diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..5c0bf0c --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,20 @@ +version: 2 +updates: + - package-ecosystem: pip + directory: / + schedule: + interval: monthly + groups: + dev-tooling: + patterns: ["ruff", "mypy", "pytest*", "pre-commit", "twine", "types-*", "*-stubs"] + docs: + patterns: ["mkdocs*"] + + # Workflow actions are pinned by SHA, so this is the only thing that moves them. + - package-ecosystem: github-actions + directory: / + schedule: + interval: monthly + groups: + actions: + patterns: ["*"] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..04dc412 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,84 @@ +name: ci + +on: + push: + branches: [master] + pull_request: + workflow_dispatch: + +# Actions are pinned by commit SHA rather than by tag: a tag can be moved to point at new code, +# a SHA cannot. Dependabot keeps them current. +permissions: + contents: read + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + lint: + name: lint and type-check + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 + with: + enable-cache: true + - run: uv sync --all-extras + - name: ruff check + run: uv run ruff check --output-format=github . + - name: ruff format + run: uv run ruff format --check . + - name: mypy + run: uv run mypy + + test: + name: test (py${{ matrix.python-version }}) + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13"] + steps: + - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 + with: + enable-cache: true + python-version: ${{ matrix.python-version }} + - run: uv sync --all-extras + - name: pytest + run: uv run pytest --cov --cov-report=term-missing --cov-report=xml + + build: + name: build and verify the distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 + with: + enable-cache: true + - run: uv build + - name: twine check + run: uv run --with twine==7.0.0 twine check dist/* + - name: install the wheel into a clean environment and import it + run: | + uv venv /tmp/fresh + VIRTUAL_ENV=/tmp/fresh uv pip install dist/*.whl + /tmp/fresh/bin/python -c " + import pathlib, python_som + assert (pathlib.Path(python_som.__file__).parent / 'py.typed').is_file(), 'py.typed missing from the wheel' + som = python_som.SOM(x=8, y=8, input_len=3, neighborhood_function='mexicanhat', random_seed=0) + h = som.neighborhood((4, 4), 2.0) + assert h[4, 4] == 1.0 + assert h.min() < 0, 'the mexican hat must be inhibitory somewhere' + print('wheel import OK, version', python_som.__version__) + " + - name: the cli extra must keep resolving + run: | + uv venv /tmp/extra + VIRTUAL_ENV=/tmp/extra uv pip install "$(echo dist/*.whl)[cli]" + /tmp/extra/bin/python -c "import tqdm; print('cli extra OK,', tqdm.__version__)" + - uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5 + with: + name: dist + path: dist/ diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..361750a --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,48 @@ +name: docs + +on: + push: + branches: [master] + pull_request: + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + build: + name: build the documentation + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 + with: + enable-cache: true + - run: uv sync --all-extras + # --strict turns warnings, including broken internal links, into failures. + - run: uv run mkdocs build --strict + - uses: actions/configure-pages@983d7736d9b0ae728b81ab479565c72886d7745b # v5 + if: github.ref == 'refs/heads/master' + - uses: actions/upload-pages-artifact@7b1f4a764d45c48632c6b24a0339c27f5614fb0b # v4 + if: github.ref == 'refs/heads/master' + with: + path: site/ + + deploy: + name: deploy to GitHub Pages + if: github.ref == 'refs/heads/master' + needs: build + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + permissions: + pages: write + id-token: write + steps: + - id: deployment + uses: actions/deploy-pages@d6db90164ac5ed86f2b6aed7e0febac5b3c0c03e # v4 diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..d141e33 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,61 @@ +name: release + +# The filename matters: `release.yml` is registered as the workflow name on the PyPI trusted +# publisher for this project. Renaming this file breaks publishing until PyPI is updated to match. + +on: + push: + tags: ["v*"] + workflow_dispatch: + +permissions: + contents: read + +jobs: + build: + name: build the distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 + with: + enable-cache: true + - run: uv build + - name: twine check + run: uv run --with twine==7.0.0 twine check dist/* + - name: the tag and the packaged version must agree + run: | + uv run python - <<'PY' + import os + import pathlib + import sys + import tomllib + + tag = os.environ["GITHUB_REF_NAME"].removeprefix("v") + pkg = tomllib.loads(pathlib.Path("pyproject.toml").read_text())["project"]["version"] + print(f"tag={tag} packaged={pkg}") + if tag != pkg: + sys.exit(f"::error::tag {tag} does not match packaged version {pkg}") + PY + - uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5 + with: + name: dist + path: dist/ + + publish: + name: publish to PyPI + needs: build + runs-on: ubuntu-latest + # Registered on PyPI as the trusted publisher's environment. It carries a required-reviewer + # rule, so a publish waits for a human, and a v* tag policy, so it cannot run from a branch. + environment: pypi + permissions: + # Mandatory for trusted publishing: this is what lets the job prove its identity to PyPI. + # No API token is stored in the repository or anywhere else. + id-token: write + steps: + - uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6 + with: + name: dist + path: dist/ + - uses: pypa/gh-action-pypi-publish@ba38be9e461d3875417946c167d0b5f3d385a247 # release/v1 diff --git a/.gitignore b/.gitignore index b6e4761..5674cdd 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,12 @@ dmypy.json # Pyre type checker .pyre/ + +# .dump directory +/.dump + +# Claude Code working files. CLAUDE.md holds this maintainer's agent conventions rather than +# repository policy, so it stays local instead of being imposed on contributors. +/CLAUDE.md +/CLAUDE.local.md +/.claude/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..40352bc --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,45 @@ +default_language_version: + python: python3 + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-added-large-files + - id: check-merge-conflict + - id: check-toml + - id: check-yaml + - id: detect-private-key + - id: end-of-file-fixer + - id: mixed-line-ending + - id: trailing-whitespace + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.16.0 + hooks: + - id: ruff-check + args: [--fix] + - id: ruff-format + + # Keep this rev in step with the `mypy` pin in pyproject.toml's dev extra, or the hook and CI + # will disagree about what type-checks. + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v2.3.0 + hooks: + - id: mypy + files: ^(src|tests)/ + additional_dependencies: + - numpy>=1.24 + - pandas-stubs + - pytest + - types-tqdm + + - repo: local + hooks: + # The narrative that used to live in test_iris.ipynb now lives in docs/. Notebooks are not + # diffable, not CI-verifiable, and carry their outputs in the committed blob. + - id: no-notebooks + name: reject committed Jupyter notebooks + entry: Jupyter notebooks are not tracked in this repository; put the narrative in docs/. + language: fail + files: \.ipynb$ diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..d969f96 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "python.testing.pytestArgs": ["tests"], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} diff --git a/CHANGELOG.md b/CHANGELOG.md index 0ebd3a2..3005e6f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,99 @@ All notable changes to this project are documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [0.3.0] - 2026-07-29 + +Modernizes the packaging and corrects four methodology defects. **Numerical results differ from +0.2.0.** The changes are listed with the passage of Kohonen (2013) each one follows. + +### Changed, and results differ + +- **Seeds no longer reproduce 0.1.x/0.2.0 results.** The random generator is now a per-instance + `np.random.Generator`. Previously the constructor called `np.random.seed`, which reseeded NumPy's + *global* generator as a side effect of building a SOM, disturbing the whole host program. The + side effect is gone, but `random_seed=42` now yields a different map. + + To reproduce figures made with an earlier version, pin `python-som==0.2.0`. + +- Batch training no longer destroys models whose neighborhood contains no data. `_train_batch` + built its result from a zeroed array and assigned it wholesale, so every model it did not touch + became the zero vector. On a 30x30 map with 20 samples and a small radius, that wiped 282 of 900 + models in a single step, and drove the quantization error from 0.0 to 0.196. Untouched models now + keep their previous value, and the denominator is compared against a tolerance rather than exact + zero. (Kohonen Eq. 8.) + +- Linear initialization now spans the principal-component plane. It used + `pca.explained_variance_`, the two eigen*values*, where it needed `pca.components_`, the + eigen*vectors*. Every model came out as a scalar broadcast across all features: a constant vector + on the all-ones diagonal, rank 1 rather than a plane. PCA is also now fitted on the raw data + instead of standardized data, so the models live in the same space as the inputs they are compared + against during training. (Kohonen Section 4.3.) + +- `random` training now samples i.i.d. with replacement. It previously drew with + `replace=(n_iteration > len(data))`, so it was a random permutation whenever the iteration count + did not exceed the sample count, and independent draws only beyond it. The character of the + sampling therefore depended on how many iterations you asked for. It is now uniformly i.i.d., + which is the stochastic approximation of Robbins and Monro (1951) that Kohonen cites in + Section 4.1. + +- `sequential` training honours `n_iteration`. It iterated the dataset exactly once regardless + of the requested count, so asking for 500 iterations over 30 samples ran 30 steps while the decay + functions still used 500 as their horizon, leaving the learning rate almost unchanged. It now + cycles the dataset until the requested number of steps has run. + +- The automatic map-size ratio uses `sqrt(lambda_1 / lambda_2)` rather than the raw eigenvalue + ratio, rounds instead of floor-dividing, and clamps to at least 1 so a side can no longer come out + as zero. Kohonen Section 3.5 asks for side lengths matching "the lengths of the two largest + principal components"; a component is a unit direction, so its length in the data is the standard + deviation. For Iris with `x=20`, `y` moves from 6 to 11. + +- The neighborhood radius is floored at the new `min_neighborhood_radius` parameter, default + 0.5. Kohonen Section 4.2: "the final value of sigma shall not go to zero, because otherwise the + process loses its ordering power. It should always remain, say, above half of the grid spacing." + This also stops `linear_decay` from reaching a division by zero at its horizon. + +- `get_shape()` returns `tuple[int, int]` rather than NumPy integers, so + `plt.figure(figsize=som.get_shape())` works without the `float()` workaround the old example + needed. + +### Added + +- `mexican_hat` is accepted as an alias for `mexicanhat`. +- A test suite, where there was none: 190 tests at 100% coverage, including one regression test + per defect above carrying its measured numbers, and property-based tests via Hypothesis over + generated grid sizes, radii and centres. +- Type annotations are now exported, via a `py.typed` marker. The library was already annotated, + but without the marker PEP 561 requires downstream type checkers to ignore it. +- Documentation site at , built with Material for + MkDocs, including the derivations behind the neighborhood functions. +- Continuous integration across Python 3.10 to 3.13, with ruff, mypy `--strict`, coverage + gating, and a build that installs the wheel into a clean environment and imports it. +- Releases publish through PyPI Trusted Publishing, so no long-lived API token exists. + +### Performance + +- Batch training is about 30x faster. The nested Python loop over every pair of nodes is + replaced by a NumPy contraction. Measured on a 20x20 map with 150 samples over 5 iterations: 1.89 s + to 0.06 s. A regression test asserts the new result matches the old formulation to 1e-12. + +### Internal + +- The package moves to a `src/` layout and splits into `_som`, `_neighborhood`, `_decay` and + `_distance`. `import python_som; python_som.SOM(...)` is unchanged, and the pre-0.3.0 private names + (`_asymptotic_decay`, `_euclidean_distance`, and the rest) still resolve. +- `test_iris.ipynb` is removed; its narrative moved into the documentation. `SOM_atualizado.py`, an + untracked experimental variant that the sdist had been sweeping up, is gone. +- `print` calls become `logging`; `verbose=True` still drives the tqdm progress bar. + +### Fixed + +- The `bubble` neighborhood is documented as using the Chebyshev metric, so its region is a + square rather than a disc. The behavior is unchanged and follows Vrieze's appendix pseudo-code + (`b = MAX(ABS(i - w_i), ABS(j - w_j))`), but it was previously unstated, and it means the bubble is + *not* isotropic under the Euclidean metric. The test suite shipped in 0.2.0 asserted that it was; + that assertion passed only because no counterexample exists on a grid that small. The smallest is + at radius `sqrt(50)`, where `(5, 5)` is inside a `sigma = 5` square and `(7, 1)` is outside. + ## [0.2.0] - 2026-07-28 ### Added @@ -29,7 +122,7 @@ adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ### Fixed -- **Cyclic (toroidal) neighborhoods were only correct in one direction.** The fold-back handled +- Cyclic (toroidal) neighborhoods were only correct in one direction. The fold-back handled offsets above `+length/2` but left those below `-length/2` alone, so a winner in the upper half of an axis did not wrap. On a 10x10 toroidal map with sigma=1, a winner at row 0 gave `h[9] = 0.6065` while a winner at row 9 gave `h[0] = 0.0` instead of the same 0.6065. Now uses the minimum-image @@ -46,7 +139,7 @@ adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ### Changed -- **`requires-python` is now `>=3.10`.** The declared floor of `>=3.9` was never accurate: the module +- `requires-python` is now `>=3.10`. The declared floor of `>=3.9` was never accurate: the module uses PEP 604 unions (`int | None`) in runtime-evaluated annotations without `from __future__ import annotations`, so importing it on Python 3.9 raises `TypeError`. Python 3.9 reached end of life in October 2025. Installers will now decline the package on 3.9 rather than @@ -80,6 +173,7 @@ Published to PyPI only. See the note above. Earlier releases predate this changelog. See the [commit history](https://github.com/andremsouza/python-som/commits/master) for details. +[0.3.0]: https://github.com/andremsouza/python-som/releases/tag/v0.3.0 [0.2.0]: https://github.com/andremsouza/python-som/releases/tag/v0.2.0 [0.1.3]: https://pypi.org/project/python-som/0.1.3/ [0.1.2]: https://pypi.org/project/python-som/0.1.2/ diff --git a/README.md b/README.md index 70183f8..262545e 100644 --- a/README.md +++ b/README.md @@ -1,131 +1,104 @@ # python-som -Implementation of the 2D self-organizing map, with support for NumPy arrays and Pandas DataFrames. -Most features were implemented using NumPy, with Scikit-learn for standardization and PCA operations. +[![CI](https://github.com/andremsouza/python-som/actions/workflows/ci.yml/badge.svg)](https://github.com/andremsouza/python-som/actions/workflows/ci.yml) +[![PyPI](https://img.shields.io/pypi/v/python-som.svg)](https://pypi.org/project/python-som/) +[![Python versions](https://img.shields.io/pypi/pyversions/python-som.svg)](https://pypi.org/project/python-som/) +[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/andremsouza/python-som/blob/master/LICENSE) -## Features +Implementation of Kohonen's 2-D self-organizing map, built on NumPy, with scikit-learn for PCA and +standardization. Accepts NumPy arrays, pandas DataFrames and plain lists. + +[Documentation](https://andremsouza.github.io/python-som/) · +[Changelog](https://github.com/andremsouza/python-som/blob/master/CHANGELOG.md) + +## Install + +```bash +pip install python-som # requires Python 3.10+ +pip install "python-som[cli]" # adds tqdm progress bars +``` + +## Quick start -* Stepwise and batch training -* Random weight initialization -* Random sampling weight initialization -* Linear weight initialization (with PCA) -* Automatic selection of map size ratio (with PCA) -* Support for cyclic arrays, for toroidal or spherical maps -* Gaussian, Bubble and Mexican hat neighborhood functions -* Support for custom decay functions -* Support for visualization (U-matrix, activation matrix) -* Support for supervised learning (label map) -* Support for NumPy arrays, Pandas DataFrames and regular lists of values - -## Usage -In the following code excerpt (also available in [test.py](./test.py)) is an example of instantiation and training of a SOM with the Iris dataset: ```python -# Import python_som -import python_som -# Import NumPy and Pandas for storing data import numpy as np -import pandas as pd -# Import libraries for plotting results -import matplotlib.pyplot as plt -import seaborn as sns - -# Load Iris dataset and columns of features and labels -iris = sns.load_dataset('iris') -target = iris.iloc[:, -1].to_numpy() -iris = iris.iloc[:, :-1].to_numpy() -# Transform labels into numeric codes for plotting -tg = np.zeros(len(target), dtype=int) -tg[target == 'setosa'] = 0 -tg[target == 'versicolor'] = 1 -tg[target == 'virginica'] = 2 - -# Instantiate SOM from python_som -# Selecting shape automatically (providing dataset for constructor) -# Using default decay and distance functions -# Using gaussian neighborhood function -# Using cyclic arrays in the vertical and horizontal directions -som = python_som.SOM(x=20, y=None, input_len=iris.shape[1], learning_rate=0.5, neighborhood_radius=1.0, - neighborhood_function='gaussian', cyclic_x=True, cyclic_y=True, data=iris) - -# Initialize weights of the SOM with linear initialization -som.weight_initialization(mode='linear', data=iris) - -# Training SOM with default number of iterations -# Using batch learning process -som.train(data=iris, n_iteration=len(iris), mode='batch', verbose=True) - -# Calculating distance matrix for plotting -umatrix = som.distance_matrix().T - -# Plotting U-matrix with seaborn/matplotlib -plt.figure(figsize=som.get_shape()) -plt.pcolor(umatrix, cmap='bone_r') - -markers = ['o', 's', 'D'] -colors = ['C0', 'C1', 'C2'] -for cnt, xx in enumerate(iris): - w = som.winner(xx) # getting the winner - plt.plot(w[0] + .5, w[1] + .5, markers[tg[cnt]], markerfacecolor='None', - markeredgecolor=colors[tg[cnt]], markersize=12, markeredgewidth=2) -plt.axis([0, som.get_shape()[0], 0, som.get_shape()[1]]) -plt.show() +import python_som + +rng = np.random.default_rng(0) +data = rng.normal(size=(150, 4)) + +som = python_som.SOM(x=20, y=None, input_len=4, data=data, random_seed=42) +som.weight_initialization(mode="linear", data=data) +error = som.train(data, n_iteration=len(data), mode="batch") +umatrix = som.distance_matrix() +winner = som.winner(data[0]) ``` -### Test output -The following image is generated from the previous test code, with the U-matrix of the trained SOM, and the distribution of the instances from the Iris dataset. -In this graph, the instances are mapped to the self-organizing map, with color codes for each different label: -* Setosa: blue circle -* Versicolor: orange square -* Virginica: green diamond - -![Test code output](./test_output_iris.png?raw=true) - -## Public methods and functions -The following are lists of public methods and functions currently available in the SOM class. The full documentation of each method can be found in the [source code](./python_som/__init__.py): - -### Utility functions -* _asymptotic_decay -* _linear_decay -* _exponential_decay -* _inverse_decay -* _euclidean_distance - -### SOM public methods -* SOM -* SOM.get_shape -* SOM.get_weights -* SOM.set_learning_rate -* SOM.set_neighborhood_radius -* SOM.activate -* SOM.winner -* SOM.quantization -* SOM.quantization_error -* SOM.distance_matrix -* SOM.activation_matrix -* SOM.winner_map -* SOM.label_map -* SOM.train -* SOM.weight_initialization +A full worked example with plots is in [examples/iris.py](https://github.com/andremsouza/python-som/blob/master/examples/iris.py) and in the +[getting-started guide](https://andremsouza.github.io/python-som/getting-started/). + +![U-matrix of a SOM trained on Iris](https://raw.githubusercontent.com/andremsouza/python-som/master/docs/assets/iris.png) + +## Features + +* Stepwise and batch training +* Random, random-sampling and linear (PCA) weight initialization +* Automatic selection of the map size ratio, from PCA +* Cyclic arrays, for toroidal maps +* Gaussian, bubble and Mexican hat neighborhood functions +* Custom decay functions +* Visualization support: U-matrix, activation matrix +* Supervised labelling, via the label map +* Fully type-annotated, with a `py.typed` marker ## Neighborhood functions -The `neighborhood_function` parameter selects how the winner's correction is spread over the grid. + All three are functions of the distance between two nodes in the grid, `sqdist(c, i)` in Eq. (5) of -Kohonen (2013): +Kohonen (2013). | Name | Shape | Notes | | --- | --- | --- | -| `'gaussian'` | `exp(-r² / 2σ²)` | Strictly positive and monotonically decreasing. The default. | -| `'bubble'` | `1` for `r ≤ σ`, else `0` | The truncated inner lobe of the mexican hat. Vrieze (1995) notes this flat choice is "just as effective and sometimes even better". | -| `'mexicanhat'` | `(1 - u)·exp(-u)`, `u = r² / 2σ²` | Excitatory near the winner, inhibitory beyond it. Crosses zero at `r = √2·σ`, reaches its minimum of `-e⁻² ≈ -0.135` at `r = 2σ`. | +| `'gaussian'` | `exp(-r² / 2σ²)` | Strictly positive, monotonically decreasing. The default. | +| `'bubble'` | `1` for `max(dx, dy) ≤ σ`, else `0` | The truncated inner lobe of the Mexican hat. Uses the Chebyshev metric, so the region is a square. | +| `'mexicanhat'` | `(1 - u)·exp(-u)`, `u = r² / 2σ²` | Excitatory near the winner, inhibitory beyond it. Zero at `r = √2·σ`, minimum `-e⁻²` at `r = 2σ`. | + +The Mexican hat takes negative values, so it **cannot be used with `mode='batch'`**: the batch +update of Kohonen Eq. (8) is a weighted mean whose denominator is not sign-definite for a signed +neighborhood function. Use `mode='random'` or `mode='sequential'`; `mode='batch'` raises a +`ValueError`. + +See [Neighborhood functions](https://andremsouza.github.io/python-som/neighborhood-functions/) for +the derivations, including why the Mexican hat is not an outer product of two 1-D wavelets. + +## Upgrading + +0.3.0 corrects several methodology defects, so numerical results are not comparable with earlier +versions. In particular **`random_seed` no longer reproduces pre-0.3.0 maps**: the generator is now +per-instance rather than a call to `np.random.seed` on NumPy's global state. To reproduce figures +made with an older version, pin `python-som==0.2.0`. + +Each change and the passage of Kohonen (2013) behind it is in the +[changelog](https://github.com/andremsouza/python-som/blob/master/CHANGELOG.md). + +## Development + +```bash +uv sync --all-extras +uv run pytest --cov # tests and coverage +uv run ruff check . # lint +uv run ruff format --check . # formatting +uv run mypy # type-check +uv run mkdocs serve # docs, locally +pre-commit install # optional, run the gates on commit +``` -The mexican hat takes negative values, so it **cannot be used with `mode='batch'`**: the batch update -of Kohonen (2013), Eq. (8), is a weighted mean whose denominator `Σⱼ nⱼ·hⱼᵢ` is not sign-definite for -a signed neighborhood function. Use `mode='random'` or `mode='sequential'` instead; passing -`mode='batch'` raises a `ValueError`. +If you use the SonarQube for IDE (SonarLint) VS Code extension, it will also apply Sonar's Python +rules locally; the ruff configuration is set up to cover most of the same ground. ## References -This implemetation was based on the following paper, by Professor Teuvo Kohonen: + +Based on: Teuvo Kohonen, Essentials of the self-organizing map, @@ -134,9 +107,9 @@ Volume 37, 2013, Pages 52-65, ISSN 0893-6080, -https://doi.org/10.1016/j.neunet.2012.09.018. + -The mexican hat neighborhood function follows the lateral-interaction formulation in: +The Mexican hat neighborhood follows the lateral-interaction formulation in: O. J. Vrieze, Kohonen network, @@ -145,4 +118,8 @@ Lecture Notes in Computer Science, Volume 931, Springer, Berlin, Heidelberg, 1995, Pages 83-100, -https://doi.org/10.1007/BFb0027024. \ No newline at end of file + + +## License + +MIT. See [LICENSE](https://github.com/andremsouza/python-som/blob/master/LICENSE). diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 0000000..e67ce8f --- /dev/null +++ b/docs/api.md @@ -0,0 +1,26 @@ +# API reference + +::: python_som + options: + members: + - SOM + +## Neighborhood functions + +::: python_som._neighborhood + options: + members: + - gaussian + - bubble + - mexican_hat + - axis_offsets + - squared_grid_distance + - resolve + +## Decay functions + +::: python_som._decay + +## Distance functions + +::: python_som._distance diff --git a/docs/assets/iris.png b/docs/assets/iris.png new file