PyODAdapter¶
- class PyODAdapter(pyod_model: BaseDetector, window_size: int = 10, stride: int = 1)[source]¶
Adapter for PyOD anomaly detection models to be used in the Aeon framework.
This adapter allows the use of PyOD models in the Aeon framework. The adapter takes a PyOD model and applies it to a sliding window of the input data. The anomaly score of each window is then averaged to obtain the final anomaly score for each data instance. If the window size is set to 1, the adapter applies the PyOD model to each data instance individually resembling the original behavior of the PyOD model. If the striding size is set to the window size, the adapter creates tumbling windows (non-overlapping) instead of sliding windows. The anomaly score for each data point is, then, computed based on the score of the single tumbling window containing the data point.
Both univariate and multivariate time series are supported. For multivariate time series the adapter concatenates the data points of each channel in the window to a single univariate feature vector per window as input to the PyOD model.
The PyOD adapter supports unsupervised and semi-supervised learning. The adapter can be fitted on a reference time series and used to detect anomalies in a different target time series with the same number of dimensions. The reference (or training) time series does not need to be clean for most PyOD models. However, knowledge in form of anomaly labels about the potential existing anomalies in the reference time series are not used during the fitting process. Use fit to fit the model on the reference time series and predict to detect anomalies in the target time series. For unsupervised anomaly detection, use fit_predict directly on the target time series.
Capabilities¶ Input data format
univariate and multivariate
Output data format
anomaly scores
Learning Type
unsupervised or semi-supervised
- Parameters:
- pyod_modelBaseDetector
Instance of a PyOD anomaly detection model used for the detection.
- window_sizeint, default=10
Size of the sliding window.
- strideint, default=1
Stride of the sliding window.
Examples
>>> import numpy as np >>> from pyod.models.lof import LOF >>> from aeon.anomaly_detection import PyODAdapter >>> X = np.random.default_rng(42).random((10, 2), dtype=np.float64) >>> detector = PyODAdapter(LOF(), window_size=2) >>> detector.fit_predict(X, axis=0) array([1.02352234 1.00193038 0.98584441 0.99630753 1.00656619 1.00682081 1.00781515 0.99709741 0.98878895 0.99723947])
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.