Catch22Classifier#

class Catch22Classifier(features='all', catch24=True, outlier_norm=False, replace_nans=True, use_pycatch22=False, estimator=None, random_state=None, n_jobs=1, parallel_backend=None)[source]#

Canonical Time-series Characteristics (catch22) classifier.

This classifier simply transforms the input data using the Catch22 [1] transformer and builds a provided estimator using the transformed data.

Parameters:

featuresint/str or List of int/str, default=”all”: The Catch22 features to extract by feature index, feature name as a str or as a list of names or indices for multiple features. If “all”, all features are extracted. Valid features are as follows:

[“DN_HistogramMode_5”, “DN_HistogramMode_10”, “SB_BinaryStats_diff_longstretch0”, “DN_OutlierInclude_p_001_mdrmd”, “DN_OutlierInclude_n_001_mdrmd”, “CO_f1ecac”, “CO_FirstMin_ac”, “SP_Summaries_welch_rect_area_5_1”, “SP_Summaries_welch_rect_centroid”, “FC_LocalSimple_mean3_stderr”, “CO_trev_1_num”, “CO_HistogramAMI_even_2_5”, “IN_AutoMutualInfoStats_40_gaussian_fmmi”, “MD_hrv_classic_pnn40”, “SB_BinaryStats_mean_longstretch1”, “SB_MotifThree_quantile_hh”, “FC_LocalSimple_mean1_tauresrat”, “CO_Embed2_Dist_tau_d_expfit_meandiff”, “SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1”, “SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1”, “SB_TransitionMatrix_3ac_sumdiagcov”, “PD_PeriodicityWang_th0_01”]
catch24bool, default=True: Extract the mean and standard deviation as well as the 22 Catch22 features if true. If a List of specific features to extract is provided, “Mean” and/or “StandardDeviation” must be added to the List to extract these features.
outlier_normbool, optional, default=False: Normalise each series during the two outlier Catch22 features, which can take a while to process for large values.
replace_nansbool, default=True: Replace NaN or inf values from the Catch22 transform with 0.
use_pycatch22bool, default=False: Wraps the C based pycatch22 implementation for aeon. (https://github.com/DynamicsAndNeuralSystems/pycatch22). This requires the pycatch22 package to be installed if True.
estimatorsklearn classifier, default=None: An sklearn estimator to be built using the transformed data. Defaults to sklearn RandomForestClassifier(n_estimators=200).
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.
n_jobsint, default=1: The number of jobs to run in parallel for both fit and predict. -1 means using all processors.
parallel_backendstr, ParallelBackendBase instance or None, default=None: Specify the parallelisation backend implementation in joblib for Catch22, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.

Attributes:

n_classes_int: Number of classes. Extracted from the data.
classes_ndarray of shape (n_classes_): Holds the label for each class.

See also

Catch22: Catch22 transformer in aeon/transformations/collection.

Notes

Authors catch22ForestClassifier.

For the Java version, see tsml.

References

[1]

Lubba, Carl H., et al. “catch22: Canonical time-series characteristics.” Data Mining and Knowledge Discovery 33.6 (2019): 1821-1852. https://link.springer.com/article/10.1007/s10618-019-00647-x

Examples

>>> from aeon.classification.feature_based import Catch22Classifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> from aeon.datasets import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12,
...                              return_y=True, random_state=0)
>>> clf = Catch22Classifier(
...     estimator=RandomForestClassifier(n_estimators=5),
...     outlier_norm=True,
...     random_state=0,
... )
>>> clf.fit(X, y)
Catch22Classifier(...)
>>> clf.predict(X)
array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])

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')[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. Catch22Classifier 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()[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)[source]#

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)[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.

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_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()[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 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[source]#

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()[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) → float[source]#

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)[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_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 settting tag values in tag_dict as dynamic tags in self.