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sktime

EAgglo

EAgglo

class EAgglo(member=None, alpha=1.0, penalty=None)[source]

Hierarchical agglomerative estimation of multiple change points.

E-Agglo is a non-parametric clustering approach for multivariate timeseries[R7d39cf41170f-1]_, where neighboring segments are sequentially merged_ to maximize a goodness-of-fit statistic. Unlike most general purpose agglomerative clustering algorithms, this procedure preserves the time ordering of the observations.

This method can detect distributional change within an independent sequence, and does not make any distributional assumptions (beyond the existence of an alpha-th moment). Estimation is performed in a manner that simultaneously identifies both the number and locations of change points.

Parameters:
memberarray_like (default=None)

Assigns points to the initial cluster membership, therefore the first dimension should be the same as for data. If None it will be initialized to dummy vector where each point is assigned to separate cluster.

alphafloat (default=1.0)

Fixed constant alpha in (0, 2] used in the divergence measure, as the alpha-th absolute moment, see equation (4) in [1].

penaltystr or callable or None (default=None)

Function that defines a penalization of the sequence of goodness-of-fit statistic, when overfitting is a concern. If None not penalty is applied. Could also be an existing penalty name, either len_penalty or mean_diff_penalty.

Attributes:
merged_array_like

2D array_like outlining which clusters were merged_ at each step.

gof_float

goodness-of-fit statistic for current clsutering.

cluster_array_like

1D array_like specifying which cluster each row of input data X belongs to.

Notes

Based on the work from [1].

References

[1] (1,2)

Matteson, David S., and Nicholas A. James. “A nonparametric approach for

multiple change point analysis of multivariate data.” Journal of the American Statistical Association 109.505 (2014): 334-345.

[2]

James, Nicholas A., and David S. Matteson. “ecp: An R package for

nonparametric multiple change point analysis of multivariate data.” arXiv preprint arXiv:1309.3295 (2013).

Examples

>>> from sktime.detection.datagen import piecewise_normal_multivariate
>>> X = piecewise_normal_multivariate(means=[[1, 3], [4, 5]], lengths=[3, 4],
... random_state = 10)
>>> from sktime.detection.eagglo import EAgglo
>>> model = EAgglo()
>>> model.fit_transform(X)
array([0, 0, 0, 1, 1, 1, 1])

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()

Test parameters.

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.