autofeat 0.1

autofeat - Linear Regression Model with Automatic Feature Engineering and Selection
===================================================================================

This library contains the `AutoFeatRegression` model with a similar interface as the `scikit-learn` models.
It has a `fit()` function to fit the model parameters, a `predict()` function to predict the target variable given the input, and a `score()` function to calculate the goodness of the fit (R^2 value). Additionally, the model has a `fit_transform()` and a `transform()` function, which extends the given data by the additional features that were engineered and selected by the model.
When calling the `fit()` function, internally the `fit_transform()` function will be called, so if you're planing to call `transform()` on the same data anyways, just call `fit_transform()` right away. `transform()` is mostly useful if you've split your data into training and test data and did not call `fit_transform()` on your whole dataset. The `predict()` and `score()` functions can be either be given data in the format of the original dataframe that was used when calling `fit()`/`fit_transformed()` or they can be given an already transformed dataframe.

The notebook contains a simple usage example - try it out! :)

For further details on the model and implementation please refer to the paper_ - and of course if any of this code was helpful for your research, please consider citing it: ::
@article{horn2019autofeat,
author = {Horn, Franziska and Pack, Robert and Rieger, Michael},
title = {The autofeat Python Library for Automatic Feature Engineering and Selection},
year = {2019},
journal = {arXiv preprint arXiv:1901.xxxxx},
}

.. _paper: https://arxiv.org/abs/1901.xxxxx

The code is intended for research purposes.

If you have any questions please don't hesitate to send me an `email <mailto:cod3licious@gmail.com>`_ and of course if you should find any bugs or want to contribute other improvements, pull requests are very welcome!

Installation
------------
You either download the code from here and include the autofeat folder in your ``$PYTHONPATH`` or install (the library components only) via pip:

``$ pip install autofeat``

The library requires Python 3! Other dependencies: `numpy`, `pandas`, `scikit-learn`, `sympy`, and `pint`