https://github.com/scikit-learn-contrib/sklearn-ann

Integration with (approximate) nearest neighbors libraries for scikit-learn + clustering based on with kNN-graphs.
https://github.com/scikit-learn-contrib/sklearn-ann

approximate-nearest-neighbor-search clustering knn knn-graphs scikit-learn

Last synced: about 4 hours ago
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Integration with (approximate) nearest neighbors libraries for scikit-learn + clustering based on with kNN-graphs.

• Host: GitHub
• URL: https://github.com/scikit-learn-contrib/sklearn-ann
• Owner: scikit-learn-contrib
• License: bsd-3-clause
• Created: 2020-12-30T14:08:33.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-10-30T09:15:29.000Z (8 days ago)
• Last Synced: 2024-10-30T10:23:18.522Z (8 days ago)
• Topics: approximate-nearest-neighbor-search, clustering, knn, knn-graphs, scikit-learn
• Language: Python
• Homepage: https://sklearn-ann.readthedocs.io/en/latest/
• Size: 194 KB
• Stars: 15
• Watchers: 4
• Forks: 5
• Open Issues: 9
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

Awesome Lists containing this project

README

        
.. -*- mode: rst -*-

|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

sklearn-ann

===========

.. inclusion-marker-do-not-remove

**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.

* RNN-DBSCAN: a variant of DBSCAN based on reverse nearest

  neighbours.

Installation

============

To install the latest release from PyPI, run:

.. code-block:: bash

    pip install sklearn-ann

To 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-ann

Why? When do I want this?

=========================

The 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.

Development

===========

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

`` 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/