ShapeletTransformClassifier

class ShapeletTransformClassifier(n_shapelet_samples=10000, max_shapelets=None, max_shapelet_length=None, estimator=None, transform_limit_in_minutes=0, time_limit_in_minutes=0, contract_max_n_shapelet_samples=inf, save_transformed_data=False, n_jobs=1, batch_size=100, random_state=None)[source]

A shapelet transform classifier (STC).

Implementation of the binary shapelet transform classifier pipeline along the lines of [1][2] but with random shapelet sampling. Transforms the data using the configurable RandomShapeletTransform and then builds a RotationForest classifier.

As some implementations and applications contract the transformation solely, contracting is available for the transform only and both classifier and transform.

Parameters:

n_shapelet_samplesint, default=10000: The number of candidate shapelets to be considered for the final transform. Filtered down to <= max_shapelets, keeping the shapelets with the most information gain.
max_shapeletsint or None, default=None: Max number of shapelets to keep for the final transform. Each class value will have its own max, set to n_classes_ / max_shapelets. If None, uses the minimum between 10 * n_instances_ and 1000.
max_shapelet_lengthint or None, default=None: Lower bound on candidate shapelet lengths for the transform. If None, no max length is used
estimatorBaseEstimator or None, default=None: Base estimator for the ensemble, can be supplied a sklearn BaseEstimator. If None a default RotationForest classifier is used.
transform_limit_in_minutesint, default=0: Time contract to limit transform time in minutes for the shapelet transform, overriding n_shapelet_samples. A value of 0 means n_shapelet_samples is used.
time_limit_in_minutesint, default=0: Time contract to limit build time in minutes, overriding n_shapelet_samples and transform_limit_in_minutes. The estimator will only be contracted if a time_limit_in_minutes parameter is present. Default of 0 means n_shapelet_samples or transform_limit_in_minutes is used.
contract_max_n_shapelet_samplesint, default=np.inf: Max number of shapelets to extract when contracting the transform with transform_limit_in_minutes or time_limit_in_minutes.
save_transformed_databool, default=False: Save the data transformed in fit in transformed_data_ for use in _get_train_probs.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
batch_sizeint or None, default=100: Number of shapelet candidates processed before being merged into the set of best shapelets in the transform.
random_stateint, RandomState instance or None, default=None: If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

classes_list: The unique class labels in the training set.
n_classes_int: The number of unique classes in the training set.
fit_time_int: The time (in milliseconds) for fit to run.
n_instances_int: The number of train cases in the training set.
n_dims_int: The number of dimensions per case in the training set.
series_length_int: The length of each series in the training set.
transformed_data_list of shape (n_estimators) of ndarray: The transformed training dataset for all classifiers. Only saved when save_transformed_data is True.

Notes

For the Java version, see 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.

Examples

>>> from sktime.classification.shapelet_based import ShapeletTransformClassifier
>>> from sktime.classification.sklearn import RotationForest
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> clf = ShapeletTransformClassifier(
...     estimator=RotationForest(n_estimators=3),
...     n_shapelet_samples=100,
...     max_shapelets=10,
...     batch_size=20,
... )
>>> clf.fit(X_train, y_train)
ShapeletTransformClassifier(...)
>>> y_pred = clf.predict(X_test)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series classifier to training data.
`fit_predict`(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
`fit_predict_proba`(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.