DWT_MLEAD¶
- class DWT_MLEAD(start_level: int = 3, quantile_boundary_type: str = 'percentile', quantile_epsilon: float = 0.01)[source]¶
DWT-MLEAD anomaly detector.
DWT-MLEAD is an anomaly detection algorithm that uses the Discrete Wavelet Transform (DWT) and Maximum Likelihood Estimation (MLE) to detect anomalies in univariate time series. The algorithm performs mutli-level DWT using the Haar wavelet, slides windows over the DWT coefficients, and estimates the likelihood of each window using a Gaussian distribution. Anomalies are detected by comparing the likelihoods to a quantile boundary in each level and passing down the anomaly counts to the individual time points, which we use as anomaly scores. The original paper [1] subsequently clusters the anomalies to determine the anomaly centers. This step is not implemented in this version.
Capabilities¶ Input data format
univariate
Output data format
anomaly scores
Learning Type
unsupervised
- Parameters:
- start_levelint, default=3
The level at which to start the anomaly detection. Must be >= 0 and less than log_2(n_timepoints).
- quantile_boundary_typestr, default=’percentile’
The type of boundary to use for the quantile. Must be ‘percentile’, ‘monte-carlo’ is not implemented yet.
- quantile_epsilonfloat, default=0.01
The epsilon value for the quantile boundary. Must be in [0, 1].
Notes
This implementation does not exactly match the original paper [1]. We make the following changes:
We use window sizes for the DWT coefficients that decrease with the level number because otherwise we would have too few items to slide the window over.
We exclude the highest level coefficients because they contain only a single entry and are, thus, not suitable for sliding a window of length 2 over it.
We have not implemented the Monte Carlo quantile boundary type yet.
We do not perform the anomaly clustering step to determine the anomaly centers. Instead, we return the anomaly scores for each timestep in the original time series.
References
Examples
>>> import numpy as np >>> from aeon.anomaly_detection import DWT_MLEAD >>> X = np.array([1, 2, 3, 4, 1, 2, 3, 3, 2, 8, 9, 8, 1, 2, 3, 4], dtype=np.float64) >>> detector = DWT_MLEAD( ... start_level=1, quantile_boundary_type='percentile', quantile_epsilon=0.01 ... ) >>> detector.fit_predict(X) array([0., 0., 0., 0., 0., 0., 0., 0., 2., 2., 2., 2., 0., 0., 0., 0.])
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.