LeftSTAMPi

class LeftSTAMPi(window_size: int = 3, n_init_train: int = 3, normalize: bool = True, p: float = 2.0, k: int = 1)

LeftSTAMPi anomaly detector.

LeftSTAMPi [1] calculates the left matrix profile of a time series, which is the distance to the nearest neighbor of all already observed subsequences (i.e. all preceding subsequences) in the time series, in an incremental manner. The matrix profile is then used to calculate the anomaly score for each time point. The larger the distance to the nearest neighbor, the more anomalous the time point is.

LeftSTAMPi supports univariate time series only.

Capabilities

Input data format	univariate
Output data format	anomaly scores
Learning Type	unsupervised

Parameters:

window_sizeint, default=3

Size of the sliding window. Defaults to the minimal possible value of 3.

n_init_train: int, default=3

The number of points used to init the matrix profile. n_init_train must not be smaller than window_size. The discord will not be found in this segment.

normalizebool, default=True

Whether to normalize the windows before computing the distance.

pfloat, default=2.0

The p-norm to use for the distance calculation.

kint, default=1

The number of top distances to return.

References

[1]

Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh: “Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets.”, In Proceedings of the International Conference on Data Mining (ICDM), 1317–1322. doi: 10.1109/ICDM.2016.0179

Examples

Calculate the anomaly score for the complete time series at once. Internally,this is applying the incremental approach outlined below.

>>> import numpy as np 
>>> from aeon.anomaly_detection import LeftSTAMPi  
>>> X = np.random.default_rng(42).random((10))  
>>> detector = LeftSTAMPi(window_size=3, n_init_train=3)  
>>> detector.fit_predict(X)  
array([0.        , 0.        , 0.        , 0.07042306, 0.15989868,
       0.68912499, 0.75398303, 0.89696118, 0.5516023 , 0.69736132])

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X[, y, axis])	Fit time series anomaly detector to X.
fit_predict(X[, y, axis])	Fit time series anomaly detector and find anomalies for X.
get_class_tag(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
get_class_tags()	Get class tags from estimator class and all its parent classes.
get_fitted_params([deep])	Get fitted parameters.
get_metadata_routing()	Sklearn metadata routing.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
predict(X[, axis])	Find anomalies in X.
reset([keep])	Reset the object to a clean post-init state.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

Instance of type(self), clone of self (see above)

fit(X, y=None, axis=1)

Fit time series anomaly detector to X.

If the tag fit_is_empty is true, this just sets the is_fitted tag to true. Otherwise, it checks self can handle X, formats X into the structure required by self then passes X (and possibly y) to _fit.

Parameters:

Xone of aeon.base._base_series.VALID_SERIES_INPUT_TYPES

The time series to fit the model to. A valid aeon time series data structure. See aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types.

yone of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None

The target values for the time series. A valid aeon time series data structure. See aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types.

axisint

The time point axis of the input series if it is 2D. If axis==0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis==1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints).

Returns:

BaseAnomalyDetector

The fitted estimator, reference to self.

fit_predict(X, y=None, axis=1) → ndarray

Fit time series anomaly detector and find anomalies for X.

Parameters:

Xone of aeon.base._base_series.VALID_SERIES_INPUT_TYPES

The time series to fit the model to. A valid aeon time series data structure. See aeon.base._base_series.VALID_INPUT_TYPES for aeon supported types.

yone of aeon.base._base_series.VALID_SERIES_INPUT_TYPES, default=None

The target values for the time series. A valid aeon time series data structure. See aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types.

axisint, default=1

The time point axis of the input series if it is 2D. If axis==0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis==1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints).

Returns:

np.ndarray

A boolean, int or float array of length len(X), where each element indicates whether the corresponding subsequence is anomalous or its anomaly score.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

get_metadata_routing()

Sklearn metadata routing.

Not supported by aeon estimators.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

get_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

predict(X, axis=1) → ndarray

Find anomalies in X.

Parameters:

Xone of aeon.base._base_series.VALID_SERIES_INPUT_TYPES

The time series to fit the model to. A valid aeon time series data structure. See aeon.base._base_series.VALID_SERIES_INPUT_TYPES for aeon supported types.

axisint, default=1

The time point axis of the input series if it is 2D. If axis==0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis==1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints).

Returns:

np.ndarray

A boolean, int or float array of length len(X), where each element indicates whether the corresponding subsequence is anomalous or its anomaly score.

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed behaviour:

removes any object attributes, except:

hyper-parameters (arguments of __init__) object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:

selfobject

Reference to self.