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https://github.com/pavelkomarov/projection-pursuit
An implementation of multivariate projection pursuit regression and univariate classification
https://github.com/pavelkomarov/projection-pursuit
classification coveralls friedman machine-learning projection-pursuit regression scikit-learn sphinx travis-ci
Last synced: about 16 hours ago
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An implementation of multivariate projection pursuit regression and univariate classification
- Host: GitHub
- URL: https://github.com/pavelkomarov/projection-pursuit
- Owner: pavelkomarov
- License: bsd-3-clause
- Created: 2018-01-29T00:50:25.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-09-12T20:21:09.000Z (4 months ago)
- Last Synced: 2025-01-15T22:20:26.193Z (8 days ago)
- Topics: classification, coveralls, friedman, machine-learning, projection-pursuit, regression, scikit-learn, sphinx, travis-ci
- Language: Python
- Homepage: https://pavelkomarov.com/projection-pursuit/skpp.html
- Size: 1.27 MB
- Stars: 33
- Watchers: 5
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION.md
Awesome Lists containing this project
README
# Projection Pursuit
[](https://github.com/pavelkomarov/projection-pursuit/actions)
[](https://coveralls.io/github/pavelkomarov/projection-pursuit?branch=master&service=github)
[](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 Github Actions, 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