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        "author": "Ulf Hamster",
        "author_email": "554c46@gmail.com",
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        "description": "[![Build Status](https://travis-ci.org/kmedian/binsel.svg?branch=master)](https://travis-ci.org/kmedian/binsel)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/kmedian/binsel/master?urlpath=lab)\n\n# binsel\nFeature selection for Hard Voting classifier.\n\n\n## Table of Contents\n* [Installation](#installation)\n* [Usage](#usage)\n* [Commands](#commands)\n* [Support](#support)\n* [Contributing](#contributing)\n\n\n## Installation\nThe `binsel` [git repo](http://github.com/kmedian/binsel) is available as [PyPi package](https://pypi.org/project/binsel)\n\n```\npip install binsel\n```\n\n\n## Usage\nCheck the [`binsel_hardvote` example](http://github.com/kmedian/binsel/examples/binsel_hardvote.ipynb) folder for notebooks.\n\n\n## Algorithm\nThe task is to select e.g. `n_select=3` binary features from a pool of many binary features.\nThese binary features might be the prediction of binary classifiers. \nThe selected binary features are then combined into one hard-voting classifier.\n\nA voting classifier should have the following properties\n\n* each voter (a binary feature) should be highly correlated to the target variable\n* the selected binary features should be uncorrelated.\n\nThe algorithm works as follows \n\n1. Generate multiple correlation matrices by bootstrapping (see [`korr.bootcorr`](https://github.com/kmedian/korr/blob/master/korr/bootcorr.py)). This includes `corr(X_i, X_j)` as well as `corr(Y, X_i)` computation. Also store the oob samples for evaluation.\n2. For each correlation matrix do ...\n    a. Preselect the `i*` with the highest `abs(corr(Y, X_i))` estimates (e.g. pick the `n_pre=?` highest absolute correlations)\n    b. Slice a correlation matrix `corr(X_i*, X_j*)` and find the least correlated combination of `n_select=?` features. (see [`korr.mincorr`](https://github.com/kmedian/korr/blob/master/korr/mincorr.py))\n    c. Compute the out-of-bag (OOB) performance (see step 1) of the hard-voter with the selected `n_select=?` binary features\n3. Select the binary feature combination with the best OOB performance as final model.\n\n\n## Commands\n* Check syntax: `flake8 --ignore=F401`\n* Run Unit Tests: `python -W ignore -m unittest discover`\n* Remove `.pyc` files: `find . -type f -name \"*.pyc\" | xargs rm`\n* Remove `__pycache__` folders: `find . -type d -name \"__pycache__\" | xargs rm -rf`\n* Upload to PyPi with twine: `python setup.py sdist && twine upload -r pypi dist/*`\n\n\n## Support\nPlease [open an issue](https://github.com/kmedian/binsel/issues/new) for support.\n\n\n## Contributing\nPlease contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/kmedian/binsel/compare/).",
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