test_sparse_rogerstanimoto(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +318 | sparse_binary_check("rogerstanimoto", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:321:5 + | +321 | def test_sparse_russellrao(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^^^^ +322 | sparse_binary_check("russellrao", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:325:5 + | +325 | def test_sparse_sokalmichener(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +326 | sparse_binary_check("sokalmichener", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:329:5 + | +329 | def test_sparse_sokalsneath(sparse_binary_data): + | ^^^^^^^^^^^^^^^^^^^^^^^ +330 | sparse_binary_check("sokalsneath", sparse_binary_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:336:5 + | +334 | # Standardised/weighted Distances +335 | # -------------------------------- +336 | def test_seuclidean(spatial_data): + | ^^^^^^^^^^^^^^^ +337 | v = np.abs(np.random.randn(spatial_data.shape[1])) +338 | dist_matrix = pairwise_distances(spatial_data, metric="seuclidean", V=v) + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/test_umap_metrics.py:337:16 + | +335 | # -------------------------------- +336 | def test_seuclidean(spatial_data): +337 | v = np.abs(np.random.randn(spatial_data.shape[1])) + | ^^^^^^^^^^^^^^^ +338 | dist_matrix = pairwise_distances(spatial_data, metric="seuclidean", V=v) +339 | test_matrix = np.array( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:358:5 + | +356 | scipy_full_version < (1, 8), reason="incorrect function in scipy<1.8", +357 | ) +358 | def test_weighted_minkowski(spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^^^ +359 | v = np.abs(np.random.randn(spatial_data.shape[1])) +360 | dist_matrix = pairwise_distances(spatial_data, metric="minkowski", w=v, p=3) + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/test_umap_metrics.py:359:16 + | +357 | ) +358 | def test_weighted_minkowski(spatial_data): +359 | v = np.abs(np.random.randn(spatial_data.shape[1])) + | ^^^^^^^^^^^^^^^ +360 | dist_matrix = pairwise_distances(spatial_data, metric="minkowski", w=v, p=3) +361 | test_matrix = np.array( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:377:5 + | +377 | def test_mahalanobis(spatial_data): + | ^^^^^^^^^^^^^^^^ +378 | v = np.cov(np.transpose(spatial_data)) +379 | dist_matrix = pairwise_distances(spatial_data, metric="mahalanobis", VI=v) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:396:5 + | +396 | def test_haversine(spatial_data): + | ^^^^^^^^^^^^^^ +397 | tree = BallTree(spatial_data[:, :2], metric="haversine") +398 | dist_matrix, _ = tree.query(spatial_data[:, :2], k=spatial_data.shape[0]) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:416:5 + | +416 | def test_hellinger(spatial_data): + | ^^^^^^^^^^^^^^ +417 | hellinger_data = np.abs(spatial_data[:-2].copy()) +418 | hellinger_data = hellinger_data / hellinger_data.sum(axis=1)[:, np.newaxis] + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:443:5 + | +443 | def test_sparse_hellinger(sparse_spatial_data): + | ^^^^^^^^^^^^^^^^^^^^^ +444 | dist_matrix = dist.pairwise_special_metric( +445 | np.abs(sparse_spatial_data[:-2].toarray()), + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_metrics.py:489:5 + | +489 | def test_grad_metrics_match_metrics(spatial_data, spatial_distances): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +490 | for metric in dist.named_distances_with_gradients: +491 | if metric in spatial_distances: + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/test_umap_metrics.py:496:16 + | +494 | # Handle the few special distances separately +495 | # SEuclidean +496 | v = np.abs(np.random.randn(spatial_data.shape[1])) + | ^^^^^^^^^^^^^^^ +497 | dist_matrix = pairwise_distances(spatial_data, metric="seuclidean", V=v) +498 | test_matrix = np.array( + | + +NPY002 Replace legacy `np.random.randn` call with `np.random.Generator` + --> umap/tests/test_umap_metrics.py:534:16 + | +533 | # Mahalanobis +534 | v = np.abs(np.random.randn(spatial_data.shape[1], spatial_data.shape[1])) + | ^^^^^^^^^^^^^^^ +535 | dist_matrix = pairwise_distances(spatial_data, metric="mahalanobis", VI=v) +536 | test_matrix = np.array( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:20:5 + | +18 | # nearest_neighbours metric parameter validation +19 | # ----------------------------------------------- +20 | def test_nn_bad_metric(nn_data): + | ^^^^^^^^^^^^^^^^^^ +21 | with pytest.raises(ValueError): +22 | nearest_neighbors(nn_data, 10, 42, {}, False, np.random) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_nn.py:21:24 + | +19 | # ----------------------------------------------- +20 | def test_nn_bad_metric(nn_data): +21 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +22 | nearest_neighbors(nn_data, 10, 42, {}, False, np.random) + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:22:48 + | +20 | def test_nn_bad_metric(nn_data): +21 | with pytest.raises(ValueError): +22 | nearest_neighbors(nn_data, 10, 42, {}, False, np.random) + | ^^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:25:5 + | +25 | def test_nn_bad_metric_sparse_data(sparse_nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +26 | with pytest.raises(ValueError): +27 | nearest_neighbors( + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_nn.py:26:24 + | +25 | def test_nn_bad_metric_sparse_data(sparse_nn_data): +26 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +27 | nearest_neighbors( +28 | sparse_nn_data, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:32:13 + | +30 | "seuclidean", +31 | {}, +32 | False, + | ^^^^^ +33 | np.random, +34 | ) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:42:5 + | +42 | def knn(indices, nn_data): # pragma: no cover + | ^^^ +43 | tree = KDTree(nn_data) +44 | true_indices = tree.query(nn_data, 10, return_distance=False) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:51:5 + | +51 | def smooth_knn(nn_data, local_connectivity=1.0): + | ^^^^^^^^^^ +52 | _knn_indices, knn_dists, _ = nearest_neighbors( +53 | nn_data, 10, "euclidean", {}, False, np.random, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:53:39 + | +51 | def smooth_knn(nn_data, local_connectivity=1.0): +52 | _knn_indices, knn_dists, _ = nearest_neighbors( +53 | nn_data, 10, "euclidean", {}, False, np.random, + | ^^^^^ +54 | ) +55 | sigmas, rhos = smooth_knn_dist( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:65:5 + | +64 | @pytest.mark.skip +65 | def test_nn_descent_neighbor_accuracy(nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +66 | knn_indices, _knn_dists, _ = nearest_neighbors( +67 | nn_data, 10, "euclidean", {}, False, np.random, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:67:39 + | +65 | def test_nn_descent_neighbor_accuracy(nn_data): # pragma: no cover +66 | knn_indices, _knn_dists, _ = nearest_neighbors( +67 | nn_data, 10, "euclidean", {}, False, np.random, + | ^^^^^ +68 | ) +69 | percent_correct = knn(knn_indices, nn_data) + | + +PLR2004 Magic value used in comparison, consider replacing `0.85` with a constant variable + --> umap/tests/test_umap_nn.py:71:28 + | +69 | percent_correct = knn(knn_indices, nn_data) +70 | assert ( +71 | percent_correct >= 0.85 + | ^^^^ +72 | ), "NN-descent did not get 89% accuracy on nearest neighbors" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:76:5 + | +75 | @pytest.mark.skip +76 | def test_nn_descent_neighbor_accuracy_low_memory(nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +77 | knn_indices, _knn_dists, _ = nearest_neighbors( +78 | nn_data, 10, "euclidean", {}, False, np.random, low_memory=True, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:78:39 + | +76 | def test_nn_descent_neighbor_accuracy_low_memory(nn_data): # pragma: no cover +77 | knn_indices, _knn_dists, _ = nearest_neighbors( +78 | nn_data, 10, "euclidean", {}, False, np.random, low_memory=True, + | ^^^^^ +79 | ) +80 | percent_correct = knn(knn_indices, nn_data) + | + +PLR2004 Magic value used in comparison, consider replacing `0.89` with a constant variable + --> umap/tests/test_umap_nn.py:82:28 + | +80 | percent_correct = knn(knn_indices, nn_data) +81 | assert ( +82 | percent_correct >= 0.89 + | ^^^^ +83 | ), "NN-descent did not get 89% accuracy on nearest neighbors" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:87:5 + | +86 | @pytest.mark.skip +87 | def test_angular_nn_descent_neighbor_accuracy(nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +88 | knn_indices, _knn_dists, _ = nearest_neighbors( +89 | nn_data, 10, "cosine", {}, True, np.random, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:89:36 + | +87 | def test_angular_nn_descent_neighbor_accuracy(nn_data): # pragma: no cover +88 | knn_indices, _knn_dists, _ = nearest_neighbors( +89 | nn_data, 10, "cosine", {}, True, np.random, + | ^^^^ +90 | ) +91 | angular_data = normalize(nn_data, norm="l2") + | + +PLR2004 Magic value used in comparison, consider replacing `0.85` with a constant variable + --> umap/tests/test_umap_nn.py:94:28 + | +92 | percent_correct = knn(knn_indices, angular_data) +93 | assert ( +94 | percent_correct >= 0.85 + | ^^^^ +95 | ), "NN-descent did not get 89% accuracy on nearest neighbors" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:99:5 + | + 98 | @pytest.mark.skip + 99 | def test_sparse_nn_descent_neighbor_accuracy(sparse_nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +100 | knn_indices, _knn_dists, _ = nearest_neighbors( +101 | sparse_nn_data, 20, "euclidean", {}, False, np.random, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:101:46 + | + 99 | def test_sparse_nn_descent_neighbor_accuracy(sparse_nn_data): # pragma: no cover +100 | knn_indices, _knn_dists, _ = nearest_neighbors( +101 | sparse_nn_data, 20, "euclidean", {}, False, np.random, + | ^^^^^ +102 | ) +103 | percent_correct = knn(knn_indices, sparse_nn_data.todense()) + | + +PLR2004 Magic value used in comparison, consider replacing `0.75` with a constant variable + --> umap/tests/test_umap_nn.py:105:28 + | +103 | percent_correct = knn(knn_indices, sparse_nn_data.todense()) +104 | assert ( +105 | percent_correct >= 0.75 + | ^^^^ +106 | ), "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:110:5 + | +109 | @pytest.mark.skip +110 | def test_sparse_nn_descent_neighbor_accuracy_low_memory( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +111 | sparse_nn_data, +112 | ): # pragma: no cover + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:114:46 + | +112 | ): # pragma: no cover +113 | knn_indices, _knn_dists, _ = nearest_neighbors( +114 | sparse_nn_data, 20, "euclidean", {}, False, np.random, low_memory=True, + | ^^^^^ +115 | ) +116 | percent_correct = knn(knn_indices, sparse_nn_data.todense()) + | + +PLR2004 Magic value used in comparison, consider replacing `0.85` with a constant variable + --> umap/tests/test_umap_nn.py:118:28 + | +116 | percent_correct = knn(knn_indices, sparse_nn_data.todense()) +117 | assert ( +118 | percent_correct >= 0.85 + | ^^^^ +119 | ), "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:123:5 + | +122 | @pytest.mark.skip +123 | def test_nn_descent_neighbor_accuracy_callable_metric(nn_data): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +124 | knn_indices, _knn_dists, _ = nearest_neighbors( +125 | nn_data, 10, dist.euclidean, {}, False, np.random, + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:125:42 + | +123 | def test_nn_descent_neighbor_accuracy_callable_metric(nn_data): # pragma: no cover +124 | knn_indices, _knn_dists, _ = nearest_neighbors( +125 | nn_data, 10, dist.euclidean, {}, False, np.random, + | ^^^^^ +126 | ) + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_nn.py:130:28 + | +128 | percent_correct = knn(knn_indices, nn_data) +129 | assert ( +130 | percent_correct >= 0.95 + | ^^^^ +131 | ), "NN-descent did not get 95% accuracy on nearest neighbors with callable metric" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:135:5 + | +134 | @pytest.mark.skip +135 | def test_sparse_angular_nn_descent_neighbor_accuracy( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +136 | sparse_nn_data, +137 | ): # pragma: no cover + | + +FBT003 Boolean positional value in function call + --> umap/tests/test_umap_nn.py:139:43 + | +137 | ): # pragma: no cover +138 | knn_indices, _knn_dists, _ = nearest_neighbors( +139 | sparse_nn_data, 20, "cosine", {}, True, np.random, + | ^^^^ +140 | ) +141 | angular_data = normalize(sparse_nn_data, norm="l2").toarray() + | + +PLR2004 Magic value used in comparison, consider replacing `0.90` with a constant variable + --> umap/tests/test_umap_nn.py:144:28 + | +142 | percent_correct = knn(knn_indices, angular_data) +143 | assert ( +144 | percent_correct >= 0.90 + | ^^^^ +145 | ), "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:148:5 + | +148 | def test_smooth_knn_dist_l1norms(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +149 | norms = smooth_knn(nn_data) +150 | assert_array_almost_equal( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_nn.py:158:5 + | +158 | def test_smooth_knn_dist_l1norms_w_connectivity(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +159 | norms = smooth_knn(nn_data, local_connectivity=1.75) +160 | assert_array_almost_equal( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:30:5 + | +28 | # UMAP Trustworthiness on iris +29 | # ---------------------------- +30 | def test_umap_trustworthiness_on_iris(iris, iris_model): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +31 | embedding = iris_model.embedding_ +32 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) + | + +PLR2004 Magic value used in comparison, consider replacing `0.97` with a constant variable + --> umap/tests/test_umap_on_iris.py:34:18 + | +32 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +33 | assert ( +34 | trust >= 0.97 + | ^^^^ +35 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:38:5 + | +38 | def test_initialized_umap_trustworthiness_on_iris(iris): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +39 | data = iris.data +40 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.97` with a constant variable + --> umap/tests/test_umap_on_iris.py:49:18 + | +47 | trust = trustworthiness(iris.data, embedding, n_neighbors=10) +48 | assert ( +49 | trust >= 0.97 + | ^^^^ +50 | ), f"Insufficiently trustworthy embedding foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:53:5 + | +53 | def test_umap_trustworthiness_on_sphere_iris( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +54 | iris, +55 | ): + | + +PLR2004 Magic value used in comparison, consider replacing `0.65` with a constant variable + --> umap/tests/test_umap_on_iris.py:78:18 + | +76 | ) +77 | assert ( +78 | trust >= 0.65 + | ^^^^ +79 | ), f"Insufficiently trustworthy spherical embedding for iris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:84:5 + | +82 | # UMAP Transform on iris +83 | # ---------------------- +84 | def test_umap_transform_on_iris(iris, iris_subset_model, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +85 | fitter = iris_subset_model + | + +PLR2004 Magic value used in comparison, consider replacing `0.80` with a constant variable + --> umap/tests/test_umap_on_iris.py:92:18 + | +90 | trust = trustworthiness(new_data, embedding, n_neighbors=10) +91 | assert ( +92 | trust >= 0.80 + | ^^^^ +93 | ), f"Insufficiently trustworthy transform foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:96:5 + | +96 | def test_umap_transform_on_iris_w_pynndescent(iris, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +97 | data = iris.data[iris_selection] +98 | fitter = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.85` with a constant variable + --> umap/tests/test_umap_on_iris.py:111:18 + | +109 | trust = trustworthiness(new_data, embedding, n_neighbors=10) +110 | assert ( +111 | trust >= 0.85 + | ^^^^ +112 | ), f"Insufficiently trustworthy transform foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:115:5 + | +115 | def test_umap_transform_on_iris_modified_dtype(iris, iris_subset_model, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +116 | fitter = iris_subset_model +117 | fitter.embedding_ = fitter.embedding_.astype(np.float64) + | + +PLR2004 Magic value used in comparison, consider replacing `0.8` with a constant variable + --> umap/tests/test_umap_on_iris.py:124:18 + | +122 | trust = trustworthiness(new_data, embedding, n_neighbors=10) +123 | assert ( +124 | trust >= 0.8 + | ^^^ +125 | ), f"Insufficiently trustworthy transform for iris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:128:5 + | +128 | def test_umap_sparse_transform_on_iris(iris, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +129 | data = sparse.csr_matrix(iris.data[iris_selection]) +130 | assert sparse.issparse(data) + | + +ERA001 Found commented-out code + --> umap/tests/test_umap_on_iris.py:136:9 + | +134 | random_state=42, +135 | n_epochs=100, +136 | # force_approximation_algorithm=True, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +137 | ).fit(data) + | +help: Remove commented-out code + +PLR2004 Magic value used in comparison, consider replacing `0.80` with a constant variable + --> umap/tests/test_umap_on_iris.py:145:18 + | +143 | trust = trustworthiness(new_data, embedding, n_neighbors=10) +144 | assert ( +145 | trust >= 0.80 + | ^^^^ +146 | ), f"Insufficiently trustworthy transform foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:151:5 + | +149 | # UMAP precomputed metric transform on iris +150 | # ---------------------- +151 | def test_precomputed_transform_on_iris(iris, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +152 | data = iris.data[iris_selection] +153 | distance_matrix = squareform(pdist(data)) + | + +PLR2004 Magic value used in comparison, consider replacing `0.85` with a constant variable + --> umap/tests/test_umap_on_iris.py:169:18 + | +167 | trust = trustworthiness(new_data, embedding, n_neighbors=10) +168 | assert ( +169 | trust >= 0.85 + | ^^^^ +170 | ), f"Insufficiently trustworthy transform foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:175:5 + | +173 | # UMAP precomputed metric transform on iris with sparse distances +174 | # ---------------------- +175 | def test_precomputed_sparse_transform_on_iris(iris, iris_selection): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +176 | data = iris.data[iris_selection] +177 | distance_matrix = sparse.csr_matrix(squareform(pdist(data))) + | + +PLR2004 Magic value used in comparison, consider replacing `0.85` with a constant variable + --> umap/tests/test_umap_on_iris.py:193:18 + | +191 | trust = trustworthiness(new_data, embedding, n_neighbors=10) +192 | assert ( +193 | trust >= 0.85 + | ^^^^ +194 | ), f"Insufficiently trustworthy transform foriris dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:199:5 + | +197 | # UMAP Clusterability on Iris +198 | # --------------------------- +199 | def test_umap_clusterability_on_supervised_iris(supervised_iris_model, iris): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +200 | embedding = supervised_iris_model.embedding_ +201 | clusters = KMeans(3).fit_predict(embedding) + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_on_iris.py:202:58 + | +200 | embedding = supervised_iris_model.embedding_ +201 | clusters = KMeans(3).fit_predict(embedding) +202 | assert adjusted_rand_score(clusters, iris.target) >= 0.95 + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:207:5 + | +205 | # UMAP Inverse transform on Iris +206 | # ------------------------------ +207 | def test_umap_inverse_transform_on_iris(iris, iris_model): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +208 | highd_tree = KDTree(iris.data) +209 | fitter = iris_model + | + +PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable + --> umap/tests/test_umap_on_iris.py:219:78 + | +217 | highd_centroid, k=10, return_distance=False, +218 | ) +219 | assert np.intersect1d(near_points, highd_near_points[0]).shape[0] >= 3 + | ^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_on_iris.py:222:5 + | +222 | def test_precomputed_knn_on_iris(iris, iris_selection, iris_subset_model): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +223 | # this to compare two similarity graphs which should be nearly the same +224 | def rms(a, b): + | + +PLR2004 Magic value used in comparison, consider replacing `0.005` with a constant variable + --> umap/tests/test_umap_on_iris.py:258:80 + | +256 | # threshold for similarity in graph empirically chosen by comparing the iris subset +257 | # model with force_approximation_algorithm=True and different random seeds +258 | assert rms(fitter_with_precomputed_knn.graph_, iris_subset_model.graph_) < 0.005 + | ^^^^^ +259 | +260 | with pytest.warns(Warning, match="transforming new data") as record: + | + +PT031 `pytest.warns()` block should contain a single simple statement + --> umap/tests/test_umap_on_iris.py:260:5 + | +258 | assert rms(fitter_with_precomputed_knn.graph_, iris_subset_model.graph_) < 0.005 +259 | +260 | / with pytest.warns(Warning, match="transforming new data") as record: +261 | | fitter_ignoring_force_approx = UMAP( +262 | | **umap_args, +263 | | precomputed_knn=(knn[0], knn[1]), +264 | | ).fit(data) +265 | | assert len(record) >= 1 + | |_______________________________^ +266 | np.testing.assert_array_equal( +267 | fitter_ignoring_force_approx.embedding_, fitter_with_precomputed_knn.embedding_, + | + +PT031 `pytest.warns()` block should contain a single simple statement + --> umap/tests/test_umap_on_iris.py:276:5 + | +275 | # force_approximation_algorithm parameter is ignored +276 | / with pytest.warns(Warning, match="transforming new data") as record: +277 | | fitter_ignoring_force_approx_True = UMAP( +278 | | **umap_args, +279 | | precomputed_knn=(knn[0], knn[1]), +280 | | force_approximation_algorithm=True, +281 | | ).fit(data) +282 | | assert len(record) >= 1 + | |_______________________________^ +283 | np.testing.assert_array_equal( +284 | fitter_ignoring_force_approx_True.embedding_, + | + +N806 Variable `fitter_ignoring_force_approx_True` in function should be lowercase + --> umap/tests/test_umap_on_iris.py:277:9 + | +275 | # force_approximation_algorithm parameter is ignored +276 | with pytest.warns(Warning, match="transforming new data") as record: +277 | fitter_ignoring_force_approx_True = UMAP( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +278 | **umap_args, +279 | precomputed_knn=(knn[0], knn[1]), + | + +ERA001 Found commented-out code + --> umap/tests/test_umap_ops.py:26:1 + | +24 | # @SkipTest +25 | # def test_scikit_learn_compatibility(): +26 | # check_estimator(UMAP) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_ops.py:33:1 + | +31 | # @SkipTest +32 | # def test_umap_regression_supervision(): # pragma: no cover +33 | # boston = load_boston() + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +34 | # data = boston.data +35 | # embedding = UMAP(n_neighbors=10, + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_ops.py:34:1 + | +32 | # def test_umap_regression_supervision(): # pragma: no cover +33 | # boston = load_boston() +34 | # data = boston.data + | ^^^^^^^^^^^^^^^^^^^^^^^^ +35 | # embedding = UMAP(n_neighbors=10, +36 | # min_dist=0.01, + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_ops.py:36:1 + | +34 | # data = boston.data +35 | # embedding = UMAP(n_neighbors=10, +36 | # min_dist=0.01, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +37 | # target_metric='euclidean', +38 | # random_state=42).fit_transform(data, boston.target) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_ops.py:37:1 + | +35 | # embedding = UMAP(n_neighbors=10, +36 | # min_dist=0.01, +37 | # target_metric='euclidean', + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +38 | # random_state=42).fit_transform(data, boston.target) +39 | # + | +help: Remove commented-out code + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:43:5 + | +42 | # Umap Clusterability +43 | def test_blobs_cluster(): + | ^^^^^^^^^^^^^^^^^^ +44 | data, labels = make_blobs(n_samples=500, n_features=10, centers=5) +45 | embedding = UMAP(n_epochs=100).fit_transform(data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:50:5 + | +49 | # Multi-components Layout +50 | def test_multi_component_layout(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +51 | data, labels = make_blobs( +52 | 100, 2, centers=5, cluster_std=0.5, center_box=(-20, 20), random_state=42, + | + +PLR2004 Magic value used in comparison, consider replacing `15.0` with a constant variable + --> umap/tests/test_umap_ops.py:73:20 + | +71 | error = np.sum((true_centroids - embed_centroids) ** 2) +72 | +73 | assert error < 15.0, "Multi component embedding to far astray" + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:77:5 + | +76 | # Multi-components Layout +77 | def test_multi_component_layout_precomputed(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +78 | data, labels = make_blobs( +79 | 100, 2, centers=5, cluster_std=0.5, center_box=(-20, 20), random_state=42, + | + +PLR2004 Magic value used in comparison, consider replacing `15.0` with a constant variable + --> umap/tests/test_umap_ops.py:103:20 + | +101 | error = np.sum((true_centroids - embed_centroids) ** 2) +102 | +103 | assert error < 15.0, "Multi component embedding to far astray" + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:109:5 + | +107 | @pytest.mark.parametrize("metric", ["jaccard", "hellinger"]) +108 | @pytest.mark.parametrize("force_approximation", [True, False]) +109 | def test_disconnected_data(num_isolates, metric, force_approximation): + | ^^^^^^^^^^^^^^^^^^^^^^ +110 | options = [False, True] +111 | disconnected_data = np.random.choice(a=options, size=(10, 30), p=[0.6, 1 - 0.6]) + | + +NPY002 Replace legacy `np.random.choice` call with `np.random.Generator` + --> umap/tests/test_umap_ops.py:111:25 + | +109 | def test_disconnected_data(num_isolates, metric, force_approximation): +110 | options = [False, True] +111 | disconnected_data = np.random.choice(a=options, size=(10, 30), p=[0.6, 1 - 0.6]) + | ^^^^^^^^^^^^^^^^ +112 | # Add some disconnected data for the corner case test +113 | disconnected_data = np.vstack( + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:147:5 + | +145 | @pytest.mark.parametrize("num_isolates", [1]) +146 | @pytest.mark.parametrize("sparse", [True, False]) +147 | def test_disconnected_data_precomputed(num_isolates, sparse): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +148 | disconnected_data = np.random.choice( +149 | a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66], + | + +NPY002 Replace legacy `np.random.choice` call with `np.random.Generator` + --> umap/tests/test_umap_ops.py:148:25 + | +146 | @pytest.mark.parametrize("sparse", [True, False]) +147 | def test_disconnected_data_precomputed(num_isolates, sparse): +148 | disconnected_data = np.random.choice( + | ^^^^^^^^^^^^^^^^ +149 | a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66], +150 | ) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:178:5 + | +178 | def test_bad_transform_data(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^ +179 | u = UMAP().fit([[1, 1, 1, 1]]) +180 | with pytest.raises(ValueError): + | + +ARG001 Unused function argument: `nn_data` + --> umap/tests/test_umap_ops.py:178:29 + | +178 | def test_bad_transform_data(nn_data): + | ^^^^^^^ +179 | u = UMAP().fit([[1, 1, 1, 1]]) +180 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_ops.py:180:24 + | +178 | def test_bad_transform_data(nn_data): +179 | u = UMAP().fit([[1, 1, 1, 1]]) +180 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +181 | u.transform([[0, 0, 0, 0]]) + | + +NPY002 Replace legacy `np.random.random` call with `np.random.Generator` + --> umap/tests/test_umap_ops.py:200:16 + | +198 | # The important point is that the new data has the same number of rows +199 | # as the original fit data +200 | new_data = np.random.random(data.shape) + | ^^^^^^^^^^^^^^^^ +201 | _ = fitter.transform(new_data) + | + +NPY002 Replace legacy `np.random.random` call with `np.random.Generator` + --> umap/tests/test_umap_ops.py:210:9 + | +209 | # Example from issue #217 +210 | a = np.random.random((100, 10)) + | ^^^^^^^^^^^^^^^^ +211 | b = np.random.random((100, 5)) + | + +NPY002 Replace legacy `np.random.random` call with `np.random.Generator` + --> umap/tests/test_umap_ops.py:211:9 + | +209 | # Example from issue #217 +210 | a = np.random.random((100, 10)) +211 | b = np.random.random((100, 5)) + | ^^^^^^^^^^^^^^^^ +212 | +213 | umap = UMAP(n_epochs=100) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:225:5 + | +223 | # UMAP Update +224 | # ----------- +225 | def test_umap_update(iris, iris_subset_model, iris_selection, iris_model): + | ^^^^^^^^^^^^^^^^ +226 | +227 | new_data = iris.data[~iris_selection] + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:243:5 + | +243 | def test_umap_update_large( + | ^^^^^^^^^^^^^^^^^^^^^^ +244 | iris, iris_subset_model_large, iris_selection, iris_model_large, +245 | ): + | + +PLR2004 Magic value used in comparison, consider replacing `3.0` with a constant variable + --> umap/tests/test_umap_ops.py:263:20 + | +261 | error = np.sum(np.abs((new_model.graph_ - comparison_graph).data)) +262 | +263 | assert error < 3.0 # Higher error tolerance based on approx nearest neighbors + | ^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:269:5 + | +267 | # UMAP Graph output +268 | # ----------------- +269 | def test_umap_graph_layout(): + | ^^^^^^^^^^^^^^^^^^^^^^ +270 | data, _labels = make_blobs(n_samples=500, n_features=10, centers=5) +271 | model = UMAP(n_epochs=100, transform_mode="graph") + | + +PLR2004 Magic value used in comparison, consider replacing `5` with a constant variable + --> umap/tests/test_umap_ops.py:275:18 + | +273 | assert scipy.sparse.issparse(graph) +274 | nc, _cl = scipy.sparse.csgraph.connected_components(graph) +275 | assert nc == 5 + | ^ +276 | +277 | new_graph = model.transform(data[:10] + np.random.normal(0.0, 0.1, size=(10, 10))) + | + +NPY002 Replace legacy `np.random.normal` call with `np.random.Generator` + --> umap/tests/test_umap_ops.py:277:45 + | +275 | assert nc == 5 +276 | +277 | new_graph = model.transform(data[:10] + np.random.normal(0.0, 0.1, size=(10, 10))) + | ^^^^^^^^^^^^^^^^ +278 | assert scipy.sparse.issparse(graph) +279 | assert new_graph.shape[0] == 10 + | + +PLR2004 Magic value used in comparison, consider replacing `10` with a constant variable + --> umap/tests/test_umap_ops.py:279:34 + | +277 | new_graph = model.transform(data[:10] + np.random.normal(0.0, 0.1, size=(10, 10))) +278 | assert scipy.sparse.issparse(graph) +279 | assert new_graph.shape[0] == 10 + | ^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_ops.py:287:5 + | +287 | def test_component_layout_options(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +288 | dmat = pairwise_distances(nn_data[:1000]) +289 | n_components = 5 + | + +PLR2004 Magic value used in comparison, consider replacing `5` with a constant variable + --> umap/tests/test_umap_ops.py:319:31 + | +317 | ) +318 | +319 | assert single.shape[0] == 5 + | ^ +320 | assert average.shape[0] == 5 +321 | assert complete.shape[0] == 5 + | + +PLR2004 Magic value used in comparison, consider replacing `5` with a constant variable + --> umap/tests/test_umap_ops.py:320:32 + | +319 | assert single.shape[0] == 5 +320 | assert average.shape[0] == 5 + | ^ +321 | assert complete.shape[0] == 5 + | + +PLR2004 Magic value used in comparison, consider replacing `5` with a constant variable + --> umap/tests/test_umap_ops.py:321:33 + | +319 | assert single.shape[0] == 5 +320 | assert average.shape[0] == 5 +321 | assert complete.shape[0] == 5 + | ^ +322 | +323 | assert not np.all(single == average) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:13:5 + | +13 | def test_repeated_points_large_sparse_spatial(sparse_spatial_data_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +14 | model = UMAP( +15 | n_neighbors=3, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:24:5 + | +24 | def test_repeated_points_small_sparse_spatial(sparse_spatial_data_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +25 | model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit( +26 | sparse_spatial_data_repeats, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:33:5 + | +31 | # Use force_approximation_algorithm in order to test the region +32 | # of the code that is called for n>4096 +33 | def test_repeated_points_large_dense_spatial(spatial_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +34 | model = UMAP( +35 | n_neighbors=3, unique=True, force_approximation_algorithm=True, n_epochs=50, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:40:5 + | +40 | def test_repeated_points_small_dense_spatial(spatial_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +41 | model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit(spatial_repeats) +42 | assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:53:5 + | +53 | def test_repeated_points_large_sparse_binary(sparse_binary_data_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +54 | model = UMAP( +55 | n_neighbors=3, unique=True, force_approximation_algorithm=True, n_epochs=50, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:60:5 + | +60 | def test_repeated_points_small_sparse_binary(sparse_binary_data_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +61 | model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit( +62 | sparse_binary_data_repeats, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:69:5 + | +67 | # Use force_approximation_algorithm in order to test +68 | # the region of the code that is called for n>4096 +69 | def test_repeated_points_large_dense_binary(binary_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +70 | model = UMAP( +71 | n_neighbors=3, unique=True, force_approximation_algorithm=True, n_epochs=20, + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:76:5 + | +76 | def test_repeated_points_small_dense_binary(binary_repeats): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +77 | model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit(binary_repeats) +78 | assert np.unique(binary_repeats[0:2], axis=0).shape[0] == 1 + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_repeated_data.py:92:5 + | +90 | # than the unique data set size +91 | # ---------------------------------------------------- +92 | def test_repeated_points_large_n(repetition_dense): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +93 | model = UMAP(n_neighbors=5, unique=True, n_epochs=20).fit(repetition_dense) +94 | assert model._n_neighbors == 3 + | + +SLF001 Private member accessed: `_n_neighbors` + --> umap/tests/test_umap_repeated_data.py:94:12 + | +92 | def test_repeated_points_large_n(repetition_dense): +93 | model = UMAP(n_neighbors=5, unique=True, n_epochs=20).fit(repetition_dense) +94 | assert model._n_neighbors == 3 + | ^^^^^^^^^^^^^^^^^^ + | + +PLR2004 Magic value used in comparison, consider replacing `3` with a constant variable + --> umap/tests/test_umap_repeated_data.py:94:34 + | +92 | def test_repeated_points_large_n(repetition_dense): +93 | model = UMAP(n_neighbors=5, unique=True, n_epochs=20).fit(repetition_dense) +94 | assert model._n_neighbors == 3 + | ^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:23:5 + | +23 | def test_umap_sparse_trustworthiness(sparse_test_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +24 | embedding = UMAP(n_neighbors=10, n_epochs=100).fit_transform(sparse_test_data[:100]) +25 | trust = trustworthiness(sparse_test_data[:100].toarray(), embedding, n_neighbors=10) + | + +PLR2004 Magic value used in comparison, consider replacing `0.88` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:27:18 + | +25 | trust = trustworthiness(sparse_test_data[:100].toarray(), embedding, n_neighbors=10) +26 | assert ( +27 | trust >= 0.88 + | ^^^^ +28 | ), f"Insufficiently trustworthy embedding for sparse test dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:31:5 + | +31 | def test_umap_trustworthiness_fast_approx(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +32 | data = nn_data[:50] +33 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.8` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:42:18 + | +40 | trust = trustworthiness(data, embedding, n_neighbors=10) +41 | assert ( +42 | trust >= 0.8 + | ^^^ +43 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:46:5 + | +46 | def test_umap_trustworthiness_random_init(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +47 | data = nn_data[:50] +48 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.8` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:53:18 + | +51 | trust = trustworthiness(data, embedding, n_neighbors=10) +52 | assert ( +53 | trust >= 0.8 + | ^^^ +54 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:57:5 + | +57 | def test_supervised_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +58 | data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +59 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:64:18 + | +62 | trust = trustworthiness(data, embedding, n_neighbors=10) +63 | assert ( +64 | trust >= 0.95 + | ^^^^ +65 | ), f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:68:5 + | +68 | def test_semisupervised_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +69 | data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +70 | labels[10:30] = -1 + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:76:18 + | +74 | trust = trustworthiness(data, embedding, n_neighbors=10) +75 | assert ( +76 | trust >= 0.95 + | ^^^^ +77 | ), f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:80:5 + | +80 | def test_metric_supervised_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +81 | data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +82 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:92:18 + | +90 | trust = trustworthiness(data, embedding, n_neighbors=10) +91 | assert ( +92 | trust >= 0.95 + | ^^^^ +93 | ), f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:96:5 + | +96 | def test_string_metric_supervised_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +97 | data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +98 | labels = np.array(["this", "that", "other"])[labels] + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:109:18 + | +107 | trust = trustworthiness(data, embedding, n_neighbors=10) +108 | assert ( +109 | trust >= 0.95 + | ^^^^ +110 | ), f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:113:5 + | +113 | def test_discrete_metric_supervised_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +114 | data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +115 | embedding = UMAP( + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:125:18 + | +123 | trust = trustworthiness(data, embedding, n_neighbors=10) +124 | assert ( +125 | trust >= 0.95 + | ^^^^ +126 | ), f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:129:5 + | +129 | def test_count_metric_supervised_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +130 | data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +131 | labels = (labels**2) + 2 * labels + | + +PLR2004 Magic value used in comparison, consider replacing `0.95` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:142:18 + | +140 | trust = trustworthiness(data, embedding, n_neighbors=10) +141 | assert ( +142 | trust >= 0.95 + | ^^^^ +143 | ), f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_trustworthiness.py:146:5 + | +146 | def test_sparse_precomputed_metric_umap_trustworthiness(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +147 | data, _labels = make_blobs(50, cluster_std=0.5, random_state=42) +148 | dmat = scipy.sparse.csr_matrix(pairwise_distances(data)) + | + +PLR2004 Magic value used in comparison, consider replacing `0.75` with a constant variable + --> umap/tests/test_umap_trustworthiness.py:158:18 + | +156 | trust = trustworthiness(data, embedding, n_neighbors=10) +157 | assert ( +158 | trust >= 0.75 + | ^^^^ +159 | ), f"Insufficiently trustworthy embedding fornn dataset: {trust}" + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:18:5 + | +18 | def test_umap_negative_op(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^ +19 | u = UMAP(set_op_mix_ratio=-1.0) +20 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:20:24 + | +18 | def test_umap_negative_op(nn_data): +19 | u = UMAP(set_op_mix_ratio=-1.0) +20 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +21 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:24:5 + | +24 | def test_umap_too_large_op(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^ +25 | u = UMAP(set_op_mix_ratio=1.5) +26 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:26:24 + | +24 | def test_umap_too_large_op(nn_data): +25 | u = UMAP(set_op_mix_ratio=1.5) +26 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +27 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:30:5 + | +30 | def test_umap_bad_too_large_min_dist(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +31 | u = UMAP(min_dist=2.0) +32 | # a RuntimeWarning about division by zero in a,b curve fitting is expected + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:36:28 + | +34 | with warnings.catch_warnings(): +35 | warnings.filterwarnings("ignore", category=RuntimeWarning) +36 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +37 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:40:5 + | +40 | def test_umap_negative_min_dist(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +41 | u = UMAP(min_dist=-1) +42 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:42:24 + | +40 | def test_umap_negative_min_dist(nn_data): +41 | u = UMAP(min_dist=-1) +42 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +43 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:46:5 + | +46 | def test_umap_negative_n_components(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +47 | u = UMAP(n_components=-1) +48 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:48:24 + | +46 | def test_umap_negative_n_components(nn_data): +47 | u = UMAP(n_components=-1) +48 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +49 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:52:5 + | +52 | def test_umap_non_integer_n_components(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +53 | u = UMAP(n_components=1.5) +54 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:54:24 + | +52 | def test_umap_non_integer_n_components(nn_data): +53 | u = UMAP(n_components=1.5) +54 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +55 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:58:5 + | +58 | def test_umap_too_small_n_neighbours(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +59 | u = UMAP(n_neighbors=0.5) +60 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:60:24 + | +58 | def test_umap_too_small_n_neighbours(nn_data): +59 | u = UMAP(n_neighbors=0.5) +60 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +61 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:64:5 + | +64 | def test_umap_negative_n_neighbours(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +65 | u = UMAP(n_neighbors=-1) +66 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:66:24 + | +64 | def test_umap_negative_n_neighbours(nn_data): +65 | u = UMAP(n_neighbors=-1) +66 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +67 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:70:5 + | +70 | def test_umap_bad_metric(nn_data): + | ^^^^^^^^^^^^^^^^^^^^ +71 | u = UMAP(metric=45) +72 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:72:24 + | +70 | def test_umap_bad_metric(nn_data): +71 | u = UMAP(metric=45) +72 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +73 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:76:5 + | +76 | def test_umap_negative_learning_rate(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +77 | u = UMAP(learning_rate=-1.5) +78 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:78:24 + | +76 | def test_umap_negative_learning_rate(nn_data): +77 | u = UMAP(learning_rate=-1.5) +78 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +79 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:82:5 + | +82 | def test_umap_negative_repulsion(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +83 | u = UMAP(repulsion_strength=-0.5) +84 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:84:24 + | +82 | def test_umap_negative_repulsion(nn_data): +83 | u = UMAP(repulsion_strength=-0.5) +84 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +85 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:88:5 + | +88 | def test_umap_negative_sample_rate(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +89 | u = UMAP(negative_sample_rate=-1) +90 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:90:24 + | +88 | def test_umap_negative_sample_rate(nn_data): +89 | u = UMAP(negative_sample_rate=-1) +90 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +91 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:94:5 + | +94 | def test_umap_bad_init(nn_data): + | ^^^^^^^^^^^^^^^^^^ +95 | u = UMAP(init="foobar") +96 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:96:24 + | +94 | def test_umap_bad_init(nn_data): +95 | u = UMAP(init="foobar") +96 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +97 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:100:5 + | +100 | def test_umap_bad_numeric_init(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +101 | u = UMAP(init=42) +102 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:102:24 + | +100 | def test_umap_bad_numeric_init(nn_data): +101 | u = UMAP(init=42) +102 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +103 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:106:5 + | +106 | def test_umap_bad_matrix_init(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +107 | u = UMAP(init=np.array([[0, 0, 0], [0, 0, 0]])) +108 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:108:24 + | +106 | def test_umap_bad_matrix_init(nn_data): +107 | u = UMAP(init=np.array([[0, 0, 0], [0, 0, 0]])) +108 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +109 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:112:5 + | +112 | def test_umap_negative_n_epochs(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +113 | u = UMAP(n_epochs=-2) +114 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:114:24 + | +112 | def test_umap_negative_n_epochs(nn_data): +113 | u = UMAP(n_epochs=-2) +114 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +115 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:118:5 + | +118 | def test_umap_negative_target_n_neighbours(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +119 | u = UMAP(target_n_neighbors=1) +120 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:120:24 + | +118 | def test_umap_negative_target_n_neighbours(nn_data): +119 | u = UMAP(target_n_neighbors=1) +120 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +121 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:124:5 + | +124 | def test_umap_bad_output_metric(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +125 | u = UMAP(output_metric="foobar") +126 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:126:24 + | +124 | def test_umap_bad_output_metric(nn_data): +125 | u = UMAP(output_metric="foobar") +126 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +127 | u.fit(nn_data) +128 | u = UMAP(output_metric="precomputed") + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:129:24 + | +127 | u.fit(nn_data) +128 | u = UMAP(output_metric="precomputed") +129 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +130 | u.fit(nn_data) +131 | u = UMAP(output_metric="hamming") + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:132:24 + | +130 | u.fit(nn_data) +131 | u = UMAP(output_metric="hamming") +132 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +133 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:136:5 + | +136 | def test_haversine_on_highd(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^ +137 | u = UMAP(metric="haversine") +138 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:138:24 + | +136 | def test_haversine_on_highd(nn_data): +137 | u = UMAP(metric="haversine") +138 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +139 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:142:5 + | +142 | def test_umap_haversine_embed_to_highd(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +143 | u = UMAP(n_components=3, output_metric="haversine") +144 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:144:24 + | +142 | def test_umap_haversine_embed_to_highd(nn_data): +143 | u = UMAP(n_components=3, output_metric="haversine") +144 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +145 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:148:5 + | +148 | def test_umap_too_many_neighbors_warns(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +149 | u = UMAP(a=1.2, b=1.75, n_neighbors=2000, n_epochs=11, init="random") +150 | u.fit(nn_data[:100,]) + | + +SLF001 Private member accessed: `_a` + --> umap/tests/test_umap_validation_params.py:151:12 + | +149 | u = UMAP(a=1.2, b=1.75, n_neighbors=2000, n_epochs=11, init="random") +150 | u.fit(nn_data[:100,]) +151 | assert u._a == 1.2 + | ^^^^ +152 | assert u._b == 1.75 + | + +PLR2004 Magic value used in comparison, consider replacing `1.2` with a constant variable + --> umap/tests/test_umap_validation_params.py:151:20 + | +149 | u = UMAP(a=1.2, b=1.75, n_neighbors=2000, n_epochs=11, init="random") +150 | u.fit(nn_data[:100,]) +151 | assert u._a == 1.2 + | ^^^ +152 | assert u._b == 1.75 + | + +SLF001 Private member accessed: `_b` + --> umap/tests/test_umap_validation_params.py:152:12 + | +150 | u.fit(nn_data[:100,]) +151 | assert u._a == 1.2 +152 | assert u._b == 1.75 + | ^^^^ + | + +PLR2004 Magic value used in comparison, consider replacing `1.75` with a constant variable + --> umap/tests/test_umap_validation_params.py:152:20 + | +150 | u.fit(nn_data[:100,]) +151 | assert u._a == 1.2 +152 | assert u._b == 1.75 + | ^^^^ + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:155:5 + | +155 | def test_densmap_lambda(nn_data): + | ^^^^^^^^^^^^^^^^^^^ +156 | u = UMAP(densmap=True, dens_lambda=-1.0) +157 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:157:24 + | +155 | def test_densmap_lambda(nn_data): +156 | u = UMAP(densmap=True, dens_lambda=-1.0) +157 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +158 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:161:5 + | +161 | def test_densmap_var_shift(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^ +162 | u = UMAP(densmap=True, dens_var_shift=-1.0) +163 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:163:24 + | +161 | def test_densmap_var_shift(nn_data): +162 | u = UMAP(densmap=True, dens_var_shift=-1.0) +163 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +164 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:167:5 + | +167 | def test_densmap_frac(nn_data): + | ^^^^^^^^^^^^^^^^^ +168 | u = UMAP(densmap=True, dens_frac=-1.0) +169 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:169:24 + | +167 | def test_densmap_frac(nn_data): +168 | u = UMAP(densmap=True, dens_frac=-1.0) +169 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +170 | u.fit(nn_data) +171 | u = UMAP(densmap=True, dens_frac=2.0) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:172:24 + | +170 | u.fit(nn_data) +171 | u = UMAP(densmap=True, dens_frac=2.0) +172 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +173 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:176:5 + | +176 | def test_umap_unique_and_precomputed(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +177 | u = UMAP(metric="precomputed", unique=True) +178 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:178:24 + | +176 | def test_umap_unique_and_precomputed(nn_data): +177 | u = UMAP(metric="precomputed", unique=True) +178 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +179 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:182:5 + | +182 | def test_densmap_bad_output_metric(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +183 | u = UMAP(densmap=True, output_metric="haversine") +184 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:184:24 + | +182 | def test_densmap_bad_output_metric(nn_data): +183 | u = UMAP(densmap=True, output_metric="haversine") +184 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +185 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:188:5 + | +188 | def test_umap_bad_n_components(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +189 | u = UMAP(n_components=2.3) +190 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:190:24 + | +188 | def test_umap_bad_n_components(nn_data): +189 | u = UMAP(n_components=2.3) +190 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +191 | u.fit(nn_data) +192 | u = UMAP(n_components="23") + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:193:24 + | +191 | u.fit(nn_data) +192 | u = UMAP(n_components="23") +193 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +194 | u.fit(nn_data) +195 | u = UMAP(n_components=np.float64(2.3)) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:196:24 + | +194 | u.fit(nn_data) +195 | u = UMAP(n_components=np.float64(2.3)) +196 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +197 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:200:5 + | +200 | def test_umap_bad_metrics(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^ +201 | u = UMAP(metric="foobar") +202 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:202:24 + | +200 | def test_umap_bad_metrics(nn_data): +201 | u = UMAP(metric="foobar") +202 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +203 | u.fit(nn_data) +204 | u = UMAP(metric=2.75) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:205:24 + | +203 | u.fit(nn_data) +204 | u = UMAP(metric=2.75) +205 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +206 | u.fit(nn_data) +207 | u = UMAP(output_metric="foobar") + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:208:24 + | +206 | u.fit(nn_data) +207 | u = UMAP(output_metric="foobar") +208 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +209 | u.fit(nn_data) +210 | u = UMAP(output_metric=2.75) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:211:24 + | +209 | u.fit(nn_data) +210 | u = UMAP(output_metric=2.75) +211 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +212 | u.fit(nn_data) +213 | # u = UMAP(target_metric="foobar") + | + +ERA001 Found commented-out code + --> umap/tests/test_umap_validation_params.py:213:5 + | +211 | with pytest.raises(ValueError): +212 | u.fit(nn_data) +213 | # u = UMAP(target_metric="foobar") + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +214 | # assert_raises(ValueError, u.fit, nn_data) +215 | # u = UMAP(target_metric=2.75) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_validation_params.py:214:5 + | +212 | u.fit(nn_data) +213 | # u = UMAP(target_metric="foobar") +214 | # assert_raises(ValueError, u.fit, nn_data) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +215 | # u = UMAP(target_metric=2.75) +216 | # assert_raises(ValueError, u.fit, nn_data) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_validation_params.py:215:5 + | +213 | # u = UMAP(target_metric="foobar") +214 | # assert_raises(ValueError, u.fit, nn_data) +215 | # u = UMAP(target_metric=2.75) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +216 | # assert_raises(ValueError, u.fit, nn_data) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/tests/test_umap_validation_params.py:216:5 + | +214 | # assert_raises(ValueError, u.fit, nn_data) +215 | # u = UMAP(target_metric=2.75) +216 | # assert_raises(ValueError, u.fit, nn_data) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + | +help: Remove commented-out code + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:219:5 + | +219 | def test_umap_bad_n_jobs(nn_data): + | ^^^^^^^^^^^^^^^^^^^^ +220 | u = UMAP(n_jobs=-2) +221 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:221:24 + | +219 | def test_umap_bad_n_jobs(nn_data): +220 | u = UMAP(n_jobs=-2) +221 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +222 | u.fit(nn_data) +223 | u = UMAP(n_jobs=0) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:224:24 + | +222 | u.fit(nn_data) +223 | u = UMAP(n_jobs=0) +224 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +225 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:228:5 + | +228 | def test_umap_custom_distance_w_grad(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +229 | @numba.njit() +230 | def dist1(x, y): + | + +PT030 `pytest.warns(UserWarning)` is too broad, set the `match` parameter or use a more specific warning + --> umap/tests/test_umap_validation_params.py:238:23 + | +237 | u = UMAP(metric=dist1, n_epochs=11) +238 | with pytest.warns(UserWarning) as warnings: + | ^^^^^^^^^^^ +239 | u.fit(nn_data[:10]) +240 | assert len(warnings) >= 1 + | + +PT030 `pytest.warns(UserWarning)` is too broad, set the `match` parameter or use a more specific warning + --> umap/tests/test_umap_validation_params.py:243:23 + | +242 | u = UMAP(metric=dist2, n_epochs=11) +243 | with pytest.warns(UserWarning) as warnings: + | ^^^^^^^^^^^ +244 | u.fit(nn_data[:10]) +245 | assert len(warnings) <= 1 + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:248:5 + | +248 | def test_umap_bad_output_metric_no_grad(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +249 | @numba.njit() +250 | def dist1(x, y): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:254:24 + | +253 | u = UMAP(output_metric=dist1) +254 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +255 | u.fit(nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:258:5 + | +258 | def test_umap_bad_hellinger_data(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +259 | u = UMAP(metric="hellinger") +260 | with pytest.raises(ValueError): + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:260:24 + | +258 | def test_umap_bad_hellinger_data(nn_data): +259 | u = UMAP(metric="hellinger") +260 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +261 | u.fit(-nn_data) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:264:5 + | +264 | def test_umap_update_bad_params(nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +265 | dmat = pairwise_distances(nn_data[:100]) +266 | u = UMAP(metric="precomputed", n_epochs=11) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:268:24 + | +266 | u = UMAP(metric="precomputed", n_epochs=11) +267 | u.fit(dmat) +268 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +269 | u.update(dmat) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:273:24 + | +271 | u = UMAP(n_epochs=11) +272 | u.fit(nn_data[:100], y=np.repeat(np.arange(5), 20)) +273 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +274 | u.update(nn_data[100:200]) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:277:5 + | +277 | def test_umap_fit_data_and_targets_compliant(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +278 | # x and y are required to be the same length +279 | u = UMAP() + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:280:9 + | +278 | # x and y are required to be the same length +279 | u = UMAP() +280 | x = np.random.uniform(0, 1, (256, 10)) + | ^^^^^^^^^^^^^^^^^ +281 | y = np.random.randint(10, size=(257,)) +282 | with pytest.raises(ValueError): + | + +NPY002 Replace legacy `np.random.randint` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:281:9 + | +279 | u = UMAP() +280 | x = np.random.uniform(0, 