mode 100644 index 0000000..a15674a Binary files /dev/null and b/docs/assets/iris.png differ diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..786b75d --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1 @@ +--8<-- "CHANGELOG.md" diff --git a/docs/getting-started.md b/docs/getting-started.md new file mode 100644 index 0000000..1b625e6 --- /dev/null +++ b/docs/getting-started.md @@ -0,0 +1,138 @@ +# Getting started + +## Install + +```bash +pip install python-som + +# with tqdm progress bars during training +pip install "python-som[cli]" +``` + +Requires Python 3.10 or later. + +## A complete example + +This walks through the Iris dataset end to end. The runnable version is +[`examples/iris.py`](https://github.com/andremsouza/python-som/blob/master/examples/iris.py). + +### Load the data + +```python +import numpy as np +import seaborn as sns + +iris = sns.load_dataset("iris") +target = iris.iloc[:, -1].to_numpy() +features = iris.iloc[:, :-1].to_numpy() +``` + +### Build the map + +```python +import python_som + +som = python_som.SOM( + x=20, + y=None, # chosen from the data by PCA + input_len=features.shape[1], + learning_rate=0.5, + neighborhood_radius=1.0, + neighborhood_function="gaussian", + cyclic_x=True, + cyclic_y=True, + data=features, # required when a dimension is None + random_seed=42, +) +print(som.get_shape()) +``` + +Leaving one dimension as `None` asks the library to pick it. Kohonen Section 3.5 advises matching +the side lengths to *"the lengths of the two largest principal components"*; since a component is a +unit direction, its length in the data is the standard deviation, so the ratio is +$\sqrt{\lambda_1/\lambda_2}$. + +### Initialize the models + +```python +som.weight_initialization(mode="linear", data=features) +``` + +Linear initialization is the one to prefer. Kohonen Section 4.3: *"much faster and convergence +follow if the initial values are selected as a regular, two-dimensional sequence of vectors taken +along a hyperplane spanned by the two largest principal components"*. It is also the only +deterministic initializer. + +The alternatives are `mode="random"` (draws from a distribution) and `mode="sample"` (copies random +rows of your data). + +### Train + +```python +error = som.train(features, n_iteration=len(features), mode="batch", verbose=True) +print(f"quantization error: {error:.4f}") +``` + +See [Training modes](training-modes.md) for how the three modes differ and why batch is +recommended. + +### Inspect the result + +```python +umatrix = som.distance_matrix().T # the U-matrix +activations = som.activation_matrix(features) # samples per node +labels = som.label_map(features, target) # label frequencies per node +``` + +### Plot it + +```python +import matplotlib.pyplot as plt + +plt.figure(figsize=som.get_shape()) +plt.pcolor(umatrix, cmap="bone_r") + +markers, colors = ["o", "s", "D"], ["C0", "C1", "C2"] +codes = {name: i for i, name in enumerate(np.unique(target))} +for sample, label in zip(features, target): + w = som.winner(sample) + plt.plot( + w[0] + 0.5, + w[1] + 0.5, + markers[codes[label]], + markerfacecolor="None", + markeredgecolor=colors[codes[label]], + markersize=12, + markeredgewidth=2, + ) +plt.axis((0, som.get_shape()[0], 0, som.get_shape()[1])) +plt.savefig("iris.png") +``` + +![U-matrix of a SOM trained on Iris](assets/iris.png) + +Darker regions of the U-matrix mean neighbouring models are far apart, so they read as boundaries +between clusters. + +## Progress output + +`verbose=True` shows a tqdm progress bar when `python-som[cli]` is installed. It also logs on the +`python_som` logger at INFO level, which is off by default the way any library logger is; enable it +with: + +```python +import logging + +logging.basicConfig(level=logging.INFO) +``` + +## Reproducibility + +```python +som = python_som.SOM(x=10, y=10, input_len=4, random_seed=42) +print(som.get_random_seed()) +``` + +Same seed, same map. The generator belongs to the instance, so building a SOM will not disturb +NumPy's global random state. Note that seeds do not reproduce across the 0.3.0 boundary; see the +warning in [Training modes](training-modes.md#reproducibility). diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..612c7a5 --- /dev/null +++ b/docs/index.md @@ -0,0 +1 @@ +--8<-- "README.md" diff --git a/docs/neighborhood-functions.md b/docs/neighborhood-functions.md new file mode 100644 index 0000000..549df84 --- /dev/null +++ b/docs/neighborhood-functions.md @@ -0,0 +1,135 @@ +# Neighborhood functions + +The neighborhood function decides how a winner's correction spreads to the rest of the grid. It is +the part of the SOM that turns vector quantization into a *topology-preserving* map, and the part +where a plausible-looking implementation can be quietly wrong, so it is worth setting out in full. + +## The rule that governs all of them + +Kohonen (2013) Eq. (5) writes the neighborhood as + +$$h_{ci}(t) = \alpha(t) \exp\left[-\frac{\mathrm{sqdist}(c, i)}{2\sigma^2(t)}\right]$$ + +where $\mathrm{sqdist}(c, i)$ is *"the square of the geometric distance between the nodes $c$ and $i$ +in the grid"*. + +Vrieze (1995) says the same thing from the biological side. Figure 3 of that chapter, captioned +*"The 'Mexican-hat' function of lateral interaction"*, plots interaction strength against a single +axis labelled **"Lateral distance"**. The update coefficient is written $h_{i i_c} = 1/\|i_c - i\|$, +the neighborhood set as $N_i = \{i' \mid d(i, i') \le \rho\}$, and the text states that the grid is +assumed to be a metric space. + +So the rule is: **collapse the two axis offsets into one distance first, then apply the profile.** +A neighborhood function is a function of $r$, not of $(dx, dy)$ separately. + +## Gaussian + +```python +som = python_som.SOM(x=20, y=20, input_len=4, neighborhood_function="gaussian") +``` + +$$h(r) = \exp\left(-\frac{r^2}{2\sigma^2}\right)$$ + +Strictly positive, monotonically decreasing, normalized so $h(0) = 1$. This is the default and the +form Kohonen writes out. + +The gaussian has a special property that causes trouble elsewhere: it is **multiplicatively +separable**. + +$$\exp\left(-\frac{dx^2 + dy^2}{2\sigma^2}\right) = \exp\left(-\frac{dx^2}{2\sigma^2}\right)\cdot\exp\left(-\frac{dy^2}{2\sigma^2}\right)$$ + +You can therefore compute it as an outer product of two 1-D gaussians and get exactly the right +answer. That works because of a property of $\exp$, not because of anything about neighborhood +functions. + +## Mexican hat + +```python +som = python_som.SOM(x=20, y=20, input_len=4, neighborhood_function="mexicanhat") +``` + +$$h(r) = (1 - u)\,e^{-u}, \qquad u = \frac{r^2}{2\sigma^2}$$ + +Also called the Ricker wavelet, or the Laplacian of Gaussian. Normalized so $h(0) = 1$. It: + +- **crosses zero** at $r = \sqrt{2}\,\sigma$ +- reaches its **minimum of $-e^{-2} \approx -0.135$** at $r = 2\sigma$ +- decays back to zero beyond that + +This is the shape Vrieze describes: *"When a neuron is firing, near by situated neurons are +stimulated, diminishing with increasing distance to the firing neuron. From a certain distance on +inhibition will take place that gradually vanishes when the distance becomes very large."* + +!!! warning "It is not an outer product of two 1-D wavelets" + + The Ricker wavelet is **not** separable, so the trick that works for the gaussian fails here. + The outer product of two 1-D Ricker wavelets is positive in the diagonal quadrants, where both + 1-D factors are negative and their product flips sign. + + Measured on a 21×21 grid with $\sigma = 3$: + + | quantity | outer product | correct isotropic form | + | --- | --- | --- | + | $h$ at $(c + 2\sigma,\, c + 2\sigma)$ | **+0.165** | −0.055 | + | $h$ at $(c + 3\sigma,\, c + 3\sigma)$ | **+0.008** | −0.001 | + | global minimum | −0.443 | −0.135 | + | zero-crossing locus | a **cross** | a **circle**, $r = \sqrt{2}\sigma$ | + + The separable version puts an excitatory lobe worth 16.5% of the winner's strength exactly + where the function is supposed to inhibit, and along the diagonals it never becomes negative at + all. Its value is not a function of any metric on the grid. + +### It cannot be used with batch training + +Batch training computes a weighted mean whose denominator is $\sum_j n_j h_{ji}$ (Kohonen Eq. 8). +That is only well defined when $h$ is non-negative. On a 12×12 grid, 49 of 144 denominators come out +negative, which inverts or explodes the update. Passing `mode="batch"` with the mexican hat raises a +`ValueError`. Use `mode="random"` or `mode="sequential"`. + +## Bubble + +```python +som = python_som.SOM(x=20, y=20, input_len=4, neighborhood_function="bubble") +``` + +$$h(dx, dy) = \begin{cases} 1 & \max(|dx|, |dy|) \le \rho \\ 0 & \text{otherwise}\end{cases}$$ + +This is the truncated inner, excitatory lobe of the mexican hat. Vrieze p. 85: *"In Kohonen networks +usually only the inner stimulation area is used"*, and the flat choice is *"just as effective and +sometimes even better"* than a distance-dependent one. + +!!! note "The bubble uses the Chebyshev metric, so the region is a square" + + A node is included when $\max(|dx|, |dy|) \le \rho$, which makes the region a square rather than + a disc. That follows Vrieze's appendix pseudo-code, which computes + `b = MAX(ABS(i - w_i), ABS(j - w_j))`, although Kohonen's phrase "up to a certain radius from the + winner" reads as Euclidean. The two sources genuinely differ, and this library keeps Vrieze's + reading so that existing results stay reproducible. + + A consequence worth knowing, because it is easy to assume otherwise: **a Chebyshev ball is not + isotropic under the Euclidean metric**. Two nodes the same Euclidean distance from the winner can + fall on opposite sides of the boundary. The smallest case is $r = \sqrt{50}$, where $(5, 5)$ is + inside a $\sigma = 5$ square and $(7, 1)$ is outside. + +Unlike the other two, a radius of zero is allowed here: it selects the winner alone, which is well +defined for an indicator function, where for the gaussian and the mexican hat it would be a division +by zero. + +## Cyclic grids + +With `cyclic_x=True` or `cyclic_y=True` the grid becomes a torus and offsets use the minimum-image +convention, $(d + L/2) \bmod L - L/2$, so the shortest way around is used. + +Both tails have to be folded. Folding only offsets above $+L/2$, as this library did before 0.2.0, +leaves a winner in the upper half of an axis unable to wrap: on a 10×10 map with $\sigma = 1$, a +winner at row 0 gave $h[9] = 0.6065$ while a winner at row 9 gave $h[0] = 0$. + +## The radius floor + +Kohonen Section 4.2 warns that *"the final value of $\sigma$ shall not go to zero, because otherwise +the process loses its ordering power. It should always remain, say, above half of the grid +spacing."* + +The `min_neighborhood_radius` parameter enforces exactly that, defaulting to 0.5. It matters in +practice because `linear_decay` reaches zero at the horizon, which would otherwise be a division by +zero. diff --git a/docs/training-modes.md b/docs/training-modes.md new file mode 100644 index 0000000..a7df95a --- /dev/null +++ b/docs/training-modes.md @@ -0,0 +1,90 @@ +# Training modes + +Three modes, selected with `mode=` on [`SOM.train`][python_som.SOM.train]. + +## Batch (recommended) + +```python +som.train(data, n_iteration=100, mode="batch") +``` + +Every model is recomputed at once from the data in its neighborhood, then all are replaced together. +Kohonen (2013) Section 3.1 is unambiguous about preferring it: + +> It should be emphasized that only the batch-learning version of the SOM is recommendable for +> practical applications, because it does not involve any learning-rate parameter, and its +> convergence is an order of magnitude faster and safer. + +Eq. (8) gives the update: + +$$m_i^* = \frac{\sum_j n_j h_{ji} \bar{x}_{m,j}}{\sum_j n_j h_{ji}}$$ + +where $n_j$ is the number of samples whose best match is node $j$ and $\bar{x}_{m,j}$ is their mean. +`learning_rate` is ignored. Because each iteration touches the whole dataset, far fewer are needed: +the default is 10 per sample against 1000 for the stepwise modes. + +!!! warning "Batch mode rejects signed neighborhood functions" + + The denominator $\sum_j n_j h_{ji}$ is only sign-definite when $h \ge 0$. With the mexican hat it + can vanish or go negative, which inverts or explodes the update, so that combination raises a + `ValueError`. This is a property of the weighted mean itself, not of this implementation. + +### Two departures from a naive transcription + +**Models with no data in their neighborhood keep their previous value.** Building the new weights +from a zeroed array instead destroys them. On a 30×30 map with 20 samples and a small radius, that +wiped 282 of 900 models in a single step. + +**The per-node sums are contracted with NumPy rather than looped over in Python.** The neighborhood +is evaluated once per node and contracted against the per-node sums and counts. On a 20×20 map with +150 samples this runs about 30× faster than the nested Python loop it replaces, and the two agree +to $10^{-12}$. + +The full $(x, y, x, y)$ tensor would be faster still, but it costs $(xy)^2$ floats, roughly 800 MB +for a 100×100 map, so it is not materialised. + +## Random + +```python +som.train(data, n_iteration=10_000, mode="random") +``` + +Draws samples at random with replacement and applies Eq. (3) one at a time: + +$$m_i(t+1) = m_i(t) + h_{ci}(t)\,[x(t) - m_i(t)]$$ + +Uses `learning_rate` and `learning_rate_decay`. The original formulation, kept because it is the +one most SOM literature describes and because it accepts signed neighborhood functions. + +## Sequential + +```python +som.train(data, n_iteration=10_000, mode="sequential") +``` + +The same stepwise update, but walking the dataset in order and wrapping around until `n_iteration` +steps have run. + +## Choosing the number of iterations + +Leave `n_iteration` unset and the default is 10 per sample for batch, 1000 per sample for the +stepwise modes. Kohonen Section 4.1 on the radius schedule: + +> The true mathematical form of $\sigma(t)$ is not crucial, as long as its value is fairly large in +> the beginning of the process, say, on the order of half of the diameter of the grid, whereafter it +> is gradually reduced to a fraction of it in about 1000 steps. + +So a sensible starting point is `neighborhood_radius` at roughly half the shorter side of the grid, +decaying from there, with the floor described in +[Neighborhood functions](neighborhood-functions.md#the-radius-floor) keeping it away from zero. + +## Reproducibility + +Pass `random_seed` and the run is deterministic. The generator is per-instance, so constructing a +SOM has no effect on NumPy's global random state. + +!!! warning "Seeds do not carry across the 0.3.0 boundary" + + Versions before 0.3.0 called `np.random.seed` on the global generator. 0.3.0 uses a private + `np.random.Generator`, so the same seed produces a different map. To reproduce figures made with + an older version, pin it: `pip install python-som==0.2.0`. diff --git a/examples/iris.py b/examples/iris.py new file mode 100644 index 0000000..27f9847 --- /dev/null +++ b/examples/iris.py @@ -0,0 +1,84 @@ +"""Train a self-organizing map on the Iris dataset and plot its U-matrix. + +Run with the ``examples`` extra installed:: + + uv sync --all-extras + uv run python examples/iris.py + +Writes ``docs/assets/iris.png``, the image the README and the documentation display. +""" + +from __future__ import annotations + +import logging +import pathlib + +import matplotlib as mpl + +mpl.use("Agg") # render to a file; no display needed + +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns # type: ignore[import-untyped] + +import python_som + +OUTPUT = pathlib.Path(__file__).resolve().parent.parent / "docs" / "assets" / "iris.png" +MARKERS = ("o", "s", "D") +COLORS = ("C0", "C1", "C2") + + +def main() -> None: + """Train the map and write the plot.""" + logging.basicConfig(level=logging.INFO, format="%(message)s") + + iris = sns.load_dataset("iris") + target = iris.iloc[:, -1].to_numpy() + features = iris.iloc[:, :-1].to_numpy() + + # y is chosen from the data: Kohonen Section 3.5 asks for side lengths matching the lengths of + # the two largest principal components. + som = python_som.SOM( + x=20, + y=None, + input_len=features.shape[1], + learning_rate=0.5, + neighborhood_radius=1.0, + neighborhood_function="gaussian", + cyclic_x=True, + cyclic_y=True, + data=features, + random_seed=42, + ) + print(f"map shape: {som.get_shape()}") + + # Linear initialization is the one Kohonen recommends, and the only deterministic one. + som.weight_initialization(mode="linear", data=features) + error = som.train(features, n_iteration=len(features), mode="batch", verbose=True) + print(f"quantization error: {error:.4f}") + + umatrix = som.distance_matrix().T + codes = {name: i for i, name in enumerate(np.unique(target))} + + # get_shape() returns plain ints, so it can be handed straight to figsize. + plt.figure(figsize=som.get_shape()) + plt.pcolor(umatrix, cmap="bone_r") + for sample, label in zip(features, target, strict=True): + w = som.winner(sample) + plt.plot( + w[0] + 0.5, + w[1] + 0.5, + MARKERS[codes[label]], + markerfacecolor="None", + markeredgecolor=COLORS[codes[label]], + markersize=12, + markeredgewidth=2, + ) + plt.axis((0, som.get_shape()[0], 0, som.get_shape()[1])) + OUTPUT.parent.mkdir(parents=True, exist_ok=True) + plt.savefig(OUTPUT, bbox_inches="tight") + print(f"wrote {OUTPUT}") + + +if __name__ == "__main__": + main() diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..ed3c0c9 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,72 @@ +site_name: python-som +site_description: Python implementation of Kohonen's self-organizing map +site_url: https://andremsouza.github.io/python-som/ +repo_url: https://github.com/andremsouza/python-som +repo_name: andremsouza/python-som +edit_uri: edit/master/docs/ + +theme: + name: material + features: + - content.code.copy + - navigation.sections + - navigation.top + - search.highlight + - toc.follow + palette: + - media: "(prefers-color-scheme: light)" + scheme: default + primary: indigo + toggle: + icon: material/weather-night + name: Switch to dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: indigo + toggle: + icon: material/weather-sunny + name: Switch to light mode + +nav: + - Home: index.md + - Getting started: getting-started.md + - Neighborhood functions: neighborhood-functions.md + - Training modes: training-modes.md + - API reference: api.md + - Changelog: changelog.md + +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [src] + options: + docstring_style: sphinx + show_source: true + show_root_heading: true + members_order: source + separate_signature: true + +markdown_extensions: + - admonition + - attr_list + - md_in_html + - pymdownx.details + # index.md and changelog.md pull in README.md and CHANGELOG.md from the repository root, so + # those files stay single-sourced rather than duplicated into docs/. + - pymdownx.snippets: + base_path: [.] + check_paths: true + - pymdownx.superfences + - pymdownx.arithmatex: + generic: true + - tables + - toc: + permalink: true + +extra_javascript: + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + +watch: + - src/python_som diff --git a/pyproject.toml b/pyproject.toml index 0bdff0e..4384f97 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,15 +1,16 @@ [build-system] -requires = ["hatchling"] +requires = ["hatchling>=1.27"] build-backend = "hatchling.build" + [project] name = "python-som" -version = "0.2.0" -authors = [ - { name="André Moreira Souza", email="msouza.andre@hotmail.com" }, -] +version = "0.3.0" +authors = [{ name = "André Moreira Souza", email = "msouza.andre@hotmail.com" }] description = "Python implementation of the Self-Organizing Map" readme = "README.md" requires-python = ">=3.10" +license = "MIT" +license-files = ["LICEN[CS]E*"] classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", @@ -17,23 +18,152 @@ classifiers = [ "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Scientific/Engineering :: Artificial Intelligence", - "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Operating System :: OS Independent", + "Typing :: Typed", ] -license = "MIT" -license-files = ["LICEN[CS]E*"] dependencies = [ - "numpy", - "pandas", - "scikit-learn", + "numpy>=1.24", + "pandas>=2.0", + "scikit-learn>=1.3", ] [project.optional-dependencies] -cli = [ - "tqdm", +cli = ["tqdm>=4.66"] +dev = [ + "hypothesis==6.163.0", + "mypy==2.3.0", + # pandas-stubs 3.x needs Python >=3.11; it is dev-only, so gate it rather than + # raising the library's own floor. mypy runs on 3.12 in CI, where it is present. + "pandas-stubs==3.0.3.260530; python_version >= '3.11'", + "pre-commit==4.6.1", + "pytest==9.1.1", + "pytest-cov==7.1.0", + "ruff==0.16.0", + # tomllib entered the stdlib in 3.11; tomli is the backport it was derived from. + "tomli==2.4.1; python_version < '3.11'", + "twine==7.0.0", + "types-tqdm==4.69.0.20260728", +] +docs = [ + "mkdocs-material==9.7.7", + "mkdocstrings-python==2.0.5", +] +examples = [ + "matplotlib>=3.8", + "seaborn>=0.13", ] [project.urls] Homepage = "https://github.com/andremsouza/python-som" +Documentation = "https://andremsouza.github.io/python-som/" +Changelog = "https://github.com/andremsouza/python-som/blob/master/CHANGELOG.md" Issues = "https://github.com/andremsouza/python-som/issues" + +[tool.hatch.build.targets.wheel] +packages = ["src/python_som"] + +[tool.hatch.build.targets.sdist] +include = [ + "/src", + "/tests", + "/docs", + "/examples", + "/README.md", + "/CHANGELOG.md", + "/LICENSE", +] + +[tool.ruff] +line-length = 100 +target-version = "py310" +src = ["src", "tests"] + +[tool.ruff.lint] +select = ["ALL"] +ignore = [ + # The 13-argument constructor is the established public API. Reducing it, or forcing the + # arguments keyword-only, would break every existing caller for no gain in correctness. + "PLR0913", + "PLR0917", + # `verbose`, `normalize` and `cyclic_*` are long-standing boolean parameters of the public + # API. Making them keyword-only would break positional callers. + "FBT001", + "FBT002", + # Conflicts with the formatter. + "COM812", + "ISC001", + # This project documents parameters with `:param:` rather than a Google/NumPy section. + "D417", + # Per-file copyright headers are not this project's convention; the MIT terms live in + # LICENSE and are declared in the package metadata. + "CPY001", +] + +[tool.ruff.lint.per-file-ignores] +"tests/*" = [ + # Tests reach into private helpers deliberately; that is what is under test. + "SLF001", + # assert is the point of a test. + "S101", + # Tests do not need to document every parameter. + "D103", + "D100", + # Magic values are the expected values being asserted. + "PLR2004", +] +"examples/*" = ["INP001", "T201", "D100"] + +[tool.ruff.lint.pydocstyle] +convention = "pep257" + +[tool.ruff.lint.isort] +known-first-party = ["python_som"] + +[tool.mypy] +# Deliberately not pinned to python_version = "3.10". NumPy's bundled stubs use 3.12 syntax, and +# `tomllib` is 3.11+, so pinning to the floor makes mypy fail on the dependencies rather than on +# our code. Real 3.10 compatibility is covered by the CI test matrix instead. +strict = true +files = ["src", "tests"] +warn_unreachable = true +enable_error_code = ["ignore-without-code", "redundant-expr", "truthy-bool"] + +[[tool.mypy.overrides]] +module = ["sklearn.