BaseAnomalyDetector

class BaseAnomalyDetector(axis)

Base class for anomaly detection algorithms.

Anomaly detection algorithms are used to identify anomalous subsequences in time series data. These algorithms take a series of length m and return a boolean, int or float array of length m, where each element indicates whether the corresponding subsequence is anomalous or its anomaly score.

Input and internal data format (where m is the number of time points and d is the number of channels):

Univariate series (default):

np.ndarray, shape (m,), (m, 1) or (1, m) depending on axis. This is converted to a 2D np.ndarray internally. pd.DataFrame, shape (m, 1) or (1, m) depending on axis. pd.Series, shape (m,).

Multivariate series:

np.ndarray array, shape (m, d) or (d, m) depending on axis. pd.DataFrame (m, d) or (d, m) depending on axis.

Output data format (one of the following):

Anomaly scores (default):

np.ndarray, shape (m,) of type float. For each point of the input time series, the anomaly score is a float value indicating the degree of anomalousness. The higher the score, the more anomalous the point.

Binary classification:

np.ndarray, shape (m,) of type bool or int. For each point of the input time series, the output is a boolean or integer value indicating whether the point is anomalous (True/1) or not (False/0).

Detector learning types:

Unsupervised (default):

Unsupervised detectors do not require any training data and can directly be used on the target time series. Their tags are set to fit_is_empty=True and requires_y=False. You would usually call the fit_predict method on these detectors.

Semi-supervised:

Semi-supervised detectors require a training step on a time series without anomalies (normal behaving time series). The target value y would consist of only zeros. Thus, these algorithms have logic in the fit method, but do not require the target values. Their tags are set to fit_is_empty=False and requires_y=False. You would usually first call the fit method on the training data and then the predict method for your target time series.

Supervised:

Supervised detectors require a training step on a time series with known anomalies (anomalies should be present and must be annotated). The detector implements the fit method, and the target value y consists of zeros and ones. Their tags are, thus, set to fit_is_empty=False and requires_y=True. You would usually first call the fit method on the training data and then the predict method for your target time series.

Parameters:

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). Setting this class variable will convert the input data to the chosen axis.

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.

final 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.

final 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.

final 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.

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)

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.

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.