binsel 0.1.2

Feature selection for Hard Voting classifier

binsel

Feature selection for Hard Voting classifier.

Table of Contents

• Installation
• Usage
• Commands
• Support
• Contributing

Installation

The binsel git repo is available as PyPi package

pip install binsel

Usage

Check the binsel_hardvote example folder for notebooks.

Algorithm

The task is to select e.g. n_select=3 binary features from a pool of many binary features. These binary features might be the prediction of binary classifiers. The selected binary features are then combined into one hard-voting classifier.

A voting classifier should have the following properties

• each voter (a binary feature) should be highly correlated to the target variable
• the selected binary features should be uncorrelated.

The algorithm works as follows

1. Generate multiple correlation matrices by bootstrapping (see korr.bootcorr). This includes corr(X_i, X_j) as well as corr(Y, X_i) computation. Also store the oob samples for evaluation.
2. For each correlation matrix do ... a. Preselect the i* with the highest abs(corr(Y, X_i)) estimates (e.g. pick the n_pre=? highest absolute correlations) b. Slice a correlation matrix corr(X_i*, X_j*) and find the least correlated combination of n_select=? features. (see korr.mincorr) c. Compute the out-of-bag (OOB) performance (see step 1) of the hard-voter with the selected n_select=? binary features
3. Select the binary feature combination with the best OOB performance as final model.

Commands

• Check syntax: flake8 --ignore=F401
• Run Unit Tests: python -W ignore -m unittest discover
• Remove .pyc files: find . -type f -name "*.pyc" | xargs rm
• Remove __pycache__ folders: find . -type d -name "__pycache__" | xargs rm -rf
• Upload to PyPi with twine: python setup.py sdist && twine upload -r pypi dist/*

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.