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===========
PyNNDescent

PyNNDescent is a Python nearest neighbor descent for approximate nearest neighbors.
It provides a python implementation of Nearest Neighbor
Descent for k-neighbor-graph construction and approximate nearest neighbor
search, as per the paper:

Dong, Wei, Charikar Moses, and Kai Li.
"Efficient k-nearest neighbor graph construction for generic similarity
measures."
Proceedings of the 20th international conference on World wide web. ACM, 2011.

This library supplements that approach with the use of random projection trees for
initialisation. This can be particularly useful for the metrics that are
amenable to such approaches (euclidean, minkowski, angular, cosine, etc.). Graph
diversification is also performed, pruning the longest edges of any triangles in the
graph.

Currently this library targets relatively high accuracy
(80%-100% accuracy rate) approximate nearest neighbor searches.

Why use PyNNDescent?

PyNNDescent provides fast approximate nearest neighbor queries. The
ann-benchmarks <https://github.com/erikbern/ann-benchmarks>_ system puts it
solidly in the mix of top performing ANN libraries:

SIFT-128 Euclidean

.. image:: https://pynndescent.readthedocs.io/en/latest/_images/sift.png
:alt: ANN benchmark performance for SIFT 128 dataset

NYTimes-256 Angular

.. image:: https://pynndescent.readthedocs.io/en/latest/_images/nytimes.png
:alt: ANN benchmark performance for NYTimes 256 dataset

While PyNNDescent is among fastest ANN library, it is also both easy to install (pip
and conda installable) with no platform or compilation issues, and is very flexible,
supporting a wide variety of distance metrics by default:

Minkowski style metrics

euclidean
manhattan
chebyshev
minkowski

Miscellaneous spatial metrics

canberra
braycurtis
haversine

Normalized spatial metrics

mahalanobis
wminkowski
seuclidean

Angular and correlation metrics

cosine
dot
correlation
spearmanr
tsss
true_angular

Probability metrics

hellinger
wasserstein

Metrics for binary data

hamming
jaccard
dice
russelrao
kulsinski
rogerstanimoto
sokalmichener
sokalsneath
yule

and also custom user defined distance metrics while still retaining performance.

PyNNDescent also integrates well with Scikit-learn, including providing support
for the KNeighborTransformer as a drop in replacement for algorithms
that make use of nearest neighbor computations.

How to use PyNNDescent

PyNNDescent aims to have a very simple interface. It is similar to (but more
limited than) KDTrees and BallTrees in sklearn. In practice there are
only two operations -- index construction, and querying an index for nearest
neighbors.

To build a new search index on some training data data you can do something
like

.. code:: python

from pynndescent import NNDescent
index = NNDescent(data)

You can then use the index for searching (and can pickle it to disk if you
wish). To search a pynndescent index for the 15 nearest neighbors of a test data
set query_data you can do something like

.. code:: python

index.query(query_data, k=15)

and that is pretty much all there is to it. You can find more details in the
documentation <https://pynndescent.readthedocs.org>_.

Installing

PyNNDescent is designed to be easy to install being a pure python module with
relatively light requirements:

numpy
scipy
scikit-learn >= 0.22
numba >= 0.51

all of which should be pip or conda installable. The easiest way to install should be
via conda:

.. code:: bash

conda install -c conda-forge pynndescent

or via pip:

.. code:: bash

pip install pynndescent

To manually install this package:

.. code:: bash

wget https://github.com/lmcinnes/pynndescent/archive/master.zip
unzip master.zip
rm master.zip
cd pynndescent-master
python setup.py install

Help and Support

This project is still young. The documentation is still growing. In the meantime please
open an issue <https://github.com/lmcinnes/pynndescent/issues/new>_
and I will try to provide any help and guidance that I can. Please also check
the docstrings on the code, which provide some descriptions of the parameters.

License

The pynndescent package is 2-clause BSD licensed. Enjoy.

Contributing

Contributions are more than welcome! There are lots of opportunities
for potential projects, so please get in touch if you would like to
help out. Everything from code to notebooks to
examples and documentation are all equally valuable so please don't feel
you can't contribute. To contribute please fork the project <https://github.com/lmcinnes/pynndescent/issues#fork-destination-box>_ make your changes and
submit a pull request. We will do our best to work through any issues with
you and get your code merged into the main branch.

Dependency	Optional
importlib-metadata	false
joblib	false
llvmlite	false
numba	false
scikit-learn	false
scipy	false

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