IndividualTDE

IndividualTDE

class IndividualTDE(window_size=10, word_length=8, norm=False, levels=1, igb=False, alphabet_size=4, bigrams=True, dim_threshold=0.85, max_dims=20, typed_dict=True, n_jobs=1, random_state=None)[source]

Single TDE classifier, an extension of the Bag of SFA Symbols (BOSS) model.

Base classifier for the TDE classifier. Implementation of single TDE base model from Middlehurst (2021). [R89e83b1e1ed5-1]

Overview: input “n” series of length “m” and IndividualTDE performs a SFA transform to form a sparse dictionary of discretised words. The resulting dictionary is used with the histogram intersection distance function in a 1-nearest neighbor.

fit involves finding “n” histograms.

predict uses 1 nearest neighbor with the histogram intersection distance function.

Parameters:

window_sizeint, default=10

Size of the window to use in the SFA transform.

word_lengthint, default=8

Length of word to use to use in the SFA transform.

normbool, default=False

Whether to normalize SFA words by dropping the first Fourier coefficient.

levelsint, default=1

The number of spatial pyramid levels for the SFA transform.

igbbool, default=False

Whether to use Information Gain Binning (IGB) or Multiple Coefficient Binning (MCB) for the SFA transform.

alphabet_sizedefault=4

Number of possible letters (values) for each word.

bigramsbool, default=False

Whether to record word bigrams in the SFA transform.

dim_thresholdfloat, default=0.85

Accuracy threshold as a proportion of the highest accuracy dimension for words extracted from each dimensions. Only applicable for multivariate data.

max_dimsint, default=20

Maximum number of dimensions words are extracted from. Only applicable for multivariate data.

typed_dictbool, default=True

Use a numba TypedDict to store word counts. May increase memory usage, but will be faster for larger datasets.

n_jobsint, default=1

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

random_stateint or None, default=None

Seed for random, integer.

Attributes:

n_classes_int

The number of classes.

classes_list

The classes labels.

n_instances_int

The number of train cases.

n_dims_int

The number of dimensions per case.

series_length_int

The length of each series.

See also

TemporalDictionaryEnsemble, SFA

Notes

For the Java version, see TSML.

References

[1]

Matthew Middlehurst, James Large, Gavin Cawley and Anthony Bagnall “The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification”, in proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.

Examples

>>> from sktime.classification.dictionary_based import IndividualTDE
>>> 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 = IndividualTDE()
>>> clf.fit(X_train, y_train)
IndividualTDE(...)
>>> 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.