# gokinjo
A feature extraction library based on k-nearest neighbor algorithm in Python - k-NN based feature has experience of being used in 1st place solution of Kaggle competition
Switchable backend of k-NN algorithm - [scikit-learn](https://github.com/scikit-learn/scikit-learn) (default) - [annoy](https://github.com/spotify/annoy)
FYI: ‘gokinjo’ is meant neighborhood in japanese.
### Prerequisite
Python 3.6 or later
setuptools >= 30.0.3.0
### How to install
#### From PyPI
`bash $ pip install gokinjo `
##### With annoy backend
`bash $ pip install "gokinjo[annoy]" `
#### From source code
`bash $ pip install git+https://github.com/momijiame/gokinjo.git `
### Usage example
Please see [examples](https://github.com/momijiame/gokinjo/tree/master/examples) in GitHub repository.
### How to setup of development environment
`bash $ pip install -e ".[develop]" $ pytest `
### References
The competition which k-NN feature was used on 1st place solution - https://www.kaggle.com/c/otto-group-product-classification-challenge/discussion/14335
R implementation - https://github.com/davpinto/fastknn/blob/master/R/extract.R
Another Python implementation - https://github.com/upura/knnFeat
1 maintainer
Metadata
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Release files for gokinjo 0.1.0
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