WEASEL

class WEASEL(anova=True, bigrams=True, binning_strategy='information-gain', window_inc=2, p_threshold=0.05, alphabet_size=2, n_jobs=1, feature_selection='chi2', support_probabilities=False, random_state=None)[source]

Word Extraction for Time Series Classification (WEASEL).

Overview: Input n series length m WEASEL is a 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
p-threshold: threshold used for chi^2-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 discretise into SFA words.

WEASEL slides a window length w along the series. The w length window is shortened to an l length word through taking a Fourier transform and keeping the best l/2 complex coefficients using an anova one-sided test. These l coefficients are then discretised into alpha possible symbols, to form a word of length l. A histogram of words for each series is formed and stored. For each window-length a bag is created and all words are joint into one bag-of-patterns. Words from different window-lengths are discriminated by different prefixes. fit involves training a logistic regression classifier on the single bag-of-patterns.

predict uses the logistic regression classifier

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
bigrams: boolean, default=True: whether to create bigrams of SFA words
binning_strategy: {“equi-depth”, “equi-width”, “information-gain”},
default=”information-gain”: The binning method used to derive the breakpoints.
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.
p_threshold: int, default=0.05 (disabled by default): 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.
alphabet_sizedefault = 2: Number of possible letters (values) for each word.
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). If set to chi2,

p_threshold is applied. Random also reduces the number significantly. None applies not feature selectiona and yields large bag of words, e.g. much memory may be needed.
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.
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. - TSML.

References

[1]

Patrick Schäfer and Ulf Leser, “Fast and Accurate Time Series Classification

with WEASEL”, in proc ACM on Conference on Information and Knowledge Management, 2017, https://dl.acm.org/doi/10.1145/3132847.3132980

Examples

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