TemporalDictionaryEnsemble

class TemporalDictionaryEnsemble(n_parameter_samples=250, max_ensemble_size=50, max_win_len_prop=1, min_window=10, randomly_selected_params=50, bigrams=None, dim_threshold=0.85, max_dims=20, time_limit_in_minutes=0.0, contract_max_n_parameter_samples=inf, typed_dict=True, save_train_predictions=False, n_jobs=1, random_state=None)

Temporal Dictionary Ensemble (TDE).

Implementation of the dictionary based Temporal Dictionary Ensemble as described in [R4a176c017ca3-1].

Overview: Input ‘n’ series length ‘m’ with ‘d’ dimensions TDE searches ‘k’ parameter values selected using a Gaussian processes regressor, evaluating each with a LOOCV. It then retains ‘s’ ensemble members. There are six primary parameters for individual classifiers:

• alpha: alphabet size

• w: window length

• l: word length

• p: normalise/no normalise

• h: levels

• b: MCB/IGB

For any combination, an individual TDE classifier slides a window of length w along the series. The w length window is shortened to an l length word through taking a Fourier transform and keeping the first l/2 complex coefficients. These lcoefficients are then discretised into alpha possible values, to form a word length l using breakpoints found using b. A histogram of words for each series is formed and stored, using a spatial pyramid of h levels. For multivariate series, accuracy from a reduced histogram is used to select dimensions.

fit involves finding n histograms. predict uses 1 nearest neighbour with the histogram intersection distance function.

Parameters:

n_parameter_samplesint, default=250

Number of parameter combinations to consider for the final ensemble.

max_ensemble_sizeint, default=50

Maximum number of estimators in the ensemble.

max_win_len_propfloat, default=1

Maximum window length as a proportion of series length, must be between 0 and 1.

min_windowint, default=10

Minimum window length.

randomly_selected_params: int, default=50

Number of parameters randomly selected before the Gaussian process parameter selection is used.

bigramsboolean or None, default=None

Whether to use bigrams, defaults to true for univariate data and false for multivariate data.

dim_thresholdfloat, default=0.85

Dimension accuracy threshold for multivariate data, must be between 0 and 1.

max_dimsint, default=20

Max number of dimensions per classifier for multivariate data.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_parameter_samples. Default of 0 means n_parameter_samples is used.

contract_max_n_parameter_samplesint, default=np.inf

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

typed_dictbool, default=True

Use a numba typed Dict to store word counts. May increase memory usage, but will be faster for larger datasets. As the Dict cannot be pickled currently, there will be some overhead converting it to a python dict with multiple threads and pickling.

save_train_predictionsbool, default=False

Save the ensemble member train predictions in fit for use in _get_train_probs leave-one-out cross-validation.

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.

classes_list

The classes labels.

n_instances_int

The number of train cases.

n_dims_int

The number of dimensions per case.

series_length_int

The length of each series.

n_estimators_int

The final number of classifiers used (<= max_ensemble_size)

estimators_list of shape (n_estimators) of IndividualTDE

The collections of estimators trained in fit.

weights_list of shape (n_estimators) of float

Weight of each estimator in the ensemble.

See also

IndividualTDE, ContractableBOSS

Notes

For the Java version, see TSML.

References

[1]

Matthew Middlehurst, James Large, Gavin Cawley and Anthony Bagnall “The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification”, in proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.

Examples

>>> from aeon.classification.dictionary_based import TemporalDictionaryEnsemble
>>> 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 = TemporalDictionaryEnsemble(
...     n_parameter_samples=10,
...     max_ensemble_size=3,
...     randomly_selected_params=5,
... )
>>> clf.fit(X_train, y_train)
TemporalDictionaryEnsemble(...)
>>> y_pred = clf.predict(X_test)

Methods

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

classmethod get_test_params(parameter_set='default')

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. TemporalDictionaryEnsemble 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

“contracting” - used in classifiers that set the

“capability:contractable” tag to True to test contacting functionality

“train_estimate” - used in some classifiers that set the

“capability:train_estimate” tag to True to allow for more efficient testing when relevant parameters are available

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:

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.

fit(X, y)

Fit time series classifier to training data.

Parameters:

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

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

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

indices correspond to instance indices in X

Returns:

selfReference to self.

Notes

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

classmethod get_class_tag(tag_name, tag_value_default=None)

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_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) → ndarray

Predicts labels for time series in X.

Parameters:

X3D np.array of shape (n_instances, n_channels, series_length)

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

Returns:

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

indices correspond to instance indices in X

predict_proba(X) → ndarray

Predicts labels probabilities for sequences in X.

Parameters:

X3D np.array of shape (n_cases, n_channels, series_length)

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

Returns:

y2D array of shape (n_cases, n_classes) - predicted class probabilities

First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

reset()

Reset the object to a clean post-init state.

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

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

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

save(path=None)

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

Scores predicted labels against ground truth labels on X.

Parameters:

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

of shape [n_instances, n_channels, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

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

indices correspond to instance indices in X

Returns:

float, accuracy score of predict(X) vs y

set_params(**params)

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.