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].

Overview: Input n series length m

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 disctrtize 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.

See also

WEASEL

Notes

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

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`()	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	Clone 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.
`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 a class tag's value.
`get_class_tags`()	Get class tags from the class and all its 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`()	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 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 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])	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_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. For classifiers, a “default” set of parameters should be provided for general testing, and a “results_comparison” set for comparing against previously recorded results if the general set does not produce suitable probabilities to compare against.

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.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

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

Clone 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 dimensions, equal length series)

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]
or pd.DataFrame with each column a dimension, each cell a pd.Series: (any number of dimensions, equal or unequal length series)
or of any other supported Panel mtype: for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

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.

fit_predict(X, y, cv=None, change_state=True) → ndarray[source]#

Fit and predict labels for sequences in X.

Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.

Writes to self, if change_state=True:: Sets self.is_fitted to True. Sets fitted model attributes ending in “_”.

Does not update state if change_state=False.

Parameters:

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

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]
or pd.DataFrame with each column a dimension, each cell a pd.Series: (any number of dimensions, equal or unequal length series)
or of any other supported Panel mtype: for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

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

indices correspond to instance indices in X

cvNone, int, or sklearn cross-validation object, optional, default=None

None : predictions are in-sample, equivalent to fit(X, y).predict(X) cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)

intequivalent to cv=KFold(cv, shuffle=True, random_state=x),: i.e., k-fold cross-validation predictions out-of-sample random_state x is taken from self if exists, otherwise x=None

change_statebool, optional (default=True)

if False, will not change the state of the classifier,: i.e., fit/predict sequence is run with a copy, self does not change
if True, will fit self to the full X and y,: end state will be equivalent to running fit(X, y)

Returns:

y1D np.array of int, of shape [n_instances] - predicted class labels: indices correspond to instance indices in X if cv is passed, -1 indicates entries not seen in union of test sets

fit_predict_proba(X, y, cv=None, change_state=True) → ndarray[source]#

Fit and predict labels probabilities for sequences in X.

Convenience method to produce in-sample predictions and cross-validated out-of-sample predictions.

Parameters:

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

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]
or pd.DataFrame with each column a dimension, each cell a pd.Series: (any number of dimensions, equal or unequal length series)
or of any other supported Panel mtype: for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

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

indices correspond to instance indices in X

cvNone, int, or sklearn cross-validation object, optional, default=None

None : predictions are in-sample, equivalent to fit(X, y).predict(X) cv : predictions are equivalent to fit(X_train, y_train).predict(X_test)

int : equivalent to cv=Kfold(int), i.e., k-fold cross-validation predictions

change_statebool, optional (default=True)

if False, will not change the state of the classifier,: i.e., fit/predict sequence is run with a copy, self does not change
if True, will fit self to the full X and y,: end state will be equivalent to running fit(X, y)

Returns:

y2D array of shape [n_instances, n_classes] - predicted class probabilities: 1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

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

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany: 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 the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:

collected_tagsdict: Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()[source]#

Get config flags for self.

Returns:

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

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 object’s parameter defaults.

Returns:

default_dict: dict[str, Any]: 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 object’s parameter names.

Returns:

param_names: list[str]: Alphabetically sorted list of parameter names of cls.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

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

Returns:

paramsdict with str-valued keys

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

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
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

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_valueAny: 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 composed of other BaseObjects.

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 an object has any parameters whose values are BaseObjects.

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 sequences in X.

Parameters:

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

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]
or pd.DataFrame with each column a dimension, each cell a pd.Series: (any number of dimensions, equal or unequal length series)
or of any other supported Panel mtype: for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

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 (any number of dimensions, equal length series)

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]
or pd.DataFrame with each column a dimension, each cell a pd.Series: (any number of dimensions, equal or unequal length series)
or of any other supported Panel mtype: for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns:

y2D array of shape [n_instances, n_classes] - predicted class probabilities: 1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

reset()[source]#

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

Class and object methods, and class attributes are also unaffected.

Returns:

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

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

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 dimensions, equal length series)

or 2D np.array (univariate, equal length series): of shape [n_instances, series_length]
or pd.DataFrame with each column a dimension, each cell a pd.Series: (any number of dimensions, equal or unequal length series)
or of any other supported Panel mtype: for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.ndarray of int, 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_config(**config_dict)[source]#

Set config flags to given values.

Parameters:

config_dictdict

Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:

displaystr, “diagram” (default), or “text”

how jupyter kernels display instances of self

“diagram” = html box diagram representation
“text” = string printout

print_changed_onlybool, default=True

whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False) does not nest, i.e., only affects self and not component estimators

warningsstr, “on” (default), or “off”

whether to raise warnings, affects warnings from sktime only

“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime

backend:parallelstr, optional, default=”None”

backend to use for parallelization when broadcasting/vectorizing, one of

“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib Parallel
“dask”: uses dask, requires dask package in environment

backend:parallel:paramsdict, optional, default={} (no parameters passed)

additional parameters passed to the parallelization backend as config. Valid keys depend on the value of backend:parallel:

“None”: no additional parameters, backend_params is ignored
“loky”, “multiprocessing” and “threading”: any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend:parallel
“dask”: any valid keys for dask.compute can be passed, e.g., scheduler

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

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:

self: Reference 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.