Welcome to gplearn’s documentation!¶
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
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 here should make the more relevant ones clear for your
gplearn currently supports regression through the
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.
gplearn is built on scikit-learn and a fairly recent copy (0.18.1+) is required
for installation. If you come across any issues in running or installing the
package, please submit a bug report.
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.
Next up, read some more details about what Genetic Programming is, and how it works…
- Introduction to GP
- API reference
- Advanced Use
- Release History