Chapydette

Chapydette contains fast Cython implementations of kernel-based change-point detection algorithms and feature generation methods. There are currently two algorithms implemented:

The kernel change-point algorithm of Harchaoui and Cappé (2007), which detects a fixed number of change points in a sequence of observations.
The change-point analysis algorithm of Harchaoui, Bach, and Moulines (2009), which detects the presence of and estimates the location of a single change point based on the Maximum Mean Discrepancy (Gretton et al., 2012).

See the full documentation for more details. The next release will include the following addition:

A data-driven model selection procedure based on Arlot, Celisse, and Harchaoui (2019), which allows one to automatically select the number of change points.

Installation

This code was written using Python 3.7 and requires Cython, Faiss, Numpy, PyTorch, Scipy, and Scikit-learn. If you are using Anaconda you can install these via

conda install cython numpy scipy scikit-learn
conda install pytorch torchvision cpuonly -c pytorch
conda install faiss-cpu -c pytorch

If you are using a Mac, you should also install llvm, gcc, and libgcc:
conda install llvm gcc libgcc
To then install this package, run
python setup.py install
from the installation directory.

This code has not been tested on a Windows operating system.

The installer will check whether you have the remainder of the required dependencies. There are two optional dependencies:

Yael http://yael.gforge.inria.fr/
Pomegranate https://pomegranate.readthedocs.io/en/latest/index.html

If you have them installed and use them, they may greatly speed up the feature generation code.

Layout

There are two tasks that Chapydette can perform:

Feature generation
Change-point estimation

The functions for the former are contained in feature_generation.py, while the functions for the latter are in cp_estimation.py. Examples of how to use Chapydette are provided in the Jupyter notebook in the examples directory.

Authors

Corinne Jones
Zaid Harchaoui

License

This code has a GPLv3 license.

References

S. Arlot, A. Celisse, and Z. Harchaoui, "A kernel multiple change-point algorithm via model selection," Journal of Machine Learning Research (to appear).
A. Gretton, K.M. Borgwardt, M.J. Rasch, B. Schölkopf, and A. Smola, "A kernel two-sample test," Journal of Machine Learning Research, vol. 13, pp. 723–773, 2012.
Z. Harchaoui and O. Cappé, "Retrospective mutiple change-point estimation with kernels," in IEEE Workshop on Statistical Signal Processing, 2007, pp. 768–772.
Z. Harchaoui, F.R. Bach, and E. Moulines, "Kernel change-point analysis," in Advances in Neural Information Processing Systems, 2008, pp. 609–616.

kronerte / chapydette