RandomShapeletTransform

class RandomShapeletTransform(n_shapelet_samples: int = 10000, max_shapelets: int | None = None, min_shapelet_length: int = 3, max_shapelet_length: int | None = None, remove_self_similar: bool = True, batch_size: int | None = 100, verbose: bool = False, time_limit_in_minutes: float = 0.0, contract_max_n_shapelet_samples: float = inf, n_jobs: int = 1, parallel_backend=None, random_state: int | None = None)[source]

Bases: BaseCollectionTransformer

Random Shapelet Transform.

Implementation of the binary shapelet transform along the lines of [1], [2], with randomly extracted shapelets. A shapelet is a subsequence from the train set. The transform finds a set of shapelets that are good at separating the classes based on the distances between shapelets and whole series. The distance between a shapelet and a series (called sDist in the literature) is defined as the minimum Euclidean distance between shapelet and all windows the same length as the shapelet.

Overview: Input n series with d channels of length m. Continuously extract candidate shapelets and filter them in batches.

For each candidate shapelet:

  • Extract a shapelet from an instance with random length, position and channel.

  • Z-normalise the shapelet.

  • Find the distance from the shapelet to all train cases.

  • Derive a binary orderline and score the shapelet by information gain.

  • Retain only the best shapelets per class.

Parameters:
n_shapelet_samplesint, default=10000

Number of candidate shapelets to assess. Ignored when time_limit_in_minutes > 0.

max_shapeletsint or None, default=None

Maximum number of shapelets to keep. Each class value will have its own max, set to n_classes / max_shapelets. If None, set to min(10 * n_cases, 1000) during fit.

min_shapelet_lengthint, default=3

Lower bound on candidate shapelet lengths.

max_shapelet_lengthint or None, default=None

Upper bound on candidate shapelet lengths. If None the length of the shortest input series is used.

remove_self_similarbool, default=True

Remove overlapping “self-similar” shapelets when merging candidate shapelets.

batch_sizeint or None, default=100

Number of shapelet candidates processed before being merged into the set of best shapelets.

verbosebool, default=False

Whether to print progress messages during fitting and transforming.

time_limit_in_minutesfloat, default=0.0

Time contract to limit build time in minutes, overriding n_shapelet_samples. Default of 0 means n_shapelet_samples is used.

contract_max_n_shapelet_samplesfloat, default=np.inf

Max number of shapelets to extract when time_limit_in_minutes is set.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and transform. -1 means using all processors.

parallel_backendstr, ParallelBackendBase instance or None, default=None

Specify the parallelisation backend implementation in joblib, if None a prefer="threads" value is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.

random_stateint or None, default=None

Seed for random number generation.

Attributes:
n_classes_int

The number of classes.

n_cases_int

The number of train cases.

n_channels_int

The number of dimensions per case.

min_n_timepoints_int

The minimum length of series in train data.

classes_list

The class labels.

shapeletslist of tuple

The stored shapelets and related information after fitting. Each tuple is stored as (quality, length, position, channel, case_index, class_label, shapelet), where shapelet is the z-normalised subsequence extracted from the source case.

See also

ShapeletTransformClassifier

Notes

Capabilities

Missing Values

No

Multithreading

Yes

Inverse Transform

No

Univariate

Yes

Multivariate

Yes

Unequal Length

Yes

For the Java version, see TSML. <https://github.com/time-series-machine-learning/tsml-java/src/java/tsml/>`_.

References

[1]

Jon Hills et al., “Classification of time series by shapelet transformation”, Data Mining and Knowledge Discovery, 28(4), 851-881, 2014.

[2]

A. Bostrom and A. Bagnall, “Binary Shapelet Transform for Multiclass Time Series Classification”, Transactions on Large-Scale Data and Knowledge Centered Systems, 32, 2017.

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_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.

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.

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

Parameters:
Xnp.ndarray or list

Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (number of channels, series length) 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.

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

Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (number of channels, series length) 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.

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_params(deep=True)

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.

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.

Raises:
TypeError

If ‘keep’ is not a string or a list of strings.

set_params(**params)

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

Data to fit transform to, of valid collection type. Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or list of numpy arrays (number of channels, series length) 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.

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