|pypi| |docs| |travis| |license|
.. begin_badges
.. |docs| image:: https://readthedocs.org/projects/n2/badge/?version=latest :target: https://n2.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status
.. |pypi| image:: https://img.shields.io/pypi/v/n2.svg?style=flat :target: https://pypi.python.org/pypi/n2 :alt: Latest Version
.. |travis| image:: https://travis-ci.org/kakao/n2.svg?branch=master :target: https://travis-ci.org/kakao/n2 :alt: Build Status
.. |license| image:: https://img.shields.io/github/license/kakao/n2 :target: https://github.com/kakao/n2/blob/master/LICENSE :alt: Apache-License 2.0
.. end_badges
.. begin_intro
Lightweight approximate N\ earest N\ eighbor algorithm library written in C++ (with Python/Go bindings).
N2 stands for two N's, which comes from 'Approximate N
\ earest
N
\ eighbor Algorithm'.
.. end_intro
.. begin_background
Before N2, there has been other great approximate nearest neighbor
libraries such as Annoy
_ and NMSLIB
_. However, each of them had
different strengths and weaknesses regarding usability, performance,
and etc. So, N2 has been developed aiming to bring the strengths of
existing aKNN libraries and supplement their weaknesses.
.. end_background
.. begin_features
.. end_features
.. Please manually sync the table below with that of docs/index.rst.
+-----------+-------------+--------------------------------------------------------------------+
| Metric | Definition | d(p, q) |
+-----------+-------------+--------------------------------------------------------------------+
| "angular" | 1 - cosθ | 1 - {sum(p :sub:i
· q :sub:i
) / |
| | | sqrt(sum(p :sub:i
· p :sub:i
) · sum(q :sub:i
· q :sub:i
))} |
+-----------+-------------+--------------------------------------------------------------------+
| "L2" | squared L2 | sum{(p :sub:i
- q :sub:i
) :sup:2
} |
+-----------+-------------+--------------------------------------------------------------------+
| "dot" | dot product | sum(p :sub:i
· q :sub:i
) |
+-----------+-------------+--------------------------------------------------------------------+
.. begin_metric_detail
N2 supports three distance metrics. For "angular" and "L2", d (distance) is defined such that the closer the vectors are, the smaller d is. However for "dot", d is defined such that the closer the vectors are, the larger d is. You may be wondering why we defined and implemented "dot" metric as plain dot product and not as (1 - dot product). The rationale for this decision was to allow users to directly interpret the d value returned from Hnsw search function as a dot product value.
.. end_metric_detail
.. code:: bash
$ pip install n2
.. code:: python
import numpy as np
from n2 import HnswIndex
N, dim = 10240, 20
samples = np.arange(N * dim).reshape(N, dim)
index = HnswIndex(dim)
for sample in samples:
index.add_data(sample)
index.build(m=5, n_threads=4)
print(index.search_by_id(0, 10))
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Visit n2.readthedocs.io
_ for full documentation.
The documentation site explains the following contents in detail.
Installation Guide
_
User Guide with Basic Examples
Python Interface
_C++ Interface
_Go Interface
_Benchmark
_
ann-benchmarks.com
.
Note that N2 version 0.1.6 is used in ann-benchmarks.com
(last checked on October 8th, 2020)
and we are continuing our efforts to improve N2 performance.Index Build Time
|image0|
Search Speed
|image1|
Memory Usage
|image2|
.. begin_footnote
References
------------------------------------------------------------------------------
- Y\. A. Malkov and D. A. Yashunin, "Efficient and robust approximate
nearest neighbor search using hierarchical navigable small world
graphs," CoRR, vol. abs/1603.09320, 2016. [Online].
Available: http://arxiv.org/abs/1603.09320
- NMSLIB: https://github.com/nmslib/nmslib
- Annoy: https://github.com/spotify/annoy
License
------------------------------------------------------------------------------
This software is licensed under the `Apache 2 license`_, quoted below.
Copyright 2017 Kakao Corp. http://www.kakaocorp.com
Licensed under the Apache License, Version 2.0 (the “License”); you may
not use this project except in compliance with the License. You may
obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0.
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
.. _Apache 2 license: https://github.com/kakao/n2/blob/master/LICENSE
.. _Annoy: https://github.com/spotify/annoy
.. _NMSLIB: https://github.com/nmslib/nmslib
.. _Installation Guide: https://n2.readthedocs.io/en/latest/install.html
.. _Python Interface: https://n2.readthedocs.io/en/latest/python_api.html
.. _C++ Interface: https://n2.readthedocs.io/en/latest/cpp_api.html
.. _Go Interface: https://n2.readthedocs.io/en/latest/go_api.html
.. _Benchmark: https://n2.readthedocs.io/en/latest/benchmark.html
.. _n2.readthedocs.io: https://n2.readthedocs.io/en/latest/
.. _ann-benchmarks.com: http://ann-benchmarks.com/
.. |image0| image:: docs/imgs/build_time/build_time_threads.png
.. |image1| image:: docs/imgs/search_time/search_time.png
.. |image2| image:: docs/imgs/mem/memory_usage.png
.. end_footnote