tsseg.algorithms.bottomup package

Bottom-Up segmentation — agglomerative merging of segments.

Description

Contrary to binary segmentation (top-down), bottom-up segmentation starts with a fine initial partition along a regular grid and successively merges the two most similar contiguous segments until a stopping criterion is met. The procedure is generous: it begins with many change points and removes the least significant ones.

Complexity is \(O(n\log n)\) where n is the number of samples, and the algorithm supports any cost function available in the vendored ruptures library.

Type: change point detection

Supervision: unsupervised or semi-supervised

Scope: univariate and multivariate

Complexity: \(O(n\log n)\)

Parameters

Name	Type	Default	Description
n_cps	int / None	None	Number of change points. When None, uses penalty or epsilon.
model	str	"l2"	Ruptures cost model ("l2", "l1", "rbf", etc.).
min_size	int	2	Minimum segment length.
jump	int	5	Grid step for candidate change points.
penalty	float / None	10	Penalty value. Mutually exclusive with n_cps / epsilon.
epsilon	float / None	None	Reconstruction budget. Mutually exclusive with n_cps / penalty.
cost_params	dict / None	None	Extra parameters passed to the cost function.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import BottomUpDetector

detector = BottomUpDetector(model="l2", n_cps=3)
labels = detector.fit_predict(X)

# Penalty-based stopping
detector = BottomUpDetector(model="l2", penalty=10)
labels = detector.fit_predict(X)

Implementation: Vendored from ruptures v1.1.8. BSD 2-Clause.

Reference: Keogh, Chu, Hart & Pazzani (2001), ICDM; Fryzlewicz (2007), JASA.

Submodules

tsseg.algorithms.bottomup.detector module

Module contents