Metadata-Version: 1.1
Name: autofeat
Version: 0.1.1
Summary: Automatic Feature Engineering and Selection Linear Regression Model
Home-page: https://github.com/cod3licious/autofeat
Author: Franziska Horn
Author-email: cod3licious@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Description: # `autofeat` library
        ### A Linear Regression Model with Automatic Feature Engineering and Selection
        
        This library contains the `AutoFeatRegression` model with a similar interface as the `scikit-learn` models:
        - `fit()` function to fit the model parameters
        - `predict()` function to predict the target variable given the input
        - `score()` function to calculate the goodness of the fit (R^2 value)
        - `fit_transform()` and `transform()` functions, which extend 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_transform()` or they can be given an already transformed dataframe.
        
        The [notebook](https://github.com/cod3licious/autofeat/blob/master/autofeat_test.ipynb) contains a simple usage example - try it out! :)
        
        For further details on the model and implementation please refer to the [paper](https://arxiv.org/abs/1901.07329)  - 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:1901.07329},
            }
        ```
        
        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`
        
        ## Acknowledgments
        
        This project was made possible thanks to support by the [BASF](https://www.basf.com).
        
Keywords: feature engineering feature selection linear regression
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
