Catch22¶
- class Catch22(features='all', catch24=False, outlier_norm=False, replace_nans=False, use_pycatch22=False, n_jobs=1, parallel_backend=None)[source]¶
Canonical Time-series Characteristics (Catch22).
Overview: Input n series with d dimensions of length m. Transforms series into the 22 Catch22 [1] features extracted from the hctsa [R0686c85ef67c-2] toolbox.
- 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”, “CO_f1ecac”,”CO_FirstMin_ac”, “CO_HistogramAMI_even_2_5”, “CO_trev_1_num”, “MD_hrv_classic_pnn40”, “SB_BinaryStats_mean_longstretch1”, “SB_TransitionMatrix_3ac_sumdiagcov”, “PD_PeriodicityWang_th0_01”, “CO_Embed2_Dist_tau_d_expfit_meandiff”, “IN_AutoMutualInfoStats_40_gaussian_fmmi”, “FC_LocalSimple_mean1_tauresrat”, “DN_OutlierInclude_p_001_mdrmd”, “DN_OutlierInclude_n_001_mdrmd”, “SP_Summaries_welch_rect_area_5_1”, “SB_BinaryStats_diff_longstretch0”, “SB_MotifThree_quantile_hh”, “SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1”, “SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1”, “SP_Summaries_welch_rect_centroid”, “FC_LocalSimple_mean3_stderr”]
- Shortened:
[“mode_5”, “mode_10”, “acf_timescale”, “acf_first_min”, “ami2”, “trev”, “high_fluctuation”, “stretch_high”, “transition_matrix”, “periodicity”, “embedding_dist”, “ami_timescale”, “whiten_timescale”, “outlier_timing_pos”, “outlier_timing_neg”, “centroid_freq”, “stretch_decreasing”, “entropy_pairs”, “rs_range”, “dfa”, “low_freq_power”, “forecast_error”]
- catch24bool, 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, default=False
Replace NaN or inf values from the Catch22 transform with 0.
- use_pycatch22bool, optional, 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.- n_jobsint, default=1
The number of jobs to run in parallel for transform. Requires multiple input cases.
-1
means using all processors.- parallel_backendstr, ParallelBackendBase instance or None, default=None
Specify the parallelisation backend implementation in joblib, 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:
get_features_arguments
Return feature names for the estimators features argument.
See also
Catch22Classifier
Notes
Original Catch22 package implementations: https://github.com/DynamicsAndNeuralSystems/Catch22
For the Java version, see https://github.com/uea-machine-learning/tsml/blob/master/src/main/java /tsml/transformers/Catch22.java
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. .. [R0686c85ef67c-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.
Examples
>>> from aeon.transformations.collection.feature_based import Catch22 >>> from aeon.testing.data_generation import make_example_3d_numpy >>> X = make_example_3d_numpy(n_cases=4, n_channels=1, n_timepoints=10, ... random_state=0, return_y=False) >>> tnf = Catch22(replace_nans=True) >>> tnf.fit(X) Catch22(...) >>> print(tnf.transform(X)[0]) [1.15639531e+00 1.31700577e+00 5.66227710e-01 2.00000000e+00 3.89048349e-01 2.33853577e-01 1.00000000e+00 3.00000000e+00 8.23045267e-03 0.00000000e+00 1.70859420e-01 2.00000000e+00 1.00000000e+00 2.00000000e-01 0.00000000e+00 1.10933565e-32 4.00000000e+00 2.04319187e+00 0.00000000e+00 0.00000000e+00 1.96349541e+00 5.51667002e-01]
Methods
clone
([random_state])Obtain a clone of the object with the same hyperparameters.
fit
(X[, y])Fit transformer to X, optionally using y if supervised.
fit_transform
(X[, y])Fit to data, then transform it.
get_class_tag
(tag_name[, raise_error, ...])Get tag value from estimator class (only class tags).
Get class tags from estimator class and all its parent classes.
get_fitted_params
([deep])Get fitted parameters.
Sklearn metadata routing.
get_params
([deep])Get parameters for this estimator.
get_tag
(tag_name[, raise_error, ...])Get tag value from estimator class.
get_tags
()Get tags from estimator.
inverse_transform
(X[, y])Inverse transform X and return an inverse transformed version.
reset
([keep])Reset the object to a clean post-init state.
set_params
(**params)Set the parameters of this estimator.
set_tags
(**tag_dict)Set dynamic tags to given values.
transform
(X[, y])Transform X and return a transformed version.
- clone(random_state=None)[source]¶
Obtain a clone of the object with the same hyperparameters.
