vfi 0.1.1

How to use VFI

The vfi package inherits from sklearn classes, and thus drops in neatly next to other sklearn classifiers with an identical calling API. Similarly it supports input in a variety of formats: an array (or pandas dataframe) of shape (num_samples x num_features).

import vfi
from sklearn.datasets import load_iris

data, target = load_iris(return_X_y=True)

model = vfi.VFI()
model.fit(data, target)

Installing

PyPI install, presuming you have an up to date pip:

pip install vfi

If pip is having difficulties pulling the dependencies then we’d suggest to first upgrade pip to at least version 10 and try again:

pip install --upgrade pip
pip install vfi

Otherwise install the dependencies manually using anaconda followed by pulling vfi from pip:

conda install numpy scipy
conda install scikit-learn
pip install vfi

For a manual install of the latest code directly from GitHub:

pip install --upgrade git+https://github.com/chkoar/vfi.git#egg=vfi

Alternatively download the package, install requirements, and manually run the installer:

wget https://github.com/chkoar/vfi/archive/master.zip
unzip master.zip
rm master.zip
cd vfi-master

pip install -r requirements.txt

python setup.py install

Running the Tests

The package tests can be run after installation using the command:

pytest vfi --cov

Python Version

The vfi package supports only Python 3.

Contributing

We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.

Licensing

The vfi package is MIT licensed. Enjoy.