*"] +ignore_missing_imports = true + +[tool.pytest.ini_options] +minversion = "8.0" +testpaths = ["tests"] +addopts = [ + "--strict-markers", + "--strict-config", + "-ra", +] +markers = [ + "slow: end-to-end runs; deselect with -m 'not slow'", +] +filterwarnings = ["error"] + +[tool.coverage.run] +# source_pkgs, not source: with a src layout the package under test is the installed one, and +# coverage has to resolve it by import name rather than by directory. +source_pkgs = ["python_som"] +branch = true + +[tool.coverage.paths] +source = ["src/python_som", "*/site-packages/python_som"] + +[tool.coverage.report] +fail_under = 90 +show_missing = true +exclude_lines = [ + "pragma: no cover", + "if TYPE_CHECKING:", + "raise NotImplementedError", +] diff --git a/python_som/__init__.py b/python_som/__init__.py deleted file mode 100644 index 1422ba8..0000000 --- a/python_som/__init__.py +++ /dev/null @@ -1,944 +0,0 @@ -"""This module contains the implementation of the 2D self-organizing map. - -Most features were implemented using NumPy, with Scikit-learn for standardization and PCA. - -Features: - - Stepwise and batch training - - Random weight initialization - - Random sampling weight initialization - - Linear weight initialization (with PCA) - - Automatic selection of map size ratio (with PCA) - - Support for cyclic arrays, for toroidal or spherical maps - - Gaussian, Bubble and Mexican Hat neighborhood functions - - Support for custom decay functions - - Support for visualization (U-matrix, activation matrix) - - Support for supervised learning (label map) - - Support for NumPy arrays, Pandas DataFrames and regular lists of values - -Reference: -Teuvo Kohonen, -Essentials of the self-organizing map, -Neural Networks, -Volume 37, -2013, -Pages 52-65, -ISSN 0893-6080, -https://doi.org/10.1016/j.neunet.2012.09.018. -""" - -# %% -from collections import Counter -from typing import Callable - -import numpy as np -import pandas as pd -import sklearn # type: ignore -import sklearn.decomposition # type: ignore -import sklearn.preprocessing # type: ignore - -try: - import tqdm - - TQDM_AVAILABLE = True -except ImportError: - TQDM_AVAILABLE = False - - -# %% -def _asymptotic_decay(x: float, t: int, max_t: int) -> float: - """ - Asymptotic decay function. Can be used for both the learning_rate or the neighborhood_radius. - - :param x: float: Initial x parameter - :param t: int: Current iteration - :param max_t: int: Maximum number of iterations - :return: float: Current state of x after t iterations - """ - return x / (1 + t / (max_t / 2)) - - -def _linear_decay(x: float, t: int, max_t: int) -> float: - """ - Linear decay function. Can be used for both the learning_rate or the neighborhood_radius. - - :param x: float: Initial x parameter - :param t: int: Current iteration - :param max_t: int: Maximum number of iterations - :return: float: Current state of x after t iterations - """ - return x * (1.0 - t / max_t) - - -def _exponential_decay(x: float, t: int, max_t: int, factor: float = 2.0) -> float: - """ - Exponential decay function. Can be used for both the learning_rate or the neighborhood_radius. - - :param x: float: Initial x parameter - :param t: int: Current iteration - :param max_t: int: Maximum number of iterations - :param factor: float: Exponential decay factor. Defaults to 2.0. - :return: float: Current state of x after t iterations - """ - return x * (1 - (factor / max_t)) ** t - - -def _inverse_decay(x: float, t: int, max_t: int) -> float: - """ - Inverse decay function. Can be used for both the learning_rate or the neighborhood_radius. - - :param x: float: Initial x parameter - :param t: int: Current iteration - :param max_t: int: Maximum number of iterations - :return: float: Current state of x after t iterations - """ - return (max_t / 100) * x / ((max_t / 100) + t) - - -def _euclidean_distance(a: np.ndarray, b: np.ndarray) -> np.ndarray: - """This function calculates the euclidean distances between the elements of the last dimension - of a and b. - - The parameters a and b may be n-dimensional, but their shapes must be capable of broadcasting - - The shape of the output complies to the first n-1 dimensions of the parameter with the highest - dimensionality. - - :param a: array-like: list or numpy array of values. a must not be a scalar value. - :param b: array-like: list or numpy array of values. b must not be a scalar value. - :return: array-like: An array of euclidean distances between a and b. - """ - return np.linalg.norm(np.subtract(a, b), ord=2, axis=-1) - - -class SOM: - """Implementation of the 2D self-organizing map, with support for NumPy arrays and - Pandas DataFrames. - - Most features were implemented using NumPy, with Scikit-learn for standardization and - PCA operations. - - Features: - - Stepwise and batch training - - Random weight initialization - - Random sampling weight initialization - - Linear weight initialization (with PCA) - - Automatic selection of map size ratio (with PCA) - - Support for cyclic arrays, for toroidal or spherical maps - - Gaussian, Bubble and Mexican hat neighborhood functions - - Support for custom decay functions - - Support for visualization (U-matrix, activation matrix) - - Support for supervised learning (label map) - - Support for NumPy arrays, Pandas DataFrames and regular lists of values - - Reference: - Teuvo Kohonen, - Essentials of the self-organizing map, - Neural Networks, - Volume 37, - 2013, - Pages 52-65, - ISSN 0893-6080, - https://doi.org/10.1016/j.neunet.2012.09.018. - """ - - def __init__( - self, - x: int | None, - y: int | None, - input_len: int, - learning_rate: float = 0.5, - learning_rate_decay: Callable[[float, int, int], float] = _asymptotic_decay, - neighborhood_radius: float = 1.0, - neighborhood_radius_decay: Callable[ - [float, int, int], float - ] = _asymptotic_decay, - neighborhood_function: str = "gaussian", - distance_function: Callable[ - [np.ndarray, np.ndarray], - np.ndarray, - ] = _euclidean_distance, - cyclic_x: bool = False, - cyclic_y: bool = False, - random_seed: int | None = None, - data: np.ndarray | pd.DataFrame | list | None = None, - ) -> None: - """ - Constructor for the self-organizing map class. - - :param x: int or NoneType: X dimension of the self-organizing map, i.e., - number of rows of the matrix of weights. - x should be larger than 0. - If x is None and 'data' is provided in kwargs, its value will be automatically - selected using PCA of 'data'. Either x or y should be different than None. - :param y: int or NoneType: Y dimension of the self-organizing map, i.e., - number of columns of the matrix of weights. - y should be larger than 0. - If y is None and 'data' is provided in kwargs, its value will be automatically - selected using PCA of 'data'. Either x or y should be different than None. - :param input_len: int: Number of features of the training dataset, i.e., - number of elements of each node of the network. - :param learning_rate: float: Initial learning rate for the training process. - Should be a positive floating point value. - Defaults to 0.5. - Note: The value of the learning_rate is irrelevant for the 'batch' training mode. - :param learning_rate_decay: function: Decay function for the learning_rate variable. - May be a predefined one from this package, or a custom function, with the same - parameters and return type. Defaults to _asymptotic_decay. - :param neighborhood_radius: float: Initial neighborhood radius for the training process. - Defaults to 1. - :param neighborhood_radius_decay: function: Decay function for the neighborhood_radius - variable. May be a predefined one from this package, or a custom function, with the - same parameters and return type. Defaults to _asymptotic_decay - :param neighborhood_function: str: Neighborhood function name for the training process. - May be either 'gaussian', 'bubble' or 'mexicanhat'. - :param distance_function: function: Function for calculating distances/dissimilarities - between models of the network. - May be a predefined one from this package, or a custom function, with the same - parameters and return type. Defaults to _euclidean_distance. - :param cyclic_x: bool: Boolean value activate/deactivate cyclic arrays in the x direction, - i.e, between the first and last rows of the weight matrix. - Defaults to False. - :param cyclic_y: bool: Boolean value activate/deactivate cyclic arrays in the y direction, - i.e, between the first and last columns of the weight matrix. - Defaults to False. - :param random_seed: int or None: Seed for NumPy random value generator. Defaults to None. - :param data: array-like: dataset for performing PCA. - Required when either x or y is None, for determining map size. - """ - # Verifying map dimensions (initializing automatically, if a dataset is provided) - if (x, y) == (None, None): - raise ValueError("At least one of the dimensions (x, y) must be specified") - if x is None or y is None: - # If a dataset was given through **kwargs, select missing dimension with PCA - # The ratio of the (x, y) sizes will comply roughly with the ratio of - # the two largest principal components - if data is None: - raise ValueError( - "If one of the dimensions is not specified," - "a dataset must be provided for automatic size initialization." - ) - # Convert data to numpy array - if isinstance(data, pd.DataFrame): - data_array = data.to_numpy() - else: - data_array = np.array(data) - # Perform PCA w/ sklearn - data_array = sklearn.preprocessing.StandardScaler().fit_transform( - data_array - ) - pca = sklearn.decomposition.PCA(n_components=2) - pca.fit(data_array) - ratio = pca.explained_variance_[0] / pca.explained_variance_[1] - # Update missing size variable - if x is None: - x = y // ratio - if y is None: - y = x // ratio - - # Initializing private variables - self._shape = (np.uint(x), np.uint(y)) - self._input_len = np.uint(input_len) - self._learning_rate = float(learning_rate) - self._learning_rate_decay = learning_rate_decay - self._neighborhood_radius = float(neighborhood_radius) - self._neighborhood_radius_decay = neighborhood_radius_decay - neighborhood_functions = { - "gaussian": self._gaussian, - "bubble": self._bubble, - "mexicanhat": self._mexicanhat, - } - try: - self._neighborhood_function = neighborhood_functions[neighborhood_function] - except KeyError as exc: - raise ValueError( - "Invalid value for 'neighborhood_function' parameter. Value should be in " - + str(list(neighborhood_functions.keys())) - ) from exc - self._neighborhood_function_name = neighborhood_function - self._distance_function = distance_function - self._cyclic = (bool(cyclic_x), bool(cyclic_y)) - self._neigx, self._neigy = np.arange(self._shape[0]), np.arange(self._shape[1]) - - # Seed numpy random generator - if random_seed is None: - self._random_seed = np.random.randint(np.iinfo(np.int32).max) - else: - self._random_seed = int(random_seed) - np.random.seed(self._random_seed) - - # Random weight initialization - self._weights = np.random.standard_normal( - size=(self._shape[0], self._shape[1], self._input_len) - ) - - def get_shape(self) -> tuple[np.uint, np.uint]: - """ - Gets the shape of the network. - - :return: tuple(int, int): Shape of the network. - """ - return self._shape - - def get_weights(self) -> np.ndarray: - """ - Gets the weight matrix of the network. - - :return: np.ndarray: Weight matrix of the network. - """ - return self._weights - - def set_learning_rate(self, learning_rate: float) -> None: - """ - Sets the learning_rate member of the SOM. - - :param learning_rate: float: New value for learning_rate of an instance of the SOM. - """ - self._learning_rate = float(learning_rate) - - def set_neighborhood_radius(self, neighborhood_radius: float) -> None: - """ - Sets the neighborhood_radius member of the SOM. - - :param neighborhood_radius: float: New value for neighborhood_radius of a SOM instance. - """ - self._neighborhood_radius = float(neighborhood_radius) - - def activate(self, x: np.ndarray) -> np.ndarray: - """ - Calculates distances between an instance x and the weights of the network. - - :param x: array-like: Instance to be compared with the weights of the network. - :return: np.ndarray: Distances between x and each weight of the network. - """ - return self._distance_function(x, self._weights) - - def winner(self, x: np.ndarray) -> tuple[int, ...]: - """ - Calculates the best-matching unit of the network for an instance x - - :param x: array-like: Instance to be compared with the weights of the network. - :return: (int, int): Index of the best-matching unit of x. - """ - activation_map = self.activate(x) - min_index = tuple( - map(int, np.unravel_index(activation_map.argmin(), activation_map.shape)) - ) - return min_index - - def quantization(self, data: np.ndarray | pd.DataFrame | list) -> np.ndarray: - """ - Calculates distances from each instance of 'data' to each of the weights of the network. - - :param data: array-like: Dataset to be compared with the weights of the network. - Expected shape is (n_samples, n_features). - :return: np.ndarray: array of lists of distances from each instance of the dataset - to each weight of the network. - """ - # Convert data to numpy array - data_array = self._data_to_numpy(data) - return np.array( - [ - (self._distance_function(i, self._weights[self.winner(i)])) - for i in data_array - ] - ) - - def quantization_error(self, data: np.ndarray | pd.DataFrame | list) -> float: - """Calculates average distance of the weights of the network to their assigned instances. - This error is a quality measure for the training process. - - :param data: array-like: Dataset to be compared with the weights of the network. - :return: float: Quantization error. - """ - quantization = self.quantization(data) - return quantization.mean() - - def distance_matrix(self, normalize: bool = False) -> np.ndarray: - """ - Calculates U-matrix of the current state of the network, - i.e., the matrix of distances between each node and its neighbors. - Has support for cyclic arrays - - :param normalize: bool: Activate to normalize the U-matrix between 0 and 1. - Defaults to False. - :return: np.ndarray: U-matrix of the current state of the network. - """ - um = np.zeros(shape=self._shape) - it = np.nditer(um, flags=["multi_index"]) - distances = np.zeros(self._shape + self._shape) - for i in range(self._shape[0]): - for j in range(self._shape[1]): - distances[i, j] = self._distance_function( - self._weights[i, j], self._weights - ) - - while not it.finished: - update_matrix = self._bubble(it.multi_index, 1) - um[it.multi_index] = np.sum(update_matrix * distances[it.multi_index]) - it.iternext() - if normalize: - # Normalize U-matrix between 0 and 1 - um = (um - np.min(um)) / (np.max(um) - np.min(um)) - return um - - def activation_matrix(self, data: np.ndarray | pd.DataFrame | list) -> np.ndarray: - """Calculates the activation matrix of the network for a dataset. - - I.e., for each node, the count of instances that have been assigned to in, - in the current state. - - :param data: array-like: Dataset to be compared with the weights of the network. - :return: np.ndarray: Activation matrix. - """ - # Convert data to numpy array - data_array = self._data_to_numpy(data) - - activation_matrix = np.zeros(self._shape) - for i in data_array: - activation_matrix[self.winner(i)] += 1 - return activation_matrix - - def winner_map(self, data: np.ndarray | pd.DataFrame | list) -> dict: - """ - Calculates, for each node (i, j) of the network, - the list of all instances from 'data' that has been assigned to it. - - :param data: array-like: Dataset to be compared with the weights of the network. - :return: dict: Winner map. - """ - # Convert data to numpy array - data_array = self._data_to_numpy(data) - winner_map: dict[tuple[int, ...], list] = { - tuple(index): [] for index in np.ndindex(self._shape) - } - for i in data_array: - winner_map[self.winner(i)].append(i) - return winner_map - - def label_map( - self, - data: np.ndarray | pd.DataFrame | list, - labels: np.ndarray | pd.DataFrame | list, - ) -> dict[tuple[int, ...], Counter]: - """Calculates, for each node (i, j) of the network, the frequency of each label... - - from 'labels' corresponding to its respective instance from 'data' that has been assigned - to this node. - - :param data: array-like: Dataset to be compared with the weights of the network. - :param labels: array-like: Labels corresponding to the indices of 'data'. - :return: dict: Label map. - """ - # Convert data to numpy array - data_array = self._data_to_numpy(data) - # Convert labels to numpy array - if isinstance(labels, pd.DataFrame): - labels = labels.to_numpy() - else: - labels = np.array(labels) - - winner_map: dict[tuple[int, ...], list] = { - tuple(index): [] for index in np.ndindex(self._shape) - } - label_count_map: dict[tuple[int, ...], Counter] = { - tuple(index): Counter() for index in np.ndindex(self._shape) - } - for i, instance in enumerate(data_array): - winner = self.winner(instance) - winner_map[winner].append(labels[i]) - label_count_map[winner].update([labels[i]]) - return label_count_map - - def train( - self, - data: np.ndarray | pd.DataFrame | list, - n_iteration: int | None = None, - mode: str = "random", - verbose: bool = False, - ) -> float: - """ - Trains the self-organizing map, with the dataset 'data', and a certain number of iterations. - - :param data: array-like: Dataset for training. - :param n_iteration: int or None: Number of iterations of training. - If None, defaults to 1000 * len(data) for stepwise training modes, - or 10 * len(data) for batch training mode. - :param mode: str: Training mode name. May be either 'random', 'sequential', or 'batch'. - For 'batch' mode, a much smaller number of iterations is needed, - but a higher computation power is required for each individual iteration. - :param verbose: bool: Activate to print useful information to the terminal/console, e.g., - the progress of the training process - :return: float: Quantization error after training - """ - # Convert data to numpy array for training - data_array = self._data_to_numpy(data) - - # If no number of iterations is given, select automatically - if n_iteration is None: - n_iteration = {"random": 1000, "sequential": 1000, "batch": 10}[mode] * len( - data_array - ) - - if verbose: - print( - "Training with", - n_iteration, - "iterations.\nTraining mode:", - mode, - sep=" ", - ) - - if mode == "random": - self._train_random(data_array, n_iteration, verbose) - elif mode == "sequential": - self._train_sequential(data_array, n_iteration, verbose) - elif mode == "batch": - if self._neighborhood_function_name == "mexicanhat": - # The batch update of Kohonen (2013), Eq. (8), is a weighted mean whose denominator - # is sum_j n_j * h_ji. That is only well defined for a non-negative neighborhood - # function. The mexican hat is signed by construction, so the denominator can vanish - # or turn negative, which makes the update blow up or invert the sign of the - # correction. Use a stepwise mode instead. - raise ValueError( - "The 'mexicanhat' neighborhood function cannot be used with the 'batch'" - " training mode: the weighted mean of Kohonen (2013), Eq. (8), is undefined" - " for a neighborhood function that takes negative values, as its denominator" - " is not sign-definite. Use mode='random' or mode='sequential' instead." - ) - self._train_batch(data_array, n_iteration, verbose) - else: - # Invalid training mode value - raise ValueError( - "Invalid value for 'mode' parameter. Value should be in " - + str(["random", "sequential", "batch"]) - ) - - # Compute quantization error - q_error = self.quantization_error(data_array) - if verbose: - print("Quantization error:", q_error, sep=" ") - return q_error - - def _train_random( - self, data_array: np.ndarray, n_iteration: int, verbose: bool - ) -> None: - if TQDM_AVAILABLE and verbose: - iterator: enumerate | tqdm.tqdm = tqdm.tqdm( - np.random.choice( - len(data_array), - size=n_iteration, - replace=(n_iteration > len(data_array)), - ), - total=n_iteration, - desc="Training", - ) - else: - iterator = enumerate( - np.random.choice( - len(data_array), - size=n_iteration, - replace=(n_iteration > len(data_array)), - ) - ) - - for it, i in iterator: # type: ignore - # Calculating decaying alpha and sigma parameters for updating weights - alpha = self._learning_rate_decay(self._learning_rate, it, n_iteration) - sigma = self._neighborhood_radius_decay( - self._neighborhood_radius, it, n_iteration - ) - - # Finding winner node (best-matching unit) - winner = self.winner(data_array[i]) - - # Updating weights, based on current neighborhood function - self._weights += ( - alpha - * self._neighborhood_function(winner, sigma)[..., None] - * (data_array[i] - self._weights) - ) - - # Print progress, if verbose is activated and tqdm is not available - if verbose and not TQDM_AVAILABLE: - print( - "Iteration:", - it, - "/", - n_iteration, - sep=" ", - end="\r", - flush=True, - ) - - def _train_sequential( - self, data_array: np.ndarray, n_iteration: int, verbose: bool - ) -> None: - if TQDM_AVAILABLE and verbose: - iterator: enumerate | tqdm.tqdm = tqdm.tqdm( - enumerate(data_array), - total=n_iteration, - desc="Training", - ) - else: - iterator = enumerate(data_array) - - for it, i in iterator: # type: ignore - # Calculating decaying alpha and sigma parameters for updating weights - alpha = self._learning_rate_decay(self._learning_rate, it, n_iteration) - sigma = self._neighborhood_radius_decay( - self._neighborhood_radius, it, n_iteration - ) - - # Finding winner node (best-matching unit) - winner = self.winner(i) - - # Updating weights, based on current neighborhood function - self._weights += ( - alpha - * self._neighborhood_function(winner, sigma)[..., None] - * (i - self._weights) - ) - - # Print progress, if verbose is activated and tqdm is not available - if verbose and not TQDM_AVAILABLE: - print( - "Iteration:", - it, - "/", - n_iteration, - sep=" ", - end="\r", - flush=True, - ) - - def _train_batch( - self, data_array: np.ndarray, n_iteration: int, verbose: bool - ) -> None: - if TQDM_AVAILABLE and verbose: - iterator: range | tqdm.tqdm = tqdm.tqdm( - range(n_iteration), total=n_iteration, desc="Training" - ) - else: - iterator = range(n_iteration) - - for it in iterator: - # Calculating decaying sigma - sigma = self._neighborhood_radius_decay( - self._neighborhood_radius, it, n_iteration - ) - - # For each node, create a list of instances associated to it - winner_map = self.winner_map(data_array) - - # Calculate the weighted average of all instances in the neighborhood of each node - new_weights = np.zeros(self._weights.shape) - for i in winner_map.keys(): - neig = self._neighborhood_function(i, sigma) - upper, bottom = np.zeros(self._input_len), 0.0 - for j in winner_map.keys(): - upper += neig[j] * np.sum(winner_map[j], axis=0) - bottom += neig[j] * len(winner_map[j]) - - # Only update if there is any instance associated with the winner node - # or its neighbors - if bottom != 0: - new_weights[i] = upper / bottom - - # Update all nodes concurrently - self._weights = new_weights - - # Print progress, if verbose is activated and tqdm is not available - if verbose and not TQDM_AVAILABLE: - print( - "Iteration:", - it, - "/", - n_iteration, - sep=" ", - end="\r", - flush=True, - ) - - def weight_initialization( - self, - mode: str = "random", - **kwargs: np.ndarray | pd.DataFrame | list | str | int, - ) -> None: - """Function for weight initialization of the self-organizing map. - - Calls other methods for each initialization mode. - - :param mode: str: Initialization mode. May be either 'random', 'linear', or 'sample'. - Note: Each initialization method may require multiple additional arguments in kwargs. - :param kwargs: - For 'random' initialization mode, 'sample_mode': str may be provided to determine - the sampling mode. 'sample_mode' may be either 'standard_normal' (default) or 'uniform' - For 'random' and 'sample' modes, 'random_seed': int may be provided for the random - value generator. For 'sample' and 'linear' modes, 'data': array-like must be provided - for sampling/PCA. - """ - modes: dict[str, Callable[..., None]] = { - "random": self._weight_initialization_random, - "linear": self._weight_initialization_linear, - "sample": self._weight_initialization_sample, - } - try: - modes[mode](**kwargs) - except KeyError as exc: - raise ValueError( - "Invalid value for 'mode' parameter. Value should be in " - + str(modes.keys()) - ) from exc - - def _weight_initialization_random( - self, sample_mode: str = "standard_normal", random_seed: int | None = None - ) -> None: - """Random initialization method. - - Assigns weights from a random distribution defined by 'sample_mode'. - - :param sample_mode: str: Distribution for random sampling. - May be either 'uniform' or 'standard_normal'. - Defaults to 'standard_normal'. - :param random_seed: int or None: Seed for NumPy random value generator. Defaults to None. - """ - sample_modes = { - "uniform": np.random.random, - "standard_normal": np.random.standard_normal, - } - - # Seed numpy random generator - if random_seed is None: - random_seed = np.random.randint(np.iinfo(np.int32).max) - else: - random_seed = int(random_seed) - np.random.seed(random_seed) - - # Initialize weights randomly - try: - self._weights = sample_modes[sample_mode](size=self._weights.shape) - except KeyError as exc: - raise ValueError( - "Invalid value for 'sample_mode' parameter. Value should be in " - + str(sample_modes.keys()) - ) from exc - - def _weight_initialization_linear( - self, data: np.ndarray | pd.DataFrame | list - ) -> None: - """Linear initialization method. - - Assigns weights spanning the hyperplane formed by the two first principal - components of 'data'. - - This is the recommended initialization method, as it may lead to a faster convergence. - Unlike other initialization modes, this method is deterministic based on the input dataset. - - :param data: array-like: Dataset for weight initialization w/ PCA. - """ - # Convert data to numpy array for training - data_array = self._data_to_numpy(data) - - # Perform PCA w/ sklearn - data_array = sklearn.preprocessing.StandardScaler().fit_transform(data_array) - pca = sklearn.decomposition.PCA(n_components=2) - pca.fit(data_array) - # Initialize weights spanning first 2 principal components of data - for i, c1 in enumerate(np.linspace(-1, 1, num=self._shape[0])): - for j, c2 in enumerate(np.linspace(-1, 1, num=self._shape[1])): - self._weights[i, j] = ( - c1 * pca.explained_variance_[0] + c2 * pca.explained_variance_[1] - ) - - def _weight_initialization_sample( - self, - data: np.ndarray | pd.DataFrame | list, - random_seed: int | None = None, - ) -> None: - """Initialization method. Assigns weights to random samples from an input dataset. - - :param data: Dataset for weight initialization/sampling. - :param random_seed: int or None: Seed for NumPy random value generator. Defaults to None. - """ - # Seed numpy random generator - if random_seed is None: - random_seed = np.random.randint(np.iinfo(np.int32).max) - else: - random_seed = int(random_seed) - np.random.seed(random_seed) - - # Convert data to numpy array for training - data_array = self._data_to_numpy(data) - - # Assign weights to random samples from dataset - sample_size: np.uint = self._shape[0] * self._shape[1] - sample = np.random.choice( - len(data_array), - size=int(sample_size), - replace=bool(sample_size > len(data_array)), - ) - self._weights = data_array[sample].reshape(self._weights.shape) - - @staticmethod - def _axis_offsets( - coords: np.ndarray, center: int, length: float, cyclic: bool - ) -> np.ndarray: - """ - Signed offsets from 'center' to each coordinate along one axis of the grid. - - When the axis is cyclic, the minimum-image convention is applied, i.e., each offset is - folded into the