WEASEL#

class WEASEL(anova=True, bigrams=True, binning_strategy='information-gain', window_inc=2, p_threshold=0.05, alphabet_size=4, n_jobs=1, feature_selection='chi2', support_probabilities=False, random_state=None)[source]#

Word Extraction for Time Series Classification (WEASEL) [1].

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 = 4: Number of possible letters (values) for each word.
feature_selection: {“chi2”, “none”, “random”}, default: chi2: Sets the feature selections strategy to be used. Large amounts of memory may be needed depending on the setting of bigrams (true is more) or alpha (larger is more). ‘chi2’ reduces the number of words, keeping those above the ‘p_threshold’. ‘random’ reduces the number to at most ‘max_feature_count’, by randomly selecting features. ‘none’ does not apply any feature selection and yields large bag of words
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 accurate. predict_proba() is needed for example in Early-Classification like TEASER.
random_state: int or None, default=None: Seed for random, integer

Attributes:

is_fitted: Whether fit has been called.

See also

MUSE

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 aeon.classification.dictionary_based import WEASEL
>>> from aeon.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`()	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	clone/mirror tags from another estimator as dynamic override.
`create_test_instance`([parameter_set])	Construct Estimator instance if possible.
`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.
`get_class_tag`(tag_name[, tag_value_default])	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_param_defaults`()	Get parameter defaults for the object.
`get_param_names`()	Get parameter names for the object.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from estimator class and dynamic tag overrides.
`get_tags`()	Get tags from estimator class and dynamic tag overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for time series in X.
`predict_proba`(X)	Predicts labels probabilities for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y)	Scores predicted labels against ground truth labels on X.
`set_params`(**params)	Set the parameters of this object.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

paramsdict or list of dict, default={}: Parameters to create testing instances of the class. Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters.

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)).

Returns:

instance of type(self), clone of self (see above)

clone_tags(estimator, tag_names=None)[source]#

clone/mirror tags from another estimator as dynamic override.

Parameters:

estimatorestimator inheriting from :class:BaseEstimator
tag_namesstr or list of str, default = None: Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}
parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

fit(X, y)[source]#

Fit time series classifier to training data.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]

y1D np.array of int, of shape [n_instances] - class labels for fitting

indices correspond to instance indices in X

Returns:

selfReference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params(deep=True)[source]#

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True

Whether to return fitted parameters of components.

If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()[source]#

Get parameter defaults for the object.

Returns:

default_dict: dict with str keys: keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__

classmethod get_param_names()[source]#

Get parameter names for the object.

Returns:

param_names: list of str, alphabetically sorted list of parameter names of cls

get_params(deep=True)[source]#

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, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

Parameters:

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

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 i.e. if tag_name is not in self.get_tags(
).keys()

get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool, whether self contains a parameter which is BaseObject

property is_fitted[source]#: Whether fit has been called.

classmethod load_from_path(serial)[source]#

Load object from file location.

Parameters:

serialresult of ZipFile(path).open(“object)

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:

serial1st element of output of cls.save(None)

Returns:

deserialized self resulting in output serial, of cls.save(None)

predict(X) → ndarray[source]#

Predicts labels for time series in X.

Parameters:

X3D np.array of shape (n_instances, n_channels, series_length): or 2D np.array of shape (n_instances, series_length)

Returns:

y1D np.array of int, of shape [n_instances] - predicted class labels: indices correspond to instance indices in X

predict_proba(X) → ndarray[source]#

Predicts labels probabilities for sequences in X.

Parameters:

X3D np.array of shape (n_cases, n_channels, series_length): or 2D np.array of shape (n_cases, series_length)

Returns:

y2D array of shape (n_cases, n_classes) - predicted class probabilities: First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

reset()[source]#

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail 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 hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]#

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path): if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file

score(X, y) → float[source]#

Scores predicted labels against ground truth labels on X.

Parameters:

X3D np.array (any number of channels, equal length series)

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]

y1D np.ndarray of shape [n_instances] - class labels (ground truth)

indices correspond to instance indices in X

Returns:

float, accuracy score of predict(X) vs y

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: BaseObject parameters

Returns:

selfreference to self (after parameters have been set)

set_tags(**tag_dict)[source]#

Set dynamic tags to given values.

Parameters:

tag_dictdict: Dictionary of tag name : tag value pairs.

Returns:

Self: Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.