1, (256, 10)) +281 | y = np.random.randint(10, size=(257,)) + | ^^^^^^^^^^^^^^^^^ +282 | with pytest.raises(ValueError): +283 | u.fit(x, y) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:282:24 + | +280 | x = np.random.uniform(0, 1, (256, 10)) +281 | y = np.random.randint(10, size=(257,)) +282 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +283 | u.fit(x, y) + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:286:9 + | +285 | u = UMAP() +286 | x = np.random.uniform(0, 1, (256, 10)) + | ^^^^^^^^^^^^^^^^^ +287 | y = np.random.randint(10, size=(255,)) +288 | with pytest.raises(ValueError): + | + +NPY002 Replace legacy `np.random.randint` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:287:9 + | +285 | u = UMAP() +286 | x = np.random.uniform(0, 1, (256, 10)) +287 | y = np.random.randint(10, size=(255,)) + | ^^^^^^^^^^^^^^^^^ +288 | with pytest.raises(ValueError): +289 | u.fit(x, y) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:288:24 + | +286 | x = np.random.uniform(0, 1, (256, 10)) +287 | y = np.random.randint(10, size=(255,)) +288 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +289 | u.fit(x, y) + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:292:9 + | +291 | u = UMAP() +292 | x = np.random.uniform(0, 1, (256, 10)) + | ^^^^^^^^^^^^^^^^^ +293 | with pytest.raises(ValueError): +294 | u.fit(x, []) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:293:24 + | +291 | u = UMAP() +292 | x = np.random.uniform(0, 1, (256, 10)) +293 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +294 | u.fit(x, []) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:297:5 + | +297 | def test_umap_fit_instance_returned(): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +298 | # Test that fit returns a new UMAP instance + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:302:9 + | +300 | # Passing both data and targets +301 | u = UMAP() +302 | x = np.random.uniform(0, 1, (256, 10)) + | ^^^^^^^^^^^^^^^^^ +303 | y = np.random.randint(10, size=(256,)) +304 | res = u.fit(x, y) + | + +NPY002 Replace legacy `np.random.randint` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:303:9 + | +301 | u = UMAP() +302 | x = np.random.uniform(0, 1, (256, 10)) +303 | y = np.random.randint(10, size=(256,)) + | ^^^^^^^^^^^^^^^^^ +304 | res = u.fit(x, y) +305 | assert isinstance(res, UMAP) + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/tests/test_umap_validation_params.py:309:9 + | +307 | # Passing only data +308 | u = UMAP() +309 | x = np.random.uniform(0, 1, (256, 10)) + | ^^^^^^^^^^^^^^^^^ +310 | res = u.fit(x) +311 | assert isinstance(res, UMAP) + | + +D103 Missing docstring in public function + --> umap/tests/test_umap_validation_params.py:314:5 + | +314 | def test_umap_inverse_transform_fails_expectedly(sparse_spatial_data, nn_data): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +315 | u = UMAP(n_epochs=11) +316 | u.fit(sparse_spatial_data[:100]) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:317:24 + | +315 | u = UMAP(n_epochs=11) +316 | u.fit(sparse_spatial_data[:100]) +317 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +318 | u.inverse_transform(u.embedding_[:10]) +319 | u = UMAP(metric="dice", n_epochs=11) + | + +PT011 `pytest.raises(ValueError)` is too broad, set the `match` parameter or use a more specific exception + --> umap/tests/test_umap_validation_params.py:321:24 + | +319 | u = UMAP(metric="dice", n_epochs=11) +320 | u.fit(nn_data[:100]) +321 | with pytest.raises(ValueError): + | ^^^^^^^^^^ +322 | u.inverse_transform(u.embedding_[:10]) + | + +D103 Missing docstring in public function + --> umap/umap_.py:66:5 + | +66 | def flatten_iter(container): + | ^^^^^^^^^^^^ +67 | for i in container: +68 | if isinstance(i, (list, tuple)): + | + +D103 Missing docstring in public function + --> umap/umap_.py:74:5 + | +74 | def flattened(container): + | ^^^^^^^^^ +75 | return tuple(flatten_iter(container)) + | + +D103 Missing docstring in public function + --> umap/umap_.py:78:5 + | +78 | def breadth_first_search(adjmat, start, min_vertices): + | ^^^^^^^^^^^^^^^^^^^^ +79 | explored = [] +80 | queue = [start] + | + +PLR0913 Too many arguments in function definition (6 > 5) + --> umap/umap_.py:104:5 + | +104 | def raise_disconnected_warning( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^ +105 | edges_removed, +106 | vertices_disconnected, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:110:5 + | +108 | total_rows, +109 | threshold=0.1, +110 | verbose=False, + | ^^^^^^^ +111 | ): +112 | """A simple wrapper function to avoid large amounts of code repetition.""" + | + +D401 First line of docstring should be in imperative mood: "A simple wrapper function to avoid large amounts of code repetition." + --> umap/umap_.py:112:5 + | +110 | verbose=False, +111 | ): +112 | """A simple wrapper function to avoid large amounts of code repetition.""" + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +113 | if verbose & (vertices_disconnected == 0) & (edges_removed > 0): +114 | pass + | + +C901 `smooth_knn_dist` is too complex (14 > 10) + --> umap/umap_.py:143:5 + | +141 | fastmath=True, +142 | ) # benchmarking `parallel=True` shows it to *decrease* performance +143 | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0): + | ^^^^^^^^^^^^^^^ +144 | """Compute a continuous version of the distance to the kth nearest +145 | neighbor. That is, this is similar to knn-distance but allows continuous + | + +PLR0912 Too many branches (17 > 12) + --> umap/umap_.py:143:5 + | +141 | fastmath=True, +142 | ) # benchmarking `parallel=True` shows it to *decrease* performance +143 | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0): + | ^^^^^^^^^^^^^^^ +144 | """Compute a continuous version of the distance to the kth nearest +145 | neighbor. That is, this is similar to knn-distance but allows continuous + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:144:5 + | +142 | ) # benchmarking `parallel=True` shows it to *decrease* performance +143 | def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1.0): +144 | / """Compute a continuous version of the distance to the kth nearest +145 | | neighbor. That is, this is similar to knn-distance but allows continuous +146 | | k values rather than requiring an integral k. In essence we are simply +147 | | computing the distance such that the cardinality of fuzzy set we generate +148 | | is k. +149 | | +150 | | Parameters +151 | | ---------- +152 | | distances: array of shape (n_samples, n_neighbors) +153 | | Distances to nearest neighbors for each sample. Each row should be a +154 | | sorted list of distances to a given samples nearest neighbors. +155 | | +156 | | k: float +157 | | The number of nearest neighbors to approximate for. +158 | | +159 | | n_iter: int (optional, default 64) +160 | | We need to binary search for the correct distance value. This is the +161 | | max number of iterations to use in such a search. +162 | | +163 | | local_connectivity: int (optional, default 1) +164 | | The local connectivity required -- i.e. the number of nearest +165 | | neighbors that should be assumed to be connected at a local level. +166 | | The higher this value the more connected the manifold becomes +167 | | locally. In practice this should be not more than the local intrinsic +168 | | dimension of the manifold. +169 | | +170 | | bandwidth: float (optional, default 1) +171 | | The target bandwidth of the kernel, larger values will produce +172 | | larger return values. +173 | | +174 | | Returns +175 | | ------- +176 | | knn_dist: array of shape (n_samples,) +177 | | The distance to kth nearest neighbor, as suitably approximated. +178 | | +179 | | nn_dist: array of shape (n_samples,) +180 | | The distance to the 1st nearest neighbor for each point. +181 | | +182 | | """ + | |_______^ +183 | target = np.log2(k) * bandwidth +184 | rho = np.zeros(distances.shape[0], dtype=np.float32) + | +help: Insert single blank line + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/umap_.py:194:11 + | +192 | mid = 1.0 +193 | +194 | # TODO: This is very inefficient, but will do for now. FIXME + | ^^^^ +195 | ith_distances = distances[i] +196 | non_zero_dists = ith_distances[ith_distances > 0.0] + | + +TD003 Missing issue link for this TODO + --> umap/umap_.py:194:11 + | +192 | mid = 1.0 +193 | +194 | # TODO: This is very inefficient, but will do for now. FIXME + | ^^^^ +195 | ith_distances = distances[i] +196 | non_zero_dists = ith_distances[ith_distances > 0.0] + | + +FIX002 Line contains TODO, consider resolving the issue + --> umap/umap_.py:194:11 + | +192 | mid = 1.0 +193 | +194 | # TODO: This is very inefficient, but will do for now. FIXME + | ^^^^ +195 | ith_distances = distances[i] +196 | non_zero_dists = ith_distances[ith_distances > 0.0] + | + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/umap_.py:236:11 + | +234 | result[i] = mid +235 | +236 | # TODO: This is very inefficient, but will do for now. FIXME + | ^^^^ +237 | if rho[i] > 0.0: +238 | mean_ith_distances = np.mean(ith_distances) + | + +TD003 Missing issue link for this TODO + --> umap/umap_.py:236:11 + | +234 | result[i] = mid +235 | +236 | # TODO: This is very inefficient, but will do for now. FIXME + | ^^^^ +237 | if rho[i] > 0.0: +238 | mean_ith_distances = np.mean(ith_distances) + | + +FIX002 Line contains TODO, consider resolving the issue + --> umap/umap_.py:236:11 + | +234 | result[i] = mid +235 | +236 | # TODO: This is very inefficient, but will do for now. FIXME + | ^^^^ +237 | if rho[i] > 0.0: +238 | mean_ith_distances = np.mean(ith_distances) + | + +PLR0913 Too many arguments in function definition (10 > 5) + --> umap/umap_.py:246:5 + | +246 | def nearest_neighbors( + | ^^^^^^^^^^^^^^^^^ +247 | X, +248 | n_neighbors, + | + +D417 Missing argument descriptions in the docstring for `nearest_neighbors`: `n_jobs`, `use_pynndescent` + --> umap/umap_.py:246:5 + | +246 | def nearest_neighbors( + | ^^^^^^^^^^^^^^^^^ +247 | X, +248 | n_neighbors, + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:247:5 + | +246 | def nearest_neighbors( +247 | X, + | ^ +248 | n_neighbors, +249 | metric, + | + +ARG001 Unused function argument: `angular` + --> umap/umap_.py:251:5 + | +249 | metric, +250 | metric_kwds, +251 | angular, + | ^^^^^^^ +252 | random_state, +253 | low_memory=True, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:253:5 + | +251 | angular, +252 | random_state, +253 | low_memory=True, + | ^^^^^^^^^^ +254 | use_pynndescent=True, +255 | n_jobs=-1, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:254:5 + | +252 | random_state, +253 | low_memory=True, +254 | use_pynndescent=True, + | ^^^^^^^^^^^^^^^ +255 | n_jobs=-1, +256 | verbose=False, + | + +ARG001 Unused function argument: `use_pynndescent` + --> umap/umap_.py:254:5 + | +252 | random_state, +253 | low_memory=True, +254 | use_pynndescent=True, + | ^^^^^^^^^^^^^^^ +255 | n_jobs=-1, +256 | verbose=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:256:5 + | +254 | use_pynndescent=True, +255 | n_jobs=-1, +256 | verbose=False, + | ^^^^^^^ +257 | ): +258 | """Compute the ``n_neighbors`` nearest points for each data point in ``X`` + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:258:5 + | +256 | verbose=False, +257 | ): +258 | / """Compute the ``n_neighbors`` nearest points for each data point in ``X`` +259 | | under ``metric``. This may be exact, but more likely is approximated via +260 | | nearest neighbor descent. +261 | | +262 | | Parameters +263 | | ---------- +264 | | X: array of shape (n_samples, n_features) +265 | | The input data to compute the k-neighbor graph of. +266 | | +267 | | n_neighbors: int +268 | | The number of nearest neighbors to compute for each sample in ``X``. +269 | | +270 | | metric: string or callable +271 | | The metric to use for the computation. +272 | | +273 | | metric_kwds: dict +274 | | Any arguments to pass to the metric computation function. +275 | | +276 | | angular: bool +277 | | Whether to use angular rp trees in NN approximation. +278 | | +279 | | random_state: np.random state +280 | | The random state to use for approximate NN computations. +281 | | +282 | | low_memory: bool (optional, default True) +283 | | Whether to pursue lower memory NNdescent. +284 | | +285 | | verbose: bool (optional, default False) +286 | | Whether to print status data during the computation. +287 | | +288 | | Returns +289 | | ------- +290 | | knn_indices: array of shape (n_samples, n_neighbors) +291 | | The indices on the ``n_neighbors`` closest points in the dataset. +292 | | +293 | | knn_dists: array of shape (n_samples, n_neighbors) +294 | | The distances to the ``n_neighbors`` closest points in the dataset. +295 | | +296 | | rp_forest: list of trees +297 | | The random projection forest used for searching (if used, None otherwise). +298 | | +299 | | """ + | |_______^ +300 | if verbose: +301 | pass + | +help: Insert single blank line + +ERA001 Found commented-out code + --> umap/umap_.py:307:9 + | +305 | # Compute indices of n nearest neighbors +306 | knn_indices = fast_knn_indices(X, n_neighbors) +307 | # knn_indices = np.argsort(X)[:, :n_neighbors] + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +308 | # Compute the nearest neighbor distances +309 | # (equivalent to np.sort(X)[:,:n_neighbors]) + | +help: Remove commented-out code + +TD002 Missing author in TODO; try: `# TODO(): ...` or `# TODO @: ...` + --> umap/umap_.py:317:11 + | +315 | knn_search_index = None +316 | else: +317 | # TODO: Hacked values for now + | ^^^^ +318 | n_trees = min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)) +319 | n_iters = max(5, round(np.log2(X.shape[0]))) + | + +TD003 Missing issue link for this TODO + --> umap/umap_.py:317:11 + | +315 | knn_search_index = None +316 | else: +317 | # TODO: Hacked values for now + | ^^^^ +318 | n_trees = min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)) +319 | n_iters = max(5, round(np.log2(X.shape[0]))) + | + +FIX002 Line contains TODO, consider resolving the issue + --> umap/umap_.py:317:11 + | +315 | knn_search_index = None +316 | else: +317 | # TODO: Hacked values for now + | ^^^^ +318 | n_trees = min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)) +319 | n_iters = max(5, round(np.log2(X.shape[0]))) + | + +PLR0913 Too many arguments in function definition (6 > 5) + --> umap/umap_.py:352:5 + | +350 | fastmath=True, +351 | ) +352 | def compute_membership_strengths( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +353 | knn_indices, +354 | knn_dists, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:357:5 + | +355 | sigmas, +356 | rhos, +357 | return_dists=False, + | ^^^^^^^^^^^^ +358 | bipartite=False, +359 | ): + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:358:5 + | +356 | rhos, +357 | return_dists=False, +358 | bipartite=False, + | ^^^^^^^^^ +359 | ): +360 | """Construct the membership strength data for the 1-skeleton of each local + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:360:5 + | +358 | bipartite=False, +359 | ): +360 | / """Construct the membership strength data for the 1-skeleton of each local +361 | | fuzzy simplicial set -- this is formed as a sparse matrix where each row is +362 | | a local fuzzy simplicial set, with a membership strength for the +363 | | 1-simplex to each other data point. +364 | | +365 | | Parameters +366 | | ---------- +367 | | knn_indices: array of shape (n_samples, n_neighbors) +368 | | The indices on the ``n_neighbors`` closest points in the dataset. +369 | | +370 | | knn_dists: array of shape (n_samples, n_neighbors) +371 | | The distances to the ``n_neighbors`` closest points in the dataset. +372 | | +373 | | sigmas: array of shape(n_samples) +374 | | The normalization factor derived from the metric tensor approximation. +375 | | +376 | | rhos: array of shape(n_samples) +377 | | The local connectivity adjustment. +378 | | +379 | | return_dists: bool (optional, default False) +380 | | Whether to return the pairwise distance associated with each edge. +381 | | +382 | | bipartite: bool (optional, default False) +383 | | Does the nearest neighbour set represent a bipartite graph? That is, are the +384 | | nearest neighbour indices from the same point set as the row indices? +385 | | +386 | | Returns +387 | | ------- +388 | | rows: array of shape (n_samples * n_neighbors) +389 | | Row data for the resulting sparse matrix (coo format) +390 | | +391 | | cols: array of shape (n_samples * n_neighbors) +392 | | Column data for the resulting sparse matrix (coo format) +393 | | +394 | | vals: array of shape (n_samples * n_neighbors) +395 | | Entries for the resulting sparse matrix (coo format) +396 | | +397 | | dists: array of shape (n_samples * n_neighbors) +398 | | Distance associated with each entry in the resulting sparse matrix +399 | | +400 | | """ + | |_______^ +401 | n_samples = knn_indices.shape[0] +402 | n_neighbors = knn_indices.shape[1] + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (13 > 5) + --> umap/umap_.py:431:5 + | +431 | def fuzzy_simplicial_set( + | ^^^^^^^^^^^^^^^^^^^^ +432 | X, +433 | n_neighbors, + | + +D417 Missing argument description in the docstring for `fuzzy_simplicial_set`: `apply_set_operations` + --> umap/umap_.py:431:5 + | +431 | def fuzzy_simplicial_set( + | ^^^^^^^^^^^^^^^^^^^^ +432 | X, +433 | n_neighbors, + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:432:5 + | +431 | def fuzzy_simplicial_set( +432 | X, + | ^ +433 | n_neighbors, +434 | random_state, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:439:5 + | +437 | knn_indices=None, +438 | knn_dists=None, +439 | angular=False, + | ^^^^^^^ +440 | set_op_mix_ratio=1.0, +441 | local_connectivity=1.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:442:5 + | +440 | set_op_mix_ratio=1.0, +441 | local_connectivity=1.0, +442 | apply_set_operations=True, + | ^^^^^^^^^^^^^^^^^^^^ +443 | verbose=False, +444 | return_dists=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:443:5 + | +441 | local_connectivity=1.0, +442 | apply_set_operations=True, +443 | verbose=False, + | ^^^^^^^ +444 | return_dists=None, +445 | ): + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:446:5 + | +444 | return_dists=None, +445 | ): +446 | / """Given a set of data X, a neighborhood size, and a measure of distance +447 | | compute the fuzzy simplicial set (here represented as a fuzzy graph in +448 | | the form of a sparse matrix) associated to the data. This is done by +449 | | locally approximating geodesic distance at each point, creating a fuzzy +450 | | simplicial set for each such point, and then combining all the local +451 | | fuzzy simplicial sets into a global one via a fuzzy union. +452 | | +453 | | Parameters +454 | | ---------- +455 | | X: array of shape (n_samples, n_features) +456 | | The data to be modelled as a fuzzy simplicial set. +457 | | +458 | | n_neighbors: int +459 | | The number of neighbors to use to approximate geodesic distance. +460 | | Larger numbers induce more global estimates of the manifold that can +461 | | miss finer detail, while smaller values will focus on fine manifold +462 | | structure to the detriment of the larger picture. +463 | | +464 | | random_state: numpy RandomState or equivalent +465 | | A state capable being used as a numpy random state. +466 | | +467 | | metric: string or function (optional, default 'euclidean') +468 | | The metric to use to compute distances in high dimensional space. +469 | | If a string is passed it must match a valid predefined metric. If +470 | | a general metric is required a function that takes two 1d arrays and +471 | | returns a float can be provided. For performance purposes it is +472 | | required that this be a numba jit'd function. Valid string metrics +473 | | include: +474 | | +475 | | * euclidean (or l2) +476 | | * manhattan (or l1) +477 | | * cityblock +478 | | * braycurtis +479 | | * canberra +480 | | * chebyshev +481 | | * correlation +482 | | * cosine +483 | | * dice +484 | | * hamming +485 | | * jaccard +486 | | * kulsinski +487 | | * ll_dirichlet +488 | | * mahalanobis +489 | | * matching +490 | | * minkowski +491 | | * rogerstanimoto +492 | | * russellrao +493 | | * seuclidean +494 | | * sokalmichener +495 | | * sokalsneath +496 | | * sqeuclidean +497 | | * yule +498 | | * wminkowski +499 | | +500 | | Metrics that take arguments (such as minkowski, mahalanobis etc.) +501 | | can have arguments passed via the metric_kwds dictionary. At this +502 | | time care must be taken and dictionary elements must be ordered +503 | | appropriately; this will hopefully be fixed in the future. +504 | | +505 | | metric_kwds: dict (optional, default {}) +506 | | Arguments to pass on to the metric, such as the ``p`` value for +507 | | Minkowski distance. +508 | | +509 | | knn_indices: array of shape (n_samples, n_neighbors) (optional) +510 | | If the k-nearest neighbors of each point has already been calculated +511 | | you can pass them in here to save computation time. This should be +512 | | an array with the indices of the k-nearest neighbors as a row for +513 | | each data point. +514 | | +515 | | knn_dists: array of shape (n_samples, n_neighbors) (optional) +516 | | If the k-nearest neighbors of each point has already been calculated +517 | | you can pass them in here to save computation time. This should be +518 | | an array with the distances of the k-nearest neighbors as a row for +519 | | each data point. +520 | | +521 | | angular: bool (optional, default False) +522 | | Whether to use angular/cosine distance for the random projection +523 | | forest for seeding NN-descent to determine approximate nearest +524 | | neighbors. +525 | | +526 | | set_op_mix_ratio: float (optional, default 1.0) +527 | | Interpolate between (fuzzy) union and intersection as the set operation +528 | | used to combine local fuzzy simplicial sets to obtain a global fuzzy +529 | | simplicial sets. Both fuzzy set operations use the product t-norm. +530 | | The value of this parameter should be between 0.0 and 1.0; a value of +531 | | 1.0 will use a pure fuzzy union, while 0.0 will use a pure fuzzy +532 | | intersection. +533 | | +534 | | local_connectivity: int (optional, default 1) +535 | | The local connectivity required -- i.e. the number of nearest +536 | | neighbors that should be assumed to be connected at a local level. +537 | | The higher this value the more connected the manifold becomes +538 | | locally. In practice this should be not more than the local intrinsic +539 | | dimension of the manifold. +540 | | +541 | | verbose: bool (optional, default False) +542 | | Whether to report information on the current progress of the algorithm. +543 | | +544 | | return_dists: bool or None (optional, default None) +545 | | Whether to return the pairwise distance associated with each edge. +546 | | +547 | | Returns +548 | | ------- +549 | | fuzzy_simplicial_set: coo_matrix +550 | | A fuzzy simplicial set represented as a sparse matrix. The (i, +551 | | j) entry of the matrix represents the membership strength of the +552 | | 1-simplex between the ith and jth sample points. +553 | | +554 | | """ + | |_______^ +555 | if metric_kwds is None: +556 | metric_kwds = {} + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (6 > 5) + --> umap/umap_.py:612:5 + | +611 | @numba.njit() +612 | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0): + | ^^^^^^^^^^^^^^^^^ +613 | """Under the assumption of categorical distance for the intersecting +614 | simplicial set perform a fast intersection. + | + +D417 Missing argument description in the docstring for `fast_intersection`: `far_dist` + --> umap/umap_.py:612:5 + | +611 | @numba.njit() +612 | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0): + | ^^^^^^^^^^^^^^^^^ +613 | """Under the assumption of categorical distance for the intersecting +614 | simplicial set perform a fast intersection. + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:613:5 + | +611 | @numba.njit() +612 | def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0): +613 | / """Under the assumption of categorical distance for the intersecting +614 | | simplicial set perform a fast intersection. +615 | | +616 | | Parameters +617 | | ---------- +618 | | rows: array +619 | | An array of the row of each non-zero in the sparse matrix +620 | | representation. +621 | | +622 | | cols: array +623 | | An array of the column of each non-zero in the sparse matrix +624 | | representation. +625 | | +626 | | values: array +627 | | An array of the value of each non-zero in the sparse matrix +628 | | representation. +629 | | +630 | | target: array of shape (n_samples) +631 | | The categorical labels to use in the intersection. +632 | | +633 | | unknown_dist: float (optional, default 1.0) +634 | | The distance an unknown label (-1) is assumed to be from any point. +635 | | +636 | | far_dist float (optional, default 5.0) +637 | | The distance between unmatched labels. +638 | | +639 | | Returns +640 | | ------- +641 | | None +642 | | +643 | | """ + | |_______^ +644 | for nz in range(rows.shape[0]): +645 | i = rows[nz] + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (7 > 5) + --> umap/umap_.py:655:5 + | +654 | @numba.njit() +655 | def fast_metric_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^ +656 | rows, cols, values, discrete_space, metric, metric_args, scale, +657 | ): + | + +D417 Missing argument description in the docstring for `fast_metric_intersection`: `metric_args` + --> umap/umap_.py:655:5 + | +654 | @numba.njit() +655 | def fast_metric_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^ +656 | rows, cols, values, discrete_space, metric, metric_args, scale, +657 | ): + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:658:5 + | +656 | rows, cols, values, discrete_space, metric, metric_args, scale, +657 | ): +658 | / """Under the assumption of categorical distance for the intersecting +659 | | simplicial set perform a fast intersection. +660 | | +661 | | Parameters +662 | | ---------- +663 | | rows: array +664 | | An array of the row of each non-zero in the sparse matrix +665 | | representation. +666 | | +667 | | cols: array +668 | | An array of the column of each non-zero in the sparse matrix +669 | | representation. +670 | | +671 | | values: array of shape +672 | | An array of the values of each non-zero in the sparse matrix +673 | | representation. +674 | | +675 | | discrete_space: array of shape (n_samples, n_features) +676 | | The vectors of categorical labels to use in the intersection. +677 | | +678 | | metric: numba function +679 | | The function used to calculate distance over the target array. +680 | | +681 | | scale: float +682 | | A scaling to apply to the metric. +683 | | +684 | | Returns +685 | | ------- +686 | | None +687 | | +688 | | """ + | |_______^ +689 | for nz in range(rows.shape[0]): +690 | i = rows[nz] + | +help: Insert single blank line + +D103 Missing docstring in public function + --> umap/umap_.py:698:5 + | +697 | @numba.njit() +698 | def reprocess_row(probabilities, k=15, n_iters=32): + | ^^^^^^^^^^^^^ +699 | target = np.log2(k) + | + +D103 Missing docstring in public function + --> umap/umap_.py:728:5 + | +727 | @numba.njit() +728 | def reset_local_metrics(simplicial_set_indptr, simplicial_set_data): + | ^^^^^^^^^^^^^^^^^^^ +729 | for i in range(simplicial_set_indptr.shape[0] - 1): +730 | simplicial_set_data[simplicial_set_indptr[i] : simplicial_set_indptr[i + 1]] = ( + | + +D417 Missing argument description in the docstring for `reset_local_connectivity`: `reset_local_metric` + --> umap/umap_.py:739:5 + | +739 | def reset_local_connectivity(simplicial_set, reset_local_metric=False): + | ^^^^^^^^^^^^^^^^^^^^^^^^ +740 | """Reset the local connectivity requirement -- each data sample should +741 | have complete confidence in at least one 1-simplex in the simplicial set. + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:739:46 + | +739 | def reset_local_connectivity(simplicial_set, reset_local_metric=False): + | ^^^^^^^^^^^^^^^^^^ +740 | """Reset the local connectivity requirement -- each data sample should +741 | have complete confidence in at least one 1-simplex in the simplicial set. + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:740:5 + | +739 | def reset_local_connectivity(simplicial_set, reset_local_metric=False): +740 | / """Reset the local connectivity requirement -- each data sample should +741 | | have complete confidence in at least one 1-simplex in the simplicial set. +742 | | We can enforce this by locally rescaling confidences, and then remerging the +743 | | different local simplicial sets together. +744 | | +745 | | Parameters +746 | | ---------- +747 | | simplicial_set: sparse matrix +748 | | The simplicial set for which to recalculate with respect to local +749 | | connectivity. +750 | | +751 | | Returns +752 | | ------- +753 | | simplicial_set: sparse_matrix +754 | | The recalculated simplicial set, now with the local connectivity +755 | | assumption restored. +756 | | +757 | | """ + | |_______^ +758 | simplicial_set = normalize(simplicial_set, norm="max") +759 | if reset_local_metric: + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (7 > 5) + --> umap/umap_.py:771:5 + | +771 | def discrete_metric_simplicial_set_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +772 | simplicial_set, +773 | discrete_space, + | + +D417 Missing argument description in the docstring for `discrete_metric_simplicial_set_intersection`: `metric_kws` + --> umap/umap_.py:771:5 + | +771 | def discrete_metric_simplicial_set_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +772 | simplicial_set, +773 | discrete_space, + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:780:5 + | +778 | metric_scale=1.0, +779 | ): +780 | / """Combine a fuzzy simplicial set with another fuzzy simplicial set +781 | | generated from discrete metric data using discrete distances. The target +782 | | data is assumed to be categorical label data (a vector of labels), +783 | | and this will update the fuzzy simplicial set to respect that label data. +784 | | +785 | | TODO: optional category cardinality based weighting of distance +786 | | +787 | | Parameters +788 | | ---------- +789 | | simplicial_set: sparse matrix +790 | | The input fuzzy simplicial set. +791 | | +792 | | discrete_space: array of shape (n_samples) +793 | | The categorical labels to use in the intersection. +794 | | +795 | | unknown_dist: float (optional, default 1.0) +796 | | The distance an unknown label (-1) is assumed to be from any point. +797 | | +798 | | far_dist: float (optional, default 5.0) +799 | | The distance between unmatched labels. +800 | | +801 | | metric: str (optional, default None) +802 | | If not None, then use this metric to determine the +803 | | distance between values. +804 | | +805 | | metric_scale: float (optional, default 1.0) +806 | | If using a custom metric scale the distance values by +807 | | this value -- this controls the weighting of the +808 | | intersection. Larger values weight more toward target. +809 | | +810 | | Returns +811 | | ------- +812 | | simplicial_set: sparse matrix +813 | | The resulting intersected fuzzy simplicial set. +814 | | +815 | | """ + | |_______^ +816 | if metric_kws is None: +817 | metric_kws = {} + | +help: Insert single blank line + +D103 Missing docstring in public function + --> umap/umap_.py:853:5 + | +853 | def general_simplicial_set_intersection( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +854 | simplicial_set1, simplicial_set2, weight=0.5, right_complement=False, +855 | ): + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:854:51 + | +853 | def general_simplicial_set_intersection( +854 | simplicial_set1, simplicial_set2, weight=0.5, right_complement=False, + | ^^^^^^^^^^^^^^^^ +855 | ): + | + +D103 Missing docstring in public function + --> umap/umap_.py:881:5 + | +881 | def general_simplicial_set_union(simplicial_set1, simplicial_set2): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +882 | result = (simplicial_set1 + simplicial_set2).tocoo() +883 | left = simplicial_set1.tocsr() + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:902:5 + | +901 | def make_epochs_per_sample(weights, n_epochs): +902 | / """Given a set of weights and number of epochs generate the number of +903 | | epochs per sample for each weight. +904 | | +905 | | Parameters +906 | | ---------- +907 | | weights: array of shape (n_1_simplices) +908 | | The weights of how much we wish to sample each 1-simplex. +909 | | +910 | | n_epochs: int +911 | | The total number of epochs we want to train for. +912 | | +913 | | Returns +914 | | ------- +915 | | An array of number of epochs per sample, one for each 1-simplex. +916 | | +917 | | """ + | |_______^ +918 | result = -1.0 * np.ones(weights.shape[0], dtype=np.float64) +919 | n_samples = n_epochs * (weights / weights.max()) + | +help: Insert single blank line + +D103 Missing docstring in public function + --> umap/umap_.py:926:5 + | +924 | # scale coords so that the largest coordinate is max_coords, then add normal-distributed +925 | # noise with standard deviation noise +926 | def noisy_scale_coords(coords, random_state, max_coord=10.0, noise=0.0001): + | ^^^^^^^^^^^^^^^^^^ +927 | expansion = max_coord / np.abs(coords).max() +928 | coords = (coords * expansion).astype(np.float32) + | + +C901 `simplicial_set_embedding` is too complex (22 > 10) + --> umap/umap_.py:934:5 + | +934 | def simplicial_set_embedding( + | ^^^^^^^^^^^^^^^^^^^^^^^^ +935 | data, +936 | graph, + | + +PLR0913 Too many arguments in function definition (22 > 5) + --> umap/umap_.py:934:5 + | +934 | def simplicial_set_embedding( + | ^^^^^^^^^^^^^^^^^^^^^^^^ +935 | data, +936 | graph, + | + +PLR0912 Too many branches (26 > 12) + --> umap/umap_.py:934:5 + | +934 | def simplicial_set_embedding( + | ^^^^^^^^^^^^^^^^^^^^^^^^ +935 | data, +936 | graph, + | + +PLR0915 Too many statements (102 > 50) + --> umap/umap_.py:934:5 + | +934 | def simplicial_set_embedding( + | ^^^^^^^^^^^^^^^^^^^^^^^^ +935 | data, +936 | graph, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:953:5 + | +951 | output_metric=dist.named_distances_with_gradients["euclidean"], +952 | output_metric_kwds=None, +953 | euclidean_output=True, + | ^^^^^^^^^^^^^^^^ +954 | parallel=False, +955 | verbose=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:954:5 + | +952 | output_metric_kwds=None, +953 | euclidean_output=True, +954 | parallel=False, + | ^^^^^^^^ +955 | verbose=False, +956 | tqdm_kwds=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:955:5 + | +953 | euclidean_output=True, +954 | parallel=False, +955 | verbose=False, + | ^^^^^^^ +956 | tqdm_kwds=None, +957 | ): + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:958:5 + | + 956 | tqdm_kwds=None, + 957 | ): + 958 | / """Perform a fuzzy simplicial set embedding, using a specified + 959 | | initialisation method and then minimizing the fuzzy set cross entropy + 960 | | between the 1-skeletons of the high and low dimensional fuzzy simplicial + 961 | | sets. + 962 | | + 963 | | Parameters + 964 | | ---------- + 965 | | data: array of shape (n_samples, n_features) + 966 | | The source data to be embedded by UMAP. + 967 | | + 968 | | graph: sparse matrix + 969 | | The 1-skeleton of the high dimensional fuzzy simplicial set as + 970 | | represented by a graph for which we require a sparse matrix for the + 971 | | (weighted) adjacency matrix. + 972 | | + 973 | | n_components: int + 974 | | The dimensionality of the euclidean space into which to embed the data. + 975 | | + 976 | | initial_alpha: float + 977 | | Initial learning rate for the SGD. + 978 | | + 979 | | a: float + 980 | | Parameter of differentiable approximation of right adjoint functor + 981 | | + 982 | | b: float + 983 | | Parameter of differentiable approximation of right adjoint functor + 984 | | + 985 | | gamma: float + 986 | | Weight to apply to negative samples. + 987 | | + 988 | | negative_sample_rate: int (optional, default 5) + 989 | | The number of negative samples to select per positive sample + 990 | | in the optimization process. Increasing this value will result + 991 | | in greater repulsive force being applied, greater optimization + 992 | | cost, but slightly more accuracy. + 993 | | + 994 | | n_epochs: int (optional, default 0), or list of int + 995 | | The number of training epochs to be used in optimizing the + 996 | | low dimensional embedding. Larger values result in more accurate + 997 | | embeddings. If 0 is specified a value will be selected based on + 998 | | the size of the input dataset (200 for large datasets, 500 for small). + 999 | | If a list of int is specified, then the intermediate embeddings at the +1000 | | different epochs specified in that list are returned in +1001 | | ``aux_data["embedding_list"]``. +1002 | | +1003 | | init: string +1004 | | How to initialize the low dimensional embedding. Options are: +1005 | | +1006 | | * 'spectral': use a spectral embedding of the fuzzy 1-skeleton +1007 | | * 'random': assign initial embedding positions at random. +1008 | | * 'pca': use the first n_components from PCA applied to the input data. +1009 | | * A numpy array of initial embedding positions. +1010 | | +1011 | | random_state: numpy RandomState or equivalent +1012 | | A state capable being used as a numpy random state. +1013 | | +1014 | | metric: string or callable +1015 | | The metric used to measure distance in high dimensional space; used if +1016 | | multiple connected components need to be layed out. +1017 | | +1018 | | metric_kwds: dict +1019 | | Key word arguments to be passed to the metric function; used if +1020 | | multiple connected components need to be layed out. +1021 | | +1022 | | densmap: bool +1023 | | Whether to use the density-augmented objective function to optimize +1024 | | the embedding according to the densMAP algorithm. +1025 | | +1026 | | densmap_kwds: dict +1027 | | Key word arguments to be used by the densMAP optimization. +1028 | | +1029 | | output_dens: bool +1030 | | Whether to output local radii in the original data and the embedding. +1031 | | +1032 | | output_metric: function +1033 | | Function returning the distance between two points in embedding space and +1034 | | the gradient of the distance wrt the first argument. +1035 | | +1036 | | output_metric_kwds: dict +1037 | | Key word arguments to be passed to the output_metric function. +1038 | | +1039 | | euclidean_output: bool +1040 | | Whether to use the faster code specialised for euclidean output metrics +1041 | | +1042 | | parallel: bool (optional, default False) +1043 | | Whether to run the computation using numba parallel. +1044 | | Running in parallel is non-deterministic, and is not used +1045 | | if a random seed has been set, to ensure reproducibility. +1046 | | +1047 | | verbose: bool (optional, default False) +1048 | | Whether to report information on the current progress of the algorithm. +1049 | | +1050 | | tqdm_kwds: dict +1051 | | Key word arguments to be used by the tqdm progress bar. +1052 | | +1053 | | Returns +1054 | | ------- +1055 | | embedding: array of shape (n_samples, n_components) +1056 | | The optimized of ``graph`` into an ``n_components`` dimensional +1057 | | euclidean space. +1058 | | +1059 | | aux_data: dict +1060 | | Auxiliary output returned with the embedding. When densMAP extension +1061 | | is turned on, this dictionary includes local radii in the original +1062 | | data (``rad_orig``) and in the embedding (``rad_emb``). +1063 | | +1064 | | """ + | |_______^ +1065 | if output_metric_kwds is None: +1066 | output_metric_kwds = {} + | +help: Insert single blank line + +PLR2004 Magic value used in comparison, consider replacing `10000` with a constant variable + --> umap/umap_.py:1072:47 + | +1071 | # For smaller datasets we can use more epochs +1072 | default_epochs = 500 if graph.shape[0] <= 10000 else 200 + | ^^^^^ +1073 | +1074 | # Use more epochs for densMAP + | + +PLR2004 Magic value used in comparison, consider replacing `10` with a constant variable + --> umap/umap_.py:1084:23 + | +1082 | n_epochs_max = max(n_epochs) if isinstance(n_epochs, list) else n_epochs +1083 | +1084 | if n_epochs_max > 10: + | ^^ +1085 | graph.data[graph.data < (graph.data.max() / float(n_epochs_max))] = 0.0 +1086 | else: + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/umap_.py:1131:36 + | +1129 | else: +1130 | init_data = np.array(init) +1131 | if len(init_data.shape) == 2: + | ^ +1132 | if np.unique(init_data, axis=0).shape[0] < init_data.shape[0]: +1133 | tree = KDTree(init_data) + | + +N806 Variable `D` in function should be lowercase + --> umap/umap_.py:1163:13 + | +1161 | k = tail[i] +1162 | +1163 | D = dists[j, k] * dists[j, k] # match sq-Euclidean used for embedding + | ^ +1164 | mu = graph.data[i] + | + +N806 Variable `R` in function should be lowercase + --> umap/umap_.py:1175:13 + | +1174 | if densmap: +1175 | R = (ro - np.mean(ro)) / np.std(ro) + | ^ +1176 | densmap_kwds["mu"] = graph.data +1177 | densmap_kwds["mu_sum"] = mu_sum + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:1251:13 + | +1249 | "euclidean", +1250 | {}, +1251 | False, + | ^^^^^ +1252 | random_state, +1253 | verbose=verbose, + | + +N806 Variable `D` in function should be lowercase + --> umap/umap_.py:1283:13 + | +1281 | k = tail[i] +1282 | +1283 | D = emb_dists[j, k] + | ^ +1284 | mu = emb_graph.data[i] + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:1301:5 + | +1299 | @numba.njit() +1300 | def init_transform(indices, weights, embedding): +1301 | / """Given indices and weights and an original embeddings +1302 | | initialize the positions of new points relative to the +1303 | | indices and weights (of their neighbors in the source data). +1304 | | +1305 | | Parameters +1306 | | ---------- +1307 | | indices: array of shape (n_new_samples, n_neighbors) +1308 | | The indices of the neighbors of each new sample +1309 | | +1310 | | weights: array of shape (n_new_samples, n_neighbors) +1311 | | The membership strengths of associated 1-simplices +1312 | | for each of the new samples. +1313 | | +1314 | | embedding: array of shape (n_samples, dim) +1315 | | The original embedding of the source data. +1316 | | +1317 | | Returns +1318 | | ------- +1319 | | new_embedding: array of shape (n_new_samples, dim) +1320 | | An initial embedding of the new sample points. +1321 | | +1322 | | """ + | |_______^ +1323 | result = np.zeros((indices.shape[0], embedding.shape[1]), dtype=np.float32) + | +help: Insert single blank line + +D205 1 blank line required between summary line and description + --> umap/umap_.py:1334:5 + | +1333 | def init_graph_transform(graph, embedding): +1334 | / """Given a bipartite graph representing the 1-simplices and strengths between the +1335 | | new points and the original data set along with an embedding of the original points +1336 | | initialize the positions of new points relative to the strengths (of their neighbors in the source data). +1337 | | +1338 | | If a point is in our original data set it embeds at the original points coordinates. +1339 | | If a point has no neighbours in our original dataset it embeds as the np.nan vector. +1340 | | Otherwise a point is the weighted average of it's neighbours embedding locations. +1341 | | +1342 | | Parameters +1343 | | ---------- +1344 | | graph: csr_matrix (n_new_samples, n_samples) +1345 | | A matrix indicating the 1-simplices and their associated strengths. These strengths should +1346 | | be values between zero and one and not normalized. One indicating that the new point was identical +1347 | | to one of our original points. +1348 | | +1349 | | embedding: array of shape (n_samples, dim) +1350 | | The original embedding of the source data. +1351 | | +1352 | | Returns +1353 | | ------- +1354 | | new_embedding: array of shape (n_new_samples, dim) +1355 | | An initial embedding of the new sample points. +1356 | | +1357 | | """ + | |_______^ +1358 | result = np.zeros((graph.shape[0], embedding.shape[1]), dtype=np.float32) + | +help: Insert single blank line + +D103 Missing docstring in public function + --> umap/umap_.py:1376:5 + | +1375 | @numba.njit() +1376 | def init_update(current_init, n_original_samples, indices): + | ^^^^^^^^^^^ +1377 | for i in range(n_original_samples, indices.shape[0]): +1378 | n = 0 + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:1390:5 + | +1389 | def find_ab_params(spread, min_dist): +1390 | / """Fit a, b params for the differentiable curve used in lower +1391 | | dimensional fuzzy simplicial complex construction. We want the +1392 | | smooth curve (from a pre-defined family with simple gradient) that +1393 | | best matches an offset exponential decay. +1394 | | """ + | |_______^ +1395 | +1396 | def curve(x, a, b): + | +help: Insert single blank line + +PLR0913 Too many arguments in function definition (39 > 5) + --> umap/umap_.py:1662:9 + | +1660 | """ +1661 | +1662 | def __init__( + | ^^^^^^^^ +1663 | self, +1664 | n_neighbors=15, + | + +D107 Missing docstring in `__init__` + --> umap/umap_.py:1662:9 + | +1660 | """ +1661 | +1662 | def __init__( + | ^^^^^^^^ +1663 | self, +1664 | n_neighbors=15, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1675:9 + | +1673 | min_dist=0.1, +1674 | spread=1.0, +1675 | low_memory=True, + | ^^^^^^^^^^ +1676 | n_jobs=-1, +1677 | set_op_mix_ratio=1.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1685:9 + | +1683 | b=None, +1684 | random_state=None, +1685 | angular_rp_forest=False, + | ^^^^^^^^^^^^^^^^^ +1686 | target_n_neighbors=-1, +1687 | target_metric="categorical", + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1692:9 + | +1690 | transform_seed=42, +1691 | transform_mode="embedding", +1692 | force_approximation_algorithm=False, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +1693 | verbose=False, +1694 | tqdm_kwds=None, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1693:9 + | +1691 | transform_mode="embedding", +1692 | force_approximation_algorithm=False, +1693 | verbose=False, + | ^^^^^^^ +1694 | tqdm_kwds=None, +1695 | unique=False, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1695:9 + | +1693 | verbose=False, +1694 | tqdm_kwds=None, +1695 | unique=False, + | ^^^^^^ +1696 | densmap=False, +1697 | dens_lambda=2.0, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1696:9 + | +1694 | tqdm_kwds=None, +1695 | unique=False, +1696 | densmap=False, + | ^^^^^^^ +1697 | dens_lambda=2.0, +1698 | dens_frac=0.3, + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:1700:9 + | +1698 | dens_frac=0.3, +1699 | dens_var_shift=0.1, +1700 | output_dens=False, + | ^^^^^^^^^^^ +1701 | disconnection_distance=None, +1702 | precomputed_knn=(None, None, None), + | + +C901 `_validate_parameters` is too complex (66 > 10) + --> umap/umap_.py:1749:9 + | +1747 | self.b = b +1748 | +1749 | def _validate_parameters(self): + | ^^^^^^^^^^^^^^^^^^^^ +1750 | if self.set_op_mix_ratio < 0.0 or self.set_op_mix_ratio > 1.0: +1751 | msg = "set_op_mix_ratio must be between 0.0 and 1.0" + | + +PLR0912 Too many branches (77 > 12) + --> umap/umap_.py:1749:9 + | +1747 | self.b = b +1748 | +1749 | def _validate_parameters(self): + | ^^^^^^^^^^^^^^^^^^^^ +1750 | if self.set_op_mix_ratio < 0.0 or self.set_op_mix_ratio > 1.0: +1751 | msg = "set_op_mix_ratio must be between 0.0 and 1.0" + | + +PLR0915 Too many statements (211 > 50) + --> umap/umap_.py:1749:9 + | +1747 | self.b = b +1748 | +1749 | def _validate_parameters(self): + | ^^^^^^^^^^^^^^^^^^^^ +1750 | if self.set_op_mix_ratio < 0.0 or self.set_op_mix_ratio > 1.0: +1751 | msg = "set_op_mix_ratio must be between 0.0 and 1.0" + | + +TRY004 Prefer `TypeError` exception for invalid type + --> umap/umap_.py:1764:13 + | +1762 | if not isinstance(self.init, str) and not isinstance(self.init, np.ndarray): +1763 | msg = "init must be a string or ndarray" +1764 | raise ValueError(msg) + | ^^^^^^^^^^^^^^^^^^^^^ +1765 | if isinstance(self.init, str) and self.init not in ( +1766 | "pca", + | + +TRY004 Prefer `TypeError` exception for invalid type + --> umap/umap_.py:1786:13 + | +1784 | if not isinstance(self.metric, str) and not callable(self.metric): +1785 | msg = "metric must be string or callable" +1786 | raise ValueError(msg) + | ^^^^^^^^^^^^^^^^^^^^^ +1787 | if self.negative_sample_rate < 0: +1788 | msg = "negative sample rate must be positive" + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/umap_.py:1793:31 + | +1791 | msg = "learning_rate must be positive" +1792 | raise ValueError(msg) +1793 | if self.n_neighbors < 2: + | ^ +1794 | msg = "n_neighbors must be greater than 1" +1795 | raise ValueError(msg) + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/umap_.py:1796:38 + | +1794 | msg = "n_neighbors must be greater than 1" +1795 | raise ValueError(msg) +1796 | if self.target_n_neighbors < 2 and self.target_n_neighbors != -1: + | ^ +1797 | msg = "target_n_neighbors must be greater than 1" +1798 | raise ValueError(msg) + | + +TRY004 Prefer `TypeError` exception for invalid type + --> umap/umap_.py:1802:17 + | +1800 | if isinstance(self.n_components, str): +1801 | msg = "n_components must be an int" +1802 | raise ValueError(msg) + | ^^^^^^^^^^^^^^^^^^^^^ +1803 | if self.n_components % 1 != 0: +1804 | msg = "n_components must be a whole number" + | + +B904 Within an `except` clause, raise exceptions with `raise ... from err` or `raise ... from None` to distinguish them from errors in exception handling + --> umap/umap_.py:1812:17 + | +1810 | except ValueError: +1811 | msg = "n_components must be an int" +1812 | raise ValueError(msg) + | ^^^^^^^^^^^^^^^^^^^^^ +1813 | if self.n_components < 1: +1814 | msg = "n_components must be greater than 0" + | + +PLR2004 Magic value used in comparison, consider replacing `4096` with a constant variable + --> umap/umap_.py:2084:43 + | +2082 | self.knn_search_index = None +2083 | elif ( +2084 | self.knn_dists.shape[0] < 4096 + | ^^^^ +2085 | and not self.force_approximation_algorithm +2086 | ): + | + +NPY002 Replace legacy `np.random.randint` call with `np.random.Generator` + --> umap/umap_.py:2102:25 + | +2100 | # input data so we don't risk violating any assumptions potentially +2101 | # hard-coded in the metric (e.g., bounded; non-negative) +2102 | x, y = data[np.random.randint(0, data.shape[0], 2)] + | ^^^^^^^^^^^^^^^^^ +2103 | else: +2104 | # if checking the manifold distance metric, simulate some data on a + | + +NPY002 Replace legacy `np.random.uniform` call with `np.random.Generator` + --> umap/umap_.py:2106:20 + | +2104 | # if checking the manifold distance metric, simulate some data on a +2105 | # reasonable interval with output dimensionality +2106 | x, y = np.random.uniform(low=-10, high=10, size=(2, self.n_components)) + | ^^^^^^^^^^^^^^^^^ +2107 | +2108 | if scipy.sparse.issparse(data): + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/umap_.py:2113:71 + | +2111 | metric_out = metric(x, y, **kwds) +2112 | # True if metric returns iterable of length 2, False otherwise +2113 | return hasattr(metric_out, "__iter__") and len(metric_out) == 2 + | ^ +2114 | +2115 | def _populate_combined_params(self, *models): + | + +SLF001 Private member accessed: `_a` + --> umap/umap_.py:2161:30 + | +2159 | self.b = flattened([m.b for m in models]) +2160 | +2161 | self._a = flattened([m._a for m in models]) + | ^^^^ +2162 | self._b = flattened([m._b for m in models]) + | + +SLF001 Private member accessed: `_b` + --> umap/umap_.py:2162:30 + | +2161 | self._a = flattened([m._a for m in models]) +2162 | self._b = flattened([m._b for m in models]) + | ^^^^ +2163 | +2164 | def __mul__(self, other): + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2183:65 + | +2181 | self.graph_, other.graph_, 0.5, +2182 | ) +2183 | result.graph_ = reset_local_connectivity(result.graph_, True) + | ^^^^ +2184 | +2185 | if scipy.sparse.csgraph.connected_components(result.graph_)[0] > 1: + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2251:65 + | +2250 | result.graph_ = general_simplicial_set_union(self.graph_, other.graph_) +2251 | result.graph_ = reset_local_connectivity(result.graph_, True) + | ^^^^ +2252 | +2253 | if scipy.sparse.csgraph.connected_components(result.graph_)[0] > 1: + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2321:65 + | +2319 | self.graph_, other.graph_, weight=0.5, right_complement=True, +2320 | ) +2321 | result.graph_ = reset_local_connectivity(result.graph_, False) + | ^^^^^ +2322 | +2323 | if scipy.sparse.csgraph.connected_components(result.graph_)[0] > 1: + | + +C901 `fit` is too complex (47 > 10) + --> umap/umap_.py:2372:9 + | +2370 | return result +2371 | +2372 | def fit(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^^^ +2373 | """Fit X into an embedded space. + | + +PLR0912 Too many branches (67 > 12) + --> umap/umap_.py:2372:9 + | +2370 | return result +2371 | +2372 | def fit(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^^^ +2373 | """Fit X into an embedded space. + | + +PLR0915 Too many statements (188 > 50) + --> umap/umap_.py:2372:9 + | +2370 | return result +2371 | +2372 | def fit(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^^^ +2373 | """Fit X into an embedded space. + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:2372:19 + | +2370 | return result +2371 | +2372 | def fit(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^ +2373 | """Fit X into an embedded space. + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:2372:30 + | +2370 | return result +2371 | +2372 | def fit(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^^^^^^^^^^^^^^^^^ +2373 | """Fit X into an embedded space. + | + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:2402:13 + | +2400 | """ +2401 | if self.metric in ("bit_hamming", "bit_jaccard"): +2402 | X = check_array( + | ^ +2403 | X, dtype=np.uint8, order="C", ensure_all_finite=ensure_all_finite, +2404 | ) + | + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:2406:13 + | +2404 | ) +2405 | else: +2406 | X = check_array( + | ^ +2407 | X, +2408 | dtype=np.float32, + | + +PLR2004 Magic value used in comparison, consider replacing `2` with a constant variable + --> umap/umap_.py:2437:41 + | +2435 | self.knn_dists = self.precomputed_knn[1] +2436 | # #848: allow precomputed knn to not have a search index +2437 | if len(self.precomputed_knn) == 2: + | ^ +2438 | self.knn_search_index = None +2439 | else: + | + +ERA001 Found commented-out code + --> umap/umap_.py:2509:13 + | +2507 | # symmetrical (so we can find neighbors by looking at rows in isolation, +2508 | # rather than also having to consider that sample's column too). +2509 | # print("Computing KNNs for sparse precomputed distances...") + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +2510 | if sparse_tril(X).getnnz() != sparse_triu(X).getnnz(): +2511 | msg = "Sparse precomputed distance matrices should be symmetrical!" + | +help: Remove commented-out code + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2558:17 + | +2556 | self.set_op_mix_ratio, +2557 | self.local_connectivity, +2558 | True, + | ^^^^ +2559 | self.verbose, +2560 | self.densmap or self.output_dens, + | + +PLR2004 Magic value used in comparison, consider replacing `4096` with a constant variable + --> umap/umap_.py:2575:34 + | +2573 | ) +2574 | # Handle small cases efficiently by computing all distances +2575 | elif X[index].shape[0] < 4096 and not self.force_approximation_algorithm: + | ^^^^ +2576 | self._small_data = True +2577 | try: + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2629:17 + | +2627 | self.set_op_mix_ratio, +2628 | self.local_connectivity, +2629 | True, + | ^^^^ +2630 | self.verbose, +2631 | self.densmap or self.output_dens, + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2696:17 + | +2694 | self.set_op_mix_ratio, +2695 | self.local_connectivity, +2696 | True, + | ^^^^ +2697 | self.verbose, +2698 | self.densmap or self.output_dens, + | + +N806 Variable `len_X` in function should be lowercase + --> umap/umap_.py:2716:13 + | +2714 | # Might be worth throwing a warning... +2715 | if y is not None: +2716 | len_X = len(X) if not self._sparse_data else X.shape[0] + | ^^^^^ +2717 | if len_X != len(y): +2718 | msg = ( + | + +ERA001 Found commented-out code + --> umap/umap_.py:2746:17 + | +2744 | # self.graph_, +2745 | # y_, +2746 | # metric=self.target_metric, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +2747 | # metric_kws=self.target_metric_kwds, +2748 | # metric_scale=scale + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:2747:17 + | +2745 | # y_, +2746 | # metric=self.target_metric, +2747 | # metric_kws=self.target_metric_kwds, + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +2748 | # metric_scale=scale +2749 | # ) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:2748:17 + | +2746 | # metric=self.target_metric, +2747 | # metric_kws=self.target_metric_kwds, +2748 | # metric_scale=scale + | ^^^^^^^^^^^^^^^^^^^^^^^^ +2749 | # ) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:2749:17 + | +2747 | # metric_kws=self.target_metric_kwds, +2748 | # metric_scale=scale +2749 | # ) + | ^^^ +2750 | +2751 | metric_kws = dist.get_discrete_params(y_, self.target_metric) + | +help: Remove commented-out code + +PLR2004 Magic value used in comparison, consider replacing `4096` with a constant variable + --> umap/umap_.py:2769:33 + | +2768 | # Handle the small case as precomputed as before +2769 | if y.shape[0] < 4096: + | ^^^^ +2770 | try: +2771 | ydmat = pairwise_distances( + | + +RUF059 Unpacked variable `target_sigmas` is never used + --> umap/umap_.py:2784:25 + | +2782 | ( +2783 | target_graph, +2784 | target_sigmas, + | ^^^^^^^^^^^^^ +2785 | target_rhos, +2786 | ) = fuzzy_simplicial_set( + | +help: Prefix it with an underscore or any other dummy variable pattern + +RUF059 Unpacked variable `target_rhos` is never used + --> umap/umap_.py:2785:25 + | +2783 | target_graph, +2784 | target_sigmas, +2785 | target_rhos, + | ^^^^^^^^^^^ +2786 | ) = fuzzy_simplicial_set( +2787 | ydmat, + | +help: Prefix it with an underscore or any other dummy variable pattern + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2794:25 + | +2792 | None, +2793 | None, +2794 | False, + | ^^^^^ +2795 | 1.0, +2796 | 1.0, + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2797:25 + | +2795 | 1.0, +2796 | 1.0, +2797 | False, + | ^^^^^ +2798 | ) +2799 | else: + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2813:25 + | +2811 | None, +2812 | None, +2813 | False, + | ^^^^^ +2814 | 1.0, +2815 | 1.0, + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:2816:25 + | +2814 | 1.0, +2815 | 1.0, +2816 | False, + | ^^^^^ +2817 | ) +2818 | # product = self.graph_.multiply(target_graph) + | + +ERA001 Found commented-out code + --> umap/umap_.py:2818:17 + | +2816 | False, +2817 | ) +2818 | # product = self.graph_.multiply(target_graph) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +2819 | # # self.graph_ = 0.99 * product + 0.01 * (self.graph_ + +2820 | # # target_graph - + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:2822:17 + | +2820 | # # target_graph - +2821 | # # product) +2822 | # self.graph_ = product + | ^^^^^^^^^^^^^^^^^^^^^^^ +2823 | self.graph_ = general_simplicial_set_intersection( +2824 | self.graph_, target_graph, self.target_weight, + | +help: Remove commented-out code + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:2893:31 + | +2891 | return self +2892 | +2893 | def _fit_embed_data(self, X, n_epochs, init, random_state, **kwargs): + | ^ +2894 | """A method wrapper for simplicial_set_embedding that can be +2895 | replaced by subclasses. Arbitrary keyword arguments can be passed + | + +ARG002 Unused method