RandomDilatedShapeletTransform¶

class RandomDilatedShapeletTransform(max_shapelets=10000, shapelet_lengths=None, proba_normalization=0.8, threshold_percentiles=None, alpha_similarity=0.5, use_prime_dilations=False, random_state=None, n_jobs=1)[source]¶

Random Dilated Shapelet Transform (RDST) as described in [R1a26faa97573-1]_[R1a26faa97573-2]_.

Overview: The input is n series with d channels of length m. First step is to extract candidate shapelets from the inputs. This is done randomly, and for each candidate shapelet:

Length is randomly selected from shapelet_lengths parameter

Dilation is sampled as a function the shapelet length and time series length

Normalization is chosen randomly given the probability given as parameter

Value is sampled randomly from an input time series given the length and

dilation parameter. - Threshold is randomly chosen between two percentiles of the distribution of the distance vector between the shapelet and another time series. This time serie is drawn from the same class if classes are given during fit. Otherwise, a random sample will be used. If there is only one sample per class, the same sample will be used.

Then, once the set of shapelets have been initialized, we extract the shapelet features from each pair of shapelets and input series. Three features are extracted:

min d(S,X): the minimum value of the distance vector between a shapelet S and

a time series X. - argmin d(S,X): the location of the minumum. - SO(d(S,X), threshold): The number of point in the distance vector that are bellow the threshold parameter of the shapelet.

Parameters:

max_shapeletsint, default=10000: The maximum number of shapelet to keep for the final transformation. A lower number of shapelets can be kept if alpha similarity have discarded the whole dataset.
shapelet_lengthsarray, default=None: The set of possible length for shapelets. Each shapelet length is uniformly drawn from this set. If None, the shapelets length will be equal to min(max(2,n_timepoints//2),11).
proba_normalizationfloat, default=0.8: This probability (between 0 and 1) indicate the chance of each shapelet to be initialized such as it will use a z-normalized distance, inducing either scale sensitivity or invariance. A value of 1 would mean that all shapelets will use a z-normalized distance.
threshold_percentilesarray, default=None: The two percentiles used to select the threshold used to compute the Shapelet Occurrence feature. If None, the 5th and the 10th percentiles (i.e. [5,10]) will be used.
alpha_similarityfloat, default=0.5: The strength of the alpha similarity pruning. The higher the value, the lower the allowed number of common indexes with previously sampled shapelets when sampling a new candidate with the same dilation parameter. It can cause the number of sampled shapelets to be lower than max_shapelets if the whole search space has been covered. The default is 0.5, and the maximum is 1. Value above it have no effect for now.
use_prime_dilationsbool, default=False: If True, restrict the value of the shapelet dilation parameter to be prime values. This can greatly speed up the algorithm for long time series and/or short shapelet length, possibly at the cost of some accuracy.
n_jobsint, default=1: The number of threads used for both fit and transform.
random_stateint or None, default=None: Seed for random number generation.

Attributes:

shapeletslist

The stored shapelets. Each item in the list is a tuple containing:

shapelet values
length parameter
dilation parameter
treshold parameter
normalization parameter
mean parameter
standard deviation parameter

max_shapelet_length_int

The maximum actual shapelet length fitted to train data.

min_n_timepoints_int

The minimum length of series in train data.

Notes

This implementation use all the features for multivariate shapelets, without affecting a random feature subsets to each shapelet as done in the original implementation. See `convst https://github.com/baraline/convst/blob/main/convst/transformers/rdst.py`_.

References

[1]

Antoine Guillaume et al. “Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets”, Pattern Recognition and Artificial Intelligence. ICPRAI 2022.

[2]

Antoine Guillaume, “Time series classification with shapelets: Application to predictive maintenance on event logs”, PhD Thesis, University of Orléans, 2023.