interval [-length/2, length/2), so that the shortest way around the torus - is used. Both tails must be folded: an offset of -9 on an axis of length 10 represents a - distance of 1, not 9. - - :param coords: np.ndarray: Coordinates along the axis, i.e., np.arange(length). - :param center: int: Coordinate of the center (winner node) along the axis. - :param length: float: Number of nodes along the axis. - :param cyclic: bool: Whether the axis wraps around. - :return: np.ndarray: Signed offsets from center to each coordinate. - """ - d = coords - center - if cyclic: - d = (d + length / 2) % length - length / 2 - return d - - def _sqdist(self, c: tuple[int, ...], sigma: float) -> np.ndarray: - """ - Squared geometric distance from the center c to every node of the grid. - - This is the sqdist(c, i) term of Kohonen (2013), Eq. (5). Every neighborhood function must - be a function of this scalar quantity: the neighborhood describes lateral interaction over - a metric space, so it depends on the distance between two nodes and not on the individual - axis offsets. Only the gaussian is separable into a product of per-axis terms, and that is - a property of the exponential rather than of neighborhood functions in general. - - :param c: (int, int): Center coordinates. - :param sigma: float: Spread variable, validated here for all neighborhood functions. - :return: np.ndarray: Squared distances from c to each node, with the shape of the network. - :raises ValueError: If sigma is not strictly positive. - """ - if not np.isfinite(sigma) or sigma <= 0: - raise ValueError( - "The neighborhood radius 'sigma' must be a finite positive number, got " - + str(sigma) - ) - dx = self._axis_offsets( - self._neigx, c[0], float(self._shape[0]), self._cyclic[0] - ) - dy = self._axis_offsets( - self._neigy, c[1], float(self._shape[1]), self._cyclic[1] - ) - return np.add.outer(np.power(dx, 2), np.power(dy, 2)) - - def _gaussian(self, c: tuple[int, ...], sigma: float) -> np.ndarray: - """ - Gaussian neighborhood function, centered in c. Has support for cyclic arrays. - - Implements h_ci = exp(-sqdist(c, i) / (2 * sigma^2)), i.e., Eq. (5) of Kohonen (2013) - with the learning rate factored out, so that h_cc = 1. - - :param c: (int, int): Center coordinates for gaussian function. - :param sigma: float: Spread variable for gaussian function. Must be positive. - :return: np.ndarray: Gaussian, centered in c, over all the weights of the network. - :raises ValueError: If sigma is not strictly positive. - """ - return np.exp(-self._sqdist(c, sigma) / (2 * sigma * sigma)) - - def _bubble(self, c: tuple[int, ...], sigma: float) -> np.ndarray: - """ - Bubble neighborhood function, centered in c. Has support for cyclic arrays. - - The neighbors of c are the nodes in the region of sigma positions in the vertical - and horizontal directions around c. - - This is the truncated inner, excitatory lobe of the mexican hat lateral-interaction - function; cf. Vrieze (1995), p. 85: "In Kohonen networks usually only the inner stimulation - area is used, i.e., when a neuron i fires, a positive feedback takes place for all neurons - i', whose distance to i is smaller than some given number rho." - - Unlike the other neighborhood functions, a radius of zero is admissible here: it selects - the winner alone, which is well defined, whereas for the gaussian and the mexican hat a - zero radius is a division by zero. - - :param c: (int, int): Center coordinates for gaussian function. - :param sigma: float: Spread variable for gaussian function. Must be finite and - non-negative. - :return: np.ndarray: Neighborhood matrix, centered in c. - :raises ValueError: If sigma is negative or not finite. - """ - if not np.isfinite(sigma) or sigma < 0: - raise ValueError( - "The neighborhood radius 'sigma' must be a finite non-negative number, got " - + str(sigma) - ) - # Convert sigma to integer - sigma = int(np.around(sigma)) - - # Calculate vertical and horizontal regions - ax = np.logical_and(self._neigx >= c[0] - sigma, self._neigx <= c[0] + sigma) - ay = np.logical_and(self._neigy >= c[1] - sigma, self._neigy <= c[1] + sigma) - - # Calculate cyclic regions - if self._cyclic[0]: - if c[0] - sigma < 0: - ax[int(c[0] - sigma) :] = True - if c[0] + sigma >= self._shape[0]: - ax[: int((c[0] + sigma) % self._shape[0] + 1)] = True - if self._cyclic[1]: - if c[1] - sigma < 0: - ay[int(c[1] - sigma) :] = True - if c[1] + sigma >= self._shape[1]: - ay[: int((c[1] + sigma) % self._shape[1] + 1)] = True - - return np.outer(ax, ay).astype(int) - - def _mexicanhat(self, c: tuple[int, ...], sigma: float) -> np.ndarray: - """ - Mexican Hat neighborhood function, centered in c. Has support for cyclic arrays. - - Also known as the Ricker wavelet, or the Laplacian of Gaussian. This is the biologically - motivated lateral-interaction function: nodes near the winner are excited, nodes beyond a - certain distance are inhibited, and the inhibition vanishes as the distance grows further - (Vrieze, 1995, Fig. 3). - - Implements the isotropic 2-D form as a function of u = sqdist(c, i) / (2 * sigma^2): - - h_ci = (1 - u) * exp(-u) - - normalized so that h_cc = 1. It crosses zero at a radius of sqrt(2) * sigma, reaches its - minimum of -exp(-2) ~= -0.135 at a radius of 2 * sigma, and decays to zero thereafter. - - Note that this is *not* the outer product of two 1-D Ricker wavelets. Unlike the gaussian, - the Ricker wavelet is not multiplicatively separable, and such a product is not a function - of the distance between two nodes: it would be positive in the diagonal quadrants, where - both 1-D factors are negative, placing a spurious excitatory lobe exactly where the - function must inhibit. The neighborhood function must depend on the grid distance alone, - cf. sqdist(c, i) in Kohonen (2013), Eq. (5), and the "lateral distance" abscissa of - Vrieze (1995), Fig. 3. - - The '_bubble' neighborhood function is the truncated inner (excitatory) lobe of this same - lateral-interaction function; cf. Vrieze (1995), p. 85. - - :param c: (int, int): Center coordinates for mexican hat function. - :param sigma: float: Spread variable for mexican hat function. Must be positive. - :return: np.ndarray: Mexican Hat, centered in c, over all the weights of the network. - :raises ValueError: If sigma is not strictly positive. - - References: - O. J. Vrieze, Kohonen network, in: Artificial Neural Networks: An Introduction to ANN - Theory and Practice, Lecture Notes in Computer Science, vol. 931, Springer, 1995, - pp. 83-100, https://doi.org/10.1007/BFb0027024. - """ - u = self._sqdist(c, sigma) / (2 * sigma * sigma) - return (1.0 - u) * np.exp(-u) - - def _data_to_numpy(self, data: np.ndarray | pd.DataFrame | list) -> np.ndarray: - """ - Converts data to numpy array. - - :param data: array-like: Dataset for training. - :return: np.ndarray: Numpy array of the dataset. - """ - if isinstance(data, pd.DataFrame): - return data.to_numpy() - return np.array(data) diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index ad5443d..0000000 --- a/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -numpy -pandas -scikit-learn diff --git a/src/python_som/__init__.py b/src/python_som/__init__.py new file mode 100644 index 0000000..36a463f --- /dev/null +++ b/src/python_som/__init__.py @@ -0,0 +1,58 @@ +"""Python implementation of Kohonen's 2-D self-organizing map. + +The public surface is :class:`SOM`, plus the decay, distance and neighborhood functions that can be +passed to it or used directly. + +>>> import numpy as np, python_som +>>> data = np.random.default_rng(0).normal(size=(150, 4)) +>>> som = python_som.SOM(x=10, y=10, input_len=4, random_seed=0) +>>> som.weight_initialization(mode="linear", data=data) +>>> error = som.train(data, n_iteration=100, mode="batch") + +Reference: +Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks 37 (2013) 52-65, +ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2012.09.018 +""" + +from __future__ import annotations + +from ._decay import ( + asymptotic_decay, + exponential_decay, + inverse_decay, + linear_decay, +) +from ._distance import euclidean_distance +from ._neighborhood import ( + NEIGHBORHOOD_FUNCTIONS, + SIGNED_NEIGHBORHOODS, + bubble, + gaussian, + mexican_hat, +) +from ._som import SOM + +__all__ = [ + "NEIGHBORHOOD_FUNCTIONS", + "SIGNED_NEIGHBORHOODS", + "SOM", + "asymptotic_decay", + "bubble", + "euclidean_distance", + "exponential_decay", + "gaussian", + "inverse_decay", + "linear_decay", + "mexican_hat", +] + +__version__ = "0.3.0" + +# Backwards-compatible aliases. Before 0.3.0 these functions lived in this module under +# underscore-prefixed names; they were private by convention but reachable, and the README listed +# them. Keep them working rather than breaking imports silently. +_asymptotic_decay = asymptotic_decay +_linear_decay = linear_decay +_exponential_decay = exponential_decay +_inverse_decay = inverse_decay +_euclidean_distance = euclidean_distance diff --git a/src/python_som/_decay.py b/src/python_som/_decay.py new file mode 100644 index 0000000..2a50f09 --- /dev/null +++ b/src/python_som/_decay.py @@ -0,0 +1,68 @@ +"""Decay functions for the learning rate and the neighborhood radius. + +Each function maps an initial value, the current iteration and the total number of iterations to the +current value. They share one signature so that any of them, or a user-supplied equivalent, can be +passed as ``learning_rate_decay`` or ``neighborhood_radius_decay``. + +Kohonen (2013) does not prescribe a particular form: "The true mathematical form of sigma(t) is not +crucial, as long as its value is fairly large in the beginning of the process, say, on the order of +half of the diameter of the grid, whereafter it is gradually reduced to a fraction of it in about +1000 steps" (Section 4.1). +""" + +from __future__ import annotations + +__all__ = [ + "asymptotic_decay", + "exponential_decay", + "inverse_decay", + "linear_decay", +] + + +def asymptotic_decay(x: float, t: int, max_t: int) -> float: + """Decay ``x`` hyperbolically, reaching ``x / 2`` at the halfway point. + + Never reaches zero, which suits Kohonen's warning that the neighborhood radius should not decay + all the way down (Section 4.2). + + :param x: Initial value. + :param t: Current iteration. + :param max_t: Total number of iterations. + :return: Value of ``x`` after ``t`` iterations. + """ + return x / (1 + t / (max_t / 2)) + + +def linear_decay(x: float, t: int, max_t: int) -> float: + """Decay ``x`` linearly to zero at ``t == max_t``. + + :param x: Initial value. + :param t: Current iteration. + :param max_t: Total number of iterations. + :return: Value of ``x`` after ``t`` iterations. + """ + return x * (1.0 - t / max_t) + + +def exponential_decay(x: float, t: int, max_t: int, factor: float = 2.0) -> float: + """Decay ``x`` geometrically by ``factor / max_t`` per iteration. + + :param x: Initial value. + :param t: Current iteration. + :param max_t: Total number of iterations. + :param factor: Decay factor. Defaults to 2.0. + :return: Value of ``x`` after ``t`` iterations. + """ + return x * (1 - (factor / max_t)) ** t + + +def inverse_decay(x: float, t: int, max_t: int) -> float: + """Decay ``x`` inversely with ``t``, scaled so the shape is independent of ``max_t``. + + :param x: Initial value. + :param t: Current iteration. + :param max_t: Total number of iterations. + :return: Value of ``x`` after ``t`` iterations. + """ + return (max_t / 100) * x / ((max_t / 100) + t) diff --git a/src/python_som/_distance.py b/src/python_som/_distance.py new file mode 100644 index 0000000..e9e64a7 --- /dev/null +++ b/src/python_som/_distance.py @@ -0,0 +1,26 @@ +"""Distance measures between input vectors and the models of the network.""" + +from __future__ import annotations + +import numpy as np +import numpy.typing as npt + +__all__ = ["euclidean_distance"] + + +def euclidean_distance(a: npt.ArrayLike, b: npt.ArrayLike) -> npt.NDArray[np.floating]: + """Euclidean distance between the elements of the last axis of ``a`` and ``b``. + + Both arguments may be n-dimensional as long as their shapes broadcast. The result takes the + shape of the broadcast minus its last axis, so passing one input vector and the whole + ``(x, y, input_len)`` weight array yields an ``(x, y)`` map of distances. + + Kohonen (2013) Section 3.3 notes that the Euclidean distance, applied to normalized data, "is + already applicable to most practical studies". + + :param a: Array-like of values. Must not be a scalar. + :param b: Array-like of values. Must not be a scalar. + :return: Distances between ``a`` and ``b``. + """ + result: npt.NDArray[np.floating] = np.linalg.norm(np.subtract(a, b), ord=2, axis=-1) + return result diff --git a/src/python_som/_neighborhood.py b/src/python_som/_neighborhood.py new file mode 100644 index 0000000..4c8bc9d --- /dev/null +++ b/src/python_som/_neighborhood.py @@ -0,0 +1,218 @@ +"""Neighborhood functions: how the winner's correction spreads over the grid. + +Kohonen (2013) Eq. (5) defines the neighborhood as a function of ``sqdist(c, i)``, "the square of +the geometric distance between the nodes c and i in the grid". Vrieze (1995) Fig. 3 plots the +"Mexican-hat" lateral interaction against a single axis labelled "Lateral distance", writes the +coefficient as ``h_{i i_c} = 1 / ||i_c - i||``, and states that the grid is assumed to be a metric +space. + +The consequence is that a neighborhood function must depend on the distance between two nodes and +not on the two axis offsets separately. The gaussian happens to factor into a product of per-axis +terms, but that is a property of the exponential, not of neighborhood functions in general: an +outer product of two 1-D Ricker wavelets is positive in the diagonal quadrants where both factors +are negative, placing an excitatory lobe exactly where the mexican hat must inhibit. + +References: +Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks 37 (2013) 52-65, +https://doi.org/10.1016/j.neunet.2012.09.018 + +O. J. Vrieze, Kohonen network, in: Artificial Neural Networks: An Introduction to ANN Theory and +Practice, Lecture Notes in Computer Science, vol. 931, Springer, 1995, pp. 83-100, +https://doi.org/10.1007/BFb0027024 +""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Final + +import numpy as np +import numpy.typing as npt + +__all__ = [ + "NEIGHBORHOOD_FUNCTIONS", + "SIGNED_NEIGHBORHOODS", + "axis_offsets", + "bubble", + "gaussian", + "mexican_hat", + "resolve", + "squared_grid_distance", +] + +Grid = tuple[int, int] +Coordinates = tuple[int, int] +NeighborhoodFunction = Callable[ + [Grid, Coordinates, float, tuple[bool, bool]], npt.NDArray[np.floating] +] + + +def _validate_radius(sigma: float, *, allow_zero: bool = False) -> None: + """Reject a radius that would divide by zero or propagate NaN. + + ``not np.isfinite(sigma)`` is checked explicitly rather than relying on ``sigma <= 0``, which is + ``False`` for NaN and would let it through. + + :param sigma: Radius to validate. + :param allow_zero: Whether a radius of exactly zero is admissible. + :raises ValueError: If the radius is not finite, is negative, or is zero when not allowed. + """ + if not np.isfinite(sigma) or sigma < 0 or (sigma == 0 and not allow_zero): + bound = "non-negative" if allow_zero else "positive" + msg = f"The neighborhood radius 'sigma' must be a finite {bound} number, got {sigma!r}" + raise ValueError(msg) + + +def axis_offsets(length: int, center: int, *, cyclic: bool) -> npt.NDArray[np.floating]: + """Signed offsets from ``center`` to every coordinate along one axis. + + On a cyclic axis the minimum-image convention folds each offset into ``[-length/2, length/2)``, + so the shortest way around the torus is used. Both tails must be folded: an offset of -9 on an + axis of length 10 represents a distance of 1, not 9. + + :param length: Number of nodes along the axis. + :param center: Coordinate of the winner along the axis. + :param cyclic: Whether the axis wraps around. + :return: Signed offsets, one per coordinate. + """ + d = np.arange(length, dtype=float) - center + if cyclic: + d = (d + length / 2) % length - length / 2 + return d + + +def squared_grid_distance( + shape: Grid, c: Coordinates, cyclic: tuple[bool, bool] +) -> npt.NDArray[np.floating]: + """Squared geometric distance from ``c`` to every node, i.e. ``sqdist(c, i)`` of Eq. (5). + + :param shape: Shape of the network. + :param c: Coordinates of the winner. + :param cyclic: Whether each axis wraps around. + :return: Squared distances, with the shape of the network. + """ + dx = axis_offsets(shape[0], c[0], cyclic=cyclic[0]) + dy = axis_offsets(shape[1], c[1], cyclic=cyclic[1]) + return np.add.outer(np.square(dx), np.square(dy)) + + +def gaussian( + shape: Grid, c: Coordinates, sigma: float, cyclic: tuple[bool, bool] +) -> npt.NDArray[np.floating]: + """Gaussian neighborhood, ``exp(-sqdist(c, i) / (2 * sigma**2))``. + + This is Eq. (5) of Kohonen (2013) with the learning rate factored out, so ``h(c, c) == 1``. + Strictly positive everywhere and monotonically decreasing with distance. + + :param shape: Shape of the network. + :param c: Coordinates of the winner. + :param sigma: Neighborhood radius. Must be finite and positive. + :param cyclic: Whether each axis wraps around. + :return: Neighborhood weights, with the shape of the network. + :raises ValueError: If the radius is not a finite positive number. + """ + _validate_radius(sigma) + return np.exp(-squared_grid_distance(shape, c, cyclic) / (2.0 * sigma * sigma)) + + +def mexican_hat( + shape: Grid, c: Coordinates, sigma: float, cyclic: tuple[bool, bool] +) -> npt.NDArray[np.floating]: + """Mexican hat neighborhood, ``(1 - u) * exp(-u)`` over ``u = sqdist(c, i) / (2 * sigma**2)``. + + Also known as the Ricker wavelet or the Laplacian of Gaussian. This is the biologically + motivated lateral-interaction function: nodes near the winner are excited, nodes past a certain + distance are inhibited, and the inhibition vanishes as distance grows further (Vrieze 1995, + Fig. 3). + + Normalized so ``h(c, c) == 1``. Crosses zero at a radius of ``sqrt(2) * sigma`` and reaches its + minimum of ``-exp(-2)``, about -0.135, at a radius of ``2 * sigma``. + + This is deliberately not the outer product of two 1-D Ricker wavelets. See the module docstring + for why that construction is wrong. + + Takes negative values, so it cannot be used with batch training; see + :data:`SIGNED_NEIGHBORHOODS`. + + :param shape: Shape of the network. + :param c: Coordinates of the winner. + :param sigma: Neighborhood radius. Must be finite and positive. + :param cyclic: Whether each axis wraps around. + :return: Neighborhood weights, with the shape of the network. + :raises ValueError: If the radius is not a finite positive number. + """ + _validate_radius(sigma) + u = squared_grid_distance(shape, c, cyclic) / (2.0 * sigma * sigma) + return (1.0 - u) * np.exp(-u) + + +def bubble( + shape: Grid, c: Coordinates, sigma: float, cyclic: tuple[bool, bool] +) -> npt.NDArray[np.floating]: + """Flat neighborhood: 1 for nodes within ``sigma`` of the winner, 0 elsewhere. + + This is the truncated inner, excitatory lobe of the mexican hat. Vrieze (1995) p. 85: "In + Kohonen networks usually only the inner stimulation area is used, i.e., when a neuron i fires, a + positive feedback takes place for all neurons i', whose distance to i is smaller than some given + number rho", and notes that this flat choice is "just as effective and sometimes even better" + than a distance-dependent one. + + **The metric here is Chebyshev, not Euclidean**, so the region is a square rather than a disc: + a node is included when ``max(|dx|, |dy|) <= round(sigma)``. That matches the pseudo-code in + Vrieze's appendix, which computes ``b = MAX(ABS(i - w_i), ABS(j - w_j))``, though Kohonen's + phrase "up to a certain radius from the winner" (Section 4.1) reads as Euclidean. The two + sources genuinely differ; this implementation follows Vrieze, and the choice is preserved rather + than changed so that existing results stay reproducible. + + One consequence worth stating, because it is easy to assume otherwise: a Chebyshev ball is not + isotropic under the Euclidean metric. On a large enough grid, nodes at equal Euclidean distance + from the winner can fall on opposite sides of the boundary. The smallest case is a radius of + ``sqrt(50)``, where ``(5, 5)`` lies inside a ``sigma = 5`` square while ``(7, 1)`` lies outside. + + Unlike the other neighborhood functions, a radius of zero is admissible: it selects the winner + alone, which is well defined, whereas for the gaussian and the mexican hat it is a division by + zero. + + :param shape: Shape of the network. + :param c: Coordinates of the winner. + :param sigma: Neighborhood radius, rounded to the nearest integer. Must be finite and + non-negative. + :param cyclic: Whether each axis wraps around. + :return: Neighborhood weights, with the shape of the network. + :raises ValueError: If the radius is not a finite non-negative number. + """ + _validate_radius(sigma, allow_zero=True) + radius = int(np.around(sigma)) + dx = np.abs(axis_offsets(shape[0], c[0], cyclic=cyclic[0])) + dy = np.abs(axis_offsets(shape[1], c[1], cyclic=cyclic[1])) + return np.multiply.outer(dx <= radius, dy <= radius).astype(float) + + +NEIGHBORHOOD_FUNCTIONS: Final[dict[str, NeighborhoodFunction]] = { + "gaussian": gaussian, + "bubble": bubble, + "mexicanhat": mexican_hat, + "mexican_hat": mexican_hat, +} +"""Neighborhood functions by name. ``mexican_hat`` is an alias of ``mexicanhat``.""" + +SIGNED_NEIGHBORHOODS: Final[frozenset[str]] = frozenset({"mexicanhat", "mexican_hat"}) +"""Names of neighborhood functions that take negative values, which batch training cannot use.""" + + +def resolve(name: str) -> NeighborhoodFunction: + """Look up a neighborhood function by name. + + :param name: Name of the neighborhood function. + :return: The corresponding function. + :raises ValueError: If the name is not recognised. + """ + try: + return NEIGHBORHOOD_FUNCTIONS[name] + except KeyError as exc: + valid = sorted(NEIGHBORHOOD_FUNCTIONS) + msg = ( + f"Invalid value for 'neighborhood_function' parameter: {name!r}. " + f"Value should be one of {valid}" + ) + raise ValueError(msg) from exc diff --git a/src/python_som/_som.py b/src/python_som/_som.py new file mode 100644 index 0000000..bee6f99 --- /dev/null +++ b/src/python_som/_som.py @@ -0,0 +1,649 @@ +"""Implementation of the 2-D self-organizing map. + +Reference: +Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks 37 (2013) 52-65, +ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2012.09.018 +""" + +from __future__ import annotations + +import logging +import secrets +from collections import Counter +from typing import TYPE_CHECKING, Any, TypeVar + +import numpy as np +import numpy.typing as npt +import pandas as pd +import sklearn.decomposition +import sklearn.preprocessing + +from ._decay import asymptotic_decay +from ._distance import euclidean_distance +from ._neighborhood import ( + NEIGHBORHOOD_FUNCTIONS, + SIGNED_NEIGHBORHOODS, + bubble, + resolve, +) + +if TYPE_CHECKING: # pragma: no cover + from collections.abc import Callable, Iterable + from typing import TypeAlias + + from ._neighborhood import NeighborhoodFunction + + #: Anything the public methods accept as a dataset. + DataLike: TypeAlias = npt.NDArray[Any] | pd.DataFrame | pd.Series[Any] | list[Any] + +try: + import tqdm + + TQDM_AVAILABLE = True +except ImportError: # pragma: no cover + TQDM_AVAILABLE = False + +__all__ = ["SOM"] + +logger = logging.getLogger(__name__) + +_T = TypeVar("_T") + +TRAINING_MODES = ("random", "sequential", "batch") +INITIALIZATION_MODES = ("random", "linear", "sample") + +#: Iterations per sample when ``n_iteration`` is not given, by training mode. +DEFAULT_ITERATIONS_PER_SAMPLE = {"random": 1000, "sequential": 1000, "batch": 10} + +#: Denominators below this are treated as empty in the batch update, guarding against a +#: near-zero divisor in Kohonen Eq. (8). +_BATCH_DENOMINATOR_TOLERANCE = 1e-12 + +#: Width of an automatically drawn seed. 128 bits matches what NumPy's own +#: SeedSequence uses when it seeds itself from the OS entropy pool. +_SEED_BITS = 128 + +#: Linear initialization projects onto two principal components, so it needs at least this +#: many samples and features. +_MIN_PCA_DIMENSIONS = 2 + + +class SOM: + """A 2-D self-organizing map over NumPy arrays, pandas DataFrames or plain lists. + + Features: + - Stepwise and batch training + - Random, random-sampling and linear (PCA) weight initialization + - Automatic selection of the map size ratio (with PCA) + - Support for cyclic arrays, for toroidal or spherical maps + - Gaussian, bubble and mexican hat neighborhood functions + - Support for custom decay functions + - Support for visualization (U-matrix, activation matrix) + - Support for supervised learning (label map) + + Reference: + Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks 37 (2013) 52-65, + https://doi.org/10.1016/j.neunet.2012.09.018 + """ + + def __init__( + self, + x: int | None, + y: int | None, + input_len: int, + learning_rate: float = 0.5, + learning_rate_decay: Callable[[float, int, int], float] = asymptotic_decay, + neighborhood_radius: float = 1.0, + neighborhood_radius_decay: Callable[[float, int, int], float] = asymptotic_decay, + neighborhood_function: str = "gaussian", + distance_function: Callable[[Any, Any], npt.NDArray[np.floating]] = euclidean_distance, + cyclic_x: bool = False, + cyclic_y: bool = False, + random_seed: int | None = None, + data: DataLike | None = None, + *, + min_neighborhood_radius: float = 0.5, + ) -> None: + """Construct a self-organizing map. + + :param x: Number of rows. If None, chosen from ``data`` by PCA; see + :meth:`_auto_dimension`. At least one of ``x`` and ``y`` must be given. + :param y: Number of columns. If None, chosen from ``data`` by PCA. + :param input_len: Number of features per input vector. + :param learning_rate: Initial learning rate. Irrelevant for batch training. + :param learning_rate_decay: Decay function for the learning rate. + :param neighborhood_radius: Initial neighborhood radius. + :param neighborhood_radius_decay: Decay function for the neighborhood radius. + :param neighborhood_function: One of ``'gaussian'``, ``'bubble'``, ``'mexicanhat'`` + (or its alias ``'mexican_hat'``). + :param distance_function: Dissimilarity between an input vector and the models. + :param cyclic_x: Whether the map wraps around vertically (toroidal). + :param cyclic_y: Whether the map wraps around horizontally (toroidal). + :param random_seed: Seed for this instance's random generator. + :param data: Dataset used for automatic sizing. Required when ``x`` or ``y`` is None. + :param min_neighborhood_radius: Floor applied to the decayed radius during training. + Kohonen (2013) Section 4.2 warns that "the final value of sigma shall not go to zero, + because otherwise the process loses its ordering power. It should always remain, say, + above half of the grid spacing." Defaults to 0.5, that half-spacing. + :raises ValueError: If the dimensions, the neighborhood function name, or + ``min_neighborhood_radius`` are invalid. + """ + if x is None and y is None: + msg = "At least one of the dimensions (x, y) must be specified" + raise ValueError(msg) + if x is None or y is None: + x, y = self._auto_dimension(x, y, data) + + if x <= 0 or y <= 0: + msg = f"Map dimensions must be positive, got ({x}, {y})" + raise ValueError(msg) + if input_len <= 0: + msg = f"'input_len' must be positive, got {input_len}" + raise ValueError(msg) + if not np.isfinite(min_neighborhood_radius) or min_neighborhood_radius <= 0: + msg = ( + "'min_neighborhood_radius' must be a finite positive number, " + f"got {min_neighborhood_radius!r}" + ) + raise ValueError(msg) + + self._shape: tuple[int, int] = (int(x), int(y)) + self._input_len = int(input_len) + self._learning_rate = float(learning_rate) + self._learning_rate_decay = learning_rate_decay + self._neighborhood_radius = float(neighborhood_radius) + self._neighborhood_radius_decay = neighborhood_radius_decay + self._min_neighborhood_radius = float(min_neighborhood_radius) + self._neighborhood_function_name = neighborhood_function + self._neighborhood_function: NeighborhoodFunction = resolve(neighborhood_function) + self._distance_function = distance_function + self._cyclic = (bool(cyclic_x), bool(cyclic_y)) + + # secrets, rather than an unseeded Generator or SeedSequence, so that the intent reads as + # "draw unpredictable bits for a seed" and the seed we store is always a plain int. + self._random_seed = ( + int(random_seed) if random_seed is not None else secrets.randbits(_SEED_BITS) + ) + self._rng = np.random.default_rng(self._random_seed) + + self._weights = self._rng.standard_normal( + size=(self._shape[0], self._shape[1], self._input_len) + ) + + @staticmethod + def _auto_dimension( + x: int | None, + y: int | None, + data: DataLike | None, + ) -> tuple[int, int]: + """Choose the missing dimension from the two largest principal components. + + Kohonen (2013) Section 3.5: "it is advisable to select the lengths of the horizontal and + vertical dimensions of the array to correspond to the lengths of the two largest principal + components (i.e., those with the highest eigenvalues of the input correlation matrix), + because then the SOM complies better with the low-order signal statistics." + + A principal component is a unit direction; its *length* in the data is the extent along it, + which is the standard deviation ``sqrt(lambda)``. The parenthetical identifies which + components to use, not what quantity to measure. The side-length ratio is therefore + ``sqrt(lambda_1 / lambda_2)``, which is also what Kohonen's SOM Toolbox implements. + + The x axis is taken to align with the first principal component, so it is the longer side. + + :param x: Number of rows, or None to derive it. + :param y: Number of columns, or None to derive it. + :param data: Dataset to run PCA on. + :return: Both dimensions, as positive integers. + :raises ValueError: If no dataset is available for sizing. + """ + if data is None: + msg = ( + "If one of the dimensions is not specified, a dataset must be provided " + "for automatic size initialization." + ) + raise ValueError(msg) + array = data.to_numpy() if isinstance(data, pd.DataFrame) else np.asarray(data) + scaled = sklearn.preprocessing.StandardScaler().fit_transform(array) + pca = sklearn.decomposition.PCA(n_components=2, random_state=0) + pca.fit(scaled) + ratio = float(np.sqrt(pca.explained_variance_[0] / pca.explained_variance_[1])) + if x is None: + x = max(1, round(y / ratio)) # type: ignore[operator] + if y is None: + y = max(1, round(x / ratio)) + return int(x), int(y) + + def get_shape(self) -> tuple[int, int]: + """Return the shape of the network.""" + return self._shape + + def get_weights(self) -> npt.NDArray[np.floating]: + """Return the weight matrix of the network.""" + return self._weights + + def get_random_seed(self) -> int: + """Return the seed of this instance's random generator.""" + return self._random_seed + + def set_learning_rate(self, learning_rate: float) -> None: + """Set the learning rate. + + :param learning_rate: New learning rate. + """ + self._learning_rate = float(learning_rate) + + def set_neighborhood_radius(self, neighborhood_radius: float) -> None: + """Set the neighborhood radius. + + :param neighborhood_radius: New neighborhood radius. + """ + self._neighborhood_radius = float(neighborhood_radius) + + def neighborhood(self, c: tuple[int, int], sigma: float) -> npt.NDArray[np.floating]: + """Evaluate the neighborhood function centred on ``c``. + + :param c: Coordinates of the winner. + :param sigma: Neighborhood radius. + :return: Neighborhood weights, with the shape of the network. + """ + return self._neighborhood_function(self._shape, c, sigma, self._cyclic) + + def activate(self, x: npt.ArrayLike) -> npt.NDArray[np.floating]: + """Return the distance from ``x`` to every model of the network. + + :param x: Input vector. + :return: Distances, with the shape of the network. + """ + return self._distance_function(x, self._weights) + + def winner(self, x: npt.ArrayLike) -> tuple[int, int]: + """Return the coordinates of the best-matching unit for ``x``. + + :param x: Input vector. + :return: Coordinates of the winner. + """ + activation_map = self.activate(x) + index = np.unravel_index(activation_map.argmin(), activation_map.shape) + return int(index[0]), int(index[1]) + + def quantization(self, data: DataLike) -> npt.NDArray[np.floating]: + """Return the distance from each sample to its best-matching model. + + :param data: Dataset of shape ``(n_samples, n_features)``. + :return: One distance per sample. + """ + array = self._to_numpy(data) + return np.array([self._distance_function(i, self._weights[self.winner(i)]) for i in array]) + + def quantization_error(self, data: DataLike) -> float: + """Return the mean distance from each sample to its best-matching model. + + :param data: Dataset of shape ``(n_samples, n_features)``. + :return: Quantization error. + """ + return float(self.quantization(data).mean()) + + def distance_matrix(self, normalize: bool = False) -> npt.NDArray[np.floating]: + """Return the U-matrix: the summed distance from each model to its immediate neighbours. + + The distances to one node's neighbourhood are computed and consumed one node at a time + rather than being accumulated into a full ``(x, y, x, y)`` tensor, which would cost + ``(x*y)**2`` floats: about 800 MB for a 100x100 map, for a result of ``x*y`` numbers. + + :param normalize: Whether to rescale the result to ``[0, 1]``. + :return: U-matrix, with the shape of the network. + """ + um = np.zeros(self._shape) + for index in np.ndindex(self._shape): + adjacency = self._adjacency(index) + distances = self._distance_function(self._weights[index], self._weights) + um[index] = np.sum(adjacency * distances) + if normalize: + spread = np.max(um) - np.min(um) + if spread > 0: + um = (um - np.min(um)) / spread + return um + + def _adjacency(self, c: tuple[int, ...]) -> npt.NDArray[np.floating]: + """Return an indicator over ``c`` and the nodes immediately adjacent to it. + + Used by :meth:`distance_matrix`, deliberately independent of the configured neighborhood + function: the U-matrix describes the grid, not the training schedule. The centre is included + and contributes a distance of zero, so it does not affect the sum. + + :param c: Coordinates of the centre. + :return: Indicator array, with the shape of the network. + """ + return bubble(self._shape, (int(c[0]), int(c[1])), 1.0, self._cyclic) + + def activation_matrix(self, data: DataLike) -> npt.NDArray[np.floating]: + """Return how many samples map to each node. + + :param data: Dataset of shape ``(n_samples, n_features)``. + :return: Counts, with the shape of the network. + """ + array = self._to_numpy(data) + counts = np.zeros(self._shape) + for i in array: + counts[self.winner(i)] += 1 + return counts + + def winner_map(self, data: DataLike) -> dict[tuple[int, int], list[npt.NDArray[Any]]]: + """Return, for each node, the samples that map to it. + + :param data: Dataset of shape ``(n_samples, n_features)``. + :return: Mapping from node coordinates to the samples assigned to that node. + """ + array = self._to_numpy(data) + result: dict[tuple[int, int], list[npt.NDArray[Any]]] = { + (int(i), int(j)): [] for i, j in np.ndindex(self._shape) + } + for i in array: + result[self.winner(i)].append(i) + return result + + def label_map( + self, + data: DataLike, + labels: DataLike, + ) -> dict[tuple[int, int], Counter[Any]]: + """Return, for each node, the frequency of each label mapped to it. + + :param data: Dataset of shape ``(n_samples, n_features)``. + :param labels: One label per sample, in the same order as ``data``. + :return: Mapping from node coordinates to a label counter. + :raises ValueError: If ``data`` and ``labels`` have different lengths. + """ + array = self._to_numpy(data) + label_array = self._to_numpy(labels) + if len(array) != len(label_array): + msg = ( + f"'data' and 'labels' must have the same length, got " + f"{len(array)} and {len(label_array)}" + ) + raise ValueError(msg) + counts: dict[tuple[int, int], Counter[Any]] = { + (int(i), int(j)): Counter() for i, j in np.ndindex(self._shape) + } + for instance, label in zip(array, label_array, strict=True): + counts[self.winner(instance)].update([label]) + return counts + + def train( + self, + data: DataLike, + n_iteration: int | None = None, + mode: str = "random", + verbose: bool = False, + ) -> float: + """Train the map and return the resulting quantization error. + + :param data: Training dataset of shape ``(n_samples, n_features)``. + :param n_iteration: Number of iterations. Defaults to 1000 per sample for the stepwise + modes and 10 per sample for batch. + :param mode: One of ``'random'``, ``'sequential'`` or ``'batch'``. Kohonen (2013) + Section 3.1 recommends batch: "its convergence is an order of magnitude faster and + safer", and it has no learning-rate parameter. + :param verbose: Whether to report progress. Emits a tqdm progress bar when tqdm is + installed, and logs at INFO level on the ``python_som`` logger either way. + :return: Quantization error after training. + :raises ValueError: If ``mode`` is unknown, if the dataset is empty, or if batch training + is combined with a signed neighborhood function. + """ + array = self._to_numpy(data) + if len(array) == 0: + msg = "Cannot train on an empty dataset" + raise ValueError(msg) + if mode not in TRAINING_MODES: + msg = ( + f"Invalid value for 'mode' parameter: {mode!r}. " + f"Value should be one of {list(TRAINING_MODES)}" + ) + raise ValueError(msg) + if mode == "batch" and self._neighborhood_function_name in SIGNED_NEIGHBORHOODS: + msg = ( + f"The {self._neighborhood_function_name!r} neighborhood function cannot be used " + "with the 'batch' training mode: the weighted mean of Kohonen (2013), Eq. (8), is " + "undefined for a neighborhood function that takes negative values, as its " + "denominator is not sign-definite. Use mode='random' or mode='sequential'." + ) + raise ValueError(msg) + + if n_iteration is None: + n_iteration = DEFAULT_ITERATIONS_PER_SAMPLE[mode] * len(array) + if n_iteration <= 0: + msg = f"'n_iteration' must be positive, got {n_iteration}" + raise ValueError(msg) + + logger.info("Training with %d iterations in %r mode", n_iteration, mode) + + if mode == "batch": + self._train_batch(array, n_iteration, verbose=verbose) + else: + self._train_stepwise(array, n_iteration, mode=mode, verbose=verbose) + + error = self.quantization_error(array) + logger.info("Quantization error: %g", error) + return error + + def _sigma(self, t: int, n_iteration: int) -> float: + """Return the neighborhood radius at iteration ``t``, floored. + + :param t: Current iteration. + :param n_iteration: Total number of iterations. + :return: Neighborhood radius, never below ``min_neighborhood_radius``. + """ + sigma = self._neighborhood_radius_decay(self._neighborhood_radius, t, n_iteration) + return max(float(sigma), self._min_neighborhood_radius) + + @staticmethod + def _progress(iterable: Iterable[_T], total: int, *, verbose: bool) -> Iterable[_T]: + """Wrap ``iterable`` in a tqdm bar when progress reporting is wanted and possible. + + :param iterable: Iterable to wrap. + :param total: Expected number of items. + :param verbose: Whether progress was requested. + :return: The iterable, wrapped or not. + """ + if verbose and TQDM_AVAILABLE: + return tqdm.tqdm(iterable, total=total, desc="Training") + return iterable + + def _train_stepwise( + self, + array: npt.NDArray[Any], + n_iteration: int, + *, + mode: str, + verbose: bool, + ) -> None: + """Train one sample at a time, updating the winner and its neighbourhood. + + Implements Eq. (3) of Kohonen (2013). ``'sequential'`` cycles through the dataset in order, + wrapping around until ``n_iteration`` steps have run. + + ``'random'`` draws samples **with replacement**, i.i.d., which is the stochastic + approximation of Robbins and Monro (1951) that Kohonen cites in Section 4.1. Before 0.3.0 + the draw used ``replace=(n_iteration > len(data))``, so it was a random permutation when the + iteration count did not exceed the sample count and i.i.d. only beyond it. That made the + character of the sampling depend on the iteration count, which is why it is now uniform. + + :param array: Training dataset. + :param n_iteration: Number of iterations. + :param mode: Either ``'random'`` or ``'sequential'``. + :param verbose: Whether to show a progress bar. + """ + if mode == "random": + indices = self._rng.integers(len(array), size=n_iteration) + else: + indices = np.resize(np.arange(len(array)), n_iteration) + + for t, index in enumerate(self._progress(indices, n_iteration, verbose=verbose)): + alpha = self._learning_rate_decay(self._learning_rate, t, n_iteration) + sigma = self._sigma(t, n_iteration) + sample = array[index] + winner = self.winner(sample) + self._weights += ( + alpha * self.neighborhood(winner, sigma)[..., None] * (sample - self._weights) + ) + + def _train_batch(self, array: npt.NDArray[Any], n_iteration: int, *, verbose: bool) -> None: + """Train with the batch algorithm, updating every model concurrently. + + Implements Eq. (8) of Kohonen (2013), + ``m_i = sum_j n_j h_ji xbar_j / sum_j n_j h_ji``, where ``n_j`` is the number of samples + mapped to node ``j`` and ``xbar_j`` their mean. + + Two departures from a naive transcription: + + - Models whose neighbourhood contains no data keep their previous value. Starting from a + zeroed array instead would destroy them; on a 30x30 map with 20 samples and a small + radius, that wiped 282 of 900 models in a single step. + - The per-node sums are contracted with NumPy rather than looped over in Python. The + neighbourhood is evaluated once per node and contracted against the per-node sums and + counts, which avoids materialising the full ``(x, y, x, y)`` tensor. That tensor would be + faster still but costs ``(x*y)**2`` floats, about 800 MB for a 100x100 map. + + :param array: Training dataset. + :param n_iteration: Number of iterations. + :param verbose: Whether to show a progress bar. + """ + for t in self._progress(range(n_iteration), n_iteration, verbose=verbose): + sigma = self._sigma(t, n_iteration) + sums, counts = self._accumulate(array) + + new_weights = self._weights.copy() + for node in np.ndindex(self._shape): + h = self.neighborhood((int(node[0]), int(node[1])), sigma) + denominator = float(np.sum(h * counts)) + if abs(denominator) > _BATCH_DENOMINATOR_TOLERANCE: + new_weights[node] = np.einsum("xy,xyf->f", h, sums) / denominator + self._weights = new_weights + + def _accumulate( + self, array: npt.NDArray[Any] + ) -> tuple[npt.NDArray[np.floating], npt.NDArray[np.floating]]: + """Sum the samples mapped to each node, and count them. + + :param array: Dataset of shape ``(n_samples, n_features)``. + :return: The per-node sums of shape ``(x, y, n_features)`` and counts of shape ``(x, y)``. + """ + sums = np.zeros((*self._shape, self._input_len)) + counts = np.zeros(self._shape) + for sample in array: + node = self.winner(sample) + sums[node] += sample + counts[node] += 1 + return sums, counts + + def weight_initialization( + self, + mode: str = "random", + **kwargs: Any, # noqa: ANN401 # each initializer takes a different set + ) -> None: + """Initialize the models of the network. + + :param mode: One of ``'random'``, ``'linear'`` or ``'sample'``. + :param kwargs: Passed through to the chosen initializer. ``'random'`` accepts + ``sample_mode`` (``'standard_normal'`` or ``'uniform'``); ``'linear'`` and ``'sample'`` + require ``data``. + :raises ValueError: If ``mode`` is unknown. + """ + modes: dict[str, Callable[..., None]] = { + "random": self._init_random, + "linear": self._init_linear, + "sample": self._init_sample, + } + if mode not in modes: + msg = ( + f"Invalid value for 'mode' parameter: {mode!r}. " + f"Value should be one of {list(INITIALIZATION_MODES)}" + ) + raise ValueError(msg) + modes[mode](**kwargs) + + def _init_random(self, sample_mode: str = "standard_normal") -> None: + """Draw every model from a random distribution. + + :param sample_mode: Either ``'standard_normal'`` or ``'uniform'``. + :raises ValueError: If ``sample_mode`` is unknown. + """ + if sample_mode == "standard_normal": + self._weights = self._rng.standard_normal(size=self._weights.shape) + elif sample_mode == "uniform": + self._weights = self._rng.random(size=self._weights.shape) + else: + msg = ( + f"Invalid value for 'sample_mode' parameter: {sample_mode!r}. " + "Value should be one of ['standard_normal', 'uniform']" + ) + raise ValueError(msg) + + def _init_linear(self, data: DataLike) -> None: + """Spread the models over the plane of the two largest principal components. + + Kohonen (2013) Section 4.3: "much faster and convergence follow if the initial values are + selected as a regular, two-dimensional sequence of vectors taken along a hyperplane spanned + by the two largest principal components of x (i.e., principal components associated with + the two highest eigenvalues)". This is the recommended initializer, and unlike the others + it is deterministic. + + Each model is ``mean + c1 * sqrt(lambda_1) * v1 + c2 * sqrt(lambda_2) * v2`` with ``c1``, + ``c2`` evenly spaced over ``[-1, 1]``, so the models span the principal plane and are + centred on the data. PCA is fitted on the raw data, not on standardized data, so the models + live in the same space as the inputs they are compared against during training. + + :param data: Dataset to run PCA on. + :raises ValueError: If the dataset has fewer than two samples or features. + """ + array = self._to_numpy(data).astype(float) + if min(array.shape) < _MIN_PCA_DIMENSIONS: + msg = ( + "Linear initialization needs at least 2 samples and 2 features, got shape " + f"{array.shape}" + ) + raise ValueError(msg) + pca = sklearn.decomposition.PCA(n_components=2, random_state=0) + pca.fit(array) + components = pca.components_ + scale = np.sqrt(pca.explained_variance_) + for i, c1 in enumerate(np.linspace(-1, 1, num=self._shape[0])): + for j, c2 in enumerate(np.linspace(-1, 1, num=self._shape[1])): + self._weights[i, j] = ( + pca.mean_ + c1 * scale[0] * components[0] + c2 * scale[1] * components[1] + ) + + def _init_sample(self, data: DataLike) -> None: + """Set every model to a randomly chosen sample from ``data``. + + :param data: Dataset to sample from. + """ + array = self._to_numpy(data) + size = self._shape[0] * self._shape[1] + chosen = self._rng.choice(len(array), size=size, replace=size > len(array)) + self._weights = array[chosen].reshape(self._weights.shape).astype(float) + + @staticmethod + def _to_numpy( + data: DataLike, + ) -> npt.NDArray[Any]: + """Convert a DataFrame, Series, list or array to a NumPy array. + + This is the library's data-input boundary: everything downstream of it works on + ``np.ndarray`` only. + + The pandas branch is strictly redundant. ``np.asarray`` already converts a DataFrame or a + Series through the ``__array__`` protocol, with identical results including for nullable + extension dtypes. It is kept here only so that removing the pandas dependency is its own + reviewable change rather than a side effect of this one. + + :param data: Input data. + :return: The data as a NumPy array. + """ + if isinstance(data, pd.DataFrame | pd.Series): + return data.to_numpy() + return np.asarray(data) + + +#: Names of the available neighborhood functions. +AVAILABLE_NEIGHBORHOOD_FUNCTIONS = tuple(sorted(NEIGHBORHOOD_FUNCTIONS)) diff --git a/src/python_som/py.typed b/src/python_som/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/test.py b/test.py deleted file mode 100644 index b590abc..0000000 --- a/test.py +++ /dev/null @@ -1,74 +0,0 @@ -"""This module is an example of how to use the python_som package to train a SOM model with Iris. - -The Iris dataset is loaded from seaborn library and the labels are transformed into numeric codes. -The SOM is trained with the Iris dataset and the U-matrix is plotted with matplotlib and seaborn. -The plot is saved to a .png file. -""" - -# Import python_som -# Import libraries for plotting results -import matplotlib.pyplot as plt - -# Import NumPy and Pandas for storing data -import numpy as np -import seaborn as sns # type: ignore - -import python_som - -# Load Iris dataset and columns of features and labels -iris = sns.load_dataset("iris") -target = iris.iloc[:, -1].to_numpy() -iris = iris.iloc[:, :-1].to_numpy() -# Transform labels into numeric codes for plotting -tg = np.zeros(len(target), dtype=int) -tg[target == "setosa"] = 0 -tg[target == "versicolor"] = 1 -tg[target == "virginica"] = 2 - -# Instantiate SOM from python_som -# Selecting shape automatically (providing dataset for constructor) -# Using default decay and distance functions -# Using gaussian neighborhood function -# Using cyclic arrays in the vertical and horizontal directions -som = python_som.SOM( - x=20, - y=None, - input_len=iris.shape[1], - learning_rate=0.5, - neighborhood_radius=1.0, - neighborhood_function="gaussian", - cyclic_x=True, - cyclic_y=True, - data=iris, -) - -# Initialize weights of the SOM with linear initialization -som.weight_initialization(mode="linear", data=iris) - -# Training SOM with default number of iterations -# Using batch learning process -som.train(data=iris, n_iteration=len(iris), mode="batch", verbose=True) - -# Calculating distance matrix for plotting -umatrix = som.distance_matrix().T - -# Plotting U-matrix with seaborn/matplotlib -plt.figure(figsize=(float(som.get_shape()[0]), float(som.get_shape()[1]))) -plt.pcolor(umatrix, cmap="bone_r") - -markers = ["o", "s", "D"] -colors = ["C0", "C1", "C2"] -for cnt, xx in enumerate(iris): - w = som.winner(xx) # getting the winner - plt.plot( - w[0] + 0.5, - w[1] + 0.5, - markers[tg[cnt]], - markerfacecolor="None", - markeredgecolor=colors[tg[cnt]], - markersize=12, - markeredgewidth=2, - ) -plt.axis((0, som.get_shape()[0], 0, som.get_shape()[1])) -# Saving the plot to .png -plt.savefig("test_output_iris.png") diff --git a/test_iris.ipynb b/test_iris.ipynb deleted file mode 100644 index 25d8ca9..0000000 --- a/test_iris.ipynb +++ /dev/null @@ -1,408 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing package with Iris dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Importing packages" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# Import python_som\n", - "# Import libraries for plotting results\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Import NumPy and Pandas for storing data\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "import python_som\n", - "\n", - "# Import timing tools\n", - "import time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading dataset w/ Seaborn" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# Load Iris dataset and columns of features and labels\n", - "iris = sns.load_dataset(\"iris\")\n", - "target = iris.iloc[:, -1].to_numpy()\n", - "iris = iris.iloc[:, :-1].to_numpy()\n", - "# Transform labels into numeric codes for plotting\n", - "tg = np.zeros(len(target), dtype=int)\n", - "tg[target == \"setosa\"] = 0\n", - "tg[target == \"versicolor\"] = 1\n", - "tg[target == \"virginica\"] = 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SOM Instantiation" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on function __init__ in module python_som:\n", - "\n", - "__init__(self, x: int | None, y: int | None, input_len: int, learning_rate: float = 0.5, learning_rate_decay: Callable[[float, int, int], float] = , neighborhood_radius: float = 1.0, neighborhood_radius_decay: Callable[[float, int, int], float] = , neighborhood_function: str = 'gaussian', distance_function: Callable[[numpy.ndarray, numpy.ndarray], numpy.ndarray] = , cyclic_x: bool = False, cyclic_y: bool = False, random_seed: int | None = None, data: numpy.ndarray | pandas.core.frame.DataFrame | list | None = None) -> None\n", - " Constructor for the self-organizing map class.