Catch22Wrapper

Catch22Wrapper

class Catch22Wrapper(features='all', catch24=False, outlier_norm=False, replace_nans=False, col_names='range')[source]

Canonical Time-series Characteristics (Catch22 and 24), using pycatch22 package.

Direct interface to the pycatch22 implementation of Catch-22 and Catch-24 feature sets (https://github.com/DynamicsAndNeuralSystems/pycatch22).

Overview: Input n series with d dimensions of length m Transforms series into the 22 Catch22 [1] features extracted from the hctsa [R653ba6b2ce48-2] toolbox.

Parameters:

featuresint/str or List of int/str, optional, 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, optional, default=False

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, optional, default=True

Replace NaN or inf values from the Catch22 transform with 0.

col_namesstr, one of {“range”, “int_feat”, “str_feat”}, optional, default=”range”

The type of column names to return. If “range”, column names will be a regular range of integers, as in a RangeIndex. If “int_feat”, column names will be the integer feature indices, as defined in pycatch22. If “str_feat”, column names will be the string feature names.

Attributes:

is_fitted

Whether fit has been called.

See also

Catch22

Catch22Classifier

References

[1]

Lubba, C. H., Sethi, S. S., Knaute, P., Schultz, S. R., Fulcher, B. D., &

Jones, N. S. (2019). catch22: Canonical time-series characteristics. Data Mining and Knowledge Discovery, 33(6), 1821-1852. .. [R653ba6b2ce48-2] Fulcher, B. D., Little, M. A., & Jones, N. S. (2013). Highly comparative time-series analysis: the empirical structure of time series and their methods. Journal of the Royal Society Interface, 10(83), 20130048.

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.