RandomDilatedShapeletTransform

class RandomDilatedShapeletTransform(max_shapelets: int = 10000, shapelet_lengths: List[int] | ndarray | None = None, proba_normalization: float = 0.8, threshold_percentiles: List[float] | ndarray | None = None, alpha_similarity: float = 0.5, use_prime_dilations: bool = False, random_state: int | None = None, n_jobs: int = 1)[source]

Random Dilated Shapelet Transform (RDST) as described in [1], [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

  • Start 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 series 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 points in the distance vector that are bellow the threshold parameter of the shapelet.

Parameters:
max_shapeletsint, default=10000

The maximum number of shapelets to keep for the final transformation. A lower number of shapelets can be kept if alpha similarity has discarded the whole dataset.

shapelet_lengthsarray, default=None

The set of possible lengths for shapelets. Each shapelet length is uniformly drawn from this set. If None, the shapelet length will be equal to min(max(2,n_timepoints//2),11).

proba_normalizationfloat, default=0.8

This probability (between 0 and 1) indicates the chance of each shapelet to be initialized such as it will use a z-normalised distance, inducing either scale sensitivity or invariance. A value of 1 would mean that all shapelets will use a z-normalised 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 fewer common indexes with previously sampled shapelets are allowed 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. Values above it have no effect for now.

use_prime_dilationsbool, default=False

If True, restricts 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 lengths, 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

  • startpoint values

  • length parameter

  • dilation parameter

  • threshold parameter

  • normalization parameter

  • mean parameter

  • standard deviation parameter

  • class value

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 uses all the features for multivariate shapelets, without affecting a random feature subset 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")
>>> t = RandomDilatedShapeletTransform(
...     max_shapelets=10
... )
>>> t.fit(X_train, y_train)
RandomDilatedShapeletTransform(...)
>>> X_t = t.transform(X_train)

Methods

clone([random_state])

Obtain a clone of the object with the same hyperparameters.

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[, 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.

inverse_transform(X[, y])

Inverse transform X and return an inverse transformed version.

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.

transform(X[, y])

Transform X and return a transformed version.

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)[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:
Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

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

Parameters:
Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:
transformed version of X
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.

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 fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:
Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:
inverse transformed version of X

of the same type as X

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.

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 fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:
Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag(“capability:multivariate”)` is False, they cannot handle multivariate series. If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:
transformed version of X