TEASER

class TEASER(estimator=None, one_class_classifier=None, one_class_param_grid=None, classification_points=None, n_jobs=1, random_state=None)

Two-tier Early and Accurate Series Classifier (TEASER).

An early classifier which uses one class SVM’s trained on prediction probabilities to determine whether an early prediction is safe or not.

Overview:

Build n classifiers, where n is the number of classification_points. For each classifier, train a one class svm used to determine prediction safety at that series length. Tune the number of consecutive safe svm predictions required to consider the prediction safe.

While a prediction is still deemed unsafe:

Make a prediction using the series length at classification point i. Decide whether the predcition is safe or not using decide_prediction_safety.

Parameters:

estimatoraeon classifier, default=None

An aeon estimator to be built at each of the classification_points time stamps. Defaults to a WEASEL classifier.

one_class_classifierone-class sklearn classifier, default=None

An sklearn one-class classifier used to determine whether an early decision is safe. Defaults to a tuned one-class SVM classifier.

one_class_param_griddict or list of dict, default=None

The hyper-parameters for the one-class classifier to learn using grid-search. Dictionary with parameters names (str) as keys and lists of parameter settings to try as values, or a list of such dictionaries.

classification_pointsList or None, default=None

List of integer time series time stamps to build classifiers and allow predictions at. Early predictions must have a series length that matches a value in the _classification_points List. Duplicate values will be removed, and the full series length will be appeneded if not present. If None, will use 20 thresholds linearly spaces from 0 to the series length.

n_jobsint, default=1

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

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

The number of classes.

n_cases_int

The number of train cases.

n_channels_int

The number of dimensions per case.

n_timepoints_int

The full length of each series.

classes_list

The unique class labels.

state_info2d np.ndarray (4 columns)

Information stored about input instances after the decision-making process in update/predict methods. Used in update methods to make decisions based on the resutls of previous method calls. Records in order: the time stamp index, the number of consecutive decisions made, the predicted class and the series length.

References

[1]

Schäfer, Patrick, and Ulf Leser. “TEASER: early and accurate time series classification.” Data mining and knowledge discovery 34, no. 5 (2020)

Examples

>>> from aeon.classification.early_classification import TEASER
>>> from aeon.classification.interval_based import TimeSeriesForestClassifier
>>> 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 = TEASER(
...     classification_points=[6, 16, 24],
...     estimator=TimeSeriesForestClassifier(n_estimators=5),
... )
>>> clf.fit(X_train, y_train)
TEASER(...)
>>> y_pred, decisions = 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.
compute_harmonic_mean(state_info, y)	Calculate harmonic mean from a state info matrix and array of class labeles.
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.
filter_X(X, decisions)	Remove True cases from X given a boolean array of decisions.
filter_X_y(X, y, decisions)	Remove True cases from X and y given a boolean array of decisions.
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_metadata_routing()	Get metadata routing of this object.
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_state_info()	Return the state information generated from the last predict/update call.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator class.
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 sequences in X.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
reset_state_info()	Reset the state information used in update methods.
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.
split_indices(indices, decisions)	Split a list of indices given a boolean array of decisions.
split_indices_and_filter(X, indices, decisions)	Remove True cases and split a list of indices given an array of decisions.
update_predict(X)	Update label prediction for sequences in X at a larger series length.
update_predict_proba(X)	Update label probabilities for sequences in X at a larger series length.

compute_harmonic_mean(state_info, y) → tuple[float, float, float]

Calculate harmonic mean from a state info matrix and array of class labeles.

Parameters:

state_info2d np.ndarray of int

The state_info from a TEASER object after a prediction or update. It is assumed the state_info is complete, and a positive decision has been returned for all cases.

y1D np.array of int

Actual class labels for predictions. indices correspond to instance indices in state_info.

Returns:

harmonic_meanfloat

Harmonic Mean represents the balance between accuracy and earliness for a set of early predictions.

accuracyfloat

Accuracy for the predictions made in the state_info.

earlinessfloat

Average time taken to make a classification. The earliness for a single case is the number of time points required divided by the total series length.

classmethod get_test_params(parameter_set='default')

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. TEASER provides the following special sets:

“results_comparison” - used in some classifiers to compare against

previously generated results where the default set of parameters cannot produce suitable probability estimates

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()

Check if the estimator has been fitted.

Raises:

NotFittedError

If the estimator has not been fitted yet.

clone()

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)

Clone/mirror tags from another estimator as dynamic override.

