DetectorAsTransformer

DetectorAsTransformer

class DetectorAsTransformer(estimator)[source]

Use an anomaly, changepoint detector, segmentation estimator as a transformer.

This adapter is used in coercions, when passing an detector to a transformer slot.

The transformation is series-to-primitives, transforming a time series into its cluster assignment.

The adapter dispatches BaseTransformer.transform to BaseDetector.transform.

Parameters:

estimatorsktime detector, i.e., estimator inheriting from BaseDetector

this is a “blueprint” clusterer, state does not change when fit is called

Attributes:

estimator_sktime detector, clone of estimator

this clone is fitted in the pipeline when fit is called

Examples

>>> from sktime.detection.compose import DetectorAsTransformer
>>> from sktime.detection.lof import SubLOF
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> X = _make_hierarchical()
>>> detector = SubLOF.create_test_instance()
>>> t = DetectorAsTransformer(detector)
>>> t.fit(X)
DetectorAsTransformer(...)
>>> Xt = t.transform(X)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.