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|PyPI| |ReadTheDocs|
.. |PyPI| image:: https://img.shields.io/pypi/v/sklearn-ann .. _PyPI: https://pypi.org/project/sklearn-ann/
.. |ReadTheDocs| image:: https://readthedocs.org/projects/sklearn-ann/badge/?version=latest .. _ReadTheDocs: https://sklearn-ann.readthedocs.io/en/latest/?badge=latest
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sklearn-ann eases integration of approximate nearest neighbours libraries such as annoy, nmslib and faiss into your sklearn pipelines. It consists of:
Transformers conforming to the same interface as
KNeighborsTransformer which can be used to transform feature matrices
into sparse distance matrices for use by any estimator that can deal with
sparse distance matrices. Many, but not all, of scikit-learn's clustering and
manifold learning algorithms can work with this kind of input.To install the latest release from PyPI, run:
.. code-block:: bash
pip install sklearn-annTo install the latest development version from GitHub, run:
.. code-block:: bash
pip install git+https://github.com/scikit-learn-contrib/sklearn-ann.git#egg=sklearn-annThe main scenarios in which this is needed is for performing clustering or manifold learning or high dimensional data. The reason is that currently the only neighbourhood algorithms which are build into scikit-learn are essentially the standard tree approaches to space partitioning: the ball tree and the K-D tree. These do not perform competitively in high dimensional spaces.
This project is managed using Hatch and pre-commit. To get started, run pre-commit install and hatch env create. Run all commands using hatch run python <command> which will ensure the environment is kept up to date. pre-commit_ comes into
play on every git commit after installation.
Consult pyproject.toml for which dependency groups and extras exist,
and the Hatch help or user guide for more info on what they are.
.. _Hatch: https://hatch.pypa.io/ .. _pre-commit: https://pre-commit.com/