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.
If you use ruptures in a scientific publication, we would appreciate citations to the following paper:
- C. Truong, L. Oudre, N. Vayatis. Selective review of offline change point detection methods. Signal Processing, 167:107299, 2020. [journal] [pdf]
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).
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With pip:
pip3 install ruptures
-
With conda:
conda install -c conda-forge ruptures
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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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