Feature selection for Hard Voting classifier
Project description
binsel
Feature selection for Hard Voting classifier.
Table of Contents
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
- Generate multiple correlation matrices by bootstrapping (see
korr.bootcorr
). This includescorr(X_i, X_j)
as well ascorr(Y, X_i)
computation. Also store the oob samples for evaluation. - For each correlation matrix do ...
a. Preselect the
i*
with the highestabs(corr(Y, X_i))
estimates (e.g. pick then_pre=?
highest absolute correlations) b. Slice a correlation matrixcorr(X_i*, X_j*)
and find the least correlated combination ofn_select=?
features. (seekorr.mincorr
) c. Compute the out-of-bag (OOB) performance (see step 1) of the hard-voter with the selectedn_select=?
binary features - Select the binary feature combination with the best OOB performance as final model.
Commands
Install a virtual environment
python3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
pip3 install jupyterlab twine
(If your git repo is stored in a folder with whitespaces, then don't use the subfolder .venv
. Use an absolute path without whitespaces.)
Python commands
- Jupyter for the examples:
jupyter lab
- Check syntax:
flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
- Run Unit Tests:
python -W ignore -m unittest discover
- Upload to PyPi with twine:
python setup.py sdist && twine upload -r pypi dist/*
Clean up
find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .venv
Support
Please open an issue for support.
Contributing
Please contribute using Github Flow. Create a branch, add commits, and open a pull request.
Project details
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