# Projection Pursuit

This repository is home to a couple scikit-learn-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:

`pip install projection-pursuit`

or

`$ python setup.py install`

If the installation is successful, you should be able to execute the following in 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.

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`

.

## References

- Friedman, Jerome. (1985). "Classification and Multiple Regression Through Projection Pursuit." http://www.slac.stanford.edu/pubs/slacpubs/3750/slac-pub-3824.pdf
- Hastie, Tibshirani, & Friedman. (2016).
*The Elements of Statistical Learning 2nd Ed.*, section 11.2. - (2017)
*Projection pursuit regression*https://en.wikipedia.org/wiki/Projection_pursuit_regression