\n", - "\n", - " :param x: int or NoneType: X dimension of the self-organizing map, i.e.,\n", - " number of rows of the matrix of weights.\n", - " x should be larger than 0.\n", - " If x is None and 'data' is provided in kwargs, its value will be automatically\n", - " selected using PCA of 'data'. Either x or y should be different than None.\n", - " :param y: int or NoneType: Y dimension of the self-organizing map, i.e.,\n", - " number of columns of the matrix of weights.\n", - " y should be larger than 0.\n", - " If y is None and 'data' is provided in kwargs, its value will be automatically\n", - " selected using PCA of 'data'. Either x or y should be different than None.\n", - " :param input_len: int: Number of features of the training dataset, i.e.,\n", - " number of elements of each node of the network.\n", - " :param learning_rate: float: Initial learning rate for the training process.\n", - " Should be a positive floating point value.\n", - " Defaults to 0.5.\n", - " Note: The value of the learning_rate is irrelevant for the 'batch' training mode.\n", - " :param learning_rate_decay: function: Decay function for the learning_rate variable.\n", - " May be a predefined one from this package, or a custom function, with the same\n", - " parameters and return type. Defaults to _asymptotic_decay.\n", - " :param neighborhood_radius: float: Initial neighborhood radius for the training process.\n", - " Defaults to 1.\n", - " :param neighborhood_radius_decay: function: Decay function for the neighborhood_radius\n", - " variable. May be a predefined one from this package, or a custom function, with the\n", - " same parameters and return type. Defaults to _asymptotic_decay\n", - " :param neighborhood_function: str: Neighborhood function name for the training process.\n", - " May be either 'gaussian' or 'bubble'.\n", - " :param distance_function: function: Function for calculating distances/dissimilarities\n", - " between models of the network.\n", - " May be a predefined one from this package, or a custom function, with the same\n", - " parameters and return type. Defaults to _euclidean_distance.\n", - " :param cyclic_x: bool: Boolean value activate/deactivate cyclic arrays in the x direction,\n", - " i.e, between the first and last rows of the weight matrix.\n", - " Defaults to False.\n", - " :param cyclic_y: bool: Boolean value activate/deactivate cyclic arrays in the y direction,\n", - " i.e, between the first and last columns of the weight matrix.\n", - " Defaults to False.\n", - " :param random_seed: int or None: Seed for NumPy random value generator. Defaults to None.\n", - " :param data: array-like: dataset for performing PCA.\n", - " Required when either x or y is None, for determining map size.\n", - "\n" - ] - } - ], - "source": [ - "help(python_som.SOM.__init__)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate SOM from python_som\n", - "# Selecting shape automatically (providing dataset for constructor)\n", - "# Using default decay and distance functions\n", - "# Using gaussian neighborhood function\n", - "# Using cyclic arrays in the vertical and horizontal directions\n", - "som = python_som.SOM(x=20, y=None, input_len=iris.shape[1], learning_rate=0.5, neighborhood_radius=1.0,\n", - " neighborhood_function='gaussian', cyclic_x=True, cyclic_y=True, data=iris)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# # Instantiate SOM from python_som\n", - "# # With fixed shape\n", - "# # Using default decay and distance functions\n", - "# # Using gaussian neighborhood function\n", - "# # Not using cyclic arrays\n", - "# som = python_som.SOM(x=8, y=8, input_len=iris.shape[1], learning_rate=0.5, neighborhood_radius=1.0,\n", - "# neighborhood_function='gaussian', cyclic_x=False, cyclic_y=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Weight initialization" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method weight_initialization in module python_som:\n", - "\n", - "weight_initialization(mode: str = 'random', **kwargs: numpy.ndarray | pandas.core.frame.DataFrame | list | str | int) -> None method of python_som.SOM instance\n", - " Function for weight initialization of the self-organizing map.\n", - "\n", - " Calls other methods for each initialization mode.\n", - "\n", - " :param mode: str: Initialization mode. May be either 'random', 'linear', or 'sample'.\n", - " Note: Each initialization method may require multiple additional arguments in kwargs.\n", - " :param kwargs:\n", - " For 'random' initialization mode, 'sample_mode': str may be provided to determine\n", - " the sampling mode. 'sample_mode' may be either 'standard_normal' (default) or 'uniform'\n", - " For 'random' and 'sample' modes, 'random_seed': int may be provided for the random\n", - " value generator. For 'sample' and 'linear' modes, 'data': array-like must be provided\n", - " for sampling/PCA.\n", - "\n" - ] - } - ], - "source": [ - "help(som.weight_initialization)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# # Initialize weights of the SOM with random initialization, from uniform distribution\n", - "# som.weight_initialization(mode='random', sample_mode='uniform')" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "# # Initialize weights of the SOM with sampling from dataset\n", - "# som.weight_initialization(mode='sample', data=iris)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize weights of the SOM with linear initialization\n", - "som.weight_initialization(mode='linear', data=iris)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method train in module python_som:\n", - "\n", - "train(data: numpy.ndarray | pandas.core.frame.DataFrame | list, n_iteration: int | None = None, mode: str = 'random', verbose: bool = False) -> float method of python_som.SOM instance\n", - " Trains the self-organizing map, with the dataset 'data', and a certain number of iterations.\n", - "\n", - " :param data: array-like: Dataset for training.\n", - " :param n_iteration: int or None: Number of iterations of training.\n", - " If None, defaults to 1000 * len(data) for stepwise training modes,\n", - " or 10 * len(data) for batch training mode.\n", - " :param mode: str: Training mode name. May be either 'random', 'sequential', or 'batch'.\n", - " For 'batch' mode, a much smaller number of iterations is needed,\n", - " but a higher computation power is required for each individual iteration.\n", - " :param verbose: bool: Activate to print useful information to the terminal/console, e.g.,\n", - " the progress of the training process\n", - " :return: float: Quantization error after training\n", - "\n" - ] - } - ], - "source": [ - "help(som.train)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "# # Training with stepwise algorithm, with random sampling from the dataset\n", - "# t_start = time.time()\n", - "# q_error = som.train(iris, 1000 * len(iris), mode='random', verbose=False)\n", - "# t_end = time.time()\n", - "# print(\"Quantization error:\", q_error)\n", - "# print(\"Execution time:\", t_end-t_start, \"s\")" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# # Training with stepwise algorithm, with sequential sampling from the dataset\n", - "# t_start = time.time()\n", - "# q_error = som.train(iris, 1000 * len(iris), mode='sequential', verbose=False)\n", - "# t_end = time.time()\n", - "# print(\"Quantization error:\", q_error)\n", - "# print(\"Execution time:\", t_end-t_start, \"s\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Quantization error: 0.11333343581048994\n", - "Execution time: 6.359957695007324 s\n" - ] - } - ], - "source": [ - "# Training with batch algorithm\n", - "t_start = time.time()\n", - "q_error = som.train(iris, len(iris), mode='batch', verbose=False)\n", - "t_end = time.time()\n", - "print(\"Quantization error:\", q_error)\n", - "print(\"Execution time:\", t_end-t_start, \"s\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting U-matrix" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method distance_matrix in module python_som:\n", - "\n", - "distance_matrix(normalize: bool = False) -> numpy.ndarray method of python_som.SOM instance\n", - " Calculates U-matrix of the current state of the network,\n", - " i.e., the matrix of distances between each node and its neighbors.\n", - " Has support for cyclic arrays\n", - "\n", - " :param normalize: bool: Activate to normalize the U-matrix between 0 and 1.\n", - " Defaults to False.\n", - " :return: np.ndarray: U-matrix of the current state of the network.\n", - "\n" - ] - } - ], - "source": [ - "help(som.distance_matrix)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculating distance matrix for plotting\n", - "umatrix = som.distance_matrix().T\n", - "\n", - "# Plotting U-matrix with seaborn/matplotlib\n", - "plt.figure(figsize=som.get_shape())\n", - "plt.pcolor(umatrix, cmap='bone_r')\n", - "\n", - "markers = ['o', 's', 'D']\n", - "colors = ['C0', 'C1', 'C2']\n", - "for cnt, xx in enumerate(iris):\n", - " w = som.winner(xx) # getting the winner\n", - " plt.plot(w[0] + .5, w[1] + .5, markers[tg[cnt]], markerfacecolor='None',\n", - " markeredgecolor=colors[tg[cnt]], markersize=12, markeredgewidth=2)\n", - "plt.axis([0, som.get_shape()[0], 0, som.get_shape()[1]])\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "torch", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/test_output_iris.png b/test_output_iris.png deleted file mode 100644 index d9fa87c..0000000 Binary files a/test_output_iris.png and /dev/null differ diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..c2b46a5 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,5 @@ +"""Test suite for python_som. + +This is a package so that shared helpers in ``conftest`` can be imported explicitly by the test +modules, rather than relying on implicit ``sys.path`` insertion. +""" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..bbe5739 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,79 @@ +"""Shared fixtures for the python_som test suite.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import numpy.typing as npt +import pandas as pd +import pytest +from hypothesis import settings + +import python_som + +# Deterministic replay in CI, so a failure reported by a run can be reproduced from the log. +settings.register_profile("ci", derandomize=True, max_examples=50, deadline=None) +settings.register_profile("dev", max_examples=25, deadline=None) +settings.load_profile("ci") + +SEED = 20260728 + + +@pytest.fixture +def rng() -> np.random.Generator: + """Return a seeded generator, so every test that draws data is reproducible.""" + return np.random.default_rng(SEED) + + +@pytest.fixture +def blobs(rng: np.random.Generator) -> npt.NDArray[np.floating]: + """Three well-separated Gaussian clusters in 3-D, 60 samples total.""" + centres = np.array([[0.0, 0.0, 0.0], [8.0, 8.0, 8.0], [-8.0, 8.0, -8.0]]) + return np.concatenate([c + rng.normal(scale=0.4, size=(20, 3)) for c in centres]) + + +@pytest.fixture +def blob_labels() -> npt.NDArray[np.int_]: + """Cluster identity for each sample of :func:`blobs`.""" + return np.repeat([0, 1, 2], 20) + + +@pytest.fixture +def frame(blobs: npt.NDArray[np.floating]) -> pd.DataFrame: + """Return the blobs fixture as a DataFrame, for the input-type parity tests.""" + return pd.DataFrame(blobs, columns=["a", "b", "c"]) + + +@pytest.fixture +def som() -> python_som.SOM: + """Return a small seeded map with the default gaussian neighborhood.""" + return python_som.SOM(x=8, y=6, input_len=3, random_seed=SEED) + + +def make_som(**kwargs: Any) -> python_som.SOM: # noqa: ANN401 + """Build a seeded map, overriding any constructor argument. + + :param kwargs: Constructor arguments to override. + :return: The constructed map. + """ + params: dict[str, Any] = { + "x": 21, + "y": 21, + "input_len": 3, + "random_seed": SEED, + } + params.update(kwargs) + return python_som.SOM(**params) + + +def grid_radius(shape: tuple[int, int], c: tuple[int, int]) -> npt.NDArray[np.floating]: + """Euclidean distance from ``c`` to every node of a non-cyclic grid. + + :param shape: Shape of the grid. + :param c: Coordinates of the centre. + :return: Distances, with the shape of the grid. + """ + dx = np.arange(shape[0]) - c[0] + dy = np.arange(shape[1]) - c[1] + return np.sqrt(np.add.outer(dx**2, dy**2)) diff --git a/tests/test_analysis.py b/tests/test_analysis.py new file mode 100644 index 0000000..383cf45 --- /dev/null +++ b/tests/test_analysis.py @@ -0,0 +1,134 @@ +"""Analysis and visualization helpers: winner, quantization, U-matrix and the maps.""" + +from __future__ import annotations + +import numpy as np +import pytest + +from python_som import euclidean_distance +from tests.conftest import make_som + + +def test_activate_returns_one_distance_per_node(blobs: np.ndarray) -> None: + som = make_som(x=7, y=4) + assert som.activate(blobs[0]).shape == (7, 4) + + +def test_winner_is_the_argmin_of_activate(blobs: np.ndarray) -> None: + som = make_som(x=7, y=4) + for sample in blobs[:10]: + activation = som.activate(sample) + expected = np.unravel_index(activation.argmin(), activation.shape) + assert som.winner(sample) == (int(expected[0]), int(expected[1])) + + +def test_quantization_returns_one_value_per_sample(blobs: np.ndarray) -> None: + som = make_som(x=6, y=6) + assert som.quantization(blobs).shape == (len(blobs),) + + +def test_quantization_error_is_the_mean_distance(blobs: np.ndarray) -> None: + som = make_som(x=6, y=6) + assert som.quantization_error(blobs) == pytest.approx(som.quantization(blobs).mean()) + + +def test_quantization_error_is_zero_for_an_exact_fit() -> None: + """A map whose models are exactly the data has no quantization error.""" + data = np.arange(12, dtype=float).reshape(4, 3) + som = make_som(x=2, y=2, input_len=3) + som.weight_initialization(mode="sample", data=data) + som._weights = data.reshape(2, 2, 3).copy() + assert som.quantization_error(data) == pytest.approx(0.0) + + +def test_activation_matrix_counts_every_sample(blobs: np.ndarray) -> None: + som = make_som(x=6, y=5) + assert som.activation_matrix(blobs).sum() == len(blobs) + + +def test_winner_map_partitions_the_dataset(blobs: np.ndarray) -> None: + som = make_som(x=6, y=5) + winner_map = som.winner_map(blobs) + assert len(winner_map) == 30 + assert sum(len(v) for v in winner_map.values()) == len(blobs) + + +def test_winner_map_agrees_with_the_activation_matrix(blobs: np.ndarray) -> None: + som = make_som(x=6, y=5) + counts = som.activation_matrix(blobs) + for node, members in som.winner_map(blobs).items(): + assert counts[node] == len(members) + + +def test_label_map_counts_labels_per_node(blobs: np.ndarray, blob_labels: np.ndarray) -> None: + som = make_som(x=6, y=5) + label_map = som.label_map(blobs, blob_labels) + assert sum(sum(c.values()) for c in label_map.values()) == len(blobs) + + +def test_label_map_separates_well_separated_clusters( + blobs: np.ndarray, blob_labels: np.ndarray +) -> None: + """After training on three distant blobs, most nodes should be label-pure.""" + som = make_som(x=8, y=8, neighborhood_radius=2.0) + som.weight_initialization(mode="linear", data=blobs) + som.train(blobs, n_iteration=40, mode="batch") + label_map = som.label_map(blobs, blob_labels) + occupied = [c for c in label_map.values() if sum(c.values()) > 0] + pure = [c for c in occupied if len(c) == 1] + assert len(pure) / len(occupied) > 0.8 + + +def test_label_map_rejects_mismatched_lengths(blobs: np.ndarray) -> None: + som = make_som(x=4, y=4) + with pytest.raises(ValueError, match="same length"): + som.label_map(blobs, [0, 1, 2]) + + +def test_distance_matrix_has_the_grid_shape() -> None: + som = make_som(x=6, y=4) + assert som.distance_matrix().shape == (6, 4) + + +def test_distance_matrix_is_non_negative() -> None: + assert (make_som(x=6, y=4).distance_matrix() >= 0).all() + + +def test_distance_matrix_normalizes_to_the_unit_interval() -> None: + um = make_som(x=6, y=4).distance_matrix(normalize=True) + assert um.min() == pytest.approx(0.0) + assert um.max() == pytest.approx(1.0) + + +def test_distance_matrix_normalization_survives_a_flat_map() -> None: + """A map whose models are all identical has zero spread; normalizing must not divide by zero.""" + som = make_som(x=4, y=4, input_len=3) + som._weights = np.zeros((4, 4, 3)) + um = som.distance_matrix(normalize=True) + assert np.isfinite(um).all() + + +def test_distance_matrix_is_larger_at_a_cluster_boundary() -> None: + """The U-matrix should peak where neighbouring models are far apart.""" + som = make_som(x=4, y=1, input_len=1) + som._weights = np.array([[0.0], [0.0], [10.0], [10.0]]).reshape(4, 1, 1) + um = som.distance_matrix() + assert um[1, 0] > um[0, 0] + assert um[2, 0] > um[3, 0] + + +def test_euclidean_distance_broadcasts() -> None: + a = np.zeros(3) + b = np.ones((4, 5, 3)) + assert euclidean_distance(a, b).shape == (4, 5) + assert euclidean_distance(a, b)[0, 0] == pytest.approx(np.sqrt(3)) + + +def test_euclidean_distance_is_zero_for_identical_vectors() -> None: + v = np.array([1.0, -2.0, 3.5]) + assert euclidean_distance(v, v) == pytest.approx(0.0) + + +def test_euclidean_distance_is_symmetric() -> None: + a, b = np.array([1.0, 2.0]), np.array([-3.0, 0.5]) + assert euclidean_distance(a, b) == pytest.approx(euclidean_distance(b, a)) diff --git a/tests/test_construction.py b/tests/test_construction.py new file mode 100644 index 0000000..0636e7d --- /dev/null +++ b/tests/test_construction.py @@ -0,0 +1,148 @@ +"""Constructor validation, automatic sizing and accessors.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np +import pytest +import sklearn.decomposition +import sklearn.preprocessing + +import python_som +from tests.conftest import SEED, make_som + +if TYPE_CHECKING: + import pandas as pd + + +def test_shape_is_plain_ints() -> None: + """Regression: ``get_shape`` used to return ``np.uint``. + + That made ``plt.figure(figsize=som.get_shape())`` fail, which the shipped example worked around + with an explicit ``float()`` and the README did not. + """ + shape = make_som(x=7, y=4).get_shape() + assert shape == (7, 4) + assert all(type(v) is int for v in shape) + + +def test_weights_have_the_declared_shape() -> None: + assert make_som(x=7, y=4, input_len=5).get_weights().shape == (7, 4, 5) + + +def test_seed_is_recorded_and_reused() -> None: + som = make_som(x=4, y=4, random_seed=99) + assert som.get_random_seed() == 99 + + +def test_omitting_the_seed_still_records_one() -> None: + som = make_som(x=4, y=4, random_seed=None) + assert isinstance(som.get_random_seed(), int) + + +def test_both_dimensions_none_raises() -> None: + with pytest.raises(ValueError, match="At least one of the dimensions"): + python_som.SOM(x=None, y=None, input_len=3) + + +def test_missing_dimension_without_data_raises() -> None: + with pytest.raises(ValueError, match="dataset must be provided"): + python_som.SOM(x=10, y=None, input_len=3) + + +@pytest.mark.parametrize(("x", "y"), [(0, 5), (5, 0), (-1, 5)]) +def test_non_positive_dimensions_raise(x: int, y: int) -> None: + with pytest.raises(ValueError, match="dimensions must be positive"): + python_som.SOM(x=x, y=y, input_len=3) + + +def test_non_positive_input_len_raises() -> None: + with pytest.raises(ValueError, match="input_len"): + python_som.SOM(x=5, y=5, input_len=0) + + +def test_unknown_neighborhood_function_raises_listing_the_options() -> None: + with pytest.raises(ValueError, match="neighborhood_function") as excinfo: + python_som.SOM(x=5, y=5, input_len=3, neighborhood_function="sombrero") + assert "gaussian" in str(excinfo.value) + + +def test_auto_dimension_uses_the_square_root_of_the_eigenvalue_ratio( + blobs: np.ndarray, +) -> None: + """Use the square root of the eigenvalue ratio for the automatic side length. + + Kohonen Section 3.5 asks for side lengths matching the *lengths* of the two largest principal + components. + + A component is a unit direction, so its length in the data is the standard deviation + ``sqrt(lambda)``; the ratio is therefore ``sqrt(lambda_1 / lambda_2)``. Using the raw eigenvalue + ratio, as the code did before, over-elongates the map. + """ + scaled = sklearn.preprocessing.StandardScaler().fit_transform(blobs) + pca = sklearn.decomposition.PCA(n_components=2, random_state=0) + pca.fit(scaled) + ratio = float(np.sqrt(pca.explained_variance_[0] / pca.explained_variance_[1])) + + som = python_som.SOM(x=20, y=None, input_len=3, data=blobs, random_seed=SEED) + assert som.get_shape() == (20, max(1, round(20 / ratio))) + + +def test_auto_dimension_derives_x_when_y_is_given(blobs: np.ndarray) -> None: + """The mirror of the case above; both branches of the ratio must behave the same way. + + Only ``y=None`` was covered before, which left half of the changed auto-sizing code untested. + """ + scaled = sklearn.preprocessing.StandardScaler().fit_transform(blobs) + pca = sklearn.decomposition.PCA(n_components=2, random_state=0) + pca.fit(scaled) + ratio = float(np.sqrt(pca.explained_variance_[0] / pca.explained_variance_[1])) + + som = python_som.SOM(x=None, y=20, input_len=3, data=blobs, random_seed=SEED) + assert som.get_shape() == (max(1, round(20 / ratio)), 20) + + +def test_auto_dimension_is_symmetric_in_its_two_branches(blobs: np.ndarray) -> None: + """Deriving x from y, or y from x, must apply the same ratio in the same direction.""" + from_y = python_som.SOM(x=None, y=20, input_len=3, data=blobs, random_seed=SEED).get_shape() + from_x = python_som.SOM(x=20, y=None, input_len=3, data=blobs, random_seed=SEED).get_shape() + assert from_y[0] == from_x[1] + assert from_y[1] == from_x[0] == 20 + + +def test_auto_dimension_never_returns_zero() -> None: + """Floor division could previously produce a zero-length side for a strongly 1-D dataset.""" + rng = np.random.default_rng(SEED) + almost_1d = np.column_stack( + [rng.normal(scale=50.0, size=200), rng.normal(scale=0.01, size=200)] + ) + som = python_som.SOM(x=3, y=None, input_len=2, data=almost_1d, random_seed=SEED) + assert som.get_shape()[1] >= 1 + + +def test_auto_dimension_accepts_a_dataframe(frame: pd.DataFrame) -> None: + som = python_som.SOM(x=12, y=None, input_len=3, data=frame, random_seed=SEED) + assert som.get_shape()[0] == 12 + + +def test_setters_update_the_hyperparameters() -> None: + som = make_som(x=4, y=4) + som.set_learning_rate(0.25) + som.set_neighborhood_radius(3.5) + assert som._learning_rate == pytest.approx(0.25) + assert som._neighborhood_radius == pytest.approx(3.5) + + +def test_neighborhood_is_reachable_from_the_public_api() -> None: + som = make_som(x=9, y=9, neighborhood_function="mexicanhat") + h = som.neighborhood((4, 4), 2.0) + assert h.shape == (9, 9) + assert h[4, 4] == pytest.approx(1.0) + + +def test_cyclic_flags_reach_the_neighborhood() -> None: + flat = make_som(x=10, y=10, cyclic_x=False).neighborhood((0, 5), 1.0) + torus = make_som(x=10, y=10, cyclic_x=True, cyclic_y=True).neighborhood((0, 5), 1.0) + assert flat[9, 5] < 1e-12 + assert torus[9, 5] > 0.5 diff --git a/tests/test_decay.py b/tests/test_decay.py new file mode 100644 index 0000000..bb5fa15 --- /dev/null +++ b/tests/test_decay.py @@ -0,0 +1,80 @@ +"""Properties of the decay functions.""" + +from __future__ import annotations + +from collections.abc import Callable +from itertools import pairwise +from typing import TypeAlias + +import numpy as np +import pytest +from hypothesis import given +from hypothesis import strategies as st + +from python_som import ( + asymptotic_decay, + exponential_decay, + inverse_decay, + linear_decay, +) + +DecayFunction: TypeAlias = Callable[..., float] + +ALL_DECAYS = [asymptotic_decay, linear_decay, exponential_decay, inverse_decay] + + +@pytest.mark.parametrize("decay", ALL_DECAYS) +def test_starts_at_the_initial_value(decay: DecayFunction) -> None: + assert decay(0.5, 0, 100) == pytest.approx(0.5) + + +@pytest.mark.parametrize("decay", ALL_DECAYS) +def test_is_non_increasing(decay: DecayFunction) -> None: + values = [decay(1.0, t, 100) for t in range(100)] + assert all(b <= a + 1e-12 for a, b in pairwise(values)) + + +@pytest.mark.parametrize("decay", ALL_DECAYS) +def test_stays_non_negative_within_the_horizon(decay: DecayFunction) -> None: + assert all(decay(1.0, t, 100) >= -1e-12 for t in range(101)) + + +@pytest.mark.parametrize("decay", ALL_DECAYS) +def test_scales_linearly_with_the_initial_value(decay: DecayFunction) -> None: + """Every decay is a multiplicative envelope, so doubling x doubles the output.""" + assert decay(2.0, 30, 100) == pytest.approx(2 * decay(1.0, 30, 100)) + + +def test_asymptotic_halves_at_the_midpoint() -> None: + assert asymptotic_decay(1.0, 50, 100) == pytest.approx(0.5) + + +def test_asymptotic_never_reaches_zero() -> None: + """Which is why it is the default: Kohonen warns sigma should not decay to zero.""" + assert asymptotic_decay(1.0, 10**6, 100) > 0 + + +def test_linear_reaches_zero_at_the_horizon() -> None: + assert linear_decay(1.0, 100, 100) == pytest.approx(0.0) + + +def test_exponential_respects_its_factor() -> None: + assert exponential_decay(1.0, 1, 100, factor=2.0) == pytest.approx(1 - 2 / 100) + + +def test_inverse_is_independent_of_the_horizon_scale() -> None: + """The ``max_t / 100`` scaling makes the shape depend on t, not on the horizon length.""" + assert inverse_decay(1.0, 100, 100) == pytest.approx(inverse_decay(1.0, 100, 100)) + assert inverse_decay(1.0, 1, 100) == pytest.approx(1 / (1 + 1)) + + +@given( + x=st.floats(min_value=0.01, max_value=100.0), + t=st.integers(min_value=0, max_value=999), + max_t=st.integers(min_value=1, max_value=1000), +) +@pytest.mark.parametrize("decay", ALL_DECAYS) +def test_output_is_finite_for_generated_inputs( + decay: DecayFunction, x: float, t: int, max_t: int +) -> None: + assert np.isfinite(decay(x, t, max_t)) diff --git a/tests/test_end_to_end.py b/tests/test_end_to_end.py new file mode 100644 index 0000000..c7defdb --- /dev/null +++ b/tests/test_end_to_end.py @@ -0,0 +1,114 @@ +"""End-to-end runs, marked ``slow``. + +These exist because every other test in the suite checks one property in isolation. A map can pass +all of them and still fail to do the thing the library is for: take a dataset with structure in it +and lay that structure out on the grid. Deselect with ``-m 'not slow'``. + +Nothing here touches the network. The example script uses seaborn's Iris loader, which downloads, +so the same code path is exercised over synthetic clusters instead. +""" + +from __future__ import annotations + +import numpy as np +import pytest + +import python_som +from tests.conftest import SEED + +pytestmark = pytest.mark.slow + + +@pytest.fixture(scope="module") +def clusters() -> tuple[np.ndarray, np.ndarray]: + """Four well-separated clusters in 5-D, 200 samples, with their labels.""" + rng = np.random.default_rng(SEED) + centres = np.array( + [ + [0.0, 0.0, 0.0, 0.0, 0.0], + [10.0, 10.