Examples

>>> from aeon.transformations.collection.shapelet_based import (
...     RandomDilatedShapeletTransform
... )
>>> from aeon.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> t = RandomDilatedShapeletTransform(
...     max_shapelets=10
... )
>>> t.fit(X_train, y_train)
RandomDilatedShapeletTransform(...)
>>> X_t = t.transform(X_train)

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	Clone/mirror tags from another estimator as dynamic override.
`create_test_instance`([parameter_set])	Construct Estimator instance if possible.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally using y if supervised.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	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`()	Get metadata routing of this object.
`get_param_defaults`()	Get parameter defaults for the object.
`get_param_names`()	Get parameter names for the object.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from estimator class.
`get_tags`()	Get tags from estimator class.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`set_params`(**params)	Set the parameters of this object.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.

classmethod get_test_params(parameter_set='default')[source]¶

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set. There are currently no reserved values for transformers.

Returns:

paramsdict or list of dict, default = {}: Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

check_is_fitted()[source]¶

Check if the estimator has been fitted.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone()[source]¶

Obtain a clone of the object with same hyper-parameters.

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)).

Returns:

instance of type(self), clone of self (see above)

clone_tags(estimator, tag_names=None)[source]¶

Clone/mirror tags from another estimator as dynamic override.

Parameters:

estimatorobject: Estimator inheriting from :class:BaseEstimator.
tag_namesstr or list of str, default = None: Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]¶

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters.

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]¶

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i]).
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}.
parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X, y=None)[source]¶

Fit transformer to X, optionally using y if supervised.

State change:: Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator

Parameters:

XInput data: Data to fit transform to, of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation

Returns:

selfa fitted instance of the estimator

fit_transform(X, y=None)[source]¶

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

State change:: Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True

possibly coerced to inner type or update_data compatible type by reference, when possible

model attributes (ending in “_”) : dependent on estimator.

Parameters:

XInput data: Data to fit transform to, of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation

Returns:

transformed version of X

classmethod get_class_tag(tag_name, tag_value_default=None)[source]¶

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

See also

get_tag: Get a single tag from an object.
get_clas_tags: Get all tags from a class.
get_class_tag: Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()

inverse_transform(X, y=None)[source]¶

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags: “input_data_type”=”Series”, “output_data_type”=”Series”,

can have an inverse_transform.

State required:: Requires state to be “fitted”.
Accesses in self:: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:

XInput data: Data to fit transform to, of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation

Returns:

inverse transformed version of X: of the same type as X

is_composite()[source]¶

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool: Whether self contains a parameter which is BaseObject.

property is_fitted[source]¶: Whether fit has been called.

classmethod load_from_path(serial)[source]¶

Load object from file location.

Parameters:

serialobject: Result of ZipFile(path).open(“object).

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]¶

Load object from serialized memory container.

Parameters:

serialobject: First element of output of cls.save(None).

Returns:

deserialized self resulting in output serial, of cls.save(None).

reset()[source]¶

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail 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 hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]¶

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path).: if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.

set_params(**params)[source]¶

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: BaseObject parameters

Returns:

selfreference to self (after parameters have been set)

set_tags(**tag_dict)[source]¶

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name : tag value pairs.

Returns:

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_dict as dynamic tags in self.

transform(X, y=None)[source]¶

Transform X and return a transformed version.

State required:: Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:

XInput data: Data to fit transform to, of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation

Returns:

transformed version of X

update(X, y=None, update_params=True)[source]¶

Update transformer with X, optionally y.

State required:: Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True fitted model attributes (ending in “_”) : must be set, accessed by _update

Writes to self: _X : set to be X, if remember_data tag is True, potentially used in _update fitted model attributes (ending in “_”) : only if update_params=True

type and nature of update are dependent on estimator

Parameters:

Xdata to update of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation
update_paramsbool, default=True: whether the model is updated. Yes if true, if false, simply skips call. argument exists for compatibility with forecasting module.

Returns:

selfa fitted instance of the estimator