ExtendedIsolationForest

class ExtendedIsolationForest(n_estimators: int = 100, max_samples='auto', extension_level: int | None = None, random_state: int | RandomState | None = None, window_size: int = 10, stride: int = 1)

Bases: BaseSeriesAnomalyDetector

Extended Isolation Forest (EIF) for anomaly detection.

The Extended Isolation Forest [1] generalises the Isolation Forest [2] by isolating observations with randomly oriented hyperplanes instead of axis-parallel cuts. Axis-parallel splitting introduces artefacts in the resulting anomaly score map (bands of low score aligned with the feature axes); using random hyperplanes removes this bias and yields a more consistent score field, while retaining the linear-time isolation mechanism.

Anomalies are isolated closer to the root of each tree and therefore have shorter expected path lengths. The anomaly score of a point x is s(x) = 2 ** (-E[h(x)] / c(psi)), where E[h(x)] is the mean path length of x across the forest, psi is the sub-sampling size and c is the expected path length of an unsuccessful binary-search-tree search. Scores approach 1 for anomalies and 0.5 or below for normal points.

The detector operates on sliding windows of the input series (see window_size and stride). For multivariate series the channels within a window are stacked into a single feature vector, so each window has window_size * n_channels features.

Parameters:

n_estimatorsint, default=100

The number of isolation trees in the ensemble.

max_samplesint, float or “auto”, default=”auto”

The number of windows to draw (without replacement) to train each tree.

• If int, draw max_samples windows.

• If float, draw max_samples * n_windows windows.

• If "auto", use min(256, n_windows).

extension_levelint or None, default=None

The degree of freedom of the splitting hyperplanes. Must be between 0 and n_features - 1, where n_features = window_size * n_channels. 0 recovers the standard axis-parallel Isolation Forest; n_features - 1 uses fully extended hyperplanes. If None, the maximum value n_features - 1 is used.

random_stateint, np.random.RandomState or None, default=None

Seed or random state for reproducibility of the sub-sampling and the random hyperplanes.

window_sizeint, default=10

Size of the sliding window.

strideint, default=1

Stride of the sliding window.

Notes

Capabilities

Missing Values	No
Multithreading	No
Univariate	Yes
Multivariate	Yes

References

[1]

Hariri, S., Kind, M. C., & Brunner, R. J. (2021). Extended Isolation Forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479-1489. https://doi.org/10.1109/TKDE.2019.2947676

[2]

Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation Forest. In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422).

Examples

>>> import numpy as np
>>> from aeon.anomaly_detection.series.outlier_detection import (
...     ExtendedIsolationForest,
... )
>>> rng = np.random.default_rng(0)
>>> X = rng.normal(size=200)
>>> X[100] = 10.0
>>> detector = ExtendedIsolationForest(window_size=1, random_state=0)
>>> scores = detector.fit_predict(X)
>>> int(np.argmax(scores))
100

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_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_fit_request(*[, axis])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_predict_request(*[, axis])	Configure whether metadata should be requested to be passed to the predict method.
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:

BaseSeriesAnomalyDetector

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_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 temporary callback context attached by scikit-learn

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_fit_request(*, axis: bool | None | str = '$UNCHANGED$') → ExtendedIsolationForest

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in fit.

Returns:

selfobject

The updated object.

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_predict_request(*, axis: bool | None | str = '$UNCHANGED$') → ExtendedIsolationForest

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to predict.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in predict.

Returns:

selfobject

The updated object.

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.