SignatureClassifier

class SignatureClassifier(estimator=None, augmentation_list=('basepoint', 'addtime'), window_name='dyadic', window_depth=3, window_length=None, window_step=None, rescaling=None, sig_tfm='signature', depth=4, random_state=None)

Classification module using signature-based features.

This simply initialises the SignatureTransformer class which builds the feature extraction pipeline, then creates a new pipeline by appending a classifier after the feature extraction step.

The default parameters are set to best practice parameters found in “A Generalised Signature Method for Multivariate TimeSeries” [1]

Note that the final classifier used on the UEA datasets involved tuning the hyper-parameters:

• depth over [1, 2, 3, 4, 5, 6]

• window_depth over [2, 3, 4]

• RandomForestClassifier hyper-parameters.

as these were found to be the most dataset dependent hyper-parameters.

Thus, we recommend always tuning at least these parameters to any given dataset.

Parameters:

estimatorsklearn estimator, default=RandomForestClassifier

This should be any sklearn-type estimator. Defaults to RandomForestClassifier.

augmentation_listlist of tuple of str, default=(“basepoint”, “addtime”)

List of augmentations to be applied before the signature transform is applied.

window_namestr, default=”dyadic”

The name of the window transform to apply.

window_depthint, default=3

The depth of the dyadic window. (Active only if window_name == ‘dyadic’].

window_lengthint, default=None

The length of the sliding/expanding window. (Active only if `window_name in [‘sliding, ‘expanding’].

window_stepint, default=None

The step of the sliding/expanding window. (Active only if `window_name in [‘sliding, ‘expanding’].

rescalingstr, default=None

The method of signature rescaling.

sig_tfmstr, default=”signature”

String to specify the type of signature transform. One of: [‘signature’, ‘logsignature’]).

depthint, default=4

Signature truncation depth.

random_stateint, default=None

Random state initialisation.

Attributes:

signature_methodsklearn.Pipeline

An sklearn pipeline that performs the signature feature extraction step.

pipelinesklearn.Pipeline

The classifier appended to the signature_method pipeline to make a classification pipeline.

n_classes_int

Number of classes. Extracted from the data.

classes_ndarray of shape (n_classes_)

Holds the label for each class.

See also

SignatureTransformer

SignatureTransformer in the transformations package.

References

[1]

Morrill, James, et al. “A generalised signature method for multivariate time series feature extraction.” arXiv preprint arXiv:2006.00873 (2020). https://arxiv.org/pdf/2006.00873.pdf

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.
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 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. SignatureClassifier 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')

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, n_timepoints)

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

of shape (n_instances, n_timepoints)

or list of numpy arrays (any number of channels, unequal length series)

of shape [n_instances], 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.array, 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.

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.

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.

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_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()
>>> 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) → ndarray

Predicts labels for time series in X.

Parameters:

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

of shape (n_instances, n_channels, n_timepoints)

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

of shape (n_instances, n_timepoints)

or list of numpy arrays (any number of channels, unequal length series)

of shape [n_instances], 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 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 (any number of channels, equal length series)

of shape (n_instances, n_channels, n_timepoints)

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

of shape (n_instances, n_timepoints)

or list of numpy arrays (any number of channels, unequal length series)

of shape [n_instances], 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

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, n_timepoints)

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

of shape (n_instances, n_timepoints)

or list of numpy arrays (any number of channels, unequal length series)

of shape [n_instances], 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 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.