LOF

class LOF(n_neighbors: int = 20, algorithm: str | None = 'auto', leaf_size: int = 30, metric: str = 'minkowski', p: int = 2, metric_params: dict | None = None, n_jobs: int = 1, window_size: int = 10, stride: int = 1)[source]

Local Outlier Factor (LOF) algorithm for anomaly detection.

This class implement metrics-based outlier detection algorithms using the Local Outlier Factor (LOF) algorithm from PyOD.

Capabilities

Input data format

univariate or multivariate

Output data format

anomaly scores

missing_values

False

Learning Type

unsupervised or semi-supervised

python_dependencies

[“pyod”]

The documentation for parameters has been adapted from the [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id586). Here, X refers to the set of sliding windows extracted from the time series using aeon.utils.windowing.sliding_windows with the parameters window_size and stride. The internal X has the shape (n_windows, window_size * n_channels).

Parameters:
n_neighborsint, optional (default=20)

Number of neighbors to use by default for kneighbors queries. If n_neighbors is larger than the number of samples provided, all samples will be used.

algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional

Algorithm used to compute the nearest neighbors: - ‘ball_tree’ will use BallTree - ‘kd_tree’ will use KDTree - ‘brute’ will use a brute-force search. - ‘auto’ will attempt to decide the most appropriate algorithm

based on the values passed to fit method.

Note: fitting on sparse input will override the setting of this parameter, using brute force.

leaf_sizeint, optional (default=30)

Leaf size passed to BallTree or KDTree. This can affect the speed of the construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.

metricstring or callable, default ‘minkowski’

metric used for the distance computation. Any metric from scikit-learn or scipy.spatial.distance can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays as input and return one value indicating the distance between them. This works for Scipy’s metrics, but is less efficient than passing the metric name as a string.

pinteger, optional (default = 2)

Parameter for the Minkowski metric

metric_paramsdict, optional (default = None)

Additional keyword arguments for the metric function.

n_jobsint, optional (default = 1)

The number of parallel jobs to run for neighbors search. If -1, then the number of jobs is set to the number of CPU cores. Affects only kneighbors and kneighbors_graph methods.

noveltybool (default=False)

By default, LocalOutlierFactor is only meant to be used for outlier detection (novelty=False). Set novelty to True if you want to use LocalOutlierFactor for novelty detection. In this case be aware that that you should only use predict, decision_function and score_samples on new unseen data and not on the training set.

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)[source]

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)[source]

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[source]

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)[source]

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()[source]

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)[source]

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()[source]

Sklearn metadata routing.

Not supported by aeon estimators.

get_params(deep=True)[source]

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)[source]

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()[source]

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[source]

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)[source]

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)[source]

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)[source]

Set dynamic tags to given values.

Parameters:
**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:
selfobject

Reference to self.