ruptures is a Python library for off-line change point detection.
This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models.
ruptures focuses on ease of use by providing a well-documented and consistent interface.
In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.
- Website (and documentation): ctruong.perso.math.cnrs.fr/ruptures.
- Statement of purpose: Truong, C., Oudre, L., & Vayatis, N. (2018). ruptures: change point detection in Python. ArXiv E-Prints arXiv:1801.00826, 1–5, arXiv:1801.00826.
- Official repository: reine.cmla.ens-cachan.fr/c.truong/ruptures/repository/latest/archive.zip.
- ML Open Source repository: mloss.org/software/view/700/
- Pypi package index: pypi.python.org/pypi/ruptures
ruptures is tested to work under Python >= 3.4.
It is written in pure Python and depends on the following libraries: numpy, scipy and matplotlib (optional).
-
With pip:
pip3 install ruptures
-
From source: download the archive and run from inside the ruptures directory:
python3 setup.py install
or
python3 setup.py develop
(Please refer to the documentation for more advanced use.)
The following snippet creates a noisy piecewise constant signal, performs a penalized kernel change point detection and displays the results (alternating colors mark true regimes and dashed lines mark estimated change points).
import matplotlib.pyplot as plt
import ruptures as rpt
# generate signal
n_samples, dim, sigma = 1000, 3, 4
n_bkps = 4 # number of breakpoints
signal, bkps = rpt.pw_constant(n_samples, dim, n_bkps, noise_std=sigma)
# detection
algo = rpt.Pelt(model="rbf").fit(signal)
result = algo.predict(pen=10)
# display
rpt.display(signal, bkps, result)
plt.show()
This project is under BSD license.
BSD 2-Clause License
Copyright (c) 2017, ENS Paris-Saclay, CNRS
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