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.

To install the latest release from PyPI, run:

pip install sklearn-ann

To install the latest development version from GitHub, run:

pip install git+https://github.com/scikit-learn-contrib/sklearn-ann.git#egg=sklearn-ann

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.

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.