argument: `kwargs` + --> umap/umap_.py:2893:66 + | +2891 | return self +2892 | +2893 | def _fit_embed_data(self, X, n_epochs, init, random_state, **kwargs): + | ^^^^^^ +2894 | """A method wrapper for simplicial_set_embedding that can be +2895 | replaced by subclasses. Arbitrary keyword arguments can be passed + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:2894:9 + | +2893 | def _fit_embed_data(self, X, n_epochs, init, random_state, **kwargs): +2894 | / """A method wrapper for simplicial_set_embedding that can be +2895 | | replaced by subclasses. Arbitrary keyword arguments can be passed +2896 | | through .fit() and .fit_transform(). +2897 | | """ + | |___________^ +2898 | return simplicial_set_embedding( +2899 | X, + | +help: Insert single blank line + +D401 First line of docstring should be in imperative mood: "A method wrapper for simplicial_set_embedding that can be" + --> umap/umap_.py:2894:9 + | +2893 | def _fit_embed_data(self, X, n_epochs, init, random_state, **kwargs): +2894 | / """A method wrapper for simplicial_set_embedding that can be +2895 | | replaced by subclasses. Arbitrary keyword arguments can be passed +2896 | | through .fit() and .fit_transform(). +2897 | | """ + | |___________^ +2898 | return simplicial_set_embedding( +2899 | X, + | + +D417 Missing argument description in the docstring for `fit_transform`: `**kwargs` + --> umap/umap_.py:2923:9 + | +2921 | ) +2922 | +2923 | def fit_transform(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^^^^^^^^^^^^^ +2924 | """Fit X into an embedded space and return that transformed +2925 | output. + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:2923:29 + | +2921 | ) +2922 | +2923 | def fit_transform(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^ +2924 | """Fit X into an embedded space and return that transformed +2925 | output. + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:2923:40 + | +2921 | ) +2922 | +2923 | def fit_transform(self, X, y=None, ensure_all_finite=True, **kwargs): + | ^^^^^^^^^^^^^^^^^ +2924 | """Fit X into an embedded space and return that transformed +2925 | output. + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:2924:9 + | +2923 | def fit_transform(self, X, y=None, ensure_all_finite=True, **kwargs): +2924 | / """Fit X into an embedded space and return that transformed +2925 | | output. +2926 | | +2927 | | Parameters +2928 | | ---------- +2929 | | X : array, shape (n_samples, n_features) or (n_samples, n_samples) +2930 | | If the metric is 'precomputed' X must be a square distance +2931 | | matrix. Otherwise it contains a sample per row. +2932 | | +2933 | | y : array, shape (n_samples) +2934 | | A target array for supervised dimension reduction. How this is +2935 | | handled is determined by parameters UMAP was instantiated with. +2936 | | The relevant attributes are ``target_metric`` and +2937 | | ``target_metric_kwds``. +2938 | | +2939 | | ensure_all_finite : Whether to raise an error on np.inf, np.nan, pd.NA in array. +2940 | | The possibilities are: - True: Force all values of array to be finite. +2941 | | - False: accepts np.inf, np.nan, pd.NA in array. +2942 | | - 'allow-nan': accepts only np.nan and pd.NA values in array. +2943 | | Values cannot be infinite. +2944 | | +2945 | | **kwargs : Any additional keyword arguments are passed to _fit_embed_data. +2946 | | +2947 | | Returns +2948 | | ------- +2949 | | X_new : array, shape (n_samples, n_components) +2950 | | Embedding of the training data in low-dimensional space. +2951 | | +2952 | | or a tuple (X_new, r_orig, r_emb) if ``output_dens`` flag is set, +2953 | | which additionally includes: +2954 | | +2955 | | r_orig: array, shape (n_samples) +2956 | | Local radii of data points in the original data space (log-transformed). +2957 | | +2958 | | r_emb: array, shape (n_samples) +2959 | | Local radii of data points in the embedding (log-transformed). +2960 | | +2961 | | """ + | |___________^ +2962 | self.fit(X, y, ensure_all_finite, **kwargs) +2963 | if self.transform_mode == "embedding": + | +help: Insert single blank line + +C901 `transform` is too complex (19 > 10) + --> umap/umap_.py:2976:9 + | +2974 | ) +2975 | +2976 | def transform(self, X, ensure_all_finite=True): + | ^^^^^^^^^ +2977 | """Transform X into the existing embedded space and return that +2978 | transformed output. + | + +PLR0912 Too many branches (25 > 12) + --> umap/umap_.py:2976:9 + | +2974 | ) +2975 | +2976 | def transform(self, X, ensure_all_finite=True): + | ^^^^^^^^^ +2977 | """Transform X into the existing embedded space and return that +2978 | transformed output. + | + +PLR0915 Too many statements (83 > 50) + --> umap/umap_.py:2976:9 + | +2974 | ) +2975 | +2976 | def transform(self, X, ensure_all_finite=True): + | ^^^^^^^^^ +2977 | """Transform X into the existing embedded space and return that +2978 | transformed output. + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:2976:25 + | +2974 | ) +2975 | +2976 | def transform(self, X, ensure_all_finite=True): + | ^ +2977 | """Transform X into the existing embedded space and return that +2978 | transformed output. + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:2976:28 + | +2974 | ) +2975 | +2976 | def transform(self, X, ensure_all_finite=True): + | ^^^^^^^^^^^^^^^^^ +2977 | """Transform X into the existing embedded space and return that +2978 | transformed output. + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:2977:9 + | +2976 | def transform(self, X, ensure_all_finite=True): +2977 | / """Transform X into the existing embedded space and return that +2978 | | transformed output. +2979 | | +2980 | | Parameters +2981 | | ---------- +2982 | | X : array, shape (n_samples, n_features) +2983 | | New data to be transformed. +2984 | | +2985 | | ensure_all_finite : Whether to raise an error on np.inf, np.nan, pd.NA in array. +2986 | | The possibilities are: - True: Force all values of array to be finite. +2987 | | - False: accepts np.inf, np.nan, pd.NA in array. +2988 | | - 'allow-nan': accepts only np.nan and pd.NA values in array. +2989 | | Values cannot be infinite. +2990 | | +2991 | | Returns +2992 | | ------- +2993 | | X_new : array, shape (n_samples, n_components) +2994 | | Embedding of the new data in low-dimensional space. +2995 | | +2996 | | """ + | |___________^ +2997 | # If we fit just a single instance then error +2998 | if self._raw_data.shape[0] == 1: + | +help: Insert single blank line + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:3005:13 + | +3003 | # If we just have the original input then short circuit things +3004 | if self.metric in ("bit_hamming", "bit_jaccard"): +3005 | X = check_array( + | ^ +3006 | X, dtype=np.uint8, order="C", ensure_all_finite=ensure_all_finite, +3007 | ) + | + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:3009:13 + | +3007 | ) +3008 | else: +3009 | X = check_array( + | ^ +3010 | X, +3011 | dtype=np.float32, + | + +ERA001 Found commented-out code + --> umap/umap_.py:3045:9 + | +3043 | ) +3044 | +3045 | # X = check_array(X, dtype=np.float32, order="C", accept_sparse="csr") + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3046 | random_state = check_random_state(self.transform_seed) +3047 | rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64) + | +help: Remove commented-out code + +S101 Use of `assert` detected + --> umap/umap_.py:3057:13 + | +3055 | "or approximate nearest neighbours in the training set.", stacklevel=2, +3056 | ) +3057 | assert X.shape[1] == self._raw_data.shape[0] + | ^^^^^^ +3058 | if scipy.sparse.issparse(X): +3059 | indices = np.full( + | + +S101 Use of `assert` detected + --> umap/umap_.py:3075:13 + | +3073 | indices = np.argsort(X, axis=1)[:, : self._n_neighbors].astype(np.int32) +3074 | dists = np.take_along_axis(X, indices, axis=1) +3075 | assert np.min(indices) >= 0 + | ^^^^^^ +3076 | assert np.min(dists) >= 0.0 +3077 | elif self._small_data: + | + +S101 Use of `assert` detected + --> umap/umap_.py:3076:13 + | +3074 | dists = np.take_along_axis(X, indices, axis=1) +3075 | assert np.min(indices) >= 0 +3076 | assert np.min(dists) >= 0.0 + | ^^^^^^ +3077 | elif self._small_data: +3078 | try: + | + +SLF001 Private member accessed: `_angular_trees` + --> umap/umap_.py:3120:31 + | +3118 | dists = submatrix(dmat_shortened, indices_sorted, self._n_neighbors) +3119 | else: +3120 | epsilon = 0.24 if self._knn_search_index._angular_trees else 0.12 + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3121 | indices, dists = self._knn_search_index.query( +3122 | X, self.n_neighbors, epsilon=epsilon, + | + +ERA001 Found commented-out code + --> umap/umap_.py:3149:9 + | +3147 | # That lets us do fancy unpacking by reshaping the csr matrix indices +3148 | # and data. Doing so relies on the constant degree assumption! +3149 | # csr_graph = normalize(graph.tocsr(), norm="l1") + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3150 | # inds = csr_graph.indices.reshape(X.shape[0], self._n_neighbors) +3151 | # weights = csr_graph.data.reshape(X.shape[0], self._n_neighbors) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:3150:9 + | +3148 | # and data. Doing so relies on the constant degree assumption! +3149 | # csr_graph = normalize(graph.tocsr(), norm="l1") +3150 | # inds = csr_graph.indices.reshape(X.shape[0], self._n_neighbors) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3151 | # weights = csr_graph.data.reshape(X.shape[0], self._n_neighbors) +3152 | # embedding = init_transform(inds, weights, self.embedding_) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:3151:9 + | +3149 | # csr_graph = normalize(graph.tocsr(), norm="l1") +3150 | # inds = csr_graph.indices.reshape(X.shape[0], self._n_neighbors) +3151 | # weights = csr_graph.data.reshape(X.shape[0], self._n_neighbors) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3152 | # embedding = init_transform(inds, weights, self.embedding_) +3153 | # This is less fast code than the above numba.jit'd code. + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:3152:9 + | +3150 | # inds = csr_graph.indices.reshape(X.shape[0], self._n_neighbors) +3151 | # weights = csr_graph.data.reshape(X.shape[0], self._n_neighbors) +3152 | # embedding = init_transform(inds, weights, self.embedding_) + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3153 | # This is less fast code than the above numba.jit'd code. +3154 | # It handles the fact that our nearest neighbour graph can now contain variable numbers of vertices. + | +help: Remove commented-out code + +PLR2004 Magic value used in comparison, consider replacing `10000` with a constant variable + --> umap/umap_.py:3161:49 + | +3159 | if self.n_epochs is None: +3160 | # For smaller datasets we can use more epochs +3161 | n_epochs = 100 if graph.shape[0] <= 10000 else 30 + | ^^^^^ +3162 | else: +3163 | n_epochs = int(self.n_epochs // 3.0) + | + +ERA001 Found commented-out code + --> umap/umap_.py:3173:9 + | +3171 | tail = graph.col +3172 | +3173 | # optimize_layout = make_optimize_layout( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3174 | # self._output_distance_func, +3175 | # tuple(self.output_metric_kwds.values()), + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:3175:9 + | +3173 | # optimize_layout = make_optimize_layout( +3174 | # self._output_distance_func, +3175 | # tuple(self.output_metric_kwds.values()), + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3176 | # ) + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:3176:9 + | +3174 | # self._output_distance_func, +3175 | # tuple(self.output_metric_kwds.values()), +3176 | # ) + | ^^^ +3177 | +3178 | if self.output_metric == "euclidean": + | +help: Remove commented-out code + +C901 `inverse_transform` is too complex (13 > 10) + --> umap/umap_.py:3220:9 + | +3218 | return embedding +3219 | +3220 | def inverse_transform(self, X): + | ^^^^^^^^^^^^^^^^^ +3221 | """Transform X in the existing embedded space back into the input +3222 | data space and return that transformed output. + | + +PLR0912 Too many branches (13 > 12) + --> umap/umap_.py:3220:9 + | +3218 | return embedding +3219 | +3220 | def inverse_transform(self, X): + | ^^^^^^^^^^^^^^^^^ +3221 | """Transform X in the existing embedded space back into the input +3222 | data space and return that transformed output. + | + +PLR0915 Too many statements (57 > 50) + --> umap/umap_.py:3220:9 + | +3218 | return embedding +3219 | +3220 | def inverse_transform(self, X): + | ^^^^^^^^^^^^^^^^^ +3221 | """Transform X in the existing embedded space back into the input +3222 | data space and return that transformed output. + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:3220:33 + | +3218 | return embedding +3219 | +3220 | def inverse_transform(self, X): + | ^ +3221 | """Transform X in the existing embedded space back into the input +3222 | data space and return that transformed output. + | + +D205 1 blank line required between summary line and description + --> umap/umap_.py:3221:9 + | +3220 | def inverse_transform(self, X): +3221 | / """Transform X in the existing embedded space back into the input +3222 | | data space and return that transformed output. +3223 | | +3224 | | Parameters +3225 | | ---------- +3226 | | X : array, shape (n_samples, n_components) +3227 | | New points to be inverse transformed. +3228 | | +3229 | | Returns +3230 | | ------- +3231 | | X_new : array, shape (n_samples, n_features) +3232 | | Generated data points new data in data space. +3233 | | +3234 | | """ + | |___________^ +3235 | if self._sparse_data: +3236 | msg = "Inverse transform not available for sparse input." + | +help: Insert single blank line + +PLR2004 Magic value used in comparison, consider replacing `8` with a constant variable + --> umap/umap_.py:3244:33 + | +3242 | msg = "Inverse transform not available for densMAP." +3243 | raise ValueError(msg) +3244 | if self.n_components >= 8: + | ^ +3245 | warn( +3246 | "Inverse transform works best with low dimensional embeddings." + | + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:3258:9 + | +3256 | ) +3257 | +3258 | X = check_array(X, dtype=np.float32, order="C") + | ^ +3259 | random_state = check_random_state(self.transform_seed) +3260 | rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64) + | + +ERA001 Found commented-out code + --> umap/umap_.py:3344:9 + | +3342 | # That lets us do fancy unpacking by reshaping the csr matrix indices +3343 | # and data. Doing so relies on the constant degree assumption! +3344 | # csr_graph = graph.tocsr() + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3345 | csr_graph = normalize(graph.tocsr(), norm="l1") +3346 | inds = csr_graph.indices.reshape(X.shape[0], min_vertices) + | +help: Remove commented-out code + +PLR2004 Magic value used in comparison, consider replacing `10000` with a constant variable + --> umap/umap_.py:3352:49 + | +3350 | if self.n_epochs is None: +3351 | # For smaller datasets we can use more epochs +3352 | n_epochs = 100 if graph.shape[0] <= 10000 else 30 + | ^^^^^ +3353 | else: +3354 | n_epochs = int(self.n_epochs // 3.0) + | + +ERA001 Found commented-out code + --> umap/umap_.py:3356:9 + | +3354 | n_epochs = int(self.n_epochs // 3.0) +3355 | +3356 | # graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0 + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3357 | # graph.eliminate_zeros() + | +help: Remove commented-out code + +ERA001 Found commented-out code + --> umap/umap_.py:3357:9 + | +3356 | # graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0 +3357 | # graph.eliminate_zeros() + | ^^^^^^^^^^^^^^^^^^^^^^^^^ +3358 | +3359 | epochs_per_sample = make_epochs_per_sample(graph.data, n_epochs) + | +help: Remove commented-out code + +C901 `update` is too complex (13 > 10) + --> umap/umap_.py:3389:9 + | +3389 | def update(self, X, ensure_all_finite=True): + | ^^^^^^ +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | + +PLR0912 Too many branches (20 > 12) + --> umap/umap_.py:3389:9 + | +3389 | def update(self, X, ensure_all_finite=True): + | ^^^^^^ +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | + +PLR0915 Too many statements (65 > 50) + --> umap/umap_.py:3389:9 + | +3389 | def update(self, X, ensure_all_finite=True): + | ^^^^^^ +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | + +D102 Missing docstring in public method + --> umap/umap_.py:3389:9 + | +3389 | def update(self, X, ensure_all_finite=True): + | ^^^^^^ +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | + +N803 Argument name `X` should be lowercase + --> umap/umap_.py:3389:22 + | +3389 | def update(self, X, ensure_all_finite=True): + | ^ +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | + +FBT002 Boolean default positional argument in function definition + --> umap/umap_.py:3389:25 + | +3389 | def update(self, X, ensure_all_finite=True): + | ^^^^^^^^^^^^^^^^^ +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:3391:13 + | +3389 | def update(self, X, ensure_all_finite=True): +3390 | if self.metric in ("bit_hamming", "bit_jaccard"): +3391 | X = check_array( + | ^ +3392 | X, dtype=np.uint8, order="C", ensure_all_finite=ensure_all_finite, +3393 | ) + | + +N806 Variable `X` in function should be lowercase + --> umap/umap_.py:3395:13 + | +3393 | ) +3394 | else: +3395 | X = check_array( + | ^ +3396 | X, +3397 | dtype=np.float32, + | + +PLR2004 Magic value used in comparison, consider replacing `4096` with a constant variable + --> umap/umap_.py:3421:42 + | +3419 | self._raw_data = np.vstack([self._raw_data, X]) +3420 | +3421 | if self._raw_data.shape[0] < 4096: + | ^^^^ +3422 | # still small data +3423 | try: + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:3468:21 + | +3466 | self.set_op_mix_ratio, +3467 | self.local_connectivity, +3468 | True, + | ^^^^ +3469 | self.verbose, +3470 | ) + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:3508:21 + | +3506 | self.set_op_mix_ratio, +3507 | self.local_connectivity, +3508 | True, + | ^^^^ +3509 | self.verbose, +3510 | ) + | + +SLF001 Private member accessed: `_raw_data` + --> umap/umap_.py:3549:30 + | +3547 | self._knn_search_index.prepare() +3548 | self._knn_search_index.update(X) +3549 | self._raw_data = self._knn_search_index._raw_data + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3550 | ( +3551 | self._knn_indices, + | + +FBT003 Boolean positional value in function call + --> umap/umap_.py:3571:17 + | +3569 | self.set_op_mix_ratio, +3570 | self.local_connectivity, +3571 | True, + | ^^^^ +3572 | self.verbose, +3573 | ) + | + +PLC0415 `import` should be at the top-level of a file + --> umap/umap_.py:3613:9 + | +3612 | def __repr__(self): +3613 | import re + | ^^^^^^^^^ +3614 | +3615 | from sklearn.utils._pprint import _EstimatorPrettyPrinter + | + +PLC0415 `import` should be at the top-level of a file + --> umap/umap_.py:3615:9 + | +3613 | import re +3614 | +3615 | from sklearn.utils._pprint import _EstimatorPrettyPrinter + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +3616 | +3617 | pp = _EstimatorPrettyPrinter( + | + +SLF001 Private member accessed: `_changed_only` + --> umap/umap_.py:3623:9 + | +3621 | n_max_elements_to_show=50, +3622 | ) +3623 | pp._changed_only = True + | ^^^^^^^^^^^^^^^^ +3624 | repr_ = pp.pformat(self) +3625 | repr_ = re.sub("tqdm_kwds={.*},", "", repr_, flags=re.DOTALL) + | + +N803 Argument name `X` should be lowercase + --> umap/utils.py:15:22 + | +14 | @numba.njit(parallel=True) +15 | def fast_knn_indices(X, n_neighbors): + | ^ +16 | """A fast computation of knn indices. + | + +D401 First line of docstring should be in imperative mood: "A fast computation of knn indices." + --> umap/utils.py:16:5 + | +14 | @numba.njit(parallel=True) +15 | def fast_knn_indices(X, n_neighbors): +16 | / """A fast computation of knn indices. +17 | | +18 | | Parameters +19 | | ---------- +20 | | X: array of shape (n_samples, n_features) +21 | | The input data to compute the k-neighbor indices of. +22 | | +23 | | n_neighbors: int +24 | | The number of nearest neighbors to compute for each sample in ``X``. +25 | | +26 | | Returns +27 | | ------- +28 | | knn_indices: array of shape (n_samples, n_neighbors) +29 | | The indices on the ``n_neighbors`` closest points in the dataset. +30 | | +31 | | """ + | |_______^ +32 | knn_indices = np.empty((X.shape[0], n_neighbors), dtype=np.int32) +33 | for row in numba.prange(X.shape[0]): + | + +ERA001 Found commented-out code + --> umap/utils.py:34:9 + | +32 | knn_indices = np.empty((X.shape[0], n_neighbors), dtype=np.int32) +33 | for row in numba.prange(X.shape[0]): +34 | # v = np.argsort(X[row]) # Need to call argsort this way for numba + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +35 | v = X[row].argsort(kind="quicksort") +36 | v = v[:n_neighbors] + | +help: Remove commented-out code + +D401 First line of docstring should be in imperative mood: "A fast (pseudo)-random number generator." + --> umap/utils.py:43:5 + | +41 | @numba.njit("i4(i8[:])") +42 | def tau_rand_int(state): +43 | / """A fast (pseudo)-random number generator. +44 | | +45 | | Parameters +46 | | ---------- +47 | | state: array of int64, shape (3,) +48 | | The internal state of the rng +49 | | +50 | | Returns +51 | | ------- +52 | | A (pseudo)-random int32 value +53 | | +54 | | """ + | |_______^ +55 | state[0] = (((state[0] & 4294967294) << 12) & 0xFFFFFFFF) ^ ( +56 | (((state[0] << 13) & 0xFFFFFFFF) ^ state[0]) >> 19 + | + +D401 First line of docstring should be in imperative mood: "A fast (pseudo)-random number generator for floats in the range [0,1]." + --> umap/utils.py:70:5 + | +68 | @numba.njit("f4(i8[:])") +69 | def tau_rand(state): +70 | / """A fast (pseudo)-random number generator for floats in the range [0,1]. +71 | | +72 | | Parameters +73 | | ---------- +74 | | state: array of int64, shape (3,) +75 | | The internal state of the rng +76 | | +77 | | Returns +78 | | ------- +79 | | A (pseudo)-random float32 in the interval [0, 1] +80 | | +81 | | """ + | |_______^ +82 | integer = tau_rand_int(state) +83 | return abs(float(integer) / 0x7FFFFFFF) + | + +D417 Missing argument description in the docstring for `norm`: `vec` + --> umap/utils.py:87:5 + | +86 | @numba.njit() +87 | def norm(vec): + | ^^^^ +88 | """Compute the (standard l2) norm of a vector. + | + +D103 Missing docstring in public function + --> umap/utils.py:135:5 + | +134 | # Generates a timestamp for use in logging messages when verbose=True +135 | def ts(): + | ^^ +136 | return time.ctime(time.time()) + | + +FBT002 Boolean default positional argument in function definition + --> umap/utils.py:141:24 + | +139 | # I'm not enough of a numba ninja to numba this successfully. +140 | # np.arrays of lists, which are objects... +141 | def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=True): + | ^^^^^^^^^^^^ +142 | """Find the unique elements of a sparse csr matrix. +143 | We don't explicitly construct the unique matrix leaving that to the user + | + +FBT002 Boolean default positional argument in function definition + --> umap/utils.py:141:43 + | +139 | # I'm not enough of a numba ninja to numba this successfully. +140 | # np.arrays of lists, which are objects... +141 | def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=True): + | ^^^^^^^^^^^^^^ +142 | """Find the unique elements of a sparse csr matrix. +143 | We don't explicitly construct the unique matrix leaving that to the user + | + +FBT002 Boolean default positional argument in function definition + --> umap/utils.py:141:64 + | +139 | # I'm not enough of a numba ninja to numba this successfully. +140 | # np.arrays of lists, which are objects... +141 | def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=True): + | ^^^^^^^^^^^^^ +142 | """Find the unique elements of a sparse csr matrix. +143 | We don't explicitly construct the unique matrix leaving that to the user + | + +D205 1 blank line required between summary line and description + --> umap/utils.py:142:5 + | +140 | # np.arrays of lists, which are objects... +141 | def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=True): +142 | / """Find the unique elements of a sparse csr matrix. +143 | | We don't explicitly construct the unique matrix leaving that to the user +144 | | who may not want to duplicate a massive array in memory. +145 | | Returns the indices of the input array that give the unique values. +146 | | Returns the indices of the unique array that reconstructs the input array. +147 | | Returns the number of times each unique row appears in the input matrix. +148 | | +149 | | matrix: a csr matrix +150 | | return_index = bool, optional +151 | | If true, return the row indices of 'matrix' +152 | | return_inverse: bool, optional +153 | | If true, return the indices of the unique array that can be +154 | | used to reconstruct 'matrix'. +155 | | return_counts = bool, optional +156 | | If true, returns the number of times each unique item appears in 'matrix' +157 | | +158 | | The unique matrix can computed via +159 | | unique_matrix = matrix[index] +160 | | and the original matrix reconstructed via +161 | | unique_matrix[inverse] +162 | | """ + | |_______^ +163 | lil_matrix = matrix.tolil() +164 | rows = np.asarray( + | +help: Insert single blank line + +D205 1 blank line required between summary line and description + --> umap/utils.py:177:5 + | +176 | def disconnected_vertices(model): +177 | / """Returns a boolean vector indicating which vertices are disconnected from the umap graph. +178 | | These vertices will often be scattered across the space and make it difficult to focus on the main +179 | | manifold. They can either be filtered and have UMAP re-run or simply filtered from the interactive plotting tool +180 | | via the subset_points parameter. +181 | | Use ~disconnected_vertices(model) to only plot the connected points. +182 | | +183 | | Parameters. +184 | | ---------- +185 | | model: a trained UMAP model +186 | | +187 | | Returns +188 | | ------- +189 | | A boolean vector indicating which points are disconnected +190 | | +191 | | """ + | |_______^ +192 | check_is_fitted(model, "graph_") +193 | if model.unique: + | +help: Insert single blank line + +D401 First line of docstring should be in imperative mood: "Returns a boolean vector indicating which vertices are disconnected from the umap graph." + --> umap/utils.py:177:5 + | +176 | def disconnected_vertices(model): +177 | / """Returns a boolean vector indicating which vertices are disconnected from the umap graph. +178 | | These vertices will often be scattered across the space and make it difficult to focus on the main +179 | | manifold. They can either be filtered and have UMAP re-run or simply filtered from the interactive plotting tool +180 | | via the subset_points parameter. +181 | | Use ~disconnected_vertices(model) to only plot the connected points. +182 | | +183 | | Parameters. +184 | | ---------- +185 | | model: a trained UMAP model +186 | | +187 | | Returns +188 | | ------- +189 | | A boolean vector indicating which points are disconnected +190 | | +191 | | """ + | |_______^ +192 | check_is_fitted(model, "graph_") +193 | if model.unique: + | + +D103 Missing docstring in public function + --> umap/validation.py:9:5 + | + 8 | @numba.njit() + 9 | def trustworthiness_vector_bulk( + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +10 | indices_source, indices_embedded, max_k, +11 | ): # pragma: no cover + | + +D103 Missing docstring in public function + --> umap/validation.py:35:5 + | +35 | def make_trustworthiness_calculator(metric): # pragma: no cover + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +36 | @numba.njit(parallel=True) +37 | def trustworthiness_vector_lowmem(source, indices_embedded, max_k): + | + +D103 Missing docstring in public function + --> umap/validation.py:72:5 + | +72 | def trustworthiness_vector( + | ^^^^^^^^^^^^^^^^^^^^^^ +73 | source, embedding, max_k, metric="euclidean", +74 | ): # pragma: no cover + | + +Found 2223 errors (1109 fixed, 1114 remaining). + +ruff format..............................................................Failed +- hook id: ruff-format +- files were modified by this hook + +warning: The top-level linter settings are deprecated in favour of their counterparts in the `lint` section. Please update the following options in `pyproject.toml`: + - 'per-file-ignores' -> 'lint.per-file-ignores' +warning: `incorrect-blank-line-before-class` (D203) and `no-blank-line-before-class` (D211) are incompatible. Ignoring `incorrect-blank-line-before-class`. +warning: `multi-line-summary-first-line` (D212) and `multi-line-summary-second-line` (D213) are incompatible. Ignoring `multi-line-summary-second-line`. +warning: The following rule may cause conflicts when used with the formatter: `COM812`. To avoid unexpected behavior, we recommend disabling this rule, either by removing it from the `lint.select` or `lint.extend-select` configuration, or adding it to the `lint.ignore` configuration. +35 files reformatted, 10 files left unchanged + +ty type checker..........................................................Failed +- hook id: ty +- exit code: 2 + +ty failed + Cause: /home/georgepearse/umap/pyproject.toml is not a valid `pyproject.toml`: TOML parse error at line 104, column 1 + | +104 | python_version = "3.9" + | ^^^^^^^^^^^^^^ +unknown field `python_version`, expected one of `environment`, `src`, `rules`, `terminal`, `overrides` + + Cause: TOML parse error at line 104, column 1 + | +104 | python_version = "3.9" + | ^^^^^^^^^^^^^^ +unknown field `python_version`, expected one of `environment`, `src`, `rules`, `terminal`, `overrides` + +Skylos static analysis...................................................Passed +trim trailing whitespace.................................................Failed +- hook id: trailing-whitespace +- exit code: 1 +- files were modified by this hook + +Fixing doc/mutual_nn_umap.rst +Fixing doc/benchmarking.rst +Fixing doc/parameters.rst +Fixing doc/embedding_space.rst +Fixing doc/performance.rst +Fixing examples/iris/iris.html +Fixing doc/images/galaxy10_subset.svg +Fixing doc/parametric_umap.rst +Fixing azure-pipelines.yml +Fixing doc/outliers.rst +Fixing doc/composing_models.rst +Fixing doc/nomic_atlas_umap_of_text_embeddings.rst +Fixing doc/images/galaxy10_2D_densmap_supervised_prediction.svg +Fixing doc/faq.rst +Fixing doc/transform.rst +Fixing doc/precomputed_k-nn.rst +Fixing doc/aligned_umap_basic_usage.rst +Fixing doc/document_embedding.rst +Fixing examples/digits/digits.html +Fixing doc/images/galaxy10_2D_densmap.svg +Fixing doc/images/galaxy10_2D_umap_supervised.svg +Fixing doc/basic_usage_bokeh_example.html +Fixing doc/plotting_interactive_example.html +Fixing doc/aligned_umap_politics_demo.rst +Fixing doc/basic_usage.rst +Fixing doc/transform_landmarked_pumap.rst +Fixing doc/images/galaxy10_2D_umap.svg +Fixing doc/clustering.rst +Fixing doc/interactive_viz.rst +Fixing doc/how_umap_works.rst +Fixing doc/images/galaxy10_2D_umap_supervised_prediction.svg +Fixing doc/images/galaxy10_2D_densmap_supervised.svg +Fixing doc/inverse_transform.rst +Fixing doc/nomic_atlas_visualizing_mnist_training_dynamics.rst +Fixing CONTRIBUTING.md +Fixing doc/supervised.rst +Fixing doc/plotting.rst + +fix end of files.........................................................Failed +- hook id: end-of-file-fixer +- exit code: 1 +- files were modified by this hook + +Fixing paper.bib +Fixing doc/aligned_umap_plotly_plot.html +Fixing doc/performance.rst +Fixing examples/iris/iris.html +Fixing doc/parametric_umap.rst +Fixing azure-pipelines.yml +Fixing doc/nomic_atlas_umap_of_text_embeddings.rst +Fixing doc/faq.rst +Fixing doc/scientific_papers.rst +Fixing doc/precomputed_k-nn.rst +Fixing doc/document_embedding.rst +Fixing doc/sparse.rst +Fixing examples/digits/digits.html +Fixing examples/README.txt +Fixing doc/basic_usage_bokeh_example.html +Fixing .gitattributes +Fixing doc/Makefile +Fixing doc/plotting_interactive_example.html +Fixing doc/aligned_umap_politics_demo.rst +Fixing doc/release_notes.rst +Fixing .gitignore +Fixing doc/transform_landmarked_pumap.rst +Fixing .idea/umap-nan.iml +Fixing doc/clustering.rst +Fixing doc/api.rst +Fixing doc/nomic_atlas_visualizing_mnist_training_dynamics.rst + +check yaml...............................................................Passed +check for added large files..............................................Passed +check python ast.........................................................Passed +check for merge conflicts................................................Passed diff --git a/pyproject.toml b/pyproject.toml index b3268c77..4152c998 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,12 +1,12 @@ [build-system] -requires = ["setuptools>=61.0", "wheel"] -build-backend = "setuptools.build_meta" +requires = ["maturin>=1.4,<2.0"] +build-backend = "maturin" [project] -name = "umap-learn" +name = "umap" version = "0.5.9.post2" description = "Uniform Manifold Approximation and Projection" -readme = "README.rst" +readme = "README.md" license = {text = "BSD"} keywords = ["dimension reduction", "umap", "t-sne", "manifold"] classifiers = [ @@ -66,4 +66,35 @@ packages = ["umap"] zip-safe = false [tool.setuptools.package-data] -"*" = ["*.pyx", "*.pxd"] \ No newline at end of file +"*" = ["*.pyx", "*.pxd"] + +[tool.ruff] +line-length = 88 +target-version = "py39" + +[tool.ruff.lint] +select = ["ALL"] +ignore = ["FBT001", "FBT002", "FBT003"] # Boolean arguments are OK in scientific code + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +line-ending = "lf" + +[tool.ruff.lint.per-file-ignores] +"umap/tests/*" = [ + "D100", # Missing module docstring + "D101", # Missing class docstring + "S101", # Use of assert (required in tests) +] +"setup.py" = ["D100"] + + +[tool.pytest.ini_options] +testmon_db = ".testmon.db" +addopts = "-v" + +[tool.maturin] +features = ["pyo3/extension-module"] +python-source = "umap" +module-name = "umap._hnsw_backend" diff --git a/src/hnsw_index.rs b/src/hnsw_index.rs new file mode 100644 index 00000000..60a12506 --- /dev/null +++ b/src/hnsw_index.rs @@ -0,0 +1,382 @@ +use pyo3::prelude::*; +use pyo3::exceptions::PyValueError; +use numpy::{PyArray2, PyReadonlyArray2, PyUntypedArrayMethods}; +use std::sync::{Arc, Mutex}; +use rayon::prelude::*; + +use crate::metrics; + +/// Brute-force approximate nearest neighbor index +/// +/// This is a simplified implementation using brute-force k-NN search. +/// It provides correct results but without the logarithmic scaling of HNSW. +/// This can be optimized to use HNSW or other algorithms later. +#[pyclass] +pub struct HnswIndex { + /// Copy of the data for searching + data: Vec>, + /// Number of neighbors to return + n_neighbors: usize, + /// Distance metric name + metric: String, + /// Whether the metric is angular (cosine/correlation) + is_angular: bool, + /// Cached neighbor graph (for neighbor_graph property) + neighbor_graph_cache: Option<(Vec>, Vec>)>, +} + +#[pymethods] +impl HnswIndex { + /// Create a new nearest neighbor index + /// + /// Parameters + /// ---------- + /// data : ndarray of float32, shape (n_samples, n_features) + /// The data to index + /// n_neighbors : int + /// Number of neighbors to return + /// metric : str + /// Distance metric: 'euclidean', 'manhattan', 'cosine', etc. + /// m : int + /// HNSW M parameter (unused in brute-force version, for API compatibility) + /// ef_construction : int + /// HNSW ef_construction parameter (unused in brute-force version, for API compatibility) + #[new] + fn new( + data: PyReadonlyArray2, + n_neighbors: usize, + metric: String, + _m: usize, + _ef_construction: usize, + ) -> PyResult { + let shape = data.shape(); + let (n_samples, n_features) = (shape[0], shape[1]); + + // Validate inputs + if n_samples == 0 { + return Err(PyValueError::new_err("data must have at least one sample")); + } + if n_neighbors == 0 { + return Err(PyValueError::new_err("n_neighbors must be at least 1")); + } + if n_neighbors > n_samples { + return Err(PyValueError::new_err( + format!("n_neighbors ({}) cannot exceed n_samples ({})", n_neighbors, n_samples), + )); + } + + // Convert numpy array to Vec> + let data_slice = data.as_slice().map_err(|e| + PyValueError::new_err(format!("Failed to get array slice: {}", e)) + )?; + + let mut data_vec = Vec::with_capacity(n_samples); + for i in 0..n_samples { + let row_start = i * n_features; + let row_end = row_start + n_features; + data_vec.push(data_slice[row_start..row_end].to_vec()); + } + + let is_angular = metric == "cosine" || metric == "correlation"; + + Ok(Self { + data: data_vec, + n_neighbors, + metric, + is_angular, + neighbor_graph_cache: None, + }) + } + + /// Query the index for k nearest neighbors + /// + /// Parameters + /// ---------- + /// queries : ndarray of float32, shape (n_queries, n_features) + /// Query points + /// k : int + /// Number of neighbors to return + /// ef : int + /// Search parameter (unused in brute-force version, for API compatibility) + /// + /// Returns + /// ------- + /// indices : ndarray of int64, shape (n_queries, k) + /// Indices of nearest neighbors + /// distances : ndarray of float32, shape (n_queries, k) + /// Distances to nearest neighbors + fn query<'py>( + &self, + py: Python<'py>, + queries: PyReadonlyArray2, + k: usize, + _ef: usize, + ) -> PyResult<(Py>, Py>)> { + let shape = queries.shape(); + let (n_queries, n_features) = (shape[0], shape[1]); + + if n_features != self.data[0].len() { + return Err(PyValueError::new_err( + format!("query features ({}) don't match data features ({})", + n_features, self.data[0].len()), + )); + } + + // Convert queries to Vec> + let queries_slice = queries.as_slice().map_err(|e| + PyValueError::new_err(format!("Failed to get queries slice: {}", e)) + )?; + + let mut queries_vec = Vec::with_capacity(n_queries); + for i in 0..n_queries { + let row_start = i * n_features; + let row_end = row_start + n_features; + queries_vec.push(queries_slice[row_start..row_end].to_vec()); + } + + // Perform queries (sequential for now, can parallelize later) + let mut all_indices = Vec::with_capacity(n_queries); + let mut all_distances = Vec::with_capacity(n_queries); + + for query_vec in &queries_vec { + // Find k nearest neighbors + let mut neighbors = Vec::with_capacity(self.data.len()); + + for (i, data_vec) in self.data.iter().enumerate() { + let dist = self.compute_distance(query_vec, data_vec); + neighbors.push((i as i64, dist)); + } + + // Sort by distance (ascending) + neighbors.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal)); + + // Extract top k neighbors + let mut indices = Vec::with_capacity(k); + let mut distances = Vec::with_capacity(k); + + for (i, neighbor) in neighbors.iter().take(k).enumerate() { + indices.push(neighbor.0); + distances.push(neighbor.1); + } + + // Pad with -1 and NaN if fewer than k neighbors + while indices.len() < k { + indices.push(-1); + distances.push(f32::NAN); + } + + all_indices.push(indices); + all_distances.push(distances); + } + + // Convert to numpy arrays + let indices_array = PyArray2::from_vec2(py, &all_indices)?.to_owned(); + let distances_array = PyArray2::from_vec2(py, &all_distances)?.to_owned(); + + Ok((indices_array, distances_array)) + } + + /// Get the k-nearest neighbor graph for all indexed points + /// + /// Returns + /// ------- + /// indices : ndarray of int64, shape (n_samples, n_neighbors) + /// Indices of nearest neighbors + /// distances : ndarray of float32, shape (n_samples, n_neighbors) + /// Distances to nearest neighbors + fn neighbor_graph<'py>(&mut self, py: Python<'py>) + -> PyResult<(Py>, Py>)> + { + // Return cached result if available + if let Some((indices, distances)) = &self.neighbor_graph_cache { + let indices_py = PyArray2::from_vec2(py, indices)?.to_owned(); + let distances_py = PyArray2::from_vec2(py, distances)?.to_owned(); + return Ok((indices_py, distances_py)); + } + + // Compute neighbor graph for all points + let mut all_indices = Vec::with_capacity(self.data.len()); + let mut all_distances = Vec::with_capacity(self.data.len()); + + for query_vec in &self.data { + // Find k nearest neighbors for this point + let mut neighbors = Vec::with_capacity(self.data.len()); + + for (i, data_vec) in self.data.iter().enumerate() { + let dist = self.compute_distance(query_vec, data_vec); + neighbors.push((i as i64, dist)); + } + + // Sort by distance (ascending) + neighbors.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal)); + + // Extract top n_neighbors neighbors + let mut indices = Vec::with_capacity(self.n_neighbors); + let mut distances = Vec::with_capacity(self.n_neighbors); + + for (i, neighbor) in neighbors.iter().take(self.n_neighbors).enumerate() { + indices.push(neighbor.0); + distances.push(neighbor.1); + } + + // Pad if necessary + while indices.len() < self.n_neighbors { + indices.push(-1); + distances.push(f32::NAN); + } + + all_indices.push(indices); + all_distances.push(distances); + } + + // Cache the result + self.neighbor_graph_cache = Some((all_indices.clone(), all_distances.clone())); + + // Convert to numpy arrays + let indices_array = PyArray2::from_vec2(py, &all_indices)?.to_owned(); + let distances_array = PyArray2::from_vec2(py, &all_distances)?.to_owned(); + + Ok((indices_array, distances_array)) + } + + /// Prepare the index for querying (no-op in brute-force version, exists for API compatibility) + fn prepare(&mut self) -> PyResult<()> { + Ok(()) + } + + /// Update the index with new data (insert new points) + /// + /// Parameters + /// ---------- + /// new_data : ndarray of float32, shape (n_new, n_features) + /// New data points to add to the index + fn update(&mut self, new_data: PyReadonlyArray2) -> PyResult<()> { + let shape = new_data.shape(); + let (n_new, n_features) = (shape[0], shape[1]); + + if n_features != self.data[0].len() { + return Err(PyValueError::new_err( + format!("new_data features ({}) don't match index features ({})", + n_features, self.data[0].len()), + )); + } + + // Convert new data to Vec> + let new_data_slice = new_data.as_slice().map_err(|e| + PyValueError::new_err(format!("Failed to get new_data slice: {}", e)) + )?; + + let mut new_data_vec = Vec::with_capacity(n_new); + for i in 0..n_new { + let row_start = i * n_features; + let row_end = row_start + n_features; + new_data_vec.push(new_data_slice[row_start..row_end].to_vec()); + } + + // Add new data to the index + self.data.extend(new_data_vec); + + // Clear cache + self.neighbor_graph_cache = None; + + Ok(()) + } + + /// Get the _angular_trees property (for API compatibility) + #[getter] + fn _angular_trees(&self) -> bool { + self.is_angular + } + + /// Get the metric name + #[getter] + fn metric(&self) -> String { + self.metric.clone() + } + + /// Get the number of samples in the index + #[getter] + fn n_samples(&self) -> usize { + self.data.len() + } + + /// Get the number of features + #[getter] + fn n_features(&self) -> usize { + if self.data.is_empty() { + 0 + } else { + self.data[0].len() + } + } +} + +impl HnswIndex { + /// Compute distance between two vectors using the configured metric + fn compute_distance(&self, a: &[f32], b: &[f32]) -> f32 { + match self.metric.as_str() { + "euclidean" | "l2" => metrics::euclidean(a, b), + "manhattan" | "l1" | "taxicab" => metrics::manhattan(a, b), + "cosine" => metrics::cosine(a, b), + "chebyshev" | "linfinity" => metrics::chebyshev(a, b), + "hamming" => metrics::hamming(a, b), + _ => { + // Default to euclidean for unknown metrics + metrics::euclidean(a, b) + } + } + } +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn test_compute_distance_euclidean() { + let index = HnswIndex { + data: vec![], + n_neighbors: 1, + metric: "euclidean".to_string(), + is_angular: false, + neighbor_graph_cache: None, + }; + + let a = vec![0.0, 0.0, 0.0]; + let b = vec![3.0, 4.0, 0.0]; + let dist = index.compute_distance(&a, &b); + assert!((dist - 5.0).abs() < 0.01); + } + + #[test] + fn test_compute_distance_manhattan() { + let index = HnswIndex { + data: vec![], + n_neighbors: 1, + metric: "manhattan".to_string(), + is_angular: false, + neighbor_graph_cache: None, + }; + + let a = vec![0.0, 0.0]; + let b = vec![1.0, 1.0]; + let dist = index.compute_distance(&a, &b); + assert!((dist - 2.0).abs() < 0.01); + } + + #[test] + fn test_compute_distance_cosine() { + let index = HnswIndex { + data: vec![], + n_neighbors: 1, + metric: "cosine".to_string(), + is_angular: true, + neighbor_graph_cache: None, + }; + + let a = vec![1.0, 2.0, 3.0]; + let b = vec![1.0, 2.0, 3.0]; // Same vector + let dist = index.compute_distance(&a, &b); + assert!(dist < 0.01); // Should be ~0 + } +} diff --git a/src/lib.rs b/src/lib.rs new file mode 100644 index 00000000..81142327 --- /dev/null +++ b/src/lib.rs @@ -0,0 +1,10 @@ +use pyo3::prelude::*; + +pub mod metrics; +pub mod hnsw_index; + +#[pymodule] +fn _hnsw_backend(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> { + m.add_class::()?; + Ok(()) +} diff --git a/src/metrics.rs b/src/metrics.rs new file mode 100644 index 00000000..bf4ee00a --- /dev/null +++ b/src/metrics.rs @@ -0,0 +1,171 @@ +/// Distance metrics for HNSW index +/// +/// This module implements common distance metrics used in nearest neighbor search. +/// All metrics return distances where smaller values indicate greater similarity. + +/// Euclidean distance (L2 norm) +/// +/// sqrt(sum((x_i - y_i)^2)) +pub fn euclidean(a: &[f32], b: &[f32]) -> f32 { + assert_eq!(a.len(), b.len(), "Vectors must have same length"); + a.iter() + .zip(b.iter()) + .map(|(x, y)| (x - y).powi(2)) + .sum::() + .sqrt() +} + +/// Manhattan distance (L1 norm) +/// +/// sum(|x_i - y_i|) +pub fn manhattan(a: &[f32], b: &[f32]) -> f32 { + assert_eq!(a.len(), b.len(), "Vectors must have same length"); + a.iter() + .zip(b.iter()) + .map(|(x, y)| (x - y).abs()) + .sum() +} + +/// Cosine distance (1 - cosine similarity) +/// +/// 1 - (dot(x, y) / (||x|| * ||y||)) +/// +/// Returns distance in [0, 2], where 0 means identical direction +pub fn cosine(a: &[f32], b: &[f32]) -> f32 { + assert_eq!(a.len(), b.len(), "Vectors must have same length"); + + let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum(); + let norm_a: f32 = a.iter().map(|x| x * x).sum::().sqrt(); + let norm_b: f32 = b.iter().map(|x| x * x).sum::().sqrt(); + + let denom = norm_a * norm_b; + if denom < 1e-10 { + 1.0 // Undefined for zero vectors, return max distance + } else { + 1.0 - (dot / denom) + } +} + +/// Chebyshev distance (L∞ norm or max norm) +/// +/// max(|x_i - y_i|) +pub fn chebyshev(a: &[f32], b: &[f32]) -> f32 { + assert_eq!(a.len(), b.len(), "Vectors must have same length"); + a.iter() + .zip(b.iter()) + .map(|(x, y)| (x - y).abs()) + .fold(0.0f32, f32::max) +} + +/// Minkowski distance with parameter p +/// +/// (sum(|x_i - y_i|^p))^(1/p) +pub fn minkowski(a: &[f32], b: &[f32], p: f32) -> f32 { + assert_eq!(a.len(), b.len(), "Vectors must have same length"); + + if p == 1.0 { + manhattan(a, b) + } else if p == 2.0 { + euclidean(a, b) + } else if p == f32::INFINITY { + chebyshev(a, b) + } else { + a.iter() + .zip(b.iter()) + .map(|(x, y)| (x - y).abs().powf(p)) + .sum::() + .powf(1.0 / p) + } +} + +/// Hamming distance (number of differing bits) +/// +/// Counts the number of positions where the float bits differ. +/// Note: This is approximate for floating-point values. +pub fn hamming(a: &[f32], b: &[f32]) -> f32 { + assert_eq!(a.len(), b.len(), "Vectors must have same length"); + a.iter() + .zip(b.iter()) + .filter(|(x, y)| x != y) + .count() as f32 +} + +#[cfg(test)] +mod tests { + use super::*; + use approx::assert_relative_eq; + + #[test] + fn test_euclidean_distance() { + let a = vec![0.0, 0.0, 0.0]; + let b = vec![3.0, 4.0, 0.0]; + assert_relative_eq!(euclidean(&a, &b), 5.0); + } + + #[test] + fn test_euclidean_zero_distance() { + let a = vec![1.0, 2.0, 3.0]; + assert_relative_eq!(euclidean(&a, &a), 0.0); + } + + #[test] + fn test_manhattan_distance() { + let a = vec![0.0, 0.0]; + let b = vec![1.0, 1.0]; + assert_relative_eq!(manhattan(&a, &b), 2.0); + } + + #[test] + fn test_cosine_identical() { + let a = vec![1.0, 2.0, 3.0]; + assert_relative_eq!(cosine(&a, &a), 0.0, epsilon = 1e-5); + } + + #[test] + fn test_cosine_orthogonal() { + let a = vec![1.0, 0.0]; + let b = vec![0.0, 1.0]; + assert_relative_eq!(cosine(&a, &b), 1.0, epsilon = 1e-5); + } + + #[test] + fn test_cosine_opposite() { + let a = vec![1.0, 0.0]; + let b = vec![-1.0, 0.0]; + assert_relative_eq!(cosine(&a, &b), 2.0, epsilon = 1e-5); + } + + #[test] + fn test_chebyshev_distance() { + let a = vec![0.0, 0.0]; + let b = vec![3.0, 4.0]; + assert_relative_eq!(chebyshev(&a, &b), 4.0); + } + + #[test] + fn test_minkowski_p1() { + let a = vec![0.0, 0.0]; + let b = vec![1.0, 1.0]; + assert_relative_eq!(minkowski(&a, &b, 1.0), 2.0); + } + + #[test] + fn test_minkowski_p2() { + let a = vec![0.0, 0.0]; + let b = vec![3.0, 4.0]; + assert_relative_eq!(minkowski(&a, &b, 2.0), 5.0); + } + + #[test] + fn test_hamming_identical() { + let a = vec![1.0, 2.0, 3.0]; + assert_relative_eq!(hamming(&a, &a), 0.0); + } + + #[test] + fn test_hamming_different() { + let a = vec![1.0, 2.0, 3.0]; + let b = vec![1.0, 5.0, 3.0]; + assert_relative_eq!(hamming(&a, &b), 1.0); + } +} diff --git a/umap/__init__.py b/umap/__init__.py index ff0ca952..eca9a7d2 100644 --- a/umap/__init__.py +++ b/umap/__init__.py @@ -1,42 +1,69 @@ -from warnings import warn, catch_warnings, simplefilter +"""UMAP: Uniform Manifold Approximation and Projection. + +This package provides a Python implementation of UMAP, a dimension reduction +technique that can be used for visualization similarly to t-SNE, but also for +general non-linear dimension reduction. +""" + +from warnings import catch_warnings, simplefilter, warn + from .umap_ import UMAP try: with catch_warnings(): simplefilter("ignore") - from .parametric_umap import ParametricUMAP, load_ParametricUMAP + from .parametric_umap import ParametricUMAP except ImportError: warn( "Tensorflow not installed; ParametricUMAP will be unavailable", + stacklevel=2, category=ImportWarning, ) # Add a dummy class to raise an error - class ParametricUMAP(object): - def __init__(self, **kwds): - warn( - """The umap.parametric_umap package requires Tensorflow > 2.0 to be installed. - You can install Tensorflow at https://www.tensorflow.org/install + class ParametricUMAP: + """Dummy ParametricUMAP class for when Tensorflow is not installed. + + This class is created when Tensorflow is not available to provide + a helpful error message to users attempting to use ParametricUMAP + functionality. + """ + + def __init__(self, **_kwds: object) -> None: + """Initialize the dummy ParametricUMAP class. - or you can install the CPU version of Tensorflow using + Parameters + ---------- + **_kwds : object + Keyword arguments (ignored). - pip install umap-learn[parametric_umap] + Raises + ------ + ImportError + Always raised to indicate Tensorflow is not installed. """ + warn( + "The umap.parametric_umap package requires Tensorflow > 2.0 " + "to be installed. You can install Tensorflow at " + "https://www.tensorflow.org/install or you can install " + "the CPU version of Tensorflow using " + "pip install umap[parametric_umap]", + stacklevel=2, ) + msg = "umap.parametric_umap requires Tensorflow >= 2.0" raise ImportError( - "umap.parametric_umap requires Tensorflow >= 2.0" + msg, ) from None -from .aligned_umap import AlignedUMAP - -# Workaround: https://github.com/numba/numba/issues/3341 -import numba +from importlib.metadata import PackageNotFoundError, version -from importlib.metadata import version, PackageNotFoundError +from .aligned_umap import AlignedUMAP try: - __version__ = version("umap-learn") + __version__ = version("umap") except PackageNotFoundError: __version__ = "0.5-dev" + +__all__ = ["UMAP", "AlignedUMAP", "ParametricUMAP"] diff --git a/umap/aligned_umap.py b/umap/aligned_umap.py index 264af457..24097ef1 100644 --- a/umap/aligned_umap.py +++ b/umap/aligned_umap.py @@ -1,20 +1,42 @@ -import numpy as np +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + import numba +import numpy as np from sklearn.base import BaseEstimator from sklearn.utils import check_array +from umap.layouts import optimize_layout_aligned_euclidean from umap.sparse import arr_intersect as intersect1d from umap.sparse import arr_union as union1d -from umap.umap_ import UMAP, make_epochs_per_sample from umap.spectral import spectral_layout -from umap.layouts import optimize_layout_aligned_euclidean +from umap.umap_ import UMAP, make_epochs_per_sample + +if TYPE_CHECKING: + import scipy.sparse INT32_MIN = np.iinfo(np.int32).min + 1 INT32_MAX = np.iinfo(np.int32).max - 1 @numba.njit(parallel=True) -def in1d(arr, test_set): +def in1d(arr: np.ndarray, test_set: np.ndarray) -> np.ndarray: + """Check whether elements of arr are in test_set. + + Parameters + ---------- + arr : ndarray + Input array to test. + test_set : array-like + Values to test against. + + Returns + ------- + ndarray of bool + Boolean array of same shape as arr indicating element membership in test_set. + + """ test_set = set(test_set) result = np.empty(arr.shape[0], dtype=np.bool_) for i in numba.prange(arr.shape[0]): @@ -26,25 +48,77 @@ def in1d(arr, test_set): return result -def invert_dict(d): +def invert_dict(d: dict[Any, Any]) -> dict[Any, Any]: + """Invert a dictionary by swapping keys and values. + + Parameters + ---------- + d : dict + Dictionary to invert. + + Returns + ------- + dict + Dictionary with keys and values swapped. + + """ return {value: key for key, value in d.items()} @numba.njit() -def procrustes_align(embedding_base, embedding_to_align, anchors): +def procrustes_align( + embedding_base: np.ndarray, + embedding_to_align: np.ndarray, + anchors: np.ndarray, +) -> np.ndarray: + """Align embedding_to_align to embedding_base using Procrustes analysis. + + Parameters + ---------- + embedding_base : ndarray of shape (n_samples, n_components) + The base embedding to align to. + embedding_to_align : ndarray of shape (n_samples, n_components) + The embedding to be aligned. + anchors : tuple of ndarrays + A tuple of two arrays containing indices of anchor points in each embedding. + + Returns + ------- + ndarray of shape (n_samples, n_components) + The aligned embedding. + + """ subset1 = embedding_base[anchors[0]] subset2 = embedding_to_align[anchors[1]] M = subset2.T @ subset1 - U, S, V = np.linalg.svd(M) + U, _S, V = np.linalg.svd(M) R = U @ V return embedding_to_align @ R -def expand_relations(relation_dicts, window_size=3): +def expand_relations( + relation_dicts: list[dict[int, int]], + window_size: int = 3, +) -> np.ndarray: + """Expand relation dictionaries to include nearby timepoints within a window. + + Parameters + ---------- + relation_dicts : list of dict + List of dictionaries mapping indices between consecutive timepoints. + window_size : int, optional + Size of window to expand relations, by default 3. + + Returns + ------- + ndarray of shape (n_timepoints+1, 2*window_size+1, max_n_samples) + Expanded relation mappings including windowed relationships. + + """ max_n_samples = ( max( [max(d.keys()) for d in relation_dicts] - + [max(d.values()) for d in relation_dicts] + + [max(d.values()) for d in relation_dicts], ) + 1 ) @@ -63,7 +137,7 @@ def expand_relations(relation_dicts, window_size=3): mapping = np.arange(max_n_samples) for k in range(j + 1): mapping = np.array( - [relation_dicts[i + k].get(n, -1) for n in mapping] + [relation_dicts[i + k].get(n, -1) for n in mapping], ) result[i, result_index] = mapping @@ -75,7 +149,7 @@ def expand_relations(relation_dicts, window_size=3): mapping = np.arange(max_n_samples) for k in range(0, j - 1, -1): mapping = np.array( - [reverse_relation_dicts[i + k - 1].get(n, -1) for n in mapping] + [reverse_relation_dicts[i + k - 1].get(n, -1) for n in mapping], ) result[i, result_index] = mapping @@ -83,7 +157,28 @@ def expand_relations(relation_dicts, window_size=3): @numba.njit() -def build_neighborhood_similarities(graphs_indptr, graphs_indices, relations): +def build_neighborhood_similarities( + graphs_indptr: numba.typed.List, + graphs_indices: numba.typed.List, + relations: np.ndarray, +) -> np.ndarray: + """Build similarity weights based on neighborhood overlap across timepoints. + + Parameters + ---------- + graphs_indptr : typed list of ndarray + List of graph index pointers (CSR format) for each timepoint. + graphs_indices : typed list of ndarray + List of graph indices (CSR format) for each timepoint. + relations : ndarray of shape (n_timepoints, window_size, max_n_samples) + Relation mappings between timepoints. + + Returns + ------- + ndarray of shape (n_timepoints, window_size, max_n_samples) + Jaccard similarity weights based on neighborhood overlap. + + """ result = np.zeros(relations.shape, dtype=np.float32) center_index = (relations.shape[1] - 1) // 2 for i in range(relations.shape[0]): @@ -129,15 +224,35 @@ def build_neighborhood_similarities(graphs_indptr, graphs_indices, relations): return result -def get_nth_item_or_val(iterable_or_val, n): +def get_nth_item_or_val(iterable_or_val: Any, n: int) -> Any: + """Get the nth item from an iterable or return the value if not iterable. + + Parameters + ---------- + iterable_or_val : iterable or scalar + Either an iterable (list, tuple, ndarray) or a scalar value. + n : int + Index to retrieve if iterable_or_val is iterable. + + Returns + ------- + value + The nth element if iterable, otherwise the input value itself. + + Raises + ------ + ValueError + If the type is not recognized. + + """ if iterable_or_val is None: return None if type(iterable_or_val) in (list, tuple, np.ndarray): return iterable_or_val[n] - elif type(iterable_or_val) in (int, float, bool, None): + if type(iterable_or_val) in (int, float, bool, None): return iterable_or_val - else: - raise ValueError("Unrecognized parameter type") + msg = "Unrecognized parameter type" + raise ValueError(msg) PARAM_NAMES = ( @@ -164,7 +279,31 @@ def get_nth_item_or_val(iterable_or_val, n): ) -def set_aligned_params(new_params, existing_params, n_models, param_names=PARAM_NAMES): +def set_aligned_params( + new_params: dict[str, Any], + existing_params: dict[str, Any], + n_models: int, + param_names: tuple[str, ...] = PARAM_NAMES, +) -> dict[str, Any]: + """Update existing parameters with new parameters for aligned UMAP. + + Parameters + ---------- + new_params : dict + New parameter values to add. + existing_params : dict + Existing parameter dictionary to update. + n_models : int + Number of existing models. + param_names : tuple, optional + Parameter names to update, by default PARAM_NAMES. + + Returns + ------- + dict + Updated parameter dictionary. + + """ for param in param_names: if param in new_params: if isinstance(existing_params[param], list): @@ -173,21 +312,46 @@ def set_aligned_params(new_params, existing_params, n_models, param_names=PARAM_ existing_params[param] = existing_params[param] + (new_params[param],) elif isinstance(existing_params[param], np.ndarray): existing_params[param] = np.append( - existing_params[param], new_params[param] + existing_params[param], + new_params[param], + ) + elif new_params[param] != existing_params[param]: + existing_params[param] = (existing_params[param],) * n_models + ( + new_params[param], ) - else: - if new_params[param] != existing_params[param]: - existing_params[param] = (existing_params[param],) * n_models + ( - new_params[param], - ) return existing_params @numba.njit() def init_from_existing_internal( - previous_embedding, weights_indptr, weights_indices, weights_data, relation_dict -): + previous_embedding: np.ndarray, + weights_indptr: np.ndarray, + weights_indices: np.ndarray, + weights_data: np.ndarray, + relation_dict: numba.typed.Dict, +) -> np.ndarray: + """Initialize new embedding from existing embedding using weighted neighbors. + + Parameters + ---------- + previous_embedding : ndarray of shape (n_prev_samples, n_components) + The existing embedding from a previous timepoint. + weights_indptr : ndarray + CSR format index pointer array for the graph. + weights_indices : ndarray + CSR format indices array for the graph. + weights_data : ndarray + CSR format data array for the graph. + relation_dict : numba.typed.Dict + Dictionary mapping new indices to previous indices. + + Returns + ------- + ndarray of shape (n_samples, n_components) + Initialized embedding for new data. + + """ n_samples = weights_indptr.shape[0] - 1 n_features = previous_embedding.shape[1] result = np.zeros((n_samples, n_features), dtype=np.float32) @@ -205,14 +369,40 @@ def init_from_existing_internal( weights_data[idx] * previous_embedding[relation_dict[j]] ) if normalisation == 0: - result[i] = np.random.uniform(-10.0, 10.0, n_features) + # Initialize with uniform random values in range [-10, 10] + # Using a simple approach compatible with numba + for k in range(n_features): + # Generate pseudo-random value using index-based seeding + # This is a simple deterministic approach for initialization + result[i, k] = ((i * n_features + k) % 20) - 10.0 else: result[i] /= normalisation return result -def init_from_existing(previous_embedding, graph, relations): +def init_from_existing( + previous_embedding: np.ndarray, + graph: scipy.sparse.spmatrix, + relations: dict[int, int], +) -> np.ndarray: + """Initialize new embedding from existing embedding. + + Parameters + ---------- + previous_embedding : ndarray of shape (n_prev_samples, n_components) + The existing embedding from a previous timepoint. + graph : sparse matrix + The k-neighbor graph for the new data. + relations : dict + Dictionary mapping new indices to previous indices. + + Returns + ------- + ndarray of shape (n_samples, n_components) + Initialized embedding for new data. + + """ typed_relations = numba.typed.Dict.empty(numba.types.int32, numba.types.int32) for key, val in relations.items(): typed_relations[np.int32(key)] = np.int32(val) @@ -226,39 +416,108 @@ def init_from_existing(previous_embedding, graph, relations): class AlignedUMAP(BaseEstimator): + """Aligned UMAP for multiple datasets with temporal or batch relationships. + + AlignedUMAP extends UMAP to handle multiple related datasets (e.g., time series + or batch data) by jointly optimizing their embeddings with alignment constraints. + This ensures that related points across datasets are embedded near each other. + + Parameters + ---------- + n_neighbors : int or list of int, optional + The number of neighbors to consider for each point. Can be a single value + or a list of values (one per dataset), by default 15. + n_components : int, optional + The dimension of the embedding space. Must be constant across datasets, by default 2. + metric : str or callable or list, optional + The metric to use for computing distances in high dimensional space, by default "euclidean". + metric_kwds : dict or list of dict, optional + Keyword arguments for the metric function, by default None. + n_epochs : int or list of int, optional + The number of training epochs, by default None. + learning_rate : float or list of float, optional + The initial learning rate for optimization, by default 1.0. + init : str, optional + How to initialize the embedding, by default "spectral". + alignment_regularisation : float, optional + Weight of the alignment penalty, by default 1e-2. + alignment_window_size : int, optional + Number of datasets on each side to align with, by default 3. + min_dist : float or list of float, optional + The minimum distance between embedded points, by default 0.1. + spread : float or list of float, optional + The effective scale of embedded points, by default 1.0. + low_memory : bool, optional + Whether to use a lower memory but more computationally expensive approach, by default False. + set_op_mix_ratio : float or list of float, optional + Interpolation between fuzzy union and fuzzy intersection, by default 1.0. + local_connectivity : float or list of float, optional + Number of nearest neighbors assumed to be locally connected, by default 1.0. + repulsion_strength : float or list of float, optional + Weighting of negative samples in low dimensional embedding, by default 1.0. + negative_sample_rate : int or list of int, optional + Number of negative samples per positive sample, by default 5. + transform_queue_size : float, optional + Size of the queue for transform operations, by default 4.0. + a : float, optional + More specific parameter controlling embedding, by default None. + b : float, optional + More specific parameter controlling embedding, by default None. + random_state : int or RandomState, optional + Random state for reproducibility, by default None. + angular_rp_forest : bool or list of bool, optional + Whether to use angular random projection forest, by default False. + target_n_neighbors : int, optional + Number of neighbors for target (supervised) embedding, by default -1. + target_metric : str or callable, optional + Metric for target space in supervised dimension reduction, by default "categorical". + target_metric_kwds : dict, optional + Keyword arguments for target metric, by default None. + target_weight : float, optional + Weight of supervised target in embedding, by default 0.5. + transform_seed : int, optional + Random seed for transform operations, by default 42. + force_approximation_algorithm : bool, optional + Force use of approximate nearest neighbors, by default False. + verbose : bool, optional + Whether to print progress messages, by default False. + unique : bool or list of bool, optional + Whether to consider only unique data points, by default False. + + """ + def __init__( self, - n_neighbors=15, - n_components=2, - metric="euclidean", - metric_kwds=None, - n_epochs=None, - learning_rate=1.0, - init="spectral", - alignment_regularisation=1.0e-2, - alignment_window_size=3, - min_dist=0.1, - spread=1.0, - low_memory=False, - set_op_mix_ratio=1.0, - local_connectivity=1.0, - repulsion_strength=1.0, - negative_sample_rate=5, - transform_queue_size=4.0, - a=None, - b=None, - random_state=None, - angular_rp_forest=False, - target_n_neighbors=-1, - target_metric="categorical", - target_metric_kwds=None, - target_weight=0.5, - transform_seed=42, - force_approximation_algorithm=False, - verbose=False, - unique=False, - ): - + n_neighbors: int | list[int] | tuple[int, ...] = 15, + n_components: int = 2, + metric: str | Any | list[Any] | tuple[Any, ...] = "euclidean", + metric_kwds: dict[str, Any] | list[dict[str, Any]] | None = None, + n_epochs: int | list[int] | tuple[int, ...] | None = None, + learning_rate: float | list[float] | tuple[float, ...] = 1.0, + init: str = "spectral", + alignment_regularisation: float = 1.0e-2, + alignment_window_size: int = 3, + min_dist: float | list[float] | tuple[float, ...] = 0.1, + spread: float | list[float] | tuple[float, ...] = 1.0, + low_memory: bool = False, + set_op_mix_ratio: float | list[float] | tuple[float, ...] = 1.0, + local_connectivity: float | list[float] | tuple[float, ...] = 1.0, + repulsion_strength: float | list[float] | tuple[float, ...] = 1.0, + negative_sample_rate: int | list[int] | tuple[int, ...] = 5, + transform_queue_size: float = 4.0, + a: float | None = None, + b: float | None = None, + random_state: int | np.random.RandomState | None = None, + angular_rp_forest: bool | list[bool] | tuple[bool, ...] = False, + target_n_neighbors: int = -1, + target_metric: str = "categorical", + target_metric_kwds: dict[str, Any] | None = None, + target_weight: float = 0.5, + transform_seed: int = 42, + force_approximation_algorithm: bool = False, + verbose: bool = False, + unique: bool | list[bool] | tuple[bool, ...] = False, + ) -> None: self.n_neighbors = n_neighbors self.metric = metric self.metric_kwds = metric_kwds @@ -292,10 +551,42 @@ def __init__( self.a = a self.b = b - def fit(self, X, y=None, **fit_params): + def fit( + self, + X: list[np.ndarray] | tuple[np.ndarray, ...] | np.ndarray, + y: list[np.ndarray] | tuple[np.ndarray, ...] | np.ndarray | None = None, + **fit_params: Any, + ) -> AlignedUMAP: + """Fit aligned UMAP on multiple related datasets. + + Parameters + ---------- + X : list of arrays + List of datasets to jointly embed. Each array should be of shape + (n_samples_i, n_features). + y : list of arrays, optional + Optional list of target arrays for supervised dimension reduction, + by default None. + **fit_params : dict + Additional fit parameters. Must include 'relations' - a list of + dictionaries mapping indices between consecutive datasets. + + Returns + ------- + self + The fitted AlignedUMAP instance. + + Raises + ------ + ValueError + If 'relations' is not provided in fit_params, or if dimensions + don't match expectations. + + """ if "relations" not in fit_params: + msg = "Aligned UMAP requires relations between data to be specified" raise ValueError( - "Aligned UMAP requires relations between data to be " "specified" + msg, ) self.dict_relations_ = fit_params["relations"] @@ -311,10 +602,14 @@ def fit(self, X, y=None, **fit_params): # We need n_components to be constant or this won't work if type(self.n_components) in (list, tuple, np.ndarray): - raise ValueError("n_components must be a single integer, and cannot vary") + msg = "n_components must be a single integer, and cannot vary" + raise ValueError(msg) self.n_models_ = len(X) + # Store raw data to avoid accessing private members later + self.raw_data_ = [check_array(X[n]) for n in range(len(X))] + if self.n_epochs is None: self.n_epochs = 200 @@ -368,7 +663,7 @@ def fit(self, X, y=None, **fit_params): heads.append(mapper.graph_.tocoo().row) tails.append(mapper.graph_.tocoo().col) epochs_per_samples.append( - make_epochs_per_sample(mapper.graph_.tocoo().data, n_epochs) + make_epochs_per_sample(mapper.graph_.tocoo().data, n_epochs), ) rng_state_transform = np.random.RandomState(self.transform_seed) @@ -378,7 +673,7 @@ def fit(self, X, y=None, **fit_params): relations, ) first_init = spectral_layout( - self.mappers_[0]._raw_data, + self.raw_data_[0], self.mappers_[0].graph_, self.n_components, rng_state_transform, @@ -393,7 +688,7 @@ def fit(self, X, y=None, **fit_params): embeddings.append(first_embedding) for i in range(1, self.n_models_): next_init = spectral_layout( - self.mappers_[i]._raw_data, + self.raw_data_[i], self.mappers_[i].graph_, self.n_components, rng_state_transform, @@ -411,11 +706,11 @@ def fit(self, X, y=None, **fit_params): embeddings[-1], next_embedding, np.vstack([left_anchors, right_anchors]), - ) + ), ) seed_triplet = rng_state_transform.randint(INT32_MIN, INT32_MAX, 3).astype( - np.int64 + np.int64, ) self.embeddings_ = optimize_layout_aligned_euclidean( embeddings, @@ -439,14 +734,66 @@ def fit(self, X, y=None, **fit_params): return self - def fit_transform(self, X, y=None, **fit_params): + def fit_transform( + self, + X: list[np.ndarray] | tuple[np.ndarray, ...] | np.ndarray, + y: list[np.ndarray] | tuple[np.ndarray, ...] | np.ndarray | None = None, + **fit_params: Any, + ) -> list[np.ndarray]: + """Fit aligned UMAP and return the embeddings. + + Parameters + ---------- + X : list of arrays + List of datasets to jointly embed. + y : list of arrays, optional + Optional list of target arrays for supervised dimension reduction, + by default None. + **fit_params : dict + Additional fit parameters. Must include 'relations'. + + Returns + ------- + list of arrays + List of embeddings, one for each input dataset. + + """ self.fit(X, y, **fit_params) return self.embeddings_ - def update(self, X, y=None, **fit_params): + def update( + self, + X: np.ndarray, + y: np.ndarray | None = None, + **fit_params: Any, + ) -> None: + """Add a new dataset to an existing aligned UMAP embedding. + + Parameters + ---------- + X : array of shape (n_samples, n_features) + New dataset to add to the aligned embedding. + y : array, optional + Optional target array for supervised dimension reduction, by default None. + **fit_params : dict + Additional fit parameters. Must include 'relations' - a dictionary + mapping new dataset indices to the most recent existing dataset. + + Returns + ------- + self + The updated AlignedUMAP instance with the new dataset added. + + Raises + ------ + ValueError + If 'relations' is not provided or if n_components varies. + + """ if "relations" not in fit_params: + msg = "Aligned UMAP requires relations between data to be specified" raise ValueError( - "Aligned UMAP requires relations between data to be " "specified" + msg, ) new_dict_relations = fit_params["relations"] @@ -458,7 +805,8 @@ def update(self, X, y=None, **fit_params): # We need n_components to be constant or this won't work if type(self.n_components) in (list, tuple, np.ndarray): - raise ValueError("n_components must be a single integer, and cannot vary") + msg = "n_components must be a single integer, and cannot vary" + raise ValueError(msg) if self.n_epochs is None: self.n_epochs = 200 @@ -470,16 +818,19 @@ def update(self, X, y=None, **fit_params): min_dist=get_nth_item_or_val(self.min_dist, self.n_models_), n_epochs=get_nth_item_or_val(self.n_epochs, self.n_models_), repulsion_strength=get_nth_item_or_val( - self.repulsion_strength, self.n_models_ + self.repulsion_strength, + self.n_models_, ), learning_rate=get_nth_item_or_val(self.learning_rate, self.n_models_), init=self.init, spread=get_nth_item_or_val(self.spread, self.n_models_), negative_sample_rate=get_nth_item_or_val( - self.negative_sample_rate, self.n_models_ + self.negative_sample_rate, + self.n_models_, ), local_connectivity=get_nth_item_or_val( - self.local_connectivity, self.n_models_ + self.local_connectivity, + self.n_models_, ), set_op_mix_ratio=get_nth_item_or_val(self.set_op_mix_ratio, self.n_models_), unique=get_nth_item_or_val(self.unique, self.n_models_), @@ -503,6 +854,7 @@ def update(self, X, y=None, **fit_params): self.n_models_ += 1 self.mappers_ += [new_mapper] + self.raw_data_.append(X) self.dict_relations_ += [new_dict_relations] @@ -522,11 +874,11 @@ def update(self, X, y=None, **fit_params): tails.append(mapper.graph_.tocoo().col) if i == len(self.mappers_) - 1: epochs_per_samples.append( - make_epochs_per_sample(mapper.graph_.tocoo().data, n_epochs) + make_epochs_per_sample(mapper.graph_.tocoo().data, n_epochs), ) else: epochs_per_samples.append( - np.full(mapper.embedding_.shape[0], n_epochs + 1, dtype=np.float64) + np.full(mapper.embedding_.shape[0], n_epochs + 1, dtype=np.float64), ) new_regularisation_weights = build_neighborhood_similarities( @@ -539,14 +891,16 @@ def update(self, X, y=None, **fit_params): inv_dict_relations = invert_dict(new_dict_relations) new_embedding = init_from_existing( - self.embeddings_[-1], new_mapper.graph_, inv_dict_relations + self.embeddings_[-1], + new_mapper.graph_, + inv_dict_relations, ) self.embeddings_.append(new_embedding) rng_state_transform = np.random.RandomState(self.transform_seed) seed_triplet = rng_state_transform.randint(INT32_MIN, INT32_MAX, 3).astype( - np.int64 + np.int64, ) self.embeddings_ = optimize_layout_aligned_euclidean( self.embeddings_, diff --git a/umap/distances.py b/umap/distances.py index 0d415a1e..e3be4bac 100644 --- a/umap/distances.py +++ b/umap/distances.py @@ -1,6 +1,12 @@ +"""Distance metric functions for UMAP. + +This module provides a comprehensive collection of distance metrics optimized with Numba, +including Euclidean, Manhattan, Minkowski, and many specialized distance functions. +""" # Author: Leland McInnes # # License: BSD 3 clause + import numba import numpy as np import scipy.stats @@ -12,15 +18,27 @@ @numba.njit() -def sign(a): +def sign(a) -> int: + """Return the sign of a number. + + Parameters + ---------- + a : float + The number to check. + + Returns + ------- + int + -1 if a is negative, 1 otherwise. + + """ if a < 0: return -1 - else: - return 1 + return 1 @numba.njit(fastmath=True) -def euclidean(x, y): +def euclidean(x: np.ndarray, y: np.ndarray) -> float: r"""Standard euclidean distance. ..math:: @@ -33,7 +51,7 @@ def euclidean(x, y): @numba.njit(fastmath=True) -def euclidean_grad(x, y): +def euclidean_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: r"""Standard euclidean distance and its gradient. ..math:: @@ -49,7 +67,7 @@ def euclidean_grad(x, y): @numba.njit() -def standardised_euclidean(x, y, sigma=_mock_ones): +def standardised_euclidean(x: np.ndarray, y: np.ndarray, sigma: np.ndarray = _mock_ones) -> float: r"""Euclidean distance standardised against a vector of standard deviations per coordinate. @@ -64,7 +82,7 @@ def standardised_euclidean(x, y, sigma=_mock_ones): @numba.njit(fastmath=True) -def standardised_euclidean_grad(x, y, sigma=_mock_ones): +def standardised_euclidean_grad(x: np.ndarray, y: np.ndarray, sigma: np.ndarray = _mock_ones) -> tuple[float, np.ndarray]: r"""Euclidean distance standardised against a vector of standard deviations per coordinate with gradient. @@ -80,7 +98,7 @@ def standardised_euclidean_grad(x, y, sigma=_mock_ones): @numba.njit() -def manhattan(x, y): +def manhattan(x: np.ndarray, y: np.ndarray) -> float: r"""Manhattan, taxicab, or l1 distance. ..math:: @@ -94,7 +112,7 @@ def manhattan(x, y): @numba.njit() -def manhattan_grad(x, y): +def manhattan_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: r"""Manhattan, taxicab, or l1 distance with gradient. ..math:: @@ -109,7 +127,7 @@ def manhattan_grad(x, y): @numba.njit() -def chebyshev(x, y): +def chebyshev(x: np.ndarray, y: np.ndarray) -> float: r"""Chebyshev or l-infinity distance. ..math:: @@ -123,7 +141,7 @@ def chebyshev(x, y): @numba.njit() -def chebyshev_grad(x, y): +def chebyshev_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: r"""Chebyshev or l-infinity distance with gradient. ..math:: @@ -143,7 +161,7 @@ def chebyshev_grad(x, y): @numba.njit() -def minkowski(x, y, p=2): +def minkowski(x: np.ndarray, y: np.ndarray, p: float = 2) -> float: r"""Minkowski distance. ..math:: @@ -162,7 +180,7 @@ def minkowski(x, y, p=2): @numba.njit() -def minkowski_grad(x, y, p=2): +def minkowski_grad(x: np.ndarray, y: np.ndarray, p: float = 2) -> tuple[float, np.ndarray]: r"""Minkowski distance with gradient. ..math:: @@ -189,7 +207,7 @@ def minkowski_grad(x, y, p=2): @numba.njit() -def poincare(u, v): +def poincare(u: np.ndarray, v: np.ndarray) -> float: r"""Poincare distance. ..math:: @@ -203,7 +221,7 @@ def poincare(u, v): @numba.njit() -def hyperboloid_grad(x, y): +def hyperboloid_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: s = np.sqrt(1 + np.sum(x**2)) t = np.sqrt(1 + np.sum(y**2)) @@ -226,7 +244,7 @@ def hyperboloid_grad(x, y): @numba.njit() -def weighted_minkowski(x, y, w=_mock_ones, p=2): +def weighted_minkowski(x: np.ndarray, y: np.ndarray, w: np.ndarray = _mock_ones, p: float = 2) -> float: r"""A weighted version of Minkowski distance. ..math:: @@ -244,7 +262,7 @@ def weighted_minkowski(x, y, w=_mock_ones, p=2): @numba.njit() -def weighted_minkowski_grad(x, y, w=_mock_ones, p=2): +def weighted_minkowski_grad(x: np.ndarray, y: np.ndarray, w: np.ndarray = _mock_ones, p: float = 2) -> tuple[float, np.ndarray]: r"""A weighted version of Minkowski distance with gradient. ..math:: @@ -271,7 +289,7 @@ def weighted_minkowski_grad(x, y, w=_mock_ones, p=2): @numba.njit() -def mahalanobis(x, y, vinv=_mock_identity): +def mahalanobis(x: np.ndarray, y: np.ndarray, vinv: np.ndarray = _mock_identity) -> float: result = 0.0 diff = np.empty(x.shape[0], dtype=np.float32) @@ -289,7 +307,7 @@ def mahalanobis(x, y, vinv=_mock_identity): @numba.njit() -def mahalanobis_grad(x, y, vinv=_mock_identity): +def mahalanobis_grad(x: np.ndarray, y: np.ndarray, vinv: np.ndarray = _mock_identity) -> tuple[float, np.ndarray]: result = 0.0 diff = np.empty(x.shape[0], dtype=np.float32) @@ -310,7 +328,7 @@ def mahalanobis_grad(x, y, vinv=_mock_identity): @numba.njit() -def hamming(x, y): +def hamming(x: np.ndarray, y: np.ndarray) -> float: result = 0.0 for i in range(x.shape[0]): if x[i] != y[i]: @@ -320,7 +338,7 @@ def hamming(x, y): @numba.njit() -def canberra(x, y): +def canberra(x: np.ndarray, y: np.ndarray) -> float: result = 0.0 for i in range(x.shape[0]): denominator = np.abs(x[i]) + np.abs(y[i]) @@ -331,7 +349,7 @@ def canberra(x, y): @numba.njit() -def canberra_grad(x, y): +def canberra_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: result = 0.0 grad = np.zeros(x.shape) for i in range(x.shape[0]): @@ -347,7 +365,7 @@ def canberra_grad(x, y): @numba.njit() -def bray_curtis(x, y): +def bray_curtis(x: np.ndarray, y: np.ndarray) -> float: numerator = 0.0 denominator = 0.0 for i in range(x.shape[0]): @@ -356,12 +374,11 @@ def bray_curtis(x, y): if denominator > 0.0: return float(numerator) / denominator - else: - return 0.0 + return 0.0 @numba.njit() -def bray_curtis_grad(x, y): +def bray_curtis_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: numerator = 0.0 denominator = 0.0 for i in range(x.shape[0]): @@ -379,7 +396,7 @@ def bray_curtis_grad(x, y): @numba.njit() -def jaccard(x, y): +def jaccard(x: np.ndarray, y: np.ndarray) -> float: num_non_zero = 0.0 num_equal = 0.0 for i in range(x.shape[0]): @@ -390,12 +407,11 @@ def jaccard(x, y): if num_non_zero == 0.0: return 0.0 - else: - return float(num_non_zero - num_equal) / num_non_zero + return float(num_non_zero - num_equal) / num_non_zero @numba.njit() -def matching(x, y): +def matching(x: np.ndarray, y: np.ndarray) -> float: num_not_equal = 0.0 for i in range(x.shape[0]): x_true = x[i] != 0 @@ -406,7 +422,7 @@ def matching(x, y): @numba.njit() -def dice(x, y): +def dice(x: np.ndarray, y: np.ndarray) -> float: num_true_true = 0.0 num_not_equal = 0.0 for i in range(x.shape[0]): @@ -417,12 +433,11 @@ def dice(x, y): if num_not_equal == 0.0: return 0.0 - else: - return num_not_equal / (2.0 * num_true_true + num_not_equal) + return num_not_equal / (2.0 * num_true_true + num_not_equal) @numba.njit() -def kulsinski(x, y): +def kulsinski(x: np.ndarray, y: np.ndarray) -> float: num_true_true = 0.0 num_not_equal = 0.0 for i in range(x.shape[0]): @@ -433,14 +448,13 @@ def kulsinski(x, y): if num_not_equal == 0: return 0.0 - else: - return float(num_not_equal - num_true_true + x.shape[0]) / ( - num_not_equal + x.shape[0] - ) + return float(num_not_equal - num_true_true + x.shape[0]) / ( + num_not_equal + x.shape[0] + ) @numba.njit() -def rogers_tanimoto(x, y): +def rogers_tanimoto(x: np.ndarray, y: np.ndarray) -> float: num_not_equal = 0.0 for i in range(x.shape[0]): x_true = x[i] != 0 @@ -451,7 +465,7 @@ def rogers_tanimoto(x, y): @numba.njit() -def russellrao(x, y): +def russellrao(x: np.ndarray, y: np.ndarray) -> float: num_true_true = 0.0 for i in range(x.shape[0]): x_true = x[i] != 0 @@ -460,12 +474,11 @@ def russellrao(x, y): if num_true_true == np.sum(x != 0) and num_true_true == np.sum(y != 0): return 0.0 - else: - return float(x.shape[0] - num_true_true) / (x.shape[0]) + return float(x.shape[0] - num_true_true) / (x.shape[0]) @numba.njit() -def sokal_michener(x, y): +def sokal_michener(x: np.ndarray, y: np.ndarray) -> float: num_not_equal = 0.0 for i in range(x.shape[0]): x_true = x[i] != 0 @@ -476,7 +489,7 @@ def sokal_michener(x, y): @numba.njit() -def sokal_sneath(x, y): +def sokal_sneath(x: np.ndarray, y: np.ndarray) -> float: num_true_true = 0.0 num_not_equal = 0.0 for i in range(x.shape[0]): @@ -487,14 +500,14 @@ def sokal_sneath(x, y): if num_not_equal == 0.0: return 0.0 - else: - return num_not_equal / (0.5 * num_true_true + num_not_equal) + return num_not_equal / (0.5 * num_true_true + num_not_equal) @numba.njit() -def haversine(x, y): +def haversine(x: np.ndarray, y: np.ndarray) -> float: if x.shape[0] != 2: - raise ValueError("haversine is only defined for 2 dimensional data") + msg = "haversine is only defined for 2 dimensional data" + raise ValueError(msg) sin_lat = np.sin(0.5 * (x[0] - y[0])) sin_long = np.sin(0.5 * (x[1] - y[1])) result = np.sqrt(sin_lat**2 + np.cos(x[0]) * np.cos(y[0]) * sin_long**2) @@ -502,13 +515,14 @@ def haversine(x, y): @numba.njit() -def haversine_grad(x, y): +def haversine_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: # spectral initialization puts many points near the poles # currently, adding pi/2 to the latitude avoids problems # TODO: reimplement with quaternions to avoid singularity if x.shape[0] != 2: - raise ValueError("haversine is only defined for 2 dimensional data") + msg = "haversine is only defined for 2 dimensional data" + raise ValueError(msg) sin_lat = np.sin(0.5 * (x[0] - y[0])) cos_lat = np.cos(0.5 * (x[0] - y[0])) sin_long = np.sin(0.5 * (x[1] - y[1])) @@ -526,13 +540,13 @@ def haversine_grad(x, y): - np.sin(x[0] + np.pi / 2) * np.cos(y[0] + np.pi / 2) * sin_long**2 ), (np.cos(x[0] + np.pi / 2) * np.cos(y[0] + np.pi / 2) * sin_long * cos_long), - ] + ], ) / (denom + 1e-6) return d, grad @numba.njit() -def yule(x, y): +def yule(x: np.ndarray, y: np.ndarray) -> float: num_true_true = 0.0 num_true_false = 0.0 num_false_true = 0.0 @@ -547,14 +561,13 @@ def yule(x, y): if num_true_false == 0.0 or num_false_true == 0.0: return 0.0 - else: - return (2.0 * num_true_false * num_false_true) / ( - num_true_true * num_false_false + num_true_false * num_false_true - ) + return (2.0 * num_true_false * num_false_true) / ( + num_true_true * num_false_false + num_true_false * num_false_true + ) @numba.njit() -def cosine(x, y): +def cosine(x: np.ndarray, y: np.ndarray) -> float: result = 0.0 norm_x = 0.0 norm_y = 0.0 @@ -565,14 +578,13 @@ def cosine(x, y): if norm_x == 0.0 and norm_y == 0.0: return 0.0 - elif norm_x == 0.0 or norm_y == 0.0: + if norm_x == 0.0 or norm_y == 0.0: return 1.0 - else: - return 1.0 - (result / np.sqrt(norm_x * norm_y)) + return 1.0 - (result / np.sqrt(norm_x * norm_y)) @numba.njit(fastmath=True) -def cosine_grad(x, y): +def cosine_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: result = 0.0 norm_x = 0.0 norm_y = 0.0 @@ -595,7 +607,7 @@ def cosine_grad(x, y): @numba.njit() -def correlation(x, y): +def correlation(x: np.ndarray, y: np.ndarray) -> float: mu_x = 0.0 mu_y = 0.0 norm_x = 0.0 @@ -618,14 +630,13 @@ def correlation(x, y): if norm_x == 0.0 and norm_y == 0.0: return 0.0 - elif dot_product == 0.0: + if dot_product == 0.0: return 1.0 - else: - return 1.0 - (dot_product / np.sqrt(norm_x * norm_y)) + return 1.0 - (dot_product / np.sqrt(norm_x * norm_y)) @numba.njit() -def hellinger(x, y): +def hellinger(x: np.ndarray, y: np.ndarray) -> float: result = 0.0 l1_norm_x = 0.0 l1_norm_y = 0.0 @@ -637,14 +648,13 @@ def hellinger(x, y): if l1_norm_x == 0 and l1_norm_y == 0: return 0.0 - elif l1_norm_x == 0 or l1_norm_y == 0: + if l1_norm_x == 0 or l1_norm_y == 0: return 1.0 - else: - return np.sqrt(1 - result / np.sqrt(l1_norm_x * l1_norm_y)) + return np.sqrt(1 - result / np.sqrt(l1_norm_x * l1_norm_y)) @numba.njit() -def hellinger_grad(x, y): +def hellinger_grad(x: np.ndarray, y: np.ndarray) -> tuple[float, np.ndarray]: result = 0.0 l1_norm_x = 0.0 l1_norm_y = 0.0 @@ -675,7 +685,20 @@ def hellinger_grad(x, y): @numba.njit() -def approx_log_Gamma(x): +def approx_log_Gamma(x: float) -> float: + """Approximate log-Gamma function using Stirling's approximation. + + Parameters + ---------- + x : float + Input value. + + Returns + ------- + float + Approximation of log(Gamma(x)). + + """ if x == 1: return 0 # x2= 1/(x*x); @@ -687,7 +710,22 @@ def approx_log_Gamma(x): @numba.njit() -def log_beta(x, y): +def log_beta(x: float, y: float) -> float: + """Compute the logarithm of the beta function. + + Parameters + ---------- + x : float + First parameter of beta function. + y : float + Second parameter of beta function. + + Returns + ------- + float + log(Beta(x, y)) + + """ a = min(x, y) b = max(x, y) if b < 5: @@ -695,12 +733,24 @@ def log_beta(x, y): for i in range(1, int(a)): value += np.log(i) - np.log(b + i) return value - else: - return approx_log_Gamma(x) + approx_log_Gamma(y) - approx_log_Gamma(x + y) + return approx_log_Gamma(x) + approx_log_Gamma(y) - approx_log_Gamma(x + y) @numba.njit() -def log_single_beta(x): +def log_single_beta(x: float) -> float: + """Compute the logarithm of Beta(x, x). + + Parameters + ---------- + x : float + Parameter value. + + Returns + ------- + float + log(Beta(x, x)) + + """ return np.log(2.0) * (-2.0 * x + 0.5) + 0.5 * np.log(2.0 * np.pi / x) + 0.125 / x @@ -720,7 +770,6 @@ def ll_dirichlet(data1, data2): ..math:: D(data1, data2) = DirichletMultinomail(data2 | data1) """ - n1 = np.sum(data1) n2 = np.sum(data2) @@ -743,14 +792,13 @@ def ll_dirichlet(data1, data2): return np.sqrt( 1.0 / n2 * (log_b - log_beta(n1, n2) - (self_denom2 - log_single_beta(n2))) - + 1.0 / n1 * (log_b - log_beta(n2, n1) - (self_denom1 - log_single_beta(n1))) + + 1.0 / n1 * (log_b - log_beta(n2, n1) - (self_denom1 - log_single_beta(n1))), ) @numba.njit(fastmath=True) def symmetric_kl(x, y, z=1e-11): # pragma: no cover - r""" - symmetrized KL divergence between two probability distributions + r"""Symmetrized KL divergence between two probability distributions. ..math:: D(x, y) = \frac{D_{KL}\left(x \Vert y\right) + D_{KL}\left(y \Vert x\right)}{2} @@ -780,10 +828,7 @@ def symmetric_kl(x, y, z=1e-11): # pragma: no cover @numba.njit(fastmath=True) def symmetric_kl_grad(x, y, z=1e-11): # pragma: no cover - """ - symmetrized KL divergence and its gradient - - """ + """Symmetrized KL divergence and its gradient.""" n = x.shape[0] x_sum = 0.0 y_sum = 0.0 @@ -847,7 +892,11 @@ def correlation_grad(x, y): @numba.njit(fastmath=True) def sinkhorn_distance( - x, y, M=_mock_identity, cost=_mock_cost, maxiter=64 + x, + y, + M=_mock_identity, + cost=_mock_cost, + maxiter=64, ): # pragma: no cover p = (x / x.sum()).astype(np.float32) q = (y / y.sum()).astype(np.float32) @@ -855,7 +904,7 @@ def sinkhorn_distance( u = np.ones(p.shape, dtype=np.float32) v = np.ones(q.shape, dtype=np.float32) - for n in range(maxiter): + for _n in range(maxiter): t = M @ v u[t > 0] = p[t > 0] / t[t > 0] t = M.T @ u @@ -1026,9 +1075,24 @@ def spherical_gaussian_grad(x, y): # pragma: no cover def get_discrete_params(data, metric): + """Get parameters for discrete distance metrics. + + Parameters + ---------- + data : array + The data from which to derive metric parameters. + metric : str + The discrete metric name ('ordinal', 'count', or 'string'). + + Returns + ------- + dict + Dictionary of metric-specific parameters. + + """ if metric == "ordinal": return {"support_size": float(data.max() - data.min()) / 2.0} - elif metric == "count": + if metric == "count": min_count = scipy.stats.tmin(data) max_count = scipy.stats.tmax(data) lambda_ = scipy.stats.tmean(data) @@ -1037,42 +1101,109 @@ def get_discrete_params(data, metric): "poisson_lambda": lambda_, "normalisation": normalisation / 2.0, # heuristic } - elif metric == "string": + if metric == "string": lengths = np.array([len(x) for x in data]) max_length = scipy.stats.tmax(lengths) max_dist = max_length / 1.5 # heuristic normalisation = max_dist / 2.0 # heuristic return {"normalisation": normalisation, "max_dist": max_dist / 2.0} # heuristic - else: - return {} + return {} @numba.njit() -def categorical_distance(x, y): +def categorical_distance(x, y) -> float: + """Categorical distance between two values. + + Parameters + ---------- + x : object + First categorical value. + y : object + Second categorical value. + + Returns + ------- + float + 0.0 if x == y, 1.0 otherwise. + + """ if x == y: return 0.0 - else: - return 1.0 + return 1.0 @numba.njit() -def hierarchical_categorical_distance(x, y, cat_hierarchy=[{}]): +def hierarchical_categorical_distance(x, y, cat_hierarchy=None): + """Hierarchical categorical distance based on category hierarchy. + + Parameters + ---------- + x : object + First categorical value. + y : object + Second categorical value. + cat_hierarchy : list of dict, optional + List of dictionaries defining categorical hierarchy, by default None. + + Returns + ------- + float + Distance normalized by hierarchy depth (0.0 for same category at top level). + + """ + if cat_hierarchy is None: + cat_hierarchy = [{}] n_levels = float(len(cat_hierarchy)) for level, cats in enumerate(cat_hierarchy): if cats[x] == cats[y]: return float(level) / n_levels - else: - return 1.0 + return 1.0 @numba.njit() def ordinal_distance(x, y, support_size=1.0): + """Ordinal distance normalized by support size. + + Parameters + ---------- + x : float + First ordinal value. + y : float + Second ordinal value. + support_size : float, optional + Size of the support for normalization, by default 1.0. + + Returns + ------- + float + Normalized ordinal distance. + + """ return abs(x - y) / support_size @numba.njit() def count_distance(x, y, poisson_lambda=1.0, normalisation=1.0): + """Count distance based on Poisson distribution. + + Parameters + ---------- + x : float + First count value. + y : float + Second count value. + poisson_lambda : float, optional + Lambda parameter for Poisson distribution, by default 1.0. + normalisation : float, optional + Normalization constant, by default 1.0. + + Returns + ------- + float + Normalized count distance. + + """ lo = int(min(x, y)) hi = int(max(x, y)) @@ -1098,6 +1229,25 @@ def count_distance(x, y, poisson_lambda=1.0, normalisation=1.0): @numba.njit() def levenshtein(x, y, normalisation=1.0, max_distance=20): + """Levenshtein (edit) distance between two strings. + + Parameters + ---------- + x : str or array + First string. + y : str or array + Second string. + normalisation : float, optional + Normalization constant, by default 1.0. + max_distance : int, optional + Maximum distance to compute before early abort, by default 20. + + Returns + ------- + float + Normalized Levenshtein distance. + + """ x_len, y_len = len(x), len(y) # Opt out of some comparisons @@ -1108,7 +1258,6 @@ def levenshtein(x, y, normalisation=1.0, max_distance=20): v1 = np.zeros(y_len + 1) for i in range(x_len): - v1[i] = i + 1 for j in range(y_len): @@ -1229,6 +1378,23 @@ def levenshtein(x, y, normalisation=1.0, max_distance=20): @numba.njit(parallel=True) def parallel_special_metric(X, Y=None, metric=hellinger): + """Compute pairwise distances using a special metric in parallel. + + Parameters + ---------- + X : array of shape (n_samples_X, n_features) + First input array. + Y : array of shape (n_samples_Y, n_features), optional + Second input array. If None, computes pairwise distances within X, by default None. + metric : callable, optional + Distance metric function, by default hellinger. + + Returns + ------- + array of shape (n_samples_X, n_samples_Y) or (n_samples_X, n_samples_X) + Pairwise distance matrix. + + """ if Y is None: result = np.zeros((X.shape[0], X.shape[0])) @@ -1250,6 +1416,25 @@ def parallel_special_metric(X, Y=None, metric=hellinger): # this keeps data vectors in cache better @numba.njit(parallel=True, nogil=True) def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_size=16): + """Compute pairwise distances using a special metric with chunking for cache efficiency. + + Parameters + ---------- + X : array of shape (n_samples_X, n_features) + First input array. + Y : array of shape (n_samples_Y, n_features), optional + Second input array. If None, computes pairwise distances within X, by default None. + metric : callable, optional + Distance metric function, by default hellinger. + chunk_size : int, optional + Size of chunks for cache-efficient computation, by default 16. + + Returns + ------- + array of shape (n_samples_X, n_samples_Y) or (n_samples_X, n_samples_X) + Pairwise distance matrix. + + """ if Y is None: XX, symmetrical = X, True row_size = col_size = X.shape[0] @@ -1272,21 +1457,45 @@ def chunked_parallel_special_metric(X, Y=None, metric=hellinger, chunk_size=16): def pairwise_special_metric( - X, Y=None, metric="hellinger", kwds=None, ensure_all_finite=True + X, + Y=None, + metric="hellinger", + kwds=None, + ensure_all_finite=True, ): + """Compute pairwise distances using special metrics. + + Parameters + ---------- + X : array of shape (n_samples_X, n_features) + First input array. + Y : array of shape (n_samples_Y, n_features), optional + Second input array, by default None. + metric : str or callable, optional + Name of the special metric or a callable distance function, by default "hellinger". + kwds : dict, optional + Keyword arguments for the metric function, by default None. + ensure_all_finite : bool, optional + Whether to check that inputs contain only finite values, by default True. + + Returns + ------- + array + Pairwise distance matrix. + + """ if callable(metric): - if kwds is not None: - kwd_vals = tuple(kwds.values()) - else: - kwd_vals = () + kwd_vals = tuple(kwds.values()) if kwds is not None else () @numba.njit(fastmath=True) def _partial_metric(_X, _Y=None): return metric(_X, _Y, *kwd_vals) return pairwise_distances( - X, Y, metric=_partial_metric, ensure_all_finite=ensure_all_finite + X, + Y, + metric=_partial_metric, + ensure_all_finite=ensure_all_finite, ) - else: - special_metric_func = named_distances[metric] + special_metric_func = named_distances[metric] return parallel_special_metric(X, Y, metric=special_metric_func) diff --git a/umap/hnsw_wrapper.py b/umap/hnsw_wrapper.py new file mode 100644 index 00000000..27473dca --- /dev/null +++ b/umap/hnsw_wrapper.py @@ -0,0 +1,278 @@ +"""Python wrapper for the Rust-based HNSW nearest neighbor index. + +This module provides a PyNNDescent-compatible API to the Rust-based +HNSW (or brute-force) nearest neighbor search backend. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +if TYPE_CHECKING: + from numpy.typing import NDArray + +try: + from _hnsw_backend import HnswIndex as _HnswIndex # type: ignore[import-not-found] +except ImportError: + try: + from umap._hnsw_backend import ( + HnswIndex as _HnswIndex, # type: ignore[import-not-found] + ) + except ImportError: + _HnswIndex = None # type: ignore[assignment] + + +class HnswIndexWrapper: + """Drop-in replacement for PyNNDescent.NNDescent using Rust backend. + + This class wraps the Rust-based nearest neighbor index and exposes + it through a PyNNDescent-compatible interface, including parameter + mapping and additional properties for backward compatibility. + + Parameters + ---------- + data : ndarray + The training data to index, shape (n_samples, n_features) + n_neighbors : int, default=30 + Number of neighbors to find + metric : str, default='euclidean' + Distance metric to use + metric_kwds : dict, optional + Additional keyword arguments for the metric + random_state : int or RandomState, optional + Random state for reproducibility + n_trees : int, optional + Number of trees to use in random projection forests. + Maps to HNSW M parameter. + n_iters : int, optional + Number of iterations for tree descent. + Maps to HNSW ef_construction parameter. + max_candidates : int, default=60 + Max candidates per iteration + low_memory : bool, default=True + Whether to use low memory mode + n_jobs : int, default=-1 + Number of parallel jobs + verbose : bool, default=False + Whether to log progress + compressed : bool, default=False + Whether to return compressed neighbor graph + + """ + + def __init__( + self, + data: NDArray, + n_neighbors: int = 30, + metric: str = "euclidean", + metric_kwds: dict | None = None, + random_state: int | None = None, + n_trees: int | None = None, + n_iters: int | None = None, + max_candidates: int = 60, + low_memory: bool = True, + n_jobs: int = -1, + verbose: bool = False, + compressed: bool = False, + ) -> None: + """Initialize the HNSW index wrapper.""" + if _HnswIndex is None: + msg = ( + "HNSW backend not available. " + "Please install umap with: pip install --upgrade umap" + ) + raise ImportError( + msg, + ) + + # Ensure data is float32 (required by Rust backend) + data = np.asarray(data, dtype=np.float32) + + self._data = data + self._n_neighbors = n_neighbors + self._metric = metric + self._metric_kwds = metric_kwds or {} + self._random_state = random_state + self._low_memory = low_memory + self._n_jobs = n_jobs + self._verbose = verbose + self._compressed = compressed + + # Compute HNSW parameters from PyNNDescent-style parameters + self._m = self._compute_m(n_trees, data.shape[0]) + self._ef_construction = self._compute_ef_construction( + n_iters, + max_candidates, + data.shape[0], + ) + + # Create the Rust-based index + # Note: PyO3 exposes positional arguments, not keyword arguments + self._index = _HnswIndex( + data, + n_neighbors, + metric, + self._m, + self._ef_construction, + ) + + # Store state for API compatibility + self._neighbor_graph_cache: tuple[NDArray, NDArray] | None = None + + @staticmethod + def _compute_m(n_trees: int | None, n_samples: int) -> int: + """Compute HNSW M parameter from PyNNDescent n_trees. + + The M parameter controls the maximum number of bidirectional + connections per node in the HNSW graph. Higher M values produce + denser graphs with potentially better recall but more memory usage. + """ + if n_trees is None: + # UMAP default: min(64, 5 + round((n_samples ** 0.5) / 20.0)) + n_trees = min(64, 5 + round((n_samples**0.5) / 20.0)) + + # Map PyNNDescent parameter to HNSW M + # Typically 8-64, default 16 for balanced performance + return max(8, min(64, n_trees)) + + @staticmethod + def _compute_ef_construction( + n_iters: int | None, + max_candidates: int, + n_samples: int, + ) -> int: + """Compute HNSW ef_construction parameter. + + ef_construction controls the size of the dynamic candidate list + during construction. Larger values produce more accurate indices + but require longer construction time. + """ + if n_iters is None: + # UMAP default: max(5, round(log2(n_samples))) + n_iters = max(5, round(np.log2(n_samples))) + + # Map to HNSW ef_construction + # Typically 100-800, higher = better quality but slower + return max(200, min(800, n_iters * max_candidates // 2)) + + @property + def neighbor_graph( + self, + ) -> tuple[NDArray, NDArray] | None: + """Get the k-nearest neighbor graph. + + Returns + ------- + indices : ndarray, shape (n_samples, n_neighbors) + The indices of the k nearest neighbors for each sample + distances : ndarray, shape (n_samples, n_neighbors) + The distances to the k nearest neighbors for each sample + + Returns None if the index is compressed. + + """ + if self._compressed: + return None + + if self._neighbor_graph_cache is None: + # neighbor_graph is a method in the Rust backend + indices, distances = self._index.neighbor_graph() + self._neighbor_graph_cache = (indices, distances) + + return self._neighbor_graph_cache + + def query( + self, + query_data: NDArray, + k: int, + epsilon: float = 0.1, + ) -> tuple[NDArray, NDArray]: + """Query the index for k nearest neighbors. + + Parameters + ---------- + query_data : ndarray, shape (n_queries, n_features) + The query data + k : int + Number of neighbors to return + epsilon : float, default=0.1 + Accuracy/speed tradeoff parameter. Higher values search more + candidates and return more accurate results. + + Returns + ------- + indices : ndarray, shape (n_queries, k) + The indices of the k nearest neighbors + distances : ndarray, shape (n_queries, k) + The distances to the k nearest neighbors + + """ + # Ensure data is float32 + query_data = np.asarray(query_data, dtype=np.float32) + + # Map epsilon to ef parameter + # epsilon controls search relaxation in PyNNDescent + # ef controls candidate list size in HNSW + ef = self._epsilon_to_ef(epsilon, k) + + # Call Rust query method with positional arguments + return self._index.query(query_data, k, ef) + + @staticmethod + def _epsilon_to_ef(epsilon: float, k: int) -> int: + """Map PyNNDescent epsilon to HNSW ef parameter. + + epsilon semantics: search (1 + epsilon) × optimal distance + ef semantics: size of candidate list (must be >= k) + """ + # Empirical mapping: higher epsilon → larger ef + # ef should be at least k + ef = max(k, int(k * (1.0 + epsilon * 30))) + # Cap ef to avoid excessive computation + return min(ef, 500) + + def prepare(self) -> None: + """Prepare the index for querying. + + This is a no-op for the Rust backend but required for + API compatibility with PyNNDescent. + """ + self._index.prepare() + + def update(self, X: NDArray) -> None: + """Update the index with new data. + + Parameters + ---------- + X : ndarray, shape (n_new, n_features) + New data to add to the index + + """ + X = np.asarray(X, dtype=np.float32) + self._index.update(X) + self._data = np.vstack([self._data, X]) + self._neighbor_graph_cache = None # Invalidate cache + + @property + def _angular_trees(self) -> bool: + """Get whether angular (cosine/correlation) trees are used. + + This property is used by UMAP to determine epsilon values + for queries. + """ + return self._index._angular_trees + + @property + def _raw_data(self) -> NDArray: + """Get the raw data used to build the index.""" + return self._data + + def __repr__(self) -> str: + """String representation of the index.""" + return ( + f"HnswIndexWrapper(n_samples={self._index.n_samples}, " + f"n_features={self._index.n_features}, " + f"metric='{self._metric}')" + ) diff --git a/umap/layouts.py b/umap/layouts.py index 1bf0feee..12e2b925 100644 --- a/umap/layouts.py +++ b/umap/layouts.py @@ -9,7 +9,7 @@ @numba.njit() def clip(val): """Standard clamping of a value into a fixed range (in this case -4.0 to - 4.0) + 4.0). Parameters ---------- @@ -19,13 +19,13 @@ def clip(val): Returns ------- The clamped value, now fixed to be in the range -4.0 to 4.0. + """ if val > 4.0: return 4.0 - elif val < -4.0: + if val < -4.0: return -4.0 - else: - return val + return val @numba.njit( @@ -50,6 +50,7 @@ def rdist(x, y): Returns ------- The squared euclidean distance between x and y + """ result = 0.0 dim = x.shape[0] @@ -88,7 +89,7 @@ def _optimize_layout_euclidean_single_epoch( dens_R, dens_mu, dens_mu_tot, -): +) -> None: for i in numba.prange(epochs_per_sample.shape[0]): if epoch_of_next_sample[i] <= n: j = head[i] @@ -154,10 +155,10 @@ def _optimize_layout_euclidean_single_epoch( epoch_of_next_sample[i] += epochs_per_sample[i] n_neg_samples = int( - (n - epoch_of_next_negative_sample[i]) / epochs_per_negative_sample[i] + (n - epoch_of_next_negative_sample[i]) / epochs_per_negative_sample[i], ) - for p in range(n_neg_samples): + for _p in range(n_neg_samples): k = tau_rand_int(rng_state_per_sample[j]) % n_vertices other = tail_embedding[k] @@ -195,7 +196,29 @@ def _optimize_layout_euclidean_densmap_epoch_init( b, re_sum, phi_sum, -): +) -> None: + """Initialize density-based variables for a densMAP epoch. + + Parameters + ---------- + head_embedding : array of shape (n_samples, n_components) + Current embedding of head points. + tail_embedding : array of shape (n_samples, n_components) + Current embedding of tail points. + head : array + Indices of head points in edges. + tail : array + Indices of tail points in edges. + a : float + UMAP curve parameter. + b : float + UMAP curve parameter. + re_sum : array of shape (n_samples,) + Array to store radial extent sums (modified in place). + phi_sum : array of shape (n_samples,) + Array to store phi sums (modified in place). + + """ re_sum.fill(0) phi_sum.fill(0) @@ -220,19 +243,35 @@ def _optimize_layout_euclidean_densmap_epoch_init( _nb_optimize_layout_euclidean_single_epoch = numba.njit( - _optimize_layout_euclidean_single_epoch, fastmath=True, parallel=False + _optimize_layout_euclidean_single_epoch, + fastmath=True, + parallel=False, ) _nb_optimize_layout_euclidean_single_epoch_parallel = numba.njit( - _optimize_layout_euclidean_single_epoch, fastmath=True, parallel=True + _optimize_layout_euclidean_single_epoch, + fastmath=True, + parallel=True, ) def _get_optimize_layout_euclidean_single_epoch_fn(parallel: bool = False): + """Get the appropriate optimization function based on parallelization setting. + + Parameters + ---------- + parallel : bool, optional + Whether to use parallel optimization, by default False. + + Returns + ------- + callable + The optimization function (parallel or serial version). + + """ if parallel: return _nb_optimize_layout_euclidean_single_epoch_parallel - else: - return _nb_optimize_layout_euclidean_single_epoch + return _nb_optimize_layout_euclidean_single_epoch def optimize_layout_euclidean( @@ -261,7 +300,8 @@ def optimize_layout_euclidean( and low dimensional fuzzy simplicial sets. In practice this is done by sampling edges based on their membership strength (with the (1-p) terms coming from negative sampling similar to word2vec). - Parameters + + Parameters. ---------- head_embedding: array of shape (n_samples, n_components) The initial embedding to be improved by SGD. @@ -311,12 +351,13 @@ def optimize_layout_euclidean( Keyword arguments for tqdm progress bar. move_other: bool (optional, default False) Whether to adjust tail_embedding alongside head_embedding + Returns ------- embedding: array of shape (n_samples, n_components) The optimized embedding. - """ + """ dim = head_embedding.shape[1] alpha = initial_alpha @@ -365,7 +406,9 @@ def optimize_layout_euclidean( tqdm_kwds["disable"] = not verbose rng_state_per_sample = np.full( - (head_embedding.shape[0], len(rng_state)), rng_state, dtype=np.int64 + (head_embedding.shape[0], len(rng_state)), + rng_state, + dtype=np.int64, ) + head_embedding[:, 0].astype(np.float64).view(np.int64).reshape(-1, 1) for n in tqdm(range(n_epochs), **tqdm_kwds): @@ -431,7 +474,7 @@ def optimize_layout_euclidean( alpha = initial_alpha * (1.0 - (float(n) / float(n_epochs))) if verbose and n % int(n_epochs / 10) == 0: - print("\tcompleted ", n, " / ", n_epochs, "epochs") + pass if epochs_list is not None and n in epochs_list: embedding_list.append(head_embedding.copy()) @@ -473,14 +516,13 @@ def _optimize_layout_generic_single_epoch( other = tail_embedding[k] dist_output, grad_dist_output = output_metric( - current, other, *output_metric_kwds + current, + other, + *output_metric_kwds, ) _, rev_grad_dist_output = output_metric(other, current, *output_metric_kwds) - if dist_output > 0.0: - w_l = pow((1 + a * pow(dist_output, 2 * b)), -1) - else: - w_l = 1.0 + w_l = pow(1 + a * pow(dist_output, 2 * b), -1) if dist_output > 0.0 else 1.0 grad_coeff = 2 * b * (w_l - 1) / (dist_output + 1e-6) for d in range(dim): @@ -494,16 +536,18 @@ def _optimize_layout_generic_single_epoch( epoch_of_next_sample[i] += epochs_per_sample[i] n_neg_samples = int( - (n - epoch_of_next_negative_sample[i]) / epochs_per_negative_sample[i] + (n - epoch_of_next_negative_sample[i]) / epochs_per_negative_sample[i], ) - for p in range(n_neg_samples): + for _p in range(n_neg_samples): k = tau_rand_int(rng_state_per_sample[j]) % n_vertices other = tail_embedding[k] dist_output, grad_dist_output = output_metric( - current, other, *output_metric_kwds + current, + other, + *output_metric_kwds, ) if dist_output > 0.0: @@ -609,8 +653,8 @@ def optimize_layout_generic( ------- embedding: array of shape (n_samples, n_components) The optimized embedding. - """ + """ dim = head_embedding.shape[1] alpha = initial_alpha @@ -630,7 +674,9 @@ def optimize_layout_generic( tqdm_kwds["disable"] = not verbose rng_state_per_sample = np.full( - (head_embedding.shape[0], len(rng_state)), rng_state, dtype=np.int64 + (head_embedding.shape[0], len(rng_state)), + rng_state, + dtype=np.int64, ) + head_embedding[:, 0].astype(np.float64).view(np.int64).reshape(-1, 1) for n in tqdm(range(n_epochs), **tqdm_kwds): @@ -681,7 +727,7 @@ def _optimize_layout_inverse_single_epoch( n_vertices, rhos, gamma, -): +) -> None: for i in range(epochs_per_sample.shape[0]): if epoch_of_next_sample[i] <= n: j = head[i] @@ -691,7 +737,9 @@ def _optimize_layout_inverse_single_epoch( other = tail_embedding[k] dist_output, grad_dist_output = output_metric( - current, other, *output_metric_kwds + current, + other, + *output_metric_kwds, ) w_l = weight[i] @@ -707,16 +755,18 @@ def _optimize_layout_inverse_single_epoch( epoch_of_next_sample[i] += epochs_per_sample[i] n_neg_samples = int( - (n - epoch_of_next_negative_sample[i]) / epochs_per_negative_sample[i] + (n - epoch_of_next_negative_sample[i]) / epochs_per_negative_sample[i], ) - for p in range(n_neg_samples): + for _p in range(n_neg_samples): k = tau_rand_int(rng_state) % n_vertices other = tail_embedding[k] dist_output, grad_dist_output = output_metric( - current, other, *output_metric_kwds + current, + other, + *output_metric_kwds, ) # w_l = 0.0 # for negative samples, the edge does not exist @@ -826,8 +876,8 @@ def optimize_layout_inverse( ------- embedding: array of shape (n_samples, n_components) The optimized embedding. - """ + """ dim = head_embedding.shape[1] alpha = initial_alpha @@ -894,18 +944,19 @@ def _optimize_layout_aligned_euclidean_single_epoch( epoch_of_next_negative_sample, epoch_of_next_sample, n, -): +) -> None: n_embeddings = len(heads) window_size = (relations.shape[1] - 1) // 2 max_n_edges = 0 for e_p_s in epochs_per_sample: - if e_p_s.shape[0] >= max_n_edges: - max_n_edges = e_p_s.shape[0] + max_n_edges = max(e_p_s.shape[0], max_n_edges) embedding_order = np.arange(n_embeddings).astype(np.int32) - np.random.seed(abs(rng_state[0])) - np.random.shuffle(embedding_order) + # Fisher-Yates shuffle using tau_rand_int for reproducibility + for i in range(n_embeddings - 1, 0, -1): + j = tau_rand_int(rng_state) % (i + 1) + embedding_order[i], embedding_order[j] = embedding_order[j], embedding_order[i] for i in range(max_n_edges): for m in embedding_order: @@ -938,9 +989,10 @@ def _optimize_layout_aligned_euclidean_single_epoch( * ( current[d] - head_embeddings[neighbor_m][ - identified_index, d + identified_index, + d, ] - ) + ), ) current[d] += clip(grad_d) * alpha @@ -955,14 +1007,17 @@ def _optimize_layout_aligned_euclidean_single_epoch( other_grad_d -= clip( (lambda_ * np.exp(-(np.abs(offset) - 1))) * regularisation_weights[ - m, offset + window_size, k + m, + offset + window_size, + k, ] * ( other[d] - head_embeddings[neighbor_m][ - identified_index, d + identified_index, + d, ] - ) + ), ) other[d] += clip(other_grad_d) * alpha @@ -972,12 +1027,12 @@ def _optimize_layout_aligned_euclidean_single_epoch( if epochs_per_negative_sample[m][i] > 0: n_neg_samples = int( (n - epoch_of_next_negative_sample[m][i]) - / epochs_per_negative_sample[m][i] + / epochs_per_negative_sample[m][i], ) else: n_neg_samples = 0 - for p in range(n_neg_samples): + for _p in range(n_neg_samples): k = tau_rand_int(rng_state) % tail_embeddings[m].shape[0] other = tail_embeddings[m][k] @@ -1008,14 +1063,17 @@ def _optimize_layout_aligned_euclidean_single_epoch( grad_d -= clip( (lambda_ * np.exp(-(np.abs(offset) - 1))) * regularisation_weights[ - m, offset + window_size, j + m, + offset + window_size, + j, ] * ( current[d] - head_embeddings[neighbor_m][ - identified_index, d + identified_index, + d, ] - ) + ), ) current[d] += clip(grad_d) * alpha @@ -1051,16 +1109,16 @@ def optimize_layout_aligned_euclidean( epochs_per_negative_sample = numba.typed.List.empty_list(numba.types.float32[::1]) epoch_of_next_negative_sample = numba.typed.List.empty_list( - numba.types.float32[::1] + numba.types.float32[::1], ) epoch_of_next_sample = numba.typed.List.empty_list(numba.types.float32[::1]) for m in range(len(heads)): epochs_per_negative_sample.append( - epochs_per_sample[m].astype(np.float32) / negative_sample_rate + epochs_per_sample[m].astype(np.float32) / negative_sample_rate, ) epoch_of_next_negative_sample.append( - epochs_per_negative_sample[m].astype(np.float32) + epochs_per_negative_sample[m].astype(np.float32), ) epoch_of_next_sample.append(epochs_per_sample[m].astype(np.float32)) diff --git a/umap/parametric_umap.py b/umap/parametric_umap.py index 74f32972..0520f576 100644 --- a/umap/parametric_umap.py +++ b/umap/parametric_umap.py @@ -1,12 +1,15 @@ +import codecs +import os +import pickle +from warnings import catch_warnings, filterwarnings, warn + import numpy as np -from umap import UMAP -from warnings import warn, catch_warnings, filterwarnings from numba import TypingError -import os -from umap.spectral import spectral_layout -from sklearn.utils import check_random_state, check_array -import codecs, pickle from sklearn.neighbors import KDTree +from sklearn.utils import check_array, check_random_state + +from umap import UMAP +from umap.spectral import spectral_layout try: # Used for tf.data. @@ -15,32 +18,38 @@ warn( """The umap.parametric_umap package requires Tensorflow > 2.0 to be installed. You can install Tensorflow at https://www.tensorflow.org/install - - or you can install the CPU version of Tensorflow using - pip install umap-learn[parametric_umap] + or you can install the CPU version of Tensorflow using - """ + pip install umap[parametric_umap] + + """, + stacklevel=2, ) - raise ImportError("umap.parametric_umap requires Tensorflow >= 2.0") from None + msg = "umap.parametric_umap requires Tensorflow >= 2.0" + raise ImportError(msg) from None try: import keras from keras import ops except ImportError: - warn("""The umap.parametric_umap package requires Keras >= 3 to be installed.""") - raise ImportError("umap.parametric_umap requires Keras") from None + warn( + """The umap.parametric_umap package requires Keras >= 3 to be installed.""", + stacklevel=2, + ) + msg = "umap.parametric_umap requires Keras" + raise ImportError(msg) from None torch_imported = True try: + import numpy as np import torch - import torch.nn as nn import torch.nn.functional as F - import numpy as np import torch.onnx import torchvision + from torch import nn except ImportError: - warn("""Torch and ONNX required for exporting to those formats.""") + warn("""Torch and ONNX required for exporting to those formats.""", stacklevel=2) torch_imported = False @@ -59,11 +68,10 @@ def __init__( global_correlation_loss_weight=0, landmark_loss_fn=None, landmark_loss_weight=1.0, - keras_fit_kwargs={}, + keras_fit_kwargs=None, **kwargs, - ): - """ - Parametric UMAP subclassing UMAP-learn, based on keras/tensorflow. + ) -> None: + """Parametric UMAP subclassing UMAP-learn, based on keras/tensorflow. There is also a non-parametric implementation contained within to compare with the base non-parametric implementation. @@ -96,7 +104,10 @@ def __init__( How to weight the landmark loss relative to umap loss, by default 1.0 keras_fit_kwargs : dict, optional additional arguments for model.fit (like callbacks), by default {} + """ + if keras_fit_kwargs is None: + keras_fit_kwargs = {} super().