IndividualTDE

class IndividualTDE(window_size=10, word_length=8, norm=False, levels=1, igb=False, alphabet_size=4, bigrams=True, dim_threshold=0.85, max_dims=20, typed_dict=True, n_jobs=1, random_state=None)[source]

Single TDE classifier, an extension of the Bag of SFA Symbols (BOSS) model.

Base classifier for the TDE classifier. Implementation of single TDE base model from [1].

Overview: input “n” series of length “m” and IndividualTDE performs a SFA transform to form a sparse dictionary of discretised words. The resulting dictionary is used with the histogram intersection distance function in a 1-nearest neighbor.

fit involves finding “n” histograms.

predict uses 1 nearest neighbor with the histogram intersection distance function.

Parameters:
window_sizeint, default=10

Size of the window to use in the SFA transform.

word_lengthint, default=8

Length of word to use to use in the SFA transform.

normbool, default=False

Whether to normalize SFA words by dropping the first Fourier coefficient.

levelsint, default=1

The number of spatial pyramid levels for the SFA transform.

igbbool, default=False

Whether to use Information Gain Binning (IGB) or Multiple Coefficient Binning (MCB) for the SFA transform.

alphabet_sizedefault=4

Number of possible letters (values) for each word.

bigramsbool, default=False

Whether to record word bigrams in the SFA transform.

dim_thresholdfloat, default=0.85

Accuracy threshold as a propotion of the highest accuracy dimension for words extracted from each dimensions. Only applicable for multivariate data.

max_dimsint, default=20

Maximum number of dimensions words are extracted from. Only applicable for multivariate data.

typed_dictbool, default=True

Use a numba TypedDict to store word counts. May increase memory usage, but will be faster for larger datasets.

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, integer.

Attributes:
n_classes_int

The number of classes.

classes_list

The classes labels.

n_cases_int

The number of train cases.

n_channels_int

The number of dimensions per case.

n_timepoints_int

The length of each series.

See also

TemporalDictinaryEnsemble, SFA

TDE extends BOSS and uses SFA.

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 IndividualTDE
>>> 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 = IndividualTDE()
>>> clf.fit(X_train, y_train)
IndividualTDE(...)
>>> 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.

fit_predict(X, y)

Fits the classifier and predicts class labels for X.

fit_predict_proba(X, y)

Fits the classifier and predicts class label probabilities for X.

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_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 class labels for time series in X.

predict_proba(X)

Predicts class label probabilities for time series 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[, metric, use_proba, metric_params])

Scores predicted labels against ground truth labels on X.

set_params(**params)

Set the parameters of this object.

set_score_request(*[, metric, ...])

Request metadata passed to the score method.

set_tags(**tag_dict)

Set dynamic tags to given values.

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. 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)[source]

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')[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) BaseCollectionEstimator[source]

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) ndarray[source]

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.

Returns:
predictionsnp.ndarray

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

fit_predict_proba(X, y) ndarray[source]

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.

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, tag_value_default=None)[source]

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.

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()[source]

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)[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.

get_metadata_routing()[source]

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()[source]

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()[source]

Get parameter names for the object.

Returns:
param_names: list of str, alphabetically sorted list of parameter names of cls
get_params(deep=True)[source]

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)[source]

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()[source]

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()
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.

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.

is_composite()[source]

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[source]

Whether fit has been called.

classmethod load_from_path(serial)[source]

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)[source]

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) ndarray[source]

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[source]

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()[source]

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)[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, metric='accuracy', use_proba=False, metric_params=None) float[source]

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)[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:
selfreference to self (after parameters have been set)
set_score_request(*, metric: bool | None | str = '$UNCHANGED$', metric_params: bool | None | str = '$UNCHANGED$', use_proba: bool | None | str = '$UNCHANGED$') IndividualTDE[source]

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
metricstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for metric parameter in score.

metric_paramsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for metric_params parameter in score.

use_probastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for use_proba parameter in score.

Returns:
selfobject

The updated object.

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 setting tag values in tag_dict as dynamic tags in self.