nanopq

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Pure python implementation of product quantization for nearest neighbor search

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최종 버전 다운로드 (.zip)

- - build.yml
  - publish.yml
- - api.rst
  - tutorial.rst
- conf.py
- index.rst
- make.bat
- Makefile
- requirements.txt
- __init__.py
- convert_faiss.py
- opq.py
- pq.py
- __init__.py
- test_convert_faiss.py
- test_opq.py
- test_pq.py
.gitignore
.readthedocs.yaml
LICENSE
Makefile
MANIFEST.in
mypy.ini
pyproject.toml
README.md
requirements-dev.txt
setup.py

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# nanopq

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Nano Product Quantization (nanopq): a vanilla implementation of Product Quantization (PQ) and Optimized Product Quantization (OPQ) written in pure python without any third party dependencies.

## Installing
You can install the package via pip. This library works with Python 3.5+ on linux.
```
pip install nanopq
```

## [Documentation](https://nanopq.readthedocs.io/en/latest/index.html)
- [Tutorial](https://nanopq.readthedocs.io/en/latest/source/tutorial.html)
- [API](https://nanopq.readthedocs.io/en/latest/source/api.html)

## Example

```python
import nanopq
import numpy as np

N, Nt, D = 10000, 2000, 128
X = np.random.random((N, D)).astype(np.float32)  # 10,000 128-dim vectors to be indexed
Xt = np.random.random((Nt, D)).astype(np.float32)  # 2,000 128-dim vectors for training
query = np.random.random((D,)).astype(np.float32)  # a 128-dim query vector

# Instantiate with M=8 sub-spaces
pq = nanopq.PQ(M=8)

# Train codewords
pq.fit(Xt)

# Encode to PQ-codes
X_code = pq.encode(X)  # (10000, 8) with dtype=np.uint8

# Results: create a distance table online, and compute Asymmetric Distance to each PQ-code 
dists = pq.dtable(query).adist(X_code)  # (10000, ) 
```

## Author
- [Yusuke Matsui](http://yusukematsui.me)

## Contributors
- [@Hiroshiba](https://github.com/Hiroshiba) fixed a bug of importlib ([#3](https://github.com/matsui528/nanopq/pull/3))
- [@calvinmccarter](https://github.com/calvinmccarter) implemented parametric initialization for OPQ ([#14](https://github.com/matsui528/nanopq/pull/14))
- [@de9uch1](https://github.com/de9uch1) exntended the interface to the faiss so that OPQ can be handled ([#19](https://github.com/matsui528/nanopq/pull/19))
- [@mpskex](https://github.com/mpskex) implemented (1) initialization of clustering and (2) dot-product for computation ([#24](https://github.com/matsui528/nanopq/pull/24))
- [@lsb](https://github.com/lsb) fixed a typo ([#26](https://github.com/matsui528/nanopq/pull/26))
- [@asukaminato0721](https://github.com/asukaminato0721) used Literal for string inputs ([#42](https://github.com/matsui528/nanopq/pull/42))

## Reference
- [H. Jegou, M. Douze, and C. Schmid, "Product Quantization for Nearest Neighbor Search", IEEE TPAMI 2011](https://ieeexplore.ieee.org/document/5432202/) (the original paper of PQ)
- [T. Ge, K. He, Q. Ke, and J. Sun, "Optimized Product Quantization", IEEE TPAMI 2014](https://ieeexplore.ieee.org/document/6678503/) (the original paper of OPQ)
- [Y. Matsui, Y. Uchida, H. Jegou, and S. Satoh, "A Survey of Product Quantization", ITE MTA 2018](https://www.jstage.jst.go.jp/article/mta/6/1/6_2/_pdf/) (a survey paper of PQ) 
- [PQ in faiss](https://github.com/facebookresearch/faiss/wiki/Faiss-building-blocks:-clustering,-PCA,-quantization#pq-encoding--decoding) (Faiss contains an optimized implementation of PQ. [See the difference to ours here](https://nanopq.readthedocs.io/en/latest/source/tutorial.html#difference-from-pq-in-faiss))
- [Rayuela.jl](https://github.com/una-dinosauria/Rayuela.jl) (Julia implementation of several encoding algorithms including PQ and OPQ)
- [PQk-means](https://github.com/DwangoMediaVillage/pqkmeans) (clustering on PQ-codes. The implementation of nanopq is compatible to [that of PQk-means](https://github.com/DwangoMediaVillage/pqkmeans/blob/master/tutorial/1_pqkmeans.ipynb))
- [Rii](https://github.com/matsui528/rii) (IVFPQ-based ANN algorithm using nanopq)
- [Product quantization in Faiss and from scratch](https://www.youtube.com/watch?v=PNVJvZEkuXo) (Related tutorial)

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