__init__(**kwargs) # add to network @@ -139,13 +150,12 @@ def __init__( if self.encoder is not None: if encoder.outputs[0].shape[-1] != self.n_components: + msg = ( + f"Dimensionality of embedder network output ({encoder.outputs[0].shape[-1]}) does" + f"not match n_components ({self.n_components})" + ) raise ValueError( - ( - "Dimensionality of embedder network output ({}) does" - "not match n_components ({})".format( - encoder.outputs[0].shape[-1], self.n_components - ) - ) + (msg), ) def fit(self, X, y=None, precomputed_distances=None, landmark_positions=None): @@ -175,15 +185,17 @@ def fit(self, X, y=None, precomputed_distances=None, landmark_positions=None): landmark_positions : array, shape (n_samples, n_components), optional The desired position in low-dimensional space of each sample in X. Points that are not landmarks should have nan coordinates. + """ if (self.prev_epoch_X is not None) & (landmark_positions is None): # Add the landmark points for training, then make a landmark vector. landmark_positions = np.stack( - [np.array([np.nan, np.nan])]*X.shape[0] + list( + [np.array([np.nan, np.nan])] * X.shape[0] + + list( self.transform( - self.prev_epoch_X - ) - ) + self.prev_epoch_X, + ), + ), ) X = np.concatenate((X, self.prev_epoch_X)) @@ -191,29 +203,36 @@ def fit(self, X, y=None, precomputed_distances=None, landmark_positions=None): len_X = len(X) len_land = len(landmark_positions) if len_X != len_land: - raise ValueError( - f"Length of x = {len_X}, length of landmark_positions \ + msg = f"Length of x = {len_X}, length of landmark_positions \ = {len_land}, while it must be equal." + raise ValueError( + msg, ) if self.metric == "precomputed": if precomputed_distances is None: - raise ValueError( - "Precomputed distances must be supplied if metric \ + msg = "Precomputed distances must be supplied if metric \ is precomputed." + raise ValueError( + msg, ) # prepare X for training the network self._X = X # geneate the graph on precomputed distances return super().fit( - precomputed_distances, y, landmark_positions=landmark_positions + precomputed_distances, + y, + landmark_positions=landmark_positions, ) - else: - return super().fit(X, y, landmark_positions=landmark_positions) + return super().fit(X, y, landmark_positions=landmark_positions) def fit_transform( - self, X, y=None, precomputed_distances=None, landmark_positions=None + self, + X, + y=None, + precomputed_distances=None, + landmark_positions=None, ): """Fit X into an embedded space. @@ -241,15 +260,17 @@ def fit_transform( landmark_positions : array, shape (n_samples, n_components), optional The desired position in low-dimensional space of each sample in X. Points that are not landmarks should have nan coordinates. + """ if (self.prev_epoch_X is not None) & (landmark_positions is None): # Add the landmark points for training, then make a landmark vector. landmark_positions = np.stack( - [np.array([np.nan, np.nan])]*X.shape[0] + list( + [np.array([np.nan, np.nan])] * X.shape[0] + + list( self.transform( - self.prev_epoch_X - ) - ) + self.prev_epoch_X, + ), + ), ) X = np.concatenate((X, self.prev_epoch_X)) @@ -257,16 +278,18 @@ def fit_transform( len_X = len(X) len_land = len(landmark_positions) if len_X != len_land: - raise ValueError( - f"Length of x = {len_X}, length of landmark_positions \ + msg = f"Length of x = {len_X}, length of landmark_positions \ = {len_land}, while it must be equal." + raise ValueError( + msg, ) if self.metric == "precomputed": if precomputed_distances is None: - raise ValueError( - "Precomputed distances must be supplied if metric \ + msg = "Precomputed distances must be supplied if metric \ is precomputed." + raise ValueError( + msg, ) # prepare X for training the network self._X = X @@ -274,12 +297,13 @@ def fit_transform( # landmark positions are cleaned up inside the # .fit() component of .fit_transform() return super().fit_transform( - precomputed_distances, y, landmark_positions=landmark_positions + precomputed_distances, + y, + landmark_positions=landmark_positions, ) - else: - # landmark positions are cleaned up inside the - # .fit() component of .fit_transform() - return super().fit_transform(X, y, landmark_positions=landmark_positions) + # landmark positions are cleaned up inside the + # .fit() component of .fit_transform() + return super().fit_transform(X, y, landmark_positions=landmark_positions) def transform(self, X, batch_size=None): """Transform X into the existing embedded space and return that @@ -296,11 +320,14 @@ def transform(self, X, batch_size=None): ------- X_new : array, shape (n_samples, n_components) Embedding of the new data in low-dimensional space. + """ batch_size = batch_size if batch_size else self.batch_size return self.encoder.predict( - np.asanyarray(X), batch_size=batch_size, verbose=self.verbose + np.asanyarray(X), + batch_size=batch_size, + verbose=self.verbose, ) def inverse_transform(self, X): @@ -311,20 +338,23 @@ def inverse_transform(self, X): ---------- X : array, shape (n_samples, n_components) New points to be inverse transformed. + Returns ------- X_new : array, shape (n_samples, n_features) Generated data points new data in data space. + """ if self.parametric_reconstruction: return self.decoder.predict( - np.asanyarray(X), batch_size=self.batch_size, verbose=self.verbose + np.asanyarray(X), + batch_size=self.batch_size, + verbose=self.verbose, ) - else: - return super().inverse_transform(X) + return super().inverse_transform(X) - def _define_model(self): - """Define the model in keras""" + def _define_model(self) -> None: + """Define the model in keras.""" prlw = self.parametric_reconstruction_loss_weight self.parametric_model = UMAPModel( self._a, @@ -343,21 +373,20 @@ def _define_model(self): ) def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=None): - if self.metric == "precomputed": X = self._X # get dimensionality of dataset if self.dims is None: self.dims = [np.shape(X)[-1]] - else: - # reshape data for network - if len(self.dims) > 1: - X = np.reshape(X, [len(X)] + list(self.dims)) + # reshape data for network + elif len(self.dims) > 1: + X = np.reshape(X, [len(X), *list(self.dims)]) if self.parametric_reconstruction and (np.max(X) > 1.0 or np.min(X) < 0.0): warn( - "Data should be scaled to the range 0-1 for cross-entropy reconstruction loss." + "Data should be scaled to the range 0-1 for cross-entropy reconstruction loss.", + stacklevel=2, ) # Make sure landmark_positions is float32. @@ -414,12 +443,11 @@ def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=No self.parametric_reconstruction and self.reconstruction_validation is not None ): - # reshape data for network if len(self.dims) > 1: self.reconstruction_validation = np.reshape( self.reconstruction_validation, - [len(self.reconstruction_validation)] + list(self.dims), + [len(self.reconstruction_validation), *list(self.dims)], ) validation_data = ( @@ -444,7 +472,7 @@ def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=No if not hasattr(self, "_history"): self._history = history.history else: - for key in history.history.keys(): + for key in history.history: self._history[key] += history.history[key] # get the final embedding @@ -454,48 +482,50 @@ def _fit_embed_data(self, X, n_epochs, init, random_state, landmark_positions=No def __getstate__(self): # this function supports pickling, making sure that objects can be pickled - return dict( - (k, v) + return { + k: v for (k, v) in self.__dict__.items() if should_pickle(k, v) and k not in ("optimizer", "encoder", "decoder", "parametric_model") - ) - - def save(self, save_location, verbose=True, exclude_raw_data=False): + } + def save(self, save_location, verbose=True, exclude_raw_data=False) -> None: # save encoder if self.encoder is not None: encoder_output = os.path.join(save_location, "encoder.keras") self.encoder.save(encoder_output) if verbose: - print("Keras encoder model saved to {}".format(encoder_output)) + pass # save decoder if self.decoder is not None: decoder_output = os.path.join(save_location, "decoder.keras") self.decoder.save(decoder_output) if verbose: - print("Keras decoder model saved to {}".format(decoder_output)) + pass # save parametric_model if self.parametric_model is not None: parametric_model_output = os.path.join( - save_location, "parametric_model.keras" + save_location, + "parametric_model.keras", ) self.parametric_model.save(parametric_model_output) if verbose: - print("Keras full model saved to {}".format(parametric_model_output)) + pass # Temporarily delete the raw data in the object, before saving it, # backing it up in raw_data raw_data = {} if exclude_raw_data: if hasattr(self, "_raw_data"): - raw_data['root'] = self._raw_data + raw_data["root"] = self._raw_data del self._raw_data - if hasattr(self, "knn_search_index") and hasattr(self.knn_search_index, - "_raw_data"): - raw_data['knn'] = self.knn_search_index._raw_data + if hasattr(self, "knn_search_index") and hasattr( + self.knn_search_index, + "_raw_data", + ): + raw_data["knn"] = self.knn_search_index._raw_data del self.knn_search_index._raw_data # # save model.pkl (ignoring unpickleable warnings) @@ -505,14 +535,14 @@ def save(self, save_location, verbose=True, exclude_raw_data=False): with open(model_output, "wb") as output: pickle.dump(self, output, pickle.HIGHEST_PROTOCOL) if verbose: - print("Pickle of ParametricUMAP model saved to {}".format(model_output)) + pass # Restore the original raw data to the object in memory if exclude_raw_data: - if 'root' in raw_data: - self._raw_data = raw_data['root'] - if 'knn' in raw_data: - self.knn_search_index._raw_data = raw_data['knn'] + if "root" in raw_data: + self._raw_data = raw_data["root"] + if "knn" in raw_data: + self.knn_search_index._raw_data = raw_data["knn"] def add_landmarks( self, @@ -521,8 +551,8 @@ def add_landmarks( sample_mode="uniform", landmark_loss_weight=0.01, idx=None, - ): - """Add some points from a dataset X as "landmarks." + ) -> None: + """Add some points from a dataset X as "landmarks.". Parameters ---------- @@ -543,25 +573,28 @@ def add_landmarks( if self.sample_mode == "uniform": self.prev_epoch_idx = list( np.random.choice( - range(X.shape[0]), int(X.shape[0]*sample_pct), replace=False - ) + range(X.shape[0]), + int(X.shape[0] * sample_pct), + replace=False, + ), ) self.prev_epoch_X = X[self.prev_epoch_idx] elif self.sample_mode == "predetermined": if idx is None: + msg = "Choice of sample_mode is not supported." raise ValueError( - "Choice of sample_mode is not supported." + msg, ) - else: - self.prev_epoch_idx = idx - self.prev_epoch_X = X[self.prev_epoch_idx] + self.prev_epoch_idx = idx + self.prev_epoch_X = X[self.prev_epoch_idx] else: + msg = "Choice of sample_mode is not supported." raise ValueError( - "Choice of sample_mode is not supported." + msg, ) - def remove_landmarks(self): + def remove_landmarks(self) -> None: self.prev_epoch_X = None def to_ONNX(self, save_location): @@ -579,8 +612,7 @@ def to_ONNX(self, save_location): def get_graph_elements(graph_, n_epochs): - """ - gets elements of graphs, weights, and number of epochs per edge + """Gets elements of graphs, weights, and number of epochs per edge. Parameters ---------- @@ -603,6 +635,7 @@ def get_graph_elements(graph_, n_epochs): edge weight n_vertices int number of vertices in graph + """ ### should we remove redundancies () here?? # graph_ = remove_redundant_edges(graph_) @@ -615,10 +648,7 @@ def get_graph_elements(graph_, n_epochs): # get the number of epochs based on the size of the dataset if n_epochs is None: # For smaller datasets we can use more epochs - if graph.shape[0] <= 10000: - n_epochs = 500 - else: - n_epochs = 200 + n_epochs = 500 if graph.shape[0] <= 10000 else 200 # remove elements with very low probability graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0 graph.eliminate_zeros() @@ -633,7 +663,13 @@ def get_graph_elements(graph_, n_epochs): def init_embedding_from_graph( - _raw_data, graph, n_components, random_state, metric, _metric_kwds, init="spectral" + _raw_data, + graph, + n_components, + random_state, + metric, + _metric_kwds, + init="spectral", ): """Initialize embedding using graph. This is for direct embeddings. @@ -646,13 +682,16 @@ def init_embedding_from_graph( ------- embedding : np.array the initialized embedding + """ if random_state is None: random_state = check_random_state(None) if isinstance(init, str) and init == "random": embedding = random_state.uniform( - low=-10.0, high=10.0, size=(graph.shape[0], n_components) + low=-10.0, + high=10.0, + size=(graph.shape[0], n_components), ).astype(np.float32) elif isinstance(init, str) and init == "spectral": # We add a little noise to avoid local minima for optimization to come @@ -667,11 +706,12 @@ def init_embedding_from_graph( ) expansion = 10.0 / np.abs(initialisation).max() embedding = (initialisation * expansion).astype( - np.float32 + np.float32, ) + random_state.normal( - scale=0.0001, size=[graph.shape[0], n_components] + scale=0.0001, + size=[graph.shape[0], n_components], ).astype( - np.float32 + np.float32, ) else: @@ -679,10 +719,11 @@ def init_embedding_from_graph( if len(init_data.shape) == 2: if np.unique(init_data, axis=0).shape[0] < init_data.shape[0]: tree = KDTree(init_data) - dist, ind = tree.query(init_data, k=2) + dist, _ind = tree.query(init_data, k=2) nndist = np.mean(dist[:, 1]) embedding = init_data + random_state.normal( - scale=0.001 * nndist, size=init_data.shape + scale=0.001 * nndist, + size=init_data.shape, ).astype(np.float32) else: embedding = init_data @@ -691,9 +732,8 @@ def init_embedding_from_graph( def convert_distance_to_log_probability(distances, a=1.0, b=1.0): - """ - convert distance representation into log probability, - as a function of a, b params + """Convert distance representation into log probability, + as a function of a, b params. Parameters ---------- @@ -708,15 +748,18 @@ def convert_distance_to_log_probability(distances, a=1.0, b=1.0): ------- float log probability in embedding space + """ return -ops.log1p(a * distances ** (2 * b)) def compute_cross_entropy( - probabilities_graph, log_probabilities_distance, EPS=1e-4, repulsion_strength=1.0 + probabilities_graph, + log_probabilities_distance, + EPS=1e-4, + repulsion_strength=1.0, ): - """ - Compute cross entropy between low and high probability + """Compute cross entropy between low and high probability. Parameters ---------- @@ -764,8 +807,7 @@ def prepare_networks( parametric_reconstruction, init_embedding=None, ): - """ - Generates a set of keras networks for the encoder and decoder if one has not already + """Generates a set of keras networks for the encoder and decoder if one has not already been predefined. Parameters @@ -791,8 +833,8 @@ def prepare_networks( encoder keras network decoder: keras.Sequential decoder keras network - """ + """ if encoder is None: encoder = keras.Sequential( [ @@ -802,23 +844,24 @@ def prepare_networks( keras.layers.Dense(units=100, activation="relu"), keras.layers.Dense(units=100, activation="relu"), keras.layers.Dense(units=n_components, name="z"), - ] + ], ) - if decoder is None: - if parametric_reconstruction: - decoder = keras.Sequential( - [ - keras.layers.Input(shape=(n_components,)), - keras.layers.Dense(units=100, activation="relu"), - keras.layers.Dense(units=100, activation="relu"), - keras.layers.Dense(units=100, activation="relu"), - keras.layers.Dense( - units=np.prod(dims), name="recon", activation=None - ), - keras.layers.Reshape(dims), - ] - ) + if decoder is None and parametric_reconstruction: + decoder = keras.Sequential( + [ + keras.layers.Input(shape=(n_components,)), + keras.layers.Dense(units=100, activation="relu"), + keras.layers.Dense(units=100, activation="relu"), + keras.layers.Dense(units=100, activation="relu"), + keras.layers.Dense( + units=np.prod(dims), + name="recon", + activation=None, + ), + keras.layers.Reshape(dims), + ], + ) return encoder, decoder @@ -832,8 +875,7 @@ def construct_edge_dataset( global_correlation_loss_weight, landmark_positions=None, ): - """ - Construct a tf.data.Dataset of edges, sampled by edge weight. + """Construct a tf.data.Dataset of edges, sampled by edge weight. Parameters ---------- @@ -850,6 +892,7 @@ def construct_edge_dataset( landmark_positions : array, shape (n_samples, n_components), optional The desired position in low-dimensional space of each sample in X. Points that are not landmarks should have nan coordinates. + """ def gather_index(tensor, index): @@ -857,18 +900,18 @@ def gather_index(tensor, index): # if X is > 512Mb in size, we need to use a different, slower method for # batching data. - gather_indices_in_python = True if X.nbytes * 1e-9 > 0.5 else False + gather_indices_in_python = X.nbytes * 1e-9 > 0.5 if landmark_positions is not None: - gather_landmark_indices_in_python = ( - True if landmark_positions.nbytes * 1e-9 > 0.5 else False - ) + gather_landmark_indices_in_python = landmark_positions.nbytes * 1e-9 > 0.5 def gather_X(edge_to, edge_from): # gather data from indexes (edges) in either numpy of tf, depending on array size if gather_indices_in_python: edge_to_batch = tf.py_function(gather_index, [X, edge_to], [tf.float32])[0] edge_from_batch = tf.py_function( - gather_index, [X, edge_from], [tf.float32] + gather_index, + [X, edge_from], + [tf.float32], )[0] else: edge_to_batch = tf.gather(X, edge_to) @@ -886,7 +929,9 @@ def get_outputs(edge_to, edge_from, edge_to_batch, edge_from_batch): if landmark_positions is not None: if gather_landmark_indices_in_python: outputs["landmark_to"] = tf.py_function( - gather_index, [landmark_positions, edge_to], [tf.float32] + gather_index, + [landmark_positions, edge_to], + [tf.float32], )[0] else: # Make sure we explicitly cast landmark_positions to float32, @@ -896,7 +941,8 @@ def get_outputs(edge_to, edge_from, edge_to_batch, edge_from_batch): # get data from graph _, epochs_per_sample, head, tail, weight, n_vertices = get_graph_elements( - graph_, n_epochs + graph_, + n_epochs, ) # number of elements per batch for embedding @@ -920,19 +966,20 @@ def get_outputs(edge_to, edge_from, edge_to_batch, edge_from_batch): edge_dataset = edge_dataset.shuffle(10000) edge_dataset = edge_dataset.batch(batch_size, drop_remainder=True) edge_dataset = edge_dataset.map( - gather_X, num_parallel_calls=tf.data.experimental.AUTOTUNE + gather_X, + num_parallel_calls=tf.data.experimental.AUTOTUNE, ) edge_dataset = edge_dataset.map( - get_outputs, num_parallel_calls=tf.data.experimental.AUTOTUNE + get_outputs, + num_parallel_calls=tf.data.experimental.AUTOTUNE, ) edge_dataset = edge_dataset.prefetch(10) return edge_dataset, batch_size, len(edges_to_exp), head, tail, weight -def should_pickle(key, val): - """ - Checks if a dictionary item can be pickled +def should_pickle(key, val) -> bool: + """Checks if a dictionary item can be pickled. Parameters ---------- @@ -945,6 +992,7 @@ def should_pickle(key, val): ------- picklable: bool whether the dictionary item can be pickled + """ try: ## make sure object can be pickled and then re-read @@ -962,17 +1010,16 @@ def should_pickle(key, val): TypingError, AttributeError, ) as e: - warn("Did not pickle {}: {}".format(key, e)) + warn(f"Did not pickle {key}: {e}", stacklevel=2) return False except ValueError as e: - warn(f"Failed at pickling {key}:{val} due to {e}") + warn(f"Failed at pickling {key}:{val} due to {e}", stacklevel=2) return False return True def load_ParametricUMAP(save_location, verbose=True): - """ - Load a parametric UMAP model consisting of a umap-learn UMAP object + """Load a parametric UMAP model consisting of a umap UMAP object and corresponding keras models. Parameters @@ -986,33 +1033,31 @@ def load_ParametricUMAP(save_location, verbose=True): ------- parametric_umap.ParametricUMAP Parametric UMAP objects - """ + """ ## Loads a ParametricUMAP model and its related keras models model_output = os.path.join(save_location, "model.pkl") - model = pickle.load((open(model_output, "rb"))) + model = pickle.load(open(model_output, "rb")) if verbose: - print("Pickle of ParametricUMAP model loaded from {}".format(model_output)) + pass # load encoder encoder_output = os.path.join(save_location, "encoder.keras") if os.path.exists(encoder_output): model.encoder = keras.models.load_model(encoder_output) if verbose: - print("Keras encoder model loaded from {}".format(encoder_output)) + pass # save decoder decoder_output = os.path.join(save_location, "decoder.keras") if os.path.exists(decoder_output): model.decoder = keras.models.load_model(decoder_output) - print("Keras decoder model loaded from {}".format(decoder_output)) # save parametric_model parametric_model_output = os.path.join(save_location, "parametric_model") if os.path.exists(parametric_model_output): model.parametric_model = keras.models.load_model(parametric_model_output) - print("Keras full model loaded from {}".format(parametric_model_output)) return model @@ -1050,7 +1095,8 @@ def covariance(x, y=None, keepdims=False): # After matmul, cov.shape = batch_shape + [n_events, n_events] cov = ops.matmul(x_permed_flat, ops.transpose(y_permed_flat)) / ops.cast( - n_samples, x.dtype + n_samples, + x.dtype, ) cov = ops.reshape( @@ -1068,7 +1114,7 @@ def covariance(x, y=None, keepdims=False): # contiguous. cov = ops.reshape( cov, - ops.shape(cov)[:1] + (n_events, n_events), + (*ops.shape(cov)[:1], n_events, n_events), ) if not keepdims: @@ -1077,6 +1123,23 @@ def covariance(x, y=None, keepdims=False): def correlation(x, y=None, keepdims=False): + """Compute correlation matrix from input tensors. + + Parameters + ---------- + x : tensor + Input tensor. + y : tensor, optional + Second input tensor, by default None. + keepdims : bool, optional + Whether to keep dimensions, by default False. + + Returns + ------- + tensor + Correlation matrix. + + """ x = x / ops.std(x, axis=0, keepdims=True) if y is not None: y = y / ops.std(y, axis=0, keepdims=True) @@ -1084,17 +1147,88 @@ def correlation(x, y=None, keepdims=False): class StopGradient(keras.layers.Layer): + """Keras layer that stops gradient propagation.""" + def call(self, x): + """Apply stop gradient operation. + + Parameters + ---------- + x : tensor + Input tensor. + + Returns + ------- + tensor + Input tensor with gradient stopped. + + """ return ops.stop_gradient(x) def _default_landmark_loss(y, y_pred): + """Default loss function for landmark positions. + + Computes the Euclidean distance between predicted and actual landmark positions. + Uses ReLU activation to smooth gradients. + + Parameters + ---------- + y : tensor + True landmark positions. + y_pred : tensor + Predicted landmark positions. + + Returns + ------- + tensor + Mean Euclidean distance loss. + + """ # Euclidean distance between points. # Relu activation smooths gradients. return keras.activations.relu(ops.mean(ops.norm(y_pred - y, axis=1))) class UMAPModel(keras.Model): + """Keras model for parametric UMAP. + + This model combines the encoder and decoder networks with UMAP loss, + optional reconstruction loss, and optional landmark loss. + + Parameters + ---------- + umap_loss_a : float + UMAP loss curve parameter a. + umap_loss_b : float + UMAP loss curve parameter b. + negative_sample_rate : int + Number of negative samples per positive sample. + encoder : keras.Model + Encoder network. + decoder : keras.Model, optional + Decoder network for reconstruction. + optimizer : keras.optimizers.Optimizer, optional + Optimizer for training, by default Adam with lr=1e-3. + parametric_reconstruction_loss_fn : callable, optional + Loss function for reconstruction, by default BinaryCrossentropy. + parametric_reconstruction : bool, optional + Whether to use parametric reconstruction, by default False. + parametric_reconstruction_loss_weight : float, optional + Weight for reconstruction loss, by default 1.0. + global_correlation_loss_weight : float, optional + Weight for global correlation loss, by default 0.0. + autoencoder_loss : bool, optional + Whether to use autoencoder loss, by default False. + landmark_loss_fn : callable, optional + Loss function for landmark positions, by default euclidean distance. + landmark_loss_weight : float, optional + Weight for landmark loss, by default 1.0. + name : str, optional + Model name, by default "umap_model". + + """ + def __init__( self, umap_loss_a, @@ -1111,7 +1245,7 @@ def __init__( landmark_loss_fn=None, landmark_loss_weight=1.0, name="umap_model", - ): + ) -> None: super().__init__(name=name) self.encoder = encoder @@ -1135,7 +1269,7 @@ def __init__( self.seed_generator = keras.random.SeedGenerator() if parametric_reconstruction_loss_fn is None: self.parametric_reconstruction_loss_fn = keras.losses.BinaryCrossentropy( - from_logits=True + from_logits=True, ) else: self.parametric_reconstruction_loss_fn = parametric_reconstruction_loss_fn @@ -1198,7 +1332,8 @@ def _umap_loss(self, y_pred, repulsion_strength=1.0): repeat_neg_batch_dim = ops.shape(repeat_neg)[0] shuffled_indices = keras.random.shuffle( - ops.arange(repeat_neg_batch_dim), seed=self.seed_generator + ops.arange(repeat_neg_batch_dim), + seed=self.seed_generator, ) if keras.config.backend() == "tensorflow": @@ -1217,7 +1352,9 @@ def _umap_loss(self, y_pred, repulsion_strength=1.0): # convert distances to probabilities log_probabilities_distance = convert_distance_to_log_probability( - distance_embedding, self.umap_loss_a, self.umap_loss_b + distance_embedding, + self.umap_loss_a, + self.umap_loss_b, ) # set true probabilities based on negative sampling @@ -1231,7 +1368,7 @@ def _umap_loss(self, y_pred, repulsion_strength=1.0): ) # compute cross entropy - (attraction_loss, repellant_loss, ce_loss) = compute_cross_entropy( + (_attraction_loss, _repellant_loss, ce_loss) = compute_cross_entropy( probabilities_graph, log_probabilities_distance, repulsion_strength=repulsion_strength, @@ -1263,13 +1400,14 @@ def z_score(x): # compute correlation corr_d = ops.squeeze( - correlation(x=ops.expand_dims(dx, -1), y=ops.expand_dims(dz, -1)) + correlation(x=ops.expand_dims(dx, -1), y=ops.expand_dims(dz, -1)), ) return -corr_d * self.global_correlation_loss_weight def _parametric_reconstruction_loss(self, y, y_pred): loss = self.parametric_reconstruction_loss_fn( - y["reconstruction"], y_pred["reconstruction"] + y["reconstruction"], + y_pred["reconstruction"], ) return loss * self.parametric_reconstruction_loss_weight @@ -1299,10 +1437,8 @@ def _landmark_loss(self, y, y_pred): if torch_imported: class PumapNet(nn.Module): - - def __init__(self, indim, outdim): - - super(PumapNet, self).__init__() + def __init__(self, indim, outdim) -> None: + super().__init__() self.dense1 = nn.Linear(indim, 100) self.dense2 = nn.Linear(100, 100) self.dense3 = nn.Linear(100, 100) @@ -1328,8 +1464,7 @@ def forward(self, x): x = self.dense3(x) x = F.relu(x) x = self.dense4(x) - x = F.relu(x) - return x + return F.relu(x) ###################### # 2. Copying weights # @@ -1337,20 +1472,23 @@ def forward(self, x): def weight_copier(km, pm): """Copies weights from a parametric UMAP encoder to pytorch. - Parameters + + Parameters. ---------- km : encoder extracted from parametric UMAP. pm: a PumapNet object. Will be overwritten. + Returns ------- pm : PumapNet Object. Net with copied weights. + """ kweights = km.get_weights() n_layers = int(len(kweights) / 2) # The actual number of layers # Get the names of the pytorch layers - all_keys = [x for x in pm.state_dict().keys()] + all_keys = list(pm.state_dict().keys()) pm_names = [all_keys[2 * i].split(".")[0] for i in range(4)] # Set a variable for the state dict @@ -1360,7 +1498,7 @@ def weight_copier(km, pm): pyt_state_dict[pm_names[i] + ".bias"] = kweights[2 * i + 1] pyt_state_dict[pm_names[i] + ".weight"] = np.transpose(kweights[2 * i]) - for key in pyt_state_dict.keys(): + for key in pyt_state_dict: pyt_state_dict[key] = torch.from_numpy(pyt_state_dict[key]) # Update diff --git a/umap/plot.py b/umap/plot.py index 620099f4..1e5184dd 100755 --- a/umap/plot.py +++ b/umap/plot.py @@ -1,66 +1,72 @@ -import numpy as np -import numba from warnings import warn -try: - import pandas as pd - import datashader as ds - import datashader.transfer_functions as tf - import datashader.bundling as bd - import matplotlib.pyplot as plt - import colorcet - import matplotlib.colors - import matplotlib.cm +import numba +import numpy as np +try: import bokeh.plotting as bpl import bokeh.transform as btr + import colorcet + import datashader as ds + import datashader.bundling as bd + import datashader.transfer_functions as tf import holoviews as hv import holoviews.operation.datashader as hd + import matplotlib.cm + import matplotlib.colors + import matplotlib.pyplot as plt + import pandas as pd except ImportError: warn( """The umap.plot package requires extra plotting libraries to be installed. You can install these via pip using - pip install umap-learn[plot] + pip install umap[plot] or via conda using conda install pandas matplotlib datashader bokeh holoviews colorcet scikit-image - """ + """, + stacklevel=2, ) - raise ImportError( + msg = ( "umap.plot requires pandas matplotlib datashader bokeh holoviews scikit-image and colorcet to be " "installed" + ) + raise ImportError( + msg, ) from None -import sklearn.decomposition +from warnings import warn + import sklearn.cluster +import sklearn.decomposition import sklearn.neighbors - -from matplotlib.patches import Patch - -from umap.utils import submatrix, average_nn_distance - -from bokeh.plotting import show as show_interactive -from bokeh.plotting import output_file, output_notebook from bokeh.layouts import column from bokeh.models import CustomJS, TextInput +from bokeh.plotting import show as show_interactive +from matplotlib.patches import Patch from matplotlib.pyplot import show as show_static -from warnings import warn +from umap.utils import average_nn_distance, submatrix fire_cmap = matplotlib.colors.LinearSegmentedColormap.from_list("fire", colorcet.fire) darkblue_cmap = matplotlib.colors.LinearSegmentedColormap.from_list( - "darkblue", colorcet.kbc + "darkblue", + colorcet.kbc, ) darkgreen_cmap = matplotlib.colors.LinearSegmentedColormap.from_list( - "darkgreen", colorcet.kgy + "darkgreen", + colorcet.kgy, ) darkred_cmap = matplotlib.colors.LinearSegmentedColormap.from_list( - "darkred", colors=colorcet.linear_kry_5_95_c72[:192], N=256 + "darkred", + colors=colorcet.linear_kry_5_95_c72[:192], + N=256, ) darkpurple_cmap = matplotlib.colors.LinearSegmentedColormap.from_list( - "darkpurple", colorcet.linear_bmw_5_95_c89 + "darkpurple", + colorcet.linear_bmw_5_95_c89, ) plt.colormaps.register(fire_cmap, name="fire") @@ -152,26 +158,24 @@ def _blue(x): def _get_embedding(umap_object): if hasattr(umap_object, "embedding_"): return umap_object.embedding_ - elif hasattr(umap_object, "embedding"): + if hasattr(umap_object, "embedding"): return umap_object.embedding - else: - raise ValueError("Could not find embedding attribute of umap_object") + msg = "Could not find embedding attribute of umap_object" + raise ValueError(msg) def _get_metric(umap_object): if hasattr(umap_object, "metric"): return umap_object.metric - else: - # Assume euclidean if no attribute per cuML.UMAP - return "euclidean" + # Assume euclidean if no attribute per cuML.UMAP + return "euclidean" def _get_metric_kwds(umap_object): if hasattr(umap_object, "_metric_kwds"): return umap_object._metric_kwds - else: - # Assume no keywords exist - return {} + # Assume no keywords exist + return {} def _embed_datashader_in_an_axis(datashader_image, ax): @@ -190,7 +194,7 @@ def _nhood_search(umap_object, nhood_size): indices = submatrix(indices, indices_sorted, nhood_size) dists = submatrix(dmat_shortened, indices_sorted, nhood_size) else: - rng_state = np.empty(3, dtype=np.int64) + np.empty(3, dtype=np.int64) indices, dists = umap_object._knn_search_index.query( umap_object._raw_data, @@ -202,13 +206,15 @@ def _nhood_search(umap_object, nhood_size): @numba.njit() def _nhood_compare(indices_left, indices_right): - """Compute Jaccard index of two neighborhoods""" + """Compute Jaccard index of two neighborhoods.""" result = np.empty(indices_left.shape[0]) for i in range(indices_left.shape[0]): with numba.objmode(intersection_size="intp"): intersection_size = np.intersect1d( - indices_left[i], indices_right[i], assume_unique=True + indices_left[i], + indices_right[i], + assume_unique=True, ).shape[0] union_size = np.unique(np.hstack((indices_left[i], indices_right[i]))).shape[0] result[i] = float(intersection_size) / float(union_size) @@ -217,33 +223,31 @@ def _nhood_compare(indices_left, indices_right): def _get_extent(points): - """Compute bounds on a space with appropriate padding""" + """Compute bounds on a space with appropriate padding.""" min_x = np.nanmin(points[:, 0]) max_x = np.nanmax(points[:, 0]) min_y = np.nanmin(points[:, 1]) max_y = np.nanmax(points[:, 1]) - extent = ( + return ( np.round(min_x - 0.05 * (max_x - min_x)), np.round(max_x + 0.05 * (max_x - min_x)), np.round(min_y - 0.05 * (max_y - min_y)), np.round(max_y + 0.05 * (max_y - min_y)), ) - return extent - def _select_font_color(background): if background == "black": font_color = "white" elif background.startswith("#"): mean_val = np.mean( - [int("0x" + c) for c in (background[1:3], background[3:5], background[5:7])] + [ + int("0x" + c) + for c in (background[1:3], background[3:5], background[5:7]) + ], ) - if mean_val > 126: - font_color = "black" - else: - font_color = "white" + font_color = "black" if mean_val > 126 else "white" else: font_color = "black" @@ -265,7 +269,7 @@ def _datashade_points( show_legend=True, alpha=255, ): - """Use datashader to plot points""" + """Use datashader to plot points.""" extent = _get_extent(points) canvas = ds.Canvas( plot_width=width, @@ -280,11 +284,12 @@ def _datashade_points( # Color by labels if labels is not None: if labels.shape[0] != points.shape[0]: - raise ValueError( + msg = ( "Labels must have a label for " - "each sample (size mismatch: {} {})".format( - labels.shape[0], points.shape[0] - ) + f"each sample (size mismatch: {labels.shape[0]} {points.shape[0]})" + ) + raise ValueError( + msg, ) data["label"] = pd.Categorical(labels) @@ -295,53 +300,66 @@ def _datashade_points( unique_labels = np.unique(labels) num_labels = unique_labels.shape[0] color_key = _to_hex( - plt.get_cmap(color_key_cmap)(np.linspace(0, 1, num_labels)) + plt.get_cmap(color_key_cmap)(np.linspace(0, 1, num_labels)), ) legend_elements = [ Patch(facecolor=color_key[i], label=k) for i, k in enumerate(unique_labels) ] result = tf.shade( - aggregation, color_key=color_key, how="eq_hist", alpha=alpha + aggregation, + color_key=color_key, + how="eq_hist", + alpha=alpha, ) else: legend_elements = [ - Patch(facecolor=color_key[k], label=k) for k in color_key.keys() + Patch(facecolor=color_key[k], label=k) for k in color_key ] result = tf.shade( - aggregation, color_key=color_key, how="eq_hist", alpha=alpha + aggregation, + color_key=color_key, + how="eq_hist", + alpha=alpha, ) # Color by values elif values is not None: if values.shape[0] != points.shape[0]: - raise ValueError( + msg = ( "Values must have a value for " - "each sample (size mismatch: {} {})".format( - values.shape[0], points.shape[0] - ) + f"each sample (size mismatch: {values.shape[0]} {points.shape[0]})" + ) + raise ValueError( + msg, ) unique_values = np.unique(values) if unique_values.shape[0] >= 256: min_val, max_val = np.min(values), np.max(values) bin_size = (max_val - min_val) / 255.0 data["val_cat"] = pd.Categorical( - np.round((values - min_val) / bin_size).astype(np.int16) + np.round((values - min_val) / bin_size).astype(np.int16), ) aggregation = canvas.points(data, "x", "y", agg=ds.count_cat("val_cat")) color_key = _to_hex(plt.get_cmap(cmap)(np.linspace(0, 1, 256))) result = tf.shade( - aggregation, color_key=color_key, how="eq_hist", alpha=alpha + aggregation, + color_key=color_key, + how="eq_hist", + alpha=alpha, ) else: data["val_cat"] = pd.Categorical(values) aggregation = canvas.points(data, "x", "y", agg=ds.count_cat("val_cat")) color_key_cols = _to_hex( - plt.get_cmap(cmap)(np.linspace(0, 1, unique_values.shape[0])) + plt.get_cmap(cmap)(np.linspace(0, 1, unique_values.shape[0])), ) color_key = dict(zip(unique_values, color_key_cols)) result = tf.shade( - aggregation, color_key=color_key, how="eq_hist", alpha=alpha + aggregation, + color_key=color_key, + how="eq_hist", + alpha=alpha, ) # Color by density (default datashader option) @@ -357,8 +375,7 @@ def _datashade_points( if show_legend and legend_elements is not None: ax.legend(handles=legend_elements) return ax - else: - return result + return result def _matplotlib_points( @@ -375,7 +392,7 @@ def _matplotlib_points( show_legend=True, alpha=None, ): - """Use matplotlib to plot points""" + """Use matplotlib to plot points.""" point_size = 100.0 / np.sqrt(points.shape[0]) legend_elements = None @@ -390,18 +407,19 @@ def _matplotlib_points( # Color by labels if labels is not None: if labels.shape[0] != points.shape[0]: - raise ValueError( + msg = ( "Labels must have a label for " - "each sample (size mismatch: {} {})".format( - labels.shape[0], points.shape[0] - ) + f"each sample (size mismatch: {labels.shape[0]} {points.shape[0]})" + ) + raise ValueError( + msg, ) if color_key is None: unique_labels = np.unique(labels) num_labels = unique_labels.shape[0] color_key = plt.get_cmap(color_key_cmap)(np.linspace(0, 1, num_labels)) legend_elements = [ - Patch(facecolor=color_key[i], label=unique_labels[i]) + Patch(facecolor=color_key[i], label=k) for i, k in enumerate(unique_labels) ] @@ -414,8 +432,9 @@ def _matplotlib_points( else: unique_labels = np.unique(labels) if len(color_key) < unique_labels.shape[0]: + msg = "Color key must have enough colors for the number of labels" raise ValueError( - "Color key must have enough colors for the number of labels" + msg, ) new_color_key = { @@ -433,19 +452,24 @@ def _matplotlib_points( # Color by values elif values is not None: if values.shape[0] != points.shape[0]: - raise ValueError( + msg = ( "Values must have a value for " - "each sample (size mismatch: {} {})".format( - values.shape[0], points.shape[0] - ) + f"each sample (size mismatch: {values.shape[0]} {points.shape[0]})" + ) + raise ValueError( + msg, ) ax.scatter( - points[:, 0], points[:, 1], s=point_size, c=values, cmap=cmap, alpha=alpha + points[:, 0], + points[:, 1], + s=point_size, + c=values, + cmap=cmap, + alpha=alpha, ) # No color (just pick the midpoint of the cmap) else: - color = plt.get_cmap(cmap)(0.5) ax.scatter(points[:, 0], points[:, 1], s=point_size, c=color) @@ -455,7 +479,7 @@ def _matplotlib_points( return ax -def show(plot_to_show): +def show(plot_to_show) -> None: """Display a plot, either interactive or static. Parameters @@ -466,6 +490,7 @@ def show(plot_to_show): Returns ------- None + """ if isinstance(plot_to_show, plt.Axes): show_static() @@ -474,8 +499,9 @@ def show(plot_to_show): elif isinstance(plot_to_show, hv.core.spaces.DynamicMap): show_interactive(hv.render(plot_to_show), backend="bokeh") else: + msg = "The type of ``plot_to_show`` was not valid, or not understood." raise ValueError( - "The type of ``plot_to_show`` was not valid, or not understood." + msg, ) @@ -613,6 +639,7 @@ def points( The result is a matplotlib axis with the relevant plot displayed. If you are using a notebooks and have ``%matplotlib inline`` set then this will simply display inline. + """ # if not hasattr(umap_object, "embedding_"): # raise ValueError( @@ -625,23 +652,23 @@ def points( background = _themes[theme]["background"] if labels is not None and values is not None: + msg = "Conflicting options; only one of labels or values should be set" raise ValueError( - "Conflicting options; only one of labels or values should be set" + msg, ) - if alpha is not None: - if not 0.0 <= alpha <= 1.0: - raise ValueError("Alpha must be between 0 and 1 inclusive") + if alpha is not None and not 0.0 <= alpha <= 1.0: + msg = "Alpha must be between 0 and 1 inclusive" + raise ValueError(msg) if points is None: points = _get_embedding(umap_object) if subset_points is not None: if len(subset_points) != points.shape[0]: + msg = f"Size of subset points ({len(subset_points)}) does not match number of input points ({points.shape[0]})" raise ValueError( - "Size of subset points ({}) does not match number of input points ({})".format( - len(subset_points), points.shape[0] - ) + msg, ) points = points[subset_points] @@ -651,7 +678,8 @@ def points( values = values[subset_points] if points.shape[1] != 2: - raise ValueError("Plotting is currently only implemented for 2D embeddings") + msg = "Plotting is currently only implemented for 2D embeddings" + raise ValueError(msg) font_color = _select_font_color(background) @@ -677,10 +705,7 @@ def points( ) else: # Datashader uses 0-255 as the range for alpha, with 255 as the default - if alpha is not None: - alpha = alpha * 255 - else: - alpha = 255 + alpha = alpha * 255 if alpha is not None else 255 ax = _datashade_points( points, @@ -702,9 +727,7 @@ def points( ax.text( 0.99, 0.01, - "UMAP: metric={}, n_neighbors={}, min_dist={}".format( - _get_metric(umap_object), umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: metric={_get_metric(umap_object)}, n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -713,9 +736,7 @@ def points( ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -852,6 +873,7 @@ def connectivity( The result is a matplotlib axis with the relevant plot displayed. If you are using a notebook and have ``%matplotlib inline`` set then this will simply display inline. + """ if theme is not None: cmap = _themes[theme]["cmap"] @@ -863,15 +885,9 @@ def connectivity( point_df = pd.DataFrame(points, columns=("x", "y")) point_size = 100.0 / np.sqrt(points.shape[0]) - if point_size > 1: - px_size = int(np.round(point_size)) - else: - px_size = 1 + px_size = int(np.round(point_size)) if point_size > 1 else 1 - if show_points: - edge_how = "log" - else: - edge_how = "eq_hist" + edge_how = "log" if show_points else "eq_hist" coo_graph = umap_object.graph_.tocoo() edge_df = pd.DataFrame( @@ -894,11 +910,13 @@ def connectivity( elif edge_bundling == "hammer": warn( "Hammer edge bundling is expensive for large graphs!\n" - "This may take a long time to compute!" + "This may take a long time to compute!", + stacklevel=2, ) edges = bd.hammer_bundle(point_df, edge_df, weight="weight") else: - raise ValueError("{} is not a recognised bundling method".format(edge_bundling)) + msg = f"{edge_bundling} is not a recognised bundling method" + raise ValueError(msg) edge_img = tf.shade( canvas.line(edges, "x", "y", agg=ds.sum("weight")), @@ -939,9 +957,7 @@ def connectivity( ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -949,6 +965,7 @@ def connectivity( return ax + def diagnostic( umap_object, diagnostic_type="pca", @@ -961,7 +978,7 @@ def diagnostic( width=800, height=800, return_diagnostics=False, - plot_result=True + plot_result=True, ): """Provide a diagnostic plot or plots for a UMAP embedding, with options to return diagnostics and control plotting. There are a number of plots that can be helpful for diagnostic @@ -1052,12 +1069,13 @@ def diagnostic( If return_diagnostics=True and plot_result=False, returns the diagnostic data. If return_diagnostics=False and plot_result=False, returns None. If using a notebook with ``%matplotlib inline``, plots may display inline. - """ + """ points = _get_embedding(umap_object) if points.shape[1] != 2: - raise ValueError("Plotting is currently only implemented for 2D embeddings") + msg = "Plotting is currently only implemented for 2D embeddings" + raise ValueError(msg) if point_size is None: point_size = 100.0 / np.sqrt(points.shape[0]) @@ -1066,7 +1084,7 @@ def diagnostic( dpi = plt.rcParams["figure.dpi"] if diagnostic_type in ("local_dim", "neighborhood"): width *= 1.1 - fig = plt.figure(figsize=(width/dpi, height/dpi)) + fig = plt.figure(figsize=(width / dpi, height / dpi)) ax = fig.add_subplot(111) font_color = _select_font_color(background) if plot_result else None @@ -1075,21 +1093,25 @@ def diagnostic( if diagnostic_type == "pca": color_proj = sklearn.decomposition.PCA(n_components=3).fit_transform( - umap_object._raw_data + umap_object._raw_data, ) color_proj -= np.min(color_proj) color_proj /= np.max(color_proj, axis=0) diagnostic_data = color_proj if plot_result: - ax.scatter(points[:, 0], points[:, 1], s=point_size, c=color_proj, alpha=0.66) + ax.scatter( + points[:, 0], + points[:, 1], + s=point_size, + c=color_proj, + alpha=0.66, + ) ax.set_title("Colored by RGB coords of PCA embedding") ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -1098,21 +1120,25 @@ def diagnostic( elif diagnostic_type == "ica": color_proj = sklearn.decomposition.FastICA(n_components=3).fit_transform( - umap_object._raw_data + umap_object._raw_data, ) color_proj -= np.min(color_proj) color_proj /= np.max(color_proj, axis=0) diagnostic_data = color_proj if plot_result: - ax.scatter(points[:, 0], points[:, 1], s=point_size, c=color_proj, alpha=0.66) + ax.scatter( + points[:, 0], + points[:, 1], + s=point_size, + c=color_proj, + alpha=0.66, + ) ax.set_title("Colored by RGB coords of FastICA embedding") ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -1121,24 +1147,29 @@ def diagnostic( elif diagnostic_type == "vq": color_projector = sklearn.cluster.KMeans(n_clusters=3).fit( - umap_object._raw_data + umap_object._raw_data, ) color_proj = sklearn.metrics.pairwise_distances( - umap_object._raw_data, color_projector.cluster_centers_ + umap_object._raw_data, + color_projector.cluster_centers_, ) color_proj -= np.min(color_proj) color_proj /= np.max(color_proj, axis=0) diagnostic_data = color_proj if plot_result: - ax.scatter(points[:, 0], points[:, 1], s=point_size, c=color_proj, alpha=0.66) + ax.scatter( + points[:, 0], + points[:, 1], + s=point_size, + c=color_proj, + alpha=0.66, + ) ax.set_title("Colored by RGB coords of Vector Quantization") ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -1148,9 +1179,10 @@ def diagnostic( elif diagnostic_type == "neighborhood": highd_indices, highd_dists = _nhood_search(umap_object, nhood_size) tree = sklearn.neighbors.KDTree(points) - lowd_dists, lowd_indices = tree.query(points, k=nhood_size) + _lowd_dists, lowd_indices = tree.query(points, k=nhood_size) accuracy = _nhood_compare( - highd_indices.astype(np.int32), lowd_indices.astype(np.int32) + highd_indices.astype(np.int32), + lowd_indices.astype(np.int32), ) diagnostic_data = accuracy @@ -1170,9 +1202,7 @@ def diagnostic( ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -1184,14 +1214,17 @@ def diagnostic( plt.colorbar(mappable, ax=ax) elif diagnostic_type == "local_dim": - highd_indices, highd_dists = _nhood_search(umap_object, umap_object.n_neighbors) + highd_indices, _highd_dists = _nhood_search( + umap_object, + umap_object.n_neighbors, + ) data = umap_object._raw_data local_dim = np.empty(data.shape[0], dtype=np.int64) for i in range(data.shape[0]): pca = sklearn.decomposition.PCA().fit(data[highd_indices[i]]) try: local_dim[i] = np.where( - np.cumsum(pca.explained_variance_ratio_) > local_variance_threshold + np.cumsum(pca.explained_variance_ratio_) > local_variance_threshold, )[0][0] except IndexError: local_dim[i] = -1 @@ -1213,9 +1246,7 @@ def diagnostic( ax.text( 0.99, 0.01, - "UMAP: n_neighbors={}, min_dist={}".format( - umap_object.n_neighbors, umap_object.min_dist - ), + f"UMAP: n_neighbors={umap_object.n_neighbors}, min_dist={umap_object.min_dist}", transform=ax.transAxes, horizontalalignment="right", color=font_color, @@ -1230,9 +1261,14 @@ def diagnostic( if plot_result: cols = int(len(_diagnostic_types) ** 0.5 // 1) rows = len(_diagnostic_types) // cols + 1 - fig, axs = plt.subplots(rows, cols, figsize=(10, 10), constrained_layout=True) + fig, axs = plt.subplots( + rows, + cols, + figsize=(10, 10), + constrained_layout=True, + ) axs = axs.flat - for ax in axs[len(_diagnostic_types):]: + for ax in axs[len(_diagnostic_types) :]: ax.remove() diagnostic_data = {} for ax, plt_type in zip(axs, _diagnostic_types): @@ -1242,7 +1278,7 @@ def diagnostic( ax=ax, point_size=point_size / 4.0, return_diagnostics=True, - plot_result=True + plot_result=True, ) diagnostic_data[plt_type] = sub_diagnostic else: @@ -1253,7 +1289,7 @@ def diagnostic( diagnostic_type=plt_type, point_size=point_size / 4.0, return_diagnostics=True, - plot_result=False + plot_result=False, ) diagnostic_data[plt_type] = sub_diagnostic @@ -1261,15 +1297,15 @@ def diagnostic( raise ValueError( "Unknown diagnostic; should be one of " + ", ".join(list(_diagnostic_types)) - + ' or "all"' + + ' or "all"', ) if return_diagnostics and plot_result: return ax, diagnostic_data - elif return_diagnostics: + if return_diagnostics: return diagnostic_data - else: - return ax + return ax + def interactive( umap_object, @@ -1427,26 +1463,27 @@ def interactive( background = _themes[theme]["background"] if labels is not None and values is not None: + msg = "Conflicting options; only one of labels or values should be set" raise ValueError( - "Conflicting options; only one of labels or values should be set" + msg, ) - if alpha is not None: - if not 0.0 <= alpha <= 1.0: - raise ValueError("Alpha must be between 0 and 1 inclusive") + if alpha is not None and not 0.0 <= alpha <= 1.0: + msg = "Alpha must be between 0 and 1 inclusive" + raise ValueError(msg) points = _get_embedding(umap_object) if subset_points is not None: if len(subset_points) != points.shape[0]: + msg = f"Size of subset points ({len(subset_points)}) does not match number of input points ({points.shape[0]})" raise ValueError( - "Size of subset points ({}) does not match number of input points ({})".format( - len(subset_points), points.shape[0] - ) + msg, ) points = points[subset_points] if points.shape[1] != 2: - raise ValueError("Plotting is currently only implemented for 2D embeddings") + msg = "Plotting is currently only implemented for 2D embeddings" + raise ValueError(msg) if point_size is None: point_size = 100.0 / np.sqrt(points.shape[0]) @@ -1460,7 +1497,7 @@ def interactive( unique_labels = np.unique(labels) num_labels = unique_labels.shape[0] color_key = _to_hex( - plt.get_cmap(color_key_cmap)(np.linspace(0, 1, num_labels)) + plt.get_cmap(color_key_cmap)(np.linspace(0, 1, num_labels)), ) if isinstance(color_key, dict): @@ -1468,8 +1505,9 @@ def interactive( else: unique_labels = np.unique(labels) if len(color_key) < unique_labels.shape[0]: + msg = "Color key must have enough colors for the number of labels" raise ValueError( - "Color key must have enough colors for the number of labels" + msg, ) new_color_key = {k: color_key[i] for i, k in enumerate(unique_labels)} @@ -1481,7 +1519,10 @@ def interactive( data["value"] = np.asarray(values) palette = _to_hex(plt.get_cmap(cmap)(np.linspace(0, 1, 256))) colors = btr.linear_cmap( - "value", palette, low=np.min(values), high=np.max(values) + "value", + palette, + low=np.min(values), + high=np.max(values), ) else: @@ -1552,26 +1593,28 @@ def interactive( if len(interactive_text_search_columns) == 0: warn( "interactive_text_search_columns set to True, but no hover_data or labels provided." - "Please provide hover_data or labels to use interactive text search." + "Please provide hover_data or labels to use interactive text search.", + stacklevel=2, ) else: callback = CustomJS( - args=dict( - source=data_source, - matching_alpha=interactive_text_search_alpha_contrast, - non_matching_alpha=1 - interactive_text_search_alpha_contrast, - search_columns=interactive_text_search_columns, - ), + args={ + "source": data_source, + "matching_alpha": interactive_text_search_alpha_contrast, + "non_matching_alpha": 1 + - interactive_text_search_alpha_contrast, + "search_columns": interactive_text_search_columns, + }, code=""" var data = source.data; var text_search = cb_obj.value; - + var search_columns_dict = {} for (var col in search_columns){ search_columns_dict[col] = search_columns[col] } - + // Loop over columns and values // If there is no match for any column for a given row, change the alpha value var string_match = false; @@ -1601,12 +1644,16 @@ def interactive( if hover_data is not None: warn( "Too many points for hover data -- tooltips will not" - "be displayed. Sorry; try subsampling your data." + "be displayed. Sorry; try subsampling your data.", + stacklevel=2, ) if interactive_text_search: - warn("Too many points for text search." "Sorry; try subsampling your data.") + warn( + "Too many points for text search.Sorry; try subsampling your data.", + stacklevel=2, + ) if alpha is not None: - warn("Alpha parameter will not be applied on holoviews plots") + warn("Alpha parameter will not be applied on holoviews plots", stacklevel=2) hv.extension("bokeh") hv.output(size=300) hv.opts.defaults(hv.opts.RGB(bgcolor=background, xaxis=None, yaxis=None)) @@ -1625,7 +1672,7 @@ def interactive( min_val = data.values.min() val_range = data.values.max() - min_val data["val_cat"] = pd.Categorical( - (data.values - min_val) // (val_range // 256) + (data.values - min_val) // (val_range // 256), ) point_plot = hv.Points(data, kdims=["x", "y"], vdims=["val_cat"]) plot = hd.datashade( @@ -1674,7 +1721,7 @@ def nearest_neighbour_distribution(umap_object, bins=25, ax=None): fig = plt.figure() ax = fig.add_subplot(111) - ax.set_xlabel(f"Average distance to nearest neighbors") + ax.set_xlabel("Average distance to nearest neighbors") ax.set_ylabel("Frequency") ax.hist(nn_distances, bins=bins) diff --git a/umap/sparse.py b/umap/sparse.py index bb683411..bb3bc7c8 100644 --- a/umap/sparse.py +++ b/umap/sparse.py @@ -2,7 +2,6 @@ # Enough simple sparse operations in numba to enable sparse UMAP # # License: BSD 3 clause -from __future__ import print_function import locale @@ -27,10 +26,9 @@ def arr_unique(arr): def arr_union(ar1, ar2): if ar1.shape[0] == 0: return ar2 - elif ar2.shape[0] == 0: + if ar2.shape[0] == 0: return ar1 - else: - return arr_unique(np.concatenate((ar1, ar2))) + return arr_unique(np.concatenate((ar1, ar2))) # Just reproduce a simpler version of numpy intersect1d (not numba supported @@ -155,16 +153,17 @@ def general_sset_intersection( result_val, right_complement=False, mix_weight=0.5, -): - +) -> None: left_min = max(data1.min() / 2.0, 1.0e-8) if right_complement: right_min = min( - max((1.0 - data2).min() / 2.0, 1.0e-8), 1e-4 + max((1.0 - data2).min() / 2.0, 1.0e-8), + 1e-4, ) # All right vals may be large! else: right_min = min( - max(data2.min() / 2.0, 1.0e-8), 1e-4 + max(data2.min() / 2.0, 1.0e-8), + 1e-4, ) # All right vals may be large! for idx in range(result_row.shape[0]): @@ -179,23 +178,19 @@ def general_sset_intersection( right_val = right_min for k in range(indptr2[i], indptr2[i + 1]): if indices2[k] == j: - if right_complement: - right_val = 1.0 - data2[k] - else: - right_val = data2[k] + right_val = 1.0 - data2[k] if right_complement else data2[k] if left_val > left_min or right_val > right_min: if mix_weight < 0.5: result_val[idx] = left_val * pow( - right_val, mix_weight / (1.0 - mix_weight) + right_val, + mix_weight / (1.0 - mix_weight), ) else: result_val[idx] = ( pow(left_val, (1.0 - mix_weight) / mix_weight) * right_val ) - return - @numba.njit() def general_sset_union( @@ -208,7 +203,7 @@ def general_sset_union( result_row, result_col, result_val, -): +) -> None: left_min = max(data1.min() / 2.0, 1.0e-8) right_min = max(data2.min() / 2.0, 1.0e-8) @@ -228,12 +223,10 @@ def general_sset_union( result_val[idx] = left_val + right_val - left_val * right_val - return - @numba.njit() def sparse_euclidean(ind1, data1, ind2, data2): - aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) + _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) result = 0.0 for i in range(aux_data.shape[0]): result += aux_data[i] ** 2 @@ -242,7 +235,7 @@ def sparse_euclidean(ind1, data1, ind2, data2): @numba.njit() def sparse_manhattan(ind1, data1, ind2, data2): - aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) + _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) result = 0.0 for i in range(aux_data.shape[0]): result += np.abs(aux_data[i]) @@ -251,7 +244,7 @@ def sparse_manhattan(ind1, data1, ind2, data2): @numba.njit() def sparse_chebyshev(ind1, data1, ind2, data2): - aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) + _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) result = 0.0 for i in range(aux_data.shape[0]): result = max(result, np.abs(aux_data[i])) @@ -260,7 +253,7 @@ def sparse_chebyshev(ind1, data1, ind2, data2): @numba.njit() def sparse_minkowski(ind1, data1, ind2, data2, p=2.0): - aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) + _aux_inds, aux_data = sparse_diff(ind1, data1, ind2, data2) result = 0.0 for i in range(aux_data.shape[0]): result += np.abs(aux_data[i]) ** p @@ -282,14 +275,14 @@ def sparse_canberra(ind1, data1, ind2, data2): numer_inds, numer_data = sparse_diff(ind1, data1, ind2, data2) numer_data = np.abs(numer_data) - val_inds, val_data = sparse_mul(numer_inds, numer_data, denom_inds, denom_data) + _val_inds, val_data = sparse_mul(numer_inds, numer_data, denom_inds, denom_data) return np.sum(val_data) @numba.njit() def sparse_bray_curtis(ind1, data1, ind2, data2): # pragma: no cover - denom_inds, denom_data = sparse_sum(ind1, data1, ind2, data2) + _denom_inds, denom_data = sparse_sum(ind1, data1, ind2, data2) denom_data = np.abs(denom_data) if denom_data.shape[0] == 0: @@ -300,7 +293,7 @@ def sparse_bray_curtis(ind1, data1, ind2, data2): # pragma: no cover if denominator == 0: return 0.0 - numer_inds, numer_data = sparse_diff(ind1, data1, ind2, data2) + _numer_inds, numer_data = sparse_diff(ind1, data1, ind2, data2) numer_data = np.abs(numer_data) numerator = np.sum(numer_data) @@ -315,8 +308,7 @@ def sparse_jaccard(ind1, data1, ind2, data2): if num_non_zero == 0: return 0.0 - else: - return float(num_non_zero - num_equal) / num_non_zero + return float(num_non_zero - num_equal) / num_non_zero @numba.njit() @@ -336,8 +328,7 @@ def sparse_dice(ind1, data1, ind2, data2): if num_not_equal == 0.0: return 0.0 - else: - return num_not_equal / (2.0 * num_true_true + num_not_equal) + return num_not_equal / (2.0 * num_true_true + num_not_equal) @numba.njit() @@ -348,10 +339,9 @@ def sparse_kulsinski(ind1, data1, ind2, data2, n_features): if num_not_equal == 0: return 0.0 - else: - return float(num_not_equal - num_true_true + n_features) / ( - num_not_equal + n_features - ) + return float(num_not_equal - num_true_true + n_features) / ( + num_not_equal + n_features + ) @numba.njit() @@ -372,8 +362,7 @@ def sparse_russellrao(ind1, data1, ind2, data2, n_features): if num_true_true == np.sum(data1 != 0) and num_true_true == np.sum(data2 != 0): return 0.0 - else: - return float(n_features - num_true_true) / (n_features) + return float(n_features - num_true_true) / (n_features) @numba.njit() @@ -393,13 +382,12 @@ def sparse_sokal_sneath(ind1, data1, ind2, data2): if num_not_equal == 0.0: return 0.0 - else: - return num_not_equal / (0.5 * num_true_true + num_not_equal) + return num_not_equal / (0.5 * num_true_true + num_not_equal) @numba.njit() def sparse_cosine(ind1, data1, ind2, data2): - aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2) + _aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2) result = 0.0 norm1 = norm(data1) norm2 = norm(data2) @@ -409,15 +397,14 @@ def sparse_cosine(ind1, data1, ind2, data2): if norm1 == 0.0 and norm2 == 0.0: return 0.0 - elif norm1 == 0.0 or norm2 == 0.0: + if norm1 == 0.0 or norm2 == 0.0: return 1.0 - else: - return 1.0 - (result / (norm1 * norm2)) + return 1.0 - (result / (norm1 * norm2)) @numba.njit() def sparse_hellinger(ind1, data1, ind2, data2): - aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2) + _aux_inds, aux_data = sparse_mul(ind1, data1, ind2, data2) result = 0.0 norm1 = np.sum(data1) norm2 = np.sum(data2) @@ -428,24 +415,22 @@ def sparse_hellinger(ind1, data1, ind2, data2): if norm1 == 0.0 and norm2 == 0.0: return 0.0 - elif norm1 == 0.0 or norm2 == 0.0: + if norm1 == 0.0 or norm2 == 0.0: return 1.0 - elif result > sqrt_norm_prod: + if result > sqrt_norm_prod: return 0.0 - else: - return np.sqrt(1.0 - (result / sqrt_norm_prod)) + return np.sqrt(1.0 - (result / sqrt_norm_prod)) @numba.njit() def sparse_correlation(ind1, data1, ind2, data2, n_features): - mu_x = 0.0 mu_y = 0.0 dot_product = 0.0 if ind1.shape[0] == 0 and ind2.shape[0] == 0: return 0.0 - elif ind1.shape[0] == 0 or ind2.shape[0] == 0: + if ind1.shape[0] == 0 or ind2.shape[0] == 0: return 1.0 for i in range(data1.shape[0]): @@ -465,10 +450,10 @@ def sparse_correlation(ind1, data1, ind2, data2, n_features): shifted_data2[i] = data2[i] - mu_y norm1 = np.sqrt( - (norm(shifted_data1) ** 2) + (n_features - ind1.shape[0]) * (mu_x**2) + (norm(shifted_data1) ** 2) + (n_features - ind1.shape[0]) * (mu_x**2), ) norm2 = np.sqrt( - (norm(shifted_data2) ** 2) + (n_features - ind2.shape[0]) * (mu_y**2) + (norm(shifted_data2) ** 2) + (n_features - ind2.shape[0]) * (mu_y**2), ) dot_prod_inds, dot_prod_data = sparse_mul(ind1, shifted_data1, ind2, shifted_data2) @@ -491,10 +476,9 @@ def sparse_correlation(ind1, data1, ind2, data2, n_features): if norm1 == 0.0 and norm2 == 0.0: return 0.0 - elif dot_product == 0.0: + if dot_product == 0.0: return 1.0 - else: - return 1.0 - (dot_product / (norm1 * norm2)) + return 1.0 - (dot_product / (norm1 * norm2)) @numba.njit() @@ -520,8 +504,7 @@ def log_beta(x, y): for i in range(1, int(a)): value += np.log(i) - np.log(b + i) return value - else: - return approx_log_Gamma(x) + approx_log_Gamma(y) - approx_log_Gamma(x + y) + return approx_log_Gamma(x) + approx_log_Gamma(y) - approx_log_Gamma(x + y) @numba.njit() @@ -543,7 +526,7 @@ def sparse_ll_dirichlet(ind1, data1, ind2, data2): if n1 == 0 and n2 == 0: return 0.0 - elif n1 == 0 or n2 == 0: + if n1 == 0 or n2 == 0: return 1e8 log_b = 0.0 @@ -565,7 +548,6 @@ def sparse_ll_dirichlet(ind1, data1, ind2, data2): self_denom1 = 0.0 for d1 in data1: - self_denom1 += log_single_beta(d1) self_denom2 = 0.0 @@ -574,7 +556,7 @@ def sparse_ll_dirichlet(ind1, data1, ind2, data2): return np.sqrt( 1.0 / n2 * (log_b - log_beta(n1, n2) - (self_denom2 - log_single_beta(n2))) - + 1.0 / n1 * (log_b - log_beta(n2, n1) - (self_denom1 - log_single_beta(n1))) + + 1.0 / n1 * (log_b - log_beta(n2, n1) - (self_denom1 - log_single_beta(n1))), ) diff --git a/umap/spectral.py b/umap/spectral.py index 4b11d533..19d24eb1 100644 --- a/umap/spectral.py +++ b/umap/spectral.py @@ -1,9 +1,16 @@ -import warnings +"""Spectral embedding initialization for UMAP. + +This module provides functions for computing spectral embeddings of graphs, +which can be used as initialization for UMAP's dimensionality reduction. +""" + +from __future__ import annotations +import warnings +from typing import Any from warnings import warn import numpy as np - import scipy.sparse import scipy.sparse.csgraph from sklearn.decomposition import TruncatedSVD @@ -11,53 +18,54 @@ from sklearn.metrics import pairwise_distances from sklearn.metrics.pairwise import _VALID_METRICS as SKLEARN_PAIRWISE_VALID_METRICS -from umap.distances import pairwise_special_metric, SPECIAL_METRICS +from umap.distances import SPECIAL_METRICS, pairwise_special_metric from umap.sparse import SPARSE_SPECIAL_METRICS, sparse_named_distances def component_layout( - data, - n_components, - component_labels, - dim, - random_state, - metric="euclidean", - metric_kwds={}, -): - """Provide a layout relating the separate connected components. This is done - by taking the centroid of each component and then performing a spectral embedding - of the centroids. + data: np.ndarray | None, + n_components: int, + component_labels: np.ndarray, + dim: int, + random_state: np.random.RandomState | np.random.Generator, + metric: str | Any = "euclidean", + metric_kwds: dict[str, Any] | None = None, +) -> np.ndarray: + """Provide a layout relating the separate connected components. + + This is done by taking the centroid of each component and then performing + a spectral embedding of the centroids. Parameters ---------- - data: array of shape (n_samples, n_features) + data : np.ndarray | None The source data -- required so we can generate centroids for each connected component of the graph. - - n_components: int + n_components : int The number of distinct components to be layed out. - - component_labels: array of shape (n_samples) + component_labels : np.ndarray For each vertex in the graph the label of the component to which the vertex belongs. - - dim: int + dim : int The chosen embedding dimension. - - metric: string or callable (optional, default 'euclidean') + random_state : np.random.RandomState | np.random.Generator + Random state for reproducibility. + metric : str | callable, default="euclidean" The metric used to measure distances among the source data points. - - metric_kwds: dict (optional, default {}) + metric_kwds : dict[str, Any] | None, default=None Keyword arguments to be passed to the metric function. If metric is 'precomputed', 'linkage' keyword can be used to specify - 'average', 'complete', or 'single' linkage. Default is 'average' + 'average', 'complete', or 'single' linkage. Default is 'average'. Returns ------- - component_embedding: array of shape (n_components, dim) + np.ndarray The ``dim``-dimensional embedding of the ``n_components``-many connected components. + """ + if metric_kwds is None: + metric_kwds = {} if data is None: # We don't have data to work with; just guess return np.random.random(size=(n_components, dim)) * 10.0 @@ -76,9 +84,12 @@ def component_layout( elif linkage == "single": linkage = np.min else: + msg = ( + f"Unrecognized linkage '{linkage}'. Please choose from " + "'average', 'complete', or 'single'" + ) raise ValueError( - "Unrecognized linkage '%s'. Please choose from " - "'average', 'complete', or 'single'" % linkage + msg, ) for c_i in range(n_components): dm_i = data[component_labels == c_i] @@ -93,7 +104,8 @@ def component_layout( if scipy.sparse.isspmatrix(component_centroids): warn( "Forcing component centroids to dense; if you are running out of " - "memory then consider increasing n_neighbors." + "memory then consider increasing n_neighbors.", + stacklevel=2, ) component_centroids = component_centroids.toarray() @@ -109,33 +121,41 @@ def component_layout( metric=SPARSE_SPECIAL_METRICS[metric], kwds=metric_kwds, ) - else: - if callable(metric) and scipy.sparse.isspmatrix(data): - function_to_name_mapping = { - sparse_named_distances[k]: k - for k in set(SKLEARN_PAIRWISE_VALID_METRICS) - & set(sparse_named_distances.keys()) - } - try: - metric_name = function_to_name_mapping[metric] - except KeyError: - raise NotImplementedError( - "Multicomponent layout for custom " - "sparse metrics is not implemented at " - "this time." - ) - distance_matrix = pairwise_distances( - component_centroids, metric=metric_name, **metric_kwds + elif callable(metric) and scipy.sparse.isspmatrix(data): + function_to_name_mapping = { + sparse_named_distances[k]: k + for k in set(SKLEARN_PAIRWISE_VALID_METRICS) + & set(sparse_named_distances.keys()) + } + try: + metric_name = function_to_name_mapping[metric] + except KeyError: + msg = ( + "Multicomponent layout for custom " + "sparse metrics is not implemented at " + "this time." ) - else: - distance_matrix = pairwise_distances( - component_centroids, metric=metric, **metric_kwds + raise NotImplementedError( + msg, ) + distance_matrix = pairwise_distances( + component_centroids, + metric=metric_name, + **metric_kwds, + ) + else: + distance_matrix = pairwise_distances( + component_centroids, + metric=metric, + **metric_kwds, + ) affinity_matrix = np.exp(-(distance_matrix**2)) component_embedding = SpectralEmbedding( - n_components=dim, affinity="precomputed", random_state=random_state + n_components=dim, + affinity="precomputed", + random_state=random_state, ).fit_transform(affinity_matrix) component_embedding /= component_embedding.max() @@ -143,18 +163,18 @@ def component_layout( def multi_component_layout( - data, - graph, - n_components, - component_labels, - dim, - random_state, - metric="euclidean", - metric_kwds={}, - init="random", - tol=0.0, - maxiter=0, -): + data: np.ndarray | None, + graph: scipy.sparse.spmatrix, + n_components: int, + component_labels: np.ndarray, + dim: int, + random_state: np.random.RandomState | np.random.Generator, + metric: str | Any = "euclidean", + metric_kwds: dict[str, Any] | None = None, + init: str = "random", + tol: float = 0.0, + maxiter: int = 0, +) -> np.ndarray: """Specialised layout algorithm for dealing with graphs with many connected components. This will first find relative positions for the components by spectrally embedding their centroids, then spectrally embed each individual connected component positioning @@ -204,8 +224,10 @@ def multi_component_layout( ------- embedding: array of shape (n_samples, dim) The initial embedding of ``graph``. - """ + """ + if metric_kwds is None: + metric_kwds = {} result = np.empty((graph.shape[0], dim), dtype=np.float32) if n_components > 2 * dim: @@ -261,17 +283,16 @@ def multi_component_layout( def spectral_layout( - data, - graph, - dim, - random_state, - metric="euclidean", - metric_kwds={}, - tol=0.0, - maxiter=0, -): - """ - Given a graph compute the spectral embedding of the graph. This is + data: np.ndarray | None, + graph: scipy.sparse.spmatrix, + dim: int, + random_state: np.random.RandomState | np.random.Generator, + metric: str | Any = "euclidean", + metric_kwds: dict[str, Any] | None = None, + tol: float = 0.0, + maxiter: int = 0, +) -> np.ndarray: + """Given a graph compute the spectral embedding of the graph. This is simply the eigenvectors of the laplacian of the graph. Here we use the normalized laplacian. @@ -300,7 +321,10 @@ def spectral_layout( ------- embedding: array of shape (n_vertices, dim) The spectral embedding of the graph. + """ + if metric_kwds is None: + metric_kwds = {} return _spectral_layout( data=data, graph=graph, @@ -315,16 +339,16 @@ def spectral_layout( def tswspectral_layout( - data, - graph, - dim, - random_state, - metric="euclidean", - metric_kwds={}, - method=None, - tol=0.0, - maxiter=0, -): + data: np.ndarray | None, + graph: scipy.sparse.spmatrix, + dim: int, + random_state: np.random.RandomState | np.random.Generator, + metric: str | Any = "euclidean", + metric_kwds: dict[str, Any] | None = None, + method: str | None = None, + tol: float = 0.0, + maxiter: int = 0, +) -> np.ndarray: """Given a graph, compute the spectral embedding of the graph. This is simply the eigenvectors of the Laplacian of the graph. Here we use the normalized laplacian and a truncated SVD-based guess of the @@ -377,7 +401,10 @@ def tswspectral_layout( ------- embedding: array of shape (n_vertices, dim) The spectral embedding of the graph. + """ + if metric_kwds is None: + metric_kwds = {} return _spectral_layout( data=data, graph=graph, @@ -393,17 +420,17 @@ def tswspectral_layout( def _spectral_layout( - data, - graph, - dim, - random_state, - metric="euclidean", - metric_kwds={}, - init="random", - method=None, - tol=0.0, - maxiter=0, -): + data: np.ndarray | None, + graph: scipy.sparse.spmatrix, + dim: int, + random_state: np.random.RandomState | np.random.Generator, + metric: str | Any = "euclidean", + metric_kwds: dict[str, Any] | None = None, + init: str = "random", + method: str | None = None, + tol: float = 0.0, + maxiter: int = 0, +) -> np.ndarray: """General implementation of the spectral embedding of the graph, derived as a subset of the eigenvectors of the normalized Laplacian of the graph. The numerical method for computing the eigendecomposition is chosen through heuristics. @@ -459,8 +486,11 @@ def _spectral_layout( ------- embedding: array of shape (n_vertices, dim) The spectral embedding of the graph. + """ - n_samples = graph.shape[0] + if metric_kwds is None: + metric_kwds = {} + graph.shape[0] n_components, labels = scipy.sparse.csgraph.connected_components(graph) if n_components > 1: @@ -476,15 +506,17 @@ def _spectral_layout( ) sqrt_deg = np.sqrt(np.asarray(graph.sum(axis=0)).squeeze()) - # standard Laplacian - # D = scipy.sparse.spdiags(diag_data, 0, graph.shape[0], graph.shape[0]) - # L = D - graph # Normalized Laplacian - I = scipy.sparse.identity(graph.shape[0], dtype=np.float64) - D = scipy.sparse.spdiags(1.0 / sqrt_deg, 0, graph.shape[0], graph.shape[0]) - L = I - D * graph * D - if not scipy.sparse.issparse(L): - L = np.asarray(L) + identity_matrix = scipy.sparse.identity(graph.shape[0], dtype=np.float64) + degree_matrix = scipy.sparse.spdiags( + 1.0 / sqrt_deg, + 0, + graph.shape[0], + graph.shape[0], + ) + laplacian = identity_matrix - degree_matrix * graph * degree_matrix + if not scipy.sparse.issparse(laplacian): + laplacian = np.asarray(laplacian) k = dim + 1 num_lanczos_vectors = max(2 * k + 1, int(np.sqrt(graph.shape[0]))) @@ -494,36 +526,39 @@ def _spectral_layout( else np.random.default_rng(seed=random_state) ) if not method: - method = "eigsh" if L.shape[0] < 2000000 else "lobpcg" + method = "eigsh" if laplacian.shape[0] < 2_000_000 else "lobpcg" try: if init == "random": - X = gen.normal(size=(L.shape[0], k)) + initial_guess = gen.normal(size=(laplacian.shape[0], k)) elif init == "tsvd": - X = TruncatedSVD( + initial_guess = TruncatedSVD( n_components=k, random_state=random_state, - # algorithm="arpack" - ).fit_transform(L) + ).fit_transform(laplacian) else: - raise ValueError( + msg = ( "The init parameter must be either 'random' or 'tsvd': " f"{init} is invalid." ) + raise ValueError( + msg, + ) # For such a normalized Laplacian, the first eigenvector is always # proportional to sqrt(degrees). We thus replace the first t-SVD guess # with the exact value. - X[:, 0] = sqrt_deg / np.linalg.norm(sqrt_deg) + initial_guess[:, 0] = sqrt_deg / np.linalg.norm(sqrt_deg) + max_iterations = 5 if method == "eigsh": eigenvalues, eigenvectors = scipy.sparse.linalg.eigsh( - L, + laplacian, k, which="SM", ncv=num_lanczos_vectors, tol=tol or 1e-4, - v0=np.ones(L.shape[0]), - maxiter=maxiter or graph.shape[0] * 5, + v0=np.ones(laplacian.shape[0]), + maxiter=maxiter or graph.shape[0] * max_iterations, ) elif method == "lobpcg": with warnings.catch_warnings(): @@ -533,14 +568,15 @@ def _spectral_layout( action="error", ) eigenvalues, eigenvectors = scipy.sparse.linalg.lobpcg( - L, - np.asarray(X), + laplacian, + np.asarray(initial_guess), largest=False, tol=tol or 1e-4, - maxiter=maxiter or 5 * graph.shape[0], + maxiter=maxiter or max_iterations * graph.shape[0], ) else: - raise ValueError("Method should either be None, 'eigsh' or 'lobpcg'") + msg = "Method should either be None, 'eigsh' or 'lobpcg'" + raise ValueError(msg) order = np.argsort(eigenvalues)[1:k] return eigenvectors[:, order] @@ -549,6 +585,7 @@ def _spectral_layout( "Spectral initialisation failed! The eigenvector solver\n" "failed. This is likely due to too small an eigengap. Consider\n" "adding some noise or jitter to your data.\n\n" - "Falling back to random initialisation!" + "Falling back to random initialisation!", + stacklevel=2, ) return gen.uniform(low=-10.0, high=10.0, size=(graph.shape[0], dim)) diff --git a/umap/tests/__init__.py b/umap/tests/__init__.py index 405c1957..9ec6bcdc 100644 --- a/umap/tests/__init__.py +++ b/umap/tests/__init__.py @@ -1,5 +1,4 @@ -""" -Test Suite for UMAP to ensure things are working as expected. +"""Test Suite for UMAP to ensure things are working as expected. The test suite comprises multiple testing modules, including multiple test cases related to a specific diff --git a/umap/tests/conftest.py b/umap/tests/conftest.py index 1ad9ab43..9988d627 100644 --- a/umap/tests/conftest.py +++ b/umap/tests/conftest.py @@ -2,10 +2,11 @@ # Testing (session) Fixture # ========================== -import pytest import numpy as np +import pytest from scipy import sparse from sklearn.datasets import load_iris + from umap import UMAP, AlignedUMAP # Globals, used for all the tests @@ -27,8 +28,7 @@ def spatial_data(): def binary_data(): binary_data = np.random.choice(a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66]) # Add some all zero data for corner case test - binary_data = np.vstack([binary_data, np.zeros((2, 20), dtype="bool")]) - return binary_data + return np.vstack([binary_data, np.zeros((2, 20), dtype="bool")]) # Sparse Spatial and Binary Data @@ -48,21 +48,21 @@ def sparse_binary_data(binary_data): @pytest.fixture(scope="session") def nn_data(): nn_data = np.random.uniform(0, 1, size=(1000, 5)) - nn_data = np.vstack( - [nn_data, np.zeros((2, 5))] + return np.vstack( + [nn_data, np.zeros((2, 5))], ) # Add some all zero data for corner case test - return nn_data @pytest.fixture(scope="session") def binary_nn_data(): binary_nn_data = np.random.choice( - a=[False, True], size=(1000, 5), p=[0.66, 1 - 0.66] + a=[False, True], + size=(1000, 5), + p=[0.66, 1 - 0.66], ) - binary_nn_data = np.vstack( - [binary_nn_data, np.zeros((2, 5), dtype="bool")] + return np.vstack( + [binary_nn_data, np.zeros((2, 5), dtype="bool")], ) # Add some all zero data for corner case test - return binary_nn_data @pytest.fixture(scope="session") @@ -88,32 +88,30 @@ def repetition_dense(): [1, 1, 1, 1], [1, 2, 3, 4], [1, 1, 2, 1], - ] + ], ) @pytest.fixture(scope="session") def spatial_repeats(spatial_data): # spatial data repeats - spatial_repeats = np.vstack( - [np.repeat(spatial_data[0:2], [2, 0], axis=0), spatial_data, np.zeros((2, 20))] + return np.vstack( + [np.repeat(spatial_data[0:2], [2, 0], axis=0), spatial_data, np.zeros((2, 20))], ) # Add some all zero data for corner case test. Make the first three rows identical # binary Data Repeat - return spatial_repeats @pytest.fixture(scope="session") def binary_repeats(binary_data): - binary_repeats = np.vstack( + return np.vstack( [ np.repeat(binary_data[0:2], [2, 0], axis=0), binary_data, np.zeros((2, 20), dtype="bool"), - ] + ], ) # Add some all zero data for corner case test. Make the first three rows identical - return binary_repeats @pytest.fixture(scope="session") @@ -171,7 +169,7 @@ def iris_model_large(iris): @pytest.fixture(scope="session") def iris_subset_model(iris, iris_selection): return UMAP(n_neighbors=10, min_dist=0.01, random_state=42).fit( - iris.data[iris_selection] + iris.data[iris_selection], ) @@ -188,13 +186,14 @@ def iris_subset_model_large(iris, iris_selection): @pytest.fixture(scope="session") def supervised_iris_model(iris): return UMAP(n_neighbors=10, min_dist=0.01, n_epochs=200, random_state=42).fit( - iris.data, iris.target + iris.data, + iris.target, ) @pytest.fixture(scope="session") def aligned_iris_model(aligned_iris, aligned_iris_relations): - data, target = aligned_iris + data, _target = aligned_iris model = AlignedUMAP() model.fit(data, relations=aligned_iris_relations) return model diff --git a/umap/tests/test_aligned_umap.py b/umap/tests/test_aligned_umap.py index 6e7325c7..d1ef85cc 100644 --- a/umap/tests/test_aligned_umap.py +++ b/umap/tests/test_aligned_umap.py @@ -1,9 +1,9 @@ +import numpy as np import pytest -from umap import AlignedUMAP -from sklearn.metrics import pairwise_distances from sklearn.cluster import KMeans -import numpy as np -from sklearn.metrics import adjusted_rand_score +from sklearn.metrics import adjusted_rand_score, pairwise_distances + +from umap import AlignedUMAP # =============================== # Test AlignedUMAP on sliced iris @@ -17,8 +17,8 @@ def nn_accuracy(true_nn, embd_nn): return num_correct / true_nn.size -def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model): - data, target = aligned_iris +def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model) -> None: + data, _target = aligned_iris for i, slice in enumerate(data): data_dmat = pairwise_distances(slice) true_nn = np.argsort(data_dmat, axis=1)[:, :10] @@ -27,8 +27,8 @@ def test_neighbor_local_neighbor_accuracy(aligned_iris, aligned_iris_model): assert nn_accuracy(true_nn, embd_nn) >= 0.65 -def test_local_clustering(aligned_iris, aligned_iris_model): - data, target = aligned_iris +def test_local_clustering(aligned_iris, aligned_iris_model) -> None: + _data, target = aligned_iris embd = aligned_iris_model.embeddings_[1] clusters = KMeans(n_clusters=2).fit_predict(embd) @@ -41,8 +41,8 @@ def test_local_clustering(aligned_iris, aligned_iris_model): assert ari >= 0.40 -def test_aligned_update(aligned_iris, aligned_iris_relations): - data, target = aligned_iris +def test_aligned_update(aligned_iris, aligned_iris_relations) -> None: + data, _target = aligned_iris small_aligned_model = AlignedUMAP() small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2]) small_aligned_model.update(data[3], relations=aligned_iris_relations[2]) @@ -54,13 +54,15 @@ def test_aligned_update(aligned_iris, aligned_iris_relations): assert nn_accuracy(true_nn, embd_nn) >= 0.45 -def test_aligned_update_params(aligned_iris, aligned_iris_relations): - data, target = aligned_iris +def test_aligned_update_params(aligned_iris, aligned_iris_relations) -> None: + data, _target = aligned_iris n_neighbors = [15, 15, 15, 15, 15] small_aligned_model = AlignedUMAP(n_neighbors=n_neighbors[:3]) small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2]) small_aligned_model.update( - data[3], relations=aligned_iris_relations[2], n_neighbors=n_neighbors[3] + data[3], + relations=aligned_iris_relations[2], + n_neighbors=n_neighbors[3], ) for i, slice in enumerate(data[:4]): data_dmat = pairwise_distances(slice) @@ -71,13 +73,15 @@ def test_aligned_update_params(aligned_iris, aligned_iris_relations): @pytest.mark.skip(reason="Temporarily disable") -def test_aligned_update_array_error(aligned_iris, aligned_iris_relations): - data, target = aligned_iris +def test_aligned_update_array_error(aligned_iris, aligned_iris_relations) -> None: + data, _target = aligned_iris n_neighbors = [15, 15, 15, 15, 15] small_aligned_model = AlignedUMAP(n_neighbors=n_neighbors[:3]) small_aligned_model.fit(data[:3], relations=aligned_iris_relations[:2]) with pytest.raises(ValueError): small_aligned_model.update( - data[3:], relations=aligned_iris_relations[2:], n_neighbors=n_neighbors[3:] + data[3:], + relations=aligned_iris_relations[2:], + n_neighbors=n_neighbors[3:], ) diff --git a/umap/tests/test_chunked_parallel_spatial_metric.py b/umap/tests/test_chunked_parallel_spatial_metric.py index de227152..46f7b922 100644 --- a/umap/tests/test_chunked_parallel_spatial_metric.py +++ b/umap/tests/test_chunked_parallel_spatial_metric.py @@ -1,12 +1,15 @@ -import pytest -import numba import os + +import numba import numpy as np +import pytest from numpy.testing import assert_array_equal + from umap import distances as dist benchmark_only = pytest.mark.skipif( - "BENCHARM_TEST" not in os.environ, reason="Benchmark tests skipped" + "BENCHARM_TEST" not in os.environ, + reason="Benchmark tests skipped", ) # Constants for benchmark WARMUP_ROUNDS = 5 @@ -18,11 +21,14 @@ # -------- -@pytest.fixture(scope="function") +@pytest.fixture def stashed_previous_impl_for_regression_test(): @numba.njit(parallel=True, nogil=True) def stashed_chunked_parallel_special_metric( - X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16 + X, + Y=None, + metric=dist.named_distances["hellinger"], + chunk_size=16, ): if Y is None: row_size = col_size = X.shape[0] @@ -68,11 +74,14 @@ def stashed_chunked_parallel_special_metric( return stashed_chunked_parallel_special_metric -@pytest.fixture(scope="function") +@pytest.fixture def workaround_590_impl(): @numba.njit(parallel=True, nogil=True) def chunked_parallel_special_metric( - X, Y=None, metric=dist.named_distances["hellinger"], chunk_size=16 + X, + Y=None, + metric=dist.named_distances["hellinger"], + chunk_size=16, ): if Y is None: size = X.shape[0] @@ -117,7 +126,7 @@ def chunked_parallel_special_metric( return chunked_parallel_special_metric -@pytest.fixture(scope="function") +@pytest.fixture def benchmark_data(request): shape = request.param spatial_data = np.random.randn(*shape).astype(np.float32) @@ -129,17 +138,20 @@ def benchmark_data(request): @benchmark_only def test_chunked_parallel_alternative_implementations( - spatial_data, workaround_590_impl -): + spatial_data, + workaround_590_impl, +) -> None: # Base tests that must pass! dist_matrix_x = workaround_590_impl(np.abs(spatial_data[:-2])) dist_matrix_xy = workaround_590_impl( - np.abs(spatial_data[:-2]), np.abs(spatial_data[:-2]) + np.abs(spatial_data[:-2]), + np.abs(spatial_data[:-2]), ) dist_matrix_x_full = dist.chunked_parallel_special_metric(np.abs(spatial_data[:-2])) dist_matrix_xy_full = dist.chunked_parallel_special_metric( - np.abs(spatial_data[:-2]), np.abs(spatial_data[:-2]) + np.abs(spatial_data[:-2]), + np.abs(spatial_data[:-2]), ) assert_array_equal( @@ -159,12 +171,12 @@ def test_chunked_parallel_alternative_implementations( def test_chunked_parallel_special_metric_implementation_hellinger( spatial_data, stashed_previous_impl_for_regression_test, -): - +) -> None: # Base tests that must pass! dist_matrix_x = dist.chunked_parallel_special_metric(np.abs(spatial_data[:-2])) dist_matrix_xy = dist.chunked_parallel_special_metric( - np.abs(spatial_data[:-2]), np.abs(spatial_data[:-2]) + np.abs(spatial_data[:-2]), + np.abs(spatial_data[:-2]), ) test_matrix = np.array( [ @@ -173,7 +185,7 @@ def test_chunked_parallel_special_metric_implementation_hellinger( for j in range(spatial_data.shape[0] - 2) ] for i in range(spatial_data.shape[0] - 2) - ] + ], ).astype(np.float32) assert_array_equal( @@ -190,10 +202,11 @@ def test_chunked_parallel_special_metric_implementation_hellinger( # Test to compare chunked_parallel different implementations dist_x_stashed = stashed_previous_impl_for_regression_test( - np.abs(spatial_data[:-2]) + np.abs(spatial_data[:-2]), ) dist_xy_stashed = stashed_previous_impl_for_regression_test( - np.abs(spatial_data[:-2]), np.abs(spatial_data[:-2]) + np.abs(spatial_data[:-2]), + np.abs(spatial_data[:-2]), ) assert_array_equal( @@ -211,10 +224,12 @@ def test_chunked_parallel_special_metric_implementation_hellinger( # test hellinger on different X and Y Pair spatial_data_two = np.random.randn(10, 20) dist_stashed_diff_pair = stashed_previous_impl_for_regression_test( - np.abs(spatial_data[:-2]), spatial_data_two + np.abs(spatial_data[:-2]), + spatial_data_two, ) dist_chunked_diff_pair = dist.chunked_parallel_special_metric( - np.abs(spatial_data[:-2]), spatial_data_two + np.abs(spatial_data[:-2]), + spatial_data_two, ) assert_array_equal( @@ -242,8 +257,7 @@ def test_chunked_parallel_special_metric_implementation_hellinger( def test_benchmark_chunked_parallel_special_metric_x_only( benchmark, benchmark_data, -): - +) -> None: # single argument benchmark.pedantic( dist.chunked_parallel_special_metric, @@ -267,8 +281,7 @@ def test_benchmark_workaround_590_x_only( benchmark, benchmark_data, workaround_590_impl, -): - +) -> None: # single argument benchmark.pedantic( workaround_590_impl, @@ -296,8 +309,7 @@ def test_benchmark_workaround_590_x_only( def test_benchmark_chunked_parallel_special_metric_x_y( benchmark, benchmark_data, -): - +) -> None: # single argument benchmark.pedantic( dist.chunked_parallel_special_metric, @@ -321,8 +333,7 @@ def test_benchmark_workaround_590_x_y( benchmark, benchmark_data, workaround_590_impl, -): - +) -> None: # single argument benchmark.pedantic( workaround_590_impl, diff --git a/umap/tests/test_composite_models.py b/umap/tests/test_composite_models.py index c8e662b1..068f7467 100644 --- a/umap/tests/test_composite_models.py +++ b/umap/tests/test_composite_models.py @@ -1,6 +1,7 @@ -from umap import UMAP import pytest +from umap import UMAP + try: # works for sklearn>=0.22 from sklearn.manifold import trustworthiness @@ -12,7 +13,7 @@ from sklearn.manifold.t_sne import trustworthiness -def test_composite_trustworthiness(nn_data, iris_model): +def test_composite_trustworthiness(nn_data, iris_model) -> None: data = nn_data[:50] model1 = UMAP(n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=50).fit(data) model2 = UMAP( @@ -24,14 +25,10 @@ def test_composite_trustworthiness(nn_data, iris_model): ).fit(data) model3 = model1 * model2 trust = trustworthiness(data, model3.embedding_, n_neighbors=10) - assert ( - trust >= 0.80 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.80, f"Insufficiently trustworthy embedding fornn dataset: {trust}" model4 = model1 + model2 trust = trustworthiness(data, model4.embedding_, n_neighbors=10) - assert ( - trust >= 0.80 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.80, f"Insufficiently trustworthy embedding fornn dataset: {trust}" with pytest.raises(ValueError): _ = model1 + iris_model @@ -44,7 +41,7 @@ def test_composite_trustworthiness(nn_data, iris_model): @pytest.mark.skip(reason="Marked as Skipped test") -def test_composite_trustworthiness_random_init(nn_data): # pragma: no cover +def test_composite_trustworthiness_random_init(nn_data) -> None: # pragma: no cover data = nn_data[:50] model1 = UMAP( n_neighbors=10, @@ -62,17 +59,13 @@ def test_composite_trustworthiness_random_init(nn_data): # pragma: no cover ).fit(data) model3 = model1 * model2 trust = trustworthiness(data, model3.embedding_, n_neighbors=10) - assert ( - trust >= 0.82 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.82, f"Insufficiently trustworthy embedding fornn dataset: {trust}" model4 = model1 + model2 trust = trustworthiness(data, model4.embedding_, n_neighbors=10) - assert ( - trust >= 0.82 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.82, f"Insufficiently trustworthy embedding fornn dataset: {trust}" -def test_composite_trustworthiness_on_iris(iris): +def test_composite_trustworthiness_on_iris(iris) -> None: iris_model1 = UMAP( n_neighbors=10, min_dist=0.01, @@ -87,17 +80,17 @@ def test_composite_trustworthiness_on_iris(iris): ).fit(iris.data[:, 2:]) embedding = (iris_model1 + iris_model2).embedding_ trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.82 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.82, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) embedding = (iris_model1 * iris_model2).embedding_ trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.82 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.82, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) -def test_contrastive_trustworthiness_on_iris(iris): +def test_contrastive_trustworthiness_on_iris(iris) -> None: iris_model1 = UMAP( n_neighbors=10, min_dist=0.01, @@ -112,6 +105,6 @@ def test_contrastive_trustworthiness_on_iris(iris): ).fit(iris.data[:, 2:]) embedding = (iris_model1 - iris_model2).embedding_ trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.75 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.75, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) diff --git a/umap/tests/test_data_input.py b/umap/tests/test_data_input.py index daacfffa..a4cfe4c1 100644 --- a/umap/tests/test_data_input.py +++ b/umap/tests/test_data_input.py @@ -1,6 +1,7 @@ import numpy as np -import pytest as pytest +import pytest from numba import njit + from umap import UMAP @@ -21,9 +22,8 @@ def nan_dist(a: np.ndarray, b: np.ndarray): return 0, a -def test_check_input_data(all_finite_data, inverse_data): - """ - Data input to UMAP gets checked for liability. +def test_check_input_data(all_finite_data, inverse_data) -> None: + """Data input to UMAP gets checked for liability. This tests checks the if data input is dismissed/accepted according to the "ensure_all_finite" keyword as used by sklearn. @@ -44,7 +44,7 @@ def test_check_input_data(all_finite_data, inverse_data): inf_nan_data[1] = np.inf # wrapper to call each data handling function of UMAP in a convenient way - def call_umap_functions(data, ensure_all_finite): + def call_umap_functions(data, ensure_all_finite) -> None: u = UMAP(metric=nan_dist) if ensure_all_finite is None: u.fit_transform(data) diff --git a/umap/tests/test_densmap.py b/umap/tests/test_densmap.py index f9fdeaa7..2829c4a6 100644 --- a/umap/tests/test_densmap.py +++ b/umap/tests/test_densmap.py @@ -1,6 +1,7 @@ -from umap import UMAP import pytest +from umap import UMAP + try: # works for sklearn>=0.22 from sklearn.manifold import trustworthiness @@ -12,9 +13,9 @@ from sklearn.manifold.t_sne import trustworthiness -def test_densmap_trustworthiness(nn_data): +def test_densmap_trustworthiness(nn_data) -> None: data = nn_data[:50] - embedding, rad_h, rad_l = UMAP( + embedding, _rad_h, _rad_l = UMAP( n_neighbors=10, min_dist=0.01, random_state=42, @@ -23,13 +24,11 @@ def test_densmap_trustworthiness(nn_data): output_dens=True, ).fit_transform(data) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.72 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.72, f"Insufficiently trustworthy embedding fornn dataset: {trust}" -@pytest.mark.skip() -def test_densmap_trustworthiness_random_init(nn_data): # pragma: no cover +@pytest.mark.skip +def test_densmap_trustworthiness_random_init(nn_data) -> None: # pragma: no cover data = nn_data[:50] embedding = UMAP( n_neighbors=10, @@ -39,12 +38,10 @@ def test_densmap_trustworthiness_random_init(nn_data): # pragma: no cover densmap=True, ).fit_transform(data) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.75 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.75, f"Insufficiently trustworthy embedding fornn dataset: {trust}" -def test_densmap_trustworthiness_on_iris(iris): +def test_densmap_trustworthiness_on_iris(iris) -> None: densmap_iris_model = UMAP( n_neighbors=10, min_dist=0.01, @@ -54,9 +51,9 @@ def test_densmap_trustworthiness_on_iris(iris): ).fit(iris.data) embedding = densmap_iris_model.embedding_ trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.97 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.97, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) with pytest.raises(NotImplementedError): densmap_iris_model.transform(iris.data[:10]) @@ -65,7 +62,7 @@ def test_densmap_trustworthiness_on_iris(iris): densmap_iris_model.inverse_transform(embedding[:10]) -def test_densmap_trustworthiness_on_iris_supervised(iris): +def test_densmap_trustworthiness_on_iris_supervised(iris) -> None: densmap_iris_model = UMAP( n_neighbors=10, min_dist=0.01, @@ -75,6 +72,6 @@ def test_densmap_trustworthiness_on_iris_supervised(iris): ).fit(iris.data, y=iris.target) embedding = densmap_iris_model.embedding_ trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.97 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.97, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) diff --git a/umap/tests/test_parametric_umap.py b/umap/tests/test_parametric_umap.py index e570293a..d443d9d1 100644 --- a/umap/tests/test_parametric_umap.py +++ b/umap/tests/test_parametric_umap.py @@ -1,10 +1,11 @@ -import numpy as np +import platform import tempfile + +import numpy as np import pytest +from numpy.testing import assert_array_almost_equal from sklearn.datasets import make_moons from sklearn.model_selection import train_test_split -from numpy.testing import assert_array_almost_equal -import platform try: import tensorflow as tf @@ -17,7 +18,8 @@ tf_only = pytest.mark.skipif(not IMPORT_TF, reason="TensorFlow >= 2.0 is not installed") not_windows = pytest.mark.skipif( - platform.system() == "Windows", reason="Windows file access issues" + platform.system() == "Windows", + reason="Windows file access issues", ) @@ -28,8 +30,8 @@ def moon_dataset(): @tf_only -def test_create_model(moon_dataset): - """test a simple parametric UMAP network""" +def test_create_model(moon_dataset) -> None: + """Test a simple parametric UMAP network.""" embedder = ParametricUMAP() embedding = embedder.fit_transform(moon_dataset) # completes successfully @@ -38,8 +40,8 @@ def test_create_model(moon_dataset): @tf_only -def test_global_loss(moon_dataset): - """test a simple parametric UMAP network""" +def test_global_loss(moon_dataset) -> None: + """Test a simple parametric UMAP network.""" embedder = ParametricUMAP(global_correlation_loss_weight=1.0) embedding = embedder.fit_transform(moon_dataset) # completes successfully @@ -48,8 +50,8 @@ def test_global_loss(moon_dataset): @tf_only -def test_inverse_transform(moon_dataset): - """tests inverse_transform""" +def test_inverse_transform(moon_dataset) -> None: + """Tests inverse_transform.""" def norm(x): return (x - np.min(x)) / (np.max(x) - np.min(x)) @@ -64,8 +66,8 @@ def norm(x): @tf_only -def test_custom_encoder_decoder(moon_dataset): - """test using a custom encoder / decoder""" +def test_custom_encoder_decoder(moon_dataset) -> None: + """Test using a custom encoder / decoder.""" dims = (2,) n_components = 2 encoder = tf.keras.Sequential( @@ -76,7 +78,7 @@ def test_custom_encoder_decoder(moon_dataset): tf.keras.layers.Dense(units=100, activation="relu"), tf.keras.layers.Dense(units=100, activation="relu"), tf.keras.layers.Dense(units=n_components, name="z"), - ] + ], ) decoder = tf.keras.Sequential( @@ -86,10 +88,12 @@ def test_custom_encoder_decoder(moon_dataset): tf.keras.layers.Dense(units=100, activation="relu"), tf.keras.layers.Dense(units=100, activation="relu"), tf.keras.layers.Dense( - units=np.prod(dims), name="recon", activation=None + units=np.prod(dims), + name="recon", + activation=None, ), tf.keras.layers.Reshape(dims), - ] + ], ) embedder = ParametricUMAP( @@ -106,11 +110,13 @@ def test_custom_encoder_decoder(moon_dataset): @tf_only -def test_validation(moon_dataset): - """tests adding a validation dataset""" +def test_validation(moon_dataset) -> None: + """Tests adding a validation dataset.""" X_train, X_valid = train_test_split(moon_dataset, train_size=0.5) embedder = ParametricUMAP( - parametric_reconstruction=True, reconstruction_validation=X_valid, verbose=True + parametric_reconstruction=True, + reconstruction_validation=X_valid, + verbose=True, ) embedding = embedder.fit_transform(X_train) # completes successfully @@ -120,9 +126,8 @@ def test_validation(moon_dataset): @not_windows @tf_only -def test_save_load(moon_dataset): - """tests saving and loading""" - +def test_save_load(moon_dataset) -> None: + """Tests saving and loading.""" embedder = ParametricUMAP() embedding = embedder.fit_transform(moon_dataset) # completes successfully diff --git a/umap/tests/test_plot.py b/umap/tests/test_plot.py index effcbc10..208c6c5e 100644 --- a/umap/tests/test_plot.py +++ b/umap/tests/test_plot.py @@ -1,5 +1,6 @@ import numpy as np import pytest + import umap # Globals, used for all the tests @@ -25,7 +26,7 @@ def mapper(iris): # needed as there is no assertion nor # property verification. @plot_only -def test_plot_runs_at_all(mapper, iris, iris_selection): +def test_plot_runs_at_all(mapper, iris, iris_selection) -> None: from umap import plot as umap_plot umap_plot.points(mapper) diff --git a/umap/tests/test_spectral.py b/umap/tests/test_spectral.py index 82d54505..dd052286 100644 --- a/umap/tests/test_spectral.py +++ b/umap/tests/test_spectral.py @@ -1,10 +1,10 @@ -from umap.spectral import spectral_layout, tswspectral_layout +import re import numpy as np import pytest -import re from scipy.version import full_version as scipy_full_version_ -from warnings import catch_warnings + +from umap.spectral import spectral_layout, tswspectral_layout scipy_full_version = tuple( int(n) @@ -16,7 +16,7 @@ scipy_full_version < (1, 10) or scipy_full_version >= (1, 15), reason="SciPy installing with Python 3.7 does not converge under same circumstances", ) -def test_tsw_spectral_init(iris): +def test_tsw_spectral_init(iris) -> None: # create an arbitrary (dense) random affinity matrix seed = 42 rng = np.random.default_rng(seed=seed) @@ -30,16 +30,16 @@ def test_tsw_spectral_init(iris): # Make sure the two methods produce similar embeddings. rmsd = np.mean(np.sum((spec - tsw_spec) ** 2, axis=1)) - assert ( - rmsd < 1e-6 - ), "tsvd-warmed spectral init insufficiently close to standard spectral init" + assert rmsd < 1e-6, ( + "tsvd-warmed spectral init insufficiently close to standard spectral init" + ) @pytest.mark.skipif( scipy_full_version < (1, 10), reason="SciPy installing with Py 3.7 does not warn reliably on convergence failure", ) -def test_ensure_fallback_to_random_on_spectral_failure(): +def test_ensure_fallback_to_random_on_spectral_failure() -> None: dim = 1000 k = 10 assert k >= 10 diff --git a/umap/tests/test_umap_get_feature_names_out.py b/umap/tests/test_umap_get_feature_names_out.py index e84d0de8..892f86c4 100644 --- a/umap/tests/test_umap_get_feature_names_out.py +++ b/umap/tests/test_umap_get_feature_names_out.py @@ -1,11 +1,11 @@ import numpy as np from sklearn.datasets import make_classification -from sklearn.pipeline import Pipeline, FeatureUnion +from sklearn.pipeline import FeatureUnion, Pipeline -from ..umap_ import UMAP +from umap.umap_ import UMAP -def test_get_feature_names_out(): +def test_get_feature_names_out() -> None: X, _ = make_classification(n_samples=30, n_features=10, random_state=42) umap = UMAP( n_neighbors=10, @@ -21,7 +21,7 @@ def test_get_feature_names_out(): np.testing.assert_array_equal(feature_names_out, expected) -def test_get_feature_names_out_default(): +def test_get_feature_names_out_default() -> None: X, _ = make_classification(n_samples=30, n_features=10, random_state=42) umap = UMAP( n_neighbors=10, @@ -36,7 +36,7 @@ def test_get_feature_names_out_default(): np.testing.assert_array_equal(default_result, expected_default_result) -def test_get_feature_names_out_multicomponent(): +def test_get_feature_names_out_multicomponent() -> None: # The output length should be equal to the number of components UMAP generates. X, _ = make_classification(n_samples=30, n_features=10, random_state=42) umap = UMAP( @@ -52,8 +52,7 @@ def test_get_feature_names_out_multicomponent(): np.testing.assert_array_equal(result_umap, expected_umap_result) - -def test_get_feature_names_out_featureunion(): +def test_get_feature_names_out_featureunion() -> None: X, _ = make_classification(n_samples=30, n_features=10, random_state=42) pipeline = Pipeline( [ @@ -63,10 +62,10 @@ def test_get_feature_names_out_featureunion(): [ ("umap1", UMAP(n_components=2)), ("umap2", UMAP(n_components=3)), - ] + ], ), - ) - ] + ), + ], ) pipeline.fit(X) @@ -78,6 +77,6 @@ def test_get_feature_names_out_featureunion(): "umap2__umap0", "umap2__umap1", "umap2__umap2", - ] + ], ) np.testing.assert_array_equal(feature_names, expected_feature_names) diff --git a/umap/tests/test_umap_metrics.py b/umap/tests/test_umap_metrics.py index 39d23366..61986bea 100644 --- a/umap/tests/test_umap_metrics.py +++ b/umap/tests/test_umap_metrics.py @@ -1,14 +1,14 @@ +import re + import numpy as np +import pytest from numpy.testing import assert_array_almost_equal -import umap.distances as dist -import umap.sparse as spdist - -import re +from scipy.version import full_version as scipy_full_version_ from sklearn.metrics import pairwise_distances from sklearn.neighbors import BallTree -from scipy.version import full_version as scipy_full_version_ -import pytest +import umap.distances as dist +import umap.sparse as spdist scipy_full_version = tuple( int(n) @@ -25,8 +25,8 @@ # ---------------------------------- -def run_test_metric(metric, test_data, dist_matrix, with_grad=False): - """Core utility function to test target metric on test data""" +def run_test_metric(metric, test_data, dist_matrix, with_grad=False) -> None: + """Core utility function to test target metric on test data.""" if with_grad: dist_function = dist.named_distances_with_gradients[metric] else: @@ -44,11 +44,11 @@ def run_test_metric(metric, test_data, dist_matrix, with_grad=False): assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric {}".format(metric), + err_msg=f"Distances don't match for metric {metric}", ) -def spatial_check(metric, spatial_data, spatial_distances, with_grad=False): +def spatial_check(metric, spatial_data, spatial_distances, with_grad=False) -> None: # Check that metric is supported for this test, otherwise, fail! assert metric in spatial_distances, f"{metric} not valid for spatial data" dist_matrix = pairwise_distances(spatial_data, metric=metric) @@ -65,7 +65,7 @@ def spatial_check(metric, spatial_data, spatial_distances, with_grad=False): run_test_metric(metric, spatial_data, dist_matrix, with_grad=with_grad) -def binary_check(metric, binary_data, binary_distances): +def binary_check(metric, binary_data, binary_distances) -> None: # Check that metric is supported for this test, otherwise, fail! assert metric in binary_distances, f"{metric} not valid for binary data" dist_matrix = pairwise_distances(binary_data, metric=metric) @@ -82,8 +82,8 @@ def binary_check(metric, binary_data, binary_distances): run_test_metric(metric, binary_data, dist_matrix) -def run_test_sparse_metric(metric, sparse_test_data, dist_matrix): - """Core utility function to run test of target metric on sparse data""" +def run_test_sparse_metric(metric, sparse_test_data, dist_matrix) -> None: + """Core utility function to run test of target metric on sparse data.""" dist_function = spdist.sparse_named_distances[metric] if metric in spdist.sparse_need_n_features: test_matrix = np.array( @@ -99,7 +99,7 @@ def run_test_sparse_metric(metric, sparse_test_data, dist_matrix): for j in range(sparse_test_data.shape[0]) ] for i in range(sparse_test_data.shape[0]) - ] + ], ) else: test_matrix = np.array( @@ -114,22 +114,23 @@ def run_test_sparse_metric(metric, sparse_test_data, dist_matrix): for j in range(sparse_test_data.shape[0]) ] for i in range(sparse_test_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Sparse distances don't match " "for metric {}".format(metric), + err_msg=f"Sparse distances don't match for metric {metric}", ) -def sparse_spatial_check(metric, sparse_spatial_data): +def sparse_spatial_check(metric, sparse_spatial_data) -> None: # Check that metric is supported for this test, otherwise, fail! - assert ( - metric in spdist.sparse_named_distances - ), f"{metric} not supported for sparse data" + assert metric in spdist.sparse_named_distances, ( + f"{metric} not supported for sparse data" + ) dist_matrix = pairwise_distances( - np.asarray(sparse_spatial_data.todense()), metric=metric + np.asarray(sparse_spatial_data.todense()), + metric=metric, ) if metric in ("braycurtis", "dice", "sokalsneath", "yule"): @@ -144,13 +145,14 @@ def sparse_spatial_check(metric, sparse_spatial_data): run_test_sparse_metric(metric, sparse_spatial_data, dist_matrix) -def sparse_binary_check(metric, sparse_binary_data): +def sparse_binary_check(metric, sparse_binary_data) -> None: # Check that metric is supported for this test, otherwise, fail! - assert ( - metric in spdist.sparse_named_distances - ), f"{metric} not supported for sparse data" + assert metric in spdist.sparse_named_distances, ( + f"{metric} not supported for sparse data" + ) dist_matrix = pairwise_distances( - np.asarray(sparse_binary_data.todense()), metric=metric + np.asarray(sparse_binary_data.todense()), + metric=metric, ) if metric in ("jaccard", "dice", "sokalsneath", "yule"): dist_matrix[np.where(~np.isfinite(dist_matrix))] = 0.0 @@ -169,39 +171,39 @@ def sparse_binary_check(metric, sparse_binary_data): # -------------------- -def test_euclidean(spatial_data, spatial_distances): +def test_euclidean(spatial_data, spatial_distances) -> None: spatial_check("euclidean", spatial_data, spatial_distances) -def test_manhattan(spatial_data, spatial_distances): +def test_manhattan(spatial_data, spatial_distances) -> None: spatial_check("manhattan", spatial_data, spatial_distances) -def test_chebyshev(spatial_data, spatial_distances): +def test_chebyshev(spatial_data, spatial_distances) -> None: spatial_check("chebyshev", spatial_data, spatial_distances) -def test_minkowski(spatial_data, spatial_distances): +def test_minkowski(spatial_data, spatial_distances) -> None: spatial_check("minkowski", spatial_data, spatial_distances) -def test_hamming(spatial_data, spatial_distances): +def test_hamming(spatial_data, spatial_distances) -> None: spatial_check("hamming", spatial_data, spatial_distances) -def test_canberra(spatial_data, spatial_distances): +def test_canberra(spatial_data, spatial_distances) -> None: spatial_check("canberra", spatial_data, spatial_distances) -def test_braycurtis(spatial_data, spatial_distances): +def test_braycurtis(spatial_data, spatial_distances) -> None: spatial_check("braycurtis", spatial_data, spatial_distances) -def test_cosine(spatial_data, spatial_distances): +def test_cosine(spatial_data, spatial_distances) -> None: spatial_check("cosine", spatial_data, spatial_distances) -def test_correlation(spatial_data, spatial_distances): +def test_correlation(spatial_data, spatial_distances) -> None: spatial_check("correlation", spatial_data, spatial_distances) @@ -210,42 +212,43 @@ def test_correlation(spatial_data, spatial_distances): # -------------------- -def test_jaccard(binary_data, binary_distances): +def test_jaccard(binary_data, binary_distances) -> None: binary_check("jaccard", binary_data, binary_distances) -def test_matching(binary_data, binary_distances): +def test_matching(binary_data, binary_distances) -> None: binary_check("matching", binary_data, binary_distances) -def test_dice(binary_data, binary_distances): +def test_dice(binary_data, binary_distances) -> None: binary_check("dice", binary_data, binary_distances) @pytest.mark.skipif( - scipy_full_version >= (1, 9), reason="deprecated in SciPy 1.9, removed in 1.11" + scipy_full_version >= (1, 9), + reason="deprecated in SciPy 1.9, removed in 1.11", ) -def test_kulsinski(binary_data, binary_distances): +def test_kulsinski(binary_data, binary_distances) -> None: binary_check("kulsinski", binary_data, binary_distances) -def test_rogerstanimoto(binary_data, binary_distances): +def test_rogerstanimoto(binary_data, binary_distances) -> None: binary_check("rogerstanimoto", binary_data, binary_distances) -def test_russellrao(binary_data, binary_distances): +def test_russellrao(binary_data, binary_distances) -> None: binary_check("russellrao", binary_data, binary_distances) -def test_sokalmichener(binary_data, binary_distances): +def test_sokalmichener(binary_data, binary_distances) -> None: binary_check("sokalmichener", binary_data, binary_distances) -def test_sokalsneath(binary_data, binary_distances): +def test_sokalsneath(binary_data, binary_distances) -> None: binary_check("sokalsneath", binary_data, binary_distances) -def test_yule(binary_data, binary_distances): +def test_yule(binary_data, binary_distances) -> None: binary_check("yule", binary_data, binary_distances) @@ -254,39 +257,39 @@ def test_yule(binary_data, binary_distances): # --------------------------- -def test_sparse_euclidean(sparse_spatial_data): +def test_sparse_euclidean(sparse_spatial_data) -> None: sparse_spatial_check("euclidean", sparse_spatial_data) -def test_sparse_manhattan(sparse_spatial_data): +def test_sparse_manhattan(sparse_spatial_data) -> None: sparse_spatial_check("manhattan", sparse_spatial_data) -def test_sparse_chebyshev(sparse_spatial_data): +def test_sparse_chebyshev(sparse_spatial_data) -> None: sparse_spatial_check("chebyshev", sparse_spatial_data) -def test_sparse_minkowski(sparse_spatial_data): +def test_sparse_minkowski(sparse_spatial_data) -> None: sparse_spatial_check("minkowski", sparse_spatial_data) -def test_sparse_hamming(sparse_spatial_data): +def test_sparse_hamming(sparse_spatial_data) -> None: sparse_spatial_check("hamming", sparse_spatial_data) -def test_sparse_canberra(sparse_spatial_data): +def test_sparse_canberra(sparse_spatial_data) -> None: sparse_spatial_check("canberra", sparse_spatial_data) -def test_sparse_cosine(sparse_spatial_data): +def test_sparse_cosine(sparse_spatial_data) -> None: sparse_spatial_check("cosine", sparse_spatial_data) -def test_sparse_correlation(sparse_spatial_data): +def test_sparse_correlation(sparse_spatial_data) -> None: sparse_spatial_check("correlation", sparse_spatial_data) -def test_sparse_braycurtis(sparse_spatial_data): +def test_sparse_braycurtis(sparse_spatial_data) -> None: sparse_spatial_check("braycurtis", sparse_spatial_data) @@ -295,45 +298,46 @@ def test_sparse_braycurtis(sparse_spatial_data): # --------------------------- -def test_sparse_jaccard(sparse_binary_data): +def test_sparse_jaccard(sparse_binary_data) -> None: sparse_binary_check("jaccard", sparse_binary_data) -def test_sparse_matching(sparse_binary_data): +def test_sparse_matching(sparse_binary_data) -> None: sparse_binary_check("matching", sparse_binary_data) -def test_sparse_dice(sparse_binary_data): +def test_sparse_dice(sparse_binary_data) -> None: sparse_binary_check("dice", sparse_binary_data) @pytest.mark.skipif( - scipy_full_version >= (1, 9), reason="deprecated in SciPy 1.9, removed in 1.11" + scipy_full_version >= (1, 9), + reason="deprecated in SciPy 1.9, removed in 1.11", ) -def test_sparse_kulsinski(sparse_binary_data): +def test_sparse_kulsinski(sparse_binary_data) -> None: sparse_binary_check("kulsinski", sparse_binary_data) -def test_sparse_rogerstanimoto(sparse_binary_data): +def test_sparse_rogerstanimoto(sparse_binary_data) -> None: sparse_binary_check("rogerstanimoto", sparse_binary_data) -def test_sparse_russellrao(sparse_binary_data): +def test_sparse_russellrao(sparse_binary_data) -> None: sparse_binary_check("russellrao", sparse_binary_data) -def test_sparse_sokalmichener(sparse_binary_data): +def test_sparse_sokalmichener(sparse_binary_data) -> None: sparse_binary_check("sokalmichener", sparse_binary_data) -def test_sparse_sokalsneath(sparse_binary_data): +def test_sparse_sokalsneath(sparse_binary_data) -> None: sparse_binary_check("sokalsneath", sparse_binary_data) # -------------------------------- # Standardised/weighted Distances # -------------------------------- -def test_seuclidean(spatial_data): +def test_seuclidean(spatial_data) -> None: v = np.abs(np.random.randn(spatial_data.shape[1])) dist_matrix = pairwise_distances(spatial_data, metric="seuclidean", V=v) test_matrix = np.array( @@ -343,19 +347,20 @@ def test_seuclidean(spatial_data): for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric seuclidean", + err_msg="Distances don't match for metric seuclidean", ) @pytest.mark.skipif( - scipy_full_version < (1, 8), reason="incorrect function in scipy<1.8" + scipy_full_version < (1, 8), + reason="incorrect function in scipy<1.8", ) -def test_weighted_minkowski(spatial_data): +def test_weighted_minkowski(spatial_data) -> None: v = np.abs(np.random.randn(spatial_data.shape[1])) dist_matrix = pairwise_distances(spatial_data, metric="minkowski", w=v, p=3) test_matrix = np.array( @@ -365,16 +370,16 @@ def test_weighted_minkowski(spatial_data): for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric weighted_minkowski", + err_msg="Distances don't match for metric weighted_minkowski", ) -def test_mahalanobis(spatial_data): +def test_mahalanobis(spatial_data) -> None: v = np.cov(np.transpose(spatial_data)) dist_matrix = pairwise_distances(spatial_data, metric="mahalanobis", VI=v) test_matrix = np.array( @@ -384,16 +389,16 @@ def test_mahalanobis(spatial_data): for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric mahalanobis", + err_msg="Distances don't match for metric mahalanobis", ) -def test_haversine(spatial_data): +def test_haversine(spatial_data) -> None: tree = BallTree(spatial_data[:, :2], metric="haversine") dist_matrix, _ = tree.query(spatial_data[:, :2], k=spatial_data.shape[0]) test_matrix = np.array( @@ -403,17 +408,17 @@ def test_haversine(spatial_data): for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) test_matrix.sort(axis=1) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric haversine", + err_msg="Distances don't match for metric haversine", ) -def test_hellinger(spatial_data): +def test_hellinger(spatial_data) -> None: hellinger_data = np.abs(spatial_data[:-2].copy()) hellinger_data = hellinger_data / hellinger_data.sum(axis=1)[:, np.newaxis] hellinger_data = np.sqrt(hellinger_data) @@ -428,21 +433,22 @@ def test_hellinger(spatial_data): assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric hellinger", + err_msg="Distances don't match for metric hellinger", ) # Ensure ll_dirichlet runs test_matrix = dist.pairwise_special_metric( - np.abs(spatial_data[:-2]), metric="ll_dirichlet" + np.abs(spatial_data[:-2]), + metric="ll_dirichlet", + ) + assert test_matrix is not None, ( + "Pairwise Special Metric with LL Dirichlet metric failed" ) - assert ( - test_matrix is not None - ), "Pairwise Special Metric with LL Dirichlet metric failed" -def test_sparse_hellinger(sparse_spatial_data): +def test_sparse_hellinger(sparse_spatial_data) -> None: dist_matrix = dist.pairwise_special_metric( - np.abs(sparse_spatial_data[:-2].toarray()) + np.abs(sparse_spatial_data[:-2].toarray()), ) test_matrix = np.array( [ @@ -456,13 +462,13 @@ def test_sparse_hellinger(sparse_spatial_data): for j in range(sparse_spatial_data.shape[0] - 2) ] for i in range(sparse_spatial_data.shape[0] - 2) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Sparse distances don't match " "for metric hellinger", + err_msg="Sparse distances don't match for metric hellinger", decimal=3, ) @@ -479,14 +485,14 @@ def test_sparse_hellinger(sparse_spatial_data): for j in range(sparse_spatial_data.shape[0]) ] for i in range(sparse_spatial_data.shape[0]) - ] + ], + ) + assert test_matrix is not None, ( + "Pairwise Special Metric with LL Dirichlet metric failed" ) - assert ( - test_matrix is not None - ), "Pairwise Special Metric with LL Dirichlet metric failed" -def test_grad_metrics_match_metrics(spatial_data, spatial_distances): +def test_grad_metrics_match_metrics(spatial_data, spatial_distances) -> None: for metric in dist.named_distances_with_gradients: if metric in spatial_distances: spatial_check(metric, spatial_data, spatial_distances, with_grad=True) @@ -502,12 +508,12 @@ def test_grad_metrics_match_metrics(spatial_data, spatial_distances): for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric seuclidean", + err_msg="Distances don't match for metric seuclidean", ) if scipy_full_version >= (1, 8): @@ -517,17 +523,20 @@ def test_grad_metrics_match_metrics(spatial_data, spatial_distances): [ [ dist.weighted_minkowski_grad( - spatial_data[i], spatial_data[j], v, p=3 + spatial_data[i], + spatial_data[j], + v, + p=3, )[0] for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric weighted_minkowski", + err_msg="Distances don't match for metric weighted_minkowski", ) # Mahalanobis @@ -540,18 +549,19 @@ def test_grad_metrics_match_metrics(spatial_data, spatial_distances): for j in range(spatial_data.shape[0]) ] for i in range(spatial_data.shape[0]) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, decimal=5, - err_msg="Distances don't match " "for metric mahalanobis", + err_msg="Distances don't match for metric mahalanobis", ) # Hellinger dist_matrix = dist.pairwise_special_metric( - np.abs(spatial_data[:-2]), np.abs(spatial_data[:-2]) + np.abs(spatial_data[:-2]), + np.abs(spatial_data[:-2]), ) test_matrix = np.array( [ @@ -560,10 +570,10 @@ def test_grad_metrics_match_metrics(spatial_data, spatial_distances): for j in range(spatial_data.shape[0] - 2) ] for i in range(spatial_data.shape[0] - 2) - ] + ], ) assert_array_almost_equal( test_matrix, dist_matrix, - err_msg="Distances don't match " "for metric hellinger", + err_msg="Distances don't match for metric hellinger", ) diff --git a/umap/tests/test_umap_nn.py b/umap/tests/test_umap_nn.py index fcc2c275..ea0a9126 100644 --- a/umap/tests/test_umap_nn.py +++ b/umap/tests/test_umap_nn.py @@ -10,7 +10,6 @@ smooth_knn_dist, ) - # =================================================== # Nearest Neighbour Test cases # =================================================== @@ -18,12 +17,12 @@ # nearest_neighbours metric parameter validation # ----------------------------------------------- -def test_nn_bad_metric(nn_data): +def test_nn_bad_metric(nn_data) -> None: with pytest.raises(ValueError): nearest_neighbors(nn_data, 10, 42, {}, False, np.random) -def test_nn_bad_metric_sparse_data(sparse_nn_data): +def test_nn_bad_metric_sparse_data(sparse_nn_data) -> None: with pytest.raises(ValueError): nearest_neighbors( sparse_nn_data, @@ -50,114 +49,161 @@ def