eyadsibai / categorical-encoding

A library of sklearn compatible categorical variable encoders

Categorical Encoding Methods

A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques.

Important Links

Documentation: http://contrib.scikit-learn.org/categorical-encoding/

Encoding Methods

• Ordinal [2][3]
• One-Hot [2][3]
• Binary [5]
• Helmert Contrast [2][3]
• Sum Contrast [2][3]
• Polynomial Contrast [2][3]
• Backward Difference Contrast [2][3]
• Hashing [1]
• BaseN [6]
• LeaveOneOut [4]

Usage

The package by itself comes with a single module and an estimator. Before installing the module you will need numpy, statsmodels, and scipy.

To install the module execute:

$ python setup.py install

or

pip install category_encoders

or

conda install -c conda-forge category_encoders

To use:

import category_encoders as ce

encoder = ce.BackwardDifferenceEncoder(cols=[...])
encoder = ce.BinaryEncoder(cols=[...])
encoder = ce.HashingEncoder(cols=[...])
encoder = ce.HelmertEncoder(cols=[...])
encoder = ce.OneHotEncoder(cols=[...])
encoder = ce.OrdinalEncoder(cols=[...])
encoder = ce.SumEncoder(cols=[...])
encoder = ce.PolynomialEncoder(cols=[...])
encoder = ce.BaseNEncoder(cols=[...])
encoder = ce.LeaveOneOutEncoder(cols=[...])

All of these are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. If the cols parameter isn't passed, every non-numeric column will be encoded. Please see the docs for transformer-specific configuration options.

Examples

In the examples directory, there is an example script used to benchmark different encoding techniques on various datasets.

The datasets used in the examples are car, mushroom, and splice datasets from the UCI dataset repository, found here:

datasets

License

BSD 3-Clause

References:

1. Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML.
2. Contrast Coding Systems for categorical variables. UCLA: Statistical Consulting Group. from https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/.
3. Gregory Carey (2003). Coding Categorical Variables, from http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
4. Strategies to encode categorical variables with many categories. from https://www.kaggle.com/c/caterpillar-tube-pricing/discussion/15748#143154.
5. Beyond One-Hot: an exploration of categorical variables. from http://www.willmcginnis.com/2015/11/29/beyond-one-hot-an-exploration-of-categorical-variables/
6. BaseN Encoding and Grid Search in categorical variables. from http://www.willmcginnis.com/2016/12/18/basen-encoding-grid-search-category_encoders/