KMeansAD¶
- class KMeansAD(n_clusters: int = 20, window_size: int = 20, stride: int = 1, random_state: int | None = None)[source]¶
KMeans anomaly detector.
The k-Means anomaly detector uses k-Means clustering to detect anomalies in time series. The time series is split into windows of a fixed size, and the k-Means algorithm is used to cluster these windows. The anomaly score for each time point is the average Euclidean distance between the time point’s windows and the windows’ corresponding cluster centers.
k-MeansAD
supports univariate and multivariate time series. It can also be fitted on a clean reference time series and used to detect anomalies in a different target time series with the same number of dimensions.Capabilities¶ Input data format
univariate and multivariate
Output data format
anomaly scores
Learning Type
unsupervised or semi-superivsed
- Parameters:
- n_clustersint, default=20
The number of clusters to use in the k-Means algorithm. The bigger the number of clusters, the less noisy the anomaly scores get. However, the number of clusters should not be too high, as this can lead to overfitting.
- window_sizeint, default=20
The size of the sliding window used to split the time series into windows. The bigger the window size, the bigger the anomaly context is. If it is too big, however, the detector marks points anomalous that are not. If it is too small, the detector might not detect larger anomalies or contextual anomalies at all. If
window_size
is smaller than the anomaly, the detector might detect only the transitions between normal data and the anomalous subsequence.- strideint, default=1
The stride of the sliding window. The stride determines how many time points the windows are spaced appart. A stride of 1 means that the window is moved one time point forward compared to the previous window. The larger the stride, the fewer windows are created, which leads to noisier anomaly scores.
- random_stateint, default=None
The random state to use in the k-Means algorithm.
Notes
This implementation is inspired by [1]. However, the original paper proposes a different kind of preprocessing and also uses advanced techniques to post-process the clustering.
References
[1]Yairi, Takehisa, Yoshikiyo Kato, and Koichi Hori. “Fault Detection by Mining Association Rules from House-Keeping Data.” In Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (-SAIRAS), Vol. 6., 2001.
Examples
>>> import numpy as np >>> from aeon.anomaly_detection import KMeansAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = KMeansAD(n_clusters=3, window_size=4, stride=1, random_state=0) >>> detector.fit_predict(X) array([1.97827709, 2.45374147, 2.51929879, 2.36979677, 2.34826601, 2.05075554, 2.57611912, 2.87642119, 3.18400743, 3.65060425, 3.36402514, 3.94053744, 3.65448197, 3.6707922 , 3.70341266, 1.97827709])
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 from estimator class and all its parent classes.
get_fitted_params
([deep])Get fitted parameters.
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 totype(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 theis_fitted
tag to true. Otherwise, it checksself
can handleX
, formatsX
into the structure required byself
then passesX
(and possiblyy
) 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 ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if
raise_error
is True andtag_name
is not inself.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 byset_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_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 ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if raise_error is
True
andtag_name
is not inself.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__
orset_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 totype(self)(**self.get_params(deep=False))
, assuming no other attributes were kept usingkeep
.- 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 ofget_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.