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 10.0, 10.0, 0.0], + [-10.0, 0.0, 0.0, 10.0, 10.0], + ] + ) + data = np.concatenate([c + rng.normal(scale=0.5, size=(50, 5)) for c in centres]) + labels = np.repeat(np.arange(4), 50) + return data, labels + + +def test_batch_training_separates_known_clusters( + clusters: tuple[np.ndarray, np.ndarray], +) -> None: + """The whole point of the library, asserted once: structure in, structure on the grid.""" + data, labels = clusters + som = python_som.SOM(x=12, y=12, input_len=5, neighborhood_radius=4.0, random_seed=SEED) + som.weight_initialization(mode="linear", data=data) + error = som.train(data, n_iteration=40, mode="batch") + + # every cluster claims at least one node, and almost every occupied node is label-pure + label_map = som.label_map(data, labels) + occupied = [c for c in label_map.values() if sum(c.values()) > 0] + pure = [c for c in occupied if len(c) == 1] + claimed = {next(iter(c)) for c in pure} + + assert len(claimed) == 4, f"expected all four clusters represented, got {claimed}" + assert len(pure) / len(occupied) > 0.9 + assert error < 1.0 + + +def test_the_u_matrix_shows_the_cluster_boundaries( + clusters: tuple[np.ndarray, np.ndarray], +) -> None: + """A trained map's U-matrix must have more contrast than an untrained one. + + If training did nothing, the U-matrix of random models would be just as structured. This is the + cheapest available check that ``distance_matrix`` reports something meaningful. + """ + data, _ = clusters + untrained = python_som.SOM(x=12, y=12, input_len=5, random_seed=SEED) + trained = python_som.SOM(x=12, y=12, input_len=5, neighborhood_radius=4.0, random_seed=SEED) + trained.weight_initialization(mode="linear", data=data) + trained.train(data, n_iteration=40, mode="batch") + + assert np.ptp(trained.distance_matrix()) > np.ptp(untrained.distance_matrix()) + + +@pytest.mark.parametrize("mode", ["random", "sequential", "batch"]) +def test_every_mode_reaches_a_usable_map( + mode: str, clusters: tuple[np.ndarray, np.ndarray] +) -> None: + """All three training modes must produce finite models and reduce the error.""" + data, _ = clusters + som = python_som.SOM(x=10, y=10, input_len=5, neighborhood_radius=3.0, random_seed=SEED) + som.weight_initialization(mode="linear", data=data) + before = som.quantization_error(data) + after = som.train(data, n_iteration=60, mode=mode) + + assert np.isfinite(som.get_weights()).all() + assert after < before + + +def test_a_toroidal_map_has_no_edge(clusters: tuple[np.ndarray, np.ndarray]) -> None: + """On a torus every node is equivalent, so no row should be systematically starved of data. + + This is the end-to-end consequence of the cyclic fold-back fix: with the old one-sided fold, + nodes in the upper half of an axis had a truncated neighbourhood and attracted fewer samples. + """ + data, _ = clusters + som = python_som.SOM( + x=8, + y=8, + input_len=5, + neighborhood_radius=3.0, + cyclic_x=True, + cyclic_y=True, + random_seed=SEED, + ) + som.weight_initialization(mode="linear", data=data) + som.train(data, n_iteration=60, mode="batch") + + counts = som.activation_matrix(data) + top_half = counts[:4].sum() + bottom_half = counts[4:].sum() + assert top_half > 0 + assert bottom_half > 0 diff --git a/tests/test_io_types.py b/tests/test_io_types.py new file mode 100644 index 0000000..ac491f7 --- /dev/null +++ b/tests/test_io_types.py @@ -0,0 +1,80 @@ +"""Parity across the accepted input types: ndarray, DataFrame and plain list.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import pandas as pd +import pytest + +from tests.conftest import SEED, make_som + + +@pytest.fixture +def variants(blobs: np.ndarray) -> dict[str, Any]: + """Return the same data as an array, a DataFrame and a nested list.""" + return { + "ndarray": blobs, + "dataframe": pd.DataFrame(blobs, columns=["a", "b", "c"]), + "list": blobs.tolist(), + } + + +def test_quantization_error_agrees_across_input_types(variants: dict[str, Any]) -> None: + errors = [make_som(x=6, y=6).quantization_error(v) for v in variants.values()] + assert errors[0] == pytest.approx(errors[1]) + assert errors[0] == pytest.approx(errors[2]) + + +def test_training_agrees_across_input_types(variants: dict[str, Any]) -> None: + weights = [] + for v in variants.values(): + som = make_som(x=6, y=6, random_seed=SEED) + som.train(v, n_iteration=20, mode="batch") + weights.append(som.get_weights()) + np.testing.assert_allclose(weights[0], weights[1]) + np.testing.assert_allclose(weights[0], weights[2]) + + +def test_activation_matrix_agrees_across_input_types(variants: dict[str, Any]) -> None: + matrices = [make_som(x=5, y=5).activation_matrix(v) for v in variants.values()] + np.testing.assert_array_equal(matrices[0], matrices[1]) + np.testing.assert_array_equal(matrices[0], matrices[2]) + + +def test_linear_init_agrees_across_input_types(variants: dict[str, Any]) -> None: + weights = [] + for v in variants.values(): + som = make_som(x=5, y=5) + som.weight_initialization(mode="linear", data=v) + weights.append(som.get_weights()) + np.testing.assert_allclose(weights[0], weights[1]) + np.testing.assert_allclose(weights[0], weights[2]) + + +def test_labels_accept_a_series(blobs: np.ndarray, blob_labels: np.ndarray) -> None: + som = make_som(x=5, y=5) + from_array = som.label_map(blobs, blob_labels) + from_series = som.label_map(blobs, pd.Series(blob_labels)) + assert from_array == from_series + + +def test_labels_accept_strings(blobs: np.ndarray) -> None: + labels = ["setosa"] * 20 + ["versicolor"] * 20 + ["virginica"] * 20 + label_map = make_som(x=5, y=5).label_map(blobs, labels) + seen = {k for counter in label_map.values() for k in counter} + assert seen <= {"setosa", "versicolor", "virginica"} + + +def test_a_single_feature_dataset_works() -> None: + data = np.linspace(0, 1, 20).reshape(-1, 1) + som = make_som(x=4, y=3, input_len=1) + som.train(data, n_iteration=10, mode="batch") + assert np.isfinite(som.get_weights()).all() + + +def test_a_single_sample_dataset_works() -> None: + som = make_som(x=3, y=3, input_len=3) + som.train(np.array([[1.0, 2.0, 3.0]]), n_iteration=3, mode="batch") + assert np.isfinite(som.get_weights()).all() diff --git a/tests/test_neighborhood.py b/tests/test_neighborhood.py index a504c49..37c04fc 100644 --- a/tests/test_neighborhood.py +++ b/tests/test_neighborhood.py @@ -1,290 +1,332 @@ -"""Analytic property tests for the neighborhood functions of the self-organizing map. +"""Analytic properties of the neighborhood functions. -These assert closed-form properties rather than golden values, so they remain meaningful if the -implementation is rewritten. The central property is *isotropy*: a neighborhood function models -lateral interaction over the grid, so it must be a function of the distance between two nodes and +These assert closed forms rather than golden values, so they stay meaningful if the implementation +is rewritten. The central property for the gaussian and the mexican hat is *isotropy*: they model +lateral interaction over a metric space, so they must depend on the distance between two nodes and nothing else. -References: -Teuvo Kohonen, Essentials of the self-organizing map, Neural Networks 37 (2013) 52-65, -https://doi.org/10.1016/j.neunet.2012.09.018 -- Eq. (5), the neighborhood as a function of -sqdist(c, i). - -O. J. Vrieze, Kohonen network, in: Artificial Neural Networks: An Introduction to ANN Theory and -Practice, LNCS 931, Springer, 1995, pp. 83-100, https://doi.org/10.1007/BFb0027024 -- Fig. 3, the -"Mexican-hat" function of lateral interaction plotted against a single "lateral distance" axis. +The bubble is deliberately excluded from the isotropy tests. It is a Chebyshev ball, not a +Euclidean one; see :func:`test_bubble_is_not_euclidean_isotropic`. """ +from __future__ import annotations + import numpy as np import pytest +from hypothesis import given +from hypothesis import strategies as st -import python_som - - -def make_som( - x=21, y=21, cyclic_x=False, cyclic_y=False, neighborhood_function="gaussian" -): - """Builds a SOM with a fixed seed; only the grid geometry matters for these tests.""" - return python_som.SOM( - x=x, - y=y, - input_len=3, - neighborhood_function=neighborhood_function, - cyclic_x=cyclic_x, - cyclic_y=cyclic_y, - random_seed=0, - ) - +from python_som._neighborhood import ( + NEIGHBORHOOD_FUNCTIONS, + NeighborhoodFunction, + axis_offsets, + bubble, + gaussian, + mexican_hat, + resolve, + squared_grid_distance, +) +from tests.conftest import grid_radius -def grid_radius(shape, c): - """Euclidean distance from c to every node of a non-cyclic grid.""" - dx = np.arange(shape[0]) - c[0] - dy = np.arange(shape[1]) - c[1] - return np.sqrt(np.add.outer(dx**2, dy**2)) +ALL_FUNCTIONS = [gaussian, bubble, mexican_hat] +SMOOTH_FUNCTIONS = [gaussian, mexican_hat] +NO_WRAP = (False, False) +WRAP = (True, True) +GRID = (21, 21) +CENTRE = (10, 10) -# -------------------------------------------------------------------------------------------- -# Properties shared by every neighborhood function -# -------------------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------------------- +# Shared by every neighborhood function +# --------------------------------------------------------------------------------------------- -@pytest.mark.parametrize("name", ["gaussian", "bubble", "mexicanhat"]) -def test_shape_matches_the_grid(name): - som = make_som(neighborhood_function=name) - assert som._neighborhood_function((10, 10), 3.0).shape == (21, 21) +@pytest.mark.parametrize("func", ALL_FUNCTIONS) +def test_shape_matches_the_grid(func: NeighborhoodFunction) -> None: + assert func(GRID, CENTRE, 3.0, NO_WRAP).shape == GRID -@pytest.mark.parametrize("name", ["gaussian", "bubble", "mexicanhat"]) -def test_winner_has_maximum_response(name): - """The winner is maximally excited: h(c, c) is the global maximum.""" - som = make_som(neighborhood_function=name) - h = som._neighborhood_function((10, 10), 3.0) - assert h[10, 10] == pytest.approx(h.max()) +@pytest.mark.parametrize("func", ALL_FUNCTIONS) +def test_winner_has_maximum_response(func: NeighborhoodFunction) -> None: + """The winner is maximally excited.""" + h = func(GRID, CENTRE, 3.0, NO_WRAP) + assert h[CENTRE] == pytest.approx(h.max()) -@pytest.mark.parametrize("name", ["gaussian", "mexicanhat"]) -def test_normalized_to_unity_at_the_winner(name): - som = make_som(neighborhood_function=name) - assert som._neighborhood_function((10, 10), 3.0)[10, 10] == pytest.approx(1.0) +@pytest.mark.parametrize("func", SMOOTH_FUNCTIONS) +def test_normalized_to_unity_at_the_winner(func: NeighborhoodFunction) -> None: + assert func(GRID, CENTRE, 3.0, NO_WRAP)[CENTRE] == pytest.approx(1.0) -@pytest.mark.parametrize("name", ["gaussian", "bubble", "mexicanhat"]) +@pytest.mark.parametrize("func", ALL_FUNCTIONS) @pytest.mark.parametrize("sigma", [-1.0, -0.5, float("nan"), float("inf")]) -def test_negative_or_non_finite_sigma_is_rejected(name, sigma): +def test_negative_or_non_finite_radius_is_rejected( + func: NeighborhoodFunction, sigma: float +) -> None: """A negative or non-finite radius is meaningless for any neighborhood function.""" - som = make_som(neighborhood_function=name) with pytest.raises(ValueError, match="sigma"): - som._neighborhood_function((10, 10), sigma) + func(GRID, CENTRE, sigma, NO_WRAP) -@pytest.mark.parametrize("name", ["gaussian", "mexicanhat"]) -def test_zero_sigma_is_rejected_where_it_divides(name): - """sigma appears in a denominator, so zero must raise rather than yield inf/nan.""" - som = make_som(neighborhood_function=name) +@pytest.mark.parametrize("func", SMOOTH_FUNCTIONS) +def test_zero_radius_is_rejected_where_it_divides(func: NeighborhoodFunction) -> None: + """The radius appears in a denominator, so zero must raise rather than yield inf or nan.""" with pytest.raises(ValueError, match="sigma"): - som._neighborhood_function((10, 10), 0.0) + func(GRID, CENTRE, 0.0, NO_WRAP) -def test_bubble_admits_a_zero_radius(): - """For the bubble a zero radius is well defined: it selects the winner alone.""" - som = make_som(neighborhood_function="bubble") - h = som._bubble((10, 10), 0.0) - assert h[10, 10] == 1 - assert h.sum() == 1 - - -@pytest.mark.parametrize("name", ["gaussian", "bubble", "mexicanhat"]) -def test_four_fold_symmetry_about_the_winner(name): - """h is symmetric under reflection in both axes when the winner is centred.""" - som = make_som(neighborhood_function=name) - h = som._neighborhood_function((10, 10), 3.0) +@pytest.mark.parametrize("func", ALL_FUNCTIONS) +def test_four_fold_symmetry_about_a_centred_winner(func: NeighborhoodFunction) -> None: + h = func(GRID, CENTRE, 3.0, NO_WRAP) np.testing.assert_allclose(h, h[::-1, :]) np.testing.assert_allclose(h, h[:, ::-1]) -@pytest.mark.parametrize("name", ["gaussian", "bubble", "mexicanhat"]) -def test_is_isotropic(name): - """h depends only on the grid distance -- the property a separable product violates. +@pytest.mark.parametrize("func", ALL_FUNCTIONS) +def test_output_is_finite(func: NeighborhoodFunction) -> None: + assert np.isfinite(func(GRID, CENTRE, 3.0, NO_WRAP)).all() + - Kohonen (2013) Eq. (5) defines the neighborhood over sqdist(c, i); Vrieze (1995) Fig. 3 plots - it against a single "lateral distance" axis. Nodes equidistant from the winner must therefore +# --------------------------------------------------------------------------------------------- +# Isotropy: the property a separable product violates +# --------------------------------------------------------------------------------------------- + + +@pytest.mark.parametrize("func", SMOOTH_FUNCTIONS) +def test_is_isotropic(func: NeighborhoodFunction) -> None: + """The response depends only on the grid distance. + + Kohonen (2013) Eq. (5) defines the neighborhood over ``sqdist(c, i)`` and Vrieze (1995) Fig. 3 + plots it against a single "lateral distance" axis, so nodes equidistant from the winner must receive equal responses. An outer product of two 1-D profiles fails this for every profile - except the gaussian, which is separable only by accident of the exponential. + except the gaussian, which separates only by a property of the exponential. """ - som = make_som(neighborhood_function=name) - h = som._neighborhood_function((10, 10), 3.0) - r = np.round(grid_radius((21, 21), (10, 10)), 9) + h = func(GRID, CENTRE, 3.0, NO_WRAP) + r = np.round(grid_radius(GRID, CENTRE), 9) for radius in np.unique(r): - responses = h[r == radius] - np.testing.assert_allclose(responses, responses[0], atol=1e-12) + shell = h[r == radius] + np.testing.assert_allclose(shell, shell[0], atol=1e-12) + + +@given( + size=st.integers(min_value=5, max_value=25), + cx=st.integers(min_value=0, max_value=24), + sigma=st.floats(min_value=0.3, max_value=8.0), +) +def test_isotropy_holds_for_generated_grids(size: int, cx: int, sigma: float) -> None: + """Isotropy under adversarial grid sizes, centres and radii, not just hand-picked ones.""" + centre = (min(cx, size - 1), min(cx, size - 1)) + h = mexican_hat((size, size), centre, sigma, NO_WRAP) + r = np.round(grid_radius((size, size), centre), 9) + for radius in np.unique(r): + shell = h[r == radius] + np.testing.assert_allclose(shell, shell[0], atol=1e-12) + + +def test_mexican_hat_is_not_the_separable_product() -> None: + """Guards against a regression to ``np.outer`` of two 1-D Ricker wavelets. + The decisive difference is on the diagonal, where both 1-D factors are negative and their + product turns positive: +0.165 where the isotropic form gives -0.055. + """ + sigma = 3.0 + dx = np.arange(GRID[0]) - CENTRE[0] + ax = (1 - dx**2 / sigma**2) * np.exp(-(dx**2) / (2 * sigma**2)) + separable = np.outer(ax, ax) + h = mexican_hat(GRID, CENTRE, sigma, NO_WRAP) -# -------------------------------------------------------------------------------------------- + assert separable[16, 16] > 0 > h[16, 16] + assert separable[16, 16] == pytest.approx(0.164840749998, rel=1e-9) + assert h[16, 16] == pytest.approx(-0.054946916666, rel=1e-9) + + # and the separable product is measurably anisotropic + r = np.round(grid_radius(GRID, CENTRE), 9) + spreads = [np.ptp(separable[r == v]) for v in np.unique(r)] + assert max(spreads) > 0.4 + + +# --------------------------------------------------------------------------------------------- # Gaussian -# -------------------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------------------- -def test_gaussian_matches_its_closed_form(): - som = make_som() +def test_gaussian_matches_its_closed_form() -> None: sigma = 3.0 - h = som._gaussian((10, 10), sigma) - expected = np.exp(-(grid_radius((21, 21), (10, 10)) ** 2) / (2 * sigma**2)) - np.testing.assert_allclose(h, expected) + expected = np.exp(-(grid_radius(GRID, CENTRE) ** 2) / (2 * sigma**2)) + np.testing.assert_allclose(gaussian(GRID, CENTRE, sigma, NO_WRAP), expected) -def test_gaussian_is_strictly_positive_and_decreasing(): - som = make_som() - h = som._gaussian((10, 10), 3.0) +def test_gaussian_is_strictly_positive_and_decreasing() -> None: + h = gaussian(GRID, CENTRE, 3.0, NO_WRAP) assert (h > 0).all() - centre_row = h[10, 10:] - assert (np.diff(centre_row) < 0).all() + assert (np.diff(h[CENTRE[0], CENTRE[1] :]) < 0).all() -# -------------------------------------------------------------------------------------------- -# Mexican hat -- the properties that define the shape -# -------------------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------------------- +# Mexican hat +# --------------------------------------------------------------------------------------------- -def test_mexican_hat_crosses_zero_at_sqrt2_sigma(): - """(1 - u) exp(-u) with u = r^2/(2 sigma^2) vanishes at u = 1, i.e. r = sqrt(2) sigma.""" - som = make_som(x=41, y=41) - sigma = 4.0 - h = som._mexicanhat((20, 20), sigma) - r = grid_radius((41, 41), (20, 20)) - inside = r < np.sqrt(2) * sigma - 1e-9 - outside_but_near = (r > np.sqrt(2) * sigma + 1e-9) & (r < 4 * sigma) - assert (h[inside] > 0).all() - assert (h[outside_but_near] < 0).all() +def test_mexican_hat_crosses_zero_at_sqrt2_sigma() -> None: + """``(1 - u) exp(-u)`` with ``u = r^2 / 2 sigma^2`` vanishes at ``u = 1``.""" + shape, centre, sigma = (41, 41), (20, 20), 4.0 + h = mexican_hat(shape, centre, sigma, NO_WRAP) + r = grid_radius(shape, centre) + assert (h[r < np.sqrt(2) * sigma - 1e-9] > 0).all() + beyond = (r > np.sqrt(2) * sigma + 1e-9) & (r < 4 * sigma) + assert (h[beyond] < 0).all() -def test_mexican_hat_minimum_is_minus_exp_minus_two_at_two_sigma(): - """d/du [(1-u) e^-u] = 0 at u = 2, giving h = -e^-2 at r = 2 sigma.""" - som = make_som(x=41, y=41) - sigma = 4.0 - h = som._mexicanhat((20, 20), sigma) - r = grid_radius((41, 41), (20, 20)) +def test_mexican_hat_minimum_is_minus_exp_minus_two_at_two_sigma() -> None: + """``d/du [(1 - u) e^-u] = 0`` at ``u = 2``, giving ``h = -e^-2`` at ``r = 2 sigma``.""" + shape, centre, sigma = (41, 41), (20, 20), 4.0 + h = mexican_hat(shape, centre, sigma, NO_WRAP) + r = grid_radius(shape, centre) assert h.min() == pytest.approx(-np.exp(-2.0), rel=1e-9) assert r.flat[h.argmin()] == pytest.approx(2 * sigma, rel=1e-9) -def test_mexican_hat_is_inhibitory_on_the_diagonal(): - """Regression for the separable outer product. +def test_mexican_hat_has_no_positive_lobe_beyond_the_zero_crossing() -> None: + """Excitation is confined to the inner disc.""" + shape, centre, sigma = (41, 41), (20, 20), 4.0 + h = mexican_hat(shape, centre, sigma, NO_WRAP) + r = grid_radius(shape, centre) + assert h[r > np.sqrt(2) * sigma + 1e-9].max() <= 0.0 - An outer product of two 1-D Ricker wavelets is positive wherever both factors are negative, - putting a spurious excitatory lobe on the diagonals. Measured on this exact grid, that - construction gives +0.165 at (c+2s, c+2s) where the isotropic form gives -0.055. - """ - som = make_som() - sigma = 3.0 - h = som._mexicanhat((10, 10), sigma) - assert h[16, 16] < 0 - assert h[16, 16] == pytest.approx(-0.054946916666, rel=1e-9) - assert h[19, 19] < 0 +# --------------------------------------------------------------------------------------------- +# Bubble +# --------------------------------------------------------------------------------------------- -def test_mexican_hat_has_no_positive_lobe_beyond_the_zero_crossing(): - """Excitation is confined to the inner disc; everything beyond it inhibits or vanishes.""" - som = make_som(x=41, y=41) - sigma = 4.0 - h = som._mexicanhat((20, 20), sigma) - r = grid_radius((41, 41), (20, 20)) - assert h[r > np.sqrt(2) * sigma + 1e-9].max() <= 0.0 +def test_bubble_is_a_chebyshev_ball() -> None: + """Vrieze (1995) p. 85, and the ``MAX(ABS(...), ABS(...))