roerich 0.1

A change point detection package

Roerich

roerich is a library for online and offline change point detection. Currently, it implements algorithms based on direct density estimation from this article:

Hushchyn, Mikhail, and Andrey Ustyuzhanin. ‘Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation’. ArXiv:2001.06386 [Cs, Stat], Jan. 2020. arXiv.org, http://arxiv.org/abs/2001.06386.

Dependencies and install

Basic usage

Make sure that your data has a shape (seq_len, n_dims) or you can generate synthetic data:

import numpy as np
import roerich

X, label = roerich.generate_dataset(period=2000, N_tot=20000)
T = np.arange(len(X))

You can use two algorithms: CLF or RuLSIF:

cpd = roerich.OnlineNNClassifier(net='default', scaler="default", metric="KL_sym",
                  periods=1, window_size=10, lag_size=500, step=10, n_epochs=10,
                  lr=0.1, lam=0.0001, optimizer="Adam"
                 )

# Detect change points
score, peaks = cpd.predict(X)

For data visualization use:

roerich.display(X, T, label, score, T, peaks)

Changelog

See the changelog for a history of notable changes to roerich.

Thanks to all our contributors

License

BSD 2-Clause License

Copyright (c) 2017, ENS Paris-Saclay, CNRS
All rights reserved.

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modification, are permitted provided that the following conditions are met:

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  this list of conditions and the following disclaimer in the documentation
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