ROCKAD

class ROCKAD(n_estimators=10, n_kernels=100, normalise=False, n_neighbors=5, metric='euclidean', power_transform=True, n_jobs=1, random_state=None)

Bases: BaseCollectionAnomalyDetector

ROCKET-based whole-series Anomaly Detector (ROCKAD).

ROCKAD [1] leverages the ROCKET transformation for feature extraction from time series data and applies the scikit learn k-nearest neighbors (k-NN) approach with bootstrap aggregation for robust semi-supervised anomaly detection. The data gets transformed into the ROCKET feature space. Then the whole-series are compared based on the feature space by finding the nearest neighbours.

This class supports both univariate and multivariate time series and provides options for normalizing features, applying power transformations, and customizing the distance metric.

Parameters:

n_estimatorsint, default=10

Number of k-NN estimators to use in the bootstrap aggregation.

n_kernelsint, default=100

Number of kernels to use in the ROCKET transformation.

normalisebool, default=False

Whether to normalize the ROCKET-transformed features.

n_neighborsint, default=5

Number of neighbors to use for the k-NN algorithm.

n_jobsint, default=1

Number of parallel jobs to use for the k-NN algorithm and ROCKET transformation.

metricstr, default=”euclidean”

Distance metric to use for the k-NN algorithm.

power_transformbool, default=True

Whether to apply a power transformation (Yeo-Johnson) to the features.

random_stateint, default=None

Random seed for reproducibility.

Attributes:

rocket_transformer_Optional[Rocket]

Instance of the ROCKET transformer used to extract features, set after fitting.

list_baggers_Optional[list[NearestNeighbors]]

List containing k-NN estimators used for anomaly scoring, set after fitting.

power_transformer_PowerTransformer

Transformer used to apply power transformation to the features.

Notes

Capabilities

Missing Values	No
Multithreading	Yes
Univariate	Yes
Multivariate	Yes

Capabilities

Missing Values	No
Multithreading	Yes
Univariate	Yes
Multivariate	Yes
Unequal Length	No

References

[1]

Theissler, A., Wengert, M., Gerschner, F. (2023). ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_33

Examples

>>> import numpy as np
>>> from aeon.anomaly_detection.collection import ROCKAD
>>> rng = np.random.default_rng(seed=42)
>>> X_train = rng.normal(loc=0.0, scale=1.0, size=(10, 100))
>>> X_test = rng.normal(loc=0.0, scale=1.0, size=(5, 100))
>>> X_test[4][50:58] -= 5
>>> detector = ROCKAD()
>>> detector.fit(X_train)
>>> detector.predict(X_test)
    array([0.        , 0.00554713, 0.06990941, 0.22881059, 0.32382585,
        0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154,
        0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154,
        0.52382585, 0.65200875, 0.80313368, 0.85194344, 1.        ])

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X[, y])	Fit collection anomaly detector to training data.
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_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)	Predicts anomalies for time series 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)

Fit collection anomaly detector to training data.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of int, of shape (n_cases) - anomaly labels (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

selfBaseCollectionAnomalyDetector

Reference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

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

Fit time series anomaly detector and find anomalies for X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of int, of shape (n_cases) - anomaly labels (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

predictionsnp.ndarray

1D np.array of float, of shape (n_cases) - predicted anomalies or anomaly scores for each time series in X. Indices correspond to instance indices in X.

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_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)

Predicts anomalies for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

Returns:

predictionsnp.ndarray

1D np.array of float, of shape (n_cases) - predicted anomalies or anomaly scores for each time series in X. Indices correspond to instance indices in X.

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.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

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.