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

estimator: sktime classifier, default=None

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

one_class_classifier: one-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_grid: dict 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 or None, default=None

Seed for random number generation.

Attributes

n_classes_int

The number of classes.

n_instances_int

The number of train cases.

n_dims_int

The number of dimensions per case.

series_length_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 sktime.classification.early_classification import TEASER
>>> from sktime.classification.interval_based import TimeSeriesForestClassifier
>>> 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 = 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.
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_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 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 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.

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.

Returns

paramsdict or list of dict, default = {}

Parameters to create testing instances of the class.

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

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’)

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’)

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

of shape [n_instances, n_dimensions, series_length]

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.

classmethod get_class_tag(tag_name, tag_value_default=None)

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()

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

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 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()

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()

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

serialresult 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

serial1st element of output of cls.save(None)

Returns

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

predict(X) → Tuple[numpy.ndarray, numpy.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.

Parameters

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

of shape [n_instances, n_dimensions, series_length]

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

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[numpy.ndarray, numpy.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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

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

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

of shape [n_instances, n_dimensions, series_length]

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

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 settting 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[numpy.ndarray, numpy.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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

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

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[numpy.ndarray, numpy.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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

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

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