ContractableBOSS

class ContractableBOSS(n_parameter_samples=250, max_ensemble_size=50, max_win_len_prop=1, min_window=10, time_limit_in_minutes=0.0, contract_max_n_parameter_samples=inf, feature_selection='none', n_jobs=1, random_state=None)

Contractable Bag of Symbolic Fourier Approximation Symbols (cBOSS).

Implementation of BOSS Ensemble from [1] with refinements described in [2].

Overview: Input “n” series of length “m” and cBOSS randomly samples n_parameter_samples parameter sets, evaluting each with LOOCV. It then retains max_ensemble_size classifiers with the highest accuracy There are three primary parameters:

• alpha: alphabet size

• w: window length

• l: word length.

For any combination, a single BOSS slides a window length “w” along the series. The “w” length window is shortened to an “l” length word by taking a Fourier transform and keeping the first l/2 complex coefficients. These “l” coefficients are then discretised into “alpha” possible values, to form a word length “l”. A histogram of words for each series is formed and stored.

Fit involves finding “n” histograms.

Predict uses 1 nearest neighbor with a bespoke BOSS distance function.

Parameters:

n_parameter_samplesint, default = 250

If search is randomised, number of parameter combos to try.

max_ensemble_sizeint or None, default = 50

Maximum number of classifiers to retain. Will limit number of retained classifiers even if more than max_ensemble_size are within threshold.

max_win_len_propint or float, default = 1

Maximum window length as a proportion of the series length.

min_windowint, default = 10

Minimum window size.

time_limit_in_minutesint, default = 0

Time contract to limit build time in minutes. Default of 0 means no limit.

contract_max_n_parameter_samplesint, default=np.inf

Max number of parameter combinations to consider when time_limit_in_minutes is set.

n_jobsint, default = 1

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

feature_selectionstr, default = “none”

Sets the feature selections strategy to be used. One of {“chi2”, “none”, “random”}. “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.

random_stateint, RandomState instance or None, default=None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Attributes:

n_classes_int

Number of classes. Extracted from the data.

classes_list

The classes labels.

n_cases_int

Number of instances. Extracted from the data.

n_timepoints_int

Length of all series (assumed equal).

estimators_list

List of DecisionTree classifiers.

n_estimators_int

The final number of classifiers used. Will be <= max_ensemble_size.

weights_

Weight of each classifier in the ensemble.

See also

BOSSEnsemble, IndividualBOSS

Variants of the BOSS classifier.

Notes

For the Java version, see TSML.

References

[1]

Patrick Schäfer, “The BOSS is concerned with time series classification in the presence of noise”, Data Mining and Knowledge Discovery, 29(6): 2015 https://link.springer.com/article/10.1007/s10618-014-0377-7

[2]

Matthew Middlehurst, William Vickers and Anthony Bagnall “Scalable Dictionary Classifiers for Time Series Classification”, in proc 20th International Conference on Intelligent Data Engineering and Automated Learning,LNCS, volume 11871 https://link.springer.com/chapter/10.1007/978-3-030-33607-3_2

Examples

>>> from aeon.classification.dictionary_based import ContractableBOSS
>>> from aeon.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> clf = ContractableBOSS(n_parameter_samples=10, max_ensemble_size=3)
>>> clf.fit(X_train, y_train)
ContractableBOSS(...)
>>> y_pred = clf.predict(X_test)

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X, y)	Fit time series classifier to training data.
fit_predict(X, y, **kwargs)	Fits the classifier and predicts class labels for X.
fit_predict_proba(X, y, **kwargs)	Fits the classifier and predicts class label probabilities for X.
get_class_tag(tag_name[, raise_error, ...])	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_metadata_routing()	Sklearn metadata routing.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
predict(X)	Predicts class labels for time series in X.
predict_proba(X)	Predicts class label probabilities for time series in X.
reset([keep])	Reset the object to a clean post-init state.
score(X, y[, metric, use_proba, metric_params])	Scores predicted labels against ground truth labels on X.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

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)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

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

fit(X, y) → BaseCollectionEstimator

Fit time series classifier to training data.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

selfBaseClassifier

Reference 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, **kwargs) → ndarray

Fits the classifier and predicts class labels for X.

fit_predict produces prediction estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function _fit_predict is not overridden. If _fit_predict is overridden, kwargs may not function as expected. If _fit_predict is not overridden, valid input is cv_size integer, which is the number of cross validation folds to use to estimate train data. If cv_size is not passed, the default is 10. If cv_size is greater than the minimum number of samples in any class, it is set to this minimum.

Returns:

predictionsnp.ndarray

shape [n_cases] - predicted class labels indices correspond to instance indices in

fit_predict_proba(X, y, **kwargs) → ndarray

Fits the classifier and predicts class label probabilities for X.

fit_predict_proba produces probability estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict_proba will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict_proba(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function _fit_predict is not overridden. If _fit_predict is overridden, kwargs may not function as expected. If _fit_predict is not overridden, valid input is cv_size integer, which is the number of cross validation folds to use to estimate train data. If cv_size is not passed, the default is 10. If cv_size is greater than the minimum number of samples in any class, it is set to this minimum.

Returns:

probabilitiesnp.ndarray

2D 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

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

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

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

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

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

get_metadata_routing()

Sklearn metadata routing.

Not supported by aeon estimators.

get_params(deep=True)

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, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

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 and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

predict(X) → ndarray

Predicts class labels for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

Returns:

predictionsnp.ndarray

1D np.array of float, of shape (n_cases) - predicted class labels indices correspond to instance indices in X

predict_proba(X) → ndarray

Predicts class label probabilities for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

Returns:

probabilitiesnp.ndarray

2D 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(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed 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 hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

score(X, y, metric='accuracy', use_proba=False, metric_params=None) → float

Scores predicted labels against ground truth labels on X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

metricUnion[str, callable], default=”accuracy”,

Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.

use_probabool, default=False,

Argument to check if scorer works on probability estimates or not.

metric_paramsdict, default=None,

Contains parameters to be passed to the scoring function. If None, no parameters are passed.

Returns:

scorefloat

Accuracy score of predict(X) vs y.

set_params(**params)

Set the parameters of this estimator.

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

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:

selfobject

Reference to self.