tsseg.algorithms.random package

Random — baseline detector for benchmarking.

Description

Emits uniformly random change points or state labels. Intended as a lower bound for benchmarks and for pipeline testing.

Two modes of operation:

Unsupervised — draws a random n_change_points and/or n_states.
Semi-supervised — given quantities, randomises locations/labels.

Type: state detection
Supervision: unsupervised or semi-supervised
Scope: univariate and multivariate

Parameters

Name	Type	Default	Description
`semi_supervised`	bool	`False`	Enable semi-supervised mode.
`n_change_points`	int / None	`None`	Number of change points to emit.
`n_states`	int / None	`None`	Number of distinct states.
`random_state`	int / None	`None`	Random seed for reproducibility.

Usage

from tsseg.algorithms import RandomDetector

detector = RandomDetector(n_change_points=5, random_state=42)
labels = detector.fit_predict(X)

Implementation: Origin: new code.

Reference: —

Submodules

tsseg.algorithms.random.detector module

Random detector producing synthetic states and change points.

class tsseg.algorithms.random.detector.RandomDetector(*, semi_supervised=False, n_change_points=None, n_states=None, random_state=None)[source]

Bases: BaseSegmenter

Random detector emitting change points and state labels.

The detector is primarily intended for testing pipelines. It can operate in a purely unsupervised fashion, where the number of change points and states is drawn at random (with bounds derived from available data), or in a semi-supervised mode where those quantities are given but locations and assignments remain random.

set_fit_request(*, axis: bool | None | str = '$UNCHANGED$') → RandomDetector

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:: axis (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for axis parameter in fit.
Returns:: self – The updated object.
Return type:: object

set_predict_request(*, axis: bool | None | str = '$UNCHANGED$') → RandomDetector

Configure whether metadata should be requested to be passed to the predict method.