MUSE

class MUSE(anova=True, variance=False, bigrams=True, window_inc=2, alphabet_size=4, use_first_order_differences=True, feature_selection='chi2', p_threshold=0.05, support_probabilities=False, n_jobs=1, random_state=None)[source]

MUSE (MUltivariate Symbolic Extension).

Also known as WEASLE-MUSE: implementation of multivariate version of WEASEL, referred to as just MUSE from [1].

WEASEL+MUSE is a multivariate dictionary classifier that builds a bag-of-patterns using SFA for different window lengths and learns a logistic regression classifier on this bag.

There are these primary parameters:

alphabet_size: alphabet size
chi2-threshold: used for feature selection to select best words
anova: select best l/2 fourier coefficients other than first ones
bigrams: using bigrams of SFA words
binning_strategy: the binning strategy used to discretize into SFA words.

Parameters:

anova: boolean, default=True: If True, the Fourier coefficient selection is done via a one-way ANOVA test. If False, the first Fourier coefficients are selected. Only applicable if labels are given
variance: boolean, default = False: If True, the Fourier coefficient selection is done via the largest variance. If False, the first Fourier coefficients are selected. Only applicable if labels are given
bigrams: boolean, default=True: whether to create bigrams of SFA words
window_inc: int, default=2: WEASEL create a BoP model for each window sizes. This is the increment used to determine the next window size.
alphabet_sizedefault = 4: Number of possible letters (values) for each word.
p_threshold: int, default=0.05 (disabled by default): Used when feature selection is applied based on the chi-squared test. This is the p-value threshold to use for chi-squared test on bag-of-words (lower means more strict). 1 indicates that the test should not be performed.
use_first_order_differences: boolean, default=True: If set to True will add the first order differences of each dimension to the data.
support_probabilities: bool, default: False: If set to False, a RidgeClassifierCV will be trained, which has higher accuracy and is faster, yet does not support predict_proba. If set to True, a LogisticRegression will be trained, which does support predict_proba(), yet is slower and typically less accuracy. predict_proba() is needed for example in Early-Classification like TEASER.
feature_selection: {“chi2”, “none”, “random”}, default: chi2: Sets the feature selections strategy to be used. Chi2 reduces the number of words significantly and is thus much faster (preferred). Random also reduces the number significantly. None applies not feature selectiona and yields large bag of words, e.g. much memory may be needed.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
random_state: int or None, default=None: Seed for random, integer

Attributes:

n_classes_int: The number of classes.
classes_list: The classes labels.

Notes

For the Java version, see - Original Publication. - `MUSE

<https://github.com/uea-machine-learning/tsml/blob/master/src/main/java/tsml/

classifiers/multivariate/WEASEL_MUSE.java>`_.

References

[1]

Patrick Schäfer and Ulf Leser, “Multivariate time series classification with WEASEL+MUSE”, in proc 3rd ECML/PKDD Workshop on AALTD}, 2018 https://arxiv.org/abs/1711.11343

Examples

>>> from sktime.classification.dictionary_based import MUSE
>>> 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 = MUSE(window_inc=4, use_first_order_differences=False)
>>> clf.fit(X_train, y_train)
MUSE(...)
>>> 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.