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Scikit-learn estimators based on projection pursuit.

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# Projection Pursuit [![Travis Status](https://travis-ci.org/pavelkomarov/projection-pursuit.svg?branch=master)](https://travis-ci.org/pavelkomarov/projection-pursuit) [![Coverage Status](https://coveralls.io/repos/github/pavelkomarov/projection-pursuit/badge.svg?branch=master&service=github)](https://coveralls.io/github/pavelkomarov/projection-pursuit?branch=master&service=github) [![Downloads](https://pepy.tech/badge/projection-pursuit)](https://pepy.tech/project/projection-pursuit)

[Documentation](https://pavelkomarov.com/projection-pursuit/skpp.html), [How it works](https://pavelkomarov.com/projection-pursuit/math.pdf).

This repository is home to a couple [scikit-learn](http://scikit-learn.org/)-compatible estimators based on Jerome Friedman’s generalizations[1] of his and Werner Stuetzle’s Projection Pursuit Regression algorithm[2][3]. A regressor capable of multivariate estimation and dimensionality reduction and a univariate classifier based on regression to a one-hot multivariate representation are included.

This repository is also meant to serve as a fairly pared-down example of how to use TravisCI, Coveralls, Sphinx, PyTest, how to deploy to PyPI and Github Pages, and how to create a Scikit-Learn Estimator that passes the sklearn checks and follows the PEP 8 style standard.

## Installation and Usage The package by itself comes with a single module containing the estimators. Before installing the module you will need numpy, scipy, scikit-learn, and matplotlib. To install the module execute:

`shell pip install projection-pursuit ` or `shell $ python setup.py install `

If the installation is successful, you should be able to execute the following in Python: `python >>> from skpp import ProjectionPursuitRegressor >>> estimator = ProjectionPursuitRegressor() >>> estimator.fit(np.arange(10).reshape(10, 1), np.arange(10)) `

Sphinx is run via continuous integration to generate [the API](https://pavelkomarov.com/projection-pursuit/skpp.html).

For a few usage examples, see the examples and benchmarks directories. For an intuition of what the learner is doing, try running viz_training_process.py. For comparisons to other learners and an intuition of why you might want to try PPR, try the benchmarks. For a deep dive in to the math and an explanation of exactly how and why this works, see [math.pdf](https://pavelkomarov.com/projection-pursuit/math.pdf).

## References

  1. Friedman, Jerome. (1985). “Classification and Multiple Regression Through Projection Pursuit.” http://www.slac.stanford.edu/pubs/slacpubs/3750/slac-pub-3824.pdf
  2. Hastie, Tibshirani, & Friedman. (2016). The Elements of Statistical Learning 2nd Ed., section 11.2.
    1. Projection pursuit regression https://en.wikipedia.org/wiki/Projection_pursuit_regression

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