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