`` of his appendix pseudo-code.""" + h = bubble(GRID, CENTRE, 2.0, NO_WRAP) + assert set(np.unique(h)) <= {0.0, 1.0} + expected = np.zeros(GRID) + expected[8:13, 8:13] = 1.0 + np.testing.assert_array_equal(h, expected) -def test_mexican_hat_differs_from_the_separable_product(): - """Guards against a regression to np.outer of two 1-D wavelets.""" - som = make_som() - sigma = 3.0 - dx = np.arange(21) - 10 - ax = (1 - dx**2 / sigma**2) * np.exp(-(dx**2) / (2 * sigma**2)) - separable = np.outer(ax, ax) - h = som._mexicanhat((10, 10), sigma) - assert not np.allclose(h, separable) - # The decisive difference: on the diagonal the separable product is excitatory where the - # isotropic form inhibits, because there both of its 1-D factors are negative. - assert separable[16, 16] > 0 > h[16, 16] - assert separable[16, 16] == pytest.approx(0.164840749998, rel=1e-9) - # The separable product is also anisotropic: (10, 14) and (13, 13) are not equidistant - # in its value despite both lying at grid radius 4 and 3*sqrt(2) respectively. - assert separable[10, 6] != pytest.approx(separable[13, 13]) +def test_bubble_admits_a_zero_radius() -> None: + """A zero radius selects the winner alone, which is well defined for an indicator.""" + h = bubble(GRID, CENTRE, 0.0, NO_WRAP) + assert h[CENTRE] == 1.0 + assert h.sum() == 1.0 -# -------------------------------------------------------------------------------------------- -# Bubble -# -------------------------------------------------------------------------------------------- +def test_bubble_is_not_euclidean_isotropic() -> None: + """Pinning down a property that is easy to assume and is false. -def test_bubble_is_the_indicator_of_a_neighbourhood_set(): - """Vrieze (1995) p. 85: N_i = {i' : d(i, i') <= rho}, the truncated inner lobe.""" - som = make_som() - h = som._bubble((10, 10), 2.0) - assert set(np.unique(h)) <= {0, 1} - expected = np.zeros((21, 21), dtype=int) - expected[8:13, 8:13] = 1 - np.testing.assert_array_equal(h, expected) + The bubble is a Chebyshev ball, so two nodes at equal *Euclidean* distance from the winner can + fall on opposite sides of its boundary. The smallest such case is a radius of ``sqrt(50)``, + where ``(5, 5)`` is inside a ``sigma = 5`` square while ``(7, 1)`` is outside. + + An earlier version of this suite asserted the bubble *was* isotropic and passed, because the + grid it used was too small for any collision to exist: you need ``a^2 + b^2 = c^2 + d^2`` with + ``max(a, b) <= sigma < max(c, d)``, and the smallest is ``50 = 25 + 25 = 49 + 1``. + """ + shape, centre, sigma = (31, 31), (15, 15), 5.0 + h = bubble(shape, centre, sigma, NO_WRAP) + assert h[15 + 5, 15 + 5] == 1.0 # Chebyshev distance 5, inside + assert h[15 + 7, 15 + 1] == 0.0 # Chebyshev distance 7, outside + # both are at Euclidean radius sqrt(50) + assert np.hypot(5, 5) == pytest.approx(np.hypot(7, 1)) -# -------------------------------------------------------------------------------------------- +def test_bubble_rounds_its_radius() -> None: + np.testing.assert_array_equal( + bubble(GRID, CENTRE, 1.4, NO_WRAP), bubble(GRID, CENTRE, 1.0, NO_WRAP) + ) + np.testing.assert_array_equal( + bubble(GRID, CENTRE, 1.6, NO_WRAP), bubble(GRID, CENTRE, 2.0, NO_WRAP) + ) + + +# --------------------------------------------------------------------------------------------- # Cyclic (toroidal) grids -# -------------------------------------------------------------------------------------------- +# --------------------------------------------------------------------------------------------- -@pytest.mark.parametrize("name", ["gaussian", "mexicanhat"]) -def test_cyclic_wraps_from_both_edges(name): +@pytest.mark.parametrize("func", ALL_FUNCTIONS) +def test_cyclic_wraps_from_both_edges(func: NeighborhoodFunction) -> None: """Regression for the one-sided fold-back. - The original fold only handled dx > shape/2, leaving offsets below -shape/2 unfolded. A winner - at row 0 wrapped correctly (h[9] = 0.6065) while a winner at row 9 did not (h[0] = 0.0). + The original fold handled ``dx > shape/2`` and left offsets below ``-shape/2`` alone, so a + winner at row 0 wrapped (``h[9] = 0.6065``) while a winner at row 9 did not (``h[0] = 0.0``). """ - som = make_som(x=10, y=10, cyclic_x=True, cyclic_y=True, neighborhood_function=name) - low = som._neighborhood_function((0, 5), 1.0)[:, 5] - high = som._neighborhood_function((9, 5), 1.0)[:, 5] + shape = (10, 10) + low = func(shape, (0, 5), 1.0, WRAP)[:, 5] + high = func(shape, (9, 5), 1.0, WRAP)[:, 5] assert low[9] == pytest.approx(low[1]) assert high[0] == pytest.approx(high[8]) np.testing.assert_allclose(high, np.roll(low, 9)) -@pytest.mark.parametrize("name", ["gaussian", "mexicanhat"]) -def test_cyclic_response_is_translation_invariant(name): - """On a torus every node is equivalent, so h depends only on the offset from the winner.""" - som = make_som(x=12, y=12, cyclic_x=True, cyclic_y=True, neighborhood_function=name) - reference = som._neighborhood_function((0, 0), 2.0) - for cx in range(12): - for cy in range(12): - shifted = som._neighborhood_function((cx, cy), 2.0) +@pytest.mark.parametrize("func", ALL_FUNCTIONS) +def test_cyclic_response_is_translation_invariant(func: NeighborhoodFunction) -> None: + """Every node of a torus is equivalent, so the response depends only on the offset.""" + shape = (12, 12) + reference = func(shape, (0, 0), 2.0, WRAP) + for cx in range(shape[0]): + for cy in range(shape[1]): np.testing.assert_allclose( - shifted, np.roll(reference, (cx, cy), axis=(0, 1)) + func(shape, (cx, cy), 2.0, WRAP), + np.roll(reference, (cx, cy), axis=(0, 1)), ) -def test_non_cyclic_grid_does_not_wrap(): - som = make_som(x=10, y=10) - h = som._gaussian((0, 5), 1.0)[:, 5] - assert h[9] < 1e-12 +def test_non_cyclic_grid_does_not_wrap() -> None: + assert gaussian((10, 10), (0, 5), 1.0, NO_WRAP)[9, 5] < 1e-12 + + +@given( + length=st.integers(min_value=2, max_value=40), + center=st.integers(min_value=0, max_value=39), +) +def test_cyclic_offsets_never_exceed_half_the_axis(length: int, center: int) -> None: + """The minimum-image convention bounds every offset by ``length / 2``.""" + d = axis_offsets(length, center % length, cyclic=True) + assert np.abs(d).max() <= length / 2 + 1e-12 + + +@given( + length=st.integers(min_value=2, max_value=40), + center=st.integers(min_value=0, max_value=39), +) +def test_cyclic_offset_matches_the_wrap_around_distance(length: int, center: int) -> None: + c = center % length + d = np.abs(axis_offsets(length, c, cyclic=True)) + direct = np.abs(np.arange(length) - c) + expected = np.minimum(direct, length - direct) + np.testing.assert_allclose(d, expected) + + +# --------------------------------------------------------------------------------------------- +# sqdist and lookup +# --------------------------------------------------------------------------------------------- + + +def test_squared_grid_distance_matches_euclidean() -> None: + np.testing.assert_allclose( + squared_grid_distance(GRID, CENTRE, NO_WRAP), grid_radius(GRID, CENTRE) ** 2 + ) -# -------------------------------------------------------------------------------------------- -# Selection and guards at the SOM level -# -------------------------------------------------------------------------------------------- +def test_squared_grid_distance_is_zero_at_the_centre() -> None: + assert squared_grid_distance(GRID, CENTRE, NO_WRAP)[CENTRE] == 0.0 -def test_unknown_neighborhood_function_raises_value_error(): - with pytest.raises(ValueError, match="neighborhood_function"): - python_som.SOM(x=8, y=8, input_len=3, neighborhood_function="mexican_hat") +@pytest.mark.parametrize("name", sorted(NEIGHBORHOOD_FUNCTIONS)) +def test_every_registered_name_resolves(name: str) -> None: + assert callable(resolve(name)) -def test_batch_training_rejects_the_mexican_hat(): - """Kohonen Eq. (8) divides by sum_j n_j h_ji, which is not sign-definite for a signed h.""" - rng = np.random.default_rng(0) - data = rng.normal(size=(30, 3)) - som = make_som(x=12, y=12, neighborhood_function="mexicanhat") - with pytest.raises(ValueError, match="batch"): - som.train(data, n_iteration=1, mode="batch") +def test_mexican_hat_alias_is_the_same_function() -> None: + assert resolve("mexican_hat") is resolve("mexicanhat") -@pytest.mark.parametrize("mode", ["random", "sequential"]) -def test_stepwise_training_accepts_the_mexican_hat(mode): - rng = np.random.default_rng(0) - som = make_som(x=12, y=12, neighborhood_function="mexicanhat") - som.train(rng.normal(size=(30, 3)), n_iteration=30, mode=mode) - assert np.isfinite(som.get_weights()).all() +def test_unknown_name_raises_value_error_listing_the_options() -> None: + with pytest.raises(ValueError, match="neighborhood_function") as excinfo: + resolve("sombrero") + assert "gaussian" in str(excinfo.value) diff --git a/tests/test_packaging.py b/tests/test_packaging.py new file mode 100644 index 0000000..a93066b --- /dev/null +++ b/tests/test_packaging.py @@ -0,0 +1,103 @@ +"""The packaging contract: typing marker, public surface, version and metadata.""" + +from __future__ import annotations + +import pathlib +import sys +from importlib.metadata import metadata, version +from typing import Any + +import pytest + +if sys.version_info >= (3, 11): + import tomllib +else: # pragma: no cover + import tomli as tomllib + +import python_som + +PROJECT_ROOT = pathlib.Path(__file__).resolve().parent.parent + + +@pytest.fixture(scope="module") +def pyproject() -> dict[str, Any]: + """Return the parsed ``pyproject.toml``.""" + with (PROJECT_ROOT / "pyproject.toml").open("rb") as fh: + return tomllib.load(fh) + + +def test_py_typed_marker_is_present() -> None: + """Without this file, PEP 561 says downstream type checkers must ignore our annotations.""" + marker = pathlib.Path(python_som.__file__).parent / "py.typed" + assert marker.is_file() + + +def test_installed_version_matches_pyproject(pyproject: dict[str, Any]) -> None: + assert version("python-som") == pyproject["project"]["version"] + + +def test_dunder_version_matches_pyproject(pyproject: dict[str, Any]) -> None: + assert python_som.__version__ == pyproject["project"]["version"] + + +def test_cli_extra_is_declared(pyproject: dict[str, Any]) -> None: + """The ``cli`` extra shipped in 0.1.3 on PyPI but was never committed. + + Releasing from the repository without it would silently break + ``pip install python-som[cli]`` for existing users. + """ + assert "cli" in pyproject["project"]["optional-dependencies"] + assert any(d.startswith("tqdm") for d in pyproject["project"]["optional-dependencies"]["cli"]) + + +def test_requires_python_is_honest(pyproject: dict[str, Any]) -> None: + """The package uses PEP 604 unions in runtime annotations, so 3.10 is the true floor.""" + assert pyproject["project"]["requires-python"] == ">=3.10" + + +def test_metadata_declares_typing_support() -> None: + classifiers = metadata("python-som").get_all("Classifier") or [] + assert "Typing :: Typed" in classifiers + + +def test_no_deprecated_license_classifier(pyproject: dict[str, Any]) -> None: + """PEP 639 replaces the classifier with the SPDX ``license`` field; having both conflicts.""" + classifiers = pyproject["project"]["classifiers"] + assert not any(c.startswith("License ::") for c in classifiers) + assert pyproject["project"]["license"] == "MIT" + + +def test_public_surface_is_importable() -> None: + for name in python_som.__all__: + assert hasattr(python_som, name), name + + +def test_som_is_constructible_from_the_package_root() -> None: + som = python_som.SOM(x=4, y=4, input_len=2, random_seed=0) + assert som.get_shape() == (4, 4) + + +@pytest.mark.parametrize( + "name", + [ + "_asymptotic_decay", + "_linear_decay", + "_exponential_decay", + "_inverse_decay", + "_euclidean_distance", + ], +) +def test_pre_0_3_private_aliases_still_resolve(name: str) -> None: + """These were underscore-prefixed but reachable, and the README listed them. + + Keeping them working avoids breaking imports that the old README effectively advertised. + """ + assert callable(getattr(python_som, name)) + + +def test_dev_dependencies_are_pinned_exactly(pyproject: dict[str, Any]) -> None: + """Reproducible tooling: a floating dev pin means CI can break without a commit.""" + for group in ("dev", "docs"): + for spec in pyproject["project"]["optional-dependencies"][group]: + requirement = spec.split(";")[0].strip() + assert "==" in requirement, f"{group} dependency is not pinned: {spec}" diff --git a/tests/test_training.py b/tests/test_training.py new file mode 100644 index 0000000..a0a9b91 --- /dev/null +++ b/tests/test_training.py @@ -0,0 +1,291 @@ +"""Training behaviour and the invariants that must hold for any input. + +The invariants are the point of this module. The bugs found in this library were not wrong formulas +so much as properties nobody had asserted: that no model is silently destroyed, that a requested +iteration count is honoured, that a training step cannot divide by a vanishing denominator. +""" + +from __future__ import annotations + +import numpy as np +import pytest + +import python_som +from tests.conftest import SEED, make_som + + +def test_batch_never_zeroes_a_model(rng: np.random.Generator) -> None: + """Regression for the batch update starting from a zeroed array. + + ``_train_batch`` used to build ``new_weights = np.zeros(...)``, fill only the nodes whose + neighbourhood contained data, and assign the result. Every other model was destroyed: on this + exact configuration, 282 of 900 models became the zero vector in a single step. + """ + data = rng.normal(size=(20, 3)) + som = make_som(x=30, y=30, neighborhood_radius=0.5) + som.weight_initialization(mode="sample", data=data) + before = som.get_weights().copy() + + som.train(data, n_iteration=1, mode="batch") + after = som.get_weights() + + zeroed = np.all(after == 0.0, axis=-1) + assert not zeroed.any(), f"{zeroed.sum()} of {zeroed.size} models were wiped to zero" + # untouched models keep their previous value exactly + untouched = np.all(np.isclose(after, before), axis=-1) + assert untouched.any(), "expected some models to be left alone at this radius" + + +def test_batch_preserves_finiteness(blobs: np.ndarray) -> None: + som = make_som(x=10, y=10, neighborhood_radius=2.0) + som.train(blobs, n_iteration=5, mode="batch") + assert np.isfinite(som.get_weights()).all() + + +@pytest.mark.parametrize("mode", ["random", "sequential", "batch"]) +def test_training_reduces_quantization_error(mode: str, blobs: np.ndarray) -> None: + """Training should fit the data better than the initial state.""" + som = make_som(x=8, y=8, neighborhood_radius=2.0, learning_rate=0.5) + before = som.quantization_error(blobs) + after = som.train(blobs, n_iteration=30, mode=mode) + assert after < before + + +@pytest.mark.parametrize("mode", ["random", "sequential"]) +def test_stepwise_runs_the_requested_number_of_iterations( + mode: str, blobs: np.ndarray, monkeypatch: pytest.MonkeyPatch +) -> None: + """Regression: sequential mode used to run ``len(data)`` steps regardless of ``n_iteration``. + + It iterated ``enumerate(data_array)`` directly, so asking for 500 iterations over 30 samples + ran 30 steps while the decay functions still used 500 as their horizon. + """ + som = make_som(x=6, y=6) + calls = 0 + original = som.winner + + def counting_winner(x: np.ndarray) -> tuple[int, int]: + nonlocal calls + calls += 1 + return original(x) + + monkeypatch.setattr(som, "winner", counting_winner) + requested = 137 + som.train(blobs, n_iteration=requested, mode=mode) + # each training step calls winner() once; quantization_error() then calls it once per sample + assert calls - len(blobs) == requested + + +def test_sequential_cycles_through_the_dataset(blobs: np.ndarray) -> None: + """More iterations than samples must wrap around rather than stop early.""" + som = make_som(x=5, y=5) + som.train(blobs, n_iteration=len(blobs) * 3, mode="sequential") + assert np.isfinite(som.get_weights()).all() + + +def test_random_samples_with_replacement( + blobs: np.ndarray, monkeypatch: pytest.MonkeyPatch +) -> None: + """Pin the sampling semantics, which changed in 0.3.0 and are easy to alter by accident. + + ``random`` mode draws i.i.d. with replacement, the Robbins-Monro stochastic approximation + Kohonen cites in Section 4.1. Before 0.3.0 the draw used + ``replace=(n_iteration > len(data))``, so asking for fewer iterations than samples produced a + permutation instead, making the character of the sampling depend on the iteration count. + + With replacement, drawing as many indices as there are samples repeats at least one of them + with overwhelming probability: for n = 60 the chance of a permutation is 60!/60**60, far below + any floating-point threshold. + """ + seen: list[int] = [] + som = make_som(x=5, y=5) + original = som.winner + + def recording_winner(x: np.ndarray) -> tuple[int, int]: + seen.append(int(np.flatnonzero((blobs == x).all(axis=1))[0])) + return original(x) + + monkeypatch.setattr(som, "winner", recording_winner) + som.train(blobs, n_iteration=len(blobs), mode="random") + drawn = seen[: len(blobs)] + assert len(drawn) == len(blobs) + assert len(set(drawn)) < len(blobs), "a with-replacement draw should repeat an index" + + +def test_sequential_visits_every_sample_exactly_once_per_epoch( + blobs: np.ndarray, monkeypatch: pytest.MonkeyPatch +) -> None: + """Sequential is the counterpart: one full pass covers every sample once, in order.""" + seen: list[int] = [] + som = make_som(x=5, y=5) + original = som.winner + + def recording_winner(x: np.ndarray) -> tuple[int, int]: + seen.append(int(np.flatnonzero((blobs == x).all(axis=1))[0])) + return original(x) + + monkeypatch.setattr(som, "winner", recording_winner) + som.train(blobs, n_iteration=len(blobs), mode="sequential") + assert seen[: len(blobs)] == list(range(len(blobs))) + + +def test_batch_rejects_a_signed_neighborhood(blobs: np.ndarray) -> None: + """Kohonen Eq. (8) divides by ``sum_j n_j h_ji``, which is not sign-definite for a signed h. + + Measured on a 12x12 grid, 49 of 144 denominators come out negative. + """ + som = make_som(x=12, y=12, neighborhood_function="mexicanhat") + with pytest.raises(ValueError, match="batch"): + som.train(blobs, n_iteration=1, mode="batch") + + +def test_batch_rejects_the_alias_too(blobs: np.ndarray) -> None: + som = make_som(x=12, y=12, neighborhood_function="mexican_hat") + with pytest.raises(ValueError, match="batch"): + som.train(blobs, n_iteration=1, mode="batch") + + +@pytest.mark.parametrize("mode", ["random", "sequential"]) +def test_stepwise_accepts_a_signed_neighborhood(mode: str, blobs: np.ndarray) -> None: + """The mexican hat is fine for stepwise training; only the batch mean is undefined.""" + som = make_som(x=12, y=12, neighborhood_function="mexicanhat") + som.train(blobs, n_iteration=30, mode=mode) + assert np.isfinite(som.get_weights()).all() + + +@pytest.mark.parametrize(("mode", "per_sample"), [("batch", 10), ("sequential", 1000)]) +def test_omitting_n_iteration_uses_the_documented_default( + mode: str, per_sample: int, monkeypatch: pytest.MonkeyPatch +) -> None: + """The default is 1000 iterations per sample for stepwise modes and 10 for batch. + + That default is documented on ``train`` but was never exercised, so the arithmetic behind it + was untested. A two-sample dataset keeps even the 1000-per-sample case quick. + """ + data = np.array([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]) + som = make_som(x=3, y=3) + steps = 0 + original = som._sigma + + def counting_sigma(t: int, n: int) -> float: + nonlocal steps + steps += 1 + return original(t, n) + + monkeypatch.setattr(som, "_sigma", counting_sigma) + som.train(data, mode=mode) + assert steps == per_sample * len(data) + + +def test_verbose_training_runs_with_a_progress_bar(blobs: np.ndarray) -> None: + """The tqdm branch of the progress wrapper, which no other test reaches. + + tqdm ships in the dev extra, so this exercises the wrapped path rather than the fallback. + """ + som = make_som(x=5, y=5) + error = som.train(blobs, n_iteration=3, mode="batch", verbose=True) + assert np.isfinite(error) + + +def test_unknown_mode_raises_value_error() -> None: + som = make_som(x=5, y=5) + with pytest.raises(ValueError, match="mode"): + som.train(np.zeros((4, 3)), n_iteration=1, mode="stochastic") + + +def test_empty_dataset_raises_value_error() -> None: + som = make_som(x=5, y=5) + with pytest.raises(ValueError, match="empty"): + som.train(np.zeros((0, 3)), n_iteration=1) + + +def test_non_positive_iteration_count_raises_value_error(blobs: np.ndarray) -> None: + som = make_som(x=5, y=5) + with pytest.raises(ValueError, match="n_iteration"): + som.train(blobs, n_iteration=0) + + +def test_same_seed_reproduces_the_same_map(blobs: np.ndarray) -> None: + a = make_som(x=8, y=8, random_seed=SEED) + b = make_som(x=8, y=8, random_seed=SEED) + a.train(blobs, n_iteration=25, mode="random") + b.train(blobs, n_iteration=25, mode="random") + np.testing.assert_array_equal(a.get_weights(), b.get_weights()) + + +def test_different_seeds_give_different_maps(blobs: np.ndarray) -> None: + a = make_som(x=8, y=8, random_seed=1) + b = make_som(x=8, y=8, random_seed=2) + a.train(blobs, n_iteration=25, mode="random") + b.train(blobs, n_iteration=25, mode="random") + assert not np.allclose(a.get_weights(), b.get_weights()) + + +def test_construction_does_not_disturb_the_global_numpy_rng() -> None: + """Regression: the constructor used to call ``np.random.seed`` on the global generator. + + Building a SOM would silently reseed NumPy for the whole host program. + """ + np.random.seed(12345) # noqa: NPY002 + expected = np.random.random(5) # noqa: NPY002 + + np.random.seed(12345) # noqa: NPY002 + make_som(x=6, y=6, random_seed=None) + actual = np.random.random(5) # noqa: NPY002 + + np.testing.assert_array_equal(expected, actual) + + +def test_radius_is_floored_during_training(blobs: np.ndarray) -> None: + """Floor the radius so it never reaches zero during training. + + Kohonen Section 4.2: sigma "shall not go to zero, because otherwise the process loses its + ordering power". + + ``linear_decay`` reaches zero at the horizon, which would be a division by zero in the + gaussian. The floor keeps it defined. + """ + som = make_som( + x=6, + y=6, + neighborhood_radius_decay=python_som.linear_decay, + min_neighborhood_radius=0.5, + ) + som.train(blobs, n_iteration=10, mode="random") + assert np.isfinite(som.get_weights()).all() + + +def test_invalid_minimum_radius_is_rejected() -> None: + with pytest.raises(ValueError, match="min_neighborhood_radius"): + make_som(x=5, y=5, min_neighborhood_radius=0.0) + + +def test_winner_is_always_inside_the_grid(blobs: np.ndarray) -> None: + som = make_som(x=7, y=4) + for sample in blobs: + i, j = som.winner(sample) + assert 0 <= i < 7 + assert 0 <= j < 4 + + +def test_batch_matches_a_reference_implementation(blobs: np.ndarray) -> None: + """The contracted batch update must agree with the literal double loop of Eq. (8).""" + som = make_som(x=6, y=5, neighborhood_radius=1.5) + som.weight_initialization(mode="sample", data=blobs) + reference = som.get_weights().copy() + + sigma = som._sigma(0, 1) + winner_map = som.winner_map(blobs) + expected = reference.copy() + for i in np.ndindex(som.get_shape()): + neig = som.neighborhood((int(i[0]), int(i[1])), sigma) + upper = np.zeros(3) + bottom = 0.0 + for j, members in winner_map.items(): + upper += neig[j] * np.sum(members, axis=0) if members else 0.0 + bottom += neig[j] * len(members) + if abs(bottom) > 1e-12: + expected[i] = upper / bottom + + som.train(blobs, n_iteration=1, mode="batch") + np.testing.assert_allclose(som.get_weights(), expected, atol=1e-12) diff --git a/tests/test_weight_init.py b/tests/test_weight_init.py new file mode 100644 index 0000000..f167746 --- /dev/null +++ b/tests/test_weight_init.py @@ -0,0 +1,125 @@ +"""Weight initialization, including the PCA hyperplane fix.""" + +from __future__ import annotations + +import numpy as np +import pytest +import sklearn.decomposition + +from tests.conftest import make_som + + +def test_linear_init_spans_the_principal_plane(blobs: np.ndarray) -> None: + """Regression: linear initialization used the eigen*values* instead of the eigen*vectors*. + + ``pca.explained_variance_[0]`` and ``[1]`` are two scalars, so every model came out as + ``c1 * var0 + c2 * var1`` broadcast across all features: a constant vector on the all-ones + diagonal, rank 1, not a plane. + + Kohonen Section 4.3 asks for "a regular, two-dimensional sequence of vectors taken along a + hyperplane spanned by the two largest principal components". + """ + som = make_som(x=6, y=5, input_len=3) + som.weight_initialization(mode="linear", data=blobs) + weights = som.get_weights().reshape(-1, 3) + + # not constant within a model + assert not np.allclose(weights, weights[:, :1]) + + # the models span a rank-2 affine subspace + centred = weights - weights.mean(axis=0) + singular = np.linalg.svd(centred, compute_uv=False) + assert singular[1] > 1e-8 + assert singular[2] < 1e-8 * max(1.0, singular[0]) + + +def test_linear_init_is_centred_on_the_data_mean(blobs: np.ndarray) -> None: + som = make_som(x=7, y=7, input_len=3) + som.weight_initialization(mode="linear", data=blobs) + np.testing.assert_allclose( + som.get_weights().reshape(-1, 3).mean(axis=0), blobs.mean(axis=0), atol=1e-8 + ) + + +def test_linear_init_aligns_with_the_principal_components(blobs: np.ndarray) -> None: + """The plane the models span must be the plane of the first two components.""" + som = make_som(x=6, y=6, input_len=3) + som.weight_initialization(mode="linear", data=blobs) + weights = som.get_weights().reshape(-1, 3) + + pca = sklearn.decomposition.PCA(n_components=2, random_state=0).fit(blobs) + centred = weights - weights.mean(axis=0) + # every model offset lies in the span of the two components + residual = centred - centred @ pca.components_.T @ pca.components_ + assert np.abs(residual).max() < 1e-8 + + +def test_linear_init_lives_in_the_data_space(blobs: np.ndarray) -> None: + """PCA is fitted on raw data, not standardized data. + + Fitting on standardized data while comparing against raw data during training put the models in + a different space entirely. + """ + som = make_som(x=6, y=6, input_len=3) + som.weight_initialization(mode="linear", data=blobs) + weights = som.get_weights().reshape(-1, 3) + assert weights.min() >= blobs.min() - 1.0 + assert weights.max() <= blobs.max() + 1.0 + + +def test_linear_init_is_deterministic(blobs: np.ndarray) -> None: + a, b = make_som(x=5, y=5), make_som(x=5, y=5, random_seed=999) + a.weight_initialization(mode="linear", data=blobs) + b.weight_initialization(mode="linear", data=blobs) + np.testing.assert_allclose(a.get_weights(), b.get_weights()) + + +def test_linear_init_rejects_a_degenerate_dataset() -> None: + som = make_som(x=4, y=4, input_len=1) + with pytest.raises(ValueError, match="at least 2 samples"): + som.weight_initialization(mode="linear", data=np.zeros((5, 1))) + + +def test_sample_init_draws_from_the_dataset(blobs: np.ndarray) -> None: + som = make_som(x=4, y=4, input_len=3) + som.weight_initialization(mode="sample", data=blobs) + for model in som.get_weights().reshape(-1, 3): + assert np.isclose(blobs, model).all(axis=1).any() + + +def test_sample_init_handles_more_nodes_than_samples() -> None: + data = np.arange(12, dtype=float).reshape(4, 3) + som = make_som(x=5, y=5, input_len=3) + som.weight_initialization(mode="sample", data=data) + assert som.get_weights().shape == (5, 5, 3) + + +@pytest.mark.parametrize("sample_mode", ["standard_normal", "uniform"]) +def test_random_init_modes(sample_mode: str) -> None: + som = make_som(x=5, y=5, input_len=3) + som.weight_initialization(mode="random", sample_mode=sample_mode) + weights = som.get_weights() + assert weights.shape == (5, 5, 3) + if sample_mode == "uniform": + assert weights.min() >= 0.0 + assert weights.max() <= 1.0 + + +def test_random_init_rejects_an_unknown_sample_mode() -> None: + som = make_som(x=4, y=4) + with pytest.raises(ValueError, match="sample_mode"): + som.weight_initialization(mode="random", sample_mode="cauchy") + + +def test_unknown_initialization_mode_raises() -> None: + som = make_som(x=4, y=4) + with pytest.raises(ValueError, match="mode"): + som.weight_initialization(mode="spectral") + + +def test_initialization_is_reproducible_from_the_seed() -> None: + a = make_som(x=5, y=5, random_seed=7) + b = make_som(x=5, y=5, random_seed=7) + a.weight_initialization(mode="random") + b.weight_initialization(mode="random") + np.testing.assert_array_equal(a.get_weights(), b.get_weights()) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..219298d --- /dev/null +++ b/uv.lock @@ -0,0 +1,3049 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.15' and sys_platform == 'win32'", + "python_full_version >= '3.15' and 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