Metadata-Version: 1.0
Name: projection-pursuit
Version: 0.9
Summary: Scikit-learn estimators based on projection pursuit.
Home-page: https://github.com/pavelkomarov/projection-pursuit
Author: Pavel Komarov
Author-email: pvlkmrv@gmail.com
License: BSD
Description: # Projection Pursuit
        [![Travis Status](https://travis-ci.org/pavelkomarov/projection-pursuit.svg?branch=master)](https://travis-ci.org/pavelkomarov/projection-pursuit)
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        [![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.
        3. (2017) *Projection pursuit regression* https://en.wikipedia.org/wiki/Projection_pursuit_regression
        
Platform: UNKNOWN