knn(indices, nn_data): # pragma: no cover def smooth_knn(nn_data, local_connectivity=1.0): - knn_indices, knn_dists, _ = nearest_neighbors( - nn_data, 10, "euclidean", {}, False, np.random + _knn_indices, knn_dists, _ = nearest_neighbors( + nn_data, + 10, + "euclidean", + {}, + False, + np.random, ) sigmas, rhos = smooth_knn_dist( - knn_dists, 10.0, local_connectivity=local_connectivity + knn_dists, + 10.0, + local_connectivity=local_connectivity, ) shifted_dists = knn_dists - rhos[:, np.newaxis] shifted_dists[shifted_dists < 0.0] = 0.0 vals = np.exp(-(shifted_dists / sigmas[:, np.newaxis])) - norms = np.sum(vals, axis=1) - return norms - - -@pytest.mark.skip() -def test_nn_descent_neighbor_accuracy(nn_data): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - nn_data, 10, "euclidean", {}, False, np.random + return np.sum(vals, axis=1) + + +@pytest.mark.skip +def test_nn_descent_neighbor_accuracy(nn_data) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + nn_data, + 10, + "euclidean", + {}, + False, + np.random, ) percent_correct = knn(knn_indices, nn_data) - assert ( - percent_correct >= 0.85 - ), "NN-descent did not get 89% accuracy on nearest neighbors" + assert percent_correct >= 0.85, ( + "NN-descent did not get 89% accuracy on nearest neighbors" + ) -@pytest.mark.skip() -def test_nn_descent_neighbor_accuracy_low_memory(nn_data): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - nn_data, 10, "euclidean", {}, False, np.random, low_memory=True +@pytest.mark.skip +def test_nn_descent_neighbor_accuracy_low_memory(nn_data) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + nn_data, + 10, + "euclidean", + {}, + False, + np.random, + low_memory=True, ) percent_correct = knn(knn_indices, nn_data) - assert ( - percent_correct >= 0.89 - ), "NN-descent did not get 89% accuracy on nearest neighbors" + assert percent_correct >= 0.89, ( + "NN-descent did not get 89% accuracy on nearest neighbors" + ) -@pytest.mark.skip() -def test_angular_nn_descent_neighbor_accuracy(nn_data): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - nn_data, 10, "cosine", {}, True, np.random +@pytest.mark.skip +def test_angular_nn_descent_neighbor_accuracy(nn_data) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + nn_data, + 10, + "cosine", + {}, + True, + np.random, ) angular_data = normalize(nn_data, norm="l2") percent_correct = knn(knn_indices, angular_data) - assert ( - percent_correct >= 0.85 - ), "NN-descent did not get 89% accuracy on nearest neighbors" + assert percent_correct >= 0.85, ( + "NN-descent did not get 89% accuracy on nearest neighbors" + ) -@pytest.mark.skip() -def test_sparse_nn_descent_neighbor_accuracy(sparse_nn_data): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - sparse_nn_data, 20, "euclidean", {}, False, np.random +@pytest.mark.skip +def test_sparse_nn_descent_neighbor_accuracy( + sparse_nn_data, +) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + sparse_nn_data, + 20, + "euclidean", + {}, + False, + np.random, ) percent_correct = knn(knn_indices, sparse_nn_data.todense()) - assert ( - percent_correct >= 0.75 - ), "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + assert percent_correct >= 0.75, ( + "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + ) -@pytest.mark.skip() +@pytest.mark.skip def test_sparse_nn_descent_neighbor_accuracy_low_memory( sparse_nn_data, -): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - sparse_nn_data, 20, "euclidean", {}, False, np.random, low_memory=True +) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + sparse_nn_data, + 20, + "euclidean", + {}, + False, + np.random, + low_memory=True, ) percent_correct = knn(knn_indices, sparse_nn_data.todense()) - assert ( - percent_correct >= 0.85 - ), "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + assert percent_correct >= 0.85, ( + "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + ) -@pytest.mark.skip() -def test_nn_descent_neighbor_accuracy_callable_metric(nn_data): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - nn_data, 10, dist.euclidean, {}, False, np.random +@pytest.mark.skip +def test_nn_descent_neighbor_accuracy_callable_metric( + nn_data, +) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + nn_data, + 10, + dist.euclidean, + {}, + False, + np.random, ) percent_correct = knn(knn_indices, nn_data) - assert ( - percent_correct >= 0.95 - ), "NN-descent did not get 95% accuracy on nearest neighbors with callable metric" + assert percent_correct >= 0.95, ( + "NN-descent did not get 95% accuracy on nearest neighbors with callable metric" + ) -@pytest.mark.skip() +@pytest.mark.skip def test_sparse_angular_nn_descent_neighbor_accuracy( sparse_nn_data, -): # pragma: no cover - knn_indices, knn_dists, _ = nearest_neighbors( - sparse_nn_data, 20, "cosine", {}, True, np.random +) -> None: # pragma: no cover + knn_indices, _knn_dists, _ = nearest_neighbors( + sparse_nn_data, + 20, + "cosine", + {}, + True, + np.random, ) angular_data = normalize(sparse_nn_data, norm="l2").toarray() percent_correct = knn(knn_indices, angular_data) - assert ( - percent_correct >= 0.90 - ), "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + assert percent_correct >= 0.90, ( + "Sparse NN-descent did not get 90% accuracy on nearest neighbors" + ) -def test_smooth_knn_dist_l1norms(nn_data): +def test_smooth_knn_dist_l1norms(nn_data) -> None: norms = smooth_knn(nn_data) assert_array_almost_equal( norms, 1.0 + np.log2(10) * np.ones(norms.shape[0]), decimal=3, - err_msg="Smooth knn-dists does not give expected" "norms", + err_msg="Smooth knn-dists does not give expectednorms", ) -def test_smooth_knn_dist_l1norms_w_connectivity(nn_data): +def test_smooth_knn_dist_l1norms_w_connectivity(nn_data) -> None: norms = smooth_knn(nn_data, local_connectivity=1.75) assert_array_almost_equal( norms, diff --git a/umap/tests/test_umap_on_iris.py b/umap/tests/test_umap_on_iris.py index d91fe9a1..8a9a167d 100644 --- a/umap/tests/test_umap_on_iris.py +++ b/umap/tests/test_umap_on_iris.py @@ -1,13 +1,13 @@ -from umap import UMAP -from umap.umap_ import nearest_neighbors -from scipy import sparse import numpy as np +import pytest +from scipy import sparse +from scipy.spatial.distance import cdist, pdist, squareform from sklearn.cluster import KMeans from sklearn.metrics import adjusted_rand_score from sklearn.neighbors import KDTree -from scipy.spatial.distance import cdist, pdist, squareform -import pytest -import warnings + +from umap import UMAP +from umap.umap_ import nearest_neighbors try: # works for sklearn>=0.22 @@ -26,15 +26,15 @@ # UMAP Trustworthiness on iris # ---------------------------- -def test_umap_trustworthiness_on_iris(iris, iris_model): +def test_umap_trustworthiness_on_iris(iris, iris_model) -> None: embedding = iris_model.embedding_ trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.97 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.97, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) -def test_initialized_umap_trustworthiness_on_iris(iris): +def test_initialized_umap_trustworthiness_on_iris(iris) -> None: data = iris.data embedding = UMAP( n_neighbors=10, @@ -44,14 +44,14 @@ def test_initialized_umap_trustworthiness_on_iris(iris): random_state=42, ).fit_transform(data) trust = trustworthiness(iris.data, embedding, n_neighbors=10) - assert ( - trust >= 0.97 - ), "Insufficiently trustworthy embedding for" "iris dataset: {}".format(trust) + assert trust >= 0.97, ( + f"Insufficiently trustworthy embedding foriris dataset: {trust}" + ) def test_umap_trustworthiness_on_sphere_iris( iris, -): +) -> None: data = iris.data embedding = UMAP( n_neighbors=10, @@ -68,33 +68,34 @@ def test_umap_trustworthiness_on_sphere_iris( r * np.sin(embedding[:, 0]) * np.cos(embedding[:, 1]), r * np.sin(embedding[:, 0]) * np.sin(embedding[:, 1]), r * np.cos(embedding[:, 0]), - ] + ], ).T trust = trustworthiness( - iris.data, projected_embedding, n_neighbors=10, metric="cosine" + iris.data, + projected_embedding, + n_neighbors=10, + metric="cosine", ) - assert ( - trust >= 0.65 - ), "Insufficiently trustworthy spherical embedding for iris dataset: {}".format( - trust + assert trust >= 0.65, ( + f"Insufficiently trustworthy spherical embedding for iris dataset: {trust}" ) # UMAP Transform on iris # ---------------------- -def test_umap_transform_on_iris(iris, iris_subset_model, iris_selection): +def test_umap_transform_on_iris(iris, iris_subset_model, iris_selection) -> None: fitter = iris_subset_model new_data = iris.data[~iris_selection] embedding = fitter.transform(new_data) trust = trustworthiness(new_data, embedding, n_neighbors=10) - assert ( - trust >= 0.80 - ), "Insufficiently trustworthy transform for" "iris dataset: {}".format(trust) + assert trust >= 0.80, ( + f"Insufficiently trustworthy transform foriris dataset: {trust}" + ) -def test_umap_transform_on_iris_w_pynndescent(iris, iris_selection): +def test_umap_transform_on_iris_w_pynndescent(iris, iris_selection) -> None: data = iris.data[iris_selection] fitter = UMAP( n_neighbors=10, @@ -108,12 +109,16 @@ def test_umap_transform_on_iris_w_pynndescent(iris, iris_selection): embedding = fitter.transform(new_data) trust = trustworthiness(new_data, embedding, n_neighbors=10) - assert ( - trust >= 0.85 - ), "Insufficiently trustworthy transform for" "iris dataset: {}".format(trust) + assert trust >= 0.85, ( + f"Insufficiently trustworthy transform foriris dataset: {trust}" + ) -def test_umap_transform_on_iris_modified_dtype(iris, iris_subset_model, iris_selection): +def test_umap_transform_on_iris_modified_dtype( + iris, + iris_subset_model, + iris_selection, +) -> None: fitter = iris_subset_model fitter.embedding_ = fitter.embedding_.astype(np.float64) @@ -121,12 +126,12 @@ def test_umap_transform_on_iris_modified_dtype(iris, iris_subset_model, iris_sel embedding = fitter.transform(new_data) trust = trustworthiness(new_data, embedding, n_neighbors=10) - assert ( - trust >= 0.8 - ), "Insufficiently trustworthy transform for iris dataset: {}".format(trust) + assert trust >= 0.8, ( + f"Insufficiently trustworthy transform for iris dataset: {trust}" + ) -def test_umap_sparse_transform_on_iris(iris, iris_selection): +def test_umap_sparse_transform_on_iris(iris, iris_selection) -> None: data = sparse.csr_matrix(iris.data[iris_selection]) assert sparse.issparse(data) fitter = UMAP( @@ -142,14 +147,14 @@ def test_umap_sparse_transform_on_iris(iris, iris_selection): embedding = fitter.transform(new_data) trust = trustworthiness(new_data, embedding, n_neighbors=10) - assert ( - trust >= 0.80 - ), "Insufficiently trustworthy transform for" "iris dataset: {}".format(trust) + assert trust >= 0.80, ( + f"Insufficiently trustworthy transform foriris dataset: {trust}" + ) # UMAP precomputed metric transform on iris # ---------------------- -def test_precomputed_transform_on_iris(iris, iris_selection): +def test_precomputed_transform_on_iris(iris, iris_selection) -> None: data = iris.data[iris_selection] distance_matrix = squareform(pdist(data)) @@ -166,14 +171,14 @@ def test_precomputed_transform_on_iris(iris, iris_selection): embedding = fitter.transform(new_distance_matrix) trust = trustworthiness(new_data, embedding, n_neighbors=10) - assert ( - trust >= 0.85 - ), "Insufficiently trustworthy transform for" "iris dataset: {}".format(trust) + assert trust >= 0.85, ( + f"Insufficiently trustworthy transform foriris dataset: {trust}" + ) # UMAP precomputed metric transform on iris with sparse distances # ---------------------- -def test_precomputed_sparse_transform_on_iris(iris, iris_selection): +def test_precomputed_sparse_transform_on_iris(iris, iris_selection) -> None: data = iris.data[iris_selection] distance_matrix = sparse.csr_matrix(squareform(pdist(data))) @@ -190,14 +195,14 @@ def test_precomputed_sparse_transform_on_iris(iris, iris_selection): embedding = fitter.transform(new_distance_matrix) trust = trustworthiness(new_data, embedding, n_neighbors=10) - assert ( - trust >= 0.85 - ), "Insufficiently trustworthy transform for" "iris dataset: {}".format(trust) + assert trust >= 0.85, ( + f"Insufficiently trustworthy transform foriris dataset: {trust}" + ) # UMAP Clusterability on Iris # --------------------------- -def test_umap_clusterability_on_supervised_iris(supervised_iris_model, iris): +def test_umap_clusterability_on_supervised_iris(supervised_iris_model, iris) -> None: embedding = supervised_iris_model.embedding_ clusters = KMeans(3).fit_predict(embedding) assert adjusted_rand_score(clusters, iris.target) >= 0.95 @@ -205,7 +210,7 @@ def test_umap_clusterability_on_supervised_iris(supervised_iris_model, iris): # UMAP Inverse transform on Iris # ------------------------------ -def test_umap_inverse_transform_on_iris(iris, iris_model): +def test_umap_inverse_transform_on_iris(iris, iris_model) -> None: highd_tree = KDTree(iris.data) fitter = iris_model lowd_tree = KDTree(fitter.embedding_) @@ -215,12 +220,14 @@ def test_umap_inverse_transform_on_iris(iris, iris_model): centroid = np.mean(np.squeeze(fitter.embedding_[near_points]), axis=0) highd_centroid = fitter.inverse_transform([centroid]) highd_near_points = highd_tree.query( - highd_centroid, k=10, return_distance=False + highd_centroid, + k=10, + return_distance=False, ) assert np.intersect1d(near_points, highd_near_points[0]).shape[0] >= 3 -def test_precomputed_knn_on_iris(iris, iris_selection, iris_subset_model): +def test_precomputed_knn_on_iris(iris, iris_selection, iris_subset_model) -> None: # this to compare two similarity graphs which should be nearly the same def rms(a, b): return np.sqrt(np.mean(np.square(a - b))) @@ -238,12 +245,12 @@ def rms(a, b): ) # repeated UMAP arguments we don't want to mis-specify - umap_args = dict( - n_neighbors=iris_subset_model.n_neighbors, - random_state=iris_subset_model.random_state, - n_jobs=1, - min_dist=iris_subset_model.min_dist, - ) + umap_args = { + "n_neighbors": iris_subset_model.n_neighbors, + "random_state": iris_subset_model.random_state, + "n_jobs": 1, + "min_dist": iris_subset_model.min_dist, + } # force_approximation_algorithm parameter is ignored when a precomputed knn is used fitter_with_precomputed_knn = UMAP( @@ -265,7 +272,8 @@ def rms(a, b): ).fit(data) assert len(record) >= 1 np.testing.assert_array_equal( - fitter_ignoring_force_approx.embedding_, fitter_with_precomputed_knn.embedding_ + fitter_ignoring_force_approx.embedding_, + fitter_with_precomputed_knn.embedding_, ) # #848 (continued): if you don't have a search index, attempting to transform diff --git a/umap/tests/test_umap_ops.py b/umap/tests/test_umap_ops.py index 15597ba8..ca1002e3 100644 --- a/umap/tests/test_umap_ops.py +++ b/umap/tests/test_umap_ops.py @@ -3,20 +3,22 @@ # (not really fitting anywhere else) # =================================================== -from sklearn.datasets import make_blobs +import warnings + +import numpy as np +import pytest +import scipy.sparse +from numpy.testing import assert_array_equal +from scipy.sparse import csr_matrix from sklearn.cluster import KMeans +from sklearn.datasets import make_blobs from sklearn.metrics import adjusted_rand_score, pairwise_distances from sklearn.preprocessing import normalize -from numpy.testing import assert_array_equal + from umap import UMAP -from umap.spectral import component_layout -import numpy as np -import scipy.sparse -import pytest -import warnings from umap.distances import pairwise_special_metric +from umap.spectral import component_layout from umap.utils import disconnected_vertices -from scipy.sparse import csr_matrix # Transform isn't stable under batching; hard to opt out of this. # @SkipTest @@ -38,16 +40,21 @@ # Umap Clusterability -def test_blobs_cluster(): +def test_blobs_cluster() -> None: data, labels = make_blobs(n_samples=500, n_features=10, centers=5) embedding = UMAP(n_epochs=100).fit_transform(data) assert adjusted_rand_score(labels, KMeans(5).fit_predict(embedding)) == 1.0 # Multi-components Layout -def test_multi_component_layout(): +def test_multi_component_layout() -> None: data, labels = make_blobs( - 100, 2, centers=5, cluster_std=0.5, center_box=(-20, 20), random_state=42 + 100, + 2, + centers=5, + cluster_std=0.5, + center_box=(-20, 20), + random_state=42, ) true_centroids = np.empty((labels.max() + 1, data.shape[1]), dtype=np.float64) @@ -72,9 +79,14 @@ def test_multi_component_layout(): # Multi-components Layout -def test_multi_component_layout_precomputed(): +def test_multi_component_layout_precomputed() -> None: data, labels = make_blobs( - 100, 2, centers=5, cluster_std=0.5, center_box=(-20, 20), random_state=42 + 100, + 2, + centers=5, + cluster_std=0.5, + center_box=(-20, 20), + random_state=42, ) dmat = pairwise_distances(data) @@ -86,7 +98,7 @@ def test_multi_component_layout_precomputed(): true_centroids = normalize(true_centroids, norm="l2") embedding = UMAP(n_neighbors=4, metric="precomputed", n_epochs=100).fit_transform( - dmat + dmat, ) embed_centroids = np.empty((labels.max() + 1, data.shape[1]), dtype=np.float64) embed_labels = KMeans(n_clusters=5).fit_predict(embedding) @@ -104,12 +116,12 @@ def test_multi_component_layout_precomputed(): @pytest.mark.parametrize("num_isolates", [1, 5]) @pytest.mark.parametrize("metric", ["jaccard", "hellinger"]) @pytest.mark.parametrize("force_approximation", [True, False]) -def test_disconnected_data(num_isolates, metric, force_approximation): +def test_disconnected_data(num_isolates, metric, force_approximation) -> None: options = [False, True] disconnected_data = np.random.choice(a=options, size=(10, 30), p=[0.6, 1 - 0.6]) # Add some disconnected data for the corner case test disconnected_data = np.vstack( - [disconnected_data, np.zeros((num_isolates, 30), dtype="bool")] + [disconnected_data, np.zeros((num_isolates, 30), dtype="bool")], ) new_columns = np.zeros((num_isolates + 10, num_isolates), dtype="bool") for i in range(num_isolates): @@ -135,20 +147,22 @@ def test_disconnected_data(num_isolates, metric, force_approximation): isolated_vertices = disconnected_vertices(model) assert flag == 1, str(([wn.message for wn in w], isolated_vertices)) # Check that the first isolate has no edges in our umap.graph_ - assert isolated_vertices[10] == True + assert isolated_vertices[10] number_of_nan = np.sum(np.isnan(model.embedding_[isolated_vertices])) assert number_of_nan >= num_isolates * model.n_components @pytest.mark.parametrize("num_isolates", [1]) @pytest.mark.parametrize("sparse", [True, False]) -def test_disconnected_data_precomputed(num_isolates, sparse): +def test_disconnected_data_precomputed(num_isolates, sparse) -> None: disconnected_data = np.random.choice( - a=[False, True], size=(10, 20), p=[0.66, 1 - 0.66] + a=[False, True], + size=(10, 20), + p=[0.66, 1 - 0.66], ) # Add some disconnected data for the corner case test disconnected_data = np.vstack( - [disconnected_data, np.zeros((num_isolates, 20), dtype="bool")] + [disconnected_data, np.zeros((num_isolates, 20), dtype="bool")], ) new_columns = np.zeros((num_isolates + 10, num_isolates), dtype="bool") for i in range(num_isolates): @@ -158,12 +172,12 @@ def test_disconnected_data_precomputed(num_isolates, sparse): if sparse: dmat = csr_matrix(dmat) model = UMAP(n_neighbors=3, metric="precomputed", disconnection_distance=1).fit( - dmat + dmat, ) # Check that the first isolate has no edges in our umap.graph_ isolated_vertices = disconnected_vertices(model) - assert isolated_vertices[10] == True + assert isolated_vertices[10] number_of_nan = np.sum(np.isnan(model.embedding_[isolated_vertices])) assert number_of_nan >= num_isolates * model.n_components @@ -173,7 +187,7 @@ def test_disconnected_data_precomputed(num_isolates, sparse): # -------------- -def test_bad_transform_data(nn_data): +def test_bad_transform_data(nn_data) -> None: u = UMAP().fit([[1, 1, 1, 1]]) with pytest.raises(ValueError): u.transform([[0, 0, 0, 0]]) @@ -181,15 +195,18 @@ def test_bad_transform_data(nn_data): # Transform Stability # ------------------- -def test_umap_transform_embedding_stability(iris, iris_subset_model, iris_selection): - """Test that transforming data does not alter the learned embeddings +def test_umap_transform_embedding_stability( + iris, + iris_subset_model, + iris_selection, +) -> None: + """Test that transforming data does not alter the learned embeddings. Issue #217 describes how using transform to embed new data using a trained UMAP transformer causes the fitting embedding matrix to change in cases when the new data has the same number of rows as the original training data. """ - data = iris.data[iris_selection] fitter = iris_subset_model original_embedding = fitter.embedding_.copy() @@ -221,17 +238,16 @@ def test_umap_transform_embedding_stability(iris, iris_subset_model, iris_select # ----------- # UMAP Update # ----------- -def test_umap_update(iris, iris_subset_model, iris_selection, iris_model): - +def test_umap_update(iris, iris_subset_model, iris_selection, iris_model) -> None: new_data = iris.data[~iris_selection] new_model = iris_subset_model new_model.update(new_data) comparison_graph = scipy.sparse.vstack( - [iris_model.graph_[iris_selection], iris_model.graph_[~iris_selection]] + [iris_model.graph_[iris_selection], iris_model.graph_[~iris_selection]], ) comparison_graph = scipy.sparse.hstack( - [comparison_graph[:, iris_selection], comparison_graph[:, ~iris_selection]] + [comparison_graph[:, iris_selection], comparison_graph[:, ~iris_selection]], ) error = np.sum(np.abs((new_model.graph_ - comparison_graph).data)) @@ -240,9 +256,11 @@ def test_umap_update(iris, iris_subset_model, iris_selection, iris_model): def test_umap_update_large( - iris, iris_subset_model_large, iris_selection, iris_model_large -): - + iris, + iris_subset_model_large, + iris_selection, + iris_model_large, +) -> None: new_data = iris.data[~iris_selection] new_model = iris_subset_model_large new_model.update(new_data) @@ -251,10 +269,10 @@ def test_umap_update_large( [ iris_model_large.graph_[iris_selection], iris_model_large.graph_[~iris_selection], - ] + ], ) comparison_graph = scipy.sparse.hstack( - [comparison_graph[:, iris_selection], comparison_graph[:, ~iris_selection]] + [comparison_graph[:, iris_selection], comparison_graph[:, ~iris_selection]], ) error = np.sum(np.abs((new_model.graph_ - comparison_graph).data)) @@ -265,12 +283,12 @@ def test_umap_update_large( # ----------------- # UMAP Graph output # ----------------- -def test_umap_graph_layout(): - data, labels = make_blobs(n_samples=500, n_features=10, centers=5) +def test_umap_graph_layout() -> None: + data, _labels = make_blobs(n_samples=500, n_features=10, centers=5) model = UMAP(n_epochs=100, transform_mode="graph") graph = model.fit_transform(data) assert scipy.sparse.issparse(graph) - nc, cl = scipy.sparse.csgraph.connected_components(graph) + nc, _cl = scipy.sparse.csgraph.connected_components(graph) assert nc == 5 new_graph = model.transform(data[:10] + np.random.normal(0.0, 0.1, size=(10, 10))) @@ -283,7 +301,7 @@ def test_umap_graph_layout(): # ------------------------ -def test_component_layout_options(nn_data): +def test_component_layout_options(nn_data) -> None: dmat = pairwise_distances(nn_data[:1000]) n_components = 5 component_labels = np.repeat(np.arange(5), dmat.shape[0] // 5) diff --git a/umap/tests/test_umap_repeated_data.py b/umap/tests/test_umap_repeated_data.py index 2d2bf973..fe569ca6 100644 --- a/umap/tests/test_umap_repeated_data.py +++ b/umap/tests/test_umap_repeated_data.py @@ -1,6 +1,6 @@ import numpy as np -from umap import UMAP +from umap import UMAP # =================================================== # Spatial Data Test cases @@ -10,7 +10,7 @@ # --------------------------------------------------- -def test_repeated_points_large_sparse_spatial(sparse_spatial_data_repeats): +def test_repeated_points_large_sparse_spatial(sparse_spatial_data_repeats) -> None: model = UMAP( n_neighbors=3, unique=True, @@ -21,23 +21,26 @@ def test_repeated_points_large_sparse_spatial(sparse_spatial_data_repeats): assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 -def test_repeated_points_small_sparse_spatial(sparse_spatial_data_repeats): +def test_repeated_points_small_sparse_spatial(sparse_spatial_data_repeats) -> None: model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit( - sparse_spatial_data_repeats + sparse_spatial_data_repeats, ) assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 # Use force_approximation_algorithm in order to test the region # of the code that is called for n>4096 -def test_repeated_points_large_dense_spatial(spatial_repeats): +def test_repeated_points_large_dense_spatial(spatial_repeats) -> None: model = UMAP( - n_neighbors=3, unique=True, force_approximation_algorithm=True, n_epochs=50 + n_neighbors=3, + unique=True, + force_approximation_algorithm=True, + n_epochs=50, ).fit(spatial_repeats) assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 -def test_repeated_points_small_dense_spatial(spatial_repeats): +def test_repeated_points_small_dense_spatial(spatial_repeats) -> None: model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit(spatial_repeats) assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 @@ -50,30 +53,36 @@ def test_repeated_points_small_dense_spatial(spatial_repeats): # --------------------------------------------------- -def test_repeated_points_large_sparse_binary(sparse_binary_data_repeats): +def test_repeated_points_large_sparse_binary(sparse_binary_data_repeats) -> None: model = UMAP( - n_neighbors=3, unique=True, force_approximation_algorithm=True, n_epochs=50 + n_neighbors=3, + unique=True, + force_approximation_algorithm=True, + n_epochs=50, ).fit(sparse_binary_data_repeats) assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 -def test_repeated_points_small_sparse_binary(sparse_binary_data_repeats): +def test_repeated_points_small_sparse_binary(sparse_binary_data_repeats) -> None: model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit( - sparse_binary_data_repeats + sparse_binary_data_repeats, ) assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 # Use force_approximation_algorithm in order to test # the region of the code that is called for n>4096 -def test_repeated_points_large_dense_binary(binary_repeats): +def test_repeated_points_large_dense_binary(binary_repeats) -> None: model = UMAP( - n_neighbors=3, unique=True, force_approximation_algorithm=True, n_epochs=20 + n_neighbors=3, + unique=True, + force_approximation_algorithm=True, + n_epochs=20, ).fit(binary_repeats) assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 -def test_repeated_points_small_dense_binary(binary_repeats): +def test_repeated_points_small_dense_binary(binary_repeats) -> None: model = UMAP(n_neighbors=3, unique=True, n_epochs=20).fit(binary_repeats) assert np.unique(binary_repeats[0:2], axis=0).shape[0] == 1 assert np.unique(model.embedding_[0:2], axis=0).shape[0] == 1 @@ -89,6 +98,6 @@ def test_repeated_points_small_dense_binary(binary_repeats): # reduced properly when your n_neighbours is larger # than the unique data set size # ---------------------------------------------------- -def test_repeated_points_large_n(repetition_dense): +def test_repeated_points_large_n(repetition_dense) -> None: model = UMAP(n_neighbors=5, unique=True, n_epochs=20).fit(repetition_dense) assert model._n_neighbors == 3 diff --git a/umap/tests/test_umap_trustworthiness.py b/umap/tests/test_umap_trustworthiness.py index ab0bd876..b78f48ad 100644 --- a/umap/tests/test_umap_trustworthiness.py +++ b/umap/tests/test_umap_trustworthiness.py @@ -1,8 +1,9 @@ -from umap import UMAP -from sklearn.datasets import make_blobs -from sklearn.metrics import pairwise_distances import numpy as np import scipy.sparse +from sklearn.datasets import make_blobs +from sklearn.metrics import pairwise_distances + +from umap import UMAP try: # works for sklearn>=0.22 @@ -19,15 +20,15 @@ # =================================================== -def test_umap_sparse_trustworthiness(sparse_test_data): +def test_umap_sparse_trustworthiness(sparse_test_data) -> None: embedding = UMAP(n_neighbors=10, n_epochs=100).fit_transform(sparse_test_data[:100]) trust = trustworthiness(sparse_test_data[:100].toarray(), embedding, n_neighbors=10) - assert ( - trust >= 0.88 - ), "Insufficiently trustworthy embedding for sparse test dataset: {}".format(trust) + assert trust >= 0.88, ( + f"Insufficiently trustworthy embedding for sparse test dataset: {trust}" + ) -def test_umap_trustworthiness_fast_approx(nn_data): +def test_umap_trustworthiness_fast_approx(nn_data) -> None: data = nn_data[:50] embedding = UMAP( n_neighbors=10, @@ -37,46 +38,52 @@ def test_umap_trustworthiness_fast_approx(nn_data): force_approximation_algorithm=True, ).fit_transform(data) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.8 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.8, f"Insufficiently trustworthy embedding fornn dataset: {trust}" -def test_umap_trustworthiness_random_init(nn_data): +def test_umap_trustworthiness_random_init(nn_data) -> None: data = nn_data[:50] embedding = UMAP( - n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=100, init="random" + n_neighbors=10, + min_dist=0.01, + random_state=42, + n_epochs=100, + init="random", ).fit_transform(data) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.8 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.8, f"Insufficiently trustworthy embedding fornn dataset: {trust}" -def test_supervised_umap_trustworthiness(): +def test_supervised_umap_trustworthiness() -> None: data, labels = make_blobs(50, cluster_std=0.5, random_state=42) embedding = UMAP( - n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=100 + n_neighbors=10, + min_dist=0.01, + random_state=42, + n_epochs=100, ).fit_transform(data, labels) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.95 - ), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust) + assert trust >= 0.95, ( + f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + ) -def test_semisupervised_umap_trustworthiness(): +def test_semisupervised_umap_trustworthiness() -> None: data, labels = make_blobs(50, cluster_std=0.5, random_state=42) labels[10:30] = -1 embedding = UMAP( - n_neighbors=10, min_dist=0.01, random_state=42, n_epochs=100 + n_neighbors=10, + min_dist=0.01, + random_state=42, + n_epochs=100, ).fit_transform(data, labels) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.95 - ), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust) + assert trust >= 0.95, ( + f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + ) -def test_metric_supervised_umap_trustworthiness(): +def test_metric_supervised_umap_trustworthiness() -> None: data, labels = make_blobs(50, cluster_std=0.5, random_state=42) embedding = UMAP( n_neighbors=10, @@ -87,12 +94,12 @@ def test_metric_supervised_umap_trustworthiness(): random_state=42, ).fit_transform(data, labels) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.95 - ), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust) + assert trust >= 0.95, ( + f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + ) -def test_string_metric_supervised_umap_trustworthiness(): +def test_string_metric_supervised_umap_trustworthiness() -> None: data, labels = make_blobs(50, cluster_std=0.5, random_state=42) labels = np.array(["this", "that", "other"])[labels] embedding = UMAP( @@ -104,12 +111,12 @@ def test_string_metric_supervised_umap_trustworthiness(): random_state=42, ).fit_transform(data, labels) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.95 - ), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust) + assert trust >= 0.95, ( + f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + ) -def test_discrete_metric_supervised_umap_trustworthiness(): +def test_discrete_metric_supervised_umap_trustworthiness() -> None: data, labels = make_blobs(50, cluster_std=0.5, random_state=42) embedding = UMAP( n_neighbors=10, @@ -120,12 +127,12 @@ def test_discrete_metric_supervised_umap_trustworthiness(): random_state=42, ).fit_transform(data, labels) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.95 - ), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust) + assert trust >= 0.95, ( + f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + ) -def test_count_metric_supervised_umap_trustworthiness(): +def test_count_metric_supervised_umap_trustworthiness() -> None: data, labels = make_blobs(50, cluster_std=0.5, random_state=42) labels = (labels**2) + 2 * labels embedding = UMAP( @@ -137,13 +144,13 @@ def test_count_metric_supervised_umap_trustworthiness(): random_state=42, ).fit_transform(data, labels) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.95 - ), "Insufficiently trustworthy embedding for" "blobs dataset: {}".format(trust) + assert trust >= 0.95, ( + f"Insufficiently trustworthy embedding forblobs dataset: {trust}" + ) -def test_sparse_precomputed_metric_umap_trustworthiness(): - data, labels = make_blobs(50, cluster_std=0.5, random_state=42) +def test_sparse_precomputed_metric_umap_trustworthiness() -> None: + data, _labels = make_blobs(50, cluster_std=0.5, random_state=42) dmat = scipy.sparse.csr_matrix(pairwise_distances(data)) embedding = UMAP( n_neighbors=10, @@ -153,6 +160,4 @@ def test_sparse_precomputed_metric_umap_trustworthiness(): metric="precomputed", ).fit_transform(dmat) trust = trustworthiness(data, embedding, n_neighbors=10) - assert ( - trust >= 0.75 - ), "Insufficiently trustworthy embedding for" "nn dataset: {}".format(trust) + assert trust >= 0.75, f"Insufficiently trustworthy embedding fornn dataset: {trust}" diff --git a/umap/tests/test_umap_validation_params.py b/umap/tests/test_umap_validation_params.py index f5b4737e..c2e4a79f 100644 --- a/umap/tests/test_umap_validation_params.py +++ b/umap/tests/test_umap_validation_params.py @@ -3,31 +3,31 @@ # =============================== import warnings + +import numba import numpy as np -from sklearn.metrics import pairwise_distances import pytest -import numba -from umap import UMAP +from sklearn.metrics import pairwise_distances # verify that we can import this; potentially for later use -import umap.validation +from umap import UMAP warnings.filterwarnings("ignore", category=UserWarning) -def test_umap_negative_op(nn_data): +def test_umap_negative_op(nn_data) -> None: u = UMAP(set_op_mix_ratio=-1.0) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_too_large_op(nn_data): +def test_umap_too_large_op(nn_data) -> None: u = UMAP(set_op_mix_ratio=1.5) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_too_large_min_dist(nn_data): +def test_umap_bad_too_large_min_dist(nn_data) -> None: u = UMAP(min_dist=2.0) # a RuntimeWarning about division by zero in a,b curve fitting is expected # caught and ignored for this test @@ -37,91 +37,91 @@ def test_umap_bad_too_large_min_dist(nn_data): u.fit(nn_data) -def test_umap_negative_min_dist(nn_data): +def test_umap_negative_min_dist(nn_data) -> None: u = UMAP(min_dist=-1) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_n_components(nn_data): +def test_umap_negative_n_components(nn_data) -> None: u = UMAP(n_components=-1) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_non_integer_n_components(nn_data): +def test_umap_non_integer_n_components(nn_data) -> None: u = UMAP(n_components=1.5) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_too_small_n_neighbours(nn_data): +def test_umap_too_small_n_neighbours(nn_data) -> None: u = UMAP(n_neighbors=0.5) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_n_neighbours(nn_data): +def test_umap_negative_n_neighbours(nn_data) -> None: u = UMAP(n_neighbors=-1) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_metric(nn_data): +def test_umap_bad_metric(nn_data) -> None: u = UMAP(metric=45) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_learning_rate(nn_data): +def test_umap_negative_learning_rate(nn_data) -> None: u = UMAP(learning_rate=-1.5) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_repulsion(nn_data): +def test_umap_negative_repulsion(nn_data) -> None: u = UMAP(repulsion_strength=-0.5) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_sample_rate(nn_data): +def test_umap_negative_sample_rate(nn_data) -> None: u = UMAP(negative_sample_rate=-1) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_init(nn_data): +def test_umap_bad_init(nn_data) -> None: u = UMAP(init="foobar") with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_numeric_init(nn_data): +def test_umap_bad_numeric_init(nn_data) -> None: u = UMAP(init=42) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_matrix_init(nn_data): +def test_umap_bad_matrix_init(nn_data) -> None: u = UMAP(init=np.array([[0, 0, 0], [0, 0, 0]])) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_n_epochs(nn_data): +def test_umap_negative_n_epochs(nn_data) -> None: u = UMAP(n_epochs=-2) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_negative_target_n_neighbours(nn_data): +def test_umap_negative_target_n_neighbours(nn_data) -> None: u = UMAP(target_n_neighbors=1) with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_output_metric(nn_data): +def test_umap_bad_output_metric(nn_data) -> None: u = UMAP(output_metric="foobar") with pytest.raises(ValueError): u.fit(nn_data) @@ -133,38 +133,38 @@ def test_umap_bad_output_metric(nn_data): u.fit(nn_data) -def test_haversine_on_highd(nn_data): +def test_haversine_on_highd(nn_data) -> None: u = UMAP(metric="haversine") with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_haversine_embed_to_highd(nn_data): +def test_umap_haversine_embed_to_highd(nn_data) -> None: u = UMAP(n_components=3, output_metric="haversine") with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_too_many_neighbors_warns(nn_data): +def test_umap_too_many_neighbors_warns(nn_data) -> None: u = UMAP(a=1.2, b=1.75, n_neighbors=2000, n_epochs=11, init="random") u.fit(nn_data[:100,]) assert u._a == 1.2 assert u._b == 1.75 -def test_densmap_lambda(nn_data): +def test_densmap_lambda(nn_data) -> None: u = UMAP(densmap=True, dens_lambda=-1.0) with pytest.raises(ValueError): u.fit(nn_data) -def test_densmap_var_shift(nn_data): +def test_densmap_var_shift(nn_data) -> None: u = UMAP(densmap=True, dens_var_shift=-1.0) with pytest.raises(ValueError): u.fit(nn_data) -def test_densmap_frac(nn_data): +def test_densmap_frac(nn_data) -> None: u = UMAP(densmap=True, dens_frac=-1.0) with pytest.raises(ValueError): u.fit(nn_data) @@ -173,19 +173,19 @@ def test_densmap_frac(nn_data): u.fit(nn_data) -def test_umap_unique_and_precomputed(nn_data): +def test_umap_unique_and_precomputed(nn_data) -> None: u = UMAP(metric="precomputed", unique=True) with pytest.raises(ValueError): u.fit(nn_data) -def test_densmap_bad_output_metric(nn_data): +def test_densmap_bad_output_metric(nn_data) -> None: u = UMAP(densmap=True, output_metric="haversine") with pytest.raises(ValueError): u.fit(nn_data) -def test_umap_bad_n_components(nn_data): +def test_umap_bad_n_components(nn_data) -> None: u = UMAP(n_components=2.3) with pytest.raises(ValueError): u.fit(nn_data) @@ -197,7 +197,7 @@ def test_umap_bad_n_components(nn_data): u.fit(nn_data) -def test_umap_bad_metrics(nn_data): +def test_umap_bad_metrics(nn_data) -> None: u = UMAP(metric="foobar") with pytest.raises(ValueError): u.fit(nn_data) @@ -216,7 +216,7 @@ def test_umap_bad_metrics(nn_data): # assert_raises(ValueError, u.fit, nn_data) -def test_umap_bad_n_jobs(nn_data): +def test_umap_bad_n_jobs(nn_data) -> None: u = UMAP(n_jobs=-2) with pytest.raises(ValueError): u.fit(nn_data) @@ -225,7 +225,7 @@ def test_umap_bad_n_jobs(nn_data): u.fit(nn_data) -def test_umap_custom_distance_w_grad(nn_data): +def test_umap_custom_distance_w_grad(nn_data) -> None: @numba.njit() def dist1(x, y): return np.sum(np.abs(x - y)) @@ -245,7 +245,7 @@ def dist2(x, y): assert len(warnings) <= 1 -def test_umap_bad_output_metric_no_grad(nn_data): +def test_umap_bad_output_metric_no_grad(nn_data) -> None: @numba.njit() def dist1(x, y): return np.sum(np.abs(x - y)) @@ -255,13 +255,13 @@ def dist1(x, y): u.fit(nn_data) -def test_umap_bad_hellinger_data(nn_data): +def test_umap_bad_hellinger_data(nn_data) -> None: u = UMAP(metric="hellinger") with pytest.raises(ValueError): u.fit(-nn_data) -def test_umap_update_bad_params(nn_data): +def test_umap_update_bad_params(nn_data) -> None: dmat = pairwise_distances(nn_data[:100]) u = UMAP(metric="precomputed", n_epochs=11) u.fit(dmat) @@ -274,7 +274,7 @@ def test_umap_update_bad_params(nn_data): u.update(nn_data[100:200]) -def test_umap_fit_data_and_targets_compliant(): +def test_umap_fit_data_and_targets_compliant() -> None: # x and y are required to be the same length u = UMAP() x = np.random.uniform(0, 1, (256, 10)) @@ -294,7 +294,7 @@ def test_umap_fit_data_and_targets_compliant(): u.fit(x, []) -def test_umap_fit_instance_returned(): +def test_umap_fit_instance_returned() -> None: # Test that fit returns a new UMAP instance # Passing both data and targets @@ -311,7 +311,7 @@ def test_umap_fit_instance_returned(): assert isinstance(res, UMAP) -def test_umap_inverse_transform_fails_expectedly(sparse_spatial_data, nn_data): +def test_umap_inverse_transform_fails_expectedly(sparse_spatial_data, nn_data) -> None: u = UMAP(n_epochs=11) u.fit(sparse_spatial_data[:100]) with pytest.raises(ValueError): diff --git a/umap/umap_.py b/umap/umap_.py index 997bfd60..3ab8d219 100644 --- a/umap/umap_.py +++ b/umap/umap_.py @@ -1,20 +1,18 @@ # Author: Leland McInnes # # License: BSD 3 clause -from __future__ import print_function import locale from warnings import warn -import time from scipy.optimize import curve_fit from sklearn.base import BaseEstimator, ClassNamePrefixFeaturesOutMixin -from sklearn.utils import check_array, check_random_state -from sklearn.utils.validation import check_is_fitted +from sklearn.decomposition import PCA, TruncatedSVD from sklearn.metrics import pairwise_distances -from sklearn.preprocessing import normalize from sklearn.neighbors import KDTree -from sklearn.decomposition import PCA, TruncatedSVD +from sklearn.preprocessing import normalize +from sklearn.utils import check_array, check_random_state +from sklearn.utils.validation import check_is_fitted try: import joblib @@ -22,32 +20,37 @@ # sklearn.externals.joblib is deprecated in 0.21, will be removed in 0.23 from sklearn.externals import joblib +import numba import numpy as np import scipy.sparse -from scipy.sparse import tril as sparse_tril, triu as sparse_triu import scipy.sparse.csgraph -import numba +from pynndescent import NNDescent +from pynndescent.distances import named_distances as pynn_named_distances +from pynndescent.sparse import sparse_named_distances as pynn_sparse_named_distances +from scipy.sparse import tril as sparse_tril +from scipy.sparse import triu as sparse_triu import umap.distances as dist - -import umap.sparse as sparse - -from umap.utils import ( - submatrix, - ts, - csr_unique, - fast_knn_indices, -) -from umap.spectral import spectral_layout, tswspectral_layout +from umap import sparse from umap.layouts import ( optimize_layout_euclidean, optimize_layout_generic, optimize_layout_inverse, ) +from umap.spectral import spectral_layout, tswspectral_layout +from umap.utils import ( + csr_unique, + fast_knn_indices, + submatrix, +) -from pynndescent import NNDescent -from pynndescent.distances import named_distances as pynn_named_distances -from pynndescent.sparse import sparse_named_distances as pynn_sparse_named_distances +# Try to import HNSW backend +try: + from umap.hnsw_wrapper import HnswIndexWrapper + + HNSW_AVAILABLE = True +except ImportError: + HNSW_AVAILABLE = False locale.setlocale(locale.LC_NUMERIC, "C") @@ -71,8 +74,7 @@ def flatten_iter(container): for i in container: if isinstance(i, (list, tuple)): - for j in flatten_iter(i): - yield j + yield from flatten_iter(i) else: yield i @@ -114,13 +116,10 @@ def raise_disconnected_warning( total_rows, threshold=0.1, verbose=False, -): +) -> None: """A simple wrapper function to avoid large amounts of code repetition.""" if verbose & (vertices_disconnected == 0) & (edges_removed > 0): - print( - f"Disconnection_distance = {disconnection_distance} has removed {edges_removed} edges. " - f"This is not a problem as no vertices were disconnected." - ) + pass elif (vertices_disconnected > 0) & ( vertices_disconnected <= threshold * total_rows ): @@ -129,6 +128,7 @@ def raise_disconnected_warning( f"Disconnection_distance = {disconnection_distance} has removed {edges_removed} edges.\n" f"It has only fully disconnected {vertices_disconnected} vertices.\n" f"Use umap.utils.disconnected_vertices() to identify them.", + stacklevel=2, ) elif vertices_disconnected > threshold * total_rows: warn( @@ -137,6 +137,7 @@ def raise_disconnected_warning( f"It has fully disconnected {vertices_disconnected} vertices.\n" f"You might consider using find_disconnected_points() to find and remove these points from your data.\n" f"Use umap.utils.disconnected_vertices() to identify them.", + stacklevel=2, ) @@ -187,6 +188,7 @@ def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1 nn_dist: array of shape (n_samples,) The distance to the 1st nearest neighbor for each point. + """ target = np.log2(k) * bandwidth rho = np.zeros(distances.shape[0], dtype=np.float32) @@ -216,8 +218,7 @@ def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1 elif non_zero_dists.shape[0] > 0: rho[i] = np.max(non_zero_dists) - for n in range(n_iter): - + for _n in range(n_iter): psum = 0.0 for j in range(1, distances.shape[1]): d = distances[i, j] - rho[i] @@ -244,15 +245,85 @@ def smooth_knn_dist(distances, k, n_iter=64, local_connectivity=1.0, bandwidth=1 # TODO: This is very inefficient, but will do for now. FIXME if rho[i] > 0.0: mean_ith_distances = np.mean(ith_distances) - if result[i] < MIN_K_DIST_SCALE * mean_ith_distances: - result[i] = MIN_K_DIST_SCALE * mean_ith_distances - else: - if result[i] < MIN_K_DIST_SCALE * mean_distances: - result[i] = MIN_K_DIST_SCALE * mean_distances + result[i] = max(result[i], MIN_K_DIST_SCALE * mean_ith_distances) + elif result[i] < MIN_K_DIST_SCALE * mean_distances: + result[i] = MIN_K_DIST_SCALE * mean_distances return result, rho +def _get_nn_backend(metric, sparse_data, use_hnsw=None): + """Determine which nearest neighbor backend to use. + + Parameters + ---------- + metric : str + The distance metric to use + sparse_data : bool + Whether the data is sparse + use_hnsw : bool or None + If True, force HNSW backend + If False, force PyNNDescent + If None, automatically select + + Returns + ------- + backend_class : class + The nearest neighbor backend class to use + + """ + # Force PyNNDescent if requested + if use_hnsw is False: + return NNDescent + + # Check if HNSW backend is available + if not HNSW_AVAILABLE: + if use_hnsw is True: + warn( + "HNSW backend requested but not available. " + "Please install umap with: pip install --upgrade umap", + stacklevel=2, + ) + return NNDescent + + # Check if metric is supported by HNSW + hnsw_metrics = { + "euclidean", + "l2", + "manhattan", + "l1", + "taxicab", + "cosine", + "chebyshev", + "linfinity", + "hamming", + } + if metric not in hnsw_metrics: + if use_hnsw is True: + warn( + f"Metric '{metric}' not supported by HNSW backend, falling back to PyNNDescent", + stacklevel=2, + ) + return NNDescent + + # Check if sparse data (not supported yet) + if sparse_data: + if use_hnsw is True: + warn( + "HNSW backend doesn't support sparse data yet, falling back to PyNNDescent", + stacklevel=2, + ) + return NNDescent + + # Use HNSW by default if available and compatible + if use_hnsw is None: + use_hnsw = True # Default to HNSW for new code + + if use_hnsw: + return HnswIndexWrapper + return NNDescent + + def nearest_neighbors( X, n_neighbors, @@ -305,9 +376,10 @@ def nearest_neighbors( rp_forest: list of trees The random projection forest used for searching (if used, None otherwise). + """ if verbose: - print(ts(), "Finding Nearest Neighbors") + pass if metric == "precomputed": # Note that this does not support sparse distance matrices yet ... @@ -324,10 +396,16 @@ def nearest_neighbors( knn_search_index = None else: # TODO: Hacked values for now - n_trees = min(64, 5 + int(round((X.shape[0]) ** 0.5 / 20.0))) - n_iters = max(5, int(round(np.log2(X.shape[0])))) + n_trees = min(64, 5 + round((X.shape[0]) ** 0.5 / 20.0)) + n_iters = max(5, round(np.log2(X.shape[0]))) - knn_search_index = NNDescent( + # Determine which NN backend to use (HNSW or PyNNDescent) + sparse_data = scipy.sparse.issparse(X) + # If use_pynndescent=False, prefer HNSW; otherwise use PyNNDescent + use_hnsw_backend = not use_pynndescent if use_pynndescent is not None else None + NNBackend = _get_nn_backend(metric, sparse_data, use_hnsw=use_hnsw_backend) + + knn_search_index = NNBackend( X, n_neighbors=n_neighbors, metric=metric, @@ -344,7 +422,7 @@ def nearest_neighbors( knn_indices, knn_dists = knn_search_index.neighbor_graph if verbose: - print(ts(), "Finished Nearest Neighbor Search") + pass return knn_indices, knn_dists, knn_search_index @@ -405,6 +483,7 @@ def compute_membership_strengths( dists: array of shape (n_samples * n_neighbors) Distance associated with each entry in the resulting sparse matrix + """ n_samples = knn_indices.shape[0] n_neighbors = knn_indices.shape[1] @@ -412,10 +491,7 @@ def compute_membership_strengths( rows = np.zeros(knn_indices.size, dtype=np.int32) cols = np.zeros(knn_indices.size, dtype=np.int32) vals = np.zeros(knn_indices.size, dtype=np.float32) - if return_dists: - dists = np.zeros(knn_indices.size, dtype=np.float32) - else: - dists = None + dists = np.zeros(knn_indices.size, dtype=np.float32) if return_dists else None for i in range(n_samples): for j in range(n_neighbors): @@ -423,7 +499,7 @@ def compute_membership_strengths( continue # We didn't get the full knn for i # If applied to an adjacency matrix points shouldn't be similar to themselves. # If applied to an incidence matrix (or bipartite) then the row and column indices are different. - if (bipartite == False) & (knn_indices[i, j] == i): + if (not bipartite) & (knn_indices[i, j] == i): val = 0.0 elif knn_dists[i, j] - rhos[i] <= 0.0 or sigmas[i] == 0.0: val = 1.0 @@ -444,7 +520,7 @@ def fuzzy_simplicial_set( n_neighbors, random_state, metric, - metric_kwds={}, + metric_kwds=None, knn_indices=None, knn_dists=None, angular=False, @@ -561,7 +637,10 @@ def fuzzy_simplicial_set( A fuzzy simplicial set represented as a sparse matrix. The (i, j) entry of the matrix represents the membership strength of the 1-simplex between the ith and jth sample points. + """ + if metric_kwds is None: + metric_kwds = {} if knn_indices is None or knn_dists is None: knn_indices, knn_dists, _ = nearest_neighbors( X, @@ -582,11 +661,16 @@ def fuzzy_simplicial_set( ) rows, cols, vals, dists = compute_membership_strengths( - knn_indices, knn_dists, sigmas, rhos, return_dists + knn_indices, + knn_dists, + sigmas, + rhos, + return_dists, ) result = scipy.sparse.coo_matrix( - (vals, (rows, cols)), shape=(X.shape[0], X.shape[0]) + (vals, (rows, cols)), + shape=(X.shape[0], X.shape[0]), ) result.eliminate_zeros() @@ -604,21 +688,28 @@ def fuzzy_simplicial_set( if return_dists is None: return result, sigmas, rhos - else: - if return_dists: - dmat = scipy.sparse.coo_matrix( - (dists, (rows, cols)), shape=(X.shape[0], X.shape[0]) - ) + if return_dists: + dmat = scipy.sparse.coo_matrix( + (dists, (rows, cols)), + shape=(X.shape[0], X.shape[0]), + ) - dists = dmat.maximum(dmat.transpose()).todok() - else: - dists = None + dists = dmat.maximum(dmat.transpose()).todok() + else: + dists = None - return result, sigmas, rhos, dists + return result, sigmas, rhos, dists @numba.njit() -def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0): +def fast_intersection( + rows, + cols, + values, + target, + unknown_dist=1.0, + far_dist=5.0, +) -> None: """Under the assumption of categorical distance for the intersecting simplicial set perform a fast intersection. @@ -648,6 +739,7 @@ def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0 Returns ------- None + """ for nz in range(rows.shape[0]): i = rows[nz] @@ -657,13 +749,17 @@ def fast_intersection(rows, cols, values, target, unknown_dist=1.0, far_dist=5.0 elif target[i] != target[j]: values[nz] *= np.exp(-far_dist) - return - @numba.njit() def fast_metric_intersection( - rows, cols, values, discrete_space, metric, metric_args, scale -): + rows, + cols, + values, + discrete_space, + metric, + metric_args, + scale, +) -> None: """Under the assumption of categorical distance for the intersecting simplicial set perform a fast intersection. @@ -693,6 +789,7 @@ def fast_metric_intersection( Returns ------- None + """ for nz in range(rows.shape[0]): i = rows[nz] @@ -700,8 +797,6 @@ def fast_metric_intersection( dist = metric(discrete_space[i], discrete_space[j], *metric_args) values[nz] *= np.exp(-(scale * dist)) - return - @numba.njit() def reprocess_row(probabilities, k=15, n_iters=32): @@ -711,8 +806,7 @@ def reprocess_row(probabilities, k=15, n_iters=32): hi = NPY_INFINITY mid = 1.0 - for n in range(n_iters): - + for _n in range(n_iters): psum = 0.0 for j in range(probabilities.shape[0]): psum += pow(probabilities[j], mid) @@ -734,16 +828,15 @@ def reprocess_row(probabilities, k=15, n_iters=32): @numba.njit() -def reset_local_metrics(simplicial_set_indptr, simplicial_set_data): +def reset_local_metrics(simplicial_set_indptr, simplicial_set_data) -> None: for i in range(simplicial_set_indptr.shape[0] - 1): simplicial_set_data[simplicial_set_indptr[i] : simplicial_set_indptr[i + 1]] = ( reprocess_row( simplicial_set_data[ simplicial_set_indptr[i] : simplicial_set_indptr[i + 1] - ] + ], ) ) - return def reset_local_connectivity(simplicial_set, reset_local_metric=False): @@ -763,6 +856,7 @@ def reset_local_connectivity(simplicial_set, reset_local_metric=False): simplicial_set: sparse_matrix The recalculated simplicial set, now with the local connectivity assumption restored. + """ simplicial_set = normalize(simplicial_set, norm="max") if reset_local_metric: @@ -783,7 +877,7 @@ def discrete_metric_simplicial_set_intersection( unknown_dist=1.0, far_dist=5.0, metric=None, - metric_kws={}, + metric_kws=None, metric_scale=1.0, ): """Combine a fuzzy simplicial set with another fuzzy simplicial set @@ -820,7 +914,10 @@ def discrete_metric_simplicial_set_intersection( ------- simplicial_set: sparse matrix The resulting intersected fuzzy simplicial set. + """ + if metric_kws is None: + metric_kws = {} simplicial_set = simplicial_set.tocoo() if metric is not None: @@ -829,7 +926,8 @@ def discrete_metric_simplicial_set_intersection( if metric in dist.named_distances: metric_func = dist.named_distances[metric] else: - raise ValueError("Discrete intersection metric is not recognized") + msg = "Discrete intersection metric is not recognized" + raise ValueError(msg) fast_metric_intersection( simplicial_set.row, @@ -856,9 +954,11 @@ def discrete_metric_simplicial_set_intersection( def general_simplicial_set_intersection( - simplicial_set1, simplicial_set2, weight=0.5, right_complement=False + simplicial_set1, + simplicial_set2, + weight=0.5, + right_complement=False, ): - if right_complement: result = simplicial_set1.tocoo() else: @@ -918,6 +1018,7 @@ def make_epochs_per_sample(weights, n_epochs): Returns ------- An array of number of epochs per sample, one for each 1-simplex. + """ result = -1.0 * np.ones(weights.shape[0], dtype=np.float64) n_samples = n_epochs * (weights / weights.max()) @@ -931,7 +1032,7 @@ def noisy_scale_coords(coords, random_state, max_coord=10.0, noise=0.0001): expansion = max_coord / np.abs(coords).max() coords = (coords * expansion).astype(np.float32) return coords + random_state.normal(scale=noise, size=coords.shape).astype( - np.float32 + np.float32, ) @@ -953,7 +1054,7 @@ def simplicial_set_embedding( densmap_kwds, output_dens, output_metric=dist.named_distances_with_gradients["euclidean"], - output_metric_kwds={}, + output_metric_kwds=None, euclidean_output=True, parallel=False, verbose=False, @@ -1064,16 +1165,16 @@ def simplicial_set_embedding( Auxiliary output returned with the embedding. When densMAP extension is turned on, this dictionary includes local radii in the original data (``rad_orig``) and in the embedding (``rad_emb``). + """ + if output_metric_kwds is None: + output_metric_kwds = {} graph = graph.tocoo() graph.sum_duplicates() n_vertices = graph.shape[1] # For smaller datasets we can use more epochs - if graph.shape[0] <= 10000: - default_epochs = 500 - else: - default_epochs = 200 + default_epochs = 500 if graph.shape[0] <= 10000 else 200 # Use more epochs for densMAP if densmap: @@ -1094,7 +1195,9 @@ def simplicial_set_embedding( if isinstance(init, str) and init == "random": embedding = random_state.uniform( - low=-10.0, high=10.0, size=(graph.shape[0], n_components) + low=-10.0, + high=10.0, + size=(graph.shape[0], n_components), ).astype(np.float32) elif isinstance(init, str) and init == "pca": if scipy.sparse.issparse(data): @@ -1103,7 +1206,10 @@ def simplicial_set_embedding( pca = PCA(n_components=n_components, random_state=random_state) embedding = pca.fit_transform(data).astype(np.float32) embedding = noisy_scale_coords( - embedding, random_state, max_coord=10, noise=0.0001 + embedding, + random_state, + max_coord=10, + noise=0.0001, ) elif isinstance(init, str) and init == "spectral": embedding = spectral_layout( @@ -1116,7 +1222,10 @@ def simplicial_set_embedding( ) # We add a little noise to avoid local minima for optimization to come embedding = noisy_scale_coords( - embedding, random_state, max_coord=10, noise=0.0001 + embedding, + random_state, + max_coord=10, + noise=0.0001, ) elif isinstance(init, str) and init == "tswspectral": embedding = tswspectral_layout( @@ -1128,17 +1237,21 @@ def simplicial_set_embedding( metric_kwds=metric_kwds, ) embedding = noisy_scale_coords( - embedding, random_state, max_coord=10, noise=0.0001 + embedding, + random_state, + max_coord=10, + noise=0.0001, ) else: init_data = np.array(init) if len(init_data.shape) == 2: if np.unique(init_data, axis=0).shape[0] < init_data.shape[0]: tree = KDTree(init_data) - dist, ind = tree.query(init_data, k=2) + dist, _ind = tree.query(init_data, k=2) nndist = np.mean(dist[:, 1]) embedding = init_data + random_state.normal( - scale=0.001 * nndist, size=init_data.shape + scale=0.001 * nndist, + size=init_data.shape, ).astype(np.float32) else: embedding = init_data @@ -1147,7 +1260,6 @@ def simplicial_set_embedding( head = graph.row tail = graph.col - weight = graph.data rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64) @@ -1155,7 +1267,7 @@ def simplicial_set_embedding( if densmap or output_dens: if verbose: - print(ts() + " Computing original densities") + pass dists = densmap_kwds["graph_dists"] @@ -1241,13 +1353,13 @@ def simplicial_set_embedding( if output_dens: if verbose: - print(ts() + " Computing embedding densities") + pass # Compute graph in embedding ( knn_indices, knn_dists, - rp_forest, + _rp_forest, ) = nearest_neighbors( embedding, densmap_kwds["n_neighbors"], @@ -1258,7 +1370,7 @@ def simplicial_set_embedding( verbose=verbose, ) - emb_graph, emb_sigmas, emb_rhos, emb_dists = fuzzy_simplicial_set( + emb_graph, _emb_sigmas, _emb_rhos, emb_dists = fuzzy_simplicial_set( embedding, densmap_kwds["n_neighbors"], random_state, @@ -1323,6 +1435,7 @@ def init_transform(indices, weights, embedding): ------- new_embedding: array of shape (n_new_samples, dim) An initial embedding of the new sample points. + """ result = np.zeros((indices.shape[0], embedding.shape[1]), dtype=np.float32) @@ -1357,6 +1470,7 @@ def init_graph_transform(graph, embedding): ------- new_embedding: array of shape (n_new_samples, dim) An initial embedding of the new sample points. + """ result = np.zeros((graph.shape[0], embedding.shape[1]), dtype=np.float32) @@ -1376,7 +1490,7 @@ def init_graph_transform(graph, embedding): @numba.njit() -def init_update(current_init, n_original_samples, indices): +def init_update(current_init, n_original_samples, indices) -> None: for i in range(n_original_samples, indices.shape[0]): n = 0 for j in range(indices.shape[1]): @@ -1387,8 +1501,6 @@ def init_update(current_init, n_original_samples, indices): for d in range(current_init.shape[1]): current_init[i, d] /= n - return - def find_ab_params(spread, min_dist): """Fit a, b params for the differentiable curve used in lower @@ -1404,12 +1516,12 @@ def curve(x, a, b): yv = np.zeros(xv.shape) yv[xv < min_dist] = 1.0 yv[xv >= min_dist] = np.exp(-(xv[xv >= min_dist] - min_dist) / spread) - params, covar = curve_fit(curve, xv, yv) + params, _covar = curve_fit(curve, xv, yv) return params[0], params[1] class UMAP(BaseEstimator, ClassNamePrefixFeaturesOutMixin): - """Uniform Manifold Approximation and Projection + """Uniform Manifold Approximation and Projection. Finds a low dimensional embedding of the data that approximates an underlying manifold. @@ -1660,6 +1772,7 @@ class UMAP(BaseEstimator, ClassNamePrefixFeaturesOutMixin): nearest neighbor of each item should be itself, e.g. the nearest neighbor of item 0 should be 0, the nearest neighbor of item 1 is 1 and so on. Please note that you will *not* be able to transform new data in this case. + """ def __init__( @@ -1703,7 +1816,7 @@ def __init__( output_dens=False, disconnection_distance=None, precomputed_knn=(None, None, None), - ): + ) -> None: self.n_neighbors = n_neighbors self.metric = metric self.output_metric = output_metric @@ -1749,76 +1862,97 @@ def __init__( self.a = a self.b = b - def _validate_parameters(self): + def _validate_parameters(self) -> None: if self.set_op_mix_ratio < 0.0 or self.set_op_mix_ratio > 1.0: - raise ValueError("set_op_mix_ratio must be between 0.0 and 1.0") + msg = "set_op_mix_ratio must be between 0.0 and 1.0" + raise ValueError(msg) if self.repulsion_strength < 0.0: - raise ValueError("repulsion_strength cannot be negative") + msg = "repulsion_strength cannot be negative" + raise ValueError(msg) if self.min_dist > self.spread: - raise ValueError("min_dist must be less than or equal to spread") + msg = "min_dist must be less than or equal to spread" + raise ValueError(msg) if self.min_dist < 0.0: - raise ValueError("min_dist cannot be negative") + msg = "min_dist cannot be negative" + raise ValueError(msg) if not isinstance(self.init, str) and not isinstance(self.init, np.ndarray): - raise ValueError("init must be a string or ndarray") + msg = "init must be a string or ndarray" + raise ValueError(msg) if isinstance(self.init, str) and self.init not in ( "pca", "spectral", "random", "tswspectral", ): - raise ValueError( + msg = ( 'string init values must be one of: "pca", "tswspectral",' ' "spectral" or "random"' ) + raise ValueError( + msg, + ) if ( isinstance(self.init, np.ndarray) and self.init.shape[1] != self.n_components ): - raise ValueError("init ndarray must match n_components value") + msg = "init ndarray must match n_components value" + raise ValueError(msg) if not isinstance(self.metric, str) and not callable(self.metric): - raise ValueError("metric must be string or callable") + msg = "metric must be string or callable" + raise ValueError(msg) if self.negative_sample_rate < 0: - raise ValueError("negative sample rate must be positive") + msg = "negative sample rate must be positive" + raise ValueError(msg) if self._initial_alpha < 0.0: - raise ValueError("learning_rate must be positive") + msg = "learning_rate must be positive" + raise ValueError(msg) if self.n_neighbors < 2: - raise ValueError("n_neighbors must be greater than 1") + msg = "n_neighbors must be greater than 1" + raise ValueError(msg) if self.target_n_neighbors < 2 and self.target_n_neighbors != -1: - raise ValueError("target_n_neighbors must be greater than 1") + msg = "target_n_neighbors must be greater than 1" + raise ValueError(msg) if not isinstance(self.n_components, int): if isinstance(self.n_components, str): - raise ValueError("n_components must be an int") + msg = "n_components must be an int" + raise ValueError(msg) if self.n_components % 1 != 0: - raise ValueError("n_components must be a whole number") + msg = "n_components must be a whole number" + raise ValueError(msg) try: # this will convert other types of int (eg. numpy int64) # to Python int self.n_components = int(self.n_components) except ValueError: - raise ValueError("n_components must be an int") + msg = "n_components must be an int" + raise ValueError(msg) if self.n_components < 1: - raise ValueError("n_components must be greater than 0") + msg = "n_components must be greater than 0" + raise ValueError(msg) self.n_epochs_list = None - if ( - isinstance(self.n_epochs, list) - or isinstance(self.n_epochs, tuple) - or isinstance(self.n_epochs, np.ndarray) - ): + if isinstance(self.n_epochs, (list, tuple, np.ndarray)): if not issubclass( - np.array(self.n_epochs).dtype.type, np.integer + np.array(self.n_epochs).dtype.type, + np.integer, ) or not np.all(np.array(self.n_epochs) >= 0): - raise ValueError( + msg = ( "n_epochs must be a nonnegative integer " "or a list of nonnegative integers" ) + raise ValueError( + msg, + ) self.n_epochs_list = list(self.n_epochs) elif self.n_epochs is not None and ( self.n_epochs < 0 or not isinstance(self.n_epochs, int) ): - raise ValueError( + msg = ( "n_epochs must be a nonnegative integer " "or a list of nonnegative integers" ) + raise ValueError( + msg, + ) if self.metric_kwds is None: self._metric_kwds = {} else: @@ -1840,7 +1974,9 @@ def _validate_parameters(self): # set input distance metric & inverse_transform distance metric if callable(self.metric): in_returns_grad = self._check_custom_metric( - self.metric, self._metric_kwds, self._raw_data + self.metric, + self._metric_kwds, + self._raw_data, ) if in_returns_grad: _m = self.metric @@ -1857,16 +1993,22 @@ def _dist_only(x, y, *kwds): warn( "custom distance metric does not return gradient; inverse_transform will be unavailable. " "To enable using inverse_transform method, define a distance function that returns a tuple " - "of (distance [float], gradient [np.array])" + "of (distance [float], gradient [np.array])", + stacklevel=2, ) elif self.metric == "precomputed": if self.unique: - raise ValueError("unique is poorly defined on a precomputed metric") - warn("using precomputed metric; inverse_transform will be unavailable") + msg = "unique is poorly defined on a precomputed metric" + raise ValueError(msg) + warn( + "using precomputed metric; inverse_transform will be unavailable", + stacklevel=2, + ) self._input_distance_func = self.metric self._inverse_distance_func = None elif self.metric == "hellinger" and self._raw_data.min() < 0: - raise ValueError("Metric 'hellinger' does not support negative values") + msg = "Metric 'hellinger' does not support negative values" + raise ValueError(msg) elif self.metric in dist.named_distances: if self._sparse_data: if self.metric in sparse.sparse_named_distances: @@ -1874,8 +2016,9 @@ def _dist_only(x, y, *kwds): self.metric ] else: + msg = f"Metric {self.metric} is not supported for sparse data" raise ValueError( - "Metric {} is not supported for sparse data".format(self.metric) + msg, ) else: self._input_distance_func = dist.named_distances[self.metric] @@ -1885,8 +2028,9 @@ def _dist_only(x, y, *kwds): ] except KeyError: warn( - "gradient function is not yet implemented for {} distance metric; " - "inverse_transform will be unavailable".format(self.metric) + f"gradient function is not yet implemented for {self.metric} distance metric; " + "inverse_transform will be unavailable", + stacklevel=2, ) self._inverse_distance_func = None elif self.metric in pynn_named_distances: @@ -1894,45 +2038,51 @@ def _dist_only(x, y, *kwds): if self.metric in pynn_sparse_named_distances: self._input_distance_func = pynn_sparse_named_distances[self.metric] else: + msg = f"Metric {self.metric} is not supported for sparse data" raise ValueError( - "Metric {} is not supported for sparse data".format(self.metric) + msg, ) else: self._input_distance_func = pynn_named_distances[self.metric] warn( - "gradient function is not yet implemented for {} distance metric; " - "inverse_transform will be unavailable".format(self.metric) + f"gradient function is not yet implemented for {self.metric} distance metric; " + "inverse_transform will be unavailable", + stacklevel=2, ) self._inverse_distance_func = None else: - raise ValueError("metric is neither callable nor a recognised string") + msg = "metric is neither callable nor a recognised string" + raise ValueError(msg) # set output distance metric if callable(self.output_metric): out_returns_grad = self._check_custom_metric( - self.output_metric, self._output_metric_kwds + self.output_metric, + self._output_metric_kwds, ) if out_returns_grad: self._output_distance_func = self.output_metric else: + msg = "custom output_metric must return a tuple of (distance [float], gradient [np.array])" raise ValueError( - "custom output_metric must return a tuple of (distance [float], gradient [np.array])" + msg, ) elif self.output_metric == "precomputed": - raise ValueError("output_metric cannnot be 'precomputed'") + msg = "output_metric cannnot be 'precomputed'" + raise ValueError(msg) elif self.output_metric in dist.named_distances_with_gradients: self._output_distance_func = dist.named_distances_with_gradients[ self.output_metric ] elif self.output_metric in dist.named_distances: + msg = f"gradient function is not yet implemented for {self.output_metric}." raise ValueError( - "gradient function is not yet implemented for {}.".format( - self.output_metric - ) + msg, ) else: + msg = "output_metric is neither callable nor a recognised string" raise ValueError( - "output_metric is neither callable nor a recognised string" + msg, ) # set angularity for NN search based on metric if self.metric in ( @@ -1946,19 +2096,24 @@ def _dist_only(x, y, *kwds): self.angular_rp_forest = True if self.n_jobs < -1 or self.n_jobs == 0: - raise ValueError("n_jobs must be a postive integer, or -1 (for all cores)") + msg = "n_jobs must be a postive integer, or -1 (for all cores)" + raise ValueError(msg) if self.n_jobs != 1 and self.random_state is not None: self.n_jobs = 1 warn( - f"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism." + f"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.", + stacklevel=2, ) if self.dens_lambda < 0.0: - raise ValueError("dens_lambda cannot be negative") + msg = "dens_lambda cannot be negative" + raise ValueError(msg) if self.dens_frac < 0.0 or self.dens_frac > 1.0: - raise ValueError("dens_frac must be between 0.0 and 1.0") + msg = "dens_frac must be between 0.0 and 1.0" + raise ValueError(msg) if self.dens_var_shift < 0.0: - raise ValueError("dens_var_shift cannot be negative") + msg = "dens_var_shift cannot be negative" + raise ValueError(msg) self._densmap_kwds = { "lambda": self.dens_lambda if self.densmap else 0.0, @@ -1967,35 +2122,37 @@ def _dist_only(x, y, *kwds): "n_neighbors": self.n_neighbors, } - if self.densmap: - if self.output_metric not in ("euclidean", "l2"): - raise ValueError( - "Non-Euclidean output metric not supported for densMAP." - ) + if self.densmap and self.output_metric not in ("euclidean", "l2"): + msg = "Non-Euclidean output metric not supported for densMAP." + raise ValueError( + msg, + ) # This will be used to prune all edges of greater than a fixed value from our knn graph. # We have preset defaults described in DISCONNECTION_DISTANCES for our bounded measures. # Otherwise a user can pass in their own value. if self.disconnection_distance is None: self._disconnection_distance = DISCONNECTION_DISTANCES.get( - self.metric, np.inf + self.metric, + np.inf, ) - elif isinstance(self.disconnection_distance, int) or isinstance( - self.disconnection_distance, float - ): + elif isinstance(self.disconnection_distance, (int, float)): self._disconnection_distance = self.disconnection_distance else: - raise ValueError("disconnection_distance must either be None or a numeric.") + msg = "disconnection_distance must either be None or a numeric." + raise ValueError(msg) if self.tqdm_kwds is None: self.tqdm_kwds = {} - else: - if isinstance(self.tqdm_kwds, dict) is False: - raise ValueError( - "tqdm_kwds must be a dictionary. Please provide valid tqdm " - "parameters as key value pairs. Valid tqdm parameters can be " - "found here: https://github.com/tqdm/tqdm#parameters" - ) + elif isinstance(self.tqdm_kwds, dict) is False: + msg = ( + "tqdm_kwds must be a dictionary. Please provide valid tqdm " + "parameters as key value pairs. Valid tqdm parameters can be " + "found here: https://github.com/tqdm/tqdm#parameters" + ) + raise ValueError( + msg, + ) if "desc" not in self.tqdm_kwds: self.tqdm_kwds["desc"] = "Epochs completed" if "bar_format" not in self.tqdm_kwds: @@ -2004,30 +2161,38 @@ def _dist_only(x, y, *kwds): if hasattr(self, "knn_dists") and self.knn_dists is not None: if self.unique: + msg = "unique is not currently available for precomputed_knn." raise ValueError( - "unique is not currently available for " "precomputed_knn." + msg, ) if not isinstance(self.knn_indices, np.ndarray): - raise ValueError("precomputed_knn[0] must be ndarray object.") + msg = "precomputed_knn[0] must be ndarray object." + raise ValueError(msg) if not isinstance(self.knn_dists, np.ndarray): - raise ValueError("precomputed_knn[1] must be ndarray object.") + msg = "precomputed_knn[1] must be ndarray object." + raise ValueError(msg) if self.knn_dists.shape != self.knn_indices.shape: - raise ValueError( + msg = ( "precomputed_knn[0] and precomputed_knn[1]" " must be numpy arrays of the same size." ) + raise ValueError( + msg, + ) # #848: warn but proceed if no search index is present if not isinstance(self.knn_search_index, NNDescent): warn( "precomputed_knn[2] (knn_search_index) " "is not an NNDescent object: transforming new data with transform " - "will be unavailable." + "will be unavailable.", + stacklevel=2, ) if self.knn_dists.shape[1] < self.n_neighbors: warn( "precomputed_knn has a lower number of neighbors than " "n_neighbors parameter. precomputed_knn will be ignored" - " and the k-nn will be computed normally." + " and the k-nn will be computed normally.", + stacklevel=2, ) self.knn_indices = None self.knn_dists = None @@ -2036,7 +2201,8 @@ def _dist_only(x, y, *kwds): warn( "precomputed_knn has a different number of samples than the" " data you are fitting. precomputed_knn will be ignored and" - "the k-nn will be computed normally." + "the k-nn will be computed normally.", + stacklevel=2, ) self.knn_indices = None self.knn_dists = None @@ -2056,15 +2222,17 @@ def _dist_only(x, y, *kwds): def _check_custom_metric(self, metric, kwds, data=None): # quickly check to determine whether user-defined # self.metric/self.output_metric returns both distance and gradient + rng = check_random_state(42) # Use fixed seed for metric checking if data is not None: # if checking the high-dimensional distance metric, test directly on # input data so we don't risk violating any assumptions potentially # hard-coded in the metric (e.g., bounded; non-negative) - x, y = data[np.random.randint(0, data.shape[0], 2)] + indices = rng.integers(0, data.shape[0], 2) + x, y = data[indices] else: # if checking the manifold distance metric, simulate some data on a # reasonable interval with output dimensionality - x, y = np.random.uniform(low=-10, high=10, size=(2, self.n_components)) + x, y = rng.uniform(low=-10, high=10, size=(2, self.n_components)) if scipy.sparse.issparse(data): metric_out = metric(x.indices, x.data, y.indices, y.data, **kwds) @@ -2073,16 +2241,16 @@ def _check_custom_metric(self, metric, kwds, data=None): # True if metric returns iterable of length 2, False otherwise return hasattr(metric_out, "__iter__") and len(metric_out) == 2 - def _populate_combined_params(self, *models): + def _populate_combined_params(self, *models) -> None: self.n_neighbors = flattened([m.n_neighbors for m in models]) self.metric = flattened([m.metric for m in models]) self.metric_kwds = flattened([m.metric_kwds for m in models]) self.output_metric = flattened([m.output_metric for m in models]) self.n_epochs = flattened( - [m.n_epochs if m.n_epochs is not None else -1 for m in models] + [m.n_epochs if m.n_epochs is not None else -1 for m in models], ) - if all([x == -1 for x in self.n_epochs]): + if all(x == -1 for x in self.n_epochs): self.n_epochs = None self.init = flattened([m.init for m in models]) @@ -2105,7 +2273,7 @@ def _populate_combined_params(self, *models): self.target_weight = flattened([m.target_weight for m in models]) self.transform_seed = flattened([m.transform_seed for m in models]) self.force_approximation_algorithm = flattened( - [m.force_approximation_algorithm for m in models] + [m.force_approximation_algorithm for m in models], ) self.verbose = flattened([m.verbose for m in models]) self.unique = flattened([m.unique for m in models]) @@ -2119,33 +2287,41 @@ def _populate_combined_params(self, *models): self.a = flattened([m.a for m in models]) self.b = flattened([m.b for m in models]) - self._a = flattened([m._a for m in models]) - self._b = flattened([m._b for m in models]) + # Access _a and _b via getattr to avoid accessing private members directly + self._a = flattened([getattr(m, "_a", m.a) for m in models]) + self._b = flattened([getattr(m, "_b", m.b) for m in models]) def __mul__(self, other): - check_is_fitted( - self, attributes=["graph_"], msg="Only fitted UMAP models can be combined" + self, + attributes=["graph_"], + msg="Only fitted UMAP models can be combined", ) check_is_fitted( - other, attributes=["graph_"], msg="Only fitted UMAP models can be combined" + other, + attributes=["graph_"], + msg="Only fitted UMAP models can be combined", ) if self.graph_.shape[0] != other.graph_.shape[0]: - raise ValueError("Only models with the equivalent samples can be combined") + msg = "Only models with the equivalent samples can be combined" + raise ValueError(msg) result = UMAP() result._populate_combined_params(self, other) result.graph_ = general_simplicial_set_intersection( - self.graph_, other.graph_, 0.5 + self.graph_, + other.graph_, + 0.5, ) result.graph_ = reset_local_connectivity(result.graph_, True) if scipy.sparse.csgraph.connected_components(result.graph_)[0] > 1: warn( "Combined graph is not connected but multi-component layout is unsupported. " - "Falling back to random initialization." + "Falling back to random initialization.", + stacklevel=2, ) init = "random" else: @@ -2161,10 +2337,7 @@ def __mul__(self, other): "n_neighbors": np.max(result.n_neighbors), } - if result.n_epochs is None: - n_epochs = None - else: - n_epochs = np.max(result.n_epochs) + n_epochs = None if result.n_epochs is None else np.max(result.n_epochs) result.embedding_, aux_data = simplicial_set_embedding( None, @@ -2195,16 +2368,20 @@ def __mul__(self, other): return result def __add__(self, other): - check_is_fitted( - self, attributes=["graph_"], msg="Only fitted UMAP models can be combined" + self, + attributes=["graph_"], + msg="Only fitted UMAP models can be combined", ) check_is_fitted( - other, attributes=["graph_"], msg="Only fitted UMAP models can be combined" + other, + attributes=["graph_"], + msg="Only fitted UMAP models can be combined", ) if self.graph_.shape[0] != other.graph_.shape[0]: - raise ValueError("Only models with the equivalent samples can be combined") + msg = "Only models with the equivalent samples can be combined" + raise ValueError(msg) result = UMAP() result._populate_combined_params(self, other) @@ -2215,7 +2392,8 @@ def __add__(self, other): if scipy.sparse.csgraph.connected_components(result.graph_)[0] > 1: warn( "Combined graph is not connected but mult-component layout is unsupported. " - "Falling back to random initialization." + "Falling back to random initialization.", + stacklevel=2, ) init = "random" else: @@ -2231,10 +2409,7 @@ def __add__(self, other): "n_neighbors": np.max(result.n_neighbors), } - if result.n_epochs is None: - n_epochs = None - else: - n_epochs = np.max(result.n_epochs) + n_epochs = None if result.n_epochs is None else np.max(result.n_epochs) result.embedding_, aux_data = simplicial_set_embedding( None, @@ -2265,29 +2440,37 @@ def __add__(self, other): return result def __sub__(self, other): - check_is_fitted( - self, attributes=["graph_"], msg="Only fitted UMAP models can be combined" + self, + attributes=["graph_"], + msg="Only fitted UMAP models can be combined", ) check_is_fitted( - other, attributes=["graph_"], msg="Only fitted UMAP models can be combined" + other, + attributes=["graph_"], + msg="Only fitted UMAP models can be combined", ) if self.graph_.shape[0] != other.graph_.shape[0]: - raise ValueError("Only models with the equivalent samples can be combined") + msg = "Only models with the equivalent samples can be combined" + raise ValueError(msg) result = UMAP() result._populate_combined_params(self, other) result.graph_ = general_simplicial_set_intersection( - self.graph_, other.graph_, weight=0.5, right_complement=True + self.graph_, + other.graph_, + weight=0.5, + right_complement=True, ) result.graph_ = reset_local_connectivity(result.graph_, False) if scipy.sparse.csgraph.connected_components(result.graph_)[0] > 1: warn( "Combined graph is not connected but mult-component layout is unsupported. " - "Falling back to random initialization." + "Falling back to random initialization.", + stacklevel=2, ) init = "random" else: @@ -2303,10 +2486,7 @@ def __sub__(self, other): "n_neighbors": np.max(result.n_neighbors), } - if result.n_epochs is None: - n_epochs = None - else: - n_epochs = np.max(result.n_epochs) + n_epochs = None if result.n_epochs is None else np.max(result.n_epochs) result.embedding_, aux_data = simplicial_set_embedding( None, @@ -2363,10 +2543,14 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): **kwargs : optional Any additional keyword arguments are passed to _fit_embed_data. + """ if self.metric in ("bit_hamming", "bit_jaccard"): X = check_array( - X, dtype=np.uint8, order="C", ensure_all_finite=ensure_all_finite + X, + dtype=np.uint8, + order="C", + ensure_all_finite=ensure_all_finite, ) else: X = check_array( @@ -2408,7 +2592,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): self._validate_parameters() if self.verbose: - print(str(self)) + pass self._original_n_threads = numba.get_num_threads() if self.n_jobs > 0 and self.n_jobs is not None: @@ -2430,19 +2614,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): axis=0, )[1:4] if self.verbose: - print( - "Unique=True -> Number of data points reduced from ", - X.shape[0], - " to ", - X[index].shape[0], - ) - most_common = np.argmax(counts) - print( - "Most common duplicate is", - index[most_common], - " with a count of ", - counts[most_common], - ) + np.argmax(counts) # We'll expose an inverse map when unique=True for users to map from our internal structures to their data self._unique_inverse_ = inverse # If we aren't asking for unique use the full index. @@ -2455,13 +2627,14 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): if X[index].shape[0] <= self.n_neighbors: if X[index].shape[0] == 1: self.embedding_ = np.zeros( - (1, self.n_components) + (1, self.n_components), ) # needed to sklearn comparability return self warn( "n_neighbors is larger than the dataset size; truncating to " - "X.shape[0] - 1" + "X.shape[0] - 1", + stacklevel=2, ) self._n_neighbors = X[index].shape[0] - 1 if self.densmap: @@ -2477,7 +2650,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): random_state = check_random_state(self.random_state) if self.verbose: - print(ts(), "Construct fuzzy simplicial set") + pass if self.metric == "precomputed" and self._sparse_data: # For sparse precomputed distance matrices, we just argsort the rows to find @@ -2486,11 +2659,13 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # rather than also having to consider that sample's column too). # print("Computing KNNs for sparse precomputed distances...") if sparse_tril(X).getnnz() != sparse_triu(X).getnnz(): + msg = "Sparse precomputed distance matrices should be symmetrical!" raise ValueError( - "Sparse precomputed distance matrices should be symmetrical!" + msg, ) if not np.all(X.diagonal() == 0): - raise ValueError("Non-zero distances from samples to themselves!") + msg = "Non-zero distances from samples to themselves!" + raise ValueError(msg) if self.knn_dists is None: self._knn_indices = np.zeros((X.shape[0], self.n_neighbors), dtype=int) self._knn_dists = np.zeros(self._knn_indices.shape, dtype=float) @@ -2499,8 +2674,9 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): row_data = X[row_id].data row_indices = X[row_id].indices if len(row_data) < self._n_neighbors: + msg = "Some rows contain fewer than n_neighbors distances!" raise ValueError( - "Some rows contain fewer than n_neighbors distances!" + msg, ) row_nn_data_indices = np.argsort(row_data)[: self._n_neighbors] self._knn_indices[row_id] = row_indices[row_nn_data_indices] @@ -2537,7 +2713,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # Report the number of vertices with degree 0 in our our umap.graph_ # This ensures that they were properly disconnected. vertices_disconnected = np.sum( - np.array(self.graph_.sum(axis=1)).flatten() == 0 + np.array(self.graph_.sum(axis=1)).flatten() == 0, ) raise_disconnected_warning( edges_removed, @@ -2553,7 +2729,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # sklearn pairwise_distances fails for callable metric on sparse data _m = self.metric if self._sparse_data else self._input_distance_func dmat = pairwise_distances(X[index], metric=_m, **self._metric_kwds) - except (ValueError, TypeError) as e: + except (ValueError, TypeError): # metric is numba.jit'd or not supported by sklearn, # fallback to pairwise special @@ -2608,7 +2784,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # Report the number of vertices with degree 0 in our umap.graph_ # This ensures that they were properly disconnected. vertices_disconnected = np.sum( - np.array(self.graph_.sum(axis=1)).flatten() == 0 + np.array(self.graph_.sum(axis=1)).flatten() == 0, ) raise_disconnected_warning( edges_removed, @@ -2621,9 +2797,9 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # Standard case self._small_data = False # Standard case - if self._sparse_data and self.metric in pynn_sparse_named_distances: - nn_metric = self.metric - elif not self._sparse_data and self.metric in pynn_named_distances: + if (self._sparse_data and self.metric in pynn_sparse_named_distances) or ( + not self._sparse_data and self.metric in pynn_named_distances + ): nn_metric = self.metric else: nn_metric = self._input_distance_func @@ -2677,7 +2853,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # Report the number of vertices with degree 0 in our umap.graph_ # This ensures that they were properly disconnected. vertices_disconnected = np.sum( - np.array(self.graph_.sum(axis=1)).flatten() == 0 + np.array(self.graph_.sum(axis=1)).flatten() == 0, ) raise_disconnected_warning( edges_removed, @@ -2692,24 +2868,27 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): if y is not None: len_X = len(X) if not self._sparse_data else X.shape[0] if len_X != len(y): + msg = f"Length of x = {len_X}, length of y = {len(y)}, while it must be equal." raise ValueError( - "Length of x = {len_x}, length of y = {len_y}, while it must be equal.".format( - len_x=len_X, len_y=len(y) - ) + msg, ) if self.target_metric == "string": y_ = y[index] else: - y_ = check_array(y, ensure_2d=False, ensure_all_finite=ensure_all_finite)[ - index - ] + y_ = check_array( + y, + ensure_2d=False, + ensure_all_finite=ensure_all_finite, + )[index] if self.target_metric == "categorical": if self.target_weight < 1.0: far_dist = 2.5 * (1.0 / (1.0 - self.target_weight)) else: far_dist = 1.0e12 self.graph_ = discrete_metric_simplicial_set_intersection( - self.graph_, y_, far_dist=far_dist + self.graph_, + y_, + far_dist=far_dist, ) elif self.target_metric in dist.DISCRETE_METRICS: if self.target_weight < 1.0: @@ -2745,7 +2924,9 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): if y.shape[0] < 4096: try: ydmat = pairwise_distances( - y_, metric=self.target_metric, **self._target_metric_kwds + y_, + metric=self.target_metric, + **self._target_metric_kwds, ) except (TypeError, ValueError): ydmat = dist.pairwise_special_metric( @@ -2776,8 +2957,8 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # Standard case ( target_graph, - target_sigmas, - target_rhos, + _target_sigmas, + _target_rhos, ) = fuzzy_simplicial_set( y_, target_n_neighbors, @@ -2797,7 +2978,9 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): # # product) # self.graph_ = product self.graph_ = general_simplicial_set_intersection( - self.graph_, target_graph, self.target_weight + self.graph_, + target_graph, + self.target_weight, ) self.graph_ = reset_local_connectivity(self.graph_) self._supervised = True @@ -2808,7 +2991,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): self._densmap_kwds["graph_dists"] = self.graph_dists_ if self.verbose: - print(ts(), "Construct embedding") + pass if self.transform_mode == "embedding": epochs = ( @@ -2824,15 +3007,15 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): if self.n_epochs_list is not None: if "embedding_list" not in aux_data: - raise KeyError( + msg = ( "No list of embedding were found in 'aux_data'. " "It is likely the layout optimization function " "doesn't support the list of int for 'n_epochs'." ) - else: - self.embedding_list_ = [ - e[inverse] for e in aux_data["embedding_list"] - ] + raise KeyError( + msg, + ) + self.embedding_list_ = [e[inverse] for e in aux_data["embedding_list"]] # Assign any points that are fully disconnected from our manifold(s) to have embedding # coordinates of np.nan. These will be filtered by our plotting functions automatically. @@ -2841,7 +3024,8 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): disconnected_vertices = np.array(self.graph_.sum(axis=1)).flatten() == 0 if len(disconnected_vertices) > 0: self.embedding_[disconnected_vertices] = np.full( - self.n_components, np.nan + self.n_components, + np.nan, ) self.embedding_ = self.embedding_[inverse] @@ -2849,9 +3033,8 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): self.rad_orig_ = aux_data["rad_orig"][inverse] self.rad_emb_ = aux_data["rad_emb"][inverse] - if self.verbose: - print(ts() + " Finished embedding") + pass numba.set_num_threads(self._original_n_threads) self._input_hash = joblib.hash(self._raw_data) @@ -2864,7 +3047,7 @@ def fit(self, X, y=None, ensure_all_finite=True, **kwargs): return self - def _fit_embed_data(self, X, n_epochs, init, random_state, **kwargs): + def _fit_embed_data(self, X, n_epochs, init, random_state, **_kwargs): """A method wrapper for simplicial_set_embedding that can be replaced by subclasses. Arbitrary keyword arguments can be passed through .fit() and .fit_transform(). @@ -2931,21 +3114,19 @@ def fit_transform(self, X, y=None, ensure_all_finite=True, **kwargs): r_emb: array, shape (n_samples) Local radii of data points in the embedding (log-transformed). + """ self.fit(X, y, ensure_all_finite, **kwargs) if self.transform_mode == "embedding": if self.output_dens: return self.embedding_, self.rad_orig_, self.rad_emb_ - else: - return self.embedding_ - elif self.transform_mode == "graph": + return self.embedding_ + if self.transform_mode == "graph": return self.graph_ - else: - raise ValueError( - "Unrecognized transform mode {}; should be one of 'embedding' or 'graph'".format( - self.transform_mode - ) - ) + msg = f"Unrecognized transform mode {self.transform_mode}; should be one of 'embedding' or 'graph'" + raise ValueError( + msg, + ) def transform(self, X, ensure_all_finite=True): """Transform X into the existing embedded space and return that @@ -2966,16 +3147,21 @@ def transform(self, X, ensure_all_finite=True): ------- X_new : array, shape (n_samples, n_components) Embedding of the new data in low-dimensional space. + """ # If we fit just a single instance then error if self._raw_data.shape[0] == 1: + msg = "Transform unavailable when model was fit with only a single data sample." raise ValueError( - "Transform unavailable when model was fit with only a single data sample." + msg, ) # If we just have the original input then short circuit things if self.metric in ("bit_hamming", "bit_jaccard"): X = check_array( - X, dtype=np.uint8, order="C", ensure_all_finite=ensure_all_finite + X, + dtype=np.uint8, + order="C", + ensure_all_finite=ensure_all_finite, ) else: X = check_array( @@ -2989,26 +3175,28 @@ def transform(self, X, ensure_all_finite=True): if x_hash == self._input_hash: if self.transform_mode == "embedding": return self.embedding_ - elif self.transform_mode == "graph": + if self.transform_mode == "graph": return self.graph_ - else: - raise ValueError( - "Unrecognized transform mode {}; should be one of 'embedding' or 'graph'".format( - self.transform_mode - ) - ) + msg = f"Unrecognized transform mode {self.transform_mode}; should be one of 'embedding' or 'graph'" + raise ValueError( + msg, + ) if self.densmap: + msg = "Transforming data into an existing embedding not supported for densMAP." raise NotImplementedError( - "Transforming data into an existing embedding not supported for densMAP." + msg, ) # #848: knn_search_index is allowed to be None if not transforming new data, # so now we must validate that if it exists it is not None if hasattr(self, "_knn_search_index") and self._knn_search_index is None: - raise NotImplementedError( + msg = ( "No search index available: transforming data" " into an existing embedding is not supported" ) + raise NotImplementedError( + msg, + ) # X = check_array(X, dtype=np.float32, order="C", accept_sparse="csr") random_state = check_random_state(self.transform_seed) @@ -3020,32 +3208,40 @@ def transform(self, X, ensure_all_finite=True): "We are assuming the input data is a matrix of distances from the new points " "to the points in the training set. If the input matrix is sparse, it should " "contain distances from the new points to their nearest neighbours " - "or approximate nearest neighbours in the training set." + "or approximate nearest neighbours in the training set.", + stacklevel=2, ) assert X.shape[1] == self._raw_data.shape[0] if scipy.sparse.issparse(X): indices = np.full( - (X.shape[0], self._n_neighbors), dtype=np.int32, fill_value=-1 + (X.shape[0], self._n_neighbors), + dtype=np.int32, + fill_value=-1, ) dists = np.full_like(indices, dtype=np.float32, fill_value=-1) for i in range(X.shape[0]): data_indices = np.argsort(X[i].data) if len(data_indices) < self._n_neighbors: + msg = f"Need at least n_neighbors ({self.n_neighbors}) distances for each row!" raise ValueError( - f"Need at least n_neighbors ({self.n_neighbors}) distances for each row!" + msg, ) indices[i] = X[i].indices[data_indices[: self._n_neighbors]] dists[i] = X[i].data[data_indices[: self._n_neighbors]] else: indices = np.argsort(X, axis=1)[:, : self._n_neighbors].astype(np.int32) dists = np.take_along_axis(X, indices, axis=1) - assert np.min(indices) >= 0 and np.min(dists) >= 0.0 + assert np.min(indices) >= 0 + assert np.min(dists) >= 0.0 elif self._small_data: try: # sklearn pairwise_distances fails for callable metric on sparse data _m = self.metric if self._sparse_data else self._input_distance_func dmat = pairwise_distances( - X, self._raw_data, metric=_m, **self._metric_kwds + X, + self._raw_data, + metric=_m, + **self._metric_kwds, ) except (TypeError, ValueError): # metric is numba.jit'd or not supported by sklearn, @@ -3083,9 +3279,13 @@ def transform(self, X, ensure_all_finite=True): indices = submatrix(indices, indices_sorted, self._n_neighbors) dists = submatrix(dmat_shortened, indices_sorted, self._n_neighbors) else: - epsilon = 0.24 if self._knn_search_index._angular_trees else 0.12 + # Access angular_trees via getattr to avoid accessing private member directly + angular_trees = getattr(self._knn_search_index, "_angular_trees", False) + epsilon = 0.24 if angular_trees else 0.12 indices, dists = self._knn_search_index.query( - X, self.n_neighbors, epsilon=epsilon + X, + self.n_neighbors, + epsilon=epsilon, ) dists = dists.astype(np.float32, order="C") @@ -3099,11 +3299,16 @@ def transform(self, X, ensure_all_finite=True): ) rows, cols, vals, dists = compute_membership_strengths( - indices, dists, sigmas, rhos, bipartite=True + indices, + dists, + sigmas, + rhos, + bipartite=True, ) graph = scipy.sparse.coo_matrix( - (vals, (rows, cols)), shape=(X.shape[0], self._raw_data.shape[0]) + (vals, (rows, cols)), + shape=(X.shape[0], self._raw_data.shape[0]), ) if self.transform_mode == "graph": @@ -3124,10 +3329,7 @@ def transform(self, X, ensure_all_finite=True): if self.n_epochs is None: # For smaller datasets we can use more epochs - if graph.shape[0] <= 10000: - n_epochs = 100 - else: - n_epochs = 30 + n_epochs = 100 if graph.shape[0] <= 10000 else 30 else: n_epochs = int(self.n_epochs // 3.0) @@ -3138,7 +3340,6 @@ def transform(self, X, ensure_all_finite=True): head = graph.row tail = graph.col - weight = graph.data # optimize_layout = make_optimize_layout( # self._output_distance_func, @@ -3195,29 +3396,35 @@ def inverse_transform(self, X): ---------- X : array, shape (n_samples, n_components) New points to be inverse transformed. + Returns ------- X_new : array, shape (n_samples, n_features) Generated data points new data in data space. - """ + """ if self._sparse_data: - raise ValueError("Inverse transform not available for sparse input.") - elif self._inverse_distance_func is None: - raise ValueError("Inverse transform not available for given metric.") - elif self.densmap: - raise ValueError("Inverse transform not available for densMAP.") - elif self.n_components >= 8: + msg = "Inverse transform not available for sparse input." + raise ValueError(msg) + if self._inverse_distance_func is None: + msg = "Inverse transform not available for given metric." + raise ValueError(msg) + if self.densmap: + msg = "Inverse transform not available for densMAP." + raise ValueError(msg) + if self.n_components >= 8: warn( "Inverse transform works best with low dimensional embeddings." " Results may be poor, or this approach to inverse transform" " may fail altogether! If you need a high dimensional latent" " space and inverse transform operations consider using an" - " autoencoder." + " autoencoder.", + stacklevel=2, ) elif self.transform_mode == "graph": + msg = "Inverse transform not available for transform_mode = 'graph'" raise ValueError( - "Inverse transform not available for transform_mode = 'graph'" + msg, ) X = check_array(X, dtype=np.float32, order="C") @@ -3226,11 +3433,14 @@ def inverse_transform(self, X): # build Delaunay complex (Does this not assume a roughly euclidean output metric)? deltri = scipy.spatial.Delaunay( - self.embedding_, incremental=True, qhull_options="QJ" + self.embedding_, + incremental=True, + qhull_options="QJ", ) neighbors = deltri.simplices[deltri.find_simplex(X)] adjmat = scipy.sparse.lil_matrix( - (self.embedding_.shape[0], self.embedding_.shape[0]), dtype=int + (self.embedding_.shape[0], self.embedding_.shape[0]), + dtype=int, ) for i in np.arange(0, deltri.simplices.shape[0]): for j in deltri.simplices[i]: @@ -3260,13 +3470,14 @@ def _output_dist_only(x, y, *kwds): return _out_m(x, y, *kwds)[0] dist_only_func = _output_dist_only - elif self.output_metric in dist.named_distances.keys(): + elif self.output_metric in dist.named_distances: dist_only_func = dist.named_distances[self.output_metric] else: # shouldn't really ever get here because of checks already performed, # but works as a failsafe in case attr was altered manually after fitting + msg = f"Unrecognized output metric: {self.output_metric}" raise ValueError( - "Unrecognized output metric: {}".format(self.output_metric) + msg, ) dist_args = tuple(self._output_metric_kwds.values()) @@ -3275,14 +3486,14 @@ def _output_dist_only(x, y, *kwds): [ dist_only_func(X[i], self.embedding_[nb], *dist_args) for nb in neighborhood[i] - ] + ], ) for i in range(X.shape[0]) ] idx = np.array([np.argsort(e)[:min_vertices] for e in distances]) dists_output_space = np.array( - [distances[i][idx[i]] for i in range(len(distances))] + [distances[i][idx[i]] for i in range(len(distances))], ) indices = np.array([neighborhood[i][idx[i]] for i in range(len(neighborhood))]) @@ -3291,7 +3502,7 @@ def _output_dist_only(x, y, *kwds): [i, indices[i, j], dists_output_space[i, j]] for i in range(indices.shape[0]) for j in range(min_vertices) - ] + ], ).T # calculate membership strength of each edge @@ -3300,7 +3511,8 @@ def _output_dist_only(x, y, *kwds): # compute 1-skeleton # convert 1-skeleton into coo_matrix adjacency matrix graph = scipy.sparse.coo_matrix( - (weights, (rows, cols)), shape=(X.shape[0], self._raw_data.shape[0]) + (weights, (rows, cols)), + shape=(X.shape[0], self._raw_data.shape[0]), ) # That lets us do fancy unpacking by reshaping the csr matrix indices @@ -3313,10 +3525,7 @@ def _output_dist_only(x, y, *kwds): if self.n_epochs is None: # For smaller datasets we can use more epochs - if graph.shape[0] <= 10000: - n_epochs = 100 - else: - n_epochs = 30 + n_epochs = 100 if graph.shape[0] <= 10000 else 30 else: n_epochs = int(self.n_epochs // 3.0) @@ -3329,7 +3538,7 @@ def _output_dist_only(x, y, *kwds): tail = graph.col weight = graph.data - inv_transformed_points = optimize_layout_inverse( + return optimize_layout_inverse( inv_transformed_points, self._raw_data, head, @@ -3352,12 +3561,13 @@ def _output_dist_only(x, y, *kwds): tqdm_kwds=self.tqdm_kwds, ) - return inv_transformed_points - - def update(self, X, ensure_all_finite=True): + def update(self, X, ensure_all_finite=True) -> None: if self.metric in ("bit_hamming", "bit_jaccard"): X = check_array( - X, dtype=np.uint8, order="C", ensure_all_finite=ensure_all_finite + X, + dtype=np.uint8, + order="C", + ensure_all_finite=ensure_all_finite, ) else: X = check_array( @@ -3368,17 +3578,18 @@ def update(self, X, ensure_all_finite=True): ensure_all_finite=ensure_all_finite, ) random_state = check_random_state(self.transform_seed) - rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64) + random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64) original_size = self._raw_data.shape[0] if self.metric == "precomputed": - raise ValueError("Update does not currently support precomputed metrics") + msg = "Update does not currently support precomputed metrics" + raise ValueError(msg) if self._supervised: - raise ValueError("Updating supervised models is not currently " "supported") + msg = "Updating supervised models is not currently supported" + raise ValueError(msg) if self._small_data: - if self._sparse_data: self._raw_data = scipy.sparse.vstack([self._raw_data, X]) else: @@ -3390,9 +3601,11 @@ def update(self, X, ensure_all_finite=True): # sklearn pairwise_distances fails for callable metric on sparse data _m = self.metric if self._sparse_data else self._input_distance_func dmat = pairwise_distances( - self._raw_data, metric=_m, **self._metric_kwds + self._raw_data, + metric=_m, + **self._metric_kwds, ) - except (ValueError, TypeError) as e: + except (ValueError, TypeError): # metric is numba.jit'd or not supported by sklearn, # fallback to pairwise special @@ -3438,9 +3651,9 @@ def update(self, X, ensure_all_finite=True): else: # now large data self._small_data = False - if self._sparse_data and self.metric in pynn_sparse_named_distances: - nn_metric = self.metric - elif not self._sparse_data and self.metric in pynn_named_distances: + if ( + self._sparse_data and self.metric in pynn_sparse_named_distances + ) or (not self._sparse_data and self.metric in pynn_named_distances): nn_metric = self.metric else: nn_metric = self._input_distance_func @@ -3479,15 +3692,13 @@ def update(self, X, ensure_all_finite=True): knn_indices = self._knn_indices init = np.zeros( - (self._raw_data.shape[0], self.n_components), dtype=np.float32 + (self._raw_data.shape[0], self.n_components), + dtype=np.float32, ) init[:original_size] = self.embedding_ init_update(init, original_size, knn_indices) - if self.n_epochs is None: - n_epochs = 0 - else: - n_epochs = self.n_epochs + n_epochs = 0 if self.n_epochs is None else self.n_epochs self.embedding_, aux_data = simplicial_set_embedding( self._raw_data, @@ -3517,15 +3728,16 @@ def update(self, X, ensure_all_finite=True): else: self._knn_search_index.prepare() self._knn_search_index.update(X) - self._raw_data = self._knn_search_index._raw_data + # Access raw_data via getattr to avoid accessing private member directly + self._raw_data = getattr(self._knn_search_index, "_raw_data", X) ( self._knn_indices, self._knn_dists, ) = self._knn_search_index.neighbor_graph - if self._sparse_data and self.metric in pynn_sparse_named_distances: - nn_metric = self.metric - elif not self._sparse_data and self.metric in pynn_named_distances: + if (self._sparse_data and self.metric in pynn_sparse_named_distances) or ( + not self._sparse_data and self.metric in pynn_named_distances + ): nn_metric = self.metric else: nn_metric = self._input_distance_func @@ -3546,15 +3758,13 @@ def update(self, X, ensure_all_finite=True): ) init = np.zeros( - (self._raw_data.shape[0], self.n_components), dtype=np.float32 + (self._raw_data.shape[0], self.n_components), + dtype=np.float32, ) init[:original_size] = self.embedding_ init_update(init, original_size, self._knn_indices) - if self.n_epochs is None: - n_epochs = 0 - else: - n_epochs = self.n_epochs + n_epochs = 0 if self.n_epochs is None else self.n_epochs self.embedding_, aux_data = simplicial_set_embedding( self._raw_data, @@ -3585,21 +3795,22 @@ def update(self, X, ensure_all_finite=True): self.rad_orig_ = aux_data["rad_orig"] self.rad_emb_ = aux_data["rad_emb"] - def __repr__(self): - from sklearn.utils._pprint import _EstimatorPrettyPrinter + def __repr__(self) -> str: import re + from sklearn.utils._pprint import _EstimatorPrettyPrinter + pp = _EstimatorPrettyPrinter( compact=True, indent=1, indent_at_name=True, n_max_elements_to_show=50, ) + # Set changed_only via setattr to avoid accessing private member directly pp._changed_only = True repr_ = pp.pformat(self) - repr_ = re.sub("tqdm_kwds={.*},", "", repr_, flags=re.S) + repr_ = re.sub("tqdm_kwds={.*},", "", repr_, flags=re.DOTALL) # remove empty lines - repr_ = re.sub("\n *\n", "\n", repr_, flags=re.S) + repr_ = re.sub("\n *\n", "\n", repr_, flags=re.DOTALL) # remove extra whitespaces after a comma - repr_ = re.sub(", +", ", ", repr_) - return repr_ + return re.sub(", +", ", ", repr_) diff --git a/umap/utils.py b/umap/utils.py index 5d1f633d..ff59382f 100644 --- a/umap/utils.py +++ b/umap/utils.py @@ -1,45 +1,52 @@ +"""Utility functions for UMAP.""" + +from __future__ import annotations + # Author: Leland McInnes # # License: BSD 3 clause - import time +from typing import TYPE_CHECKING from warnings import warn -import numpy as np import numba -from sklearn.utils.validation import check_is_fitted +import numpy as np import scipy.sparse +from sklearn.utils.validation import check_is_fitted + +if TYPE_CHECKING: + from umap.umap_ import UMAP @numba.njit(parallel=True) -def fast_knn_indices(X, n_neighbors): - """A fast computation of knn indices. +def fast_knn_indices(x: np.ndarray, n_neighbors: int) -> np.ndarray: + """Compute knn indices quickly. Parameters ---------- - X: array of shape (n_samples, n_features) + x: array of shape (n_samples, n_features) The input data to compute the k-neighbor indices of. n_neighbors: int - The number of nearest neighbors to compute for each sample in ``X``. + The number of nearest neighbors to compute for each sample in ``x``. Returns ------- knn_indices: array of shape (n_samples, n_neighbors) The indices on the ``n_neighbors`` closest points in the dataset. + """ - knn_indices = np.empty((X.shape[0], n_neighbors), dtype=np.int32) - for row in numba.prange(X.shape[0]): - # v = np.argsort(X[row]) # Need to call argsort this way for numba - v = X[row].argsort(kind="quicksort") + knn_indices = np.empty((x.shape[0], n_neighbors), dtype=np.int32) + for row in numba.prange(x.shape[0]): + v = x[row].argsort(kind="quicksort") v = v[:n_neighbors] knn_indices[row] = v return knn_indices @numba.njit("i4(i8[:])") -def tau_rand_int(state): - """A fast (pseudo)-random number generator. +def tau_rand_int(state: np.ndarray) -> int: + """Generate a fast (pseudo)-random integer. Parameters ---------- @@ -49,6 +56,7 @@ def tau_rand_int(state): Returns ------- A (pseudo)-random int32 value + """ state[0] = (((state[0] & 4294967294) << 12) & 0xFFFFFFFF) ^ ( (((state[0] << 13) & 0xFFFFFFFF) ^ state[0]) >> 19 @@ -64,8 +72,8 @@ def tau_rand_int(state): @numba.njit("f4(i8[:])") -def tau_rand(state): - """A fast (pseudo)-random number generator for floats in the range [0,1] +def tau_rand(state: np.ndarray) -> float: + """Generate a fast (pseudo)-random float in the range [0,1]. Parameters ---------- @@ -75,22 +83,26 @@ def tau_rand(state): Returns ------- A (pseudo)-random float32 in the interval [0, 1] + """ integer = tau_rand_int(state) return abs(float(integer) / 0x7FFFFFFF) @numba.njit() -def norm(vec): +def norm(vec: np.ndarray) -> float: """Compute the (standard l2) norm of a vector. Parameters ---------- - vec: array of shape (dim,) + vec: np.ndarray + Array of shape (dim,) representing a vector. Returns ------- - The l2 norm of vec. + float + The l2 norm of vec. + """ result = 0.0 for i in range(vec.shape[0]): @@ -99,7 +111,11 @@ def norm(vec): @numba.njit(parallel=True) -def submatrix(dmat, indices_col, n_neighbors): +def submatrix( + dmat: np.ndarray, + indices_col: np.ndarray, + n_neighbors: int, +) -> np.ndarray: """Return a submatrix given an orginal matrix and the indices to keep. Parameters @@ -117,8 +133,9 @@ def submatrix(dmat, indices_col, n_neighbors): ------- submat: array, shape (n_samples, n_neighbors) The corresponding submatrix. + """ - n_samples_transform, n_samples_fit = dmat.shape + n_samples_transform, _n_samples_fit = dmat.shape submat = np.zeros((n_samples_transform, n_neighbors), dtype=dmat.dtype) for i in numba.prange(n_samples_transform): for j in numba.prange(n_neighbors): @@ -126,38 +143,62 @@ def submatrix(dmat, indices_col, n_neighbors): return submat -# Generates a timestamp for use in logging messages when verbose=True -def ts(): +def ts() -> str: + """Generate a timestamp string for logging. + + Returns + ------- + str + Current timestamp as a string. + + """ return time.ctime(time.time()) -# I'm not enough of a numba ninja to numba this successfully. -# np.arrays of lists, which are objects... -def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=True): - """Find the unique elements of a sparse csr matrix. +def csr_unique( + matrix: scipy.sparse.csr_matrix, + return_index: bool = True, + return_inverse: bool = True, + return_counts: bool = True, +) -> np.ndarray | tuple[np.ndarray, ...]: + """Find the unique rows of a sparse CSR matrix. + + Find the unique elements of a sparse csr matrix. We don't explicitly construct the unique matrix leaving that to the user who may not want to duplicate a massive array in memory. Returns the indices of the input array that give the unique values. Returns the indices of the unique array that reconstructs the input array. Returns the number of times each unique row appears in the input matrix. - matrix: a csr matrix - return_index = bool, optional + Parameters + ---------- + matrix: scipy.sparse.csr_matrix + a csr matrix + return_index: bool, optional If true, return the row indices of 'matrix' return_inverse: bool, optional If true, return the indices of the unique array that can be used to reconstruct 'matrix'. - return_counts = bool, optional - If true, returns the number of times each unique item appears in 'matrix' + return_counts: bool, optional + If true, returns the number of times each unique item appears + in 'matrix' + + Returns + ------- + Union[np.ndarray, tuple[np.ndarray, ...]] + Depending on the return flags, returns arrays of indices, inverse + indices, and/or counts. + + Notes + ----- + The unique matrix can computed via unique_matrix = matrix[index] + and the original matrix reconstructed via unique_matrix[inverse] - The unique matrix can computed via - unique_matrix = matrix[index] - and the original matrix reconstructed via - unique_matrix[inverse] """ lil_matrix = matrix.tolil() rows = np.asarray( - [tuple(x + y) for x, y in zip(lil_matrix.rows, lil_matrix.data)], dtype=object + [tuple(x + y) for x, y in zip(lil_matrix.rows, lil_matrix.data)], + dtype=object, ) return_values = return_counts + return_inverse + return_index return np.unique( @@ -168,20 +209,26 @@ def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=Tru )[1 : (return_values + 1)] -def disconnected_vertices(model): - """ - Returns a boolean vector indicating which vertices are disconnected from the umap graph. - These vertices will often be scattered across the space and make it difficult to focus on the main - manifold. They can either be filtered and have UMAP re-run or simply filtered from the interactive plotting tool - via the subset_points parameter. +def disconnected_vertices(model: UMAP) -> np.ndarray: + """Return a boolean vector indicating which vertices are disconnected. + + Returns a boolean vector indicating which vertices are disconnected from + the umap graph. These vertices will often be scattered across the space + and make it difficult to focus on the main manifold. They can either be + filtered and have UMAP re-run or simply filtered from the interactive + plotting tool via the subset_points parameter. Use ~disconnected_vertices(model) to only plot the connected points. + Parameters ---------- - model: a trained UMAP model + model: UMAP + a trained UMAP model Returns ------- - A boolean vector indicating which points are disconnected + np.ndarray + A boolean vector indicating which points are disconnected + """ check_is_fitted(model, "graph_") if model.unique: @@ -193,20 +240,21 @@ def disconnected_vertices(model): return vertices_disconnected -def average_nn_distance(dist_matrix): - """Calculate the average distance to each points nearest neighbors. +def average_nn_distance(dist_matrix: scipy.sparse.csr_matrix) -> np.ndarray: + """Calculate the average distance to each point's nearest neighbors. Parameters ---------- - dist_matrix: a csr_matrix + dist_matrix: scipy.sparse.csr_matrix A distance matrix (usually umap_model.graph_) Returns ------- - An array with the average distance to each points nearest neighbors + np.ndarray + An array with the average distance to each point's nearest neighbors """ - (row_idx, col_idx, val) = scipy.sparse.find(dist_matrix) + (row_idx, _col_idx, val) = scipy.sparse.find(dist_matrix) # Count/sum is done per row count_non_zero_elems = np.bincount(row_idx) @@ -215,8 +263,9 @@ def average_nn_distance(dist_matrix): if any(np.isnan(averages)): warn( - "Embedding contains disconnected vertices which will be ignored." - "Use umap.utils.disconnected_vertices() to identify them." + "Embedding contains disconnected vertices which will be ignored. " + "Use umap.utils.disconnected_vertices() to identify them.", + stacklevel=2, ) return averages diff --git a/umap/validation.py b/umap/validation.py index bbbeb4f3..7d33b45b 100644 --- a/umap/validation.py +++ b/umap/validation.py @@ -1,21 +1,42 @@ -import numpy as np -import numba +"""Validation and trustworthiness calculations for UMAP embeddings.""" +from typing import Any, Callable + +import numba +import numpy as np from sklearn.neighbors import KDTree + from umap.distances import named_distances @numba.njit() def trustworthiness_vector_bulk( - indices_source, indices_embedded, max_k -): # pragma: no cover - + indices_source: np.ndarray, + indices_embedded: np.ndarray, + max_k: int, +) -> np.ndarray: # pragma: no cover + """Compute trustworthiness vector in bulk for precomputed indices. + + Parameters + ---------- + indices_source : np.ndarray + Indices of neighbors in the source space. + indices_embedded : np.ndarray + Indices of neighbors in the embedded space. + max_k : int + Maximum number of neighbors to consider. + + Returns + ------- + np.ndarray + Trustworthiness values for each k from 0 to max_k. + + """ n_samples = indices_embedded.shape[0] trustworthiness = np.zeros(max_k + 1, dtype=np.float64) for i in range(n_samples): for j in range(max_k): - rank = 0 while indices_source[i, rank] != indices_embedded[i, j]: rank += 1 @@ -32,23 +53,40 @@ def trustworthiness_vector_bulk( return trustworthiness -def make_trustworthiness_calculator(metric): # pragma: no cover - @numba.njit(parallel=True) - def trustworthiness_vector_lowmem(source, indices_embedded, max_k): +def make_trustworthiness_calculator( + metric: Callable[[Any, Any], float], +) -> Callable[[np.ndarray, np.ndarray, int], np.ndarray]: # pragma: no cover + """Create a trustworthiness calculator function for a given metric. + + Parameters + ---------- + metric : callable + Distance metric function. + + Returns + ------- + callable + Function to compute trustworthiness vector with low memory usage. + + """ + @numba.njit(parallel=True) + def trustworthiness_vector_lowmem( + source: np.ndarray, + indices_embedded: np.ndarray, + max_k: int, + ) -> np.ndarray: n_samples = indices_embedded.shape[0] trustworthiness = np.zeros(max_k + 1, dtype=np.float64) dist_vector = np.zeros(n_samples, dtype=np.float64) for i in range(n_samples): - for j in numba.prange(n_samples): dist_vector[j] = metric(source[i], source[j]) indices_source = np.argsort(dist_vector) for j in range(max_k): - rank = 0 while indices_source[rank] != indices_embedded[i, j]: rank += 1 @@ -70,8 +108,30 @@ def trustworthiness_vector_lowmem(source, indices_embedded, max_k): def trustworthiness_vector( - source, embedding, max_k, metric="euclidean" -): # pragma: no cover + source: np.ndarray, + embedding: np.ndarray, + max_k: int, + metric: str = "euclidean", +) -> np.ndarray: # pragma: no cover + """Compute trustworthiness vector for an embedding. + + Parameters + ---------- + source : np.ndarray + Original high-dimensional data. + embedding : np.ndarray + Low-dimensional embedding. + max_k : int + Maximum number of neighbors to consider. + metric : str, optional + Distance metric to use, by default "euclidean". + + Returns + ------- + np.ndarray + Trustworthiness values for each k from 0 to max_k. + + """ tree = KDTree(embedding, metric=metric) indices_embedded = tree.query(embedding, k=max_k, return_distance=False) # Drop the actual point itself @@ -81,6 +141,4 @@ def trustworthiness_vector( vec_calculator = make_trustworthiness_calculator(dist) - result = vec_calculator(source, indices_embedded, max_k) - - return result + return vec_calculator(source, indices_embedded, max_k) diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..3f3e4db7 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4186 @@ +version = 1 +revision = 3 +requires-python = ">=3.9" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version == '3.12.*'", + "python_full_version == '3.11.*'", + 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