Parameters:

estimatorobject

Estimator 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', return_first=True)

Construct Estimator instance if possible.

Calls the get_test_params method and returns an instance or list of instances using the returned dict or list of dict.

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.

return_firstbool, default=True

If True, return the first instance of the list of instances. If False, return the list of instances.

Returns:

instanceBaseEstimator or list of BaseEstimator

Instance of the class with default parameters. If return_first is False, returns list of instances.

classmethod create_test_instances_and_names(parameter_set='default')

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.

static filter_X(X, decisions)

Remove True cases from X given a boolean array of decisions.

static filter_X_y(X, y, decisions)

Remove True cases from X and y given a boolean array of decisions.

fit(X, y)

Fit time series classifier to training data.

Parameters:

X3D np.ndarray

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.

np.array

shape (n_cases) - 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, raise_error=False)

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.

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()

See also

get_tag

Get a single tag from an object.

get_tags

Get all tags from an object.

get_class_tag

Get a single tag from a class.

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 : 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)

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.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

classmethod get_param_defaults()

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()

Get parameter names for the object.

Returns:

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

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_state_info()

Return the state information generated from the last predict/update call.

Returns:

An array containing the state info for each decision in X from update and

predict methods. Contains classifier dependant information for future decisions

on the data and information on when a cases decision has been made. Each row

contains information for a case from the latest decision on its safety made in

update/predict. Successive updates are likely to remove rows from the

state_info, as it will only store as many rows as there are input instances to

update/predict.

get_tag(tag_name, tag_value_default=None, raise_error=True)

Get tag value from estimator class.

Uses dynamic tag overrides.

Parameters:

tag_namestr

Name of tag to be retrieved.

tag_value_defaultany type, 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()

See also

get_tags

Get all tags from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

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

get_tags()

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:

dict

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.

See also

get_tag

Get a single tag from an object.

get_class_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()

is_composite()

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

Whether fit has been called.

classmethod load_from_path(serial)

Load object from file location.

Parameters:

serialobject

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

Returns:

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

classmethod load_from_serial(serial)

Load object from serialized memory container.

Parameters:

serialobject

First element of output of cls.save(None).

Returns:

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

predict(X) → tuple[ndarray, ndarray]

Predicts labels for sequences in X.

Early classifiers can predict at series lengths shorter than the train data series length.

Predict will return -1 for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

X3D np.ndarray

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.

Returns:

ynp.array

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

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use.

predict_proba(X) → tuple[ndarray, ndarray]

Predicts labels probabilities for sequences in X.

Early classifiers can predict at series lengths shorter than the train data series length.

Probability predictions will return [-1]*n_classes_ for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

Parameters:

X3D np.ndarray

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.

Returns:

y2D array of shape [n_cases, 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

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use

reset()

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

reset_state_info()

Reset the state information used in update methods.

save(path=None)

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) → tuple[float, float, float]

Scores predicted labels against ground truth labels on X.

Parameters:

X3D np.ndarray

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.

y1D np.ndarray of int, of shape [n_cases] - class labels (ground truth)

indices correspond to instance indices in X

Returns:

Tuple of floats, harmonic mean, accuracy and earliness scores of predict(X) vs y

set_params(**params)

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)

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 setting tag values in tag_dict as dynamic tags in self.

static split_indices(indices, decisions)

Split a list of indices given a boolean array of decisions.

static split_indices_and_filter(X, indices, decisions)

Remove True cases and split a list of indices given an array of decisions.

update_predict(X) → tuple[ndarray, ndarray]

Update label prediction for sequences in X at a larger series length.

Uses information stored in the classifiers state from previous predictions and updates at shorter series lengths. Update will only accept cases which have not yet had a decision made, cases which have had a positive decision should be removed from the input with the row ordering preserved.

If no state information is present, predict will be called instead.

Prediction updates will return -1 for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

Parameters:

X3D np.ndarray

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.

Returns:

y1D np.array of int, of shape [n_cases] - predicted class labels

indices correspond to instance indices in X

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use

update_predict_proba(X) → tuple[ndarray, ndarray]

Update label probabilities for sequences in X at a larger series length.

Uses information stored in the classifiers state from previous predictions and updates at shorter series lengths. Update will only accept cases which have not yet had a decision made, cases which have had a positive decision should be removed from the input with the row ordering preserved.

If no state information is present, predict_proba will be called instead.

Probability predictions updates will return [-1]*n_classes_ for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

Parameters:

X3D np.ndarray

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.

Returns:

y2D array of shape [n_cases, 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

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use