Genetic Programming in Python, with a scikit-learn inspired API
Welcome to gplearn!
gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API.
While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.
Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.
gplearn retains the familiar scikit-learn fit/predict API and works with the existing scikit-learn pipeline and grid search modules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak, reading the documentation should make the more relevant ones clear for your problem.
gplearn currently supports regression through the SymbolicRegressor as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification. Future versions of the package will expand this class to support more complicated multi-target classification problems, and much more is planned too.
I hope you get some excellent results from using gplearn! If you do, please drop me a line on my blog about how you used it.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|gplearn-0.3.0-py2.py3-none-any.whl (33.5 kB) Copy SHA256 hash SHA256||Wheel||2.7||Nov 23, 2017|
|gplearn-0.3.0.tar.gz (27.8 kB) Copy SHA256 hash SHA256||Source||None||Nov 23, 2017|