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 totype(self)(**self.get_params(deep=False))
.- Parameters:
- random_stateint, RandomState instance, or None, default=None
Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.
- Returns:
- estimatorobject
Instance of
type(self)
, clone of self (see above)
- fit(X, y=None)[source]¶
Fit transformer to X, optionally using y if supervised.
- State change:
Changes state to “fitted”.
Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, 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)
, wheren_timepoints_i
is length of seriesi
. 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. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- selfa fitted instance of the estimator
- fit_transform(X, y=None)[source]¶
Fit to data, then transform it.
Fits the transformer to X and y and returns a transformed version of X.
- State change:
Changes state to “fitted”.
Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator.
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, 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)
, wheren_timepoints_i
is length of seriesi
. 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. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- transformed version of X
- classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶
Get tag value from estimator class (only class tags).
- Parameters:
- tag_namestr
Name of tag value.
- raise_errorbool, default=True
Whether a ValueError is raised when the tag is not found.
- tag_value_defaultany type, default=None
Default/fallback value if tag is not found and error is not raised.
- Returns:
- tag_value
Value of the
tag_name
tag in cls. If not found, returns an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if
raise_error
is True andtag_name
is not inself.get_tags().keys()
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 and tag value pairs. Collected from
_tags
class attribute via nested inheritance. These are not overridden by dynamic tags set byset_tags
or class__init__
calls.
- get_fitted_params(deep=True)[source]¶
Get fitted parameters.
- State required:
Requires state to be “fitted”.
- Parameters:
- deepbool, default=True
If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.
- Returns:
- fitted_paramsdict
Fitted parameter names mapped to their values.
- 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, raise_error=True, tag_value_default=None)[source]¶
Get tag value from estimator class.
Includes dynamic and overridden tags.
- Parameters:
- tag_namestr
Name of tag to be retrieved.
- raise_errorbool, default=True
Whether a ValueError is raised when the tag is not found.
- tag_value_defaultany type, default=None
Default/fallback value if tag is not found and error is not raised.
- Returns:
- tag_value
Value of the
tag_name
tag in self. If not found, returns an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if raise_error is
True
andtag_name
is not inself.get_tags().keys()
Examples
>>> from aeon.classification import DummyClassifier >>> d = DummyClassifier() >>> d.get_tag("capability:multivariate") True
- get_tags()[source]¶
Get tags from estimator.
Includes dynamic and overridden tags.
- Returns:
- collected_tagsdict
Dictionary of tag name and tag value pairs. Collected from
_tags
class attribute via nested inheritance and then any overridden and new tags from__init__
orset_tags
.
- inverse_transform(X, y=None)[source]¶
Inverse transform X and return an inverse transformed version.
- Currently it is assumed that only transformers with tags
“input_data_type”=”Series”, “output_data_type”=”Series”,
can have an inverse_transform.
- State required:
Requires state to be “fitted”.
- Accesses in self:
_is_fitted : must be True fitted model attributes (ending in “_”) : accessed by _inverse_transform
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, 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)
, wheren_timepoints_i
is length of seriesi
. 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. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- inverse transformed version of X
of the same type as X
- reset(keep=None)[source]¶
Reset the object to a clean post-init state.
After a
self.reset()
call, self is equal or similar in value totype(self)(**self.get_params(deep=False))
, assuming no other attributes were kept usingkeep
.- Detailed 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 hyperparameters (result ofget_params
)- Not affected by the reset are:
object attributes containing double-underscores class and object methods, class attributes any attributes specified in the
keep
argument
- Parameters:
- keepNone, str, or list of str, default=None
If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.
- Returns:
- selfobject
Reference to self.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_tags(**tag_dict)[source]¶
Set dynamic tags to given values.
- Parameters:
- **tag_dictdict
Dictionary of tag name and tag value pairs.
- Returns:
- selfobject
Reference to self.
- transform(X, y=None)[source]¶
Transform X and return a transformed version.
- State required:
Requires state to be “fitted”.
Accesses in self: _is_fitted : must be True fitted model attributes (ending in “_”) : must be set, accessed by _transform
- Parameters:
- Xnp.ndarray or list
Input data, any number of channels, equal length series of shape
( n_cases, n_channels, 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)
, wheren_timepoints_i
is length of seriesi
. 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. If
self.get_tag( "capability:unequal_length")
is False, they cannot handle unequal length input. In both situations, aValueError
is raised if X has a characteristic that the estimator does not have the capability to handle.Data to fit transform to, of valid collection type.
- ynp.ndarray, default=None
1D np.array of float or str, of shape
(n_cases)
- class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.
- Returns:
- transformed version of X